diff --git a/.gitattributes b/.gitattributes index c7d9f3332a950355d5a77d85000f05e6f45435ea..6f8cccd61748f4948f88e8baa28a56816b115afe 100644 --- a/.gitattributes +++ b/.gitattributes @@ -32,3 +32,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +*.gif filter=lfs diff=lfs merge=lfs -text +*.mp4 filter=lfs diff=lfs merge=lfs -text +*.jpg filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text +*.jpeg filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..4629b51e01e38f3f1961c24814853d3c2b0af8c9 --- /dev/null +++ b/.gitignore @@ -0,0 +1,27 @@ +# compilation and distribution +__pycache__ +_ext +*.pyc +*.pyd +*.so +*.dll +*.egg-info/ +build/ +dist/ +wheels/ + +# Editor temporaries +*.swn +*.swo +*.swp +*~ + +# editor settings +.idea +.vscode +_darcs + +# custom files +./image/ +./tmp_files/ + diff --git a/README.md b/README.md index d3f61c429bcdf4a16d692ba15425199b378fd6f2..d57e907c80c756bba2902a1b06113994abc2e3d0 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,166 @@ ---- title: InternChat -emoji: 😻 -colorFrom: green -colorTo: indigo +emoji: 🤖💬 +colorFrom: indigo +colorTo: pink sdk: gradio -sdk_version: 3.29.0 -app_file: app.py +sdk_version: 3.28.1 +app_file: iChatApp.py pinned: false license: apache-2.0 ---- -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference + +[[中文文档]](README_CN.md) + +**The project is still under construction, we will continue to update it and welcome contributions/pull requests from the community.** + + + +

+ + + | + | + + + + +# InternChat [[paper](https://pjlab-gvm-data.oss-cn-shanghai.aliyuncs.com/papers/ichat.pdf)] + + + +**InternChat**(short for **iChat**) is pointing-language-driven visual interactive system, allowing you to interact with ChatGPT by clicking, dragging and drawing using a pointing device. The name InternChat stands for **inter**action, **n**onverbal, and **chat**bots. Different from existing interactive systems that rely on pure language, by incorporating pointing instructions, iChat significantly improves the efficiency of communication between users and chatbots, as well as the accuracy of chatbots in vision-centric tasks, especially in complicated visual scenarios. Additionally, in iChat, an auxiliary control mechanism is used to improve the control capability of LLM, and a large vision-language model termed **Husky** is fine-tuned for high-quality multi-modal dialogue (impressing ChatGPT-3.5-turbo with **93.89% GPT-4 Quality**). + +## Online Demo +[**InternChat**](https://ichat.opengvlab.com/) is online. Let's try it! + +[**NOTE**] It is possible that you are waiting in a lengthy queue. You can clone our repo and run it with your private GPU. + +https://github.com/OpenGVLab/InternChat/assets/13723743/3270b05f-0823-4f13-9966-4010fd855643 + + + +## Schedule +- [ ] Support Chinese +- [ ] Support MOSS +- [ ] More powerful foundation models based on [InternImage](https://github.com/OpenGVLab/InternImage) and [InternVideo](https://github.com/OpenGVLab/InternVideo) +- [ ] More accurate interactive experience +- [ ] Web Page & Code Generation +- [x] Support voice assistant +- [x] Support click interaction +- [x] Interactive image editing +- [x] Interactive image generation +- [x] Interactive visual question answering +- [x] Segment Anything +- [x] Image inpainting +- [x] Image caption +- [x] image matting +- [x] Optical character recognition +- [x] Action recognition +- [x] Video caption +- [x] Video dense caption +- [x] video highlight interpretation + + + +## System Overview +

Logo

+ +## 🎁 Major Features + +

(a) Remove the masked object

+

+ +

(b) Interactive image editing +

+ +

(c) Image generation

+

+ +

(d) Interactive visual question answer

+

+ + +

(e) Interactive image generation

+

image

+ + +

(f) Video highlight interpretation

+

+ + + + +## 🛠️ Installation + +### Basic requirements + +- Linux +- Python 3.8+ +- PyTorch 1.12+ +- CUDA 11.6+ +- GCC & G++ 5.4+ +- GPU Memory >= 17G for loading basic tools (HuskyVQA, SegmentAnything, ImageOCRRecognition) + +### Install Python dependencies +```shell +pip install -r requirements.txt +``` + +### Model zoo +Coming soon... + +## 👨‍🏫 Get Started +Running the following shell can start a gradio service: +```shell +python -u iChatApp.py --load "HuskyVQA_cuda:0,SegmentAnything_cuda:0,ImageOCRRecognition_cuda:0" --port 3456 +``` + +if you want to enable the voice assistant, please use `openssl` to generate the certificate: +```shell +openssl req -x509 -newkey rsa:4096 -keyout ./key.pem -out ./cert.pem -sha256 -days 365 -nodes +``` + +and then run: +```shell +python -u iChatApp.py --load "HuskyVQA_cuda:0,SegmentAnything_cuda:0,ImageOCRRecognition_cuda:0" --port 3456 --https +``` + + + + +## 🎫 License + +This project is released under the [Apache 2.0 license](LICENSE). + +## 🖊️ Citation + +If you find this project useful in your research, please consider cite: +```BibTeX +@misc{2023internchat, + title={InternChat: Solving Vision-Centric Tasks by Interacting with Chatbots Beyond Language}, + author={Zhaoyang Liu and Yinan He and Wenhai Wang and Weiyun Wang and Yi Wang and Shoufa Chen and Qinglong Zhang and Yang Yang and Qingyun Li and Jiashuo Yu and Kunchang Li and Zhe Chen and Xue Yang and Xizhou Zhu and Yali Wang and Limin Wang and Ping Luo and Jifeng Dai and Yu Qiao}, + howpublished = {\url{https://arxiv.org/abs/2305.05662}}, + year={2023} +} +``` + +## 🤝 Acknowledgement +Thanks to the open source of the following projects: + +[Hugging Face](https://github.com/huggingface)   +[LangChain](https://github.com/hwchase17/langchain)   +[TaskMatrix](https://github.com/microsoft/TaskMatrix)   +[SAM](https://github.com/facebookresearch/segment-anything)   +[Stable Diffusion](https://github.com/CompVis/stable-diffusion)   +[ControlNet](https://github.com/lllyasviel/ControlNet)   +[InstructPix2Pix](https://github.com/timothybrooks/instruct-pix2pix)   +[BLIP](https://github.com/salesforce/BLIP)   +[Latent Diffusion Models](https://github.com/CompVis/latent-diffusion)   +[EasyOCR](https://github.com/JaidedAI/EasyOCR)   + +Welcome to discuss with us and continuously improve the user experience of InternChat. + +WeChat QR Code + +

image

+ diff --git a/README_CN.md b/README_CN.md new file mode 100644 index 0000000000000000000000000000000000000000..21134b6d876ae4a5138ee022aecffafa725f0a9d --- /dev/null +++ b/README_CN.md @@ -0,0 +1,147 @@ +[[English Document](README.md)] + +**[NOTE] 该项目仍在建设中，我们将继续更新，并欢迎社区的贡献/拉取请求。** + +

+ + + | + | + + +# InternChat [[论文](https://arxiv.org/pdf/2305.05662.pdf)] + +**InternChat**（简称 **iChat**）是一种基于指向语言驱动的视觉交互系统，允许您使用指向设备通过点击、拖动和绘制与 ChatGPT 进行互动。InternChat 的名称代表了 **inter**action（交互）、**n**onverbal（非语言）和 **chat**bots（聊天机器人）。与依赖纯语言的现有交互系统不同，通过整合指向指令，iChat 显著提高了用户与聊天机器人之间的沟通效率，以及聊天机器人在视觉为中心任务中的准确性，特别是在复杂的视觉场景中。此外，在 iChat 中，采用辅助控制机制来提高 LLM 的控制能力，并对一个大型视觉-语言模型 **Husky** 进行微调，以实现高质量的多模态对话（在ChatGPT-3.5-turbo评测中达到 **93.89% GPT-4 质量**）。 + +## 在线Demo + +[注意] 可能会出现排队等待较长时间。您可以clone我们的仓库并使用您自己的GPU运行。 + +[**InternChat**已上线，尝试一下！](https://ichat.opengvlab.com) + + +https://github.com/OpenGVLab/InternChat/assets/13723743/3270b05f-0823-4f13-9966-4010fd855643 + +## Schedule +- [ ] 支持中文 +- [ ] 支持 MOSS +- [ ] 基于 InternImage 和 InternVideo 的更强大的基础模型 +- [ ] 更准确的交互体验 +- [ ] 网页 & 代码生成 +- [x] 支持语音助手 +- [x] 支持点击交互 +- [x] 交互式图像编辑 +- [x] 交互式图像生成 +- [x] 交互式视觉问答 +- [x] Segment Anything模型 +- [x] 图像修复 +- [x] 图像描述 +- [x] 图像抠图 +- [x] 光学字符识别（OCR） +- [x] 动作识别 +- [x] 视频描述 +- [x] 视频密集描述 +- [x] 视频高光时刻截取 + +## 系统概览 +

Logo

+ +## 🎁 主要特点 + + +

(a) 移除遮盖的对象

+

+ +

(b) 交互式图像编辑 +

+ +

(c) 图像生成

+

+ +

(d) 交互式视觉问答

+

+ +

(e) 交互式图像生成

+

image

+ +

(f) 视频高光解释

+

+ + + +## 🛠️ 安装 + +### 基本要求 + +- Linux +- Python 3.8+ +- PyTorch 1.12+ +- CUDA 11.6+ +- GCC & G++ 5.4+ +- GPU 内存 >= 17G 用于加载基本工具 (HuskyVQA, SegmentAnything, ImageOCRRecognition) + +### 安装Python的依赖项 +```shell +pip install -r requirements.txt +``` + +### 模型库 + +即将推出... + +## 👨‍🏫 运行指南 + +运行以下 shell 可启动一个 gradio 服务： + +```shell +python -u iChatApp.py --load "HuskyVQA_cuda:0,SegmentAnything_cuda:0,ImageOCRRecognition_cuda:0" --port 3456 +``` + +如果您想启用语音助手，请使用 openssl 生成证书： + +```shell +openssl req -x509 -newkey rsa:4096 -keyout ./key.pem -out ./cert.pem -sha256 -days 365 -nodes +``` +然后运行： + +```shell +python -u iChatApp.py --load "HuskyVQA_cuda:0,SegmentAnything_cuda:0,ImageOCRRecognition_cuda:0" --port 3456 --https +``` + + +## 🎫 许可 + +该项目根据[Apache 2.0 license](LICENSE)发布。 + +## 🖊️ 引用 + +如果您在研究中发现这个项目有用，请考虑引用： +```BibTeX +@misc{2023internchat, + title={InternChat: Solving Vision-Centric Tasks by Interacting with Chatbots Beyond Language}, + author={Zhaoyang Liu and Yinan He and Wenhai Wang and Weiyun Wang and Yi Wang and Shoufa Chen and Qinglong Zhang and Yang Yang and Qingyun Li and Jiashuo Yu and Kunchang Li and Zhe Chen and Xue Yang and Xizhou Zhu and Yali Wang and Limin Wang and Ping Luo and Jifeng Dai and Yu Qiao}, + howpublished = {\url{https://arxiv.org/abs/2305.05662}}, + year={2023} +} +``` + +## 🤝 致谢 + +感谢以下开源项目: + +[Hugging Face](https://github.com/huggingface)   +[LangChain](https://github.com/hwchase17/langchain)   +[TaskMatrix](https://github.com/microsoft/TaskMatrix)   +[SAM](https://github.com/facebookresearch/segment-anything)   +[Stable Diffusion](https://github.com/CompVis/stable-diffusion)   +[ControlNet](https://github.com/lllyasviel/ControlNet)   +[InstructPix2Pix](https://github.com/timothybrooks/instruct-pix2pix)   +[BLIP](https://github.com/salesforce/BLIP)   +[Latent Diffusion Models](https://github.com/CompVis/latent-diffusion)   +[EasyOCR](https://github.com/JaidedAI/EasyOCR)   + + + +如果您在试用、运行、部署中有任何问题，欢迎加入我们的微信群讨论！如果您对项目有任何的想法和建议，欢迎加入我们的微信群讨论！ + +

image

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adam + lr: 0.001 + discriminator: + kind: adam + lr: 0.0001 +visualizer: + key_order: + - image + - predicted_image + - discr_output_fake + - discr_output_real + - inpainted + rescale_keys: + - discr_output_fake + - discr_output_real + kind: directory + outdir: /group-volume/User-Driven-Content-Generation/r.suvorov/inpainting/experiments/r.suvorov_2021-04-30_14-41-12_train_simple_pix2pix2_gap_sdpl_novgg_large_b18_ffc075_batch8x15/samples +location: + data_root_dir: /group-volume/User-Driven-Content-Generation/datasets/inpainting_data_root_large + out_root_dir: /group-volume/User-Driven-Content-Generation/${env:USER}/inpainting/experiments + tb_dir: /group-volume/User-Driven-Content-Generation/${env:USER}/inpainting/tb_logs +data: + batch_size: 15 + val_batch_size: 2 + num_workers: 3 + train: + indir: ${location.data_root_dir}/train + out_size: 256 + mask_gen_kwargs: + irregular_proba: 1 + irregular_kwargs: + max_angle: 4 + max_len: 200 + max_width: 100 + max_times: 5 + min_times: 1 + box_proba: 1 + box_kwargs: + margin: 10 + bbox_min_size: 30 + bbox_max_size: 150 + max_times: 3 + min_times: 1 + segm_proba: 0 + segm_kwargs: + confidence_threshold: 0.5 + max_object_area: 0.5 + min_mask_area: 0.07 + downsample_levels: 6 + num_variants_per_mask: 1 + rigidness_mode: 1 + max_foreground_coverage: 0.3 + max_foreground_intersection: 0.7 + max_mask_intersection: 0.1 + max_hidden_area: 0.1 + max_scale_change: 0.25 + horizontal_flip: true + max_vertical_shift: 0.2 + position_shuffle: true + transform_variant: distortions + dataloader_kwargs: + batch_size: ${data.batch_size} + shuffle: true + num_workers: ${data.num_workers} + val: + indir: ${location.data_root_dir}/val + img_suffix: .png + dataloader_kwargs: + batch_size: ${data.val_batch_size} + shuffle: false + num_workers: ${data.num_workers} + visual_test: + indir: ${location.data_root_dir}/korean_test + img_suffix: _input.png + pad_out_to_modulo: 32 + dataloader_kwargs: + batch_size: 1 + shuffle: false + num_workers: ${data.num_workers} +generator: + kind: ffc_resnet + input_nc: 4 + output_nc: 3 + ngf: 64 + n_downsampling: 3 + n_blocks: 18 + add_out_act: sigmoid + init_conv_kwargs: + ratio_gin: 0 + ratio_gout: 0 + enable_lfu: false + downsample_conv_kwargs: + ratio_gin: ${generator.init_conv_kwargs.ratio_gout} + ratio_gout: ${generator.downsample_conv_kwargs.ratio_gin} + enable_lfu: false + resnet_conv_kwargs: + ratio_gin: 0.75 + ratio_gout: ${generator.resnet_conv_kwargs.ratio_gin} + enable_lfu: false +discriminator: + kind: pix2pixhd_nlayer + input_nc: 3 + ndf: 64 + n_layers: 4 +evaluator: + kind: default + inpainted_key: inpainted + integral_kind: ssim_fid100_f1 +trainer: + kwargs: + gpus: -1 + accelerator: ddp + max_epochs: 200 + gradient_clip_val: 1 + log_gpu_memory: None + limit_train_batches: 25000 + val_check_interval: ${trainer.kwargs.limit_train_batches} + log_every_n_steps: 1000 + precision: 32 + terminate_on_nan: false + check_val_every_n_epoch: 1 + num_sanity_val_steps: 8 + limit_val_batches: 1000 + replace_sampler_ddp: false + checkpoint_kwargs: + verbose: true + save_top_k: 5 + save_last: true + period: 1 + monitor: val_ssim_fid100_f1_total_mean + mode: max diff --git a/configs/med_config.json b/configs/med_config.json new file mode 100644 index 0000000000000000000000000000000000000000..0ffad0a6f3c2f9f11b8faa84529d9860bb70327a --- /dev/null +++ b/configs/med_config.json @@ -0,0 +1,21 @@ +{ + "architectures": [ + "BertModel" + ], + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-12, + "max_position_embeddings": 512, + "model_type": "bert", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 0, + "type_vocab_size": 2, + "vocab_size": 30524, + "encoder_width": 768, + "add_cross_attention": true +} diff --git a/configs/q2l_config.json b/configs/q2l_config.json new file mode 100644 index 0000000000000000000000000000000000000000..adbfea1199e42633b91b2f129cecbdb79f8ed3cb --- /dev/null +++ b/configs/q2l_config.json @@ -0,0 +1,23 @@ +{ + "architectures": [ + "BertModel" + ], + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-12, + "max_position_embeddings": 512, + "model_type": "bert", + "num_attention_heads": 4, + "num_hidden_layers": 2, + "pad_token_id": 0, + "type_vocab_size": 2, + "vocab_size": 30522, + "encoder_width": 768, + "add_cross_attention": true, + "add_tag_cross_attention": false + } + \ No newline at end of file diff --git a/configs/swin/config_swinB_224.json b/configs/swin/config_swinB_224.json new file mode 100644 index 0000000000000000000000000000000000000000..331dd101266f0ba246e539eb50948cfca78b8753 --- /dev/null +++ b/configs/swin/config_swinB_224.json @@ -0,0 +1,10 @@ +{ + "ckpt": "pretrain_model/swin_base_patch4_window7_224_22k.pth", + "vision_width": 1024, + "image_res": 224, + "window_size": 7, + "embed_dim": 128, + "depths": [ 2, 2, 18, 2 ], + "num_heads": [ 4, 8, 16, 32 ] + } + \ No newline at end of file diff --git a/configs/swin/config_swinB_384.json b/configs/swin/config_swinB_384.json new file mode 100644 index 0000000000000000000000000000000000000000..7910c99721e75a51ddbcc0e5822f2e7a6920a5cd --- /dev/null +++ b/configs/swin/config_swinB_384.json @@ -0,0 +1,10 @@ +{ + "ckpt": "pretrain_model/swin_base_patch4_window7_224_22k.pth", + "vision_width": 1024, + "image_res": 384, + "window_size": 12, + "embed_dim": 128, + "depths": [ 2, 2, 18, 2 ], + "num_heads": [ 4, 8, 16, 32 ] + } + \ No newline at end of file diff --git a/configs/swin/config_swinB_480.json b/configs/swin/config_swinB_480.json new file mode 100644 index 0000000000000000000000000000000000000000..ce0972f1ff6135b26a0a4ca0410f98f1fb80470e --- /dev/null +++ b/configs/swin/config_swinB_480.json @@ -0,0 +1,9 @@ +{ + "ckpt": "pretrain_model/swin_base_patch4_window7_224_22k.pth", + "vision_width": 1024, + "image_res": 480, + "window_size": 15, + "embed_dim": 128, + "depths": [ 2, 2, 18, 2 ], + "num_heads": [ 4, 8, 16, 32 ] +} \ No newline at end of file diff --git a/configs/swin/config_swinB_576.json b/configs/swin/config_swinB_576.json new file mode 100644 index 0000000000000000000000000000000000000000..8ba05f6ce690ae3ea59e76d2e7546162907a2c23 --- /dev/null +++ b/configs/swin/config_swinB_576.json @@ -0,0 +1,9 @@ +{ + "ckpt": "pretrain_model/swin_base_patch4_window7_224_22k.pth", + "vision_width": 1024, + "image_res": 576, + "window_size": 18, + "embed_dim": 128, + "depths": [ 2, 2, 18, 2 ], + "num_heads": [ 4, 8, 16, 32 ] +} \ No newline at end of file diff --git a/configs/swin/config_swinB_608.json b/configs/swin/config_swinB_608.json new file mode 100644 index 0000000000000000000000000000000000000000..74409a72ff423560ab481513a6f8712d7eae36bc --- /dev/null +++ b/configs/swin/config_swinB_608.json @@ -0,0 +1,9 @@ +{ + "ckpt": "pretrain_model/swin_base_patch4_window7_224_22k.pth", + "vision_width": 1024, + "image_res": 608, + "window_size": 19, + "embed_dim": 128, + "depths": [ 2, 2, 18, 2 ], + "num_heads": [ 4, 8, 16, 32 ] +} \ No newline at end of file diff --git a/configs/tag2text_caption.yaml b/configs/tag2text_caption.yaml new file mode 100644 index 0000000000000000000000000000000000000000..eefbb7de93299b5ab86cada1081bfaf35cdb2680 --- /dev/null +++ b/configs/tag2text_caption.yaml @@ -0,0 +1,33 @@ +image_root: '/home/notebook/data/group/projects/tagging/caption/datasets/public/coco/' + +ann_root: 'dataset/caption_dataset' +coco_gt_root: 'dataset/caption_dataset' + +pretrained: '/home/notebook/code/personal/S9049611/BLIP/output/pretrain_caption_tagtotext_v2_bert_asl' + +# size of vit model; base or large +vit: 'swin_b' +vit_grad_ckpt: False +vit_ckpt_layer: 0 + +batch_size: 35 +init_lr: 5e-6 + +image_size: 384 + +# generation configs +max_length: 20 +min_length: 5 +num_beams: 3 +prompt: 'a picture of ' + +# optimizer +weight_decay: 0.05 +min_lr: 0 +max_epoch: 10 + +text_pretrain: 'bert' + +class_num: 3429 +threshold: 0.7 + diff --git a/iChat/__init__.py b/iChat/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..aed4fa323c2c8001593fdfdcd878a21718800167 --- /dev/null +++ b/iChat/__init__.py @@ -0,0 +1 @@ +from .models import * diff --git a/iChat/chatbot/__init__.py b/iChat/chatbot/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3b9b92f088214e5de1573f1610f941b787495adb --- /dev/null +++ b/iChat/chatbot/__init__.py @@ -0,0 +1 @@ +from .chatbot import ConversationBot \ No newline at end of file diff --git a/iChat/chatbot/chatbot.py b/iChat/chatbot/chatbot.py new file mode 100644 index 0000000000000000000000000000000000000000..389909ef052bb56de6a579e847a1b272a5bfe9ec --- /dev/null +++ b/iChat/chatbot/chatbot.py @@ -0,0 +1,440 @@ +import inspect +import re +import os +import numpy as np +import uuid +import shutil +import whisper +import gradio as gr + +from PIL import Image + +from langchain.agents.initialize import initialize_agent +from langchain.agents.tools import Tool +from langchain.chains.conversation.memory import ConversationBufferMemory +from langchain.llms.openai import OpenAI + +from ..models import * +from iGPT.models.utils import gen_new_name + +GLOBAL_SEED=1912 + + +''' +INTERN_CHAT_PREFIX = """InternChat is designed to be able to assist with a wide range of text and visual related tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. InternChat is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand. + +InternChat is able to process and understand large amounts of text and images. As a language model, InternChat can not directly read images, but it has a list of tools to finish different visual tasks. Each image will have a file name formed as "image/xxx.png", and InternChat can invoke different tools to indirectly understand pictures. When talking about images, InternChat is very strict to the file name and will never fabricate nonexistent files. When using tools to generate new image files, InternChat is also known that the image may not be the same as the user's demand, and will use other visual question answering tools or description tools to observe the real image. InternChat is able to use tools in a sequence, and is loyal to the tool observation outputs rather than faking the image content and image file name. It will remember to provide the file name from the last tool observation, if a new image is generated. + +Human may provide new figures to InternChat with a description. The description helps InternChat to understand this image, but InternChat should use tools to finish following tasks, rather than directly imagine from the description. + +Overall, InternChat is a powerful visual dialogue assistant tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. + + +TOOLS: +------ + +InternChat has access to the following tools:""" + +INTERN_CHAT_FORMAT_INSTRUCTIONS = """To use a tool, please use the following format: + +``` +Thought: Do I need to use a tool? Yes +Action: the action to take, should be one of [{tool_names}] +Action Input: the input to the action +Observation: the result of the action +``` + +When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format: + +``` +Thought: Do I need to use a tool? No +{ai_prefix}: [your response here] +``` +""" + +INTERN_CHAT_SUFFIX = """You are very strict to the filename correctness and will never fake a file name if it does not exist. +You will remember to provide the image file name loyally if it's provided in the last tool observation. + +Begin! + +Previous conversation history: +{chat_history} + +New input: {input} +Since InternChat is a text language model, InternChat must use tools to observe images rather than imagination. +The thoughts and observations are only visible for InternChat, InternChat should remember to repeat important information in the final response for Human. +Thought: Do I need to use a tool? {agent_scratchpad} Let's think step by step. +""" + +INTERN_CHAT_PREFIX_CN = """InternChat 旨在能够协助完成范围广泛的文本和视觉相关任务，从回答简单的问题到提供对广泛主题的深入解释和讨论。 InternChat 能够根据收到的输入生成类似人类的文本，使其能够进行听起来自然的对话，并提供连贯且与手头主题相关的响应。 + +InternChat 能够处理和理解大量文本和图像。作为一种语言模型，InternChat 不能直接读取图像，但它有一系列工具来完成不同的视觉任务。每张图片都会有一个文件名，格式为“image/xxx.png”，InternChat可以调用不同的工具来间接理解图片。在谈论图片时，InternChat 对文件名的要求非常严格，绝不会伪造不存在的文件。在使用工具生成新的图像文件时，InternChat也知道图像可能与用户需求不一样，会使用其他视觉问答工具或描述工具来观察真实图像。 InternChat 能够按顺序使用工具，并且忠于工具观察输出，而不是伪造图像内容和图像文件名。如果生成新图像，它将记得提供上次工具观察的文件名。 + +Human 可能会向 InternChat 提供带有描述的新图形。描述帮助 InternChat 理解这个图像，但 InternChat 应该使用工具来完成以下任务，而不是直接从描述中想象。有些工具将会返回英文描述，但你对用户的聊天应当采用中文。 + +总的来说，InternChat 是一个强大的可视化对话辅助工具，可以帮助处理范围广泛的任务，并提供关于范围广泛的主题的有价值的见解和信息。 + +工具列表: +------ + +InternChat 可以使用这些工具:""" + +INTERN_CHAT_FORMAT_INSTRUCTIONS_CN = """用户使用中文和你进行聊天，但是工具的参数应当使用英文。如果要调用工具，你必须遵循如下格式: + +``` +Thought: Do I need to use a tool? Yes +Action: the action to take, should be one of [{tool_names}] +Action Input: the input to the action +Observation: the result of the action +``` + +当你不再需要继续调用工具，而是对观察结果进行总结回复时，你必须使用如下格式： + + +``` +Thought: Do I need to use a tool? No +{ai_prefix}: [your response here] +``` +""" + +INTERN_CHAT_SUFFIX_CN = """你对文件名的正确性非常严格，而且永远不会伪造不存在的文件。 + +开始! + +因为InternChat是一个文本语言模型，必须使用工具去观察图片而不是依靠想象。 +推理想法和观察结果只对InternChat可见，需要记得在最终回复时把重要的信息重复给用户，你只能给用户返回中文句子。我们一步一步思考。在你使用工具时，工具的参数只能是英文。 + +聊天历史: +{chat_history} + +新输入: {input} +Thought: Do I need to use a tool? {agent_scratchpad} +""" +''' + + +VISUAL_CHATGPT_PREFIX = """Visual ChatGPT is designed to be able to assist with a wide range of text and visual related tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. Visual ChatGPT is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand. + +Visual ChatGPT is able to process and understand large amounts of text and images. As a language model, Visual ChatGPT can not directly read images, but it has a list of tools to finish different visual tasks. Each image will have a file name formed as "image/xxx.png", and Visual ChatGPT can invoke different tools to indirectly understand pictures. When talking about images, Visual ChatGPT is very strict to the file name and will never fabricate nonexistent files. When using tools to generate new image files, Visual ChatGPT is also known that the image may not be the same as the user's demand, and will use other visual question answering tools or description tools to observe the real image. Visual ChatGPT is able to use tools in a sequence, and is loyal to the tool observation outputs rather than faking the image content and image file name. It will remember to provide the file name from the last tool observation, if a new image is generated. + +Human may provide new figures to Visual ChatGPT with a description. The description helps Visual ChatGPT to understand this image, but Visual ChatGPT should use tools to finish following tasks, rather than directly imagine from the description. + +Overall, Visual ChatGPT is a powerful visual dialogue assistant tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. + + +TOOLS: +------ + +Visual ChatGPT has access to the following tools:""" + +VISUAL_CHATGPT_FORMAT_INSTRUCTIONS = """To use a tool, please use the following format: + +``` +Thought: Do I need to use a tool? Yes +Action: the action to take, should be one of [{tool_names}] +Action Input: the input to the action +Observation: the result of the action +``` + +When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format: + +``` +Thought: Do I need to use a tool? No +{ai_prefix}: [your response here] +``` +""" + +VISUAL_CHATGPT_SUFFIX = """You are very strict to the filename correctness and will never fake a file name if it does not exist. +You will remember to provide the image file name loyally if it's provided in the last tool observation. + +Begin! + +Previous conversation history: +{chat_history} + +New input: {input} +Since Visual ChatGPT is a text language model, Visual ChatGPT must use tools to observe images rather than imagination. +The thoughts and observations are only visible for Visual ChatGPT, Visual ChatGPT should remember to repeat important information in the final response for Human. +Thought: Do I need to use a tool? {agent_scratchpad} Let's think step by step. +""" + +VISUAL_CHATGPT_PREFIX_CN = """Visual ChatGPT 旨在能够协助完成范围广泛的文本和视觉相关任务，从回答简单的问题到提供对广泛主题的深入解释和讨论。 Visual ChatGPT 能够根据收到的输入生成类似人类的文本，使其能够进行听起来自然的对话，并提供连贯且与手头主题相关的响应。 + +Visual ChatGPT 能够处理和理解大量文本和图像。作为一种语言模型，Visual ChatGPT 不能直接读取图像，但它有一系列工具来完成不同的视觉任务。每张图片都会有一个文件名，格式为“image/xxx.png”，Visual ChatGPT可以调用不同的工具来间接理解图片。在谈论图片时，Visual ChatGPT 对文件名的要求非常严格，绝不会伪造不存在的文件。在使用工具生成新的图像文件时，Visual ChatGPT也知道图像可能与用户需求不一样，会使用其他视觉问答工具或描述工具来观察真实图像。 Visual ChatGPT 能够按顺序使用工具，并且忠于工具观察输出，而不是伪造图像内容和图像文件名。如果生成新图像，它将记得提供上次工具观察的文件名。 + +Human 可能会向 Visual ChatGPT 提供带有描述的新图形。描述帮助 Visual ChatGPT 理解这个图像，但 Visual ChatGPT 应该使用工具来完成以下任务，而不是直接从描述中想象。有些工具将会返回英文描述，但你对用户的聊天应当采用中文。 + +总的来说，Visual ChatGPT 是一个强大的可视化对话辅助工具，可以帮助处理范围广泛的任务，并提供关于范围广泛的主题的有价值的见解和信息。 + +工具列表: +------ + +Visual ChatGPT 可以使用这些工具:""" + +VISUAL_CHATGPT_FORMAT_INSTRUCTIONS_CN = """用户使用中文和你进行聊天，但是工具的参数应当使用英文。如果要调用工具，你必须遵循如下格式: + +``` +Thought: Do I need to use a tool? Yes +Action: the action to take, should be one of [{tool_names}] +Action Input: the input to the action +Observation: the result of the action +``` + +当你不再需要继续调用工具，而是对观察结果进行总结回复时，你必须使用如下格式： + + +``` +Thought: Do I need to use a tool? No +{ai_prefix}: [your response here] +``` +""" + +VISUAL_CHATGPT_SUFFIX_CN = """你对文件名的正确性非常严格，而且永远不会伪造不存在的文件。 + +开始! + +因为Visual ChatGPT是一个文本语言模型，必须使用工具去观察图片而不是依靠想象。 +推理想法和观察结果只对Visual ChatGPT可见，需要记得在最终回复时把重要的信息重复给用户，你只能给用户返回中文句子。我们一步一步思考。在你使用工具时，工具的参数只能是英文。 + +聊天历史: +{chat_history} + +新输入: {input} +Thought: Do I need to use a tool? {agent_scratchpad} +""" + + + +def cut_dialogue_history(history_memory, keep_last_n_words=500): + if history_memory is None or len(history_memory) == 0: + return history_memory + tokens = history_memory.split() + n_tokens = len(tokens) + print(f"history_memory:{history_memory}, n_tokens: {n_tokens}") + if n_tokens < keep_last_n_words: + return history_memory + paragraphs = history_memory.split('\n') + last_n_tokens = n_tokens + while last_n_tokens >= keep_last_n_words: + last_n_tokens -= len(paragraphs[0].split(' ')) + paragraphs = paragraphs[1:] + return '\n' + '\n'.join(paragraphs) + + +class ConversationBot: + def __init__(self, load_dict): + # load_dict = {'VisualQuestionAnswering':'cuda:0', 'ImageCaptioning':'cuda:1',...} + print(f"Initializing VisualChatGPT, load_dict={load_dict}") + if 'ImageCaptioning' not in load_dict: + raise ValueError("You have to load ImageCaptioning as a basic function for i-GPT") + # if 'SegmentAnything' not in load_dict: + # raise ValueError("You have to load SegmentAnything as a basic function for i-GPT") + + self.models = {} + self.uploaded_image_filename = None + # self.segmented_image_filename = None + self.history_mask = None + self.load_dict = load_dict + # self.llm = None + # Load Basic Foundation Models + for class_name, device in load_dict.items(): + self.models[class_name] = globals()[class_name](device=device) + # self.models['models'] = self.models + + # Load Template Foundation Models + for class_name, module in globals().items(): + if getattr(module, 'template_model', False): + template_required_names = {k for k in inspect.signature(module.__init__).parameters.keys() if k!='self'} + loaded_names = set([type(e).__name__ for e in self.models.values()]) + if template_required_names.issubset(loaded_names): + self.models[class_name] = globals()[class_name]( + **{name: self.models[name] for name in template_required_names}) + # elif 'models' in template_required_names: + # self.models[class_name] = globals()[class_name]( + # **{name: self.models[name] for name in template_required_names}) + + self.tools = [] + for instance in self.models.values(): + for e in dir(instance): + if e.startswith('inference'): + func = getattr(instance, e) + self.tools.append(Tool(name=func.name, description=func.description, func=func)) + self.llm = None + self.memory = ConversationBufferMemory(memory_key="chat_history", output_key='output') + # self.first_init=True + self.audio_model = None + + def init_agent(self): + self.memory.clear() #clear previous history + self.reset() + self.llm = OpenAI(temperature=0) + self.agent = initialize_agent( + self.tools, + self.llm, + agent="conversational-react-description", + verbose=True, + memory=self.memory, + return_intermediate_steps=True, + agent_kwargs={'prefix': VISUAL_CHATGPT_PREFIX, 'format_instructions': VISUAL_CHATGPT_FORMAT_INSTRUCTIONS, + 'suffix': VISUAL_CHATGPT_SUFFIX}, ) + + def run_text(self, text, state): + # print(f'text = {text}') + self.agent.memory.buffer = cut_dialogue_history(self.agent.memory.buffer, keep_last_n_words=500) + try: + print(f'text = {text}') + res = self.agent({"input": text.strip()}) + print('ab'* 30) + print(res['output']) + print('cd'* 30) + except Exception as err: + # Human_prompt = text + # self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + ' AI: ' + AI_prompt + state += [(text, 'I can not understand your instruction. Could you provide more information?')] + print(err) + return state, state + + res['output'] = res['output'].replace("\\", "/") + # response = re.sub('(tmp_files/[-\w]*.[png|mp4])', lambda m: f'![](file={m.group(0)})*{m.group(0)}*', res['output']) + + # print("res['output'] = ", res['output']) + # response = re.sub('(tmp_files/[-\w]*.(png|mp4))', replace_path, res['output']) + pattern = re.compile('(image/[-\\w]*.(png|mp4))') + out_filenames = pattern.findall(res['output']) + response = res['output'] + state = state + [(text, response)] + for f in out_filenames: + state = state + [(None, f'{f[0]} is as following: ')] + state = state + [(None, (f[0], ))] + # if len(out_filenames) > 1: + # state = state + [(None, (out_filenames[-1][0], ))] + # print('out_filename[-1][0] = ', out_filenames[-1][0]) + print(f"\nProcessed run_text, Input text: {text}\nCurrent state: {state}\n" + f"Current Memory: {self.agent.memory.buffer}") + return state, state + + def run_audio(self, audio_path, state): + print(f'audio_path = {audio_path}') + if self.audio_model is None: + self.audio_model = whisper.load_model("small").to('cuda:0') + text = self.audio_model.transcribe(audio_path)["text"] + res = self.run_text(text, state) + print(f"\nProcessed run_audio, Input transcribed audio: {text}\nCurrent state: {state}\n" + f"Current Memory: {self.agent.memory.buffer}") + return res[0], res[1] + + def upload_image(self, image, state, txt): + self.reset() + img = image['image'] + image_filename = os.path.join('image/', f"{str(uuid.uuid4())[:6]}.png") + self.uploaded_image_filename=image_filename + img = img.convert('RGB') + img.save(image_filename, "PNG") + # print(f"Resize image form {width}x{height} to {width_new}x{height_new}") + # let some foundation models preprocess image + # NEED_PREPROCESSING_LIST = ["SegmentAnything", "ImageOCRRecognition"] + # for model_name in NEED_PREPROCESSING_LIST: + # if model_name in self.models.keys(): + # self.models[model_name].preprocess(np.array(img), image_filename) + + description = self.models['ImageCaptioning'].inference(image_filename) + # description = 'Debug' + + Human_prompt = f'\nHuman: provide a figure named {image_filename}. The description is: {description}. This information helps you to understand this image, but you should use tools to finish following tasks, rather than directly imagine from my description. If you understand, say \"Received\". \n' + AI_prompt = "Received. " + self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + ' AI: ' + AI_prompt + state = state + [(f"![](file={image_filename})*{image_filename}*", AI_prompt)] + print(f"\nProcessed upload_image, Input image: {image_filename}\nCurrent state: {state}\n" + f"Current Memory: {self.agent.memory.buffer}") + return state, state, f'{txt} {image_filename} ', gr.update(visible=True), gr.update(visible=True) + + def upload_video(self, video_path, state, txt): + # self.cur_file = video_path + vid_name = os.path.basename(video_path) + # vid_name = gen_new_name(vid_name, '', vid_name.split('.')[-1]) + new_video_path = os.path.join('./image/', f"{str(uuid.uuid4())[:6]}.mp4") + new_video_path = gen_new_name(new_video_path, '', vid_name.split('.')[-1]) + shutil.copy(video_path, new_video_path) + + if "VideoCaption" in self.models.keys(): + description = self.models['VideoCaption'].inference(new_video_path) + else: + description = 'A video.' + Human_prompt = f'\nHuman: provide a video named {new_video_path}. The description is: {description}. This information helps you to understand this video, but you should use tools to finish following tasks, rather than directly imagine from my description. If you understand, say \"Received\". \n' + AI_prompt = f"Received video: {new_video_path} " + self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + 'AI: ' + AI_prompt + # state = state + [(f"![](file={new_video_path})*{new_video_path}*", AI_prompt)] + # state = state + [(f"![](file={video_path})*{new_video_path}*", AI_prompt)] + state = state + [((new_video_path, ), AI_prompt)] + # print('exists = ', os.path.exists("./tmp_files/1e7f_f4236666_tmp.mp4")) + print(f"\nProcessed upload_video, Input video: {new_video_path}\nCurrent state: {state}\n" + f"Current Memory: {self.agent.memory.buffer}") + return state, state, f'{txt} {new_video_path} ' + + def blend_mask(self, img, mask): + mask = mask.astype(np.uint8) + transparency_ratio = mask.astype(np.float32) / 3 + transparency_ratio = transparency_ratio[:, :, np.newaxis] + mask = mask[:, :, np.newaxis] * 255 + mask= mask.repeat(3, axis=2) + mask[:,:,0] = 0 + mask[:,:,2] = 0 + new_img_arr = np.array(img) * (1 - transparency_ratio) + mask * transparency_ratio + new_img_arr = np.clip(new_img_arr, 0, 255).astype(np.uint8) + # print(new_img_arr.shape) + return Image.fromarray(new_img_arr) + + def process_image(self, image, state): + img = Image.open(self.uploaded_image_filename).convert('RGB') + # img = image['image'].convert('RGB') + mask = image['mask'].convert('L') + mask = np.array(mask, dtype=np.uint8) + + Human_prompt="Please process this image based on given mask." + if self.uploaded_image_filename is None: + AI_prompt = "Please upload an image for processing." + state += [(Human_prompt, AI_prompt)] + return state, state, None + if mask.sum() == 0: + AI_prompt = "You can click the image in the right and ask me some questions." + state += [(Human_prompt, AI_prompt)] + return state, state, image['image'] + + if self.history_mask is None: + self.history_mask = mask + else: + self.history_mask = np.logical_or(self.history_mask, mask) + + if 'SegmentAnything' in self.models.keys(): + self.models['SegmentAnything'].clicked_region = self.history_mask + if 'ImageOCRRecognition' in self.models.keys(): + self.models['ImageOCRRecognition'].clicked_region = mask + + # self.models['SegmentAnything'].mask = self.history_mask + # history_mask = self.history_mask.astype(np.uint8) * 255 + res_mask = self.models['SegmentAnything'].segment_by_mask(self.history_mask) + + img = self.blend_mask(img, res_mask) + + AI_prompt = f"I have finished processing. Now, you can ask me some questions." + state = state + [(Human_prompt, AI_prompt)] + # AI_prompt = f"Received. I found {ocr_text} in this position. The sgemented figure is named {seg_filename}." + self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + ' AI: ' + AI_prompt + # state = state + [(Human_prompt, f"![](file={seg_filename})*{AI_prompt}*")] + # print() + print(f"\nProcessed run_image, Input image: {self.uploaded_image_filename}\nCurrent state: {state}\n" + f"Current Memory: {self.agent.memory.buffer}") + return state, state, img + + def reset(self, clear_history_memory=False): + print('reset the model cache.') + NEED_RESET_LIST = ['SegmentAnything', 'ImageOCRRecognition'] + for model_name in NEED_RESET_LIST: + if model_name in self.models.keys(): + self.models[model_name].reset() + + self.history_mask = None + self.uploaded_image_filename = None + if clear_history_memory: + self.agent.memory.clear() + return None \ No newline at end of file diff --git a/iChat/models/__init__.py b/iChat/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..aecd534b29ddf363577139eace8e06d7833f2a3a --- /dev/null +++ b/iChat/models/__init__.py @@ -0,0 +1,37 @@ +# from .image import (MaskFormer, ImageEditing, InstructPix2Pix, \ +# Text2Image, ImageCaptioning, Image2Canny, CannyText2Image, \ +# Image2Line, LineText2Image, Image2Hed, HedText2Image, Image2Scribble, \ +# ScribbleText2Image, Image2Pose, PoseText2Image, SegText2Image, \ +# Image2Depth, DepthText2Image, Image2Normal, NormalText2Image, \ +# VisualQuestionAnswering, InfinityOutPainting, \ +# SegmentAnything, InpaintMaskedAnything, ExtractMaskedAnything, \ +# ReplaceMaskedAnything, ImageOCRRecognition) + +from .husky import HuskyVQA + +from .video import (ActionRecognition, DenseCaption, VideoCaption, + Summarization, GenerateTikTokVideo) + +from .lang import SimpleLanguageModel + +from .inpainting import LDMInpainting + +# __all__ = [ +# 'MaskFormer', 'ImageEditing', 'InstructPix2Pix', \ +# 'Text2Image', 'ImageCaptioning', 'Image2Canny', 'CannyText2Image', \ +# 'Image2Line', 'LineText2Image', 'Image2Hed', 'HedText2Image', \ +# 'Image2Scribble', 'ScribbleText2Image', 'Image2Pose', 'PoseText2Image', \ +# 'SegText2Image', 'Image2Depth', 'DepthText2Image', 'Image2Normal', \ +# 'NormalText2Image', 'VisualQuestionAnswering', 'InfinityOutPainting', \ +# 'SegmentAnything', 'InpaintMaskedAnything', 'ExtractMaskedAnything', \ +# 'ReplaceMaskedAnything', 'ImageOCRRecognition', "SimpleLanguageModel", \ +# 'ActionRecognition', 'DenseCaption', 'VideoCaption', 'Summarization', \ +# 'GenerateTikTokVideo' +# ] + +__all__ = [ + 'HuskyVQA', "SimpleLanguageModel", 'GenerateTikTokVideo', \ + 'LDMInpainting', + 'ActionRecognition', 'DenseCaption', 'VideoCaption', 'Summarization' +] + diff --git a/iChat/models/grit_model.py b/iChat/models/grit_model.py new file mode 100644 index 0000000000000000000000000000000000000000..9d2ed9dd3788713aef3a02e45f5e8e0e16202cf7 --- /dev/null +++ b/iChat/models/grit_model.py @@ -0,0 +1,46 @@ +import os +import sys + +from .grit_src.image_dense_captions import image_caption_api, init_demo, dense_pred_to_caption, dense_pred_to_caption_only_name +from detectron2.data.detection_utils import read_image + +class DenseCaptioning(): + def __init__(self, device): + self.device = device + self.demo = None + + + def initialize_model(self): + self.demo = init_demo(self.device) + + def image_dense_caption_debug(self, image_src): + dense_caption = """ + 1. the broccoli is green, [0, 0, 333, 325]; + 2. a piece of broccoli, [0, 147, 143, 324]; + 3. silver fork on plate, [4, 547, 252, 612]; + """ + return dense_caption + + def image_dense_caption(self, image_src): + dense_caption = image_caption_api(image_src, self.device) + print('\033[1;35m' + '*' * 100 + '\033[0m') + print("Step2, Dense Caption:\n") + print(dense_caption) + print('\033[1;35m' + '*' * 100 + '\033[0m') + return dense_caption + + def run_caption_api(self,image_src): + img = read_image(image_src, format="BGR") + print(img.shape) + predictions, visualized_output = self.demo.run_on_image(img) + new_caption = dense_pred_to_caption_only_name(predictions) + return new_caption + + def run_caption_tensor(self,img): + # img = read_image(image_src, format="BGR") + # print(img.shape) + predictions, visualized_output = self.demo.run_on_image(img) + new_caption = dense_pred_to_caption_only_name(predictions) + return new_caption + + diff --git a/iChat/models/grit_src/configs/Base.yaml b/iChat/models/grit_src/configs/Base.yaml new file mode 100644 index 0000000000000000000000000000000000000000..445690acaafacfba6b54f28b4cf32e40c4bcae9d --- /dev/null +++ b/iChat/models/grit_src/configs/Base.yaml @@ -0,0 +1,77 @@ +MODEL: + META_ARCHITECTURE: "GRiT" + MASK_ON: True + PROPOSAL_GENERATOR: + NAME: "CenterNet" + FPN: + IN_FEATURES: ["layer3", "layer4", "layer5"] + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.12, 57.375] + ROI_HEADS: + NAME: GRiTROIHeadsAndTextDecoder + IN_FEATURES: ["p3", "p4", "p5"] + IOU_THRESHOLDS: [0.6] + NUM_CLASSES: 1 + SCORE_THRESH_TEST: 0.02 + NMS_THRESH_TEST: 0.5 + OBJECT_FEAT_POOLER_RES: 14 + ROI_BOX_CASCADE_HEAD: + IOUS: [0.6, 0.7, 0.8] + ROI_BOX_HEAD: + NAME: "FastRCNNConvFCHead" + NUM_FC: 2 + POOLER_RESOLUTION: 7 + CLS_AGNOSTIC_BBOX_REG: True + MULT_PROPOSAL_SCORE: True + ROI_MASK_HEAD: + NAME: "MaskRCNNConvUpsampleHead" + NUM_CONV: 4 + POOLER_RESOLUTION: 14 + CLS_AGNOSTIC_MASK: True + CENTERNET: + NUM_CLASSES: 1 + REG_WEIGHT: 1. + NOT_NORM_REG: True + ONLY_PROPOSAL: True + WITH_AGN_HM: True + INFERENCE_TH: 0.0001 + PRE_NMS_TOPK_TRAIN: 4000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TEST: 256 + NMS_TH_TRAIN: 0.9 + NMS_TH_TEST: 0.9 + POS_WEIGHT: 0.5 + NEG_WEIGHT: 0.5 + IGNORE_HIGH_FP: 0.85 +DATASETS: + TRAIN: ("coco_2017_train",) + TEST: ("coco_2017_val",) +DATALOADER: + SAMPLER_TRAIN: "MultiDatasetSampler" + DATASET_RATIO: [1] + DATASET_INPUT_SIZE: [1024] + DATASET_INPUT_SCALE: [[0.1, 2.0]] + FILTER_EMPTY_ANNOTATIONS: False + NUM_WORKERS: 8 +TEST: + DETECTIONS_PER_IMAGE: 256 +SOLVER: + LR_SCHEDULER_NAME: "WarmupCosineLR" + CHECKPOINT_PERIOD: 10000 + WARMUP_ITERS: 1000 + WARMUP_FACTOR: 0.001 + USE_CUSTOM_SOLVER: True + OPTIMIZER: "ADAMW" + MAX_ITER: 180000 + IMS_PER_BATCH: 64 + BASE_LR: 0.00008 + VIT_LAYER_DECAY: True + CLIP_GRADIENTS: + ENABLED: True +INPUT: + FORMAT: RGB + CUSTOM_AUG: EfficientDetResizeCrop + TRAIN_SIZE: 640 +USE_ACT_CHECKPOINT: True +VERSION: 2 \ No newline at end of file diff --git a/iChat/models/grit_src/configs/GRiT_B_DenseCap.yaml b/iChat/models/grit_src/configs/GRiT_B_DenseCap.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0e7d2d2c7448d330d9356b3af90b975b2ce7d528 --- /dev/null +++ b/iChat/models/grit_src/configs/GRiT_B_DenseCap.yaml @@ -0,0 +1,20 @@ +_BASE_: "Base.yaml" +MODEL: + TRAIN_TASK: ["DenseCap"] + TEST_TASK: "DenseCap" + MASK_ON: False + ROI_HEADS: + SOFT_NMS_ENABLED: False + BEAM_SIZE: 1 + WEIGHTS: "detectron2://ImageNetPretrained/MAE/mae_pretrain_vit_base.pth" + BACKBONE: + NAME: build_vit_fpn_backbone + VIT_LAYERS: 12 +SOLVER: + VIT_LAYER_DECAY_RATE: 0.7 +DATASETS: + TRAIN: ("vg_train",) + TEST: ("vg_test",) +DATALOADER: + DATASET_BS: 2 +OUTPUT_DIR: "./output/GRiT_B_DenseCap" \ No newline at end of file diff --git a/iChat/models/grit_src/configs/GRiT_B_DenseCap_ObjectDet.yaml b/iChat/models/grit_src/configs/GRiT_B_DenseCap_ObjectDet.yaml new file mode 100644 index 0000000000000000000000000000000000000000..49f3ef13ab8bf0eb8515c009e70e1d33687efd39 --- /dev/null +++ b/iChat/models/grit_src/configs/GRiT_B_DenseCap_ObjectDet.yaml @@ -0,0 +1,23 @@ +_BASE_: "Base.yaml" +MODEL: + TRAIN_TASK: ["ObjectDet", "DenseCap"] + TEST_TASK: "DenseCap" # DenseCap or ObjectDet: Choose one for testing + MASK_ON: True + ROI_HEADS: + SOFT_NMS_ENABLED: False + BEAM_SIZE: 1 + WEIGHTS: "detectron2://ImageNetPretrained/MAE/mae_pretrain_vit_base.pth" + BACKBONE: + NAME: build_vit_fpn_backbone + VIT_LAYERS: 12 +SOLVER: + VIT_LAYER_DECAY_RATE: 0.7 +DATASETS: + TRAIN: ("GRiT_coco2017_train", "vg_train") + TEST: ("coco_2017_test-dev",) +DATALOADER: + DATASET_RATIO: [1, 1] + DATASET_BS: 2 + DATASET_INPUT_SIZE: [1024, 1024] + DATASET_INPUT_SCALE: [[0.1, 2.0], [0.1, 2.0]] +OUTPUT_DIR: "./output/GRiT_B_DenseCap_ObjectDet" \ No newline at end of file diff --git a/iChat/models/grit_src/configs/GRiT_B_ObjectDet.yaml b/iChat/models/grit_src/configs/GRiT_B_ObjectDet.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e7a75052f84b7913480cc5ca0e29c03e4dbea4ef --- /dev/null +++ b/iChat/models/grit_src/configs/GRiT_B_ObjectDet.yaml @@ -0,0 +1,20 @@ +_BASE_: "Base.yaml" +MODEL: + TRAIN_TASK: ["ObjectDet"] + TEST_TASK: "ObjectDet" + MASK_ON: True + ROI_HEADS: + SOFT_NMS_ENABLED: True + BEAM_SIZE: 3 + WEIGHTS: "detectron2://ImageNetPretrained/MAE/mae_pretrain_vit_base.pth" + BACKBONE: + NAME: build_vit_fpn_backbone + VIT_LAYERS: 12 +SOLVER: + VIT_LAYER_DECAY_RATE: 0.7 +DATASETS: + TRAIN: ("GRiT_coco2017_train",) + TEST: ("coco_2017_val",) +DATALOADER: + DATASET_BS: 2 +OUTPUT_DIR: "./output/GRiT_B_ObjectDet" \ No newline at end of file diff --git a/iChat/models/grit_src/configs/GRiT_H_ObjectDet.yaml b/iChat/models/grit_src/configs/GRiT_H_ObjectDet.yaml new file mode 100644 index 0000000000000000000000000000000000000000..000a1d4629b695f5c4b4741fe28d0b8561c11cdb --- /dev/null +++ b/iChat/models/grit_src/configs/GRiT_H_ObjectDet.yaml @@ -0,0 +1,21 @@ +_BASE_: "Base.yaml" +MODEL: + TRAIN_TASK: ["ObjectDet"] + TEST_TASK: "ObjectDet" + MASK_ON: True + ROI_HEADS: + SOFT_NMS_ENABLED: True + BEAM_SIZE: 3 + WEIGHTS: "detectron2://ImageNetPretrained/MAE/mae_pretrain_vit_huge_p14to16.pth" + BACKBONE: + NAME: build_vit_fpn_backbone_huge + VIT_LAYERS: 32 +SOLVER: + MAX_ITER: 135000 + VIT_LAYER_DECAY_RATE: 0.9 +DATASETS: + TRAIN: ("GRiT_coco2017_train",) + TEST: ("coco_2017_val",) +DATALOADER: + DATASET_BS: 1 +OUTPUT_DIR: "./output/GRiT_H_ObjectDet" \ No newline at end of file diff --git a/iChat/models/grit_src/configs/GRiT_L_ObjectDet.yaml b/iChat/models/grit_src/configs/GRiT_L_ObjectDet.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b6e3b97f08fe4671e1a686b6cb6a83f8fc52f9a7 --- /dev/null +++ b/iChat/models/grit_src/configs/GRiT_L_ObjectDet.yaml @@ -0,0 +1,20 @@ +_BASE_: "Base.yaml" +MODEL: + TRAIN_TASK: ["ObjectDet"] + TEST_TASK: "ObjectDet" + MASK_ON: True + ROI_HEADS: + SOFT_NMS_ENABLED: True + BEAM_SIZE: 3 + WEIGHTS: "detectron2://ImageNetPretrained/MAE/mae_pretrain_vit_large.pth" + BACKBONE: + NAME: build_vit_fpn_backbone_large + VIT_LAYERS: 24 +SOLVER: + VIT_LAYER_DECAY_RATE: 0.8 +DATASETS: + TRAIN: ("GRiT_coco2017_train",) + TEST: ("coco_2017_val",) +DATALOADER: + DATASET_BS: 1 +OUTPUT_DIR: "./output/GRiT_L_ObjectDet" \ No newline at end of file diff --git a/iChat/models/grit_src/grit/__init__.py b/iChat/models/grit_src/grit/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..81f24566b0093edc133440090715b20ee569ca37 --- /dev/null +++ b/iChat/models/grit_src/grit/__init__.py @@ -0,0 +1,7 @@ +from .modeling.meta_arch import grit +from .modeling.roi_heads import grit_roi_heads +from .modeling.backbone import vit + +from .data.datasets import object365 +from .data.datasets import vg +from .data.datasets import grit_coco \ No newline at end of file diff --git a/iChat/models/grit_src/grit/config.py b/iChat/models/grit_src/grit/config.py new file mode 100644 index 0000000000000000000000000000000000000000..fabe7f0fbe1e41c6eb280f8f7d6ae2e9c4911135 --- /dev/null +++ b/iChat/models/grit_src/grit/config.py @@ -0,0 +1,50 @@ +from detectron2.config import CfgNode as CN + + +def add_grit_config(cfg): + _C = cfg + + _C.MODEL.BEAM_SIZE = 1 + _C.MODEL.TRAIN_TASK = ["ObjectDet", "DenseCap"] + _C.MODEL.TEST_TASK = "DenseCap" # This can be varied if the model is jointly trained on multiple tasks + + _C.MODEL.ROI_BOX_HEAD.USE_BIAS = 0.0 # >= 0: not use + _C.MODEL.ROI_BOX_HEAD.MULT_PROPOSAL_SCORE = False + + _C.MODEL.ROI_HEADS.MASK_WEIGHT = 1.0 + _C.MODEL.ROI_HEADS.OBJECT_FEAT_POOLER_RES = 14 + _C.MODEL.ROI_HEADS.SOFT_NMS_ENABLED = False + + # Backbones + _C.MODEL.VIT_LAYERS = 12 + + # Text Decoder + _C.TEXT_DECODER = CN() + _C.TEXT_DECODER.VOCAB_SIZE = 30522 + _C.TEXT_DECODER.HIDDEN_SIZE = 768 + _C.TEXT_DECODER.NUM_LAYERS = 6 + _C.TEXT_DECODER.ATTENTION_HEADS = 12 + _C.TEXT_DECODER.FEEDFORWARD_SIZE = 768 * 4 + + # Multi-dataset dataloader + _C.DATALOADER.DATASET_RATIO = [1, 1] # sample ratio + _C.DATALOADER.DATASET_BS = 1 + _C.DATALOADER.DATASET_INPUT_SIZE = [1024, 1024] + _C.DATALOADER.DATASET_INPUT_SCALE = [(0.1, 2.0), (0.1, 2.0)] + _C.DATALOADER.DATASET_MIN_SIZES = [(640, 800), (640, 800)] + _C.DATALOADER.DATASET_MAX_SIZES = [1333, 1333] + + _C.SOLVER.USE_CUSTOM_SOLVER = True + _C.SOLVER.OPTIMIZER = 'ADAMW' + _C.SOLVER.VIT_LAYER_DECAY = True + _C.SOLVER.VIT_LAYER_DECAY_RATE = 0.7 + + _C.INPUT.CUSTOM_AUG = 'EfficientDetResizeCrop' + _C.INPUT.TRAIN_SIZE = 1024 + _C.INPUT.TEST_SIZE = 1024 + _C.INPUT.SCALE_RANGE = (0.1, 2.) + # 'default' for fixed short / long edge + _C.INPUT.TEST_INPUT_TYPE = 'default' + + _C.FIND_UNUSED_PARAM = True + _C.USE_ACT_CHECKPOINT = True \ No newline at end of file diff --git a/iChat/models/grit_src/grit/custom_solver.py b/iChat/models/grit_src/grit/custom_solver.py new file mode 100644 index 0000000000000000000000000000000000000000..87f7d61ed756acf9326b7ab4097a989a9e6c7532 --- /dev/null +++ b/iChat/models/grit_src/grit/custom_solver.py @@ -0,0 +1,88 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# Modified by Jialian Wu from https://github.com/facebookresearch/Detic/blob/main/detic/custom_solver.py +import itertools +from typing import Any, Callable, Dict, Iterable, List, Set, Type, Union +import torch + +from detectron2.config import CfgNode + +from detectron2.solver.build import maybe_add_gradient_clipping + + +def build_custom_optimizer(cfg: CfgNode, model: torch.nn.Module) -> torch.optim.Optimizer: + params: List[Dict[str, Any]] = [] + memo: Set[torch.nn.parameter.Parameter] = set() + optimizer_type = cfg.SOLVER.OPTIMIZER + + for key, value in model.named_parameters(recurse=True): + if not value.requires_grad: + continue + # Avoid duplicating parameters + if value in memo: + continue + memo.add(value) + lr = cfg.SOLVER.BASE_LR + weight_decay = cfg.SOLVER.WEIGHT_DECAY + + if cfg.SOLVER.VIT_LAYER_DECAY: + lr = lr * get_vit_lr_decay_rate(key, cfg.SOLVER.VIT_LAYER_DECAY_RATE, cfg.MODEL.VIT_LAYERS) + + param = {"params": [value], "lr": lr} + if optimizer_type != 'ADAMW': + param['weight_decay'] = weight_decay + params += [param] + + def maybe_add_full_model_gradient_clipping(optim): # optim: the optimizer class + # detectron2 doesn't have full model gradient clipping now + clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE + enable = ( + cfg.SOLVER.CLIP_GRADIENTS.ENABLED + and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model" + and clip_norm_val > 0.0 + ) + + class FullModelGradientClippingOptimizer(optim): + def step(self, closure=None): + all_params = itertools.chain(*[x["params"] for x in self.param_groups]) + torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val) + super().step(closure=closure) + + return FullModelGradientClippingOptimizer if enable else optim + + + if optimizer_type == 'SGD': + optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)( + params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM, + nesterov=cfg.SOLVER.NESTEROV + ) + elif optimizer_type == 'ADAMW': + optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)( + params, cfg.SOLVER.BASE_LR, + weight_decay=cfg.SOLVER.WEIGHT_DECAY + ) + else: + raise NotImplementedError(f"no optimizer type {optimizer_type}") + if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model": + optimizer = maybe_add_gradient_clipping(cfg, optimizer) + return optimizer + + +def get_vit_lr_decay_rate(name, lr_decay_rate=1.0, num_layers=12): + """ + Calculate lr decay rate for different ViT blocks. + Args: + name (string): parameter name. + lr_decay_rate (float): base lr decay rate. + num_layers (int): number of ViT blocks. + + Returns: + lr decay rate for the given parameter. + """ + layer_id = num_layers + 1 + if name.startswith("backbone"): + if ".pos_embed" in name or ".patch_embed" in name: + layer_id = 0 + elif ".blocks." in name and ".residual." not in name: + layer_id = int(name[name.find(".blocks.") :].split(".")[2]) + 1 + + return lr_decay_rate ** (num_layers + 1 - layer_id) \ No newline at end of file diff --git a/iChat/models/grit_src/grit/data/custom_build_augmentation.py b/iChat/models/grit_src/grit/data/custom_build_augmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..49a52d011c09dbe027d41ee7e50127c392a8bf33 --- /dev/null +++ b/iChat/models/grit_src/grit/data/custom_build_augmentation.py @@ -0,0 +1,44 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from detectron2.data import transforms as T +from .transforms.custom_augmentation_impl import EfficientDetResizeCrop + + +def build_custom_augmentation(cfg, is_train, scale=None, size=None, \ + min_size=None, max_size=None): + """ + Create a list of default :class:`Augmentation` from config. + Now it includes resizing and flipping. + + Returns: + list[Augmentation] + """ + if cfg.INPUT.CUSTOM_AUG == 'ResizeShortestEdge': + if is_train: + min_size = cfg.INPUT.MIN_SIZE_TRAIN if min_size is None else min_size + max_size = cfg.INPUT.MAX_SIZE_TRAIN if max_size is None else max_size + sample_style = cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING + else: + min_size = cfg.INPUT.MIN_SIZE_TEST + max_size = cfg.INPUT.MAX_SIZE_TEST + sample_style = "choice" + augmentation = [T.ResizeShortestEdge(min_size, max_size, sample_style)] + elif cfg.INPUT.CUSTOM_AUG == 'EfficientDetResizeCrop': + if is_train: + scale = cfg.INPUT.SCALE_RANGE if scale is None else scale + size = cfg.INPUT.TRAIN_SIZE if size is None else size + else: + scale = (1, 1) + size = cfg.INPUT.TEST_SIZE + augmentation = [EfficientDetResizeCrop(size, scale)] + else: + assert 0, cfg.INPUT.CUSTOM_AUG + + if is_train: + augmentation.append(T.RandomFlip()) + return augmentation + + +build_custom_transform_gen = build_custom_augmentation +""" +Alias for backward-compatibility. +""" \ No newline at end of file diff --git a/iChat/models/grit_src/grit/data/custom_dataset_dataloader.py b/iChat/models/grit_src/grit/data/custom_dataset_dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..ea9c4172f838d130df297bed9c0755669720c39d --- /dev/null +++ b/iChat/models/grit_src/grit/data/custom_dataset_dataloader.py @@ -0,0 +1,250 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Jialian Wu from https://github.com/facebookresearch/Detic/blob/main/detic/data/custom_dataset_dataloader.py +import operator +import torch +import torch.utils.data +from detectron2.utils.comm import get_world_size + +from detectron2.config import configurable +from torch.utils.data.sampler import BatchSampler, Sampler +from detectron2.data.common import DatasetFromList, MapDataset +from detectron2.data.dataset_mapper import DatasetMapper +from detectron2.data.build import get_detection_dataset_dicts, build_batch_data_loader +from detectron2.data.samplers import TrainingSampler +from detectron2.data.build import worker_init_reset_seed, print_instances_class_histogram +from detectron2.data.build import filter_images_with_only_crowd_annotations +from detectron2.data.build import filter_images_with_few_keypoints +from detectron2.data.build import check_metadata_consistency +from detectron2.data.catalog import MetadataCatalog, DatasetCatalog +from detectron2.utils import comm +import itertools +from typing import Optional + + +def _custom_train_loader_from_config(cfg, mapper=None, *, dataset=None, sampler=None): + sampler_name = cfg.DATALOADER.SAMPLER_TRAIN + if 'MultiDataset' in sampler_name: + dataset_dicts = get_detection_dataset_dicts_with_source( + cfg.DATASETS.TRAIN, + filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS, + min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE + if cfg.MODEL.KEYPOINT_ON else 0, + proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None, + ) + else: + dataset_dicts = get_detection_dataset_dicts( + cfg.DATASETS.TRAIN, + filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS, + min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE + if cfg.MODEL.KEYPOINT_ON else 0, + proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None, + ) + + if mapper is None: + mapper = DatasetMapper(cfg, True) + + if sampler is not None: + pass + elif sampler_name == "TrainingSampler": + sampler = TrainingSampler(len(dataset)) + elif sampler_name == "MultiDatasetSampler": + sampler = MultiDatasetSampler( + dataset_dicts, + dataset_ratio=cfg.DATALOADER.DATASET_RATIO, + ) + else: + raise ValueError("Unknown training sampler: {}".format(sampler_name)) + + return { + "dataset": dataset_dicts, + "sampler": sampler, + "mapper": mapper, + "total_batch_size": cfg.SOLVER.IMS_PER_BATCH, + "num_workers": cfg.DATALOADER.NUM_WORKERS, + 'dataset_bs': cfg.DATALOADER.DATASET_BS, + 'num_datasets': len(cfg.DATASETS.TRAIN) + } + + +@configurable(from_config=_custom_train_loader_from_config) +def build_custom_train_loader( + dataset, *, mapper, sampler, + total_batch_size=16, + num_workers=0, + num_datasets=1, + dataset_bs=1 +): + + if isinstance(dataset, list): + dataset = DatasetFromList(dataset, copy=False) + if mapper is not None: + dataset = MapDataset(dataset, mapper) + if sampler is None: + sampler = TrainingSampler(len(dataset)) + assert isinstance(sampler, torch.utils.data.sampler.Sampler) + + return build_dataset_batch_data_loader( + dataset_bs, + dataset, + sampler, + total_batch_size, + num_datasets=num_datasets, + num_workers=num_workers, + ) + + +def build_dataset_batch_data_loader( + dataset_bs, dataset, sampler, total_batch_size, num_datasets, num_workers=0 +): + + world_size = get_world_size() + assert ( + total_batch_size > 0 and total_batch_size % world_size == 0 + ), "Total batch size ({}) must be divisible by the number of gpus ({}).".format( + total_batch_size, world_size + ) + + data_loader = torch.utils.data.DataLoader( + dataset, + sampler=sampler, + num_workers=num_workers, + batch_sampler=None, + collate_fn=operator.itemgetter(0), # don't batch, but yield individual elements + worker_init_fn=worker_init_reset_seed, + ) + + if num_datasets > 1: + return MultiDatasets(data_loader, dataset_bs, num_datasets) + else: + return SingleDataset(data_loader, dataset_bs) + + +def get_detection_dataset_dicts_with_source( + dataset_names, filter_empty=True, min_keypoints=0, proposal_files=None +): + assert len(dataset_names) + dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in dataset_names] + for dataset_name, dicts in zip(dataset_names, dataset_dicts): + assert len(dicts), "Dataset '{}' is empty!".format(dataset_name) + + for source_id, (dataset_name, dicts) in \ + enumerate(zip(dataset_names, dataset_dicts)): + assert len(dicts), "Dataset '{}' is empty!".format(dataset_name) + for d in dicts: + d['dataset_source'] = source_id + + if "annotations" in dicts[0]: + try: + class_names = MetadataCatalog.get(dataset_name).thing_classes + check_metadata_consistency("thing_classes", dataset_name) + print_instances_class_histogram(dicts, class_names) + except AttributeError: # class names are not available for this dataset + pass + + assert proposal_files is None + + dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts)) + + has_instances = "annotations" in dataset_dicts[0] + if filter_empty and has_instances: + dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts) + if min_keypoints > 0 and has_instances: + dataset_dicts = filter_images_with_few_keypoints(dataset_dicts, min_keypoints) + + return dataset_dicts + + +class MultiDatasetSampler(Sampler): + def __init__( + self, + dataset_dicts, + dataset_ratio, + seed: Optional[int] = None, + ): + sizes = [0 for _ in range(len(dataset_ratio))] + for d in dataset_dicts: + sizes[d['dataset_source']] += 1 + print('dataset sizes', sizes) + self.sizes = sizes + assert len(dataset_ratio) == len(sizes), \ + 'length of dataset ratio {} should be equal to number if dataset {}'.format( + len(dataset_ratio), len(sizes) + ) + if seed is None: + seed = comm.shared_random_seed() + self._seed = int(seed) + self._rank = comm.get_rank() + self._world_size = comm.get_world_size() + + self.dataset_ids = torch.tensor( + [d['dataset_source'] for d in dataset_dicts], dtype=torch.long) + self.dataset_ratio = dataset_ratio + + dataset_weight = [torch.ones(s) * max(sizes) / s * r / sum(dataset_ratio) \ + for i, (r, s) in enumerate(zip(dataset_ratio, sizes))] + dataset_weight = torch.cat(dataset_weight) + + self.weights = dataset_weight + self.sample_epoch_size = len(self.weights) + + def __iter__(self): + start = self._rank + yield from itertools.islice( + self._infinite_indices(), start, None, self._world_size) + + def _infinite_indices(self): + g = torch.Generator() + g.manual_seed(self._seed) + while True: + if len(self.dataset_ratio) > 1: + # multiple datasets + ids = torch.multinomial( + self.weights, self.sample_epoch_size, generator=g, + replacement=True) + nums = [(self.dataset_ids[ids] == i).sum().int().item() \ + for i in range(len(self.sizes))] + yield from ids + else: + # single dataset + yield from torch.randperm(self.sizes[0], generator=g).tolist() + + +class SingleDataset(torch.utils.data.IterableDataset): + def __init__(self, dataset, batch_sizes): + self.dataset = dataset + self.batch_sizes = batch_sizes + self._buckets = [[] for _ in range(2)] + + def __iter__(self): + for d in self.dataset: + w, h = d["width"], d["height"] + aspect_ratio_bucket_id = 0 if w > h else 1 + bucket_id = aspect_ratio_bucket_id + bucket = self._buckets[bucket_id] + bucket.append(d) + if len(bucket) == self.batch_sizes: + yield bucket[:] + del bucket[:] + + +class MultiDatasets(torch.utils.data.IterableDataset): + def __init__(self, dataset, batch_sizes, num_datasets): + self.dataset = dataset + self.batch_sizes = batch_sizes + self._buckets = [[] for _ in range(2 * num_datasets)] + self.iter_idx = 0 + self.num_datasets = num_datasets + + def __iter__(self): + for d in self.dataset: + w, h = d["width"], d["height"] + aspect_ratio_bucket_id = 0 if w > h else 1 + bucket_id = d['dataset_source'] * 2 + aspect_ratio_bucket_id + bucket = self._buckets[bucket_id] + if len(bucket) < self.batch_sizes: + bucket.append(d) + selected_dataset = self.iter_idx % self.num_datasets + if len(bucket) == self.batch_sizes and selected_dataset == d['dataset_source']: + self.iter_idx += 1 + yield bucket[:] + del bucket[:] \ No newline at end of file diff --git a/iChat/models/grit_src/grit/data/custom_dataset_mapper.py b/iChat/models/grit_src/grit/data/custom_dataset_mapper.py new file mode 100644 index 0000000000000000000000000000000000000000..1e21edb3d151dafdca5c4debfb7341a9ed0efdd9 --- /dev/null +++ b/iChat/models/grit_src/grit/data/custom_dataset_mapper.py @@ -0,0 +1,149 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# Modified by Jialian Wu from https://github.com/facebookresearch/Detic/blob/main/detic/data/custom_dataset_mapper.py +import copy +import numpy as np +import torch + +from detectron2.config import configurable + +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T +from detectron2.data.dataset_mapper import DatasetMapper +from .custom_build_augmentation import build_custom_augmentation +from itertools import compress +import logging + +__all__ = ["CustomDatasetMapper", "ObjDescription"] +logger = logging.getLogger(__name__) + + +class CustomDatasetMapper(DatasetMapper): + @configurable + def __init__(self, is_train: bool, + dataset_augs=[], + **kwargs): + if is_train: + self.dataset_augs = [T.AugmentationList(x) for x in dataset_augs] + super().__init__(is_train, **kwargs) + + @classmethod + def from_config(cls, cfg, is_train: bool = True): + ret = super().from_config(cfg, is_train) + if is_train: + if cfg.INPUT.CUSTOM_AUG == 'EfficientDetResizeCrop': + dataset_scales = cfg.DATALOADER.DATASET_INPUT_SCALE + dataset_sizes = cfg.DATALOADER.DATASET_INPUT_SIZE + ret['dataset_augs'] = [ + build_custom_augmentation(cfg, True, scale, size) \ + for scale, size in zip(dataset_scales, dataset_sizes)] + else: + assert cfg.INPUT.CUSTOM_AUG == 'ResizeShortestEdge' + min_sizes = cfg.DATALOADER.DATASET_MIN_SIZES + max_sizes = cfg.DATALOADER.DATASET_MAX_SIZES + ret['dataset_augs'] = [ + build_custom_augmentation( + cfg, True, min_size=mi, max_size=ma) \ + for mi, ma in zip(min_sizes, max_sizes)] + else: + ret['dataset_augs'] = [] + + return ret + + def __call__(self, dataset_dict): + dataset_dict_out = self.prepare_data(dataset_dict) + + # When augmented image is too small, do re-augmentation + retry = 0 + while (dataset_dict_out["image"].shape[1] < 32 or dataset_dict_out["image"].shape[2] < 32): + retry += 1 + if retry == 100: + logger.info('Retry 100 times for augmentation. Make sure the image size is not too small.') + logger.info('Find image information below') + logger.info(dataset_dict) + dataset_dict_out = self.prepare_data(dataset_dict) + + return dataset_dict_out + + def prepare_data(self, dataset_dict_in): + dataset_dict = copy.deepcopy(dataset_dict_in) + if 'file_name' in dataset_dict: + ori_image = utils.read_image( + dataset_dict["file_name"], format=self.image_format) + else: + ori_image, _, _ = self.tar_dataset[dataset_dict["tar_index"]] + ori_image = utils._apply_exif_orientation(ori_image) + ori_image = utils.convert_PIL_to_numpy(ori_image, self.image_format) + utils.check_image_size(dataset_dict, ori_image) + + aug_input = T.AugInput(copy.deepcopy(ori_image), sem_seg=None) + if self.is_train: + transforms = \ + self.dataset_augs[dataset_dict['dataset_source']](aug_input) + else: + transforms = self.augmentations(aug_input) + image, sem_seg_gt = aug_input.image, aug_input.sem_seg + + image_shape = image.shape[:2] + dataset_dict["image"] = torch.as_tensor( + np.ascontiguousarray(image.transpose(2, 0, 1))) + + if not self.is_train: + # USER: Modify this if you want to keep them for some reason. + dataset_dict.pop("annotations", None) + return dataset_dict + + if "annotations" in dataset_dict: + if len(dataset_dict["annotations"]) > 0: + object_descriptions = [an['object_description'] for an in dataset_dict["annotations"]] + else: + object_descriptions = [] + # USER: Modify this if you want to keep them for some reason. + for anno in dataset_dict["annotations"]: + if not self.use_instance_mask: + anno.pop("segmentation", None) + if not self.use_keypoint: + anno.pop("keypoints", None) + + all_annos = [ + (utils.transform_instance_annotations( + obj, transforms, image_shape, + keypoint_hflip_indices=self.keypoint_hflip_indices, + ), obj.get("iscrowd", 0)) + for obj in dataset_dict.pop("annotations") + ] + annos = [ann[0] for ann in all_annos if ann[1] == 0] + instances = utils.annotations_to_instances( + annos, image_shape, mask_format=self.instance_mask_format + ) + + instances.gt_object_descriptions = ObjDescription(object_descriptions) + + del all_annos + if self.recompute_boxes: + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + dataset_dict["instances"] = utils.filter_empty_instances(instances) + + return dataset_dict + + +class ObjDescription: + def __init__(self, object_descriptions): + self.data = object_descriptions + + def __getitem__(self, item): + assert type(item) == torch.Tensor + assert item.dim() == 1 + if len(item) > 0: + assert item.dtype == torch.int64 or item.dtype == torch.bool + if item.dtype == torch.int64: + return ObjDescription([self.data[x.item()] for x in item]) + elif item.dtype == torch.bool: + return ObjDescription(list(compress(self.data, item))) + + return ObjDescription(list(compress(self.data, item))) + + def __len__(self): + return len(self.data) + + def __repr__(self): + return "ObjDescription({})".format(self.data) \ No newline at end of file diff --git a/iChat/models/grit_src/grit/data/datasets/grit_coco.py b/iChat/models/grit_src/grit/data/datasets/grit_coco.py new file mode 100644 index 0000000000000000000000000000000000000000..fea81f7dd8ad2c27dac8438753b845ab64cef81e --- /dev/null +++ b/iChat/models/grit_src/grit/data/datasets/grit_coco.py @@ -0,0 +1,112 @@ +import logging +import os +from fvcore.common.timer import Timer +from detectron2.structures import BoxMode +from fvcore.common.file_io import PathManager +from detectron2.data import DatasetCatalog, MetadataCatalog +from lvis import LVIS + +logger = logging.getLogger(__name__) + +__all__ = ["load_GRiTcoco_json", "register_GRiTcoco_instances"] + + +def register_GRiTcoco_instances(name, metadata, json_file, image_root): + """ + """ + DatasetCatalog.register(name, lambda: load_GRiTcoco_json( + json_file, image_root, name)) + MetadataCatalog.get(name).set( + json_file=json_file, image_root=image_root, + evaluator_type="coco", **metadata + ) + + +def get_GRiTcoco_meta(): + categories = [{'supercategory': 'object', 'id': 1, 'name': 'object'}] + categories = sorted(categories, key=lambda x: x["id"]) + thing_classes = [k["name"] for k in categories] + meta = {"thing_classes": thing_classes} + return meta + + +def load_GRiTcoco_json(json_file, image_root, dataset_name=None): + ''' + Load COCO class name text for object description for GRiT + ''' + + json_file = PathManager.get_local_path(json_file) + + timer = Timer() + lvis_api = LVIS(json_file) + if timer.seconds() > 1: + logger.info("Loading {} takes {:.2f} seconds.".format( + json_file, timer.seconds())) + + class_names = {} + sort_cat = sorted(lvis_api.dataset['categories'], key=lambda x: x['id']) + for x in sort_cat: + class_names[x['id']] = x['name'] + + img_ids = sorted(lvis_api.imgs.keys()) + imgs = lvis_api.load_imgs(img_ids) + anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids] + + ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] + assert len(set(ann_ids)) == len(ann_ids), \ + "Annotation ids in '{}' are not unique".format(json_file) + + imgs_anns = list(zip(imgs, anns)) + logger.info("Loaded {} images in the LVIS v1 format from {}".format( + len(imgs_anns), json_file)) + + dataset_dicts = [] + + for (img_dict, anno_dict_list) in imgs_anns: + record = {} + if "file_name" in img_dict: + file_name = img_dict["file_name"] + record["file_name"] = os.path.join(image_root, file_name) + + record["height"] = int(img_dict["height"]) + record["width"] = int(img_dict["width"]) + image_id = record["image_id"] = img_dict["id"] + + objs = [] + for anno in anno_dict_list: + assert anno["image_id"] == image_id + if anno.get('iscrowd', 0) > 0: + continue + obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS} + obj["category_id"] = 0 + obj["object_description"] = class_names[anno['category_id']] + if 'segmentation' in anno: + segm = anno["segmentation"] + valid_segm = [poly for poly in segm \ + if len(poly) % 2 == 0 and len(poly) >= 6] + if not len(segm) == len(valid_segm): + print('Annotation contains an invalid polygon with < 3 points') + assert len(segm) > 0 + obj["segmentation"] = segm + objs.append(obj) + record["annotations"] = objs + if len(record["annotations"]) == 0: + continue + record["task"] = "ObjectDet" + dataset_dicts.append(record) + + return dataset_dicts + + +_CUSTOM_SPLITS_LVIS = { + "GRiT_coco2017_train": ("coco/train2017/", "coco/annotations/instances_train2017.json"), +} + + +for key, (image_root, json_file) in _CUSTOM_SPLITS_LVIS.items(): + register_GRiTcoco_instances( + key, + get_GRiTcoco_meta(), + os.path.join("datasets", json_file) if "://" not in json_file else json_file, + os.path.join("datasets", image_root), + ) \ No newline at end of file diff --git a/iChat/models/grit_src/grit/data/datasets/object365.py b/iChat/models/grit_src/grit/data/datasets/object365.py new file mode 100644 index 0000000000000000000000000000000000000000..8b8cc19da23d8397284b50588ee46e750b5b7552 --- /dev/null +++ b/iChat/models/grit_src/grit/data/datasets/object365.py @@ -0,0 +1,111 @@ +import logging +import os +from fvcore.common.timer import Timer +from detectron2.structures import BoxMode +from fvcore.common.file_io import PathManager +from detectron2.data import DatasetCatalog, MetadataCatalog +from lvis import LVIS + +logger = logging.getLogger(__name__) + +__all__ = ["load_o365_json", "register_o365_instances"] + + +def register_o365_instances(name, metadata, json_file, image_root): + DatasetCatalog.register(name, lambda: load_o365_json( + json_file, image_root, name)) + MetadataCatalog.get(name).set( + json_file=json_file, image_root=image_root, + evaluator_type="lvis", **metadata + ) + + +def get_o365_meta(): + categories = [{'supercategory': 'object', 'id': 1, 'name': 'object'}] + o365_categories = sorted(categories, key=lambda x: x["id"]) + thing_classes = [k["name"] for k in o365_categories] + meta = {"thing_classes": thing_classes} + return meta + + +def load_o365_json(json_file, image_root, dataset_name=None): + ''' + Load Object365 class name text for object description for GRiT + ''' + + json_file = PathManager.get_local_path(json_file) + + timer = Timer() + lvis_api = LVIS(json_file) + if timer.seconds() > 1: + logger.info("Loading {} takes {:.2f} seconds.".format( + json_file, timer.seconds())) + + class_names = {} + sort_cat = sorted(lvis_api.dataset['categories'], key=lambda x: x['id']) + for x in sort_cat: + if '/' in x['name']: + text = '' + for xx in x['name'].split('/'): + text += xx + text += ' ' + text = text[:-1] + else: + text = x['name'] + class_names[x['id']] = text + + img_ids = sorted(lvis_api.imgs.keys()) + imgs = lvis_api.load_imgs(img_ids) + anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids] + + ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] + assert len(set(ann_ids)) == len(ann_ids), \ + "Annotation ids in '{}' are not unique".format(json_file) + + imgs_anns = list(zip(imgs, anns)) + logger.info("Loaded {} images in the LVIS v1 format from {}".format( + len(imgs_anns), json_file)) + + dataset_dicts = [] + + for (img_dict, anno_dict_list) in imgs_anns: + record = {} + if "file_name" in img_dict: + file_name = img_dict["file_name"] + record["file_name"] = os.path.join(image_root, file_name) + + record["height"] = int(img_dict["height"]) + record["width"] = int(img_dict["width"]) + image_id = record["image_id"] = img_dict["id"] + + objs = [] + for anno in anno_dict_list: + assert anno["image_id"] == image_id + if anno.get('iscrowd', 0) > 0: + continue + obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS} + obj["category_id"] = 0 + obj["object_description"] = class_names[anno['category_id']] + + objs.append(obj) + record["annotations"] = objs + if len(record["annotations"]) == 0: + continue + record["task"] = "ObjectDet" + dataset_dicts.append(record) + + return dataset_dicts + + +_CUSTOM_SPLITS_LVIS = { + "object365_train": ("object365/images/train/", "object365/annotations/train_v1.json"), +} + + +for key, (image_root, json_file) in _CUSTOM_SPLITS_LVIS.items(): + register_o365_instances( + key, + get_o365_meta(), + os.path.join("datasets", json_file) if "://" not in json_file else json_file, + os.path.join("datasets", image_root), + ) \ No newline at end of file diff --git a/iChat/models/grit_src/grit/data/datasets/vg.py b/iChat/models/grit_src/grit/data/datasets/vg.py new file mode 100644 index 0000000000000000000000000000000000000000..4d47a80d9f88b89ca3064dbc4945b0246162e5d1 --- /dev/null +++ b/iChat/models/grit_src/grit/data/datasets/vg.py @@ -0,0 +1,98 @@ +import logging +import os +from fvcore.common.timer import Timer +from detectron2.structures import BoxMode +from fvcore.common.file_io import PathManager +from detectron2.data import DatasetCatalog, MetadataCatalog +from lvis import LVIS + +logger = logging.getLogger(__name__) + +__all__ = ["load_vg_json", "register_vg_instances"] + + +def register_vg_instances(name, metadata, json_file, image_root): + """ + """ + DatasetCatalog.register(name, lambda: load_vg_json( + json_file, image_root, name)) + MetadataCatalog.get(name).set( + json_file=json_file, image_root=image_root, + evaluator_type="vg", **metadata + ) + + +def get_vg_meta(): + categories = [{'supercategory': 'object', 'id': 1, 'name': 'object'}] + vg_categories = sorted(categories, key=lambda x: x["id"]) + thing_classes = [k["name"] for k in vg_categories] + meta = {"thing_classes": thing_classes} + return meta + + +def load_vg_json(json_file, image_root, dataset_name=None): + + json_file = PathManager.get_local_path(json_file) + + timer = Timer() + lvis_api = LVIS(json_file) + if timer.seconds() > 1: + logger.info("Loading {} takes {:.2f} seconds.".format( + json_file, timer.seconds())) + + img_ids = sorted(lvis_api.imgs.keys()) + imgs = lvis_api.load_imgs(img_ids) + anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids] + + ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] + assert len(set(ann_ids)) == len(ann_ids), \ + "Annotation ids in '{}' are not unique".format(json_file) + + imgs_anns = list(zip(imgs, anns)) + logger.info("Loaded {} images in the LVIS v1 format from {}".format( + len(imgs_anns), json_file)) + + dataset_dicts = [] + + for (img_dict, anno_dict_list) in imgs_anns: + record = {} + if "file_name" in img_dict: + file_name = img_dict["file_name"] + record["file_name"] = os.path.join(image_root, file_name) + + record["height"] = int(img_dict["height"]) + record["width"] = int(img_dict["width"]) + image_id = record["image_id"] = img_dict["id"] + + objs = [] + for anno in anno_dict_list: + assert anno["image_id"] == image_id + if anno.get('iscrowd', 0) > 0: + continue + obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS} + obj["category_id"] = 0 + obj["object_description"] = anno["caption"] + + objs.append(obj) + record["annotations"] = objs + if len(record["annotations"]) == 0: + continue + record["task"] = "DenseCap" + dataset_dicts.append(record) + + return dataset_dicts + + +_CUSTOM_SPLITS_LVIS = { + "vg_train": ("vg/images", "vg/annotations/train.json"), + "vg_test": ("vg/images", "vg/annotations/test.json"), +} + + +for key, (image_root, json_file) in _CUSTOM_SPLITS_LVIS.items(): + register_vg_instances( + key, + get_vg_meta(), + os.path.join("datasets", json_file) if "://" not in json_file else json_file, + os.path.join("datasets", image_root), + ) \ No newline at end of file diff --git a/iChat/models/grit_src/grit/data/transforms/custom_augmentation_impl.py b/iChat/models/grit_src/grit/data/transforms/custom_augmentation_impl.py new file mode 100644 index 0000000000000000000000000000000000000000..6b9637f3ad41e3ba513636219e49371296d9ab9f --- /dev/null +++ b/iChat/models/grit_src/grit/data/transforms/custom_augmentation_impl.py @@ -0,0 +1,52 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# Part of the code is from https://github.com/rwightman/efficientdet-pytorch/blob/master/effdet/data/transforms.py +# Modified by Xingyi Zhou +# The original code is under Apache-2.0 License +import numpy as np +from PIL import Image + +from detectron2.data.transforms.augmentation import Augmentation +from .custom_transform import EfficientDetResizeCropTransform + +__all__ = [ + "EfficientDetResizeCrop", +] + + +class EfficientDetResizeCrop(Augmentation): + """ + Scale the shorter edge to the given size, with a limit of `max_size` on the longer edge. + If `max_size` is reached, then downscale so that the longer edge does not exceed max_size. + """ + + def __init__( + self, size, scale, interp=Image.BILINEAR + ): + """ + """ + super().__init__() + self.target_size = (size, size) + self.scale = scale + self.interp = interp + + def get_transform(self, img): + # Select a random scale factor. + scale_factor = np.random.uniform(*self.scale) + scaled_target_height = scale_factor * self.target_size[0] + scaled_target_width = scale_factor * self.target_size[1] + # Recompute the accurate scale_factor using rounded scaled image size. + width, height = img.shape[1], img.shape[0] + img_scale_y = scaled_target_height / height + img_scale_x = scaled_target_width / width + img_scale = min(img_scale_y, img_scale_x) + + # Select non-zero random offset (x, y) if scaled image is larger than target size + scaled_h = int(height * img_scale) + scaled_w = int(width * img_scale) + offset_y = scaled_h - self.target_size[0] + offset_x = scaled_w - self.target_size[1] + offset_y = int(max(0.0, float(offset_y)) * np.random.uniform(0, 1)) + offset_x = int(max(0.0, float(offset_x)) * np.random.uniform(0, 1)) + return EfficientDetResizeCropTransform( + scaled_h, scaled_w, offset_y, offset_x, img_scale, self.target_size, self.interp) diff --git a/iChat/models/grit_src/grit/data/transforms/custom_transform.py b/iChat/models/grit_src/grit/data/transforms/custom_transform.py new file mode 100644 index 0000000000000000000000000000000000000000..423063a4ea14fe92caaed7efc69d8596a597485e --- /dev/null +++ b/iChat/models/grit_src/grit/data/transforms/custom_transform.py @@ -0,0 +1,115 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# Part of the code is from https://github.com/rwightman/efficientdet-pytorch/blob/master/effdet/data/transforms.py +# Modified by Xingyi Zhou +# The original code is under Apache-2.0 License +import numpy as np +import torch +import torch.nn.functional as F +from fvcore.transforms.transform import ( + CropTransform, + HFlipTransform, + NoOpTransform, + Transform, + TransformList, +) +from PIL import Image + +try: + import cv2 # noqa +except ImportError: + # OpenCV is an optional dependency at the moment + pass + +__all__ = [ + "EfficientDetResizeCropTransform", +] + + +class EfficientDetResizeCropTransform(Transform): + """ + """ + + def __init__(self, scaled_h, scaled_w, offset_y, offset_x, img_scale, \ + target_size, interp=None): + """ + Args: + h, w (int): original image size + new_h, new_w (int): new image size + interp: PIL interpolation methods, defaults to bilinear. + """ + # TODO decide on PIL vs opencv + super().__init__() + if interp is None: + interp = Image.BILINEAR + self._set_attributes(locals()) + + def apply_image(self, img, interp=None): + assert len(img.shape) <= 4 + + if img.dtype == np.uint8: + pil_image = Image.fromarray(img) + interp_method = interp if interp is not None else self.interp + pil_image = pil_image.resize((self.scaled_w, self.scaled_h), interp_method) + ret = np.asarray(pil_image) + right = min(self.scaled_w, self.offset_x + self.target_size[1]) + lower = min(self.scaled_h, self.offset_y + self.target_size[0]) + if len(ret.shape) <= 3: + ret = ret[self.offset_y: lower, self.offset_x: right] + else: + ret = ret[..., self.offset_y: lower, self.offset_x: right, :] + else: + # PIL only supports uint8 + img = torch.from_numpy(img) + shape = list(img.shape) + shape_4d = shape[:2] + [1] * (4 - len(shape)) + shape[2:] + img = img.view(shape_4d).permute(2, 3, 0, 1) # hw(c) -> nchw + _PIL_RESIZE_TO_INTERPOLATE_MODE = {Image.BILINEAR: "bilinear", Image.BICUBIC: "bicubic"} + mode = _PIL_RESIZE_TO_INTERPOLATE_MODE[self.interp] + img = F.interpolate(img, (self.scaled_h, self.scaled_w), mode=mode, align_corners=False) + shape[:2] = (self.scaled_h, self.scaled_w) + ret = img.permute(2, 3, 0, 1).view(shape).numpy() # nchw -> hw(c) + right = min(self.scaled_w, self.offset_x + self.target_size[1]) + lower = min(self.scaled_h, self.offset_y + self.target_size[0]) + if len(ret.shape) <= 3: + ret = ret[self.offset_y: lower, self.offset_x: right] + else: + ret = ret[..., self.offset_y: lower, self.offset_x: right, :] + return ret + + + def apply_coords(self, coords): + coords[:, 0] = coords[:, 0] * self.img_scale + coords[:, 1] = coords[:, 1] * self.img_scale + coords[:, 0] -= self.offset_x + coords[:, 1] -= self.offset_y + return coords + + + def apply_segmentation(self, segmentation): + segmentation = self.apply_image(segmentation, interp=Image.NEAREST) + return segmentation + + + def inverse(self): + raise NotImplementedError + + + def inverse_apply_coords(self, coords): + coords[:, 0] += self.offset_x + coords[:, 1] += self.offset_y + coords[:, 0] = coords[:, 0] / self.img_scale + coords[:, 1] = coords[:, 1] / self.img_scale + return coords + + + def inverse_apply_box(self, box: np.ndarray) -> np.ndarray: + """ + """ + idxs = np.array([(0, 1), (2, 1), (0, 3), (2, 3)]).flatten() + coords = np.asarray(box).reshape(-1, 4)[:, idxs].reshape(-1, 2) + coords = self.inverse_apply_coords(coords).reshape((-1, 4, 2)) + minxy = coords.min(axis=1) + maxxy = coords.max(axis=1) + trans_boxes = np.concatenate((minxy, maxxy), axis=1) + return trans_boxes \ No newline at end of file diff --git a/iChat/models/grit_src/grit/evaluation/eval.py b/iChat/models/grit_src/grit/evaluation/eval.py new file mode 100644 index 0000000000000000000000000000000000000000..951a0920ec3d93703245562d4f76ec597e672ad9 --- /dev/null +++ b/iChat/models/grit_src/grit/evaluation/eval.py @@ -0,0 +1,156 @@ +import itertools +import json +import os +from detectron2.structures import Boxes, BoxMode, pairwise_iou +from detectron2.utils.file_io import PathManager +import numpy as np +import pycocotools.mask as mask_util +from detectron2.evaluation.coco_evaluation import COCOEvaluator +from detectron2.evaluation.coco_evaluation import _evaluate_predictions_on_coco + + +class GRiTCOCOEvaluator(COCOEvaluator): + def process(self, inputs, outputs): + for input, output in zip(inputs, outputs): + prediction = {"image_id": input["image_id"]} + + if "instances" in output: + instances = output["instances"].to(self._cpu_device) + prediction["instances"] = instances_to_coco_json(instances, input["image_id"]) + + if len(prediction) > 1: + self._predictions.append(prediction) + + def _eval_predictions(self, predictions, img_ids=None): + self._logger.info("Preparing results for COCO format ...") + coco_results = list(itertools.chain(*[x["instances"] for x in predictions])) + tasks = self._tasks or self._tasks_from_predictions(coco_results) + + if self._output_dir: + file_path = os.path.join(self._output_dir, "coco_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(coco_results)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info( + "Evaluating predictions with {} COCO API...".format( + "unofficial" if self._use_fast_impl else "official" + ) + ) + + coco_results = self.convert_classname_to_id(coco_results) + + for task in sorted(tasks): + assert task in {"bbox", "segm", "keypoints"}, f"Got unknown task: {task}!" + coco_eval = ( + _evaluate_predictions_on_coco( + self._coco_api, + coco_results, + task, + kpt_oks_sigmas=self._kpt_oks_sigmas, + use_fast_impl=self._use_fast_impl, + img_ids=img_ids, + max_dets_per_image=self._max_dets_per_image, + ) + if len(coco_results) > 0 + else None # cocoapi does not handle empty results very well + ) + + res = self._derive_coco_results( + coco_eval, task, class_names=self._metadata.get("thing_classes") + ) + self._results[task] = res + + def convert_classname_to_id(self, results): + outputs = [] + class_name_to_id = {} + categories = sorted(self._coco_api.dataset['categories'], key=lambda x: x['id']) + + for cat in categories: + class_name_to_id[cat['name']] = cat['id'] + + for pred in results: + if pred['object_descriptions'] in class_name_to_id: + pred['category_id'] = class_name_to_id[pred['object_descriptions']] + del pred['object_descriptions'] + outputs.append(pred) + + return outputs + + +class GRiTVGEvaluator(COCOEvaluator): + def process(self, inputs, outputs): + for input, output in zip(inputs, outputs): + assert input["image_id"] == int(input['file_name'].split('/')[-1].split('.')[0]) + prediction = {"image_id": input["image_id"]} + + if "instances" in output: + instances = output["instances"].to(self._cpu_device) + prediction["instances"] = instances_to_coco_json(instances, input["image_id"], output_logits=True) + h = input['height'] + w = input['width'] + scale = 720.0 / max(h, w) + scaled_inst = [] + for inst in prediction["instances"]: + inst['bbox'][0] = inst['bbox'][0] * scale + inst['bbox'][1] = inst['bbox'][1] * scale + inst['bbox'][2] = inst['bbox'][2] * scale + inst['bbox'][3] = inst['bbox'][3] * scale + scaled_inst.append(inst) + if len(scaled_inst) > 0: + prediction["instances"] = scaled_inst + if len(prediction) > 1: + self._predictions.append(prediction) + + def _eval_predictions(self, predictions, img_ids=None): + ''' + This is only for saving the results to json file + ''' + self._logger.info("Preparing results for COCO format ...") + coco_results = list(itertools.chain(*[x["instances"] for x in predictions])) + + if self._output_dir: + file_path = os.path.join(self._output_dir, "vg_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(coco_results)) + f.flush() + + +def instances_to_coco_json(instances, img_id, output_logits=False): + """ + Add object_descriptions and logit (if applicable) to + detectron2's instances_to_coco_json + """ + num_instance = len(instances) + if num_instance == 0: + return [] + + boxes = instances.pred_boxes.tensor.numpy() + boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) + boxes = boxes.tolist() + scores = instances.scores.tolist() + classes = instances.pred_classes.tolist() + object_descriptions = instances.pred_object_descriptions.data + if output_logits: + logits = instances.logits.tolist() + + results = [] + for k in range(num_instance): + result = { + "image_id": img_id, + "category_id": classes[k], + "bbox": boxes[k], + "score": scores[k], + 'object_descriptions': object_descriptions[k], + } + if output_logits: + result["logit"] = logits[k] + + results.append(result) + return results \ No newline at end of file diff --git a/iChat/models/grit_src/grit/modeling/backbone/utils.py b/iChat/models/grit_src/grit/modeling/backbone/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e71db21f1223c87cceeb422a70888f7bac42bb18 --- /dev/null +++ b/iChat/models/grit_src/grit/modeling/backbone/utils.py @@ -0,0 +1,186 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# This code is from https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/utils.py +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + +__all__ = [ + "window_partition", + "window_unpartition", + "add_decomposed_rel_pos", + "get_abs_pos", + "PatchEmbed", +] + +def window_partition(x, window_size): + """ + Partition into non-overlapping windows with padding if needed. + Args: + x (tensor): input tokens with [B, H, W, C]. + window_size (int): window size. + + Returns: + windows: windows after partition with [B * num_windows, window_size, window_size, C]. + (Hp, Wp): padded height and width before partition + """ + B, H, W, C = x.shape + + pad_h = (window_size - H % window_size) % window_size + pad_w = (window_size - W % window_size) % window_size + if pad_h > 0 or pad_w > 0: + x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h)) + Hp, Wp = H + pad_h, W + pad_w + + x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows, (Hp, Wp) + + +def window_unpartition(windows, window_size, pad_hw, hw): + """ + Window unpartition into original sequences and removing padding. + Args: + x (tensor): input tokens with [B * num_windows, window_size, window_size, C]. + window_size (int): window size. + pad_hw (Tuple): padded height and width (Hp, Wp). + hw (Tuple): original height and width (H, W) before padding. + + Returns: + x: unpartitioned sequences with [B, H, W, C]. + """ + Hp, Wp = pad_hw + H, W = hw + B = windows.shape[0] // (Hp * Wp // window_size // window_size) + x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1) + + if Hp > H or Wp > W: + x = x[:, :H, :W, :].contiguous() + return x + + +def get_rel_pos(q_size, k_size, rel_pos): + """ + Get relative positional embeddings according to the relative positions of + query and key sizes. + Args: + q_size (int): size of query q. + k_size (int): size of key k. + rel_pos (Tensor): relative position embeddings (L, C). + + Returns: + Extracted positional embeddings according to relative positions. + """ + max_rel_dist = int(2 * max(q_size, k_size) - 1) + # Interpolate rel pos if needed. + if rel_pos.shape[0] != max_rel_dist: + # Interpolate rel pos. + rel_pos_resized = F.interpolate( + rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), + size=max_rel_dist, + mode="linear", + ) + rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0) + else: + rel_pos_resized = rel_pos + + # Scale the coords with short length if shapes for q and k are different. + q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0) + k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0) + relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0) + + return rel_pos_resized[relative_coords.long()] + + +def add_decomposed_rel_pos(attn, q, rel_pos_h, rel_pos_w, q_size, k_size): + """ + Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. + https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950 + Args: + attn (Tensor): attention map. + q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C). + rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis. + rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis. + q_size (Tuple): spatial sequence size of query q with (q_h, q_w). + k_size (Tuple): spatial sequence size of key k with (k_h, k_w). + + Returns: + attn (Tensor): attention map with added relative positional embeddings. + """ + q_h, q_w = q_size + k_h, k_w = k_size + Rh = get_rel_pos(q_h, k_h, rel_pos_h) + Rw = get_rel_pos(q_w, k_w, rel_pos_w) + + B, _, dim = q.shape + r_q = q.reshape(B, q_h, q_w, dim) + rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh) + rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw) + + attn = ( + attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :] + ).view(B, q_h * q_w, k_h * k_w) + + return attn + + +def get_abs_pos(abs_pos, has_cls_token, hw): + """ + Calculate absolute positional embeddings. If needed, resize embeddings and remove cls_token + dimension for the original embeddings. + Args: + abs_pos (Tensor): absolute positional embeddings with (1, num_position, C). + has_cls_token (bool): If true, has 1 embedding in abs_pos for cls token. + hw (Tuple): size of input image tokens. + + Returns: + Absolute positional embeddings after processing with shape (1, H, W, C) + """ + h, w = hw + if has_cls_token: + abs_pos = abs_pos[:, 1:] + xy_num = abs_pos.shape[1] + size = int(math.sqrt(xy_num)) + assert size * size == xy_num + + if size != h or size != w: + new_abs_pos = F.interpolate( + abs_pos.reshape(1, size, size, -1).permute(0, 3, 1, 2), + size=(h, w), + mode="bicubic", + align_corners=False, + ) + + return new_abs_pos.permute(0, 2, 3, 1) + else: + return abs_pos.reshape(1, h, w, -1) + + +class PatchEmbed(nn.Module): + """ + Image to Patch Embedding. + """ + + def __init__( + self, kernel_size=(16, 16), stride=(16, 16), padding=(0, 0), in_chans=3, embed_dim=768 + ): + """ + Args: + kernel_size (Tuple): kernel size of the projection layer. + stride (Tuple): stride of the projection layer. + padding (Tuple): padding size of the projection layer. + in_chans (int): Number of input image channels. + embed_dim (int): embed_dim (int): Patch embedding dimension. + """ + super().__init__() + + self.proj = nn.Conv2d( + in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding + ) + + def forward(self, x): + x = self.proj(x) + # B C H W -> B H W C + x = x.permute(0, 2, 3, 1) + return x diff --git a/iChat/models/grit_src/grit/modeling/backbone/vit.py b/iChat/models/grit_src/grit/modeling/backbone/vit.py new file mode 100644 index 0000000000000000000000000000000000000000..36d1207f1b5a59030b77984e424a4b1e8b3c761a --- /dev/null +++ b/iChat/models/grit_src/grit/modeling/backbone/vit.py @@ -0,0 +1,538 @@ +# Modified by Jialian Wu from https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py +import logging +import math +import fvcore.nn.weight_init as weight_init +import torch +import torch.nn as nn +from functools import partial + +from detectron2.layers import CNNBlockBase, Conv2d, get_norm +from detectron2.modeling.backbone.build import BACKBONE_REGISTRY +from detectron2.layers import ShapeSpec +from centernet.modeling.backbone.fpn_p5 import LastLevelP6P7_P5 + +import torch.utils.checkpoint as checkpoint +from timm.models.layers import DropPath, Mlp, trunc_normal_ + +from detectron2.modeling.backbone.backbone import Backbone +from .utils import ( + PatchEmbed, + add_decomposed_rel_pos, + get_abs_pos, + window_partition, + window_unpartition, +) + +logger = logging.getLogger(__name__) + + +__all__ = ["ViT"] + + +class Attention(nn.Module): + """Multi-head Attention block with relative position embeddings.""" + + def __init__( + self, + dim, + num_heads=8, + qkv_bias=True, + use_rel_pos=False, + rel_pos_zero_init=True, + input_size=None, + ): + """ + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + qkv_bias (bool: If True, add a learnable bias to query, key, value. + rel_pos (bool): If True, add relative positional embeddings to the attention map. + rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. + input_size (int or None): Input resolution for calculating the relative positional + parameter size. + """ + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim**-0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.proj = nn.Linear(dim, dim) + + self.use_rel_pos = use_rel_pos + if self.use_rel_pos: + # initialize relative positional embeddings + self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim)) + self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim)) + + if not rel_pos_zero_init: + trunc_normal_(self.rel_pos_h, std=0.02) + trunc_normal_(self.rel_pos_w, std=0.02) + + def forward(self, x): + B, H, W, _ = x.shape + # qkv with shape (3, B, nHead, H * W, C) + qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + # q, k, v with shape (B * nHead, H * W, C) + q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0) + + attn = (q * self.scale) @ k.transpose(-2, -1) + + if self.use_rel_pos: + attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W)) + + attn = attn.softmax(dim=-1) + x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1) + x = self.proj(x) + + return x + + +class ResBottleneckBlock(CNNBlockBase): + """ + The standard bottleneck residual block without the last activation layer. + It contains 3 conv layers with kernels 1x1, 3x3, 1x1. + """ + + def __init__( + self, + in_channels, + out_channels, + bottleneck_channels, + norm="LN", + act_layer=nn.GELU, + ): + """ + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + bottleneck_channels (int): number of output channels for the 3x3 + "bottleneck" conv layers. + norm (str or callable): normalization for all conv layers. + See :func:`layers.get_norm` for supported format. + act_layer (callable): activation for all conv layers. + """ + super().__init__(in_channels, out_channels, 1) + + self.conv1 = Conv2d(in_channels, bottleneck_channels, 1, bias=False) + self.norm1 = get_norm(norm, bottleneck_channels) + self.act1 = act_layer() + + self.conv2 = Conv2d( + bottleneck_channels, + bottleneck_channels, + 3, + padding=1, + bias=False, + ) + self.norm2 = get_norm(norm, bottleneck_channels) + self.act2 = act_layer() + + self.conv3 = Conv2d(bottleneck_channels, out_channels, 1, bias=False) + self.norm3 = get_norm(norm, out_channels) + + for layer in [self.conv1, self.conv2, self.conv3]: + weight_init.c2_msra_fill(layer) + for layer in [self.norm1, self.norm2]: + layer.weight.data.fill_(1.0) + layer.bias.data.zero_() + # zero init last norm layer. + self.norm3.weight.data.zero_() + self.norm3.bias.data.zero_() + + def forward(self, x): + out = x + for layer in self.children(): + out = layer(out) + + out = x + out + return out + + +class Block(nn.Module): + """Transformer blocks with support of window attention and residual propagation blocks""" + + def __init__( + self, + dim, + num_heads, + mlp_ratio=4.0, + qkv_bias=True, + drop_path=0.0, + norm_layer=nn.LayerNorm, + act_layer=nn.GELU, + use_rel_pos=False, + rel_pos_zero_init=True, + window_size=0, + use_residual_block=False, + input_size=None, + ): + """ + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads in each ViT block. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool): If True, add a learnable bias to query, key, value. + drop_path (float): Stochastic depth rate. + norm_layer (nn.Module): Normalization layer. + act_layer (nn.Module): Activation layer. + use_rel_pos (bool): If True, add relative positional embeddings to the attention map. + rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. + window_size (int): Window size for window attention blocks. If it equals 0, then not + use window attention. + use_residual_block (bool): If True, use a residual block after the MLP block. + input_size (int or None): Input resolution for calculating the relative positional + parameter size. + """ + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + use_rel_pos=use_rel_pos, + rel_pos_zero_init=rel_pos_zero_init, + input_size=input_size if window_size == 0 else (window_size, window_size), + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer) + + self.window_size = window_size + + self.use_residual_block = use_residual_block + if use_residual_block: + # Use a residual block with bottleneck channel as dim // 2 + self.residual = ResBottleneckBlock( + in_channels=dim, + out_channels=dim, + bottleneck_channels=dim // 2, + norm="LN", + act_layer=act_layer, + ) + + def forward(self, x): + shortcut = x + x = self.norm1(x) + # Window partition + if self.window_size > 0: + H, W = x.shape[1], x.shape[2] + x, pad_hw = window_partition(x, self.window_size) + + x = self.attn(x) + # Reverse window partition + if self.window_size > 0: + x = window_unpartition(x, self.window_size, pad_hw, (H, W)) + + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + if self.use_residual_block: + x = self.residual(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1) + + return x + + +class ViT(Backbone): + """ + This module implements Vision Transformer (ViT) backbone in :paper:`vitdet`. + "Exploring Plain Vision Transformer Backbones for Object Detection", + https://arxiv.org/abs/2203.16527 + """ + + def __init__( + self, + img_size=1024, + patch_size=16, + in_chans=3, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4.0, + qkv_bias=True, + drop_path_rate=0.0, + norm_layer=nn.LayerNorm, + act_layer=nn.GELU, + use_abs_pos=True, + use_rel_pos=False, + rel_pos_zero_init=True, + window_size=0, + window_block_indexes=(), + residual_block_indexes=(), + use_act_checkpoint=True, + pretrain_img_size=224, + pretrain_use_cls_token=True, + out_feature="last_feat", + ): + """ + Args: + img_size (int): Input image size. + patch_size (int): Patch size. + in_chans (int): Number of input image channels. + embed_dim (int): Patch embedding dimension. + depth (int): Depth of ViT. + num_heads (int): Number of attention heads in each ViT block. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool): If True, add a learnable bias to query, key, value. + drop_path_rate (float): Stochastic depth rate. + norm_layer (nn.Module): Normalization layer. + act_layer (nn.Module): Activation layer. + use_abs_pos (bool): If True, use absolute positional embeddings. + use_rel_pos (bool): If True, add relative positional embeddings to the attention map. + rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. + window_size (int): Window size for window attention blocks. + window_block_indexes (list): Indexes for blocks using window attention. + residual_block_indexes (list): Indexes for blocks using conv propagation. + use_act_checkpoint (bool): If True, use activation checkpointing. + pretrain_img_size (int): input image size for pretraining models. + pretrain_use_cls_token (bool): If True, pretrainig models use class token. + out_feature (str): name of the feature from the last block. + """ + super().__init__() + self.pretrain_use_cls_token = pretrain_use_cls_token + self.use_act_checkpoint = use_act_checkpoint + + self.patch_embed = PatchEmbed( + kernel_size=(patch_size, patch_size), + stride=(patch_size, patch_size), + in_chans=in_chans, + embed_dim=embed_dim, + ) + + if use_abs_pos: + # Initialize absolute positional embedding with pretrain image size. + num_patches = (pretrain_img_size // patch_size) * (pretrain_img_size // patch_size) + num_positions = (num_patches + 1) if pretrain_use_cls_token else num_patches + self.pos_embed = nn.Parameter(torch.zeros(1, num_positions, embed_dim)) + else: + self.pos_embed = None + + # stochastic depth decay rule + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] + + self.blocks = nn.ModuleList() + for i in range(depth): + block = Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop_path=dpr[i], + norm_layer=norm_layer, + act_layer=act_layer, + use_rel_pos=use_rel_pos, + rel_pos_zero_init=rel_pos_zero_init, + window_size=window_size if i in window_block_indexes else 0, + use_residual_block=i in residual_block_indexes, + input_size=(img_size // patch_size, img_size // patch_size), + ) + self.blocks.append(block) + + self._out_feature_channels = {out_feature: embed_dim} + self._out_feature_strides = {out_feature: patch_size} + self._out_features = [out_feature] + + if self.pos_embed is not None: + trunc_normal_(self.pos_embed, std=0.02) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def forward(self, x): + x = self.patch_embed(x) + if self.pos_embed is not None: + x = x + get_abs_pos( + self.pos_embed, self.pretrain_use_cls_token, (x.shape[1], x.shape[2]) + ) + + for blk in self.blocks: + if self.use_act_checkpoint: + x = checkpoint.checkpoint(blk, x) + else: + x = blk(x) + + return x.permute(0, 3, 1, 2) + + +class ViT_FPN(Backbone): + def __init__(self, bottom_up=None, top_block=None, out_channels=None, strides=None, vit_out_dim=None): + super(ViT_FPN, self).__init__() + assert isinstance(bottom_up, Backbone) + self.bottom_up = bottom_up + self.top_block = top_block + + self._out_feature_strides = {"p{}".format(int(math.log2(s))): s for s in strides} + self._out_features = list(self._out_feature_strides.keys()) + self._out_feature_channels = {k: out_channels for k in self._out_features} + self._size_divisibility = strides[2] + + self.maxpool = nn.MaxPool2d(2, stride=2) + self.fpn_stride_16_8 = nn.ConvTranspose2d(vit_out_dim, vit_out_dim, 2, stride=2, bias=False) + self.fpn_stride8_conv1 = nn.Conv2d(in_channels=vit_out_dim, out_channels=out_channels, kernel_size=1, bias=False) + self.fpn_stride8_norm1 = nn.LayerNorm(out_channels) + self.fpn_stride8_conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1, bias=False) + self.fpn_stride8_norm2 = nn.LayerNorm(out_channels) + + self.fpn_stride16_conv1 = nn.Conv2d(in_channels=vit_out_dim, out_channels=out_channels, kernel_size=1, bias=False) + self.fpn_stride16_norm1 = nn.LayerNorm(out_channels) + self.fpn_stride16_conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1, bias=False) + self.fpn_stride16_norm2 = nn.LayerNorm(out_channels) + + self.fpn_stride32_conv1 = nn.Conv2d(in_channels=vit_out_dim, out_channels=out_channels, kernel_size=1, bias=False) + self.fpn_stride32_norm1 = nn.LayerNorm(out_channels) + self.fpn_stride32_conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1, bias=False) + self.fpn_stride32_norm2 = nn.LayerNorm(out_channels) + + def forward(self, x): + vit_output_featuremap = self.bottom_up(x) + + stride8_feature = self.fpn_stride_16_8(vit_output_featuremap) + stride8_feature = self.fpn_stride8_norm1(self.fpn_stride8_conv1(stride8_feature) + .permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + stride8_feature = self.fpn_stride8_norm2(self.fpn_stride8_conv2(stride8_feature) + .permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + + stride32_feature = self.maxpool(vit_output_featuremap) + stride32_feature = self.fpn_stride32_norm1(self.fpn_stride32_conv1(stride32_feature) + .permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + stride32_feature = self.fpn_stride32_norm2(self.fpn_stride32_conv2(stride32_feature) + .permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + + stride16_feature = self.fpn_stride16_norm1(self.fpn_stride16_conv1(vit_output_featuremap). + permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + stride16_feature = self.fpn_stride16_norm2(self.fpn_stride16_conv2(stride16_feature) + .permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + + results = [stride8_feature, stride16_feature, stride32_feature] + + results.extend(self.top_block(stride32_feature)) + + assert len(self._out_features) == len(results) + fpn_out = {f: res for f, res in zip(self._out_features, results)} + + return fpn_out + @property + def size_divisibility(self): + return self._size_divisibility + + def output_shape(self): + return { + name: ShapeSpec( + channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] + ) + for name in self._out_features + } + + +@BACKBONE_REGISTRY.register() +def build_vit_fpn_backbone(cfg, input_shape: ShapeSpec): + embed_dim = 768 + vit_out_dim = embed_dim + bottom_up = ViT( # Single-scale ViT backbone + img_size=1024, + patch_size=16, + embed_dim=embed_dim, + depth=12, + num_heads=12, + drop_path_rate=0.1, + window_size=14, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=[ + # 2, 5, 8 11 for global attention + 0, + 1, + 3, + 4, + 6, + 7, + 9, + 10, + ], + residual_block_indexes=[], + use_act_checkpoint=cfg.USE_ACT_CHECKPOINT, + use_rel_pos=True, + out_feature="last_feat",) + + out_channels = cfg.MODEL.FPN.OUT_CHANNELS + assert out_channels == 256 or out_channels == 768 or out_channels == 1024 + backbone = ViT_FPN(bottom_up=bottom_up, + top_block=LastLevelP6P7_P5(out_channels, out_channels), + out_channels=out_channels, + strides=[8, 16, 32, 64, 128], + vit_out_dim=vit_out_dim) + return backbone + + +@BACKBONE_REGISTRY.register() +def build_vit_fpn_backbone_large(cfg, input_shape: ShapeSpec): + window_block_indexes = (list(range(0, 5)) + list(range(6, 11)) + list(range(12, 17)) + list(range(18, 23))) + embed_dim = 1024 + vit_out_dim = embed_dim + bottom_up = ViT( # Single-scale ViT backbone + img_size=1024, + patch_size=16, + embed_dim=embed_dim, + depth=24, + num_heads=16, + drop_path_rate=0.4, + window_size=14, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=window_block_indexes, + residual_block_indexes=[], + use_act_checkpoint=cfg.USE_ACT_CHECKPOINT, + use_rel_pos=True, + out_feature="last_feat",) + + out_channels = cfg.MODEL.FPN.OUT_CHANNELS + assert out_channels == 256 or out_channels == 768 or out_channels == 1024 + backbone = ViT_FPN(bottom_up=bottom_up, + top_block=LastLevelP6P7_P5(out_channels, out_channels), + out_channels=out_channels, + strides=[8, 16, 32, 64, 128], + vit_out_dim=vit_out_dim) + return backbone + + +@BACKBONE_REGISTRY.register() +def build_vit_fpn_backbone_huge(cfg, input_shape: ShapeSpec): + window_block_indexes = (list(range(0, 7)) + list(range(8, 15)) + list(range(16, 23)) + list(range(24, 31))) + embed_dim = 1280 + vit_out_dim = embed_dim + bottom_up = ViT( # Single-scale ViT backbone + img_size=1024, + patch_size=16, + embed_dim=embed_dim, + depth=32, + num_heads=16, + drop_path_rate=0.5, + window_size=14, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + window_block_indexes=window_block_indexes, + residual_block_indexes=[], + use_act_checkpoint=cfg.USE_ACT_CHECKPOINT, + use_rel_pos=True, + out_feature="last_feat",) + + out_channels = cfg.MODEL.FPN.OUT_CHANNELS + assert out_channels == 256 or out_channels == 768 or out_channels == 1024 + backbone = ViT_FPN(bottom_up=bottom_up, + top_block=LastLevelP6P7_P5(out_channels, out_channels), + out_channels=out_channels, + strides=[8, 16, 32, 64, 128], + vit_out_dim=vit_out_dim) + return backbone diff --git a/iChat/models/grit_src/grit/modeling/meta_arch/grit.py b/iChat/models/grit_src/grit/modeling/meta_arch/grit.py new file mode 100644 index 0000000000000000000000000000000000000000..101725fd455e723360eaafc26db37beb226a9233 --- /dev/null +++ b/iChat/models/grit_src/grit/modeling/meta_arch/grit.py @@ -0,0 +1,66 @@ +from typing import Dict, List, Optional, Tuple +import torch +from detectron2.config import configurable +from detectron2.structures import ImageList, Instances, Boxes +from detectron2.modeling.meta_arch.build import META_ARCH_REGISTRY +from detectron2.modeling.meta_arch.rcnn import GeneralizedRCNN + + +@META_ARCH_REGISTRY.register() +class GRiT(GeneralizedRCNN): + @configurable + def __init__( + self, + **kwargs): + super().__init__(**kwargs) + assert self.proposal_generator is not None + + @classmethod + def from_config(cls, cfg): + ret = super().from_config(cfg) + return ret + + def inference( + self, + batched_inputs: Tuple[Dict[str, torch.Tensor]], + detected_instances: Optional[List[Instances]] = None, + do_postprocess: bool = True, + ): + assert not self.training + assert detected_instances is None + + images = self.preprocess_image(batched_inputs) + features = self.backbone(images.tensor) + proposals, _ = self.proposal_generator(images, features, None) + results, _ = self.roi_heads(features, proposals) + if do_postprocess: + assert not torch.jit.is_scripting(), \ + "Scripting is not supported for postprocess." + return GRiT._postprocess( + results, batched_inputs, images.image_sizes) + else: + return results + + def forward(self, batched_inputs: List[Dict[str, torch.Tensor]]): + if not self.training: + return self.inference(batched_inputs) + + images = self.preprocess_image(batched_inputs) + + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + + targets_task = batched_inputs[0]['task'] + for anno_per_image in batched_inputs: + assert targets_task == anno_per_image['task'] + + features = self.backbone(images.tensor) + proposals, proposal_losses = self.proposal_generator( + images, features, gt_instances) + proposals, roihead_textdecoder_losses = self.roi_heads( + features, proposals, gt_instances, targets_task=targets_task) + + losses = {} + losses.update(roihead_textdecoder_losses) + losses.update(proposal_losses) + + return losses \ No newline at end of file diff --git a/iChat/models/grit_src/grit/modeling/roi_heads/grit_fast_rcnn.py b/iChat/models/grit_src/grit/modeling/roi_heads/grit_fast_rcnn.py new file mode 100644 index 0000000000000000000000000000000000000000..5d03daabac26aecf214baf1f743c97a5d7486bf7 --- /dev/null +++ b/iChat/models/grit_src/grit/modeling/roi_heads/grit_fast_rcnn.py @@ -0,0 +1,126 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Jialian Wu from https://github.com/facebookresearch/Detic/blob/main/detic/modeling/roi_heads/detic_fast_rcnn.py +import torch +from fvcore.nn import giou_loss, smooth_l1_loss +from torch import nn +from torch.nn import functional as F +import fvcore.nn.weight_init as weight_init +from detectron2.config import configurable +from detectron2.layers import ShapeSpec, batched_nms, cat, cross_entropy, nonzero_tuple +from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers +from detectron2.modeling.roi_heads.fast_rcnn import _log_classification_stats + + +__all__ = ["GRiTFastRCNNOutputLayers"] + + +class GRiTFastRCNNOutputLayers(FastRCNNOutputLayers): + @configurable + def __init__( + self, + input_shape: ShapeSpec, + **kwargs, + ): + super().__init__( + input_shape=input_shape, + **kwargs, + ) + + input_size = input_shape.channels * \ + (input_shape.width or 1) * (input_shape.height or 1) + + self.bbox_pred = nn.Sequential( + nn.Linear(input_size, input_size), + nn.ReLU(inplace=True), + nn.Linear(input_size, 4) + ) + weight_init.c2_xavier_fill(self.bbox_pred[0]) + nn.init.normal_(self.bbox_pred[-1].weight, std=0.001) + nn.init.constant_(self.bbox_pred[-1].bias, 0) + + @classmethod + def from_config(cls, cfg, input_shape): + ret = super().from_config(cfg, input_shape) + return ret + + def losses(self, predictions, proposals): + scores, proposal_deltas = predictions + gt_classes = ( + cat([p.gt_classes for p in proposals], dim=0) if len(proposals) else torch.empty(0) + ) + num_classes = self.num_classes + _log_classification_stats(scores, gt_classes) + + if len(proposals): + proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0) # Nx4 + assert not proposal_boxes.requires_grad, "Proposals should not require gradients!" + gt_boxes = cat( + [(p.gt_boxes if p.has("gt_boxes") else p.proposal_boxes).tensor for p in proposals], + dim=0, + ) + else: + proposal_boxes = gt_boxes = torch.empty((0, 4), device=proposal_deltas.device) + + loss_cls = self.softmax_cross_entropy_loss(scores, gt_classes) + return { + "loss_cls": loss_cls, + "loss_box_reg": self.box_reg_loss( + proposal_boxes, gt_boxes, proposal_deltas, gt_classes, + num_classes=num_classes) + } + + def softmax_cross_entropy_loss(self, pred_class_logits, gt_classes): + if pred_class_logits.numel() == 0: + return pred_class_logits.new_zeros([1])[0] + + loss = F.cross_entropy( + pred_class_logits, gt_classes, reduction="mean") + return loss + + def box_reg_loss( + self, proposal_boxes, gt_boxes, pred_deltas, gt_classes, + num_classes=-1): + num_classes = num_classes if num_classes > 0 else self.num_classes + box_dim = proposal_boxes.shape[1] + fg_inds = nonzero_tuple((gt_classes >= 0) & (gt_classes < num_classes))[0] + if pred_deltas.shape[1] == box_dim: + fg_pred_deltas = pred_deltas[fg_inds] + else: + fg_pred_deltas = pred_deltas.view(-1, self.num_classes, box_dim)[ + fg_inds, gt_classes[fg_inds] + ] + + if self.box_reg_loss_type == "smooth_l1": + gt_pred_deltas = self.box2box_transform.get_deltas( + proposal_boxes[fg_inds], + gt_boxes[fg_inds], + ) + loss_box_reg = smooth_l1_loss( + fg_pred_deltas, gt_pred_deltas, self.smooth_l1_beta, reduction="sum" + ) + elif self.box_reg_loss_type == "giou": + fg_pred_boxes = self.box2box_transform.apply_deltas( + fg_pred_deltas, proposal_boxes[fg_inds] + ) + loss_box_reg = giou_loss(fg_pred_boxes, gt_boxes[fg_inds], reduction="sum") + else: + raise ValueError(f"Invalid bbox reg loss type '{self.box_reg_loss_type}'") + return loss_box_reg / max(gt_classes.numel(), 1.0) + + def predict_probs(self, predictions, proposals): + scores = predictions[0] + num_inst_per_image = [len(p) for p in proposals] + probs = F.softmax(scores, dim=-1) + return probs.split(num_inst_per_image, dim=0) + + def forward(self, x): + if x.dim() > 2: + x = torch.flatten(x, start_dim=1) + scores = [] + + cls_scores = self.cls_score(x) + scores.append(cls_scores) + scores = torch.cat(scores, dim=1) + + proposal_deltas = self.bbox_pred(x) + return scores, proposal_deltas \ No newline at end of file diff --git a/iChat/models/grit_src/grit/modeling/roi_heads/grit_roi_heads.py b/iChat/models/grit_src/grit/modeling/roi_heads/grit_roi_heads.py new file mode 100644 index 0000000000000000000000000000000000000000..c67354bde77e81c1334e7f6846a753ee13f89b41 --- /dev/null +++ b/iChat/models/grit_src/grit/modeling/roi_heads/grit_roi_heads.py @@ -0,0 +1,478 @@ +import math +import torch +from typing import Dict, List, Optional, Tuple, Union + +from detectron2.config import configurable +from detectron2.structures import Boxes, Instances, pairwise_iou +from detectron2.utils.events import get_event_storage + +from detectron2.modeling.box_regression import Box2BoxTransform +from detectron2.modeling.roi_heads.roi_heads import ROI_HEADS_REGISTRY, StandardROIHeads +from detectron2.modeling.roi_heads.cascade_rcnn import CascadeROIHeads, _ScaleGradient +from detectron2.modeling.poolers import ROIPooler +from detectron2.layers import batched_nms +from .grit_fast_rcnn import GRiTFastRCNNOutputLayers + +from ..text.text_decoder import TransformerDecoderTextualHead, GRiTTextDecoder, AutoRegressiveBeamSearch +from ..text.load_text_token import LoadTextTokens +from transformers import BertTokenizer +from iChat.models.grit_src.grit.data.custom_dataset_mapper import ObjDescription +from ..soft_nms import batched_soft_nms + +import logging +logger = logging.getLogger(__name__) + + +@ROI_HEADS_REGISTRY.register() +class GRiTROIHeadsAndTextDecoder(CascadeROIHeads): + @configurable + def __init__( + self, + *, + text_decoder_transformer, + train_task: list, + test_task: str, + mult_proposal_score: bool = False, + mask_weight: float = 1.0, + object_feat_pooler=None, + soft_nms_enabled=False, + beam_size=1, + **kwargs, + ): + super().__init__(**kwargs) + self.mult_proposal_score = mult_proposal_score + self.mask_weight = mask_weight + self.object_feat_pooler = object_feat_pooler + self.soft_nms_enabled = soft_nms_enabled + self.test_task = test_task + self.beam_size = beam_size + + tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True) + self.tokenizer = tokenizer + + assert test_task in train_task, 'GRiT has not been trained on {} task, ' \ + 'please verify the task name or train a new ' \ + 'GRiT on {} task'.format(test_task, test_task) + task_begin_tokens = {} + for i, task in enumerate(train_task): + if i == 0: + task_begin_tokens[task] = tokenizer.cls_token_id + else: + task_begin_tokens[task] = 103 + i + self.task_begin_tokens = task_begin_tokens + + beamsearch_decode = AutoRegressiveBeamSearch( + end_token_id=tokenizer.sep_token_id, + max_steps=40, + beam_size=beam_size, + objectdet=test_task == "ObjectDet", + per_node_beam_size=1, + ) + self.text_decoder = GRiTTextDecoder( + text_decoder_transformer, + beamsearch_decode=beamsearch_decode, + begin_token_id=task_begin_tokens[test_task], + loss_type='smooth', + tokenizer=tokenizer, + ) + self.get_target_text_tokens = LoadTextTokens(tokenizer, max_text_len=40, padding='do_not_pad') + + @classmethod + def from_config(cls, cfg, input_shape): + ret = super().from_config(cfg, input_shape) + text_decoder_transformer = TransformerDecoderTextualHead( + object_feature_size=cfg.MODEL.FPN.OUT_CHANNELS, + vocab_size=cfg.TEXT_DECODER.VOCAB_SIZE, + hidden_size=cfg.TEXT_DECODER.HIDDEN_SIZE, + num_layers=cfg.TEXT_DECODER.NUM_LAYERS, + attention_heads=cfg.TEXT_DECODER.ATTENTION_HEADS, + feedforward_size=cfg.TEXT_DECODER.FEEDFORWARD_SIZE, + mask_future_positions=True, + padding_idx=0, + decoder_type='bert_en', + use_act_checkpoint=cfg.USE_ACT_CHECKPOINT, + ) + ret.update({ + 'text_decoder_transformer': text_decoder_transformer, + 'train_task': cfg.MODEL.TRAIN_TASK, + 'test_task': cfg.MODEL.TEST_TASK, + 'mult_proposal_score': cfg.MODEL.ROI_BOX_HEAD.MULT_PROPOSAL_SCORE, + 'mask_weight': cfg.MODEL.ROI_HEADS.MASK_WEIGHT, + 'soft_nms_enabled': cfg.MODEL.ROI_HEADS.SOFT_NMS_ENABLED, + 'beam_size': cfg.MODEL.BEAM_SIZE, + }) + return ret + + @classmethod + def _init_box_head(self, cfg, input_shape): + ret = super()._init_box_head(cfg, input_shape) + del ret['box_predictors'] + cascade_bbox_reg_weights = cfg.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS + box_predictors = [] + for box_head, bbox_reg_weights in zip(ret['box_heads'], \ + cascade_bbox_reg_weights): + box_predictors.append( + GRiTFastRCNNOutputLayers( + cfg, box_head.output_shape, + box2box_transform=Box2BoxTransform(weights=bbox_reg_weights) + )) + ret['box_predictors'] = box_predictors + + in_features = cfg.MODEL.ROI_HEADS.IN_FEATURES + pooler_scales = tuple(1.0 / input_shape[k].stride for k in in_features) + sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO + pooler_type = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE + object_feat_pooler = ROIPooler( + output_size=cfg.MODEL.ROI_HEADS.OBJECT_FEAT_POOLER_RES, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + ret['object_feat_pooler'] = object_feat_pooler + return ret + + def check_if_all_background(self, proposals, targets, stage): + all_background = True + for proposals_per_image in proposals: + if not (proposals_per_image.gt_classes == self.num_classes).all(): + all_background = False + + if all_background: + logger.info('all proposals are background at stage {}'.format(stage)) + proposals[0].proposal_boxes.tensor[0, :] = targets[0].gt_boxes.tensor[0, :] + proposals[0].gt_boxes.tensor[0, :] = targets[0].gt_boxes.tensor[0, :] + proposals[0].objectness_logits[0] = math.log((1.0 - 1e-10) / (1 - (1.0 - 1e-10))) + proposals[0].gt_classes[0] = targets[0].gt_classes[0] + proposals[0].gt_object_descriptions.data[0] = targets[0].gt_object_descriptions.data[0] + if 'foreground' in proposals[0].get_fields().keys(): + proposals[0].foreground[0] = 1 + return proposals + + def _forward_box(self, features, proposals, targets=None, task="ObjectDet"): + if self.training: + proposals = self.check_if_all_background(proposals, targets, 0) + if (not self.training) and self.mult_proposal_score: + if len(proposals) > 0 and proposals[0].has('scores'): + proposal_scores = [p.get('scores') for p in proposals] + else: + proposal_scores = [p.get('objectness_logits') for p in proposals] + + features = [features[f] for f in self.box_in_features] + head_outputs = [] + prev_pred_boxes = None + image_sizes = [x.image_size for x in proposals] + + for k in range(self.num_cascade_stages): + if k > 0: + proposals = self._create_proposals_from_boxes( + prev_pred_boxes, image_sizes, + logits=[p.objectness_logits for p in proposals]) + if self.training: + proposals = self._match_and_label_boxes_GRiT( + proposals, k, targets) + proposals = self.check_if_all_background(proposals, targets, k) + predictions = self._run_stage(features, proposals, k) + prev_pred_boxes = self.box_predictor[k].predict_boxes( + (predictions[0], predictions[1]), proposals) + head_outputs.append((self.box_predictor[k], predictions, proposals)) + + if self.training: + object_features = self.object_feat_pooler(features, [x.proposal_boxes for x in proposals]) + object_features = _ScaleGradient.apply(object_features, 1.0 / self.num_cascade_stages) + foreground = torch.cat([x.foreground for x in proposals]) + object_features = object_features[foreground > 0] + + object_descriptions = [] + for x in proposals: + object_descriptions += x.gt_object_descriptions[x.foreground > 0].data + object_descriptions = ObjDescription(object_descriptions) + object_descriptions = object_descriptions.data + + if len(object_descriptions) > 0: + begin_token = self.task_begin_tokens[task] + text_decoder_inputs = self.get_target_text_tokens(object_descriptions, object_features, begin_token) + object_features = object_features.view( + object_features.shape[0], object_features.shape[1], -1).permute(0, 2, 1).contiguous() + text_decoder_inputs.update({'object_features': object_features}) + text_decoder_loss = self.text_decoder(text_decoder_inputs) + else: + text_decoder_loss = head_outputs[0][1][0].new_zeros([1])[0] + + losses = {} + storage = get_event_storage() + # RoI Head losses (For the proposal generator loss, please find it in grit.py) + for stage, (predictor, predictions, proposals) in enumerate(head_outputs): + with storage.name_scope("stage{}".format(stage)): + stage_losses = predictor.losses( + (predictions[0], predictions[1]), proposals) + losses.update({k + "_stage{}".format(stage): v for k, v in stage_losses.items()}) + # Text Decoder loss + losses.update({'text_decoder_loss': text_decoder_loss}) + return losses + else: + scores_per_stage = [h[0].predict_probs(h[1], h[2]) for h in head_outputs] + logits_per_stage = [(h[1][0],) for h in head_outputs] + scores = [ + sum(list(scores_per_image)) * (1.0 / self.num_cascade_stages) + for scores_per_image in zip(*scores_per_stage) + ] + logits = [ + sum(list(logits_per_image)) * (1.0 / self.num_cascade_stages) + for logits_per_image in zip(*logits_per_stage) + ] + if self.mult_proposal_score: + scores = [(s * ps[:, None]) ** 0.5 for s, ps in zip(scores, proposal_scores)] + predictor, predictions, proposals = head_outputs[-1] + boxes = predictor.predict_boxes( + (predictions[0], predictions[1]), proposals) + assert len(boxes) == 1 + pred_instances, _ = self.fast_rcnn_inference_GRiT( + boxes, + scores, + logits, + image_sizes, + predictor.test_score_thresh, + predictor.test_nms_thresh, + predictor.test_topk_per_image, + self.soft_nms_enabled, + ) + + assert len(pred_instances) == 1, "Only support one image" + for i, pred_instance in enumerate(pred_instances): + if len(pred_instance.pred_boxes) > 0: + object_features = self.object_feat_pooler(features, [pred_instance.pred_boxes]) + object_features = object_features.view( + object_features.shape[0], object_features.shape[1], -1).permute(0, 2, 1).contiguous() + text_decoder_output = self.text_decoder({'object_features': object_features}) + if self.beam_size > 1 and self.test_task == "ObjectDet": + pred_boxes = [] + pred_scores = [] + pred_classes = [] + pred_object_descriptions = [] + + for beam_id in range(self.beam_size): + pred_boxes.append(pred_instance.pred_boxes.tensor) + # object score = sqrt(objectness score x description score) + pred_scores.append((pred_instance.scores * + torch.exp(text_decoder_output['logprobs'])[:, beam_id]) ** 0.5) + pred_classes.append(pred_instance.pred_classes) + for prediction in text_decoder_output['predictions'][:, beam_id, :]: + # convert text tokens to words + description = self.tokenizer.decode(prediction.tolist()[1:], skip_special_tokens=True) + pred_object_descriptions.append(description) + + merged_instances = Instances(image_sizes[0]) + if torch.cat(pred_scores, dim=0).shape[0] <= predictor.test_topk_per_image: + merged_instances.scores = torch.cat(pred_scores, dim=0) + merged_instances.pred_boxes = Boxes(torch.cat(pred_boxes, dim=0)) + merged_instances.pred_classes = torch.cat(pred_classes, dim=0) + merged_instances.pred_object_descriptions = ObjDescription(pred_object_descriptions) + else: + pred_scores, top_idx = torch.topk( + torch.cat(pred_scores, dim=0), predictor.test_topk_per_image) + merged_instances.scores = pred_scores + merged_instances.pred_boxes = Boxes(torch.cat(pred_boxes, dim=0)[top_idx, :]) + merged_instances.pred_classes = torch.cat(pred_classes, dim=0)[top_idx] + merged_instances.pred_object_descriptions = \ + ObjDescription(ObjDescription(pred_object_descriptions)[top_idx].data) + + pred_instances[i] = merged_instances + else: + # object score = sqrt(objectness score x description score) + pred_instance.scores = (pred_instance.scores * + torch.exp(text_decoder_output['logprobs'])) ** 0.5 + + pred_object_descriptions = [] + for prediction in text_decoder_output['predictions']: + # convert text tokens to words + description = self.tokenizer.decode(prediction.tolist()[1:], skip_special_tokens=True) + pred_object_descriptions.append(description) + pred_instance.pred_object_descriptions = ObjDescription(pred_object_descriptions) + else: + pred_instance.pred_object_descriptions = ObjDescription([]) + + return pred_instances + + + def forward(self, features, proposals, targets=None, targets_task="ObjectDet"): + if self.training: + proposals = self.label_and_sample_proposals( + proposals, targets) + + losses = self._forward_box(features, proposals, targets, task=targets_task) + if targets[0].has('gt_masks'): + mask_losses = self._forward_mask(features, proposals) + losses.update({k: v * self.mask_weight \ + for k, v in mask_losses.items()}) + else: + losses.update(self._get_empty_mask_loss(device=proposals[0].objectness_logits.device)) + return proposals, losses + else: + pred_instances = self._forward_box(features, proposals, task=self.test_task) + pred_instances = self.forward_with_given_boxes(features, pred_instances) + return pred_instances, {} + + @torch.no_grad() + def _match_and_label_boxes_GRiT(self, proposals, stage, targets): + """ + Add "gt_object_description" and "foreground" to detectron2's _match_and_label_boxes + """ + num_fg_samples, num_bg_samples = [], [] + for proposals_per_image, targets_per_image in zip(proposals, targets): + match_quality_matrix = pairwise_iou( + targets_per_image.gt_boxes, proposals_per_image.proposal_boxes + ) + # proposal_labels are 0 or 1 + matched_idxs, proposal_labels = self.proposal_matchers[stage](match_quality_matrix) + if len(targets_per_image) > 0: + gt_classes = targets_per_image.gt_classes[matched_idxs] + # Label unmatched proposals (0 label from matcher) as background (label=num_classes) + gt_classes[proposal_labels == 0] = self.num_classes + foreground = torch.ones_like(gt_classes) + foreground[proposal_labels == 0] = 0 + gt_boxes = targets_per_image.gt_boxes[matched_idxs] + gt_object_descriptions = targets_per_image.gt_object_descriptions[matched_idxs] + else: + gt_classes = torch.zeros_like(matched_idxs) + self.num_classes + foreground = torch.zeros_like(gt_classes) + gt_boxes = Boxes( + targets_per_image.gt_boxes.tensor.new_zeros((len(proposals_per_image), 4)) + ) + gt_object_descriptions = ObjDescription(['None' for i in range(len(proposals_per_image))]) + proposals_per_image.gt_classes = gt_classes + proposals_per_image.gt_boxes = gt_boxes + proposals_per_image.gt_object_descriptions = gt_object_descriptions + proposals_per_image.foreground = foreground + + num_fg_samples.append((proposal_labels == 1).sum().item()) + num_bg_samples.append(proposal_labels.numel() - num_fg_samples[-1]) + + # Log the number of fg/bg samples in each stage + storage = get_event_storage() + storage.put_scalar( + "stage{}/roi_head/num_fg_samples".format(stage), + sum(num_fg_samples) / len(num_fg_samples), + ) + storage.put_scalar( + "stage{}/roi_head/num_bg_samples".format(stage), + sum(num_bg_samples) / len(num_bg_samples), + ) + return proposals + + def fast_rcnn_inference_GRiT( + self, + boxes: List[torch.Tensor], + scores: List[torch.Tensor], + logits: List[torch.Tensor], + image_shapes: List[Tuple[int, int]], + score_thresh: float, + nms_thresh: float, + topk_per_image: int, + soft_nms_enabled: bool, + ): + result_per_image = [ + self.fast_rcnn_inference_single_image_GRiT( + boxes_per_image, scores_per_image, logits_per_image, image_shape, + score_thresh, nms_thresh, topk_per_image, soft_nms_enabled + ) + for scores_per_image, boxes_per_image, image_shape, logits_per_image \ + in zip(scores, boxes, image_shapes, logits) + ] + return [x[0] for x in result_per_image], [x[1] for x in result_per_image] + + def fast_rcnn_inference_single_image_GRiT( + self, + boxes, + scores, + logits, + image_shape: Tuple[int, int], + score_thresh: float, + nms_thresh: float, + topk_per_image: int, + soft_nms_enabled, + ): + """ + Add soft NMS to detectron2's fast_rcnn_inference_single_image + """ + valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) + if not valid_mask.all(): + boxes = boxes[valid_mask] + scores = scores[valid_mask] + logits = logits[valid_mask] + + scores = scores[:, :-1] + logits = logits[:, :-1] + num_bbox_reg_classes = boxes.shape[1] // 4 + # Convert to Boxes to use the `clip` function ... + boxes = Boxes(boxes.reshape(-1, 4)) + boxes.clip(image_shape) + boxes = boxes.tensor.view(-1, num_bbox_reg_classes, 4) # R x C x 4 + + # 1. Filter results based on detection scores. It can make NMS more efficient + # by filtering out low-confidence detections. + filter_mask = scores > score_thresh # R x K + # R' x 2. First column contains indices of the R predictions; + # Second column contains indices of classes. + filter_inds = filter_mask.nonzero() + if num_bbox_reg_classes == 1: + boxes = boxes[filter_inds[:, 0], 0] + else: + boxes = boxes[filter_mask] + scores = scores[filter_mask] + logits = logits[filter_mask] + + # 2. Apply NMS for each class independently. + if not soft_nms_enabled: + keep = batched_nms(boxes, scores, filter_inds[:, 1], nms_thresh) + else: + keep, soft_nms_scores = batched_soft_nms( + boxes, + scores, + filter_inds[:, 1], + "linear", + 0.5, + nms_thresh, + 0.001, + ) + scores[keep] = soft_nms_scores + if topk_per_image >= 0: + keep = keep[:topk_per_image] + boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep] + logits = logits[keep] + + result = Instances(image_shape) + result.pred_boxes = Boxes(boxes) + result.scores = scores + result.pred_classes = filter_inds[:, 1] + result.logits = logits + return result, filter_inds[:, 0] + + def _get_empty_mask_loss(self, device): + if self.mask_on: + return {'loss_mask': torch.zeros( + (1, ), device=device, dtype=torch.float32)[0]} + else: + return {} + + def _create_proposals_from_boxes(self, boxes, image_sizes, logits): + boxes = [Boxes(b.detach()) for b in boxes] + proposals = [] + for boxes_per_image, image_size, logit in zip( + boxes, image_sizes, logits): + boxes_per_image.clip(image_size) + if self.training: + inds = boxes_per_image.nonempty() + boxes_per_image = boxes_per_image[inds] + logit = logit[inds] + prop = Instances(image_size) + prop.proposal_boxes = boxes_per_image + prop.objectness_logits = logit + proposals.append(prop) + return proposals + + def _run_stage(self, features, proposals, stage): + pool_boxes = [x.proposal_boxes for x in proposals] + box_features = self.box_pooler(features, pool_boxes) + box_features = _ScaleGradient.apply(box_features, 1.0 / self.num_cascade_stages) + box_features = self.box_head[stage](box_features) + return self.box_predictor[stage](box_features) diff --git a/iChat/models/grit_src/grit/modeling/soft_nms.py b/iChat/models/grit_src/grit/modeling/soft_nms.py new file mode 100644 index 0000000000000000000000000000000000000000..6a5aae7c4261191b8e07e0fd25055d8917f7f97d --- /dev/null +++ b/iChat/models/grit_src/grit/modeling/soft_nms.py @@ -0,0 +1,177 @@ +import torch + +from detectron2.structures import Boxes, RotatedBoxes, pairwise_iou, pairwise_iou_rotated + + +def soft_nms(boxes, scores, method, gaussian_sigma, linear_threshold, prune_threshold): + """ + Performs soft non-maximum suppression algorithm on axis aligned boxes + + Args: + boxes (Tensor[N, 5]): + boxes where NMS will be performed. They + are expected to be in (x_ctr, y_ctr, width, height, angle_degrees) format + scores (Tensor[N]): + scores for each one of the boxes + method (str): + one of ['gaussian', 'linear', 'hard'] + see paper for details. users encouraged not to use "hard", as this is the + same nms available elsewhere in detectron2 + gaussian_sigma (float): + parameter for Gaussian penalty function + linear_threshold (float): + iou threshold for applying linear decay. Nt from the paper + re-used as threshold for standard "hard" nms + prune_threshold (float): + boxes with scores below this threshold are pruned at each iteration. + Dramatically reduces computation time. Authors use values in [10e-4, 10e-2] + + Returns: + tuple(Tensor, Tensor): + [0]: int64 tensor with the indices of the elements that have been kept + by Soft NMS, sorted in decreasing order of scores + [1]: float tensor with the re-scored scores of the elements that were kept +""" + return _soft_nms( + Boxes, + pairwise_iou, + boxes, + scores, + method, + gaussian_sigma, + linear_threshold, + prune_threshold, + ) + + +def batched_soft_nms( + boxes, scores, idxs, method, gaussian_sigma, linear_threshold, prune_threshold +): + """ + Performs soft non-maximum suppression in a batched fashion. + + Each index value correspond to a category, and NMS + will not be applied between elements of different categories. + + Args: + boxes (Tensor[N, 4]): + boxes where NMS will be performed. They + are expected to be in (x1, y1, x2, y2) format + scores (Tensor[N]): + scores for each one of the boxes + idxs (Tensor[N]): + indices of the categories for each one of the boxes. + method (str): + one of ['gaussian', 'linear', 'hard'] + see paper for details. users encouraged not to use "hard", as this is the + same nms available elsewhere in detectron2 + gaussian_sigma (float): + parameter for Gaussian penalty function + linear_threshold (float): + iou threshold for applying linear decay. Nt from the paper + re-used as threshold for standard "hard" nms + prune_threshold (float): + boxes with scores below this threshold are pruned at each iteration. + Dramatically reduces computation time. Authors use values in [10e-4, 10e-2] + Returns: + tuple(Tensor, Tensor): + [0]: int64 tensor with the indices of the elements that have been kept + by Soft NMS, sorted in decreasing order of scores + [1]: float tensor with the re-scored scores of the elements that were kept + """ + if boxes.numel() == 0: + return ( + torch.empty((0,), dtype=torch.int64, device=boxes.device), + torch.empty((0,), dtype=torch.float32, device=scores.device), + ) + # strategy: in order to perform NMS independently per class. + # we add an offset to all the boxes. The offset is dependent + # only on the class idx, and is large enough so that boxes + # from different classes do not overlap + max_coordinate = boxes.max() + offsets = idxs.to(boxes) * (max_coordinate + 1) + boxes_for_nms = boxes + offsets[:, None] + return soft_nms( + boxes_for_nms, scores, method, gaussian_sigma, linear_threshold, prune_threshold + ) + + +def _soft_nms( + box_class, + pairwise_iou_func, + boxes, + scores, + method, + gaussian_sigma, + linear_threshold, + prune_threshold, +): + """ + Soft non-max suppression algorithm. + + Implementation of [Soft-NMS -- Improving Object Detection With One Line of Codec] + (https://arxiv.org/abs/1704.04503) + + Args: + box_class (cls): one of Box, RotatedBoxes + pairwise_iou_func (func): one of pairwise_iou, pairwise_iou_rotated + boxes (Tensor[N, ?]): + boxes where NMS will be performed + if Boxes, in (x1, y1, x2, y2) format + if RotatedBoxes, in (x_ctr, y_ctr, width, height, angle_degrees) format + scores (Tensor[N]): + scores for each one of the boxes + method (str): + one of ['gaussian', 'linear', 'hard'] + see paper for details. users encouraged not to use "hard", as this is the + same nms available elsewhere in detectron2 + gaussian_sigma (float): + parameter for Gaussian penalty function + linear_threshold (float): + iou threshold for applying linear decay. Nt from the paper + re-used as threshold for standard "hard" nms + prune_threshold (float): + boxes with scores below this threshold are pruned at each iteration. + Dramatically reduces computation time. Authors use values in [10e-4, 10e-2] + + Returns: + tuple(Tensor, Tensor): + [0]: int64 tensor with the indices of the elements that have been kept + by Soft NMS, sorted in decreasing order of scores + [1]: float tensor with the re-scored scores of the elements that were kept + """ + boxes = boxes.clone() + scores = scores.clone() + idxs = torch.arange(scores.size()[0]) + + idxs_out = [] + scores_out = [] + + while scores.numel() > 0: + top_idx = torch.argmax(scores) + idxs_out.append(idxs[top_idx].item()) + scores_out.append(scores[top_idx].item()) + + top_box = boxes[top_idx] + ious = pairwise_iou_func(box_class(top_box.unsqueeze(0)), box_class(boxes))[0] + + if method == "linear": + decay = torch.ones_like(ious) + decay_mask = ious > linear_threshold + decay[decay_mask] = 1 - ious[decay_mask] + elif method == "gaussian": + decay = torch.exp(-torch.pow(ious, 2) / gaussian_sigma) + elif method == "hard": # standard NMS + decay = (ious < linear_threshold).float() + else: + raise NotImplementedError("{} soft nms method not implemented.".format(method)) + + scores *= decay + keep = scores > prune_threshold + keep[top_idx] = False + + boxes = boxes[keep] + scores = scores[keep] + idxs = idxs[keep] + + return torch.tensor(idxs_out).to(boxes.device), torch.tensor(scores_out).to(scores.device) \ No newline at end of file diff --git a/iChat/models/grit_src/grit/modeling/text/file_utils.py b/iChat/models/grit_src/grit/modeling/text/file_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..51918cf3857471e4ffb5b617d73ee8b9eed0989e --- /dev/null +++ b/iChat/models/grit_src/grit/modeling/text/file_utils.py @@ -0,0 +1,256 @@ +# Utilities for working with the local dataset cache. +# This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp +# Copyright by the AllenNLP authors. + +from __future__ import absolute_import, division, print_function, unicode_literals + +import sys +import json +import logging +import os +import shutil +import tempfile +import fnmatch +from functools import wraps +from hashlib import sha256 +from io import open + +import boto3 +import requests +from botocore.exceptions import ClientError +from tqdm import tqdm + +try: + from torch.hub import _get_torch_home + torch_cache_home = _get_torch_home() +except ImportError: + torch_cache_home = os.path.expanduser( + os.getenv('TORCH_HOME', os.path.join( + os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch'))) +default_cache_path = os.path.join(torch_cache_home, 'pytorch_transformers') + +try: + from urllib.parse import urlparse +except ImportError: + from urlparse import urlparse + +try: + from pathlib import Path + PYTORCH_PRETRAINED_BERT_CACHE = Path( + os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', default_cache_path)) +except (AttributeError, ImportError): + PYTORCH_PRETRAINED_BERT_CACHE = os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', + default_cache_path) + +logger = logging.getLogger(__name__) # pylint: disable=invalid-name + + +def url_to_filename(url, etag=None): + """ + Convert `url` into a hashed filename in a repeatable way. + If `etag` is specified, append its hash to the url's, delimited + by a period. + """ + url_bytes = url.encode('utf-8') + url_hash = sha256(url_bytes) + filename = url_hash.hexdigest() + + if etag: + etag_bytes = etag.encode('utf-8') + etag_hash = sha256(etag_bytes) + filename += '.' + etag_hash.hexdigest() + + return filename + + +def filename_to_url(filename, cache_dir=None): + """ + Return the url and etag (which may be ``None``) stored for `filename`. + Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist. + """ + if cache_dir is None: + cache_dir = PYTORCH_PRETRAINED_BERT_CACHE + if sys.version_info[0] == 3 and isinstance(cache_dir, Path): + cache_dir = str(cache_dir) + + cache_path = os.path.join(cache_dir, filename) + if not os.path.exists(cache_path): + raise EnvironmentError("file {} not found".format(cache_path)) + + meta_path = cache_path + '.json' + if not os.path.exists(meta_path): + raise EnvironmentError("file {} not found".format(meta_path)) + + with open(meta_path, encoding="utf-8") as meta_file: + metadata = json.load(meta_file) + url = metadata['url'] + etag = metadata['etag'] + + return url, etag + + +def cached_path(url_or_filename, cache_dir=None): + """ + Given something that might be a URL (or might be a local path), + determine which. If it's a URL, download the file and cache it, and + return the path to the cached file. If it's already a local path, + make sure the file exists and then return the path. + """ + if cache_dir is None: + cache_dir = PYTORCH_PRETRAINED_BERT_CACHE + if sys.version_info[0] == 3 and isinstance(url_or_filename, Path): + url_or_filename = str(url_or_filename) + if sys.version_info[0] == 3 and isinstance(cache_dir, Path): + cache_dir = str(cache_dir) + + parsed = urlparse(url_or_filename) + + if parsed.scheme in ('http', 'https', 's3'): + # URL, so get it from the cache (downloading if necessary) + return get_from_cache(url_or_filename, cache_dir) + elif os.path.exists(url_or_filename): + # File, and it exists. + return url_or_filename + elif parsed.scheme == '': + # File, but it doesn't exist. + raise EnvironmentError("file {} not found".format(url_or_filename)) + else: + # Something unknown + raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename)) + + +def split_s3_path(url): + """Split a full s3 path into the bucket name and path.""" + parsed = urlparse(url) + if not parsed.netloc or not parsed.path: + raise ValueError("bad s3 path {}".format(url)) + bucket_name = parsed.netloc + s3_path = parsed.path + # Remove '/' at beginning of path. + if s3_path.startswith("/"): + s3_path = s3_path[1:] + return bucket_name, s3_path + + +def s3_request(func): + """ + Wrapper function for s3 requests in order to create more helpful error + messages. + """ + + @wraps(func) + def wrapper(url, *args, **kwargs): + try: + return func(url, *args, **kwargs) + except ClientError as exc: + if int(exc.response["Error"]["Code"]) == 404: + raise EnvironmentError("file {} not found".format(url)) + else: + raise + + return wrapper + + +@s3_request +def s3_etag(url): + """Check ETag on S3 object.""" + s3_resource = boto3.resource("s3") + bucket_name, s3_path = split_s3_path(url) + s3_object = s3_resource.Object(bucket_name, s3_path) + return s3_object.e_tag + + +@s3_request +def s3_get(url, temp_file): + """Pull a file directly from S3.""" + s3_resource = boto3.resource("s3") + bucket_name, s3_path = split_s3_path(url) + s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file) + + +def http_get(url, temp_file): + req = requests.get(url, stream=True) + content_length = req.headers.get('Content-Length') + total = int(content_length) if content_length is not None else None + progress = tqdm(unit="B", total=total) + for chunk in req.iter_content(chunk_size=1024): + if chunk: # filter out keep-alive new chunks + progress.update(len(chunk)) + temp_file.write(chunk) + progress.close() + + +def get_from_cache(url, cache_dir=None): + """ + Given a URL, look for the corresponding dataset in the local cache. + If it's not there, download it. Then return the path to the cached file. + """ + if cache_dir is None: + cache_dir = PYTORCH_PRETRAINED_BERT_CACHE + if sys.version_info[0] == 3 and isinstance(cache_dir, Path): + cache_dir = str(cache_dir) + if sys.version_info[0] == 2 and not isinstance(cache_dir, str): + cache_dir = str(cache_dir) + + if not os.path.exists(cache_dir): + os.makedirs(cache_dir) + + # Get eTag to add to filename, if it exists. + if url.startswith("s3://"): + etag = s3_etag(url) + else: + try: + response = requests.head(url, allow_redirects=True) + if response.status_code != 200: + etag = None + else: + etag = response.headers.get("ETag") + except EnvironmentError: + etag = None + + if sys.version_info[0] == 2 and etag is not None: + etag = etag.decode('utf-8') + filename = url_to_filename(url, etag) + + # get cache path to put the file + cache_path = os.path.join(cache_dir, filename) + + # If we don't have a connection (etag is None) and can't identify the file + # try to get the last downloaded one + if not os.path.exists(cache_path) and etag is None: + matching_files = fnmatch.filter(os.listdir(cache_dir), filename + '.*') + matching_files = list(filter(lambda s: not s.endswith('.json'), matching_files)) + if matching_files: + cache_path = os.path.join(cache_dir, matching_files[-1]) + + if not os.path.exists(cache_path): + # Download to temporary file, then copy to cache dir once finished. + # Otherwise you get corrupt cache entries if the download gets interrupted. + with tempfile.NamedTemporaryFile() as temp_file: + logger.info("%s not found in cache, downloading to %s", url, temp_file.name) + + # GET file object + if url.startswith("s3://"): + s3_get(url, temp_file) + else: + http_get(url, temp_file) + + # we are copying the file before closing it, so flush to avoid truncation + temp_file.flush() + # shutil.copyfileobj() starts at the current position, so go to the start + temp_file.seek(0) + + logger.info("copying %s to cache at %s", temp_file.name, cache_path) + with open(cache_path, 'wb') as cache_file: + shutil.copyfileobj(temp_file, cache_file) + + logger.info("creating metadata file for %s", cache_path) + meta = {'url': url, 'etag': etag} + meta_path = cache_path + '.json' + with open(meta_path, 'w') as meta_file: + output_string = json.dumps(meta) + meta_file.write(output_string) + + logger.info("removing temp file %s", temp_file.name) + + return cache_path diff --git a/iChat/models/grit_src/grit/modeling/text/load_text_token.py b/iChat/models/grit_src/grit/modeling/text/load_text_token.py new file mode 100644 index 0000000000000000000000000000000000000000..8491021bf5d7d23d7f3826395f270dccad30df36 --- /dev/null +++ b/iChat/models/grit_src/grit/modeling/text/load_text_token.py @@ -0,0 +1,80 @@ +import torch + + +class LoadTextTokens(object): + def __init__(self, tokenizer, max_text_len=40, padding='do_not_pad'): + self.tokenizer = tokenizer + self.max_text_len = max_text_len + self.padding = padding + + def descriptions_to_text_tokens(self, target, begin_token): + target_encoding = self.tokenizer( + target, padding=self.padding, + add_special_tokens=False, + truncation=True, max_length=self.max_text_len) + + need_predict = [1] * len(target_encoding['input_ids']) + payload = target_encoding['input_ids'] + if len(payload) > self.max_text_len - 2: + payload = payload[-(self.max_text_len - 2):] + need_predict = payload[-(self.max_text_len - 2):] + + input_ids = [begin_token] + payload + [self.tokenizer.sep_token_id] + + need_predict = [0] + need_predict + [1] + data = { + 'text_tokens': torch.tensor(input_ids), + 'text_lengths': len(input_ids), + 'need_predict': torch.tensor(need_predict), + } + + return data + + def __call__(self, object_descriptions, box_features, begin_token): + text_tokens = [] + text_lengths = [] + need_predict = [] + for description in object_descriptions: + tokens = self.descriptions_to_text_tokens(description, begin_token) + text_tokens.append(tokens['text_tokens']) + text_lengths.append(tokens['text_lengths']) + need_predict.append(tokens['need_predict']) + + text_tokens = torch.cat(self.collate(text_tokens), dim=0).to(box_features.device) + text_lengths = torch.tensor(text_lengths).to(box_features.device) + need_predict = torch.cat(self.collate(need_predict), dim=0).to(box_features.device) + + assert text_tokens.dim() == 2 and need_predict.dim() == 2 + data = {'text_tokens': text_tokens, + 'text_lengths': text_lengths, + 'need_predict': need_predict} + + return data + + def collate(self, batch): + if all(isinstance(b, torch.Tensor) for b in batch) and len(batch) > 0: + if not all(b.shape == batch[0].shape for b in batch[1:]): + assert all(len(b.shape) == len(batch[0].shape) for b in batch[1:]) + shape = torch.tensor([b.shape for b in batch]) + max_shape = tuple(shape.max(dim=0)[0].tolist()) + batch2 = [] + for b in batch: + if any(c < m for c, m in zip(b.shape, max_shape)): + b2 = torch.zeros(max_shape, dtype=b.dtype, device=b.device) + if b.dim() == 1: + b2[:b.shape[0]] = b + elif b.dim() == 2: + b2[:b.shape[0], :b.shape[1]] = b + elif b.dim() == 3: + b2[:b.shape[0], :b.shape[1], :b.shape[2]] = b + else: + raise NotImplementedError + b = b2 + batch2.append(b[None, ...]) + else: + batch2 = [] + for b in batch: + batch2.append(b[None, ...]) + return batch2 + else: + raise NotImplementedError diff --git a/iChat/models/grit_src/grit/modeling/text/modeling_bert.py b/iChat/models/grit_src/grit/modeling/text/modeling_bert.py new file mode 100644 index 0000000000000000000000000000000000000000..3f8bf2d5d7552ee6c314da86a19a56eb0bdaa03e --- /dev/null +++ b/iChat/models/grit_src/grit/modeling/text/modeling_bert.py @@ -0,0 +1,529 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch BERT model. """ +# Adapted from https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py + +from __future__ import absolute_import, division, print_function, unicode_literals +import copy +import os +import json +import logging +import math +import sys +from io import open +import torch +from torch import nn +import torch.utils.checkpoint as checkpoint +from .file_utils import cached_path + + +logger = logging.getLogger() + + +BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { + 'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json", + 'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-config.json", + 'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-config.json", + 'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-config.json", + 'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-config.json", + 'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-config.json", + 'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-config.json", + 'bert-base-german-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-cased-config.json", + 'bert-large-uncased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-config.json", + 'bert-large-cased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-config.json", + 'bert-large-uncased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-config.json", + 'bert-large-cased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-config.json", + 'bert-base-cased-finetuned-mrpc': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-config.json", +} + + +def qk2attn(query, key, attention_mask, gamma): + query = query / gamma + attention_scores = torch.matmul(query, key.transpose(-1, -2)) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + attention_scores = attention_scores + attention_mask + return attention_scores.softmax(dim=-1) + + +class QK2Attention(nn.Module): + def forward(self, query, key, attention_mask, gamma): + return qk2attn(query, key, attention_mask, gamma) + + +LayerNormClass = torch.nn.LayerNorm + + +class BertSelfAttention(nn.Module): + def __init__(self, config): + super(BertSelfAttention, self).__init__() + if config.hidden_size % config.num_attention_heads != 0: + raise ValueError( + "The hidden size (%d) is not a multiple of the number of attention " + "heads (%d)" % (config.hidden_size, config.num_attention_heads)) + self.output_attentions = config.output_attentions + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.softmax = nn.Softmax(dim=-1) + self.qk2attn = QK2Attention() + + def transpose_for_scores(self, x): + if torch._C._get_tracing_state(): + # exporter is not smart enough to detect dynamic size for some paths + x = x.view(x.shape[0], -1, self.num_attention_heads, self.attention_head_size) + else: + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward(self, hidden_states, attention_mask, head_mask=None, + history_state=None): + if history_state is not None: + x_states = torch.cat([history_state, hidden_states], dim=1) + mixed_query_layer = self.query(hidden_states) + mixed_key_layer = self.key(x_states) + mixed_value_layer = self.value(x_states) + else: + mixed_query_layer = self.query(hidden_states) + mixed_key_layer = self.key(hidden_states) + mixed_value_layer = self.value(hidden_states) + + query_layer = self.transpose_for_scores(mixed_query_layer) + key_layer = self.transpose_for_scores(mixed_key_layer) + value_layer = self.transpose_for_scores(mixed_value_layer) + + attention_probs = self.qk2attn(query_layer, key_layer, attention_mask, math.sqrt(self.attention_head_size)) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = (context_layer, attention_probs) if self.output_attentions else (context_layer,) + return outputs + + +class BertSelfOutput(nn.Module): + def __init__(self, config): + super(BertSelfOutput, self).__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.pre_norm = hasattr(config, 'pre_norm') and config.pre_norm + if not self.pre_norm: + self.LayerNorm = LayerNormClass(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + if not self.pre_norm: + hidden_states = self.LayerNorm(hidden_states + input_tensor) + else: + hidden_states = hidden_states + input_tensor + return hidden_states + + +class BertAttention(nn.Module): + def __init__(self, config): + super(BertAttention, self).__init__() + self.pre_norm = hasattr(config, 'pre_norm') and config.pre_norm + if self.pre_norm: + self.LayerNorm = LayerNormClass(config.hidden_size, eps=config.layer_norm_eps) + self.self = BertSelfAttention(config) + self.output = BertSelfOutput(config) + + def forward(self, input_tensor, attention_mask, head_mask=None, + history_state=None): + if self.pre_norm: + self_outputs = self.self(self.LayerNorm(input_tensor), attention_mask, head_mask, + self.layerNorm(history_state) if history_state else history_state) + else: + self_outputs = self.self(input_tensor, attention_mask, head_mask, + history_state) + attention_output = self.output(self_outputs[0], input_tensor) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +class BertIntermediate(nn.Module): + def __init__(self, config): + super(BertIntermediate, self).__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + assert config.hidden_act == 'gelu', 'Please implement other activation functions' + self.intermediate_act_fn = _gelu_python + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +class BertOutput(nn.Module): + def __init__(self, config): + super(BertOutput, self).__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.pre_norm = hasattr(config, 'pre_norm') and config.pre_norm + self.dropout = nn.Dropout(config.hidden_dropout_prob) + if not self.pre_norm: + self.LayerNorm = LayerNormClass(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + if not self.pre_norm: + hidden_states = self.LayerNorm(hidden_states + input_tensor) + else: + hidden_states = hidden_states + input_tensor + return hidden_states + + +class Mlp(nn.Module): + def __init__(self, config): + super().__init__() + self.pre_norm = hasattr(config, 'pre_norm') and config.pre_norm + self.intermediate = BertIntermediate(config) + if self.pre_norm: + self.LayerNorm = LayerNormClass(config.hidden_size, eps=config.layer_norm_eps) + self.output = BertOutput(config) + + def forward(self, attention_output): + if not self.pre_norm: + intermediate_output = self.intermediate(attention_output) + else: + intermediate_output = self.intermediate(self.LayerNorm(attention_output)) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +class BertLayer(nn.Module): + def __init__(self, config, use_act_checkpoint=True): + super(BertLayer, self).__init__() + self.pre_norm = hasattr(config, 'pre_norm') and config.pre_norm + self.use_mlp_wrapper = hasattr(config, 'use_mlp_wrapper') and config.use_mlp_wrapper + self.attention = BertAttention(config) + self.use_act_checkpoint = use_act_checkpoint + if self.use_mlp_wrapper: + self.mlp = Mlp(config) + else: + self.intermediate = BertIntermediate(config) + if self.pre_norm: + self.LayerNorm = LayerNormClass(config.hidden_size, eps=config.layer_norm_eps) + self.output = BertOutput(config) + + def forward(self, hidden_states, attention_mask, head_mask=None, + history_state=None): + if self.use_act_checkpoint: + attention_outputs = checkpoint.checkpoint(self.attention, hidden_states, + attention_mask, head_mask, history_state) + else: + attention_outputs = self.attention(hidden_states, attention_mask, + head_mask, history_state) + attention_output = attention_outputs[0] + if self.use_mlp_wrapper: + layer_output = self.mlp(attention_output) + else: + if not self.pre_norm: + intermediate_output = self.intermediate(attention_output) + else: + intermediate_output = self.intermediate(self.LayerNorm(attention_output)) + layer_output = self.output(intermediate_output, attention_output) + outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them + return outputs + + +class BertEncoder(nn.Module): + def __init__(self, config, use_act_checkpoint=True): + super(BertEncoder, self).__init__() + self.output_attentions = config.output_attentions + self.output_hidden_states = config.output_hidden_states + self.layer = nn.ModuleList([BertLayer(config, use_act_checkpoint=use_act_checkpoint) for _ in range(config.num_hidden_layers)]) + self.pre_norm = hasattr(config, 'pre_norm') and config.pre_norm + if self.pre_norm: + self.LayerNorm = LayerNormClass(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states, attention_mask, head_mask=None, + encoder_history_states=None): + all_hidden_states = () + all_attentions = () + for i, layer_module in enumerate(self.layer): + if self.output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + history_state = None if encoder_history_states is None else encoder_history_states[i] + layer_outputs = layer_module( + hidden_states, attention_mask, + (None if head_mask is None else head_mask[i]), + history_state, + ) + hidden_states = layer_outputs[0] + + if self.output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + if self.pre_norm: + hidden_states = self.LayerNorm(hidden_states) + outputs = (hidden_states,) + if self.output_hidden_states: + outputs = outputs + (all_hidden_states,) + if self.output_attentions: + outputs = outputs + (all_attentions,) + return outputs + +CONFIG_NAME = "config.json" + +class PretrainedConfig(object): + """ Base class for all configuration classes. + Handle a few common parameters and methods for loading/downloading/saving configurations. + """ + pretrained_config_archive_map = {} + + def __init__(self, **kwargs): + self.finetuning_task = kwargs.pop('finetuning_task', None) + self.num_labels = kwargs.pop('num_labels', 2) + self.output_attentions = kwargs.pop('output_attentions', False) + self.output_hidden_states = kwargs.pop('output_hidden_states', False) + self.torchscript = kwargs.pop('torchscript', False) + + def save_pretrained(self, save_directory): + """ Save a configuration object to a directory, so that it + can be re-loaded using the `from_pretrained(save_directory)` class method. + """ + assert os.path.isdir(save_directory), "Saving path should be a directory where the model and configuration can be saved" + + # If we save using the predefined names, we can load using `from_pretrained` + output_config_file = os.path.join(save_directory, CONFIG_NAME) + + self.to_json_file(output_config_file) + + @classmethod + def from_pretrained(cls, pretrained_model_name_or_path, **kwargs): + r""" Instantiate a PretrainedConfig from a pre-trained model configuration. + + Params: + **pretrained_model_name_or_path**: either: + - a string with the `shortcut name` of a pre-trained model configuration to load from cache + or download and cache if not already stored in cache (e.g. 'bert-base-uncased'). + - a path to a `directory` containing a configuration file saved + using the `save_pretrained(save_directory)` method. + - a path or url to a saved configuration `file`. + **cache_dir**: (`optional`) string: + Path to a directory in which a downloaded pre-trained model + configuration should be cached if the standard cache should not be used. + **return_unused_kwargs**: (`optional`) bool: + - If False, then this function returns just the final configuration object. + - If True, then this functions returns a tuple `(config, unused_kwargs)` where `unused_kwargs` + is a dictionary consisting of the key/value pairs whose keys are not configuration attributes: + ie the part of kwargs which has not been used to update `config` and is otherwise ignored. + **kwargs**: (`optional`) dict: + Dictionary of key/value pairs with which to update the configuration object after loading. + - The values in kwargs of any keys which are configuration attributes will be used + to override the loaded values. + - Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled + by the `return_unused_kwargs` keyword parameter. + + Examples:: + + >>> config = BertConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache. + >>> config = BertConfig.from_pretrained('./test/saved_model/') # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')` + >>> config = BertConfig.from_pretrained('./test/saved_model/my_configuration.json') + >>> config = BertConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False) + >>> assert config.output_attention == True + >>> config, unused_kwargs = BertConfig.from_pretrained('bert-base-uncased', output_attention=True, + >>> foo=False, return_unused_kwargs=True) + >>> assert config.output_attention == True + >>> assert unused_kwargs == {'foo': False} + + """ + cache_dir = kwargs.pop('cache_dir', None) + return_unused_kwargs = kwargs.pop('return_unused_kwargs', False) + + if pretrained_model_name_or_path in cls.pretrained_config_archive_map: + config_file = cls.pretrained_config_archive_map[pretrained_model_name_or_path] + elif os.path.isdir(pretrained_model_name_or_path): + config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME) + else: + config_file = pretrained_model_name_or_path + # redirect to the cache, if necessary + try: + resolved_config_file = cached_path(config_file, cache_dir=cache_dir) + except EnvironmentError: + if pretrained_model_name_or_path in cls.pretrained_config_archive_map: + logger.error( + "Couldn't reach server at '{}' to download pretrained model configuration file.".format( + config_file)) + else: + logger.error( + "Model name '{}' was not found in model name list ({}). " + "We assumed '{}' was a path or url but couldn't find any file " + "associated to this path or url.".format( + pretrained_model_name_or_path, + ', '.join(cls.pretrained_config_archive_map.keys()), + config_file)) + return None + if resolved_config_file == config_file: + logger.info("loading configuration file {}".format(config_file)) + else: + logger.info("loading configuration file {} from cache at {}".format( + config_file, resolved_config_file)) + + # Load config + config = cls.from_json_file(resolved_config_file) + + # Update config with kwargs if needed + to_remove = [] + for key, value in kwargs.items(): + if hasattr(config, key): + setattr(config, key, value) + to_remove.append(key) + # add img_layer_norm_eps, use_img_layernorm + if "img_layer_norm_eps" in kwargs: + setattr(config, "img_layer_norm_eps", kwargs["img_layer_norm_eps"]) + to_remove.append("img_layer_norm_eps") + if "use_img_layernorm" in kwargs: + setattr(config, "use_img_layernorm", kwargs["use_img_layernorm"]) + to_remove.append("use_img_layernorm") + for key in to_remove: + kwargs.pop(key, None) + + logger.info("Model config %s", config) + if return_unused_kwargs: + return config, kwargs + else: + return config + + @classmethod + def from_dict(cls, json_object): + """Constructs a `Config` from a Python dictionary of parameters.""" + config = cls(vocab_size_or_config_json_file=-1) + for key, value in json_object.items(): + config.__dict__[key] = value + return config + + @classmethod + def from_json_file(cls, json_file): + """Constructs a `BertConfig` from a json file of parameters.""" + with open(json_file, "r", encoding='utf-8') as reader: + text = reader.read() + return cls.from_dict(json.loads(text)) + + def __eq__(self, other): + return self.__dict__ == other.__dict__ + + def __repr__(self): + return str(self.to_json_string()) + + def to_dict(self): + """Serializes this instance to a Python dictionary.""" + output = copy.deepcopy(self.__dict__) + return output + + def to_json_string(self): + """Serializes this instance to a JSON string.""" + return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n" + + def to_json_file(self, json_file_path): + """ Save this instance to a json file.""" + with open(json_file_path, "w", encoding='utf-8') as writer: + writer.write(self.to_json_string()) + + +class BertConfig(PretrainedConfig): + r""" + :class:`~pytorch_transformers.BertConfig` is the configuration class to store the configuration of a + `BertModel`. + + + Arguments: + vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`. + hidden_size: Size of the encoder layers and the pooler layer. + num_hidden_layers: Number of hidden layers in the Transformer encoder. + num_attention_heads: Number of attention heads for each attention layer in + the Transformer encoder. + intermediate_size: The size of the "intermediate" (i.e., feed-forward) + layer in the Transformer encoder. + hidden_act: The non-linear activation function (function or string) in the + encoder and pooler. If string, "gelu", "relu" and "swish" are supported. + hidden_dropout_prob: The dropout probabilitiy for all fully connected + layers in the embeddings, encoder, and pooler. + attention_probs_dropout_prob: The dropout ratio for the attention + probabilities. + max_position_embeddings: The maximum sequence length that this model might + ever be used with. Typically set this to something large just in case + (e.g., 512 or 1024 or 2048). + type_vocab_size: The vocabulary size of the `token_type_ids` passed into + `BertModel`. + initializer_range: The sttdev of the truncated_normal_initializer for + initializing all weight matrices. + layer_norm_eps: The epsilon used by LayerNorm. + """ + pretrained_config_archive_map = BERT_PRETRAINED_CONFIG_ARCHIVE_MAP + + def __init__(self, + vocab_size_or_config_json_file=30522, + hidden_size=768, + num_hidden_layers=12, + num_attention_heads=12, + intermediate_size=3072, + hidden_act="gelu", + hidden_dropout_prob=0.1, + attention_probs_dropout_prob=0.1, + max_position_embeddings=512, + type_vocab_size=2, + initializer_range=0.02, + layer_norm_eps=1e-12, + **kwargs): + super(BertConfig, self).__init__(**kwargs) + if isinstance(vocab_size_or_config_json_file, str): + with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader: + json_config = json.loads(reader.read()) + for key, value in json_config.items(): + self.__dict__[key] = value + elif isinstance(vocab_size_or_config_json_file, int): + self.vocab_size = vocab_size_or_config_json_file + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.hidden_act = hidden_act + self.intermediate_size = intermediate_size + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.max_position_embeddings = max_position_embeddings + self.type_vocab_size = type_vocab_size + self.initializer_range = initializer_range + self.layer_norm_eps = layer_norm_eps + else: + raise ValueError("First argument must be either a vocabulary size (int)" + "or the path to a pretrained model config file (str)") + + +def _gelu_python(x): + + return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0))) \ No newline at end of file diff --git a/iChat/models/grit_src/grit/modeling/text/text_decoder.py b/iChat/models/grit_src/grit/modeling/text/text_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..071baa7a52d21d7132cc492f070cba066d17aa43 --- /dev/null +++ b/iChat/models/grit_src/grit/modeling/text/text_decoder.py @@ -0,0 +1,672 @@ +# Modified by Jialian Wu from +# https://github.com/microsoft/GenerativeImage2Text/blob/main/generativeimage2text/layers/decoder.py +# and https://github.com/kdexd/virtex +from torch import nn +import torch +import functools +from torch.nn import functional as F +import warnings + + +class TextualHead(nn.Module): + def __init__(self, + visual_feature_size: int, vocab_size: int, hidden_size: int): + super().__init__() + self.visual_feature_size = visual_feature_size + self.vocab_size = vocab_size + self.hidden_size = hidden_size + + @property + def textual_feature_size(self): + return self.hidden_size + + +class WordAndPositionalEmbedding(nn.Module): + def __init__( + self, + vocab_size: int, + hidden_size: int, + dropout: float = 0.0, + max_caption_length: int = 30, + padding_idx: int = 0, + ): + super().__init__() + self.vocab_size = vocab_size + self.padding_idx = padding_idx + + #self.words = nn.Embedding(vocab_size, hidden_size, padding_idx=padding_idx) + self.words = nn.Embedding(vocab_size, hidden_size) + + # We provide no "padding index" for positional embeddings. We zero out + # the positional embeddings of padded positions as a post-processing. + self.positions = nn.Embedding(max_caption_length, hidden_size) + self.layer_norm = nn.LayerNorm( + hidden_size, eps=1e-8, elementwise_affine=True + ) + self.dropout = nn.Dropout(p=dropout) + + def forward(self, tokens: torch.Tensor): + position_indices = self._create_position_indices(tokens) + + # shape: (batch_size, max_caption_length, hidden_size) + word_embeddings = self.words(tokens) + position_embeddings = self.positions(position_indices) + + # shape: (batch_size, max_caption_length, hidden_size) + embeddings = self.layer_norm(word_embeddings + position_embeddings) + embeddings = self.dropout(embeddings) + + return embeddings + + @functools.lru_cache(maxsize=128) + def _create_position_indices(self, tokens: torch.Tensor): + + # Create position indices of the same size as token indices. + batch_size, max_caption_length = tokens.size() + positions = torch.arange( + max_caption_length, dtype=tokens.dtype, device=tokens.device + ) + # shape: (batch_size, max_caption_length) + positions = positions.unsqueeze(0).expand(batch_size, max_caption_length) + return positions + + +class BertEncoderAsDecoder(nn.Module): + def __init__(self, encoder): + super().__init__() + self.encoder = encoder + + def forward(self, tgt, memory, + tgt_mask=None, + tgt_key_padding_mask=None, + memory_key_padding_mask=None, + tgt_bi_valid_mask=None, + encoder_history_states=None, + ): + assert tgt_key_padding_mask is None, 'not supported' + assert tgt_mask.dim() == 2 + assert tgt_mask.shape[0] == tgt_mask.shape[1] + # tgt_mask should always be 0/negative infinity + tgt = tgt.transpose(0, 1) + memory = memory.transpose(0, 1) + + hidden_states = torch.cat((memory, tgt), dim=1) + num_tgt = tgt.shape[1] + num_memory = memory.shape[1] + device = tgt.device + dtype = tgt.dtype + top_left = torch.zeros((num_memory, num_memory), device=device, dtype=dtype) + top_right = torch.full((num_memory, num_tgt), float('-inf'), device=tgt.device, dtype=dtype,) + bottom_left = torch.zeros((num_tgt, num_memory), dtype=dtype, device=tgt_mask.device,) + left = torch.cat((top_left, bottom_left), dim=0) + right = torch.cat((top_right, tgt_mask.to(dtype)), dim=0) + + full_attention_mask = torch.cat((left, right), dim=1)[None, :] + + if memory_key_padding_mask is None: + memory_key_padding_mask = torch.full((memory.shape[0], memory.shape[1]), fill_value=False, device=device) + # if it is False, it means valid. That is, it is not a padding + assert memory_key_padding_mask.dtype == torch.bool + zero_negative_infinity = torch.zeros_like(memory_key_padding_mask, dtype=tgt.dtype) + zero_negative_infinity[memory_key_padding_mask] = float('-inf') + full_attention_mask = full_attention_mask.expand((memory_key_padding_mask.shape[0], num_memory + num_tgt, num_memory + num_tgt)) + full_attention_mask = full_attention_mask.clone() + origin_left = full_attention_mask[:, :, :num_memory] + update = zero_negative_infinity[:, None, :] + full_attention_mask[:, :, :num_memory] = origin_left + update + + if tgt_bi_valid_mask is not None: + # verify the correctness + bs = full_attention_mask.shape[0] + # during inference, tgt_bi_valid_mask's length is not changed, but + # num_tgt can be increased + max_valid_target = tgt_bi_valid_mask.shape[1] + mask = tgt_bi_valid_mask[:, None, :].expand((bs, num_memory+num_tgt, max_valid_target)) + full_attention_mask[:, :, num_memory:(num_memory+max_valid_target)][mask] = 0 + + # add axis for multi-head + full_attention_mask = full_attention_mask[:, None, :, :] + + if encoder_history_states is None: + result = self.encoder( + hidden_states=hidden_states, + attention_mask=full_attention_mask, + encoder_history_states=encoder_history_states, + ) + result = list(result) + result[0] = result[0][:, num_memory:].transpose(0, 1) + if self.encoder.output_hidden_states: + return result[0], result[1] + else: + # make it back-compatible + return result[0] + else: + encoder_out = self.encoder( + hidden_states=hidden_states[:, -1:], + attention_mask=full_attention_mask[:, :, -1:], + encoder_history_states=encoder_history_states, + ) + result = encoder_out[0].transpose(0, 1) + if self.encoder.output_hidden_states: + return result, encoder_out[1] + else: + return result + + +def create_transformer(decoder_type, norm_type, + textual_feature_size, + attention_heads, + feedforward_size, + dropout, + num_layers, + output_hidden_states=False, + use_mlp_wrapper=None, + use_act_checkpoint=True, + ): + assert norm_type in ['post', 'pre'] + if decoder_type is None: + LayerClass = ( + nn.TransformerDecoderLayer + if norm_type == "post" + else PreNormTransformerDecoderLayer + ) + _layer = LayerClass( + textual_feature_size, + attention_heads, + dim_feedforward=feedforward_size, + dropout=dropout, + activation="gelu", + ) + return nn.TransformerDecoder(_layer, num_layers) + elif decoder_type == 'bert_en': + from .modeling_bert import BertConfig, BertEncoder + config = BertConfig( + vocab_size_or_config_json_file=30522, + hidden_size=textual_feature_size, + num_hidden_layers=num_layers, + num_attention_heads=attention_heads, + intermediate_size=feedforward_size, + hidden_act="gelu", + hidden_dropout_prob=0.1, + attention_probs_dropout_prob=0.1, + layer_norm_eps=1e-12, + ) + config.pre_norm = (norm_type == 'pre') + config.use_mlp_wrapper = use_mlp_wrapper + config.output_hidden_states = output_hidden_states + encoder = BertEncoder(config, use_act_checkpoint=use_act_checkpoint) + return BertEncoderAsDecoder(encoder) + + +class PreNormTransformerDecoderLayer(nn.TransformerDecoderLayer): + def forward(self, tgt, memory, tgt_mask=None, memory_mask=None, + tgt_key_padding_mask=None, memory_key_padding_mask=None): + # fmt: off + # We use the members (modules) from super-class, just the order of + # operations is changed here. First layernorm, then attention. + tgt2 = self.norm1(tgt) + tgt2, _ = self.self_attn( + tgt2, tgt2, tgt2, attn_mask=tgt_mask, + key_padding_mask=tgt_key_padding_mask + ) + tgt = tgt + self.dropout1(tgt2) + + # Layernorm first, then decoder attention. + tgt2 = self.norm2(tgt) + tgt2, _ = self.multihead_attn( + tgt2, memory, memory, attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask + ) + tgt = tgt + self.dropout2(tgt2) + + # Layernorm first, then transformation through feedforward network. + tgt2 = self.norm3(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) + tgt = tgt + self.dropout3(tgt2) + return tgt + + +class TransformerDecoderTextualHead(TextualHead): + def __init__( + self, + object_feature_size: int, + vocab_size: int, + hidden_size: int, + num_layers: int, + attention_heads: int, + feedforward_size: int, + dropout: float = 0.1, + norm_type: str = "post", + mask_future_positions: bool = True, + max_caption_length: int = 1024, + padding_idx: int = 0, + decoder_type=None, + not_tie_weight=None, + output_hidden_states=None, + use_mlp_wrapper=None, + use_act_checkpoint=True, + ): + super().__init__(object_feature_size, vocab_size, hidden_size) + self.num_layers = num_layers + self.attention_heads = attention_heads + self.feedforward_size = feedforward_size + self.dropout = dropout + assert mask_future_positions + self.padding_idx = padding_idx + + self.object_feature_projection = nn.Sequential( + nn.Linear(object_feature_size, self.textual_feature_size), + nn.LayerNorm(self.textual_feature_size)) + + self.embedding = WordAndPositionalEmbedding( + self.vocab_size, + self.textual_feature_size, + dropout=dropout, + max_caption_length=max_caption_length, + padding_idx=padding_idx, + ) + self.transformer = create_transformer( + decoder_type=decoder_type, + norm_type=norm_type, + textual_feature_size=self.textual_feature_size, + attention_heads=self.attention_heads, + feedforward_size=self.feedforward_size, + dropout=dropout, + num_layers=self.num_layers, + output_hidden_states=output_hidden_states, + use_mlp_wrapper=use_mlp_wrapper, + use_act_checkpoint=use_act_checkpoint, + ) + self.apply(self._init_weights) + + # Create an output linear layer and tie the input and output word + # embeddings to reduce parametejs. + self.output = nn.Linear(self.textual_feature_size, vocab_size) + if not not_tie_weight: + self.output.weight = self.embedding.words.weight + + @staticmethod + def _init_weights(module): + """Initialize weights like BERT - N(0.0, 0.02), bias = 0.""" + + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=0.02) + elif isinstance(module, nn.MultiheadAttention): + module.in_proj_weight.data.normal_(mean=0.0, std=0.02) + module.out_proj.weight.data.normal_(mean=0.0, std=0.02) + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=0.02) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + def forward( + self, + hidden_states, + text_tokens, + ): + projected_object_features = self.object_feature_projection(hidden_states) if hidden_states is not None else None + batch_size, max_text_length = text_tokens.size() + text_embeddings = self.embedding(text_tokens) + + # An additive mask for masking the future (one direction). + uni_mask_zero_neg = self._generate_future_mask( + max_text_length, text_embeddings.dtype, text_embeddings.device + ) + + # We transpose the first two dimensions of tokens embeddings and visual + # features, as required by decoder. + text_embeddings = text_embeddings.transpose(0, 1) + + projected_object_features = projected_object_features.transpose(0, 1) + + # if transformer here is the pytorch/decoder, there is no chance, the + # output is always tensor + trans_out = self.transformer( + text_embeddings, + projected_object_features, + tgt_mask=uni_mask_zero_neg, + ) + if isinstance(trans_out, tuple): + textual_features = trans_out[0] + else: + assert isinstance(trans_out, torch.Tensor) + textual_features = trans_out + # Undo the transpose and bring batch to dim 0. + # shape: (batch_size, max_caption_length, hidden_size) + textual_features = textual_features.transpose(0, 1) + + # shape: (batch_size, max_caption_length, vocab_size) + output_logits = self.output(textual_features) + if isinstance(trans_out, tuple): + return output_logits, trans_out[1] + else: + return output_logits + + def _generate_future_mask( + self, size: int, dtype: torch.dtype, device: torch.device + ): + # Default mask is for forward direction. Flip for backward direction. + mask = torch.triu( + torch.ones(size, size, device=device, dtype=dtype), diagonal=1 + ) + mask = mask.masked_fill(mask == 1, float("-inf")) + return mask + + +class AutoRegressiveBeamSearch(object): + def __init__( + self, + end_token_id: int, + max_steps: int = 50, + beam_size: int = 5, + objectdet=True, + per_node_beam_size: int = 2, + ): + self._eos_index = end_token_id + self.max_steps = max_steps + self.beam_size = beam_size + self.objectdet = objectdet + self.per_node_beam_size = per_node_beam_size or beam_size + + def search(self, begin_tokens, step): + if self.beam_size > 1 and self.objectdet: + only_return_best = False + else: + only_return_best = True + + batch_size = begin_tokens.size()[0] + + predictions = begin_tokens.unsqueeze(1).expand((batch_size, self.beam_size, begin_tokens.shape[-1])) + # Calculate the first timestep. This is done outside the main loop + # because we are going from a single decoder input (the output from the + # encoder) to the top `beam_size` decoder outputs. On the other hand, + # within the main loop we are going from the `beam_size` elements of the + # beam to `beam_size`^2 candidates from which we will select the top + # `beam_size` elements for the next iteration. + # shape: (batch_size, num_classes) + start_class_logits = step(begin_tokens) + + # Convert logits to logprobs. + # shape: (batch_size * beam_size, vocab_size) + start_class_logprobs = F.log_softmax(start_class_logits, dim=1) + + num_classes = start_class_logprobs.size()[1] + + # shape: (batch_size, beam_size), (batch_size, beam_size) + start_top_logprobs, start_predicted_classes = start_class_logprobs.topk( + self.beam_size + ) + + if ( + self.beam_size == 1 + and (start_predicted_classes == self._eos_index).all() + ): + warnings.warn( + "Empty object description predicted. You may want to increase beam" + "size or ensure your step function is working properly.", + RuntimeWarning, + ) + if only_return_best: + return start_predicted_classes, start_top_logprobs + else: + return start_predicted_classes.unsqueeze(-1), start_top_logprobs + + # The log probs for the last time step. + # shape: (batch_size, beam_size) + last_logprobs = start_top_logprobs + + # shape: (batch_size, beam_size, sequence_length) + predictions = torch.cat([predictions, start_predicted_classes.unsqueeze(-1)], dim=-1) + + # Log probability tensor that mandates that the end token is selected. + # shape: (batch_size * beam_size, num_classes) + logprobs_after_end = start_class_logprobs.new_full( + (batch_size * self.beam_size, num_classes), float("-inf") + ) + logprobs_after_end[:, self._eos_index] = 0.0 + + logits_after_end = start_class_logprobs.new_full( + (batch_size * self.beam_size, num_classes), float("-inf") + ) + logits_after_end[:, self._eos_index] = 0 + + while predictions.shape[-1] < self.max_steps: + # shape: (batch_size * beam_size,) + last_predictions = predictions[:, :, -1].reshape(batch_size * self.beam_size) + + # If every predicted token from the last step is `self._eos_index`, + # then we can stop early. + if (last_predictions == self._eos_index).all(): + break + + predictions_so_far = predictions.view( + batch_size * self.beam_size, -1 + ) + # shape: (batch_size * beam_size, num_classes) + class_logits = step(predictions_so_far) + + # Set logprobs of last predicted tokens as high negative value to avoid + # repetition in description. + class_logits = class_logits.scatter(1, predictions_so_far[:, -1].view((-1, 1)), -10000) + + # shape: (batch_size * beam_size, num_classes) + last_predictions_expanded = last_predictions.unsqueeze(-1).expand( + batch_size * self.beam_size, num_classes + ) + + # Here we are finding any beams where we predicted the end token in + # the previous timestep and replacing the distribution with a + # one-hot distribution, forcing the beam to predict the end token + # this timestep as well. + class_logits = torch.where( + last_predictions_expanded == self._eos_index, + logits_after_end, + class_logits, + ) + + # Convert logits to logprobs. + # shape: (batch_size * beam_size, vocab_size) + class_logprobs = F.log_softmax(class_logits, dim=1) + + # shape (both): (batch_size * beam_size, per_node_beam_size) + top_logprobs, predicted_classes = class_logprobs.topk( + self.per_node_beam_size + ) + + # Here we expand the last log probs to `(batch_size * beam_size, + # per_node_beam_size)` so that we can add them to the current log + # probs for this timestep. This lets us maintain the log + # probability of each element on the beam. + # shape: (batch_size * beam_size, per_node_beam_size) + expanded_last_logprobs = ( + last_logprobs.unsqueeze(2) + .expand(batch_size, self.beam_size, self.per_node_beam_size) + .reshape(batch_size * self.beam_size, self.per_node_beam_size) + ) + # shape: (batch_size * beam_size, per_node_beam_size) + summed_top_logprobs = top_logprobs + expanded_last_logprobs + + # shape: (batch_size, beam_size * per_node_beam_size) + reshaped_summed = summed_top_logprobs.reshape( + batch_size, self.beam_size * self.per_node_beam_size + ) + # shape: (batch_size, beam_size * per_node_beam_size) + reshaped_predicted_classes = predicted_classes.reshape( + batch_size, self.beam_size * self.per_node_beam_size + ) + # Append the predictions to the current beam. + reshaped_beam = ( + predictions.view(batch_size * self.beam_size, 1, -1) + .repeat(1, self.per_node_beam_size, 1) + .reshape(batch_size, self.beam_size * self.per_node_beam_size, -1) + ) + # batch_size, (beam_size * per_node_beach_size), #token + reshaped_beam = torch.cat([reshaped_beam, reshaped_predicted_classes.unsqueeze(-1)], dim=-1) + + # Keep only the top `beam_size` beam indices. + # shape: (batch_size, beam_size), (batch_size, beam_size) + restricted_beam_logprobs, restricted_beam_indices = reshaped_summed.topk( + self.beam_size + ) + predictions = reshaped_beam.gather( + 1, restricted_beam_indices.unsqueeze(-1).repeat(1,1,reshaped_beam.shape[-1]) + ) + + # shape: (batch_size, beam_size) + last_logprobs = restricted_beam_logprobs + + if not torch.isfinite(last_logprobs).all(): + warnings.warn( + "Infinite log probs encountered. Some final descriptions may not " + "make sense. This can happen when the beam size is larger than" + " the number of valid (non-zero probability) transitions that " + "the step function produces.", + RuntimeWarning, + ) + + # Optionally select best beam and its logprobs. + if only_return_best: + # shape: (batch_size, sequence_length) + predictions = predictions[:, 0, :] + last_logprobs = last_logprobs[:, 0] + num_valid = (predictions != self._eos_index).sum(dim=-1) + num_valid += (predictions == self._eos_index).sum(dim=-1) > 0 + num_valid = num_valid - begin_tokens.shape[1] + num_valid = num_valid.clip(min=1) + + last_logprobs = last_logprobs / num_valid + + return predictions, last_logprobs + + +class GRiTTextDecoder(nn.Module): + def __init__( + self, + transformer, + begin_token_id=101, + beamsearch_decode=None, + loss_type=None, + tokenizer=None, + ): + super().__init__() + self.textual = transformer + self.padding_idx = self.textual.padding_idx + + self.begin_token_id = begin_token_id + self.beamsearch_decode = beamsearch_decode + self.tokenizer = tokenizer + + if loss_type is None: + self.loss = nn.CrossEntropyLoss(ignore_index=self.padding_idx) + elif loss_type == 'smooth': + self.loss = SmoothLabelCrossEntropyLoss(ignore_index=self.padding_idx) + else: + raise NotImplementedError(loss_type) + + def forward(self, batch): + object_features = batch['object_features'] + + if self.training: + caption_token_input = batch["text_tokens"] + + output_logits = self.textual( + object_features, + caption_token_input, + ) + + if 'need_predict' in batch: + # in place should also be good, but we do not choose that for + # safety as we may use it in prediction results in future + target = batch["text_tokens"].clone() + target[batch['need_predict'] == 0] = self.padding_idx + else: + target = batch["text_tokens"] + + feat = output_logits[:, :-1].contiguous() + target = target[:, 1:].contiguous() + feat = feat.view(-1, self.textual.vocab_size) + target = target.view(-1) + + valid_mask = target != self.padding_idx + target = target[valid_mask] + feat = feat[valid_mask] + loss = self.loss(feat, target) + + return loss + else: + output_dict = self.infer(object_features) + return output_dict + + def infer(self, object_features): + batch_size = object_features.size(0) + begin_tokens = object_features.new_full( + (batch_size, 1), self.begin_token_id + ).long() + + decoding_step = functools.partial( + self.decoding_step, object_features + ) + + object_description_tokens, logprobs = self.beamsearch_decode.search( + begin_tokens, decoding_step + ) + + output_dict = { + 'predictions': object_description_tokens, + 'logprobs': logprobs, + } + + return output_dict + + def decoding_step(self, object_features, partial_text): + batch_size = object_features.shape[0] + beam_size = int(partial_text.size(0) / batch_size) + if beam_size > 1: + batch_size, num_token, channels = object_features.size() + object_features = object_features.unsqueeze(1).repeat(1, beam_size, 1, 1) + object_features = object_features.view( + batch_size * beam_size, num_token, channels + ) + + text_lengths = torch.ones_like(partial_text) + if len(text_lengths.size()) != 2: + partial_text = partial_text.unsqueeze(1) + + # shape: (batch_size * beam_size, partial_caption_length, vocab_size) + logits = self.textual( + object_features, + partial_text, + ) + + return logits[:, -1, :].float() + + +class SmoothLabelCrossEntropyLoss(nn.Module): + def __init__(self, eps=0.1, log_prefix='', ignore_index=None): + super().__init__() + self.eps = eps + self.log_soft = nn.LogSoftmax(dim=1) + self.kl = nn.KLDivLoss(reduction='none') + + self.iter = 0 + self.max_loss = 0 + self.min_loss = 0 + self.log_prefix = log_prefix + self.ignore_index = ignore_index + + def forward(self, feature, target): + feature = feature.float() + if self.ignore_index is not None: + valid_mask = target != self.ignore_index + target = target[valid_mask] + feature = feature[valid_mask] + assert target.numel() > 0 + self.iter += 1 + eps = self.eps + n_class = feature.size(1) + one_hot = torch.zeros_like(feature).scatter(1, target.view(-1, 1), 1) + one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1) + log_prb = self.log_soft(feature) + loss = self.kl(log_prb, one_hot) + return loss.sum(dim=1).mean() + diff --git a/iChat/models/grit_src/grit/predictor.py b/iChat/models/grit_src/grit/predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..6c95bfa4168af4376be065db8e22e2f70e937896 --- /dev/null +++ b/iChat/models/grit_src/grit/predictor.py @@ -0,0 +1,90 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Jialian Wu from https://github.com/facebookresearch/detectron2/blob/main/detectron2/utils/visualizer.py +import torch + +from detectron2.engine.defaults import DefaultPredictor +from detectron2.utils.visualizer import ColorMode, Visualizer + + +class BatchDefaultPredictor(DefaultPredictor): + def __call__(self, original_images): + """ + Args: + original_image (np.ndarray): an image of shape (H, W, C) (in BGR order). + + Returns: + predictions (dict): + the output of the model for one image only. + See :doc:`/tutorials/models` for details about the format. + """ + with torch.no_grad(): # https://github.com/sphinx-doc/sphinx/issues/4258 + # Apply pre-processing to image. + height, width = original_images.shape[1:3] + batch_inputs = [] + for original_image in original_images: + image = self.aug.get_transform(original_image).apply_image(original_image) + image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1)) + + inputs = {"image": image, "height": height, "width": width} + batch_inputs.append(inputs) + predictions = self.model(batch_inputs)[0] + return predictions + +class Visualizer_GRiT(Visualizer): + def __init__(self, image, instance_mode=None): + super().__init__(image, instance_mode=instance_mode) + + def draw_instance_predictions(self, predictions): + boxes = predictions.pred_boxes if predictions.has("pred_boxes") else None + scores = predictions.scores if predictions.has("scores") else None + classes = predictions.pred_classes.tolist() if predictions.has("pred_classes") else None + object_description = predictions.pred_object_descriptions.data + # uncomment to output scores in visualized images + # object_description = [c + '|' + str(round(s.item(), 1)) for c, s in zip(object_description, scores)] + + if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("thing_colors"): + colors = [ + self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in classes + ] + alpha = 0.8 + else: + colors = None + alpha = 0.5 + + if self._instance_mode == ColorMode.IMAGE_BW: + self.output.reset_image( + self._create_grayscale_image( + (predictions.pred_masks.any(dim=0) > 0).numpy() + if predictions.has("pred_masks") + else None + ) + ) + alpha = 0.3 + + self.overlay_instances( + masks=None, + boxes=boxes, + labels=object_description, + keypoints=None, + assigned_colors=colors, + alpha=alpha, + ) + return self.output + + +class VisualizationDemo(object): + def __init__(self, cfg, instance_mode=ColorMode.IMAGE): + self.cpu_device = torch.device("cpu") + self.instance_mode = instance_mode + + self.predictor = DefaultPredictor(cfg) + + def run_on_image(self, image): + predictions = self.predictor(image) + # Convert image from OpenCV BGR format to Matplotlib RGB format. + image = image[:, :, ::-1] + visualizer = Visualizer_GRiT(image, instance_mode=self.instance_mode) + instances = predictions["instances"].to(self.cpu_device) + vis_output = visualizer.draw_instance_predictions(predictions=instances) + + return predictions, vis_output \ No newline at end of file diff --git a/iChat/models/grit_src/image_dense_captions.py b/iChat/models/grit_src/image_dense_captions.py new file mode 100644 index 0000000000000000000000000000000000000000..f52763ce36827ccbcdbf27840163371fc98d805e --- /dev/null +++ b/iChat/models/grit_src/image_dense_captions.py @@ -0,0 +1,83 @@ +import argparse +import multiprocessing as mp +import os +import time +import cv2 +import tqdm +import sys + +from detectron2.config import get_cfg +from detectron2.data.detection_utils import read_image +from detectron2.utils.logger import setup_logger + +sys.path.insert(0, 'iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/') +from centernet.config import add_centernet_config +from ..grit_src.grit.config import add_grit_config + +from ..grit_src.grit.predictor import VisualizationDemo + + +# constants +WINDOW_NAME = "GRiT" + + +def dense_pred_to_caption(predictions): + boxes = predictions["instances"].pred_boxes if predictions["instances"].has("pred_boxes") else None + object_description = predictions["instances"].pred_object_descriptions.data + new_caption = "" + for i in range(len(object_description)): + new_caption += (object_description[i] + ": " + str([int(a) for a in boxes[i].tensor.cpu().detach().numpy()[0]])) + "; " + return new_caption + +def dense_pred_to_caption_only_name(predictions): + # boxes = predictions["instances"].pred_boxes if predictions["instances"].has("pred_boxes") else None + object_description = predictions["instances"].pred_object_descriptions.data + new_caption = ",".join(object_description) + del predictions + # for i in range(len(object_description)): + # new_caption += (object_description[i] + ": " + str([int(a) for a in boxes[i].tensor.cpu().detach().numpy()[0]])) + "; " + return new_caption + +def setup_cfg(args): + cfg = get_cfg() + # if args["cpu"]: + # cfg.MODEL.DEVICE="cpu" + add_centernet_config(cfg) + add_grit_config(cfg) + cfg.merge_from_file(args["config_file"]) + cfg.merge_from_list(args["opts"]) + # Set score_threshold for builtin models + cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args["confidence_threshold"] + cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args["confidence_threshold"] + if args["test_task"]: + cfg.MODEL.TEST_TASK = args["test_task"] + cfg.MODEL.BEAM_SIZE = 1 + cfg.MODEL.ROI_HEADS.SOFT_NMS_ENABLED = False + cfg.USE_ACT_CHECKPOINT = False + cfg.freeze() + return cfg + +def get_parser(device): + arg_dict = {'config_file': "iChat/models/grit_src/configs/GRiT_B_DenseCap_ObjectDet.yaml", 'device': device, 'confidence_threshold': 0.5, 'test_task': 'DenseCap', 'opts': ["MODEL.WEIGHTS", "model_zoo/grit_b_densecap_objectdet.pth"]} + return arg_dict + +def image_caption_api(image_src, device): + args2 = get_parser(device) + cfg = setup_cfg(args2) + demo = VisualizationDemo(cfg) + if image_src: + img = read_image(image_src, format="BGR") + predictions, visualized_output = demo.run_on_image(img) + new_caption = dense_pred_to_caption(predictions) + return new_caption + +def init_demo(device): + args2 = get_parser(device) + cfg = setup_cfg(args2) + demo = VisualizationDemo(cfg) + return demo + +if __name__=="__main__": + import os + os.environ['CUDA_VISIBLE_DEVICES']='7' + print(image_caption_api("images/dancing_example_4.mp4_20230417_135359.263.jpg",'cuda')) \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/.circleci/config.yml b/iChat/models/grit_src/third_party/CenterNet2/.circleci/config.yml new file mode 100644 index 0000000000000000000000000000000000000000..097afade9aa0a48b54ab7c7aae93058227901e7a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/.circleci/config.yml @@ -0,0 +1,256 @@ +version: 2.1 + +# ------------------------------------------------------------------------------------- +# Environments to run the jobs in +# ------------------------------------------------------------------------------------- +cpu: &cpu + machine: + image: ubuntu-2004:202107-02 + resource_class: medium + +gpu: &gpu + machine: + # NOTE: use a cuda vesion that's supported by all our pytorch versions + image: ubuntu-1604-cuda-11.1:202012-01 + resource_class: gpu.nvidia.small + +windows-cpu: &windows_cpu + machine: + resource_class: windows.medium + image: windows-server-2019-vs2019:stable + shell: powershell.exe + +# windows-gpu: &windows_gpu +# machine: +# resource_class: windows.gpu.nvidia.medium +# image: windows-server-2019-nvidia:stable + +version_parameters: &version_parameters + parameters: + pytorch_version: + type: string + torchvision_version: + type: string + pytorch_index: + type: string + # use test wheels index to have access to RC wheels + # https://download.pytorch.org/whl/test/torch_test.html + default: "https://download.pytorch.org/whl/torch_stable.html" + python_version: # NOTE: only affect linux + type: string + default: '3.6.8' + + environment: + PYTORCH_VERSION: << parameters.pytorch_version >> + TORCHVISION_VERSION: << parameters.torchvision_version >> + PYTORCH_INDEX: << parameters.pytorch_index >> + PYTHON_VERSION: << parameters.python_version>> + # point datasets to ~/.torch so it's cached in CI + DETECTRON2_DATASETS: ~/.torch/datasets + +# ------------------------------------------------------------------------------------- +# Re-usable commands +# ------------------------------------------------------------------------------------- +# install_nvidia_driver: &install_nvidia_driver +# - run: +# name: Install nvidia driver +# working_directory: ~/ +# command: | +# wget -q 'https://s3.amazonaws.com/ossci-linux/nvidia_driver/NVIDIA-Linux-x86_64-430.40.run' +# sudo /bin/bash ./NVIDIA-Linux-x86_64-430.40.run -s --no-drm +# nvidia-smi + +add_ssh_keys: &add_ssh_keys + # https://circleci.com/docs/2.0/add-ssh-key/ + - add_ssh_keys: + fingerprints: + - "e4:13:f2:22:d4:49:e8:e4:57:5a:ac:20:2f:3f:1f:ca" + +install_python: &install_python + - run: + name: Install Python + working_directory: ~/ + command: | + # upgrade pyenv + cd /opt/circleci/.pyenv/plugins/python-build/../.. && git pull && cd - + pyenv install -s $PYTHON_VERSION + pyenv global $PYTHON_VERSION + python --version + which python + pip install --upgrade pip + +setup_venv: &setup_venv + - run: + name: Setup Virtual Env + working_directory: ~/ + command: | + python -m venv ~/venv + echo ". ~/venv/bin/activate" >> $BASH_ENV + . ~/venv/bin/activate + python --version + which python + which pip + pip install --upgrade pip + +setup_venv_win: &setup_venv_win + - run: + name: Setup Virutal Env for Windows + command: | + pip install virtualenv + python -m virtualenv env + .\env\Scripts\activate + python --version + which python + which pip + +install_linux_dep: &install_linux_dep + - run: + name: Install Dependencies + command: | + # disable crash coredump, so unittests fail fast + sudo systemctl stop apport.service + # install from github to get latest; install iopath first since fvcore depends on it + pip install --progress-bar off -U 'git+https://github.com/facebookresearch/iopath' + pip install --progress-bar off -U 'git+https://github.com/facebookresearch/fvcore' + # Don't use pytest-xdist: cuda tests are unstable under multi-process workers. + pip install --progress-bar off ninja opencv-python-headless pytest tensorboard pycocotools + pip install --progress-bar off torch==$PYTORCH_VERSION -f $PYTORCH_INDEX + if [[ "$TORCHVISION_VERSION" == "master" ]]; then + pip install git+https://github.com/pytorch/vision.git + else + pip install --progress-bar off torchvision==$TORCHVISION_VERSION -f $PYTORCH_INDEX + fi + + python -c 'import torch; print("CUDA:", torch.cuda.is_available())' + gcc --version + +install_detectron2: &install_detectron2 + - run: + name: Install Detectron2 + command: | + # Remove first, in case it's in the CI cache + pip uninstall -y detectron2 + pip install --progress-bar off -e .[all] + python -m detectron2.utils.collect_env + ./datasets/prepare_for_tests.sh + +run_unittests: &run_unittests + - run: + name: Run Unit Tests + command: | + pytest -v --durations=15 tests # parallel causes some random failures + +# ------------------------------------------------------------------------------------- +# Jobs to run +# ------------------------------------------------------------------------------------- +jobs: + linux_cpu_tests: + <<: *cpu + <<: *version_parameters + + working_directory: ~/detectron2 + + steps: + - checkout + + # Cache the venv directory that contains python, dependencies, and checkpoints + # Refresh the key when dependencies should be updated (e.g. when pytorch releases) + - restore_cache: + keys: + - cache-{{ arch }}-<< parameters.pytorch_version >>-{{ .Branch }}-20210827 + + - <<: *install_python + - <<: *install_linux_dep + - <<: *install_detectron2 + - <<: *run_unittests + + - save_cache: + paths: + - /opt/circleci/.pyenv + - ~/.torch + key: cache-{{ arch }}-<< parameters.pytorch_version >>-{{ .Branch }}-20210827 + + + linux_gpu_tests: + <<: *gpu + <<: *version_parameters + + working_directory: ~/detectron2 + + steps: + - checkout + + - restore_cache: + keys: + - cache-{{ arch }}-<< parameters.pytorch_version >>-{{ .Branch }}-20210827 + + - <<: *install_python + - <<: *install_linux_dep + - <<: *install_detectron2 + - <<: *run_unittests + + - save_cache: + paths: + - /opt/circleci/.pyenv + - ~/.torch + key: cache-{{ arch }}-<< parameters.pytorch_version >>-{{ .Branch }}-20210827 + + windows_cpu_build: + <<: *windows_cpu + <<: *version_parameters + steps: + - <<: *add_ssh_keys + - checkout + - <<: *setup_venv_win + + # Cache the env directory that contains dependencies + - restore_cache: + keys: + - cache-{{ arch }}-<< parameters.pytorch_version >>-{{ .Branch }}-20210404 + + - run: + name: Install Dependencies + command: | + pip install certifi --ignore-installed # required on windows to workaround some cert issue + pip install numpy cython # required on windows before pycocotools + pip install opencv-python-headless pytest-xdist pycocotools tensorboard + pip install -U git+https://github.com/facebookresearch/iopath + pip install -U git+https://github.com/facebookresearch/fvcore + pip install torch==$env:PYTORCH_VERSION torchvision==$env:TORCHVISION_VERSION -f $env:PYTORCH_INDEX + + - save_cache: + paths: + - env + key: cache-{{ arch }}-<< parameters.pytorch_version >>-{{ .Branch }}-20210404 + + - <<: *install_detectron2 + # TODO: unittest fails for now + +workflows: + version: 2 + regular_test: + jobs: + - linux_cpu_tests: + name: linux_cpu_tests_pytorch1.10 + pytorch_version: '1.10.0+cpu' + torchvision_version: '0.11.1+cpu' + - linux_gpu_tests: + name: linux_gpu_tests_pytorch1.8 + pytorch_version: '1.8.1+cu111' + torchvision_version: '0.9.1+cu111' + - linux_gpu_tests: + name: linux_gpu_tests_pytorch1.9 + pytorch_version: '1.9+cu111' + torchvision_version: '0.10+cu111' + - linux_gpu_tests: + name: linux_gpu_tests_pytorch1.10 + pytorch_version: '1.10+cu111' + torchvision_version: '0.11.1+cu111' + - linux_gpu_tests: + name: linux_gpu_tests_pytorch1.10_python39 + pytorch_version: '1.10+cu111' + torchvision_version: '0.11.1+cu111' + python_version: '3.9.6' + - windows_cpu_build: + pytorch_version: '1.10+cpu' + torchvision_version: '0.11.1+cpu' diff --git a/iChat/models/grit_src/third_party/CenterNet2/.clang-format b/iChat/models/grit_src/third_party/CenterNet2/.clang-format new file mode 100644 index 0000000000000000000000000000000000000000..39b1b3d603ed0cf6b7f94c9c08067f148f35613f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/.clang-format @@ -0,0 +1,85 @@ +AccessModifierOffset: -1 +AlignAfterOpenBracket: AlwaysBreak +AlignConsecutiveAssignments: false +AlignConsecutiveDeclarations: false +AlignEscapedNewlinesLeft: true +AlignOperands: false +AlignTrailingComments: false +AllowAllParametersOfDeclarationOnNextLine: false +AllowShortBlocksOnASingleLine: false +AllowShortCaseLabelsOnASingleLine: false +AllowShortFunctionsOnASingleLine: Empty +AllowShortIfStatementsOnASingleLine: false +AllowShortLoopsOnASingleLine: false +AlwaysBreakAfterReturnType: None +AlwaysBreakBeforeMultilineStrings: true +AlwaysBreakTemplateDeclarations: true +BinPackArguments: false +BinPackParameters: false +BraceWrapping: + AfterClass: false + AfterControlStatement: false + AfterEnum: false + AfterFunction: false + AfterNamespace: false + AfterObjCDeclaration: false + AfterStruct: false + AfterUnion: false + BeforeCatch: false + BeforeElse: false + IndentBraces: false +BreakBeforeBinaryOperators: None +BreakBeforeBraces: Attach +BreakBeforeTernaryOperators: true +BreakConstructorInitializersBeforeComma: false +BreakAfterJavaFieldAnnotations: false +BreakStringLiterals: false +ColumnLimit: 80 +CommentPragmas: '^ IWYU pragma:' +ConstructorInitializerAllOnOneLineOrOnePerLine: true +ConstructorInitializerIndentWidth: 4 +ContinuationIndentWidth: 4 +Cpp11BracedListStyle: true +DerivePointerAlignment: false +DisableFormat: false +ForEachMacros: [ FOR_EACH, FOR_EACH_R, FOR_EACH_RANGE, ] +IncludeCategories: + - Regex: '^<.*\.h(pp)?>' + Priority: 1 + - Regex: '^<.*' + Priority: 2 + - Regex: '.*' + Priority: 3 +IndentCaseLabels: true +IndentWidth: 2 +IndentWrappedFunctionNames: false +KeepEmptyLinesAtTheStartOfBlocks: false +MacroBlockBegin: '' +MacroBlockEnd: '' +MaxEmptyLinesToKeep: 1 +NamespaceIndentation: None +ObjCBlockIndentWidth: 2 +ObjCSpaceAfterProperty: false +ObjCSpaceBeforeProtocolList: false +PenaltyBreakBeforeFirstCallParameter: 1 +PenaltyBreakComment: 300 +PenaltyBreakFirstLessLess: 120 +PenaltyBreakString: 1000 +PenaltyExcessCharacter: 1000000 +PenaltyReturnTypeOnItsOwnLine: 200 +PointerAlignment: Left +ReflowComments: true +SortIncludes: true +SpaceAfterCStyleCast: false +SpaceBeforeAssignmentOperators: true +SpaceBeforeParens: ControlStatements +SpaceInEmptyParentheses: false +SpacesBeforeTrailingComments: 1 +SpacesInAngles: false +SpacesInContainerLiterals: true +SpacesInCStyleCastParentheses: false +SpacesInParentheses: false +SpacesInSquareBrackets: false +Standard: Cpp11 +TabWidth: 8 +UseTab: Never diff --git a/iChat/models/grit_src/third_party/CenterNet2/.flake8 b/iChat/models/grit_src/third_party/CenterNet2/.flake8 new file mode 100644 index 0000000000000000000000000000000000000000..ae8edda517fa7e2adc003299f18c880a823b1343 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/.flake8 @@ -0,0 +1,15 @@ +# This is an example .flake8 config, used when developing *Black* itself. +# Keep in sync with setup.cfg which is used for source packages. + +[flake8] +ignore = W503, E203, E221, C901, C408, E741, C407, B017 +max-line-length = 100 +max-complexity = 18 +select = B,C,E,F,W,T4,B9 +exclude = build +per-file-ignores = + **/__init__.py:F401,F403,E402 + **/configs/**.py:F401,E402 + configs/**.py:F401,E402 + **/tests/config/**.py:F401,E402 + tests/config/**.py:F401,E402 diff --git a/iChat/models/grit_src/third_party/CenterNet2/.gitignore b/iChat/models/grit_src/third_party/CenterNet2/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..8ca283cb5a1cf828456b3ed3b95e947ca1ba1426 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/.gitignore @@ -0,0 +1,57 @@ +slurm* +# output dir +output +instant_test_output +inference_test_output + + +*.png +*.json +*.diff +# *.jpg +!/projects/DensePose/doc/images/*.jpg + +# compilation and distribution +__pycache__ +_ext +*.pyc +*.pyd +*.so +*.dll +*.egg-info/ +build/ +dist/ +wheels/ + +# pytorch/python/numpy formats +*.pth +*.pkl +*.npy +*.ts +model_ts*.txt + +# ipython/jupyter notebooks +*.ipynb +**/.ipynb_checkpoints/ + +# Editor temporaries +*.swn +*.swo +*.swp +*~ + +# editor settings +.idea +.vscode +_darcs + +# project dirs +/detectron2/model_zoo/configs +/datasets/* +!/datasets/*.* +!/datasets/lvis/ +/datasets/lvis/* +!/datasets/lvis/lvis_v1_train_cat_info.json +/projects/*/datasets +/models +/snippet diff --git a/iChat/models/grit_src/third_party/CenterNet2/GETTING_STARTED.md b/iChat/models/grit_src/third_party/CenterNet2/GETTING_STARTED.md new file mode 100644 index 0000000000000000000000000000000000000000..404b0c8f467264d1adf61e8274e5f864e24018e8 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/GETTING_STARTED.md @@ -0,0 +1,79 @@ +## Getting Started with Detectron2 + +This document provides a brief intro of the usage of builtin command-line tools in detectron2. + +For a tutorial that involves actual coding with the API, +see our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5) +which covers how to run inference with an +existing model, and how to train a builtin model on a custom dataset. + + +### Inference Demo with Pre-trained Models + +1. Pick a model and its config file from + [model zoo](MODEL_ZOO.md), + for example, `mask_rcnn_R_50_FPN_3x.yaml`. +2. We provide `demo.py` that is able to demo builtin configs. Run it with: +``` +cd demo/ +python demo.py --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \ + --input input1.jpg input2.jpg \ + [--other-options] + --opts MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl +``` +The configs are made for training, therefore we need to specify `MODEL.WEIGHTS` to a model from model zoo for evaluation. +This command will run the inference and show visualizations in an OpenCV window. + +For details of the command line arguments, see `demo.py -h` or look at its source code +to understand its behavior. Some common arguments are: +* To run __on your webcam__, replace `--input files` with `--webcam`. +* To run __on a video__, replace `--input files` with `--video-input video.mp4`. +* To run __on cpu__, add `MODEL.DEVICE cpu` after `--opts`. +* To save outputs to a directory (for images) or a file (for webcam or video), use `--output`. + + +### Training & Evaluation in Command Line + +We provide two scripts in "tools/plain_train_net.py" and "tools/train_net.py", +that are made to train all the configs provided in detectron2. You may want to +use it as a reference to write your own training script. + +Compared to "train_net.py", "plain_train_net.py" supports fewer default +features. It also includes fewer abstraction, therefore is easier to add custom +logic. + +To train a model with "train_net.py", first +setup the corresponding datasets following +[datasets/README.md](./datasets/README.md), +then run: +``` +cd tools/ +./train_net.py --num-gpus 8 \ + --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml +``` + +The configs are made for 8-GPU training. +To train on 1 GPU, you may need to [change some parameters](https://arxiv.org/abs/1706.02677), e.g.: +``` +./train_net.py \ + --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \ + --num-gpus 1 SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025 +``` + +To evaluate a model's performance, use +``` +./train_net.py \ + --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \ + --eval-only MODEL.WEIGHTS /path/to/checkpoint_file +``` +For more options, see `./train_net.py -h`. + +### Use Detectron2 APIs in Your Code + +See our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5) +to learn how to use detectron2 APIs to: +1. run inference with an existing model +2. train a builtin model on a custom dataset + +See [detectron2/projects](https://github.com/facebookresearch/detectron2/tree/main/projects) +for more ways to build your project on detectron2. diff --git a/iChat/models/grit_src/third_party/CenterNet2/INSTALL.md b/iChat/models/grit_src/third_party/CenterNet2/INSTALL.md new file mode 100644 index 0000000000000000000000000000000000000000..b40768913742ca2b2e11c74d5944561931ecb326 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/INSTALL.md @@ -0,0 +1,261 @@ +## Installation + +### Requirements +- Linux or macOS with Python ≥ 3.6 +- PyTorch ≥ 1.8 and [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation. + Install them together at [pytorch.org](https://pytorch.org) to make sure of this +- OpenCV is optional but needed by demo and visualization + + +### Build Detectron2 from Source + +gcc & g++ ≥ 5.4 are required. [ninja](https://ninja-build.org/) is optional but recommended for faster build. +After having them, run: +``` +python -m pip install 'git+https://github.com/facebookresearch/detectron2.git' +# (add --user if you don't have permission) + +# Or, to install it from a local clone: +git clone https://github.com/facebookresearch/detectron2.git +python -m pip install -e detectron2 + +# On macOS, you may need to prepend the above commands with a few environment variables: +CC=clang CXX=clang++ ARCHFLAGS="-arch x86_64" python -m pip install ... +``` + +To __rebuild__ detectron2 that's built from a local clone, use `rm -rf build/ **/*.so` to clean the +old build first. You often need to rebuild detectron2 after reinstalling PyTorch. + +### Install Pre-Built Detectron2 (Linux only) + +Choose from this table to install [v0.6 (Oct 2021)](https://github.com/facebookresearch/detectron2/releases): + +
CUDA torch 1.10torch 1.9torch 1.8
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+
+ +Note that: +1. The pre-built packages have to be used with corresponding version of CUDA and the official package of PyTorch. + Otherwise, please build detectron2 from source. +2. New packages are released every few months. Therefore, packages may not contain latest features in the main + branch and may not be compatible with the main branch of a research project that uses detectron2 + (e.g. those in [projects](projects)). + +### Common Installation Issues + +Click each issue for its solutions: + +
+ +Undefined symbols that looks like "TH..","at::Tensor...","torch..." + +
+ +This usually happens when detectron2 or torchvision is not +compiled with the version of PyTorch you're running. + +If the error comes from a pre-built torchvision, uninstall torchvision and pytorch and reinstall them +following [pytorch.org](http://pytorch.org). So the versions will match. + +If the error comes from a pre-built detectron2, check [release notes](https://github.com/facebookresearch/detectron2/releases), +uninstall and reinstall the correct pre-built detectron2 that matches pytorch version. + +If the error comes from detectron2 or torchvision that you built manually from source, +remove files you built (`build/`, `**/*.so`) and rebuild it so it can pick up the version of pytorch currently in your environment. + +If the above instructions do not resolve this problem, please provide an environment (e.g. a dockerfile) that can reproduce the issue. +
+ +
+ +Missing torch dynamic libraries, OR segmentation fault immediately when using detectron2. + +This usually happens when detectron2 or torchvision is not +compiled with the version of PyTorch you're running. See the previous common issue for the solution. +
+ +
+ +Undefined C++ symbols (e.g. "GLIBCXX..") or C++ symbols not found. + +
+Usually it's because the library is compiled with a newer C++ compiler but run with an old C++ runtime. + +This often happens with old anaconda. +It may help to run `conda update libgcc` to upgrade its runtime. + +The fundamental solution is to avoid the mismatch, either by compiling using older version of C++ +compiler, or run the code with proper C++ runtime. +To run the code with a specific C++ runtime, you can use environment variable `LD_PRELOAD=/path/to/libstdc++.so`. + +
+ +
+ +"nvcc not found" or "Not compiled with GPU support" or "Detectron2 CUDA Compiler: not available". + +
+CUDA is not found when building detectron2. +You should make sure + +``` +python -c 'import torch; from torch.utils.cpp_extension import CUDA_HOME; print(torch.cuda.is_available(), CUDA_HOME)' +``` + +print `(True, a directory with cuda)` at the time you build detectron2. + +Most models can run inference (but not training) without GPU support. To use CPUs, set `MODEL.DEVICE='cpu'` in the config. +
+ +
+ +"invalid device function" or "no kernel image is available for execution". + +
+Two possibilities: + +* You build detectron2 with one version of CUDA but run it with a different version. + + To check whether it is the case, + use `python -m detectron2.utils.collect_env` to find out inconsistent CUDA versions. + In the output of this command, you should expect "Detectron2 CUDA Compiler", "CUDA_HOME", "PyTorch built with - CUDA" + to contain cuda libraries of the same version. + + When they are inconsistent, + you need to either install a different build of PyTorch (or build by yourself) + to match your local CUDA installation, or install a different version of CUDA to match PyTorch. + +* PyTorch/torchvision/Detectron2 is not built for the correct GPU SM architecture (aka. compute capability). + + The architecture included by PyTorch/detectron2/torchvision is available in the "architecture flags" in + `python -m detectron2.utils.collect_env`. It must include + the architecture of your GPU, which can be found at [developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus). + + If you're using pre-built PyTorch/detectron2/torchvision, they have included support for most popular GPUs already. + If not supported, you need to build them from source. + + When building detectron2/torchvision from source, they detect the GPU device and build for only the device. + This means the compiled code may not work on a different GPU device. + To recompile them for the correct architecture, remove all installed/compiled files, + and rebuild them with the `TORCH_CUDA_ARCH_LIST` environment variable set properly. + For example, `export TORCH_CUDA_ARCH_LIST="6.0;7.0"` makes it compile for both P100s and V100s. +
+ +
+ +Undefined CUDA symbols; Cannot open libcudart.so + +
+The version of NVCC you use to build detectron2 or torchvision does +not match the version of CUDA you are running with. +This often happens when using anaconda's CUDA runtime. + +Use `python -m detectron2.utils.collect_env` to find out inconsistent CUDA versions. +In the output of this command, you should expect "Detectron2 CUDA Compiler", "CUDA_HOME", "PyTorch built with - CUDA" +to contain cuda libraries of the same version. + +When they are inconsistent, +you need to either install a different build of PyTorch (or build by yourself) +to match your local CUDA installation, or install a different version of CUDA to match PyTorch. +
+ + +
+ +C++ compilation errors from NVCC / NVRTC, or "Unsupported gpu architecture" + +
+A few possibilities: + +1. Local CUDA/NVCC version has to match the CUDA version of your PyTorch. Both can be found in `python collect_env.py`. + When they are inconsistent, you need to either install a different build of PyTorch (or build by yourself) + to match your local CUDA installation, or install a different version of CUDA to match PyTorch. + +2. Local CUDA/NVCC version shall support the SM architecture (a.k.a. compute capability) of your GPU. + The capability of your GPU can be found at [developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus). + The capability supported by NVCC is listed at [here](https://gist.github.com/ax3l/9489132). + If your NVCC version is too old, this can be workaround by setting environment variable + `TORCH_CUDA_ARCH_LIST` to a lower, supported capability. + +3. The combination of NVCC and GCC you use is incompatible. You need to change one of their versions. + See [here](https://gist.github.com/ax3l/9489132) for some valid combinations. + Notably, CUDA<=10.1.105 doesn't support GCC>7.3. + + The CUDA/GCC version used by PyTorch can be found by `print(torch.__config__.show())`. + +
+ + +
+ +"ImportError: cannot import name '_C'". + +
+Please build and install detectron2 following the instructions above. + +Or, if you are running code from detectron2's root directory, `cd` to a different one. +Otherwise you may not import the code that you installed. +
+ + +
+ +Any issue on windows. + +
+ +Detectron2 is continuously built on windows with [CircleCI](https://app.circleci.com/pipelines/github/facebookresearch/detectron2?branch=main). +However we do not provide official support for it. +PRs that improves code compatibility on windows are welcome. +
+ +
+ +ONNX conversion segfault after some "TraceWarning". + +
+The ONNX package is compiled with a too old compiler. + +Please build and install ONNX from its source code using a compiler +whose version is closer to what's used by PyTorch (available in `torch.__config__.show()`). +
+ + +
+ +"library not found for -lstdc++" on older version of MacOS + +
+See +[this stackoverflow answer](https://stackoverflow.com/questions/56083725/macos-build-issues-lstdc-not-found-while-building-python-package). + +
+ + +### Installation inside specific environments: + +* __Colab__: see our [Colab Tutorial](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5) + which has step-by-step instructions. + +* __Docker__: The official [Dockerfile](docker) installs detectron2 with a few simple commands. + diff --git a/iChat/models/grit_src/third_party/CenterNet2/LICENSE b/iChat/models/grit_src/third_party/CenterNet2/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..cd1b070674331757508398d99c830664dce6eaec --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/LICENSE @@ -0,0 +1,202 @@ +Apache License +Version 2.0, January 2004 +http://www.apache.org/licenses/ + +TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + +1. 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We also recommend that a +file or class name and description of purpose be included on the +same "printed page" as the copyright notice for easier +identification within third-party archives. + +Copyright [yyyy] [name of copyright owner] + + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. diff --git a/iChat/models/grit_src/third_party/CenterNet2/MODEL_ZOO.md b/iChat/models/grit_src/third_party/CenterNet2/MODEL_ZOO.md new file mode 100644 index 0000000000000000000000000000000000000000..69db2728563c680e89a0d5d3e6ba272b8d78bdbd --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/MODEL_ZOO.md @@ -0,0 +1,1052 @@ +# Detectron2 Model Zoo and Baselines + +## Introduction + +This file documents a large collection of baselines trained +with detectron2 in Sep-Oct, 2019. +All numbers were obtained on [Big Basin](https://engineering.fb.com/data-center-engineering/introducing-big-basin-our-next-generation-ai-hardware/) +servers with 8 NVIDIA V100 GPUs & NVLink. The speed numbers are periodically updated with latest PyTorch/CUDA/cuDNN versions. +You can access these models from code using [detectron2.model_zoo](https://detectron2.readthedocs.io/modules/model_zoo.html) APIs. + +In addition to these official baseline models, you can find more models in [projects/](projects/). + +#### How to Read the Tables +* The "Name" column contains a link to the config file. Models can be reproduced using `tools/train_net.py` with the corresponding yaml config file, + or `tools/lazyconfig_train_net.py` for python config files. +* Training speed is averaged across the entire training. + We keep updating the speed with latest version of detectron2/pytorch/etc., + so they might be different from the `metrics` file. + Training speed for multi-machine jobs is not provided. +* Inference speed is measured by `tools/train_net.py --eval-only`, or [inference_on_dataset()](https://detectron2.readthedocs.io/modules/evaluation.html#detectron2.evaluation.inference_on_dataset), + with batch size 1 in detectron2 directly. + Measuring it with custom code may introduce other overhead. + Actual deployment in production should in general be faster than the given inference + speed due to more optimizations. +* The *model id* column is provided for ease of reference. + To check downloaded file integrity, any model on this page contains its md5 prefix in its file name. +* Training curves and other statistics can be found in `metrics` for each model. + +#### Common Settings for COCO Models +* All COCO models were trained on `train2017` and evaluated on `val2017`. +* The default settings are __not directly comparable__ with Detectron's standard settings. + For example, our default training data augmentation uses scale jittering in addition to horizontal flipping. + + To make fair comparisons with Detectron's settings, see + [Detectron1-Comparisons](configs/Detectron1-Comparisons/) for accuracy comparison, + and [benchmarks](https://detectron2.readthedocs.io/notes/benchmarks.html) + for speed comparison. +* For Faster/Mask R-CNN, we provide baselines based on __3 different backbone combinations__: + * __FPN__: Use a ResNet+FPN backbone with standard conv and FC heads for mask and box prediction, + respectively. It obtains the best + speed/accuracy tradeoff, but the other two are still useful for research. + * __C4__: Use a ResNet conv4 backbone with conv5 head. The original baseline in the Faster R-CNN paper. + * __DC5__ (Dilated-C5): Use a ResNet conv5 backbone with dilations in conv5, and standard conv and FC heads + for mask and box prediction, respectively. + This is used by the Deformable ConvNet paper. +* Most models are trained with the 3x schedule (~37 COCO epochs). + Although 1x models are heavily under-trained, we provide some ResNet-50 models with the 1x (~12 COCO epochs) + training schedule for comparison when doing quick research iteration. + +#### ImageNet Pretrained Models + +It's common to initialize from backbone models pre-trained on ImageNet classification tasks. The following backbone models are available: + +* [R-50.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-50.pkl): converted copy of [MSRA's original ResNet-50](https://github.com/KaimingHe/deep-residual-networks) model. +* [R-101.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-101.pkl): converted copy of [MSRA's original ResNet-101](https://github.com/KaimingHe/deep-residual-networks) model. +* [X-101-32x8d.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/FAIR/X-101-32x8d.pkl): ResNeXt-101-32x8d model trained with Caffe2 at FB. +* [R-50.pkl (torchvision)](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/torchvision/R-50.pkl): converted copy of [torchvision's ResNet-50](https://pytorch.org/docs/stable/torchvision/models.html#torchvision.models.resnet50) model. + More details can be found in [the conversion script](tools/convert-torchvision-to-d2.py). + +Note that the above models have __different__ format from those provided in Detectron: we do not fuse BatchNorm into an affine layer. +Pretrained models in Detectron's format can still be used. For example: +* [X-152-32x8d-IN5k.pkl](https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/25093814/X-152-32x8d-IN5k.pkl): + ResNeXt-152-32x8d model trained on ImageNet-5k with Caffe2 at FB (see ResNeXt paper for details on ImageNet-5k). +* [R-50-GN.pkl](https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/47261647/R-50-GN.pkl): + ResNet-50 with Group Normalization. +* [R-101-GN.pkl](https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/47592356/R-101-GN.pkl): + ResNet-101 with Group Normalization. + +These models require slightly different settings regarding normalization and architecture. See the model zoo configs for reference. + +#### License + +All models available for download through this document are licensed under the +[Creative Commons Attribution-ShareAlike 3.0 license](https://creativecommons.org/licenses/by-sa/3.0/). + +### COCO Object Detection Baselines + +#### Faster R-CNN: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Namelr
sched		train
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		model iddownload
R50-C41x0.5510.1024.835.7137257644model | metrics
R50-DC51x0.3800.0685.037.3137847829model | metrics
R50-FPN1x0.2100.0383.037.9137257794model | metrics
R50-C43x0.5430.1044.838.4137849393model | metrics
R50-DC53x0.3780.0705.039.0137849425model | metrics
R50-FPN3x0.2090.0383.040.2137849458model | metrics
R101-C43x0.6190.1395.941.1138204752model | metrics
R101-DC53x0.4520.0866.140.6138204841model | metrics
R101-FPN3x0.2860.0514.142.0137851257model | metrics
X101-FPN3x0.6380.0986.743.0139173657model | metrics
+ +#### RetinaNet: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Namelr
sched		train
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		model iddownload
R501x0.2050.0414.137.4190397773model | metrics
R503x0.2050.0414.138.7190397829model | metrics
R1013x0.2910.0545.240.4190397697model | metrics
+ + +#### RPN & Fast R-CNN: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Namelr
sched		train
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		prop.
AR		model iddownload
RPN R50-C41x0.1300.0341.551.6137258005model | metrics
RPN R50-FPN1x0.1860.0322.758.0137258492model | metrics
Fast R-CNN R50-FPN1x0.1400.0292.637.8137635226model | metrics
+ +### COCO Instance Segmentation Baselines with Mask R-CNN + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Namelr
sched		train
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		mask
AP		model iddownload
R50-C41x0.5840.1105.236.832.2137259246model | metrics
R50-DC51x0.4710.0766.538.334.2137260150model | metrics
R50-FPN1x0.2610.0433.438.635.2137260431model | metrics
R50-C43x0.5750.1115.239.834.4137849525model | metrics
R50-DC53x0.4700.0766.540.035.9137849551model | metrics
R50-FPN3x0.2610.0433.441.037.2137849600model | metrics
R101-C43x0.6520.1456.342.636.7138363239model | metrics
R101-DC53x0.5450.0927.641.937.3138363294model | metrics
R101-FPN3x0.3400.0564.642.938.6138205316model | metrics
X101-FPN3x0.6900.1037.244.339.5139653917model | metrics
+ + + +#### New baselines using Large-Scale Jitter and Longer Training Schedule + +The following baselines of COCO Instance Segmentation with Mask R-CNN are generated +using a longer training schedule and large-scale jitter as described in Google's +[Simple Copy-Paste Data Augmentation](https://arxiv.org/pdf/2012.07177.pdf) paper. These +models are trained from scratch using random initialization. These baselines exceed the +previous Mask R-CNN baselines. + +In the following table, one epoch consists of training on 118000 COCO images. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Nameepochstrain
time
(s/im)		inference
time
(s/im)		box
AP		mask
AP		model iddownload
R50-FPN1000.3760.06944.640.342047764model | metrics
R50-FPN2000.3760.06946.341.742047638model | metrics
R50-FPN4000.3760.06947.442.542019571model | metrics
R101-FPN1000.5180.07346.441.642025812model | metrics
R101-FPN2000.5180.07348.043.142131867model | metrics
R101-FPN4000.5180.07348.943.742073830model | metrics
regnetx_4gf_dds_FPN1000.4740.07146.041.342047771model | metrics
regnetx_4gf_dds_FPN2000.4740.07148.143.142132721model | metrics
regnetx_4gf_dds_FPN4000.4740.07148.643.542025447model | metrics
regnety_4gf_dds_FPN1000.4870.07346.141.642047784model | metrics
regnety_4gf_dds_FPN2000.4870.07247.843.042047642model | metrics
regnety_4gf_dds_FPN4000.4870.07248.243.342045954model | metrics
+ +### COCO Person Keypoint Detection Baselines with Keypoint R-CNN + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Namelr
sched		train
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		kp.
AP		model iddownload
R50-FPN1x0.3150.0725.053.664.0137261548model | metrics
R50-FPN3x0.3160.0665.055.465.5137849621model | metrics
R101-FPN3x0.3900.0766.156.466.1138363331model | metrics
X101-FPN3x0.7380.1218.757.366.0139686956model | metrics
+ +### COCO Panoptic Segmentation Baselines with Panoptic FPN + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Namelr
sched		train
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		mask
AP		PQmodel iddownload
R50-FPN1x0.3040.0534.837.634.739.4139514544model | metrics
R50-FPN3x0.3020.0534.840.036.541.5139514569model | metrics
R101-FPN3x0.3920.0666.042.438.543.0139514519model | metrics
+ + +### LVIS Instance Segmentation Baselines with Mask R-CNN + +Mask R-CNN baselines on the [LVIS dataset](https://lvisdataset.org), v0.5. +These baselines are described in Table 3(c) of the [LVIS paper](https://arxiv.org/abs/1908.03195). + +NOTE: the 1x schedule here has the same amount of __iterations__ as the COCO 1x baselines. +They are roughly 24 epochs of LVISv0.5 data. +The final results of these configs have large variance across different runs. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Namelr
sched		train
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		mask
AP		model iddownload
R50-FPN1x0.2920.1077.123.624.4144219072model | metrics
R101-FPN1x0.3710.1147.825.625.9144219035model | metrics
X101-FPN1x0.7120.15110.226.727.1144219108model | metrics
+ + + +### Cityscapes & Pascal VOC Baselines + +Simple baselines for +* Mask R-CNN on Cityscapes instance segmentation (initialized from COCO pre-training, then trained on Cityscapes fine annotations only) +* Faster R-CNN on PASCAL VOC object detection (trained on VOC 2007 train+val + VOC 2012 train+val, tested on VOC 2007 using 11-point interpolated AP) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Nametrain
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		box
AP50		mask
AP		model iddownload
R50-FPN, Cityscapes0.2400.0784.436.5142423278model | metrics
R50-C4, VOC0.5370.0814.851.980.3142202221model | metrics
+ + + +### Other Settings + +Ablations for Deformable Conv and Cascade R-CNN: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Namelr
sched		train
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		mask
AP		model iddownload
Baseline R50-FPN1x0.2610.0433.438.635.2137260431model | metrics
Deformable Conv1x0.3420.0483.541.537.5138602867model | metrics
Cascade R-CNN1x0.3170.0524.042.136.4138602847model | metrics
Baseline R50-FPN3x0.2610.0433.441.037.2137849600model | metrics
Deformable Conv3x0.3490.0473.542.738.5144998336model | metrics
Cascade R-CNN3x0.3280.0534.044.338.5144998488model | metrics
+ + +Ablations for normalization methods, and a few models trained from scratch following [Rethinking ImageNet Pre-training](https://arxiv.org/abs/1811.08883). +(Note: The baseline uses `2fc` head while the others use [`4conv1fc` head](https://arxiv.org/abs/1803.08494)) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Namelr
sched		train
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		mask
AP		model iddownload
Baseline R50-FPN3x0.2610.0433.441.037.2137849600model | metrics
GN3x0.3090.0605.642.638.6138602888model | metrics
SyncBN3x0.3450.0535.541.937.8169527823model | metrics
GN (from scratch)3x0.3380.0617.239.936.6138602908model | metrics
GN (from scratch)9xN/A0.0617.243.739.6183808979model | metrics
SyncBN (from scratch)9xN/A0.0557.243.639.3184226666model | metrics
+ + +A few very large models trained for a long time, for demo purposes. They are trained using multiple machines: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Nameinference
time
(s/im)		train
mem
(GB)		box
AP		mask
AP		PQmodel iddownload
Panoptic FPN R1010.09811.447.441.346.1139797668model | metrics
Mask R-CNN X1520.23415.150.244.018131413model | metrics
above + test-time aug.51.945.9
diff --git a/iChat/models/grit_src/third_party/CenterNet2/README.md b/iChat/models/grit_src/third_party/CenterNet2/README.md new file mode 100644 index 0000000000000000000000000000000000000000..d3e1d5cf533555e19c6326777f792ac82a560a84 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/README.md @@ -0,0 +1,85 @@ +# Probabilistic two-stage detection +Two-stage object detectors that use class-agnostic one-stage detectors as the proposal network. + + +

+ +> [**Probabilistic two-stage detection**](http://arxiv.org/abs/2103.07461), +> Xingyi Zhou, Vladlen Koltun, Philipp Krähenbühl, +> *arXiv technical report ([arXiv 2103.07461](http://arxiv.org/abs/2103.07461))* + +Contact: [zhouxy@cs.utexas.edu](mailto:zhouxy@cs.utexas.edu). Any questions or discussions are welcomed! + +## Abstract + +We develop a probabilistic interpretation of two-stage object detection. We show that this probabilistic interpretation motivates a number of common empirical training practices. It also suggests changes to two-stage detection pipelines. Specifically, the first stage should infer proper object-vs-background likelihoods, which should then inform the overall score of the detector. A standard region proposal network (RPN) cannot infer this likelihood sufficiently well, but many one-stage detectors can. We show how to build a probabilistic two-stage detector from any state-of-the-art one-stage detector. The resulting detectors are faster and more accurate than both their one- and two-stage precursors. Our detector achieves 56.4 mAP on COCO test-dev with single-scale testing, outperforming all published results. Using a lightweight backbone, our detector achieves 49.2 mAP on COCO at 33 fps on a Titan Xp. + +## Summary + +- Two-stage CenterNet: First stage estimates object probabilities, second stage conditionally classifies objects. + +- Resulting detector is faster and more accurate than both traditional two-stage detectors (fewer proposals required), and one-stage detectors (lighter first stage head). + +- Our best model achieves 56.4 mAP on COCO test-dev. + +- This repo also includes a detectron2-based CenterNet implementation with better accuracy (42.5 mAP at 70FPS) and a new FPN version of CenterNet (40.2 mAP with Res50_1x). + +## Main results + +All models are trained with multi-scale training, and tested with a single scale. The FPS is tested on a Titan RTX GPU. +More models and details can be found in the [MODEL_ZOO](projects/CenterNet2/centernet2_docs/MODEL_ZOO.md). + +#### COCO + +| Model | COCO val mAP | FPS | +|-------------------------------------------|---------------|-------| +| CenterNet-S4_DLA_8x | 42.5 | 71 | +| CenterNet2_R50_1x | 42.9 | 24 | +| CenterNet2_X101-DCN_2x | 49.9 | 8 | +| CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST | 56.1 | 5 | +| CenterNet2_DLA-BiFPN-P5_24x_ST | 49.2 | 38 | + + +#### LVIS + +| Model | val mAP box | +| ------------------------- | ----------- | +| CenterNet2_R50_1x | 26.5 | +| CenterNet2_FedLoss_R50_1x | 28.3 | + + +#### Objects365 + +| Model | val mAP | +|-------------------------------------------|----------| +| CenterNet2_R50_1x | 22.6 | + +## Installation + +Our project is developed on [detectron2](https://github.com/facebookresearch/detectron2). Please follow the official detectron2 [installation](https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md). All our code is under `projects/CenterNet2/`. In theory, you should be able to copy-paste `projects/CenterNet2/` to the latest detectron2 release or your own detectron2 repo to run our project. There might be API changes in future detectron2 releases that make it incompatible. + +We use the default detectron2 demo script. To run inference on an image folder using our pre-trained model, run + +~~~ +python projects/CenterNet2/demo/demo.py --config-file projects/CenterNet2/configs/CenterNet2_R50_1x.yaml --input path/to/image/ --opts MODEL.WEIGHTS models/CenterNet2_R50_1x.pth +~~~ + +## Benchmark evaluation and training + +Please check detectron2 [GETTING_STARTED.md](https://github.com/facebookresearch/detectron2/blob/master/GETTING_STARTED.md) for running evaluation and training. Our config files are under `projects/CenterNet2/configs` and the pre-trained models are in the [MODEL_ZOO](projects/CenterNet2/centernet2_docs/MODEL_ZOO.md). + + +## License + +Our code under `projects/CenterNet2/` is under [Apache 2.0 license](projects/CenterNet2/LICENSE). `projects/CenterNet2/centernet/modeling/backbone/bifpn_fcos.py` are from [AdelaiDet](https://github.com/aim-uofa/AdelaiDet), which follows the original [non-commercial license](https://github.com/aim-uofa/AdelaiDet/blob/master/LICENSE). The code from detectron2 follows the original [Apache 2.0 license](LICENSE). + +## Citation + +If you find this project useful for your research, please use the following BibTeX entry. + + @inproceedings{zhou2021probablistic, + title={Probabilistic two-stage detection}, + author={Zhou, Xingyi and Koltun, Vladlen and Kr{\"a}henb{\"u}hl, Philipp}, + booktitle={arXiv preprint arXiv:2103.07461}, + year={2021} + } diff --git a/iChat/models/grit_src/third_party/CenterNet2/README_D2.md b/iChat/models/grit_src/third_party/CenterNet2/README_D2.md new file mode 100644 index 0000000000000000000000000000000000000000..a88ad7e21ce1d8651ec0d73848ce6dcd17f19d00 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/README_D2.md @@ -0,0 +1,62 @@ + + +Detectron2 is Facebook AI Research's next generation software system +that implements state-of-the-art object detection algorithms. +It is a ground-up rewrite of the previous version, +[Detectron](https://github.com/facebookresearch/Detectron/), +and it originates from [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/). + +
+ +
+ +### What's New +* It is powered by the [PyTorch](https://pytorch.org) deep learning framework. +* Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, + DeepLab, etc. +* Can be used as a library to support [different projects](projects/) on top of it. + We'll open source more research projects in this way. +* It [trains much faster](https://detectron2.readthedocs.io/notes/benchmarks.html). +* Models can be exported to TorchScript format or Caffe2 format for deployment. + +See our [blog post](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/) +to see more demos and learn about detectron2. + +## Installation + +See [INSTALL.md](INSTALL.md). + +## Getting Started + +Follow the [installation instructions](https://detectron2.readthedocs.io/tutorials/install.html) to +install detectron2. + +See [Getting Started with Detectron2](https://detectron2.readthedocs.io/tutorials/getting_started.html), +and the [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5) +to learn about basic usage. + +Learn more at our [documentation](https://detectron2.readthedocs.org). +And see [projects/](projects/) for some projects that are built on top of detectron2. + +## Model Zoo and Baselines + +We provide a large set of baseline results and trained models available for download in the [Detectron2 Model Zoo](MODEL_ZOO.md). + + +## License + +Detectron2 is released under the [Apache 2.0 license](LICENSE). + +## Citing Detectron2 + +If you use Detectron2 in your research or wish to refer to the baseline results published in the [Model Zoo](MODEL_ZOO.md), please use the following BibTeX entry. + +```BibTeX +@misc{wu2019detectron2, + author = {Yuxin Wu and Alexander Kirillov and Francisco Massa and + Wan-Yen Lo and Ross Girshick}, + title = {Detectron2}, + howpublished = {\url{https://github.com/facebookresearch/detectron2}}, + year = {2019} +} +``` diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Base-RCNN-C4.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/Base-RCNN-C4.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fbf34a0ea57a587e09997edd94c4012d69d0b6ad --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Base-RCNN-C4.yaml @@ -0,0 +1,18 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedRCNN" + RPN: + PRE_NMS_TOPK_TEST: 6000 + POST_NMS_TOPK_TEST: 1000 + ROI_HEADS: + NAME: "Res5ROIHeads" +DATASETS: + TRAIN: ("coco_2017_train",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.02 + STEPS: (60000, 80000) + MAX_ITER: 90000 +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +VERSION: 2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Base-RCNN-DilatedC5.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/Base-RCNN-DilatedC5.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c0d6d16bdaf532f09e4976f0aa240a49e748da27 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Base-RCNN-DilatedC5.yaml @@ -0,0 +1,31 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedRCNN" + RESNETS: + OUT_FEATURES: ["res5"] + RES5_DILATION: 2 + RPN: + IN_FEATURES: ["res5"] + PRE_NMS_TOPK_TEST: 6000 + POST_NMS_TOPK_TEST: 1000 + ROI_HEADS: + NAME: "StandardROIHeads" + IN_FEATURES: ["res5"] + ROI_BOX_HEAD: + NAME: "FastRCNNConvFCHead" + NUM_FC: 2 + POOLER_RESOLUTION: 7 + ROI_MASK_HEAD: + NAME: "MaskRCNNConvUpsampleHead" + NUM_CONV: 4 + POOLER_RESOLUTION: 14 +DATASETS: + TRAIN: ("coco_2017_train",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.02 + STEPS: (60000, 80000) + MAX_ITER: 90000 +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +VERSION: 2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Base-RCNN-FPN.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/Base-RCNN-FPN.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3e020f2e7b2f26765be317f907126a1556621abf --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Base-RCNN-FPN.yaml @@ -0,0 +1,42 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedRCNN" + BACKBONE: + NAME: "build_resnet_fpn_backbone" + RESNETS: + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + FPN: + IN_FEATURES: ["res2", "res3", "res4", "res5"] + ANCHOR_GENERATOR: + SIZES: [[32], [64], [128], [256], [512]] # One size for each in feature map + ASPECT_RATIOS: [[0.5, 1.0, 2.0]] # Three aspect ratios (same for all in feature maps) + RPN: + IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"] + PRE_NMS_TOPK_TRAIN: 2000 # Per FPN level + PRE_NMS_TOPK_TEST: 1000 # Per FPN level + # Detectron1 uses 2000 proposals per-batch, + # (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue) + # which is approximately 1000 proposals per-image since the default batch size for FPN is 2. + POST_NMS_TOPK_TRAIN: 1000 + POST_NMS_TOPK_TEST: 1000 + ROI_HEADS: + NAME: "StandardROIHeads" + IN_FEATURES: ["p2", "p3", "p4", "p5"] + ROI_BOX_HEAD: + NAME: "FastRCNNConvFCHead" + NUM_FC: 2 + POOLER_RESOLUTION: 7 + ROI_MASK_HEAD: + NAME: "MaskRCNNConvUpsampleHead" + NUM_CONV: 4 + POOLER_RESOLUTION: 14 +DATASETS: + TRAIN: ("coco_2017_train",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.02 + STEPS: (60000, 80000) + MAX_ITER: 90000 +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +VERSION: 2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Base-RetinaNet.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/Base-RetinaNet.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8b45b982bbf84b34d2a6a172ab0a946b1029f7c8 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Base-RetinaNet.yaml @@ -0,0 +1,25 @@ +MODEL: + META_ARCHITECTURE: "RetinaNet" + BACKBONE: + NAME: "build_retinanet_resnet_fpn_backbone" + RESNETS: + OUT_FEATURES: ["res3", "res4", "res5"] + ANCHOR_GENERATOR: + SIZES: !!python/object/apply:eval ["[[x, x * 2**(1.0/3), x * 2**(2.0/3) ] for x in [32, 64, 128, 256, 512 ]]"] + FPN: + IN_FEATURES: ["res3", "res4", "res5"] + RETINANET: + IOU_THRESHOLDS: [0.4, 0.5] + IOU_LABELS: [0, -1, 1] + SMOOTH_L1_LOSS_BETA: 0.0 +DATASETS: + TRAIN: ("coco_2017_train",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.01 # Note that RetinaNet uses a different default learning rate + STEPS: (60000, 80000) + MAX_ITER: 90000 +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +VERSION: 2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..773ac10e87c626760d00d831bf664ce9ff073c49 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml @@ -0,0 +1,17 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + LOAD_PROPOSALS: True + RESNETS: + DEPTH: 50 + PROPOSAL_GENERATOR: + NAME: "PrecomputedProposals" +DATASETS: + TRAIN: ("coco_2017_train",) + PROPOSAL_FILES_TRAIN: ("detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/coco_2017_train_box_proposals_21bc3a.pkl", ) + TEST: ("coco_2017_val",) + PROPOSAL_FILES_TEST: ("detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/coco_2017_val_box_proposals_ee0dad.pkl", ) +DATALOADER: + # proposals are part of the dataset_dicts, and take a lot of RAM + NUM_WORKERS: 2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..db142cd671c1841b4f64cf130bee7f7954ecdd28 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-C4.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + MASK_ON: False + RESNETS: + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bceb6b343618d8cd9a6c414ff9eb86ab31cc230a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-DilatedC5.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + MASK_ON: False + RESNETS: + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..57a098f53ee8c54ecfa354cc96efefd890dc1b72 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + MASK_ON: False + RESNETS: + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f96130105c3ba6ab393e0932870903875f5cb732 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml @@ -0,0 +1,6 @@ +_BASE_: "../Base-RCNN-C4.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_50_C4_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_50_C4_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bc51bce390a85ee3529ffdcebde05748e1646be0 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_50_C4_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-C4.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_50_DC5_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_50_DC5_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0fe96f57febdac5790ea4cec168fa4b97ac4807a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_50_DC5_1x.yaml @@ -0,0 +1,6 @@ +_BASE_: "../Base-RCNN-DilatedC5.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..33fadeb87d1ef67ab2b55926b9a652ab4ac4a27d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-DilatedC5.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3262019a1211b910d3b371569199ed1afaacf6a4 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml @@ -0,0 +1,6 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..41395182bf5c9dd8ab1241c4414068817298d554 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9c9b5ab77157baa581d90d9847c045c19ed6ffa3 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml @@ -0,0 +1,13 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + MASK_ON: False + WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl" + PIXEL_STD: [57.375, 57.120, 58.395] + RESNETS: + STRIDE_IN_1X1: False # this is a C2 model + NUM_GROUPS: 32 + WIDTH_PER_GROUP: 8 + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/fcos_R_50_FPN_1x.py b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/fcos_R_50_FPN_1x.py new file mode 100644 index 0000000000000000000000000000000000000000..86f83c68786f5995c462ade5f3067072d69f047e --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/fcos_R_50_FPN_1x.py @@ -0,0 +1,11 @@ +from ..common.optim import SGD as optimizer +from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier +from ..common.data.coco import dataloader +from ..common.models.fcos import model +from ..common.train import train + +dataloader.train.mapper.use_instance_mask = False +optimizer.lr = 0.01 + +model.backbone.bottom_up.freeze_at = 2 +train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl" diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4abb1b9a547957aa6afc0b29129e00f89cf98d59 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml @@ -0,0 +1,8 @@ +_BASE_: "../Base-RetinaNet.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + RESNETS: + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/retinanet_R_50_FPN_1x.py b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/retinanet_R_50_FPN_1x.py new file mode 100644 index 0000000000000000000000000000000000000000..43057a8eeed38c78183e26d21b74261eb4dbc1b9 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/retinanet_R_50_FPN_1x.py @@ -0,0 +1,11 @@ +from ..common.optim import SGD as optimizer +from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier +from ..common.data.coco import dataloader +from ..common.models.retinanet import model +from ..common.train import train + +dataloader.train.mapper.use_instance_mask = False +model.backbone.bottom_up.freeze_at = 2 +optimizer.lr = 0.01 + +train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl" diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4a24ce3a9a108a8792e18c8aabfb7b712f0d3725 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml @@ -0,0 +1,5 @@ +_BASE_: "../Base-RetinaNet.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + RESNETS: + DEPTH: 50 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/retinanet_R_50_FPN_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/retinanet_R_50_FPN_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3b5412d4a7aef1d6c3f7c1e34f94007de639b833 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/retinanet_R_50_FPN_3x.yaml @@ -0,0 +1,8 @@ +_BASE_: "../Base-RetinaNet.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + RESNETS: + DEPTH: 50 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/rpn_R_50_C4_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/rpn_R_50_C4_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e04821156b0376ba5215d5ce5b7010a36b43e6a1 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/rpn_R_50_C4_1x.yaml @@ -0,0 +1,10 @@ +_BASE_: "../Base-RCNN-C4.yaml" +MODEL: + META_ARCHITECTURE: "ProposalNetwork" + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 + RPN: + PRE_NMS_TOPK_TEST: 12000 + POST_NMS_TOPK_TEST: 2000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/rpn_R_50_FPN_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/rpn_R_50_FPN_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dc9c95203b1c3c9cd9bb9876bb8d9a5dd9b31d9a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/rpn_R_50_FPN_1x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + META_ARCHITECTURE: "ProposalNetwork" + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 + RPN: + POST_NMS_TOPK_TEST: 2000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1a94cc45a0f2aaa8c92e14871c553b736545e327 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-C4.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + MASK_ON: True + RESNETS: + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..67b70cf4be8c19f5dc735b6f55a8690698f34b69 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-DilatedC5.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + MASK_ON: True + RESNETS: + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1935a302d2d0fa7f69553b3fd50b5a7082c6c0d1 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + MASK_ON: True + RESNETS: + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.py b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.py new file mode 100644 index 0000000000000000000000000000000000000000..22016be150df4abbe912700d7ca29f8b7b72554a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.py @@ -0,0 +1,8 @@ +from ..common.train import train +from ..common.optim import SGD as optimizer +from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier +from ..common.data.coco import dataloader +from ..common.models.mask_rcnn_c4 import model + +model.backbone.freeze_at = 2 +train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl" diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a9aeb4eac38026dbb867e799f9fd3a8d8eb3af80 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml @@ -0,0 +1,6 @@ +_BASE_: "../Base-RCNN-C4.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..38ed867d897dfec839cbcf11a2e2dc8abb92f07c --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-C4.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b13eefab2a049c48d94d5051c82ceb6dbde40579 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x.yaml @@ -0,0 +1,6 @@ +_BASE_: "../Base-RCNN-DilatedC5.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d401016358f967f6619d88b1c9bd5673a1cdeba8 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-DilatedC5.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.py b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.py new file mode 100644 index 0000000000000000000000000000000000000000..40844ddeb8d47ff58a6af49ab35bad84e14f5721 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.py @@ -0,0 +1,8 @@ +from ..common.optim import SGD as optimizer +from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier +from ..common.data.coco import dataloader +from ..common.models.mask_rcnn_fpn import model +from ..common.train import train + +model.backbone.bottom_up.freeze_at = 2 +train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl" diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d50fb866ca7811a87b42555c7213f88e00bf6df1 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml @@ -0,0 +1,6 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x_giou.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x_giou.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bec680ee17a474fefe527b7b79d26266e75c09f0 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x_giou.yaml @@ -0,0 +1,12 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 + RPN: + BBOX_REG_LOSS_TYPE: "giou" + BBOX_REG_LOSS_WEIGHT: 2.0 + ROI_BOX_HEAD: + BBOX_REG_LOSS_TYPE: "giou" + BBOX_REG_LOSS_WEIGHT: 10.0 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..be7d06b8e0f032ee7fcaabd7c122158518489fd2 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d14c63f74383bfc308750f51d51344398b02a239 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml @@ -0,0 +1,13 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + MASK_ON: True + WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl" + PIXEL_STD: [57.375, 57.120, 58.395] + RESNETS: + STRIDE_IN_1X1: False # this is a C2 model + NUM_GROUPS: 32 + WIDTH_PER_GROUP: 8 + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_regnetx_4gf_dds_fpn_1x.py b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_regnetx_4gf_dds_fpn_1x.py new file mode 100644 index 0000000000000000000000000000000000000000..d7bbdd7d00505f1e51154379c99ab621cb648a6d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_regnetx_4gf_dds_fpn_1x.py @@ -0,0 +1,34 @@ +from ..common.optim import SGD as optimizer +from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier +from ..common.data.coco import dataloader +from ..common.models.mask_rcnn_fpn import model +from ..common.train import train + +from detectron2.config import LazyCall as L +from detectron2.modeling.backbone import RegNet +from detectron2.modeling.backbone.regnet import SimpleStem, ResBottleneckBlock + + +# Replace default ResNet with RegNetX-4GF from the DDS paper. Config source: +# https://github.com/facebookresearch/pycls/blob/2c152a6e5d913e898cca4f0a758f41e6b976714d/configs/dds_baselines/regnetx/RegNetX-4.0GF_dds_8gpu.yaml#L4-L9 # noqa +model.backbone.bottom_up = L(RegNet)( + stem_class=SimpleStem, + stem_width=32, + block_class=ResBottleneckBlock, + depth=23, + w_a=38.65, + w_0=96, + w_m=2.43, + group_width=40, + freeze_at=2, + norm="FrozenBN", + out_features=["s1", "s2", "s3", "s4"], +) +model.pixel_std = [57.375, 57.120, 58.395] + +optimizer.weight_decay = 5e-5 +train.init_checkpoint = ( + "https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906383/RegNetX-4.0GF_dds_8gpu.pyth" +) +# RegNets benefit from enabling cudnn benchmark mode +train.cudnn_benchmark = True diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_regnety_4gf_dds_fpn_1x.py b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_regnety_4gf_dds_fpn_1x.py new file mode 100644 index 0000000000000000000000000000000000000000..72c6b7a5c8939970bd0e1e4a3c1155695943b19a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_regnety_4gf_dds_fpn_1x.py @@ -0,0 +1,35 @@ +from ..common.optim import SGD as optimizer +from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier +from ..common.data.coco import dataloader +from ..common.models.mask_rcnn_fpn import model +from ..common.train import train + +from detectron2.config import LazyCall as L +from detectron2.modeling.backbone import RegNet +from detectron2.modeling.backbone.regnet import SimpleStem, ResBottleneckBlock + + +# Replace default ResNet with RegNetY-4GF from the DDS paper. Config source: +# https://github.com/facebookresearch/pycls/blob/2c152a6e5d913e898cca4f0a758f41e6b976714d/configs/dds_baselines/regnety/RegNetY-4.0GF_dds_8gpu.yaml#L4-L10 # noqa +model.backbone.bottom_up = L(RegNet)( + stem_class=SimpleStem, + stem_width=32, + block_class=ResBottleneckBlock, + depth=22, + w_a=31.41, + w_0=96, + w_m=2.24, + group_width=64, + se_ratio=0.25, + freeze_at=2, + norm="FrozenBN", + out_features=["s1", "s2", "s3", "s4"], +) +model.pixel_std = [57.375, 57.120, 58.395] + +optimizer.weight_decay = 5e-5 +train.init_checkpoint = ( + "https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906838/RegNetY-4.0GF_dds_8gpu.pyth" +) +# RegNets benefit from enabling cudnn benchmark mode +train.cudnn_benchmark = True diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/Base-Keypoint-RCNN-FPN.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/Base-Keypoint-RCNN-FPN.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4e03944a42d2e497da5ceca17c8fda797dac3f82 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/Base-Keypoint-RCNN-FPN.yaml @@ -0,0 +1,15 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + KEYPOINT_ON: True + ROI_HEADS: + NUM_CLASSES: 1 + ROI_BOX_HEAD: + SMOOTH_L1_BETA: 0.5 # Keypoint AP degrades (though box AP improves) when using plain L1 loss + RPN: + # Detectron1 uses 2000 proposals per-batch, but this option is per-image in detectron2. + # 1000 proposals per-image is found to hurt box AP. + # Therefore we increase it to 1500 per-image. + POST_NMS_TOPK_TRAIN: 1500 +DATASETS: + TRAIN: ("keypoints_coco_2017_train",) + TEST: ("keypoints_coco_2017_val",) diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9309535c57a1aa7d23297aac80a9bd78a6c79fcc --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x.yaml @@ -0,0 +1,8 @@ +_BASE_: "Base-Keypoint-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + RESNETS: + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.py b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.py new file mode 100644 index 0000000000000000000000000000000000000000..1aad53bfef62fb584d5022585d567e346f671a55 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.py @@ -0,0 +1,8 @@ +from ..common.optim import SGD as optimizer +from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier +from ..common.data.coco_keypoint import dataloader +from ..common.models.keypoint_rcnn_fpn import model +from ..common.train import train + +model.backbone.bottom_up.freeze_at = 2 +train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl" diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7bf85cf745b53b3e7ab28fe94b7f4f9e7fe6e335 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.yaml @@ -0,0 +1,5 @@ +_BASE_: "Base-Keypoint-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + RESNETS: + DEPTH: 50 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a07f243f650a497b9372501e3face75194cf0941 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml @@ -0,0 +1,8 @@ +_BASE_: "Base-Keypoint-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + RESNETS: + DEPTH: 50 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d4bfa20a98c0a65c6bd60e93b07e8f4b7d92a867 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml @@ -0,0 +1,12 @@ +_BASE_: "Base-Keypoint-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl" + PIXEL_STD: [57.375, 57.120, 58.395] + RESNETS: + STRIDE_IN_1X1: False # this is a C2 model + NUM_GROUPS: 32 + WIDTH_PER_GROUP: 8 + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f00d54b760c2b9271c75643e0a1ab1ffc0d9543a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml @@ -0,0 +1,11 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + META_ARCHITECTURE: "PanopticFPN" + MASK_ON: True + SEM_SEG_HEAD: + LOSS_WEIGHT: 0.5 +DATASETS: + TRAIN: ("coco_2017_train_panoptic_separated",) + TEST: ("coco_2017_val_panoptic_separated",) +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: False diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0e01f6fb31e9b00b1857b7de3b5074184d1f4a21 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml @@ -0,0 +1,8 @@ +_BASE_: "Base-Panoptic-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + RESNETS: + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.py b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.py new file mode 100644 index 0000000000000000000000000000000000000000..40cf18131810307157a9a7d1f6d5922b00fd73d5 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.py @@ -0,0 +1,8 @@ +from ..common.optim import SGD as optimizer +from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier +from ..common.data.coco_panoptic_separated import dataloader +from ..common.models.panoptic_fpn import model +from ..common.train import train + +model.backbone.bottom_up.freeze_at = 2 +train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl" diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6afa2c1cc92495309ed1553a17359fe5d7d6566e --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.yaml @@ -0,0 +1,5 @@ +_BASE_: "Base-Panoptic-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + RESNETS: + DEPTH: 50 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b956b3f673e78649184fe2c50e2700b3f1f14794 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml @@ -0,0 +1,8 @@ +_BASE_: "Base-Panoptic-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + RESNETS: + DEPTH: 50 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Cityscapes/mask_rcnn_R_50_FPN.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/Cityscapes/mask_rcnn_R_50_FPN.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1a7aaeb961581ed9492c4cfe5a69a1eb60495b3e --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Cityscapes/mask_rcnn_R_50_FPN.yaml @@ -0,0 +1,27 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + # WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + # For better, more stable performance initialize from COCO + WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl" + MASK_ON: True + ROI_HEADS: + NUM_CLASSES: 8 +# This is similar to the setting used in Mask R-CNN paper, Appendix A +# But there are some differences, e.g., we did not initialize the output +# layer using the corresponding classes from COCO +INPUT: + MIN_SIZE_TRAIN: (800, 832, 864, 896, 928, 960, 992, 1024) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 1024 + MAX_SIZE_TRAIN: 2048 + MAX_SIZE_TEST: 2048 +DATASETS: + TRAIN: ("cityscapes_fine_instance_seg_train",) + TEST: ("cityscapes_fine_instance_seg_val",) +SOLVER: + BASE_LR: 0.01 + STEPS: (18000,) + MAX_ITER: 24000 + IMS_PER_BATCH: 8 +TEST: + EVAL_PERIOD: 8000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Detectron1-Comparisons/README.md b/iChat/models/grit_src/third_party/CenterNet2/configs/Detectron1-Comparisons/README.md new file mode 100644 index 0000000000000000000000000000000000000000..924fd00af642ddf1a4ff4c4f5947f676134eb7de --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Detectron1-Comparisons/README.md @@ -0,0 +1,84 @@ + +Detectron2 model zoo's experimental settings and a few implementation details are different from Detectron. + +The differences in implementation details are shared in +[Compatibility with Other Libraries](../../docs/notes/compatibility.md). + +The differences in model zoo's experimental settings include: +* Use scale augmentation during training. This improves AP with lower training cost. +* Use L1 loss instead of smooth L1 loss for simplicity. This sometimes improves box AP but may + affect other AP. +* Use `POOLER_SAMPLING_RATIO=0` instead of 2. This does not significantly affect AP. +* Use `ROIAlignV2`. This does not significantly affect AP. + +In this directory, we provide a few configs that __do not__ have the above changes. +They mimic Detectron's behavior as close as possible, +and provide a fair comparison of accuracy and speed against Detectron. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Namelr
sched		train
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		mask
AP		kp.
AP		model iddownload
Faster R-CNN1x0.2190.0383.136.9137781054model | metrics
Keypoint R-CNN1x0.3130.0715.053.164.2137781195model | metrics
Mask R-CNN1x0.2730.0433.437.834.9137781281model | metrics
+ +## Comparisons: + +* Faster R-CNN: Detectron's AP is 36.7, similar to ours. +* Keypoint R-CNN: Detectron's AP is box 53.6, keypoint 64.2. Fixing a Detectron's + [bug](https://github.com/facebookresearch/Detectron/issues/459) lead to a drop in box AP, and can be + compensated back by some parameter tuning. +* Mask R-CNN: Detectron's AP is box 37.7, mask 33.9. We're 1 AP better in mask AP, due to more correct implementation. + See [this article](https://ppwwyyxx.com/blog/2021/Where-are-Pixels/) for details. + +For speed comparison, see [benchmarks](https://detectron2.readthedocs.io/notes/benchmarks.html). diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6ce77f137fa2c4e5254a62b58c18b8b76096f2aa --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x.yaml @@ -0,0 +1,17 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 + # Detectron1 uses smooth L1 loss with some magic beta values. + # The defaults are changed to L1 loss in Detectron2. + RPN: + SMOOTH_L1_BETA: 0.1111 + ROI_BOX_HEAD: + SMOOTH_L1_BETA: 1.0 + POOLER_SAMPLING_RATIO: 2 + POOLER_TYPE: "ROIAlign" +INPUT: + # no scale augmentation + MIN_SIZE_TRAIN: (800, ) diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..aacf868ba5290c752031c130a2081af48afc0808 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x.yaml @@ -0,0 +1,27 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + KEYPOINT_ON: True + RESNETS: + DEPTH: 50 + ROI_HEADS: + NUM_CLASSES: 1 + ROI_KEYPOINT_HEAD: + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 2 + POOLER_TYPE: "ROIAlign" + # Detectron1 uses smooth L1 loss with some magic beta values. + # The defaults are changed to L1 loss in Detectron2. + ROI_BOX_HEAD: + SMOOTH_L1_BETA: 1.0 + POOLER_SAMPLING_RATIO: 2 + POOLER_TYPE: "ROIAlign" + RPN: + SMOOTH_L1_BETA: 0.1111 + # Detectron1 uses 2000 proposals per-batch, but this option is per-image in detectron2 + # 1000 proposals per-image is found to hurt box AP. + # Therefore we increase it to 1500 per-image. + POST_NMS_TOPK_TRAIN: 1500 +DATASETS: + TRAIN: ("keypoints_coco_2017_train",) + TEST: ("keypoints_coco_2017_val",) diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4ea86a8d8e2cd3e51cbc7311b0d00710c07d01f6 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml @@ -0,0 +1,20 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 + # Detectron1 uses smooth L1 loss with some magic beta values. + # The defaults are changed to L1 loss in Detectron2. + RPN: + SMOOTH_L1_BETA: 0.1111 + ROI_BOX_HEAD: + SMOOTH_L1_BETA: 1.0 + POOLER_SAMPLING_RATIO: 2 + POOLER_TYPE: "ROIAlign" + ROI_MASK_HEAD: + POOLER_SAMPLING_RATIO: 2 + POOLER_TYPE: "ROIAlign" +INPUT: + # no scale augmentation + MIN_SIZE_TRAIN: (800, ) diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/LVISv0.5-InstanceSegmentation/mask_rcnn_R_101_FPN_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/LVISv0.5-InstanceSegmentation/mask_rcnn_R_101_FPN_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f0c3a1bbc0a09e1384de522f30c443ba1e36fafa --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/LVISv0.5-InstanceSegmentation/mask_rcnn_R_101_FPN_1x.yaml @@ -0,0 +1,19 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + MASK_ON: True + RESNETS: + DEPTH: 101 + ROI_HEADS: + NUM_CLASSES: 1230 + SCORE_THRESH_TEST: 0.0001 +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +DATASETS: + TRAIN: ("lvis_v0.5_train",) + TEST: ("lvis_v0.5_val",) +TEST: + DETECTIONS_PER_IMAGE: 300 # LVIS allows up to 300 +DATALOADER: + SAMPLER_TRAIN: "RepeatFactorTrainingSampler" + REPEAT_THRESHOLD: 0.001 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/LVISv0.5-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/LVISv0.5-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..64b4caa4ef2b284782367ea702e1ae6653472630 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/LVISv0.5-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml @@ -0,0 +1,19 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 + ROI_HEADS: + NUM_CLASSES: 1230 + SCORE_THRESH_TEST: 0.0001 +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +DATASETS: + TRAIN: ("lvis_v0.5_train",) + TEST: ("lvis_v0.5_val",) +TEST: + DETECTIONS_PER_IMAGE: 300 # LVIS allows up to 300 +DATALOADER: + SAMPLER_TRAIN: "RepeatFactorTrainingSampler" + REPEAT_THRESHOLD: 0.001 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/LVISv0.5-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/LVISv0.5-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c8b822c6c006ba642f4caf9b55e7983f6797427a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/LVISv0.5-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x.yaml @@ -0,0 +1,23 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl" + PIXEL_STD: [57.375, 57.120, 58.395] + MASK_ON: True + RESNETS: + STRIDE_IN_1X1: False # this is a C2 model + NUM_GROUPS: 32 + WIDTH_PER_GROUP: 8 + DEPTH: 101 + ROI_HEADS: + NUM_CLASSES: 1230 + SCORE_THRESH_TEST: 0.0001 +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +DATASETS: + TRAIN: ("lvis_v0.5_train",) + TEST: ("lvis_v0.5_val",) +TEST: + DETECTIONS_PER_IMAGE: 300 # LVIS allows up to 300 +DATALOADER: + SAMPLER_TRAIN: "RepeatFactorTrainingSampler" + REPEAT_THRESHOLD: 0.001 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/LVISv1-InstanceSegmentation/mask_rcnn_R_101_FPN_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/LVISv1-InstanceSegmentation/mask_rcnn_R_101_FPN_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ca4dd97144561276ecaabbb6c254e3a7737ac157 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/LVISv1-InstanceSegmentation/mask_rcnn_R_101_FPN_1x.yaml @@ -0,0 +1,22 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + MASK_ON: True + RESNETS: + DEPTH: 101 + ROI_HEADS: + NUM_CLASSES: 1203 + SCORE_THRESH_TEST: 0.0001 +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +DATASETS: + TRAIN: ("lvis_v1_train",) + TEST: ("lvis_v1_val",) +TEST: + DETECTIONS_PER_IMAGE: 300 # LVIS allows up to 300 +SOLVER: + STEPS: (120000, 160000) + MAX_ITER: 180000 # 180000 * 16 / 100000 ~ 28.8 epochs +DATALOADER: + SAMPLER_TRAIN: "RepeatFactorTrainingSampler" + REPEAT_THRESHOLD: 0.001 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/LVISv1-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/LVISv1-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f313295ee5f0d553d394ce2efe003810c79af47d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/LVISv1-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml @@ -0,0 +1,22 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 + ROI_HEADS: + NUM_CLASSES: 1203 + SCORE_THRESH_TEST: 0.0001 +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +DATASETS: + TRAIN: ("lvis_v1_train",) + TEST: ("lvis_v1_val",) +TEST: + DETECTIONS_PER_IMAGE: 300 # LVIS allows up to 300 +SOLVER: + STEPS: (120000, 160000) + MAX_ITER: 180000 # 180000 * 16 / 100000 ~ 28.8 epochs +DATALOADER: + SAMPLER_TRAIN: "RepeatFactorTrainingSampler" + REPEAT_THRESHOLD: 0.001 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/LVISv1-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/LVISv1-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f6528f7c31c8cfbf139c14fd0cae598592d8e898 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/LVISv1-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x.yaml @@ -0,0 +1,26 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl" + PIXEL_STD: [57.375, 57.120, 58.395] + MASK_ON: True + RESNETS: + STRIDE_IN_1X1: False # this is a C2 model + NUM_GROUPS: 32 + WIDTH_PER_GROUP: 8 + DEPTH: 101 + ROI_HEADS: + NUM_CLASSES: 1203 + SCORE_THRESH_TEST: 0.0001 +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +DATASETS: + TRAIN: ("lvis_v1_train",) + TEST: ("lvis_v1_val",) +SOLVER: + STEPS: (120000, 160000) + MAX_ITER: 180000 # 180000 * 16 / 100000 ~ 28.8 epochs +TEST: + DETECTIONS_PER_IMAGE: 300 # LVIS allows up to 300 +DATALOADER: + SAMPLER_TRAIN: "RepeatFactorTrainingSampler" + REPEAT_THRESHOLD: 0.001 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/cascade_mask_rcnn_R_50_FPN_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/cascade_mask_rcnn_R_50_FPN_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..abb33b618932e94b66239945ac892f4c84a6e8f8 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/cascade_mask_rcnn_R_50_FPN_1x.yaml @@ -0,0 +1,12 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 + ROI_HEADS: + NAME: CascadeROIHeads + ROI_BOX_HEAD: + CLS_AGNOSTIC_BBOX_REG: True + RPN: + POST_NMS_TOPK_TRAIN: 2000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e2201ad5c46ded91ccfa47b7698a521625c5e447 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml @@ -0,0 +1,15 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 + ROI_HEADS: + NAME: CascadeROIHeads + ROI_BOX_HEAD: + CLS_AGNOSTIC_BBOX_REG: True + RPN: + POST_NMS_TOPK_TRAIN: 2000 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fc117f6b5e3e51558ec2f01b73c5365622e5ce25 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv.yaml @@ -0,0 +1,36 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + MASK_ON: True + WEIGHTS: "catalog://ImageNetPretrained/FAIR/X-152-32x8d-IN5k" + RESNETS: + STRIDE_IN_1X1: False # this is a C2 model + NUM_GROUPS: 32 + WIDTH_PER_GROUP: 8 + DEPTH: 152 + DEFORM_ON_PER_STAGE: [False, True, True, True] + ROI_HEADS: + NAME: "CascadeROIHeads" + ROI_BOX_HEAD: + NAME: "FastRCNNConvFCHead" + NUM_CONV: 4 + NUM_FC: 1 + NORM: "GN" + CLS_AGNOSTIC_BBOX_REG: True + ROI_MASK_HEAD: + NUM_CONV: 8 + NORM: "GN" + RPN: + POST_NMS_TOPK_TRAIN: 2000 +SOLVER: + IMS_PER_BATCH: 128 + STEPS: (35000, 45000) + MAX_ITER: 50000 + BASE_LR: 0.16 +INPUT: + MIN_SIZE_TRAIN: (640, 864) + MIN_SIZE_TRAIN_SAMPLING: "range" + MAX_SIZE_TRAIN: 1440 + CROP: + ENABLED: True +TEST: + EVAL_PERIOD: 2500 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/mask_rcnn_R_50_FPN_1x_cls_agnostic.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/mask_rcnn_R_50_FPN_1x_cls_agnostic.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4c3b767ff473bbab7225cc8a4a92608543d78246 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/mask_rcnn_R_50_FPN_1x_cls_agnostic.yaml @@ -0,0 +1,10 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 + ROI_BOX_HEAD: + CLS_AGNOSTIC_BBOX_REG: True + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: True diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml new file mode 100644 index 0000000000000000000000000000000000000000..04ff988d073ef9169ee4ca2cbce0d6f030c15232 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml @@ -0,0 +1,8 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 + DEFORM_ON_PER_STAGE: [False, True, True, True] # on Res3,Res4,Res5 + DEFORM_MODULATED: False diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5.yaml new file mode 100644 index 0000000000000000000000000000000000000000..68c0ca58d7df97ca728c339da0ca9828fe6be318 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5.yaml @@ -0,0 +1,11 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 + DEFORM_ON_PER_STAGE: [False, True, True, True] # on Res3,Res4,Res5 + DEFORM_MODULATED: False +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/mask_rcnn_R_50_FPN_3x_gn.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/mask_rcnn_R_50_FPN_3x_gn.yaml new file mode 100644 index 0000000000000000000000000000000000000000..74d274e5a529b5a8afe186940868f9d48c6112b3 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/mask_rcnn_R_50_FPN_3x_gn.yaml @@ -0,0 +1,21 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "catalog://ImageNetPretrained/FAIR/R-50-GN" + MASK_ON: True + RESNETS: + DEPTH: 50 + NORM: "GN" + STRIDE_IN_1X1: False + FPN: + NORM: "GN" + ROI_BOX_HEAD: + NAME: "FastRCNNConvFCHead" + NUM_CONV: 4 + NUM_FC: 1 + NORM: "GN" + ROI_MASK_HEAD: + NORM: "GN" +SOLVER: + # 3x schedule + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/mask_rcnn_R_50_FPN_3x_syncbn.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/mask_rcnn_R_50_FPN_3x_syncbn.yaml new file mode 100644 index 0000000000000000000000000000000000000000..11ebb076ba529f26c71a0d972e96ca4c2d6a830b --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/mask_rcnn_R_50_FPN_3x_syncbn.yaml @@ -0,0 +1,24 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 + NORM: "SyncBN" + STRIDE_IN_1X1: True + FPN: + NORM: "SyncBN" + ROI_BOX_HEAD: + NAME: "FastRCNNConvFCHead" + NUM_CONV: 4 + NUM_FC: 1 + NORM: "SyncBN" + ROI_MASK_HEAD: + NORM: "SyncBN" +SOLVER: + # 3x schedule + STEPS: (210000, 250000) + MAX_ITER: 270000 +TEST: + PRECISE_BN: + ENABLED: True diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/mmdet_mask_rcnn_R_50_FPN_1x.py b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/mmdet_mask_rcnn_R_50_FPN_1x.py new file mode 100644 index 0000000000000000000000000000000000000000..0f2464be744c083985898a25f9e71d00104f689d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/mmdet_mask_rcnn_R_50_FPN_1x.py @@ -0,0 +1,151 @@ +# An example config to train a mmdetection model using detectron2. + +from ..common.data.coco import dataloader +from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier +from ..common.optim import SGD as optimizer +from ..common.train import train + +from detectron2.modeling.mmdet_wrapper import MMDetDetector +from detectron2.config import LazyCall as L + +model = L(MMDetDetector)( + detector=dict( + type="MaskRCNN", + pretrained="torchvision://resnet50", + backbone=dict( + type="ResNet", + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type="BN", requires_grad=True), + norm_eval=True, + style="pytorch", + ), + neck=dict(type="FPN", in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5), + rpn_head=dict( + type="RPNHead", + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type="AnchorGenerator", + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64], + ), + bbox_coder=dict( + type="DeltaXYWHBBoxCoder", + target_means=[0.0, 0.0, 0.0, 0.0], + target_stds=[1.0, 1.0, 1.0, 1.0], + ), + loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type="L1Loss", loss_weight=1.0), + ), + roi_head=dict( + type="StandardRoIHead", + bbox_roi_extractor=dict( + type="SingleRoIExtractor", + roi_layer=dict(type="RoIAlign", output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + ), + bbox_head=dict( + type="Shared2FCBBoxHead", + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type="DeltaXYWHBBoxCoder", + target_means=[0.0, 0.0, 0.0, 0.0], + target_stds=[0.1, 0.1, 0.2, 0.2], + ), + reg_class_agnostic=False, + loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type="L1Loss", loss_weight=1.0), + ), + mask_roi_extractor=dict( + type="SingleRoIExtractor", + roi_layer=dict(type="RoIAlign", output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + ), + mask_head=dict( + type="FCNMaskHead", + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_mask=dict(type="CrossEntropyLoss", use_mask=True, loss_weight=1.0), + ), + ), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type="MaxIoUAssigner", + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1, + ), + sampler=dict( + type="RandomSampler", + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False, + ), + allowed_border=-1, + pos_weight=-1, + debug=False, + ), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type="nms", iou_threshold=0.7), + min_bbox_size=0, + ), + rcnn=dict( + assigner=dict( + type="MaxIoUAssigner", + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=True, + ignore_iof_thr=-1, + ), + sampler=dict( + type="RandomSampler", + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True, + ), + mask_size=28, + pos_weight=-1, + debug=False, + ), + ), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type="nms", iou_threshold=0.7), + min_bbox_size=0, + ), + rcnn=dict( + score_thr=0.05, + nms=dict(type="nms", iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5, + ), + ), + ), + pixel_mean=[123.675, 116.280, 103.530], + pixel_std=[58.395, 57.120, 57.375], +) + +dataloader.train.mapper.image_format = "RGB" # torchvision pretrained model +train.init_checkpoint = None # pretrained model is loaded inside backbone diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..34016cea3ca9d7fb69ef4fe01d6b47ee8690a13b --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x.yaml @@ -0,0 +1,26 @@ +# A large PanopticFPN for demo purposes. +# Use GN on backbone to support semantic seg. +# Use Cascade + Deform Conv to improve localization. +_BASE_: "../COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml" +MODEL: + WEIGHTS: "catalog://ImageNetPretrained/FAIR/R-101-GN" + RESNETS: + DEPTH: 101 + NORM: "GN" + DEFORM_ON_PER_STAGE: [False, True, True, True] + STRIDE_IN_1X1: False + FPN: + NORM: "GN" + ROI_HEADS: + NAME: CascadeROIHeads + ROI_BOX_HEAD: + CLS_AGNOSTIC_BBOX_REG: True + ROI_MASK_HEAD: + NORM: "GN" + RPN: + POST_NMS_TOPK_TRAIN: 2000 +SOLVER: + STEPS: (105000, 125000) + MAX_ITER: 135000 + IMS_PER_BATCH: 32 + BASE_LR: 0.04 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f3400288cde242fcf66eef7f63b5a9165ca663c5 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml @@ -0,0 +1,13 @@ +_BASE_: "mask_rcnn_R_50_FPN_3x_gn.yaml" +MODEL: + # Train from random initialization. + WEIGHTS: "" + # It makes sense to divide by STD when training from scratch + # But it seems to make no difference on the results and C2's models didn't do this. + # So we keep things consistent with C2. + # PIXEL_STD: [57.375, 57.12, 58.395] + MASK_ON: True + BACKBONE: + FREEZE_AT: 0 +# NOTE: Please refer to Rethinking ImageNet Pre-training https://arxiv.org/abs/1811.08883 +# to learn what you need for training from scratch. diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_gn.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_gn.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d90c9ff0ef4573252ee165b4c958ec5f74178176 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_gn.yaml @@ -0,0 +1,19 @@ +_BASE_: "mask_rcnn_R_50_FPN_3x_gn.yaml" +MODEL: + PIXEL_STD: [57.375, 57.12, 58.395] + WEIGHTS: "" + MASK_ON: True + RESNETS: + STRIDE_IN_1X1: False + BACKBONE: + FREEZE_AT: 0 +SOLVER: + # 9x schedule + IMS_PER_BATCH: 64 # 4x the standard + STEPS: (187500, 197500) # last 60/4==15k and last 20/4==5k + MAX_ITER: 202500 # 90k * 9 / 4 + BASE_LR: 0.08 +TEST: + EVAL_PERIOD: 2500 +# NOTE: Please refer to Rethinking ImageNet Pre-training https://arxiv.org/abs/1811.08883 +# to learn what you need for training from scratch. diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn.yaml new file mode 100644 index 0000000000000000000000000000000000000000..60d4e42330e396a1901437df8e17b262d5ad547a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn.yaml @@ -0,0 +1,19 @@ +_BASE_: "mask_rcnn_R_50_FPN_3x_syncbn.yaml" +MODEL: + PIXEL_STD: [57.375, 57.12, 58.395] + WEIGHTS: "" + MASK_ON: True + RESNETS: + STRIDE_IN_1X1: False + BACKBONE: + FREEZE_AT: 0 +SOLVER: + # 9x schedule + IMS_PER_BATCH: 64 # 4x the standard + STEPS: (187500, 197500) # last 60/4==15k and last 20/4==5k + MAX_ITER: 202500 # 90k * 9 / 4 + BASE_LR: 0.08 +TEST: + EVAL_PERIOD: 2500 +# NOTE: Please refer to Rethinking ImageNet Pre-training https://arxiv.org/abs/1811.08883 +# to learn what you need for training from scratch. diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/semantic_R_50_FPN_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/semantic_R_50_FPN_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ac256e1372770ab3d9ae522c962de0fd0dbceeb5 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/semantic_R_50_FPN_1x.yaml @@ -0,0 +1,11 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + META_ARCHITECTURE: "SemanticSegmentor" + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + RESNETS: + DEPTH: 50 +DATASETS: + TRAIN: ("coco_2017_train_panoptic_stuffonly",) + TEST: ("coco_2017_val_panoptic_stuffonly",) +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/torchvision_imagenet_R_50.py b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/torchvision_imagenet_R_50.py new file mode 100644 index 0000000000000000000000000000000000000000..0d75305bcf7445b98db84b3d489a1505d2fce5af --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/Misc/torchvision_imagenet_R_50.py @@ -0,0 +1,150 @@ +""" +An example config file to train a ImageNet classifier with detectron2. +Model and dataloader both come from torchvision. +This shows how to use detectron2 as a general engine for any new models and tasks. + +To run, use the following command: + +python tools/lazyconfig_train_net.py --config-file configs/Misc/torchvision_imagenet_R_50.py \ + --num-gpus 8 dataloader.train.dataset.root=/path/to/imagenet/ + +""" + + +import torch +from torch import nn +from torch.nn import functional as F +from omegaconf import OmegaConf +import torchvision +from torchvision.transforms import transforms as T +from torchvision.models.resnet import ResNet, Bottleneck +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from detectron2.solver import WarmupParamScheduler +from detectron2.solver.build import get_default_optimizer_params +from detectron2.config import LazyCall as L +from detectron2.model_zoo import get_config +from detectron2.data.samplers import TrainingSampler, InferenceSampler +from detectron2.evaluation import DatasetEvaluator +from detectron2.utils import comm + + +""" +Note: Here we put reusable code (models, evaluation, data) together with configs just as a +proof-of-concept, to easily demonstrate what's needed to train a ImageNet classifier in detectron2. +Writing code in configs offers extreme flexibility but is often not a good engineering practice. +In practice, you might want to put code in your project and import them instead. +""" + + +def build_data_loader(dataset, batch_size, num_workers, training=True): + return torch.utils.data.DataLoader( + dataset, + sampler=(TrainingSampler if training else InferenceSampler)(len(dataset)), + batch_size=batch_size, + num_workers=num_workers, + pin_memory=True, + ) + + +class ClassificationNet(nn.Module): + def __init__(self, model: nn.Module): + super().__init__() + self.model = model + + @property + def device(self): + return list(self.model.parameters())[0].device + + def forward(self, inputs): + image, label = inputs + pred = self.model(image.to(self.device)) + if self.training: + label = label.to(self.device) + return F.cross_entropy(pred, label) + else: + return pred + + +class ClassificationAcc(DatasetEvaluator): + def reset(self): + self.corr = self.total = 0 + + def process(self, inputs, outputs): + image, label = inputs + self.corr += (outputs.argmax(dim=1).cpu() == label.cpu()).sum().item() + self.total += len(label) + + def evaluate(self): + all_corr_total = comm.all_gather([self.corr, self.total]) + corr = sum(x[0] for x in all_corr_total) + total = sum(x[1] for x in all_corr_total) + return {"accuracy": corr / total} + + +# --- End of code that could be in a project and be imported + + +dataloader = OmegaConf.create() +dataloader.train = L(build_data_loader)( + dataset=L(torchvision.datasets.ImageNet)( + root="/path/to/imagenet", + split="train", + transform=L(T.Compose)( + transforms=[ + L(T.RandomResizedCrop)(size=224), + L(T.RandomHorizontalFlip)(), + T.ToTensor(), + L(T.Normalize)(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + ] + ), + ), + batch_size=256 // 8, + num_workers=4, + training=True, +) + +dataloader.test = L(build_data_loader)( + dataset=L(torchvision.datasets.ImageNet)( + root="${...train.dataset.root}", + split="val", + transform=L(T.Compose)( + transforms=[ + L(T.Resize)(size=256), + L(T.CenterCrop)(size=224), + T.ToTensor(), + L(T.Normalize)(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + ] + ), + ), + batch_size=256 // 8, + num_workers=4, + training=False, +) + +dataloader.evaluator = L(ClassificationAcc)() + +model = L(ClassificationNet)( + model=(ResNet)(block=Bottleneck, layers=[3, 4, 6, 3], zero_init_residual=True) +) + + +optimizer = L(torch.optim.SGD)( + params=L(get_default_optimizer_params)(), + lr=0.1, + momentum=0.9, + weight_decay=1e-4, +) + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1, 0.01, 0.001], milestones=[30, 60, 90, 100] + ), + warmup_length=1 / 100, + warmup_factor=0.1, +) + + +train = get_config("common/train.py").train +train.init_checkpoint = None +train.max_iter = 100 * 1281167 // 256 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/PascalVOC-Detection/faster_rcnn_R_50_C4.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/PascalVOC-Detection/faster_rcnn_R_50_C4.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ea2a6baaebd1a186db18f2904430ffb25901898e --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/PascalVOC-Detection/faster_rcnn_R_50_C4.yaml @@ -0,0 +1,18 @@ +_BASE_: "../Base-RCNN-C4.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 + ROI_HEADS: + NUM_CLASSES: 20 +INPUT: + MIN_SIZE_TRAIN: (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800) + MIN_SIZE_TEST: 800 +DATASETS: + TRAIN: ('voc_2007_trainval', 'voc_2012_trainval') + TEST: ('voc_2007_test',) +SOLVER: + STEPS: (12000, 16000) + MAX_ITER: 18000 # 17.4 epochs + WARMUP_ITERS: 100 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/PascalVOC-Detection/faster_rcnn_R_50_FPN.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/PascalVOC-Detection/faster_rcnn_R_50_FPN.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e554cab18a358a27b630c1ab0c2359666b0e1514 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/PascalVOC-Detection/faster_rcnn_R_50_FPN.yaml @@ -0,0 +1,18 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 + ROI_HEADS: + NUM_CLASSES: 20 +INPUT: + MIN_SIZE_TRAIN: (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800) + MIN_SIZE_TEST: 800 +DATASETS: + TRAIN: ('voc_2007_trainval', 'voc_2012_trainval') + TEST: ('voc_2007_test',) +SOLVER: + STEPS: (12000, 16000) + MAX_ITER: 18000 # 17.4 epochs + WARMUP_ITERS: 100 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/common/README.md b/iChat/models/grit_src/third_party/CenterNet2/configs/common/README.md new file mode 100644 index 0000000000000000000000000000000000000000..912cc29927542bfe4258d3208cf52d73cb0ea477 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/common/README.md @@ -0,0 +1,6 @@ +This directory provides definitions for a few common models, dataloaders, scheduler, +and optimizers that are often used in training. +The definition of these objects are provided in the form of lazy instantiation: +their arguments can be edited by users before constructing the objects. + +They can be imported, or loaded by `model_zoo.get_config` API in users' own configs. diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/common/coco_schedule.py b/iChat/models/grit_src/third_party/CenterNet2/configs/common/coco_schedule.py new file mode 100644 index 0000000000000000000000000000000000000000..355e66a1d213cb599a7ffe55089d854089c8ead2 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/common/coco_schedule.py @@ -0,0 +1,47 @@ +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from detectron2.config import LazyCall as L +from detectron2.solver import WarmupParamScheduler + + +def default_X_scheduler(num_X): + """ + Returns the config for a default multi-step LR scheduler such as "1x", "3x", + commonly referred to in papers, where every 1x has the total length of 1440k + training images (~12 COCO epochs). LR is decayed twice at the end of training + following the strategy defined in "Rethinking ImageNet Pretraining", Sec 4. + + Args: + num_X: a positive real number + + Returns: + DictConfig: configs that define the multiplier for LR during training + """ + # total number of iterations assuming 16 batch size, using 1440000/16=90000 + total_steps_16bs = num_X * 90000 + + if num_X <= 2: + scheduler = L(MultiStepParamScheduler)( + values=[1.0, 0.1, 0.01], + # note that scheduler is scale-invariant. This is equivalent to + # milestones=[6, 8, 9] + milestones=[60000, 80000, 90000], + ) + else: + scheduler = L(MultiStepParamScheduler)( + values=[1.0, 0.1, 0.01], + milestones=[total_steps_16bs - 60000, total_steps_16bs - 20000, total_steps_16bs], + ) + return L(WarmupParamScheduler)( + scheduler=scheduler, + warmup_length=1000 / total_steps_16bs, + warmup_method="linear", + warmup_factor=0.001, + ) + + +lr_multiplier_1x = default_X_scheduler(1) +lr_multiplier_2x = default_X_scheduler(2) +lr_multiplier_3x = default_X_scheduler(3) +lr_multiplier_6x = default_X_scheduler(6) +lr_multiplier_9x = default_X_scheduler(9) diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/common/data/coco.py b/iChat/models/grit_src/third_party/CenterNet2/configs/common/data/coco.py new file mode 100644 index 0000000000000000000000000000000000000000..703c4385c7ddc7eb0759c98d102ab2384d6a9e3e --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/common/data/coco.py @@ -0,0 +1,48 @@ +from omegaconf import OmegaConf + +import detectron2.data.transforms as T +from detectron2.config import LazyCall as L +from detectron2.data import ( + DatasetMapper, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, +) +from detectron2.evaluation import COCOEvaluator + +dataloader = OmegaConf.create() + +dataloader.train = L(build_detection_train_loader)( + dataset=L(get_detection_dataset_dicts)(names="coco_2017_train"), + mapper=L(DatasetMapper)( + is_train=True, + augmentations=[ + L(T.ResizeShortestEdge)( + short_edge_length=(640, 672, 704, 736, 768, 800), + sample_style="choice", + max_size=1333, + ), + L(T.RandomFlip)(horizontal=True), + ], + image_format="BGR", + use_instance_mask=True, + ), + total_batch_size=16, + num_workers=4, +) + +dataloader.test = L(build_detection_test_loader)( + dataset=L(get_detection_dataset_dicts)(names="coco_2017_val", filter_empty=False), + mapper=L(DatasetMapper)( + is_train=False, + augmentations=[ + L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333), + ], + image_format="${...train.mapper.image_format}", + ), + num_workers=4, +) + +dataloader.evaluator = L(COCOEvaluator)( + dataset_name="${..test.dataset.names}", +) diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/common/data/coco_keypoint.py b/iChat/models/grit_src/third_party/CenterNet2/configs/common/data/coco_keypoint.py new file mode 100644 index 0000000000000000000000000000000000000000..b4ceb066faf696954244205dc75376b767071217 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/common/data/coco_keypoint.py @@ -0,0 +1,13 @@ +from detectron2.data.detection_utils import create_keypoint_hflip_indices + +from .coco import dataloader + +dataloader.train.dataset.min_keypoints = 1 +dataloader.train.dataset.names = "keypoints_coco_2017_train" +dataloader.test.dataset.names = "keypoints_coco_2017_val" + +dataloader.train.mapper.update( + use_instance_mask=False, + use_keypoint=True, + keypoint_hflip_indices=create_keypoint_hflip_indices(dataloader.train.dataset.names), +) diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/common/data/coco_panoptic_separated.py b/iChat/models/grit_src/third_party/CenterNet2/configs/common/data/coco_panoptic_separated.py new file mode 100644 index 0000000000000000000000000000000000000000..5ccbc77e64d1c92c99cbd7158d047bab54cb9f3d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/common/data/coco_panoptic_separated.py @@ -0,0 +1,26 @@ +from detectron2.config import LazyCall as L +from detectron2.evaluation import ( + COCOEvaluator, + COCOPanopticEvaluator, + DatasetEvaluators, + SemSegEvaluator, +) + +from .coco import dataloader + +dataloader.train.dataset.names = "coco_2017_train_panoptic_separated" +dataloader.train.dataset.filter_empty = False +dataloader.test.dataset.names = "coco_2017_val_panoptic_separated" + + +dataloader.evaluator = [ + L(COCOEvaluator)( + dataset_name="${...test.dataset.names}", + ), + L(SemSegEvaluator)( + dataset_name="${...test.dataset.names}", + ), + L(COCOPanopticEvaluator)( + dataset_name="${...test.dataset.names}", + ), +] diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/common/models/cascade_rcnn.py b/iChat/models/grit_src/third_party/CenterNet2/configs/common/models/cascade_rcnn.py new file mode 100644 index 0000000000000000000000000000000000000000..c7372a801dc00d7fec4db8cda8c2612ce281d48a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/common/models/cascade_rcnn.py @@ -0,0 +1,36 @@ +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.modeling.box_regression import Box2BoxTransform +from detectron2.modeling.matcher import Matcher +from detectron2.modeling.roi_heads import FastRCNNOutputLayers, FastRCNNConvFCHead, CascadeROIHeads + +from .mask_rcnn_fpn import model + +# arguments that don't exist for Cascade R-CNN +[model.roi_heads.pop(k) for k in ["box_head", "box_predictor", "proposal_matcher"]] + +model.roi_heads.update( + _target_=CascadeROIHeads, + box_heads=[ + L(FastRCNNConvFCHead)( + input_shape=ShapeSpec(channels=256, height=7, width=7), + conv_dims=[], + fc_dims=[1024, 1024], + ) + for k in range(3) + ], + box_predictors=[ + L(FastRCNNOutputLayers)( + input_shape=ShapeSpec(channels=1024), + test_score_thresh=0.05, + box2box_transform=L(Box2BoxTransform)(weights=(w1, w1, w2, w2)), + cls_agnostic_bbox_reg=True, + num_classes="${...num_classes}", + ) + for (w1, w2) in [(10, 5), (20, 10), (30, 15)] + ], + proposal_matchers=[ + L(Matcher)(thresholds=[th], labels=[0, 1], allow_low_quality_matches=False) + for th in [0.5, 0.6, 0.7] + ], +) diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/common/models/fcos.py b/iChat/models/grit_src/third_party/CenterNet2/configs/common/models/fcos.py new file mode 100644 index 0000000000000000000000000000000000000000..1c752029b7fc64ec375a55182e5342c9eb48bb33 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/common/models/fcos.py @@ -0,0 +1,23 @@ +from detectron2.modeling.meta_arch.fcos import FCOS, FCOSHead + +from .retinanet import model + +model._target_ = FCOS + +del model.anchor_generator +del model.box2box_transform +del model.anchor_matcher +del model.input_format + +# Use P5 instead of C5 to compute P6/P7 +# (Sec 2.2 of https://arxiv.org/abs/2006.09214) +model.backbone.top_block.in_feature = "p5" +model.backbone.top_block.in_channels = 256 + +# New score threshold determined based on sqrt(cls_score * centerness) +model.test_score_thresh = 0.2 +model.test_nms_thresh = 0.6 + +model.head._target_ = FCOSHead +del model.head.num_anchors +model.head.norm = "GN" diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/common/models/keypoint_rcnn_fpn.py b/iChat/models/grit_src/third_party/CenterNet2/configs/common/models/keypoint_rcnn_fpn.py new file mode 100644 index 0000000000000000000000000000000000000000..56b3994df249884d4816fc9a5c7f553a9ab6f400 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/common/models/keypoint_rcnn_fpn.py @@ -0,0 +1,33 @@ +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.modeling.poolers import ROIPooler +from detectron2.modeling.roi_heads import KRCNNConvDeconvUpsampleHead + +from .mask_rcnn_fpn import model + +[model.roi_heads.pop(x) for x in ["mask_in_features", "mask_pooler", "mask_head"]] + +model.roi_heads.update( + num_classes=1, + keypoint_in_features=["p2", "p3", "p4", "p5"], + keypoint_pooler=L(ROIPooler)( + output_size=14, + scales=(1.0 / 4, 1.0 / 8, 1.0 / 16, 1.0 / 32), + sampling_ratio=0, + pooler_type="ROIAlignV2", + ), + keypoint_head=L(KRCNNConvDeconvUpsampleHead)( + input_shape=ShapeSpec(channels=256, width=14, height=14), + num_keypoints=17, + conv_dims=[512] * 8, + loss_normalizer="visible", + ), +) + +# Detectron1 uses 2000 proposals per-batch, but this option is per-image in detectron2. +# 1000 proposals per-image is found to hurt box AP. +# Therefore we increase it to 1500 per-image. +model.proposal_generator.post_nms_topk = (1500, 1000) + +# Keypoint AP degrades (though box AP improves) when using plain L1 loss +model.roi_heads.box_predictor.smooth_l1_beta = 0.5 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/common/models/mask_rcnn_c4.py b/iChat/models/grit_src/third_party/CenterNet2/configs/common/models/mask_rcnn_c4.py new file mode 100644 index 0000000000000000000000000000000000000000..a3dcf8be42a39c6e5f6e76e3ab23adeccb33085d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/common/models/mask_rcnn_c4.py @@ -0,0 +1,88 @@ +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.modeling.meta_arch import GeneralizedRCNN +from detectron2.modeling.anchor_generator import DefaultAnchorGenerator +from detectron2.modeling.backbone import BasicStem, BottleneckBlock, ResNet +from detectron2.modeling.box_regression import Box2BoxTransform +from detectron2.modeling.matcher import Matcher +from detectron2.modeling.poolers import ROIPooler +from detectron2.modeling.proposal_generator import RPN, StandardRPNHead +from detectron2.modeling.roi_heads import ( + FastRCNNOutputLayers, + MaskRCNNConvUpsampleHead, + Res5ROIHeads, +) + +model = L(GeneralizedRCNN)( + backbone=L(ResNet)( + stem=L(BasicStem)(in_channels=3, out_channels=64, norm="FrozenBN"), + stages=L(ResNet.make_default_stages)( + depth=50, + stride_in_1x1=True, + norm="FrozenBN", + ), + out_features=["res4"], + ), + proposal_generator=L(RPN)( + in_features=["res4"], + head=L(StandardRPNHead)(in_channels=1024, num_anchors=15), + anchor_generator=L(DefaultAnchorGenerator)( + sizes=[[32, 64, 128, 256, 512]], + aspect_ratios=[0.5, 1.0, 2.0], + strides=[16], + offset=0.0, + ), + anchor_matcher=L(Matcher)( + thresholds=[0.3, 0.7], labels=[0, -1, 1], allow_low_quality_matches=True + ), + box2box_transform=L(Box2BoxTransform)(weights=[1.0, 1.0, 1.0, 1.0]), + batch_size_per_image=256, + positive_fraction=0.5, + pre_nms_topk=(12000, 6000), + post_nms_topk=(2000, 1000), + nms_thresh=0.7, + ), + roi_heads=L(Res5ROIHeads)( + num_classes=80, + batch_size_per_image=512, + positive_fraction=0.25, + proposal_matcher=L(Matcher)( + thresholds=[0.5], labels=[0, 1], allow_low_quality_matches=False + ), + in_features=["res4"], + pooler=L(ROIPooler)( + output_size=14, + scales=(1.0 / 16,), + sampling_ratio=0, + pooler_type="ROIAlignV2", + ), + res5=L(ResNet.make_stage)( + block_class=BottleneckBlock, + num_blocks=3, + stride_per_block=[2, 1, 1], + in_channels=1024, + bottleneck_channels=512, + out_channels=2048, + norm="FrozenBN", + stride_in_1x1=True, + ), + box_predictor=L(FastRCNNOutputLayers)( + input_shape=L(ShapeSpec)(channels="${...res5.out_channels}", height=1, width=1), + test_score_thresh=0.05, + box2box_transform=L(Box2BoxTransform)(weights=(10, 10, 5, 5)), + num_classes="${..num_classes}", + ), + mask_head=L(MaskRCNNConvUpsampleHead)( + input_shape=L(ShapeSpec)( + channels="${...res5.out_channels}", + width="${...pooler.output_size}", + height="${...pooler.output_size}", + ), + num_classes="${..num_classes}", + conv_dims=[256], + ), + ), + pixel_mean=[103.530, 116.280, 123.675], + pixel_std=[1.0, 1.0, 1.0], + input_format="BGR", +) diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/common/models/mask_rcnn_fpn.py b/iChat/models/grit_src/third_party/CenterNet2/configs/common/models/mask_rcnn_fpn.py new file mode 100644 index 0000000000000000000000000000000000000000..744d5306f5b0ba4cf508731bd790bad823b520fa --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/common/models/mask_rcnn_fpn.py @@ -0,0 +1,93 @@ +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.modeling.meta_arch import GeneralizedRCNN +from detectron2.modeling.anchor_generator import DefaultAnchorGenerator +from detectron2.modeling.backbone.fpn import LastLevelMaxPool +from detectron2.modeling.backbone import BasicStem, FPN, ResNet +from detectron2.modeling.box_regression import Box2BoxTransform +from detectron2.modeling.matcher import Matcher +from detectron2.modeling.poolers import ROIPooler +from detectron2.modeling.proposal_generator import RPN, StandardRPNHead +from detectron2.modeling.roi_heads import ( + StandardROIHeads, + FastRCNNOutputLayers, + MaskRCNNConvUpsampleHead, + FastRCNNConvFCHead, +) + +model = L(GeneralizedRCNN)( + backbone=L(FPN)( + bottom_up=L(ResNet)( + stem=L(BasicStem)(in_channels=3, out_channels=64, norm="FrozenBN"), + stages=L(ResNet.make_default_stages)( + depth=50, + stride_in_1x1=True, + norm="FrozenBN", + ), + out_features=["res2", "res3", "res4", "res5"], + ), + in_features="${.bottom_up.out_features}", + out_channels=256, + top_block=L(LastLevelMaxPool)(), + ), + proposal_generator=L(RPN)( + in_features=["p2", "p3", "p4", "p5", "p6"], + head=L(StandardRPNHead)(in_channels=256, num_anchors=3), + anchor_generator=L(DefaultAnchorGenerator)( + sizes=[[32], [64], [128], [256], [512]], + aspect_ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64], + offset=0.0, + ), + anchor_matcher=L(Matcher)( + thresholds=[0.3, 0.7], labels=[0, -1, 1], allow_low_quality_matches=True + ), + box2box_transform=L(Box2BoxTransform)(weights=[1.0, 1.0, 1.0, 1.0]), + batch_size_per_image=256, + positive_fraction=0.5, + pre_nms_topk=(2000, 1000), + post_nms_topk=(1000, 1000), + nms_thresh=0.7, + ), + roi_heads=L(StandardROIHeads)( + num_classes=80, + batch_size_per_image=512, + positive_fraction=0.25, + proposal_matcher=L(Matcher)( + thresholds=[0.5], labels=[0, 1], allow_low_quality_matches=False + ), + box_in_features=["p2", "p3", "p4", "p5"], + box_pooler=L(ROIPooler)( + output_size=7, + scales=(1.0 / 4, 1.0 / 8, 1.0 / 16, 1.0 / 32), + sampling_ratio=0, + pooler_type="ROIAlignV2", + ), + box_head=L(FastRCNNConvFCHead)( + input_shape=ShapeSpec(channels=256, height=7, width=7), + conv_dims=[], + fc_dims=[1024, 1024], + ), + box_predictor=L(FastRCNNOutputLayers)( + input_shape=ShapeSpec(channels=1024), + test_score_thresh=0.05, + box2box_transform=L(Box2BoxTransform)(weights=(10, 10, 5, 5)), + num_classes="${..num_classes}", + ), + mask_in_features=["p2", "p3", "p4", "p5"], + mask_pooler=L(ROIPooler)( + output_size=14, + scales=(1.0 / 4, 1.0 / 8, 1.0 / 16, 1.0 / 32), + sampling_ratio=0, + pooler_type="ROIAlignV2", + ), + mask_head=L(MaskRCNNConvUpsampleHead)( + input_shape=ShapeSpec(channels=256, width=14, height=14), + num_classes="${..num_classes}", + conv_dims=[256, 256, 256, 256, 256], + ), + ), + pixel_mean=[103.530, 116.280, 123.675], + pixel_std=[1.0, 1.0, 1.0], + input_format="BGR", +) diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/common/models/panoptic_fpn.py b/iChat/models/grit_src/third_party/CenterNet2/configs/common/models/panoptic_fpn.py new file mode 100644 index 0000000000000000000000000000000000000000..88f55d2ce9db62e61445d6a3700067d9d864ecae --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/common/models/panoptic_fpn.py @@ -0,0 +1,20 @@ +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.modeling import PanopticFPN +from detectron2.modeling.meta_arch.semantic_seg import SemSegFPNHead + +from .mask_rcnn_fpn import model + +model._target_ = PanopticFPN +model.sem_seg_head = L(SemSegFPNHead)( + input_shape={ + f: L(ShapeSpec)(stride=s, channels="${....backbone.out_channels}") + for f, s in zip(["p2", "p3", "p4", "p5"], [4, 8, 16, 32]) + }, + ignore_value=255, + num_classes=54, # COCO stuff + 1 + conv_dims=128, + common_stride=4, + loss_weight=0.5, + norm="GN", +) diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/common/models/retinanet.py b/iChat/models/grit_src/third_party/CenterNet2/configs/common/models/retinanet.py new file mode 100644 index 0000000000000000000000000000000000000000..83cfda4b6001750c676c22feb5e3560cba394140 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/common/models/retinanet.py @@ -0,0 +1,53 @@ +# -*- coding: utf-8 -*- + +from detectron2.config import LazyCall as L +from detectron2.layers import ShapeSpec +from detectron2.modeling.meta_arch import RetinaNet +from detectron2.modeling.anchor_generator import DefaultAnchorGenerator +from detectron2.modeling.backbone.fpn import LastLevelP6P7 +from detectron2.modeling.backbone import BasicStem, FPN, ResNet +from detectron2.modeling.box_regression import Box2BoxTransform +from detectron2.modeling.matcher import Matcher +from detectron2.modeling.meta_arch.retinanet import RetinaNetHead + +model = L(RetinaNet)( + backbone=L(FPN)( + bottom_up=L(ResNet)( + stem=L(BasicStem)(in_channels=3, out_channels=64, norm="FrozenBN"), + stages=L(ResNet.make_default_stages)( + depth=50, + stride_in_1x1=True, + norm="FrozenBN", + ), + out_features=["res3", "res4", "res5"], + ), + in_features=["res3", "res4", "res5"], + out_channels=256, + top_block=L(LastLevelP6P7)(in_channels=2048, out_channels="${..out_channels}"), + ), + head=L(RetinaNetHead)( + # Shape for each input feature map + input_shape=[ShapeSpec(channels=256)] * 5, + num_classes="${..num_classes}", + conv_dims=[256, 256, 256, 256], + prior_prob=0.01, + num_anchors=9, + ), + anchor_generator=L(DefaultAnchorGenerator)( + sizes=[[x, x * 2 ** (1.0 / 3), x * 2 ** (2.0 / 3)] for x in [32, 64, 128, 256, 512]], + aspect_ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128], + offset=0.0, + ), + box2box_transform=L(Box2BoxTransform)(weights=[1.0, 1.0, 1.0, 1.0]), + anchor_matcher=L(Matcher)( + thresholds=[0.4, 0.5], labels=[0, -1, 1], allow_low_quality_matches=True + ), + num_classes=80, + head_in_features=["p3", "p4", "p5", "p6", "p7"], + focal_loss_alpha=0.25, + focal_loss_gamma=2.0, + pixel_mean=[103.530, 116.280, 123.675], + pixel_std=[1.0, 1.0, 1.0], + input_format="BGR", +) diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/common/optim.py b/iChat/models/grit_src/third_party/CenterNet2/configs/common/optim.py new file mode 100644 index 0000000000000000000000000000000000000000..d39d3aaa546c17e831d21d1758b69e8c1609415e --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/common/optim.py @@ -0,0 +1,15 @@ +import torch + +from detectron2.config import LazyCall as L +from detectron2.solver.build import get_default_optimizer_params + +SGD = L(torch.optim.SGD)( + params=L(get_default_optimizer_params)( + # params.model is meant to be set to the model object, before instantiating + # the optimizer. + weight_decay_norm=0.0 + ), + lr=0.02, + momentum=0.9, + weight_decay=1e-4, +) diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/common/train.py b/iChat/models/grit_src/third_party/CenterNet2/configs/common/train.py new file mode 100644 index 0000000000000000000000000000000000000000..b6ed02bd59f540ca58df20bf72d462f195210a32 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/common/train.py @@ -0,0 +1,18 @@ +# Common training-related configs that are designed for "tools/lazyconfig_train_net.py" +# You can use your own instead, together with your own train_net.py +train = dict( + output_dir="./output", + init_checkpoint="", + max_iter=90000, + amp=dict(enabled=False), # options for Automatic Mixed Precision + ddp=dict( # options for DistributedDataParallel + broadcast_buffers=False, + find_unused_parameters=False, + fp16_compression=False, + ), + checkpointer=dict(period=5000, max_to_keep=100), # options for PeriodicCheckpointer + eval_period=5000, + log_period=20, + device="cuda" + # ... +) diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_101_FPN_100ep_LSJ.py b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_101_FPN_100ep_LSJ.py new file mode 100644 index 0000000000000000000000000000000000000000..3740e9bb08c5f168a9ab3a6d94561678bad1775c --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_101_FPN_100ep_LSJ.py @@ -0,0 +1,9 @@ +from .mask_rcnn_R_50_FPN_100ep_LSJ import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +model.backbone.bottom_up.stages.depth = 101 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_101_FPN_200ep_LSJ.py b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_101_FPN_200ep_LSJ.py new file mode 100644 index 0000000000000000000000000000000000000000..18e5f0720c568db4ef0c97b59688b5e7866df606 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_101_FPN_200ep_LSJ.py @@ -0,0 +1,14 @@ +from .mask_rcnn_R_101_FPN_100ep_LSJ import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +train.max_iter *= 2 # 100ep -> 200ep + +lr_multiplier.scheduler.milestones = [ + milestone * 2 for milestone in lr_multiplier.scheduler.milestones +] +lr_multiplier.scheduler.num_updates = train.max_iter diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ.py b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ.py new file mode 100644 index 0000000000000000000000000000000000000000..63c54ee9a5ce2368494b775cc90fada1439feaa5 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ.py @@ -0,0 +1,14 @@ +from .mask_rcnn_R_101_FPN_100ep_LSJ import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +train.max_iter *= 4 # 100ep -> 400ep + +lr_multiplier.scheduler.milestones = [ + milestone * 4 for milestone in lr_multiplier.scheduler.milestones +] +lr_multiplier.scheduler.num_updates = train.max_iter diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ.py b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ.py new file mode 100644 index 0000000000000000000000000000000000000000..df7a2aedf480ed8dc4aa3645e37420e9b893fae4 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ.py @@ -0,0 +1,72 @@ +import detectron2.data.transforms as T +from detectron2.config.lazy import LazyCall as L +from detectron2.layers.batch_norm import NaiveSyncBatchNorm +from detectron2.solver import WarmupParamScheduler +from fvcore.common.param_scheduler import MultiStepParamScheduler + +from ..common.data.coco import dataloader +from ..common.models.mask_rcnn_fpn import model +from ..common.optim import SGD as optimizer +from ..common.train import train + +# train from scratch +train.init_checkpoint = "" +train.amp.enabled = True +train.ddp.fp16_compression = True +model.backbone.bottom_up.freeze_at = 0 + +# SyncBN +# fmt: off +model.backbone.bottom_up.stem.norm = \ + model.backbone.bottom_up.stages.norm = \ + model.backbone.norm = "SyncBN" + +# Using NaiveSyncBatchNorm becase heads may have empty input. That is not supported by +# torch.nn.SyncBatchNorm. We can remove this after +# https://github.com/pytorch/pytorch/issues/36530 is fixed. +model.roi_heads.box_head.conv_norm = \ + model.roi_heads.mask_head.conv_norm = lambda c: NaiveSyncBatchNorm(c, + stats_mode="N") +# fmt: on + +# 2conv in RPN: +# https://github.com/tensorflow/tpu/blob/b24729de804fdb751b06467d3dce0637fa652060/models/official/detection/modeling/architecture/heads.py#L95-L97 # noqa: E501, B950 +model.proposal_generator.head.conv_dims = [-1, -1] + +# 4conv1fc box head +model.roi_heads.box_head.conv_dims = [256, 256, 256, 256] +model.roi_heads.box_head.fc_dims = [1024] + +# resize_and_crop_image in: +# https://github.com/tensorflow/tpu/blob/b24729de804fdb751b06467d3dce0637fa652060/models/official/detection/utils/input_utils.py#L127 # noqa: E501, B950 +image_size = 1024 +dataloader.train.mapper.augmentations = [ + L(T.ResizeScale)( + min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size + ), + L(T.FixedSizeCrop)(crop_size=(image_size, image_size)), + L(T.RandomFlip)(horizontal=True), +] + +# recompute boxes due to cropping +dataloader.train.mapper.recompute_boxes = True + +# larger batch-size. +dataloader.train.total_batch_size = 64 + +# Equivalent to 100 epochs. +# 100 ep = 184375 iters * 64 images/iter / 118000 images/ep +train.max_iter = 184375 + +lr_multiplier = L(WarmupParamScheduler)( + scheduler=L(MultiStepParamScheduler)( + values=[1.0, 0.1, 0.01], + milestones=[163889, 177546], + num_updates=train.max_iter, + ), + warmup_length=500 / train.max_iter, + warmup_factor=0.067, +) + +optimizer.lr = 0.1 +optimizer.weight_decay = 4e-5 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ.py b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ.py new file mode 100644 index 0000000000000000000000000000000000000000..2a7c376da5f9269197c44079f3e0f3b09cdc63fa --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ.py @@ -0,0 +1,14 @@ +from .mask_rcnn_R_50_FPN_100ep_LSJ import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +train.max_iter *= 2 # 100ep -> 200ep + +lr_multiplier.scheduler.milestones = [ + milestone * 2 for milestone in lr_multiplier.scheduler.milestones +] +lr_multiplier.scheduler.num_updates = train.max_iter diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_400ep_LSJ.py b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_400ep_LSJ.py new file mode 100644 index 0000000000000000000000000000000000000000..97586b8f5330a9d995a0bffd1f5e7bd5b5656462 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_400ep_LSJ.py @@ -0,0 +1,14 @@ +from .mask_rcnn_R_50_FPN_100ep_LSJ import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +train.max_iter *= 4 # 100ep -> 400ep + +lr_multiplier.scheduler.milestones = [ + milestone * 4 for milestone in lr_multiplier.scheduler.milestones +] +lr_multiplier.scheduler.num_updates = train.max_iter diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_50ep_LSJ.py b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_50ep_LSJ.py new file mode 100644 index 0000000000000000000000000000000000000000..2ca1ede262cf5c37a3a54778458c74aff1479411 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_50ep_LSJ.py @@ -0,0 +1,14 @@ +from .mask_rcnn_R_50_FPN_100ep_LSJ import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +train.max_iter //= 2 # 100ep -> 50ep + +lr_multiplier.scheduler.milestones = [ + milestone // 2 for milestone in lr_multiplier.scheduler.milestones +] +lr_multiplier.scheduler.num_updates = train.max_iter diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ.py b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ.py new file mode 100644 index 0000000000000000000000000000000000000000..ef0b6d16d4403fb5d16a3aeb71a22621a0be5e21 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ.py @@ -0,0 +1,29 @@ +from .mask_rcnn_R_50_FPN_100ep_LSJ import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) +from detectron2.config import LazyCall as L +from detectron2.modeling.backbone import RegNet +from detectron2.modeling.backbone.regnet import SimpleStem, ResBottleneckBlock + +# Config source: +# https://github.com/facebookresearch/detectron2/blob/main/configs/COCO-InstanceSegmentation/mask_rcnn_regnetx_4gf_dds_fpn_1x.py # noqa +model.backbone.bottom_up = L(RegNet)( + stem_class=SimpleStem, + stem_width=32, + block_class=ResBottleneckBlock, + depth=23, + w_a=38.65, + w_0=96, + w_m=2.43, + group_width=40, + norm="SyncBN", + out_features=["s1", "s2", "s3", "s4"], +) +model.pixel_std = [57.375, 57.120, 58.395] + +# RegNets benefit from enabling cudnn benchmark mode +train.cudnn_benchmark = True diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ.py b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ.py new file mode 100644 index 0000000000000000000000000000000000000000..731320e74ebed4d8ceec58c07cb906542b8b021b --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ.py @@ -0,0 +1,14 @@ +from .mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +train.max_iter *= 2 # 100ep -> 200ep + +lr_multiplier.scheduler.milestones = [ + milestone * 2 for milestone in lr_multiplier.scheduler.milestones +] +lr_multiplier.scheduler.num_updates = train.max_iter diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ.py b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ.py new file mode 100644 index 0000000000000000000000000000000000000000..8f369a2afedb6c6e69fd52ff9a9a6b1cdf965937 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ.py @@ -0,0 +1,14 @@ +from .mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +train.max_iter *= 4 # 100ep -> 400ep + +lr_multiplier.scheduler.milestones = [ + milestone * 4 for milestone in lr_multiplier.scheduler.milestones +] +lr_multiplier.scheduler.num_updates = train.max_iter diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ.py b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ.py new file mode 100644 index 0000000000000000000000000000000000000000..ba2c3274a493d5136507364558c8289eb6ee6259 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ.py @@ -0,0 +1,30 @@ +from .mask_rcnn_R_50_FPN_100ep_LSJ import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) +from detectron2.config import LazyCall as L +from detectron2.modeling.backbone import RegNet +from detectron2.modeling.backbone.regnet import SimpleStem, ResBottleneckBlock + +# Config source: +# https://github.com/facebookresearch/detectron2/blob/main/configs/COCO-InstanceSegmentation/mask_rcnn_regnety_4gf_dds_fpn_1x.py # noqa +model.backbone.bottom_up = L(RegNet)( + stem_class=SimpleStem, + stem_width=32, + block_class=ResBottleneckBlock, + depth=22, + w_a=31.41, + w_0=96, + w_m=2.24, + group_width=64, + se_ratio=0.25, + norm="SyncBN", + out_features=["s1", "s2", "s3", "s4"], +) +model.pixel_std = [57.375, 57.120, 58.395] + +# RegNets benefit from enabling cudnn benchmark mode +train.cudnn_benchmark = True diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ.py b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ.py new file mode 100644 index 0000000000000000000000000000000000000000..b867cc865e5ac4d7b70221da141894efd7cbd75c --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ.py @@ -0,0 +1,14 @@ +from .mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +train.max_iter *= 2 # 100ep -> 200ep + +lr_multiplier.scheduler.milestones = [ + milestone * 2 for milestone in lr_multiplier.scheduler.milestones +] +lr_multiplier.scheduler.num_updates = train.max_iter diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ.py b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ.py new file mode 100644 index 0000000000000000000000000000000000000000..7b86ea8c6c5c48f5d26c9e0df7cf96e745b17b34 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ.py @@ -0,0 +1,14 @@ +from .mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ import ( + dataloader, + lr_multiplier, + model, + optimizer, + train, +) + +train.max_iter *= 4 # 100ep -> 400ep + +lr_multiplier.scheduler.milestones = [ + milestone * 4 for milestone in lr_multiplier.scheduler.milestones +] +lr_multiplier.scheduler.num_updates = train.max_iter diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/README.md b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/README.md new file mode 100644 index 0000000000000000000000000000000000000000..4e6c82ef3f75a73c7006f33d7c850a0d4781a58f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/README.md @@ -0,0 +1,8 @@ +These are quick configs for performance or accuracy regression tracking purposes. + +* `*instance_test.yaml`: can train on 2 GPUs. They are used to test whether the training can + successfully finish. They are not expected to produce reasonable training results. +* `*inference_acc_test.yaml`: They should be run using `--eval-only`. They run inference using pre-trained models and verify + the results are as expected. +* `*training_acc_test.yaml`: They should be trained on 8 GPUs. They finish in about an hour and verify the training accuracy + is within the normal range. diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_inference_acc_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_inference_acc_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fc5a4116cb096278823049c1f823e99f8e16e97e --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_inference_acc_test.yaml @@ -0,0 +1,7 @@ +_BASE_: "../Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml" +MODEL: + WEIGHTS: "detectron2://Misc/cascade_mask_rcnn_R_50_FPN_3x/144998488/model_final_480dd8.pkl" +DATASETS: + TEST: ("coco_2017_val_100",) +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 50.18, 0.02], ["segm", "AP", 43.87, 0.02]] diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_instant_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_instant_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e41a0fe7ffe9c3531741df49e546aa45cfe4fdee --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_instant_test.yaml @@ -0,0 +1,11 @@ +_BASE_: "../Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml" +DATASETS: + TRAIN: ("coco_2017_val_100",) + TEST: ("coco_2017_val_100",) +SOLVER: + BASE_LR: 0.005 + STEPS: (30,) + MAX_ITER: 40 + IMS_PER_BATCH: 4 +DATALOADER: + NUM_WORKERS: 2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/fast_rcnn_R_50_FPN_inference_acc_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/fast_rcnn_R_50_FPN_inference_acc_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a2f37e5e2cc2a9e195e13703e9930e67e0f9a896 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/fast_rcnn_R_50_FPN_inference_acc_test.yaml @@ -0,0 +1,7 @@ +_BASE_: "../COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml" +MODEL: + WEIGHTS: "detectron2://COCO-Detection/fast_rcnn_R_50_FPN_1x/137635226/model_final_e5f7ce.pkl" +DATASETS: + TEST: ("coco_2017_val_100",) +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 45.70, 0.02]] diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/fast_rcnn_R_50_FPN_instant_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/fast_rcnn_R_50_FPN_instant_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..52fc0ec03c8b87ab2be1dda97bec1e8c93e6bb5c --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/fast_rcnn_R_50_FPN_instant_test.yaml @@ -0,0 +1,15 @@ +_BASE_: "../COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" +DATASETS: + TRAIN: ("coco_2017_val_100",) + PROPOSAL_FILES_TRAIN: ("detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/coco_2017_val_box_proposals_ee0dad.pkl", ) + TEST: ("coco_2017_val_100",) + PROPOSAL_FILES_TEST: ("detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/coco_2017_val_box_proposals_ee0dad.pkl", ) +SOLVER: + BASE_LR: 0.005 + STEPS: (30,) + MAX_ITER: 40 + IMS_PER_BATCH: 4 +DATALOADER: + NUM_WORKERS: 2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/keypoint_rcnn_R_50_FPN_inference_acc_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/keypoint_rcnn_R_50_FPN_inference_acc_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..14cf2aa82aec52ad44e28ead0665dad811d55457 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/keypoint_rcnn_R_50_FPN_inference_acc_test.yaml @@ -0,0 +1,7 @@ +_BASE_: "../COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml" +MODEL: + WEIGHTS: "detectron2://COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x/137849621/model_final_a6e10b.pkl" +DATASETS: + TEST: ("keypoints_coco_2017_val_100",) +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 52.47, 0.02], ["keypoints", "AP", 67.36, 0.02]] diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/keypoint_rcnn_R_50_FPN_instant_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/keypoint_rcnn_R_50_FPN_instant_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3dd209f693bd0bfdd46a2c9e7e750dede3abc141 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/keypoint_rcnn_R_50_FPN_instant_test.yaml @@ -0,0 +1,16 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + KEYPOINT_ON: True + ROI_HEADS: + NUM_CLASSES: 1 +DATASETS: + TRAIN: ("keypoints_coco_2017_val_100",) + TEST: ("keypoints_coco_2017_val_100",) +SOLVER: + BASE_LR: 0.005 + STEPS: (30,) + MAX_ITER: 40 + IMS_PER_BATCH: 4 +DATALOADER: + NUM_WORKERS: 2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/keypoint_rcnn_R_50_FPN_normalized_training_acc_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/keypoint_rcnn_R_50_FPN_normalized_training_acc_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4b92392f1c4457033ae4c87a521e339fe9e184ce --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/keypoint_rcnn_R_50_FPN_normalized_training_acc_test.yaml @@ -0,0 +1,30 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + KEYPOINT_ON: True + RESNETS: + DEPTH: 50 + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 256 + NUM_CLASSES: 1 + ROI_KEYPOINT_HEAD: + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 2 + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: False + LOSS_WEIGHT: 4.0 + ROI_BOX_HEAD: + SMOOTH_L1_BETA: 1.0 # Keypoint AP degrades when using plain L1 loss + RPN: + SMOOTH_L1_BETA: 0.2 # Keypoint AP degrades when using plain L1 loss +DATASETS: + TRAIN: ("keypoints_coco_2017_val",) + TEST: ("keypoints_coco_2017_val",) +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +SOLVER: + WARMUP_FACTOR: 0.33333333 + WARMUP_ITERS: 100 + STEPS: (5500, 5800) + MAX_ITER: 6000 +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 55.35, 1.0], ["keypoints", "AP", 76.91, 1.0]] diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/keypoint_rcnn_R_50_FPN_training_acc_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/keypoint_rcnn_R_50_FPN_training_acc_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9bd962878fea64035887c48981beeb8d41bfdbd0 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/keypoint_rcnn_R_50_FPN_training_acc_test.yaml @@ -0,0 +1,28 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + KEYPOINT_ON: True + RESNETS: + DEPTH: 50 + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 256 + NUM_CLASSES: 1 + ROI_KEYPOINT_HEAD: + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 2 + ROI_BOX_HEAD: + SMOOTH_L1_BETA: 1.0 # Keypoint AP degrades when using plain L1 loss + RPN: + SMOOTH_L1_BETA: 0.2 # Keypoint AP degrades when using plain L1 loss +DATASETS: + TRAIN: ("keypoints_coco_2017_val",) + TEST: ("keypoints_coco_2017_val",) +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +SOLVER: + WARMUP_FACTOR: 0.33333333 + WARMUP_ITERS: 100 + STEPS: (5500, 5800) + MAX_ITER: 6000 +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 53.5, 1.0], ["keypoints", "AP", 72.4, 1.0]] diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_C4_GCV_instant_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_C4_GCV_instant_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ab6e69812b94ea7e071f29d9a6937d5c70805b5b --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_C4_GCV_instant_test.yaml @@ -0,0 +1,18 @@ +_BASE_: "../Base-RCNN-C4.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True +DATASETS: + TRAIN: ("coco_2017_val_100",) + TEST: ("coco_2017_val_100",) +SOLVER: + BASE_LR: 0.001 + STEPS: (30,) + MAX_ITER: 40 + IMS_PER_BATCH: 4 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "value" + CLIP_VALUE: 1.0 +DATALOADER: + NUM_WORKERS: 2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_C4_inference_acc_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_C4_inference_acc_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b2d5b7ff87e069f8c774a230bdfd47b8c12d18a3 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_C4_inference_acc_test.yaml @@ -0,0 +1,7 @@ +_BASE_: "../COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml" +MODEL: + WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x/137849525/model_final_4ce675.pkl" +DATASETS: + TEST: ("coco_2017_val_100",) +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 47.37, 0.02], ["segm", "AP", 40.99, 0.02]] diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_C4_instant_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_C4_instant_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6c4f1214efa520944fd941daec082ad45c164a23 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_C4_instant_test.yaml @@ -0,0 +1,14 @@ +_BASE_: "../Base-RCNN-C4.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True +DATASETS: + TRAIN: ("coco_2017_val_100",) + TEST: ("coco_2017_val_100",) +SOLVER: + BASE_LR: 0.001 + STEPS: (30,) + MAX_ITER: 40 + IMS_PER_BATCH: 4 +DATALOADER: + NUM_WORKERS: 2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_C4_training_acc_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_C4_training_acc_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f68dd8f96c7896b5fc95d694a399f2ce417c1deb --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_C4_training_acc_test.yaml @@ -0,0 +1,22 @@ +_BASE_: "../Base-RCNN-C4.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 256 + MASK_ON: True +DATASETS: + TRAIN: ("coco_2017_val",) + TEST: ("coco_2017_val",) +INPUT: + MIN_SIZE_TRAIN: (600,) + MAX_SIZE_TRAIN: 1000 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1000 +SOLVER: + IMS_PER_BATCH: 8 # base uses 16 + WARMUP_FACTOR: 0.33333 + WARMUP_ITERS: 100 + STEPS: (11000, 11600) + MAX_ITER: 12000 +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 41.88, 0.7], ["segm", "AP", 33.79, 0.5]] diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_DC5_inference_acc_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_DC5_inference_acc_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e3ce6cf922ae07fba5b5e01edbac19bf58a8e9dd --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_DC5_inference_acc_test.yaml @@ -0,0 +1,7 @@ +_BASE_: "../COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml" +MODEL: + WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x/137849551/model_final_84107b.pkl" +DATASETS: + TEST: ("coco_2017_val_100",) +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 47.44, 0.02], ["segm", "AP", 42.94, 0.02]] diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e5454bfd95cc37749c50aec7866f32d9a80ca2b7 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml @@ -0,0 +1,10 @@ +_BASE_: "../COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml" +MODEL: + WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl" +DATASETS: + TEST: ("coco_2017_val_100",) +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 47.34, 0.02], ["segm", "AP", 42.67, 0.02], ["bbox_TTA", "AP", 49.11, 0.02], ["segm_TTA", "AP", 45.04, 0.02]] + AUG: + ENABLED: True + MIN_SIZES: (700, 800) # to save some time diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_FPN_instant_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_FPN_instant_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6dbfcde0bf837990634d419a6dda1e2909c3cd7f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_FPN_instant_test.yaml @@ -0,0 +1,14 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True +DATASETS: + TRAIN: ("coco_2017_val_100",) + TEST: ("coco_2017_val_100",) +SOLVER: + BASE_LR: 0.005 + STEPS: (30,) + MAX_ITER: 40 + IMS_PER_BATCH: 4 +DATALOADER: + NUM_WORKERS: 2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_FPN_pred_boxes_training_acc_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_FPN_pred_boxes_training_acc_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..52f78762bda23331c97afd523cf98a5c118b113e --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_FPN_pred_boxes_training_acc_test.yaml @@ -0,0 +1,6 @@ +_BASE_: "./mask_rcnn_R_50_FPN_training_acc_test.yaml" +MODEL: + ROI_BOX_HEAD: + TRAIN_ON_PRED_BOXES: True +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 42.6, 1.0], ["segm", "AP", 35.8, 0.8]] diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_FPN_training_acc_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_FPN_training_acc_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..aadae4ce898761e1e40e5af65a9e5ea01053b936 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/mask_rcnn_R_50_FPN_training_acc_test.yaml @@ -0,0 +1,21 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 256 + MASK_ON: True +DATASETS: + TRAIN: ("coco_2017_val",) + TEST: ("coco_2017_val",) +INPUT: + MIN_SIZE_TRAIN: (600,) + MAX_SIZE_TRAIN: 1000 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1000 +SOLVER: + WARMUP_FACTOR: 0.3333333 + WARMUP_ITERS: 100 + STEPS: (5500, 5800) + MAX_ITER: 6000 +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 42.5, 1.0], ["segm", "AP", 35.8, 0.8]] diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/panoptic_fpn_R_50_inference_acc_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/panoptic_fpn_R_50_inference_acc_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..70874e3a92c9034d75cbbebb145b61084ba15e42 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/panoptic_fpn_R_50_inference_acc_test.yaml @@ -0,0 +1,7 @@ +_BASE_: "../COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml" +MODEL: + WEIGHTS: "detectron2://COCO-PanopticSegmentation/panoptic_fpn_R_50_3x/139514569/model_final_c10459.pkl" +DATASETS: + TEST: ("coco_2017_val_100_panoptic_separated",) +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 46.47, 0.02], ["segm", "AP", 43.39, 0.02], ["sem_seg", "mIoU", 42.55, 0.02], ["panoptic_seg", "PQ", 38.99, 0.02]] diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/panoptic_fpn_R_50_instant_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/panoptic_fpn_R_50_instant_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7cdee7bfcf6dc75dda52602a0d9177ad0a9cc6ed --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/panoptic_fpn_R_50_instant_test.yaml @@ -0,0 +1,19 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + META_ARCHITECTURE: "PanopticFPN" + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 + SEM_SEG_HEAD: + LOSS_WEIGHT: 0.5 +DATASETS: + TRAIN: ("coco_2017_val_100_panoptic_separated",) + TEST: ("coco_2017_val_100_panoptic_separated",) +SOLVER: + BASE_LR: 0.005 + STEPS: (30,) + MAX_ITER: 40 + IMS_PER_BATCH: 4 +DATALOADER: + NUM_WORKERS: 1 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/panoptic_fpn_R_50_training_acc_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/panoptic_fpn_R_50_training_acc_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f3bbf30196cb35434340d4c343cab0c96283cd4f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/panoptic_fpn_R_50_training_acc_test.yaml @@ -0,0 +1,20 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + META_ARCHITECTURE: "PanopticFPN" + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 + SEM_SEG_HEAD: + LOSS_WEIGHT: 0.5 +DATASETS: + TRAIN: ("coco_2017_val_panoptic_separated",) + TEST: ("coco_2017_val_panoptic_separated",) +SOLVER: + BASE_LR: 0.01 + WARMUP_FACTOR: 0.001 + WARMUP_ITERS: 500 + STEPS: (5500,) + MAX_ITER: 7000 +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 46.70, 1.1], ["segm", "AP", 39.0, 0.7], ["sem_seg", "mIoU", 64.73, 1.3], ["panoptic_seg", "PQ", 48.13, 0.8]] diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/retinanet_R_50_FPN_inference_acc_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/retinanet_R_50_FPN_inference_acc_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cb666c1a6b3e351227046bc9c2af8799408858e8 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/retinanet_R_50_FPN_inference_acc_test.yaml @@ -0,0 +1,7 @@ +_BASE_: "../COCO-Detection/retinanet_R_50_FPN_3x.yaml" +MODEL: + WEIGHTS: "detectron2://COCO-Detection/retinanet_R_50_FPN_3x/190397829/model_final_5bd44e.pkl" +DATASETS: + TEST: ("coco_2017_val_100",) +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 44.45, 0.02]] diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/retinanet_R_50_FPN_instant_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/retinanet_R_50_FPN_instant_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8d95c1f614296716374686b22055a587ccd052b9 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/retinanet_R_50_FPN_instant_test.yaml @@ -0,0 +1,13 @@ +_BASE_: "../COCO-Detection/retinanet_R_50_FPN_1x.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" +DATASETS: + TRAIN: ("coco_2017_val_100",) + TEST: ("coco_2017_val_100",) +SOLVER: + BASE_LR: 0.005 + STEPS: (30,) + MAX_ITER: 40 + IMS_PER_BATCH: 4 +DATALOADER: + NUM_WORKERS: 2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/rpn_R_50_FPN_inference_acc_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/rpn_R_50_FPN_inference_acc_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c7c3f908a9e80e98b2d25b6d384a60acaba9d4f8 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/rpn_R_50_FPN_inference_acc_test.yaml @@ -0,0 +1,7 @@ +_BASE_: "../COCO-Detection/rpn_R_50_FPN_1x.yaml" +MODEL: + WEIGHTS: "detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/model_final_02ce48.pkl" +DATASETS: + TEST: ("coco_2017_val_100",) +TEST: + EXPECTED_RESULTS: [["box_proposals", "AR@1000", 58.16, 0.02]] diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/rpn_R_50_FPN_instant_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/rpn_R_50_FPN_instant_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..402d432477507dc36f04c4a9777cb80fe06b2809 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/rpn_R_50_FPN_instant_test.yaml @@ -0,0 +1,13 @@ +_BASE_: "../COCO-Detection/rpn_R_50_FPN_1x.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" +DATASETS: + TRAIN: ("coco_2017_val_100",) + TEST: ("coco_2017_val_100",) +SOLVER: + STEPS: (30,) + MAX_ITER: 40 + BASE_LR: 0.005 + IMS_PER_BATCH: 4 +DATALOADER: + NUM_WORKERS: 2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/semantic_R_50_FPN_inference_acc_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/semantic_R_50_FPN_inference_acc_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bca74987d5218736983617883e0fe37f79d219b7 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/semantic_R_50_FPN_inference_acc_test.yaml @@ -0,0 +1,10 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + META_ARCHITECTURE: "SemanticSegmentor" + WEIGHTS: "detectron2://semantic_R_50_FPN_1x/111802073/model_final_c18079783c55a94968edc28b7101c5f0.pkl" + RESNETS: + DEPTH: 50 +DATASETS: + TEST: ("coco_2017_val_100_panoptic_stuffonly",) +TEST: + EXPECTED_RESULTS: [["sem_seg", "mIoU", 39.53, 0.02], ["sem_seg", "mACC", 51.50, 0.02]] diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/semantic_R_50_FPN_instant_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/semantic_R_50_FPN_instant_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..14ab606f219b462fe37fcc7d5fbdbe65cb5c2642 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/semantic_R_50_FPN_instant_test.yaml @@ -0,0 +1,18 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + META_ARCHITECTURE: "SemanticSegmentor" + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + RESNETS: + DEPTH: 50 +DATASETS: + TRAIN: ("coco_2017_val_100_panoptic_stuffonly",) + TEST: ("coco_2017_val_100_panoptic_stuffonly",) +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +SOLVER: + BASE_LR: 0.005 + STEPS: (30,) + MAX_ITER: 40 + IMS_PER_BATCH: 4 +DATALOADER: + NUM_WORKERS: 2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/semantic_R_50_FPN_training_acc_test.yaml b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/semantic_R_50_FPN_training_acc_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1f78d775889b11e9e76743de5ddb8139198edf61 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/configs/quick_schedules/semantic_R_50_FPN_training_acc_test.yaml @@ -0,0 +1,20 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + META_ARCHITECTURE: "SemanticSegmentor" + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + RESNETS: + DEPTH: 50 +DATASETS: + TRAIN: ("coco_2017_val_panoptic_stuffonly",) + TEST: ("coco_2017_val_panoptic_stuffonly",) +SOLVER: + BASE_LR: 0.01 + WARMUP_FACTOR: 0.001 + WARMUP_ITERS: 300 + STEPS: (5500,) + MAX_ITER: 7000 +TEST: + EXPECTED_RESULTS: [["sem_seg", "mIoU", 76.51, 1.0], ["sem_seg", "mACC", 83.25, 1.0]] +INPUT: + # no scale augmentation + MIN_SIZE_TRAIN: (800, ) diff --git a/iChat/models/grit_src/third_party/CenterNet2/datasets/README.md b/iChat/models/grit_src/third_party/CenterNet2/datasets/README.md new file mode 100644 index 0000000000000000000000000000000000000000..0eb44cc3b23beeb1755ab8d12002d26f13434235 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/datasets/README.md @@ -0,0 +1,140 @@ +# Use Builtin Datasets + +A dataset can be used by accessing [DatasetCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.DatasetCatalog) +for its data, or [MetadataCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.MetadataCatalog) for its metadata (class names, etc). +This document explains how to setup the builtin datasets so they can be used by the above APIs. +[Use Custom Datasets](https://detectron2.readthedocs.io/tutorials/datasets.html) gives a deeper dive on how to use `DatasetCatalog` and `MetadataCatalog`, +and how to add new datasets to them. + +Detectron2 has builtin support for a few datasets. +The datasets are assumed to exist in a directory specified by the environment variable +`DETECTRON2_DATASETS`. +Under this directory, detectron2 will look for datasets in the structure described below, if needed. +``` +$DETECTRON2_DATASETS/ + coco/ + lvis/ + cityscapes/ + VOC20{07,12}/ +``` + +You can set the location for builtin datasets by `export DETECTRON2_DATASETS=/path/to/datasets`. +If left unset, the default is `./datasets` relative to your current working directory. + +The [model zoo](https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md) +contains configs and models that use these builtin datasets. + +## Expected dataset structure for [COCO instance/keypoint detection](https://cocodataset.org/#download): + +``` +coco/ + annotations/ + instances_{train,val}2017.json + person_keypoints_{train,val}2017.json + {train,val}2017/ + # image files that are mentioned in the corresponding json +``` + +You can use the 2014 version of the dataset as well. + +Some of the builtin tests (`dev/run_*_tests.sh`) uses a tiny version of the COCO dataset, +which you can download with `./datasets/prepare_for_tests.sh`. + +## Expected dataset structure for PanopticFPN: + +Extract panoptic annotations from [COCO website](https://cocodataset.org/#download) +into the following structure: +``` +coco/ + annotations/ + panoptic_{train,val}2017.json + panoptic_{train,val}2017/ # png annotations + panoptic_stuff_{train,val}2017/ # generated by the script mentioned below +``` + +Install panopticapi by: +``` +pip install git+https://github.com/cocodataset/panopticapi.git +``` +Then, run `python datasets/prepare_panoptic_fpn.py`, to extract semantic annotations from panoptic annotations. + +## Expected dataset structure for [LVIS instance segmentation](https://www.lvisdataset.org/dataset): +``` +coco/ + {train,val,test}2017/ +lvis/ + lvis_v0.5_{train,val}.json + lvis_v0.5_image_info_test.json + lvis_v1_{train,val}.json + lvis_v1_image_info_test{,_challenge}.json +``` + +Install lvis-api by: +``` +pip install git+https://github.com/lvis-dataset/lvis-api.git +``` + +To evaluate models trained on the COCO dataset using LVIS annotations, +run `python datasets/prepare_cocofied_lvis.py` to prepare "cocofied" LVIS annotations. + +## Expected dataset structure for [cityscapes](https://www.cityscapes-dataset.com/downloads/): +``` +cityscapes/ + gtFine/ + train/ + aachen/ + color.png, instanceIds.png, labelIds.png, polygons.json, + labelTrainIds.png + ... + val/ + test/ + # below are generated Cityscapes panoptic annotation + cityscapes_panoptic_train.json + cityscapes_panoptic_train/ + cityscapes_panoptic_val.json + cityscapes_panoptic_val/ + cityscapes_panoptic_test.json + cityscapes_panoptic_test/ + leftImg8bit/ + train/ + val/ + test/ +``` +Install cityscapes scripts by: +``` +pip install git+https://github.com/mcordts/cityscapesScripts.git +``` + +Note: to create labelTrainIds.png, first prepare the above structure, then run cityscapesescript with: +``` +CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createTrainIdLabelImgs.py +``` +These files are not needed for instance segmentation. + +Note: to generate Cityscapes panoptic dataset, run cityscapesescript with: +``` +CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createPanopticImgs.py +``` +These files are not needed for semantic and instance segmentation. + +## Expected dataset structure for [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/index.html): +``` +VOC20{07,12}/ + Annotations/ + ImageSets/ + Main/ + trainval.txt + test.txt + # train.txt or val.txt, if you use these splits + JPEGImages/ +``` + +## Expected dataset structure for [ADE20k Scene Parsing](http://sceneparsing.csail.mit.edu/): +``` +ADEChallengeData2016/ + annotations/ + annotations_detectron2/ + images/ + objectInfo150.txt +``` +The directory `annotations_detectron2` is generated by running `python datasets/prepare_ade20k_sem_seg.py`. diff --git a/iChat/models/grit_src/third_party/CenterNet2/datasets/lvis/lvis_v1_train_cat_info.json b/iChat/models/grit_src/third_party/CenterNet2/datasets/lvis/lvis_v1_train_cat_info.json new file mode 100644 index 0000000000000000000000000000000000000000..95fef0923366adbf4378da64113f6bc63a1265a6 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/datasets/lvis/lvis_v1_train_cat_info.json @@ -0,0 +1 @@ +[{"name": "aerosol_can", "instance_count": 109, "def": "a dispenser that holds a substance under pressure", "synonyms": ["aerosol_can", "spray_can"], "image_count": 64, "id": 1, "frequency": "c", "synset": "aerosol.n.02"}, {"name": "air_conditioner", "instance_count": 1081, "def": "a machine that keeps air cool and dry", "synonyms": ["air_conditioner"], "image_count": 364, "id": 2, "frequency": "f", "synset": "air_conditioner.n.01"}, {"name": "airplane", "instance_count": 3720, "def": "an aircraft that has a 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"urinal.n.01"}, {"name": "urn", "instance_count": 81, "def": "a large vase that usually has a pedestal or feet", "synonyms": ["urn"], "image_count": 12, "id": 1137, "frequency": "c", "synset": "urn.n.01"}, {"name": "vacuum_cleaner", "instance_count": 38, "def": "an electrical home appliance that cleans by suction", "synonyms": ["vacuum_cleaner"], "image_count": 37, "id": 1138, "frequency": "c", "synset": "vacuum.n.04"}, {"name": "vase", "instance_count": 4971, "def": "an open jar of glass or porcelain used as an ornament or to hold flowers", "synonyms": ["vase"], "image_count": 1866, "id": 1139, "frequency": "f", "synset": "vase.n.01"}, {"name": "vending_machine", "instance_count": 65, "def": "a slot machine for selling goods", "synonyms": ["vending_machine"], "image_count": 47, "id": 1140, "frequency": "c", "synset": "vending_machine.n.01"}, {"name": "vent", "instance_count": 3370, "def": "a hole for the escape of gas or air", "synonyms": ["vent", "blowhole", "air_vent"], "image_count": 1468, "id": 1141, "frequency": "f", "synset": "vent.n.01"}, {"name": "vest", "instance_count": 1313, "def": "a man's sleeveless garment worn underneath a coat", "synonyms": ["vest", "waistcoat"], "image_count": 729, "id": 1142, "frequency": "f", "synset": "vest.n.01"}, {"name": "videotape", "instance_count": 228, "def": "a video recording made on magnetic tape", "synonyms": ["videotape"], "image_count": 24, "id": 1143, "frequency": "c", "synset": "videotape.n.01"}, {"name": "vinegar", "instance_count": 1, "def": "sour-tasting liquid produced usually by oxidation of the alcohol in wine or cider and used as a condiment or food preservative", "synonyms": ["vinegar"], "image_count": 1, "id": 1144, "frequency": "r", "synset": "vinegar.n.01"}, {"name": "violin", "instance_count": 10, "def": "bowed stringed instrument that is the highest member of the violin family", "synonyms": ["violin", "fiddle"], "image_count": 10, "id": 1145, "frequency": "r", "synset": "violin.n.01"}, {"name": "vodka", "instance_count": 3, "def": "unaged colorless liquor originating in Russia", "synonyms": ["vodka"], "image_count": 3, "id": 1146, "frequency": "r", "synset": "vodka.n.01"}, {"name": "volleyball", "instance_count": 33, "def": "an inflated ball used in playing volleyball", "synonyms": ["volleyball"], "image_count": 14, "id": 1147, "frequency": "c", "synset": "volleyball.n.02"}, {"name": "vulture", "instance_count": 16, "def": "any of various large birds of prey having naked heads and weak claws and feeding chiefly on carrion", "synonyms": ["vulture"], "image_count": 4, "id": 1148, "frequency": "r", "synset": "vulture.n.01"}, {"name": "waffle", "instance_count": 61, "def": "pancake batter baked in a waffle iron", "synonyms": ["waffle"], "image_count": 29, "id": 1149, "frequency": "c", "synset": "waffle.n.01"}, {"name": "waffle_iron", "instance_count": 4, "def": "a kitchen appliance for baking waffles", "synonyms": ["waffle_iron"], "image_count": 4, "id": 1150, "frequency": "r", "synset": "waffle_iron.n.01"}, {"name": "wagon", "instance_count": 121, "def": "any of various kinds of wheeled vehicles drawn by an animal or a tractor", "synonyms": ["wagon"], "image_count": 70, "id": 1151, "frequency": "c", "synset": "wagon.n.01"}, {"name": "wagon_wheel", "instance_count": 209, "def": "a wheel of a wagon", "synonyms": ["wagon_wheel"], "image_count": 46, "id": 1152, "frequency": "c", "synset": "wagon_wheel.n.01"}, {"name": "walking_stick", "instance_count": 21, "def": "a stick carried in the hand for support in walking", "synonyms": ["walking_stick"], "image_count": 14, "id": 1153, "frequency": "c", "synset": "walking_stick.n.01"}, {"name": "wall_clock", "instance_count": 100, "def": "a clock mounted on a wall", "synonyms": ["wall_clock"], "image_count": 48, "id": 1154, "frequency": "c", "synset": "wall_clock.n.01"}, {"name": "wall_socket", "instance_count": 3069, "def": "receptacle providing a place in a wiring system where current can be taken to run electrical devices", "synonyms": ["wall_socket", "wall_plug", "electric_outlet", "electrical_outlet", "outlet", "electric_receptacle"], "image_count": 1855, "id": 1155, "frequency": "f", "synset": "wall_socket.n.01"}, {"name": "wallet", "instance_count": 123, "def": "a pocket-size case for holding papers and paper money", "synonyms": ["wallet", "billfold"], "image_count": 113, "id": 1156, "frequency": "f", "synset": "wallet.n.01"}, {"name": "walrus", "instance_count": 1, "def": "either of two large northern marine mammals having ivory tusks and tough hide over thick blubber", "synonyms": ["walrus"], "image_count": 1, "id": 1157, "frequency": "r", "synset": "walrus.n.01"}, {"name": "wardrobe", "instance_count": 1, "def": "a tall piece of furniture that provides storage space for clothes; has a door and rails or hooks for hanging clothes", "synonyms": ["wardrobe"], "image_count": 1, "id": 1158, "frequency": "r", "synset": "wardrobe.n.01"}, {"name": "washbasin", "instance_count": 15, "def": "a bathroom sink that is permanently installed and connected to a water supply and drainpipe; where you can wash your hands and face", "synonyms": ["washbasin", "basin_(for_washing)", "washbowl", "washstand", "handbasin"], "image_count": 10, "id": 1159, "frequency": "r", "synset": "washbasin.n.01"}, {"name": "automatic_washer", "instance_count": 68, "def": "a home appliance for washing clothes and linens automatically", "synonyms": ["automatic_washer", "washing_machine"], "image_count": 54, "id": 1160, "frequency": "c", "synset": "washer.n.03"}, {"name": "watch", "instance_count": 2703, "def": "a small, portable timepiece", "synonyms": ["watch", "wristwatch"], "image_count": 1923, "id": 1161, "frequency": "f", "synset": "watch.n.01"}, {"name": "water_bottle", "instance_count": 1449, "def": "a bottle for holding water", "synonyms": ["water_bottle"], "image_count": 630, "id": 1162, "frequency": "f", "synset": "water_bottle.n.01"}, {"name": "water_cooler", "instance_count": 39, "def": "a device for cooling and dispensing drinking water", "synonyms": ["water_cooler"], "image_count": 31, "id": 1163, "frequency": "c", "synset": "water_cooler.n.01"}, {"name": "water_faucet", "instance_count": 109, "def": "a faucet for drawing water from a pipe or cask", "synonyms": ["water_faucet", "water_tap", "tap_(water_faucet)"], "image_count": 69, "id": 1164, "frequency": "c", "synset": "water_faucet.n.01"}, {"name": "water_heater", "instance_count": 7, "def": "a heater and storage tank to supply heated water", "synonyms": ["water_heater", "hot-water_heater"], "image_count": 7, "id": 1165, "frequency": "r", "synset": "water_heater.n.01"}, {"name": "water_jug", "instance_count": 23, "def": "a jug that holds water", "synonyms": ["water_jug"], "image_count": 11, "id": 1166, "frequency": "c", "synset": "water_jug.n.01"}, {"name": "water_gun", "instance_count": 1, "def": "plaything consisting of a toy pistol that squirts water", "synonyms": ["water_gun", "squirt_gun"], "image_count": 1, "id": 1167, "frequency": "r", "synset": "water_pistol.n.01"}, {"name": "water_scooter", "instance_count": 54, "def": "a motorboat resembling a motor scooter (NOT A SURFBOARD OR WATER SKI)", "synonyms": ["water_scooter", "sea_scooter", "jet_ski"], "image_count": 30, "id": 1168, "frequency": "c", "synset": "water_scooter.n.01"}, {"name": "water_ski", "instance_count": 98, "def": "broad ski for skimming over water towed by a speedboat (DO NOT MARK WATER)", "synonyms": ["water_ski"], "image_count": 50, "id": 1169, "frequency": "c", "synset": "water_ski.n.01"}, {"name": "water_tower", "instance_count": 60, "def": "a large reservoir for water", "synonyms": ["water_tower"], "image_count": 45, "id": 1170, "frequency": "c", "synset": "water_tower.n.01"}, {"name": "watering_can", "instance_count": 44, "def": "a container with a handle and a spout with a perforated nozzle; used to sprinkle water over plants", "synonyms": ["watering_can"], "image_count": 28, "id": 1171, "frequency": "c", "synset": "watering_can.n.01"}, {"name": "watermelon", "instance_count": 814, "def": "large oblong or roundish melon with a hard green rind and sweet watery red or occasionally yellowish pulp", "synonyms": ["watermelon"], "image_count": 114, "id": 1172, "frequency": "f", "synset": "watermelon.n.02"}, {"name": "weathervane", "instance_count": 237, "def": "mechanical device attached to an elevated structure; rotates freely to show the direction of the wind", "synonyms": ["weathervane", "vane_(weathervane)", "wind_vane"], "image_count": 193, "id": 1173, "frequency": "f", "synset": "weathervane.n.01"}, {"name": "webcam", "instance_count": 27, "def": "a digital camera designed to take digital photographs and transmit them over the internet", "synonyms": ["webcam"], "image_count": 21, "id": 1174, "frequency": "c", "synset": "webcam.n.01"}, {"name": "wedding_cake", "instance_count": 140, "def": "a rich cake with two or more tiers and covered with frosting and decorations; served at a wedding reception", "synonyms": ["wedding_cake", "bridecake"], "image_count": 91, "id": 1175, "frequency": "c", "synset": "wedding_cake.n.01"}, {"name": "wedding_ring", "instance_count": 49, "def": "a ring given to the bride and/or groom at the wedding", "synonyms": ["wedding_ring", "wedding_band"], "image_count": 31, "id": 1176, "frequency": "c", "synset": "wedding_ring.n.01"}, {"name": "wet_suit", "instance_count": 2907, "def": "a close-fitting garment made of a permeable material; worn in cold water to retain body heat", "synonyms": ["wet_suit"], "image_count": 1469, "id": 1177, "frequency": "f", "synset": "wet_suit.n.01"}, {"name": "wheel", "instance_count": 11272, "def": "a circular frame with spokes (or a solid disc) that can rotate on a shaft or axle", "synonyms": ["wheel"], "image_count": 1924, "id": 1178, "frequency": "f", "synset": "wheel.n.01"}, {"name": "wheelchair", "instance_count": 107, "def": "a movable chair mounted on large wheels", "synonyms": ["wheelchair"], "image_count": 87, "id": 1179, "frequency": "c", "synset": "wheelchair.n.01"}, {"name": "whipped_cream", "instance_count": 201, "def": "cream that has been beaten until light and fluffy", "synonyms": ["whipped_cream"], "image_count": 77, "id": 1180, "frequency": "c", "synset": "whipped_cream.n.01"}, {"name": "whistle", "instance_count": 13, "def": "a small wind instrument that produces a whistling sound by blowing into it", "synonyms": ["whistle"], "image_count": 11, "id": 1181, "frequency": "c", "synset": "whistle.n.03"}, {"name": "wig", "instance_count": 69, "def": "hairpiece covering the head and made of real or synthetic hair", "synonyms": ["wig"], "image_count": 47, "id": 1182, "frequency": "c", "synset": "wig.n.01"}, {"name": "wind_chime", "instance_count": 28, "def": "a decorative arrangement of pieces of metal or glass or pottery that hang together loosely so the wind can cause them to tinkle", "synonyms": ["wind_chime"], "image_count": 21, "id": 1183, "frequency": "c", "synset": "wind_chime.n.01"}, {"name": "windmill", "instance_count": 202, "def": "A mill or turbine that is powered by wind", "synonyms": ["windmill"], "image_count": 47, "id": 1184, "frequency": "c", "synset": "windmill.n.01"}, {"name": "window_box_(for_plants)", "instance_count": 253, "def": "a container for growing plants on a windowsill", "synonyms": ["window_box_(for_plants)"], "image_count": 70, "id": 1185, "frequency": "c", "synset": "window_box.n.01"}, {"name": "windshield_wiper", "instance_count": 4793, "def": "a mechanical device that cleans the windshield", "synonyms": ["windshield_wiper", "windscreen_wiper", "wiper_(for_windshield/screen)"], "image_count": 1838, "id": 1186, "frequency": "f", "synset": "windshield_wiper.n.01"}, {"name": "windsock", "instance_count": 26, "def": "a truncated cloth cone mounted on a mast/pole; shows wind direction", "synonyms": ["windsock", "air_sock", "air-sleeve", "wind_sleeve", "wind_cone"], "image_count": 19, "id": 1187, "frequency": "c", "synset": "windsock.n.01"}, {"name": "wine_bottle", "instance_count": 4449, "def": "a bottle for holding wine", "synonyms": ["wine_bottle"], "image_count": 531, "id": 1188, "frequency": "f", "synset": "wine_bottle.n.01"}, {"name": "wine_bucket", "instance_count": 21, "def": "a bucket of ice used to chill a bottle of wine", "synonyms": ["wine_bucket", "wine_cooler"], "image_count": 11, "id": 1189, "frequency": "c", "synset": "wine_bucket.n.01"}, {"name": "wineglass", "instance_count": 4259, "def": "a glass that has a stem and in which wine is served", "synonyms": ["wineglass"], "image_count": 941, "id": 1190, "frequency": "f", "synset": "wineglass.n.01"}, {"name": "blinder_(for_horses)", "instance_count": 271, "def": "blinds that prevent a horse from seeing something on either side", "synonyms": ["blinder_(for_horses)"], "image_count": 113, "id": 1191, "frequency": "f", "synset": "winker.n.02"}, {"name": "wok", "instance_count": 60, "def": "pan with a convex bottom; used for frying in Chinese cooking", "synonyms": ["wok"], "image_count": 26, "id": 1192, "frequency": "c", "synset": "wok.n.01"}, {"name": "wolf", "instance_count": 16, "def": "a wild carnivorous mammal of the dog family, living and hunting in packs", "synonyms": ["wolf"], "image_count": 5, "id": 1193, "frequency": "r", "synset": "wolf.n.01"}, {"name": "wooden_spoon", "instance_count": 123, "def": "a spoon made of wood", "synonyms": ["wooden_spoon"], "image_count": 56, "id": 1194, "frequency": "c", "synset": "wooden_spoon.n.02"}, {"name": "wreath", "instance_count": 119, "def": "an arrangement of flowers, leaves, or stems fastened in a ring", "synonyms": ["wreath"], "image_count": 73, "id": 1195, "frequency": "c", "synset": "wreath.n.01"}, {"name": "wrench", "instance_count": 80, "def": "a hand tool that is used to hold or twist a nut or bolt", "synonyms": ["wrench", "spanner"], "image_count": 32, "id": 1196, "frequency": "c", "synset": "wrench.n.03"}, {"name": "wristband", "instance_count": 268, "def": "band consisting of a part of a sleeve that covers the wrist", "synonyms": ["wristband"], "image_count": 128, "id": 1197, "frequency": "f", "synset": "wristband.n.01"}, {"name": "wristlet", "instance_count": 1330, "def": "a band or bracelet worn around the wrist", "synonyms": ["wristlet", "wrist_band"], "image_count": 623, "id": 1198, "frequency": "f", "synset": "wristlet.n.01"}, {"name": "yacht", "instance_count": 50, "def": "an expensive vessel propelled by sail or power and used for cruising or racing", "synonyms": ["yacht"], "image_count": 12, "id": 1199, "frequency": "c", "synset": "yacht.n.01"}, {"name": "yogurt", "instance_count": 116, "def": "a custard-like food made from curdled milk", "synonyms": ["yogurt", "yoghurt", "yoghourt"], "image_count": 52, "id": 1200, "frequency": "c", "synset": "yogurt.n.01"}, {"name": "yoke_(animal_equipment)", "instance_count": 20, "def": "gear joining two animals at the neck; NOT egg yolk", "synonyms": ["yoke_(animal_equipment)"], "image_count": 11, "id": 1201, "frequency": "c", "synset": "yoke.n.07"}, {"name": "zebra", "instance_count": 5443, "def": "any of several fleet black-and-white striped African equines", "synonyms": ["zebra"], "image_count": 1674, "id": 1202, "frequency": "f", "synset": "zebra.n.01"}, {"name": "zucchini", "instance_count": 798, "def": "small cucumber-shaped vegetable marrow; typically dark green", "synonyms": ["zucchini", "courgette"], "image_count": 81, "id": 1203, "frequency": "c", "synset": "zucchini.n.02"}] \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/datasets/prepare_ade20k_sem_seg.py b/iChat/models/grit_src/third_party/CenterNet2/datasets/prepare_ade20k_sem_seg.py new file mode 100644 index 0000000000000000000000000000000000000000..8b4a58d8f2877544498e328b6d269f23aa1eb59f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/datasets/prepare_ade20k_sem_seg.py @@ -0,0 +1,26 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +import os +from pathlib import Path +import tqdm +from PIL import Image + + +def convert(input, output): + img = np.asarray(Image.open(input)) + assert img.dtype == np.uint8 + img = img - 1 # 0 (ignore) becomes 255. others are shifted by 1 + Image.fromarray(img).save(output) + + +if __name__ == "__main__": + dataset_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets")) / "ADEChallengeData2016" + for name in ["training", "validation"]: + annotation_dir = dataset_dir / "annotations" / name + output_dir = dataset_dir / "annotations_detectron2" / name + output_dir.mkdir(parents=True, exist_ok=True) + for file in tqdm.tqdm(list(annotation_dir.iterdir())): + output_file = output_dir / file.name + convert(file, output_file) diff --git a/iChat/models/grit_src/third_party/CenterNet2/datasets/prepare_cocofied_lvis.py b/iChat/models/grit_src/third_party/CenterNet2/datasets/prepare_cocofied_lvis.py new file mode 100644 index 0000000000000000000000000000000000000000..245c88482a9e2405e5a912b5c560aed78a614a13 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/datasets/prepare_cocofied_lvis.py @@ -0,0 +1,176 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import copy +import json +import os +from collections import defaultdict + +# This mapping is extracted from the official LVIS mapping: +# https://github.com/lvis-dataset/lvis-api/blob/master/data/coco_to_synset.json +COCO_SYNSET_CATEGORIES = [ + {"synset": "person.n.01", "coco_cat_id": 1}, + {"synset": "bicycle.n.01", "coco_cat_id": 2}, + {"synset": "car.n.01", "coco_cat_id": 3}, + {"synset": "motorcycle.n.01", "coco_cat_id": 4}, + {"synset": "airplane.n.01", "coco_cat_id": 5}, + {"synset": "bus.n.01", "coco_cat_id": 6}, + {"synset": "train.n.01", "coco_cat_id": 7}, + {"synset": "truck.n.01", "coco_cat_id": 8}, + {"synset": "boat.n.01", "coco_cat_id": 9}, + {"synset": "traffic_light.n.01", "coco_cat_id": 10}, + {"synset": "fireplug.n.01", "coco_cat_id": 11}, + {"synset": "stop_sign.n.01", "coco_cat_id": 13}, + {"synset": "parking_meter.n.01", "coco_cat_id": 14}, + {"synset": "bench.n.01", "coco_cat_id": 15}, + {"synset": "bird.n.01", "coco_cat_id": 16}, + {"synset": "cat.n.01", "coco_cat_id": 17}, + {"synset": "dog.n.01", "coco_cat_id": 18}, + {"synset": "horse.n.01", "coco_cat_id": 19}, + {"synset": "sheep.n.01", "coco_cat_id": 20}, + {"synset": "beef.n.01", "coco_cat_id": 21}, + {"synset": "elephant.n.01", "coco_cat_id": 22}, + {"synset": "bear.n.01", "coco_cat_id": 23}, + {"synset": "zebra.n.01", "coco_cat_id": 24}, + {"synset": "giraffe.n.01", "coco_cat_id": 25}, + {"synset": "backpack.n.01", "coco_cat_id": 27}, + {"synset": "umbrella.n.01", "coco_cat_id": 28}, + {"synset": "bag.n.04", "coco_cat_id": 31}, + {"synset": "necktie.n.01", "coco_cat_id": 32}, + {"synset": "bag.n.06", "coco_cat_id": 33}, + {"synset": "frisbee.n.01", "coco_cat_id": 34}, + {"synset": "ski.n.01", "coco_cat_id": 35}, + {"synset": "snowboard.n.01", "coco_cat_id": 36}, + {"synset": "ball.n.06", "coco_cat_id": 37}, + {"synset": "kite.n.03", "coco_cat_id": 38}, + {"synset": "baseball_bat.n.01", "coco_cat_id": 39}, + {"synset": "baseball_glove.n.01", "coco_cat_id": 40}, + {"synset": "skateboard.n.01", "coco_cat_id": 41}, + {"synset": "surfboard.n.01", "coco_cat_id": 42}, + {"synset": "tennis_racket.n.01", "coco_cat_id": 43}, + {"synset": "bottle.n.01", "coco_cat_id": 44}, + {"synset": "wineglass.n.01", "coco_cat_id": 46}, + {"synset": "cup.n.01", "coco_cat_id": 47}, + {"synset": "fork.n.01", "coco_cat_id": 48}, + {"synset": "knife.n.01", "coco_cat_id": 49}, + {"synset": "spoon.n.01", "coco_cat_id": 50}, + {"synset": "bowl.n.03", "coco_cat_id": 51}, + {"synset": "banana.n.02", "coco_cat_id": 52}, + {"synset": "apple.n.01", "coco_cat_id": 53}, + {"synset": "sandwich.n.01", "coco_cat_id": 54}, + {"synset": "orange.n.01", "coco_cat_id": 55}, + {"synset": "broccoli.n.01", "coco_cat_id": 56}, + {"synset": "carrot.n.01", "coco_cat_id": 57}, + {"synset": "frank.n.02", "coco_cat_id": 58}, + {"synset": "pizza.n.01", "coco_cat_id": 59}, + {"synset": "doughnut.n.02", "coco_cat_id": 60}, + {"synset": "cake.n.03", "coco_cat_id": 61}, + {"synset": "chair.n.01", "coco_cat_id": 62}, + {"synset": "sofa.n.01", "coco_cat_id": 63}, + {"synset": "pot.n.04", "coco_cat_id": 64}, + {"synset": "bed.n.01", "coco_cat_id": 65}, + {"synset": "dining_table.n.01", "coco_cat_id": 67}, + {"synset": "toilet.n.02", "coco_cat_id": 70}, + {"synset": "television_receiver.n.01", "coco_cat_id": 72}, + {"synset": "laptop.n.01", "coco_cat_id": 73}, + {"synset": "mouse.n.04", "coco_cat_id": 74}, + {"synset": "remote_control.n.01", "coco_cat_id": 75}, + {"synset": "computer_keyboard.n.01", "coco_cat_id": 76}, + {"synset": "cellular_telephone.n.01", "coco_cat_id": 77}, + {"synset": "microwave.n.02", "coco_cat_id": 78}, + {"synset": "oven.n.01", "coco_cat_id": 79}, + {"synset": "toaster.n.02", "coco_cat_id": 80}, + {"synset": "sink.n.01", "coco_cat_id": 81}, + {"synset": "electric_refrigerator.n.01", "coco_cat_id": 82}, + {"synset": "book.n.01", "coco_cat_id": 84}, + {"synset": "clock.n.01", "coco_cat_id": 85}, + {"synset": "vase.n.01", "coco_cat_id": 86}, + {"synset": "scissors.n.01", "coco_cat_id": 87}, + {"synset": "teddy.n.01", "coco_cat_id": 88}, + {"synset": "hand_blower.n.01", "coco_cat_id": 89}, + {"synset": "toothbrush.n.01", "coco_cat_id": 90}, +] + + +def cocofy_lvis(input_filename, output_filename): + """ + Filter LVIS instance segmentation annotations to remove all categories that are not included in + COCO. The new json files can be used to evaluate COCO AP using `lvis-api`. The category ids in + the output json are the incontiguous COCO dataset ids. + + Args: + input_filename (str): path to the LVIS json file. + output_filename (str): path to the COCOfied json file. + """ + + with open(input_filename, "r") as f: + lvis_json = json.load(f) + + lvis_annos = lvis_json.pop("annotations") + cocofied_lvis = copy.deepcopy(lvis_json) + lvis_json["annotations"] = lvis_annos + + # Mapping from lvis cat id to coco cat id via synset + lvis_cat_id_to_synset = {cat["id"]: cat["synset"] for cat in lvis_json["categories"]} + synset_to_coco_cat_id = {x["synset"]: x["coco_cat_id"] for x in COCO_SYNSET_CATEGORIES} + # Synsets that we will keep in the dataset + synsets_to_keep = set(synset_to_coco_cat_id.keys()) + coco_cat_id_with_instances = defaultdict(int) + + new_annos = [] + ann_id = 1 + for ann in lvis_annos: + lvis_cat_id = ann["category_id"] + synset = lvis_cat_id_to_synset[lvis_cat_id] + if synset not in synsets_to_keep: + continue + coco_cat_id = synset_to_coco_cat_id[synset] + new_ann = copy.deepcopy(ann) + new_ann["category_id"] = coco_cat_id + new_ann["id"] = ann_id + ann_id += 1 + new_annos.append(new_ann) + coco_cat_id_with_instances[coco_cat_id] += 1 + cocofied_lvis["annotations"] = new_annos + + for image in cocofied_lvis["images"]: + for key in ["not_exhaustive_category_ids", "neg_category_ids"]: + new_category_list = [] + for lvis_cat_id in image[key]: + synset = lvis_cat_id_to_synset[lvis_cat_id] + if synset not in synsets_to_keep: + continue + coco_cat_id = synset_to_coco_cat_id[synset] + new_category_list.append(coco_cat_id) + coco_cat_id_with_instances[coco_cat_id] += 1 + image[key] = new_category_list + + coco_cat_id_with_instances = set(coco_cat_id_with_instances.keys()) + + new_categories = [] + for cat in lvis_json["categories"]: + synset = cat["synset"] + if synset not in synsets_to_keep: + continue + coco_cat_id = synset_to_coco_cat_id[synset] + if coco_cat_id not in coco_cat_id_with_instances: + continue + new_cat = copy.deepcopy(cat) + new_cat["id"] = coco_cat_id + new_categories.append(new_cat) + cocofied_lvis["categories"] = new_categories + + with open(output_filename, "w") as f: + json.dump(cocofied_lvis, f) + print("{} is COCOfied and stored in {}.".format(input_filename, output_filename)) + + +if __name__ == "__main__": + dataset_dir = os.path.join(os.getenv("DETECTRON2_DATASETS", "datasets"), "lvis") + for s in ["lvis_v0.5_train", "lvis_v0.5_val"]: + print("Start COCOfing {}.".format(s)) + cocofy_lvis( + os.path.join(dataset_dir, "{}.json".format(s)), + os.path.join(dataset_dir, "{}_cocofied.json".format(s)), + ) diff --git a/iChat/models/grit_src/third_party/CenterNet2/datasets/prepare_for_tests.sh b/iChat/models/grit_src/third_party/CenterNet2/datasets/prepare_for_tests.sh new file mode 100644 index 0000000000000000000000000000000000000000..67e875a41da652b2fcae6631b76d94584935ddb9 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/datasets/prepare_for_tests.sh @@ -0,0 +1,31 @@ +#!/bin/bash -e +# Copyright (c) Facebook, Inc. and its affiliates. + +# Download the mini dataset (coco val2017_100, with only 100 images) +# to be used in unittests & integration tests. + +cd "${0%/*}" + +BASE=https://dl.fbaipublicfiles.com/detectron2 +ROOT=${DETECTRON2_DATASETS:-./} +ROOT=${ROOT/#\~/$HOME} # expand ~ to HOME +mkdir -p $ROOT/coco/annotations + +for anno in instances_val2017_100 \ + person_keypoints_val2017_100 ; do + + dest=$ROOT/coco/annotations/$anno.json + [[ -s $dest ]] && { + echo "$dest exists. Skipping ..." + } || { + wget $BASE/annotations/coco/$anno.json -O $dest + } +done + +dest=$ROOT/coco/val2017_100.tgz +[[ -d $ROOT/coco/val2017 ]] && { + echo "$ROOT/coco/val2017 exists. Skipping ..." +} || { + wget $BASE/annotations/coco/val2017_100.tgz -O $dest + tar xzf $dest -C $ROOT/coco/ && rm -f $dest +} diff --git a/iChat/models/grit_src/third_party/CenterNet2/datasets/prepare_panoptic_fpn.py b/iChat/models/grit_src/third_party/CenterNet2/datasets/prepare_panoptic_fpn.py new file mode 100644 index 0000000000000000000000000000000000000000..597d791afab1bcc0013203a66c7fba225065eebe --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/datasets/prepare_panoptic_fpn.py @@ -0,0 +1,116 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import functools +import json +import multiprocessing as mp +import numpy as np +import os +import time +from fvcore.common.download import download +from panopticapi.utils import rgb2id +from PIL import Image + +from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES + + +def _process_panoptic_to_semantic(input_panoptic, output_semantic, segments, id_map): + panoptic = np.asarray(Image.open(input_panoptic), dtype=np.uint32) + panoptic = rgb2id(panoptic) + output = np.zeros_like(panoptic, dtype=np.uint8) + 255 + for seg in segments: + cat_id = seg["category_id"] + new_cat_id = id_map[cat_id] + output[panoptic == seg["id"]] = new_cat_id + Image.fromarray(output).save(output_semantic) + + +def separate_coco_semantic_from_panoptic(panoptic_json, panoptic_root, sem_seg_root, categories): + """ + Create semantic segmentation annotations from panoptic segmentation + annotations, to be used by PanopticFPN. + + It maps all thing categories to class 0, and maps all unlabeled pixels to class 255. + It maps all stuff categories to contiguous ids starting from 1. + + Args: + panoptic_json (str): path to the panoptic json file, in COCO's format. + panoptic_root (str): a directory with panoptic annotation files, in COCO's format. + sem_seg_root (str): a directory to output semantic annotation files + categories (list[dict]): category metadata. Each dict needs to have: + "id": corresponds to the "category_id" in the json annotations + "isthing": 0 or 1 + """ + os.makedirs(sem_seg_root, exist_ok=True) + + stuff_ids = [k["id"] for k in categories if k["isthing"] == 0] + thing_ids = [k["id"] for k in categories if k["isthing"] == 1] + id_map = {} # map from category id to id in the output semantic annotation + assert len(stuff_ids) <= 254 + for i, stuff_id in enumerate(stuff_ids): + id_map[stuff_id] = i + 1 + for thing_id in thing_ids: + id_map[thing_id] = 0 + id_map[0] = 255 + + with open(panoptic_json) as f: + obj = json.load(f) + + pool = mp.Pool(processes=max(mp.cpu_count() // 2, 4)) + + def iter_annotations(): + for anno in obj["annotations"]: + file_name = anno["file_name"] + segments = anno["segments_info"] + input = os.path.join(panoptic_root, file_name) + output = os.path.join(sem_seg_root, file_name) + yield input, output, segments + + print("Start writing to {} ...".format(sem_seg_root)) + start = time.time() + pool.starmap( + functools.partial(_process_panoptic_to_semantic, id_map=id_map), + iter_annotations(), + chunksize=100, + ) + print("Finished. time: {:.2f}s".format(time.time() - start)) + + +if __name__ == "__main__": + dataset_dir = os.path.join(os.getenv("DETECTRON2_DATASETS", "datasets"), "coco") + for s in ["val2017", "train2017"]: + separate_coco_semantic_from_panoptic( + os.path.join(dataset_dir, "annotations/panoptic_{}.json".format(s)), + os.path.join(dataset_dir, "panoptic_{}".format(s)), + os.path.join(dataset_dir, "panoptic_stuff_{}".format(s)), + COCO_CATEGORIES, + ) + + # Prepare val2017_100 for quick testing: + + dest_dir = os.path.join(dataset_dir, "annotations/") + URL_PREFIX = "https://dl.fbaipublicfiles.com/detectron2/" + download(URL_PREFIX + "annotations/coco/panoptic_val2017_100.json", dest_dir) + with open(os.path.join(dest_dir, "panoptic_val2017_100.json")) as f: + obj = json.load(f) + + def link_val100(dir_full, dir_100): + print("Creating " + dir_100 + " ...") + os.makedirs(dir_100, exist_ok=True) + for img in obj["images"]: + basename = os.path.splitext(img["file_name"])[0] + src = os.path.join(dir_full, basename + ".png") + dst = os.path.join(dir_100, basename + ".png") + src = os.path.relpath(src, start=dir_100) + os.symlink(src, dst) + + link_val100( + os.path.join(dataset_dir, "panoptic_val2017"), + os.path.join(dataset_dir, "panoptic_val2017_100"), + ) + + link_val100( + os.path.join(dataset_dir, "panoptic_stuff_val2017"), + os.path.join(dataset_dir, "panoptic_stuff_val2017_100"), + ) diff --git a/iChat/models/grit_src/third_party/CenterNet2/demo/README.md b/iChat/models/grit_src/third_party/CenterNet2/demo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..133d8d38e5e9f5f44aca92c59f73309e166d7132 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/demo/README.md @@ -0,0 +1,8 @@ + +## Detectron2 Demo + +We provide a command line tool to run a simple demo of builtin configs. +The usage is explained in [GETTING_STARTED.md](../GETTING_STARTED.md). + +See our [blog post](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-) +for a high-quality demo generated with this tool. diff --git a/iChat/models/grit_src/third_party/CenterNet2/demo/demo.py b/iChat/models/grit_src/third_party/CenterNet2/demo/demo.py new file mode 100644 index 0000000000000000000000000000000000000000..4baa8767f7b299f18253aadb15a9bac5b9cc07fc --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/demo/demo.py @@ -0,0 +1,188 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import argparse +import glob +import multiprocessing as mp +import numpy as np +import os +import tempfile +import time +import warnings +import cv2 +import tqdm + +from detectron2.config import get_cfg +from detectron2.data.detection_utils import read_image +from detectron2.utils.logger import setup_logger + +from predictor import VisualizationDemo + +# constants +WINDOW_NAME = "COCO detections" + + +def setup_cfg(args): + # load config from file and command-line arguments + cfg = get_cfg() + # To use demo for Panoptic-DeepLab, please uncomment the following two lines. + # from detectron2.projects.panoptic_deeplab import add_panoptic_deeplab_config # noqa + # add_panoptic_deeplab_config(cfg) + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + # Set score_threshold for builtin models + cfg.MODEL.RETINANET.SCORE_THRESH_TEST = args.confidence_threshold + cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold + cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.confidence_threshold + cfg.freeze() + return cfg + + +def get_parser(): + parser = argparse.ArgumentParser(description="Detectron2 demo for builtin configs") + parser.add_argument( + "--config-file", + default="configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml", + metavar="FILE", + help="path to config file", + ) + parser.add_argument("--webcam", action="store_true", help="Take inputs from webcam.") + parser.add_argument("--video-input", help="Path to video file.") + parser.add_argument( + "--input", + nargs="+", + help="A list of space separated input images; " + "or a single glob pattern such as 'directory/*.jpg'", + ) + parser.add_argument( + "--output", + help="A file or directory to save output visualizations. " + "If not given, will show output in an OpenCV window.", + ) + + parser.add_argument( + "--confidence-threshold", + type=float, + default=0.5, + help="Minimum score for instance predictions to be shown", + ) + parser.add_argument( + "--opts", + help="Modify config options using the command-line 'KEY VALUE' pairs", + default=[], + nargs=argparse.REMAINDER, + ) + return parser + + +def test_opencv_video_format(codec, file_ext): + with tempfile.TemporaryDirectory(prefix="video_format_test") as dir: + filename = os.path.join(dir, "test_file" + file_ext) + writer = cv2.VideoWriter( + filename=filename, + fourcc=cv2.VideoWriter_fourcc(*codec), + fps=float(30), + frameSize=(10, 10), + isColor=True, + ) + [writer.write(np.zeros((10, 10, 3), np.uint8)) for _ in range(30)] + writer.release() + if os.path.isfile(filename): + return True + return False + + +if __name__ == "__main__": + mp.set_start_method("spawn", force=True) + args = get_parser().parse_args() + setup_logger(name="fvcore") + logger = setup_logger() + logger.info("Arguments: " + str(args)) + + cfg = setup_cfg(args) + + demo = VisualizationDemo(cfg) + + if args.input: + if len(args.input) == 1: + args.input = glob.glob(os.path.expanduser(args.input[0])) + assert args.input, "The input path(s) was not found" + for path in tqdm.tqdm(args.input, disable=not args.output): + # use PIL, to be consistent with evaluation + img = read_image(path, format="BGR") + start_time = time.time() + predictions, visualized_output = demo.run_on_image(img) + logger.info( + "{}: {} in {:.2f}s".format( + path, + "detected {} instances".format(len(predictions["instances"])) + if "instances" in predictions + else "finished", + time.time() - start_time, + ) + ) + + if args.output: + if os.path.isdir(args.output): + assert os.path.isdir(args.output), args.output + out_filename = os.path.join(args.output, os.path.basename(path)) + else: + assert len(args.input) == 1, "Please specify a directory with args.output" + out_filename = args.output + visualized_output.save(out_filename) + else: + cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) + cv2.imshow(WINDOW_NAME, visualized_output.get_image()[:, :, ::-1]) + if cv2.waitKey(0) == 27: + break # esc to quit + elif args.webcam: + assert args.input is None, "Cannot have both --input and --webcam!" + assert args.output is None, "output not yet supported with --webcam!" + cam = cv2.VideoCapture(0) + for vis in tqdm.tqdm(demo.run_on_video(cam)): + cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) + cv2.imshow(WINDOW_NAME, vis) + if cv2.waitKey(1) == 27: + break # esc to quit + cam.release() + cv2.destroyAllWindows() + elif args.video_input: + video = cv2.VideoCapture(args.video_input) + width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) + height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) + frames_per_second = video.get(cv2.CAP_PROP_FPS) + num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) + basename = os.path.basename(args.video_input) + codec, file_ext = ( + ("x264", ".mkv") if test_opencv_video_format("x264", ".mkv") else ("mp4v", ".mp4") + ) + if codec == ".mp4v": + warnings.warn("x264 codec not available, switching to mp4v") + if args.output: + if os.path.isdir(args.output): + output_fname = os.path.join(args.output, basename) + output_fname = os.path.splitext(output_fname)[0] + file_ext + else: + output_fname = args.output + assert not os.path.isfile(output_fname), output_fname + output_file = cv2.VideoWriter( + filename=output_fname, + # some installation of opencv may not support x264 (due to its license), + # you can try other format (e.g. MPEG) + fourcc=cv2.VideoWriter_fourcc(*codec), + fps=float(frames_per_second), + frameSize=(width, height), + isColor=True, + ) + assert os.path.isfile(args.video_input) + for vis_frame in tqdm.tqdm(demo.run_on_video(video), total=num_frames): + if args.output: + output_file.write(vis_frame) + else: + cv2.namedWindow(basename, cv2.WINDOW_NORMAL) + cv2.imshow(basename, vis_frame) + if cv2.waitKey(1) == 27: + break # esc to quit + video.release() + if args.output: + output_file.release() + else: + cv2.destroyAllWindows() diff --git a/iChat/models/grit_src/third_party/CenterNet2/demo/predictor.py b/iChat/models/grit_src/third_party/CenterNet2/demo/predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..7b7ebd3f846850172c1f560f8492d51e5667f76d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/demo/predictor.py @@ -0,0 +1,220 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import atexit +import bisect +import multiprocessing as mp +from collections import deque +import cv2 +import torch + +from detectron2.data import MetadataCatalog +from detectron2.engine.defaults import DefaultPredictor +from detectron2.utils.video_visualizer import VideoVisualizer +from detectron2.utils.visualizer import ColorMode, Visualizer + + +class VisualizationDemo(object): + def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False): + """ + Args: + cfg (CfgNode): + instance_mode (ColorMode): + parallel (bool): whether to run the model in different processes from visualization. + Useful since the visualization logic can be slow. + """ + self.metadata = MetadataCatalog.get( + cfg.DATASETS.TEST[0] if len(cfg.DATASETS.TEST) else "__unused" + ) + self.cpu_device = torch.device("cpu") + self.instance_mode = instance_mode + + self.parallel = parallel + if parallel: + num_gpu = torch.cuda.device_count() + self.predictor = AsyncPredictor(cfg, num_gpus=num_gpu) + else: + self.predictor = DefaultPredictor(cfg) + + def run_on_image(self, image): + """ + Args: + image (np.ndarray): an image of shape (H, W, C) (in BGR order). + This is the format used by OpenCV. + + Returns: + predictions (dict): the output of the model. + vis_output (VisImage): the visualized image output. + """ + vis_output = None + predictions = self.predictor(image) + # Convert image from OpenCV BGR format to Matplotlib RGB format. + image = image[:, :, ::-1] + visualizer = Visualizer(image, self.metadata, instance_mode=self.instance_mode) + if "panoptic_seg" in predictions: + panoptic_seg, segments_info = predictions["panoptic_seg"] + vis_output = visualizer.draw_panoptic_seg_predictions( + panoptic_seg.to(self.cpu_device), segments_info + ) + else: + if "sem_seg" in predictions: + vis_output = visualizer.draw_sem_seg( + predictions["sem_seg"].argmax(dim=0).to(self.cpu_device) + ) + if "instances" in predictions: + instances = predictions["instances"].to(self.cpu_device) + vis_output = visualizer.draw_instance_predictions(predictions=instances) + + return predictions, vis_output + + def _frame_from_video(self, video): + while video.isOpened(): + success, frame = video.read() + if success: + yield frame + else: + break + + def run_on_video(self, video): + """ + Visualizes predictions on frames of the input video. + + Args: + video (cv2.VideoCapture): a :class:`VideoCapture` object, whose source can be + either a webcam or a video file. + + Yields: + ndarray: BGR visualizations of each video frame. + """ + video_visualizer = VideoVisualizer(self.metadata, self.instance_mode) + + def process_predictions(frame, predictions): + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + if "panoptic_seg" in predictions: + panoptic_seg, segments_info = predictions["panoptic_seg"] + vis_frame = video_visualizer.draw_panoptic_seg_predictions( + frame, panoptic_seg.to(self.cpu_device), segments_info + ) + elif "instances" in predictions: + predictions = predictions["instances"].to(self.cpu_device) + vis_frame = video_visualizer.draw_instance_predictions(frame, predictions) + elif "sem_seg" in predictions: + vis_frame = video_visualizer.draw_sem_seg( + frame, predictions["sem_seg"].argmax(dim=0).to(self.cpu_device) + ) + + # Converts Matplotlib RGB format to OpenCV BGR format + vis_frame = cv2.cvtColor(vis_frame.get_image(), cv2.COLOR_RGB2BGR) + return vis_frame + + frame_gen = self._frame_from_video(video) + if self.parallel: + buffer_size = self.predictor.default_buffer_size + + frame_data = deque() + + for cnt, frame in enumerate(frame_gen): + frame_data.append(frame) + self.predictor.put(frame) + + if cnt >= buffer_size: + frame = frame_data.popleft() + predictions = self.predictor.get() + yield process_predictions(frame, predictions) + + while len(frame_data): + frame = frame_data.popleft() + predictions = self.predictor.get() + yield process_predictions(frame, predictions) + else: + for frame in frame_gen: + yield process_predictions(frame, self.predictor(frame)) + + +class AsyncPredictor: + """ + A predictor that runs the model asynchronously, possibly on >1 GPUs. + Because rendering the visualization takes considerably amount of time, + this helps improve throughput a little bit when rendering videos. + """ + + class _StopToken: + pass + + class _PredictWorker(mp.Process): + def __init__(self, cfg, task_queue, result_queue): + self.cfg = cfg + self.task_queue = task_queue + self.result_queue = result_queue + super().__init__() + + def run(self): + predictor = DefaultPredictor(self.cfg) + + while True: + task = self.task_queue.get() + if isinstance(task, AsyncPredictor._StopToken): + break + idx, data = task + result = predictor(data) + self.result_queue.put((idx, result)) + + def __init__(self, cfg, num_gpus: int = 1): + """ + Args: + cfg (CfgNode): + num_gpus (int): if 0, will run on CPU + """ + num_workers = max(num_gpus, 1) + self.task_queue = mp.Queue(maxsize=num_workers * 3) + self.result_queue = mp.Queue(maxsize=num_workers * 3) + self.procs = [] + for gpuid in range(max(num_gpus, 1)): + cfg = cfg.clone() + cfg.defrost() + cfg.MODEL.DEVICE = "cuda:{}".format(gpuid) if num_gpus > 0 else "cpu" + self.procs.append( + AsyncPredictor._PredictWorker(cfg, self.task_queue, self.result_queue) + ) + + self.put_idx = 0 + self.get_idx = 0 + self.result_rank = [] + self.result_data = [] + + for p in self.procs: + p.start() + atexit.register(self.shutdown) + + def put(self, image): + self.put_idx += 1 + self.task_queue.put((self.put_idx, image)) + + def get(self): + self.get_idx += 1 # the index needed for this request + if len(self.result_rank) and self.result_rank[0] == self.get_idx: + res = self.result_data[0] + del self.result_data[0], self.result_rank[0] + return res + + while True: + # make sure the results are returned in the correct order + idx, res = self.result_queue.get() + if idx == self.get_idx: + return res + insert = bisect.bisect(self.result_rank, idx) + self.result_rank.insert(insert, idx) + self.result_data.insert(insert, res) + + def __len__(self): + return self.put_idx - self.get_idx + + def __call__(self, image): + self.put(image) + return self.get() + + def shutdown(self): + for _ in self.procs: + self.task_queue.put(AsyncPredictor._StopToken()) + + @property + def default_buffer_size(self): + return len(self.procs) * 5 diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bdd994b49294485c27610772f97f177741f5518f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from .utils.env import setup_environment + +setup_environment() + + +# This line will be programatically read/write by setup.py. +# Leave them at the bottom of this file and don't touch them. +__version__ = "0.6" diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..99da0469ae7e169d8970e4b642fed3f870076860 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/__init__.py @@ -0,0 +1,10 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +# File: + + +from . import catalog as _UNUSED # register the handler +from .detection_checkpoint import DetectionCheckpointer +from fvcore.common.checkpoint import Checkpointer, PeriodicCheckpointer + +__all__ = ["Checkpointer", "PeriodicCheckpointer", "DetectionCheckpointer"] diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/c2_model_loading.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/c2_model_loading.py new file mode 100644 index 0000000000000000000000000000000000000000..8c8d181bd7200bd3fd38446e743f8f16780d6e76 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/c2_model_loading.py @@ -0,0 +1,407 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import logging +import re +from typing import Dict, List +import torch +from tabulate import tabulate + + +def convert_basic_c2_names(original_keys): + """ + Apply some basic name conversion to names in C2 weights. + It only deals with typical backbone models. + + Args: + original_keys (list[str]): + Returns: + list[str]: The same number of strings matching those in original_keys. + """ + layer_keys = copy.deepcopy(original_keys) + layer_keys = [ + {"pred_b": "linear_b", "pred_w": "linear_w"}.get(k, k) for k in layer_keys + ] # some hard-coded mappings + + layer_keys = [k.replace("_", ".") for k in layer_keys] + layer_keys = [re.sub("\\.b$", ".bias", k) for k in layer_keys] + layer_keys = [re.sub("\\.w$", ".weight", k) for k in layer_keys] + # Uniform both bn and gn names to "norm" + layer_keys = [re.sub("bn\\.s$", "norm.weight", k) for k in layer_keys] + layer_keys = [re.sub("bn\\.bias$", "norm.bias", k) for k in layer_keys] + layer_keys = [re.sub("bn\\.rm", "norm.running_mean", k) for k in layer_keys] + layer_keys = [re.sub("bn\\.running.mean$", "norm.running_mean", k) for k in layer_keys] + layer_keys = [re.sub("bn\\.riv$", "norm.running_var", k) for k in layer_keys] + layer_keys = [re.sub("bn\\.running.var$", "norm.running_var", k) for k in layer_keys] + layer_keys = [re.sub("bn\\.gamma$", "norm.weight", k) for k in layer_keys] + layer_keys = [re.sub("bn\\.beta$", "norm.bias", k) for k in layer_keys] + layer_keys = [re.sub("gn\\.s$", "norm.weight", k) for k in layer_keys] + layer_keys = [re.sub("gn\\.bias$", "norm.bias", k) for k in layer_keys] + + # stem + layer_keys = [re.sub("^res\\.conv1\\.norm\\.", "conv1.norm.", k) for k in layer_keys] + # to avoid mis-matching with "conv1" in other components (e.g. detection head) + layer_keys = [re.sub("^conv1\\.", "stem.conv1.", k) for k in layer_keys] + + # layer1-4 is used by torchvision, however we follow the C2 naming strategy (res2-5) + # layer_keys = [re.sub("^res2.", "layer1.", k) for k in layer_keys] + # layer_keys = [re.sub("^res3.", "layer2.", k) for k in layer_keys] + # layer_keys = [re.sub("^res4.", "layer3.", k) for k in layer_keys] + # layer_keys = [re.sub("^res5.", "layer4.", k) for k in layer_keys] + + # blocks + layer_keys = [k.replace(".branch1.", ".shortcut.") for k in layer_keys] + layer_keys = [k.replace(".branch2a.", ".conv1.") for k in layer_keys] + layer_keys = [k.replace(".branch2b.", ".conv2.") for k in layer_keys] + layer_keys = [k.replace(".branch2c.", ".conv3.") for k in layer_keys] + + # DensePose substitutions + layer_keys = [re.sub("^body.conv.fcn", "body_conv_fcn", k) for k in layer_keys] + layer_keys = [k.replace("AnnIndex.lowres", "ann_index_lowres") for k in layer_keys] + layer_keys = [k.replace("Index.UV.lowres", "index_uv_lowres") for k in layer_keys] + layer_keys = [k.replace("U.lowres", "u_lowres") for k in layer_keys] + layer_keys = [k.replace("V.lowres", "v_lowres") for k in layer_keys] + return layer_keys + + +def convert_c2_detectron_names(weights): + """ + Map Caffe2 Detectron weight names to Detectron2 names. + + Args: + weights (dict): name -> tensor + + Returns: + dict: detectron2 names -> tensor + dict: detectron2 names -> C2 names + """ + logger = logging.getLogger(__name__) + logger.info("Renaming Caffe2 weights ......") + original_keys = sorted(weights.keys()) + layer_keys = copy.deepcopy(original_keys) + + layer_keys = convert_basic_c2_names(layer_keys) + + # -------------------------------------------------------------------------- + # RPN hidden representation conv + # -------------------------------------------------------------------------- + # FPN case + # In the C2 model, the RPN hidden layer conv is defined for FPN level 2 and then + # shared for all other levels, hence the appearance of "fpn2" + layer_keys = [ + k.replace("conv.rpn.fpn2", "proposal_generator.rpn_head.conv") for k in layer_keys + ] + # Non-FPN case + layer_keys = [k.replace("conv.rpn", "proposal_generator.rpn_head.conv") for k in layer_keys] + + # -------------------------------------------------------------------------- + # RPN box transformation conv + # -------------------------------------------------------------------------- + # FPN case (see note above about "fpn2") + layer_keys = [ + k.replace("rpn.bbox.pred.fpn2", "proposal_generator.rpn_head.anchor_deltas") + for k in layer_keys + ] + layer_keys = [ + k.replace("rpn.cls.logits.fpn2", "proposal_generator.rpn_head.objectness_logits") + for k in layer_keys + ] + # Non-FPN case + layer_keys = [ + k.replace("rpn.bbox.pred", "proposal_generator.rpn_head.anchor_deltas") for k in layer_keys + ] + layer_keys = [ + k.replace("rpn.cls.logits", "proposal_generator.rpn_head.objectness_logits") + for k in layer_keys + ] + + # -------------------------------------------------------------------------- + # Fast R-CNN box head + # -------------------------------------------------------------------------- + layer_keys = [re.sub("^bbox\\.pred", "bbox_pred", k) for k in layer_keys] + layer_keys = [re.sub("^cls\\.score", "cls_score", k) for k in layer_keys] + layer_keys = [re.sub("^fc6\\.", "box_head.fc1.", k) for k in layer_keys] + layer_keys = [re.sub("^fc7\\.", "box_head.fc2.", k) for k in layer_keys] + # 4conv1fc head tensor names: head_conv1_w, head_conv1_gn_s + layer_keys = [re.sub("^head\\.conv", "box_head.conv", k) for k in layer_keys] + + # -------------------------------------------------------------------------- + # FPN lateral and output convolutions + # -------------------------------------------------------------------------- + def fpn_map(name): + """ + Look for keys with the following patterns: + 1) Starts with "fpn.inner." + Example: "fpn.inner.res2.2.sum.lateral.weight" + Meaning: These are lateral pathway convolutions + 2) Starts with "fpn.res" + Example: "fpn.res2.2.sum.weight" + Meaning: These are FPN output convolutions + """ + splits = name.split(".") + norm = ".norm" if "norm" in splits else "" + if name.startswith("fpn.inner."): + # splits example: ['fpn', 'inner', 'res2', '2', 'sum', 'lateral', 'weight'] + stage = int(splits[2][len("res") :]) + return "fpn_lateral{}{}.{}".format(stage, norm, splits[-1]) + elif name.startswith("fpn.res"): + # splits example: ['fpn', 'res2', '2', 'sum', 'weight'] + stage = int(splits[1][len("res") :]) + return "fpn_output{}{}.{}".format(stage, norm, splits[-1]) + return name + + layer_keys = [fpn_map(k) for k in layer_keys] + + # -------------------------------------------------------------------------- + # Mask R-CNN mask head + # -------------------------------------------------------------------------- + # roi_heads.StandardROIHeads case + layer_keys = [k.replace(".[mask].fcn", "mask_head.mask_fcn") for k in layer_keys] + layer_keys = [re.sub("^\\.mask\\.fcn", "mask_head.mask_fcn", k) for k in layer_keys] + layer_keys = [k.replace("mask.fcn.logits", "mask_head.predictor") for k in layer_keys] + # roi_heads.Res5ROIHeads case + layer_keys = [k.replace("conv5.mask", "mask_head.deconv") for k in layer_keys] + + # -------------------------------------------------------------------------- + # Keypoint R-CNN head + # -------------------------------------------------------------------------- + # interestingly, the keypoint head convs have blob names that are simply "conv_fcnX" + layer_keys = [k.replace("conv.fcn", "roi_heads.keypoint_head.conv_fcn") for k in layer_keys] + layer_keys = [ + k.replace("kps.score.lowres", "roi_heads.keypoint_head.score_lowres") for k in layer_keys + ] + layer_keys = [k.replace("kps.score.", "roi_heads.keypoint_head.score.") for k in layer_keys] + + # -------------------------------------------------------------------------- + # Done with replacements + # -------------------------------------------------------------------------- + assert len(set(layer_keys)) == len(layer_keys) + assert len(original_keys) == len(layer_keys) + + new_weights = {} + new_keys_to_original_keys = {} + for orig, renamed in zip(original_keys, layer_keys): + new_keys_to_original_keys[renamed] = orig + if renamed.startswith("bbox_pred.") or renamed.startswith("mask_head.predictor."): + # remove the meaningless prediction weight for background class + new_start_idx = 4 if renamed.startswith("bbox_pred.") else 1 + new_weights[renamed] = weights[orig][new_start_idx:] + logger.info( + "Remove prediction weight for background class in {}. The shape changes from " + "{} to {}.".format( + renamed, tuple(weights[orig].shape), tuple(new_weights[renamed].shape) + ) + ) + elif renamed.startswith("cls_score."): + # move weights of bg class from original index 0 to last index + logger.info( + "Move classification weights for background class in {} from index 0 to " + "index {}.".format(renamed, weights[orig].shape[0] - 1) + ) + new_weights[renamed] = torch.cat([weights[orig][1:], weights[orig][:1]]) + else: + new_weights[renamed] = weights[orig] + + return new_weights, new_keys_to_original_keys + + +# Note the current matching is not symmetric. +# it assumes model_state_dict will have longer names. +def align_and_update_state_dicts(model_state_dict, ckpt_state_dict, c2_conversion=True): + """ + Match names between the two state-dict, and returns a new chkpt_state_dict with names + converted to match model_state_dict with heuristics. The returned dict can be later + loaded with fvcore checkpointer. + If `c2_conversion==True`, `ckpt_state_dict` is assumed to be a Caffe2 + model and will be renamed at first. + + Strategy: suppose that the models that we will create will have prefixes appended + to each of its keys, for example due to an extra level of nesting that the original + pre-trained weights from ImageNet won't contain. For example, model.state_dict() + might return backbone[0].body.res2.conv1.weight, while the pre-trained model contains + res2.conv1.weight. We thus want to match both parameters together. + For that, we look for each model weight, look among all loaded keys if there is one + that is a suffix of the current weight name, and use it if that's the case. + If multiple matches exist, take the one with longest size + of the corresponding name. For example, for the same model as before, the pretrained + weight file can contain both res2.conv1.weight, as well as conv1.weight. In this case, + we want to match backbone[0].body.conv1.weight to conv1.weight, and + backbone[0].body.res2.conv1.weight to res2.conv1.weight. + """ + model_keys = sorted(model_state_dict.keys()) + if c2_conversion: + ckpt_state_dict, original_keys = convert_c2_detectron_names(ckpt_state_dict) + # original_keys: the name in the original dict (before renaming) + else: + original_keys = {x: x for x in ckpt_state_dict.keys()} + ckpt_keys = sorted(ckpt_state_dict.keys()) + + def match(a, b): + # Matched ckpt_key should be a complete (starts with '.') suffix. + # For example, roi_heads.mesh_head.whatever_conv1 does not match conv1, + # but matches whatever_conv1 or mesh_head.whatever_conv1. + return a == b or a.endswith("." + b) + + # get a matrix of string matches, where each (i, j) entry correspond to the size of the + # ckpt_key string, if it matches + match_matrix = [len(j) if match(i, j) else 0 for i in model_keys for j in ckpt_keys] + match_matrix = torch.as_tensor(match_matrix).view(len(model_keys), len(ckpt_keys)) + # use the matched one with longest size in case of multiple matches + max_match_size, idxs = match_matrix.max(1) + # remove indices that correspond to no-match + idxs[max_match_size == 0] = -1 + + logger = logging.getLogger(__name__) + # matched_pairs (matched checkpoint key --> matched model key) + matched_keys = {} + result_state_dict = {} + for idx_model, idx_ckpt in enumerate(idxs.tolist()): + if idx_ckpt == -1: + continue + key_model = model_keys[idx_model] + key_ckpt = ckpt_keys[idx_ckpt] + value_ckpt = ckpt_state_dict[key_ckpt] + shape_in_model = model_state_dict[key_model].shape + + if shape_in_model != value_ckpt.shape: + logger.warning( + "Shape of {} in checkpoint is {}, while shape of {} in model is {}.".format( + key_ckpt, value_ckpt.shape, key_model, shape_in_model + ) + ) + logger.warning( + "{} will not be loaded. Please double check and see if this is desired.".format( + key_ckpt + ) + ) + continue + + assert key_model not in result_state_dict + result_state_dict[key_model] = value_ckpt + if key_ckpt in matched_keys: # already added to matched_keys + logger.error( + "Ambiguity found for {} in checkpoint!" + "It matches at least two keys in the model ({} and {}).".format( + key_ckpt, key_model, matched_keys[key_ckpt] + ) + ) + raise ValueError("Cannot match one checkpoint key to multiple keys in the model.") + + matched_keys[key_ckpt] = key_model + + # logging: + matched_model_keys = sorted(matched_keys.values()) + if len(matched_model_keys) == 0: + logger.warning("No weights in checkpoint matched with model.") + return ckpt_state_dict + common_prefix = _longest_common_prefix(matched_model_keys) + rev_matched_keys = {v: k for k, v in matched_keys.items()} + original_keys = {k: original_keys[rev_matched_keys[k]] for k in matched_model_keys} + + model_key_groups = _group_keys_by_module(matched_model_keys, original_keys) + table = [] + memo = set() + for key_model in matched_model_keys: + if key_model in memo: + continue + if key_model in model_key_groups: + group = model_key_groups[key_model] + memo |= set(group) + shapes = [tuple(model_state_dict[k].shape) for k in group] + table.append( + ( + _longest_common_prefix([k[len(common_prefix) :] for k in group]) + "*", + _group_str([original_keys[k] for k in group]), + " ".join([str(x).replace(" ", "") for x in shapes]), + ) + ) + else: + key_checkpoint = original_keys[key_model] + shape = str(tuple(model_state_dict[key_model].shape)) + table.append((key_model[len(common_prefix) :], key_checkpoint, shape)) + table_str = tabulate( + table, tablefmt="pipe", headers=["Names in Model", "Names in Checkpoint", "Shapes"] + ) + logger.info( + "Following weights matched with " + + (f"submodule {common_prefix[:-1]}" if common_prefix else "model") + + ":\n" + + table_str + ) + + unmatched_ckpt_keys = [k for k in ckpt_keys if k not in set(matched_keys.keys())] + for k in unmatched_ckpt_keys: + result_state_dict[k] = ckpt_state_dict[k] + return result_state_dict + + +def _group_keys_by_module(keys: List[str], original_names: Dict[str, str]): + """ + Params in the same submodule are grouped together. + + Args: + keys: names of all parameters + original_names: mapping from parameter name to their name in the checkpoint + + Returns: + dict[name -> all other names in the same group] + """ + + def _submodule_name(key): + pos = key.rfind(".") + if pos < 0: + return None + prefix = key[: pos + 1] + return prefix + + all_submodules = [_submodule_name(k) for k in keys] + all_submodules = [x for x in all_submodules if x] + all_submodules = sorted(all_submodules, key=len) + + ret = {} + for prefix in all_submodules: + group = [k for k in keys if k.startswith(prefix)] + if len(group) <= 1: + continue + original_name_lcp = _longest_common_prefix_str([original_names[k] for k in group]) + if len(original_name_lcp) == 0: + # don't group weights if original names don't share prefix + continue + + for k in group: + if k in ret: + continue + ret[k] = group + return ret + + +def _longest_common_prefix(names: List[str]) -> str: + """ + ["abc.zfg", "abc.zef"] -> "abc." + """ + names = [n.split(".") for n in names] + m1, m2 = min(names), max(names) + ret = [a for a, b in zip(m1, m2) if a == b] + ret = ".".join(ret) + "." if len(ret) else "" + return ret + + +def _longest_common_prefix_str(names: List[str]) -> str: + m1, m2 = min(names), max(names) + lcp = [a for a, b in zip(m1, m2) if a == b] + lcp = "".join(lcp) + return lcp + + +def _group_str(names: List[str]) -> str: + """ + Turn "common1", "common2", "common3" into "common{1,2,3}" + """ + lcp = _longest_common_prefix_str(names) + rest = [x[len(lcp) :] for x in names] + rest = "{" + ",".join(rest) + "}" + ret = lcp + rest + + # add some simplification for BN specifically + ret = ret.replace("bn_{beta,running_mean,running_var,gamma}", "bn_*") + ret = ret.replace("bn_beta,bn_running_mean,bn_running_var,bn_gamma", "bn_*") + return ret diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/catalog.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/catalog.py new file mode 100644 index 0000000000000000000000000000000000000000..9a85736754a0de4550df96c22f38fc515bd02d71 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/catalog.py @@ -0,0 +1,115 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging + +from detectron2.utils.file_io import PathHandler, PathManager + + +class ModelCatalog(object): + """ + Store mappings from names to third-party models. + """ + + S3_C2_DETECTRON_PREFIX = "https://dl.fbaipublicfiles.com/detectron" + + # MSRA models have STRIDE_IN_1X1=True. False otherwise. + # NOTE: all BN models here have fused BN into an affine layer. + # As a result, you should only load them to a model with "FrozenBN". + # Loading them to a model with regular BN or SyncBN is wrong. + # Even when loaded to FrozenBN, it is still different from affine by an epsilon, + # which should be negligible for training. + # NOTE: all models here uses PIXEL_STD=[1,1,1] + # NOTE: Most of the BN models here are no longer used. We use the + # re-converted pre-trained models under detectron2 model zoo instead. + C2_IMAGENET_MODELS = { + "MSRA/R-50": "ImageNetPretrained/MSRA/R-50.pkl", + "MSRA/R-101": "ImageNetPretrained/MSRA/R-101.pkl", + "FAIR/R-50-GN": "ImageNetPretrained/47261647/R-50-GN.pkl", + "FAIR/R-101-GN": "ImageNetPretrained/47592356/R-101-GN.pkl", + "FAIR/X-101-32x8d": "ImageNetPretrained/20171220/X-101-32x8d.pkl", + "FAIR/X-101-64x4d": "ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl", + "FAIR/X-152-32x8d-IN5k": "ImageNetPretrained/25093814/X-152-32x8d-IN5k.pkl", + } + + C2_DETECTRON_PATH_FORMAT = ( + "{prefix}/{url}/output/train/{dataset}/{type}/model_final.pkl" # noqa B950 + ) + + C2_DATASET_COCO = "coco_2014_train%3Acoco_2014_valminusminival" + C2_DATASET_COCO_KEYPOINTS = "keypoints_coco_2014_train%3Akeypoints_coco_2014_valminusminival" + + # format: {model_name} -> part of the url + C2_DETECTRON_MODELS = { + "35857197/e2e_faster_rcnn_R-50-C4_1x": "35857197/12_2017_baselines/e2e_faster_rcnn_R-50-C4_1x.yaml.01_33_49.iAX0mXvW", # noqa B950 + "35857345/e2e_faster_rcnn_R-50-FPN_1x": "35857345/12_2017_baselines/e2e_faster_rcnn_R-50-FPN_1x.yaml.01_36_30.cUF7QR7I", # noqa B950 + "35857890/e2e_faster_rcnn_R-101-FPN_1x": "35857890/12_2017_baselines/e2e_faster_rcnn_R-101-FPN_1x.yaml.01_38_50.sNxI7sX7", # noqa B950 + "36761737/e2e_faster_rcnn_X-101-32x8d-FPN_1x": "36761737/12_2017_baselines/e2e_faster_rcnn_X-101-32x8d-FPN_1x.yaml.06_31_39.5MIHi1fZ", # noqa B950 + "35858791/e2e_mask_rcnn_R-50-C4_1x": "35858791/12_2017_baselines/e2e_mask_rcnn_R-50-C4_1x.yaml.01_45_57.ZgkA7hPB", # noqa B950 + "35858933/e2e_mask_rcnn_R-50-FPN_1x": "35858933/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml.01_48_14.DzEQe4wC", # noqa B950 + "35861795/e2e_mask_rcnn_R-101-FPN_1x": "35861795/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_1x.yaml.02_31_37.KqyEK4tT", # noqa B950 + "36761843/e2e_mask_rcnn_X-101-32x8d-FPN_1x": "36761843/12_2017_baselines/e2e_mask_rcnn_X-101-32x8d-FPN_1x.yaml.06_35_59.RZotkLKI", # noqa B950 + "48616381/e2e_mask_rcnn_R-50-FPN_2x_gn": "GN/48616381/04_2018_gn_baselines/e2e_mask_rcnn_R-50-FPN_2x_gn_0416.13_23_38.bTlTI97Q", # noqa B950 + "37697547/e2e_keypoint_rcnn_R-50-FPN_1x": "37697547/12_2017_baselines/e2e_keypoint_rcnn_R-50-FPN_1x.yaml.08_42_54.kdzV35ao", # noqa B950 + "35998355/rpn_R-50-C4_1x": "35998355/12_2017_baselines/rpn_R-50-C4_1x.yaml.08_00_43.njH5oD9L", # noqa B950 + "35998814/rpn_R-50-FPN_1x": "35998814/12_2017_baselines/rpn_R-50-FPN_1x.yaml.08_06_03.Axg0r179", # noqa B950 + "36225147/fast_R-50-FPN_1x": "36225147/12_2017_baselines/fast_rcnn_R-50-FPN_1x.yaml.08_39_09.L3obSdQ2", # noqa B950 + } + + @staticmethod + def get(name): + if name.startswith("Caffe2Detectron/COCO"): + return ModelCatalog._get_c2_detectron_baseline(name) + if name.startswith("ImageNetPretrained/"): + return ModelCatalog._get_c2_imagenet_pretrained(name) + raise RuntimeError("model not present in the catalog: {}".format(name)) + + @staticmethod + def _get_c2_imagenet_pretrained(name): + prefix = ModelCatalog.S3_C2_DETECTRON_PREFIX + name = name[len("ImageNetPretrained/") :] + name = ModelCatalog.C2_IMAGENET_MODELS[name] + url = "/".join([prefix, name]) + return url + + @staticmethod + def _get_c2_detectron_baseline(name): + name = name[len("Caffe2Detectron/COCO/") :] + url = ModelCatalog.C2_DETECTRON_MODELS[name] + if "keypoint_rcnn" in name: + dataset = ModelCatalog.C2_DATASET_COCO_KEYPOINTS + else: + dataset = ModelCatalog.C2_DATASET_COCO + + if "35998355/rpn_R-50-C4_1x" in name: + # this one model is somehow different from others .. + type = "rpn" + else: + type = "generalized_rcnn" + + # Detectron C2 models are stored in the structure defined in `C2_DETECTRON_PATH_FORMAT`. + url = ModelCatalog.C2_DETECTRON_PATH_FORMAT.format( + prefix=ModelCatalog.S3_C2_DETECTRON_PREFIX, url=url, type=type, dataset=dataset + ) + return url + + +class ModelCatalogHandler(PathHandler): + """ + Resolve URL like catalog://. + """ + + PREFIX = "catalog://" + + def _get_supported_prefixes(self): + return [self.PREFIX] + + def _get_local_path(self, path, **kwargs): + logger = logging.getLogger(__name__) + catalog_path = ModelCatalog.get(path[len(self.PREFIX) :]) + logger.info("Catalog entry {} points to {}".format(path, catalog_path)) + return PathManager.get_local_path(catalog_path, **kwargs) + + def _open(self, path, mode="r", **kwargs): + return PathManager.open(self._get_local_path(path), mode, **kwargs) + + +PathManager.register_handler(ModelCatalogHandler()) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/detection_checkpoint.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/detection_checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..82fd3b2d40054573917a445b138d29a6dabfb907 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/detection_checkpoint.py @@ -0,0 +1,120 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import os +import pickle +import torch +from fvcore.common.checkpoint import Checkpointer +from torch.nn.parallel import DistributedDataParallel + +import detectron2.utils.comm as comm +from detectron2.utils.file_io import PathManager + +from .c2_model_loading import align_and_update_state_dicts + + +class DetectionCheckpointer(Checkpointer): + """ + Same as :class:`Checkpointer`, but is able to: + 1. handle models in detectron & detectron2 model zoo, and apply conversions for legacy models. + 2. correctly load checkpoints that are only available on the master worker + """ + + def __init__(self, model, save_dir="", *, save_to_disk=None, **checkpointables): + is_main_process = comm.is_main_process() + super().__init__( + model, + save_dir, + save_to_disk=is_main_process if save_to_disk is None else save_to_disk, + **checkpointables, + ) + self.path_manager = PathManager + + def load(self, path, *args, **kwargs): + need_sync = False + + if path and isinstance(self.model, DistributedDataParallel): + logger = logging.getLogger(__name__) + path = self.path_manager.get_local_path(path) + has_file = os.path.isfile(path) + all_has_file = comm.all_gather(has_file) + if not all_has_file[0]: + raise OSError(f"File {path} not found on main worker.") + if not all(all_has_file): + logger.warning( + f"Not all workers can read checkpoint {path}. " + "Training may fail to fully resume." + ) + # TODO: broadcast the checkpoint file contents from main + # worker, and load from it instead. + need_sync = True + if not has_file: + path = None # don't load if not readable + ret = super().load(path, *args, **kwargs) + + if need_sync: + logger.info("Broadcasting model states from main worker ...") + self.model._sync_params_and_buffers() + return ret + + def _load_file(self, filename): + if filename.endswith(".pkl"): + with PathManager.open(filename, "rb") as f: + data = pickle.load(f, encoding="latin1") + if "model" in data and "__author__" in data: + # file is in Detectron2 model zoo format + self.logger.info("Reading a file from '{}'".format(data["__author__"])) + return data + else: + # assume file is from Caffe2 / Detectron1 model zoo + if "blobs" in data: + # Detection models have "blobs", but ImageNet models don't + data = data["blobs"] + data = {k: v for k, v in data.items() if not k.endswith("_momentum")} + return {"model": data, "__author__": "Caffe2", "matching_heuristics": True} + elif filename.endswith(".pyth"): + # assume file is from pycls; no one else seems to use the ".pyth" extension + with PathManager.open(filename, "rb") as f: + data = torch.load(f) + assert ( + "model_state" in data + ), f"Cannot load .pyth file {filename}; pycls checkpoints must contain 'model_state'." + model_state = { + k: v + for k, v in data["model_state"].items() + if not k.endswith("num_batches_tracked") + } + return {"model": model_state, "__author__": "pycls", "matching_heuristics": True} + + loaded = super()._load_file(filename) # load native pth checkpoint + if "model" not in loaded: + loaded = {"model": loaded} + return loaded + + def _load_model(self, checkpoint): + if checkpoint.get("matching_heuristics", False): + self._convert_ndarray_to_tensor(checkpoint["model"]) + # convert weights by name-matching heuristics + checkpoint["model"] = align_and_update_state_dicts( + self.model.state_dict(), + checkpoint["model"], + c2_conversion=checkpoint.get("__author__", None) == "Caffe2", + ) + # for non-caffe2 models, use standard ways to load it + incompatible = super()._load_model(checkpoint) + + model_buffers = dict(self.model.named_buffers(recurse=False)) + for k in ["pixel_mean", "pixel_std"]: + # Ignore missing key message about pixel_mean/std. + # Though they may be missing in old checkpoints, they will be correctly + # initialized from config anyway. + if k in model_buffers: + try: + incompatible.missing_keys.remove(k) + except ValueError: + pass + for k in incompatible.unexpected_keys[:]: + # Ignore unexpected keys about cell anchors. They exist in old checkpoints + # but now they are non-persistent buffers and will not be in new checkpoints. + if "anchor_generator.cell_anchors" in k: + incompatible.unexpected_keys.remove(k) + return incompatible diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/config/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4e648e632d55c70f160d49630378d202fbde4e45 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/config/__init__.py @@ -0,0 +1,24 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .compat import downgrade_config, upgrade_config +from .config import CfgNode, get_cfg, global_cfg, set_global_cfg, configurable +from .instantiate import instantiate +from .lazy import LazyCall, LazyConfig + +__all__ = [ + "CfgNode", + "get_cfg", + "global_cfg", + "set_global_cfg", + "downgrade_config", + "upgrade_config", + "configurable", + "instantiate", + "LazyCall", + "LazyConfig", +] + + +from detectron2.utils.env import fixup_module_metadata + +fixup_module_metadata(__name__, globals(), __all__) +del fixup_module_metadata diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/config/compat.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/config/compat.py new file mode 100644 index 0000000000000000000000000000000000000000..11a08c439bf14defd880e37a938fab8a08e68eeb --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/config/compat.py @@ -0,0 +1,229 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +""" +Backward compatibility of configs. + +Instructions to bump version: ++ It's not needed to bump version if new keys are added. + It's only needed when backward-incompatible changes happen + (i.e., some existing keys disappear, or the meaning of a key changes) ++ To bump version, do the following: + 1. Increment _C.VERSION in defaults.py + 2. Add a converter in this file. + + Each ConverterVX has a function "upgrade" which in-place upgrades config from X-1 to X, + and a function "downgrade" which in-place downgrades config from X to X-1 + + In each function, VERSION is left unchanged. + + Each converter assumes that its input has the relevant keys + (i.e., the input is not a partial config). + 3. Run the tests (test_config.py) to make sure the upgrade & downgrade + functions are consistent. +""" + +import logging +from typing import List, Optional, Tuple + +from .config import CfgNode as CN +from .defaults import _C + +__all__ = ["upgrade_config", "downgrade_config"] + + +def upgrade_config(cfg: CN, to_version: Optional[int] = None) -> CN: + """ + Upgrade a config from its current version to a newer version. + + Args: + cfg (CfgNode): + to_version (int): defaults to the latest version. + """ + cfg = cfg.clone() + if to_version is None: + to_version = _C.VERSION + + assert cfg.VERSION <= to_version, "Cannot upgrade from v{} to v{}!".format( + cfg.VERSION, to_version + ) + for k in range(cfg.VERSION, to_version): + converter = globals()["ConverterV" + str(k + 1)] + converter.upgrade(cfg) + cfg.VERSION = k + 1 + return cfg + + +def downgrade_config(cfg: CN, to_version: int) -> CN: + """ + Downgrade a config from its current version to an older version. + + Args: + cfg (CfgNode): + to_version (int): + + Note: + A general downgrade of arbitrary configs is not always possible due to the + different functionalities in different versions. + The purpose of downgrade is only to recover the defaults in old versions, + allowing it to load an old partial yaml config. + Therefore, the implementation only needs to fill in the default values + in the old version when a general downgrade is not possible. + """ + cfg = cfg.clone() + assert cfg.VERSION >= to_version, "Cannot downgrade from v{} to v{}!".format( + cfg.VERSION, to_version + ) + for k in range(cfg.VERSION, to_version, -1): + converter = globals()["ConverterV" + str(k)] + converter.downgrade(cfg) + cfg.VERSION = k - 1 + return cfg + + +def guess_version(cfg: CN, filename: str) -> int: + """ + Guess the version of a partial config where the VERSION field is not specified. + Returns the version, or the latest if cannot make a guess. + + This makes it easier for users to migrate. + """ + logger = logging.getLogger(__name__) + + def _has(name: str) -> bool: + cur = cfg + for n in name.split("."): + if n not in cur: + return False + cur = cur[n] + return True + + # Most users' partial configs have "MODEL.WEIGHT", so guess on it + ret = None + if _has("MODEL.WEIGHT") or _has("TEST.AUG_ON"): + ret = 1 + + if ret is not None: + logger.warning("Config '{}' has no VERSION. Assuming it to be v{}.".format(filename, ret)) + else: + ret = _C.VERSION + logger.warning( + "Config '{}' has no VERSION. Assuming it to be compatible with latest v{}.".format( + filename, ret + ) + ) + return ret + + +def _rename(cfg: CN, old: str, new: str) -> None: + old_keys = old.split(".") + new_keys = new.split(".") + + def _set(key_seq: List[str], val: str) -> None: + cur = cfg + for k in key_seq[:-1]: + if k not in cur: + cur[k] = CN() + cur = cur[k] + cur[key_seq[-1]] = val + + def _get(key_seq: List[str]) -> CN: + cur = cfg + for k in key_seq: + cur = cur[k] + return cur + + def _del(key_seq: List[str]) -> None: + cur = cfg + for k in key_seq[:-1]: + cur = cur[k] + del cur[key_seq[-1]] + if len(cur) == 0 and len(key_seq) > 1: + _del(key_seq[:-1]) + + _set(new_keys, _get(old_keys)) + _del(old_keys) + + +class _RenameConverter: + """ + A converter that handles simple rename. + """ + + RENAME: List[Tuple[str, str]] = [] # list of tuples of (old name, new name) + + @classmethod + def upgrade(cls, cfg: CN) -> None: + for old, new in cls.RENAME: + _rename(cfg, old, new) + + @classmethod + def downgrade(cls, cfg: CN) -> None: + for old, new in cls.RENAME[::-1]: + _rename(cfg, new, old) + + +class ConverterV1(_RenameConverter): + RENAME = [("MODEL.RPN_HEAD.NAME", "MODEL.RPN.HEAD_NAME")] + + +class ConverterV2(_RenameConverter): + """ + A large bulk of rename, before public release. + """ + + RENAME = [ + ("MODEL.WEIGHT", "MODEL.WEIGHTS"), + ("MODEL.PANOPTIC_FPN.SEMANTIC_LOSS_SCALE", "MODEL.SEM_SEG_HEAD.LOSS_WEIGHT"), + ("MODEL.PANOPTIC_FPN.RPN_LOSS_SCALE", "MODEL.RPN.LOSS_WEIGHT"), + ("MODEL.PANOPTIC_FPN.INSTANCE_LOSS_SCALE", "MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT"), + ("MODEL.PANOPTIC_FPN.COMBINE_ON", "MODEL.PANOPTIC_FPN.COMBINE.ENABLED"), + ( + "MODEL.PANOPTIC_FPN.COMBINE_OVERLAP_THRESHOLD", + "MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH", + ), + ( + "MODEL.PANOPTIC_FPN.COMBINE_STUFF_AREA_LIMIT", + "MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT", + ), + ( + "MODEL.PANOPTIC_FPN.COMBINE_INSTANCES_CONFIDENCE_THRESHOLD", + "MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH", + ), + ("MODEL.ROI_HEADS.SCORE_THRESH", "MODEL.ROI_HEADS.SCORE_THRESH_TEST"), + ("MODEL.ROI_HEADS.NMS", "MODEL.ROI_HEADS.NMS_THRESH_TEST"), + ("MODEL.RETINANET.INFERENCE_SCORE_THRESHOLD", "MODEL.RETINANET.SCORE_THRESH_TEST"), + ("MODEL.RETINANET.INFERENCE_TOPK_CANDIDATES", "MODEL.RETINANET.TOPK_CANDIDATES_TEST"), + ("MODEL.RETINANET.INFERENCE_NMS_THRESHOLD", "MODEL.RETINANET.NMS_THRESH_TEST"), + ("TEST.DETECTIONS_PER_IMG", "TEST.DETECTIONS_PER_IMAGE"), + ("TEST.AUG_ON", "TEST.AUG.ENABLED"), + ("TEST.AUG_MIN_SIZES", "TEST.AUG.MIN_SIZES"), + ("TEST.AUG_MAX_SIZE", "TEST.AUG.MAX_SIZE"), + ("TEST.AUG_FLIP", "TEST.AUG.FLIP"), + ] + + @classmethod + def upgrade(cls, cfg: CN) -> None: + super().upgrade(cfg) + + if cfg.MODEL.META_ARCHITECTURE == "RetinaNet": + _rename( + cfg, "MODEL.RETINANET.ANCHOR_ASPECT_RATIOS", "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS" + ) + _rename(cfg, "MODEL.RETINANET.ANCHOR_SIZES", "MODEL.ANCHOR_GENERATOR.SIZES") + del cfg["MODEL"]["RPN"]["ANCHOR_SIZES"] + del cfg["MODEL"]["RPN"]["ANCHOR_ASPECT_RATIOS"] + else: + _rename(cfg, "MODEL.RPN.ANCHOR_ASPECT_RATIOS", "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS") + _rename(cfg, "MODEL.RPN.ANCHOR_SIZES", "MODEL.ANCHOR_GENERATOR.SIZES") + del cfg["MODEL"]["RETINANET"]["ANCHOR_SIZES"] + del cfg["MODEL"]["RETINANET"]["ANCHOR_ASPECT_RATIOS"] + del cfg["MODEL"]["RETINANET"]["ANCHOR_STRIDES"] + + @classmethod + def downgrade(cls, cfg: CN) -> None: + super().downgrade(cfg) + + _rename(cfg, "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS", "MODEL.RPN.ANCHOR_ASPECT_RATIOS") + _rename(cfg, "MODEL.ANCHOR_GENERATOR.SIZES", "MODEL.RPN.ANCHOR_SIZES") + cfg.MODEL.RETINANET.ANCHOR_ASPECT_RATIOS = cfg.MODEL.RPN.ANCHOR_ASPECT_RATIOS + cfg.MODEL.RETINANET.ANCHOR_SIZES = cfg.MODEL.RPN.ANCHOR_SIZES + cfg.MODEL.RETINANET.ANCHOR_STRIDES = [] # this is not used anywhere in any version diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/config/config.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/config/config.py new file mode 100644 index 0000000000000000000000000000000000000000..49a55b1bc87509e2bb24b902ae12c21d5aaeda81 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/config/config.py @@ -0,0 +1,265 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import functools +import inspect +import logging +from fvcore.common.config import CfgNode as _CfgNode + +from detectron2.utils.file_io import PathManager + + +class CfgNode(_CfgNode): + """ + The same as `fvcore.common.config.CfgNode`, but different in: + + 1. Use unsafe yaml loading by default. + Note that this may lead to arbitrary code execution: you must not + load a config file from untrusted sources before manually inspecting + the content of the file. + 2. Support config versioning. + When attempting to merge an old config, it will convert the old config automatically. + + .. automethod:: clone + .. automethod:: freeze + .. automethod:: defrost + .. automethod:: is_frozen + .. automethod:: load_yaml_with_base + .. automethod:: merge_from_list + .. automethod:: merge_from_other_cfg + """ + + @classmethod + def _open_cfg(cls, filename): + return PathManager.open(filename, "r") + + # Note that the default value of allow_unsafe is changed to True + def merge_from_file(self, cfg_filename: str, allow_unsafe: bool = True) -> None: + """ + Load content from the given config file and merge it into self. + + Args: + cfg_filename: config filename + allow_unsafe: allow unsafe yaml syntax + """ + assert PathManager.isfile(cfg_filename), f"Config file '{cfg_filename}' does not exist!" + loaded_cfg = self.load_yaml_with_base(cfg_filename, allow_unsafe=allow_unsafe) + loaded_cfg = type(self)(loaded_cfg) + + # defaults.py needs to import CfgNode + from .defaults import _C + + latest_ver = _C.VERSION + assert ( + latest_ver == self.VERSION + ), "CfgNode.merge_from_file is only allowed on a config object of latest version!" + + logger = logging.getLogger(__name__) + + loaded_ver = loaded_cfg.get("VERSION", None) + if loaded_ver is None: + from .compat import guess_version + + loaded_ver = guess_version(loaded_cfg, cfg_filename) + assert loaded_ver <= self.VERSION, "Cannot merge a v{} config into a v{} config.".format( + loaded_ver, self.VERSION + ) + + if loaded_ver == self.VERSION: + self.merge_from_other_cfg(loaded_cfg) + else: + # compat.py needs to import CfgNode + from .compat import upgrade_config, downgrade_config + + logger.warning( + "Loading an old v{} config file '{}' by automatically upgrading to v{}. " + "See docs/CHANGELOG.md for instructions to update your files.".format( + loaded_ver, cfg_filename, self.VERSION + ) + ) + # To convert, first obtain a full config at an old version + old_self = downgrade_config(self, to_version=loaded_ver) + old_self.merge_from_other_cfg(loaded_cfg) + new_config = upgrade_config(old_self) + self.clear() + self.update(new_config) + + def dump(self, *args, **kwargs): + """ + Returns: + str: a yaml string representation of the config + """ + # to make it show up in docs + return super().dump(*args, **kwargs) + + +global_cfg = CfgNode() + + +def get_cfg() -> CfgNode: + """ + Get a copy of the default config. + + Returns: + a detectron2 CfgNode instance. + """ + from .defaults import _C + + return _C.clone() + + +def set_global_cfg(cfg: CfgNode) -> None: + """ + Let the global config point to the given cfg. + + Assume that the given "cfg" has the key "KEY", after calling + `set_global_cfg(cfg)`, the key can be accessed by: + :: + from detectron2.config import global_cfg + print(global_cfg.KEY) + + By using a hacky global config, you can access these configs anywhere, + without having to pass the config object or the values deep into the code. + This is a hacky feature introduced for quick prototyping / research exploration. + """ + global global_cfg + global_cfg.clear() + global_cfg.update(cfg) + + +def configurable(init_func=None, *, from_config=None): + """ + Decorate a function or a class's __init__ method so that it can be called + with a :class:`CfgNode` object using a :func:`from_config` function that translates + :class:`CfgNode` to arguments. + + Examples: + :: + # Usage 1: Decorator on __init__: + class A: + @configurable + def __init__(self, a, b=2, c=3): + pass + + @classmethod + def from_config(cls, cfg): # 'cfg' must be the first argument + # Returns kwargs to be passed to __init__ + return {"a": cfg.A, "b": cfg.B} + + a1 = A(a=1, b=2) # regular construction + a2 = A(cfg) # construct with a cfg + a3 = A(cfg, b=3, c=4) # construct with extra overwrite + + # Usage 2: Decorator on any function. Needs an extra from_config argument: + @configurable(from_config=lambda cfg: {"a: cfg.A, "b": cfg.B}) + def a_func(a, b=2, c=3): + pass + + a1 = a_func(a=1, b=2) # regular call + a2 = a_func(cfg) # call with a cfg + a3 = a_func(cfg, b=3, c=4) # call with extra overwrite + + Args: + init_func (callable): a class's ``__init__`` method in usage 1. The + class must have a ``from_config`` classmethod which takes `cfg` as + the first argument. + from_config (callable): the from_config function in usage 2. It must take `cfg` + as its first argument. + """ + + if init_func is not None: + assert ( + inspect.isfunction(init_func) + and from_config is None + and init_func.__name__ == "__init__" + ), "Incorrect use of @configurable. Check API documentation for examples." + + @functools.wraps(init_func) + def wrapped(self, *args, **kwargs): + try: + from_config_func = type(self).from_config + except AttributeError as e: + raise AttributeError( + "Class with @configurable must have a 'from_config' classmethod." + ) from e + if not inspect.ismethod(from_config_func): + raise TypeError("Class with @configurable must have a 'from_config' classmethod.") + + if _called_with_cfg(*args, **kwargs): + explicit_args = _get_args_from_config(from_config_func, *args, **kwargs) + init_func(self, **explicit_args) + else: + init_func(self, *args, **kwargs) + + return wrapped + + else: + if from_config is None: + return configurable # @configurable() is made equivalent to @configurable + assert inspect.isfunction( + from_config + ), "from_config argument of configurable must be a function!" + + def wrapper(orig_func): + @functools.wraps(orig_func) + def wrapped(*args, **kwargs): + if _called_with_cfg(*args, **kwargs): + explicit_args = _get_args_from_config(from_config, *args, **kwargs) + return orig_func(**explicit_args) + else: + return orig_func(*args, **kwargs) + + wrapped.from_config = from_config + return wrapped + + return wrapper + + +def _get_args_from_config(from_config_func, *args, **kwargs): + """ + Use `from_config` to obtain explicit arguments. + + Returns: + dict: arguments to be used for cls.__init__ + """ + signature = inspect.signature(from_config_func) + if list(signature.parameters.keys())[0] != "cfg": + if inspect.isfunction(from_config_func): + name = from_config_func.__name__ + else: + name = f"{from_config_func.__self__}.from_config" + raise TypeError(f"{name} must take 'cfg' as the first argument!") + support_var_arg = any( + param.kind in [param.VAR_POSITIONAL, param.VAR_KEYWORD] + for param in signature.parameters.values() + ) + if support_var_arg: # forward all arguments to from_config, if from_config accepts them + ret = from_config_func(*args, **kwargs) + else: + # forward supported arguments to from_config + supported_arg_names = set(signature.parameters.keys()) + extra_kwargs = {} + for name in list(kwargs.keys()): + if name not in supported_arg_names: + extra_kwargs[name] = kwargs.pop(name) + ret = from_config_func(*args, **kwargs) + # forward the other arguments to __init__ + ret.update(extra_kwargs) + return ret + + +def _called_with_cfg(*args, **kwargs): + """ + Returns: + bool: whether the arguments contain CfgNode and should be considered + forwarded to from_config. + """ + from omegaconf import DictConfig + + if len(args) and isinstance(args[0], (_CfgNode, DictConfig)): + return True + if isinstance(kwargs.pop("cfg", None), (_CfgNode, DictConfig)): + return True + # `from_config`'s first argument is forced to be "cfg". + # So the above check covers all cases. + return False diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/config/defaults.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/config/defaults.py new file mode 100644 index 0000000000000000000000000000000000000000..848486dfe91a62559e6ae35120a4dac26d4bd66d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/config/defaults.py @@ -0,0 +1,635 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .config import CfgNode as CN + +# NOTE: given the new config system +# (https://detectron2.readthedocs.io/en/latest/tutorials/lazyconfigs.html), +# we will stop adding new functionalities to default CfgNode. + +# ----------------------------------------------------------------------------- +# Convention about Training / Test specific parameters +# ----------------------------------------------------------------------------- +# Whenever an argument can be either used for training or for testing, the +# corresponding name will be post-fixed by a _TRAIN for a training parameter, +# or _TEST for a test-specific parameter. +# For example, the number of images during training will be +# IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be +# IMAGES_PER_BATCH_TEST + +# ----------------------------------------------------------------------------- +# Config definition +# ----------------------------------------------------------------------------- + +_C = CN() + +# The version number, to upgrade from old configs to new ones if any +# changes happen. It's recommended to keep a VERSION in your config file. +_C.VERSION = 2 + +_C.MODEL = CN() +_C.MODEL.LOAD_PROPOSALS = False +_C.MODEL.MASK_ON = False +_C.MODEL.KEYPOINT_ON = False +_C.MODEL.DEVICE = "cuda" +_C.MODEL.META_ARCHITECTURE = "GeneralizedRCNN" + +# Path (a file path, or URL like detectron2://.., https://..) to a checkpoint file +# to be loaded to the model. You can find available models in the model zoo. +_C.MODEL.WEIGHTS = "" + +# Values to be used for image normalization (BGR order, since INPUT.FORMAT defaults to BGR). +# To train on images of different number of channels, just set different mean & std. +# Default values are the mean pixel value from ImageNet: [103.53, 116.28, 123.675] +_C.MODEL.PIXEL_MEAN = [103.530, 116.280, 123.675] +# When using pre-trained models in Detectron1 or any MSRA models, +# std has been absorbed into its conv1 weights, so the std needs to be set 1. +# Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std) +_C.MODEL.PIXEL_STD = [1.0, 1.0, 1.0] + + +# ----------------------------------------------------------------------------- +# INPUT +# ----------------------------------------------------------------------------- +_C.INPUT = CN() +# By default, {MIN,MAX}_SIZE options are used in transforms.ResizeShortestEdge. +# Please refer to ResizeShortestEdge for detailed definition. +# Size of the smallest side of the image during training +_C.INPUT.MIN_SIZE_TRAIN = (800,) +# Sample size of smallest side by choice or random selection from range give by +# INPUT.MIN_SIZE_TRAIN +_C.INPUT.MIN_SIZE_TRAIN_SAMPLING = "choice" +# Maximum size of the side of the image during training +_C.INPUT.MAX_SIZE_TRAIN = 1333 +# Size of the smallest side of the image during testing. Set to zero to disable resize in testing. +_C.INPUT.MIN_SIZE_TEST = 800 +# Maximum size of the side of the image during testing +_C.INPUT.MAX_SIZE_TEST = 1333 +# Mode for flipping images used in data augmentation during training +# choose one of ["horizontal, "vertical", "none"] +_C.INPUT.RANDOM_FLIP = "horizontal" + +# `True` if cropping is used for data augmentation during training +_C.INPUT.CROP = CN({"ENABLED": False}) +# Cropping type. See documentation of `detectron2.data.transforms.RandomCrop` for explanation. +_C.INPUT.CROP.TYPE = "relative_range" +# Size of crop in range (0, 1] if CROP.TYPE is "relative" or "relative_range" and in number of +# pixels if CROP.TYPE is "absolute" +_C.INPUT.CROP.SIZE = [0.9, 0.9] + + +# Whether the model needs RGB, YUV, HSV etc. +# Should be one of the modes defined here, as we use PIL to read the image: +# https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes +# with BGR being the one exception. One can set image format to BGR, we will +# internally use RGB for conversion and flip the channels over +_C.INPUT.FORMAT = "BGR" +# The ground truth mask format that the model will use. +# Mask R-CNN supports either "polygon" or "bitmask" as ground truth. +_C.INPUT.MASK_FORMAT = "polygon" # alternative: "bitmask" + + +# ----------------------------------------------------------------------------- +# Dataset +# ----------------------------------------------------------------------------- +_C.DATASETS = CN() +# List of the dataset names for training. Must be registered in DatasetCatalog +# Samples from these datasets will be merged and used as one dataset. +_C.DATASETS.TRAIN = () +# List of the pre-computed proposal files for training, which must be consistent +# with datasets listed in DATASETS.TRAIN. +_C.DATASETS.PROPOSAL_FILES_TRAIN = () +# Number of top scoring precomputed proposals to keep for training +_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN = 2000 +# List of the dataset names for testing. Must be registered in DatasetCatalog +_C.DATASETS.TEST = () +# List of the pre-computed proposal files for test, which must be consistent +# with datasets listed in DATASETS.TEST. +_C.DATASETS.PROPOSAL_FILES_TEST = () +# Number of top scoring precomputed proposals to keep for test +_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST = 1000 + +# ----------------------------------------------------------------------------- +# DataLoader +# ----------------------------------------------------------------------------- +_C.DATALOADER = CN() +# Number of data loading threads +_C.DATALOADER.NUM_WORKERS = 4 +# If True, each batch should contain only images for which the aspect ratio +# is compatible. This groups portrait images together, and landscape images +# are not batched with portrait images. +_C.DATALOADER.ASPECT_RATIO_GROUPING = True +# Options: TrainingSampler, RepeatFactorTrainingSampler +_C.DATALOADER.SAMPLER_TRAIN = "TrainingSampler" +# Repeat threshold for RepeatFactorTrainingSampler +_C.DATALOADER.REPEAT_THRESHOLD = 0.0 +# Tf True, when working on datasets that have instance annotations, the +# training dataloader will filter out images without associated annotations +_C.DATALOADER.FILTER_EMPTY_ANNOTATIONS = True + +# ---------------------------------------------------------------------------- # +# Backbone options +# ---------------------------------------------------------------------------- # +_C.MODEL.BACKBONE = CN() + +_C.MODEL.BACKBONE.NAME = "build_resnet_backbone" +# Freeze the first several stages so they are not trained. +# There are 5 stages in ResNet. The first is a convolution, and the following +# stages are each group of residual blocks. +_C.MODEL.BACKBONE.FREEZE_AT = 2 + + +# ---------------------------------------------------------------------------- # +# FPN options +# ---------------------------------------------------------------------------- # +_C.MODEL.FPN = CN() +# Names of the input feature maps to be used by FPN +# They must have contiguous power of 2 strides +# e.g., ["res2", "res3", "res4", "res5"] +_C.MODEL.FPN.IN_FEATURES = [] +_C.MODEL.FPN.OUT_CHANNELS = 256 + +# Options: "" (no norm), "GN" +_C.MODEL.FPN.NORM = "" + +# Types for fusing the FPN top-down and lateral features. Can be either "sum" or "avg" +_C.MODEL.FPN.FUSE_TYPE = "sum" + + +# ---------------------------------------------------------------------------- # +# Proposal generator options +# ---------------------------------------------------------------------------- # +_C.MODEL.PROPOSAL_GENERATOR = CN() +# Current proposal generators include "RPN", "RRPN" and "PrecomputedProposals" +_C.MODEL.PROPOSAL_GENERATOR.NAME = "RPN" +# Proposal height and width both need to be greater than MIN_SIZE +# (a the scale used during training or inference) +_C.MODEL.PROPOSAL_GENERATOR.MIN_SIZE = 0 + + +# ---------------------------------------------------------------------------- # +# Anchor generator options +# ---------------------------------------------------------------------------- # +_C.MODEL.ANCHOR_GENERATOR = CN() +# The generator can be any name in the ANCHOR_GENERATOR registry +_C.MODEL.ANCHOR_GENERATOR.NAME = "DefaultAnchorGenerator" +# Anchor sizes (i.e. sqrt of area) in absolute pixels w.r.t. the network input. +# Format: list[list[float]]. SIZES[i] specifies the list of sizes to use for +# IN_FEATURES[i]; len(SIZES) must be equal to len(IN_FEATURES) or 1. +# When len(SIZES) == 1, SIZES[0] is used for all IN_FEATURES. +_C.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64, 128, 256, 512]] +# Anchor aspect ratios. For each area given in `SIZES`, anchors with different aspect +# ratios are generated by an anchor generator. +# Format: list[list[float]]. ASPECT_RATIOS[i] specifies the list of aspect ratios (H/W) +# to use for IN_FEATURES[i]; len(ASPECT_RATIOS) == len(IN_FEATURES) must be true, +# or len(ASPECT_RATIOS) == 1 is true and aspect ratio list ASPECT_RATIOS[0] is used +# for all IN_FEATURES. +_C.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.5, 1.0, 2.0]] +# Anchor angles. +# list[list[float]], the angle in degrees, for each input feature map. +# ANGLES[i] specifies the list of angles for IN_FEATURES[i]. +_C.MODEL.ANCHOR_GENERATOR.ANGLES = [[-90, 0, 90]] +# Relative offset between the center of the first anchor and the top-left corner of the image +# Value has to be in [0, 1). Recommend to use 0.5, which means half stride. +# The value is not expected to affect model accuracy. +_C.MODEL.ANCHOR_GENERATOR.OFFSET = 0.0 + +# ---------------------------------------------------------------------------- # +# RPN options +# ---------------------------------------------------------------------------- # +_C.MODEL.RPN = CN() +_C.MODEL.RPN.HEAD_NAME = "StandardRPNHead" # used by RPN_HEAD_REGISTRY + +# Names of the input feature maps to be used by RPN +# e.g., ["p2", "p3", "p4", "p5", "p6"] for FPN +_C.MODEL.RPN.IN_FEATURES = ["res4"] +# Remove RPN anchors that go outside the image by BOUNDARY_THRESH pixels +# Set to -1 or a large value, e.g. 100000, to disable pruning anchors +_C.MODEL.RPN.BOUNDARY_THRESH = -1 +# IOU overlap ratios [BG_IOU_THRESHOLD, FG_IOU_THRESHOLD] +# Minimum overlap required between an anchor and ground-truth box for the +# (anchor, gt box) pair to be a positive example (IoU >= FG_IOU_THRESHOLD +# ==> positive RPN example: 1) +# Maximum overlap allowed between an anchor and ground-truth box for the +# (anchor, gt box) pair to be a negative examples (IoU < BG_IOU_THRESHOLD +# ==> negative RPN example: 0) +# Anchors with overlap in between (BG_IOU_THRESHOLD <= IoU < FG_IOU_THRESHOLD) +# are ignored (-1) +_C.MODEL.RPN.IOU_THRESHOLDS = [0.3, 0.7] +_C.MODEL.RPN.IOU_LABELS = [0, -1, 1] +# Number of regions per image used to train RPN +_C.MODEL.RPN.BATCH_SIZE_PER_IMAGE = 256 +# Target fraction of foreground (positive) examples per RPN minibatch +_C.MODEL.RPN.POSITIVE_FRACTION = 0.5 +# Options are: "smooth_l1", "giou", "diou", "ciou" +_C.MODEL.RPN.BBOX_REG_LOSS_TYPE = "smooth_l1" +_C.MODEL.RPN.BBOX_REG_LOSS_WEIGHT = 1.0 +# Weights on (dx, dy, dw, dh) for normalizing RPN anchor regression targets +_C.MODEL.RPN.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0) +# The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1. +_C.MODEL.RPN.SMOOTH_L1_BETA = 0.0 +_C.MODEL.RPN.LOSS_WEIGHT = 1.0 +# Number of top scoring RPN proposals to keep before applying NMS +# When FPN is used, this is *per FPN level* (not total) +_C.MODEL.RPN.PRE_NMS_TOPK_TRAIN = 12000 +_C.MODEL.RPN.PRE_NMS_TOPK_TEST = 6000 +# Number of top scoring RPN proposals to keep after applying NMS +# When FPN is used, this limit is applied per level and then again to the union +# of proposals from all levels +# NOTE: When FPN is used, the meaning of this config is different from Detectron1. +# It means per-batch topk in Detectron1, but per-image topk here. +# See the "find_top_rpn_proposals" function for details. +_C.MODEL.RPN.POST_NMS_TOPK_TRAIN = 2000 +_C.MODEL.RPN.POST_NMS_TOPK_TEST = 1000 +# NMS threshold used on RPN proposals +_C.MODEL.RPN.NMS_THRESH = 0.7 +# Set this to -1 to use the same number of output channels as input channels. +_C.MODEL.RPN.CONV_DIMS = [-1] + +# ---------------------------------------------------------------------------- # +# ROI HEADS options +# ---------------------------------------------------------------------------- # +_C.MODEL.ROI_HEADS = CN() +_C.MODEL.ROI_HEADS.NAME = "Res5ROIHeads" +# Number of foreground classes +_C.MODEL.ROI_HEADS.NUM_CLASSES = 80 +# Names of the input feature maps to be used by ROI heads +# Currently all heads (box, mask, ...) use the same input feature map list +# e.g., ["p2", "p3", "p4", "p5"] is commonly used for FPN +_C.MODEL.ROI_HEADS.IN_FEATURES = ["res4"] +# IOU overlap ratios [IOU_THRESHOLD] +# Overlap threshold for an RoI to be considered background (if < IOU_THRESHOLD) +# Overlap threshold for an RoI to be considered foreground (if >= IOU_THRESHOLD) +_C.MODEL.ROI_HEADS.IOU_THRESHOLDS = [0.5] +_C.MODEL.ROI_HEADS.IOU_LABELS = [0, 1] +# RoI minibatch size *per image* (number of regions of interest [ROIs]) during training +# Total number of RoIs per training minibatch = +# ROI_HEADS.BATCH_SIZE_PER_IMAGE * SOLVER.IMS_PER_BATCH +# E.g., a common configuration is: 512 * 16 = 8192 +_C.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512 +# Target fraction of RoI minibatch that is labeled foreground (i.e. class > 0) +_C.MODEL.ROI_HEADS.POSITIVE_FRACTION = 0.25 + +# Only used on test mode + +# Minimum score threshold (assuming scores in a [0, 1] range); a value chosen to +# balance obtaining high recall with not having too many low precision +# detections that will slow down inference post processing steps (like NMS) +# A default threshold of 0.0 increases AP by ~0.2-0.3 but significantly slows down +# inference. +_C.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.05 +# Overlap threshold used for non-maximum suppression (suppress boxes with +# IoU >= this threshold) +_C.MODEL.ROI_HEADS.NMS_THRESH_TEST = 0.5 +# If True, augment proposals with ground-truth boxes before sampling proposals to +# train ROI heads. +_C.MODEL.ROI_HEADS.PROPOSAL_APPEND_GT = True + +# ---------------------------------------------------------------------------- # +# Box Head +# ---------------------------------------------------------------------------- # +_C.MODEL.ROI_BOX_HEAD = CN() +# C4 don't use head name option +# Options for non-C4 models: FastRCNNConvFCHead, +_C.MODEL.ROI_BOX_HEAD.NAME = "" +# Options are: "smooth_l1", "giou", "diou", "ciou" +_C.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_TYPE = "smooth_l1" +# The final scaling coefficient on the box regression loss, used to balance the magnitude of its +# gradients with other losses in the model. See also `MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT`. +_C.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_WEIGHT = 1.0 +# Default weights on (dx, dy, dw, dh) for normalizing bbox regression targets +# These are empirically chosen to approximately lead to unit variance targets +_C.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10.0, 10.0, 5.0, 5.0) +# The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1. +_C.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA = 0.0 +_C.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = 14 +_C.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO = 0 +# Type of pooling operation applied to the incoming feature map for each RoI +_C.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignV2" + +_C.MODEL.ROI_BOX_HEAD.NUM_FC = 0 +# Hidden layer dimension for FC layers in the RoI box head +_C.MODEL.ROI_BOX_HEAD.FC_DIM = 1024 +_C.MODEL.ROI_BOX_HEAD.NUM_CONV = 0 +# Channel dimension for Conv layers in the RoI box head +_C.MODEL.ROI_BOX_HEAD.CONV_DIM = 256 +# Normalization method for the convolution layers. +# Options: "" (no norm), "GN", "SyncBN". +_C.MODEL.ROI_BOX_HEAD.NORM = "" +# Whether to use class agnostic for bbox regression +_C.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG = False +# If true, RoI heads use bounding boxes predicted by the box head rather than proposal boxes. +_C.MODEL.ROI_BOX_HEAD.TRAIN_ON_PRED_BOXES = False + +# ---------------------------------------------------------------------------- # +# Cascaded Box Head +# ---------------------------------------------------------------------------- # +_C.MODEL.ROI_BOX_CASCADE_HEAD = CN() +# The number of cascade stages is implicitly defined by the length of the following two configs. +_C.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS = ( + (10.0, 10.0, 5.0, 5.0), + (20.0, 20.0, 10.0, 10.0), + (30.0, 30.0, 15.0, 15.0), +) +_C.MODEL.ROI_BOX_CASCADE_HEAD.IOUS = (0.5, 0.6, 0.7) + + +# ---------------------------------------------------------------------------- # +# Mask Head +# ---------------------------------------------------------------------------- # +_C.MODEL.ROI_MASK_HEAD = CN() +_C.MODEL.ROI_MASK_HEAD.NAME = "MaskRCNNConvUpsampleHead" +_C.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION = 14 +_C.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO = 0 +_C.MODEL.ROI_MASK_HEAD.NUM_CONV = 0 # The number of convs in the mask head +_C.MODEL.ROI_MASK_HEAD.CONV_DIM = 256 +# Normalization method for the convolution layers. +# Options: "" (no norm), "GN", "SyncBN". +_C.MODEL.ROI_MASK_HEAD.NORM = "" +# Whether to use class agnostic for mask prediction +_C.MODEL.ROI_MASK_HEAD.CLS_AGNOSTIC_MASK = False +# Type of pooling operation applied to the incoming feature map for each RoI +_C.MODEL.ROI_MASK_HEAD.POOLER_TYPE = "ROIAlignV2" + + +# ---------------------------------------------------------------------------- # +# Keypoint Head +# ---------------------------------------------------------------------------- # +_C.MODEL.ROI_KEYPOINT_HEAD = CN() +_C.MODEL.ROI_KEYPOINT_HEAD.NAME = "KRCNNConvDeconvUpsampleHead" +_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION = 14 +_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO = 0 +_C.MODEL.ROI_KEYPOINT_HEAD.CONV_DIMS = tuple(512 for _ in range(8)) +_C.MODEL.ROI_KEYPOINT_HEAD.NUM_KEYPOINTS = 17 # 17 is the number of keypoints in COCO. + +# Images with too few (or no) keypoints are excluded from training. +_C.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE = 1 +# Normalize by the total number of visible keypoints in the minibatch if True. +# Otherwise, normalize by the total number of keypoints that could ever exist +# in the minibatch. +# The keypoint softmax loss is only calculated on visible keypoints. +# Since the number of visible keypoints can vary significantly between +# minibatches, this has the effect of up-weighting the importance of +# minibatches with few visible keypoints. (Imagine the extreme case of +# only one visible keypoint versus N: in the case of N, each one +# contributes 1/N to the gradient compared to the single keypoint +# determining the gradient direction). Instead, we can normalize the +# loss by the total number of keypoints, if it were the case that all +# keypoints were visible in a full minibatch. (Returning to the example, +# this means that the one visible keypoint contributes as much as each +# of the N keypoints.) +_C.MODEL.ROI_KEYPOINT_HEAD.NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS = True +# Multi-task loss weight to use for keypoints +# Recommended values: +# - use 1.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is True +# - use 4.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is False +_C.MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT = 1.0 +# Type of pooling operation applied to the incoming feature map for each RoI +_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_TYPE = "ROIAlignV2" + +# ---------------------------------------------------------------------------- # +# Semantic Segmentation Head +# ---------------------------------------------------------------------------- # +_C.MODEL.SEM_SEG_HEAD = CN() +_C.MODEL.SEM_SEG_HEAD.NAME = "SemSegFPNHead" +_C.MODEL.SEM_SEG_HEAD.IN_FEATURES = ["p2", "p3", "p4", "p5"] +# Label in the semantic segmentation ground truth that is ignored, i.e., no loss is calculated for +# the correposnding pixel. +_C.MODEL.SEM_SEG_HEAD.IGNORE_VALUE = 255 +# Number of classes in the semantic segmentation head +_C.MODEL.SEM_SEG_HEAD.NUM_CLASSES = 54 +# Number of channels in the 3x3 convs inside semantic-FPN heads. +_C.MODEL.SEM_SEG_HEAD.CONVS_DIM = 128 +# Outputs from semantic-FPN heads are up-scaled to the COMMON_STRIDE stride. +_C.MODEL.SEM_SEG_HEAD.COMMON_STRIDE = 4 +# Normalization method for the convolution layers. Options: "" (no norm), "GN". +_C.MODEL.SEM_SEG_HEAD.NORM = "GN" +_C.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT = 1.0 + +_C.MODEL.PANOPTIC_FPN = CN() +# Scaling of all losses from instance detection / segmentation head. +_C.MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT = 1.0 + +# options when combining instance & semantic segmentation outputs +_C.MODEL.PANOPTIC_FPN.COMBINE = CN({"ENABLED": True}) # "COMBINE.ENABLED" is deprecated & not used +_C.MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH = 0.5 +_C.MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT = 4096 +_C.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = 0.5 + + +# ---------------------------------------------------------------------------- # +# RetinaNet Head +# ---------------------------------------------------------------------------- # +_C.MODEL.RETINANET = CN() + +# This is the number of foreground classes. +_C.MODEL.RETINANET.NUM_CLASSES = 80 + +_C.MODEL.RETINANET.IN_FEATURES = ["p3", "p4", "p5", "p6", "p7"] + +# Convolutions to use in the cls and bbox tower +# NOTE: this doesn't include the last conv for logits +_C.MODEL.RETINANET.NUM_CONVS = 4 + +# IoU overlap ratio [bg, fg] for labeling anchors. +# Anchors with < bg are labeled negative (0) +# Anchors with >= bg and < fg are ignored (-1) +# Anchors with >= fg are labeled positive (1) +_C.MODEL.RETINANET.IOU_THRESHOLDS = [0.4, 0.5] +_C.MODEL.RETINANET.IOU_LABELS = [0, -1, 1] + +# Prior prob for rare case (i.e. foreground) at the beginning of training. +# This is used to set the bias for the logits layer of the classifier subnet. +# This improves training stability in the case of heavy class imbalance. +_C.MODEL.RETINANET.PRIOR_PROB = 0.01 + +# Inference cls score threshold, only anchors with score > INFERENCE_TH are +# considered for inference (to improve speed) +_C.MODEL.RETINANET.SCORE_THRESH_TEST = 0.05 +# Select topk candidates before NMS +_C.MODEL.RETINANET.TOPK_CANDIDATES_TEST = 1000 +_C.MODEL.RETINANET.NMS_THRESH_TEST = 0.5 + +# Weights on (dx, dy, dw, dh) for normalizing Retinanet anchor regression targets +_C.MODEL.RETINANET.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0) + +# Loss parameters +_C.MODEL.RETINANET.FOCAL_LOSS_GAMMA = 2.0 +_C.MODEL.RETINANET.FOCAL_LOSS_ALPHA = 0.25 +_C.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA = 0.1 +# Options are: "smooth_l1", "giou", "diou", "ciou" +_C.MODEL.RETINANET.BBOX_REG_LOSS_TYPE = "smooth_l1" + +# One of BN, SyncBN, FrozenBN, GN +# Only supports GN until unshared norm is implemented +_C.MODEL.RETINANET.NORM = "" + + +# ---------------------------------------------------------------------------- # +# ResNe[X]t options (ResNets = {ResNet, ResNeXt} +# Note that parts of a resnet may be used for both the backbone and the head +# These options apply to both +# ---------------------------------------------------------------------------- # +_C.MODEL.RESNETS = CN() + +_C.MODEL.RESNETS.DEPTH = 50 +_C.MODEL.RESNETS.OUT_FEATURES = ["res4"] # res4 for C4 backbone, res2..5 for FPN backbone + +# Number of groups to use; 1 ==> ResNet; > 1 ==> ResNeXt +_C.MODEL.RESNETS.NUM_GROUPS = 1 + +# Options: FrozenBN, GN, "SyncBN", "BN" +_C.MODEL.RESNETS.NORM = "FrozenBN" + +# Baseline width of each group. +# Scaling this parameters will scale the width of all bottleneck layers. +_C.MODEL.RESNETS.WIDTH_PER_GROUP = 64 + +# Place the stride 2 conv on the 1x1 filter +# Use True only for the original MSRA ResNet; use False for C2 and Torch models +_C.MODEL.RESNETS.STRIDE_IN_1X1 = True + +# Apply dilation in stage "res5" +_C.MODEL.RESNETS.RES5_DILATION = 1 + +# Output width of res2. Scaling this parameters will scale the width of all 1x1 convs in ResNet +# For R18 and R34, this needs to be set to 64 +_C.MODEL.RESNETS.RES2_OUT_CHANNELS = 256 +_C.MODEL.RESNETS.STEM_OUT_CHANNELS = 64 + +# Apply Deformable Convolution in stages +# Specify if apply deform_conv on Res2, Res3, Res4, Res5 +_C.MODEL.RESNETS.DEFORM_ON_PER_STAGE = [False, False, False, False] +# Use True to use modulated deform_conv (DeformableV2, https://arxiv.org/abs/1811.11168); +# Use False for DeformableV1. +_C.MODEL.RESNETS.DEFORM_MODULATED = False +# Number of groups in deformable conv. +_C.MODEL.RESNETS.DEFORM_NUM_GROUPS = 1 + + +# ---------------------------------------------------------------------------- # +# Solver +# ---------------------------------------------------------------------------- # +_C.SOLVER = CN() + +# Options: WarmupMultiStepLR, WarmupCosineLR. +# See detectron2/solver/build.py for definition. +_C.SOLVER.LR_SCHEDULER_NAME = "WarmupMultiStepLR" + +_C.SOLVER.MAX_ITER = 40000 + +_C.SOLVER.BASE_LR = 0.001 + +_C.SOLVER.MOMENTUM = 0.9 + +_C.SOLVER.NESTEROV = False + +_C.SOLVER.WEIGHT_DECAY = 0.0001 +# The weight decay that's applied to parameters of normalization layers +# (typically the affine transformation) +_C.SOLVER.WEIGHT_DECAY_NORM = 0.0 + +_C.SOLVER.GAMMA = 0.1 +# The iteration number to decrease learning rate by GAMMA. +_C.SOLVER.STEPS = (30000,) + +_C.SOLVER.WARMUP_FACTOR = 1.0 / 1000 +_C.SOLVER.WARMUP_ITERS = 1000 +_C.SOLVER.WARMUP_METHOD = "linear" + +# Save a checkpoint after every this number of iterations +_C.SOLVER.CHECKPOINT_PERIOD = 5000 + +# Number of images per batch across all machines. This is also the number +# of training images per step (i.e. per iteration). If we use 16 GPUs +# and IMS_PER_BATCH = 32, each GPU will see 2 images per batch. +# May be adjusted automatically if REFERENCE_WORLD_SIZE is set. +_C.SOLVER.IMS_PER_BATCH = 16 + +# The reference number of workers (GPUs) this config is meant to train with. +# It takes no effect when set to 0. +# With a non-zero value, it will be used by DefaultTrainer to compute a desired +# per-worker batch size, and then scale the other related configs (total batch size, +# learning rate, etc) to match the per-worker batch size. +# See documentation of `DefaultTrainer.auto_scale_workers` for details: +_C.SOLVER.REFERENCE_WORLD_SIZE = 0 + +# Detectron v1 (and previous detection code) used a 2x higher LR and 0 WD for +# biases. This is not useful (at least for recent models). You should avoid +# changing these and they exist only to reproduce Detectron v1 training if +# desired. +_C.SOLVER.BIAS_LR_FACTOR = 1.0 +_C.SOLVER.WEIGHT_DECAY_BIAS = None # None means following WEIGHT_DECAY + +# Gradient clipping +_C.SOLVER.CLIP_GRADIENTS = CN({"ENABLED": False}) +# Type of gradient clipping, currently 2 values are supported: +# - "value": the absolute values of elements of each gradients are clipped +# - "norm": the norm of the gradient for each parameter is clipped thus +# affecting all elements in the parameter +_C.SOLVER.CLIP_GRADIENTS.CLIP_TYPE = "value" +# Maximum absolute value used for clipping gradients +_C.SOLVER.CLIP_GRADIENTS.CLIP_VALUE = 1.0 +# Floating point number p for L-p norm to be used with the "norm" +# gradient clipping type; for L-inf, please specify .inf +_C.SOLVER.CLIP_GRADIENTS.NORM_TYPE = 2.0 + +# Enable automatic mixed precision for training +# Note that this does not change model's inference behavior. +# To use AMP in inference, run inference under autocast() +_C.SOLVER.AMP = CN({"ENABLED": False}) + +# ---------------------------------------------------------------------------- # +# Specific test options +# ---------------------------------------------------------------------------- # +_C.TEST = CN() +# For end-to-end tests to verify the expected accuracy. +# Each item is [task, metric, value, tolerance] +# e.g.: [['bbox', 'AP', 38.5, 0.2]] +_C.TEST.EXPECTED_RESULTS = [] +# The period (in terms of steps) to evaluate the model during training. +# Set to 0 to disable. +_C.TEST.EVAL_PERIOD = 0 +# The sigmas used to calculate keypoint OKS. See http://cocodataset.org/#keypoints-eval +# When empty, it will use the defaults in COCO. +# Otherwise it should be a list[float] with the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS. +_C.TEST.KEYPOINT_OKS_SIGMAS = [] +# Maximum number of detections to return per image during inference (100 is +# based on the limit established for the COCO dataset). +_C.TEST.DETECTIONS_PER_IMAGE = 100 + +_C.TEST.AUG = CN({"ENABLED": False}) +_C.TEST.AUG.MIN_SIZES = (400, 500, 600, 700, 800, 900, 1000, 1100, 1200) +_C.TEST.AUG.MAX_SIZE = 4000 +_C.TEST.AUG.FLIP = True + +_C.TEST.PRECISE_BN = CN({"ENABLED": False}) +_C.TEST.PRECISE_BN.NUM_ITER = 200 + +# ---------------------------------------------------------------------------- # +# Misc options +# ---------------------------------------------------------------------------- # +# Directory where output files are written +_C.OUTPUT_DIR = "./output" +# Set seed to negative to fully randomize everything. +# Set seed to positive to use a fixed seed. Note that a fixed seed increases +# reproducibility but does not guarantee fully deterministic behavior. +# Disabling all parallelism further increases reproducibility. +_C.SEED = -1 +# Benchmark different cudnn algorithms. +# If input images have very different sizes, this option will have large overhead +# for about 10k iterations. It usually hurts total time, but can benefit for certain models. +# If input images have the same or similar sizes, benchmark is often helpful. +_C.CUDNN_BENCHMARK = False +# The period (in terms of steps) for minibatch visualization at train time. +# Set to 0 to disable. +_C.VIS_PERIOD = 0 + +# global config is for quick hack purposes. +# You can set them in command line or config files, +# and access it with: +# +# from detectron2.config import global_cfg +# print(global_cfg.HACK) +# +# Do not commit any configs into it. +_C.GLOBAL = CN() +_C.GLOBAL.HACK = 1.0 diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/config/instantiate.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/config/instantiate.py new file mode 100644 index 0000000000000000000000000000000000000000..cbb32e19ea518eee84941b20f58d1054e84d1937 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/config/instantiate.py @@ -0,0 +1,82 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import dataclasses +import logging +from collections import abc +from typing import Any + +from detectron2.utils.registry import _convert_target_to_string, locate + +__all__ = ["dump_dataclass", "instantiate"] + + +def dump_dataclass(obj: Any): + """ + Dump a dataclass recursively into a dict that can be later instantiated. + + Args: + obj: a dataclass object + + Returns: + dict + """ + assert dataclasses.is_dataclass(obj) and not isinstance( + obj, type + ), "dump_dataclass() requires an instance of a dataclass." + ret = {"_target_": _convert_target_to_string(type(obj))} + for f in dataclasses.fields(obj): + v = getattr(obj, f.name) + if dataclasses.is_dataclass(v): + v = dump_dataclass(v) + if isinstance(v, (list, tuple)): + v = [dump_dataclass(x) if dataclasses.is_dataclass(x) else x for x in v] + ret[f.name] = v + return ret + + +def instantiate(cfg): + """ + Recursively instantiate objects defined in dictionaries by + "_target_" and arguments. + + Args: + cfg: a dict-like object with "_target_" that defines the caller, and + other keys that define the arguments + + Returns: + object instantiated by cfg + """ + from omegaconf import ListConfig + + if isinstance(cfg, ListConfig): + lst = [instantiate(x) for x in cfg] + return ListConfig(lst, flags={"allow_objects": True}) + if isinstance(cfg, list): + # Specialize for list, because many classes take + # list[objects] as arguments, such as ResNet, DatasetMapper + return [instantiate(x) for x in cfg] + + if isinstance(cfg, abc.Mapping) and "_target_" in cfg: + # conceptually equivalent to hydra.utils.instantiate(cfg) with _convert_=all, + # but faster: https://github.com/facebookresearch/hydra/issues/1200 + cfg = {k: instantiate(v) for k, v in cfg.items()} + cls = cfg.pop("_target_") + cls = instantiate(cls) + + if isinstance(cls, str): + cls_name = cls + cls = locate(cls_name) + assert cls is not None, cls_name + else: + try: + cls_name = cls.__module__ + "." + cls.__qualname__ + except Exception: + # target could be anything, so the above could fail + cls_name = str(cls) + assert callable(cls), f"_target_ {cls} does not define a callable object" + try: + return cls(**cfg) + except TypeError: + logger = logging.getLogger(__name__) + logger.error(f"Error when instantiating {cls_name}!") + raise + return cfg # return as-is if don't know what to do diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/config/lazy.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/config/lazy.py new file mode 100644 index 0000000000000000000000000000000000000000..fa5d86b427ab53ecbe992842cce71d12c5e3a141 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/config/lazy.py @@ -0,0 +1,399 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import ast +import builtins +import importlib +import inspect +import logging +import os +import uuid +from collections import abc +from contextlib import contextmanager +from copy import deepcopy +from dataclasses import is_dataclass +from typing import List, Tuple, Union +import cloudpickle +import yaml +from omegaconf import DictConfig, ListConfig, OmegaConf + +from detectron2.utils.file_io import PathManager +from detectron2.utils.registry import _convert_target_to_string + +__all__ = ["LazyCall", "LazyConfig"] + + +class LazyCall: + """ + Wrap a callable so that when it's called, the call will not be executed, + but returns a dict that describes the call. + + LazyCall object has to be called with only keyword arguments. Positional + arguments are not yet supported. + + Examples: + :: + from detectron2.config import instantiate, LazyCall + + layer_cfg = LazyCall(nn.Conv2d)(in_channels=32, out_channels=32) + layer_cfg.out_channels = 64 # can edit it afterwards + layer = instantiate(layer_cfg) + """ + + def __init__(self, target): + if not (callable(target) or isinstance(target, (str, abc.Mapping))): + raise TypeError( + f"target of LazyCall must be a callable or defines a callable! Got {target}" + ) + self._target = target + + def __call__(self, **kwargs): + if is_dataclass(self._target): + # omegaconf object cannot hold dataclass type + # https://github.com/omry/omegaconf/issues/784 + target = _convert_target_to_string(self._target) + else: + target = self._target + kwargs["_target_"] = target + + return DictConfig(content=kwargs, flags={"allow_objects": True}) + + +def _visit_dict_config(cfg, func): + """ + Apply func recursively to all DictConfig in cfg. + """ + if isinstance(cfg, DictConfig): + func(cfg) + for v in cfg.values(): + _visit_dict_config(v, func) + elif isinstance(cfg, ListConfig): + for v in cfg: + _visit_dict_config(v, func) + + +def _validate_py_syntax(filename): + # see also https://github.com/open-mmlab/mmcv/blob/master/mmcv/utils/config.py + with PathManager.open(filename, "r") as f: + content = f.read() + try: + ast.parse(content) + except SyntaxError as e: + raise SyntaxError(f"Config file {filename} has syntax error!") from e + + +def _cast_to_config(obj): + # if given a dict, return DictConfig instead + if isinstance(obj, dict): + return DictConfig(obj, flags={"allow_objects": True}) + return obj + + +_CFG_PACKAGE_NAME = "detectron2._cfg_loader" +""" +A namespace to put all imported config into. +""" + + +def _random_package_name(filename): + # generate a random package name when loading config files + return _CFG_PACKAGE_NAME + str(uuid.uuid4())[:4] + "." + os.path.basename(filename) + + +@contextmanager +def _patch_import(): + """ + Enhance relative import statements in config files, so that they: + 1. locate files purely based on relative location, regardless of packages. + e.g. you can import file without having __init__ + 2. do not cache modules globally; modifications of module states has no side effect + 3. support other storage system through PathManager + 4. imported dict are turned into omegaconf.DictConfig automatically + """ + old_import = builtins.__import__ + + def find_relative_file(original_file, relative_import_path, level): + cur_file = os.path.dirname(original_file) + for _ in range(level - 1): + cur_file = os.path.dirname(cur_file) + cur_name = relative_import_path.lstrip(".") + for part in cur_name.split("."): + cur_file = os.path.join(cur_file, part) + # NOTE: directory import is not handled. Because then it's unclear + # if such import should produce python module or DictConfig. This can + # be discussed further if needed. + if not cur_file.endswith(".py"): + cur_file += ".py" + if not PathManager.isfile(cur_file): + raise ImportError( + f"Cannot import name {relative_import_path} from " + f"{original_file}: {cur_file} has to exist." + ) + return cur_file + + def new_import(name, globals=None, locals=None, fromlist=(), level=0): + if ( + # Only deal with relative imports inside config files + level != 0 + and globals is not None + and (globals.get("__package__", "") or "").startswith(_CFG_PACKAGE_NAME) + ): + cur_file = find_relative_file(globals["__file__"], name, level) + _validate_py_syntax(cur_file) + spec = importlib.machinery.ModuleSpec( + _random_package_name(cur_file), None, origin=cur_file + ) + module = importlib.util.module_from_spec(spec) + module.__file__ = cur_file + with PathManager.open(cur_file) as f: + content = f.read() + exec(compile(content, cur_file, "exec"), module.__dict__) + for name in fromlist: # turn imported dict into DictConfig automatically + val = _cast_to_config(module.__dict__[name]) + module.__dict__[name] = val + return module + return old_import(name, globals, locals, fromlist=fromlist, level=level) + + builtins.__import__ = new_import + yield new_import + builtins.__import__ = old_import + + +class LazyConfig: + """ + Provide methods to save, load, and overrides an omegaconf config object + which may contain definition of lazily-constructed objects. + """ + + @staticmethod + def load_rel(filename: str, keys: Union[None, str, Tuple[str, ...]] = None): + """ + Similar to :meth:`load()`, but load path relative to the caller's + source file. + + This has the same functionality as a relative import, except that this method + accepts filename as a string, so more characters are allowed in the filename. + """ + caller_frame = inspect.stack()[1] + caller_fname = caller_frame[0].f_code.co_filename + assert caller_fname != "", "load_rel Unable to find caller" + caller_dir = os.path.dirname(caller_fname) + filename = os.path.join(caller_dir, filename) + return LazyConfig.load(filename, keys) + + @staticmethod + def load(filename: str, keys: Union[None, str, Tuple[str, ...]] = None): + """ + Load a config file. + + Args: + filename: absolute path or relative path w.r.t. the current working directory + keys: keys to load and return. If not given, return all keys + (whose values are config objects) in a dict. + """ + has_keys = keys is not None + filename = filename.replace("/./", "/") # redundant + if os.path.splitext(filename)[1] not in [".py", ".yaml", ".yml"]: + raise ValueError(f"Config file {filename} has to be a python or yaml file.") + if filename.endswith(".py"): + _validate_py_syntax(filename) + + with _patch_import(): + # Record the filename + module_namespace = { + "__file__": filename, + "__package__": _random_package_name(filename), + } + with PathManager.open(filename) as f: + content = f.read() + # Compile first with filename to: + # 1. make filename appears in stacktrace + # 2. make load_rel able to find its parent's (possibly remote) location + exec(compile(content, filename, "exec"), module_namespace) + + ret = module_namespace + else: + with PathManager.open(filename) as f: + obj = yaml.unsafe_load(f) + ret = OmegaConf.create(obj, flags={"allow_objects": True}) + + if has_keys: + if isinstance(keys, str): + return _cast_to_config(ret[keys]) + else: + return tuple(_cast_to_config(ret[a]) for a in keys) + else: + if filename.endswith(".py"): + # when not specified, only load those that are config objects + ret = DictConfig( + { + name: _cast_to_config(value) + for name, value in ret.items() + if isinstance(value, (DictConfig, ListConfig, dict)) + and not name.startswith("_") + }, + flags={"allow_objects": True}, + ) + return ret + + @staticmethod + def save(cfg, filename: str): + """ + Save a config object to a yaml file. + Note that when the config dictionary contains complex objects (e.g. lambda), + it can't be saved to yaml. In that case we will print an error and + attempt to save to a pkl file instead. + + Args: + cfg: an omegaconf config object + filename: yaml file name to save the config file + """ + logger = logging.getLogger(__name__) + try: + cfg = deepcopy(cfg) + except Exception: + pass + else: + # if it's deep-copyable, then... + def _replace_type_by_name(x): + if "_target_" in x and callable(x._target_): + try: + x._target_ = _convert_target_to_string(x._target_) + except AttributeError: + pass + + # not necessary, but makes yaml looks nicer + _visit_dict_config(cfg, _replace_type_by_name) + + save_pkl = False + try: + dict = OmegaConf.to_container(cfg, resolve=False) + dumped = yaml.dump(dict, default_flow_style=None, allow_unicode=True, width=9999) + with PathManager.open(filename, "w") as f: + f.write(dumped) + + try: + _ = yaml.unsafe_load(dumped) # test that it is loadable + except Exception: + logger.warning( + "The config contains objects that cannot serialize to a valid yaml. " + f"{filename} is human-readable but cannot be loaded." + ) + save_pkl = True + except Exception: + logger.exception("Unable to serialize the config to yaml. Error:") + save_pkl = True + + if save_pkl: + new_filename = filename + ".pkl" + try: + # retry by pickle + with PathManager.open(new_filename, "wb") as f: + cloudpickle.dump(cfg, f) + logger.warning(f"Config is saved using cloudpickle at {new_filename}.") + except Exception: + pass + + @staticmethod + def apply_overrides(cfg, overrides: List[str]): + """ + In-place override contents of cfg. + + Args: + cfg: an omegaconf config object + overrides: list of strings in the format of "a=b" to override configs. + See https://hydra.cc/docs/next/advanced/override_grammar/basic/ + for syntax. + + Returns: + the cfg object + """ + + def safe_update(cfg, key, value): + parts = key.split(".") + for idx in range(1, len(parts)): + prefix = ".".join(parts[:idx]) + v = OmegaConf.select(cfg, prefix, default=None) + if v is None: + break + if not OmegaConf.is_config(v): + raise KeyError( + f"Trying to update key {key}, but {prefix} " + f"is not a config, but has type {type(v)}." + ) + OmegaConf.update(cfg, key, value, merge=True) + + from hydra.core.override_parser.overrides_parser import OverridesParser + + parser = OverridesParser.create() + overrides = parser.parse_overrides(overrides) + for o in overrides: + key = o.key_or_group + value = o.value() + if o.is_delete(): + # TODO support this + raise NotImplementedError("deletion is not yet a supported override") + safe_update(cfg, key, value) + return cfg + + @staticmethod + def to_py(cfg, prefix: str = "cfg."): + """ + Try to convert a config object into Python-like psuedo code. + + Note that perfect conversion is not always possible. So the returned + results are mainly meant to be human-readable, and not meant to be executed. + + Args: + cfg: an omegaconf config object + prefix: root name for the resulting code (default: "cfg.") + + + Returns: + str of formatted Python code + """ + import black + + cfg = OmegaConf.to_container(cfg, resolve=True) + + def _to_str(obj, prefix=None, inside_call=False): + if prefix is None: + prefix = [] + if isinstance(obj, abc.Mapping) and "_target_" in obj: + # Dict representing a function call + target = _convert_target_to_string(obj.pop("_target_")) + args = [] + for k, v in sorted(obj.items()): + args.append(f"{k}={_to_str(v, inside_call=True)}") + args = ", ".join(args) + call = f"{target}({args})" + return "".join(prefix) + call + elif isinstance(obj, abc.Mapping) and not inside_call: + # Dict that is not inside a call is a list of top-level config objects that we + # render as one object per line with dot separated prefixes + key_list = [] + for k, v in sorted(obj.items()): + if isinstance(v, abc.Mapping) and "_target_" not in v: + key_list.append(_to_str(v, prefix=prefix + [k + "."])) + else: + key = "".join(prefix) + k + key_list.append(f"{key}={_to_str(v)}") + return "\n".join(key_list) + elif isinstance(obj, abc.Mapping): + # Dict that is inside a call is rendered as a regular dict + return ( + "{" + + ",".join( + f"{repr(k)}: {_to_str(v, inside_call=inside_call)}" + for k, v in sorted(obj.items()) + ) + + "}" + ) + elif isinstance(obj, list): + return "[" + ",".join(_to_str(x, inside_call=inside_call) for x in obj) + "]" + else: + return repr(obj) + + py_str = _to_str(cfg, prefix=[prefix]) + try: + return black.format_str(py_str, mode=black.Mode()) + except black.InvalidInput: + return py_str diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..259f669b78bd05815cb8d3351fd6c5fc9a1b85a1 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/__init__.py @@ -0,0 +1,19 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from . import transforms # isort:skip + +from .build import ( + build_batch_data_loader, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, + load_proposals_into_dataset, + print_instances_class_histogram, +) +from .catalog import DatasetCatalog, MetadataCatalog, Metadata +from .common import DatasetFromList, MapDataset, ToIterableDataset +from .dataset_mapper import DatasetMapper + +# ensure the builtin datasets are registered +from . import datasets, samplers # isort:skip + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/benchmark.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..ac2f372a4b111ad40b8e720adea208608271bab6 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/benchmark.py @@ -0,0 +1,225 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import numpy as np +from itertools import count +from typing import List, Tuple +import torch +import tqdm +from fvcore.common.timer import Timer + +from detectron2.utils import comm + +from .build import build_batch_data_loader +from .common import DatasetFromList, MapDataset +from .samplers import TrainingSampler + +logger = logging.getLogger(__name__) + + +class _EmptyMapDataset(torch.utils.data.Dataset): + """ + Map anything to emptiness. + """ + + def __init__(self, dataset): + self.ds = dataset + + def __len__(self): + return len(self.ds) + + def __getitem__(self, idx): + _ = self.ds[idx] + return [0] + + +def iter_benchmark( + iterator, num_iter: int, warmup: int = 5, max_time_seconds: float = 60 +) -> Tuple[float, List[float]]: + """ + Benchmark an iterator/iterable for `num_iter` iterations with an extra + `warmup` iterations of warmup. + End early if `max_time_seconds` time is spent on iterations. + + Returns: + float: average time (seconds) per iteration + list[float]: time spent on each iteration. Sometimes useful for further analysis. + """ + num_iter, warmup = int(num_iter), int(warmup) + + iterator = iter(iterator) + for _ in range(warmup): + next(iterator) + timer = Timer() + all_times = [] + for curr_iter in tqdm.trange(num_iter): + start = timer.seconds() + if start > max_time_seconds: + num_iter = curr_iter + break + next(iterator) + all_times.append(timer.seconds() - start) + avg = timer.seconds() / num_iter + return avg, all_times + + +class DataLoaderBenchmark: + """ + Some common benchmarks that help understand perf bottleneck of a standard dataloader + made of dataset, mapper and sampler. + """ + + def __init__( + self, + dataset, + *, + mapper, + sampler=None, + total_batch_size, + num_workers=0, + max_time_seconds: int = 90, + ): + """ + Args: + max_time_seconds (int): maximum time to spent for each benchmark + other args: same as in `build.py:build_detection_train_loader` + """ + if isinstance(dataset, list): + dataset = DatasetFromList(dataset, copy=False, serialize=True) + if sampler is None: + sampler = TrainingSampler(len(dataset)) + + self.dataset = dataset + self.mapper = mapper + self.sampler = sampler + self.total_batch_size = total_batch_size + self.num_workers = num_workers + self.per_gpu_batch_size = self.total_batch_size // comm.get_world_size() + + self.max_time_seconds = max_time_seconds + + def _benchmark(self, iterator, num_iter, warmup, msg=None): + avg, all_times = iter_benchmark(iterator, num_iter, warmup, self.max_time_seconds) + if msg is not None: + self._log_time(msg, avg, all_times) + return avg, all_times + + def _log_time(self, msg, avg, all_times, distributed=False): + percentiles = [np.percentile(all_times, k, interpolation="nearest") for k in [1, 5, 95, 99]] + if not distributed: + logger.info( + f"{msg}: avg={1.0/avg:.1f} it/s, " + f"p1={percentiles[0]:.2g}s, p5={percentiles[1]:.2g}s, " + f"p95={percentiles[2]:.2g}s, p99={percentiles[3]:.2g}s." + ) + return + avg_per_gpu = comm.all_gather(avg) + percentiles_per_gpu = comm.all_gather(percentiles) + if comm.get_rank() > 0: + return + for idx, avg, percentiles in zip(count(), avg_per_gpu, percentiles_per_gpu): + logger.info( + f"GPU{idx} {msg}: avg={1.0/avg:.1f} it/s, " + f"p1={percentiles[0]:.2g}s, p5={percentiles[1]:.2g}s, " + f"p95={percentiles[2]:.2g}s, p99={percentiles[3]:.2g}s." + ) + + def benchmark_dataset(self, num_iter, warmup=5): + """ + Benchmark the speed of taking raw samples from the dataset. + """ + + def loader(): + while True: + for k in self.sampler: + yield self.dataset[k] + + self._benchmark(loader(), num_iter, warmup, "Dataset Alone") + + def benchmark_mapper(self, num_iter, warmup=5): + """ + Benchmark the speed of taking raw samples from the dataset and map + them in a single process. + """ + + def loader(): + while True: + for k in self.sampler: + yield self.mapper(self.dataset[k]) + + self._benchmark(loader(), num_iter, warmup, "Single Process Mapper (sec/sample)") + + def benchmark_workers(self, num_iter, warmup=10): + """ + Benchmark the dataloader by tuning num_workers to [0, 1, self.num_workers]. + """ + candidates = [0, 1] + if self.num_workers not in candidates: + candidates.append(self.num_workers) + + dataset = MapDataset(self.dataset, self.mapper) + for n in candidates: + loader = build_batch_data_loader( + dataset, + self.sampler, + self.total_batch_size, + num_workers=n, + ) + self._benchmark( + iter(loader), + num_iter * max(n, 1), + warmup * max(n, 1), + f"DataLoader ({n} workers, bs={self.per_gpu_batch_size})", + ) + del loader + + def benchmark_IPC(self, num_iter, warmup=10): + """ + Benchmark the dataloader where each worker outputs nothing. This + eliminates the IPC overhead compared to the regular dataloader. + + PyTorch multiprocessing's IPC only optimizes for torch tensors. + Large numpy arrays or other data structure may incur large IPC overhead. + """ + n = self.num_workers + dataset = _EmptyMapDataset(MapDataset(self.dataset, self.mapper)) + loader = build_batch_data_loader( + dataset, self.sampler, self.total_batch_size, num_workers=n + ) + self._benchmark( + iter(loader), + num_iter * max(n, 1), + warmup * max(n, 1), + f"DataLoader ({n} workers, bs={self.per_gpu_batch_size}) w/o comm", + ) + + def benchmark_distributed(self, num_iter, warmup=10): + """ + Benchmark the dataloader in each distributed worker, and log results of + all workers. This helps understand the final performance as well as + the variances among workers. + + It also prints startup time (first iter) of the dataloader. + """ + gpu = comm.get_world_size() + dataset = MapDataset(self.dataset, self.mapper) + n = self.num_workers + loader = build_batch_data_loader( + dataset, self.sampler, self.total_batch_size, num_workers=n + ) + + timer = Timer() + loader = iter(loader) + next(loader) + startup_time = timer.seconds() + logger.info("Dataloader startup time: {:.2f} seconds".format(startup_time)) + + comm.synchronize() + + avg, all_times = self._benchmark(loader, num_iter * max(n, 1), warmup * max(n, 1)) + del loader + self._log_time( + f"DataLoader ({gpu} GPUs x {n} workers, total bs={self.total_batch_size})", + avg, + all_times, + True, + ) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/build.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/build.py new file mode 100644 index 0000000000000000000000000000000000000000..a31369d1693f86154a7a9249fc043d49f3e9f390 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/build.py @@ -0,0 +1,542 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import itertools +import logging +import numpy as np +import operator +import pickle +from typing import Any, Callable, Dict, List, Optional, Union +import torch +import torch.utils.data as torchdata +from tabulate import tabulate +from termcolor import colored + +from detectron2.config import configurable +from detectron2.structures import BoxMode +from detectron2.utils.comm import get_world_size +from detectron2.utils.env import seed_all_rng +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import _log_api_usage, log_first_n + +from .catalog import DatasetCatalog, MetadataCatalog +from .common import AspectRatioGroupedDataset, DatasetFromList, MapDataset, ToIterableDataset +from .dataset_mapper import DatasetMapper +from .detection_utils import check_metadata_consistency +from .samplers import ( + InferenceSampler, + RandomSubsetTrainingSampler, + RepeatFactorTrainingSampler, + TrainingSampler, +) + +""" +This file contains the default logic to build a dataloader for training or testing. +""" + +__all__ = [ + "build_batch_data_loader", + "build_detection_train_loader", + "build_detection_test_loader", + "get_detection_dataset_dicts", + "load_proposals_into_dataset", + "print_instances_class_histogram", +] + + +def filter_images_with_only_crowd_annotations(dataset_dicts): + """ + Filter out images with none annotations or only crowd annotations + (i.e., images without non-crowd annotations). + A common training-time preprocessing on COCO dataset. + + Args: + dataset_dicts (list[dict]): annotations in Detectron2 Dataset format. + + Returns: + list[dict]: the same format, but filtered. + """ + num_before = len(dataset_dicts) + + def valid(anns): + for ann in anns: + if ann.get("iscrowd", 0) == 0: + return True + return False + + dataset_dicts = [x for x in dataset_dicts if valid(x["annotations"])] + num_after = len(dataset_dicts) + logger = logging.getLogger(__name__) + logger.info( + "Removed {} images with no usable annotations. {} images left.".format( + num_before - num_after, num_after + ) + ) + return dataset_dicts + + +def filter_images_with_few_keypoints(dataset_dicts, min_keypoints_per_image): + """ + Filter out images with too few number of keypoints. + + Args: + dataset_dicts (list[dict]): annotations in Detectron2 Dataset format. + + Returns: + list[dict]: the same format as dataset_dicts, but filtered. + """ + num_before = len(dataset_dicts) + + def visible_keypoints_in_image(dic): + # Each keypoints field has the format [x1, y1, v1, ...], where v is visibility + annotations = dic["annotations"] + return sum( + (np.array(ann["keypoints"][2::3]) > 0).sum() + for ann in annotations + if "keypoints" in ann + ) + + dataset_dicts = [ + x for x in dataset_dicts if visible_keypoints_in_image(x) >= min_keypoints_per_image + ] + num_after = len(dataset_dicts) + logger = logging.getLogger(__name__) + logger.info( + "Removed {} images with fewer than {} keypoints.".format( + num_before - num_after, min_keypoints_per_image + ) + ) + return dataset_dicts + + +def load_proposals_into_dataset(dataset_dicts, proposal_file): + """ + Load precomputed object proposals into the dataset. + + The proposal file should be a pickled dict with the following keys: + + - "ids": list[int] or list[str], the image ids + - "boxes": list[np.ndarray], each is an Nx4 array of boxes corresponding to the image id + - "objectness_logits": list[np.ndarray], each is an N sized array of objectness scores + corresponding to the boxes. + - "bbox_mode": the BoxMode of the boxes array. Defaults to ``BoxMode.XYXY_ABS``. + + Args: + dataset_dicts (list[dict]): annotations in Detectron2 Dataset format. + proposal_file (str): file path of pre-computed proposals, in pkl format. + + Returns: + list[dict]: the same format as dataset_dicts, but added proposal field. + """ + logger = logging.getLogger(__name__) + logger.info("Loading proposals from: {}".format(proposal_file)) + + with PathManager.open(proposal_file, "rb") as f: + proposals = pickle.load(f, encoding="latin1") + + # Rename the key names in D1 proposal files + rename_keys = {"indexes": "ids", "scores": "objectness_logits"} + for key in rename_keys: + if key in proposals: + proposals[rename_keys[key]] = proposals.pop(key) + + # Fetch the indexes of all proposals that are in the dataset + # Convert image_id to str since they could be int. + img_ids = set({str(record["image_id"]) for record in dataset_dicts}) + id_to_index = {str(id): i for i, id in enumerate(proposals["ids"]) if str(id) in img_ids} + + # Assuming default bbox_mode of precomputed proposals are 'XYXY_ABS' + bbox_mode = BoxMode(proposals["bbox_mode"]) if "bbox_mode" in proposals else BoxMode.XYXY_ABS + + for record in dataset_dicts: + # Get the index of the proposal + i = id_to_index[str(record["image_id"])] + + boxes = proposals["boxes"][i] + objectness_logits = proposals["objectness_logits"][i] + # Sort the proposals in descending order of the scores + inds = objectness_logits.argsort()[::-1] + record["proposal_boxes"] = boxes[inds] + record["proposal_objectness_logits"] = objectness_logits[inds] + record["proposal_bbox_mode"] = bbox_mode + + return dataset_dicts + + +def print_instances_class_histogram(dataset_dicts, class_names): + """ + Args: + dataset_dicts (list[dict]): list of dataset dicts. + class_names (list[str]): list of class names (zero-indexed). + """ + num_classes = len(class_names) + hist_bins = np.arange(num_classes + 1) + histogram = np.zeros((num_classes,), dtype=np.int) + for entry in dataset_dicts: + annos = entry["annotations"] + classes = np.asarray( + [x["category_id"] for x in annos if not x.get("iscrowd", 0)], dtype=np.int + ) + if len(classes): + assert classes.min() >= 0, f"Got an invalid category_id={classes.min()}" + assert ( + classes.max() < num_classes + ), f"Got an invalid category_id={classes.max()} for a dataset of {num_classes} classes" + histogram += np.histogram(classes, bins=hist_bins)[0] + + N_COLS = min(6, len(class_names) * 2) + + def short_name(x): + # make long class names shorter. useful for lvis + if len(x) > 13: + return x[:11] + ".." + return x + + data = list( + itertools.chain(*[[short_name(class_names[i]), int(v)] for i, v in enumerate(histogram)]) + ) + total_num_instances = sum(data[1::2]) + data.extend([None] * (N_COLS - (len(data) % N_COLS))) + if num_classes > 1: + data.extend(["total", total_num_instances]) + data = itertools.zip_longest(*[data[i::N_COLS] for i in range(N_COLS)]) + table = tabulate( + data, + headers=["category", "#instances"] * (N_COLS // 2), + tablefmt="pipe", + numalign="left", + stralign="center", + ) + log_first_n( + logging.INFO, + "Distribution of instances among all {} categories:\n".format(num_classes) + + colored(table, "cyan"), + key="message", + ) + + +def get_detection_dataset_dicts( + names, + filter_empty=True, + min_keypoints=0, + proposal_files=None, + check_consistency=True, +): + """ + Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation. + + Args: + names (str or list[str]): a dataset name or a list of dataset names + filter_empty (bool): whether to filter out images without instance annotations + min_keypoints (int): filter out images with fewer keypoints than + `min_keypoints`. Set to 0 to do nothing. + proposal_files (list[str]): if given, a list of object proposal files + that match each dataset in `names`. + check_consistency (bool): whether to check if datasets have consistent metadata. + + Returns: + list[dict]: a list of dicts following the standard dataset dict format. + """ + if isinstance(names, str): + names = [names] + assert len(names), names + dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in names] + for dataset_name, dicts in zip(names, dataset_dicts): + assert len(dicts), "Dataset '{}' is empty!".format(dataset_name) + + if proposal_files is not None: + assert len(names) == len(proposal_files) + # load precomputed proposals from proposal files + dataset_dicts = [ + load_proposals_into_dataset(dataset_i_dicts, proposal_file) + for dataset_i_dicts, proposal_file in zip(dataset_dicts, proposal_files) + ] + + if isinstance(dataset_dicts[0], torchdata.Dataset): + return torchdata.ConcatDataset(dataset_dicts) + + dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts)) + + has_instances = "annotations" in dataset_dicts[0] + if filter_empty and has_instances: + dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts) + if min_keypoints > 0 and has_instances: + dataset_dicts = filter_images_with_few_keypoints(dataset_dicts, min_keypoints) + + if check_consistency and has_instances: + try: + class_names = MetadataCatalog.get(names[0]).thing_classes + check_metadata_consistency("thing_classes", names) + print_instances_class_histogram(dataset_dicts, class_names) + except AttributeError: # class names are not available for this dataset + pass + + assert len(dataset_dicts), "No valid data found in {}.".format(",".join(names)) + return dataset_dicts + + +def build_batch_data_loader( + dataset, + sampler, + total_batch_size, + *, + aspect_ratio_grouping=False, + num_workers=0, + collate_fn=None, +): + """ + Build a batched dataloader. The main differences from `torch.utils.data.DataLoader` are: + 1. support aspect ratio grouping options + 2. use no "batch collation", because this is common for detection training + + Args: + dataset (torch.utils.data.Dataset): a pytorch map-style or iterable dataset. + sampler (torch.utils.data.sampler.Sampler or None): a sampler that produces indices. + Must be provided iff. ``dataset`` is a map-style dataset. + total_batch_size, aspect_ratio_grouping, num_workers, collate_fn: see + :func:`build_detection_train_loader`. + + Returns: + iterable[list]. Length of each list is the batch size of the current + GPU. Each element in the list comes from the dataset. + """ + world_size = get_world_size() + assert ( + total_batch_size > 0 and total_batch_size % world_size == 0 + ), "Total batch size ({}) must be divisible by the number of gpus ({}).".format( + total_batch_size, world_size + ) + batch_size = total_batch_size // world_size + + if isinstance(dataset, torchdata.IterableDataset): + assert sampler is None, "sampler must be None if dataset is IterableDataset" + else: + dataset = ToIterableDataset(dataset, sampler) + + if aspect_ratio_grouping: + data_loader = torchdata.DataLoader( + dataset, + num_workers=num_workers, + collate_fn=operator.itemgetter(0), # don't batch, but yield individual elements + worker_init_fn=worker_init_reset_seed, + ) # yield individual mapped dict + data_loader = AspectRatioGroupedDataset(data_loader, batch_size) + if collate_fn is None: + return data_loader + return MapDataset(data_loader, collate_fn) + else: + return torchdata.DataLoader( + dataset, + batch_size=batch_size, + drop_last=True, + num_workers=num_workers, + collate_fn=trivial_batch_collator if collate_fn is None else collate_fn, + worker_init_fn=worker_init_reset_seed, + ) + + +def _train_loader_from_config(cfg, mapper=None, *, dataset=None, sampler=None): + if dataset is None: + dataset = get_detection_dataset_dicts( + cfg.DATASETS.TRAIN, + filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS, + min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE + if cfg.MODEL.KEYPOINT_ON + else 0, + proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None, + ) + _log_api_usage("dataset." + cfg.DATASETS.TRAIN[0]) + + if mapper is None: + mapper = DatasetMapper(cfg, True) + + if sampler is None: + sampler_name = cfg.DATALOADER.SAMPLER_TRAIN + logger = logging.getLogger(__name__) + logger.info("Using training sampler {}".format(sampler_name)) + if sampler_name == "TrainingSampler": + sampler = TrainingSampler(len(dataset)) + elif sampler_name == "RepeatFactorTrainingSampler": + repeat_factors = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset, cfg.DATALOADER.REPEAT_THRESHOLD + ) + sampler = RepeatFactorTrainingSampler(repeat_factors) + elif sampler_name == "RandomSubsetTrainingSampler": + sampler = RandomSubsetTrainingSampler(len(dataset), cfg.DATALOADER.RANDOM_SUBSET_RATIO) + else: + raise ValueError("Unknown training sampler: {}".format(sampler_name)) + + return { + "dataset": dataset, + "sampler": sampler, + "mapper": mapper, + "total_batch_size": cfg.SOLVER.IMS_PER_BATCH, + "aspect_ratio_grouping": cfg.DATALOADER.ASPECT_RATIO_GROUPING, + "num_workers": cfg.DATALOADER.NUM_WORKERS, + } + + +@configurable(from_config=_train_loader_from_config) +def build_detection_train_loader( + dataset, + *, + mapper, + sampler=None, + total_batch_size, + aspect_ratio_grouping=True, + num_workers=0, + collate_fn=None, +): + """ + Build a dataloader for object detection with some default features. + + Args: + dataset (list or torch.utils.data.Dataset): a list of dataset dicts, + or a pytorch dataset (either map-style or iterable). It can be obtained + by using :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`. + mapper (callable): a callable which takes a sample (dict) from dataset and + returns the format to be consumed by the model. + When using cfg, the default choice is ``DatasetMapper(cfg, is_train=True)``. + sampler (torch.utils.data.sampler.Sampler or None): a sampler that produces + indices to be applied on ``dataset``. + If ``dataset`` is map-style, the default sampler is a :class:`TrainingSampler`, + which coordinates an infinite random shuffle sequence across all workers. + Sampler must be None if ``dataset`` is iterable. + total_batch_size (int): total batch size across all workers. + aspect_ratio_grouping (bool): whether to group images with similar + aspect ratio for efficiency. When enabled, it requires each + element in dataset be a dict with keys "width" and "height". + num_workers (int): number of parallel data loading workers + collate_fn: a function that determines how to do batching, same as the argument of + `torch.utils.data.DataLoader`. Defaults to do no collation and return a list of + data. No collation is OK for small batch size and simple data structures. + If your batch size is large and each sample contains too many small tensors, + it's more efficient to collate them in data loader. + + Returns: + torch.utils.data.DataLoader: + a dataloader. Each output from it is a ``list[mapped_element]`` of length + ``total_batch_size / num_workers``, where ``mapped_element`` is produced + by the ``mapper``. + """ + if isinstance(dataset, list): + dataset = DatasetFromList(dataset, copy=False) + if mapper is not None: + dataset = MapDataset(dataset, mapper) + + if isinstance(dataset, torchdata.IterableDataset): + assert sampler is None, "sampler must be None if dataset is IterableDataset" + else: + if sampler is None: + sampler = TrainingSampler(len(dataset)) + assert isinstance(sampler, torchdata.Sampler), f"Expect a Sampler but got {type(sampler)}" + return build_batch_data_loader( + dataset, + sampler, + total_batch_size, + aspect_ratio_grouping=aspect_ratio_grouping, + num_workers=num_workers, + collate_fn=collate_fn, + ) + + +def _test_loader_from_config(cfg, dataset_name, mapper=None): + """ + Uses the given `dataset_name` argument (instead of the names in cfg), because the + standard practice is to evaluate each test set individually (not combining them). + """ + if isinstance(dataset_name, str): + dataset_name = [dataset_name] + + dataset = get_detection_dataset_dicts( + dataset_name, + filter_empty=False, + proposal_files=[ + cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(x)] for x in dataset_name + ] + if cfg.MODEL.LOAD_PROPOSALS + else None, + ) + if mapper is None: + mapper = DatasetMapper(cfg, False) + return { + "dataset": dataset, + "mapper": mapper, + "num_workers": cfg.DATALOADER.NUM_WORKERS, + "sampler": InferenceSampler(len(dataset)), + } + + +@configurable(from_config=_test_loader_from_config) +def build_detection_test_loader( + dataset: Union[List[Any], torchdata.Dataset], + *, + mapper: Callable[[Dict[str, Any]], Any], + sampler: Optional[torchdata.Sampler] = None, + batch_size: int = 1, + num_workers: int = 0, + collate_fn: Optional[Callable[[List[Any]], Any]] = None, +) -> torchdata.DataLoader: + """ + Similar to `build_detection_train_loader`, with default batch size = 1, + and sampler = :class:`InferenceSampler`. This sampler coordinates all workers + to produce the exact set of all samples. + + Args: + dataset: a list of dataset dicts, + or a pytorch dataset (either map-style or iterable). They can be obtained + by using :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`. + mapper: a callable which takes a sample (dict) from dataset + and returns the format to be consumed by the model. + When using cfg, the default choice is ``DatasetMapper(cfg, is_train=False)``. + sampler: a sampler that produces + indices to be applied on ``dataset``. Default to :class:`InferenceSampler`, + which splits the dataset across all workers. Sampler must be None + if `dataset` is iterable. + batch_size: the batch size of the data loader to be created. + Default to 1 image per worker since this is the standard when reporting + inference time in papers. + num_workers: number of parallel data loading workers + collate_fn: same as the argument of `torch.utils.data.DataLoader`. + Defaults to do no collation and return a list of data. + + Returns: + DataLoader: a torch DataLoader, that loads the given detection + dataset, with test-time transformation and batching. + + Examples: + :: + data_loader = build_detection_test_loader( + DatasetRegistry.get("my_test"), + mapper=DatasetMapper(...)) + + # or, instantiate with a CfgNode: + data_loader = build_detection_test_loader(cfg, "my_test") + """ + if isinstance(dataset, list): + dataset = DatasetFromList(dataset, copy=False) + if mapper is not None: + dataset = MapDataset(dataset, mapper) + if isinstance(dataset, torchdata.IterableDataset): + assert sampler is None, "sampler must be None if dataset is IterableDataset" + else: + if sampler is None: + sampler = InferenceSampler(len(dataset)) + return torchdata.DataLoader( + dataset, + batch_size=batch_size, + sampler=sampler, + drop_last=False, + num_workers=num_workers, + collate_fn=trivial_batch_collator if collate_fn is None else collate_fn, + ) + + +def trivial_batch_collator(batch): + """ + A batch collator that does nothing. + """ + return batch + + +def worker_init_reset_seed(worker_id): + initial_seed = torch.initial_seed() % 2 ** 31 + seed_all_rng(initial_seed + worker_id) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/catalog.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/catalog.py new file mode 100644 index 0000000000000000000000000000000000000000..45c110c19508f23921b9033cdaf0aa8056f0c125 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/catalog.py @@ -0,0 +1,236 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import logging +import types +from collections import UserDict +from typing import List + +from detectron2.utils.logger import log_first_n + +__all__ = ["DatasetCatalog", "MetadataCatalog", "Metadata"] + + +class _DatasetCatalog(UserDict): + """ + A global dictionary that stores information about the datasets and how to obtain them. + + It contains a mapping from strings + (which are names that identify a dataset, e.g. "coco_2014_train") + to a function which parses the dataset and returns the samples in the + format of `list[dict]`. + + The returned dicts should be in Detectron2 Dataset format (See DATASETS.md for details) + if used with the data loader functionalities in `data/build.py,data/detection_transform.py`. + + The purpose of having this catalog is to make it easy to choose + different datasets, by just using the strings in the config. + """ + + def register(self, name, func): + """ + Args: + name (str): the name that identifies a dataset, e.g. "coco_2014_train". + func (callable): a callable which takes no arguments and returns a list of dicts. + It must return the same results if called multiple times. + """ + assert callable(func), "You must register a function with `DatasetCatalog.register`!" + assert name not in self, "Dataset '{}' is already registered!".format(name) + self[name] = func + + def get(self, name): + """ + Call the registered function and return its results. + + Args: + name (str): the name that identifies a dataset, e.g. "coco_2014_train". + + Returns: + list[dict]: dataset annotations. + """ + try: + f = self[name] + except KeyError as e: + raise KeyError( + "Dataset '{}' is not registered! Available datasets are: {}".format( + name, ", ".join(list(self.keys())) + ) + ) from e + return f() + + def list(self) -> List[str]: + """ + List all registered datasets. + + Returns: + list[str] + """ + return list(self.keys()) + + def remove(self, name): + """ + Alias of ``pop``. + """ + self.pop(name) + + def __str__(self): + return "DatasetCatalog(registered datasets: {})".format(", ".join(self.keys())) + + __repr__ = __str__ + + +DatasetCatalog = _DatasetCatalog() +DatasetCatalog.__doc__ = ( + _DatasetCatalog.__doc__ + + """ + .. automethod:: detectron2.data.catalog.DatasetCatalog.register + .. automethod:: detectron2.data.catalog.DatasetCatalog.get +""" +) + + +class Metadata(types.SimpleNamespace): + """ + A class that supports simple attribute setter/getter. + It is intended for storing metadata of a dataset and make it accessible globally. + + Examples: + :: + # somewhere when you load the data: + MetadataCatalog.get("mydataset").thing_classes = ["person", "dog"] + + # somewhere when you print statistics or visualize: + classes = MetadataCatalog.get("mydataset").thing_classes + """ + + # the name of the dataset + # set default to N/A so that `self.name` in the errors will not trigger getattr again + name: str = "N/A" + + _RENAMED = { + "class_names": "thing_classes", + "dataset_id_to_contiguous_id": "thing_dataset_id_to_contiguous_id", + "stuff_class_names": "stuff_classes", + } + + def __getattr__(self, key): + if key in self._RENAMED: + log_first_n( + logging.WARNING, + "Metadata '{}' was renamed to '{}'!".format(key, self._RENAMED[key]), + n=10, + ) + return getattr(self, self._RENAMED[key]) + + # "name" exists in every metadata + if len(self.__dict__) > 1: + raise AttributeError( + "Attribute '{}' does not exist in the metadata of dataset '{}'. Available " + "keys are {}.".format(key, self.name, str(self.__dict__.keys())) + ) + else: + raise AttributeError( + f"Attribute '{key}' does not exist in the metadata of dataset '{self.name}': " + "metadata is empty." + ) + + def __setattr__(self, key, val): + if key in self._RENAMED: + log_first_n( + logging.WARNING, + "Metadata '{}' was renamed to '{}'!".format(key, self._RENAMED[key]), + n=10, + ) + setattr(self, self._RENAMED[key], val) + + # Ensure that metadata of the same name stays consistent + try: + oldval = getattr(self, key) + assert oldval == val, ( + "Attribute '{}' in the metadata of '{}' cannot be set " + "to a different value!\n{} != {}".format(key, self.name, oldval, val) + ) + except AttributeError: + super().__setattr__(key, val) + + def as_dict(self): + """ + Returns all the metadata as a dict. + Note that modifications to the returned dict will not reflect on the Metadata object. + """ + return copy.copy(self.__dict__) + + def set(self, **kwargs): + """ + Set multiple metadata with kwargs. + """ + for k, v in kwargs.items(): + setattr(self, k, v) + return self + + def get(self, key, default=None): + """ + Access an attribute and return its value if exists. + Otherwise return default. + """ + try: + return getattr(self, key) + except AttributeError: + return default + + +class _MetadataCatalog(UserDict): + """ + MetadataCatalog is a global dictionary that provides access to + :class:`Metadata` of a given dataset. + + The metadata associated with a certain name is a singleton: once created, the + metadata will stay alive and will be returned by future calls to ``get(name)``. + + It's like global variables, so don't abuse it. + It's meant for storing knowledge that's constant and shared across the execution + of the program, e.g.: the class names in COCO. + """ + + def get(self, name): + """ + Args: + name (str): name of a dataset (e.g. coco_2014_train). + + Returns: + Metadata: The :class:`Metadata` instance associated with this name, + or create an empty one if none is available. + """ + assert len(name) + r = super().get(name, None) + if r is None: + r = self[name] = Metadata(name=name) + return r + + def list(self): + """ + List all registered metadata. + + Returns: + list[str]: keys (names of datasets) of all registered metadata + """ + return list(self.keys()) + + def remove(self, name): + """ + Alias of ``pop``. + """ + self.pop(name) + + def __str__(self): + return "MetadataCatalog(registered metadata: {})".format(", ".join(self.keys())) + + __repr__ = __str__ + + +MetadataCatalog = _MetadataCatalog() +MetadataCatalog.__doc__ = ( + _MetadataCatalog.__doc__ + + """ + .. automethod:: detectron2.data.catalog.MetadataCatalog.get +""" +) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/common.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/common.py new file mode 100644 index 0000000000000000000000000000000000000000..d6b8742417abc897f5faa190db1341bbe7b2940d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/common.py @@ -0,0 +1,241 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import itertools +import logging +import numpy as np +import pickle +import random +import torch.utils.data as data +from torch.utils.data.sampler import Sampler + +from detectron2.utils.serialize import PicklableWrapper + +__all__ = ["MapDataset", "DatasetFromList", "AspectRatioGroupedDataset", "ToIterableDataset"] + + +def _shard_iterator_dataloader_worker(iterable): + # Shard the iterable if we're currently inside pytorch dataloader worker. + worker_info = data.get_worker_info() + if worker_info is None or worker_info.num_workers == 1: + # do nothing + yield from iterable + else: + yield from itertools.islice(iterable, worker_info.id, None, worker_info.num_workers) + + +class _MapIterableDataset(data.IterableDataset): + """ + Map a function over elements in an IterableDataset. + + Similar to pytorch's MapIterDataPipe, but support filtering when map_func + returns None. + + This class is not public-facing. Will be called by `MapDataset`. + """ + + def __init__(self, dataset, map_func): + self._dataset = dataset + self._map_func = PicklableWrapper(map_func) # wrap so that a lambda will work + + def __len__(self): + return len(self._dataset) + + def __iter__(self): + for x in map(self._map_func, self._dataset): + if x is not None: + yield x + + +class MapDataset(data.Dataset): + """ + Map a function over the elements in a dataset. + """ + + def __init__(self, dataset, map_func): + """ + Args: + dataset: a dataset where map function is applied. Can be either + map-style or iterable dataset. When given an iterable dataset, + the returned object will also be an iterable dataset. + map_func: a callable which maps the element in dataset. map_func can + return None to skip the data (e.g. in case of errors). + How None is handled depends on the style of `dataset`. + If `dataset` is map-style, it randomly tries other elements. + If `dataset` is iterable, it skips the data and tries the next. + """ + self._dataset = dataset + self._map_func = PicklableWrapper(map_func) # wrap so that a lambda will work + + self._rng = random.Random(42) + self._fallback_candidates = set(range(len(dataset))) + + def __new__(cls, dataset, map_func): + is_iterable = isinstance(dataset, data.IterableDataset) + if is_iterable: + return _MapIterableDataset(dataset, map_func) + else: + return super().__new__(cls) + + def __getnewargs__(self): + return self._dataset, self._map_func + + def __len__(self): + return len(self._dataset) + + def __getitem__(self, idx): + retry_count = 0 + cur_idx = int(idx) + + while True: + data = self._map_func(self._dataset[cur_idx]) + if data is not None: + self._fallback_candidates.add(cur_idx) + return data + + # _map_func fails for this idx, use a random new index from the pool + retry_count += 1 + self._fallback_candidates.discard(cur_idx) + cur_idx = self._rng.sample(self._fallback_candidates, k=1)[0] + + if retry_count >= 3: + logger = logging.getLogger(__name__) + logger.warning( + "Failed to apply `_map_func` for idx: {}, retry count: {}".format( + idx, retry_count + ) + ) + + +class DatasetFromList(data.Dataset): + """ + Wrap a list to a torch Dataset. It produces elements of the list as data. + """ + + def __init__(self, lst: list, copy: bool = True, serialize: bool = True): + """ + Args: + lst (list): a list which contains elements to produce. + copy (bool): whether to deepcopy the element when producing it, + so that the result can be modified in place without affecting the + source in the list. + serialize (bool): whether to hold memory using serialized objects, when + enabled, data loader workers can use shared RAM from master + process instead of making a copy. + """ + self._lst = lst + self._copy = copy + self._serialize = serialize + + def _serialize(data): + buffer = pickle.dumps(data, protocol=-1) + return np.frombuffer(buffer, dtype=np.uint8) + + if self._serialize: + logger = logging.getLogger(__name__) + logger.info( + "Serializing {} elements to byte tensors and concatenating them all ...".format( + len(self._lst) + ) + ) + self._lst = [_serialize(x) for x in self._lst] + self._addr = np.asarray([len(x) for x in self._lst], dtype=np.int64) + self._addr = np.cumsum(self._addr) + self._lst = np.concatenate(self._lst) + logger.info("Serialized dataset takes {:.2f} MiB".format(len(self._lst) / 1024 ** 2)) + + def __len__(self): + if self._serialize: + return len(self._addr) + else: + return len(self._lst) + + def __getitem__(self, idx): + if self._serialize: + start_addr = 0 if idx == 0 else self._addr[idx - 1].item() + end_addr = self._addr[idx].item() + bytes = memoryview(self._lst[start_addr:end_addr]) + return pickle.loads(bytes) + elif self._copy: + return copy.deepcopy(self._lst[idx]) + else: + return self._lst[idx] + + +class ToIterableDataset(data.IterableDataset): + """ + Convert an old indices-based (also called map-style) dataset + to an iterable-style dataset. + """ + + def __init__(self, dataset: data.Dataset, sampler: Sampler, shard_sampler: bool = True): + """ + Args: + dataset: an old-style dataset with ``__getitem__`` + sampler: a cheap iterable that produces indices to be applied on ``dataset``. + shard_sampler: whether to shard the sampler based on the current pytorch data loader + worker id. When an IterableDataset is forked by pytorch's DataLoader into multiple + workers, it is responsible for sharding its data based on worker id so that workers + don't produce identical data. + + Most samplers (like our TrainingSampler) do not shard based on dataloader worker id + and this argument should be set to True. But certain samplers may be already + sharded, in that case this argument should be set to False. + """ + assert not isinstance(dataset, data.IterableDataset), dataset + assert isinstance(sampler, Sampler), sampler + self.dataset = dataset + self.sampler = sampler + self.shard_sampler = shard_sampler + + def __iter__(self): + if not self.shard_sampler: + sampler = self.sampler + else: + # With map-style dataset, `DataLoader(dataset, sampler)` runs the + # sampler in main process only. But `DataLoader(ToIterableDataset(dataset, sampler))` + # will run sampler in every of the N worker. So we should only keep 1/N of the ids on + # each worker. The assumption is that sampler is cheap to iterate so it's fine to + # discard ids in workers. + sampler = _shard_iterator_dataloader_worker(self.sampler) + for idx in sampler: + yield self.dataset[idx] + + def __len__(self): + return len(self.sampler) + + +class AspectRatioGroupedDataset(data.IterableDataset): + """ + Batch data that have similar aspect ratio together. + In this implementation, images whose aspect ratio < (or >) 1 will + be batched together. + This improves training speed because the images then need less padding + to form a batch. + + It assumes the underlying dataset produces dicts with "width" and "height" keys. + It will then produce a list of original dicts with length = batch_size, + all with similar aspect ratios. + """ + + def __init__(self, dataset, batch_size): + """ + Args: + dataset: an iterable. Each element must be a dict with keys + "width" and "height", which will be used to batch data. + batch_size (int): + """ + self.dataset = dataset + self.batch_size = batch_size + self._buckets = [[] for _ in range(2)] + # Hard-coded two aspect ratio groups: w > h and w < h. + # Can add support for more aspect ratio groups, but doesn't seem useful + + def __iter__(self): + for d in self.dataset: + w, h = d["width"], d["height"] + bucket_id = 0 if w > h else 1 + bucket = self._buckets[bucket_id] + bucket.append(d) + if len(bucket) == self.batch_size: + yield bucket[:] + del bucket[:] diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/dataset_mapper.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/dataset_mapper.py new file mode 100644 index 0000000000000000000000000000000000000000..a8714f7990f11e146a01e03d108518e0356b50c4 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/dataset_mapper.py @@ -0,0 +1,191 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import logging +import numpy as np +from typing import List, Optional, Union +import torch + +from detectron2.config import configurable + +from . import detection_utils as utils +from . import transforms as T + +""" +This file contains the default mapping that's applied to "dataset dicts". +""" + +__all__ = ["DatasetMapper"] + + +class DatasetMapper: + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by the model. + + This is the default callable to be used to map your dataset dict into training data. + You may need to follow it to implement your own one for customized logic, + such as a different way to read or transform images. + See :doc:`/tutorials/data_loading` for details. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies cropping/geometric transforms to the image and annotations + 3. Prepare data and annotations to Tensor and :class:`Instances` + """ + + @configurable + def __init__( + self, + is_train: bool, + *, + augmentations: List[Union[T.Augmentation, T.Transform]], + image_format: str, + use_instance_mask: bool = False, + use_keypoint: bool = False, + instance_mask_format: str = "polygon", + keypoint_hflip_indices: Optional[np.ndarray] = None, + precomputed_proposal_topk: Optional[int] = None, + recompute_boxes: bool = False, + ): + """ + NOTE: this interface is experimental. + + Args: + is_train: whether it's used in training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + use_instance_mask: whether to process instance segmentation annotations, if available + use_keypoint: whether to process keypoint annotations if available + instance_mask_format: one of "polygon" or "bitmask". Process instance segmentation + masks into this format. + keypoint_hflip_indices: see :func:`detection_utils.create_keypoint_hflip_indices` + precomputed_proposal_topk: if given, will load pre-computed + proposals from dataset_dict and keep the top k proposals for each image. + recompute_boxes: whether to overwrite bounding box annotations + by computing tight bounding boxes from instance mask annotations. + """ + if recompute_boxes: + assert use_instance_mask, "recompute_boxes requires instance masks" + # fmt: off + self.is_train = is_train + self.augmentations = T.AugmentationList(augmentations) + self.image_format = image_format + self.use_instance_mask = use_instance_mask + self.instance_mask_format = instance_mask_format + self.use_keypoint = use_keypoint + self.keypoint_hflip_indices = keypoint_hflip_indices + self.proposal_topk = precomputed_proposal_topk + self.recompute_boxes = recompute_boxes + # fmt: on + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}") + + @classmethod + def from_config(cls, cfg, is_train: bool = True): + augs = utils.build_augmentation(cfg, is_train) + if cfg.INPUT.CROP.ENABLED and is_train: + augs.insert(0, T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE)) + recompute_boxes = cfg.MODEL.MASK_ON + else: + recompute_boxes = False + + ret = { + "is_train": is_train, + "augmentations": augs, + "image_format": cfg.INPUT.FORMAT, + "use_instance_mask": cfg.MODEL.MASK_ON, + "instance_mask_format": cfg.INPUT.MASK_FORMAT, + "use_keypoint": cfg.MODEL.KEYPOINT_ON, + "recompute_boxes": recompute_boxes, + } + + if cfg.MODEL.KEYPOINT_ON: + ret["keypoint_hflip_indices"] = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN) + + if cfg.MODEL.LOAD_PROPOSALS: + ret["precomputed_proposal_topk"] = ( + cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN + if is_train + else cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST + ) + return ret + + def _transform_annotations(self, dataset_dict, transforms, image_shape): + # USER: Modify this if you want to keep them for some reason. + for anno in dataset_dict["annotations"]: + if not self.use_instance_mask: + anno.pop("segmentation", None) + if not self.use_keypoint: + anno.pop("keypoints", None) + + # USER: Implement additional transformations if you have other types of data + annos = [ + utils.transform_instance_annotations( + obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices + ) + for obj in dataset_dict.pop("annotations") + if obj.get("iscrowd", 0) == 0 + ] + instances = utils.annotations_to_instances( + annos, image_shape, mask_format=self.instance_mask_format + ) + + # After transforms such as cropping are applied, the bounding box may no longer + # tightly bound the object. As an example, imagine a triangle object + # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight + # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to + # the intersection of original bounding box and the cropping box. + if self.recompute_boxes: + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + dataset_dict["instances"] = utils.filter_empty_instances(instances) + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + # USER: Write your own image loading if it's not from a file + image = utils.read_image(dataset_dict["file_name"], format=self.image_format) + utils.check_image_size(dataset_dict, image) + + # USER: Remove if you don't do semantic/panoptic segmentation. + if "sem_seg_file_name" in dataset_dict: + sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2) + else: + sem_seg_gt = None + + aug_input = T.AugInput(image, sem_seg=sem_seg_gt) + transforms = self.augmentations(aug_input) + image, sem_seg_gt = aug_input.image, aug_input.sem_seg + + image_shape = image.shape[:2] # h, w + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + if sem_seg_gt is not None: + dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long")) + + # USER: Remove if you don't use pre-computed proposals. + # Most users would not need this feature. + if self.proposal_topk is not None: + utils.transform_proposals( + dataset_dict, image_shape, transforms, proposal_topk=self.proposal_topk + ) + + if not self.is_train: + # USER: Modify this if you want to keep them for some reason. + dataset_dict.pop("annotations", None) + dataset_dict.pop("sem_seg_file_name", None) + return dataset_dict + + if "annotations" in dataset_dict: + self._transform_annotations(dataset_dict, transforms, image_shape) + + return dataset_dict diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/README.md b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9fb3e4f7afec17137c95c78be6ef06d520ec8032 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/README.md @@ -0,0 +1,9 @@ + + +### Common Datasets + +The dataset implemented here do not need to load the data into the final format. +It should provide the minimal data structure needed to use the dataset, so it can be very efficient. + +For example, for an image dataset, just provide the file names and labels, but don't read the images. +Let the downstream decide how to read. diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a44bedc15e5f0e762fc4d77efd6f1b07c6ff77d0 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .coco import load_coco_json, load_sem_seg, register_coco_instances, convert_to_coco_json +from .coco_panoptic import register_coco_panoptic, register_coco_panoptic_separated +from .lvis import load_lvis_json, register_lvis_instances, get_lvis_instances_meta +from .pascal_voc import load_voc_instances, register_pascal_voc +from . import builtin as _builtin # ensure the builtin datasets are registered + + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/builtin.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/builtin.py new file mode 100644 index 0000000000000000000000000000000000000000..c3a68aa833f12f0fa324a269c36190f21b8a75bd --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/builtin.py @@ -0,0 +1,259 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + + +""" +This file registers pre-defined datasets at hard-coded paths, and their metadata. + +We hard-code metadata for common datasets. This will enable: +1. Consistency check when loading the datasets +2. Use models on these standard datasets directly and run demos, + without having to download the dataset annotations + +We hard-code some paths to the dataset that's assumed to +exist in "./datasets/". + +Users SHOULD NOT use this file to create new dataset / metadata for new dataset. +To add new dataset, refer to the tutorial "docs/DATASETS.md". +""" + +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog + +from .builtin_meta import ADE20K_SEM_SEG_CATEGORIES, _get_builtin_metadata +from .cityscapes import load_cityscapes_instances, load_cityscapes_semantic +from .cityscapes_panoptic import register_all_cityscapes_panoptic +from .coco import load_sem_seg, register_coco_instances +from .coco_panoptic import register_coco_panoptic, register_coco_panoptic_separated +from .lvis import get_lvis_instances_meta, register_lvis_instances +from .pascal_voc import register_pascal_voc + +# ==== Predefined datasets and splits for COCO ========== + +_PREDEFINED_SPLITS_COCO = {} +_PREDEFINED_SPLITS_COCO["coco"] = { + "coco_2014_train": ("coco/train2014", "coco/annotations/instances_train2014.json"), + "coco_2014_val": ("coco/val2014", "coco/annotations/instances_val2014.json"), + "coco_2014_minival": ("coco/val2014", "coco/annotations/instances_minival2014.json"), + "coco_2014_valminusminival": ( + "coco/val2014", + "coco/annotations/instances_valminusminival2014.json", + ), + "coco_2017_train": ("coco/train2017", "coco/annotations/instances_train2017.json"), + "coco_2017_val": ("coco/val2017", "coco/annotations/instances_val2017.json"), + "coco_2017_test": ("coco/test2017", "coco/annotations/image_info_test2017.json"), + "coco_2017_test-dev": ("coco/test2017", "coco/annotations/image_info_test-dev2017.json"), + "coco_2017_val_100": ("coco/val2017", "coco/annotations/instances_val2017_100.json"), +} + +_PREDEFINED_SPLITS_COCO["coco_person"] = { + "keypoints_coco_2014_train": ( + "coco/train2014", + "coco/annotations/person_keypoints_train2014.json", + ), + "keypoints_coco_2014_val": ("coco/val2014", "coco/annotations/person_keypoints_val2014.json"), + "keypoints_coco_2014_minival": ( + "coco/val2014", + "coco/annotations/person_keypoints_minival2014.json", + ), + "keypoints_coco_2014_valminusminival": ( + "coco/val2014", + "coco/annotations/person_keypoints_valminusminival2014.json", + ), + "keypoints_coco_2017_train": ( + "coco/train2017", + "coco/annotations/person_keypoints_train2017.json", + ), + "keypoints_coco_2017_val": ("coco/val2017", "coco/annotations/person_keypoints_val2017.json"), + "keypoints_coco_2017_val_100": ( + "coco/val2017", + "coco/annotations/person_keypoints_val2017_100.json", + ), +} + + +_PREDEFINED_SPLITS_COCO_PANOPTIC = { + "coco_2017_train_panoptic": ( + # This is the original panoptic annotation directory + "coco/panoptic_train2017", + "coco/annotations/panoptic_train2017.json", + # This directory contains semantic annotations that are + # converted from panoptic annotations. + # It is used by PanopticFPN. + # You can use the script at detectron2/datasets/prepare_panoptic_fpn.py + # to create these directories. + "coco/panoptic_stuff_train2017", + ), + "coco_2017_val_panoptic": ( + "coco/panoptic_val2017", + "coco/annotations/panoptic_val2017.json", + "coco/panoptic_stuff_val2017", + ), + "coco_2017_val_100_panoptic": ( + "coco/panoptic_val2017_100", + "coco/annotations/panoptic_val2017_100.json", + "coco/panoptic_stuff_val2017_100", + ), +} + + +def register_all_coco(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_COCO.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + # Assume pre-defined datasets live in `./datasets`. + register_coco_instances( + key, + _get_builtin_metadata(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + for ( + prefix, + (panoptic_root, panoptic_json, semantic_root), + ) in _PREDEFINED_SPLITS_COCO_PANOPTIC.items(): + prefix_instances = prefix[: -len("_panoptic")] + instances_meta = MetadataCatalog.get(prefix_instances) + image_root, instances_json = instances_meta.image_root, instances_meta.json_file + # The "separated" version of COCO panoptic segmentation dataset, + # e.g. used by Panoptic FPN + register_coco_panoptic_separated( + prefix, + _get_builtin_metadata("coco_panoptic_separated"), + image_root, + os.path.join(root, panoptic_root), + os.path.join(root, panoptic_json), + os.path.join(root, semantic_root), + instances_json, + ) + # The "standard" version of COCO panoptic segmentation dataset, + # e.g. used by Panoptic-DeepLab + register_coco_panoptic( + prefix, + _get_builtin_metadata("coco_panoptic_standard"), + image_root, + os.path.join(root, panoptic_root), + os.path.join(root, panoptic_json), + instances_json, + ) + + +# ==== Predefined datasets and splits for LVIS ========== + + +_PREDEFINED_SPLITS_LVIS = { + "lvis_v1": { + "lvis_v1_train": ("coco/", "lvis/lvis_v1_train.json"), + "lvis_v1_val": ("coco/", "lvis/lvis_v1_val.json"), + "lvis_v1_test_dev": ("coco/", "lvis/lvis_v1_image_info_test_dev.json"), + "lvis_v1_test_challenge": ("coco/", "lvis/lvis_v1_image_info_test_challenge.json"), + }, + "lvis_v0.5": { + "lvis_v0.5_train": ("coco/", "lvis/lvis_v0.5_train.json"), + "lvis_v0.5_val": ("coco/", "lvis/lvis_v0.5_val.json"), + "lvis_v0.5_val_rand_100": ("coco/", "lvis/lvis_v0.5_val_rand_100.json"), + "lvis_v0.5_test": ("coco/", "lvis/lvis_v0.5_image_info_test.json"), + }, + "lvis_v0.5_cocofied": { + "lvis_v0.5_train_cocofied": ("coco/", "lvis/lvis_v0.5_train_cocofied.json"), + "lvis_v0.5_val_cocofied": ("coco/", "lvis/lvis_v0.5_val_cocofied.json"), + }, +} + + +def register_all_lvis(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_LVIS.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + register_lvis_instances( + key, + get_lvis_instances_meta(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +# ==== Predefined splits for raw cityscapes images =========== +_RAW_CITYSCAPES_SPLITS = { + "cityscapes_fine_{task}_train": ("cityscapes/leftImg8bit/train/", "cityscapes/gtFine/train/"), + "cityscapes_fine_{task}_val": ("cityscapes/leftImg8bit/val/", "cityscapes/gtFine/val/"), + "cityscapes_fine_{task}_test": ("cityscapes/leftImg8bit/test/", "cityscapes/gtFine/test/"), +} + + +def register_all_cityscapes(root): + for key, (image_dir, gt_dir) in _RAW_CITYSCAPES_SPLITS.items(): + meta = _get_builtin_metadata("cityscapes") + image_dir = os.path.join(root, image_dir) + gt_dir = os.path.join(root, gt_dir) + + inst_key = key.format(task="instance_seg") + DatasetCatalog.register( + inst_key, + lambda x=image_dir, y=gt_dir: load_cityscapes_instances( + x, y, from_json=True, to_polygons=True + ), + ) + MetadataCatalog.get(inst_key).set( + image_dir=image_dir, gt_dir=gt_dir, evaluator_type="cityscapes_instance", **meta + ) + + sem_key = key.format(task="sem_seg") + DatasetCatalog.register( + sem_key, lambda x=image_dir, y=gt_dir: load_cityscapes_semantic(x, y) + ) + MetadataCatalog.get(sem_key).set( + image_dir=image_dir, + gt_dir=gt_dir, + evaluator_type="cityscapes_sem_seg", + ignore_label=255, + **meta, + ) + + +# ==== Predefined splits for PASCAL VOC =========== +def register_all_pascal_voc(root): + SPLITS = [ + ("voc_2007_trainval", "VOC2007", "trainval"), + ("voc_2007_train", "VOC2007", "train"), + ("voc_2007_val", "VOC2007", "val"), + ("voc_2007_test", "VOC2007", "test"), + ("voc_2012_trainval", "VOC2012", "trainval"), + ("voc_2012_train", "VOC2012", "train"), + ("voc_2012_val", "VOC2012", "val"), + ] + for name, dirname, split in SPLITS: + year = 2007 if "2007" in name else 2012 + register_pascal_voc(name, os.path.join(root, dirname), split, year) + MetadataCatalog.get(name).evaluator_type = "pascal_voc" + + +def register_all_ade20k(root): + root = os.path.join(root, "ADEChallengeData2016") + for name, dirname in [("train", "training"), ("val", "validation")]: + image_dir = os.path.join(root, "images", dirname) + gt_dir = os.path.join(root, "annotations_detectron2", dirname) + name = f"ade20k_sem_seg_{name}" + DatasetCatalog.register( + name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="png", image_ext="jpg") + ) + MetadataCatalog.get(name).set( + stuff_classes=ADE20K_SEM_SEG_CATEGORIES[:], + image_root=image_dir, + sem_seg_root=gt_dir, + evaluator_type="sem_seg", + ignore_label=255, + ) + + +# True for open source; +# Internally at fb, we register them elsewhere +if __name__.endswith(".builtin"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.path.expanduser(os.getenv("DETECTRON2_DATASETS", "datasets")) + register_all_coco(_root) + register_all_lvis(_root) + register_all_cityscapes(_root) + register_all_cityscapes_panoptic(_root) + register_all_pascal_voc(_root) + register_all_ade20k(_root) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/builtin_meta.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/builtin_meta.py new file mode 100644 index 0000000000000000000000000000000000000000..63c7a1a31b31dd89b82011effee26471faccacf5 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/builtin_meta.py @@ -0,0 +1,350 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +""" +Note: +For your custom dataset, there is no need to hard-code metadata anywhere in the code. +For example, for COCO-format dataset, metadata will be obtained automatically +when calling `load_coco_json`. For other dataset, metadata may also be obtained in other ways +during loading. + +However, we hard-coded metadata for a few common dataset here. +The only goal is to allow users who don't have these dataset to use pre-trained models. +Users don't have to download a COCO json (which contains metadata), in order to visualize a +COCO model (with correct class names and colors). +""" + + +# All coco categories, together with their nice-looking visualization colors +# It's from https://github.com/cocodataset/panopticapi/blob/master/panoptic_coco_categories.json +COCO_CATEGORIES = [ + {"color": [220, 20, 60], "isthing": 1, "id": 1, "name": "person"}, + {"color": [119, 11, 32], "isthing": 1, "id": 2, "name": "bicycle"}, + {"color": [0, 0, 142], "isthing": 1, "id": 3, "name": "car"}, + {"color": [0, 0, 230], "isthing": 1, "id": 4, "name": "motorcycle"}, + {"color": [106, 0, 228], "isthing": 1, "id": 5, "name": "airplane"}, + {"color": [0, 60, 100], "isthing": 1, "id": 6, "name": "bus"}, + {"color": [0, 80, 100], "isthing": 1, "id": 7, "name": "train"}, + {"color": [0, 0, 70], "isthing": 1, "id": 8, "name": "truck"}, + {"color": [0, 0, 192], "isthing": 1, "id": 9, "name": "boat"}, + {"color": [250, 170, 30], "isthing": 1, "id": 10, "name": "traffic light"}, + {"color": [100, 170, 30], "isthing": 1, "id": 11, "name": "fire hydrant"}, + {"color": [220, 220, 0], "isthing": 1, "id": 13, "name": "stop sign"}, + {"color": [175, 116, 175], "isthing": 1, "id": 14, "name": "parking meter"}, + {"color": [250, 0, 30], "isthing": 1, "id": 15, "name": "bench"}, + {"color": [165, 42, 42], "isthing": 1, "id": 16, "name": "bird"}, + {"color": [255, 77, 255], "isthing": 1, "id": 17, "name": "cat"}, + {"color": [0, 226, 252], "isthing": 1, "id": 18, "name": "dog"}, + {"color": [182, 182, 255], "isthing": 1, "id": 19, "name": "horse"}, + {"color": [0, 82, 0], "isthing": 1, "id": 20, "name": "sheep"}, + {"color": [120, 166, 157], "isthing": 1, "id": 21, "name": "cow"}, + {"color": [110, 76, 0], "isthing": 1, "id": 22, "name": "elephant"}, + {"color": [174, 57, 255], "isthing": 1, "id": 23, "name": "bear"}, + {"color": [199, 100, 0], "isthing": 1, "id": 24, "name": "zebra"}, + {"color": [72, 0, 118], "isthing": 1, "id": 25, "name": "giraffe"}, + {"color": [255, 179, 240], "isthing": 1, "id": 27, "name": "backpack"}, + {"color": [0, 125, 92], "isthing": 1, "id": 28, "name": "umbrella"}, + {"color": [209, 0, 151], "isthing": 1, "id": 31, "name": "handbag"}, + {"color": [188, 208, 182], "isthing": 1, "id": 32, "name": "tie"}, + {"color": [0, 220, 176], "isthing": 1, "id": 33, "name": "suitcase"}, + {"color": [255, 99, 164], "isthing": 1, "id": 34, "name": "frisbee"}, + {"color": [92, 0, 73], "isthing": 1, "id": 35, "name": "skis"}, + {"color": [133, 129, 255], "isthing": 1, "id": 36, "name": "snowboard"}, + {"color": [78, 180, 255], "isthing": 1, "id": 37, "name": "sports ball"}, + {"color": [0, 228, 0], "isthing": 1, "id": 38, "name": "kite"}, + {"color": [174, 255, 243], "isthing": 1, "id": 39, "name": "baseball bat"}, + {"color": [45, 89, 255], "isthing": 1, "id": 40, "name": "baseball glove"}, + {"color": [134, 134, 103], "isthing": 1, "id": 41, "name": "skateboard"}, + {"color": [145, 148, 174], "isthing": 1, "id": 42, "name": "surfboard"}, + {"color": [255, 208, 186], "isthing": 1, "id": 43, "name": "tennis racket"}, + {"color": [197, 226, 255], "isthing": 1, "id": 44, "name": "bottle"}, + {"color": [171, 134, 1], "isthing": 1, "id": 46, "name": "wine glass"}, + {"color": [109, 63, 54], "isthing": 1, "id": 47, "name": "cup"}, + {"color": [207, 138, 255], "isthing": 1, "id": 48, "name": "fork"}, + {"color": [151, 0, 95], "isthing": 1, "id": 49, "name": "knife"}, + {"color": [9, 80, 61], "isthing": 1, "id": 50, "name": "spoon"}, + {"color": [84, 105, 51], "isthing": 1, "id": 51, "name": "bowl"}, + {"color": [74, 65, 105], "isthing": 1, "id": 52, "name": "banana"}, + {"color": [166, 196, 102], "isthing": 1, "id": 53, "name": "apple"}, + {"color": [208, 195, 210], "isthing": 1, "id": 54, "name": "sandwich"}, + {"color": [255, 109, 65], "isthing": 1, "id": 55, "name": "orange"}, + {"color": [0, 143, 149], "isthing": 1, "id": 56, "name": "broccoli"}, + {"color": [179, 0, 194], "isthing": 1, "id": 57, "name": "carrot"}, + {"color": [209, 99, 106], "isthing": 1, "id": 58, "name": "hot dog"}, + {"color": [5, 121, 0], "isthing": 1, "id": 59, "name": "pizza"}, + {"color": [227, 255, 205], "isthing": 1, "id": 60, "name": "donut"}, + {"color": [147, 186, 208], "isthing": 1, "id": 61, "name": "cake"}, + {"color": [153, 69, 1], "isthing": 1, "id": 62, "name": "chair"}, + {"color": [3, 95, 161], "isthing": 1, "id": 63, "name": "couch"}, + {"color": [163, 255, 0], "isthing": 1, "id": 64, "name": "potted plant"}, + {"color": [119, 0, 170], "isthing": 1, "id": 65, "name": "bed"}, + {"color": [0, 182, 199], "isthing": 1, "id": 67, "name": "dining table"}, + {"color": [0, 165, 120], "isthing": 1, "id": 70, "name": "toilet"}, + {"color": [183, 130, 88], "isthing": 1, "id": 72, "name": "tv"}, + {"color": [95, 32, 0], "isthing": 1, "id": 73, "name": "laptop"}, + {"color": [130, 114, 135], "isthing": 1, "id": 74, "name": "mouse"}, + {"color": [110, 129, 133], "isthing": 1, "id": 75, "name": "remote"}, + {"color": [166, 74, 118], "isthing": 1, "id": 76, "name": "keyboard"}, + {"color": [219, 142, 185], "isthing": 1, "id": 77, "name": "cell phone"}, + {"color": [79, 210, 114], "isthing": 1, "id": 78, "name": "microwave"}, + {"color": [178, 90, 62], "isthing": 1, "id": 79, "name": "oven"}, + {"color": [65, 70, 15], "isthing": 1, "id": 80, "name": "toaster"}, + {"color": [127, 167, 115], "isthing": 1, "id": 81, "name": "sink"}, + {"color": [59, 105, 106], "isthing": 1, "id": 82, "name": "refrigerator"}, + {"color": [142, 108, 45], "isthing": 1, "id": 84, "name": "book"}, + {"color": [196, 172, 0], "isthing": 1, "id": 85, "name": "clock"}, + {"color": [95, 54, 80], "isthing": 1, "id": 86, "name": "vase"}, + {"color": [128, 76, 255], "isthing": 1, "id": 87, "name": "scissors"}, + {"color": [201, 57, 1], "isthing": 1, "id": 88, "name": "teddy bear"}, + {"color": [246, 0, 122], "isthing": 1, "id": 89, "name": "hair drier"}, + {"color": [191, 162, 208], "isthing": 1, "id": 90, "name": "toothbrush"}, + {"color": [255, 255, 128], "isthing": 0, "id": 92, "name": "banner"}, + {"color": [147, 211, 203], "isthing": 0, "id": 93, "name": "blanket"}, + {"color": [150, 100, 100], "isthing": 0, "id": 95, "name": "bridge"}, + {"color": [168, 171, 172], "isthing": 0, "id": 100, "name": "cardboard"}, + {"color": [146, 112, 198], "isthing": 0, "id": 107, "name": "counter"}, + {"color": [210, 170, 100], "isthing": 0, "id": 109, "name": "curtain"}, + {"color": [92, 136, 89], "isthing": 0, "id": 112, "name": "door-stuff"}, + {"color": [218, 88, 184], "isthing": 0, "id": 118, "name": "floor-wood"}, + {"color": [241, 129, 0], "isthing": 0, "id": 119, "name": "flower"}, + {"color": [217, 17, 255], "isthing": 0, "id": 122, "name": "fruit"}, + {"color": [124, 74, 181], "isthing": 0, "id": 125, "name": "gravel"}, + {"color": [70, 70, 70], "isthing": 0, "id": 128, "name": "house"}, + {"color": [255, 228, 255], "isthing": 0, "id": 130, "name": "light"}, + {"color": [154, 208, 0], "isthing": 0, "id": 133, "name": "mirror-stuff"}, + {"color": [193, 0, 92], "isthing": 0, "id": 138, "name": "net"}, + {"color": [76, 91, 113], "isthing": 0, "id": 141, "name": "pillow"}, + {"color": [255, 180, 195], "isthing": 0, "id": 144, "name": "platform"}, + {"color": [106, 154, 176], "isthing": 0, "id": 145, "name": "playingfield"}, + {"color": [230, 150, 140], "isthing": 0, "id": 147, "name": "railroad"}, + {"color": [60, 143, 255], "isthing": 0, "id": 148, "name": "river"}, + {"color": [128, 64, 128], "isthing": 0, "id": 149, "name": "road"}, + {"color": [92, 82, 55], "isthing": 0, "id": 151, "name": "roof"}, + {"color": [254, 212, 124], "isthing": 0, "id": 154, "name": "sand"}, + {"color": [73, 77, 174], "isthing": 0, "id": 155, "name": "sea"}, + {"color": [255, 160, 98], "isthing": 0, "id": 156, "name": "shelf"}, + {"color": [255, 255, 255], "isthing": 0, "id": 159, "name": "snow"}, + {"color": [104, 84, 109], "isthing": 0, "id": 161, "name": "stairs"}, + {"color": [169, 164, 131], "isthing": 0, "id": 166, "name": "tent"}, + {"color": [225, 199, 255], "isthing": 0, "id": 168, "name": "towel"}, + {"color": [137, 54, 74], "isthing": 0, "id": 171, "name": "wall-brick"}, + {"color": [135, 158, 223], "isthing": 0, "id": 175, "name": "wall-stone"}, + {"color": [7, 246, 231], "isthing": 0, "id": 176, "name": "wall-tile"}, + {"color": [107, 255, 200], "isthing": 0, "id": 177, "name": "wall-wood"}, + {"color": [58, 41, 149], "isthing": 0, "id": 178, "name": "water-other"}, + {"color": [183, 121, 142], "isthing": 0, "id": 180, "name": "window-blind"}, + {"color": [255, 73, 97], "isthing": 0, "id": 181, "name": "window-other"}, + {"color": [107, 142, 35], "isthing": 0, "id": 184, "name": "tree-merged"}, + {"color": [190, 153, 153], "isthing": 0, "id": 185, "name": "fence-merged"}, + {"color": [146, 139, 141], "isthing": 0, "id": 186, "name": "ceiling-merged"}, + {"color": [70, 130, 180], "isthing": 0, "id": 187, "name": "sky-other-merged"}, + {"color": [134, 199, 156], "isthing": 0, "id": 188, "name": "cabinet-merged"}, + {"color": [209, 226, 140], "isthing": 0, "id": 189, "name": "table-merged"}, + {"color": [96, 36, 108], "isthing": 0, "id": 190, "name": "floor-other-merged"}, + {"color": [96, 96, 96], "isthing": 0, "id": 191, "name": "pavement-merged"}, + {"color": [64, 170, 64], "isthing": 0, "id": 192, "name": "mountain-merged"}, + {"color": [152, 251, 152], "isthing": 0, "id": 193, "name": "grass-merged"}, + {"color": [208, 229, 228], "isthing": 0, "id": 194, "name": "dirt-merged"}, + {"color": [206, 186, 171], "isthing": 0, "id": 195, "name": "paper-merged"}, + {"color": [152, 161, 64], "isthing": 0, "id": 196, "name": "food-other-merged"}, + {"color": [116, 112, 0], "isthing": 0, "id": 197, "name": "building-other-merged"}, + {"color": [0, 114, 143], "isthing": 0, "id": 198, "name": "rock-merged"}, + {"color": [102, 102, 156], "isthing": 0, "id": 199, "name": "wall-other-merged"}, + {"color": [250, 141, 255], "isthing": 0, "id": 200, "name": "rug-merged"}, +] + +# fmt: off +COCO_PERSON_KEYPOINT_NAMES = ( + "nose", + "left_eye", "right_eye", + "left_ear", "right_ear", + "left_shoulder", "right_shoulder", + "left_elbow", "right_elbow", + "left_wrist", "right_wrist", + "left_hip", "right_hip", + "left_knee", "right_knee", + "left_ankle", "right_ankle", +) +# fmt: on + +# Pairs of keypoints that should be exchanged under horizontal flipping +COCO_PERSON_KEYPOINT_FLIP_MAP = ( + ("left_eye", "right_eye"), + ("left_ear", "right_ear"), + ("left_shoulder", "right_shoulder"), + ("left_elbow", "right_elbow"), + ("left_wrist", "right_wrist"), + ("left_hip", "right_hip"), + ("left_knee", "right_knee"), + ("left_ankle", "right_ankle"), +) + +# rules for pairs of keypoints to draw a line between, and the line color to use. +KEYPOINT_CONNECTION_RULES = [ + # face + ("left_ear", "left_eye", (102, 204, 255)), + ("right_ear", "right_eye", (51, 153, 255)), + ("left_eye", "nose", (102, 0, 204)), + ("nose", "right_eye", (51, 102, 255)), + # upper-body + ("left_shoulder", "right_shoulder", (255, 128, 0)), + ("left_shoulder", "left_elbow", (153, 255, 204)), + ("right_shoulder", "right_elbow", (128, 229, 255)), + ("left_elbow", "left_wrist", (153, 255, 153)), + ("right_elbow", "right_wrist", (102, 255, 224)), + # lower-body + ("left_hip", "right_hip", (255, 102, 0)), + ("left_hip", "left_knee", (255, 255, 77)), + ("right_hip", "right_knee", (153, 255, 204)), + ("left_knee", "left_ankle", (191, 255, 128)), + ("right_knee", "right_ankle", (255, 195, 77)), +] + +# All Cityscapes categories, together with their nice-looking visualization colors +# It's from https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py # noqa +CITYSCAPES_CATEGORIES = [ + {"color": (128, 64, 128), "isthing": 0, "id": 7, "trainId": 0, "name": "road"}, + {"color": (244, 35, 232), "isthing": 0, "id": 8, "trainId": 1, "name": "sidewalk"}, + {"color": (70, 70, 70), "isthing": 0, "id": 11, "trainId": 2, "name": "building"}, + {"color": (102, 102, 156), "isthing": 0, "id": 12, "trainId": 3, "name": "wall"}, + {"color": (190, 153, 153), "isthing": 0, "id": 13, "trainId": 4, "name": "fence"}, + {"color": (153, 153, 153), "isthing": 0, "id": 17, "trainId": 5, "name": "pole"}, + {"color": (250, 170, 30), "isthing": 0, "id": 19, "trainId": 6, "name": "traffic light"}, + {"color": (220, 220, 0), "isthing": 0, "id": 20, "trainId": 7, "name": "traffic sign"}, + {"color": (107, 142, 35), "isthing": 0, "id": 21, "trainId": 8, "name": "vegetation"}, + {"color": (152, 251, 152), "isthing": 0, "id": 22, "trainId": 9, "name": "terrain"}, + {"color": (70, 130, 180), "isthing": 0, "id": 23, "trainId": 10, "name": "sky"}, + {"color": (220, 20, 60), "isthing": 1, "id": 24, "trainId": 11, "name": "person"}, + {"color": (255, 0, 0), "isthing": 1, "id": 25, "trainId": 12, "name": "rider"}, + {"color": (0, 0, 142), "isthing": 1, "id": 26, "trainId": 13, "name": "car"}, + {"color": (0, 0, 70), "isthing": 1, "id": 27, "trainId": 14, "name": "truck"}, + {"color": (0, 60, 100), "isthing": 1, "id": 28, "trainId": 15, "name": "bus"}, + {"color": (0, 80, 100), "isthing": 1, "id": 31, "trainId": 16, "name": "train"}, + {"color": (0, 0, 230), "isthing": 1, "id": 32, "trainId": 17, "name": "motorcycle"}, + {"color": (119, 11, 32), "isthing": 1, "id": 33, "trainId": 18, "name": "bicycle"}, +] + +# fmt: off +ADE20K_SEM_SEG_CATEGORIES = [ + "wall", "building", "sky", "floor", "tree", "ceiling", "road, route", "bed", "window ", "grass", "cabinet", "sidewalk, pavement", "person", "earth, ground", "door", "table", "mountain, mount", "plant", "curtain", "chair", "car", "water", "painting, picture", "sofa", "shelf", "house", "sea", "mirror", "rug", "field", "armchair", "seat", "fence", "desk", "rock, stone", "wardrobe, closet, press", "lamp", "tub", "rail", "cushion", "base, pedestal, stand", "box", "column, pillar", "signboard, sign", "chest of drawers, chest, bureau, dresser", "counter", "sand", "sink", "skyscraper", "fireplace", "refrigerator, icebox", "grandstand, covered stand", "path", "stairs", "runway", "case, display case, showcase, vitrine", "pool table, billiard table, snooker table", "pillow", "screen door, screen", "stairway, staircase", "river", "bridge, span", "bookcase", "blind, screen", "coffee table", "toilet, can, commode, crapper, pot, potty, stool, throne", "flower", "book", "hill", "bench", "countertop", "stove", "palm, palm tree", "kitchen island", "computer", "swivel chair", "boat", "bar", "arcade machine", "hovel, hut, hutch, shack, shanty", "bus", "towel", "light", "truck", "tower", "chandelier", "awning, sunshade, sunblind", "street lamp", "booth", "tv", "plane", "dirt track", "clothes", "pole", "land, ground, soil", "bannister, banister, balustrade, balusters, handrail", "escalator, moving staircase, moving stairway", "ottoman, pouf, pouffe, puff, hassock", "bottle", "buffet, counter, sideboard", "poster, posting, placard, notice, bill, card", "stage", "van", "ship", "fountain", "conveyer belt, conveyor belt, conveyer, conveyor, transporter", "canopy", "washer, automatic washer, washing machine", "plaything, toy", "pool", "stool", "barrel, cask", "basket, handbasket", "falls", "tent", "bag", "minibike, motorbike", "cradle", "oven", "ball", "food, solid food", "step, stair", "tank, storage tank", "trade name", "microwave", "pot", "animal", "bicycle", "lake", "dishwasher", "screen", "blanket, cover", "sculpture", "hood, exhaust hood", "sconce", "vase", "traffic light", "tray", "trash can", "fan", "pier", "crt screen", "plate", "monitor", "bulletin board", "shower", "radiator", "glass, drinking glass", "clock", "flag", # noqa +] +# After processed by `prepare_ade20k_sem_seg.py`, id 255 means ignore +# fmt: on + + +def _get_coco_instances_meta(): + thing_ids = [k["id"] for k in COCO_CATEGORIES if k["isthing"] == 1] + thing_colors = [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 1] + assert len(thing_ids) == 80, len(thing_ids) + # Mapping from the incontiguous COCO category id to an id in [0, 79] + thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)} + thing_classes = [k["name"] for k in COCO_CATEGORIES if k["isthing"] == 1] + ret = { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + "thing_colors": thing_colors, + } + return ret + + +def _get_coco_panoptic_separated_meta(): + """ + Returns metadata for "separated" version of the panoptic segmentation dataset. + """ + stuff_ids = [k["id"] for k in COCO_CATEGORIES if k["isthing"] == 0] + assert len(stuff_ids) == 53, len(stuff_ids) + + # For semantic segmentation, this mapping maps from contiguous stuff id + # (in [0, 53], used in models) to ids in the dataset (used for processing results) + # The id 0 is mapped to an extra category "thing". + stuff_dataset_id_to_contiguous_id = {k: i + 1 for i, k in enumerate(stuff_ids)} + # When converting COCO panoptic annotations to semantic annotations + # We label the "thing" category to 0 + stuff_dataset_id_to_contiguous_id[0] = 0 + + # 54 names for COCO stuff categories (including "things") + stuff_classes = ["things"] + [ + k["name"].replace("-other", "").replace("-merged", "") + for k in COCO_CATEGORIES + if k["isthing"] == 0 + ] + + # NOTE: I randomly picked a color for things + stuff_colors = [[82, 18, 128]] + [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 0] + ret = { + "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id, + "stuff_classes": stuff_classes, + "stuff_colors": stuff_colors, + } + ret.update(_get_coco_instances_meta()) + return ret + + +def _get_builtin_metadata(dataset_name): + if dataset_name == "coco": + return _get_coco_instances_meta() + if dataset_name == "coco_panoptic_separated": + return _get_coco_panoptic_separated_meta() + elif dataset_name == "coco_panoptic_standard": + meta = {} + # The following metadata maps contiguous id from [0, #thing categories + + # #stuff categories) to their names and colors. We have to replica of the + # same name and color under "thing_*" and "stuff_*" because the current + # visualization function in D2 handles thing and class classes differently + # due to some heuristic used in Panoptic FPN. We keep the same naming to + # enable reusing existing visualization functions. + thing_classes = [k["name"] for k in COCO_CATEGORIES] + thing_colors = [k["color"] for k in COCO_CATEGORIES] + stuff_classes = [k["name"] for k in COCO_CATEGORIES] + stuff_colors = [k["color"] for k in COCO_CATEGORIES] + + meta["thing_classes"] = thing_classes + meta["thing_colors"] = thing_colors + meta["stuff_classes"] = stuff_classes + meta["stuff_colors"] = stuff_colors + + # Convert category id for training: + # category id: like semantic segmentation, it is the class id for each + # pixel. Since there are some classes not used in evaluation, the category + # id is not always contiguous and thus we have two set of category ids: + # - original category id: category id in the original dataset, mainly + # used for evaluation. + # - contiguous category id: [0, #classes), in order to train the linear + # softmax classifier. + thing_dataset_id_to_contiguous_id = {} + stuff_dataset_id_to_contiguous_id = {} + + for i, cat in enumerate(COCO_CATEGORIES): + if cat["isthing"]: + thing_dataset_id_to_contiguous_id[cat["id"]] = i + else: + stuff_dataset_id_to_contiguous_id[cat["id"]] = i + + meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id + meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id + + return meta + elif dataset_name == "coco_person": + return { + "thing_classes": ["person"], + "keypoint_names": COCO_PERSON_KEYPOINT_NAMES, + "keypoint_flip_map": COCO_PERSON_KEYPOINT_FLIP_MAP, + "keypoint_connection_rules": KEYPOINT_CONNECTION_RULES, + } + elif dataset_name == "cityscapes": + # fmt: off + CITYSCAPES_THING_CLASSES = [ + "person", "rider", "car", "truck", + "bus", "train", "motorcycle", "bicycle", + ] + CITYSCAPES_STUFF_CLASSES = [ + "road", "sidewalk", "building", "wall", "fence", "pole", "traffic light", + "traffic sign", "vegetation", "terrain", "sky", "person", "rider", "car", + "truck", "bus", "train", "motorcycle", "bicycle", + ] + # fmt: on + return { + "thing_classes": CITYSCAPES_THING_CLASSES, + "stuff_classes": CITYSCAPES_STUFF_CLASSES, + } + raise KeyError("No built-in metadata for dataset {}".format(dataset_name)) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/cityscapes.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..1e84a5bdb3d4e410d8eef4b80a5d4c099a180104 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/cityscapes.py @@ -0,0 +1,329 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import functools +import json +import logging +import multiprocessing as mp +import numpy as np +import os +from itertools import chain +import pycocotools.mask as mask_util +from PIL import Image + +from detectron2.structures import BoxMode +from detectron2.utils.comm import get_world_size +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import setup_logger + +try: + import cv2 # noqa +except ImportError: + # OpenCV is an optional dependency at the moment + pass + + +logger = logging.getLogger(__name__) + + +def _get_cityscapes_files(image_dir, gt_dir): + files = [] + # scan through the directory + cities = PathManager.ls(image_dir) + logger.info(f"{len(cities)} cities found in '{image_dir}'.") + for city in cities: + city_img_dir = os.path.join(image_dir, city) + city_gt_dir = os.path.join(gt_dir, city) + for basename in PathManager.ls(city_img_dir): + image_file = os.path.join(city_img_dir, basename) + + suffix = "leftImg8bit.png" + assert basename.endswith(suffix), basename + basename = basename[: -len(suffix)] + + instance_file = os.path.join(city_gt_dir, basename + "gtFine_instanceIds.png") + label_file = os.path.join(city_gt_dir, basename + "gtFine_labelIds.png") + json_file = os.path.join(city_gt_dir, basename + "gtFine_polygons.json") + + files.append((image_file, instance_file, label_file, json_file)) + assert len(files), "No images found in {}".format(image_dir) + for f in files[0]: + assert PathManager.isfile(f), f + return files + + +def load_cityscapes_instances(image_dir, gt_dir, from_json=True, to_polygons=True): + """ + Args: + image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train". + gt_dir (str): path to the raw annotations. e.g., "~/cityscapes/gtFine/train". + from_json (bool): whether to read annotations from the raw json file or the png files. + to_polygons (bool): whether to represent the segmentation as polygons + (COCO's format) instead of masks (cityscapes's format). + + Returns: + list[dict]: a list of dicts in Detectron2 standard format. (See + `Using Custom Datasets `_ ) + """ + if from_json: + assert to_polygons, ( + "Cityscapes's json annotations are in polygon format. " + "Converting to mask format is not supported now." + ) + files = _get_cityscapes_files(image_dir, gt_dir) + + logger.info("Preprocessing cityscapes annotations ...") + # This is still not fast: all workers will execute duplicate works and will + # take up to 10m on a 8GPU server. + pool = mp.Pool(processes=max(mp.cpu_count() // get_world_size() // 2, 4)) + + ret = pool.map( + functools.partial(_cityscapes_files_to_dict, from_json=from_json, to_polygons=to_polygons), + files, + ) + logger.info("Loaded {} images from {}".format(len(ret), image_dir)) + + # Map cityscape ids to contiguous ids + from cityscapesscripts.helpers.labels import labels + + labels = [l for l in labels if l.hasInstances and not l.ignoreInEval] + dataset_id_to_contiguous_id = {l.id: idx for idx, l in enumerate(labels)} + for dict_per_image in ret: + for anno in dict_per_image["annotations"]: + anno["category_id"] = dataset_id_to_contiguous_id[anno["category_id"]] + return ret + + +def load_cityscapes_semantic(image_dir, gt_dir): + """ + Args: + image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train". + gt_dir (str): path to the raw annotations. e.g., "~/cityscapes/gtFine/train". + + Returns: + list[dict]: a list of dict, each has "file_name" and + "sem_seg_file_name". + """ + ret = [] + # gt_dir is small and contain many small files. make sense to fetch to local first + gt_dir = PathManager.get_local_path(gt_dir) + for image_file, _, label_file, json_file in _get_cityscapes_files(image_dir, gt_dir): + label_file = label_file.replace("labelIds", "labelTrainIds") + + with PathManager.open(json_file, "r") as f: + jsonobj = json.load(f) + ret.append( + { + "file_name": image_file, + "sem_seg_file_name": label_file, + "height": jsonobj["imgHeight"], + "width": jsonobj["imgWidth"], + } + ) + assert len(ret), f"No images found in {image_dir}!" + assert PathManager.isfile( + ret[0]["sem_seg_file_name"] + ), "Please generate labelTrainIds.png with cityscapesscripts/preparation/createTrainIdLabelImgs.py" # noqa + return ret + + +def _cityscapes_files_to_dict(files, from_json, to_polygons): + """ + Parse cityscapes annotation files to a instance segmentation dataset dict. + + Args: + files (tuple): consists of (image_file, instance_id_file, label_id_file, json_file) + from_json (bool): whether to read annotations from the raw json file or the png files. + to_polygons (bool): whether to represent the segmentation as polygons + (COCO's format) instead of masks (cityscapes's format). + + Returns: + A dict in Detectron2 Dataset format. + """ + from cityscapesscripts.helpers.labels import id2label, name2label + + image_file, instance_id_file, _, json_file = files + + annos = [] + + if from_json: + from shapely.geometry import MultiPolygon, Polygon + + with PathManager.open(json_file, "r") as f: + jsonobj = json.load(f) + ret = { + "file_name": image_file, + "image_id": os.path.basename(image_file), + "height": jsonobj["imgHeight"], + "width": jsonobj["imgWidth"], + } + + # `polygons_union` contains the union of all valid polygons. + polygons_union = Polygon() + + # CityscapesScripts draw the polygons in sequential order + # and each polygon *overwrites* existing ones. See + # (https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/preparation/json2instanceImg.py) # noqa + # We use reverse order, and each polygon *avoids* early ones. + # This will resolve the ploygon overlaps in the same way as CityscapesScripts. + for obj in jsonobj["objects"][::-1]: + if "deleted" in obj: # cityscapes data format specific + continue + label_name = obj["label"] + + try: + label = name2label[label_name] + except KeyError: + if label_name.endswith("group"): # crowd area + label = name2label[label_name[: -len("group")]] + else: + raise + if label.id < 0: # cityscapes data format + continue + + # Cityscapes's raw annotations uses integer coordinates + # Therefore +0.5 here + poly_coord = np.asarray(obj["polygon"], dtype="f4") + 0.5 + # CityscapesScript uses PIL.ImageDraw.polygon to rasterize + # polygons for evaluation. This function operates in integer space + # and draws each pixel whose center falls into the polygon. + # Therefore it draws a polygon which is 0.5 "fatter" in expectation. + # We therefore dilate the input polygon by 0.5 as our input. + poly = Polygon(poly_coord).buffer(0.5, resolution=4) + + if not label.hasInstances or label.ignoreInEval: + # even if we won't store the polygon it still contributes to overlaps resolution + polygons_union = polygons_union.union(poly) + continue + + # Take non-overlapping part of the polygon + poly_wo_overlaps = poly.difference(polygons_union) + if poly_wo_overlaps.is_empty: + continue + polygons_union = polygons_union.union(poly) + + anno = {} + anno["iscrowd"] = label_name.endswith("group") + anno["category_id"] = label.id + + if isinstance(poly_wo_overlaps, Polygon): + poly_list = [poly_wo_overlaps] + elif isinstance(poly_wo_overlaps, MultiPolygon): + poly_list = poly_wo_overlaps.geoms + else: + raise NotImplementedError("Unknown geometric structure {}".format(poly_wo_overlaps)) + + poly_coord = [] + for poly_el in poly_list: + # COCO API can work only with exterior boundaries now, hence we store only them. + # TODO: store both exterior and interior boundaries once other parts of the + # codebase support holes in polygons. + poly_coord.append(list(chain(*poly_el.exterior.coords))) + anno["segmentation"] = poly_coord + (xmin, ymin, xmax, ymax) = poly_wo_overlaps.bounds + + anno["bbox"] = (xmin, ymin, xmax, ymax) + anno["bbox_mode"] = BoxMode.XYXY_ABS + + annos.append(anno) + else: + # See also the official annotation parsing scripts at + # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/instances2dict.py # noqa + with PathManager.open(instance_id_file, "rb") as f: + inst_image = np.asarray(Image.open(f), order="F") + # ids < 24 are stuff labels (filtering them first is about 5% faster) + flattened_ids = np.unique(inst_image[inst_image >= 24]) + + ret = { + "file_name": image_file, + "image_id": os.path.basename(image_file), + "height": inst_image.shape[0], + "width": inst_image.shape[1], + } + + for instance_id in flattened_ids: + # For non-crowd annotations, instance_id // 1000 is the label_id + # Crowd annotations have <1000 instance ids + label_id = instance_id // 1000 if instance_id >= 1000 else instance_id + label = id2label[label_id] + if not label.hasInstances or label.ignoreInEval: + continue + + anno = {} + anno["iscrowd"] = instance_id < 1000 + anno["category_id"] = label.id + + mask = np.asarray(inst_image == instance_id, dtype=np.uint8, order="F") + + inds = np.nonzero(mask) + ymin, ymax = inds[0].min(), inds[0].max() + xmin, xmax = inds[1].min(), inds[1].max() + anno["bbox"] = (xmin, ymin, xmax, ymax) + if xmax <= xmin or ymax <= ymin: + continue + anno["bbox_mode"] = BoxMode.XYXY_ABS + if to_polygons: + # This conversion comes from D4809743 and D5171122, + # when Mask-RCNN was first developed. + contours = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[ + -2 + ] + polygons = [c.reshape(-1).tolist() for c in contours if len(c) >= 3] + # opencv's can produce invalid polygons + if len(polygons) == 0: + continue + anno["segmentation"] = polygons + else: + anno["segmentation"] = mask_util.encode(mask[:, :, None])[0] + annos.append(anno) + ret["annotations"] = annos + return ret + + +if __name__ == "__main__": + """ + Test the cityscapes dataset loader. + + Usage: + python -m detectron2.data.datasets.cityscapes \ + cityscapes/leftImg8bit/train cityscapes/gtFine/train + """ + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("image_dir") + parser.add_argument("gt_dir") + parser.add_argument("--type", choices=["instance", "semantic"], default="instance") + args = parser.parse_args() + from detectron2.data.catalog import Metadata + from detectron2.utils.visualizer import Visualizer + from cityscapesscripts.helpers.labels import labels + + logger = setup_logger(name=__name__) + + dirname = "cityscapes-data-vis" + os.makedirs(dirname, exist_ok=True) + + if args.type == "instance": + dicts = load_cityscapes_instances( + args.image_dir, args.gt_dir, from_json=True, to_polygons=True + ) + logger.info("Done loading {} samples.".format(len(dicts))) + + thing_classes = [k.name for k in labels if k.hasInstances and not k.ignoreInEval] + meta = Metadata().set(thing_classes=thing_classes) + + else: + dicts = load_cityscapes_semantic(args.image_dir, args.gt_dir) + logger.info("Done loading {} samples.".format(len(dicts))) + + stuff_classes = [k.name for k in labels if k.trainId != 255] + stuff_colors = [k.color for k in labels if k.trainId != 255] + meta = Metadata().set(stuff_classes=stuff_classes, stuff_colors=stuff_colors) + + for d in dicts: + img = np.array(Image.open(PathManager.open(d["file_name"], "rb"))) + visualizer = Visualizer(img, metadata=meta) + vis = visualizer.draw_dataset_dict(d) + # cv2.imshow("a", vis.get_image()[:, :, ::-1]) + # cv2.waitKey() + fpath = os.path.join(dirname, os.path.basename(d["file_name"])) + vis.save(fpath) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/cityscapes_panoptic.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/cityscapes_panoptic.py new file mode 100644 index 0000000000000000000000000000000000000000..48c136f1623261b079591065fec7c7fc38165076 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/cityscapes_panoptic.py @@ -0,0 +1,187 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import json +import logging +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets.builtin_meta import CITYSCAPES_CATEGORIES +from detectron2.utils.file_io import PathManager + +""" +This file contains functions to register the Cityscapes panoptic dataset to the DatasetCatalog. +""" + + +logger = logging.getLogger(__name__) + + +def get_cityscapes_panoptic_files(image_dir, gt_dir, json_info): + files = [] + # scan through the directory + cities = PathManager.ls(image_dir) + logger.info(f"{len(cities)} cities found in '{image_dir}'.") + image_dict = {} + for city in cities: + city_img_dir = os.path.join(image_dir, city) + for basename in PathManager.ls(city_img_dir): + image_file = os.path.join(city_img_dir, basename) + + suffix = "_leftImg8bit.png" + assert basename.endswith(suffix), basename + basename = os.path.basename(basename)[: -len(suffix)] + + image_dict[basename] = image_file + + for ann in json_info["annotations"]: + image_file = image_dict.get(ann["image_id"], None) + assert image_file is not None, "No image {} found for annotation {}".format( + ann["image_id"], ann["file_name"] + ) + label_file = os.path.join(gt_dir, ann["file_name"]) + segments_info = ann["segments_info"] + + files.append((image_file, label_file, segments_info)) + + assert len(files), "No images found in {}".format(image_dir) + assert PathManager.isfile(files[0][0]), files[0][0] + assert PathManager.isfile(files[0][1]), files[0][1] + return files + + +def load_cityscapes_panoptic(image_dir, gt_dir, gt_json, meta): + """ + Args: + image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train". + gt_dir (str): path to the raw annotations. e.g., + "~/cityscapes/gtFine/cityscapes_panoptic_train". + gt_json (str): path to the json file. e.g., + "~/cityscapes/gtFine/cityscapes_panoptic_train.json". + meta (dict): dictionary containing "thing_dataset_id_to_contiguous_id" + and "stuff_dataset_id_to_contiguous_id" to map category ids to + contiguous ids for training. + + Returns: + list[dict]: a list of dicts in Detectron2 standard format. (See + `Using Custom Datasets `_ ) + """ + + def _convert_category_id(segment_info, meta): + if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]: + segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + else: + segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + return segment_info + + assert os.path.exists( + gt_json + ), "Please run `python cityscapesscripts/preparation/createPanopticImgs.py` to generate label files." # noqa + with open(gt_json) as f: + json_info = json.load(f) + files = get_cityscapes_panoptic_files(image_dir, gt_dir, json_info) + ret = [] + for image_file, label_file, segments_info in files: + sem_label_file = ( + image_file.replace("leftImg8bit", "gtFine").split(".")[0] + "_labelTrainIds.png" + ) + segments_info = [_convert_category_id(x, meta) for x in segments_info] + ret.append( + { + "file_name": image_file, + "image_id": "_".join( + os.path.splitext(os.path.basename(image_file))[0].split("_")[:3] + ), + "sem_seg_file_name": sem_label_file, + "pan_seg_file_name": label_file, + "segments_info": segments_info, + } + ) + assert len(ret), f"No images found in {image_dir}!" + assert PathManager.isfile( + ret[0]["sem_seg_file_name"] + ), "Please generate labelTrainIds.png with cityscapesscripts/preparation/createTrainIdLabelImgs.py" # noqa + assert PathManager.isfile( + ret[0]["pan_seg_file_name"] + ), "Please generate panoptic annotation with python cityscapesscripts/preparation/createPanopticImgs.py" # noqa + return ret + + +_RAW_CITYSCAPES_PANOPTIC_SPLITS = { + "cityscapes_fine_panoptic_train": ( + "cityscapes/leftImg8bit/train", + "cityscapes/gtFine/cityscapes_panoptic_train", + "cityscapes/gtFine/cityscapes_panoptic_train.json", + ), + "cityscapes_fine_panoptic_val": ( + "cityscapes/leftImg8bit/val", + "cityscapes/gtFine/cityscapes_panoptic_val", + "cityscapes/gtFine/cityscapes_panoptic_val.json", + ), + # "cityscapes_fine_panoptic_test": not supported yet +} + + +def register_all_cityscapes_panoptic(root): + meta = {} + # The following metadata maps contiguous id from [0, #thing categories + + # #stuff categories) to their names and colors. We have to replica of the + # same name and color under "thing_*" and "stuff_*" because the current + # visualization function in D2 handles thing and class classes differently + # due to some heuristic used in Panoptic FPN. We keep the same naming to + # enable reusing existing visualization functions. + thing_classes = [k["name"] for k in CITYSCAPES_CATEGORIES] + thing_colors = [k["color"] for k in CITYSCAPES_CATEGORIES] + stuff_classes = [k["name"] for k in CITYSCAPES_CATEGORIES] + stuff_colors = [k["color"] for k in CITYSCAPES_CATEGORIES] + + meta["thing_classes"] = thing_classes + meta["thing_colors"] = thing_colors + meta["stuff_classes"] = stuff_classes + meta["stuff_colors"] = stuff_colors + + # There are three types of ids in cityscapes panoptic segmentation: + # (1) category id: like semantic segmentation, it is the class id for each + # pixel. Since there are some classes not used in evaluation, the category + # id is not always contiguous and thus we have two set of category ids: + # - original category id: category id in the original dataset, mainly + # used for evaluation. + # - contiguous category id: [0, #classes), in order to train the classifier + # (2) instance id: this id is used to differentiate different instances from + # the same category. For "stuff" classes, the instance id is always 0; for + # "thing" classes, the instance id starts from 1 and 0 is reserved for + # ignored instances (e.g. crowd annotation). + # (3) panoptic id: this is the compact id that encode both category and + # instance id by: category_id * 1000 + instance_id. + thing_dataset_id_to_contiguous_id = {} + stuff_dataset_id_to_contiguous_id = {} + + for k in CITYSCAPES_CATEGORIES: + if k["isthing"] == 1: + thing_dataset_id_to_contiguous_id[k["id"]] = k["trainId"] + else: + stuff_dataset_id_to_contiguous_id[k["id"]] = k["trainId"] + + meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id + meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id + + for key, (image_dir, gt_dir, gt_json) in _RAW_CITYSCAPES_PANOPTIC_SPLITS.items(): + image_dir = os.path.join(root, image_dir) + gt_dir = os.path.join(root, gt_dir) + gt_json = os.path.join(root, gt_json) + + DatasetCatalog.register( + key, lambda x=image_dir, y=gt_dir, z=gt_json: load_cityscapes_panoptic(x, y, z, meta) + ) + MetadataCatalog.get(key).set( + panoptic_root=gt_dir, + image_root=image_dir, + panoptic_json=gt_json, + gt_dir=gt_dir.replace("cityscapes_panoptic_", ""), + evaluator_type="cityscapes_panoptic_seg", + ignore_label=255, + label_divisor=1000, + **meta, + ) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/coco.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/coco.py new file mode 100644 index 0000000000000000000000000000000000000000..ed4f7ccb20efa3b54c719783e279c381ca5d8587 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/coco.py @@ -0,0 +1,539 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import contextlib +import datetime +import io +import json +import logging +import numpy as np +import os +import shutil +import pycocotools.mask as mask_util +from fvcore.common.timer import Timer +from iopath.common.file_io import file_lock +from PIL import Image + +from detectron2.structures import Boxes, BoxMode, PolygonMasks, RotatedBoxes +from detectron2.utils.file_io import PathManager + +from .. import DatasetCatalog, MetadataCatalog + +""" +This file contains functions to parse COCO-format annotations into dicts in "Detectron2 format". +""" + + +logger = logging.getLogger(__name__) + +__all__ = ["load_coco_json", "load_sem_seg", "convert_to_coco_json", "register_coco_instances"] + + +def load_coco_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None): + """ + Load a json file with COCO's instances annotation format. + Currently supports instance detection, instance segmentation, + and person keypoints annotations. + + Args: + json_file (str): full path to the json file in COCO instances annotation format. + image_root (str or path-like): the directory where the images in this json file exists. + dataset_name (str or None): the name of the dataset (e.g., coco_2017_train). + When provided, this function will also do the following: + + * Put "thing_classes" into the metadata associated with this dataset. + * Map the category ids into a contiguous range (needed by standard dataset format), + and add "thing_dataset_id_to_contiguous_id" to the metadata associated + with this dataset. + + This option should usually be provided, unless users need to load + the original json content and apply more processing manually. + extra_annotation_keys (list[str]): list of per-annotation keys that should also be + loaded into the dataset dict (besides "iscrowd", "bbox", "keypoints", + "category_id", "segmentation"). The values for these keys will be returned as-is. + For example, the densepose annotations are loaded in this way. + + Returns: + list[dict]: a list of dicts in Detectron2 standard dataset dicts format (See + `Using Custom Datasets `_ ) when `dataset_name` is not None. + If `dataset_name` is None, the returned `category_ids` may be + incontiguous and may not conform to the Detectron2 standard format. + + Notes: + 1. This function does not read the image files. + The results do not have the "image" field. + """ + from pycocotools.coco import COCO + + timer = Timer() + json_file = PathManager.get_local_path(json_file) + with contextlib.redirect_stdout(io.StringIO()): + coco_api = COCO(json_file) + if timer.seconds() > 1: + logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) + + id_map = None + if dataset_name is not None: + meta = MetadataCatalog.get(dataset_name) + cat_ids = sorted(coco_api.getCatIds()) + cats = coco_api.loadCats(cat_ids) + # The categories in a custom json file may not be sorted. + thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])] + meta.thing_classes = thing_classes + + # In COCO, certain category ids are artificially removed, + # and by convention they are always ignored. + # We deal with COCO's id issue and translate + # the category ids to contiguous ids in [0, 80). + + # It works by looking at the "categories" field in the json, therefore + # if users' own json also have incontiguous ids, we'll + # apply this mapping as well but print a warning. + if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)): + if "coco" not in dataset_name: + logger.warning( + """ +Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you. +""" + ) + id_map = {v: i for i, v in enumerate(cat_ids)} + meta.thing_dataset_id_to_contiguous_id = id_map + + # sort indices for reproducible results + img_ids = sorted(coco_api.imgs.keys()) + # imgs is a list of dicts, each looks something like: + # {'license': 4, + # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg', + # 'file_name': 'COCO_val2014_000000001268.jpg', + # 'height': 427, + # 'width': 640, + # 'date_captured': '2013-11-17 05:57:24', + # 'id': 1268} + imgs = coco_api.loadImgs(img_ids) + # anns is a list[list[dict]], where each dict is an annotation + # record for an object. The inner list enumerates the objects in an image + # and the outer list enumerates over images. Example of anns[0]: + # [{'segmentation': [[192.81, + # 247.09, + # ... + # 219.03, + # 249.06]], + # 'area': 1035.749, + # 'iscrowd': 0, + # 'image_id': 1268, + # 'bbox': [192.81, 224.8, 74.73, 33.43], + # 'category_id': 16, + # 'id': 42986}, + # ...] + anns = [coco_api.imgToAnns[img_id] for img_id in img_ids] + total_num_valid_anns = sum([len(x) for x in anns]) + total_num_anns = len(coco_api.anns) + if total_num_valid_anns < total_num_anns: + logger.warning( + f"{json_file} contains {total_num_anns} annotations, but only " + f"{total_num_valid_anns} of them match to images in the file." + ) + + if "minival" not in json_file: + # The popular valminusminival & minival annotations for COCO2014 contain this bug. + # However the ratio of buggy annotations there is tiny and does not affect accuracy. + # Therefore we explicitly white-list them. + ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] + assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique!".format( + json_file + ) + + imgs_anns = list(zip(imgs, anns)) + logger.info("Loaded {} images in COCO format from {}".format(len(imgs_anns), json_file)) + + dataset_dicts = [] + + ann_keys = ["iscrowd", "bbox", "keypoints", "category_id"] + (extra_annotation_keys or []) + + num_instances_without_valid_segmentation = 0 + + for (img_dict, anno_dict_list) in imgs_anns: + record = {} + record["file_name"] = os.path.join(image_root, img_dict["file_name"]) + record["height"] = img_dict["height"] + record["width"] = img_dict["width"] + image_id = record["image_id"] = img_dict["id"] + + objs = [] + for anno in anno_dict_list: + # Check that the image_id in this annotation is the same as + # the image_id we're looking at. + # This fails only when the data parsing logic or the annotation file is buggy. + + # The original COCO valminusminival2014 & minival2014 annotation files + # actually contains bugs that, together with certain ways of using COCO API, + # can trigger this assertion. + assert anno["image_id"] == image_id + + assert anno.get("ignore", 0) == 0, '"ignore" in COCO json file is not supported.' + + obj = {key: anno[key] for key in ann_keys if key in anno} + if "bbox" in obj and len(obj["bbox"]) == 0: + raise ValueError( + f"One annotation of image {image_id} contains empty 'bbox' value! " + "This json does not have valid COCO format." + ) + + segm = anno.get("segmentation", None) + if segm: # either list[list[float]] or dict(RLE) + if isinstance(segm, dict): + if isinstance(segm["counts"], list): + # convert to compressed RLE + segm = mask_util.frPyObjects(segm, *segm["size"]) + else: + # filter out invalid polygons (< 3 points) + segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] + if len(segm) == 0: + num_instances_without_valid_segmentation += 1 + continue # ignore this instance + obj["segmentation"] = segm + + keypts = anno.get("keypoints", None) + if keypts: # list[int] + for idx, v in enumerate(keypts): + if idx % 3 != 2: + # COCO's segmentation coordinates are floating points in [0, H or W], + # but keypoint coordinates are integers in [0, H-1 or W-1] + # Therefore we assume the coordinates are "pixel indices" and + # add 0.5 to convert to floating point coordinates. + keypts[idx] = v + 0.5 + obj["keypoints"] = keypts + + obj["bbox_mode"] = BoxMode.XYWH_ABS + if id_map: + annotation_category_id = obj["category_id"] + try: + obj["category_id"] = id_map[annotation_category_id] + except KeyError as e: + raise KeyError( + f"Encountered category_id={annotation_category_id} " + "but this id does not exist in 'categories' of the json file." + ) from e + objs.append(obj) + record["annotations"] = objs + dataset_dicts.append(record) + + if num_instances_without_valid_segmentation > 0: + logger.warning( + "Filtered out {} instances without valid segmentation. ".format( + num_instances_without_valid_segmentation + ) + + "There might be issues in your dataset generation process. Please " + "check https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html carefully" + ) + return dataset_dicts + + +def load_sem_seg(gt_root, image_root, gt_ext="png", image_ext="jpg"): + """ + Load semantic segmentation datasets. All files under "gt_root" with "gt_ext" extension are + treated as ground truth annotations and all files under "image_root" with "image_ext" extension + as input images. Ground truth and input images are matched using file paths relative to + "gt_root" and "image_root" respectively without taking into account file extensions. + This works for COCO as well as some other datasets. + + Args: + gt_root (str): full path to ground truth semantic segmentation files. Semantic segmentation + annotations are stored as images with integer values in pixels that represent + corresponding semantic labels. + image_root (str): the directory where the input images are. + gt_ext (str): file extension for ground truth annotations. + image_ext (str): file extension for input images. + + Returns: + list[dict]: + a list of dicts in detectron2 standard format without instance-level + annotation. + + Notes: + 1. This function does not read the image and ground truth files. + The results do not have the "image" and "sem_seg" fields. + """ + + # We match input images with ground truth based on their relative filepaths (without file + # extensions) starting from 'image_root' and 'gt_root' respectively. + def file2id(folder_path, file_path): + # extract relative path starting from `folder_path` + image_id = os.path.normpath(os.path.relpath(file_path, start=folder_path)) + # remove file extension + image_id = os.path.splitext(image_id)[0] + return image_id + + input_files = sorted( + (os.path.join(image_root, f) for f in PathManager.ls(image_root) if f.endswith(image_ext)), + key=lambda file_path: file2id(image_root, file_path), + ) + gt_files = sorted( + (os.path.join(gt_root, f) for f in PathManager.ls(gt_root) if f.endswith(gt_ext)), + key=lambda file_path: file2id(gt_root, file_path), + ) + + assert len(gt_files) > 0, "No annotations found in {}.".format(gt_root) + + # Use the intersection, so that val2017_100 annotations can run smoothly with val2017 images + if len(input_files) != len(gt_files): + logger.warn( + "Directory {} and {} has {} and {} files, respectively.".format( + image_root, gt_root, len(input_files), len(gt_files) + ) + ) + input_basenames = [os.path.basename(f)[: -len(image_ext)] for f in input_files] + gt_basenames = [os.path.basename(f)[: -len(gt_ext)] for f in gt_files] + intersect = list(set(input_basenames) & set(gt_basenames)) + # sort, otherwise each worker may obtain a list[dict] in different order + intersect = sorted(intersect) + logger.warn("Will use their intersection of {} files.".format(len(intersect))) + input_files = [os.path.join(image_root, f + image_ext) for f in intersect] + gt_files = [os.path.join(gt_root, f + gt_ext) for f in intersect] + + logger.info( + "Loaded {} images with semantic segmentation from {}".format(len(input_files), image_root) + ) + + dataset_dicts = [] + for (img_path, gt_path) in zip(input_files, gt_files): + record = {} + record["file_name"] = img_path + record["sem_seg_file_name"] = gt_path + dataset_dicts.append(record) + + return dataset_dicts + + +def convert_to_coco_dict(dataset_name): + """ + Convert an instance detection/segmentation or keypoint detection dataset + in detectron2's standard format into COCO json format. + + Generic dataset description can be found here: + https://detectron2.readthedocs.io/tutorials/datasets.html#register-a-dataset + + COCO data format description can be found here: + http://cocodataset.org/#format-data + + Args: + dataset_name (str): + name of the source dataset + Must be registered in DatastCatalog and in detectron2's standard format. + Must have corresponding metadata "thing_classes" + Returns: + coco_dict: serializable dict in COCO json format + """ + + dataset_dicts = DatasetCatalog.get(dataset_name) + metadata = MetadataCatalog.get(dataset_name) + + # unmap the category mapping ids for COCO + if hasattr(metadata, "thing_dataset_id_to_contiguous_id"): + reverse_id_mapping = {v: k for k, v in metadata.thing_dataset_id_to_contiguous_id.items()} + reverse_id_mapper = lambda contiguous_id: reverse_id_mapping[contiguous_id] # noqa + else: + reverse_id_mapper = lambda contiguous_id: contiguous_id # noqa + + categories = [ + {"id": reverse_id_mapper(id), "name": name} + for id, name in enumerate(metadata.thing_classes) + ] + + logger.info("Converting dataset dicts into COCO format") + coco_images = [] + coco_annotations = [] + + for image_id, image_dict in enumerate(dataset_dicts): + coco_image = { + "id": image_dict.get("image_id", image_id), + "width": int(image_dict["width"]), + "height": int(image_dict["height"]), + "file_name": str(image_dict["file_name"]), + } + coco_images.append(coco_image) + + anns_per_image = image_dict.get("annotations", []) + for annotation in anns_per_image: + # create a new dict with only COCO fields + coco_annotation = {} + + # COCO requirement: XYWH box format for axis-align and XYWHA for rotated + bbox = annotation["bbox"] + if isinstance(bbox, np.ndarray): + if bbox.ndim != 1: + raise ValueError(f"bbox has to be 1-dimensional. Got shape={bbox.shape}.") + bbox = bbox.tolist() + if len(bbox) not in [4, 5]: + raise ValueError(f"bbox has to has length 4 or 5. Got {bbox}.") + from_bbox_mode = annotation["bbox_mode"] + to_bbox_mode = BoxMode.XYWH_ABS if len(bbox) == 4 else BoxMode.XYWHA_ABS + bbox = BoxMode.convert(bbox, from_bbox_mode, to_bbox_mode) + + # COCO requirement: instance area + if "segmentation" in annotation: + # Computing areas for instances by counting the pixels + segmentation = annotation["segmentation"] + # TODO: check segmentation type: RLE, BinaryMask or Polygon + if isinstance(segmentation, list): + polygons = PolygonMasks([segmentation]) + area = polygons.area()[0].item() + elif isinstance(segmentation, dict): # RLE + area = mask_util.area(segmentation).item() + else: + raise TypeError(f"Unknown segmentation type {type(segmentation)}!") + else: + # Computing areas using bounding boxes + if to_bbox_mode == BoxMode.XYWH_ABS: + bbox_xy = BoxMode.convert(bbox, to_bbox_mode, BoxMode.XYXY_ABS) + area = Boxes([bbox_xy]).area()[0].item() + else: + area = RotatedBoxes([bbox]).area()[0].item() + + if "keypoints" in annotation: + keypoints = annotation["keypoints"] # list[int] + for idx, v in enumerate(keypoints): + if idx % 3 != 2: + # COCO's segmentation coordinates are floating points in [0, H or W], + # but keypoint coordinates are integers in [0, H-1 or W-1] + # For COCO format consistency we substract 0.5 + # https://github.com/facebookresearch/detectron2/pull/175#issuecomment-551202163 + keypoints[idx] = v - 0.5 + if "num_keypoints" in annotation: + num_keypoints = annotation["num_keypoints"] + else: + num_keypoints = sum(kp > 0 for kp in keypoints[2::3]) + + # COCO requirement: + # linking annotations to images + # "id" field must start with 1 + coco_annotation["id"] = len(coco_annotations) + 1 + coco_annotation["image_id"] = coco_image["id"] + coco_annotation["bbox"] = [round(float(x), 3) for x in bbox] + coco_annotation["area"] = float(area) + coco_annotation["iscrowd"] = int(annotation.get("iscrowd", 0)) + coco_annotation["category_id"] = int(reverse_id_mapper(annotation["category_id"])) + + # Add optional fields + if "keypoints" in annotation: + coco_annotation["keypoints"] = keypoints + coco_annotation["num_keypoints"] = num_keypoints + + if "segmentation" in annotation: + seg = coco_annotation["segmentation"] = annotation["segmentation"] + if isinstance(seg, dict): # RLE + counts = seg["counts"] + if not isinstance(counts, str): + # make it json-serializable + seg["counts"] = counts.decode("ascii") + + coco_annotations.append(coco_annotation) + + logger.info( + "Conversion finished, " + f"#images: {len(coco_images)}, #annotations: {len(coco_annotations)}" + ) + + info = { + "date_created": str(datetime.datetime.now()), + "description": "Automatically generated COCO json file for Detectron2.", + } + coco_dict = {"info": info, "images": coco_images, "categories": categories, "licenses": None} + if len(coco_annotations) > 0: + coco_dict["annotations"] = coco_annotations + return coco_dict + + +def convert_to_coco_json(dataset_name, output_file, allow_cached=True): + """ + Converts dataset into COCO format and saves it to a json file. + dataset_name must be registered in DatasetCatalog and in detectron2's standard format. + + Args: + dataset_name: + reference from the config file to the catalogs + must be registered in DatasetCatalog and in detectron2's standard format + output_file: path of json file that will be saved to + allow_cached: if json file is already present then skip conversion + """ + + # TODO: The dataset or the conversion script *may* change, + # a checksum would be useful for validating the cached data + + PathManager.mkdirs(os.path.dirname(output_file)) + with file_lock(output_file): + if PathManager.exists(output_file) and allow_cached: + logger.warning( + f"Using previously cached COCO format annotations at '{output_file}'. " + "You need to clear the cache file if your dataset has been modified." + ) + else: + logger.info(f"Converting annotations of dataset '{dataset_name}' to COCO format ...)") + coco_dict = convert_to_coco_dict(dataset_name) + + logger.info(f"Caching COCO format annotations at '{output_file}' ...") + tmp_file = output_file + ".tmp" + with PathManager.open(tmp_file, "w") as f: + json.dump(coco_dict, f) + shutil.move(tmp_file, output_file) + + +def register_coco_instances(name, metadata, json_file, image_root): + """ + Register a dataset in COCO's json annotation format for + instance detection, instance segmentation and keypoint detection. + (i.e., Type 1 and 2 in http://cocodataset.org/#format-data. + `instances*.json` and `person_keypoints*.json` in the dataset). + + This is an example of how to register a new dataset. + You can do something similar to this function, to register new datasets. + + Args: + name (str): the name that identifies a dataset, e.g. "coco_2014_train". + metadata (dict): extra metadata associated with this dataset. You can + leave it as an empty dict. + json_file (str): path to the json instance annotation file. + image_root (str or path-like): directory which contains all the images. + """ + assert isinstance(name, str), name + assert isinstance(json_file, (str, os.PathLike)), json_file + assert isinstance(image_root, (str, os.PathLike)), image_root + # 1. register a function which returns dicts + DatasetCatalog.register(name, lambda: load_coco_json(json_file, image_root, name)) + + # 2. Optionally, add metadata about this dataset, + # since they might be useful in evaluation, visualization or logging + MetadataCatalog.get(name).set( + json_file=json_file, image_root=image_root, evaluator_type="coco", **metadata + ) + + +if __name__ == "__main__": + """ + Test the COCO json dataset loader. + + Usage: + python -m detectron2.data.datasets.coco \ + path/to/json path/to/image_root dataset_name + + "dataset_name" can be "coco_2014_minival_100", or other + pre-registered ones + """ + from detectron2.utils.logger import setup_logger + from detectron2.utils.visualizer import Visualizer + import detectron2.data.datasets # noqa # add pre-defined metadata + import sys + + logger = setup_logger(name=__name__) + assert sys.argv[3] in DatasetCatalog.list() + meta = MetadataCatalog.get(sys.argv[3]) + + dicts = load_coco_json(sys.argv[1], sys.argv[2], sys.argv[3]) + logger.info("Done loading {} samples.".format(len(dicts))) + + dirname = "coco-data-vis" + os.makedirs(dirname, exist_ok=True) + for d in dicts: + img = np.array(Image.open(d["file_name"])) + visualizer = Visualizer(img, metadata=meta) + vis = visualizer.draw_dataset_dict(d) + fpath = os.path.join(dirname, os.path.basename(d["file_name"])) + vis.save(fpath) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/coco_panoptic.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/coco_panoptic.py new file mode 100644 index 0000000000000000000000000000000000000000..b8dae44317b556610d7fed39017e082d7e855956 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/coco_panoptic.py @@ -0,0 +1,228 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import json +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.utils.file_io import PathManager + +from .coco import load_coco_json, load_sem_seg + +__all__ = ["register_coco_panoptic", "register_coco_panoptic_separated"] + + +def load_coco_panoptic_json(json_file, image_dir, gt_dir, meta): + """ + Args: + image_dir (str): path to the raw dataset. e.g., "~/coco/train2017". + gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017". + json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json". + + Returns: + list[dict]: a list of dicts in Detectron2 standard format. (See + `Using Custom Datasets `_ ) + """ + + def _convert_category_id(segment_info, meta): + if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]: + segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = True + else: + segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = False + return segment_info + + with PathManager.open(json_file) as f: + json_info = json.load(f) + + ret = [] + for ann in json_info["annotations"]: + image_id = int(ann["image_id"]) + # TODO: currently we assume image and label has the same filename but + # different extension, and images have extension ".jpg" for COCO. Need + # to make image extension a user-provided argument if we extend this + # function to support other COCO-like datasets. + image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg") + label_file = os.path.join(gt_dir, ann["file_name"]) + segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]] + ret.append( + { + "file_name": image_file, + "image_id": image_id, + "pan_seg_file_name": label_file, + "segments_info": segments_info, + } + ) + assert len(ret), f"No images found in {image_dir}!" + assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"] + assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"] + return ret + + +def register_coco_panoptic( + name, metadata, image_root, panoptic_root, panoptic_json, instances_json=None +): + """ + Register a "standard" version of COCO panoptic segmentation dataset named `name`. + The dictionaries in this registered dataset follows detectron2's standard format. + Hence it's called "standard". + + Args: + name (str): the name that identifies a dataset, + e.g. "coco_2017_train_panoptic" + metadata (dict): extra metadata associated with this dataset. + image_root (str): directory which contains all the images + panoptic_root (str): directory which contains panoptic annotation images in COCO format + panoptic_json (str): path to the json panoptic annotation file in COCO format + sem_seg_root (none): not used, to be consistent with + `register_coco_panoptic_separated`. + instances_json (str): path to the json instance annotation file + """ + panoptic_name = name + DatasetCatalog.register( + panoptic_name, + lambda: load_coco_panoptic_json(panoptic_json, image_root, panoptic_root, metadata), + ) + MetadataCatalog.get(panoptic_name).set( + panoptic_root=panoptic_root, + image_root=image_root, + panoptic_json=panoptic_json, + json_file=instances_json, + evaluator_type="coco_panoptic_seg", + ignore_label=255, + label_divisor=1000, + **metadata, + ) + + +def register_coco_panoptic_separated( + name, metadata, image_root, panoptic_root, panoptic_json, sem_seg_root, instances_json +): + """ + Register a "separated" version of COCO panoptic segmentation dataset named `name`. + The annotations in this registered dataset will contain both instance annotations and + semantic annotations, each with its own contiguous ids. Hence it's called "separated". + + It follows the setting used by the PanopticFPN paper: + + 1. The instance annotations directly come from polygons in the COCO + instances annotation task, rather than from the masks in the COCO panoptic annotations. + + The two format have small differences: + Polygons in the instance annotations may have overlaps. + The mask annotations are produced by labeling the overlapped polygons + with depth ordering. + + 2. The semantic annotations are converted from panoptic annotations, where + all "things" are assigned a semantic id of 0. + All semantic categories will therefore have ids in contiguous + range [1, #stuff_categories]. + + This function will also register a pure semantic segmentation dataset + named ``name + '_stuffonly'``. + + Args: + name (str): the name that identifies a dataset, + e.g. "coco_2017_train_panoptic" + metadata (dict): extra metadata associated with this dataset. + image_root (str): directory which contains all the images + panoptic_root (str): directory which contains panoptic annotation images + panoptic_json (str): path to the json panoptic annotation file + sem_seg_root (str): directory which contains all the ground truth segmentation annotations. + instances_json (str): path to the json instance annotation file + """ + panoptic_name = name + "_separated" + DatasetCatalog.register( + panoptic_name, + lambda: merge_to_panoptic( + load_coco_json(instances_json, image_root, panoptic_name), + load_sem_seg(sem_seg_root, image_root), + ), + ) + MetadataCatalog.get(panoptic_name).set( + panoptic_root=panoptic_root, + image_root=image_root, + panoptic_json=panoptic_json, + sem_seg_root=sem_seg_root, + json_file=instances_json, # TODO rename + evaluator_type="coco_panoptic_seg", + ignore_label=255, + **metadata, + ) + + semantic_name = name + "_stuffonly" + DatasetCatalog.register(semantic_name, lambda: load_sem_seg(sem_seg_root, image_root)) + MetadataCatalog.get(semantic_name).set( + sem_seg_root=sem_seg_root, + image_root=image_root, + evaluator_type="sem_seg", + ignore_label=255, + **metadata, + ) + + +def merge_to_panoptic(detection_dicts, sem_seg_dicts): + """ + Create dataset dicts for panoptic segmentation, by + merging two dicts using "file_name" field to match their entries. + + Args: + detection_dicts (list[dict]): lists of dicts for object detection or instance segmentation. + sem_seg_dicts (list[dict]): lists of dicts for semantic segmentation. + + Returns: + list[dict] (one per input image): Each dict contains all (key, value) pairs from dicts in + both detection_dicts and sem_seg_dicts that correspond to the same image. + The function assumes that the same key in different dicts has the same value. + """ + results = [] + sem_seg_file_to_entry = {x["file_name"]: x for x in sem_seg_dicts} + assert len(sem_seg_file_to_entry) > 0 + + for det_dict in detection_dicts: + dic = copy.copy(det_dict) + dic.update(sem_seg_file_to_entry[dic["file_name"]]) + results.append(dic) + return results + + +if __name__ == "__main__": + """ + Test the COCO panoptic dataset loader. + + Usage: + python -m detectron2.data.datasets.coco_panoptic \ + path/to/image_root path/to/panoptic_root path/to/panoptic_json dataset_name 10 + + "dataset_name" can be "coco_2017_train_panoptic", or other + pre-registered ones + """ + from detectron2.utils.logger import setup_logger + from detectron2.utils.visualizer import Visualizer + import detectron2.data.datasets # noqa # add pre-defined metadata + import sys + from PIL import Image + import numpy as np + + logger = setup_logger(name=__name__) + assert sys.argv[4] in DatasetCatalog.list() + meta = MetadataCatalog.get(sys.argv[4]) + + dicts = load_coco_panoptic_json(sys.argv[3], sys.argv[1], sys.argv[2], meta.as_dict()) + logger.info("Done loading {} samples.".format(len(dicts))) + + dirname = "coco-data-vis" + os.makedirs(dirname, exist_ok=True) + num_imgs_to_vis = int(sys.argv[5]) + for i, d in enumerate(dicts): + img = np.array(Image.open(d["file_name"])) + visualizer = Visualizer(img, metadata=meta) + vis = visualizer.draw_dataset_dict(d) + fpath = os.path.join(dirname, os.path.basename(d["file_name"])) + vis.save(fpath) + if i + 1 >= num_imgs_to_vis: + break diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/lvis.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/lvis.py new file mode 100644 index 0000000000000000000000000000000000000000..78b396534cc1a119677d2af1015fc78a18b83846 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/lvis.py @@ -0,0 +1,240 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import os +from fvcore.common.timer import Timer + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.structures import BoxMode +from detectron2.utils.file_io import PathManager + +from .builtin_meta import _get_coco_instances_meta +from .lvis_v0_5_categories import LVIS_CATEGORIES as LVIS_V0_5_CATEGORIES +from .lvis_v1_categories import LVIS_CATEGORIES as LVIS_V1_CATEGORIES + +""" +This file contains functions to parse LVIS-format annotations into dicts in the +"Detectron2 format". +""" + +logger = logging.getLogger(__name__) + +__all__ = ["load_lvis_json", "register_lvis_instances", "get_lvis_instances_meta"] + + +def register_lvis_instances(name, metadata, json_file, image_root): + """ + Register a dataset in LVIS's json annotation format for instance detection and segmentation. + + Args: + name (str): a name that identifies the dataset, e.g. "lvis_v0.5_train". + metadata (dict): extra metadata associated with this dataset. It can be an empty dict. + json_file (str): path to the json instance annotation file. + image_root (str or path-like): directory which contains all the images. + """ + DatasetCatalog.register(name, lambda: load_lvis_json(json_file, image_root, name)) + MetadataCatalog.get(name).set( + json_file=json_file, image_root=image_root, evaluator_type="lvis", **metadata + ) + + +def load_lvis_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None): + """ + Load a json file in LVIS's annotation format. + + Args: + json_file (str): full path to the LVIS json annotation file. + image_root (str): the directory where the images in this json file exists. + dataset_name (str): the name of the dataset (e.g., "lvis_v0.5_train"). + If provided, this function will put "thing_classes" into the metadata + associated with this dataset. + extra_annotation_keys (list[str]): list of per-annotation keys that should also be + loaded into the dataset dict (besides "bbox", "bbox_mode", "category_id", + "segmentation"). The values for these keys will be returned as-is. + + Returns: + list[dict]: a list of dicts in Detectron2 standard format. (See + `Using Custom Datasets `_ ) + + Notes: + 1. This function does not read the image files. + The results do not have the "image" field. + """ + from lvis import LVIS + + json_file = PathManager.get_local_path(json_file) + + timer = Timer() + lvis_api = LVIS(json_file) + if timer.seconds() > 1: + logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) + + if dataset_name is not None: + meta = get_lvis_instances_meta(dataset_name) + MetadataCatalog.get(dataset_name).set(**meta) + + # sort indices for reproducible results + img_ids = sorted(lvis_api.imgs.keys()) + # imgs is a list of dicts, each looks something like: + # {'license': 4, + # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg', + # 'file_name': 'COCO_val2014_000000001268.jpg', + # 'height': 427, + # 'width': 640, + # 'date_captured': '2013-11-17 05:57:24', + # 'id': 1268} + imgs = lvis_api.load_imgs(img_ids) + # anns is a list[list[dict]], where each dict is an annotation + # record for an object. The inner list enumerates the objects in an image + # and the outer list enumerates over images. Example of anns[0]: + # [{'segmentation': [[192.81, + # 247.09, + # ... + # 219.03, + # 249.06]], + # 'area': 1035.749, + # 'image_id': 1268, + # 'bbox': [192.81, 224.8, 74.73, 33.43], + # 'category_id': 16, + # 'id': 42986}, + # ...] + anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids] + + # Sanity check that each annotation has a unique id + ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] + assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique".format( + json_file + ) + + imgs_anns = list(zip(imgs, anns)) + + logger.info("Loaded {} images in the LVIS format from {}".format(len(imgs_anns), json_file)) + + if extra_annotation_keys: + logger.info( + "The following extra annotation keys will be loaded: {} ".format(extra_annotation_keys) + ) + else: + extra_annotation_keys = [] + + def get_file_name(img_root, img_dict): + # Determine the path including the split folder ("train2017", "val2017", "test2017") from + # the coco_url field. Example: + # 'coco_url': 'http://images.cocodataset.org/train2017/000000155379.jpg' + split_folder, file_name = img_dict["coco_url"].split("/")[-2:] + return os.path.join(img_root + split_folder, file_name) + + dataset_dicts = [] + + for (img_dict, anno_dict_list) in imgs_anns: + record = {} + record["file_name"] = get_file_name(image_root, img_dict) + record["height"] = img_dict["height"] + record["width"] = img_dict["width"] + record["not_exhaustive_category_ids"] = img_dict.get("not_exhaustive_category_ids", []) + record["neg_category_ids"] = img_dict.get("neg_category_ids", []) + image_id = record["image_id"] = img_dict["id"] + + objs = [] + for anno in anno_dict_list: + # Check that the image_id in this annotation is the same as + # the image_id we're looking at. + # This fails only when the data parsing logic or the annotation file is buggy. + assert anno["image_id"] == image_id + obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS} + # LVIS data loader can be used to load COCO dataset categories. In this case `meta` + # variable will have a field with COCO-specific category mapping. + if dataset_name is not None and "thing_dataset_id_to_contiguous_id" in meta: + obj["category_id"] = meta["thing_dataset_id_to_contiguous_id"][anno["category_id"]] + else: + obj["category_id"] = anno["category_id"] - 1 # Convert 1-indexed to 0-indexed + segm = anno["segmentation"] # list[list[float]] + # filter out invalid polygons (< 3 points) + valid_segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] + assert len(segm) == len( + valid_segm + ), "Annotation contains an invalid polygon with < 3 points" + assert len(segm) > 0 + obj["segmentation"] = segm + for extra_ann_key in extra_annotation_keys: + obj[extra_ann_key] = anno[extra_ann_key] + objs.append(obj) + record["annotations"] = objs + dataset_dicts.append(record) + + return dataset_dicts + + +def get_lvis_instances_meta(dataset_name): + """ + Load LVIS metadata. + + Args: + dataset_name (str): LVIS dataset name without the split name (e.g., "lvis_v0.5"). + + Returns: + dict: LVIS metadata with keys: thing_classes + """ + if "cocofied" in dataset_name: + return _get_coco_instances_meta() + if "v0.5" in dataset_name: + return _get_lvis_instances_meta_v0_5() + elif "v1" in dataset_name: + return _get_lvis_instances_meta_v1() + raise ValueError("No built-in metadata for dataset {}".format(dataset_name)) + + +def _get_lvis_instances_meta_v0_5(): + assert len(LVIS_V0_5_CATEGORIES) == 1230 + cat_ids = [k["id"] for k in LVIS_V0_5_CATEGORIES] + assert min(cat_ids) == 1 and max(cat_ids) == len( + cat_ids + ), "Category ids are not in [1, #categories], as expected" + # Ensure that the category list is sorted by id + lvis_categories = sorted(LVIS_V0_5_CATEGORIES, key=lambda x: x["id"]) + thing_classes = [k["synonyms"][0] for k in lvis_categories] + meta = {"thing_classes": thing_classes} + return meta + + +def _get_lvis_instances_meta_v1(): + assert len(LVIS_V1_CATEGORIES) == 1203 + cat_ids = [k["id"] for k in LVIS_V1_CATEGORIES] + assert min(cat_ids) == 1 and max(cat_ids) == len( + cat_ids + ), "Category ids are not in [1, #categories], as expected" + # Ensure that the category list is sorted by id + lvis_categories = sorted(LVIS_V1_CATEGORIES, key=lambda x: x["id"]) + thing_classes = [k["synonyms"][0] for k in lvis_categories] + meta = {"thing_classes": thing_classes} + return meta + + +if __name__ == "__main__": + """ + Test the LVIS json dataset loader. + + Usage: + python -m detectron2.data.datasets.lvis \ + path/to/json path/to/image_root dataset_name vis_limit + """ + import sys + import numpy as np + from detectron2.utils.logger import setup_logger + from PIL import Image + import detectron2.data.datasets # noqa # add pre-defined metadata + from detectron2.utils.visualizer import Visualizer + + logger = setup_logger(name=__name__) + meta = MetadataCatalog.get(sys.argv[3]) + + dicts = load_lvis_json(sys.argv[1], sys.argv[2], sys.argv[3]) + logger.info("Done loading {} samples.".format(len(dicts))) + + dirname = "lvis-data-vis" + os.makedirs(dirname, exist_ok=True) + for d in dicts[: int(sys.argv[4])]: + img = np.array(Image.open(d["file_name"])) + visualizer = Visualizer(img, metadata=meta) + vis = visualizer.draw_dataset_dict(d) + fpath = os.path.join(dirname, os.path.basename(d["file_name"])) + vis.save(fpath) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/lvis_v0_5_categories.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/lvis_v0_5_categories.py new file mode 100644 index 0000000000000000000000000000000000000000..d3dab6198da614937b08682f4c9edf52bdf1d236 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/lvis_v0_5_categories.py @@ -0,0 +1,13 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Autogen with +# with open("lvis_v0.5_val.json", "r") as f: +# a = json.load(f) +# c = a["categories"] +# for x in c: +# del x["image_count"] +# del x["instance_count"] +# LVIS_CATEGORIES = repr(c) + " # noqa" + +# fmt: off +LVIS_CATEGORIES = [{'frequency': 'r', 'id': 1, 'synset': 'acorn.n.01', 'synonyms': ['acorn'], 'def': 'nut from an oak tree', 'name': 'acorn'}, {'frequency': 'c', 'id': 2, 'synset': 'aerosol.n.02', 'synonyms': ['aerosol_can', 'spray_can'], 'def': 'a dispenser that holds a substance under pressure', 'name': 'aerosol_can'}, {'frequency': 'f', 'id': 3, 'synset': 'air_conditioner.n.01', 'synonyms': ['air_conditioner'], 'def': 'a machine that keeps air cool and dry', 'name': 'air_conditioner'}, {'frequency': 'f', 'id': 4, 'synset': 'airplane.n.01', 'synonyms': ['airplane', 'aeroplane'], 'def': 'an aircraft that has a fixed wing and is powered by propellers or jets', 'name': 'airplane'}, {'frequency': 'c', 'id': 5, 'synset': 'alarm_clock.n.01', 'synonyms': ['alarm_clock'], 'def': 'a clock that wakes a sleeper at some preset time', 'name': 'alarm_clock'}, {'frequency': 'c', 'id': 6, 'synset': 'alcohol.n.01', 'synonyms': ['alcohol', 'alcoholic_beverage'], 'def': 'a liquor or brew containing alcohol as the active agent', 'name': 'alcohol'}, {'frequency': 'r', 'id': 7, 'synset': 'alligator.n.02', 'synonyms': ['alligator', 'gator'], 'def': 'amphibious reptiles related to crocodiles but with shorter broader snouts', 'name': 'alligator'}, {'frequency': 'c', 'id': 8, 'synset': 'almond.n.02', 'synonyms': ['almond'], 'def': 'oval-shaped edible seed of the almond tree', 'name': 'almond'}, {'frequency': 'c', 'id': 9, 'synset': 'ambulance.n.01', 'synonyms': ['ambulance'], 'def': 'a vehicle that takes people to and from hospitals', 'name': 'ambulance'}, {'frequency': 'r', 'id': 10, 'synset': 'amplifier.n.01', 'synonyms': ['amplifier'], 'def': 'electronic equipment that increases strength of signals', 'name': 'amplifier'}, {'frequency': 'c', 'id': 11, 'synset': 'anklet.n.03', 'synonyms': ['anklet', 'ankle_bracelet'], 'def': 'an ornament worn around the ankle', 'name': 'anklet'}, {'frequency': 'f', 'id': 12, 'synset': 'antenna.n.01', 'synonyms': ['antenna', 'aerial', 'transmitting_aerial'], 'def': 'an electrical device that sends or receives radio or television signals', 'name': 'antenna'}, {'frequency': 'f', 'id': 13, 'synset': 'apple.n.01', 'synonyms': ['apple'], 'def': 'fruit with red or yellow or green skin and sweet to tart crisp whitish flesh', 'name': 'apple'}, {'frequency': 'r', 'id': 14, 'synset': 'apple_juice.n.01', 'synonyms': ['apple_juice'], 'def': 'the juice of apples', 'name': 'apple_juice'}, {'frequency': 'r', 'id': 15, 'synset': 'applesauce.n.01', 'synonyms': ['applesauce'], 'def': 'puree of stewed apples usually sweetened and spiced', 'name': 'applesauce'}, {'frequency': 'r', 'id': 16, 'synset': 'apricot.n.02', 'synonyms': ['apricot'], 'def': 'downy yellow to rosy-colored fruit resembling a small peach', 'name': 'apricot'}, {'frequency': 'f', 'id': 17, 'synset': 'apron.n.01', 'synonyms': ['apron'], 'def': 'a garment of cloth that is tied about the waist and worn to protect clothing', 'name': 'apron'}, {'frequency': 'c', 'id': 18, 'synset': 'aquarium.n.01', 'synonyms': ['aquarium', 'fish_tank'], 'def': 'a tank/pool/bowl filled with water for keeping live fish and underwater animals', 'name': 'aquarium'}, {'frequency': 'c', 'id': 19, 'synset': 'armband.n.02', 'synonyms': ['armband'], 'def': 'a band worn around the upper arm', 'name': 'armband'}, {'frequency': 'f', 'id': 20, 'synset': 'armchair.n.01', 'synonyms': ['armchair'], 'def': 'chair with a support on each side for arms', 'name': 'armchair'}, {'frequency': 'r', 'id': 21, 'synset': 'armoire.n.01', 'synonyms': ['armoire'], 'def': 'a large wardrobe or cabinet', 'name': 'armoire'}, {'frequency': 'r', 'id': 22, 'synset': 'armor.n.01', 'synonyms': ['armor', 'armour'], 'def': 'protective covering made of metal and used in combat', 'name': 'armor'}, {'frequency': 'c', 'id': 23, 'synset': 'artichoke.n.02', 'synonyms': ['artichoke'], 'def': 'a thistlelike flower head with edible fleshy leaves and heart', 'name': 'artichoke'}, {'frequency': 'f', 'id': 24, 'synset': 'ashcan.n.01', 'synonyms': ['trash_can', 'garbage_can', 'wastebin', 'dustbin', 'trash_barrel', 'trash_bin'], 'def': 'a bin that holds rubbish until it is collected', 'name': 'trash_can'}, {'frequency': 'c', 'id': 25, 'synset': 'ashtray.n.01', 'synonyms': ['ashtray'], 'def': "a receptacle for the ash from smokers' cigars or cigarettes", 'name': 'ashtray'}, {'frequency': 'c', 'id': 26, 'synset': 'asparagus.n.02', 'synonyms': ['asparagus'], 'def': 'edible young shoots of the asparagus plant', 'name': 'asparagus'}, {'frequency': 'c', 'id': 27, 'synset': 'atomizer.n.01', 'synonyms': ['atomizer', 'atomiser', 'spray', 'sprayer', 'nebulizer', 'nebuliser'], 'def': 'a dispenser that turns a liquid (such as perfume) into a fine mist', 'name': 'atomizer'}, {'frequency': 'c', 'id': 28, 'synset': 'avocado.n.01', 'synonyms': ['avocado'], 'def': 'a pear-shaped fruit with green or blackish skin and rich yellowish pulp enclosing a single large seed', 'name': 'avocado'}, {'frequency': 'c', 'id': 29, 'synset': 'award.n.02', 'synonyms': ['award', 'accolade'], 'def': 'a tangible symbol signifying approval or distinction', 'name': 'award'}, {'frequency': 'f', 'id': 30, 'synset': 'awning.n.01', 'synonyms': ['awning'], 'def': 'a canopy made of canvas to shelter people or things from rain or sun', 'name': 'awning'}, {'frequency': 'r', 'id': 31, 'synset': 'ax.n.01', 'synonyms': ['ax', 'axe'], 'def': 'an edge tool with a heavy bladed head mounted across a handle', 'name': 'ax'}, {'frequency': 'f', 'id': 32, 'synset': 'baby_buggy.n.01', 'synonyms': ['baby_buggy', 'baby_carriage', 'perambulator', 'pram', 'stroller'], 'def': 'a small vehicle with four wheels in which a baby or child is pushed around', 'name': 'baby_buggy'}, {'frequency': 'c', 'id': 33, 'synset': 'backboard.n.01', 'synonyms': ['basketball_backboard'], 'def': 'a raised vertical board with basket attached; used to play basketball', 'name': 'basketball_backboard'}, {'frequency': 'f', 'id': 34, 'synset': 'backpack.n.01', 'synonyms': ['backpack', 'knapsack', 'packsack', 'rucksack', 'haversack'], 'def': 'a bag carried by a strap on your back or shoulder', 'name': 'backpack'}, {'frequency': 'f', 'id': 35, 'synset': 'bag.n.04', 'synonyms': ['handbag', 'purse', 'pocketbook'], 'def': 'a container used for carrying money and small personal items or accessories', 'name': 'handbag'}, {'frequency': 'f', 'id': 36, 'synset': 'bag.n.06', 'synonyms': ['suitcase', 'baggage', 'luggage'], 'def': 'cases used to carry belongings when traveling', 'name': 'suitcase'}, {'frequency': 'c', 'id': 37, 'synset': 'bagel.n.01', 'synonyms': ['bagel', 'beigel'], 'def': 'glazed yeast-raised doughnut-shaped roll with hard crust', 'name': 'bagel'}, {'frequency': 'r', 'id': 38, 'synset': 'bagpipe.n.01', 'synonyms': ['bagpipe'], 'def': 'a tubular wind instrument; the player blows air into a bag and squeezes it out', 'name': 'bagpipe'}, {'frequency': 'r', 'id': 39, 'synset': 'baguet.n.01', 'synonyms': ['baguet', 'baguette'], 'def': 'narrow French stick loaf', 'name': 'baguet'}, {'frequency': 'r', 'id': 40, 'synset': 'bait.n.02', 'synonyms': ['bait', 'lure'], 'def': 'something used to lure fish or other animals into danger so they can be trapped or killed', 'name': 'bait'}, {'frequency': 'f', 'id': 41, 'synset': 'ball.n.06', 'synonyms': ['ball'], 'def': 'a spherical object used as a plaything', 'name': 'ball'}, {'frequency': 'r', 'id': 42, 'synset': 'ballet_skirt.n.01', 'synonyms': ['ballet_skirt', 'tutu'], 'def': 'very short skirt worn by ballerinas', 'name': 'ballet_skirt'}, {'frequency': 'f', 'id': 43, 'synset': 'balloon.n.01', 'synonyms': ['balloon'], 'def': 'large tough nonrigid bag filled with gas or heated air', 'name': 'balloon'}, {'frequency': 'c', 'id': 44, 'synset': 'bamboo.n.02', 'synonyms': ['bamboo'], 'def': 'woody tropical grass having hollow woody stems', 'name': 'bamboo'}, {'frequency': 'f', 'id': 45, 'synset': 'banana.n.02', 'synonyms': ['banana'], 'def': 'elongated crescent-shaped yellow fruit with soft sweet flesh', 'name': 'banana'}, {'frequency': 'r', 'id': 46, 'synset': 'band_aid.n.01', 'synonyms': ['Band_Aid'], 'def': 'trade name for an adhesive bandage to cover small cuts or blisters', 'name': 'Band_Aid'}, {'frequency': 'c', 'id': 47, 'synset': 'bandage.n.01', 'synonyms': ['bandage'], 'def': 'a piece of soft material that covers and protects an injured part of the body', 'name': 'bandage'}, {'frequency': 'c', 'id': 48, 'synset': 'bandanna.n.01', 'synonyms': ['bandanna', 'bandana'], 'def': 'large and brightly colored handkerchief; often used as a neckerchief', 'name': 'bandanna'}, {'frequency': 'r', 'id': 49, 'synset': 'banjo.n.01', 'synonyms': ['banjo'], 'def': 'a stringed instrument of the guitar family with a long neck and circular body', 'name': 'banjo'}, {'frequency': 'f', 'id': 50, 'synset': 'banner.n.01', 'synonyms': ['banner', 'streamer'], 'def': 'long strip of cloth or paper used for decoration or advertising', 'name': 'banner'}, {'frequency': 'r', 'id': 51, 'synset': 'barbell.n.01', 'synonyms': ['barbell'], 'def': 'a bar to which heavy discs are attached at each end; used in weightlifting', 'name': 'barbell'}, {'frequency': 'r', 'id': 52, 'synset': 'barge.n.01', 'synonyms': ['barge'], 'def': 'a flatbottom boat for carrying heavy loads (especially on canals)', 'name': 'barge'}, {'frequency': 'f', 'id': 53, 'synset': 'barrel.n.02', 'synonyms': ['barrel', 'cask'], 'def': 'a cylindrical container that holds liquids', 'name': 'barrel'}, {'frequency': 'c', 'id': 54, 'synset': 'barrette.n.01', 'synonyms': ['barrette'], 'def': "a pin for holding women's hair in place", 'name': 'barrette'}, {'frequency': 'c', 'id': 55, 'synset': 'barrow.n.03', 'synonyms': ['barrow', 'garden_cart', 'lawn_cart', 'wheelbarrow'], 'def': 'a cart for carrying small loads; has handles and one or more wheels', 'name': 'barrow'}, {'frequency': 'f', 'id': 56, 'synset': 'base.n.03', 'synonyms': ['baseball_base'], 'def': 'a place that the runner must touch before scoring', 'name': 'baseball_base'}, {'frequency': 'f', 'id': 57, 'synset': 'baseball.n.02', 'synonyms': ['baseball'], 'def': 'a ball used in playing baseball', 'name': 'baseball'}, {'frequency': 'f', 'id': 58, 'synset': 'baseball_bat.n.01', 'synonyms': ['baseball_bat'], 'def': 'an implement used in baseball by the batter', 'name': 'baseball_bat'}, {'frequency': 'f', 'id': 59, 'synset': 'baseball_cap.n.01', 'synonyms': ['baseball_cap', 'jockey_cap', 'golf_cap'], 'def': 'a cap with a bill', 'name': 'baseball_cap'}, {'frequency': 'f', 'id': 60, 'synset': 'baseball_glove.n.01', 'synonyms': ['baseball_glove', 'baseball_mitt'], 'def': 'the handwear used by fielders in playing baseball', 'name': 'baseball_glove'}, {'frequency': 'f', 'id': 61, 'synset': 'basket.n.01', 'synonyms': ['basket', 'handbasket'], 'def': 'a container that is usually woven and has handles', 'name': 'basket'}, {'frequency': 'c', 'id': 62, 'synset': 'basket.n.03', 'synonyms': ['basketball_hoop'], 'def': 'metal hoop supporting a net through which players try to throw the basketball', 'name': 'basketball_hoop'}, {'frequency': 'c', 'id': 63, 'synset': 'basketball.n.02', 'synonyms': ['basketball'], 'def': 'an inflated ball used in playing basketball', 'name': 'basketball'}, {'frequency': 'r', 'id': 64, 'synset': 'bass_horn.n.01', 'synonyms': ['bass_horn', 'sousaphone', 'tuba'], 'def': 'the lowest brass wind instrument', 'name': 'bass_horn'}, {'frequency': 'r', 'id': 65, 'synset': 'bat.n.01', 'synonyms': ['bat_(animal)'], 'def': 'nocturnal mouselike mammal with forelimbs modified to form membranous wings', 'name': 'bat_(animal)'}, {'frequency': 'f', 'id': 66, 'synset': 'bath_mat.n.01', 'synonyms': ['bath_mat'], 'def': 'a heavy towel or mat to stand on while drying yourself after a bath', 'name': 'bath_mat'}, {'frequency': 'f', 'id': 67, 'synset': 'bath_towel.n.01', 'synonyms': ['bath_towel'], 'def': 'a large towel; to dry yourself after a bath', 'name': 'bath_towel'}, {'frequency': 'c', 'id': 68, 'synset': 'bathrobe.n.01', 'synonyms': ['bathrobe'], 'def': 'a loose-fitting robe of towelling; worn after a bath or swim', 'name': 'bathrobe'}, {'frequency': 'f', 'id': 69, 'synset': 'bathtub.n.01', 'synonyms': ['bathtub', 'bathing_tub'], 'def': 'a large open container that you fill with water and use to wash the body', 'name': 'bathtub'}, {'frequency': 'r', 'id': 70, 'synset': 'batter.n.02', 'synonyms': ['batter_(food)'], 'def': 'a liquid or semiliquid mixture, as of flour, eggs, and milk, used in cooking', 'name': 'batter_(food)'}, {'frequency': 'c', 'id': 71, 'synset': 'battery.n.02', 'synonyms': ['battery'], 'def': 'a portable device that produces electricity', 'name': 'battery'}, {'frequency': 'r', 'id': 72, 'synset': 'beach_ball.n.01', 'synonyms': ['beachball'], 'def': 'large and light ball; for play at the seaside', 'name': 'beachball'}, {'frequency': 'c', 'id': 73, 'synset': 'bead.n.01', 'synonyms': ['bead'], 'def': 'a small ball with a hole through the middle used for ornamentation, jewellery, etc.', 'name': 'bead'}, {'frequency': 'r', 'id': 74, 'synset': 'beaker.n.01', 'synonyms': ['beaker'], 'def': 'a flatbottomed jar made of glass or plastic; used for chemistry', 'name': 'beaker'}, {'frequency': 'c', 'id': 75, 'synset': 'bean_curd.n.01', 'synonyms': ['bean_curd', 'tofu'], 'def': 'cheeselike food made of curdled soybean milk', 'name': 'bean_curd'}, {'frequency': 'c', 'id': 76, 'synset': 'beanbag.n.01', 'synonyms': ['beanbag'], 'def': 'a bag filled with dried beans or similar items; used in games or to sit on', 'name': 'beanbag'}, {'frequency': 'f', 'id': 77, 'synset': 'beanie.n.01', 'synonyms': ['beanie', 'beany'], 'def': 'a small skullcap; formerly worn by schoolboys and college freshmen', 'name': 'beanie'}, {'frequency': 'f', 'id': 78, 'synset': 'bear.n.01', 'synonyms': ['bear'], 'def': 'large carnivorous or omnivorous mammals with shaggy coats and claws', 'name': 'bear'}, {'frequency': 'f', 'id': 79, 'synset': 'bed.n.01', 'synonyms': ['bed'], 'def': 'a piece of furniture that provides a place to sleep', 'name': 'bed'}, {'frequency': 'c', 'id': 80, 'synset': 'bedspread.n.01', 'synonyms': ['bedspread', 'bedcover', 'bed_covering', 'counterpane', 'spread'], 'def': 'decorative cover for a bed', 'name': 'bedspread'}, {'frequency': 'f', 'id': 81, 'synset': 'beef.n.01', 'synonyms': ['cow'], 'def': 'cattle that are reared for their meat', 'name': 'cow'}, {'frequency': 'c', 'id': 82, 'synset': 'beef.n.02', 'synonyms': ['beef_(food)', 'boeuf_(food)'], 'def': 'meat from an adult domestic bovine', 'name': 'beef_(food)'}, {'frequency': 'r', 'id': 83, 'synset': 'beeper.n.01', 'synonyms': ['beeper', 'pager'], 'def': 'an device that beeps when the person carrying it is being paged', 'name': 'beeper'}, {'frequency': 'f', 'id': 84, 'synset': 'beer_bottle.n.01', 'synonyms': ['beer_bottle'], 'def': 'a bottle that holds beer', 'name': 'beer_bottle'}, {'frequency': 'c', 'id': 85, 'synset': 'beer_can.n.01', 'synonyms': ['beer_can'], 'def': 'a can that holds beer', 'name': 'beer_can'}, {'frequency': 'r', 'id': 86, 'synset': 'beetle.n.01', 'synonyms': ['beetle'], 'def': 'insect with hard wing covers', 'name': 'beetle'}, {'frequency': 'f', 'id': 87, 'synset': 'bell.n.01', 'synonyms': ['bell'], 'def': 'a hollow device made of metal that makes a ringing sound when struck', 'name': 'bell'}, {'frequency': 'f', 'id': 88, 'synset': 'bell_pepper.n.02', 'synonyms': ['bell_pepper', 'capsicum'], 'def': 'large bell-shaped sweet pepper in green or red or yellow or orange or black varieties', 'name': 'bell_pepper'}, {'frequency': 'f', 'id': 89, 'synset': 'belt.n.02', 'synonyms': ['belt'], 'def': 'a band to tie or buckle around the body (usually at the waist)', 'name': 'belt'}, {'frequency': 'f', 'id': 90, 'synset': 'belt_buckle.n.01', 'synonyms': ['belt_buckle'], 'def': 'the buckle used to fasten a belt', 'name': 'belt_buckle'}, {'frequency': 'f', 'id': 91, 'synset': 'bench.n.01', 'synonyms': ['bench'], 'def': 'a long seat for more than one person', 'name': 'bench'}, {'frequency': 'c', 'id': 92, 'synset': 'beret.n.01', 'synonyms': ['beret'], 'def': 'a cap with no brim or bill; made of soft cloth', 'name': 'beret'}, {'frequency': 'c', 'id': 93, 'synset': 'bib.n.02', 'synonyms': ['bib'], 'def': 'a napkin tied under the chin of a child while eating', 'name': 'bib'}, {'frequency': 'r', 'id': 94, 'synset': 'bible.n.01', 'synonyms': ['Bible'], 'def': 'the sacred writings of the Christian religions', 'name': 'Bible'}, {'frequency': 'f', 'id': 95, 'synset': 'bicycle.n.01', 'synonyms': ['bicycle', 'bike_(bicycle)'], 'def': 'a wheeled vehicle that has two wheels and is moved by foot pedals', 'name': 'bicycle'}, {'frequency': 'f', 'id': 96, 'synset': 'bill.n.09', 'synonyms': ['visor', 'vizor'], 'def': 'a brim that projects to the front to shade the eyes', 'name': 'visor'}, {'frequency': 'c', 'id': 97, 'synset': 'binder.n.03', 'synonyms': ['binder', 'ring-binder'], 'def': 'holds loose papers or magazines', 'name': 'binder'}, {'frequency': 'c', 'id': 98, 'synset': 'binoculars.n.01', 'synonyms': ['binoculars', 'field_glasses', 'opera_glasses'], 'def': 'an optical instrument designed for simultaneous use by both eyes', 'name': 'binoculars'}, {'frequency': 'f', 'id': 99, 'synset': 'bird.n.01', 'synonyms': ['bird'], 'def': 'animal characterized by feathers and wings', 'name': 'bird'}, {'frequency': 'r', 'id': 100, 'synset': 'bird_feeder.n.01', 'synonyms': ['birdfeeder'], 'def': 'an outdoor device that supplies food for wild birds', 'name': 'birdfeeder'}, {'frequency': 'r', 'id': 101, 'synset': 'birdbath.n.01', 'synonyms': ['birdbath'], 'def': 'an ornamental basin (usually in a garden) for birds to bathe in', 'name': 'birdbath'}, {'frequency': 'c', 'id': 102, 'synset': 'birdcage.n.01', 'synonyms': ['birdcage'], 'def': 'a cage in which a bird can be kept', 'name': 'birdcage'}, {'frequency': 'c', 'id': 103, 'synset': 'birdhouse.n.01', 'synonyms': ['birdhouse'], 'def': 'a shelter for birds', 'name': 'birdhouse'}, {'frequency': 'f', 'id': 104, 'synset': 'birthday_cake.n.01', 'synonyms': ['birthday_cake'], 'def': 'decorated cake served at a birthday party', 'name': 'birthday_cake'}, {'frequency': 'r', 'id': 105, 'synset': 'birthday_card.n.01', 'synonyms': ['birthday_card'], 'def': 'a card expressing a birthday greeting', 'name': 'birthday_card'}, {'frequency': 'r', 'id': 106, 'synset': 'biscuit.n.01', 'synonyms': ['biscuit_(bread)'], 'def': 'small round bread leavened with baking-powder or soda', 'name': 'biscuit_(bread)'}, {'frequency': 'r', 'id': 107, 'synset': 'black_flag.n.01', 'synonyms': ['pirate_flag'], 'def': 'a flag usually bearing a white skull and crossbones on a black background', 'name': 'pirate_flag'}, {'frequency': 'c', 'id': 108, 'synset': 'black_sheep.n.02', 'synonyms': ['black_sheep'], 'def': 'sheep with a black coat', 'name': 'black_sheep'}, {'frequency': 'c', 'id': 109, 'synset': 'blackboard.n.01', 'synonyms': ['blackboard', 'chalkboard'], 'def': 'sheet of slate; for writing with chalk', 'name': 'blackboard'}, {'frequency': 'f', 'id': 110, 'synset': 'blanket.n.01', 'synonyms': ['blanket'], 'def': 'bedding that keeps a person warm in bed', 'name': 'blanket'}, {'frequency': 'c', 'id': 111, 'synset': 'blazer.n.01', 'synonyms': ['blazer', 'sport_jacket', 'sport_coat', 'sports_jacket', 'sports_coat'], 'def': 'lightweight jacket; often striped in the colors of a club or school', 'name': 'blazer'}, {'frequency': 'f', 'id': 112, 'synset': 'blender.n.01', 'synonyms': ['blender', 'liquidizer', 'liquidiser'], 'def': 'an electrically powered mixer that mix or chop or liquefy foods', 'name': 'blender'}, {'frequency': 'r', 'id': 113, 'synset': 'blimp.n.02', 'synonyms': ['blimp'], 'def': 'a small nonrigid airship used for observation or as a barrage balloon', 'name': 'blimp'}, {'frequency': 'c', 'id': 114, 'synset': 'blinker.n.01', 'synonyms': ['blinker', 'flasher'], 'def': 'a light that flashes on and off; used as a signal or to send messages', 'name': 'blinker'}, {'frequency': 'c', 'id': 115, 'synset': 'blueberry.n.02', 'synonyms': ['blueberry'], 'def': 'sweet edible dark-blue berries of blueberry plants', 'name': 'blueberry'}, {'frequency': 'r', 'id': 116, 'synset': 'boar.n.02', 'synonyms': ['boar'], 'def': 'an uncastrated male hog', 'name': 'boar'}, {'frequency': 'r', 'id': 117, 'synset': 'board.n.09', 'synonyms': ['gameboard'], 'def': 'a flat portable surface (usually rectangular) designed for board games', 'name': 'gameboard'}, {'frequency': 'f', 'id': 118, 'synset': 'boat.n.01', 'synonyms': ['boat', 'ship_(boat)'], 'def': 'a vessel for travel on water', 'name': 'boat'}, {'frequency': 'c', 'id': 119, 'synset': 'bobbin.n.01', 'synonyms': ['bobbin', 'spool', 'reel'], 'def': 'a thing around which thread/tape/film or other flexible materials can be wound', 'name': 'bobbin'}, {'frequency': 'r', 'id': 120, 'synset': 'bobby_pin.n.01', 'synonyms': ['bobby_pin', 'hairgrip'], 'def': 'a flat wire hairpin used to hold bobbed hair in place', 'name': 'bobby_pin'}, {'frequency': 'c', 'id': 121, 'synset': 'boiled_egg.n.01', 'synonyms': ['boiled_egg', 'coddled_egg'], 'def': 'egg cooked briefly in the shell in gently boiling water', 'name': 'boiled_egg'}, {'frequency': 'r', 'id': 122, 'synset': 'bolo_tie.n.01', 'synonyms': ['bolo_tie', 'bolo', 'bola_tie', 'bola'], 'def': 'a cord fastened around the neck with an ornamental clasp and worn as a necktie', 'name': 'bolo_tie'}, {'frequency': 'c', 'id': 123, 'synset': 'bolt.n.03', 'synonyms': ['deadbolt'], 'def': 'the part of a lock that is engaged or withdrawn with a key', 'name': 'deadbolt'}, {'frequency': 'f', 'id': 124, 'synset': 'bolt.n.06', 'synonyms': ['bolt'], 'def': 'a screw that screws into a nut to form a fastener', 'name': 'bolt'}, {'frequency': 'r', 'id': 125, 'synset': 'bonnet.n.01', 'synonyms': ['bonnet'], 'def': 'a hat tied under the chin', 'name': 'bonnet'}, {'frequency': 'f', 'id': 126, 'synset': 'book.n.01', 'synonyms': ['book'], 'def': 'a written work or composition that has been published', 'name': 'book'}, {'frequency': 'r', 'id': 127, 'synset': 'book_bag.n.01', 'synonyms': ['book_bag'], 'def': 'a bag in which students carry their books', 'name': 'book_bag'}, {'frequency': 'c', 'id': 128, 'synset': 'bookcase.n.01', 'synonyms': ['bookcase'], 'def': 'a piece of furniture with shelves for storing books', 'name': 'bookcase'}, {'frequency': 'c', 'id': 129, 'synset': 'booklet.n.01', 'synonyms': ['booklet', 'brochure', 'leaflet', 'pamphlet'], 'def': 'a small book usually having a paper cover', 'name': 'booklet'}, {'frequency': 'r', 'id': 130, 'synset': 'bookmark.n.01', 'synonyms': ['bookmark', 'bookmarker'], 'def': 'a marker (a piece of paper or ribbon) placed between the pages of a book', 'name': 'bookmark'}, {'frequency': 'r', 'id': 131, 'synset': 'boom.n.04', 'synonyms': ['boom_microphone', 'microphone_boom'], 'def': 'a pole carrying an overhead microphone projected over a film or tv set', 'name': 'boom_microphone'}, {'frequency': 'f', 'id': 132, 'synset': 'boot.n.01', 'synonyms': ['boot'], 'def': 'footwear that covers the whole foot and lower leg', 'name': 'boot'}, {'frequency': 'f', 'id': 133, 'synset': 'bottle.n.01', 'synonyms': ['bottle'], 'def': 'a glass or plastic vessel used for storing drinks or other liquids', 'name': 'bottle'}, {'frequency': 'c', 'id': 134, 'synset': 'bottle_opener.n.01', 'synonyms': ['bottle_opener'], 'def': 'an opener for removing caps or corks from bottles', 'name': 'bottle_opener'}, {'frequency': 'c', 'id': 135, 'synset': 'bouquet.n.01', 'synonyms': ['bouquet'], 'def': 'an arrangement of flowers that is usually given as a present', 'name': 'bouquet'}, {'frequency': 'r', 'id': 136, 'synset': 'bow.n.04', 'synonyms': ['bow_(weapon)'], 'def': 'a weapon for shooting arrows', 'name': 'bow_(weapon)'}, {'frequency': 'f', 'id': 137, 'synset': 'bow.n.08', 'synonyms': ['bow_(decorative_ribbons)'], 'def': 'a decorative interlacing of ribbons', 'name': 'bow_(decorative_ribbons)'}, {'frequency': 'f', 'id': 138, 'synset': 'bow_tie.n.01', 'synonyms': ['bow-tie', 'bowtie'], 'def': "a man's tie that ties in a bow", 'name': 'bow-tie'}, {'frequency': 'f', 'id': 139, 'synset': 'bowl.n.03', 'synonyms': ['bowl'], 'def': 'a dish that is round and open at the top for serving foods', 'name': 'bowl'}, {'frequency': 'r', 'id': 140, 'synset': 'bowl.n.08', 'synonyms': ['pipe_bowl'], 'def': 'a small round container that is open at the top for holding tobacco', 'name': 'pipe_bowl'}, {'frequency': 'c', 'id': 141, 'synset': 'bowler_hat.n.01', 'synonyms': ['bowler_hat', 'bowler', 'derby_hat', 'derby', 'plug_hat'], 'def': 'a felt hat that is round and hard with a narrow brim', 'name': 'bowler_hat'}, {'frequency': 'r', 'id': 142, 'synset': 'bowling_ball.n.01', 'synonyms': ['bowling_ball'], 'def': 'a large ball with finger holes used in the sport of bowling', 'name': 'bowling_ball'}, {'frequency': 'r', 'id': 143, 'synset': 'bowling_pin.n.01', 'synonyms': ['bowling_pin'], 'def': 'a club-shaped wooden object used in bowling', 'name': 'bowling_pin'}, {'frequency': 'r', 'id': 144, 'synset': 'boxing_glove.n.01', 'synonyms': ['boxing_glove'], 'def': 'large glove coverings the fists of a fighter worn for the sport of boxing', 'name': 'boxing_glove'}, {'frequency': 'c', 'id': 145, 'synset': 'brace.n.06', 'synonyms': ['suspenders'], 'def': 'elastic straps that hold trousers up (usually used in the plural)', 'name': 'suspenders'}, {'frequency': 'f', 'id': 146, 'synset': 'bracelet.n.02', 'synonyms': ['bracelet', 'bangle'], 'def': 'jewelry worn around the wrist for decoration', 'name': 'bracelet'}, {'frequency': 'r', 'id': 147, 'synset': 'brass.n.07', 'synonyms': ['brass_plaque'], 'def': 'a memorial made of brass', 'name': 'brass_plaque'}, {'frequency': 'c', 'id': 148, 'synset': 'brassiere.n.01', 'synonyms': ['brassiere', 'bra', 'bandeau'], 'def': 'an undergarment worn by women to support their breasts', 'name': 'brassiere'}, {'frequency': 'c', 'id': 149, 'synset': 'bread-bin.n.01', 'synonyms': ['bread-bin', 'breadbox'], 'def': 'a container used to keep bread or cake in', 'name': 'bread-bin'}, {'frequency': 'r', 'id': 150, 'synset': 'breechcloth.n.01', 'synonyms': ['breechcloth', 'breechclout', 'loincloth'], 'def': 'a garment that provides covering for the loins', 'name': 'breechcloth'}, {'frequency': 'c', 'id': 151, 'synset': 'bridal_gown.n.01', 'synonyms': ['bridal_gown', 'wedding_gown', 'wedding_dress'], 'def': 'a gown worn by the bride at a wedding', 'name': 'bridal_gown'}, {'frequency': 'c', 'id': 152, 'synset': 'briefcase.n.01', 'synonyms': ['briefcase'], 'def': 'a case with a handle; for carrying papers or files or books', 'name': 'briefcase'}, {'frequency': 'c', 'id': 153, 'synset': 'bristle_brush.n.01', 'synonyms': ['bristle_brush'], 'def': 'a brush that is made with the short stiff hairs of an animal or plant', 'name': 'bristle_brush'}, {'frequency': 'f', 'id': 154, 'synset': 'broccoli.n.01', 'synonyms': ['broccoli'], 'def': 'plant with dense clusters of tight green flower buds', 'name': 'broccoli'}, {'frequency': 'r', 'id': 155, 'synset': 'brooch.n.01', 'synonyms': ['broach'], 'def': 'a decorative pin worn by women', 'name': 'broach'}, {'frequency': 'c', 'id': 156, 'synset': 'broom.n.01', 'synonyms': ['broom'], 'def': 'bundle of straws or twigs attached to a long handle; used for cleaning', 'name': 'broom'}, {'frequency': 'c', 'id': 157, 'synset': 'brownie.n.03', 'synonyms': ['brownie'], 'def': 'square or bar of very rich chocolate cake usually with nuts', 'name': 'brownie'}, {'frequency': 'c', 'id': 158, 'synset': 'brussels_sprouts.n.01', 'synonyms': ['brussels_sprouts'], 'def': 'the small edible cabbage-like buds growing along a stalk', 'name': 'brussels_sprouts'}, {'frequency': 'r', 'id': 159, 'synset': 'bubble_gum.n.01', 'synonyms': ['bubble_gum'], 'def': 'a kind of chewing gum that can be blown into bubbles', 'name': 'bubble_gum'}, {'frequency': 'f', 'id': 160, 'synset': 'bucket.n.01', 'synonyms': ['bucket', 'pail'], 'def': 'a roughly cylindrical vessel that is open at the top', 'name': 'bucket'}, {'frequency': 'r', 'id': 161, 'synset': 'buggy.n.01', 'synonyms': ['horse_buggy'], 'def': 'a small lightweight carriage; drawn by a single horse', 'name': 'horse_buggy'}, {'frequency': 'c', 'id': 162, 'synset': 'bull.n.11', 'synonyms': ['bull'], 'def': 'mature male cow', 'name': 'bull'}, {'frequency': 'r', 'id': 163, 'synset': 'bulldog.n.01', 'synonyms': ['bulldog'], 'def': 'a thickset short-haired dog with a large head and strong undershot lower jaw', 'name': 'bulldog'}, {'frequency': 'r', 'id': 164, 'synset': 'bulldozer.n.01', 'synonyms': ['bulldozer', 'dozer'], 'def': 'large powerful tractor; a large blade in front flattens areas of ground', 'name': 'bulldozer'}, {'frequency': 'c', 'id': 165, 'synset': 'bullet_train.n.01', 'synonyms': ['bullet_train'], 'def': 'a high-speed passenger train', 'name': 'bullet_train'}, {'frequency': 'c', 'id': 166, 'synset': 'bulletin_board.n.02', 'synonyms': ['bulletin_board', 'notice_board'], 'def': 'a board that hangs on a wall; displays announcements', 'name': 'bulletin_board'}, {'frequency': 'r', 'id': 167, 'synset': 'bulletproof_vest.n.01', 'synonyms': ['bulletproof_vest'], 'def': 'a vest capable of resisting the impact of a bullet', 'name': 'bulletproof_vest'}, {'frequency': 'c', 'id': 168, 'synset': 'bullhorn.n.01', 'synonyms': ['bullhorn', 'megaphone'], 'def': 'a portable loudspeaker with built-in microphone and amplifier', 'name': 'bullhorn'}, {'frequency': 'r', 'id': 169, 'synset': 'bully_beef.n.01', 'synonyms': ['corned_beef', 'corn_beef'], 'def': 'beef cured or pickled in brine', 'name': 'corned_beef'}, {'frequency': 'f', 'id': 170, 'synset': 'bun.n.01', 'synonyms': ['bun', 'roll'], 'def': 'small rounded bread either plain or sweet', 'name': 'bun'}, {'frequency': 'c', 'id': 171, 'synset': 'bunk_bed.n.01', 'synonyms': ['bunk_bed'], 'def': 'beds built one above the other', 'name': 'bunk_bed'}, {'frequency': 'f', 'id': 172, 'synset': 'buoy.n.01', 'synonyms': ['buoy'], 'def': 'a float attached by rope to the seabed to mark channels in a harbor or underwater hazards', 'name': 'buoy'}, {'frequency': 'r', 'id': 173, 'synset': 'burrito.n.01', 'synonyms': ['burrito'], 'def': 'a flour tortilla folded around a filling', 'name': 'burrito'}, {'frequency': 'f', 'id': 174, 'synset': 'bus.n.01', 'synonyms': ['bus_(vehicle)', 'autobus', 'charabanc', 'double-decker', 'motorbus', 'motorcoach'], 'def': 'a vehicle carrying many passengers; used for public transport', 'name': 'bus_(vehicle)'}, {'frequency': 'c', 'id': 175, 'synset': 'business_card.n.01', 'synonyms': ['business_card'], 'def': "a card on which are printed the person's name and business affiliation", 'name': 'business_card'}, {'frequency': 'c', 'id': 176, 'synset': 'butcher_knife.n.01', 'synonyms': ['butcher_knife'], 'def': 'a large sharp knife for cutting or trimming meat', 'name': 'butcher_knife'}, {'frequency': 'c', 'id': 177, 'synset': 'butter.n.01', 'synonyms': ['butter'], 'def': 'an edible emulsion of fat globules made by churning milk or cream; for cooking and table use', 'name': 'butter'}, {'frequency': 'c', 'id': 178, 'synset': 'butterfly.n.01', 'synonyms': ['butterfly'], 'def': 'insect typically having a slender body with knobbed antennae and broad colorful wings', 'name': 'butterfly'}, {'frequency': 'f', 'id': 179, 'synset': 'button.n.01', 'synonyms': ['button'], 'def': 'a round fastener sewn to shirts and coats etc to fit through buttonholes', 'name': 'button'}, {'frequency': 'f', 'id': 180, 'synset': 'cab.n.03', 'synonyms': ['cab_(taxi)', 'taxi', 'taxicab'], 'def': 'a car that takes passengers where they want to go in exchange for money', 'name': 'cab_(taxi)'}, {'frequency': 'r', 'id': 181, 'synset': 'cabana.n.01', 'synonyms': ['cabana'], 'def': 'a small tent used as a dressing room beside the sea or a swimming pool', 'name': 'cabana'}, {'frequency': 'r', 'id': 182, 'synset': 'cabin_car.n.01', 'synonyms': ['cabin_car', 'caboose'], 'def': 'a car on a freight train for use of the train crew; usually the last car on the train', 'name': 'cabin_car'}, {'frequency': 'f', 'id': 183, 'synset': 'cabinet.n.01', 'synonyms': ['cabinet'], 'def': 'a piece of furniture resembling a cupboard with doors and shelves and drawers', 'name': 'cabinet'}, {'frequency': 'r', 'id': 184, 'synset': 'cabinet.n.03', 'synonyms': ['locker', 'storage_locker'], 'def': 'a storage compartment for clothes and valuables; usually it has a lock', 'name': 'locker'}, {'frequency': 'f', 'id': 185, 'synset': 'cake.n.03', 'synonyms': ['cake'], 'def': 'baked goods made from or based on a mixture of flour, sugar, eggs, and fat', 'name': 'cake'}, {'frequency': 'c', 'id': 186, 'synset': 'calculator.n.02', 'synonyms': ['calculator'], 'def': 'a small machine that is used for mathematical calculations', 'name': 'calculator'}, {'frequency': 'f', 'id': 187, 'synset': 'calendar.n.02', 'synonyms': ['calendar'], 'def': 'a list or register of events (appointments/social events/court cases, etc)', 'name': 'calendar'}, {'frequency': 'c', 'id': 188, 'synset': 'calf.n.01', 'synonyms': ['calf'], 'def': 'young of domestic cattle', 'name': 'calf'}, {'frequency': 'c', 'id': 189, 'synset': 'camcorder.n.01', 'synonyms': ['camcorder'], 'def': 'a portable television camera and videocassette recorder', 'name': 'camcorder'}, {'frequency': 'c', 'id': 190, 'synset': 'camel.n.01', 'synonyms': ['camel'], 'def': 'cud-chewing mammal used as a draft or saddle animal in desert regions', 'name': 'camel'}, {'frequency': 'f', 'id': 191, 'synset': 'camera.n.01', 'synonyms': ['camera'], 'def': 'equipment for taking photographs', 'name': 'camera'}, {'frequency': 'c', 'id': 192, 'synset': 'camera_lens.n.01', 'synonyms': ['camera_lens'], 'def': 'a lens that focuses the image in a camera', 'name': 'camera_lens'}, {'frequency': 'c', 'id': 193, 'synset': 'camper.n.02', 'synonyms': ['camper_(vehicle)', 'camping_bus', 'motor_home'], 'def': 'a recreational vehicle equipped for camping out while traveling', 'name': 'camper_(vehicle)'}, {'frequency': 'f', 'id': 194, 'synset': 'can.n.01', 'synonyms': ['can', 'tin_can'], 'def': 'airtight sealed metal container for food or drink or paint etc.', 'name': 'can'}, {'frequency': 'c', 'id': 195, 'synset': 'can_opener.n.01', 'synonyms': ['can_opener', 'tin_opener'], 'def': 'a device for cutting cans open', 'name': 'can_opener'}, {'frequency': 'r', 'id': 196, 'synset': 'candelabrum.n.01', 'synonyms': ['candelabrum', 'candelabra'], 'def': 'branched candlestick; ornamental; has several lights', 'name': 'candelabrum'}, {'frequency': 'f', 'id': 197, 'synset': 'candle.n.01', 'synonyms': ['candle', 'candlestick'], 'def': 'stick of wax with a wick in the middle', 'name': 'candle'}, {'frequency': 'f', 'id': 198, 'synset': 'candlestick.n.01', 'synonyms': ['candle_holder'], 'def': 'a holder with sockets for candles', 'name': 'candle_holder'}, {'frequency': 'r', 'id': 199, 'synset': 'candy_bar.n.01', 'synonyms': ['candy_bar'], 'def': 'a candy shaped as a bar', 'name': 'candy_bar'}, {'frequency': 'c', 'id': 200, 'synset': 'candy_cane.n.01', 'synonyms': ['candy_cane'], 'def': 'a hard candy in the shape of a rod (usually with stripes)', 'name': 'candy_cane'}, {'frequency': 'c', 'id': 201, 'synset': 'cane.n.01', 'synonyms': ['walking_cane'], 'def': 'a stick that people can lean on to help them walk', 'name': 'walking_cane'}, {'frequency': 'c', 'id': 202, 'synset': 'canister.n.02', 'synonyms': ['canister', 'cannister'], 'def': 'metal container for storing dry foods such as tea or flour', 'name': 'canister'}, {'frequency': 'r', 'id': 203, 'synset': 'cannon.n.02', 'synonyms': ['cannon'], 'def': 'heavy gun fired from a tank', 'name': 'cannon'}, {'frequency': 'c', 'id': 204, 'synset': 'canoe.n.01', 'synonyms': ['canoe'], 'def': 'small and light boat; pointed at both ends; propelled with a paddle', 'name': 'canoe'}, {'frequency': 'r', 'id': 205, 'synset': 'cantaloup.n.02', 'synonyms': ['cantaloup', 'cantaloupe'], 'def': 'the fruit of a cantaloup vine; small to medium-sized melon with yellowish flesh', 'name': 'cantaloup'}, {'frequency': 'r', 'id': 206, 'synset': 'canteen.n.01', 'synonyms': ['canteen'], 'def': 'a flask for carrying water; used by soldiers or travelers', 'name': 'canteen'}, {'frequency': 'c', 'id': 207, 'synset': 'cap.n.01', 'synonyms': ['cap_(headwear)'], 'def': 'a tight-fitting headwear', 'name': 'cap_(headwear)'}, {'frequency': 'f', 'id': 208, 'synset': 'cap.n.02', 'synonyms': ['bottle_cap', 'cap_(container_lid)'], 'def': 'a top (as for a bottle)', 'name': 'bottle_cap'}, {'frequency': 'r', 'id': 209, 'synset': 'cape.n.02', 'synonyms': ['cape'], 'def': 'a sleeveless garment like a cloak but shorter', 'name': 'cape'}, {'frequency': 'c', 'id': 210, 'synset': 'cappuccino.n.01', 'synonyms': ['cappuccino', 'coffee_cappuccino'], 'def': 'equal parts of espresso and steamed milk', 'name': 'cappuccino'}, {'frequency': 'f', 'id': 211, 'synset': 'car.n.01', 'synonyms': ['car_(automobile)', 'auto_(automobile)', 'automobile'], 'def': 'a motor vehicle with four wheels', 'name': 'car_(automobile)'}, {'frequency': 'f', 'id': 212, 'synset': 'car.n.02', 'synonyms': ['railcar_(part_of_a_train)', 'railway_car_(part_of_a_train)', 'railroad_car_(part_of_a_train)'], 'def': 'a wheeled vehicle adapted to the rails of railroad', 'name': 'railcar_(part_of_a_train)'}, {'frequency': 'r', 'id': 213, 'synset': 'car.n.04', 'synonyms': ['elevator_car'], 'def': 'where passengers ride up and down', 'name': 'elevator_car'}, {'frequency': 'r', 'id': 214, 'synset': 'car_battery.n.01', 'synonyms': ['car_battery', 'automobile_battery'], 'def': 'a battery in a motor vehicle', 'name': 'car_battery'}, {'frequency': 'c', 'id': 215, 'synset': 'card.n.02', 'synonyms': ['identity_card'], 'def': 'a card certifying the identity of the bearer', 'name': 'identity_card'}, {'frequency': 'c', 'id': 216, 'synset': 'card.n.03', 'synonyms': ['card'], 'def': 'a rectangular piece of paper used to send messages (e.g. greetings or pictures)', 'name': 'card'}, {'frequency': 'r', 'id': 217, 'synset': 'cardigan.n.01', 'synonyms': ['cardigan'], 'def': 'knitted jacket that is fastened up the front with buttons or a zipper', 'name': 'cardigan'}, {'frequency': 'r', 'id': 218, 'synset': 'cargo_ship.n.01', 'synonyms': ['cargo_ship', 'cargo_vessel'], 'def': 'a ship designed to carry cargo', 'name': 'cargo_ship'}, {'frequency': 'r', 'id': 219, 'synset': 'carnation.n.01', 'synonyms': ['carnation'], 'def': 'plant with pink to purple-red spice-scented usually double flowers', 'name': 'carnation'}, {'frequency': 'c', 'id': 220, 'synset': 'carriage.n.02', 'synonyms': ['horse_carriage'], 'def': 'a vehicle with wheels drawn by one or more horses', 'name': 'horse_carriage'}, {'frequency': 'f', 'id': 221, 'synset': 'carrot.n.01', 'synonyms': ['carrot'], 'def': 'deep orange edible root of the cultivated carrot plant', 'name': 'carrot'}, {'frequency': 'c', 'id': 222, 'synset': 'carryall.n.01', 'synonyms': ['tote_bag'], 'def': 'a capacious bag or basket', 'name': 'tote_bag'}, {'frequency': 'c', 'id': 223, 'synset': 'cart.n.01', 'synonyms': ['cart'], 'def': 'a heavy open wagon usually having two wheels and drawn by an animal', 'name': 'cart'}, {'frequency': 'c', 'id': 224, 'synset': 'carton.n.02', 'synonyms': ['carton'], 'def': 'a box made of cardboard; opens by flaps on top', 'name': 'carton'}, {'frequency': 'c', 'id': 225, 'synset': 'cash_register.n.01', 'synonyms': ['cash_register', 'register_(for_cash_transactions)'], 'def': 'a cashbox with an adding machine to register transactions', 'name': 'cash_register'}, {'frequency': 'r', 'id': 226, 'synset': 'casserole.n.01', 'synonyms': ['casserole'], 'def': 'food cooked and served in a casserole', 'name': 'casserole'}, {'frequency': 'r', 'id': 227, 'synset': 'cassette.n.01', 'synonyms': ['cassette'], 'def': 'a container that holds a magnetic tape used for recording or playing sound or video', 'name': 'cassette'}, {'frequency': 'c', 'id': 228, 'synset': 'cast.n.05', 'synonyms': ['cast', 'plaster_cast', 'plaster_bandage'], 'def': 'bandage consisting of a firm covering that immobilizes broken bones while they heal', 'name': 'cast'}, {'frequency': 'f', 'id': 229, 'synset': 'cat.n.01', 'synonyms': ['cat'], 'def': 'a domestic house cat', 'name': 'cat'}, {'frequency': 'c', 'id': 230, 'synset': 'cauliflower.n.02', 'synonyms': ['cauliflower'], 'def': 'edible compact head of white undeveloped flowers', 'name': 'cauliflower'}, {'frequency': 'r', 'id': 231, 'synset': 'caviar.n.01', 'synonyms': ['caviar', 'caviare'], 'def': "salted roe of sturgeon or other large fish; usually served as an hors d'oeuvre", 'name': 'caviar'}, {'frequency': 'c', 'id': 232, 'synset': 'cayenne.n.02', 'synonyms': ['cayenne_(spice)', 'cayenne_pepper_(spice)', 'red_pepper_(spice)'], 'def': 'ground pods and seeds of pungent red peppers of the genus Capsicum', 'name': 'cayenne_(spice)'}, {'frequency': 'c', 'id': 233, 'synset': 'cd_player.n.01', 'synonyms': ['CD_player'], 'def': 'electronic equipment for playing compact discs (CDs)', 'name': 'CD_player'}, {'frequency': 'c', 'id': 234, 'synset': 'celery.n.01', 'synonyms': ['celery'], 'def': 'widely cultivated herb with aromatic leaf stalks that are eaten raw or cooked', 'name': 'celery'}, {'frequency': 'f', 'id': 235, 'synset': 'cellular_telephone.n.01', 'synonyms': ['cellular_telephone', 'cellular_phone', 'cellphone', 'mobile_phone', 'smart_phone'], 'def': 'a hand-held mobile telephone', 'name': 'cellular_telephone'}, {'frequency': 'r', 'id': 236, 'synset': 'chain_mail.n.01', 'synonyms': ['chain_mail', 'ring_mail', 'chain_armor', 'chain_armour', 'ring_armor', 'ring_armour'], 'def': '(Middle Ages) flexible armor made of interlinked metal rings', 'name': 'chain_mail'}, {'frequency': 'f', 'id': 237, 'synset': 'chair.n.01', 'synonyms': ['chair'], 'def': 'a seat for one person, with a support for the back', 'name': 'chair'}, {'frequency': 'r', 'id': 238, 'synset': 'chaise_longue.n.01', 'synonyms': ['chaise_longue', 'chaise', 'daybed'], 'def': 'a long chair; for reclining', 'name': 'chaise_longue'}, {'frequency': 'r', 'id': 239, 'synset': 'champagne.n.01', 'synonyms': ['champagne'], 'def': 'a white sparkling wine produced in Champagne or resembling that produced there', 'name': 'champagne'}, {'frequency': 'f', 'id': 240, 'synset': 'chandelier.n.01', 'synonyms': ['chandelier'], 'def': 'branched lighting fixture; often ornate; hangs from the ceiling', 'name': 'chandelier'}, {'frequency': 'r', 'id': 241, 'synset': 'chap.n.04', 'synonyms': ['chap'], 'def': 'leather leggings without a seat; worn over trousers by cowboys to protect their legs', 'name': 'chap'}, {'frequency': 'r', 'id': 242, 'synset': 'checkbook.n.01', 'synonyms': ['checkbook', 'chequebook'], 'def': 'a book issued to holders of checking accounts', 'name': 'checkbook'}, {'frequency': 'r', 'id': 243, 'synset': 'checkerboard.n.01', 'synonyms': ['checkerboard'], 'def': 'a board having 64 squares of two alternating colors', 'name': 'checkerboard'}, {'frequency': 'c', 'id': 244, 'synset': 'cherry.n.03', 'synonyms': ['cherry'], 'def': 'a red fruit with a single hard stone', 'name': 'cherry'}, {'frequency': 'r', 'id': 245, 'synset': 'chessboard.n.01', 'synonyms': ['chessboard'], 'def': 'a checkerboard used to play chess', 'name': 'chessboard'}, {'frequency': 'r', 'id': 246, 'synset': 'chest_of_drawers.n.01', 'synonyms': ['chest_of_drawers_(furniture)', 'bureau_(furniture)', 'chest_(furniture)'], 'def': 'furniture with drawers for keeping clothes', 'name': 'chest_of_drawers_(furniture)'}, {'frequency': 'c', 'id': 247, 'synset': 'chicken.n.02', 'synonyms': ['chicken_(animal)'], 'def': 'a domestic fowl bred for flesh or eggs', 'name': 'chicken_(animal)'}, {'frequency': 'c', 'id': 248, 'synset': 'chicken_wire.n.01', 'synonyms': ['chicken_wire'], 'def': 'a galvanized wire network with a hexagonal mesh; used to build fences', 'name': 'chicken_wire'}, {'frequency': 'r', 'id': 249, 'synset': 'chickpea.n.01', 'synonyms': ['chickpea', 'garbanzo'], 'def': 'the seed of the chickpea plant; usually dried', 'name': 'chickpea'}, {'frequency': 'r', 'id': 250, 'synset': 'chihuahua.n.03', 'synonyms': ['Chihuahua'], 'def': 'an old breed of tiny short-haired dog with protruding eyes from Mexico', 'name': 'Chihuahua'}, {'frequency': 'r', 'id': 251, 'synset': 'chili.n.02', 'synonyms': ['chili_(vegetable)', 'chili_pepper_(vegetable)', 'chilli_(vegetable)', 'chilly_(vegetable)', 'chile_(vegetable)'], 'def': 'very hot and finely tapering pepper of special pungency', 'name': 'chili_(vegetable)'}, {'frequency': 'r', 'id': 252, 'synset': 'chime.n.01', 'synonyms': ['chime', 'gong'], 'def': 'an instrument consisting of a set of bells that are struck with a hammer', 'name': 'chime'}, {'frequency': 'r', 'id': 253, 'synset': 'chinaware.n.01', 'synonyms': ['chinaware'], 'def': 'dishware made of high quality porcelain', 'name': 'chinaware'}, {'frequency': 'c', 'id': 254, 'synset': 'chip.n.04', 'synonyms': ['crisp_(potato_chip)', 'potato_chip'], 'def': 'a thin crisp slice of potato fried in deep fat', 'name': 'crisp_(potato_chip)'}, {'frequency': 'r', 'id': 255, 'synset': 'chip.n.06', 'synonyms': ['poker_chip'], 'def': 'a small disk-shaped counter used to represent money when gambling', 'name': 'poker_chip'}, {'frequency': 'c', 'id': 256, 'synset': 'chocolate_bar.n.01', 'synonyms': ['chocolate_bar'], 'def': 'a bar of chocolate candy', 'name': 'chocolate_bar'}, {'frequency': 'c', 'id': 257, 'synset': 'chocolate_cake.n.01', 'synonyms': ['chocolate_cake'], 'def': 'cake containing chocolate', 'name': 'chocolate_cake'}, {'frequency': 'r', 'id': 258, 'synset': 'chocolate_milk.n.01', 'synonyms': ['chocolate_milk'], 'def': 'milk flavored with chocolate syrup', 'name': 'chocolate_milk'}, {'frequency': 'r', 'id': 259, 'synset': 'chocolate_mousse.n.01', 'synonyms': ['chocolate_mousse'], 'def': 'dessert mousse made with chocolate', 'name': 'chocolate_mousse'}, {'frequency': 'f', 'id': 260, 'synset': 'choker.n.03', 'synonyms': ['choker', 'collar', 'neckband'], 'def': 'necklace that fits tightly around the neck', 'name': 'choker'}, {'frequency': 'f', 'id': 261, 'synset': 'chopping_board.n.01', 'synonyms': ['chopping_board', 'cutting_board', 'chopping_block'], 'def': 'a wooden board where meats or vegetables can be cut', 'name': 'chopping_board'}, {'frequency': 'c', 'id': 262, 'synset': 'chopstick.n.01', 'synonyms': ['chopstick'], 'def': 'one of a pair of slender sticks used as oriental tableware to eat food with', 'name': 'chopstick'}, {'frequency': 'f', 'id': 263, 'synset': 'christmas_tree.n.05', 'synonyms': ['Christmas_tree'], 'def': 'an ornamented evergreen used as a Christmas decoration', 'name': 'Christmas_tree'}, {'frequency': 'c', 'id': 264, 'synset': 'chute.n.02', 'synonyms': ['slide'], 'def': 'sloping channel through which things can descend', 'name': 'slide'}, {'frequency': 'r', 'id': 265, 'synset': 'cider.n.01', 'synonyms': ['cider', 'cyder'], 'def': 'a beverage made from juice pressed from apples', 'name': 'cider'}, {'frequency': 'r', 'id': 266, 'synset': 'cigar_box.n.01', 'synonyms': ['cigar_box'], 'def': 'a box for holding cigars', 'name': 'cigar_box'}, {'frequency': 'c', 'id': 267, 'synset': 'cigarette.n.01', 'synonyms': ['cigarette'], 'def': 'finely ground tobacco wrapped in paper; for smoking', 'name': 'cigarette'}, {'frequency': 'c', 'id': 268, 'synset': 'cigarette_case.n.01', 'synonyms': ['cigarette_case', 'cigarette_pack'], 'def': 'a small flat case for holding cigarettes', 'name': 'cigarette_case'}, {'frequency': 'f', 'id': 269, 'synset': 'cistern.n.02', 'synonyms': ['cistern', 'water_tank'], 'def': 'a tank that holds the water used to flush a toilet', 'name': 'cistern'}, {'frequency': 'r', 'id': 270, 'synset': 'clarinet.n.01', 'synonyms': ['clarinet'], 'def': 'a single-reed instrument with a straight tube', 'name': 'clarinet'}, {'frequency': 'r', 'id': 271, 'synset': 'clasp.n.01', 'synonyms': ['clasp'], 'def': 'a fastener (as a buckle or hook) that is used to hold two things together', 'name': 'clasp'}, {'frequency': 'c', 'id': 272, 'synset': 'cleansing_agent.n.01', 'synonyms': ['cleansing_agent', 'cleanser', 'cleaner'], 'def': 'a preparation used in cleaning something', 'name': 'cleansing_agent'}, {'frequency': 'r', 'id': 273, 'synset': 'clementine.n.01', 'synonyms': ['clementine'], 'def': 'a variety of mandarin orange', 'name': 'clementine'}, {'frequency': 'c', 'id': 274, 'synset': 'clip.n.03', 'synonyms': ['clip'], 'def': 'any of various small fasteners used to hold loose articles together', 'name': 'clip'}, {'frequency': 'c', 'id': 275, 'synset': 'clipboard.n.01', 'synonyms': ['clipboard'], 'def': 'a small writing board with a clip at the top for holding papers', 'name': 'clipboard'}, {'frequency': 'f', 'id': 276, 'synset': 'clock.n.01', 'synonyms': ['clock', 'timepiece', 'timekeeper'], 'def': 'a timepiece that shows the time of day', 'name': 'clock'}, {'frequency': 'f', 'id': 277, 'synset': 'clock_tower.n.01', 'synonyms': ['clock_tower'], 'def': 'a tower with a large clock visible high up on an outside face', 'name': 'clock_tower'}, {'frequency': 'c', 'id': 278, 'synset': 'clothes_hamper.n.01', 'synonyms': ['clothes_hamper', 'laundry_basket', 'clothes_basket'], 'def': 'a hamper that holds dirty clothes to be washed or wet clothes to be dried', 'name': 'clothes_hamper'}, {'frequency': 'c', 'id': 279, 'synset': 'clothespin.n.01', 'synonyms': ['clothespin', 'clothes_peg'], 'def': 'wood or plastic fastener; for holding clothes on a clothesline', 'name': 'clothespin'}, {'frequency': 'r', 'id': 280, 'synset': 'clutch_bag.n.01', 'synonyms': ['clutch_bag'], 'def': "a woman's strapless purse that is carried in the hand", 'name': 'clutch_bag'}, {'frequency': 'f', 'id': 281, 'synset': 'coaster.n.03', 'synonyms': ['coaster'], 'def': 'a covering (plate or mat) that protects the surface of a table', 'name': 'coaster'}, {'frequency': 'f', 'id': 282, 'synset': 'coat.n.01', 'synonyms': ['coat'], 'def': 'an outer garment that has sleeves and covers the body from shoulder down', 'name': 'coat'}, {'frequency': 'c', 'id': 283, 'synset': 'coat_hanger.n.01', 'synonyms': ['coat_hanger', 'clothes_hanger', 'dress_hanger'], 'def': "a hanger that is shaped like a person's shoulders", 'name': 'coat_hanger'}, {'frequency': 'r', 'id': 284, 'synset': 'coatrack.n.01', 'synonyms': ['coatrack', 'hatrack'], 'def': 'a rack with hooks for temporarily holding coats and hats', 'name': 'coatrack'}, {'frequency': 'c', 'id': 285, 'synset': 'cock.n.04', 'synonyms': ['cock', 'rooster'], 'def': 'adult male chicken', 'name': 'cock'}, {'frequency': 'c', 'id': 286, 'synset': 'coconut.n.02', 'synonyms': ['coconut', 'cocoanut'], 'def': 'large hard-shelled brown oval nut with a fibrous husk', 'name': 'coconut'}, {'frequency': 'r', 'id': 287, 'synset': 'coffee_filter.n.01', 'synonyms': ['coffee_filter'], 'def': 'filter (usually of paper) that passes the coffee and retains the coffee grounds', 'name': 'coffee_filter'}, {'frequency': 'f', 'id': 288, 'synset': 'coffee_maker.n.01', 'synonyms': ['coffee_maker', 'coffee_machine'], 'def': 'a kitchen appliance for brewing coffee automatically', 'name': 'coffee_maker'}, {'frequency': 'f', 'id': 289, 'synset': 'coffee_table.n.01', 'synonyms': ['coffee_table', 'cocktail_table'], 'def': 'low table where magazines can be placed and coffee or cocktails are served', 'name': 'coffee_table'}, {'frequency': 'c', 'id': 290, 'synset': 'coffeepot.n.01', 'synonyms': ['coffeepot'], 'def': 'tall pot in which coffee is brewed', 'name': 'coffeepot'}, {'frequency': 'r', 'id': 291, 'synset': 'coil.n.05', 'synonyms': ['coil'], 'def': 'tubing that is wound in a spiral', 'name': 'coil'}, {'frequency': 'c', 'id': 292, 'synset': 'coin.n.01', 'synonyms': ['coin'], 'def': 'a flat metal piece (usually a disc) used as money', 'name': 'coin'}, {'frequency': 'r', 'id': 293, 'synset': 'colander.n.01', 'synonyms': ['colander', 'cullender'], 'def': 'bowl-shaped strainer; used to wash or drain foods', 'name': 'colander'}, {'frequency': 'c', 'id': 294, 'synset': 'coleslaw.n.01', 'synonyms': ['coleslaw', 'slaw'], 'def': 'basically shredded cabbage', 'name': 'coleslaw'}, {'frequency': 'r', 'id': 295, 'synset': 'coloring_material.n.01', 'synonyms': ['coloring_material', 'colouring_material'], 'def': 'any material used for its color', 'name': 'coloring_material'}, {'frequency': 'r', 'id': 296, 'synset': 'combination_lock.n.01', 'synonyms': ['combination_lock'], 'def': 'lock that can be opened only by turning dials in a special sequence', 'name': 'combination_lock'}, {'frequency': 'c', 'id': 297, 'synset': 'comforter.n.04', 'synonyms': ['pacifier', 'teething_ring'], 'def': 'device used for an infant to suck or bite on', 'name': 'pacifier'}, {'frequency': 'r', 'id': 298, 'synset': 'comic_book.n.01', 'synonyms': ['comic_book'], 'def': 'a magazine devoted to comic strips', 'name': 'comic_book'}, {'frequency': 'f', 'id': 299, 'synset': 'computer_keyboard.n.01', 'synonyms': ['computer_keyboard', 'keyboard_(computer)'], 'def': 'a keyboard that is a data input device for computers', 'name': 'computer_keyboard'}, {'frequency': 'r', 'id': 300, 'synset': 'concrete_mixer.n.01', 'synonyms': ['concrete_mixer', 'cement_mixer'], 'def': 'a machine with a large revolving drum in which cement/concrete is mixed', 'name': 'concrete_mixer'}, {'frequency': 'f', 'id': 301, 'synset': 'cone.n.01', 'synonyms': ['cone', 'traffic_cone'], 'def': 'a cone-shaped object used to direct traffic', 'name': 'cone'}, {'frequency': 'f', 'id': 302, 'synset': 'control.n.09', 'synonyms': ['control', 'controller'], 'def': 'a mechanism that controls the operation of a machine', 'name': 'control'}, {'frequency': 'r', 'id': 303, 'synset': 'convertible.n.01', 'synonyms': ['convertible_(automobile)'], 'def': 'a car that has top that can be folded or removed', 'name': 'convertible_(automobile)'}, {'frequency': 'r', 'id': 304, 'synset': 'convertible.n.03', 'synonyms': ['sofa_bed'], 'def': 'a sofa that can be converted into a bed', 'name': 'sofa_bed'}, {'frequency': 'c', 'id': 305, 'synset': 'cookie.n.01', 'synonyms': ['cookie', 'cooky', 'biscuit_(cookie)'], 'def': "any of various small flat sweet cakes (`biscuit' is the British term)", 'name': 'cookie'}, {'frequency': 'r', 'id': 306, 'synset': 'cookie_jar.n.01', 'synonyms': ['cookie_jar', 'cooky_jar'], 'def': 'a jar in which cookies are kept (and sometimes money is hidden)', 'name': 'cookie_jar'}, {'frequency': 'r', 'id': 307, 'synset': 'cooking_utensil.n.01', 'synonyms': ['cooking_utensil'], 'def': 'a kitchen utensil made of material that does not melt easily; used for cooking', 'name': 'cooking_utensil'}, {'frequency': 'f', 'id': 308, 'synset': 'cooler.n.01', 'synonyms': ['cooler_(for_food)', 'ice_chest'], 'def': 'an insulated box for storing food often with ice', 'name': 'cooler_(for_food)'}, {'frequency': 'c', 'id': 309, 'synset': 'cork.n.04', 'synonyms': ['cork_(bottle_plug)', 'bottle_cork'], 'def': 'the plug in the mouth of a bottle (especially a wine bottle)', 'name': 'cork_(bottle_plug)'}, {'frequency': 'r', 'id': 310, 'synset': 'corkboard.n.01', 'synonyms': ['corkboard'], 'def': 'a sheet consisting of cork granules', 'name': 'corkboard'}, {'frequency': 'r', 'id': 311, 'synset': 'corkscrew.n.01', 'synonyms': ['corkscrew', 'bottle_screw'], 'def': 'a bottle opener that pulls corks', 'name': 'corkscrew'}, {'frequency': 'c', 'id': 312, 'synset': 'corn.n.03', 'synonyms': ['edible_corn', 'corn', 'maize'], 'def': 'ears of corn that can be prepared and served for human food', 'name': 'edible_corn'}, {'frequency': 'r', 'id': 313, 'synset': 'cornbread.n.01', 'synonyms': ['cornbread'], 'def': 'bread made primarily of cornmeal', 'name': 'cornbread'}, {'frequency': 'c', 'id': 314, 'synset': 'cornet.n.01', 'synonyms': ['cornet', 'horn', 'trumpet'], 'def': 'a brass musical instrument with a narrow tube and a flared bell and many valves', 'name': 'cornet'}, {'frequency': 'c', 'id': 315, 'synset': 'cornice.n.01', 'synonyms': ['cornice', 'valance', 'valance_board', 'pelmet'], 'def': 'a decorative framework to conceal curtain fixtures at the top of a window casing', 'name': 'cornice'}, {'frequency': 'r', 'id': 316, 'synset': 'cornmeal.n.01', 'synonyms': ['cornmeal'], 'def': 'coarsely ground corn', 'name': 'cornmeal'}, {'frequency': 'r', 'id': 317, 'synset': 'corset.n.01', 'synonyms': ['corset', 'girdle'], 'def': "a woman's close-fitting foundation garment", 'name': 'corset'}, {'frequency': 'r', 'id': 318, 'synset': 'cos.n.02', 'synonyms': ['romaine_lettuce'], 'def': 'lettuce with long dark-green leaves in a loosely packed elongated head', 'name': 'romaine_lettuce'}, {'frequency': 'c', 'id': 319, 'synset': 'costume.n.04', 'synonyms': ['costume'], 'def': 'the attire characteristic of a country or a time or a social class', 'name': 'costume'}, {'frequency': 'r', 'id': 320, 'synset': 'cougar.n.01', 'synonyms': ['cougar', 'puma', 'catamount', 'mountain_lion', 'panther'], 'def': 'large American feline resembling a lion', 'name': 'cougar'}, {'frequency': 'r', 'id': 321, 'synset': 'coverall.n.01', 'synonyms': ['coverall'], 'def': 'a loose-fitting protective garment that is worn over other clothing', 'name': 'coverall'}, {'frequency': 'r', 'id': 322, 'synset': 'cowbell.n.01', 'synonyms': ['cowbell'], 'def': 'a bell hung around the neck of cow so that the cow can be easily located', 'name': 'cowbell'}, {'frequency': 'f', 'id': 323, 'synset': 'cowboy_hat.n.01', 'synonyms': ['cowboy_hat', 'ten-gallon_hat'], 'def': 'a hat with a wide brim and a soft crown; worn by American ranch hands', 'name': 'cowboy_hat'}, {'frequency': 'r', 'id': 324, 'synset': 'crab.n.01', 'synonyms': ['crab_(animal)'], 'def': 'decapod having eyes on short stalks and a broad flattened shell and pincers', 'name': 'crab_(animal)'}, {'frequency': 'c', 'id': 325, 'synset': 'cracker.n.01', 'synonyms': ['cracker'], 'def': 'a thin crisp wafer', 'name': 'cracker'}, {'frequency': 'r', 'id': 326, 'synset': 'crape.n.01', 'synonyms': ['crape', 'crepe', 'French_pancake'], 'def': 'small very thin pancake', 'name': 'crape'}, {'frequency': 'f', 'id': 327, 'synset': 'crate.n.01', 'synonyms': ['crate'], 'def': 'a rugged box (usually made of wood); used for shipping', 'name': 'crate'}, {'frequency': 'r', 'id': 328, 'synset': 'crayon.n.01', 'synonyms': ['crayon', 'wax_crayon'], 'def': 'writing or drawing implement made of a colored stick of composition wax', 'name': 'crayon'}, {'frequency': 'r', 'id': 329, 'synset': 'cream_pitcher.n.01', 'synonyms': ['cream_pitcher'], 'def': 'a small pitcher for serving cream', 'name': 'cream_pitcher'}, {'frequency': 'r', 'id': 330, 'synset': 'credit_card.n.01', 'synonyms': ['credit_card', 'charge_card', 'debit_card'], 'def': 'a card, usually plastic, used to pay for goods and services', 'name': 'credit_card'}, {'frequency': 'c', 'id': 331, 'synset': 'crescent_roll.n.01', 'synonyms': ['crescent_roll', 'croissant'], 'def': 'very rich flaky crescent-shaped roll', 'name': 'crescent_roll'}, {'frequency': 'c', 'id': 332, 'synset': 'crib.n.01', 'synonyms': ['crib', 'cot'], 'def': 'baby bed with high sides made of slats', 'name': 'crib'}, {'frequency': 'c', 'id': 333, 'synset': 'crock.n.03', 'synonyms': ['crock_pot', 'earthenware_jar'], 'def': 'an earthen jar (made of baked clay)', 'name': 'crock_pot'}, {'frequency': 'f', 'id': 334, 'synset': 'crossbar.n.01', 'synonyms': ['crossbar'], 'def': 'a horizontal bar that goes across something', 'name': 'crossbar'}, {'frequency': 'r', 'id': 335, 'synset': 'crouton.n.01', 'synonyms': ['crouton'], 'def': 'a small piece of toasted or fried bread; served in soup or salads', 'name': 'crouton'}, {'frequency': 'r', 'id': 336, 'synset': 'crow.n.01', 'synonyms': ['crow'], 'def': 'black birds having a raucous call', 'name': 'crow'}, {'frequency': 'c', 'id': 337, 'synset': 'crown.n.04', 'synonyms': ['crown'], 'def': 'an ornamental jeweled headdress signifying sovereignty', 'name': 'crown'}, {'frequency': 'c', 'id': 338, 'synset': 'crucifix.n.01', 'synonyms': ['crucifix'], 'def': 'representation of the cross on which Jesus died', 'name': 'crucifix'}, {'frequency': 'c', 'id': 339, 'synset': 'cruise_ship.n.01', 'synonyms': ['cruise_ship', 'cruise_liner'], 'def': 'a passenger ship used commercially for pleasure cruises', 'name': 'cruise_ship'}, {'frequency': 'c', 'id': 340, 'synset': 'cruiser.n.01', 'synonyms': ['police_cruiser', 'patrol_car', 'police_car', 'squad_car'], 'def': 'a car in which policemen cruise the streets', 'name': 'police_cruiser'}, {'frequency': 'c', 'id': 341, 'synset': 'crumb.n.03', 'synonyms': ['crumb'], 'def': 'small piece of e.g. bread or cake', 'name': 'crumb'}, {'frequency': 'r', 'id': 342, 'synset': 'crutch.n.01', 'synonyms': ['crutch'], 'def': 'a wooden or metal staff that fits under the armpit and reaches to the ground', 'name': 'crutch'}, {'frequency': 'c', 'id': 343, 'synset': 'cub.n.03', 'synonyms': ['cub_(animal)'], 'def': 'the young of certain carnivorous mammals such as the bear or wolf or lion', 'name': 'cub_(animal)'}, {'frequency': 'r', 'id': 344, 'synset': 'cube.n.05', 'synonyms': ['cube', 'square_block'], 'def': 'a block in the (approximate) shape of a cube', 'name': 'cube'}, {'frequency': 'f', 'id': 345, 'synset': 'cucumber.n.02', 'synonyms': ['cucumber', 'cuke'], 'def': 'cylindrical green fruit with thin green rind and white flesh eaten as a vegetable', 'name': 'cucumber'}, {'frequency': 'c', 'id': 346, 'synset': 'cufflink.n.01', 'synonyms': ['cufflink'], 'def': 'jewelry consisting of linked buttons used to fasten the cuffs of a shirt', 'name': 'cufflink'}, {'frequency': 'f', 'id': 347, 'synset': 'cup.n.01', 'synonyms': ['cup'], 'def': 'a small open container usually used for drinking; usually has a handle', 'name': 'cup'}, {'frequency': 'c', 'id': 348, 'synset': 'cup.n.08', 'synonyms': ['trophy_cup'], 'def': 'a metal vessel with handles that is awarded as a trophy to a competition winner', 'name': 'trophy_cup'}, {'frequency': 'c', 'id': 349, 'synset': 'cupcake.n.01', 'synonyms': ['cupcake'], 'def': 'small cake baked in a muffin tin', 'name': 'cupcake'}, {'frequency': 'r', 'id': 350, 'synset': 'curler.n.01', 'synonyms': ['hair_curler', 'hair_roller', 'hair_crimper'], 'def': 'a cylindrical tube around which the hair is wound to curl it', 'name': 'hair_curler'}, {'frequency': 'r', 'id': 351, 'synset': 'curling_iron.n.01', 'synonyms': ['curling_iron'], 'def': 'a cylindrical home appliance that heats hair that has been curled around it', 'name': 'curling_iron'}, {'frequency': 'f', 'id': 352, 'synset': 'curtain.n.01', 'synonyms': ['curtain', 'drapery'], 'def': 'hanging cloth used as a blind (especially for a window)', 'name': 'curtain'}, {'frequency': 'f', 'id': 353, 'synset': 'cushion.n.03', 'synonyms': ['cushion'], 'def': 'a soft bag filled with air or padding such as feathers or foam rubber', 'name': 'cushion'}, {'frequency': 'r', 'id': 354, 'synset': 'custard.n.01', 'synonyms': ['custard'], 'def': 'sweetened mixture of milk and eggs baked or boiled or frozen', 'name': 'custard'}, {'frequency': 'c', 'id': 355, 'synset': 'cutter.n.06', 'synonyms': ['cutting_tool'], 'def': 'a cutting implement; a tool for cutting', 'name': 'cutting_tool'}, {'frequency': 'r', 'id': 356, 'synset': 'cylinder.n.04', 'synonyms': ['cylinder'], 'def': 'a cylindrical container', 'name': 'cylinder'}, {'frequency': 'r', 'id': 357, 'synset': 'cymbal.n.01', 'synonyms': ['cymbal'], 'def': 'a percussion instrument consisting of a concave brass disk', 'name': 'cymbal'}, {'frequency': 'r', 'id': 358, 'synset': 'dachshund.n.01', 'synonyms': ['dachshund', 'dachsie', 'badger_dog'], 'def': 'small long-bodied short-legged breed of dog having a short sleek coat and long drooping ears', 'name': 'dachshund'}, {'frequency': 'r', 'id': 359, 'synset': 'dagger.n.01', 'synonyms': ['dagger'], 'def': 'a short knife with a pointed blade used for piercing or stabbing', 'name': 'dagger'}, {'frequency': 'r', 'id': 360, 'synset': 'dartboard.n.01', 'synonyms': ['dartboard'], 'def': 'a circular board of wood or cork used as the target in the game of darts', 'name': 'dartboard'}, {'frequency': 'r', 'id': 361, 'synset': 'date.n.08', 'synonyms': ['date_(fruit)'], 'def': 'sweet edible fruit of the date palm with a single long woody seed', 'name': 'date_(fruit)'}, {'frequency': 'f', 'id': 362, 'synset': 'deck_chair.n.01', 'synonyms': ['deck_chair', 'beach_chair'], 'def': 'a folding chair for use outdoors; a wooden frame supports a length of canvas', 'name': 'deck_chair'}, {'frequency': 'c', 'id': 363, 'synset': 'deer.n.01', 'synonyms': ['deer', 'cervid'], 'def': "distinguished from Bovidae by the male's having solid deciduous antlers", 'name': 'deer'}, {'frequency': 'c', 'id': 364, 'synset': 'dental_floss.n.01', 'synonyms': ['dental_floss', 'floss'], 'def': 'a soft thread for cleaning the spaces between the teeth', 'name': 'dental_floss'}, {'frequency': 'f', 'id': 365, 'synset': 'desk.n.01', 'synonyms': ['desk'], 'def': 'a piece of furniture with a writing surface and usually drawers or other compartments', 'name': 'desk'}, {'frequency': 'r', 'id': 366, 'synset': 'detergent.n.01', 'synonyms': ['detergent'], 'def': 'a surface-active chemical widely used in industry and laundering', 'name': 'detergent'}, {'frequency': 'c', 'id': 367, 'synset': 'diaper.n.01', 'synonyms': ['diaper'], 'def': 'garment consisting of a folded cloth drawn up between the legs and fastened at the waist', 'name': 'diaper'}, {'frequency': 'r', 'id': 368, 'synset': 'diary.n.01', 'synonyms': ['diary', 'journal'], 'def': 'a daily written record of (usually personal) experiences and observations', 'name': 'diary'}, {'frequency': 'r', 'id': 369, 'synset': 'die.n.01', 'synonyms': ['die', 'dice'], 'def': 'a small cube with 1 to 6 spots on the six faces; used in gambling', 'name': 'die'}, {'frequency': 'r', 'id': 370, 'synset': 'dinghy.n.01', 'synonyms': ['dinghy', 'dory', 'rowboat'], 'def': 'a small boat of shallow draft with seats and oars with which it is propelled', 'name': 'dinghy'}, {'frequency': 'f', 'id': 371, 'synset': 'dining_table.n.01', 'synonyms': ['dining_table'], 'def': 'a table at which meals are served', 'name': 'dining_table'}, {'frequency': 'r', 'id': 372, 'synset': 'dinner_jacket.n.01', 'synonyms': ['tux', 'tuxedo'], 'def': 'semiformal evening dress for men', 'name': 'tux'}, {'frequency': 'c', 'id': 373, 'synset': 'dish.n.01', 'synonyms': ['dish'], 'def': 'a piece of dishware normally used as a container for holding or serving food', 'name': 'dish'}, {'frequency': 'c', 'id': 374, 'synset': 'dish.n.05', 'synonyms': ['dish_antenna'], 'def': 'directional antenna consisting of a parabolic reflector', 'name': 'dish_antenna'}, {'frequency': 'c', 'id': 375, 'synset': 'dishrag.n.01', 'synonyms': ['dishrag', 'dishcloth'], 'def': 'a cloth for washing dishes', 'name': 'dishrag'}, {'frequency': 'c', 'id': 376, 'synset': 'dishtowel.n.01', 'synonyms': ['dishtowel', 'tea_towel'], 'def': 'a towel for drying dishes', 'name': 'dishtowel'}, {'frequency': 'f', 'id': 377, 'synset': 'dishwasher.n.01', 'synonyms': ['dishwasher', 'dishwashing_machine'], 'def': 'a machine for washing dishes', 'name': 'dishwasher'}, {'frequency': 'r', 'id': 378, 'synset': 'dishwasher_detergent.n.01', 'synonyms': ['dishwasher_detergent', 'dishwashing_detergent', 'dishwashing_liquid'], 'def': 'a low-sudsing detergent designed for use in dishwashers', 'name': 'dishwasher_detergent'}, {'frequency': 'r', 'id': 379, 'synset': 'diskette.n.01', 'synonyms': ['diskette', 'floppy', 'floppy_disk'], 'def': 'a small plastic magnetic disk enclosed in a stiff envelope used to store data', 'name': 'diskette'}, {'frequency': 'c', 'id': 380, 'synset': 'dispenser.n.01', 'synonyms': ['dispenser'], 'def': 'a container so designed that the contents can be used in prescribed amounts', 'name': 'dispenser'}, {'frequency': 'c', 'id': 381, 'synset': 'dixie_cup.n.01', 'synonyms': ['Dixie_cup', 'paper_cup'], 'def': 'a disposable cup made of paper; for holding drinks', 'name': 'Dixie_cup'}, {'frequency': 'f', 'id': 382, 'synset': 'dog.n.01', 'synonyms': ['dog'], 'def': 'a common domesticated dog', 'name': 'dog'}, {'frequency': 'f', 'id': 383, 'synset': 'dog_collar.n.01', 'synonyms': ['dog_collar'], 'def': 'a collar for a dog', 'name': 'dog_collar'}, {'frequency': 'c', 'id': 384, 'synset': 'doll.n.01', 'synonyms': ['doll'], 'def': 'a toy replica of a HUMAN (NOT AN ANIMAL)', 'name': 'doll'}, {'frequency': 'r', 'id': 385, 'synset': 'dollar.n.02', 'synonyms': ['dollar', 'dollar_bill', 'one_dollar_bill'], 'def': 'a piece of paper money worth one dollar', 'name': 'dollar'}, {'frequency': 'r', 'id': 386, 'synset': 'dolphin.n.02', 'synonyms': ['dolphin'], 'def': 'any of various small toothed whales with a beaklike snout; larger than porpoises', 'name': 'dolphin'}, {'frequency': 'c', 'id': 387, 'synset': 'domestic_ass.n.01', 'synonyms': ['domestic_ass', 'donkey'], 'def': 'domestic beast of burden descended from the African wild ass; patient but stubborn', 'name': 'domestic_ass'}, {'frequency': 'r', 'id': 388, 'synset': 'domino.n.03', 'synonyms': ['eye_mask'], 'def': 'a mask covering the upper part of the face but with holes for the eyes', 'name': 'eye_mask'}, {'frequency': 'r', 'id': 389, 'synset': 'doorbell.n.01', 'synonyms': ['doorbell', 'buzzer'], 'def': 'a button at an outer door that gives a ringing or buzzing signal when pushed', 'name': 'doorbell'}, {'frequency': 'f', 'id': 390, 'synset': 'doorknob.n.01', 'synonyms': ['doorknob', 'doorhandle'], 'def': "a knob used to open a door (often called `doorhandle' in Great Britain)", 'name': 'doorknob'}, {'frequency': 'c', 'id': 391, 'synset': 'doormat.n.02', 'synonyms': ['doormat', 'welcome_mat'], 'def': 'a mat placed outside an exterior door for wiping the shoes before entering', 'name': 'doormat'}, {'frequency': 'f', 'id': 392, 'synset': 'doughnut.n.02', 'synonyms': ['doughnut', 'donut'], 'def': 'a small ring-shaped friedcake', 'name': 'doughnut'}, {'frequency': 'r', 'id': 393, 'synset': 'dove.n.01', 'synonyms': ['dove'], 'def': 'any of numerous small pigeons', 'name': 'dove'}, {'frequency': 'r', 'id': 394, 'synset': 'dragonfly.n.01', 'synonyms': ['dragonfly'], 'def': 'slender-bodied non-stinging insect having iridescent wings that are outspread at rest', 'name': 'dragonfly'}, {'frequency': 'f', 'id': 395, 'synset': 'drawer.n.01', 'synonyms': ['drawer'], 'def': 'a boxlike container in a piece of furniture; made so as to slide in and out', 'name': 'drawer'}, {'frequency': 'c', 'id': 396, 'synset': 'drawers.n.01', 'synonyms': ['underdrawers', 'boxers', 'boxershorts'], 'def': 'underpants worn by men', 'name': 'underdrawers'}, {'frequency': 'f', 'id': 397, 'synset': 'dress.n.01', 'synonyms': ['dress', 'frock'], 'def': 'a one-piece garment for a woman; has skirt and bodice', 'name': 'dress'}, {'frequency': 'c', 'id': 398, 'synset': 'dress_hat.n.01', 'synonyms': ['dress_hat', 'high_hat', 'opera_hat', 'silk_hat', 'top_hat'], 'def': "a man's hat with a tall crown; usually covered with silk or with beaver fur", 'name': 'dress_hat'}, {'frequency': 'c', 'id': 399, 'synset': 'dress_suit.n.01', 'synonyms': ['dress_suit'], 'def': 'formalwear consisting of full evening dress for men', 'name': 'dress_suit'}, {'frequency': 'c', 'id': 400, 'synset': 'dresser.n.05', 'synonyms': ['dresser'], 'def': 'a cabinet with shelves', 'name': 'dresser'}, {'frequency': 'c', 'id': 401, 'synset': 'drill.n.01', 'synonyms': ['drill'], 'def': 'a tool with a sharp rotating point for making holes in hard materials', 'name': 'drill'}, {'frequency': 'r', 'id': 402, 'synset': 'drinking_fountain.n.01', 'synonyms': ['drinking_fountain'], 'def': 'a public fountain to provide a jet of drinking water', 'name': 'drinking_fountain'}, {'frequency': 'r', 'id': 403, 'synset': 'drone.n.04', 'synonyms': ['drone'], 'def': 'an aircraft without a pilot that is operated by remote control', 'name': 'drone'}, {'frequency': 'r', 'id': 404, 'synset': 'dropper.n.01', 'synonyms': ['dropper', 'eye_dropper'], 'def': 'pipet consisting of a small tube with a vacuum bulb at one end for drawing liquid in and releasing it a drop at a time', 'name': 'dropper'}, {'frequency': 'c', 'id': 405, 'synset': 'drum.n.01', 'synonyms': ['drum_(musical_instrument)'], 'def': 'a musical percussion instrument; usually consists of a hollow cylinder with a membrane stretched across each end', 'name': 'drum_(musical_instrument)'}, {'frequency': 'r', 'id': 406, 'synset': 'drumstick.n.02', 'synonyms': ['drumstick'], 'def': 'a stick used for playing a drum', 'name': 'drumstick'}, {'frequency': 'f', 'id': 407, 'synset': 'duck.n.01', 'synonyms': ['duck'], 'def': 'small web-footed broad-billed swimming bird', 'name': 'duck'}, {'frequency': 'r', 'id': 408, 'synset': 'duckling.n.02', 'synonyms': ['duckling'], 'def': 'young duck', 'name': 'duckling'}, {'frequency': 'c', 'id': 409, 'synset': 'duct_tape.n.01', 'synonyms': ['duct_tape'], 'def': 'a wide silvery adhesive tape', 'name': 'duct_tape'}, {'frequency': 'f', 'id': 410, 'synset': 'duffel_bag.n.01', 'synonyms': ['duffel_bag', 'duffle_bag', 'duffel', 'duffle'], 'def': 'a large cylindrical bag of heavy cloth', 'name': 'duffel_bag'}, {'frequency': 'r', 'id': 411, 'synset': 'dumbbell.n.01', 'synonyms': ['dumbbell'], 'def': 'an exercising weight with two ball-like ends connected by a short handle', 'name': 'dumbbell'}, {'frequency': 'c', 'id': 412, 'synset': 'dumpster.n.01', 'synonyms': ['dumpster'], 'def': 'a container designed to receive and transport and dump waste', 'name': 'dumpster'}, {'frequency': 'r', 'id': 413, 'synset': 'dustpan.n.02', 'synonyms': ['dustpan'], 'def': 'a short-handled receptacle into which dust can be swept', 'name': 'dustpan'}, {'frequency': 'r', 'id': 414, 'synset': 'dutch_oven.n.02', 'synonyms': ['Dutch_oven'], 'def': 'iron or earthenware cooking pot; used for stews', 'name': 'Dutch_oven'}, {'frequency': 'c', 'id': 415, 'synset': 'eagle.n.01', 'synonyms': ['eagle'], 'def': 'large birds of prey noted for their broad wings and strong soaring flight', 'name': 'eagle'}, {'frequency': 'f', 'id': 416, 'synset': 'earphone.n.01', 'synonyms': ['earphone', 'earpiece', 'headphone'], 'def': 'device for listening to audio that is held over or inserted into the ear', 'name': 'earphone'}, {'frequency': 'r', 'id': 417, 'synset': 'earplug.n.01', 'synonyms': ['earplug'], 'def': 'a soft plug that is inserted into the ear canal to block sound', 'name': 'earplug'}, {'frequency': 'f', 'id': 418, 'synset': 'earring.n.01', 'synonyms': ['earring'], 'def': 'jewelry to ornament the ear', 'name': 'earring'}, {'frequency': 'c', 'id': 419, 'synset': 'easel.n.01', 'synonyms': ['easel'], 'def': "an upright tripod for displaying something (usually an artist's canvas)", 'name': 'easel'}, {'frequency': 'r', 'id': 420, 'synset': 'eclair.n.01', 'synonyms': ['eclair'], 'def': 'oblong cream puff', 'name': 'eclair'}, {'frequency': 'r', 'id': 421, 'synset': 'eel.n.01', 'synonyms': ['eel'], 'def': 'an elongate fish with fatty flesh', 'name': 'eel'}, {'frequency': 'f', 'id': 422, 'synset': 'egg.n.02', 'synonyms': ['egg', 'eggs'], 'def': 'oval reproductive body of a fowl (especially a hen) used as food', 'name': 'egg'}, {'frequency': 'r', 'id': 423, 'synset': 'egg_roll.n.01', 'synonyms': ['egg_roll', 'spring_roll'], 'def': 'minced vegetables and meat wrapped in a pancake and fried', 'name': 'egg_roll'}, {'frequency': 'c', 'id': 424, 'synset': 'egg_yolk.n.01', 'synonyms': ['egg_yolk', 'yolk_(egg)'], 'def': 'the yellow spherical part of an egg', 'name': 'egg_yolk'}, {'frequency': 'c', 'id': 425, 'synset': 'eggbeater.n.02', 'synonyms': ['eggbeater', 'eggwhisk'], 'def': 'a mixer for beating eggs or whipping cream', 'name': 'eggbeater'}, {'frequency': 'c', 'id': 426, 'synset': 'eggplant.n.01', 'synonyms': ['eggplant', 'aubergine'], 'def': 'egg-shaped vegetable having a shiny skin typically dark purple', 'name': 'eggplant'}, {'frequency': 'r', 'id': 427, 'synset': 'electric_chair.n.01', 'synonyms': ['electric_chair'], 'def': 'a chair-shaped instrument of execution by electrocution', 'name': 'electric_chair'}, {'frequency': 'f', 'id': 428, 'synset': 'electric_refrigerator.n.01', 'synonyms': ['refrigerator'], 'def': 'a refrigerator in which the coolant is pumped around by an electric motor', 'name': 'refrigerator'}, {'frequency': 'f', 'id': 429, 'synset': 'elephant.n.01', 'synonyms': ['elephant'], 'def': 'a common elephant', 'name': 'elephant'}, {'frequency': 'r', 'id': 430, 'synset': 'elk.n.01', 'synonyms': ['elk', 'moose'], 'def': 'large northern deer with enormous flattened antlers in the male', 'name': 'elk'}, {'frequency': 'c', 'id': 431, 'synset': 'envelope.n.01', 'synonyms': ['envelope'], 'def': 'a flat (usually rectangular) container for a letter, thin package, etc.', 'name': 'envelope'}, {'frequency': 'c', 'id': 432, 'synset': 'eraser.n.01', 'synonyms': ['eraser'], 'def': 'an implement used to erase something', 'name': 'eraser'}, {'frequency': 'r', 'id': 433, 'synset': 'escargot.n.01', 'synonyms': ['escargot'], 'def': 'edible snail usually served in the shell with a sauce of melted butter and garlic', 'name': 'escargot'}, {'frequency': 'r', 'id': 434, 'synset': 'eyepatch.n.01', 'synonyms': ['eyepatch'], 'def': 'a protective cloth covering for an injured eye', 'name': 'eyepatch'}, {'frequency': 'r', 'id': 435, 'synset': 'falcon.n.01', 'synonyms': ['falcon'], 'def': 'birds of prey having long pointed powerful wings adapted for swift flight', 'name': 'falcon'}, {'frequency': 'f', 'id': 436, 'synset': 'fan.n.01', 'synonyms': ['fan'], 'def': 'a device for creating a current of air by movement of a surface or surfaces', 'name': 'fan'}, {'frequency': 'f', 'id': 437, 'synset': 'faucet.n.01', 'synonyms': ['faucet', 'spigot', 'tap'], 'def': 'a regulator for controlling the flow of a liquid from a reservoir', 'name': 'faucet'}, {'frequency': 'r', 'id': 438, 'synset': 'fedora.n.01', 'synonyms': ['fedora'], 'def': 'a hat made of felt with a creased crown', 'name': 'fedora'}, {'frequency': 'r', 'id': 439, 'synset': 'ferret.n.02', 'synonyms': ['ferret'], 'def': 'domesticated albino variety of the European polecat bred for hunting rats and rabbits', 'name': 'ferret'}, {'frequency': 'c', 'id': 440, 'synset': 'ferris_wheel.n.01', 'synonyms': ['Ferris_wheel'], 'def': 'a large wheel with suspended seats that remain upright as the wheel rotates', 'name': 'Ferris_wheel'}, {'frequency': 'r', 'id': 441, 'synset': 'ferry.n.01', 'synonyms': ['ferry', 'ferryboat'], 'def': 'a boat that transports people or vehicles across a body of water and operates on a regular schedule', 'name': 'ferry'}, {'frequency': 'r', 'id': 442, 'synset': 'fig.n.04', 'synonyms': ['fig_(fruit)'], 'def': 'fleshy sweet pear-shaped yellowish or purple fruit eaten fresh or preserved or dried', 'name': 'fig_(fruit)'}, {'frequency': 'c', 'id': 443, 'synset': 'fighter.n.02', 'synonyms': ['fighter_jet', 'fighter_aircraft', 'attack_aircraft'], 'def': 'a high-speed military or naval airplane designed to destroy enemy targets', 'name': 'fighter_jet'}, {'frequency': 'f', 'id': 444, 'synset': 'figurine.n.01', 'synonyms': ['figurine'], 'def': 'a small carved or molded figure', 'name': 'figurine'}, {'frequency': 'c', 'id': 445, 'synset': 'file.n.03', 'synonyms': ['file_cabinet', 'filing_cabinet'], 'def': 'office furniture consisting of a container for keeping papers in order', 'name': 'file_cabinet'}, {'frequency': 'r', 'id': 446, 'synset': 'file.n.04', 'synonyms': ['file_(tool)'], 'def': 'a steel hand tool with small sharp teeth on some or all of its surfaces; used for smoothing wood or metal', 'name': 'file_(tool)'}, {'frequency': 'f', 'id': 447, 'synset': 'fire_alarm.n.02', 'synonyms': ['fire_alarm', 'smoke_alarm'], 'def': 'an alarm that is tripped off by fire or smoke', 'name': 'fire_alarm'}, {'frequency': 'c', 'id': 448, 'synset': 'fire_engine.n.01', 'synonyms': ['fire_engine', 'fire_truck'], 'def': 'large trucks that carry firefighters and equipment to the site of a fire', 'name': 'fire_engine'}, {'frequency': 'c', 'id': 449, 'synset': 'fire_extinguisher.n.01', 'synonyms': ['fire_extinguisher', 'extinguisher'], 'def': 'a manually operated device for extinguishing small fires', 'name': 'fire_extinguisher'}, {'frequency': 'c', 'id': 450, 'synset': 'fire_hose.n.01', 'synonyms': ['fire_hose'], 'def': 'a large hose that carries water from a fire hydrant to the site of the fire', 'name': 'fire_hose'}, {'frequency': 'f', 'id': 451, 'synset': 'fireplace.n.01', 'synonyms': ['fireplace'], 'def': 'an open recess in a wall at the base of a chimney where a fire can be built', 'name': 'fireplace'}, {'frequency': 'f', 'id': 452, 'synset': 'fireplug.n.01', 'synonyms': ['fireplug', 'fire_hydrant', 'hydrant'], 'def': 'an upright hydrant for drawing water to use in fighting a fire', 'name': 'fireplug'}, {'frequency': 'c', 'id': 453, 'synset': 'fish.n.01', 'synonyms': ['fish'], 'def': 'any of various mostly cold-blooded aquatic vertebrates usually having scales and breathing through gills', 'name': 'fish'}, {'frequency': 'r', 'id': 454, 'synset': 'fish.n.02', 'synonyms': ['fish_(food)'], 'def': 'the flesh of fish used as food', 'name': 'fish_(food)'}, {'frequency': 'r', 'id': 455, 'synset': 'fishbowl.n.02', 'synonyms': ['fishbowl', 'goldfish_bowl'], 'def': 'a transparent bowl in which small fish are kept', 'name': 'fishbowl'}, {'frequency': 'r', 'id': 456, 'synset': 'fishing_boat.n.01', 'synonyms': ['fishing_boat', 'fishing_vessel'], 'def': 'a vessel for fishing', 'name': 'fishing_boat'}, {'frequency': 'c', 'id': 457, 'synset': 'fishing_rod.n.01', 'synonyms': ['fishing_rod', 'fishing_pole'], 'def': 'a rod that is used in fishing to extend the fishing line', 'name': 'fishing_rod'}, {'frequency': 'f', 'id': 458, 'synset': 'flag.n.01', 'synonyms': ['flag'], 'def': 'emblem usually consisting of a rectangular piece of cloth of distinctive design (do not include pole)', 'name': 'flag'}, {'frequency': 'f', 'id': 459, 'synset': 'flagpole.n.02', 'synonyms': ['flagpole', 'flagstaff'], 'def': 'a tall staff or pole on which a flag is raised', 'name': 'flagpole'}, {'frequency': 'c', 'id': 460, 'synset': 'flamingo.n.01', 'synonyms': ['flamingo'], 'def': 'large pink web-footed bird with down-bent bill', 'name': 'flamingo'}, {'frequency': 'c', 'id': 461, 'synset': 'flannel.n.01', 'synonyms': ['flannel'], 'def': 'a soft light woolen fabric; used for clothing', 'name': 'flannel'}, {'frequency': 'r', 'id': 462, 'synset': 'flash.n.10', 'synonyms': ['flash', 'flashbulb'], 'def': 'a lamp for providing momentary light to take a photograph', 'name': 'flash'}, {'frequency': 'c', 'id': 463, 'synset': 'flashlight.n.01', 'synonyms': ['flashlight', 'torch'], 'def': 'a small portable battery-powered electric lamp', 'name': 'flashlight'}, {'frequency': 'r', 'id': 464, 'synset': 'fleece.n.03', 'synonyms': ['fleece'], 'def': 'a soft bulky fabric with deep pile; used chiefly for clothing', 'name': 'fleece'}, {'frequency': 'f', 'id': 465, 'synset': 'flip-flop.n.02', 'synonyms': ['flip-flop_(sandal)'], 'def': 'a backless sandal held to the foot by a thong between two toes', 'name': 'flip-flop_(sandal)'}, {'frequency': 'c', 'id': 466, 'synset': 'flipper.n.01', 'synonyms': ['flipper_(footwear)', 'fin_(footwear)'], 'def': 'a shoe to aid a person in swimming', 'name': 'flipper_(footwear)'}, {'frequency': 'f', 'id': 467, 'synset': 'flower_arrangement.n.01', 'synonyms': ['flower_arrangement', 'floral_arrangement'], 'def': 'a decorative arrangement of flowers', 'name': 'flower_arrangement'}, {'frequency': 'c', 'id': 468, 'synset': 'flute.n.02', 'synonyms': ['flute_glass', 'champagne_flute'], 'def': 'a tall narrow wineglass', 'name': 'flute_glass'}, {'frequency': 'r', 'id': 469, 'synset': 'foal.n.01', 'synonyms': ['foal'], 'def': 'a young horse', 'name': 'foal'}, {'frequency': 'c', 'id': 470, 'synset': 'folding_chair.n.01', 'synonyms': ['folding_chair'], 'def': 'a chair that can be folded flat for storage', 'name': 'folding_chair'}, {'frequency': 'c', 'id': 471, 'synset': 'food_processor.n.01', 'synonyms': ['food_processor'], 'def': 'a kitchen appliance for shredding, blending, chopping, or slicing food', 'name': 'food_processor'}, {'frequency': 'c', 'id': 472, 'synset': 'football.n.02', 'synonyms': ['football_(American)'], 'def': 'the inflated oblong ball used in playing American football', 'name': 'football_(American)'}, {'frequency': 'r', 'id': 473, 'synset': 'football_helmet.n.01', 'synonyms': ['football_helmet'], 'def': 'a padded helmet with a face mask to protect the head of football players', 'name': 'football_helmet'}, {'frequency': 'c', 'id': 474, 'synset': 'footstool.n.01', 'synonyms': ['footstool', 'footrest'], 'def': 'a low seat or a stool to rest the feet of a seated person', 'name': 'footstool'}, {'frequency': 'f', 'id': 475, 'synset': 'fork.n.01', 'synonyms': ['fork'], 'def': 'cutlery used for serving and eating food', 'name': 'fork'}, {'frequency': 'r', 'id': 476, 'synset': 'forklift.n.01', 'synonyms': ['forklift'], 'def': 'an industrial vehicle with a power operated fork in front that can be inserted under loads to lift and move them', 'name': 'forklift'}, {'frequency': 'r', 'id': 477, 'synset': 'freight_car.n.01', 'synonyms': ['freight_car'], 'def': 'a railway car that carries freight', 'name': 'freight_car'}, {'frequency': 'r', 'id': 478, 'synset': 'french_toast.n.01', 'synonyms': ['French_toast'], 'def': 'bread slice dipped in egg and milk and fried', 'name': 'French_toast'}, {'frequency': 'c', 'id': 479, 'synset': 'freshener.n.01', 'synonyms': ['freshener', 'air_freshener'], 'def': 'anything that freshens', 'name': 'freshener'}, {'frequency': 'f', 'id': 480, 'synset': 'frisbee.n.01', 'synonyms': ['frisbee'], 'def': 'a light, plastic disk propelled with a flip of the wrist for recreation or competition', 'name': 'frisbee'}, {'frequency': 'c', 'id': 481, 'synset': 'frog.n.01', 'synonyms': ['frog', 'toad', 'toad_frog'], 'def': 'a tailless stout-bodied amphibians with long hind limbs for leaping', 'name': 'frog'}, {'frequency': 'c', 'id': 482, 'synset': 'fruit_juice.n.01', 'synonyms': ['fruit_juice'], 'def': 'drink produced by squeezing or crushing fruit', 'name': 'fruit_juice'}, {'frequency': 'r', 'id': 483, 'synset': 'fruit_salad.n.01', 'synonyms': ['fruit_salad'], 'def': 'salad composed of fruits', 'name': 'fruit_salad'}, {'frequency': 'c', 'id': 484, 'synset': 'frying_pan.n.01', 'synonyms': ['frying_pan', 'frypan', 'skillet'], 'def': 'a pan used for frying foods', 'name': 'frying_pan'}, {'frequency': 'r', 'id': 485, 'synset': 'fudge.n.01', 'synonyms': ['fudge'], 'def': 'soft creamy candy', 'name': 'fudge'}, {'frequency': 'r', 'id': 486, 'synset': 'funnel.n.02', 'synonyms': ['funnel'], 'def': 'a cone-shaped utensil used to channel a substance into a container with a small mouth', 'name': 'funnel'}, {'frequency': 'c', 'id': 487, 'synset': 'futon.n.01', 'synonyms': ['futon'], 'def': 'a pad that is used for sleeping on the floor or on a raised frame', 'name': 'futon'}, {'frequency': 'r', 'id': 488, 'synset': 'gag.n.02', 'synonyms': ['gag', 'muzzle'], 'def': "restraint put into a person's mouth to prevent speaking or shouting", 'name': 'gag'}, {'frequency': 'r', 'id': 489, 'synset': 'garbage.n.03', 'synonyms': ['garbage'], 'def': 'a receptacle where waste can be discarded', 'name': 'garbage'}, {'frequency': 'c', 'id': 490, 'synset': 'garbage_truck.n.01', 'synonyms': ['garbage_truck'], 'def': 'a truck for collecting domestic refuse', 'name': 'garbage_truck'}, {'frequency': 'c', 'id': 491, 'synset': 'garden_hose.n.01', 'synonyms': ['garden_hose'], 'def': 'a hose used for watering a lawn or garden', 'name': 'garden_hose'}, {'frequency': 'c', 'id': 492, 'synset': 'gargle.n.01', 'synonyms': ['gargle', 'mouthwash'], 'def': 'a medicated solution used for gargling and rinsing the mouth', 'name': 'gargle'}, {'frequency': 'r', 'id': 493, 'synset': 'gargoyle.n.02', 'synonyms': ['gargoyle'], 'def': 'an ornament consisting of a grotesquely carved figure of a person or animal', 'name': 'gargoyle'}, {'frequency': 'c', 'id': 494, 'synset': 'garlic.n.02', 'synonyms': ['garlic', 'ail'], 'def': 'aromatic bulb used as seasoning', 'name': 'garlic'}, {'frequency': 'r', 'id': 495, 'synset': 'gasmask.n.01', 'synonyms': ['gasmask', 'respirator', 'gas_helmet'], 'def': 'a protective face mask with a filter', 'name': 'gasmask'}, {'frequency': 'r', 'id': 496, 'synset': 'gazelle.n.01', 'synonyms': ['gazelle'], 'def': 'small swift graceful antelope of Africa and Asia having lustrous eyes', 'name': 'gazelle'}, {'frequency': 'c', 'id': 497, 'synset': 'gelatin.n.02', 'synonyms': ['gelatin', 'jelly'], 'def': 'an edible jelly made with gelatin and used as a dessert or salad base or a coating for foods', 'name': 'gelatin'}, {'frequency': 'r', 'id': 498, 'synset': 'gem.n.02', 'synonyms': ['gemstone'], 'def': 'a crystalline rock that can be cut and polished for jewelry', 'name': 'gemstone'}, {'frequency': 'c', 'id': 499, 'synset': 'giant_panda.n.01', 'synonyms': ['giant_panda', 'panda', 'panda_bear'], 'def': 'large black-and-white herbivorous mammal of bamboo forests of China and Tibet', 'name': 'giant_panda'}, {'frequency': 'c', 'id': 500, 'synset': 'gift_wrap.n.01', 'synonyms': ['gift_wrap'], 'def': 'attractive wrapping paper suitable for wrapping gifts', 'name': 'gift_wrap'}, {'frequency': 'c', 'id': 501, 'synset': 'ginger.n.03', 'synonyms': ['ginger', 'gingerroot'], 'def': 'the root of the common ginger plant; used fresh as a seasoning', 'name': 'ginger'}, {'frequency': 'f', 'id': 502, 'synset': 'giraffe.n.01', 'synonyms': ['giraffe'], 'def': 'tall animal having a spotted coat and small horns and very long neck and legs', 'name': 'giraffe'}, {'frequency': 'c', 'id': 503, 'synset': 'girdle.n.02', 'synonyms': ['cincture', 'sash', 'waistband', 'waistcloth'], 'def': 'a band of material around the waist that strengthens a skirt or trousers', 'name': 'cincture'}, {'frequency': 'f', 'id': 504, 'synset': 'glass.n.02', 'synonyms': ['glass_(drink_container)', 'drinking_glass'], 'def': 'a container for holding liquids while drinking', 'name': 'glass_(drink_container)'}, {'frequency': 'c', 'id': 505, 'synset': 'globe.n.03', 'synonyms': ['globe'], 'def': 'a sphere on which a map (especially of the earth) is represented', 'name': 'globe'}, {'frequency': 'f', 'id': 506, 'synset': 'glove.n.02', 'synonyms': ['glove'], 'def': 'handwear covering the hand', 'name': 'glove'}, {'frequency': 'c', 'id': 507, 'synset': 'goat.n.01', 'synonyms': ['goat'], 'def': 'a common goat', 'name': 'goat'}, {'frequency': 'f', 'id': 508, 'synset': 'goggles.n.01', 'synonyms': ['goggles'], 'def': 'tight-fitting spectacles worn to protect the eyes', 'name': 'goggles'}, {'frequency': 'r', 'id': 509, 'synset': 'goldfish.n.01', 'synonyms': ['goldfish'], 'def': 'small golden or orange-red freshwater fishes used as pond or aquarium pets', 'name': 'goldfish'}, {'frequency': 'r', 'id': 510, 'synset': 'golf_club.n.02', 'synonyms': ['golf_club', 'golf-club'], 'def': 'golf equipment used by a golfer to hit a golf ball', 'name': 'golf_club'}, {'frequency': 'c', 'id': 511, 'synset': 'golfcart.n.01', 'synonyms': ['golfcart'], 'def': 'a small motor vehicle in which golfers can ride between shots', 'name': 'golfcart'}, {'frequency': 'r', 'id': 512, 'synset': 'gondola.n.02', 'synonyms': ['gondola_(boat)'], 'def': 'long narrow flat-bottomed boat propelled by sculling; traditionally used on canals of Venice', 'name': 'gondola_(boat)'}, {'frequency': 'c', 'id': 513, 'synset': 'goose.n.01', 'synonyms': ['goose'], 'def': 'loud, web-footed long-necked aquatic birds usually larger than ducks', 'name': 'goose'}, {'frequency': 'r', 'id': 514, 'synset': 'gorilla.n.01', 'synonyms': ['gorilla'], 'def': 'largest ape', 'name': 'gorilla'}, {'frequency': 'r', 'id': 515, 'synset': 'gourd.n.02', 'synonyms': ['gourd'], 'def': 'any of numerous inedible fruits with hard rinds', 'name': 'gourd'}, {'frequency': 'r', 'id': 516, 'synset': 'gown.n.04', 'synonyms': ['surgical_gown', 'scrubs_(surgical_clothing)'], 'def': 'protective garment worn by surgeons during operations', 'name': 'surgical_gown'}, {'frequency': 'f', 'id': 517, 'synset': 'grape.n.01', 'synonyms': ['grape'], 'def': 'any of various juicy fruit with green or purple skins; grow in clusters', 'name': 'grape'}, {'frequency': 'r', 'id': 518, 'synset': 'grasshopper.n.01', 'synonyms': ['grasshopper'], 'def': 'plant-eating insect with hind legs adapted for leaping', 'name': 'grasshopper'}, {'frequency': 'c', 'id': 519, 'synset': 'grater.n.01', 'synonyms': ['grater'], 'def': 'utensil with sharp perforations for shredding foods (as vegetables or cheese)', 'name': 'grater'}, {'frequency': 'c', 'id': 520, 'synset': 'gravestone.n.01', 'synonyms': ['gravestone', 'headstone', 'tombstone'], 'def': 'a stone that is used to mark a grave', 'name': 'gravestone'}, {'frequency': 'r', 'id': 521, 'synset': 'gravy_boat.n.01', 'synonyms': ['gravy_boat', 'gravy_holder'], 'def': 'a dish (often boat-shaped) for serving gravy or sauce', 'name': 'gravy_boat'}, {'frequency': 'c', 'id': 522, 'synset': 'green_bean.n.02', 'synonyms': ['green_bean'], 'def': 'a common bean plant cultivated for its slender green edible pods', 'name': 'green_bean'}, {'frequency': 'c', 'id': 523, 'synset': 'green_onion.n.01', 'synonyms': ['green_onion', 'spring_onion', 'scallion'], 'def': 'a young onion before the bulb has enlarged', 'name': 'green_onion'}, {'frequency': 'r', 'id': 524, 'synset': 'griddle.n.01', 'synonyms': ['griddle'], 'def': 'cooking utensil consisting of a flat heated surface on which food is cooked', 'name': 'griddle'}, {'frequency': 'r', 'id': 525, 'synset': 'grillroom.n.01', 'synonyms': ['grillroom', 'grill_(restaurant)'], 'def': 'a restaurant where food is cooked on a grill', 'name': 'grillroom'}, {'frequency': 'r', 'id': 526, 'synset': 'grinder.n.04', 'synonyms': ['grinder_(tool)'], 'def': 'a machine tool that polishes metal', 'name': 'grinder_(tool)'}, {'frequency': 'r', 'id': 527, 'synset': 'grits.n.01', 'synonyms': ['grits', 'hominy_grits'], 'def': 'coarsely ground corn boiled as a breakfast dish', 'name': 'grits'}, {'frequency': 'c', 'id': 528, 'synset': 'grizzly.n.01', 'synonyms': ['grizzly', 'grizzly_bear'], 'def': 'powerful brownish-yellow bear of the uplands of western North America', 'name': 'grizzly'}, {'frequency': 'c', 'id': 529, 'synset': 'grocery_bag.n.01', 'synonyms': ['grocery_bag'], 'def': "a sack for holding customer's groceries", 'name': 'grocery_bag'}, {'frequency': 'r', 'id': 530, 'synset': 'guacamole.n.01', 'synonyms': ['guacamole'], 'def': 'a dip made of mashed avocado mixed with chopped onions and other seasonings', 'name': 'guacamole'}, {'frequency': 'f', 'id': 531, 'synset': 'guitar.n.01', 'synonyms': ['guitar'], 'def': 'a stringed instrument usually having six strings; played by strumming or plucking', 'name': 'guitar'}, {'frequency': 'c', 'id': 532, 'synset': 'gull.n.02', 'synonyms': ['gull', 'seagull'], 'def': 'mostly white aquatic bird having long pointed wings and short legs', 'name': 'gull'}, {'frequency': 'c', 'id': 533, 'synset': 'gun.n.01', 'synonyms': ['gun'], 'def': 'a weapon that discharges a bullet at high velocity from a metal tube', 'name': 'gun'}, {'frequency': 'r', 'id': 534, 'synset': 'hair_spray.n.01', 'synonyms': ['hair_spray'], 'def': 'substance sprayed on the hair to hold it in place', 'name': 'hair_spray'}, {'frequency': 'c', 'id': 535, 'synset': 'hairbrush.n.01', 'synonyms': ['hairbrush'], 'def': "a brush used to groom a person's hair", 'name': 'hairbrush'}, {'frequency': 'c', 'id': 536, 'synset': 'hairnet.n.01', 'synonyms': ['hairnet'], 'def': 'a small net that someone wears over their hair to keep it in place', 'name': 'hairnet'}, {'frequency': 'c', 'id': 537, 'synset': 'hairpin.n.01', 'synonyms': ['hairpin'], 'def': "a double pronged pin used to hold women's hair in place", 'name': 'hairpin'}, {'frequency': 'f', 'id': 538, 'synset': 'ham.n.01', 'synonyms': ['ham', 'jambon', 'gammon'], 'def': 'meat cut from the thigh of a hog (usually smoked)', 'name': 'ham'}, {'frequency': 'c', 'id': 539, 'synset': 'hamburger.n.01', 'synonyms': ['hamburger', 'beefburger', 'burger'], 'def': 'a sandwich consisting of a patty of minced beef served on a bun', 'name': 'hamburger'}, {'frequency': 'c', 'id': 540, 'synset': 'hammer.n.02', 'synonyms': ['hammer'], 'def': 'a hand tool with a heavy head and a handle; used to deliver an impulsive force by striking', 'name': 'hammer'}, {'frequency': 'r', 'id': 541, 'synset': 'hammock.n.02', 'synonyms': ['hammock'], 'def': 'a hanging bed of canvas or rope netting (usually suspended between two trees)', 'name': 'hammock'}, {'frequency': 'r', 'id': 542, 'synset': 'hamper.n.02', 'synonyms': ['hamper'], 'def': 'a basket usually with a cover', 'name': 'hamper'}, {'frequency': 'r', 'id': 543, 'synset': 'hamster.n.01', 'synonyms': ['hamster'], 'def': 'short-tailed burrowing rodent with large cheek pouches', 'name': 'hamster'}, {'frequency': 'c', 'id': 544, 'synset': 'hand_blower.n.01', 'synonyms': ['hair_dryer'], 'def': 'a hand-held electric blower that can blow warm air onto the hair', 'name': 'hair_dryer'}, {'frequency': 'r', 'id': 545, 'synset': 'hand_glass.n.01', 'synonyms': ['hand_glass', 'hand_mirror'], 'def': 'a mirror intended to be held in the hand', 'name': 'hand_glass'}, {'frequency': 'f', 'id': 546, 'synset': 'hand_towel.n.01', 'synonyms': ['hand_towel', 'face_towel'], 'def': 'a small towel used to dry the hands or face', 'name': 'hand_towel'}, {'frequency': 'c', 'id': 547, 'synset': 'handcart.n.01', 'synonyms': ['handcart', 'pushcart', 'hand_truck'], 'def': 'wheeled vehicle that can be pushed by a person', 'name': 'handcart'}, {'frequency': 'r', 'id': 548, 'synset': 'handcuff.n.01', 'synonyms': ['handcuff'], 'def': 'shackle that consists of a metal loop that can be locked around the wrist', 'name': 'handcuff'}, {'frequency': 'c', 'id': 549, 'synset': 'handkerchief.n.01', 'synonyms': ['handkerchief'], 'def': 'a square piece of cloth used for wiping the eyes or nose or as a costume accessory', 'name': 'handkerchief'}, {'frequency': 'f', 'id': 550, 'synset': 'handle.n.01', 'synonyms': ['handle', 'grip', 'handgrip'], 'def': 'the appendage to an object that is designed to be held in order to use or move it', 'name': 'handle'}, {'frequency': 'r', 'id': 551, 'synset': 'handsaw.n.01', 'synonyms': ['handsaw', "carpenter's_saw"], 'def': 'a saw used with one hand for cutting wood', 'name': 'handsaw'}, {'frequency': 'r', 'id': 552, 'synset': 'hardback.n.01', 'synonyms': ['hardback_book', 'hardcover_book'], 'def': 'a book with cardboard or cloth or leather covers', 'name': 'hardback_book'}, {'frequency': 'r', 'id': 553, 'synset': 'harmonium.n.01', 'synonyms': ['harmonium', 'organ_(musical_instrument)', 'reed_organ_(musical_instrument)'], 'def': 'a free-reed instrument in which air is forced through the reeds by bellows', 'name': 'harmonium'}, {'frequency': 'f', 'id': 554, 'synset': 'hat.n.01', 'synonyms': ['hat'], 'def': 'headwear that protects the head from bad weather, sun, or worn for fashion', 'name': 'hat'}, {'frequency': 'r', 'id': 555, 'synset': 'hatbox.n.01', 'synonyms': ['hatbox'], 'def': 'a round piece of luggage for carrying hats', 'name': 'hatbox'}, {'frequency': 'r', 'id': 556, 'synset': 'hatch.n.03', 'synonyms': ['hatch'], 'def': 'a movable barrier covering a hatchway', 'name': 'hatch'}, {'frequency': 'c', 'id': 557, 'synset': 'head_covering.n.01', 'synonyms': ['veil'], 'def': 'a garment that covers the head and face', 'name': 'veil'}, {'frequency': 'f', 'id': 558, 'synset': 'headband.n.01', 'synonyms': ['headband'], 'def': 'a band worn around or over the head', 'name': 'headband'}, {'frequency': 'f', 'id': 559, 'synset': 'headboard.n.01', 'synonyms': ['headboard'], 'def': 'a vertical board or panel forming the head of a bedstead', 'name': 'headboard'}, {'frequency': 'f', 'id': 560, 'synset': 'headlight.n.01', 'synonyms': ['headlight', 'headlamp'], 'def': 'a powerful light with reflector; attached to the front of an automobile or locomotive', 'name': 'headlight'}, {'frequency': 'c', 'id': 561, 'synset': 'headscarf.n.01', 'synonyms': ['headscarf'], 'def': 'a kerchief worn over the head and tied under the chin', 'name': 'headscarf'}, {'frequency': 'r', 'id': 562, 'synset': 'headset.n.01', 'synonyms': ['headset'], 'def': 'receiver consisting of a pair of headphones', 'name': 'headset'}, {'frequency': 'c', 'id': 563, 'synset': 'headstall.n.01', 'synonyms': ['headstall_(for_horses)', 'headpiece_(for_horses)'], 'def': "the band that is the part of a bridle that fits around a horse's head", 'name': 'headstall_(for_horses)'}, {'frequency': 'r', 'id': 564, 'synset': 'hearing_aid.n.02', 'synonyms': ['hearing_aid'], 'def': 'an acoustic device used to direct sound to the ear of a hearing-impaired person', 'name': 'hearing_aid'}, {'frequency': 'c', 'id': 565, 'synset': 'heart.n.02', 'synonyms': ['heart'], 'def': 'a muscular organ; its contractions move the blood through the body', 'name': 'heart'}, {'frequency': 'c', 'id': 566, 'synset': 'heater.n.01', 'synonyms': ['heater', 'warmer'], 'def': 'device that heats water or supplies warmth to a room', 'name': 'heater'}, {'frequency': 'c', 'id': 567, 'synset': 'helicopter.n.01', 'synonyms': ['helicopter'], 'def': 'an aircraft without wings that obtains its lift from the rotation of overhead blades', 'name': 'helicopter'}, {'frequency': 'f', 'id': 568, 'synset': 'helmet.n.02', 'synonyms': ['helmet'], 'def': 'a protective headgear made of hard material to resist blows', 'name': 'helmet'}, {'frequency': 'r', 'id': 569, 'synset': 'heron.n.02', 'synonyms': ['heron'], 'def': 'grey or white wading bird with long neck and long legs and (usually) long bill', 'name': 'heron'}, {'frequency': 'c', 'id': 570, 'synset': 'highchair.n.01', 'synonyms': ['highchair', 'feeding_chair'], 'def': 'a chair for feeding a very young child', 'name': 'highchair'}, {'frequency': 'f', 'id': 571, 'synset': 'hinge.n.01', 'synonyms': ['hinge'], 'def': 'a joint that holds two parts together so that one can swing relative to the other', 'name': 'hinge'}, {'frequency': 'r', 'id': 572, 'synset': 'hippopotamus.n.01', 'synonyms': ['hippopotamus'], 'def': 'massive thick-skinned animal living in or around rivers of tropical Africa', 'name': 'hippopotamus'}, {'frequency': 'r', 'id': 573, 'synset': 'hockey_stick.n.01', 'synonyms': ['hockey_stick'], 'def': 'sports implement consisting of a stick used by hockey players to move the puck', 'name': 'hockey_stick'}, {'frequency': 'c', 'id': 574, 'synset': 'hog.n.03', 'synonyms': ['hog', 'pig'], 'def': 'domestic swine', 'name': 'hog'}, {'frequency': 'f', 'id': 575, 'synset': 'home_plate.n.01', 'synonyms': ['home_plate_(baseball)', 'home_base_(baseball)'], 'def': '(baseball) a rubber slab where the batter stands; it must be touched by a base runner in order to score', 'name': 'home_plate_(baseball)'}, {'frequency': 'c', 'id': 576, 'synset': 'honey.n.01', 'synonyms': ['honey'], 'def': 'a sweet yellow liquid produced by bees', 'name': 'honey'}, {'frequency': 'f', 'id': 577, 'synset': 'hood.n.06', 'synonyms': ['fume_hood', 'exhaust_hood'], 'def': 'metal covering leading to a vent that exhausts smoke or fumes', 'name': 'fume_hood'}, {'frequency': 'f', 'id': 578, 'synset': 'hook.n.05', 'synonyms': ['hook'], 'def': 'a curved or bent implement for suspending or pulling something', 'name': 'hook'}, {'frequency': 'f', 'id': 579, 'synset': 'horse.n.01', 'synonyms': ['horse'], 'def': 'a common horse', 'name': 'horse'}, {'frequency': 'f', 'id': 580, 'synset': 'hose.n.03', 'synonyms': ['hose', 'hosepipe'], 'def': 'a flexible pipe for conveying a liquid or gas', 'name': 'hose'}, {'frequency': 'r', 'id': 581, 'synset': 'hot-air_balloon.n.01', 'synonyms': ['hot-air_balloon'], 'def': 'balloon for travel through the air in a basket suspended below a large bag of heated air', 'name': 'hot-air_balloon'}, {'frequency': 'r', 'id': 582, 'synset': 'hot_plate.n.01', 'synonyms': ['hotplate'], 'def': 'a portable electric appliance for heating or cooking or keeping food warm', 'name': 'hotplate'}, {'frequency': 'c', 'id': 583, 'synset': 'hot_sauce.n.01', 'synonyms': ['hot_sauce'], 'def': 'a pungent peppery sauce', 'name': 'hot_sauce'}, {'frequency': 'r', 'id': 584, 'synset': 'hourglass.n.01', 'synonyms': ['hourglass'], 'def': 'a sandglass timer that runs for sixty minutes', 'name': 'hourglass'}, {'frequency': 'r', 'id': 585, 'synset': 'houseboat.n.01', 'synonyms': ['houseboat'], 'def': 'a barge that is designed and equipped for use as a dwelling', 'name': 'houseboat'}, {'frequency': 'r', 'id': 586, 'synset': 'hummingbird.n.01', 'synonyms': ['hummingbird'], 'def': 'tiny American bird having brilliant iridescent plumage and long slender bills', 'name': 'hummingbird'}, {'frequency': 'r', 'id': 587, 'synset': 'hummus.n.01', 'synonyms': ['hummus', 'humus', 'hommos', 'hoummos', 'humous'], 'def': 'a thick spread made from mashed chickpeas', 'name': 'hummus'}, {'frequency': 'c', 'id': 588, 'synset': 'ice_bear.n.01', 'synonyms': ['polar_bear'], 'def': 'white bear of Arctic regions', 'name': 'polar_bear'}, {'frequency': 'c', 'id': 589, 'synset': 'ice_cream.n.01', 'synonyms': ['icecream'], 'def': 'frozen dessert containing cream and sugar and flavoring', 'name': 'icecream'}, {'frequency': 'r', 'id': 590, 'synset': 'ice_lolly.n.01', 'synonyms': ['popsicle'], 'def': 'ice cream or water ice on a small wooden stick', 'name': 'popsicle'}, {'frequency': 'c', 'id': 591, 'synset': 'ice_maker.n.01', 'synonyms': ['ice_maker'], 'def': 'an appliance included in some electric refrigerators for making ice cubes', 'name': 'ice_maker'}, {'frequency': 'r', 'id': 592, 'synset': 'ice_pack.n.01', 'synonyms': ['ice_pack', 'ice_bag'], 'def': 'a waterproof bag filled with ice: applied to the body (especially the head) to cool or reduce swelling', 'name': 'ice_pack'}, {'frequency': 'r', 'id': 593, 'synset': 'ice_skate.n.01', 'synonyms': ['ice_skate'], 'def': 'skate consisting of a boot with a steel blade fitted to the sole', 'name': 'ice_skate'}, {'frequency': 'r', 'id': 594, 'synset': 'ice_tea.n.01', 'synonyms': ['ice_tea', 'iced_tea'], 'def': 'strong tea served over ice', 'name': 'ice_tea'}, {'frequency': 'c', 'id': 595, 'synset': 'igniter.n.01', 'synonyms': ['igniter', 'ignitor', 'lighter'], 'def': 'a substance or device used to start a fire', 'name': 'igniter'}, {'frequency': 'r', 'id': 596, 'synset': 'incense.n.01', 'synonyms': ['incense'], 'def': 'a substance that produces a fragrant odor when burned', 'name': 'incense'}, {'frequency': 'r', 'id': 597, 'synset': 'inhaler.n.01', 'synonyms': ['inhaler', 'inhalator'], 'def': 'a dispenser that produces a chemical vapor to be inhaled through mouth or nose', 'name': 'inhaler'}, {'frequency': 'c', 'id': 598, 'synset': 'ipod.n.01', 'synonyms': ['iPod'], 'def': 'a pocket-sized device used to play music files', 'name': 'iPod'}, {'frequency': 'c', 'id': 599, 'synset': 'iron.n.04', 'synonyms': ['iron_(for_clothing)', 'smoothing_iron_(for_clothing)'], 'def': 'home appliance consisting of a flat metal base that is heated and used to smooth cloth', 'name': 'iron_(for_clothing)'}, {'frequency': 'r', 'id': 600, 'synset': 'ironing_board.n.01', 'synonyms': ['ironing_board'], 'def': 'narrow padded board on collapsible supports; used for ironing clothes', 'name': 'ironing_board'}, {'frequency': 'f', 'id': 601, 'synset': 'jacket.n.01', 'synonyms': ['jacket'], 'def': 'a waist-length coat', 'name': 'jacket'}, {'frequency': 'r', 'id': 602, 'synset': 'jam.n.01', 'synonyms': ['jam'], 'def': 'preserve of crushed fruit', 'name': 'jam'}, {'frequency': 'f', 'id': 603, 'synset': 'jean.n.01', 'synonyms': ['jean', 'blue_jean', 'denim'], 'def': '(usually plural) close-fitting trousers of heavy denim for manual work or casual wear', 'name': 'jean'}, {'frequency': 'c', 'id': 604, 'synset': 'jeep.n.01', 'synonyms': ['jeep', 'landrover'], 'def': 'a car suitable for traveling over rough terrain', 'name': 'jeep'}, {'frequency': 'r', 'id': 605, 'synset': 'jelly_bean.n.01', 'synonyms': ['jelly_bean', 'jelly_egg'], 'def': 'sugar-glazed jellied candy', 'name': 'jelly_bean'}, {'frequency': 'f', 'id': 606, 'synset': 'jersey.n.03', 'synonyms': ['jersey', 'T-shirt', 'tee_shirt'], 'def': 'a close-fitting pullover shirt', 'name': 'jersey'}, {'frequency': 'c', 'id': 607, 'synset': 'jet.n.01', 'synonyms': ['jet_plane', 'jet-propelled_plane'], 'def': 'an airplane powered by one or more jet engines', 'name': 'jet_plane'}, {'frequency': 'c', 'id': 608, 'synset': 'jewelry.n.01', 'synonyms': ['jewelry', 'jewellery'], 'def': 'an adornment (as a bracelet or ring or necklace) made of precious metals and set with gems (or imitation gems)', 'name': 'jewelry'}, {'frequency': 'r', 'id': 609, 'synset': 'joystick.n.02', 'synonyms': ['joystick'], 'def': 'a control device for computers consisting of a vertical handle that can move freely in two directions', 'name': 'joystick'}, {'frequency': 'r', 'id': 610, 'synset': 'jump_suit.n.01', 'synonyms': ['jumpsuit'], 'def': "one-piece garment fashioned after a parachutist's uniform", 'name': 'jumpsuit'}, {'frequency': 'c', 'id': 611, 'synset': 'kayak.n.01', 'synonyms': ['kayak'], 'def': 'a small canoe consisting of a light frame made watertight with animal skins', 'name': 'kayak'}, {'frequency': 'r', 'id': 612, 'synset': 'keg.n.02', 'synonyms': ['keg'], 'def': 'small cask or barrel', 'name': 'keg'}, {'frequency': 'r', 'id': 613, 'synset': 'kennel.n.01', 'synonyms': ['kennel', 'doghouse'], 'def': 'outbuilding that serves as a shelter for a dog', 'name': 'kennel'}, {'frequency': 'c', 'id': 614, 'synset': 'kettle.n.01', 'synonyms': ['kettle', 'boiler'], 'def': 'a metal pot for stewing or boiling; usually has a lid', 'name': 'kettle'}, {'frequency': 'f', 'id': 615, 'synset': 'key.n.01', 'synonyms': ['key'], 'def': 'metal instrument used to unlock a lock', 'name': 'key'}, {'frequency': 'r', 'id': 616, 'synset': 'keycard.n.01', 'synonyms': ['keycard'], 'def': 'a plastic card used to gain access typically to a door', 'name': 'keycard'}, {'frequency': 'r', 'id': 617, 'synset': 'kilt.n.01', 'synonyms': ['kilt'], 'def': 'a knee-length pleated tartan skirt worn by men as part of the traditional dress in the Highlands of northern Scotland', 'name': 'kilt'}, {'frequency': 'c', 'id': 618, 'synset': 'kimono.n.01', 'synonyms': ['kimono'], 'def': 'a loose robe; imitated from robes originally worn by Japanese', 'name': 'kimono'}, {'frequency': 'f', 'id': 619, 'synset': 'kitchen_sink.n.01', 'synonyms': ['kitchen_sink'], 'def': 'a sink in a kitchen', 'name': 'kitchen_sink'}, {'frequency': 'c', 'id': 620, 'synset': 'kitchen_table.n.01', 'synonyms': ['kitchen_table'], 'def': 'a table in the kitchen', 'name': 'kitchen_table'}, {'frequency': 'f', 'id': 621, 'synset': 'kite.n.03', 'synonyms': ['kite'], 'def': 'plaything consisting of a light frame covered with tissue paper; flown in wind at end of a string', 'name': 'kite'}, {'frequency': 'c', 'id': 622, 'synset': 'kitten.n.01', 'synonyms': ['kitten', 'kitty'], 'def': 'young domestic cat', 'name': 'kitten'}, {'frequency': 'c', 'id': 623, 'synset': 'kiwi.n.03', 'synonyms': ['kiwi_fruit'], 'def': 'fuzzy brown egg-shaped fruit with slightly tart green flesh', 'name': 'kiwi_fruit'}, {'frequency': 'f', 'id': 624, 'synset': 'knee_pad.n.01', 'synonyms': ['knee_pad'], 'def': 'protective garment consisting of a pad worn by football or baseball or hockey players', 'name': 'knee_pad'}, {'frequency': 'f', 'id': 625, 'synset': 'knife.n.01', 'synonyms': ['knife'], 'def': 'tool with a blade and point used as a cutting instrument', 'name': 'knife'}, {'frequency': 'r', 'id': 626, 'synset': 'knight.n.02', 'synonyms': ['knight_(chess_piece)', 'horse_(chess_piece)'], 'def': 'a chess game piece shaped to resemble the head of a horse', 'name': 'knight_(chess_piece)'}, {'frequency': 'r', 'id': 627, 'synset': 'knitting_needle.n.01', 'synonyms': ['knitting_needle'], 'def': 'needle consisting of a slender rod with pointed ends; usually used in pairs', 'name': 'knitting_needle'}, {'frequency': 'f', 'id': 628, 'synset': 'knob.n.02', 'synonyms': ['knob'], 'def': 'a round handle often found on a door', 'name': 'knob'}, {'frequency': 'r', 'id': 629, 'synset': 'knocker.n.05', 'synonyms': ['knocker_(on_a_door)', 'doorknocker'], 'def': 'a device (usually metal and ornamental) attached by a hinge to a door', 'name': 'knocker_(on_a_door)'}, {'frequency': 'r', 'id': 630, 'synset': 'koala.n.01', 'synonyms': ['koala', 'koala_bear'], 'def': 'sluggish tailless Australian marsupial with grey furry ears and coat', 'name': 'koala'}, {'frequency': 'r', 'id': 631, 'synset': 'lab_coat.n.01', 'synonyms': ['lab_coat', 'laboratory_coat'], 'def': 'a light coat worn to protect clothing from substances used while working in a laboratory', 'name': 'lab_coat'}, {'frequency': 'f', 'id': 632, 'synset': 'ladder.n.01', 'synonyms': ['ladder'], 'def': 'steps consisting of two parallel members connected by rungs', 'name': 'ladder'}, {'frequency': 'c', 'id': 633, 'synset': 'ladle.n.01', 'synonyms': ['ladle'], 'def': 'a spoon-shaped vessel with a long handle frequently used to transfer liquids', 'name': 'ladle'}, {'frequency': 'r', 'id': 634, 'synset': 'ladybug.n.01', 'synonyms': ['ladybug', 'ladybeetle', 'ladybird_beetle'], 'def': 'small round bright-colored and spotted beetle, typically red and black', 'name': 'ladybug'}, {'frequency': 'c', 'id': 635, 'synset': 'lamb.n.01', 'synonyms': ['lamb_(animal)'], 'def': 'young sheep', 'name': 'lamb_(animal)'}, {'frequency': 'r', 'id': 636, 'synset': 'lamb_chop.n.01', 'synonyms': ['lamb-chop', 'lambchop'], 'def': 'chop cut from a lamb', 'name': 'lamb-chop'}, {'frequency': 'f', 'id': 637, 'synset': 'lamp.n.02', 'synonyms': ['lamp'], 'def': 'a piece of furniture holding one or more electric light bulbs', 'name': 'lamp'}, {'frequency': 'f', 'id': 638, 'synset': 'lamppost.n.01', 'synonyms': ['lamppost'], 'def': 'a metal post supporting an outdoor lamp (such as a streetlight)', 'name': 'lamppost'}, {'frequency': 'f', 'id': 639, 'synset': 'lampshade.n.01', 'synonyms': ['lampshade'], 'def': 'a protective ornamental shade used to screen a light bulb from direct view', 'name': 'lampshade'}, {'frequency': 'c', 'id': 640, 'synset': 'lantern.n.01', 'synonyms': ['lantern'], 'def': 'light in a transparent protective case', 'name': 'lantern'}, {'frequency': 'f', 'id': 641, 'synset': 'lanyard.n.02', 'synonyms': ['lanyard', 'laniard'], 'def': 'a cord worn around the neck to hold a knife or whistle, etc.', 'name': 'lanyard'}, {'frequency': 'f', 'id': 642, 'synset': 'laptop.n.01', 'synonyms': ['laptop_computer', 'notebook_computer'], 'def': 'a portable computer small enough to use in your lap', 'name': 'laptop_computer'}, {'frequency': 'r', 'id': 643, 'synset': 'lasagna.n.01', 'synonyms': ['lasagna', 'lasagne'], 'def': 'baked dish of layers of lasagna pasta with sauce and cheese and meat or vegetables', 'name': 'lasagna'}, {'frequency': 'c', 'id': 644, 'synset': 'latch.n.02', 'synonyms': ['latch'], 'def': 'a bar that can be lowered or slid into a groove to fasten a door or gate', 'name': 'latch'}, {'frequency': 'r', 'id': 645, 'synset': 'lawn_mower.n.01', 'synonyms': ['lawn_mower'], 'def': 'garden tool for mowing grass on lawns', 'name': 'lawn_mower'}, {'frequency': 'r', 'id': 646, 'synset': 'leather.n.01', 'synonyms': ['leather'], 'def': 'an animal skin made smooth and flexible by removing the hair and then tanning', 'name': 'leather'}, {'frequency': 'c', 'id': 647, 'synset': 'legging.n.01', 'synonyms': ['legging_(clothing)', 'leging_(clothing)', 'leg_covering'], 'def': 'a garment covering the leg (usually extending from the knee to the ankle)', 'name': 'legging_(clothing)'}, {'frequency': 'c', 'id': 648, 'synset': 'lego.n.01', 'synonyms': ['Lego', 'Lego_set'], 'def': "a child's plastic construction set for making models from blocks", 'name': 'Lego'}, {'frequency': 'f', 'id': 649, 'synset': 'lemon.n.01', 'synonyms': ['lemon'], 'def': 'yellow oval fruit with juicy acidic flesh', 'name': 'lemon'}, {'frequency': 'r', 'id': 650, 'synset': 'lemonade.n.01', 'synonyms': ['lemonade'], 'def': 'sweetened beverage of diluted lemon juice', 'name': 'lemonade'}, {'frequency': 'f', 'id': 651, 'synset': 'lettuce.n.02', 'synonyms': ['lettuce'], 'def': 'leafy plant commonly eaten in salad or on sandwiches', 'name': 'lettuce'}, {'frequency': 'f', 'id': 652, 'synset': 'license_plate.n.01', 'synonyms': ['license_plate', 'numberplate'], 'def': "a plate mounted on the front and back of car and bearing the car's registration number", 'name': 'license_plate'}, {'frequency': 'f', 'id': 653, 'synset': 'life_buoy.n.01', 'synonyms': ['life_buoy', 'lifesaver', 'life_belt', 'life_ring'], 'def': 'a ring-shaped life preserver used to prevent drowning (NOT a life-jacket or vest)', 'name': 'life_buoy'}, {'frequency': 'f', 'id': 654, 'synset': 'life_jacket.n.01', 'synonyms': ['life_jacket', 'life_vest'], 'def': 'life preserver consisting of a sleeveless jacket of buoyant or inflatable design', 'name': 'life_jacket'}, {'frequency': 'f', 'id': 655, 'synset': 'light_bulb.n.01', 'synonyms': ['lightbulb'], 'def': 'glass bulb or tube shaped electric device that emits light (DO NOT MARK LAMPS AS A WHOLE)', 'name': 'lightbulb'}, {'frequency': 'r', 'id': 656, 'synset': 'lightning_rod.n.02', 'synonyms': ['lightning_rod', 'lightning_conductor'], 'def': 'a metallic conductor that is attached to a high point and leads to the ground', 'name': 'lightning_rod'}, {'frequency': 'c', 'id': 657, 'synset': 'lime.n.06', 'synonyms': ['lime'], 'def': 'the green acidic fruit of any of various lime trees', 'name': 'lime'}, {'frequency': 'r', 'id': 658, 'synset': 'limousine.n.01', 'synonyms': ['limousine'], 'def': 'long luxurious car; usually driven by a chauffeur', 'name': 'limousine'}, {'frequency': 'r', 'id': 659, 'synset': 'linen.n.02', 'synonyms': ['linen_paper'], 'def': 'a high-quality paper made of linen fibers or with a linen finish', 'name': 'linen_paper'}, {'frequency': 'c', 'id': 660, 'synset': 'lion.n.01', 'synonyms': ['lion'], 'def': 'large gregarious predatory cat of Africa and India', 'name': 'lion'}, {'frequency': 'c', 'id': 661, 'synset': 'lip_balm.n.01', 'synonyms': ['lip_balm'], 'def': 'a balm applied to the lips', 'name': 'lip_balm'}, {'frequency': 'c', 'id': 662, 'synset': 'lipstick.n.01', 'synonyms': ['lipstick', 'lip_rouge'], 'def': 'makeup that is used to color the lips', 'name': 'lipstick'}, {'frequency': 'r', 'id': 663, 'synset': 'liquor.n.01', 'synonyms': ['liquor', 'spirits', 'hard_liquor', 'liqueur', 'cordial'], 'def': 'an alcoholic beverage that is distilled rather than fermented', 'name': 'liquor'}, {'frequency': 'r', 'id': 664, 'synset': 'lizard.n.01', 'synonyms': ['lizard'], 'def': 'a reptile with usually two pairs of legs and a tapering tail', 'name': 'lizard'}, {'frequency': 'r', 'id': 665, 'synset': 'loafer.n.02', 'synonyms': ['Loafer_(type_of_shoe)'], 'def': 'a low leather step-in shoe', 'name': 'Loafer_(type_of_shoe)'}, {'frequency': 'f', 'id': 666, 'synset': 'log.n.01', 'synonyms': ['log'], 'def': 'a segment of the trunk of a tree when stripped of branches', 'name': 'log'}, {'frequency': 'c', 'id': 667, 'synset': 'lollipop.n.02', 'synonyms': ['lollipop'], 'def': 'hard candy on a stick', 'name': 'lollipop'}, {'frequency': 'c', 'id': 668, 'synset': 'lotion.n.01', 'synonyms': ['lotion'], 'def': 'any of various cosmetic preparations that are applied to the skin', 'name': 'lotion'}, {'frequency': 'f', 'id': 669, 'synset': 'loudspeaker.n.01', 'synonyms': ['speaker_(stero_equipment)'], 'def': 'electronic device that produces sound often as part of a stereo system', 'name': 'speaker_(stero_equipment)'}, {'frequency': 'c', 'id': 670, 'synset': 'love_seat.n.01', 'synonyms': ['loveseat'], 'def': 'small sofa that seats two people', 'name': 'loveseat'}, {'frequency': 'r', 'id': 671, 'synset': 'machine_gun.n.01', 'synonyms': ['machine_gun'], 'def': 'a rapidly firing automatic gun', 'name': 'machine_gun'}, {'frequency': 'f', 'id': 672, 'synset': 'magazine.n.02', 'synonyms': ['magazine'], 'def': 'a paperback periodic publication', 'name': 'magazine'}, {'frequency': 'f', 'id': 673, 'synset': 'magnet.n.01', 'synonyms': ['magnet'], 'def': 'a device that attracts iron and produces a magnetic field', 'name': 'magnet'}, {'frequency': 'r', 'id': 674, 'synset': 'mail_slot.n.01', 'synonyms': ['mail_slot'], 'def': 'a slot (usually in a door) through which mail can be delivered', 'name': 'mail_slot'}, {'frequency': 'c', 'id': 675, 'synset': 'mailbox.n.01', 'synonyms': ['mailbox_(at_home)', 'letter_box_(at_home)'], 'def': 'a private box for delivery of mail', 'name': 'mailbox_(at_home)'}, {'frequency': 'r', 'id': 676, 'synset': 'mallet.n.01', 'synonyms': ['mallet'], 'def': 'a sports implement with a long handle and a hammer-like head used to hit a ball', 'name': 'mallet'}, {'frequency': 'r', 'id': 677, 'synset': 'mammoth.n.01', 'synonyms': ['mammoth'], 'def': 'any of numerous extinct elephants widely distributed in the Pleistocene', 'name': 'mammoth'}, {'frequency': 'c', 'id': 678, 'synset': 'mandarin.n.05', 'synonyms': ['mandarin_orange'], 'def': 'a somewhat flat reddish-orange loose skinned citrus of China', 'name': 'mandarin_orange'}, {'frequency': 'c', 'id': 679, 'synset': 'manger.n.01', 'synonyms': ['manger', 'trough'], 'def': 'a container (usually in a barn or stable) from which cattle or horses feed', 'name': 'manger'}, {'frequency': 'f', 'id': 680, 'synset': 'manhole.n.01', 'synonyms': ['manhole'], 'def': 'a hole (usually with a flush cover) through which a person can gain access to an underground structure', 'name': 'manhole'}, {'frequency': 'c', 'id': 681, 'synset': 'map.n.01', 'synonyms': ['map'], 'def': "a diagrammatic representation of the earth's surface (or part of it)", 'name': 'map'}, {'frequency': 'c', 'id': 682, 'synset': 'marker.n.03', 'synonyms': ['marker'], 'def': 'a writing implement for making a mark', 'name': 'marker'}, {'frequency': 'r', 'id': 683, 'synset': 'martini.n.01', 'synonyms': ['martini'], 'def': 'a cocktail made of gin (or vodka) with dry vermouth', 'name': 'martini'}, {'frequency': 'r', 'id': 684, 'synset': 'mascot.n.01', 'synonyms': ['mascot'], 'def': 'a person or animal that is adopted by a team or other group as a symbolic figure', 'name': 'mascot'}, {'frequency': 'c', 'id': 685, 'synset': 'mashed_potato.n.01', 'synonyms': ['mashed_potato'], 'def': 'potato that has been peeled and boiled and then mashed', 'name': 'mashed_potato'}, {'frequency': 'r', 'id': 686, 'synset': 'masher.n.02', 'synonyms': ['masher'], 'def': 'a kitchen utensil used for mashing (e.g. potatoes)', 'name': 'masher'}, {'frequency': 'f', 'id': 687, 'synset': 'mask.n.04', 'synonyms': ['mask', 'facemask'], 'def': 'a protective covering worn over the face', 'name': 'mask'}, {'frequency': 'f', 'id': 688, 'synset': 'mast.n.01', 'synonyms': ['mast'], 'def': 'a vertical spar for supporting sails', 'name': 'mast'}, {'frequency': 'c', 'id': 689, 'synset': 'mat.n.03', 'synonyms': ['mat_(gym_equipment)', 'gym_mat'], 'def': 'sports equipment consisting of a piece of thick padding on the floor for gymnastics', 'name': 'mat_(gym_equipment)'}, {'frequency': 'r', 'id': 690, 'synset': 'matchbox.n.01', 'synonyms': ['matchbox'], 'def': 'a box for holding matches', 'name': 'matchbox'}, {'frequency': 'f', 'id': 691, 'synset': 'mattress.n.01', 'synonyms': ['mattress'], 'def': 'a thick pad filled with resilient material used as a bed or part of a bed', 'name': 'mattress'}, {'frequency': 'c', 'id': 692, 'synset': 'measuring_cup.n.01', 'synonyms': ['measuring_cup'], 'def': 'graduated cup used to measure liquid or granular ingredients', 'name': 'measuring_cup'}, {'frequency': 'c', 'id': 693, 'synset': 'measuring_stick.n.01', 'synonyms': ['measuring_stick', 'ruler_(measuring_stick)', 'measuring_rod'], 'def': 'measuring instrument having a sequence of marks at regular intervals', 'name': 'measuring_stick'}, {'frequency': 'c', 'id': 694, 'synset': 'meatball.n.01', 'synonyms': ['meatball'], 'def': 'ground meat formed into a ball and fried or simmered in broth', 'name': 'meatball'}, {'frequency': 'c', 'id': 695, 'synset': 'medicine.n.02', 'synonyms': ['medicine'], 'def': 'something that treats or prevents or alleviates the symptoms of disease', 'name': 'medicine'}, {'frequency': 'r', 'id': 696, 'synset': 'melon.n.01', 'synonyms': ['melon'], 'def': 'fruit of the gourd family having a hard rind and sweet juicy flesh', 'name': 'melon'}, {'frequency': 'f', 'id': 697, 'synset': 'microphone.n.01', 'synonyms': ['microphone'], 'def': 'device for converting sound waves into electrical energy', 'name': 'microphone'}, {'frequency': 'r', 'id': 698, 'synset': 'microscope.n.01', 'synonyms': ['microscope'], 'def': 'magnifier of the image of small objects', 'name': 'microscope'}, {'frequency': 'f', 'id': 699, 'synset': 'microwave.n.02', 'synonyms': ['microwave_oven'], 'def': 'kitchen appliance that cooks food by passing an electromagnetic wave through it', 'name': 'microwave_oven'}, {'frequency': 'r', 'id': 700, 'synset': 'milestone.n.01', 'synonyms': ['milestone', 'milepost'], 'def': 'stone post at side of a road to show distances', 'name': 'milestone'}, {'frequency': 'c', 'id': 701, 'synset': 'milk.n.01', 'synonyms': ['milk'], 'def': 'a white nutritious liquid secreted by mammals and used as food by human beings', 'name': 'milk'}, {'frequency': 'f', 'id': 702, 'synset': 'minivan.n.01', 'synonyms': ['minivan'], 'def': 'a small box-shaped passenger van', 'name': 'minivan'}, {'frequency': 'r', 'id': 703, 'synset': 'mint.n.05', 'synonyms': ['mint_candy'], 'def': 'a candy that is flavored with a mint oil', 'name': 'mint_candy'}, {'frequency': 'f', 'id': 704, 'synset': 'mirror.n.01', 'synonyms': ['mirror'], 'def': 'polished surface that forms images by reflecting light', 'name': 'mirror'}, {'frequency': 'c', 'id': 705, 'synset': 'mitten.n.01', 'synonyms': ['mitten'], 'def': 'glove that encases the thumb separately and the other four fingers together', 'name': 'mitten'}, {'frequency': 'c', 'id': 706, 'synset': 'mixer.n.04', 'synonyms': ['mixer_(kitchen_tool)', 'stand_mixer'], 'def': 'a kitchen utensil that is used for mixing foods', 'name': 'mixer_(kitchen_tool)'}, {'frequency': 'c', 'id': 707, 'synset': 'money.n.03', 'synonyms': ['money'], 'def': 'the official currency issued by a government or national bank', 'name': 'money'}, {'frequency': 'f', 'id': 708, 'synset': 'monitor.n.04', 'synonyms': ['monitor_(computer_equipment) computer_monitor'], 'def': 'a computer monitor', 'name': 'monitor_(computer_equipment) computer_monitor'}, {'frequency': 'c', 'id': 709, 'synset': 'monkey.n.01', 'synonyms': ['monkey'], 'def': 'any of various long-tailed primates', 'name': 'monkey'}, {'frequency': 'f', 'id': 710, 'synset': 'motor.n.01', 'synonyms': ['motor'], 'def': 'machine that converts other forms of energy into mechanical energy and so imparts motion', 'name': 'motor'}, {'frequency': 'f', 'id': 711, 'synset': 'motor_scooter.n.01', 'synonyms': ['motor_scooter', 'scooter'], 'def': 'a wheeled vehicle with small wheels and a low-powered engine', 'name': 'motor_scooter'}, {'frequency': 'r', 'id': 712, 'synset': 'motor_vehicle.n.01', 'synonyms': ['motor_vehicle', 'automotive_vehicle'], 'def': 'a self-propelled wheeled vehicle that does not run on rails', 'name': 'motor_vehicle'}, {'frequency': 'r', 'id': 713, 'synset': 'motorboat.n.01', 'synonyms': ['motorboat', 'powerboat'], 'def': 'a boat propelled by an internal-combustion engine', 'name': 'motorboat'}, {'frequency': 'f', 'id': 714, 'synset': 'motorcycle.n.01', 'synonyms': ['motorcycle'], 'def': 'a motor vehicle with two wheels and a strong frame', 'name': 'motorcycle'}, {'frequency': 'f', 'id': 715, 'synset': 'mound.n.01', 'synonyms': ['mound_(baseball)', "pitcher's_mound"], 'def': '(baseball) the slight elevation on which the pitcher stands', 'name': 'mound_(baseball)'}, {'frequency': 'r', 'id': 716, 'synset': 'mouse.n.01', 'synonyms': ['mouse_(animal_rodent)'], 'def': 'a small rodent with pointed snouts and small ears on elongated bodies with slender usually hairless tails', 'name': 'mouse_(animal_rodent)'}, {'frequency': 'f', 'id': 717, 'synset': 'mouse.n.04', 'synonyms': ['mouse_(computer_equipment)', 'computer_mouse'], 'def': 'a computer input device that controls an on-screen pointer', 'name': 'mouse_(computer_equipment)'}, {'frequency': 'f', 'id': 718, 'synset': 'mousepad.n.01', 'synonyms': ['mousepad'], 'def': 'a small portable pad that provides an operating surface for a computer mouse', 'name': 'mousepad'}, {'frequency': 'c', 'id': 719, 'synset': 'muffin.n.01', 'synonyms': ['muffin'], 'def': 'a sweet quick bread baked in a cup-shaped pan', 'name': 'muffin'}, {'frequency': 'f', 'id': 720, 'synset': 'mug.n.04', 'synonyms': ['mug'], 'def': 'with handle and usually cylindrical', 'name': 'mug'}, {'frequency': 'f', 'id': 721, 'synset': 'mushroom.n.02', 'synonyms': ['mushroom'], 'def': 'a common mushroom', 'name': 'mushroom'}, {'frequency': 'r', 'id': 722, 'synset': 'music_stool.n.01', 'synonyms': ['music_stool', 'piano_stool'], 'def': 'a stool for piano players; usually adjustable in height', 'name': 'music_stool'}, {'frequency': 'r', 'id': 723, 'synset': 'musical_instrument.n.01', 'synonyms': ['musical_instrument', 'instrument_(musical)'], 'def': 'any of various devices or contrivances that can be used to produce musical tones or sounds', 'name': 'musical_instrument'}, {'frequency': 'r', 'id': 724, 'synset': 'nailfile.n.01', 'synonyms': ['nailfile'], 'def': 'a small flat file for shaping the nails', 'name': 'nailfile'}, {'frequency': 'r', 'id': 725, 'synset': 'nameplate.n.01', 'synonyms': ['nameplate'], 'def': 'a plate bearing a name', 'name': 'nameplate'}, {'frequency': 'f', 'id': 726, 'synset': 'napkin.n.01', 'synonyms': ['napkin', 'table_napkin', 'serviette'], 'def': 'a small piece of table linen or paper that is used to wipe the mouth and to cover the lap in order to protect clothing', 'name': 'napkin'}, {'frequency': 'r', 'id': 727, 'synset': 'neckerchief.n.01', 'synonyms': ['neckerchief'], 'def': 'a kerchief worn around the neck', 'name': 'neckerchief'}, {'frequency': 'f', 'id': 728, 'synset': 'necklace.n.01', 'synonyms': ['necklace'], 'def': 'jewelry consisting of a cord or chain (often bearing gems) worn about the neck as an ornament', 'name': 'necklace'}, {'frequency': 'f', 'id': 729, 'synset': 'necktie.n.01', 'synonyms': ['necktie', 'tie_(necktie)'], 'def': 'neckwear consisting of a long narrow piece of material worn under a collar and tied in knot at the front', 'name': 'necktie'}, {'frequency': 'r', 'id': 730, 'synset': 'needle.n.03', 'synonyms': ['needle'], 'def': 'a sharp pointed implement (usually metal)', 'name': 'needle'}, {'frequency': 'c', 'id': 731, 'synset': 'nest.n.01', 'synonyms': ['nest'], 'def': 'a structure in which animals lay eggs or give birth to their young', 'name': 'nest'}, {'frequency': 'r', 'id': 732, 'synset': 'newsstand.n.01', 'synonyms': ['newsstand'], 'def': 'a stall where newspapers and other periodicals are sold', 'name': 'newsstand'}, {'frequency': 'c', 'id': 733, 'synset': 'nightwear.n.01', 'synonyms': ['nightshirt', 'nightwear', 'sleepwear', 'nightclothes'], 'def': 'garments designed to be worn in bed', 'name': 'nightshirt'}, {'frequency': 'r', 'id': 734, 'synset': 'nosebag.n.01', 'synonyms': ['nosebag_(for_animals)', 'feedbag'], 'def': 'a canvas bag that is used to feed an animal (such as a horse); covers the muzzle and fastens at the top of the head', 'name': 'nosebag_(for_animals)'}, {'frequency': 'r', 'id': 735, 'synset': 'noseband.n.01', 'synonyms': ['noseband_(for_animals)', 'nosepiece_(for_animals)'], 'def': "a strap that is the part of a bridle that goes over the animal's nose", 'name': 'noseband_(for_animals)'}, {'frequency': 'f', 'id': 736, 'synset': 'notebook.n.01', 'synonyms': ['notebook'], 'def': 'a book with blank pages for recording notes or memoranda', 'name': 'notebook'}, {'frequency': 'c', 'id': 737, 'synset': 'notepad.n.01', 'synonyms': ['notepad'], 'def': 'a pad of paper for keeping notes', 'name': 'notepad'}, {'frequency': 'c', 'id': 738, 'synset': 'nut.n.03', 'synonyms': ['nut'], 'def': 'a small metal block (usually square or hexagonal) with internal screw thread to be fitted onto a bolt', 'name': 'nut'}, {'frequency': 'r', 'id': 739, 'synset': 'nutcracker.n.01', 'synonyms': ['nutcracker'], 'def': 'a hand tool used to crack nuts open', 'name': 'nutcracker'}, {'frequency': 'c', 'id': 740, 'synset': 'oar.n.01', 'synonyms': ['oar'], 'def': 'an implement used to propel or steer a boat', 'name': 'oar'}, {'frequency': 'r', 'id': 741, 'synset': 'octopus.n.01', 'synonyms': ['octopus_(food)'], 'def': 'tentacles of octopus prepared as food', 'name': 'octopus_(food)'}, {'frequency': 'r', 'id': 742, 'synset': 'octopus.n.02', 'synonyms': ['octopus_(animal)'], 'def': 'bottom-living cephalopod having a soft oval body with eight long tentacles', 'name': 'octopus_(animal)'}, {'frequency': 'c', 'id': 743, 'synset': 'oil_lamp.n.01', 'synonyms': ['oil_lamp', 'kerosene_lamp', 'kerosine_lamp'], 'def': 'a lamp that burns oil (as kerosine) for light', 'name': 'oil_lamp'}, {'frequency': 'c', 'id': 744, 'synset': 'olive_oil.n.01', 'synonyms': ['olive_oil'], 'def': 'oil from olives', 'name': 'olive_oil'}, {'frequency': 'r', 'id': 745, 'synset': 'omelet.n.01', 'synonyms': ['omelet', 'omelette'], 'def': 'beaten eggs cooked until just set; may be folded around e.g. ham or cheese or jelly', 'name': 'omelet'}, {'frequency': 'f', 'id': 746, 'synset': 'onion.n.01', 'synonyms': ['onion'], 'def': 'the bulb of an onion plant', 'name': 'onion'}, {'frequency': 'f', 'id': 747, 'synset': 'orange.n.01', 'synonyms': ['orange_(fruit)'], 'def': 'orange (FRUIT of an orange tree)', 'name': 'orange_(fruit)'}, {'frequency': 'c', 'id': 748, 'synset': 'orange_juice.n.01', 'synonyms': ['orange_juice'], 'def': 'bottled or freshly squeezed juice of oranges', 'name': 'orange_juice'}, {'frequency': 'r', 'id': 749, 'synset': 'oregano.n.01', 'synonyms': ['oregano', 'marjoram'], 'def': 'aromatic Eurasian perennial herb used in cooking and baking', 'name': 'oregano'}, {'frequency': 'c', 'id': 750, 'synset': 'ostrich.n.02', 'synonyms': ['ostrich'], 'def': 'fast-running African flightless bird with two-toed feet; largest living bird', 'name': 'ostrich'}, {'frequency': 'c', 'id': 751, 'synset': 'ottoman.n.03', 'synonyms': ['ottoman', 'pouf', 'pouffe', 'hassock'], 'def': 'thick cushion used as a seat', 'name': 'ottoman'}, {'frequency': 'c', 'id': 752, 'synset': 'overall.n.01', 'synonyms': ['overalls_(clothing)'], 'def': 'work clothing consisting of denim trousers usually with a bib and shoulder straps', 'name': 'overalls_(clothing)'}, {'frequency': 'c', 'id': 753, 'synset': 'owl.n.01', 'synonyms': ['owl'], 'def': 'nocturnal bird of prey with hawk-like beak and claws and large head with front-facing eyes', 'name': 'owl'}, {'frequency': 'c', 'id': 754, 'synset': 'packet.n.03', 'synonyms': ['packet'], 'def': 'a small package or bundle', 'name': 'packet'}, {'frequency': 'r', 'id': 755, 'synset': 'pad.n.03', 'synonyms': ['inkpad', 'inking_pad', 'stamp_pad'], 'def': 'absorbent material saturated with ink used to transfer ink evenly to a rubber stamp', 'name': 'inkpad'}, {'frequency': 'c', 'id': 756, 'synset': 'pad.n.04', 'synonyms': ['pad'], 'def': 'a flat mass of soft material used for protection, stuffing, or comfort', 'name': 'pad'}, {'frequency': 'c', 'id': 757, 'synset': 'paddle.n.04', 'synonyms': ['paddle', 'boat_paddle'], 'def': 'a short light oar used without an oarlock to propel a canoe or small boat', 'name': 'paddle'}, {'frequency': 'c', 'id': 758, 'synset': 'padlock.n.01', 'synonyms': ['padlock'], 'def': 'a detachable, portable lock', 'name': 'padlock'}, {'frequency': 'r', 'id': 759, 'synset': 'paintbox.n.01', 'synonyms': ['paintbox'], 'def': "a box containing a collection of cubes or tubes of artists' paint", 'name': 'paintbox'}, {'frequency': 'c', 'id': 760, 'synset': 'paintbrush.n.01', 'synonyms': ['paintbrush'], 'def': 'a brush used as an applicator to apply paint', 'name': 'paintbrush'}, {'frequency': 'f', 'id': 761, 'synset': 'painting.n.01', 'synonyms': ['painting'], 'def': 'graphic art consisting of an artistic composition made by applying paints to a surface', 'name': 'painting'}, {'frequency': 'c', 'id': 762, 'synset': 'pajama.n.02', 'synonyms': ['pajamas', 'pyjamas'], 'def': 'loose-fitting nightclothes worn for sleeping or lounging', 'name': 'pajamas'}, {'frequency': 'c', 'id': 763, 'synset': 'palette.n.02', 'synonyms': ['palette', 'pallet'], 'def': 'board that provides a flat surface on which artists mix paints and the range of colors used', 'name': 'palette'}, {'frequency': 'f', 'id': 764, 'synset': 'pan.n.01', 'synonyms': ['pan_(for_cooking)', 'cooking_pan'], 'def': 'cooking utensil consisting of a wide metal vessel', 'name': 'pan_(for_cooking)'}, {'frequency': 'r', 'id': 765, 'synset': 'pan.n.03', 'synonyms': ['pan_(metal_container)'], 'def': 'shallow container made of metal', 'name': 'pan_(metal_container)'}, {'frequency': 'c', 'id': 766, 'synset': 'pancake.n.01', 'synonyms': ['pancake'], 'def': 'a flat cake of thin batter fried on both sides on a griddle', 'name': 'pancake'}, {'frequency': 'r', 'id': 767, 'synset': 'pantyhose.n.01', 'synonyms': ['pantyhose'], 'def': "a woman's tights consisting of underpants and stockings", 'name': 'pantyhose'}, {'frequency': 'r', 'id': 768, 'synset': 'papaya.n.02', 'synonyms': ['papaya'], 'def': 'large oval melon-like tropical fruit with yellowish flesh', 'name': 'papaya'}, {'frequency': 'r', 'id': 769, 'synset': 'paper_clip.n.01', 'synonyms': ['paperclip'], 'def': 'a wire or plastic clip for holding sheets of paper together', 'name': 'paperclip'}, {'frequency': 'f', 'id': 770, 'synset': 'paper_plate.n.01', 'synonyms': ['paper_plate'], 'def': 'a disposable plate made of cardboard', 'name': 'paper_plate'}, {'frequency': 'f', 'id': 771, 'synset': 'paper_towel.n.01', 'synonyms': ['paper_towel'], 'def': 'a disposable towel made of absorbent paper', 'name': 'paper_towel'}, {'frequency': 'r', 'id': 772, 'synset': 'paperback_book.n.01', 'synonyms': ['paperback_book', 'paper-back_book', 'softback_book', 'soft-cover_book'], 'def': 'a book with paper covers', 'name': 'paperback_book'}, {'frequency': 'r', 'id': 773, 'synset': 'paperweight.n.01', 'synonyms': ['paperweight'], 'def': 'a weight used to hold down a stack of papers', 'name': 'paperweight'}, {'frequency': 'c', 'id': 774, 'synset': 'parachute.n.01', 'synonyms': ['parachute'], 'def': 'rescue equipment consisting of a device that fills with air and retards your fall', 'name': 'parachute'}, {'frequency': 'r', 'id': 775, 'synset': 'parakeet.n.01', 'synonyms': ['parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet', 'parroquet'], 'def': 'any of numerous small slender long-tailed parrots', 'name': 'parakeet'}, {'frequency': 'c', 'id': 776, 'synset': 'parasail.n.01', 'synonyms': ['parasail_(sports)'], 'def': 'parachute that will lift a person up into the air when it is towed by a motorboat or a car', 'name': 'parasail_(sports)'}, {'frequency': 'r', 'id': 777, 'synset': 'parchment.n.01', 'synonyms': ['parchment'], 'def': 'a superior paper resembling sheepskin', 'name': 'parchment'}, {'frequency': 'r', 'id': 778, 'synset': 'parka.n.01', 'synonyms': ['parka', 'anorak'], 'def': "a kind of heavy jacket (`windcheater' is a British term)", 'name': 'parka'}, {'frequency': 'f', 'id': 779, 'synset': 'parking_meter.n.01', 'synonyms': ['parking_meter'], 'def': 'a coin-operated timer located next to a parking space', 'name': 'parking_meter'}, {'frequency': 'c', 'id': 780, 'synset': 'parrot.n.01', 'synonyms': ['parrot'], 'def': 'usually brightly colored tropical birds with short hooked beaks and the ability to mimic sounds', 'name': 'parrot'}, {'frequency': 'c', 'id': 781, 'synset': 'passenger_car.n.01', 'synonyms': ['passenger_car_(part_of_a_train)', 'coach_(part_of_a_train)'], 'def': 'a railcar where passengers ride', 'name': 'passenger_car_(part_of_a_train)'}, {'frequency': 'r', 'id': 782, 'synset': 'passenger_ship.n.01', 'synonyms': ['passenger_ship'], 'def': 'a ship built to carry passengers', 'name': 'passenger_ship'}, {'frequency': 'r', 'id': 783, 'synset': 'passport.n.02', 'synonyms': ['passport'], 'def': 'a document issued by a country to a citizen allowing that person to travel abroad and re-enter the home country', 'name': 'passport'}, {'frequency': 'f', 'id': 784, 'synset': 'pastry.n.02', 'synonyms': ['pastry'], 'def': 'any of various baked foods made of dough or batter', 'name': 'pastry'}, {'frequency': 'r', 'id': 785, 'synset': 'patty.n.01', 'synonyms': ['patty_(food)'], 'def': 'small flat mass of chopped food', 'name': 'patty_(food)'}, {'frequency': 'c', 'id': 786, 'synset': 'pea.n.01', 'synonyms': ['pea_(food)'], 'def': 'seed of a pea plant used for food', 'name': 'pea_(food)'}, {'frequency': 'c', 'id': 787, 'synset': 'peach.n.03', 'synonyms': ['peach'], 'def': 'downy juicy fruit with sweet yellowish or whitish flesh', 'name': 'peach'}, {'frequency': 'c', 'id': 788, 'synset': 'peanut_butter.n.01', 'synonyms': ['peanut_butter'], 'def': 'a spread made from ground peanuts', 'name': 'peanut_butter'}, {'frequency': 'c', 'id': 789, 'synset': 'pear.n.01', 'synonyms': ['pear'], 'def': 'sweet juicy gritty-textured fruit available in many varieties', 'name': 'pear'}, {'frequency': 'r', 'id': 790, 'synset': 'peeler.n.03', 'synonyms': ['peeler_(tool_for_fruit_and_vegetables)'], 'def': 'a device for peeling vegetables or fruits', 'name': 'peeler_(tool_for_fruit_and_vegetables)'}, {'frequency': 'r', 'id': 791, 'synset': 'pegboard.n.01', 'synonyms': ['pegboard'], 'def': 'a board perforated with regularly spaced holes into which pegs can be fitted', 'name': 'pegboard'}, {'frequency': 'c', 'id': 792, 'synset': 'pelican.n.01', 'synonyms': ['pelican'], 'def': 'large long-winged warm-water seabird having a large bill with a distensible pouch for fish', 'name': 'pelican'}, {'frequency': 'f', 'id': 793, 'synset': 'pen.n.01', 'synonyms': ['pen'], 'def': 'a writing implement with a point from which ink flows', 'name': 'pen'}, {'frequency': 'c', 'id': 794, 'synset': 'pencil.n.01', 'synonyms': ['pencil'], 'def': 'a thin cylindrical pointed writing implement made of wood and graphite', 'name': 'pencil'}, {'frequency': 'r', 'id': 795, 'synset': 'pencil_box.n.01', 'synonyms': ['pencil_box', 'pencil_case'], 'def': 'a box for holding pencils', 'name': 'pencil_box'}, {'frequency': 'r', 'id': 796, 'synset': 'pencil_sharpener.n.01', 'synonyms': ['pencil_sharpener'], 'def': 'a rotary implement for sharpening the point on pencils', 'name': 'pencil_sharpener'}, {'frequency': 'r', 'id': 797, 'synset': 'pendulum.n.01', 'synonyms': ['pendulum'], 'def': 'an apparatus consisting of an object mounted so that it swings freely under the influence of gravity', 'name': 'pendulum'}, {'frequency': 'c', 'id': 798, 'synset': 'penguin.n.01', 'synonyms': ['penguin'], 'def': 'short-legged flightless birds of cold southern regions having webbed feet and wings modified as flippers', 'name': 'penguin'}, {'frequency': 'r', 'id': 799, 'synset': 'pennant.n.02', 'synonyms': ['pennant'], 'def': 'a flag longer than it is wide (and often tapering)', 'name': 'pennant'}, {'frequency': 'r', 'id': 800, 'synset': 'penny.n.02', 'synonyms': ['penny_(coin)'], 'def': 'a coin worth one-hundredth of the value of the basic unit', 'name': 'penny_(coin)'}, {'frequency': 'c', 'id': 801, 'synset': 'pepper.n.03', 'synonyms': ['pepper', 'peppercorn'], 'def': 'pungent seasoning from the berry of the common pepper plant; whole or ground', 'name': 'pepper'}, {'frequency': 'c', 'id': 802, 'synset': 'pepper_mill.n.01', 'synonyms': ['pepper_mill', 'pepper_grinder'], 'def': 'a mill for grinding pepper', 'name': 'pepper_mill'}, {'frequency': 'c', 'id': 803, 'synset': 'perfume.n.02', 'synonyms': ['perfume'], 'def': 'a toiletry that emits and diffuses a fragrant odor', 'name': 'perfume'}, {'frequency': 'r', 'id': 804, 'synset': 'persimmon.n.02', 'synonyms': ['persimmon'], 'def': 'orange fruit resembling a plum; edible when fully ripe', 'name': 'persimmon'}, {'frequency': 'f', 'id': 805, 'synset': 'person.n.01', 'synonyms': ['baby', 'child', 'boy', 'girl', 'man', 'woman', 'person', 'human'], 'def': 'a human being', 'name': 'baby'}, {'frequency': 'r', 'id': 806, 'synset': 'pet.n.01', 'synonyms': ['pet'], 'def': 'a domesticated animal kept for companionship or amusement', 'name': 'pet'}, {'frequency': 'r', 'id': 807, 'synset': 'petfood.n.01', 'synonyms': ['petfood', 'pet-food'], 'def': 'food prepared for animal pets', 'name': 'petfood'}, {'frequency': 'r', 'id': 808, 'synset': 'pew.n.01', 'synonyms': ['pew_(church_bench)', 'church_bench'], 'def': 'long bench with backs; used in church by the congregation', 'name': 'pew_(church_bench)'}, {'frequency': 'r', 'id': 809, 'synset': 'phonebook.n.01', 'synonyms': ['phonebook', 'telephone_book', 'telephone_directory'], 'def': 'a directory containing an alphabetical list of telephone subscribers and their telephone numbers', 'name': 'phonebook'}, {'frequency': 'c', 'id': 810, 'synset': 'phonograph_record.n.01', 'synonyms': ['phonograph_record', 'phonograph_recording', 'record_(phonograph_recording)'], 'def': 'sound recording consisting of a typically black disk with a continuous groove', 'name': 'phonograph_record'}, {'frequency': 'c', 'id': 811, 'synset': 'piano.n.01', 'synonyms': ['piano'], 'def': 'a keyboard instrument that is played by depressing keys that cause hammers to strike tuned strings and produce sounds', 'name': 'piano'}, {'frequency': 'f', 'id': 812, 'synset': 'pickle.n.01', 'synonyms': ['pickle'], 'def': 'vegetables (especially cucumbers) preserved in brine or vinegar', 'name': 'pickle'}, {'frequency': 'f', 'id': 813, 'synset': 'pickup.n.01', 'synonyms': ['pickup_truck'], 'def': 'a light truck with an open body and low sides and a tailboard', 'name': 'pickup_truck'}, {'frequency': 'c', 'id': 814, 'synset': 'pie.n.01', 'synonyms': ['pie'], 'def': 'dish baked in pastry-lined pan often with a pastry top', 'name': 'pie'}, {'frequency': 'c', 'id': 815, 'synset': 'pigeon.n.01', 'synonyms': ['pigeon'], 'def': 'wild and domesticated birds having a heavy body and short legs', 'name': 'pigeon'}, {'frequency': 'r', 'id': 816, 'synset': 'piggy_bank.n.01', 'synonyms': ['piggy_bank', 'penny_bank'], 'def': "a child's coin bank (often shaped like a pig)", 'name': 'piggy_bank'}, {'frequency': 'f', 'id': 817, 'synset': 'pillow.n.01', 'synonyms': ['pillow'], 'def': 'a cushion to support the head of a sleeping person', 'name': 'pillow'}, {'frequency': 'r', 'id': 818, 'synset': 'pin.n.09', 'synonyms': ['pin_(non_jewelry)'], 'def': 'a small slender (often pointed) piece of wood or metal used to support or fasten or attach things', 'name': 'pin_(non_jewelry)'}, {'frequency': 'f', 'id': 819, 'synset': 'pineapple.n.02', 'synonyms': ['pineapple'], 'def': 'large sweet fleshy tropical fruit with a tuft of stiff leaves', 'name': 'pineapple'}, {'frequency': 'c', 'id': 820, 'synset': 'pinecone.n.01', 'synonyms': ['pinecone'], 'def': 'the seed-producing cone of a pine tree', 'name': 'pinecone'}, {'frequency': 'r', 'id': 821, 'synset': 'ping-pong_ball.n.01', 'synonyms': ['ping-pong_ball'], 'def': 'light hollow ball used in playing table tennis', 'name': 'ping-pong_ball'}, {'frequency': 'r', 'id': 822, 'synset': 'pinwheel.n.03', 'synonyms': ['pinwheel'], 'def': 'a toy consisting of vanes of colored paper or plastic that is pinned to a stick and spins when it is pointed into the wind', 'name': 'pinwheel'}, {'frequency': 'r', 'id': 823, 'synset': 'pipe.n.01', 'synonyms': ['tobacco_pipe'], 'def': 'a tube with a small bowl at one end; used for smoking tobacco', 'name': 'tobacco_pipe'}, {'frequency': 'f', 'id': 824, 'synset': 'pipe.n.02', 'synonyms': ['pipe', 'piping'], 'def': 'a long tube made of metal or plastic that is used to carry water or oil or gas etc.', 'name': 'pipe'}, {'frequency': 'r', 'id': 825, 'synset': 'pistol.n.01', 'synonyms': ['pistol', 'handgun'], 'def': 'a firearm that is held and fired with one hand', 'name': 'pistol'}, {'frequency': 'r', 'id': 826, 'synset': 'pita.n.01', 'synonyms': ['pita_(bread)', 'pocket_bread'], 'def': 'usually small round bread that can open into a pocket for filling', 'name': 'pita_(bread)'}, {'frequency': 'f', 'id': 827, 'synset': 'pitcher.n.02', 'synonyms': ['pitcher_(vessel_for_liquid)', 'ewer'], 'def': 'an open vessel with a handle and a spout for pouring', 'name': 'pitcher_(vessel_for_liquid)'}, {'frequency': 'r', 'id': 828, 'synset': 'pitchfork.n.01', 'synonyms': ['pitchfork'], 'def': 'a long-handled hand tool with sharp widely spaced prongs for lifting and pitching hay', 'name': 'pitchfork'}, {'frequency': 'f', 'id': 829, 'synset': 'pizza.n.01', 'synonyms': ['pizza'], 'def': 'Italian open pie made of thin bread dough spread with a spiced mixture of e.g. tomato sauce and cheese', 'name': 'pizza'}, {'frequency': 'f', 'id': 830, 'synset': 'place_mat.n.01', 'synonyms': ['place_mat'], 'def': 'a mat placed on a table for an individual place setting', 'name': 'place_mat'}, {'frequency': 'f', 'id': 831, 'synset': 'plate.n.04', 'synonyms': ['plate'], 'def': 'dish on which food is served or from which food is eaten', 'name': 'plate'}, {'frequency': 'c', 'id': 832, 'synset': 'platter.n.01', 'synonyms': ['platter'], 'def': 'a large shallow dish used for serving food', 'name': 'platter'}, {'frequency': 'r', 'id': 833, 'synset': 'playing_card.n.01', 'synonyms': ['playing_card'], 'def': 'one of a pack of cards that are used to play card games', 'name': 'playing_card'}, {'frequency': 'r', 'id': 834, 'synset': 'playpen.n.01', 'synonyms': ['playpen'], 'def': 'a portable enclosure in which babies may be left to play', 'name': 'playpen'}, {'frequency': 'c', 'id': 835, 'synset': 'pliers.n.01', 'synonyms': ['pliers', 'plyers'], 'def': 'a gripping hand tool with two hinged arms and (usually) serrated jaws', 'name': 'pliers'}, {'frequency': 'r', 'id': 836, 'synset': 'plow.n.01', 'synonyms': ['plow_(farm_equipment)', 'plough_(farm_equipment)'], 'def': 'a farm tool having one or more heavy blades to break the soil and cut a furrow prior to sowing', 'name': 'plow_(farm_equipment)'}, {'frequency': 'r', 'id': 837, 'synset': 'pocket_watch.n.01', 'synonyms': ['pocket_watch'], 'def': 'a watch that is carried in a small watch pocket', 'name': 'pocket_watch'}, {'frequency': 'c', 'id': 838, 'synset': 'pocketknife.n.01', 'synonyms': ['pocketknife'], 'def': 'a knife with a blade that folds into the handle; suitable for carrying in the pocket', 'name': 'pocketknife'}, {'frequency': 'c', 'id': 839, 'synset': 'poker.n.01', 'synonyms': ['poker_(fire_stirring_tool)', 'stove_poker', 'fire_hook'], 'def': 'fire iron consisting of a metal rod with a handle; used to stir a fire', 'name': 'poker_(fire_stirring_tool)'}, {'frequency': 'f', 'id': 840, 'synset': 'pole.n.01', 'synonyms': ['pole', 'post'], 'def': 'a long (usually round) rod of wood or metal or plastic', 'name': 'pole'}, {'frequency': 'r', 'id': 841, 'synset': 'police_van.n.01', 'synonyms': ['police_van', 'police_wagon', 'paddy_wagon', 'patrol_wagon'], 'def': 'van used by police to transport prisoners', 'name': 'police_van'}, {'frequency': 'f', 'id': 842, 'synset': 'polo_shirt.n.01', 'synonyms': ['polo_shirt', 'sport_shirt'], 'def': 'a shirt with short sleeves designed for comfort and casual wear', 'name': 'polo_shirt'}, {'frequency': 'r', 'id': 843, 'synset': 'poncho.n.01', 'synonyms': ['poncho'], 'def': 'a blanket-like cloak with a hole in the center for the head', 'name': 'poncho'}, {'frequency': 'c', 'id': 844, 'synset': 'pony.n.05', 'synonyms': ['pony'], 'def': 'any of various breeds of small gentle horses usually less than five feet high at the shoulder', 'name': 'pony'}, {'frequency': 'r', 'id': 845, 'synset': 'pool_table.n.01', 'synonyms': ['pool_table', 'billiard_table', 'snooker_table'], 'def': 'game equipment consisting of a heavy table on which pool is played', 'name': 'pool_table'}, {'frequency': 'f', 'id': 846, 'synset': 'pop.n.02', 'synonyms': ['pop_(soda)', 'soda_(pop)', 'tonic', 'soft_drink'], 'def': 'a sweet drink containing carbonated water and flavoring', 'name': 'pop_(soda)'}, {'frequency': 'r', 'id': 847, 'synset': 'portrait.n.02', 'synonyms': ['portrait', 'portrayal'], 'def': 'any likeness of a person, in any medium', 'name': 'portrait'}, {'frequency': 'c', 'id': 848, 'synset': 'postbox.n.01', 'synonyms': ['postbox_(public)', 'mailbox_(public)'], 'def': 'public box for deposit of mail', 'name': 'postbox_(public)'}, {'frequency': 'c', 'id': 849, 'synset': 'postcard.n.01', 'synonyms': ['postcard', 'postal_card', 'mailing-card'], 'def': 'a card for sending messages by post without an envelope', 'name': 'postcard'}, {'frequency': 'f', 'id': 850, 'synset': 'poster.n.01', 'synonyms': ['poster', 'placard'], 'def': 'a sign posted in a public place as an advertisement', 'name': 'poster'}, {'frequency': 'f', 'id': 851, 'synset': 'pot.n.01', 'synonyms': ['pot'], 'def': 'metal or earthenware cooking vessel that is usually round and deep; often has a handle and lid', 'name': 'pot'}, {'frequency': 'f', 'id': 852, 'synset': 'pot.n.04', 'synonyms': ['flowerpot'], 'def': 'a container in which plants are cultivated', 'name': 'flowerpot'}, {'frequency': 'f', 'id': 853, 'synset': 'potato.n.01', 'synonyms': ['potato'], 'def': 'an edible tuber native to South America', 'name': 'potato'}, {'frequency': 'c', 'id': 854, 'synset': 'potholder.n.01', 'synonyms': ['potholder'], 'def': 'an insulated pad for holding hot pots', 'name': 'potholder'}, {'frequency': 'c', 'id': 855, 'synset': 'pottery.n.01', 'synonyms': ['pottery', 'clayware'], 'def': 'ceramic ware made from clay and baked in a kiln', 'name': 'pottery'}, {'frequency': 'c', 'id': 856, 'synset': 'pouch.n.01', 'synonyms': ['pouch'], 'def': 'a small or medium size container for holding or carrying things', 'name': 'pouch'}, {'frequency': 'r', 'id': 857, 'synset': 'power_shovel.n.01', 'synonyms': ['power_shovel', 'excavator', 'digger'], 'def': 'a machine for excavating', 'name': 'power_shovel'}, {'frequency': 'c', 'id': 858, 'synset': 'prawn.n.01', 'synonyms': ['prawn', 'shrimp'], 'def': 'any of various edible decapod crustaceans', 'name': 'prawn'}, {'frequency': 'f', 'id': 859, 'synset': 'printer.n.03', 'synonyms': ['printer', 'printing_machine'], 'def': 'a machine that prints', 'name': 'printer'}, {'frequency': 'c', 'id': 860, 'synset': 'projectile.n.01', 'synonyms': ['projectile_(weapon)', 'missile'], 'def': 'a weapon that is forcibly thrown or projected at a targets', 'name': 'projectile_(weapon)'}, {'frequency': 'c', 'id': 861, 'synset': 'projector.n.02', 'synonyms': ['projector'], 'def': 'an optical instrument that projects an enlarged image onto a screen', 'name': 'projector'}, {'frequency': 'f', 'id': 862, 'synset': 'propeller.n.01', 'synonyms': ['propeller', 'propellor'], 'def': 'a mechanical device that rotates to push against air or water', 'name': 'propeller'}, {'frequency': 'r', 'id': 863, 'synset': 'prune.n.01', 'synonyms': ['prune'], 'def': 'dried plum', 'name': 'prune'}, {'frequency': 'r', 'id': 864, 'synset': 'pudding.n.01', 'synonyms': ['pudding'], 'def': 'any of various soft thick unsweetened baked dishes', 'name': 'pudding'}, {'frequency': 'r', 'id': 865, 'synset': 'puffer.n.02', 'synonyms': ['puffer_(fish)', 'pufferfish', 'blowfish', 'globefish'], 'def': 'fishes whose elongated spiny body can inflate itself with water or air to form a globe', 'name': 'puffer_(fish)'}, {'frequency': 'r', 'id': 866, 'synset': 'puffin.n.01', 'synonyms': ['puffin'], 'def': 'seabirds having short necks and brightly colored compressed bills', 'name': 'puffin'}, {'frequency': 'r', 'id': 867, 'synset': 'pug.n.01', 'synonyms': ['pug-dog'], 'def': 'small compact smooth-coated breed of Asiatic origin having a tightly curled tail and broad flat wrinkled muzzle', 'name': 'pug-dog'}, {'frequency': 'c', 'id': 868, 'synset': 'pumpkin.n.02', 'synonyms': ['pumpkin'], 'def': 'usually large pulpy deep-yellow round fruit of the squash family maturing in late summer or early autumn', 'name': 'pumpkin'}, {'frequency': 'r', 'id': 869, 'synset': 'punch.n.03', 'synonyms': ['puncher'], 'def': 'a tool for making holes or indentations', 'name': 'puncher'}, {'frequency': 'r', 'id': 870, 'synset': 'puppet.n.01', 'synonyms': ['puppet', 'marionette'], 'def': 'a small figure of a person operated from above with strings by a puppeteer', 'name': 'puppet'}, {'frequency': 'r', 'id': 871, 'synset': 'puppy.n.01', 'synonyms': ['puppy'], 'def': 'a young dog', 'name': 'puppy'}, {'frequency': 'r', 'id': 872, 'synset': 'quesadilla.n.01', 'synonyms': ['quesadilla'], 'def': 'a tortilla that is filled with cheese and heated', 'name': 'quesadilla'}, {'frequency': 'r', 'id': 873, 'synset': 'quiche.n.02', 'synonyms': ['quiche'], 'def': 'a tart filled with rich unsweetened custard; often contains other ingredients (as cheese or ham or seafood or vegetables)', 'name': 'quiche'}, {'frequency': 'f', 'id': 874, 'synset': 'quilt.n.01', 'synonyms': ['quilt', 'comforter'], 'def': 'bedding made of two layers of cloth filled with stuffing and stitched together', 'name': 'quilt'}, {'frequency': 'c', 'id': 875, 'synset': 'rabbit.n.01', 'synonyms': ['rabbit'], 'def': 'any of various burrowing animals of the family Leporidae having long ears and short tails', 'name': 'rabbit'}, {'frequency': 'r', 'id': 876, 'synset': 'racer.n.02', 'synonyms': ['race_car', 'racing_car'], 'def': 'a fast car that competes in races', 'name': 'race_car'}, {'frequency': 'c', 'id': 877, 'synset': 'racket.n.04', 'synonyms': ['racket', 'racquet'], 'def': 'a sports implement used to strike a ball in various games', 'name': 'racket'}, {'frequency': 'r', 'id': 878, 'synset': 'radar.n.01', 'synonyms': ['radar'], 'def': 'measuring instrument in which the echo of a pulse of microwave radiation is used to detect and locate distant objects', 'name': 'radar'}, {'frequency': 'c', 'id': 879, 'synset': 'radiator.n.03', 'synonyms': ['radiator'], 'def': 'a mechanism consisting of a metal honeycomb through which hot fluids circulate', 'name': 'radiator'}, {'frequency': 'c', 'id': 880, 'synset': 'radio_receiver.n.01', 'synonyms': ['radio_receiver', 'radio_set', 'radio', 'tuner_(radio)'], 'def': 'an electronic receiver that detects and demodulates and amplifies transmitted radio signals', 'name': 'radio_receiver'}, {'frequency': 'c', 'id': 881, 'synset': 'radish.n.03', 'synonyms': ['radish', 'daikon'], 'def': 'pungent edible root of any of various cultivated radish plants', 'name': 'radish'}, {'frequency': 'c', 'id': 882, 'synset': 'raft.n.01', 'synonyms': ['raft'], 'def': 'a flat float (usually made of logs or planks) that can be used for transport or as a platform for swimmers', 'name': 'raft'}, {'frequency': 'r', 'id': 883, 'synset': 'rag_doll.n.01', 'synonyms': ['rag_doll'], 'def': 'a cloth doll that is stuffed and (usually) painted', 'name': 'rag_doll'}, {'frequency': 'c', 'id': 884, 'synset': 'raincoat.n.01', 'synonyms': ['raincoat', 'waterproof_jacket'], 'def': 'a water-resistant coat', 'name': 'raincoat'}, {'frequency': 'c', 'id': 885, 'synset': 'ram.n.05', 'synonyms': ['ram_(animal)'], 'def': 'uncastrated adult male sheep', 'name': 'ram_(animal)'}, {'frequency': 'c', 'id': 886, 'synset': 'raspberry.n.02', 'synonyms': ['raspberry'], 'def': 'red or black edible aggregate berries usually smaller than the related blackberries', 'name': 'raspberry'}, {'frequency': 'r', 'id': 887, 'synset': 'rat.n.01', 'synonyms': ['rat'], 'def': 'any of various long-tailed rodents similar to but larger than a mouse', 'name': 'rat'}, {'frequency': 'c', 'id': 888, 'synset': 'razorblade.n.01', 'synonyms': ['razorblade'], 'def': 'a blade that has very sharp edge', 'name': 'razorblade'}, {'frequency': 'c', 'id': 889, 'synset': 'reamer.n.01', 'synonyms': ['reamer_(juicer)', 'juicer', 'juice_reamer'], 'def': 'a squeezer with a conical ridged center that is used for squeezing juice from citrus fruit', 'name': 'reamer_(juicer)'}, {'frequency': 'f', 'id': 890, 'synset': 'rearview_mirror.n.01', 'synonyms': ['rearview_mirror'], 'def': 'car mirror that reflects the view out of the rear window', 'name': 'rearview_mirror'}, {'frequency': 'c', 'id': 891, 'synset': 'receipt.n.02', 'synonyms': ['receipt'], 'def': 'an acknowledgment (usually tangible) that payment has been made', 'name': 'receipt'}, {'frequency': 'c', 'id': 892, 'synset': 'recliner.n.01', 'synonyms': ['recliner', 'reclining_chair', 'lounger_(chair)'], 'def': 'an armchair whose back can be lowered and foot can be raised to allow the sitter to recline in it', 'name': 'recliner'}, {'frequency': 'r', 'id': 893, 'synset': 'record_player.n.01', 'synonyms': ['record_player', 'phonograph_(record_player)', 'turntable'], 'def': 'machine in which rotating records cause a stylus to vibrate and the vibrations are amplified acoustically or electronically', 'name': 'record_player'}, {'frequency': 'r', 'id': 894, 'synset': 'red_cabbage.n.02', 'synonyms': ['red_cabbage'], 'def': 'compact head of purplish-red leaves', 'name': 'red_cabbage'}, {'frequency': 'f', 'id': 895, 'synset': 'reflector.n.01', 'synonyms': ['reflector'], 'def': 'device that reflects light, radiation, etc.', 'name': 'reflector'}, {'frequency': 'f', 'id': 896, 'synset': 'remote_control.n.01', 'synonyms': ['remote_control'], 'def': 'a device that can be used to control a machine or apparatus from a distance', 'name': 'remote_control'}, {'frequency': 'c', 'id': 897, 'synset': 'rhinoceros.n.01', 'synonyms': ['rhinoceros'], 'def': 'massive powerful herbivorous odd-toed ungulate of southeast Asia and Africa having very thick skin and one or two horns on the snout', 'name': 'rhinoceros'}, {'frequency': 'r', 'id': 898, 'synset': 'rib.n.03', 'synonyms': ['rib_(food)'], 'def': 'cut of meat including one or more ribs', 'name': 'rib_(food)'}, {'frequency': 'r', 'id': 899, 'synset': 'rifle.n.01', 'synonyms': ['rifle'], 'def': 'a shoulder firearm with a long barrel', 'name': 'rifle'}, {'frequency': 'f', 'id': 900, 'synset': 'ring.n.08', 'synonyms': ['ring'], 'def': 'jewelry consisting of a circlet of precious metal (often set with jewels) worn on the finger', 'name': 'ring'}, {'frequency': 'r', 'id': 901, 'synset': 'river_boat.n.01', 'synonyms': ['river_boat'], 'def': 'a boat used on rivers or to ply a river', 'name': 'river_boat'}, {'frequency': 'r', 'id': 902, 'synset': 'road_map.n.02', 'synonyms': ['road_map'], 'def': '(NOT A ROAD) a MAP showing roads (for automobile travel)', 'name': 'road_map'}, {'frequency': 'c', 'id': 903, 'synset': 'robe.n.01', 'synonyms': ['robe'], 'def': 'any loose flowing garment', 'name': 'robe'}, {'frequency': 'c', 'id': 904, 'synset': 'rocking_chair.n.01', 'synonyms': ['rocking_chair'], 'def': 'a chair mounted on rockers', 'name': 'rocking_chair'}, {'frequency': 'r', 'id': 905, 'synset': 'roller_skate.n.01', 'synonyms': ['roller_skate'], 'def': 'a shoe with pairs of rollers (small hard wheels) fixed to the sole', 'name': 'roller_skate'}, {'frequency': 'r', 'id': 906, 'synset': 'rollerblade.n.01', 'synonyms': ['Rollerblade'], 'def': 'an in-line variant of a roller skate', 'name': 'Rollerblade'}, {'frequency': 'c', 'id': 907, 'synset': 'rolling_pin.n.01', 'synonyms': ['rolling_pin'], 'def': 'utensil consisting of a cylinder (usually of wood) with a handle at each end; used to roll out dough', 'name': 'rolling_pin'}, {'frequency': 'r', 'id': 908, 'synset': 'root_beer.n.01', 'synonyms': ['root_beer'], 'def': 'carbonated drink containing extracts of roots and herbs', 'name': 'root_beer'}, {'frequency': 'c', 'id': 909, 'synset': 'router.n.02', 'synonyms': ['router_(computer_equipment)'], 'def': 'a device that forwards data packets between computer networks', 'name': 'router_(computer_equipment)'}, {'frequency': 'f', 'id': 910, 'synset': 'rubber_band.n.01', 'synonyms': ['rubber_band', 'elastic_band'], 'def': 'a narrow band of elastic rubber used to hold things (such as papers) together', 'name': 'rubber_band'}, {'frequency': 'c', 'id': 911, 'synset': 'runner.n.08', 'synonyms': ['runner_(carpet)'], 'def': 'a long narrow carpet', 'name': 'runner_(carpet)'}, {'frequency': 'f', 'id': 912, 'synset': 'sack.n.01', 'synonyms': ['plastic_bag', 'paper_bag'], 'def': "a bag made of paper or plastic for holding customer's purchases", 'name': 'plastic_bag'}, {'frequency': 'f', 'id': 913, 'synset': 'saddle.n.01', 'synonyms': ['saddle_(on_an_animal)'], 'def': 'a seat for the rider of a horse or camel', 'name': 'saddle_(on_an_animal)'}, {'frequency': 'f', 'id': 914, 'synset': 'saddle_blanket.n.01', 'synonyms': ['saddle_blanket', 'saddlecloth', 'horse_blanket'], 'def': 'stable gear consisting of a blanket placed under the saddle', 'name': 'saddle_blanket'}, {'frequency': 'c', 'id': 915, 'synset': 'saddlebag.n.01', 'synonyms': ['saddlebag'], 'def': 'a large bag (or pair of bags) hung over a saddle', 'name': 'saddlebag'}, {'frequency': 'r', 'id': 916, 'synset': 'safety_pin.n.01', 'synonyms': ['safety_pin'], 'def': 'a pin in the form of a clasp; has a guard so the point of the pin will not stick the user', 'name': 'safety_pin'}, {'frequency': 'c', 'id': 917, 'synset': 'sail.n.01', 'synonyms': ['sail'], 'def': 'a large piece of fabric by means of which wind is used to propel a sailing vessel', 'name': 'sail'}, {'frequency': 'c', 'id': 918, 'synset': 'salad.n.01', 'synonyms': ['salad'], 'def': 'food mixtures either arranged on a plate or tossed and served with a moist dressing; usually consisting of or including greens', 'name': 'salad'}, {'frequency': 'r', 'id': 919, 'synset': 'salad_plate.n.01', 'synonyms': ['salad_plate', 'salad_bowl'], 'def': 'a plate or bowl for individual servings of salad', 'name': 'salad_plate'}, {'frequency': 'r', 'id': 920, 'synset': 'salami.n.01', 'synonyms': ['salami'], 'def': 'highly seasoned fatty sausage of pork and beef usually dried', 'name': 'salami'}, {'frequency': 'r', 'id': 921, 'synset': 'salmon.n.01', 'synonyms': ['salmon_(fish)'], 'def': 'any of various large food and game fishes of northern waters', 'name': 'salmon_(fish)'}, {'frequency': 'r', 'id': 922, 'synset': 'salmon.n.03', 'synonyms': ['salmon_(food)'], 'def': 'flesh of any of various marine or freshwater fish of the family Salmonidae', 'name': 'salmon_(food)'}, {'frequency': 'r', 'id': 923, 'synset': 'salsa.n.01', 'synonyms': ['salsa'], 'def': 'spicy sauce of tomatoes and onions and chili peppers to accompany Mexican foods', 'name': 'salsa'}, {'frequency': 'f', 'id': 924, 'synset': 'saltshaker.n.01', 'synonyms': ['saltshaker'], 'def': 'a shaker with a perforated top for sprinkling salt', 'name': 'saltshaker'}, {'frequency': 'f', 'id': 925, 'synset': 'sandal.n.01', 'synonyms': ['sandal_(type_of_shoe)'], 'def': 'a shoe consisting of a sole fastened by straps to the foot', 'name': 'sandal_(type_of_shoe)'}, {'frequency': 'f', 'id': 926, 'synset': 'sandwich.n.01', 'synonyms': ['sandwich'], 'def': 'two (or more) slices of bread with a filling between them', 'name': 'sandwich'}, {'frequency': 'r', 'id': 927, 'synset': 'satchel.n.01', 'synonyms': ['satchel'], 'def': 'luggage consisting of a small case with a flat bottom and (usually) a shoulder strap', 'name': 'satchel'}, {'frequency': 'r', 'id': 928, 'synset': 'saucepan.n.01', 'synonyms': ['saucepan'], 'def': 'a deep pan with a handle; used for stewing or boiling', 'name': 'saucepan'}, {'frequency': 'f', 'id': 929, 'synset': 'saucer.n.02', 'synonyms': ['saucer'], 'def': 'a small shallow dish for holding a cup at the table', 'name': 'saucer'}, {'frequency': 'f', 'id': 930, 'synset': 'sausage.n.01', 'synonyms': ['sausage'], 'def': 'highly seasoned minced meat stuffed in casings', 'name': 'sausage'}, {'frequency': 'r', 'id': 931, 'synset': 'sawhorse.n.01', 'synonyms': ['sawhorse', 'sawbuck'], 'def': 'a framework for holding wood that is being sawed', 'name': 'sawhorse'}, {'frequency': 'r', 'id': 932, 'synset': 'sax.n.02', 'synonyms': ['saxophone'], 'def': "a wind instrument with a `J'-shaped form typically made of brass", 'name': 'saxophone'}, {'frequency': 'f', 'id': 933, 'synset': 'scale.n.07', 'synonyms': ['scale_(measuring_instrument)'], 'def': 'a measuring instrument for weighing; shows amount of mass', 'name': 'scale_(measuring_instrument)'}, {'frequency': 'r', 'id': 934, 'synset': 'scarecrow.n.01', 'synonyms': ['scarecrow', 'strawman'], 'def': 'an effigy in the shape of a man to frighten birds away from seeds', 'name': 'scarecrow'}, {'frequency': 'f', 'id': 935, 'synset': 'scarf.n.01', 'synonyms': ['scarf'], 'def': 'a garment worn around the head or neck or shoulders for warmth or decoration', 'name': 'scarf'}, {'frequency': 'c', 'id': 936, 'synset': 'school_bus.n.01', 'synonyms': ['school_bus'], 'def': 'a bus used to transport children to or from school', 'name': 'school_bus'}, {'frequency': 'f', 'id': 937, 'synset': 'scissors.n.01', 'synonyms': ['scissors'], 'def': 'a tool having two crossed pivoting blades with looped handles', 'name': 'scissors'}, {'frequency': 'c', 'id': 938, 'synset': 'scoreboard.n.01', 'synonyms': ['scoreboard'], 'def': 'a large board for displaying the score of a contest (and some other information)', 'name': 'scoreboard'}, {'frequency': 'c', 'id': 939, 'synset': 'scrambled_eggs.n.01', 'synonyms': ['scrambled_eggs'], 'def': 'eggs beaten and cooked to a soft firm consistency while stirring', 'name': 'scrambled_eggs'}, {'frequency': 'r', 'id': 940, 'synset': 'scraper.n.01', 'synonyms': ['scraper'], 'def': 'any of various hand tools for scraping', 'name': 'scraper'}, {'frequency': 'r', 'id': 941, 'synset': 'scratcher.n.03', 'synonyms': ['scratcher'], 'def': 'a device used for scratching', 'name': 'scratcher'}, {'frequency': 'c', 'id': 942, 'synset': 'screwdriver.n.01', 'synonyms': ['screwdriver'], 'def': 'a hand tool for driving screws; has a tip that fits into the head of a screw', 'name': 'screwdriver'}, {'frequency': 'c', 'id': 943, 'synset': 'scrub_brush.n.01', 'synonyms': ['scrubbing_brush'], 'def': 'a brush with short stiff bristles for heavy cleaning', 'name': 'scrubbing_brush'}, {'frequency': 'c', 'id': 944, 'synset': 'sculpture.n.01', 'synonyms': ['sculpture'], 'def': 'a three-dimensional work of art', 'name': 'sculpture'}, {'frequency': 'r', 'id': 945, 'synset': 'seabird.n.01', 'synonyms': ['seabird', 'seafowl'], 'def': 'a bird that frequents coastal waters and the open ocean: gulls; pelicans; gannets; cormorants; albatrosses; petrels; etc.', 'name': 'seabird'}, {'frequency': 'r', 'id': 946, 'synset': 'seahorse.n.02', 'synonyms': ['seahorse'], 'def': 'small fish with horse-like heads bent sharply downward and curled tails', 'name': 'seahorse'}, {'frequency': 'r', 'id': 947, 'synset': 'seaplane.n.01', 'synonyms': ['seaplane', 'hydroplane'], 'def': 'an airplane that can land on or take off from water', 'name': 'seaplane'}, {'frequency': 'c', 'id': 948, 'synset': 'seashell.n.01', 'synonyms': ['seashell'], 'def': 'the shell of a marine organism', 'name': 'seashell'}, {'frequency': 'r', 'id': 949, 'synset': 'seedling.n.01', 'synonyms': ['seedling'], 'def': 'young plant or tree grown from a seed', 'name': 'seedling'}, {'frequency': 'c', 'id': 950, 'synset': 'serving_dish.n.01', 'synonyms': ['serving_dish'], 'def': 'a dish used for serving food', 'name': 'serving_dish'}, {'frequency': 'r', 'id': 951, 'synset': 'sewing_machine.n.01', 'synonyms': ['sewing_machine'], 'def': 'a textile machine used as a home appliance for sewing', 'name': 'sewing_machine'}, {'frequency': 'r', 'id': 952, 'synset': 'shaker.n.03', 'synonyms': ['shaker'], 'def': 'a container in which something can be shaken', 'name': 'shaker'}, {'frequency': 'c', 'id': 953, 'synset': 'shampoo.n.01', 'synonyms': ['shampoo'], 'def': 'cleansing agent consisting of soaps or detergents used for washing the hair', 'name': 'shampoo'}, {'frequency': 'r', 'id': 954, 'synset': 'shark.n.01', 'synonyms': ['shark'], 'def': 'typically large carnivorous fishes with sharpe teeth', 'name': 'shark'}, {'frequency': 'r', 'id': 955, 'synset': 'sharpener.n.01', 'synonyms': ['sharpener'], 'def': 'any implement that is used to make something (an edge or a point) sharper', 'name': 'sharpener'}, {'frequency': 'r', 'id': 956, 'synset': 'sharpie.n.03', 'synonyms': ['Sharpie'], 'def': 'a pen with indelible ink that will write on any surface', 'name': 'Sharpie'}, {'frequency': 'r', 'id': 957, 'synset': 'shaver.n.03', 'synonyms': ['shaver_(electric)', 'electric_shaver', 'electric_razor'], 'def': 'a razor powered by an electric motor', 'name': 'shaver_(electric)'}, {'frequency': 'c', 'id': 958, 'synset': 'shaving_cream.n.01', 'synonyms': ['shaving_cream', 'shaving_soap'], 'def': 'toiletry consisting that forms a rich lather for softening the beard before shaving', 'name': 'shaving_cream'}, {'frequency': 'r', 'id': 959, 'synset': 'shawl.n.01', 'synonyms': ['shawl'], 'def': 'cloak consisting of an oblong piece of cloth used to cover the head and shoulders', 'name': 'shawl'}, {'frequency': 'r', 'id': 960, 'synset': 'shears.n.01', 'synonyms': ['shears'], 'def': 'large scissors with strong blades', 'name': 'shears'}, {'frequency': 'f', 'id': 961, 'synset': 'sheep.n.01', 'synonyms': ['sheep'], 'def': 'woolly usually horned ruminant mammal related to the goat', 'name': 'sheep'}, {'frequency': 'r', 'id': 962, 'synset': 'shepherd_dog.n.01', 'synonyms': ['shepherd_dog', 'sheepdog'], 'def': 'any of various usually long-haired breeds of dog reared to herd and guard sheep', 'name': 'shepherd_dog'}, {'frequency': 'r', 'id': 963, 'synset': 'sherbert.n.01', 'synonyms': ['sherbert', 'sherbet'], 'def': 'a frozen dessert made primarily of fruit juice and sugar', 'name': 'sherbert'}, {'frequency': 'r', 'id': 964, 'synset': 'shield.n.02', 'synonyms': ['shield'], 'def': 'armor carried on the arm to intercept blows', 'name': 'shield'}, {'frequency': 'f', 'id': 965, 'synset': 'shirt.n.01', 'synonyms': ['shirt'], 'def': 'a garment worn on the upper half of the body', 'name': 'shirt'}, {'frequency': 'f', 'id': 966, 'synset': 'shoe.n.01', 'synonyms': ['shoe', 'sneaker_(type_of_shoe)', 'tennis_shoe'], 'def': 'common footwear covering the foot', 'name': 'shoe'}, {'frequency': 'c', 'id': 967, 'synset': 'shopping_bag.n.01', 'synonyms': ['shopping_bag'], 'def': 'a bag made of plastic or strong paper (often with handles); used to transport goods after shopping', 'name': 'shopping_bag'}, {'frequency': 'c', 'id': 968, 'synset': 'shopping_cart.n.01', 'synonyms': ['shopping_cart'], 'def': 'a handcart that holds groceries or other goods while shopping', 'name': 'shopping_cart'}, {'frequency': 'f', 'id': 969, 'synset': 'short_pants.n.01', 'synonyms': ['short_pants', 'shorts_(clothing)', 'trunks_(clothing)'], 'def': 'trousers that end at or above the knee', 'name': 'short_pants'}, {'frequency': 'r', 'id': 970, 'synset': 'shot_glass.n.01', 'synonyms': ['shot_glass'], 'def': 'a small glass adequate to hold a single swallow of whiskey', 'name': 'shot_glass'}, {'frequency': 'c', 'id': 971, 'synset': 'shoulder_bag.n.01', 'synonyms': ['shoulder_bag'], 'def': 'a large handbag that can be carried by a strap looped over the shoulder', 'name': 'shoulder_bag'}, {'frequency': 'c', 'id': 972, 'synset': 'shovel.n.01', 'synonyms': ['shovel'], 'def': 'a hand tool for lifting loose material such as snow, dirt, etc.', 'name': 'shovel'}, {'frequency': 'f', 'id': 973, 'synset': 'shower.n.01', 'synonyms': ['shower_head'], 'def': 'a plumbing fixture that sprays water over you', 'name': 'shower_head'}, {'frequency': 'f', 'id': 974, 'synset': 'shower_curtain.n.01', 'synonyms': ['shower_curtain'], 'def': 'a curtain that keeps water from splashing out of the shower area', 'name': 'shower_curtain'}, {'frequency': 'r', 'id': 975, 'synset': 'shredder.n.01', 'synonyms': ['shredder_(for_paper)'], 'def': 'a device that shreds documents', 'name': 'shredder_(for_paper)'}, {'frequency': 'r', 'id': 976, 'synset': 'sieve.n.01', 'synonyms': ['sieve', 'screen_(sieve)'], 'def': 'a strainer for separating lumps from powdered material or grading particles', 'name': 'sieve'}, {'frequency': 'f', 'id': 977, 'synset': 'signboard.n.01', 'synonyms': ['signboard'], 'def': 'structure displaying a board on which advertisements can be posted', 'name': 'signboard'}, {'frequency': 'c', 'id': 978, 'synset': 'silo.n.01', 'synonyms': ['silo'], 'def': 'a cylindrical tower used for storing goods', 'name': 'silo'}, {'frequency': 'f', 'id': 979, 'synset': 'sink.n.01', 'synonyms': ['sink'], 'def': 'plumbing fixture consisting of a water basin fixed to a wall or floor and having a drainpipe', 'name': 'sink'}, {'frequency': 'f', 'id': 980, 'synset': 'skateboard.n.01', 'synonyms': ['skateboard'], 'def': 'a board with wheels that is ridden in a standing or crouching position and propelled by foot', 'name': 'skateboard'}, {'frequency': 'c', 'id': 981, 'synset': 'skewer.n.01', 'synonyms': ['skewer'], 'def': 'a long pin for holding meat in position while it is being roasted', 'name': 'skewer'}, {'frequency': 'f', 'id': 982, 'synset': 'ski.n.01', 'synonyms': ['ski'], 'def': 'sports equipment for skiing on snow', 'name': 'ski'}, {'frequency': 'f', 'id': 983, 'synset': 'ski_boot.n.01', 'synonyms': ['ski_boot'], 'def': 'a stiff boot that is fastened to a ski with a ski binding', 'name': 'ski_boot'}, {'frequency': 'f', 'id': 984, 'synset': 'ski_parka.n.01', 'synonyms': ['ski_parka', 'ski_jacket'], 'def': 'a parka to be worn while skiing', 'name': 'ski_parka'}, {'frequency': 'f', 'id': 985, 'synset': 'ski_pole.n.01', 'synonyms': ['ski_pole'], 'def': 'a pole with metal points used as an aid in skiing', 'name': 'ski_pole'}, {'frequency': 'f', 'id': 986, 'synset': 'skirt.n.02', 'synonyms': ['skirt'], 'def': 'a garment hanging from the waist; worn mainly by girls and women', 'name': 'skirt'}, {'frequency': 'c', 'id': 987, 'synset': 'sled.n.01', 'synonyms': ['sled', 'sledge', 'sleigh'], 'def': 'a vehicle or flat object for transportation over snow by sliding or pulled by dogs, etc.', 'name': 'sled'}, {'frequency': 'c', 'id': 988, 'synset': 'sleeping_bag.n.01', 'synonyms': ['sleeping_bag'], 'def': 'large padded bag designed to be slept in outdoors', 'name': 'sleeping_bag'}, {'frequency': 'r', 'id': 989, 'synset': 'sling.n.05', 'synonyms': ['sling_(bandage)', 'triangular_bandage'], 'def': 'bandage to support an injured forearm; slung over the shoulder or neck', 'name': 'sling_(bandage)'}, {'frequency': 'c', 'id': 990, 'synset': 'slipper.n.01', 'synonyms': ['slipper_(footwear)', 'carpet_slipper_(footwear)'], 'def': 'low footwear that can be slipped on and off easily; usually worn indoors', 'name': 'slipper_(footwear)'}, {'frequency': 'r', 'id': 991, 'synset': 'smoothie.n.02', 'synonyms': ['smoothie'], 'def': 'a thick smooth drink consisting of fresh fruit pureed with ice cream or yoghurt or milk', 'name': 'smoothie'}, {'frequency': 'r', 'id': 992, 'synset': 'snake.n.01', 'synonyms': ['snake', 'serpent'], 'def': 'limbless scaly elongate reptile; some are venomous', 'name': 'snake'}, {'frequency': 'f', 'id': 993, 'synset': 'snowboard.n.01', 'synonyms': ['snowboard'], 'def': 'a board that resembles a broad ski or a small surfboard; used in a standing position to slide down snow-covered slopes', 'name': 'snowboard'}, {'frequency': 'c', 'id': 994, 'synset': 'snowman.n.01', 'synonyms': ['snowman'], 'def': 'a figure of a person made of packed snow', 'name': 'snowman'}, {'frequency': 'c', 'id': 995, 'synset': 'snowmobile.n.01', 'synonyms': ['snowmobile'], 'def': 'tracked vehicle for travel on snow having skis in front', 'name': 'snowmobile'}, {'frequency': 'f', 'id': 996, 'synset': 'soap.n.01', 'synonyms': ['soap'], 'def': 'a cleansing agent made from the salts of vegetable or animal fats', 'name': 'soap'}, {'frequency': 'f', 'id': 997, 'synset': 'soccer_ball.n.01', 'synonyms': ['soccer_ball'], 'def': "an inflated ball used in playing soccer (called `football' outside of the United States)", 'name': 'soccer_ball'}, {'frequency': 'f', 'id': 998, 'synset': 'sock.n.01', 'synonyms': ['sock'], 'def': 'cloth covering for the foot; worn inside the shoe; reaches to between the ankle and the knee', 'name': 'sock'}, {'frequency': 'r', 'id': 999, 'synset': 'soda_fountain.n.02', 'synonyms': ['soda_fountain'], 'def': 'an apparatus for dispensing soda water', 'name': 'soda_fountain'}, {'frequency': 'r', 'id': 1000, 'synset': 'soda_water.n.01', 'synonyms': ['carbonated_water', 'club_soda', 'seltzer', 'sparkling_water'], 'def': 'effervescent beverage artificially charged with carbon dioxide', 'name': 'carbonated_water'}, {'frequency': 'f', 'id': 1001, 'synset': 'sofa.n.01', 'synonyms': ['sofa', 'couch', 'lounge'], 'def': 'an upholstered seat for more than one person', 'name': 'sofa'}, {'frequency': 'r', 'id': 1002, 'synset': 'softball.n.01', 'synonyms': ['softball'], 'def': 'ball used in playing softball', 'name': 'softball'}, {'frequency': 'c', 'id': 1003, 'synset': 'solar_array.n.01', 'synonyms': ['solar_array', 'solar_battery', 'solar_panel'], 'def': 'electrical device consisting of a large array of connected solar cells', 'name': 'solar_array'}, {'frequency': 'r', 'id': 1004, 'synset': 'sombrero.n.02', 'synonyms': ['sombrero'], 'def': 'a straw hat with a tall crown and broad brim; worn in American southwest and in Mexico', 'name': 'sombrero'}, {'frequency': 'c', 'id': 1005, 'synset': 'soup.n.01', 'synonyms': ['soup'], 'def': 'liquid food especially of meat or fish or vegetable stock often containing pieces of solid food', 'name': 'soup'}, {'frequency': 'r', 'id': 1006, 'synset': 'soup_bowl.n.01', 'synonyms': ['soup_bowl'], 'def': 'a bowl for serving soup', 'name': 'soup_bowl'}, {'frequency': 'c', 'id': 1007, 'synset': 'soupspoon.n.01', 'synonyms': ['soupspoon'], 'def': 'a spoon with a rounded bowl for eating soup', 'name': 'soupspoon'}, {'frequency': 'c', 'id': 1008, 'synset': 'sour_cream.n.01', 'synonyms': ['sour_cream', 'soured_cream'], 'def': 'soured light cream', 'name': 'sour_cream'}, {'frequency': 'r', 'id': 1009, 'synset': 'soya_milk.n.01', 'synonyms': ['soya_milk', 'soybean_milk', 'soymilk'], 'def': 'a milk substitute containing soybean flour and water; used in some infant formulas and in making tofu', 'name': 'soya_milk'}, {'frequency': 'r', 'id': 1010, 'synset': 'space_shuttle.n.01', 'synonyms': ['space_shuttle'], 'def': "a reusable spacecraft with wings for a controlled descent through the Earth's atmosphere", 'name': 'space_shuttle'}, {'frequency': 'r', 'id': 1011, 'synset': 'sparkler.n.02', 'synonyms': ['sparkler_(fireworks)'], 'def': 'a firework that burns slowly and throws out a shower of sparks', 'name': 'sparkler_(fireworks)'}, {'frequency': 'f', 'id': 1012, 'synset': 'spatula.n.02', 'synonyms': ['spatula'], 'def': 'a hand tool with a thin flexible blade used to mix or spread soft substances', 'name': 'spatula'}, {'frequency': 'r', 'id': 1013, 'synset': 'spear.n.01', 'synonyms': ['spear', 'lance'], 'def': 'a long pointed rod used as a tool or weapon', 'name': 'spear'}, {'frequency': 'f', 'id': 1014, 'synset': 'spectacles.n.01', 'synonyms': ['spectacles', 'specs', 'eyeglasses', 'glasses'], 'def': 'optical instrument consisting of a frame that holds a pair of lenses for correcting defective vision', 'name': 'spectacles'}, {'frequency': 'c', 'id': 1015, 'synset': 'spice_rack.n.01', 'synonyms': ['spice_rack'], 'def': 'a rack for displaying containers filled with spices', 'name': 'spice_rack'}, {'frequency': 'r', 'id': 1016, 'synset': 'spider.n.01', 'synonyms': ['spider'], 'def': 'predatory arachnid with eight legs, two poison fangs, two feelers, and usually two silk-spinning organs at the back end of the body', 'name': 'spider'}, {'frequency': 'c', 'id': 1017, 'synset': 'sponge.n.01', 'synonyms': ['sponge'], 'def': 'a porous mass usable to absorb water typically used for cleaning', 'name': 'sponge'}, {'frequency': 'f', 'id': 1018, 'synset': 'spoon.n.01', 'synonyms': ['spoon'], 'def': 'a piece of cutlery with a shallow bowl-shaped container and a handle', 'name': 'spoon'}, {'frequency': 'c', 'id': 1019, 'synset': 'sportswear.n.01', 'synonyms': ['sportswear', 'athletic_wear', 'activewear'], 'def': 'attire worn for sport or for casual wear', 'name': 'sportswear'}, {'frequency': 'c', 'id': 1020, 'synset': 'spotlight.n.02', 'synonyms': ['spotlight'], 'def': 'a lamp that produces a strong beam of light to illuminate a restricted area; used to focus attention of a stage performer', 'name': 'spotlight'}, {'frequency': 'r', 'id': 1021, 'synset': 'squirrel.n.01', 'synonyms': ['squirrel'], 'def': 'a kind of arboreal rodent having a long bushy tail', 'name': 'squirrel'}, {'frequency': 'c', 'id': 1022, 'synset': 'stapler.n.01', 'synonyms': ['stapler_(stapling_machine)'], 'def': 'a machine that inserts staples into sheets of paper in order to fasten them together', 'name': 'stapler_(stapling_machine)'}, {'frequency': 'r', 'id': 1023, 'synset': 'starfish.n.01', 'synonyms': ['starfish', 'sea_star'], 'def': 'echinoderms characterized by five arms extending from a central disk', 'name': 'starfish'}, {'frequency': 'f', 'id': 1024, 'synset': 'statue.n.01', 'synonyms': ['statue_(sculpture)'], 'def': 'a sculpture representing a human or animal', 'name': 'statue_(sculpture)'}, {'frequency': 'c', 'id': 1025, 'synset': 'steak.n.01', 'synonyms': ['steak_(food)'], 'def': 'a slice of meat cut from the fleshy part of an animal or large fish', 'name': 'steak_(food)'}, {'frequency': 'r', 'id': 1026, 'synset': 'steak_knife.n.01', 'synonyms': ['steak_knife'], 'def': 'a sharp table knife used in eating steak', 'name': 'steak_knife'}, {'frequency': 'r', 'id': 1027, 'synset': 'steamer.n.02', 'synonyms': ['steamer_(kitchen_appliance)'], 'def': 'a cooking utensil that can be used to cook food by steaming it', 'name': 'steamer_(kitchen_appliance)'}, {'frequency': 'f', 'id': 1028, 'synset': 'steering_wheel.n.01', 'synonyms': ['steering_wheel'], 'def': 'a handwheel that is used for steering', 'name': 'steering_wheel'}, {'frequency': 'r', 'id': 1029, 'synset': 'stencil.n.01', 'synonyms': ['stencil'], 'def': 'a sheet of material (metal, plastic, etc.) that has been perforated with a pattern; ink or paint can pass through the perforations to create the printed pattern on the surface below', 'name': 'stencil'}, {'frequency': 'r', 'id': 1030, 'synset': 'step_ladder.n.01', 'synonyms': ['stepladder'], 'def': 'a folding portable ladder hinged at the top', 'name': 'stepladder'}, {'frequency': 'c', 'id': 1031, 'synset': 'step_stool.n.01', 'synonyms': ['step_stool'], 'def': 'a stool that has one or two steps that fold under the seat', 'name': 'step_stool'}, {'frequency': 'c', 'id': 1032, 'synset': 'stereo.n.01', 'synonyms': ['stereo_(sound_system)'], 'def': 'electronic device for playing audio', 'name': 'stereo_(sound_system)'}, {'frequency': 'r', 'id': 1033, 'synset': 'stew.n.02', 'synonyms': ['stew'], 'def': 'food prepared by stewing especially meat or fish with vegetables', 'name': 'stew'}, {'frequency': 'r', 'id': 1034, 'synset': 'stirrer.n.02', 'synonyms': ['stirrer'], 'def': 'an implement used for stirring', 'name': 'stirrer'}, {'frequency': 'f', 'id': 1035, 'synset': 'stirrup.n.01', 'synonyms': ['stirrup'], 'def': "support consisting of metal loops into which rider's feet go", 'name': 'stirrup'}, {'frequency': 'c', 'id': 1036, 'synset': 'stocking.n.01', 'synonyms': ['stockings_(leg_wear)'], 'def': 'close-fitting hosiery to cover the foot and leg; come in matched pairs', 'name': 'stockings_(leg_wear)'}, {'frequency': 'f', 'id': 1037, 'synset': 'stool.n.01', 'synonyms': ['stool'], 'def': 'a simple seat without a back or arms', 'name': 'stool'}, {'frequency': 'f', 'id': 1038, 'synset': 'stop_sign.n.01', 'synonyms': ['stop_sign'], 'def': 'a traffic sign to notify drivers that they must come to a complete stop', 'name': 'stop_sign'}, {'frequency': 'f', 'id': 1039, 'synset': 'stoplight.n.01', 'synonyms': ['brake_light'], 'def': 'a red light on the rear of a motor vehicle that signals when the brakes are applied', 'name': 'brake_light'}, {'frequency': 'f', 'id': 1040, 'synset': 'stove.n.01', 'synonyms': ['stove', 'kitchen_stove', 'range_(kitchen_appliance)', 'kitchen_range', 'cooking_stove'], 'def': 'a kitchen appliance used for cooking food', 'name': 'stove'}, {'frequency': 'c', 'id': 1041, 'synset': 'strainer.n.01', 'synonyms': ['strainer'], 'def': 'a filter to retain larger pieces while smaller pieces and liquids pass through', 'name': 'strainer'}, {'frequency': 'f', 'id': 1042, 'synset': 'strap.n.01', 'synonyms': ['strap'], 'def': 'an elongated strip of material for binding things together or holding', 'name': 'strap'}, {'frequency': 'f', 'id': 1043, 'synset': 'straw.n.04', 'synonyms': ['straw_(for_drinking)', 'drinking_straw'], 'def': 'a thin paper or plastic tube used to suck liquids into the mouth', 'name': 'straw_(for_drinking)'}, {'frequency': 'f', 'id': 1044, 'synset': 'strawberry.n.01', 'synonyms': ['strawberry'], 'def': 'sweet fleshy red fruit', 'name': 'strawberry'}, {'frequency': 'f', 'id': 1045, 'synset': 'street_sign.n.01', 'synonyms': ['street_sign'], 'def': 'a sign visible from the street', 'name': 'street_sign'}, {'frequency': 'f', 'id': 1046, 'synset': 'streetlight.n.01', 'synonyms': ['streetlight', 'street_lamp'], 'def': 'a lamp supported on a lamppost; for illuminating a street', 'name': 'streetlight'}, {'frequency': 'r', 'id': 1047, 'synset': 'string_cheese.n.01', 'synonyms': ['string_cheese'], 'def': 'cheese formed in long strings twisted together', 'name': 'string_cheese'}, {'frequency': 'r', 'id': 1048, 'synset': 'stylus.n.02', 'synonyms': ['stylus'], 'def': 'a pointed tool for writing or drawing or engraving', 'name': 'stylus'}, {'frequency': 'r', 'id': 1049, 'synset': 'subwoofer.n.01', 'synonyms': ['subwoofer'], 'def': 'a loudspeaker that is designed to reproduce very low bass frequencies', 'name': 'subwoofer'}, {'frequency': 'r', 'id': 1050, 'synset': 'sugar_bowl.n.01', 'synonyms': ['sugar_bowl'], 'def': 'a dish in which sugar is served', 'name': 'sugar_bowl'}, {'frequency': 'r', 'id': 1051, 'synset': 'sugarcane.n.01', 'synonyms': ['sugarcane_(plant)'], 'def': 'juicy canes whose sap is a source of molasses and commercial sugar; fresh canes are sometimes chewed for the juice', 'name': 'sugarcane_(plant)'}, {'frequency': 'c', 'id': 1052, 'synset': 'suit.n.01', 'synonyms': ['suit_(clothing)'], 'def': 'a set of garments (usually including a jacket and trousers or skirt) for outerwear all of the same fabric and color', 'name': 'suit_(clothing)'}, {'frequency': 'c', 'id': 1053, 'synset': 'sunflower.n.01', 'synonyms': ['sunflower'], 'def': 'any plant of the genus Helianthus having large flower heads with dark disk florets and showy yellow rays', 'name': 'sunflower'}, {'frequency': 'f', 'id': 1054, 'synset': 'sunglasses.n.01', 'synonyms': ['sunglasses'], 'def': 'spectacles that are darkened or polarized to protect the eyes from the glare of the sun', 'name': 'sunglasses'}, {'frequency': 'c', 'id': 1055, 'synset': 'sunhat.n.01', 'synonyms': ['sunhat'], 'def': 'a hat with a broad brim that protects the face from direct exposure to the sun', 'name': 'sunhat'}, {'frequency': 'r', 'id': 1056, 'synset': 'sunscreen.n.01', 'synonyms': ['sunscreen', 'sunblock'], 'def': 'a cream spread on the skin; contains a chemical to filter out ultraviolet light and so protect from sunburn', 'name': 'sunscreen'}, {'frequency': 'f', 'id': 1057, 'synset': 'surfboard.n.01', 'synonyms': ['surfboard'], 'def': 'a narrow buoyant board for riding surf', 'name': 'surfboard'}, {'frequency': 'c', 'id': 1058, 'synset': 'sushi.n.01', 'synonyms': ['sushi'], 'def': 'rice (with raw fish) wrapped in seaweed', 'name': 'sushi'}, {'frequency': 'c', 'id': 1059, 'synset': 'swab.n.02', 'synonyms': ['mop'], 'def': 'cleaning implement consisting of absorbent material fastened to a handle; for cleaning floors', 'name': 'mop'}, {'frequency': 'c', 'id': 1060, 'synset': 'sweat_pants.n.01', 'synonyms': ['sweat_pants'], 'def': 'loose-fitting trousers with elastic cuffs; worn by athletes', 'name': 'sweat_pants'}, {'frequency': 'c', 'id': 1061, 'synset': 'sweatband.n.02', 'synonyms': ['sweatband'], 'def': 'a band of material tied around the forehead or wrist to absorb sweat', 'name': 'sweatband'}, {'frequency': 'f', 'id': 1062, 'synset': 'sweater.n.01', 'synonyms': ['sweater'], 'def': 'a crocheted or knitted garment covering the upper part of the body', 'name': 'sweater'}, {'frequency': 'f', 'id': 1063, 'synset': 'sweatshirt.n.01', 'synonyms': ['sweatshirt'], 'def': 'cotton knit pullover with long sleeves worn during athletic activity', 'name': 'sweatshirt'}, {'frequency': 'c', 'id': 1064, 'synset': 'sweet_potato.n.02', 'synonyms': ['sweet_potato'], 'def': 'the edible tuberous root of the sweet potato vine', 'name': 'sweet_potato'}, {'frequency': 'f', 'id': 1065, 'synset': 'swimsuit.n.01', 'synonyms': ['swimsuit', 'swimwear', 'bathing_suit', 'swimming_costume', 'bathing_costume', 'swimming_trunks', 'bathing_trunks'], 'def': 'garment worn for swimming', 'name': 'swimsuit'}, {'frequency': 'c', 'id': 1066, 'synset': 'sword.n.01', 'synonyms': ['sword'], 'def': 'a cutting or thrusting weapon that has a long metal blade', 'name': 'sword'}, {'frequency': 'r', 'id': 1067, 'synset': 'syringe.n.01', 'synonyms': ['syringe'], 'def': 'a medical instrument used to inject or withdraw fluids', 'name': 'syringe'}, {'frequency': 'r', 'id': 1068, 'synset': 'tabasco.n.02', 'synonyms': ['Tabasco_sauce'], 'def': 'very spicy sauce (trade name Tabasco) made from fully-aged red peppers', 'name': 'Tabasco_sauce'}, {'frequency': 'r', 'id': 1069, 'synset': 'table-tennis_table.n.01', 'synonyms': ['table-tennis_table', 'ping-pong_table'], 'def': 'a table used for playing table tennis', 'name': 'table-tennis_table'}, {'frequency': 'f', 'id': 1070, 'synset': 'table.n.02', 'synonyms': ['table'], 'def': 'a piece of furniture having a smooth flat top that is usually supported by one or more vertical legs', 'name': 'table'}, {'frequency': 'c', 'id': 1071, 'synset': 'table_lamp.n.01', 'synonyms': ['table_lamp'], 'def': 'a lamp that sits on a table', 'name': 'table_lamp'}, {'frequency': 'f', 'id': 1072, 'synset': 'tablecloth.n.01', 'synonyms': ['tablecloth'], 'def': 'a covering spread over a dining table', 'name': 'tablecloth'}, {'frequency': 'r', 'id': 1073, 'synset': 'tachometer.n.01', 'synonyms': ['tachometer'], 'def': 'measuring instrument for indicating speed of rotation', 'name': 'tachometer'}, {'frequency': 'r', 'id': 1074, 'synset': 'taco.n.02', 'synonyms': ['taco'], 'def': 'a small tortilla cupped around a filling', 'name': 'taco'}, {'frequency': 'f', 'id': 1075, 'synset': 'tag.n.02', 'synonyms': ['tag'], 'def': 'a label associated with something for the purpose of identification or information', 'name': 'tag'}, {'frequency': 'f', 'id': 1076, 'synset': 'taillight.n.01', 'synonyms': ['taillight', 'rear_light'], 'def': 'lamp (usually red) mounted at the rear of a motor vehicle', 'name': 'taillight'}, {'frequency': 'r', 'id': 1077, 'synset': 'tambourine.n.01', 'synonyms': ['tambourine'], 'def': 'a shallow drum with a single drumhead and with metallic disks in the sides', 'name': 'tambourine'}, {'frequency': 'r', 'id': 1078, 'synset': 'tank.n.01', 'synonyms': ['army_tank', 'armored_combat_vehicle', 'armoured_combat_vehicle'], 'def': 'an enclosed armored military vehicle; has a cannon and moves on caterpillar treads', 'name': 'army_tank'}, {'frequency': 'c', 'id': 1079, 'synset': 'tank.n.02', 'synonyms': ['tank_(storage_vessel)', 'storage_tank'], 'def': 'a large (usually metallic) vessel for holding gases or liquids', 'name': 'tank_(storage_vessel)'}, {'frequency': 'f', 'id': 1080, 'synset': 'tank_top.n.01', 'synonyms': ['tank_top_(clothing)'], 'def': 'a tight-fitting sleeveless shirt with wide shoulder straps and low neck and no front opening', 'name': 'tank_top_(clothing)'}, {'frequency': 'c', 'id': 1081, 'synset': 'tape.n.01', 'synonyms': ['tape_(sticky_cloth_or_paper)'], 'def': 'a long thin piece of cloth or paper as used for binding or fastening', 'name': 'tape_(sticky_cloth_or_paper)'}, {'frequency': 'c', 'id': 1082, 'synset': 'tape.n.04', 'synonyms': ['tape_measure', 'measuring_tape'], 'def': 'measuring instrument consisting of a narrow strip (cloth or metal) marked in inches or centimeters and used for measuring lengths', 'name': 'tape_measure'}, {'frequency': 'c', 'id': 1083, 'synset': 'tapestry.n.02', 'synonyms': ['tapestry'], 'def': 'a heavy textile with a woven design; used for curtains and upholstery', 'name': 'tapestry'}, {'frequency': 'f', 'id': 1084, 'synset': 'tarpaulin.n.01', 'synonyms': ['tarp'], 'def': 'waterproofed canvas', 'name': 'tarp'}, {'frequency': 'c', 'id': 1085, 'synset': 'tartan.n.01', 'synonyms': ['tartan', 'plaid'], 'def': 'a cloth having a crisscross design', 'name': 'tartan'}, {'frequency': 'c', 'id': 1086, 'synset': 'tassel.n.01', 'synonyms': ['tassel'], 'def': 'adornment consisting of a bunch of cords fastened at one end', 'name': 'tassel'}, {'frequency': 'r', 'id': 1087, 'synset': 'tea_bag.n.01', 'synonyms': ['tea_bag'], 'def': 'a measured amount of tea in a bag for an individual serving of tea', 'name': 'tea_bag'}, {'frequency': 'c', 'id': 1088, 'synset': 'teacup.n.02', 'synonyms': ['teacup'], 'def': 'a cup from which tea is drunk', 'name': 'teacup'}, {'frequency': 'c', 'id': 1089, 'synset': 'teakettle.n.01', 'synonyms': ['teakettle'], 'def': 'kettle for boiling water to make tea', 'name': 'teakettle'}, {'frequency': 'c', 'id': 1090, 'synset': 'teapot.n.01', 'synonyms': ['teapot'], 'def': 'pot for brewing tea; usually has a spout and handle', 'name': 'teapot'}, {'frequency': 'f', 'id': 1091, 'synset': 'teddy.n.01', 'synonyms': ['teddy_bear'], 'def': "plaything consisting of a child's toy bear (usually plush and stuffed with soft materials)", 'name': 'teddy_bear'}, {'frequency': 'f', 'id': 1092, 'synset': 'telephone.n.01', 'synonyms': ['telephone', 'phone', 'telephone_set'], 'def': 'electronic device for communicating by voice over long distances', 'name': 'telephone'}, {'frequency': 'c', 'id': 1093, 'synset': 'telephone_booth.n.01', 'synonyms': ['telephone_booth', 'phone_booth', 'call_box', 'telephone_box', 'telephone_kiosk'], 'def': 'booth for using a telephone', 'name': 'telephone_booth'}, {'frequency': 'f', 'id': 1094, 'synset': 'telephone_pole.n.01', 'synonyms': ['telephone_pole', 'telegraph_pole', 'telegraph_post'], 'def': 'tall pole supporting telephone wires', 'name': 'telephone_pole'}, {'frequency': 'r', 'id': 1095, 'synset': 'telephoto_lens.n.01', 'synonyms': ['telephoto_lens', 'zoom_lens'], 'def': 'a camera lens that magnifies the image', 'name': 'telephoto_lens'}, {'frequency': 'c', 'id': 1096, 'synset': 'television_camera.n.01', 'synonyms': ['television_camera', 'tv_camera'], 'def': 'television equipment for capturing and recording video', 'name': 'television_camera'}, {'frequency': 'f', 'id': 1097, 'synset': 'television_receiver.n.01', 'synonyms': ['television_set', 'tv', 'tv_set'], 'def': 'an electronic device that receives television signals and displays them on a screen', 'name': 'television_set'}, {'frequency': 'f', 'id': 1098, 'synset': 'tennis_ball.n.01', 'synonyms': ['tennis_ball'], 'def': 'ball about the size of a fist used in playing tennis', 'name': 'tennis_ball'}, {'frequency': 'f', 'id': 1099, 'synset': 'tennis_racket.n.01', 'synonyms': ['tennis_racket'], 'def': 'a racket used to play tennis', 'name': 'tennis_racket'}, {'frequency': 'r', 'id': 1100, 'synset': 'tequila.n.01', 'synonyms': ['tequila'], 'def': 'Mexican liquor made from fermented juices of an agave plant', 'name': 'tequila'}, {'frequency': 'c', 'id': 1101, 'synset': 'thermometer.n.01', 'synonyms': ['thermometer'], 'def': 'measuring instrument for measuring temperature', 'name': 'thermometer'}, {'frequency': 'c', 'id': 1102, 'synset': 'thermos.n.01', 'synonyms': ['thermos_bottle'], 'def': 'vacuum flask that preserves temperature of hot or cold drinks', 'name': 'thermos_bottle'}, {'frequency': 'c', 'id': 1103, 'synset': 'thermostat.n.01', 'synonyms': ['thermostat'], 'def': 'a regulator for automatically regulating temperature by starting or stopping the supply of heat', 'name': 'thermostat'}, {'frequency': 'r', 'id': 1104, 'synset': 'thimble.n.02', 'synonyms': ['thimble'], 'def': 'a small metal cap to protect the finger while sewing; can be used as a small container', 'name': 'thimble'}, {'frequency': 'c', 'id': 1105, 'synset': 'thread.n.01', 'synonyms': ['thread', 'yarn'], 'def': 'a fine cord of twisted fibers (of cotton or silk or wool or nylon etc.) used in sewing and weaving', 'name': 'thread'}, {'frequency': 'c', 'id': 1106, 'synset': 'thumbtack.n.01', 'synonyms': ['thumbtack', 'drawing_pin', 'pushpin'], 'def': 'a tack for attaching papers to a bulletin board or drawing board', 'name': 'thumbtack'}, {'frequency': 'c', 'id': 1107, 'synset': 'tiara.n.01', 'synonyms': ['tiara'], 'def': 'a jeweled headdress worn by women on formal occasions', 'name': 'tiara'}, {'frequency': 'c', 'id': 1108, 'synset': 'tiger.n.02', 'synonyms': ['tiger'], 'def': 'large feline of forests in most of Asia having a tawny coat with black stripes', 'name': 'tiger'}, {'frequency': 'c', 'id': 1109, 'synset': 'tights.n.01', 'synonyms': ['tights_(clothing)', 'leotards'], 'def': 'skintight knit hose covering the body from the waist to the feet worn by acrobats and dancers and as stockings by women and girls', 'name': 'tights_(clothing)'}, {'frequency': 'c', 'id': 1110, 'synset': 'timer.n.01', 'synonyms': ['timer', 'stopwatch'], 'def': 'a timepiece that measures a time interval and signals its end', 'name': 'timer'}, {'frequency': 'f', 'id': 1111, 'synset': 'tinfoil.n.01', 'synonyms': ['tinfoil'], 'def': 'foil made of tin or an alloy of tin and lead', 'name': 'tinfoil'}, {'frequency': 'r', 'id': 1112, 'synset': 'tinsel.n.01', 'synonyms': ['tinsel'], 'def': 'a showy decoration that is basically valueless', 'name': 'tinsel'}, {'frequency': 'f', 'id': 1113, 'synset': 'tissue.n.02', 'synonyms': ['tissue_paper'], 'def': 'a soft thin (usually translucent) paper', 'name': 'tissue_paper'}, {'frequency': 'c', 'id': 1114, 'synset': 'toast.n.01', 'synonyms': ['toast_(food)'], 'def': 'slice of bread that has been toasted', 'name': 'toast_(food)'}, {'frequency': 'f', 'id': 1115, 'synset': 'toaster.n.02', 'synonyms': ['toaster'], 'def': 'a kitchen appliance (usually electric) for toasting bread', 'name': 'toaster'}, {'frequency': 'c', 'id': 1116, 'synset': 'toaster_oven.n.01', 'synonyms': ['toaster_oven'], 'def': 'kitchen appliance consisting of a small electric oven for toasting or warming food', 'name': 'toaster_oven'}, {'frequency': 'f', 'id': 1117, 'synset': 'toilet.n.02', 'synonyms': ['toilet'], 'def': 'a plumbing fixture for defecation and urination', 'name': 'toilet'}, {'frequency': 'f', 'id': 1118, 'synset': 'toilet_tissue.n.01', 'synonyms': ['toilet_tissue', 'toilet_paper', 'bathroom_tissue'], 'def': 'a soft thin absorbent paper for use in toilets', 'name': 'toilet_tissue'}, {'frequency': 'f', 'id': 1119, 'synset': 'tomato.n.01', 'synonyms': ['tomato'], 'def': 'mildly acid red or yellow pulpy fruit eaten as a vegetable', 'name': 'tomato'}, {'frequency': 'c', 'id': 1120, 'synset': 'tongs.n.01', 'synonyms': ['tongs'], 'def': 'any of various devices for taking hold of objects; usually have two hinged legs with handles above and pointed hooks below', 'name': 'tongs'}, {'frequency': 'c', 'id': 1121, 'synset': 'toolbox.n.01', 'synonyms': ['toolbox'], 'def': 'a box or chest or cabinet for holding hand tools', 'name': 'toolbox'}, {'frequency': 'f', 'id': 1122, 'synset': 'toothbrush.n.01', 'synonyms': ['toothbrush'], 'def': 'small brush; has long handle; used to clean teeth', 'name': 'toothbrush'}, {'frequency': 'f', 'id': 1123, 'synset': 'toothpaste.n.01', 'synonyms': ['toothpaste'], 'def': 'a dentifrice in the form of a paste', 'name': 'toothpaste'}, {'frequency': 'c', 'id': 1124, 'synset': 'toothpick.n.01', 'synonyms': ['toothpick'], 'def': 'pick consisting of a small strip of wood or plastic; used to pick food from between the teeth', 'name': 'toothpick'}, {'frequency': 'c', 'id': 1125, 'synset': 'top.n.09', 'synonyms': ['cover'], 'def': 'covering for a hole (especially a hole in the top of a container)', 'name': 'cover'}, {'frequency': 'c', 'id': 1126, 'synset': 'tortilla.n.01', 'synonyms': ['tortilla'], 'def': 'thin unleavened pancake made from cornmeal or wheat flour', 'name': 'tortilla'}, {'frequency': 'c', 'id': 1127, 'synset': 'tow_truck.n.01', 'synonyms': ['tow_truck'], 'def': 'a truck equipped to hoist and pull wrecked cars (or to remove cars from no-parking zones)', 'name': 'tow_truck'}, {'frequency': 'f', 'id': 1128, 'synset': 'towel.n.01', 'synonyms': ['towel'], 'def': 'a rectangular piece of absorbent cloth (or paper) for drying or wiping', 'name': 'towel'}, {'frequency': 'f', 'id': 1129, 'synset': 'towel_rack.n.01', 'synonyms': ['towel_rack', 'towel_rail', 'towel_bar'], 'def': 'a rack consisting of one or more bars on which towels can be hung', 'name': 'towel_rack'}, {'frequency': 'f', 'id': 1130, 'synset': 'toy.n.03', 'synonyms': ['toy'], 'def': 'a device regarded as providing amusement', 'name': 'toy'}, {'frequency': 'c', 'id': 1131, 'synset': 'tractor.n.01', 'synonyms': ['tractor_(farm_equipment)'], 'def': 'a wheeled vehicle with large wheels; used in farming and other applications', 'name': 'tractor_(farm_equipment)'}, {'frequency': 'f', 'id': 1132, 'synset': 'traffic_light.n.01', 'synonyms': ['traffic_light'], 'def': 'a device to control vehicle traffic often consisting of three or more lights', 'name': 'traffic_light'}, {'frequency': 'r', 'id': 1133, 'synset': 'trail_bike.n.01', 'synonyms': ['dirt_bike'], 'def': 'a lightweight motorcycle equipped with rugged tires and suspension for off-road use', 'name': 'dirt_bike'}, {'frequency': 'c', 'id': 1134, 'synset': 'trailer_truck.n.01', 'synonyms': ['trailer_truck', 'tractor_trailer', 'trucking_rig', 'articulated_lorry', 'semi_truck'], 'def': 'a truck consisting of a tractor and trailer together', 'name': 'trailer_truck'}, {'frequency': 'f', 'id': 1135, 'synset': 'train.n.01', 'synonyms': ['train_(railroad_vehicle)', 'railroad_train'], 'def': 'public or private transport provided by a line of railway cars coupled together and drawn by a locomotive', 'name': 'train_(railroad_vehicle)'}, {'frequency': 'r', 'id': 1136, 'synset': 'trampoline.n.01', 'synonyms': ['trampoline'], 'def': 'gymnastic apparatus consisting of a strong canvas sheet attached with springs to a metal frame', 'name': 'trampoline'}, {'frequency': 'f', 'id': 1137, 'synset': 'tray.n.01', 'synonyms': ['tray'], 'def': 'an open receptacle for holding or displaying or serving articles or food', 'name': 'tray'}, {'frequency': 'r', 'id': 1138, 'synset': 'tree_house.n.01', 'synonyms': ['tree_house'], 'def': '(NOT A TREE) a PLAYHOUSE built in the branches of a tree', 'name': 'tree_house'}, {'frequency': 'r', 'id': 1139, 'synset': 'trench_coat.n.01', 'synonyms': ['trench_coat'], 'def': 'a military style raincoat; belted with deep pockets', 'name': 'trench_coat'}, {'frequency': 'r', 'id': 1140, 'synset': 'triangle.n.05', 'synonyms': ['triangle_(musical_instrument)'], 'def': 'a percussion instrument consisting of a metal bar bent in the shape of an open triangle', 'name': 'triangle_(musical_instrument)'}, {'frequency': 'r', 'id': 1141, 'synset': 'tricycle.n.01', 'synonyms': ['tricycle'], 'def': 'a vehicle with three wheels that is moved by foot pedals', 'name': 'tricycle'}, {'frequency': 'c', 'id': 1142, 'synset': 'tripod.n.01', 'synonyms': ['tripod'], 'def': 'a three-legged rack used for support', 'name': 'tripod'}, {'frequency': 'f', 'id': 1143, 'synset': 'trouser.n.01', 'synonyms': ['trousers', 'pants_(clothing)'], 'def': 'a garment extending from the waist to the knee or ankle, covering each leg separately', 'name': 'trousers'}, {'frequency': 'f', 'id': 1144, 'synset': 'truck.n.01', 'synonyms': ['truck'], 'def': 'an automotive vehicle suitable for hauling', 'name': 'truck'}, {'frequency': 'r', 'id': 1145, 'synset': 'truffle.n.03', 'synonyms': ['truffle_(chocolate)', 'chocolate_truffle'], 'def': 'creamy chocolate candy', 'name': 'truffle_(chocolate)'}, {'frequency': 'c', 'id': 1146, 'synset': 'trunk.n.02', 'synonyms': ['trunk'], 'def': 'luggage consisting of a large strong case used when traveling or for storage', 'name': 'trunk'}, {'frequency': 'r', 'id': 1147, 'synset': 'tub.n.02', 'synonyms': ['vat'], 'def': 'a large open vessel for holding or storing liquids', 'name': 'vat'}, {'frequency': 'c', 'id': 1148, 'synset': 'turban.n.01', 'synonyms': ['turban'], 'def': 'a traditional headdress consisting of a long scarf wrapped around the head', 'name': 'turban'}, {'frequency': 'r', 'id': 1149, 'synset': 'turkey.n.01', 'synonyms': ['turkey_(bird)'], 'def': 'large gallinaceous bird with fan-shaped tail; widely domesticated for food', 'name': 'turkey_(bird)'}, {'frequency': 'c', 'id': 1150, 'synset': 'turkey.n.04', 'synonyms': ['turkey_(food)'], 'def': 'flesh of large domesticated fowl usually roasted', 'name': 'turkey_(food)'}, {'frequency': 'r', 'id': 1151, 'synset': 'turnip.n.01', 'synonyms': ['turnip'], 'def': 'widely cultivated plant having a large fleshy edible white or yellow root', 'name': 'turnip'}, {'frequency': 'c', 'id': 1152, 'synset': 'turtle.n.02', 'synonyms': ['turtle'], 'def': 'any of various aquatic and land reptiles having a bony shell and flipper-like limbs for swimming', 'name': 'turtle'}, {'frequency': 'r', 'id': 1153, 'synset': 'turtleneck.n.01', 'synonyms': ['turtleneck_(clothing)', 'polo-neck'], 'def': 'a sweater or jersey with a high close-fitting collar', 'name': 'turtleneck_(clothing)'}, {'frequency': 'r', 'id': 1154, 'synset': 'typewriter.n.01', 'synonyms': ['typewriter'], 'def': 'hand-operated character printer for printing written messages one character at a time', 'name': 'typewriter'}, {'frequency': 'f', 'id': 1155, 'synset': 'umbrella.n.01', 'synonyms': ['umbrella'], 'def': 'a lightweight handheld collapsible canopy', 'name': 'umbrella'}, {'frequency': 'c', 'id': 1156, 'synset': 'underwear.n.01', 'synonyms': ['underwear', 'underclothes', 'underclothing', 'underpants'], 'def': 'undergarment worn next to the skin and under the outer garments', 'name': 'underwear'}, {'frequency': 'r', 'id': 1157, 'synset': 'unicycle.n.01', 'synonyms': ['unicycle'], 'def': 'a vehicle with a single wheel that is driven by pedals', 'name': 'unicycle'}, {'frequency': 'c', 'id': 1158, 'synset': 'urinal.n.01', 'synonyms': ['urinal'], 'def': 'a plumbing fixture (usually attached to the wall) used by men to urinate', 'name': 'urinal'}, {'frequency': 'r', 'id': 1159, 'synset': 'urn.n.01', 'synonyms': ['urn'], 'def': 'a large vase that usually has a pedestal or feet', 'name': 'urn'}, {'frequency': 'c', 'id': 1160, 'synset': 'vacuum.n.04', 'synonyms': ['vacuum_cleaner'], 'def': 'an electrical home appliance that cleans by suction', 'name': 'vacuum_cleaner'}, {'frequency': 'c', 'id': 1161, 'synset': 'valve.n.03', 'synonyms': ['valve'], 'def': 'control consisting of a mechanical device for controlling the flow of a fluid', 'name': 'valve'}, {'frequency': 'f', 'id': 1162, 'synset': 'vase.n.01', 'synonyms': ['vase'], 'def': 'an open jar of glass or porcelain used as an ornament or to hold flowers', 'name': 'vase'}, {'frequency': 'c', 'id': 1163, 'synset': 'vending_machine.n.01', 'synonyms': ['vending_machine'], 'def': 'a slot machine for selling goods', 'name': 'vending_machine'}, {'frequency': 'f', 'id': 1164, 'synset': 'vent.n.01', 'synonyms': ['vent', 'blowhole', 'air_vent'], 'def': 'a hole for the escape of gas or air', 'name': 'vent'}, {'frequency': 'c', 'id': 1165, 'synset': 'videotape.n.01', 'synonyms': ['videotape'], 'def': 'a video recording made on magnetic tape', 'name': 'videotape'}, {'frequency': 'r', 'id': 1166, 'synset': 'vinegar.n.01', 'synonyms': ['vinegar'], 'def': 'sour-tasting liquid produced usually by oxidation of the alcohol in wine or cider and used as a condiment or food preservative', 'name': 'vinegar'}, {'frequency': 'r', 'id': 1167, 'synset': 'violin.n.01', 'synonyms': ['violin', 'fiddle'], 'def': 'bowed stringed instrument that is the highest member of the violin family', 'name': 'violin'}, {'frequency': 'r', 'id': 1168, 'synset': 'vodka.n.01', 'synonyms': ['vodka'], 'def': 'unaged colorless liquor originating in Russia', 'name': 'vodka'}, {'frequency': 'r', 'id': 1169, 'synset': 'volleyball.n.02', 'synonyms': ['volleyball'], 'def': 'an inflated ball used in playing volleyball', 'name': 'volleyball'}, {'frequency': 'r', 'id': 1170, 'synset': 'vulture.n.01', 'synonyms': ['vulture'], 'def': 'any of various large birds of prey having naked heads and weak claws and feeding chiefly on carrion', 'name': 'vulture'}, {'frequency': 'c', 'id': 1171, 'synset': 'waffle.n.01', 'synonyms': ['waffle'], 'def': 'pancake batter baked in a waffle iron', 'name': 'waffle'}, {'frequency': 'r', 'id': 1172, 'synset': 'waffle_iron.n.01', 'synonyms': ['waffle_iron'], 'def': 'a kitchen appliance for baking waffles', 'name': 'waffle_iron'}, {'frequency': 'c', 'id': 1173, 'synset': 'wagon.n.01', 'synonyms': ['wagon'], 'def': 'any of various kinds of wheeled vehicles drawn by an animal or a tractor', 'name': 'wagon'}, {'frequency': 'c', 'id': 1174, 'synset': 'wagon_wheel.n.01', 'synonyms': ['wagon_wheel'], 'def': 'a wheel of a wagon', 'name': 'wagon_wheel'}, {'frequency': 'c', 'id': 1175, 'synset': 'walking_stick.n.01', 'synonyms': ['walking_stick'], 'def': 'a stick carried in the hand for support in walking', 'name': 'walking_stick'}, {'frequency': 'c', 'id': 1176, 'synset': 'wall_clock.n.01', 'synonyms': ['wall_clock'], 'def': 'a clock mounted on a wall', 'name': 'wall_clock'}, {'frequency': 'f', 'id': 1177, 'synset': 'wall_socket.n.01', 'synonyms': ['wall_socket', 'wall_plug', 'electric_outlet', 'electrical_outlet', 'outlet', 'electric_receptacle'], 'def': 'receptacle providing a place in a wiring system where current can be taken to run electrical devices', 'name': 'wall_socket'}, {'frequency': 'c', 'id': 1178, 'synset': 'wallet.n.01', 'synonyms': ['wallet', 'billfold'], 'def': 'a pocket-size case for holding papers and paper money', 'name': 'wallet'}, {'frequency': 'r', 'id': 1179, 'synset': 'walrus.n.01', 'synonyms': ['walrus'], 'def': 'either of two large northern marine mammals having ivory tusks and tough hide over thick blubber', 'name': 'walrus'}, {'frequency': 'r', 'id': 1180, 'synset': 'wardrobe.n.01', 'synonyms': ['wardrobe'], 'def': 'a tall piece of furniture that provides storage space for clothes; has a door and rails or hooks for hanging clothes', 'name': 'wardrobe'}, {'frequency': 'r', 'id': 1181, 'synset': 'wasabi.n.02', 'synonyms': ['wasabi'], 'def': 'the thick green root of the wasabi plant that the Japanese use in cooking and that tastes like strong horseradish', 'name': 'wasabi'}, {'frequency': 'c', 'id': 1182, 'synset': 'washer.n.03', 'synonyms': ['automatic_washer', 'washing_machine'], 'def': 'a home appliance for washing clothes and linens automatically', 'name': 'automatic_washer'}, {'frequency': 'f', 'id': 1183, 'synset': 'watch.n.01', 'synonyms': ['watch', 'wristwatch'], 'def': 'a small, portable timepiece', 'name': 'watch'}, {'frequency': 'f', 'id': 1184, 'synset': 'water_bottle.n.01', 'synonyms': ['water_bottle'], 'def': 'a bottle for holding water', 'name': 'water_bottle'}, {'frequency': 'c', 'id': 1185, 'synset': 'water_cooler.n.01', 'synonyms': ['water_cooler'], 'def': 'a device for cooling and dispensing drinking water', 'name': 'water_cooler'}, {'frequency': 'c', 'id': 1186, 'synset': 'water_faucet.n.01', 'synonyms': ['water_faucet', 'water_tap', 'tap_(water_faucet)'], 'def': 'a faucet for drawing water from a pipe or cask', 'name': 'water_faucet'}, {'frequency': 'r', 'id': 1187, 'synset': 'water_filter.n.01', 'synonyms': ['water_filter'], 'def': 'a filter to remove impurities from the water supply', 'name': 'water_filter'}, {'frequency': 'r', 'id': 1188, 'synset': 'water_heater.n.01', 'synonyms': ['water_heater', 'hot-water_heater'], 'def': 'a heater and storage tank to supply heated water', 'name': 'water_heater'}, {'frequency': 'r', 'id': 1189, 'synset': 'water_jug.n.01', 'synonyms': ['water_jug'], 'def': 'a jug that holds water', 'name': 'water_jug'}, {'frequency': 'r', 'id': 1190, 'synset': 'water_pistol.n.01', 'synonyms': ['water_gun', 'squirt_gun'], 'def': 'plaything consisting of a toy pistol that squirts water', 'name': 'water_gun'}, {'frequency': 'c', 'id': 1191, 'synset': 'water_scooter.n.01', 'synonyms': ['water_scooter', 'sea_scooter', 'jet_ski'], 'def': 'a motorboat resembling a motor scooter (NOT A SURFBOARD OR WATER SKI)', 'name': 'water_scooter'}, {'frequency': 'c', 'id': 1192, 'synset': 'water_ski.n.01', 'synonyms': ['water_ski'], 'def': 'broad ski for skimming over water towed by a speedboat (DO NOT MARK WATER)', 'name': 'water_ski'}, {'frequency': 'c', 'id': 1193, 'synset': 'water_tower.n.01', 'synonyms': ['water_tower'], 'def': 'a large reservoir for water', 'name': 'water_tower'}, {'frequency': 'c', 'id': 1194, 'synset': 'watering_can.n.01', 'synonyms': ['watering_can'], 'def': 'a container with a handle and a spout with a perforated nozzle; used to sprinkle water over plants', 'name': 'watering_can'}, {'frequency': 'c', 'id': 1195, 'synset': 'watermelon.n.02', 'synonyms': ['watermelon'], 'def': 'large oblong or roundish melon with a hard green rind and sweet watery red or occasionally yellowish pulp', 'name': 'watermelon'}, {'frequency': 'f', 'id': 1196, 'synset': 'weathervane.n.01', 'synonyms': ['weathervane', 'vane_(weathervane)', 'wind_vane'], 'def': 'mechanical device attached to an elevated structure; rotates freely to show the direction of the wind', 'name': 'weathervane'}, {'frequency': 'c', 'id': 1197, 'synset': 'webcam.n.01', 'synonyms': ['webcam'], 'def': 'a digital camera designed to take digital photographs and transmit them over the internet', 'name': 'webcam'}, {'frequency': 'c', 'id': 1198, 'synset': 'wedding_cake.n.01', 'synonyms': ['wedding_cake', 'bridecake'], 'def': 'a rich cake with two or more tiers and covered with frosting and decorations; served at a wedding reception', 'name': 'wedding_cake'}, {'frequency': 'c', 'id': 1199, 'synset': 'wedding_ring.n.01', 'synonyms': ['wedding_ring', 'wedding_band'], 'def': 'a ring given to the bride and/or groom at the wedding', 'name': 'wedding_ring'}, {'frequency': 'f', 'id': 1200, 'synset': 'wet_suit.n.01', 'synonyms': ['wet_suit'], 'def': 'a close-fitting garment made of a permeable material; worn in cold water to retain body heat', 'name': 'wet_suit'}, {'frequency': 'f', 'id': 1201, 'synset': 'wheel.n.01', 'synonyms': ['wheel'], 'def': 'a circular frame with spokes (or a solid disc) that can rotate on a shaft or axle', 'name': 'wheel'}, {'frequency': 'c', 'id': 1202, 'synset': 'wheelchair.n.01', 'synonyms': ['wheelchair'], 'def': 'a movable chair mounted on large wheels', 'name': 'wheelchair'}, {'frequency': 'c', 'id': 1203, 'synset': 'whipped_cream.n.01', 'synonyms': ['whipped_cream'], 'def': 'cream that has been beaten until light and fluffy', 'name': 'whipped_cream'}, {'frequency': 'r', 'id': 1204, 'synset': 'whiskey.n.01', 'synonyms': ['whiskey'], 'def': 'a liquor made from fermented mash of grain', 'name': 'whiskey'}, {'frequency': 'r', 'id': 1205, 'synset': 'whistle.n.03', 'synonyms': ['whistle'], 'def': 'a small wind instrument that produces a whistling sound by blowing into it', 'name': 'whistle'}, {'frequency': 'r', 'id': 1206, 'synset': 'wick.n.02', 'synonyms': ['wick'], 'def': 'a loosely woven cord in a candle or oil lamp that is lit on fire', 'name': 'wick'}, {'frequency': 'c', 'id': 1207, 'synset': 'wig.n.01', 'synonyms': ['wig'], 'def': 'hairpiece covering the head and made of real or synthetic hair', 'name': 'wig'}, {'frequency': 'c', 'id': 1208, 'synset': 'wind_chime.n.01', 'synonyms': ['wind_chime'], 'def': 'a decorative arrangement of pieces of metal or glass or pottery that hang together loosely so the wind can cause them to tinkle', 'name': 'wind_chime'}, {'frequency': 'c', 'id': 1209, 'synset': 'windmill.n.01', 'synonyms': ['windmill'], 'def': 'a mill that is powered by the wind', 'name': 'windmill'}, {'frequency': 'c', 'id': 1210, 'synset': 'window_box.n.01', 'synonyms': ['window_box_(for_plants)'], 'def': 'a container for growing plants on a windowsill', 'name': 'window_box_(for_plants)'}, {'frequency': 'f', 'id': 1211, 'synset': 'windshield_wiper.n.01', 'synonyms': ['windshield_wiper', 'windscreen_wiper', 'wiper_(for_windshield/screen)'], 'def': 'a mechanical device that cleans the windshield', 'name': 'windshield_wiper'}, {'frequency': 'c', 'id': 1212, 'synset': 'windsock.n.01', 'synonyms': ['windsock', 'air_sock', 'air-sleeve', 'wind_sleeve', 'wind_cone'], 'def': 'a truncated cloth cone mounted on a mast/pole; shows wind direction', 'name': 'windsock'}, {'frequency': 'f', 'id': 1213, 'synset': 'wine_bottle.n.01', 'synonyms': ['wine_bottle'], 'def': 'a bottle for holding wine', 'name': 'wine_bottle'}, {'frequency': 'r', 'id': 1214, 'synset': 'wine_bucket.n.01', 'synonyms': ['wine_bucket', 'wine_cooler'], 'def': 'a bucket of ice used to chill a bottle of wine', 'name': 'wine_bucket'}, {'frequency': 'f', 'id': 1215, 'synset': 'wineglass.n.01', 'synonyms': ['wineglass'], 'def': 'a glass that has a stem and in which wine is served', 'name': 'wineglass'}, {'frequency': 'r', 'id': 1216, 'synset': 'wing_chair.n.01', 'synonyms': ['wing_chair'], 'def': 'easy chair having wings on each side of a high back', 'name': 'wing_chair'}, {'frequency': 'c', 'id': 1217, 'synset': 'winker.n.02', 'synonyms': ['blinder_(for_horses)'], 'def': 'blinds that prevent a horse from seeing something on either side', 'name': 'blinder_(for_horses)'}, {'frequency': 'c', 'id': 1218, 'synset': 'wok.n.01', 'synonyms': ['wok'], 'def': 'pan with a convex bottom; used for frying in Chinese cooking', 'name': 'wok'}, {'frequency': 'r', 'id': 1219, 'synset': 'wolf.n.01', 'synonyms': ['wolf'], 'def': 'a wild carnivorous mammal of the dog family, living and hunting in packs', 'name': 'wolf'}, {'frequency': 'c', 'id': 1220, 'synset': 'wooden_spoon.n.02', 'synonyms': ['wooden_spoon'], 'def': 'a spoon made of wood', 'name': 'wooden_spoon'}, {'frequency': 'c', 'id': 1221, 'synset': 'wreath.n.01', 'synonyms': ['wreath'], 'def': 'an arrangement of flowers, leaves, or stems fastened in a ring', 'name': 'wreath'}, {'frequency': 'c', 'id': 1222, 'synset': 'wrench.n.03', 'synonyms': ['wrench', 'spanner'], 'def': 'a hand tool that is used to hold or twist a nut or bolt', 'name': 'wrench'}, {'frequency': 'c', 'id': 1223, 'synset': 'wristband.n.01', 'synonyms': ['wristband'], 'def': 'band consisting of a part of a sleeve that covers the wrist', 'name': 'wristband'}, {'frequency': 'f', 'id': 1224, 'synset': 'wristlet.n.01', 'synonyms': ['wristlet', 'wrist_band'], 'def': 'a band or bracelet worn around the wrist', 'name': 'wristlet'}, {'frequency': 'r', 'id': 1225, 'synset': 'yacht.n.01', 'synonyms': ['yacht'], 'def': 'an expensive vessel propelled by sail or power and used for cruising or racing', 'name': 'yacht'}, {'frequency': 'r', 'id': 1226, 'synset': 'yak.n.02', 'synonyms': ['yak'], 'def': 'large long-haired wild ox of Tibet often domesticated', 'name': 'yak'}, {'frequency': 'c', 'id': 1227, 'synset': 'yogurt.n.01', 'synonyms': ['yogurt', 'yoghurt', 'yoghourt'], 'def': 'a custard-like food made from curdled milk', 'name': 'yogurt'}, {'frequency': 'r', 'id': 1228, 'synset': 'yoke.n.07', 'synonyms': ['yoke_(animal_equipment)'], 'def': 'gear joining two animals at the neck; NOT egg yolk', 'name': 'yoke_(animal_equipment)'}, {'frequency': 'f', 'id': 1229, 'synset': 'zebra.n.01', 'synonyms': ['zebra'], 'def': 'any of several fleet black-and-white striped African equines', 'name': 'zebra'}, {'frequency': 'c', 'id': 1230, 'synset': 'zucchini.n.02', 'synonyms': ['zucchini', 'courgette'], 'def': 'small cucumber-shaped vegetable marrow; typically dark green', 'name': 'zucchini'}] # noqa +# fmt: on diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/lvis_v1_categories.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/lvis_v1_categories.py new file mode 100644 index 0000000000000000000000000000000000000000..7374e6968bb006f5d8c49e75d9d3b31ea3d77d05 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/lvis_v1_categories.py @@ -0,0 +1,16 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Autogen with +# with open("lvis_v1_val.json", "r") as f: +# a = json.load(f) +# c = a["categories"] +# for x in c: +# del x["image_count"] +# del x["instance_count"] +# LVIS_CATEGORIES = repr(c) + " # noqa" +# with open("/tmp/lvis_categories.py", "wt") as f: +# f.write(f"LVIS_CATEGORIES = {LVIS_CATEGORIES}") +# Then paste the contents of that file below + +# fmt: off +LVIS_CATEGORIES = [{'frequency': 'c', 'synset': 'aerosol.n.02', 'synonyms': ['aerosol_can', 'spray_can'], 'id': 1, 'def': 'a dispenser that holds a substance under pressure', 'name': 'aerosol_can'}, {'frequency': 'f', 'synset': 'air_conditioner.n.01', 'synonyms': ['air_conditioner'], 'id': 2, 'def': 'a machine that keeps air cool and dry', 'name': 'air_conditioner'}, {'frequency': 'f', 'synset': 'airplane.n.01', 'synonyms': ['airplane', 'aeroplane'], 'id': 3, 'def': 'an aircraft that has a fixed wing and is powered by propellers or jets', 'name': 'airplane'}, {'frequency': 'f', 'synset': 'alarm_clock.n.01', 'synonyms': ['alarm_clock'], 'id': 4, 'def': 'a clock that wakes a sleeper at some preset time', 'name': 'alarm_clock'}, {'frequency': 'c', 'synset': 'alcohol.n.01', 'synonyms': ['alcohol', 'alcoholic_beverage'], 'id': 5, 'def': 'a liquor or brew containing alcohol as the active agent', 'name': 'alcohol'}, {'frequency': 'c', 'synset': 'alligator.n.02', 'synonyms': ['alligator', 'gator'], 'id': 6, 'def': 'amphibious reptiles related to crocodiles but with shorter broader snouts', 'name': 'alligator'}, {'frequency': 'c', 'synset': 'almond.n.02', 'synonyms': ['almond'], 'id': 7, 'def': 'oval-shaped edible seed of the almond tree', 'name': 'almond'}, {'frequency': 'c', 'synset': 'ambulance.n.01', 'synonyms': ['ambulance'], 'id': 8, 'def': 'a vehicle that takes people to and from hospitals', 'name': 'ambulance'}, {'frequency': 'c', 'synset': 'amplifier.n.01', 'synonyms': ['amplifier'], 'id': 9, 'def': 'electronic equipment that increases strength of signals', 'name': 'amplifier'}, {'frequency': 'c', 'synset': 'anklet.n.03', 'synonyms': ['anklet', 'ankle_bracelet'], 'id': 10, 'def': 'an ornament worn around the ankle', 'name': 'anklet'}, {'frequency': 'f', 'synset': 'antenna.n.01', 'synonyms': ['antenna', 'aerial', 'transmitting_aerial'], 'id': 11, 'def': 'an electrical device that sends or receives radio or television signals', 'name': 'antenna'}, {'frequency': 'f', 'synset': 'apple.n.01', 'synonyms': ['apple'], 'id': 12, 'def': 'fruit with red or yellow or green skin and sweet to tart crisp whitish flesh', 'name': 'apple'}, {'frequency': 'r', 'synset': 'applesauce.n.01', 'synonyms': ['applesauce'], 'id': 13, 'def': 'puree of stewed apples usually sweetened and spiced', 'name': 'applesauce'}, {'frequency': 'r', 'synset': 'apricot.n.02', 'synonyms': ['apricot'], 'id': 14, 'def': 'downy yellow to rosy-colored fruit resembling a small peach', 'name': 'apricot'}, {'frequency': 'f', 'synset': 'apron.n.01', 'synonyms': ['apron'], 'id': 15, 'def': 'a garment of cloth that is tied about the waist and worn to protect clothing', 'name': 'apron'}, {'frequency': 'c', 'synset': 'aquarium.n.01', 'synonyms': ['aquarium', 'fish_tank'], 'id': 16, 'def': 'a tank/pool/bowl filled with water for keeping live fish and underwater animals', 'name': 'aquarium'}, {'frequency': 'r', 'synset': 'arctic.n.02', 'synonyms': ['arctic_(type_of_shoe)', 'galosh', 'golosh', 'rubber_(type_of_shoe)', 'gumshoe'], 'id': 17, 'def': 'a waterproof overshoe that protects shoes from water or snow', 'name': 'arctic_(type_of_shoe)'}, {'frequency': 'c', 'synset': 'armband.n.02', 'synonyms': ['armband'], 'id': 18, 'def': 'a band worn around the upper arm', 'name': 'armband'}, {'frequency': 'f', 'synset': 'armchair.n.01', 'synonyms': ['armchair'], 'id': 19, 'def': 'chair with a support on each side for arms', 'name': 'armchair'}, {'frequency': 'r', 'synset': 'armoire.n.01', 'synonyms': ['armoire'], 'id': 20, 'def': 'a large wardrobe or cabinet', 'name': 'armoire'}, {'frequency': 'r', 'synset': 'armor.n.01', 'synonyms': ['armor', 'armour'], 'id': 21, 'def': 'protective covering made of metal and used in combat', 'name': 'armor'}, {'frequency': 'c', 'synset': 'artichoke.n.02', 'synonyms': ['artichoke'], 'id': 22, 'def': 'a thistlelike flower head with edible fleshy leaves and heart', 'name': 'artichoke'}, {'frequency': 'f', 'synset': 'ashcan.n.01', 'synonyms': ['trash_can', 'garbage_can', 'wastebin', 'dustbin', 'trash_barrel', 'trash_bin'], 'id': 23, 'def': 'a bin that holds rubbish until it is collected', 'name': 'trash_can'}, {'frequency': 'c', 'synset': 'ashtray.n.01', 'synonyms': ['ashtray'], 'id': 24, 'def': "a receptacle for the ash from smokers' cigars or cigarettes", 'name': 'ashtray'}, {'frequency': 'c', 'synset': 'asparagus.n.02', 'synonyms': ['asparagus'], 'id': 25, 'def': 'edible young shoots of the asparagus plant', 'name': 'asparagus'}, {'frequency': 'c', 'synset': 'atomizer.n.01', 'synonyms': ['atomizer', 'atomiser', 'spray', 'sprayer', 'nebulizer', 'nebuliser'], 'id': 26, 'def': 'a dispenser that turns a liquid (such as perfume) into a fine mist', 'name': 'atomizer'}, {'frequency': 'f', 'synset': 'avocado.n.01', 'synonyms': ['avocado'], 'id': 27, 'def': 'a pear-shaped fruit with green or blackish skin and rich yellowish pulp enclosing a single large seed', 'name': 'avocado'}, {'frequency': 'c', 'synset': 'award.n.02', 'synonyms': ['award', 'accolade'], 'id': 28, 'def': 'a tangible symbol signifying approval or distinction', 'name': 'award'}, {'frequency': 'f', 'synset': 'awning.n.01', 'synonyms': ['awning'], 'id': 29, 'def': 'a canopy made of canvas to shelter people or things from rain or sun', 'name': 'awning'}, {'frequency': 'r', 'synset': 'ax.n.01', 'synonyms': ['ax', 'axe'], 'id': 30, 'def': 'an edge tool with a heavy bladed head mounted across a handle', 'name': 'ax'}, {'frequency': 'r', 'synset': 'baboon.n.01', 'synonyms': ['baboon'], 'id': 31, 'def': 'large terrestrial monkeys having doglike muzzles', 'name': 'baboon'}, {'frequency': 'f', 'synset': 'baby_buggy.n.01', 'synonyms': ['baby_buggy', 'baby_carriage', 'perambulator', 'pram', 'stroller'], 'id': 32, 'def': 'a small vehicle with four wheels in which a baby or child is pushed around', 'name': 'baby_buggy'}, {'frequency': 'c', 'synset': 'backboard.n.01', 'synonyms': ['basketball_backboard'], 'id': 33, 'def': 'a raised vertical board with basket attached; used to play basketball', 'name': 'basketball_backboard'}, {'frequency': 'f', 'synset': 'backpack.n.01', 'synonyms': ['backpack', 'knapsack', 'packsack', 'rucksack', 'haversack'], 'id': 34, 'def': 'a bag carried by a strap on your back or shoulder', 'name': 'backpack'}, {'frequency': 'f', 'synset': 'bag.n.04', 'synonyms': ['handbag', 'purse', 'pocketbook'], 'id': 35, 'def': 'a container used for carrying money and small personal items or accessories', 'name': 'handbag'}, {'frequency': 'f', 'synset': 'bag.n.06', 'synonyms': ['suitcase', 'baggage', 'luggage'], 'id': 36, 'def': 'cases used to carry belongings when traveling', 'name': 'suitcase'}, {'frequency': 'c', 'synset': 'bagel.n.01', 'synonyms': ['bagel', 'beigel'], 'id': 37, 'def': 'glazed yeast-raised doughnut-shaped roll with hard crust', 'name': 'bagel'}, {'frequency': 'r', 'synset': 'bagpipe.n.01', 'synonyms': ['bagpipe'], 'id': 38, 'def': 'a tubular wind instrument; the player blows air into a bag and squeezes it out', 'name': 'bagpipe'}, {'frequency': 'r', 'synset': 'baguet.n.01', 'synonyms': ['baguet', 'baguette'], 'id': 39, 'def': 'narrow French stick loaf', 'name': 'baguet'}, {'frequency': 'r', 'synset': 'bait.n.02', 'synonyms': ['bait', 'lure'], 'id': 40, 'def': 'something used to lure fish or other animals into danger so they can be trapped or killed', 'name': 'bait'}, {'frequency': 'f', 'synset': 'ball.n.06', 'synonyms': ['ball'], 'id': 41, 'def': 'a spherical object used as a plaything', 'name': 'ball'}, {'frequency': 'r', 'synset': 'ballet_skirt.n.01', 'synonyms': ['ballet_skirt', 'tutu'], 'id': 42, 'def': 'very short skirt worn by ballerinas', 'name': 'ballet_skirt'}, {'frequency': 'f', 'synset': 'balloon.n.01', 'synonyms': ['balloon'], 'id': 43, 'def': 'large tough nonrigid bag filled with gas or heated air', 'name': 'balloon'}, {'frequency': 'c', 'synset': 'bamboo.n.02', 'synonyms': ['bamboo'], 'id': 44, 'def': 'woody tropical grass having hollow woody stems', 'name': 'bamboo'}, {'frequency': 'f', 'synset': 'banana.n.02', 'synonyms': ['banana'], 'id': 45, 'def': 'elongated crescent-shaped yellow fruit with soft sweet flesh', 'name': 'banana'}, {'frequency': 'c', 'synset': 'band_aid.n.01', 'synonyms': ['Band_Aid'], 'id': 46, 'def': 'trade name for an adhesive bandage to cover small cuts or blisters', 'name': 'Band_Aid'}, {'frequency': 'c', 'synset': 'bandage.n.01', 'synonyms': ['bandage'], 'id': 47, 'def': 'a piece of soft material that covers and protects an injured part of the body', 'name': 'bandage'}, {'frequency': 'f', 'synset': 'bandanna.n.01', 'synonyms': ['bandanna', 'bandana'], 'id': 48, 'def': 'large and brightly colored handkerchief; often used as a neckerchief', 'name': 'bandanna'}, {'frequency': 'r', 'synset': 'banjo.n.01', 'synonyms': ['banjo'], 'id': 49, 'def': 'a stringed instrument of the guitar family with a long neck and circular body', 'name': 'banjo'}, {'frequency': 'f', 'synset': 'banner.n.01', 'synonyms': ['banner', 'streamer'], 'id': 50, 'def': 'long strip of cloth or paper used for decoration or advertising', 'name': 'banner'}, {'frequency': 'r', 'synset': 'barbell.n.01', 'synonyms': ['barbell'], 'id': 51, 'def': 'a bar to which heavy discs are attached at each end; used in weightlifting', 'name': 'barbell'}, {'frequency': 'r', 'synset': 'barge.n.01', 'synonyms': ['barge'], 'id': 52, 'def': 'a flatbottom boat for carrying heavy loads (especially on canals)', 'name': 'barge'}, {'frequency': 'f', 'synset': 'barrel.n.02', 'synonyms': ['barrel', 'cask'], 'id': 53, 'def': 'a cylindrical container that holds liquids', 'name': 'barrel'}, {'frequency': 'c', 'synset': 'barrette.n.01', 'synonyms': ['barrette'], 'id': 54, 'def': "a pin for holding women's hair in place", 'name': 'barrette'}, {'frequency': 'c', 'synset': 'barrow.n.03', 'synonyms': ['barrow', 'garden_cart', 'lawn_cart', 'wheelbarrow'], 'id': 55, 'def': 'a cart for carrying small loads; has handles and one or more wheels', 'name': 'barrow'}, {'frequency': 'f', 'synset': 'base.n.03', 'synonyms': ['baseball_base'], 'id': 56, 'def': 'a place that the runner must touch before scoring', 'name': 'baseball_base'}, {'frequency': 'f', 'synset': 'baseball.n.02', 'synonyms': ['baseball'], 'id': 57, 'def': 'a ball used in playing baseball', 'name': 'baseball'}, {'frequency': 'f', 'synset': 'baseball_bat.n.01', 'synonyms': ['baseball_bat'], 'id': 58, 'def': 'an implement used in baseball by the batter', 'name': 'baseball_bat'}, {'frequency': 'f', 'synset': 'baseball_cap.n.01', 'synonyms': ['baseball_cap', 'jockey_cap', 'golf_cap'], 'id': 59, 'def': 'a cap with a bill', 'name': 'baseball_cap'}, {'frequency': 'f', 'synset': 'baseball_glove.n.01', 'synonyms': ['baseball_glove', 'baseball_mitt'], 'id': 60, 'def': 'the handwear used by fielders in playing baseball', 'name': 'baseball_glove'}, {'frequency': 'f', 'synset': 'basket.n.01', 'synonyms': ['basket', 'handbasket'], 'id': 61, 'def': 'a container that is usually woven and has handles', 'name': 'basket'}, {'frequency': 'c', 'synset': 'basketball.n.02', 'synonyms': ['basketball'], 'id': 62, 'def': 'an inflated ball used in playing basketball', 'name': 'basketball'}, {'frequency': 'r', 'synset': 'bass_horn.n.01', 'synonyms': ['bass_horn', 'sousaphone', 'tuba'], 'id': 63, 'def': 'the lowest brass wind instrument', 'name': 'bass_horn'}, {'frequency': 'c', 'synset': 'bat.n.01', 'synonyms': ['bat_(animal)'], 'id': 64, 'def': 'nocturnal mouselike mammal with forelimbs modified to form membranous wings', 'name': 'bat_(animal)'}, {'frequency': 'f', 'synset': 'bath_mat.n.01', 'synonyms': ['bath_mat'], 'id': 65, 'def': 'a heavy towel or mat to stand on while drying yourself after a bath', 'name': 'bath_mat'}, {'frequency': 'f', 'synset': 'bath_towel.n.01', 'synonyms': ['bath_towel'], 'id': 66, 'def': 'a large towel; to dry yourself after a bath', 'name': 'bath_towel'}, {'frequency': 'c', 'synset': 'bathrobe.n.01', 'synonyms': ['bathrobe'], 'id': 67, 'def': 'a loose-fitting robe of towelling; worn after a bath or swim', 'name': 'bathrobe'}, {'frequency': 'f', 'synset': 'bathtub.n.01', 'synonyms': ['bathtub', 'bathing_tub'], 'id': 68, 'def': 'a large open container that you fill with water and use to wash the body', 'name': 'bathtub'}, {'frequency': 'r', 'synset': 'batter.n.02', 'synonyms': ['batter_(food)'], 'id': 69, 'def': 'a liquid or semiliquid mixture, as of flour, eggs, and milk, used in cooking', 'name': 'batter_(food)'}, {'frequency': 'c', 'synset': 'battery.n.02', 'synonyms': ['battery'], 'id': 70, 'def': 'a portable device that produces electricity', 'name': 'battery'}, {'frequency': 'r', 'synset': 'beach_ball.n.01', 'synonyms': ['beachball'], 'id': 71, 'def': 'large and light ball; for play at the seaside', 'name': 'beachball'}, {'frequency': 'c', 'synset': 'bead.n.01', 'synonyms': ['bead'], 'id': 72, 'def': 'a small ball with a hole through the middle used for ornamentation, jewellery, etc.', 'name': 'bead'}, {'frequency': 'c', 'synset': 'bean_curd.n.01', 'synonyms': ['bean_curd', 'tofu'], 'id': 73, 'def': 'cheeselike food made of curdled soybean milk', 'name': 'bean_curd'}, {'frequency': 'c', 'synset': 'beanbag.n.01', 'synonyms': ['beanbag'], 'id': 74, 'def': 'a bag filled with dried beans or similar items; used in games or to sit on', 'name': 'beanbag'}, {'frequency': 'f', 'synset': 'beanie.n.01', 'synonyms': ['beanie', 'beany'], 'id': 75, 'def': 'a small skullcap; formerly worn by schoolboys and college freshmen', 'name': 'beanie'}, {'frequency': 'f', 'synset': 'bear.n.01', 'synonyms': ['bear'], 'id': 76, 'def': 'large carnivorous or omnivorous mammals with shaggy coats and claws', 'name': 'bear'}, {'frequency': 'f', 'synset': 'bed.n.01', 'synonyms': ['bed'], 'id': 77, 'def': 'a piece of furniture that provides a place to sleep', 'name': 'bed'}, {'frequency': 'r', 'synset': 'bedpan.n.01', 'synonyms': ['bedpan'], 'id': 78, 'def': 'a shallow vessel used by a bedridden patient for defecation and urination', 'name': 'bedpan'}, {'frequency': 'f', 'synset': 'bedspread.n.01', 'synonyms': ['bedspread', 'bedcover', 'bed_covering', 'counterpane', 'spread'], 'id': 79, 'def': 'decorative cover for a bed', 'name': 'bedspread'}, {'frequency': 'f', 'synset': 'beef.n.01', 'synonyms': ['cow'], 'id': 80, 'def': 'cattle/cow', 'name': 'cow'}, {'frequency': 'f', 'synset': 'beef.n.02', 'synonyms': ['beef_(food)', 'boeuf_(food)'], 'id': 81, 'def': 'meat from an adult domestic bovine', 'name': 'beef_(food)'}, {'frequency': 'r', 'synset': 'beeper.n.01', 'synonyms': ['beeper', 'pager'], 'id': 82, 'def': 'an device that beeps when the person carrying it is being paged', 'name': 'beeper'}, {'frequency': 'f', 'synset': 'beer_bottle.n.01', 'synonyms': ['beer_bottle'], 'id': 83, 'def': 'a bottle that holds beer', 'name': 'beer_bottle'}, {'frequency': 'c', 'synset': 'beer_can.n.01', 'synonyms': ['beer_can'], 'id': 84, 'def': 'a can that holds beer', 'name': 'beer_can'}, {'frequency': 'r', 'synset': 'beetle.n.01', 'synonyms': ['beetle'], 'id': 85, 'def': 'insect with hard wing covers', 'name': 'beetle'}, {'frequency': 'f', 'synset': 'bell.n.01', 'synonyms': ['bell'], 'id': 86, 'def': 'a hollow device made of metal that makes a ringing sound when struck', 'name': 'bell'}, {'frequency': 'f', 'synset': 'bell_pepper.n.02', 'synonyms': ['bell_pepper', 'capsicum'], 'id': 87, 'def': 'large bell-shaped sweet pepper in green or red or yellow or orange or black varieties', 'name': 'bell_pepper'}, {'frequency': 'f', 'synset': 'belt.n.02', 'synonyms': ['belt'], 'id': 88, 'def': 'a band to tie or buckle around the body (usually at the waist)', 'name': 'belt'}, {'frequency': 'f', 'synset': 'belt_buckle.n.01', 'synonyms': ['belt_buckle'], 'id': 89, 'def': 'the buckle used to fasten a belt', 'name': 'belt_buckle'}, {'frequency': 'f', 'synset': 'bench.n.01', 'synonyms': ['bench'], 'id': 90, 'def': 'a long seat for more than one person', 'name': 'bench'}, {'frequency': 'c', 'synset': 'beret.n.01', 'synonyms': ['beret'], 'id': 91, 'def': 'a cap with no brim or bill; made of soft cloth', 'name': 'beret'}, {'frequency': 'c', 'synset': 'bib.n.02', 'synonyms': ['bib'], 'id': 92, 'def': 'a napkin tied under the chin of a child while eating', 'name': 'bib'}, {'frequency': 'r', 'synset': 'bible.n.01', 'synonyms': ['Bible'], 'id': 93, 'def': 'the sacred writings of the Christian religions', 'name': 'Bible'}, {'frequency': 'f', 'synset': 'bicycle.n.01', 'synonyms': ['bicycle', 'bike_(bicycle)'], 'id': 94, 'def': 'a wheeled vehicle that has two wheels and is moved by foot pedals', 'name': 'bicycle'}, {'frequency': 'f', 'synset': 'bill.n.09', 'synonyms': ['visor', 'vizor'], 'id': 95, 'def': 'a brim that projects to the front to shade the eyes', 'name': 'visor'}, {'frequency': 'f', 'synset': 'billboard.n.01', 'synonyms': ['billboard'], 'id': 96, 'def': 'large outdoor signboard', 'name': 'billboard'}, {'frequency': 'c', 'synset': 'binder.n.03', 'synonyms': ['binder', 'ring-binder'], 'id': 97, 'def': 'holds loose papers or magazines', 'name': 'binder'}, {'frequency': 'c', 'synset': 'binoculars.n.01', 'synonyms': ['binoculars', 'field_glasses', 'opera_glasses'], 'id': 98, 'def': 'an optical instrument designed for simultaneous use by both eyes', 'name': 'binoculars'}, {'frequency': 'f', 'synset': 'bird.n.01', 'synonyms': ['bird'], 'id': 99, 'def': 'animal characterized by feathers and wings', 'name': 'bird'}, {'frequency': 'c', 'synset': 'bird_feeder.n.01', 'synonyms': ['birdfeeder'], 'id': 100, 'def': 'an outdoor device that supplies food for wild birds', 'name': 'birdfeeder'}, {'frequency': 'c', 'synset': 'birdbath.n.01', 'synonyms': ['birdbath'], 'id': 101, 'def': 'an ornamental basin (usually in a garden) for birds to bathe in', 'name': 'birdbath'}, {'frequency': 'c', 'synset': 'birdcage.n.01', 'synonyms': ['birdcage'], 'id': 102, 'def': 'a cage in which a bird can be kept', 'name': 'birdcage'}, {'frequency': 'c', 'synset': 'birdhouse.n.01', 'synonyms': ['birdhouse'], 'id': 103, 'def': 'a shelter for birds', 'name': 'birdhouse'}, {'frequency': 'f', 'synset': 'birthday_cake.n.01', 'synonyms': ['birthday_cake'], 'id': 104, 'def': 'decorated cake served at a birthday party', 'name': 'birthday_cake'}, {'frequency': 'r', 'synset': 'birthday_card.n.01', 'synonyms': ['birthday_card'], 'id': 105, 'def': 'a card expressing a birthday greeting', 'name': 'birthday_card'}, {'frequency': 'r', 'synset': 'black_flag.n.01', 'synonyms': ['pirate_flag'], 'id': 106, 'def': 'a flag usually bearing a white skull and crossbones on a black background', 'name': 'pirate_flag'}, {'frequency': 'c', 'synset': 'black_sheep.n.02', 'synonyms': ['black_sheep'], 'id': 107, 'def': 'sheep with a black coat', 'name': 'black_sheep'}, {'frequency': 'c', 'synset': 'blackberry.n.01', 'synonyms': ['blackberry'], 'id': 108, 'def': 'large sweet black or very dark purple edible aggregate fruit', 'name': 'blackberry'}, {'frequency': 'f', 'synset': 'blackboard.n.01', 'synonyms': ['blackboard', 'chalkboard'], 'id': 109, 'def': 'sheet of slate; for writing with chalk', 'name': 'blackboard'}, {'frequency': 'f', 'synset': 'blanket.n.01', 'synonyms': ['blanket'], 'id': 110, 'def': 'bedding that keeps a person warm in bed', 'name': 'blanket'}, {'frequency': 'c', 'synset': 'blazer.n.01', 'synonyms': ['blazer', 'sport_jacket', 'sport_coat', 'sports_jacket', 'sports_coat'], 'id': 111, 'def': 'lightweight jacket; often striped in the colors of a club or school', 'name': 'blazer'}, {'frequency': 'f', 'synset': 'blender.n.01', 'synonyms': ['blender', 'liquidizer', 'liquidiser'], 'id': 112, 'def': 'an electrically powered mixer that mix or chop or liquefy foods', 'name': 'blender'}, {'frequency': 'r', 'synset': 'blimp.n.02', 'synonyms': ['blimp'], 'id': 113, 'def': 'a small nonrigid airship used for observation or as a barrage balloon', 'name': 'blimp'}, {'frequency': 'f', 'synset': 'blinker.n.01', 'synonyms': ['blinker', 'flasher'], 'id': 114, 'def': 'a light that flashes on and off; used as a signal or to send messages', 'name': 'blinker'}, {'frequency': 'f', 'synset': 'blouse.n.01', 'synonyms': ['blouse'], 'id': 115, 'def': 'a top worn by women', 'name': 'blouse'}, {'frequency': 'f', 'synset': 'blueberry.n.02', 'synonyms': ['blueberry'], 'id': 116, 'def': 'sweet edible dark-blue berries of blueberry plants', 'name': 'blueberry'}, {'frequency': 'r', 'synset': 'board.n.09', 'synonyms': ['gameboard'], 'id': 117, 'def': 'a flat portable surface (usually rectangular) designed for board games', 'name': 'gameboard'}, {'frequency': 'f', 'synset': 'boat.n.01', 'synonyms': ['boat', 'ship_(boat)'], 'id': 118, 'def': 'a vessel for travel on water', 'name': 'boat'}, {'frequency': 'r', 'synset': 'bob.n.05', 'synonyms': ['bob', 'bobber', 'bobfloat'], 'id': 119, 'def': 'a small float usually made of cork; attached to a fishing line', 'name': 'bob'}, {'frequency': 'c', 'synset': 'bobbin.n.01', 'synonyms': ['bobbin', 'spool', 'reel'], 'id': 120, 'def': 'a thing around which thread/tape/film or other flexible materials can be wound', 'name': 'bobbin'}, {'frequency': 'c', 'synset': 'bobby_pin.n.01', 'synonyms': ['bobby_pin', 'hairgrip'], 'id': 121, 'def': 'a flat wire hairpin used to hold bobbed hair in place', 'name': 'bobby_pin'}, {'frequency': 'c', 'synset': 'boiled_egg.n.01', 'synonyms': ['boiled_egg', 'coddled_egg'], 'id': 122, 'def': 'egg cooked briefly in the shell in gently boiling water', 'name': 'boiled_egg'}, {'frequency': 'r', 'synset': 'bolo_tie.n.01', 'synonyms': ['bolo_tie', 'bolo', 'bola_tie', 'bola'], 'id': 123, 'def': 'a cord fastened around the neck with an ornamental clasp and worn as a necktie', 'name': 'bolo_tie'}, {'frequency': 'c', 'synset': 'bolt.n.03', 'synonyms': ['deadbolt'], 'id': 124, 'def': 'the part of a lock that is engaged or withdrawn with a key', 'name': 'deadbolt'}, {'frequency': 'f', 'synset': 'bolt.n.06', 'synonyms': ['bolt'], 'id': 125, 'def': 'a screw that screws into a nut to form a fastener', 'name': 'bolt'}, {'frequency': 'r', 'synset': 'bonnet.n.01', 'synonyms': ['bonnet'], 'id': 126, 'def': 'a hat tied under the chin', 'name': 'bonnet'}, {'frequency': 'f', 'synset': 'book.n.01', 'synonyms': ['book'], 'id': 127, 'def': 'a written work or composition that has been published', 'name': 'book'}, {'frequency': 'c', 'synset': 'bookcase.n.01', 'synonyms': ['bookcase'], 'id': 128, 'def': 'a piece of furniture with shelves for storing books', 'name': 'bookcase'}, {'frequency': 'c', 'synset': 'booklet.n.01', 'synonyms': ['booklet', 'brochure', 'leaflet', 'pamphlet'], 'id': 129, 'def': 'a small book usually having a paper cover', 'name': 'booklet'}, {'frequency': 'r', 'synset': 'bookmark.n.01', 'synonyms': ['bookmark', 'bookmarker'], 'id': 130, 'def': 'a marker (a piece of paper or ribbon) placed between the pages of a book', 'name': 'bookmark'}, {'frequency': 'r', 'synset': 'boom.n.04', 'synonyms': ['boom_microphone', 'microphone_boom'], 'id': 131, 'def': 'a pole carrying an overhead microphone projected over a film or tv set', 'name': 'boom_microphone'}, {'frequency': 'f', 'synset': 'boot.n.01', 'synonyms': ['boot'], 'id': 132, 'def': 'footwear that covers the whole foot and lower leg', 'name': 'boot'}, {'frequency': 'f', 'synset': 'bottle.n.01', 'synonyms': ['bottle'], 'id': 133, 'def': 'a glass or plastic vessel used for storing drinks or other liquids', 'name': 'bottle'}, {'frequency': 'c', 'synset': 'bottle_opener.n.01', 'synonyms': ['bottle_opener'], 'id': 134, 'def': 'an opener for removing caps or corks from bottles', 'name': 'bottle_opener'}, {'frequency': 'c', 'synset': 'bouquet.n.01', 'synonyms': ['bouquet'], 'id': 135, 'def': 'an arrangement of flowers that is usually given as a present', 'name': 'bouquet'}, {'frequency': 'r', 'synset': 'bow.n.04', 'synonyms': ['bow_(weapon)'], 'id': 136, 'def': 'a weapon for shooting arrows', 'name': 'bow_(weapon)'}, {'frequency': 'f', 'synset': 'bow.n.08', 'synonyms': ['bow_(decorative_ribbons)'], 'id': 137, 'def': 'a decorative interlacing of ribbons', 'name': 'bow_(decorative_ribbons)'}, {'frequency': 'f', 'synset': 'bow_tie.n.01', 'synonyms': ['bow-tie', 'bowtie'], 'id': 138, 'def': "a man's tie that ties in a bow", 'name': 'bow-tie'}, {'frequency': 'f', 'synset': 'bowl.n.03', 'synonyms': ['bowl'], 'id': 139, 'def': 'a dish that is round and open at the top for serving foods', 'name': 'bowl'}, {'frequency': 'r', 'synset': 'bowl.n.08', 'synonyms': ['pipe_bowl'], 'id': 140, 'def': 'a small round container that is open at the top for holding tobacco', 'name': 'pipe_bowl'}, {'frequency': 'c', 'synset': 'bowler_hat.n.01', 'synonyms': ['bowler_hat', 'bowler', 'derby_hat', 'derby', 'plug_hat'], 'id': 141, 'def': 'a felt hat that is round and hard with a narrow brim', 'name': 'bowler_hat'}, {'frequency': 'r', 'synset': 'bowling_ball.n.01', 'synonyms': ['bowling_ball'], 'id': 142, 'def': 'a large ball with finger holes used in the sport of bowling', 'name': 'bowling_ball'}, {'frequency': 'f', 'synset': 'box.n.01', 'synonyms': ['box'], 'id': 143, 'def': 'a (usually rectangular) container; may have a lid', 'name': 'box'}, {'frequency': 'r', 'synset': 'boxing_glove.n.01', 'synonyms': ['boxing_glove'], 'id': 144, 'def': 'large glove coverings the fists of a fighter worn for the sport of boxing', 'name': 'boxing_glove'}, {'frequency': 'c', 'synset': 'brace.n.06', 'synonyms': ['suspenders'], 'id': 145, 'def': 'elastic straps that hold trousers up (usually used in the plural)', 'name': 'suspenders'}, {'frequency': 'f', 'synset': 'bracelet.n.02', 'synonyms': ['bracelet', 'bangle'], 'id': 146, 'def': 'jewelry worn around the wrist for decoration', 'name': 'bracelet'}, {'frequency': 'r', 'synset': 'brass.n.07', 'synonyms': ['brass_plaque'], 'id': 147, 'def': 'a memorial made of brass', 'name': 'brass_plaque'}, {'frequency': 'c', 'synset': 'brassiere.n.01', 'synonyms': ['brassiere', 'bra', 'bandeau'], 'id': 148, 'def': 'an undergarment worn by women to support their breasts', 'name': 'brassiere'}, {'frequency': 'c', 'synset': 'bread-bin.n.01', 'synonyms': ['bread-bin', 'breadbox'], 'id': 149, 'def': 'a container used to keep bread or cake in', 'name': 'bread-bin'}, {'frequency': 'f', 'synset': 'bread.n.01', 'synonyms': ['bread'], 'id': 150, 'def': 'food made from dough of flour or meal and usually raised with yeast or baking powder and then baked', 'name': 'bread'}, {'frequency': 'r', 'synset': 'breechcloth.n.01', 'synonyms': ['breechcloth', 'breechclout', 'loincloth'], 'id': 151, 'def': 'a garment that provides covering for the loins', 'name': 'breechcloth'}, {'frequency': 'f', 'synset': 'bridal_gown.n.01', 'synonyms': ['bridal_gown', 'wedding_gown', 'wedding_dress'], 'id': 152, 'def': 'a gown worn by the bride at a wedding', 'name': 'bridal_gown'}, {'frequency': 'c', 'synset': 'briefcase.n.01', 'synonyms': ['briefcase'], 'id': 153, 'def': 'a case with a handle; for carrying papers or files or books', 'name': 'briefcase'}, {'frequency': 'f', 'synset': 'broccoli.n.01', 'synonyms': ['broccoli'], 'id': 154, 'def': 'plant with dense clusters of tight green flower buds', 'name': 'broccoli'}, {'frequency': 'r', 'synset': 'brooch.n.01', 'synonyms': ['broach'], 'id': 155, 'def': 'a decorative pin worn by women', 'name': 'broach'}, {'frequency': 'c', 'synset': 'broom.n.01', 'synonyms': ['broom'], 'id': 156, 'def': 'bundle of straws or twigs attached to a long handle; used for cleaning', 'name': 'broom'}, {'frequency': 'c', 'synset': 'brownie.n.03', 'synonyms': ['brownie'], 'id': 157, 'def': 'square or bar of very rich chocolate cake usually with nuts', 'name': 'brownie'}, {'frequency': 'c', 'synset': 'brussels_sprouts.n.01', 'synonyms': ['brussels_sprouts'], 'id': 158, 'def': 'the small edible cabbage-like buds growing along a stalk', 'name': 'brussels_sprouts'}, {'frequency': 'r', 'synset': 'bubble_gum.n.01', 'synonyms': ['bubble_gum'], 'id': 159, 'def': 'a kind of chewing gum that can be blown into bubbles', 'name': 'bubble_gum'}, {'frequency': 'f', 'synset': 'bucket.n.01', 'synonyms': ['bucket', 'pail'], 'id': 160, 'def': 'a roughly cylindrical vessel that is open at the top', 'name': 'bucket'}, {'frequency': 'r', 'synset': 'buggy.n.01', 'synonyms': ['horse_buggy'], 'id': 161, 'def': 'a small lightweight carriage; drawn by a single horse', 'name': 'horse_buggy'}, {'frequency': 'c', 'synset': 'bull.n.11', 'synonyms': ['horned_cow'], 'id': 162, 'def': 'a cow with horns', 'name': 'bull'}, {'frequency': 'c', 'synset': 'bulldog.n.01', 'synonyms': ['bulldog'], 'id': 163, 'def': 'a thickset short-haired dog with a large head and strong undershot lower jaw', 'name': 'bulldog'}, {'frequency': 'r', 'synset': 'bulldozer.n.01', 'synonyms': ['bulldozer', 'dozer'], 'id': 164, 'def': 'large powerful tractor; a large blade in front flattens areas of ground', 'name': 'bulldozer'}, {'frequency': 'c', 'synset': 'bullet_train.n.01', 'synonyms': ['bullet_train'], 'id': 165, 'def': 'a high-speed passenger train', 'name': 'bullet_train'}, {'frequency': 'c', 'synset': 'bulletin_board.n.02', 'synonyms': ['bulletin_board', 'notice_board'], 'id': 166, 'def': 'a board that hangs on a wall; displays announcements', 'name': 'bulletin_board'}, {'frequency': 'r', 'synset': 'bulletproof_vest.n.01', 'synonyms': ['bulletproof_vest'], 'id': 167, 'def': 'a vest capable of resisting the impact of a bullet', 'name': 'bulletproof_vest'}, {'frequency': 'c', 'synset': 'bullhorn.n.01', 'synonyms': ['bullhorn', 'megaphone'], 'id': 168, 'def': 'a portable loudspeaker with built-in microphone and amplifier', 'name': 'bullhorn'}, {'frequency': 'f', 'synset': 'bun.n.01', 'synonyms': ['bun', 'roll'], 'id': 169, 'def': 'small rounded bread either plain or sweet', 'name': 'bun'}, {'frequency': 'c', 'synset': 'bunk_bed.n.01', 'synonyms': ['bunk_bed'], 'id': 170, 'def': 'beds built one above the other', 'name': 'bunk_bed'}, {'frequency': 'f', 'synset': 'buoy.n.01', 'synonyms': ['buoy'], 'id': 171, 'def': 'a float attached by rope to the seabed to mark channels in a harbor or underwater hazards', 'name': 'buoy'}, {'frequency': 'r', 'synset': 'burrito.n.01', 'synonyms': ['burrito'], 'id': 172, 'def': 'a flour tortilla folded around a filling', 'name': 'burrito'}, {'frequency': 'f', 'synset': 'bus.n.01', 'synonyms': ['bus_(vehicle)', 'autobus', 'charabanc', 'double-decker', 'motorbus', 'motorcoach'], 'id': 173, 'def': 'a vehicle carrying many passengers; used for public transport', 'name': 'bus_(vehicle)'}, {'frequency': 'c', 'synset': 'business_card.n.01', 'synonyms': ['business_card'], 'id': 174, 'def': "a card on which are printed the person's name and business affiliation", 'name': 'business_card'}, {'frequency': 'f', 'synset': 'butter.n.01', 'synonyms': ['butter'], 'id': 175, 'def': 'an edible emulsion of fat globules made by churning milk or cream; for cooking and table use', 'name': 'butter'}, {'frequency': 'c', 'synset': 'butterfly.n.01', 'synonyms': ['butterfly'], 'id': 176, 'def': 'insect typically having a slender body with knobbed antennae and broad colorful wings', 'name': 'butterfly'}, {'frequency': 'f', 'synset': 'button.n.01', 'synonyms': ['button'], 'id': 177, 'def': 'a round fastener sewn to shirts and coats etc to fit through buttonholes', 'name': 'button'}, {'frequency': 'f', 'synset': 'cab.n.03', 'synonyms': ['cab_(taxi)', 'taxi', 'taxicab'], 'id': 178, 'def': 'a car that takes passengers where they want to go in exchange for money', 'name': 'cab_(taxi)'}, {'frequency': 'r', 'synset': 'cabana.n.01', 'synonyms': ['cabana'], 'id': 179, 'def': 'a small tent used as a dressing room beside the sea or a swimming pool', 'name': 'cabana'}, {'frequency': 'c', 'synset': 'cabin_car.n.01', 'synonyms': ['cabin_car', 'caboose'], 'id': 180, 'def': 'a car on a freight train for use of the train crew; usually the last car on the train', 'name': 'cabin_car'}, {'frequency': 'f', 'synset': 'cabinet.n.01', 'synonyms': ['cabinet'], 'id': 181, 'def': 'a piece of furniture resembling a cupboard with doors and shelves and drawers', 'name': 'cabinet'}, {'frequency': 'r', 'synset': 'cabinet.n.03', 'synonyms': ['locker', 'storage_locker'], 'id': 182, 'def': 'a storage compartment for clothes and valuables; usually it has a lock', 'name': 'locker'}, {'frequency': 'f', 'synset': 'cake.n.03', 'synonyms': ['cake'], 'id': 183, 'def': 'baked goods made from or based on a mixture of flour, sugar, eggs, and fat', 'name': 'cake'}, {'frequency': 'c', 'synset': 'calculator.n.02', 'synonyms': ['calculator'], 'id': 184, 'def': 'a small machine that is used for mathematical calculations', 'name': 'calculator'}, {'frequency': 'f', 'synset': 'calendar.n.02', 'synonyms': ['calendar'], 'id': 185, 'def': 'a list or register of events (appointments/social events/court cases, etc)', 'name': 'calendar'}, {'frequency': 'c', 'synset': 'calf.n.01', 'synonyms': ['calf'], 'id': 186, 'def': 'young of domestic cattle', 'name': 'calf'}, {'frequency': 'c', 'synset': 'camcorder.n.01', 'synonyms': ['camcorder'], 'id': 187, 'def': 'a portable television camera and videocassette recorder', 'name': 'camcorder'}, {'frequency': 'c', 'synset': 'camel.n.01', 'synonyms': ['camel'], 'id': 188, 'def': 'cud-chewing mammal used as a draft or saddle animal in desert regions', 'name': 'camel'}, {'frequency': 'f', 'synset': 'camera.n.01', 'synonyms': ['camera'], 'id': 189, 'def': 'equipment for taking photographs', 'name': 'camera'}, {'frequency': 'c', 'synset': 'camera_lens.n.01', 'synonyms': ['camera_lens'], 'id': 190, 'def': 'a lens that focuses the image in a camera', 'name': 'camera_lens'}, {'frequency': 'c', 'synset': 'camper.n.02', 'synonyms': ['camper_(vehicle)', 'camping_bus', 'motor_home'], 'id': 191, 'def': 'a recreational vehicle equipped for camping out while traveling', 'name': 'camper_(vehicle)'}, {'frequency': 'f', 'synset': 'can.n.01', 'synonyms': ['can', 'tin_can'], 'id': 192, 'def': 'airtight sealed metal container for food or drink or paint etc.', 'name': 'can'}, {'frequency': 'c', 'synset': 'can_opener.n.01', 'synonyms': ['can_opener', 'tin_opener'], 'id': 193, 'def': 'a device for cutting cans open', 'name': 'can_opener'}, {'frequency': 'f', 'synset': 'candle.n.01', 'synonyms': ['candle', 'candlestick'], 'id': 194, 'def': 'stick of wax with a wick in the middle', 'name': 'candle'}, {'frequency': 'f', 'synset': 'candlestick.n.01', 'synonyms': ['candle_holder'], 'id': 195, 'def': 'a holder with sockets for candles', 'name': 'candle_holder'}, {'frequency': 'r', 'synset': 'candy_bar.n.01', 'synonyms': ['candy_bar'], 'id': 196, 'def': 'a candy shaped as a bar', 'name': 'candy_bar'}, {'frequency': 'c', 'synset': 'candy_cane.n.01', 'synonyms': ['candy_cane'], 'id': 197, 'def': 'a hard candy in the shape of a rod (usually with stripes)', 'name': 'candy_cane'}, {'frequency': 'c', 'synset': 'cane.n.01', 'synonyms': ['walking_cane'], 'id': 198, 'def': 'a stick that people can lean on to help them walk', 'name': 'walking_cane'}, {'frequency': 'c', 'synset': 'canister.n.02', 'synonyms': ['canister', 'cannister'], 'id': 199, 'def': 'metal container for storing dry foods such as tea or flour', 'name': 'canister'}, {'frequency': 'c', 'synset': 'canoe.n.01', 'synonyms': ['canoe'], 'id': 200, 'def': 'small and light boat; pointed at both ends; propelled with a paddle', 'name': 'canoe'}, {'frequency': 'c', 'synset': 'cantaloup.n.02', 'synonyms': ['cantaloup', 'cantaloupe'], 'id': 201, 'def': 'the fruit of a cantaloup vine; small to medium-sized melon with yellowish flesh', 'name': 'cantaloup'}, {'frequency': 'r', 'synset': 'canteen.n.01', 'synonyms': ['canteen'], 'id': 202, 'def': 'a flask for carrying water; used by soldiers or travelers', 'name': 'canteen'}, {'frequency': 'f', 'synset': 'cap.n.01', 'synonyms': ['cap_(headwear)'], 'id': 203, 'def': 'a tight-fitting headwear', 'name': 'cap_(headwear)'}, {'frequency': 'f', 'synset': 'cap.n.02', 'synonyms': ['bottle_cap', 'cap_(container_lid)'], 'id': 204, 'def': 'a top (as for a bottle)', 'name': 'bottle_cap'}, {'frequency': 'c', 'synset': 'cape.n.02', 'synonyms': ['cape'], 'id': 205, 'def': 'a sleeveless garment like a cloak but shorter', 'name': 'cape'}, {'frequency': 'c', 'synset': 'cappuccino.n.01', 'synonyms': ['cappuccino', 'coffee_cappuccino'], 'id': 206, 'def': 'equal parts of espresso and steamed milk', 'name': 'cappuccino'}, {'frequency': 'f', 'synset': 'car.n.01', 'synonyms': ['car_(automobile)', 'auto_(automobile)', 'automobile'], 'id': 207, 'def': 'a motor vehicle with four wheels', 'name': 'car_(automobile)'}, {'frequency': 'f', 'synset': 'car.n.02', 'synonyms': ['railcar_(part_of_a_train)', 'railway_car_(part_of_a_train)', 'railroad_car_(part_of_a_train)'], 'id': 208, 'def': 'a wheeled vehicle adapted to the rails of railroad (mark each individual railcar separately)', 'name': 'railcar_(part_of_a_train)'}, {'frequency': 'r', 'synset': 'car.n.04', 'synonyms': ['elevator_car'], 'id': 209, 'def': 'where passengers ride up and down', 'name': 'elevator_car'}, {'frequency': 'r', 'synset': 'car_battery.n.01', 'synonyms': ['car_battery', 'automobile_battery'], 'id': 210, 'def': 'a battery in a motor vehicle', 'name': 'car_battery'}, {'frequency': 'c', 'synset': 'card.n.02', 'synonyms': ['identity_card'], 'id': 211, 'def': 'a card certifying the identity of the bearer', 'name': 'identity_card'}, {'frequency': 'c', 'synset': 'card.n.03', 'synonyms': ['card'], 'id': 212, 'def': 'a rectangular piece of paper used to send messages (e.g. greetings or pictures)', 'name': 'card'}, {'frequency': 'c', 'synset': 'cardigan.n.01', 'synonyms': ['cardigan'], 'id': 213, 'def': 'knitted jacket that is fastened up the front with buttons or a zipper', 'name': 'cardigan'}, {'frequency': 'r', 'synset': 'cargo_ship.n.01', 'synonyms': ['cargo_ship', 'cargo_vessel'], 'id': 214, 'def': 'a ship designed to carry cargo', 'name': 'cargo_ship'}, {'frequency': 'r', 'synset': 'carnation.n.01', 'synonyms': ['carnation'], 'id': 215, 'def': 'plant with pink to purple-red spice-scented usually double flowers', 'name': 'carnation'}, {'frequency': 'c', 'synset': 'carriage.n.02', 'synonyms': ['horse_carriage'], 'id': 216, 'def': 'a vehicle with wheels drawn by one or more horses', 'name': 'horse_carriage'}, {'frequency': 'f', 'synset': 'carrot.n.01', 'synonyms': ['carrot'], 'id': 217, 'def': 'deep orange edible root of the cultivated carrot plant', 'name': 'carrot'}, {'frequency': 'f', 'synset': 'carryall.n.01', 'synonyms': ['tote_bag'], 'id': 218, 'def': 'a capacious bag or basket', 'name': 'tote_bag'}, {'frequency': 'c', 'synset': 'cart.n.01', 'synonyms': ['cart'], 'id': 219, 'def': 'a heavy open wagon usually having two wheels and drawn by an animal', 'name': 'cart'}, {'frequency': 'c', 'synset': 'carton.n.02', 'synonyms': ['carton'], 'id': 220, 'def': 'a container made of cardboard for holding food or drink', 'name': 'carton'}, {'frequency': 'c', 'synset': 'cash_register.n.01', 'synonyms': ['cash_register', 'register_(for_cash_transactions)'], 'id': 221, 'def': 'a cashbox with an adding machine to register transactions', 'name': 'cash_register'}, {'frequency': 'r', 'synset': 'casserole.n.01', 'synonyms': ['casserole'], 'id': 222, 'def': 'food cooked and served in a casserole', 'name': 'casserole'}, {'frequency': 'r', 'synset': 'cassette.n.01', 'synonyms': ['cassette'], 'id': 223, 'def': 'a container that holds a magnetic tape used for recording or playing sound or video', 'name': 'cassette'}, {'frequency': 'c', 'synset': 'cast.n.05', 'synonyms': ['cast', 'plaster_cast', 'plaster_bandage'], 'id': 224, 'def': 'bandage consisting of a firm covering that immobilizes broken bones while they heal', 'name': 'cast'}, {'frequency': 'f', 'synset': 'cat.n.01', 'synonyms': ['cat'], 'id': 225, 'def': 'a domestic house cat', 'name': 'cat'}, {'frequency': 'f', 'synset': 'cauliflower.n.02', 'synonyms': ['cauliflower'], 'id': 226, 'def': 'edible compact head of white undeveloped flowers', 'name': 'cauliflower'}, {'frequency': 'c', 'synset': 'cayenne.n.02', 'synonyms': ['cayenne_(spice)', 'cayenne_pepper_(spice)', 'red_pepper_(spice)'], 'id': 227, 'def': 'ground pods and seeds of pungent red peppers of the genus Capsicum', 'name': 'cayenne_(spice)'}, {'frequency': 'c', 'synset': 'cd_player.n.01', 'synonyms': ['CD_player'], 'id': 228, 'def': 'electronic equipment for playing compact discs (CDs)', 'name': 'CD_player'}, {'frequency': 'f', 'synset': 'celery.n.01', 'synonyms': ['celery'], 'id': 229, 'def': 'widely cultivated herb with aromatic leaf stalks that are eaten raw or cooked', 'name': 'celery'}, {'frequency': 'f', 'synset': 'cellular_telephone.n.01', 'synonyms': ['cellular_telephone', 'cellular_phone', 'cellphone', 'mobile_phone', 'smart_phone'], 'id': 230, 'def': 'a hand-held mobile telephone', 'name': 'cellular_telephone'}, {'frequency': 'r', 'synset': 'chain_mail.n.01', 'synonyms': ['chain_mail', 'ring_mail', 'chain_armor', 'chain_armour', 'ring_armor', 'ring_armour'], 'id': 231, 'def': '(Middle Ages) flexible armor made of interlinked metal rings', 'name': 'chain_mail'}, {'frequency': 'f', 'synset': 'chair.n.01', 'synonyms': ['chair'], 'id': 232, 'def': 'a seat for one person, with a support for the back', 'name': 'chair'}, {'frequency': 'r', 'synset': 'chaise_longue.n.01', 'synonyms': ['chaise_longue', 'chaise', 'daybed'], 'id': 233, 'def': 'a long chair; for reclining', 'name': 'chaise_longue'}, {'frequency': 'r', 'synset': 'chalice.n.01', 'synonyms': ['chalice'], 'id': 234, 'def': 'a bowl-shaped drinking vessel; especially the Eucharistic cup', 'name': 'chalice'}, {'frequency': 'f', 'synset': 'chandelier.n.01', 'synonyms': ['chandelier'], 'id': 235, 'def': 'branched lighting fixture; often ornate; hangs from the ceiling', 'name': 'chandelier'}, {'frequency': 'r', 'synset': 'chap.n.04', 'synonyms': ['chap'], 'id': 236, 'def': 'leather leggings without a seat; worn over trousers by cowboys to protect their legs', 'name': 'chap'}, {'frequency': 'r', 'synset': 'checkbook.n.01', 'synonyms': ['checkbook', 'chequebook'], 'id': 237, 'def': 'a book issued to holders of checking accounts', 'name': 'checkbook'}, {'frequency': 'r', 'synset': 'checkerboard.n.01', 'synonyms': ['checkerboard'], 'id': 238, 'def': 'a board having 64 squares of two alternating colors', 'name': 'checkerboard'}, {'frequency': 'c', 'synset': 'cherry.n.03', 'synonyms': ['cherry'], 'id': 239, 'def': 'a red fruit with a single hard stone', 'name': 'cherry'}, {'frequency': 'r', 'synset': 'chessboard.n.01', 'synonyms': ['chessboard'], 'id': 240, 'def': 'a checkerboard used to play chess', 'name': 'chessboard'}, {'frequency': 'c', 'synset': 'chicken.n.02', 'synonyms': ['chicken_(animal)'], 'id': 241, 'def': 'a domestic fowl bred for flesh or eggs', 'name': 'chicken_(animal)'}, {'frequency': 'c', 'synset': 'chickpea.n.01', 'synonyms': ['chickpea', 'garbanzo'], 'id': 242, 'def': 'the seed of the chickpea plant; usually dried', 'name': 'chickpea'}, {'frequency': 'c', 'synset': 'chili.n.02', 'synonyms': ['chili_(vegetable)', 'chili_pepper_(vegetable)', 'chilli_(vegetable)', 'chilly_(vegetable)', 'chile_(vegetable)'], 'id': 243, 'def': 'very hot and finely tapering pepper of special pungency', 'name': 'chili_(vegetable)'}, {'frequency': 'r', 'synset': 'chime.n.01', 'synonyms': ['chime', 'gong'], 'id': 244, 'def': 'an instrument consisting of a set of bells that are struck with a hammer', 'name': 'chime'}, {'frequency': 'r', 'synset': 'chinaware.n.01', 'synonyms': ['chinaware'], 'id': 245, 'def': 'dishware made of high quality porcelain', 'name': 'chinaware'}, {'frequency': 'c', 'synset': 'chip.n.04', 'synonyms': ['crisp_(potato_chip)', 'potato_chip'], 'id': 246, 'def': 'a thin crisp slice of potato fried in deep fat', 'name': 'crisp_(potato_chip)'}, {'frequency': 'r', 'synset': 'chip.n.06', 'synonyms': ['poker_chip'], 'id': 247, 'def': 'a small disk-shaped counter used to represent money when gambling', 'name': 'poker_chip'}, {'frequency': 'c', 'synset': 'chocolate_bar.n.01', 'synonyms': ['chocolate_bar'], 'id': 248, 'def': 'a bar of chocolate candy', 'name': 'chocolate_bar'}, {'frequency': 'c', 'synset': 'chocolate_cake.n.01', 'synonyms': ['chocolate_cake'], 'id': 249, 'def': 'cake containing chocolate', 'name': 'chocolate_cake'}, {'frequency': 'r', 'synset': 'chocolate_milk.n.01', 'synonyms': ['chocolate_milk'], 'id': 250, 'def': 'milk flavored with chocolate syrup', 'name': 'chocolate_milk'}, {'frequency': 'r', 'synset': 'chocolate_mousse.n.01', 'synonyms': ['chocolate_mousse'], 'id': 251, 'def': 'dessert mousse made with chocolate', 'name': 'chocolate_mousse'}, {'frequency': 'f', 'synset': 'choker.n.03', 'synonyms': ['choker', 'collar', 'neckband'], 'id': 252, 'def': 'shirt collar, animal collar, or tight-fitting necklace', 'name': 'choker'}, {'frequency': 'f', 'synset': 'chopping_board.n.01', 'synonyms': ['chopping_board', 'cutting_board', 'chopping_block'], 'id': 253, 'def': 'a wooden board where meats or vegetables can be cut', 'name': 'chopping_board'}, {'frequency': 'f', 'synset': 'chopstick.n.01', 'synonyms': ['chopstick'], 'id': 254, 'def': 'one of a pair of slender sticks used as oriental tableware to eat food with', 'name': 'chopstick'}, {'frequency': 'f', 'synset': 'christmas_tree.n.05', 'synonyms': ['Christmas_tree'], 'id': 255, 'def': 'an ornamented evergreen used as a Christmas decoration', 'name': 'Christmas_tree'}, {'frequency': 'c', 'synset': 'chute.n.02', 'synonyms': ['slide'], 'id': 256, 'def': 'sloping channel through which things can descend', 'name': 'slide'}, {'frequency': 'r', 'synset': 'cider.n.01', 'synonyms': ['cider', 'cyder'], 'id': 257, 'def': 'a beverage made from juice pressed from apples', 'name': 'cider'}, {'frequency': 'r', 'synset': 'cigar_box.n.01', 'synonyms': ['cigar_box'], 'id': 258, 'def': 'a box for holding cigars', 'name': 'cigar_box'}, {'frequency': 'f', 'synset': 'cigarette.n.01', 'synonyms': ['cigarette'], 'id': 259, 'def': 'finely ground tobacco wrapped in paper; for smoking', 'name': 'cigarette'}, {'frequency': 'c', 'synset': 'cigarette_case.n.01', 'synonyms': ['cigarette_case', 'cigarette_pack'], 'id': 260, 'def': 'a small flat case for holding cigarettes', 'name': 'cigarette_case'}, {'frequency': 'f', 'synset': 'cistern.n.02', 'synonyms': ['cistern', 'water_tank'], 'id': 261, 'def': 'a tank that holds the water used to flush a toilet', 'name': 'cistern'}, {'frequency': 'r', 'synset': 'clarinet.n.01', 'synonyms': ['clarinet'], 'id': 262, 'def': 'a single-reed instrument with a straight tube', 'name': 'clarinet'}, {'frequency': 'c', 'synset': 'clasp.n.01', 'synonyms': ['clasp'], 'id': 263, 'def': 'a fastener (as a buckle or hook) that is used to hold two things together', 'name': 'clasp'}, {'frequency': 'c', 'synset': 'cleansing_agent.n.01', 'synonyms': ['cleansing_agent', 'cleanser', 'cleaner'], 'id': 264, 'def': 'a preparation used in cleaning something', 'name': 'cleansing_agent'}, {'frequency': 'r', 'synset': 'cleat.n.02', 'synonyms': ['cleat_(for_securing_rope)'], 'id': 265, 'def': 'a fastener (usually with two projecting horns) around which a rope can be secured', 'name': 'cleat_(for_securing_rope)'}, {'frequency': 'r', 'synset': 'clementine.n.01', 'synonyms': ['clementine'], 'id': 266, 'def': 'a variety of mandarin orange', 'name': 'clementine'}, {'frequency': 'c', 'synset': 'clip.n.03', 'synonyms': ['clip'], 'id': 267, 'def': 'any of various small fasteners used to hold loose articles together', 'name': 'clip'}, {'frequency': 'c', 'synset': 'clipboard.n.01', 'synonyms': ['clipboard'], 'id': 268, 'def': 'a small writing board with a clip at the top for holding papers', 'name': 'clipboard'}, {'frequency': 'r', 'synset': 'clipper.n.03', 'synonyms': ['clippers_(for_plants)'], 'id': 269, 'def': 'shears for cutting grass or shrubbery (often used in the plural)', 'name': 'clippers_(for_plants)'}, {'frequency': 'r', 'synset': 'cloak.n.02', 'synonyms': ['cloak'], 'id': 270, 'def': 'a loose outer garment', 'name': 'cloak'}, {'frequency': 'f', 'synset': 'clock.n.01', 'synonyms': ['clock', 'timepiece', 'timekeeper'], 'id': 271, 'def': 'a timepiece that shows the time of day', 'name': 'clock'}, {'frequency': 'f', 'synset': 'clock_tower.n.01', 'synonyms': ['clock_tower'], 'id': 272, 'def': 'a tower with a large clock visible high up on an outside face', 'name': 'clock_tower'}, {'frequency': 'c', 'synset': 'clothes_hamper.n.01', 'synonyms': ['clothes_hamper', 'laundry_basket', 'clothes_basket'], 'id': 273, 'def': 'a hamper that holds dirty clothes to be washed or wet clothes to be dried', 'name': 'clothes_hamper'}, {'frequency': 'c', 'synset': 'clothespin.n.01', 'synonyms': ['clothespin', 'clothes_peg'], 'id': 274, 'def': 'wood or plastic fastener; for holding clothes on a clothesline', 'name': 'clothespin'}, {'frequency': 'r', 'synset': 'clutch_bag.n.01', 'synonyms': ['clutch_bag'], 'id': 275, 'def': "a woman's strapless purse that is carried in the hand", 'name': 'clutch_bag'}, {'frequency': 'f', 'synset': 'coaster.n.03', 'synonyms': ['coaster'], 'id': 276, 'def': 'a covering (plate or mat) that protects the surface of a table', 'name': 'coaster'}, {'frequency': 'f', 'synset': 'coat.n.01', 'synonyms': ['coat'], 'id': 277, 'def': 'an outer garment that has sleeves and covers the body from shoulder down', 'name': 'coat'}, {'frequency': 'c', 'synset': 'coat_hanger.n.01', 'synonyms': ['coat_hanger', 'clothes_hanger', 'dress_hanger'], 'id': 278, 'def': "a hanger that is shaped like a person's shoulders", 'name': 'coat_hanger'}, {'frequency': 'c', 'synset': 'coatrack.n.01', 'synonyms': ['coatrack', 'hatrack'], 'id': 279, 'def': 'a rack with hooks for temporarily holding coats and hats', 'name': 'coatrack'}, {'frequency': 'c', 'synset': 'cock.n.04', 'synonyms': ['cock', 'rooster'], 'id': 280, 'def': 'adult male chicken', 'name': 'cock'}, {'frequency': 'r', 'synset': 'cockroach.n.01', 'synonyms': ['cockroach'], 'id': 281, 'def': 'any of numerous chiefly nocturnal insects; some are domestic pests', 'name': 'cockroach'}, {'frequency': 'r', 'synset': 'cocoa.n.01', 'synonyms': ['cocoa_(beverage)', 'hot_chocolate_(beverage)', 'drinking_chocolate'], 'id': 282, 'def': 'a beverage made from cocoa powder and milk and sugar; usually drunk hot', 'name': 'cocoa_(beverage)'}, {'frequency': 'c', 'synset': 'coconut.n.02', 'synonyms': ['coconut', 'cocoanut'], 'id': 283, 'def': 'large hard-shelled brown oval nut with a fibrous husk', 'name': 'coconut'}, {'frequency': 'f', 'synset': 'coffee_maker.n.01', 'synonyms': ['coffee_maker', 'coffee_machine'], 'id': 284, 'def': 'a kitchen appliance for brewing coffee automatically', 'name': 'coffee_maker'}, {'frequency': 'f', 'synset': 'coffee_table.n.01', 'synonyms': ['coffee_table', 'cocktail_table'], 'id': 285, 'def': 'low table where magazines can be placed and coffee or cocktails are served', 'name': 'coffee_table'}, {'frequency': 'c', 'synset': 'coffeepot.n.01', 'synonyms': ['coffeepot'], 'id': 286, 'def': 'tall pot in which coffee is brewed', 'name': 'coffeepot'}, {'frequency': 'r', 'synset': 'coil.n.05', 'synonyms': ['coil'], 'id': 287, 'def': 'tubing that is wound in a spiral', 'name': 'coil'}, {'frequency': 'c', 'synset': 'coin.n.01', 'synonyms': ['coin'], 'id': 288, 'def': 'a flat metal piece (usually a disc) used as money', 'name': 'coin'}, {'frequency': 'c', 'synset': 'colander.n.01', 'synonyms': ['colander', 'cullender'], 'id': 289, 'def': 'bowl-shaped strainer; used to wash or drain foods', 'name': 'colander'}, {'frequency': 'c', 'synset': 'coleslaw.n.01', 'synonyms': ['coleslaw', 'slaw'], 'id': 290, 'def': 'basically shredded cabbage', 'name': 'coleslaw'}, {'frequency': 'r', 'synset': 'coloring_material.n.01', 'synonyms': ['coloring_material', 'colouring_material'], 'id': 291, 'def': 'any material used for its color', 'name': 'coloring_material'}, {'frequency': 'r', 'synset': 'combination_lock.n.01', 'synonyms': ['combination_lock'], 'id': 292, 'def': 'lock that can be opened only by turning dials in a special sequence', 'name': 'combination_lock'}, {'frequency': 'c', 'synset': 'comforter.n.04', 'synonyms': ['pacifier', 'teething_ring'], 'id': 293, 'def': 'device used for an infant to suck or bite on', 'name': 'pacifier'}, {'frequency': 'r', 'synset': 'comic_book.n.01', 'synonyms': ['comic_book'], 'id': 294, 'def': 'a magazine devoted to comic strips', 'name': 'comic_book'}, {'frequency': 'r', 'synset': 'compass.n.01', 'synonyms': ['compass'], 'id': 295, 'def': 'navigational instrument for finding directions', 'name': 'compass'}, {'frequency': 'f', 'synset': 'computer_keyboard.n.01', 'synonyms': ['computer_keyboard', 'keyboard_(computer)'], 'id': 296, 'def': 'a keyboard that is a data input device for computers', 'name': 'computer_keyboard'}, {'frequency': 'f', 'synset': 'condiment.n.01', 'synonyms': ['condiment'], 'id': 297, 'def': 'a preparation (a sauce or relish or spice) to enhance flavor or enjoyment', 'name': 'condiment'}, {'frequency': 'f', 'synset': 'cone.n.01', 'synonyms': ['cone', 'traffic_cone'], 'id': 298, 'def': 'a cone-shaped object used to direct traffic', 'name': 'cone'}, {'frequency': 'f', 'synset': 'control.n.09', 'synonyms': ['control', 'controller'], 'id': 299, 'def': 'a mechanism that controls the operation of a machine', 'name': 'control'}, {'frequency': 'r', 'synset': 'convertible.n.01', 'synonyms': ['convertible_(automobile)'], 'id': 300, 'def': 'a car that has top that can be folded or removed', 'name': 'convertible_(automobile)'}, {'frequency': 'r', 'synset': 'convertible.n.03', 'synonyms': ['sofa_bed'], 'id': 301, 'def': 'a sofa that can be converted into a bed', 'name': 'sofa_bed'}, {'frequency': 'r', 'synset': 'cooker.n.01', 'synonyms': ['cooker'], 'id': 302, 'def': 'a utensil for cooking', 'name': 'cooker'}, {'frequency': 'f', 'synset': 'cookie.n.01', 'synonyms': ['cookie', 'cooky', 'biscuit_(cookie)'], 'id': 303, 'def': "any of various small flat sweet cakes (`biscuit' is the British term)", 'name': 'cookie'}, {'frequency': 'r', 'synset': 'cooking_utensil.n.01', 'synonyms': ['cooking_utensil'], 'id': 304, 'def': 'a kitchen utensil made of material that does not melt easily; used for cooking', 'name': 'cooking_utensil'}, {'frequency': 'f', 'synset': 'cooler.n.01', 'synonyms': ['cooler_(for_food)', 'ice_chest'], 'id': 305, 'def': 'an insulated box for storing food often with ice', 'name': 'cooler_(for_food)'}, {'frequency': 'f', 'synset': 'cork.n.04', 'synonyms': ['cork_(bottle_plug)', 'bottle_cork'], 'id': 306, 'def': 'the plug in the mouth of a bottle (especially a wine bottle)', 'name': 'cork_(bottle_plug)'}, {'frequency': 'r', 'synset': 'corkboard.n.01', 'synonyms': ['corkboard'], 'id': 307, 'def': 'a sheet consisting of cork granules', 'name': 'corkboard'}, {'frequency': 'c', 'synset': 'corkscrew.n.01', 'synonyms': ['corkscrew', 'bottle_screw'], 'id': 308, 'def': 'a bottle opener that pulls corks', 'name': 'corkscrew'}, {'frequency': 'f', 'synset': 'corn.n.03', 'synonyms': ['edible_corn', 'corn', 'maize'], 'id': 309, 'def': 'ears or kernels of corn that can be prepared and served for human food (only mark individual ears or kernels)', 'name': 'edible_corn'}, {'frequency': 'r', 'synset': 'cornbread.n.01', 'synonyms': ['cornbread'], 'id': 310, 'def': 'bread made primarily of cornmeal', 'name': 'cornbread'}, {'frequency': 'c', 'synset': 'cornet.n.01', 'synonyms': ['cornet', 'horn', 'trumpet'], 'id': 311, 'def': 'a brass musical instrument with a narrow tube and a flared bell and many valves', 'name': 'cornet'}, {'frequency': 'c', 'synset': 'cornice.n.01', 'synonyms': ['cornice', 'valance', 'valance_board', 'pelmet'], 'id': 312, 'def': 'a decorative framework to conceal curtain fixtures at the top of a window casing', 'name': 'cornice'}, {'frequency': 'r', 'synset': 'cornmeal.n.01', 'synonyms': ['cornmeal'], 'id': 313, 'def': 'coarsely ground corn', 'name': 'cornmeal'}, {'frequency': 'c', 'synset': 'corset.n.01', 'synonyms': ['corset', 'girdle'], 'id': 314, 'def': "a woman's close-fitting foundation garment", 'name': 'corset'}, {'frequency': 'c', 'synset': 'costume.n.04', 'synonyms': ['costume'], 'id': 315, 'def': 'the attire characteristic of a country or a time or a social class', 'name': 'costume'}, {'frequency': 'r', 'synset': 'cougar.n.01', 'synonyms': ['cougar', 'puma', 'catamount', 'mountain_lion', 'panther'], 'id': 316, 'def': 'large American feline resembling a lion', 'name': 'cougar'}, {'frequency': 'r', 'synset': 'coverall.n.01', 'synonyms': ['coverall'], 'id': 317, 'def': 'a loose-fitting protective garment that is worn over other clothing', 'name': 'coverall'}, {'frequency': 'c', 'synset': 'cowbell.n.01', 'synonyms': ['cowbell'], 'id': 318, 'def': 'a bell hung around the neck of cow so that the cow can be easily located', 'name': 'cowbell'}, {'frequency': 'f', 'synset': 'cowboy_hat.n.01', 'synonyms': ['cowboy_hat', 'ten-gallon_hat'], 'id': 319, 'def': 'a hat with a wide brim and a soft crown; worn by American ranch hands', 'name': 'cowboy_hat'}, {'frequency': 'c', 'synset': 'crab.n.01', 'synonyms': ['crab_(animal)'], 'id': 320, 'def': 'decapod having eyes on short stalks and a broad flattened shell and pincers', 'name': 'crab_(animal)'}, {'frequency': 'r', 'synset': 'crab.n.05', 'synonyms': ['crabmeat'], 'id': 321, 'def': 'the edible flesh of any of various crabs', 'name': 'crabmeat'}, {'frequency': 'c', 'synset': 'cracker.n.01', 'synonyms': ['cracker'], 'id': 322, 'def': 'a thin crisp wafer', 'name': 'cracker'}, {'frequency': 'r', 'synset': 'crape.n.01', 'synonyms': ['crape', 'crepe', 'French_pancake'], 'id': 323, 'def': 'small very thin pancake', 'name': 'crape'}, {'frequency': 'f', 'synset': 'crate.n.01', 'synonyms': ['crate'], 'id': 324, 'def': 'a rugged box (usually made of wood); used for shipping', 'name': 'crate'}, {'frequency': 'c', 'synset': 'crayon.n.01', 'synonyms': ['crayon', 'wax_crayon'], 'id': 325, 'def': 'writing or drawing implement made of a colored stick of composition wax', 'name': 'crayon'}, {'frequency': 'r', 'synset': 'cream_pitcher.n.01', 'synonyms': ['cream_pitcher'], 'id': 326, 'def': 'a small pitcher for serving cream', 'name': 'cream_pitcher'}, {'frequency': 'c', 'synset': 'crescent_roll.n.01', 'synonyms': ['crescent_roll', 'croissant'], 'id': 327, 'def': 'very rich flaky crescent-shaped roll', 'name': 'crescent_roll'}, {'frequency': 'c', 'synset': 'crib.n.01', 'synonyms': ['crib', 'cot'], 'id': 328, 'def': 'baby bed with high sides made of slats', 'name': 'crib'}, {'frequency': 'c', 'synset': 'crock.n.03', 'synonyms': ['crock_pot', 'earthenware_jar'], 'id': 329, 'def': 'an earthen jar (made of baked clay) or a modern electric crockpot', 'name': 'crock_pot'}, {'frequency': 'f', 'synset': 'crossbar.n.01', 'synonyms': ['crossbar'], 'id': 330, 'def': 'a horizontal bar that goes across something', 'name': 'crossbar'}, {'frequency': 'r', 'synset': 'crouton.n.01', 'synonyms': ['crouton'], 'id': 331, 'def': 'a small piece of toasted or fried bread; served in soup or salads', 'name': 'crouton'}, {'frequency': 'c', 'synset': 'crow.n.01', 'synonyms': ['crow'], 'id': 332, 'def': 'black birds having a raucous call', 'name': 'crow'}, {'frequency': 'r', 'synset': 'crowbar.n.01', 'synonyms': ['crowbar', 'wrecking_bar', 'pry_bar'], 'id': 333, 'def': 'a heavy iron lever with one end forged into a wedge', 'name': 'crowbar'}, {'frequency': 'c', 'synset': 'crown.n.04', 'synonyms': ['crown'], 'id': 334, 'def': 'an ornamental jeweled headdress signifying sovereignty', 'name': 'crown'}, {'frequency': 'c', 'synset': 'crucifix.n.01', 'synonyms': ['crucifix'], 'id': 335, 'def': 'representation of the cross on which Jesus died', 'name': 'crucifix'}, {'frequency': 'c', 'synset': 'cruise_ship.n.01', 'synonyms': ['cruise_ship', 'cruise_liner'], 'id': 336, 'def': 'a passenger ship used commercially for pleasure cruises', 'name': 'cruise_ship'}, {'frequency': 'c', 'synset': 'cruiser.n.01', 'synonyms': ['police_cruiser', 'patrol_car', 'police_car', 'squad_car'], 'id': 337, 'def': 'a car in which policemen cruise the streets', 'name': 'police_cruiser'}, {'frequency': 'f', 'synset': 'crumb.n.03', 'synonyms': ['crumb'], 'id': 338, 'def': 'small piece of e.g. bread or cake', 'name': 'crumb'}, {'frequency': 'c', 'synset': 'crutch.n.01', 'synonyms': ['crutch'], 'id': 339, 'def': 'a wooden or metal staff that fits under the armpit and reaches to the ground', 'name': 'crutch'}, {'frequency': 'c', 'synset': 'cub.n.03', 'synonyms': ['cub_(animal)'], 'id': 340, 'def': 'the young of certain carnivorous mammals such as the bear or wolf or lion', 'name': 'cub_(animal)'}, {'frequency': 'c', 'synset': 'cube.n.05', 'synonyms': ['cube', 'square_block'], 'id': 341, 'def': 'a block in the (approximate) shape of a cube', 'name': 'cube'}, {'frequency': 'f', 'synset': 'cucumber.n.02', 'synonyms': ['cucumber', 'cuke'], 'id': 342, 'def': 'cylindrical green fruit with thin green rind and white flesh eaten as a vegetable', 'name': 'cucumber'}, {'frequency': 'c', 'synset': 'cufflink.n.01', 'synonyms': ['cufflink'], 'id': 343, 'def': 'jewelry consisting of linked buttons used to fasten the cuffs of a shirt', 'name': 'cufflink'}, {'frequency': 'f', 'synset': 'cup.n.01', 'synonyms': ['cup'], 'id': 344, 'def': 'a small open container usually used for drinking; usually has a handle', 'name': 'cup'}, {'frequency': 'c', 'synset': 'cup.n.08', 'synonyms': ['trophy_cup'], 'id': 345, 'def': 'a metal award or cup-shaped vessel with handles that is awarded as a trophy to a competition winner', 'name': 'trophy_cup'}, {'frequency': 'f', 'synset': 'cupboard.n.01', 'synonyms': ['cupboard', 'closet'], 'id': 346, 'def': 'a small room (or recess) or cabinet used for storage space', 'name': 'cupboard'}, {'frequency': 'f', 'synset': 'cupcake.n.01', 'synonyms': ['cupcake'], 'id': 347, 'def': 'small cake baked in a muffin tin', 'name': 'cupcake'}, {'frequency': 'r', 'synset': 'curler.n.01', 'synonyms': ['hair_curler', 'hair_roller', 'hair_crimper'], 'id': 348, 'def': 'a cylindrical tube around which the hair is wound to curl it', 'name': 'hair_curler'}, {'frequency': 'r', 'synset': 'curling_iron.n.01', 'synonyms': ['curling_iron'], 'id': 349, 'def': 'a cylindrical home appliance that heats hair that has been curled around it', 'name': 'curling_iron'}, {'frequency': 'f', 'synset': 'curtain.n.01', 'synonyms': ['curtain', 'drapery'], 'id': 350, 'def': 'hanging cloth used as a blind (especially for a window)', 'name': 'curtain'}, {'frequency': 'f', 'synset': 'cushion.n.03', 'synonyms': ['cushion'], 'id': 351, 'def': 'a soft bag filled with air or padding such as feathers or foam rubber', 'name': 'cushion'}, {'frequency': 'r', 'synset': 'cylinder.n.04', 'synonyms': ['cylinder'], 'id': 352, 'def': 'a cylindrical container', 'name': 'cylinder'}, {'frequency': 'r', 'synset': 'cymbal.n.01', 'synonyms': ['cymbal'], 'id': 353, 'def': 'a percussion instrument consisting of a concave brass disk', 'name': 'cymbal'}, {'frequency': 'r', 'synset': 'dagger.n.01', 'synonyms': ['dagger'], 'id': 354, 'def': 'a short knife with a pointed blade used for piercing or stabbing', 'name': 'dagger'}, {'frequency': 'r', 'synset': 'dalmatian.n.02', 'synonyms': ['dalmatian'], 'id': 355, 'def': 'a large breed having a smooth white coat with black or brown spots', 'name': 'dalmatian'}, {'frequency': 'c', 'synset': 'dartboard.n.01', 'synonyms': ['dartboard'], 'id': 356, 'def': 'a circular board of wood or cork used as the target in the game of darts', 'name': 'dartboard'}, {'frequency': 'r', 'synset': 'date.n.08', 'synonyms': ['date_(fruit)'], 'id': 357, 'def': 'sweet edible fruit of the date palm with a single long woody seed', 'name': 'date_(fruit)'}, {'frequency': 'f', 'synset': 'deck_chair.n.01', 'synonyms': ['deck_chair', 'beach_chair'], 'id': 358, 'def': 'a folding chair for use outdoors; a wooden frame supports a length of canvas', 'name': 'deck_chair'}, {'frequency': 'c', 'synset': 'deer.n.01', 'synonyms': ['deer', 'cervid'], 'id': 359, 'def': "distinguished from Bovidae by the male's having solid deciduous antlers", 'name': 'deer'}, {'frequency': 'c', 'synset': 'dental_floss.n.01', 'synonyms': ['dental_floss', 'floss'], 'id': 360, 'def': 'a soft thread for cleaning the spaces between the teeth', 'name': 'dental_floss'}, {'frequency': 'f', 'synset': 'desk.n.01', 'synonyms': ['desk'], 'id': 361, 'def': 'a piece of furniture with a writing surface and usually drawers or other compartments', 'name': 'desk'}, {'frequency': 'r', 'synset': 'detergent.n.01', 'synonyms': ['detergent'], 'id': 362, 'def': 'a surface-active chemical widely used in industry and laundering', 'name': 'detergent'}, {'frequency': 'c', 'synset': 'diaper.n.01', 'synonyms': ['diaper'], 'id': 363, 'def': 'garment consisting of a folded cloth drawn up between the legs and fastened at the waist', 'name': 'diaper'}, {'frequency': 'r', 'synset': 'diary.n.01', 'synonyms': ['diary', 'journal'], 'id': 364, 'def': 'yearly planner book', 'name': 'diary'}, {'frequency': 'r', 'synset': 'die.n.01', 'synonyms': ['die', 'dice'], 'id': 365, 'def': 'a small cube with 1 to 6 spots on the six faces; used in gambling', 'name': 'die'}, {'frequency': 'r', 'synset': 'dinghy.n.01', 'synonyms': ['dinghy', 'dory', 'rowboat'], 'id': 366, 'def': 'a small boat of shallow draft with seats and oars with which it is propelled', 'name': 'dinghy'}, {'frequency': 'f', 'synset': 'dining_table.n.01', 'synonyms': ['dining_table'], 'id': 367, 'def': 'a table at which meals are served', 'name': 'dining_table'}, {'frequency': 'r', 'synset': 'dinner_jacket.n.01', 'synonyms': ['tux', 'tuxedo'], 'id': 368, 'def': 'semiformal evening dress for men', 'name': 'tux'}, {'frequency': 'f', 'synset': 'dish.n.01', 'synonyms': ['dish'], 'id': 369, 'def': 'a piece of dishware normally used as a container for holding or serving food', 'name': 'dish'}, {'frequency': 'c', 'synset': 'dish.n.05', 'synonyms': ['dish_antenna'], 'id': 370, 'def': 'directional antenna consisting of a parabolic reflector', 'name': 'dish_antenna'}, {'frequency': 'c', 'synset': 'dishrag.n.01', 'synonyms': ['dishrag', 'dishcloth'], 'id': 371, 'def': 'a cloth for washing dishes or cleaning in general', 'name': 'dishrag'}, {'frequency': 'f', 'synset': 'dishtowel.n.01', 'synonyms': ['dishtowel', 'tea_towel'], 'id': 372, 'def': 'a towel for drying dishes', 'name': 'dishtowel'}, {'frequency': 'f', 'synset': 'dishwasher.n.01', 'synonyms': ['dishwasher', 'dishwashing_machine'], 'id': 373, 'def': 'a machine for washing dishes', 'name': 'dishwasher'}, {'frequency': 'r', 'synset': 'dishwasher_detergent.n.01', 'synonyms': ['dishwasher_detergent', 'dishwashing_detergent', 'dishwashing_liquid', 'dishsoap'], 'id': 374, 'def': 'dishsoap or dish detergent designed for use in dishwashers', 'name': 'dishwasher_detergent'}, {'frequency': 'f', 'synset': 'dispenser.n.01', 'synonyms': ['dispenser'], 'id': 375, 'def': 'a container so designed that the contents can be used in prescribed amounts', 'name': 'dispenser'}, {'frequency': 'r', 'synset': 'diving_board.n.01', 'synonyms': ['diving_board'], 'id': 376, 'def': 'a springboard from which swimmers can dive', 'name': 'diving_board'}, {'frequency': 'f', 'synset': 'dixie_cup.n.01', 'synonyms': ['Dixie_cup', 'paper_cup'], 'id': 377, 'def': 'a disposable cup made of paper; for holding drinks', 'name': 'Dixie_cup'}, {'frequency': 'f', 'synset': 'dog.n.01', 'synonyms': ['dog'], 'id': 378, 'def': 'a common domesticated dog', 'name': 'dog'}, {'frequency': 'f', 'synset': 'dog_collar.n.01', 'synonyms': ['dog_collar'], 'id': 379, 'def': 'a collar for a dog', 'name': 'dog_collar'}, {'frequency': 'f', 'synset': 'doll.n.01', 'synonyms': ['doll'], 'id': 380, 'def': 'a toy replica of a HUMAN (NOT AN ANIMAL)', 'name': 'doll'}, {'frequency': 'r', 'synset': 'dollar.n.02', 'synonyms': ['dollar', 'dollar_bill', 'one_dollar_bill'], 'id': 381, 'def': 'a piece of paper money worth one dollar', 'name': 'dollar'}, {'frequency': 'r', 'synset': 'dollhouse.n.01', 'synonyms': ['dollhouse', "doll's_house"], 'id': 382, 'def': "a house so small that it is likened to a child's plaything", 'name': 'dollhouse'}, {'frequency': 'c', 'synset': 'dolphin.n.02', 'synonyms': ['dolphin'], 'id': 383, 'def': 'any of various small toothed whales with a beaklike snout; larger than porpoises', 'name': 'dolphin'}, {'frequency': 'c', 'synset': 'domestic_ass.n.01', 'synonyms': ['domestic_ass', 'donkey'], 'id': 384, 'def': 'domestic beast of burden descended from the African wild ass; patient but stubborn', 'name': 'domestic_ass'}, {'frequency': 'f', 'synset': 'doorknob.n.01', 'synonyms': ['doorknob', 'doorhandle'], 'id': 385, 'def': "a knob used to open a door (often called `doorhandle' in Great Britain)", 'name': 'doorknob'}, {'frequency': 'c', 'synset': 'doormat.n.02', 'synonyms': ['doormat', 'welcome_mat'], 'id': 386, 'def': 'a mat placed outside an exterior door for wiping the shoes before entering', 'name': 'doormat'}, {'frequency': 'f', 'synset': 'doughnut.n.02', 'synonyms': ['doughnut', 'donut'], 'id': 387, 'def': 'a small ring-shaped friedcake', 'name': 'doughnut'}, {'frequency': 'r', 'synset': 'dove.n.01', 'synonyms': ['dove'], 'id': 388, 'def': 'any of numerous small pigeons', 'name': 'dove'}, {'frequency': 'r', 'synset': 'dragonfly.n.01', 'synonyms': ['dragonfly'], 'id': 389, 'def': 'slender-bodied non-stinging insect having iridescent wings that are outspread at rest', 'name': 'dragonfly'}, {'frequency': 'f', 'synset': 'drawer.n.01', 'synonyms': ['drawer'], 'id': 390, 'def': 'a boxlike container in a piece of furniture; made so as to slide in and out', 'name': 'drawer'}, {'frequency': 'c', 'synset': 'drawers.n.01', 'synonyms': ['underdrawers', 'boxers', 'boxershorts'], 'id': 391, 'def': 'underpants worn by men', 'name': 'underdrawers'}, {'frequency': 'f', 'synset': 'dress.n.01', 'synonyms': ['dress', 'frock'], 'id': 392, 'def': 'a one-piece garment for a woman; has skirt and bodice', 'name': 'dress'}, {'frequency': 'c', 'synset': 'dress_hat.n.01', 'synonyms': ['dress_hat', 'high_hat', 'opera_hat', 'silk_hat', 'top_hat'], 'id': 393, 'def': "a man's hat with a tall crown; usually covered with silk or with beaver fur", 'name': 'dress_hat'}, {'frequency': 'f', 'synset': 'dress_suit.n.01', 'synonyms': ['dress_suit'], 'id': 394, 'def': 'formalwear consisting of full evening dress for men', 'name': 'dress_suit'}, {'frequency': 'f', 'synset': 'dresser.n.05', 'synonyms': ['dresser'], 'id': 395, 'def': 'a cabinet with shelves', 'name': 'dresser'}, {'frequency': 'c', 'synset': 'drill.n.01', 'synonyms': ['drill'], 'id': 396, 'def': 'a tool with a sharp rotating point for making holes in hard materials', 'name': 'drill'}, {'frequency': 'r', 'synset': 'drone.n.04', 'synonyms': ['drone'], 'id': 397, 'def': 'an aircraft without a pilot that is operated by remote control', 'name': 'drone'}, {'frequency': 'r', 'synset': 'dropper.n.01', 'synonyms': ['dropper', 'eye_dropper'], 'id': 398, 'def': 'pipet consisting of a small tube with a vacuum bulb at one end for drawing liquid in and releasing it a drop at a time', 'name': 'dropper'}, {'frequency': 'c', 'synset': 'drum.n.01', 'synonyms': ['drum_(musical_instrument)'], 'id': 399, 'def': 'a musical percussion instrument; usually consists of a hollow cylinder with a membrane stretched across each end', 'name': 'drum_(musical_instrument)'}, {'frequency': 'r', 'synset': 'drumstick.n.02', 'synonyms': ['drumstick'], 'id': 400, 'def': 'a stick used for playing a drum', 'name': 'drumstick'}, {'frequency': 'f', 'synset': 'duck.n.01', 'synonyms': ['duck'], 'id': 401, 'def': 'small web-footed broad-billed swimming bird', 'name': 'duck'}, {'frequency': 'c', 'synset': 'duckling.n.02', 'synonyms': ['duckling'], 'id': 402, 'def': 'young duck', 'name': 'duckling'}, {'frequency': 'c', 'synset': 'duct_tape.n.01', 'synonyms': ['duct_tape'], 'id': 403, 'def': 'a wide silvery adhesive tape', 'name': 'duct_tape'}, {'frequency': 'f', 'synset': 'duffel_bag.n.01', 'synonyms': ['duffel_bag', 'duffle_bag', 'duffel', 'duffle'], 'id': 404, 'def': 'a large cylindrical bag of heavy cloth (does not include suitcases)', 'name': 'duffel_bag'}, {'frequency': 'r', 'synset': 'dumbbell.n.01', 'synonyms': ['dumbbell'], 'id': 405, 'def': 'an exercising weight with two ball-like ends connected by a short handle', 'name': 'dumbbell'}, {'frequency': 'c', 'synset': 'dumpster.n.01', 'synonyms': ['dumpster'], 'id': 406, 'def': 'a container designed to receive and transport and dump waste', 'name': 'dumpster'}, {'frequency': 'r', 'synset': 'dustpan.n.02', 'synonyms': ['dustpan'], 'id': 407, 'def': 'a short-handled receptacle into which dust can be swept', 'name': 'dustpan'}, {'frequency': 'c', 'synset': 'eagle.n.01', 'synonyms': ['eagle'], 'id': 408, 'def': 'large birds of prey noted for their broad wings and strong soaring flight', 'name': 'eagle'}, {'frequency': 'f', 'synset': 'earphone.n.01', 'synonyms': ['earphone', 'earpiece', 'headphone'], 'id': 409, 'def': 'device for listening to audio that is held over or inserted into the ear', 'name': 'earphone'}, {'frequency': 'r', 'synset': 'earplug.n.01', 'synonyms': ['earplug'], 'id': 410, 'def': 'a soft plug that is inserted into the ear canal to block sound', 'name': 'earplug'}, {'frequency': 'f', 'synset': 'earring.n.01', 'synonyms': ['earring'], 'id': 411, 'def': 'jewelry to ornament the ear', 'name': 'earring'}, {'frequency': 'c', 'synset': 'easel.n.01', 'synonyms': ['easel'], 'id': 412, 'def': "an upright tripod for displaying something (usually an artist's canvas)", 'name': 'easel'}, {'frequency': 'r', 'synset': 'eclair.n.01', 'synonyms': ['eclair'], 'id': 413, 'def': 'oblong cream puff', 'name': 'eclair'}, {'frequency': 'r', 'synset': 'eel.n.01', 'synonyms': ['eel'], 'id': 414, 'def': 'an elongate fish with fatty flesh', 'name': 'eel'}, {'frequency': 'f', 'synset': 'egg.n.02', 'synonyms': ['egg', 'eggs'], 'id': 415, 'def': 'oval reproductive body of a fowl (especially a hen) used as food', 'name': 'egg'}, {'frequency': 'r', 'synset': 'egg_roll.n.01', 'synonyms': ['egg_roll', 'spring_roll'], 'id': 416, 'def': 'minced vegetables and meat wrapped in a pancake and fried', 'name': 'egg_roll'}, {'frequency': 'c', 'synset': 'egg_yolk.n.01', 'synonyms': ['egg_yolk', 'yolk_(egg)'], 'id': 417, 'def': 'the yellow spherical part of an egg', 'name': 'egg_yolk'}, {'frequency': 'c', 'synset': 'eggbeater.n.02', 'synonyms': ['eggbeater', 'eggwhisk'], 'id': 418, 'def': 'a mixer for beating eggs or whipping cream', 'name': 'eggbeater'}, {'frequency': 'c', 'synset': 'eggplant.n.01', 'synonyms': ['eggplant', 'aubergine'], 'id': 419, 'def': 'egg-shaped vegetable having a shiny skin typically dark purple', 'name': 'eggplant'}, {'frequency': 'r', 'synset': 'electric_chair.n.01', 'synonyms': ['electric_chair'], 'id': 420, 'def': 'a chair-shaped instrument of execution by electrocution', 'name': 'electric_chair'}, {'frequency': 'f', 'synset': 'electric_refrigerator.n.01', 'synonyms': ['refrigerator'], 'id': 421, 'def': 'a refrigerator in which the coolant is pumped around by an electric motor', 'name': 'refrigerator'}, {'frequency': 'f', 'synset': 'elephant.n.01', 'synonyms': ['elephant'], 'id': 422, 'def': 'a common elephant', 'name': 'elephant'}, {'frequency': 'c', 'synset': 'elk.n.01', 'synonyms': ['elk', 'moose'], 'id': 423, 'def': 'large northern deer with enormous flattened antlers in the male', 'name': 'elk'}, {'frequency': 'c', 'synset': 'envelope.n.01', 'synonyms': ['envelope'], 'id': 424, 'def': 'a flat (usually rectangular) container for a letter, thin package, etc.', 'name': 'envelope'}, {'frequency': 'c', 'synset': 'eraser.n.01', 'synonyms': ['eraser'], 'id': 425, 'def': 'an implement used to erase something', 'name': 'eraser'}, {'frequency': 'r', 'synset': 'escargot.n.01', 'synonyms': ['escargot'], 'id': 426, 'def': 'edible snail usually served in the shell with a sauce of melted butter and garlic', 'name': 'escargot'}, {'frequency': 'r', 'synset': 'eyepatch.n.01', 'synonyms': ['eyepatch'], 'id': 427, 'def': 'a protective cloth covering for an injured eye', 'name': 'eyepatch'}, {'frequency': 'r', 'synset': 'falcon.n.01', 'synonyms': ['falcon'], 'id': 428, 'def': 'birds of prey having long pointed powerful wings adapted for swift flight', 'name': 'falcon'}, {'frequency': 'f', 'synset': 'fan.n.01', 'synonyms': ['fan'], 'id': 429, 'def': 'a device for creating a current of air by movement of a surface or surfaces', 'name': 'fan'}, {'frequency': 'f', 'synset': 'faucet.n.01', 'synonyms': ['faucet', 'spigot', 'tap'], 'id': 430, 'def': 'a regulator for controlling the flow of a liquid from a reservoir', 'name': 'faucet'}, {'frequency': 'r', 'synset': 'fedora.n.01', 'synonyms': ['fedora'], 'id': 431, 'def': 'a hat made of felt with a creased crown', 'name': 'fedora'}, {'frequency': 'r', 'synset': 'ferret.n.02', 'synonyms': ['ferret'], 'id': 432, 'def': 'domesticated albino variety of the European polecat bred for hunting rats and rabbits', 'name': 'ferret'}, {'frequency': 'c', 'synset': 'ferris_wheel.n.01', 'synonyms': ['Ferris_wheel'], 'id': 433, 'def': 'a large wheel with suspended seats that remain upright as the wheel rotates', 'name': 'Ferris_wheel'}, {'frequency': 'c', 'synset': 'ferry.n.01', 'synonyms': ['ferry', 'ferryboat'], 'id': 434, 'def': 'a boat that transports people or vehicles across a body of water and operates on a regular schedule', 'name': 'ferry'}, {'frequency': 'r', 'synset': 'fig.n.04', 'synonyms': ['fig_(fruit)'], 'id': 435, 'def': 'fleshy sweet pear-shaped yellowish or purple fruit eaten fresh or preserved or dried', 'name': 'fig_(fruit)'}, {'frequency': 'c', 'synset': 'fighter.n.02', 'synonyms': ['fighter_jet', 'fighter_aircraft', 'attack_aircraft'], 'id': 436, 'def': 'a high-speed military or naval airplane designed to destroy enemy targets', 'name': 'fighter_jet'}, {'frequency': 'f', 'synset': 'figurine.n.01', 'synonyms': ['figurine'], 'id': 437, 'def': 'a small carved or molded figure', 'name': 'figurine'}, {'frequency': 'c', 'synset': 'file.n.03', 'synonyms': ['file_cabinet', 'filing_cabinet'], 'id': 438, 'def': 'office furniture consisting of a container for keeping papers in order', 'name': 'file_cabinet'}, {'frequency': 'r', 'synset': 'file.n.04', 'synonyms': ['file_(tool)'], 'id': 439, 'def': 'a steel hand tool with small sharp teeth on some or all of its surfaces; used for smoothing wood or metal', 'name': 'file_(tool)'}, {'frequency': 'f', 'synset': 'fire_alarm.n.02', 'synonyms': ['fire_alarm', 'smoke_alarm'], 'id': 440, 'def': 'an alarm that is tripped off by fire or smoke', 'name': 'fire_alarm'}, {'frequency': 'f', 'synset': 'fire_engine.n.01', 'synonyms': ['fire_engine', 'fire_truck'], 'id': 441, 'def': 'large trucks that carry firefighters and equipment to the site of a fire', 'name': 'fire_engine'}, {'frequency': 'f', 'synset': 'fire_extinguisher.n.01', 'synonyms': ['fire_extinguisher', 'extinguisher'], 'id': 442, 'def': 'a manually operated device for extinguishing small fires', 'name': 'fire_extinguisher'}, {'frequency': 'c', 'synset': 'fire_hose.n.01', 'synonyms': ['fire_hose'], 'id': 443, 'def': 'a large hose that carries water from a fire hydrant to the site of the fire', 'name': 'fire_hose'}, {'frequency': 'f', 'synset': 'fireplace.n.01', 'synonyms': ['fireplace'], 'id': 444, 'def': 'an open recess in a wall at the base of a chimney where a fire can be built', 'name': 'fireplace'}, {'frequency': 'f', 'synset': 'fireplug.n.01', 'synonyms': ['fireplug', 'fire_hydrant', 'hydrant'], 'id': 445, 'def': 'an upright hydrant for drawing water to use in fighting a fire', 'name': 'fireplug'}, {'frequency': 'r', 'synset': 'first-aid_kit.n.01', 'synonyms': ['first-aid_kit'], 'id': 446, 'def': 'kit consisting of a set of bandages and medicines for giving first aid', 'name': 'first-aid_kit'}, {'frequency': 'f', 'synset': 'fish.n.01', 'synonyms': ['fish'], 'id': 447, 'def': 'any of various mostly cold-blooded aquatic vertebrates usually having scales and breathing through gills', 'name': 'fish'}, {'frequency': 'c', 'synset': 'fish.n.02', 'synonyms': ['fish_(food)'], 'id': 448, 'def': 'the flesh of fish used as food', 'name': 'fish_(food)'}, {'frequency': 'r', 'synset': 'fishbowl.n.02', 'synonyms': ['fishbowl', 'goldfish_bowl'], 'id': 449, 'def': 'a transparent bowl in which small fish are kept', 'name': 'fishbowl'}, {'frequency': 'c', 'synset': 'fishing_rod.n.01', 'synonyms': ['fishing_rod', 'fishing_pole'], 'id': 450, 'def': 'a rod that is used in fishing to extend the fishing line', 'name': 'fishing_rod'}, {'frequency': 'f', 'synset': 'flag.n.01', 'synonyms': ['flag'], 'id': 451, 'def': 'emblem usually consisting of a rectangular piece of cloth of distinctive design (do not include pole)', 'name': 'flag'}, {'frequency': 'f', 'synset': 'flagpole.n.02', 'synonyms': ['flagpole', 'flagstaff'], 'id': 452, 'def': 'a tall staff or pole on which a flag is raised', 'name': 'flagpole'}, {'frequency': 'c', 'synset': 'flamingo.n.01', 'synonyms': ['flamingo'], 'id': 453, 'def': 'large pink web-footed bird with down-bent bill', 'name': 'flamingo'}, {'frequency': 'c', 'synset': 'flannel.n.01', 'synonyms': ['flannel'], 'id': 454, 'def': 'a soft light woolen fabric; used for clothing', 'name': 'flannel'}, {'frequency': 'c', 'synset': 'flap.n.01', 'synonyms': ['flap'], 'id': 455, 'def': 'any broad thin covering attached at one edge, such as a mud flap next to a wheel or a flap on an airplane wing', 'name': 'flap'}, {'frequency': 'r', 'synset': 'flash.n.10', 'synonyms': ['flash', 'flashbulb'], 'id': 456, 'def': 'a lamp for providing momentary light to take a photograph', 'name': 'flash'}, {'frequency': 'c', 'synset': 'flashlight.n.01', 'synonyms': ['flashlight', 'torch'], 'id': 457, 'def': 'a small portable battery-powered electric lamp', 'name': 'flashlight'}, {'frequency': 'r', 'synset': 'fleece.n.03', 'synonyms': ['fleece'], 'id': 458, 'def': 'a soft bulky fabric with deep pile; used chiefly for clothing', 'name': 'fleece'}, {'frequency': 'f', 'synset': 'flip-flop.n.02', 'synonyms': ['flip-flop_(sandal)'], 'id': 459, 'def': 'a backless sandal held to the foot by a thong between two toes', 'name': 'flip-flop_(sandal)'}, {'frequency': 'c', 'synset': 'flipper.n.01', 'synonyms': ['flipper_(footwear)', 'fin_(footwear)'], 'id': 460, 'def': 'a shoe to aid a person in swimming', 'name': 'flipper_(footwear)'}, {'frequency': 'f', 'synset': 'flower_arrangement.n.01', 'synonyms': ['flower_arrangement', 'floral_arrangement'], 'id': 461, 'def': 'a decorative arrangement of flowers', 'name': 'flower_arrangement'}, {'frequency': 'c', 'synset': 'flute.n.02', 'synonyms': ['flute_glass', 'champagne_flute'], 'id': 462, 'def': 'a tall narrow wineglass', 'name': 'flute_glass'}, {'frequency': 'c', 'synset': 'foal.n.01', 'synonyms': ['foal'], 'id': 463, 'def': 'a young horse', 'name': 'foal'}, {'frequency': 'c', 'synset': 'folding_chair.n.01', 'synonyms': ['folding_chair'], 'id': 464, 'def': 'a chair that can be folded flat for storage', 'name': 'folding_chair'}, {'frequency': 'c', 'synset': 'food_processor.n.01', 'synonyms': ['food_processor'], 'id': 465, 'def': 'a kitchen appliance for shredding, blending, chopping, or slicing food', 'name': 'food_processor'}, {'frequency': 'c', 'synset': 'football.n.02', 'synonyms': ['football_(American)'], 'id': 466, 'def': 'the inflated oblong ball used in playing American football', 'name': 'football_(American)'}, {'frequency': 'r', 'synset': 'football_helmet.n.01', 'synonyms': ['football_helmet'], 'id': 467, 'def': 'a padded helmet with a face mask to protect the head of football players', 'name': 'football_helmet'}, {'frequency': 'c', 'synset': 'footstool.n.01', 'synonyms': ['footstool', 'footrest'], 'id': 468, 'def': 'a low seat or a stool to rest the feet of a seated person', 'name': 'footstool'}, {'frequency': 'f', 'synset': 'fork.n.01', 'synonyms': ['fork'], 'id': 469, 'def': 'cutlery used for serving and eating food', 'name': 'fork'}, {'frequency': 'c', 'synset': 'forklift.n.01', 'synonyms': ['forklift'], 'id': 470, 'def': 'an industrial vehicle with a power operated fork in front that can be inserted under loads to lift and move them', 'name': 'forklift'}, {'frequency': 'c', 'synset': 'freight_car.n.01', 'synonyms': ['freight_car'], 'id': 471, 'def': 'a railway car that carries freight', 'name': 'freight_car'}, {'frequency': 'c', 'synset': 'french_toast.n.01', 'synonyms': ['French_toast'], 'id': 472, 'def': 'bread slice dipped in egg and milk and fried', 'name': 'French_toast'}, {'frequency': 'c', 'synset': 'freshener.n.01', 'synonyms': ['freshener', 'air_freshener'], 'id': 473, 'def': 'anything that freshens air by removing or covering odor', 'name': 'freshener'}, {'frequency': 'f', 'synset': 'frisbee.n.01', 'synonyms': ['frisbee'], 'id': 474, 'def': 'a light, plastic disk propelled with a flip of the wrist for recreation or competition', 'name': 'frisbee'}, {'frequency': 'c', 'synset': 'frog.n.01', 'synonyms': ['frog', 'toad', 'toad_frog'], 'id': 475, 'def': 'a tailless stout-bodied amphibians with long hind limbs for leaping', 'name': 'frog'}, {'frequency': 'c', 'synset': 'fruit_juice.n.01', 'synonyms': ['fruit_juice'], 'id': 476, 'def': 'drink produced by squeezing or crushing fruit', 'name': 'fruit_juice'}, {'frequency': 'f', 'synset': 'frying_pan.n.01', 'synonyms': ['frying_pan', 'frypan', 'skillet'], 'id': 477, 'def': 'a pan used for frying foods', 'name': 'frying_pan'}, {'frequency': 'r', 'synset': 'fudge.n.01', 'synonyms': ['fudge'], 'id': 478, 'def': 'soft creamy candy', 'name': 'fudge'}, {'frequency': 'r', 'synset': 'funnel.n.02', 'synonyms': ['funnel'], 'id': 479, 'def': 'a cone-shaped utensil used to channel a substance into a container with a small mouth', 'name': 'funnel'}, {'frequency': 'r', 'synset': 'futon.n.01', 'synonyms': ['futon'], 'id': 480, 'def': 'a pad that is used for sleeping on the floor or on a raised frame', 'name': 'futon'}, {'frequency': 'r', 'synset': 'gag.n.02', 'synonyms': ['gag', 'muzzle'], 'id': 481, 'def': "restraint put into a person's mouth to prevent speaking or shouting", 'name': 'gag'}, {'frequency': 'r', 'synset': 'garbage.n.03', 'synonyms': ['garbage'], 'id': 482, 'def': 'a receptacle where waste can be discarded', 'name': 'garbage'}, {'frequency': 'c', 'synset': 'garbage_truck.n.01', 'synonyms': ['garbage_truck'], 'id': 483, 'def': 'a truck for collecting domestic refuse', 'name': 'garbage_truck'}, {'frequency': 'c', 'synset': 'garden_hose.n.01', 'synonyms': ['garden_hose'], 'id': 484, 'def': 'a hose used for watering a lawn or garden', 'name': 'garden_hose'}, {'frequency': 'c', 'synset': 'gargle.n.01', 'synonyms': ['gargle', 'mouthwash'], 'id': 485, 'def': 'a medicated solution used for gargling and rinsing the mouth', 'name': 'gargle'}, {'frequency': 'r', 'synset': 'gargoyle.n.02', 'synonyms': ['gargoyle'], 'id': 486, 'def': 'an ornament consisting of a grotesquely carved figure of a person or animal', 'name': 'gargoyle'}, {'frequency': 'c', 'synset': 'garlic.n.02', 'synonyms': ['garlic', 'ail'], 'id': 487, 'def': 'aromatic bulb used as seasoning', 'name': 'garlic'}, {'frequency': 'r', 'synset': 'gasmask.n.01', 'synonyms': ['gasmask', 'respirator', 'gas_helmet'], 'id': 488, 'def': 'a protective face mask with a filter', 'name': 'gasmask'}, {'frequency': 'c', 'synset': 'gazelle.n.01', 'synonyms': ['gazelle'], 'id': 489, 'def': 'small swift graceful antelope of Africa and Asia having lustrous eyes', 'name': 'gazelle'}, {'frequency': 'c', 'synset': 'gelatin.n.02', 'synonyms': ['gelatin', 'jelly'], 'id': 490, 'def': 'an edible jelly made with gelatin and used as a dessert or salad base or a coating for foods', 'name': 'gelatin'}, {'frequency': 'r', 'synset': 'gem.n.02', 'synonyms': ['gemstone'], 'id': 491, 'def': 'a crystalline rock that can be cut and polished for jewelry', 'name': 'gemstone'}, {'frequency': 'r', 'synset': 'generator.n.02', 'synonyms': ['generator'], 'id': 492, 'def': 'engine that converts mechanical energy into electrical energy by electromagnetic induction', 'name': 'generator'}, {'frequency': 'c', 'synset': 'giant_panda.n.01', 'synonyms': ['giant_panda', 'panda', 'panda_bear'], 'id': 493, 'def': 'large black-and-white herbivorous mammal of bamboo forests of China and Tibet', 'name': 'giant_panda'}, {'frequency': 'c', 'synset': 'gift_wrap.n.01', 'synonyms': ['gift_wrap'], 'id': 494, 'def': 'attractive wrapping paper suitable for wrapping gifts', 'name': 'gift_wrap'}, {'frequency': 'c', 'synset': 'ginger.n.03', 'synonyms': ['ginger', 'gingerroot'], 'id': 495, 'def': 'the root of the common ginger plant; used fresh as a seasoning', 'name': 'ginger'}, {'frequency': 'f', 'synset': 'giraffe.n.01', 'synonyms': ['giraffe'], 'id': 496, 'def': 'tall animal having a spotted coat and small horns and very long neck and legs', 'name': 'giraffe'}, {'frequency': 'c', 'synset': 'girdle.n.02', 'synonyms': ['cincture', 'sash', 'waistband', 'waistcloth'], 'id': 497, 'def': 'a band of material around the waist that strengthens a skirt or trousers', 'name': 'cincture'}, {'frequency': 'f', 'synset': 'glass.n.02', 'synonyms': ['glass_(drink_container)', 'drinking_glass'], 'id': 498, 'def': 'a container for holding liquids while drinking', 'name': 'glass_(drink_container)'}, {'frequency': 'c', 'synset': 'globe.n.03', 'synonyms': ['globe'], 'id': 499, 'def': 'a sphere on which a map (especially of the earth) is represented', 'name': 'globe'}, {'frequency': 'f', 'synset': 'glove.n.02', 'synonyms': ['glove'], 'id': 500, 'def': 'handwear covering the hand', 'name': 'glove'}, {'frequency': 'c', 'synset': 'goat.n.01', 'synonyms': ['goat'], 'id': 501, 'def': 'a common goat', 'name': 'goat'}, {'frequency': 'f', 'synset': 'goggles.n.01', 'synonyms': ['goggles'], 'id': 502, 'def': 'tight-fitting spectacles worn to protect the eyes', 'name': 'goggles'}, {'frequency': 'r', 'synset': 'goldfish.n.01', 'synonyms': ['goldfish'], 'id': 503, 'def': 'small golden or orange-red freshwater fishes used as pond or aquarium pets', 'name': 'goldfish'}, {'frequency': 'c', 'synset': 'golf_club.n.02', 'synonyms': ['golf_club', 'golf-club'], 'id': 504, 'def': 'golf equipment used by a golfer to hit a golf ball', 'name': 'golf_club'}, {'frequency': 'c', 'synset': 'golfcart.n.01', 'synonyms': ['golfcart'], 'id': 505, 'def': 'a small motor vehicle in which golfers can ride between shots', 'name': 'golfcart'}, {'frequency': 'r', 'synset': 'gondola.n.02', 'synonyms': ['gondola_(boat)'], 'id': 506, 'def': 'long narrow flat-bottomed boat propelled by sculling; traditionally used on canals of Venice', 'name': 'gondola_(boat)'}, {'frequency': 'c', 'synset': 'goose.n.01', 'synonyms': ['goose'], 'id': 507, 'def': 'loud, web-footed long-necked aquatic birds usually larger than ducks', 'name': 'goose'}, {'frequency': 'r', 'synset': 'gorilla.n.01', 'synonyms': ['gorilla'], 'id': 508, 'def': 'largest ape', 'name': 'gorilla'}, {'frequency': 'r', 'synset': 'gourd.n.02', 'synonyms': ['gourd'], 'id': 509, 'def': 'any of numerous inedible fruits with hard rinds', 'name': 'gourd'}, {'frequency': 'f', 'synset': 'grape.n.01', 'synonyms': ['grape'], 'id': 510, 'def': 'any of various juicy fruit with green or purple skins; grow in clusters', 'name': 'grape'}, {'frequency': 'c', 'synset': 'grater.n.01', 'synonyms': ['grater'], 'id': 511, 'def': 'utensil with sharp perforations for shredding foods (as vegetables or cheese)', 'name': 'grater'}, {'frequency': 'c', 'synset': 'gravestone.n.01', 'synonyms': ['gravestone', 'headstone', 'tombstone'], 'id': 512, 'def': 'a stone that is used to mark a grave', 'name': 'gravestone'}, {'frequency': 'r', 'synset': 'gravy_boat.n.01', 'synonyms': ['gravy_boat', 'gravy_holder'], 'id': 513, 'def': 'a dish (often boat-shaped) for serving gravy or sauce', 'name': 'gravy_boat'}, {'frequency': 'f', 'synset': 'green_bean.n.02', 'synonyms': ['green_bean'], 'id': 514, 'def': 'a common bean plant cultivated for its slender green edible pods', 'name': 'green_bean'}, {'frequency': 'f', 'synset': 'green_onion.n.01', 'synonyms': ['green_onion', 'spring_onion', 'scallion'], 'id': 515, 'def': 'a young onion before the bulb has enlarged', 'name': 'green_onion'}, {'frequency': 'r', 'synset': 'griddle.n.01', 'synonyms': ['griddle'], 'id': 516, 'def': 'cooking utensil consisting of a flat heated surface on which food is cooked', 'name': 'griddle'}, {'frequency': 'f', 'synset': 'grill.n.02', 'synonyms': ['grill', 'grille', 'grillwork', 'radiator_grille'], 'id': 517, 'def': 'a framework of metal bars used as a partition or a grate', 'name': 'grill'}, {'frequency': 'r', 'synset': 'grits.n.01', 'synonyms': ['grits', 'hominy_grits'], 'id': 518, 'def': 'coarsely ground corn boiled as a breakfast dish', 'name': 'grits'}, {'frequency': 'c', 'synset': 'grizzly.n.01', 'synonyms': ['grizzly', 'grizzly_bear'], 'id': 519, 'def': 'powerful brownish-yellow bear of the uplands of western North America', 'name': 'grizzly'}, {'frequency': 'c', 'synset': 'grocery_bag.n.01', 'synonyms': ['grocery_bag'], 'id': 520, 'def': "a sack for holding customer's groceries", 'name': 'grocery_bag'}, {'frequency': 'f', 'synset': 'guitar.n.01', 'synonyms': ['guitar'], 'id': 521, 'def': 'a stringed instrument usually having six strings; played by strumming or plucking', 'name': 'guitar'}, {'frequency': 'c', 'synset': 'gull.n.02', 'synonyms': ['gull', 'seagull'], 'id': 522, 'def': 'mostly white aquatic bird having long pointed wings and short legs', 'name': 'gull'}, {'frequency': 'c', 'synset': 'gun.n.01', 'synonyms': ['gun'], 'id': 523, 'def': 'a weapon that discharges a bullet at high velocity from a metal tube', 'name': 'gun'}, {'frequency': 'f', 'synset': 'hairbrush.n.01', 'synonyms': ['hairbrush'], 'id': 524, 'def': "a brush used to groom a person's hair", 'name': 'hairbrush'}, {'frequency': 'c', 'synset': 'hairnet.n.01', 'synonyms': ['hairnet'], 'id': 525, 'def': 'a small net that someone wears over their hair to keep it in place', 'name': 'hairnet'}, {'frequency': 'c', 'synset': 'hairpin.n.01', 'synonyms': ['hairpin'], 'id': 526, 'def': "a double pronged pin used to hold women's hair in place", 'name': 'hairpin'}, {'frequency': 'r', 'synset': 'halter.n.03', 'synonyms': ['halter_top'], 'id': 527, 'def': "a woman's top that fastens behind the back and neck leaving the back and arms uncovered", 'name': 'halter_top'}, {'frequency': 'f', 'synset': 'ham.n.01', 'synonyms': ['ham', 'jambon', 'gammon'], 'id': 528, 'def': 'meat cut from the thigh of a hog (usually smoked)', 'name': 'ham'}, {'frequency': 'c', 'synset': 'hamburger.n.01', 'synonyms': ['hamburger', 'beefburger', 'burger'], 'id': 529, 'def': 'a sandwich consisting of a patty of minced beef served on a bun', 'name': 'hamburger'}, {'frequency': 'c', 'synset': 'hammer.n.02', 'synonyms': ['hammer'], 'id': 530, 'def': 'a hand tool with a heavy head and a handle; used to deliver an impulsive force by striking', 'name': 'hammer'}, {'frequency': 'c', 'synset': 'hammock.n.02', 'synonyms': ['hammock'], 'id': 531, 'def': 'a hanging bed of canvas or rope netting (usually suspended between two trees)', 'name': 'hammock'}, {'frequency': 'r', 'synset': 'hamper.n.02', 'synonyms': ['hamper'], 'id': 532, 'def': 'a basket usually with a cover', 'name': 'hamper'}, {'frequency': 'c', 'synset': 'hamster.n.01', 'synonyms': ['hamster'], 'id': 533, 'def': 'short-tailed burrowing rodent with large cheek pouches', 'name': 'hamster'}, {'frequency': 'f', 'synset': 'hand_blower.n.01', 'synonyms': ['hair_dryer'], 'id': 534, 'def': 'a hand-held electric blower that can blow warm air onto the hair', 'name': 'hair_dryer'}, {'frequency': 'r', 'synset': 'hand_glass.n.01', 'synonyms': ['hand_glass', 'hand_mirror'], 'id': 535, 'def': 'a mirror intended to be held in the hand', 'name': 'hand_glass'}, {'frequency': 'f', 'synset': 'hand_towel.n.01', 'synonyms': ['hand_towel', 'face_towel'], 'id': 536, 'def': 'a small towel used to dry the hands or face', 'name': 'hand_towel'}, {'frequency': 'c', 'synset': 'handcart.n.01', 'synonyms': ['handcart', 'pushcart', 'hand_truck'], 'id': 537, 'def': 'wheeled vehicle that can be pushed by a person', 'name': 'handcart'}, {'frequency': 'r', 'synset': 'handcuff.n.01', 'synonyms': ['handcuff'], 'id': 538, 'def': 'shackle that consists of a metal loop that can be locked around the wrist', 'name': 'handcuff'}, {'frequency': 'c', 'synset': 'handkerchief.n.01', 'synonyms': ['handkerchief'], 'id': 539, 'def': 'a square piece of cloth used for wiping the eyes or nose or as a costume accessory', 'name': 'handkerchief'}, {'frequency': 'f', 'synset': 'handle.n.01', 'synonyms': ['handle', 'grip', 'handgrip'], 'id': 540, 'def': 'the appendage to an object that is designed to be held in order to use or move it', 'name': 'handle'}, {'frequency': 'r', 'synset': 'handsaw.n.01', 'synonyms': ['handsaw', "carpenter's_saw"], 'id': 541, 'def': 'a saw used with one hand for cutting wood', 'name': 'handsaw'}, {'frequency': 'r', 'synset': 'hardback.n.01', 'synonyms': ['hardback_book', 'hardcover_book'], 'id': 542, 'def': 'a book with cardboard or cloth or leather covers', 'name': 'hardback_book'}, {'frequency': 'r', 'synset': 'harmonium.n.01', 'synonyms': ['harmonium', 'organ_(musical_instrument)', 'reed_organ_(musical_instrument)'], 'id': 543, 'def': 'a free-reed instrument in which air is forced through the reeds by bellows', 'name': 'harmonium'}, {'frequency': 'f', 'synset': 'hat.n.01', 'synonyms': ['hat'], 'id': 544, 'def': 'headwear that protects the head from bad weather, sun, or worn for fashion', 'name': 'hat'}, {'frequency': 'r', 'synset': 'hatbox.n.01', 'synonyms': ['hatbox'], 'id': 545, 'def': 'a round piece of luggage for carrying hats', 'name': 'hatbox'}, {'frequency': 'c', 'synset': 'head_covering.n.01', 'synonyms': ['veil'], 'id': 546, 'def': 'a garment that covers the head OR face', 'name': 'veil'}, {'frequency': 'f', 'synset': 'headband.n.01', 'synonyms': ['headband'], 'id': 547, 'def': 'a band worn around or over the head', 'name': 'headband'}, {'frequency': 'f', 'synset': 'headboard.n.01', 'synonyms': ['headboard'], 'id': 548, 'def': 'a vertical board or panel forming the head of a bedstead', 'name': 'headboard'}, {'frequency': 'f', 'synset': 'headlight.n.01', 'synonyms': ['headlight', 'headlamp'], 'id': 549, 'def': 'a powerful light with reflector; attached to the front of an automobile or locomotive', 'name': 'headlight'}, {'frequency': 'c', 'synset': 'headscarf.n.01', 'synonyms': ['headscarf'], 'id': 550, 'def': 'a kerchief worn over the head and tied under the chin', 'name': 'headscarf'}, {'frequency': 'r', 'synset': 'headset.n.01', 'synonyms': ['headset'], 'id': 551, 'def': 'receiver consisting of a pair of headphones', 'name': 'headset'}, {'frequency': 'c', 'synset': 'headstall.n.01', 'synonyms': ['headstall_(for_horses)', 'headpiece_(for_horses)'], 'id': 552, 'def': "the band that is the part of a bridle that fits around a horse's head", 'name': 'headstall_(for_horses)'}, {'frequency': 'c', 'synset': 'heart.n.02', 'synonyms': ['heart'], 'id': 553, 'def': 'a muscular organ; its contractions move the blood through the body', 'name': 'heart'}, {'frequency': 'c', 'synset': 'heater.n.01', 'synonyms': ['heater', 'warmer'], 'id': 554, 'def': 'device that heats water or supplies warmth to a room', 'name': 'heater'}, {'frequency': 'c', 'synset': 'helicopter.n.01', 'synonyms': ['helicopter'], 'id': 555, 'def': 'an aircraft without wings that obtains its lift from the rotation of overhead blades', 'name': 'helicopter'}, {'frequency': 'f', 'synset': 'helmet.n.02', 'synonyms': ['helmet'], 'id': 556, 'def': 'a protective headgear made of hard material to resist blows', 'name': 'helmet'}, {'frequency': 'r', 'synset': 'heron.n.02', 'synonyms': ['heron'], 'id': 557, 'def': 'grey or white wading bird with long neck and long legs and (usually) long bill', 'name': 'heron'}, {'frequency': 'c', 'synset': 'highchair.n.01', 'synonyms': ['highchair', 'feeding_chair'], 'id': 558, 'def': 'a chair for feeding a very young child', 'name': 'highchair'}, {'frequency': 'f', 'synset': 'hinge.n.01', 'synonyms': ['hinge'], 'id': 559, 'def': 'a joint that holds two parts together so that one can swing relative to the other', 'name': 'hinge'}, {'frequency': 'r', 'synset': 'hippopotamus.n.01', 'synonyms': ['hippopotamus'], 'id': 560, 'def': 'massive thick-skinned animal living in or around rivers of tropical Africa', 'name': 'hippopotamus'}, {'frequency': 'r', 'synset': 'hockey_stick.n.01', 'synonyms': ['hockey_stick'], 'id': 561, 'def': 'sports implement consisting of a stick used by hockey players to move the puck', 'name': 'hockey_stick'}, {'frequency': 'c', 'synset': 'hog.n.03', 'synonyms': ['hog', 'pig'], 'id': 562, 'def': 'domestic swine', 'name': 'hog'}, {'frequency': 'f', 'synset': 'home_plate.n.01', 'synonyms': ['home_plate_(baseball)', 'home_base_(baseball)'], 'id': 563, 'def': '(baseball) a rubber slab where the batter stands; it must be touched by a base runner in order to score', 'name': 'home_plate_(baseball)'}, {'frequency': 'c', 'synset': 'honey.n.01', 'synonyms': ['honey'], 'id': 564, 'def': 'a sweet yellow liquid produced by bees', 'name': 'honey'}, {'frequency': 'f', 'synset': 'hood.n.06', 'synonyms': ['fume_hood', 'exhaust_hood'], 'id': 565, 'def': 'metal covering leading to a vent that exhausts smoke or fumes', 'name': 'fume_hood'}, {'frequency': 'f', 'synset': 'hook.n.05', 'synonyms': ['hook'], 'id': 566, 'def': 'a curved or bent implement for suspending or pulling something', 'name': 'hook'}, {'frequency': 'r', 'synset': 'hookah.n.01', 'synonyms': ['hookah', 'narghile', 'nargileh', 'sheesha', 'shisha', 'water_pipe'], 'id': 567, 'def': 'a tobacco pipe with a long flexible tube connected to a container where the smoke is cooled by passing through water', 'name': 'hookah'}, {'frequency': 'r', 'synset': 'hornet.n.01', 'synonyms': ['hornet'], 'id': 568, 'def': 'large stinging wasp', 'name': 'hornet'}, {'frequency': 'f', 'synset': 'horse.n.01', 'synonyms': ['horse'], 'id': 569, 'def': 'a common horse', 'name': 'horse'}, {'frequency': 'f', 'synset': 'hose.n.03', 'synonyms': ['hose', 'hosepipe'], 'id': 570, 'def': 'a flexible pipe for conveying a liquid or gas', 'name': 'hose'}, {'frequency': 'r', 'synset': 'hot-air_balloon.n.01', 'synonyms': ['hot-air_balloon'], 'id': 571, 'def': 'balloon for travel through the air in a basket suspended below a large bag of heated air', 'name': 'hot-air_balloon'}, {'frequency': 'r', 'synset': 'hot_plate.n.01', 'synonyms': ['hotplate'], 'id': 572, 'def': 'a portable electric appliance for heating or cooking or keeping food warm', 'name': 'hotplate'}, {'frequency': 'c', 'synset': 'hot_sauce.n.01', 'synonyms': ['hot_sauce'], 'id': 573, 'def': 'a pungent peppery sauce', 'name': 'hot_sauce'}, {'frequency': 'r', 'synset': 'hourglass.n.01', 'synonyms': ['hourglass'], 'id': 574, 'def': 'a sandglass timer that runs for sixty minutes', 'name': 'hourglass'}, {'frequency': 'r', 'synset': 'houseboat.n.01', 'synonyms': ['houseboat'], 'id': 575, 'def': 'a barge that is designed and equipped for use as a dwelling', 'name': 'houseboat'}, {'frequency': 'c', 'synset': 'hummingbird.n.01', 'synonyms': ['hummingbird'], 'id': 576, 'def': 'tiny American bird having brilliant iridescent plumage and long slender bills', 'name': 'hummingbird'}, {'frequency': 'r', 'synset': 'hummus.n.01', 'synonyms': ['hummus', 'humus', 'hommos', 'hoummos', 'humous'], 'id': 577, 'def': 'a thick spread made from mashed chickpeas', 'name': 'hummus'}, {'frequency': 'f', 'synset': 'ice_bear.n.01', 'synonyms': ['polar_bear'], 'id': 578, 'def': 'white bear of Arctic regions', 'name': 'polar_bear'}, {'frequency': 'c', 'synset': 'ice_cream.n.01', 'synonyms': ['icecream'], 'id': 579, 'def': 'frozen dessert containing cream and sugar and flavoring', 'name': 'icecream'}, {'frequency': 'r', 'synset': 'ice_lolly.n.01', 'synonyms': ['popsicle'], 'id': 580, 'def': 'ice cream or water ice on a small wooden stick', 'name': 'popsicle'}, {'frequency': 'c', 'synset': 'ice_maker.n.01', 'synonyms': ['ice_maker'], 'id': 581, 'def': 'an appliance included in some electric refrigerators for making ice cubes', 'name': 'ice_maker'}, {'frequency': 'r', 'synset': 'ice_pack.n.01', 'synonyms': ['ice_pack', 'ice_bag'], 'id': 582, 'def': 'a waterproof bag filled with ice: applied to the body (especially the head) to cool or reduce swelling', 'name': 'ice_pack'}, {'frequency': 'r', 'synset': 'ice_skate.n.01', 'synonyms': ['ice_skate'], 'id': 583, 'def': 'skate consisting of a boot with a steel blade fitted to the sole', 'name': 'ice_skate'}, {'frequency': 'c', 'synset': 'igniter.n.01', 'synonyms': ['igniter', 'ignitor', 'lighter'], 'id': 584, 'def': 'a substance or device used to start a fire', 'name': 'igniter'}, {'frequency': 'r', 'synset': 'inhaler.n.01', 'synonyms': ['inhaler', 'inhalator'], 'id': 585, 'def': 'a dispenser that produces a chemical vapor to be inhaled through mouth or nose', 'name': 'inhaler'}, {'frequency': 'f', 'synset': 'ipod.n.01', 'synonyms': ['iPod'], 'id': 586, 'def': 'a pocket-sized device used to play music files', 'name': 'iPod'}, {'frequency': 'c', 'synset': 'iron.n.04', 'synonyms': ['iron_(for_clothing)', 'smoothing_iron_(for_clothing)'], 'id': 587, 'def': 'home appliance consisting of a flat metal base that is heated and used to smooth cloth', 'name': 'iron_(for_clothing)'}, {'frequency': 'c', 'synset': 'ironing_board.n.01', 'synonyms': ['ironing_board'], 'id': 588, 'def': 'narrow padded board on collapsible supports; used for ironing clothes', 'name': 'ironing_board'}, {'frequency': 'f', 'synset': 'jacket.n.01', 'synonyms': ['jacket'], 'id': 589, 'def': 'a waist-length coat', 'name': 'jacket'}, {'frequency': 'c', 'synset': 'jam.n.01', 'synonyms': ['jam'], 'id': 590, 'def': 'preserve of crushed fruit', 'name': 'jam'}, {'frequency': 'f', 'synset': 'jar.n.01', 'synonyms': ['jar'], 'id': 591, 'def': 'a vessel (usually cylindrical) with a wide mouth and without handles', 'name': 'jar'}, {'frequency': 'f', 'synset': 'jean.n.01', 'synonyms': ['jean', 'blue_jean', 'denim'], 'id': 592, 'def': '(usually plural) close-fitting trousers of heavy denim for manual work or casual wear', 'name': 'jean'}, {'frequency': 'c', 'synset': 'jeep.n.01', 'synonyms': ['jeep', 'landrover'], 'id': 593, 'def': 'a car suitable for traveling over rough terrain', 'name': 'jeep'}, {'frequency': 'r', 'synset': 'jelly_bean.n.01', 'synonyms': ['jelly_bean', 'jelly_egg'], 'id': 594, 'def': 'sugar-glazed jellied candy', 'name': 'jelly_bean'}, {'frequency': 'f', 'synset': 'jersey.n.03', 'synonyms': ['jersey', 'T-shirt', 'tee_shirt'], 'id': 595, 'def': 'a close-fitting pullover shirt', 'name': 'jersey'}, {'frequency': 'c', 'synset': 'jet.n.01', 'synonyms': ['jet_plane', 'jet-propelled_plane'], 'id': 596, 'def': 'an airplane powered by one or more jet engines', 'name': 'jet_plane'}, {'frequency': 'r', 'synset': 'jewel.n.01', 'synonyms': ['jewel', 'gem', 'precious_stone'], 'id': 597, 'def': 'a precious or semiprecious stone incorporated into a piece of jewelry', 'name': 'jewel'}, {'frequency': 'c', 'synset': 'jewelry.n.01', 'synonyms': ['jewelry', 'jewellery'], 'id': 598, 'def': 'an adornment (as a bracelet or ring or necklace) made of precious metals and set with gems (or imitation gems)', 'name': 'jewelry'}, {'frequency': 'r', 'synset': 'joystick.n.02', 'synonyms': ['joystick'], 'id': 599, 'def': 'a control device for computers consisting of a vertical handle that can move freely in two directions', 'name': 'joystick'}, {'frequency': 'c', 'synset': 'jump_suit.n.01', 'synonyms': ['jumpsuit'], 'id': 600, 'def': "one-piece garment fashioned after a parachutist's uniform", 'name': 'jumpsuit'}, {'frequency': 'c', 'synset': 'kayak.n.01', 'synonyms': ['kayak'], 'id': 601, 'def': 'a small canoe consisting of a light frame made watertight with animal skins', 'name': 'kayak'}, {'frequency': 'r', 'synset': 'keg.n.02', 'synonyms': ['keg'], 'id': 602, 'def': 'small cask or barrel', 'name': 'keg'}, {'frequency': 'r', 'synset': 'kennel.n.01', 'synonyms': ['kennel', 'doghouse'], 'id': 603, 'def': 'outbuilding that serves as a shelter for a dog', 'name': 'kennel'}, {'frequency': 'c', 'synset': 'kettle.n.01', 'synonyms': ['kettle', 'boiler'], 'id': 604, 'def': 'a metal pot for stewing or boiling; usually has a lid', 'name': 'kettle'}, {'frequency': 'f', 'synset': 'key.n.01', 'synonyms': ['key'], 'id': 605, 'def': 'metal instrument used to unlock a lock', 'name': 'key'}, {'frequency': 'r', 'synset': 'keycard.n.01', 'synonyms': ['keycard'], 'id': 606, 'def': 'a plastic card used to gain access typically to a door', 'name': 'keycard'}, {'frequency': 'c', 'synset': 'kilt.n.01', 'synonyms': ['kilt'], 'id': 607, 'def': 'a knee-length pleated tartan skirt worn by men as part of the traditional dress in the Highlands of northern Scotland', 'name': 'kilt'}, {'frequency': 'c', 'synset': 'kimono.n.01', 'synonyms': ['kimono'], 'id': 608, 'def': 'a loose robe; imitated from robes originally worn by Japanese', 'name': 'kimono'}, {'frequency': 'f', 'synset': 'kitchen_sink.n.01', 'synonyms': ['kitchen_sink'], 'id': 609, 'def': 'a sink in a kitchen', 'name': 'kitchen_sink'}, {'frequency': 'r', 'synset': 'kitchen_table.n.01', 'synonyms': ['kitchen_table'], 'id': 610, 'def': 'a table in the kitchen', 'name': 'kitchen_table'}, {'frequency': 'f', 'synset': 'kite.n.03', 'synonyms': ['kite'], 'id': 611, 'def': 'plaything consisting of a light frame covered with tissue paper; flown in wind at end of a string', 'name': 'kite'}, {'frequency': 'c', 'synset': 'kitten.n.01', 'synonyms': ['kitten', 'kitty'], 'id': 612, 'def': 'young domestic cat', 'name': 'kitten'}, {'frequency': 'c', 'synset': 'kiwi.n.03', 'synonyms': ['kiwi_fruit'], 'id': 613, 'def': 'fuzzy brown egg-shaped fruit with slightly tart green flesh', 'name': 'kiwi_fruit'}, {'frequency': 'f', 'synset': 'knee_pad.n.01', 'synonyms': ['knee_pad'], 'id': 614, 'def': 'protective garment consisting of a pad worn by football or baseball or hockey players', 'name': 'knee_pad'}, {'frequency': 'f', 'synset': 'knife.n.01', 'synonyms': ['knife'], 'id': 615, 'def': 'tool with a blade and point used as a cutting instrument', 'name': 'knife'}, {'frequency': 'r', 'synset': 'knitting_needle.n.01', 'synonyms': ['knitting_needle'], 'id': 616, 'def': 'needle consisting of a slender rod with pointed ends; usually used in pairs', 'name': 'knitting_needle'}, {'frequency': 'f', 'synset': 'knob.n.02', 'synonyms': ['knob'], 'id': 617, 'def': 'a round handle often found on a door', 'name': 'knob'}, {'frequency': 'r', 'synset': 'knocker.n.05', 'synonyms': ['knocker_(on_a_door)', 'doorknocker'], 'id': 618, 'def': 'a device (usually metal and ornamental) attached by a hinge to a door', 'name': 'knocker_(on_a_door)'}, {'frequency': 'r', 'synset': 'koala.n.01', 'synonyms': ['koala', 'koala_bear'], 'id': 619, 'def': 'sluggish tailless Australian marsupial with grey furry ears and coat', 'name': 'koala'}, {'frequency': 'r', 'synset': 'lab_coat.n.01', 'synonyms': ['lab_coat', 'laboratory_coat'], 'id': 620, 'def': 'a light coat worn to protect clothing from substances used while working in a laboratory', 'name': 'lab_coat'}, {'frequency': 'f', 'synset': 'ladder.n.01', 'synonyms': ['ladder'], 'id': 621, 'def': 'steps consisting of two parallel members connected by rungs', 'name': 'ladder'}, {'frequency': 'c', 'synset': 'ladle.n.01', 'synonyms': ['ladle'], 'id': 622, 'def': 'a spoon-shaped vessel with a long handle frequently used to transfer liquids', 'name': 'ladle'}, {'frequency': 'c', 'synset': 'ladybug.n.01', 'synonyms': ['ladybug', 'ladybeetle', 'ladybird_beetle'], 'id': 623, 'def': 'small round bright-colored and spotted beetle, typically red and black', 'name': 'ladybug'}, {'frequency': 'f', 'synset': 'lamb.n.01', 'synonyms': ['lamb_(animal)'], 'id': 624, 'def': 'young sheep', 'name': 'lamb_(animal)'}, {'frequency': 'r', 'synset': 'lamb_chop.n.01', 'synonyms': ['lamb-chop', 'lambchop'], 'id': 625, 'def': 'chop cut from a lamb', 'name': 'lamb-chop'}, {'frequency': 'f', 'synset': 'lamp.n.02', 'synonyms': ['lamp'], 'id': 626, 'def': 'a piece of furniture holding one or more electric light bulbs', 'name': 'lamp'}, {'frequency': 'f', 'synset': 'lamppost.n.01', 'synonyms': ['lamppost'], 'id': 627, 'def': 'a metal post supporting an outdoor lamp (such as a streetlight)', 'name': 'lamppost'}, {'frequency': 'f', 'synset': 'lampshade.n.01', 'synonyms': ['lampshade'], 'id': 628, 'def': 'a protective ornamental shade used to screen a light bulb from direct view', 'name': 'lampshade'}, {'frequency': 'c', 'synset': 'lantern.n.01', 'synonyms': ['lantern'], 'id': 629, 'def': 'light in a transparent protective case', 'name': 'lantern'}, {'frequency': 'f', 'synset': 'lanyard.n.02', 'synonyms': ['lanyard', 'laniard'], 'id': 630, 'def': 'a cord worn around the neck to hold a knife or whistle, etc.', 'name': 'lanyard'}, {'frequency': 'f', 'synset': 'laptop.n.01', 'synonyms': ['laptop_computer', 'notebook_computer'], 'id': 631, 'def': 'a portable computer small enough to use in your lap', 'name': 'laptop_computer'}, {'frequency': 'r', 'synset': 'lasagna.n.01', 'synonyms': ['lasagna', 'lasagne'], 'id': 632, 'def': 'baked dish of layers of lasagna pasta with sauce and cheese and meat or vegetables', 'name': 'lasagna'}, {'frequency': 'f', 'synset': 'latch.n.02', 'synonyms': ['latch'], 'id': 633, 'def': 'a bar that can be lowered or slid into a groove to fasten a door or gate', 'name': 'latch'}, {'frequency': 'r', 'synset': 'lawn_mower.n.01', 'synonyms': ['lawn_mower'], 'id': 634, 'def': 'garden tool for mowing grass on lawns', 'name': 'lawn_mower'}, {'frequency': 'r', 'synset': 'leather.n.01', 'synonyms': ['leather'], 'id': 635, 'def': 'an animal skin made smooth and flexible by removing the hair and then tanning', 'name': 'leather'}, {'frequency': 'c', 'synset': 'legging.n.01', 'synonyms': ['legging_(clothing)', 'leging_(clothing)', 'leg_covering'], 'id': 636, 'def': 'a garment covering the leg (usually extending from the knee to the ankle)', 'name': 'legging_(clothing)'}, {'frequency': 'c', 'synset': 'lego.n.01', 'synonyms': ['Lego', 'Lego_set'], 'id': 637, 'def': "a child's plastic construction set for making models from blocks", 'name': 'Lego'}, {'frequency': 'r', 'synset': 'legume.n.02', 'synonyms': ['legume'], 'id': 638, 'def': 'the fruit or seed of bean or pea plants', 'name': 'legume'}, {'frequency': 'f', 'synset': 'lemon.n.01', 'synonyms': ['lemon'], 'id': 639, 'def': 'yellow oval fruit with juicy acidic flesh', 'name': 'lemon'}, {'frequency': 'r', 'synset': 'lemonade.n.01', 'synonyms': ['lemonade'], 'id': 640, 'def': 'sweetened beverage of diluted lemon juice', 'name': 'lemonade'}, {'frequency': 'f', 'synset': 'lettuce.n.02', 'synonyms': ['lettuce'], 'id': 641, 'def': 'leafy plant commonly eaten in salad or on sandwiches', 'name': 'lettuce'}, {'frequency': 'f', 'synset': 'license_plate.n.01', 'synonyms': ['license_plate', 'numberplate'], 'id': 642, 'def': "a plate mounted on the front and back of car and bearing the car's registration number", 'name': 'license_plate'}, {'frequency': 'f', 'synset': 'life_buoy.n.01', 'synonyms': ['life_buoy', 'lifesaver', 'life_belt', 'life_ring'], 'id': 643, 'def': 'a ring-shaped life preserver used to prevent drowning (NOT a life-jacket or vest)', 'name': 'life_buoy'}, {'frequency': 'f', 'synset': 'life_jacket.n.01', 'synonyms': ['life_jacket', 'life_vest'], 'id': 644, 'def': 'life preserver consisting of a sleeveless jacket of buoyant or inflatable design', 'name': 'life_jacket'}, {'frequency': 'f', 'synset': 'light_bulb.n.01', 'synonyms': ['lightbulb'], 'id': 645, 'def': 'lightblub/source of light', 'name': 'lightbulb'}, {'frequency': 'r', 'synset': 'lightning_rod.n.02', 'synonyms': ['lightning_rod', 'lightning_conductor'], 'id': 646, 'def': 'a metallic conductor that is attached to a high point and leads to the ground', 'name': 'lightning_rod'}, {'frequency': 'f', 'synset': 'lime.n.06', 'synonyms': ['lime'], 'id': 647, 'def': 'the green acidic fruit of any of various lime trees', 'name': 'lime'}, {'frequency': 'r', 'synset': 'limousine.n.01', 'synonyms': ['limousine'], 'id': 648, 'def': 'long luxurious car; usually driven by a chauffeur', 'name': 'limousine'}, {'frequency': 'c', 'synset': 'lion.n.01', 'synonyms': ['lion'], 'id': 649, 'def': 'large gregarious predatory cat of Africa and India', 'name': 'lion'}, {'frequency': 'c', 'synset': 'lip_balm.n.01', 'synonyms': ['lip_balm'], 'id': 650, 'def': 'a balm applied to the lips', 'name': 'lip_balm'}, {'frequency': 'r', 'synset': 'liquor.n.01', 'synonyms': ['liquor', 'spirits', 'hard_liquor', 'liqueur', 'cordial'], 'id': 651, 'def': 'liquor or beer', 'name': 'liquor'}, {'frequency': 'c', 'synset': 'lizard.n.01', 'synonyms': ['lizard'], 'id': 652, 'def': 'a reptile with usually two pairs of legs and a tapering tail', 'name': 'lizard'}, {'frequency': 'f', 'synset': 'log.n.01', 'synonyms': ['log'], 'id': 653, 'def': 'a segment of the trunk of a tree when stripped of branches', 'name': 'log'}, {'frequency': 'c', 'synset': 'lollipop.n.02', 'synonyms': ['lollipop'], 'id': 654, 'def': 'hard candy on a stick', 'name': 'lollipop'}, {'frequency': 'f', 'synset': 'loudspeaker.n.01', 'synonyms': ['speaker_(stero_equipment)'], 'id': 655, 'def': 'electronic device that produces sound often as part of a stereo system', 'name': 'speaker_(stero_equipment)'}, {'frequency': 'c', 'synset': 'love_seat.n.01', 'synonyms': ['loveseat'], 'id': 656, 'def': 'small sofa that seats two people', 'name': 'loveseat'}, {'frequency': 'r', 'synset': 'machine_gun.n.01', 'synonyms': ['machine_gun'], 'id': 657, 'def': 'a rapidly firing automatic gun', 'name': 'machine_gun'}, {'frequency': 'f', 'synset': 'magazine.n.02', 'synonyms': ['magazine'], 'id': 658, 'def': 'a paperback periodic publication', 'name': 'magazine'}, {'frequency': 'f', 'synset': 'magnet.n.01', 'synonyms': ['magnet'], 'id': 659, 'def': 'a device that attracts iron and produces a magnetic field', 'name': 'magnet'}, {'frequency': 'c', 'synset': 'mail_slot.n.01', 'synonyms': ['mail_slot'], 'id': 660, 'def': 'a slot (usually in a door) through which mail can be delivered', 'name': 'mail_slot'}, {'frequency': 'f', 'synset': 'mailbox.n.01', 'synonyms': ['mailbox_(at_home)', 'letter_box_(at_home)'], 'id': 661, 'def': 'a private box for delivery of mail', 'name': 'mailbox_(at_home)'}, {'frequency': 'r', 'synset': 'mallard.n.01', 'synonyms': ['mallard'], 'id': 662, 'def': 'wild dabbling duck from which domestic ducks are descended', 'name': 'mallard'}, {'frequency': 'r', 'synset': 'mallet.n.01', 'synonyms': ['mallet'], 'id': 663, 'def': 'a sports implement with a long handle and a hammer-like head used to hit a ball', 'name': 'mallet'}, {'frequency': 'r', 'synset': 'mammoth.n.01', 'synonyms': ['mammoth'], 'id': 664, 'def': 'any of numerous extinct elephants widely distributed in the Pleistocene', 'name': 'mammoth'}, {'frequency': 'r', 'synset': 'manatee.n.01', 'synonyms': ['manatee'], 'id': 665, 'def': 'sirenian mammal of tropical coastal waters of America', 'name': 'manatee'}, {'frequency': 'c', 'synset': 'mandarin.n.05', 'synonyms': ['mandarin_orange'], 'id': 666, 'def': 'a somewhat flat reddish-orange loose skinned citrus of China', 'name': 'mandarin_orange'}, {'frequency': 'c', 'synset': 'manger.n.01', 'synonyms': ['manger', 'trough'], 'id': 667, 'def': 'a container (usually in a barn or stable) from which cattle or horses feed', 'name': 'manger'}, {'frequency': 'f', 'synset': 'manhole.n.01', 'synonyms': ['manhole'], 'id': 668, 'def': 'a hole (usually with a flush cover) through which a person can gain access to an underground structure', 'name': 'manhole'}, {'frequency': 'f', 'synset': 'map.n.01', 'synonyms': ['map'], 'id': 669, 'def': "a diagrammatic representation of the earth's surface (or part of it)", 'name': 'map'}, {'frequency': 'f', 'synset': 'marker.n.03', 'synonyms': ['marker'], 'id': 670, 'def': 'a writing implement for making a mark', 'name': 'marker'}, {'frequency': 'r', 'synset': 'martini.n.01', 'synonyms': ['martini'], 'id': 671, 'def': 'a cocktail made of gin (or vodka) with dry vermouth', 'name': 'martini'}, {'frequency': 'r', 'synset': 'mascot.n.01', 'synonyms': ['mascot'], 'id': 672, 'def': 'a person or animal that is adopted by a team or other group as a symbolic figure', 'name': 'mascot'}, {'frequency': 'c', 'synset': 'mashed_potato.n.01', 'synonyms': ['mashed_potato'], 'id': 673, 'def': 'potato that has been peeled and boiled and then mashed', 'name': 'mashed_potato'}, {'frequency': 'r', 'synset': 'masher.n.02', 'synonyms': ['masher'], 'id': 674, 'def': 'a kitchen utensil used for mashing (e.g. potatoes)', 'name': 'masher'}, {'frequency': 'f', 'synset': 'mask.n.04', 'synonyms': ['mask', 'facemask'], 'id': 675, 'def': 'a protective covering worn over the face', 'name': 'mask'}, {'frequency': 'f', 'synset': 'mast.n.01', 'synonyms': ['mast'], 'id': 676, 'def': 'a vertical spar for supporting sails', 'name': 'mast'}, {'frequency': 'c', 'synset': 'mat.n.03', 'synonyms': ['mat_(gym_equipment)', 'gym_mat'], 'id': 677, 'def': 'sports equipment consisting of a piece of thick padding on the floor for gymnastics', 'name': 'mat_(gym_equipment)'}, {'frequency': 'r', 'synset': 'matchbox.n.01', 'synonyms': ['matchbox'], 'id': 678, 'def': 'a box for holding matches', 'name': 'matchbox'}, {'frequency': 'f', 'synset': 'mattress.n.01', 'synonyms': ['mattress'], 'id': 679, 'def': 'a thick pad filled with resilient material used as a bed or part of a bed', 'name': 'mattress'}, {'frequency': 'c', 'synset': 'measuring_cup.n.01', 'synonyms': ['measuring_cup'], 'id': 680, 'def': 'graduated cup used to measure liquid or granular ingredients', 'name': 'measuring_cup'}, {'frequency': 'c', 'synset': 'measuring_stick.n.01', 'synonyms': ['measuring_stick', 'ruler_(measuring_stick)', 'measuring_rod'], 'id': 681, 'def': 'measuring instrument having a sequence of marks at regular intervals', 'name': 'measuring_stick'}, {'frequency': 'c', 'synset': 'meatball.n.01', 'synonyms': ['meatball'], 'id': 682, 'def': 'ground meat formed into a ball and fried or simmered in broth', 'name': 'meatball'}, {'frequency': 'c', 'synset': 'medicine.n.02', 'synonyms': ['medicine'], 'id': 683, 'def': 'something that treats or prevents or alleviates the symptoms of disease', 'name': 'medicine'}, {'frequency': 'c', 'synset': 'melon.n.01', 'synonyms': ['melon'], 'id': 684, 'def': 'fruit of the gourd family having a hard rind and sweet juicy flesh', 'name': 'melon'}, {'frequency': 'f', 'synset': 'microphone.n.01', 'synonyms': ['microphone'], 'id': 685, 'def': 'device for converting sound waves into electrical energy', 'name': 'microphone'}, {'frequency': 'r', 'synset': 'microscope.n.01', 'synonyms': ['microscope'], 'id': 686, 'def': 'magnifier of the image of small objects', 'name': 'microscope'}, {'frequency': 'f', 'synset': 'microwave.n.02', 'synonyms': ['microwave_oven'], 'id': 687, 'def': 'kitchen appliance that cooks food by passing an electromagnetic wave through it', 'name': 'microwave_oven'}, {'frequency': 'r', 'synset': 'milestone.n.01', 'synonyms': ['milestone', 'milepost'], 'id': 688, 'def': 'stone post at side of a road to show distances', 'name': 'milestone'}, {'frequency': 'f', 'synset': 'milk.n.01', 'synonyms': ['milk'], 'id': 689, 'def': 'a white nutritious liquid secreted by mammals and used as food by human beings', 'name': 'milk'}, {'frequency': 'r', 'synset': 'milk_can.n.01', 'synonyms': ['milk_can'], 'id': 690, 'def': 'can for transporting milk', 'name': 'milk_can'}, {'frequency': 'r', 'synset': 'milkshake.n.01', 'synonyms': ['milkshake'], 'id': 691, 'def': 'frothy drink of milk and flavoring and sometimes fruit or ice cream', 'name': 'milkshake'}, {'frequency': 'f', 'synset': 'minivan.n.01', 'synonyms': ['minivan'], 'id': 692, 'def': 'a small box-shaped passenger van', 'name': 'minivan'}, {'frequency': 'r', 'synset': 'mint.n.05', 'synonyms': ['mint_candy'], 'id': 693, 'def': 'a candy that is flavored with a mint oil', 'name': 'mint_candy'}, {'frequency': 'f', 'synset': 'mirror.n.01', 'synonyms': ['mirror'], 'id': 694, 'def': 'polished surface that forms images by reflecting light', 'name': 'mirror'}, {'frequency': 'c', 'synset': 'mitten.n.01', 'synonyms': ['mitten'], 'id': 695, 'def': 'glove that encases the thumb separately and the other four fingers together', 'name': 'mitten'}, {'frequency': 'c', 'synset': 'mixer.n.04', 'synonyms': ['mixer_(kitchen_tool)', 'stand_mixer'], 'id': 696, 'def': 'a kitchen utensil that is used for mixing foods', 'name': 'mixer_(kitchen_tool)'}, {'frequency': 'c', 'synset': 'money.n.03', 'synonyms': ['money'], 'id': 697, 'def': 'the official currency issued by a government or national bank', 'name': 'money'}, {'frequency': 'f', 'synset': 'monitor.n.04', 'synonyms': ['monitor_(computer_equipment) computer_monitor'], 'id': 698, 'def': 'a computer monitor', 'name': 'monitor_(computer_equipment) computer_monitor'}, {'frequency': 'c', 'synset': 'monkey.n.01', 'synonyms': ['monkey'], 'id': 699, 'def': 'any of various long-tailed primates', 'name': 'monkey'}, {'frequency': 'f', 'synset': 'motor.n.01', 'synonyms': ['motor'], 'id': 700, 'def': 'machine that converts other forms of energy into mechanical energy and so imparts motion', 'name': 'motor'}, {'frequency': 'f', 'synset': 'motor_scooter.n.01', 'synonyms': ['motor_scooter', 'scooter'], 'id': 701, 'def': 'a wheeled vehicle with small wheels and a low-powered engine', 'name': 'motor_scooter'}, {'frequency': 'r', 'synset': 'motor_vehicle.n.01', 'synonyms': ['motor_vehicle', 'automotive_vehicle'], 'id': 702, 'def': 'a self-propelled wheeled vehicle that does not run on rails', 'name': 'motor_vehicle'}, {'frequency': 'f', 'synset': 'motorcycle.n.01', 'synonyms': ['motorcycle'], 'id': 703, 'def': 'a motor vehicle with two wheels and a strong frame', 'name': 'motorcycle'}, {'frequency': 'f', 'synset': 'mound.n.01', 'synonyms': ['mound_(baseball)', "pitcher's_mound"], 'id': 704, 'def': '(baseball) the slight elevation on which the pitcher stands', 'name': 'mound_(baseball)'}, {'frequency': 'f', 'synset': 'mouse.n.04', 'synonyms': ['mouse_(computer_equipment)', 'computer_mouse'], 'id': 705, 'def': 'a computer input device that controls an on-screen pointer (does not include trackpads / touchpads)', 'name': 'mouse_(computer_equipment)'}, {'frequency': 'f', 'synset': 'mousepad.n.01', 'synonyms': ['mousepad'], 'id': 706, 'def': 'a small portable pad that provides an operating surface for a computer mouse', 'name': 'mousepad'}, {'frequency': 'c', 'synset': 'muffin.n.01', 'synonyms': ['muffin'], 'id': 707, 'def': 'a sweet quick bread baked in a cup-shaped pan', 'name': 'muffin'}, {'frequency': 'f', 'synset': 'mug.n.04', 'synonyms': ['mug'], 'id': 708, 'def': 'with handle and usually cylindrical', 'name': 'mug'}, {'frequency': 'f', 'synset': 'mushroom.n.02', 'synonyms': ['mushroom'], 'id': 709, 'def': 'a common mushroom', 'name': 'mushroom'}, {'frequency': 'r', 'synset': 'music_stool.n.01', 'synonyms': ['music_stool', 'piano_stool'], 'id': 710, 'def': 'a stool for piano players; usually adjustable in height', 'name': 'music_stool'}, {'frequency': 'c', 'synset': 'musical_instrument.n.01', 'synonyms': ['musical_instrument', 'instrument_(musical)'], 'id': 711, 'def': 'any of various devices or contrivances that can be used to produce musical tones or sounds', 'name': 'musical_instrument'}, {'frequency': 'r', 'synset': 'nailfile.n.01', 'synonyms': ['nailfile'], 'id': 712, 'def': 'a small flat file for shaping the nails', 'name': 'nailfile'}, {'frequency': 'f', 'synset': 'napkin.n.01', 'synonyms': ['napkin', 'table_napkin', 'serviette'], 'id': 713, 'def': 'a small piece of table linen or paper that is used to wipe the mouth and to cover the lap in order to protect clothing', 'name': 'napkin'}, {'frequency': 'r', 'synset': 'neckerchief.n.01', 'synonyms': ['neckerchief'], 'id': 714, 'def': 'a kerchief worn around the neck', 'name': 'neckerchief'}, {'frequency': 'f', 'synset': 'necklace.n.01', 'synonyms': ['necklace'], 'id': 715, 'def': 'jewelry consisting of a cord or chain (often bearing gems) worn about the neck as an ornament', 'name': 'necklace'}, {'frequency': 'f', 'synset': 'necktie.n.01', 'synonyms': ['necktie', 'tie_(necktie)'], 'id': 716, 'def': 'neckwear consisting of a long narrow piece of material worn under a collar and tied in knot at the front', 'name': 'necktie'}, {'frequency': 'c', 'synset': 'needle.n.03', 'synonyms': ['needle'], 'id': 717, 'def': 'a sharp pointed implement (usually metal)', 'name': 'needle'}, {'frequency': 'c', 'synset': 'nest.n.01', 'synonyms': ['nest'], 'id': 718, 'def': 'a structure in which animals lay eggs or give birth to their young', 'name': 'nest'}, {'frequency': 'f', 'synset': 'newspaper.n.01', 'synonyms': ['newspaper', 'paper_(newspaper)'], 'id': 719, 'def': 'a daily or weekly publication on folded sheets containing news, articles, and advertisements', 'name': 'newspaper'}, {'frequency': 'c', 'synset': 'newsstand.n.01', 'synonyms': ['newsstand'], 'id': 720, 'def': 'a stall where newspapers and other periodicals are sold', 'name': 'newsstand'}, {'frequency': 'c', 'synset': 'nightwear.n.01', 'synonyms': ['nightshirt', 'nightwear', 'sleepwear', 'nightclothes'], 'id': 721, 'def': 'garments designed to be worn in bed', 'name': 'nightshirt'}, {'frequency': 'r', 'synset': 'nosebag.n.01', 'synonyms': ['nosebag_(for_animals)', 'feedbag'], 'id': 722, 'def': 'a canvas bag that is used to feed an animal (such as a horse); covers the muzzle and fastens at the top of the head', 'name': 'nosebag_(for_animals)'}, {'frequency': 'c', 'synset': 'noseband.n.01', 'synonyms': ['noseband_(for_animals)', 'nosepiece_(for_animals)'], 'id': 723, 'def': "a strap that is the part of a bridle that goes over the animal's nose", 'name': 'noseband_(for_animals)'}, {'frequency': 'f', 'synset': 'notebook.n.01', 'synonyms': ['notebook'], 'id': 724, 'def': 'a book with blank pages for recording notes or memoranda', 'name': 'notebook'}, {'frequency': 'c', 'synset': 'notepad.n.01', 'synonyms': ['notepad'], 'id': 725, 'def': 'a pad of paper for keeping notes', 'name': 'notepad'}, {'frequency': 'f', 'synset': 'nut.n.03', 'synonyms': ['nut'], 'id': 726, 'def': 'a small metal block (usually square or hexagonal) with internal screw thread to be fitted onto a bolt', 'name': 'nut'}, {'frequency': 'r', 'synset': 'nutcracker.n.01', 'synonyms': ['nutcracker'], 'id': 727, 'def': 'a hand tool used to crack nuts open', 'name': 'nutcracker'}, {'frequency': 'f', 'synset': 'oar.n.01', 'synonyms': ['oar'], 'id': 728, 'def': 'an implement used to propel or steer a boat', 'name': 'oar'}, {'frequency': 'r', 'synset': 'octopus.n.01', 'synonyms': ['octopus_(food)'], 'id': 729, 'def': 'tentacles of octopus prepared as food', 'name': 'octopus_(food)'}, {'frequency': 'r', 'synset': 'octopus.n.02', 'synonyms': ['octopus_(animal)'], 'id': 730, 'def': 'bottom-living cephalopod having a soft oval body with eight long tentacles', 'name': 'octopus_(animal)'}, {'frequency': 'c', 'synset': 'oil_lamp.n.01', 'synonyms': ['oil_lamp', 'kerosene_lamp', 'kerosine_lamp'], 'id': 731, 'def': 'a lamp that burns oil (as kerosine) for light', 'name': 'oil_lamp'}, {'frequency': 'c', 'synset': 'olive_oil.n.01', 'synonyms': ['olive_oil'], 'id': 732, 'def': 'oil from olives', 'name': 'olive_oil'}, {'frequency': 'r', 'synset': 'omelet.n.01', 'synonyms': ['omelet', 'omelette'], 'id': 733, 'def': 'beaten eggs cooked until just set; may be folded around e.g. ham or cheese or jelly', 'name': 'omelet'}, {'frequency': 'f', 'synset': 'onion.n.01', 'synonyms': ['onion'], 'id': 734, 'def': 'the bulb of an onion plant', 'name': 'onion'}, {'frequency': 'f', 'synset': 'orange.n.01', 'synonyms': ['orange_(fruit)'], 'id': 735, 'def': 'orange (FRUIT of an orange tree)', 'name': 'orange_(fruit)'}, {'frequency': 'c', 'synset': 'orange_juice.n.01', 'synonyms': ['orange_juice'], 'id': 736, 'def': 'bottled or freshly squeezed juice of oranges', 'name': 'orange_juice'}, {'frequency': 'c', 'synset': 'ostrich.n.02', 'synonyms': ['ostrich'], 'id': 737, 'def': 'fast-running African flightless bird with two-toed feet; largest living bird', 'name': 'ostrich'}, {'frequency': 'f', 'synset': 'ottoman.n.03', 'synonyms': ['ottoman', 'pouf', 'pouffe', 'hassock'], 'id': 738, 'def': 'a thick standalone cushion used as a seat or footrest, often next to a chair', 'name': 'ottoman'}, {'frequency': 'f', 'synset': 'oven.n.01', 'synonyms': ['oven'], 'id': 739, 'def': 'kitchen appliance used for baking or roasting', 'name': 'oven'}, {'frequency': 'c', 'synset': 'overall.n.01', 'synonyms': ['overalls_(clothing)'], 'id': 740, 'def': 'work clothing consisting of denim trousers usually with a bib and shoulder straps', 'name': 'overalls_(clothing)'}, {'frequency': 'c', 'synset': 'owl.n.01', 'synonyms': ['owl'], 'id': 741, 'def': 'nocturnal bird of prey with hawk-like beak and claws and large head with front-facing eyes', 'name': 'owl'}, {'frequency': 'c', 'synset': 'packet.n.03', 'synonyms': ['packet'], 'id': 742, 'def': 'a small package or bundle', 'name': 'packet'}, {'frequency': 'r', 'synset': 'pad.n.03', 'synonyms': ['inkpad', 'inking_pad', 'stamp_pad'], 'id': 743, 'def': 'absorbent material saturated with ink used to transfer ink evenly to a rubber stamp', 'name': 'inkpad'}, {'frequency': 'c', 'synset': 'pad.n.04', 'synonyms': ['pad'], 'id': 744, 'def': 'mostly arm/knee pads labeled', 'name': 'pad'}, {'frequency': 'f', 'synset': 'paddle.n.04', 'synonyms': ['paddle', 'boat_paddle'], 'id': 745, 'def': 'a short light oar used without an oarlock to propel a canoe or small boat', 'name': 'paddle'}, {'frequency': 'c', 'synset': 'padlock.n.01', 'synonyms': ['padlock'], 'id': 746, 'def': 'a detachable, portable lock', 'name': 'padlock'}, {'frequency': 'c', 'synset': 'paintbrush.n.01', 'synonyms': ['paintbrush'], 'id': 747, 'def': 'a brush used as an applicator to apply paint', 'name': 'paintbrush'}, {'frequency': 'f', 'synset': 'painting.n.01', 'synonyms': ['painting'], 'id': 748, 'def': 'graphic art consisting of an artistic composition made by applying paints to a surface', 'name': 'painting'}, {'frequency': 'f', 'synset': 'pajama.n.02', 'synonyms': ['pajamas', 'pyjamas'], 'id': 749, 'def': 'loose-fitting nightclothes worn for sleeping or lounging', 'name': 'pajamas'}, {'frequency': 'c', 'synset': 'palette.n.02', 'synonyms': ['palette', 'pallet'], 'id': 750, 'def': 'board that provides a flat surface on which artists mix paints and the range of colors used', 'name': 'palette'}, {'frequency': 'f', 'synset': 'pan.n.01', 'synonyms': ['pan_(for_cooking)', 'cooking_pan'], 'id': 751, 'def': 'cooking utensil consisting of a wide metal vessel', 'name': 'pan_(for_cooking)'}, {'frequency': 'r', 'synset': 'pan.n.03', 'synonyms': ['pan_(metal_container)'], 'id': 752, 'def': 'shallow container made of metal', 'name': 'pan_(metal_container)'}, {'frequency': 'c', 'synset': 'pancake.n.01', 'synonyms': ['pancake'], 'id': 753, 'def': 'a flat cake of thin batter fried on both sides on a griddle', 'name': 'pancake'}, {'frequency': 'r', 'synset': 'pantyhose.n.01', 'synonyms': ['pantyhose'], 'id': 754, 'def': "a woman's tights consisting of underpants and stockings", 'name': 'pantyhose'}, {'frequency': 'r', 'synset': 'papaya.n.02', 'synonyms': ['papaya'], 'id': 755, 'def': 'large oval melon-like tropical fruit with yellowish flesh', 'name': 'papaya'}, {'frequency': 'f', 'synset': 'paper_plate.n.01', 'synonyms': ['paper_plate'], 'id': 756, 'def': 'a disposable plate made of cardboard', 'name': 'paper_plate'}, {'frequency': 'f', 'synset': 'paper_towel.n.01', 'synonyms': ['paper_towel'], 'id': 757, 'def': 'a disposable towel made of absorbent paper', 'name': 'paper_towel'}, {'frequency': 'r', 'synset': 'paperback_book.n.01', 'synonyms': ['paperback_book', 'paper-back_book', 'softback_book', 'soft-cover_book'], 'id': 758, 'def': 'a book with paper covers', 'name': 'paperback_book'}, {'frequency': 'r', 'synset': 'paperweight.n.01', 'synonyms': ['paperweight'], 'id': 759, 'def': 'a weight used to hold down a stack of papers', 'name': 'paperweight'}, {'frequency': 'c', 'synset': 'parachute.n.01', 'synonyms': ['parachute'], 'id': 760, 'def': 'rescue equipment consisting of a device that fills with air and retards your fall', 'name': 'parachute'}, {'frequency': 'c', 'synset': 'parakeet.n.01', 'synonyms': ['parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet', 'parroquet'], 'id': 761, 'def': 'any of numerous small slender long-tailed parrots', 'name': 'parakeet'}, {'frequency': 'c', 'synset': 'parasail.n.01', 'synonyms': ['parasail_(sports)'], 'id': 762, 'def': 'parachute that will lift a person up into the air when it is towed by a motorboat or a car', 'name': 'parasail_(sports)'}, {'frequency': 'c', 'synset': 'parasol.n.01', 'synonyms': ['parasol', 'sunshade'], 'id': 763, 'def': 'a handheld collapsible source of shade', 'name': 'parasol'}, {'frequency': 'r', 'synset': 'parchment.n.01', 'synonyms': ['parchment'], 'id': 764, 'def': 'a superior paper resembling sheepskin', 'name': 'parchment'}, {'frequency': 'c', 'synset': 'parka.n.01', 'synonyms': ['parka', 'anorak'], 'id': 765, 'def': "a kind of heavy jacket (`windcheater' is a British term)", 'name': 'parka'}, {'frequency': 'f', 'synset': 'parking_meter.n.01', 'synonyms': ['parking_meter'], 'id': 766, 'def': 'a coin-operated timer located next to a parking space', 'name': 'parking_meter'}, {'frequency': 'c', 'synset': 'parrot.n.01', 'synonyms': ['parrot'], 'id': 767, 'def': 'usually brightly colored tropical birds with short hooked beaks and the ability to mimic sounds', 'name': 'parrot'}, {'frequency': 'c', 'synset': 'passenger_car.n.01', 'synonyms': ['passenger_car_(part_of_a_train)', 'coach_(part_of_a_train)'], 'id': 768, 'def': 'a railcar where passengers ride', 'name': 'passenger_car_(part_of_a_train)'}, {'frequency': 'r', 'synset': 'passenger_ship.n.01', 'synonyms': ['passenger_ship'], 'id': 769, 'def': 'a ship built to carry passengers', 'name': 'passenger_ship'}, {'frequency': 'c', 'synset': 'passport.n.02', 'synonyms': ['passport'], 'id': 770, 'def': 'a document issued by a country to a citizen allowing that person to travel abroad and re-enter the home country', 'name': 'passport'}, {'frequency': 'f', 'synset': 'pastry.n.02', 'synonyms': ['pastry'], 'id': 771, 'def': 'any of various baked foods made of dough or batter', 'name': 'pastry'}, {'frequency': 'r', 'synset': 'patty.n.01', 'synonyms': ['patty_(food)'], 'id': 772, 'def': 'small flat mass of chopped food', 'name': 'patty_(food)'}, {'frequency': 'c', 'synset': 'pea.n.01', 'synonyms': ['pea_(food)'], 'id': 773, 'def': 'seed of a pea plant used for food', 'name': 'pea_(food)'}, {'frequency': 'c', 'synset': 'peach.n.03', 'synonyms': ['peach'], 'id': 774, 'def': 'downy juicy fruit with sweet yellowish or whitish flesh', 'name': 'peach'}, {'frequency': 'c', 'synset': 'peanut_butter.n.01', 'synonyms': ['peanut_butter'], 'id': 775, 'def': 'a spread made from ground peanuts', 'name': 'peanut_butter'}, {'frequency': 'f', 'synset': 'pear.n.01', 'synonyms': ['pear'], 'id': 776, 'def': 'sweet juicy gritty-textured fruit available in many varieties', 'name': 'pear'}, {'frequency': 'c', 'synset': 'peeler.n.03', 'synonyms': ['peeler_(tool_for_fruit_and_vegetables)'], 'id': 777, 'def': 'a device for peeling vegetables or fruits', 'name': 'peeler_(tool_for_fruit_and_vegetables)'}, {'frequency': 'r', 'synset': 'peg.n.04', 'synonyms': ['wooden_leg', 'pegleg'], 'id': 778, 'def': 'a prosthesis that replaces a missing leg', 'name': 'wooden_leg'}, {'frequency': 'r', 'synset': 'pegboard.n.01', 'synonyms': ['pegboard'], 'id': 779, 'def': 'a board perforated with regularly spaced holes into which pegs can be fitted', 'name': 'pegboard'}, {'frequency': 'c', 'synset': 'pelican.n.01', 'synonyms': ['pelican'], 'id': 780, 'def': 'large long-winged warm-water seabird having a large bill with a distensible pouch for fish', 'name': 'pelican'}, {'frequency': 'f', 'synset': 'pen.n.01', 'synonyms': ['pen'], 'id': 781, 'def': 'a writing implement with a point from which ink flows', 'name': 'pen'}, {'frequency': 'f', 'synset': 'pencil.n.01', 'synonyms': ['pencil'], 'id': 782, 'def': 'a thin cylindrical pointed writing implement made of wood and graphite', 'name': 'pencil'}, {'frequency': 'r', 'synset': 'pencil_box.n.01', 'synonyms': ['pencil_box', 'pencil_case'], 'id': 783, 'def': 'a box for holding pencils', 'name': 'pencil_box'}, {'frequency': 'r', 'synset': 'pencil_sharpener.n.01', 'synonyms': ['pencil_sharpener'], 'id': 784, 'def': 'a rotary implement for sharpening the point on pencils', 'name': 'pencil_sharpener'}, {'frequency': 'r', 'synset': 'pendulum.n.01', 'synonyms': ['pendulum'], 'id': 785, 'def': 'an apparatus consisting of an object mounted so that it swings freely under the influence of gravity', 'name': 'pendulum'}, {'frequency': 'c', 'synset': 'penguin.n.01', 'synonyms': ['penguin'], 'id': 786, 'def': 'short-legged flightless birds of cold southern regions having webbed feet and wings modified as flippers', 'name': 'penguin'}, {'frequency': 'r', 'synset': 'pennant.n.02', 'synonyms': ['pennant'], 'id': 787, 'def': 'a flag longer than it is wide (and often tapering)', 'name': 'pennant'}, {'frequency': 'r', 'synset': 'penny.n.02', 'synonyms': ['penny_(coin)'], 'id': 788, 'def': 'a coin worth one-hundredth of the value of the basic unit', 'name': 'penny_(coin)'}, {'frequency': 'f', 'synset': 'pepper.n.03', 'synonyms': ['pepper', 'peppercorn'], 'id': 789, 'def': 'pungent seasoning from the berry of the common pepper plant; whole or ground', 'name': 'pepper'}, {'frequency': 'c', 'synset': 'pepper_mill.n.01', 'synonyms': ['pepper_mill', 'pepper_grinder'], 'id': 790, 'def': 'a mill for grinding pepper', 'name': 'pepper_mill'}, {'frequency': 'c', 'synset': 'perfume.n.02', 'synonyms': ['perfume'], 'id': 791, 'def': 'a toiletry that emits and diffuses a fragrant odor', 'name': 'perfume'}, {'frequency': 'r', 'synset': 'persimmon.n.02', 'synonyms': ['persimmon'], 'id': 792, 'def': 'orange fruit resembling a plum; edible when fully ripe', 'name': 'persimmon'}, {'frequency': 'f', 'synset': 'person.n.01', 'synonyms': ['person', 'baby', 'child', 'boy', 'girl', 'man', 'woman', 'human'], 'id': 793, 'def': 'a human being', 'name': 'person'}, {'frequency': 'c', 'synset': 'pet.n.01', 'synonyms': ['pet'], 'id': 794, 'def': 'a domesticated animal kept for companionship or amusement', 'name': 'pet'}, {'frequency': 'c', 'synset': 'pew.n.01', 'synonyms': ['pew_(church_bench)', 'church_bench'], 'id': 795, 'def': 'long bench with backs; used in church by the congregation', 'name': 'pew_(church_bench)'}, {'frequency': 'r', 'synset': 'phonebook.n.01', 'synonyms': ['phonebook', 'telephone_book', 'telephone_directory'], 'id': 796, 'def': 'a directory containing an alphabetical list of telephone subscribers and their telephone numbers', 'name': 'phonebook'}, {'frequency': 'c', 'synset': 'phonograph_record.n.01', 'synonyms': ['phonograph_record', 'phonograph_recording', 'record_(phonograph_recording)'], 'id': 797, 'def': 'sound recording consisting of a typically black disk with a continuous groove', 'name': 'phonograph_record'}, {'frequency': 'f', 'synset': 'piano.n.01', 'synonyms': ['piano'], 'id': 798, 'def': 'a keyboard instrument that is played by depressing keys that cause hammers to strike tuned strings and produce sounds', 'name': 'piano'}, {'frequency': 'f', 'synset': 'pickle.n.01', 'synonyms': ['pickle'], 'id': 799, 'def': 'vegetables (especially cucumbers) preserved in brine or vinegar', 'name': 'pickle'}, {'frequency': 'f', 'synset': 'pickup.n.01', 'synonyms': ['pickup_truck'], 'id': 800, 'def': 'a light truck with an open body and low sides and a tailboard', 'name': 'pickup_truck'}, {'frequency': 'c', 'synset': 'pie.n.01', 'synonyms': ['pie'], 'id': 801, 'def': 'dish baked in pastry-lined pan often with a pastry top', 'name': 'pie'}, {'frequency': 'c', 'synset': 'pigeon.n.01', 'synonyms': ['pigeon'], 'id': 802, 'def': 'wild and domesticated birds having a heavy body and short legs', 'name': 'pigeon'}, {'frequency': 'r', 'synset': 'piggy_bank.n.01', 'synonyms': ['piggy_bank', 'penny_bank'], 'id': 803, 'def': "a child's coin bank (often shaped like a pig)", 'name': 'piggy_bank'}, {'frequency': 'f', 'synset': 'pillow.n.01', 'synonyms': ['pillow'], 'id': 804, 'def': 'a cushion to support the head of a sleeping person', 'name': 'pillow'}, {'frequency': 'r', 'synset': 'pin.n.09', 'synonyms': ['pin_(non_jewelry)'], 'id': 805, 'def': 'a small slender (often pointed) piece of wood or metal used to support or fasten or attach things', 'name': 'pin_(non_jewelry)'}, {'frequency': 'f', 'synset': 'pineapple.n.02', 'synonyms': ['pineapple'], 'id': 806, 'def': 'large sweet fleshy tropical fruit with a tuft of stiff leaves', 'name': 'pineapple'}, {'frequency': 'c', 'synset': 'pinecone.n.01', 'synonyms': ['pinecone'], 'id': 807, 'def': 'the seed-producing cone of a pine tree', 'name': 'pinecone'}, {'frequency': 'r', 'synset': 'ping-pong_ball.n.01', 'synonyms': ['ping-pong_ball'], 'id': 808, 'def': 'light hollow ball used in playing table tennis', 'name': 'ping-pong_ball'}, {'frequency': 'r', 'synset': 'pinwheel.n.03', 'synonyms': ['pinwheel'], 'id': 809, 'def': 'a toy consisting of vanes of colored paper or plastic that is pinned to a stick and spins when it is pointed into the wind', 'name': 'pinwheel'}, {'frequency': 'r', 'synset': 'pipe.n.01', 'synonyms': ['tobacco_pipe'], 'id': 810, 'def': 'a tube with a small bowl at one end; used for smoking tobacco', 'name': 'tobacco_pipe'}, {'frequency': 'f', 'synset': 'pipe.n.02', 'synonyms': ['pipe', 'piping'], 'id': 811, 'def': 'a long tube made of metal or plastic that is used to carry water or oil or gas etc.', 'name': 'pipe'}, {'frequency': 'r', 'synset': 'pistol.n.01', 'synonyms': ['pistol', 'handgun'], 'id': 812, 'def': 'a firearm that is held and fired with one hand', 'name': 'pistol'}, {'frequency': 'c', 'synset': 'pita.n.01', 'synonyms': ['pita_(bread)', 'pocket_bread'], 'id': 813, 'def': 'usually small round bread that can open into a pocket for filling', 'name': 'pita_(bread)'}, {'frequency': 'f', 'synset': 'pitcher.n.02', 'synonyms': ['pitcher_(vessel_for_liquid)', 'ewer'], 'id': 814, 'def': 'an open vessel with a handle and a spout for pouring', 'name': 'pitcher_(vessel_for_liquid)'}, {'frequency': 'r', 'synset': 'pitchfork.n.01', 'synonyms': ['pitchfork'], 'id': 815, 'def': 'a long-handled hand tool with sharp widely spaced prongs for lifting and pitching hay', 'name': 'pitchfork'}, {'frequency': 'f', 'synset': 'pizza.n.01', 'synonyms': ['pizza'], 'id': 816, 'def': 'Italian open pie made of thin bread dough spread with a spiced mixture of e.g. tomato sauce and cheese', 'name': 'pizza'}, {'frequency': 'f', 'synset': 'place_mat.n.01', 'synonyms': ['place_mat'], 'id': 817, 'def': 'a mat placed on a table for an individual place setting', 'name': 'place_mat'}, {'frequency': 'f', 'synset': 'plate.n.04', 'synonyms': ['plate'], 'id': 818, 'def': 'dish on which food is served or from which food is eaten', 'name': 'plate'}, {'frequency': 'c', 'synset': 'platter.n.01', 'synonyms': ['platter'], 'id': 819, 'def': 'a large shallow dish used for serving food', 'name': 'platter'}, {'frequency': 'r', 'synset': 'playpen.n.01', 'synonyms': ['playpen'], 'id': 820, 'def': 'a portable enclosure in which babies may be left to play', 'name': 'playpen'}, {'frequency': 'c', 'synset': 'pliers.n.01', 'synonyms': ['pliers', 'plyers'], 'id': 821, 'def': 'a gripping hand tool with two hinged arms and (usually) serrated jaws', 'name': 'pliers'}, {'frequency': 'r', 'synset': 'plow.n.01', 'synonyms': ['plow_(farm_equipment)', 'plough_(farm_equipment)'], 'id': 822, 'def': 'a farm tool having one or more heavy blades to break the soil and cut a furrow prior to sowing', 'name': 'plow_(farm_equipment)'}, {'frequency': 'r', 'synset': 'plume.n.02', 'synonyms': ['plume'], 'id': 823, 'def': 'a feather or cluster of feathers worn as an ornament', 'name': 'plume'}, {'frequency': 'r', 'synset': 'pocket_watch.n.01', 'synonyms': ['pocket_watch'], 'id': 824, 'def': 'a watch that is carried in a small watch pocket', 'name': 'pocket_watch'}, {'frequency': 'c', 'synset': 'pocketknife.n.01', 'synonyms': ['pocketknife'], 'id': 825, 'def': 'a knife with a blade that folds into the handle; suitable for carrying in the pocket', 'name': 'pocketknife'}, {'frequency': 'c', 'synset': 'poker.n.01', 'synonyms': ['poker_(fire_stirring_tool)', 'stove_poker', 'fire_hook'], 'id': 826, 'def': 'fire iron consisting of a metal rod with a handle; used to stir a fire', 'name': 'poker_(fire_stirring_tool)'}, {'frequency': 'f', 'synset': 'pole.n.01', 'synonyms': ['pole', 'post'], 'id': 827, 'def': 'a long (usually round) rod of wood or metal or plastic', 'name': 'pole'}, {'frequency': 'f', 'synset': 'polo_shirt.n.01', 'synonyms': ['polo_shirt', 'sport_shirt'], 'id': 828, 'def': 'a shirt with short sleeves designed for comfort and casual wear', 'name': 'polo_shirt'}, {'frequency': 'r', 'synset': 'poncho.n.01', 'synonyms': ['poncho'], 'id': 829, 'def': 'a blanket-like cloak with a hole in the center for the head', 'name': 'poncho'}, {'frequency': 'c', 'synset': 'pony.n.05', 'synonyms': ['pony'], 'id': 830, 'def': 'any of various breeds of small gentle horses usually less than five feet high at the shoulder', 'name': 'pony'}, {'frequency': 'r', 'synset': 'pool_table.n.01', 'synonyms': ['pool_table', 'billiard_table', 'snooker_table'], 'id': 831, 'def': 'game equipment consisting of a heavy table on which pool is played', 'name': 'pool_table'}, {'frequency': 'f', 'synset': 'pop.n.02', 'synonyms': ['pop_(soda)', 'soda_(pop)', 'tonic', 'soft_drink'], 'id': 832, 'def': 'a sweet drink containing carbonated water and flavoring', 'name': 'pop_(soda)'}, {'frequency': 'c', 'synset': 'postbox.n.01', 'synonyms': ['postbox_(public)', 'mailbox_(public)'], 'id': 833, 'def': 'public box for deposit of mail', 'name': 'postbox_(public)'}, {'frequency': 'c', 'synset': 'postcard.n.01', 'synonyms': ['postcard', 'postal_card', 'mailing-card'], 'id': 834, 'def': 'a card for sending messages by post without an envelope', 'name': 'postcard'}, {'frequency': 'f', 'synset': 'poster.n.01', 'synonyms': ['poster', 'placard'], 'id': 835, 'def': 'a sign posted in a public place as an advertisement', 'name': 'poster'}, {'frequency': 'f', 'synset': 'pot.n.01', 'synonyms': ['pot'], 'id': 836, 'def': 'metal or earthenware cooking vessel that is usually round and deep; often has a handle and lid', 'name': 'pot'}, {'frequency': 'f', 'synset': 'pot.n.04', 'synonyms': ['flowerpot'], 'id': 837, 'def': 'a container in which plants are cultivated', 'name': 'flowerpot'}, {'frequency': 'f', 'synset': 'potato.n.01', 'synonyms': ['potato'], 'id': 838, 'def': 'an edible tuber native to South America', 'name': 'potato'}, {'frequency': 'c', 'synset': 'potholder.n.01', 'synonyms': ['potholder'], 'id': 839, 'def': 'an insulated pad for holding hot pots', 'name': 'potholder'}, {'frequency': 'c', 'synset': 'pottery.n.01', 'synonyms': ['pottery', 'clayware'], 'id': 840, 'def': 'ceramic ware made from clay and baked in a kiln', 'name': 'pottery'}, {'frequency': 'c', 'synset': 'pouch.n.01', 'synonyms': ['pouch'], 'id': 841, 'def': 'a small or medium size container for holding or carrying things', 'name': 'pouch'}, {'frequency': 'c', 'synset': 'power_shovel.n.01', 'synonyms': ['power_shovel', 'excavator', 'digger'], 'id': 842, 'def': 'a machine for excavating', 'name': 'power_shovel'}, {'frequency': 'c', 'synset': 'prawn.n.01', 'synonyms': ['prawn', 'shrimp'], 'id': 843, 'def': 'any of various edible decapod crustaceans', 'name': 'prawn'}, {'frequency': 'c', 'synset': 'pretzel.n.01', 'synonyms': ['pretzel'], 'id': 844, 'def': 'glazed and salted cracker typically in the shape of a loose knot', 'name': 'pretzel'}, {'frequency': 'f', 'synset': 'printer.n.03', 'synonyms': ['printer', 'printing_machine'], 'id': 845, 'def': 'a machine that prints', 'name': 'printer'}, {'frequency': 'c', 'synset': 'projectile.n.01', 'synonyms': ['projectile_(weapon)', 'missile'], 'id': 846, 'def': 'a weapon that is forcibly thrown or projected at a targets', 'name': 'projectile_(weapon)'}, {'frequency': 'c', 'synset': 'projector.n.02', 'synonyms': ['projector'], 'id': 847, 'def': 'an optical instrument that projects an enlarged image onto a screen', 'name': 'projector'}, {'frequency': 'f', 'synset': 'propeller.n.01', 'synonyms': ['propeller', 'propellor'], 'id': 848, 'def': 'a mechanical device that rotates to push against air or water', 'name': 'propeller'}, {'frequency': 'r', 'synset': 'prune.n.01', 'synonyms': ['prune'], 'id': 849, 'def': 'dried plum', 'name': 'prune'}, {'frequency': 'r', 'synset': 'pudding.n.01', 'synonyms': ['pudding'], 'id': 850, 'def': 'any of various soft thick unsweetened baked dishes', 'name': 'pudding'}, {'frequency': 'r', 'synset': 'puffer.n.02', 'synonyms': ['puffer_(fish)', 'pufferfish', 'blowfish', 'globefish'], 'id': 851, 'def': 'fishes whose elongated spiny body can inflate itself with water or air to form a globe', 'name': 'puffer_(fish)'}, {'frequency': 'r', 'synset': 'puffin.n.01', 'synonyms': ['puffin'], 'id': 852, 'def': 'seabirds having short necks and brightly colored compressed bills', 'name': 'puffin'}, {'frequency': 'r', 'synset': 'pug.n.01', 'synonyms': ['pug-dog'], 'id': 853, 'def': 'small compact smooth-coated breed of Asiatic origin having a tightly curled tail and broad flat wrinkled muzzle', 'name': 'pug-dog'}, {'frequency': 'c', 'synset': 'pumpkin.n.02', 'synonyms': ['pumpkin'], 'id': 854, 'def': 'usually large pulpy deep-yellow round fruit of the squash family maturing in late summer or early autumn', 'name': 'pumpkin'}, {'frequency': 'r', 'synset': 'punch.n.03', 'synonyms': ['puncher'], 'id': 855, 'def': 'a tool for making holes or indentations', 'name': 'puncher'}, {'frequency': 'r', 'synset': 'puppet.n.01', 'synonyms': ['puppet', 'marionette'], 'id': 856, 'def': 'a small figure of a person operated from above with strings by a puppeteer', 'name': 'puppet'}, {'frequency': 'c', 'synset': 'puppy.n.01', 'synonyms': ['puppy'], 'id': 857, 'def': 'a young dog', 'name': 'puppy'}, {'frequency': 'r', 'synset': 'quesadilla.n.01', 'synonyms': ['quesadilla'], 'id': 858, 'def': 'a tortilla that is filled with cheese and heated', 'name': 'quesadilla'}, {'frequency': 'r', 'synset': 'quiche.n.02', 'synonyms': ['quiche'], 'id': 859, 'def': 'a tart filled with rich unsweetened custard; often contains other ingredients (as cheese or ham or seafood or vegetables)', 'name': 'quiche'}, {'frequency': 'f', 'synset': 'quilt.n.01', 'synonyms': ['quilt', 'comforter'], 'id': 860, 'def': 'bedding made of two layers of cloth filled with stuffing and stitched together', 'name': 'quilt'}, {'frequency': 'c', 'synset': 'rabbit.n.01', 'synonyms': ['rabbit'], 'id': 861, 'def': 'any of various burrowing animals of the family Leporidae having long ears and short tails', 'name': 'rabbit'}, {'frequency': 'r', 'synset': 'racer.n.02', 'synonyms': ['race_car', 'racing_car'], 'id': 862, 'def': 'a fast car that competes in races', 'name': 'race_car'}, {'frequency': 'c', 'synset': 'racket.n.04', 'synonyms': ['racket', 'racquet'], 'id': 863, 'def': 'a sports implement used to strike a ball in various games', 'name': 'racket'}, {'frequency': 'r', 'synset': 'radar.n.01', 'synonyms': ['radar'], 'id': 864, 'def': 'measuring instrument in which the echo of a pulse of microwave radiation is used to detect and locate distant objects', 'name': 'radar'}, {'frequency': 'f', 'synset': 'radiator.n.03', 'synonyms': ['radiator'], 'id': 865, 'def': 'a mechanism consisting of a metal honeycomb through which hot fluids circulate', 'name': 'radiator'}, {'frequency': 'c', 'synset': 'radio_receiver.n.01', 'synonyms': ['radio_receiver', 'radio_set', 'radio', 'tuner_(radio)'], 'id': 866, 'def': 'an electronic receiver that detects and demodulates and amplifies transmitted radio signals', 'name': 'radio_receiver'}, {'frequency': 'c', 'synset': 'radish.n.03', 'synonyms': ['radish', 'daikon'], 'id': 867, 'def': 'pungent edible root of any of various cultivated radish plants', 'name': 'radish'}, {'frequency': 'c', 'synset': 'raft.n.01', 'synonyms': ['raft'], 'id': 868, 'def': 'a flat float (usually made of logs or planks) that can be used for transport or as a platform for swimmers', 'name': 'raft'}, {'frequency': 'r', 'synset': 'rag_doll.n.01', 'synonyms': ['rag_doll'], 'id': 869, 'def': 'a cloth doll that is stuffed and (usually) painted', 'name': 'rag_doll'}, {'frequency': 'c', 'synset': 'raincoat.n.01', 'synonyms': ['raincoat', 'waterproof_jacket'], 'id': 870, 'def': 'a water-resistant coat', 'name': 'raincoat'}, {'frequency': 'c', 'synset': 'ram.n.05', 'synonyms': ['ram_(animal)'], 'id': 871, 'def': 'uncastrated adult male sheep', 'name': 'ram_(animal)'}, {'frequency': 'c', 'synset': 'raspberry.n.02', 'synonyms': ['raspberry'], 'id': 872, 'def': 'red or black edible aggregate berries usually smaller than the related blackberries', 'name': 'raspberry'}, {'frequency': 'r', 'synset': 'rat.n.01', 'synonyms': ['rat'], 'id': 873, 'def': 'any of various long-tailed rodents similar to but larger than a mouse', 'name': 'rat'}, {'frequency': 'c', 'synset': 'razorblade.n.01', 'synonyms': ['razorblade'], 'id': 874, 'def': 'a blade that has very sharp edge', 'name': 'razorblade'}, {'frequency': 'c', 'synset': 'reamer.n.01', 'synonyms': ['reamer_(juicer)', 'juicer', 'juice_reamer'], 'id': 875, 'def': 'a squeezer with a conical ridged center that is used for squeezing juice from citrus fruit', 'name': 'reamer_(juicer)'}, {'frequency': 'f', 'synset': 'rearview_mirror.n.01', 'synonyms': ['rearview_mirror'], 'id': 876, 'def': 'vehicle mirror (side or rearview)', 'name': 'rearview_mirror'}, {'frequency': 'c', 'synset': 'receipt.n.02', 'synonyms': ['receipt'], 'id': 877, 'def': 'an acknowledgment (usually tangible) that payment has been made', 'name': 'receipt'}, {'frequency': 'c', 'synset': 'recliner.n.01', 'synonyms': ['recliner', 'reclining_chair', 'lounger_(chair)'], 'id': 878, 'def': 'an armchair whose back can be lowered and foot can be raised to allow the sitter to recline in it', 'name': 'recliner'}, {'frequency': 'c', 'synset': 'record_player.n.01', 'synonyms': ['record_player', 'phonograph_(record_player)', 'turntable'], 'id': 879, 'def': 'machine in which rotating records cause a stylus to vibrate and the vibrations are amplified acoustically or electronically', 'name': 'record_player'}, {'frequency': 'f', 'synset': 'reflector.n.01', 'synonyms': ['reflector'], 'id': 880, 'def': 'device that reflects light, radiation, etc.', 'name': 'reflector'}, {'frequency': 'f', 'synset': 'remote_control.n.01', 'synonyms': ['remote_control'], 'id': 881, 'def': 'a device that can be used to control a machine or apparatus from a distance', 'name': 'remote_control'}, {'frequency': 'c', 'synset': 'rhinoceros.n.01', 'synonyms': ['rhinoceros'], 'id': 882, 'def': 'massive powerful herbivorous odd-toed ungulate of southeast Asia and Africa having very thick skin and one or two horns on the snout', 'name': 'rhinoceros'}, {'frequency': 'r', 'synset': 'rib.n.03', 'synonyms': ['rib_(food)'], 'id': 883, 'def': 'cut of meat including one or more ribs', 'name': 'rib_(food)'}, {'frequency': 'c', 'synset': 'rifle.n.01', 'synonyms': ['rifle'], 'id': 884, 'def': 'a shoulder firearm with a long barrel', 'name': 'rifle'}, {'frequency': 'f', 'synset': 'ring.n.08', 'synonyms': ['ring'], 'id': 885, 'def': 'jewelry consisting of a circlet of precious metal (often set with jewels) worn on the finger', 'name': 'ring'}, {'frequency': 'r', 'synset': 'river_boat.n.01', 'synonyms': ['river_boat'], 'id': 886, 'def': 'a boat used on rivers or to ply a river', 'name': 'river_boat'}, {'frequency': 'r', 'synset': 'road_map.n.02', 'synonyms': ['road_map'], 'id': 887, 'def': '(NOT A ROAD) a MAP showing roads (for automobile travel)', 'name': 'road_map'}, {'frequency': 'c', 'synset': 'robe.n.01', 'synonyms': ['robe'], 'id': 888, 'def': 'any loose flowing garment', 'name': 'robe'}, {'frequency': 'c', 'synset': 'rocking_chair.n.01', 'synonyms': ['rocking_chair'], 'id': 889, 'def': 'a chair mounted on rockers', 'name': 'rocking_chair'}, {'frequency': 'r', 'synset': 'rodent.n.01', 'synonyms': ['rodent'], 'id': 890, 'def': 'relatively small placental mammals having a single pair of constantly growing incisor teeth specialized for gnawing', 'name': 'rodent'}, {'frequency': 'r', 'synset': 'roller_skate.n.01', 'synonyms': ['roller_skate'], 'id': 891, 'def': 'a shoe with pairs of rollers (small hard wheels) fixed to the sole', 'name': 'roller_skate'}, {'frequency': 'r', 'synset': 'rollerblade.n.01', 'synonyms': ['Rollerblade'], 'id': 892, 'def': 'an in-line variant of a roller skate', 'name': 'Rollerblade'}, {'frequency': 'c', 'synset': 'rolling_pin.n.01', 'synonyms': ['rolling_pin'], 'id': 893, 'def': 'utensil consisting of a cylinder (usually of wood) with a handle at each end; used to roll out dough', 'name': 'rolling_pin'}, {'frequency': 'r', 'synset': 'root_beer.n.01', 'synonyms': ['root_beer'], 'id': 894, 'def': 'carbonated drink containing extracts of roots and herbs', 'name': 'root_beer'}, {'frequency': 'c', 'synset': 'router.n.02', 'synonyms': ['router_(computer_equipment)'], 'id': 895, 'def': 'a device that forwards data packets between computer networks', 'name': 'router_(computer_equipment)'}, {'frequency': 'f', 'synset': 'rubber_band.n.01', 'synonyms': ['rubber_band', 'elastic_band'], 'id': 896, 'def': 'a narrow band of elastic rubber used to hold things (such as papers) together', 'name': 'rubber_band'}, {'frequency': 'c', 'synset': 'runner.n.08', 'synonyms': ['runner_(carpet)'], 'id': 897, 'def': 'a long narrow carpet', 'name': 'runner_(carpet)'}, {'frequency': 'f', 'synset': 'sack.n.01', 'synonyms': ['plastic_bag', 'paper_bag'], 'id': 898, 'def': "a bag made of paper or plastic for holding customer's purchases", 'name': 'plastic_bag'}, {'frequency': 'f', 'synset': 'saddle.n.01', 'synonyms': ['saddle_(on_an_animal)'], 'id': 899, 'def': 'a seat for the rider of a horse or camel', 'name': 'saddle_(on_an_animal)'}, {'frequency': 'f', 'synset': 'saddle_blanket.n.01', 'synonyms': ['saddle_blanket', 'saddlecloth', 'horse_blanket'], 'id': 900, 'def': 'stable gear consisting of a blanket placed under the saddle', 'name': 'saddle_blanket'}, {'frequency': 'c', 'synset': 'saddlebag.n.01', 'synonyms': ['saddlebag'], 'id': 901, 'def': 'a large bag (or pair of bags) hung over a saddle', 'name': 'saddlebag'}, {'frequency': 'r', 'synset': 'safety_pin.n.01', 'synonyms': ['safety_pin'], 'id': 902, 'def': 'a pin in the form of a clasp; has a guard so the point of the pin will not stick the user', 'name': 'safety_pin'}, {'frequency': 'f', 'synset': 'sail.n.01', 'synonyms': ['sail'], 'id': 903, 'def': 'a large piece of fabric by means of which wind is used to propel a sailing vessel', 'name': 'sail'}, {'frequency': 'f', 'synset': 'salad.n.01', 'synonyms': ['salad'], 'id': 904, 'def': 'food mixtures either arranged on a plate or tossed and served with a moist dressing; usually consisting of or including greens', 'name': 'salad'}, {'frequency': 'r', 'synset': 'salad_plate.n.01', 'synonyms': ['salad_plate', 'salad_bowl'], 'id': 905, 'def': 'a plate or bowl for individual servings of salad', 'name': 'salad_plate'}, {'frequency': 'c', 'synset': 'salami.n.01', 'synonyms': ['salami'], 'id': 906, 'def': 'highly seasoned fatty sausage of pork and beef usually dried', 'name': 'salami'}, {'frequency': 'c', 'synset': 'salmon.n.01', 'synonyms': ['salmon_(fish)'], 'id': 907, 'def': 'any of various large food and game fishes of northern waters', 'name': 'salmon_(fish)'}, {'frequency': 'r', 'synset': 'salmon.n.03', 'synonyms': ['salmon_(food)'], 'id': 908, 'def': 'flesh of any of various marine or freshwater fish of the family Salmonidae', 'name': 'salmon_(food)'}, {'frequency': 'c', 'synset': 'salsa.n.01', 'synonyms': ['salsa'], 'id': 909, 'def': 'spicy sauce of tomatoes and onions and chili peppers to accompany Mexican foods', 'name': 'salsa'}, {'frequency': 'f', 'synset': 'saltshaker.n.01', 'synonyms': ['saltshaker'], 'id': 910, 'def': 'a shaker with a perforated top for sprinkling salt', 'name': 'saltshaker'}, {'frequency': 'f', 'synset': 'sandal.n.01', 'synonyms': ['sandal_(type_of_shoe)'], 'id': 911, 'def': 'a shoe consisting of a sole fastened by straps to the foot', 'name': 'sandal_(type_of_shoe)'}, {'frequency': 'f', 'synset': 'sandwich.n.01', 'synonyms': ['sandwich'], 'id': 912, 'def': 'two (or more) slices of bread with a filling between them', 'name': 'sandwich'}, {'frequency': 'r', 'synset': 'satchel.n.01', 'synonyms': ['satchel'], 'id': 913, 'def': 'luggage consisting of a small case with a flat bottom and (usually) a shoulder strap', 'name': 'satchel'}, {'frequency': 'r', 'synset': 'saucepan.n.01', 'synonyms': ['saucepan'], 'id': 914, 'def': 'a deep pan with a handle; used for stewing or boiling', 'name': 'saucepan'}, {'frequency': 'f', 'synset': 'saucer.n.02', 'synonyms': ['saucer'], 'id': 915, 'def': 'a small shallow dish for holding a cup at the table', 'name': 'saucer'}, {'frequency': 'f', 'synset': 'sausage.n.01', 'synonyms': ['sausage'], 'id': 916, 'def': 'highly seasoned minced meat stuffed in casings', 'name': 'sausage'}, {'frequency': 'r', 'synset': 'sawhorse.n.01', 'synonyms': ['sawhorse', 'sawbuck'], 'id': 917, 'def': 'a framework for holding wood that is being sawed', 'name': 'sawhorse'}, {'frequency': 'r', 'synset': 'sax.n.02', 'synonyms': ['saxophone'], 'id': 918, 'def': "a wind instrument with a `J'-shaped form typically made of brass", 'name': 'saxophone'}, {'frequency': 'f', 'synset': 'scale.n.07', 'synonyms': ['scale_(measuring_instrument)'], 'id': 919, 'def': 'a measuring instrument for weighing; shows amount of mass', 'name': 'scale_(measuring_instrument)'}, {'frequency': 'r', 'synset': 'scarecrow.n.01', 'synonyms': ['scarecrow', 'strawman'], 'id': 920, 'def': 'an effigy in the shape of a man to frighten birds away from seeds', 'name': 'scarecrow'}, {'frequency': 'f', 'synset': 'scarf.n.01', 'synonyms': ['scarf'], 'id': 921, 'def': 'a garment worn around the head or neck or shoulders for warmth or decoration', 'name': 'scarf'}, {'frequency': 'c', 'synset': 'school_bus.n.01', 'synonyms': ['school_bus'], 'id': 922, 'def': 'a bus used to transport children to or from school', 'name': 'school_bus'}, {'frequency': 'f', 'synset': 'scissors.n.01', 'synonyms': ['scissors'], 'id': 923, 'def': 'a tool having two crossed pivoting blades with looped handles', 'name': 'scissors'}, {'frequency': 'f', 'synset': 'scoreboard.n.01', 'synonyms': ['scoreboard'], 'id': 924, 'def': 'a large board for displaying the score of a contest (and some other information)', 'name': 'scoreboard'}, {'frequency': 'r', 'synset': 'scraper.n.01', 'synonyms': ['scraper'], 'id': 925, 'def': 'any of various hand tools for scraping', 'name': 'scraper'}, {'frequency': 'c', 'synset': 'screwdriver.n.01', 'synonyms': ['screwdriver'], 'id': 926, 'def': 'a hand tool for driving screws; has a tip that fits into the head of a screw', 'name': 'screwdriver'}, {'frequency': 'f', 'synset': 'scrub_brush.n.01', 'synonyms': ['scrubbing_brush'], 'id': 927, 'def': 'a brush with short stiff bristles for heavy cleaning', 'name': 'scrubbing_brush'}, {'frequency': 'c', 'synset': 'sculpture.n.01', 'synonyms': ['sculpture'], 'id': 928, 'def': 'a three-dimensional work of art', 'name': 'sculpture'}, {'frequency': 'c', 'synset': 'seabird.n.01', 'synonyms': ['seabird', 'seafowl'], 'id': 929, 'def': 'a bird that frequents coastal waters and the open ocean: gulls; pelicans; gannets; cormorants; albatrosses; petrels; etc.', 'name': 'seabird'}, {'frequency': 'c', 'synset': 'seahorse.n.02', 'synonyms': ['seahorse'], 'id': 930, 'def': 'small fish with horse-like heads bent sharply downward and curled tails', 'name': 'seahorse'}, {'frequency': 'r', 'synset': 'seaplane.n.01', 'synonyms': ['seaplane', 'hydroplane'], 'id': 931, 'def': 'an airplane that can land on or take off from water', 'name': 'seaplane'}, {'frequency': 'c', 'synset': 'seashell.n.01', 'synonyms': ['seashell'], 'id': 932, 'def': 'the shell of a marine organism', 'name': 'seashell'}, {'frequency': 'c', 'synset': 'sewing_machine.n.01', 'synonyms': ['sewing_machine'], 'id': 933, 'def': 'a textile machine used as a home appliance for sewing', 'name': 'sewing_machine'}, {'frequency': 'c', 'synset': 'shaker.n.03', 'synonyms': ['shaker'], 'id': 934, 'def': 'a container in which something can be shaken', 'name': 'shaker'}, {'frequency': 'c', 'synset': 'shampoo.n.01', 'synonyms': ['shampoo'], 'id': 935, 'def': 'cleansing agent consisting of soaps or detergents used for washing the hair', 'name': 'shampoo'}, {'frequency': 'c', 'synset': 'shark.n.01', 'synonyms': ['shark'], 'id': 936, 'def': 'typically large carnivorous fishes with sharpe teeth', 'name': 'shark'}, {'frequency': 'r', 'synset': 'sharpener.n.01', 'synonyms': ['sharpener'], 'id': 937, 'def': 'any implement that is used to make something (an edge or a point) sharper', 'name': 'sharpener'}, {'frequency': 'r', 'synset': 'sharpie.n.03', 'synonyms': ['Sharpie'], 'id': 938, 'def': 'a pen with indelible ink that will write on any surface', 'name': 'Sharpie'}, {'frequency': 'r', 'synset': 'shaver.n.03', 'synonyms': ['shaver_(electric)', 'electric_shaver', 'electric_razor'], 'id': 939, 'def': 'a razor powered by an electric motor', 'name': 'shaver_(electric)'}, {'frequency': 'c', 'synset': 'shaving_cream.n.01', 'synonyms': ['shaving_cream', 'shaving_soap'], 'id': 940, 'def': 'toiletry consisting that forms a rich lather for softening the beard before shaving', 'name': 'shaving_cream'}, {'frequency': 'r', 'synset': 'shawl.n.01', 'synonyms': ['shawl'], 'id': 941, 'def': 'cloak consisting of an oblong piece of cloth used to cover the head and shoulders', 'name': 'shawl'}, {'frequency': 'r', 'synset': 'shears.n.01', 'synonyms': ['shears'], 'id': 942, 'def': 'large scissors with strong blades', 'name': 'shears'}, {'frequency': 'f', 'synset': 'sheep.n.01', 'synonyms': ['sheep'], 'id': 943, 'def': 'woolly usually horned ruminant mammal related to the goat', 'name': 'sheep'}, {'frequency': 'r', 'synset': 'shepherd_dog.n.01', 'synonyms': ['shepherd_dog', 'sheepdog'], 'id': 944, 'def': 'any of various usually long-haired breeds of dog reared to herd and guard sheep', 'name': 'shepherd_dog'}, {'frequency': 'r', 'synset': 'sherbert.n.01', 'synonyms': ['sherbert', 'sherbet'], 'id': 945, 'def': 'a frozen dessert made primarily of fruit juice and sugar', 'name': 'sherbert'}, {'frequency': 'c', 'synset': 'shield.n.02', 'synonyms': ['shield'], 'id': 946, 'def': 'armor carried on the arm to intercept blows', 'name': 'shield'}, {'frequency': 'f', 'synset': 'shirt.n.01', 'synonyms': ['shirt'], 'id': 947, 'def': 'a garment worn on the upper half of the body', 'name': 'shirt'}, {'frequency': 'f', 'synset': 'shoe.n.01', 'synonyms': ['shoe', 'sneaker_(type_of_shoe)', 'tennis_shoe'], 'id': 948, 'def': 'common footwear covering the foot', 'name': 'shoe'}, {'frequency': 'f', 'synset': 'shopping_bag.n.01', 'synonyms': ['shopping_bag'], 'id': 949, 'def': 'a bag made of plastic or strong paper (often with handles); used to transport goods after shopping', 'name': 'shopping_bag'}, {'frequency': 'c', 'synset': 'shopping_cart.n.01', 'synonyms': ['shopping_cart'], 'id': 950, 'def': 'a handcart that holds groceries or other goods while shopping', 'name': 'shopping_cart'}, {'frequency': 'f', 'synset': 'short_pants.n.01', 'synonyms': ['short_pants', 'shorts_(clothing)', 'trunks_(clothing)'], 'id': 951, 'def': 'trousers that end at or above the knee', 'name': 'short_pants'}, {'frequency': 'r', 'synset': 'shot_glass.n.01', 'synonyms': ['shot_glass'], 'id': 952, 'def': 'a small glass adequate to hold a single swallow of whiskey', 'name': 'shot_glass'}, {'frequency': 'f', 'synset': 'shoulder_bag.n.01', 'synonyms': ['shoulder_bag'], 'id': 953, 'def': 'a large handbag that can be carried by a strap looped over the shoulder', 'name': 'shoulder_bag'}, {'frequency': 'c', 'synset': 'shovel.n.01', 'synonyms': ['shovel'], 'id': 954, 'def': 'a hand tool for lifting loose material such as snow, dirt, etc.', 'name': 'shovel'}, {'frequency': 'f', 'synset': 'shower.n.01', 'synonyms': ['shower_head'], 'id': 955, 'def': 'a plumbing fixture that sprays water over you', 'name': 'shower_head'}, {'frequency': 'r', 'synset': 'shower_cap.n.01', 'synonyms': ['shower_cap'], 'id': 956, 'def': 'a tight cap worn to keep hair dry while showering', 'name': 'shower_cap'}, {'frequency': 'f', 'synset': 'shower_curtain.n.01', 'synonyms': ['shower_curtain'], 'id': 957, 'def': 'a curtain that keeps water from splashing out of the shower area', 'name': 'shower_curtain'}, {'frequency': 'r', 'synset': 'shredder.n.01', 'synonyms': ['shredder_(for_paper)'], 'id': 958, 'def': 'a device that shreds documents', 'name': 'shredder_(for_paper)'}, {'frequency': 'f', 'synset': 'signboard.n.01', 'synonyms': ['signboard'], 'id': 959, 'def': 'structure displaying a board on which advertisements can be posted', 'name': 'signboard'}, {'frequency': 'c', 'synset': 'silo.n.01', 'synonyms': ['silo'], 'id': 960, 'def': 'a cylindrical tower used for storing goods', 'name': 'silo'}, {'frequency': 'f', 'synset': 'sink.n.01', 'synonyms': ['sink'], 'id': 961, 'def': 'plumbing fixture consisting of a water basin fixed to a wall or floor and having a drainpipe', 'name': 'sink'}, {'frequency': 'f', 'synset': 'skateboard.n.01', 'synonyms': ['skateboard'], 'id': 962, 'def': 'a board with wheels that is ridden in a standing or crouching position and propelled by foot', 'name': 'skateboard'}, {'frequency': 'c', 'synset': 'skewer.n.01', 'synonyms': ['skewer'], 'id': 963, 'def': 'a long pin for holding meat in position while it is being roasted', 'name': 'skewer'}, {'frequency': 'f', 'synset': 'ski.n.01', 'synonyms': ['ski'], 'id': 964, 'def': 'sports equipment for skiing on snow', 'name': 'ski'}, {'frequency': 'f', 'synset': 'ski_boot.n.01', 'synonyms': ['ski_boot'], 'id': 965, 'def': 'a stiff boot that is fastened to a ski with a ski binding', 'name': 'ski_boot'}, {'frequency': 'f', 'synset': 'ski_parka.n.01', 'synonyms': ['ski_parka', 'ski_jacket'], 'id': 966, 'def': 'a parka to be worn while skiing', 'name': 'ski_parka'}, {'frequency': 'f', 'synset': 'ski_pole.n.01', 'synonyms': ['ski_pole'], 'id': 967, 'def': 'a pole with metal points used as an aid in skiing', 'name': 'ski_pole'}, {'frequency': 'f', 'synset': 'skirt.n.02', 'synonyms': ['skirt'], 'id': 968, 'def': 'a garment hanging from the waist; worn mainly by girls and women', 'name': 'skirt'}, {'frequency': 'r', 'synset': 'skullcap.n.01', 'synonyms': ['skullcap'], 'id': 969, 'def': 'rounded brimless cap fitting the crown of the head', 'name': 'skullcap'}, {'frequency': 'c', 'synset': 'sled.n.01', 'synonyms': ['sled', 'sledge', 'sleigh'], 'id': 970, 'def': 'a vehicle or flat object for transportation over snow by sliding or pulled by dogs, etc.', 'name': 'sled'}, {'frequency': 'c', 'synset': 'sleeping_bag.n.01', 'synonyms': ['sleeping_bag'], 'id': 971, 'def': 'large padded bag designed to be slept in outdoors', 'name': 'sleeping_bag'}, {'frequency': 'r', 'synset': 'sling.n.05', 'synonyms': ['sling_(bandage)', 'triangular_bandage'], 'id': 972, 'def': 'bandage to support an injured forearm; slung over the shoulder or neck', 'name': 'sling_(bandage)'}, {'frequency': 'c', 'synset': 'slipper.n.01', 'synonyms': ['slipper_(footwear)', 'carpet_slipper_(footwear)'], 'id': 973, 'def': 'low footwear that can be slipped on and off easily; usually worn indoors', 'name': 'slipper_(footwear)'}, {'frequency': 'r', 'synset': 'smoothie.n.02', 'synonyms': ['smoothie'], 'id': 974, 'def': 'a thick smooth drink consisting of fresh fruit pureed with ice cream or yoghurt or milk', 'name': 'smoothie'}, {'frequency': 'r', 'synset': 'snake.n.01', 'synonyms': ['snake', 'serpent'], 'id': 975, 'def': 'limbless scaly elongate reptile; some are venomous', 'name': 'snake'}, {'frequency': 'f', 'synset': 'snowboard.n.01', 'synonyms': ['snowboard'], 'id': 976, 'def': 'a board that resembles a broad ski or a small surfboard; used in a standing position to slide down snow-covered slopes', 'name': 'snowboard'}, {'frequency': 'c', 'synset': 'snowman.n.01', 'synonyms': ['snowman'], 'id': 977, 'def': 'a figure of a person made of packed snow', 'name': 'snowman'}, {'frequency': 'c', 'synset': 'snowmobile.n.01', 'synonyms': ['snowmobile'], 'id': 978, 'def': 'tracked vehicle for travel on snow having skis in front', 'name': 'snowmobile'}, {'frequency': 'f', 'synset': 'soap.n.01', 'synonyms': ['soap'], 'id': 979, 'def': 'a cleansing agent made from the salts of vegetable or animal fats', 'name': 'soap'}, {'frequency': 'f', 'synset': 'soccer_ball.n.01', 'synonyms': ['soccer_ball'], 'id': 980, 'def': "an inflated ball used in playing soccer (called `football' outside of the United States)", 'name': 'soccer_ball'}, {'frequency': 'f', 'synset': 'sock.n.01', 'synonyms': ['sock'], 'id': 981, 'def': 'cloth covering for the foot; worn inside the shoe; reaches to between the ankle and the knee', 'name': 'sock'}, {'frequency': 'f', 'synset': 'sofa.n.01', 'synonyms': ['sofa', 'couch', 'lounge'], 'id': 982, 'def': 'an upholstered seat for more than one person', 'name': 'sofa'}, {'frequency': 'r', 'synset': 'softball.n.01', 'synonyms': ['softball'], 'id': 983, 'def': 'ball used in playing softball', 'name': 'softball'}, {'frequency': 'c', 'synset': 'solar_array.n.01', 'synonyms': ['solar_array', 'solar_battery', 'solar_panel'], 'id': 984, 'def': 'electrical device consisting of a large array of connected solar cells', 'name': 'solar_array'}, {'frequency': 'r', 'synset': 'sombrero.n.02', 'synonyms': ['sombrero'], 'id': 985, 'def': 'a straw hat with a tall crown and broad brim; worn in American southwest and in Mexico', 'name': 'sombrero'}, {'frequency': 'f', 'synset': 'soup.n.01', 'synonyms': ['soup'], 'id': 986, 'def': 'liquid food especially of meat or fish or vegetable stock often containing pieces of solid food', 'name': 'soup'}, {'frequency': 'r', 'synset': 'soup_bowl.n.01', 'synonyms': ['soup_bowl'], 'id': 987, 'def': 'a bowl for serving soup', 'name': 'soup_bowl'}, {'frequency': 'c', 'synset': 'soupspoon.n.01', 'synonyms': ['soupspoon'], 'id': 988, 'def': 'a spoon with a rounded bowl for eating soup', 'name': 'soupspoon'}, {'frequency': 'c', 'synset': 'sour_cream.n.01', 'synonyms': ['sour_cream', 'soured_cream'], 'id': 989, 'def': 'soured light cream', 'name': 'sour_cream'}, {'frequency': 'r', 'synset': 'soya_milk.n.01', 'synonyms': ['soya_milk', 'soybean_milk', 'soymilk'], 'id': 990, 'def': 'a milk substitute containing soybean flour and water; used in some infant formulas and in making tofu', 'name': 'soya_milk'}, {'frequency': 'r', 'synset': 'space_shuttle.n.01', 'synonyms': ['space_shuttle'], 'id': 991, 'def': "a reusable spacecraft with wings for a controlled descent through the Earth's atmosphere", 'name': 'space_shuttle'}, {'frequency': 'r', 'synset': 'sparkler.n.02', 'synonyms': ['sparkler_(fireworks)'], 'id': 992, 'def': 'a firework that burns slowly and throws out a shower of sparks', 'name': 'sparkler_(fireworks)'}, {'frequency': 'f', 'synset': 'spatula.n.02', 'synonyms': ['spatula'], 'id': 993, 'def': 'a hand tool with a thin flexible blade used to mix or spread soft substances', 'name': 'spatula'}, {'frequency': 'r', 'synset': 'spear.n.01', 'synonyms': ['spear', 'lance'], 'id': 994, 'def': 'a long pointed rod used as a tool or weapon', 'name': 'spear'}, {'frequency': 'f', 'synset': 'spectacles.n.01', 'synonyms': ['spectacles', 'specs', 'eyeglasses', 'glasses'], 'id': 995, 'def': 'optical instrument consisting of a frame that holds a pair of lenses for correcting defective vision', 'name': 'spectacles'}, {'frequency': 'c', 'synset': 'spice_rack.n.01', 'synonyms': ['spice_rack'], 'id': 996, 'def': 'a rack for displaying containers filled with spices', 'name': 'spice_rack'}, {'frequency': 'c', 'synset': 'spider.n.01', 'synonyms': ['spider'], 'id': 997, 'def': 'predatory arachnid with eight legs, two poison fangs, two feelers, and usually two silk-spinning organs at the back end of the body', 'name': 'spider'}, {'frequency': 'r', 'synset': 'spiny_lobster.n.02', 'synonyms': ['crawfish', 'crayfish'], 'id': 998, 'def': 'large edible marine crustacean having a spiny carapace but lacking the large pincers of true lobsters', 'name': 'crawfish'}, {'frequency': 'c', 'synset': 'sponge.n.01', 'synonyms': ['sponge'], 'id': 999, 'def': 'a porous mass usable to absorb water typically used for cleaning', 'name': 'sponge'}, {'frequency': 'f', 'synset': 'spoon.n.01', 'synonyms': ['spoon'], 'id': 1000, 'def': 'a piece of cutlery with a shallow bowl-shaped container and a handle', 'name': 'spoon'}, {'frequency': 'c', 'synset': 'sportswear.n.01', 'synonyms': ['sportswear', 'athletic_wear', 'activewear'], 'id': 1001, 'def': 'attire worn for sport or for casual wear', 'name': 'sportswear'}, {'frequency': 'c', 'synset': 'spotlight.n.02', 'synonyms': ['spotlight'], 'id': 1002, 'def': 'a lamp that produces a strong beam of light to illuminate a restricted area; used to focus attention of a stage performer', 'name': 'spotlight'}, {'frequency': 'r', 'synset': 'squid.n.01', 'synonyms': ['squid_(food)', 'calamari', 'calamary'], 'id': 1003, 'def': '(Italian cuisine) squid prepared as food', 'name': 'squid_(food)'}, {'frequency': 'c', 'synset': 'squirrel.n.01', 'synonyms': ['squirrel'], 'id': 1004, 'def': 'a kind of arboreal rodent having a long bushy tail', 'name': 'squirrel'}, {'frequency': 'r', 'synset': 'stagecoach.n.01', 'synonyms': ['stagecoach'], 'id': 1005, 'def': 'a large coach-and-four formerly used to carry passengers and mail on regular routes between towns', 'name': 'stagecoach'}, {'frequency': 'c', 'synset': 'stapler.n.01', 'synonyms': ['stapler_(stapling_machine)'], 'id': 1006, 'def': 'a machine that inserts staples into sheets of paper in order to fasten them together', 'name': 'stapler_(stapling_machine)'}, {'frequency': 'c', 'synset': 'starfish.n.01', 'synonyms': ['starfish', 'sea_star'], 'id': 1007, 'def': 'echinoderms characterized by five arms extending from a central disk', 'name': 'starfish'}, {'frequency': 'f', 'synset': 'statue.n.01', 'synonyms': ['statue_(sculpture)'], 'id': 1008, 'def': 'a sculpture representing a human or animal', 'name': 'statue_(sculpture)'}, {'frequency': 'c', 'synset': 'steak.n.01', 'synonyms': ['steak_(food)'], 'id': 1009, 'def': 'a slice of meat cut from the fleshy part of an animal or large fish', 'name': 'steak_(food)'}, {'frequency': 'r', 'synset': 'steak_knife.n.01', 'synonyms': ['steak_knife'], 'id': 1010, 'def': 'a sharp table knife used in eating steak', 'name': 'steak_knife'}, {'frequency': 'f', 'synset': 'steering_wheel.n.01', 'synonyms': ['steering_wheel'], 'id': 1011, 'def': 'a handwheel that is used for steering', 'name': 'steering_wheel'}, {'frequency': 'r', 'synset': 'step_ladder.n.01', 'synonyms': ['stepladder'], 'id': 1012, 'def': 'a folding portable ladder hinged at the top', 'name': 'stepladder'}, {'frequency': 'c', 'synset': 'step_stool.n.01', 'synonyms': ['step_stool'], 'id': 1013, 'def': 'a stool that has one or two steps that fold under the seat', 'name': 'step_stool'}, {'frequency': 'c', 'synset': 'stereo.n.01', 'synonyms': ['stereo_(sound_system)'], 'id': 1014, 'def': 'electronic device for playing audio', 'name': 'stereo_(sound_system)'}, {'frequency': 'r', 'synset': 'stew.n.02', 'synonyms': ['stew'], 'id': 1015, 'def': 'food prepared by stewing especially meat or fish with vegetables', 'name': 'stew'}, {'frequency': 'r', 'synset': 'stirrer.n.02', 'synonyms': ['stirrer'], 'id': 1016, 'def': 'an implement used for stirring', 'name': 'stirrer'}, {'frequency': 'f', 'synset': 'stirrup.n.01', 'synonyms': ['stirrup'], 'id': 1017, 'def': "support consisting of metal loops into which rider's feet go", 'name': 'stirrup'}, {'frequency': 'f', 'synset': 'stool.n.01', 'synonyms': ['stool'], 'id': 1018, 'def': 'a simple seat without a back or arms', 'name': 'stool'}, {'frequency': 'f', 'synset': 'stop_sign.n.01', 'synonyms': ['stop_sign'], 'id': 1019, 'def': 'a traffic sign to notify drivers that they must come to a complete stop', 'name': 'stop_sign'}, {'frequency': 'f', 'synset': 'stoplight.n.01', 'synonyms': ['brake_light'], 'id': 1020, 'def': 'a red light on the rear of a motor vehicle that signals when the brakes are applied', 'name': 'brake_light'}, {'frequency': 'f', 'synset': 'stove.n.01', 'synonyms': ['stove', 'kitchen_stove', 'range_(kitchen_appliance)', 'kitchen_range', 'cooking_stove'], 'id': 1021, 'def': 'a kitchen appliance used for cooking food', 'name': 'stove'}, {'frequency': 'c', 'synset': 'strainer.n.01', 'synonyms': ['strainer'], 'id': 1022, 'def': 'a filter to retain larger pieces while smaller pieces and liquids pass through', 'name': 'strainer'}, {'frequency': 'f', 'synset': 'strap.n.01', 'synonyms': ['strap'], 'id': 1023, 'def': 'an elongated strip of material for binding things together or holding', 'name': 'strap'}, {'frequency': 'f', 'synset': 'straw.n.04', 'synonyms': ['straw_(for_drinking)', 'drinking_straw'], 'id': 1024, 'def': 'a thin paper or plastic tube used to suck liquids into the mouth', 'name': 'straw_(for_drinking)'}, {'frequency': 'f', 'synset': 'strawberry.n.01', 'synonyms': ['strawberry'], 'id': 1025, 'def': 'sweet fleshy red fruit', 'name': 'strawberry'}, {'frequency': 'f', 'synset': 'street_sign.n.01', 'synonyms': ['street_sign'], 'id': 1026, 'def': 'a sign visible from the street', 'name': 'street_sign'}, {'frequency': 'f', 'synset': 'streetlight.n.01', 'synonyms': ['streetlight', 'street_lamp'], 'id': 1027, 'def': 'a lamp supported on a lamppost; for illuminating a street', 'name': 'streetlight'}, {'frequency': 'r', 'synset': 'string_cheese.n.01', 'synonyms': ['string_cheese'], 'id': 1028, 'def': 'cheese formed in long strings twisted together', 'name': 'string_cheese'}, {'frequency': 'r', 'synset': 'stylus.n.02', 'synonyms': ['stylus'], 'id': 1029, 'def': 'a pointed tool for writing or drawing or engraving, including pens', 'name': 'stylus'}, {'frequency': 'r', 'synset': 'subwoofer.n.01', 'synonyms': ['subwoofer'], 'id': 1030, 'def': 'a loudspeaker that is designed to reproduce very low bass frequencies', 'name': 'subwoofer'}, {'frequency': 'r', 'synset': 'sugar_bowl.n.01', 'synonyms': ['sugar_bowl'], 'id': 1031, 'def': 'a dish in which sugar is served', 'name': 'sugar_bowl'}, {'frequency': 'r', 'synset': 'sugarcane.n.01', 'synonyms': ['sugarcane_(plant)'], 'id': 1032, 'def': 'juicy canes whose sap is a source of molasses and commercial sugar; fresh canes are sometimes chewed for the juice', 'name': 'sugarcane_(plant)'}, {'frequency': 'f', 'synset': 'suit.n.01', 'synonyms': ['suit_(clothing)'], 'id': 1033, 'def': 'a set of garments (usually including a jacket and trousers or skirt) for outerwear all of the same fabric and color', 'name': 'suit_(clothing)'}, {'frequency': 'c', 'synset': 'sunflower.n.01', 'synonyms': ['sunflower'], 'id': 1034, 'def': 'any plant of the genus Helianthus having large flower heads with dark disk florets and showy yellow rays', 'name': 'sunflower'}, {'frequency': 'f', 'synset': 'sunglasses.n.01', 'synonyms': ['sunglasses'], 'id': 1035, 'def': 'spectacles that are darkened or polarized to protect the eyes from the glare of the sun', 'name': 'sunglasses'}, {'frequency': 'c', 'synset': 'sunhat.n.01', 'synonyms': ['sunhat'], 'id': 1036, 'def': 'a hat with a broad brim that protects the face from direct exposure to the sun', 'name': 'sunhat'}, {'frequency': 'f', 'synset': 'surfboard.n.01', 'synonyms': ['surfboard'], 'id': 1037, 'def': 'a narrow buoyant board for riding surf', 'name': 'surfboard'}, {'frequency': 'c', 'synset': 'sushi.n.01', 'synonyms': ['sushi'], 'id': 1038, 'def': 'rice (with raw fish) wrapped in seaweed', 'name': 'sushi'}, {'frequency': 'c', 'synset': 'swab.n.02', 'synonyms': ['mop'], 'id': 1039, 'def': 'cleaning implement consisting of absorbent material fastened to a handle; for cleaning floors', 'name': 'mop'}, {'frequency': 'c', 'synset': 'sweat_pants.n.01', 'synonyms': ['sweat_pants'], 'id': 1040, 'def': 'loose-fitting trousers with elastic cuffs; worn by athletes', 'name': 'sweat_pants'}, {'frequency': 'c', 'synset': 'sweatband.n.02', 'synonyms': ['sweatband'], 'id': 1041, 'def': 'a band of material tied around the forehead or wrist to absorb sweat', 'name': 'sweatband'}, {'frequency': 'f', 'synset': 'sweater.n.01', 'synonyms': ['sweater'], 'id': 1042, 'def': 'a crocheted or knitted garment covering the upper part of the body', 'name': 'sweater'}, {'frequency': 'f', 'synset': 'sweatshirt.n.01', 'synonyms': ['sweatshirt'], 'id': 1043, 'def': 'cotton knit pullover with long sleeves worn during athletic activity', 'name': 'sweatshirt'}, {'frequency': 'c', 'synset': 'sweet_potato.n.02', 'synonyms': ['sweet_potato'], 'id': 1044, 'def': 'the edible tuberous root of the sweet potato vine', 'name': 'sweet_potato'}, {'frequency': 'f', 'synset': 'swimsuit.n.01', 'synonyms': ['swimsuit', 'swimwear', 'bathing_suit', 'swimming_costume', 'bathing_costume', 'swimming_trunks', 'bathing_trunks'], 'id': 1045, 'def': 'garment worn for swimming', 'name': 'swimsuit'}, {'frequency': 'c', 'synset': 'sword.n.01', 'synonyms': ['sword'], 'id': 1046, 'def': 'a cutting or thrusting weapon that has a long metal blade', 'name': 'sword'}, {'frequency': 'r', 'synset': 'syringe.n.01', 'synonyms': ['syringe'], 'id': 1047, 'def': 'a medical instrument used to inject or withdraw fluids', 'name': 'syringe'}, {'frequency': 'r', 'synset': 'tabasco.n.02', 'synonyms': ['Tabasco_sauce'], 'id': 1048, 'def': 'very spicy sauce (trade name Tabasco) made from fully-aged red peppers', 'name': 'Tabasco_sauce'}, {'frequency': 'r', 'synset': 'table-tennis_table.n.01', 'synonyms': ['table-tennis_table', 'ping-pong_table'], 'id': 1049, 'def': 'a table used for playing table tennis', 'name': 'table-tennis_table'}, {'frequency': 'f', 'synset': 'table.n.02', 'synonyms': ['table'], 'id': 1050, 'def': 'a piece of furniture having a smooth flat top that is usually supported by one or more vertical legs', 'name': 'table'}, {'frequency': 'c', 'synset': 'table_lamp.n.01', 'synonyms': ['table_lamp'], 'id': 1051, 'def': 'a lamp that sits on a table', 'name': 'table_lamp'}, {'frequency': 'f', 'synset': 'tablecloth.n.01', 'synonyms': ['tablecloth'], 'id': 1052, 'def': 'a covering spread over a dining table', 'name': 'tablecloth'}, {'frequency': 'r', 'synset': 'tachometer.n.01', 'synonyms': ['tachometer'], 'id': 1053, 'def': 'measuring instrument for indicating speed of rotation', 'name': 'tachometer'}, {'frequency': 'r', 'synset': 'taco.n.02', 'synonyms': ['taco'], 'id': 1054, 'def': 'a small tortilla cupped around a filling', 'name': 'taco'}, {'frequency': 'f', 'synset': 'tag.n.02', 'synonyms': ['tag'], 'id': 1055, 'def': 'a label associated with something for the purpose of identification or information', 'name': 'tag'}, {'frequency': 'f', 'synset': 'taillight.n.01', 'synonyms': ['taillight', 'rear_light'], 'id': 1056, 'def': 'lamp (usually red) mounted at the rear of a motor vehicle', 'name': 'taillight'}, {'frequency': 'r', 'synset': 'tambourine.n.01', 'synonyms': ['tambourine'], 'id': 1057, 'def': 'a shallow drum with a single drumhead and with metallic disks in the sides', 'name': 'tambourine'}, {'frequency': 'r', 'synset': 'tank.n.01', 'synonyms': ['army_tank', 'armored_combat_vehicle', 'armoured_combat_vehicle'], 'id': 1058, 'def': 'an enclosed armored military vehicle; has a cannon and moves on caterpillar treads', 'name': 'army_tank'}, {'frequency': 'f', 'synset': 'tank.n.02', 'synonyms': ['tank_(storage_vessel)', 'storage_tank'], 'id': 1059, 'def': 'a large (usually metallic) vessel for holding gases or liquids', 'name': 'tank_(storage_vessel)'}, {'frequency': 'f', 'synset': 'tank_top.n.01', 'synonyms': ['tank_top_(clothing)'], 'id': 1060, 'def': 'a tight-fitting sleeveless shirt with wide shoulder straps and low neck and no front opening', 'name': 'tank_top_(clothing)'}, {'frequency': 'f', 'synset': 'tape.n.01', 'synonyms': ['tape_(sticky_cloth_or_paper)'], 'id': 1061, 'def': 'a long thin piece of cloth or paper as used for binding or fastening', 'name': 'tape_(sticky_cloth_or_paper)'}, {'frequency': 'c', 'synset': 'tape.n.04', 'synonyms': ['tape_measure', 'measuring_tape'], 'id': 1062, 'def': 'measuring instrument consisting of a narrow strip (cloth or metal) marked in inches or centimeters and used for measuring lengths', 'name': 'tape_measure'}, {'frequency': 'c', 'synset': 'tapestry.n.02', 'synonyms': ['tapestry'], 'id': 1063, 'def': 'a heavy textile with a woven design; used for curtains and upholstery', 'name': 'tapestry'}, {'frequency': 'f', 'synset': 'tarpaulin.n.01', 'synonyms': ['tarp'], 'id': 1064, 'def': 'waterproofed canvas', 'name': 'tarp'}, {'frequency': 'c', 'synset': 'tartan.n.01', 'synonyms': ['tartan', 'plaid'], 'id': 1065, 'def': 'a cloth having a crisscross design', 'name': 'tartan'}, {'frequency': 'c', 'synset': 'tassel.n.01', 'synonyms': ['tassel'], 'id': 1066, 'def': 'adornment consisting of a bunch of cords fastened at one end', 'name': 'tassel'}, {'frequency': 'c', 'synset': 'tea_bag.n.01', 'synonyms': ['tea_bag'], 'id': 1067, 'def': 'a measured amount of tea in a bag for an individual serving of tea', 'name': 'tea_bag'}, {'frequency': 'c', 'synset': 'teacup.n.02', 'synonyms': ['teacup'], 'id': 1068, 'def': 'a cup from which tea is drunk', 'name': 'teacup'}, {'frequency': 'c', 'synset': 'teakettle.n.01', 'synonyms': ['teakettle'], 'id': 1069, 'def': 'kettle for boiling water to make tea', 'name': 'teakettle'}, {'frequency': 'f', 'synset': 'teapot.n.01', 'synonyms': ['teapot'], 'id': 1070, 'def': 'pot for brewing tea; usually has a spout and handle', 'name': 'teapot'}, {'frequency': 'f', 'synset': 'teddy.n.01', 'synonyms': ['teddy_bear'], 'id': 1071, 'def': "plaything consisting of a child's toy bear (usually plush and stuffed with soft materials)", 'name': 'teddy_bear'}, {'frequency': 'f', 'synset': 'telephone.n.01', 'synonyms': ['telephone', 'phone', 'telephone_set'], 'id': 1072, 'def': 'electronic device for communicating by voice over long distances (includes wired and wireless/cell phones)', 'name': 'telephone'}, {'frequency': 'c', 'synset': 'telephone_booth.n.01', 'synonyms': ['telephone_booth', 'phone_booth', 'call_box', 'telephone_box', 'telephone_kiosk'], 'id': 1073, 'def': 'booth for using a telephone', 'name': 'telephone_booth'}, {'frequency': 'f', 'synset': 'telephone_pole.n.01', 'synonyms': ['telephone_pole', 'telegraph_pole', 'telegraph_post'], 'id': 1074, 'def': 'tall pole supporting telephone wires', 'name': 'telephone_pole'}, {'frequency': 'r', 'synset': 'telephoto_lens.n.01', 'synonyms': ['telephoto_lens', 'zoom_lens'], 'id': 1075, 'def': 'a camera lens that magnifies the image', 'name': 'telephoto_lens'}, {'frequency': 'c', 'synset': 'television_camera.n.01', 'synonyms': ['television_camera', 'tv_camera'], 'id': 1076, 'def': 'television equipment for capturing and recording video', 'name': 'television_camera'}, {'frequency': 'f', 'synset': 'television_receiver.n.01', 'synonyms': ['television_set', 'tv', 'tv_set'], 'id': 1077, 'def': 'an electronic device that receives television signals and displays them on a screen', 'name': 'television_set'}, {'frequency': 'f', 'synset': 'tennis_ball.n.01', 'synonyms': ['tennis_ball'], 'id': 1078, 'def': 'ball about the size of a fist used in playing tennis', 'name': 'tennis_ball'}, {'frequency': 'f', 'synset': 'tennis_racket.n.01', 'synonyms': ['tennis_racket'], 'id': 1079, 'def': 'a racket used to play tennis', 'name': 'tennis_racket'}, {'frequency': 'r', 'synset': 'tequila.n.01', 'synonyms': ['tequila'], 'id': 1080, 'def': 'Mexican liquor made from fermented juices of an agave plant', 'name': 'tequila'}, {'frequency': 'c', 'synset': 'thermometer.n.01', 'synonyms': ['thermometer'], 'id': 1081, 'def': 'measuring instrument for measuring temperature', 'name': 'thermometer'}, {'frequency': 'c', 'synset': 'thermos.n.01', 'synonyms': ['thermos_bottle'], 'id': 1082, 'def': 'vacuum flask that preserves temperature of hot or cold drinks', 'name': 'thermos_bottle'}, {'frequency': 'f', 'synset': 'thermostat.n.01', 'synonyms': ['thermostat'], 'id': 1083, 'def': 'a regulator for automatically regulating temperature by starting or stopping the supply of heat', 'name': 'thermostat'}, {'frequency': 'r', 'synset': 'thimble.n.02', 'synonyms': ['thimble'], 'id': 1084, 'def': 'a small metal cap to protect the finger while sewing; can be used as a small container', 'name': 'thimble'}, {'frequency': 'c', 'synset': 'thread.n.01', 'synonyms': ['thread', 'yarn'], 'id': 1085, 'def': 'a fine cord of twisted fibers (of cotton or silk or wool or nylon etc.) used in sewing and weaving', 'name': 'thread'}, {'frequency': 'c', 'synset': 'thumbtack.n.01', 'synonyms': ['thumbtack', 'drawing_pin', 'pushpin'], 'id': 1086, 'def': 'a tack for attaching papers to a bulletin board or drawing board', 'name': 'thumbtack'}, {'frequency': 'c', 'synset': 'tiara.n.01', 'synonyms': ['tiara'], 'id': 1087, 'def': 'a jeweled headdress worn by women on formal occasions', 'name': 'tiara'}, {'frequency': 'c', 'synset': 'tiger.n.02', 'synonyms': ['tiger'], 'id': 1088, 'def': 'large feline of forests in most of Asia having a tawny coat with black stripes', 'name': 'tiger'}, {'frequency': 'c', 'synset': 'tights.n.01', 'synonyms': ['tights_(clothing)', 'leotards'], 'id': 1089, 'def': 'skintight knit hose covering the body from the waist to the feet worn by acrobats and dancers and as stockings by women and girls', 'name': 'tights_(clothing)'}, {'frequency': 'c', 'synset': 'timer.n.01', 'synonyms': ['timer', 'stopwatch'], 'id': 1090, 'def': 'a timepiece that measures a time interval and signals its end', 'name': 'timer'}, {'frequency': 'f', 'synset': 'tinfoil.n.01', 'synonyms': ['tinfoil'], 'id': 1091, 'def': 'foil made of tin or an alloy of tin and lead', 'name': 'tinfoil'}, {'frequency': 'c', 'synset': 'tinsel.n.01', 'synonyms': ['tinsel'], 'id': 1092, 'def': 'a showy decoration that is basically valueless', 'name': 'tinsel'}, {'frequency': 'f', 'synset': 'tissue.n.02', 'synonyms': ['tissue_paper'], 'id': 1093, 'def': 'a soft thin (usually translucent) paper', 'name': 'tissue_paper'}, {'frequency': 'c', 'synset': 'toast.n.01', 'synonyms': ['toast_(food)'], 'id': 1094, 'def': 'slice of bread that has been toasted', 'name': 'toast_(food)'}, {'frequency': 'f', 'synset': 'toaster.n.02', 'synonyms': ['toaster'], 'id': 1095, 'def': 'a kitchen appliance (usually electric) for toasting bread', 'name': 'toaster'}, {'frequency': 'f', 'synset': 'toaster_oven.n.01', 'synonyms': ['toaster_oven'], 'id': 1096, 'def': 'kitchen appliance consisting of a small electric oven for toasting or warming food', 'name': 'toaster_oven'}, {'frequency': 'f', 'synset': 'toilet.n.02', 'synonyms': ['toilet'], 'id': 1097, 'def': 'a plumbing fixture for defecation and urination', 'name': 'toilet'}, {'frequency': 'f', 'synset': 'toilet_tissue.n.01', 'synonyms': ['toilet_tissue', 'toilet_paper', 'bathroom_tissue'], 'id': 1098, 'def': 'a soft thin absorbent paper for use in toilets', 'name': 'toilet_tissue'}, {'frequency': 'f', 'synset': 'tomato.n.01', 'synonyms': ['tomato'], 'id': 1099, 'def': 'mildly acid red or yellow pulpy fruit eaten as a vegetable', 'name': 'tomato'}, {'frequency': 'f', 'synset': 'tongs.n.01', 'synonyms': ['tongs'], 'id': 1100, 'def': 'any of various devices for taking hold of objects; usually have two hinged legs with handles above and pointed hooks below', 'name': 'tongs'}, {'frequency': 'c', 'synset': 'toolbox.n.01', 'synonyms': ['toolbox'], 'id': 1101, 'def': 'a box or chest or cabinet for holding hand tools', 'name': 'toolbox'}, {'frequency': 'f', 'synset': 'toothbrush.n.01', 'synonyms': ['toothbrush'], 'id': 1102, 'def': 'small brush; has long handle; used to clean teeth', 'name': 'toothbrush'}, {'frequency': 'f', 'synset': 'toothpaste.n.01', 'synonyms': ['toothpaste'], 'id': 1103, 'def': 'a dentifrice in the form of a paste', 'name': 'toothpaste'}, {'frequency': 'f', 'synset': 'toothpick.n.01', 'synonyms': ['toothpick'], 'id': 1104, 'def': 'pick consisting of a small strip of wood or plastic; used to pick food from between the teeth', 'name': 'toothpick'}, {'frequency': 'f', 'synset': 'top.n.09', 'synonyms': ['cover'], 'id': 1105, 'def': 'covering for a hole (especially a hole in the top of a container)', 'name': 'cover'}, {'frequency': 'c', 'synset': 'tortilla.n.01', 'synonyms': ['tortilla'], 'id': 1106, 'def': 'thin unleavened pancake made from cornmeal or wheat flour', 'name': 'tortilla'}, {'frequency': 'c', 'synset': 'tow_truck.n.01', 'synonyms': ['tow_truck'], 'id': 1107, 'def': 'a truck equipped to hoist and pull wrecked cars (or to remove cars from no-parking zones)', 'name': 'tow_truck'}, {'frequency': 'f', 'synset': 'towel.n.01', 'synonyms': ['towel'], 'id': 1108, 'def': 'a rectangular piece of absorbent cloth (or paper) for drying or wiping', 'name': 'towel'}, {'frequency': 'f', 'synset': 'towel_rack.n.01', 'synonyms': ['towel_rack', 'towel_rail', 'towel_bar'], 'id': 1109, 'def': 'a rack consisting of one or more bars on which towels can be hung', 'name': 'towel_rack'}, {'frequency': 'f', 'synset': 'toy.n.03', 'synonyms': ['toy'], 'id': 1110, 'def': 'a device regarded as providing amusement', 'name': 'toy'}, {'frequency': 'c', 'synset': 'tractor.n.01', 'synonyms': ['tractor_(farm_equipment)'], 'id': 1111, 'def': 'a wheeled vehicle with large wheels; used in farming and other applications', 'name': 'tractor_(farm_equipment)'}, {'frequency': 'f', 'synset': 'traffic_light.n.01', 'synonyms': ['traffic_light'], 'id': 1112, 'def': 'a device to control vehicle traffic often consisting of three or more lights', 'name': 'traffic_light'}, {'frequency': 'c', 'synset': 'trail_bike.n.01', 'synonyms': ['dirt_bike'], 'id': 1113, 'def': 'a lightweight motorcycle equipped with rugged tires and suspension for off-road use', 'name': 'dirt_bike'}, {'frequency': 'f', 'synset': 'trailer_truck.n.01', 'synonyms': ['trailer_truck', 'tractor_trailer', 'trucking_rig', 'articulated_lorry', 'semi_truck'], 'id': 1114, 'def': 'a truck consisting of a tractor and trailer together', 'name': 'trailer_truck'}, {'frequency': 'f', 'synset': 'train.n.01', 'synonyms': ['train_(railroad_vehicle)', 'railroad_train'], 'id': 1115, 'def': 'public or private transport provided by a line of railway cars coupled together and drawn by a locomotive', 'name': 'train_(railroad_vehicle)'}, {'frequency': 'r', 'synset': 'trampoline.n.01', 'synonyms': ['trampoline'], 'id': 1116, 'def': 'gymnastic apparatus consisting of a strong canvas sheet attached with springs to a metal frame', 'name': 'trampoline'}, {'frequency': 'f', 'synset': 'tray.n.01', 'synonyms': ['tray'], 'id': 1117, 'def': 'an open receptacle for holding or displaying or serving articles or food', 'name': 'tray'}, {'frequency': 'r', 'synset': 'trench_coat.n.01', 'synonyms': ['trench_coat'], 'id': 1118, 'def': 'a military style raincoat; belted with deep pockets', 'name': 'trench_coat'}, {'frequency': 'r', 'synset': 'triangle.n.05', 'synonyms': ['triangle_(musical_instrument)'], 'id': 1119, 'def': 'a percussion instrument consisting of a metal bar bent in the shape of an open triangle', 'name': 'triangle_(musical_instrument)'}, {'frequency': 'c', 'synset': 'tricycle.n.01', 'synonyms': ['tricycle'], 'id': 1120, 'def': 'a vehicle with three wheels that is moved by foot pedals', 'name': 'tricycle'}, {'frequency': 'f', 'synset': 'tripod.n.01', 'synonyms': ['tripod'], 'id': 1121, 'def': 'a three-legged rack used for support', 'name': 'tripod'}, {'frequency': 'f', 'synset': 'trouser.n.01', 'synonyms': ['trousers', 'pants_(clothing)'], 'id': 1122, 'def': 'a garment extending from the waist to the knee or ankle, covering each leg separately', 'name': 'trousers'}, {'frequency': 'f', 'synset': 'truck.n.01', 'synonyms': ['truck'], 'id': 1123, 'def': 'an automotive vehicle suitable for hauling', 'name': 'truck'}, {'frequency': 'r', 'synset': 'truffle.n.03', 'synonyms': ['truffle_(chocolate)', 'chocolate_truffle'], 'id': 1124, 'def': 'creamy chocolate candy', 'name': 'truffle_(chocolate)'}, {'frequency': 'c', 'synset': 'trunk.n.02', 'synonyms': ['trunk'], 'id': 1125, 'def': 'luggage consisting of a large strong case used when traveling or for storage', 'name': 'trunk'}, {'frequency': 'r', 'synset': 'tub.n.02', 'synonyms': ['vat'], 'id': 1126, 'def': 'a large vessel for holding or storing liquids', 'name': 'vat'}, {'frequency': 'c', 'synset': 'turban.n.01', 'synonyms': ['turban'], 'id': 1127, 'def': 'a traditional headdress consisting of a long scarf wrapped around the head', 'name': 'turban'}, {'frequency': 'c', 'synset': 'turkey.n.04', 'synonyms': ['turkey_(food)'], 'id': 1128, 'def': 'flesh of large domesticated fowl usually roasted', 'name': 'turkey_(food)'}, {'frequency': 'r', 'synset': 'turnip.n.01', 'synonyms': ['turnip'], 'id': 1129, 'def': 'widely cultivated plant having a large fleshy edible white or yellow root', 'name': 'turnip'}, {'frequency': 'c', 'synset': 'turtle.n.02', 'synonyms': ['turtle'], 'id': 1130, 'def': 'any of various aquatic and land reptiles having a bony shell and flipper-like limbs for swimming', 'name': 'turtle'}, {'frequency': 'c', 'synset': 'turtleneck.n.01', 'synonyms': ['turtleneck_(clothing)', 'polo-neck'], 'id': 1131, 'def': 'a sweater or jersey with a high close-fitting collar', 'name': 'turtleneck_(clothing)'}, {'frequency': 'c', 'synset': 'typewriter.n.01', 'synonyms': ['typewriter'], 'id': 1132, 'def': 'hand-operated character printer for printing written messages one character at a time', 'name': 'typewriter'}, {'frequency': 'f', 'synset': 'umbrella.n.01', 'synonyms': ['umbrella'], 'id': 1133, 'def': 'a lightweight handheld collapsible canopy', 'name': 'umbrella'}, {'frequency': 'f', 'synset': 'underwear.n.01', 'synonyms': ['underwear', 'underclothes', 'underclothing', 'underpants'], 'id': 1134, 'def': 'undergarment worn next to the skin and under the outer garments', 'name': 'underwear'}, {'frequency': 'r', 'synset': 'unicycle.n.01', 'synonyms': ['unicycle'], 'id': 1135, 'def': 'a vehicle with a single wheel that is driven by pedals', 'name': 'unicycle'}, {'frequency': 'f', 'synset': 'urinal.n.01', 'synonyms': ['urinal'], 'id': 1136, 'def': 'a plumbing fixture (usually attached to the wall) used by men to urinate', 'name': 'urinal'}, {'frequency': 'c', 'synset': 'urn.n.01', 'synonyms': ['urn'], 'id': 1137, 'def': 'a large vase that usually has a pedestal or feet', 'name': 'urn'}, {'frequency': 'c', 'synset': 'vacuum.n.04', 'synonyms': ['vacuum_cleaner'], 'id': 1138, 'def': 'an electrical home appliance that cleans by suction', 'name': 'vacuum_cleaner'}, {'frequency': 'f', 'synset': 'vase.n.01', 'synonyms': ['vase'], 'id': 1139, 'def': 'an open jar of glass or porcelain used as an ornament or to hold flowers', 'name': 'vase'}, {'frequency': 'c', 'synset': 'vending_machine.n.01', 'synonyms': ['vending_machine'], 'id': 1140, 'def': 'a slot machine for selling goods', 'name': 'vending_machine'}, {'frequency': 'f', 'synset': 'vent.n.01', 'synonyms': ['vent', 'blowhole', 'air_vent'], 'id': 1141, 'def': 'a hole for the escape of gas or air', 'name': 'vent'}, {'frequency': 'f', 'synset': 'vest.n.01', 'synonyms': ['vest', 'waistcoat'], 'id': 1142, 'def': "a man's sleeveless garment worn underneath a coat", 'name': 'vest'}, {'frequency': 'c', 'synset': 'videotape.n.01', 'synonyms': ['videotape'], 'id': 1143, 'def': 'a video recording made on magnetic tape', 'name': 'videotape'}, {'frequency': 'r', 'synset': 'vinegar.n.01', 'synonyms': ['vinegar'], 'id': 1144, 'def': 'sour-tasting liquid produced usually by oxidation of the alcohol in wine or cider and used as a condiment or food preservative', 'name': 'vinegar'}, {'frequency': 'r', 'synset': 'violin.n.01', 'synonyms': ['violin', 'fiddle'], 'id': 1145, 'def': 'bowed stringed instrument that is the highest member of the violin family', 'name': 'violin'}, {'frequency': 'r', 'synset': 'vodka.n.01', 'synonyms': ['vodka'], 'id': 1146, 'def': 'unaged colorless liquor originating in Russia', 'name': 'vodka'}, {'frequency': 'c', 'synset': 'volleyball.n.02', 'synonyms': ['volleyball'], 'id': 1147, 'def': 'an inflated ball used in playing volleyball', 'name': 'volleyball'}, {'frequency': 'r', 'synset': 'vulture.n.01', 'synonyms': ['vulture'], 'id': 1148, 'def': 'any of various large birds of prey having naked heads and weak claws and feeding chiefly on carrion', 'name': 'vulture'}, {'frequency': 'c', 'synset': 'waffle.n.01', 'synonyms': ['waffle'], 'id': 1149, 'def': 'pancake batter baked in a waffle iron', 'name': 'waffle'}, {'frequency': 'r', 'synset': 'waffle_iron.n.01', 'synonyms': ['waffle_iron'], 'id': 1150, 'def': 'a kitchen appliance for baking waffles', 'name': 'waffle_iron'}, {'frequency': 'c', 'synset': 'wagon.n.01', 'synonyms': ['wagon'], 'id': 1151, 'def': 'any of various kinds of wheeled vehicles drawn by an animal or a tractor', 'name': 'wagon'}, {'frequency': 'c', 'synset': 'wagon_wheel.n.01', 'synonyms': ['wagon_wheel'], 'id': 1152, 'def': 'a wheel of a wagon', 'name': 'wagon_wheel'}, {'frequency': 'c', 'synset': 'walking_stick.n.01', 'synonyms': ['walking_stick'], 'id': 1153, 'def': 'a stick carried in the hand for support in walking', 'name': 'walking_stick'}, {'frequency': 'c', 'synset': 'wall_clock.n.01', 'synonyms': ['wall_clock'], 'id': 1154, 'def': 'a clock mounted on a wall', 'name': 'wall_clock'}, {'frequency': 'f', 'synset': 'wall_socket.n.01', 'synonyms': ['wall_socket', 'wall_plug', 'electric_outlet', 'electrical_outlet', 'outlet', 'electric_receptacle'], 'id': 1155, 'def': 'receptacle providing a place in a wiring system where current can be taken to run electrical devices', 'name': 'wall_socket'}, {'frequency': 'f', 'synset': 'wallet.n.01', 'synonyms': ['wallet', 'billfold'], 'id': 1156, 'def': 'a pocket-size case for holding papers and paper money', 'name': 'wallet'}, {'frequency': 'r', 'synset': 'walrus.n.01', 'synonyms': ['walrus'], 'id': 1157, 'def': 'either of two large northern marine mammals having ivory tusks and tough hide over thick blubber', 'name': 'walrus'}, {'frequency': 'r', 'synset': 'wardrobe.n.01', 'synonyms': ['wardrobe'], 'id': 1158, 'def': 'a tall piece of furniture that provides storage space for clothes; has a door and rails or hooks for hanging clothes', 'name': 'wardrobe'}, {'frequency': 'r', 'synset': 'washbasin.n.01', 'synonyms': ['washbasin', 'basin_(for_washing)', 'washbowl', 'washstand', 'handbasin'], 'id': 1159, 'def': 'a bathroom sink that is permanently installed and connected to a water supply and drainpipe; where you can wash your hands and face', 'name': 'washbasin'}, {'frequency': 'c', 'synset': 'washer.n.03', 'synonyms': ['automatic_washer', 'washing_machine'], 'id': 1160, 'def': 'a home appliance for washing clothes and linens automatically', 'name': 'automatic_washer'}, {'frequency': 'f', 'synset': 'watch.n.01', 'synonyms': ['watch', 'wristwatch'], 'id': 1161, 'def': 'a small, portable timepiece', 'name': 'watch'}, {'frequency': 'f', 'synset': 'water_bottle.n.01', 'synonyms': ['water_bottle'], 'id': 1162, 'def': 'a bottle for holding water', 'name': 'water_bottle'}, {'frequency': 'c', 'synset': 'water_cooler.n.01', 'synonyms': ['water_cooler'], 'id': 1163, 'def': 'a device for cooling and dispensing drinking water', 'name': 'water_cooler'}, {'frequency': 'c', 'synset': 'water_faucet.n.01', 'synonyms': ['water_faucet', 'water_tap', 'tap_(water_faucet)'], 'id': 1164, 'def': 'a faucet for drawing water from a pipe or cask', 'name': 'water_faucet'}, {'frequency': 'r', 'synset': 'water_heater.n.01', 'synonyms': ['water_heater', 'hot-water_heater'], 'id': 1165, 'def': 'a heater and storage tank to supply heated water', 'name': 'water_heater'}, {'frequency': 'c', 'synset': 'water_jug.n.01', 'synonyms': ['water_jug'], 'id': 1166, 'def': 'a jug that holds water', 'name': 'water_jug'}, {'frequency': 'r', 'synset': 'water_pistol.n.01', 'synonyms': ['water_gun', 'squirt_gun'], 'id': 1167, 'def': 'plaything consisting of a toy pistol that squirts water', 'name': 'water_gun'}, {'frequency': 'c', 'synset': 'water_scooter.n.01', 'synonyms': ['water_scooter', 'sea_scooter', 'jet_ski'], 'id': 1168, 'def': 'a motorboat resembling a motor scooter (NOT A SURFBOARD OR WATER SKI)', 'name': 'water_scooter'}, {'frequency': 'c', 'synset': 'water_ski.n.01', 'synonyms': ['water_ski'], 'id': 1169, 'def': 'broad ski for skimming over water towed by a speedboat (DO NOT MARK WATER)', 'name': 'water_ski'}, {'frequency': 'c', 'synset': 'water_tower.n.01', 'synonyms': ['water_tower'], 'id': 1170, 'def': 'a large reservoir for water', 'name': 'water_tower'}, {'frequency': 'c', 'synset': 'watering_can.n.01', 'synonyms': ['watering_can'], 'id': 1171, 'def': 'a container with a handle and a spout with a perforated nozzle; used to sprinkle water over plants', 'name': 'watering_can'}, {'frequency': 'f', 'synset': 'watermelon.n.02', 'synonyms': ['watermelon'], 'id': 1172, 'def': 'large oblong or roundish melon with a hard green rind and sweet watery red or occasionally yellowish pulp', 'name': 'watermelon'}, {'frequency': 'f', 'synset': 'weathervane.n.01', 'synonyms': ['weathervane', 'vane_(weathervane)', 'wind_vane'], 'id': 1173, 'def': 'mechanical device attached to an elevated structure; rotates freely to show the direction of the wind', 'name': 'weathervane'}, {'frequency': 'c', 'synset': 'webcam.n.01', 'synonyms': ['webcam'], 'id': 1174, 'def': 'a digital camera designed to take digital photographs and transmit them over the internet', 'name': 'webcam'}, {'frequency': 'c', 'synset': 'wedding_cake.n.01', 'synonyms': ['wedding_cake', 'bridecake'], 'id': 1175, 'def': 'a rich cake with two or more tiers and covered with frosting and decorations; served at a wedding reception', 'name': 'wedding_cake'}, {'frequency': 'c', 'synset': 'wedding_ring.n.01', 'synonyms': ['wedding_ring', 'wedding_band'], 'id': 1176, 'def': 'a ring given to the bride and/or groom at the wedding', 'name': 'wedding_ring'}, {'frequency': 'f', 'synset': 'wet_suit.n.01', 'synonyms': ['wet_suit'], 'id': 1177, 'def': 'a close-fitting garment made of a permeable material; worn in cold water to retain body heat', 'name': 'wet_suit'}, {'frequency': 'f', 'synset': 'wheel.n.01', 'synonyms': ['wheel'], 'id': 1178, 'def': 'a circular frame with spokes (or a solid disc) that can rotate on a shaft or axle', 'name': 'wheel'}, {'frequency': 'c', 'synset': 'wheelchair.n.01', 'synonyms': ['wheelchair'], 'id': 1179, 'def': 'a movable chair mounted on large wheels', 'name': 'wheelchair'}, {'frequency': 'c', 'synset': 'whipped_cream.n.01', 'synonyms': ['whipped_cream'], 'id': 1180, 'def': 'cream that has been beaten until light and fluffy', 'name': 'whipped_cream'}, {'frequency': 'c', 'synset': 'whistle.n.03', 'synonyms': ['whistle'], 'id': 1181, 'def': 'a small wind instrument that produces a whistling sound by blowing into it', 'name': 'whistle'}, {'frequency': 'c', 'synset': 'wig.n.01', 'synonyms': ['wig'], 'id': 1182, 'def': 'hairpiece covering the head and made of real or synthetic hair', 'name': 'wig'}, {'frequency': 'c', 'synset': 'wind_chime.n.01', 'synonyms': ['wind_chime'], 'id': 1183, 'def': 'a decorative arrangement of pieces of metal or glass or pottery that hang together loosely so the wind can cause them to tinkle', 'name': 'wind_chime'}, {'frequency': 'c', 'synset': 'windmill.n.01', 'synonyms': ['windmill'], 'id': 1184, 'def': 'A mill or turbine that is powered by wind', 'name': 'windmill'}, {'frequency': 'c', 'synset': 'window_box.n.01', 'synonyms': ['window_box_(for_plants)'], 'id': 1185, 'def': 'a container for growing plants on a windowsill', 'name': 'window_box_(for_plants)'}, {'frequency': 'f', 'synset': 'windshield_wiper.n.01', 'synonyms': ['windshield_wiper', 'windscreen_wiper', 'wiper_(for_windshield/screen)'], 'id': 1186, 'def': 'a mechanical device that cleans the windshield', 'name': 'windshield_wiper'}, {'frequency': 'c', 'synset': 'windsock.n.01', 'synonyms': ['windsock', 'air_sock', 'air-sleeve', 'wind_sleeve', 'wind_cone'], 'id': 1187, 'def': 'a truncated cloth cone mounted on a mast/pole; shows wind direction', 'name': 'windsock'}, {'frequency': 'f', 'synset': 'wine_bottle.n.01', 'synonyms': ['wine_bottle'], 'id': 1188, 'def': 'a bottle for holding wine', 'name': 'wine_bottle'}, {'frequency': 'c', 'synset': 'wine_bucket.n.01', 'synonyms': ['wine_bucket', 'wine_cooler'], 'id': 1189, 'def': 'a bucket of ice used to chill a bottle of wine', 'name': 'wine_bucket'}, {'frequency': 'f', 'synset': 'wineglass.n.01', 'synonyms': ['wineglass'], 'id': 1190, 'def': 'a glass that has a stem and in which wine is served', 'name': 'wineglass'}, {'frequency': 'f', 'synset': 'winker.n.02', 'synonyms': ['blinder_(for_horses)'], 'id': 1191, 'def': 'blinds that prevent a horse from seeing something on either side', 'name': 'blinder_(for_horses)'}, {'frequency': 'c', 'synset': 'wok.n.01', 'synonyms': ['wok'], 'id': 1192, 'def': 'pan with a convex bottom; used for frying in Chinese cooking', 'name': 'wok'}, {'frequency': 'r', 'synset': 'wolf.n.01', 'synonyms': ['wolf'], 'id': 1193, 'def': 'a wild carnivorous mammal of the dog family, living and hunting in packs', 'name': 'wolf'}, {'frequency': 'c', 'synset': 'wooden_spoon.n.02', 'synonyms': ['wooden_spoon'], 'id': 1194, 'def': 'a spoon made of wood', 'name': 'wooden_spoon'}, {'frequency': 'c', 'synset': 'wreath.n.01', 'synonyms': ['wreath'], 'id': 1195, 'def': 'an arrangement of flowers, leaves, or stems fastened in a ring', 'name': 'wreath'}, {'frequency': 'c', 'synset': 'wrench.n.03', 'synonyms': ['wrench', 'spanner'], 'id': 1196, 'def': 'a hand tool that is used to hold or twist a nut or bolt', 'name': 'wrench'}, {'frequency': 'f', 'synset': 'wristband.n.01', 'synonyms': ['wristband'], 'id': 1197, 'def': 'band consisting of a part of a sleeve that covers the wrist', 'name': 'wristband'}, {'frequency': 'f', 'synset': 'wristlet.n.01', 'synonyms': ['wristlet', 'wrist_band'], 'id': 1198, 'def': 'a band or bracelet worn around the wrist', 'name': 'wristlet'}, {'frequency': 'c', 'synset': 'yacht.n.01', 'synonyms': ['yacht'], 'id': 1199, 'def': 'an expensive vessel propelled by sail or power and used for cruising or racing', 'name': 'yacht'}, {'frequency': 'c', 'synset': 'yogurt.n.01', 'synonyms': ['yogurt', 'yoghurt', 'yoghourt'], 'id': 1200, 'def': 'a custard-like food made from curdled milk', 'name': 'yogurt'}, {'frequency': 'c', 'synset': 'yoke.n.07', 'synonyms': ['yoke_(animal_equipment)'], 'id': 1201, 'def': 'gear joining two animals at the neck; NOT egg yolk', 'name': 'yoke_(animal_equipment)'}, {'frequency': 'f', 'synset': 'zebra.n.01', 'synonyms': ['zebra'], 'id': 1202, 'def': 'any of several fleet black-and-white striped African equines', 'name': 'zebra'}, {'frequency': 'c', 'synset': 'zucchini.n.02', 'synonyms': ['zucchini', 'courgette'], 'id': 1203, 'def': 'small cucumber-shaped vegetable marrow; typically dark green', 'name': 'zucchini'}] # noqa +# fmt: on diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/pascal_voc.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/pascal_voc.py new file mode 100644 index 0000000000000000000000000000000000000000..dbbf82cb96442bfa0cf05ed0f4dddf3645434b7e --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/pascal_voc.py @@ -0,0 +1,82 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import numpy as np +import os +import xml.etree.ElementTree as ET +from typing import List, Tuple, Union + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.structures import BoxMode +from detectron2.utils.file_io import PathManager + +__all__ = ["load_voc_instances", "register_pascal_voc"] + + +# fmt: off +CLASS_NAMES = ( + "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", + "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", + "pottedplant", "sheep", "sofa", "train", "tvmonitor" +) +# fmt: on + + +def load_voc_instances(dirname: str, split: str, class_names: Union[List[str], Tuple[str, ...]]): + """ + Load Pascal VOC detection annotations to Detectron2 format. + + Args: + dirname: Contain "Annotations", "ImageSets", "JPEGImages" + split (str): one of "train", "test", "val", "trainval" + class_names: list or tuple of class names + """ + with PathManager.open(os.path.join(dirname, "ImageSets", "Main", split + ".txt")) as f: + fileids = np.loadtxt(f, dtype=np.str) + + # Needs to read many small annotation files. Makes sense at local + annotation_dirname = PathManager.get_local_path(os.path.join(dirname, "Annotations/")) + dicts = [] + for fileid in fileids: + anno_file = os.path.join(annotation_dirname, fileid + ".xml") + jpeg_file = os.path.join(dirname, "JPEGImages", fileid + ".jpg") + + with PathManager.open(anno_file) as f: + tree = ET.parse(f) + + r = { + "file_name": jpeg_file, + "image_id": fileid, + "height": int(tree.findall("./size/height")[0].text), + "width": int(tree.findall("./size/width")[0].text), + } + instances = [] + + for obj in tree.findall("object"): + cls = obj.find("name").text + # We include "difficult" samples in training. + # Based on limited experiments, they don't hurt accuracy. + # difficult = int(obj.find("difficult").text) + # if difficult == 1: + # continue + bbox = obj.find("bndbox") + bbox = [float(bbox.find(x).text) for x in ["xmin", "ymin", "xmax", "ymax"]] + # Original annotations are integers in the range [1, W or H] + # Assuming they mean 1-based pixel indices (inclusive), + # a box with annotation (xmin=1, xmax=W) covers the whole image. + # In coordinate space this is represented by (xmin=0, xmax=W) + bbox[0] -= 1.0 + bbox[1] -= 1.0 + instances.append( + {"category_id": class_names.index(cls), "bbox": bbox, "bbox_mode": BoxMode.XYXY_ABS} + ) + r["annotations"] = instances + dicts.append(r) + return dicts + + +def register_pascal_voc(name, dirname, split, year, class_names=CLASS_NAMES): + DatasetCatalog.register(name, lambda: load_voc_instances(dirname, split, class_names)) + MetadataCatalog.get(name).set( + thing_classes=list(class_names), dirname=dirname, year=year, split=split + ) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/register_coco.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/register_coco.py new file mode 100644 index 0000000000000000000000000000000000000000..e564438d5bf016bcdbb65b4bbdc215d79f579f8a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/register_coco.py @@ -0,0 +1,3 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .coco import register_coco_instances # noqa +from .coco_panoptic import register_coco_panoptic_separated # noqa diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/detection_utils.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/detection_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..2707eb430f4474c4a8a8968e5bf4caf2124d9f36 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/detection_utils.py @@ -0,0 +1,623 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +""" +Common data processing utilities that are used in a +typical object detection data pipeline. +""" +import logging +import numpy as np +from typing import List, Union +import pycocotools.mask as mask_util +import torch +from PIL import Image + +from detectron2.structures import ( + BitMasks, + Boxes, + BoxMode, + Instances, + Keypoints, + PolygonMasks, + RotatedBoxes, + polygons_to_bitmask, +) +from detectron2.utils.file_io import PathManager + +from . import transforms as T +from .catalog import MetadataCatalog + +__all__ = [ + "SizeMismatchError", + "convert_image_to_rgb", + "check_image_size", + "transform_proposals", + "transform_instance_annotations", + "annotations_to_instances", + "annotations_to_instances_rotated", + "build_augmentation", + "build_transform_gen", + "create_keypoint_hflip_indices", + "filter_empty_instances", + "read_image", +] + + +class SizeMismatchError(ValueError): + """ + When loaded image has difference width/height compared with annotation. + """ + + +# https://en.wikipedia.org/wiki/YUV#SDTV_with_BT.601 +_M_RGB2YUV = [[0.299, 0.587, 0.114], [-0.14713, -0.28886, 0.436], [0.615, -0.51499, -0.10001]] +_M_YUV2RGB = [[1.0, 0.0, 1.13983], [1.0, -0.39465, -0.58060], [1.0, 2.03211, 0.0]] + +# https://www.exiv2.org/tags.html +_EXIF_ORIENT = 274 # exif 'Orientation' tag + + +def convert_PIL_to_numpy(image, format): + """ + Convert PIL image to numpy array of target format. + + Args: + image (PIL.Image): a PIL image + format (str): the format of output image + + Returns: + (np.ndarray): also see `read_image` + """ + if format is not None: + # PIL only supports RGB, so convert to RGB and flip channels over below + conversion_format = format + if format in ["BGR", "YUV-BT.601"]: + conversion_format = "RGB" + image = image.convert(conversion_format) + image = np.asarray(image) + # PIL squeezes out the channel dimension for "L", so make it HWC + if format == "L": + image = np.expand_dims(image, -1) + + # handle formats not supported by PIL + elif format == "BGR": + # flip channels if needed + image = image[:, :, ::-1] + elif format == "YUV-BT.601": + image = image / 255.0 + image = np.dot(image, np.array(_M_RGB2YUV).T) + + return image + + +def convert_image_to_rgb(image, format): + """ + Convert an image from given format to RGB. + + Args: + image (np.ndarray or Tensor): an HWC image + format (str): the format of input image, also see `read_image` + + Returns: + (np.ndarray): (H,W,3) RGB image in 0-255 range, can be either float or uint8 + """ + if isinstance(image, torch.Tensor): + image = image.cpu().numpy() + if format == "BGR": + image = image[:, :, [2, 1, 0]] + elif format == "YUV-BT.601": + image = np.dot(image, np.array(_M_YUV2RGB).T) + image = image * 255.0 + else: + if format == "L": + image = image[:, :, 0] + image = image.astype(np.uint8) + image = np.asarray(Image.fromarray(image, mode=format).convert("RGB")) + return image + + +def _apply_exif_orientation(image): + """ + Applies the exif orientation correctly. + + This code exists per the bug: + https://github.com/python-pillow/Pillow/issues/3973 + with the function `ImageOps.exif_transpose`. The Pillow source raises errors with + various methods, especially `tobytes` + + Function based on: + https://github.com/wkentaro/labelme/blob/v4.5.4/labelme/utils/image.py#L59 + https://github.com/python-pillow/Pillow/blob/7.1.2/src/PIL/ImageOps.py#L527 + + Args: + image (PIL.Image): a PIL image + + Returns: + (PIL.Image): the PIL image with exif orientation applied, if applicable + """ + if not hasattr(image, "getexif"): + return image + + try: + exif = image.getexif() + except Exception: # https://github.com/facebookresearch/detectron2/issues/1885 + exif = None + + if exif is None: + return image + + orientation = exif.get(_EXIF_ORIENT) + + method = { + 2: Image.FLIP_LEFT_RIGHT, + 3: Image.ROTATE_180, + 4: Image.FLIP_TOP_BOTTOM, + 5: Image.TRANSPOSE, + 6: Image.ROTATE_270, + 7: Image.TRANSVERSE, + 8: Image.ROTATE_90, + }.get(orientation) + + if method is not None: + return image.transpose(method) + return image + + +def read_image(file_name, format=None): + """ + Read an image into the given format. + Will apply rotation and flipping if the image has such exif information. + + Args: + file_name (str): image file path + format (str): one of the supported image modes in PIL, or "BGR" or "YUV-BT.601". + + Returns: + image (np.ndarray): + an HWC image in the given format, which is 0-255, uint8 for + supported image modes in PIL or "BGR"; float (0-1 for Y) for YUV-BT.601. + """ + with PathManager.open(file_name, "rb") as f: + image = Image.open(f) + + # work around this bug: https://github.com/python-pillow/Pillow/issues/3973 + image = _apply_exif_orientation(image) + return convert_PIL_to_numpy(image, format) + + +def check_image_size(dataset_dict, image): + """ + Raise an error if the image does not match the size specified in the dict. + """ + if "width" in dataset_dict or "height" in dataset_dict: + image_wh = (image.shape[1], image.shape[0]) + expected_wh = (dataset_dict["width"], dataset_dict["height"]) + if not image_wh == expected_wh: + raise SizeMismatchError( + "Mismatched image shape{}, got {}, expect {}.".format( + " for image " + dataset_dict["file_name"] + if "file_name" in dataset_dict + else "", + image_wh, + expected_wh, + ) + + " Please check the width/height in your annotation." + ) + + # To ensure bbox always remap to original image size + if "width" not in dataset_dict: + dataset_dict["width"] = image.shape[1] + if "height" not in dataset_dict: + dataset_dict["height"] = image.shape[0] + + +def transform_proposals(dataset_dict, image_shape, transforms, *, proposal_topk, min_box_size=0): + """ + Apply transformations to the proposals in dataset_dict, if any. + + Args: + dataset_dict (dict): a dict read from the dataset, possibly + contains fields "proposal_boxes", "proposal_objectness_logits", "proposal_bbox_mode" + image_shape (tuple): height, width + transforms (TransformList): + proposal_topk (int): only keep top-K scoring proposals + min_box_size (int): proposals with either side smaller than this + threshold are removed + + The input dict is modified in-place, with abovementioned keys removed. A new + key "proposals" will be added. Its value is an `Instances` + object which contains the transformed proposals in its field + "proposal_boxes" and "objectness_logits". + """ + if "proposal_boxes" in dataset_dict: + # Transform proposal boxes + boxes = transforms.apply_box( + BoxMode.convert( + dataset_dict.pop("proposal_boxes"), + dataset_dict.pop("proposal_bbox_mode"), + BoxMode.XYXY_ABS, + ) + ) + boxes = Boxes(boxes) + objectness_logits = torch.as_tensor( + dataset_dict.pop("proposal_objectness_logits").astype("float32") + ) + + boxes.clip(image_shape) + keep = boxes.nonempty(threshold=min_box_size) + boxes = boxes[keep] + objectness_logits = objectness_logits[keep] + + proposals = Instances(image_shape) + proposals.proposal_boxes = boxes[:proposal_topk] + proposals.objectness_logits = objectness_logits[:proposal_topk] + dataset_dict["proposals"] = proposals + + +def transform_instance_annotations( + annotation, transforms, image_size, *, keypoint_hflip_indices=None +): + """ + Apply transforms to box, segmentation and keypoints annotations of a single instance. + + It will use `transforms.apply_box` for the box, and + `transforms.apply_coords` for segmentation polygons & keypoints. + If you need anything more specially designed for each data structure, + you'll need to implement your own version of this function or the transforms. + + Args: + annotation (dict): dict of instance annotations for a single instance. + It will be modified in-place. + transforms (TransformList or list[Transform]): + image_size (tuple): the height, width of the transformed image + keypoint_hflip_indices (ndarray[int]): see `create_keypoint_hflip_indices`. + + Returns: + dict: + the same input dict with fields "bbox", "segmentation", "keypoints" + transformed according to `transforms`. + The "bbox_mode" field will be set to XYXY_ABS. + """ + if isinstance(transforms, (tuple, list)): + transforms = T.TransformList(transforms) + # bbox is 1d (per-instance bounding box) + bbox = BoxMode.convert(annotation["bbox"], annotation["bbox_mode"], BoxMode.XYXY_ABS) + # clip transformed bbox to image size + bbox = transforms.apply_box(np.array([bbox]))[0].clip(min=0) + annotation["bbox"] = np.minimum(bbox, list(image_size + image_size)[::-1]) + annotation["bbox_mode"] = BoxMode.XYXY_ABS + + if "segmentation" in annotation: + # each instance contains 1 or more polygons + segm = annotation["segmentation"] + if isinstance(segm, list): + # polygons + polygons = [np.asarray(p).reshape(-1, 2) for p in segm] + annotation["segmentation"] = [ + p.reshape(-1) for p in transforms.apply_polygons(polygons) + ] + elif isinstance(segm, dict): + # RLE + mask = mask_util.decode(segm) + mask = transforms.apply_segmentation(mask) + assert tuple(mask.shape[:2]) == image_size + annotation["segmentation"] = mask + else: + raise ValueError( + "Cannot transform segmentation of type '{}'!" + "Supported types are: polygons as list[list[float] or ndarray]," + " COCO-style RLE as a dict.".format(type(segm)) + ) + + if "keypoints" in annotation: + keypoints = transform_keypoint_annotations( + annotation["keypoints"], transforms, image_size, keypoint_hflip_indices + ) + annotation["keypoints"] = keypoints + + return annotation + + +def transform_keypoint_annotations(keypoints, transforms, image_size, keypoint_hflip_indices=None): + """ + Transform keypoint annotations of an image. + If a keypoint is transformed out of image boundary, it will be marked "unlabeled" (visibility=0) + + Args: + keypoints (list[float]): Nx3 float in Detectron2's Dataset format. + Each point is represented by (x, y, visibility). + transforms (TransformList): + image_size (tuple): the height, width of the transformed image + keypoint_hflip_indices (ndarray[int]): see `create_keypoint_hflip_indices`. + When `transforms` includes horizontal flip, will use the index + mapping to flip keypoints. + """ + # (N*3,) -> (N, 3) + keypoints = np.asarray(keypoints, dtype="float64").reshape(-1, 3) + keypoints_xy = transforms.apply_coords(keypoints[:, :2]) + + # Set all out-of-boundary points to "unlabeled" + inside = (keypoints_xy >= np.array([0, 0])) & (keypoints_xy <= np.array(image_size[::-1])) + inside = inside.all(axis=1) + keypoints[:, :2] = keypoints_xy + keypoints[:, 2][~inside] = 0 + + # This assumes that HorizFlipTransform is the only one that does flip + do_hflip = sum(isinstance(t, T.HFlipTransform) for t in transforms.transforms) % 2 == 1 + + # Alternative way: check if probe points was horizontally flipped. + # probe = np.asarray([[0.0, 0.0], [image_width, 0.0]]) + # probe_aug = transforms.apply_coords(probe.copy()) + # do_hflip = np.sign(probe[1][0] - probe[0][0]) != np.sign(probe_aug[1][0] - probe_aug[0][0]) # noqa + + # If flipped, swap each keypoint with its opposite-handed equivalent + if do_hflip: + if keypoint_hflip_indices is None: + raise ValueError("Cannot flip keypoints without providing flip indices!") + if len(keypoints) != len(keypoint_hflip_indices): + raise ValueError( + "Keypoint data has {} points, but metadata " + "contains {} points!".format(len(keypoints), len(keypoint_hflip_indices)) + ) + keypoints = keypoints[np.asarray(keypoint_hflip_indices, dtype=np.int32), :] + + # Maintain COCO convention that if visibility == 0 (unlabeled), then x, y = 0 + keypoints[keypoints[:, 2] == 0] = 0 + return keypoints + + +def annotations_to_instances(annos, image_size, mask_format="polygon"): + """ + Create an :class:`Instances` object used by the models, + from instance annotations in the dataset dict. + + Args: + annos (list[dict]): a list of instance annotations in one image, each + element for one instance. + image_size (tuple): height, width + + Returns: + Instances: + It will contain fields "gt_boxes", "gt_classes", + "gt_masks", "gt_keypoints", if they can be obtained from `annos`. + This is the format that builtin models expect. + """ + boxes = ( + np.stack( + [BoxMode.convert(obj["bbox"], obj["bbox_mode"], BoxMode.XYXY_ABS) for obj in annos] + ) + if len(annos) + else np.zeros((0, 4)) + ) + target = Instances(image_size) + target.gt_boxes = Boxes(boxes) + + classes = [int(obj["category_id"]) for obj in annos] + classes = torch.tensor(classes, dtype=torch.int64) + target.gt_classes = classes + + if len(annos) and "segmentation" in annos[0]: + segms = [obj["segmentation"] for obj in annos] + if mask_format == "polygon": + try: + masks = PolygonMasks(segms) + except ValueError as e: + raise ValueError( + "Failed to use mask_format=='polygon' from the given annotations!" + ) from e + else: + assert mask_format == "bitmask", mask_format + masks = [] + for segm in segms: + if isinstance(segm, list): + # polygon + masks.append(polygons_to_bitmask(segm, *image_size)) + elif isinstance(segm, dict): + # COCO RLE + masks.append(mask_util.decode(segm)) + elif isinstance(segm, np.ndarray): + assert segm.ndim == 2, "Expect segmentation of 2 dimensions, got {}.".format( + segm.ndim + ) + # mask array + masks.append(segm) + else: + raise ValueError( + "Cannot convert segmentation of type '{}' to BitMasks!" + "Supported types are: polygons as list[list[float] or ndarray]," + " COCO-style RLE as a dict, or a binary segmentation mask " + " in a 2D numpy array of shape HxW.".format(type(segm)) + ) + # torch.from_numpy does not support array with negative stride. + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x)) for x in masks]) + ) + target.gt_masks = masks + + if len(annos) and "keypoints" in annos[0]: + kpts = [obj.get("keypoints", []) for obj in annos] + target.gt_keypoints = Keypoints(kpts) + + return target + + +def annotations_to_instances_rotated(annos, image_size): + """ + Create an :class:`Instances` object used by the models, + from instance annotations in the dataset dict. + Compared to `annotations_to_instances`, this function is for rotated boxes only + + Args: + annos (list[dict]): a list of instance annotations in one image, each + element for one instance. + image_size (tuple): height, width + + Returns: + Instances: + Containing fields "gt_boxes", "gt_classes", + if they can be obtained from `annos`. + This is the format that builtin models expect. + """ + boxes = [obj["bbox"] for obj in annos] + target = Instances(image_size) + boxes = target.gt_boxes = RotatedBoxes(boxes) + boxes.clip(image_size) + + classes = [obj["category_id"] for obj in annos] + classes = torch.tensor(classes, dtype=torch.int64) + target.gt_classes = classes + + return target + + +def filter_empty_instances( + instances, by_box=True, by_mask=True, box_threshold=1e-5, return_mask=False +): + """ + Filter out empty instances in an `Instances` object. + + Args: + instances (Instances): + by_box (bool): whether to filter out instances with empty boxes + by_mask (bool): whether to filter out instances with empty masks + box_threshold (float): minimum width and height to be considered non-empty + return_mask (bool): whether to return boolean mask of filtered instances + + Returns: + Instances: the filtered instances. + tensor[bool], optional: boolean mask of filtered instances + """ + assert by_box or by_mask + r = [] + if by_box: + r.append(instances.gt_boxes.nonempty(threshold=box_threshold)) + if instances.has("gt_masks") and by_mask: + r.append(instances.gt_masks.nonempty()) + + # TODO: can also filter visible keypoints + + if not r: + return instances + m = r[0] + for x in r[1:]: + m = m & x + if return_mask: + return instances[m], m + return instances[m] + + +def create_keypoint_hflip_indices(dataset_names: Union[str, List[str]]) -> List[int]: + """ + Args: + dataset_names: list of dataset names + + Returns: + list[int]: a list of size=#keypoints, storing the + horizontally-flipped keypoint indices. + """ + if isinstance(dataset_names, str): + dataset_names = [dataset_names] + + check_metadata_consistency("keypoint_names", dataset_names) + check_metadata_consistency("keypoint_flip_map", dataset_names) + + meta = MetadataCatalog.get(dataset_names[0]) + names = meta.keypoint_names + # TODO flip -> hflip + flip_map = dict(meta.keypoint_flip_map) + flip_map.update({v: k for k, v in flip_map.items()}) + flipped_names = [i if i not in flip_map else flip_map[i] for i in names] + flip_indices = [names.index(i) for i in flipped_names] + return flip_indices + + +def gen_crop_transform_with_instance(crop_size, image_size, instance): + """ + Generate a CropTransform so that the cropping region contains + the center of the given instance. + + Args: + crop_size (tuple): h, w in pixels + image_size (tuple): h, w + instance (dict): an annotation dict of one instance, in Detectron2's + dataset format. + """ + crop_size = np.asarray(crop_size, dtype=np.int32) + bbox = BoxMode.convert(instance["bbox"], instance["bbox_mode"], BoxMode.XYXY_ABS) + center_yx = (bbox[1] + bbox[3]) * 0.5, (bbox[0] + bbox[2]) * 0.5 + assert ( + image_size[0] >= center_yx[0] and image_size[1] >= center_yx[1] + ), "The annotation bounding box is outside of the image!" + assert ( + image_size[0] >= crop_size[0] and image_size[1] >= crop_size[1] + ), "Crop size is larger than image size!" + + min_yx = np.maximum(np.floor(center_yx).astype(np.int32) - crop_size, 0) + max_yx = np.maximum(np.asarray(image_size, dtype=np.int32) - crop_size, 0) + max_yx = np.minimum(max_yx, np.ceil(center_yx).astype(np.int32)) + + y0 = np.random.randint(min_yx[0], max_yx[0] + 1) + x0 = np.random.randint(min_yx[1], max_yx[1] + 1) + return T.CropTransform(x0, y0, crop_size[1], crop_size[0]) + + +def check_metadata_consistency(key, dataset_names): + """ + Check that the datasets have consistent metadata. + + Args: + key (str): a metadata key + dataset_names (list[str]): a list of dataset names + + Raises: + AttributeError: if the key does not exist in the metadata + ValueError: if the given datasets do not have the same metadata values defined by key + """ + if len(dataset_names) == 0: + return + logger = logging.getLogger(__name__) + entries_per_dataset = [getattr(MetadataCatalog.get(d), key) for d in dataset_names] + for idx, entry in enumerate(entries_per_dataset): + if entry != entries_per_dataset[0]: + logger.error( + "Metadata '{}' for dataset '{}' is '{}'".format(key, dataset_names[idx], str(entry)) + ) + logger.error( + "Metadata '{}' for dataset '{}' is '{}'".format( + key, dataset_names[0], str(entries_per_dataset[0]) + ) + ) + raise ValueError("Datasets have different metadata '{}'!".format(key)) + + +def build_augmentation(cfg, is_train): + """ + Create a list of default :class:`Augmentation` from config. + Now it includes resizing and flipping. + + Returns: + list[Augmentation] + """ + if is_train: + min_size = cfg.INPUT.MIN_SIZE_TRAIN + max_size = cfg.INPUT.MAX_SIZE_TRAIN + sample_style = cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING + else: + min_size = cfg.INPUT.MIN_SIZE_TEST + max_size = cfg.INPUT.MAX_SIZE_TEST + sample_style = "choice" + augmentation = [T.ResizeShortestEdge(min_size, max_size, sample_style)] + if is_train and cfg.INPUT.RANDOM_FLIP != "none": + augmentation.append( + T.RandomFlip( + horizontal=cfg.INPUT.RANDOM_FLIP == "horizontal", + vertical=cfg.INPUT.RANDOM_FLIP == "vertical", + ) + ) + return augmentation + + +build_transform_gen = build_augmentation +""" +Alias for backward-compatibility. +""" diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/samplers/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/samplers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..85c9f1a9df8a4038fbd4246239b699402e382309 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/samplers/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .distributed_sampler import ( + InferenceSampler, + RandomSubsetTrainingSampler, + RepeatFactorTrainingSampler, + TrainingSampler, +) + +from .grouped_batch_sampler import GroupedBatchSampler + +__all__ = [ + "GroupedBatchSampler", + "TrainingSampler", + "RandomSubsetTrainingSampler", + "InferenceSampler", + "RepeatFactorTrainingSampler", +] diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/samplers/distributed_sampler.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/samplers/distributed_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..a098e6ac07c1b193fddcb69e6e54aced82e6081c --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/samplers/distributed_sampler.py @@ -0,0 +1,278 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import itertools +import logging +import math +from collections import defaultdict +from typing import Optional +import torch +from torch.utils.data.sampler import Sampler + +from detectron2.utils import comm + +logger = logging.getLogger(__name__) + + +class TrainingSampler(Sampler): + """ + In training, we only care about the "infinite stream" of training data. + So this sampler produces an infinite stream of indices and + all workers cooperate to correctly shuffle the indices and sample different indices. + + The samplers in each worker effectively produces `indices[worker_id::num_workers]` + where `indices` is an infinite stream of indices consisting of + `shuffle(range(size)) + shuffle(range(size)) + ...` (if shuffle is True) + or `range(size) + range(size) + ...` (if shuffle is False) + + Note that this sampler does not shard based on pytorch DataLoader worker id. + A sampler passed to pytorch DataLoader is used only with map-style dataset + and will not be executed inside workers. + But if this sampler is used in a way that it gets execute inside a dataloader + worker, then extra work needs to be done to shard its outputs based on worker id. + This is required so that workers don't produce identical data. + :class:`ToIterableDataset` implements this logic. + This note is true for all samplers in detectron2. + """ + + def __init__(self, size: int, shuffle: bool = True, seed: Optional[int] = None): + """ + Args: + size (int): the total number of data of the underlying dataset to sample from + shuffle (bool): whether to shuffle the indices or not + seed (int): the initial seed of the shuffle. Must be the same + across all workers. If None, will use a random seed shared + among workers (require synchronization among all workers). + """ + if not isinstance(size, int): + raise TypeError(f"TrainingSampler(size=) expects an int. Got type {type(size)}.") + if size <= 0: + raise ValueError(f"TrainingSampler(size=) expects a positive int. Got {size}.") + self._size = size + self._shuffle = shuffle + if seed is None: + seed = comm.shared_random_seed() + self._seed = int(seed) + + self._rank = comm.get_rank() + self._world_size = comm.get_world_size() + + def __iter__(self): + start = self._rank + yield from itertools.islice(self._infinite_indices(), start, None, self._world_size) + + def _infinite_indices(self): + g = torch.Generator() + g.manual_seed(self._seed) + while True: + if self._shuffle: + yield from torch.randperm(self._size, generator=g).tolist() + else: + yield from torch.arange(self._size).tolist() + + +class RandomSubsetTrainingSampler(TrainingSampler): + """ + Similar to TrainingSampler, but only sample a random subset of indices. + This is useful when you want to estimate the accuracy vs data-number curves by + training the model with different subset_ratio. + """ + + def __init__( + self, + size: int, + subset_ratio: float, + shuffle: bool = True, + seed_shuffle: Optional[int] = None, + seed_subset: Optional[int] = None, + ): + """ + Args: + size (int): the total number of data of the underlying dataset to sample from + subset_ratio (float): the ratio of subset data to sample from the underlying dataset + shuffle (bool): whether to shuffle the indices or not + seed_shuffle (int): the initial seed of the shuffle. Must be the same + across all workers. If None, will use a random seed shared + among workers (require synchronization among all workers). + seed_subset (int): the seed to randomize the subset to be sampled. + Must be the same across all workers. If None, will use a random seed shared + among workers (require synchronization among all workers). + """ + super().__init__(size=size, shuffle=shuffle, seed=seed_shuffle) + + assert 0.0 < subset_ratio <= 1.0 + self._size_subset = int(size * subset_ratio) + assert self._size_subset > 0 + if seed_subset is None: + seed_subset = comm.shared_random_seed() + self._seed_subset = int(seed_subset) + + # randomly generate the subset indexes to be sampled from + g = torch.Generator() + g.manual_seed(self._seed_subset) + indexes_randperm = torch.randperm(self._size, generator=g) + self._indexes_subset = indexes_randperm[: self._size_subset] + + logger.info("Using RandomSubsetTrainingSampler......") + logger.info(f"Randomly sample {self._size_subset} data from the original {self._size} data") + + def _infinite_indices(self): + g = torch.Generator() + g.manual_seed(self._seed) # self._seed equals seed_shuffle from __init__() + while True: + if self._shuffle: + # generate a random permutation to shuffle self._indexes_subset + randperm = torch.randperm(self._size_subset, generator=g) + yield from self._indexes_subset[randperm].tolist() + else: + yield from self._indexes_subset.tolist() + + +class RepeatFactorTrainingSampler(Sampler): + """ + Similar to TrainingSampler, but a sample may appear more times than others based + on its "repeat factor". This is suitable for training on class imbalanced datasets like LVIS. + """ + + def __init__(self, repeat_factors, *, shuffle=True, seed=None): + """ + Args: + repeat_factors (Tensor): a float vector, the repeat factor for each indice. When it's + full of ones, it is equivalent to ``TrainingSampler(len(repeat_factors), ...)``. + shuffle (bool): whether to shuffle the indices or not + seed (int): the initial seed of the shuffle. Must be the same + across all workers. If None, will use a random seed shared + among workers (require synchronization among all workers). + """ + self._shuffle = shuffle + if seed is None: + seed = comm.shared_random_seed() + self._seed = int(seed) + + self._rank = comm.get_rank() + self._world_size = comm.get_world_size() + + # Split into whole number (_int_part) and fractional (_frac_part) parts. + self._int_part = torch.trunc(repeat_factors) + self._frac_part = repeat_factors - self._int_part + + @staticmethod + def repeat_factors_from_category_frequency(dataset_dicts, repeat_thresh): + """ + Compute (fractional) per-image repeat factors based on category frequency. + The repeat factor for an image is a function of the frequency of the rarest + category labeled in that image. The "frequency of category c" in [0, 1] is defined + as the fraction of images in the training set (without repeats) in which category c + appears. + See :paper:`lvis` (>= v2) Appendix B.2. + + Args: + dataset_dicts (list[dict]): annotations in Detectron2 dataset format. + repeat_thresh (float): frequency threshold below which data is repeated. + If the frequency is half of `repeat_thresh`, the image will be + repeated twice. + + Returns: + torch.Tensor: + the i-th element is the repeat factor for the dataset image at index i. + """ + # 1. For each category c, compute the fraction of images that contain it: f(c) + category_freq = defaultdict(int) + for dataset_dict in dataset_dicts: # For each image (without repeats) + cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]} + for cat_id in cat_ids: + category_freq[cat_id] += 1 + num_images = len(dataset_dicts) + for k, v in category_freq.items(): + category_freq[k] = v / num_images + + # 2. For each category c, compute the category-level repeat factor: + # r(c) = max(1, sqrt(t / f(c))) + category_rep = { + cat_id: max(1.0, math.sqrt(repeat_thresh / cat_freq)) + for cat_id, cat_freq in category_freq.items() + } + + # 3. For each image I, compute the image-level repeat factor: + # r(I) = max_{c in I} r(c) + rep_factors = [] + for dataset_dict in dataset_dicts: + cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]} + rep_factor = max({category_rep[cat_id] for cat_id in cat_ids}, default=1.0) + rep_factors.append(rep_factor) + + return torch.tensor(rep_factors, dtype=torch.float32) + + def _get_epoch_indices(self, generator): + """ + Create a list of dataset indices (with repeats) to use for one epoch. + + Args: + generator (torch.Generator): pseudo random number generator used for + stochastic rounding. + + Returns: + torch.Tensor: list of dataset indices to use in one epoch. Each index + is repeated based on its calculated repeat factor. + """ + # Since repeat factors are fractional, we use stochastic rounding so + # that the target repeat factor is achieved in expectation over the + # course of training + rands = torch.rand(len(self._frac_part), generator=generator) + rep_factors = self._int_part + (rands < self._frac_part).float() + # Construct a list of indices in which we repeat images as specified + indices = [] + for dataset_index, rep_factor in enumerate(rep_factors): + indices.extend([dataset_index] * int(rep_factor.item())) + return torch.tensor(indices, dtype=torch.int64) + + def __iter__(self): + start = self._rank + yield from itertools.islice(self._infinite_indices(), start, None, self._world_size) + + def _infinite_indices(self): + g = torch.Generator() + g.manual_seed(self._seed) + while True: + # Sample indices with repeats determined by stochastic rounding; each + # "epoch" may have a slightly different size due to the rounding. + indices = self._get_epoch_indices(g) + if self._shuffle: + randperm = torch.randperm(len(indices), generator=g) + yield from indices[randperm].tolist() + else: + yield from indices.tolist() + + +class InferenceSampler(Sampler): + """ + Produce indices for inference across all workers. + Inference needs to run on the __exact__ set of samples, + therefore when the total number of samples is not divisible by the number of workers, + this sampler produces different number of samples on different workers. + """ + + def __init__(self, size: int): + """ + Args: + size (int): the total number of data of the underlying dataset to sample from + """ + self._size = size + assert size > 0 + self._rank = comm.get_rank() + self._world_size = comm.get_world_size() + self._local_indices = self._get_local_indices(size, self._world_size, self._rank) + + @staticmethod + def _get_local_indices(total_size, world_size, rank): + shard_size = total_size // world_size + left = total_size % world_size + shard_sizes = [shard_size + int(r < left) for r in range(world_size)] + + begin = sum(shard_sizes[:rank]) + end = min(sum(shard_sizes[: rank + 1]), total_size) + return range(begin, end) + + def __iter__(self): + yield from self._local_indices + + def __len__(self): + return len(self._local_indices) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/samplers/grouped_batch_sampler.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/samplers/grouped_batch_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..5b247730aacd04dd0c752664acde3257c4eddd71 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/samplers/grouped_batch_sampler.py @@ -0,0 +1,47 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +from torch.utils.data.sampler import BatchSampler, Sampler + + +class GroupedBatchSampler(BatchSampler): + """ + Wraps another sampler to yield a mini-batch of indices. + It enforces that the batch only contain elements from the same group. + It also tries to provide mini-batches which follows an ordering which is + as close as possible to the ordering from the original sampler. + """ + + def __init__(self, sampler, group_ids, batch_size): + """ + Args: + sampler (Sampler): Base sampler. + group_ids (list[int]): If the sampler produces indices in range [0, N), + `group_ids` must be a list of `N` ints which contains the group id of each sample. + The group ids must be a set of integers in the range [0, num_groups). + batch_size (int): Size of mini-batch. + """ + if not isinstance(sampler, Sampler): + raise ValueError( + "sampler should be an instance of " + "torch.utils.data.Sampler, but got sampler={}".format(sampler) + ) + self.sampler = sampler + self.group_ids = np.asarray(group_ids) + assert self.group_ids.ndim == 1 + self.batch_size = batch_size + groups = np.unique(self.group_ids).tolist() + + # buffer the indices of each group until batch size is reached + self.buffer_per_group = {k: [] for k in groups} + + def __iter__(self): + for idx in self.sampler: + group_id = self.group_ids[idx] + group_buffer = self.buffer_per_group[group_id] + group_buffer.append(idx) + if len(group_buffer) == self.batch_size: + yield group_buffer[:] # yield a copy of the list + del group_buffer[:] + + def __len__(self): + raise NotImplementedError("len() of GroupedBatchSampler is not well-defined.") diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/transforms/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/transforms/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ab3c63b5b456a7fb878757e25768a3634f76ae5b --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/transforms/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from fvcore.transforms.transform import Transform, TransformList # order them first +from fvcore.transforms.transform import * +from .transform import * +from .augmentation import * +from .augmentation_impl import * + +__all__ = [k for k in globals().keys() if not k.startswith("_")] + + +from detectron2.utils.env import fixup_module_metadata + +fixup_module_metadata(__name__, globals(), __all__) +del fixup_module_metadata diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/transforms/augmentation.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/transforms/augmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..48be5b1bd66617dfca41f1e915259ffd485bcdd6 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/transforms/augmentation.py @@ -0,0 +1,377 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import inspect +import numpy as np +import pprint +from typing import Any, List, Optional, Tuple, Union +from fvcore.transforms.transform import Transform, TransformList + +""" +See "Data Augmentation" tutorial for an overview of the system: +https://detectron2.readthedocs.io/tutorials/augmentation.html +""" + + +__all__ = [ + "Augmentation", + "AugmentationList", + "AugInput", + "TransformGen", + "apply_transform_gens", + "StandardAugInput", + "apply_augmentations", +] + + +def _check_img_dtype(img): + assert isinstance(img, np.ndarray), "[Augmentation] Needs an numpy array, but got a {}!".format( + type(img) + ) + assert not isinstance(img.dtype, np.integer) or ( + img.dtype == np.uint8 + ), "[Augmentation] Got image of type {}, use uint8 or floating points instead!".format( + img.dtype + ) + assert img.ndim in [2, 3], img.ndim + + +def _get_aug_input_args(aug, aug_input) -> List[Any]: + """ + Get the arguments to be passed to ``aug.get_transform`` from the input ``aug_input``. + """ + if aug.input_args is None: + # Decide what attributes are needed automatically + prms = list(inspect.signature(aug.get_transform).parameters.items()) + # The default behavior is: if there is one parameter, then its "image" + # (work automatically for majority of use cases, and also avoid BC breaking), + # Otherwise, use the argument names. + if len(prms) == 1: + names = ("image",) + else: + names = [] + for name, prm in prms: + if prm.kind in (inspect.Parameter.VAR_POSITIONAL, inspect.Parameter.VAR_KEYWORD): + raise TypeError( + f""" \ +The default implementation of `{type(aug)}.__call__` does not allow \ +`{type(aug)}.get_transform` to use variable-length arguments (*args, **kwargs)! \ +If arguments are unknown, reimplement `__call__` instead. \ +""" + ) + names.append(name) + aug.input_args = tuple(names) + + args = [] + for f in aug.input_args: + try: + args.append(getattr(aug_input, f)) + except AttributeError as e: + raise AttributeError( + f"{type(aug)}.get_transform needs input attribute '{f}', " + f"but it is not an attribute of {type(aug_input)}!" + ) from e + return args + + +class Augmentation: + """ + Augmentation defines (often random) policies/strategies to generate :class:`Transform` + from data. It is often used for pre-processing of input data. + + A "policy" that generates a :class:`Transform` may, in the most general case, + need arbitrary information from input data in order to determine what transforms + to apply. Therefore, each :class:`Augmentation` instance defines the arguments + needed by its :meth:`get_transform` method. When called with the positional arguments, + the :meth:`get_transform` method executes the policy. + + Note that :class:`Augmentation` defines the policies to create a :class:`Transform`, + but not how to execute the actual transform operations to those data. + Its :meth:`__call__` method will use :meth:`AugInput.transform` to execute the transform. + + The returned `Transform` object is meant to describe deterministic transformation, which means + it can be re-applied on associated data, e.g. the geometry of an image and its segmentation + masks need to be transformed together. + (If such re-application is not needed, then determinism is not a crucial requirement.) + """ + + input_args: Optional[Tuple[str]] = None + """ + Stores the attribute names needed by :meth:`get_transform`, e.g. ``("image", "sem_seg")``. + By default, it is just a tuple of argument names in :meth:`self.get_transform`, which often only + contain "image". As long as the argument name convention is followed, there is no need for + users to touch this attribute. + """ + + def _init(self, params=None): + if params: + for k, v in params.items(): + if k != "self" and not k.startswith("_"): + setattr(self, k, v) + + def get_transform(self, *args) -> Transform: + """ + Execute the policy based on input data, and decide what transform to apply to inputs. + + Args: + args: Any fixed-length positional arguments. By default, the name of the arguments + should exist in the :class:`AugInput` to be used. + + Returns: + Transform: Returns the deterministic transform to apply to the input. + + Examples: + :: + class MyAug: + # if a policy needs to know both image and semantic segmentation + def get_transform(image, sem_seg) -> T.Transform: + pass + tfm: Transform = MyAug().get_transform(image, sem_seg) + new_image = tfm.apply_image(image) + + Notes: + Users can freely use arbitrary new argument names in custom + :meth:`get_transform` method, as long as they are available in the + input data. In detectron2 we use the following convention: + + * image: (H,W) or (H,W,C) ndarray of type uint8 in range [0, 255], or + floating point in range [0, 1] or [0, 255]. + * boxes: (N,4) ndarray of float32. It represents the instance bounding boxes + of N instances. Each is in XYXY format in unit of absolute coordinates. + * sem_seg: (H,W) ndarray of type uint8. Each element is an integer label of pixel. + + We do not specify convention for other types and do not include builtin + :class:`Augmentation` that uses other types in detectron2. + """ + raise NotImplementedError + + def __call__(self, aug_input) -> Transform: + """ + Augment the given `aug_input` **in-place**, and return the transform that's used. + + This method will be called to apply the augmentation. In most augmentation, it + is enough to use the default implementation, which calls :meth:`get_transform` + using the inputs. But a subclass can overwrite it to have more complicated logic. + + Args: + aug_input (AugInput): an object that has attributes needed by this augmentation + (defined by ``self.get_transform``). Its ``transform`` method will be called + to in-place transform it. + + Returns: + Transform: the transform that is applied on the input. + """ + args = _get_aug_input_args(self, aug_input) + tfm = self.get_transform(*args) + assert isinstance(tfm, (Transform, TransformList)), ( + f"{type(self)}.get_transform must return an instance of Transform! " + f"Got {type(tfm)} instead." + ) + aug_input.transform(tfm) + return tfm + + def _rand_range(self, low=1.0, high=None, size=None): + """ + Uniform float random number between low and high. + """ + if high is None: + low, high = 0, low + if size is None: + size = [] + return np.random.uniform(low, high, size) + + def __repr__(self): + """ + Produce something like: + "MyAugmentation(field1={self.field1}, field2={self.field2})" + """ + try: + sig = inspect.signature(self.__init__) + classname = type(self).__name__ + argstr = [] + for name, param in sig.parameters.items(): + assert ( + param.kind != param.VAR_POSITIONAL and param.kind != param.VAR_KEYWORD + ), "The default __repr__ doesn't support *args or **kwargs" + assert hasattr(self, name), ( + "Attribute {} not found! " + "Default __repr__ only works if attributes match the constructor.".format(name) + ) + attr = getattr(self, name) + default = param.default + if default is attr: + continue + attr_str = pprint.pformat(attr) + if "\n" in attr_str: + # don't show it if pformat decides to use >1 lines + attr_str = "..." + argstr.append("{}={}".format(name, attr_str)) + return "{}({})".format(classname, ", ".join(argstr)) + except AssertionError: + return super().__repr__() + + __str__ = __repr__ + + +def _transform_to_aug(tfm_or_aug): + """ + Wrap Transform into Augmentation. + Private, used internally to implement augmentations. + """ + assert isinstance(tfm_or_aug, (Transform, Augmentation)), tfm_or_aug + if isinstance(tfm_or_aug, Augmentation): + return tfm_or_aug + else: + + class _TransformToAug(Augmentation): + def __init__(self, tfm: Transform): + self.tfm = tfm + + def get_transform(self, *args): + return self.tfm + + def __repr__(self): + return repr(self.tfm) + + __str__ = __repr__ + + return _TransformToAug(tfm_or_aug) + + +class AugmentationList(Augmentation): + """ + Apply a sequence of augmentations. + + It has ``__call__`` method to apply the augmentations. + + Note that :meth:`get_transform` method is impossible (will throw error if called) + for :class:`AugmentationList`, because in order to apply a sequence of augmentations, + the kth augmentation must be applied first, to provide inputs needed by the (k+1)th + augmentation. + """ + + def __init__(self, augs): + """ + Args: + augs (list[Augmentation or Transform]): + """ + super().__init__() + self.augs = [_transform_to_aug(x) for x in augs] + + def __call__(self, aug_input) -> Transform: + tfms = [] + for x in self.augs: + tfm = x(aug_input) + tfms.append(tfm) + return TransformList(tfms) + + def __repr__(self): + msgs = [str(x) for x in self.augs] + return "AugmentationList[{}]".format(", ".join(msgs)) + + __str__ = __repr__ + + +class AugInput: + """ + Input that can be used with :meth:`Augmentation.__call__`. + This is a standard implementation for the majority of use cases. + This class provides the standard attributes **"image", "boxes", "sem_seg"** + defined in :meth:`__init__` and they may be needed by different augmentations. + Most augmentation policies do not need attributes beyond these three. + + After applying augmentations to these attributes (using :meth:`AugInput.transform`), + the returned transforms can then be used to transform other data structures that users have. + + Examples: + :: + input = AugInput(image, boxes=boxes) + tfms = augmentation(input) + transformed_image = input.image + transformed_boxes = input.boxes + transformed_other_data = tfms.apply_other(other_data) + + An extended project that works with new data types may implement augmentation policies + that need other inputs. An algorithm may need to transform inputs in a way different + from the standard approach defined in this class. In those rare situations, users can + implement a class similar to this class, that satify the following condition: + + * The input must provide access to these data in the form of attribute access + (``getattr``). For example, if an :class:`Augmentation` to be applied needs "image" + and "sem_seg" arguments, its input must have the attribute "image" and "sem_seg". + * The input must have a ``transform(tfm: Transform) -> None`` method which + in-place transforms all its attributes. + """ + + # TODO maybe should support more builtin data types here + def __init__( + self, + image: np.ndarray, + *, + boxes: Optional[np.ndarray] = None, + sem_seg: Optional[np.ndarray] = None, + ): + """ + Args: + image (ndarray): (H,W) or (H,W,C) ndarray of type uint8 in range [0, 255], or + floating point in range [0, 1] or [0, 255]. The meaning of C is up + to users. + boxes (ndarray or None): Nx4 float32 boxes in XYXY_ABS mode + sem_seg (ndarray or None): HxW uint8 semantic segmentation mask. Each element + is an integer label of pixel. + """ + _check_img_dtype(image) + self.image = image + self.boxes = boxes + self.sem_seg = sem_seg + + def transform(self, tfm: Transform) -> None: + """ + In-place transform all attributes of this class. + + By "in-place", it means after calling this method, accessing an attribute such + as ``self.image`` will return transformed data. + """ + self.image = tfm.apply_image(self.image) + if self.boxes is not None: + self.boxes = tfm.apply_box(self.boxes) + if self.sem_seg is not None: + self.sem_seg = tfm.apply_segmentation(self.sem_seg) + + def apply_augmentations( + self, augmentations: List[Union[Augmentation, Transform]] + ) -> TransformList: + """ + Equivalent of ``AugmentationList(augmentations)(self)`` + """ + return AugmentationList(augmentations)(self) + + +def apply_augmentations(augmentations: List[Union[Transform, Augmentation]], inputs): + """ + Use ``T.AugmentationList(augmentations)(inputs)`` instead. + """ + if isinstance(inputs, np.ndarray): + # handle the common case of image-only Augmentation, also for backward compatibility + image_only = True + inputs = AugInput(inputs) + else: + image_only = False + tfms = inputs.apply_augmentations(augmentations) + return inputs.image if image_only else inputs, tfms + + +apply_transform_gens = apply_augmentations +""" +Alias for backward-compatibility. +""" + +TransformGen = Augmentation +""" +Alias for Augmentation, since it is something that generates :class:`Transform`s +""" + +StandardAugInput = AugInput +""" +Alias for compatibility. It's not worth the complexity to have two classes. +""" diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/transforms/augmentation_impl.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/transforms/augmentation_impl.py new file mode 100644 index 0000000000000000000000000000000000000000..652a34a9aef2d4004f46ad7814befe6d1c230bc4 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/transforms/augmentation_impl.py @@ -0,0 +1,614 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +""" +Implement many useful :class:`Augmentation`. +""" +import numpy as np +import sys +from typing import Tuple +import torch +from fvcore.transforms.transform import ( + BlendTransform, + CropTransform, + HFlipTransform, + NoOpTransform, + PadTransform, + Transform, + TransformList, + VFlipTransform, +) +from PIL import Image + +from .augmentation import Augmentation, _transform_to_aug +from .transform import ExtentTransform, ResizeTransform, RotationTransform + +__all__ = [ + "FixedSizeCrop", + "RandomApply", + "RandomBrightness", + "RandomContrast", + "RandomCrop", + "RandomExtent", + "RandomFlip", + "RandomSaturation", + "RandomLighting", + "RandomRotation", + "Resize", + "ResizeScale", + "ResizeShortestEdge", + "RandomCrop_CategoryAreaConstraint", +] + + +class RandomApply(Augmentation): + """ + Randomly apply an augmentation with a given probability. + """ + + def __init__(self, tfm_or_aug, prob=0.5): + """ + Args: + tfm_or_aug (Transform, Augmentation): the transform or augmentation + to be applied. It can either be a `Transform` or `Augmentation` + instance. + prob (float): probability between 0.0 and 1.0 that + the wrapper transformation is applied + """ + super().__init__() + self.aug = _transform_to_aug(tfm_or_aug) + assert 0.0 <= prob <= 1.0, f"Probablity must be between 0.0 and 1.0 (given: {prob})" + self.prob = prob + + def get_transform(self, *args): + do = self._rand_range() < self.prob + if do: + return self.aug.get_transform(*args) + else: + return NoOpTransform() + + def __call__(self, aug_input): + do = self._rand_range() < self.prob + if do: + return self.aug(aug_input) + else: + return NoOpTransform() + + +class RandomFlip(Augmentation): + """ + Flip the image horizontally or vertically with the given probability. + """ + + def __init__(self, prob=0.5, *, horizontal=True, vertical=False): + """ + Args: + prob (float): probability of flip. + horizontal (boolean): whether to apply horizontal flipping + vertical (boolean): whether to apply vertical flipping + """ + super().__init__() + + if horizontal and vertical: + raise ValueError("Cannot do both horiz and vert. Please use two Flip instead.") + if not horizontal and not vertical: + raise ValueError("At least one of horiz or vert has to be True!") + self._init(locals()) + + def get_transform(self, image): + h, w = image.shape[:2] + do = self._rand_range() < self.prob + if do: + if self.horizontal: + return HFlipTransform(w) + elif self.vertical: + return VFlipTransform(h) + else: + return NoOpTransform() + + +class Resize(Augmentation): + """Resize image to a fixed target size""" + + def __init__(self, shape, interp=Image.BILINEAR): + """ + Args: + shape: (h, w) tuple or a int + interp: PIL interpolation method + """ + if isinstance(shape, int): + shape = (shape, shape) + shape = tuple(shape) + self._init(locals()) + + def get_transform(self, image): + return ResizeTransform( + image.shape[0], image.shape[1], self.shape[0], self.shape[1], self.interp + ) + + +class ResizeShortestEdge(Augmentation): + """ + Resize the image while keeping the aspect ratio unchanged. + It attempts to scale the shorter edge to the given `short_edge_length`, + as long as the longer edge does not exceed `max_size`. + If `max_size` is reached, then downscale so that the longer edge does not exceed max_size. + """ + + @torch.jit.unused + def __init__( + self, short_edge_length, max_size=sys.maxsize, sample_style="range", interp=Image.BILINEAR + ): + """ + Args: + short_edge_length (list[int]): If ``sample_style=="range"``, + a [min, max] interval from which to sample the shortest edge length. + If ``sample_style=="choice"``, a list of shortest edge lengths to sample from. + max_size (int): maximum allowed longest edge length. + sample_style (str): either "range" or "choice". + """ + super().__init__() + assert sample_style in ["range", "choice"], sample_style + + self.is_range = sample_style == "range" + if isinstance(short_edge_length, int): + short_edge_length = (short_edge_length, short_edge_length) + if self.is_range: + assert len(short_edge_length) == 2, ( + "short_edge_length must be two values using 'range' sample style." + f" Got {short_edge_length}!" + ) + self._init(locals()) + + @torch.jit.unused + def get_transform(self, image): + h, w = image.shape[:2] + if self.is_range: + size = np.random.randint(self.short_edge_length[0], self.short_edge_length[1] + 1) + else: + size = np.random.choice(self.short_edge_length) + if size == 0: + return NoOpTransform() + + newh, neww = ResizeShortestEdge.get_output_shape(h, w, size, self.max_size) + return ResizeTransform(h, w, newh, neww, self.interp) + + @staticmethod + def get_output_shape( + oldh: int, oldw: int, short_edge_length: int, max_size: int + ) -> Tuple[int, int]: + """ + Compute the output size given input size and target short edge length. + """ + h, w = oldh, oldw + size = short_edge_length * 1.0 + scale = size / min(h, w) + if h < w: + newh, neww = size, scale * w + else: + newh, neww = scale * h, size + if max(newh, neww) > max_size: + scale = max_size * 1.0 / max(newh, neww) + newh = newh * scale + neww = neww * scale + neww = int(neww + 0.5) + newh = int(newh + 0.5) + return (newh, neww) + + +class ResizeScale(Augmentation): + """ + Takes target size as input and randomly scales the given target size between `min_scale` + and `max_scale`. It then scales the input image such that it fits inside the scaled target + box, keeping the aspect ratio constant. + This implements the resize part of the Google's 'resize_and_crop' data augmentation: + https://github.com/tensorflow/tpu/blob/master/models/official/detection/utils/input_utils.py#L127 + """ + + def __init__( + self, + min_scale: float, + max_scale: float, + target_height: int, + target_width: int, + interp: int = Image.BILINEAR, + ): + """ + Args: + min_scale: minimum image scale range. + max_scale: maximum image scale range. + target_height: target image height. + target_width: target image width. + interp: image interpolation method. + """ + super().__init__() + self._init(locals()) + + def _get_resize(self, image: np.ndarray, scale: float) -> Transform: + input_size = image.shape[:2] + + # Compute new target size given a scale. + target_size = (self.target_height, self.target_width) + target_scale_size = np.multiply(target_size, scale) + + # Compute actual rescaling applied to input image and output size. + output_scale = np.minimum( + target_scale_size[0] / input_size[0], target_scale_size[1] / input_size[1] + ) + output_size = np.round(np.multiply(input_size, output_scale)).astype(int) + + return ResizeTransform( + input_size[0], input_size[1], output_size[0], output_size[1], self.interp + ) + + def get_transform(self, image: np.ndarray) -> Transform: + random_scale = np.random.uniform(self.min_scale, self.max_scale) + return self._get_resize(image, random_scale) + + +class RandomRotation(Augmentation): + """ + This method returns a copy of this image, rotated the given + number of degrees counter clockwise around the given center. + """ + + def __init__(self, angle, expand=True, center=None, sample_style="range", interp=None): + """ + Args: + angle (list[float]): If ``sample_style=="range"``, + a [min, max] interval from which to sample the angle (in degrees). + If ``sample_style=="choice"``, a list of angles to sample from + expand (bool): choose if the image should be resized to fit the whole + rotated image (default), or simply cropped + center (list[[float, float]]): If ``sample_style=="range"``, + a [[minx, miny], [maxx, maxy]] relative interval from which to sample the center, + [0, 0] being the top left of the image and [1, 1] the bottom right. + If ``sample_style=="choice"``, a list of centers to sample from + Default: None, which means that the center of rotation is the center of the image + center has no effect if expand=True because it only affects shifting + """ + super().__init__() + assert sample_style in ["range", "choice"], sample_style + self.is_range = sample_style == "range" + if isinstance(angle, (float, int)): + angle = (angle, angle) + if center is not None and isinstance(center[0], (float, int)): + center = (center, center) + self._init(locals()) + + def get_transform(self, image): + h, w = image.shape[:2] + center = None + if self.is_range: + angle = np.random.uniform(self.angle[0], self.angle[1]) + if self.center is not None: + center = ( + np.random.uniform(self.center[0][0], self.center[1][0]), + np.random.uniform(self.center[0][1], self.center[1][1]), + ) + else: + angle = np.random.choice(self.angle) + if self.center is not None: + center = np.random.choice(self.center) + + if center is not None: + center = (w * center[0], h * center[1]) # Convert to absolute coordinates + + if angle % 360 == 0: + return NoOpTransform() + + return RotationTransform(h, w, angle, expand=self.expand, center=center, interp=self.interp) + + +class FixedSizeCrop(Augmentation): + """ + If `crop_size` is smaller than the input image size, then it uses a random crop of + the crop size. If `crop_size` is larger than the input image size, then it pads + the right and the bottom of the image to the crop size if `pad` is True, otherwise + it returns the smaller image. + """ + + def __init__(self, crop_size: Tuple[int], pad: bool = True, pad_value: float = 128.0): + """ + Args: + crop_size: target image (height, width). + pad: if True, will pad images smaller than `crop_size` up to `crop_size` + pad_value: the padding value. + """ + super().__init__() + self._init(locals()) + + def _get_crop(self, image: np.ndarray) -> Transform: + # Compute the image scale and scaled size. + input_size = image.shape[:2] + output_size = self.crop_size + + # Add random crop if the image is scaled up. + max_offset = np.subtract(input_size, output_size) + max_offset = np.maximum(max_offset, 0) + offset = np.multiply(max_offset, np.random.uniform(0.0, 1.0)) + offset = np.round(offset).astype(int) + return CropTransform( + offset[1], offset[0], output_size[1], output_size[0], input_size[1], input_size[0] + ) + + def _get_pad(self, image: np.ndarray) -> Transform: + # Compute the image scale and scaled size. + input_size = image.shape[:2] + output_size = self.crop_size + + # Add padding if the image is scaled down. + pad_size = np.subtract(output_size, input_size) + pad_size = np.maximum(pad_size, 0) + original_size = np.minimum(input_size, output_size) + return PadTransform( + 0, 0, pad_size[1], pad_size[0], original_size[1], original_size[0], self.pad_value + ) + + def get_transform(self, image: np.ndarray) -> TransformList: + transforms = [self._get_crop(image)] + if self.pad: + transforms.append(self._get_pad(image)) + return TransformList(transforms) + + +class RandomCrop(Augmentation): + """ + Randomly crop a rectangle region out of an image. + """ + + def __init__(self, crop_type: str, crop_size): + """ + Args: + crop_type (str): one of "relative_range", "relative", "absolute", "absolute_range". + crop_size (tuple[float, float]): two floats, explained below. + + - "relative": crop a (H * crop_size[0], W * crop_size[1]) region from an input image of + size (H, W). crop size should be in (0, 1] + - "relative_range": uniformly sample two values from [crop_size[0], 1] + and [crop_size[1]], 1], and use them as in "relative" crop type. + - "absolute" crop a (crop_size[0], crop_size[1]) region from input image. + crop_size must be smaller than the input image size. + - "absolute_range", for an input of size (H, W), uniformly sample H_crop in + [crop_size[0], min(H, crop_size[1])] and W_crop in [crop_size[0], min(W, crop_size[1])]. + Then crop a region (H_crop, W_crop). + """ + # TODO style of relative_range and absolute_range are not consistent: + # one takes (h, w) but another takes (min, max) + super().__init__() + assert crop_type in ["relative_range", "relative", "absolute", "absolute_range"] + self._init(locals()) + + def get_transform(self, image): + h, w = image.shape[:2] + croph, cropw = self.get_crop_size((h, w)) + assert h >= croph and w >= cropw, "Shape computation in {} has bugs.".format(self) + h0 = np.random.randint(h - croph + 1) + w0 = np.random.randint(w - cropw + 1) + return CropTransform(w0, h0, cropw, croph) + + def get_crop_size(self, image_size): + """ + Args: + image_size (tuple): height, width + + Returns: + crop_size (tuple): height, width in absolute pixels + """ + h, w = image_size + if self.crop_type == "relative": + ch, cw = self.crop_size + return int(h * ch + 0.5), int(w * cw + 0.5) + elif self.crop_type == "relative_range": + crop_size = np.asarray(self.crop_size, dtype=np.float32) + ch, cw = crop_size + np.random.rand(2) * (1 - crop_size) + return int(h * ch + 0.5), int(w * cw + 0.5) + elif self.crop_type == "absolute": + return (min(self.crop_size[0], h), min(self.crop_size[1], w)) + elif self.crop_type == "absolute_range": + assert self.crop_size[0] <= self.crop_size[1] + ch = np.random.randint(min(h, self.crop_size[0]), min(h, self.crop_size[1]) + 1) + cw = np.random.randint(min(w, self.crop_size[0]), min(w, self.crop_size[1]) + 1) + return ch, cw + else: + raise NotImplementedError("Unknown crop type {}".format(self.crop_type)) + + +class RandomCrop_CategoryAreaConstraint(Augmentation): + """ + Similar to :class:`RandomCrop`, but find a cropping window such that no single category + occupies a ratio of more than `single_category_max_area` in semantic segmentation ground + truth, which can cause unstability in training. The function attempts to find such a valid + cropping window for at most 10 times. + """ + + def __init__( + self, + crop_type: str, + crop_size, + single_category_max_area: float = 1.0, + ignored_category: int = None, + ): + """ + Args: + crop_type, crop_size: same as in :class:`RandomCrop` + single_category_max_area: the maximum allowed area ratio of a + category. Set to 1.0 to disable + ignored_category: allow this category in the semantic segmentation + ground truth to exceed the area ratio. Usually set to the category + that's ignored in training. + """ + self.crop_aug = RandomCrop(crop_type, crop_size) + self._init(locals()) + + def get_transform(self, image, sem_seg): + if self.single_category_max_area >= 1.0: + return self.crop_aug.get_transform(image) + else: + h, w = sem_seg.shape + for _ in range(10): + crop_size = self.crop_aug.get_crop_size((h, w)) + y0 = np.random.randint(h - crop_size[0] + 1) + x0 = np.random.randint(w - crop_size[1] + 1) + sem_seg_temp = sem_seg[y0 : y0 + crop_size[0], x0 : x0 + crop_size[1]] + labels, cnt = np.unique(sem_seg_temp, return_counts=True) + if self.ignored_category is not None: + cnt = cnt[labels != self.ignored_category] + if len(cnt) > 1 and np.max(cnt) < np.sum(cnt) * self.single_category_max_area: + break + crop_tfm = CropTransform(x0, y0, crop_size[1], crop_size[0]) + return crop_tfm + + +class RandomExtent(Augmentation): + """ + Outputs an image by cropping a random "subrect" of the source image. + + The subrect can be parameterized to include pixels outside the source image, + in which case they will be set to zeros (i.e. black). The size of the output + image will vary with the size of the random subrect. + """ + + def __init__(self, scale_range, shift_range): + """ + Args: + output_size (h, w): Dimensions of output image + scale_range (l, h): Range of input-to-output size scaling factor + shift_range (x, y): Range of shifts of the cropped subrect. The rect + is shifted by [w / 2 * Uniform(-x, x), h / 2 * Uniform(-y, y)], + where (w, h) is the (width, height) of the input image. Set each + component to zero to crop at the image's center. + """ + super().__init__() + self._init(locals()) + + def get_transform(self, image): + img_h, img_w = image.shape[:2] + + # Initialize src_rect to fit the input image. + src_rect = np.array([-0.5 * img_w, -0.5 * img_h, 0.5 * img_w, 0.5 * img_h]) + + # Apply a random scaling to the src_rect. + src_rect *= np.random.uniform(self.scale_range[0], self.scale_range[1]) + + # Apply a random shift to the coordinates origin. + src_rect[0::2] += self.shift_range[0] * img_w * (np.random.rand() - 0.5) + src_rect[1::2] += self.shift_range[1] * img_h * (np.random.rand() - 0.5) + + # Map src_rect coordinates into image coordinates (center at corner). + src_rect[0::2] += 0.5 * img_w + src_rect[1::2] += 0.5 * img_h + + return ExtentTransform( + src_rect=(src_rect[0], src_rect[1], src_rect[2], src_rect[3]), + output_size=(int(src_rect[3] - src_rect[1]), int(src_rect[2] - src_rect[0])), + ) + + +class RandomContrast(Augmentation): + """ + Randomly transforms image contrast. + + Contrast intensity is uniformly sampled in (intensity_min, intensity_max). + - intensity < 1 will reduce contrast + - intensity = 1 will preserve the input image + - intensity > 1 will increase contrast + + See: https://pillow.readthedocs.io/en/3.0.x/reference/ImageEnhance.html + """ + + def __init__(self, intensity_min, intensity_max): + """ + Args: + intensity_min (float): Minimum augmentation + intensity_max (float): Maximum augmentation + """ + super().__init__() + self._init(locals()) + + def get_transform(self, image): + w = np.random.uniform(self.intensity_min, self.intensity_max) + return BlendTransform(src_image=image.mean(), src_weight=1 - w, dst_weight=w) + + +class RandomBrightness(Augmentation): + """ + Randomly transforms image brightness. + + Brightness intensity is uniformly sampled in (intensity_min, intensity_max). + - intensity < 1 will reduce brightness + - intensity = 1 will preserve the input image + - intensity > 1 will increase brightness + + See: https://pillow.readthedocs.io/en/3.0.x/reference/ImageEnhance.html + """ + + def __init__(self, intensity_min, intensity_max): + """ + Args: + intensity_min (float): Minimum augmentation + intensity_max (float): Maximum augmentation + """ + super().__init__() + self._init(locals()) + + def get_transform(self, image): + w = np.random.uniform(self.intensity_min, self.intensity_max) + return BlendTransform(src_image=0, src_weight=1 - w, dst_weight=w) + + +class RandomSaturation(Augmentation): + """ + Randomly transforms saturation of an RGB image. + Input images are assumed to have 'RGB' channel order. + + Saturation intensity is uniformly sampled in (intensity_min, intensity_max). + - intensity < 1 will reduce saturation (make the image more grayscale) + - intensity = 1 will preserve the input image + - intensity > 1 will increase saturation + + See: https://pillow.readthedocs.io/en/3.0.x/reference/ImageEnhance.html + """ + + def __init__(self, intensity_min, intensity_max): + """ + Args: + intensity_min (float): Minimum augmentation (1 preserves input). + intensity_max (float): Maximum augmentation (1 preserves input). + """ + super().__init__() + self._init(locals()) + + def get_transform(self, image): + assert image.shape[-1] == 3, "RandomSaturation only works on RGB images" + w = np.random.uniform(self.intensity_min, self.intensity_max) + grayscale = image.dot([0.299, 0.587, 0.114])[:, :, np.newaxis] + return BlendTransform(src_image=grayscale, src_weight=1 - w, dst_weight=w) + + +class RandomLighting(Augmentation): + """ + The "lighting" augmentation described in AlexNet, using fixed PCA over ImageNet. + Input images are assumed to have 'RGB' channel order. + + The degree of color jittering is randomly sampled via a normal distribution, + with standard deviation given by the scale parameter. + """ + + def __init__(self, scale): + """ + Args: + scale (float): Standard deviation of principal component weighting. + """ + super().__init__() + self._init(locals()) + self.eigen_vecs = np.array( + [[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.8140], [-0.5836, -0.6948, 0.4203]] + ) + self.eigen_vals = np.array([0.2175, 0.0188, 0.0045]) + + def get_transform(self, image): + assert image.shape[-1] == 3, "RandomLighting only works on RGB images" + weights = np.random.normal(scale=self.scale, size=3) + return BlendTransform( + src_image=self.eigen_vecs.dot(weights * self.eigen_vals), src_weight=1.0, dst_weight=1.0 + ) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/transforms/transform.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/transforms/transform.py new file mode 100644 index 0000000000000000000000000000000000000000..de44b991d7ab0d920ffb769e1402f08e358d37f7 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/data/transforms/transform.py @@ -0,0 +1,351 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +""" +See "Data Augmentation" tutorial for an overview of the system: +https://detectron2.readthedocs.io/tutorials/augmentation.html +""" + +import numpy as np +import torch +import torch.nn.functional as F +from fvcore.transforms.transform import ( + CropTransform, + HFlipTransform, + NoOpTransform, + Transform, + TransformList, +) +from PIL import Image + +try: + import cv2 # noqa +except ImportError: + # OpenCV is an optional dependency at the moment + pass + +__all__ = [ + "ExtentTransform", + "ResizeTransform", + "RotationTransform", + "ColorTransform", + "PILColorTransform", +] + + +class ExtentTransform(Transform): + """ + Extracts a subregion from the source image and scales it to the output size. + + The fill color is used to map pixels from the source rect that fall outside + the source image. + + See: https://pillow.readthedocs.io/en/latest/PIL.html#PIL.ImageTransform.ExtentTransform + """ + + def __init__(self, src_rect, output_size, interp=Image.LINEAR, fill=0): + """ + Args: + src_rect (x0, y0, x1, y1): src coordinates + output_size (h, w): dst image size + interp: PIL interpolation methods + fill: Fill color used when src_rect extends outside image + """ + super().__init__() + self._set_attributes(locals()) + + def apply_image(self, img, interp=None): + h, w = self.output_size + if len(img.shape) > 2 and img.shape[2] == 1: + pil_image = Image.fromarray(img[:, :, 0], mode="L") + else: + pil_image = Image.fromarray(img) + pil_image = pil_image.transform( + size=(w, h), + method=Image.EXTENT, + data=self.src_rect, + resample=interp if interp else self.interp, + fill=self.fill, + ) + ret = np.asarray(pil_image) + if len(img.shape) > 2 and img.shape[2] == 1: + ret = np.expand_dims(ret, -1) + return ret + + def apply_coords(self, coords): + # Transform image center from source coordinates into output coordinates + # and then map the new origin to the corner of the output image. + h, w = self.output_size + x0, y0, x1, y1 = self.src_rect + new_coords = coords.astype(np.float32) + new_coords[:, 0] -= 0.5 * (x0 + x1) + new_coords[:, 1] -= 0.5 * (y0 + y1) + new_coords[:, 0] *= w / (x1 - x0) + new_coords[:, 1] *= h / (y1 - y0) + new_coords[:, 0] += 0.5 * w + new_coords[:, 1] += 0.5 * h + return new_coords + + def apply_segmentation(self, segmentation): + segmentation = self.apply_image(segmentation, interp=Image.NEAREST) + return segmentation + + +class ResizeTransform(Transform): + """ + Resize the image to a target size. + """ + + def __init__(self, h, w, new_h, new_w, interp=None): + """ + Args: + h, w (int): original image size + new_h, new_w (int): new image size + interp: PIL interpolation methods, defaults to bilinear. + """ + # TODO decide on PIL vs opencv + super().__init__() + if interp is None: + interp = Image.BILINEAR + self._set_attributes(locals()) + + def apply_image(self, img, interp=None): + assert img.shape[:2] == (self.h, self.w) + assert len(img.shape) <= 4 + interp_method = interp if interp is not None else self.interp + + if img.dtype == np.uint8: + if len(img.shape) > 2 and img.shape[2] == 1: + pil_image = Image.fromarray(img[:, :, 0], mode="L") + else: + pil_image = Image.fromarray(img) + pil_image = pil_image.resize((self.new_w, self.new_h), interp_method) + ret = np.asarray(pil_image) + if len(img.shape) > 2 and img.shape[2] == 1: + ret = np.expand_dims(ret, -1) + else: + # PIL only supports uint8 + if any(x < 0 for x in img.strides): + img = np.ascontiguousarray(img) + img = torch.from_numpy(img) + shape = list(img.shape) + shape_4d = shape[:2] + [1] * (4 - len(shape)) + shape[2:] + img = img.view(shape_4d).permute(2, 3, 0, 1) # hw(c) -> nchw + _PIL_RESIZE_TO_INTERPOLATE_MODE = { + Image.NEAREST: "nearest", + Image.BILINEAR: "bilinear", + Image.BICUBIC: "bicubic", + } + mode = _PIL_RESIZE_TO_INTERPOLATE_MODE[interp_method] + align_corners = None if mode == "nearest" else False + img = F.interpolate( + img, (self.new_h, self.new_w), mode=mode, align_corners=align_corners + ) + shape[:2] = (self.new_h, self.new_w) + ret = img.permute(2, 3, 0, 1).view(shape).numpy() # nchw -> hw(c) + + return ret + + def apply_coords(self, coords): + coords[:, 0] = coords[:, 0] * (self.new_w * 1.0 / self.w) + coords[:, 1] = coords[:, 1] * (self.new_h * 1.0 / self.h) + return coords + + def apply_segmentation(self, segmentation): + segmentation = self.apply_image(segmentation, interp=Image.NEAREST) + return segmentation + + def inverse(self): + return ResizeTransform(self.new_h, self.new_w, self.h, self.w, self.interp) + + +class RotationTransform(Transform): + """ + This method returns a copy of this image, rotated the given + number of degrees counter clockwise around its center. + """ + + def __init__(self, h, w, angle, expand=True, center=None, interp=None): + """ + Args: + h, w (int): original image size + angle (float): degrees for rotation + expand (bool): choose if the image should be resized to fit the whole + rotated image (default), or simply cropped + center (tuple (width, height)): coordinates of the rotation center + if left to None, the center will be fit to the center of each image + center has no effect if expand=True because it only affects shifting + interp: cv2 interpolation method, default cv2.INTER_LINEAR + """ + super().__init__() + image_center = np.array((w / 2, h / 2)) + if center is None: + center = image_center + if interp is None: + interp = cv2.INTER_LINEAR + abs_cos, abs_sin = (abs(np.cos(np.deg2rad(angle))), abs(np.sin(np.deg2rad(angle)))) + if expand: + # find the new width and height bounds + bound_w, bound_h = np.rint( + [h * abs_sin + w * abs_cos, h * abs_cos + w * abs_sin] + ).astype(int) + else: + bound_w, bound_h = w, h + + self._set_attributes(locals()) + self.rm_coords = self.create_rotation_matrix() + # Needed because of this problem https://github.com/opencv/opencv/issues/11784 + self.rm_image = self.create_rotation_matrix(offset=-0.5) + + def apply_image(self, img, interp=None): + """ + img should be a numpy array, formatted as Height * Width * Nchannels + """ + if len(img) == 0 or self.angle % 360 == 0: + return img + assert img.shape[:2] == (self.h, self.w) + interp = interp if interp is not None else self.interp + return cv2.warpAffine(img, self.rm_image, (self.bound_w, self.bound_h), flags=interp) + + def apply_coords(self, coords): + """ + coords should be a N * 2 array-like, containing N couples of (x, y) points + """ + coords = np.asarray(coords, dtype=float) + if len(coords) == 0 or self.angle % 360 == 0: + return coords + return cv2.transform(coords[:, np.newaxis, :], self.rm_coords)[:, 0, :] + + def apply_segmentation(self, segmentation): + segmentation = self.apply_image(segmentation, interp=cv2.INTER_NEAREST) + return segmentation + + def create_rotation_matrix(self, offset=0): + center = (self.center[0] + offset, self.center[1] + offset) + rm = cv2.getRotationMatrix2D(tuple(center), self.angle, 1) + if self.expand: + # Find the coordinates of the center of rotation in the new image + # The only point for which we know the future coordinates is the center of the image + rot_im_center = cv2.transform(self.image_center[None, None, :] + offset, rm)[0, 0, :] + new_center = np.array([self.bound_w / 2, self.bound_h / 2]) + offset - rot_im_center + # shift the rotation center to the new coordinates + rm[:, 2] += new_center + return rm + + def inverse(self): + """ + The inverse is to rotate it back with expand, and crop to get the original shape. + """ + if not self.expand: # Not possible to inverse if a part of the image is lost + raise NotImplementedError() + rotation = RotationTransform( + self.bound_h, self.bound_w, -self.angle, True, None, self.interp + ) + crop = CropTransform( + (rotation.bound_w - self.w) // 2, (rotation.bound_h - self.h) // 2, self.w, self.h + ) + return TransformList([rotation, crop]) + + +class ColorTransform(Transform): + """ + Generic wrapper for any photometric transforms. + These transformations should only affect the color space and + not the coordinate space of the image (e.g. annotation + coordinates such as bounding boxes should not be changed) + """ + + def __init__(self, op): + """ + Args: + op (Callable): operation to be applied to the image, + which takes in an ndarray and returns an ndarray. + """ + if not callable(op): + raise ValueError("op parameter should be callable") + super().__init__() + self._set_attributes(locals()) + + def apply_image(self, img): + return self.op(img) + + def apply_coords(self, coords): + return coords + + def inverse(self): + return NoOpTransform() + + def apply_segmentation(self, segmentation): + return segmentation + + +class PILColorTransform(ColorTransform): + """ + Generic wrapper for PIL Photometric image transforms, + which affect the color space and not the coordinate + space of the image + """ + + def __init__(self, op): + """ + Args: + op (Callable): operation to be applied to the image, + which takes in a PIL Image and returns a transformed + PIL Image. + For reference on possible operations see: + - https://pillow.readthedocs.io/en/stable/ + """ + if not callable(op): + raise ValueError("op parameter should be callable") + super().__init__(op) + + def apply_image(self, img): + img = Image.fromarray(img) + return np.asarray(super().apply_image(img)) + + +def HFlip_rotated_box(transform, rotated_boxes): + """ + Apply the horizontal flip transform on rotated boxes. + + Args: + rotated_boxes (ndarray): Nx5 floating point array of + (x_center, y_center, width, height, angle_degrees) format + in absolute coordinates. + """ + # Transform x_center + rotated_boxes[:, 0] = transform.width - rotated_boxes[:, 0] + # Transform angle + rotated_boxes[:, 4] = -rotated_boxes[:, 4] + return rotated_boxes + + +def Resize_rotated_box(transform, rotated_boxes): + """ + Apply the resizing transform on rotated boxes. For details of how these (approximation) + formulas are derived, please refer to :meth:`RotatedBoxes.scale`. + + Args: + rotated_boxes (ndarray): Nx5 floating point array of + (x_center, y_center, width, height, angle_degrees) format + in absolute coordinates. + """ + scale_factor_x = transform.new_w * 1.0 / transform.w + scale_factor_y = transform.new_h * 1.0 / transform.h + rotated_boxes[:, 0] *= scale_factor_x + rotated_boxes[:, 1] *= scale_factor_y + theta = rotated_boxes[:, 4] * np.pi / 180.0 + c = np.cos(theta) + s = np.sin(theta) + rotated_boxes[:, 2] *= np.sqrt(np.square(scale_factor_x * c) + np.square(scale_factor_y * s)) + rotated_boxes[:, 3] *= np.sqrt(np.square(scale_factor_x * s) + np.square(scale_factor_y * c)) + rotated_boxes[:, 4] = np.arctan2(scale_factor_x * s, scale_factor_y * c) * 180 / np.pi + + return rotated_boxes + + +HFlipTransform.register_type("rotated_box", HFlip_rotated_box) +ResizeTransform.register_type("rotated_box", Resize_rotated_box) + +# not necessary any more with latest fvcore +NoOpTransform.register_type("rotated_box", lambda t, x: x) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/engine/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/engine/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..08a61572b4c7d09c8d400e903a96cbf5b2cc4763 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/engine/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from .launch import * +from .train_loop import * + +__all__ = [k for k in globals().keys() if not k.startswith("_")] + + +# prefer to let hooks and defaults live in separate namespaces (therefore not in __all__) +# but still make them available here +from .hooks import * +from .defaults import * diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/engine/defaults.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/engine/defaults.py new file mode 100644 index 0000000000000000000000000000000000000000..cc3faa15550a348dbe1445f7c7c91b26ba59d01b --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/engine/defaults.py @@ -0,0 +1,715 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +""" +This file contains components with some default boilerplate logic user may need +in training / testing. They will not work for everyone, but many users may find them useful. + +The behavior of functions/classes in this file is subject to change, +since they are meant to represent the "common default behavior" people need in their projects. +""" + +import argparse +import logging +import os +import sys +import weakref +from collections import OrderedDict +from typing import Optional +import torch +from fvcore.nn.precise_bn import get_bn_modules +from omegaconf import OmegaConf +from torch.nn.parallel import DistributedDataParallel + +import detectron2.data.transforms as T +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import CfgNode, LazyConfig +from detectron2.data import ( + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, +) +from detectron2.evaluation import ( + DatasetEvaluator, + inference_on_dataset, + print_csv_format, + verify_results, +) +from detectron2.modeling import build_model +from detectron2.solver import build_lr_scheduler, build_optimizer +from detectron2.utils import comm +from detectron2.utils.collect_env import collect_env_info +from detectron2.utils.env import seed_all_rng +from detectron2.utils.events import CommonMetricPrinter, JSONWriter, TensorboardXWriter +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import setup_logger + +from . import hooks +from .train_loop import AMPTrainer, SimpleTrainer, TrainerBase + +__all__ = [ + "create_ddp_model", + "default_argument_parser", + "default_setup", + "default_writers", + "DefaultPredictor", + "DefaultTrainer", +] + + +def create_ddp_model(model, *, fp16_compression=False, **kwargs): + """ + Create a DistributedDataParallel model if there are >1 processes. + + Args: + model: a torch.nn.Module + fp16_compression: add fp16 compression hooks to the ddp object. + See more at https://pytorch.org/docs/stable/ddp_comm_hooks.html#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_hook + kwargs: other arguments of :module:`torch.nn.parallel.DistributedDataParallel`. + """ # noqa + if comm.get_world_size() == 1: + return model + if "device_ids" not in kwargs: + kwargs["device_ids"] = [comm.get_local_rank()] + ddp = DistributedDataParallel(model, **kwargs) + if fp16_compression: + from torch.distributed.algorithms.ddp_comm_hooks import default as comm_hooks + + ddp.register_comm_hook(state=None, hook=comm_hooks.fp16_compress_hook) + return ddp + + +def default_argument_parser(epilog=None): + """ + Create a parser with some common arguments used by detectron2 users. + + Args: + epilog (str): epilog passed to ArgumentParser describing the usage. + + Returns: + argparse.ArgumentParser: + """ + parser = argparse.ArgumentParser( + epilog=epilog + or f""" +Examples: + +Run on single machine: + $ {sys.argv[0]} --num-gpus 8 --config-file cfg.yaml + +Change some config options: + $ {sys.argv[0]} --config-file cfg.yaml MODEL.WEIGHTS /path/to/weight.pth SOLVER.BASE_LR 0.001 + +Run on multiple machines: + (machine0)$ {sys.argv[0]} --machine-rank 0 --num-machines 2 --dist-url [--other-flags] + (machine1)$ {sys.argv[0]} --machine-rank 1 --num-machines 2 --dist-url [--other-flags] +""", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file") + parser.add_argument( + "--resume", + action="store_true", + help="Whether to attempt to resume from the checkpoint directory. " + "See documentation of `DefaultTrainer.resume_or_load()` for what it means.", + ) + parser.add_argument("--eval-only", action="store_true", help="perform evaluation only") + parser.add_argument("--num-gpus", type=int, default=1, help="number of gpus *per machine*") + parser.add_argument("--num-machines", type=int, default=1, help="total number of machines") + parser.add_argument( + "--machine-rank", type=int, default=0, help="the rank of this machine (unique per machine)" + ) + + # PyTorch still may leave orphan processes in multi-gpu training. + # Therefore we use a deterministic way to obtain port, + # so that users are aware of orphan processes by seeing the port occupied. + port = 2 ** 15 + 2 ** 14 + hash(os.getuid() if sys.platform != "win32" else 1) % 2 ** 14 + parser.add_argument( + "--dist-url", + default="tcp://127.0.0.1:{}".format(port), + help="initialization URL for pytorch distributed backend. See " + "https://pytorch.org/docs/stable/distributed.html for details.", + ) + parser.add_argument( + "opts", + help=""" +Modify config options at the end of the command. For Yacs configs, use +space-separated "PATH.KEY VALUE" pairs. +For python-based LazyConfig, use "path.key=value". + """.strip(), + default=None, + nargs=argparse.REMAINDER, + ) + return parser + + +def _try_get_key(cfg, *keys, default=None): + """ + Try select keys from cfg until the first key that exists. Otherwise return default. + """ + if isinstance(cfg, CfgNode): + cfg = OmegaConf.create(cfg.dump()) + for k in keys: + none = object() + p = OmegaConf.select(cfg, k, default=none) + if p is not none: + return p + return default + + +def _highlight(code, filename): + try: + import pygments + except ImportError: + return code + + from pygments.lexers import Python3Lexer, YamlLexer + from pygments.formatters import Terminal256Formatter + + lexer = Python3Lexer() if filename.endswith(".py") else YamlLexer() + code = pygments.highlight(code, lexer, Terminal256Formatter(style="monokai")) + return code + + +def default_setup(cfg, args): + """ + Perform some basic common setups at the beginning of a job, including: + + 1. Set up the detectron2 logger + 2. Log basic information about environment, cmdline arguments, and config + 3. Backup the config to the output directory + + Args: + cfg (CfgNode or omegaconf.DictConfig): the full config to be used + args (argparse.NameSpace): the command line arguments to be logged + """ + output_dir = _try_get_key(cfg, "OUTPUT_DIR", "output_dir", "train.output_dir") + if comm.is_main_process() and output_dir: + PathManager.mkdirs(output_dir) + + rank = comm.get_rank() + setup_logger(output_dir, distributed_rank=rank, name="fvcore") + logger = setup_logger(output_dir, distributed_rank=rank) + + logger.info("Rank of current process: {}. World size: {}".format(rank, comm.get_world_size())) + logger.info("Environment info:\n" + collect_env_info()) + + logger.info("Command line arguments: " + str(args)) + if hasattr(args, "config_file") and args.config_file != "": + logger.info( + "Contents of args.config_file={}:\n{}".format( + args.config_file, + _highlight(PathManager.open(args.config_file, "r").read(), args.config_file), + ) + ) + + if comm.is_main_process() and output_dir: + # Note: some of our scripts may expect the existence of + # config.yaml in output directory + path = os.path.join(output_dir, "config.yaml") + if isinstance(cfg, CfgNode): + logger.info("Running with full config:\n{}".format(_highlight(cfg.dump(), ".yaml"))) + with PathManager.open(path, "w") as f: + f.write(cfg.dump()) + else: + LazyConfig.save(cfg, path) + logger.info("Full config saved to {}".format(path)) + + # make sure each worker has a different, yet deterministic seed if specified + seed = _try_get_key(cfg, "SEED", "train.seed", default=-1) + seed_all_rng(None if seed < 0 else seed + rank) + + # cudnn benchmark has large overhead. It shouldn't be used considering the small size of + # typical validation set. + if not (hasattr(args, "eval_only") and args.eval_only): + torch.backends.cudnn.benchmark = _try_get_key( + cfg, "CUDNN_BENCHMARK", "train.cudnn_benchmark", default=False + ) + + +def default_writers(output_dir: str, max_iter: Optional[int] = None): + """ + Build a list of :class:`EventWriter` to be used. + It now consists of a :class:`CommonMetricPrinter`, + :class:`TensorboardXWriter` and :class:`JSONWriter`. + + Args: + output_dir: directory to store JSON metrics and tensorboard events + max_iter: the total number of iterations + + Returns: + list[EventWriter]: a list of :class:`EventWriter` objects. + """ + PathManager.mkdirs(output_dir) + return [ + # It may not always print what you want to see, since it prints "common" metrics only. + CommonMetricPrinter(max_iter), + JSONWriter(os.path.join(output_dir, "metrics.json")), + TensorboardXWriter(output_dir), + ] + + +class DefaultPredictor: + """ + Create a simple end-to-end predictor with the given config that runs on + single device for a single input image. + + Compared to using the model directly, this class does the following additions: + + 1. Load checkpoint from `cfg.MODEL.WEIGHTS`. + 2. Always take BGR image as the input and apply conversion defined by `cfg.INPUT.FORMAT`. + 3. Apply resizing defined by `cfg.INPUT.{MIN,MAX}_SIZE_TEST`. + 4. Take one input image and produce a single output, instead of a batch. + + This is meant for simple demo purposes, so it does the above steps automatically. + This is not meant for benchmarks or running complicated inference logic. + If you'd like to do anything more complicated, please refer to its source code as + examples to build and use the model manually. + + Attributes: + metadata (Metadata): the metadata of the underlying dataset, obtained from + cfg.DATASETS.TEST. + + Examples: + :: + pred = DefaultPredictor(cfg) + inputs = cv2.imread("input.jpg") + outputs = pred(inputs) + """ + + def __init__(self, cfg): + self.cfg = cfg.clone() # cfg can be modified by model + self.model = build_model(self.cfg) + self.model.eval() + if len(cfg.DATASETS.TEST): + self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0]) + + checkpointer = DetectionCheckpointer(self.model) + checkpointer.load(cfg.MODEL.WEIGHTS) + + self.aug = T.ResizeShortestEdge( + [cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST + ) + + self.input_format = cfg.INPUT.FORMAT + assert self.input_format in ["RGB", "BGR"], self.input_format + + def __call__(self, original_image): + """ + Args: + original_image (np.ndarray): an image of shape (H, W, C) (in BGR order). + + Returns: + predictions (dict): + the output of the model for one image only. + See :doc:`/tutorials/models` for details about the format. + """ + with torch.no_grad(): # https://github.com/sphinx-doc/sphinx/issues/4258 + # Apply pre-processing to image. + if self.input_format == "RGB": + # whether the model expects BGR inputs or RGB + original_image = original_image[:, :, ::-1] + height, width = original_image.shape[:2] + image = self.aug.get_transform(original_image).apply_image(original_image) + image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1)) + + inputs = {"image": image, "height": height, "width": width} + predictions = self.model([inputs])[0] + return predictions + + +class DefaultTrainer(TrainerBase): + """ + A trainer with default training logic. It does the following: + + 1. Create a :class:`SimpleTrainer` using model, optimizer, dataloader + defined by the given config. Create a LR scheduler defined by the config. + 2. Load the last checkpoint or `cfg.MODEL.WEIGHTS`, if exists, when + `resume_or_load` is called. + 3. Register a few common hooks defined by the config. + + It is created to simplify the **standard model training workflow** and reduce code boilerplate + for users who only need the standard training workflow, with standard features. + It means this class makes *many assumptions* about your training logic that + may easily become invalid in a new research. In fact, any assumptions beyond those made in the + :class:`SimpleTrainer` are too much for research. + + The code of this class has been annotated about restrictive assumptions it makes. + When they do not work for you, you're encouraged to: + + 1. Overwrite methods of this class, OR: + 2. Use :class:`SimpleTrainer`, which only does minimal SGD training and + nothing else. You can then add your own hooks if needed. OR: + 3. Write your own training loop similar to `tools/plain_train_net.py`. + + See the :doc:`/tutorials/training` tutorials for more details. + + Note that the behavior of this class, like other functions/classes in + this file, is not stable, since it is meant to represent the "common default behavior". + It is only guaranteed to work well with the standard models and training workflow in detectron2. + To obtain more stable behavior, write your own training logic with other public APIs. + + Examples: + :: + trainer = DefaultTrainer(cfg) + trainer.resume_or_load() # load last checkpoint or MODEL.WEIGHTS + trainer.train() + + Attributes: + scheduler: + checkpointer (DetectionCheckpointer): + cfg (CfgNode): + """ + + def __init__(self, cfg): + """ + Args: + cfg (CfgNode): + """ + super().__init__() + logger = logging.getLogger("detectron2") + if not logger.isEnabledFor(logging.INFO): # setup_logger is not called for d2 + setup_logger() + cfg = DefaultTrainer.auto_scale_workers(cfg, comm.get_world_size()) + + # Assume these objects must be constructed in this order. + model = self.build_model(cfg) + optimizer = self.build_optimizer(cfg, model) + data_loader = self.build_train_loader(cfg) + + model = create_ddp_model(model, broadcast_buffers=False) + self._trainer = (AMPTrainer if cfg.SOLVER.AMP.ENABLED else SimpleTrainer)( + model, data_loader, optimizer + ) + + self.scheduler = self.build_lr_scheduler(cfg, optimizer) + self.checkpointer = DetectionCheckpointer( + # Assume you want to save checkpoints together with logs/statistics + model, + cfg.OUTPUT_DIR, + trainer=weakref.proxy(self), + ) + self.start_iter = 0 + self.max_iter = cfg.SOLVER.MAX_ITER + self.cfg = cfg + + self.register_hooks(self.build_hooks()) + + def resume_or_load(self, resume=True): + """ + If `resume==True` and `cfg.OUTPUT_DIR` contains the last checkpoint (defined by + a `last_checkpoint` file), resume from the file. Resuming means loading all + available states (eg. optimizer and scheduler) and update iteration counter + from the checkpoint. ``cfg.MODEL.WEIGHTS`` will not be used. + + Otherwise, this is considered as an independent training. The method will load model + weights from the file `cfg.MODEL.WEIGHTS` (but will not load other states) and start + from iteration 0. + + Args: + resume (bool): whether to do resume or not + """ + self.checkpointer.resume_or_load(self.cfg.MODEL.WEIGHTS, resume=resume) + if resume and self.checkpointer.has_checkpoint(): + # The checkpoint stores the training iteration that just finished, thus we start + # at the next iteration + self.start_iter = self.iter + 1 + + def build_hooks(self): + """ + Build a list of default hooks, including timing, evaluation, + checkpointing, lr scheduling, precise BN, writing events. + + Returns: + list[HookBase]: + """ + cfg = self.cfg.clone() + cfg.defrost() + cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN + + ret = [ + hooks.IterationTimer(), + hooks.LRScheduler(), + hooks.PreciseBN( + # Run at the same freq as (but before) evaluation. + cfg.TEST.EVAL_PERIOD, + self.model, + # Build a new data loader to not affect training + self.build_train_loader(cfg), + cfg.TEST.PRECISE_BN.NUM_ITER, + ) + if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model) + else None, + ] + + # Do PreciseBN before checkpointer, because it updates the model and need to + # be saved by checkpointer. + # This is not always the best: if checkpointing has a different frequency, + # some checkpoints may have more precise statistics than others. + if comm.is_main_process(): + ret.append(hooks.PeriodicCheckpointer(self.checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD)) + + def test_and_save_results(): + self._last_eval_results = self.test(self.cfg, self.model) + return self._last_eval_results + + # Do evaluation after checkpointer, because then if it fails, + # we can use the saved checkpoint to debug. + ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results)) + + if comm.is_main_process(): + # Here the default print/log frequency of each writer is used. + # run writers in the end, so that evaluation metrics are written + ret.append(hooks.PeriodicWriter(self.build_writers(), period=20)) + return ret + + def build_writers(self): + """ + Build a list of writers to be used using :func:`default_writers()`. + If you'd like a different list of writers, you can overwrite it in + your trainer. + + Returns: + list[EventWriter]: a list of :class:`EventWriter` objects. + """ + return default_writers(self.cfg.OUTPUT_DIR, self.max_iter) + + def train(self): + """ + Run training. + + Returns: + OrderedDict of results, if evaluation is enabled. Otherwise None. + """ + super().train(self.start_iter, self.max_iter) + if len(self.cfg.TEST.EXPECTED_RESULTS) and comm.is_main_process(): + assert hasattr( + self, "_last_eval_results" + ), "No evaluation results obtained during training!" + verify_results(self.cfg, self._last_eval_results) + return self._last_eval_results + + def run_step(self): + self._trainer.iter = self.iter + self._trainer.run_step() + + def state_dict(self): + ret = super().state_dict() + ret["_trainer"] = self._trainer.state_dict() + return ret + + def load_state_dict(self, state_dict): + super().load_state_dict(state_dict) + self._trainer.load_state_dict(state_dict["_trainer"]) + + @classmethod + def build_model(cls, cfg): + """ + Returns: + torch.nn.Module: + + It now calls :func:`detectron2.modeling.build_model`. + Overwrite it if you'd like a different model. + """ + model = build_model(cfg) + logger = logging.getLogger(__name__) + logger.info("Model:\n{}".format(model)) + return model + + @classmethod + def build_optimizer(cls, cfg, model): + """ + Returns: + torch.optim.Optimizer: + + It now calls :func:`detectron2.solver.build_optimizer`. + Overwrite it if you'd like a different optimizer. + """ + return build_optimizer(cfg, model) + + @classmethod + def build_lr_scheduler(cls, cfg, optimizer): + """ + It now calls :func:`detectron2.solver.build_lr_scheduler`. + Overwrite it if you'd like a different scheduler. + """ + return build_lr_scheduler(cfg, optimizer) + + @classmethod + def build_train_loader(cls, cfg): + """ + Returns: + iterable + + It now calls :func:`detectron2.data.build_detection_train_loader`. + Overwrite it if you'd like a different data loader. + """ + return build_detection_train_loader(cfg) + + @classmethod + def build_test_loader(cls, cfg, dataset_name): + """ + Returns: + iterable + + It now calls :func:`detectron2.data.build_detection_test_loader`. + Overwrite it if you'd like a different data loader. + """ + return build_detection_test_loader(cfg, dataset_name) + + @classmethod + def build_evaluator(cls, cfg, dataset_name): + """ + Returns: + DatasetEvaluator or None + + It is not implemented by default. + """ + raise NotImplementedError( + """ +If you want DefaultTrainer to automatically run evaluation, +please implement `build_evaluator()` in subclasses (see train_net.py for example). +Alternatively, you can call evaluation functions yourself (see Colab balloon tutorial for example). +""" + ) + + @classmethod + def test(cls, cfg, model, evaluators=None): + """ + Evaluate the given model. The given model is expected to already contain + weights to evaluate. + + Args: + cfg (CfgNode): + model (nn.Module): + evaluators (list[DatasetEvaluator] or None): if None, will call + :meth:`build_evaluator`. Otherwise, must have the same length as + ``cfg.DATASETS.TEST``. + + Returns: + dict: a dict of result metrics + """ + logger = logging.getLogger(__name__) + if isinstance(evaluators, DatasetEvaluator): + evaluators = [evaluators] + if evaluators is not None: + assert len(cfg.DATASETS.TEST) == len(evaluators), "{} != {}".format( + len(cfg.DATASETS.TEST), len(evaluators) + ) + + results = OrderedDict() + for idx, dataset_name in enumerate(cfg.DATASETS.TEST): + data_loader = cls.build_test_loader(cfg, dataset_name) + # When evaluators are passed in as arguments, + # implicitly assume that evaluators can be created before data_loader. + if evaluators is not None: + evaluator = evaluators[idx] + else: + try: + evaluator = cls.build_evaluator(cfg, dataset_name) + except NotImplementedError: + logger.warn( + "No evaluator found. Use `DefaultTrainer.test(evaluators=)`, " + "or implement its `build_evaluator` method." + ) + results[dataset_name] = {} + continue + results_i = inference_on_dataset(model, data_loader, evaluator) + results[dataset_name] = results_i + if comm.is_main_process(): + assert isinstance( + results_i, dict + ), "Evaluator must return a dict on the main process. Got {} instead.".format( + results_i + ) + logger.info("Evaluation results for {} in csv format:".format(dataset_name)) + print_csv_format(results_i) + + if len(results) == 1: + results = list(results.values())[0] + return results + + @staticmethod + def auto_scale_workers(cfg, num_workers: int): + """ + When the config is defined for certain number of workers (according to + ``cfg.SOLVER.REFERENCE_WORLD_SIZE``) that's different from the number of + workers currently in use, returns a new cfg where the total batch size + is scaled so that the per-GPU batch size stays the same as the + original ``IMS_PER_BATCH // REFERENCE_WORLD_SIZE``. + + Other config options are also scaled accordingly: + * training steps and warmup steps are scaled inverse proportionally. + * learning rate are scaled proportionally, following :paper:`ImageNet in 1h`. + + For example, with the original config like the following: + + .. code-block:: yaml + + IMS_PER_BATCH: 16 + BASE_LR: 0.1 + REFERENCE_WORLD_SIZE: 8 + MAX_ITER: 5000 + STEPS: (4000,) + CHECKPOINT_PERIOD: 1000 + + When this config is used on 16 GPUs instead of the reference number 8, + calling this method will return a new config with: + + .. code-block:: yaml + + IMS_PER_BATCH: 32 + BASE_LR: 0.2 + REFERENCE_WORLD_SIZE: 16 + MAX_ITER: 2500 + STEPS: (2000,) + CHECKPOINT_PERIOD: 500 + + Note that both the original config and this new config can be trained on 16 GPUs. + It's up to user whether to enable this feature (by setting ``REFERENCE_WORLD_SIZE``). + + Returns: + CfgNode: a new config. Same as original if ``cfg.SOLVER.REFERENCE_WORLD_SIZE==0``. + """ + old_world_size = cfg.SOLVER.REFERENCE_WORLD_SIZE + if old_world_size == 0 or old_world_size == num_workers: + return cfg + cfg = cfg.clone() + frozen = cfg.is_frozen() + cfg.defrost() + + assert ( + cfg.SOLVER.IMS_PER_BATCH % old_world_size == 0 + ), "Invalid REFERENCE_WORLD_SIZE in config!" + scale = num_workers / old_world_size + bs = cfg.SOLVER.IMS_PER_BATCH = int(round(cfg.SOLVER.IMS_PER_BATCH * scale)) + lr = cfg.SOLVER.BASE_LR = cfg.SOLVER.BASE_LR * scale + max_iter = cfg.SOLVER.MAX_ITER = int(round(cfg.SOLVER.MAX_ITER / scale)) + warmup_iter = cfg.SOLVER.WARMUP_ITERS = int(round(cfg.SOLVER.WARMUP_ITERS / scale)) + cfg.SOLVER.STEPS = tuple(int(round(s / scale)) for s in cfg.SOLVER.STEPS) + cfg.TEST.EVAL_PERIOD = int(round(cfg.TEST.EVAL_PERIOD / scale)) + cfg.SOLVER.CHECKPOINT_PERIOD = int(round(cfg.SOLVER.CHECKPOINT_PERIOD / scale)) + cfg.SOLVER.REFERENCE_WORLD_SIZE = num_workers # maintain invariant + logger = logging.getLogger(__name__) + logger.info( + f"Auto-scaling the config to batch_size={bs}, learning_rate={lr}, " + f"max_iter={max_iter}, warmup={warmup_iter}." + ) + + if frozen: + cfg.freeze() + return cfg + + +# Access basic attributes from the underlying trainer +for _attr in ["model", "data_loader", "optimizer"]: + setattr( + DefaultTrainer, + _attr, + property( + # getter + lambda self, x=_attr: getattr(self._trainer, x), + # setter + lambda self, value, x=_attr: setattr(self._trainer, x, value), + ), + ) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/engine/hooks.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/engine/hooks.py new file mode 100644 index 0000000000000000000000000000000000000000..52c321f979726b8aa89ba34874bc6729a75b70b4 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/engine/hooks.py @@ -0,0 +1,686 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import datetime +import itertools +import logging +import math +import operator +import os +import tempfile +import time +import warnings +from collections import Counter +import torch +from fvcore.common.checkpoint import Checkpointer +from fvcore.common.checkpoint import PeriodicCheckpointer as _PeriodicCheckpointer +from fvcore.common.param_scheduler import ParamScheduler +from fvcore.common.timer import Timer +from fvcore.nn.precise_bn import get_bn_modules, update_bn_stats + +import detectron2.utils.comm as comm +from detectron2.evaluation.testing import flatten_results_dict +from detectron2.solver import LRMultiplier +from detectron2.utils.events import EventStorage, EventWriter +from detectron2.utils.file_io import PathManager + +from .train_loop import HookBase + +__all__ = [ + "CallbackHook", + "IterationTimer", + "PeriodicWriter", + "PeriodicCheckpointer", + "BestCheckpointer", + "LRScheduler", + "AutogradProfiler", + "EvalHook", + "PreciseBN", + "TorchProfiler", + "TorchMemoryStats", +] + + +""" +Implement some common hooks. +""" + + +class CallbackHook(HookBase): + """ + Create a hook using callback functions provided by the user. + """ + + def __init__(self, *, before_train=None, after_train=None, before_step=None, after_step=None): + """ + Each argument is a function that takes one argument: the trainer. + """ + self._before_train = before_train + self._before_step = before_step + self._after_step = after_step + self._after_train = after_train + + def before_train(self): + if self._before_train: + self._before_train(self.trainer) + + def after_train(self): + if self._after_train: + self._after_train(self.trainer) + # The functions may be closures that hold reference to the trainer + # Therefore, delete them to avoid circular reference. + del self._before_train, self._after_train + del self._before_step, self._after_step + + def before_step(self): + if self._before_step: + self._before_step(self.trainer) + + def after_step(self): + if self._after_step: + self._after_step(self.trainer) + + +class IterationTimer(HookBase): + """ + Track the time spent for each iteration (each run_step call in the trainer). + Print a summary in the end of training. + + This hook uses the time between the call to its :meth:`before_step` + and :meth:`after_step` methods. + Under the convention that :meth:`before_step` of all hooks should only + take negligible amount of time, the :class:`IterationTimer` hook should be + placed at the beginning of the list of hooks to obtain accurate timing. + """ + + def __init__(self, warmup_iter=3): + """ + Args: + warmup_iter (int): the number of iterations at the beginning to exclude + from timing. + """ + self._warmup_iter = warmup_iter + self._step_timer = Timer() + self._start_time = time.perf_counter() + self._total_timer = Timer() + + def before_train(self): + self._start_time = time.perf_counter() + self._total_timer.reset() + self._total_timer.pause() + + def after_train(self): + logger = logging.getLogger(__name__) + total_time = time.perf_counter() - self._start_time + total_time_minus_hooks = self._total_timer.seconds() + hook_time = total_time - total_time_minus_hooks + + num_iter = self.trainer.storage.iter + 1 - self.trainer.start_iter - self._warmup_iter + + if num_iter > 0 and total_time_minus_hooks > 0: + # Speed is meaningful only after warmup + # NOTE this format is parsed by grep in some scripts + logger.info( + "Overall training speed: {} iterations in {} ({:.4f} s / it)".format( + num_iter, + str(datetime.timedelta(seconds=int(total_time_minus_hooks))), + total_time_minus_hooks / num_iter, + ) + ) + + logger.info( + "Total training time: {} ({} on hooks)".format( + str(datetime.timedelta(seconds=int(total_time))), + str(datetime.timedelta(seconds=int(hook_time))), + ) + ) + + def before_step(self): + self._step_timer.reset() + self._total_timer.resume() + + def after_step(self): + # +1 because we're in after_step, the current step is done + # but not yet counted + iter_done = self.trainer.storage.iter - self.trainer.start_iter + 1 + if iter_done >= self._warmup_iter: + sec = self._step_timer.seconds() + self.trainer.storage.put_scalars(time=sec) + else: + self._start_time = time.perf_counter() + self._total_timer.reset() + + self._total_timer.pause() + + +class PeriodicWriter(HookBase): + """ + Write events to EventStorage (by calling ``writer.write()``) periodically. + + It is executed every ``period`` iterations and after the last iteration. + Note that ``period`` does not affect how data is smoothed by each writer. + """ + + def __init__(self, writers, period=20): + """ + Args: + writers (list[EventWriter]): a list of EventWriter objects + period (int): + """ + self._writers = writers + for w in writers: + assert isinstance(w, EventWriter), w + self._period = period + + def after_step(self): + if (self.trainer.iter + 1) % self._period == 0 or ( + self.trainer.iter == self.trainer.max_iter - 1 + ): + for writer in self._writers: + writer.write() + + def after_train(self): + for writer in self._writers: + # If any new data is found (e.g. produced by other after_train), + # write them before closing + writer.write() + writer.close() + + +class PeriodicCheckpointer(_PeriodicCheckpointer, HookBase): + """ + Same as :class:`detectron2.checkpoint.PeriodicCheckpointer`, but as a hook. + + Note that when used as a hook, + it is unable to save additional data other than what's defined + by the given `checkpointer`. + + It is executed every ``period`` iterations and after the last iteration. + """ + + def before_train(self): + self.max_iter = self.trainer.max_iter + + def after_step(self): + # No way to use **kwargs + self.step(self.trainer.iter) + + +class BestCheckpointer(HookBase): + """ + Checkpoints best weights based off given metric. + + This hook should be used in conjunction to and executed after the hook + that produces the metric, e.g. `EvalHook`. + """ + + def __init__( + self, + eval_period: int, + checkpointer: Checkpointer, + val_metric: str, + mode: str = "max", + file_prefix: str = "model_best", + ) -> None: + """ + Args: + eval_period (int): the period `EvalHook` is set to run. + checkpointer: the checkpointer object used to save checkpoints. + val_metric (str): validation metric to track for best checkpoint, e.g. "bbox/AP50" + mode (str): one of {'max', 'min'}. controls whether the chosen val metric should be + maximized or minimized, e.g. for "bbox/AP50" it should be "max" + file_prefix (str): the prefix of checkpoint's filename, defaults to "model_best" + """ + self._logger = logging.getLogger(__name__) + self._period = eval_period + self._val_metric = val_metric + assert mode in [ + "max", + "min", + ], f'Mode "{mode}" to `BestCheckpointer` is unknown. It should be one of {"max", "min"}.' + if mode == "max": + self._compare = operator.gt + else: + self._compare = operator.lt + self._checkpointer = checkpointer + self._file_prefix = file_prefix + self.best_metric = None + self.best_iter = None + + def _update_best(self, val, iteration): + if math.isnan(val) or math.isinf(val): + return False + self.best_metric = val + self.best_iter = iteration + return True + + def _best_checking(self): + metric_tuple = self.trainer.storage.latest().get(self._val_metric) + if metric_tuple is None: + self._logger.warning( + f"Given val metric {self._val_metric} does not seem to be computed/stored." + "Will not be checkpointing based on it." + ) + return + else: + latest_metric, metric_iter = metric_tuple + + if self.best_metric is None: + if self._update_best(latest_metric, metric_iter): + additional_state = {"iteration": metric_iter} + self._checkpointer.save(f"{self._file_prefix}", **additional_state) + self._logger.info( + f"Saved first model at {self.best_metric:0.5f} @ {self.best_iter} steps" + ) + elif self._compare(latest_metric, self.best_metric): + additional_state = {"iteration": metric_iter} + self._checkpointer.save(f"{self._file_prefix}", **additional_state) + self._logger.info( + f"Saved best model as latest eval score for {self._val_metric} is " + f"{latest_metric:0.5f}, better than last best score " + f"{self.best_metric:0.5f} @ iteration {self.best_iter}." + ) + self._update_best(latest_metric, metric_iter) + else: + self._logger.info( + f"Not saving as latest eval score for {self._val_metric} is {latest_metric:0.5f}, " + f"not better than best score {self.best_metric:0.5f} @ iteration {self.best_iter}." + ) + + def after_step(self): + # same conditions as `EvalHook` + next_iter = self.trainer.iter + 1 + if ( + self._period > 0 + and next_iter % self._period == 0 + and next_iter != self.trainer.max_iter + ): + self._best_checking() + + def after_train(self): + # same conditions as `EvalHook` + if self.trainer.iter + 1 >= self.trainer.max_iter: + self._best_checking() + + +class LRScheduler(HookBase): + """ + A hook which executes a torch builtin LR scheduler and summarizes the LR. + It is executed after every iteration. + """ + + def __init__(self, optimizer=None, scheduler=None): + """ + Args: + optimizer (torch.optim.Optimizer): + scheduler (torch.optim.LRScheduler or fvcore.common.param_scheduler.ParamScheduler): + if a :class:`ParamScheduler` object, it defines the multiplier over the base LR + in the optimizer. + + If any argument is not given, will try to obtain it from the trainer. + """ + self._optimizer = optimizer + self._scheduler = scheduler + + def before_train(self): + self._optimizer = self._optimizer or self.trainer.optimizer + if isinstance(self.scheduler, ParamScheduler): + self._scheduler = LRMultiplier( + self._optimizer, + self.scheduler, + self.trainer.max_iter, + last_iter=self.trainer.iter - 1, + ) + self._best_param_group_id = LRScheduler.get_best_param_group_id(self._optimizer) + + @staticmethod + def get_best_param_group_id(optimizer): + # NOTE: some heuristics on what LR to summarize + # summarize the param group with most parameters + largest_group = max(len(g["params"]) for g in optimizer.param_groups) + + if largest_group == 1: + # If all groups have one parameter, + # then find the most common initial LR, and use it for summary + lr_count = Counter([g["lr"] for g in optimizer.param_groups]) + lr = lr_count.most_common()[0][0] + for i, g in enumerate(optimizer.param_groups): + if g["lr"] == lr: + return i + else: + for i, g in enumerate(optimizer.param_groups): + if len(g["params"]) == largest_group: + return i + + def after_step(self): + lr = self._optimizer.param_groups[self._best_param_group_id]["lr"] + self.trainer.storage.put_scalar("lr", lr, smoothing_hint=False) + self.scheduler.step() + + @property + def scheduler(self): + return self._scheduler or self.trainer.scheduler + + def state_dict(self): + if isinstance(self.scheduler, torch.optim.lr_scheduler._LRScheduler): + return self.scheduler.state_dict() + return {} + + def load_state_dict(self, state_dict): + if isinstance(self.scheduler, torch.optim.lr_scheduler._LRScheduler): + logger = logging.getLogger(__name__) + logger.info("Loading scheduler from state_dict ...") + self.scheduler.load_state_dict(state_dict) + + +class TorchProfiler(HookBase): + """ + A hook which runs `torch.profiler.profile`. + + Examples: + :: + hooks.TorchProfiler( + lambda trainer: 10 < trainer.iter < 20, self.cfg.OUTPUT_DIR + ) + + The above example will run the profiler for iteration 10~20 and dump + results to ``OUTPUT_DIR``. We did not profile the first few iterations + because they are typically slower than the rest. + The result files can be loaded in the ``chrome://tracing`` page in chrome browser, + and the tensorboard visualizations can be visualized using + ``tensorboard --logdir OUTPUT_DIR/log`` + """ + + def __init__(self, enable_predicate, output_dir, *, activities=None, save_tensorboard=True): + """ + Args: + enable_predicate (callable[trainer -> bool]): a function which takes a trainer, + and returns whether to enable the profiler. + It will be called once every step, and can be used to select which steps to profile. + output_dir (str): the output directory to dump tracing files. + activities (iterable): same as in `torch.profiler.profile`. + save_tensorboard (bool): whether to save tensorboard visualizations at (output_dir)/log/ + """ + self._enable_predicate = enable_predicate + self._activities = activities + self._output_dir = output_dir + self._save_tensorboard = save_tensorboard + + def before_step(self): + if self._enable_predicate(self.trainer): + if self._save_tensorboard: + on_trace_ready = torch.profiler.tensorboard_trace_handler( + os.path.join( + self._output_dir, + "log", + "profiler-tensorboard-iter{}".format(self.trainer.iter), + ), + f"worker{comm.get_rank()}", + ) + else: + on_trace_ready = None + self._profiler = torch.profiler.profile( + activities=self._activities, + on_trace_ready=on_trace_ready, + record_shapes=True, + profile_memory=True, + with_stack=True, + with_flops=True, + ) + self._profiler.__enter__() + else: + self._profiler = None + + def after_step(self): + if self._profiler is None: + return + self._profiler.__exit__(None, None, None) + if not self._save_tensorboard: + PathManager.mkdirs(self._output_dir) + out_file = os.path.join( + self._output_dir, "profiler-trace-iter{}.json".format(self.trainer.iter) + ) + if "://" not in out_file: + self._profiler.export_chrome_trace(out_file) + else: + # Support non-posix filesystems + with tempfile.TemporaryDirectory(prefix="detectron2_profiler") as d: + tmp_file = os.path.join(d, "tmp.json") + self._profiler.export_chrome_trace(tmp_file) + with open(tmp_file) as f: + content = f.read() + with PathManager.open(out_file, "w") as f: + f.write(content) + + +class AutogradProfiler(TorchProfiler): + """ + A hook which runs `torch.autograd.profiler.profile`. + + Examples: + :: + hooks.AutogradProfiler( + lambda trainer: 10 < trainer.iter < 20, self.cfg.OUTPUT_DIR + ) + + The above example will run the profiler for iteration 10~20 and dump + results to ``OUTPUT_DIR``. We did not profile the first few iterations + because they are typically slower than the rest. + The result files can be loaded in the ``chrome://tracing`` page in chrome browser. + + Note: + When used together with NCCL on older version of GPUs, + autograd profiler may cause deadlock because it unnecessarily allocates + memory on every device it sees. The memory management calls, if + interleaved with NCCL calls, lead to deadlock on GPUs that do not + support ``cudaLaunchCooperativeKernelMultiDevice``. + """ + + def __init__(self, enable_predicate, output_dir, *, use_cuda=True): + """ + Args: + enable_predicate (callable[trainer -> bool]): a function which takes a trainer, + and returns whether to enable the profiler. + It will be called once every step, and can be used to select which steps to profile. + output_dir (str): the output directory to dump tracing files. + use_cuda (bool): same as in `torch.autograd.profiler.profile`. + """ + warnings.warn("AutogradProfiler has been deprecated in favor of TorchProfiler.") + self._enable_predicate = enable_predicate + self._use_cuda = use_cuda + self._output_dir = output_dir + + def before_step(self): + if self._enable_predicate(self.trainer): + self._profiler = torch.autograd.profiler.profile(use_cuda=self._use_cuda) + self._profiler.__enter__() + else: + self._profiler = None + + +class EvalHook(HookBase): + """ + Run an evaluation function periodically, and at the end of training. + + It is executed every ``eval_period`` iterations and after the last iteration. + """ + + def __init__(self, eval_period, eval_function): + """ + Args: + eval_period (int): the period to run `eval_function`. Set to 0 to + not evaluate periodically (but still after the last iteration). + eval_function (callable): a function which takes no arguments, and + returns a nested dict of evaluation metrics. + + Note: + This hook must be enabled in all or none workers. + If you would like only certain workers to perform evaluation, + give other workers a no-op function (`eval_function=lambda: None`). + """ + self._period = eval_period + self._func = eval_function + + def _do_eval(self): + results = self._func() + + if results: + assert isinstance( + results, dict + ), "Eval function must return a dict. Got {} instead.".format(results) + + flattened_results = flatten_results_dict(results) + for k, v in flattened_results.items(): + try: + v = float(v) + except Exception as e: + raise ValueError( + "[EvalHook] eval_function should return a nested dict of float. " + "Got '{}: {}' instead.".format(k, v) + ) from e + self.trainer.storage.put_scalars(**flattened_results, smoothing_hint=False) + + # Evaluation may take different time among workers. + # A barrier make them start the next iteration together. + comm.synchronize() + + def after_step(self): + next_iter = self.trainer.iter + 1 + if self._period > 0 and next_iter % self._period == 0: + # do the last eval in after_train + if next_iter != self.trainer.max_iter: + self._do_eval() + + def after_train(self): + # This condition is to prevent the eval from running after a failed training + if self.trainer.iter + 1 >= self.trainer.max_iter: + self._do_eval() + # func is likely a closure that holds reference to the trainer + # therefore we clean it to avoid circular reference in the end + del self._func + + +class PreciseBN(HookBase): + """ + The standard implementation of BatchNorm uses EMA in inference, which is + sometimes suboptimal. + This class computes the true average of statistics rather than the moving average, + and put true averages to every BN layer in the given model. + + It is executed every ``period`` iterations and after the last iteration. + """ + + def __init__(self, period, model, data_loader, num_iter): + """ + Args: + period (int): the period this hook is run, or 0 to not run during training. + The hook will always run in the end of training. + model (nn.Module): a module whose all BN layers in training mode will be + updated by precise BN. + Note that user is responsible for ensuring the BN layers to be + updated are in training mode when this hook is triggered. + data_loader (iterable): it will produce data to be run by `model(data)`. + num_iter (int): number of iterations used to compute the precise + statistics. + """ + self._logger = logging.getLogger(__name__) + if len(get_bn_modules(model)) == 0: + self._logger.info( + "PreciseBN is disabled because model does not contain BN layers in training mode." + ) + self._disabled = True + return + + self._model = model + self._data_loader = data_loader + self._num_iter = num_iter + self._period = period + self._disabled = False + + self._data_iter = None + + def after_step(self): + next_iter = self.trainer.iter + 1 + is_final = next_iter == self.trainer.max_iter + if is_final or (self._period > 0 and next_iter % self._period == 0): + self.update_stats() + + def update_stats(self): + """ + Update the model with precise statistics. Users can manually call this method. + """ + if self._disabled: + return + + if self._data_iter is None: + self._data_iter = iter(self._data_loader) + + def data_loader(): + for num_iter in itertools.count(1): + if num_iter % 100 == 0: + self._logger.info( + "Running precise-BN ... {}/{} iterations.".format(num_iter, self._num_iter) + ) + # This way we can reuse the same iterator + yield next(self._data_iter) + + with EventStorage(): # capture events in a new storage to discard them + self._logger.info( + "Running precise-BN for {} iterations... ".format(self._num_iter) + + "Note that this could produce different statistics every time." + ) + update_bn_stats(self._model, data_loader(), self._num_iter) + + +class TorchMemoryStats(HookBase): + """ + Writes pytorch's cuda memory statistics periodically. + """ + + def __init__(self, period=20, max_runs=10): + """ + Args: + period (int): Output stats each 'period' iterations + max_runs (int): Stop the logging after 'max_runs' + """ + + self._logger = logging.getLogger(__name__) + self._period = period + self._max_runs = max_runs + self._runs = 0 + + def after_step(self): + if self._runs > self._max_runs: + return + + if (self.trainer.iter + 1) % self._period == 0 or ( + self.trainer.iter == self.trainer.max_iter - 1 + ): + if torch.cuda.is_available(): + max_reserved_mb = torch.cuda.max_memory_reserved() / 1024.0 / 1024.0 + reserved_mb = torch.cuda.memory_reserved() / 1024.0 / 1024.0 + max_allocated_mb = torch.cuda.max_memory_allocated() / 1024.0 / 1024.0 + allocated_mb = torch.cuda.memory_allocated() / 1024.0 / 1024.0 + + self._logger.info( + ( + " iter: {} " + " max_reserved_mem: {:.0f}MB " + " reserved_mem: {:.0f}MB " + " max_allocated_mem: {:.0f}MB " + " allocated_mem: {:.0f}MB " + ).format( + self.trainer.iter, + max_reserved_mb, + reserved_mb, + max_allocated_mb, + allocated_mb, + ) + ) + + self._runs += 1 + if self._runs == self._max_runs: + mem_summary = torch.cuda.memory_summary() + self._logger.info("\n" + mem_summary) + + torch.cuda.reset_peak_memory_stats() diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/engine/launch.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/engine/launch.py new file mode 100644 index 0000000000000000000000000000000000000000..46f98691f163a82fdfcf75d910b28590af042de9 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/engine/launch.py @@ -0,0 +1,126 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +from datetime import timedelta +import torch +import torch.distributed as dist +import torch.multiprocessing as mp + +from detectron2.utils import comm + +__all__ = ["DEFAULT_TIMEOUT", "launch"] + +DEFAULT_TIMEOUT = timedelta(minutes=30) + + +def _find_free_port(): + import socket + + sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + # Binding to port 0 will cause the OS to find an available port for us + sock.bind(("", 0)) + port = sock.getsockname()[1] + sock.close() + # NOTE: there is still a chance the port could be taken by other processes. + return port + + +def launch( + main_func, + num_gpus_per_machine, + num_machines=1, + machine_rank=0, + dist_url=None, + args=(), + timeout=DEFAULT_TIMEOUT, +): + """ + Launch multi-gpu or distributed training. + This function must be called on all machines involved in the training. + It will spawn child processes (defined by ``num_gpus_per_machine``) on each machine. + + Args: + main_func: a function that will be called by `main_func(*args)` + num_gpus_per_machine (int): number of GPUs per machine + num_machines (int): the total number of machines + machine_rank (int): the rank of this machine + dist_url (str): url to connect to for distributed jobs, including protocol + e.g. "tcp://127.0.0.1:8686". + Can be set to "auto" to automatically select a free port on localhost + timeout (timedelta): timeout of the distributed workers + args (tuple): arguments passed to main_func + """ + world_size = num_machines * num_gpus_per_machine + if world_size > 1: + # https://github.com/pytorch/pytorch/pull/14391 + # TODO prctl in spawned processes + + if dist_url == "auto": + assert num_machines == 1, "dist_url=auto not supported in multi-machine jobs." + port = _find_free_port() + dist_url = f"tcp://127.0.0.1:{port}" + if num_machines > 1 and dist_url.startswith("file://"): + logger = logging.getLogger(__name__) + logger.warning( + "file:// is not a reliable init_method in multi-machine jobs. Prefer tcp://" + ) + + mp.spawn( + _distributed_worker, + nprocs=num_gpus_per_machine, + args=( + main_func, + world_size, + num_gpus_per_machine, + machine_rank, + dist_url, + args, + timeout, + ), + daemon=False, + ) + else: + main_func(*args) + + +def _distributed_worker( + local_rank, + main_func, + world_size, + num_gpus_per_machine, + machine_rank, + dist_url, + args, + timeout=DEFAULT_TIMEOUT, +): + assert torch.cuda.is_available(), "cuda is not available. Please check your installation." + global_rank = machine_rank * num_gpus_per_machine + local_rank + try: + dist.init_process_group( + backend="NCCL", + init_method=dist_url, + world_size=world_size, + rank=global_rank, + timeout=timeout, + ) + except Exception as e: + logger = logging.getLogger(__name__) + logger.error("Process group URL: {}".format(dist_url)) + raise e + + # Setup the local process group (which contains ranks within the same machine) + assert comm._LOCAL_PROCESS_GROUP is None + num_machines = world_size // num_gpus_per_machine + for i in range(num_machines): + ranks_on_i = list(range(i * num_gpus_per_machine, (i + 1) * num_gpus_per_machine)) + pg = dist.new_group(ranks_on_i) + if i == machine_rank: + comm._LOCAL_PROCESS_GROUP = pg + + assert num_gpus_per_machine <= torch.cuda.device_count() + torch.cuda.set_device(local_rank) + + # synchronize is needed here to prevent a possible timeout after calling init_process_group + # See: https://github.com/facebookresearch/maskrcnn-benchmark/issues/172 + comm.synchronize() + + main_func(*args) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/engine/train_loop.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/engine/train_loop.py new file mode 100644 index 0000000000000000000000000000000000000000..c4a86b52a5604f2b5799abac299ca4726345b7a6 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/engine/train_loop.py @@ -0,0 +1,417 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import logging +import numpy as np +import time +import weakref +from typing import List, Mapping, Optional +import torch +from torch.nn.parallel import DataParallel, DistributedDataParallel + +import detectron2.utils.comm as comm +from detectron2.utils.events import EventStorage, get_event_storage +from detectron2.utils.logger import _log_api_usage + +__all__ = ["HookBase", "TrainerBase", "SimpleTrainer", "AMPTrainer"] + + +class HookBase: + """ + Base class for hooks that can be registered with :class:`TrainerBase`. + + Each hook can implement 4 methods. The way they are called is demonstrated + in the following snippet: + :: + hook.before_train() + for iter in range(start_iter, max_iter): + hook.before_step() + trainer.run_step() + hook.after_step() + iter += 1 + hook.after_train() + + Notes: + 1. In the hook method, users can access ``self.trainer`` to access more + properties about the context (e.g., model, current iteration, or config + if using :class:`DefaultTrainer`). + + 2. A hook that does something in :meth:`before_step` can often be + implemented equivalently in :meth:`after_step`. + If the hook takes non-trivial time, it is strongly recommended to + implement the hook in :meth:`after_step` instead of :meth:`before_step`. + The convention is that :meth:`before_step` should only take negligible time. + + Following this convention will allow hooks that do care about the difference + between :meth:`before_step` and :meth:`after_step` (e.g., timer) to + function properly. + + """ + + trainer: "TrainerBase" = None + """ + A weak reference to the trainer object. Set by the trainer when the hook is registered. + """ + + def before_train(self): + """ + Called before the first iteration. + """ + pass + + def after_train(self): + """ + Called after the last iteration. + """ + pass + + def before_step(self): + """ + Called before each iteration. + """ + pass + + def after_step(self): + """ + Called after each iteration. + """ + pass + + def state_dict(self): + """ + Hooks are stateless by default, but can be made checkpointable by + implementing `state_dict` and `load_state_dict`. + """ + return {} + + +class TrainerBase: + """ + Base class for iterative trainer with hooks. + + The only assumption we made here is: the training runs in a loop. + A subclass can implement what the loop is. + We made no assumptions about the existence of dataloader, optimizer, model, etc. + + Attributes: + iter(int): the current iteration. + + start_iter(int): The iteration to start with. + By convention the minimum possible value is 0. + + max_iter(int): The iteration to end training. + + storage(EventStorage): An EventStorage that's opened during the course of training. + """ + + def __init__(self) -> None: + self._hooks: List[HookBase] = [] + self.iter: int = 0 + self.start_iter: int = 0 + self.max_iter: int + self.storage: EventStorage + _log_api_usage("trainer." + self.__class__.__name__) + + def register_hooks(self, hooks: List[Optional[HookBase]]) -> None: + """ + Register hooks to the trainer. The hooks are executed in the order + they are registered. + + Args: + hooks (list[Optional[HookBase]]): list of hooks + """ + hooks = [h for h in hooks if h is not None] + for h in hooks: + assert isinstance(h, HookBase) + # To avoid circular reference, hooks and trainer cannot own each other. + # This normally does not matter, but will cause memory leak if the + # involved objects contain __del__: + # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/ + h.trainer = weakref.proxy(self) + self._hooks.extend(hooks) + + def train(self, start_iter: int, max_iter: int): + """ + Args: + start_iter, max_iter (int): See docs above + """ + logger = logging.getLogger(__name__) + logger.info("Starting training from iteration {}".format(start_iter)) + + self.iter = self.start_iter = start_iter + self.max_iter = max_iter + + with EventStorage(start_iter) as self.storage: + try: + self.before_train() + for self.iter in range(start_iter, max_iter): + self.before_step() + self.run_step() + self.after_step() + # self.iter == max_iter can be used by `after_train` to + # tell whether the training successfully finished or failed + # due to exceptions. + self.iter += 1 + except Exception: + logger.exception("Exception during training:") + raise + finally: + self.after_train() + + def before_train(self): + for h in self._hooks: + h.before_train() + + def after_train(self): + self.storage.iter = self.iter + for h in self._hooks: + h.after_train() + + def before_step(self): + # Maintain the invariant that storage.iter == trainer.iter + # for the entire execution of each step + self.storage.iter = self.iter + + for h in self._hooks: + h.before_step() + + def after_step(self): + for h in self._hooks: + h.after_step() + + def run_step(self): + raise NotImplementedError + + def state_dict(self): + ret = {"iteration": self.iter} + hooks_state = {} + for h in self._hooks: + sd = h.state_dict() + if sd: + name = type(h).__qualname__ + if name in hooks_state: + # TODO handle repetitive stateful hooks + continue + hooks_state[name] = sd + if hooks_state: + ret["hooks"] = hooks_state + return ret + + def load_state_dict(self, state_dict): + logger = logging.getLogger(__name__) + self.iter = state_dict["iteration"] + for key, value in state_dict.get("hooks", {}).items(): + for h in self._hooks: + try: + name = type(h).__qualname__ + except AttributeError: + continue + if name == key: + h.load_state_dict(value) + break + else: + logger.warning(f"Cannot find the hook '{key}', its state_dict is ignored.") + + +class SimpleTrainer(TrainerBase): + """ + A simple trainer for the most common type of task: + single-cost single-optimizer single-data-source iterative optimization, + optionally using data-parallelism. + It assumes that every step, you: + + 1. Compute the loss with a data from the data_loader. + 2. Compute the gradients with the above loss. + 3. Update the model with the optimizer. + + All other tasks during training (checkpointing, logging, evaluation, LR schedule) + are maintained by hooks, which can be registered by :meth:`TrainerBase.register_hooks`. + + If you want to do anything fancier than this, + either subclass TrainerBase and implement your own `run_step`, + or write your own training loop. + """ + + def __init__(self, model, data_loader, optimizer): + """ + Args: + model: a torch Module. Takes a data from data_loader and returns a + dict of losses. + data_loader: an iterable. Contains data to be used to call model. + optimizer: a torch optimizer. + """ + super().__init__() + + """ + We set the model to training mode in the trainer. + However it's valid to train a model that's in eval mode. + If you want your model (or a submodule of it) to behave + like evaluation during training, you can overwrite its train() method. + """ + model.train() + + self.model = model + self.data_loader = data_loader + self._data_loader_iter = iter(data_loader) + self.optimizer = optimizer + + def run_step(self): + """ + Implement the standard training logic described above. + """ + assert self.model.training, "[SimpleTrainer] model was changed to eval mode!" + start = time.perf_counter() + """ + If you want to do something with the data, you can wrap the dataloader. + """ + data = next(self._data_loader_iter) + data_time = time.perf_counter() - start + + """ + If you want to do something with the losses, you can wrap the model. + """ + loss_dict = self.model(data) + if isinstance(loss_dict, torch.Tensor): + losses = loss_dict + loss_dict = {"total_loss": loss_dict} + else: + losses = sum(loss_dict.values()) + + """ + If you need to accumulate gradients or do something similar, you can + wrap the optimizer with your custom `zero_grad()` method. + """ + self.optimizer.zero_grad() + losses.backward() + + self._write_metrics(loss_dict, data_time) + + """ + If you need gradient clipping/scaling or other processing, you can + wrap the optimizer with your custom `step()` method. But it is + suboptimal as explained in https://arxiv.org/abs/2006.15704 Sec 3.2.4 + """ + self.optimizer.step() + + def _write_metrics( + self, + loss_dict: Mapping[str, torch.Tensor], + data_time: float, + prefix: str = "", + ) -> None: + SimpleTrainer.write_metrics(loss_dict, data_time, prefix) + + @staticmethod + def write_metrics( + loss_dict: Mapping[str, torch.Tensor], + data_time: float, + prefix: str = "", + ) -> None: + """ + Args: + loss_dict (dict): dict of scalar losses + data_time (float): time taken by the dataloader iteration + prefix (str): prefix for logging keys + """ + metrics_dict = {k: v.detach().cpu().item() for k, v in loss_dict.items()} + metrics_dict["data_time"] = data_time + + # Gather metrics among all workers for logging + # This assumes we do DDP-style training, which is currently the only + # supported method in detectron2. + all_metrics_dict = comm.gather(metrics_dict) + + if comm.is_main_process(): + storage = get_event_storage() + + # data_time among workers can have high variance. The actual latency + # caused by data_time is the maximum among workers. + data_time = np.max([x.pop("data_time") for x in all_metrics_dict]) + storage.put_scalar("data_time", data_time) + + # average the rest metrics + metrics_dict = { + k: np.mean([x[k] for x in all_metrics_dict]) for k in all_metrics_dict[0].keys() + } + total_losses_reduced = sum(metrics_dict.values()) + if not np.isfinite(total_losses_reduced): + raise FloatingPointError( + f"Loss became infinite or NaN at iteration={storage.iter}!\n" + f"loss_dict = {metrics_dict}" + ) + + storage.put_scalar("{}total_loss".format(prefix), total_losses_reduced) + if len(metrics_dict) > 1: + storage.put_scalars(**metrics_dict) + + def state_dict(self): + ret = super().state_dict() + ret["optimizer"] = self.optimizer.state_dict() + return ret + + def load_state_dict(self, state_dict): + super().load_state_dict(state_dict) + self.optimizer.load_state_dict(state_dict["optimizer"]) + + +class AMPTrainer(SimpleTrainer): + """ + Like :class:`SimpleTrainer`, but uses PyTorch's native automatic mixed precision + in the training loop. + """ + + def __init__(self, model, data_loader, optimizer, grad_scaler=None): + """ + Args: + model, data_loader, optimizer: same as in :class:`SimpleTrainer`. + grad_scaler: torch GradScaler to automatically scale gradients. + """ + unsupported = "AMPTrainer does not support single-process multi-device training!" + if isinstance(model, DistributedDataParallel): + assert not (model.device_ids and len(model.device_ids) > 1), unsupported + assert not isinstance(model, DataParallel), unsupported + + super().__init__(model, data_loader, optimizer) + + if grad_scaler is None: + from torch.cuda.amp import GradScaler + + grad_scaler = GradScaler() + self.grad_scaler = grad_scaler + + def run_step(self): + """ + Implement the AMP training logic. + """ + assert self.model.training, "[AMPTrainer] model was changed to eval mode!" + assert torch.cuda.is_available(), "[AMPTrainer] CUDA is required for AMP training!" + from torch.cuda.amp import autocast + + start = time.perf_counter() + data = next(self._data_loader_iter) + data_time = time.perf_counter() - start + + with autocast(): + loss_dict = self.model(data) + if isinstance(loss_dict, torch.Tensor): + losses = loss_dict + loss_dict = {"total_loss": loss_dict} + else: + losses = sum(loss_dict.values()) + + self.optimizer.zero_grad() + self.grad_scaler.scale(losses).backward() + + self._write_metrics(loss_dict, data_time) + + self.grad_scaler.step(self.optimizer) + self.grad_scaler.update() + + def state_dict(self): + ret = super().state_dict() + ret["grad_scaler"] = self.grad_scaler.state_dict() + return ret + + def load_state_dict(self, state_dict): + super().load_state_dict(state_dict) + self.grad_scaler.load_state_dict(state_dict["grad_scaler"]) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d96609e8f2261a6800fe85fcf3e1eaeaa44455c6 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .cityscapes_evaluation import CityscapesInstanceEvaluator, CityscapesSemSegEvaluator +from .coco_evaluation import COCOEvaluator +from .rotated_coco_evaluation import RotatedCOCOEvaluator +from .evaluator import DatasetEvaluator, DatasetEvaluators, inference_context, inference_on_dataset +from .lvis_evaluation import LVISEvaluator +from .panoptic_evaluation import COCOPanopticEvaluator +from .pascal_voc_evaluation import PascalVOCDetectionEvaluator +from .sem_seg_evaluation import SemSegEvaluator +from .testing import print_csv_format, verify_results + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/cityscapes_evaluation.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/cityscapes_evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..3fb6c4cd5f752d639570d022cb23ce18491c370a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/cityscapes_evaluation.py @@ -0,0 +1,194 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import glob +import logging +import numpy as np +import os +import tempfile +from collections import OrderedDict +import torch +from PIL import Image + +from detectron2.data import MetadataCatalog +from detectron2.utils import comm +from detectron2.utils.file_io import PathManager + +from .evaluator import DatasetEvaluator + + +class CityscapesEvaluator(DatasetEvaluator): + """ + Base class for evaluation using cityscapes API. + """ + + def __init__(self, dataset_name): + """ + Args: + dataset_name (str): the name of the dataset. + It must have the following metadata associated with it: + "thing_classes", "gt_dir". + """ + self._metadata = MetadataCatalog.get(dataset_name) + self._cpu_device = torch.device("cpu") + self._logger = logging.getLogger(__name__) + + def reset(self): + self._working_dir = tempfile.TemporaryDirectory(prefix="cityscapes_eval_") + self._temp_dir = self._working_dir.name + # All workers will write to the same results directory + # TODO this does not work in distributed training + self._temp_dir = comm.all_gather(self._temp_dir)[0] + if self._temp_dir != self._working_dir.name: + self._working_dir.cleanup() + self._logger.info( + "Writing cityscapes results to temporary directory {} ...".format(self._temp_dir) + ) + + +class CityscapesInstanceEvaluator(CityscapesEvaluator): + """ + Evaluate instance segmentation results on cityscapes dataset using cityscapes API. + + Note: + * It does not work in multi-machine distributed training. + * It contains a synchronization, therefore has to be used on all ranks. + * Only the main process runs evaluation. + """ + + def process(self, inputs, outputs): + from cityscapesscripts.helpers.labels import name2label + + for input, output in zip(inputs, outputs): + file_name = input["file_name"] + basename = os.path.splitext(os.path.basename(file_name))[0] + pred_txt = os.path.join(self._temp_dir, basename + "_pred.txt") + + if "instances" in output: + output = output["instances"].to(self._cpu_device) + num_instances = len(output) + with open(pred_txt, "w") as fout: + for i in range(num_instances): + pred_class = output.pred_classes[i] + classes = self._metadata.thing_classes[pred_class] + class_id = name2label[classes].id + score = output.scores[i] + mask = output.pred_masks[i].numpy().astype("uint8") + png_filename = os.path.join( + self._temp_dir, basename + "_{}_{}.png".format(i, classes) + ) + + Image.fromarray(mask * 255).save(png_filename) + fout.write( + "{} {} {}\n".format(os.path.basename(png_filename), class_id, score) + ) + else: + # Cityscapes requires a prediction file for every ground truth image. + with open(pred_txt, "w") as fout: + pass + + def evaluate(self): + """ + Returns: + dict: has a key "segm", whose value is a dict of "AP" and "AP50". + """ + comm.synchronize() + if comm.get_rank() > 0: + return + import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval + + self._logger.info("Evaluating results under {} ...".format(self._temp_dir)) + + # set some global states in cityscapes evaluation API, before evaluating + cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir) + cityscapes_eval.args.predictionWalk = None + cityscapes_eval.args.JSONOutput = False + cityscapes_eval.args.colorized = False + cityscapes_eval.args.gtInstancesFile = os.path.join(self._temp_dir, "gtInstances.json") + + # These lines are adopted from + # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa + gt_dir = PathManager.get_local_path(self._metadata.gt_dir) + groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_instanceIds.png")) + assert len( + groundTruthImgList + ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format( + cityscapes_eval.args.groundTruthSearch + ) + predictionImgList = [] + for gt in groundTruthImgList: + predictionImgList.append(cityscapes_eval.getPrediction(gt, cityscapes_eval.args)) + results = cityscapes_eval.evaluateImgLists( + predictionImgList, groundTruthImgList, cityscapes_eval.args + )["averages"] + + ret = OrderedDict() + ret["segm"] = {"AP": results["allAp"] * 100, "AP50": results["allAp50%"] * 100} + self._working_dir.cleanup() + return ret + + +class CityscapesSemSegEvaluator(CityscapesEvaluator): + """ + Evaluate semantic segmentation results on cityscapes dataset using cityscapes API. + + Note: + * It does not work in multi-machine distributed training. + * It contains a synchronization, therefore has to be used on all ranks. + * Only the main process runs evaluation. + """ + + def process(self, inputs, outputs): + from cityscapesscripts.helpers.labels import trainId2label + + for input, output in zip(inputs, outputs): + file_name = input["file_name"] + basename = os.path.splitext(os.path.basename(file_name))[0] + pred_filename = os.path.join(self._temp_dir, basename + "_pred.png") + + output = output["sem_seg"].argmax(dim=0).to(self._cpu_device).numpy() + pred = 255 * np.ones(output.shape, dtype=np.uint8) + for train_id, label in trainId2label.items(): + if label.ignoreInEval: + continue + pred[output == train_id] = label.id + Image.fromarray(pred).save(pred_filename) + + def evaluate(self): + comm.synchronize() + if comm.get_rank() > 0: + return + # Load the Cityscapes eval script *after* setting the required env var, + # since the script reads CITYSCAPES_DATASET into global variables at load time. + import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as cityscapes_eval + + self._logger.info("Evaluating results under {} ...".format(self._temp_dir)) + + # set some global states in cityscapes evaluation API, before evaluating + cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir) + cityscapes_eval.args.predictionWalk = None + cityscapes_eval.args.JSONOutput = False + cityscapes_eval.args.colorized = False + + # These lines are adopted from + # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalPixelLevelSemanticLabeling.py # noqa + gt_dir = PathManager.get_local_path(self._metadata.gt_dir) + groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_labelIds.png")) + assert len( + groundTruthImgList + ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format( + cityscapes_eval.args.groundTruthSearch + ) + predictionImgList = [] + for gt in groundTruthImgList: + predictionImgList.append(cityscapes_eval.getPrediction(cityscapes_eval.args, gt)) + results = cityscapes_eval.evaluateImgLists( + predictionImgList, groundTruthImgList, cityscapes_eval.args + ) + ret = OrderedDict() + ret["sem_seg"] = { + "IoU": 100.0 * results["averageScoreClasses"], + "iIoU": 100.0 * results["averageScoreInstClasses"], + "IoU_sup": 100.0 * results["averageScoreCategories"], + "iIoU_sup": 100.0 * results["averageScoreInstCategories"], + } + self._working_dir.cleanup() + return ret diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/coco_evaluation.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/coco_evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..aad7f5a6e79a9047e7eea623ecc761ea9655b8d6 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/coco_evaluation.py @@ -0,0 +1,710 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import contextlib +import copy +import io +import itertools +import json +import logging +import numpy as np +import os +import pickle +from collections import OrderedDict +import pycocotools.mask as mask_util +import torch +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval +from tabulate import tabulate + +import detectron2.utils.comm as comm +from detectron2.config import CfgNode +from detectron2.data import MetadataCatalog +from detectron2.data.datasets.coco import convert_to_coco_json +from detectron2.evaluation.fast_eval_api import COCOeval_opt +from detectron2.structures import Boxes, BoxMode, pairwise_iou +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import create_small_table + +from .evaluator import DatasetEvaluator + + +class COCOEvaluator(DatasetEvaluator): + """ + Evaluate AR for object proposals, AP for instance detection/segmentation, AP + for keypoint detection outputs using COCO's metrics. + See http://cocodataset.org/#detection-eval and + http://cocodataset.org/#keypoints-eval to understand its metrics. + The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means + the metric cannot be computed (e.g. due to no predictions made). + + In addition to COCO, this evaluator is able to support any bounding box detection, + instance segmentation, or keypoint detection dataset. + """ + + def __init__( + self, + dataset_name, + tasks=None, + distributed=True, + output_dir=None, + *, + max_dets_per_image=None, + use_fast_impl=True, + kpt_oks_sigmas=(), + ): + """ + Args: + dataset_name (str): name of the dataset to be evaluated. + It must have either the following corresponding metadata: + + "json_file": the path to the COCO format annotation + + Or it must be in detectron2's standard dataset format + so it can be converted to COCO format automatically. + tasks (tuple[str]): tasks that can be evaluated under the given + configuration. A task is one of "bbox", "segm", "keypoints". + By default, will infer this automatically from predictions. + distributed (True): if True, will collect results from all ranks and run evaluation + in the main process. + Otherwise, will only evaluate the results in the current process. + output_dir (str): optional, an output directory to dump all + results predicted on the dataset. The dump contains two files: + + 1. "instances_predictions.pth" a file that can be loaded with `torch.load` and + contains all the results in the format they are produced by the model. + 2. "coco_instances_results.json" a json file in COCO's result format. + max_dets_per_image (int): limit on the maximum number of detections per image. + By default in COCO, this limit is to 100, but this can be customized + to be greater, as is needed in evaluation metrics AP fixed and AP pool + (see https://arxiv.org/pdf/2102.01066.pdf) + This doesn't affect keypoint evaluation. + use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP. + Although the results should be very close to the official implementation in COCO + API, it is still recommended to compute results with the official API for use in + papers. The faster implementation also uses more RAM. + kpt_oks_sigmas (list[float]): The sigmas used to calculate keypoint OKS. + See http://cocodataset.org/#keypoints-eval + When empty, it will use the defaults in COCO. + Otherwise it should be the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS. + """ + self._logger = logging.getLogger(__name__) + self._distributed = distributed + self._output_dir = output_dir + self._use_fast_impl = use_fast_impl + + # COCOeval requires the limit on the number of detections per image (maxDets) to be a list + # with at least 3 elements. The default maxDets in COCOeval is [1, 10, 100], in which the + # 3rd element (100) is used as the limit on the number of detections per image when + # evaluating AP. COCOEvaluator expects an integer for max_dets_per_image, so for COCOeval, + # we reformat max_dets_per_image into [1, 10, max_dets_per_image], based on the defaults. + if max_dets_per_image is None: + max_dets_per_image = [1, 10, 100] + else: + max_dets_per_image = [1, 10, max_dets_per_image] + self._max_dets_per_image = max_dets_per_image + + if tasks is not None and isinstance(tasks, CfgNode): + kpt_oks_sigmas = ( + tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas + ) + self._logger.warn( + "COCO Evaluator instantiated using config, this is deprecated behavior." + " Please pass in explicit arguments instead." + ) + self._tasks = None # Infering it from predictions should be better + else: + self._tasks = tasks + + self._cpu_device = torch.device("cpu") + + self._metadata = MetadataCatalog.get(dataset_name) + if not hasattr(self._metadata, "json_file"): + if output_dir is None: + raise ValueError( + "output_dir must be provided to COCOEvaluator " + "for datasets not in COCO format." + ) + self._logger.info(f"Trying to convert '{dataset_name}' to COCO format ...") + + cache_path = os.path.join(output_dir, f"{dataset_name}_coco_format.json") + self._metadata.json_file = cache_path + convert_to_coco_json(dataset_name, cache_path) + + json_file = PathManager.get_local_path(self._metadata.json_file) + with contextlib.redirect_stdout(io.StringIO()): + self._coco_api = COCO(json_file) + + # Test set json files do not contain annotations (evaluation must be + # performed using the COCO evaluation server). + self._do_evaluation = "annotations" in self._coco_api.dataset + if self._do_evaluation: + self._kpt_oks_sigmas = kpt_oks_sigmas + + def reset(self): + self._predictions = [] + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a COCO model (e.g., GeneralizedRCNN). + It is a list of dict. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a COCO model. It is a list of dicts with key + "instances" that contains :class:`Instances`. + """ + for input, output in zip(inputs, outputs): + prediction = {"image_id": input["image_id"]} + + if "instances" in output: + instances = output["instances"].to(self._cpu_device) + prediction["instances"] = instances_to_coco_json(instances, input["image_id"]) + if "proposals" in output: + prediction["proposals"] = output["proposals"].to(self._cpu_device) + if len(prediction) > 1: + self._predictions.append(prediction) + + def evaluate(self, img_ids=None): + """ + Args: + img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset + """ + if self._distributed: + comm.synchronize() + predictions = comm.gather(self._predictions, dst=0) + predictions = list(itertools.chain(*predictions)) + + if not comm.is_main_process(): + return {} + else: + predictions = self._predictions + + if len(predictions) == 0: + self._logger.warning("[COCOEvaluator] Did not receive valid predictions.") + return {} + + if self._output_dir: + PathManager.mkdirs(self._output_dir) + file_path = os.path.join(self._output_dir, "instances_predictions.pth") + with PathManager.open(file_path, "wb") as f: + torch.save(predictions, f) + + self._results = OrderedDict() + if "proposals" in predictions[0]: + self._eval_box_proposals(predictions) + if "instances" in predictions[0]: + self._eval_predictions(predictions, img_ids=img_ids) + # Copy so the caller can do whatever with results + return copy.deepcopy(self._results) + + def _tasks_from_predictions(self, predictions): + """ + Get COCO API "tasks" (i.e. iou_type) from COCO-format predictions. + """ + tasks = {"bbox"} + for pred in predictions: + if "segmentation" in pred: + tasks.add("segm") + if "keypoints" in pred: + tasks.add("keypoints") + return sorted(tasks) + + def _eval_predictions(self, predictions, img_ids=None): + """ + Evaluate predictions. Fill self._results with the metrics of the tasks. + """ + self._logger.info("Preparing results for COCO format ...") + coco_results = list(itertools.chain(*[x["instances"] for x in predictions])) + tasks = self._tasks or self._tasks_from_predictions(coco_results) + + # unmap the category ids for COCO + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id + all_contiguous_ids = list(dataset_id_to_contiguous_id.values()) + num_classes = len(all_contiguous_ids) + assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1 + + reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()} + for result in coco_results: + category_id = result["category_id"] + assert category_id < num_classes, ( + f"A prediction has class={category_id}, " + f"but the dataset only has {num_classes} classes and " + f"predicted class id should be in [0, {num_classes - 1}]." + ) + result["category_id"] = reverse_id_mapping[category_id] + + if self._output_dir: + file_path = os.path.join(self._output_dir, "coco_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(coco_results)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info( + "Evaluating predictions with {} COCO API...".format( + "unofficial" if self._use_fast_impl else "official" + ) + ) + for task in sorted(tasks): + assert task in {"bbox", "segm", "keypoints"}, f"Got unknown task: {task}!" + coco_eval = ( + _evaluate_predictions_on_coco( + self._coco_api, + coco_results, + task, + kpt_oks_sigmas=self._kpt_oks_sigmas, + use_fast_impl=self._use_fast_impl, + img_ids=img_ids, + max_dets_per_image=self._max_dets_per_image, + ) + if len(coco_results) > 0 + else None # cocoapi does not handle empty results very well + ) + + res = self._derive_coco_results( + coco_eval, task, class_names=self._metadata.get("thing_classes") + ) + self._results[task] = res + + def _eval_box_proposals(self, predictions): + """ + Evaluate the box proposals in predictions. + Fill self._results with the metrics for "box_proposals" task. + """ + if self._output_dir: + # Saving generated box proposals to file. + # Predicted box_proposals are in XYXY_ABS mode. + bbox_mode = BoxMode.XYXY_ABS.value + ids, boxes, objectness_logits = [], [], [] + for prediction in predictions: + ids.append(prediction["image_id"]) + boxes.append(prediction["proposals"].proposal_boxes.tensor.numpy()) + objectness_logits.append(prediction["proposals"].objectness_logits.numpy()) + + proposal_data = { + "boxes": boxes, + "objectness_logits": objectness_logits, + "ids": ids, + "bbox_mode": bbox_mode, + } + with PathManager.open(os.path.join(self._output_dir, "box_proposals.pkl"), "wb") as f: + pickle.dump(proposal_data, f) + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info("Evaluating bbox proposals ...") + res = {} + areas = {"all": "", "small": "s", "medium": "m", "large": "l"} + for limit in [100, 1000]: + for area, suffix in areas.items(): + stats = _evaluate_box_proposals(predictions, self._coco_api, area=area, limit=limit) + key = "AR{}@{:d}".format(suffix, limit) + res[key] = float(stats["ar"].item() * 100) + self._logger.info("Proposal metrics: \n" + create_small_table(res)) + self._results["box_proposals"] = res + + def _derive_coco_results(self, coco_eval, iou_type, class_names=None): + """ + Derive the desired score numbers from summarized COCOeval. + + Args: + coco_eval (None or COCOEval): None represents no predictions from model. + iou_type (str): + class_names (None or list[str]): if provided, will use it to predict + per-category AP. + + Returns: + a dict of {metric name: score} + """ + + metrics = { + "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl"], + "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl"], + "keypoints": ["AP", "AP50", "AP75", "APm", "APl"], + }[iou_type] + + if coco_eval is None: + self._logger.warn("No predictions from the model!") + return {metric: float("nan") for metric in metrics} + + # the standard metrics + results = { + metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan") + for idx, metric in enumerate(metrics) + } + self._logger.info( + "Evaluation results for {}: \n".format(iou_type) + create_small_table(results) + ) + if not np.isfinite(sum(results.values())): + self._logger.info("Some metrics cannot be computed and is shown as NaN.") + + if class_names is None or len(class_names) <= 1: + return results + # Compute per-category AP + # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa + precisions = coco_eval.eval["precision"] + # precision has dims (iou, recall, cls, area range, max dets) + assert len(class_names) == precisions.shape[2] + + results_per_category = [] + for idx, name in enumerate(class_names): + # area range index 0: all area ranges + # max dets index -1: typically 100 per image + precision = precisions[:, :, idx, 0, -1] + precision = precision[precision > -1] + ap = np.mean(precision) if precision.size else float("nan") + results_per_category.append(("{}".format(name), float(ap * 100))) + + # tabulate it + N_COLS = min(6, len(results_per_category) * 2) + results_flatten = list(itertools.chain(*results_per_category)) + results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)]) + table = tabulate( + results_2d, + tablefmt="pipe", + floatfmt=".3f", + headers=["category", "AP"] * (N_COLS // 2), + numalign="left", + ) + self._logger.info("Per-category {} AP: \n".format(iou_type) + table) + + results.update({"AP-" + name: ap for name, ap in results_per_category}) + return results + + +def instances_to_coco_json(instances, img_id): + """ + Dump an "Instances" object to a COCO-format json that's used for evaluation. + + Args: + instances (Instances): + img_id (int): the image id + + Returns: + list[dict]: list of json annotations in COCO format. + """ + num_instance = len(instances) + if num_instance == 0: + return [] + + boxes = instances.pred_boxes.tensor.numpy() + boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) + boxes = boxes.tolist() + scores = instances.scores.tolist() + classes = instances.pred_classes.tolist() + + has_mask = instances.has("pred_masks") + if has_mask: + # use RLE to encode the masks, because they are too large and takes memory + # since this evaluator stores outputs of the entire dataset + rles = [ + mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0] + for mask in instances.pred_masks + ] + for rle in rles: + # "counts" is an array encoded by mask_util as a byte-stream. Python3's + # json writer which always produces strings cannot serialize a bytestream + # unless you decode it. Thankfully, utf-8 works out (which is also what + # the pycocotools/_mask.pyx does). + rle["counts"] = rle["counts"].decode("utf-8") + + has_keypoints = instances.has("pred_keypoints") + if has_keypoints: + keypoints = instances.pred_keypoints + + results = [] + for k in range(num_instance): + result = { + "image_id": img_id, + "category_id": classes[k], + "bbox": boxes[k], + "score": scores[k], + } + if has_mask: + result["segmentation"] = rles[k] + if has_keypoints: + # In COCO annotations, + # keypoints coordinates are pixel indices. + # However our predictions are floating point coordinates. + # Therefore we subtract 0.5 to be consistent with the annotation format. + # This is the inverse of data loading logic in `datasets/coco.py`. + keypoints[k][:, :2] -= 0.5 + result["keypoints"] = keypoints[k].flatten().tolist() + results.append(result) + return results + + +# inspired from Detectron: +# https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L255 # noqa +def _evaluate_box_proposals(dataset_predictions, coco_api, thresholds=None, area="all", limit=None): + """ + Evaluate detection proposal recall metrics. This function is a much + faster alternative to the official COCO API recall evaluation code. However, + it produces slightly different results. + """ + # Record max overlap value for each gt box + # Return vector of overlap values + areas = { + "all": 0, + "small": 1, + "medium": 2, + "large": 3, + "96-128": 4, + "128-256": 5, + "256-512": 6, + "512-inf": 7, + } + area_ranges = [ + [0 ** 2, 1e5 ** 2], # all + [0 ** 2, 32 ** 2], # small + [32 ** 2, 96 ** 2], # medium + [96 ** 2, 1e5 ** 2], # large + [96 ** 2, 128 ** 2], # 96-128 + [128 ** 2, 256 ** 2], # 128-256 + [256 ** 2, 512 ** 2], # 256-512 + [512 ** 2, 1e5 ** 2], + ] # 512-inf + assert area in areas, "Unknown area range: {}".format(area) + area_range = area_ranges[areas[area]] + gt_overlaps = [] + num_pos = 0 + + for prediction_dict in dataset_predictions: + predictions = prediction_dict["proposals"] + + # sort predictions in descending order + # TODO maybe remove this and make it explicit in the documentation + inds = predictions.objectness_logits.sort(descending=True)[1] + predictions = predictions[inds] + + ann_ids = coco_api.getAnnIds(imgIds=prediction_dict["image_id"]) + anno = coco_api.loadAnns(ann_ids) + gt_boxes = [ + BoxMode.convert(obj["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) + for obj in anno + if obj["iscrowd"] == 0 + ] + gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4) # guard against no boxes + gt_boxes = Boxes(gt_boxes) + gt_areas = torch.as_tensor([obj["area"] for obj in anno if obj["iscrowd"] == 0]) + + if len(gt_boxes) == 0 or len(predictions) == 0: + continue + + valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1]) + gt_boxes = gt_boxes[valid_gt_inds] + + num_pos += len(gt_boxes) + + if len(gt_boxes) == 0: + continue + + if limit is not None and len(predictions) > limit: + predictions = predictions[:limit] + + overlaps = pairwise_iou(predictions.proposal_boxes, gt_boxes) + + _gt_overlaps = torch.zeros(len(gt_boxes)) + for j in range(min(len(predictions), len(gt_boxes))): + # find which proposal box maximally covers each gt box + # and get the iou amount of coverage for each gt box + max_overlaps, argmax_overlaps = overlaps.max(dim=0) + + # find which gt box is 'best' covered (i.e. 'best' = most iou) + gt_ovr, gt_ind = max_overlaps.max(dim=0) + assert gt_ovr >= 0 + # find the proposal box that covers the best covered gt box + box_ind = argmax_overlaps[gt_ind] + # record the iou coverage of this gt box + _gt_overlaps[j] = overlaps[box_ind, gt_ind] + assert _gt_overlaps[j] == gt_ovr + # mark the proposal box and the gt box as used + overlaps[box_ind, :] = -1 + overlaps[:, gt_ind] = -1 + + # append recorded iou coverage level + gt_overlaps.append(_gt_overlaps) + gt_overlaps = ( + torch.cat(gt_overlaps, dim=0) if len(gt_overlaps) else torch.zeros(0, dtype=torch.float32) + ) + gt_overlaps, _ = torch.sort(gt_overlaps) + + if thresholds is None: + step = 0.05 + thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32) + recalls = torch.zeros_like(thresholds) + # compute recall for each iou threshold + for i, t in enumerate(thresholds): + recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos) + # ar = 2 * np.trapz(recalls, thresholds) + ar = recalls.mean() + return { + "ar": ar, + "recalls": recalls, + "thresholds": thresholds, + "gt_overlaps": gt_overlaps, + "num_pos": num_pos, + } + + +def _evaluate_predictions_on_coco( + coco_gt, + coco_results, + iou_type, + kpt_oks_sigmas=None, + use_fast_impl=True, + img_ids=None, + max_dets_per_image=None, +): + """ + Evaluate the coco results using COCOEval API. + """ + assert len(coco_results) > 0 + + if iou_type == "segm": + coco_results = copy.deepcopy(coco_results) + # When evaluating mask AP, if the results contain bbox, cocoapi will + # use the box area as the area of the instance, instead of the mask area. + # This leads to a different definition of small/medium/large. + # We remove the bbox field to let mask AP use mask area. + for c in coco_results: + c.pop("bbox", None) + + coco_dt = coco_gt.loadRes(coco_results) + coco_eval = (COCOeval_opt if use_fast_impl else COCOeval)(coco_gt, coco_dt, iou_type) + # For COCO, the default max_dets_per_image is [1, 10, 100]. + if max_dets_per_image is None: + max_dets_per_image = [1, 10, 100] # Default from COCOEval + else: + assert ( + len(max_dets_per_image) >= 3 + ), "COCOeval requires maxDets (and max_dets_per_image) to have length at least 3" + # In the case that user supplies a custom input for max_dets_per_image, + # apply COCOevalMaxDets to evaluate AP with the custom input. + if max_dets_per_image[2] != 100: + coco_eval = COCOevalMaxDets(coco_gt, coco_dt, iou_type) + if iou_type != "keypoints": + coco_eval.params.maxDets = max_dets_per_image + + if img_ids is not None: + coco_eval.params.imgIds = img_ids + + if iou_type == "keypoints": + # Use the COCO default keypoint OKS sigmas unless overrides are specified + if kpt_oks_sigmas: + assert hasattr(coco_eval.params, "kpt_oks_sigmas"), "pycocotools is too old!" + coco_eval.params.kpt_oks_sigmas = np.array(kpt_oks_sigmas) + # COCOAPI requires every detection and every gt to have keypoints, so + # we just take the first entry from both + num_keypoints_dt = len(coco_results[0]["keypoints"]) // 3 + num_keypoints_gt = len(next(iter(coco_gt.anns.values()))["keypoints"]) // 3 + num_keypoints_oks = len(coco_eval.params.kpt_oks_sigmas) + assert num_keypoints_oks == num_keypoints_dt == num_keypoints_gt, ( + f"[COCOEvaluator] Prediction contain {num_keypoints_dt} keypoints. " + f"Ground truth contains {num_keypoints_gt} keypoints. " + f"The length of cfg.TEST.KEYPOINT_OKS_SIGMAS is {num_keypoints_oks}. " + "They have to agree with each other. For meaning of OKS, please refer to " + "http://cocodataset.org/#keypoints-eval." + ) + + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() + + return coco_eval + + +class COCOevalMaxDets(COCOeval): + """ + Modified version of COCOeval for evaluating AP with a custom + maxDets (by default for COCO, maxDets is 100) + """ + + def summarize(self): + """ + Compute and display summary metrics for evaluation results given + a custom value for max_dets_per_image + """ + + def _summarize(ap=1, iouThr=None, areaRng="all", maxDets=100): + p = self.params + iStr = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}" + titleStr = "Average Precision" if ap == 1 else "Average Recall" + typeStr = "(AP)" if ap == 1 else "(AR)" + iouStr = ( + "{:0.2f}:{:0.2f}".format(p.iouThrs[0], p.iouThrs[-1]) + if iouThr is None + else "{:0.2f}".format(iouThr) + ) + + aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng] + mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] + if ap == 1: + # dimension of precision: [TxRxKxAxM] + s = self.eval["precision"] + # IoU + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:, :, :, aind, mind] + else: + # dimension of recall: [TxKxAxM] + s = self.eval["recall"] + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:, :, aind, mind] + if len(s[s > -1]) == 0: + mean_s = -1 + else: + mean_s = np.mean(s[s > -1]) + print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s)) + return mean_s + + def _summarizeDets(): + stats = np.zeros((12,)) + # Evaluate AP using the custom limit on maximum detections per image + stats[0] = _summarize(1, maxDets=self.params.maxDets[2]) + stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2]) + stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2]) + stats[3] = _summarize(1, areaRng="small", maxDets=self.params.maxDets[2]) + stats[4] = _summarize(1, areaRng="medium", maxDets=self.params.maxDets[2]) + stats[5] = _summarize(1, areaRng="large", maxDets=self.params.maxDets[2]) + stats[6] = _summarize(0, maxDets=self.params.maxDets[0]) + stats[7] = _summarize(0, maxDets=self.params.maxDets[1]) + stats[8] = _summarize(0, maxDets=self.params.maxDets[2]) + stats[9] = _summarize(0, areaRng="small", maxDets=self.params.maxDets[2]) + stats[10] = _summarize(0, areaRng="medium", maxDets=self.params.maxDets[2]) + stats[11] = _summarize(0, areaRng="large", maxDets=self.params.maxDets[2]) + return stats + + def _summarizeKps(): + stats = np.zeros((10,)) + stats[0] = _summarize(1, maxDets=20) + stats[1] = _summarize(1, maxDets=20, iouThr=0.5) + stats[2] = _summarize(1, maxDets=20, iouThr=0.75) + stats[3] = _summarize(1, maxDets=20, areaRng="medium") + stats[4] = _summarize(1, maxDets=20, areaRng="large") + stats[5] = _summarize(0, maxDets=20) + stats[6] = _summarize(0, maxDets=20, iouThr=0.5) + stats[7] = _summarize(0, maxDets=20, iouThr=0.75) + stats[8] = _summarize(0, maxDets=20, areaRng="medium") + stats[9] = _summarize(0, maxDets=20, areaRng="large") + return stats + + if not self.eval: + raise Exception("Please run accumulate() first") + iouType = self.params.iouType + if iouType == "segm" or iouType == "bbox": + summarize = _summarizeDets + elif iouType == "keypoints": + summarize = _summarizeKps + self.stats = summarize() + + def __str__(self): + self.summarize() diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/evaluator.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/evaluator.py new file mode 100644 index 0000000000000000000000000000000000000000..baf996002b2fddc8c1952408d450b5bf69394f0a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/evaluator.py @@ -0,0 +1,224 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import datetime +import logging +import time +from collections import OrderedDict, abc +from contextlib import ExitStack, contextmanager +from typing import List, Union +import torch +from torch import nn + +from detectron2.utils.comm import get_world_size, is_main_process +from detectron2.utils.logger import log_every_n_seconds + + +class DatasetEvaluator: + """ + Base class for a dataset evaluator. + + The function :func:`inference_on_dataset` runs the model over + all samples in the dataset, and have a DatasetEvaluator to process the inputs/outputs. + + This class will accumulate information of the inputs/outputs (by :meth:`process`), + and produce evaluation results in the end (by :meth:`evaluate`). + """ + + def reset(self): + """ + Preparation for a new round of evaluation. + Should be called before starting a round of evaluation. + """ + pass + + def process(self, inputs, outputs): + """ + Process the pair of inputs and outputs. + If they contain batches, the pairs can be consumed one-by-one using `zip`: + + .. code-block:: python + + for input_, output in zip(inputs, outputs): + # do evaluation on single input/output pair + ... + + Args: + inputs (list): the inputs that's used to call the model. + outputs (list): the return value of `model(inputs)` + """ + pass + + def evaluate(self): + """ + Evaluate/summarize the performance, after processing all input/output pairs. + + Returns: + dict: + A new evaluator class can return a dict of arbitrary format + as long as the user can process the results. + In our train_net.py, we expect the following format: + + * key: the name of the task (e.g., bbox) + * value: a dict of {metric name: score}, e.g.: {"AP50": 80} + """ + pass + + +class DatasetEvaluators(DatasetEvaluator): + """ + Wrapper class to combine multiple :class:`DatasetEvaluator` instances. + + This class dispatches every evaluation call to + all of its :class:`DatasetEvaluator`. + """ + + def __init__(self, evaluators): + """ + Args: + evaluators (list): the evaluators to combine. + """ + super().__init__() + self._evaluators = evaluators + + def reset(self): + for evaluator in self._evaluators: + evaluator.reset() + + def process(self, inputs, outputs): + for evaluator in self._evaluators: + evaluator.process(inputs, outputs) + + def evaluate(self): + results = OrderedDict() + for evaluator in self._evaluators: + result = evaluator.evaluate() + if is_main_process() and result is not None: + for k, v in result.items(): + assert ( + k not in results + ), "Different evaluators produce results with the same key {}".format(k) + results[k] = v + return results + + +def inference_on_dataset( + model, data_loader, evaluator: Union[DatasetEvaluator, List[DatasetEvaluator], None] +): + """ + Run model on the data_loader and evaluate the metrics with evaluator. + Also benchmark the inference speed of `model.__call__` accurately. + The model will be used in eval mode. + + Args: + model (callable): a callable which takes an object from + `data_loader` and returns some outputs. + + If it's an nn.Module, it will be temporarily set to `eval` mode. + If you wish to evaluate a model in `training` mode instead, you can + wrap the given model and override its behavior of `.eval()` and `.train()`. + data_loader: an iterable object with a length. + The elements it generates will be the inputs to the model. + evaluator: the evaluator(s) to run. Use `None` if you only want to benchmark, + but don't want to do any evaluation. + + Returns: + The return value of `evaluator.evaluate()` + """ + num_devices = get_world_size() + logger = logging.getLogger(__name__) + logger.info("Start inference on {} batches".format(len(data_loader))) + + total = len(data_loader) # inference data loader must have a fixed length + if evaluator is None: + # create a no-op evaluator + evaluator = DatasetEvaluators([]) + if isinstance(evaluator, abc.MutableSequence): + evaluator = DatasetEvaluators(evaluator) + evaluator.reset() + + num_warmup = min(5, total - 1) + start_time = time.perf_counter() + total_data_time = 0 + total_compute_time = 0 + total_eval_time = 0 + with ExitStack() as stack: + if isinstance(model, nn.Module): + stack.enter_context(inference_context(model)) + stack.enter_context(torch.no_grad()) + + start_data_time = time.perf_counter() + for idx, inputs in enumerate(data_loader): + total_data_time += time.perf_counter() - start_data_time + if idx == num_warmup: + start_time = time.perf_counter() + total_data_time = 0 + total_compute_time = 0 + total_eval_time = 0 + + start_compute_time = time.perf_counter() + outputs = model(inputs) + if torch.cuda.is_available(): + torch.cuda.synchronize() + total_compute_time += time.perf_counter() - start_compute_time + + start_eval_time = time.perf_counter() + evaluator.process(inputs, outputs) + total_eval_time += time.perf_counter() - start_eval_time + + iters_after_start = idx + 1 - num_warmup * int(idx >= num_warmup) + data_seconds_per_iter = total_data_time / iters_after_start + compute_seconds_per_iter = total_compute_time / iters_after_start + eval_seconds_per_iter = total_eval_time / iters_after_start + total_seconds_per_iter = (time.perf_counter() - start_time) / iters_after_start + if idx >= num_warmup * 2 or compute_seconds_per_iter > 5: + eta = datetime.timedelta(seconds=int(total_seconds_per_iter * (total - idx - 1))) + log_every_n_seconds( + logging.INFO, + ( + f"Inference done {idx + 1}/{total}. " + f"Dataloading: {data_seconds_per_iter:.4f} s/iter. " + f"Inference: {compute_seconds_per_iter:.4f} s/iter. " + f"Eval: {eval_seconds_per_iter:.4f} s/iter. " + f"Total: {total_seconds_per_iter:.4f} s/iter. " + f"ETA={eta}" + ), + n=5, + ) + start_data_time = time.perf_counter() + + # Measure the time only for this worker (before the synchronization barrier) + total_time = time.perf_counter() - start_time + total_time_str = str(datetime.timedelta(seconds=total_time)) + # NOTE this format is parsed by grep + logger.info( + "Total inference time: {} ({:.6f} s / iter per device, on {} devices)".format( + total_time_str, total_time / (total - num_warmup), num_devices + ) + ) + total_compute_time_str = str(datetime.timedelta(seconds=int(total_compute_time))) + logger.info( + "Total inference pure compute time: {} ({:.6f} s / iter per device, on {} devices)".format( + total_compute_time_str, total_compute_time / (total - num_warmup), num_devices + ) + ) + + results = evaluator.evaluate() + # An evaluator may return None when not in main process. + # Replace it by an empty dict instead to make it easier for downstream code to handle + if results is None: + results = {} + return results + + +@contextmanager +def inference_context(model): + """ + A context where the model is temporarily changed to eval mode, + and restored to previous mode afterwards. + + Args: + model: a torch Module + """ + training_mode = model.training + model.eval() + yield + model.train(training_mode) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/fast_eval_api.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/fast_eval_api.py new file mode 100644 index 0000000000000000000000000000000000000000..2eb202bd5efa3ec3d366027b1debffc269ae8b17 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/fast_eval_api.py @@ -0,0 +1,121 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import logging +import numpy as np +import time +from pycocotools.cocoeval import COCOeval + +from detectron2 import _C + +logger = logging.getLogger(__name__) + + +class COCOeval_opt(COCOeval): + """ + This is a slightly modified version of the original COCO API, where the functions evaluateImg() + and accumulate() are implemented in C++ to speedup evaluation + """ + + def evaluate(self): + """ + Run per image evaluation on given images and store results in self.evalImgs_cpp, a + datastructure that isn't readable from Python but is used by a c++ implementation of + accumulate(). Unlike the original COCO PythonAPI, we don't populate the datastructure + self.evalImgs because this datastructure is a computational bottleneck. + :return: None + """ + tic = time.time() + + p = self.params + # add backward compatibility if useSegm is specified in params + if p.useSegm is not None: + p.iouType = "segm" if p.useSegm == 1 else "bbox" + logger.info("Evaluate annotation type *{}*".format(p.iouType)) + p.imgIds = list(np.unique(p.imgIds)) + if p.useCats: + p.catIds = list(np.unique(p.catIds)) + p.maxDets = sorted(p.maxDets) + self.params = p + + self._prepare() # bottleneck + + # loop through images, area range, max detection number + catIds = p.catIds if p.useCats else [-1] + + if p.iouType == "segm" or p.iouType == "bbox": + computeIoU = self.computeIoU + elif p.iouType == "keypoints": + computeIoU = self.computeOks + self.ious = { + (imgId, catId): computeIoU(imgId, catId) for imgId in p.imgIds for catId in catIds + } # bottleneck + + maxDet = p.maxDets[-1] + + # <<<< Beginning of code differences with original COCO API + def convert_instances_to_cpp(instances, is_det=False): + # Convert annotations for a list of instances in an image to a format that's fast + # to access in C++ + instances_cpp = [] + for instance in instances: + instance_cpp = _C.InstanceAnnotation( + int(instance["id"]), + instance["score"] if is_det else instance.get("score", 0.0), + instance["area"], + bool(instance.get("iscrowd", 0)), + bool(instance.get("ignore", 0)), + ) + instances_cpp.append(instance_cpp) + return instances_cpp + + # Convert GT annotations, detections, and IOUs to a format that's fast to access in C++ + ground_truth_instances = [ + [convert_instances_to_cpp(self._gts[imgId, catId]) for catId in p.catIds] + for imgId in p.imgIds + ] + detected_instances = [ + [convert_instances_to_cpp(self._dts[imgId, catId], is_det=True) for catId in p.catIds] + for imgId in p.imgIds + ] + ious = [[self.ious[imgId, catId] for catId in catIds] for imgId in p.imgIds] + + if not p.useCats: + # For each image, flatten per-category lists into a single list + ground_truth_instances = [[[o for c in i for o in c]] for i in ground_truth_instances] + detected_instances = [[[o for c in i for o in c]] for i in detected_instances] + + # Call C++ implementation of self.evaluateImgs() + self._evalImgs_cpp = _C.COCOevalEvaluateImages( + p.areaRng, maxDet, p.iouThrs, ious, ground_truth_instances, detected_instances + ) + self._evalImgs = None + + self._paramsEval = copy.deepcopy(self.params) + toc = time.time() + logger.info("COCOeval_opt.evaluate() finished in {:0.2f} seconds.".format(toc - tic)) + # >>>> End of code differences with original COCO API + + def accumulate(self): + """ + Accumulate per image evaluation results and store the result in self.eval. Does not + support changing parameter settings from those used by self.evaluate() + """ + logger.info("Accumulating evaluation results...") + tic = time.time() + assert hasattr( + self, "_evalImgs_cpp" + ), "evaluate() must be called before accmulate() is called." + + self.eval = _C.COCOevalAccumulate(self._paramsEval, self._evalImgs_cpp) + + # recall is num_iou_thresholds X num_categories X num_area_ranges X num_max_detections + self.eval["recall"] = np.array(self.eval["recall"]).reshape( + self.eval["counts"][:1] + self.eval["counts"][2:] + ) + + # precision and scores are num_iou_thresholds X num_recall_thresholds X num_categories X + # num_area_ranges X num_max_detections + self.eval["precision"] = np.array(self.eval["precision"]).reshape(self.eval["counts"]) + self.eval["scores"] = np.array(self.eval["scores"]).reshape(self.eval["counts"]) + toc = time.time() + logger.info("COCOeval_opt.accumulate() finished in {:0.2f} seconds.".format(toc - tic)) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/lvis_evaluation.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/lvis_evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..0604feaaf42ffd072e3cb91f395204f818fa709a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/lvis_evaluation.py @@ -0,0 +1,380 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import itertools +import json +import logging +import os +import pickle +from collections import OrderedDict +import torch + +import detectron2.utils.comm as comm +from detectron2.config import CfgNode +from detectron2.data import MetadataCatalog +from detectron2.structures import Boxes, BoxMode, pairwise_iou +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import create_small_table + +from .coco_evaluation import instances_to_coco_json +from .evaluator import DatasetEvaluator + + +class LVISEvaluator(DatasetEvaluator): + """ + Evaluate object proposal and instance detection/segmentation outputs using + LVIS's metrics and evaluation API. + """ + + def __init__( + self, + dataset_name, + tasks=None, + distributed=True, + output_dir=None, + *, + max_dets_per_image=None, + ): + """ + Args: + dataset_name (str): name of the dataset to be evaluated. + It must have the following corresponding metadata: + "json_file": the path to the LVIS format annotation + tasks (tuple[str]): tasks that can be evaluated under the given + configuration. A task is one of "bbox", "segm". + By default, will infer this automatically from predictions. + distributed (True): if True, will collect results from all ranks for evaluation. + Otherwise, will evaluate the results in the current process. + output_dir (str): optional, an output directory to dump results. + max_dets_per_image (None or int): limit on maximum detections per image in evaluating AP + This limit, by default of the LVIS dataset, is 300. + """ + from lvis import LVIS + + self._logger = logging.getLogger(__name__) + + if tasks is not None and isinstance(tasks, CfgNode): + self._logger.warn( + "COCO Evaluator instantiated using config, this is deprecated behavior." + " Please pass in explicit arguments instead." + ) + self._tasks = None # Infering it from predictions should be better + else: + self._tasks = tasks + + self._distributed = distributed + self._output_dir = output_dir + self._max_dets_per_image = max_dets_per_image + + self._cpu_device = torch.device("cpu") + + self._metadata = MetadataCatalog.get(dataset_name) + json_file = PathManager.get_local_path(self._metadata.json_file) + self._lvis_api = LVIS(json_file) + # Test set json files do not contain annotations (evaluation must be + # performed using the LVIS evaluation server). + self._do_evaluation = len(self._lvis_api.get_ann_ids()) > 0 + + def reset(self): + self._predictions = [] + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a LVIS model (e.g., GeneralizedRCNN). + It is a list of dict. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a LVIS model. It is a list of dicts with key + "instances" that contains :class:`Instances`. + """ + for input, output in zip(inputs, outputs): + prediction = {"image_id": input["image_id"]} + + if "instances" in output: + instances = output["instances"].to(self._cpu_device) + prediction["instances"] = instances_to_coco_json(instances, input["image_id"]) + if "proposals" in output: + prediction["proposals"] = output["proposals"].to(self._cpu_device) + self._predictions.append(prediction) + + def evaluate(self): + if self._distributed: + comm.synchronize() + predictions = comm.gather(self._predictions, dst=0) + predictions = list(itertools.chain(*predictions)) + + if not comm.is_main_process(): + return + else: + predictions = self._predictions + + if len(predictions) == 0: + self._logger.warning("[LVISEvaluator] Did not receive valid predictions.") + return {} + + if self._output_dir: + PathManager.mkdirs(self._output_dir) + file_path = os.path.join(self._output_dir, "instances_predictions.pth") + with PathManager.open(file_path, "wb") as f: + torch.save(predictions, f) + + self._results = OrderedDict() + if "proposals" in predictions[0]: + self._eval_box_proposals(predictions) + if "instances" in predictions[0]: + self._eval_predictions(predictions) + # Copy so the caller can do whatever with results + return copy.deepcopy(self._results) + + def _tasks_from_predictions(self, predictions): + for pred in predictions: + if "segmentation" in pred: + return ("bbox", "segm") + return ("bbox",) + + def _eval_predictions(self, predictions): + """ + Evaluate predictions. Fill self._results with the metrics of the tasks. + + Args: + predictions (list[dict]): list of outputs from the model + """ + self._logger.info("Preparing results in the LVIS format ...") + lvis_results = list(itertools.chain(*[x["instances"] for x in predictions])) + tasks = self._tasks or self._tasks_from_predictions(lvis_results) + + # LVIS evaluator can be used to evaluate results for COCO dataset categories. + # In this case `_metadata` variable will have a field with COCO-specific category mapping. + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + reverse_id_mapping = { + v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items() + } + for result in lvis_results: + result["category_id"] = reverse_id_mapping[result["category_id"]] + else: + # unmap the category ids for LVIS (from 0-indexed to 1-indexed) + for result in lvis_results: + result["category_id"] += 1 + + if self._output_dir: + file_path = os.path.join(self._output_dir, "lvis_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(lvis_results)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info("Evaluating predictions ...") + for task in sorted(tasks): + res = _evaluate_predictions_on_lvis( + self._lvis_api, + lvis_results, + task, + max_dets_per_image=self._max_dets_per_image, + class_names=self._metadata.get("thing_classes"), + ) + self._results[task] = res + + def _eval_box_proposals(self, predictions): + """ + Evaluate the box proposals in predictions. + Fill self._results with the metrics for "box_proposals" task. + """ + if self._output_dir: + # Saving generated box proposals to file. + # Predicted box_proposals are in XYXY_ABS mode. + bbox_mode = BoxMode.XYXY_ABS.value + ids, boxes, objectness_logits = [], [], [] + for prediction in predictions: + ids.append(prediction["image_id"]) + boxes.append(prediction["proposals"].proposal_boxes.tensor.numpy()) + objectness_logits.append(prediction["proposals"].objectness_logits.numpy()) + + proposal_data = { + "boxes": boxes, + "objectness_logits": objectness_logits, + "ids": ids, + "bbox_mode": bbox_mode, + } + with PathManager.open(os.path.join(self._output_dir, "box_proposals.pkl"), "wb") as f: + pickle.dump(proposal_data, f) + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info("Evaluating bbox proposals ...") + res = {} + areas = {"all": "", "small": "s", "medium": "m", "large": "l"} + for limit in [100, 1000]: + for area, suffix in areas.items(): + stats = _evaluate_box_proposals(predictions, self._lvis_api, area=area, limit=limit) + key = "AR{}@{:d}".format(suffix, limit) + res[key] = float(stats["ar"].item() * 100) + self._logger.info("Proposal metrics: \n" + create_small_table(res)) + self._results["box_proposals"] = res + + +# inspired from Detectron: +# https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L255 # noqa +def _evaluate_box_proposals(dataset_predictions, lvis_api, thresholds=None, area="all", limit=None): + """ + Evaluate detection proposal recall metrics. This function is a much + faster alternative to the official LVIS API recall evaluation code. However, + it produces slightly different results. + """ + # Record max overlap value for each gt box + # Return vector of overlap values + areas = { + "all": 0, + "small": 1, + "medium": 2, + "large": 3, + "96-128": 4, + "128-256": 5, + "256-512": 6, + "512-inf": 7, + } + area_ranges = [ + [0 ** 2, 1e5 ** 2], # all + [0 ** 2, 32 ** 2], # small + [32 ** 2, 96 ** 2], # medium + [96 ** 2, 1e5 ** 2], # large + [96 ** 2, 128 ** 2], # 96-128 + [128 ** 2, 256 ** 2], # 128-256 + [256 ** 2, 512 ** 2], # 256-512 + [512 ** 2, 1e5 ** 2], + ] # 512-inf + assert area in areas, "Unknown area range: {}".format(area) + area_range = area_ranges[areas[area]] + gt_overlaps = [] + num_pos = 0 + + for prediction_dict in dataset_predictions: + predictions = prediction_dict["proposals"] + + # sort predictions in descending order + # TODO maybe remove this and make it explicit in the documentation + inds = predictions.objectness_logits.sort(descending=True)[1] + predictions = predictions[inds] + + ann_ids = lvis_api.get_ann_ids(img_ids=[prediction_dict["image_id"]]) + anno = lvis_api.load_anns(ann_ids) + gt_boxes = [ + BoxMode.convert(obj["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) for obj in anno + ] + gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4) # guard against no boxes + gt_boxes = Boxes(gt_boxes) + gt_areas = torch.as_tensor([obj["area"] for obj in anno]) + + if len(gt_boxes) == 0 or len(predictions) == 0: + continue + + valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1]) + gt_boxes = gt_boxes[valid_gt_inds] + + num_pos += len(gt_boxes) + + if len(gt_boxes) == 0: + continue + + if limit is not None and len(predictions) > limit: + predictions = predictions[:limit] + + overlaps = pairwise_iou(predictions.proposal_boxes, gt_boxes) + + _gt_overlaps = torch.zeros(len(gt_boxes)) + for j in range(min(len(predictions), len(gt_boxes))): + # find which proposal box maximally covers each gt box + # and get the iou amount of coverage for each gt box + max_overlaps, argmax_overlaps = overlaps.max(dim=0) + + # find which gt box is 'best' covered (i.e. 'best' = most iou) + gt_ovr, gt_ind = max_overlaps.max(dim=0) + assert gt_ovr >= 0 + # find the proposal box that covers the best covered gt box + box_ind = argmax_overlaps[gt_ind] + # record the iou coverage of this gt box + _gt_overlaps[j] = overlaps[box_ind, gt_ind] + assert _gt_overlaps[j] == gt_ovr + # mark the proposal box and the gt box as used + overlaps[box_ind, :] = -1 + overlaps[:, gt_ind] = -1 + + # append recorded iou coverage level + gt_overlaps.append(_gt_overlaps) + gt_overlaps = ( + torch.cat(gt_overlaps, dim=0) if len(gt_overlaps) else torch.zeros(0, dtype=torch.float32) + ) + gt_overlaps, _ = torch.sort(gt_overlaps) + + if thresholds is None: + step = 0.05 + thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32) + recalls = torch.zeros_like(thresholds) + # compute recall for each iou threshold + for i, t in enumerate(thresholds): + recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos) + # ar = 2 * np.trapz(recalls, thresholds) + ar = recalls.mean() + return { + "ar": ar, + "recalls": recalls, + "thresholds": thresholds, + "gt_overlaps": gt_overlaps, + "num_pos": num_pos, + } + + +def _evaluate_predictions_on_lvis( + lvis_gt, lvis_results, iou_type, max_dets_per_image=None, class_names=None +): + """ + Args: + iou_type (str): + max_dets_per_image (None or int): limit on maximum detections per image in evaluating AP + This limit, by default of the LVIS dataset, is 300. + class_names (None or list[str]): if provided, will use it to predict + per-category AP. + + Returns: + a dict of {metric name: score} + """ + metrics = { + "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"], + "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"], + }[iou_type] + + logger = logging.getLogger(__name__) + + if len(lvis_results) == 0: # TODO: check if needed + logger.warn("No predictions from the model!") + return {metric: float("nan") for metric in metrics} + + if iou_type == "segm": + lvis_results = copy.deepcopy(lvis_results) + # When evaluating mask AP, if the results contain bbox, LVIS API will + # use the box area as the area of the instance, instead of the mask area. + # This leads to a different definition of small/medium/large. + # We remove the bbox field to let mask AP use mask area. + for c in lvis_results: + c.pop("bbox", None) + + if max_dets_per_image is None: + max_dets_per_image = 300 # Default for LVIS dataset + + from lvis import LVISEval, LVISResults + + logger.info(f"Evaluating with max detections per image = {max_dets_per_image}") + lvis_results = LVISResults(lvis_gt, lvis_results, max_dets=max_dets_per_image) + lvis_eval = LVISEval(lvis_gt, lvis_results, iou_type) + lvis_eval.run() + lvis_eval.print_results() + + # Pull the standard metrics from the LVIS results + results = lvis_eval.get_results() + results = {metric: float(results[metric] * 100) for metric in metrics} + logger.info("Evaluation results for {}: \n".format(iou_type) + create_small_table(results)) + return results diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/panoptic_evaluation.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/panoptic_evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..9fb3462b7f9abf6feaa499976bfed526ebd17e31 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/panoptic_evaluation.py @@ -0,0 +1,199 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import contextlib +import io +import itertools +import json +import logging +import numpy as np +import os +import tempfile +from collections import OrderedDict +from typing import Optional +from PIL import Image +from tabulate import tabulate + +from detectron2.data import MetadataCatalog +from detectron2.utils import comm +from detectron2.utils.file_io import PathManager + +from .evaluator import DatasetEvaluator + +logger = logging.getLogger(__name__) + + +class COCOPanopticEvaluator(DatasetEvaluator): + """ + Evaluate Panoptic Quality metrics on COCO using PanopticAPI. + It saves panoptic segmentation prediction in `output_dir` + + It contains a synchronize call and has to be called from all workers. + """ + + def __init__(self, dataset_name: str, output_dir: Optional[str] = None): + """ + Args: + dataset_name: name of the dataset + output_dir: output directory to save results for evaluation. + """ + self._metadata = MetadataCatalog.get(dataset_name) + self._thing_contiguous_id_to_dataset_id = { + v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items() + } + self._stuff_contiguous_id_to_dataset_id = { + v: k for k, v in self._metadata.stuff_dataset_id_to_contiguous_id.items() + } + + self._output_dir = output_dir + if self._output_dir is not None: + PathManager.mkdirs(self._output_dir) + + def reset(self): + self._predictions = [] + + def _convert_category_id(self, segment_info): + isthing = segment_info.pop("isthing", None) + if isthing is None: + # the model produces panoptic category id directly. No more conversion needed + return segment_info + if isthing is True: + segment_info["category_id"] = self._thing_contiguous_id_to_dataset_id[ + segment_info["category_id"] + ] + else: + segment_info["category_id"] = self._stuff_contiguous_id_to_dataset_id[ + segment_info["category_id"] + ] + return segment_info + + def process(self, inputs, outputs): + from panopticapi.utils import id2rgb + + for input, output in zip(inputs, outputs): + panoptic_img, segments_info = output["panoptic_seg"] + panoptic_img = panoptic_img.cpu().numpy() + if segments_info is None: + # If "segments_info" is None, we assume "panoptic_img" is a + # H*W int32 image storing the panoptic_id in the format of + # category_id * label_divisor + instance_id. We reserve -1 for + # VOID label, and add 1 to panoptic_img since the official + # evaluation script uses 0 for VOID label. + label_divisor = self._metadata.label_divisor + segments_info = [] + for panoptic_label in np.unique(panoptic_img): + if panoptic_label == -1: + # VOID region. + continue + pred_class = panoptic_label // label_divisor + isthing = ( + pred_class in self._metadata.thing_dataset_id_to_contiguous_id.values() + ) + segments_info.append( + { + "id": int(panoptic_label) + 1, + "category_id": int(pred_class), + "isthing": bool(isthing), + } + ) + # Official evaluation script uses 0 for VOID label. + panoptic_img += 1 + + file_name = os.path.basename(input["file_name"]) + file_name_png = os.path.splitext(file_name)[0] + ".png" + with io.BytesIO() as out: + Image.fromarray(id2rgb(panoptic_img)).save(out, format="PNG") + segments_info = [self._convert_category_id(x) for x in segments_info] + self._predictions.append( + { + "image_id": input["image_id"], + "file_name": file_name_png, + "png_string": out.getvalue(), + "segments_info": segments_info, + } + ) + + def evaluate(self): + comm.synchronize() + + self._predictions = comm.gather(self._predictions) + self._predictions = list(itertools.chain(*self._predictions)) + if not comm.is_main_process(): + return + + # PanopticApi requires local files + gt_json = PathManager.get_local_path(self._metadata.panoptic_json) + gt_folder = PathManager.get_local_path(self._metadata.panoptic_root) + + with tempfile.TemporaryDirectory(prefix="panoptic_eval") as pred_dir: + logger.info("Writing all panoptic predictions to {} ...".format(pred_dir)) + for p in self._predictions: + with open(os.path.join(pred_dir, p["file_name"]), "wb") as f: + f.write(p.pop("png_string")) + + with open(gt_json, "r") as f: + json_data = json.load(f) + json_data["annotations"] = self._predictions + + output_dir = self._output_dir or pred_dir + predictions_json = os.path.join(output_dir, "predictions.json") + with PathManager.open(predictions_json, "w") as f: + f.write(json.dumps(json_data)) + + from panopticapi.evaluation import pq_compute + + with contextlib.redirect_stdout(io.StringIO()): + pq_res = pq_compute( + gt_json, + PathManager.get_local_path(predictions_json), + gt_folder=gt_folder, + pred_folder=pred_dir, + ) + + res = {} + res["PQ"] = 100 * pq_res["All"]["pq"] + res["SQ"] = 100 * pq_res["All"]["sq"] + res["RQ"] = 100 * pq_res["All"]["rq"] + res["PQ_th"] = 100 * pq_res["Things"]["pq"] + res["SQ_th"] = 100 * pq_res["Things"]["sq"] + res["RQ_th"] = 100 * pq_res["Things"]["rq"] + res["PQ_st"] = 100 * pq_res["Stuff"]["pq"] + res["SQ_st"] = 100 * pq_res["Stuff"]["sq"] + res["RQ_st"] = 100 * pq_res["Stuff"]["rq"] + + results = OrderedDict({"panoptic_seg": res}) + _print_panoptic_results(pq_res) + + return results + + +def _print_panoptic_results(pq_res): + headers = ["", "PQ", "SQ", "RQ", "#categories"] + data = [] + for name in ["All", "Things", "Stuff"]: + row = [name] + [pq_res[name][k] * 100 for k in ["pq", "sq", "rq"]] + [pq_res[name]["n"]] + data.append(row) + table = tabulate( + data, headers=headers, tablefmt="pipe", floatfmt=".3f", stralign="center", numalign="center" + ) + logger.info("Panoptic Evaluation Results:\n" + table) + + +if __name__ == "__main__": + from detectron2.utils.logger import setup_logger + + logger = setup_logger() + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--gt-json") + parser.add_argument("--gt-dir") + parser.add_argument("--pred-json") + parser.add_argument("--pred-dir") + args = parser.parse_args() + + from panopticapi.evaluation import pq_compute + + with contextlib.redirect_stdout(io.StringIO()): + pq_res = pq_compute( + args.gt_json, args.pred_json, gt_folder=args.gt_dir, pred_folder=args.pred_dir + ) + _print_panoptic_results(pq_res) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/pascal_voc_evaluation.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/pascal_voc_evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..1d1abcde2f87bb5f103e73cb364aaabbecb6e619 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/pascal_voc_evaluation.py @@ -0,0 +1,300 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import logging +import numpy as np +import os +import tempfile +import xml.etree.ElementTree as ET +from collections import OrderedDict, defaultdict +from functools import lru_cache +import torch + +from detectron2.data import MetadataCatalog +from detectron2.utils import comm +from detectron2.utils.file_io import PathManager + +from .evaluator import DatasetEvaluator + + +class PascalVOCDetectionEvaluator(DatasetEvaluator): + """ + Evaluate Pascal VOC style AP for Pascal VOC dataset. + It contains a synchronization, therefore has to be called from all ranks. + + Note that the concept of AP can be implemented in different ways and may not + produce identical results. This class mimics the implementation of the official + Pascal VOC Matlab API, and should produce similar but not identical results to the + official API. + """ + + def __init__(self, dataset_name): + """ + Args: + dataset_name (str): name of the dataset, e.g., "voc_2007_test" + """ + self._dataset_name = dataset_name + meta = MetadataCatalog.get(dataset_name) + + # Too many tiny files, download all to local for speed. + annotation_dir_local = PathManager.get_local_path( + os.path.join(meta.dirname, "Annotations/") + ) + self._anno_file_template = os.path.join(annotation_dir_local, "{}.xml") + self._image_set_path = os.path.join(meta.dirname, "ImageSets", "Main", meta.split + ".txt") + self._class_names = meta.thing_classes + assert meta.year in [2007, 2012], meta.year + self._is_2007 = meta.year == 2007 + self._cpu_device = torch.device("cpu") + self._logger = logging.getLogger(__name__) + + def reset(self): + self._predictions = defaultdict(list) # class name -> list of prediction strings + + def process(self, inputs, outputs): + for input, output in zip(inputs, outputs): + image_id = input["image_id"] + instances = output["instances"].to(self._cpu_device) + boxes = instances.pred_boxes.tensor.numpy() + scores = instances.scores.tolist() + classes = instances.pred_classes.tolist() + for box, score, cls in zip(boxes, scores, classes): + xmin, ymin, xmax, ymax = box + # The inverse of data loading logic in `datasets/pascal_voc.py` + xmin += 1 + ymin += 1 + self._predictions[cls].append( + f"{image_id} {score:.3f} {xmin:.1f} {ymin:.1f} {xmax:.1f} {ymax:.1f}" + ) + + def evaluate(self): + """ + Returns: + dict: has a key "segm", whose value is a dict of "AP", "AP50", and "AP75". + """ + all_predictions = comm.gather(self._predictions, dst=0) + if not comm.is_main_process(): + return + predictions = defaultdict(list) + for predictions_per_rank in all_predictions: + for clsid, lines in predictions_per_rank.items(): + predictions[clsid].extend(lines) + del all_predictions + + self._logger.info( + "Evaluating {} using {} metric. " + "Note that results do not use the official Matlab API.".format( + self._dataset_name, 2007 if self._is_2007 else 2012 + ) + ) + + with tempfile.TemporaryDirectory(prefix="pascal_voc_eval_") as dirname: + res_file_template = os.path.join(dirname, "{}.txt") + + aps = defaultdict(list) # iou -> ap per class + for cls_id, cls_name in enumerate(self._class_names): + lines = predictions.get(cls_id, [""]) + + with open(res_file_template.format(cls_name), "w") as f: + f.write("\n".join(lines)) + + for thresh in range(50, 100, 5): + rec, prec, ap = voc_eval( + res_file_template, + self._anno_file_template, + self._image_set_path, + cls_name, + ovthresh=thresh / 100.0, + use_07_metric=self._is_2007, + ) + aps[thresh].append(ap * 100) + + ret = OrderedDict() + mAP = {iou: np.mean(x) for iou, x in aps.items()} + ret["bbox"] = {"AP": np.mean(list(mAP.values())), "AP50": mAP[50], "AP75": mAP[75]} + return ret + + +############################################################################## +# +# Below code is modified from +# https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/voc_eval.py +# -------------------------------------------------------- +# Fast/er R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Bharath Hariharan +# -------------------------------------------------------- + +"""Python implementation of the PASCAL VOC devkit's AP evaluation code.""" + + +@lru_cache(maxsize=None) +def parse_rec(filename): + """Parse a PASCAL VOC xml file.""" + with PathManager.open(filename) as f: + tree = ET.parse(f) + objects = [] + for obj in tree.findall("object"): + obj_struct = {} + obj_struct["name"] = obj.find("name").text + obj_struct["pose"] = obj.find("pose").text + obj_struct["truncated"] = int(obj.find("truncated").text) + obj_struct["difficult"] = int(obj.find("difficult").text) + bbox = obj.find("bndbox") + obj_struct["bbox"] = [ + int(bbox.find("xmin").text), + int(bbox.find("ymin").text), + int(bbox.find("xmax").text), + int(bbox.find("ymax").text), + ] + objects.append(obj_struct) + + return objects + + +def voc_ap(rec, prec, use_07_metric=False): + """Compute VOC AP given precision and recall. If use_07_metric is true, uses + the VOC 07 11-point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0.0 + for t in np.arange(0.0, 1.1, 0.1): + if np.sum(rec >= t) == 0: + p = 0 + else: + p = np.max(prec[rec >= t]) + ap = ap + p / 11.0 + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.0], rec, [1.0])) + mpre = np.concatenate(([0.0], prec, [0.0])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + + +def voc_eval(detpath, annopath, imagesetfile, classname, ovthresh=0.5, use_07_metric=False): + """rec, prec, ap = voc_eval(detpath, + annopath, + imagesetfile, + classname, + [ovthresh], + [use_07_metric]) + + Top level function that does the PASCAL VOC evaluation. + + detpath: Path to detections + detpath.format(classname) should produce the detection results file. + annopath: Path to annotations + annopath.format(imagename) should be the xml annotations file. + imagesetfile: Text file containing the list of images, one image per line. + classname: Category name (duh) + [ovthresh]: Overlap threshold (default = 0.5) + [use_07_metric]: Whether to use VOC07's 11 point AP computation + (default False) + """ + # assumes detections are in detpath.format(classname) + # assumes annotations are in annopath.format(imagename) + # assumes imagesetfile is a text file with each line an image name + + # first load gt + # read list of images + with PathManager.open(imagesetfile, "r") as f: + lines = f.readlines() + imagenames = [x.strip() for x in lines] + + # load annots + recs = {} + for imagename in imagenames: + recs[imagename] = parse_rec(annopath.format(imagename)) + + # extract gt objects for this class + class_recs = {} + npos = 0 + for imagename in imagenames: + R = [obj for obj in recs[imagename] if obj["name"] == classname] + bbox = np.array([x["bbox"] for x in R]) + difficult = np.array([x["difficult"] for x in R]).astype(np.bool) + # difficult = np.array([False for x in R]).astype(np.bool) # treat all "difficult" as GT + det = [False] * len(R) + npos = npos + sum(~difficult) + class_recs[imagename] = {"bbox": bbox, "difficult": difficult, "det": det} + + # read dets + detfile = detpath.format(classname) + with open(detfile, "r") as f: + lines = f.readlines() + + splitlines = [x.strip().split(" ") for x in lines] + image_ids = [x[0] for x in splitlines] + confidence = np.array([float(x[1]) for x in splitlines]) + BB = np.array([[float(z) for z in x[2:]] for x in splitlines]).reshape(-1, 4) + + # sort by confidence + sorted_ind = np.argsort(-confidence) + BB = BB[sorted_ind, :] + image_ids = [image_ids[x] for x in sorted_ind] + + # go down dets and mark TPs and FPs + nd = len(image_ids) + tp = np.zeros(nd) + fp = np.zeros(nd) + for d in range(nd): + R = class_recs[image_ids[d]] + bb = BB[d, :].astype(float) + ovmax = -np.inf + BBGT = R["bbox"].astype(float) + + if BBGT.size > 0: + # compute overlaps + # intersection + ixmin = np.maximum(BBGT[:, 0], bb[0]) + iymin = np.maximum(BBGT[:, 1], bb[1]) + ixmax = np.minimum(BBGT[:, 2], bb[2]) + iymax = np.minimum(BBGT[:, 3], bb[3]) + iw = np.maximum(ixmax - ixmin + 1.0, 0.0) + ih = np.maximum(iymax - iymin + 1.0, 0.0) + inters = iw * ih + + # union + uni = ( + (bb[2] - bb[0] + 1.0) * (bb[3] - bb[1] + 1.0) + + (BBGT[:, 2] - BBGT[:, 0] + 1.0) * (BBGT[:, 3] - BBGT[:, 1] + 1.0) + - inters + ) + + overlaps = inters / uni + ovmax = np.max(overlaps) + jmax = np.argmax(overlaps) + + if ovmax > ovthresh: + if not R["difficult"][jmax]: + if not R["det"][jmax]: + tp[d] = 1.0 + R["det"][jmax] = 1 + else: + fp[d] = 1.0 + else: + fp[d] = 1.0 + + # compute precision recall + fp = np.cumsum(fp) + tp = np.cumsum(tp) + rec = tp / float(npos) + # avoid divide by zero in case the first detection matches a difficult + # ground truth + prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) + ap = voc_ap(rec, prec, use_07_metric) + + return rec, prec, ap diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/rotated_coco_evaluation.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/rotated_coco_evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..ea6d1b381dcf106339a03f08577df673ad439c46 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/rotated_coco_evaluation.py @@ -0,0 +1,207 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import itertools +import json +import numpy as np +import os +import torch +from pycocotools.cocoeval import COCOeval, maskUtils + +from detectron2.structures import BoxMode, RotatedBoxes, pairwise_iou_rotated +from detectron2.utils.file_io import PathManager + +from .coco_evaluation import COCOEvaluator + + +class RotatedCOCOeval(COCOeval): + @staticmethod + def is_rotated(box_list): + if type(box_list) == np.ndarray: + return box_list.shape[1] == 5 + elif type(box_list) == list: + if box_list == []: # cannot decide the box_dim + return False + return np.all( + np.array( + [ + (len(obj) == 5) and ((type(obj) == list) or (type(obj) == np.ndarray)) + for obj in box_list + ] + ) + ) + return False + + @staticmethod + def boxlist_to_tensor(boxlist, output_box_dim): + if type(boxlist) == np.ndarray: + box_tensor = torch.from_numpy(boxlist) + elif type(boxlist) == list: + if boxlist == []: + return torch.zeros((0, output_box_dim), dtype=torch.float32) + else: + box_tensor = torch.FloatTensor(boxlist) + else: + raise Exception("Unrecognized boxlist type") + + input_box_dim = box_tensor.shape[1] + if input_box_dim != output_box_dim: + if input_box_dim == 4 and output_box_dim == 5: + box_tensor = BoxMode.convert(box_tensor, BoxMode.XYWH_ABS, BoxMode.XYWHA_ABS) + else: + raise Exception( + "Unable to convert from {}-dim box to {}-dim box".format( + input_box_dim, output_box_dim + ) + ) + return box_tensor + + def compute_iou_dt_gt(self, dt, gt, is_crowd): + if self.is_rotated(dt) or self.is_rotated(gt): + # TODO: take is_crowd into consideration + assert all(c == 0 for c in is_crowd) + dt = RotatedBoxes(self.boxlist_to_tensor(dt, output_box_dim=5)) + gt = RotatedBoxes(self.boxlist_to_tensor(gt, output_box_dim=5)) + return pairwise_iou_rotated(dt, gt) + else: + # This is the same as the classical COCO evaluation + return maskUtils.iou(dt, gt, is_crowd) + + def computeIoU(self, imgId, catId): + p = self.params + if p.useCats: + gt = self._gts[imgId, catId] + dt = self._dts[imgId, catId] + else: + gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]] + dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]] + if len(gt) == 0 and len(dt) == 0: + return [] + inds = np.argsort([-d["score"] for d in dt], kind="mergesort") + dt = [dt[i] for i in inds] + if len(dt) > p.maxDets[-1]: + dt = dt[0 : p.maxDets[-1]] + + assert p.iouType == "bbox", "unsupported iouType for iou computation" + + g = [g["bbox"] for g in gt] + d = [d["bbox"] for d in dt] + + # compute iou between each dt and gt region + iscrowd = [int(o["iscrowd"]) for o in gt] + + # Note: this function is copied from cocoeval.py in cocoapi + # and the major difference is here. + ious = self.compute_iou_dt_gt(d, g, iscrowd) + return ious + + +class RotatedCOCOEvaluator(COCOEvaluator): + """ + Evaluate object proposal/instance detection outputs using COCO-like metrics and APIs, + with rotated boxes support. + Note: this uses IOU only and does not consider angle differences. + """ + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a COCO model (e.g., GeneralizedRCNN). + It is a list of dict. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a COCO model. It is a list of dicts with key + "instances" that contains :class:`Instances`. + """ + for input, output in zip(inputs, outputs): + prediction = {"image_id": input["image_id"]} + + if "instances" in output: + instances = output["instances"].to(self._cpu_device) + + prediction["instances"] = self.instances_to_json(instances, input["image_id"]) + if "proposals" in output: + prediction["proposals"] = output["proposals"].to(self._cpu_device) + self._predictions.append(prediction) + + def instances_to_json(self, instances, img_id): + num_instance = len(instances) + if num_instance == 0: + return [] + + boxes = instances.pred_boxes.tensor.numpy() + if boxes.shape[1] == 4: + boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) + boxes = boxes.tolist() + scores = instances.scores.tolist() + classes = instances.pred_classes.tolist() + + results = [] + for k in range(num_instance): + result = { + "image_id": img_id, + "category_id": classes[k], + "bbox": boxes[k], + "score": scores[k], + } + + results.append(result) + return results + + def _eval_predictions(self, predictions, img_ids=None): # img_ids: unused + """ + Evaluate predictions on the given tasks. + Fill self._results with the metrics of the tasks. + """ + self._logger.info("Preparing results for COCO format ...") + coco_results = list(itertools.chain(*[x["instances"] for x in predictions])) + + # unmap the category ids for COCO + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + reverse_id_mapping = { + v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items() + } + for result in coco_results: + result["category_id"] = reverse_id_mapping[result["category_id"]] + + if self._output_dir: + file_path = os.path.join(self._output_dir, "coco_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(coco_results)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info("Evaluating predictions ...") + + assert self._tasks is None or set(self._tasks) == { + "bbox" + }, "[RotatedCOCOEvaluator] Only bbox evaluation is supported" + coco_eval = ( + self._evaluate_predictions_on_coco(self._coco_api, coco_results) + if len(coco_results) > 0 + else None # cocoapi does not handle empty results very well + ) + + task = "bbox" + res = self._derive_coco_results( + coco_eval, task, class_names=self._metadata.get("thing_classes") + ) + self._results[task] = res + + def _evaluate_predictions_on_coco(self, coco_gt, coco_results): + """ + Evaluate the coco results using COCOEval API. + """ + assert len(coco_results) > 0 + + coco_dt = coco_gt.loadRes(coco_results) + + # Only bbox is supported for now + coco_eval = RotatedCOCOeval(coco_gt, coco_dt, iouType="bbox") + + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() + + return coco_eval diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/sem_seg_evaluation.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/sem_seg_evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..7a19db71562ef47569dc7f77ec616af85447f0ec --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/sem_seg_evaluation.py @@ -0,0 +1,184 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import itertools +import json +import logging +import numpy as np +import os +from collections import OrderedDict +import PIL.Image as Image +import pycocotools.mask as mask_util +import torch + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.utils.comm import all_gather, is_main_process, synchronize +from detectron2.utils.file_io import PathManager + +from .evaluator import DatasetEvaluator + + +class SemSegEvaluator(DatasetEvaluator): + """ + Evaluate semantic segmentation metrics. + """ + + def __init__( + self, + dataset_name, + distributed=True, + output_dir=None, + *, + num_classes=None, + ignore_label=None, + ): + """ + Args: + dataset_name (str): name of the dataset to be evaluated. + distributed (bool): if True, will collect results from all ranks for evaluation. + Otherwise, will evaluate the results in the current process. + output_dir (str): an output directory to dump results. + num_classes, ignore_label: deprecated argument + """ + self._logger = logging.getLogger(__name__) + if num_classes is not None: + self._logger.warn( + "SemSegEvaluator(num_classes) is deprecated! It should be obtained from metadata." + ) + if ignore_label is not None: + self._logger.warn( + "SemSegEvaluator(ignore_label) is deprecated! It should be obtained from metadata." + ) + self._dataset_name = dataset_name + self._distributed = distributed + self._output_dir = output_dir + + self._cpu_device = torch.device("cpu") + + self.input_file_to_gt_file = { + dataset_record["file_name"]: dataset_record["sem_seg_file_name"] + for dataset_record in DatasetCatalog.get(dataset_name) + } + + meta = MetadataCatalog.get(dataset_name) + # Dict that maps contiguous training ids to COCO category ids + try: + c2d = meta.stuff_dataset_id_to_contiguous_id + self._contiguous_id_to_dataset_id = {v: k for k, v in c2d.items()} + except AttributeError: + self._contiguous_id_to_dataset_id = None + self._class_names = meta.stuff_classes + self._num_classes = len(meta.stuff_classes) + if num_classes is not None: + assert self._num_classes == num_classes, f"{self._num_classes} != {num_classes}" + self._ignore_label = ignore_label if ignore_label is not None else meta.ignore_label + + def reset(self): + self._conf_matrix = np.zeros((self._num_classes + 1, self._num_classes + 1), dtype=np.int64) + self._predictions = [] + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a model. + It is a list of dicts. Each dict corresponds to an image and + contains keys like "height", "width", "file_name". + outputs: the outputs of a model. It is either list of semantic segmentation predictions + (Tensor [H, W]) or list of dicts with key "sem_seg" that contains semantic + segmentation prediction in the same format. + """ + for input, output in zip(inputs, outputs): + output = output["sem_seg"].argmax(dim=0).to(self._cpu_device) + pred = np.array(output, dtype=np.int) + with PathManager.open(self.input_file_to_gt_file[input["file_name"]], "rb") as f: + gt = np.array(Image.open(f), dtype=np.int) + + gt[gt == self._ignore_label] = self._num_classes + + self._conf_matrix += np.bincount( + (self._num_classes + 1) * pred.reshape(-1) + gt.reshape(-1), + minlength=self._conf_matrix.size, + ).reshape(self._conf_matrix.shape) + + self._predictions.extend(self.encode_json_sem_seg(pred, input["file_name"])) + + def evaluate(self): + """ + Evaluates standard semantic segmentation metrics (http://cocodataset.org/#stuff-eval): + + * Mean intersection-over-union averaged across classes (mIoU) + * Frequency Weighted IoU (fwIoU) + * Mean pixel accuracy averaged across classes (mACC) + * Pixel Accuracy (pACC) + """ + if self._distributed: + synchronize() + conf_matrix_list = all_gather(self._conf_matrix) + self._predictions = all_gather(self._predictions) + self._predictions = list(itertools.chain(*self._predictions)) + if not is_main_process(): + return + + self._conf_matrix = np.zeros_like(self._conf_matrix) + for conf_matrix in conf_matrix_list: + self._conf_matrix += conf_matrix + + if self._output_dir: + PathManager.mkdirs(self._output_dir) + file_path = os.path.join(self._output_dir, "sem_seg_predictions.json") + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(self._predictions)) + + acc = np.full(self._num_classes, np.nan, dtype=np.float) + iou = np.full(self._num_classes, np.nan, dtype=np.float) + tp = self._conf_matrix.diagonal()[:-1].astype(np.float) + pos_gt = np.sum(self._conf_matrix[:-1, :-1], axis=0).astype(np.float) + class_weights = pos_gt / np.sum(pos_gt) + pos_pred = np.sum(self._conf_matrix[:-1, :-1], axis=1).astype(np.float) + acc_valid = pos_gt > 0 + acc[acc_valid] = tp[acc_valid] / pos_gt[acc_valid] + iou_valid = (pos_gt + pos_pred) > 0 + union = pos_gt + pos_pred - tp + iou[acc_valid] = tp[acc_valid] / union[acc_valid] + macc = np.sum(acc[acc_valid]) / np.sum(acc_valid) + miou = np.sum(iou[acc_valid]) / np.sum(iou_valid) + fiou = np.sum(iou[acc_valid] * class_weights[acc_valid]) + pacc = np.sum(tp) / np.sum(pos_gt) + + res = {} + res["mIoU"] = 100 * miou + res["fwIoU"] = 100 * fiou + for i, name in enumerate(self._class_names): + res["IoU-{}".format(name)] = 100 * iou[i] + res["mACC"] = 100 * macc + res["pACC"] = 100 * pacc + for i, name in enumerate(self._class_names): + res["ACC-{}".format(name)] = 100 * acc[i] + + if self._output_dir: + file_path = os.path.join(self._output_dir, "sem_seg_evaluation.pth") + with PathManager.open(file_path, "wb") as f: + torch.save(res, f) + results = OrderedDict({"sem_seg": res}) + self._logger.info(results) + return results + + def encode_json_sem_seg(self, sem_seg, input_file_name): + """ + Convert semantic segmentation to COCO stuff format with segments encoded as RLEs. + See http://cocodataset.org/#format-results + """ + json_list = [] + for label in np.unique(sem_seg): + if self._contiguous_id_to_dataset_id is not None: + assert ( + label in self._contiguous_id_to_dataset_id + ), "Label {} is not in the metadata info for {}".format(label, self._dataset_name) + dataset_id = self._contiguous_id_to_dataset_id[label] + else: + dataset_id = int(label) + mask = (sem_seg == label).astype(np.uint8) + mask_rle = mask_util.encode(np.array(mask[:, :, None], order="F"))[0] + mask_rle["counts"] = mask_rle["counts"].decode("utf-8") + json_list.append( + {"file_name": input_file_name, "category_id": dataset_id, "segmentation": mask_rle} + ) + return json_list diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/testing.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/testing.py new file mode 100644 index 0000000000000000000000000000000000000000..9e5ae625bb0593fc20739dd3ea549157e4df4f3d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/evaluation/testing.py @@ -0,0 +1,85 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import numpy as np +import pprint +import sys +from collections.abc import Mapping + + +def print_csv_format(results): + """ + Print main metrics in a format similar to Detectron, + so that they are easy to copypaste into a spreadsheet. + + Args: + results (OrderedDict[dict]): task_name -> {metric -> score} + unordered dict can also be printed, but in arbitrary order + """ + assert isinstance(results, Mapping) or not len(results), results + logger = logging.getLogger(__name__) + for task, res in results.items(): + if isinstance(res, Mapping): + # Don't print "AP-category" metrics since they are usually not tracked. + important_res = [(k, v) for k, v in res.items() if "-" not in k] + logger.info("copypaste: Task: {}".format(task)) + logger.info("copypaste: " + ",".join([k[0] for k in important_res])) + logger.info("copypaste: " + ",".join(["{0:.4f}".format(k[1]) for k in important_res])) + else: + logger.info(f"copypaste: {task}={res}") + + +def verify_results(cfg, results): + """ + Args: + results (OrderedDict[dict]): task_name -> {metric -> score} + + Returns: + bool: whether the verification succeeds or not + """ + expected_results = cfg.TEST.EXPECTED_RESULTS + if not len(expected_results): + return True + + ok = True + for task, metric, expected, tolerance in expected_results: + actual = results[task].get(metric, None) + if actual is None: + ok = False + continue + if not np.isfinite(actual): + ok = False + continue + diff = abs(actual - expected) + if diff > tolerance: + ok = False + + logger = logging.getLogger(__name__) + if not ok: + logger.error("Result verification failed!") + logger.error("Expected Results: " + str(expected_results)) + logger.error("Actual Results: " + pprint.pformat(results)) + + sys.exit(1) + else: + logger.info("Results verification passed.") + return ok + + +def flatten_results_dict(results): + """ + Expand a hierarchical dict of scalars into a flat dict of scalars. + If results[k1][k2][k3] = v, the returned dict will have the entry + {"k1/k2/k3": v}. + + Args: + results (dict): + """ + r = {} + for k, v in results.items(): + if isinstance(v, Mapping): + v = flatten_results_dict(v) + for kk, vv in v.items(): + r[k + "/" + kk] = vv + else: + r[k] = v + return r diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/README.md b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9fcd33513fb81ef3aeb4d3c8d9732324dffa2646 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/README.md @@ -0,0 +1,13 @@ + +This directory contains code to prepare a detectron2 model for deployment. +Currently it supports exporting a detectron2 model to Caffe2 format through ONNX. + +Please see [documentation](https://detectron2.readthedocs.io/tutorials/deployment.html) for its usage. + + +### Acknowledgements + +Thanks to Mobile Vision team at Facebook for developing the Caffe2 conversion tools. + +Thanks to Computing Platform Department - PAI team at Alibaba Group (@bddpqq, @chenbohua3) who +help export Detectron2 models to TorchScript. diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..25e5c94618a71cc584756ca2e17d6233a072dd87 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/__init__.py @@ -0,0 +1,15 @@ +# -*- coding: utf-8 -*- + +try: + from caffe2.proto import caffe2_pb2 as _tmp + + # caffe2 is optional +except ImportError: + pass +else: + from .api import * + +from .flatten import TracingAdapter +from .torchscript import scripting_with_instances, dump_torchscript_IR + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/api.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/api.py new file mode 100644 index 0000000000000000000000000000000000000000..ad4272183f2a533dbb68f6e65cf42144f4b69fc4 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/api.py @@ -0,0 +1,235 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import logging +import os +import torch +from caffe2.proto import caffe2_pb2 +from torch import nn + +from detectron2.config import CfgNode +from detectron2.utils.file_io import PathManager + +from .caffe2_inference import ProtobufDetectionModel +from .caffe2_modeling import META_ARCH_CAFFE2_EXPORT_TYPE_MAP, convert_batched_inputs_to_c2_format +from .shared import get_pb_arg_vali, get_pb_arg_vals, save_graph + +__all__ = [ + "add_export_config", + "Caffe2Model", + "Caffe2Tracer", +] + + +def add_export_config(cfg): + return cfg + + +class Caffe2Tracer: + """ + Make a detectron2 model traceable with Caffe2 operators. + This class creates a traceable version of a detectron2 model which: + + 1. Rewrite parts of the model using ops in Caffe2. Note that some ops do + not have GPU implementation in Caffe2. + 2. Remove post-processing and only produce raw layer outputs + + After making a traceable model, the class provide methods to export such a + model to different deployment formats. + Exported graph produced by this class take two input tensors: + + 1. (1, C, H, W) float "data" which is an image (usually in [0, 255]). + (H, W) often has to be padded to multiple of 32 (depend on the model + architecture). + 2. 1x3 float "im_info", each row of which is (height, width, 1.0). + Height and width are true image shapes before padding. + + The class currently only supports models using builtin meta architectures. + Batch inference is not supported, and contributions are welcome. + """ + + def __init__(self, cfg: CfgNode, model: nn.Module, inputs): + """ + Args: + cfg (CfgNode): a detectron2 config used to construct caffe2-compatible model. + model (nn.Module): An original pytorch model. Must be among a few official models + in detectron2 that can be converted to become caffe2-compatible automatically. + Weights have to be already loaded to this model. + inputs: sample inputs that the given model takes for inference. + Will be used to trace the model. For most models, random inputs with + no detected objects will not work as they lead to wrong traces. + """ + assert isinstance(cfg, CfgNode), cfg + assert isinstance(model, torch.nn.Module), type(model) + + # TODO make it support custom models, by passing in c2 model directly + C2MetaArch = META_ARCH_CAFFE2_EXPORT_TYPE_MAP[cfg.MODEL.META_ARCHITECTURE] + self.traceable_model = C2MetaArch(cfg, copy.deepcopy(model)) + self.inputs = inputs + self.traceable_inputs = self.traceable_model.get_caffe2_inputs(inputs) + + def export_caffe2(self): + """ + Export the model to Caffe2's protobuf format. + The returned object can be saved with its :meth:`.save_protobuf()` method. + The result can be loaded and executed using Caffe2 runtime. + + Returns: + :class:`Caffe2Model` + """ + from .caffe2_export import export_caffe2_detection_model + + predict_net, init_net = export_caffe2_detection_model( + self.traceable_model, self.traceable_inputs + ) + return Caffe2Model(predict_net, init_net) + + def export_onnx(self): + """ + Export the model to ONNX format. + Note that the exported model contains custom ops only available in caffe2, therefore it + cannot be directly executed by other runtime (such as onnxruntime or TensorRT). + Post-processing or transformation passes may be applied on the model to accommodate + different runtimes, but we currently do not provide support for them. + + Returns: + onnx.ModelProto: an onnx model. + """ + from .caffe2_export import export_onnx_model as export_onnx_model_impl + + return export_onnx_model_impl(self.traceable_model, (self.traceable_inputs,)) + + def export_torchscript(self): + """ + Export the model to a ``torch.jit.TracedModule`` by tracing. + The returned object can be saved to a file by ``.save()``. + + Returns: + torch.jit.TracedModule: a torch TracedModule + """ + logger = logging.getLogger(__name__) + logger.info("Tracing the model with torch.jit.trace ...") + with torch.no_grad(): + return torch.jit.trace(self.traceable_model, (self.traceable_inputs,)) + + +class Caffe2Model(nn.Module): + """ + A wrapper around the traced model in Caffe2's protobuf format. + The exported graph has different inputs/outputs from the original Pytorch + model, as explained in :class:`Caffe2Tracer`. This class wraps around the + exported graph to simulate the same interface as the original Pytorch model. + It also provides functions to save/load models in Caffe2's format.' + + Examples: + :: + c2_model = Caffe2Tracer(cfg, torch_model, inputs).export_caffe2() + inputs = [{"image": img_tensor_CHW}] + outputs = c2_model(inputs) + orig_outputs = torch_model(inputs) + """ + + def __init__(self, predict_net, init_net): + super().__init__() + self.eval() # always in eval mode + self._predict_net = predict_net + self._init_net = init_net + self._predictor = None + + __init__.__HIDE_SPHINX_DOC__ = True + + @property + def predict_net(self): + """ + caffe2.core.Net: the underlying caffe2 predict net + """ + return self._predict_net + + @property + def init_net(self): + """ + caffe2.core.Net: the underlying caffe2 init net + """ + return self._init_net + + def save_protobuf(self, output_dir): + """ + Save the model as caffe2's protobuf format. + It saves the following files: + + * "model.pb": definition of the graph. Can be visualized with + tools like `netron `_. + * "model_init.pb": model parameters + * "model.pbtxt": human-readable definition of the graph. Not + needed for deployment. + + Args: + output_dir (str): the output directory to save protobuf files. + """ + logger = logging.getLogger(__name__) + logger.info("Saving model to {} ...".format(output_dir)) + if not PathManager.exists(output_dir): + PathManager.mkdirs(output_dir) + + with PathManager.open(os.path.join(output_dir, "model.pb"), "wb") as f: + f.write(self._predict_net.SerializeToString()) + with PathManager.open(os.path.join(output_dir, "model.pbtxt"), "w") as f: + f.write(str(self._predict_net)) + with PathManager.open(os.path.join(output_dir, "model_init.pb"), "wb") as f: + f.write(self._init_net.SerializeToString()) + + def save_graph(self, output_file, inputs=None): + """ + Save the graph as SVG format. + + Args: + output_file (str): a SVG file + inputs: optional inputs given to the model. + If given, the inputs will be used to run the graph to record + shape of every tensor. The shape information will be + saved together with the graph. + """ + from .caffe2_export import run_and_save_graph + + if inputs is None: + save_graph(self._predict_net, output_file, op_only=False) + else: + size_divisibility = get_pb_arg_vali(self._predict_net, "size_divisibility", 0) + device = get_pb_arg_vals(self._predict_net, "device", b"cpu").decode("ascii") + inputs = convert_batched_inputs_to_c2_format(inputs, size_divisibility, device) + inputs = [x.cpu().numpy() for x in inputs] + run_and_save_graph(self._predict_net, self._init_net, inputs, output_file) + + @staticmethod + def load_protobuf(dir): + """ + Args: + dir (str): a directory used to save Caffe2Model with + :meth:`save_protobuf`. + The files "model.pb" and "model_init.pb" are needed. + + Returns: + Caffe2Model: the caffe2 model loaded from this directory. + """ + predict_net = caffe2_pb2.NetDef() + with PathManager.open(os.path.join(dir, "model.pb"), "rb") as f: + predict_net.ParseFromString(f.read()) + + init_net = caffe2_pb2.NetDef() + with PathManager.open(os.path.join(dir, "model_init.pb"), "rb") as f: + init_net.ParseFromString(f.read()) + + return Caffe2Model(predict_net, init_net) + + def __call__(self, inputs): + """ + An interface that wraps around a Caffe2 model and mimics detectron2's models' + input/output format. See details about the format at :doc:`/tutorials/models`. + This is used to compare the outputs of caffe2 model with its original torch model. + + Due to the extra conversion between Pytorch/Caffe2, this method is not meant for + benchmark. Because of the conversion, this method also has dependency + on detectron2 in order to convert to detectron2's output format. + """ + if self._predictor is None: + self._predictor = ProtobufDetectionModel(self._predict_net, self._init_net) + return self._predictor(inputs) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/c10.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/c10.py new file mode 100644 index 0000000000000000000000000000000000000000..25ee23009547913733dc528fb8a39ca995fd9e31 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/c10.py @@ -0,0 +1,534 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import math +import torch +import torch.nn.functional as F + +from detectron2.layers import cat +from detectron2.layers.roi_align_rotated import ROIAlignRotated +from detectron2.modeling import poolers +from detectron2.modeling.proposal_generator import rpn +from detectron2.modeling.roi_heads.mask_head import mask_rcnn_inference +from detectron2.structures import Boxes, ImageList, Instances, Keypoints + +from .shared import alias, to_device + + +""" +This file contains caffe2-compatible implementation of several detectron2 components. +""" + + +class Caffe2Boxes(Boxes): + """ + Representing a list of detectron2.structures.Boxes from minibatch, each box + is represented by a 5d vector (batch index + 4 coordinates), or a 6d vector + (batch index + 5 coordinates) for RotatedBoxes. + """ + + def __init__(self, tensor): + assert isinstance(tensor, torch.Tensor) + assert tensor.dim() == 2 and tensor.size(-1) in [4, 5, 6], tensor.size() + # TODO: make tensor immutable when dim is Nx5 for Boxes, + # and Nx6 for RotatedBoxes? + self.tensor = tensor + + +# TODO clean up this class, maybe just extend Instances +class InstancesList(object): + """ + Tensor representation of a list of Instances object for a batch of images. + + When dealing with a batch of images with Caffe2 ops, a list of bboxes + (instances) are usually represented by single Tensor with size + (sigma(Ni), 5) or (sigma(Ni), 4) plus a batch split Tensor. This class is + for providing common functions to convert between these two representations. + """ + + def __init__(self, im_info, indices, extra_fields=None): + # [N, 3] -> (H, W, Scale) + self.im_info = im_info + # [N,] -> indice of batch to which the instance belongs + self.indices = indices + # [N, ...] + self.batch_extra_fields = extra_fields or {} + + self.image_size = self.im_info + + def get_fields(self): + """like `get_fields` in the Instances object, + but return each field in tensor representations""" + ret = {} + for k, v in self.batch_extra_fields.items(): + # if isinstance(v, torch.Tensor): + # tensor_rep = v + # elif isinstance(v, (Boxes, Keypoints)): + # tensor_rep = v.tensor + # else: + # raise ValueError("Can't find tensor representation for: {}".format()) + ret[k] = v + return ret + + def has(self, name): + return name in self.batch_extra_fields + + def set(self, name, value): + data_len = len(value) + if len(self.batch_extra_fields): + assert ( + len(self) == data_len + ), "Adding a field of length {} to a Instances of length {}".format(data_len, len(self)) + self.batch_extra_fields[name] = value + + def __setattr__(self, name, val): + if name in ["im_info", "indices", "batch_extra_fields", "image_size"]: + super().__setattr__(name, val) + else: + self.set(name, val) + + def __getattr__(self, name): + if name not in self.batch_extra_fields: + raise AttributeError("Cannot find field '{}' in the given Instances!".format(name)) + return self.batch_extra_fields[name] + + def __len__(self): + return len(self.indices) + + def flatten(self): + ret = [] + for _, v in self.batch_extra_fields.items(): + if isinstance(v, (Boxes, Keypoints)): + ret.append(v.tensor) + else: + ret.append(v) + return ret + + @staticmethod + def to_d2_instances_list(instances_list): + """ + Convert InstancesList to List[Instances]. The input `instances_list` can + also be a List[Instances], in this case this method is a non-op. + """ + if not isinstance(instances_list, InstancesList): + assert all(isinstance(x, Instances) for x in instances_list) + return instances_list + + ret = [] + for i, info in enumerate(instances_list.im_info): + instances = Instances(torch.Size([int(info[0].item()), int(info[1].item())])) + + ids = instances_list.indices == i + for k, v in instances_list.batch_extra_fields.items(): + if isinstance(v, torch.Tensor): + instances.set(k, v[ids]) + continue + elif isinstance(v, Boxes): + instances.set(k, v[ids, -4:]) + continue + + target_type, tensor_source = v + assert isinstance(tensor_source, torch.Tensor) + assert tensor_source.shape[0] == instances_list.indices.shape[0] + tensor_source = tensor_source[ids] + + if issubclass(target_type, Boxes): + instances.set(k, Boxes(tensor_source[:, -4:])) + elif issubclass(target_type, Keypoints): + instances.set(k, Keypoints(tensor_source)) + elif issubclass(target_type, torch.Tensor): + instances.set(k, tensor_source) + else: + raise ValueError("Can't handle targe type: {}".format(target_type)) + + ret.append(instances) + return ret + + +class Caffe2Compatible(object): + """ + A model can inherit this class to indicate that it can be traced and deployed with caffe2. + """ + + def _get_tensor_mode(self): + return self._tensor_mode + + def _set_tensor_mode(self, v): + self._tensor_mode = v + + tensor_mode = property(_get_tensor_mode, _set_tensor_mode) + """ + If true, the model expects C2-style tensor only inputs/outputs format. + """ + + +class Caffe2RPN(Caffe2Compatible, rpn.RPN): + def _generate_proposals( + self, images, objectness_logits_pred, anchor_deltas_pred, gt_instances=None + ): + assert isinstance(images, ImageList) + if self.tensor_mode: + im_info = images.image_sizes + else: + im_info = torch.tensor([[im_sz[0], im_sz[1], 1.0] for im_sz in images.image_sizes]).to( + images.tensor.device + ) + assert isinstance(im_info, torch.Tensor) + + rpn_rois_list = [] + rpn_roi_probs_list = [] + for scores, bbox_deltas, cell_anchors_tensor, feat_stride in zip( + objectness_logits_pred, + anchor_deltas_pred, + iter(self.anchor_generator.cell_anchors), + self.anchor_generator.strides, + ): + scores = scores.detach() + bbox_deltas = bbox_deltas.detach() + + rpn_rois, rpn_roi_probs = torch.ops._caffe2.GenerateProposals( + scores, + bbox_deltas, + im_info, + cell_anchors_tensor, + spatial_scale=1.0 / feat_stride, + pre_nms_topN=self.pre_nms_topk[self.training], + post_nms_topN=self.post_nms_topk[self.training], + nms_thresh=self.nms_thresh, + min_size=self.min_box_size, + # correct_transform_coords=True, # deprecated argument + angle_bound_on=True, # Default + angle_bound_lo=-180, + angle_bound_hi=180, + clip_angle_thresh=1.0, # Default + legacy_plus_one=False, + ) + rpn_rois_list.append(rpn_rois) + rpn_roi_probs_list.append(rpn_roi_probs) + + # For FPN in D2, in RPN all proposals from different levels are concated + # together, ranked and picked by top post_nms_topk. Then in ROIPooler + # it calculates level_assignments and calls the RoIAlign from + # the corresponding level. + + if len(objectness_logits_pred) == 1: + rpn_rois = rpn_rois_list[0] + rpn_roi_probs = rpn_roi_probs_list[0] + else: + assert len(rpn_rois_list) == len(rpn_roi_probs_list) + rpn_post_nms_topN = self.post_nms_topk[self.training] + + device = rpn_rois_list[0].device + input_list = [to_device(x, "cpu") for x in (rpn_rois_list + rpn_roi_probs_list)] + + # TODO remove this after confirming rpn_max_level/rpn_min_level + # is not needed in CollectRpnProposals. + feature_strides = list(self.anchor_generator.strides) + rpn_min_level = int(math.log2(feature_strides[0])) + rpn_max_level = int(math.log2(feature_strides[-1])) + assert (rpn_max_level - rpn_min_level + 1) == len( + rpn_rois_list + ), "CollectRpnProposals requires continuous levels" + + rpn_rois = torch.ops._caffe2.CollectRpnProposals( + input_list, + # NOTE: in current implementation, rpn_max_level and rpn_min_level + # are not needed, only the subtraction of two matters and it + # can be infer from the number of inputs. Keep them now for + # consistency. + rpn_max_level=2 + len(rpn_rois_list) - 1, + rpn_min_level=2, + rpn_post_nms_topN=rpn_post_nms_topN, + ) + rpn_rois = to_device(rpn_rois, device) + rpn_roi_probs = [] + + proposals = self.c2_postprocess(im_info, rpn_rois, rpn_roi_probs, self.tensor_mode) + return proposals, {} + + def forward(self, images, features, gt_instances=None): + assert not self.training + features = [features[f] for f in self.in_features] + objectness_logits_pred, anchor_deltas_pred = self.rpn_head(features) + return self._generate_proposals( + images, + objectness_logits_pred, + anchor_deltas_pred, + gt_instances, + ) + + @staticmethod + def c2_postprocess(im_info, rpn_rois, rpn_roi_probs, tensor_mode): + proposals = InstancesList( + im_info=im_info, + indices=rpn_rois[:, 0], + extra_fields={ + "proposal_boxes": Caffe2Boxes(rpn_rois), + "objectness_logits": (torch.Tensor, rpn_roi_probs), + }, + ) + if not tensor_mode: + proposals = InstancesList.to_d2_instances_list(proposals) + else: + proposals = [proposals] + return proposals + + +class Caffe2ROIPooler(Caffe2Compatible, poolers.ROIPooler): + @staticmethod + def c2_preprocess(box_lists): + assert all(isinstance(x, Boxes) for x in box_lists) + if all(isinstance(x, Caffe2Boxes) for x in box_lists): + # input is pure-tensor based + assert len(box_lists) == 1 + pooler_fmt_boxes = box_lists[0].tensor + else: + pooler_fmt_boxes = poolers.convert_boxes_to_pooler_format(box_lists) + return pooler_fmt_boxes + + def forward(self, x, box_lists): + assert not self.training + + pooler_fmt_boxes = self.c2_preprocess(box_lists) + num_level_assignments = len(self.level_poolers) + + if num_level_assignments == 1: + if isinstance(self.level_poolers[0], ROIAlignRotated): + c2_roi_align = torch.ops._caffe2.RoIAlignRotated + aligned = True + else: + c2_roi_align = torch.ops._caffe2.RoIAlign + aligned = self.level_poolers[0].aligned + + x0 = x[0] + if x0.is_quantized: + x0 = x0.dequantize() + + out = c2_roi_align( + x0, + pooler_fmt_boxes, + order="NCHW", + spatial_scale=float(self.level_poolers[0].spatial_scale), + pooled_h=int(self.output_size[0]), + pooled_w=int(self.output_size[1]), + sampling_ratio=int(self.level_poolers[0].sampling_ratio), + aligned=aligned, + ) + return out + + device = pooler_fmt_boxes.device + assert ( + self.max_level - self.min_level + 1 == 4 + ), "Currently DistributeFpnProposals only support 4 levels" + fpn_outputs = torch.ops._caffe2.DistributeFpnProposals( + to_device(pooler_fmt_boxes, "cpu"), + roi_canonical_scale=self.canonical_box_size, + roi_canonical_level=self.canonical_level, + roi_max_level=self.max_level, + roi_min_level=self.min_level, + legacy_plus_one=False, + ) + fpn_outputs = [to_device(x, device) for x in fpn_outputs] + + rois_fpn_list = fpn_outputs[:-1] + rois_idx_restore_int32 = fpn_outputs[-1] + + roi_feat_fpn_list = [] + for roi_fpn, x_level, pooler in zip(rois_fpn_list, x, self.level_poolers): + if isinstance(pooler, ROIAlignRotated): + c2_roi_align = torch.ops._caffe2.RoIAlignRotated + aligned = True + else: + c2_roi_align = torch.ops._caffe2.RoIAlign + aligned = bool(pooler.aligned) + + if x_level.is_quantized: + x_level = x_level.dequantize() + + roi_feat_fpn = c2_roi_align( + x_level, + roi_fpn, + order="NCHW", + spatial_scale=float(pooler.spatial_scale), + pooled_h=int(self.output_size[0]), + pooled_w=int(self.output_size[1]), + sampling_ratio=int(pooler.sampling_ratio), + aligned=aligned, + ) + roi_feat_fpn_list.append(roi_feat_fpn) + + roi_feat_shuffled = cat(roi_feat_fpn_list, dim=0) + assert roi_feat_shuffled.numel() > 0 and rois_idx_restore_int32.numel() > 0, ( + "Caffe2 export requires tracing with a model checkpoint + input that can produce valid" + " detections. But no detections were obtained with the given checkpoint and input!" + ) + roi_feat = torch.ops._caffe2.BatchPermutation(roi_feat_shuffled, rois_idx_restore_int32) + return roi_feat + + +class Caffe2FastRCNNOutputsInference: + def __init__(self, tensor_mode): + self.tensor_mode = tensor_mode # whether the output is caffe2 tensor mode + + def __call__(self, box_predictor, predictions, proposals): + """equivalent to FastRCNNOutputLayers.inference""" + num_classes = box_predictor.num_classes + score_thresh = box_predictor.test_score_thresh + nms_thresh = box_predictor.test_nms_thresh + topk_per_image = box_predictor.test_topk_per_image + is_rotated = len(box_predictor.box2box_transform.weights) == 5 + + if is_rotated: + box_dim = 5 + assert box_predictor.box2box_transform.weights[4] == 1, ( + "The weights for Rotated BBoxTransform in C2 have only 4 dimensions," + + " thus enforcing the angle weight to be 1 for now" + ) + box2box_transform_weights = box_predictor.box2box_transform.weights[:4] + else: + box_dim = 4 + box2box_transform_weights = box_predictor.box2box_transform.weights + + class_logits, box_regression = predictions + if num_classes + 1 == class_logits.shape[1]: + class_prob = F.softmax(class_logits, -1) + else: + assert num_classes == class_logits.shape[1] + class_prob = F.sigmoid(class_logits) + # BoxWithNMSLimit will infer num_classes from the shape of the class_prob + # So append a zero column as placeholder for the background class + class_prob = torch.cat((class_prob, torch.zeros(class_prob.shape[0], 1)), dim=1) + + assert box_regression.shape[1] % box_dim == 0 + cls_agnostic_bbox_reg = box_regression.shape[1] // box_dim == 1 + + input_tensor_mode = proposals[0].proposal_boxes.tensor.shape[1] == box_dim + 1 + + rois = type(proposals[0].proposal_boxes).cat([p.proposal_boxes for p in proposals]) + device, dtype = rois.tensor.device, rois.tensor.dtype + if input_tensor_mode: + im_info = proposals[0].image_size + rois = rois.tensor + else: + im_info = torch.tensor( + [[sz[0], sz[1], 1.0] for sz in [x.image_size for x in proposals]] + ) + batch_ids = cat( + [ + torch.full((b, 1), i, dtype=dtype, device=device) + for i, b in enumerate(len(p) for p in proposals) + ], + dim=0, + ) + rois = torch.cat([batch_ids, rois.tensor], dim=1) + + roi_pred_bbox, roi_batch_splits = torch.ops._caffe2.BBoxTransform( + to_device(rois, "cpu"), + to_device(box_regression, "cpu"), + to_device(im_info, "cpu"), + weights=box2box_transform_weights, + apply_scale=True, + rotated=is_rotated, + angle_bound_on=True, + angle_bound_lo=-180, + angle_bound_hi=180, + clip_angle_thresh=1.0, + legacy_plus_one=False, + ) + roi_pred_bbox = to_device(roi_pred_bbox, device) + roi_batch_splits = to_device(roi_batch_splits, device) + + nms_outputs = torch.ops._caffe2.BoxWithNMSLimit( + to_device(class_prob, "cpu"), + to_device(roi_pred_bbox, "cpu"), + to_device(roi_batch_splits, "cpu"), + score_thresh=float(score_thresh), + nms=float(nms_thresh), + detections_per_im=int(topk_per_image), + soft_nms_enabled=False, + soft_nms_method="linear", + soft_nms_sigma=0.5, + soft_nms_min_score_thres=0.001, + rotated=is_rotated, + cls_agnostic_bbox_reg=cls_agnostic_bbox_reg, + input_boxes_include_bg_cls=False, + output_classes_include_bg_cls=False, + legacy_plus_one=False, + ) + roi_score_nms = to_device(nms_outputs[0], device) + roi_bbox_nms = to_device(nms_outputs[1], device) + roi_class_nms = to_device(nms_outputs[2], device) + roi_batch_splits_nms = to_device(nms_outputs[3], device) + roi_keeps_nms = to_device(nms_outputs[4], device) + roi_keeps_size_nms = to_device(nms_outputs[5], device) + if not self.tensor_mode: + roi_class_nms = roi_class_nms.to(torch.int64) + + roi_batch_ids = cat( + [ + torch.full((b, 1), i, dtype=dtype, device=device) + for i, b in enumerate(int(x.item()) for x in roi_batch_splits_nms) + ], + dim=0, + ) + + roi_class_nms = alias(roi_class_nms, "class_nms") + roi_score_nms = alias(roi_score_nms, "score_nms") + roi_bbox_nms = alias(roi_bbox_nms, "bbox_nms") + roi_batch_splits_nms = alias(roi_batch_splits_nms, "batch_splits_nms") + roi_keeps_nms = alias(roi_keeps_nms, "keeps_nms") + roi_keeps_size_nms = alias(roi_keeps_size_nms, "keeps_size_nms") + + results = InstancesList( + im_info=im_info, + indices=roi_batch_ids[:, 0], + extra_fields={ + "pred_boxes": Caffe2Boxes(roi_bbox_nms), + "scores": roi_score_nms, + "pred_classes": roi_class_nms, + }, + ) + + if not self.tensor_mode: + results = InstancesList.to_d2_instances_list(results) + batch_splits = roi_batch_splits_nms.int().tolist() + kept_indices = list(roi_keeps_nms.to(torch.int64).split(batch_splits)) + else: + results = [results] + kept_indices = [roi_keeps_nms] + + return results, kept_indices + + +class Caffe2MaskRCNNInference: + def __call__(self, pred_mask_logits, pred_instances): + """equivalent to mask_head.mask_rcnn_inference""" + if all(isinstance(x, InstancesList) for x in pred_instances): + assert len(pred_instances) == 1 + mask_probs_pred = pred_mask_logits.sigmoid() + mask_probs_pred = alias(mask_probs_pred, "mask_fcn_probs") + pred_instances[0].pred_masks = mask_probs_pred + else: + mask_rcnn_inference(pred_mask_logits, pred_instances) + + +class Caffe2KeypointRCNNInference: + def __init__(self, use_heatmap_max_keypoint): + self.use_heatmap_max_keypoint = use_heatmap_max_keypoint + + def __call__(self, pred_keypoint_logits, pred_instances): + # just return the keypoint heatmap for now, + # there will be option to call HeatmapMaxKeypointOp + output = alias(pred_keypoint_logits, "kps_score") + if all(isinstance(x, InstancesList) for x in pred_instances): + assert len(pred_instances) == 1 + if self.use_heatmap_max_keypoint: + device = output.device + output = torch.ops._caffe2.HeatmapMaxKeypoint( + to_device(output, "cpu"), + pred_instances[0].pred_boxes.tensor, + should_output_softmax=True, # worth make it configerable? + ) + output = to_device(output, device) + output = alias(output, "keypoints_out") + pred_instances[0].pred_keypoints = output + return pred_keypoint_logits diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_export.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_export.py new file mode 100644 index 0000000000000000000000000000000000000000..74ac123a7aed6cd77d6d833446a831d9048745b2 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_export.py @@ -0,0 +1,207 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import copy +import io +import logging +import numpy as np +from typing import List +import onnx +import torch +from caffe2.proto import caffe2_pb2 +from caffe2.python import core +from caffe2.python.onnx.backend import Caffe2Backend +from tabulate import tabulate +from termcolor import colored +from torch.onnx import OperatorExportTypes + +from .shared import ( + ScopedWS, + construct_init_net_from_params, + fuse_alias_placeholder, + fuse_copy_between_cpu_and_gpu, + get_params_from_init_net, + group_norm_replace_aten_with_caffe2, + infer_device_type, + remove_dead_end_ops, + remove_reshape_for_fc, + save_graph, +) + +logger = logging.getLogger(__name__) + + +def export_onnx_model(model, inputs): + """ + Trace and export a model to onnx format. + + Args: + model (nn.Module): + inputs (tuple[args]): the model will be called by `model(*inputs)` + + Returns: + an onnx model + """ + assert isinstance(model, torch.nn.Module) + + # make sure all modules are in eval mode, onnx may change the training state + # of the module if the states are not consistent + def _check_eval(module): + assert not module.training + + model.apply(_check_eval) + + # Export the model to ONNX + with torch.no_grad(): + with io.BytesIO() as f: + torch.onnx.export( + model, + inputs, + f, + operator_export_type=OperatorExportTypes.ONNX_ATEN_FALLBACK, + # verbose=True, # NOTE: uncomment this for debugging + # export_params=True, + ) + onnx_model = onnx.load_from_string(f.getvalue()) + + # Apply ONNX's Optimization + all_passes = onnx.optimizer.get_available_passes() + passes = ["fuse_bn_into_conv"] + assert all(p in all_passes for p in passes) + onnx_model = onnx.optimizer.optimize(onnx_model, passes) + return onnx_model + + +def _op_stats(net_def): + type_count = {} + for t in [op.type for op in net_def.op]: + type_count[t] = type_count.get(t, 0) + 1 + type_count_list = sorted(type_count.items(), key=lambda kv: kv[0]) # alphabet + type_count_list = sorted(type_count_list, key=lambda kv: -kv[1]) # count + return "\n".join("{:>4}x {}".format(count, name) for name, count in type_count_list) + + +def _assign_device_option( + predict_net: caffe2_pb2.NetDef, init_net: caffe2_pb2.NetDef, tensor_inputs: List[torch.Tensor] +): + """ + ONNX exported network doesn't have concept of device, assign necessary + device option for each op in order to make it runable on GPU runtime. + """ + + def _get_device_type(torch_tensor): + assert torch_tensor.device.type in ["cpu", "cuda"] + assert torch_tensor.device.index == 0 + return torch_tensor.device.type + + def _assign_op_device_option(net_proto, net_ssa, blob_device_types): + for op, ssa_i in zip(net_proto.op, net_ssa): + if op.type in ["CopyCPUToGPU", "CopyGPUToCPU"]: + op.device_option.CopyFrom(core.DeviceOption(caffe2_pb2.CUDA, 0)) + else: + devices = [blob_device_types[b] for b in ssa_i[0] + ssa_i[1]] + assert all(d == devices[0] for d in devices) + if devices[0] == "cuda": + op.device_option.CopyFrom(core.DeviceOption(caffe2_pb2.CUDA, 0)) + + # update ops in predict_net + predict_net_input_device_types = { + (name, 0): _get_device_type(tensor) + for name, tensor in zip(predict_net.external_input, tensor_inputs) + } + predict_net_device_types = infer_device_type( + predict_net, known_status=predict_net_input_device_types, device_name_style="pytorch" + ) + predict_net_ssa, _ = core.get_ssa(predict_net) + _assign_op_device_option(predict_net, predict_net_ssa, predict_net_device_types) + + # update ops in init_net + init_net_ssa, versions = core.get_ssa(init_net) + init_net_output_device_types = { + (name, versions[name]): predict_net_device_types[(name, 0)] + for name in init_net.external_output + } + init_net_device_types = infer_device_type( + init_net, known_status=init_net_output_device_types, device_name_style="pytorch" + ) + _assign_op_device_option(init_net, init_net_ssa, init_net_device_types) + + +def export_caffe2_detection_model(model: torch.nn.Module, tensor_inputs: List[torch.Tensor]): + """ + Export a caffe2-compatible Detectron2 model to caffe2 format via ONNX. + + Arg: + model: a caffe2-compatible version of detectron2 model, defined in caffe2_modeling.py + tensor_inputs: a list of tensors that caffe2 model takes as input. + """ + model = copy.deepcopy(model) + assert isinstance(model, torch.nn.Module) + assert hasattr(model, "encode_additional_info") + + # Export via ONNX + logger.info( + "Exporting a {} model via ONNX ...".format(type(model).__name__) + + " Some warnings from ONNX are expected and are usually not to worry about." + ) + onnx_model = export_onnx_model(model, (tensor_inputs,)) + # Convert ONNX model to Caffe2 protobuf + init_net, predict_net = Caffe2Backend.onnx_graph_to_caffe2_net(onnx_model) + ops_table = [[op.type, op.input, op.output] for op in predict_net.op] + table = tabulate(ops_table, headers=["type", "input", "output"], tablefmt="pipe") + logger.info( + "ONNX export Done. Exported predict_net (before optimizations):\n" + colored(table, "cyan") + ) + + # Apply protobuf optimization + fuse_alias_placeholder(predict_net, init_net) + if any(t.device.type != "cpu" for t in tensor_inputs): + fuse_copy_between_cpu_and_gpu(predict_net) + remove_dead_end_ops(init_net) + _assign_device_option(predict_net, init_net, tensor_inputs) + params, device_options = get_params_from_init_net(init_net) + predict_net, params = remove_reshape_for_fc(predict_net, params) + init_net = construct_init_net_from_params(params, device_options) + group_norm_replace_aten_with_caffe2(predict_net) + + # Record necessary information for running the pb model in Detectron2 system. + model.encode_additional_info(predict_net, init_net) + + logger.info("Operators used in predict_net: \n{}".format(_op_stats(predict_net))) + logger.info("Operators used in init_net: \n{}".format(_op_stats(init_net))) + + return predict_net, init_net + + +def run_and_save_graph(predict_net, init_net, tensor_inputs, graph_save_path): + """ + Run the caffe2 model on given inputs, recording the shape and draw the graph. + + predict_net/init_net: caffe2 model. + tensor_inputs: a list of tensors that caffe2 model takes as input. + graph_save_path: path for saving graph of exported model. + """ + + logger.info("Saving graph of ONNX exported model to {} ...".format(graph_save_path)) + save_graph(predict_net, graph_save_path, op_only=False) + + # Run the exported Caffe2 net + logger.info("Running ONNX exported model ...") + with ScopedWS("__ws_tmp__", True) as ws: + ws.RunNetOnce(init_net) + initialized_blobs = set(ws.Blobs()) + uninitialized = [inp for inp in predict_net.external_input if inp not in initialized_blobs] + for name, blob in zip(uninitialized, tensor_inputs): + ws.FeedBlob(name, blob) + + try: + ws.RunNetOnce(predict_net) + except RuntimeError as e: + logger.warning("Encountered RuntimeError: \n{}".format(str(e))) + + ws_blobs = {b: ws.FetchBlob(b) for b in ws.Blobs()} + blob_sizes = {b: ws_blobs[b].shape for b in ws_blobs if isinstance(ws_blobs[b], np.ndarray)} + + logger.info("Saving graph with blob shapes to {} ...".format(graph_save_path)) + save_graph(predict_net, graph_save_path, op_only=False, blob_sizes=blob_sizes) + + return ws_blobs diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_inference.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..deb886c0417285ed1d5ad85eb941fa1ac757cdab --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_inference.py @@ -0,0 +1,161 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import logging +import numpy as np +from itertools import count +import torch +from caffe2.proto import caffe2_pb2 +from caffe2.python import core + +from .caffe2_modeling import META_ARCH_CAFFE2_EXPORT_TYPE_MAP, convert_batched_inputs_to_c2_format +from .shared import ScopedWS, get_pb_arg_vali, get_pb_arg_vals, infer_device_type + +logger = logging.getLogger(__name__) + + +# ===== ref: mobile-vision predictor's 'Caffe2Wrapper' class ====== +class ProtobufModel(torch.nn.Module): + """ + Wrapper of a caffe2's protobuf model. + It works just like nn.Module, but running caffe2 under the hood. + Input/Output are tuple[tensor] that match the caffe2 net's external_input/output. + """ + + _ids = count(0) + + def __init__(self, predict_net, init_net): + logger.info(f"Initializing ProtobufModel for: {predict_net.name} ...") + super().__init__() + assert isinstance(predict_net, caffe2_pb2.NetDef) + assert isinstance(init_net, caffe2_pb2.NetDef) + # create unique temporary workspace for each instance + self.ws_name = "__tmp_ProtobufModel_{}__".format(next(self._ids)) + self.net = core.Net(predict_net) + + logger.info("Running init_net once to fill the parameters ...") + with ScopedWS(self.ws_name, is_reset=True, is_cleanup=False) as ws: + ws.RunNetOnce(init_net) + uninitialized_external_input = [] + for blob in self.net.Proto().external_input: + if blob not in ws.Blobs(): + uninitialized_external_input.append(blob) + ws.CreateBlob(blob) + ws.CreateNet(self.net) + + self._error_msgs = set() + self._input_blobs = uninitialized_external_input + + def _infer_output_devices(self, inputs): + """ + Returns: + list[str]: list of device for each external output + """ + + def _get_device_type(torch_tensor): + assert torch_tensor.device.type in ["cpu", "cuda"] + assert torch_tensor.device.index == 0 + return torch_tensor.device.type + + predict_net = self.net.Proto() + input_device_types = { + (name, 0): _get_device_type(tensor) for name, tensor in zip(self._input_blobs, inputs) + } + device_type_map = infer_device_type( + predict_net, known_status=input_device_types, device_name_style="pytorch" + ) + ssa, versions = core.get_ssa(predict_net) + versioned_outputs = [(name, versions[name]) for name in predict_net.external_output] + output_devices = [device_type_map[outp] for outp in versioned_outputs] + return output_devices + + def forward(self, inputs): + """ + Args: + inputs (tuple[torch.Tensor]) + + Returns: + tuple[torch.Tensor] + """ + assert len(inputs) == len(self._input_blobs), ( + f"Length of inputs ({len(inputs)}) " + f"doesn't match the required input blobs: {self._input_blobs}" + ) + + with ScopedWS(self.ws_name, is_reset=False, is_cleanup=False) as ws: + for b, tensor in zip(self._input_blobs, inputs): + ws.FeedBlob(b, tensor) + + try: + ws.RunNet(self.net.Proto().name) + except RuntimeError as e: + if not str(e) in self._error_msgs: + self._error_msgs.add(str(e)) + logger.warning("Encountered new RuntimeError: \n{}".format(str(e))) + logger.warning("Catch the error and use partial results.") + + c2_outputs = [ws.FetchBlob(b) for b in self.net.Proto().external_output] + # Remove outputs of current run, this is necessary in order to + # prevent fetching the result from previous run if the model fails + # in the middle. + for b in self.net.Proto().external_output: + # Needs to create uninitialized blob to make the net runable. + # This is "equivalent" to: ws.RemoveBlob(b) then ws.CreateBlob(b), + # but there'no such API. + ws.FeedBlob(b, f"{b}, a C++ native class of type nullptr (uninitialized).") + + # Cast output to torch.Tensor on the desired device + output_devices = ( + self._infer_output_devices(inputs) + if any(t.device.type != "cpu" for t in inputs) + else ["cpu" for _ in self.net.Proto().external_output] + ) + + outputs = [] + for name, c2_output, device in zip( + self.net.Proto().external_output, c2_outputs, output_devices + ): + if not isinstance(c2_output, np.ndarray): + raise RuntimeError( + "Invalid output for blob {}, received: {}".format(name, c2_output) + ) + outputs.append(torch.tensor(c2_output).to(device=device)) + return tuple(outputs) + + +class ProtobufDetectionModel(torch.nn.Module): + """ + A class works just like a pytorch meta arch in terms of inference, but running + caffe2 model under the hood. + """ + + def __init__(self, predict_net, init_net, *, convert_outputs=None): + """ + Args: + predict_net, init_net (core.Net): caffe2 nets + convert_outptus (callable): a function that converts caffe2 + outputs to the same format of the original pytorch model. + By default, use the one defined in the caffe2 meta_arch. + """ + super().__init__() + self.protobuf_model = ProtobufModel(predict_net, init_net) + self.size_divisibility = get_pb_arg_vali(predict_net, "size_divisibility", 0) + self.device = get_pb_arg_vals(predict_net, "device", b"cpu").decode("ascii") + + if convert_outputs is None: + meta_arch = get_pb_arg_vals(predict_net, "meta_architecture", b"GeneralizedRCNN") + meta_arch = META_ARCH_CAFFE2_EXPORT_TYPE_MAP[meta_arch.decode("ascii")] + self._convert_outputs = meta_arch.get_outputs_converter(predict_net, init_net) + else: + self._convert_outputs = convert_outputs + + def _convert_inputs(self, batched_inputs): + # currently all models convert inputs in the same way + return convert_batched_inputs_to_c2_format( + batched_inputs, self.size_divisibility, self.device + ) + + def forward(self, batched_inputs): + c2_inputs = self._convert_inputs(batched_inputs) + c2_results = self.protobuf_model(c2_inputs) + c2_results = dict(zip(self.protobuf_model.net.Proto().external_output, c2_results)) + return self._convert_outputs(batched_inputs, c2_inputs, c2_results) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_modeling.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_modeling.py new file mode 100644 index 0000000000000000000000000000000000000000..e00de4ad28fd81483c9e1161394b7b508fdad91f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_modeling.py @@ -0,0 +1,419 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import functools +import io +import struct +import types +import torch + +from detectron2.modeling import meta_arch +from detectron2.modeling.box_regression import Box2BoxTransform +from detectron2.modeling.roi_heads import keypoint_head +from detectron2.structures import Boxes, ImageList, Instances, RotatedBoxes + +from .c10 import Caffe2Compatible +from .caffe2_patch import ROIHeadsPatcher, patch_generalized_rcnn +from .shared import ( + alias, + check_set_pb_arg, + get_pb_arg_floats, + get_pb_arg_valf, + get_pb_arg_vali, + get_pb_arg_vals, + mock_torch_nn_functional_interpolate, +) + + +def assemble_rcnn_outputs_by_name(image_sizes, tensor_outputs, force_mask_on=False): + """ + A function to assemble caffe2 model's outputs (i.e. Dict[str, Tensor]) + to detectron2's format (i.e. list of Instances instance). + This only works when the model follows the Caffe2 detectron's naming convention. + + Args: + image_sizes (List[List[int, int]]): [H, W] of every image. + tensor_outputs (Dict[str, Tensor]): external_output to its tensor. + + force_mask_on (Bool): if true, the it make sure there'll be pred_masks even + if the mask is not found from tensor_outputs (usually due to model crash) + """ + + results = [Instances(image_size) for image_size in image_sizes] + + batch_splits = tensor_outputs.get("batch_splits", None) + if batch_splits: + raise NotImplementedError() + assert len(image_sizes) == 1 + result = results[0] + + bbox_nms = tensor_outputs["bbox_nms"] + score_nms = tensor_outputs["score_nms"] + class_nms = tensor_outputs["class_nms"] + # Detection will always success because Conv support 0-batch + assert bbox_nms is not None + assert score_nms is not None + assert class_nms is not None + if bbox_nms.shape[1] == 5: + result.pred_boxes = RotatedBoxes(bbox_nms) + else: + result.pred_boxes = Boxes(bbox_nms) + result.scores = score_nms + result.pred_classes = class_nms.to(torch.int64) + + mask_fcn_probs = tensor_outputs.get("mask_fcn_probs", None) + if mask_fcn_probs is not None: + # finish the mask pred + mask_probs_pred = mask_fcn_probs + num_masks = mask_probs_pred.shape[0] + class_pred = result.pred_classes + indices = torch.arange(num_masks, device=class_pred.device) + mask_probs_pred = mask_probs_pred[indices, class_pred][:, None] + result.pred_masks = mask_probs_pred + elif force_mask_on: + # NOTE: there's no way to know the height/width of mask here, it won't be + # used anyway when batch size is 0, so just set them to 0. + result.pred_masks = torch.zeros([0, 1, 0, 0], dtype=torch.uint8) + + keypoints_out = tensor_outputs.get("keypoints_out", None) + kps_score = tensor_outputs.get("kps_score", None) + if keypoints_out is not None: + # keypoints_out: [N, 4, #kypoints], where 4 is in order of (x, y, score, prob) + keypoints_tensor = keypoints_out + # NOTE: it's possible that prob is not calculated if "should_output_softmax" + # is set to False in HeatmapMaxKeypoint, so just using raw score, seems + # it doesn't affect mAP. TODO: check more carefully. + keypoint_xyp = keypoints_tensor.transpose(1, 2)[:, :, [0, 1, 2]] + result.pred_keypoints = keypoint_xyp + elif kps_score is not None: + # keypoint heatmap to sparse data structure + pred_keypoint_logits = kps_score + keypoint_head.keypoint_rcnn_inference(pred_keypoint_logits, [result]) + + return results + + +def _cast_to_f32(f64): + return struct.unpack("f", struct.pack("f", f64))[0] + + +def set_caffe2_compatible_tensor_mode(model, enable=True): + def _fn(m): + if isinstance(m, Caffe2Compatible): + m.tensor_mode = enable + + model.apply(_fn) + + +def convert_batched_inputs_to_c2_format(batched_inputs, size_divisibility, device): + """ + See get_caffe2_inputs() below. + """ + assert all(isinstance(x, dict) for x in batched_inputs) + assert all(x["image"].dim() == 3 for x in batched_inputs) + + images = [x["image"] for x in batched_inputs] + images = ImageList.from_tensors(images, size_divisibility) + + im_info = [] + for input_per_image, image_size in zip(batched_inputs, images.image_sizes): + target_height = input_per_image.get("height", image_size[0]) + target_width = input_per_image.get("width", image_size[1]) # noqa + # NOTE: The scale inside im_info is kept as convention and for providing + # post-processing information if further processing is needed. For + # current Caffe2 model definitions that don't include post-processing inside + # the model, this number is not used. + # NOTE: There can be a slight difference between width and height + # scales, using a single number can results in numerical difference + # compared with D2's post-processing. + scale = target_height / image_size[0] + im_info.append([image_size[0], image_size[1], scale]) + im_info = torch.Tensor(im_info) + + return images.tensor.to(device), im_info.to(device) + + +class Caffe2MetaArch(Caffe2Compatible, torch.nn.Module): + """ + Base class for caffe2-compatible implementation of a meta architecture. + The forward is traceable and its traced graph can be converted to caffe2 + graph through ONNX. + """ + + def __init__(self, cfg, torch_model): + """ + Args: + cfg (CfgNode): + torch_model (nn.Module): the detectron2 model (meta_arch) to be + converted. + """ + super().__init__() + self._wrapped_model = torch_model + self.eval() + set_caffe2_compatible_tensor_mode(self, True) + + def get_caffe2_inputs(self, batched_inputs): + """ + Convert pytorch-style structured inputs to caffe2-style inputs that + are tuples of tensors. + + Args: + batched_inputs (list[dict]): inputs to a detectron2 model + in its standard format. Each dict has "image" (CHW tensor), and optionally + "height" and "width". + + Returns: + tuple[Tensor]: + tuple of tensors that will be the inputs to the + :meth:`forward` method. For existing models, the first + is an NCHW tensor (padded and batched); the second is + a im_info Nx3 tensor, where the rows are + (height, width, unused legacy parameter) + """ + return convert_batched_inputs_to_c2_format( + batched_inputs, + self._wrapped_model.backbone.size_divisibility, + self._wrapped_model.device, + ) + + def encode_additional_info(self, predict_net, init_net): + """ + Save extra metadata that will be used by inference in the output protobuf. + """ + pass + + def forward(self, inputs): + """ + Run the forward in caffe2-style. It has to use caffe2-compatible ops + and the method will be used for tracing. + + Args: + inputs (tuple[Tensor]): inputs defined by :meth:`get_caffe2_input`. + They will be the inputs of the converted caffe2 graph. + + Returns: + tuple[Tensor]: output tensors. They will be the outputs of the + converted caffe2 graph. + """ + raise NotImplementedError + + def _caffe2_preprocess_image(self, inputs): + """ + Caffe2 implementation of preprocess_image, which is called inside each MetaArch's forward. + It normalizes the input images, and the final caffe2 graph assumes the + inputs have been batched already. + """ + data, im_info = inputs + data = alias(data, "data") + im_info = alias(im_info, "im_info") + mean, std = self._wrapped_model.pixel_mean, self._wrapped_model.pixel_std + normalized_data = (data - mean) / std + normalized_data = alias(normalized_data, "normalized_data") + + # Pack (data, im_info) into ImageList which is recognized by self.inference. + images = ImageList(tensor=normalized_data, image_sizes=im_info) + return images + + @staticmethod + def get_outputs_converter(predict_net, init_net): + """ + Creates a function that converts outputs of the caffe2 model to + detectron2's standard format. + The function uses information in `predict_net` and `init_net` that are + available at inferene time. Therefore the function logic can be used in inference. + + The returned function has the following signature: + + def convert(batched_inputs, c2_inputs, c2_results) -> detectron2_outputs + + Where + + * batched_inputs (list[dict]): the original input format of the meta arch + * c2_inputs (tuple[Tensor]): the caffe2 inputs. + * c2_results (dict[str, Tensor]): the caffe2 output format, + corresponding to the outputs of the :meth:`forward` function. + * detectron2_outputs: the original output format of the meta arch. + + This function can be used to compare the outputs of the original meta arch and + the converted caffe2 graph. + + Returns: + callable: a callable of the above signature. + """ + raise NotImplementedError + + +class Caffe2GeneralizedRCNN(Caffe2MetaArch): + def __init__(self, cfg, torch_model): + assert isinstance(torch_model, meta_arch.GeneralizedRCNN) + torch_model = patch_generalized_rcnn(torch_model) + super().__init__(cfg, torch_model) + + try: + use_heatmap_max_keypoint = cfg.EXPORT_CAFFE2.USE_HEATMAP_MAX_KEYPOINT + except AttributeError: + use_heatmap_max_keypoint = False + self.roi_heads_patcher = ROIHeadsPatcher( + self._wrapped_model.roi_heads, use_heatmap_max_keypoint + ) + + def encode_additional_info(self, predict_net, init_net): + size_divisibility = self._wrapped_model.backbone.size_divisibility + check_set_pb_arg(predict_net, "size_divisibility", "i", size_divisibility) + check_set_pb_arg( + predict_net, "device", "s", str.encode(str(self._wrapped_model.device), "ascii") + ) + check_set_pb_arg(predict_net, "meta_architecture", "s", b"GeneralizedRCNN") + + @mock_torch_nn_functional_interpolate() + def forward(self, inputs): + if not self.tensor_mode: + return self._wrapped_model.inference(inputs) + images = self._caffe2_preprocess_image(inputs) + features = self._wrapped_model.backbone(images.tensor) + proposals, _ = self._wrapped_model.proposal_generator(images, features) + with self.roi_heads_patcher.mock_roi_heads(): + detector_results, _ = self._wrapped_model.roi_heads(images, features, proposals) + return tuple(detector_results[0].flatten()) + + @staticmethod + def get_outputs_converter(predict_net, init_net): + def f(batched_inputs, c2_inputs, c2_results): + _, im_info = c2_inputs + image_sizes = [[int(im[0]), int(im[1])] for im in im_info] + results = assemble_rcnn_outputs_by_name(image_sizes, c2_results) + return meta_arch.GeneralizedRCNN._postprocess(results, batched_inputs, image_sizes) + + return f + + +class Caffe2RetinaNet(Caffe2MetaArch): + def __init__(self, cfg, torch_model): + assert isinstance(torch_model, meta_arch.RetinaNet) + super().__init__(cfg, torch_model) + + @mock_torch_nn_functional_interpolate() + def forward(self, inputs): + assert self.tensor_mode + images = self._caffe2_preprocess_image(inputs) + + # explicitly return the images sizes to avoid removing "im_info" by ONNX + # since it's not used in the forward path + return_tensors = [images.image_sizes] + + features = self._wrapped_model.backbone(images.tensor) + features = [features[f] for f in self._wrapped_model.head_in_features] + for i, feature_i in enumerate(features): + features[i] = alias(feature_i, "feature_{}".format(i), is_backward=True) + return_tensors.append(features[i]) + + pred_logits, pred_anchor_deltas = self._wrapped_model.head(features) + for i, (box_cls_i, box_delta_i) in enumerate(zip(pred_logits, pred_anchor_deltas)): + return_tensors.append(alias(box_cls_i, "box_cls_{}".format(i))) + return_tensors.append(alias(box_delta_i, "box_delta_{}".format(i))) + + return tuple(return_tensors) + + def encode_additional_info(self, predict_net, init_net): + size_divisibility = self._wrapped_model.backbone.size_divisibility + check_set_pb_arg(predict_net, "size_divisibility", "i", size_divisibility) + check_set_pb_arg( + predict_net, "device", "s", str.encode(str(self._wrapped_model.device), "ascii") + ) + check_set_pb_arg(predict_net, "meta_architecture", "s", b"RetinaNet") + + # Inference parameters: + check_set_pb_arg( + predict_net, "score_threshold", "f", _cast_to_f32(self._wrapped_model.test_score_thresh) + ) + check_set_pb_arg( + predict_net, "topk_candidates", "i", self._wrapped_model.test_topk_candidates + ) + check_set_pb_arg( + predict_net, "nms_threshold", "f", _cast_to_f32(self._wrapped_model.test_nms_thresh) + ) + check_set_pb_arg( + predict_net, + "max_detections_per_image", + "i", + self._wrapped_model.max_detections_per_image, + ) + + check_set_pb_arg( + predict_net, + "bbox_reg_weights", + "floats", + [_cast_to_f32(w) for w in self._wrapped_model.box2box_transform.weights], + ) + self._encode_anchor_generator_cfg(predict_net) + + def _encode_anchor_generator_cfg(self, predict_net): + # serialize anchor_generator for future use + serialized_anchor_generator = io.BytesIO() + torch.save(self._wrapped_model.anchor_generator, serialized_anchor_generator) + # Ideally we can put anchor generating inside the model, then we don't + # need to store this information. + bytes = serialized_anchor_generator.getvalue() + check_set_pb_arg(predict_net, "serialized_anchor_generator", "s", bytes) + + @staticmethod + def get_outputs_converter(predict_net, init_net): + self = types.SimpleNamespace() + serialized_anchor_generator = io.BytesIO( + get_pb_arg_vals(predict_net, "serialized_anchor_generator", None) + ) + self.anchor_generator = torch.load(serialized_anchor_generator) + bbox_reg_weights = get_pb_arg_floats(predict_net, "bbox_reg_weights", None) + self.box2box_transform = Box2BoxTransform(weights=tuple(bbox_reg_weights)) + self.test_score_thresh = get_pb_arg_valf(predict_net, "score_threshold", None) + self.test_topk_candidates = get_pb_arg_vali(predict_net, "topk_candidates", None) + self.test_nms_thresh = get_pb_arg_valf(predict_net, "nms_threshold", None) + self.max_detections_per_image = get_pb_arg_vali( + predict_net, "max_detections_per_image", None + ) + + # hack to reuse inference code from RetinaNet + for meth in [ + "forward_inference", + "inference_single_image", + "_transpose_dense_predictions", + "_decode_multi_level_predictions", + "_decode_per_level_predictions", + ]: + setattr(self, meth, functools.partial(getattr(meta_arch.RetinaNet, meth), self)) + + def f(batched_inputs, c2_inputs, c2_results): + _, im_info = c2_inputs + image_sizes = [[int(im[0]), int(im[1])] for im in im_info] + dummy_images = ImageList( + torch.randn( + ( + len(im_info), + 3, + ) + + tuple(image_sizes[0]) + ), + image_sizes, + ) + + num_features = len([x for x in c2_results.keys() if x.startswith("box_cls_")]) + pred_logits = [c2_results["box_cls_{}".format(i)] for i in range(num_features)] + pred_anchor_deltas = [c2_results["box_delta_{}".format(i)] for i in range(num_features)] + + # For each feature level, feature should have the same batch size and + # spatial dimension as the box_cls and box_delta. + dummy_features = [x.clone()[:, 0:0, :, :] for x in pred_logits] + # self.num_classess can be inferred + self.num_classes = pred_logits[0].shape[1] // (pred_anchor_deltas[0].shape[1] // 4) + + results = self.forward_inference( + dummy_images, dummy_features, [pred_logits, pred_anchor_deltas] + ) + return meta_arch.GeneralizedRCNN._postprocess(results, batched_inputs, image_sizes) + + return f + + +META_ARCH_CAFFE2_EXPORT_TYPE_MAP = { + "GeneralizedRCNN": Caffe2GeneralizedRCNN, + "RetinaNet": Caffe2RetinaNet, +} diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_patch.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_patch.py new file mode 100644 index 0000000000000000000000000000000000000000..c9eee594a27cdec29ce5f2b6f7730171eda3805e --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_patch.py @@ -0,0 +1,152 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import contextlib +from unittest import mock +import torch + +from detectron2.modeling import poolers +from detectron2.modeling.proposal_generator import rpn +from detectron2.modeling.roi_heads import keypoint_head, mask_head +from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers + +from .c10 import ( + Caffe2Compatible, + Caffe2FastRCNNOutputsInference, + Caffe2KeypointRCNNInference, + Caffe2MaskRCNNInference, + Caffe2ROIPooler, + Caffe2RPN, +) + + +class GenericMixin(object): + pass + + +class Caffe2CompatibleConverter(object): + """ + A GenericUpdater which implements the `create_from` interface, by modifying + module object and assign it with another class replaceCls. + """ + + def __init__(self, replaceCls): + self.replaceCls = replaceCls + + def create_from(self, module): + # update module's class to the new class + assert isinstance(module, torch.nn.Module) + if issubclass(self.replaceCls, GenericMixin): + # replaceCls should act as mixin, create a new class on-the-fly + new_class = type( + "{}MixedWith{}".format(self.replaceCls.__name__, module.__class__.__name__), + (self.replaceCls, module.__class__), + {}, # {"new_method": lambda self: ...}, + ) + module.__class__ = new_class + else: + # replaceCls is complete class, this allow arbitrary class swap + module.__class__ = self.replaceCls + + # initialize Caffe2Compatible + if isinstance(module, Caffe2Compatible): + module.tensor_mode = False + + return module + + +def patch(model, target, updater, *args, **kwargs): + """ + recursively (post-order) update all modules with the target type and its + subclasses, make a initialization/composition/inheritance/... via the + updater.create_from. + """ + for name, module in model.named_children(): + model._modules[name] = patch(module, target, updater, *args, **kwargs) + if isinstance(model, target): + return updater.create_from(model, *args, **kwargs) + return model + + +def patch_generalized_rcnn(model): + ccc = Caffe2CompatibleConverter + model = patch(model, rpn.RPN, ccc(Caffe2RPN)) + model = patch(model, poolers.ROIPooler, ccc(Caffe2ROIPooler)) + + return model + + +@contextlib.contextmanager +def mock_fastrcnn_outputs_inference( + tensor_mode, check=True, box_predictor_type=FastRCNNOutputLayers +): + with mock.patch.object( + box_predictor_type, + "inference", + autospec=True, + side_effect=Caffe2FastRCNNOutputsInference(tensor_mode), + ) as mocked_func: + yield + if check: + assert mocked_func.call_count > 0 + + +@contextlib.contextmanager +def mock_mask_rcnn_inference(tensor_mode, patched_module, check=True): + with mock.patch( + "{}.mask_rcnn_inference".format(patched_module), side_effect=Caffe2MaskRCNNInference() + ) as mocked_func: + yield + if check: + assert mocked_func.call_count > 0 + + +@contextlib.contextmanager +def mock_keypoint_rcnn_inference(tensor_mode, patched_module, use_heatmap_max_keypoint, check=True): + with mock.patch( + "{}.keypoint_rcnn_inference".format(patched_module), + side_effect=Caffe2KeypointRCNNInference(use_heatmap_max_keypoint), + ) as mocked_func: + yield + if check: + assert mocked_func.call_count > 0 + + +class ROIHeadsPatcher: + def __init__(self, heads, use_heatmap_max_keypoint): + self.heads = heads + self.use_heatmap_max_keypoint = use_heatmap_max_keypoint + + @contextlib.contextmanager + def mock_roi_heads(self, tensor_mode=True): + """ + Patching several inference functions inside ROIHeads and its subclasses + + Args: + tensor_mode (bool): whether the inputs/outputs are caffe2's tensor + format or not. Default to True. + """ + # NOTE: this requries the `keypoint_rcnn_inference` and `mask_rcnn_inference` + # are called inside the same file as BaseXxxHead due to using mock.patch. + kpt_heads_mod = keypoint_head.BaseKeypointRCNNHead.__module__ + mask_head_mod = mask_head.BaseMaskRCNNHead.__module__ + + mock_ctx_managers = [ + mock_fastrcnn_outputs_inference( + tensor_mode=tensor_mode, + check=True, + box_predictor_type=type(self.heads.box_predictor), + ) + ] + if getattr(self.heads, "keypoint_on", False): + mock_ctx_managers += [ + mock_keypoint_rcnn_inference( + tensor_mode, kpt_heads_mod, self.use_heatmap_max_keypoint + ) + ] + if getattr(self.heads, "mask_on", False): + mock_ctx_managers += [mock_mask_rcnn_inference(tensor_mode, mask_head_mod)] + + with contextlib.ExitStack() as stack: # python 3.3+ + for mgr in mock_ctx_managers: + stack.enter_context(mgr) + yield diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/flatten.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/flatten.py new file mode 100644 index 0000000000000000000000000000000000000000..f5ba4297567d650f147eebeed361e9d62fab899d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/flatten.py @@ -0,0 +1,330 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import collections +from dataclasses import dataclass +from typing import Callable, List, Optional, Tuple +import torch +from torch import nn + +from detectron2.structures import Boxes, Instances, ROIMasks +from detectron2.utils.registry import _convert_target_to_string, locate + +from .torchscript_patch import patch_builtin_len + + +@dataclass +class Schema: + """ + A Schema defines how to flatten a possibly hierarchical object into tuple of + primitive objects, so it can be used as inputs/outputs of PyTorch's tracing. + + PyTorch does not support tracing a function that produces rich output + structures (e.g. dict, Instances, Boxes). To trace such a function, we + flatten the rich object into tuple of tensors, and return this tuple of tensors + instead. Meanwhile, we also need to know how to "rebuild" the original object + from the flattened results, so we can evaluate the flattened results. + A Schema defines how to flatten an object, and while flattening it, it records + necessary schemas so that the object can be rebuilt using the flattened outputs. + + The flattened object and the schema object is returned by ``.flatten`` classmethod. + Then the original object can be rebuilt with the ``__call__`` method of schema. + + A Schema is a dataclass that can be serialized easily. + """ + + # inspired by FetchMapper in tensorflow/python/client/session.py + + @classmethod + def flatten(cls, obj): + raise NotImplementedError + + def __call__(self, values): + raise NotImplementedError + + @staticmethod + def _concat(values): + ret = () + sizes = [] + for v in values: + assert isinstance(v, tuple), "Flattened results must be a tuple" + ret = ret + v + sizes.append(len(v)) + return ret, sizes + + @staticmethod + def _split(values, sizes): + if len(sizes): + expected_len = sum(sizes) + assert ( + len(values) == expected_len + ), f"Values has length {len(values)} but expect length {expected_len}." + ret = [] + for k in range(len(sizes)): + begin, end = sum(sizes[:k]), sum(sizes[: k + 1]) + ret.append(values[begin:end]) + return ret + + +@dataclass +class ListSchema(Schema): + schemas: List[Schema] # the schemas that define how to flatten each element in the list + sizes: List[int] # the flattened length of each element + + def __call__(self, values): + values = self._split(values, self.sizes) + if len(values) != len(self.schemas): + raise ValueError( + f"Values has length {len(values)} but schemas " f"has length {len(self.schemas)}!" + ) + values = [m(v) for m, v in zip(self.schemas, values)] + return list(values) + + @classmethod + def flatten(cls, obj): + res = [flatten_to_tuple(k) for k in obj] + values, sizes = cls._concat([k[0] for k in res]) + return values, cls([k[1] for k in res], sizes) + + +@dataclass +class TupleSchema(ListSchema): + def __call__(self, values): + return tuple(super().__call__(values)) + + +@dataclass +class IdentitySchema(Schema): + def __call__(self, values): + return values[0] + + @classmethod + def flatten(cls, obj): + return (obj,), cls() + + +@dataclass +class DictSchema(ListSchema): + keys: List[str] + + def __call__(self, values): + values = super().__call__(values) + return dict(zip(self.keys, values)) + + @classmethod + def flatten(cls, obj): + for k in obj.keys(): + if not isinstance(k, str): + raise KeyError("Only support flattening dictionaries if keys are str.") + keys = sorted(obj.keys()) + values = [obj[k] for k in keys] + ret, schema = ListSchema.flatten(values) + return ret, cls(schema.schemas, schema.sizes, keys) + + +@dataclass +class InstancesSchema(DictSchema): + def __call__(self, values): + image_size, fields = values[-1], values[:-1] + fields = super().__call__(fields) + return Instances(image_size, **fields) + + @classmethod + def flatten(cls, obj): + ret, schema = super().flatten(obj.get_fields()) + size = obj.image_size + if not isinstance(size, torch.Tensor): + size = torch.tensor(size) + return ret + (size,), schema + + +@dataclass +class TensorWrapSchema(Schema): + """ + For classes that are simple wrapper of tensors, e.g. + Boxes, RotatedBoxes, BitMasks + """ + + class_name: str + + def __call__(self, values): + return locate(self.class_name)(values[0]) + + @classmethod + def flatten(cls, obj): + return (obj.tensor,), cls(_convert_target_to_string(type(obj))) + + +# if more custom structures needed in the future, can allow +# passing in extra schemas for custom types +def flatten_to_tuple(obj): + """ + Flatten an object so it can be used for PyTorch tracing. + Also returns how to rebuild the original object from the flattened outputs. + + Returns: + res (tuple): the flattened results that can be used as tracing outputs + schema: an object with a ``__call__`` method such that ``schema(res) == obj``. + It is a pure dataclass that can be serialized. + """ + schemas = [ + ((str, bytes), IdentitySchema), + (list, ListSchema), + (tuple, TupleSchema), + (collections.abc.Mapping, DictSchema), + (Instances, InstancesSchema), + ((Boxes, ROIMasks), TensorWrapSchema), + ] + for klass, schema in schemas: + if isinstance(obj, klass): + F = schema + break + else: + F = IdentitySchema + + return F.flatten(obj) + + +class TracingAdapter(nn.Module): + """ + A model may take rich input/output format (e.g. dict or custom classes), + but `torch.jit.trace` requires tuple of tensors as input/output. + This adapter flattens input/output format of a model so it becomes traceable. + + It also records the necessary schema to rebuild model's inputs/outputs from flattened + inputs/outputs. + + Example: + :: + outputs = model(inputs) # inputs/outputs may be rich structure + adapter = TracingAdapter(model, inputs) + + # can now trace the model, with adapter.flattened_inputs, or another + # tuple of tensors with the same length and meaning + traced = torch.jit.trace(adapter, adapter.flattened_inputs) + + # traced model can only produce flattened outputs (tuple of tensors) + flattened_outputs = traced(*adapter.flattened_inputs) + # adapter knows the schema to convert it back (new_outputs == outputs) + new_outputs = adapter.outputs_schema(flattened_outputs) + """ + + flattened_inputs: Tuple[torch.Tensor] = None + """ + Flattened version of inputs given to this class's constructor. + """ + + inputs_schema: Schema = None + """ + Schema of the inputs given to this class's constructor. + """ + + outputs_schema: Schema = None + """ + Schema of the output produced by calling the given model with inputs. + """ + + def __init__( + self, + model: nn.Module, + inputs, + inference_func: Optional[Callable] = None, + allow_non_tensor: bool = False, + ): + """ + Args: + model: an nn.Module + inputs: An input argument or a tuple of input arguments used to call model. + After flattening, it has to only consist of tensors. + inference_func: a callable that takes (model, *inputs), calls the + model with inputs, and return outputs. By default it + is ``lambda model, *inputs: model(*inputs)``. Can be override + if you need to call the model differently. + allow_non_tensor: allow inputs/outputs to contain non-tensor objects. + This option will filter out non-tensor objects to make the + model traceable, but ``inputs_schema``/``outputs_schema`` cannot be + used anymore because inputs/outputs cannot be rebuilt from pure tensors. + This is useful when you're only interested in the single trace of + execution (e.g. for flop count), but not interested in + generalizing the traced graph to new inputs. + """ + super().__init__() + if isinstance(model, (nn.parallel.distributed.DistributedDataParallel, nn.DataParallel)): + model = model.module + self.model = model + if not isinstance(inputs, tuple): + inputs = (inputs,) + self.inputs = inputs + self.allow_non_tensor = allow_non_tensor + + if inference_func is None: + inference_func = lambda model, *inputs: model(*inputs) # noqa + self.inference_func = inference_func + + self.flattened_inputs, self.inputs_schema = flatten_to_tuple(inputs) + + if all(isinstance(x, torch.Tensor) for x in self.flattened_inputs): + return + if self.allow_non_tensor: + self.flattened_inputs = tuple( + [x for x in self.flattened_inputs if isinstance(x, torch.Tensor)] + ) + self.inputs_schema = None + else: + for input in self.flattened_inputs: + if not isinstance(input, torch.Tensor): + raise ValueError( + "Inputs for tracing must only contain tensors. " + f"Got a {type(input)} instead." + ) + + def forward(self, *args: torch.Tensor): + with torch.no_grad(), patch_builtin_len(): + if self.inputs_schema is not None: + inputs_orig_format = self.inputs_schema(args) + else: + if len(args) != len(self.flattened_inputs) or any( + x is not y for x, y in zip(args, self.flattened_inputs) + ): + raise ValueError( + "TracingAdapter does not contain valid inputs_schema." + " So it cannot generalize to other inputs and must be" + " traced with `.flattened_inputs`." + ) + inputs_orig_format = self.inputs + + outputs = self.inference_func(self.model, *inputs_orig_format) + flattened_outputs, schema = flatten_to_tuple(outputs) + + flattened_output_tensors = tuple( + [x for x in flattened_outputs if isinstance(x, torch.Tensor)] + ) + if len(flattened_output_tensors) < len(flattened_outputs): + if self.allow_non_tensor: + flattened_outputs = flattened_output_tensors + self.outputs_schema = None + else: + raise ValueError( + "Model cannot be traced because some model outputs " + "cannot flatten to tensors." + ) + else: # schema is valid + if self.outputs_schema is None: + self.outputs_schema = schema + else: + assert self.outputs_schema == schema, ( + "Model should always return outputs with the same " + "structure so it can be traced!" + ) + return flattened_outputs + + def _create_wrapper(self, traced_model): + """ + Return a function that has an input/output interface the same as the + original model, but it calls the given traced model under the hood. + """ + + def forward(*args): + flattened_inputs, _ = flatten_to_tuple(args) + flattened_outputs = traced_model(*flattened_inputs) + return self.outputs_schema(flattened_outputs) + + return forward diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/shared.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/shared.py new file mode 100644 index 0000000000000000000000000000000000000000..2d0f7bf3999064a68f28a1207d65a2de7ae98c0a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/shared.py @@ -0,0 +1,1034 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import collections +import contextlib +import copy +import functools +import logging +import numpy as np +import os +from typing import Any, Callable, Dict, List, Optional, Tuple, Union +from unittest import mock +import caffe2.python.utils as putils +import torch +import torch.nn.functional as F +from caffe2.proto import caffe2_pb2 +from caffe2.python import core, net_drawer, workspace +from torch.nn.functional import interpolate as interp + +logger = logging.getLogger(__name__) + + +# ==== torch/utils_toffee/cast.py ======================================= + + +def to_device(t, device_str): + """ + This function is a replacement of .to(another_device) such that it allows the + casting to be traced properly by explicitly calling the underlying copy ops. + It also avoids introducing unncessary op when casting to the same device. + """ + src = t.device + dst = torch.device(device_str) + + if src == dst: + return t + elif src.type == "cuda" and dst.type == "cpu": + return torch.ops._caffe2.CopyGPUToCPU(t) + elif src.type == "cpu" and dst.type == "cuda": + return torch.ops._caffe2.CopyCPUToGPU(t) + else: + raise RuntimeError("Can't cast tensor from device {} to device {}".format(src, dst)) + + +# ==== torch/utils_toffee/interpolate.py ======================================= + + +# Note: borrowed from vision/detection/fair/detectron/detectron/modeling/detector.py +def BilinearInterpolation(tensor_in, up_scale): + assert up_scale % 2 == 0, "Scale should be even" + + def upsample_filt(size): + factor = (size + 1) // 2 + if size % 2 == 1: + center = factor - 1 + else: + center = factor - 0.5 + + og = np.ogrid[:size, :size] + return (1 - abs(og[0] - center) / factor) * (1 - abs(og[1] - center) / factor) + + kernel_size = int(up_scale) * 2 + bil_filt = upsample_filt(kernel_size) + + dim = int(tensor_in.shape[1]) + kernel = np.zeros((dim, dim, kernel_size, kernel_size), dtype=np.float32) + kernel[range(dim), range(dim), :, :] = bil_filt + + tensor_out = F.conv_transpose2d( + tensor_in, + weight=to_device(torch.Tensor(kernel), tensor_in.device), + bias=None, + stride=int(up_scale), + padding=int(up_scale / 2), + ) + + return tensor_out + + +# NOTE: ONNX is incompatible with traced torch.nn.functional.interpolate if +# using dynamic `scale_factor` rather than static `size`. (T43166860) +# NOTE: Caffe2 Int8 conversion might not be able to quantize `size` properly. +def onnx_compatibale_interpolate( + input, size=None, scale_factor=None, mode="nearest", align_corners=None +): + # NOTE: The input dimensions are interpreted in the form: + # `mini-batch x channels x [optional depth] x [optional height] x width`. + if size is None and scale_factor is not None: + if input.dim() == 4: + if isinstance(scale_factor, (int, float)): + height_scale, width_scale = (scale_factor, scale_factor) + else: + assert isinstance(scale_factor, (tuple, list)) + assert len(scale_factor) == 2 + height_scale, width_scale = scale_factor + + assert not align_corners, "No matching C2 op for align_corners == True" + if mode == "nearest": + return torch.ops._caffe2.ResizeNearest( + input, order="NCHW", width_scale=width_scale, height_scale=height_scale + ) + elif mode == "bilinear": + logger.warning( + "Use F.conv_transpose2d for bilinear interpolate" + " because there's no such C2 op, this may cause significant" + " slowdown and the boundary pixels won't be as same as" + " using F.interpolate due to padding." + ) + assert height_scale == width_scale + return BilinearInterpolation(input, up_scale=height_scale) + logger.warning("Output size is not static, it might cause ONNX conversion issue") + + return interp(input, size, scale_factor, mode, align_corners) + + +@contextlib.contextmanager +def mock_torch_nn_functional_interpolate(): + if torch.onnx.is_in_onnx_export(): + with mock.patch( + "torch.nn.functional.interpolate", side_effect=onnx_compatibale_interpolate + ): + yield + else: + yield + + +# ==== torch/utils_caffe2/ws_utils.py ========================================== + + +class ScopedWS(object): + def __init__(self, ws_name, is_reset, is_cleanup=False): + self.ws_name = ws_name + self.is_reset = is_reset + self.is_cleanup = is_cleanup + self.org_ws = "" + + def __enter__(self): + self.org_ws = workspace.CurrentWorkspace() + if self.ws_name is not None: + workspace.SwitchWorkspace(self.ws_name, True) + if self.is_reset: + workspace.ResetWorkspace() + + return workspace + + def __exit__(self, *args): + if self.is_cleanup: + workspace.ResetWorkspace() + if self.ws_name is not None: + workspace.SwitchWorkspace(self.org_ws) + + +def fetch_any_blob(name): + bb = None + try: + bb = workspace.FetchBlob(name) + except TypeError: + bb = workspace.FetchInt8Blob(name) + except Exception as e: + logger.error("Get blob {} error: {}".format(name, e)) + + return bb + + +# ==== torch/utils_caffe2/protobuf.py ========================================== + + +def get_pb_arg(pb, arg_name): + for x in pb.arg: + if x.name == arg_name: + return x + return None + + +def get_pb_arg_valf(pb, arg_name, default_val): + arg = get_pb_arg(pb, arg_name) + return arg.f if arg is not None else default_val + + +def get_pb_arg_floats(pb, arg_name, default_val): + arg = get_pb_arg(pb, arg_name) + return list(map(float, arg.floats)) if arg is not None else default_val + + +def get_pb_arg_ints(pb, arg_name, default_val): + arg = get_pb_arg(pb, arg_name) + return list(map(int, arg.ints)) if arg is not None else default_val + + +def get_pb_arg_vali(pb, arg_name, default_val): + arg = get_pb_arg(pb, arg_name) + return arg.i if arg is not None else default_val + + +def get_pb_arg_vals(pb, arg_name, default_val): + arg = get_pb_arg(pb, arg_name) + return arg.s if arg is not None else default_val + + +def get_pb_arg_valstrings(pb, arg_name, default_val): + arg = get_pb_arg(pb, arg_name) + return list(arg.strings) if arg is not None else default_val + + +def check_set_pb_arg(pb, arg_name, arg_attr, arg_value, allow_override=False): + arg = get_pb_arg(pb, arg_name) + if arg is None: + arg = putils.MakeArgument(arg_name, arg_value) + assert hasattr(arg, arg_attr) + pb.arg.extend([arg]) + if allow_override and getattr(arg, arg_attr) != arg_value: + logger.warning( + "Override argument {}: {} -> {}".format(arg_name, getattr(arg, arg_attr), arg_value) + ) + setattr(arg, arg_attr, arg_value) + else: + assert arg is not None + assert getattr(arg, arg_attr) == arg_value, "Existing value {}, new value {}".format( + getattr(arg, arg_attr), arg_value + ) + + +def _create_const_fill_op_from_numpy(name, tensor, device_option=None): + assert type(tensor) == np.ndarray + kTypeNameMapper = { + np.dtype("float32"): "GivenTensorFill", + np.dtype("int32"): "GivenTensorIntFill", + np.dtype("int64"): "GivenTensorInt64Fill", + np.dtype("uint8"): "GivenTensorStringFill", + } + + args_dict = {} + if tensor.dtype == np.dtype("uint8"): + args_dict.update({"values": [str(tensor.data)], "shape": [1]}) + else: + args_dict.update({"values": tensor, "shape": tensor.shape}) + + if device_option is not None: + args_dict["device_option"] = device_option + + return core.CreateOperator(kTypeNameMapper[tensor.dtype], [], [name], **args_dict) + + +def _create_const_fill_op_from_c2_int8_tensor(name, int8_tensor): + assert type(int8_tensor) == workspace.Int8Tensor + kTypeNameMapper = { + np.dtype("int32"): "Int8GivenIntTensorFill", + np.dtype("uint8"): "Int8GivenTensorFill", + } + + tensor = int8_tensor.data + assert tensor.dtype in [np.dtype("uint8"), np.dtype("int32")] + values = tensor.tobytes() if tensor.dtype == np.dtype("uint8") else tensor + + return core.CreateOperator( + kTypeNameMapper[tensor.dtype], + [], + [name], + values=values, + shape=tensor.shape, + Y_scale=int8_tensor.scale, + Y_zero_point=int8_tensor.zero_point, + ) + + +def create_const_fill_op( + name: str, + blob: Union[np.ndarray, workspace.Int8Tensor], + device_option: Optional[caffe2_pb2.DeviceOption] = None, +) -> caffe2_pb2.OperatorDef: + """ + Given a blob object, return the Caffe2 operator that creates this blob + as constant. Currently support NumPy tensor and Caffe2 Int8Tensor. + """ + + tensor_type = type(blob) + assert tensor_type in [ + np.ndarray, + workspace.Int8Tensor, + ], 'Error when creating const fill op for "{}", unsupported blob type: {}'.format( + name, type(blob) + ) + + if tensor_type == np.ndarray: + return _create_const_fill_op_from_numpy(name, blob, device_option) + elif tensor_type == workspace.Int8Tensor: + assert device_option is None + return _create_const_fill_op_from_c2_int8_tensor(name, blob) + + +def construct_init_net_from_params( + params: Dict[str, Any], device_options: Optional[Dict[str, caffe2_pb2.DeviceOption]] = None +) -> caffe2_pb2.NetDef: + """ + Construct the init_net from params dictionary + """ + init_net = caffe2_pb2.NetDef() + device_options = device_options or {} + for name, blob in params.items(): + if isinstance(blob, str): + logger.warning( + ( + "Blob {} with type {} is not supported in generating init net," + " skipped.".format(name, type(blob)) + ) + ) + continue + init_net.op.extend( + [create_const_fill_op(name, blob, device_option=device_options.get(name, None))] + ) + init_net.external_output.append(name) + return init_net + + +def get_producer_map(ssa): + """ + Return dict from versioned blob to (i, j), + where i is index of producer op, j is the index of output of that op. + """ + producer_map = {} + for i in range(len(ssa)): + outputs = ssa[i][1] + for j, outp in enumerate(outputs): + producer_map[outp] = (i, j) + return producer_map + + +def get_consumer_map(ssa): + """ + Return dict from versioned blob to list of (i, j), + where i is index of consumer op, j is the index of input of that op. + """ + consumer_map = collections.defaultdict(list) + for i in range(len(ssa)): + inputs = ssa[i][0] + for j, inp in enumerate(inputs): + consumer_map[inp].append((i, j)) + return consumer_map + + +def get_params_from_init_net( + init_net: caffe2_pb2.NetDef, +) -> [Dict[str, Any], Dict[str, caffe2_pb2.DeviceOption]]: + """ + Take the output blobs from init_net by running it. + Outputs: + params: dict from blob name to numpy array + device_options: dict from blob name to the device option of its creating op + """ + # NOTE: this assumes that the params is determined by producer op with the + # only exception be CopyGPUToCPU which is CUDA op but returns CPU tensor. + def _get_device_option(producer_op): + if producer_op.type == "CopyGPUToCPU": + return caffe2_pb2.DeviceOption() + else: + return producer_op.device_option + + with ScopedWS("__get_params_from_init_net__", is_reset=True, is_cleanup=True) as ws: + ws.RunNetOnce(init_net) + params = {b: fetch_any_blob(b) for b in init_net.external_output} + ssa, versions = core.get_ssa(init_net) + producer_map = get_producer_map(ssa) + device_options = { + b: _get_device_option(init_net.op[producer_map[(b, versions[b])][0]]) + for b in init_net.external_output + } + return params, device_options + + +def _updater_raise(op, input_types, output_types): + raise RuntimeError( + "Failed to apply updater for op {} given input_types {} and" + " output_types {}".format(op, input_types, output_types) + ) + + +def _generic_status_identifier( + predict_net: caffe2_pb2.NetDef, + status_updater: Callable, + known_status: Dict[Tuple[str, int], Any], +) -> Dict[Tuple[str, int], Any]: + """ + Statically infer the status of each blob, the status can be such as device type + (CPU/GPU), layout (NCHW/NHWC), data type (float32/int8), etc. "Blob" here + is versioned blob (Tuple[str, int]) in the format compatible with ssa. + Inputs: + predict_net: the caffe2 network + status_updater: a callable, given an op and the status of its input/output, + it returns the updated status of input/output. `None` is used for + representing unknown status. + known_status: a dict containing known status, used as initialization. + Outputs: + A dict mapping from versioned blob to its status + """ + ssa, versions = core.get_ssa(predict_net) + versioned_ext_input = [(b, 0) for b in predict_net.external_input] + versioned_ext_output = [(b, versions[b]) for b in predict_net.external_output] + all_versioned_blobs = set().union(*[set(x[0] + x[1]) for x in ssa]) + + allowed_vbs = all_versioned_blobs.union(versioned_ext_input).union(versioned_ext_output) + assert all(k in allowed_vbs for k in known_status) + assert all(v is not None for v in known_status.values()) + _known_status = copy.deepcopy(known_status) + + def _check_and_update(key, value): + assert value is not None + if key in _known_status: + if not _known_status[key] == value: + raise RuntimeError( + "Confilict status for {}, existing status {}, new status {}".format( + key, _known_status[key], value + ) + ) + _known_status[key] = value + + def _update_i(op, ssa_i): + versioned_inputs = ssa_i[0] + versioned_outputs = ssa_i[1] + + inputs_status = [_known_status.get(b, None) for b in versioned_inputs] + outputs_status = [_known_status.get(b, None) for b in versioned_outputs] + + new_inputs_status, new_outputs_status = status_updater(op, inputs_status, outputs_status) + + for versioned_blob, status in zip( + versioned_inputs + versioned_outputs, new_inputs_status + new_outputs_status + ): + if status is not None: + _check_and_update(versioned_blob, status) + + for op, ssa_i in zip(predict_net.op, ssa): + _update_i(op, ssa_i) + for op, ssa_i in zip(reversed(predict_net.op), reversed(ssa)): + _update_i(op, ssa_i) + + # NOTE: This strictly checks all the blob from predict_net must be assgined + # a known status. However sometimes it's impossible (eg. having deadend op), + # we may relax this constraint if + for k in all_versioned_blobs: + if k not in _known_status: + raise NotImplementedError( + "Can not infer the status for {}. Currently only support the case where" + " a single forward and backward pass can identify status for all blobs.".format(k) + ) + + return _known_status + + +def infer_device_type( + predict_net: caffe2_pb2.NetDef, + known_status: Dict[Tuple[str, int], Any], + device_name_style: str = "caffe2", +) -> Dict[Tuple[str, int], str]: + """Return the device type ("cpu" or "gpu"/"cuda") of each (versioned) blob""" + + assert device_name_style in ["caffe2", "pytorch"] + _CPU_STR = "cpu" + _GPU_STR = "gpu" if device_name_style == "caffe2" else "cuda" + + def _copy_cpu_to_gpu_updater(op, input_types, output_types): + if input_types[0] == _GPU_STR or output_types[0] == _CPU_STR: + _updater_raise(op, input_types, output_types) + return ([_CPU_STR], [_GPU_STR]) + + def _copy_gpu_to_cpu_updater(op, input_types, output_types): + if input_types[0] == _CPU_STR or output_types[0] == _GPU_STR: + _updater_raise(op, input_types, output_types) + return ([_GPU_STR], [_CPU_STR]) + + def _other_ops_updater(op, input_types, output_types): + non_none_types = [x for x in input_types + output_types if x is not None] + if len(non_none_types) > 0: + the_type = non_none_types[0] + if not all(x == the_type for x in non_none_types): + _updater_raise(op, input_types, output_types) + else: + the_type = None + return ([the_type for _ in op.input], [the_type for _ in op.output]) + + def _device_updater(op, *args, **kwargs): + return { + "CopyCPUToGPU": _copy_cpu_to_gpu_updater, + "CopyGPUToCPU": _copy_gpu_to_cpu_updater, + }.get(op.type, _other_ops_updater)(op, *args, **kwargs) + + return _generic_status_identifier(predict_net, _device_updater, known_status) + + +# ==== torch/utils_caffe2/vis.py =============================================== + + +def _modify_blob_names(ops, blob_rename_f): + ret = [] + + def _replace_list(blob_list, replaced_list): + del blob_list[:] + blob_list.extend(replaced_list) + + for x in ops: + cur = copy.deepcopy(x) + _replace_list(cur.input, list(map(blob_rename_f, cur.input))) + _replace_list(cur.output, list(map(blob_rename_f, cur.output))) + ret.append(cur) + + return ret + + +def _rename_blob(name, blob_sizes, blob_ranges): + def _list_to_str(bsize): + ret = ", ".join([str(x) for x in bsize]) + ret = "[" + ret + "]" + return ret + + ret = name + if blob_sizes is not None and name in blob_sizes: + ret += "\n" + _list_to_str(blob_sizes[name]) + if blob_ranges is not None and name in blob_ranges: + ret += "\n" + _list_to_str(blob_ranges[name]) + + return ret + + +# graph_name could not contain word 'graph' +def save_graph(net, file_name, graph_name="net", op_only=True, blob_sizes=None, blob_ranges=None): + blob_rename_f = functools.partial(_rename_blob, blob_sizes=blob_sizes, blob_ranges=blob_ranges) + return save_graph_base(net, file_name, graph_name, op_only, blob_rename_f) + + +def save_graph_base(net, file_name, graph_name="net", op_only=True, blob_rename_func=None): + graph = None + ops = net.op + if blob_rename_func is not None: + ops = _modify_blob_names(ops, blob_rename_func) + if not op_only: + graph = net_drawer.GetPydotGraph(ops, graph_name, rankdir="TB") + else: + graph = net_drawer.GetPydotGraphMinimal( + ops, graph_name, rankdir="TB", minimal_dependency=True + ) + + try: + par_dir = os.path.dirname(file_name) + if not os.path.exists(par_dir): + os.makedirs(par_dir) + + format = os.path.splitext(os.path.basename(file_name))[-1] + if format == ".png": + graph.write_png(file_name) + elif format == ".pdf": + graph.write_pdf(file_name) + elif format == ".svg": + graph.write_svg(file_name) + else: + print("Incorrect format {}".format(format)) + except Exception as e: + print("Error when writing graph to image {}".format(e)) + + return graph + + +# ==== torch/utils_toffee/aten_to_caffe2.py ==================================== + + +def group_norm_replace_aten_with_caffe2(predict_net: caffe2_pb2.NetDef): + """ + For ONNX exported model, GroupNorm will be represented as ATen op, + this can be a drop in replacement from ATen to GroupNorm + """ + count = 0 + for op in predict_net.op: + if op.type == "ATen": + op_name = get_pb_arg_vals(op, "operator", None) # return byte in py3 + if op_name and op_name.decode() == "group_norm": + op.arg.remove(get_pb_arg(op, "operator")) + + if get_pb_arg_vali(op, "cudnn_enabled", None): + op.arg.remove(get_pb_arg(op, "cudnn_enabled")) + + num_groups = get_pb_arg_vali(op, "num_groups", None) + if num_groups is not None: + op.arg.remove(get_pb_arg(op, "num_groups")) + check_set_pb_arg(op, "group", "i", num_groups) + + op.type = "GroupNorm" + count += 1 + if count > 1: + logger.info("Replaced {} ATen operator to GroupNormOp".format(count)) + + +# ==== torch/utils_toffee/alias.py ============================================= + + +def alias(x, name, is_backward=False): + if not torch.onnx.is_in_onnx_export(): + return x + assert isinstance(x, torch.Tensor) + return torch.ops._caffe2.AliasWithName(x, name, is_backward=is_backward) + + +def fuse_alias_placeholder(predict_net, init_net): + """Remove AliasWithName placeholder and rename the input/output of it""" + # First we finish all the re-naming + for i, op in enumerate(predict_net.op): + if op.type == "AliasWithName": + assert len(op.input) == 1 + assert len(op.output) == 1 + name = get_pb_arg_vals(op, "name", None).decode() + is_backward = bool(get_pb_arg_vali(op, "is_backward", 0)) + rename_op_input(predict_net, init_net, i, 0, name, from_producer=is_backward) + rename_op_output(predict_net, i, 0, name) + + # Remove AliasWithName, should be very safe since it's a non-op + new_ops = [] + for op in predict_net.op: + if op.type != "AliasWithName": + new_ops.append(op) + else: + # safety check + assert op.input == op.output + assert op.input[0] == op.arg[0].s.decode() + del predict_net.op[:] + predict_net.op.extend(new_ops) + + +# ==== torch/utils_caffe2/graph_transform.py =================================== + + +class IllegalGraphTransformError(ValueError): + """When a graph transform function call can't be executed.""" + + +def _rename_versioned_blob_in_proto( + proto: caffe2_pb2.NetDef, + old_name: str, + new_name: str, + version: int, + ssa: List[Tuple[List[Tuple[str, int]], List[Tuple[str, int]]]], + start_versions: Dict[str, int], + end_versions: Dict[str, int], +): + """In given proto, rename all blobs with matched version""" + # Operater list + for op, i_th_ssa in zip(proto.op, ssa): + versioned_inputs, versioned_outputs = i_th_ssa + for i in range(len(op.input)): + if versioned_inputs[i] == (old_name, version): + op.input[i] = new_name + for i in range(len(op.output)): + if versioned_outputs[i] == (old_name, version): + op.output[i] = new_name + # external_input + if start_versions.get(old_name, 0) == version: + for i in range(len(proto.external_input)): + if proto.external_input[i] == old_name: + proto.external_input[i] = new_name + # external_output + if end_versions.get(old_name, 0) == version: + for i in range(len(proto.external_output)): + if proto.external_output[i] == old_name: + proto.external_output[i] = new_name + + +def rename_op_input( + predict_net: caffe2_pb2.NetDef, + init_net: caffe2_pb2.NetDef, + op_id: int, + input_id: int, + new_name: str, + from_producer: bool = False, +): + """ + Rename the op_id-th operator in predict_net, change it's input_id-th input's + name to the new_name. It also does automatic re-route and change + external_input and init_net if necessary. + - It requires the input is only consumed by this op. + - This function modifies predict_net and init_net in-place. + - When from_producer is enable, this also updates other operators that consumes + the same input. Be cautious because may trigger unintended behavior. + """ + assert isinstance(predict_net, caffe2_pb2.NetDef) + assert isinstance(init_net, caffe2_pb2.NetDef) + + init_net_ssa, init_net_versions = core.get_ssa(init_net) + predict_net_ssa, predict_net_versions = core.get_ssa( + predict_net, copy.deepcopy(init_net_versions) + ) + + versioned_inputs, versioned_outputs = predict_net_ssa[op_id] + old_name, version = versioned_inputs[input_id] + + if from_producer: + producer_map = get_producer_map(predict_net_ssa) + if not (old_name, version) in producer_map: + raise NotImplementedError( + "Can't find producer, the input {} is probably from" + " init_net, this is not supported yet.".format(old_name) + ) + producer = producer_map[(old_name, version)] + rename_op_output(predict_net, producer[0], producer[1], new_name) + return + + def contain_targets(op_ssa): + return (old_name, version) in op_ssa[0] + + is_consumer = [contain_targets(op_ssa) for op_ssa in predict_net_ssa] + if sum(is_consumer) > 1: + raise IllegalGraphTransformError( + ( + "Input '{}' of operator(#{}) are consumed by other ops, please use" + + " rename_op_output on the producer instead. Offending op: \n{}" + ).format(old_name, op_id, predict_net.op[op_id]) + ) + + # update init_net + _rename_versioned_blob_in_proto( + init_net, old_name, new_name, version, init_net_ssa, {}, init_net_versions + ) + # update predict_net + _rename_versioned_blob_in_proto( + predict_net, + old_name, + new_name, + version, + predict_net_ssa, + init_net_versions, + predict_net_versions, + ) + + +def rename_op_output(predict_net: caffe2_pb2.NetDef, op_id: int, output_id: int, new_name: str): + """ + Rename the op_id-th operator in predict_net, change it's output_id-th input's + name to the new_name. It also does automatic re-route and change + external_output and if necessary. + - It allows multiple consumers of its output. + - This function modifies predict_net in-place, doesn't need init_net. + """ + assert isinstance(predict_net, caffe2_pb2.NetDef) + + ssa, blob_versions = core.get_ssa(predict_net) + + versioned_inputs, versioned_outputs = ssa[op_id] + old_name, version = versioned_outputs[output_id] + + # update predict_net + _rename_versioned_blob_in_proto( + predict_net, old_name, new_name, version, ssa, {}, blob_versions + ) + + +def get_sub_graph_external_input_output( + predict_net: caffe2_pb2.NetDef, sub_graph_op_indices: List[int] +) -> Tuple[List[Tuple[str, int]], List[Tuple[str, int]]]: + """ + Return the list of external input/output of sub-graph, + each element is tuple of the name and corresponding version in predict_net. + + external input/output is defined the same way as caffe2 NetDef. + """ + ssa, versions = core.get_ssa(predict_net) + + all_inputs = [] + all_outputs = [] + for op_id in sub_graph_op_indices: + all_inputs += [inp for inp in ssa[op_id][0] if inp not in all_inputs] + all_outputs += list(ssa[op_id][1]) # ssa output won't repeat + + # for versioned blobs, external inputs are just those blob in all_inputs + # but not in all_outputs + ext_inputs = [inp for inp in all_inputs if inp not in all_outputs] + + # external outputs are essentially outputs of this subgraph that are used + # outside of this sub-graph (including predict_net.external_output) + all_other_inputs = sum( + (ssa[i][0] for i in range(len(ssa)) if i not in sub_graph_op_indices), + [(outp, versions[outp]) for outp in predict_net.external_output], + ) + ext_outputs = [outp for outp in all_outputs if outp in set(all_other_inputs)] + + return ext_inputs, ext_outputs + + +class DiGraph: + """A DAG representation of caffe2 graph, each vertice is a versioned blob.""" + + def __init__(self): + self.vertices = set() + self.graph = collections.defaultdict(list) + + def add_edge(self, u, v): + self.graph[u].append(v) + self.vertices.add(u) + self.vertices.add(v) + + # grab from https://www.geeksforgeeks.org/find-paths-given-source-destination/ + def get_all_paths(self, s, d): + visited = {k: False for k in self.vertices} + path = [] + all_paths = [] + + def _get_all_paths_util(graph, u, d, visited, path): + visited[u] = True + path.append(u) + if u == d: + all_paths.append(copy.deepcopy(path)) + else: + for i in graph[u]: + if not visited[i]: + _get_all_paths_util(graph, i, d, visited, path) + path.pop() + visited[u] = False + + _get_all_paths_util(self.graph, s, d, visited, path) + return all_paths + + @staticmethod + def from_ssa(ssa): + graph = DiGraph() + for op_id in range(len(ssa)): + for inp in ssa[op_id][0]: + for outp in ssa[op_id][1]: + graph.add_edge(inp, outp) + return graph + + +def _get_dependency_chain(ssa, versioned_target, versioned_source): + """ + Return the index list of relevant operator to produce target blob from source blob, + if there's no dependency, return empty list. + """ + + # finding all paths between nodes can be O(N!), thus we can only search + # in the subgraph using the op starting from the first consumer of source blob + # to the producer of the target blob. + consumer_map = get_consumer_map(ssa) + producer_map = get_producer_map(ssa) + start_op = min(x[0] for x in consumer_map[versioned_source]) - 15 + end_op = ( + producer_map[versioned_target][0] + 15 if versioned_target in producer_map else start_op + ) + sub_graph_ssa = ssa[start_op : end_op + 1] + if len(sub_graph_ssa) > 30: + logger.warning( + "Subgraph bebetween {} and {} is large (from op#{} to op#{}), it" + " might take non-trival time to find all paths between them.".format( + versioned_source, versioned_target, start_op, end_op + ) + ) + + dag = DiGraph.from_ssa(sub_graph_ssa) + paths = dag.get_all_paths(versioned_source, versioned_target) # include two ends + ops_in_paths = [[producer_map[blob][0] for blob in path[1:]] for path in paths] + return sorted(set().union(*[set(ops) for ops in ops_in_paths])) + + +def identify_reshape_sub_graph(predict_net: caffe2_pb2.NetDef) -> List[List[int]]: + """ + Idenfity the reshape sub-graph in a protobuf. + The reshape sub-graph is defined as matching the following pattern: + + (input_blob) -> Op_1 -> ... -> Op_N -> (new_shape) -─┐ + └-------------------------------------------> Reshape -> (output_blob) + + Return: + List of sub-graphs, each sub-graph is represented as a list of indices + of the relavent ops, [Op_1, Op_2, ..., Op_N, Reshape] + """ + + ssa, _ = core.get_ssa(predict_net) + + ret = [] + for i, op in enumerate(predict_net.op): + if op.type == "Reshape": + assert len(op.input) == 2 + input_ssa = ssa[i][0] + data_source = input_ssa[0] + shape_source = input_ssa[1] + op_indices = _get_dependency_chain(ssa, shape_source, data_source) + ret.append(op_indices + [i]) + return ret + + +def remove_reshape_for_fc(predict_net, params): + """ + In PyTorch nn.Linear has to take 2D tensor, this often leads to reshape + a 4D tensor to 2D by calling .view(). However this (dynamic) reshaping + doesn't work well with ONNX and Int8 tools, and cause using extra + ops (eg. ExpandDims) that might not be available on mobile. + Luckily Caffe2 supports 4D tensor for FC, so we can remove those reshape + after exporting ONNX model. + """ + from caffe2.python import core + + # find all reshape sub-graph that can be removed, which is now all Reshape + # sub-graph whose output is only consumed by FC. + # TODO: to make it safer, we may need the actually value to better determine + # if a Reshape before FC is removable. + reshape_sub_graphs = identify_reshape_sub_graph(predict_net) + sub_graphs_to_remove = [] + for reshape_sub_graph in reshape_sub_graphs: + reshape_op_id = reshape_sub_graph[-1] + assert predict_net.op[reshape_op_id].type == "Reshape" + ssa, _ = core.get_ssa(predict_net) + reshape_output = ssa[reshape_op_id][1][0] + consumers = [i for i in range(len(ssa)) if reshape_output in ssa[i][0]] + if all(predict_net.op[consumer].type == "FC" for consumer in consumers): + # safety check if the sub-graph is isolated, for this reshape sub-graph, + # it means it has one non-param external input and one external output. + ext_inputs, ext_outputs = get_sub_graph_external_input_output( + predict_net, reshape_sub_graph + ) + non_params_ext_inputs = [inp for inp in ext_inputs if inp[1] != 0] + if len(non_params_ext_inputs) == 1 and len(ext_outputs) == 1: + sub_graphs_to_remove.append(reshape_sub_graph) + + # perform removing subgraph by: + # 1: rename the Reshape's output to its input, then the graph can be + # seen as in-place itentify, meaning whose external input/output are the same. + # 2: simply remove those ops. + remove_op_ids = [] + params_to_remove = [] + for sub_graph in sub_graphs_to_remove: + logger.info( + "Remove Reshape sub-graph:\n{}".format( + "".join(["(#{:>4})\n{}".format(i, predict_net.op[i]) for i in sub_graph]) + ) + ) + reshape_op_id = sub_graph[-1] + new_reshap_output = predict_net.op[reshape_op_id].input[0] + rename_op_output(predict_net, reshape_op_id, 0, new_reshap_output) + ext_inputs, ext_outputs = get_sub_graph_external_input_output(predict_net, sub_graph) + non_params_ext_inputs = [inp for inp in ext_inputs if inp[1] != 0] + params_ext_inputs = [inp for inp in ext_inputs if inp[1] == 0] + assert len(non_params_ext_inputs) == 1 and len(ext_outputs) == 1 + assert ext_outputs[0][0] == non_params_ext_inputs[0][0] + assert ext_outputs[0][1] == non_params_ext_inputs[0][1] + 1 + remove_op_ids.extend(sub_graph) + params_to_remove.extend(params_ext_inputs) + + predict_net = copy.deepcopy(predict_net) + new_ops = [op for i, op in enumerate(predict_net.op) if i not in remove_op_ids] + del predict_net.op[:] + predict_net.op.extend(new_ops) + for versioned_params in params_to_remove: + name = versioned_params[0] + logger.info("Remove params: {} from init_net and predict_net.external_input".format(name)) + del params[name] + predict_net.external_input.remove(name) + + return predict_net, params + + +def fuse_copy_between_cpu_and_gpu(predict_net: caffe2_pb2.NetDef): + """ + In-place fuse extra copy ops between cpu/gpu for the following case: + a -CopyAToB-> b -CopyBToA> c1 -NextOp1-> d1 + -CopyBToA> c2 -NextOp2-> d2 + The fused network will look like: + a -NextOp1-> d1 + -NextOp2-> d2 + """ + + _COPY_OPS = ["CopyCPUToGPU", "CopyGPUToCPU"] + + def _fuse_once(predict_net): + ssa, blob_versions = core.get_ssa(predict_net) + consumer_map = get_consumer_map(ssa) + versioned_external_output = [ + (name, blob_versions[name]) for name in predict_net.external_output + ] + + for op_id, op in enumerate(predict_net.op): + if op.type in _COPY_OPS: + fw_copy_versioned_output = ssa[op_id][1][0] + consumer_ids = [x[0] for x in consumer_map[fw_copy_versioned_output]] + reverse_op_type = _COPY_OPS[1 - _COPY_OPS.index(op.type)] + + is_fusable = ( + len(consumer_ids) > 0 + and fw_copy_versioned_output not in versioned_external_output + and all( + predict_net.op[_op_id].type == reverse_op_type + and ssa[_op_id][1][0] not in versioned_external_output + for _op_id in consumer_ids + ) + ) + + if is_fusable: + for rv_copy_op_id in consumer_ids: + # making each NextOp uses "a" directly and removing Copy ops + rs_copy_versioned_output = ssa[rv_copy_op_id][1][0] + next_op_id, inp_id = consumer_map[rs_copy_versioned_output][0] + predict_net.op[next_op_id].input[inp_id] = op.input[0] + # remove CopyOps + new_ops = [ + op + for i, op in enumerate(predict_net.op) + if i != op_id and i not in consumer_ids + ] + del predict_net.op[:] + predict_net.op.extend(new_ops) + return True + + return False + + # _fuse_once returns False is nothing can be fused + while _fuse_once(predict_net): + pass + + +def remove_dead_end_ops(net_def: caffe2_pb2.NetDef): + """remove ops if its output is not used or not in external_output""" + ssa, versions = core.get_ssa(net_def) + versioned_external_output = [(name, versions[name]) for name in net_def.external_output] + consumer_map = get_consumer_map(ssa) + removed_op_ids = set() + + def _is_dead_end(versioned_blob): + return not ( + versioned_blob in versioned_external_output + or ( + len(consumer_map[versioned_blob]) > 0 + and all(x[0] not in removed_op_ids for x in consumer_map[versioned_blob]) + ) + ) + + for i, ssa_i in reversed(list(enumerate(ssa))): + versioned_outputs = ssa_i[1] + if all(_is_dead_end(outp) for outp in versioned_outputs): + removed_op_ids.add(i) + + # simply removing those deadend ops should have no effect to external_output + new_ops = [op for i, op in enumerate(net_def.op) if i not in removed_op_ids] + del net_def.op[:] + net_def.op.extend(new_ops) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/torchscript.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/torchscript.py new file mode 100644 index 0000000000000000000000000000000000000000..24fe59bda44225324928542df3f2ef1745375dfd --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/torchscript.py @@ -0,0 +1,132 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import os +import torch + +from detectron2.utils.file_io import PathManager + +from .torchscript_patch import freeze_training_mode, patch_instances + +__all__ = ["scripting_with_instances", "dump_torchscript_IR"] + + +def scripting_with_instances(model, fields): + """ + Run :func:`torch.jit.script` on a model that uses the :class:`Instances` class. Since + attributes of :class:`Instances` are "dynamically" added in eager mode，it is difficult + for scripting to support it out of the box. This function is made to support scripting + a model that uses :class:`Instances`. It does the following: + + 1. Create a scriptable ``new_Instances`` class which behaves similarly to ``Instances``, + but with all attributes been "static". + The attributes need to be statically declared in the ``fields`` argument. + 2. Register ``new_Instances``, and force scripting compiler to + use it when trying to compile ``Instances``. + + After this function, the process will be reverted. User should be able to script another model + using different fields. + + Example: + Assume that ``Instances`` in the model consist of two attributes named + ``proposal_boxes`` and ``objectness_logits`` with type :class:`Boxes` and + :class:`Tensor` respectively during inference. You can call this function like: + :: + fields = {"proposal_boxes": Boxes, "objectness_logits": torch.Tensor} + torchscipt_model = scripting_with_instances(model, fields) + + Note: + It only support models in evaluation mode. + + Args: + model (nn.Module): The input model to be exported by scripting. + fields (Dict[str, type]): Attribute names and corresponding type that + ``Instances`` will use in the model. Note that all attributes used in ``Instances`` + need to be added, regardless of whether they are inputs/outputs of the model. + Data type not defined in detectron2 is not supported for now. + + Returns: + torch.jit.ScriptModule: the model in torchscript format + """ + assert ( + not model.training + ), "Currently we only support exporting models in evaluation mode to torchscript" + + with freeze_training_mode(model), patch_instances(fields): + scripted_model = torch.jit.script(model) + return scripted_model + + +# alias for old name +export_torchscript_with_instances = scripting_with_instances + + +def dump_torchscript_IR(model, dir): + """ + Dump IR of a TracedModule/ScriptModule/Function in various format (code, graph, + inlined graph). Useful for debugging. + + Args: + model (TracedModule/ScriptModule/ScriptFUnction): traced or scripted module + dir (str): output directory to dump files. + """ + dir = os.path.expanduser(dir) + PathManager.mkdirs(dir) + + def _get_script_mod(mod): + if isinstance(mod, torch.jit.TracedModule): + return mod._actual_script_module + return mod + + # Dump pretty-printed code: https://pytorch.org/docs/stable/jit.html#inspecting-code + with PathManager.open(os.path.join(dir, "model_ts_code.txt"), "w") as f: + + def get_code(mod): + # Try a few ways to get code using private attributes. + try: + # This contains more information than just `mod.code` + return _get_script_mod(mod)._c.code + except AttributeError: + pass + try: + return mod.code + except AttributeError: + return None + + def dump_code(prefix, mod): + code = get_code(mod) + name = prefix or "root model" + if code is None: + f.write(f"Could not found code for {name} (type={mod.original_name})\n") + f.write("\n") + else: + f.write(f"\nCode for {name}, type={mod.original_name}:\n") + f.write(code) + f.write("\n") + f.write("-" * 80) + + for name, m in mod.named_children(): + dump_code(prefix + "." + name, m) + + if isinstance(model, torch.jit.ScriptFunction): + f.write(get_code(model)) + else: + dump_code("", model) + + def _get_graph(model): + try: + # Recursively dump IR of all modules + return _get_script_mod(model)._c.dump_to_str(True, False, False) + except AttributeError: + return model.graph.str() + + with PathManager.open(os.path.join(dir, "model_ts_IR.txt"), "w") as f: + f.write(_get_graph(model)) + + # Dump IR of the entire graph (all submodules inlined) + with PathManager.open(os.path.join(dir, "model_ts_IR_inlined.txt"), "w") as f: + f.write(str(model.inlined_graph)) + + if not isinstance(model, torch.jit.ScriptFunction): + # Dump the model structure in pytorch style + with PathManager.open(os.path.join(dir, "model.txt"), "w") as f: + f.write(str(model)) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/torchscript_patch.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/torchscript_patch.py new file mode 100644 index 0000000000000000000000000000000000000000..da9b324f1582e31d1a16d2fe462ac2989bea56ea --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/export/torchscript_patch.py @@ -0,0 +1,406 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import os +import sys +import tempfile +from contextlib import ExitStack, contextmanager +from copy import deepcopy +from unittest import mock +import torch +from torch import nn + +# need some explicit imports due to https://github.com/pytorch/pytorch/issues/38964 +import detectron2 # noqa F401 +from detectron2.structures import Boxes, Instances +from detectron2.utils.env import _import_file + +_counter = 0 + + +def _clear_jit_cache(): + from torch.jit._recursive import concrete_type_store + from torch.jit._state import _jit_caching_layer + + concrete_type_store.type_store.clear() # for modules + _jit_caching_layer.clear() # for free functions + + +def _add_instances_conversion_methods(newInstances): + """ + Add from_instances methods to the scripted Instances class. + """ + cls_name = newInstances.__name__ + + @torch.jit.unused + def from_instances(instances: Instances): + """ + Create scripted Instances from original Instances + """ + fields = instances.get_fields() + image_size = instances.image_size + ret = newInstances(image_size) + for name, val in fields.items(): + assert hasattr(ret, f"_{name}"), f"No attribute named {name} in {cls_name}" + setattr(ret, name, deepcopy(val)) + return ret + + newInstances.from_instances = from_instances + + +@contextmanager +def patch_instances(fields): + """ + A contextmanager, under which the Instances class in detectron2 is replaced + by a statically-typed scriptable class, defined by `fields`. + See more in `scripting_with_instances`. + """ + + with tempfile.TemporaryDirectory(prefix="detectron2") as dir, tempfile.NamedTemporaryFile( + mode="w", encoding="utf-8", suffix=".py", dir=dir, delete=False + ) as f: + try: + # Objects that use Instances should not reuse previously-compiled + # results in cache, because `Instances` could be a new class each time. + _clear_jit_cache() + + cls_name, s = _gen_instance_module(fields) + f.write(s) + f.flush() + f.close() + + module = _import(f.name) + new_instances = getattr(module, cls_name) + _ = torch.jit.script(new_instances) + # let torchscript think Instances was scripted already + Instances.__torch_script_class__ = True + # let torchscript find new_instances when looking for the jit type of Instances + Instances._jit_override_qualname = torch._jit_internal._qualified_name(new_instances) + + _add_instances_conversion_methods(new_instances) + yield new_instances + finally: + try: + del Instances.__torch_script_class__ + del Instances._jit_override_qualname + except AttributeError: + pass + sys.modules.pop(module.__name__) + + +def _gen_instance_class(fields): + """ + Args: + fields (dict[name: type]) + """ + + class _FieldType: + def __init__(self, name, type_): + assert isinstance(name, str), f"Field name must be str, got {name}" + self.name = name + self.type_ = type_ + self.annotation = f"{type_.__module__}.{type_.__name__}" + + fields = [_FieldType(k, v) for k, v in fields.items()] + + def indent(level, s): + return " " * 4 * level + s + + lines = [] + + global _counter + _counter += 1 + + cls_name = "ScriptedInstances{}".format(_counter) + + field_names = tuple(x.name for x in fields) + extra_args = ", ".join([f"{f.name}: Optional[{f.annotation}] = None" for f in fields]) + lines.append( + f""" +class {cls_name}: + def __init__(self, image_size: Tuple[int, int], {extra_args}): + self.image_size = image_size + self._field_names = {field_names} +""" + ) + + for f in fields: + lines.append( + indent(2, f"self._{f.name} = torch.jit.annotate(Optional[{f.annotation}], {f.name})") + ) + + for f in fields: + lines.append( + f""" + @property + def {f.name}(self) -> {f.annotation}: + # has to use a local for type refinement + # https://pytorch.org/docs/stable/jit_language_reference.html#optional-type-refinement + t = self._{f.name} + assert t is not None, "{f.name} is None and cannot be accessed!" + return t + + @{f.name}.setter + def {f.name}(self, value: {f.annotation}) -> None: + self._{f.name} = value +""" + ) + + # support method `__len__` + lines.append( + """ + def __len__(self) -> int: +""" + ) + for f in fields: + lines.append( + f""" + t = self._{f.name} + if t is not None: + return len(t) +""" + ) + lines.append( + """ + raise NotImplementedError("Empty Instances does not support __len__!") +""" + ) + + # support method `has` + lines.append( + """ + def has(self, name: str) -> bool: +""" + ) + for f in fields: + lines.append( + f""" + if name == "{f.name}": + return self._{f.name} is not None +""" + ) + lines.append( + """ + return False +""" + ) + + # support method `to` + none_args = ", None" * len(fields) + lines.append( + f""" + def to(self, device: torch.device) -> "{cls_name}": + ret = {cls_name}(self.image_size{none_args}) +""" + ) + for f in fields: + if hasattr(f.type_, "to"): + lines.append( + f""" + t = self._{f.name} + if t is not None: + ret._{f.name} = t.to(device) +""" + ) + else: + # For now, ignore fields that cannot be moved to devices. + # Maybe can support other tensor-like classes (e.g. __torch_function__) + pass + lines.append( + """ + return ret +""" + ) + + # support method `getitem` + none_args = ", None" * len(fields) + lines.append( + f""" + def __getitem__(self, item) -> "{cls_name}": + ret = {cls_name}(self.image_size{none_args}) +""" + ) + for f in fields: + lines.append( + f""" + t = self._{f.name} + if t is not None: + ret._{f.name} = t[item] +""" + ) + lines.append( + """ + return ret +""" + ) + + # support method `cat` + # this version does not contain checks that all instances have same size and fields + none_args = ", None" * len(fields) + lines.append( + f""" + def cat(self, instances: List["{cls_name}"]) -> "{cls_name}": + ret = {cls_name}(self.image_size{none_args}) +""" + ) + for f in fields: + lines.append( + f""" + t = self._{f.name} + if t is not None: + values: List[{f.annotation}] = [x.{f.name} for x in instances] + if torch.jit.isinstance(t, torch.Tensor): + ret._{f.name} = torch.cat(values, dim=0) + else: + ret._{f.name} = t.cat(values) +""" + ) + lines.append( + """ + return ret""" + ) + + # support method `get_fields()` + lines.append( + """ + def get_fields(self) -> Dict[str, Tensor]: + ret = {} + """ + ) + for f in fields: + if f.type_ == Boxes: + stmt = "t.tensor" + elif f.type_ == torch.Tensor: + stmt = "t" + else: + stmt = f'assert False, "unsupported type {str(f.type_)}"' + lines.append( + f""" + t = self._{f.name} + if t is not None: + ret["{f.name}"] = {stmt} + """ + ) + lines.append( + """ + return ret""" + ) + return cls_name, os.linesep.join(lines) + + +def _gen_instance_module(fields): + # TODO: find a more automatic way to enable import of other classes + s = """ +from copy import deepcopy +import torch +from torch import Tensor +import typing +from typing import * + +import detectron2 +from detectron2.structures import Boxes, Instances + +""" + + cls_name, cls_def = _gen_instance_class(fields) + s += cls_def + return cls_name, s + + +def _import(path): + return _import_file( + "{}{}".format(sys.modules[__name__].__name__, _counter), path, make_importable=True + ) + + +@contextmanager +def patch_builtin_len(modules=()): + """ + Patch the builtin len() function of a few detectron2 modules + to use __len__ instead, because __len__ does not convert values to + integers and therefore is friendly to tracing. + + Args: + modules (list[stsr]): names of extra modules to patch len(), in + addition to those in detectron2. + """ + + def _new_len(obj): + return obj.__len__() + + with ExitStack() as stack: + MODULES = [ + "detectron2.modeling.roi_heads.fast_rcnn", + "detectron2.modeling.roi_heads.mask_head", + "detectron2.modeling.roi_heads.keypoint_head", + ] + list(modules) + ctxs = [stack.enter_context(mock.patch(mod + ".len")) for mod in MODULES] + for m in ctxs: + m.side_effect = _new_len + yield + + +def patch_nonscriptable_classes(): + """ + Apply patches on a few nonscriptable detectron2 classes. + Should not have side-effects on eager usage. + """ + # __prepare_scriptable__ can also be added to models for easier maintenance. + # But it complicates the clean model code. + + from detectron2.modeling.backbone import ResNet, FPN + + # Due to https://github.com/pytorch/pytorch/issues/36061, + # we change backbone to use ModuleList for scripting. + # (note: this changes param names in state_dict) + + def prepare_resnet(self): + ret = deepcopy(self) + ret.stages = nn.ModuleList(ret.stages) + for k in self.stage_names: + delattr(ret, k) + return ret + + ResNet.__prepare_scriptable__ = prepare_resnet + + def prepare_fpn(self): + ret = deepcopy(self) + ret.lateral_convs = nn.ModuleList(ret.lateral_convs) + ret.output_convs = nn.ModuleList(ret.output_convs) + for name, _ in self.named_children(): + if name.startswith("fpn_"): + delattr(ret, name) + return ret + + FPN.__prepare_scriptable__ = prepare_fpn + + # Annotate some attributes to be constants for the purpose of scripting, + # even though they are not constants in eager mode. + from detectron2.modeling.roi_heads import StandardROIHeads + + if hasattr(StandardROIHeads, "__annotations__"): + # copy first to avoid editing annotations of base class + StandardROIHeads.__annotations__ = deepcopy(StandardROIHeads.__annotations__) + StandardROIHeads.__annotations__["mask_on"] = torch.jit.Final[bool] + StandardROIHeads.__annotations__["keypoint_on"] = torch.jit.Final[bool] + + +# These patches are not supposed to have side-effects. +patch_nonscriptable_classes() + + +@contextmanager +def freeze_training_mode(model): + """ + A context manager that annotates the "training" attribute of every submodule + to constant, so that the training codepath in these modules can be + meta-compiled away. Upon exiting, the annotations are reverted. + """ + classes = {type(x) for x in model.modules()} + # __constants__ is the old way to annotate constants and not compatible + # with __annotations__ . + classes = {x for x in classes if not hasattr(x, "__constants__")} + for cls in classes: + cls.__annotations__["training"] = torch.jit.Final[bool] + yield + for cls in classes: + cls.__annotations__["training"] = bool diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3d015c530b3e33de8ea60943a0a98b135f013dd7 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/__init__.py @@ -0,0 +1,24 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .batch_norm import FrozenBatchNorm2d, get_norm, NaiveSyncBatchNorm, CycleBatchNormList +from .deform_conv import DeformConv, ModulatedDeformConv +from .mask_ops import paste_masks_in_image +from .nms import batched_nms, batched_nms_rotated, nms, nms_rotated +from .roi_align import ROIAlign, roi_align +from .roi_align_rotated import ROIAlignRotated, roi_align_rotated +from .shape_spec import ShapeSpec +from .wrappers import ( + BatchNorm2d, + Conv2d, + ConvTranspose2d, + cat, + interpolate, + Linear, + nonzero_tuple, + cross_entropy, + shapes_to_tensor, +) +from .blocks import CNNBlockBase, DepthwiseSeparableConv2d +from .aspp import ASPP +from .losses import ciou_loss, diou_loss + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/aspp.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/aspp.py new file mode 100644 index 0000000000000000000000000000000000000000..14861aa9ede4fea6a69a49f189bcab997b558148 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/aspp.py @@ -0,0 +1,144 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from copy import deepcopy +import fvcore.nn.weight_init as weight_init +import torch +from torch import nn +from torch.nn import functional as F + +from .batch_norm import get_norm +from .blocks import DepthwiseSeparableConv2d +from .wrappers import Conv2d + + +class ASPP(nn.Module): + """ + Atrous Spatial Pyramid Pooling (ASPP). + """ + + def __init__( + self, + in_channels, + out_channels, + dilations, + *, + norm, + activation, + pool_kernel_size=None, + dropout: float = 0.0, + use_depthwise_separable_conv=False, + ): + """ + Args: + in_channels (int): number of input channels for ASPP. + out_channels (int): number of output channels. + dilations (list): a list of 3 dilations in ASPP. + norm (str or callable): normalization for all conv layers. + See :func:`layers.get_norm` for supported format. norm is + applied to all conv layers except the conv following + global average pooling. + activation (callable): activation function. + pool_kernel_size (tuple, list): the average pooling size (kh, kw) + for image pooling layer in ASPP. If set to None, it always + performs global average pooling. If not None, it must be + divisible by the shape of inputs in forward(). It is recommended + to use a fixed input feature size in training, and set this + option to match this size, so that it performs global average + pooling in training, and the size of the pooling window stays + consistent in inference. + dropout (float): apply dropout on the output of ASPP. It is used in + the official DeepLab implementation with a rate of 0.1: + https://github.com/tensorflow/models/blob/21b73d22f3ed05b650e85ac50849408dd36de32e/research/deeplab/model.py#L532 # noqa + use_depthwise_separable_conv (bool): use DepthwiseSeparableConv2d + for 3x3 convs in ASPP, proposed in :paper:`DeepLabV3+`. + """ + super(ASPP, self).__init__() + assert len(dilations) == 3, "ASPP expects 3 dilations, got {}".format(len(dilations)) + self.pool_kernel_size = pool_kernel_size + self.dropout = dropout + use_bias = norm == "" + self.convs = nn.ModuleList() + # conv 1x1 + self.convs.append( + Conv2d( + in_channels, + out_channels, + kernel_size=1, + bias=use_bias, + norm=get_norm(norm, out_channels), + activation=deepcopy(activation), + ) + ) + weight_init.c2_xavier_fill(self.convs[-1]) + # atrous convs + for dilation in dilations: + if use_depthwise_separable_conv: + self.convs.append( + DepthwiseSeparableConv2d( + in_channels, + out_channels, + kernel_size=3, + padding=dilation, + dilation=dilation, + norm1=norm, + activation1=deepcopy(activation), + norm2=norm, + activation2=deepcopy(activation), + ) + ) + else: + self.convs.append( + Conv2d( + in_channels, + out_channels, + kernel_size=3, + padding=dilation, + dilation=dilation, + bias=use_bias, + norm=get_norm(norm, out_channels), + activation=deepcopy(activation), + ) + ) + weight_init.c2_xavier_fill(self.convs[-1]) + # image pooling + # We do not add BatchNorm because the spatial resolution is 1x1, + # the original TF implementation has BatchNorm. + if pool_kernel_size is None: + image_pooling = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + Conv2d(in_channels, out_channels, 1, bias=True, activation=deepcopy(activation)), + ) + else: + image_pooling = nn.Sequential( + nn.AvgPool2d(kernel_size=pool_kernel_size, stride=1), + Conv2d(in_channels, out_channels, 1, bias=True, activation=deepcopy(activation)), + ) + weight_init.c2_xavier_fill(image_pooling[1]) + self.convs.append(image_pooling) + + self.project = Conv2d( + 5 * out_channels, + out_channels, + kernel_size=1, + bias=use_bias, + norm=get_norm(norm, out_channels), + activation=deepcopy(activation), + ) + weight_init.c2_xavier_fill(self.project) + + def forward(self, x): + size = x.shape[-2:] + if self.pool_kernel_size is not None: + if size[0] % self.pool_kernel_size[0] or size[1] % self.pool_kernel_size[1]: + raise ValueError( + "`pool_kernel_size` must be divisible by the shape of inputs. " + "Input size: {} `pool_kernel_size`: {}".format(size, self.pool_kernel_size) + ) + res = [] + for conv in self.convs: + res.append(conv(x)) + res[-1] = F.interpolate(res[-1], size=size, mode="bilinear", align_corners=False) + res = torch.cat(res, dim=1) + res = self.project(res) + res = F.dropout(res, self.dropout, training=self.training) if self.dropout > 0 else res + return res diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/batch_norm.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/batch_norm.py new file mode 100644 index 0000000000000000000000000000000000000000..09a6c66cf6f4b21c38a7829b029f0ab5deda1f9e --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/batch_norm.py @@ -0,0 +1,276 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import torch +import torch.distributed as dist +from fvcore.nn.distributed import differentiable_all_reduce +from torch import nn +from torch.nn import functional as F + +from detectron2.utils import comm, env + +from .wrappers import BatchNorm2d + + +class FrozenBatchNorm2d(nn.Module): + """ + BatchNorm2d where the batch statistics and the affine parameters are fixed. + + It contains non-trainable buffers called + "weight" and "bias", "running_mean", "running_var", + initialized to perform identity transformation. + + The pre-trained backbone models from Caffe2 only contain "weight" and "bias", + which are computed from the original four parameters of BN. + The affine transform `x * weight + bias` will perform the equivalent + computation of `(x - running_mean) / sqrt(running_var) * weight + bias`. + When loading a backbone model from Caffe2, "running_mean" and "running_var" + will be left unchanged as identity transformation. + + Other pre-trained backbone models may contain all 4 parameters. + + The forward is implemented by `F.batch_norm(..., training=False)`. + """ + + _version = 3 + + def __init__(self, num_features, eps=1e-5): + super().__init__() + self.num_features = num_features + self.eps = eps + self.register_buffer("weight", torch.ones(num_features)) + self.register_buffer("bias", torch.zeros(num_features)) + self.register_buffer("running_mean", torch.zeros(num_features)) + self.register_buffer("running_var", torch.ones(num_features) - eps) + + def forward(self, x): + if x.requires_grad: + # When gradients are needed, F.batch_norm will use extra memory + # because its backward op computes gradients for weight/bias as well. + scale = self.weight * (self.running_var + self.eps).rsqrt() + bias = self.bias - self.running_mean * scale + scale = scale.reshape(1, -1, 1, 1) + bias = bias.reshape(1, -1, 1, 1) + out_dtype = x.dtype # may be half + return x * scale.to(out_dtype) + bias.to(out_dtype) + else: + # When gradients are not needed, F.batch_norm is a single fused op + # and provide more optimization opportunities. + return F.batch_norm( + x, + self.running_mean, + self.running_var, + self.weight, + self.bias, + training=False, + eps=self.eps, + ) + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + version = local_metadata.get("version", None) + + if version is None or version < 2: + # No running_mean/var in early versions + # This will silent the warnings + if prefix + "running_mean" not in state_dict: + state_dict[prefix + "running_mean"] = torch.zeros_like(self.running_mean) + if prefix + "running_var" not in state_dict: + state_dict[prefix + "running_var"] = torch.ones_like(self.running_var) + + super()._load_from_state_dict( + state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ) + + def __repr__(self): + return "FrozenBatchNorm2d(num_features={}, eps={})".format(self.num_features, self.eps) + + @classmethod + def convert_frozen_batchnorm(cls, module): + """ + Convert all BatchNorm/SyncBatchNorm in module into FrozenBatchNorm. + + Args: + module (torch.nn.Module): + + Returns: + If module is BatchNorm/SyncBatchNorm, returns a new module. + Otherwise, in-place convert module and return it. + + Similar to convert_sync_batchnorm in + https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/batchnorm.py + """ + bn_module = nn.modules.batchnorm + bn_module = (bn_module.BatchNorm2d, bn_module.SyncBatchNorm) + res = module + if isinstance(module, bn_module): + res = cls(module.num_features) + if module.affine: + res.weight.data = module.weight.data.clone().detach() + res.bias.data = module.bias.data.clone().detach() + res.running_mean.data = module.running_mean.data + res.running_var.data = module.running_var.data + res.eps = module.eps + else: + for name, child in module.named_children(): + new_child = cls.convert_frozen_batchnorm(child) + if new_child is not child: + res.add_module(name, new_child) + return res + + +def get_norm(norm, out_channels): + """ + Args: + norm (str or callable): either one of BN, SyncBN, FrozenBN, GN; + or a callable that takes a channel number and returns + the normalization layer as a nn.Module. + + Returns: + nn.Module or None: the normalization layer + """ + if norm is None: + return None + if isinstance(norm, str): + if len(norm) == 0: + return None + norm = { + "BN": BatchNorm2d, + # Fixed in https://github.com/pytorch/pytorch/pull/36382 + "SyncBN": NaiveSyncBatchNorm if env.TORCH_VERSION <= (1, 5) else nn.SyncBatchNorm, + "FrozenBN": FrozenBatchNorm2d, + "GN": lambda channels: nn.GroupNorm(32, channels), + # for debugging: + "nnSyncBN": nn.SyncBatchNorm, + "naiveSyncBN": NaiveSyncBatchNorm, + # expose stats_mode N as an option to caller, required for zero-len inputs + "naiveSyncBN_N": lambda channels: NaiveSyncBatchNorm(channels, stats_mode="N"), + }[norm] + return norm(out_channels) + + +class NaiveSyncBatchNorm(BatchNorm2d): + """ + In PyTorch<=1.5, ``nn.SyncBatchNorm`` has incorrect gradient + when the batch size on each worker is different. + (e.g., when scale augmentation is used, or when it is applied to mask head). + + This is a slower but correct alternative to `nn.SyncBatchNorm`. + + Note: + There isn't a single definition of Sync BatchNorm. + + When ``stats_mode==""``, this module computes overall statistics by using + statistics of each worker with equal weight. The result is true statistics + of all samples (as if they are all on one worker) only when all workers + have the same (N, H, W). This mode does not support inputs with zero batch size. + + When ``stats_mode=="N"``, this module computes overall statistics by weighting + the statistics of each worker by their ``N``. The result is true statistics + of all samples (as if they are all on one worker) only when all workers + have the same (H, W). It is slower than ``stats_mode==""``. + + Even though the result of this module may not be the true statistics of all samples, + it may still be reasonable because it might be preferrable to assign equal weights + to all workers, regardless of their (H, W) dimension, instead of putting larger weight + on larger images. From preliminary experiments, little difference is found between such + a simplified implementation and an accurate computation of overall mean & variance. + """ + + def __init__(self, *args, stats_mode="", **kwargs): + super().__init__(*args, **kwargs) + assert stats_mode in ["", "N"] + self._stats_mode = stats_mode + + def forward(self, input): + if comm.get_world_size() == 1 or not self.training: + return super().forward(input) + + B, C = input.shape[0], input.shape[1] + + half_input = input.dtype == torch.float16 + if half_input: + # fp16 does not have good enough numerics for the reduction here + input = input.float() + mean = torch.mean(input, dim=[0, 2, 3]) + meansqr = torch.mean(input * input, dim=[0, 2, 3]) + + if self._stats_mode == "": + assert B > 0, 'SyncBatchNorm(stats_mode="") does not support zero batch size.' + vec = torch.cat([mean, meansqr], dim=0) + vec = differentiable_all_reduce(vec) * (1.0 / dist.get_world_size()) + mean, meansqr = torch.split(vec, C) + momentum = self.momentum + else: + if B == 0: + vec = torch.zeros([2 * C + 1], device=mean.device, dtype=mean.dtype) + vec = vec + input.sum() # make sure there is gradient w.r.t input + else: + vec = torch.cat( + [mean, meansqr, torch.ones([1], device=mean.device, dtype=mean.dtype)], dim=0 + ) + vec = differentiable_all_reduce(vec * B) + + total_batch = vec[-1].detach() + momentum = total_batch.clamp(max=1) * self.momentum # no update if total_batch is 0 + mean, meansqr, _ = torch.split(vec / total_batch.clamp(min=1), C) # avoid div-by-zero + + var = meansqr - mean * mean + invstd = torch.rsqrt(var + self.eps) + scale = self.weight * invstd + bias = self.bias - mean * scale + scale = scale.reshape(1, -1, 1, 1) + bias = bias.reshape(1, -1, 1, 1) + + self.running_mean += momentum * (mean.detach() - self.running_mean) + self.running_var += momentum * (var.detach() - self.running_var) + ret = input * scale + bias + if half_input: + ret = ret.half() + return ret + + +class CycleBatchNormList(nn.ModuleList): + """ + Implement domain-specific BatchNorm by cycling. + + When a BatchNorm layer is used for multiple input domains or input + features, it might need to maintain a separate test-time statistics + for each domain. See Sec 5.2 in :paper:`rethinking-batchnorm`. + + This module implements it by using N separate BN layers + and it cycles through them every time a forward() is called. + + NOTE: The caller of this module MUST guarantee to always call + this module by multiple of N times. Otherwise its test-time statistics + will be incorrect. + """ + + def __init__(self, length: int, bn_class=nn.BatchNorm2d, **kwargs): + """ + Args: + length: number of BatchNorm layers to cycle. + bn_class: the BatchNorm class to use + kwargs: arguments of the BatchNorm class, such as num_features. + """ + self._affine = kwargs.pop("affine", True) + super().__init__([bn_class(**kwargs, affine=False) for k in range(length)]) + if self._affine: + # shared affine, domain-specific BN + channels = self[0].num_features + self.weight = nn.Parameter(torch.ones(channels)) + self.bias = nn.Parameter(torch.zeros(channels)) + self._pos = 0 + + def forward(self, x): + ret = self[self._pos](x) + self._pos = (self._pos + 1) % len(self) + + if self._affine: + w = self.weight.reshape(1, -1, 1, 1) + b = self.bias.reshape(1, -1, 1, 1) + return ret * w + b + else: + return ret + + def extra_repr(self): + return f"affine={self._affine}" diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/blocks.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..1995a4bf7339e8deb7eaaffda4f819dda55e7ac7 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/blocks.py @@ -0,0 +1,111 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import fvcore.nn.weight_init as weight_init +from torch import nn + +from .batch_norm import FrozenBatchNorm2d, get_norm +from .wrappers import Conv2d + + +""" +CNN building blocks. +""" + + +class CNNBlockBase(nn.Module): + """ + A CNN block is assumed to have input channels, output channels and a stride. + The input and output of `forward()` method must be NCHW tensors. + The method can perform arbitrary computation but must match the given + channels and stride specification. + + Attribute: + in_channels (int): + out_channels (int): + stride (int): + """ + + def __init__(self, in_channels, out_channels, stride): + """ + The `__init__` method of any subclass should also contain these arguments. + + Args: + in_channels (int): + out_channels (int): + stride (int): + """ + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.stride = stride + + def freeze(self): + """ + Make this block not trainable. + This method sets all parameters to `requires_grad=False`, + and convert all BatchNorm layers to FrozenBatchNorm + + Returns: + the block itself + """ + for p in self.parameters(): + p.requires_grad = False + FrozenBatchNorm2d.convert_frozen_batchnorm(self) + return self + + +class DepthwiseSeparableConv2d(nn.Module): + """ + A kxk depthwise convolution + a 1x1 convolution. + + In :paper:`xception`, norm & activation are applied on the second conv. + :paper:`mobilenet` uses norm & activation on both convs. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size=3, + padding=1, + dilation=1, + *, + norm1=None, + activation1=None, + norm2=None, + activation2=None, + ): + """ + Args: + norm1, norm2 (str or callable): normalization for the two conv layers. + activation1, activation2 (callable(Tensor) -> Tensor): activation + function for the two conv layers. + """ + super().__init__() + self.depthwise = Conv2d( + in_channels, + in_channels, + kernel_size=kernel_size, + padding=padding, + dilation=dilation, + groups=in_channels, + bias=not norm1, + norm=get_norm(norm1, in_channels), + activation=activation1, + ) + self.pointwise = Conv2d( + in_channels, + out_channels, + kernel_size=1, + bias=not norm2, + norm=get_norm(norm2, out_channels), + activation=activation2, + ) + + # default initialization + weight_init.c2_msra_fill(self.depthwise) + weight_init.c2_msra_fill(self.pointwise) + + def forward(self, x): + return self.pointwise(self.depthwise(x)) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/README.md b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/README.md new file mode 100644 index 0000000000000000000000000000000000000000..778ed3da0bae89820831bcd8a72ff7b9cad8d4dd --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/README.md @@ -0,0 +1,7 @@ + + +To add a new Op: + +1. Create a new directory +2. Implement new ops there +3. Delcare its Python interface in `vision.cpp`. diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated.h b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated.h new file mode 100644 index 0000000000000000000000000000000000000000..03f4211003f42f601f0cfcf4a690f5da4a0a1f67 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated.h @@ -0,0 +1,115 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#pragma once +#include + +namespace detectron2 { + +at::Tensor ROIAlignRotated_forward_cpu( + const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio); + +at::Tensor ROIAlignRotated_backward_cpu( + const at::Tensor& grad, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width, + const int sampling_ratio); + +#if defined(WITH_CUDA) || defined(WITH_HIP) +at::Tensor ROIAlignRotated_forward_cuda( + const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio); + +at::Tensor ROIAlignRotated_backward_cuda( + const at::Tensor& grad, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width, + const int sampling_ratio); +#endif + +// Interface for Python +inline at::Tensor ROIAlignRotated_forward( + const at::Tensor& input, + const at::Tensor& rois, + const double spatial_scale, + const int64_t pooled_height, + const int64_t pooled_width, + const int64_t sampling_ratio) { + if (input.is_cuda()) { +#if defined(WITH_CUDA) || defined(WITH_HIP) + return ROIAlignRotated_forward_cuda( + input, + rois, + spatial_scale, + pooled_height, + pooled_width, + sampling_ratio); +#else + AT_ERROR("Detectron2 is not compiled with GPU support!"); +#endif + } + return ROIAlignRotated_forward_cpu( + input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio); +} + +inline at::Tensor ROIAlignRotated_backward( + const at::Tensor& grad, + const at::Tensor& rois, + const double spatial_scale, + const int64_t pooled_height, + const int64_t pooled_width, + const int64_t batch_size, + const int64_t channels, + const int64_t height, + const int64_t width, + const int64_t sampling_ratio) { + if (grad.is_cuda()) { +#if defined(WITH_CUDA) || defined(WITH_HIP) + return ROIAlignRotated_backward_cuda( + grad, + rois, + spatial_scale, + pooled_height, + pooled_width, + batch_size, + channels, + height, + width, + sampling_ratio); +#else + AT_ERROR("Detectron2 is not compiled with GPU support!"); +#endif + } + return ROIAlignRotated_backward_cpu( + grad, + rois, + spatial_scale, + pooled_height, + pooled_width, + batch_size, + channels, + height, + width, + sampling_ratio); +} + +} // namespace detectron2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cpu.cpp b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cpu.cpp new file mode 100644 index 0000000000000000000000000000000000000000..2a3d3056cc71a4acaafb570739a9dd247a7eb1ed --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cpu.cpp @@ -0,0 +1,522 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#include +#include "ROIAlignRotated.h" + +// Note: this implementation originates from the Caffe2 ROIAlignRotated Op +// and PyTorch ROIAlign (non-rotated) Op implementations. +// The key difference between this implementation and those ones is +// we don't do "legacy offset" in this version, as there aren't many previous +// works, if any, using the "legacy" ROIAlignRotated Op. +// This would make the interface a bit cleaner. + +namespace detectron2 { + +namespace { +template +struct PreCalc { + int pos1; + int pos2; + int pos3; + int pos4; + T w1; + T w2; + T w3; + T w4; +}; + +template +void pre_calc_for_bilinear_interpolate( + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int iy_upper, + const int ix_upper, + T roi_start_h, + T roi_start_w, + T bin_size_h, + T bin_size_w, + int roi_bin_grid_h, + int roi_bin_grid_w, + T roi_center_h, + T roi_center_w, + T cos_theta, + T sin_theta, + std::vector>& pre_calc) { + int pre_calc_index = 0; + for (int ph = 0; ph < pooled_height; ph++) { + for (int pw = 0; pw < pooled_width; pw++) { + for (int iy = 0; iy < iy_upper; iy++) { + const T yy = roi_start_h + ph * bin_size_h + + static_cast(iy + .5f) * bin_size_h / + static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5 + for (int ix = 0; ix < ix_upper; ix++) { + const T xx = roi_start_w + pw * bin_size_w + + static_cast(ix + .5f) * bin_size_w / + static_cast(roi_bin_grid_w); + + // Rotate by theta around the center and translate + // In image space, (y, x) is the order for Right Handed System, + // and this is essentially multiplying the point by a rotation matrix + // to rotate it counterclockwise through angle theta. + T y = yy * cos_theta - xx * sin_theta + roi_center_h; + T x = yy * sin_theta + xx * cos_theta + roi_center_w; + // deal with: inverse elements are out of feature map boundary + if (y < -1.0 || y > height || x < -1.0 || x > width) { + // empty + PreCalc pc; + pc.pos1 = 0; + pc.pos2 = 0; + pc.pos3 = 0; + pc.pos4 = 0; + pc.w1 = 0; + pc.w2 = 0; + pc.w3 = 0; + pc.w4 = 0; + pre_calc[pre_calc_index] = pc; + pre_calc_index += 1; + continue; + } + + if (y < 0) { + y = 0; + } + if (x < 0) { + x = 0; + } + + int y_low = (int)y; + int x_low = (int)x; + int y_high; + int x_high; + + if (y_low >= height - 1) { + y_high = y_low = height - 1; + y = (T)y_low; + } else { + y_high = y_low + 1; + } + + if (x_low >= width - 1) { + x_high = x_low = width - 1; + x = (T)x_low; + } else { + x_high = x_low + 1; + } + + T ly = y - y_low; + T lx = x - x_low; + T hy = 1. - ly, hx = 1. - lx; + T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; + + // save weights and indices + PreCalc pc; + pc.pos1 = y_low * width + x_low; + pc.pos2 = y_low * width + x_high; + pc.pos3 = y_high * width + x_low; + pc.pos4 = y_high * width + x_high; + pc.w1 = w1; + pc.w2 = w2; + pc.w3 = w3; + pc.w4 = w4; + pre_calc[pre_calc_index] = pc; + + pre_calc_index += 1; + } + } + } + } +} + +template +void bilinear_interpolate_gradient( + const int height, + const int width, + T y, + T x, + T& w1, + T& w2, + T& w3, + T& w4, + int& x_low, + int& x_high, + int& y_low, + int& y_high) { + // deal with cases that inverse elements are out of feature map boundary + if (y < -1.0 || y > height || x < -1.0 || x > width) { + // empty + w1 = w2 = w3 = w4 = 0.; + x_low = x_high = y_low = y_high = -1; + return; + } + + if (y < 0) { + y = 0; + } + + if (x < 0) { + x = 0; + } + + y_low = (int)y; + x_low = (int)x; + + if (y_low >= height - 1) { + y_high = y_low = height - 1; + y = (T)y_low; + } else { + y_high = y_low + 1; + } + + if (x_low >= width - 1) { + x_high = x_low = width - 1; + x = (T)x_low; + } else { + x_high = x_low + 1; + } + + T ly = y - y_low; + T lx = x - x_low; + T hy = 1. - ly, hx = 1. - lx; + + // reference in forward + // T v1 = input[y_low * width + x_low]; + // T v2 = input[y_low * width + x_high]; + // T v3 = input[y_high * width + x_low]; + // T v4 = input[y_high * width + x_high]; + // T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + + w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; + + return; +} + +template +inline void add(T* address, const T& val) { + *address += val; +} + +} // namespace + +template +void ROIAlignRotatedForward( + const int nthreads, + const T* input, + const T& spatial_scale, + const int channels, + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + const T* rois, + T* output) { + int n_rois = nthreads / channels / pooled_width / pooled_height; + // (n, c, ph, pw) is an element in the pooled output + // can be parallelized using omp + // #pragma omp parallel for num_threads(32) + for (int n = 0; n < n_rois; n++) { + int index_n = n * channels * pooled_width * pooled_height; + + const T* current_roi = rois + n * 6; + int roi_batch_ind = current_roi[0]; + + // Do not use rounding; this implementation detail is critical + // ROIAlignRotated supports align == true, i.e., continuous coordinate + // by default, thus the 0.5 offset + T offset = (T)0.5; + T roi_center_w = current_roi[1] * spatial_scale - offset; + T roi_center_h = current_roi[2] * spatial_scale - offset; + T roi_width = current_roi[3] * spatial_scale; + T roi_height = current_roi[4] * spatial_scale; + T theta = current_roi[5] * M_PI / 180.0; + T cos_theta = cos(theta); + T sin_theta = sin(theta); + + AT_ASSERTM( + roi_width >= 0 && roi_height >= 0, + "ROIs in ROIAlignRotated do not have non-negative size!"); + + T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); + T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); + + // We use roi_bin_grid to sample the grid and mimic integral + int roi_bin_grid_h = (sampling_ratio > 0) + ? sampling_ratio + : ceil(roi_height / pooled_height); // e.g., = 2 + int roi_bin_grid_w = + (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); + + // We do average (integral) pooling inside a bin + const T count = std::max(roi_bin_grid_h * roi_bin_grid_w, 1); // e.g. = 4 + + // we want to precalculate indices and weights shared by all channels, + // this is the key point of optimization + std::vector> pre_calc( + roi_bin_grid_h * roi_bin_grid_w * pooled_width * pooled_height); + + // roi_start_h and roi_start_w are computed wrt the center of RoI (x, y). + // Appropriate translation needs to be applied after. + T roi_start_h = -roi_height / 2.0; + T roi_start_w = -roi_width / 2.0; + + pre_calc_for_bilinear_interpolate( + height, + width, + pooled_height, + pooled_width, + roi_bin_grid_h, + roi_bin_grid_w, + roi_start_h, + roi_start_w, + bin_size_h, + bin_size_w, + roi_bin_grid_h, + roi_bin_grid_w, + roi_center_h, + roi_center_w, + cos_theta, + sin_theta, + pre_calc); + + for (int c = 0; c < channels; c++) { + int index_n_c = index_n + c * pooled_width * pooled_height; + const T* offset_input = + input + (roi_batch_ind * channels + c) * height * width; + int pre_calc_index = 0; + + for (int ph = 0; ph < pooled_height; ph++) { + for (int pw = 0; pw < pooled_width; pw++) { + int index = index_n_c + ph * pooled_width + pw; + + T output_val = 0.; + for (int iy = 0; iy < roi_bin_grid_h; iy++) { + for (int ix = 0; ix < roi_bin_grid_w; ix++) { + PreCalc pc = pre_calc[pre_calc_index]; + output_val += pc.w1 * offset_input[pc.pos1] + + pc.w2 * offset_input[pc.pos2] + + pc.w3 * offset_input[pc.pos3] + pc.w4 * offset_input[pc.pos4]; + + pre_calc_index += 1; + } + } + output_val /= count; + + output[index] = output_val; + } // for pw + } // for ph + } // for c + } // for n +} + +template +void ROIAlignRotatedBackward( + const int nthreads, + // may not be contiguous. should index using n_stride, etc + const T* grad_output, + const T& spatial_scale, + const int channels, + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + T* grad_input, + const T* rois, + const int n_stride, + const int c_stride, + const int h_stride, + const int w_stride) { + for (int index = 0; index < nthreads; index++) { + // (n, c, ph, pw) is an element in the pooled output + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int c = (index / pooled_width / pooled_height) % channels; + int n = index / pooled_width / pooled_height / channels; + + const T* current_roi = rois + n * 6; + int roi_batch_ind = current_roi[0]; + + // Do not use rounding; this implementation detail is critical + // ROIAlignRotated supports align == true, i.e., continuous coordinate + // by default, thus the 0.5 offset + T offset = (T)0.5; + T roi_center_w = current_roi[1] * spatial_scale - offset; + T roi_center_h = current_roi[2] * spatial_scale - offset; + T roi_width = current_roi[3] * spatial_scale; + T roi_height = current_roi[4] * spatial_scale; + T theta = current_roi[5] * M_PI / 180.0; + T cos_theta = cos(theta); + T sin_theta = sin(theta); + + AT_ASSERTM( + roi_width >= 0 && roi_height >= 0, + "ROIs in ROIAlignRotated do not have non-negative size!"); + + T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); + T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); + + T* offset_grad_input = + grad_input + ((roi_batch_ind * channels + c) * height * width); + + int output_offset = n * n_stride + c * c_stride; + const T* offset_grad_output = grad_output + output_offset; + const T grad_output_this_bin = + offset_grad_output[ph * h_stride + pw * w_stride]; + + // We use roi_bin_grid to sample the grid and mimic integral + int roi_bin_grid_h = (sampling_ratio > 0) + ? sampling_ratio + : ceil(roi_height / pooled_height); // e.g., = 2 + int roi_bin_grid_w = + (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); + + // roi_start_h and roi_start_w are computed wrt the center of RoI (x, y). + // Appropriate translation needs to be applied after. + T roi_start_h = -roi_height / 2.0; + T roi_start_w = -roi_width / 2.0; + + // We do average (integral) pooling inside a bin + const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4 + + for (int iy = 0; iy < roi_bin_grid_h; iy++) { + const T yy = roi_start_h + ph * bin_size_h + + static_cast(iy + .5f) * bin_size_h / + static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5 + for (int ix = 0; ix < roi_bin_grid_w; ix++) { + const T xx = roi_start_w + pw * bin_size_w + + static_cast(ix + .5f) * bin_size_w / + static_cast(roi_bin_grid_w); + + // Rotate by theta around the center and translate + T y = yy * cos_theta - xx * sin_theta + roi_center_h; + T x = yy * sin_theta + xx * cos_theta + roi_center_w; + + T w1, w2, w3, w4; + int x_low, x_high, y_low, y_high; + + bilinear_interpolate_gradient( + height, width, y, x, w1, w2, w3, w4, x_low, x_high, y_low, y_high); + + T g1 = grad_output_this_bin * w1 / count; + T g2 = grad_output_this_bin * w2 / count; + T g3 = grad_output_this_bin * w3 / count; + T g4 = grad_output_this_bin * w4 / count; + + if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) { + // atomic add is not needed for now since it is single threaded + add(offset_grad_input + y_low * width + x_low, static_cast(g1)); + add(offset_grad_input + y_low * width + x_high, static_cast(g2)); + add(offset_grad_input + y_high * width + x_low, static_cast(g3)); + add(offset_grad_input + y_high * width + x_high, static_cast(g4)); + } // if + } // ix + } // iy + } // for +} // ROIAlignRotatedBackward + +at::Tensor ROIAlignRotated_forward_cpu( + const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio) { + AT_ASSERTM(input.device().is_cpu(), "input must be a CPU tensor"); + AT_ASSERTM(rois.device().is_cpu(), "rois must be a CPU tensor"); + + at::TensorArg input_t{input, "input", 1}, rois_t{rois, "rois", 2}; + + at::CheckedFrom c = "ROIAlign_forward_cpu"; + at::checkAllSameType(c, {input_t, rois_t}); + + auto num_rois = rois.size(0); + auto channels = input.size(1); + auto height = input.size(2); + auto width = input.size(3); + + at::Tensor output = at::zeros( + {num_rois, channels, pooled_height, pooled_width}, input.options()); + + auto output_size = num_rois * pooled_height * pooled_width * channels; + + if (output.numel() == 0) { + return output; + } + + auto input_ = input.contiguous(), rois_ = rois.contiguous(); + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + input.scalar_type(), "ROIAlignRotated_forward", [&] { + ROIAlignRotatedForward( + output_size, + input_.data_ptr(), + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + sampling_ratio, + rois_.data_ptr(), + output.data_ptr()); + }); + return output; +} + +at::Tensor ROIAlignRotated_backward_cpu( + const at::Tensor& grad, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width, + const int sampling_ratio) { + AT_ASSERTM(grad.device().is_cpu(), "grad must be a CPU tensor"); + AT_ASSERTM(rois.device().is_cpu(), "rois must be a CPU tensor"); + + at::TensorArg grad_t{grad, "grad", 1}, rois_t{rois, "rois", 2}; + + at::CheckedFrom c = "ROIAlignRotated_backward_cpu"; + at::checkAllSameType(c, {grad_t, rois_t}); + + at::Tensor grad_input = + at::zeros({batch_size, channels, height, width}, grad.options()); + + // handle possibly empty gradients + if (grad.numel() == 0) { + return grad_input; + } + + // get stride values to ensure indexing into gradients is correct. + int n_stride = grad.stride(0); + int c_stride = grad.stride(1); + int h_stride = grad.stride(2); + int w_stride = grad.stride(3); + + auto rois_ = rois.contiguous(); + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + grad.scalar_type(), "ROIAlignRotated_forward", [&] { + ROIAlignRotatedBackward( + grad.numel(), + grad.data_ptr(), + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + sampling_ratio, + grad_input.data_ptr(), + rois_.data_ptr(), + n_stride, + c_stride, + h_stride, + w_stride); + }); + return grad_input; +} + +} // namespace detectron2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cuda.cu b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cuda.cu new file mode 100644 index 0000000000000000000000000000000000000000..fca186519143b168a912c880a4cf495a0a5a9322 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cuda.cu @@ -0,0 +1,443 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#include +#include +#include +#include + +// TODO make it in a common file +#define CUDA_1D_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; \ + i += blockDim.x * gridDim.x) + +// Note: this implementation originates from the Caffe2 ROIAlignRotated Op +// and PyTorch ROIAlign (non-rotated) Op implementations. +// The key difference between this implementation and those ones is +// we don't do "legacy offset" in this version, as there aren't many previous +// works, if any, using the "legacy" ROIAlignRotated Op. +// This would make the interface a bit cleaner. + +namespace detectron2 { + +namespace { + +template +__device__ T bilinear_interpolate( + const T* input, + const int height, + const int width, + T y, + T x) { + // deal with cases that inverse elements are out of feature map boundary + if (y < -1.0 || y > height || x < -1.0 || x > width) { + // empty + return 0; + } + + if (y < 0) { + y = 0; + } + + if (x < 0) { + x = 0; + } + + int y_low = (int)y; + int x_low = (int)x; + int y_high; + int x_high; + + if (y_low >= height - 1) { + y_high = y_low = height - 1; + y = (T)y_low; + } else { + y_high = y_low + 1; + } + + if (x_low >= width - 1) { + x_high = x_low = width - 1; + x = (T)x_low; + } else { + x_high = x_low + 1; + } + + T ly = y - y_low; + T lx = x - x_low; + T hy = 1. - ly, hx = 1. - lx; + // do bilinear interpolation + T v1 = input[y_low * width + x_low]; + T v2 = input[y_low * width + x_high]; + T v3 = input[y_high * width + x_low]; + T v4 = input[y_high * width + x_high]; + T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; + + T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + + return val; +} + +template +__device__ void bilinear_interpolate_gradient( + const int height, + const int width, + T y, + T x, + T& w1, + T& w2, + T& w3, + T& w4, + int& x_low, + int& x_high, + int& y_low, + int& y_high) { + // deal with cases that inverse elements are out of feature map boundary + if (y < -1.0 || y > height || x < -1.0 || x > width) { + // empty + w1 = w2 = w3 = w4 = 0.; + x_low = x_high = y_low = y_high = -1; + return; + } + + if (y < 0) { + y = 0; + } + + if (x < 0) { + x = 0; + } + + y_low = (int)y; + x_low = (int)x; + + if (y_low >= height - 1) { + y_high = y_low = height - 1; + y = (T)y_low; + } else { + y_high = y_low + 1; + } + + if (x_low >= width - 1) { + x_high = x_low = width - 1; + x = (T)x_low; + } else { + x_high = x_low + 1; + } + + T ly = y - y_low; + T lx = x - x_low; + T hy = 1. - ly, hx = 1. - lx; + + // reference in forward + // T v1 = input[y_low * width + x_low]; + // T v2 = input[y_low * width + x_high]; + // T v3 = input[y_high * width + x_low]; + // T v4 = input[y_high * width + x_high]; + // T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + + w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; + + return; +} + +} // namespace + +template +__global__ void RoIAlignRotatedForward( + const int nthreads, + const T* input, + const T spatial_scale, + const int channels, + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + const T* rois, + T* top_data) { + CUDA_1D_KERNEL_LOOP(index, nthreads) { + // (n, c, ph, pw) is an element in the pooled output + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int c = (index / pooled_width / pooled_height) % channels; + int n = index / pooled_width / pooled_height / channels; + + const T* current_roi = rois + n * 6; + int roi_batch_ind = current_roi[0]; + + // Do not use rounding; this implementation detail is critical + // ROIAlignRotated supports align == true, i.e., continuous coordinate + // by default, thus the 0.5 offset + T offset = (T)0.5; + T roi_center_w = current_roi[1] * spatial_scale - offset; + T roi_center_h = current_roi[2] * spatial_scale - offset; + T roi_width = current_roi[3] * spatial_scale; + T roi_height = current_roi[4] * spatial_scale; + T theta = current_roi[5] * M_PI / 180.0; + T cos_theta = cos(theta); + T sin_theta = sin(theta); + + T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); + T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); + + const T* offset_input = + input + (roi_batch_ind * channels + c) * height * width; + + // We use roi_bin_grid to sample the grid and mimic integral + int roi_bin_grid_h = (sampling_ratio > 0) + ? sampling_ratio + : ceil(roi_height / pooled_height); // e.g., = 2 + int roi_bin_grid_w = + (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); + + // roi_start_h and roi_start_w are computed wrt the center of RoI (x, y). + // Appropriate translation needs to be applied after. + T roi_start_h = -roi_height / 2.0; + T roi_start_w = -roi_width / 2.0; + + // We do average (inte gral) pooling inside a bin + const T count = max(roi_bin_grid_h * roi_bin_grid_w, 1); // e.g. = 4 + + T output_val = 0.; + for (int iy = 0; iy < roi_bin_grid_h; iy++) // e.g., iy = 0, 1 + { + const T yy = roi_start_h + ph * bin_size_h + + static_cast(iy + .5f) * bin_size_h / + static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5 + for (int ix = 0; ix < roi_bin_grid_w; ix++) { + const T xx = roi_start_w + pw * bin_size_w + + static_cast(ix + .5f) * bin_size_w / + static_cast(roi_bin_grid_w); + + // Rotate by theta around the center and translate + T y = yy * cos_theta - xx * sin_theta + roi_center_h; + T x = yy * sin_theta + xx * cos_theta + roi_center_w; + + T val = bilinear_interpolate(offset_input, height, width, y, x); + output_val += val; + } + } + output_val /= count; + + top_data[index] = output_val; + } +} + +template +__global__ void RoIAlignRotatedBackwardFeature( + const int nthreads, + const T* top_diff, + const int num_rois, + const T spatial_scale, + const int channels, + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + T* bottom_diff, + const T* rois) { + CUDA_1D_KERNEL_LOOP(index, nthreads) { + // (n, c, ph, pw) is an element in the pooled output + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int c = (index / pooled_width / pooled_height) % channels; + int n = index / pooled_width / pooled_height / channels; + + const T* current_roi = rois + n * 6; + int roi_batch_ind = current_roi[0]; + + // Do not use rounding; this implementation detail is critical + // ROIAlignRotated supports align == true, i.e., continuous coordinate + // by default, thus the 0.5 offset + T offset = (T)0.5; + T roi_center_w = current_roi[1] * spatial_scale - offset; + T roi_center_h = current_roi[2] * spatial_scale - offset; + T roi_width = current_roi[3] * spatial_scale; + T roi_height = current_roi[4] * spatial_scale; + T theta = current_roi[5] * M_PI / 180.0; + T cos_theta = cos(theta); + T sin_theta = sin(theta); + + T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); + T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); + + T* offset_bottom_diff = + bottom_diff + (roi_batch_ind * channels + c) * height * width; + + int top_offset = (n * channels + c) * pooled_height * pooled_width; + const T* offset_top_diff = top_diff + top_offset; + const T top_diff_this_bin = offset_top_diff[ph * pooled_width + pw]; + + // We use roi_bin_grid to sample the grid and mimic integral + int roi_bin_grid_h = (sampling_ratio > 0) + ? sampling_ratio + : ceil(roi_height / pooled_height); // e.g., = 2 + int roi_bin_grid_w = + (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); + + // roi_start_h and roi_start_w are computed wrt the center of RoI (x, y). + // Appropriate translation needs to be applied after. + T roi_start_h = -roi_height / 2.0; + T roi_start_w = -roi_width / 2.0; + + // We do average (integral) pooling inside a bin + const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4 + + for (int iy = 0; iy < roi_bin_grid_h; iy++) // e.g., iy = 0, 1 + { + const T yy = roi_start_h + ph * bin_size_h + + static_cast(iy + .5f) * bin_size_h / + static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5 + for (int ix = 0; ix < roi_bin_grid_w; ix++) { + const T xx = roi_start_w + pw * bin_size_w + + static_cast(ix + .5f) * bin_size_w / + static_cast(roi_bin_grid_w); + + // Rotate by theta around the center and translate + T y = yy * cos_theta - xx * sin_theta + roi_center_h; + T x = yy * sin_theta + xx * cos_theta + roi_center_w; + + T w1, w2, w3, w4; + int x_low, x_high, y_low, y_high; + + bilinear_interpolate_gradient( + height, width, y, x, w1, w2, w3, w4, x_low, x_high, y_low, y_high); + + T g1 = top_diff_this_bin * w1 / count; + T g2 = top_diff_this_bin * w2 / count; + T g3 = top_diff_this_bin * w3 / count; + T g4 = top_diff_this_bin * w4 / count; + + if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) { + atomicAdd( + offset_bottom_diff + y_low * width + x_low, static_cast(g1)); + atomicAdd( + offset_bottom_diff + y_low * width + x_high, static_cast(g2)); + atomicAdd( + offset_bottom_diff + y_high * width + x_low, static_cast(g3)); + atomicAdd( + offset_bottom_diff + y_high * width + x_high, static_cast(g4)); + } // if + } // ix + } // iy + } // CUDA_1D_KERNEL_LOOP +} // RoIAlignRotatedBackward + +at::Tensor ROIAlignRotated_forward_cuda( + const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio) { + AT_ASSERTM(input.device().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(rois.device().is_cuda(), "rois must be a CUDA tensor"); + at::TensorArg input_t{input, "input", 1}, rois_t{rois, "rois", 2}; + + at::CheckedFrom c = "ROIAlignRotated_forward_cuda"; + at::checkAllSameGPU(c, {input_t, rois_t}); + at::checkAllSameType(c, {input_t, rois_t}); + at::cuda::CUDAGuard device_guard(input.device()); + + auto num_rois = rois.size(0); + auto channels = input.size(1); + auto height = input.size(2); + auto width = input.size(3); + + auto output = at::empty( + {num_rois, channels, pooled_height, pooled_width}, input.options()); + auto output_size = num_rois * pooled_height * pooled_width * channels; + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + dim3 grid(std::min( + at::cuda::ATenCeilDiv( + static_cast(output_size), static_cast(512)), + static_cast(4096))); + dim3 block(512); + + if (output.numel() == 0) { + AT_CUDA_CHECK(cudaGetLastError()); + return output; + } + + auto input_ = input.contiguous(), rois_ = rois.contiguous(); + AT_DISPATCH_FLOATING_TYPES( + input.scalar_type(), "ROIAlignRotated_forward", [&] { + RoIAlignRotatedForward<<>>( + output_size, + input_.data_ptr(), + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + sampling_ratio, + rois_.data_ptr(), + output.data_ptr()); + }); + cudaDeviceSynchronize(); + AT_CUDA_CHECK(cudaGetLastError()); + return output; +} + +// TODO remove the dependency on input and use instead its sizes -> save memory +at::Tensor ROIAlignRotated_backward_cuda( + const at::Tensor& grad, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width, + const int sampling_ratio) { + AT_ASSERTM(grad.device().is_cuda(), "grad must be a CUDA tensor"); + AT_ASSERTM(rois.device().is_cuda(), "rois must be a CUDA tensor"); + + at::TensorArg grad_t{grad, "grad", 1}, rois_t{rois, "rois", 2}; + at::CheckedFrom c = "ROIAlign_backward_cuda"; + at::checkAllSameGPU(c, {grad_t, rois_t}); + at::checkAllSameType(c, {grad_t, rois_t}); + at::cuda::CUDAGuard device_guard(grad.device()); + + auto num_rois = rois.size(0); + auto grad_input = + at::zeros({batch_size, channels, height, width}, grad.options()); + + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + dim3 grid(std::min( + at::cuda::ATenCeilDiv( + static_cast(grad.numel()), static_cast(512)), + static_cast(4096))); + dim3 block(512); + + // handle possibly empty gradients + if (grad.numel() == 0) { + AT_CUDA_CHECK(cudaGetLastError()); + return grad_input; + } + + auto grad_ = grad.contiguous(), rois_ = rois.contiguous(); + AT_DISPATCH_FLOATING_TYPES( + grad.scalar_type(), "ROIAlignRotated_backward", [&] { + RoIAlignRotatedBackwardFeature<<>>( + grad.numel(), + grad_.data_ptr(), + num_rois, + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + sampling_ratio, + grad_input.data_ptr(), + rois_.data_ptr()); + }); + AT_CUDA_CHECK(cudaGetLastError()); + return grad_input; +} + +} // namespace detectron2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated.h b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated.h new file mode 100644 index 0000000000000000000000000000000000000000..3bf383b8ed9b358b5313d433a9682c294dfb77e4 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated.h @@ -0,0 +1,35 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#pragma once +#include + +namespace detectron2 { + +at::Tensor box_iou_rotated_cpu( + const at::Tensor& boxes1, + const at::Tensor& boxes2); + +#if defined(WITH_CUDA) || defined(WITH_HIP) +at::Tensor box_iou_rotated_cuda( + const at::Tensor& boxes1, + const at::Tensor& boxes2); +#endif + +// Interface for Python +// inline is needed to prevent multiple function definitions when this header is +// included by different cpps +inline at::Tensor box_iou_rotated( + const at::Tensor& boxes1, + const at::Tensor& boxes2) { + assert(boxes1.device().is_cuda() == boxes2.device().is_cuda()); + if (boxes1.device().is_cuda()) { +#if defined(WITH_CUDA) || defined(WITH_HIP) + return box_iou_rotated_cuda(boxes1.contiguous(), boxes2.contiguous()); +#else + AT_ERROR("Detectron2 is not compiled with GPU support!"); +#endif + } + + return box_iou_rotated_cpu(boxes1.contiguous(), boxes2.contiguous()); +} + +} // namespace detectron2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cpu.cpp b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cpu.cpp new file mode 100644 index 0000000000000000000000000000000000000000..c843487b5fa4e8077dd27402ec99009266ddda8d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cpu.cpp @@ -0,0 +1,39 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#include "box_iou_rotated.h" +#include "box_iou_rotated_utils.h" + +namespace detectron2 { + +template +void box_iou_rotated_cpu_kernel( + const at::Tensor& boxes1, + const at::Tensor& boxes2, + at::Tensor& ious) { + auto num_boxes1 = boxes1.size(0); + auto num_boxes2 = boxes2.size(0); + + for (int i = 0; i < num_boxes1; i++) { + for (int j = 0; j < num_boxes2; j++) { + ious[i * num_boxes2 + j] = single_box_iou_rotated( + boxes1[i].data_ptr(), boxes2[j].data_ptr()); + } + } +} + +at::Tensor box_iou_rotated_cpu( + // input must be contiguous: + const at::Tensor& boxes1, + const at::Tensor& boxes2) { + auto num_boxes1 = boxes1.size(0); + auto num_boxes2 = boxes2.size(0); + at::Tensor ious = + at::empty({num_boxes1 * num_boxes2}, boxes1.options().dtype(at::kFloat)); + + box_iou_rotated_cpu_kernel(boxes1, boxes2, ious); + + // reshape from 1d array to 2d array + auto shape = std::vector{num_boxes1, num_boxes2}; + return ious.reshape(shape); +} + +} // namespace detectron2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cuda.cu b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cuda.cu new file mode 100644 index 0000000000000000000000000000000000000000..952710e53041187907fbd113f8d0d0fa24134a86 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cuda.cu @@ -0,0 +1,130 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#include +#include +#include +#include +#include "box_iou_rotated_utils.h" + +namespace detectron2 { + +// 2D block with 32 * 16 = 512 threads per block +const int BLOCK_DIM_X = 32; +const int BLOCK_DIM_Y = 16; + +template +__global__ void box_iou_rotated_cuda_kernel( + const int n_boxes1, + const int n_boxes2, + const T* dev_boxes1, + const T* dev_boxes2, + T* dev_ious) { + const int row_start = blockIdx.x * blockDim.x; + const int col_start = blockIdx.y * blockDim.y; + + const int row_size = min(n_boxes1 - row_start, blockDim.x); + const int col_size = min(n_boxes2 - col_start, blockDim.y); + + __shared__ float block_boxes1[BLOCK_DIM_X * 5]; + __shared__ float block_boxes2[BLOCK_DIM_Y * 5]; + + // It's safe to copy using threadIdx.x since BLOCK_DIM_X >= BLOCK_DIM_Y + if (threadIdx.x < row_size && threadIdx.y == 0) { + block_boxes1[threadIdx.x * 5 + 0] = + dev_boxes1[(row_start + threadIdx.x) * 5 + 0]; + block_boxes1[threadIdx.x * 5 + 1] = + dev_boxes1[(row_start + threadIdx.x) * 5 + 1]; + block_boxes1[threadIdx.x * 5 + 2] = + dev_boxes1[(row_start + threadIdx.x) * 5 + 2]; + block_boxes1[threadIdx.x * 5 + 3] = + dev_boxes1[(row_start + threadIdx.x) * 5 + 3]; + block_boxes1[threadIdx.x * 5 + 4] = + dev_boxes1[(row_start + threadIdx.x) * 5 + 4]; + } + + if (threadIdx.x < col_size && threadIdx.y == 0) { + block_boxes2[threadIdx.x * 5 + 0] = + dev_boxes2[(col_start + threadIdx.x) * 5 + 0]; + block_boxes2[threadIdx.x * 5 + 1] = + dev_boxes2[(col_start + threadIdx.x) * 5 + 1]; + block_boxes2[threadIdx.x * 5 + 2] = + dev_boxes2[(col_start + threadIdx.x) * 5 + 2]; + block_boxes2[threadIdx.x * 5 + 3] = + dev_boxes2[(col_start + threadIdx.x) * 5 + 3]; + block_boxes2[threadIdx.x * 5 + 4] = + dev_boxes2[(col_start + threadIdx.x) * 5 + 4]; + } + __syncthreads(); + + if (threadIdx.x < row_size && threadIdx.y < col_size) { + int offset = (row_start + threadIdx.x) * n_boxes2 + col_start + threadIdx.y; + dev_ious[offset] = single_box_iou_rotated( + block_boxes1 + threadIdx.x * 5, block_boxes2 + threadIdx.y * 5); + } +} + +at::Tensor box_iou_rotated_cuda( + // input must be contiguous + const at::Tensor& boxes1, + const at::Tensor& boxes2) { + using scalar_t = float; + AT_ASSERTM( + boxes1.scalar_type() == at::kFloat, "boxes1 must be a float tensor"); + AT_ASSERTM( + boxes2.scalar_type() == at::kFloat, "boxes2 must be a float tensor"); + AT_ASSERTM(boxes1.is_cuda(), "boxes1 must be a CUDA tensor"); + AT_ASSERTM(boxes2.is_cuda(), "boxes2 must be a CUDA tensor"); + at::cuda::CUDAGuard device_guard(boxes1.device()); + + auto num_boxes1 = boxes1.size(0); + auto num_boxes2 = boxes2.size(0); + + at::Tensor ious = + at::empty({num_boxes1 * num_boxes2}, boxes1.options().dtype(at::kFloat)); + + bool transpose = false; + if (num_boxes1 > 0 && num_boxes2 > 0) { + scalar_t *data1 = boxes1.data_ptr(), + *data2 = boxes2.data_ptr(); + + if (num_boxes2 > 65535 * BLOCK_DIM_Y) { + AT_ASSERTM( + num_boxes1 <= 65535 * BLOCK_DIM_Y, + "Too many boxes for box_iou_rotated_cuda!"); + // x dim is allowed to be large, but y dim cannot, + // so we transpose the two to avoid "invalid configuration argument" + // error. We assume one of them is small. Otherwise the result is hard to + // fit in memory anyway. + std::swap(num_boxes1, num_boxes2); + std::swap(data1, data2); + transpose = true; + } + + const int blocks_x = + at::cuda::ATenCeilDiv(static_cast(num_boxes1), BLOCK_DIM_X); + const int blocks_y = + at::cuda::ATenCeilDiv(static_cast(num_boxes2), BLOCK_DIM_Y); + + dim3 blocks(blocks_x, blocks_y); + dim3 threads(BLOCK_DIM_X, BLOCK_DIM_Y); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + box_iou_rotated_cuda_kernel<<>>( + num_boxes1, + num_boxes2, + data1, + data2, + (scalar_t*)ious.data_ptr()); + + AT_CUDA_CHECK(cudaGetLastError()); + } + + // reshape from 1d array to 2d array + auto shape = std::vector{num_boxes1, num_boxes2}; + if (transpose) { + return ious.view(shape).t(); + } else { + return ious.view(shape); + } +} + +} // namespace detectron2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_utils.h b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_utils.h new file mode 100644 index 0000000000000000000000000000000000000000..b54a5dde2ca11a74d29c4d8adb7fe1634f5baf9c --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_utils.h @@ -0,0 +1,370 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#pragma once + +#include +#include + +#if defined(__CUDACC__) || __HCC__ == 1 || __HIP__ == 1 +// Designates functions callable from the host (CPU) and the device (GPU) +#define HOST_DEVICE __host__ __device__ +#define HOST_DEVICE_INLINE HOST_DEVICE __forceinline__ +#else +#include +#define HOST_DEVICE +#define HOST_DEVICE_INLINE HOST_DEVICE inline +#endif + +namespace detectron2 { + +namespace { + +template +struct RotatedBox { + T x_ctr, y_ctr, w, h, a; +}; + +template +struct Point { + T x, y; + HOST_DEVICE_INLINE Point(const T& px = 0, const T& py = 0) : x(px), y(py) {} + HOST_DEVICE_INLINE Point operator+(const Point& p) const { + return Point(x + p.x, y + p.y); + } + HOST_DEVICE_INLINE Point& operator+=(const Point& p) { + x += p.x; + y += p.y; + return *this; + } + HOST_DEVICE_INLINE Point operator-(const Point& p) const { + return Point(x - p.x, y - p.y); + } + HOST_DEVICE_INLINE Point operator*(const T coeff) const { + return Point(x * coeff, y * coeff); + } +}; + +template +HOST_DEVICE_INLINE T dot_2d(const Point& A, const Point& B) { + return A.x * B.x + A.y * B.y; +} + +// R: result type. can be different from input type +template +HOST_DEVICE_INLINE R cross_2d(const Point& A, const Point& B) { + return static_cast(A.x) * static_cast(B.y) - + static_cast(B.x) * static_cast(A.y); +} + +template +HOST_DEVICE_INLINE void get_rotated_vertices( + const RotatedBox& box, + Point (&pts)[4]) { + // M_PI / 180. == 0.01745329251 + double theta = box.a * 0.01745329251; + T cosTheta2 = (T)cos(theta) * 0.5f; + T sinTheta2 = (T)sin(theta) * 0.5f; + + // y: top --> down; x: left --> right + pts[0].x = box.x_ctr + sinTheta2 * box.h + cosTheta2 * box.w; + pts[0].y = box.y_ctr + cosTheta2 * box.h - sinTheta2 * box.w; + pts[1].x = box.x_ctr - sinTheta2 * box.h + cosTheta2 * box.w; + pts[1].y = box.y_ctr - cosTheta2 * box.h - sinTheta2 * box.w; + pts[2].x = 2 * box.x_ctr - pts[0].x; + pts[2].y = 2 * box.y_ctr - pts[0].y; + pts[3].x = 2 * box.x_ctr - pts[1].x; + pts[3].y = 2 * box.y_ctr - pts[1].y; +} + +template +HOST_DEVICE_INLINE int get_intersection_points( + const Point (&pts1)[4], + const Point (&pts2)[4], + Point (&intersections)[24]) { + // Line vector + // A line from p1 to p2 is: p1 + (p2-p1)*t, t=[0,1] + Point vec1[4], vec2[4]; + for (int i = 0; i < 4; i++) { + vec1[i] = pts1[(i + 1) % 4] - pts1[i]; + vec2[i] = pts2[(i + 1) % 4] - pts2[i]; + } + + // When computing the intersection area, it doesn't hurt if we have + // more (duplicated/approximate) intersections/vertices than needed, + // while it can cause drastic difference if we miss an intersection/vertex. + // Therefore, we add an epsilon to relax the comparisons between + // the float point numbers that decide the intersection points. + double EPS = 1e-5; + + // Line test - test all line combos for intersection + int num = 0; // number of intersections + for (int i = 0; i < 4; i++) { + for (int j = 0; j < 4; j++) { + // Solve for 2x2 Ax=b + T det = cross_2d(vec2[j], vec1[i]); + + // This takes care of parallel lines + if (fabs(det) <= 1e-14) { + continue; + } + + auto vec12 = pts2[j] - pts1[i]; + + T t1 = cross_2d(vec2[j], vec12) / det; + T t2 = cross_2d(vec1[i], vec12) / det; + + if (t1 > -EPS && t1 < 1.0f + EPS && t2 > -EPS && t2 < 1.0f + EPS) { + intersections[num++] = pts1[i] + vec1[i] * t1; + } + } + } + + // Check for vertices of rect1 inside rect2 + { + const auto& AB = vec2[0]; + const auto& DA = vec2[3]; + auto ABdotAB = dot_2d(AB, AB); + auto ADdotAD = dot_2d(DA, DA); + for (int i = 0; i < 4; i++) { + // assume ABCD is the rectangle, and P is the point to be judged + // P is inside ABCD iff. P's projection on AB lies within AB + // and P's projection on AD lies within AD + + auto AP = pts1[i] - pts2[0]; + + auto APdotAB = dot_2d(AP, AB); + auto APdotAD = -dot_2d(AP, DA); + + if ((APdotAB > -EPS) && (APdotAD > -EPS) && (APdotAB < ABdotAB + EPS) && + (APdotAD < ADdotAD + EPS)) { + intersections[num++] = pts1[i]; + } + } + } + + // Reverse the check - check for vertices of rect2 inside rect1 + { + const auto& AB = vec1[0]; + const auto& DA = vec1[3]; + auto ABdotAB = dot_2d(AB, AB); + auto ADdotAD = dot_2d(DA, DA); + for (int i = 0; i < 4; i++) { + auto AP = pts2[i] - pts1[0]; + + auto APdotAB = dot_2d(AP, AB); + auto APdotAD = -dot_2d(AP, DA); + + if ((APdotAB > -EPS) && (APdotAD > -EPS) && (APdotAB < ABdotAB + EPS) && + (APdotAD < ADdotAD + EPS)) { + intersections[num++] = pts2[i]; + } + } + } + + return num; +} + +template +HOST_DEVICE_INLINE int convex_hull_graham( + const Point (&p)[24], + const int& num_in, + Point (&q)[24], + bool shift_to_zero = false) { + assert(num_in >= 2); + + // Step 1: + // Find point with minimum y + // if more than 1 points have the same minimum y, + // pick the one with the minimum x. + int t = 0; + for (int i = 1; i < num_in; i++) { + if (p[i].y < p[t].y || (p[i].y == p[t].y && p[i].x < p[t].x)) { + t = i; + } + } + auto& start = p[t]; // starting point + + // Step 2: + // Subtract starting point from every points (for sorting in the next step) + for (int i = 0; i < num_in; i++) { + q[i] = p[i] - start; + } + + // Swap the starting point to position 0 + auto tmp = q[0]; + q[0] = q[t]; + q[t] = tmp; + + // Step 3: + // Sort point 1 ~ num_in according to their relative cross-product values + // (essentially sorting according to angles) + // If the angles are the same, sort according to their distance to origin + T dist[24]; +#if defined(__CUDACC__) || __HCC__ == 1 || __HIP__ == 1 + // compute distance to origin before sort, and sort them together with the + // points + for (int i = 0; i < num_in; i++) { + dist[i] = dot_2d(q[i], q[i]); + } + + // CUDA version + // In the future, we can potentially use thrust + // for sorting here to improve speed (though not guaranteed) + for (int i = 1; i < num_in - 1; i++) { + for (int j = i + 1; j < num_in; j++) { + T crossProduct = cross_2d(q[i], q[j]); + if ((crossProduct < -1e-6) || + (fabs(crossProduct) < 1e-6 && dist[i] > dist[j])) { + auto q_tmp = q[i]; + q[i] = q[j]; + q[j] = q_tmp; + auto dist_tmp = dist[i]; + dist[i] = dist[j]; + dist[j] = dist_tmp; + } + } + } +#else + // CPU version + std::sort( + q + 1, q + num_in, [](const Point& A, const Point& B) -> bool { + T temp = cross_2d(A, B); + if (fabs(temp) < 1e-6) { + return dot_2d(A, A) < dot_2d(B, B); + } else { + return temp > 0; + } + }); + // compute distance to origin after sort, since the points are now different. + for (int i = 0; i < num_in; i++) { + dist[i] = dot_2d(q[i], q[i]); + } +#endif + + // Step 4: + // Make sure there are at least 2 points (that don't overlap with each other) + // in the stack + int k; // index of the non-overlapped second point + for (k = 1; k < num_in; k++) { + if (dist[k] > 1e-8) { + break; + } + } + if (k == num_in) { + // We reach the end, which means the convex hull is just one point + q[0] = p[t]; + return 1; + } + q[1] = q[k]; + int m = 2; // 2 points in the stack + // Step 5: + // Finally we can start the scanning process. + // When a non-convex relationship between the 3 points is found + // (either concave shape or duplicated points), + // we pop the previous point from the stack + // until the 3-point relationship is convex again, or + // until the stack only contains two points + for (int i = k + 1; i < num_in; i++) { + while (m > 1) { + auto q1 = q[i] - q[m - 2], q2 = q[m - 1] - q[m - 2]; + // cross_2d() uses FMA and therefore computes round(round(q1.x*q2.y) - + // q2.x*q1.y) So it may not return 0 even when q1==q2. Therefore we + // compare round(q1.x*q2.y) and round(q2.x*q1.y) directly. (round means + // round to nearest floating point). + if (q1.x * q2.y >= q2.x * q1.y) + m--; + else + break; + } + // Using double also helps, but float can solve the issue for now. + // while (m > 1 && cross_2d(q[i] - q[m - 2], q[m - 1] - q[m - 2]) + // >= 0) { + // m--; + // } + q[m++] = q[i]; + } + + // Step 6 (Optional): + // In general sense we need the original coordinates, so we + // need to shift the points back (reverting Step 2) + // But if we're only interested in getting the area/perimeter of the shape + // We can simply return. + if (!shift_to_zero) { + for (int i = 0; i < m; i++) { + q[i] += start; + } + } + + return m; +} + +template +HOST_DEVICE_INLINE T polygon_area(const Point (&q)[24], const int& m) { + if (m <= 2) { + return 0; + } + + T area = 0; + for (int i = 1; i < m - 1; i++) { + area += fabs(cross_2d(q[i] - q[0], q[i + 1] - q[0])); + } + + return area / 2.0; +} + +template +HOST_DEVICE_INLINE T rotated_boxes_intersection( + const RotatedBox& box1, + const RotatedBox& box2) { + // There are up to 4 x 4 + 4 + 4 = 24 intersections (including dups) returned + // from rotated_rect_intersection_pts + Point intersectPts[24], orderedPts[24]; + + Point pts1[4]; + Point pts2[4]; + get_rotated_vertices(box1, pts1); + get_rotated_vertices(box2, pts2); + + int num = get_intersection_points(pts1, pts2, intersectPts); + + if (num <= 2) { + return 0.0; + } + + // Convex Hull to order the intersection points in clockwise order and find + // the contour area. + int num_convex = convex_hull_graham(intersectPts, num, orderedPts, true); + return polygon_area(orderedPts, num_convex); +} + +} // namespace + +template +HOST_DEVICE_INLINE T +single_box_iou_rotated(T const* const box1_raw, T const* const box2_raw) { + // shift center to the middle point to achieve higher precision in result + RotatedBox box1, box2; + auto center_shift_x = (box1_raw[0] + box2_raw[0]) / 2.0; + auto center_shift_y = (box1_raw[1] + box2_raw[1]) / 2.0; + box1.x_ctr = box1_raw[0] - center_shift_x; + box1.y_ctr = box1_raw[1] - center_shift_y; + box1.w = box1_raw[2]; + box1.h = box1_raw[3]; + box1.a = box1_raw[4]; + box2.x_ctr = box2_raw[0] - center_shift_x; + box2.y_ctr = box2_raw[1] - center_shift_y; + box2.w = box2_raw[2]; + box2.h = box2_raw[3]; + box2.a = box2_raw[4]; + + T area1 = box1.w * box1.h; + T area2 = box2.w * box2.h; + if (area1 < 1e-14 || area2 < 1e-14) { + return 0.f; + } + + T intersection = rotated_boxes_intersection(box1, box2); + T iou = intersection / (area1 + area2 - intersection); + return iou; +} + +} // namespace detectron2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/cocoeval/cocoeval.cpp b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/cocoeval/cocoeval.cpp new file mode 100644 index 0000000000000000000000000000000000000000..0a5b7b907c06720fefc77b0dfd921b8ec3ecf2be --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/cocoeval/cocoeval.cpp @@ -0,0 +1,507 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#include "cocoeval.h" +#include +#include +#include +#include + +using namespace pybind11::literals; + +namespace detectron2 { + +namespace COCOeval { + +// Sort detections from highest score to lowest, such that +// detection_instances[detection_sorted_indices[t]] >= +// detection_instances[detection_sorted_indices[t+1]]. Use stable_sort to match +// original COCO API +void SortInstancesByDetectionScore( + const std::vector& detection_instances, + std::vector* detection_sorted_indices) { + detection_sorted_indices->resize(detection_instances.size()); + std::iota( + detection_sorted_indices->begin(), detection_sorted_indices->end(), 0); + std::stable_sort( + detection_sorted_indices->begin(), + detection_sorted_indices->end(), + [&detection_instances](size_t j1, size_t j2) { + return detection_instances[j1].score > detection_instances[j2].score; + }); +} + +// Partition the ground truth objects based on whether or not to ignore them +// based on area +void SortInstancesByIgnore( + const std::array& area_range, + const std::vector& ground_truth_instances, + std::vector* ground_truth_sorted_indices, + std::vector* ignores) { + ignores->clear(); + ignores->reserve(ground_truth_instances.size()); + for (auto o : ground_truth_instances) { + ignores->push_back( + o.ignore || o.area < area_range[0] || o.area > area_range[1]); + } + + ground_truth_sorted_indices->resize(ground_truth_instances.size()); + std::iota( + ground_truth_sorted_indices->begin(), + ground_truth_sorted_indices->end(), + 0); + std::stable_sort( + ground_truth_sorted_indices->begin(), + ground_truth_sorted_indices->end(), + [&ignores](size_t j1, size_t j2) { + return (int)(*ignores)[j1] < (int)(*ignores)[j2]; + }); +} + +// For each IOU threshold, greedily match each detected instance to a ground +// truth instance (if possible) and store the results +void MatchDetectionsToGroundTruth( + const std::vector& detection_instances, + const std::vector& detection_sorted_indices, + const std::vector& ground_truth_instances, + const std::vector& ground_truth_sorted_indices, + const std::vector& ignores, + const std::vector>& ious, + const std::vector& iou_thresholds, + const std::array& area_range, + ImageEvaluation* results) { + // Initialize memory to store return data matches and ignore + const int num_iou_thresholds = iou_thresholds.size(); + const int num_ground_truth = ground_truth_sorted_indices.size(); + const int num_detections = detection_sorted_indices.size(); + std::vector ground_truth_matches( + num_iou_thresholds * num_ground_truth, 0); + std::vector& detection_matches = results->detection_matches; + std::vector& detection_ignores = results->detection_ignores; + std::vector& ground_truth_ignores = results->ground_truth_ignores; + detection_matches.resize(num_iou_thresholds * num_detections, 0); + detection_ignores.resize(num_iou_thresholds * num_detections, false); + ground_truth_ignores.resize(num_ground_truth); + for (auto g = 0; g < num_ground_truth; ++g) { + ground_truth_ignores[g] = ignores[ground_truth_sorted_indices[g]]; + } + + for (auto t = 0; t < num_iou_thresholds; ++t) { + for (auto d = 0; d < num_detections; ++d) { + // information about best match so far (match=-1 -> unmatched) + double best_iou = std::min(iou_thresholds[t], 1 - 1e-10); + int match = -1; + for (auto g = 0; g < num_ground_truth; ++g) { + // if this ground truth instance is already matched and not a + // crowd, it cannot be matched to another detection + if (ground_truth_matches[t * num_ground_truth + g] > 0 && + !ground_truth_instances[ground_truth_sorted_indices[g]].is_crowd) { + continue; + } + + // if detected instance matched to a regular ground truth + // instance, we can break on the first ground truth instance + // tagged as ignore (because they are sorted by the ignore tag) + if (match >= 0 && !ground_truth_ignores[match] && + ground_truth_ignores[g]) { + break; + } + + // if IOU overlap is the best so far, store the match appropriately + if (ious[d][ground_truth_sorted_indices[g]] >= best_iou) { + best_iou = ious[d][ground_truth_sorted_indices[g]]; + match = g; + } + } + // if match was made, store id of match for both detection and + // ground truth + if (match >= 0) { + detection_ignores[t * num_detections + d] = ground_truth_ignores[match]; + detection_matches[t * num_detections + d] = + ground_truth_instances[ground_truth_sorted_indices[match]].id; + ground_truth_matches[t * num_ground_truth + match] = + detection_instances[detection_sorted_indices[d]].id; + } + + // set unmatched detections outside of area range to ignore + const InstanceAnnotation& detection = + detection_instances[detection_sorted_indices[d]]; + detection_ignores[t * num_detections + d] = + detection_ignores[t * num_detections + d] || + (detection_matches[t * num_detections + d] == 0 && + (detection.area < area_range[0] || detection.area > area_range[1])); + } + } + + // store detection score results + results->detection_scores.resize(detection_sorted_indices.size()); + for (size_t d = 0; d < detection_sorted_indices.size(); ++d) { + results->detection_scores[d] = + detection_instances[detection_sorted_indices[d]].score; + } +} + +std::vector EvaluateImages( + const std::vector>& area_ranges, + int max_detections, + const std::vector& iou_thresholds, + const ImageCategoryInstances>& image_category_ious, + const ImageCategoryInstances& + image_category_ground_truth_instances, + const ImageCategoryInstances& + image_category_detection_instances) { + const int num_area_ranges = area_ranges.size(); + const int num_images = image_category_ground_truth_instances.size(); + const int num_categories = + image_category_ious.size() > 0 ? image_category_ious[0].size() : 0; + std::vector detection_sorted_indices; + std::vector ground_truth_sorted_indices; + std::vector ignores; + std::vector results_all( + num_images * num_area_ranges * num_categories); + + // Store results for each image, category, and area range combination. Results + // for each IOU threshold are packed into the same ImageEvaluation object + for (auto i = 0; i < num_images; ++i) { + for (auto c = 0; c < num_categories; ++c) { + const std::vector& ground_truth_instances = + image_category_ground_truth_instances[i][c]; + const std::vector& detection_instances = + image_category_detection_instances[i][c]; + + SortInstancesByDetectionScore( + detection_instances, &detection_sorted_indices); + if ((int)detection_sorted_indices.size() > max_detections) { + detection_sorted_indices.resize(max_detections); + } + + for (size_t a = 0; a < area_ranges.size(); ++a) { + SortInstancesByIgnore( + area_ranges[a], + ground_truth_instances, + &ground_truth_sorted_indices, + &ignores); + + MatchDetectionsToGroundTruth( + detection_instances, + detection_sorted_indices, + ground_truth_instances, + ground_truth_sorted_indices, + ignores, + image_category_ious[i][c], + iou_thresholds, + area_ranges[a], + &results_all + [c * num_area_ranges * num_images + a * num_images + i]); + } + } + } + + return results_all; +} + +// Convert a python list to a vector +template +std::vector list_to_vec(const py::list& l) { + std::vector v(py::len(l)); + for (int i = 0; i < (int)py::len(l); ++i) { + v[i] = l[i].cast(); + } + return v; +} + +// Helper function to Accumulate() +// Considers the evaluation results applicable to a particular category, area +// range, and max_detections parameter setting, which begin at +// evaluations[evaluation_index]. Extracts a sorted list of length n of all +// applicable detection instances concatenated across all images in the dataset, +// which are represented by the outputs evaluation_indices, detection_scores, +// image_detection_indices, and detection_sorted_indices--all of which are +// length n. evaluation_indices[i] stores the applicable index into +// evaluations[] for instance i, which has detection score detection_score[i], +// and is the image_detection_indices[i]'th of the list of detections +// for the image containing i. detection_sorted_indices[] defines a sorted +// permutation of the 3 other outputs +int BuildSortedDetectionList( + const std::vector& evaluations, + const int64_t evaluation_index, + const int64_t num_images, + const int max_detections, + std::vector* evaluation_indices, + std::vector* detection_scores, + std::vector* detection_sorted_indices, + std::vector* image_detection_indices) { + assert(evaluations.size() >= evaluation_index + num_images); + + // Extract a list of object instances of the applicable category, area + // range, and max detections requirements such that they can be sorted + image_detection_indices->clear(); + evaluation_indices->clear(); + detection_scores->clear(); + image_detection_indices->reserve(num_images * max_detections); + evaluation_indices->reserve(num_images * max_detections); + detection_scores->reserve(num_images * max_detections); + int num_valid_ground_truth = 0; + for (auto i = 0; i < num_images; ++i) { + const ImageEvaluation& evaluation = evaluations[evaluation_index + i]; + + for (int d = 0; + d < (int)evaluation.detection_scores.size() && d < max_detections; + ++d) { // detected instances + evaluation_indices->push_back(evaluation_index + i); + image_detection_indices->push_back(d); + detection_scores->push_back(evaluation.detection_scores[d]); + } + for (auto ground_truth_ignore : evaluation.ground_truth_ignores) { + if (!ground_truth_ignore) { + ++num_valid_ground_truth; + } + } + } + + // Sort detections by decreasing score, using stable sort to match + // python implementation + detection_sorted_indices->resize(detection_scores->size()); + std::iota( + detection_sorted_indices->begin(), detection_sorted_indices->end(), 0); + std::stable_sort( + detection_sorted_indices->begin(), + detection_sorted_indices->end(), + [&detection_scores](size_t j1, size_t j2) { + return (*detection_scores)[j1] > (*detection_scores)[j2]; + }); + + return num_valid_ground_truth; +} + +// Helper function to Accumulate() +// Compute a precision recall curve given a sorted list of detected instances +// encoded in evaluations, evaluation_indices, detection_scores, +// detection_sorted_indices, image_detection_indices (see +// BuildSortedDetectionList()). Using vectors precisions and recalls +// and temporary storage, output the results into precisions_out, recalls_out, +// and scores_out, which are large buffers containing many precion/recall curves +// for all possible parameter settings, with precisions_out_index and +// recalls_out_index defining the applicable indices to store results. +void ComputePrecisionRecallCurve( + const int64_t precisions_out_index, + const int64_t precisions_out_stride, + const int64_t recalls_out_index, + const std::vector& recall_thresholds, + const int iou_threshold_index, + const int num_iou_thresholds, + const int num_valid_ground_truth, + const std::vector& evaluations, + const std::vector& evaluation_indices, + const std::vector& detection_scores, + const std::vector& detection_sorted_indices, + const std::vector& image_detection_indices, + std::vector* precisions, + std::vector* recalls, + std::vector* precisions_out, + std::vector* scores_out, + std::vector* recalls_out) { + assert(recalls_out->size() > recalls_out_index); + + // Compute precision/recall for each instance in the sorted list of detections + int64_t true_positives_sum = 0, false_positives_sum = 0; + precisions->clear(); + recalls->clear(); + precisions->reserve(detection_sorted_indices.size()); + recalls->reserve(detection_sorted_indices.size()); + assert(!evaluations.empty() || detection_sorted_indices.empty()); + for (auto detection_sorted_index : detection_sorted_indices) { + const ImageEvaluation& evaluation = + evaluations[evaluation_indices[detection_sorted_index]]; + const auto num_detections = + evaluation.detection_matches.size() / num_iou_thresholds; + const auto detection_index = iou_threshold_index * num_detections + + image_detection_indices[detection_sorted_index]; + assert(evaluation.detection_matches.size() > detection_index); + assert(evaluation.detection_ignores.size() > detection_index); + const int64_t detection_match = + evaluation.detection_matches[detection_index]; + const bool detection_ignores = + evaluation.detection_ignores[detection_index]; + const auto true_positive = detection_match > 0 && !detection_ignores; + const auto false_positive = detection_match == 0 && !detection_ignores; + if (true_positive) { + ++true_positives_sum; + } + if (false_positive) { + ++false_positives_sum; + } + + const double recall = + static_cast(true_positives_sum) / num_valid_ground_truth; + recalls->push_back(recall); + const int64_t num_valid_detections = + true_positives_sum + false_positives_sum; + const double precision = num_valid_detections > 0 + ? static_cast(true_positives_sum) / num_valid_detections + : 0.0; + precisions->push_back(precision); + } + + (*recalls_out)[recalls_out_index] = !recalls->empty() ? recalls->back() : 0; + + for (int64_t i = static_cast(precisions->size()) - 1; i > 0; --i) { + if ((*precisions)[i] > (*precisions)[i - 1]) { + (*precisions)[i - 1] = (*precisions)[i]; + } + } + + // Sample the per instance precision/recall list at each recall threshold + for (size_t r = 0; r < recall_thresholds.size(); ++r) { + // first index in recalls >= recall_thresholds[r] + std::vector::iterator low = std::lower_bound( + recalls->begin(), recalls->end(), recall_thresholds[r]); + size_t precisions_index = low - recalls->begin(); + + const auto results_ind = precisions_out_index + r * precisions_out_stride; + assert(results_ind < precisions_out->size()); + assert(results_ind < scores_out->size()); + if (precisions_index < precisions->size()) { + (*precisions_out)[results_ind] = (*precisions)[precisions_index]; + (*scores_out)[results_ind] = + detection_scores[detection_sorted_indices[precisions_index]]; + } else { + (*precisions_out)[results_ind] = 0; + (*scores_out)[results_ind] = 0; + } + } +} +py::dict Accumulate( + const py::object& params, + const std::vector& evaluations) { + const std::vector recall_thresholds = + list_to_vec(params.attr("recThrs")); + const std::vector max_detections = + list_to_vec(params.attr("maxDets")); + const int num_iou_thresholds = py::len(params.attr("iouThrs")); + const int num_recall_thresholds = py::len(params.attr("recThrs")); + const int num_categories = params.attr("useCats").cast() == 1 + ? py::len(params.attr("catIds")) + : 1; + const int num_area_ranges = py::len(params.attr("areaRng")); + const int num_max_detections = py::len(params.attr("maxDets")); + const int num_images = py::len(params.attr("imgIds")); + + std::vector precisions_out( + num_iou_thresholds * num_recall_thresholds * num_categories * + num_area_ranges * num_max_detections, + -1); + std::vector recalls_out( + num_iou_thresholds * num_categories * num_area_ranges * + num_max_detections, + -1); + std::vector scores_out( + num_iou_thresholds * num_recall_thresholds * num_categories * + num_area_ranges * num_max_detections, + -1); + + // Consider the list of all detected instances in the entire dataset in one + // large list. evaluation_indices, detection_scores, + // image_detection_indices, and detection_sorted_indices all have the same + // length as this list, such that each entry corresponds to one detected + // instance + std::vector evaluation_indices; // indices into evaluations[] + std::vector detection_scores; // detection scores of each instance + std::vector detection_sorted_indices; // sorted indices of all + // instances in the dataset + std::vector + image_detection_indices; // indices into the list of detected instances in + // the same image as each instance + std::vector precisions, recalls; + + for (auto c = 0; c < num_categories; ++c) { + for (auto a = 0; a < num_area_ranges; ++a) { + for (auto m = 0; m < num_max_detections; ++m) { + // The COCO PythonAPI assumes evaluations[] (the return value of + // COCOeval::EvaluateImages() is one long list storing results for each + // combination of category, area range, and image id, with categories in + // the outermost loop and images in the innermost loop. + const int64_t evaluations_index = + c * num_area_ranges * num_images + a * num_images; + int num_valid_ground_truth = BuildSortedDetectionList( + evaluations, + evaluations_index, + num_images, + max_detections[m], + &evaluation_indices, + &detection_scores, + &detection_sorted_indices, + &image_detection_indices); + + if (num_valid_ground_truth == 0) { + continue; + } + + for (auto t = 0; t < num_iou_thresholds; ++t) { + // recalls_out is a flattened vectors representing a + // num_iou_thresholds X num_categories X num_area_ranges X + // num_max_detections matrix + const int64_t recalls_out_index = + t * num_categories * num_area_ranges * num_max_detections + + c * num_area_ranges * num_max_detections + + a * num_max_detections + m; + + // precisions_out and scores_out are flattened vectors + // representing a num_iou_thresholds X num_recall_thresholds X + // num_categories X num_area_ranges X num_max_detections matrix + const int64_t precisions_out_stride = + num_categories * num_area_ranges * num_max_detections; + const int64_t precisions_out_index = t * num_recall_thresholds * + num_categories * num_area_ranges * num_max_detections + + c * num_area_ranges * num_max_detections + + a * num_max_detections + m; + + ComputePrecisionRecallCurve( + precisions_out_index, + precisions_out_stride, + recalls_out_index, + recall_thresholds, + t, + num_iou_thresholds, + num_valid_ground_truth, + evaluations, + evaluation_indices, + detection_scores, + detection_sorted_indices, + image_detection_indices, + &precisions, + &recalls, + &precisions_out, + &scores_out, + &recalls_out); + } + } + } + } + + time_t rawtime; + struct tm local_time; + std::array buffer; + time(&rawtime); +#ifdef _WIN32 + localtime_s(&local_time, &rawtime); +#else + localtime_r(&rawtime, &local_time); +#endif + strftime( + buffer.data(), 200, "%Y-%m-%d %H:%num_max_detections:%S", &local_time); + return py::dict( + "params"_a = params, + "counts"_a = std::vector( + {num_iou_thresholds, + num_recall_thresholds, + num_categories, + num_area_ranges, + num_max_detections}), + "date"_a = buffer, + "precision"_a = precisions_out, + "recall"_a = recalls_out, + "scores"_a = scores_out); +} + +} // namespace COCOeval + +} // namespace detectron2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/cocoeval/cocoeval.h b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/cocoeval/cocoeval.h new file mode 100644 index 0000000000000000000000000000000000000000..db246e49a026b7cd989b305f4d3d98100be3c912 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/cocoeval/cocoeval.h @@ -0,0 +1,88 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#pragma once + +#include +#include +#include +#include +#include + +namespace py = pybind11; + +namespace detectron2 { + +namespace COCOeval { + +// Annotation data for a single object instance in an image +struct InstanceAnnotation { + InstanceAnnotation( + uint64_t id, + double score, + double area, + bool is_crowd, + bool ignore) + : id{id}, score{score}, area{area}, is_crowd{is_crowd}, ignore{ignore} {} + uint64_t id; + double score = 0.; + double area = 0.; + bool is_crowd = false; + bool ignore = false; +}; + +// Stores intermediate results for evaluating detection results for a single +// image that has D detected instances and G ground truth instances. This stores +// matches between detected and ground truth instances +struct ImageEvaluation { + // For each of the D detected instances, the id of the matched ground truth + // instance, or 0 if unmatched + std::vector detection_matches; + + // The detection score of each of the D detected instances + std::vector detection_scores; + + // Marks whether or not each of G instances was ignored from evaluation (e.g., + // because it's outside area_range) + std::vector ground_truth_ignores; + + // Marks whether or not each of D instances was ignored from evaluation (e.g., + // because it's outside aRng) + std::vector detection_ignores; +}; + +template +using ImageCategoryInstances = std::vector>>; + +// C++ implementation of COCO API cocoeval.py::COCOeval.evaluateImg(). For each +// combination of image, category, area range settings, and IOU thresholds to +// evaluate, it matches detected instances to ground truth instances and stores +// the results into a vector of ImageEvaluation results, which will be +// interpreted by the COCOeval::Accumulate() function to produce precion-recall +// curves. The parameters of nested vectors have the following semantics: +// image_category_ious[i][c][d][g] is the intersection over union of the d'th +// detected instance and g'th ground truth instance of +// category category_ids[c] in image image_ids[i] +// image_category_ground_truth_instances[i][c] is a vector of ground truth +// instances in image image_ids[i] of category category_ids[c] +// image_category_detection_instances[i][c] is a vector of detected +// instances in image image_ids[i] of category category_ids[c] +std::vector EvaluateImages( + const std::vector>& area_ranges, // vector of 2-tuples + int max_detections, + const std::vector& iou_thresholds, + const ImageCategoryInstances>& image_category_ious, + const ImageCategoryInstances& + image_category_ground_truth_instances, + const ImageCategoryInstances& + image_category_detection_instances); + +// C++ implementation of COCOeval.accumulate(), which generates precision +// recall curves for each set of category, IOU threshold, detection area range, +// and max number of detections parameters. It is assumed that the parameter +// evaluations is the return value of the functon COCOeval::EvaluateImages(), +// which was called with the same parameter settings params +py::dict Accumulate( + const py::object& params, + const std::vector& evalutations); + +} // namespace COCOeval +} // namespace detectron2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/cuda_version.cu b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/cuda_version.cu new file mode 100644 index 0000000000000000000000000000000000000000..6dfe1b90c1f65c443681813fd3e3386c9faa3360 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/cuda_version.cu @@ -0,0 +1,26 @@ +// Copyright (c) Facebook, Inc. and its affiliates. + +#include + +namespace detectron2 { +int get_cudart_version() { +// Not a ROCM platform: Either HIP is not used, or +// it is used, but platform is not ROCM (i.e. it is CUDA) +#if !defined(__HIP_PLATFORM_HCC__) + return CUDART_VERSION; +#else + int version = 0; + +#if HIP_VERSION_MAJOR != 0 + // Create a convention similar to that of CUDA, as assumed by other + // parts of the code. + + version = HIP_VERSION_MINOR; + version += (HIP_VERSION_MAJOR * 100); +#else + hipRuntimeGetVersion(&version); +#endif + return version; +#endif +} +} // namespace detectron2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/deformable/deform_conv.h b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/deformable/deform_conv.h new file mode 100644 index 0000000000000000000000000000000000000000..965c1bfd47b58f9802d1c3fd69a5962517b2da61 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/deformable/deform_conv.h @@ -0,0 +1,377 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#pragma once +#include + +namespace detectron2 { + +#if defined(WITH_CUDA) || defined(WITH_HIP) +int deform_conv_forward_cuda( + at::Tensor input, + at::Tensor weight, + at::Tensor offset, + at::Tensor output, + at::Tensor columns, + at::Tensor ones, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + int im2col_step); + +int deform_conv_backward_input_cuda( + at::Tensor input, + at::Tensor offset, + at::Tensor gradOutput, + at::Tensor gradInput, + at::Tensor gradOffset, + at::Tensor weight, + at::Tensor columns, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + int im2col_step); + +int deform_conv_backward_parameters_cuda( + at::Tensor input, + at::Tensor offset, + at::Tensor gradOutput, + at::Tensor gradWeight, // at::Tensor gradBias, + at::Tensor columns, + at::Tensor ones, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + float scale, + int im2col_step); + +void modulated_deform_conv_cuda_forward( + at::Tensor input, + at::Tensor weight, + at::Tensor bias, + at::Tensor ones, + at::Tensor offset, + at::Tensor mask, + at::Tensor output, + at::Tensor columns, + int kernel_h, + int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int group, + const int deformable_group, + const bool with_bias); + +void modulated_deform_conv_cuda_backward( + at::Tensor input, + at::Tensor weight, + at::Tensor bias, + at::Tensor ones, + at::Tensor offset, + at::Tensor mask, + at::Tensor columns, + at::Tensor grad_input, + at::Tensor grad_weight, + at::Tensor grad_bias, + at::Tensor grad_offset, + at::Tensor grad_mask, + at::Tensor grad_output, + int kernel_h, + int kernel_w, + int stride_h, + int stride_w, + int pad_h, + int pad_w, + int dilation_h, + int dilation_w, + int group, + int deformable_group, + const bool with_bias); + +#endif + +inline int deform_conv_forward( + at::Tensor input, + at::Tensor weight, + at::Tensor offset, + at::Tensor output, + at::Tensor columns, + at::Tensor ones, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + int im2col_step) { + if (input.is_cuda()) { +#if defined(WITH_CUDA) || defined(WITH_HIP) + TORCH_CHECK(weight.is_cuda(), "weight tensor is not on GPU!"); + TORCH_CHECK(offset.is_cuda(), "offset tensor is not on GPU!"); + return deform_conv_forward_cuda( + input, + weight, + offset, + output, + columns, + ones, + kW, + kH, + dW, + dH, + padW, + padH, + dilationW, + dilationH, + group, + deformable_group, + im2col_step); +#else + AT_ERROR("Detectron2 is not compiled with GPU support!"); +#endif + } + AT_ERROR("This operator is not implemented on CPU"); +} + +inline int deform_conv_backward_input( + at::Tensor input, + at::Tensor offset, + at::Tensor gradOutput, + at::Tensor gradInput, + at::Tensor gradOffset, + at::Tensor weight, + at::Tensor columns, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + int im2col_step) { + if (gradOutput.is_cuda()) { +#if defined(WITH_CUDA) || defined(WITH_HIP) + TORCH_CHECK(input.is_cuda(), "input tensor is not on GPU!"); + TORCH_CHECK(weight.is_cuda(), "weight tensor is not on GPU!"); + TORCH_CHECK(offset.is_cuda(), "offset tensor is not on GPU!"); + return deform_conv_backward_input_cuda( + input, + offset, + gradOutput, + gradInput, + gradOffset, + weight, + columns, + kW, + kH, + dW, + dH, + padW, + padH, + dilationW, + dilationH, + group, + deformable_group, + im2col_step); +#else + AT_ERROR("Detectron2 is not compiled with GPU support!"); +#endif + } + AT_ERROR("This operator is not implemented on CPU"); +} + +inline int deform_conv_backward_filter( + at::Tensor input, + at::Tensor offset, + at::Tensor gradOutput, + at::Tensor gradWeight, // at::Tensor gradBias, + at::Tensor columns, + at::Tensor ones, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + float scale, + int im2col_step) { + if (gradOutput.is_cuda()) { +#if defined(WITH_CUDA) || defined(WITH_HIP) + TORCH_CHECK(input.is_cuda(), "input tensor is not on GPU!"); + TORCH_CHECK(offset.is_cuda(), "offset tensor is not on GPU!"); + return deform_conv_backward_parameters_cuda( + input, + offset, + gradOutput, + gradWeight, + columns, + ones, + kW, + kH, + dW, + dH, + padW, + padH, + dilationW, + dilationH, + group, + deformable_group, + scale, + im2col_step); +#else + AT_ERROR("Detectron2 is not compiled with GPU support!"); +#endif + } + AT_ERROR("This operator is not implemented on CPU"); +} + +inline void modulated_deform_conv_forward( + at::Tensor input, + at::Tensor weight, + at::Tensor bias, + at::Tensor ones, + at::Tensor offset, + at::Tensor mask, + at::Tensor output, + at::Tensor columns, + int kernel_h, + int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int group, + const int deformable_group, + const bool with_bias) { + if (input.is_cuda()) { +#if defined(WITH_CUDA) || defined(WITH_HIP) + TORCH_CHECK(weight.is_cuda(), "weight tensor is not on GPU!"); + TORCH_CHECK(bias.is_cuda(), "bias tensor is not on GPU!"); + TORCH_CHECK(offset.is_cuda(), "offset tensor is not on GPU!"); + return modulated_deform_conv_cuda_forward( + input, + weight, + bias, + ones, + offset, + mask, + output, + columns, + kernel_h, + kernel_w, + stride_h, + stride_w, + pad_h, + pad_w, + dilation_h, + dilation_w, + group, + deformable_group, + with_bias); +#else + AT_ERROR("Detectron2 is not compiled with GPU support!"); +#endif + } + AT_ERROR("This operator is not implemented on CPU"); +} + +inline void modulated_deform_conv_backward( + at::Tensor input, + at::Tensor weight, + at::Tensor bias, + at::Tensor ones, + at::Tensor offset, + at::Tensor mask, + at::Tensor columns, + at::Tensor grad_input, + at::Tensor grad_weight, + at::Tensor grad_bias, + at::Tensor grad_offset, + at::Tensor grad_mask, + at::Tensor grad_output, + int kernel_h, + int kernel_w, + int stride_h, + int stride_w, + int pad_h, + int pad_w, + int dilation_h, + int dilation_w, + int group, + int deformable_group, + const bool with_bias) { + if (grad_output.is_cuda()) { +#if defined(WITH_CUDA) || defined(WITH_HIP) + TORCH_CHECK(input.is_cuda(), "input tensor is not on GPU!"); + TORCH_CHECK(weight.is_cuda(), "weight tensor is not on GPU!"); + TORCH_CHECK(bias.is_cuda(), "bias tensor is not on GPU!"); + TORCH_CHECK(offset.is_cuda(), "offset tensor is not on GPU!"); + return modulated_deform_conv_cuda_backward( + input, + weight, + bias, + ones, + offset, + mask, + columns, + grad_input, + grad_weight, + grad_bias, + grad_offset, + grad_mask, + grad_output, + kernel_h, + kernel_w, + stride_h, + stride_w, + pad_h, + pad_w, + dilation_h, + dilation_w, + group, + deformable_group, + with_bias); +#else + AT_ERROR("Detectron2 is not compiled with GPU support!"); +#endif + } + AT_ERROR("This operator is not implemented on CPU"); +} + +} // namespace detectron2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/deformable/deform_conv_cuda.cu b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/deformable/deform_conv_cuda.cu new file mode 100644 index 0000000000000000000000000000000000000000..2072bb856ec40b61c3826cead2fb7bb7c971a089 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/deformable/deform_conv_cuda.cu @@ -0,0 +1,1223 @@ +// Copyright (c) Facebook, Inc. and its affiliates. + +// modified from +// https://github.com/open-mmlab/mmdetection/blob/master/mmdet/ops/dcn/src/deform_conv_cuda.cpp +// Original license: Apache 2.0 + +// modify from +// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c +// Original license: Apache 2.0 + +#include + +#include "deform_conv.h" + +#include +#include + +namespace detectron2 { + +void deformable_im2col( + const at::Tensor data_im, + const at::Tensor data_offset, + const int channels, + const int height, + const int width, + const int ksize_h, + const int ksize_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int parallel_imgs, + const int deformable_group, + at::Tensor data_col); + +void deformable_col2im( + const at::Tensor data_col, + const at::Tensor data_offset, + const int channels, + const int height, + const int width, + const int ksize_h, + const int ksize_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int parallel_imgs, + const int deformable_group, + at::Tensor grad_im); + +void deformable_col2im_coord( + const at::Tensor data_col, + const at::Tensor data_im, + const at::Tensor data_offset, + const int channels, + const int height, + const int width, + const int ksize_h, + const int ksize_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int parallel_imgs, + const int deformable_group, + at::Tensor grad_offset); + +void modulated_deformable_im2col_cuda( + const at::Tensor data_im, + const at::Tensor data_offset, + const at::Tensor data_mask, + const int batch_size, + const int channels, + const int height_im, + const int width_im, + const int height_col, + const int width_col, + const int kernel_h, + const int kenerl_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int deformable_group, + at::Tensor data_col); + +void modulated_deformable_col2im_cuda( + const at::Tensor data_col, + const at::Tensor data_offset, + const at::Tensor data_mask, + const int batch_size, + const int channels, + const int height_im, + const int width_im, + const int height_col, + const int width_col, + const int kernel_h, + const int kenerl_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int deformable_group, + at::Tensor grad_im); + +void modulated_deformable_col2im_coord_cuda( + const at::Tensor data_col, + const at::Tensor data_im, + const at::Tensor data_offset, + const at::Tensor data_mask, + const int batch_size, + const int channels, + const int height_im, + const int width_im, + const int height_col, + const int width_col, + const int kernel_h, + const int kenerl_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int deformable_group, + at::Tensor grad_offset, + at::Tensor grad_mask); + +void shape_check( + at::Tensor input, + at::Tensor offset, + at::Tensor* gradOutput, + at::Tensor weight, + int kH, + int kW, + int dH, + int dW, + int padH, + int padW, + int dilationH, + int dilationW, + int group, + int deformable_group) { + TORCH_CHECK( + weight.ndimension() == 4, + "4D weight tensor (nOutputPlane,nInputPlane,kH,kW) expected, " + "but got: %s", + weight.ndimension()); + + TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); + + TORCH_CHECK( + kW > 0 && kH > 0, + "kernel size should be greater than zero, but got kH: %d kW: %d", + kH, + kW); + + TORCH_CHECK( + (weight.size(2) == kH && weight.size(3) == kW), + "kernel size should be consistent with weight, ", + "but got kH: %d kW: %d weight.size(2): %d, weight.size(3): %d", + kH, + kW, + weight.size(2), + weight.size(3)); + + TORCH_CHECK( + dW > 0 && dH > 0, + "stride should be greater than zero, but got dH: %d dW: %d", + dH, + dW); + + TORCH_CHECK( + dilationW > 0 && dilationH > 0, + "dilation should be greater than 0, but got dilationH: %d dilationW: %d", + dilationH, + dilationW); + + int ndim = input.ndimension(); + int dimf = 0; + int dimh = 1; + int dimw = 2; + + if (ndim == 4) { + dimf++; + dimh++; + dimw++; + } + + TORCH_CHECK( + ndim == 3 || ndim == 4, + "3D or 4D input tensor expected but got: %s", + ndim); + + long nInputPlane = weight.size(1) * group; + long inputHeight = input.size(dimh); + long inputWidth = input.size(dimw); + long nOutputPlane = weight.size(0); + long outputHeight = + (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + long outputWidth = + (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + + TORCH_CHECK( + nInputPlane % deformable_group == 0, + "input channels must divide deformable group size"); + + if (outputWidth < 1 || outputHeight < 1) + AT_ERROR( + "Given input size: (%ld x %ld x %ld). " + "Calculated output size: (%ld x %ld x %ld). Output size is too small", + nInputPlane, + inputHeight, + inputWidth, + nOutputPlane, + outputHeight, + outputWidth); + + TORCH_CHECK( + input.size(1) == nInputPlane, + "invalid number of input planes, expected: %d, but got: %d", + nInputPlane, + input.size(1)); + + TORCH_CHECK( + (inputHeight + 2 * padH >= kH && inputWidth + 2 * padW >= kW), + "input image is smaller than kernel"); + + TORCH_CHECK( + (offset.size(2) == outputHeight && offset.size(3) == outputWidth), + "invalid spatial size of offset, expected height: %d width: %d, but " + "got height: %d width: %d", + outputHeight, + outputWidth, + offset.size(2), + offset.size(3)); + + TORCH_CHECK( + (offset.size(1) == deformable_group * 2 * kH * kW), + "invalid number of channels of offset"); + + if (gradOutput != NULL) { + TORCH_CHECK( + gradOutput->size(dimf) == nOutputPlane, + "invalid number of gradOutput planes, expected: %d, but got: %d", + nOutputPlane, + gradOutput->size(dimf)); + + TORCH_CHECK( + (gradOutput->size(dimh) == outputHeight && + gradOutput->size(dimw) == outputWidth), + "invalid size of gradOutput, expected height: %d width: %d , but " + "got height: %d width: %d", + outputHeight, + outputWidth, + gradOutput->size(dimh), + gradOutput->size(dimw)); + } +} + +int deform_conv_forward_cuda( + at::Tensor input, + at::Tensor weight, + at::Tensor offset, + at::Tensor output, + at::Tensor columns, + at::Tensor ones, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + int im2col_step) { + // todo: resize columns to include im2col: done + // todo: add im2col_step as input + // todo: add new output buffer and transpose it to output (or directly + // transpose output) todo: possibly change data indexing because of + // parallel_imgs + + shape_check( + input, + offset, + NULL, + weight, + kH, + kW, + dH, + dW, + padH, + padW, + dilationH, + dilationW, + group, + deformable_group); + + input = input.contiguous(); + offset = offset.contiguous(); + weight = weight.contiguous(); + + int batch = 1; + if (input.ndimension() == 3) { + // Force batch + batch = 0; + input.unsqueeze_(0); + offset.unsqueeze_(0); + } + + // todo: assert batchsize dividable by im2col_step + + long batchSize = input.size(0); + long nInputPlane = input.size(1); + long inputHeight = input.size(2); + long inputWidth = input.size(3); + + long nOutputPlane = weight.size(0); + + long outputWidth = + (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + long outputHeight = + (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + + TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset"); + + output = output.view( + {batchSize / im2col_step, + im2col_step, + nOutputPlane, + outputHeight, + outputWidth}); + columns = at::zeros( + {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, + input.options()); + + if (ones.ndimension() != 2 || + ones.size(0) * ones.size(1) < outputHeight * outputWidth) { + ones = at::ones({outputHeight, outputWidth}, input.options()); + } + + input = input.view( + {batchSize / im2col_step, + im2col_step, + nInputPlane, + inputHeight, + inputWidth}); + offset = offset.view( + {batchSize / im2col_step, + im2col_step, + deformable_group * 2 * kH * kW, + outputHeight, + outputWidth}); + + at::Tensor output_buffer = at::zeros( + {batchSize / im2col_step, + nOutputPlane, + im2col_step * outputHeight, + outputWidth}, + output.options()); + + output_buffer = output_buffer.view( + {output_buffer.size(0), + group, + output_buffer.size(1) / group, + output_buffer.size(2), + output_buffer.size(3)}); + + for (int elt = 0; elt < batchSize / im2col_step; elt++) { + deformable_im2col( + input[elt], + offset[elt], + nInputPlane, + inputHeight, + inputWidth, + kH, + kW, + padH, + padW, + dH, + dW, + dilationH, + dilationW, + im2col_step, + deformable_group, + columns); + + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + weight = weight.view( + {group, + weight.size(0) / group, + weight.size(1), + weight.size(2), + weight.size(3)}); + + for (int g = 0; g < group; g++) { + output_buffer[elt][g] = output_buffer[elt][g] + .flatten(1) + .addmm_(weight[g].flatten(1), columns[g]) + .view_as(output_buffer[elt][g]); + } + } + + output_buffer = output_buffer.view( + {output_buffer.size(0), + output_buffer.size(1) * output_buffer.size(2), + output_buffer.size(3), + output_buffer.size(4)}); + + output_buffer = output_buffer.view( + {batchSize / im2col_step, + nOutputPlane, + im2col_step, + outputHeight, + outputWidth}); + output_buffer.transpose_(1, 2); + output.copy_(output_buffer); + output = output.view({batchSize, nOutputPlane, outputHeight, outputWidth}); + + input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); + offset = offset.view( + {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + + if (batch == 0) { + output = output.view({nOutputPlane, outputHeight, outputWidth}); + input = input.view({nInputPlane, inputHeight, inputWidth}); + offset = offset.view({offset.size(1), offset.size(2), offset.size(3)}); + } + + return 1; +} + +int deform_conv_backward_input_cuda( + at::Tensor input, + at::Tensor offset, + at::Tensor gradOutput, + at::Tensor gradInput, + at::Tensor gradOffset, + at::Tensor weight, + at::Tensor columns, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + int im2col_step) { + shape_check( + input, + offset, + &gradOutput, + weight, + kH, + kW, + dH, + dW, + padH, + padW, + dilationH, + dilationW, + group, + deformable_group); + + input = input.contiguous(); + offset = offset.contiguous(); + gradOutput = gradOutput.contiguous(); + weight = weight.contiguous(); + + int batch = 1; + + if (input.ndimension() == 3) { + // Force batch + batch = 0; + input = input.view({1, input.size(0), input.size(1), input.size(2)}); + offset = offset.view({1, offset.size(0), offset.size(1), offset.size(2)}); + gradOutput = gradOutput.view( + {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)}); + } + + long batchSize = input.size(0); + long nInputPlane = input.size(1); + long inputHeight = input.size(2); + long inputWidth = input.size(3); + + long nOutputPlane = weight.size(0); + + long outputWidth = + (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + long outputHeight = + (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + + TORCH_CHECK((offset.size(0) == batchSize), 3, "invalid batch size of offset"); + gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); + columns = at::zeros( + {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, + input.options()); + + // change order of grad output + gradOutput = gradOutput.view( + {batchSize / im2col_step, + im2col_step, + nOutputPlane, + outputHeight, + outputWidth}); + gradOutput.transpose_(1, 2); + + gradInput = gradInput.view( + {batchSize / im2col_step, + im2col_step, + nInputPlane, + inputHeight, + inputWidth}); + input = input.view( + {batchSize / im2col_step, + im2col_step, + nInputPlane, + inputHeight, + inputWidth}); + gradOffset = gradOffset.view( + {batchSize / im2col_step, + im2col_step, + deformable_group * 2 * kH * kW, + outputHeight, + outputWidth}); + offset = offset.view( + {batchSize / im2col_step, + im2col_step, + deformable_group * 2 * kH * kW, + outputHeight, + outputWidth}); + + for (int elt = 0; elt < batchSize / im2col_step; elt++) { + // divide into groups + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + weight = weight.view( + {group, + weight.size(0) / group, + weight.size(1), + weight.size(2), + weight.size(3)}); + gradOutput = gradOutput.view( + {gradOutput.size(0), + group, + gradOutput.size(1) / group, + gradOutput.size(2), + gradOutput.size(3), + gradOutput.size(4)}); + + for (int g = 0; g < group; g++) { + columns[g] = columns[g].addmm_( + weight[g].flatten(1).transpose(0, 1), + gradOutput[elt][g].flatten(1), + 0.0f, + 1.0f); + } + + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + gradOutput = gradOutput.view( + {gradOutput.size(0), + gradOutput.size(1) * gradOutput.size(2), + gradOutput.size(3), + gradOutput.size(4), + gradOutput.size(5)}); + + deformable_col2im_coord( + columns, + input[elt], + offset[elt], + nInputPlane, + inputHeight, + inputWidth, + kH, + kW, + padH, + padW, + dH, + dW, + dilationH, + dilationW, + im2col_step, + deformable_group, + gradOffset[elt]); + + deformable_col2im( + columns, + offset[elt], + nInputPlane, + inputHeight, + inputWidth, + kH, + kW, + padH, + padW, + dH, + dW, + dilationH, + dilationW, + im2col_step, + deformable_group, + gradInput[elt]); + } + + gradOutput.transpose_(1, 2); + gradOutput = + gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); + + gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); + input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); + gradOffset = gradOffset.view( + {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + offset = offset.view( + {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + + if (batch == 0) { + gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); + input = input.view({nInputPlane, inputHeight, inputWidth}); + gradInput = gradInput.view({nInputPlane, inputHeight, inputWidth}); + offset = offset.view({offset.size(1), offset.size(2), offset.size(3)}); + gradOffset = + gradOffset.view({offset.size(1), offset.size(2), offset.size(3)}); + } + + return 1; +} + +int deform_conv_backward_parameters_cuda( + at::Tensor input, + at::Tensor offset, + at::Tensor gradOutput, + at::Tensor gradWeight, // at::Tensor gradBias, + at::Tensor columns, + at::Tensor ones, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + float scale, + int im2col_step) { + // todo: transpose and reshape outGrad + // todo: reshape columns + // todo: add im2col_step as input + + shape_check( + input, + offset, + &gradOutput, + gradWeight, + kH, + kW, + dH, + dW, + padH, + padW, + dilationH, + dilationW, + group, + deformable_group); + + input = input.contiguous(); + offset = offset.contiguous(); + gradOutput = gradOutput.contiguous(); + + int batch = 1; + + if (input.ndimension() == 3) { + // Force batch + batch = 0; + input = input.view( + at::IntList({1, input.size(0), input.size(1), input.size(2)})); + gradOutput = gradOutput.view( + {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)}); + } + + long batchSize = input.size(0); + long nInputPlane = input.size(1); + long inputHeight = input.size(2); + long inputWidth = input.size(3); + + long nOutputPlane = gradWeight.size(0); + + long outputWidth = + (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + long outputHeight = + (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + + TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset"); + + columns = at::zeros( + {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, + input.options()); + + gradOutput = gradOutput.view( + {batchSize / im2col_step, + im2col_step, + nOutputPlane, + outputHeight, + outputWidth}); + gradOutput.transpose_(1, 2); + + at::Tensor gradOutputBuffer = at::zeros_like(gradOutput); + gradOutputBuffer = gradOutputBuffer.view( + {batchSize / im2col_step, + nOutputPlane, + im2col_step, + outputHeight, + outputWidth}); + gradOutputBuffer.copy_(gradOutput); + // gradOutput is not contiguous, so we do reshape (instead of view) next + gradOutputBuffer = gradOutputBuffer.reshape( + {batchSize / im2col_step, + nOutputPlane, + im2col_step * outputHeight, + outputWidth}); + + gradOutput.transpose_(1, 2); + gradOutput = + gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); + + input = input.view( + {batchSize / im2col_step, + im2col_step, + nInputPlane, + inputHeight, + inputWidth}); + offset = offset.view( + {batchSize / im2col_step, + im2col_step, + deformable_group * 2 * kH * kW, + outputHeight, + outputWidth}); + + for (int elt = 0; elt < batchSize / im2col_step; elt++) { + deformable_im2col( + input[elt], + offset[elt], + nInputPlane, + inputHeight, + inputWidth, + kH, + kW, + padH, + padW, + dH, + dW, + dilationH, + dilationW, + im2col_step, + deformable_group, + columns); + + // divide into group + gradOutputBuffer = gradOutputBuffer.view( + {gradOutputBuffer.size(0), + group, + gradOutputBuffer.size(1) / group, + gradOutputBuffer.size(2), + gradOutputBuffer.size(3)}); + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + gradWeight = gradWeight.view( + {group, + gradWeight.size(0) / group, + gradWeight.size(1), + gradWeight.size(2), + gradWeight.size(3)}); + + for (int g = 0; g < group; g++) { + gradWeight[g] = gradWeight[g] + .flatten(1) + .addmm_( + gradOutputBuffer[elt][g].flatten(1), + columns[g].transpose(1, 0), + 1.0, + scale) + .view_as(gradWeight[g]); + } + gradOutputBuffer = gradOutputBuffer.view( + {gradOutputBuffer.size(0), + gradOutputBuffer.size(1) * gradOutputBuffer.size(2), + gradOutputBuffer.size(3), + gradOutputBuffer.size(4)}); + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + gradWeight = gradWeight.view( + {gradWeight.size(0) * gradWeight.size(1), + gradWeight.size(2), + gradWeight.size(3), + gradWeight.size(4)}); + } + + input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); + offset = offset.view( + {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + + if (batch == 0) { + gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); + input = input.view({nInputPlane, inputHeight, inputWidth}); + } + + return 1; +} + +void modulated_deform_conv_cuda_forward( + at::Tensor input, + at::Tensor weight, + at::Tensor bias, + at::Tensor ones, + at::Tensor offset, + at::Tensor mask, + at::Tensor output, + at::Tensor columns, + int kernel_h, + int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int group, + const int deformable_group, + const bool with_bias) { + shape_check( + input, + offset, + NULL, + weight, + kernel_h, + kernel_w, + stride_h, + stride_w, + pad_h, + pad_w, + dilation_h, + dilation_w, + group, + deformable_group); + + TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous"); + TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_out = weight.size(0); + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + + if (kernel_h_ != kernel_h || kernel_w_ != kernel_w) + AT_ERROR( + "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", + kernel_h_, + kernel_w, + kernel_h_, + kernel_w_); + if (channels != channels_kernel * group) + AT_ERROR( + "Input shape and kernel channels wont match: (%d vs %d).", + channels, + channels_kernel * group); + + const int height_out = + (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = + (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + // mask shape check + TORCH_CHECK( + (mask.size(2) == height_out && mask.size(3) == width_out), + "invalid spatial size of mask, expected height: %d width: %d, but " + "got height: %d width: %d", + height_out, + width_out, + mask.size(2), + mask.size(3)); + + TORCH_CHECK( + (mask.size(1) == deformable_group * kernel_h * kernel_w), + "invalid number of channels of mask"); + + if (ones.ndimension() != 2 || + ones.size(0) * ones.size(1) < height_out * width_out) { + // Resize plane and fill with ones... + ones = at::ones({height_out, width_out}, input.options()); + } + + // resize output + output = output.view({batch, channels_out, height_out, width_out}).zero_(); + // resize temporary columns + columns = at::zeros( + {channels * kernel_h * kernel_w, 1 * height_out * width_out}, + input.options()); + + output = output.view( + {output.size(0), + group, + output.size(1) / group, + output.size(2), + output.size(3)}); + + for (int b = 0; b < batch; b++) { + modulated_deformable_im2col_cuda( + input[b], + offset[b], + mask[b], + 1, + channels, + height, + width, + height_out, + width_out, + kernel_h, + kernel_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + deformable_group, + columns); + + // divide into group + weight = weight.view( + {group, + weight.size(0) / group, + weight.size(1), + weight.size(2), + weight.size(3)}); + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + + for (int g = 0; g < group; g++) { + output[b][g] = output[b][g] + .flatten(1) + .addmm_(weight[g].flatten(1), columns[g]) + .view_as(output[b][g]); + } + + weight = weight.view( + {weight.size(0) * weight.size(1), + weight.size(2), + weight.size(3), + weight.size(4)}); + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + } + + output = output.view( + {output.size(0), + output.size(1) * output.size(2), + output.size(3), + output.size(4)}); + + if (with_bias) { + output += bias.view({1, bias.size(0), 1, 1}); + } +} + +void modulated_deform_conv_cuda_backward( + at::Tensor input, + at::Tensor weight, + at::Tensor bias, + at::Tensor ones, + at::Tensor offset, + at::Tensor mask, + at::Tensor columns, + at::Tensor grad_input, + at::Tensor grad_weight, + at::Tensor grad_bias, + at::Tensor grad_offset, + at::Tensor grad_mask, + at::Tensor grad_output, + int kernel_h, + int kernel_w, + int stride_h, + int stride_w, + int pad_h, + int pad_w, + int dilation_h, + int dilation_w, + int group, + int deformable_group, + const bool with_bias) { + shape_check( + input, + offset, + &grad_output, + weight, + kernel_h, + kernel_w, + stride_h, + stride_w, + pad_h, + pad_w, + dilation_h, + dilation_w, + group, + deformable_group); + + TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous"); + TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + if (kernel_h_ != kernel_h || kernel_w_ != kernel_w) + AT_ERROR( + "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", + kernel_h_, + kernel_w, + kernel_h_, + kernel_w_); + if (channels != channels_kernel * group) + AT_ERROR( + "Input shape and kernel channels wont match: (%d vs %d).", + channels, + channels_kernel * group); + + const int height_out = + (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = + (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + // mask shape check + TORCH_CHECK( + (mask.size(2) == height_out && mask.size(3) == width_out), + "invalid spatial size of mask, expected height: %d width: %d, but " + "got height: %d width: %d", + height_out, + width_out, + mask.size(2), + mask.size(3)); + + TORCH_CHECK( + (mask.size(1) == deformable_group * kernel_h * kernel_w), + "invalid number of channels of mask"); + + if (ones.ndimension() != 2 || + ones.size(0) * ones.size(1) < height_out * width_out) { + // Resize plane and fill with ones... + ones = at::ones({height_out, width_out}, input.options()); + } + + grad_input = grad_input.view({batch, channels, height, width}); + columns = at::zeros( + {channels * kernel_h * kernel_w, height_out * width_out}, + input.options()); + + grad_output = grad_output.view( + {grad_output.size(0), + group, + grad_output.size(1) / group, + grad_output.size(2), + grad_output.size(3)}); + + for (int b = 0; b < batch; b++) { + // divide int group + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + weight = weight.view( + {group, + weight.size(0) / group, + weight.size(1), + weight.size(2), + weight.size(3)}); + + for (int g = 0; g < group; g++) { + columns[g].addmm_( + weight[g].flatten(1).transpose(0, 1), + grad_output[b][g].flatten(1), + 0.0f, + 1.0f); + } + + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + weight = weight.view( + {weight.size(0) * weight.size(1), + weight.size(2), + weight.size(3), + weight.size(4)}); + + // gradient w.r.t. input coordinate data + modulated_deformable_col2im_coord_cuda( + columns, + input[b], + offset[b], + mask[b], + 1, + channels, + height, + width, + height_out, + width_out, + kernel_h, + kernel_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + deformable_group, + grad_offset[b], + grad_mask[b]); + // gradient w.r.t. input data + modulated_deformable_col2im_cuda( + columns, + offset[b], + mask[b], + 1, + channels, + height, + width, + height_out, + width_out, + kernel_h, + kernel_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + deformable_group, + grad_input[b]); + + // gradient w.r.t. weight, dWeight should accumulate across the batch and + // group + modulated_deformable_im2col_cuda( + input[b], + offset[b], + mask[b], + 1, + channels, + height, + width, + height_out, + width_out, + kernel_h, + kernel_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + deformable_group, + columns); + + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + grad_weight = grad_weight.view( + {group, + grad_weight.size(0) / group, + grad_weight.size(1), + grad_weight.size(2), + grad_weight.size(3)}); + if (with_bias) + grad_bias = grad_bias.view({group, grad_bias.size(0) / group}); + + for (int g = 0; g < group; g++) { + grad_weight[g] = + grad_weight[g] + .flatten(1) + .addmm_(grad_output[b][g].flatten(1), columns[g].transpose(0, 1)) + .view_as(grad_weight[g]); + if (with_bias) { + grad_bias[g] = + grad_bias[g] + .view({-1, 1}) + .addmm_(grad_output[b][g].flatten(1), ones.view({-1, 1})) + .view(-1); + } + } + + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + grad_weight = grad_weight.view( + {grad_weight.size(0) * grad_weight.size(1), + grad_weight.size(2), + grad_weight.size(3), + grad_weight.size(4)}); + if (with_bias) + grad_bias = grad_bias.view({grad_bias.size(0) * grad_bias.size(1)}); + } + grad_output = grad_output.view( + {grad_output.size(0) * grad_output.size(1), + grad_output.size(2), + grad_output.size(3), + grad_output.size(4)}); +} + +} // namespace detectron2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/deformable/deform_conv_cuda_kernel.cu b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/deformable/deform_conv_cuda_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..f299c7add116685e9c87a187a85ea63f9f808867 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/deformable/deform_conv_cuda_kernel.cu @@ -0,0 +1,1288 @@ +// Copyright (c) Facebook, Inc. and its affiliates. + +// modified from +// https://github.com/open-mmlab/mmdetection/blob/master/mmdet/ops/dcn/src/deform_conv_cuda_kernel.cu +// Original license: Apache 2.0 +// clang-format off + +// modify from +// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda_kernel.cu + +/*! + ******************* BEGIN Caffe Copyright Notice and Disclaimer ***************** + * + * COPYRIGHT + * + * All contributions by the University of California: + * Copyright (c) 2014-2017 The Regents of the University of California (Regents) + * All rights reserved. + * + * All other contributions: + * Copyright (c) 2014-2017, the respective contributors + * All rights reserved. + * + * Caffe uses a shared copyright model: each contributor holds copyright over + * their contributions to Caffe. The project versioning records all such + * contribution and copyright details. If a contributor wants to further mark + * their specific copyright on a particular contribution, they should indicate + * their copyright solely in the commit message of the change when it is + * committed. + * + * LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, this + * list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + *AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + *IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE + *FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL + *DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + *SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + *CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, + *OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + *OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * CONTRIBUTION AGREEMENT + * + * By contributing to the BVLC/caffe repository through pull-request, comment, + * or otherwise, the contributor releases their content to the + * license and copyright terms herein. + * + ***************** END Caffe Copyright Notice and Disclaimer ********************* + * + * Copyright (c) 2018 Microsoft + * Licensed under The MIT License [see LICENSE for details] + * \file modulated_deformable_im2col.cuh + * \brief Function definitions of converting an image to + * column matrix based on kernel, padding, dilation, and offset. + * These functions are mainly used in deformable convolution operators. + * \ref: https://arxiv.org/abs/1703.06211 + * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu, Dazhi Cheng + */ + +#include +#include +#include +#include +#include +#include + +using namespace at; + +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \ + i += blockDim.x * gridDim.x) + + +namespace { + +const int CUDA_NUM_THREADS = 1024; +const int kMaxGridNum = 65535; + +inline int GET_BLOCKS(const int N) { + return std::min(kMaxGridNum, (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS); +} + +} + +template +__device__ scalar_t deformable_im2col_bilinear( + const scalar_t* bottom_data, + const int data_width, + const int height, + const int width, + scalar_t h, + scalar_t w) { + int h_low = floor(h); + int w_low = floor(w); + int h_high = h_low + 1; + int w_high = w_low + 1; + + scalar_t lh = h - h_low; + scalar_t lw = w - w_low; + scalar_t hh = 1 - lh, hw = 1 - lw; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + v1 = bottom_data[h_low * data_width + w_low]; + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + v2 = bottom_data[h_low * data_width + w_high]; + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + v3 = bottom_data[h_high * data_width + w_low]; + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + v4 = bottom_data[h_high * data_width + w_high]; + + scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + +template +__device__ scalar_t get_gradient_weight( + scalar_t argmax_h, + scalar_t argmax_w, + const int h, + const int w, + const int height, + const int width) { + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || + argmax_w >= width) { + // empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + scalar_t weight = 0; + if (h == argmax_h_low && w == argmax_w_low) + weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); + if (h == argmax_h_low && w == argmax_w_high) + weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); + if (h == argmax_h_high && w == argmax_w_low) + weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); + if (h == argmax_h_high && w == argmax_w_high) + weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); + return weight; +} + +template +__device__ scalar_t get_coordinate_weight( + scalar_t argmax_h, + scalar_t argmax_w, + const int height, + const int width, + const scalar_t* im_data, + const int data_width, + const int bp_dir) { + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || + argmax_w >= width) { + // empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + scalar_t weight = 0; + + if (bp_dir == 0) { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_w_low + 1 - argmax_w) * + im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += -1 * (argmax_w - argmax_w_low) * + im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += (argmax_w_low + 1 - argmax_w) * + im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_w - argmax_w_low) * + im_data[argmax_h_high * data_width + argmax_w_high]; + } else if (bp_dir == 1) { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_h_low + 1 - argmax_h) * + im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += (argmax_h_low + 1 - argmax_h) * + im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += -1 * (argmax_h - argmax_h_low) * + im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_h - argmax_h_low) * + im_data[argmax_h_high * data_width + argmax_w_high]; + } + + return weight; +} + +template +__global__ void deformable_im2col_gpu_kernel( + const int n, + const scalar_t* data_im, + const scalar_t* data_offset, + const int height, + const int width, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, + const int num_channels, + const int deformable_group, + const int height_col, + const int width_col, + scalar_t* data_col) { + CUDA_KERNEL_LOOP(index, n) { + // index index of output matrix + const int w_col = index % width_col; + const int h_col = (index / width_col) % height_col; + const int b_col = (index / width_col / height_col) % batch_size; + const int c_im = (index / width_col / height_col) / batch_size; + const int c_col = c_im * kernel_h * kernel_w; + + // compute deformable group index + const int deformable_group_index = c_im / channel_per_deformable_group; + + const int h_in = h_col * stride_h - pad_h; + const int w_in = w_col * stride_w - pad_w; + scalar_t* data_col_ptr = data_col + + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; + // const scalar_t* data_im_ptr = data_im + ((b_col * num_channels + c_im) * + // height + h_in) * width + w_in; + const scalar_t* data_im_ptr = + data_im + (b_col * num_channels + c_im) * height * width; + const scalar_t* data_offset_ptr = data_offset + + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * + kernel_w * height_col * width_col; + + for (int i = 0; i < kernel_h; ++i) { + for (int j = 0; j < kernel_w; ++j) { + const int data_offset_h_ptr = + ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; + const int data_offset_w_ptr = + ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + + w_col; + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + scalar_t val = static_cast(0); + const scalar_t h_im = h_in + i * dilation_h + offset_h; + const scalar_t w_im = w_in + j * dilation_w + offset_w; + if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) { + // const scalar_t map_h = i * dilation_h + offset_h; + // const scalar_t map_w = j * dilation_w + offset_w; + // const int cur_height = height - h_in; + // const int cur_width = width - w_in; + // val = deformable_im2col_bilinear(data_im_ptr, width, cur_height, + // cur_width, map_h, map_w); + val = deformable_im2col_bilinear( + data_im_ptr, width, height, width, h_im, w_im); + } + *data_col_ptr = val; + data_col_ptr += batch_size * height_col * width_col; + } + } + } +} + + +template +__global__ void deformable_col2im_gpu_kernel( + const int n, + const scalar_t* data_col, + const scalar_t* data_offset, + const int channels, + const int height, + const int width, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, + const int deformable_group, + const int height_col, + const int width_col, + scalar_t* grad_im) { + CUDA_KERNEL_LOOP(index, n) { + const int j = (index / width_col / height_col / batch_size) % kernel_w; + const int i = + (index / width_col / height_col / batch_size / kernel_w) % kernel_h; + const int c = + index / width_col / height_col / batch_size / kernel_w / kernel_h; + // compute the start and end of the output + + const int deformable_group_index = c / channel_per_deformable_group; + + int w_out = index % width_col; + int h_out = (index / width_col) % height_col; + int b = (index / width_col / height_col) % batch_size; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + + const scalar_t* data_offset_ptr = data_offset + + (b * deformable_group + deformable_group_index) * 2 * kernel_h * + kernel_w * height_col * width_col; + const int data_offset_h_ptr = + ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; + const int data_offset_w_ptr = + ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h; + const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w; + + const scalar_t cur_top_grad = data_col[index]; + const int cur_h = (int)cur_inv_h_data; + const int cur_w = (int)cur_inv_w_data; + for (int dy = -2; dy <= 2; dy++) { + for (int dx = -2; dx <= 2; dx++) { + if (cur_h + dy >= 0 && cur_h + dy < height && cur_w + dx >= 0 && + cur_w + dx < width && abs(cur_inv_h_data - (cur_h + dy)) < 1 && + abs(cur_inv_w_data - (cur_w + dx)) < 1) { + int cur_bottom_grad_pos = + ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; + scalar_t weight = get_gradient_weight( + cur_inv_h_data, + cur_inv_w_data, + cur_h + dy, + cur_w + dx, + height, + width); + atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); + } + } + } + } +} + + +template +__global__ void deformable_col2im_coord_gpu_kernel( + const int n, + const scalar_t* data_col, + const scalar_t* data_im, + const scalar_t* data_offset, + const int channels, + const int height, + const int width, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, + const int offset_channels, + const int deformable_group, + const int height_col, + const int width_col, + scalar_t* grad_offset) { + CUDA_KERNEL_LOOP(index, n) { + scalar_t val = 0; + int w = index % width_col; + int h = (index / width_col) % height_col; + int c = (index / width_col / height_col) % offset_channels; + int b = (index / width_col / height_col) / offset_channels; + // compute the start and end of the output + + const int deformable_group_index = c / (2 * kernel_h * kernel_w); + const int col_step = kernel_h * kernel_w; + int cnt = 0; + const scalar_t* data_col_ptr = data_col + + deformable_group_index * channel_per_deformable_group * batch_size * + width_col * height_col; + const scalar_t* data_im_ptr = data_im + + (b * deformable_group + deformable_group_index) * + channel_per_deformable_group / kernel_h / kernel_w * height * width; + const scalar_t* data_offset_ptr = data_offset + + (b * deformable_group + deformable_group_index) * 2 * kernel_h * + kernel_w * height_col * width_col; + + const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; + + for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; + col_c += col_step) { + const int col_pos = + (((col_c * batch_size + b) * height_col) + h) * width_col + w; + const int bp_dir = offset_c % 2; + + int j = (col_pos / width_col / height_col / batch_size) % kernel_w; + int i = + (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; + int w_out = col_pos % width_col; + int h_out = (col_pos / width_col) % height_col; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + const int data_offset_h_ptr = + (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); + const int data_offset_w_ptr = + (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + + w_out); + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + scalar_t inv_h = h_in + i * dilation_h + offset_h; + scalar_t inv_w = w_in + j * dilation_w + offset_w; + if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) { + inv_h = inv_w = -2; + } + const scalar_t weight = get_coordinate_weight( + inv_h, + inv_w, + height, + width, + data_im_ptr + cnt * height * width, + width, + bp_dir); + val += weight * data_col_ptr[col_pos]; + cnt += 1; + } + + grad_offset[index] = val; + } +} + + +namespace detectron2 { + +void deformable_im2col( + const at::Tensor data_im, + const at::Tensor data_offset, + const int channels, + const int height, + const int width, + const int ksize_h, + const int ksize_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int parallel_imgs, + const int deformable_group, + at::Tensor data_col) { + // num_axes should be smaller than block size + // todo: check parallel_imgs is correctly passed in + int height_col = + (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; + int width_col = + (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; + int num_kernels = channels * height_col * width_col * parallel_imgs; + int channel_per_deformable_group = channels / deformable_group; + + at::cuda::CUDAGuard device_guard(data_im.device()); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_im.scalar_type(), "deformable_im2col_gpu", ([&] { + const scalar_t* data_im_ = data_im.data_ptr(); + const scalar_t* data_offset_ = data_offset.data_ptr(); + scalar_t* data_col_ = data_col.data_ptr(); + + deformable_im2col_gpu_kernel<<< + GET_BLOCKS(num_kernels), + CUDA_NUM_THREADS, + 0, + stream>>>( + num_kernels, + data_im_, + data_offset_, + height, + width, + ksize_h, + ksize_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + channel_per_deformable_group, + parallel_imgs, + channels, + deformable_group, + height_col, + width_col, + data_col_); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf("error in deformable_im2col: %s\n", cudaGetErrorString(err)); + } +} + + +void deformable_col2im( + const at::Tensor data_col, + const at::Tensor data_offset, + const int channels, + const int height, + const int width, + const int ksize_h, + const int ksize_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int parallel_imgs, + const int deformable_group, + at::Tensor grad_im) { + // todo: make sure parallel_imgs is passed in correctly + int height_col = + (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; + int width_col = + (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; + int num_kernels = + channels * ksize_h * ksize_w * height_col * width_col * parallel_imgs; + int channel_per_deformable_group = channels / deformable_group; + + at::cuda::CUDAGuard device_guard(data_col.device()); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_col.scalar_type(), "deformable_col2im_gpu", ([&] { + const scalar_t* data_col_ = data_col.data_ptr(); + const scalar_t* data_offset_ = data_offset.data_ptr(); + scalar_t* grad_im_ = grad_im.data_ptr(); + + deformable_col2im_gpu_kernel<<< + GET_BLOCKS(num_kernels), + CUDA_NUM_THREADS, + 0, + stream>>>( + num_kernels, + data_col_, + data_offset_, + channels, + height, + width, + ksize_h, + ksize_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + channel_per_deformable_group, + parallel_imgs, + deformable_group, + height_col, + width_col, + grad_im_); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf("error in deformable_col2im: %s\n", cudaGetErrorString(err)); + } +} + + +void deformable_col2im_coord( + const at::Tensor data_col, + const at::Tensor data_im, + const at::Tensor data_offset, + const int channels, + const int height, + const int width, + const int ksize_h, + const int ksize_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int parallel_imgs, + const int deformable_group, + at::Tensor grad_offset) { + int height_col = + (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; + int width_col = + (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; + int num_kernels = height_col * width_col * 2 * ksize_h * ksize_w * + deformable_group * parallel_imgs; + int channel_per_deformable_group = + channels * ksize_h * ksize_w / deformable_group; + + at::cuda::CUDAGuard device_guard(data_col.device()); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_col.scalar_type(), "deformable_col2im_coord_gpu", ([&] { + const scalar_t* data_col_ = data_col.data_ptr(); + const scalar_t* data_im_ = data_im.data_ptr(); + const scalar_t* data_offset_ = data_offset.data_ptr(); + scalar_t* grad_offset_ = grad_offset.data_ptr(); + + deformable_col2im_coord_gpu_kernel<<< + GET_BLOCKS(num_kernels), + CUDA_NUM_THREADS, + 0, + stream>>>( + num_kernels, + data_col_, + data_im_, + data_offset_, + channels, + height, + width, + ksize_h, + ksize_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + channel_per_deformable_group, + parallel_imgs, + 2 * ksize_h * ksize_w * deformable_group, + deformable_group, + height_col, + width_col, + grad_offset_); + })); +} + +} // namespace detectron2 + + +template +__device__ scalar_t dmcn_im2col_bilinear( + const scalar_t* bottom_data, + const int data_width, + const int height, + const int width, + scalar_t h, + scalar_t w) { + int h_low = floor(h); + int w_low = floor(w); + int h_high = h_low + 1; + int w_high = w_low + 1; + + scalar_t lh = h - h_low; + scalar_t lw = w - w_low; + scalar_t hh = 1 - lh, hw = 1 - lw; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + v1 = bottom_data[h_low * data_width + w_low]; + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + v2 = bottom_data[h_low * data_width + w_high]; + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + v3 = bottom_data[h_high * data_width + w_low]; + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + v4 = bottom_data[h_high * data_width + w_high]; + + scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + +template +__device__ scalar_t dmcn_get_gradient_weight( + scalar_t argmax_h, + scalar_t argmax_w, + const int h, + const int w, + const int height, + const int width) { + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || + argmax_w >= width) { + // empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + scalar_t weight = 0; + if (h == argmax_h_low && w == argmax_w_low) + weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); + if (h == argmax_h_low && w == argmax_w_high) + weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); + if (h == argmax_h_high && w == argmax_w_low) + weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); + if (h == argmax_h_high && w == argmax_w_high) + weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); + return weight; +} + +template +__device__ scalar_t dmcn_get_coordinate_weight( + scalar_t argmax_h, + scalar_t argmax_w, + const int height, + const int width, + const scalar_t* im_data, + const int data_width, + const int bp_dir) { + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || + argmax_w >= width) { + // empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + scalar_t weight = 0; + + if (bp_dir == 0) { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_w_low + 1 - argmax_w) * + im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += -1 * (argmax_w - argmax_w_low) * + im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += (argmax_w_low + 1 - argmax_w) * + im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_w - argmax_w_low) * + im_data[argmax_h_high * data_width + argmax_w_high]; + } else if (bp_dir == 1) { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_h_low + 1 - argmax_h) * + im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += (argmax_h_low + 1 - argmax_h) * + im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += -1 * (argmax_h - argmax_h_low) * + im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_h - argmax_h_low) * + im_data[argmax_h_high * data_width + argmax_w_high]; + } + + return weight; +} + +template +__global__ void modulated_deformable_im2col_gpu_kernel( + const int n, + const scalar_t* data_im, + const scalar_t* data_offset, + const scalar_t* data_mask, + const int height, + const int width, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, + const int num_channels, + const int deformable_group, + const int height_col, + const int width_col, + scalar_t* data_col) { + CUDA_KERNEL_LOOP(index, n) { + // index index of output matrix + const int w_col = index % width_col; + const int h_col = (index / width_col) % height_col; + const int b_col = (index / width_col / height_col) % batch_size; + const int c_im = (index / width_col / height_col) / batch_size; + const int c_col = c_im * kernel_h * kernel_w; + + // compute deformable group index + const int deformable_group_index = c_im / channel_per_deformable_group; + + const int h_in = h_col * stride_h - pad_h; + const int w_in = w_col * stride_w - pad_w; + + scalar_t* data_col_ptr = data_col + + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; + // const float* data_im_ptr = data_im + ((b_col * num_channels + c_im) * + // height + h_in) * width + w_in; + const scalar_t* data_im_ptr = + data_im + (b_col * num_channels + c_im) * height * width; + const scalar_t* data_offset_ptr = data_offset + + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * + kernel_w * height_col * width_col; + + const scalar_t* data_mask_ptr = data_mask + + (b_col * deformable_group + deformable_group_index) * kernel_h * + kernel_w * height_col * width_col; + + for (int i = 0; i < kernel_h; ++i) { + for (int j = 0; j < kernel_w; ++j) { + const int data_offset_h_ptr = + ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; + const int data_offset_w_ptr = + ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + + w_col; + const int data_mask_hw_ptr = + ((i * kernel_w + j) * height_col + h_col) * width_col + w_col; + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; + scalar_t val = static_cast(0); + const scalar_t h_im = h_in + i * dilation_h + offset_h; + const scalar_t w_im = w_in + j * dilation_w + offset_w; + // if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) { + if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) { + // const float map_h = i * dilation_h + offset_h; + // const float map_w = j * dilation_w + offset_w; + // const int cur_height = height - h_in; + // const int cur_width = width - w_in; + // val = dmcn_im2col_bilinear(data_im_ptr, width, cur_height, + // cur_width, map_h, map_w); + val = dmcn_im2col_bilinear( + data_im_ptr, width, height, width, h_im, w_im); + } + *data_col_ptr = val * mask; + data_col_ptr += batch_size * height_col * width_col; + // data_col_ptr += height_col * width_col; + } + } + } +} + +template +__global__ void modulated_deformable_col2im_gpu_kernel( + const int n, + const scalar_t* data_col, + const scalar_t* data_offset, + const scalar_t* data_mask, + const int channels, + const int height, + const int width, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, + const int deformable_group, + const int height_col, + const int width_col, + scalar_t* grad_im) { + CUDA_KERNEL_LOOP(index, n) { + const int j = (index / width_col / height_col / batch_size) % kernel_w; + const int i = + (index / width_col / height_col / batch_size / kernel_w) % kernel_h; + const int c = + index / width_col / height_col / batch_size / kernel_w / kernel_h; + // compute the start and end of the output + + const int deformable_group_index = c / channel_per_deformable_group; + + int w_out = index % width_col; + int h_out = (index / width_col) % height_col; + int b = (index / width_col / height_col) % batch_size; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + + const scalar_t* data_offset_ptr = data_offset + + (b * deformable_group + deformable_group_index) * 2 * kernel_h * + kernel_w * height_col * width_col; + const scalar_t* data_mask_ptr = data_mask + + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * + height_col * width_col; + const int data_offset_h_ptr = + ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; + const int data_offset_w_ptr = + ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; + const int data_mask_hw_ptr = + ((i * kernel_w + j) * height_col + h_out) * width_col + w_out; + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; + const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h; + const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w; + + const scalar_t cur_top_grad = data_col[index] * mask; + const int cur_h = (int)cur_inv_h_data; + const int cur_w = (int)cur_inv_w_data; + for (int dy = -2; dy <= 2; dy++) { + for (int dx = -2; dx <= 2; dx++) { + if (cur_h + dy >= 0 && cur_h + dy < height && cur_w + dx >= 0 && + cur_w + dx < width && abs(cur_inv_h_data - (cur_h + dy)) < 1 && + abs(cur_inv_w_data - (cur_w + dx)) < 1) { + int cur_bottom_grad_pos = + ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; + scalar_t weight = dmcn_get_gradient_weight( + cur_inv_h_data, + cur_inv_w_data, + cur_h + dy, + cur_w + dx, + height, + width); + atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); + } + } + } + } +} + +template +__global__ void modulated_deformable_col2im_coord_gpu_kernel( + const int n, + const scalar_t* data_col, + const scalar_t* data_im, + const scalar_t* data_offset, + const scalar_t* data_mask, + const int channels, + const int height, + const int width, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, + const int offset_channels, + const int deformable_group, + const int height_col, + const int width_col, + scalar_t* grad_offset, + scalar_t* grad_mask) { + CUDA_KERNEL_LOOP(index, n) { + scalar_t val = 0, mval = 0; + int w = index % width_col; + int h = (index / width_col) % height_col; + int c = (index / width_col / height_col) % offset_channels; + int b = (index / width_col / height_col) / offset_channels; + // compute the start and end of the output + + const int deformable_group_index = c / (2 * kernel_h * kernel_w); + const int col_step = kernel_h * kernel_w; + int cnt = 0; + const scalar_t* data_col_ptr = data_col + + deformable_group_index * channel_per_deformable_group * batch_size * + width_col * height_col; + const scalar_t* data_im_ptr = data_im + + (b * deformable_group + deformable_group_index) * + channel_per_deformable_group / kernel_h / kernel_w * height * width; + const scalar_t* data_offset_ptr = data_offset + + (b * deformable_group + deformable_group_index) * 2 * kernel_h * + kernel_w * height_col * width_col; + const scalar_t* data_mask_ptr = data_mask + + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * + height_col * width_col; + + const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; + + for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; + col_c += col_step) { + const int col_pos = + (((col_c * batch_size + b) * height_col) + h) * width_col + w; + const int bp_dir = offset_c % 2; + + int j = (col_pos / width_col / height_col / batch_size) % kernel_w; + int i = + (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; + int w_out = col_pos % width_col; + int h_out = (col_pos / width_col) % height_col; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + const int data_offset_h_ptr = + (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); + const int data_offset_w_ptr = + (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + + w_out); + const int data_mask_hw_ptr = + (((i * kernel_w + j) * height_col + h_out) * width_col + w_out); + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; + scalar_t inv_h = h_in + i * dilation_h + offset_h; + scalar_t inv_w = w_in + j * dilation_w + offset_w; + if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) { + inv_h = inv_w = -2; + } else { + mval += data_col_ptr[col_pos] * + dmcn_im2col_bilinear( + data_im_ptr + cnt * height * width, + width, + height, + width, + inv_h, + inv_w); + } + const scalar_t weight = dmcn_get_coordinate_weight( + inv_h, + inv_w, + height, + width, + data_im_ptr + cnt * height * width, + width, + bp_dir); + val += weight * data_col_ptr[col_pos] * mask; + cnt += 1; + } + // KERNEL_ASSIGN(grad_offset[index], offset_req, val); + grad_offset[index] = val; + if (offset_c % 2 == 0) + // KERNEL_ASSIGN(grad_mask[(((b * deformable_group + + // deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * + // height_col + h) * width_col + w], mask_req, mval); + grad_mask + [(((b * deformable_group + deformable_group_index) * kernel_h * + kernel_w + + offset_c / 2) * + height_col + + h) * + width_col + + w] = mval; + } +} + + +namespace detectron2 { + +void modulated_deformable_im2col_cuda( + const at::Tensor data_im, + const at::Tensor data_offset, + const at::Tensor data_mask, + const int batch_size, + const int channels, + const int height_im, + const int width_im, + const int height_col, + const int width_col, + const int kernel_h, + const int kenerl_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int deformable_group, + at::Tensor data_col) { + // num_axes should be smaller than block size + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = channels * batch_size * height_col * width_col; + + at::cuda::CUDAGuard device_guard(data_im.device()); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_im.scalar_type(), "modulated_deformable_im2col_gpu", ([&] { + const scalar_t* data_im_ = data_im.data_ptr(); + const scalar_t* data_offset_ = data_offset.data_ptr(); + const scalar_t* data_mask_ = data_mask.data_ptr(); + scalar_t* data_col_ = data_col.data_ptr(); + + modulated_deformable_im2col_gpu_kernel<<< + GET_BLOCKS(num_kernels), + CUDA_NUM_THREADS, + 0, + stream>>>( + num_kernels, + data_im_, + data_offset_, + data_mask_, + height_im, + width_im, + kernel_h, + kenerl_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + channel_per_deformable_group, + batch_size, + channels, + deformable_group, + height_col, + width_col, + data_col_); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf( + "error in modulated_deformable_im2col_cuda: %s\n", + cudaGetErrorString(err)); + } +} + +void modulated_deformable_col2im_cuda( + const at::Tensor data_col, + const at::Tensor data_offset, + const at::Tensor data_mask, + const int batch_size, + const int channels, + const int height_im, + const int width_im, + const int height_col, + const int width_col, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int deformable_group, + at::Tensor grad_im) { + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = + channels * kernel_h * kernel_w * batch_size * height_col * width_col; + + at::cuda::CUDAGuard device_guard(data_col.device()); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_col.scalar_type(), "modulated_deformable_col2im_gpu", ([&] { + const scalar_t* data_col_ = data_col.data_ptr(); + const scalar_t* data_offset_ = data_offset.data_ptr(); + const scalar_t* data_mask_ = data_mask.data_ptr(); + scalar_t* grad_im_ = grad_im.data_ptr(); + + modulated_deformable_col2im_gpu_kernel<<< + GET_BLOCKS(num_kernels), + CUDA_NUM_THREADS, + 0, + stream>>>( + num_kernels, + data_col_, + data_offset_, + data_mask_, + channels, + height_im, + width_im, + kernel_h, + kernel_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + channel_per_deformable_group, + batch_size, + deformable_group, + height_col, + width_col, + grad_im_); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf( + "error in modulated_deformable_col2im_cuda: %s\n", + cudaGetErrorString(err)); + } +} + +void modulated_deformable_col2im_coord_cuda( + const at::Tensor data_col, + const at::Tensor data_im, + const at::Tensor data_offset, + const at::Tensor data_mask, + const int batch_size, + const int channels, + const int height_im, + const int width_im, + const int height_col, + const int width_col, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int deformable_group, + at::Tensor grad_offset, + at::Tensor grad_mask) { + const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * + kernel_w * deformable_group; + const int channel_per_deformable_group = + channels * kernel_h * kernel_w / deformable_group; + + at::cuda::CUDAGuard device_guard(data_col.device()); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_col.scalar_type(), "modulated_deformable_col2im_coord_gpu", ([&] { + const scalar_t* data_col_ = data_col.data_ptr(); + const scalar_t* data_im_ = data_im.data_ptr(); + const scalar_t* data_offset_ = data_offset.data_ptr(); + const scalar_t* data_mask_ = data_mask.data_ptr(); + scalar_t* grad_offset_ = grad_offset.data_ptr(); + scalar_t* grad_mask_ = grad_mask.data_ptr(); + + modulated_deformable_col2im_coord_gpu_kernel<<< + GET_BLOCKS(num_kernels), + CUDA_NUM_THREADS, + 0, + stream>>>( + num_kernels, + data_col_, + data_im_, + data_offset_, + data_mask_, + channels, + height_im, + width_im, + kernel_h, + kernel_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + channel_per_deformable_group, + batch_size, + 2 * kernel_h * kernel_w * deformable_group, + deformable_group, + height_col, + width_col, + grad_offset_, + grad_mask_); + })); + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf( + "error in modulated_deformable_col2im_coord_cuda: %s\n", + cudaGetErrorString(err)); + } +} + +} // namespace detectron2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/nms_rotated/nms_rotated.h b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/nms_rotated/nms_rotated.h new file mode 100644 index 0000000000000000000000000000000000000000..12aca388e47b12dafd20999f2991a9d42f4b904b --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/nms_rotated/nms_rotated.h @@ -0,0 +1,39 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#pragma once +#include + +namespace detectron2 { + +at::Tensor nms_rotated_cpu( + const at::Tensor& dets, + const at::Tensor& scores, + const double iou_threshold); + +#if defined(WITH_CUDA) || defined(WITH_HIP) +at::Tensor nms_rotated_cuda( + const at::Tensor& dets, + const at::Tensor& scores, + const double iou_threshold); +#endif + +// Interface for Python +// inline is needed to prevent multiple function definitions when this header is +// included by different cpps +inline at::Tensor nms_rotated( + const at::Tensor& dets, + const at::Tensor& scores, + const double iou_threshold) { + assert(dets.device().is_cuda() == scores.device().is_cuda()); + if (dets.device().is_cuda()) { +#if defined(WITH_CUDA) || defined(WITH_HIP) + return nms_rotated_cuda( + dets.contiguous(), scores.contiguous(), iou_threshold); +#else + AT_ERROR("Detectron2 is not compiled with GPU support!"); +#endif + } + + return nms_rotated_cpu(dets.contiguous(), scores.contiguous(), iou_threshold); +} + +} // namespace detectron2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/nms_rotated/nms_rotated_cpu.cpp b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/nms_rotated/nms_rotated_cpu.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d7556e645b604aa83d86cc702b783fd8ecedffcc --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/nms_rotated/nms_rotated_cpu.cpp @@ -0,0 +1,75 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#include "../box_iou_rotated/box_iou_rotated_utils.h" +#include "nms_rotated.h" + +namespace detectron2 { + +template +at::Tensor nms_rotated_cpu_kernel( + const at::Tensor& dets, + const at::Tensor& scores, + const double iou_threshold) { + // nms_rotated_cpu_kernel is modified from torchvision's nms_cpu_kernel, + // however, the code in this function is much shorter because + // we delegate the IoU computation for rotated boxes to + // the single_box_iou_rotated function in box_iou_rotated_utils.h + AT_ASSERTM(dets.device().is_cpu(), "dets must be a CPU tensor"); + AT_ASSERTM(scores.device().is_cpu(), "scores must be a CPU tensor"); + AT_ASSERTM( + dets.scalar_type() == scores.scalar_type(), + "dets should have the same type as scores"); + + if (dets.numel() == 0) { + return at::empty({0}, dets.options().dtype(at::kLong)); + } + + auto order_t = std::get<1>(scores.sort(0, /* descending=*/true)); + + auto ndets = dets.size(0); + at::Tensor suppressed_t = at::zeros({ndets}, dets.options().dtype(at::kByte)); + at::Tensor keep_t = at::zeros({ndets}, dets.options().dtype(at::kLong)); + + auto suppressed = suppressed_t.data_ptr(); + auto keep = keep_t.data_ptr(); + auto order = order_t.data_ptr(); + + int64_t num_to_keep = 0; + + for (int64_t _i = 0; _i < ndets; _i++) { + auto i = order[_i]; + if (suppressed[i] == 1) { + continue; + } + + keep[num_to_keep++] = i; + + for (int64_t _j = _i + 1; _j < ndets; _j++) { + auto j = order[_j]; + if (suppressed[j] == 1) { + continue; + } + + auto ovr = single_box_iou_rotated( + dets[i].data_ptr(), dets[j].data_ptr()); + if (ovr >= iou_threshold) { + suppressed[j] = 1; + } + } + } + return keep_t.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep); +} + +at::Tensor nms_rotated_cpu( + // input must be contiguous + const at::Tensor& dets, + const at::Tensor& scores, + const double iou_threshold) { + auto result = at::empty({0}, dets.options()); + + AT_DISPATCH_FLOATING_TYPES(dets.scalar_type(), "nms_rotated", [&] { + result = nms_rotated_cpu_kernel(dets, scores, iou_threshold); + }); + return result; +} + +} // namespace detectron2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/nms_rotated/nms_rotated_cuda.cu b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/nms_rotated/nms_rotated_cuda.cu new file mode 100644 index 0000000000000000000000000000000000000000..2a3db5c62e7a2da52ccf5bac980653c943d630fd --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/nms_rotated/nms_rotated_cuda.cu @@ -0,0 +1,145 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#include +#include +#include +#include +#ifdef WITH_CUDA +#include "../box_iou_rotated/box_iou_rotated_utils.h" +#endif +// TODO avoid this when pytorch supports "same directory" hipification +#ifdef WITH_HIP +#include "box_iou_rotated/box_iou_rotated_utils.h" +#endif + +using namespace detectron2; + +namespace { +int const threadsPerBlock = sizeof(unsigned long long) * 8; +} + +template +__global__ void nms_rotated_cuda_kernel( + const int n_boxes, + const double iou_threshold, + const T* dev_boxes, + unsigned long long* dev_mask) { + // nms_rotated_cuda_kernel is modified from torchvision's nms_cuda_kernel + + const int row_start = blockIdx.y; + const int col_start = blockIdx.x; + + // if (row_start > col_start) return; + + const int row_size = + min(n_boxes - row_start * threadsPerBlock, threadsPerBlock); + const int col_size = + min(n_boxes - col_start * threadsPerBlock, threadsPerBlock); + + // Compared to nms_cuda_kernel, where each box is represented with 4 values + // (x1, y1, x2, y2), each rotated box is represented with 5 values + // (x_center, y_center, width, height, angle_degrees) here. + __shared__ T block_boxes[threadsPerBlock * 5]; + if (threadIdx.x < col_size) { + block_boxes[threadIdx.x * 5 + 0] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0]; + block_boxes[threadIdx.x * 5 + 1] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1]; + block_boxes[threadIdx.x * 5 + 2] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2]; + block_boxes[threadIdx.x * 5 + 3] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3]; + block_boxes[threadIdx.x * 5 + 4] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4]; + } + __syncthreads(); + + if (threadIdx.x < row_size) { + const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x; + const T* cur_box = dev_boxes + cur_box_idx * 5; + int i = 0; + unsigned long long t = 0; + int start = 0; + if (row_start == col_start) { + start = threadIdx.x + 1; + } + for (i = start; i < col_size; i++) { + // Instead of devIoU used by original horizontal nms, here + // we use the single_box_iou_rotated function from box_iou_rotated_utils.h + if (single_box_iou_rotated(cur_box, block_boxes + i * 5) > + iou_threshold) { + t |= 1ULL << i; + } + } + const int col_blocks = at::cuda::ATenCeilDiv(n_boxes, threadsPerBlock); + dev_mask[cur_box_idx * col_blocks + col_start] = t; + } +} + +namespace detectron2 { + +at::Tensor nms_rotated_cuda( + // input must be contiguous + const at::Tensor& dets, + const at::Tensor& scores, + double iou_threshold) { + // using scalar_t = float; + AT_ASSERTM(dets.is_cuda(), "dets must be a CUDA tensor"); + AT_ASSERTM(scores.is_cuda(), "scores must be a CUDA tensor"); + at::cuda::CUDAGuard device_guard(dets.device()); + + auto order_t = std::get<1>(scores.sort(0, /* descending=*/true)); + auto dets_sorted = dets.index_select(0, order_t); + + auto dets_num = dets.size(0); + + const int col_blocks = + at::cuda::ATenCeilDiv(static_cast(dets_num), threadsPerBlock); + + at::Tensor mask = + at::empty({dets_num * col_blocks}, dets.options().dtype(at::kLong)); + + dim3 blocks(col_blocks, col_blocks); + dim3 threads(threadsPerBlock); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES( + dets_sorted.scalar_type(), "nms_rotated_kernel_cuda", [&] { + nms_rotated_cuda_kernel<<>>( + dets_num, + iou_threshold, + dets_sorted.data_ptr(), + (unsigned long long*)mask.data_ptr()); + }); + + at::Tensor mask_cpu = mask.to(at::kCPU); + unsigned long long* mask_host = + (unsigned long long*)mask_cpu.data_ptr(); + + std::vector remv(col_blocks); + memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks); + + at::Tensor keep = + at::empty({dets_num}, dets.options().dtype(at::kLong).device(at::kCPU)); + int64_t* keep_out = keep.data_ptr(); + + int num_to_keep = 0; + for (int i = 0; i < dets_num; i++) { + int nblock = i / threadsPerBlock; + int inblock = i % threadsPerBlock; + + if (!(remv[nblock] & (1ULL << inblock))) { + keep_out[num_to_keep++] = i; + unsigned long long* p = mask_host + i * col_blocks; + for (int j = nblock; j < col_blocks; j++) { + remv[j] |= p[j]; + } + } + } + + AT_CUDA_CHECK(cudaGetLastError()); + return order_t.index( + {keep.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep) + .to(order_t.device(), keep.scalar_type())}); +} + +} // namespace detectron2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/vision.cpp b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/vision.cpp new file mode 100644 index 0000000000000000000000000000000000000000..c9a2cd4f20e6f58be1c5783d67c64232dd59b560 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/vision.cpp @@ -0,0 +1,117 @@ +// Copyright (c) Facebook, Inc. and its affiliates. + +#include +#include "ROIAlignRotated/ROIAlignRotated.h" +#include "box_iou_rotated/box_iou_rotated.h" +#include "cocoeval/cocoeval.h" +#include "deformable/deform_conv.h" +#include "nms_rotated/nms_rotated.h" + +namespace detectron2 { + +#if defined(WITH_CUDA) || defined(WITH_HIP) +extern int get_cudart_version(); +#endif + +std::string get_cuda_version() { +#if defined(WITH_CUDA) || defined(WITH_HIP) + std::ostringstream oss; + +#if defined(WITH_CUDA) + oss << "CUDA "; +#else + oss << "HIP "; +#endif + + // copied from + // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/detail/CUDAHooks.cpp#L231 + auto printCudaStyleVersion = [&](int v) { + oss << (v / 1000) << "." << (v / 10 % 100); + if (v % 10 != 0) { + oss << "." << (v % 10); + } + }; + printCudaStyleVersion(get_cudart_version()); + return oss.str(); +#else // neither CUDA nor HIP + return std::string("not available"); +#endif +} + +bool has_cuda() { +#if defined(WITH_CUDA) + return true; +#else + return false; +#endif +} + +// similar to +// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp +std::string get_compiler_version() { + std::ostringstream ss; +#if defined(__GNUC__) +#ifndef __clang__ + +#if ((__GNUC__ <= 4) && (__GNUC_MINOR__ <= 8)) +#error "GCC >= 4.9 is required!" +#endif + + { ss << "GCC " << __GNUC__ << "." << __GNUC_MINOR__; } +#endif +#endif + +#if defined(__clang_major__) + { + ss << "clang " << __clang_major__ << "." << __clang_minor__ << "." + << __clang_patchlevel__; + } +#endif + +#if defined(_MSC_VER) + { ss << "MSVC " << _MSC_FULL_VER; } +#endif + return ss.str(); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("get_compiler_version", &get_compiler_version, "get_compiler_version"); + m.def("get_cuda_version", &get_cuda_version, "get_cuda_version"); + m.def("has_cuda", &has_cuda, "has_cuda"); + + m.def("deform_conv_forward", &deform_conv_forward, "deform_conv_forward"); + m.def( + "deform_conv_backward_input", + &deform_conv_backward_input, + "deform_conv_backward_input"); + m.def( + "deform_conv_backward_filter", + &deform_conv_backward_filter, + "deform_conv_backward_filter"); + m.def( + "modulated_deform_conv_forward", + &modulated_deform_conv_forward, + "modulated_deform_conv_forward"); + m.def( + "modulated_deform_conv_backward", + &modulated_deform_conv_backward, + "modulated_deform_conv_backward"); + + m.def("COCOevalAccumulate", &COCOeval::Accumulate, "COCOeval::Accumulate"); + m.def( + "COCOevalEvaluateImages", + &COCOeval::EvaluateImages, + "COCOeval::EvaluateImages"); + pybind11::class_(m, "InstanceAnnotation") + .def(pybind11::init()); + pybind11::class_(m, "ImageEvaluation") + .def(pybind11::init<>()); +} + +TORCH_LIBRARY(detectron2, m) { + m.def("nms_rotated", &nms_rotated); + m.def("box_iou_rotated", &box_iou_rotated); + m.def("roi_align_rotated_forward", &ROIAlignRotated_forward); + m.def("roi_align_rotated_backward", &ROIAlignRotated_backward); +} +} // namespace detectron2 diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/deform_conv.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/deform_conv.py new file mode 100644 index 0000000000000000000000000000000000000000..eca070f59645af4c9ccd003d99678f19538f355d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/deform_conv.py @@ -0,0 +1,501 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import math +from functools import lru_cache +import torch +from torch import nn +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from torch.nn.modules.utils import _pair +from torchvision.ops import deform_conv2d + +from detectron2 import _C + +from .wrappers import _NewEmptyTensorOp + + +class _DeformConv(Function): + @staticmethod + def forward( + ctx, + input, + offset, + weight, + stride=1, + padding=0, + dilation=1, + groups=1, + deformable_groups=1, + im2col_step=64, + ): + if input is not None and input.dim() != 4: + raise ValueError( + "Expected 4D tensor as input, got {}D tensor instead.".format(input.dim()) + ) + ctx.stride = _pair(stride) + ctx.padding = _pair(padding) + ctx.dilation = _pair(dilation) + ctx.groups = groups + ctx.deformable_groups = deformable_groups + ctx.im2col_step = im2col_step + + ctx.save_for_backward(input, offset, weight) + + output = input.new_empty( + _DeformConv._output_size(input, weight, ctx.padding, ctx.dilation, ctx.stride) + ) + + ctx.bufs_ = [input.new_empty(0), input.new_empty(0)] # columns, ones + + if not input.is_cuda: + if deformable_groups != 1: + raise NotImplementedError( + "Deformable Conv with deformable_groups != 1 is not supported on CPUs!" + ) + return deform_conv2d( + input, offset, weight, stride=stride, padding=padding, dilation=dilation + ) + else: + cur_im2col_step = _DeformConv._cal_im2col_step(input.shape[0], ctx.im2col_step) + assert (input.shape[0] % cur_im2col_step) == 0, "im2col step must divide batchsize" + + _C.deform_conv_forward( + input, + weight, + offset, + output, + ctx.bufs_[0], + ctx.bufs_[1], + weight.size(3), + weight.size(2), + ctx.stride[1], + ctx.stride[0], + ctx.padding[1], + ctx.padding[0], + ctx.dilation[1], + ctx.dilation[0], + ctx.groups, + ctx.deformable_groups, + cur_im2col_step, + ) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + input, offset, weight = ctx.saved_tensors + + grad_input = grad_offset = grad_weight = None + + if not grad_output.is_cuda: + raise NotImplementedError("Deformable Conv is not supported on CPUs!") + else: + cur_im2col_step = _DeformConv._cal_im2col_step(input.shape[0], ctx.im2col_step) + assert (input.shape[0] % cur_im2col_step) == 0, "im2col step must divide batchsize" + + if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]: + grad_input = torch.zeros_like(input) + grad_offset = torch.zeros_like(offset) + _C.deform_conv_backward_input( + input, + offset, + grad_output, + grad_input, + grad_offset, + weight, + ctx.bufs_[0], + weight.size(3), + weight.size(2), + ctx.stride[1], + ctx.stride[0], + ctx.padding[1], + ctx.padding[0], + ctx.dilation[1], + ctx.dilation[0], + ctx.groups, + ctx.deformable_groups, + cur_im2col_step, + ) + + if ctx.needs_input_grad[2]: + grad_weight = torch.zeros_like(weight) + _C.deform_conv_backward_filter( + input, + offset, + grad_output, + grad_weight, + ctx.bufs_[0], + ctx.bufs_[1], + weight.size(3), + weight.size(2), + ctx.stride[1], + ctx.stride[0], + ctx.padding[1], + ctx.padding[0], + ctx.dilation[1], + ctx.dilation[0], + ctx.groups, + ctx.deformable_groups, + 1, + cur_im2col_step, + ) + + return grad_input, grad_offset, grad_weight, None, None, None, None, None, None + + @staticmethod + def _output_size(input, weight, padding, dilation, stride): + channels = weight.size(0) + output_size = (input.size(0), channels) + for d in range(input.dim() - 2): + in_size = input.size(d + 2) + pad = padding[d] + kernel = dilation[d] * (weight.size(d + 2) - 1) + 1 + stride_ = stride[d] + output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1,) + if not all(map(lambda s: s > 0, output_size)): + raise ValueError( + "convolution input is too small (output would be {})".format( + "x".join(map(str, output_size)) + ) + ) + return output_size + + @staticmethod + @lru_cache(maxsize=128) + def _cal_im2col_step(input_size, default_size): + """ + Calculate proper im2col step size, which should be divisible by input_size and not larger + than prefer_size. Meanwhile the step size should be as large as possible to be more + efficient. So we choose the largest one among all divisors of input_size which are smaller + than prefer_size. + :param input_size: input batch size . + :param default_size: default preferred im2col step size. + :return: the largest proper step size. + """ + if input_size <= default_size: + return input_size + best_step = 1 + for step in range(2, min(int(math.sqrt(input_size)) + 1, default_size)): + if input_size % step == 0: + if input_size // step <= default_size: + return input_size // step + best_step = step + + return best_step + + +class _ModulatedDeformConv(Function): + @staticmethod + def forward( + ctx, + input, + offset, + mask, + weight, + bias=None, + stride=1, + padding=0, + dilation=1, + groups=1, + deformable_groups=1, + ): + ctx.stride = stride + ctx.padding = padding + ctx.dilation = dilation + ctx.groups = groups + ctx.deformable_groups = deformable_groups + ctx.with_bias = bias is not None + if not ctx.with_bias: + bias = input.new_empty(1) # fake tensor + if not input.is_cuda: + raise NotImplementedError("Deformable Conv is not supported on CPUs!") + if ( + weight.requires_grad + or mask.requires_grad + or offset.requires_grad + or input.requires_grad + ): + ctx.save_for_backward(input, offset, mask, weight, bias) + output = input.new_empty(_ModulatedDeformConv._infer_shape(ctx, input, weight)) + ctx._bufs = [input.new_empty(0), input.new_empty(0)] + _C.modulated_deform_conv_forward( + input, + weight, + bias, + ctx._bufs[0], + offset, + mask, + output, + ctx._bufs[1], + weight.shape[2], + weight.shape[3], + ctx.stride, + ctx.stride, + ctx.padding, + ctx.padding, + ctx.dilation, + ctx.dilation, + ctx.groups, + ctx.deformable_groups, + ctx.with_bias, + ) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + if not grad_output.is_cuda: + raise NotImplementedError("Deformable Conv is not supported on CPUs!") + input, offset, mask, weight, bias = ctx.saved_tensors + grad_input = torch.zeros_like(input) + grad_offset = torch.zeros_like(offset) + grad_mask = torch.zeros_like(mask) + grad_weight = torch.zeros_like(weight) + grad_bias = torch.zeros_like(bias) + _C.modulated_deform_conv_backward( + input, + weight, + bias, + ctx._bufs[0], + offset, + mask, + ctx._bufs[1], + grad_input, + grad_weight, + grad_bias, + grad_offset, + grad_mask, + grad_output, + weight.shape[2], + weight.shape[3], + ctx.stride, + ctx.stride, + ctx.padding, + ctx.padding, + ctx.dilation, + ctx.dilation, + ctx.groups, + ctx.deformable_groups, + ctx.with_bias, + ) + if not ctx.with_bias: + grad_bias = None + + return ( + grad_input, + grad_offset, + grad_mask, + grad_weight, + grad_bias, + None, + None, + None, + None, + None, + ) + + @staticmethod + def _infer_shape(ctx, input, weight): + n = input.size(0) + channels_out = weight.size(0) + height, width = input.shape[2:4] + kernel_h, kernel_w = weight.shape[2:4] + height_out = ( + height + 2 * ctx.padding - (ctx.dilation * (kernel_h - 1) + 1) + ) // ctx.stride + 1 + width_out = ( + width + 2 * ctx.padding - (ctx.dilation * (kernel_w - 1) + 1) + ) // ctx.stride + 1 + return n, channels_out, height_out, width_out + + +deform_conv = _DeformConv.apply +modulated_deform_conv = _ModulatedDeformConv.apply + + +class DeformConv(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + deformable_groups=1, + bias=False, + norm=None, + activation=None, + ): + """ + Deformable convolution from :paper:`deformconv`. + + Arguments are similar to :class:`Conv2D`. Extra arguments: + + Args: + deformable_groups (int): number of groups used in deformable convolution. + norm (nn.Module, optional): a normalization layer + activation (callable(Tensor) -> Tensor): a callable activation function + """ + super(DeformConv, self).__init__() + + assert not bias + assert in_channels % groups == 0, "in_channels {} cannot be divisible by groups {}".format( + in_channels, groups + ) + assert ( + out_channels % groups == 0 + ), "out_channels {} cannot be divisible by groups {}".format(out_channels, groups) + + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = _pair(kernel_size) + self.stride = _pair(stride) + self.padding = _pair(padding) + self.dilation = _pair(dilation) + self.groups = groups + self.deformable_groups = deformable_groups + self.norm = norm + self.activation = activation + + self.weight = nn.Parameter( + torch.Tensor(out_channels, in_channels // self.groups, *self.kernel_size) + ) + self.bias = None + + nn.init.kaiming_uniform_(self.weight, nonlinearity="relu") + + def forward(self, x, offset): + if x.numel() == 0: + # When input is empty, we want to return a empty tensor with "correct" shape, + # So that the following operations will not panic + # if they check for the shape of the tensor. + # This computes the height and width of the output tensor + output_shape = [ + (i + 2 * p - (di * (k - 1) + 1)) // s + 1 + for i, p, di, k, s in zip( + x.shape[-2:], self.padding, self.dilation, self.kernel_size, self.stride + ) + ] + output_shape = [x.shape[0], self.weight.shape[0]] + output_shape + return _NewEmptyTensorOp.apply(x, output_shape) + + x = deform_conv( + x, + offset, + self.weight, + self.stride, + self.padding, + self.dilation, + self.groups, + self.deformable_groups, + ) + if self.norm is not None: + x = self.norm(x) + if self.activation is not None: + x = self.activation(x) + return x + + def extra_repr(self): + tmpstr = "in_channels=" + str(self.in_channels) + tmpstr += ", out_channels=" + str(self.out_channels) + tmpstr += ", kernel_size=" + str(self.kernel_size) + tmpstr += ", stride=" + str(self.stride) + tmpstr += ", padding=" + str(self.padding) + tmpstr += ", dilation=" + str(self.dilation) + tmpstr += ", groups=" + str(self.groups) + tmpstr += ", deformable_groups=" + str(self.deformable_groups) + tmpstr += ", bias=False" + return tmpstr + + +class ModulatedDeformConv(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + deformable_groups=1, + bias=True, + norm=None, + activation=None, + ): + """ + Modulated deformable convolution from :paper:`deformconv2`. + + Arguments are similar to :class:`Conv2D`. Extra arguments: + + Args: + deformable_groups (int): number of groups used in deformable convolution. + norm (nn.Module, optional): a normalization layer + activation (callable(Tensor) -> Tensor): a callable activation function + """ + super(ModulatedDeformConv, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = _pair(kernel_size) + self.stride = stride + self.padding = padding + self.dilation = dilation + self.groups = groups + self.deformable_groups = deformable_groups + self.with_bias = bias + self.norm = norm + self.activation = activation + + self.weight = nn.Parameter( + torch.Tensor(out_channels, in_channels // groups, *self.kernel_size) + ) + if bias: + self.bias = nn.Parameter(torch.Tensor(out_channels)) + else: + self.bias = None + + nn.init.kaiming_uniform_(self.weight, nonlinearity="relu") + if self.bias is not None: + nn.init.constant_(self.bias, 0) + + def forward(self, x, offset, mask): + if x.numel() == 0: + output_shape = [ + (i + 2 * p - (di * (k - 1) + 1)) // s + 1 + for i, p, di, k, s in zip( + x.shape[-2:], self.padding, self.dilation, self.kernel_size, self.stride + ) + ] + output_shape = [x.shape[0], self.weight.shape[0]] + output_shape + return _NewEmptyTensorOp.apply(x, output_shape) + + x = modulated_deform_conv( + x, + offset, + mask, + self.weight, + self.bias, + self.stride, + self.padding, + self.dilation, + self.groups, + self.deformable_groups, + ) + if self.norm is not None: + x = self.norm(x) + if self.activation is not None: + x = self.activation(x) + return x + + def extra_repr(self): + tmpstr = "in_channels=" + str(self.in_channels) + tmpstr += ", out_channels=" + str(self.out_channels) + tmpstr += ", kernel_size=" + str(self.kernel_size) + tmpstr += ", stride=" + str(self.stride) + tmpstr += ", padding=" + str(self.padding) + tmpstr += ", dilation=" + str(self.dilation) + tmpstr += ", groups=" + str(self.groups) + tmpstr += ", deformable_groups=" + str(self.deformable_groups) + tmpstr += ", bias=" + str(self.with_bias) + return tmpstr diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/losses.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/losses.py new file mode 100644 index 0000000000000000000000000000000000000000..cf4d5e9b165659bd9dc5d80939ea1384da7b11ef --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/losses.py @@ -0,0 +1,133 @@ +import math +import torch + + +def diou_loss( + boxes1: torch.Tensor, + boxes2: torch.Tensor, + reduction: str = "none", + eps: float = 1e-7, +) -> torch.Tensor: + """ + Distance Intersection over Union Loss (Zhaohui Zheng et. al) + https://arxiv.org/abs/1911.08287 + Args: + boxes1, boxes2 (Tensor): box locations in XYXY format, shape (N, 4) or (4,). + reduction: 'none' | 'mean' | 'sum' + 'none': No reduction will be applied to the output. + 'mean': The output will be averaged. + 'sum': The output will be summed. + eps (float): small number to prevent division by zero + """ + + x1, y1, x2, y2 = boxes1.unbind(dim=-1) + x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1) + + # TODO: use torch._assert_async() when pytorch 1.8 support is dropped + assert (x2 >= x1).all(), "bad box: x1 larger than x2" + assert (y2 >= y1).all(), "bad box: y1 larger than y2" + + # Intersection keypoints + xkis1 = torch.max(x1, x1g) + ykis1 = torch.max(y1, y1g) + xkis2 = torch.min(x2, x2g) + ykis2 = torch.min(y2, y2g) + + intsct = torch.zeros_like(x1) + mask = (ykis2 > ykis1) & (xkis2 > xkis1) + intsct[mask] = (xkis2[mask] - xkis1[mask]) * (ykis2[mask] - ykis1[mask]) + union = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g) - intsct + eps + iou = intsct / union + + # smallest enclosing box + xc1 = torch.min(x1, x1g) + yc1 = torch.min(y1, y1g) + xc2 = torch.max(x2, x2g) + yc2 = torch.max(y2, y2g) + diag_len = ((xc2 - xc1) ** 2) + ((yc2 - yc1) ** 2) + eps + + # centers of boxes + x_p = (x2 + x1) / 2 + y_p = (y2 + y1) / 2 + x_g = (x1g + x2g) / 2 + y_g = (y1g + y2g) / 2 + distance = ((x_p - x_g) ** 2) + ((y_p - y_g) ** 2) + + # Eqn. (7) + loss = 1 - iou + (distance / diag_len) + if reduction == "mean": + loss = loss.mean() if loss.numel() > 0 else 0.0 * loss.sum() + elif reduction == "sum": + loss = loss.sum() + + return loss + + +def ciou_loss( + boxes1: torch.Tensor, + boxes2: torch.Tensor, + reduction: str = "none", + eps: float = 1e-7, +) -> torch.Tensor: + """ + Complete Intersection over Union Loss (Zhaohui Zheng et. al) + https://arxiv.org/abs/1911.08287 + Args: + boxes1, boxes2 (Tensor): box locations in XYXY format, shape (N, 4) or (4,). + reduction: 'none' | 'mean' | 'sum' + 'none': No reduction will be applied to the output. + 'mean': The output will be averaged. + 'sum': The output will be summed. + eps (float): small number to prevent division by zero + """ + + x1, y1, x2, y2 = boxes1.unbind(dim=-1) + x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1) + + # TODO: use torch._assert_async() when pytorch 1.8 support is dropped + assert (x2 >= x1).all(), "bad box: x1 larger than x2" + assert (y2 >= y1).all(), "bad box: y1 larger than y2" + + # Intersection keypoints + xkis1 = torch.max(x1, x1g) + ykis1 = torch.max(y1, y1g) + xkis2 = torch.min(x2, x2g) + ykis2 = torch.min(y2, y2g) + + intsct = torch.zeros_like(x1) + mask = (ykis2 > ykis1) & (xkis2 > xkis1) + intsct[mask] = (xkis2[mask] - xkis1[mask]) * (ykis2[mask] - ykis1[mask]) + union = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g) - intsct + eps + iou = intsct / union + + # smallest enclosing box + xc1 = torch.min(x1, x1g) + yc1 = torch.min(y1, y1g) + xc2 = torch.max(x2, x2g) + yc2 = torch.max(y2, y2g) + diag_len = ((xc2 - xc1) ** 2) + ((yc2 - yc1) ** 2) + eps + + # centers of boxes + x_p = (x2 + x1) / 2 + y_p = (y2 + y1) / 2 + x_g = (x1g + x2g) / 2 + y_g = (y1g + y2g) / 2 + distance = ((x_p - x_g) ** 2) + ((y_p - y_g) ** 2) + + # width and height of boxes + w_pred = x2 - x1 + h_pred = y2 - y1 + w_gt = x2g - x1g + h_gt = y2g - y1g + v = (4 / (math.pi ** 2)) * torch.pow((torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), 2) + with torch.no_grad(): + alpha = v / (1 - iou + v + eps) + + # Eqn. (10) + loss = 1 - iou + (distance / diag_len) + alpha * v + if reduction == "mean": + loss = loss.mean() if loss.numel() > 0 else 0.0 * loss.sum() + elif reduction == "sum": + loss = loss.sum() + + return loss diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/mask_ops.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/mask_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..e7a9f3a323ddbe75845b668ee6b40c5385d206c3 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/mask_ops.py @@ -0,0 +1,275 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +from typing import Tuple +import torch +from PIL import Image +from torch.nn import functional as F + +__all__ = ["paste_masks_in_image"] + + +BYTES_PER_FLOAT = 4 +# TODO: This memory limit may be too much or too little. It would be better to +# determine it based on available resources. +GPU_MEM_LIMIT = 1024 ** 3 # 1 GB memory limit + + +def _do_paste_mask(masks, boxes, img_h: int, img_w: int, skip_empty: bool = True): + """ + Args: + masks: N, 1, H, W + boxes: N, 4 + img_h, img_w (int): + skip_empty (bool): only paste masks within the region that + tightly bound all boxes, and returns the results this region only. + An important optimization for CPU. + + Returns: + if skip_empty == False, a mask of shape (N, img_h, img_w) + if skip_empty == True, a mask of shape (N, h', w'), and the slice + object for the corresponding region. + """ + # On GPU, paste all masks together (up to chunk size) + # by using the entire image to sample the masks + # Compared to pasting them one by one, + # this has more operations but is faster on COCO-scale dataset. + device = masks.device + + if skip_empty and not torch.jit.is_scripting(): + x0_int, y0_int = torch.clamp(boxes.min(dim=0).values.floor()[:2] - 1, min=0).to( + dtype=torch.int32 + ) + x1_int = torch.clamp(boxes[:, 2].max().ceil() + 1, max=img_w).to(dtype=torch.int32) + y1_int = torch.clamp(boxes[:, 3].max().ceil() + 1, max=img_h).to(dtype=torch.int32) + else: + x0_int, y0_int = 0, 0 + x1_int, y1_int = img_w, img_h + x0, y0, x1, y1 = torch.split(boxes, 1, dim=1) # each is Nx1 + + N = masks.shape[0] + + img_y = torch.arange(y0_int, y1_int, device=device, dtype=torch.float32) + 0.5 + img_x = torch.arange(x0_int, x1_int, device=device, dtype=torch.float32) + 0.5 + img_y = (img_y - y0) / (y1 - y0) * 2 - 1 + img_x = (img_x - x0) / (x1 - x0) * 2 - 1 + # img_x, img_y have shapes (N, w), (N, h) + + gx = img_x[:, None, :].expand(N, img_y.size(1), img_x.size(1)) + gy = img_y[:, :, None].expand(N, img_y.size(1), img_x.size(1)) + grid = torch.stack([gx, gy], dim=3) + + if not torch.jit.is_scripting(): + if not masks.dtype.is_floating_point: + masks = masks.float() + img_masks = F.grid_sample(masks, grid.to(masks.dtype), align_corners=False) + + if skip_empty and not torch.jit.is_scripting(): + return img_masks[:, 0], (slice(y0_int, y1_int), slice(x0_int, x1_int)) + else: + return img_masks[:, 0], () + + +# Annotate boxes as Tensor (but not Boxes) in order to use scripting +@torch.jit.script_if_tracing +def paste_masks_in_image( + masks: torch.Tensor, boxes: torch.Tensor, image_shape: Tuple[int, int], threshold: float = 0.5 +): + """ + Paste a set of masks that are of a fixed resolution (e.g., 28 x 28) into an image. + The location, height, and width for pasting each mask is determined by their + corresponding bounding boxes in boxes. + + Note: + This is a complicated but more accurate implementation. In actual deployment, it is + often enough to use a faster but less accurate implementation. + See :func:`paste_mask_in_image_old` in this file for an alternative implementation. + + Args: + masks (tensor): Tensor of shape (Bimg, Hmask, Wmask), where Bimg is the number of + detected object instances in the image and Hmask, Wmask are the mask width and mask + height of the predicted mask (e.g., Hmask = Wmask = 28). Values are in [0, 1]. + boxes (Boxes or Tensor): A Boxes of length Bimg or Tensor of shape (Bimg, 4). + boxes[i] and masks[i] correspond to the same object instance. + image_shape (tuple): height, width + threshold (float): A threshold in [0, 1] for converting the (soft) masks to + binary masks. + + Returns: + img_masks (Tensor): A tensor of shape (Bimg, Himage, Wimage), where Bimg is the + number of detected object instances and Himage, Wimage are the image width + and height. img_masks[i] is a binary mask for object instance i. + """ + + assert masks.shape[-1] == masks.shape[-2], "Only square mask predictions are supported" + N = len(masks) + if N == 0: + return masks.new_empty((0,) + image_shape, dtype=torch.uint8) + if not isinstance(boxes, torch.Tensor): + boxes = boxes.tensor + device = boxes.device + assert len(boxes) == N, boxes.shape + + img_h, img_w = image_shape + + # The actual implementation split the input into chunks, + # and paste them chunk by chunk. + if device.type == "cpu" or torch.jit.is_scripting(): + # CPU is most efficient when they are pasted one by one with skip_empty=True + # so that it performs minimal number of operations. + num_chunks = N + else: + # GPU benefits from parallelism for larger chunks, but may have memory issue + # int(img_h) because shape may be tensors in tracing + num_chunks = int(np.ceil(N * int(img_h) * int(img_w) * BYTES_PER_FLOAT / GPU_MEM_LIMIT)) + assert ( + num_chunks <= N + ), "Default GPU_MEM_LIMIT in mask_ops.py is too small; try increasing it" + chunks = torch.chunk(torch.arange(N, device=device), num_chunks) + + img_masks = torch.zeros( + N, img_h, img_w, device=device, dtype=torch.bool if threshold >= 0 else torch.uint8 + ) + for inds in chunks: + masks_chunk, spatial_inds = _do_paste_mask( + masks[inds, None, :, :], boxes[inds], img_h, img_w, skip_empty=device.type == "cpu" + ) + + if threshold >= 0: + masks_chunk = (masks_chunk >= threshold).to(dtype=torch.bool) + else: + # for visualization and debugging + masks_chunk = (masks_chunk * 255).to(dtype=torch.uint8) + + if torch.jit.is_scripting(): # Scripting does not use the optimized codepath + img_masks[inds] = masks_chunk + else: + img_masks[(inds,) + spatial_inds] = masks_chunk + return img_masks + + +# The below are the original paste function (from Detectron1) which has +# larger quantization error. +# It is faster on CPU, while the aligned one is faster on GPU thanks to grid_sample. + + +def paste_mask_in_image_old(mask, box, img_h, img_w, threshold): + """ + Paste a single mask in an image. + This is a per-box implementation of :func:`paste_masks_in_image`. + This function has larger quantization error due to incorrect pixel + modeling and is not used any more. + + Args: + mask (Tensor): A tensor of shape (Hmask, Wmask) storing the mask of a single + object instance. Values are in [0, 1]. + box (Tensor): A tensor of shape (4, ) storing the x0, y0, x1, y1 box corners + of the object instance. + img_h, img_w (int): Image height and width. + threshold (float): Mask binarization threshold in [0, 1]. + + Returns: + im_mask (Tensor): + The resized and binarized object mask pasted into the original + image plane (a tensor of shape (img_h, img_w)). + """ + # Conversion from continuous box coordinates to discrete pixel coordinates + # via truncation (cast to int32). This determines which pixels to paste the + # mask onto. + box = box.to(dtype=torch.int32) # Continuous to discrete coordinate conversion + # An example (1D) box with continuous coordinates (x0=0.7, x1=4.3) will map to + # a discrete coordinates (x0=0, x1=4). Note that box is mapped to 5 = x1 - x0 + 1 + # pixels (not x1 - x0 pixels). + samples_w = box[2] - box[0] + 1 # Number of pixel samples, *not* geometric width + samples_h = box[3] - box[1] + 1 # Number of pixel samples, *not* geometric height + + # Resample the mask from it's original grid to the new samples_w x samples_h grid + mask = Image.fromarray(mask.cpu().numpy()) + mask = mask.resize((samples_w, samples_h), resample=Image.BILINEAR) + mask = np.array(mask, copy=False) + + if threshold >= 0: + mask = np.array(mask > threshold, dtype=np.uint8) + mask = torch.from_numpy(mask) + else: + # for visualization and debugging, we also + # allow it to return an unmodified mask + mask = torch.from_numpy(mask * 255).to(torch.uint8) + + im_mask = torch.zeros((img_h, img_w), dtype=torch.uint8) + x_0 = max(box[0], 0) + x_1 = min(box[2] + 1, img_w) + y_0 = max(box[1], 0) + y_1 = min(box[3] + 1, img_h) + + im_mask[y_0:y_1, x_0:x_1] = mask[ + (y_0 - box[1]) : (y_1 - box[1]), (x_0 - box[0]) : (x_1 - box[0]) + ] + return im_mask + + +# Our pixel modeling requires extrapolation for any continuous +# coordinate < 0.5 or > length - 0.5. When sampling pixels on the masks, +# we would like this extrapolation to be an interpolation between boundary values and zero, +# instead of using absolute zero or boundary values. +# Therefore `paste_mask_in_image_old` is often used with zero padding around the masks like this: +# masks, scale = pad_masks(masks[:, 0, :, :], 1) +# boxes = scale_boxes(boxes.tensor, scale) + + +def pad_masks(masks, padding): + """ + Args: + masks (tensor): A tensor of shape (B, M, M) representing B masks. + padding (int): Number of cells to pad on all sides. + + Returns: + The padded masks and the scale factor of the padding size / original size. + """ + B = masks.shape[0] + M = masks.shape[-1] + pad2 = 2 * padding + scale = float(M + pad2) / M + padded_masks = masks.new_zeros((B, M + pad2, M + pad2)) + padded_masks[:, padding:-padding, padding:-padding] = masks + return padded_masks, scale + + +def scale_boxes(boxes, scale): + """ + Args: + boxes (tensor): A tensor of shape (B, 4) representing B boxes with 4 + coords representing the corners x0, y0, x1, y1, + scale (float): The box scaling factor. + + Returns: + Scaled boxes. + """ + w_half = (boxes[:, 2] - boxes[:, 0]) * 0.5 + h_half = (boxes[:, 3] - boxes[:, 1]) * 0.5 + x_c = (boxes[:, 2] + boxes[:, 0]) * 0.5 + y_c = (boxes[:, 3] + boxes[:, 1]) * 0.5 + + w_half *= scale + h_half *= scale + + scaled_boxes = torch.zeros_like(boxes) + scaled_boxes[:, 0] = x_c - w_half + scaled_boxes[:, 2] = x_c + w_half + scaled_boxes[:, 1] = y_c - h_half + scaled_boxes[:, 3] = y_c + h_half + return scaled_boxes + + +@torch.jit.script_if_tracing +def _paste_masks_tensor_shape( + masks: torch.Tensor, + boxes: torch.Tensor, + image_shape: Tuple[torch.Tensor, torch.Tensor], + threshold: float = 0.5, +): + """ + A wrapper of paste_masks_in_image where image_shape is Tensor. + During tracing, shapes might be tensors instead of ints. The Tensor->int + conversion should be scripted rather than traced. + """ + return paste_masks_in_image(masks, boxes, (int(image_shape[0]), int(image_shape[1])), threshold) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/nms.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/nms.py new file mode 100644 index 0000000000000000000000000000000000000000..6b6be71c7832d188aaa20bd7e1b16964cab7a731 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/nms.py @@ -0,0 +1,139 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import torch +from torchvision.ops import boxes as box_ops +from torchvision.ops import nms # noqa . for compatibility + + +def batched_nms( + boxes: torch.Tensor, scores: torch.Tensor, idxs: torch.Tensor, iou_threshold: float +): + """ + Same as torchvision.ops.boxes.batched_nms, but with float(). + """ + assert boxes.shape[-1] == 4 + # Note: Torchvision already has a strategy (https://github.com/pytorch/vision/issues/1311) + # to decide whether to use coordinate trick or for loop to implement batched_nms. So we + # just call it directly. + # Fp16 does not have enough range for batched NMS, so adding float(). + return box_ops.batched_nms(boxes.float(), scores, idxs, iou_threshold) + + +# Note: this function (nms_rotated) might be moved into +# torchvision/ops/boxes.py in the future +def nms_rotated(boxes, scores, iou_threshold): + """ + Performs non-maximum suppression (NMS) on the rotated boxes according + to their intersection-over-union (IoU). + + Rotated NMS iteratively removes lower scoring rotated boxes which have an + IoU greater than iou_threshold with another (higher scoring) rotated box. + + Note that RotatedBox (5, 3, 4, 2, -90) covers exactly the same region as + RotatedBox (5, 3, 4, 2, 90) does, and their IoU will be 1. However, they + can be representing completely different objects in certain tasks, e.g., OCR. + + As for the question of whether rotated-NMS should treat them as faraway boxes + even though their IOU is 1, it depends on the application and/or ground truth annotation. + + As an extreme example, consider a single character v and the square box around it. + + If the angle is 0 degree, the object (text) would be read as 'v'; + + If the angle is 90 degrees, the object (text) would become '>'; + + If the angle is 180 degrees, the object (text) would become '^'; + + If the angle is 270/-90 degrees, the object (text) would become '<' + + All of these cases have IoU of 1 to each other, and rotated NMS that only + uses IoU as criterion would only keep one of them with the highest score - + which, practically, still makes sense in most cases because typically + only one of theses orientations is the correct one. Also, it does not matter + as much if the box is only used to classify the object (instead of transcribing + them with a sequential OCR recognition model) later. + + On the other hand, when we use IoU to filter proposals that are close to the + ground truth during training, we should definitely take the angle into account if + we know the ground truth is labeled with the strictly correct orientation (as in, + upside-down words are annotated with -180 degrees even though they can be covered + with a 0/90/-90 degree box, etc.) + + The way the original dataset is annotated also matters. For example, if the dataset + is a 4-point polygon dataset that does not enforce ordering of vertices/orientation, + we can estimate a minimum rotated bounding box to this polygon, but there's no way + we can tell the correct angle with 100% confidence (as shown above, there could be 4 different + rotated boxes, with angles differed by 90 degrees to each other, covering the exactly + same region). In that case we have to just use IoU to determine the box + proximity (as many detection benchmarks (even for text) do) unless there're other + assumptions we can make (like width is always larger than height, or the object is not + rotated by more than 90 degrees CCW/CW, etc.) + + In summary, not considering angles in rotated NMS seems to be a good option for now, + but we should be aware of its implications. + + Args: + boxes (Tensor[N, 5]): Rotated boxes to perform NMS on. They are expected to be in + (x_center, y_center, width, height, angle_degrees) format. + scores (Tensor[N]): Scores for each one of the rotated boxes + iou_threshold (float): Discards all overlapping rotated boxes with IoU < iou_threshold + + Returns: + keep (Tensor): int64 tensor with the indices of the elements that have been kept + by Rotated NMS, sorted in decreasing order of scores + """ + return torch.ops.detectron2.nms_rotated(boxes, scores, iou_threshold) + + +# Note: this function (batched_nms_rotated) might be moved into +# torchvision/ops/boxes.py in the future +def batched_nms_rotated(boxes, scores, idxs, iou_threshold): + """ + Performs non-maximum suppression in a batched fashion. + + Each index value correspond to a category, and NMS + will not be applied between elements of different categories. + + Args: + boxes (Tensor[N, 5]): + boxes where NMS will be performed. They + are expected to be in (x_ctr, y_ctr, width, height, angle_degrees) format + scores (Tensor[N]): + scores for each one of the boxes + idxs (Tensor[N]): + indices of the categories for each one of the boxes. + iou_threshold (float): + discards all overlapping boxes + with IoU < iou_threshold + + Returns: + Tensor: + int64 tensor with the indices of the elements that have been kept + by NMS, sorted in decreasing order of scores + """ + assert boxes.shape[-1] == 5 + + if boxes.numel() == 0: + return torch.empty((0,), dtype=torch.int64, device=boxes.device) + boxes = boxes.float() # fp16 does not have enough range for batched NMS + # Strategy: in order to perform NMS independently per class, + # we add an offset to all the boxes. The offset is dependent + # only on the class idx, and is large enough so that boxes + # from different classes do not overlap + + # Note that batched_nms in torchvision/ops/boxes.py only uses max_coordinate, + # which won't handle negative coordinates correctly. + # Here by using min_coordinate we can make sure the negative coordinates are + # correctly handled. + max_coordinate = ( + torch.max(boxes[:, 0], boxes[:, 1]) + torch.max(boxes[:, 2], boxes[:, 3]) / 2 + ).max() + min_coordinate = ( + torch.min(boxes[:, 0], boxes[:, 1]) - torch.max(boxes[:, 2], boxes[:, 3]) / 2 + ).min() + offsets = idxs.to(boxes) * (max_coordinate - min_coordinate + 1) + boxes_for_nms = boxes.clone() # avoid modifying the original values in boxes + boxes_for_nms[:, :2] += offsets[:, None] + keep = nms_rotated(boxes_for_nms, scores, iou_threshold) + return keep diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/roi_align.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/roi_align.py new file mode 100644 index 0000000000000000000000000000000000000000..163462e1f194e1e4100da92d76d9516f7cc22e35 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/roi_align.py @@ -0,0 +1,74 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from torch import nn +from torchvision.ops import roi_align + + +# NOTE: torchvision's RoIAlign has a different default aligned=False +class ROIAlign(nn.Module): + def __init__(self, output_size, spatial_scale, sampling_ratio, aligned=True): + """ + Args: + output_size (tuple): h, w + spatial_scale (float): scale the input boxes by this number + sampling_ratio (int): number of inputs samples to take for each output + sample. 0 to take samples densely. + aligned (bool): if False, use the legacy implementation in + Detectron. If True, align the results more perfectly. + + Note: + The meaning of aligned=True: + + Given a continuous coordinate c, its two neighboring pixel indices (in our + pixel model) are computed by floor(c - 0.5) and ceil(c - 0.5). For example, + c=1.3 has pixel neighbors with discrete indices [0] and [1] (which are sampled + from the underlying signal at continuous coordinates 0.5 and 1.5). But the original + roi_align (aligned=False) does not subtract the 0.5 when computing neighboring + pixel indices and therefore it uses pixels with a slightly incorrect alignment + (relative to our pixel model) when performing bilinear interpolation. + + With `aligned=True`, + we first appropriately scale the ROI and then shift it by -0.5 + prior to calling roi_align. This produces the correct neighbors; see + detectron2/tests/test_roi_align.py for verification. + + The difference does not make a difference to the model's performance if + ROIAlign is used together with conv layers. + """ + super().__init__() + self.output_size = output_size + self.spatial_scale = spatial_scale + self.sampling_ratio = sampling_ratio + self.aligned = aligned + + from torchvision import __version__ + + version = tuple(int(x) for x in __version__.split(".")[:2]) + # https://github.com/pytorch/vision/pull/2438 + assert version >= (0, 7), "Require torchvision >= 0.7" + + def forward(self, input, rois): + """ + Args: + input: NCHW images + rois: Bx5 boxes. First column is the index into N. The other 4 columns are xyxy. + """ + assert rois.dim() == 2 and rois.size(1) == 5 + if input.is_quantized: + input = input.dequantize() + return roi_align( + input, + rois.to(dtype=input.dtype), + self.output_size, + self.spatial_scale, + self.sampling_ratio, + self.aligned, + ) + + def __repr__(self): + tmpstr = self.__class__.__name__ + "(" + tmpstr += "output_size=" + str(self.output_size) + tmpstr += ", spatial_scale=" + str(self.spatial_scale) + tmpstr += ", sampling_ratio=" + str(self.sampling_ratio) + tmpstr += ", aligned=" + str(self.aligned) + tmpstr += ")" + return tmpstr diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/roi_align_rotated.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/roi_align_rotated.py new file mode 100644 index 0000000000000000000000000000000000000000..d097326c3a6116e872cecf0d675b42958f359b14 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/roi_align_rotated.py @@ -0,0 +1,91 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import torch +from torch import nn +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from torch.nn.modules.utils import _pair + + +class _ROIAlignRotated(Function): + @staticmethod + def forward(ctx, input, roi, output_size, spatial_scale, sampling_ratio): + ctx.save_for_backward(roi) + ctx.output_size = _pair(output_size) + ctx.spatial_scale = spatial_scale + ctx.sampling_ratio = sampling_ratio + ctx.input_shape = input.size() + output = torch.ops.detectron2.roi_align_rotated_forward( + input, roi, spatial_scale, output_size[0], output_size[1], sampling_ratio + ) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + (rois,) = ctx.saved_tensors + output_size = ctx.output_size + spatial_scale = ctx.spatial_scale + sampling_ratio = ctx.sampling_ratio + bs, ch, h, w = ctx.input_shape + grad_input = torch.ops.detectron2.roi_align_rotated_backward( + grad_output, + rois, + spatial_scale, + output_size[0], + output_size[1], + bs, + ch, + h, + w, + sampling_ratio, + ) + return grad_input, None, None, None, None, None + + +roi_align_rotated = _ROIAlignRotated.apply + + +class ROIAlignRotated(nn.Module): + def __init__(self, output_size, spatial_scale, sampling_ratio): + """ + Args: + output_size (tuple): h, w + spatial_scale (float): scale the input boxes by this number + sampling_ratio (int): number of inputs samples to take for each output + sample. 0 to take samples densely. + + Note: + ROIAlignRotated supports continuous coordinate by default: + Given a continuous coordinate c, its two neighboring pixel indices (in our + pixel model) are computed by floor(c - 0.5) and ceil(c - 0.5). For example, + c=1.3 has pixel neighbors with discrete indices [0] and [1] (which are sampled + from the underlying signal at continuous coordinates 0.5 and 1.5). + """ + super(ROIAlignRotated, self).__init__() + self.output_size = output_size + self.spatial_scale = spatial_scale + self.sampling_ratio = sampling_ratio + + def forward(self, input, rois): + """ + Args: + input: NCHW images + rois: Bx6 boxes. First column is the index into N. + The other 5 columns are (x_ctr, y_ctr, width, height, angle_degrees). + """ + assert rois.dim() == 2 and rois.size(1) == 6 + orig_dtype = input.dtype + if orig_dtype == torch.float16: + input = input.float() + rois = rois.float() + return roi_align_rotated( + input, rois, self.output_size, self.spatial_scale, self.sampling_ratio + ).to(dtype=orig_dtype) + + def __repr__(self): + tmpstr = self.__class__.__name__ + "(" + tmpstr += "output_size=" + str(self.output_size) + tmpstr += ", spatial_scale=" + str(self.spatial_scale) + tmpstr += ", sampling_ratio=" + str(self.sampling_ratio) + tmpstr += ")" + return tmpstr diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/rotated_boxes.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/rotated_boxes.py new file mode 100644 index 0000000000000000000000000000000000000000..03f73b3bb99275931a887ad9b2d8c0ac9f412bf3 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/rotated_boxes.py @@ -0,0 +1,21 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from __future__ import absolute_import, division, print_function, unicode_literals +import torch + + +def pairwise_iou_rotated(boxes1, boxes2): + """ + Return intersection-over-union (Jaccard index) of boxes. + + Both sets of boxes are expected to be in + (x_center, y_center, width, height, angle) format. + + Arguments: + boxes1 (Tensor[N, 5]) + boxes2 (Tensor[M, 5]) + + Returns: + iou (Tensor[N, M]): the NxM matrix containing the pairwise + IoU values for every element in boxes1 and boxes2 + """ + return torch.ops.detectron2.box_iou_rotated(boxes1, boxes2) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/shape_spec.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/shape_spec.py new file mode 100644 index 0000000000000000000000000000000000000000..fe7e8e261c1ab1bb1636bd7a245068d64e632306 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/shape_spec.py @@ -0,0 +1,20 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +from collections import namedtuple + + +class ShapeSpec(namedtuple("_ShapeSpec", ["channels", "height", "width", "stride"])): + """ + A simple structure that contains basic shape specification about a tensor. + It is often used as the auxiliary inputs/outputs of models, + to complement the lack of shape inference ability among pytorch modules. + + Attributes: + channels: + height: + width: + stride: + """ + + def __new__(cls, channels=None, height=None, width=None, stride=None): + return super().__new__(cls, channels, height, width, stride) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/wrappers.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/wrappers.py new file mode 100644 index 0000000000000000000000000000000000000000..29d0ef9102b2db0ffbf723c168aa32d2451b9419 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/layers/wrappers.py @@ -0,0 +1,132 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +""" +Wrappers around on some nn functions, mainly to support empty tensors. + +Ideally, add support directly in PyTorch to empty tensors in those functions. + +These can be removed once https://github.com/pytorch/pytorch/issues/12013 +is implemented +""" + +from typing import List, Optional +import torch +from torch.nn import functional as F + + +def shapes_to_tensor(x: List[int], device: Optional[torch.device] = None) -> torch.Tensor: + """ + Turn a list of integer scalars or integer Tensor scalars into a vector, + in a way that's both traceable and scriptable. + + In tracing, `x` should be a list of scalar Tensor, so the output can trace to the inputs. + In scripting or eager, `x` should be a list of int. + """ + if torch.jit.is_scripting(): + return torch.as_tensor(x, device=device) + if torch.jit.is_tracing(): + assert all( + [isinstance(t, torch.Tensor) for t in x] + ), "Shape should be tensor during tracing!" + # as_tensor should not be used in tracing because it records a constant + ret = torch.stack(x) + if ret.device != device: # avoid recording a hard-coded device if not necessary + ret = ret.to(device=device) + return ret + return torch.as_tensor(x, device=device) + + +def cat(tensors: List[torch.Tensor], dim: int = 0): + """ + Efficient version of torch.cat that avoids a copy if there is only a single element in a list + """ + assert isinstance(tensors, (list, tuple)) + if len(tensors) == 1: + return tensors[0] + return torch.cat(tensors, dim) + + +def cross_entropy(input, target, *, reduction="mean", **kwargs): + """ + Same as `torch.nn.functional.cross_entropy`, but returns 0 (instead of nan) + for empty inputs. + """ + if target.numel() == 0 and reduction == "mean": + return input.sum() * 0.0 # connect the gradient + return F.cross_entropy(input, target, reduction=reduction, **kwargs) + + +class _NewEmptyTensorOp(torch.autograd.Function): + @staticmethod + def forward(ctx, x, new_shape): + ctx.shape = x.shape + return x.new_empty(new_shape) + + @staticmethod + def backward(ctx, grad): + shape = ctx.shape + return _NewEmptyTensorOp.apply(grad, shape), None + + +class Conv2d(torch.nn.Conv2d): + """ + A wrapper around :class:`torch.nn.Conv2d` to support empty inputs and more features. + """ + + def __init__(self, *args, **kwargs): + """ + Extra keyword arguments supported in addition to those in `torch.nn.Conv2d`: + + Args: + norm (nn.Module, optional): a normalization layer + activation (callable(Tensor) -> Tensor): a callable activation function + + It assumes that norm layer is used before activation. + """ + norm = kwargs.pop("norm", None) + activation = kwargs.pop("activation", None) + super().__init__(*args, **kwargs) + + self.norm = norm + self.activation = activation + + def forward(self, x): + # torchscript does not support SyncBatchNorm yet + # https://github.com/pytorch/pytorch/issues/40507 + # and we skip these codes in torchscript since: + # 1. currently we only support torchscript in evaluation mode + # 2. features needed by exporting module to torchscript are added in PyTorch 1.6 or + # later version, `Conv2d` in these PyTorch versions has already supported empty inputs. + if not torch.jit.is_scripting(): + if x.numel() == 0 and self.training: + # https://github.com/pytorch/pytorch/issues/12013 + assert not isinstance( + self.norm, torch.nn.SyncBatchNorm + ), "SyncBatchNorm does not support empty inputs!" + + x = F.conv2d( + x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups + ) + if self.norm is not None: + x = self.norm(x) + if self.activation is not None: + x = self.activation(x) + return x + + +ConvTranspose2d = torch.nn.ConvTranspose2d +BatchNorm2d = torch.nn.BatchNorm2d +interpolate = F.interpolate +Linear = torch.nn.Linear + + +def nonzero_tuple(x): + """ + A 'as_tuple=True' version of torch.nonzero to support torchscript. + because of https://github.com/pytorch/pytorch/issues/38718 + """ + if torch.jit.is_scripting(): + if x.dim() == 0: + return x.unsqueeze(0).nonzero().unbind(1) + return x.nonzero().unbind(1) + else: + return x.nonzero(as_tuple=True) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/model_zoo/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/model_zoo/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6204208198d813728cf6419e8eef4a733f20c18f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/model_zoo/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +""" +Model Zoo API for Detectron2: a collection of functions to create common model architectures +listed in `MODEL_ZOO.md `_, +and optionally load their pre-trained weights. +""" + +from .model_zoo import get, get_config_file, get_checkpoint_url, get_config + +__all__ = ["get_checkpoint_url", "get", "get_config_file", "get_config"] diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/model_zoo/model_zoo.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/model_zoo/model_zoo.py new file mode 100644 index 0000000000000000000000000000000000000000..5b90bc9a165ea46ada72ed0e71f1e80e71ea9f40 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/model_zoo/model_zoo.py @@ -0,0 +1,213 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import os +from typing import Optional +import pkg_resources +import torch + +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate +from detectron2.modeling import build_model + + +class _ModelZooUrls(object): + """ + Mapping from names to officially released Detectron2 pre-trained models. + """ + + S3_PREFIX = "https://dl.fbaipublicfiles.com/detectron2/" + + # format: {config_path.yaml} -> model_id/model_final_{commit}.pkl + CONFIG_PATH_TO_URL_SUFFIX = { + # COCO Detection with Faster R-CNN + "COCO-Detection/faster_rcnn_R_50_C4_1x": "137257644/model_final_721ade.pkl", + "COCO-Detection/faster_rcnn_R_50_DC5_1x": "137847829/model_final_51d356.pkl", + "COCO-Detection/faster_rcnn_R_50_FPN_1x": "137257794/model_final_b275ba.pkl", + "COCO-Detection/faster_rcnn_R_50_C4_3x": "137849393/model_final_f97cb7.pkl", + "COCO-Detection/faster_rcnn_R_50_DC5_3x": "137849425/model_final_68d202.pkl", + "COCO-Detection/faster_rcnn_R_50_FPN_3x": "137849458/model_final_280758.pkl", + "COCO-Detection/faster_rcnn_R_101_C4_3x": "138204752/model_final_298dad.pkl", + "COCO-Detection/faster_rcnn_R_101_DC5_3x": "138204841/model_final_3e0943.pkl", + "COCO-Detection/faster_rcnn_R_101_FPN_3x": "137851257/model_final_f6e8b1.pkl", + "COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x": "139173657/model_final_68b088.pkl", + # COCO Detection with RetinaNet + "COCO-Detection/retinanet_R_50_FPN_1x": "190397773/model_final_bfca0b.pkl", + "COCO-Detection/retinanet_R_50_FPN_3x": "190397829/model_final_5bd44e.pkl", + "COCO-Detection/retinanet_R_101_FPN_3x": "190397697/model_final_971ab9.pkl", + # COCO Detection with RPN and Fast R-CNN + "COCO-Detection/rpn_R_50_C4_1x": "137258005/model_final_450694.pkl", + "COCO-Detection/rpn_R_50_FPN_1x": "137258492/model_final_02ce48.pkl", + "COCO-Detection/fast_rcnn_R_50_FPN_1x": "137635226/model_final_e5f7ce.pkl", + # COCO Instance Segmentation Baselines with Mask R-CNN + "COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x": "137259246/model_final_9243eb.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x": "137260150/model_final_4f86c3.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x": "137260431/model_final_a54504.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x": "137849525/model_final_4ce675.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x": "137849551/model_final_84107b.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x": "137849600/model_final_f10217.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x": "138363239/model_final_a2914c.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x": "138363294/model_final_0464b7.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x": "138205316/model_final_a3ec72.pkl", + "COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x": "139653917/model_final_2d9806.pkl", # noqa + # New baselines using Large-Scale Jitter and Longer Training Schedule + "new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ": "42047764/model_final_bb69de.pkl", + "new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ": "42047638/model_final_89a8d3.pkl", + "new_baselines/mask_rcnn_R_50_FPN_400ep_LSJ": "42019571/model_final_14d201.pkl", + "new_baselines/mask_rcnn_R_101_FPN_100ep_LSJ": "42025812/model_final_4f7b58.pkl", + "new_baselines/mask_rcnn_R_101_FPN_200ep_LSJ": "42131867/model_final_0bb7ae.pkl", + "new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ": "42073830/model_final_f96b26.pkl", + "new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ": "42047771/model_final_b7fbab.pkl", # noqa + "new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ": "42132721/model_final_5d87c1.pkl", # noqa + "new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ": "42025447/model_final_f1362d.pkl", # noqa + "new_baselines/mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ": "42047784/model_final_6ba57e.pkl", # noqa + "new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ": "42047642/model_final_27b9c1.pkl", # noqa + "new_baselines/mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ": "42045954/model_final_ef3a80.pkl", # noqa + # COCO Person Keypoint Detection Baselines with Keypoint R-CNN + "COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x": "137261548/model_final_04e291.pkl", + "COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x": "137849621/model_final_a6e10b.pkl", + "COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x": "138363331/model_final_997cc7.pkl", + "COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x": "139686956/model_final_5ad38f.pkl", + # COCO Panoptic Segmentation Baselines with Panoptic FPN + "COCO-PanopticSegmentation/panoptic_fpn_R_50_1x": "139514544/model_final_dbfeb4.pkl", + "COCO-PanopticSegmentation/panoptic_fpn_R_50_3x": "139514569/model_final_c10459.pkl", + "COCO-PanopticSegmentation/panoptic_fpn_R_101_3x": "139514519/model_final_cafdb1.pkl", + # LVIS Instance Segmentation Baselines with Mask R-CNN + "LVISv0.5-InstanceSegmentation/mask_rcnn_R_50_FPN_1x": "144219072/model_final_571f7c.pkl", # noqa + "LVISv0.5-InstanceSegmentation/mask_rcnn_R_101_FPN_1x": "144219035/model_final_824ab5.pkl", # noqa + "LVISv0.5-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x": "144219108/model_final_5e3439.pkl", # noqa + # Cityscapes & Pascal VOC Baselines + "Cityscapes/mask_rcnn_R_50_FPN": "142423278/model_final_af9cf5.pkl", + "PascalVOC-Detection/faster_rcnn_R_50_C4": "142202221/model_final_b1acc2.pkl", + # Other Settings + "Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5": "138602867/model_final_65c703.pkl", + "Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5": "144998336/model_final_821d0b.pkl", + "Misc/cascade_mask_rcnn_R_50_FPN_1x": "138602847/model_final_e9d89b.pkl", + "Misc/cascade_mask_rcnn_R_50_FPN_3x": "144998488/model_final_480dd8.pkl", + "Misc/mask_rcnn_R_50_FPN_3x_syncbn": "169527823/model_final_3b3c51.pkl", + "Misc/mask_rcnn_R_50_FPN_3x_gn": "138602888/model_final_dc5d9e.pkl", + "Misc/scratch_mask_rcnn_R_50_FPN_3x_gn": "138602908/model_final_01ca85.pkl", + "Misc/scratch_mask_rcnn_R_50_FPN_9x_gn": "183808979/model_final_da7b4c.pkl", + "Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn": "184226666/model_final_5ce33e.pkl", + "Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x": "139797668/model_final_be35db.pkl", + "Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv": "18131413/model_0039999_e76410.pkl", # noqa + # D1 Comparisons + "Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x": "137781054/model_final_7ab50c.pkl", # noqa + "Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x": "137781281/model_final_62ca52.pkl", # noqa + "Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x": "137781195/model_final_cce136.pkl", + } + + @staticmethod + def query(config_path: str) -> Optional[str]: + """ + Args: + config_path: relative config filename + """ + name = config_path.replace(".yaml", "").replace(".py", "") + if name in _ModelZooUrls.CONFIG_PATH_TO_URL_SUFFIX: + suffix = _ModelZooUrls.CONFIG_PATH_TO_URL_SUFFIX[name] + return _ModelZooUrls.S3_PREFIX + name + "/" + suffix + return None + + +def get_checkpoint_url(config_path): + """ + Returns the URL to the model trained using the given config + + Args: + config_path (str): config file name relative to detectron2's "configs/" + directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml" + + Returns: + str: a URL to the model + """ + url = _ModelZooUrls.query(config_path) + if url is None: + raise RuntimeError("Pretrained model for {} is not available!".format(config_path)) + return url + + +def get_config_file(config_path): + """ + Returns path to a builtin config file. + + Args: + config_path (str): config file name relative to detectron2's "configs/" + directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml" + + Returns: + str: the real path to the config file. + """ + cfg_file = pkg_resources.resource_filename( + "detectron2.model_zoo", os.path.join("configs", config_path) + ) + if not os.path.exists(cfg_file): + raise RuntimeError("{} not available in Model Zoo!".format(config_path)) + return cfg_file + + +def get_config(config_path, trained: bool = False): + """ + Returns a config object for a model in model zoo. + + Args: + config_path (str): config file name relative to detectron2's "configs/" + directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml" + trained (bool): If True, will set ``MODEL.WEIGHTS`` to trained model zoo weights. + If False, the checkpoint specified in the config file's ``MODEL.WEIGHTS`` is used + instead; this will typically (though not always) initialize a subset of weights using + an ImageNet pre-trained model, while randomly initializing the other weights. + + Returns: + CfgNode or omegaconf.DictConfig: a config object + """ + cfg_file = get_config_file(config_path) + if cfg_file.endswith(".yaml"): + cfg = get_cfg() + cfg.merge_from_file(cfg_file) + if trained: + cfg.MODEL.WEIGHTS = get_checkpoint_url(config_path) + return cfg + elif cfg_file.endswith(".py"): + cfg = LazyConfig.load(cfg_file) + if trained: + url = get_checkpoint_url(config_path) + if "train" in cfg and "init_checkpoint" in cfg.train: + cfg.train.init_checkpoint = url + else: + raise NotImplementedError + return cfg + + +def get(config_path, trained: bool = False, device: Optional[str] = None): + """ + Get a model specified by relative path under Detectron2's official ``configs/`` directory. + + Args: + config_path (str): config file name relative to detectron2's "configs/" + directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml" + trained (bool): see :func:`get_config`. + device (str or None): overwrite the device in config, if given. + + Returns: + nn.Module: a detectron2 model. Will be in training mode. + + Example: + :: + from detectron2 import model_zoo + model = model_zoo.get("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml", trained=True) + """ + cfg = get_config(config_path, trained) + if device is None and not torch.cuda.is_available(): + device = "cpu" + if device is not None and isinstance(cfg, CfgNode): + cfg.MODEL.DEVICE = device + + if isinstance(cfg, CfgNode): + model = build_model(cfg) + DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS) + else: + model = instantiate(cfg.model) + if device is not None: + model = model.to(device) + if "train" in cfg and "init_checkpoint" in cfg.train: + DetectionCheckpointer(model).load(cfg.train.init_checkpoint) + return model diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..576493de77c361928ebd2491cb490113522f42d6 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/__init__.py @@ -0,0 +1,59 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from detectron2.layers import ShapeSpec + +from .anchor_generator import build_anchor_generator, ANCHOR_GENERATOR_REGISTRY +from .backbone import ( + BACKBONE_REGISTRY, + FPN, + Backbone, + ResNet, + ResNetBlockBase, + build_backbone, + build_resnet_backbone, + make_stage, +) +from .meta_arch import ( + META_ARCH_REGISTRY, + SEM_SEG_HEADS_REGISTRY, + GeneralizedRCNN, + PanopticFPN, + ProposalNetwork, + RetinaNet, + SemanticSegmentor, + build_model, + build_sem_seg_head, + FCOS, +) +from .postprocessing import detector_postprocess +from .proposal_generator import ( + PROPOSAL_GENERATOR_REGISTRY, + build_proposal_generator, + RPN_HEAD_REGISTRY, + build_rpn_head, +) +from .roi_heads import ( + ROI_BOX_HEAD_REGISTRY, + ROI_HEADS_REGISTRY, + ROI_KEYPOINT_HEAD_REGISTRY, + ROI_MASK_HEAD_REGISTRY, + ROIHeads, + StandardROIHeads, + BaseMaskRCNNHead, + BaseKeypointRCNNHead, + FastRCNNOutputLayers, + build_box_head, + build_keypoint_head, + build_mask_head, + build_roi_heads, +) +from .test_time_augmentation import DatasetMapperTTA, GeneralizedRCNNWithTTA +from .mmdet_wrapper import MMDetBackbone, MMDetDetector + +_EXCLUDE = {"ShapeSpec"} +__all__ = [k for k in globals().keys() if k not in _EXCLUDE and not k.startswith("_")] + + +from detectron2.utils.env import fixup_module_metadata + +fixup_module_metadata(__name__, globals(), __all__) +del fixup_module_metadata diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/anchor_generator.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/anchor_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..ee4b98819445f95982ca89a72cdd3e27b39b367f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/anchor_generator.py @@ -0,0 +1,382 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import collections +import math +from typing import List +import torch +from torch import nn + +from detectron2.config import configurable +from detectron2.layers import ShapeSpec +from detectron2.structures import Boxes, RotatedBoxes +from detectron2.utils.registry import Registry + +ANCHOR_GENERATOR_REGISTRY = Registry("ANCHOR_GENERATOR") +ANCHOR_GENERATOR_REGISTRY.__doc__ = """ +Registry for modules that creates object detection anchors for feature maps. + +The registered object will be called with `obj(cfg, input_shape)`. +""" + + +class BufferList(nn.Module): + """ + Similar to nn.ParameterList, but for buffers + """ + + def __init__(self, buffers): + super().__init__() + for i, buffer in enumerate(buffers): + # Use non-persistent buffer so the values are not saved in checkpoint + self.register_buffer(str(i), buffer, persistent=False) + + def __len__(self): + return len(self._buffers) + + def __iter__(self): + return iter(self._buffers.values()) + + +def _create_grid_offsets(size: List[int], stride: int, offset: float, device: torch.device): + grid_height, grid_width = size + shifts_x = torch.arange( + offset * stride, grid_width * stride, step=stride, dtype=torch.float32, device=device + ) + shifts_y = torch.arange( + offset * stride, grid_height * stride, step=stride, dtype=torch.float32, device=device + ) + + shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) + shift_x = shift_x.reshape(-1) + shift_y = shift_y.reshape(-1) + return shift_x, shift_y + + +def _broadcast_params(params, num_features, name): + """ + If one size (or aspect ratio) is specified and there are multiple feature + maps, we "broadcast" anchors of that single size (or aspect ratio) + over all feature maps. + + If params is list[float], or list[list[float]] with len(params) == 1, repeat + it num_features time. + + Returns: + list[list[float]]: param for each feature + """ + assert isinstance( + params, collections.abc.Sequence + ), f"{name} in anchor generator has to be a list! Got {params}." + assert len(params), f"{name} in anchor generator cannot be empty!" + if not isinstance(params[0], collections.abc.Sequence): # params is list[float] + return [params] * num_features + if len(params) == 1: + return list(params) * num_features + assert len(params) == num_features, ( + f"Got {name} of length {len(params)} in anchor generator, " + f"but the number of input features is {num_features}!" + ) + return params + + +@ANCHOR_GENERATOR_REGISTRY.register() +class DefaultAnchorGenerator(nn.Module): + """ + Compute anchors in the standard ways described in + "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks". + """ + + box_dim: torch.jit.Final[int] = 4 + """ + the dimension of each anchor box. + """ + + @configurable + def __init__(self, *, sizes, aspect_ratios, strides, offset=0.5): + """ + This interface is experimental. + + Args: + sizes (list[list[float]] or list[float]): + If ``sizes`` is list[list[float]], ``sizes[i]`` is the list of anchor sizes + (i.e. sqrt of anchor area) to use for the i-th feature map. + If ``sizes`` is list[float], ``sizes`` is used for all feature maps. + Anchor sizes are given in absolute lengths in units of + the input image; they do not dynamically scale if the input image size changes. + aspect_ratios (list[list[float]] or list[float]): list of aspect ratios + (i.e. height / width) to use for anchors. Same "broadcast" rule for `sizes` applies. + strides (list[int]): stride of each input feature. + offset (float): Relative offset between the center of the first anchor and the top-left + corner of the image. Value has to be in [0, 1). + Recommend to use 0.5, which means half stride. + """ + super().__init__() + + self.strides = strides + self.num_features = len(self.strides) + sizes = _broadcast_params(sizes, self.num_features, "sizes") + aspect_ratios = _broadcast_params(aspect_ratios, self.num_features, "aspect_ratios") + self.cell_anchors = self._calculate_anchors(sizes, aspect_ratios) + + self.offset = offset + assert 0.0 <= self.offset < 1.0, self.offset + + @classmethod + def from_config(cls, cfg, input_shape: List[ShapeSpec]): + return { + "sizes": cfg.MODEL.ANCHOR_GENERATOR.SIZES, + "aspect_ratios": cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS, + "strides": [x.stride for x in input_shape], + "offset": cfg.MODEL.ANCHOR_GENERATOR.OFFSET, + } + + def _calculate_anchors(self, sizes, aspect_ratios): + cell_anchors = [ + self.generate_cell_anchors(s, a).float() for s, a in zip(sizes, aspect_ratios) + ] + return BufferList(cell_anchors) + + @property + @torch.jit.unused + def num_cell_anchors(self): + """ + Alias of `num_anchors`. + """ + return self.num_anchors + + @property + @torch.jit.unused + def num_anchors(self): + """ + Returns: + list[int]: Each int is the number of anchors at every pixel + location, on that feature map. + For example, if at every pixel we use anchors of 3 aspect + ratios and 5 sizes, the number of anchors is 15. + (See also ANCHOR_GENERATOR.SIZES and ANCHOR_GENERATOR.ASPECT_RATIOS in config) + + In standard RPN models, `num_anchors` on every feature map is the same. + """ + return [len(cell_anchors) for cell_anchors in self.cell_anchors] + + def _grid_anchors(self, grid_sizes: List[List[int]]): + """ + Returns: + list[Tensor]: #featuremap tensors, each is (#locations x #cell_anchors) x 4 + """ + anchors = [] + # buffers() not supported by torchscript. use named_buffers() instead + buffers: List[torch.Tensor] = [x[1] for x in self.cell_anchors.named_buffers()] + for size, stride, base_anchors in zip(grid_sizes, self.strides, buffers): + shift_x, shift_y = _create_grid_offsets(size, stride, self.offset, base_anchors.device) + shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) + + anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) + + return anchors + + def generate_cell_anchors(self, sizes=(32, 64, 128, 256, 512), aspect_ratios=(0.5, 1, 2)): + """ + Generate a tensor storing canonical anchor boxes, which are all anchor + boxes of different sizes and aspect_ratios centered at (0, 0). + We can later build the set of anchors for a full feature map by + shifting and tiling these tensors (see `meth:_grid_anchors`). + + Args: + sizes (tuple[float]): + aspect_ratios (tuple[float]]): + + Returns: + Tensor of shape (len(sizes) * len(aspect_ratios), 4) storing anchor boxes + in XYXY format. + """ + + # This is different from the anchor generator defined in the original Faster R-CNN + # code or Detectron. They yield the same AP, however the old version defines cell + # anchors in a less natural way with a shift relative to the feature grid and + # quantization that results in slightly different sizes for different aspect ratios. + # See also https://github.com/facebookresearch/Detectron/issues/227 + + anchors = [] + for size in sizes: + area = size ** 2.0 + for aspect_ratio in aspect_ratios: + # s * s = w * h + # a = h / w + # ... some algebra ... + # w = sqrt(s * s / a) + # h = a * w + w = math.sqrt(area / aspect_ratio) + h = aspect_ratio * w + x0, y0, x1, y1 = -w / 2.0, -h / 2.0, w / 2.0, h / 2.0 + anchors.append([x0, y0, x1, y1]) + return torch.tensor(anchors) + + def forward(self, features: List[torch.Tensor]): + """ + Args: + features (list[Tensor]): list of backbone feature maps on which to generate anchors. + + Returns: + list[Boxes]: a list of Boxes containing all the anchors for each feature map + (i.e. the cell anchors repeated over all locations in the feature map). + The number of anchors of each feature map is Hi x Wi x num_cell_anchors, + where Hi, Wi are resolution of the feature map divided by anchor stride. + """ + grid_sizes = [feature_map.shape[-2:] for feature_map in features] + anchors_over_all_feature_maps = self._grid_anchors(grid_sizes) + return [Boxes(x) for x in anchors_over_all_feature_maps] + + +@ANCHOR_GENERATOR_REGISTRY.register() +class RotatedAnchorGenerator(nn.Module): + """ + Compute rotated anchors used by Rotated RPN (RRPN), described in + "Arbitrary-Oriented Scene Text Detection via Rotation Proposals". + """ + + box_dim: int = 5 + """ + the dimension of each anchor box. + """ + + @configurable + def __init__(self, *, sizes, aspect_ratios, strides, angles, offset=0.5): + """ + This interface is experimental. + + Args: + sizes (list[list[float]] or list[float]): + If sizes is list[list[float]], sizes[i] is the list of anchor sizes + (i.e. sqrt of anchor area) to use for the i-th feature map. + If sizes is list[float], the sizes are used for all feature maps. + Anchor sizes are given in absolute lengths in units of + the input image; they do not dynamically scale if the input image size changes. + aspect_ratios (list[list[float]] or list[float]): list of aspect ratios + (i.e. height / width) to use for anchors. Same "broadcast" rule for `sizes` applies. + strides (list[int]): stride of each input feature. + angles (list[list[float]] or list[float]): list of angles (in degrees CCW) + to use for anchors. Same "broadcast" rule for `sizes` applies. + offset (float): Relative offset between the center of the first anchor and the top-left + corner of the image. Value has to be in [0, 1). + Recommend to use 0.5, which means half stride. + """ + super().__init__() + + self.strides = strides + self.num_features = len(self.strides) + sizes = _broadcast_params(sizes, self.num_features, "sizes") + aspect_ratios = _broadcast_params(aspect_ratios, self.num_features, "aspect_ratios") + angles = _broadcast_params(angles, self.num_features, "angles") + self.cell_anchors = self._calculate_anchors(sizes, aspect_ratios, angles) + + self.offset = offset + assert 0.0 <= self.offset < 1.0, self.offset + + @classmethod + def from_config(cls, cfg, input_shape: List[ShapeSpec]): + return { + "sizes": cfg.MODEL.ANCHOR_GENERATOR.SIZES, + "aspect_ratios": cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS, + "strides": [x.stride for x in input_shape], + "offset": cfg.MODEL.ANCHOR_GENERATOR.OFFSET, + "angles": cfg.MODEL.ANCHOR_GENERATOR.ANGLES, + } + + def _calculate_anchors(self, sizes, aspect_ratios, angles): + cell_anchors = [ + self.generate_cell_anchors(size, aspect_ratio, angle).float() + for size, aspect_ratio, angle in zip(sizes, aspect_ratios, angles) + ] + return BufferList(cell_anchors) + + @property + def num_cell_anchors(self): + """ + Alias of `num_anchors`. + """ + return self.num_anchors + + @property + def num_anchors(self): + """ + Returns: + list[int]: Each int is the number of anchors at every pixel + location, on that feature map. + For example, if at every pixel we use anchors of 3 aspect + ratios, 2 sizes and 5 angles, the number of anchors is 30. + (See also ANCHOR_GENERATOR.SIZES, ANCHOR_GENERATOR.ASPECT_RATIOS + and ANCHOR_GENERATOR.ANGLES in config) + + In standard RRPN models, `num_anchors` on every feature map is the same. + """ + return [len(cell_anchors) for cell_anchors in self.cell_anchors] + + def _grid_anchors(self, grid_sizes): + anchors = [] + for size, stride, base_anchors in zip(grid_sizes, self.strides, self.cell_anchors): + shift_x, shift_y = _create_grid_offsets(size, stride, self.offset, base_anchors.device) + zeros = torch.zeros_like(shift_x) + shifts = torch.stack((shift_x, shift_y, zeros, zeros, zeros), dim=1) + + anchors.append((shifts.view(-1, 1, 5) + base_anchors.view(1, -1, 5)).reshape(-1, 5)) + + return anchors + + def generate_cell_anchors( + self, + sizes=(32, 64, 128, 256, 512), + aspect_ratios=(0.5, 1, 2), + angles=(-90, -60, -30, 0, 30, 60, 90), + ): + """ + Generate a tensor storing canonical anchor boxes, which are all anchor + boxes of different sizes, aspect_ratios, angles centered at (0, 0). + We can later build the set of anchors for a full feature map by + shifting and tiling these tensors (see `meth:_grid_anchors`). + + Args: + sizes (tuple[float]): + aspect_ratios (tuple[float]]): + angles (tuple[float]]): + + Returns: + Tensor of shape (len(sizes) * len(aspect_ratios) * len(angles), 5) + storing anchor boxes in (x_ctr, y_ctr, w, h, angle) format. + """ + anchors = [] + for size in sizes: + area = size ** 2.0 + for aspect_ratio in aspect_ratios: + # s * s = w * h + # a = h / w + # ... some algebra ... + # w = sqrt(s * s / a) + # h = a * w + w = math.sqrt(area / aspect_ratio) + h = aspect_ratio * w + anchors.extend([0, 0, w, h, a] for a in angles) + + return torch.tensor(anchors) + + def forward(self, features): + """ + Args: + features (list[Tensor]): list of backbone feature maps on which to generate anchors. + + Returns: + list[RotatedBoxes]: a list of Boxes containing all the anchors for each feature map + (i.e. the cell anchors repeated over all locations in the feature map). + The number of anchors of each feature map is Hi x Wi x num_cell_anchors, + where Hi, Wi are resolution of the feature map divided by anchor stride. + """ + grid_sizes = [feature_map.shape[-2:] for feature_map in features] + anchors_over_all_feature_maps = self._grid_anchors(grid_sizes) + return [RotatedBoxes(x) for x in anchors_over_all_feature_maps] + + +def build_anchor_generator(cfg, input_shape): + """ + Built an anchor generator from `cfg.MODEL.ANCHOR_GENERATOR.NAME`. + """ + anchor_generator = cfg.MODEL.ANCHOR_GENERATOR.NAME + return ANCHOR_GENERATOR_REGISTRY.get(anchor_generator)(cfg, input_shape) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..55b265d5fa574ed2a3c3c7c97850ec9b3fa5fede --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .build import build_backbone, BACKBONE_REGISTRY # noqa F401 isort:skip + +from .backbone import Backbone +from .fpn import FPN +from .regnet import RegNet +from .resnet import ( + BasicStem, + ResNet, + ResNetBlockBase, + build_resnet_backbone, + make_stage, + BottleneckBlock, +) + +__all__ = [k for k in globals().keys() if not k.startswith("_")] +# TODO can expose more resnet blocks after careful consideration diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/backbone.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/backbone.py new file mode 100644 index 0000000000000000000000000000000000000000..369fb884930c5dd82f94024c45303dafaab14d66 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/backbone.py @@ -0,0 +1,53 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from abc import ABCMeta, abstractmethod +import torch.nn as nn + +from detectron2.layers import ShapeSpec + +__all__ = ["Backbone"] + + +class Backbone(nn.Module, metaclass=ABCMeta): + """ + Abstract base class for network backbones. + """ + + def __init__(self): + """ + The `__init__` method of any subclass can specify its own set of arguments. + """ + super().__init__() + + @abstractmethod + def forward(self): + """ + Subclasses must override this method, but adhere to the same return type. + + Returns: + dict[str->Tensor]: mapping from feature name (e.g., "res2") to tensor + """ + pass + + @property + def size_divisibility(self) -> int: + """ + Some backbones require the input height and width to be divisible by a + specific integer. This is typically true for encoder / decoder type networks + with lateral connection (e.g., FPN) for which feature maps need to match + dimension in the "bottom up" and "top down" paths. Set to 0 if no specific + input size divisibility is required. + """ + return 0 + + def output_shape(self): + """ + Returns: + dict[str->ShapeSpec] + """ + # this is a backward-compatible default + return { + name: ShapeSpec( + channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] + ) + for name in self._out_features + } diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/build.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/build.py new file mode 100644 index 0000000000000000000000000000000000000000..af02141172bebe9a2a27a88c81673c2710b4d73f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/build.py @@ -0,0 +1,33 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from detectron2.layers import ShapeSpec +from detectron2.utils.registry import Registry + +from .backbone import Backbone + +BACKBONE_REGISTRY = Registry("BACKBONE") +BACKBONE_REGISTRY.__doc__ = """ +Registry for backbones, which extract feature maps from images + +The registered object must be a callable that accepts two arguments: + +1. A :class:`detectron2.config.CfgNode` +2. A :class:`detectron2.layers.ShapeSpec`, which contains the input shape specification. + +Registered object must return instance of :class:`Backbone`. +""" + + +def build_backbone(cfg, input_shape=None): + """ + Build a backbone from `cfg.MODEL.BACKBONE.NAME`. + + Returns: + an instance of :class:`Backbone` + """ + if input_shape is None: + input_shape = ShapeSpec(channels=len(cfg.MODEL.PIXEL_MEAN)) + + backbone_name = cfg.MODEL.BACKBONE.NAME + backbone = BACKBONE_REGISTRY.get(backbone_name)(cfg, input_shape) + assert isinstance(backbone, Backbone) + return backbone diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/fpn.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/fpn.py new file mode 100644 index 0000000000000000000000000000000000000000..d0bdfc9da8cb7afc9ef421baef2c173a63ff1743 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/fpn.py @@ -0,0 +1,255 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import math +import fvcore.nn.weight_init as weight_init +import torch +import torch.nn.functional as F +from torch import nn + +from detectron2.layers import Conv2d, ShapeSpec, get_norm + +from .backbone import Backbone +from .build import BACKBONE_REGISTRY +from .resnet import build_resnet_backbone + +__all__ = ["build_resnet_fpn_backbone", "build_retinanet_resnet_fpn_backbone", "FPN"] + + +class FPN(Backbone): + """ + This module implements :paper:`FPN`. + It creates pyramid features built on top of some input feature maps. + """ + + _fuse_type: torch.jit.Final[str] + + def __init__( + self, bottom_up, in_features, out_channels, norm="", top_block=None, fuse_type="sum" + ): + """ + Args: + bottom_up (Backbone): module representing the bottom up subnetwork. + Must be a subclass of :class:`Backbone`. The multi-scale feature + maps generated by the bottom up network, and listed in `in_features`, + are used to generate FPN levels. + in_features (list[str]): names of the input feature maps coming + from the backbone to which FPN is attached. For example, if the + backbone produces ["res2", "res3", "res4"], any *contiguous* sublist + of these may be used; order must be from high to low resolution. + out_channels (int): number of channels in the output feature maps. + norm (str): the normalization to use. + top_block (nn.Module or None): if provided, an extra operation will + be performed on the output of the last (smallest resolution) + FPN output, and the result will extend the result list. The top_block + further downsamples the feature map. It must have an attribute + "num_levels", meaning the number of extra FPN levels added by + this block, and "in_feature", which is a string representing + its input feature (e.g., p5). + fuse_type (str): types for fusing the top down features and the lateral + ones. It can be "sum" (default), which sums up element-wise; or "avg", + which takes the element-wise mean of the two. + """ + super(FPN, self).__init__() + assert isinstance(bottom_up, Backbone) + assert in_features, in_features + + # Feature map strides and channels from the bottom up network (e.g. ResNet) + input_shapes = bottom_up.output_shape() + strides = [input_shapes[f].stride for f in in_features] + in_channels_per_feature = [input_shapes[f].channels for f in in_features] + + _assert_strides_are_log2_contiguous(strides) + lateral_convs = [] + output_convs = [] + + use_bias = norm == "" + for idx, in_channels in enumerate(in_channels_per_feature): + lateral_norm = get_norm(norm, out_channels) + output_norm = get_norm(norm, out_channels) + + lateral_conv = Conv2d( + in_channels, out_channels, kernel_size=1, bias=use_bias, norm=lateral_norm + ) + output_conv = Conv2d( + out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + bias=use_bias, + norm=output_norm, + ) + weight_init.c2_xavier_fill(lateral_conv) + weight_init.c2_xavier_fill(output_conv) + stage = int(math.log2(strides[idx])) + self.add_module("fpn_lateral{}".format(stage), lateral_conv) + self.add_module("fpn_output{}".format(stage), output_conv) + + lateral_convs.append(lateral_conv) + output_convs.append(output_conv) + # Place convs into top-down order (from low to high resolution) + # to make the top-down computation in forward clearer. + self.lateral_convs = lateral_convs[::-1] + self.output_convs = output_convs[::-1] + self.top_block = top_block + self.in_features = tuple(in_features) + self.bottom_up = bottom_up + # Return feature names are "p", like ["p2", "p3", ..., "p6"] + self._out_feature_strides = {"p{}".format(int(math.log2(s))): s for s in strides} + # top block output feature maps. + if self.top_block is not None: + for s in range(stage, stage + self.top_block.num_levels): + self._out_feature_strides["p{}".format(s + 1)] = 2 ** (s + 1) + + self._out_features = list(self._out_feature_strides.keys()) + self._out_feature_channels = {k: out_channels for k in self._out_features} + self._size_divisibility = strides[-1] + assert fuse_type in {"avg", "sum"} + self._fuse_type = fuse_type + + @property + def size_divisibility(self): + return self._size_divisibility + + def forward(self, x): + """ + Args: + input (dict[str->Tensor]): mapping feature map name (e.g., "res5") to + feature map tensor for each feature level in high to low resolution order. + + Returns: + dict[str->Tensor]: + mapping from feature map name to FPN feature map tensor + in high to low resolution order. Returned feature names follow the FPN + paper convention: "p", where stage has stride = 2 ** stage e.g., + ["p2", "p3", ..., "p6"]. + """ + bottom_up_features = self.bottom_up(x) + results = [] + prev_features = self.lateral_convs[0](bottom_up_features[self.in_features[-1]]) + results.append(self.output_convs[0](prev_features)) + + # Reverse feature maps into top-down order (from low to high resolution) + for idx, (lateral_conv, output_conv) in enumerate( + zip(self.lateral_convs, self.output_convs) + ): + # Slicing of ModuleList is not supported https://github.com/pytorch/pytorch/issues/47336 + # Therefore we loop over all modules but skip the first one + if idx > 0: + features = self.in_features[-idx - 1] + features = bottom_up_features[features] + top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") + lateral_features = lateral_conv(features) + prev_features = lateral_features + top_down_features + if self._fuse_type == "avg": + prev_features /= 2 + results.insert(0, output_conv(prev_features)) + + if self.top_block is not None: + if self.top_block.in_feature in bottom_up_features: + top_block_in_feature = bottom_up_features[self.top_block.in_feature] + else: + top_block_in_feature = results[self._out_features.index(self.top_block.in_feature)] + results.extend(self.top_block(top_block_in_feature)) + assert len(self._out_features) == len(results) + return {f: res for f, res in zip(self._out_features, results)} + + def output_shape(self): + return { + name: ShapeSpec( + channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] + ) + for name in self._out_features + } + + +def _assert_strides_are_log2_contiguous(strides): + """ + Assert that each stride is 2x times its preceding stride, i.e. "contiguous in log2". + """ + for i, stride in enumerate(strides[1:], 1): + assert stride == 2 * strides[i - 1], "Strides {} {} are not log2 contiguous".format( + stride, strides[i - 1] + ) + + +class LastLevelMaxPool(nn.Module): + """ + This module is used in the original FPN to generate a downsampled + P6 feature from P5. + """ + + def __init__(self): + super().__init__() + self.num_levels = 1 + self.in_feature = "p5" + + def forward(self, x): + return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] + + +class LastLevelP6P7(nn.Module): + """ + This module is used in RetinaNet to generate extra layers, P6 and P7 from + C5 feature. + """ + + def __init__(self, in_channels, out_channels, in_feature="res5"): + super().__init__() + self.num_levels = 2 + self.in_feature = in_feature + self.p6 = nn.Conv2d(in_channels, out_channels, 3, 2, 1) + self.p7 = nn.Conv2d(out_channels, out_channels, 3, 2, 1) + for module in [self.p6, self.p7]: + weight_init.c2_xavier_fill(module) + + def forward(self, c5): + p6 = self.p6(c5) + p7 = self.p7(F.relu(p6)) + return [p6, p7] + + +@BACKBONE_REGISTRY.register() +def build_resnet_fpn_backbone(cfg, input_shape: ShapeSpec): + """ + Args: + cfg: a detectron2 CfgNode + + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + bottom_up = build_resnet_backbone(cfg, input_shape) + in_features = cfg.MODEL.FPN.IN_FEATURES + out_channels = cfg.MODEL.FPN.OUT_CHANNELS + backbone = FPN( + bottom_up=bottom_up, + in_features=in_features, + out_channels=out_channels, + norm=cfg.MODEL.FPN.NORM, + top_block=LastLevelMaxPool(), + fuse_type=cfg.MODEL.FPN.FUSE_TYPE, + ) + return backbone + + +@BACKBONE_REGISTRY.register() +def build_retinanet_resnet_fpn_backbone(cfg, input_shape: ShapeSpec): + """ + Args: + cfg: a detectron2 CfgNode + + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + bottom_up = build_resnet_backbone(cfg, input_shape) + in_features = cfg.MODEL.FPN.IN_FEATURES + out_channels = cfg.MODEL.FPN.OUT_CHANNELS + in_channels_p6p7 = bottom_up.output_shape()["res5"].channels + backbone = FPN( + bottom_up=bottom_up, + in_features=in_features, + out_channels=out_channels, + norm=cfg.MODEL.FPN.NORM, + top_block=LastLevelP6P7(in_channels_p6p7, out_channels), + fuse_type=cfg.MODEL.FPN.FUSE_TYPE, + ) + return backbone diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/regnet.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/regnet.py new file mode 100644 index 0000000000000000000000000000000000000000..3533d63385d1324cfc1559eae9576b3fa52585af --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/regnet.py @@ -0,0 +1,452 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Implementation of RegNet models from :paper:`dds` and :paper:`scaling`. + +This code is adapted from https://github.com/facebookresearch/pycls with minimal modifications. +Some code duplication exists between RegNet and ResNets (e.g., ResStem) in order to simplify +model loading. +""" + +import numpy as np +from torch import nn + +from detectron2.layers import CNNBlockBase, ShapeSpec, get_norm + +from .backbone import Backbone + +__all__ = [ + "AnyNet", + "RegNet", + "ResStem", + "SimpleStem", + "VanillaBlock", + "ResBasicBlock", + "ResBottleneckBlock", +] + + +def conv2d(w_in, w_out, k, *, stride=1, groups=1, bias=False): + """Helper for building a conv2d layer.""" + assert k % 2 == 1, "Only odd size kernels supported to avoid padding issues." + s, p, g, b = stride, (k - 1) // 2, groups, bias + return nn.Conv2d(w_in, w_out, k, stride=s, padding=p, groups=g, bias=b) + + +def gap2d(): + """Helper for building a global average pooling layer.""" + return nn.AdaptiveAvgPool2d((1, 1)) + + +def pool2d(k, *, stride=1): + """Helper for building a pool2d layer.""" + assert k % 2 == 1, "Only odd size kernels supported to avoid padding issues." + return nn.MaxPool2d(k, stride=stride, padding=(k - 1) // 2) + + +def init_weights(m): + """Performs ResNet-style weight initialization.""" + if isinstance(m, nn.Conv2d): + # Note that there is no bias due to BN + fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(mean=0.0, std=np.sqrt(2.0 / fan_out)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1.0) + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + m.weight.data.normal_(mean=0.0, std=0.01) + m.bias.data.zero_() + + +class ResStem(CNNBlockBase): + """ResNet stem for ImageNet: 7x7, BN, AF, MaxPool.""" + + def __init__(self, w_in, w_out, norm, activation_class): + super().__init__(w_in, w_out, 4) + self.conv = conv2d(w_in, w_out, 7, stride=2) + self.bn = get_norm(norm, w_out) + self.af = activation_class() + self.pool = pool2d(3, stride=2) + + def forward(self, x): + for layer in self.children(): + x = layer(x) + return x + + +class SimpleStem(CNNBlockBase): + """Simple stem for ImageNet: 3x3, BN, AF.""" + + def __init__(self, w_in, w_out, norm, activation_class): + super().__init__(w_in, w_out, 2) + self.conv = conv2d(w_in, w_out, 3, stride=2) + self.bn = get_norm(norm, w_out) + self.af = activation_class() + + def forward(self, x): + for layer in self.children(): + x = layer(x) + return x + + +class SE(nn.Module): + """Squeeze-and-Excitation (SE) block: AvgPool, FC, Act, FC, Sigmoid.""" + + def __init__(self, w_in, w_se, activation_class): + super().__init__() + self.avg_pool = gap2d() + self.f_ex = nn.Sequential( + conv2d(w_in, w_se, 1, bias=True), + activation_class(), + conv2d(w_se, w_in, 1, bias=True), + nn.Sigmoid(), + ) + + def forward(self, x): + return x * self.f_ex(self.avg_pool(x)) + + +class VanillaBlock(CNNBlockBase): + """Vanilla block: [3x3 conv, BN, Relu] x2.""" + + def __init__(self, w_in, w_out, stride, norm, activation_class, _params): + super().__init__(w_in, w_out, stride) + self.a = conv2d(w_in, w_out, 3, stride=stride) + self.a_bn = get_norm(norm, w_out) + self.a_af = activation_class() + self.b = conv2d(w_out, w_out, 3) + self.b_bn = get_norm(norm, w_out) + self.b_af = activation_class() + + def forward(self, x): + for layer in self.children(): + x = layer(x) + return x + + +class BasicTransform(nn.Module): + """Basic transformation: [3x3 conv, BN, Relu] x2.""" + + def __init__(self, w_in, w_out, stride, norm, activation_class, _params): + super().__init__() + self.a = conv2d(w_in, w_out, 3, stride=stride) + self.a_bn = get_norm(norm, w_out) + self.a_af = activation_class() + self.b = conv2d(w_out, w_out, 3) + self.b_bn = get_norm(norm, w_out) + self.b_bn.final_bn = True + + def forward(self, x): + for layer in self.children(): + x = layer(x) + return x + + +class ResBasicBlock(CNNBlockBase): + """Residual basic block: x + f(x), f = basic transform.""" + + def __init__(self, w_in, w_out, stride, norm, activation_class, params): + super().__init__(w_in, w_out, stride) + self.proj, self.bn = None, None + if (w_in != w_out) or (stride != 1): + self.proj = conv2d(w_in, w_out, 1, stride=stride) + self.bn = get_norm(norm, w_out) + self.f = BasicTransform(w_in, w_out, stride, norm, activation_class, params) + self.af = activation_class() + + def forward(self, x): + x_p = self.bn(self.proj(x)) if self.proj else x + return self.af(x_p + self.f(x)) + + +class BottleneckTransform(nn.Module): + """Bottleneck transformation: 1x1, 3x3 [+SE], 1x1.""" + + def __init__(self, w_in, w_out, stride, norm, activation_class, params): + super().__init__() + w_b = int(round(w_out * params["bot_mul"])) + w_se = int(round(w_in * params["se_r"])) + groups = w_b // params["group_w"] + self.a = conv2d(w_in, w_b, 1) + self.a_bn = get_norm(norm, w_b) + self.a_af = activation_class() + self.b = conv2d(w_b, w_b, 3, stride=stride, groups=groups) + self.b_bn = get_norm(norm, w_b) + self.b_af = activation_class() + self.se = SE(w_b, w_se, activation_class) if w_se else None + self.c = conv2d(w_b, w_out, 1) + self.c_bn = get_norm(norm, w_out) + self.c_bn.final_bn = True + + def forward(self, x): + for layer in self.children(): + x = layer(x) + return x + + +class ResBottleneckBlock(CNNBlockBase): + """Residual bottleneck block: x + f(x), f = bottleneck transform.""" + + def __init__(self, w_in, w_out, stride, norm, activation_class, params): + super().__init__(w_in, w_out, stride) + self.proj, self.bn = None, None + if (w_in != w_out) or (stride != 1): + self.proj = conv2d(w_in, w_out, 1, stride=stride) + self.bn = get_norm(norm, w_out) + self.f = BottleneckTransform(w_in, w_out, stride, norm, activation_class, params) + self.af = activation_class() + + def forward(self, x): + x_p = self.bn(self.proj(x)) if self.proj else x + return self.af(x_p + self.f(x)) + + +class AnyStage(nn.Module): + """AnyNet stage (sequence of blocks w/ the same output shape).""" + + def __init__(self, w_in, w_out, stride, d, block_class, norm, activation_class, params): + super().__init__() + for i in range(d): + block = block_class(w_in, w_out, stride, norm, activation_class, params) + self.add_module("b{}".format(i + 1), block) + stride, w_in = 1, w_out + + def forward(self, x): + for block in self.children(): + x = block(x) + return x + + +class AnyNet(Backbone): + """AnyNet model. See :paper:`dds`.""" + + def __init__( + self, + *, + stem_class, + stem_width, + block_class, + depths, + widths, + group_widths, + strides, + bottleneck_ratios, + se_ratio, + activation_class, + freeze_at=0, + norm="BN", + out_features=None, + ): + """ + Args: + stem_class (callable): A callable taking 4 arguments (channels in, channels out, + normalization, callable returning an activation function) that returns another + callable implementing the stem module. + stem_width (int): The number of output channels that the stem produces. + block_class (callable): A callable taking 6 arguments (channels in, channels out, + stride, normalization, callable returning an activation function, a dict of + block-specific parameters) that returns another callable implementing the repeated + block module. + depths (list[int]): Number of blocks in each stage. + widths (list[int]): For each stage, the number of output channels of each block. + group_widths (list[int]): For each stage, the number of channels per group in group + convolution, if the block uses group convolution. + strides (list[int]): The stride that each network stage applies to its input. + bottleneck_ratios (list[float]): For each stage, the ratio of the number of bottleneck + channels to the number of block input channels (or, equivalently, output channels), + if the block uses a bottleneck. + se_ratio (float): The ratio of the number of channels used inside the squeeze-excitation + (SE) module to it number of input channels, if SE the block uses SE. + activation_class (callable): A callable taking no arguments that returns another + callable implementing an activation function. + freeze_at (int): The number of stages at the beginning to freeze. + see :meth:`freeze` for detailed explanation. + norm (str or callable): normalization for all conv layers. + See :func:`layers.get_norm` for supported format. + out_features (list[str]): name of the layers whose outputs should + be returned in forward. RegNet's use "stem" and "s1", "s2", etc for the stages after + the stem. If None, will return the output of the last layer. + """ + super().__init__() + self.stem = stem_class(3, stem_width, norm, activation_class) + + current_stride = self.stem.stride + self._out_feature_strides = {"stem": current_stride} + self._out_feature_channels = {"stem": self.stem.out_channels} + self.stages_and_names = [] + prev_w = stem_width + + for i, (d, w, s, b, g) in enumerate( + zip(depths, widths, strides, bottleneck_ratios, group_widths) + ): + params = {"bot_mul": b, "group_w": g, "se_r": se_ratio} + stage = AnyStage(prev_w, w, s, d, block_class, norm, activation_class, params) + name = "s{}".format(i + 1) + self.add_module(name, stage) + self.stages_and_names.append((stage, name)) + self._out_feature_strides[name] = current_stride = int( + current_stride * np.prod([k.stride for k in stage.children()]) + ) + self._out_feature_channels[name] = list(stage.children())[-1].out_channels + prev_w = w + + self.apply(init_weights) + + if out_features is None: + out_features = [name] + self._out_features = out_features + assert len(self._out_features) + children = [x[0] for x in self.named_children()] + for out_feature in self._out_features: + assert out_feature in children, "Available children: {} does not include {}".format( + ", ".join(children), out_feature + ) + self.freeze(freeze_at) + + def forward(self, x): + """ + Args: + x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``. + + Returns: + dict[str->Tensor]: names and the corresponding features + """ + assert x.dim() == 4, f"Model takes an input of shape (N, C, H, W). Got {x.shape} instead!" + outputs = {} + x = self.stem(x) + if "stem" in self._out_features: + outputs["stem"] = x + for stage, name in self.stages_and_names: + x = stage(x) + if name in self._out_features: + outputs[name] = x + return outputs + + def output_shape(self): + return { + name: ShapeSpec( + channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] + ) + for name in self._out_features + } + + def freeze(self, freeze_at=0): + """ + Freeze the first several stages of the model. Commonly used in fine-tuning. + + Layers that produce the same feature map spatial size are defined as one + "stage" by :paper:`FPN`. + + Args: + freeze_at (int): number of stages to freeze. + `1` means freezing the stem. `2` means freezing the stem and + one residual stage, etc. + + Returns: + nn.Module: this model itself + """ + if freeze_at >= 1: + self.stem.freeze() + for idx, (stage, _) in enumerate(self.stages_and_names, start=2): + if freeze_at >= idx: + for block in stage.children(): + block.freeze() + return self + + +def adjust_block_compatibility(ws, bs, gs): + """Adjusts the compatibility of widths, bottlenecks, and groups.""" + assert len(ws) == len(bs) == len(gs) + assert all(w > 0 and b > 0 and g > 0 for w, b, g in zip(ws, bs, gs)) + vs = [int(max(1, w * b)) for w, b in zip(ws, bs)] + gs = [int(min(g, v)) for g, v in zip(gs, vs)] + ms = [np.lcm(g, b) if b > 1 else g for g, b in zip(gs, bs)] + vs = [max(m, int(round(v / m) * m)) for v, m in zip(vs, ms)] + ws = [int(v / b) for v, b in zip(vs, bs)] + assert all(w * b % g == 0 for w, b, g in zip(ws, bs, gs)) + return ws, bs, gs + + +def generate_regnet_parameters(w_a, w_0, w_m, d, q=8): + """Generates per stage widths and depths from RegNet parameters.""" + assert w_a >= 0 and w_0 > 0 and w_m > 1 and w_0 % q == 0 + # Generate continuous per-block ws + ws_cont = np.arange(d) * w_a + w_0 + # Generate quantized per-block ws + ks = np.round(np.log(ws_cont / w_0) / np.log(w_m)) + ws_all = w_0 * np.power(w_m, ks) + ws_all = np.round(np.divide(ws_all, q)).astype(int) * q + # Generate per stage ws and ds (assumes ws_all are sorted) + ws, ds = np.unique(ws_all, return_counts=True) + # Compute number of actual stages and total possible stages + num_stages, total_stages = len(ws), ks.max() + 1 + # Convert numpy arrays to lists and return + ws, ds, ws_all, ws_cont = (x.tolist() for x in (ws, ds, ws_all, ws_cont)) + return ws, ds, num_stages, total_stages, ws_all, ws_cont + + +class RegNet(AnyNet): + """RegNet model. See :paper:`dds`.""" + + def __init__( + self, + *, + stem_class, + stem_width, + block_class, + depth, + w_a, + w_0, + w_m, + group_width, + stride=2, + bottleneck_ratio=1.0, + se_ratio=0.0, + activation_class=None, + freeze_at=0, + norm="BN", + out_features=None, + ): + """ + Build a RegNet from the parameterization described in :paper:`dds` Section 3.3. + + Args: + See :class:`AnyNet` for arguments that are not listed here. + depth (int): Total number of blocks in the RegNet. + w_a (float): Factor by which block width would increase prior to quantizing block widths + by stage. See :paper:`dds` Section 3.3. + w_0 (int): Initial block width. See :paper:`dds` Section 3.3. + w_m (float): Parameter controlling block width quantization. + See :paper:`dds` Section 3.3. + group_width (int): Number of channels per group in group convolution, if the block uses + group convolution. + bottleneck_ratio (float): The ratio of the number of bottleneck channels to the number + of block input channels (or, equivalently, output channels), if the block uses a + bottleneck. + stride (int): The stride that each network stage applies to its input. + """ + ws, ds = generate_regnet_parameters(w_a, w_0, w_m, depth)[0:2] + ss = [stride for _ in ws] + bs = [bottleneck_ratio for _ in ws] + gs = [group_width for _ in ws] + ws, bs, gs = adjust_block_compatibility(ws, bs, gs) + + def default_activation_class(): + return nn.ReLU(inplace=True) + + super().__init__( + stem_class=stem_class, + stem_width=stem_width, + block_class=block_class, + depths=ds, + widths=ws, + strides=ss, + group_widths=gs, + bottleneck_ratios=bs, + se_ratio=se_ratio, + activation_class=default_activation_class + if activation_class is None + else activation_class, + freeze_at=freeze_at, + norm=norm, + out_features=out_features, + ) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/resnet.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..5b8e842c585a81b5345ade4ca1da62a4904a122a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/resnet.py @@ -0,0 +1,694 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +import fvcore.nn.weight_init as weight_init +import torch +import torch.nn.functional as F +from torch import nn + +from detectron2.layers import ( + CNNBlockBase, + Conv2d, + DeformConv, + ModulatedDeformConv, + ShapeSpec, + get_norm, +) + +from .backbone import Backbone +from .build import BACKBONE_REGISTRY + +__all__ = [ + "ResNetBlockBase", + "BasicBlock", + "BottleneckBlock", + "DeformBottleneckBlock", + "BasicStem", + "ResNet", + "make_stage", + "build_resnet_backbone", +] + + +class BasicBlock(CNNBlockBase): + """ + The basic residual block for ResNet-18 and ResNet-34 defined in :paper:`ResNet`, + with two 3x3 conv layers and a projection shortcut if needed. + """ + + def __init__(self, in_channels, out_channels, *, stride=1, norm="BN"): + """ + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + stride (int): Stride for the first conv. + norm (str or callable): normalization for all conv layers. + See :func:`layers.get_norm` for supported format. + """ + super().__init__(in_channels, out_channels, stride) + + if in_channels != out_channels: + self.shortcut = Conv2d( + in_channels, + out_channels, + kernel_size=1, + stride=stride, + bias=False, + norm=get_norm(norm, out_channels), + ) + else: + self.shortcut = None + + self.conv1 = Conv2d( + in_channels, + out_channels, + kernel_size=3, + stride=stride, + padding=1, + bias=False, + norm=get_norm(norm, out_channels), + ) + + self.conv2 = Conv2d( + out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + bias=False, + norm=get_norm(norm, out_channels), + ) + + for layer in [self.conv1, self.conv2, self.shortcut]: + if layer is not None: # shortcut can be None + weight_init.c2_msra_fill(layer) + + def forward(self, x): + out = self.conv1(x) + out = F.relu_(out) + out = self.conv2(out) + + if self.shortcut is not None: + shortcut = self.shortcut(x) + else: + shortcut = x + + out += shortcut + out = F.relu_(out) + return out + + +class BottleneckBlock(CNNBlockBase): + """ + The standard bottleneck residual block used by ResNet-50, 101 and 152 + defined in :paper:`ResNet`. It contains 3 conv layers with kernels + 1x1, 3x3, 1x1, and a projection shortcut if needed. + """ + + def __init__( + self, + in_channels, + out_channels, + *, + bottleneck_channels, + stride=1, + num_groups=1, + norm="BN", + stride_in_1x1=False, + dilation=1, + ): + """ + Args: + bottleneck_channels (int): number of output channels for the 3x3 + "bottleneck" conv layers. + num_groups (int): number of groups for the 3x3 conv layer. + norm (str or callable): normalization for all conv layers. + See :func:`layers.get_norm` for supported format. + stride_in_1x1 (bool): when stride>1, whether to put stride in the + first 1x1 convolution or the bottleneck 3x3 convolution. + dilation (int): the dilation rate of the 3x3 conv layer. + """ + super().__init__(in_channels, out_channels, stride) + + if in_channels != out_channels: + self.shortcut = Conv2d( + in_channels, + out_channels, + kernel_size=1, + stride=stride, + bias=False, + norm=get_norm(norm, out_channels), + ) + else: + self.shortcut = None + + # The original MSRA ResNet models have stride in the first 1x1 conv + # The subsequent fb.torch.resnet and Caffe2 ResNe[X]t implementations have + # stride in the 3x3 conv + stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride) + + self.conv1 = Conv2d( + in_channels, + bottleneck_channels, + kernel_size=1, + stride=stride_1x1, + bias=False, + norm=get_norm(norm, bottleneck_channels), + ) + + self.conv2 = Conv2d( + bottleneck_channels, + bottleneck_channels, + kernel_size=3, + stride=stride_3x3, + padding=1 * dilation, + bias=False, + groups=num_groups, + dilation=dilation, + norm=get_norm(norm, bottleneck_channels), + ) + + self.conv3 = Conv2d( + bottleneck_channels, + out_channels, + kernel_size=1, + bias=False, + norm=get_norm(norm, out_channels), + ) + + for layer in [self.conv1, self.conv2, self.conv3, self.shortcut]: + if layer is not None: # shortcut can be None + weight_init.c2_msra_fill(layer) + + # Zero-initialize the last normalization in each residual branch, + # so that at the beginning, the residual branch starts with zeros, + # and each residual block behaves like an identity. + # See Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour": + # "For BN layers, the learnable scaling coefficient γ is initialized + # to be 1, except for each residual block's last BN + # where γ is initialized to be 0." + + # nn.init.constant_(self.conv3.norm.weight, 0) + # TODO this somehow hurts performance when training GN models from scratch. + # Add it as an option when we need to use this code to train a backbone. + + def forward(self, x): + out = self.conv1(x) + out = F.relu_(out) + + out = self.conv2(out) + out = F.relu_(out) + + out = self.conv3(out) + + if self.shortcut is not None: + shortcut = self.shortcut(x) + else: + shortcut = x + + out += shortcut + out = F.relu_(out) + return out + + +class DeformBottleneckBlock(CNNBlockBase): + """ + Similar to :class:`BottleneckBlock`, but with :paper:`deformable conv ` + in the 3x3 convolution. + """ + + def __init__( + self, + in_channels, + out_channels, + *, + bottleneck_channels, + stride=1, + num_groups=1, + norm="BN", + stride_in_1x1=False, + dilation=1, + deform_modulated=False, + deform_num_groups=1, + ): + super().__init__(in_channels, out_channels, stride) + self.deform_modulated = deform_modulated + + if in_channels != out_channels: + self.shortcut = Conv2d( + in_channels, + out_channels, + kernel_size=1, + stride=stride, + bias=False, + norm=get_norm(norm, out_channels), + ) + else: + self.shortcut = None + + stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride) + + self.conv1 = Conv2d( + in_channels, + bottleneck_channels, + kernel_size=1, + stride=stride_1x1, + bias=False, + norm=get_norm(norm, bottleneck_channels), + ) + + if deform_modulated: + deform_conv_op = ModulatedDeformConv + # offset channels are 2 or 3 (if with modulated) * kernel_size * kernel_size + offset_channels = 27 + else: + deform_conv_op = DeformConv + offset_channels = 18 + + self.conv2_offset = Conv2d( + bottleneck_channels, + offset_channels * deform_num_groups, + kernel_size=3, + stride=stride_3x3, + padding=1 * dilation, + dilation=dilation, + ) + self.conv2 = deform_conv_op( + bottleneck_channels, + bottleneck_channels, + kernel_size=3, + stride=stride_3x3, + padding=1 * dilation, + bias=False, + groups=num_groups, + dilation=dilation, + deformable_groups=deform_num_groups, + norm=get_norm(norm, bottleneck_channels), + ) + + self.conv3 = Conv2d( + bottleneck_channels, + out_channels, + kernel_size=1, + bias=False, + norm=get_norm(norm, out_channels), + ) + + for layer in [self.conv1, self.conv2, self.conv3, self.shortcut]: + if layer is not None: # shortcut can be None + weight_init.c2_msra_fill(layer) + + nn.init.constant_(self.conv2_offset.weight, 0) + nn.init.constant_(self.conv2_offset.bias, 0) + + def forward(self, x): + out = self.conv1(x) + out = F.relu_(out) + + if self.deform_modulated: + offset_mask = self.conv2_offset(out) + offset_x, offset_y, mask = torch.chunk(offset_mask, 3, dim=1) + offset = torch.cat((offset_x, offset_y), dim=1) + mask = mask.sigmoid() + out = self.conv2(out, offset, mask) + else: + offset = self.conv2_offset(out) + out = self.conv2(out, offset) + out = F.relu_(out) + + out = self.conv3(out) + + if self.shortcut is not None: + shortcut = self.shortcut(x) + else: + shortcut = x + + out += shortcut + out = F.relu_(out) + return out + + +class BasicStem(CNNBlockBase): + """ + The standard ResNet stem (layers before the first residual block), + with a conv, relu and max_pool. + """ + + def __init__(self, in_channels=3, out_channels=64, norm="BN"): + """ + Args: + norm (str or callable): norm after the first conv layer. + See :func:`layers.get_norm` for supported format. + """ + super().__init__(in_channels, out_channels, 4) + self.in_channels = in_channels + self.conv1 = Conv2d( + in_channels, + out_channels, + kernel_size=7, + stride=2, + padding=3, + bias=False, + norm=get_norm(norm, out_channels), + ) + weight_init.c2_msra_fill(self.conv1) + + def forward(self, x): + x = self.conv1(x) + x = F.relu_(x) + x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) + return x + + +class ResNet(Backbone): + """ + Implement :paper:`ResNet`. + """ + + def __init__(self, stem, stages, num_classes=None, out_features=None, freeze_at=0): + """ + Args: + stem (nn.Module): a stem module + stages (list[list[CNNBlockBase]]): several (typically 4) stages, + each contains multiple :class:`CNNBlockBase`. + num_classes (None or int): if None, will not perform classification. + Otherwise, will create a linear layer. + out_features (list[str]): name of the layers whose outputs should + be returned in forward. Can be anything in "stem", "linear", or "res2" ... + If None, will return the output of the last layer. + freeze_at (int): The number of stages at the beginning to freeze. + see :meth:`freeze` for detailed explanation. + """ + super().__init__() + self.stem = stem + self.num_classes = num_classes + + current_stride = self.stem.stride + self._out_feature_strides = {"stem": current_stride} + self._out_feature_channels = {"stem": self.stem.out_channels} + + self.stage_names, self.stages = [], [] + + if out_features is not None: + # Avoid keeping unused layers in this module. They consume extra memory + # and may cause allreduce to fail + num_stages = max( + [{"res2": 1, "res3": 2, "res4": 3, "res5": 4}.get(f, 0) for f in out_features] + ) + stages = stages[:num_stages] + for i, blocks in enumerate(stages): + assert len(blocks) > 0, len(blocks) + for block in blocks: + assert isinstance(block, CNNBlockBase), block + + name = "res" + str(i + 2) + stage = nn.Sequential(*blocks) + + self.add_module(name, stage) + self.stage_names.append(name) + self.stages.append(stage) + + self._out_feature_strides[name] = current_stride = int( + current_stride * np.prod([k.stride for k in blocks]) + ) + self._out_feature_channels[name] = curr_channels = blocks[-1].out_channels + self.stage_names = tuple(self.stage_names) # Make it static for scripting + + if num_classes is not None: + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.linear = nn.Linear(curr_channels, num_classes) + + # Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour": + # "The 1000-way fully-connected layer is initialized by + # drawing weights from a zero-mean Gaussian with standard deviation of 0.01." + nn.init.normal_(self.linear.weight, std=0.01) + name = "linear" + + if out_features is None: + out_features = [name] + self._out_features = out_features + assert len(self._out_features) + children = [x[0] for x in self.named_children()] + for out_feature in self._out_features: + assert out_feature in children, "Available children: {}".format(", ".join(children)) + self.freeze(freeze_at) + + def forward(self, x): + """ + Args: + x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``. + + Returns: + dict[str->Tensor]: names and the corresponding features + """ + assert x.dim() == 4, f"ResNet takes an input of shape (N, C, H, W). Got {x.shape} instead!" + outputs = {} + x = self.stem(x) + if "stem" in self._out_features: + outputs["stem"] = x + for name, stage in zip(self.stage_names, self.stages): + x = stage(x) + if name in self._out_features: + outputs[name] = x + if self.num_classes is not None: + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.linear(x) + if "linear" in self._out_features: + outputs["linear"] = x + return outputs + + def output_shape(self): + return { + name: ShapeSpec( + channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] + ) + for name in self._out_features + } + + def freeze(self, freeze_at=0): + """ + Freeze the first several stages of the ResNet. Commonly used in + fine-tuning. + + Layers that produce the same feature map spatial size are defined as one + "stage" by :paper:`FPN`. + + Args: + freeze_at (int): number of stages to freeze. + `1` means freezing the stem. `2` means freezing the stem and + one residual stage, etc. + + Returns: + nn.Module: this ResNet itself + """ + if freeze_at >= 1: + self.stem.freeze() + for idx, stage in enumerate(self.stages, start=2): + if freeze_at >= idx: + for block in stage.children(): + block.freeze() + return self + + @staticmethod + def make_stage(block_class, num_blocks, *, in_channels, out_channels, **kwargs): + """ + Create a list of blocks of the same type that forms one ResNet stage. + + Args: + block_class (type): a subclass of CNNBlockBase that's used to create all blocks in this + stage. A module of this type must not change spatial resolution of inputs unless its + stride != 1. + num_blocks (int): number of blocks in this stage + in_channels (int): input channels of the entire stage. + out_channels (int): output channels of **every block** in the stage. + kwargs: other arguments passed to the constructor of + `block_class`. If the argument name is "xx_per_block", the + argument is a list of values to be passed to each block in the + stage. Otherwise, the same argument is passed to every block + in the stage. + + Returns: + list[CNNBlockBase]: a list of block module. + + Examples: + :: + stage = ResNet.make_stage( + BottleneckBlock, 3, in_channels=16, out_channels=64, + bottleneck_channels=16, num_groups=1, + stride_per_block=[2, 1, 1], + dilations_per_block=[1, 1, 2] + ) + + Usually, layers that produce the same feature map spatial size are defined as one + "stage" (in :paper:`FPN`). Under such definition, ``stride_per_block[1:]`` should + all be 1. + """ + blocks = [] + for i in range(num_blocks): + curr_kwargs = {} + for k, v in kwargs.items(): + if k.endswith("_per_block"): + assert len(v) == num_blocks, ( + f"Argument '{k}' of make_stage should have the " + f"same length as num_blocks={num_blocks}." + ) + newk = k[: -len("_per_block")] + assert newk not in kwargs, f"Cannot call make_stage with both {k} and {newk}!" + curr_kwargs[newk] = v[i] + else: + curr_kwargs[k] = v + + blocks.append( + block_class(in_channels=in_channels, out_channels=out_channels, **curr_kwargs) + ) + in_channels = out_channels + return blocks + + @staticmethod + def make_default_stages(depth, block_class=None, **kwargs): + """ + Created list of ResNet stages from pre-defined depth (one of 18, 34, 50, 101, 152). + If it doesn't create the ResNet variant you need, please use :meth:`make_stage` + instead for fine-grained customization. + + Args: + depth (int): depth of ResNet + block_class (type): the CNN block class. Has to accept + `bottleneck_channels` argument for depth > 50. + By default it is BasicBlock or BottleneckBlock, based on the + depth. + kwargs: + other arguments to pass to `make_stage`. Should not contain + stride and channels, as they are predefined for each depth. + + Returns: + list[list[CNNBlockBase]]: modules in all stages; see arguments of + :class:`ResNet.__init__`. + """ + num_blocks_per_stage = { + 18: [2, 2, 2, 2], + 34: [3, 4, 6, 3], + 50: [3, 4, 6, 3], + 101: [3, 4, 23, 3], + 152: [3, 8, 36, 3], + }[depth] + if block_class is None: + block_class = BasicBlock if depth < 50 else BottleneckBlock + if depth < 50: + in_channels = [64, 64, 128, 256] + out_channels = [64, 128, 256, 512] + else: + in_channels = [64, 256, 512, 1024] + out_channels = [256, 512, 1024, 2048] + ret = [] + for (n, s, i, o) in zip(num_blocks_per_stage, [1, 2, 2, 2], in_channels, out_channels): + if depth >= 50: + kwargs["bottleneck_channels"] = o // 4 + ret.append( + ResNet.make_stage( + block_class=block_class, + num_blocks=n, + stride_per_block=[s] + [1] * (n - 1), + in_channels=i, + out_channels=o, + **kwargs, + ) + ) + return ret + + +ResNetBlockBase = CNNBlockBase +""" +Alias for backward compatibiltiy. +""" + + +def make_stage(*args, **kwargs): + """ + Deprecated alias for backward compatibiltiy. + """ + return ResNet.make_stage(*args, **kwargs) + + +@BACKBONE_REGISTRY.register() +def build_resnet_backbone(cfg, input_shape): + """ + Create a ResNet instance from config. + + Returns: + ResNet: a :class:`ResNet` instance. + """ + # need registration of new blocks/stems? + norm = cfg.MODEL.RESNETS.NORM + stem = BasicStem( + in_channels=input_shape.channels, + out_channels=cfg.MODEL.RESNETS.STEM_OUT_CHANNELS, + norm=norm, + ) + + # fmt: off + freeze_at = cfg.MODEL.BACKBONE.FREEZE_AT + out_features = cfg.MODEL.RESNETS.OUT_FEATURES + depth = cfg.MODEL.RESNETS.DEPTH + num_groups = cfg.MODEL.RESNETS.NUM_GROUPS + width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP + bottleneck_channels = num_groups * width_per_group + in_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS + out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS + stride_in_1x1 = cfg.MODEL.RESNETS.STRIDE_IN_1X1 + res5_dilation = cfg.MODEL.RESNETS.RES5_DILATION + deform_on_per_stage = cfg.MODEL.RESNETS.DEFORM_ON_PER_STAGE + deform_modulated = cfg.MODEL.RESNETS.DEFORM_MODULATED + deform_num_groups = cfg.MODEL.RESNETS.DEFORM_NUM_GROUPS + # fmt: on + assert res5_dilation in {1, 2}, "res5_dilation cannot be {}.".format(res5_dilation) + + num_blocks_per_stage = { + 18: [2, 2, 2, 2], + 34: [3, 4, 6, 3], + 50: [3, 4, 6, 3], + 101: [3, 4, 23, 3], + 152: [3, 8, 36, 3], + }[depth] + + if depth in [18, 34]: + assert out_channels == 64, "Must set MODEL.RESNETS.RES2_OUT_CHANNELS = 64 for R18/R34" + assert not any( + deform_on_per_stage + ), "MODEL.RESNETS.DEFORM_ON_PER_STAGE unsupported for R18/R34" + assert res5_dilation == 1, "Must set MODEL.RESNETS.RES5_DILATION = 1 for R18/R34" + assert num_groups == 1, "Must set MODEL.RESNETS.NUM_GROUPS = 1 for R18/R34" + + stages = [] + + for idx, stage_idx in enumerate(range(2, 6)): + # res5_dilation is used this way as a convention in R-FCN & Deformable Conv paper + dilation = res5_dilation if stage_idx == 5 else 1 + first_stride = 1 if idx == 0 or (stage_idx == 5 and dilation == 2) else 2 + stage_kargs = { + "num_blocks": num_blocks_per_stage[idx], + "stride_per_block": [first_stride] + [1] * (num_blocks_per_stage[idx] - 1), + "in_channels": in_channels, + "out_channels": out_channels, + "norm": norm, + } + # Use BasicBlock for R18 and R34. + if depth in [18, 34]: + stage_kargs["block_class"] = BasicBlock + else: + stage_kargs["bottleneck_channels"] = bottleneck_channels + stage_kargs["stride_in_1x1"] = stride_in_1x1 + stage_kargs["dilation"] = dilation + stage_kargs["num_groups"] = num_groups + if deform_on_per_stage[idx]: + stage_kargs["block_class"] = DeformBottleneckBlock + stage_kargs["deform_modulated"] = deform_modulated + stage_kargs["deform_num_groups"] = deform_num_groups + else: + stage_kargs["block_class"] = BottleneckBlock + blocks = ResNet.make_stage(**stage_kargs) + in_channels = out_channels + out_channels *= 2 + bottleneck_channels *= 2 + stages.append(blocks) + return ResNet(stem, stages, out_features=out_features, freeze_at=freeze_at) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/box_regression.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/box_regression.py new file mode 100644 index 0000000000000000000000000000000000000000..b24c123f26faa5f17975fe13b6756151da229b2f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/box_regression.py @@ -0,0 +1,369 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import math +from typing import List, Tuple, Union +import torch +from fvcore.nn import giou_loss, smooth_l1_loss +from torch.nn import functional as F + +from detectron2.layers import cat, ciou_loss, diou_loss +from detectron2.structures import Boxes + +# Value for clamping large dw and dh predictions. The heuristic is that we clamp +# such that dw and dh are no larger than what would transform a 16px box into a +# 1000px box (based on a small anchor, 16px, and a typical image size, 1000px). +_DEFAULT_SCALE_CLAMP = math.log(1000.0 / 16) + + +__all__ = ["Box2BoxTransform", "Box2BoxTransformRotated", "Box2BoxTransformLinear"] + + +@torch.jit.script +class Box2BoxTransform(object): + """ + The box-to-box transform defined in R-CNN. The transformation is parameterized + by 4 deltas: (dx, dy, dw, dh). The transformation scales the box's width and height + by exp(dw), exp(dh) and shifts a box's center by the offset (dx * width, dy * height). + """ + + def __init__( + self, weights: Tuple[float, float, float, float], scale_clamp: float = _DEFAULT_SCALE_CLAMP + ): + """ + Args: + weights (4-element tuple): Scaling factors that are applied to the + (dx, dy, dw, dh) deltas. In Fast R-CNN, these were originally set + such that the deltas have unit variance; now they are treated as + hyperparameters of the system. + scale_clamp (float): When predicting deltas, the predicted box scaling + factors (dw and dh) are clamped such that they are <= scale_clamp. + """ + self.weights = weights + self.scale_clamp = scale_clamp + + def get_deltas(self, src_boxes, target_boxes): + """ + Get box regression transformation deltas (dx, dy, dw, dh) that can be used + to transform the `src_boxes` into the `target_boxes`. That is, the relation + ``target_boxes == self.apply_deltas(deltas, src_boxes)`` is true (unless + any delta is too large and is clamped). + + Args: + src_boxes (Tensor): source boxes, e.g., object proposals + target_boxes (Tensor): target of the transformation, e.g., ground-truth + boxes. + """ + assert isinstance(src_boxes, torch.Tensor), type(src_boxes) + assert isinstance(target_boxes, torch.Tensor), type(target_boxes) + + src_widths = src_boxes[:, 2] - src_boxes[:, 0] + src_heights = src_boxes[:, 3] - src_boxes[:, 1] + src_ctr_x = src_boxes[:, 0] + 0.5 * src_widths + src_ctr_y = src_boxes[:, 1] + 0.5 * src_heights + + target_widths = target_boxes[:, 2] - target_boxes[:, 0] + target_heights = target_boxes[:, 3] - target_boxes[:, 1] + target_ctr_x = target_boxes[:, 0] + 0.5 * target_widths + target_ctr_y = target_boxes[:, 1] + 0.5 * target_heights + + wx, wy, ww, wh = self.weights + dx = wx * (target_ctr_x - src_ctr_x) / src_widths + dy = wy * (target_ctr_y - src_ctr_y) / src_heights + dw = ww * torch.log(target_widths / src_widths) + dh = wh * torch.log(target_heights / src_heights) + + deltas = torch.stack((dx, dy, dw, dh), dim=1) + assert (src_widths > 0).all().item(), "Input boxes to Box2BoxTransform are not valid!" + return deltas + + def apply_deltas(self, deltas, boxes): + """ + Apply transformation `deltas` (dx, dy, dw, dh) to `boxes`. + + Args: + deltas (Tensor): transformation deltas of shape (N, k*4), where k >= 1. + deltas[i] represents k potentially different class-specific + box transformations for the single box boxes[i]. + boxes (Tensor): boxes to transform, of shape (N, 4) + """ + deltas = deltas.float() # ensure fp32 for decoding precision + boxes = boxes.to(deltas.dtype) + + widths = boxes[:, 2] - boxes[:, 0] + heights = boxes[:, 3] - boxes[:, 1] + ctr_x = boxes[:, 0] + 0.5 * widths + ctr_y = boxes[:, 1] + 0.5 * heights + + wx, wy, ww, wh = self.weights + dx = deltas[:, 0::4] / wx + dy = deltas[:, 1::4] / wy + dw = deltas[:, 2::4] / ww + dh = deltas[:, 3::4] / wh + + # Prevent sending too large values into torch.exp() + dw = torch.clamp(dw, max=self.scale_clamp) + dh = torch.clamp(dh, max=self.scale_clamp) + + pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] + pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] + pred_w = torch.exp(dw) * widths[:, None] + pred_h = torch.exp(dh) * heights[:, None] + + x1 = pred_ctr_x - 0.5 * pred_w + y1 = pred_ctr_y - 0.5 * pred_h + x2 = pred_ctr_x + 0.5 * pred_w + y2 = pred_ctr_y + 0.5 * pred_h + pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) + return pred_boxes.reshape(deltas.shape) + + +@torch.jit.script +class Box2BoxTransformRotated(object): + """ + The box-to-box transform defined in Rotated R-CNN. The transformation is parameterized + by 5 deltas: (dx, dy, dw, dh, da). The transformation scales the box's width and height + by exp(dw), exp(dh), shifts a box's center by the offset (dx * width, dy * height), + and rotate a box's angle by da (radians). + Note: angles of deltas are in radians while angles of boxes are in degrees. + """ + + def __init__( + self, + weights: Tuple[float, float, float, float, float], + scale_clamp: float = _DEFAULT_SCALE_CLAMP, + ): + """ + Args: + weights (5-element tuple): Scaling factors that are applied to the + (dx, dy, dw, dh, da) deltas. These are treated as + hyperparameters of the system. + scale_clamp (float): When predicting deltas, the predicted box scaling + factors (dw and dh) are clamped such that they are <= scale_clamp. + """ + self.weights = weights + self.scale_clamp = scale_clamp + + def get_deltas(self, src_boxes, target_boxes): + """ + Get box regression transformation deltas (dx, dy, dw, dh, da) that can be used + to transform the `src_boxes` into the `target_boxes`. That is, the relation + ``target_boxes == self.apply_deltas(deltas, src_boxes)`` is true (unless + any delta is too large and is clamped). + + Args: + src_boxes (Tensor): Nx5 source boxes, e.g., object proposals + target_boxes (Tensor): Nx5 target of the transformation, e.g., ground-truth + boxes. + """ + assert isinstance(src_boxes, torch.Tensor), type(src_boxes) + assert isinstance(target_boxes, torch.Tensor), type(target_boxes) + + src_ctr_x, src_ctr_y, src_widths, src_heights, src_angles = torch.unbind(src_boxes, dim=1) + + target_ctr_x, target_ctr_y, target_widths, target_heights, target_angles = torch.unbind( + target_boxes, dim=1 + ) + + wx, wy, ww, wh, wa = self.weights + dx = wx * (target_ctr_x - src_ctr_x) / src_widths + dy = wy * (target_ctr_y - src_ctr_y) / src_heights + dw = ww * torch.log(target_widths / src_widths) + dh = wh * torch.log(target_heights / src_heights) + # Angles of deltas are in radians while angles of boxes are in degrees. + # the conversion to radians serve as a way to normalize the values + da = target_angles - src_angles + da = (da + 180.0) % 360.0 - 180.0 # make it in [-180, 180) + da *= wa * math.pi / 180.0 + + deltas = torch.stack((dx, dy, dw, dh, da), dim=1) + assert ( + (src_widths > 0).all().item() + ), "Input boxes to Box2BoxTransformRotated are not valid!" + return deltas + + def apply_deltas(self, deltas, boxes): + """ + Apply transformation `deltas` (dx, dy, dw, dh, da) to `boxes`. + + Args: + deltas (Tensor): transformation deltas of shape (N, k*5). + deltas[i] represents box transformation for the single box boxes[i]. + boxes (Tensor): boxes to transform, of shape (N, 5) + """ + assert deltas.shape[1] % 5 == 0 and boxes.shape[1] == 5 + + boxes = boxes.to(deltas.dtype).unsqueeze(2) + + ctr_x = boxes[:, 0] + ctr_y = boxes[:, 1] + widths = boxes[:, 2] + heights = boxes[:, 3] + angles = boxes[:, 4] + + wx, wy, ww, wh, wa = self.weights + + dx = deltas[:, 0::5] / wx + dy = deltas[:, 1::5] / wy + dw = deltas[:, 2::5] / ww + dh = deltas[:, 3::5] / wh + da = deltas[:, 4::5] / wa + + # Prevent sending too large values into torch.exp() + dw = torch.clamp(dw, max=self.scale_clamp) + dh = torch.clamp(dh, max=self.scale_clamp) + + pred_boxes = torch.zeros_like(deltas) + pred_boxes[:, 0::5] = dx * widths + ctr_x # x_ctr + pred_boxes[:, 1::5] = dy * heights + ctr_y # y_ctr + pred_boxes[:, 2::5] = torch.exp(dw) * widths # width + pred_boxes[:, 3::5] = torch.exp(dh) * heights # height + + # Following original RRPN implementation, + # angles of deltas are in radians while angles of boxes are in degrees. + pred_angle = da * 180.0 / math.pi + angles + pred_angle = (pred_angle + 180.0) % 360.0 - 180.0 # make it in [-180, 180) + + pred_boxes[:, 4::5] = pred_angle + + return pred_boxes + + +class Box2BoxTransformLinear(object): + """ + The linear box-to-box transform defined in FCOS. The transformation is parameterized + by the distance from the center of (square) src box to 4 edges of the target box. + """ + + def __init__(self, normalize_by_size=True): + """ + Args: + normalize_by_size: normalize deltas by the size of src (anchor) boxes. + """ + self.normalize_by_size = normalize_by_size + + def get_deltas(self, src_boxes, target_boxes): + """ + Get box regression transformation deltas (dx1, dy1, dx2, dy2) that can be used + to transform the `src_boxes` into the `target_boxes`. That is, the relation + ``target_boxes == self.apply_deltas(deltas, src_boxes)`` is true. + The center of src must be inside target boxes. + + Args: + src_boxes (Tensor): square source boxes, e.g., anchors + target_boxes (Tensor): target of the transformation, e.g., ground-truth + boxes. + """ + assert isinstance(src_boxes, torch.Tensor), type(src_boxes) + assert isinstance(target_boxes, torch.Tensor), type(target_boxes) + + src_ctr_x = 0.5 * (src_boxes[:, 0] + src_boxes[:, 2]) + src_ctr_y = 0.5 * (src_boxes[:, 1] + src_boxes[:, 3]) + + target_l = src_ctr_x - target_boxes[:, 0] + target_t = src_ctr_y - target_boxes[:, 1] + target_r = target_boxes[:, 2] - src_ctr_x + target_b = target_boxes[:, 3] - src_ctr_y + + deltas = torch.stack((target_l, target_t, target_r, target_b), dim=1) + if self.normalize_by_size: + stride_w = src_boxes[:, 2] - src_boxes[:, 0] + stride_h = src_boxes[:, 3] - src_boxes[:, 1] + strides = torch.stack([stride_w, stride_h, stride_w, stride_h], axis=1) + deltas = deltas / strides + + return deltas + + def apply_deltas(self, deltas, boxes): + """ + Apply transformation `deltas` (dx1, dy1, dx2, dy2) to `boxes`. + + Args: + deltas (Tensor): transformation deltas of shape (N, k*4), where k >= 1. + deltas[i] represents k potentially different class-specific + box transformations for the single box boxes[i]. + boxes (Tensor): boxes to transform, of shape (N, 4) + """ + # Ensure the output is a valid box. See Sec 2.1 of https://arxiv.org/abs/2006.09214 + deltas = F.relu(deltas) + boxes = boxes.to(deltas.dtype) + + ctr_x = 0.5 * (boxes[:, 0] + boxes[:, 2]) + ctr_y = 0.5 * (boxes[:, 1] + boxes[:, 3]) + if self.normalize_by_size: + stride_w = boxes[:, 2] - boxes[:, 0] + stride_h = boxes[:, 3] - boxes[:, 1] + strides = torch.stack([stride_w, stride_h, stride_w, stride_h], axis=1) + deltas = deltas * strides + + l = deltas[:, 0::4] + t = deltas[:, 1::4] + r = deltas[:, 2::4] + b = deltas[:, 3::4] + + pred_boxes = torch.zeros_like(deltas) + pred_boxes[:, 0::4] = ctr_x[:, None] - l # x1 + pred_boxes[:, 1::4] = ctr_y[:, None] - t # y1 + pred_boxes[:, 2::4] = ctr_x[:, None] + r # x2 + pred_boxes[:, 3::4] = ctr_y[:, None] + b # y2 + return pred_boxes + + +def _dense_box_regression_loss( + anchors: List[Union[Boxes, torch.Tensor]], + box2box_transform: Box2BoxTransform, + pred_anchor_deltas: List[torch.Tensor], + gt_boxes: List[torch.Tensor], + fg_mask: torch.Tensor, + box_reg_loss_type="smooth_l1", + smooth_l1_beta=0.0, +): + """ + Compute loss for dense multi-level box regression. + Loss is accumulated over ``fg_mask``. + + Args: + anchors: #lvl anchor boxes, each is (HixWixA, 4) + pred_anchor_deltas: #lvl predictions, each is (N, HixWixA, 4) + gt_boxes: N ground truth boxes, each has shape (R, 4) (R = sum(Hi * Wi * A)) + fg_mask: the foreground boolean mask of shape (N, R) to compute loss on + box_reg_loss_type (str): Loss type to use. Supported losses: "smooth_l1", "giou", + "diou", "ciou". + smooth_l1_beta (float): beta parameter for the smooth L1 regression loss. Default to + use L1 loss. Only used when `box_reg_loss_type` is "smooth_l1" + """ + if isinstance(anchors[0], Boxes): + anchors = type(anchors[0]).cat(anchors).tensor # (R, 4) + else: + anchors = cat(anchors) + if box_reg_loss_type == "smooth_l1": + gt_anchor_deltas = [box2box_transform.get_deltas(anchors, k) for k in gt_boxes] + gt_anchor_deltas = torch.stack(gt_anchor_deltas) # (N, R, 4) + loss_box_reg = smooth_l1_loss( + cat(pred_anchor_deltas, dim=1)[fg_mask], + gt_anchor_deltas[fg_mask], + beta=smooth_l1_beta, + reduction="sum", + ) + elif box_reg_loss_type == "giou": + pred_boxes = [ + box2box_transform.apply_deltas(k, anchors) for k in cat(pred_anchor_deltas, dim=1) + ] + loss_box_reg = giou_loss( + torch.stack(pred_boxes)[fg_mask], torch.stack(gt_boxes)[fg_mask], reduction="sum" + ) + elif box_reg_loss_type == "diou": + pred_boxes = [ + box2box_transform.apply_deltas(k, anchors) for k in cat(pred_anchor_deltas, dim=1) + ] + loss_box_reg = diou_loss( + torch.stack(pred_boxes)[fg_mask], torch.stack(gt_boxes)[fg_mask], reduction="sum" + ) + elif box_reg_loss_type == "ciou": + pred_boxes = [ + box2box_transform.apply_deltas(k, anchors) for k in cat(pred_anchor_deltas, dim=1) + ] + loss_box_reg = ciou_loss( + torch.stack(pred_boxes)[fg_mask], torch.stack(gt_boxes)[fg_mask], reduction="sum" + ) + else: + raise ValueError(f"Invalid dense box regression loss type '{box_reg_loss_type}'") + return loss_box_reg diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/matcher.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/matcher.py new file mode 100644 index 0000000000000000000000000000000000000000..c7597cab5a89a7e828b8eee53d1a3712be6dbc62 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/matcher.py @@ -0,0 +1,127 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from typing import List +import torch + +from detectron2.layers import nonzero_tuple + + +# TODO: the name is too general +class Matcher(object): + """ + This class assigns to each predicted "element" (e.g., a box) a ground-truth + element. Each predicted element will have exactly zero or one matches; each + ground-truth element may be matched to zero or more predicted elements. + + The matching is determined by the MxN match_quality_matrix, that characterizes + how well each (ground-truth, prediction)-pair match each other. For example, + if the elements are boxes, this matrix may contain box intersection-over-union + overlap values. + + The matcher returns (a) a vector of length N containing the index of the + ground-truth element m in [0, M) that matches to prediction n in [0, N). + (b) a vector of length N containing the labels for each prediction. + """ + + def __init__( + self, thresholds: List[float], labels: List[int], allow_low_quality_matches: bool = False + ): + """ + Args: + thresholds (list): a list of thresholds used to stratify predictions + into levels. + labels (list): a list of values to label predictions belonging at + each level. A label can be one of {-1, 0, 1} signifying + {ignore, negative class, positive class}, respectively. + allow_low_quality_matches (bool): if True, produce additional matches + for predictions with maximum match quality lower than high_threshold. + See set_low_quality_matches_ for more details. + + For example, + thresholds = [0.3, 0.5] + labels = [0, -1, 1] + All predictions with iou < 0.3 will be marked with 0 and + thus will be considered as false positives while training. + All predictions with 0.3 <= iou < 0.5 will be marked with -1 and + thus will be ignored. + All predictions with 0.5 <= iou will be marked with 1 and + thus will be considered as true positives. + """ + # Add -inf and +inf to first and last position in thresholds + thresholds = thresholds[:] + assert thresholds[0] > 0 + thresholds.insert(0, -float("inf")) + thresholds.append(float("inf")) + # Currently torchscript does not support all + generator + assert all([low <= high for (low, high) in zip(thresholds[:-1], thresholds[1:])]) + assert all([l in [-1, 0, 1] for l in labels]) + assert len(labels) == len(thresholds) - 1 + self.thresholds = thresholds + self.labels = labels + self.allow_low_quality_matches = allow_low_quality_matches + + def __call__(self, match_quality_matrix): + """ + Args: + match_quality_matrix (Tensor[float]): an MxN tensor, containing the + pairwise quality between M ground-truth elements and N predicted + elements. All elements must be >= 0 (due to the us of `torch.nonzero` + for selecting indices in :meth:`set_low_quality_matches_`). + + Returns: + matches (Tensor[int64]): a vector of length N, where matches[i] is a matched + ground-truth index in [0, M) + match_labels (Tensor[int8]): a vector of length N, where pred_labels[i] indicates + whether a prediction is a true or false positive or ignored + """ + assert match_quality_matrix.dim() == 2 + if match_quality_matrix.numel() == 0: + default_matches = match_quality_matrix.new_full( + (match_quality_matrix.size(1),), 0, dtype=torch.int64 + ) + # When no gt boxes exist, we define IOU = 0 and therefore set labels + # to `self.labels[0]`, which usually defaults to background class 0 + # To choose to ignore instead, can make labels=[-1,0,-1,1] + set appropriate thresholds + default_match_labels = match_quality_matrix.new_full( + (match_quality_matrix.size(1),), self.labels[0], dtype=torch.int8 + ) + return default_matches, default_match_labels + + assert torch.all(match_quality_matrix >= 0) + + # match_quality_matrix is M (gt) x N (predicted) + # Max over gt elements (dim 0) to find best gt candidate for each prediction + matched_vals, matches = match_quality_matrix.max(dim=0) + + match_labels = matches.new_full(matches.size(), 1, dtype=torch.int8) + + for (l, low, high) in zip(self.labels, self.thresholds[:-1], self.thresholds[1:]): + low_high = (matched_vals >= low) & (matched_vals < high) + match_labels[low_high] = l + + if self.allow_low_quality_matches: + self.set_low_quality_matches_(match_labels, match_quality_matrix) + + return matches, match_labels + + def set_low_quality_matches_(self, match_labels, match_quality_matrix): + """ + Produce additional matches for predictions that have only low-quality matches. + Specifically, for each ground-truth G find the set of predictions that have + maximum overlap with it (including ties); for each prediction in that set, if + it is unmatched, then match it to the ground-truth G. + + This function implements the RPN assignment case (i) in Sec. 3.1.2 of + :paper:`Faster R-CNN`. + """ + # For each gt, find the prediction with which it has highest quality + highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1) + # Find the highest quality match available, even if it is low, including ties. + # Note that the matches qualities must be positive due to the use of + # `torch.nonzero`. + _, pred_inds_with_highest_quality = nonzero_tuple( + match_quality_matrix == highest_quality_foreach_gt[:, None] + ) + # If an anchor was labeled positive only due to a low-quality match + # with gt_A, but it has larger overlap with gt_B, it's matched index will still be gt_B. + # This follows the implementation in Detectron, and is found to have no significant impact. + match_labels[pred_inds_with_highest_quality] = 1 diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6b0668157052ce7b796ef50bc7ee85361e7605b9 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/__init__.py @@ -0,0 +1,16 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +from .build import META_ARCH_REGISTRY, build_model # isort:skip + +from .panoptic_fpn import PanopticFPN + +# import all the meta_arch, so they will be registered +from .rcnn import GeneralizedRCNN, ProposalNetwork +from .dense_detector import DenseDetector +from .retinanet import RetinaNet +from .fcos import FCOS +from .semantic_seg import SEM_SEG_HEADS_REGISTRY, SemanticSegmentor, build_sem_seg_head + + +__all__ = list(globals().keys()) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/build.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/build.py new file mode 100644 index 0000000000000000000000000000000000000000..3427215746c9a146bd902f22ea9b26d121c36b27 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/build.py @@ -0,0 +1,25 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import torch + +from detectron2.utils.logger import _log_api_usage +from detectron2.utils.registry import Registry + +META_ARCH_REGISTRY = Registry("META_ARCH") # noqa F401 isort:skip +META_ARCH_REGISTRY.__doc__ = """ +Registry for meta-architectures, i.e. the whole model. + +The registered object will be called with `obj(cfg)` +and expected to return a `nn.Module` object. +""" + + +def build_model(cfg): + """ + Build the whole model architecture, defined by ``cfg.MODEL.META_ARCHITECTURE``. + Note that it does not load any weights from ``cfg``. + """ + meta_arch = cfg.MODEL.META_ARCHITECTURE + model = META_ARCH_REGISTRY.get(meta_arch)(cfg) + model.to(torch.device(cfg.MODEL.DEVICE)) + _log_api_usage("modeling.meta_arch." + meta_arch) + return model diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/dense_detector.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/dense_detector.py new file mode 100644 index 0000000000000000000000000000000000000000..382eab976f4426496f6a54ce7d47b093db477f91 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/dense_detector.py @@ -0,0 +1,282 @@ +import numpy as np +from typing import Dict, List, Optional, Tuple +import torch +from torch import Tensor, nn + +from detectron2.data.detection_utils import convert_image_to_rgb +from detectron2.modeling import Backbone +from detectron2.structures import Boxes, ImageList, Instances +from detectron2.utils.events import get_event_storage + +from ..postprocessing import detector_postprocess + + +def permute_to_N_HWA_K(tensor, K: int): + """ + Transpose/reshape a tensor from (N, (Ai x K), H, W) to (N, (HxWxAi), K) + """ + assert tensor.dim() == 4, tensor.shape + N, _, H, W = tensor.shape + tensor = tensor.view(N, -1, K, H, W) + tensor = tensor.permute(0, 3, 4, 1, 2) + tensor = tensor.reshape(N, -1, K) # Size=(N,HWA,K) + return tensor + + +class DenseDetector(nn.Module): + """ + Base class for dense detector. We define a dense detector as a fully-convolutional model that + makes per-pixel (i.e. dense) predictions. + """ + + def __init__( + self, + backbone: Backbone, + head: nn.Module, + head_in_features: Optional[List[str]] = None, + *, + pixel_mean, + pixel_std, + ): + """ + Args: + backbone: backbone module + head: head module + head_in_features: backbone features to use in head. Default to all backbone features. + pixel_mean (Tuple[float]): + Values to be used for image normalization (BGR order). + To train on images of different number of channels, set different mean & std. + Default values are the mean pixel value from ImageNet: [103.53, 116.28, 123.675] + pixel_std (Tuple[float]): + When using pre-trained models in Detectron1 or any MSRA models, + std has been absorbed into its conv1 weights, so the std needs to be set 1. + Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std) + """ + super().__init__() + + self.backbone = backbone + self.head = head + if head_in_features is None: + shapes = self.backbone.output_shape() + self.head_in_features = sorted(shapes.keys(), key=lambda x: shapes[x].stride) + else: + self.head_in_features = head_in_features + + self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False) + self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False) + + @property + def device(self): + return self.pixel_mean.device + + def forward(self, batched_inputs: List[Dict[str, Tensor]]): + """ + Args: + batched_inputs: a list, batched outputs of :class:`DatasetMapper` . + Each item in the list contains the inputs for one image. + For now, each item in the list is a dict that contains: + + * image: Tensor, image in (C, H, W) format. + * instances: Instances + + Other information that's included in the original dicts, such as: + + * "height", "width" (int): the output resolution of the model, used in inference. + See :meth:`postprocess` for details. + + Returns: + In training, dict[str, Tensor]: mapping from a named loss to a tensor storing the + loss. Used during training only. In inference, the standard output format, described + in :doc:`/tutorials/models`. + """ + images = self.preprocess_image(batched_inputs) + features = self.backbone(images.tensor) + features = [features[f] for f in self.head_in_features] + predictions = self.head(features) + + if self.training: + assert not torch.jit.is_scripting(), "Not supported" + assert "instances" in batched_inputs[0], "Instance annotations are missing in training!" + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + return self.forward_training(images, features, predictions, gt_instances) + else: + results = self.forward_inference(images, features, predictions) + if torch.jit.is_scripting(): + return results + + processed_results = [] + for results_per_image, input_per_image, image_size in zip( + results, batched_inputs, images.image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + r = detector_postprocess(results_per_image, height, width) + processed_results.append({"instances": r}) + return processed_results + + def forward_training(self, images, features, predictions, gt_instances): + raise NotImplementedError() + + def preprocess_image(self, batched_inputs: List[Dict[str, Tensor]]): + """ + Normalize, pad and batch the input images. + """ + images = [x["image"].to(self.device) for x in batched_inputs] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors(images, self.backbone.size_divisibility) + return images + + def _transpose_dense_predictions( + self, predictions: List[List[Tensor]], dims_per_anchor: List[int] + ) -> List[List[Tensor]]: + """ + Transpose the dense per-level predictions. + + Args: + predictions: a list of outputs, each is a list of per-level + predictions with shape (N, Ai x K, Hi, Wi), where N is the + number of images, Ai is the number of anchors per location on + level i, K is the dimension of predictions per anchor. + dims_per_anchor: the value of K for each predictions. e.g. 4 for + box prediction, #classes for classification prediction. + + Returns: + List[List[Tensor]]: each prediction is transposed to (N, Hi x Wi x Ai, K). + """ + assert len(predictions) == len(dims_per_anchor) + res: List[List[Tensor]] = [] + for pred, dim_per_anchor in zip(predictions, dims_per_anchor): + pred = [permute_to_N_HWA_K(x, dim_per_anchor) for x in pred] + res.append(pred) + return res + + def _ema_update(self, name: str, value: float, initial_value: float, momentum: float = 0.9): + """ + Apply EMA update to `self.name` using `value`. + + This is mainly used for loss normalizer. In Detectron1, loss is normalized by number + of foreground samples in the batch. When batch size is 1 per GPU, #foreground has a + large variance and using it lead to lower performance. Therefore we maintain an EMA of + #foreground to stabilize the normalizer. + + Args: + name: name of the normalizer + value: the new value to update + initial_value: the initial value to start with + momentum: momentum of EMA + + Returns: + float: the updated EMA value + """ + if hasattr(self, name): + old = getattr(self, name) + else: + old = initial_value + new = old * momentum + value * (1 - momentum) + setattr(self, name, new) + return new + + def _decode_per_level_predictions( + self, + anchors: Boxes, + pred_scores: Tensor, + pred_deltas: Tensor, + score_thresh: float, + topk_candidates: int, + image_size: Tuple[int, int], + ) -> Instances: + """ + Decode boxes and classification predictions of one featuer level, by + the following steps: + 1. filter the predictions based on score threshold and top K scores. + 2. transform the box regression outputs + 3. return the predicted scores, classes and boxes + + Args: + anchors: Boxes, anchor for this feature level + pred_scores: HxWxA,K + pred_deltas: HxWxA,4 + + Returns: + Instances: with field "scores", "pred_boxes", "pred_classes". + """ + # Apply two filtering to make NMS faster. + # 1. Keep boxes with confidence score higher than threshold + keep_idxs = pred_scores > score_thresh + pred_scores = pred_scores[keep_idxs] + topk_idxs = torch.nonzero(keep_idxs) # Kx2 + + # 2. Keep top k top scoring boxes only + num_topk = min(topk_candidates, topk_idxs.size(0)) + pred_scores, idxs = pred_scores.topk(num_topk) + topk_idxs = topk_idxs[idxs] + + anchor_idxs, classes_idxs = topk_idxs.unbind(dim=1) + + pred_boxes = self.box2box_transform.apply_deltas( + pred_deltas[anchor_idxs], anchors.tensor[anchor_idxs] + ) + return Instances( + image_size, pred_boxes=Boxes(pred_boxes), scores=pred_scores, pred_classes=classes_idxs + ) + + def _decode_multi_level_predictions( + self, + anchors: List[Boxes], + pred_scores: List[Tensor], + pred_deltas: List[Tensor], + score_thresh: float, + topk_candidates: int, + image_size: Tuple[int, int], + ) -> Instances: + """ + Run `_decode_per_level_predictions` for all feature levels and concat the results. + """ + predictions = [ + self._decode_per_level_predictions( + anchors_i, + box_cls_i, + box_reg_i, + self.test_score_thresh, + self.test_topk_candidates, + image_size, + ) + # Iterate over every feature level + for box_cls_i, box_reg_i, anchors_i in zip(pred_scores, pred_deltas, anchors) + ] + return predictions[0].cat(predictions) # 'Instances.cat' is not scriptale but this is + + def visualize_training(self, batched_inputs, results): + """ + A function used to visualize ground truth images and final network predictions. + It shows ground truth bounding boxes on the original image and up to 20 + predicted object bounding boxes on the original image. + + Args: + batched_inputs (list): a list that contains input to the model. + results (List[Instances]): a list of #images elements returned by forward_inference(). + """ + from detectron2.utils.visualizer import Visualizer + + assert len(batched_inputs) == len( + results + ), "Cannot visualize inputs and results of different sizes" + storage = get_event_storage() + max_boxes = 20 + + image_index = 0 # only visualize a single image + img = batched_inputs[image_index]["image"] + img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format) + v_gt = Visualizer(img, None) + v_gt = v_gt.overlay_instances(boxes=batched_inputs[image_index]["instances"].gt_boxes) + anno_img = v_gt.get_image() + processed_results = detector_postprocess(results[image_index], img.shape[0], img.shape[1]) + predicted_boxes = processed_results.pred_boxes.tensor.detach().cpu().numpy() + + v_pred = Visualizer(img, None) + v_pred = v_pred.overlay_instances(boxes=predicted_boxes[0:max_boxes]) + prop_img = v_pred.get_image() + vis_img = np.vstack((anno_img, prop_img)) + vis_img = vis_img.transpose(2, 0, 1) + vis_name = f"Top: GT bounding boxes; Bottom: {max_boxes} Highest Scoring Results" + storage.put_image(vis_name, vis_img) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/fcos.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/fcos.py new file mode 100644 index 0000000000000000000000000000000000000000..55cdb76e836214a2b5a7a4a5a5c47e3382dee86d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/fcos.py @@ -0,0 +1,303 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import logging +from typing import List, Optional, Tuple +import torch +from fvcore.nn import sigmoid_focal_loss_jit +from torch import Tensor, nn +from torch.nn import functional as F + +from detectron2.layers import ShapeSpec, batched_nms +from detectron2.structures import Boxes, ImageList, Instances, pairwise_point_box_distance +from detectron2.utils.events import get_event_storage + +from ..anchor_generator import DefaultAnchorGenerator +from ..backbone import Backbone +from ..box_regression import Box2BoxTransformLinear, _dense_box_regression_loss +from .dense_detector import DenseDetector +from .retinanet import RetinaNetHead + +__all__ = ["FCOS"] + + +logger = logging.getLogger(__name__) + + +class FCOS(DenseDetector): + """ + Implement FCOS in :paper:`fcos`. + """ + + def __init__( + self, + *, + backbone: Backbone, + head: nn.Module, + head_in_features: Optional[List[str]] = None, + box2box_transform=None, + num_classes, + center_sampling_radius: float = 1.5, + focal_loss_alpha=0.25, + focal_loss_gamma=2.0, + test_score_thresh=0.2, + test_topk_candidates=1000, + test_nms_thresh=0.6, + max_detections_per_image=100, + pixel_mean, + pixel_std, + ): + """ + Args: + center_sampling_radius: radius of the "center" of a groundtruth box, + within which all anchor points are labeled positive. + Other arguments mean the same as in :class:`RetinaNet`. + """ + super().__init__( + backbone, head, head_in_features, pixel_mean=pixel_mean, pixel_std=pixel_std + ) + + self.num_classes = num_classes + + # FCOS uses one anchor point per location. + # We represent the anchor point by a box whose size equals the anchor stride. + feature_shapes = backbone.output_shape() + fpn_strides = [feature_shapes[k].stride for k in self.head_in_features] + self.anchor_generator = DefaultAnchorGenerator( + sizes=[[k] for k in fpn_strides], aspect_ratios=[1.0], strides=fpn_strides + ) + + # FCOS parameterizes box regression by a linear transform, + # where predictions are normalized by anchor stride (equal to anchor size). + if box2box_transform is None: + box2box_transform = Box2BoxTransformLinear(normalize_by_size=True) + self.box2box_transform = box2box_transform + + self.center_sampling_radius = float(center_sampling_radius) + + # Loss parameters: + self.focal_loss_alpha = focal_loss_alpha + self.focal_loss_gamma = focal_loss_gamma + + # Inference parameters: + self.test_score_thresh = test_score_thresh + self.test_topk_candidates = test_topk_candidates + self.test_nms_thresh = test_nms_thresh + self.max_detections_per_image = max_detections_per_image + + def forward_training(self, images, features, predictions, gt_instances): + # Transpose the Hi*Wi*A dimension to the middle: + pred_logits, pred_anchor_deltas, pred_centerness = self._transpose_dense_predictions( + predictions, [self.num_classes, 4, 1] + ) + anchors = self.anchor_generator(features) + gt_labels, gt_boxes = self.label_anchors(anchors, gt_instances) + return self.losses( + anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes, pred_centerness + ) + + @torch.no_grad() + def match_anchors(self, anchors: List[Boxes], gt_instances: List[Instances]): + """ + Match anchors with ground truth boxes. + + Args: + anchors: #level boxes, from the highest resolution to lower resolution + gt_instances: ground truth instances per image + + Returns: + List[Tensor]: + #image tensors, each is a vector of matched gt + indices (or -1 for unmatched anchors) for all anchors. + """ + num_anchors_per_level = [len(x) for x in anchors] + anchors = Boxes.cat(anchors) # Rx4 + anchor_centers = anchors.get_centers() # Rx2 + anchor_sizes = anchors.tensor[:, 2] - anchors.tensor[:, 0] # R + + lower_bound = anchor_sizes * 4 + lower_bound[: num_anchors_per_level[0]] = 0 + upper_bound = anchor_sizes * 8 + upper_bound[-num_anchors_per_level[-1] :] = float("inf") + + matched_indices = [] + for gt_per_image in gt_instances: + gt_centers = gt_per_image.gt_boxes.get_centers() # Nx2 + # FCOS with center sampling: anchor point must be close enough to gt center. + pairwise_match = (anchor_centers[:, None, :] - gt_centers[None, :, :]).abs_().max( + dim=2 + ).values < self.center_sampling_radius * anchor_sizes[:, None] + pairwise_dist = pairwise_point_box_distance(anchor_centers, gt_per_image.gt_boxes) + + # The original FCOS anchor matching rule: anchor point must be inside gt + pairwise_match &= pairwise_dist.min(dim=2).values > 0 + + # Multilevel anchor matching in FCOS: each anchor is only responsible + # for certain scale range. + pairwise_dist = pairwise_dist.max(dim=2).values + pairwise_match &= (pairwise_dist > lower_bound[:, None]) & ( + pairwise_dist < upper_bound[:, None] + ) + + # Match the GT box with minimum area, if there are multiple GT matches + gt_areas = gt_per_image.gt_boxes.area() # N + pairwise_match = pairwise_match.to(torch.float32) * (1e8 - gt_areas[None, :]) + min_values, matched_idx = pairwise_match.max(dim=1) # R, per-anchor match + matched_idx[min_values < 1e-5] = -1 # Unmatched anchors are assigned -1 + + matched_indices.append(matched_idx) + return matched_indices + + @torch.no_grad() + def label_anchors(self, anchors, gt_instances): + """ + Same interface as :meth:`RetinaNet.label_anchors`, but implemented with FCOS + anchor matching rule. + + Unlike RetinaNet, there are no ignored anchors. + """ + matched_indices = self.match_anchors(anchors, gt_instances) + + matched_labels, matched_boxes = [], [] + for gt_index, gt_per_image in zip(matched_indices, gt_instances): + label = gt_per_image.gt_classes[gt_index.clip(min=0)] + label[gt_index < 0] = self.num_classes # background + + matched_gt_boxes = gt_per_image.gt_boxes[gt_index.clip(min=0)] + + matched_labels.append(label) + matched_boxes.append(matched_gt_boxes) + return matched_labels, matched_boxes + + def losses( + self, anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes, pred_centerness + ): + """ + This method is almost identical to :meth:`RetinaNet.losses`, with an extra + "loss_centerness" in the returned dict. + """ + num_images = len(gt_labels) + gt_labels = torch.stack(gt_labels) # (N, R) + + pos_mask = (gt_labels >= 0) & (gt_labels != self.num_classes) + num_pos_anchors = pos_mask.sum().item() + get_event_storage().put_scalar("num_pos_anchors", num_pos_anchors / num_images) + normalizer = self._ema_update("loss_normalizer", max(num_pos_anchors, 1), 300) + + # classification and regression loss + gt_labels_target = F.one_hot(gt_labels, num_classes=self.num_classes + 1)[ + :, :, :-1 + ] # no loss for the last (background) class + loss_cls = sigmoid_focal_loss_jit( + torch.cat(pred_logits, dim=1), + gt_labels_target.to(pred_logits[0].dtype), + alpha=self.focal_loss_alpha, + gamma=self.focal_loss_gamma, + reduction="sum", + ) + + loss_box_reg = _dense_box_regression_loss( + anchors, + self.box2box_transform, + pred_anchor_deltas, + [x.tensor for x in gt_boxes], + pos_mask, + box_reg_loss_type="giou", + ) + + ctrness_targets = self.compute_ctrness_targets(anchors, gt_boxes) # NxR + pred_centerness = torch.cat(pred_centerness, dim=1).squeeze(dim=2) # NxR + ctrness_loss = F.binary_cross_entropy_with_logits( + pred_centerness[pos_mask], ctrness_targets[pos_mask], reduction="sum" + ) + return { + "loss_fcos_cls": loss_cls / normalizer, + "loss_fcos_loc": loss_box_reg / normalizer, + "loss_fcos_ctr": ctrness_loss / normalizer, + } + + def compute_ctrness_targets(self, anchors, gt_boxes): # NxR + anchors = Boxes.cat(anchors).tensor # Rx4 + reg_targets = [self.box2box_transform.get_deltas(anchors, m.tensor) for m in gt_boxes] + reg_targets = torch.stack(reg_targets, dim=0) # NxRx4 + if len(reg_targets) == 0: + return reg_targets.new_zeros(len(reg_targets)) + left_right = reg_targets[:, :, [0, 2]] + top_bottom = reg_targets[:, :, [1, 3]] + ctrness = (left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) * ( + top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0] + ) + return torch.sqrt(ctrness) + + def forward_inference( + self, images: ImageList, features: List[Tensor], predictions: List[List[Tensor]] + ): + pred_logits, pred_anchor_deltas, pred_centerness = self._transpose_dense_predictions( + predictions, [self.num_classes, 4, 1] + ) + anchors = self.anchor_generator(features) + + results: List[Instances] = [] + for img_idx, image_size in enumerate(images.image_sizes): + scores_per_image = [ + # Multiply and sqrt centerness & classification scores + # (See eqn. 4 in https://arxiv.org/abs/2006.09214) + torch.sqrt(x[img_idx].sigmoid_() * y[img_idx].sigmoid_()) + for x, y in zip(pred_logits, pred_centerness) + ] + deltas_per_image = [x[img_idx] for x in pred_anchor_deltas] + results_per_image = self.inference_single_image( + anchors, scores_per_image, deltas_per_image, image_size + ) + results.append(results_per_image) + return results + + def inference_single_image( + self, + anchors: List[Boxes], + box_cls: List[Tensor], + box_delta: List[Tensor], + image_size: Tuple[int, int], + ): + """ + Identical to :meth:`RetinaNet.inference_single_image. + """ + pred = self._decode_multi_level_predictions( + anchors, + box_cls, + box_delta, + self.test_score_thresh, + self.test_topk_candidates, + image_size, + ) + keep = batched_nms( + pred.pred_boxes.tensor, pred.scores, pred.pred_classes, self.test_nms_thresh + ) + return pred[keep[: self.max_detections_per_image]] + + +class FCOSHead(RetinaNetHead): + """ + The head used in :paper:`fcos`. It adds an additional centerness + prediction branch on top of :class:`RetinaNetHead`. + """ + + def __init__(self, *, input_shape: List[ShapeSpec], conv_dims: List[int], **kwargs): + super().__init__(input_shape=input_shape, conv_dims=conv_dims, num_anchors=1, **kwargs) + # Unlike original FCOS, we do not add an additional learnable scale layer + # because it's found to have no benefits after normalizing regression targets by stride. + self._num_features = len(input_shape) + self.ctrness = nn.Conv2d(conv_dims[-1], 1, kernel_size=3, stride=1, padding=1) + torch.nn.init.normal_(self.ctrness.weight, std=0.01) + torch.nn.init.constant_(self.ctrness.bias, 0) + + def forward(self, features): + assert len(features) == self._num_features + logits = [] + bbox_reg = [] + ctrness = [] + for feature in features: + logits.append(self.cls_score(self.cls_subnet(feature))) + bbox_feature = self.bbox_subnet(feature) + bbox_reg.append(self.bbox_pred(bbox_feature)) + ctrness.append(self.ctrness(bbox_feature)) + return logits, bbox_reg, ctrness diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/panoptic_fpn.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/panoptic_fpn.py new file mode 100644 index 0000000000000000000000000000000000000000..13aeabce162f4114109efe2c7fb4770b89087ab0 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/panoptic_fpn.py @@ -0,0 +1,266 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import logging +from typing import Dict, List +import torch +from torch import nn + +from detectron2.config import configurable +from detectron2.structures import ImageList + +from ..postprocessing import detector_postprocess, sem_seg_postprocess +from .build import META_ARCH_REGISTRY +from .rcnn import GeneralizedRCNN +from .semantic_seg import build_sem_seg_head + +__all__ = ["PanopticFPN"] + + +@META_ARCH_REGISTRY.register() +class PanopticFPN(GeneralizedRCNN): + """ + Implement the paper :paper:`PanopticFPN`. + """ + + @configurable + def __init__( + self, + *, + sem_seg_head: nn.Module, + combine_overlap_thresh: float = 0.5, + combine_stuff_area_thresh: float = 4096, + combine_instances_score_thresh: float = 0.5, + **kwargs, + ): + """ + NOTE: this interface is experimental. + + Args: + sem_seg_head: a module for the semantic segmentation head. + combine_overlap_thresh: combine masks into one instances if + they have enough overlap + combine_stuff_area_thresh: ignore stuff areas smaller than this threshold + combine_instances_score_thresh: ignore instances whose score is + smaller than this threshold + + Other arguments are the same as :class:`GeneralizedRCNN`. + """ + super().__init__(**kwargs) + self.sem_seg_head = sem_seg_head + # options when combining instance & semantic outputs + self.combine_overlap_thresh = combine_overlap_thresh + self.combine_stuff_area_thresh = combine_stuff_area_thresh + self.combine_instances_score_thresh = combine_instances_score_thresh + + @classmethod + def from_config(cls, cfg): + ret = super().from_config(cfg) + ret.update( + { + "combine_overlap_thresh": cfg.MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH, + "combine_stuff_area_thresh": cfg.MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT, + "combine_instances_score_thresh": cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH, # noqa + } + ) + ret["sem_seg_head"] = build_sem_seg_head(cfg, ret["backbone"].output_shape()) + logger = logging.getLogger(__name__) + if not cfg.MODEL.PANOPTIC_FPN.COMBINE.ENABLED: + logger.warning( + "PANOPTIC_FPN.COMBINED.ENABLED is no longer used. " + " model.inference(do_postprocess=) should be used to toggle postprocessing." + ) + if cfg.MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT != 1.0: + w = cfg.MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT + logger.warning( + "PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT should be replaced by weights on each ROI head." + ) + + def update_weight(x): + if isinstance(x, dict): + return {k: v * w for k, v in x.items()} + else: + return x * w + + roi_heads = ret["roi_heads"] + roi_heads.box_predictor.loss_weight = update_weight(roi_heads.box_predictor.loss_weight) + roi_heads.mask_head.loss_weight = update_weight(roi_heads.mask_head.loss_weight) + return ret + + def forward(self, batched_inputs): + """ + Args: + batched_inputs: a list, batched outputs of :class:`DatasetMapper`. + Each item in the list contains the inputs for one image. + + For now, each item in the list is a dict that contains: + + * "image": Tensor, image in (C, H, W) format. + * "instances": Instances + * "sem_seg": semantic segmentation ground truth. + * Other information that's included in the original dicts, such as: + "height", "width" (int): the output resolution of the model, used in inference. + See :meth:`postprocess` for details. + + Returns: + list[dict]: + each dict has the results for one image. The dict contains the following keys: + + * "instances": see :meth:`GeneralizedRCNN.forward` for its format. + * "sem_seg": see :meth:`SemanticSegmentor.forward` for its format. + * "panoptic_seg": See the return value of + :func:`combine_semantic_and_instance_outputs` for its format. + """ + if not self.training: + return self.inference(batched_inputs) + images = self.preprocess_image(batched_inputs) + features = self.backbone(images.tensor) + + assert "sem_seg" in batched_inputs[0] + gt_sem_seg = [x["sem_seg"].to(self.device) for x in batched_inputs] + gt_sem_seg = ImageList.from_tensors( + gt_sem_seg, self.backbone.size_divisibility, self.sem_seg_head.ignore_value + ).tensor + sem_seg_results, sem_seg_losses = self.sem_seg_head(features, gt_sem_seg) + + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + proposals, proposal_losses = self.proposal_generator(images, features, gt_instances) + detector_results, detector_losses = self.roi_heads( + images, features, proposals, gt_instances + ) + + losses = sem_seg_losses + losses.update(proposal_losses) + losses.update(detector_losses) + return losses + + def inference(self, batched_inputs: List[Dict[str, torch.Tensor]], do_postprocess: bool = True): + """ + Run inference on the given inputs. + + Args: + batched_inputs (list[dict]): same as in :meth:`forward` + do_postprocess (bool): whether to apply post-processing on the outputs. + + Returns: + When do_postprocess=True, see docs in :meth:`forward`. + Otherwise, returns a (list[Instances], list[Tensor]) that contains + the raw detector outputs, and raw semantic segmentation outputs. + """ + images = self.preprocess_image(batched_inputs) + features = self.backbone(images.tensor) + sem_seg_results, sem_seg_losses = self.sem_seg_head(features, None) + proposals, _ = self.proposal_generator(images, features, None) + detector_results, _ = self.roi_heads(images, features, proposals, None) + + if do_postprocess: + processed_results = [] + for sem_seg_result, detector_result, input_per_image, image_size in zip( + sem_seg_results, detector_results, batched_inputs, images.image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + sem_seg_r = sem_seg_postprocess(sem_seg_result, image_size, height, width) + detector_r = detector_postprocess(detector_result, height, width) + + processed_results.append({"sem_seg": sem_seg_r, "instances": detector_r}) + + panoptic_r = combine_semantic_and_instance_outputs( + detector_r, + sem_seg_r.argmax(dim=0), + self.combine_overlap_thresh, + self.combine_stuff_area_thresh, + self.combine_instances_score_thresh, + ) + processed_results[-1]["panoptic_seg"] = panoptic_r + return processed_results + else: + return detector_results, sem_seg_results + + +def combine_semantic_and_instance_outputs( + instance_results, + semantic_results, + overlap_threshold, + stuff_area_thresh, + instances_score_thresh, +): + """ + Implement a simple combining logic following + "combine_semantic_and_instance_predictions.py" in panopticapi + to produce panoptic segmentation outputs. + + Args: + instance_results: output of :func:`detector_postprocess`. + semantic_results: an (H, W) tensor, each element is the contiguous semantic + category id + + Returns: + panoptic_seg (Tensor): of shape (height, width) where the values are ids for each segment. + segments_info (list[dict]): Describe each segment in `panoptic_seg`. + Each dict contains keys "id", "category_id", "isthing". + """ + panoptic_seg = torch.zeros_like(semantic_results, dtype=torch.int32) + + # sort instance outputs by scores + sorted_inds = torch.argsort(-instance_results.scores) + + current_segment_id = 0 + segments_info = [] + + instance_masks = instance_results.pred_masks.to(dtype=torch.bool, device=panoptic_seg.device) + + # Add instances one-by-one, check for overlaps with existing ones + for inst_id in sorted_inds: + score = instance_results.scores[inst_id].item() + if score < instances_score_thresh: + break + mask = instance_masks[inst_id] # H,W + mask_area = mask.sum().item() + + if mask_area == 0: + continue + + intersect = (mask > 0) & (panoptic_seg > 0) + intersect_area = intersect.sum().item() + + if intersect_area * 1.0 / mask_area > overlap_threshold: + continue + + if intersect_area > 0: + mask = mask & (panoptic_seg == 0) + + current_segment_id += 1 + panoptic_seg[mask] = current_segment_id + segments_info.append( + { + "id": current_segment_id, + "isthing": True, + "score": score, + "category_id": instance_results.pred_classes[inst_id].item(), + "instance_id": inst_id.item(), + } + ) + + # Add semantic results to remaining empty areas + semantic_labels = torch.unique(semantic_results).cpu().tolist() + for semantic_label in semantic_labels: + if semantic_label == 0: # 0 is a special "thing" class + continue + mask = (semantic_results == semantic_label) & (panoptic_seg == 0) + mask_area = mask.sum().item() + if mask_area < stuff_area_thresh: + continue + + current_segment_id += 1 + panoptic_seg[mask] = current_segment_id + segments_info.append( + { + "id": current_segment_id, + "isthing": False, + "category_id": semantic_label, + "area": mask_area, + } + ) + + return panoptic_seg, segments_info diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/rcnn.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/rcnn.py new file mode 100644 index 0000000000000000000000000000000000000000..7b45363e6eba306c519b5deeca2bc38d6535cec8 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/rcnn.py @@ -0,0 +1,327 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import numpy as np +from typing import Dict, List, Optional, Tuple +import torch +from torch import nn + +from detectron2.config import configurable +from detectron2.data.detection_utils import convert_image_to_rgb +from detectron2.structures import ImageList, Instances +from detectron2.utils.events import get_event_storage +from detectron2.utils.logger import log_first_n + +from ..backbone import Backbone, build_backbone +from ..postprocessing import detector_postprocess +from ..proposal_generator import build_proposal_generator +from ..roi_heads import build_roi_heads +from .build import META_ARCH_REGISTRY + +__all__ = ["GeneralizedRCNN", "ProposalNetwork"] + + +@META_ARCH_REGISTRY.register() +class GeneralizedRCNN(nn.Module): + """ + Generalized R-CNN. Any models that contains the following three components: + 1. Per-image feature extraction (aka backbone) + 2. Region proposal generation + 3. Per-region feature extraction and prediction + """ + + @configurable + def __init__( + self, + *, + backbone: Backbone, + proposal_generator: nn.Module, + roi_heads: nn.Module, + pixel_mean: Tuple[float], + pixel_std: Tuple[float], + input_format: Optional[str] = None, + vis_period: int = 0, + ): + """ + Args: + backbone: a backbone module, must follow detectron2's backbone interface + proposal_generator: a module that generates proposals using backbone features + roi_heads: a ROI head that performs per-region computation + pixel_mean, pixel_std: list or tuple with #channels element, representing + the per-channel mean and std to be used to normalize the input image + input_format: describe the meaning of channels of input. Needed by visualization + vis_period: the period to run visualization. Set to 0 to disable. + """ + super().__init__() + self.backbone = backbone + self.proposal_generator = proposal_generator + self.roi_heads = roi_heads + + self.input_format = input_format + self.vis_period = vis_period + if vis_period > 0: + assert input_format is not None, "input_format is required for visualization!" + + self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False) + self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False) + assert ( + self.pixel_mean.shape == self.pixel_std.shape + ), f"{self.pixel_mean} and {self.pixel_std} have different shapes!" + + @classmethod + def from_config(cls, cfg): + backbone = build_backbone(cfg) + return { + "backbone": backbone, + "proposal_generator": build_proposal_generator(cfg, backbone.output_shape()), + "roi_heads": build_roi_heads(cfg, backbone.output_shape()), + "input_format": cfg.INPUT.FORMAT, + "vis_period": cfg.VIS_PERIOD, + "pixel_mean": cfg.MODEL.PIXEL_MEAN, + "pixel_std": cfg.MODEL.PIXEL_STD, + } + + @property + def device(self): + return self.pixel_mean.device + + def visualize_training(self, batched_inputs, proposals): + """ + A function used to visualize images and proposals. It shows ground truth + bounding boxes on the original image and up to 20 top-scoring predicted + object proposals on the original image. Users can implement different + visualization functions for different models. + + Args: + batched_inputs (list): a list that contains input to the model. + proposals (list): a list that contains predicted proposals. Both + batched_inputs and proposals should have the same length. + """ + from detectron2.utils.visualizer import Visualizer + + storage = get_event_storage() + max_vis_prop = 20 + + for input, prop in zip(batched_inputs, proposals): + img = input["image"] + img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format) + v_gt = Visualizer(img, None) + v_gt = v_gt.overlay_instances(boxes=input["instances"].gt_boxes) + anno_img = v_gt.get_image() + box_size = min(len(prop.proposal_boxes), max_vis_prop) + v_pred = Visualizer(img, None) + v_pred = v_pred.overlay_instances( + boxes=prop.proposal_boxes[0:box_size].tensor.cpu().numpy() + ) + prop_img = v_pred.get_image() + vis_img = np.concatenate((anno_img, prop_img), axis=1) + vis_img = vis_img.transpose(2, 0, 1) + vis_name = "Left: GT bounding boxes; Right: Predicted proposals" + storage.put_image(vis_name, vis_img) + break # only visualize one image in a batch + + def forward(self, batched_inputs: List[Dict[str, torch.Tensor]]): + """ + Args: + batched_inputs: a list, batched outputs of :class:`DatasetMapper` . + Each item in the list contains the inputs for one image. + For now, each item in the list is a dict that contains: + + * image: Tensor, image in (C, H, W) format. + * instances (optional): groundtruth :class:`Instances` + * proposals (optional): :class:`Instances`, precomputed proposals. + + Other information that's included in the original dicts, such as: + + * "height", "width" (int): the output resolution of the model, used in inference. + See :meth:`postprocess` for details. + + Returns: + list[dict]: + Each dict is the output for one input image. + The dict contains one key "instances" whose value is a :class:`Instances`. + The :class:`Instances` object has the following keys: + "pred_boxes", "pred_classes", "scores", "pred_masks", "pred_keypoints" + """ + if not self.training: + return self.inference(batched_inputs) + + images = self.preprocess_image(batched_inputs) + if "instances" in batched_inputs[0]: + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + else: + gt_instances = None + + features = self.backbone(images.tensor) + + if self.proposal_generator is not None: + proposals, proposal_losses = self.proposal_generator(images, features, gt_instances) + else: + assert "proposals" in batched_inputs[0] + proposals = [x["proposals"].to(self.device) for x in batched_inputs] + proposal_losses = {} + + _, detector_losses = self.roi_heads(images, features, proposals, gt_instances) + if self.vis_period > 0: + storage = get_event_storage() + if storage.iter % self.vis_period == 0: + self.visualize_training(batched_inputs, proposals) + + losses = {} + losses.update(detector_losses) + losses.update(proposal_losses) + return losses + + def inference( + self, + batched_inputs: List[Dict[str, torch.Tensor]], + detected_instances: Optional[List[Instances]] = None, + do_postprocess: bool = True, + ): + """ + Run inference on the given inputs. + + Args: + batched_inputs (list[dict]): same as in :meth:`forward` + detected_instances (None or list[Instances]): if not None, it + contains an `Instances` object per image. The `Instances` + object contains "pred_boxes" and "pred_classes" which are + known boxes in the image. + The inference will then skip the detection of bounding boxes, + and only predict other per-ROI outputs. + do_postprocess (bool): whether to apply post-processing on the outputs. + + Returns: + When do_postprocess=True, same as in :meth:`forward`. + Otherwise, a list[Instances] containing raw network outputs. + """ + assert not self.training + + images = self.preprocess_image(batched_inputs) + features = self.backbone(images.tensor) + + if detected_instances is None: + if self.proposal_generator is not None: + proposals, _ = self.proposal_generator(images, features, None) + else: + assert "proposals" in batched_inputs[0] + proposals = [x["proposals"].to(self.device) for x in batched_inputs] + + results, _ = self.roi_heads(images, features, proposals, None) + else: + detected_instances = [x.to(self.device) for x in detected_instances] + results = self.roi_heads.forward_with_given_boxes(features, detected_instances) + + if do_postprocess: + assert not torch.jit.is_scripting(), "Scripting is not supported for postprocess." + return GeneralizedRCNN._postprocess(results, batched_inputs, images.image_sizes) + else: + return results + + def preprocess_image(self, batched_inputs: List[Dict[str, torch.Tensor]]): + """ + Normalize, pad and batch the input images. + """ + images = [x["image"].to(self.device) for x in batched_inputs] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors(images, self.backbone.size_divisibility) + return images + + @staticmethod + def _postprocess(instances, batched_inputs: List[Dict[str, torch.Tensor]], image_sizes): + """ + Rescale the output instances to the target size. + """ + # note: private function; subject to changes + processed_results = [] + for results_per_image, input_per_image, image_size in zip( + instances, batched_inputs, image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + r = detector_postprocess(results_per_image, height, width) + processed_results.append({"instances": r}) + return processed_results + + +@META_ARCH_REGISTRY.register() +class ProposalNetwork(nn.Module): + """ + A meta architecture that only predicts object proposals. + """ + + @configurable + def __init__( + self, + *, + backbone: Backbone, + proposal_generator: nn.Module, + pixel_mean: Tuple[float], + pixel_std: Tuple[float], + ): + """ + Args: + backbone: a backbone module, must follow detectron2's backbone interface + proposal_generator: a module that generates proposals using backbone features + pixel_mean, pixel_std: list or tuple with #channels element, representing + the per-channel mean and std to be used to normalize the input image + """ + super().__init__() + self.backbone = backbone + self.proposal_generator = proposal_generator + self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False) + self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False) + + @classmethod + def from_config(cls, cfg): + backbone = build_backbone(cfg) + return { + "backbone": backbone, + "proposal_generator": build_proposal_generator(cfg, backbone.output_shape()), + "pixel_mean": cfg.MODEL.PIXEL_MEAN, + "pixel_std": cfg.MODEL.PIXEL_STD, + } + + @property + def device(self): + return self.pixel_mean.device + + def forward(self, batched_inputs): + """ + Args: + Same as in :class:`GeneralizedRCNN.forward` + + Returns: + list[dict]: + Each dict is the output for one input image. + The dict contains one key "proposals" whose value is a + :class:`Instances` with keys "proposal_boxes" and "objectness_logits". + """ + images = [x["image"].to(self.device) for x in batched_inputs] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors(images, self.backbone.size_divisibility) + features = self.backbone(images.tensor) + + if "instances" in batched_inputs[0]: + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + elif "targets" in batched_inputs[0]: + log_first_n( + logging.WARN, "'targets' in the model inputs is now renamed to 'instances'!", n=10 + ) + gt_instances = [x["targets"].to(self.device) for x in batched_inputs] + else: + gt_instances = None + proposals, proposal_losses = self.proposal_generator(images, features, gt_instances) + # In training, the proposals are not useful at all but we generate them anyway. + # This makes RPN-only models about 5% slower. + if self.training: + return proposal_losses + + processed_results = [] + for results_per_image, input_per_image, image_size in zip( + proposals, batched_inputs, images.image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + r = detector_postprocess(results_per_image, height, width) + processed_results.append({"proposals": r}) + return processed_results diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/retinanet.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/retinanet.py new file mode 100644 index 0000000000000000000000000000000000000000..3ea88f61759e497ca629d1d1add43b7bd44e8072 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/retinanet.py @@ -0,0 +1,439 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import math +from typing import List, Tuple +import torch +from fvcore.nn import sigmoid_focal_loss_jit +from torch import Tensor, nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import CycleBatchNormList, ShapeSpec, batched_nms, cat, get_norm +from detectron2.structures import Boxes, ImageList, Instances, pairwise_iou +from detectron2.utils.events import get_event_storage + +from ..anchor_generator import build_anchor_generator +from ..backbone import Backbone, build_backbone +from ..box_regression import Box2BoxTransform, _dense_box_regression_loss +from ..matcher import Matcher +from .build import META_ARCH_REGISTRY +from .dense_detector import DenseDetector, permute_to_N_HWA_K # noqa + +__all__ = ["RetinaNet"] + + +logger = logging.getLogger(__name__) + + +@META_ARCH_REGISTRY.register() +class RetinaNet(DenseDetector): + """ + Implement RetinaNet in :paper:`RetinaNet`. + """ + + @configurable + def __init__( + self, + *, + backbone: Backbone, + head: nn.Module, + head_in_features, + anchor_generator, + box2box_transform, + anchor_matcher, + num_classes, + focal_loss_alpha=0.25, + focal_loss_gamma=2.0, + smooth_l1_beta=0.0, + box_reg_loss_type="smooth_l1", + test_score_thresh=0.05, + test_topk_candidates=1000, + test_nms_thresh=0.5, + max_detections_per_image=100, + pixel_mean, + pixel_std, + vis_period=0, + input_format="BGR", + ): + """ + NOTE: this interface is experimental. + + Args: + backbone: a backbone module, must follow detectron2's backbone interface + head (nn.Module): a module that predicts logits and regression deltas + for each level from a list of per-level features + head_in_features (Tuple[str]): Names of the input feature maps to be used in head + anchor_generator (nn.Module): a module that creates anchors from a + list of features. Usually an instance of :class:`AnchorGenerator` + box2box_transform (Box2BoxTransform): defines the transform from anchors boxes to + instance boxes + anchor_matcher (Matcher): label the anchors by matching them with ground truth. + num_classes (int): number of classes. Used to label background proposals. + + # Loss parameters: + focal_loss_alpha (float): focal_loss_alpha + focal_loss_gamma (float): focal_loss_gamma + smooth_l1_beta (float): smooth_l1_beta + box_reg_loss_type (str): Options are "smooth_l1", "giou", "diou", "ciou" + + # Inference parameters: + test_score_thresh (float): Inference cls score threshold, only anchors with + score > INFERENCE_TH are considered for inference (to improve speed) + test_topk_candidates (int): Select topk candidates before NMS + test_nms_thresh (float): Overlap threshold used for non-maximum suppression + (suppress boxes with IoU >= this threshold) + max_detections_per_image (int): + Maximum number of detections to return per image during inference + (100 is based on the limit established for the COCO dataset). + + pixel_mean, pixel_std: see :class:`DenseDetector`. + """ + super().__init__( + backbone, head, head_in_features, pixel_mean=pixel_mean, pixel_std=pixel_std + ) + self.num_classes = num_classes + + # Anchors + self.anchor_generator = anchor_generator + self.box2box_transform = box2box_transform + self.anchor_matcher = anchor_matcher + + # Loss parameters: + self.focal_loss_alpha = focal_loss_alpha + self.focal_loss_gamma = focal_loss_gamma + self.smooth_l1_beta = smooth_l1_beta + self.box_reg_loss_type = box_reg_loss_type + # Inference parameters: + self.test_score_thresh = test_score_thresh + self.test_topk_candidates = test_topk_candidates + self.test_nms_thresh = test_nms_thresh + self.max_detections_per_image = max_detections_per_image + # Vis parameters + self.vis_period = vis_period + self.input_format = input_format + + @classmethod + def from_config(cls, cfg): + backbone = build_backbone(cfg) + backbone_shape = backbone.output_shape() + feature_shapes = [backbone_shape[f] for f in cfg.MODEL.RETINANET.IN_FEATURES] + head = RetinaNetHead(cfg, feature_shapes) + anchor_generator = build_anchor_generator(cfg, feature_shapes) + return { + "backbone": backbone, + "head": head, + "anchor_generator": anchor_generator, + "box2box_transform": Box2BoxTransform(weights=cfg.MODEL.RETINANET.BBOX_REG_WEIGHTS), + "anchor_matcher": Matcher( + cfg.MODEL.RETINANET.IOU_THRESHOLDS, + cfg.MODEL.RETINANET.IOU_LABELS, + allow_low_quality_matches=True, + ), + "pixel_mean": cfg.MODEL.PIXEL_MEAN, + "pixel_std": cfg.MODEL.PIXEL_STD, + "num_classes": cfg.MODEL.RETINANET.NUM_CLASSES, + "head_in_features": cfg.MODEL.RETINANET.IN_FEATURES, + # Loss parameters: + "focal_loss_alpha": cfg.MODEL.RETINANET.FOCAL_LOSS_ALPHA, + "focal_loss_gamma": cfg.MODEL.RETINANET.FOCAL_LOSS_GAMMA, + "smooth_l1_beta": cfg.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA, + "box_reg_loss_type": cfg.MODEL.RETINANET.BBOX_REG_LOSS_TYPE, + # Inference parameters: + "test_score_thresh": cfg.MODEL.RETINANET.SCORE_THRESH_TEST, + "test_topk_candidates": cfg.MODEL.RETINANET.TOPK_CANDIDATES_TEST, + "test_nms_thresh": cfg.MODEL.RETINANET.NMS_THRESH_TEST, + "max_detections_per_image": cfg.TEST.DETECTIONS_PER_IMAGE, + # Vis parameters + "vis_period": cfg.VIS_PERIOD, + "input_format": cfg.INPUT.FORMAT, + } + + def forward_training(self, images, features, predictions, gt_instances): + # Transpose the Hi*Wi*A dimension to the middle: + pred_logits, pred_anchor_deltas = self._transpose_dense_predictions( + predictions, [self.num_classes, 4] + ) + anchors = self.anchor_generator(features) + gt_labels, gt_boxes = self.label_anchors(anchors, gt_instances) + return self.losses(anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes) + + def losses(self, anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes): + """ + Args: + anchors (list[Boxes]): a list of #feature level Boxes + gt_labels, gt_boxes: see output of :meth:`RetinaNet.label_anchors`. + Their shapes are (N, R) and (N, R, 4), respectively, where R is + the total number of anchors across levels, i.e. sum(Hi x Wi x Ai) + pred_logits, pred_anchor_deltas: both are list[Tensor]. Each element in the + list corresponds to one level and has shape (N, Hi * Wi * Ai, K or 4). + Where K is the number of classes used in `pred_logits`. + + Returns: + dict[str, Tensor]: + mapping from a named loss to a scalar tensor storing the loss. + Used during training only. The dict keys are: "loss_cls" and "loss_box_reg" + """ + num_images = len(gt_labels) + gt_labels = torch.stack(gt_labels) # (N, R) + + valid_mask = gt_labels >= 0 + pos_mask = (gt_labels >= 0) & (gt_labels != self.num_classes) + num_pos_anchors = pos_mask.sum().item() + get_event_storage().put_scalar("num_pos_anchors", num_pos_anchors / num_images) + normalizer = self._ema_update("loss_normalizer", max(num_pos_anchors, 1), 100) + + # classification and regression loss + gt_labels_target = F.one_hot(gt_labels[valid_mask], num_classes=self.num_classes + 1)[ + :, :-1 + ] # no loss for the last (background) class + loss_cls = sigmoid_focal_loss_jit( + cat(pred_logits, dim=1)[valid_mask], + gt_labels_target.to(pred_logits[0].dtype), + alpha=self.focal_loss_alpha, + gamma=self.focal_loss_gamma, + reduction="sum", + ) + + loss_box_reg = _dense_box_regression_loss( + anchors, + self.box2box_transform, + pred_anchor_deltas, + gt_boxes, + pos_mask, + box_reg_loss_type=self.box_reg_loss_type, + smooth_l1_beta=self.smooth_l1_beta, + ) + + return { + "loss_cls": loss_cls / normalizer, + "loss_box_reg": loss_box_reg / normalizer, + } + + @torch.no_grad() + def label_anchors(self, anchors, gt_instances): + """ + Args: + anchors (list[Boxes]): A list of #feature level Boxes. + The Boxes contains anchors of this image on the specific feature level. + gt_instances (list[Instances]): a list of N `Instances`s. The i-th + `Instances` contains the ground-truth per-instance annotations + for the i-th input image. + + Returns: + list[Tensor]: List of #img tensors. i-th element is a vector of labels whose length is + the total number of anchors across all feature maps (sum(Hi * Wi * A)). + Label values are in {-1, 0, ..., K}, with -1 means ignore, and K means background. + + list[Tensor]: i-th element is a Rx4 tensor, where R is the total number of anchors + across feature maps. The values are the matched gt boxes for each anchor. + Values are undefined for those anchors not labeled as foreground. + """ + anchors = Boxes.cat(anchors) # Rx4 + + gt_labels = [] + matched_gt_boxes = [] + for gt_per_image in gt_instances: + match_quality_matrix = pairwise_iou(gt_per_image.gt_boxes, anchors) + matched_idxs, anchor_labels = self.anchor_matcher(match_quality_matrix) + del match_quality_matrix + + if len(gt_per_image) > 0: + matched_gt_boxes_i = gt_per_image.gt_boxes.tensor[matched_idxs] + + gt_labels_i = gt_per_image.gt_classes[matched_idxs] + # Anchors with label 0 are treated as background. + gt_labels_i[anchor_labels == 0] = self.num_classes + # Anchors with label -1 are ignored. + gt_labels_i[anchor_labels == -1] = -1 + else: + matched_gt_boxes_i = torch.zeros_like(anchors.tensor) + gt_labels_i = torch.zeros_like(matched_idxs) + self.num_classes + + gt_labels.append(gt_labels_i) + matched_gt_boxes.append(matched_gt_boxes_i) + + return gt_labels, matched_gt_boxes + + def forward_inference( + self, images: ImageList, features: List[Tensor], predictions: List[List[Tensor]] + ): + pred_logits, pred_anchor_deltas = self._transpose_dense_predictions( + predictions, [self.num_classes, 4] + ) + anchors = self.anchor_generator(features) + + results: List[Instances] = [] + for img_idx, image_size in enumerate(images.image_sizes): + scores_per_image = [x[img_idx].sigmoid_() for x in pred_logits] + deltas_per_image = [x[img_idx] for x in pred_anchor_deltas] + results_per_image = self.inference_single_image( + anchors, scores_per_image, deltas_per_image, image_size + ) + results.append(results_per_image) + return results + + def inference_single_image( + self, + anchors: List[Boxes], + box_cls: List[Tensor], + box_delta: List[Tensor], + image_size: Tuple[int, int], + ): + """ + Single-image inference. Return bounding-box detection results by thresholding + on scores and applying non-maximum suppression (NMS). + + Arguments: + anchors (list[Boxes]): list of #feature levels. Each entry contains + a Boxes object, which contains all the anchors in that feature level. + box_cls (list[Tensor]): list of #feature levels. Each entry contains + tensor of size (H x W x A, K) + box_delta (list[Tensor]): Same shape as 'box_cls' except that K becomes 4. + image_size (tuple(H, W)): a tuple of the image height and width. + + Returns: + Same as `inference`, but for only one image. + """ + pred = self._decode_multi_level_predictions( + anchors, + box_cls, + box_delta, + self.test_score_thresh, + self.test_topk_candidates, + image_size, + ) + keep = batched_nms( # per-class NMS + pred.pred_boxes.tensor, pred.scores, pred.pred_classes, self.test_nms_thresh + ) + return pred[keep[: self.max_detections_per_image]] + + +class RetinaNetHead(nn.Module): + """ + The head used in RetinaNet for object classification and box regression. + It has two subnets for the two tasks, with a common structure but separate parameters. + """ + + @configurable + def __init__( + self, + *, + input_shape: List[ShapeSpec], + num_classes, + num_anchors, + conv_dims: List[int], + norm="", + prior_prob=0.01, + ): + """ + NOTE: this interface is experimental. + + Args: + input_shape (List[ShapeSpec]): input shape + num_classes (int): number of classes. Used to label background proposals. + num_anchors (int): number of generated anchors + conv_dims (List[int]): dimensions for each convolution layer + norm (str or callable): + Normalization for conv layers except for the two output layers. + See :func:`detectron2.layers.get_norm` for supported types. + prior_prob (float): Prior weight for computing bias + """ + super().__init__() + + self._num_features = len(input_shape) + if norm == "BN" or norm == "SyncBN": + logger.info( + f"Using domain-specific {norm} in RetinaNetHead with len={self._num_features}." + ) + bn_class = nn.BatchNorm2d if norm == "BN" else nn.SyncBatchNorm + + def norm(c): + return CycleBatchNormList( + length=self._num_features, bn_class=bn_class, num_features=c + ) + + else: + norm_name = str(type(get_norm(norm, 1))) + if "BN" in norm_name: + logger.warning( + f"Shared BatchNorm (type={norm_name}) may not work well in RetinaNetHead." + ) + + cls_subnet = [] + bbox_subnet = [] + for in_channels, out_channels in zip( + [input_shape[0].channels] + list(conv_dims), conv_dims + ): + cls_subnet.append( + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + ) + if norm: + cls_subnet.append(get_norm(norm, out_channels)) + cls_subnet.append(nn.ReLU()) + bbox_subnet.append( + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + ) + if norm: + bbox_subnet.append(get_norm(norm, out_channels)) + bbox_subnet.append(nn.ReLU()) + + self.cls_subnet = nn.Sequential(*cls_subnet) + self.bbox_subnet = nn.Sequential(*bbox_subnet) + self.cls_score = nn.Conv2d( + conv_dims[-1], num_anchors * num_classes, kernel_size=3, stride=1, padding=1 + ) + self.bbox_pred = nn.Conv2d( + conv_dims[-1], num_anchors * 4, kernel_size=3, stride=1, padding=1 + ) + + # Initialization + for modules in [self.cls_subnet, self.bbox_subnet, self.cls_score, self.bbox_pred]: + for layer in modules.modules(): + if isinstance(layer, nn.Conv2d): + torch.nn.init.normal_(layer.weight, mean=0, std=0.01) + torch.nn.init.constant_(layer.bias, 0) + + # Use prior in model initialization to improve stability + bias_value = -(math.log((1 - prior_prob) / prior_prob)) + torch.nn.init.constant_(self.cls_score.bias, bias_value) + + @classmethod + def from_config(cls, cfg, input_shape: List[ShapeSpec]): + num_anchors = build_anchor_generator(cfg, input_shape).num_cell_anchors + assert ( + len(set(num_anchors)) == 1 + ), "Using different number of anchors between levels is not currently supported!" + num_anchors = num_anchors[0] + + return { + "input_shape": input_shape, + "num_classes": cfg.MODEL.RETINANET.NUM_CLASSES, + "conv_dims": [input_shape[0].channels] * cfg.MODEL.RETINANET.NUM_CONVS, + "prior_prob": cfg.MODEL.RETINANET.PRIOR_PROB, + "norm": cfg.MODEL.RETINANET.NORM, + "num_anchors": num_anchors, + } + + def forward(self, features: List[Tensor]): + """ + Arguments: + features (list[Tensor]): FPN feature map tensors in high to low resolution. + Each tensor in the list correspond to different feature levels. + + Returns: + logits (list[Tensor]): #lvl tensors, each has shape (N, AxK, Hi, Wi). + The tensor predicts the classification probability + at each spatial position for each of the A anchors and K object + classes. + bbox_reg (list[Tensor]): #lvl tensors, each has shape (N, Ax4, Hi, Wi). + The tensor predicts 4-vector (dx,dy,dw,dh) box + regression values for every anchor. These values are the + relative offset between the anchor and the ground truth box. + """ + assert len(features) == self._num_features + logits = [] + bbox_reg = [] + for feature in features: + logits.append(self.cls_score(self.cls_subnet(feature))) + bbox_reg.append(self.bbox_pred(self.bbox_subnet(feature))) + return logits, bbox_reg diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/semantic_seg.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/semantic_seg.py new file mode 100644 index 0000000000000000000000000000000000000000..6dd3dc23f5a333e1170ab317875551f852a0b53f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/semantic_seg.py @@ -0,0 +1,260 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +from typing import Callable, Dict, Optional, Tuple, Union +import fvcore.nn.weight_init as weight_init +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import Conv2d, ShapeSpec, get_norm +from detectron2.structures import ImageList +from detectron2.utils.registry import Registry + +from ..backbone import Backbone, build_backbone +from ..postprocessing import sem_seg_postprocess +from .build import META_ARCH_REGISTRY + +__all__ = [ + "SemanticSegmentor", + "SEM_SEG_HEADS_REGISTRY", + "SemSegFPNHead", + "build_sem_seg_head", +] + + +SEM_SEG_HEADS_REGISTRY = Registry("SEM_SEG_HEADS") +SEM_SEG_HEADS_REGISTRY.__doc__ = """ +Registry for semantic segmentation heads, which make semantic segmentation predictions +from feature maps. +""" + + +@META_ARCH_REGISTRY.register() +class SemanticSegmentor(nn.Module): + """ + Main class for semantic segmentation architectures. + """ + + @configurable + def __init__( + self, + *, + backbone: Backbone, + sem_seg_head: nn.Module, + pixel_mean: Tuple[float], + pixel_std: Tuple[float], + ): + """ + Args: + backbone: a backbone module, must follow detectron2's backbone interface + sem_seg_head: a module that predicts semantic segmentation from backbone features + pixel_mean, pixel_std: list or tuple with #channels element, representing + the per-channel mean and std to be used to normalize the input image + """ + super().__init__() + self.backbone = backbone + self.sem_seg_head = sem_seg_head + self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False) + self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False) + + @classmethod + def from_config(cls, cfg): + backbone = build_backbone(cfg) + sem_seg_head = build_sem_seg_head(cfg, backbone.output_shape()) + return { + "backbone": backbone, + "sem_seg_head": sem_seg_head, + "pixel_mean": cfg.MODEL.PIXEL_MEAN, + "pixel_std": cfg.MODEL.PIXEL_STD, + } + + @property + def device(self): + return self.pixel_mean.device + + def forward(self, batched_inputs): + """ + Args: + batched_inputs: a list, batched outputs of :class:`DatasetMapper`. + Each item in the list contains the inputs for one image. + + For now, each item in the list is a dict that contains: + + * "image": Tensor, image in (C, H, W) format. + * "sem_seg": semantic segmentation ground truth + * Other information that's included in the original dicts, such as: + "height", "width" (int): the output resolution of the model (may be different + from input resolution), used in inference. + + + Returns: + list[dict]: + Each dict is the output for one input image. + The dict contains one key "sem_seg" whose value is a + Tensor that represents the + per-pixel segmentation prediced by the head. + The prediction has shape KxHxW that represents the logits of + each class for each pixel. + """ + images = [x["image"].to(self.device) for x in batched_inputs] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors(images, self.backbone.size_divisibility) + + features = self.backbone(images.tensor) + + if "sem_seg" in batched_inputs[0]: + targets = [x["sem_seg"].to(self.device) for x in batched_inputs] + targets = ImageList.from_tensors( + targets, self.backbone.size_divisibility, self.sem_seg_head.ignore_value + ).tensor + else: + targets = None + results, losses = self.sem_seg_head(features, targets) + + if self.training: + return losses + + processed_results = [] + for result, input_per_image, image_size in zip(results, batched_inputs, images.image_sizes): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + r = sem_seg_postprocess(result, image_size, height, width) + processed_results.append({"sem_seg": r}) + return processed_results + + +def build_sem_seg_head(cfg, input_shape): + """ + Build a semantic segmentation head from `cfg.MODEL.SEM_SEG_HEAD.NAME`. + """ + name = cfg.MODEL.SEM_SEG_HEAD.NAME + return SEM_SEG_HEADS_REGISTRY.get(name)(cfg, input_shape) + + +@SEM_SEG_HEADS_REGISTRY.register() +class SemSegFPNHead(nn.Module): + """ + A semantic segmentation head described in :paper:`PanopticFPN`. + It takes a list of FPN features as input, and applies a sequence of + 3x3 convs and upsampling to scale all of them to the stride defined by + ``common_stride``. Then these features are added and used to make final + predictions by another 1x1 conv layer. + """ + + @configurable + def __init__( + self, + input_shape: Dict[str, ShapeSpec], + *, + num_classes: int, + conv_dims: int, + common_stride: int, + loss_weight: float = 1.0, + norm: Optional[Union[str, Callable]] = None, + ignore_value: int = -1, + ): + """ + NOTE: this interface is experimental. + + Args: + input_shape: shapes (channels and stride) of the input features + num_classes: number of classes to predict + conv_dims: number of output channels for the intermediate conv layers. + common_stride: the common stride that all features will be upscaled to + loss_weight: loss weight + norm (str or callable): normalization for all conv layers + ignore_value: category id to be ignored during training. + """ + super().__init__() + input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride) + if not len(input_shape): + raise ValueError("SemSegFPNHead(input_shape=) cannot be empty!") + self.in_features = [k for k, v in input_shape] + feature_strides = [v.stride for k, v in input_shape] + feature_channels = [v.channels for k, v in input_shape] + + self.ignore_value = ignore_value + self.common_stride = common_stride + self.loss_weight = loss_weight + + self.scale_heads = [] + for in_feature, stride, channels in zip( + self.in_features, feature_strides, feature_channels + ): + head_ops = [] + head_length = max(1, int(np.log2(stride) - np.log2(self.common_stride))) + for k in range(head_length): + norm_module = get_norm(norm, conv_dims) + conv = Conv2d( + channels if k == 0 else conv_dims, + conv_dims, + kernel_size=3, + stride=1, + padding=1, + bias=not norm, + norm=norm_module, + activation=F.relu, + ) + weight_init.c2_msra_fill(conv) + head_ops.append(conv) + if stride != self.common_stride: + head_ops.append( + nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False) + ) + self.scale_heads.append(nn.Sequential(*head_ops)) + self.add_module(in_feature, self.scale_heads[-1]) + self.predictor = Conv2d(conv_dims, num_classes, kernel_size=1, stride=1, padding=0) + weight_init.c2_msra_fill(self.predictor) + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + return { + "input_shape": { + k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES + }, + "ignore_value": cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE, + "num_classes": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES, + "conv_dims": cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM, + "common_stride": cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE, + "norm": cfg.MODEL.SEM_SEG_HEAD.NORM, + "loss_weight": cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT, + } + + def forward(self, features, targets=None): + """ + Returns: + In training, returns (None, dict of losses) + In inference, returns (CxHxW logits, {}) + """ + x = self.layers(features) + if self.training: + return None, self.losses(x, targets) + else: + x = F.interpolate( + x, scale_factor=self.common_stride, mode="bilinear", align_corners=False + ) + return x, {} + + def layers(self, features): + for i, f in enumerate(self.in_features): + if i == 0: + x = self.scale_heads[i](features[f]) + else: + x = x + self.scale_heads[i](features[f]) + x = self.predictor(x) + return x + + def losses(self, predictions, targets): + predictions = predictions.float() # https://github.com/pytorch/pytorch/issues/48163 + predictions = F.interpolate( + predictions, + scale_factor=self.common_stride, + mode="bilinear", + align_corners=False, + ) + loss = F.cross_entropy( + predictions, targets, reduction="mean", ignore_index=self.ignore_value + ) + losses = {"loss_sem_seg": loss * self.loss_weight} + return losses diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/mmdet_wrapper.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/mmdet_wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..386e92960511a782f48ec9350d19c3851e43849f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/mmdet_wrapper.py @@ -0,0 +1,274 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import itertools +import logging +import numpy as np +from collections import OrderedDict +from collections.abc import Mapping +from typing import Dict, List, Optional, Tuple, Union +import torch +from omegaconf import DictConfig, OmegaConf +from torch import Tensor, nn + +from detectron2.layers import ShapeSpec +from detectron2.structures import BitMasks, Boxes, ImageList, Instances +from detectron2.utils.events import get_event_storage + +from .backbone import Backbone + +logger = logging.getLogger(__name__) + + +def _to_container(cfg): + """ + mmdet will assert the type of dict/list. + So convert omegaconf objects to dict/list. + """ + if isinstance(cfg, DictConfig): + cfg = OmegaConf.to_container(cfg, resolve=True) + from mmcv.utils import ConfigDict + + return ConfigDict(cfg) + + +class MMDetBackbone(Backbone): + """ + Wrapper of mmdetection backbones to use in detectron2. + + mmdet backbones produce list/tuple of tensors, while detectron2 backbones + produce a dict of tensors. This class wraps the given backbone to produce + output in detectron2's convention, so it can be used in place of detectron2 + backbones. + """ + + def __init__( + self, + backbone: Union[nn.Module, Mapping], + neck: Union[nn.Module, Mapping, None] = None, + *, + output_shapes: List[ShapeSpec], + output_names: Optional[List[str]] = None, + ): + """ + Args: + backbone: either a backbone module or a mmdet config dict that defines a + backbone. The backbone takes a 4D image tensor and returns a + sequence of tensors. + neck: either a backbone module or a mmdet config dict that defines a + neck. The neck takes outputs of backbone and returns a + sequence of tensors. If None, no neck is used. + pretrained_backbone: defines the backbone weights that can be loaded by + mmdet, such as "torchvision://resnet50". + output_shapes: shape for every output of the backbone (or neck, if given). + stride and channels are often needed. + output_names: names for every output of the backbone (or neck, if given). + By default, will use "out0", "out1", ... + """ + super().__init__() + if isinstance(backbone, Mapping): + from mmdet.models import build_backbone + + backbone = build_backbone(_to_container(backbone)) + self.backbone = backbone + + if isinstance(neck, Mapping): + from mmdet.models import build_neck + + neck = build_neck(_to_container(neck)) + self.neck = neck + + # "Neck" weights, if any, are part of neck itself. This is the interface + # of mmdet so we follow it. Reference: + # https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/detectors/two_stage.py + logger.info("Initializing mmdet backbone weights...") + self.backbone.init_weights() + # train() in mmdet modules is non-trivial, and has to be explicitly + # called. Reference: + # https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/backbones/resnet.py + self.backbone.train() + if self.neck is not None: + logger.info("Initializing mmdet neck weights ...") + if isinstance(self.neck, nn.Sequential): + for m in self.neck: + m.init_weights() + else: + self.neck.init_weights() + self.neck.train() + + self._output_shapes = output_shapes + if not output_names: + output_names = [f"out{i}" for i in range(len(output_shapes))] + self._output_names = output_names + + def forward(self, x) -> Dict[str, Tensor]: + outs = self.backbone(x) + if self.neck is not None: + outs = self.neck(outs) + assert isinstance( + outs, (list, tuple) + ), "mmdet backbone should return a list/tuple of tensors!" + if len(outs) != len(self._output_shapes): + raise ValueError( + "Length of output_shapes does not match outputs from the mmdet backbone: " + f"{len(outs)} != {len(self._output_shapes)}" + ) + return {k: v for k, v in zip(self._output_names, outs)} + + def output_shape(self) -> Dict[str, ShapeSpec]: + return {k: v for k, v in zip(self._output_names, self._output_shapes)} + + +class MMDetDetector(nn.Module): + """ + Wrapper of a mmdetection detector model, for detection and instance segmentation. + Input/output formats of this class follow detectron2's convention, so a + mmdetection model can be trained and evaluated in detectron2. + """ + + def __init__( + self, + detector: Union[nn.Module, Mapping], + *, + # Default is 32 regardless of model: + # https://github.com/open-mmlab/mmdetection/tree/master/configs/_base_/datasets + size_divisibility=32, + pixel_mean: Tuple[float], + pixel_std: Tuple[float], + ): + """ + Args: + detector: a mmdet detector, or a mmdet config dict that defines a detector. + size_divisibility: pad input images to multiple of this number + pixel_mean: per-channel mean to normalize input image + pixel_std: per-channel stddev to normalize input image + """ + super().__init__() + if isinstance(detector, Mapping): + from mmdet.models import build_detector + + detector = build_detector(_to_container(detector)) + self.detector = detector + self.size_divisibility = size_divisibility + + self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False) + self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False) + assert ( + self.pixel_mean.shape == self.pixel_std.shape + ), f"{self.pixel_mean} and {self.pixel_std} have different shapes!" + + def forward(self, batched_inputs: List[Dict[str, torch.Tensor]]): + images = [x["image"].to(self.device) for x in batched_inputs] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors(images, size_divisibility=self.size_divisibility).tensor + metas = [] + rescale = {"height" in x for x in batched_inputs} + if len(rescale) != 1: + raise ValueError("Some inputs have original height/width, but some don't!") + rescale = list(rescale)[0] + output_shapes = [] + for input in batched_inputs: + meta = {} + c, h, w = input["image"].shape + meta["img_shape"] = meta["ori_shape"] = (h, w, c) + if rescale: + scale_factor = np.array( + [w / input["width"], h / input["height"]] * 2, dtype="float32" + ) + ori_shape = (input["height"], input["width"]) + output_shapes.append(ori_shape) + meta["ori_shape"] = ori_shape + (c,) + else: + scale_factor = 1.0 + output_shapes.append((h, w)) + meta["scale_factor"] = scale_factor + meta["flip"] = False + padh, padw = images.shape[-2:] + meta["pad_shape"] = (padh, padw, c) + metas.append(meta) + + if self.training: + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + if gt_instances[0].has("gt_masks"): + from mmdet.core import PolygonMasks as mm_PolygonMasks, BitmapMasks as mm_BitMasks + + def convert_mask(m, shape): + # mmdet mask format + if isinstance(m, BitMasks): + return mm_BitMasks(m.tensor.cpu().numpy(), shape[0], shape[1]) + else: + return mm_PolygonMasks(m.polygons, shape[0], shape[1]) + + gt_masks = [convert_mask(x.gt_masks, x.image_size) for x in gt_instances] + losses_and_metrics = self.detector.forward_train( + images, + metas, + [x.gt_boxes.tensor for x in gt_instances], + [x.gt_classes for x in gt_instances], + gt_masks=gt_masks, + ) + else: + losses_and_metrics = self.detector.forward_train( + images, + metas, + [x.gt_boxes.tensor for x in gt_instances], + [x.gt_classes for x in gt_instances], + ) + return _parse_losses(losses_and_metrics) + else: + results = self.detector.simple_test(images, metas, rescale=rescale) + results = [ + {"instances": _convert_mmdet_result(r, shape)} + for r, shape in zip(results, output_shapes) + ] + return results + + @property + def device(self): + return self.pixel_mean.device + + +# Reference: show_result() in +# https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/detectors/base.py +def _convert_mmdet_result(result, shape: Tuple[int, int]) -> Instances: + if isinstance(result, tuple): + bbox_result, segm_result = result + if isinstance(segm_result, tuple): + segm_result = segm_result[0] + else: + bbox_result, segm_result = result, None + + bboxes = torch.from_numpy(np.vstack(bbox_result)) # Nx5 + bboxes, scores = bboxes[:, :4], bboxes[:, -1] + labels = [ + torch.full((bbox.shape[0],), i, dtype=torch.int32) for i, bbox in enumerate(bbox_result) + ] + labels = torch.cat(labels) + inst = Instances(shape) + inst.pred_boxes = Boxes(bboxes) + inst.scores = scores + inst.pred_classes = labels + + if segm_result is not None and len(labels) > 0: + segm_result = list(itertools.chain(*segm_result)) + segm_result = [torch.from_numpy(x) if isinstance(x, np.ndarray) else x for x in segm_result] + segm_result = torch.stack(segm_result, dim=0) + inst.pred_masks = segm_result + return inst + + +# reference: https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/detectors/base.py +def _parse_losses(losses: Dict[str, Tensor]) -> Dict[str, Tensor]: + log_vars = OrderedDict() + for loss_name, loss_value in losses.items(): + if isinstance(loss_value, torch.Tensor): + log_vars[loss_name] = loss_value.mean() + elif isinstance(loss_value, list): + log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value) + else: + raise TypeError(f"{loss_name} is not a tensor or list of tensors") + + if "loss" not in loss_name: + # put metrics to storage; don't return them + storage = get_event_storage() + value = log_vars.pop(loss_name).cpu().item() + storage.put_scalar(loss_name, value) + return log_vars diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/poolers.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/poolers.py new file mode 100644 index 0000000000000000000000000000000000000000..6bea77af779ce97c770ef0e529ede51adeb76b8b --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/poolers.py @@ -0,0 +1,245 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import math +from typing import List +import torch +from torch import nn +from torchvision.ops import RoIPool + +from detectron2.layers import ROIAlign, ROIAlignRotated, cat, nonzero_tuple, shapes_to_tensor +from detectron2.structures import Boxes + +""" +To export ROIPooler to torchscript, in this file, variables that should be annotated with +`Union[List[Boxes], List[RotatedBoxes]]` are only annotated with `List[Boxes]`. + +TODO: Correct these annotations when torchscript support `Union`. +https://github.com/pytorch/pytorch/issues/41412 +""" + +__all__ = ["ROIPooler"] + + +def assign_boxes_to_levels( + box_lists: List[Boxes], + min_level: int, + max_level: int, + canonical_box_size: int, + canonical_level: int, +): + """ + Map each box in `box_lists` to a feature map level index and return the assignment + vector. + + Args: + box_lists (list[Boxes] | list[RotatedBoxes]): A list of N Boxes or N RotatedBoxes, + where N is the number of images in the batch. + min_level (int): Smallest feature map level index. The input is considered index 0, + the output of stage 1 is index 1, and so. + max_level (int): Largest feature map level index. + canonical_box_size (int): A canonical box size in pixels (sqrt(box area)). + canonical_level (int): The feature map level index on which a canonically-sized box + should be placed. + + Returns: + A tensor of length M, where M is the total number of boxes aggregated over all + N batch images. The memory layout corresponds to the concatenation of boxes + from all images. Each element is the feature map index, as an offset from + `self.min_level`, for the corresponding box (so value i means the box is at + `self.min_level + i`). + """ + box_sizes = torch.sqrt(cat([boxes.area() for boxes in box_lists])) + # Eqn.(1) in FPN paper + level_assignments = torch.floor( + canonical_level + torch.log2(box_sizes / canonical_box_size + 1e-8) + ) + # clamp level to (min, max), in case the box size is too large or too small + # for the available feature maps + level_assignments = torch.clamp(level_assignments, min=min_level, max=max_level) + return level_assignments.to(torch.int64) - min_level + + +def convert_boxes_to_pooler_format(box_lists: List[Boxes]): + """ + Convert all boxes in `box_lists` to the low-level format used by ROI pooling ops + (see description under Returns). + + Args: + box_lists (list[Boxes] | list[RotatedBoxes]): + A list of N Boxes or N RotatedBoxes, where N is the number of images in the batch. + + Returns: + When input is list[Boxes]: + A tensor of shape (M, 5), where M is the total number of boxes aggregated over all + N batch images. + The 5 columns are (batch index, x0, y0, x1, y1), where batch index + is the index in [0, N) identifying which batch image the box with corners at + (x0, y0, x1, y1) comes from. + When input is list[RotatedBoxes]: + A tensor of shape (M, 6), where M is the total number of boxes aggregated over all + N batch images. + The 6 columns are (batch index, x_ctr, y_ctr, width, height, angle_degrees), + where batch index is the index in [0, N) identifying which batch image the + rotated box (x_ctr, y_ctr, width, height, angle_degrees) comes from. + """ + boxes = torch.cat([x.tensor for x in box_lists], dim=0) + # __len__ returns Tensor in tracing. + sizes = shapes_to_tensor([x.__len__() for x in box_lists], device=boxes.device) + indices = torch.repeat_interleave( + torch.arange(len(box_lists), dtype=boxes.dtype, device=boxes.device), sizes + ) + return cat([indices[:, None], boxes], dim=1) + + +class ROIPooler(nn.Module): + """ + Region of interest feature map pooler that supports pooling from one or more + feature maps. + """ + + def __init__( + self, + output_size, + scales, + sampling_ratio, + pooler_type, + canonical_box_size=224, + canonical_level=4, + ): + """ + Args: + output_size (int, tuple[int] or list[int]): output size of the pooled region, + e.g., 14 x 14. If tuple or list is given, the length must be 2. + scales (list[float]): The scale for each low-level pooling op relative to + the input image. For a feature map with stride s relative to the input + image, scale is defined as 1/s. The stride must be power of 2. + When there are multiple scales, they must form a pyramid, i.e. they must be + a monotically decreasing geometric sequence with a factor of 1/2. + sampling_ratio (int): The `sampling_ratio` parameter for the ROIAlign op. + pooler_type (string): Name of the type of pooling operation that should be applied. + For instance, "ROIPool" or "ROIAlignV2". + canonical_box_size (int): A canonical box size in pixels (sqrt(box area)). The default + is heuristically defined as 224 pixels in the FPN paper (based on ImageNet + pre-training). + canonical_level (int): The feature map level index from which a canonically-sized box + should be placed. The default is defined as level 4 (stride=16) in the FPN paper, + i.e., a box of size 224x224 will be placed on the feature with stride=16. + The box placement for all boxes will be determined from their sizes w.r.t + canonical_box_size. For example, a box whose area is 4x that of a canonical box + should be used to pool features from feature level ``canonical_level+1``. + + Note that the actual input feature maps given to this module may not have + sufficiently many levels for the input boxes. If the boxes are too large or too + small for the input feature maps, the closest level will be used. + """ + super().__init__() + + if isinstance(output_size, int): + output_size = (output_size, output_size) + assert len(output_size) == 2 + assert isinstance(output_size[0], int) and isinstance(output_size[1], int) + self.output_size = output_size + + if pooler_type == "ROIAlign": + self.level_poolers = nn.ModuleList( + ROIAlign( + output_size, spatial_scale=scale, sampling_ratio=sampling_ratio, aligned=False + ) + for scale in scales + ) + elif pooler_type == "ROIAlignV2": + self.level_poolers = nn.ModuleList( + ROIAlign( + output_size, spatial_scale=scale, sampling_ratio=sampling_ratio, aligned=True + ) + for scale in scales + ) + elif pooler_type == "ROIPool": + self.level_poolers = nn.ModuleList( + RoIPool(output_size, spatial_scale=scale) for scale in scales + ) + elif pooler_type == "ROIAlignRotated": + self.level_poolers = nn.ModuleList( + ROIAlignRotated(output_size, spatial_scale=scale, sampling_ratio=sampling_ratio) + for scale in scales + ) + else: + raise ValueError("Unknown pooler type: {}".format(pooler_type)) + + # Map scale (defined as 1 / stride) to its feature map level under the + # assumption that stride is a power of 2. + min_level = -(math.log2(scales[0])) + max_level = -(math.log2(scales[-1])) + assert math.isclose(min_level, int(min_level)) and math.isclose( + max_level, int(max_level) + ), "Featuremap stride is not power of 2!" + self.min_level = int(min_level) + self.max_level = int(max_level) + assert ( + len(scales) == self.max_level - self.min_level + 1 + ), "[ROIPooler] Sizes of input featuremaps do not form a pyramid!" + assert 0 <= self.min_level and self.min_level <= self.max_level + self.canonical_level = canonical_level + assert canonical_box_size > 0 + self.canonical_box_size = canonical_box_size + + def forward(self, x: List[torch.Tensor], box_lists: List[Boxes]): + """ + Args: + x (list[Tensor]): A list of feature maps of NCHW shape, with scales matching those + used to construct this module. + box_lists (list[Boxes] | list[RotatedBoxes]): + A list of N Boxes or N RotatedBoxes, where N is the number of images in the batch. + The box coordinates are defined on the original image and + will be scaled by the `scales` argument of :class:`ROIPooler`. + + Returns: + Tensor: + A tensor of shape (M, C, output_size, output_size) where M is the total number of + boxes aggregated over all N batch images and C is the number of channels in `x`. + """ + num_level_assignments = len(self.level_poolers) + + assert isinstance(x, list) and isinstance( + box_lists, list + ), "Arguments to pooler must be lists" + assert ( + len(x) == num_level_assignments + ), "unequal value, num_level_assignments={}, but x is list of {} Tensors".format( + num_level_assignments, len(x) + ) + + assert len(box_lists) == x[0].size( + 0 + ), "unequal value, x[0] batch dim 0 is {}, but box_list has length {}".format( + x[0].size(0), len(box_lists) + ) + if len(box_lists) == 0: + return torch.zeros( + (0, x[0].shape[1]) + self.output_size, device=x[0].device, dtype=x[0].dtype + ) + + pooler_fmt_boxes = convert_boxes_to_pooler_format(box_lists) + + if num_level_assignments == 1: + return self.level_poolers[0](x[0], pooler_fmt_boxes) + + level_assignments = assign_boxes_to_levels( + box_lists, self.min_level, self.max_level, self.canonical_box_size, self.canonical_level + ) + + num_boxes = pooler_fmt_boxes.size(0) + num_channels = x[0].shape[1] + output_size = self.output_size[0] + + dtype, device = x[0].dtype, x[0].device + output = torch.zeros( + (num_boxes, num_channels, output_size, output_size), dtype=dtype, device=device + ) + + for level, pooler in enumerate(self.level_poolers): + inds = nonzero_tuple(level_assignments == level)[0] + pooler_fmt_boxes_level = pooler_fmt_boxes[inds] + # Use index_put_ instead of advance indexing, to avoid pytorch/issues/49852 + output.index_put_((inds,), pooler(x[level], pooler_fmt_boxes_level)) + + return output diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/postprocessing.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/postprocessing.py new file mode 100644 index 0000000000000000000000000000000000000000..52f273bb5ee150ed7252207312320f0a9bdf0e65 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/postprocessing.py @@ -0,0 +1,101 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import torch +from torch.nn import functional as F + +from detectron2.structures import Instances, ROIMasks + + +# perhaps should rename to "resize_instance" +def detector_postprocess( + results: Instances, output_height: int, output_width: int, mask_threshold: float = 0.5 +): + """ + Resize the output instances. + The input images are often resized when entering an object detector. + As a result, we often need the outputs of the detector in a different + resolution from its inputs. + + This function will resize the raw outputs of an R-CNN detector + to produce outputs according to the desired output resolution. + + Args: + results (Instances): the raw outputs from the detector. + `results.image_size` contains the input image resolution the detector sees. + This object might be modified in-place. + output_height, output_width: the desired output resolution. + + Returns: + Instances: the resized output from the model, based on the output resolution + """ + if isinstance(output_width, torch.Tensor): + # This shape might (but not necessarily) be tensors during tracing. + # Converts integer tensors to float temporaries to ensure true + # division is performed when computing scale_x and scale_y. + output_width_tmp = output_width.float() + output_height_tmp = output_height.float() + new_size = torch.stack([output_height, output_width]) + else: + new_size = (output_height, output_width) + output_width_tmp = output_width + output_height_tmp = output_height + + scale_x, scale_y = ( + output_width_tmp / results.image_size[1], + output_height_tmp / results.image_size[0], + ) + results = Instances(new_size, **results.get_fields()) + + if results.has("pred_boxes"): + output_boxes = results.pred_boxes + elif results.has("proposal_boxes"): + output_boxes = results.proposal_boxes + else: + output_boxes = None + assert output_boxes is not None, "Predictions must contain boxes!" + + output_boxes.scale(scale_x, scale_y) + output_boxes.clip(results.image_size) + + results = results[output_boxes.nonempty()] + + if results.has("pred_masks"): + if isinstance(results.pred_masks, ROIMasks): + roi_masks = results.pred_masks + else: + # pred_masks is a tensor of shape (N, 1, M, M) + roi_masks = ROIMasks(results.pred_masks[:, 0, :, :]) + results.pred_masks = roi_masks.to_bitmasks( + results.pred_boxes, output_height, output_width, mask_threshold + ).tensor # TODO return ROIMasks/BitMask object in the future + + if results.has("pred_keypoints"): + results.pred_keypoints[:, :, 0] *= scale_x + results.pred_keypoints[:, :, 1] *= scale_y + + return results + + +def sem_seg_postprocess(result, img_size, output_height, output_width): + """ + Return semantic segmentation predictions in the original resolution. + + The input images are often resized when entering semantic segmentor. Moreover, in same + cases, they also padded inside segmentor to be divisible by maximum network stride. + As a result, we often need the predictions of the segmentor in a different + resolution from its inputs. + + Args: + result (Tensor): semantic segmentation prediction logits. A tensor of shape (C, H, W), + where C is the number of classes, and H, W are the height and width of the prediction. + img_size (tuple): image size that segmentor is taking as input. + output_height, output_width: the desired output resolution. + + Returns: + semantic segmentation prediction (Tensor): A tensor of the shape + (C, output_height, output_width) that contains per-pixel soft predictions. + """ + result = result[:, : img_size[0], : img_size[1]].expand(1, -1, -1, -1) + result = F.interpolate( + result, size=(output_height, output_width), mode="bilinear", align_corners=False + )[0] + return result diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3f4e4df7645c67b7a013295207b98fe70b2e574c --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .build import PROPOSAL_GENERATOR_REGISTRY, build_proposal_generator +from .rpn import RPN_HEAD_REGISTRY, build_rpn_head, RPN, StandardRPNHead + +__all__ = list(globals().keys()) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/build.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/build.py new file mode 100644 index 0000000000000000000000000000000000000000..34eb12d00d94ff905b796e75e2c4c5845257c8e9 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/build.py @@ -0,0 +1,24 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from detectron2.utils.registry import Registry + +PROPOSAL_GENERATOR_REGISTRY = Registry("PROPOSAL_GENERATOR") +PROPOSAL_GENERATOR_REGISTRY.__doc__ = """ +Registry for proposal generator, which produces object proposals from feature maps. + +The registered object will be called with `obj(cfg, input_shape)`. +The call should return a `nn.Module` object. +""" + +from . import rpn, rrpn # noqa F401 isort:skip + + +def build_proposal_generator(cfg, input_shape): + """ + Build a proposal generator from `cfg.MODEL.PROPOSAL_GENERATOR.NAME`. + The name can be "PrecomputedProposals" to use no proposal generator. + """ + name = cfg.MODEL.PROPOSAL_GENERATOR.NAME + if name == "PrecomputedProposals": + return None + + return PROPOSAL_GENERATOR_REGISTRY.get(name)(cfg, input_shape) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/proposal_utils.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/proposal_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4703219899c12223069ce322d679361bbbf96064 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/proposal_utils.py @@ -0,0 +1,196 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import math +from typing import List, Tuple, Union +import torch + +from detectron2.layers import batched_nms, cat +from detectron2.structures import Boxes, Instances + +logger = logging.getLogger(__name__) + + +def _is_tracing(): + # (fixed in TORCH_VERSION >= 1.9) + if torch.jit.is_scripting(): + # https://github.com/pytorch/pytorch/issues/47379 + return False + else: + return torch.jit.is_tracing() + + +def find_top_rpn_proposals( + proposals: List[torch.Tensor], + pred_objectness_logits: List[torch.Tensor], + image_sizes: List[Tuple[int, int]], + nms_thresh: float, + pre_nms_topk: int, + post_nms_topk: int, + min_box_size: float, + training: bool, +): + """ + For each feature map, select the `pre_nms_topk` highest scoring proposals, + apply NMS, clip proposals, and remove small boxes. Return the `post_nms_topk` + highest scoring proposals among all the feature maps for each image. + + Args: + proposals (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A, 4). + All proposal predictions on the feature maps. + pred_objectness_logits (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A). + image_sizes (list[tuple]): sizes (h, w) for each image + nms_thresh (float): IoU threshold to use for NMS + pre_nms_topk (int): number of top k scoring proposals to keep before applying NMS. + When RPN is run on multiple feature maps (as in FPN) this number is per + feature map. + post_nms_topk (int): number of top k scoring proposals to keep after applying NMS. + When RPN is run on multiple feature maps (as in FPN) this number is total, + over all feature maps. + min_box_size (float): minimum proposal box side length in pixels (absolute units + wrt input images). + training (bool): True if proposals are to be used in training, otherwise False. + This arg exists only to support a legacy bug; look for the "NB: Legacy bug ..." + comment. + + Returns: + list[Instances]: list of N Instances. The i-th Instances + stores post_nms_topk object proposals for image i, sorted by their + objectness score in descending order. + """ + num_images = len(image_sizes) + device = proposals[0].device + + # 1. Select top-k anchor for every level and every image + topk_scores = [] # #lvl Tensor, each of shape N x topk + topk_proposals = [] + level_ids = [] # #lvl Tensor, each of shape (topk,) + batch_idx = torch.arange(num_images, device=device) + for level_id, (proposals_i, logits_i) in enumerate(zip(proposals, pred_objectness_logits)): + Hi_Wi_A = logits_i.shape[1] + if isinstance(Hi_Wi_A, torch.Tensor): # it's a tensor in tracing + num_proposals_i = torch.clamp(Hi_Wi_A, max=pre_nms_topk) + else: + num_proposals_i = min(Hi_Wi_A, pre_nms_topk) + + topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) + + # each is N x topk + topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 + + topk_proposals.append(topk_proposals_i) + topk_scores.append(topk_scores_i) + level_ids.append(torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device)) + + # 2. Concat all levels together + topk_scores = cat(topk_scores, dim=1) + topk_proposals = cat(topk_proposals, dim=1) + level_ids = cat(level_ids, dim=0) + + # 3. For each image, run a per-level NMS, and choose topk results. + results: List[Instances] = [] + for n, image_size in enumerate(image_sizes): + boxes = Boxes(topk_proposals[n]) + scores_per_img = topk_scores[n] + lvl = level_ids + + valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) + if not valid_mask.all(): + if training: + raise FloatingPointError( + "Predicted boxes or scores contain Inf/NaN. Training has diverged." + ) + boxes = boxes[valid_mask] + scores_per_img = scores_per_img[valid_mask] + lvl = lvl[valid_mask] + boxes.clip(image_size) + + # filter empty boxes + keep = boxes.nonempty(threshold=min_box_size) + if _is_tracing() or keep.sum().item() != len(boxes): + boxes, scores_per_img, lvl = boxes[keep], scores_per_img[keep], lvl[keep] + + keep = batched_nms(boxes.tensor, scores_per_img, lvl, nms_thresh) + # In Detectron1, there was different behavior during training vs. testing. + # (https://github.com/facebookresearch/Detectron/issues/459) + # During training, topk is over the proposals from *all* images in the training batch. + # During testing, it is over the proposals for each image separately. + # As a result, the training behavior becomes batch-dependent, + # and the configuration "POST_NMS_TOPK_TRAIN" end up relying on the batch size. + # This bug is addressed in Detectron2 to make the behavior independent of batch size. + keep = keep[:post_nms_topk] # keep is already sorted + + res = Instances(image_size) + res.proposal_boxes = boxes[keep] + res.objectness_logits = scores_per_img[keep] + results.append(res) + return results + + +def add_ground_truth_to_proposals( + gt: Union[List[Instances], List[Boxes]], proposals: List[Instances] +) -> List[Instances]: + """ + Call `add_ground_truth_to_proposals_single_image` for all images. + + Args: + gt(Union[List[Instances], List[Boxes]): list of N elements. Element i is a Instances + representing the ground-truth for image i. + proposals (list[Instances]): list of N elements. Element i is a Instances + representing the proposals for image i. + + Returns: + list[Instances]: list of N Instances. Each is the proposals for the image, + with field "proposal_boxes" and "objectness_logits". + """ + assert gt is not None + + if len(proposals) != len(gt): + raise ValueError("proposals and gt should have the same length as the number of images!") + if len(proposals) == 0: + return proposals + + return [ + add_ground_truth_to_proposals_single_image(gt_i, proposals_i) + for gt_i, proposals_i in zip(gt, proposals) + ] + + +def add_ground_truth_to_proposals_single_image( + gt: Union[Instances, Boxes], proposals: Instances +) -> Instances: + """ + Augment `proposals` with `gt`. + + Args: + Same as `add_ground_truth_to_proposals`, but with gt and proposals + per image. + + Returns: + Same as `add_ground_truth_to_proposals`, but for only one image. + """ + if isinstance(gt, Boxes): + # convert Boxes to Instances + gt = Instances(proposals.image_size, gt_boxes=gt) + + gt_boxes = gt.gt_boxes + device = proposals.objectness_logits.device + # Assign all ground-truth boxes an objectness logit corresponding to + # P(object) = sigmoid(logit) =~ 1. + gt_logit_value = math.log((1.0 - 1e-10) / (1 - (1.0 - 1e-10))) + gt_logits = gt_logit_value * torch.ones(len(gt_boxes), device=device) + + # Concatenating gt_boxes with proposals requires them to have the same fields + gt_proposal = Instances(proposals.image_size, **gt.get_fields()) + gt_proposal.proposal_boxes = gt_boxes + gt_proposal.objectness_logits = gt_logits + + for key in proposals.get_fields().keys(): + assert gt_proposal.has( + key + ), "The attribute '{}' in `proposals` does not exist in `gt`".format(key) + + # NOTE: Instances.cat only use fields from the first item. Extra fields in latter items + # will be thrown away. + new_proposals = Instances.cat([proposals, gt_proposal]) + + return new_proposals diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/rpn.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/rpn.py new file mode 100644 index 0000000000000000000000000000000000000000..99cd536d2f9880d2049390c45f73eb22335e1b82 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/rpn.py @@ -0,0 +1,533 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from typing import Dict, List, Optional, Tuple, Union +import torch +import torch.nn.functional as F +from torch import nn + +from detectron2.config import configurable +from detectron2.layers import Conv2d, ShapeSpec, cat +from detectron2.structures import Boxes, ImageList, Instances, pairwise_iou +from detectron2.utils.events import get_event_storage +from detectron2.utils.memory import retry_if_cuda_oom +from detectron2.utils.registry import Registry + +from ..anchor_generator import build_anchor_generator +from ..box_regression import Box2BoxTransform, _dense_box_regression_loss +from ..matcher import Matcher +from ..sampling import subsample_labels +from .build import PROPOSAL_GENERATOR_REGISTRY +from .proposal_utils import find_top_rpn_proposals + +RPN_HEAD_REGISTRY = Registry("RPN_HEAD") +RPN_HEAD_REGISTRY.__doc__ = """ +Registry for RPN heads, which take feature maps and perform +objectness classification and bounding box regression for anchors. + +The registered object will be called with `obj(cfg, input_shape)`. +The call should return a `nn.Module` object. +""" + + +""" +Shape shorthand in this module: + + N: number of images in the minibatch + L: number of feature maps per image on which RPN is run + A: number of cell anchors (must be the same for all feature maps) + Hi, Wi: height and width of the i-th feature map + B: size of the box parameterization + +Naming convention: + + objectness: refers to the binary classification of an anchor as object vs. not object. + + deltas: refers to the 4-d (dx, dy, dw, dh) deltas that parameterize the box2box + transform (see :class:`box_regression.Box2BoxTransform`), or 5d for rotated boxes. + + pred_objectness_logits: predicted objectness scores in [-inf, +inf]; use + sigmoid(pred_objectness_logits) to estimate P(object). + + gt_labels: ground-truth binary classification labels for objectness + + pred_anchor_deltas: predicted box2box transform deltas + + gt_anchor_deltas: ground-truth box2box transform deltas +""" + + +def build_rpn_head(cfg, input_shape): + """ + Build an RPN head defined by `cfg.MODEL.RPN.HEAD_NAME`. + """ + name = cfg.MODEL.RPN.HEAD_NAME + return RPN_HEAD_REGISTRY.get(name)(cfg, input_shape) + + +@RPN_HEAD_REGISTRY.register() +class StandardRPNHead(nn.Module): + """ + Standard RPN classification and regression heads described in :paper:`Faster R-CNN`. + Uses a 3x3 conv to produce a shared hidden state from which one 1x1 conv predicts + objectness logits for each anchor and a second 1x1 conv predicts bounding-box deltas + specifying how to deform each anchor into an object proposal. + """ + + @configurable + def __init__( + self, *, in_channels: int, num_anchors: int, box_dim: int = 4, conv_dims: List[int] = (-1,) + ): + """ + NOTE: this interface is experimental. + + Args: + in_channels (int): number of input feature channels. When using multiple + input features, they must have the same number of channels. + num_anchors (int): number of anchors to predict for *each spatial position* + on the feature map. The total number of anchors for each + feature map will be `num_anchors * H * W`. + box_dim (int): dimension of a box, which is also the number of box regression + predictions to make for each anchor. An axis aligned box has + box_dim=4, while a rotated box has box_dim=5. + conv_dims (list[int]): a list of integers representing the output channels + of N conv layers. Set it to -1 to use the same number of output channels + as input channels. + """ + super().__init__() + cur_channels = in_channels + # Keeping the old variable names and structure for backwards compatiblity. + # Otherwise the old checkpoints will fail to load. + if len(conv_dims) == 1: + out_channels = cur_channels if conv_dims[0] == -1 else conv_dims[0] + # 3x3 conv for the hidden representation + self.conv = self._get_rpn_conv(cur_channels, out_channels) + cur_channels = out_channels + else: + self.conv = nn.Sequential() + for k, conv_dim in enumerate(conv_dims): + out_channels = cur_channels if conv_dim == -1 else conv_dim + if out_channels <= 0: + raise ValueError( + f"Conv output channels should be greater than 0. Got {out_channels}" + ) + conv = self._get_rpn_conv(cur_channels, out_channels) + self.conv.add_module(f"conv{k}", conv) + cur_channels = out_channels + # 1x1 conv for predicting objectness logits + self.objectness_logits = nn.Conv2d(cur_channels, num_anchors, kernel_size=1, stride=1) + # 1x1 conv for predicting box2box transform deltas + self.anchor_deltas = nn.Conv2d(cur_channels, num_anchors * box_dim, kernel_size=1, stride=1) + + # Keeping the order of weights initialization same for backwards compatiblility. + for layer in self.modules(): + if isinstance(layer, nn.Conv2d): + nn.init.normal_(layer.weight, std=0.01) + nn.init.constant_(layer.bias, 0) + + def _get_rpn_conv(self, in_channels, out_channels): + return Conv2d( + in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + activation=nn.ReLU(), + ) + + @classmethod + def from_config(cls, cfg, input_shape): + # Standard RPN is shared across levels: + in_channels = [s.channels for s in input_shape] + assert len(set(in_channels)) == 1, "Each level must have the same channel!" + in_channels = in_channels[0] + + # RPNHead should take the same input as anchor generator + # NOTE: it assumes that creating an anchor generator does not have unwanted side effect. + anchor_generator = build_anchor_generator(cfg, input_shape) + num_anchors = anchor_generator.num_anchors + box_dim = anchor_generator.box_dim + assert ( + len(set(num_anchors)) == 1 + ), "Each level must have the same number of anchors per spatial position" + return { + "in_channels": in_channels, + "num_anchors": num_anchors[0], + "box_dim": box_dim, + "conv_dims": cfg.MODEL.RPN.CONV_DIMS, + } + + def forward(self, features: List[torch.Tensor]): + """ + Args: + features (list[Tensor]): list of feature maps + + Returns: + list[Tensor]: A list of L elements. + Element i is a tensor of shape (N, A, Hi, Wi) representing + the predicted objectness logits for all anchors. A is the number of cell anchors. + list[Tensor]: A list of L elements. Element i is a tensor of shape + (N, A*box_dim, Hi, Wi) representing the predicted "deltas" used to transform anchors + to proposals. + """ + pred_objectness_logits = [] + pred_anchor_deltas = [] + for x in features: + t = self.conv(x) + pred_objectness_logits.append(self.objectness_logits(t)) + pred_anchor_deltas.append(self.anchor_deltas(t)) + return pred_objectness_logits, pred_anchor_deltas + + +@PROPOSAL_GENERATOR_REGISTRY.register() +class RPN(nn.Module): + """ + Region Proposal Network, introduced by :paper:`Faster R-CNN`. + """ + + @configurable + def __init__( + self, + *, + in_features: List[str], + head: nn.Module, + anchor_generator: nn.Module, + anchor_matcher: Matcher, + box2box_transform: Box2BoxTransform, + batch_size_per_image: int, + positive_fraction: float, + pre_nms_topk: Tuple[float, float], + post_nms_topk: Tuple[float, float], + nms_thresh: float = 0.7, + min_box_size: float = 0.0, + anchor_boundary_thresh: float = -1.0, + loss_weight: Union[float, Dict[str, float]] = 1.0, + box_reg_loss_type: str = "smooth_l1", + smooth_l1_beta: float = 0.0, + ): + """ + NOTE: this interface is experimental. + + Args: + in_features (list[str]): list of names of input features to use + head (nn.Module): a module that predicts logits and regression deltas + for each level from a list of per-level features + anchor_generator (nn.Module): a module that creates anchors from a + list of features. Usually an instance of :class:`AnchorGenerator` + anchor_matcher (Matcher): label the anchors by matching them with ground truth. + box2box_transform (Box2BoxTransform): defines the transform from anchors boxes to + instance boxes + batch_size_per_image (int): number of anchors per image to sample for training + positive_fraction (float): fraction of foreground anchors to sample for training + pre_nms_topk (tuple[float]): (train, test) that represents the + number of top k proposals to select before NMS, in + training and testing. + post_nms_topk (tuple[float]): (train, test) that represents the + number of top k proposals to select after NMS, in + training and testing. + nms_thresh (float): NMS threshold used to de-duplicate the predicted proposals + min_box_size (float): remove proposal boxes with any side smaller than this threshold, + in the unit of input image pixels + anchor_boundary_thresh (float): legacy option + loss_weight (float|dict): weights to use for losses. Can be single float for weighting + all rpn losses together, or a dict of individual weightings. Valid dict keys are: + "loss_rpn_cls" - applied to classification loss + "loss_rpn_loc" - applied to box regression loss + box_reg_loss_type (str): Loss type to use. Supported losses: "smooth_l1", "giou". + smooth_l1_beta (float): beta parameter for the smooth L1 regression loss. Default to + use L1 loss. Only used when `box_reg_loss_type` is "smooth_l1" + """ + super().__init__() + self.in_features = in_features + self.rpn_head = head + self.anchor_generator = anchor_generator + self.anchor_matcher = anchor_matcher + self.box2box_transform = box2box_transform + self.batch_size_per_image = batch_size_per_image + self.positive_fraction = positive_fraction + # Map from self.training state to train/test settings + self.pre_nms_topk = {True: pre_nms_topk[0], False: pre_nms_topk[1]} + self.post_nms_topk = {True: post_nms_topk[0], False: post_nms_topk[1]} + self.nms_thresh = nms_thresh + self.min_box_size = float(min_box_size) + self.anchor_boundary_thresh = anchor_boundary_thresh + if isinstance(loss_weight, float): + loss_weight = {"loss_rpn_cls": loss_weight, "loss_rpn_loc": loss_weight} + self.loss_weight = loss_weight + self.box_reg_loss_type = box_reg_loss_type + self.smooth_l1_beta = smooth_l1_beta + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + in_features = cfg.MODEL.RPN.IN_FEATURES + ret = { + "in_features": in_features, + "min_box_size": cfg.MODEL.PROPOSAL_GENERATOR.MIN_SIZE, + "nms_thresh": cfg.MODEL.RPN.NMS_THRESH, + "batch_size_per_image": cfg.MODEL.RPN.BATCH_SIZE_PER_IMAGE, + "positive_fraction": cfg.MODEL.RPN.POSITIVE_FRACTION, + "loss_weight": { + "loss_rpn_cls": cfg.MODEL.RPN.LOSS_WEIGHT, + "loss_rpn_loc": cfg.MODEL.RPN.BBOX_REG_LOSS_WEIGHT * cfg.MODEL.RPN.LOSS_WEIGHT, + }, + "anchor_boundary_thresh": cfg.MODEL.RPN.BOUNDARY_THRESH, + "box2box_transform": Box2BoxTransform(weights=cfg.MODEL.RPN.BBOX_REG_WEIGHTS), + "box_reg_loss_type": cfg.MODEL.RPN.BBOX_REG_LOSS_TYPE, + "smooth_l1_beta": cfg.MODEL.RPN.SMOOTH_L1_BETA, + } + + ret["pre_nms_topk"] = (cfg.MODEL.RPN.PRE_NMS_TOPK_TRAIN, cfg.MODEL.RPN.PRE_NMS_TOPK_TEST) + ret["post_nms_topk"] = (cfg.MODEL.RPN.POST_NMS_TOPK_TRAIN, cfg.MODEL.RPN.POST_NMS_TOPK_TEST) + + ret["anchor_generator"] = build_anchor_generator(cfg, [input_shape[f] for f in in_features]) + ret["anchor_matcher"] = Matcher( + cfg.MODEL.RPN.IOU_THRESHOLDS, cfg.MODEL.RPN.IOU_LABELS, allow_low_quality_matches=True + ) + ret["head"] = build_rpn_head(cfg, [input_shape[f] for f in in_features]) + return ret + + def _subsample_labels(self, label): + """ + Randomly sample a subset of positive and negative examples, and overwrite + the label vector to the ignore value (-1) for all elements that are not + included in the sample. + + Args: + labels (Tensor): a vector of -1, 0, 1. Will be modified in-place and returned. + """ + pos_idx, neg_idx = subsample_labels( + label, self.batch_size_per_image, self.positive_fraction, 0 + ) + # Fill with the ignore label (-1), then set positive and negative labels + label.fill_(-1) + label.scatter_(0, pos_idx, 1) + label.scatter_(0, neg_idx, 0) + return label + + @torch.jit.unused + @torch.no_grad() + def label_and_sample_anchors( + self, anchors: List[Boxes], gt_instances: List[Instances] + ) -> Tuple[List[torch.Tensor], List[torch.Tensor]]: + """ + Args: + anchors (list[Boxes]): anchors for each feature map. + gt_instances: the ground-truth instances for each image. + + Returns: + list[Tensor]: + List of #img tensors. i-th element is a vector of labels whose length is + the total number of anchors across all feature maps R = sum(Hi * Wi * A). + Label values are in {-1, 0, 1}, with meanings: -1 = ignore; 0 = negative + class; 1 = positive class. + list[Tensor]: + i-th element is a Rx4 tensor. The values are the matched gt boxes for each + anchor. Values are undefined for those anchors not labeled as 1. + """ + anchors = Boxes.cat(anchors) + + gt_boxes = [x.gt_boxes for x in gt_instances] + image_sizes = [x.image_size for x in gt_instances] + del gt_instances + + gt_labels = [] + matched_gt_boxes = [] + for image_size_i, gt_boxes_i in zip(image_sizes, gt_boxes): + """ + image_size_i: (h, w) for the i-th image + gt_boxes_i: ground-truth boxes for i-th image + """ + + match_quality_matrix = retry_if_cuda_oom(pairwise_iou)(gt_boxes_i, anchors) + matched_idxs, gt_labels_i = retry_if_cuda_oom(self.anchor_matcher)(match_quality_matrix) + # Matching is memory-expensive and may result in CPU tensors. But the result is small + gt_labels_i = gt_labels_i.to(device=gt_boxes_i.device) + del match_quality_matrix + + if self.anchor_boundary_thresh >= 0: + # Discard anchors that go out of the boundaries of the image + # NOTE: This is legacy functionality that is turned off by default in Detectron2 + anchors_inside_image = anchors.inside_box(image_size_i, self.anchor_boundary_thresh) + gt_labels_i[~anchors_inside_image] = -1 + + # A vector of labels (-1, 0, 1) for each anchor + gt_labels_i = self._subsample_labels(gt_labels_i) + + if len(gt_boxes_i) == 0: + # These values won't be used anyway since the anchor is labeled as background + matched_gt_boxes_i = torch.zeros_like(anchors.tensor) + else: + # TODO wasted indexing computation for ignored boxes + matched_gt_boxes_i = gt_boxes_i[matched_idxs].tensor + + gt_labels.append(gt_labels_i) # N,AHW + matched_gt_boxes.append(matched_gt_boxes_i) + return gt_labels, matched_gt_boxes + + @torch.jit.unused + def losses( + self, + anchors: List[Boxes], + pred_objectness_logits: List[torch.Tensor], + gt_labels: List[torch.Tensor], + pred_anchor_deltas: List[torch.Tensor], + gt_boxes: List[torch.Tensor], + ) -> Dict[str, torch.Tensor]: + """ + Return the losses from a set of RPN predictions and their associated ground-truth. + + Args: + anchors (list[Boxes or RotatedBoxes]): anchors for each feature map, each + has shape (Hi*Wi*A, B), where B is box dimension (4 or 5). + pred_objectness_logits (list[Tensor]): A list of L elements. + Element i is a tensor of shape (N, Hi*Wi*A) representing + the predicted objectness logits for all anchors. + gt_labels (list[Tensor]): Output of :meth:`label_and_sample_anchors`. + pred_anchor_deltas (list[Tensor]): A list of L elements. Element i is a tensor of shape + (N, Hi*Wi*A, 4 or 5) representing the predicted "deltas" used to transform anchors + to proposals. + gt_boxes (list[Tensor]): Output of :meth:`label_and_sample_anchors`. + + Returns: + dict[loss name -> loss value]: A dict mapping from loss name to loss value. + Loss names are: `loss_rpn_cls` for objectness classification and + `loss_rpn_loc` for proposal localization. + """ + num_images = len(gt_labels) + gt_labels = torch.stack(gt_labels) # (N, sum(Hi*Wi*Ai)) + + # Log the number of positive/negative anchors per-image that's used in training + pos_mask = gt_labels == 1 + num_pos_anchors = pos_mask.sum().item() + num_neg_anchors = (gt_labels == 0).sum().item() + storage = get_event_storage() + storage.put_scalar("rpn/num_pos_anchors", num_pos_anchors / num_images) + storage.put_scalar("rpn/num_neg_anchors", num_neg_anchors / num_images) + + localization_loss = _dense_box_regression_loss( + anchors, + self.box2box_transform, + pred_anchor_deltas, + gt_boxes, + pos_mask, + box_reg_loss_type=self.box_reg_loss_type, + smooth_l1_beta=self.smooth_l1_beta, + ) + + valid_mask = gt_labels >= 0 + objectness_loss = F.binary_cross_entropy_with_logits( + cat(pred_objectness_logits, dim=1)[valid_mask], + gt_labels[valid_mask].to(torch.float32), + reduction="sum", + ) + normalizer = self.batch_size_per_image * num_images + losses = { + "loss_rpn_cls": objectness_loss / normalizer, + # The original Faster R-CNN paper uses a slightly different normalizer + # for loc loss. But it doesn't matter in practice + "loss_rpn_loc": localization_loss / normalizer, + } + losses = {k: v * self.loss_weight.get(k, 1.0) for k, v in losses.items()} + return losses + + def forward( + self, + images: ImageList, + features: Dict[str, torch.Tensor], + gt_instances: Optional[List[Instances]] = None, + ): + """ + Args: + images (ImageList): input images of length `N` + features (dict[str, Tensor]): input data as a mapping from feature + map name to tensor. Axis 0 represents the number of images `N` in + the input data; axes 1-3 are channels, height, and width, which may + vary between feature maps (e.g., if a feature pyramid is used). + gt_instances (list[Instances], optional): a length `N` list of `Instances`s. + Each `Instances` stores ground-truth instances for the corresponding image. + + Returns: + proposals: list[Instances]: contains fields "proposal_boxes", "objectness_logits" + loss: dict[Tensor] or None + """ + features = [features[f] for f in self.in_features] + anchors = self.anchor_generator(features) + + pred_objectness_logits, pred_anchor_deltas = self.rpn_head(features) + # Transpose the Hi*Wi*A dimension to the middle: + pred_objectness_logits = [ + # (N, A, Hi, Wi) -> (N, Hi, Wi, A) -> (N, Hi*Wi*A) + score.permute(0, 2, 3, 1).flatten(1) + for score in pred_objectness_logits + ] + pred_anchor_deltas = [ + # (N, A*B, Hi, Wi) -> (N, A, B, Hi, Wi) -> (N, Hi, Wi, A, B) -> (N, Hi*Wi*A, B) + x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) + .permute(0, 3, 4, 1, 2) + .flatten(1, -2) + for x in pred_anchor_deltas + ] + + if self.training: + assert gt_instances is not None, "RPN requires gt_instances in training!" + gt_labels, gt_boxes = self.label_and_sample_anchors(anchors, gt_instances) + losses = self.losses( + anchors, pred_objectness_logits, gt_labels, pred_anchor_deltas, gt_boxes + ) + else: + losses = {} + proposals = self.predict_proposals( + anchors, pred_objectness_logits, pred_anchor_deltas, images.image_sizes + ) + return proposals, losses + + def predict_proposals( + self, + anchors: List[Boxes], + pred_objectness_logits: List[torch.Tensor], + pred_anchor_deltas: List[torch.Tensor], + image_sizes: List[Tuple[int, int]], + ): + """ + Decode all the predicted box regression deltas to proposals. Find the top proposals + by applying NMS and removing boxes that are too small. + + Returns: + proposals (list[Instances]): list of N Instances. The i-th Instances + stores post_nms_topk object proposals for image i, sorted by their + objectness score in descending order. + """ + # The proposals are treated as fixed for joint training with roi heads. + # This approach ignores the derivative w.r.t. the proposal boxes’ coordinates that + # are also network responses. + with torch.no_grad(): + pred_proposals = self._decode_proposals(anchors, pred_anchor_deltas) + return find_top_rpn_proposals( + pred_proposals, + pred_objectness_logits, + image_sizes, + self.nms_thresh, + self.pre_nms_topk[self.training], + self.post_nms_topk[self.training], + self.min_box_size, + self.training, + ) + + def _decode_proposals(self, anchors: List[Boxes], pred_anchor_deltas: List[torch.Tensor]): + """ + Transform anchors into proposals by applying the predicted anchor deltas. + + Returns: + proposals (list[Tensor]): A list of L tensors. Tensor i has shape + (N, Hi*Wi*A, B) + """ + N = pred_anchor_deltas[0].shape[0] + proposals = [] + # For each feature map + for anchors_i, pred_anchor_deltas_i in zip(anchors, pred_anchor_deltas): + B = anchors_i.tensor.size(1) + pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) + # Expand anchors to shape (N*Hi*Wi*A, B) + anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) + proposals_i = self.box2box_transform.apply_deltas(pred_anchor_deltas_i, anchors_i) + # Append feature map proposals with shape (N, Hi*Wi*A, B) + proposals.append(proposals_i.view(N, -1, B)) + return proposals diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/rrpn.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/rrpn.py new file mode 100644 index 0000000000000000000000000000000000000000..d51b92b7d25865a950e28cfb9ae284e600495888 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/rrpn.py @@ -0,0 +1,203 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import itertools +import logging +from typing import Dict, List +import torch + +from detectron2.config import configurable +from detectron2.layers import ShapeSpec, batched_nms_rotated, cat +from detectron2.structures import Instances, RotatedBoxes, pairwise_iou_rotated +from detectron2.utils.memory import retry_if_cuda_oom + +from ..box_regression import Box2BoxTransformRotated +from .build import PROPOSAL_GENERATOR_REGISTRY +from .proposal_utils import _is_tracing +from .rpn import RPN + +logger = logging.getLogger(__name__) + + +def find_top_rrpn_proposals( + proposals, + pred_objectness_logits, + image_sizes, + nms_thresh, + pre_nms_topk, + post_nms_topk, + min_box_size, + training, +): + """ + For each feature map, select the `pre_nms_topk` highest scoring proposals, + apply NMS, clip proposals, and remove small boxes. Return the `post_nms_topk` + highest scoring proposals among all the feature maps if `training` is True, + otherwise, returns the highest `post_nms_topk` scoring proposals for each + feature map. + + Args: + proposals (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A, 5). + All proposal predictions on the feature maps. + pred_objectness_logits (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A). + image_sizes (list[tuple]): sizes (h, w) for each image + nms_thresh (float): IoU threshold to use for NMS + pre_nms_topk (int): number of top k scoring proposals to keep before applying NMS. + When RRPN is run on multiple feature maps (as in FPN) this number is per + feature map. + post_nms_topk (int): number of top k scoring proposals to keep after applying NMS. + When RRPN is run on multiple feature maps (as in FPN) this number is total, + over all feature maps. + min_box_size(float): minimum proposal box side length in pixels (absolute units wrt + input images). + training (bool): True if proposals are to be used in training, otherwise False. + This arg exists only to support a legacy bug; look for the "NB: Legacy bug ..." + comment. + + Returns: + proposals (list[Instances]): list of N Instances. The i-th Instances + stores post_nms_topk object proposals for image i. + """ + num_images = len(image_sizes) + device = proposals[0].device + + # 1. Select top-k anchor for every level and every image + topk_scores = [] # #lvl Tensor, each of shape N x topk + topk_proposals = [] + level_ids = [] # #lvl Tensor, each of shape (topk,) + batch_idx = torch.arange(num_images, device=device) + for level_id, proposals_i, logits_i in zip( + itertools.count(), proposals, pred_objectness_logits + ): + Hi_Wi_A = logits_i.shape[1] + if isinstance(Hi_Wi_A, torch.Tensor): # it's a tensor in tracing + num_proposals_i = torch.clamp(Hi_Wi_A, max=pre_nms_topk) + else: + num_proposals_i = min(Hi_Wi_A, pre_nms_topk) + + topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) + + # each is N x topk + topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 5 + + topk_proposals.append(topk_proposals_i) + topk_scores.append(topk_scores_i) + level_ids.append(torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device)) + + # 2. Concat all levels together + topk_scores = cat(topk_scores, dim=1) + topk_proposals = cat(topk_proposals, dim=1) + level_ids = cat(level_ids, dim=0) + + # 3. For each image, run a per-level NMS, and choose topk results. + results = [] + for n, image_size in enumerate(image_sizes): + boxes = RotatedBoxes(topk_proposals[n]) + scores_per_img = topk_scores[n] + valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) + if not valid_mask.all(): + boxes = boxes[valid_mask] + scores_per_img = scores_per_img[valid_mask] + boxes.clip(image_size) + + # filter empty boxes + keep = boxes.nonempty(threshold=min_box_size) + lvl = level_ids + if _is_tracing() or keep.sum().item() != len(boxes): + boxes, scores_per_img, lvl = (boxes[keep], scores_per_img[keep], level_ids[keep]) + + keep = batched_nms_rotated(boxes.tensor, scores_per_img, lvl, nms_thresh) + # In Detectron1, there was different behavior during training vs. testing. + # (https://github.com/facebookresearch/Detectron/issues/459) + # During training, topk is over the proposals from *all* images in the training batch. + # During testing, it is over the proposals for each image separately. + # As a result, the training behavior becomes batch-dependent, + # and the configuration "POST_NMS_TOPK_TRAIN" end up relying on the batch size. + # This bug is addressed in Detectron2 to make the behavior independent of batch size. + keep = keep[:post_nms_topk] + + res = Instances(image_size) + res.proposal_boxes = boxes[keep] + res.objectness_logits = scores_per_img[keep] + results.append(res) + return results + + +@PROPOSAL_GENERATOR_REGISTRY.register() +class RRPN(RPN): + """ + Rotated Region Proposal Network described in :paper:`RRPN`. + """ + + @configurable + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + if self.anchor_boundary_thresh >= 0: + raise NotImplementedError( + "anchor_boundary_thresh is a legacy option not implemented for RRPN." + ) + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + ret = super().from_config(cfg, input_shape) + ret["box2box_transform"] = Box2BoxTransformRotated(weights=cfg.MODEL.RPN.BBOX_REG_WEIGHTS) + return ret + + @torch.no_grad() + def label_and_sample_anchors(self, anchors: List[RotatedBoxes], gt_instances: List[Instances]): + """ + Args: + anchors (list[RotatedBoxes]): anchors for each feature map. + gt_instances: the ground-truth instances for each image. + + Returns: + list[Tensor]: + List of #img tensors. i-th element is a vector of labels whose length is + the total number of anchors across feature maps. Label values are in {-1, 0, 1}, + with meanings: -1 = ignore; 0 = negative class; 1 = positive class. + list[Tensor]: + i-th element is a Nx5 tensor, where N is the total number of anchors across + feature maps. The values are the matched gt boxes for each anchor. + Values are undefined for those anchors not labeled as 1. + """ + anchors = RotatedBoxes.cat(anchors) + + gt_boxes = [x.gt_boxes for x in gt_instances] + del gt_instances + + gt_labels = [] + matched_gt_boxes = [] + for gt_boxes_i in gt_boxes: + """ + gt_boxes_i: ground-truth boxes for i-th image + """ + match_quality_matrix = retry_if_cuda_oom(pairwise_iou_rotated)(gt_boxes_i, anchors) + matched_idxs, gt_labels_i = retry_if_cuda_oom(self.anchor_matcher)(match_quality_matrix) + # Matching is memory-expensive and may result in CPU tensors. But the result is small + gt_labels_i = gt_labels_i.to(device=gt_boxes_i.device) + + # A vector of labels (-1, 0, 1) for each anchor + gt_labels_i = self._subsample_labels(gt_labels_i) + + if len(gt_boxes_i) == 0: + # These values won't be used anyway since the anchor is labeled as background + matched_gt_boxes_i = torch.zeros_like(anchors.tensor) + else: + # TODO wasted indexing computation for ignored boxes + matched_gt_boxes_i = gt_boxes_i[matched_idxs].tensor + + gt_labels.append(gt_labels_i) # N,AHW + matched_gt_boxes.append(matched_gt_boxes_i) + return gt_labels, matched_gt_boxes + + @torch.no_grad() + def predict_proposals(self, anchors, pred_objectness_logits, pred_anchor_deltas, image_sizes): + pred_proposals = self._decode_proposals(anchors, pred_anchor_deltas) + return find_top_rrpn_proposals( + pred_proposals, + pred_objectness_logits, + image_sizes, + self.nms_thresh, + self.pre_nms_topk[self.training], + self.post_nms_topk[self.training], + self.min_box_size, + self.training, + ) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d13e9c57235b982f3e0645bc316de2b75755dfda --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/__init__.py @@ -0,0 +1,29 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .box_head import ROI_BOX_HEAD_REGISTRY, build_box_head, FastRCNNConvFCHead +from .keypoint_head import ( + ROI_KEYPOINT_HEAD_REGISTRY, + build_keypoint_head, + BaseKeypointRCNNHead, + KRCNNConvDeconvUpsampleHead, +) +from .mask_head import ( + ROI_MASK_HEAD_REGISTRY, + build_mask_head, + BaseMaskRCNNHead, + MaskRCNNConvUpsampleHead, +) +from .roi_heads import ( + ROI_HEADS_REGISTRY, + ROIHeads, + Res5ROIHeads, + StandardROIHeads, + build_roi_heads, + select_foreground_proposals, +) +from .cascade_rcnn import CascadeROIHeads +from .rotated_fast_rcnn import RROIHeads +from .fast_rcnn import FastRCNNOutputLayers + +from . import cascade_rcnn # isort:skip + +__all__ = list(globals().keys()) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/box_head.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/box_head.py new file mode 100644 index 0000000000000000000000000000000000000000..5d0370b0400d9268f13c905e4096a84ce42e9bfd --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/box_head.py @@ -0,0 +1,118 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +from typing import List +import fvcore.nn.weight_init as weight_init +import torch +from torch import nn + +from detectron2.config import configurable +from detectron2.layers import Conv2d, ShapeSpec, get_norm +from detectron2.utils.registry import Registry + +__all__ = ["FastRCNNConvFCHead", "build_box_head", "ROI_BOX_HEAD_REGISTRY"] + +ROI_BOX_HEAD_REGISTRY = Registry("ROI_BOX_HEAD") +ROI_BOX_HEAD_REGISTRY.__doc__ = """ +Registry for box heads, which make box predictions from per-region features. + +The registered object will be called with `obj(cfg, input_shape)`. +""" + + +# To get torchscript support, we make the head a subclass of `nn.Sequential`. +# Therefore, to add new layers in this head class, please make sure they are +# added in the order they will be used in forward(). +@ROI_BOX_HEAD_REGISTRY.register() +class FastRCNNConvFCHead(nn.Sequential): + """ + A head with several 3x3 conv layers (each followed by norm & relu) and then + several fc layers (each followed by relu). + """ + + @configurable + def __init__( + self, input_shape: ShapeSpec, *, conv_dims: List[int], fc_dims: List[int], conv_norm="" + ): + """ + NOTE: this interface is experimental. + + Args: + input_shape (ShapeSpec): shape of the input feature. + conv_dims (list[int]): the output dimensions of the conv layers + fc_dims (list[int]): the output dimensions of the fc layers + conv_norm (str or callable): normalization for the conv layers. + See :func:`detectron2.layers.get_norm` for supported types. + """ + super().__init__() + assert len(conv_dims) + len(fc_dims) > 0 + + self._output_size = (input_shape.channels, input_shape.height, input_shape.width) + + self.conv_norm_relus = [] + for k, conv_dim in enumerate(conv_dims): + conv = Conv2d( + self._output_size[0], + conv_dim, + kernel_size=3, + padding=1, + bias=not conv_norm, + norm=get_norm(conv_norm, conv_dim), + activation=nn.ReLU(), + ) + self.add_module("conv{}".format(k + 1), conv) + self.conv_norm_relus.append(conv) + self._output_size = (conv_dim, self._output_size[1], self._output_size[2]) + + self.fcs = [] + for k, fc_dim in enumerate(fc_dims): + if k == 0: + self.add_module("flatten", nn.Flatten()) + fc = nn.Linear(int(np.prod(self._output_size)), fc_dim) + self.add_module("fc{}".format(k + 1), fc) + self.add_module("fc_relu{}".format(k + 1), nn.ReLU()) + self.fcs.append(fc) + self._output_size = fc_dim + + for layer in self.conv_norm_relus: + weight_init.c2_msra_fill(layer) + for layer in self.fcs: + weight_init.c2_xavier_fill(layer) + + @classmethod + def from_config(cls, cfg, input_shape): + num_conv = cfg.MODEL.ROI_BOX_HEAD.NUM_CONV + conv_dim = cfg.MODEL.ROI_BOX_HEAD.CONV_DIM + num_fc = cfg.MODEL.ROI_BOX_HEAD.NUM_FC + fc_dim = cfg.MODEL.ROI_BOX_HEAD.FC_DIM + return { + "input_shape": input_shape, + "conv_dims": [conv_dim] * num_conv, + "fc_dims": [fc_dim] * num_fc, + "conv_norm": cfg.MODEL.ROI_BOX_HEAD.NORM, + } + + def forward(self, x): + for layer in self: + x = layer(x) + return x + + @property + @torch.jit.unused + def output_shape(self): + """ + Returns: + ShapeSpec: the output feature shape + """ + o = self._output_size + if isinstance(o, int): + return ShapeSpec(channels=o) + else: + return ShapeSpec(channels=o[0], height=o[1], width=o[2]) + + +def build_box_head(cfg, input_shape): + """ + Build a box head defined by `cfg.MODEL.ROI_BOX_HEAD.NAME`. + """ + name = cfg.MODEL.ROI_BOX_HEAD.NAME + return ROI_BOX_HEAD_REGISTRY.get(name)(cfg, input_shape) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/cascade_rcnn.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/cascade_rcnn.py new file mode 100644 index 0000000000000000000000000000000000000000..a0ca70fe23a1d406ee9bed6204a987d7e0708b91 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/cascade_rcnn.py @@ -0,0 +1,299 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from typing import List +import torch +from torch import nn +from torch.autograd.function import Function + +from detectron2.config import configurable +from detectron2.layers import ShapeSpec +from detectron2.structures import Boxes, Instances, pairwise_iou +from detectron2.utils.events import get_event_storage + +from ..box_regression import Box2BoxTransform +from ..matcher import Matcher +from ..poolers import ROIPooler +from .box_head import build_box_head +from .fast_rcnn import FastRCNNOutputLayers, fast_rcnn_inference +from .roi_heads import ROI_HEADS_REGISTRY, StandardROIHeads + + +class _ScaleGradient(Function): + @staticmethod + def forward(ctx, input, scale): + ctx.scale = scale + return input + + @staticmethod + def backward(ctx, grad_output): + return grad_output * ctx.scale, None + + +@ROI_HEADS_REGISTRY.register() +class CascadeROIHeads(StandardROIHeads): + """ + The ROI heads that implement :paper:`Cascade R-CNN`. + """ + + @configurable + def __init__( + self, + *, + box_in_features: List[str], + box_pooler: ROIPooler, + box_heads: List[nn.Module], + box_predictors: List[nn.Module], + proposal_matchers: List[Matcher], + **kwargs, + ): + """ + NOTE: this interface is experimental. + + Args: + box_pooler (ROIPooler): pooler that extracts region features from given boxes + box_heads (list[nn.Module]): box head for each cascade stage + box_predictors (list[nn.Module]): box predictor for each cascade stage + proposal_matchers (list[Matcher]): matcher with different IoU thresholds to + match boxes with ground truth for each stage. The first matcher matches + RPN proposals with ground truth, the other matchers use boxes predicted + by the previous stage as proposals and match them with ground truth. + """ + assert "proposal_matcher" not in kwargs, ( + "CascadeROIHeads takes 'proposal_matchers=' for each stage instead " + "of one 'proposal_matcher='." + ) + # The first matcher matches RPN proposals with ground truth, done in the base class + kwargs["proposal_matcher"] = proposal_matchers[0] + num_stages = self.num_cascade_stages = len(box_heads) + box_heads = nn.ModuleList(box_heads) + box_predictors = nn.ModuleList(box_predictors) + assert len(box_predictors) == num_stages, f"{len(box_predictors)} != {num_stages}!" + assert len(proposal_matchers) == num_stages, f"{len(proposal_matchers)} != {num_stages}!" + super().__init__( + box_in_features=box_in_features, + box_pooler=box_pooler, + box_head=box_heads, + box_predictor=box_predictors, + **kwargs, + ) + self.proposal_matchers = proposal_matchers + + @classmethod + def from_config(cls, cfg, input_shape): + ret = super().from_config(cfg, input_shape) + ret.pop("proposal_matcher") + return ret + + @classmethod + def _init_box_head(cls, cfg, input_shape): + # fmt: off + in_features = cfg.MODEL.ROI_HEADS.IN_FEATURES + pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION + pooler_scales = tuple(1.0 / input_shape[k].stride for k in in_features) + sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO + pooler_type = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE + cascade_bbox_reg_weights = cfg.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS + cascade_ious = cfg.MODEL.ROI_BOX_CASCADE_HEAD.IOUS + assert len(cascade_bbox_reg_weights) == len(cascade_ious) + assert cfg.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG, \ + "CascadeROIHeads only support class-agnostic regression now!" + assert cascade_ious[0] == cfg.MODEL.ROI_HEADS.IOU_THRESHOLDS[0] + # fmt: on + + in_channels = [input_shape[f].channels for f in in_features] + # Check all channel counts are equal + assert len(set(in_channels)) == 1, in_channels + in_channels = in_channels[0] + + box_pooler = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + pooled_shape = ShapeSpec( + channels=in_channels, width=pooler_resolution, height=pooler_resolution + ) + + box_heads, box_predictors, proposal_matchers = [], [], [] + for match_iou, bbox_reg_weights in zip(cascade_ious, cascade_bbox_reg_weights): + box_head = build_box_head(cfg, pooled_shape) + box_heads.append(box_head) + box_predictors.append( + FastRCNNOutputLayers( + cfg, + box_head.output_shape, + box2box_transform=Box2BoxTransform(weights=bbox_reg_weights), + ) + ) + proposal_matchers.append(Matcher([match_iou], [0, 1], allow_low_quality_matches=False)) + return { + "box_in_features": in_features, + "box_pooler": box_pooler, + "box_heads": box_heads, + "box_predictors": box_predictors, + "proposal_matchers": proposal_matchers, + } + + def forward(self, images, features, proposals, targets=None): + del images + if self.training: + proposals = self.label_and_sample_proposals(proposals, targets) + + if self.training: + # Need targets to box head + losses = self._forward_box(features, proposals, targets) + losses.update(self._forward_mask(features, proposals)) + losses.update(self._forward_keypoint(features, proposals)) + return proposals, losses + else: + pred_instances = self._forward_box(features, proposals) + pred_instances = self.forward_with_given_boxes(features, pred_instances) + return pred_instances, {} + + def _forward_box(self, features, proposals, targets=None): + """ + Args: + features, targets: the same as in + Same as in :meth:`ROIHeads.forward`. + proposals (list[Instances]): the per-image object proposals with + their matching ground truth. + Each has fields "proposal_boxes", and "objectness_logits", + "gt_classes", "gt_boxes". + """ + features = [features[f] for f in self.box_in_features] + head_outputs = [] # (predictor, predictions, proposals) + prev_pred_boxes = None + image_sizes = [x.image_size for x in proposals] + for k in range(self.num_cascade_stages): + if k > 0: + # The output boxes of the previous stage are used to create the input + # proposals of the next stage. + proposals = self._create_proposals_from_boxes(prev_pred_boxes, image_sizes) + if self.training: + proposals = self._match_and_label_boxes(proposals, k, targets) + predictions = self._run_stage(features, proposals, k) + prev_pred_boxes = self.box_predictor[k].predict_boxes(predictions, proposals) + head_outputs.append((self.box_predictor[k], predictions, proposals)) + + if self.training: + losses = {} + storage = get_event_storage() + for stage, (predictor, predictions, proposals) in enumerate(head_outputs): + with storage.name_scope("stage{}".format(stage)): + stage_losses = predictor.losses(predictions, proposals) + losses.update({k + "_stage{}".format(stage): v for k, v in stage_losses.items()}) + return losses + else: + # Each is a list[Tensor] of length #image. Each tensor is Ri x (K+1) + scores_per_stage = [h[0].predict_probs(h[1], h[2]) for h in head_outputs] + + # Average the scores across heads + scores = [ + sum(list(scores_per_image)) * (1.0 / self.num_cascade_stages) + for scores_per_image in zip(*scores_per_stage) + ] + # Use the boxes of the last head + predictor, predictions, proposals = head_outputs[-1] + boxes = predictor.predict_boxes(predictions, proposals) + pred_instances, _ = fast_rcnn_inference( + boxes, + scores, + image_sizes, + predictor.test_score_thresh, + predictor.test_nms_thresh, + predictor.test_topk_per_image, + ) + return pred_instances + + @torch.no_grad() + def _match_and_label_boxes(self, proposals, stage, targets): + """ + Match proposals with groundtruth using the matcher at the given stage. + Label the proposals as foreground or background based on the match. + + Args: + proposals (list[Instances]): One Instances for each image, with + the field "proposal_boxes". + stage (int): the current stage + targets (list[Instances]): the ground truth instances + + Returns: + list[Instances]: the same proposals, but with fields "gt_classes" and "gt_boxes" + """ + num_fg_samples, num_bg_samples = [], [] + for proposals_per_image, targets_per_image in zip(proposals, targets): + match_quality_matrix = pairwise_iou( + targets_per_image.gt_boxes, proposals_per_image.proposal_boxes + ) + # proposal_labels are 0 or 1 + matched_idxs, proposal_labels = self.proposal_matchers[stage](match_quality_matrix) + if len(targets_per_image) > 0: + gt_classes = targets_per_image.gt_classes[matched_idxs] + # Label unmatched proposals (0 label from matcher) as background (label=num_classes) + gt_classes[proposal_labels == 0] = self.num_classes + gt_boxes = targets_per_image.gt_boxes[matched_idxs] + else: + gt_classes = torch.zeros_like(matched_idxs) + self.num_classes + gt_boxes = Boxes( + targets_per_image.gt_boxes.tensor.new_zeros((len(proposals_per_image), 4)) + ) + proposals_per_image.gt_classes = gt_classes + proposals_per_image.gt_boxes = gt_boxes + + num_fg_samples.append((proposal_labels == 1).sum().item()) + num_bg_samples.append(proposal_labels.numel() - num_fg_samples[-1]) + + # Log the number of fg/bg samples in each stage + storage = get_event_storage() + storage.put_scalar( + "stage{}/roi_head/num_fg_samples".format(stage), + sum(num_fg_samples) / len(num_fg_samples), + ) + storage.put_scalar( + "stage{}/roi_head/num_bg_samples".format(stage), + sum(num_bg_samples) / len(num_bg_samples), + ) + return proposals + + def _run_stage(self, features, proposals, stage): + """ + Args: + features (list[Tensor]): #lvl input features to ROIHeads + proposals (list[Instances]): #image Instances, with the field "proposal_boxes" + stage (int): the current stage + + Returns: + Same output as `FastRCNNOutputLayers.forward()`. + """ + box_features = self.box_pooler(features, [x.proposal_boxes for x in proposals]) + # The original implementation averages the losses among heads, + # but scale up the parameter gradients of the heads. + # This is equivalent to adding the losses among heads, + # but scale down the gradients on features. + if self.training: + box_features = _ScaleGradient.apply(box_features, 1.0 / self.num_cascade_stages) + box_features = self.box_head[stage](box_features) + return self.box_predictor[stage](box_features) + + def _create_proposals_from_boxes(self, boxes, image_sizes): + """ + Args: + boxes (list[Tensor]): per-image predicted boxes, each of shape Ri x 4 + image_sizes (list[tuple]): list of image shapes in (h, w) + + Returns: + list[Instances]: per-image proposals with the given boxes. + """ + # Just like RPN, the proposals should not have gradients + boxes = [Boxes(b.detach()) for b in boxes] + proposals = [] + for boxes_per_image, image_size in zip(boxes, image_sizes): + boxes_per_image.clip(image_size) + if self.training: + # do not filter empty boxes at inference time, + # because the scores from each stage need to be aligned and added later + boxes_per_image = boxes_per_image[boxes_per_image.nonempty()] + prop = Instances(image_size) + prop.proposal_boxes = boxes_per_image + proposals.append(prop) + return proposals diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/fast_rcnn.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/fast_rcnn.py new file mode 100644 index 0000000000000000000000000000000000000000..42eba21092af71bafdc1875fdbe3a5ae1e3fd543 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/fast_rcnn.py @@ -0,0 +1,462 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +from typing import Dict, List, Tuple, Union +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import ShapeSpec, batched_nms, cat, cross_entropy, nonzero_tuple +from detectron2.modeling.box_regression import Box2BoxTransform, _dense_box_regression_loss +from detectron2.structures import Boxes, Instances +from detectron2.utils.events import get_event_storage + +__all__ = ["fast_rcnn_inference", "FastRCNNOutputLayers"] + + +logger = logging.getLogger(__name__) + +""" +Shape shorthand in this module: + + N: number of images in the minibatch + R: number of ROIs, combined over all images, in the minibatch + Ri: number of ROIs in image i + K: number of foreground classes. E.g.,there are 80 foreground classes in COCO. + +Naming convention: + + deltas: refers to the 4-d (dx, dy, dw, dh) deltas that parameterize the box2box + transform (see :class:`box_regression.Box2BoxTransform`). + + pred_class_logits: predicted class scores in [-inf, +inf]; use + softmax(pred_class_logits) to estimate P(class). + + gt_classes: ground-truth classification labels in [0, K], where [0, K) represent + foreground object classes and K represents the background class. + + pred_proposal_deltas: predicted box2box transform deltas for transforming proposals + to detection box predictions. + + gt_proposal_deltas: ground-truth box2box transform deltas +""" + + +def fast_rcnn_inference( + boxes: List[torch.Tensor], + scores: List[torch.Tensor], + image_shapes: List[Tuple[int, int]], + score_thresh: float, + nms_thresh: float, + topk_per_image: int, +): + """ + Call `fast_rcnn_inference_single_image` for all images. + + Args: + boxes (list[Tensor]): A list of Tensors of predicted class-specific or class-agnostic + boxes for each image. Element i has shape (Ri, K * 4) if doing + class-specific regression, or (Ri, 4) if doing class-agnostic + regression, where Ri is the number of predicted objects for image i. + This is compatible with the output of :meth:`FastRCNNOutputLayers.predict_boxes`. + scores (list[Tensor]): A list of Tensors of predicted class scores for each image. + Element i has shape (Ri, K + 1), where Ri is the number of predicted objects + for image i. Compatible with the output of :meth:`FastRCNNOutputLayers.predict_probs`. + image_shapes (list[tuple]): A list of (width, height) tuples for each image in the batch. + score_thresh (float): Only return detections with a confidence score exceeding this + threshold. + nms_thresh (float): The threshold to use for box non-maximum suppression. Value in [0, 1]. + topk_per_image (int): The number of top scoring detections to return. Set < 0 to return + all detections. + + Returns: + instances: (list[Instances]): A list of N instances, one for each image in the batch, + that stores the topk most confidence detections. + kept_indices: (list[Tensor]): A list of 1D tensor of length of N, each element indicates + the corresponding boxes/scores index in [0, Ri) from the input, for image i. + """ + result_per_image = [ + fast_rcnn_inference_single_image( + boxes_per_image, scores_per_image, image_shape, score_thresh, nms_thresh, topk_per_image + ) + for scores_per_image, boxes_per_image, image_shape in zip(scores, boxes, image_shapes) + ] + return [x[0] for x in result_per_image], [x[1] for x in result_per_image] + + +def _log_classification_stats(pred_logits, gt_classes, prefix="fast_rcnn"): + """ + Log the classification metrics to EventStorage. + + Args: + pred_logits: Rx(K+1) logits. The last column is for background class. + gt_classes: R labels + """ + num_instances = gt_classes.numel() + if num_instances == 0: + return + pred_classes = pred_logits.argmax(dim=1) + bg_class_ind = pred_logits.shape[1] - 1 + + fg_inds = (gt_classes >= 0) & (gt_classes < bg_class_ind) + num_fg = fg_inds.nonzero().numel() + fg_gt_classes = gt_classes[fg_inds] + fg_pred_classes = pred_classes[fg_inds] + + num_false_negative = (fg_pred_classes == bg_class_ind).nonzero().numel() + num_accurate = (pred_classes == gt_classes).nonzero().numel() + fg_num_accurate = (fg_pred_classes == fg_gt_classes).nonzero().numel() + + storage = get_event_storage() + storage.put_scalar(f"{prefix}/cls_accuracy", num_accurate / num_instances) + if num_fg > 0: + storage.put_scalar(f"{prefix}/fg_cls_accuracy", fg_num_accurate / num_fg) + storage.put_scalar(f"{prefix}/false_negative", num_false_negative / num_fg) + + +def fast_rcnn_inference_single_image( + boxes, + scores, + image_shape: Tuple[int, int], + score_thresh: float, + nms_thresh: float, + topk_per_image: int, +): + """ + Single-image inference. Return bounding-box detection results by thresholding + on scores and applying non-maximum suppression (NMS). + + Args: + Same as `fast_rcnn_inference`, but with boxes, scores, and image shapes + per image. + + Returns: + Same as `fast_rcnn_inference`, but for only one image. + """ + valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) + if not valid_mask.all(): + boxes = boxes[valid_mask] + scores = scores[valid_mask] + + scores = scores[:, :-1] + num_bbox_reg_classes = boxes.shape[1] // 4 + # Convert to Boxes to use the `clip` function ... + boxes = Boxes(boxes.reshape(-1, 4)) + boxes.clip(image_shape) + boxes = boxes.tensor.view(-1, num_bbox_reg_classes, 4) # R x C x 4 + + # 1. Filter results based on detection scores. It can make NMS more efficient + # by filtering out low-confidence detections. + filter_mask = scores > score_thresh # R x K + # R' x 2. First column contains indices of the R predictions; + # Second column contains indices of classes. + filter_inds = filter_mask.nonzero() + if num_bbox_reg_classes == 1: + boxes = boxes[filter_inds[:, 0], 0] + else: + boxes = boxes[filter_mask] + scores = scores[filter_mask] + + # 2. Apply NMS for each class independently. + keep = batched_nms(boxes, scores, filter_inds[:, 1], nms_thresh) + if topk_per_image >= 0: + keep = keep[:topk_per_image] + boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep] + + result = Instances(image_shape) + result.pred_boxes = Boxes(boxes) + result.scores = scores + result.pred_classes = filter_inds[:, 1] + return result, filter_inds[:, 0] + + +class FastRCNNOutputLayers(nn.Module): + """ + Two linear layers for predicting Fast R-CNN outputs: + + 1. proposal-to-detection box regression deltas + 2. classification scores + """ + + @configurable + def __init__( + self, + input_shape: ShapeSpec, + *, + box2box_transform, + num_classes: int, + test_score_thresh: float = 0.0, + test_nms_thresh: float = 0.5, + test_topk_per_image: int = 100, + cls_agnostic_bbox_reg: bool = False, + smooth_l1_beta: float = 0.0, + box_reg_loss_type: str = "smooth_l1", + loss_weight: Union[float, Dict[str, float]] = 1.0, + ): + """ + NOTE: this interface is experimental. + + Args: + input_shape (ShapeSpec): shape of the input feature to this module + box2box_transform (Box2BoxTransform or Box2BoxTransformRotated): + num_classes (int): number of foreground classes + test_score_thresh (float): threshold to filter predictions results. + test_nms_thresh (float): NMS threshold for prediction results. + test_topk_per_image (int): number of top predictions to produce per image. + cls_agnostic_bbox_reg (bool): whether to use class agnostic for bbox regression + smooth_l1_beta (float): transition point from L1 to L2 loss. Only used if + `box_reg_loss_type` is "smooth_l1" + box_reg_loss_type (str): Box regression loss type. One of: "smooth_l1", "giou", + "diou", "ciou" + loss_weight (float|dict): weights to use for losses. Can be single float for weighting + all losses, or a dict of individual weightings. Valid dict keys are: + * "loss_cls": applied to classification loss + * "loss_box_reg": applied to box regression loss + """ + super().__init__() + if isinstance(input_shape, int): # some backward compatibility + input_shape = ShapeSpec(channels=input_shape) + self.num_classes = num_classes + input_size = input_shape.channels * (input_shape.width or 1) * (input_shape.height or 1) + # prediction layer for num_classes foreground classes and one background class (hence + 1) + self.cls_score = nn.Linear(input_size, num_classes + 1) + num_bbox_reg_classes = 1 if cls_agnostic_bbox_reg else num_classes + box_dim = len(box2box_transform.weights) + self.bbox_pred = nn.Linear(input_size, num_bbox_reg_classes * box_dim) + + nn.init.normal_(self.cls_score.weight, std=0.01) + nn.init.normal_(self.bbox_pred.weight, std=0.001) + for l in [self.cls_score, self.bbox_pred]: + nn.init.constant_(l.bias, 0) + + self.box2box_transform = box2box_transform + self.smooth_l1_beta = smooth_l1_beta + self.test_score_thresh = test_score_thresh + self.test_nms_thresh = test_nms_thresh + self.test_topk_per_image = test_topk_per_image + self.box_reg_loss_type = box_reg_loss_type + if isinstance(loss_weight, float): + loss_weight = {"loss_cls": loss_weight, "loss_box_reg": loss_weight} + self.loss_weight = loss_weight + + @classmethod + def from_config(cls, cfg, input_shape): + return { + "input_shape": input_shape, + "box2box_transform": Box2BoxTransform(weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS), + # fmt: off + "num_classes" : cfg.MODEL.ROI_HEADS.NUM_CLASSES, + "cls_agnostic_bbox_reg" : cfg.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG, + "smooth_l1_beta" : cfg.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA, + "test_score_thresh" : cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST, + "test_nms_thresh" : cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST, + "test_topk_per_image" : cfg.TEST.DETECTIONS_PER_IMAGE, + "box_reg_loss_type" : cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_TYPE, + "loss_weight" : {"loss_box_reg": cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_WEIGHT}, + # fmt: on + } + + def forward(self, x): + """ + Args: + x: per-region features of shape (N, ...) for N bounding boxes to predict. + + Returns: + (Tensor, Tensor): + First tensor: shape (N,K+1), scores for each of the N box. Each row contains the + scores for K object categories and 1 background class. + + Second tensor: bounding box regression deltas for each box. Shape is shape (N,Kx4), + or (N,4) for class-agnostic regression. + """ + if x.dim() > 2: + x = torch.flatten(x, start_dim=1) + scores = self.cls_score(x) + proposal_deltas = self.bbox_pred(x) + return scores, proposal_deltas + + def losses(self, predictions, proposals): + """ + Args: + predictions: return values of :meth:`forward()`. + proposals (list[Instances]): proposals that match the features that were used + to compute predictions. The fields ``proposal_boxes``, ``gt_boxes``, + ``gt_classes`` are expected. + + Returns: + Dict[str, Tensor]: dict of losses + """ + scores, proposal_deltas = predictions + + # parse classification outputs + gt_classes = ( + cat([p.gt_classes for p in proposals], dim=0) if len(proposals) else torch.empty(0) + ) + _log_classification_stats(scores, gt_classes) + + # parse box regression outputs + if len(proposals): + proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0) # Nx4 + assert not proposal_boxes.requires_grad, "Proposals should not require gradients!" + # If "gt_boxes" does not exist, the proposals must be all negative and + # should not be included in regression loss computation. + # Here we just use proposal_boxes as an arbitrary placeholder because its + # value won't be used in self.box_reg_loss(). + gt_boxes = cat( + [(p.gt_boxes if p.has("gt_boxes") else p.proposal_boxes).tensor for p in proposals], + dim=0, + ) + else: + proposal_boxes = gt_boxes = torch.empty((0, 4), device=proposal_deltas.device) + + losses = { + "loss_cls": cross_entropy(scores, gt_classes, reduction="mean"), + "loss_box_reg": self.box_reg_loss( + proposal_boxes, gt_boxes, proposal_deltas, gt_classes + ), + } + return {k: v * self.loss_weight.get(k, 1.0) for k, v in losses.items()} + + def box_reg_loss(self, proposal_boxes, gt_boxes, pred_deltas, gt_classes): + """ + Args: + proposal_boxes/gt_boxes are tensors with the same shape (R, 4 or 5). + pred_deltas has shape (R, 4 or 5), or (R, num_classes * (4 or 5)). + gt_classes is a long tensor of shape R, the gt class label of each proposal. + R shall be the number of proposals. + """ + box_dim = proposal_boxes.shape[1] # 4 or 5 + # Regression loss is only computed for foreground proposals (those matched to a GT) + fg_inds = nonzero_tuple((gt_classes >= 0) & (gt_classes < self.num_classes))[0] + if pred_deltas.shape[1] == box_dim: # cls-agnostic regression + fg_pred_deltas = pred_deltas[fg_inds] + else: + fg_pred_deltas = pred_deltas.view(-1, self.num_classes, box_dim)[ + fg_inds, gt_classes[fg_inds] + ] + + loss_box_reg = _dense_box_regression_loss( + [proposal_boxes[fg_inds]], + self.box2box_transform, + [fg_pred_deltas.unsqueeze(0)], + [gt_boxes[fg_inds]], + ..., + self.box_reg_loss_type, + self.smooth_l1_beta, + ) + + # The reg loss is normalized using the total number of regions (R), not the number + # of foreground regions even though the box regression loss is only defined on + # foreground regions. Why? Because doing so gives equal training influence to + # each foreground example. To see how, consider two different minibatches: + # (1) Contains a single foreground region + # (2) Contains 100 foreground regions + # If we normalize by the number of foreground regions, the single example in + # minibatch (1) will be given 100 times as much influence as each foreground + # example in minibatch (2). Normalizing by the total number of regions, R, + # means that the single example in minibatch (1) and each of the 100 examples + # in minibatch (2) are given equal influence. + return loss_box_reg / max(gt_classes.numel(), 1.0) # return 0 if empty + + def inference(self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances]): + """ + Args: + predictions: return values of :meth:`forward()`. + proposals (list[Instances]): proposals that match the features that were + used to compute predictions. The ``proposal_boxes`` field is expected. + + Returns: + list[Instances]: same as `fast_rcnn_inference`. + list[Tensor]: same as `fast_rcnn_inference`. + """ + boxes = self.predict_boxes(predictions, proposals) + scores = self.predict_probs(predictions, proposals) + image_shapes = [x.image_size for x in proposals] + return fast_rcnn_inference( + boxes, + scores, + image_shapes, + self.test_score_thresh, + self.test_nms_thresh, + self.test_topk_per_image, + ) + + def predict_boxes_for_gt_classes(self, predictions, proposals): + """ + Args: + predictions: return values of :meth:`forward()`. + proposals (list[Instances]): proposals that match the features that were used + to compute predictions. The fields ``proposal_boxes``, ``gt_classes`` are expected. + + Returns: + list[Tensor]: + A list of Tensors of predicted boxes for GT classes in case of + class-specific box head. Element i of the list has shape (Ri, B), where Ri is + the number of proposals for image i and B is the box dimension (4 or 5) + """ + if not len(proposals): + return [] + scores, proposal_deltas = predictions + proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0) + N, B = proposal_boxes.shape + predict_boxes = self.box2box_transform.apply_deltas( + proposal_deltas, proposal_boxes + ) # Nx(KxB) + + K = predict_boxes.shape[1] // B + if K > 1: + gt_classes = torch.cat([p.gt_classes for p in proposals], dim=0) + # Some proposals are ignored or have a background class. Their gt_classes + # cannot be used as index. + gt_classes = gt_classes.clamp_(0, K - 1) + + predict_boxes = predict_boxes.view(N, K, B)[ + torch.arange(N, dtype=torch.long, device=predict_boxes.device), gt_classes + ] + num_prop_per_image = [len(p) for p in proposals] + return predict_boxes.split(num_prop_per_image) + + def predict_boxes( + self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances] + ): + """ + Args: + predictions: return values of :meth:`forward()`. + proposals (list[Instances]): proposals that match the features that were + used to compute predictions. The ``proposal_boxes`` field is expected. + + Returns: + list[Tensor]: + A list of Tensors of predicted class-specific or class-agnostic boxes + for each image. Element i has shape (Ri, K * B) or (Ri, B), where Ri is + the number of proposals for image i and B is the box dimension (4 or 5) + """ + if not len(proposals): + return [] + _, proposal_deltas = predictions + num_prop_per_image = [len(p) for p in proposals] + proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0) + predict_boxes = self.box2box_transform.apply_deltas( + proposal_deltas, + proposal_boxes, + ) # Nx(KxB) + return predict_boxes.split(num_prop_per_image) + + def predict_probs( + self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances] + ): + """ + Args: + predictions: return values of :meth:`forward()`. + proposals (list[Instances]): proposals that match the features that were + used to compute predictions. + + Returns: + list[Tensor]: + A list of Tensors of predicted class probabilities for each image. + Element i has shape (Ri, K + 1), where Ri is the number of proposals for image i. + """ + scores, _ = predictions + num_inst_per_image = [len(p) for p in proposals] + probs = F.softmax(scores, dim=-1) + return probs.split(num_inst_per_image, dim=0) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/keypoint_head.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/keypoint_head.py new file mode 100644 index 0000000000000000000000000000000000000000..e0acc138e72fcb188e4ffb3d156358b8ca59babf --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/keypoint_head.py @@ -0,0 +1,272 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from typing import List +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import Conv2d, ConvTranspose2d, cat, interpolate +from detectron2.structures import Instances, heatmaps_to_keypoints +from detectron2.utils.events import get_event_storage +from detectron2.utils.registry import Registry + +_TOTAL_SKIPPED = 0 + + +__all__ = [ + "ROI_KEYPOINT_HEAD_REGISTRY", + "build_keypoint_head", + "BaseKeypointRCNNHead", + "KRCNNConvDeconvUpsampleHead", +] + + +ROI_KEYPOINT_HEAD_REGISTRY = Registry("ROI_KEYPOINT_HEAD") +ROI_KEYPOINT_HEAD_REGISTRY.__doc__ = """ +Registry for keypoint heads, which make keypoint predictions from per-region features. + +The registered object will be called with `obj(cfg, input_shape)`. +""" + + +def build_keypoint_head(cfg, input_shape): + """ + Build a keypoint head from `cfg.MODEL.ROI_KEYPOINT_HEAD.NAME`. + """ + name = cfg.MODEL.ROI_KEYPOINT_HEAD.NAME + return ROI_KEYPOINT_HEAD_REGISTRY.get(name)(cfg, input_shape) + + +def keypoint_rcnn_loss(pred_keypoint_logits, instances, normalizer): + """ + Arguments: + pred_keypoint_logits (Tensor): A tensor of shape (N, K, S, S) where N is the total number + of instances in the batch, K is the number of keypoints, and S is the side length + of the keypoint heatmap. The values are spatial logits. + instances (list[Instances]): A list of M Instances, where M is the batch size. + These instances are predictions from the model + that are in 1:1 correspondence with pred_keypoint_logits. + Each Instances should contain a `gt_keypoints` field containing a `structures.Keypoint` + instance. + normalizer (float): Normalize the loss by this amount. + If not specified, we normalize by the number of visible keypoints in the minibatch. + + Returns a scalar tensor containing the loss. + """ + heatmaps = [] + valid = [] + + keypoint_side_len = pred_keypoint_logits.shape[2] + for instances_per_image in instances: + if len(instances_per_image) == 0: + continue + keypoints = instances_per_image.gt_keypoints + heatmaps_per_image, valid_per_image = keypoints.to_heatmap( + instances_per_image.proposal_boxes.tensor, keypoint_side_len + ) + heatmaps.append(heatmaps_per_image.view(-1)) + valid.append(valid_per_image.view(-1)) + + if len(heatmaps): + keypoint_targets = cat(heatmaps, dim=0) + valid = cat(valid, dim=0).to(dtype=torch.uint8) + valid = torch.nonzero(valid).squeeze(1) + + # torch.mean (in binary_cross_entropy_with_logits) doesn't + # accept empty tensors, so handle it separately + if len(heatmaps) == 0 or valid.numel() == 0: + global _TOTAL_SKIPPED + _TOTAL_SKIPPED += 1 + storage = get_event_storage() + storage.put_scalar("kpts_num_skipped_batches", _TOTAL_SKIPPED, smoothing_hint=False) + return pred_keypoint_logits.sum() * 0 + + N, K, H, W = pred_keypoint_logits.shape + pred_keypoint_logits = pred_keypoint_logits.view(N * K, H * W) + + keypoint_loss = F.cross_entropy( + pred_keypoint_logits[valid], keypoint_targets[valid], reduction="sum" + ) + + # If a normalizer isn't specified, normalize by the number of visible keypoints in the minibatch + if normalizer is None: + normalizer = valid.numel() + keypoint_loss /= normalizer + + return keypoint_loss + + +def keypoint_rcnn_inference(pred_keypoint_logits: torch.Tensor, pred_instances: List[Instances]): + """ + Post process each predicted keypoint heatmap in `pred_keypoint_logits` into (x, y, score) + and add it to the `pred_instances` as a `pred_keypoints` field. + + Args: + pred_keypoint_logits (Tensor): A tensor of shape (R, K, S, S) where R is the total number + of instances in the batch, K is the number of keypoints, and S is the side length of + the keypoint heatmap. The values are spatial logits. + pred_instances (list[Instances]): A list of N Instances, where N is the number of images. + + Returns: + None. Each element in pred_instances will contain extra "pred_keypoints" and + "pred_keypoint_heatmaps" fields. "pred_keypoints" is a tensor of shape + (#instance, K, 3) where the last dimension corresponds to (x, y, score). + The scores are larger than 0. "pred_keypoint_heatmaps" contains the raw + keypoint logits as passed to this function. + """ + # flatten all bboxes from all images together (list[Boxes] -> Rx4 tensor) + bboxes_flat = cat([b.pred_boxes.tensor for b in pred_instances], dim=0) + + pred_keypoint_logits = pred_keypoint_logits.detach() + keypoint_results = heatmaps_to_keypoints(pred_keypoint_logits, bboxes_flat.detach()) + num_instances_per_image = [len(i) for i in pred_instances] + keypoint_results = keypoint_results[:, :, [0, 1, 3]].split(num_instances_per_image, dim=0) + heatmap_results = pred_keypoint_logits.split(num_instances_per_image, dim=0) + + for keypoint_results_per_image, heatmap_results_per_image, instances_per_image in zip( + keypoint_results, heatmap_results, pred_instances + ): + # keypoint_results_per_image is (num instances)x(num keypoints)x(x, y, score) + # heatmap_results_per_image is (num instances)x(num keypoints)x(side)x(side) + instances_per_image.pred_keypoints = keypoint_results_per_image + instances_per_image.pred_keypoint_heatmaps = heatmap_results_per_image + + +class BaseKeypointRCNNHead(nn.Module): + """ + Implement the basic Keypoint R-CNN losses and inference logic described in + Sec. 5 of :paper:`Mask R-CNN`. + """ + + @configurable + def __init__(self, *, num_keypoints, loss_weight=1.0, loss_normalizer=1.0): + """ + NOTE: this interface is experimental. + + Args: + num_keypoints (int): number of keypoints to predict + loss_weight (float): weight to multiple on the keypoint loss + loss_normalizer (float or str): + If float, divide the loss by `loss_normalizer * #images`. + If 'visible', the loss is normalized by the total number of + visible keypoints across images. + """ + super().__init__() + self.num_keypoints = num_keypoints + self.loss_weight = loss_weight + assert loss_normalizer == "visible" or isinstance(loss_normalizer, float), loss_normalizer + self.loss_normalizer = loss_normalizer + + @classmethod + def from_config(cls, cfg, input_shape): + ret = { + "loss_weight": cfg.MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT, + "num_keypoints": cfg.MODEL.ROI_KEYPOINT_HEAD.NUM_KEYPOINTS, + } + normalize_by_visible = ( + cfg.MODEL.ROI_KEYPOINT_HEAD.NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS + ) # noqa + if not normalize_by_visible: + batch_size_per_image = cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE + positive_sample_fraction = cfg.MODEL.ROI_HEADS.POSITIVE_FRACTION + ret["loss_normalizer"] = ( + ret["num_keypoints"] * batch_size_per_image * positive_sample_fraction + ) + else: + ret["loss_normalizer"] = "visible" + return ret + + def forward(self, x, instances: List[Instances]): + """ + Args: + x: input 4D region feature(s) provided by :class:`ROIHeads`. + instances (list[Instances]): contains the boxes & labels corresponding + to the input features. + Exact format is up to its caller to decide. + Typically, this is the foreground instances in training, with + "proposal_boxes" field and other gt annotations. + In inference, it contains boxes that are already predicted. + + Returns: + A dict of losses if in training. The predicted "instances" if in inference. + """ + x = self.layers(x) + if self.training: + num_images = len(instances) + normalizer = ( + None if self.loss_normalizer == "visible" else num_images * self.loss_normalizer + ) + return { + "loss_keypoint": keypoint_rcnn_loss(x, instances, normalizer=normalizer) + * self.loss_weight + } + else: + keypoint_rcnn_inference(x, instances) + return instances + + def layers(self, x): + """ + Neural network layers that makes predictions from regional input features. + """ + raise NotImplementedError + + +# To get torchscript support, we make the head a subclass of `nn.Sequential`. +# Therefore, to add new layers in this head class, please make sure they are +# added in the order they will be used in forward(). +@ROI_KEYPOINT_HEAD_REGISTRY.register() +class KRCNNConvDeconvUpsampleHead(BaseKeypointRCNNHead, nn.Sequential): + """ + A standard keypoint head containing a series of 3x3 convs, followed by + a transpose convolution and bilinear interpolation for upsampling. + It is described in Sec. 5 of :paper:`Mask R-CNN`. + """ + + @configurable + def __init__(self, input_shape, *, num_keypoints, conv_dims, **kwargs): + """ + NOTE: this interface is experimental. + + Args: + input_shape (ShapeSpec): shape of the input feature + conv_dims: an iterable of output channel counts for each conv in the head + e.g. (512, 512, 512) for three convs outputting 512 channels. + """ + super().__init__(num_keypoints=num_keypoints, **kwargs) + + # default up_scale to 2.0 (this can be made an option) + up_scale = 2.0 + in_channels = input_shape.channels + + for idx, layer_channels in enumerate(conv_dims, 1): + module = Conv2d(in_channels, layer_channels, 3, stride=1, padding=1) + self.add_module("conv_fcn{}".format(idx), module) + self.add_module("conv_fcn_relu{}".format(idx), nn.ReLU()) + in_channels = layer_channels + + deconv_kernel = 4 + self.score_lowres = ConvTranspose2d( + in_channels, num_keypoints, deconv_kernel, stride=2, padding=deconv_kernel // 2 - 1 + ) + self.up_scale = up_scale + + for name, param in self.named_parameters(): + if "bias" in name: + nn.init.constant_(param, 0) + elif "weight" in name: + # Caffe2 implementation uses MSRAFill, which in fact + # corresponds to kaiming_normal_ in PyTorch + nn.init.kaiming_normal_(param, mode="fan_out", nonlinearity="relu") + + @classmethod + def from_config(cls, cfg, input_shape): + ret = super().from_config(cfg, input_shape) + ret["input_shape"] = input_shape + ret["conv_dims"] = cfg.MODEL.ROI_KEYPOINT_HEAD.CONV_DIMS + return ret + + def layers(self, x): + for layer in self: + x = layer(x) + x = interpolate(x, scale_factor=self.up_scale, mode="bilinear", align_corners=False) + return x diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/mask_head.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/mask_head.py new file mode 100644 index 0000000000000000000000000000000000000000..5ac5c4b9aaa34653d6c50e512a5a4300da450c7f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/mask_head.py @@ -0,0 +1,292 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from typing import List +import fvcore.nn.weight_init as weight_init +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import Conv2d, ConvTranspose2d, ShapeSpec, cat, get_norm +from detectron2.structures import Instances +from detectron2.utils.events import get_event_storage +from detectron2.utils.registry import Registry + +__all__ = [ + "BaseMaskRCNNHead", + "MaskRCNNConvUpsampleHead", + "build_mask_head", + "ROI_MASK_HEAD_REGISTRY", +] + + +ROI_MASK_HEAD_REGISTRY = Registry("ROI_MASK_HEAD") +ROI_MASK_HEAD_REGISTRY.__doc__ = """ +Registry for mask heads, which predicts instance masks given +per-region features. + +The registered object will be called with `obj(cfg, input_shape)`. +""" + + +@torch.jit.unused +def mask_rcnn_loss(pred_mask_logits: torch.Tensor, instances: List[Instances], vis_period: int = 0): + """ + Compute the mask prediction loss defined in the Mask R-CNN paper. + + Args: + pred_mask_logits (Tensor): A tensor of shape (B, C, Hmask, Wmask) or (B, 1, Hmask, Wmask) + for class-specific or class-agnostic, where B is the total number of predicted masks + in all images, C is the number of foreground classes, and Hmask, Wmask are the height + and width of the mask predictions. The values are logits. + instances (list[Instances]): A list of N Instances, where N is the number of images + in the batch. These instances are in 1:1 + correspondence with the pred_mask_logits. The ground-truth labels (class, box, mask, + ...) associated with each instance are stored in fields. + vis_period (int): the period (in steps) to dump visualization. + + Returns: + mask_loss (Tensor): A scalar tensor containing the loss. + """ + cls_agnostic_mask = pred_mask_logits.size(1) == 1 + total_num_masks = pred_mask_logits.size(0) + mask_side_len = pred_mask_logits.size(2) + assert pred_mask_logits.size(2) == pred_mask_logits.size(3), "Mask prediction must be square!" + + gt_classes = [] + gt_masks = [] + for instances_per_image in instances: + if len(instances_per_image) == 0: + continue + if not cls_agnostic_mask: + gt_classes_per_image = instances_per_image.gt_classes.to(dtype=torch.int64) + gt_classes.append(gt_classes_per_image) + + gt_masks_per_image = instances_per_image.gt_masks.crop_and_resize( + instances_per_image.proposal_boxes.tensor, mask_side_len + ).to(device=pred_mask_logits.device) + # A tensor of shape (N, M, M), N=#instances in the image; M=mask_side_len + gt_masks.append(gt_masks_per_image) + + if len(gt_masks) == 0: + return pred_mask_logits.sum() * 0 + + gt_masks = cat(gt_masks, dim=0) + + if cls_agnostic_mask: + pred_mask_logits = pred_mask_logits[:, 0] + else: + indices = torch.arange(total_num_masks) + gt_classes = cat(gt_classes, dim=0) + pred_mask_logits = pred_mask_logits[indices, gt_classes] + + if gt_masks.dtype == torch.bool: + gt_masks_bool = gt_masks + else: + # Here we allow gt_masks to be float as well (depend on the implementation of rasterize()) + gt_masks_bool = gt_masks > 0.5 + gt_masks = gt_masks.to(dtype=torch.float32) + + # Log the training accuracy (using gt classes and 0.5 threshold) + mask_incorrect = (pred_mask_logits > 0.0) != gt_masks_bool + mask_accuracy = 1 - (mask_incorrect.sum().item() / max(mask_incorrect.numel(), 1.0)) + num_positive = gt_masks_bool.sum().item() + false_positive = (mask_incorrect & ~gt_masks_bool).sum().item() / max( + gt_masks_bool.numel() - num_positive, 1.0 + ) + false_negative = (mask_incorrect & gt_masks_bool).sum().item() / max(num_positive, 1.0) + + storage = get_event_storage() + storage.put_scalar("mask_rcnn/accuracy", mask_accuracy) + storage.put_scalar("mask_rcnn/false_positive", false_positive) + storage.put_scalar("mask_rcnn/false_negative", false_negative) + if vis_period > 0 and storage.iter % vis_period == 0: + pred_masks = pred_mask_logits.sigmoid() + vis_masks = torch.cat([pred_masks, gt_masks], axis=2) + name = "Left: mask prediction; Right: mask GT" + for idx, vis_mask in enumerate(vis_masks): + vis_mask = torch.stack([vis_mask] * 3, axis=0) + storage.put_image(name + f" ({idx})", vis_mask) + + mask_loss = F.binary_cross_entropy_with_logits(pred_mask_logits, gt_masks, reduction="mean") + return mask_loss + + +def mask_rcnn_inference(pred_mask_logits: torch.Tensor, pred_instances: List[Instances]): + """ + Convert pred_mask_logits to estimated foreground probability masks while also + extracting only the masks for the predicted classes in pred_instances. For each + predicted box, the mask of the same class is attached to the instance by adding a + new "pred_masks" field to pred_instances. + + Args: + pred_mask_logits (Tensor): A tensor of shape (B, C, Hmask, Wmask) or (B, 1, Hmask, Wmask) + for class-specific or class-agnostic, where B is the total number of predicted masks + in all images, C is the number of foreground classes, and Hmask, Wmask are the height + and width of the mask predictions. The values are logits. + pred_instances (list[Instances]): A list of N Instances, where N is the number of images + in the batch. Each Instances must have field "pred_classes". + + Returns: + None. pred_instances will contain an extra "pred_masks" field storing a mask of size (Hmask, + Wmask) for predicted class. Note that the masks are returned as a soft (non-quantized) + masks the resolution predicted by the network; post-processing steps, such as resizing + the predicted masks to the original image resolution and/or binarizing them, is left + to the caller. + """ + cls_agnostic_mask = pred_mask_logits.size(1) == 1 + + if cls_agnostic_mask: + mask_probs_pred = pred_mask_logits.sigmoid() + else: + # Select masks corresponding to the predicted classes + num_masks = pred_mask_logits.shape[0] + class_pred = cat([i.pred_classes for i in pred_instances]) + indices = torch.arange(num_masks, device=class_pred.device) + mask_probs_pred = pred_mask_logits[indices, class_pred][:, None].sigmoid() + # mask_probs_pred.shape: (B, 1, Hmask, Wmask) + + num_boxes_per_image = [len(i) for i in pred_instances] + mask_probs_pred = mask_probs_pred.split(num_boxes_per_image, dim=0) + + for prob, instances in zip(mask_probs_pred, pred_instances): + instances.pred_masks = prob # (1, Hmask, Wmask) + + +class BaseMaskRCNNHead(nn.Module): + """ + Implement the basic Mask R-CNN losses and inference logic described in :paper:`Mask R-CNN` + """ + + @configurable + def __init__(self, *, loss_weight: float = 1.0, vis_period: int = 0): + """ + NOTE: this interface is experimental. + + Args: + loss_weight (float): multiplier of the loss + vis_period (int): visualization period + """ + super().__init__() + self.vis_period = vis_period + self.loss_weight = loss_weight + + @classmethod + def from_config(cls, cfg, input_shape): + return {"vis_period": cfg.VIS_PERIOD} + + def forward(self, x, instances: List[Instances]): + """ + Args: + x: input region feature(s) provided by :class:`ROIHeads`. + instances (list[Instances]): contains the boxes & labels corresponding + to the input features. + Exact format is up to its caller to decide. + Typically, this is the foreground instances in training, with + "proposal_boxes" field and other gt annotations. + In inference, it contains boxes that are already predicted. + + Returns: + A dict of losses in training. The predicted "instances" in inference. + """ + x = self.layers(x) + if self.training: + return {"loss_mask": mask_rcnn_loss(x, instances, self.vis_period) * self.loss_weight} + else: + mask_rcnn_inference(x, instances) + return instances + + def layers(self, x): + """ + Neural network layers that makes predictions from input features. + """ + raise NotImplementedError + + +# To get torchscript support, we make the head a subclass of `nn.Sequential`. +# Therefore, to add new layers in this head class, please make sure they are +# added in the order they will be used in forward(). +@ROI_MASK_HEAD_REGISTRY.register() +class MaskRCNNConvUpsampleHead(BaseMaskRCNNHead, nn.Sequential): + """ + A mask head with several conv layers, plus an upsample layer (with `ConvTranspose2d`). + Predictions are made with a final 1x1 conv layer. + """ + + @configurable + def __init__(self, input_shape: ShapeSpec, *, num_classes, conv_dims, conv_norm="", **kwargs): + """ + NOTE: this interface is experimental. + + Args: + input_shape (ShapeSpec): shape of the input feature + num_classes (int): the number of foreground classes (i.e. background is not + included). 1 if using class agnostic prediction. + conv_dims (list[int]): a list of N>0 integers representing the output dimensions + of N-1 conv layers and the last upsample layer. + conv_norm (str or callable): normalization for the conv layers. + See :func:`detectron2.layers.get_norm` for supported types. + """ + super().__init__(**kwargs) + assert len(conv_dims) >= 1, "conv_dims have to be non-empty!" + + self.conv_norm_relus = [] + + cur_channels = input_shape.channels + for k, conv_dim in enumerate(conv_dims[:-1]): + conv = Conv2d( + cur_channels, + conv_dim, + kernel_size=3, + stride=1, + padding=1, + bias=not conv_norm, + norm=get_norm(conv_norm, conv_dim), + activation=nn.ReLU(), + ) + self.add_module("mask_fcn{}".format(k + 1), conv) + self.conv_norm_relus.append(conv) + cur_channels = conv_dim + + self.deconv = ConvTranspose2d( + cur_channels, conv_dims[-1], kernel_size=2, stride=2, padding=0 + ) + self.add_module("deconv_relu", nn.ReLU()) + cur_channels = conv_dims[-1] + + self.predictor = Conv2d(cur_channels, num_classes, kernel_size=1, stride=1, padding=0) + + for layer in self.conv_norm_relus + [self.deconv]: + weight_init.c2_msra_fill(layer) + # use normal distribution initialization for mask prediction layer + nn.init.normal_(self.predictor.weight, std=0.001) + if self.predictor.bias is not None: + nn.init.constant_(self.predictor.bias, 0) + + @classmethod + def from_config(cls, cfg, input_shape): + ret = super().from_config(cfg, input_shape) + conv_dim = cfg.MODEL.ROI_MASK_HEAD.CONV_DIM + num_conv = cfg.MODEL.ROI_MASK_HEAD.NUM_CONV + ret.update( + conv_dims=[conv_dim] * (num_conv + 1), # +1 for ConvTranspose + conv_norm=cfg.MODEL.ROI_MASK_HEAD.NORM, + input_shape=input_shape, + ) + if cfg.MODEL.ROI_MASK_HEAD.CLS_AGNOSTIC_MASK: + ret["num_classes"] = 1 + else: + ret["num_classes"] = cfg.MODEL.ROI_HEADS.NUM_CLASSES + return ret + + def layers(self, x): + for layer in self: + x = layer(x) + return x + + +def build_mask_head(cfg, input_shape): + """ + Build a mask head defined by `cfg.MODEL.ROI_MASK_HEAD.NAME`. + """ + name = cfg.MODEL.ROI_MASK_HEAD.NAME + return ROI_MASK_HEAD_REGISTRY.get(name)(cfg, input_shape) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/roi_heads.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/roi_heads.py new file mode 100644 index 0000000000000000000000000000000000000000..13dd57a0478917001841f6c6299f380e1198e63a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/roi_heads.py @@ -0,0 +1,877 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import inspect +import logging +import numpy as np +from typing import Dict, List, Optional, Tuple +import torch +from torch import nn + +from detectron2.config import configurable +from detectron2.layers import ShapeSpec, nonzero_tuple +from detectron2.structures import Boxes, ImageList, Instances, pairwise_iou +from detectron2.utils.events import get_event_storage +from detectron2.utils.registry import Registry + +from ..backbone.resnet import BottleneckBlock, ResNet +from ..matcher import Matcher +from ..poolers import ROIPooler +from ..proposal_generator.proposal_utils import add_ground_truth_to_proposals +from ..sampling import subsample_labels +from .box_head import build_box_head +from .fast_rcnn import FastRCNNOutputLayers +from .keypoint_head import build_keypoint_head +from .mask_head import build_mask_head + +ROI_HEADS_REGISTRY = Registry("ROI_HEADS") +ROI_HEADS_REGISTRY.__doc__ = """ +Registry for ROI heads in a generalized R-CNN model. +ROIHeads take feature maps and region proposals, and +perform per-region computation. + +The registered object will be called with `obj(cfg, input_shape)`. +The call is expected to return an :class:`ROIHeads`. +""" + +logger = logging.getLogger(__name__) + + +def build_roi_heads(cfg, input_shape): + """ + Build ROIHeads defined by `cfg.MODEL.ROI_HEADS.NAME`. + """ + name = cfg.MODEL.ROI_HEADS.NAME + return ROI_HEADS_REGISTRY.get(name)(cfg, input_shape) + + +def select_foreground_proposals( + proposals: List[Instances], bg_label: int +) -> Tuple[List[Instances], List[torch.Tensor]]: + """ + Given a list of N Instances (for N images), each containing a `gt_classes` field, + return a list of Instances that contain only instances with `gt_classes != -1 && + gt_classes != bg_label`. + + Args: + proposals (list[Instances]): A list of N Instances, where N is the number of + images in the batch. + bg_label: label index of background class. + + Returns: + list[Instances]: N Instances, each contains only the selected foreground instances. + list[Tensor]: N boolean vector, correspond to the selection mask of + each Instances object. True for selected instances. + """ + assert isinstance(proposals, (list, tuple)) + assert isinstance(proposals[0], Instances) + assert proposals[0].has("gt_classes") + fg_proposals = [] + fg_selection_masks = [] + for proposals_per_image in proposals: + gt_classes = proposals_per_image.gt_classes + fg_selection_mask = (gt_classes != -1) & (gt_classes != bg_label) + fg_idxs = fg_selection_mask.nonzero().squeeze(1) + fg_proposals.append(proposals_per_image[fg_idxs]) + fg_selection_masks.append(fg_selection_mask) + return fg_proposals, fg_selection_masks + + +def select_proposals_with_visible_keypoints(proposals: List[Instances]) -> List[Instances]: + """ + Args: + proposals (list[Instances]): a list of N Instances, where N is the + number of images. + + Returns: + proposals: only contains proposals with at least one visible keypoint. + + Note that this is still slightly different from Detectron. + In Detectron, proposals for training keypoint head are re-sampled from + all the proposals with IOU>threshold & >=1 visible keypoint. + + Here, the proposals are first sampled from all proposals with + IOU>threshold, then proposals with no visible keypoint are filtered out. + This strategy seems to make no difference on Detectron and is easier to implement. + """ + ret = [] + all_num_fg = [] + for proposals_per_image in proposals: + # If empty/unannotated image (hard negatives), skip filtering for train + if len(proposals_per_image) == 0: + ret.append(proposals_per_image) + continue + gt_keypoints = proposals_per_image.gt_keypoints.tensor + # #fg x K x 3 + vis_mask = gt_keypoints[:, :, 2] >= 1 + xs, ys = gt_keypoints[:, :, 0], gt_keypoints[:, :, 1] + proposal_boxes = proposals_per_image.proposal_boxes.tensor.unsqueeze(dim=1) # #fg x 1 x 4 + kp_in_box = ( + (xs >= proposal_boxes[:, :, 0]) + & (xs <= proposal_boxes[:, :, 2]) + & (ys >= proposal_boxes[:, :, 1]) + & (ys <= proposal_boxes[:, :, 3]) + ) + selection = (kp_in_box & vis_mask).any(dim=1) + selection_idxs = nonzero_tuple(selection)[0] + all_num_fg.append(selection_idxs.numel()) + ret.append(proposals_per_image[selection_idxs]) + + storage = get_event_storage() + storage.put_scalar("keypoint_head/num_fg_samples", np.mean(all_num_fg)) + return ret + + +class ROIHeads(torch.nn.Module): + """ + ROIHeads perform all per-region computation in an R-CNN. + + It typically contains logic to + + 1. (in training only) match proposals with ground truth and sample them + 2. crop the regions and extract per-region features using proposals + 3. make per-region predictions with different heads + + It can have many variants, implemented as subclasses of this class. + This base class contains the logic to match/sample proposals. + But it is not necessary to inherit this class if the sampling logic is not needed. + """ + + @configurable + def __init__( + self, + *, + num_classes, + batch_size_per_image, + positive_fraction, + proposal_matcher, + proposal_append_gt=True, + ): + """ + NOTE: this interface is experimental. + + Args: + num_classes (int): number of foreground classes (i.e. background is not included) + batch_size_per_image (int): number of proposals to sample for training + positive_fraction (float): fraction of positive (foreground) proposals + to sample for training. + proposal_matcher (Matcher): matcher that matches proposals and ground truth + proposal_append_gt (bool): whether to include ground truth as proposals as well + """ + super().__init__() + self.batch_size_per_image = batch_size_per_image + self.positive_fraction = positive_fraction + self.num_classes = num_classes + self.proposal_matcher = proposal_matcher + self.proposal_append_gt = proposal_append_gt + + @classmethod + def from_config(cls, cfg): + return { + "batch_size_per_image": cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE, + "positive_fraction": cfg.MODEL.ROI_HEADS.POSITIVE_FRACTION, + "num_classes": cfg.MODEL.ROI_HEADS.NUM_CLASSES, + "proposal_append_gt": cfg.MODEL.ROI_HEADS.PROPOSAL_APPEND_GT, + # Matcher to assign box proposals to gt boxes + "proposal_matcher": Matcher( + cfg.MODEL.ROI_HEADS.IOU_THRESHOLDS, + cfg.MODEL.ROI_HEADS.IOU_LABELS, + allow_low_quality_matches=False, + ), + } + + def _sample_proposals( + self, matched_idxs: torch.Tensor, matched_labels: torch.Tensor, gt_classes: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Based on the matching between N proposals and M groundtruth, + sample the proposals and set their classification labels. + + Args: + matched_idxs (Tensor): a vector of length N, each is the best-matched + gt index in [0, M) for each proposal. + matched_labels (Tensor): a vector of length N, the matcher's label + (one of cfg.MODEL.ROI_HEADS.IOU_LABELS) for each proposal. + gt_classes (Tensor): a vector of length M. + + Returns: + Tensor: a vector of indices of sampled proposals. Each is in [0, N). + Tensor: a vector of the same length, the classification label for + each sampled proposal. Each sample is labeled as either a category in + [0, num_classes) or the background (num_classes). + """ + has_gt = gt_classes.numel() > 0 + # Get the corresponding GT for each proposal + if has_gt: + gt_classes = gt_classes[matched_idxs] + # Label unmatched proposals (0 label from matcher) as background (label=num_classes) + gt_classes[matched_labels == 0] = self.num_classes + # Label ignore proposals (-1 label) + gt_classes[matched_labels == -1] = -1 + else: + gt_classes = torch.zeros_like(matched_idxs) + self.num_classes + + sampled_fg_idxs, sampled_bg_idxs = subsample_labels( + gt_classes, self.batch_size_per_image, self.positive_fraction, self.num_classes + ) + + sampled_idxs = torch.cat([sampled_fg_idxs, sampled_bg_idxs], dim=0) + return sampled_idxs, gt_classes[sampled_idxs] + + @torch.no_grad() + def label_and_sample_proposals( + self, proposals: List[Instances], targets: List[Instances] + ) -> List[Instances]: + """ + Prepare some proposals to be used to train the ROI heads. + It performs box matching between `proposals` and `targets`, and assigns + training labels to the proposals. + It returns ``self.batch_size_per_image`` random samples from proposals and groundtruth + boxes, with a fraction of positives that is no larger than + ``self.positive_fraction``. + + Args: + See :meth:`ROIHeads.forward` + + Returns: + list[Instances]: + length `N` list of `Instances`s containing the proposals + sampled for training. Each `Instances` has the following fields: + + - proposal_boxes: the proposal boxes + - gt_boxes: the ground-truth box that the proposal is assigned to + (this is only meaningful if the proposal has a label > 0; if label = 0 + then the ground-truth box is random) + + Other fields such as "gt_classes", "gt_masks", that's included in `targets`. + """ + # Augment proposals with ground-truth boxes. + # In the case of learned proposals (e.g., RPN), when training starts + # the proposals will be low quality due to random initialization. + # It's possible that none of these initial + # proposals have high enough overlap with the gt objects to be used + # as positive examples for the second stage components (box head, + # cls head, mask head). Adding the gt boxes to the set of proposals + # ensures that the second stage components will have some positive + # examples from the start of training. For RPN, this augmentation improves + # convergence and empirically improves box AP on COCO by about 0.5 + # points (under one tested configuration). + if self.proposal_append_gt: + proposals = add_ground_truth_to_proposals(targets, proposals) + + proposals_with_gt = [] + + num_fg_samples = [] + num_bg_samples = [] + for proposals_per_image, targets_per_image in zip(proposals, targets): + has_gt = len(targets_per_image) > 0 + match_quality_matrix = pairwise_iou( + targets_per_image.gt_boxes, proposals_per_image.proposal_boxes + ) + matched_idxs, matched_labels = self.proposal_matcher(match_quality_matrix) + sampled_idxs, gt_classes = self._sample_proposals( + matched_idxs, matched_labels, targets_per_image.gt_classes + ) + + # Set target attributes of the sampled proposals: + proposals_per_image = proposals_per_image[sampled_idxs] + proposals_per_image.gt_classes = gt_classes + + if has_gt: + sampled_targets = matched_idxs[sampled_idxs] + # We index all the attributes of targets that start with "gt_" + # and have not been added to proposals yet (="gt_classes"). + # NOTE: here the indexing waste some compute, because heads + # like masks, keypoints, etc, will filter the proposals again, + # (by foreground/background, or number of keypoints in the image, etc) + # so we essentially index the data twice. + for (trg_name, trg_value) in targets_per_image.get_fields().items(): + if trg_name.startswith("gt_") and not proposals_per_image.has(trg_name): + proposals_per_image.set(trg_name, trg_value[sampled_targets]) + # If no GT is given in the image, we don't know what a dummy gt value can be. + # Therefore the returned proposals won't have any gt_* fields, except for a + # gt_classes full of background label. + + num_bg_samples.append((gt_classes == self.num_classes).sum().item()) + num_fg_samples.append(gt_classes.numel() - num_bg_samples[-1]) + proposals_with_gt.append(proposals_per_image) + + # Log the number of fg/bg samples that are selected for training ROI heads + storage = get_event_storage() + storage.put_scalar("roi_head/num_fg_samples", np.mean(num_fg_samples)) + storage.put_scalar("roi_head/num_bg_samples", np.mean(num_bg_samples)) + + return proposals_with_gt + + def forward( + self, + images: ImageList, + features: Dict[str, torch.Tensor], + proposals: List[Instances], + targets: Optional[List[Instances]] = None, + ) -> Tuple[List[Instances], Dict[str, torch.Tensor]]: + """ + Args: + images (ImageList): + features (dict[str,Tensor]): input data as a mapping from feature + map name to tensor. Axis 0 represents the number of images `N` in + the input data; axes 1-3 are channels, height, and width, which may + vary between feature maps (e.g., if a feature pyramid is used). + proposals (list[Instances]): length `N` list of `Instances`. The i-th + `Instances` contains object proposals for the i-th input image, + with fields "proposal_boxes" and "objectness_logits". + targets (list[Instances], optional): length `N` list of `Instances`. The i-th + `Instances` contains the ground-truth per-instance annotations + for the i-th input image. Specify `targets` during training only. + It may have the following fields: + + - gt_boxes: the bounding box of each instance. + - gt_classes: the label for each instance with a category ranging in [0, #class]. + - gt_masks: PolygonMasks or BitMasks, the ground-truth masks of each instance. + - gt_keypoints: NxKx3, the groud-truth keypoints for each instance. + + Returns: + list[Instances]: length `N` list of `Instances` containing the + detected instances. Returned during inference only; may be [] during training. + + dict[str->Tensor]: + mapping from a named loss to a tensor storing the loss. Used during training only. + """ + raise NotImplementedError() + + +@ROI_HEADS_REGISTRY.register() +class Res5ROIHeads(ROIHeads): + """ + The ROIHeads in a typical "C4" R-CNN model, where + the box and mask head share the cropping and + the per-region feature computation by a Res5 block. + See :paper:`ResNet` Appendix A. + """ + + @configurable + def __init__( + self, + *, + in_features: List[str], + pooler: ROIPooler, + res5: nn.Module, + box_predictor: nn.Module, + mask_head: Optional[nn.Module] = None, + **kwargs, + ): + """ + NOTE: this interface is experimental. + + Args: + in_features (list[str]): list of backbone feature map names to use for + feature extraction + pooler (ROIPooler): pooler to extra region features from backbone + res5 (nn.Sequential): a CNN to compute per-region features, to be used by + ``box_predictor`` and ``mask_head``. Typically this is a "res5" + block from a ResNet. + box_predictor (nn.Module): make box predictions from the feature. + Should have the same interface as :class:`FastRCNNOutputLayers`. + mask_head (nn.Module): transform features to make mask predictions + """ + super().__init__(**kwargs) + self.in_features = in_features + self.pooler = pooler + if isinstance(res5, (list, tuple)): + res5 = nn.Sequential(*res5) + self.res5 = res5 + self.box_predictor = box_predictor + self.mask_on = mask_head is not None + if self.mask_on: + self.mask_head = mask_head + + @classmethod + def from_config(cls, cfg, input_shape): + # fmt: off + ret = super().from_config(cfg) + in_features = ret["in_features"] = cfg.MODEL.ROI_HEADS.IN_FEATURES + pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION + pooler_type = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE + pooler_scales = (1.0 / input_shape[in_features[0]].stride, ) + sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO + mask_on = cfg.MODEL.MASK_ON + # fmt: on + assert not cfg.MODEL.KEYPOINT_ON + assert len(in_features) == 1 + + ret["pooler"] = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + + # Compatbility with old moco code. Might be useful. + # See notes in StandardROIHeads.from_config + if not inspect.ismethod(cls._build_res5_block): + logger.warning( + "The behavior of _build_res5_block may change. " + "Please do not depend on private methods." + ) + cls._build_res5_block = classmethod(cls._build_res5_block) + + ret["res5"], out_channels = cls._build_res5_block(cfg) + ret["box_predictor"] = FastRCNNOutputLayers( + cfg, ShapeSpec(channels=out_channels, height=1, width=1) + ) + + if mask_on: + ret["mask_head"] = build_mask_head( + cfg, + ShapeSpec(channels=out_channels, width=pooler_resolution, height=pooler_resolution), + ) + return ret + + @classmethod + def _build_res5_block(cls, cfg): + # fmt: off + stage_channel_factor = 2 ** 3 # res5 is 8x res2 + num_groups = cfg.MODEL.RESNETS.NUM_GROUPS + width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP + bottleneck_channels = num_groups * width_per_group * stage_channel_factor + out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS * stage_channel_factor + stride_in_1x1 = cfg.MODEL.RESNETS.STRIDE_IN_1X1 + norm = cfg.MODEL.RESNETS.NORM + assert not cfg.MODEL.RESNETS.DEFORM_ON_PER_STAGE[-1], \ + "Deformable conv is not yet supported in res5 head." + # fmt: on + + blocks = ResNet.make_stage( + BottleneckBlock, + 3, + stride_per_block=[2, 1, 1], + in_channels=out_channels // 2, + bottleneck_channels=bottleneck_channels, + out_channels=out_channels, + num_groups=num_groups, + norm=norm, + stride_in_1x1=stride_in_1x1, + ) + return nn.Sequential(*blocks), out_channels + + def _shared_roi_transform(self, features: List[torch.Tensor], boxes: List[Boxes]): + x = self.pooler(features, boxes) + return self.res5(x) + + def forward( + self, + images: ImageList, + features: Dict[str, torch.Tensor], + proposals: List[Instances], + targets: Optional[List[Instances]] = None, + ): + """ + See :meth:`ROIHeads.forward`. + """ + del images + + if self.training: + assert targets + proposals = self.label_and_sample_proposals(proposals, targets) + del targets + + proposal_boxes = [x.proposal_boxes for x in proposals] + box_features = self._shared_roi_transform( + [features[f] for f in self.in_features], proposal_boxes + ) + predictions = self.box_predictor(box_features.mean(dim=[2, 3])) + + if self.training: + del features + losses = self.box_predictor.losses(predictions, proposals) + if self.mask_on: + proposals, fg_selection_masks = select_foreground_proposals( + proposals, self.num_classes + ) + # Since the ROI feature transform is shared between boxes and masks, + # we don't need to recompute features. The mask loss is only defined + # on foreground proposals, so we need to select out the foreground + # features. + mask_features = box_features[torch.cat(fg_selection_masks, dim=0)] + del box_features + losses.update(self.mask_head(mask_features, proposals)) + return [], losses + else: + pred_instances, _ = self.box_predictor.inference(predictions, proposals) + pred_instances = self.forward_with_given_boxes(features, pred_instances) + return pred_instances, {} + + def forward_with_given_boxes( + self, features: Dict[str, torch.Tensor], instances: List[Instances] + ) -> List[Instances]: + """ + Use the given boxes in `instances` to produce other (non-box) per-ROI outputs. + + Args: + features: same as in `forward()` + instances (list[Instances]): instances to predict other outputs. Expect the keys + "pred_boxes" and "pred_classes" to exist. + + Returns: + instances (Instances): + the same `Instances` object, with extra + fields such as `pred_masks` or `pred_keypoints`. + """ + assert not self.training + assert instances[0].has("pred_boxes") and instances[0].has("pred_classes") + + if self.mask_on: + feature_list = [features[f] for f in self.in_features] + x = self._shared_roi_transform(feature_list, [x.pred_boxes for x in instances]) + return self.mask_head(x, instances) + else: + return instances + + +@ROI_HEADS_REGISTRY.register() +class StandardROIHeads(ROIHeads): + """ + It's "standard" in a sense that there is no ROI transform sharing + or feature sharing between tasks. + Each head independently processes the input features by each head's + own pooler and head. + + This class is used by most models, such as FPN and C5. + To implement more models, you can subclass it and implement a different + :meth:`forward()` or a head. + """ + + @configurable + def __init__( + self, + *, + box_in_features: List[str], + box_pooler: ROIPooler, + box_head: nn.Module, + box_predictor: nn.Module, + mask_in_features: Optional[List[str]] = None, + mask_pooler: Optional[ROIPooler] = None, + mask_head: Optional[nn.Module] = None, + keypoint_in_features: Optional[List[str]] = None, + keypoint_pooler: Optional[ROIPooler] = None, + keypoint_head: Optional[nn.Module] = None, + train_on_pred_boxes: bool = False, + **kwargs, + ): + """ + NOTE: this interface is experimental. + + Args: + box_in_features (list[str]): list of feature names to use for the box head. + box_pooler (ROIPooler): pooler to extra region features for box head + box_head (nn.Module): transform features to make box predictions + box_predictor (nn.Module): make box predictions from the feature. + Should have the same interface as :class:`FastRCNNOutputLayers`. + mask_in_features (list[str]): list of feature names to use for the mask + pooler or mask head. None if not using mask head. + mask_pooler (ROIPooler): pooler to extract region features from image features. + The mask head will then take region features to make predictions. + If None, the mask head will directly take the dict of image features + defined by `mask_in_features` + mask_head (nn.Module): transform features to make mask predictions + keypoint_in_features, keypoint_pooler, keypoint_head: similar to ``mask_*``. + train_on_pred_boxes (bool): whether to use proposal boxes or + predicted boxes from the box head to train other heads. + """ + super().__init__(**kwargs) + # keep self.in_features for backward compatibility + self.in_features = self.box_in_features = box_in_features + self.box_pooler = box_pooler + self.box_head = box_head + self.box_predictor = box_predictor + + self.mask_on = mask_in_features is not None + if self.mask_on: + self.mask_in_features = mask_in_features + self.mask_pooler = mask_pooler + self.mask_head = mask_head + + self.keypoint_on = keypoint_in_features is not None + if self.keypoint_on: + self.keypoint_in_features = keypoint_in_features + self.keypoint_pooler = keypoint_pooler + self.keypoint_head = keypoint_head + + self.train_on_pred_boxes = train_on_pred_boxes + + @classmethod + def from_config(cls, cfg, input_shape): + ret = super().from_config(cfg) + ret["train_on_pred_boxes"] = cfg.MODEL.ROI_BOX_HEAD.TRAIN_ON_PRED_BOXES + # Subclasses that have not been updated to use from_config style construction + # may have overridden _init_*_head methods. In this case, those overridden methods + # will not be classmethods and we need to avoid trying to call them here. + # We test for this with ismethod which only returns True for bound methods of cls. + # Such subclasses will need to handle calling their overridden _init_*_head methods. + if inspect.ismethod(cls._init_box_head): + ret.update(cls._init_box_head(cfg, input_shape)) + if inspect.ismethod(cls._init_mask_head): + ret.update(cls._init_mask_head(cfg, input_shape)) + if inspect.ismethod(cls._init_keypoint_head): + ret.update(cls._init_keypoint_head(cfg, input_shape)) + return ret + + @classmethod + def _init_box_head(cls, cfg, input_shape): + # fmt: off + in_features = cfg.MODEL.ROI_HEADS.IN_FEATURES + pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION + pooler_scales = tuple(1.0 / input_shape[k].stride for k in in_features) + sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO + pooler_type = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE + # fmt: on + + # If StandardROIHeads is applied on multiple feature maps (as in FPN), + # then we share the same predictors and therefore the channel counts must be the same + in_channels = [input_shape[f].channels for f in in_features] + # Check all channel counts are equal + assert len(set(in_channels)) == 1, in_channels + in_channels = in_channels[0] + + box_pooler = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + # Here we split "box head" and "box predictor", which is mainly due to historical reasons. + # They are used together so the "box predictor" layers should be part of the "box head". + # New subclasses of ROIHeads do not need "box predictor"s. + box_head = build_box_head( + cfg, ShapeSpec(channels=in_channels, height=pooler_resolution, width=pooler_resolution) + ) + box_predictor = FastRCNNOutputLayers(cfg, box_head.output_shape) + return { + "box_in_features": in_features, + "box_pooler": box_pooler, + "box_head": box_head, + "box_predictor": box_predictor, + } + + @classmethod + def _init_mask_head(cls, cfg, input_shape): + if not cfg.MODEL.MASK_ON: + return {} + # fmt: off + in_features = cfg.MODEL.ROI_HEADS.IN_FEATURES + pooler_resolution = cfg.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION + pooler_scales = tuple(1.0 / input_shape[k].stride for k in in_features) + sampling_ratio = cfg.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO + pooler_type = cfg.MODEL.ROI_MASK_HEAD.POOLER_TYPE + # fmt: on + + in_channels = [input_shape[f].channels for f in in_features][0] + + ret = {"mask_in_features": in_features} + ret["mask_pooler"] = ( + ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + if pooler_type + else None + ) + if pooler_type: + shape = ShapeSpec( + channels=in_channels, width=pooler_resolution, height=pooler_resolution + ) + else: + shape = {f: input_shape[f] for f in in_features} + ret["mask_head"] = build_mask_head(cfg, shape) + return ret + + @classmethod + def _init_keypoint_head(cls, cfg, input_shape): + if not cfg.MODEL.KEYPOINT_ON: + return {} + # fmt: off + in_features = cfg.MODEL.ROI_HEADS.IN_FEATURES + pooler_resolution = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION + pooler_scales = tuple(1.0 / input_shape[k].stride for k in in_features) # noqa + sampling_ratio = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO + pooler_type = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_TYPE + # fmt: on + + in_channels = [input_shape[f].channels for f in in_features][0] + + ret = {"keypoint_in_features": in_features} + ret["keypoint_pooler"] = ( + ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + if pooler_type + else None + ) + if pooler_type: + shape = ShapeSpec( + channels=in_channels, width=pooler_resolution, height=pooler_resolution + ) + else: + shape = {f: input_shape[f] for f in in_features} + ret["keypoint_head"] = build_keypoint_head(cfg, shape) + return ret + + def forward( + self, + images: ImageList, + features: Dict[str, torch.Tensor], + proposals: List[Instances], + targets: Optional[List[Instances]] = None, + ) -> Tuple[List[Instances], Dict[str, torch.Tensor]]: + """ + See :class:`ROIHeads.forward`. + """ + del images + if self.training: + assert targets, "'targets' argument is required during training" + proposals = self.label_and_sample_proposals(proposals, targets) + del targets + + if self.training: + losses = self._forward_box(features, proposals) + # Usually the original proposals used by the box head are used by the mask, keypoint + # heads. But when `self.train_on_pred_boxes is True`, proposals will contain boxes + # predicted by the box head. + losses.update(self._forward_mask(features, proposals)) + losses.update(self._forward_keypoint(features, proposals)) + return proposals, losses + else: + pred_instances = self._forward_box(features, proposals) + # During inference cascaded prediction is used: the mask and keypoints heads are only + # applied to the top scoring box detections. + pred_instances = self.forward_with_given_boxes(features, pred_instances) + return pred_instances, {} + + def forward_with_given_boxes( + self, features: Dict[str, torch.Tensor], instances: List[Instances] + ) -> List[Instances]: + """ + Use the given boxes in `instances` to produce other (non-box) per-ROI outputs. + + This is useful for downstream tasks where a box is known, but need to obtain + other attributes (outputs of other heads). + Test-time augmentation also uses this. + + Args: + features: same as in `forward()` + instances (list[Instances]): instances to predict other outputs. Expect the keys + "pred_boxes" and "pred_classes" to exist. + + Returns: + list[Instances]: + the same `Instances` objects, with extra + fields such as `pred_masks` or `pred_keypoints`. + """ + assert not self.training + assert instances[0].has("pred_boxes") and instances[0].has("pred_classes") + + instances = self._forward_mask(features, instances) + instances = self._forward_keypoint(features, instances) + return instances + + def _forward_box(self, features: Dict[str, torch.Tensor], proposals: List[Instances]): + """ + Forward logic of the box prediction branch. If `self.train_on_pred_boxes is True`, + the function puts predicted boxes in the `proposal_boxes` field of `proposals` argument. + + Args: + features (dict[str, Tensor]): mapping from feature map names to tensor. + Same as in :meth:`ROIHeads.forward`. + proposals (list[Instances]): the per-image object proposals with + their matching ground truth. + Each has fields "proposal_boxes", and "objectness_logits", + "gt_classes", "gt_boxes". + + Returns: + In training, a dict of losses. + In inference, a list of `Instances`, the predicted instances. + """ + features = [features[f] for f in self.box_in_features] + box_features = self.box_pooler(features, [x.proposal_boxes for x in proposals]) + box_features = self.box_head(box_features) + predictions = self.box_predictor(box_features) + del box_features + + if self.training: + losses = self.box_predictor.losses(predictions, proposals) + # proposals is modified in-place below, so losses must be computed first. + if self.train_on_pred_boxes: + with torch.no_grad(): + pred_boxes = self.box_predictor.predict_boxes_for_gt_classes( + predictions, proposals + ) + for proposals_per_image, pred_boxes_per_image in zip(proposals, pred_boxes): + proposals_per_image.proposal_boxes = Boxes(pred_boxes_per_image) + return losses + else: + pred_instances, _ = self.box_predictor.inference(predictions, proposals) + return pred_instances + + def _forward_mask(self, features: Dict[str, torch.Tensor], instances: List[Instances]): + """ + Forward logic of the mask prediction branch. + + Args: + features (dict[str, Tensor]): mapping from feature map names to tensor. + Same as in :meth:`ROIHeads.forward`. + instances (list[Instances]): the per-image instances to train/predict masks. + In training, they can be the proposals. + In inference, they can be the boxes predicted by R-CNN box head. + + Returns: + In training, a dict of losses. + In inference, update `instances` with new fields "pred_masks" and return it. + """ + if not self.mask_on: + return {} if self.training else instances + + if self.training: + # head is only trained on positive proposals. + instances, _ = select_foreground_proposals(instances, self.num_classes) + + if self.mask_pooler is not None: + features = [features[f] for f in self.mask_in_features] + boxes = [x.proposal_boxes if self.training else x.pred_boxes for x in instances] + features = self.mask_pooler(features, boxes) + else: + features = {f: features[f] for f in self.mask_in_features} + return self.mask_head(features, instances) + + def _forward_keypoint(self, features: Dict[str, torch.Tensor], instances: List[Instances]): + """ + Forward logic of the keypoint prediction branch. + + Args: + features (dict[str, Tensor]): mapping from feature map names to tensor. + Same as in :meth:`ROIHeads.forward`. + instances (list[Instances]): the per-image instances to train/predict keypoints. + In training, they can be the proposals. + In inference, they can be the boxes predicted by R-CNN box head. + + Returns: + In training, a dict of losses. + In inference, update `instances` with new fields "pred_keypoints" and return it. + """ + if not self.keypoint_on: + return {} if self.training else instances + + if self.training: + # head is only trained on positive proposals with >=1 visible keypoints. + instances, _ = select_foreground_proposals(instances, self.num_classes) + instances = select_proposals_with_visible_keypoints(instances) + + if self.keypoint_pooler is not None: + features = [features[f] for f in self.keypoint_in_features] + boxes = [x.proposal_boxes if self.training else x.pred_boxes for x in instances] + features = self.keypoint_pooler(features, boxes) + else: + features = {f: features[f] for f in self.keypoint_in_features} + return self.keypoint_head(features, instances) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/rotated_fast_rcnn.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/rotated_fast_rcnn.py new file mode 100644 index 0000000000000000000000000000000000000000..b1eedeebf8e3bde80722fc4acf51be6ca212cb3d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/rotated_fast_rcnn.py @@ -0,0 +1,270 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import numpy as np +import torch + +from detectron2.config import configurable +from detectron2.layers import ShapeSpec, batched_nms_rotated +from detectron2.structures import Instances, RotatedBoxes, pairwise_iou_rotated +from detectron2.utils.events import get_event_storage + +from ..box_regression import Box2BoxTransformRotated +from ..poolers import ROIPooler +from ..proposal_generator.proposal_utils import add_ground_truth_to_proposals +from .box_head import build_box_head +from .fast_rcnn import FastRCNNOutputLayers +from .roi_heads import ROI_HEADS_REGISTRY, StandardROIHeads + +logger = logging.getLogger(__name__) + +""" +Shape shorthand in this module: + + N: number of images in the minibatch + R: number of ROIs, combined over all images, in the minibatch + Ri: number of ROIs in image i + K: number of foreground classes. E.g.,there are 80 foreground classes in COCO. + +Naming convention: + + deltas: refers to the 5-d (dx, dy, dw, dh, da) deltas that parameterize the box2box + transform (see :class:`box_regression.Box2BoxTransformRotated`). + + pred_class_logits: predicted class scores in [-inf, +inf]; use + softmax(pred_class_logits) to estimate P(class). + + gt_classes: ground-truth classification labels in [0, K], where [0, K) represent + foreground object classes and K represents the background class. + + pred_proposal_deltas: predicted rotated box2box transform deltas for transforming proposals + to detection box predictions. + + gt_proposal_deltas: ground-truth rotated box2box transform deltas +""" + + +def fast_rcnn_inference_rotated( + boxes, scores, image_shapes, score_thresh, nms_thresh, topk_per_image +): + """ + Call `fast_rcnn_inference_single_image_rotated` for all images. + + Args: + boxes (list[Tensor]): A list of Tensors of predicted class-specific or class-agnostic + boxes for each image. Element i has shape (Ri, K * 5) if doing + class-specific regression, or (Ri, 5) if doing class-agnostic + regression, where Ri is the number of predicted objects for image i. + This is compatible with the output of :meth:`FastRCNNOutputLayers.predict_boxes`. + scores (list[Tensor]): A list of Tensors of predicted class scores for each image. + Element i has shape (Ri, K + 1), where Ri is the number of predicted objects + for image i. Compatible with the output of :meth:`FastRCNNOutputLayers.predict_probs`. + image_shapes (list[tuple]): A list of (width, height) tuples for each image in the batch. + score_thresh (float): Only return detections with a confidence score exceeding this + threshold. + nms_thresh (float): The threshold to use for box non-maximum suppression. Value in [0, 1]. + topk_per_image (int): The number of top scoring detections to return. Set < 0 to return + all detections. + + Returns: + instances: (list[Instances]): A list of N instances, one for each image in the batch, + that stores the topk most confidence detections. + kept_indices: (list[Tensor]): A list of 1D tensor of length of N, each element indicates + the corresponding boxes/scores index in [0, Ri) from the input, for image i. + """ + result_per_image = [ + fast_rcnn_inference_single_image_rotated( + boxes_per_image, scores_per_image, image_shape, score_thresh, nms_thresh, topk_per_image + ) + for scores_per_image, boxes_per_image, image_shape in zip(scores, boxes, image_shapes) + ] + return [x[0] for x in result_per_image], [x[1] for x in result_per_image] + + +def fast_rcnn_inference_single_image_rotated( + boxes, scores, image_shape, score_thresh, nms_thresh, topk_per_image +): + """ + Single-image inference. Return rotated bounding-box detection results by thresholding + on scores and applying rotated non-maximum suppression (Rotated NMS). + + Args: + Same as `fast_rcnn_inference_rotated`, but with rotated boxes, scores, and image shapes + per image. + + Returns: + Same as `fast_rcnn_inference_rotated`, but for only one image. + """ + valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) + if not valid_mask.all(): + boxes = boxes[valid_mask] + scores = scores[valid_mask] + + B = 5 # box dimension + scores = scores[:, :-1] + num_bbox_reg_classes = boxes.shape[1] // B + # Convert to Boxes to use the `clip` function ... + boxes = RotatedBoxes(boxes.reshape(-1, B)) + boxes.clip(image_shape) + boxes = boxes.tensor.view(-1, num_bbox_reg_classes, B) # R x C x B + # Filter results based on detection scores + filter_mask = scores > score_thresh # R x K + # R' x 2. First column contains indices of the R predictions; + # Second column contains indices of classes. + filter_inds = filter_mask.nonzero() + if num_bbox_reg_classes == 1: + boxes = boxes[filter_inds[:, 0], 0] + else: + boxes = boxes[filter_mask] + scores = scores[filter_mask] + + # Apply per-class Rotated NMS + keep = batched_nms_rotated(boxes, scores, filter_inds[:, 1], nms_thresh) + if topk_per_image >= 0: + keep = keep[:topk_per_image] + boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep] + + result = Instances(image_shape) + result.pred_boxes = RotatedBoxes(boxes) + result.scores = scores + result.pred_classes = filter_inds[:, 1] + + return result, filter_inds[:, 0] + + +class RotatedFastRCNNOutputLayers(FastRCNNOutputLayers): + """ + Two linear layers for predicting Rotated Fast R-CNN outputs. + """ + + @classmethod + def from_config(cls, cfg, input_shape): + args = super().from_config(cfg, input_shape) + args["box2box_transform"] = Box2BoxTransformRotated( + weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS + ) + return args + + def inference(self, predictions, proposals): + """ + Returns: + list[Instances]: same as `fast_rcnn_inference_rotated`. + list[Tensor]: same as `fast_rcnn_inference_rotated`. + """ + boxes = self.predict_boxes(predictions, proposals) + scores = self.predict_probs(predictions, proposals) + image_shapes = [x.image_size for x in proposals] + + return fast_rcnn_inference_rotated( + boxes, + scores, + image_shapes, + self.test_score_thresh, + self.test_nms_thresh, + self.test_topk_per_image, + ) + + +@ROI_HEADS_REGISTRY.register() +class RROIHeads(StandardROIHeads): + """ + This class is used by Rotated Fast R-CNN to detect rotated boxes. + For now, it only supports box predictions but not mask or keypoints. + """ + + @configurable + def __init__(self, **kwargs): + """ + NOTE: this interface is experimental. + """ + super().__init__(**kwargs) + assert ( + not self.mask_on and not self.keypoint_on + ), "Mask/Keypoints not supported in Rotated ROIHeads." + assert not self.train_on_pred_boxes, "train_on_pred_boxes not implemented for RROIHeads!" + + @classmethod + def _init_box_head(cls, cfg, input_shape): + # fmt: off + in_features = cfg.MODEL.ROI_HEADS.IN_FEATURES + pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION + pooler_scales = tuple(1.0 / input_shape[k].stride for k in in_features) + sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO + pooler_type = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE + # fmt: on + assert pooler_type in ["ROIAlignRotated"], pooler_type + # assume all channel counts are equal + in_channels = [input_shape[f].channels for f in in_features][0] + + box_pooler = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + box_head = build_box_head( + cfg, ShapeSpec(channels=in_channels, height=pooler_resolution, width=pooler_resolution) + ) + # This line is the only difference v.s. StandardROIHeads + box_predictor = RotatedFastRCNNOutputLayers(cfg, box_head.output_shape) + return { + "box_in_features": in_features, + "box_pooler": box_pooler, + "box_head": box_head, + "box_predictor": box_predictor, + } + + @torch.no_grad() + def label_and_sample_proposals(self, proposals, targets): + """ + Prepare some proposals to be used to train the RROI heads. + It performs box matching between `proposals` and `targets`, and assigns + training labels to the proposals. + It returns `self.batch_size_per_image` random samples from proposals and groundtruth boxes, + with a fraction of positives that is no larger than `self.positive_sample_fraction. + + Args: + See :meth:`StandardROIHeads.forward` + + Returns: + list[Instances]: length `N` list of `Instances`s containing the proposals + sampled for training. Each `Instances` has the following fields: + - proposal_boxes: the rotated proposal boxes + - gt_boxes: the ground-truth rotated boxes that the proposal is assigned to + (this is only meaningful if the proposal has a label > 0; if label = 0 + then the ground-truth box is random) + - gt_classes: the ground-truth classification lable for each proposal + """ + if self.proposal_append_gt: + proposals = add_ground_truth_to_proposals(targets, proposals) + + proposals_with_gt = [] + + num_fg_samples = [] + num_bg_samples = [] + for proposals_per_image, targets_per_image in zip(proposals, targets): + has_gt = len(targets_per_image) > 0 + match_quality_matrix = pairwise_iou_rotated( + targets_per_image.gt_boxes, proposals_per_image.proposal_boxes + ) + matched_idxs, matched_labels = self.proposal_matcher(match_quality_matrix) + sampled_idxs, gt_classes = self._sample_proposals( + matched_idxs, matched_labels, targets_per_image.gt_classes + ) + + proposals_per_image = proposals_per_image[sampled_idxs] + proposals_per_image.gt_classes = gt_classes + + if has_gt: + sampled_targets = matched_idxs[sampled_idxs] + proposals_per_image.gt_boxes = targets_per_image.gt_boxes[sampled_targets] + + num_bg_samples.append((gt_classes == self.num_classes).sum().item()) + num_fg_samples.append(gt_classes.numel() - num_bg_samples[-1]) + proposals_with_gt.append(proposals_per_image) + + # Log the number of fg/bg samples that are selected for training ROI heads + storage = get_event_storage() + storage.put_scalar("roi_head/num_fg_samples", np.mean(num_fg_samples)) + storage.put_scalar("roi_head/num_bg_samples", np.mean(num_bg_samples)) + + return proposals_with_gt diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/sampling.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/sampling.py new file mode 100644 index 0000000000000000000000000000000000000000..a2d0f6648b349c5ea39fd29785b77c961a58fa22 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/sampling.py @@ -0,0 +1,54 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import torch + +from detectron2.layers import nonzero_tuple + +__all__ = ["subsample_labels"] + + +def subsample_labels( + labels: torch.Tensor, num_samples: int, positive_fraction: float, bg_label: int +): + """ + Return `num_samples` (or fewer, if not enough found) + random samples from `labels` which is a mixture of positives & negatives. + It will try to return as many positives as possible without + exceeding `positive_fraction * num_samples`, and then try to + fill the remaining slots with negatives. + + Args: + labels (Tensor): (N, ) label vector with values: + * -1: ignore + * bg_label: background ("negative") class + * otherwise: one or more foreground ("positive") classes + num_samples (int): The total number of labels with value >= 0 to return. + Values that are not sampled will be filled with -1 (ignore). + positive_fraction (float): The number of subsampled labels with values > 0 + is `min(num_positives, int(positive_fraction * num_samples))`. The number + of negatives sampled is `min(num_negatives, num_samples - num_positives_sampled)`. + In order words, if there are not enough positives, the sample is filled with + negatives. If there are also not enough negatives, then as many elements are + sampled as is possible. + bg_label (int): label index of background ("negative") class. + + Returns: + pos_idx, neg_idx (Tensor): + 1D vector of indices. The total length of both is `num_samples` or fewer. + """ + positive = nonzero_tuple((labels != -1) & (labels != bg_label))[0] + negative = nonzero_tuple(labels == bg_label)[0] + + num_pos = int(num_samples * positive_fraction) + # protect against not enough positive examples + num_pos = min(positive.numel(), num_pos) + num_neg = num_samples - num_pos + # protect against not enough negative examples + num_neg = min(negative.numel(), num_neg) + + # randomly select positive and negative examples + perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos] + perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg] + + pos_idx = positive[perm1] + neg_idx = negative[perm2] + return pos_idx, neg_idx diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/test_time_augmentation.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/test_time_augmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..373e6bf00a39c040ff1da49d6dcd39a54a0b69a7 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/modeling/test_time_augmentation.py @@ -0,0 +1,307 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import numpy as np +from contextlib import contextmanager +from itertools import count +from typing import List +import torch +from fvcore.transforms import HFlipTransform, NoOpTransform +from torch import nn +from torch.nn.parallel import DistributedDataParallel + +from detectron2.config import configurable +from detectron2.data.detection_utils import read_image +from detectron2.data.transforms import ( + RandomFlip, + ResizeShortestEdge, + ResizeTransform, + apply_augmentations, +) +from detectron2.structures import Boxes, Instances + +from .meta_arch import GeneralizedRCNN +from .postprocessing import detector_postprocess +from .roi_heads.fast_rcnn import fast_rcnn_inference_single_image + +__all__ = ["DatasetMapperTTA", "GeneralizedRCNNWithTTA"] + + +class DatasetMapperTTA: + """ + Implement test-time augmentation for detection data. + It is a callable which takes a dataset dict from a detection dataset, + and returns a list of dataset dicts where the images + are augmented from the input image by the transformations defined in the config. + This is used for test-time augmentation. + """ + + @configurable + def __init__(self, min_sizes: List[int], max_size: int, flip: bool): + """ + Args: + min_sizes: list of short-edge size to resize the image to + max_size: maximum height or width of resized images + flip: whether to apply flipping augmentation + """ + self.min_sizes = min_sizes + self.max_size = max_size + self.flip = flip + + @classmethod + def from_config(cls, cfg): + return { + "min_sizes": cfg.TEST.AUG.MIN_SIZES, + "max_size": cfg.TEST.AUG.MAX_SIZE, + "flip": cfg.TEST.AUG.FLIP, + } + + def __call__(self, dataset_dict): + """ + Args: + dict: a dict in standard model input format. See tutorials for details. + + Returns: + list[dict]: + a list of dicts, which contain augmented version of the input image. + The total number of dicts is ``len(min_sizes) * (2 if flip else 1)``. + Each dict has field "transforms" which is a TransformList, + containing the transforms that are used to generate this image. + """ + numpy_image = dataset_dict["image"].permute(1, 2, 0).numpy() + shape = numpy_image.shape + orig_shape = (dataset_dict["height"], dataset_dict["width"]) + if shape[:2] != orig_shape: + # It transforms the "original" image in the dataset to the input image + pre_tfm = ResizeTransform(orig_shape[0], orig_shape[1], shape[0], shape[1]) + else: + pre_tfm = NoOpTransform() + + # Create all combinations of augmentations to use + aug_candidates = [] # each element is a list[Augmentation] + for min_size in self.min_sizes: + resize = ResizeShortestEdge(min_size, self.max_size) + aug_candidates.append([resize]) # resize only + if self.flip: + flip = RandomFlip(prob=1.0) + aug_candidates.append([resize, flip]) # resize + flip + + # Apply all the augmentations + ret = [] + for aug in aug_candidates: + new_image, tfms = apply_augmentations(aug, np.copy(numpy_image)) + torch_image = torch.from_numpy(np.ascontiguousarray(new_image.transpose(2, 0, 1))) + + dic = copy.deepcopy(dataset_dict) + dic["transforms"] = pre_tfm + tfms + dic["image"] = torch_image + ret.append(dic) + return ret + + +class GeneralizedRCNNWithTTA(nn.Module): + """ + A GeneralizedRCNN with test-time augmentation enabled. + Its :meth:`__call__` method has the same interface as :meth:`GeneralizedRCNN.forward`. + """ + + def __init__(self, cfg, model, tta_mapper=None, batch_size=3): + """ + Args: + cfg (CfgNode): + model (GeneralizedRCNN): a GeneralizedRCNN to apply TTA on. + tta_mapper (callable): takes a dataset dict and returns a list of + augmented versions of the dataset dict. Defaults to + `DatasetMapperTTA(cfg)`. + batch_size (int): batch the augmented images into this batch size for inference. + """ + super().__init__() + if isinstance(model, DistributedDataParallel): + model = model.module + assert isinstance( + model, GeneralizedRCNN + ), "TTA is only supported on GeneralizedRCNN. Got a model of type {}".format(type(model)) + self.cfg = cfg.clone() + assert not self.cfg.MODEL.KEYPOINT_ON, "TTA for keypoint is not supported yet" + assert ( + not self.cfg.MODEL.LOAD_PROPOSALS + ), "TTA for pre-computed proposals is not supported yet" + + self.model = model + + if tta_mapper is None: + tta_mapper = DatasetMapperTTA(cfg) + self.tta_mapper = tta_mapper + self.batch_size = batch_size + + @contextmanager + def _turn_off_roi_heads(self, attrs): + """ + Open a context where some heads in `model.roi_heads` are temporarily turned off. + Args: + attr (list[str]): the attribute in `model.roi_heads` which can be used + to turn off a specific head, e.g., "mask_on", "keypoint_on". + """ + roi_heads = self.model.roi_heads + old = {} + for attr in attrs: + try: + old[attr] = getattr(roi_heads, attr) + except AttributeError: + # The head may not be implemented in certain ROIHeads + pass + + if len(old.keys()) == 0: + yield + else: + for attr in old.keys(): + setattr(roi_heads, attr, False) + yield + for attr in old.keys(): + setattr(roi_heads, attr, old[attr]) + + def _batch_inference(self, batched_inputs, detected_instances=None): + """ + Execute inference on a list of inputs, + using batch size = self.batch_size, instead of the length of the list. + + Inputs & outputs have the same format as :meth:`GeneralizedRCNN.inference` + """ + if detected_instances is None: + detected_instances = [None] * len(batched_inputs) + + outputs = [] + inputs, instances = [], [] + for idx, input, instance in zip(count(), batched_inputs, detected_instances): + inputs.append(input) + instances.append(instance) + if len(inputs) == self.batch_size or idx == len(batched_inputs) - 1: + outputs.extend( + self.model.inference( + inputs, + instances if instances[0] is not None else None, + do_postprocess=False, + ) + ) + inputs, instances = [], [] + return outputs + + def __call__(self, batched_inputs): + """ + Same input/output format as :meth:`GeneralizedRCNN.forward` + """ + + def _maybe_read_image(dataset_dict): + ret = copy.copy(dataset_dict) + if "image" not in ret: + image = read_image(ret.pop("file_name"), self.model.input_format) + image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1))) # CHW + ret["image"] = image + if "height" not in ret and "width" not in ret: + ret["height"] = image.shape[1] + ret["width"] = image.shape[2] + return ret + + return [self._inference_one_image(_maybe_read_image(x)) for x in batched_inputs] + + def _inference_one_image(self, input): + """ + Args: + input (dict): one dataset dict with "image" field being a CHW tensor + + Returns: + dict: one output dict + """ + orig_shape = (input["height"], input["width"]) + augmented_inputs, tfms = self._get_augmented_inputs(input) + # Detect boxes from all augmented versions + with self._turn_off_roi_heads(["mask_on", "keypoint_on"]): + # temporarily disable roi heads + all_boxes, all_scores, all_classes = self._get_augmented_boxes(augmented_inputs, tfms) + # merge all detected boxes to obtain final predictions for boxes + merged_instances = self._merge_detections(all_boxes, all_scores, all_classes, orig_shape) + + if self.cfg.MODEL.MASK_ON: + # Use the detected boxes to obtain masks + augmented_instances = self._rescale_detected_boxes( + augmented_inputs, merged_instances, tfms + ) + # run forward on the detected boxes + outputs = self._batch_inference(augmented_inputs, augmented_instances) + # Delete now useless variables to avoid being out of memory + del augmented_inputs, augmented_instances + # average the predictions + merged_instances.pred_masks = self._reduce_pred_masks(outputs, tfms) + merged_instances = detector_postprocess(merged_instances, *orig_shape) + return {"instances": merged_instances} + else: + return {"instances": merged_instances} + + def _get_augmented_inputs(self, input): + augmented_inputs = self.tta_mapper(input) + tfms = [x.pop("transforms") for x in augmented_inputs] + return augmented_inputs, tfms + + def _get_augmented_boxes(self, augmented_inputs, tfms): + # 1: forward with all augmented images + outputs = self._batch_inference(augmented_inputs) + # 2: union the results + all_boxes = [] + all_scores = [] + all_classes = [] + for output, tfm in zip(outputs, tfms): + # Need to inverse the transforms on boxes, to obtain results on original image + pred_boxes = output.pred_boxes.tensor + original_pred_boxes = tfm.inverse().apply_box(pred_boxes.cpu().numpy()) + all_boxes.append(torch.from_numpy(original_pred_boxes).to(pred_boxes.device)) + + all_scores.extend(output.scores) + all_classes.extend(output.pred_classes) + all_boxes = torch.cat(all_boxes, dim=0) + return all_boxes, all_scores, all_classes + + def _merge_detections(self, all_boxes, all_scores, all_classes, shape_hw): + # select from the union of all results + num_boxes = len(all_boxes) + num_classes = self.cfg.MODEL.ROI_HEADS.NUM_CLASSES + # +1 because fast_rcnn_inference expects background scores as well + all_scores_2d = torch.zeros(num_boxes, num_classes + 1, device=all_boxes.device) + for idx, cls, score in zip(count(), all_classes, all_scores): + all_scores_2d[idx, cls] = score + + merged_instances, _ = fast_rcnn_inference_single_image( + all_boxes, + all_scores_2d, + shape_hw, + 1e-8, + self.cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST, + self.cfg.TEST.DETECTIONS_PER_IMAGE, + ) + + return merged_instances + + def _rescale_detected_boxes(self, augmented_inputs, merged_instances, tfms): + augmented_instances = [] + for input, tfm in zip(augmented_inputs, tfms): + # Transform the target box to the augmented image's coordinate space + pred_boxes = merged_instances.pred_boxes.tensor.cpu().numpy() + pred_boxes = torch.from_numpy(tfm.apply_box(pred_boxes)) + + aug_instances = Instances( + image_size=input["image"].shape[1:3], + pred_boxes=Boxes(pred_boxes), + pred_classes=merged_instances.pred_classes, + scores=merged_instances.scores, + ) + augmented_instances.append(aug_instances) + return augmented_instances + + def _reduce_pred_masks(self, outputs, tfms): + # Should apply inverse transforms on masks. + # We assume only resize & flip are used. pred_masks is a scale-invariant + # representation, so we handle flip specially + for output, tfm in zip(outputs, tfms): + if any(isinstance(t, HFlipTransform) for t in tfm.transforms): + output.pred_masks = output.pred_masks.flip(dims=[3]) + all_pred_masks = torch.stack([o.pred_masks for o in outputs], dim=0) + avg_pred_masks = torch.mean(all_pred_masks, dim=0) + return avg_pred_masks diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/projects/README.md b/iChat/models/grit_src/third_party/CenterNet2/detectron2/projects/README.md new file mode 100644 index 0000000000000000000000000000000000000000..95afe7ff8c8a9bd2f56621fcc3c1bdac11c256a9 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/projects/README.md @@ -0,0 +1,2 @@ + +Projects live in the [`projects` directory](../../projects) under the root of this repository, but not here. diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/projects/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/projects/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a68207db4ee3c2578e1042b00b3071a946b7adea --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/projects/__init__.py @@ -0,0 +1,31 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import importlib +from pathlib import Path + +_PROJECTS = { + "point_rend": "PointRend", + "deeplab": "DeepLab", + "panoptic_deeplab": "Panoptic-DeepLab", +} +_PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent / "projects" + +if _PROJECT_ROOT.is_dir(): + # This is true only for in-place installation (pip install -e, setup.py develop), + # where setup(package_dir=) does not work: https://github.com/pypa/setuptools/issues/230 + + class _D2ProjectsFinder(importlib.abc.MetaPathFinder): + def find_spec(self, name, path, target=None): + if not name.startswith("detectron2.projects."): + return + project_name = name.split(".")[-1] + project_dir = _PROJECTS.get(project_name) + if not project_dir: + return + target_file = _PROJECT_ROOT / f"{project_dir}/{project_name}/__init__.py" + if not target_file.is_file(): + return + return importlib.util.spec_from_file_location(name, target_file) + + import sys + + sys.meta_path.append(_D2ProjectsFinder()) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/solver/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/solver/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9a2dbd35bb24f0d4a979bc8f304142376d87e7ec --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/solver/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .build import build_lr_scheduler, build_optimizer, get_default_optimizer_params +from .lr_scheduler import WarmupCosineLR, WarmupMultiStepLR, LRMultiplier, WarmupParamScheduler + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/solver/build.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/solver/build.py new file mode 100644 index 0000000000000000000000000000000000000000..1989dfcd0855d833a75e403f6a5e88725d78022f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/solver/build.py @@ -0,0 +1,285 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import itertools +import logging +from collections import defaultdict +from enum import Enum +from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Type, Union +import torch +from fvcore.common.param_scheduler import CosineParamScheduler, MultiStepParamScheduler + +from detectron2.config import CfgNode + +from .lr_scheduler import LRMultiplier, WarmupParamScheduler + +_GradientClipperInput = Union[torch.Tensor, Iterable[torch.Tensor]] +_GradientClipper = Callable[[_GradientClipperInput], None] + + +class GradientClipType(Enum): + VALUE = "value" + NORM = "norm" + + +def _create_gradient_clipper(cfg: CfgNode) -> _GradientClipper: + """ + Creates gradient clipping closure to clip by value or by norm, + according to the provided config. + """ + cfg = copy.deepcopy(cfg) + + def clip_grad_norm(p: _GradientClipperInput): + torch.nn.utils.clip_grad_norm_(p, cfg.CLIP_VALUE, cfg.NORM_TYPE) + + def clip_grad_value(p: _GradientClipperInput): + torch.nn.utils.clip_grad_value_(p, cfg.CLIP_VALUE) + + _GRADIENT_CLIP_TYPE_TO_CLIPPER = { + GradientClipType.VALUE: clip_grad_value, + GradientClipType.NORM: clip_grad_norm, + } + return _GRADIENT_CLIP_TYPE_TO_CLIPPER[GradientClipType(cfg.CLIP_TYPE)] + + +def _generate_optimizer_class_with_gradient_clipping( + optimizer: Type[torch.optim.Optimizer], + *, + per_param_clipper: Optional[_GradientClipper] = None, + global_clipper: Optional[_GradientClipper] = None, +) -> Type[torch.optim.Optimizer]: + """ + Dynamically creates a new type that inherits the type of a given instance + and overrides the `step` method to add gradient clipping + """ + assert ( + per_param_clipper is None or global_clipper is None + ), "Not allowed to use both per-parameter clipping and global clipping" + + def optimizer_wgc_step(self, closure=None): + if per_param_clipper is not None: + for group in self.param_groups: + for p in group["params"]: + per_param_clipper(p) + else: + # global clipper for future use with detr + # (https://github.com/facebookresearch/detr/pull/287) + all_params = itertools.chain(*[g["params"] for g in self.param_groups]) + global_clipper(all_params) + super(type(self), self).step(closure) + + OptimizerWithGradientClip = type( + optimizer.__name__ + "WithGradientClip", + (optimizer,), + {"step": optimizer_wgc_step}, + ) + return OptimizerWithGradientClip + + +def maybe_add_gradient_clipping( + cfg: CfgNode, optimizer: Type[torch.optim.Optimizer] +) -> Type[torch.optim.Optimizer]: + """ + If gradient clipping is enabled through config options, wraps the existing + optimizer type to become a new dynamically created class OptimizerWithGradientClip + that inherits the given optimizer and overrides the `step` method to + include gradient clipping. + + Args: + cfg: CfgNode, configuration options + optimizer: type. A subclass of torch.optim.Optimizer + + Return: + type: either the input `optimizer` (if gradient clipping is disabled), or + a subclass of it with gradient clipping included in the `step` method. + """ + if not cfg.SOLVER.CLIP_GRADIENTS.ENABLED: + return optimizer + if isinstance(optimizer, torch.optim.Optimizer): + optimizer_type = type(optimizer) + else: + assert issubclass(optimizer, torch.optim.Optimizer), optimizer + optimizer_type = optimizer + + grad_clipper = _create_gradient_clipper(cfg.SOLVER.CLIP_GRADIENTS) + OptimizerWithGradientClip = _generate_optimizer_class_with_gradient_clipping( + optimizer_type, per_param_clipper=grad_clipper + ) + if isinstance(optimizer, torch.optim.Optimizer): + optimizer.__class__ = OptimizerWithGradientClip # a bit hacky, not recommended + return optimizer + else: + return OptimizerWithGradientClip + + +def build_optimizer(cfg: CfgNode, model: torch.nn.Module) -> torch.optim.Optimizer: + """ + Build an optimizer from config. + """ + params = get_default_optimizer_params( + model, + base_lr=cfg.SOLVER.BASE_LR, + weight_decay_norm=cfg.SOLVER.WEIGHT_DECAY_NORM, + bias_lr_factor=cfg.SOLVER.BIAS_LR_FACTOR, + weight_decay_bias=cfg.SOLVER.WEIGHT_DECAY_BIAS, + ) + return maybe_add_gradient_clipping(cfg, torch.optim.SGD)( + params, + lr=cfg.SOLVER.BASE_LR, + momentum=cfg.SOLVER.MOMENTUM, + nesterov=cfg.SOLVER.NESTEROV, + weight_decay=cfg.SOLVER.WEIGHT_DECAY, + ) + + +def get_default_optimizer_params( + model: torch.nn.Module, + base_lr: Optional[float] = None, + weight_decay: Optional[float] = None, + weight_decay_norm: Optional[float] = None, + bias_lr_factor: Optional[float] = 1.0, + weight_decay_bias: Optional[float] = None, + overrides: Optional[Dict[str, Dict[str, float]]] = None, +) -> List[Dict[str, Any]]: + """ + Get default param list for optimizer, with support for a few types of + overrides. If no overrides needed, this is equivalent to `model.parameters()`. + + Args: + base_lr: lr for every group by default. Can be omitted to use the one in optimizer. + weight_decay: weight decay for every group by default. Can be omitted to use the one + in optimizer. + weight_decay_norm: override weight decay for params in normalization layers + bias_lr_factor: multiplier of lr for bias parameters. + weight_decay_bias: override weight decay for bias parameters + overrides: if not `None`, provides values for optimizer hyperparameters + (LR, weight decay) for module parameters with a given name; e.g. + ``{"embedding": {"lr": 0.01, "weight_decay": 0.1}}`` will set the LR and + weight decay values for all module parameters named `embedding`. + + For common detection models, ``weight_decay_norm`` is the only option + needed to be set. ``bias_lr_factor,weight_decay_bias`` are legacy settings + from Detectron1 that are not found useful. + + Example: + :: + torch.optim.SGD(get_default_optimizer_params(model, weight_decay_norm=0), + lr=0.01, weight_decay=1e-4, momentum=0.9) + """ + if overrides is None: + overrides = {} + defaults = {} + if base_lr is not None: + defaults["lr"] = base_lr + if weight_decay is not None: + defaults["weight_decay"] = weight_decay + bias_overrides = {} + if bias_lr_factor is not None and bias_lr_factor != 1.0: + # NOTE: unlike Detectron v1, we now by default make bias hyperparameters + # exactly the same as regular weights. + if base_lr is None: + raise ValueError("bias_lr_factor requires base_lr") + bias_overrides["lr"] = base_lr * bias_lr_factor + if weight_decay_bias is not None: + bias_overrides["weight_decay"] = weight_decay_bias + if len(bias_overrides): + if "bias" in overrides: + raise ValueError("Conflicting overrides for 'bias'") + overrides["bias"] = bias_overrides + + norm_module_types = ( + torch.nn.BatchNorm1d, + torch.nn.BatchNorm2d, + torch.nn.BatchNorm3d, + torch.nn.SyncBatchNorm, + # NaiveSyncBatchNorm inherits from BatchNorm2d + torch.nn.GroupNorm, + torch.nn.InstanceNorm1d, + torch.nn.InstanceNorm2d, + torch.nn.InstanceNorm3d, + torch.nn.LayerNorm, + torch.nn.LocalResponseNorm, + ) + params: List[Dict[str, Any]] = [] + memo: Set[torch.nn.parameter.Parameter] = set() + for module in model.modules(): + for module_param_name, value in module.named_parameters(recurse=False): + if not value.requires_grad: + continue + # Avoid duplicating parameters + if value in memo: + continue + memo.add(value) + + hyperparams = copy.copy(defaults) + if isinstance(module, norm_module_types) and weight_decay_norm is not None: + hyperparams["weight_decay"] = weight_decay_norm + hyperparams.update(overrides.get(module_param_name, {})) + params.append({"params": [value], **hyperparams}) + return reduce_param_groups(params) + + +def _expand_param_groups(params: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + # Transform parameter groups into per-parameter structure. + # Later items in `params` can overwrite parameters set in previous items. + ret = defaultdict(dict) + for item in params: + assert "params" in item + cur_params = {x: y for x, y in item.items() if x != "params"} + for param in item["params"]: + ret[param].update({"params": [param], **cur_params}) + return list(ret.values()) + + +def reduce_param_groups(params: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + # Reorganize the parameter groups and merge duplicated groups. + # The number of parameter groups needs to be as small as possible in order + # to efficiently use the PyTorch multi-tensor optimizer. Therefore instead + # of using a parameter_group per single parameter, we reorganize the + # parameter groups and merge duplicated groups. This approach speeds + # up multi-tensor optimizer significantly. + params = _expand_param_groups(params) + groups = defaultdict(list) # re-group all parameter groups by their hyperparams + for item in params: + cur_params = tuple((x, y) for x, y in item.items() if x != "params") + groups[cur_params].extend(item["params"]) + ret = [] + for param_keys, param_values in groups.items(): + cur = {kv[0]: kv[1] for kv in param_keys} + cur["params"] = param_values + ret.append(cur) + return ret + + +def build_lr_scheduler( + cfg: CfgNode, optimizer: torch.optim.Optimizer +) -> torch.optim.lr_scheduler._LRScheduler: + """ + Build a LR scheduler from config. + """ + name = cfg.SOLVER.LR_SCHEDULER_NAME + + if name == "WarmupMultiStepLR": + steps = [x for x in cfg.SOLVER.STEPS if x <= cfg.SOLVER.MAX_ITER] + if len(steps) != len(cfg.SOLVER.STEPS): + logger = logging.getLogger(__name__) + logger.warning( + "SOLVER.STEPS contains values larger than SOLVER.MAX_ITER. " + "These values will be ignored." + ) + sched = MultiStepParamScheduler( + values=[cfg.SOLVER.GAMMA ** k for k in range(len(steps) + 1)], + milestones=steps, + num_updates=cfg.SOLVER.MAX_ITER, + ) + elif name == "WarmupCosineLR": + sched = CosineParamScheduler(1, 0) + else: + raise ValueError("Unknown LR scheduler: {}".format(name)) + + sched = WarmupParamScheduler( + sched, + cfg.SOLVER.WARMUP_FACTOR, + min(cfg.SOLVER.WARMUP_ITERS / cfg.SOLVER.MAX_ITER, 1.0), + cfg.SOLVER.WARMUP_METHOD, + ) + return LRMultiplier(optimizer, multiplier=sched, max_iter=cfg.SOLVER.MAX_ITER) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/solver/lr_scheduler.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/solver/lr_scheduler.py new file mode 100644 index 0000000000000000000000000000000000000000..8803e87b9e60cffdbe048c97c282d353191ae4c8 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/solver/lr_scheduler.py @@ -0,0 +1,238 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import math +from bisect import bisect_right +from typing import List +import torch +from fvcore.common.param_scheduler import ( + CompositeParamScheduler, + ConstantParamScheduler, + LinearParamScheduler, + ParamScheduler, +) + +logger = logging.getLogger(__name__) + + +class WarmupParamScheduler(CompositeParamScheduler): + """ + Add an initial warmup stage to another scheduler. + """ + + def __init__( + self, + scheduler: ParamScheduler, + warmup_factor: float, + warmup_length: float, + warmup_method: str = "linear", + ): + """ + Args: + scheduler: warmup will be added at the beginning of this scheduler + warmup_factor: the factor w.r.t the initial value of ``scheduler``, e.g. 0.001 + warmup_length: the relative length (in [0, 1]) of warmup steps w.r.t the entire + training, e.g. 0.01 + warmup_method: one of "linear" or "constant" + """ + end_value = scheduler(warmup_length) # the value to reach when warmup ends + start_value = warmup_factor * scheduler(0.0) + if warmup_method == "constant": + warmup = ConstantParamScheduler(start_value) + elif warmup_method == "linear": + warmup = LinearParamScheduler(start_value, end_value) + else: + raise ValueError("Unknown warmup method: {}".format(warmup_method)) + super().__init__( + [warmup, scheduler], + interval_scaling=["rescaled", "fixed"], + lengths=[warmup_length, 1 - warmup_length], + ) + + +class LRMultiplier(torch.optim.lr_scheduler._LRScheduler): + """ + A LRScheduler which uses fvcore :class:`ParamScheduler` to multiply the + learning rate of each param in the optimizer. + Every step, the learning rate of each parameter becomes its initial value + multiplied by the output of the given :class:`ParamScheduler`. + + The absolute learning rate value of each parameter can be different. + This scheduler can be used as long as the relative scale among them do + not change during training. + + Examples: + :: + LRMultiplier( + opt, + WarmupParamScheduler( + MultiStepParamScheduler( + [1, 0.1, 0.01], + milestones=[60000, 80000], + num_updates=90000, + ), 0.001, 100 / 90000 + ), + max_iter=90000 + ) + """ + + # NOTES: in the most general case, every LR can use its own scheduler. + # Supporting this requires interaction with the optimizer when its parameter + # group is initialized. For example, classyvision implements its own optimizer + # that allows different schedulers for every parameter group. + # To avoid this complexity, we use this class to support the most common cases + # where the relative scale among all LRs stay unchanged during training. In this + # case we only need a total of one scheduler that defines the relative LR multiplier. + + def __init__( + self, + optimizer: torch.optim.Optimizer, + multiplier: ParamScheduler, + max_iter: int, + last_iter: int = -1, + ): + """ + Args: + optimizer, last_iter: See ``torch.optim.lr_scheduler._LRScheduler``. + ``last_iter`` is the same as ``last_epoch``. + multiplier: a fvcore ParamScheduler that defines the multiplier on + every LR of the optimizer + max_iter: the total number of training iterations + """ + if not isinstance(multiplier, ParamScheduler): + raise ValueError( + "_LRMultiplier(multiplier=) must be an instance of fvcore " + f"ParamScheduler. Got {multiplier} instead." + ) + self._multiplier = multiplier + self._max_iter = max_iter + super().__init__(optimizer, last_epoch=last_iter) + + def state_dict(self): + # fvcore schedulers are stateless. Only keep pytorch scheduler states + return {"base_lrs": self.base_lrs, "last_epoch": self.last_epoch} + + def get_lr(self) -> List[float]: + multiplier = self._multiplier(self.last_epoch / self._max_iter) + return [base_lr * multiplier for base_lr in self.base_lrs] + + +""" +Content below is no longer needed! +""" + +# NOTE: PyTorch's LR scheduler interface uses names that assume the LR changes +# only on epoch boundaries. We typically use iteration based schedules instead. +# As a result, "epoch" (e.g., as in self.last_epoch) should be understood to mean +# "iteration" instead. + +# FIXME: ideally this would be achieved with a CombinedLRScheduler, separating +# MultiStepLR with WarmupLR but the current LRScheduler design doesn't allow it. + + +class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler): + def __init__( + self, + optimizer: torch.optim.Optimizer, + milestones: List[int], + gamma: float = 0.1, + warmup_factor: float = 0.001, + warmup_iters: int = 1000, + warmup_method: str = "linear", + last_epoch: int = -1, + ): + logger.warning( + "WarmupMultiStepLR is deprecated! Use LRMultipilier with fvcore ParamScheduler instead!" + ) + if not list(milestones) == sorted(milestones): + raise ValueError( + "Milestones should be a list of" " increasing integers. Got {}", milestones + ) + self.milestones = milestones + self.gamma = gamma + self.warmup_factor = warmup_factor + self.warmup_iters = warmup_iters + self.warmup_method = warmup_method + super().__init__(optimizer, last_epoch) + + def get_lr(self) -> List[float]: + warmup_factor = _get_warmup_factor_at_iter( + self.warmup_method, self.last_epoch, self.warmup_iters, self.warmup_factor + ) + return [ + base_lr * warmup_factor * self.gamma ** bisect_right(self.milestones, self.last_epoch) + for base_lr in self.base_lrs + ] + + def _compute_values(self) -> List[float]: + # The new interface + return self.get_lr() + + +class WarmupCosineLR(torch.optim.lr_scheduler._LRScheduler): + def __init__( + self, + optimizer: torch.optim.Optimizer, + max_iters: int, + warmup_factor: float = 0.001, + warmup_iters: int = 1000, + warmup_method: str = "linear", + last_epoch: int = -1, + ): + logger.warning( + "WarmupCosineLR is deprecated! Use LRMultipilier with fvcore ParamScheduler instead!" + ) + self.max_iters = max_iters + self.warmup_factor = warmup_factor + self.warmup_iters = warmup_iters + self.warmup_method = warmup_method + super().__init__(optimizer, last_epoch) + + def get_lr(self) -> List[float]: + warmup_factor = _get_warmup_factor_at_iter( + self.warmup_method, self.last_epoch, self.warmup_iters, self.warmup_factor + ) + # Different definitions of half-cosine with warmup are possible. For + # simplicity we multiply the standard half-cosine schedule by the warmup + # factor. An alternative is to start the period of the cosine at warmup_iters + # instead of at 0. In the case that warmup_iters << max_iters the two are + # very close to each other. + return [ + base_lr + * warmup_factor + * 0.5 + * (1.0 + math.cos(math.pi * self.last_epoch / self.max_iters)) + for base_lr in self.base_lrs + ] + + def _compute_values(self) -> List[float]: + # The new interface + return self.get_lr() + + +def _get_warmup_factor_at_iter( + method: str, iter: int, warmup_iters: int, warmup_factor: float +) -> float: + """ + Return the learning rate warmup factor at a specific iteration. + See :paper:`ImageNet in 1h` for more details. + + Args: + method (str): warmup method; either "constant" or "linear". + iter (int): iteration at which to calculate the warmup factor. + warmup_iters (int): the number of warmup iterations. + warmup_factor (float): the base warmup factor (the meaning changes according + to the method used). + + Returns: + float: the effective warmup factor at the given iteration. + """ + if iter >= warmup_iters: + return 1.0 + + if method == "constant": + return warmup_factor + elif method == "linear": + alpha = iter / warmup_iters + return warmup_factor * (1 - alpha) + alpha + else: + raise ValueError("Unknown warmup method: {}".format(method)) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/structures/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/structures/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f3ee6057e3ec2731984ce8203c6eaf5348d08260 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/structures/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .boxes import Boxes, BoxMode, pairwise_iou, pairwise_ioa, pairwise_point_box_distance +from .image_list import ImageList + +from .instances import Instances +from .keypoints import Keypoints, heatmaps_to_keypoints +from .masks import BitMasks, PolygonMasks, polygons_to_bitmask, ROIMasks +from .rotated_boxes import RotatedBoxes +from .rotated_boxes import pairwise_iou as pairwise_iou_rotated + +__all__ = [k for k in globals().keys() if not k.startswith("_")] + + +from detectron2.utils.env import fixup_module_metadata + +fixup_module_metadata(__name__, globals(), __all__) +del fixup_module_metadata diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/structures/boxes.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/structures/boxes.py new file mode 100644 index 0000000000000000000000000000000000000000..ae543c617a33245075b001f383f41775efb0b63d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/structures/boxes.py @@ -0,0 +1,423 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import math +import numpy as np +from enum import IntEnum, unique +from typing import List, Tuple, Union +import torch +from torch import device + +_RawBoxType = Union[List[float], Tuple[float, ...], torch.Tensor, np.ndarray] + + +@unique +class BoxMode(IntEnum): + """ + Enum of different ways to represent a box. + """ + + XYXY_ABS = 0 + """ + (x0, y0, x1, y1) in absolute floating points coordinates. + The coordinates in range [0, width or height]. + """ + XYWH_ABS = 1 + """ + (x0, y0, w, h) in absolute floating points coordinates. + """ + XYXY_REL = 2 + """ + Not yet supported! + (x0, y0, x1, y1) in range [0, 1]. They are relative to the size of the image. + """ + XYWH_REL = 3 + """ + Not yet supported! + (x0, y0, w, h) in range [0, 1]. They are relative to the size of the image. + """ + XYWHA_ABS = 4 + """ + (xc, yc, w, h, a) in absolute floating points coordinates. + (xc, yc) is the center of the rotated box, and the angle a is in degrees ccw. + """ + + @staticmethod + def convert(box: _RawBoxType, from_mode: "BoxMode", to_mode: "BoxMode") -> _RawBoxType: + """ + Args: + box: can be a k-tuple, k-list or an Nxk array/tensor, where k = 4 or 5 + from_mode, to_mode (BoxMode) + + Returns: + The converted box of the same type. + """ + if from_mode == to_mode: + return box + + original_type = type(box) + is_numpy = isinstance(box, np.ndarray) + single_box = isinstance(box, (list, tuple)) + if single_box: + assert len(box) == 4 or len(box) == 5, ( + "BoxMode.convert takes either a k-tuple/list or an Nxk array/tensor," + " where k == 4 or 5" + ) + arr = torch.tensor(box)[None, :] + else: + # avoid modifying the input box + if is_numpy: + arr = torch.from_numpy(np.asarray(box)).clone() + else: + arr = box.clone() + + assert to_mode not in [BoxMode.XYXY_REL, BoxMode.XYWH_REL] and from_mode not in [ + BoxMode.XYXY_REL, + BoxMode.XYWH_REL, + ], "Relative mode not yet supported!" + + if from_mode == BoxMode.XYWHA_ABS and to_mode == BoxMode.XYXY_ABS: + assert ( + arr.shape[-1] == 5 + ), "The last dimension of input shape must be 5 for XYWHA format" + original_dtype = arr.dtype + arr = arr.double() + + w = arr[:, 2] + h = arr[:, 3] + a = arr[:, 4] + c = torch.abs(torch.cos(a * math.pi / 180.0)) + s = torch.abs(torch.sin(a * math.pi / 180.0)) + # This basically computes the horizontal bounding rectangle of the rotated box + new_w = c * w + s * h + new_h = c * h + s * w + + # convert center to top-left corner + arr[:, 0] -= new_w / 2.0 + arr[:, 1] -= new_h / 2.0 + # bottom-right corner + arr[:, 2] = arr[:, 0] + new_w + arr[:, 3] = arr[:, 1] + new_h + + arr = arr[:, :4].to(dtype=original_dtype) + elif from_mode == BoxMode.XYWH_ABS and to_mode == BoxMode.XYWHA_ABS: + original_dtype = arr.dtype + arr = arr.double() + arr[:, 0] += arr[:, 2] / 2.0 + arr[:, 1] += arr[:, 3] / 2.0 + angles = torch.zeros((arr.shape[0], 1), dtype=arr.dtype) + arr = torch.cat((arr, angles), axis=1).to(dtype=original_dtype) + else: + if to_mode == BoxMode.XYXY_ABS and from_mode == BoxMode.XYWH_ABS: + arr[:, 2] += arr[:, 0] + arr[:, 3] += arr[:, 1] + elif from_mode == BoxMode.XYXY_ABS and to_mode == BoxMode.XYWH_ABS: + arr[:, 2] -= arr[:, 0] + arr[:, 3] -= arr[:, 1] + else: + raise NotImplementedError( + "Conversion from BoxMode {} to {} is not supported yet".format( + from_mode, to_mode + ) + ) + + if single_box: + return original_type(arr.flatten().tolist()) + if is_numpy: + return arr.numpy() + else: + return arr + + +class Boxes: + """ + This structure stores a list of boxes as a Nx4 torch.Tensor. + It supports some common methods about boxes + (`area`, `clip`, `nonempty`, etc), + and also behaves like a Tensor + (support indexing, `to(device)`, `.device`, and iteration over all boxes) + + Attributes: + tensor (torch.Tensor): float matrix of Nx4. Each row is (x1, y1, x2, y2). + """ + + def __init__(self, tensor: torch.Tensor): + """ + Args: + tensor (Tensor[float]): a Nx4 matrix. Each row is (x1, y1, x2, y2). + """ + device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device("cpu") + tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device) + if tensor.numel() == 0: + # Use reshape, so we don't end up creating a new tensor that does not depend on + # the inputs (and consequently confuses jit) + tensor = tensor.reshape((-1, 4)).to(dtype=torch.float32, device=device) + assert tensor.dim() == 2 and tensor.size(-1) == 4, tensor.size() + + self.tensor = tensor + + def clone(self) -> "Boxes": + """ + Clone the Boxes. + + Returns: + Boxes + """ + return Boxes(self.tensor.clone()) + + def to(self, device: torch.device): + # Boxes are assumed float32 and does not support to(dtype) + return Boxes(self.tensor.to(device=device)) + + def area(self) -> torch.Tensor: + """ + Computes the area of all the boxes. + + Returns: + torch.Tensor: a vector with areas of each box. + """ + box = self.tensor + area = (box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1]) + return area + + def clip(self, box_size: Tuple[int, int]) -> None: + """ + Clip (in place) the boxes by limiting x coordinates to the range [0, width] + and y coordinates to the range [0, height]. + + Args: + box_size (height, width): The clipping box's size. + """ + assert torch.isfinite(self.tensor).all(), "Box tensor contains infinite or NaN!" + h, w = box_size + x1 = self.tensor[:, 0].clamp(min=0, max=w) + y1 = self.tensor[:, 1].clamp(min=0, max=h) + x2 = self.tensor[:, 2].clamp(min=0, max=w) + y2 = self.tensor[:, 3].clamp(min=0, max=h) + self.tensor = torch.stack((x1, y1, x2, y2), dim=-1) + + def nonempty(self, threshold: float = 0.0) -> torch.Tensor: + """ + Find boxes that are non-empty. + A box is considered empty, if either of its side is no larger than threshold. + + Returns: + Tensor: + a binary vector which represents whether each box is empty + (False) or non-empty (True). + """ + box = self.tensor + widths = box[:, 2] - box[:, 0] + heights = box[:, 3] - box[:, 1] + keep = (widths > threshold) & (heights > threshold) + return keep + + def __getitem__(self, item) -> "Boxes": + """ + Args: + item: int, slice, or a BoolTensor + + Returns: + Boxes: Create a new :class:`Boxes` by indexing. + + The following usage are allowed: + + 1. `new_boxes = boxes[3]`: return a `Boxes` which contains only one box. + 2. `new_boxes = boxes[2:10]`: return a slice of boxes. + 3. `new_boxes = boxes[vector]`, where vector is a torch.BoolTensor + with `length = len(boxes)`. Nonzero elements in the vector will be selected. + + Note that the returned Boxes might share storage with this Boxes, + subject to Pytorch's indexing semantics. + """ + if isinstance(item, int): + return Boxes(self.tensor[item].view(1, -1)) + b = self.tensor[item] + assert b.dim() == 2, "Indexing on Boxes with {} failed to return a matrix!".format(item) + return Boxes(b) + + def __len__(self) -> int: + return self.tensor.shape[0] + + def __repr__(self) -> str: + return "Boxes(" + str(self.tensor) + ")" + + def inside_box(self, box_size: Tuple[int, int], boundary_threshold: int = 0) -> torch.Tensor: + """ + Args: + box_size (height, width): Size of the reference box. + boundary_threshold (int): Boxes that extend beyond the reference box + boundary by more than boundary_threshold are considered "outside". + + Returns: + a binary vector, indicating whether each box is inside the reference box. + """ + height, width = box_size + inds_inside = ( + (self.tensor[..., 0] >= -boundary_threshold) + & (self.tensor[..., 1] >= -boundary_threshold) + & (self.tensor[..., 2] < width + boundary_threshold) + & (self.tensor[..., 3] < height + boundary_threshold) + ) + return inds_inside + + def get_centers(self) -> torch.Tensor: + """ + Returns: + The box centers in a Nx2 array of (x, y). + """ + return (self.tensor[:, :2] + self.tensor[:, 2:]) / 2 + + def scale(self, scale_x: float, scale_y: float) -> None: + """ + Scale the box with horizontal and vertical scaling factors + """ + self.tensor[:, 0::2] *= scale_x + self.tensor[:, 1::2] *= scale_y + + @classmethod + def cat(cls, boxes_list: List["Boxes"]) -> "Boxes": + """ + Concatenates a list of Boxes into a single Boxes + + Arguments: + boxes_list (list[Boxes]) + + Returns: + Boxes: the concatenated Boxes + """ + assert isinstance(boxes_list, (list, tuple)) + if len(boxes_list) == 0: + return cls(torch.empty(0)) + assert all([isinstance(box, Boxes) for box in boxes_list]) + + # use torch.cat (v.s. layers.cat) so the returned boxes never share storage with input + cat_boxes = cls(torch.cat([b.tensor for b in boxes_list], dim=0)) + return cat_boxes + + @property + def device(self) -> device: + return self.tensor.device + + # type "Iterator[torch.Tensor]", yield, and iter() not supported by torchscript + # https://github.com/pytorch/pytorch/issues/18627 + @torch.jit.unused + def __iter__(self): + """ + Yield a box as a Tensor of shape (4,) at a time. + """ + yield from self.tensor + + +def pairwise_intersection(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor: + """ + Given two lists of boxes of size N and M, + compute the intersection area between __all__ N x M pairs of boxes. + The box order must be (xmin, ymin, xmax, ymax) + + Args: + boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively. + + Returns: + Tensor: intersection, sized [N,M]. + """ + boxes1, boxes2 = boxes1.tensor, boxes2.tensor + width_height = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) - torch.max( + boxes1[:, None, :2], boxes2[:, :2] + ) # [N,M,2] + + width_height.clamp_(min=0) # [N,M,2] + intersection = width_height.prod(dim=2) # [N,M] + return intersection + + +# implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py +# with slight modifications +def pairwise_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor: + """ + Given two lists of boxes of size N and M, compute the IoU + (intersection over union) between **all** N x M pairs of boxes. + The box order must be (xmin, ymin, xmax, ymax). + + Args: + boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively. + + Returns: + Tensor: IoU, sized [N,M]. + """ + area1 = boxes1.area() # [N] + area2 = boxes2.area() # [M] + inter = pairwise_intersection(boxes1, boxes2) + + # handle empty boxes + iou = torch.where( + inter > 0, + inter / (area1[:, None] + area2 - inter), + torch.zeros(1, dtype=inter.dtype, device=inter.device), + ) + return iou + + +def pairwise_ioa(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor: + """ + Similar to :func:`pariwise_iou` but compute the IoA (intersection over boxes2 area). + + Args: + boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively. + + Returns: + Tensor: IoA, sized [N,M]. + """ + area2 = boxes2.area() # [M] + inter = pairwise_intersection(boxes1, boxes2) + + # handle empty boxes + ioa = torch.where( + inter > 0, inter / area2, torch.zeros(1, dtype=inter.dtype, device=inter.device) + ) + return ioa + + +def pairwise_point_box_distance(points: torch.Tensor, boxes: Boxes): + """ + Pairwise distance between N points and M boxes. The distance between a + point and a box is represented by the distance from the point to 4 edges + of the box. Distances are all positive when the point is inside the box. + + Args: + points: Nx2 coordinates. Each row is (x, y) + boxes: M boxes + + Returns: + Tensor: distances of size (N, M, 4). The 4 values are distances from + the point to the left, top, right, bottom of the box. + """ + x, y = points.unsqueeze(dim=2).unbind(dim=1) # (N, 1) + x0, y0, x1, y1 = boxes.tensor.unsqueeze(dim=0).unbind(dim=2) # (1, M) + return torch.stack([x - x0, y - y0, x1 - x, y1 - y], dim=2) + + +def matched_pairwise_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor: + """ + Compute pairwise intersection over union (IOU) of two sets of matched + boxes that have the same number of boxes. + Similar to :func:`pairwise_iou`, but computes only diagonal elements of the matrix. + + Args: + boxes1 (Boxes): bounding boxes, sized [N,4]. + boxes2 (Boxes): same length as boxes1 + Returns: + Tensor: iou, sized [N]. + """ + assert len(boxes1) == len( + boxes2 + ), "boxlists should have the same" "number of entries, got {}, {}".format( + len(boxes1), len(boxes2) + ) + area1 = boxes1.area() # [N] + area2 = boxes2.area() # [N] + box1, box2 = boxes1.tensor, boxes2.tensor + lt = torch.max(box1[:, :2], box2[:, :2]) # [N,2] + rb = torch.min(box1[:, 2:], box2[:, 2:]) # [N,2] + wh = (rb - lt).clamp(min=0) # [N,2] + inter = wh[:, 0] * wh[:, 1] # [N] + iou = inter / (area1 + area2 - inter) # [N] + return iou diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/structures/image_list.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/structures/image_list.py new file mode 100644 index 0000000000000000000000000000000000000000..b31b2d395dffb9d3694239d1aa73615899975f4e --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/structures/image_list.py @@ -0,0 +1,110 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from __future__ import division +from typing import Any, List, Tuple +import torch +from torch import device +from torch.nn import functional as F + +from detectron2.layers.wrappers import shapes_to_tensor + + +class ImageList(object): + """ + Structure that holds a list of images (of possibly + varying sizes) as a single tensor. + This works by padding the images to the same size. + The original sizes of each image is stored in `image_sizes`. + + Attributes: + image_sizes (list[tuple[int, int]]): each tuple is (h, w). + During tracing, it becomes list[Tensor] instead. + """ + + def __init__(self, tensor: torch.Tensor, image_sizes: List[Tuple[int, int]]): + """ + Arguments: + tensor (Tensor): of shape (N, H, W) or (N, C_1, ..., C_K, H, W) where K >= 1 + image_sizes (list[tuple[int, int]]): Each tuple is (h, w). It can + be smaller than (H, W) due to padding. + """ + self.tensor = tensor + self.image_sizes = image_sizes + + def __len__(self) -> int: + return len(self.image_sizes) + + def __getitem__(self, idx) -> torch.Tensor: + """ + Access the individual image in its original size. + + Args: + idx: int or slice + + Returns: + Tensor: an image of shape (H, W) or (C_1, ..., C_K, H, W) where K >= 1 + """ + size = self.image_sizes[idx] + return self.tensor[idx, ..., : size[0], : size[1]] + + @torch.jit.unused + def to(self, *args: Any, **kwargs: Any) -> "ImageList": + cast_tensor = self.tensor.to(*args, **kwargs) + return ImageList(cast_tensor, self.image_sizes) + + @property + def device(self) -> device: + return self.tensor.device + + @staticmethod + def from_tensors( + tensors: List[torch.Tensor], size_divisibility: int = 0, pad_value: float = 0.0 + ) -> "ImageList": + """ + Args: + tensors: a tuple or list of `torch.Tensor`, each of shape (Hi, Wi) or + (C_1, ..., C_K, Hi, Wi) where K >= 1. The Tensors will be padded + to the same shape with `pad_value`. + size_divisibility (int): If `size_divisibility > 0`, add padding to ensure + the common height and width is divisible by `size_divisibility`. + This depends on the model and many models need a divisibility of 32. + pad_value (float): value to pad + + Returns: + an `ImageList`. + """ + assert len(tensors) > 0 + assert isinstance(tensors, (tuple, list)) + for t in tensors: + assert isinstance(t, torch.Tensor), type(t) + assert t.shape[:-2] == tensors[0].shape[:-2], t.shape + + image_sizes = [(im.shape[-2], im.shape[-1]) for im in tensors] + image_sizes_tensor = [shapes_to_tensor(x) for x in image_sizes] + max_size = torch.stack(image_sizes_tensor).max(0).values + + if size_divisibility > 1: + stride = size_divisibility + # the last two dims are H,W, both subject to divisibility requirement + max_size = (max_size + (stride - 1)).div(stride, rounding_mode="floor") * stride + + # handle weirdness of scripting and tracing ... + if torch.jit.is_scripting(): + max_size: List[int] = max_size.to(dtype=torch.long).tolist() + else: + if torch.jit.is_tracing(): + image_sizes = image_sizes_tensor + + if len(tensors) == 1: + # This seems slightly (2%) faster. + # TODO: check whether it's faster for multiple images as well + image_size = image_sizes[0] + padding_size = [0, max_size[-1] - image_size[1], 0, max_size[-2] - image_size[0]] + batched_imgs = F.pad(tensors[0], padding_size, value=pad_value).unsqueeze_(0) + else: + # max_size can be a tensor in tracing mode, therefore convert to list + batch_shape = [len(tensors)] + list(tensors[0].shape[:-2]) + list(max_size) + batched_imgs = tensors[0].new_full(batch_shape, pad_value) + for img, pad_img in zip(tensors, batched_imgs): + pad_img[..., : img.shape[-2], : img.shape[-1]].copy_(img) + + return ImageList(batched_imgs.contiguous(), image_sizes) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/structures/instances.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/structures/instances.py new file mode 100644 index 0000000000000000000000000000000000000000..612e66f527397b0e940d716f4ad4f799b962954a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/structures/instances.py @@ -0,0 +1,192 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import itertools +from typing import Any, Dict, List, Tuple, Union +import torch + + +class Instances: + """ + This class represents a list of instances in an image. + It stores the attributes of instances (e.g., boxes, masks, labels, scores) as "fields". + All fields must have the same ``__len__`` which is the number of instances. + + All other (non-field) attributes of this class are considered private: + they must start with '_' and are not modifiable by a user. + + Some basic usage: + + 1. Set/get/check a field: + + .. code-block:: python + + instances.gt_boxes = Boxes(...) + print(instances.pred_masks) # a tensor of shape (N, H, W) + print('gt_masks' in instances) + + 2. ``len(instances)`` returns the number of instances + 3. Indexing: ``instances[indices]`` will apply the indexing on all the fields + and returns a new :class:`Instances`. + Typically, ``indices`` is a integer vector of indices, + or a binary mask of length ``num_instances`` + + .. code-block:: python + + category_3_detections = instances[instances.pred_classes == 3] + confident_detections = instances[instances.scores > 0.9] + """ + + def __init__(self, image_size: Tuple[int, int], **kwargs: Any): + """ + Args: + image_size (height, width): the spatial size of the image. + kwargs: fields to add to this `Instances`. + """ + self._image_size = image_size + self._fields: Dict[str, Any] = {} + for k, v in kwargs.items(): + self.set(k, v) + + @property + def image_size(self) -> Tuple[int, int]: + """ + Returns: + tuple: height, width + """ + return self._image_size + + def __setattr__(self, name: str, val: Any) -> None: + if name.startswith("_"): + super().__setattr__(name, val) + else: + self.set(name, val) + + def __getattr__(self, name: str) -> Any: + if name == "_fields" or name not in self._fields: + raise AttributeError("Cannot find field '{}' in the given Instances!".format(name)) + return self._fields[name] + + def set(self, name: str, value: Any) -> None: + """ + Set the field named `name` to `value`. + The length of `value` must be the number of instances, + and must agree with other existing fields in this object. + """ + data_len = len(value) + if len(self._fields): + assert ( + len(self) == data_len + ), "Adding a field of length {} to a Instances of length {}".format(data_len, len(self)) + self._fields[name] = value + + def has(self, name: str) -> bool: + """ + Returns: + bool: whether the field called `name` exists. + """ + return name in self._fields + + def remove(self, name: str) -> None: + """ + Remove the field called `name`. + """ + del self._fields[name] + + def get(self, name: str) -> Any: + """ + Returns the field called `name`. + """ + return self._fields[name] + + def get_fields(self) -> Dict[str, Any]: + """ + Returns: + dict: a dict which maps names (str) to data of the fields + + Modifying the returned dict will modify this instance. + """ + return self._fields + + # Tensor-like methods + def to(self, *args: Any, **kwargs: Any) -> "Instances": + """ + Returns: + Instances: all fields are called with a `to(device)`, if the field has this method. + """ + ret = Instances(self._image_size) + for k, v in self._fields.items(): + if hasattr(v, "to"): + v = v.to(*args, **kwargs) + ret.set(k, v) + return ret + + def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "Instances": + """ + Args: + item: an index-like object and will be used to index all the fields. + + Returns: + If `item` is a string, return the data in the corresponding field. + Otherwise, returns an `Instances` where all fields are indexed by `item`. + """ + if type(item) == int: + if item >= len(self) or item < -len(self): + raise IndexError("Instances index out of range!") + else: + item = slice(item, None, len(self)) + + ret = Instances(self._image_size) + for k, v in self._fields.items(): + ret.set(k, v[item]) + return ret + + def __len__(self) -> int: + for v in self._fields.values(): + # use __len__ because len() has to be int and is not friendly to tracing + return v.__len__() + raise NotImplementedError("Empty Instances does not support __len__!") + + def __iter__(self): + raise NotImplementedError("`Instances` object is not iterable!") + + @staticmethod + def cat(instance_lists: List["Instances"]) -> "Instances": + """ + Args: + instance_lists (list[Instances]) + + Returns: + Instances + """ + assert all(isinstance(i, Instances) for i in instance_lists) + assert len(instance_lists) > 0 + if len(instance_lists) == 1: + return instance_lists[0] + + image_size = instance_lists[0].image_size + if not isinstance(image_size, torch.Tensor): # could be a tensor in tracing + for i in instance_lists[1:]: + assert i.image_size == image_size + ret = Instances(image_size) + for k in instance_lists[0]._fields.keys(): + values = [i.get(k) for i in instance_lists] + v0 = values[0] + if isinstance(v0, torch.Tensor): + values = torch.cat(values, dim=0) + elif isinstance(v0, list): + values = list(itertools.chain(*values)) + elif hasattr(type(v0), "cat"): + values = type(v0).cat(values) + else: + raise ValueError("Unsupported type {} for concatenation".format(type(v0))) + ret.set(k, values) + return ret + + def __str__(self) -> str: + s = self.__class__.__name__ + "(" + s += "num_instances={}, ".format(len(self)) + s += "image_height={}, ".format(self._image_size[0]) + s += "image_width={}, ".format(self._image_size[1]) + s += "fields=[{}])".format(", ".join((f"{k}: {v}" for k, v in self._fields.items()))) + return s + + __repr__ = __str__ diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/structures/keypoints.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/structures/keypoints.py new file mode 100644 index 0000000000000000000000000000000000000000..d0ee8724ac42087e4ec770a3dfb8e040a62b4c15 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/structures/keypoints.py @@ -0,0 +1,239 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +from typing import Any, List, Tuple, Union +import torch +from torch.nn import functional as F + + +class Keypoints: + """ + Stores keypoint **annotation** data. GT Instances have a `gt_keypoints` property + containing the x,y location and visibility flag of each keypoint. This tensor has shape + (N, K, 3) where N is the number of instances and K is the number of keypoints per instance. + + The visibility flag follows the COCO format and must be one of three integers: + + * v=0: not labeled (in which case x=y=0) + * v=1: labeled but not visible + * v=2: labeled and visible + """ + + def __init__(self, keypoints: Union[torch.Tensor, np.ndarray, List[List[float]]]): + """ + Arguments: + keypoints: A Tensor, numpy array, or list of the x, y, and visibility of each keypoint. + The shape should be (N, K, 3) where N is the number of + instances, and K is the number of keypoints per instance. + """ + device = keypoints.device if isinstance(keypoints, torch.Tensor) else torch.device("cpu") + keypoints = torch.as_tensor(keypoints, dtype=torch.float32, device=device) + assert keypoints.dim() == 3 and keypoints.shape[2] == 3, keypoints.shape + self.tensor = keypoints + + def __len__(self) -> int: + return self.tensor.size(0) + + def to(self, *args: Any, **kwargs: Any) -> "Keypoints": + return type(self)(self.tensor.to(*args, **kwargs)) + + @property + def device(self) -> torch.device: + return self.tensor.device + + def to_heatmap(self, boxes: torch.Tensor, heatmap_size: int) -> torch.Tensor: + """ + Convert keypoint annotations to a heatmap of one-hot labels for training, + as described in :paper:`Mask R-CNN`. + + Arguments: + boxes: Nx4 tensor, the boxes to draw the keypoints to + + Returns: + heatmaps: + A tensor of shape (N, K), each element is integer spatial label + in the range [0, heatmap_size**2 - 1] for each keypoint in the input. + valid: + A tensor of shape (N, K) containing whether each keypoint is in the roi or not. + """ + return _keypoints_to_heatmap(self.tensor, boxes, heatmap_size) + + def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "Keypoints": + """ + Create a new `Keypoints` by indexing on this `Keypoints`. + + The following usage are allowed: + + 1. `new_kpts = kpts[3]`: return a `Keypoints` which contains only one instance. + 2. `new_kpts = kpts[2:10]`: return a slice of key points. + 3. `new_kpts = kpts[vector]`, where vector is a torch.ByteTensor + with `length = len(kpts)`. Nonzero elements in the vector will be selected. + + Note that the returned Keypoints might share storage with this Keypoints, + subject to Pytorch's indexing semantics. + """ + if isinstance(item, int): + return Keypoints([self.tensor[item]]) + return Keypoints(self.tensor[item]) + + def __repr__(self) -> str: + s = self.__class__.__name__ + "(" + s += "num_instances={})".format(len(self.tensor)) + return s + + @staticmethod + def cat(keypoints_list: List["Keypoints"]) -> "Keypoints": + """ + Concatenates a list of Keypoints into a single Keypoints + + Arguments: + keypoints_list (list[Keypoints]) + + Returns: + Keypoints: the concatenated Keypoints + """ + assert isinstance(keypoints_list, (list, tuple)) + assert len(keypoints_list) > 0 + assert all(isinstance(keypoints, Keypoints) for keypoints in keypoints_list) + + cat_kpts = type(keypoints_list[0])( + torch.cat([kpts.tensor for kpts in keypoints_list], dim=0) + ) + return cat_kpts + + +# TODO make this nicer, this is a direct translation from C2 (but removing the inner loop) +def _keypoints_to_heatmap( + keypoints: torch.Tensor, rois: torch.Tensor, heatmap_size: int +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Encode keypoint locations into a target heatmap for use in SoftmaxWithLoss across space. + + Maps keypoints from the half-open interval [x1, x2) on continuous image coordinates to the + closed interval [0, heatmap_size - 1] on discrete image coordinates. We use the + continuous-discrete conversion from Heckbert 1990 ("What is the coordinate of a pixel?"): + d = floor(c) and c = d + 0.5, where d is a discrete coordinate and c is a continuous coordinate. + + Arguments: + keypoints: tensor of keypoint locations in of shape (N, K, 3). + rois: Nx4 tensor of rois in xyxy format + heatmap_size: integer side length of square heatmap. + + Returns: + heatmaps: A tensor of shape (N, K) containing an integer spatial label + in the range [0, heatmap_size**2 - 1] for each keypoint in the input. + valid: A tensor of shape (N, K) containing whether each keypoint is in + the roi or not. + """ + + if rois.numel() == 0: + return rois.new().long(), rois.new().long() + offset_x = rois[:, 0] + offset_y = rois[:, 1] + scale_x = heatmap_size / (rois[:, 2] - rois[:, 0]) + scale_y = heatmap_size / (rois[:, 3] - rois[:, 1]) + + offset_x = offset_x[:, None] + offset_y = offset_y[:, None] + scale_x = scale_x[:, None] + scale_y = scale_y[:, None] + + x = keypoints[..., 0] + y = keypoints[..., 1] + + x_boundary_inds = x == rois[:, 2][:, None] + y_boundary_inds = y == rois[:, 3][:, None] + + x = (x - offset_x) * scale_x + x = x.floor().long() + y = (y - offset_y) * scale_y + y = y.floor().long() + + x[x_boundary_inds] = heatmap_size - 1 + y[y_boundary_inds] = heatmap_size - 1 + + valid_loc = (x >= 0) & (y >= 0) & (x < heatmap_size) & (y < heatmap_size) + vis = keypoints[..., 2] > 0 + valid = (valid_loc & vis).long() + + lin_ind = y * heatmap_size + x + heatmaps = lin_ind * valid + + return heatmaps, valid + + +@torch.jit.script_if_tracing +def heatmaps_to_keypoints(maps: torch.Tensor, rois: torch.Tensor) -> torch.Tensor: + """ + Extract predicted keypoint locations from heatmaps. + + Args: + maps (Tensor): (#ROIs, #keypoints, POOL_H, POOL_W). The predicted heatmap of logits for + each ROI and each keypoint. + rois (Tensor): (#ROIs, 4). The box of each ROI. + + Returns: + Tensor of shape (#ROIs, #keypoints, 4) with the last dimension corresponding to + (x, y, logit, score) for each keypoint. + + When converting discrete pixel indices in an NxN image to a continuous keypoint coordinate, + we maintain consistency with :meth:`Keypoints.to_heatmap` by using the conversion from + Heckbert 1990: c = d + 0.5, where d is a discrete coordinate and c is a continuous coordinate. + """ + # The decorator use of torch.no_grad() was not supported by torchscript. + # https://github.com/pytorch/pytorch/issues/44768 + maps = maps.detach() + rois = rois.detach() + + offset_x = rois[:, 0] + offset_y = rois[:, 1] + + widths = (rois[:, 2] - rois[:, 0]).clamp(min=1) + heights = (rois[:, 3] - rois[:, 1]).clamp(min=1) + widths_ceil = widths.ceil() + heights_ceil = heights.ceil() + + num_rois, num_keypoints = maps.shape[:2] + xy_preds = maps.new_zeros(rois.shape[0], num_keypoints, 4) + + width_corrections = widths / widths_ceil + height_corrections = heights / heights_ceil + + keypoints_idx = torch.arange(num_keypoints, device=maps.device) + + for i in range(num_rois): + outsize = (int(heights_ceil[i]), int(widths_ceil[i])) + roi_map = F.interpolate( + maps[[i]], size=outsize, mode="bicubic", align_corners=False + ).squeeze( + 0 + ) # #keypoints x H x W + + # softmax over the spatial region + max_score, _ = roi_map.view(num_keypoints, -1).max(1) + max_score = max_score.view(num_keypoints, 1, 1) + tmp_full_resolution = (roi_map - max_score).exp_() + tmp_pool_resolution = (maps[i] - max_score).exp_() + # Produce scores over the region H x W, but normalize with POOL_H x POOL_W, + # so that the scores of objects of different absolute sizes will be more comparable + roi_map_scores = tmp_full_resolution / tmp_pool_resolution.sum((1, 2), keepdim=True) + + w = roi_map.shape[2] + pos = roi_map.view(num_keypoints, -1).argmax(1) + + x_int = pos % w + y_int = (pos - x_int) // w + + assert ( + roi_map_scores[keypoints_idx, y_int, x_int] + == roi_map_scores.view(num_keypoints, -1).max(1)[0] + ).all() + + x = (x_int.float() + 0.5) * width_corrections[i] + y = (y_int.float() + 0.5) * height_corrections[i] + + xy_preds[i, :, 0] = x + offset_x[i] + xy_preds[i, :, 1] = y + offset_y[i] + xy_preds[i, :, 2] = roi_map[keypoints_idx, y_int, x_int] + xy_preds[i, :, 3] = roi_map_scores[keypoints_idx, y_int, x_int] + + return xy_preds diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/structures/masks.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/structures/masks.py new file mode 100644 index 0000000000000000000000000000000000000000..8f8e72dd9f953ddd2ac1a8a301b1f990d4dd770a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/structures/masks.py @@ -0,0 +1,532 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import itertools +import numpy as np +from typing import Any, Iterator, List, Union +import pycocotools.mask as mask_util +import torch +from torch import device + +from detectron2.layers.roi_align import ROIAlign +from detectron2.utils.memory import retry_if_cuda_oom + +from .boxes import Boxes + + +def polygon_area(x, y): + # Using the shoelace formula + # https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates + return 0.5 * np.abs(np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1))) + + +def polygons_to_bitmask(polygons: List[np.ndarray], height: int, width: int) -> np.ndarray: + """ + Args: + polygons (list[ndarray]): each array has shape (Nx2,) + height, width (int) + + Returns: + ndarray: a bool mask of shape (height, width) + """ + if len(polygons) == 0: + # COCOAPI does not support empty polygons + return np.zeros((height, width)).astype(np.bool) + rles = mask_util.frPyObjects(polygons, height, width) + rle = mask_util.merge(rles) + return mask_util.decode(rle).astype(np.bool) + + +def rasterize_polygons_within_box( + polygons: List[np.ndarray], box: np.ndarray, mask_size: int +) -> torch.Tensor: + """ + Rasterize the polygons into a mask image and + crop the mask content in the given box. + The cropped mask is resized to (mask_size, mask_size). + + This function is used when generating training targets for mask head in Mask R-CNN. + Given original ground-truth masks for an image, new ground-truth mask + training targets in the size of `mask_size x mask_size` + must be provided for each predicted box. This function will be called to + produce such targets. + + Args: + polygons (list[ndarray[float]]): a list of polygons, which represents an instance. + box: 4-element numpy array + mask_size (int): + + Returns: + Tensor: BoolTensor of shape (mask_size, mask_size) + """ + # 1. Shift the polygons w.r.t the boxes + w, h = box[2] - box[0], box[3] - box[1] + + polygons = copy.deepcopy(polygons) + for p in polygons: + p[0::2] = p[0::2] - box[0] + p[1::2] = p[1::2] - box[1] + + # 2. Rescale the polygons to the new box size + # max() to avoid division by small number + ratio_h = mask_size / max(h, 0.1) + ratio_w = mask_size / max(w, 0.1) + + if ratio_h == ratio_w: + for p in polygons: + p *= ratio_h + else: + for p in polygons: + p[0::2] *= ratio_w + p[1::2] *= ratio_h + + # 3. Rasterize the polygons with coco api + mask = polygons_to_bitmask(polygons, mask_size, mask_size) + mask = torch.from_numpy(mask) + return mask + + +class BitMasks: + """ + This class stores the segmentation masks for all objects in one image, in + the form of bitmaps. + + Attributes: + tensor: bool Tensor of N,H,W, representing N instances in the image. + """ + + def __init__(self, tensor: Union[torch.Tensor, np.ndarray]): + """ + Args: + tensor: bool Tensor of N,H,W, representing N instances in the image. + """ + device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device("cpu") + tensor = torch.as_tensor(tensor, dtype=torch.bool, device=device) + assert tensor.dim() == 3, tensor.size() + self.image_size = tensor.shape[1:] + self.tensor = tensor + + @torch.jit.unused + def to(self, *args: Any, **kwargs: Any) -> "BitMasks": + return BitMasks(self.tensor.to(*args, **kwargs)) + + @property + def device(self) -> torch.device: + return self.tensor.device + + @torch.jit.unused + def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "BitMasks": + """ + Returns: + BitMasks: Create a new :class:`BitMasks` by indexing. + + The following usage are allowed: + + 1. `new_masks = masks[3]`: return a `BitMasks` which contains only one mask. + 2. `new_masks = masks[2:10]`: return a slice of masks. + 3. `new_masks = masks[vector]`, where vector is a torch.BoolTensor + with `length = len(masks)`. Nonzero elements in the vector will be selected. + + Note that the returned object might share storage with this object, + subject to Pytorch's indexing semantics. + """ + if isinstance(item, int): + return BitMasks(self.tensor[item].unsqueeze(0)) + m = self.tensor[item] + assert m.dim() == 3, "Indexing on BitMasks with {} returns a tensor with shape {}!".format( + item, m.shape + ) + return BitMasks(m) + + @torch.jit.unused + def __iter__(self) -> torch.Tensor: + yield from self.tensor + + @torch.jit.unused + def __repr__(self) -> str: + s = self.__class__.__name__ + "(" + s += "num_instances={})".format(len(self.tensor)) + return s + + def __len__(self) -> int: + return self.tensor.shape[0] + + def nonempty(self) -> torch.Tensor: + """ + Find masks that are non-empty. + + Returns: + Tensor: a BoolTensor which represents + whether each mask is empty (False) or non-empty (True). + """ + return self.tensor.flatten(1).any(dim=1) + + @staticmethod + def from_polygon_masks( + polygon_masks: Union["PolygonMasks", List[List[np.ndarray]]], height: int, width: int + ) -> "BitMasks": + """ + Args: + polygon_masks (list[list[ndarray]] or PolygonMasks) + height, width (int) + """ + if isinstance(polygon_masks, PolygonMasks): + polygon_masks = polygon_masks.polygons + masks = [polygons_to_bitmask(p, height, width) for p in polygon_masks] + if len(masks): + return BitMasks(torch.stack([torch.from_numpy(x) for x in masks])) + else: + return BitMasks(torch.empty(0, height, width, dtype=torch.bool)) + + @staticmethod + def from_roi_masks(roi_masks: "ROIMasks", height: int, width: int) -> "BitMasks": + """ + Args: + roi_masks: + height, width (int): + """ + return roi_masks.to_bitmasks(height, width) + + def crop_and_resize(self, boxes: torch.Tensor, mask_size: int) -> torch.Tensor: + """ + Crop each bitmask by the given box, and resize results to (mask_size, mask_size). + This can be used to prepare training targets for Mask R-CNN. + It has less reconstruction error compared to rasterization with polygons. + However we observe no difference in accuracy, + but BitMasks requires more memory to store all the masks. + + Args: + boxes (Tensor): Nx4 tensor storing the boxes for each mask + mask_size (int): the size of the rasterized mask. + + Returns: + Tensor: + A bool tensor of shape (N, mask_size, mask_size), where + N is the number of predicted boxes for this image. + """ + assert len(boxes) == len(self), "{} != {}".format(len(boxes), len(self)) + device = self.tensor.device + + batch_inds = torch.arange(len(boxes), device=device).to(dtype=boxes.dtype)[:, None] + rois = torch.cat([batch_inds, boxes], dim=1) # Nx5 + + bit_masks = self.tensor.to(dtype=torch.float32) + rois = rois.to(device=device) + output = ( + ROIAlign((mask_size, mask_size), 1.0, 0, aligned=True) + .forward(bit_masks[:, None, :, :], rois) + .squeeze(1) + ) + output = output >= 0.5 + return output + + def get_bounding_boxes(self) -> Boxes: + """ + Returns: + Boxes: tight bounding boxes around bitmasks. + If a mask is empty, it's bounding box will be all zero. + """ + boxes = torch.zeros(self.tensor.shape[0], 4, dtype=torch.float32) + x_any = torch.any(self.tensor, dim=1) + y_any = torch.any(self.tensor, dim=2) + for idx in range(self.tensor.shape[0]): + x = torch.where(x_any[idx, :])[0] + y = torch.where(y_any[idx, :])[0] + if len(x) > 0 and len(y) > 0: + boxes[idx, :] = torch.as_tensor( + [x[0], y[0], x[-1] + 1, y[-1] + 1], dtype=torch.float32 + ) + return Boxes(boxes) + + @staticmethod + def cat(bitmasks_list: List["BitMasks"]) -> "BitMasks": + """ + Concatenates a list of BitMasks into a single BitMasks + + Arguments: + bitmasks_list (list[BitMasks]) + + Returns: + BitMasks: the concatenated BitMasks + """ + assert isinstance(bitmasks_list, (list, tuple)) + assert len(bitmasks_list) > 0 + assert all(isinstance(bitmask, BitMasks) for bitmask in bitmasks_list) + + cat_bitmasks = type(bitmasks_list[0])(torch.cat([bm.tensor for bm in bitmasks_list], dim=0)) + return cat_bitmasks + + +class PolygonMasks: + """ + This class stores the segmentation masks for all objects in one image, in the form of polygons. + + Attributes: + polygons: list[list[ndarray]]. Each ndarray is a float64 vector representing a polygon. + """ + + def __init__(self, polygons: List[List[Union[torch.Tensor, np.ndarray]]]): + """ + Arguments: + polygons (list[list[np.ndarray]]): The first + level of the list correspond to individual instances, + the second level to all the polygons that compose the + instance, and the third level to the polygon coordinates. + The third level array should have the format of + [x0, y0, x1, y1, ..., xn, yn] (n >= 3). + """ + if not isinstance(polygons, list): + raise ValueError( + "Cannot create PolygonMasks: Expect a list of list of polygons per image. " + "Got '{}' instead.".format(type(polygons)) + ) + + def _make_array(t: Union[torch.Tensor, np.ndarray]) -> np.ndarray: + # Use float64 for higher precision, because why not? + # Always put polygons on CPU (self.to is a no-op) since they + # are supposed to be small tensors. + # May need to change this assumption if GPU placement becomes useful + if isinstance(t, torch.Tensor): + t = t.cpu().numpy() + return np.asarray(t).astype("float64") + + def process_polygons( + polygons_per_instance: List[Union[torch.Tensor, np.ndarray]] + ) -> List[np.ndarray]: + if not isinstance(polygons_per_instance, list): + raise ValueError( + "Cannot create polygons: Expect a list of polygons per instance. " + "Got '{}' instead.".format(type(polygons_per_instance)) + ) + # transform each polygon to a numpy array + polygons_per_instance = [_make_array(p) for p in polygons_per_instance] + for polygon in polygons_per_instance: + if len(polygon) % 2 != 0 or len(polygon) < 6: + raise ValueError(f"Cannot create a polygon from {len(polygon)} coordinates.") + return polygons_per_instance + + self.polygons: List[List[np.ndarray]] = [ + process_polygons(polygons_per_instance) for polygons_per_instance in polygons + ] + + def to(self, *args: Any, **kwargs: Any) -> "PolygonMasks": + return self + + @property + def device(self) -> torch.device: + return torch.device("cpu") + + def get_bounding_boxes(self) -> Boxes: + """ + Returns: + Boxes: tight bounding boxes around polygon masks. + """ + boxes = torch.zeros(len(self.polygons), 4, dtype=torch.float32) + for idx, polygons_per_instance in enumerate(self.polygons): + minxy = torch.as_tensor([float("inf"), float("inf")], dtype=torch.float32) + maxxy = torch.zeros(2, dtype=torch.float32) + for polygon in polygons_per_instance: + coords = torch.from_numpy(polygon).view(-1, 2).to(dtype=torch.float32) + minxy = torch.min(minxy, torch.min(coords, dim=0).values) + maxxy = torch.max(maxxy, torch.max(coords, dim=0).values) + boxes[idx, :2] = minxy + boxes[idx, 2:] = maxxy + return Boxes(boxes) + + def nonempty(self) -> torch.Tensor: + """ + Find masks that are non-empty. + + Returns: + Tensor: + a BoolTensor which represents whether each mask is empty (False) or not (True). + """ + keep = [1 if len(polygon) > 0 else 0 for polygon in self.polygons] + return torch.from_numpy(np.asarray(keep, dtype=np.bool)) + + def __getitem__(self, item: Union[int, slice, List[int], torch.BoolTensor]) -> "PolygonMasks": + """ + Support indexing over the instances and return a `PolygonMasks` object. + `item` can be: + + 1. An integer. It will return an object with only one instance. + 2. A slice. It will return an object with the selected instances. + 3. A list[int]. It will return an object with the selected instances, + correpsonding to the indices in the list. + 4. A vector mask of type BoolTensor, whose length is num_instances. + It will return an object with the instances whose mask is nonzero. + """ + if isinstance(item, int): + selected_polygons = [self.polygons[item]] + elif isinstance(item, slice): + selected_polygons = self.polygons[item] + elif isinstance(item, list): + selected_polygons = [self.polygons[i] for i in item] + elif isinstance(item, torch.Tensor): + # Polygons is a list, so we have to move the indices back to CPU. + if item.dtype == torch.bool: + assert item.dim() == 1, item.shape + item = item.nonzero().squeeze(1).cpu().numpy().tolist() + elif item.dtype in [torch.int32, torch.int64]: + item = item.cpu().numpy().tolist() + else: + raise ValueError("Unsupported tensor dtype={} for indexing!".format(item.dtype)) + selected_polygons = [self.polygons[i] for i in item] + return PolygonMasks(selected_polygons) + + def __iter__(self) -> Iterator[List[np.ndarray]]: + """ + Yields: + list[ndarray]: the polygons for one instance. + Each Tensor is a float64 vector representing a polygon. + """ + return iter(self.polygons) + + def __repr__(self) -> str: + s = self.__class__.__name__ + "(" + s += "num_instances={})".format(len(self.polygons)) + return s + + def __len__(self) -> int: + return len(self.polygons) + + def crop_and_resize(self, boxes: torch.Tensor, mask_size: int) -> torch.Tensor: + """ + Crop each mask by the given box, and resize results to (mask_size, mask_size). + This can be used to prepare training targets for Mask R-CNN. + + Args: + boxes (Tensor): Nx4 tensor storing the boxes for each mask + mask_size (int): the size of the rasterized mask. + + Returns: + Tensor: A bool tensor of shape (N, mask_size, mask_size), where + N is the number of predicted boxes for this image. + """ + assert len(boxes) == len(self), "{} != {}".format(len(boxes), len(self)) + + device = boxes.device + # Put boxes on the CPU, as the polygon representation is not efficient GPU-wise + # (several small tensors for representing a single instance mask) + boxes = boxes.to(torch.device("cpu")) + + results = [ + rasterize_polygons_within_box(poly, box.numpy(), mask_size) + for poly, box in zip(self.polygons, boxes) + ] + """ + poly: list[list[float]], the polygons for one instance + box: a tensor of shape (4,) + """ + if len(results) == 0: + return torch.empty(0, mask_size, mask_size, dtype=torch.bool, device=device) + return torch.stack(results, dim=0).to(device=device) + + def area(self): + """ + Computes area of the mask. + Only works with Polygons, using the shoelace formula: + https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates + + Returns: + Tensor: a vector, area for each instance + """ + + area = [] + for polygons_per_instance in self.polygons: + area_per_instance = 0 + for p in polygons_per_instance: + area_per_instance += polygon_area(p[0::2], p[1::2]) + area.append(area_per_instance) + + return torch.tensor(area) + + @staticmethod + def cat(polymasks_list: List["PolygonMasks"]) -> "PolygonMasks": + """ + Concatenates a list of PolygonMasks into a single PolygonMasks + + Arguments: + polymasks_list (list[PolygonMasks]) + + Returns: + PolygonMasks: the concatenated PolygonMasks + """ + assert isinstance(polymasks_list, (list, tuple)) + assert len(polymasks_list) > 0 + assert all(isinstance(polymask, PolygonMasks) for polymask in polymasks_list) + + cat_polymasks = type(polymasks_list[0])( + list(itertools.chain.from_iterable(pm.polygons for pm in polymasks_list)) + ) + return cat_polymasks + + +class ROIMasks: + """ + Represent masks by N smaller masks defined in some ROIs. Once ROI boxes are given, + full-image bitmask can be obtained by "pasting" the mask on the region defined + by the corresponding ROI box. + """ + + def __init__(self, tensor: torch.Tensor): + """ + Args: + tensor: (N, M, M) mask tensor that defines the mask within each ROI. + """ + if tensor.dim() != 3: + raise ValueError("ROIMasks must take a masks of 3 dimension.") + self.tensor = tensor + + def to(self, device: torch.device) -> "ROIMasks": + return ROIMasks(self.tensor.to(device)) + + @property + def device(self) -> device: + return self.tensor.device + + def __len__(self): + return self.tensor.shape[0] + + def __getitem__(self, item) -> "ROIMasks": + """ + Returns: + ROIMasks: Create a new :class:`ROIMasks` by indexing. + + The following usage are allowed: + + 1. `new_masks = masks[2:10]`: return a slice of masks. + 2. `new_masks = masks[vector]`, where vector is a torch.BoolTensor + with `length = len(masks)`. Nonzero elements in the vector will be selected. + + Note that the returned object might share storage with this object, + subject to Pytorch's indexing semantics. + """ + t = self.tensor[item] + if t.dim() != 3: + raise ValueError( + f"Indexing on ROIMasks with {item} returns a tensor with shape {t.shape}!" + ) + return ROIMasks(t) + + @torch.jit.unused + def __repr__(self) -> str: + s = self.__class__.__name__ + "(" + s += "num_instances={})".format(len(self.tensor)) + return s + + @torch.jit.unused + def to_bitmasks(self, boxes: torch.Tensor, height, width, threshold=0.5): + """ + Args: see documentation of :func:`paste_masks_in_image`. + """ + from detectron2.layers.mask_ops import paste_masks_in_image, _paste_masks_tensor_shape + + if torch.jit.is_tracing(): + if isinstance(height, torch.Tensor): + paste_func = _paste_masks_tensor_shape + else: + paste_func = paste_masks_in_image + else: + paste_func = retry_if_cuda_oom(paste_masks_in_image) + bitmasks = paste_func(self.tensor, boxes.tensor, (height, width), threshold=threshold) + return BitMasks(bitmasks) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/structures/rotated_boxes.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/structures/rotated_boxes.py new file mode 100644 index 0000000000000000000000000000000000000000..4ec8e4c7e3f8eb9173fa21db3ca0bc29fd96834a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/structures/rotated_boxes.py @@ -0,0 +1,503 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import math +from typing import List, Tuple +import torch + +from detectron2.layers.rotated_boxes import pairwise_iou_rotated + +from .boxes import Boxes + + +class RotatedBoxes(Boxes): + """ + This structure stores a list of rotated boxes as a Nx5 torch.Tensor. + It supports some common methods about boxes + (`area`, `clip`, `nonempty`, etc), + and also behaves like a Tensor + (support indexing, `to(device)`, `.device`, and iteration over all boxes) + """ + + def __init__(self, tensor: torch.Tensor): + """ + Args: + tensor (Tensor[float]): a Nx5 matrix. Each row is + (x_center, y_center, width, height, angle), + in which angle is represented in degrees. + While there's no strict range restriction for it, + the recommended principal range is between [-180, 180) degrees. + + Assume we have a horizontal box B = (x_center, y_center, width, height), + where width is along the x-axis and height is along the y-axis. + The rotated box B_rot (x_center, y_center, width, height, angle) + can be seen as: + + 1. When angle == 0: + B_rot == B + 2. When angle > 0: + B_rot is obtained by rotating B w.r.t its center by :math:`|angle|` degrees CCW; + 3. When angle < 0: + B_rot is obtained by rotating B w.r.t its center by :math:`|angle|` degrees CW. + + Mathematically, since the right-handed coordinate system for image space + is (y, x), where y is top->down and x is left->right, the 4 vertices of the + rotated rectangle :math:`(yr_i, xr_i)` (i = 1, 2, 3, 4) can be obtained from + the vertices of the horizontal rectangle :math:`(y_i, x_i)` (i = 1, 2, 3, 4) + in the following way (:math:`\\theta = angle*\\pi/180` is the angle in radians, + :math:`(y_c, x_c)` is the center of the rectangle): + + .. math:: + + yr_i = \\cos(\\theta) (y_i - y_c) - \\sin(\\theta) (x_i - x_c) + y_c, + + xr_i = \\sin(\\theta) (y_i - y_c) + \\cos(\\theta) (x_i - x_c) + x_c, + + which is the standard rigid-body rotation transformation. + + Intuitively, the angle is + (1) the rotation angle from y-axis in image space + to the height vector (top->down in the box's local coordinate system) + of the box in CCW, and + (2) the rotation angle from x-axis in image space + to the width vector (left->right in the box's local coordinate system) + of the box in CCW. + + More intuitively, consider the following horizontal box ABCD represented + in (x1, y1, x2, y2): (3, 2, 7, 4), + covering the [3, 7] x [2, 4] region of the continuous coordinate system + which looks like this: + + .. code:: none + + O--------> x + | + | A---B + | | | + | D---C + | + v y + + Note that each capital letter represents one 0-dimensional geometric point + instead of a 'square pixel' here. + + In the example above, using (x, y) to represent a point we have: + + .. math:: + + O = (0, 0), A = (3, 2), B = (7, 2), C = (7, 4), D = (3, 4) + + We name vector AB = vector DC as the width vector in box's local coordinate system, and + vector AD = vector BC as the height vector in box's local coordinate system. Initially, + when angle = 0 degree, they're aligned with the positive directions of x-axis and y-axis + in the image space, respectively. + + For better illustration, we denote the center of the box as E, + + .. code:: none + + O--------> x + | + | A---B + | | E | + | D---C + | + v y + + where the center E = ((3+7)/2, (2+4)/2) = (5, 3). + + Also, + + .. math:: + + width = |AB| = |CD| = 7 - 3 = 4, + height = |AD| = |BC| = 4 - 2 = 2. + + Therefore, the corresponding representation for the same shape in rotated box in + (x_center, y_center, width, height, angle) format is: + + (5, 3, 4, 2, 0), + + Now, let's consider (5, 3, 4, 2, 90), which is rotated by 90 degrees + CCW (counter-clockwise) by definition. It looks like this: + + .. code:: none + + O--------> x + | B-C + | | | + | |E| + | | | + | A-D + v y + + The center E is still located at the same point (5, 3), while the vertices + ABCD are rotated by 90 degrees CCW with regard to E: + A = (4, 5), B = (4, 1), C = (6, 1), D = (6, 5) + + Here, 90 degrees can be seen as the CCW angle to rotate from y-axis to + vector AD or vector BC (the top->down height vector in box's local coordinate system), + or the CCW angle to rotate from x-axis to vector AB or vector DC (the left->right + width vector in box's local coordinate system). + + .. math:: + + width = |AB| = |CD| = 5 - 1 = 4, + height = |AD| = |BC| = 6 - 4 = 2. + + Next, how about (5, 3, 4, 2, -90), which is rotated by 90 degrees CW (clockwise) + by definition? It looks like this: + + .. code:: none + + O--------> x + | D-A + | | | + | |E| + | | | + | C-B + v y + + The center E is still located at the same point (5, 3), while the vertices + ABCD are rotated by 90 degrees CW with regard to E: + A = (6, 1), B = (6, 5), C = (4, 5), D = (4, 1) + + .. math:: + + width = |AB| = |CD| = 5 - 1 = 4, + height = |AD| = |BC| = 6 - 4 = 2. + + This covers exactly the same region as (5, 3, 4, 2, 90) does, and their IoU + will be 1. However, these two will generate different RoI Pooling results and + should not be treated as an identical box. + + On the other hand, it's easy to see that (X, Y, W, H, A) is identical to + (X, Y, W, H, A+360N), for any integer N. For example (5, 3, 4, 2, 270) would be + identical to (5, 3, 4, 2, -90), because rotating the shape 270 degrees CCW is + equivalent to rotating the same shape 90 degrees CW. + + We could rotate further to get (5, 3, 4, 2, 180), or (5, 3, 4, 2, -180): + + .. code:: none + + O--------> x + | + | C---D + | | E | + | B---A + | + v y + + .. math:: + + A = (7, 4), B = (3, 4), C = (3, 2), D = (7, 2), + + width = |AB| = |CD| = 7 - 3 = 4, + height = |AD| = |BC| = 4 - 2 = 2. + + Finally, this is a very inaccurate (heavily quantized) illustration of + how (5, 3, 4, 2, 60) looks like in case anyone wonders: + + .. code:: none + + O--------> x + | B\ + | / C + | /E / + | A / + | `D + v y + + It's still a rectangle with center of (5, 3), width of 4 and height of 2, + but its angle (and thus orientation) is somewhere between + (5, 3, 4, 2, 0) and (5, 3, 4, 2, 90). + """ + device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device("cpu") + tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device) + if tensor.numel() == 0: + # Use reshape, so we don't end up creating a new tensor that does not depend on + # the inputs (and consequently confuses jit) + tensor = tensor.reshape((0, 5)).to(dtype=torch.float32, device=device) + assert tensor.dim() == 2 and tensor.size(-1) == 5, tensor.size() + + self.tensor = tensor + + def clone(self) -> "RotatedBoxes": + """ + Clone the RotatedBoxes. + + Returns: + RotatedBoxes + """ + return RotatedBoxes(self.tensor.clone()) + + def to(self, device: torch.device): + # Boxes are assumed float32 and does not support to(dtype) + return RotatedBoxes(self.tensor.to(device=device)) + + def area(self) -> torch.Tensor: + """ + Computes the area of all the boxes. + + Returns: + torch.Tensor: a vector with areas of each box. + """ + box = self.tensor + area = box[:, 2] * box[:, 3] + return area + + def normalize_angles(self) -> None: + """ + Restrict angles to the range of [-180, 180) degrees + """ + self.tensor[:, 4] = (self.tensor[:, 4] + 180.0) % 360.0 - 180.0 + + def clip(self, box_size: Tuple[int, int], clip_angle_threshold: float = 1.0) -> None: + """ + Clip (in place) the boxes by limiting x coordinates to the range [0, width] + and y coordinates to the range [0, height]. + + For RRPN: + Only clip boxes that are almost horizontal with a tolerance of + clip_angle_threshold to maintain backward compatibility. + + Rotated boxes beyond this threshold are not clipped for two reasons: + + 1. There are potentially multiple ways to clip a rotated box to make it + fit within the image. + 2. It's tricky to make the entire rectangular box fit within the image + and still be able to not leave out pixels of interest. + + Therefore we rely on ops like RoIAlignRotated to safely handle this. + + Args: + box_size (height, width): The clipping box's size. + clip_angle_threshold: + Iff. abs(normalized(angle)) <= clip_angle_threshold (in degrees), + we do the clipping as horizontal boxes. + """ + h, w = box_size + + # normalize angles to be within (-180, 180] degrees + self.normalize_angles() + + idx = torch.where(torch.abs(self.tensor[:, 4]) <= clip_angle_threshold)[0] + + # convert to (x1, y1, x2, y2) + x1 = self.tensor[idx, 0] - self.tensor[idx, 2] / 2.0 + y1 = self.tensor[idx, 1] - self.tensor[idx, 3] / 2.0 + x2 = self.tensor[idx, 0] + self.tensor[idx, 2] / 2.0 + y2 = self.tensor[idx, 1] + self.tensor[idx, 3] / 2.0 + + # clip + x1.clamp_(min=0, max=w) + y1.clamp_(min=0, max=h) + x2.clamp_(min=0, max=w) + y2.clamp_(min=0, max=h) + + # convert back to (xc, yc, w, h) + self.tensor[idx, 0] = (x1 + x2) / 2.0 + self.tensor[idx, 1] = (y1 + y2) / 2.0 + # make sure widths and heights do not increase due to numerical errors + self.tensor[idx, 2] = torch.min(self.tensor[idx, 2], x2 - x1) + self.tensor[idx, 3] = torch.min(self.tensor[idx, 3], y2 - y1) + + def nonempty(self, threshold: float = 0.0) -> torch.Tensor: + """ + Find boxes that are non-empty. + A box is considered empty, if either of its side is no larger than threshold. + + Returns: + Tensor: a binary vector which represents + whether each box is empty (False) or non-empty (True). + """ + box = self.tensor + widths = box[:, 2] + heights = box[:, 3] + keep = (widths > threshold) & (heights > threshold) + return keep + + def __getitem__(self, item) -> "RotatedBoxes": + """ + Returns: + RotatedBoxes: Create a new :class:`RotatedBoxes` by indexing. + + The following usage are allowed: + + 1. `new_boxes = boxes[3]`: return a `RotatedBoxes` which contains only one box. + 2. `new_boxes = boxes[2:10]`: return a slice of boxes. + 3. `new_boxes = boxes[vector]`, where vector is a torch.ByteTensor + with `length = len(boxes)`. Nonzero elements in the vector will be selected. + + Note that the returned RotatedBoxes might share storage with this RotatedBoxes, + subject to Pytorch's indexing semantics. + """ + if isinstance(item, int): + return RotatedBoxes(self.tensor[item].view(1, -1)) + b = self.tensor[item] + assert b.dim() == 2, "Indexing on RotatedBoxes with {} failed to return a matrix!".format( + item + ) + return RotatedBoxes(b) + + def __len__(self) -> int: + return self.tensor.shape[0] + + def __repr__(self) -> str: + return "RotatedBoxes(" + str(self.tensor) + ")" + + def inside_box(self, box_size: Tuple[int, int], boundary_threshold: int = 0) -> torch.Tensor: + """ + Args: + box_size (height, width): Size of the reference box covering + [0, width] x [0, height] + boundary_threshold (int): Boxes that extend beyond the reference box + boundary by more than boundary_threshold are considered "outside". + + For RRPN, it might not be necessary to call this function since it's common + for rotated box to extend to outside of the image boundaries + (the clip function only clips the near-horizontal boxes) + + Returns: + a binary vector, indicating whether each box is inside the reference box. + """ + height, width = box_size + + cnt_x = self.tensor[..., 0] + cnt_y = self.tensor[..., 1] + half_w = self.tensor[..., 2] / 2.0 + half_h = self.tensor[..., 3] / 2.0 + a = self.tensor[..., 4] + c = torch.abs(torch.cos(a * math.pi / 180.0)) + s = torch.abs(torch.sin(a * math.pi / 180.0)) + # This basically computes the horizontal bounding rectangle of the rotated box + max_rect_dx = c * half_w + s * half_h + max_rect_dy = c * half_h + s * half_w + + inds_inside = ( + (cnt_x - max_rect_dx >= -boundary_threshold) + & (cnt_y - max_rect_dy >= -boundary_threshold) + & (cnt_x + max_rect_dx < width + boundary_threshold) + & (cnt_y + max_rect_dy < height + boundary_threshold) + ) + + return inds_inside + + def get_centers(self) -> torch.Tensor: + """ + Returns: + The box centers in a Nx2 array of (x, y). + """ + return self.tensor[:, :2] + + def scale(self, scale_x: float, scale_y: float) -> None: + """ + Scale the rotated box with horizontal and vertical scaling factors + Note: when scale_factor_x != scale_factor_y, + the rotated box does not preserve the rectangular shape when the angle + is not a multiple of 90 degrees under resize transformation. + Instead, the shape is a parallelogram (that has skew) + Here we make an approximation by fitting a rotated rectangle to the parallelogram. + """ + self.tensor[:, 0] *= scale_x + self.tensor[:, 1] *= scale_y + theta = self.tensor[:, 4] * math.pi / 180.0 + c = torch.cos(theta) + s = torch.sin(theta) + + # In image space, y is top->down and x is left->right + # Consider the local coordintate system for the rotated box, + # where the box center is located at (0, 0), and the four vertices ABCD are + # A(-w / 2, -h / 2), B(w / 2, -h / 2), C(w / 2, h / 2), D(-w / 2, h / 2) + # the midpoint of the left edge AD of the rotated box E is: + # E = (A+D)/2 = (-w / 2, 0) + # the midpoint of the top edge AB of the rotated box F is: + # F(0, -h / 2) + # To get the old coordinates in the global system, apply the rotation transformation + # (Note: the right-handed coordinate system for image space is yOx): + # (old_x, old_y) = (s * y + c * x, c * y - s * x) + # E(old) = (s * 0 + c * (-w/2), c * 0 - s * (-w/2)) = (-c * w / 2, s * w / 2) + # F(old) = (s * (-h / 2) + c * 0, c * (-h / 2) - s * 0) = (-s * h / 2, -c * h / 2) + # After applying the scaling factor (sfx, sfy): + # E(new) = (-sfx * c * w / 2, sfy * s * w / 2) + # F(new) = (-sfx * s * h / 2, -sfy * c * h / 2) + # The new width after scaling tranformation becomes: + + # w(new) = |E(new) - O| * 2 + # = sqrt[(sfx * c * w / 2)^2 + (sfy * s * w / 2)^2] * 2 + # = sqrt[(sfx * c)^2 + (sfy * s)^2] * w + # i.e., scale_factor_w = sqrt[(sfx * c)^2 + (sfy * s)^2] + # + # For example, + # when angle = 0 or 180, |c| = 1, s = 0, scale_factor_w == scale_factor_x; + # when |angle| = 90, c = 0, |s| = 1, scale_factor_w == scale_factor_y + self.tensor[:, 2] *= torch.sqrt((scale_x * c) ** 2 + (scale_y * s) ** 2) + + # h(new) = |F(new) - O| * 2 + # = sqrt[(sfx * s * h / 2)^2 + (sfy * c * h / 2)^2] * 2 + # = sqrt[(sfx * s)^2 + (sfy * c)^2] * h + # i.e., scale_factor_h = sqrt[(sfx * s)^2 + (sfy * c)^2] + # + # For example, + # when angle = 0 or 180, |c| = 1, s = 0, scale_factor_h == scale_factor_y; + # when |angle| = 90, c = 0, |s| = 1, scale_factor_h == scale_factor_x + self.tensor[:, 3] *= torch.sqrt((scale_x * s) ** 2 + (scale_y * c) ** 2) + + # The angle is the rotation angle from y-axis in image space to the height + # vector (top->down in the box's local coordinate system) of the box in CCW. + # + # angle(new) = angle_yOx(O - F(new)) + # = angle_yOx( (sfx * s * h / 2, sfy * c * h / 2) ) + # = atan2(sfx * s * h / 2, sfy * c * h / 2) + # = atan2(sfx * s, sfy * c) + # + # For example, + # when sfx == sfy, angle(new) == atan2(s, c) == angle(old) + self.tensor[:, 4] = torch.atan2(scale_x * s, scale_y * c) * 180 / math.pi + + @classmethod + def cat(cls, boxes_list: List["RotatedBoxes"]) -> "RotatedBoxes": + """ + Concatenates a list of RotatedBoxes into a single RotatedBoxes + + Arguments: + boxes_list (list[RotatedBoxes]) + + Returns: + RotatedBoxes: the concatenated RotatedBoxes + """ + assert isinstance(boxes_list, (list, tuple)) + if len(boxes_list) == 0: + return cls(torch.empty(0)) + assert all([isinstance(box, RotatedBoxes) for box in boxes_list]) + + # use torch.cat (v.s. layers.cat) so the returned boxes never share storage with input + cat_boxes = cls(torch.cat([b.tensor for b in boxes_list], dim=0)) + return cat_boxes + + @property + def device(self) -> torch.device: + return self.tensor.device + + @torch.jit.unused + def __iter__(self): + """ + Yield a box as a Tensor of shape (5,) at a time. + """ + yield from self.tensor + + +def pairwise_iou(boxes1: RotatedBoxes, boxes2: RotatedBoxes) -> None: + """ + Given two lists of rotated boxes of size N and M, + compute the IoU (intersection over union) + between **all** N x M pairs of boxes. + The box order must be (x_center, y_center, width, height, angle). + + Args: + boxes1, boxes2 (RotatedBoxes): + two `RotatedBoxes`. Contains N & M rotated boxes, respectively. + + Returns: + Tensor: IoU, sized [N,M]. + """ + + return pairwise_iou_rotated(boxes1.tensor, boxes2.tensor) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/README.md b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9765b24a730b77556104187ac3ef5439ab0859fd --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/README.md @@ -0,0 +1,5 @@ +# Utility functions + +This folder contain utility functions that are not used in the +core library, but are useful for building models or training +code using the config system. diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9020c2df23e2af280b7bb168b996ae9eaf312eb8 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/analysis.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/analysis.py new file mode 100644 index 0000000000000000000000000000000000000000..178da7968cc08c29ec61b823bba8b74e8d97e1d6 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/analysis.py @@ -0,0 +1,188 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# -*- coding: utf-8 -*- + +import typing +from typing import Any, List +import fvcore +from fvcore.nn import activation_count, flop_count, parameter_count, parameter_count_table +from torch import nn + +from detectron2.export import TracingAdapter + +__all__ = [ + "activation_count_operators", + "flop_count_operators", + "parameter_count_table", + "parameter_count", + "FlopCountAnalysis", +] + +FLOPS_MODE = "flops" +ACTIVATIONS_MODE = "activations" + + +# Some extra ops to ignore from counting, including elementwise and reduction ops +_IGNORED_OPS = { + "aten::add", + "aten::add_", + "aten::argmax", + "aten::argsort", + "aten::batch_norm", + "aten::constant_pad_nd", + "aten::div", + "aten::div_", + "aten::exp", + "aten::log2", + "aten::max_pool2d", + "aten::meshgrid", + "aten::mul", + "aten::mul_", + "aten::neg", + "aten::nonzero_numpy", + "aten::reciprocal", + "aten::repeat_interleave", + "aten::rsub", + "aten::sigmoid", + "aten::sigmoid_", + "aten::softmax", + "aten::sort", + "aten::sqrt", + "aten::sub", + "torchvision::nms", # TODO estimate flop for nms +} + + +class FlopCountAnalysis(fvcore.nn.FlopCountAnalysis): + """ + Same as :class:`fvcore.nn.FlopCountAnalysis`, but supports detectron2 models. + """ + + def __init__(self, model, inputs): + """ + Args: + model (nn.Module): + inputs (Any): inputs of the given model. Does not have to be tuple of tensors. + """ + wrapper = TracingAdapter(model, inputs, allow_non_tensor=True) + super().__init__(wrapper, wrapper.flattened_inputs) + self.set_op_handle(**{k: None for k in _IGNORED_OPS}) + + +def flop_count_operators(model: nn.Module, inputs: list) -> typing.DefaultDict[str, float]: + """ + Implement operator-level flops counting using jit. + This is a wrapper of :func:`fvcore.nn.flop_count` and adds supports for standard + detection models in detectron2. + Please use :class:`FlopCountAnalysis` for more advanced functionalities. + + Note: + The function runs the input through the model to compute flops. + The flops of a detection model is often input-dependent, for example, + the flops of box & mask head depends on the number of proposals & + the number of detected objects. + Therefore, the flops counting using a single input may not accurately + reflect the computation cost of a model. It's recommended to average + across a number of inputs. + + Args: + model: a detectron2 model that takes `list[dict]` as input. + inputs (list[dict]): inputs to model, in detectron2's standard format. + Only "image" key will be used. + supported_ops (dict[str, Handle]): see documentation of :func:`fvcore.nn.flop_count` + + Returns: + Counter: Gflop count per operator + """ + old_train = model.training + model.eval() + ret = FlopCountAnalysis(model, inputs).by_operator() + model.train(old_train) + return {k: v / 1e9 for k, v in ret.items()} + + +def activation_count_operators( + model: nn.Module, inputs: list, **kwargs +) -> typing.DefaultDict[str, float]: + """ + Implement operator-level activations counting using jit. + This is a wrapper of fvcore.nn.activation_count, that supports standard detection models + in detectron2. + + Note: + The function runs the input through the model to compute activations. + The activations of a detection model is often input-dependent, for example, + the activations of box & mask head depends on the number of proposals & + the number of detected objects. + + Args: + model: a detectron2 model that takes `list[dict]` as input. + inputs (list[dict]): inputs to model, in detectron2's standard format. + Only "image" key will be used. + + Returns: + Counter: activation count per operator + """ + return _wrapper_count_operators(model=model, inputs=inputs, mode=ACTIVATIONS_MODE, **kwargs) + + +def _wrapper_count_operators( + model: nn.Module, inputs: list, mode: str, **kwargs +) -> typing.DefaultDict[str, float]: + # ignore some ops + supported_ops = {k: lambda *args, **kwargs: {} for k in _IGNORED_OPS} + supported_ops.update(kwargs.pop("supported_ops", {})) + kwargs["supported_ops"] = supported_ops + + assert len(inputs) == 1, "Please use batch size=1" + tensor_input = inputs[0]["image"] + inputs = [{"image": tensor_input}] # remove other keys, in case there are any + + old_train = model.training + if isinstance(model, (nn.parallel.distributed.DistributedDataParallel, nn.DataParallel)): + model = model.module + wrapper = TracingAdapter(model, inputs) + wrapper.eval() + if mode == FLOPS_MODE: + ret = flop_count(wrapper, (tensor_input,), **kwargs) + elif mode == ACTIVATIONS_MODE: + ret = activation_count(wrapper, (tensor_input,), **kwargs) + else: + raise NotImplementedError("Count for mode {} is not supported yet.".format(mode)) + # compatible with change in fvcore + if isinstance(ret, tuple): + ret = ret[0] + model.train(old_train) + return ret + + +def find_unused_parameters(model: nn.Module, inputs: Any) -> List[str]: + """ + Given a model, find parameters that do not contribute + to the loss. + + Args: + model: a model in training mode that returns losses + inputs: argument or a tuple of arguments. Inputs of the model + + Returns: + list[str]: the name of unused parameters + """ + assert model.training + for _, prm in model.named_parameters(): + prm.grad = None + + if isinstance(inputs, tuple): + losses = model(*inputs) + else: + losses = model(inputs) + + if isinstance(losses, dict): + losses = sum(losses.values()) + losses.backward() + + unused: List[str] = [] + for name, prm in model.named_parameters(): + if prm.grad is None: + unused.append(name) + prm.grad = None + return unused diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/collect_env.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/collect_env.py new file mode 100644 index 0000000000000000000000000000000000000000..807b6c7e6245d0a21221b1b8d29b841ec8251761 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/collect_env.py @@ -0,0 +1,242 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import importlib +import numpy as np +import os +import re +import subprocess +import sys +from collections import defaultdict +import PIL +import torch +import torchvision +from tabulate import tabulate + +__all__ = ["collect_env_info"] + + +def collect_torch_env(): + try: + import torch.__config__ + + return torch.__config__.show() + except ImportError: + # compatible with older versions of pytorch + from torch.utils.collect_env import get_pretty_env_info + + return get_pretty_env_info() + + +def get_env_module(): + var_name = "DETECTRON2_ENV_MODULE" + return var_name, os.environ.get(var_name, "") + + +def detect_compute_compatibility(CUDA_HOME, so_file): + try: + cuobjdump = os.path.join(CUDA_HOME, "bin", "cuobjdump") + if os.path.isfile(cuobjdump): + output = subprocess.check_output( + "'{}' --list-elf '{}'".format(cuobjdump, so_file), shell=True + ) + output = output.decode("utf-8").strip().split("\n") + arch = [] + for line in output: + line = re.findall(r"\.sm_([0-9]*)\.", line)[0] + arch.append(".".join(line)) + arch = sorted(set(arch)) + return ", ".join(arch) + else: + return so_file + "; cannot find cuobjdump" + except Exception: + # unhandled failure + return so_file + + +def collect_env_info(): + has_gpu = torch.cuda.is_available() # true for both CUDA & ROCM + torch_version = torch.__version__ + + # NOTE that CUDA_HOME/ROCM_HOME could be None even when CUDA runtime libs are functional + from torch.utils.cpp_extension import CUDA_HOME, ROCM_HOME + + has_rocm = False + if (getattr(torch.version, "hip", None) is not None) and (ROCM_HOME is not None): + has_rocm = True + has_cuda = has_gpu and (not has_rocm) + + data = [] + data.append(("sys.platform", sys.platform)) # check-template.yml depends on it + data.append(("Python", sys.version.replace("\n", ""))) + data.append(("numpy", np.__version__)) + + try: + import detectron2 # noqa + + data.append( + ("detectron2", detectron2.__version__ + " @" + os.path.dirname(detectron2.__file__)) + ) + except ImportError: + data.append(("detectron2", "failed to import")) + except AttributeError: + data.append(("detectron2", "imported a wrong installation")) + + try: + import detectron2._C as _C + except ImportError as e: + data.append(("detectron2._C", f"not built correctly: {e}")) + + # print system compilers when extension fails to build + if sys.platform != "win32": # don't know what to do for windows + try: + # this is how torch/utils/cpp_extensions.py choose compiler + cxx = os.environ.get("CXX", "c++") + cxx = subprocess.check_output("'{}' --version".format(cxx), shell=True) + cxx = cxx.decode("utf-8").strip().split("\n")[0] + except subprocess.SubprocessError: + cxx = "Not found" + data.append(("Compiler ($CXX)", cxx)) + + if has_cuda and CUDA_HOME is not None: + try: + nvcc = os.path.join(CUDA_HOME, "bin", "nvcc") + nvcc = subprocess.check_output("'{}' -V".format(nvcc), shell=True) + nvcc = nvcc.decode("utf-8").strip().split("\n")[-1] + except subprocess.SubprocessError: + nvcc = "Not found" + data.append(("CUDA compiler", nvcc)) + if has_cuda and sys.platform != "win32": + try: + so_file = importlib.util.find_spec("detectron2._C").origin + except (ImportError, AttributeError): + pass + else: + data.append( + ("detectron2 arch flags", detect_compute_compatibility(CUDA_HOME, so_file)) + ) + else: + # print compilers that are used to build extension + data.append(("Compiler", _C.get_compiler_version())) + data.append(("CUDA compiler", _C.get_cuda_version())) # cuda or hip + if has_cuda and getattr(_C, "has_cuda", lambda: True)(): + data.append( + ("detectron2 arch flags", detect_compute_compatibility(CUDA_HOME, _C.__file__)) + ) + + data.append(get_env_module()) + data.append(("PyTorch", torch_version + " @" + os.path.dirname(torch.__file__))) + data.append(("PyTorch debug build", torch.version.debug)) + + if not has_gpu: + has_gpu_text = "No: torch.cuda.is_available() == False" + else: + has_gpu_text = "Yes" + data.append(("GPU available", has_gpu_text)) + if has_gpu: + devices = defaultdict(list) + for k in range(torch.cuda.device_count()): + cap = ".".join((str(x) for x in torch.cuda.get_device_capability(k))) + name = torch.cuda.get_device_name(k) + f" (arch={cap})" + devices[name].append(str(k)) + for name, devids in devices.items(): + data.append(("GPU " + ",".join(devids), name)) + + if has_rocm: + msg = " - invalid!" if not (ROCM_HOME and os.path.isdir(ROCM_HOME)) else "" + data.append(("ROCM_HOME", str(ROCM_HOME) + msg)) + else: + try: + from torch.utils.collect_env import get_nvidia_driver_version, run as _run + + data.append(("Driver version", get_nvidia_driver_version(_run))) + except Exception: + pass + msg = " - invalid!" if not (CUDA_HOME and os.path.isdir(CUDA_HOME)) else "" + data.append(("CUDA_HOME", str(CUDA_HOME) + msg)) + + cuda_arch_list = os.environ.get("TORCH_CUDA_ARCH_LIST", None) + if cuda_arch_list: + data.append(("TORCH_CUDA_ARCH_LIST", cuda_arch_list)) + data.append(("Pillow", PIL.__version__)) + + try: + data.append( + ( + "torchvision", + str(torchvision.__version__) + " @" + os.path.dirname(torchvision.__file__), + ) + ) + if has_cuda: + try: + torchvision_C = importlib.util.find_spec("torchvision._C").origin + msg = detect_compute_compatibility(CUDA_HOME, torchvision_C) + data.append(("torchvision arch flags", msg)) + except (ImportError, AttributeError): + data.append(("torchvision._C", "Not found")) + except AttributeError: + data.append(("torchvision", "unknown")) + + try: + import fvcore + + data.append(("fvcore", fvcore.__version__)) + except (ImportError, AttributeError): + pass + + try: + import iopath + + data.append(("iopath", iopath.__version__)) + except (ImportError, AttributeError): + pass + + try: + import cv2 + + data.append(("cv2", cv2.__version__)) + except (ImportError, AttributeError): + data.append(("cv2", "Not found")) + env_str = tabulate(data) + "\n" + env_str += collect_torch_env() + return env_str + + +def test_nccl_ops(): + num_gpu = torch.cuda.device_count() + if os.access("/tmp", os.W_OK): + import torch.multiprocessing as mp + + dist_url = "file:///tmp/nccl_tmp_file" + print("Testing NCCL connectivity ... this should not hang.") + mp.spawn(_test_nccl_worker, nprocs=num_gpu, args=(num_gpu, dist_url), daemon=False) + print("NCCL succeeded.") + + +def _test_nccl_worker(rank, num_gpu, dist_url): + import torch.distributed as dist + + dist.init_process_group(backend="NCCL", init_method=dist_url, rank=rank, world_size=num_gpu) + dist.barrier(device_ids=[rank]) + + +if __name__ == "__main__": + try: + from detectron2.utils.collect_env import collect_env_info as f + + print(f()) + except ImportError: + print(collect_env_info()) + + if torch.cuda.is_available(): + num_gpu = torch.cuda.device_count() + for k in range(num_gpu): + device = f"cuda:{k}" + try: + x = torch.tensor([1, 2.0], dtype=torch.float32) + x = x.to(device) + except Exception as e: + print( + f"Unable to copy tensor to device={device}: {e}. " + "Your CUDA environment is broken." + ) + if num_gpu > 1: + test_nccl_ops() diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/colormap.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/colormap.py new file mode 100644 index 0000000000000000000000000000000000000000..150ccc372262ec4de0b36db66a303cae9495e67f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/colormap.py @@ -0,0 +1,140 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +""" +An awesome colormap for really neat visualizations. +Copied from Detectron, and removed gray colors. +""" + +import numpy as np + +__all__ = ["colormap", "random_color"] + +# fmt: off +# RGB: +_COLORS = np.array( + [ + 0.000, 0.447, 0.741, + 0.850, 0.325, 0.098, + 0.929, 0.694, 0.125, + 0.494, 0.184, 0.556, + 0.466, 0.674, 0.188, + 0.301, 0.745, 0.933, + 0.635, 0.078, 0.184, + 0.300, 0.300, 0.300, + 0.600, 0.600, 0.600, + 1.000, 0.000, 0.000, + 1.000, 0.500, 0.000, + 0.749, 0.749, 0.000, + 0.000, 1.000, 0.000, + 0.000, 0.000, 1.000, + 0.667, 0.000, 1.000, + 0.333, 0.333, 0.000, + 0.333, 0.667, 0.000, + 0.333, 1.000, 0.000, + 0.667, 0.333, 0.000, + 0.667, 0.667, 0.000, + 0.667, 1.000, 0.000, + 1.000, 0.333, 0.000, + 1.000, 0.667, 0.000, + 1.000, 1.000, 0.000, + 0.000, 0.333, 0.500, + 0.000, 0.667, 0.500, + 0.000, 1.000, 0.500, + 0.333, 0.000, 0.500, + 0.333, 0.333, 0.500, + 0.333, 0.667, 0.500, + 0.333, 1.000, 0.500, + 0.667, 0.000, 0.500, + 0.667, 0.333, 0.500, + 0.667, 0.667, 0.500, + 0.667, 1.000, 0.500, + 1.000, 0.000, 0.500, + 1.000, 0.333, 0.500, + 1.000, 0.667, 0.500, + 1.000, 1.000, 0.500, + 0.000, 0.333, 1.000, + 0.000, 0.667, 1.000, + 0.000, 1.000, 1.000, + 0.333, 0.000, 1.000, + 0.333, 0.333, 1.000, + 0.333, 0.667, 1.000, + 0.333, 1.000, 1.000, + 0.667, 0.000, 1.000, + 0.667, 0.333, 1.000, + 0.667, 0.667, 1.000, + 0.667, 1.000, 1.000, + 1.000, 0.000, 1.000, + 1.000, 0.333, 1.000, + 1.000, 0.667, 1.000, + 0.333, 0.000, 0.000, + 0.500, 0.000, 0.000, + 0.667, 0.000, 0.000, + 0.833, 0.000, 0.000, + 1.000, 0.000, 0.000, + 0.000, 0.167, 0.000, + 0.000, 0.333, 0.000, + 0.000, 0.500, 0.000, + 0.000, 0.667, 0.000, + 0.000, 0.833, 0.000, + 0.000, 1.000, 0.000, + 0.000, 0.000, 0.167, + 0.000, 0.000, 0.333, + 0.000, 0.000, 0.500, + 0.000, 0.000, 0.667, + 0.000, 0.000, 0.833, + 0.000, 0.000, 1.000, + 0.000, 0.000, 0.000, + 0.143, 0.143, 0.143, + 0.857, 0.857, 0.857, + 1.000, 1.000, 1.000 + ] +).astype(np.float32).reshape(-1, 3) +# fmt: on + + +def colormap(rgb=False, maximum=255): + """ + Args: + rgb (bool): whether to return RGB colors or BGR colors. + maximum (int): either 255 or 1 + + Returns: + ndarray: a float32 array of Nx3 colors, in range [0, 255] or [0, 1] + """ + assert maximum in [255, 1], maximum + c = _COLORS * maximum + if not rgb: + c = c[:, ::-1] + return c + + +def random_color(rgb=False, maximum=255): + """ + Args: + rgb (bool): whether to return RGB colors or BGR colors. + maximum (int): either 255 or 1 + + Returns: + ndarray: a vector of 3 numbers + """ + idx = np.random.randint(0, len(_COLORS)) + ret = _COLORS[idx] * maximum + if not rgb: + ret = ret[::-1] + return ret + + +if __name__ == "__main__": + import cv2 + + size = 100 + H, W = 10, 10 + canvas = np.random.rand(H * size, W * size, 3).astype("float32") + for h in range(H): + for w in range(W): + idx = h * W + w + if idx >= len(_COLORS): + break + canvas[h * size : (h + 1) * size, w * size : (w + 1) * size] = _COLORS[idx] + cv2.imshow("a", canvas) + cv2.waitKey(0) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/comm.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/comm.py new file mode 100644 index 0000000000000000000000000000000000000000..7e2a0c44278cf00c16dcf360da4779d8f0c6e8e6 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/comm.py @@ -0,0 +1,199 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +""" +This file contains primitives for multi-gpu communication. +This is useful when doing distributed training. +""" + +import functools +import numpy as np +import torch +import torch.distributed as dist + +_LOCAL_PROCESS_GROUP = None +""" +A torch process group which only includes processes that on the same machine as the current process. +This variable is set when processes are spawned by `launch()` in "engine/launch.py". +""" + + +def get_world_size() -> int: + if not dist.is_available(): + return 1 + if not dist.is_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank() -> int: + if not dist.is_available(): + return 0 + if not dist.is_initialized(): + return 0 + return dist.get_rank() + + +def get_local_rank() -> int: + """ + Returns: + The rank of the current process within the local (per-machine) process group. + """ + if not dist.is_available(): + return 0 + if not dist.is_initialized(): + return 0 + assert ( + _LOCAL_PROCESS_GROUP is not None + ), "Local process group is not created! Please use launch() to spawn processes!" + return dist.get_rank(group=_LOCAL_PROCESS_GROUP) + + +def get_local_size() -> int: + """ + Returns: + The size of the per-machine process group, + i.e. the number of processes per machine. + """ + if not dist.is_available(): + return 1 + if not dist.is_initialized(): + return 1 + return dist.get_world_size(group=_LOCAL_PROCESS_GROUP) + + +def is_main_process() -> bool: + return get_rank() == 0 + + +def synchronize(): + """ + Helper function to synchronize (barrier) among all processes when + using distributed training + """ + if not dist.is_available(): + return + if not dist.is_initialized(): + return + world_size = dist.get_world_size() + if world_size == 1: + return + if dist.get_backend() == dist.Backend.NCCL: + # This argument is needed to avoid warnings. + # It's valid only for NCCL backend. + dist.barrier(device_ids=[torch.cuda.current_device()]) + else: + dist.barrier() + + +@functools.lru_cache() +def _get_global_gloo_group(): + """ + Return a process group based on gloo backend, containing all the ranks + The result is cached. + """ + if dist.get_backend() == "nccl": + return dist.new_group(backend="gloo") + else: + return dist.group.WORLD + + +def all_gather(data, group=None): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors). + + Args: + data: any picklable object + group: a torch process group. By default, will use a group which + contains all ranks on gloo backend. + + Returns: + list[data]: list of data gathered from each rank + """ + if get_world_size() == 1: + return [data] + if group is None: + group = _get_global_gloo_group() # use CPU group by default, to reduce GPU RAM usage. + world_size = dist.get_world_size(group) + if world_size == 1: + return [data] + + output = [None for _ in range(world_size)] + dist.all_gather_object(output, data, group=group) + return output + + +def gather(data, dst=0, group=None): + """ + Run gather on arbitrary picklable data (not necessarily tensors). + + Args: + data: any picklable object + dst (int): destination rank + group: a torch process group. By default, will use a group which + contains all ranks on gloo backend. + + Returns: + list[data]: on dst, a list of data gathered from each rank. Otherwise, + an empty list. + """ + if get_world_size() == 1: + return [data] + if group is None: + group = _get_global_gloo_group() + world_size = dist.get_world_size(group=group) + if world_size == 1: + return [data] + rank = dist.get_rank(group=group) + + if rank == dst: + output = [None for _ in range(world_size)] + dist.gather_object(data, output, dst=dst, group=group) + return output + else: + dist.gather_object(data, None, dst=dst, group=group) + return [] + + +def shared_random_seed(): + """ + Returns: + int: a random number that is the same across all workers. + If workers need a shared RNG, they can use this shared seed to + create one. + + All workers must call this function, otherwise it will deadlock. + """ + ints = np.random.randint(2 ** 31) + all_ints = all_gather(ints) + return all_ints[0] + + +def reduce_dict(input_dict, average=True): + """ + Reduce the values in the dictionary from all processes so that process with rank + 0 has the reduced results. + + Args: + input_dict (dict): inputs to be reduced. All the values must be scalar CUDA Tensor. + average (bool): whether to do average or sum + + Returns: + a dict with the same keys as input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.reduce(values, dst=0) + if dist.get_rank() == 0 and average: + # only main process gets accumulated, so only divide by + # world_size in this case + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/env.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/env.py new file mode 100644 index 0000000000000000000000000000000000000000..40634c17c73273ac8927632be164f466cfe7d1fa --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/env.py @@ -0,0 +1,170 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import importlib +import importlib.util +import logging +import numpy as np +import os +import random +import sys +from datetime import datetime +import torch + +__all__ = ["seed_all_rng"] + + +TORCH_VERSION = tuple(int(x) for x in torch.__version__.split(".")[:2]) +""" +PyTorch version as a tuple of 2 ints. Useful for comparison. +""" + + +DOC_BUILDING = os.getenv("_DOC_BUILDING", False) # set in docs/conf.py +""" +Whether we're building documentation. +""" + + +def seed_all_rng(seed=None): + """ + Set the random seed for the RNG in torch, numpy and python. + + Args: + seed (int): if None, will use a strong random seed. + """ + if seed is None: + seed = ( + os.getpid() + + int(datetime.now().strftime("%S%f")) + + int.from_bytes(os.urandom(2), "big") + ) + logger = logging.getLogger(__name__) + logger.info("Using a generated random seed {}".format(seed)) + np.random.seed(seed) + torch.manual_seed(seed) + random.seed(seed) + os.environ["PYTHONHASHSEED"] = str(seed) + + +# from https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path +def _import_file(module_name, file_path, make_importable=False): + spec = importlib.util.spec_from_file_location(module_name, file_path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + if make_importable: + sys.modules[module_name] = module + return module + + +def _configure_libraries(): + """ + Configurations for some libraries. + """ + # An environment option to disable `import cv2` globally, + # in case it leads to negative performance impact + disable_cv2 = int(os.environ.get("DETECTRON2_DISABLE_CV2", False)) + if disable_cv2: + sys.modules["cv2"] = None + else: + # Disable opencl in opencv since its interaction with cuda often has negative effects + # This envvar is supported after OpenCV 3.4.0 + os.environ["OPENCV_OPENCL_RUNTIME"] = "disabled" + try: + import cv2 + + if int(cv2.__version__.split(".")[0]) >= 3: + cv2.ocl.setUseOpenCL(False) + except ModuleNotFoundError: + # Other types of ImportError, if happened, should not be ignored. + # Because a failed opencv import could mess up address space + # https://github.com/skvark/opencv-python/issues/381 + pass + + def get_version(module, digit=2): + return tuple(map(int, module.__version__.split(".")[:digit])) + + # fmt: off + assert get_version(torch) >= (1, 4), "Requires torch>=1.4" + import fvcore + assert get_version(fvcore, 3) >= (0, 1, 2), "Requires fvcore>=0.1.2" + import yaml + assert get_version(yaml) >= (5, 1), "Requires pyyaml>=5.1" + # fmt: on + + +_ENV_SETUP_DONE = False + + +def setup_environment(): + """Perform environment setup work. The default setup is a no-op, but this + function allows the user to specify a Python source file or a module in + the $DETECTRON2_ENV_MODULE environment variable, that performs + custom setup work that may be necessary to their computing environment. + """ + global _ENV_SETUP_DONE + if _ENV_SETUP_DONE: + return + _ENV_SETUP_DONE = True + + _configure_libraries() + + custom_module_path = os.environ.get("DETECTRON2_ENV_MODULE") + + if custom_module_path: + setup_custom_environment(custom_module_path) + else: + # The default setup is a no-op + pass + + +def setup_custom_environment(custom_module): + """ + Load custom environment setup by importing a Python source file or a + module, and run the setup function. + """ + if custom_module.endswith(".py"): + module = _import_file("detectron2.utils.env.custom_module", custom_module) + else: + module = importlib.import_module(custom_module) + assert hasattr(module, "setup_environment") and callable(module.setup_environment), ( + "Custom environment module defined in {} does not have the " + "required callable attribute 'setup_environment'." + ).format(custom_module) + module.setup_environment() + + +def fixup_module_metadata(module_name, namespace, keys=None): + """ + Fix the __qualname__ of module members to be their exported api name, so + when they are referenced in docs, sphinx can find them. Reference: + https://github.com/python-trio/trio/blob/6754c74eacfad9cc5c92d5c24727a2f3b620624e/trio/_util.py#L216-L241 + """ + if not DOC_BUILDING: + return + seen_ids = set() + + def fix_one(qualname, name, obj): + # avoid infinite recursion (relevant when using + # typing.Generic, for example) + if id(obj) in seen_ids: + return + seen_ids.add(id(obj)) + + mod = getattr(obj, "__module__", None) + if mod is not None and (mod.startswith(module_name) or mod.startswith("fvcore.")): + obj.__module__ = module_name + # Modules, unlike everything else in Python, put fully-qualitied + # names into their __name__ attribute. We check for "." to avoid + # rewriting these. + if hasattr(obj, "__name__") and "." not in obj.__name__: + obj.__name__ = name + obj.__qualname__ = qualname + if isinstance(obj, type): + for attr_name, attr_value in obj.__dict__.items(): + fix_one(objname + "." + attr_name, attr_name, attr_value) + + if keys is None: + keys = namespace.keys() + for objname in keys: + if not objname.startswith("_"): + obj = namespace[objname] + fix_one(objname, objname, obj) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/events.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/events.py new file mode 100644 index 0000000000000000000000000000000000000000..5dee954bdd6ad7dc5ea999562d1d2b03c3a520d9 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/events.py @@ -0,0 +1,486 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import datetime +import json +import logging +import os +import time +from collections import defaultdict +from contextlib import contextmanager +from typing import Optional +import torch +from fvcore.common.history_buffer import HistoryBuffer + +from detectron2.utils.file_io import PathManager + +__all__ = [ + "get_event_storage", + "JSONWriter", + "TensorboardXWriter", + "CommonMetricPrinter", + "EventStorage", +] + +_CURRENT_STORAGE_STACK = [] + + +def get_event_storage(): + """ + Returns: + The :class:`EventStorage` object that's currently being used. + Throws an error if no :class:`EventStorage` is currently enabled. + """ + assert len( + _CURRENT_STORAGE_STACK + ), "get_event_storage() has to be called inside a 'with EventStorage(...)' context!" + return _CURRENT_STORAGE_STACK[-1] + + +class EventWriter: + """ + Base class for writers that obtain events from :class:`EventStorage` and process them. + """ + + def write(self): + raise NotImplementedError + + def close(self): + pass + + +class JSONWriter(EventWriter): + """ + Write scalars to a json file. + + It saves scalars as one json per line (instead of a big json) for easy parsing. + + Examples parsing such a json file: + :: + $ cat metrics.json | jq -s '.[0:2]' + [ + { + "data_time": 0.008433341979980469, + "iteration": 19, + "loss": 1.9228371381759644, + "loss_box_reg": 0.050025828182697296, + "loss_classifier": 0.5316952466964722, + "loss_mask": 0.7236229181289673, + "loss_rpn_box": 0.0856662318110466, + "loss_rpn_cls": 0.48198649287223816, + "lr": 0.007173333333333333, + "time": 0.25401854515075684 + }, + { + "data_time": 0.007216215133666992, + "iteration": 39, + "loss": 1.282649278640747, + "loss_box_reg": 0.06222952902317047, + "loss_classifier": 0.30682939291000366, + "loss_mask": 0.6970193982124329, + "loss_rpn_box": 0.038663312792778015, + "loss_rpn_cls": 0.1471673548221588, + "lr": 0.007706666666666667, + "time": 0.2490077018737793 + } + ] + + $ cat metrics.json | jq '.loss_mask' + 0.7126231789588928 + 0.689423680305481 + 0.6776131987571716 + ... + + """ + + def __init__(self, json_file, window_size=20): + """ + Args: + json_file (str): path to the json file. New data will be appended if the file exists. + window_size (int): the window size of median smoothing for the scalars whose + `smoothing_hint` are True. + """ + self._file_handle = PathManager.open(json_file, "a") + self._window_size = window_size + self._last_write = -1 + + def write(self): + storage = get_event_storage() + to_save = defaultdict(dict) + + for k, (v, iter) in storage.latest_with_smoothing_hint(self._window_size).items(): + # keep scalars that have not been written + if iter <= self._last_write: + continue + to_save[iter][k] = v + if len(to_save): + all_iters = sorted(to_save.keys()) + self._last_write = max(all_iters) + + for itr, scalars_per_iter in to_save.items(): + scalars_per_iter["iteration"] = itr + self._file_handle.write(json.dumps(scalars_per_iter, sort_keys=True) + "\n") + self._file_handle.flush() + try: + os.fsync(self._file_handle.fileno()) + except AttributeError: + pass + + def close(self): + self._file_handle.close() + + +class TensorboardXWriter(EventWriter): + """ + Write all scalars to a tensorboard file. + """ + + def __init__(self, log_dir: str, window_size: int = 20, **kwargs): + """ + Args: + log_dir (str): the directory to save the output events + window_size (int): the scalars will be median-smoothed by this window size + + kwargs: other arguments passed to `torch.utils.tensorboard.SummaryWriter(...)` + """ + self._window_size = window_size + from torch.utils.tensorboard import SummaryWriter + + self._writer = SummaryWriter(log_dir, **kwargs) + self._last_write = -1 + + def write(self): + storage = get_event_storage() + new_last_write = self._last_write + for k, (v, iter) in storage.latest_with_smoothing_hint(self._window_size).items(): + if iter > self._last_write: + self._writer.add_scalar(k, v, iter) + new_last_write = max(new_last_write, iter) + self._last_write = new_last_write + + # storage.put_{image,histogram} is only meant to be used by + # tensorboard writer. So we access its internal fields directly from here. + if len(storage._vis_data) >= 1: + for img_name, img, step_num in storage._vis_data: + self._writer.add_image(img_name, img, step_num) + # Storage stores all image data and rely on this writer to clear them. + # As a result it assumes only one writer will use its image data. + # An alternative design is to let storage store limited recent + # data (e.g. only the most recent image) that all writers can access. + # In that case a writer may not see all image data if its period is long. + storage.clear_images() + + if len(storage._histograms) >= 1: + for params in storage._histograms: + self._writer.add_histogram_raw(**params) + storage.clear_histograms() + + def close(self): + if hasattr(self, "_writer"): # doesn't exist when the code fails at import + self._writer.close() + + +class CommonMetricPrinter(EventWriter): + """ + Print **common** metrics to the terminal, including + iteration time, ETA, memory, all losses, and the learning rate. + It also applies smoothing using a window of 20 elements. + + It's meant to print common metrics in common ways. + To print something in more customized ways, please implement a similar printer by yourself. + """ + + def __init__(self, max_iter: Optional[int] = None, window_size: int = 20): + """ + Args: + max_iter: the maximum number of iterations to train. + Used to compute ETA. If not given, ETA will not be printed. + window_size (int): the losses will be median-smoothed by this window size + """ + self.logger = logging.getLogger(__name__) + self._max_iter = max_iter + self._window_size = window_size + self._last_write = None # (step, time) of last call to write(). Used to compute ETA + + def _get_eta(self, storage) -> Optional[str]: + if self._max_iter is None: + return "" + iteration = storage.iter + try: + eta_seconds = storage.history("time").median(1000) * (self._max_iter - iteration - 1) + storage.put_scalar("eta_seconds", eta_seconds, smoothing_hint=False) + return str(datetime.timedelta(seconds=int(eta_seconds))) + except KeyError: + # estimate eta on our own - more noisy + eta_string = None + if self._last_write is not None: + estimate_iter_time = (time.perf_counter() - self._last_write[1]) / ( + iteration - self._last_write[0] + ) + eta_seconds = estimate_iter_time * (self._max_iter - iteration - 1) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + self._last_write = (iteration, time.perf_counter()) + return eta_string + + def write(self): + storage = get_event_storage() + iteration = storage.iter + if iteration == self._max_iter: + # This hook only reports training progress (loss, ETA, etc) but not other data, + # therefore do not write anything after training succeeds, even if this method + # is called. + return + + try: + data_time = storage.history("data_time").avg(20) + except KeyError: + # they may not exist in the first few iterations (due to warmup) + # or when SimpleTrainer is not used + data_time = None + try: + iter_time = storage.history("time").global_avg() + except KeyError: + iter_time = None + try: + lr = "{:.5g}".format(storage.history("lr").latest()) + except KeyError: + lr = "N/A" + + eta_string = self._get_eta(storage) + + if torch.cuda.is_available(): + max_mem_mb = torch.cuda.max_memory_allocated() / 1024.0 / 1024.0 + else: + max_mem_mb = None + + # NOTE: max_mem is parsed by grep in "dev/parse_results.sh" + self.logger.info( + " {eta}iter: {iter} {losses} {time}{data_time}lr: {lr} {memory}".format( + eta=f"eta: {eta_string} " if eta_string else "", + iter=iteration, + losses=" ".join( + [ + "{}: {:.4g}".format(k, v.median(self._window_size)) + for k, v in storage.histories().items() + if "loss" in k + ] + ), + time="time: {:.4f} ".format(iter_time) if iter_time is not None else "", + data_time="data_time: {:.4f} ".format(data_time) if data_time is not None else "", + lr=lr, + memory="max_mem: {:.0f}M".format(max_mem_mb) if max_mem_mb is not None else "", + ) + ) + + +class EventStorage: + """ + The user-facing class that provides metric storage functionalities. + + In the future we may add support for storing / logging other types of data if needed. + """ + + def __init__(self, start_iter=0): + """ + Args: + start_iter (int): the iteration number to start with + """ + self._history = defaultdict(HistoryBuffer) + self._smoothing_hints = {} + self._latest_scalars = {} + self._iter = start_iter + self._current_prefix = "" + self._vis_data = [] + self._histograms = [] + + def put_image(self, img_name, img_tensor): + """ + Add an `img_tensor` associated with `img_name`, to be shown on + tensorboard. + + Args: + img_name (str): The name of the image to put into tensorboard. + img_tensor (torch.Tensor or numpy.array): An `uint8` or `float` + Tensor of shape `[channel, height, width]` where `channel` is + 3. The image format should be RGB. The elements in img_tensor + can either have values in [0, 1] (float32) or [0, 255] (uint8). + The `img_tensor` will be visualized in tensorboard. + """ + self._vis_data.append((img_name, img_tensor, self._iter)) + + def put_scalar(self, name, value, smoothing_hint=True): + """ + Add a scalar `value` to the `HistoryBuffer` associated with `name`. + + Args: + smoothing_hint (bool): a 'hint' on whether this scalar is noisy and should be + smoothed when logged. The hint will be accessible through + :meth:`EventStorage.smoothing_hints`. A writer may ignore the hint + and apply custom smoothing rule. + + It defaults to True because most scalars we save need to be smoothed to + provide any useful signal. + """ + name = self._current_prefix + name + history = self._history[name] + value = float(value) + history.update(value, self._iter) + self._latest_scalars[name] = (value, self._iter) + + existing_hint = self._smoothing_hints.get(name) + if existing_hint is not None: + assert ( + existing_hint == smoothing_hint + ), "Scalar {} was put with a different smoothing_hint!".format(name) + else: + self._smoothing_hints[name] = smoothing_hint + + def put_scalars(self, *, smoothing_hint=True, **kwargs): + """ + Put multiple scalars from keyword arguments. + + Examples: + + storage.put_scalars(loss=my_loss, accuracy=my_accuracy, smoothing_hint=True) + """ + for k, v in kwargs.items(): + self.put_scalar(k, v, smoothing_hint=smoothing_hint) + + def put_histogram(self, hist_name, hist_tensor, bins=1000): + """ + Create a histogram from a tensor. + + Args: + hist_name (str): The name of the histogram to put into tensorboard. + hist_tensor (torch.Tensor): A Tensor of arbitrary shape to be converted + into a histogram. + bins (int): Number of histogram bins. + """ + ht_min, ht_max = hist_tensor.min().item(), hist_tensor.max().item() + + # Create a histogram with PyTorch + hist_counts = torch.histc(hist_tensor, bins=bins) + hist_edges = torch.linspace(start=ht_min, end=ht_max, steps=bins + 1, dtype=torch.float32) + + # Parameter for the add_histogram_raw function of SummaryWriter + hist_params = dict( + tag=hist_name, + min=ht_min, + max=ht_max, + num=len(hist_tensor), + sum=float(hist_tensor.sum()), + sum_squares=float(torch.sum(hist_tensor ** 2)), + bucket_limits=hist_edges[1:].tolist(), + bucket_counts=hist_counts.tolist(), + global_step=self._iter, + ) + self._histograms.append(hist_params) + + def history(self, name): + """ + Returns: + HistoryBuffer: the scalar history for name + """ + ret = self._history.get(name, None) + if ret is None: + raise KeyError("No history metric available for {}!".format(name)) + return ret + + def histories(self): + """ + Returns: + dict[name -> HistoryBuffer]: the HistoryBuffer for all scalars + """ + return self._history + + def latest(self): + """ + Returns: + dict[str -> (float, int)]: mapping from the name of each scalar to the most + recent value and the iteration number its added. + """ + return self._latest_scalars + + def latest_with_smoothing_hint(self, window_size=20): + """ + Similar to :meth:`latest`, but the returned values + are either the un-smoothed original latest value, + or a median of the given window_size, + depend on whether the smoothing_hint is True. + + This provides a default behavior that other writers can use. + """ + result = {} + for k, (v, itr) in self._latest_scalars.items(): + result[k] = ( + self._history[k].median(window_size) if self._smoothing_hints[k] else v, + itr, + ) + return result + + def smoothing_hints(self): + """ + Returns: + dict[name -> bool]: the user-provided hint on whether the scalar + is noisy and needs smoothing. + """ + return self._smoothing_hints + + def step(self): + """ + User should either: (1) Call this function to increment storage.iter when needed. Or + (2) Set `storage.iter` to the correct iteration number before each iteration. + + The storage will then be able to associate the new data with an iteration number. + """ + self._iter += 1 + + @property + def iter(self): + """ + Returns: + int: The current iteration number. When used together with a trainer, + this is ensured to be the same as trainer.iter. + """ + return self._iter + + @iter.setter + def iter(self, val): + self._iter = int(val) + + @property + def iteration(self): + # for backward compatibility + return self._iter + + def __enter__(self): + _CURRENT_STORAGE_STACK.append(self) + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + assert _CURRENT_STORAGE_STACK[-1] == self + _CURRENT_STORAGE_STACK.pop() + + @contextmanager + def name_scope(self, name): + """ + Yields: + A context within which all the events added to this storage + will be prefixed by the name scope. + """ + old_prefix = self._current_prefix + self._current_prefix = name.rstrip("/") + "/" + yield + self._current_prefix = old_prefix + + def clear_images(self): + """ + Delete all the stored images for visualization. This should be called + after images are written to tensorboard. + """ + self._vis_data = [] + + def clear_histograms(self): + """ + Delete all the stored histograms for visualization. + This should be called after histograms are written to tensorboard. + """ + self._histograms = [] diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/file_io.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/file_io.py new file mode 100644 index 0000000000000000000000000000000000000000..46ee4ec31d04eee77976ff3edbbf84762a3409ed --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/file_io.py @@ -0,0 +1,37 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from iopath.common.file_io import HTTPURLHandler, OneDrivePathHandler, PathHandler +from iopath.common.file_io import PathManager as PathManagerBase + +__all__ = ["PathManager", "PathHandler"] + + +PathManager = PathManagerBase() +""" +This is a detectron2 project-specific PathManager. +We try to stay away from global PathManager in fvcore as it +introduces potential conflicts among other libraries. +""" + + +class Detectron2Handler(PathHandler): + """ + Resolve anything that's hosted under detectron2's namespace. + """ + + PREFIX = "detectron2://" + S3_DETECTRON2_PREFIX = "https://dl.fbaipublicfiles.com/detectron2/" + + def _get_supported_prefixes(self): + return [self.PREFIX] + + def _get_local_path(self, path, **kwargs): + name = path[len(self.PREFIX) :] + return PathManager.get_local_path(self.S3_DETECTRON2_PREFIX + name, **kwargs) + + def _open(self, path, mode="r", **kwargs): + return PathManager.open(self._get_local_path(path), mode, **kwargs) + + +PathManager.register_handler(HTTPURLHandler()) +PathManager.register_handler(OneDrivePathHandler()) +PathManager.register_handler(Detectron2Handler()) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/logger.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/logger.py new file mode 100644 index 0000000000000000000000000000000000000000..7c7890f8bec5db44098fe1a38d26eb13231f7063 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/logger.py @@ -0,0 +1,237 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import atexit +import functools +import logging +import os +import sys +import time +from collections import Counter +import torch +from tabulate import tabulate +from termcolor import colored + +from detectron2.utils.file_io import PathManager + +__all__ = ["setup_logger", "log_first_n", "log_every_n", "log_every_n_seconds"] + + +class _ColorfulFormatter(logging.Formatter): + def __init__(self, *args, **kwargs): + self._root_name = kwargs.pop("root_name") + "." + self._abbrev_name = kwargs.pop("abbrev_name", "") + if len(self._abbrev_name): + self._abbrev_name = self._abbrev_name + "." + super(_ColorfulFormatter, self).__init__(*args, **kwargs) + + def formatMessage(self, record): + record.name = record.name.replace(self._root_name, self._abbrev_name) + log = super(_ColorfulFormatter, self).formatMessage(record) + if record.levelno == logging.WARNING: + prefix = colored("WARNING", "red", attrs=["blink"]) + elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL: + prefix = colored("ERROR", "red", attrs=["blink", "underline"]) + else: + return log + return prefix + " " + log + + +@functools.lru_cache() # so that calling setup_logger multiple times won't add many handlers +def setup_logger( + output=None, distributed_rank=0, *, color=True, name="detectron2", abbrev_name=None +): + """ + Initialize the detectron2 logger and set its verbosity level to "DEBUG". + + Args: + output (str): a file name or a directory to save log. If None, will not save log file. + If ends with ".txt" or ".log", assumed to be a file name. + Otherwise, logs will be saved to `output/log.txt`. + name (str): the root module name of this logger + abbrev_name (str): an abbreviation of the module, to avoid long names in logs. + Set to "" to not log the root module in logs. + By default, will abbreviate "detectron2" to "d2" and leave other + modules unchanged. + + Returns: + logging.Logger: a logger + """ + logger = logging.getLogger(name) + logger.setLevel(logging.DEBUG) + logger.propagate = False + + if abbrev_name is None: + abbrev_name = "d2" if name == "detectron2" else name + + plain_formatter = logging.Formatter( + "[%(asctime)s] %(name)s %(levelname)s: %(message)s", datefmt="%m/%d %H:%M:%S" + ) + # stdout logging: master only + if distributed_rank == 0: + ch = logging.StreamHandler(stream=sys.stdout) + ch.setLevel(logging.DEBUG) + if color: + formatter = _ColorfulFormatter( + colored("[%(asctime)s %(name)s]: ", "green") + "%(message)s", + datefmt="%m/%d %H:%M:%S", + root_name=name, + abbrev_name=str(abbrev_name), + ) + else: + formatter = plain_formatter + ch.setFormatter(formatter) + logger.addHandler(ch) + + # file logging: all workers + if output is not None: + if output.endswith(".txt") or output.endswith(".log"): + filename = output + else: + filename = os.path.join(output, "log.txt") + if distributed_rank > 0: + filename = filename + ".rank{}".format(distributed_rank) + PathManager.mkdirs(os.path.dirname(filename)) + + fh = logging.StreamHandler(_cached_log_stream(filename)) + fh.setLevel(logging.DEBUG) + fh.setFormatter(plain_formatter) + logger.addHandler(fh) + + return logger + + +# cache the opened file object, so that different calls to `setup_logger` +# with the same file name can safely write to the same file. +@functools.lru_cache(maxsize=None) +def _cached_log_stream(filename): + # use 1K buffer if writing to cloud storage + io = PathManager.open(filename, "a", buffering=1024 if "://" in filename else -1) + atexit.register(io.close) + return io + + +""" +Below are some other convenient logging methods. +They are mainly adopted from +https://github.com/abseil/abseil-py/blob/master/absl/logging/__init__.py +""" + + +def _find_caller(): + """ + Returns: + str: module name of the caller + tuple: a hashable key to be used to identify different callers + """ + frame = sys._getframe(2) + while frame: + code = frame.f_code + if os.path.join("utils", "logger.") not in code.co_filename: + mod_name = frame.f_globals["__name__"] + if mod_name == "__main__": + mod_name = "detectron2" + return mod_name, (code.co_filename, frame.f_lineno, code.co_name) + frame = frame.f_back + + +_LOG_COUNTER = Counter() +_LOG_TIMER = {} + + +def log_first_n(lvl, msg, n=1, *, name=None, key="caller"): + """ + Log only for the first n times. + + Args: + lvl (int): the logging level + msg (str): + n (int): + name (str): name of the logger to use. Will use the caller's module by default. + key (str or tuple[str]): the string(s) can be one of "caller" or + "message", which defines how to identify duplicated logs. + For example, if called with `n=1, key="caller"`, this function + will only log the first call from the same caller, regardless of + the message content. + If called with `n=1, key="message"`, this function will log the + same content only once, even if they are called from different places. + If called with `n=1, key=("caller", "message")`, this function + will not log only if the same caller has logged the same message before. + """ + if isinstance(key, str): + key = (key,) + assert len(key) > 0 + + caller_module, caller_key = _find_caller() + hash_key = () + if "caller" in key: + hash_key = hash_key + caller_key + if "message" in key: + hash_key = hash_key + (msg,) + + _LOG_COUNTER[hash_key] += 1 + if _LOG_COUNTER[hash_key] <= n: + logging.getLogger(name or caller_module).log(lvl, msg) + + +def log_every_n(lvl, msg, n=1, *, name=None): + """ + Log once per n times. + + Args: + lvl (int): the logging level + msg (str): + n (int): + name (str): name of the logger to use. Will use the caller's module by default. + """ + caller_module, key = _find_caller() + _LOG_COUNTER[key] += 1 + if n == 1 or _LOG_COUNTER[key] % n == 1: + logging.getLogger(name or caller_module).log(lvl, msg) + + +def log_every_n_seconds(lvl, msg, n=1, *, name=None): + """ + Log no more than once per n seconds. + + Args: + lvl (int): the logging level + msg (str): + n (int): + name (str): name of the logger to use. Will use the caller's module by default. + """ + caller_module, key = _find_caller() + last_logged = _LOG_TIMER.get(key, None) + current_time = time.time() + if last_logged is None or current_time - last_logged >= n: + logging.getLogger(name or caller_module).log(lvl, msg) + _LOG_TIMER[key] = current_time + + +def create_small_table(small_dict): + """ + Create a small table using the keys of small_dict as headers. This is only + suitable for small dictionaries. + + Args: + small_dict (dict): a result dictionary of only a few items. + + Returns: + str: the table as a string. + """ + keys, values = tuple(zip(*small_dict.items())) + table = tabulate( + [values], + headers=keys, + tablefmt="pipe", + floatfmt=".3f", + stralign="center", + numalign="center", + ) + return table + + +def _log_api_usage(identifier: str): + """ + Internal function used to log the usage of different detectron2 components + inside facebook's infra. + """ + torch._C._log_api_usage_once("detectron2." + identifier) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/memory.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/memory.py new file mode 100644 index 0000000000000000000000000000000000000000..bd494780b9dbbd1571688cd270bb9b53d113c13e --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/memory.py @@ -0,0 +1,84 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import logging +from contextlib import contextmanager +from functools import wraps +import torch + +__all__ = ["retry_if_cuda_oom"] + + +@contextmanager +def _ignore_torch_cuda_oom(): + """ + A context which ignores CUDA OOM exception from pytorch. + """ + try: + yield + except RuntimeError as e: + # NOTE: the string may change? + if "CUDA out of memory. " in str(e): + pass + else: + raise + + +def retry_if_cuda_oom(func): + """ + Makes a function retry itself after encountering + pytorch's CUDA OOM error. + It will first retry after calling `torch.cuda.empty_cache()`. + + If that still fails, it will then retry by trying to convert inputs to CPUs. + In this case, it expects the function to dispatch to CPU implementation. + The return values may become CPU tensors as well and it's user's + responsibility to convert it back to CUDA tensor if needed. + + Args: + func: a stateless callable that takes tensor-like objects as arguments + + Returns: + a callable which retries `func` if OOM is encountered. + + Examples: + :: + output = retry_if_cuda_oom(some_torch_function)(input1, input2) + # output may be on CPU even if inputs are on GPU + + Note: + 1. When converting inputs to CPU, it will only look at each argument and check + if it has `.device` and `.to` for conversion. Nested structures of tensors + are not supported. + + 2. Since the function might be called more than once, it has to be + stateless. + """ + + def maybe_to_cpu(x): + try: + like_gpu_tensor = x.device.type == "cuda" and hasattr(x, "to") + except AttributeError: + like_gpu_tensor = False + if like_gpu_tensor: + return x.to(device="cpu") + else: + return x + + @wraps(func) + def wrapped(*args, **kwargs): + with _ignore_torch_cuda_oom(): + return func(*args, **kwargs) + + # Clear cache and retry + torch.cuda.empty_cache() + with _ignore_torch_cuda_oom(): + return func(*args, **kwargs) + + # Try on CPU. This slows down the code significantly, therefore print a notice. + logger = logging.getLogger(__name__) + logger.info("Attempting to copy inputs of {} to CPU due to CUDA OOM".format(str(func))) + new_args = (maybe_to_cpu(x) for x in args) + new_kwargs = {k: maybe_to_cpu(v) for k, v in kwargs.items()} + return func(*new_args, **new_kwargs) + + return wrapped diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/registry.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/registry.py new file mode 100644 index 0000000000000000000000000000000000000000..4b01e9007c2578a7b5ae555c926cc06c8a3010f9 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/registry.py @@ -0,0 +1,60 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from typing import Any +import pydoc +from fvcore.common.registry import Registry # for backward compatibility. + +""" +``Registry`` and `locate` provide ways to map a string (typically found +in config files) to callable objects. +""" + +__all__ = ["Registry", "locate"] + + +def _convert_target_to_string(t: Any) -> str: + """ + Inverse of ``locate()``. + + Args: + t: any object with ``__module__`` and ``__qualname__`` + """ + module, qualname = t.__module__, t.__qualname__ + + # Compress the path to this object, e.g. ``module.submodule._impl.class`` + # may become ``module.submodule.class``, if the later also resolves to the same + # object. This simplifies the string, and also is less affected by moving the + # class implementation. + module_parts = module.split(".") + for k in range(1, len(module_parts)): + prefix = ".".join(module_parts[:k]) + candidate = f"{prefix}.{qualname}" + try: + if locate(candidate) is t: + return candidate + except ImportError: + pass + return f"{module}.{qualname}" + + +def locate(name: str) -> Any: + """ + Locate and return an object ``x`` using an input string ``{x.__module__}.{x.__qualname__}``, + such as "module.submodule.class_name". + + Raise Exception if it cannot be found. + """ + obj = pydoc.locate(name) + + # Some cases (e.g. torch.optim.sgd.SGD) not handled correctly + # by pydoc.locate. Try a private function from hydra. + if obj is None: + try: + # from hydra.utils import get_method - will print many errors + from hydra.utils import _locate + except ImportError as e: + raise ImportError(f"Cannot dynamically locate object {name}!") from e + else: + obj = _locate(name) # it raises if fails + + return obj diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/serialize.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/serialize.py new file mode 100644 index 0000000000000000000000000000000000000000..0b38862804b70cf1159a9bc93acdef73c184d883 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/serialize.py @@ -0,0 +1,32 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import cloudpickle + + +class PicklableWrapper(object): + """ + Wrap an object to make it more picklable, note that it uses + heavy weight serialization libraries that are slower than pickle. + It's best to use it only on closures (which are usually not picklable). + + This is a simplified version of + https://github.com/joblib/joblib/blob/master/joblib/externals/loky/cloudpickle_wrapper.py + """ + + def __init__(self, obj): + while isinstance(obj, PicklableWrapper): + # Wrapping an object twice is no-op + obj = obj._obj + self._obj = obj + + def __reduce__(self): + s = cloudpickle.dumps(self._obj) + return cloudpickle.loads, (s,) + + def __call__(self, *args, **kwargs): + return self._obj(*args, **kwargs) + + def __getattr__(self, attr): + # Ensure that the wrapped object can be used seamlessly as the previous object. + if attr not in ["_obj"]: + return getattr(self._obj, attr) + return getattr(self, attr) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/testing.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/testing.py new file mode 100644 index 0000000000000000000000000000000000000000..161fa6b80845ecabb6f71f28aa3333c3178c8756 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/testing.py @@ -0,0 +1,137 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import io +import numpy as np +import torch + +from detectron2 import model_zoo +from detectron2.data import DatasetCatalog +from detectron2.data.detection_utils import read_image +from detectron2.modeling import build_model +from detectron2.structures import Boxes, Instances, ROIMasks +from detectron2.utils.file_io import PathManager + + +""" +Internal utilities for tests. Don't use except for writing tests. +""" + + +def get_model_no_weights(config_path): + """ + Like model_zoo.get, but do not load any weights (even pretrained) + """ + cfg = model_zoo.get_config(config_path) + if not torch.cuda.is_available(): + cfg.MODEL.DEVICE = "cpu" + return build_model(cfg) + + +def random_boxes(num_boxes, max_coord=100, device="cpu"): + """ + Create a random Nx4 boxes tensor, with coordinates < max_coord. + """ + boxes = torch.rand(num_boxes, 4, device=device) * (max_coord * 0.5) + boxes.clamp_(min=1.0) # tiny boxes cause numerical instability in box regression + # Note: the implementation of this function in torchvision is: + # boxes[:, 2:] += torch.rand(N, 2) * 100 + # but it does not guarantee non-negative widths/heights constraints: + # boxes[:, 2] >= boxes[:, 0] and boxes[:, 3] >= boxes[:, 1]: + boxes[:, 2:] += boxes[:, :2] + return boxes + + +def get_sample_coco_image(tensor=True): + """ + Args: + tensor (bool): if True, returns 3xHxW tensor. + else, returns a HxWx3 numpy array. + + Returns: + an image, in BGR color. + """ + try: + file_name = DatasetCatalog.get("coco_2017_val_100")[0]["file_name"] + if not PathManager.exists(file_name): + raise FileNotFoundError() + except IOError: + # for public CI to run + file_name = PathManager.get_local_path( + "http://images.cocodataset.org/train2017/000000000009.jpg" + ) + ret = read_image(file_name, format="BGR") + if tensor: + ret = torch.from_numpy(np.ascontiguousarray(ret.transpose(2, 0, 1))) + return ret + + +def convert_scripted_instances(instances): + """ + Convert a scripted Instances object to a regular :class:`Instances` object + """ + assert hasattr( + instances, "image_size" + ), f"Expect an Instances object, but got {type(instances)}!" + ret = Instances(instances.image_size) + for name in instances._field_names: + val = getattr(instances, "_" + name, None) + if val is not None: + ret.set(name, val) + return ret + + +def assert_instances_allclose(input, other, *, rtol=1e-5, msg="", size_as_tensor=False): + """ + Args: + input, other (Instances): + size_as_tensor: compare image_size of the Instances as tensors (instead of tuples). + Useful for comparing outputs of tracing. + """ + if not isinstance(input, Instances): + input = convert_scripted_instances(input) + if not isinstance(other, Instances): + other = convert_scripted_instances(other) + + if not msg: + msg = "Two Instances are different! " + else: + msg = msg.rstrip() + " " + + size_error_msg = msg + f"image_size is {input.image_size} vs. {other.image_size}!" + if size_as_tensor: + assert torch.equal( + torch.tensor(input.image_size), torch.tensor(other.image_size) + ), size_error_msg + else: + assert input.image_size == other.image_size, size_error_msg + fields = sorted(input.get_fields().keys()) + fields_other = sorted(other.get_fields().keys()) + assert fields == fields_other, msg + f"Fields are {fields} vs {fields_other}!" + + for f in fields: + val1, val2 = input.get(f), other.get(f) + if isinstance(val1, (Boxes, ROIMasks)): + # boxes in the range of O(100) and can have a larger tolerance + assert torch.allclose(val1.tensor, val2.tensor, atol=100 * rtol), ( + msg + f"Field {f} differs too much!" + ) + elif isinstance(val1, torch.Tensor): + if val1.dtype.is_floating_point: + mag = torch.abs(val1).max().cpu().item() + assert torch.allclose(val1, val2, atol=mag * rtol), ( + msg + f"Field {f} differs too much!" + ) + else: + assert torch.equal(val1, val2), msg + f"Field {f} is different!" + else: + raise ValueError(f"Don't know how to compare type {type(val1)}") + + +def reload_script_model(module): + """ + Save a jit module and load it back. + Similar to the `getExportImportCopy` function in torch/testing/ + """ + buffer = io.BytesIO() + torch.jit.save(module, buffer) + buffer.seek(0) + return torch.jit.load(buffer) diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/video_visualizer.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/video_visualizer.py new file mode 100644 index 0000000000000000000000000000000000000000..9d8a366d3ca78c1824eff62f6fe422542075f055 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/video_visualizer.py @@ -0,0 +1,252 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +import pycocotools.mask as mask_util + +from detectron2.utils.visualizer import ( + ColorMode, + Visualizer, + _create_text_labels, + _PanopticPrediction, +) + +from .colormap import random_color + + +class _DetectedInstance: + """ + Used to store data about detected objects in video frame, + in order to transfer color to objects in the future frames. + + Attributes: + label (int): + bbox (tuple[float]): + mask_rle (dict): + color (tuple[float]): RGB colors in range (0, 1) + ttl (int): time-to-live for the instance. For example, if ttl=2, + the instance color can be transferred to objects in the next two frames. + """ + + __slots__ = ["label", "bbox", "mask_rle", "color", "ttl"] + + def __init__(self, label, bbox, mask_rle, color, ttl): + self.label = label + self.bbox = bbox + self.mask_rle = mask_rle + self.color = color + self.ttl = ttl + + +class VideoVisualizer: + def __init__(self, metadata, instance_mode=ColorMode.IMAGE): + """ + Args: + metadata (MetadataCatalog): image metadata. + """ + self.metadata = metadata + self._old_instances = [] + assert instance_mode in [ + ColorMode.IMAGE, + ColorMode.IMAGE_BW, + ], "Other mode not supported yet." + self._instance_mode = instance_mode + + def draw_instance_predictions(self, frame, predictions): + """ + Draw instance-level prediction results on an image. + + Args: + frame (ndarray): an RGB image of shape (H, W, C), in the range [0, 255]. + predictions (Instances): the output of an instance detection/segmentation + model. Following fields will be used to draw: + "pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle"). + + Returns: + output (VisImage): image object with visualizations. + """ + frame_visualizer = Visualizer(frame, self.metadata) + num_instances = len(predictions) + if num_instances == 0: + return frame_visualizer.output + + boxes = predictions.pred_boxes.tensor.numpy() if predictions.has("pred_boxes") else None + scores = predictions.scores if predictions.has("scores") else None + classes = predictions.pred_classes.numpy() if predictions.has("pred_classes") else None + keypoints = predictions.pred_keypoints if predictions.has("pred_keypoints") else None + colors = predictions.COLOR if predictions.has("COLOR") else [None] * len(predictions) + durations = predictions.ID_duration if predictions.has("ID_duration") else None + duration_threshold = self.metadata.get("duration_threshold", 0) + visibilities = None if durations is None else [x > duration_threshold for x in durations] + + if predictions.has("pred_masks"): + masks = predictions.pred_masks + # mask IOU is not yet enabled + # masks_rles = mask_util.encode(np.asarray(masks.permute(1, 2, 0), order="F")) + # assert len(masks_rles) == num_instances + else: + masks = None + + detected = [ + _DetectedInstance(classes[i], boxes[i], mask_rle=None, color=colors[i], ttl=8) + for i in range(num_instances) + ] + if not predictions.has("COLOR"): + colors = self._assign_colors(detected) + + labels = _create_text_labels(classes, scores, self.metadata.get("thing_classes", None)) + + if self._instance_mode == ColorMode.IMAGE_BW: + # any() returns uint8 tensor + frame_visualizer.output.reset_image( + frame_visualizer._create_grayscale_image( + (masks.any(dim=0) > 0).numpy() if masks is not None else None + ) + ) + alpha = 0.3 + else: + alpha = 0.5 + + labels = ( + None + if labels is None + else [y[0] for y in filter(lambda x: x[1], zip(labels, visibilities))] + ) # noqa + assigned_colors = ( + None + if colors is None + else [y[0] for y in filter(lambda x: x[1], zip(colors, visibilities))] + ) # noqa + frame_visualizer.overlay_instances( + boxes=None if masks is not None else boxes[visibilities], # boxes are a bit distracting + masks=None if masks is None else masks[visibilities], + labels=labels, + keypoints=None if keypoints is None else keypoints[visibilities], + assigned_colors=assigned_colors, + alpha=alpha, + ) + + return frame_visualizer.output + + def draw_sem_seg(self, frame, sem_seg, area_threshold=None): + """ + Args: + sem_seg (ndarray or Tensor): semantic segmentation of shape (H, W), + each value is the integer label. + area_threshold (Optional[int]): only draw segmentations larger than the threshold + """ + # don't need to do anything special + frame_visualizer = Visualizer(frame, self.metadata) + frame_visualizer.draw_sem_seg(sem_seg, area_threshold=None) + return frame_visualizer.output + + def draw_panoptic_seg_predictions( + self, frame, panoptic_seg, segments_info, area_threshold=None, alpha=0.5 + ): + frame_visualizer = Visualizer(frame, self.metadata) + pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata) + + if self._instance_mode == ColorMode.IMAGE_BW: + frame_visualizer.output.reset_image( + frame_visualizer._create_grayscale_image(pred.non_empty_mask()) + ) + + # draw mask for all semantic segments first i.e. "stuff" + for mask, sinfo in pred.semantic_masks(): + category_idx = sinfo["category_id"] + try: + mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]] + except AttributeError: + mask_color = None + + frame_visualizer.draw_binary_mask( + mask, + color=mask_color, + text=self.metadata.stuff_classes[category_idx], + alpha=alpha, + area_threshold=area_threshold, + ) + + all_instances = list(pred.instance_masks()) + if len(all_instances) == 0: + return frame_visualizer.output + # draw mask for all instances second + masks, sinfo = list(zip(*all_instances)) + num_instances = len(masks) + masks_rles = mask_util.encode( + np.asarray(np.asarray(masks).transpose(1, 2, 0), dtype=np.uint8, order="F") + ) + assert len(masks_rles) == num_instances + + category_ids = [x["category_id"] for x in sinfo] + detected = [ + _DetectedInstance(category_ids[i], bbox=None, mask_rle=masks_rles[i], color=None, ttl=8) + for i in range(num_instances) + ] + colors = self._assign_colors(detected) + labels = [self.metadata.thing_classes[k] for k in category_ids] + + frame_visualizer.overlay_instances( + boxes=None, + masks=masks, + labels=labels, + keypoints=None, + assigned_colors=colors, + alpha=alpha, + ) + return frame_visualizer.output + + def _assign_colors(self, instances): + """ + Naive tracking heuristics to assign same color to the same instance, + will update the internal state of tracked instances. + + Returns: + list[tuple[float]]: list of colors. + """ + + # Compute iou with either boxes or masks: + is_crowd = np.zeros((len(instances),), dtype=np.bool) + if instances[0].bbox is None: + assert instances[0].mask_rle is not None + # use mask iou only when box iou is None + # because box seems good enough + rles_old = [x.mask_rle for x in self._old_instances] + rles_new = [x.mask_rle for x in instances] + ious = mask_util.iou(rles_old, rles_new, is_crowd) + threshold = 0.5 + else: + boxes_old = [x.bbox for x in self._old_instances] + boxes_new = [x.bbox for x in instances] + ious = mask_util.iou(boxes_old, boxes_new, is_crowd) + threshold = 0.6 + if len(ious) == 0: + ious = np.zeros((len(self._old_instances), len(instances)), dtype="float32") + + # Only allow matching instances of the same label: + for old_idx, old in enumerate(self._old_instances): + for new_idx, new in enumerate(instances): + if old.label != new.label: + ious[old_idx, new_idx] = 0 + + matched_new_per_old = np.asarray(ious).argmax(axis=1) + max_iou_per_old = np.asarray(ious).max(axis=1) + + # Try to find match for each old instance: + extra_instances = [] + for idx, inst in enumerate(self._old_instances): + if max_iou_per_old[idx] > threshold: + newidx = matched_new_per_old[idx] + if instances[newidx].color is None: + instances[newidx].color = inst.color + continue + # If an old instance does not match any new instances, + # keep it for the next frame in case it is just missed by the detector + inst.ttl -= 1 + if inst.ttl > 0: + extra_instances.append(inst) + + # Assign random color to newly-detected instances: + for inst in instances: + if inst.color is None: + inst.color = random_color(rgb=True, maximum=1) + self._old_instances = instances[:] + extra_instances + return [d.color for d in instances] diff --git a/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/visualizer.py b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/visualizer.py new file mode 100644 index 0000000000000000000000000000000000000000..8e145181871d1981e41db3c8cbc7e8f4cc7b5833 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/detectron2/utils/visualizer.py @@ -0,0 +1,1267 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import colorsys +import logging +import math +import numpy as np +from enum import Enum, unique +import cv2 +import matplotlib as mpl +import matplotlib.colors as mplc +import matplotlib.figure as mplfigure +import pycocotools.mask as mask_util +import torch +from matplotlib.backends.backend_agg import FigureCanvasAgg +from PIL import Image + +from detectron2.data import MetadataCatalog +from detectron2.structures import BitMasks, Boxes, BoxMode, Keypoints, PolygonMasks, RotatedBoxes +from detectron2.utils.file_io import PathManager + +from .colormap import random_color + +logger = logging.getLogger(__name__) + +__all__ = ["ColorMode", "VisImage", "Visualizer"] + + +_SMALL_OBJECT_AREA_THRESH = 1000 +_LARGE_MASK_AREA_THRESH = 120000 +_OFF_WHITE = (1.0, 1.0, 240.0 / 255) +_BLACK = (0, 0, 0) +_RED = (1.0, 0, 0) + +_KEYPOINT_THRESHOLD = 0.05 + + +@unique +class ColorMode(Enum): + """ + Enum of different color modes to use for instance visualizations. + """ + + IMAGE = 0 + """ + Picks a random color for every instance and overlay segmentations with low opacity. + """ + SEGMENTATION = 1 + """ + Let instances of the same category have similar colors + (from metadata.thing_colors), and overlay them with + high opacity. This provides more attention on the quality of segmentation. + """ + IMAGE_BW = 2 + """ + Same as IMAGE, but convert all areas without masks to gray-scale. + Only available for drawing per-instance mask predictions. + """ + + +class GenericMask: + """ + Attribute: + polygons (list[ndarray]): list[ndarray]: polygons for this mask. + Each ndarray has format [x, y, x, y, ...] + mask (ndarray): a binary mask + """ + + def __init__(self, mask_or_polygons, height, width): + self._mask = self._polygons = self._has_holes = None + self.height = height + self.width = width + + m = mask_or_polygons + if isinstance(m, dict): + # RLEs + assert "counts" in m and "size" in m + if isinstance(m["counts"], list): # uncompressed RLEs + h, w = m["size"] + assert h == height and w == width + m = mask_util.frPyObjects(m, h, w) + self._mask = mask_util.decode(m)[:, :] + return + + if isinstance(m, list): # list[ndarray] + self._polygons = [np.asarray(x).reshape(-1) for x in m] + return + + if isinstance(m, np.ndarray): # assumed to be a binary mask + assert m.shape[1] != 2, m.shape + assert m.shape == ( + height, + width, + ), f"mask shape: {m.shape}, target dims: {height}, {width}" + self._mask = m.astype("uint8") + return + + raise ValueError("GenericMask cannot handle object {} of type '{}'".format(m, type(m))) + + @property + def mask(self): + if self._mask is None: + self._mask = self.polygons_to_mask(self._polygons) + return self._mask + + @property + def polygons(self): + if self._polygons is None: + self._polygons, self._has_holes = self.mask_to_polygons(self._mask) + return self._polygons + + @property + def has_holes(self): + if self._has_holes is None: + if self._mask is not None: + self._polygons, self._has_holes = self.mask_to_polygons(self._mask) + else: + self._has_holes = False # if original format is polygon, does not have holes + return self._has_holes + + def mask_to_polygons(self, mask): + # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level + # hierarchy. External contours (boundary) of the object are placed in hierarchy-1. + # Internal contours (holes) are placed in hierarchy-2. + # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours. + mask = np.ascontiguousarray(mask) # some versions of cv2 does not support incontiguous arr + res = cv2.findContours(mask.astype("uint8"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE) + hierarchy = res[-1] + if hierarchy is None: # empty mask + return [], False + has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0 + res = res[-2] + res = [x.flatten() for x in res] + # These coordinates from OpenCV are integers in range [0, W-1 or H-1]. + # We add 0.5 to turn them into real-value coordinate space. A better solution + # would be to first +0.5 and then dilate the returned polygon by 0.5. + res = [x + 0.5 for x in res if len(x) >= 6] + return res, has_holes + + def polygons_to_mask(self, polygons): + rle = mask_util.frPyObjects(polygons, self.height, self.width) + rle = mask_util.merge(rle) + return mask_util.decode(rle)[:, :] + + def area(self): + return self.mask.sum() + + def bbox(self): + p = mask_util.frPyObjects(self.polygons, self.height, self.width) + p = mask_util.merge(p) + bbox = mask_util.toBbox(p) + bbox[2] += bbox[0] + bbox[3] += bbox[1] + return bbox + + +class _PanopticPrediction: + """ + Unify different panoptic annotation/prediction formats + """ + + def __init__(self, panoptic_seg, segments_info, metadata=None): + if segments_info is None: + assert metadata is not None + # If "segments_info" is None, we assume "panoptic_img" is a + # H*W int32 image storing the panoptic_id in the format of + # category_id * label_divisor + instance_id. We reserve -1 for + # VOID label. + label_divisor = metadata.label_divisor + segments_info = [] + for panoptic_label in np.unique(panoptic_seg.numpy()): + if panoptic_label == -1: + # VOID region. + continue + pred_class = panoptic_label // label_divisor + isthing = pred_class in metadata.thing_dataset_id_to_contiguous_id.values() + segments_info.append( + { + "id": int(panoptic_label), + "category_id": int(pred_class), + "isthing": bool(isthing), + } + ) + del metadata + + self._seg = panoptic_seg + + self._sinfo = {s["id"]: s for s in segments_info} # seg id -> seg info + segment_ids, areas = torch.unique(panoptic_seg, sorted=True, return_counts=True) + areas = areas.numpy() + sorted_idxs = np.argsort(-areas) + self._seg_ids, self._seg_areas = segment_ids[sorted_idxs], areas[sorted_idxs] + self._seg_ids = self._seg_ids.tolist() + for sid, area in zip(self._seg_ids, self._seg_areas): + if sid in self._sinfo: + self._sinfo[sid]["area"] = float(area) + + def non_empty_mask(self): + """ + Returns: + (H, W) array, a mask for all pixels that have a prediction + """ + empty_ids = [] + for id in self._seg_ids: + if id not in self._sinfo: + empty_ids.append(id) + if len(empty_ids) == 0: + return np.zeros(self._seg.shape, dtype=np.uint8) + assert ( + len(empty_ids) == 1 + ), ">1 ids corresponds to no labels. This is currently not supported" + return (self._seg != empty_ids[0]).numpy().astype(np.bool) + + def semantic_masks(self): + for sid in self._seg_ids: + sinfo = self._sinfo.get(sid) + if sinfo is None or sinfo["isthing"]: + # Some pixels (e.g. id 0 in PanopticFPN) have no instance or semantic predictions. + continue + yield (self._seg == sid).numpy().astype(np.bool), sinfo + + def instance_masks(self): + for sid in self._seg_ids: + sinfo = self._sinfo.get(sid) + if sinfo is None or not sinfo["isthing"]: + continue + mask = (self._seg == sid).numpy().astype(np.bool) + if mask.sum() > 0: + yield mask, sinfo + + +def _create_text_labels(classes, scores, class_names, is_crowd=None): + """ + Args: + classes (list[int] or None): + scores (list[float] or None): + class_names (list[str] or None): + is_crowd (list[bool] or None): + + Returns: + list[str] or None + """ + labels = None + if classes is not None: + if class_names is not None and len(class_names) > 0: + labels = [class_names[i] for i in classes] + else: + labels = [str(i) for i in classes] + if scores is not None: + if labels is None: + labels = ["{:.0f}%".format(s * 100) for s in scores] + else: + labels = ["{} {:.0f}%".format(l, s * 100) for l, s in zip(labels, scores)] + if labels is not None and is_crowd is not None: + labels = [l + ("|crowd" if crowd else "") for l, crowd in zip(labels, is_crowd)] + return labels + + +class VisImage: + def __init__(self, img, scale=1.0): + """ + Args: + img (ndarray): an RGB image of shape (H, W, 3) in range [0, 255]. + scale (float): scale the input image + """ + self.img = img + self.scale = scale + self.width, self.height = img.shape[1], img.shape[0] + self._setup_figure(img) + + def _setup_figure(self, img): + """ + Args: + Same as in :meth:`__init__()`. + + Returns: + fig (matplotlib.pyplot.figure): top level container for all the image plot elements. + ax (matplotlib.pyplot.Axes): contains figure elements and sets the coordinate system. + """ + fig = mplfigure.Figure(frameon=False) + self.dpi = fig.get_dpi() + # add a small 1e-2 to avoid precision lost due to matplotlib's truncation + # (https://github.com/matplotlib/matplotlib/issues/15363) + fig.set_size_inches( + (self.width * self.scale + 1e-2) / self.dpi, + (self.height * self.scale + 1e-2) / self.dpi, + ) + self.canvas = FigureCanvasAgg(fig) + # self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig) + ax = fig.add_axes([0.0, 0.0, 1.0, 1.0]) + ax.axis("off") + self.fig = fig + self.ax = ax + self.reset_image(img) + + def reset_image(self, img): + """ + Args: + img: same as in __init__ + """ + img = img.astype("uint8") + self.ax.imshow(img, extent=(0, self.width, self.height, 0), interpolation="nearest") + + def save(self, filepath): + """ + Args: + filepath (str): a string that contains the absolute path, including the file name, where + the visualized image will be saved. + """ + self.fig.savefig(filepath) + + def get_image(self): + """ + Returns: + ndarray: + the visualized image of shape (H, W, 3) (RGB) in uint8 type. + The shape is scaled w.r.t the input image using the given `scale` argument. + """ + canvas = self.canvas + s, (width, height) = canvas.print_to_buffer() + # buf = io.BytesIO() # works for cairo backend + # canvas.print_rgba(buf) + # width, height = self.width, self.height + # s = buf.getvalue() + + buffer = np.frombuffer(s, dtype="uint8") + + img_rgba = buffer.reshape(height, width, 4) + rgb, alpha = np.split(img_rgba, [3], axis=2) + return rgb.astype("uint8") + + +class Visualizer: + """ + Visualizer that draws data about detection/segmentation on images. + + It contains methods like `draw_{text,box,circle,line,binary_mask,polygon}` + that draw primitive objects to images, as well as high-level wrappers like + `draw_{instance_predictions,sem_seg,panoptic_seg_predictions,dataset_dict}` + that draw composite data in some pre-defined style. + + Note that the exact visualization style for the high-level wrappers are subject to change. + Style such as color, opacity, label contents, visibility of labels, or even the visibility + of objects themselves (e.g. when the object is too small) may change according + to different heuristics, as long as the results still look visually reasonable. + + To obtain a consistent style, you can implement custom drawing functions with the + abovementioned primitive methods instead. If you need more customized visualization + styles, you can process the data yourself following their format documented in + tutorials (:doc:`/tutorials/models`, :doc:`/tutorials/datasets`). This class does not + intend to satisfy everyone's preference on drawing styles. + + This visualizer focuses on high rendering quality rather than performance. It is not + designed to be used for real-time applications. + """ + + # TODO implement a fast, rasterized version using OpenCV + + def __init__(self, img_rgb, metadata=None, scale=1.0, instance_mode=ColorMode.IMAGE): + """ + Args: + img_rgb: a numpy array of shape (H, W, C), where H and W correspond to + the height and width of the image respectively. C is the number of + color channels. The image is required to be in RGB format since that + is a requirement of the Matplotlib library. The image is also expected + to be in the range [0, 255]. + metadata (Metadata): dataset metadata (e.g. class names and colors) + instance_mode (ColorMode): defines one of the pre-defined style for drawing + instances on an image. + """ + self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8) + if metadata is None: + metadata = MetadataCatalog.get("__nonexist__") + self.metadata = metadata + self.output = VisImage(self.img, scale=scale) + self.cpu_device = torch.device("cpu") + + # too small texts are useless, therefore clamp to 9 + self._default_font_size = max( + np.sqrt(self.output.height * self.output.width) // 90, 10 // scale + ) + self._instance_mode = instance_mode + self.keypoint_threshold = _KEYPOINT_THRESHOLD + + def draw_instance_predictions(self, predictions): + """ + Draw instance-level prediction results on an image. + + Args: + predictions (Instances): the output of an instance detection/segmentation + model. Following fields will be used to draw: + "pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle"). + + Returns: + output (VisImage): image object with visualizations. + """ + boxes = predictions.pred_boxes if predictions.has("pred_boxes") else None + scores = predictions.scores if predictions.has("scores") else None + classes = predictions.pred_classes.tolist() if predictions.has("pred_classes") else None + labels = _create_text_labels(classes, scores, self.metadata.get("thing_classes", None)) + keypoints = predictions.pred_keypoints if predictions.has("pred_keypoints") else None + + if predictions.has("pred_masks"): + masks = np.asarray(predictions.pred_masks) + masks = [GenericMask(x, self.output.height, self.output.width) for x in masks] + else: + masks = None + + if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("thing_colors"): + colors = [ + self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in classes + ] + alpha = 0.8 + else: + colors = None + alpha = 0.5 + + if self._instance_mode == ColorMode.IMAGE_BW: + self.output.reset_image( + self._create_grayscale_image( + (predictions.pred_masks.any(dim=0) > 0).numpy() + if predictions.has("pred_masks") + else None + ) + ) + alpha = 0.3 + + self.overlay_instances( + masks=masks, + boxes=boxes, + labels=labels, + keypoints=keypoints, + assigned_colors=colors, + alpha=alpha, + ) + return self.output + + def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.8): + """ + Draw semantic segmentation predictions/labels. + + Args: + sem_seg (Tensor or ndarray): the segmentation of shape (H, W). + Each value is the integer label of the pixel. + area_threshold (int): segments with less than `area_threshold` are not drawn. + alpha (float): the larger it is, the more opaque the segmentations are. + + Returns: + output (VisImage): image object with visualizations. + """ + if isinstance(sem_seg, torch.Tensor): + sem_seg = sem_seg.numpy() + labels, areas = np.unique(sem_seg, return_counts=True) + sorted_idxs = np.argsort(-areas).tolist() + labels = labels[sorted_idxs] + for label in filter(lambda l: l < len(self.metadata.stuff_classes), labels): + try: + mask_color = [x / 255 for x in self.metadata.stuff_colors[label]] + except (AttributeError, IndexError): + mask_color = None + + binary_mask = (sem_seg == label).astype(np.uint8) + text = self.metadata.stuff_classes[label] + self.draw_binary_mask( + binary_mask, + color=mask_color, + edge_color=_OFF_WHITE, + text=text, + alpha=alpha, + area_threshold=area_threshold, + ) + return self.output + + def draw_panoptic_seg(self, panoptic_seg, segments_info, area_threshold=None, alpha=0.7): + """ + Draw panoptic prediction annotations or results. + + Args: + panoptic_seg (Tensor): of shape (height, width) where the values are ids for each + segment. + segments_info (list[dict] or None): Describe each segment in `panoptic_seg`. + If it is a ``list[dict]``, each dict contains keys "id", "category_id". + If None, category id of each pixel is computed by + ``pixel // metadata.label_divisor``. + area_threshold (int): stuff segments with less than `area_threshold` are not drawn. + + Returns: + output (VisImage): image object with visualizations. + """ + pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata) + + if self._instance_mode == ColorMode.IMAGE_BW: + self.output.reset_image(self._create_grayscale_image(pred.non_empty_mask())) + + # draw mask for all semantic segments first i.e. "stuff" + for mask, sinfo in pred.semantic_masks(): + category_idx = sinfo["category_id"] + try: + mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]] + except AttributeError: + mask_color = None + + text = self.metadata.stuff_classes[category_idx] + self.draw_binary_mask( + mask, + color=mask_color, + edge_color=_OFF_WHITE, + text=text, + alpha=alpha, + area_threshold=area_threshold, + ) + + # draw mask for all instances second + all_instances = list(pred.instance_masks()) + if len(all_instances) == 0: + return self.output + masks, sinfo = list(zip(*all_instances)) + category_ids = [x["category_id"] for x in sinfo] + + try: + scores = [x["score"] for x in sinfo] + except KeyError: + scores = None + labels = _create_text_labels( + category_ids, scores, self.metadata.thing_classes, [x.get("iscrowd", 0) for x in sinfo] + ) + + try: + colors = [ + self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in category_ids + ] + except AttributeError: + colors = None + self.overlay_instances(masks=masks, labels=labels, assigned_colors=colors, alpha=alpha) + + return self.output + + draw_panoptic_seg_predictions = draw_panoptic_seg # backward compatibility + + def draw_dataset_dict(self, dic): + """ + Draw annotations/segmentaions in Detectron2 Dataset format. + + Args: + dic (dict): annotation/segmentation data of one image, in Detectron2 Dataset format. + + Returns: + output (VisImage): image object with visualizations. + """ + annos = dic.get("annotations", None) + if annos: + if "segmentation" in annos[0]: + masks = [x["segmentation"] for x in annos] + else: + masks = None + if "keypoints" in annos[0]: + keypts = [x["keypoints"] for x in annos] + keypts = np.array(keypts).reshape(len(annos), -1, 3) + else: + keypts = None + + boxes = [ + BoxMode.convert(x["bbox"], x["bbox_mode"], BoxMode.XYXY_ABS) + if len(x["bbox"]) == 4 + else x["bbox"] + for x in annos + ] + + colors = None + category_ids = [x["category_id"] for x in annos] + if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("thing_colors"): + colors = [ + self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) + for c in category_ids + ] + names = self.metadata.get("thing_classes", None) + labels = _create_text_labels( + category_ids, + scores=None, + class_names=names, + is_crowd=[x.get("iscrowd", 0) for x in annos], + ) + self.overlay_instances( + labels=labels, boxes=boxes, masks=masks, keypoints=keypts, assigned_colors=colors + ) + + sem_seg = dic.get("sem_seg", None) + if sem_seg is None and "sem_seg_file_name" in dic: + with PathManager.open(dic["sem_seg_file_name"], "rb") as f: + sem_seg = Image.open(f) + sem_seg = np.asarray(sem_seg, dtype="uint8") + if sem_seg is not None: + self.draw_sem_seg(sem_seg, area_threshold=0, alpha=0.5) + + pan_seg = dic.get("pan_seg", None) + if pan_seg is None and "pan_seg_file_name" in dic: + with PathManager.open(dic["pan_seg_file_name"], "rb") as f: + pan_seg = Image.open(f) + pan_seg = np.asarray(pan_seg) + from panopticapi.utils import rgb2id + + pan_seg = rgb2id(pan_seg) + if pan_seg is not None: + segments_info = dic["segments_info"] + pan_seg = torch.tensor(pan_seg) + self.draw_panoptic_seg(pan_seg, segments_info, area_threshold=0, alpha=0.5) + return self.output + + def overlay_instances( + self, + *, + boxes=None, + labels=None, + masks=None, + keypoints=None, + assigned_colors=None, + alpha=0.5, + ): + """ + Args: + boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`, + or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image, + or a :class:`RotatedBoxes`, + or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format + for the N objects in a single image, + labels (list[str]): the text to be displayed for each instance. + masks (masks-like object): Supported types are: + + * :class:`detectron2.structures.PolygonMasks`, + :class:`detectron2.structures.BitMasks`. + * list[list[ndarray]]: contains the segmentation masks for all objects in one image. + The first level of the list corresponds to individual instances. The second + level to all the polygon that compose the instance, and the third level + to the polygon coordinates. The third level should have the format of + [x0, y0, x1, y1, ..., xn, yn] (n >= 3). + * list[ndarray]: each ndarray is a binary mask of shape (H, W). + * list[dict]: each dict is a COCO-style RLE. + keypoints (Keypoint or array like): an array-like object of shape (N, K, 3), + where the N is the number of instances and K is the number of keypoints. + The last dimension corresponds to (x, y, visibility or score). + assigned_colors (list[matplotlib.colors]): a list of colors, where each color + corresponds to each mask or box in the image. Refer to 'matplotlib.colors' + for full list of formats that the colors are accepted in. + Returns: + output (VisImage): image object with visualizations. + """ + num_instances = 0 + if boxes is not None: + boxes = self._convert_boxes(boxes) + num_instances = len(boxes) + if masks is not None: + masks = self._convert_masks(masks) + if num_instances: + assert len(masks) == num_instances + else: + num_instances = len(masks) + if keypoints is not None: + if num_instances: + assert len(keypoints) == num_instances + else: + num_instances = len(keypoints) + keypoints = self._convert_keypoints(keypoints) + if labels is not None: + assert len(labels) == num_instances + if assigned_colors is None: + assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)] + if num_instances == 0: + return self.output + if boxes is not None and boxes.shape[1] == 5: + return self.overlay_rotated_instances( + boxes=boxes, labels=labels, assigned_colors=assigned_colors + ) + + # Display in largest to smallest order to reduce occlusion. + areas = None + if boxes is not None: + areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1) + elif masks is not None: + areas = np.asarray([x.area() for x in masks]) + + if areas is not None: + sorted_idxs = np.argsort(-areas).tolist() + # Re-order overlapped instances in descending order. + boxes = boxes[sorted_idxs] if boxes is not None else None + labels = [labels[k] for k in sorted_idxs] if labels is not None else None + masks = [masks[idx] for idx in sorted_idxs] if masks is not None else None + assigned_colors = [assigned_colors[idx] for idx in sorted_idxs] + keypoints = keypoints[sorted_idxs] if keypoints is not None else None + + for i in range(num_instances): + color = assigned_colors[i] + if boxes is not None: + self.draw_box(boxes[i], edge_color=color) + + if masks is not None: + for segment in masks[i].polygons: + self.draw_polygon(segment.reshape(-1, 2), color, alpha=alpha) + + if labels is not None: + # first get a box + if boxes is not None: + x0, y0, x1, y1 = boxes[i] + text_pos = (x0, y0) # if drawing boxes, put text on the box corner. + horiz_align = "left" + elif masks is not None: + # skip small mask without polygon + if len(masks[i].polygons) == 0: + continue + + x0, y0, x1, y1 = masks[i].bbox() + + # draw text in the center (defined by median) when box is not drawn + # median is less sensitive to outliers. + text_pos = np.median(masks[i].mask.nonzero(), axis=1)[::-1] + horiz_align = "center" + else: + continue # drawing the box confidence for keypoints isn't very useful. + # for small objects, draw text at the side to avoid occlusion + instance_area = (y1 - y0) * (x1 - x0) + if ( + instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale + or y1 - y0 < 40 * self.output.scale + ): + if y1 >= self.output.height - 5: + text_pos = (x1, y0) + else: + text_pos = (x0, y1) + + height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width) + lighter_color = self._change_color_brightness(color, brightness_factor=0.7) + font_size = ( + np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2) + * 0.5 + * self._default_font_size + ) + self.draw_text( + labels[i], + text_pos, + color=lighter_color, + horizontal_alignment=horiz_align, + font_size=font_size, + ) + + # draw keypoints + if keypoints is not None: + for keypoints_per_instance in keypoints: + self.draw_and_connect_keypoints(keypoints_per_instance) + + return self.output + + def overlay_rotated_instances(self, boxes=None, labels=None, assigned_colors=None): + """ + Args: + boxes (ndarray): an Nx5 numpy array of + (x_center, y_center, width, height, angle_degrees) format + for the N objects in a single image. + labels (list[str]): the text to be displayed for each instance. + assigned_colors (list[matplotlib.colors]): a list of colors, where each color + corresponds to each mask or box in the image. Refer to 'matplotlib.colors' + for full list of formats that the colors are accepted in. + + Returns: + output (VisImage): image object with visualizations. + """ + num_instances = len(boxes) + + if assigned_colors is None: + assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)] + if num_instances == 0: + return self.output + + # Display in largest to smallest order to reduce occlusion. + if boxes is not None: + areas = boxes[:, 2] * boxes[:, 3] + + sorted_idxs = np.argsort(-areas).tolist() + # Re-order overlapped instances in descending order. + boxes = boxes[sorted_idxs] + labels = [labels[k] for k in sorted_idxs] if labels is not None else None + colors = [assigned_colors[idx] for idx in sorted_idxs] + + for i in range(num_instances): + self.draw_rotated_box_with_label( + boxes[i], edge_color=colors[i], label=labels[i] if labels is not None else None + ) + + return self.output + + def draw_and_connect_keypoints(self, keypoints): + """ + Draws keypoints of an instance and follows the rules for keypoint connections + to draw lines between appropriate keypoints. This follows color heuristics for + line color. + + Args: + keypoints (Tensor): a tensor of shape (K, 3), where K is the number of keypoints + and the last dimension corresponds to (x, y, probability). + + Returns: + output (VisImage): image object with visualizations. + """ + visible = {} + keypoint_names = self.metadata.get("keypoint_names") + for idx, keypoint in enumerate(keypoints): + + # draw keypoint + x, y, prob = keypoint + if prob > self.keypoint_threshold: + self.draw_circle((x, y), color=_RED) + if keypoint_names: + keypoint_name = keypoint_names[idx] + visible[keypoint_name] = (x, y) + + if self.metadata.get("keypoint_connection_rules"): + for kp0, kp1, color in self.metadata.keypoint_connection_rules: + if kp0 in visible and kp1 in visible: + x0, y0 = visible[kp0] + x1, y1 = visible[kp1] + color = tuple(x / 255.0 for x in color) + self.draw_line([x0, x1], [y0, y1], color=color) + + # draw lines from nose to mid-shoulder and mid-shoulder to mid-hip + # Note that this strategy is specific to person keypoints. + # For other keypoints, it should just do nothing + try: + ls_x, ls_y = visible["left_shoulder"] + rs_x, rs_y = visible["right_shoulder"] + mid_shoulder_x, mid_shoulder_y = (ls_x + rs_x) / 2, (ls_y + rs_y) / 2 + except KeyError: + pass + else: + # draw line from nose to mid-shoulder + nose_x, nose_y = visible.get("nose", (None, None)) + if nose_x is not None: + self.draw_line([nose_x, mid_shoulder_x], [nose_y, mid_shoulder_y], color=_RED) + + try: + # draw line from mid-shoulder to mid-hip + lh_x, lh_y = visible["left_hip"] + rh_x, rh_y = visible["right_hip"] + except KeyError: + pass + else: + mid_hip_x, mid_hip_y = (lh_x + rh_x) / 2, (lh_y + rh_y) / 2 + self.draw_line([mid_hip_x, mid_shoulder_x], [mid_hip_y, mid_shoulder_y], color=_RED) + return self.output + + """ + Primitive drawing functions: + """ + + def draw_text( + self, + text, + position, + *, + font_size=None, + color="g", + horizontal_alignment="center", + rotation=0, + ): + """ + Args: + text (str): class label + position (tuple): a tuple of the x and y coordinates to place text on image. + font_size (int, optional): font of the text. If not provided, a font size + proportional to the image width is calculated and used. + color: color of the text. Refer to `matplotlib.colors` for full list + of formats that are accepted. + horizontal_alignment (str): see `matplotlib.text.Text` + rotation: rotation angle in degrees CCW + + Returns: + output (VisImage): image object with text drawn. + """ + if not font_size: + font_size = self._default_font_size + + # since the text background is dark, we don't want the text to be dark + color = np.maximum(list(mplc.to_rgb(color)), 0.2) + color[np.argmax(color)] = max(0.8, np.max(color)) + + x, y = position + self.output.ax.text( + x, + y, + text, + size=font_size * self.output.scale, + family="sans-serif", + bbox={"facecolor": "black", "alpha": 0.8, "pad": 0.7, "edgecolor": "none"}, + verticalalignment="top", + horizontalalignment=horizontal_alignment, + color=color, + zorder=10, + rotation=rotation, + ) + return self.output + + def draw_box(self, box_coord, alpha=0.5, edge_color="g", line_style="-"): + """ + Args: + box_coord (tuple): a tuple containing x0, y0, x1, y1 coordinates, where x0 and y0 + are the coordinates of the image's top left corner. x1 and y1 are the + coordinates of the image's bottom right corner. + alpha (float): blending efficient. Smaller values lead to more transparent masks. + edge_color: color of the outline of the box. Refer to `matplotlib.colors` + for full list of formats that are accepted. + line_style (string): the string to use to create the outline of the boxes. + + Returns: + output (VisImage): image object with box drawn. + """ + x0, y0, x1, y1 = box_coord + width = x1 - x0 + height = y1 - y0 + + linewidth = max(self._default_font_size / 4, 1) + + self.output.ax.add_patch( + mpl.patches.Rectangle( + (x0, y0), + width, + height, + fill=False, + edgecolor=edge_color, + linewidth=linewidth * self.output.scale, + alpha=alpha, + linestyle=line_style, + ) + ) + return self.output + + def draw_rotated_box_with_label( + self, rotated_box, alpha=0.5, edge_color="g", line_style="-", label=None + ): + """ + Draw a rotated box with label on its top-left corner. + + Args: + rotated_box (tuple): a tuple containing (cnt_x, cnt_y, w, h, angle), + where cnt_x and cnt_y are the center coordinates of the box. + w and h are the width and height of the box. angle represents how + many degrees the box is rotated CCW with regard to the 0-degree box. + alpha (float): blending efficient. Smaller values lead to more transparent masks. + edge_color: color of the outline of the box. Refer to `matplotlib.colors` + for full list of formats that are accepted. + line_style (string): the string to use to create the outline of the boxes. + label (string): label for rotated box. It will not be rendered when set to None. + + Returns: + output (VisImage): image object with box drawn. + """ + cnt_x, cnt_y, w, h, angle = rotated_box + area = w * h + # use thinner lines when the box is small + linewidth = self._default_font_size / ( + 6 if area < _SMALL_OBJECT_AREA_THRESH * self.output.scale else 3 + ) + + theta = angle * math.pi / 180.0 + c = math.cos(theta) + s = math.sin(theta) + rect = [(-w / 2, h / 2), (-w / 2, -h / 2), (w / 2, -h / 2), (w / 2, h / 2)] + # x: left->right ; y: top->down + rotated_rect = [(s * yy + c * xx + cnt_x, c * yy - s * xx + cnt_y) for (xx, yy) in rect] + for k in range(4): + j = (k + 1) % 4 + self.draw_line( + [rotated_rect[k][0], rotated_rect[j][0]], + [rotated_rect[k][1], rotated_rect[j][1]], + color=edge_color, + linestyle="--" if k == 1 else line_style, + linewidth=linewidth, + ) + + if label is not None: + text_pos = rotated_rect[1] # topleft corner + + height_ratio = h / np.sqrt(self.output.height * self.output.width) + label_color = self._change_color_brightness(edge_color, brightness_factor=0.7) + font_size = ( + np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2) * 0.5 * self._default_font_size + ) + self.draw_text(label, text_pos, color=label_color, font_size=font_size, rotation=angle) + + return self.output + + def draw_circle(self, circle_coord, color, radius=3): + """ + Args: + circle_coord (list(int) or tuple(int)): contains the x and y coordinates + of the center of the circle. + color: color of the polygon. Refer to `matplotlib.colors` for a full list of + formats that are accepted. + radius (int): radius of the circle. + + Returns: + output (VisImage): image object with box drawn. + """ + x, y = circle_coord + self.output.ax.add_patch( + mpl.patches.Circle(circle_coord, radius=radius, fill=True, color=color) + ) + return self.output + + def draw_line(self, x_data, y_data, color, linestyle="-", linewidth=None): + """ + Args: + x_data (list[int]): a list containing x values of all the points being drawn. + Length of list should match the length of y_data. + y_data (list[int]): a list containing y values of all the points being drawn. + Length of list should match the length of x_data. + color: color of the line. Refer to `matplotlib.colors` for a full list of + formats that are accepted. + linestyle: style of the line. Refer to `matplotlib.lines.Line2D` + for a full list of formats that are accepted. + linewidth (float or None): width of the line. When it's None, + a default value will be computed and used. + + Returns: + output (VisImage): image object with line drawn. + """ + if linewidth is None: + linewidth = self._default_font_size / 3 + linewidth = max(linewidth, 1) + self.output.ax.add_line( + mpl.lines.Line2D( + x_data, + y_data, + linewidth=linewidth * self.output.scale, + color=color, + linestyle=linestyle, + ) + ) + return self.output + + def draw_binary_mask( + self, binary_mask, color=None, *, edge_color=None, text=None, alpha=0.5, area_threshold=10 + ): + """ + Args: + binary_mask (ndarray): numpy array of shape (H, W), where H is the image height and + W is the image width. Each value in the array is either a 0 or 1 value of uint8 + type. + color: color of the mask. Refer to `matplotlib.colors` for a full list of + formats that are accepted. If None, will pick a random color. + edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a + full list of formats that are accepted. + text (str): if None, will be drawn on the object + alpha (float): blending efficient. Smaller values lead to more transparent masks. + area_threshold (float): a connected component smaller than this area will not be shown. + + Returns: + output (VisImage): image object with mask drawn. + """ + if color is None: + color = random_color(rgb=True, maximum=1) + color = mplc.to_rgb(color) + + has_valid_segment = False + binary_mask = binary_mask.astype("uint8") # opencv needs uint8 + mask = GenericMask(binary_mask, self.output.height, self.output.width) + shape2d = (binary_mask.shape[0], binary_mask.shape[1]) + + if not mask.has_holes: + # draw polygons for regular masks + for segment in mask.polygons: + area = mask_util.area(mask_util.frPyObjects([segment], shape2d[0], shape2d[1])) + if area < (area_threshold or 0): + continue + has_valid_segment = True + segment = segment.reshape(-1, 2) + self.draw_polygon(segment, color=color, edge_color=edge_color, alpha=alpha) + else: + # TODO: Use Path/PathPatch to draw vector graphics: + # https://stackoverflow.com/questions/8919719/how-to-plot-a-complex-polygon + rgba = np.zeros(shape2d + (4,), dtype="float32") + rgba[:, :, :3] = color + rgba[:, :, 3] = (mask.mask == 1).astype("float32") * alpha + has_valid_segment = True + self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0)) + + if text is not None and has_valid_segment: + lighter_color = self._change_color_brightness(color, brightness_factor=0.7) + self._draw_text_in_mask(binary_mask, text, lighter_color) + return self.output + + def draw_soft_mask(self, soft_mask, color=None, *, text=None, alpha=0.5): + """ + Args: + soft_mask (ndarray): float array of shape (H, W), each value in [0, 1]. + color: color of the mask. Refer to `matplotlib.colors` for a full list of + formats that are accepted. If None, will pick a random color. + text (str): if None, will be drawn on the object + alpha (float): blending efficient. Smaller values lead to more transparent masks. + + Returns: + output (VisImage): image object with mask drawn. + """ + if color is None: + color = random_color(rgb=True, maximum=1) + color = mplc.to_rgb(color) + + shape2d = (soft_mask.shape[0], soft_mask.shape[1]) + rgba = np.zeros(shape2d + (4,), dtype="float32") + rgba[:, :, :3] = color + rgba[:, :, 3] = soft_mask * alpha + self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0)) + + if text is not None: + lighter_color = self._change_color_brightness(color, brightness_factor=0.7) + binary_mask = (soft_mask > 0.5).astype("uint8") + self._draw_text_in_mask(binary_mask, text, lighter_color) + return self.output + + def draw_polygon(self, segment, color, edge_color=None, alpha=0.5): + """ + Args: + segment: numpy array of shape Nx2, containing all the points in the polygon. + color: color of the polygon. Refer to `matplotlib.colors` for a full list of + formats that are accepted. + edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a + full list of formats that are accepted. If not provided, a darker shade + of the polygon color will be used instead. + alpha (float): blending efficient. Smaller values lead to more transparent masks. + + Returns: + output (VisImage): image object with polygon drawn. + """ + if edge_color is None: + # make edge color darker than the polygon color + if alpha > 0.8: + edge_color = self._change_color_brightness(color, brightness_factor=-0.7) + else: + edge_color = color + edge_color = mplc.to_rgb(edge_color) + (1,) + + polygon = mpl.patches.Polygon( + segment, + fill=True, + facecolor=mplc.to_rgb(color) + (alpha,), + edgecolor=edge_color, + linewidth=max(self._default_font_size // 15 * self.output.scale, 1), + ) + self.output.ax.add_patch(polygon) + return self.output + + """ + Internal methods: + """ + + def _jitter(self, color): + """ + Randomly modifies given color to produce a slightly different color than the color given. + + Args: + color (tuple[double]): a tuple of 3 elements, containing the RGB values of the color + picked. The values in the list are in the [0.0, 1.0] range. + + Returns: + jittered_color (tuple[double]): a tuple of 3 elements, containing the RGB values of the + color after being jittered. The values in the list are in the [0.0, 1.0] range. + """ + color = mplc.to_rgb(color) + vec = np.random.rand(3) + # better to do it in another color space + vec = vec / np.linalg.norm(vec) * 0.5 + res = np.clip(vec + color, 0, 1) + return tuple(res) + + def _create_grayscale_image(self, mask=None): + """ + Create a grayscale version of the original image. + The colors in masked area, if given, will be kept. + """ + img_bw = self.img.astype("f4").mean(axis=2) + img_bw = np.stack([img_bw] * 3, axis=2) + if mask is not None: + img_bw[mask] = self.img[mask] + return img_bw + + def _change_color_brightness(self, color, brightness_factor): + """ + Depending on the brightness_factor, gives a lighter or darker color i.e. a color with + less or more saturation than the original color. + + Args: + color: color of the polygon. Refer to `matplotlib.colors` for a full list of + formats that are accepted. + brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of + 0 will correspond to no change, a factor in [-1.0, 0) range will result in + a darker color and a factor in (0, 1.0] range will result in a lighter color. + + Returns: + modified_color (tuple[double]): a tuple containing the RGB values of the + modified color. Each value in the tuple is in the [0.0, 1.0] range. + """ + assert brightness_factor >= -1.0 and brightness_factor <= 1.0 + color = mplc.to_rgb(color) + polygon_color = colorsys.rgb_to_hls(*mplc.to_rgb(color)) + modified_lightness = polygon_color[1] + (brightness_factor * polygon_color[1]) + modified_lightness = 0.0 if modified_lightness < 0.0 else modified_lightness + modified_lightness = 1.0 if modified_lightness > 1.0 else modified_lightness + modified_color = colorsys.hls_to_rgb(polygon_color[0], modified_lightness, polygon_color[2]) + return modified_color + + def _convert_boxes(self, boxes): + """ + Convert different format of boxes to an NxB array, where B = 4 or 5 is the box dimension. + """ + if isinstance(boxes, Boxes) or isinstance(boxes, RotatedBoxes): + return boxes.tensor.detach().numpy() + else: + return np.asarray(boxes) + + def _convert_masks(self, masks_or_polygons): + """ + Convert different format of masks or polygons to a tuple of masks and polygons. + + Returns: + list[GenericMask]: + """ + + m = masks_or_polygons + if isinstance(m, PolygonMasks): + m = m.polygons + if isinstance(m, BitMasks): + m = m.tensor.numpy() + if isinstance(m, torch.Tensor): + m = m.numpy() + ret = [] + for x in m: + if isinstance(x, GenericMask): + ret.append(x) + else: + ret.append(GenericMask(x, self.output.height, self.output.width)) + return ret + + def _draw_text_in_mask(self, binary_mask, text, color): + """ + Find proper places to draw text given a binary mask. + """ + # TODO sometimes drawn on wrong objects. the heuristics here can improve. + _num_cc, cc_labels, stats, centroids = cv2.connectedComponentsWithStats(binary_mask, 8) + if stats[1:, -1].size == 0: + return + largest_component_id = np.argmax(stats[1:, -1]) + 1 + + # draw text on the largest component, as well as other very large components. + for cid in range(1, _num_cc): + if cid == largest_component_id or stats[cid, -1] > _LARGE_MASK_AREA_THRESH: + # median is more stable than centroid + # center = centroids[largest_component_id] + center = np.median((cc_labels == cid).nonzero(), axis=1)[::-1] + self.draw_text(text, center, color=color) + + def _convert_keypoints(self, keypoints): + if isinstance(keypoints, Keypoints): + keypoints = keypoints.tensor + keypoints = np.asarray(keypoints) + return keypoints + + def get_output(self): + """ + Returns: + output (VisImage): the image output containing the visualizations added + to the image. + """ + return self.output diff --git a/iChat/models/grit_src/third_party/CenterNet2/dev/README.md b/iChat/models/grit_src/third_party/CenterNet2/dev/README.md new file mode 100644 index 0000000000000000000000000000000000000000..bec811ad002a016f2137d9d0ea61c27ee5e78992 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/dev/README.md @@ -0,0 +1,7 @@ + +## Some scripts for developers to use, include: + +- `linter.sh`: lint the codebase before commit. +- `run_{inference,instant}_tests.sh`: run inference/training for a few iterations. + Note that these tests require 2 GPUs. +- `parse_results.sh`: parse results from a log file. diff --git a/iChat/models/grit_src/third_party/CenterNet2/dev/linter.sh b/iChat/models/grit_src/third_party/CenterNet2/dev/linter.sh new file mode 100644 index 0000000000000000000000000000000000000000..e873186fe3ccf146630884255de0f7b98434abdc --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/dev/linter.sh @@ -0,0 +1,42 @@ +#!/bin/bash -e +# Copyright (c) Facebook, Inc. and its affiliates. + +# cd to detectron2 project root +cd "$(dirname "${BASH_SOURCE[0]}")/.." + +{ + black --version | grep -E "21\." > /dev/null +} || { + echo "Linter requires 'black==21.*' !" + exit 1 +} + +ISORT_VERSION=$(isort --version-number) +if [[ "$ISORT_VERSION" != 4.3* ]]; then + echo "Linter requires isort==4.3.21 !" + exit 1 +fi + +set -v + +echo "Running isort ..." +isort -y -sp . --atomic + +echo "Running black ..." +black -l 100 . + +echo "Running flake8 ..." +if [ -x "$(command -v flake8-3)" ]; then + flake8-3 . +else + python3 -m flake8 . +fi + +# echo "Running mypy ..." +# Pytorch does not have enough type annotations +# mypy detectron2/solver detectron2/structures detectron2/config + +echo "Running clang-format ..." +find . -regex ".*\.\(cpp\|c\|cc\|cu\|cxx\|h\|hh\|hpp\|hxx\|tcc\|mm\|m\)" -print0 | xargs -0 clang-format -i + +command -v arc > /dev/null && arc lint diff --git a/iChat/models/grit_src/third_party/CenterNet2/dev/packaging/README.md b/iChat/models/grit_src/third_party/CenterNet2/dev/packaging/README.md new file mode 100644 index 0000000000000000000000000000000000000000..0174b7dd528efcaa0fe27d46f40a3866f03e7c41 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/dev/packaging/README.md @@ -0,0 +1,17 @@ + +## To build a cu101 wheel for release: + +``` +$ nvidia-docker run -it --storage-opt "size=20GB" --name pt pytorch/manylinux-cuda101 +# inside the container: +# git clone https://github.com/facebookresearch/detectron2/ +# cd detectron2 +# export CU_VERSION=cu101 D2_VERSION_SUFFIX= PYTHON_VERSION=3.7 PYTORCH_VERSION=1.8 +# ./dev/packaging/build_wheel.sh +``` + +## To build all wheels for combinations of CUDA and Python +``` +./dev/packaging/build_all_wheels.sh +./dev/packaging/gen_wheel_index.sh /path/to/wheels +``` diff --git a/iChat/models/grit_src/third_party/CenterNet2/dev/packaging/build_all_wheels.sh b/iChat/models/grit_src/third_party/CenterNet2/dev/packaging/build_all_wheels.sh new file mode 100644 index 0000000000000000000000000000000000000000..98b5e4444828b48c8a54229ee04a44d8c7d38090 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/dev/packaging/build_all_wheels.sh @@ -0,0 +1,65 @@ +#!/bin/bash -e +# Copyright (c) Facebook, Inc. and its affiliates. + +[[ -d "dev/packaging" ]] || { + echo "Please run this script at detectron2 root!" + exit 1 +} + +build_one() { + cu=$1 + pytorch_ver=$2 + + case "$cu" in + cu*) + container_name=manylinux-cuda${cu/cu/} + ;; + cpu) + container_name=manylinux-cuda101 + ;; + *) + echo "Unrecognized cu=$cu" + exit 1 + ;; + esac + + echo "Launching container $container_name ..." + container_id="$container_name"_"$cu"_"$pytorch_ver" + + py_versions=(3.6 3.7 3.8 3.9) + + for py in "${py_versions[@]}"; do + docker run -itd \ + --name "$container_id" \ + --mount type=bind,source="$(pwd)",target=/detectron2 \ + pytorch/$container_name + + cat </dev/null 2>&1 && pwd )" +. "$script_dir/pkg_helpers.bash" + +echo "Build Settings:" +echo "CU_VERSION: $CU_VERSION" # e.g. cu101 +echo "D2_VERSION_SUFFIX: $D2_VERSION_SUFFIX" # e.g. +cu101 or "" +echo "PYTHON_VERSION: $PYTHON_VERSION" # e.g. 3.6 +echo "PYTORCH_VERSION: $PYTORCH_VERSION" # e.g. 1.4 + +setup_cuda +setup_wheel_python + +yum install ninja-build -y +ln -sv /usr/bin/ninja-build /usr/bin/ninja || true + +pip_install pip numpy -U +pip_install "torch==$PYTORCH_VERSION" \ + -f https://download.pytorch.org/whl/"$CU_VERSION"/torch_stable.html + +# use separate directories to allow parallel build +BASE_BUILD_DIR=build/$CU_VERSION-py$PYTHON_VERSION-pt$PYTORCH_VERSION +python setup.py \ + build -b "$BASE_BUILD_DIR" \ + bdist_wheel -b "$BASE_BUILD_DIR/build_dist" -d "wheels/$CU_VERSION/torch$PYTORCH_VERSION" +rm -rf "$BASE_BUILD_DIR" diff --git a/iChat/models/grit_src/third_party/CenterNet2/dev/packaging/gen_install_table.py b/iChat/models/grit_src/third_party/CenterNet2/dev/packaging/gen_install_table.py new file mode 100644 index 0000000000000000000000000000000000000000..b4c852dc53de613707b9668f748184c2b63b9dea --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/dev/packaging/gen_install_table.py @@ -0,0 +1,63 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. +# -*- coding: utf-8 -*- + +import argparse + +template = """
install 
\
+python -m pip install detectron2{d2_version} -f \\
+  https://dl.fbaipublicfiles.com/detectron2/wheels/{cuda}/torch{torch}/index.html
+
""" +CUDA_SUFFIX = { + "11.3": "cu113", + "11.1": "cu111", + "11.0": "cu110", + "10.2": "cu102", + "10.1": "cu101", + "10.0": "cu100", + "9.2": "cu92", + "cpu": "cpu", +} + + +def gen_header(torch_versions): + return '' + "".join( + [ + ''.format(t) + for t in torch_versions + ] + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--d2-version", help="detectron2 version number, default to empty") + args = parser.parse_args() + d2_version = f"=={args.d2_version}" if args.d2_version else "" + + all_versions = ( + [("1.8", k) for k in ["11.1", "10.2", "10.1", "cpu"]] + + [("1.9", k) for k in ["11.1", "10.2", "cpu"]] + + [("1.10", k) for k in ["11.3", "11.1", "10.2", "cpu"]] + ) + + torch_versions = sorted( + {k[0] for k in all_versions}, key=lambda x: int(x.split(".")[1]), reverse=True + ) + cuda_versions = sorted( + {k[1] for k in all_versions}, key=lambda x: float(x) if x != "cpu" else 0, reverse=True + ) + + table = gen_header(torch_versions) + for cu in cuda_versions: + table += f""" """ + cu_suffix = CUDA_SUFFIX[cu] + for torch in torch_versions: + if (torch, cu) in all_versions: + cell = template.format(d2_version=d2_version, cuda=cu_suffix, torch=torch) + else: + cell = "" + table += f""" """ + table += "" + table += "
CUDA torch {}
{cu}{cell}
" + print(table) diff --git a/iChat/models/grit_src/third_party/CenterNet2/dev/packaging/gen_wheel_index.sh b/iChat/models/grit_src/third_party/CenterNet2/dev/packaging/gen_wheel_index.sh new file mode 100644 index 0000000000000000000000000000000000000000..ec96a27d809fe87ad963f3ffa7147ca4afbc1711 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/dev/packaging/gen_wheel_index.sh @@ -0,0 +1,46 @@ +#!/bin/bash -e +# Copyright (c) Facebook, Inc. and its affiliates. + + +root=$(readlink -f $1) +if [[ -z "$root" ]]; then + echo "Usage: ./gen_wheel_index.sh /absolute/path/to/wheels" + exit +fi + +export LC_ALL=C # reproducible sort +# NOTE: all sort in this script might not work when xx.10 is released + +index=$root/index.html + +cd "$root" +for cu in cpu cu92 cu100 cu101 cu102 cu110 cu111 cu113; do + mkdir -p "$root/$cu" + cd "$root/$cu" + echo "Creating $PWD/index.html ..." + # First sort by torch version, then stable sort by d2 version with unique. + # As a result, the latest torch version for each d2 version is kept. + for whl in $(find -type f -name '*.whl' -printf '%P\n' \ + | sort -k 1 -r | sort -t '/' -k 2 --stable -r --unique); do + echo "$whl
" + done > index.html + + + for torch in torch*; do + cd "$root/$cu/$torch" + + # list all whl for each cuda,torch version + echo "Creating $PWD/index.html ..." + for whl in $(find . -type f -name '*.whl' -printf '%P\n' | sort -r); do + echo "$whl
" + done > index.html + done +done + +cd "$root" +# Just list everything: +echo "Creating $index ..." +for whl in $(find . -type f -name '*.whl' -printf '%P\n' | sort -r); do + echo "$whl
" +done > "$index" + diff --git a/iChat/models/grit_src/third_party/CenterNet2/dev/packaging/pkg_helpers.bash b/iChat/models/grit_src/third_party/CenterNet2/dev/packaging/pkg_helpers.bash new file mode 100644 index 0000000000000000000000000000000000000000..ed9acb00ae8627b96c057b4493d368c7dfeda8ae --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/dev/packaging/pkg_helpers.bash @@ -0,0 +1,76 @@ +#!/bin/bash -e +# Copyright (c) Facebook, Inc. and its affiliates. + +# Function to retry functions that sometimes timeout or have flaky failures +retry () { + $* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*) +} +# Install with pip a bit more robustly than the default +pip_install() { + retry pip install --progress-bar off "$@" +} + + +setup_cuda() { + # Now work out the CUDA settings + # Like other torch domain libraries, we choose common GPU architectures only. + # See https://github.com/pytorch/pytorch/blob/master/torch/utils/cpp_extension.py + # and https://github.com/pytorch/vision/blob/main/packaging/pkg_helpers.bash for reference. + export FORCE_CUDA=1 + case "$CU_VERSION" in + cu113) + export CUDA_HOME=/usr/local/cuda-11.3/ + export TORCH_CUDA_ARCH_LIST="3.7;5.0;5.2;6.0;6.1+PTX;7.0;7.5+PTX;8.0;8.6+PTX" + ;; + cu112) + export CUDA_HOME=/usr/local/cuda-11.2/ + export TORCH_CUDA_ARCH_LIST="3.7;5.0;5.2;6.0;6.1+PTX;7.0;7.5+PTX;8.0;8.6+PTX" + ;; + cu111) + export CUDA_HOME=/usr/local/cuda-11.1/ + export TORCH_CUDA_ARCH_LIST="3.7;5.0;5.2;6.0;6.1+PTX;7.0;7.5+PTX;8.0;8.6+PTX" + ;; + cu110) + export CUDA_HOME=/usr/local/cuda-11.0/ + export TORCH_CUDA_ARCH_LIST="3.7;5.0;5.2;6.0;6.1+PTX;7.0;7.5+PTX;8.0+PTX" + ;; + cu102) + export CUDA_HOME=/usr/local/cuda-10.2/ + export TORCH_CUDA_ARCH_LIST="3.7;5.0;5.2;6.0;6.1+PTX;7.0;7.5+PTX" + ;; + cu101) + export CUDA_HOME=/usr/local/cuda-10.1/ + export TORCH_CUDA_ARCH_LIST="3.7;5.0;5.2;6.0;6.1+PTX;7.0;7.5+PTX" + ;; + cu100) + export CUDA_HOME=/usr/local/cuda-10.0/ + export TORCH_CUDA_ARCH_LIST="3.7;5.0;5.2;6.0;6.1+PTX;7.0;7.5+PTX" + ;; + cu92) + export CUDA_HOME=/usr/local/cuda-9.2/ + export TORCH_CUDA_ARCH_LIST="3.7;5.0;5.2;6.0;6.1+PTX;7.0+PTX" + ;; + cpu) + unset FORCE_CUDA + export CUDA_VISIBLE_DEVICES= + ;; + *) + echo "Unrecognized CU_VERSION=$CU_VERSION" + exit 1 + ;; + esac +} + +setup_wheel_python() { + case "$PYTHON_VERSION" in + 3.6) python_abi=cp36-cp36m ;; + 3.7) python_abi=cp37-cp37m ;; + 3.8) python_abi=cp38-cp38 ;; + 3.9) python_abi=cp39-cp39 ;; + *) + echo "Unrecognized PYTHON_VERSION=$PYTHON_VERSION" + exit 1 + ;; + esac + export PATH="/opt/python/$python_abi/bin:$PATH" +} diff --git a/iChat/models/grit_src/third_party/CenterNet2/dev/parse_results.sh b/iChat/models/grit_src/third_party/CenterNet2/dev/parse_results.sh new file mode 100644 index 0000000000000000000000000000000000000000..80768a4005753447c49339790fe66c9b82a80aaf --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/dev/parse_results.sh @@ -0,0 +1,45 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. + +# A shell script that parses metrics from the log file. +# Make it easier for developers to track performance of models. + +LOG="$1" + +if [[ -z "$LOG" ]]; then + echo "Usage: $0 /path/to/log/file" + exit 1 +fi + +# [12/15 11:47:32] trainer INFO: Total training time: 12:15:04.446477 (0.4900 s / it) +# [12/15 11:49:03] inference INFO: Total inference time: 0:01:25.326167 (0.13652186737060548 s / img per device, on 8 devices) +# [12/15 11:49:03] inference INFO: Total inference pure compute time: ..... + +# training time +trainspeed=$(grep -o 'Overall training.*' "$LOG" | grep -Eo '\(.*\)' | grep -o '[0-9\.]*') +echo "Training speed: $trainspeed s/it" + +# inference time: there could be multiple inference during training +inferencespeed=$(grep -o 'Total inference pure.*' "$LOG" | tail -n1 | grep -Eo '\(.*\)' | grep -o '[0-9\.]*' | head -n1) +echo "Inference speed: $inferencespeed s/it" + +# [12/15 11:47:18] trainer INFO: eta: 0:00:00 iter: 90000 loss: 0.5407 (0.7256) loss_classifier: 0.1744 (0.2446) loss_box_reg: 0.0838 (0.1160) loss_mask: 0.2159 (0.2722) loss_objectness: 0.0244 (0.0429) loss_rpn_box_reg: 0.0279 (0.0500) time: 0.4487 (0.4899) data: 0.0076 (0.0975) lr: 0.000200 max mem: 4161 +memory=$(grep -o 'max[_ ]mem: [0-9]*' "$LOG" | tail -n1 | grep -o '[0-9]*') +echo "Training memory: $memory MB" + +echo "Easy to copypaste:" +echo "$trainspeed","$inferencespeed","$memory" + +echo "------------------------------" + +# [12/26 17:26:32] engine.coco_evaluation: copypaste: Task: bbox +# [12/26 17:26:32] engine.coco_evaluation: copypaste: AP,AP50,AP75,APs,APm,APl +# [12/26 17:26:32] engine.coco_evaluation: copypaste: 0.0017,0.0024,0.0017,0.0005,0.0019,0.0011 +# [12/26 17:26:32] engine.coco_evaluation: copypaste: Task: segm +# [12/26 17:26:32] engine.coco_evaluation: copypaste: AP,AP50,AP75,APs,APm,APl +# [12/26 17:26:32] engine.coco_evaluation: copypaste: 0.0014,0.0021,0.0016,0.0005,0.0016,0.0011 + +echo "COCO Results:" +num_tasks=$(grep -o 'copypaste:.*Task.*' "$LOG" | sort -u | wc -l) +# each task has 3 lines +grep -o 'copypaste:.*' "$LOG" | cut -d ' ' -f 2- | tail -n $((num_tasks * 3)) diff --git a/iChat/models/grit_src/third_party/CenterNet2/dev/run_inference_tests.sh b/iChat/models/grit_src/third_party/CenterNet2/dev/run_inference_tests.sh new file mode 100644 index 0000000000000000000000000000000000000000..bc9dcc56f06f79fc5efa42c04ffdc07c2787e3ac --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/dev/run_inference_tests.sh @@ -0,0 +1,44 @@ +#!/bin/bash -e +# Copyright (c) Facebook, Inc. and its affiliates. + +BIN="python tools/train_net.py" +OUTPUT="inference_test_output" +NUM_GPUS=2 + +CFG_LIST=( "${@:1}" ) + +if [ ${#CFG_LIST[@]} -eq 0 ]; then + CFG_LIST=( ./configs/quick_schedules/*inference_acc_test.yaml ) +fi + +echo "========================================================================" +echo "Configs to run:" +echo "${CFG_LIST[@]}" +echo "========================================================================" + + +for cfg in "${CFG_LIST[@]}"; do + echo "========================================================================" + echo "Running $cfg ..." + echo "========================================================================" + $BIN \ + --eval-only \ + --num-gpus $NUM_GPUS \ + --config-file "$cfg" \ + OUTPUT_DIR $OUTPUT + rm -rf $OUTPUT +done + + +echo "========================================================================" +echo "Running demo.py ..." +echo "========================================================================" +DEMO_BIN="python demo/demo.py" +COCO_DIR=datasets/coco/val2014 +mkdir -pv $OUTPUT + +set -v + +$DEMO_BIN --config-file ./configs/quick_schedules/panoptic_fpn_R_50_inference_acc_test.yaml \ + --input $COCO_DIR/COCO_val2014_0000001933* --output $OUTPUT +rm -rf $OUTPUT diff --git a/iChat/models/grit_src/third_party/CenterNet2/dev/run_instant_tests.sh b/iChat/models/grit_src/third_party/CenterNet2/dev/run_instant_tests.sh new file mode 100644 index 0000000000000000000000000000000000000000..9fd9ba0c239d3e982c17711c9db872de3730decf --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/dev/run_instant_tests.sh @@ -0,0 +1,27 @@ +#!/bin/bash -e +# Copyright (c) Facebook, Inc. and its affiliates. + +BIN="python tools/train_net.py" +OUTPUT="instant_test_output" +NUM_GPUS=2 + +CFG_LIST=( "${@:1}" ) +if [ ${#CFG_LIST[@]} -eq 0 ]; then + CFG_LIST=( ./configs/quick_schedules/*instant_test.yaml ) +fi + +echo "========================================================================" +echo "Configs to run:" +echo "${CFG_LIST[@]}" +echo "========================================================================" + +for cfg in "${CFG_LIST[@]}"; do + echo "========================================================================" + echo "Running $cfg ..." + echo "========================================================================" + $BIN --num-gpus $NUM_GPUS --config-file "$cfg" \ + SOLVER.IMS_PER_BATCH $(($NUM_GPUS * 2)) \ + OUTPUT_DIR "$OUTPUT" + rm -rf "$OUTPUT" +done + diff --git a/iChat/models/grit_src/third_party/CenterNet2/docker/Dockerfile b/iChat/models/grit_src/third_party/CenterNet2/docker/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..4eec16dd0beac8b80c5446c9dd6cf15feaf87303 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docker/Dockerfile @@ -0,0 +1,47 @@ +FROM nvidia/cuda:11.1.1-cudnn8-devel-ubuntu18.04 +# use an older system (18.04) to avoid opencv incompatibility (issue#3524) + +ENV DEBIAN_FRONTEND noninteractive +RUN apt-get update && apt-get install -y \ + python3-opencv ca-certificates python3-dev git wget sudo ninja-build +RUN ln -sv /usr/bin/python3 /usr/bin/python + +# create a non-root user +ARG USER_ID=1000 +RUN useradd -m --no-log-init --system --uid ${USER_ID} appuser -g sudo +RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers +USER appuser +WORKDIR /home/appuser + +ENV PATH="/home/appuser/.local/bin:${PATH}" +RUN wget https://bootstrap.pypa.io/get-pip.py && \ + python3 get-pip.py --user && \ + rm get-pip.py + +# install dependencies +# See https://pytorch.org/ for other options if you use a different version of CUDA +RUN pip install --user tensorboard cmake # cmake from apt-get is too old +RUN pip install --user torch==1.10 torchvision==0.11.1 -f https://download.pytorch.org/whl/cu111/torch_stable.html + +RUN pip install --user 'git+https://github.com/facebookresearch/fvcore' +# install detectron2 +RUN git clone https://github.com/facebookresearch/detectron2 detectron2_repo +# set FORCE_CUDA because during `docker build` cuda is not accessible +ENV FORCE_CUDA="1" +# This will by default build detectron2 for all common cuda architectures and take a lot more time, +# because inside `docker build`, there is no way to tell which architecture will be used. +ARG TORCH_CUDA_ARCH_LIST="Kepler;Kepler+Tesla;Maxwell;Maxwell+Tegra;Pascal;Volta;Turing" +ENV TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST}" + +RUN pip install --user -e detectron2_repo + +# Set a fixed model cache directory. +ENV FVCORE_CACHE="/tmp" +WORKDIR /home/appuser/detectron2_repo + +# run detectron2 under user "appuser": +# wget http://images.cocodataset.org/val2017/000000439715.jpg -O input.jpg +# python3 demo/demo.py \ + #--config-file configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \ + #--input input.jpg --output outputs/ \ + #--opts MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl diff --git a/iChat/models/grit_src/third_party/CenterNet2/docker/README.md b/iChat/models/grit_src/third_party/CenterNet2/docker/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ea709f33b007abd2de044a0338659ec003330725 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docker/README.md @@ -0,0 +1,45 @@ + +## Use the container (with docker ≥ 19.03) + +``` +cd docker/ +# Build: +docker build --build-arg USER_ID=$UID -t detectron2:v0 . +# Launch (require GPUs): +docker run --gpus all -it \ + --shm-size=8gb --env="DISPLAY" --volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" \ + --name=detectron2 detectron2:v0 + +# Grant docker access to host X server to show images +xhost +local:`docker inspect --format='{{ .Config.Hostname }}' detectron2` +``` + +## Use the container (with docker-compose ≥ 1.28.0) + +Install docker-compose and nvidia-docker-toolkit, then run: +``` +cd docker && USER_ID=$UID docker-compose run detectron2 +``` + +## Use the deployment container (to test C++ examples) +After building the base detectron2 container as above, do: +``` +# Build: +docker build -t detectron2-deploy:v0 -f deploy.Dockerfile . +# Launch: +docker run --gpus all -it detectron2-deploy:v0 +``` + +#### Using a persistent cache directory + +You can prevent models from being re-downloaded on every run, +by storing them in a cache directory. + +To do this, add `--volume=$HOME/.torch/fvcore_cache:/tmp:rw` in the run command. + +## Install new dependencies +Add the following to `Dockerfile` to make persistent changes. +``` +RUN sudo apt-get update && sudo apt-get install -y vim +``` +Or run them in the container to make temporary changes. diff --git a/iChat/models/grit_src/third_party/CenterNet2/docker/deploy.Dockerfile b/iChat/models/grit_src/third_party/CenterNet2/docker/deploy.Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..30b4ed774368af89d654c9f01850d769e6cf9f52 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docker/deploy.Dockerfile @@ -0,0 +1,32 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# This file defines a container that compiles the C++ examples of detectron2. +# See docker/README.md for usage. + +# Depends on the image produced by "./Dockerfile" +FROM detectron2:v0 + +USER appuser +ENV HOME=/home/appuser +WORKDIR $HOME + +# Let torchvision find libtorch +ENV CMAKE_PREFIX_PATH=$HOME/.local/lib/python3.6/site-packages/torch/ + +RUN sudo apt-get update && sudo apt-get install libopencv-dev --yes + +# install libtorchvision +RUN git clone --branch v0.11.1 https://github.com/pytorch/vision/ +RUN mkdir vision/build && cd vision/build && \ + cmake .. -DCMAKE_INSTALL_PREFIX=$HOME/.local -DCMAKE_BUILD_TYPE=Release -DWITH_CUDA=on -DTORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST && \ + make -j && make install + +# make our installation take effect +ENV CPATH=$HOME/.local/include \ + LIBRARY_PATH=$HOME/.local/lib \ + LD_LIBRARY_PATH=$HOME/.local/lib + + +# build C++ examples of detectron2 +RUN cd detectron2_repo/tools/deploy && mkdir build && cd build && \ + cmake -DTORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST .. && make +# binaries will be available under tools/deploy/build diff --git a/iChat/models/grit_src/third_party/CenterNet2/docker/docker-compose.yml b/iChat/models/grit_src/third_party/CenterNet2/docker/docker-compose.yml new file mode 100644 index 0000000000000000000000000000000000000000..6665ab4c4bd40cae9973417b5b8d4c0c1edd7fc7 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docker/docker-compose.yml @@ -0,0 +1,26 @@ +version: "2.3" +services: + detectron2: + build: + context: . + dockerfile: Dockerfile + args: + USER_ID: ${USER_ID:-1000} + deploy: + resources: + reservations: + devices: + - capabilities: + - gpu + shm_size: "8gb" + ulimits: + memlock: -1 + stack: 67108864 + volumes: + - /tmp/.X11-unix:/tmp/.X11-unix:ro + environment: + - DISPLAY=$DISPLAY + - NVIDIA_VISIBLE_DEVICES=all + # Uncomment with proper source to access webcam from docker + # devices: + # - /dev/video0:/dev/video0 diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/.gitignore b/iChat/models/grit_src/third_party/CenterNet2/docs/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..e35d8850c9688b1ce82711694692cc574a799396 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/.gitignore @@ -0,0 +1 @@ +_build diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/Makefile b/iChat/models/grit_src/third_party/CenterNet2/docs/Makefile new file mode 100644 index 0000000000000000000000000000000000000000..718eddce170fe13b67216baf9d4d25b20e860506 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/Makefile @@ -0,0 +1,19 @@ +# Minimal makefile for Sphinx documentation +# Copyright (c) Facebook, Inc. and its affiliates. + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/README.md b/iChat/models/grit_src/third_party/CenterNet2/docs/README.md new file mode 100644 index 0000000000000000000000000000000000000000..8531cafd4d1aae0267f4fc5e7212f7db5ed90686 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/README.md @@ -0,0 +1,15 @@ +# Read the docs: + +The latest documentation built from this directory is available at [detectron2.readthedocs.io](https://detectron2.readthedocs.io/). +Documents in this directory are not meant to be read on github. + +# Build the docs: + +1. Install detectron2 according to [INSTALL.md](../INSTALL.md). +2. Install additional libraries required to build docs: + - docutils==0.16 + - Sphinx==3.2.0 + - recommonmark==0.6.0 + - sphinx_rtd_theme + +3. Run `make html` from this directory. diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/_static/css/custom.css b/iChat/models/grit_src/third_party/CenterNet2/docs/_static/css/custom.css new file mode 100644 index 0000000000000000000000000000000000000000..6c511764cf4c1d55a227619a98e5ba6578619ad7 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/_static/css/custom.css @@ -0,0 +1,30 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * some extra css to make markdown look similar between github/sphinx + */ + +/* + * Below is for install.md: + */ +.rst-content code { + white-space: pre; + border: 0px; +} + +.rst-content th { + border: 1px solid #e1e4e5; +} + +.rst-content th p { + /* otherwise will be default 24px for regular paragraph */ + margin-bottom: 0px; +} + +.rst-content .line-block { + /* otherwise will be 24px */ + margin-bottom: 0px; +} + +div.section > details { + padding-bottom: 1em; +} diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/conf.py b/iChat/models/grit_src/third_party/CenterNet2/docs/conf.py new file mode 100644 index 0000000000000000000000000000000000000000..c7232f41f7540d6d0c2e36827c9bd18d36fffac7 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/conf.py @@ -0,0 +1,382 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +# flake8: noqa + +# Configuration file for the Sphinx documentation builder. +# +# This file does only contain a selection of the most common options. For a +# full list see the documentation: +# http://www.sphinx-doc.org/en/master/config + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import sys +from unittest import mock +from sphinx.domains import Domain +from typing import Dict, List, Tuple + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +import sphinx_rtd_theme + + +class GithubURLDomain(Domain): + """ + Resolve certain links in markdown files to github source. + """ + + name = "githuburl" + ROOT = "https://github.com/facebookresearch/detectron2/blob/main/" + LINKED_DOC = ["tutorials/install", "tutorials/getting_started"] + + def resolve_any_xref(self, env, fromdocname, builder, target, node, contnode): + github_url = None + if not target.endswith("html") and target.startswith("../../"): + url = target.replace("../", "") + github_url = url + if fromdocname in self.LINKED_DOC: + # unresolved links in these docs are all github links + github_url = target + + if github_url is not None: + if github_url.endswith("MODEL_ZOO") or github_url.endswith("README"): + # bug of recommonmark. + # https://github.com/readthedocs/recommonmark/blob/ddd56e7717e9745f11300059e4268e204138a6b1/recommonmark/parser.py#L152-L155 + github_url += ".md" + print("Ref {} resolved to github:{}".format(target, github_url)) + contnode["refuri"] = self.ROOT + github_url + return [("githuburl:any", contnode)] + else: + return [] + + +# to support markdown +from recommonmark.parser import CommonMarkParser + +sys.path.insert(0, os.path.abspath("../")) +os.environ["_DOC_BUILDING"] = "True" +DEPLOY = os.environ.get("READTHEDOCS") == "True" + + +# -- Project information ----------------------------------------------------- + +# fmt: off +try: + import torch # noqa +except ImportError: + for m in [ + "torch", "torchvision", "torch.nn", "torch.nn.parallel", "torch.distributed", "torch.multiprocessing", "torch.autograd", + "torch.autograd.function", "torch.nn.modules", "torch.nn.modules.utils", "torch.utils", "torch.utils.data", "torch.onnx", + "torchvision", "torchvision.ops", + ]: + sys.modules[m] = mock.Mock(name=m) + sys.modules['torch'].__version__ = "1.7" # fake version + HAS_TORCH = False +else: + try: + torch.ops.detectron2 = mock.Mock(name="torch.ops.detectron2") + except: + pass + HAS_TORCH = True + +for m in [ + "cv2", "scipy", "portalocker", "detectron2._C", + "pycocotools", "pycocotools.mask", "pycocotools.coco", "pycocotools.cocoeval", + "google", "google.protobuf", "google.protobuf.internal", "onnx", + "caffe2", "caffe2.proto", "caffe2.python", "caffe2.python.utils", "caffe2.python.onnx", "caffe2.python.onnx.backend", +]: + sys.modules[m] = mock.Mock(name=m) +# fmt: on +sys.modules["cv2"].__version__ = "3.4" + +import detectron2 # isort: skip + +if HAS_TORCH: + from detectron2.utils.env import fixup_module_metadata + + fixup_module_metadata("torch.nn", torch.nn.__dict__) + fixup_module_metadata("torch.utils.data", torch.utils.data.__dict__) + + +project = "detectron2" +copyright = "2019-2020, detectron2 contributors" +author = "detectron2 contributors" + +# The short X.Y version +version = detectron2.__version__ +# The full version, including alpha/beta/rc tags +release = version + + +# -- General configuration --------------------------------------------------- + +# If your documentation needs a minimal Sphinx version, state it here. +# +needs_sphinx = "3.0" + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + "recommonmark", + "sphinx.ext.autodoc", + "sphinx.ext.napoleon", + "sphinx.ext.intersphinx", + "sphinx.ext.todo", + "sphinx.ext.coverage", + "sphinx.ext.mathjax", + "sphinx.ext.viewcode", + "sphinx.ext.githubpages", +] + +# -- Configurations for plugins ------------ +napoleon_google_docstring = True +napoleon_include_init_with_doc = True +napoleon_include_special_with_doc = True +napoleon_numpy_docstring = False +napoleon_use_rtype = False +autodoc_inherit_docstrings = False +autodoc_member_order = "bysource" + +if DEPLOY: + intersphinx_timeout = 10 +else: + # skip this when building locally + intersphinx_timeout = 0.5 +intersphinx_mapping = { + "python": ("https://docs.python.org/3.6", None), + "numpy": ("https://docs.scipy.org/doc/numpy/", None), + "torch": ("https://pytorch.org/docs/master/", None), +} +# ------------------------- + + +# Add any paths that contain templates here, relative to this directory. +templates_path = ["_templates"] + +source_suffix = [".rst", ".md"] + +# The master toctree document. +master_doc = "index" + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = None + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "build", "README.md", "tutorials/README.md"] + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = "sphinx" + + +# -- Options for HTML output ------------------------------------------------- + +html_theme = "sphinx_rtd_theme" +html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +# +# html_theme_options = {} + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ["_static"] +html_css_files = ["css/custom.css"] + +# Custom sidebar templates, must be a dictionary that maps document names +# to template names. +# +# The default sidebars (for documents that don't match any pattern) are +# defined by theme itself. Builtin themes are using these templates by +# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', +# 'searchbox.html']``. +# +# html_sidebars = {} + + +# -- Options for HTMLHelp output --------------------------------------------- + +# Output file base name for HTML help builder. +htmlhelp_basename = "detectron2doc" + + +# -- Options for LaTeX output ------------------------------------------------ + +latex_elements = { + # The paper size ('letterpaper' or 'a4paper'). + # + # 'papersize': 'letterpaper', + # The font size ('10pt', '11pt' or '12pt'). + # + # 'pointsize': '10pt', + # Additional stuff for the LaTeX preamble. + # + # 'preamble': '', + # Latex figure (float) alignment + # + # 'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, "detectron2.tex", "detectron2 Documentation", "detectron2 contributors", "manual") +] + + +# -- Options for manual page output ------------------------------------------ + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [(master_doc, "detectron2", "detectron2 Documentation", [author], 1)] + + +# -- Options for Texinfo output ---------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + ( + master_doc, + "detectron2", + "detectron2 Documentation", + author, + "detectron2", + "One line description of project.", + "Miscellaneous", + ) +] + + +# -- Options for todo extension ---------------------------------------------- + +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = True + + +def autodoc_skip_member(app, what, name, obj, skip, options): + # we hide something deliberately + if getattr(obj, "__HIDE_SPHINX_DOC__", False): + return True + + # Hide some that are deprecated or not intended to be used + HIDDEN = { + "ResNetBlockBase", + "GroupedBatchSampler", + "build_transform_gen", + "apply_transform_gens", + "TransformGen", + "apply_augmentations", + "StandardAugInput", + "build_batch_data_loader", + "draw_panoptic_seg_predictions", + "WarmupCosineLR", + "WarmupMultiStepLR", + "downgrade_config", + "upgrade_config", + "add_export_config", + } + try: + if name in HIDDEN or ( + hasattr(obj, "__doc__") and obj.__doc__.lower().strip().startswith("deprecated") + ): + print("Skipping deprecated object: {}".format(name)) + return True + except: + pass + return skip + + +_PAPER_DATA = { + "resnet": ("1512.03385", "Deep Residual Learning for Image Recognition"), + "fpn": ("1612.03144", "Feature Pyramid Networks for Object Detection"), + "mask r-cnn": ("1703.06870", "Mask R-CNN"), + "faster r-cnn": ( + "1506.01497", + "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", + ), + "deformconv": ("1703.06211", "Deformable Convolutional Networks"), + "deformconv2": ("1811.11168", "Deformable ConvNets v2: More Deformable, Better Results"), + "panopticfpn": ("1901.02446", "Panoptic Feature Pyramid Networks"), + "retinanet": ("1708.02002", "Focal Loss for Dense Object Detection"), + "cascade r-cnn": ("1712.00726", "Cascade R-CNN: Delving into High Quality Object Detection"), + "lvis": ("1908.03195", "LVIS: A Dataset for Large Vocabulary Instance Segmentation"), + "rrpn": ("1703.01086", "Arbitrary-Oriented Scene Text Detection via Rotation Proposals"), + "imagenet in 1h": ("1706.02677", "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour"), + "xception": ("1610.02357", "Xception: Deep Learning with Depthwise Separable Convolutions"), + "mobilenet": ( + "1704.04861", + "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications", + ), + "deeplabv3+": ( + "1802.02611", + "Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation", + ), + "dds": ("2003.13678", "Designing Network Design Spaces"), + "scaling": ("2103.06877", "Fast and Accurate Model Scaling"), + "fcos": ("2006.09214", "FCOS: A Simple and Strong Anchor-free Object Detector"), + "rethinking-batchnorm": ("2105.07576", 'Rethinking "Batch" in BatchNorm'), +} + + +def paper_ref_role( + typ: str, + rawtext: str, + text: str, + lineno: int, + inliner, + options: Dict = {}, + content: List[str] = [], +): + """ + Parse :paper:`xxx`. Similar to the "extlinks" sphinx extension. + """ + from docutils import nodes, utils + from sphinx.util.nodes import split_explicit_title + + text = utils.unescape(text) + has_explicit_title, title, link = split_explicit_title(text) + link = link.lower() + if link not in _PAPER_DATA: + inliner.reporter.warning("Cannot find paper " + link) + paper_url, paper_title = "#", link + else: + paper_url, paper_title = _PAPER_DATA[link] + if "/" not in paper_url: + paper_url = "https://arxiv.org/abs/" + paper_url + if not has_explicit_title: + title = paper_title + pnode = nodes.reference(title, title, internal=False, refuri=paper_url) + return [pnode], [] + + +def setup(app): + from recommonmark.transform import AutoStructify + + app.add_domain(GithubURLDomain) + app.connect("autodoc-skip-member", autodoc_skip_member) + app.add_role("paper", paper_ref_role) + app.add_config_value( + "recommonmark_config", + {"enable_math": True, "enable_inline_math": True, "enable_eval_rst": True}, + True, + ) + app.add_transform(AutoStructify) diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/index.rst b/iChat/models/grit_src/third_party/CenterNet2/docs/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..8634b7b12ab906c10a78d6053428029799282ffd --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/index.rst @@ -0,0 +1,14 @@ +.. detectron2 documentation master file, created by + sphinx-quickstart on Sat Sep 21 13:46:45 2019. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to detectron2's documentation! +====================================== + +.. toctree:: + :maxdepth: 2 + + tutorials/index + notes/index + modules/index diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/modules/checkpoint.rst b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/checkpoint.rst new file mode 100644 index 0000000000000000000000000000000000000000..449caaffd8a9d5e13040cb64aca073703c579a5d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/checkpoint.rst @@ -0,0 +1,7 @@ +detectron2.checkpoint +============================= + +.. automodule:: detectron2.checkpoint + :members: + :undoc-members: + :show-inheritance: diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/modules/config.rst b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/config.rst new file mode 100644 index 0000000000000000000000000000000000000000..c76913d83e696dfb02a8c25e8cd38bb25ad121f9 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/config.rst @@ -0,0 +1,18 @@ +detectron2.config +========================= + +Related tutorials: :doc:`../tutorials/configs`, :doc:`../tutorials/extend`. + +.. automodule:: detectron2.config + :members: + :undoc-members: + :show-inheritance: + + +Yaml Config References +----------------- + +.. literalinclude:: ../../detectron2/config/defaults.py + :language: python + :linenos: + :lines: 7- diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/modules/data.rst b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/data.rst new file mode 100644 index 0000000000000000000000000000000000000000..0d5bd89166fe6ad1d414c85055081f3fa9145764 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/data.rst @@ -0,0 +1,37 @@ +detectron2.data +======================= + +.. autodata:: detectron2.data.DatasetCatalog(dict) + :annotation: + +.. autodata:: detectron2.data.MetadataCatalog(dict) + :annotation: + +.. automodule:: detectron2.data + :members: + :undoc-members: + :show-inheritance: + +detectron2.data.detection\_utils module +--------------------------------------- + +.. automodule:: detectron2.data.detection_utils + :members: + :undoc-members: + :show-inheritance: + +detectron2.data.datasets module +--------------------------------------- + +.. automodule:: detectron2.data.datasets + :members: + :undoc-members: + :show-inheritance: + +detectron2.data.samplers module +--------------------------------------- + +.. automodule:: detectron2.data.samplers + :members: + :undoc-members: + :show-inheritance: diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/modules/data_transforms.rst b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/data_transforms.rst new file mode 100644 index 0000000000000000000000000000000000000000..1533a434bc1374a9825aa4fed0fab8abb2e8c02f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/data_transforms.rst @@ -0,0 +1,10 @@ +detectron2.data.transforms +==================================== + +Related tutorial: :doc:`../tutorials/augmentation`. + +.. automodule:: detectron2.data.transforms + :members: + :undoc-members: + :show-inheritance: + :imported-members: diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/modules/engine.rst b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/engine.rst new file mode 100644 index 0000000000000000000000000000000000000000..7e0d2b0762a601566772b97aaedb3c55b447fab5 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/engine.rst @@ -0,0 +1,26 @@ +detectron2.engine +========================= + +Related tutorial: :doc:`../tutorials/training`. + +.. automodule:: detectron2.engine + :members: + :undoc-members: + :show-inheritance: + + +detectron2.engine.defaults module +--------------------------------- + +.. automodule:: detectron2.engine.defaults + :members: + :undoc-members: + :show-inheritance: + +detectron2.engine.hooks module +--------------------------------- + +.. automodule:: detectron2.engine.hooks + :members: + :undoc-members: + :show-inheritance: diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/modules/evaluation.rst b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/evaluation.rst new file mode 100644 index 0000000000000000000000000000000000000000..69bfc4b9ef52ed26c61ec3d3feb5aa9bfa28da26 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/evaluation.rst @@ -0,0 +1,7 @@ +detectron2.evaluation +============================= + +.. automodule:: detectron2.evaluation + :members: + :undoc-members: + :show-inheritance: diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/modules/export.rst b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/export.rst new file mode 100644 index 0000000000000000000000000000000000000000..dcee14f869a7c0e60a1e861e07ecf1c49d272dac --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/export.rst @@ -0,0 +1,9 @@ +detectron2.export +========================= + +Related tutorial: :doc:`../tutorials/deployment`. + +.. automodule:: detectron2.export + :members: + :undoc-members: + :show-inheritance: diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/modules/fvcore.rst b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/fvcore.rst new file mode 100644 index 0000000000000000000000000000000000000000..c8bf9f58aea97cfad6430dd3c30924603cecf7ce --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/fvcore.rst @@ -0,0 +1,49 @@ +fvcore documentation +==================== + +Detectron2 depends on utilities in +`fvcore `_. +We include part of fvcore documentation here for easier reference. + +fvcore.nn +----------------- + +.. automodule:: fvcore.nn + :members: + :inherited-members: + :undoc-members: + :show-inheritance: + +fvcore.common +--------------------- + +.. automodule:: fvcore.common.checkpoint + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: fvcore.common.config + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: fvcore.common.history_buffer + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: fvcore.common.param_scheduler + :members: + :inherited-members: + :undoc-members: + :show-inheritance: + +.. automodule:: fvcore.common.registry + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: fvcore.common.timer + :members: + :undoc-members: + :show-inheritance: diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/modules/index.rst b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..14b754395bfbc581a181c7062acc47311103969d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/index.rst @@ -0,0 +1,19 @@ +API Documentation +================== + +.. toctree:: + + checkpoint + config + data + data_transforms + engine + evaluation + layers + model_zoo + modeling + solver + structures + utils + export + fvcore diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/modules/layers.rst b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/layers.rst new file mode 100644 index 0000000000000000000000000000000000000000..b43b42a7d9d01ec9fa8ef8a56019efa2bc494677 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/layers.rst @@ -0,0 +1,7 @@ +detectron2.layers +========================= + +.. automodule:: detectron2.layers + :members: + :undoc-members: + :show-inheritance: diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/modules/model_zoo.rst b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/model_zoo.rst new file mode 100644 index 0000000000000000000000000000000000000000..5abbad1ffe191480177e2173308cdc946159cf46 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/model_zoo.rst @@ -0,0 +1,7 @@ +detectron2.model_zoo +============================ + +.. automodule:: detectron2.model_zoo + :members: + :undoc-members: + :show-inheritance: diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/modules/modeling.rst b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/modeling.rst new file mode 100644 index 0000000000000000000000000000000000000000..a22c7ed35f4b694264c49c854109eb2fa85c20ea --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/modeling.rst @@ -0,0 +1,58 @@ +detectron2.modeling +=========================== + +.. automodule:: detectron2.modeling + :members: + :undoc-members: + :show-inheritance: + + +detectron2.modeling.poolers module +--------------------------------------- + +.. automodule:: detectron2.modeling.poolers + :members: + :undoc-members: + :show-inheritance: + + +detectron2.modeling.sampling module +------------------------------------ + +.. automodule:: detectron2.modeling.sampling + :members: + :undoc-members: + :show-inheritance: + + +detectron2.modeling.box_regression module +------------------------------------------ + +.. automodule:: detectron2.modeling.box_regression + :members: + :undoc-members: + :show-inheritance: + + +Model Registries +----------------- + +These are different registries provided in modeling. +Each registry provide you the ability to replace it with your customized component, +without having to modify detectron2's code. + +Note that it is impossible to allow users to customize any line of code directly. +Even just to add one line at some place, +you'll likely need to find out the smallest registry which contains that line, +and register your component to that registry. + + +.. autodata:: detectron2.modeling.META_ARCH_REGISTRY +.. autodata:: detectron2.modeling.BACKBONE_REGISTRY +.. autodata:: detectron2.modeling.PROPOSAL_GENERATOR_REGISTRY +.. autodata:: detectron2.modeling.RPN_HEAD_REGISTRY +.. autodata:: detectron2.modeling.ANCHOR_GENERATOR_REGISTRY +.. autodata:: detectron2.modeling.ROI_HEADS_REGISTRY +.. autodata:: detectron2.modeling.ROI_BOX_HEAD_REGISTRY +.. autodata:: detectron2.modeling.ROI_MASK_HEAD_REGISTRY +.. autodata:: detectron2.modeling.ROI_KEYPOINT_HEAD_REGISTRY diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/modules/solver.rst b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/solver.rst new file mode 100644 index 0000000000000000000000000000000000000000..59d98c72cceca33831681b5392d8bbec53fe70ad --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/solver.rst @@ -0,0 +1,7 @@ +detectron2.solver +========================= + +.. automodule:: detectron2.solver + :members: + :undoc-members: + :show-inheritance: diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/modules/structures.rst b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/structures.rst new file mode 100644 index 0000000000000000000000000000000000000000..1369dc0882d387930cd4f571f80c3c3157af6de6 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/structures.rst @@ -0,0 +1,7 @@ +detectron2.structures +============================= + +.. automodule:: detectron2.structures + :members: + :undoc-members: + :show-inheritance: diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/modules/utils.rst b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/utils.rst new file mode 100644 index 0000000000000000000000000000000000000000..ab58f2caf26b3beb08f72dd93d06485af5ace5c0 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/modules/utils.rst @@ -0,0 +1,80 @@ +detectron2.utils +======================== + +detectron2.utils.colormap module +-------------------------------- + +.. automodule:: detectron2.utils.colormap + :members: + :undoc-members: + :show-inheritance: + +detectron2.utils.comm module +---------------------------- + +.. automodule:: detectron2.utils.comm + :members: + :undoc-members: + :show-inheritance: + + +detectron2.utils.events module +------------------------------ + +.. automodule:: detectron2.utils.events + :members: + :undoc-members: + :show-inheritance: + + +detectron2.utils.logger module +------------------------------ + +.. automodule:: detectron2.utils.logger + :members: + :undoc-members: + :show-inheritance: + + +detectron2.utils.registry module +-------------------------------- + +.. automodule:: detectron2.utils.registry + :members: + :undoc-members: + :show-inheritance: + +detectron2.utils.memory module +---------------------------------- + +.. automodule:: detectron2.utils.memory + :members: + :undoc-members: + :show-inheritance: + + +detectron2.utils.analysis module +---------------------------------- + +.. automodule:: detectron2.utils.analysis + :members: + :undoc-members: + :show-inheritance: + + +detectron2.utils.visualizer module +---------------------------------- + +.. automodule:: detectron2.utils.visualizer + :members: + :undoc-members: + :show-inheritance: + +detectron2.utils.video\_visualizer module +----------------------------------------- + +.. automodule:: detectron2.utils.video_visualizer + :members: + :undoc-members: + :show-inheritance: + diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/notes/benchmarks.md b/iChat/models/grit_src/third_party/CenterNet2/docs/notes/benchmarks.md new file mode 100644 index 0000000000000000000000000000000000000000..b41588daf3a039b9034e80366c2710e90ba3e056 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/notes/benchmarks.md @@ -0,0 +1,196 @@ + +# Benchmarks + +Here we benchmark the training speed of a Mask R-CNN in detectron2, +with some other popular open source Mask R-CNN implementations. + + +### Settings + +* Hardware: 8 NVIDIA V100s with NVLink. +* Software: Python 3.7, CUDA 10.1, cuDNN 7.6.5, PyTorch 1.5, + TensorFlow 1.15.0rc2, Keras 2.2.5, MxNet 1.6.0b20190820. +* Model: an end-to-end R-50-FPN Mask-RCNN model, using the same hyperparameter as the + [Detectron baseline config](https://github.com/facebookresearch/Detectron/blob/master/configs/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml) + (it does not have scale augmentation). +* Metrics: We use the average throughput in iterations 100-500 to skip GPU warmup time. + Note that for R-CNN-style models, the throughput of a model typically changes during training, because + it depends on the predictions of the model. Therefore this metric is not directly comparable with + "train speed" in model zoo, which is the average speed of the entire training run. + + +### Main Results + +```eval_rst ++-------------------------------+--------------------+ +| Implementation | Throughput (img/s) | ++===============================+====================+ +| |D2| |PT| | 62 | ++-------------------------------+--------------------+ +| mmdetection_ |PT| | 53 | ++-------------------------------+--------------------+ +| maskrcnn-benchmark_ |PT| | 53 | ++-------------------------------+--------------------+ +| tensorpack_ |TF| | 50 | ++-------------------------------+--------------------+ +| simpledet_ |mxnet| | 39 | ++-------------------------------+--------------------+ +| Detectron_ |C2| | 19 | ++-------------------------------+--------------------+ +| `matterport/Mask_RCNN`__ |TF| | 14 | ++-------------------------------+--------------------+ + +.. _maskrcnn-benchmark: https://github.com/facebookresearch/maskrcnn-benchmark/ +.. _tensorpack: https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN +.. _mmdetection: https://github.com/open-mmlab/mmdetection/ +.. _simpledet: https://github.com/TuSimple/simpledet/ +.. _Detectron: https://github.com/facebookresearch/Detectron +__ https://github.com/matterport/Mask_RCNN/ + +.. |D2| image:: https://github.com/facebookresearch/detectron2/raw/main/.github/Detectron2-Logo-Horz.svg?sanitize=true + :height: 15pt + :target: https://github.com/facebookresearch/detectron2/ +.. |PT| image:: https://pytorch.org/assets/images/logo-icon.svg + :width: 15pt + :height: 15pt + :target: https://pytorch.org +.. |TF| image:: https://static.nvidiagrid.net/ngc/containers/tensorflow.png + :width: 15pt + :height: 15pt + :target: https://tensorflow.org +.. |mxnet| image:: https://github.com/dmlc/web-data/raw/master/mxnet/image/mxnet_favicon.png + :width: 15pt + :height: 15pt + :target: https://mxnet.apache.org/ +.. |C2| image:: https://caffe2.ai/static/logo.svg + :width: 15pt + :height: 15pt + :target: https://caffe2.ai +``` + + +Details for each implementation: + +* __Detectron2__: with release v0.1.2, run: + ``` + python tools/train_net.py --config-file configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml --num-gpus 8 + ``` + +* __mmdetection__: at commit `b0d845f`, run + ``` + ./tools/dist_train.sh configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py 8 + ``` + +* __maskrcnn-benchmark__: use commit `0ce8f6f` with `sed -i 's/torch.uint8/torch.bool/g' **/*.py; sed -i 's/AT_CHECK/TORCH_CHECK/g' **/*.cu` + to make it compatible with PyTorch 1.5. Then, run training with + ``` + python -m torch.distributed.launch --nproc_per_node=8 tools/train_net.py --config-file configs/e2e_mask_rcnn_R_50_FPN_1x.yaml + ``` + The speed we observed is faster than its model zoo, likely due to different software versions. + +* __tensorpack__: at commit `caafda`, `export TF_CUDNN_USE_AUTOTUNE=0`, then run + ``` + mpirun -np 8 ./train.py --config DATA.BASEDIR=/data/coco TRAINER=horovod BACKBONE.STRIDE_1X1=True TRAIN.STEPS_PER_EPOCH=50 --load ImageNet-R50-AlignPadding.npz + ``` + +* __SimpleDet__: at commit `9187a1`, run + ``` + python detection_train.py --config config/mask_r50v1_fpn_1x.py + ``` + +* __Detectron__: run + ``` + python tools/train_net.py --cfg configs/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml + ``` + Note that many of its ops run on CPUs, therefore the performance is limited. + +* __matterport/Mask_RCNN__: at commit `3deaec`, apply the following diff, `export TF_CUDNN_USE_AUTOTUNE=0`, then run + ``` + python coco.py train --dataset=/data/coco/ --model=imagenet + ``` + Note that many small details in this implementation might be different + from Detectron's standards. + +
+ + (diff to make it use the same hyperparameters - click to expand) + + + ```diff + diff --git i/mrcnn/model.py w/mrcnn/model.py + index 62cb2b0..61d7779 100644 + --- i/mrcnn/model.py + +++ w/mrcnn/model.py + @@ -2367,8 +2367,8 @@ class MaskRCNN(): + epochs=epochs, + steps_per_epoch=self.config.STEPS_PER_EPOCH, + callbacks=callbacks, + - validation_data=val_generator, + - validation_steps=self.config.VALIDATION_STEPS, + + #validation_data=val_generator, + + #validation_steps=self.config.VALIDATION_STEPS, + max_queue_size=100, + workers=workers, + use_multiprocessing=True, + diff --git i/mrcnn/parallel_model.py w/mrcnn/parallel_model.py + index d2bf53b..060172a 100644 + --- i/mrcnn/parallel_model.py + +++ w/mrcnn/parallel_model.py + @@ -32,6 +32,7 @@ class ParallelModel(KM.Model): + keras_model: The Keras model to parallelize + gpu_count: Number of GPUs. Must be > 1 + """ + + super().__init__() + self.inner_model = keras_model + self.gpu_count = gpu_count + merged_outputs = self.make_parallel() + diff --git i/samples/coco/coco.py w/samples/coco/coco.py + index 5d172b5..239ed75 100644 + --- i/samples/coco/coco.py + +++ w/samples/coco/coco.py + @@ -81,7 +81,10 @@ class CocoConfig(Config): + IMAGES_PER_GPU = 2 + + # Uncomment to train on 8 GPUs (default is 1) + - # GPU_COUNT = 8 + + GPU_COUNT = 8 + + BACKBONE = "resnet50" + + STEPS_PER_EPOCH = 50 + + TRAIN_ROIS_PER_IMAGE = 512 + + # Number of classes (including background) + NUM_CLASSES = 1 + 80 # COCO has 80 classes + @@ -496,29 +499,10 @@ if __name__ == '__main__': + # *** This training schedule is an example. Update to your needs *** + + # Training - Stage 1 + - print("Training network heads") + model.train(dataset_train, dataset_val, + learning_rate=config.LEARNING_RATE, + epochs=40, + - layers='heads', + - augmentation=augmentation) + - + - # Training - Stage 2 + - # Finetune layers from ResNet stage 4 and up + - print("Fine tune Resnet stage 4 and up") + - model.train(dataset_train, dataset_val, + - learning_rate=config.LEARNING_RATE, + - epochs=120, + - layers='4+', + - augmentation=augmentation) + - + - # Training - Stage 3 + - # Fine tune all layers + - print("Fine tune all layers") + - model.train(dataset_train, dataset_val, + - learning_rate=config.LEARNING_RATE / 10, + - epochs=160, + - layers='all', + + layers='3+', + augmentation=augmentation) + + elif args.command == "evaluate": + ``` + +
diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/notes/changelog.md b/iChat/models/grit_src/third_party/CenterNet2/docs/notes/changelog.md new file mode 100644 index 0000000000000000000000000000000000000000..000e9f8898dba53f54121a5325ba5165e45ddea2 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/notes/changelog.md @@ -0,0 +1,48 @@ +# Change Log and Backward Compatibility + +### Releases +See release logs at +[https://github.com/facebookresearch/detectron2/releases](https://github.com/facebookresearch/detectron2/releases) +for new updates. + +### Backward Compatibility + +Due to the research nature of what the library does, there might be backward incompatible changes. +But we try to reduce users' disruption by the following ways: +* APIs listed in [API documentation](https://detectron2.readthedocs.io/modules/index.html), including + function/class names, their arguments, and documented class attributes, are considered *stable* unless + otherwise noted in the documentation. + They are less likely to be broken, but if needed, will trigger a deprecation warning for a reasonable period + before getting broken, and will be documented in release logs. +* Others functions/classses/attributes are considered internal, and are more likely to change. + However, we're aware that some of them may be already used by other projects, and in particular we may + use them for convenience among projects under `detectron2/projects`. + For such APIs, we may treat them as stable APIs and also apply the above strategies. + They may be promoted to stable when we're ready. +* Projects under "detectron2/projects" or imported with "detectron2.projects" are research projects + and are all considered experimental. +* Classes/functions that contain the word "default" or are explicitly documented to produce + "default behavior" may change their behaviors when new features are added. + +Despite of the possible breakage, if a third-party project would like to keep up with the latest updates +in detectron2, using it as a library will still be less disruptive than forking, because +the frequency and scope of API changes will be much smaller than code changes. + +To see such changes, search for "incompatible changes" in [release logs](https://github.com/facebookresearch/detectron2/releases). + +### Config Version Change Log + +Detectron2's config version has not been changed since open source. +There is no need for an open source user to worry about this. + +* v1: Rename `RPN_HEAD.NAME` to `RPN.HEAD_NAME`. +* v2: A batch of rename of many configurations before release. + +### Silent Regressions in Historical Versions: + +We list a few silent regressions, since they may silently produce incorrect results and will be hard to debug. + +* 04/01/2020 - 05/11/2020: Bad accuracy if `TRAIN_ON_PRED_BOXES` is set to True. +* 03/30/2020 - 04/01/2020: ResNets are not correctly built. +* 12/19/2019 - 12/26/2019: Using aspect ratio grouping causes a drop in accuracy. +* - 11/9/2019: Test time augmentation does not predict the last category. diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/notes/compatibility.md b/iChat/models/grit_src/third_party/CenterNet2/docs/notes/compatibility.md new file mode 100644 index 0000000000000000000000000000000000000000..83d93f51c056c598c1209f9a21a4e04407b827f0 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/notes/compatibility.md @@ -0,0 +1,84 @@ +# Compatibility with Other Libraries + +## Compatibility with Detectron (and maskrcnn-benchmark) + +Detectron2 addresses some legacy issues left in Detectron. As a result, their models +are not compatible: +running inference with the same model weights will produce different results in the two code bases. + +The major differences regarding inference are: + +- The height and width of a box with corners (x1, y1) and (x2, y2) is now computed more naturally as + width = x2 - x1 and height = y2 - y1; + In Detectron, a "+ 1" was added both height and width. + + Note that the relevant ops in Caffe2 have [adopted this change of convention](https://github.com/pytorch/pytorch/pull/20550) + with an extra option. + So it is still possible to run inference with a Detectron2-trained model in Caffe2. + + The change in height/width calculations most notably changes: + - encoding/decoding in bounding box regression. + - non-maximum suppression. The effect here is very negligible, though. + +- RPN now uses simpler anchors with fewer quantization artifacts. + + In Detectron, the anchors were quantized and + [do not have accurate areas](https://github.com/facebookresearch/Detectron/issues/227). + In Detectron2, the anchors are center-aligned to feature grid points and not quantized. + +- Classification layers have a different ordering of class labels. + + This involves any trainable parameter with shape (..., num_categories + 1, ...). + In Detectron2, integer labels [0, K-1] correspond to the K = num_categories object categories + and the label "K" corresponds to the special "background" category. + In Detectron, label "0" means background, and labels [1, K] correspond to the K categories. + +- ROIAlign is implemented differently. The new implementation is [available in Caffe2](https://github.com/pytorch/pytorch/pull/23706). + + 1. All the ROIs are shifted by half a pixel compared to Detectron in order to create better image-feature-map alignment. + See `layers/roi_align.py` for details. + To enable the old behavior, use `ROIAlign(aligned=False)`, or `POOLER_TYPE=ROIAlign` instead of + `ROIAlignV2` (the default). + + 1. The ROIs are not required to have a minimum size of 1. + This will lead to tiny differences in the output, but should be negligible. + +- Mask inference function is different. + + In Detectron2, the "paste_mask" function is different and should be more accurate than in Detectron. This change + can improve mask AP on COCO by ~0.5% absolute. + +There are some other differences in training as well, but they won't affect +model-level compatibility. The major ones are: + +- We fixed a [bug](https://github.com/facebookresearch/Detectron/issues/459) in + Detectron, by making `RPN.POST_NMS_TOPK_TRAIN` per-image, rather than per-batch. + The fix may lead to a small accuracy drop for a few models (e.g. keypoint + detection) and will require some parameter tuning to match the Detectron results. +- For simplicity, we change the default loss in bounding box regression to L1 loss, instead of smooth L1 loss. + We have observed that this tends to slightly decrease box AP50 while improving box AP for higher + overlap thresholds (and leading to a slight overall improvement in box AP). +- We interpret the coordinates in COCO bounding box and segmentation annotations + as coordinates in range `[0, width]` or `[0, height]`. The coordinates in + COCO keypoint annotations are interpreted as pixel indices in range `[0, width - 1]` or `[0, height - 1]`. + Note that this affects how flip augmentation is implemented. + + +[This article](https://ppwwyyxx.com/blog/2021/Where-are-Pixels/) +explains more details on the above mentioned issues +about pixels, coordinates, and "+1"s. + + +## Compatibility with Caffe2 + +As mentioned above, despite the incompatibilities with Detectron, the relevant +ops have been implemented in Caffe2. +Therefore, models trained with detectron2 can be converted in Caffe2. +See [Deployment](../tutorials/deployment.md) for the tutorial. + +## Compatibility with TensorFlow + +Most ops are available in TensorFlow, although some tiny differences in +the implementation of resize / ROIAlign / padding need to be addressed. +A working conversion script is provided by [tensorpack Faster R-CNN](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN/convert_d2) +to run a standard detectron2 model in TensorFlow. diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/notes/contributing.md b/iChat/models/grit_src/third_party/CenterNet2/docs/notes/contributing.md new file mode 100644 index 0000000000000000000000000000000000000000..95181235eaff1cb5cbb2dc554e8d4991b603d0e5 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/notes/contributing.md @@ -0,0 +1 @@ +../../.github/CONTRIBUTING.md \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/notes/index.rst b/iChat/models/grit_src/third_party/CenterNet2/docs/notes/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..63cf907be7bb15f5316af6d44a46df601755a86b --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/notes/index.rst @@ -0,0 +1,10 @@ +Notes +====================================== + +.. toctree:: + :maxdepth: 2 + + benchmarks + compatibility + contributing + changelog diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/requirements.txt b/iChat/models/grit_src/third_party/CenterNet2/docs/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..58d3c2a5673d0ff4ce43a486350eb0056cf331c6 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/requirements.txt @@ -0,0 +1,21 @@ +docutils==0.16 +# https://github.com/sphinx-doc/sphinx/commit/7acd3ada3f38076af7b2b5c9f3b60bb9c2587a3d +sphinx==3.2.0 +recommonmark==0.6.0 +sphinx_rtd_theme +# Dependencies here are only those required by import +termcolor +numpy +tqdm +matplotlib +termcolor +yacs +tabulate +cloudpickle +Pillow +future +git+git://github.com/facebookresearch/fvcore.git +https://download.pytorch.org/whl/cpu/torch-1.8.1%2Bcpu-cp37-cp37m-linux_x86_64.whl +https://download.pytorch.org/whl/cpu/torchvision-0.9.1%2Bcpu-cp37-cp37m-linux_x86_64.whl +omegaconf>=2.1.0.dev24 +hydra-core>=1.1.0.dev5 diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/README.md b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/README.md new file mode 100644 index 0000000000000000000000000000000000000000..1ca9c94d042ef838143a45490fe6b4556c19f3c9 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/README.md @@ -0,0 +1,4 @@ +# Read the docs: + +The latest documentation built from this directory is available at [detectron2.readthedocs.io](https://detectron2.readthedocs.io/). +Documents in this directory are not meant to be read on github. diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/augmentation.md b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/augmentation.md new file mode 100644 index 0000000000000000000000000000000000000000..7601a082ceadf645e32468c2045dfe50c1216efc --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/augmentation.md @@ -0,0 +1,186 @@ + +# Data Augmentation + +Augmentation is an important part of training. +Detectron2's data augmentation system aims at addressing the following goals: + +1. Allow augmenting multiple data types together + (e.g., images together with their bounding boxes and masks) +2. Allow applying a sequence of statically-declared augmentation +3. Allow adding custom new data types to augment (rotated bounding boxes, video clips, etc.) +4. Process and manipulate the __operations__ that are applied by augmentations + +The first two features cover most of the common use cases, and is also +available in other libraries such as [albumentations](https://medium.com/pytorch/multi-target-in-albumentations-16a777e9006e). +Supporting other features adds some overhead to detectron2's augmentation API, +which we'll explain in this tutorial. + +This tutorial focuses on how to use augmentations when writing new data loaders, +and how to write new augmentations. +If you use the default data loader in detectron2, it already supports taking a user-provided list of custom augmentations, +as explained in the [Dataloader tutorial](data_loading). + +## Basic Usage + +The basic usage of feature (1) and (2) is like the following: +```python +from detectron2.data import transforms as T +# Define a sequence of augmentations: +augs = T.AugmentationList([ + T.RandomBrightness(0.9, 1.1), + T.RandomFlip(prob=0.5), + T.RandomCrop("absolute", (640, 640)) +]) # type: T.Augmentation + +# Define the augmentation input ("image" required, others optional): +input = T.AugInput(image, boxes=boxes, sem_seg=sem_seg) +# Apply the augmentation: +transform = augs(input) # type: T.Transform +image_transformed = input.image # new image +sem_seg_transformed = input.sem_seg # new semantic segmentation + +# For any extra data that needs to be augmented together, use transform, e.g.: +image2_transformed = transform.apply_image(image2) +polygons_transformed = transform.apply_polygons(polygons) +``` + +Three basic concepts are involved here. They are: +* [T.Augmentation](../modules/data_transforms.html#detectron2.data.transforms.Augmentation) defines the __"policy"__ to modify inputs. + * its `__call__(AugInput) -> Transform` method augments the inputs in-place, and returns the operation that is applied +* [T.Transform](../modules/data_transforms.html#detectron2.data.transforms.Transform) + implements the actual __operations__ to transform data + * it has methods such as `apply_image`, `apply_coords` that define how to transform each data type +* [T.AugInput](../modules/data_transforms.html#detectron2.data.transforms.AugInput) + stores inputs needed by `T.Augmentation` and how they should be transformed. + This concept is needed for some advanced usage. + Using this class directly should be sufficient for all common use cases, + since extra data not in `T.AugInput` can be augmented using the returned + `transform`, as shown in the above example. + +## Write New Augmentations + +Most 2D augmentations only need to know about the input image. Such augmentation can be implemented easily like this: + +```python +class MyColorAugmentation(T.Augmentation): + def get_transform(self, image): + r = np.random.rand(2) + return T.ColorTransform(lambda x: x * r[0] + r[1] * 10) + +class MyCustomResize(T.Augmentation): + def get_transform(self, image): + old_h, old_w = image.shape[:2] + new_h, new_w = int(old_h * np.random.rand()), int(old_w * 1.5) + return T.ResizeTransform(old_h, old_w, new_h, new_w) + +augs = MyCustomResize() +transform = augs(input) +``` + +In addition to image, any attributes of the given `AugInput` can be used as long +as they are part of the function signature, e.g.: + +```python +class MyCustomCrop(T.Augmentation): + def get_transform(self, image, sem_seg): + # decide where to crop using both image and sem_seg + return T.CropTransform(...) + +augs = MyCustomCrop() +assert hasattr(input, "image") and hasattr(input, "sem_seg") +transform = augs(input) +``` + +New transform operation can also be added by subclassing +[T.Transform](../modules/data_transforms.html#detectron2.data.transforms.Transform). + +## Advanced Usage + +We give a few examples of advanced usages that +are enabled by our system. +These options can be interesting to new research, +although changing them is often not needed +for standard use cases. + +### Custom transform strategy + +Instead of only returning the augmented data, detectron2's `Augmentation` returns the __operations__ as `T.Transform`. +This allows users to apply custom transform strategy on their data. +We use keypoints data as an example. + +Keypoints are (x, y) coordinates, but they are not so trivial to augment due to the semantic meaning they carry. +Such meaning is only known to the users, therefore users may want to augment them manually +by looking at the returned `transform`. +For example, when an image is horizontally flipped, we'd like to swap the keypoint annotations for "left eye" and "right eye". +This can be done like this (included by default in detectron2's default data loader): +```python +# augs, input are defined as in previous examples +transform = augs(input) # type: T.Transform +keypoints_xy = transform.apply_coords(keypoints_xy) # transform the coordinates + +# get a list of all transforms that were applied +transforms = T.TransformList([transform]).transforms +# check if it is flipped for odd number of times +do_hflip = sum(isinstance(t, T.HFlipTransform) for t in transforms) % 2 == 1 +if do_hflip: + keypoints_xy = keypoints_xy[flip_indices_mapping] +``` + +As another example, keypoints annotations often have a "visibility" field. +A sequence of augmentations might augment a visible keypoint out of the image boundary (e.g. with cropping), +but then bring it back within the boundary afterwards (e.g. with image padding). +If users decide to label such keypoints "invisible", +then the visibility check has to happen after every transform step. +This can be achieved by: + +```python +transform = augs(input) # type: T.TransformList +assert isinstance(transform, T.TransformList) +for t in transform.transforms: + keypoints_xy = t.apply_coords(keypoints_xy) + visibility &= (keypoints_xy >= [0, 0] & keypoints_xy <= [W, H]).all(axis=1) + +# btw, detectron2's `transform_keypoint_annotations` function chooses to label such keypoints "visible": +# keypoints_xy = transform.apply_coords(keypoints_xy) +# visibility &= (keypoints_xy >= [0, 0] & keypoints_xy <= [W, H]).all(axis=1) +``` + + +### Geometrically invert the transform +If images are pre-processed by augmentations before inference, the predicted results +such as segmentation masks are localized on the augmented image. +We'd like to invert the applied augmentation with the [inverse()](../modules/data_transforms.html#detectron2.data.transforms.Transform.inverse) +API, to obtain results on the original image: +```python +transform = augs(input) +pred_mask = make_prediction(input.image) +inv_transform = transform.inverse() +pred_mask_orig = inv_transform.apply_segmentation(pred_mask) +``` + +### Add new data types + +[T.Transform](../modules/data_transforms.html#detectron2.data.transforms.Transform) +supports a few common data types to transform, including images, coordinates, masks, boxes, polygons. +It allows registering new data types, e.g.: +```python +@T.HFlipTransform.register_type("rotated_boxes") +def func(flip_transform: T.HFlipTransform, rotated_boxes: Any): + # do the work + return flipped_rotated_boxes + +t = HFlipTransform(width=800) +transformed_rotated_boxes = t.apply_rotated_boxes(rotated_boxes) # func will be called +``` + +### Extend T.AugInput + +An augmentation can only access attributes available in the given input. +[T.AugInput](../modules/data_transforms.html#detectron2.data.transforms.StandardAugInput) defines "image", "boxes", "sem_seg", +which are sufficient for common augmentation strategies to decide how to augment. +If not, a custom implementation is needed. + +By re-implement the "transform()" method in AugInput, it is also possible to +augment different fields in ways that are dependent on each other. +Such use case is uncommon (e.g. post-process bounding box based on augmented masks), but allowed by the system. + diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/builtin_datasets.md b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/builtin_datasets.md new file mode 100644 index 0000000000000000000000000000000000000000..0ba82423ad498bdd86274ada56a201134a590d94 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/builtin_datasets.md @@ -0,0 +1 @@ +../../datasets/README.md \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/configs.md b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/configs.md new file mode 100644 index 0000000000000000000000000000000000000000..751e4eb638baeae0e8ff5c65869163a1d64e6b66 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/configs.md @@ -0,0 +1,62 @@ +# Yacs Configs + +Detectron2 provides a key-value based config system that can be +used to obtain standard, common behaviors. + +This system uses YAML and [yacs](https://github.com/rbgirshick/yacs). +Yaml is a very limited language, +so we do not expect all features in detectron2 to be available through configs. +If you need something that's not available in the config space, +please write code using detectron2's API. + +With the introduction of a more powerful [LazyConfig system](lazyconfigs.md), +we no longer add functionality / new keys to the Yacs/Yaml-based config system. + +### Basic Usage + +Some basic usage of the `CfgNode` object is shown here. See more in [documentation](../modules/config.html#detectron2.config.CfgNode). +```python +from detectron2.config import get_cfg +cfg = get_cfg() # obtain detectron2's default config +cfg.xxx = yyy # add new configs for your own custom components +cfg.merge_from_file("my_cfg.yaml") # load values from a file + +cfg.merge_from_list(["MODEL.WEIGHTS", "weights.pth"]) # can also load values from a list of str +print(cfg.dump()) # print formatted configs +with open("output.yaml", "w") as f: + f.write(cfg.dump()) # save config to file +``` + +In addition to the basic Yaml syntax, the config file can +define a `_BASE_: base.yaml` field, which will load a base config file first. +Values in the base config will be overwritten in sub-configs, if there are any conflicts. +We provided several base configs for standard model architectures. + +Many builtin tools in detectron2 accept command line config overwrite: +Key-value pairs provided in the command line will overwrite the existing values in the config file. +For example, [demo.py](../../demo/demo.py) can be used with +``` +./demo.py --config-file config.yaml [--other-options] \ + --opts MODEL.WEIGHTS /path/to/weights INPUT.MIN_SIZE_TEST 1000 +``` + +To see a list of available configs in detectron2 and what they mean, +check [Config References](../modules/config.html#config-references) + +### Configs in Projects + +A project that lives outside the detectron2 library may define its own configs, which will need to be added +for the project to be functional, e.g.: +```python +from detectron2.projects.point_rend import add_pointrend_config +cfg = get_cfg() # obtain detectron2's default config +add_pointrend_config(cfg) # add pointrend's default config +# ... ... +``` + +### Best Practice with Configs + +1. Treat the configs you write as "code": avoid copying them or duplicating them; use `_BASE_` + to share common parts between configs. + +2. Keep the configs you write simple: don't include keys that do not affect the experimental setting. diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/data_loading.md b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/data_loading.md new file mode 100644 index 0000000000000000000000000000000000000000..1d2769fc513abb0981a140f3a6b6432538704261 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/data_loading.md @@ -0,0 +1,95 @@ + +# Dataloader + +Dataloader is the component that provides data to models. +A dataloader usually (but not necessarily) takes raw information from [datasets](./datasets.md), +and process them into a format needed by the model. + +## How the Existing Dataloader Works + +Detectron2 contains a builtin data loading pipeline. +It's good to understand how it works, in case you need to write a custom one. + +Detectron2 provides two functions +[build_detection_{train,test}_loader](../modules/data.html#detectron2.data.build_detection_train_loader) +that create a default data loader from a given config. +Here is how `build_detection_{train,test}_loader` work: + +1. It takes the name of a registered dataset (e.g., "coco_2017_train") and loads a `list[dict]` representing the dataset items + in a lightweight format. These dataset items are not yet ready to be used by the model (e.g., images are + not loaded into memory, random augmentations have not been applied, etc.). + Details about the dataset format and dataset registration can be found in + [datasets](./datasets.md). +2. Each dict in this list is mapped by a function ("mapper"): + * Users can customize this mapping function by specifying the "mapper" argument in + `build_detection_{train,test}_loader`. The default mapper is [DatasetMapper](../modules/data.html#detectron2.data.DatasetMapper). + * The output format of the mapper can be arbitrary, as long as it is accepted by the consumer of this data loader (usually the model). + The outputs of the default mapper, after batching, follow the default model input format documented in + [Use Models](./models.html#model-input-format). + * The role of the mapper is to transform the lightweight representation of a dataset item into a format + that is ready for the model to consume (including, e.g., read images, perform random data augmentation and convert to torch Tensors). + If you would like to perform custom transformations to data, you often want a custom mapper. +3. The outputs of the mapper are batched (simply into a list). +4. This batched data is the output of the data loader. Typically, it's also the input of + `model.forward()`. + + +## Write a Custom Dataloader + +Using a different "mapper" with `build_detection_{train,test}_loader(mapper=)` works for most use cases +of custom data loading. +For example, if you want to resize all images to a fixed size for training, use: + +```python +import detectron2.data.transforms as T +from detectron2.data import DatasetMapper # the default mapper +dataloader = build_detection_train_loader(cfg, + mapper=DatasetMapper(cfg, is_train=True, augmentations=[ + T.Resize((800, 800)) + ])) +# use this dataloader instead of the default +``` +If the arguments of the default [DatasetMapper](../modules/data.html#detectron2.data.DatasetMapper) +does not provide what you need, you may write a custom mapper function and use it instead, e.g.: + +```python +from detectron2.data import detection_utils as utils + # Show how to implement a minimal mapper, similar to the default DatasetMapper +def mapper(dataset_dict): + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + # can use other ways to read image + image = utils.read_image(dataset_dict["file_name"], format="BGR") + # See "Data Augmentation" tutorial for details usage + auginput = T.AugInput(image) + transform = T.Resize((800, 800))(auginput) + image = torch.from_numpy(auginput.image.transpose(2, 0, 1)) + annos = [ + utils.transform_instance_annotations(annotation, [transform], image.shape[1:]) + for annotation in dataset_dict.pop("annotations") + ] + return { + # create the format that the model expects + "image": image, + "instances": utils.annotations_to_instances(annos, image.shape[1:]) + } +dataloader = build_detection_train_loader(cfg, mapper=mapper) +``` + +If you want to change not only the mapper (e.g., in order to implement different sampling or batching logic), +`build_detection_train_loader` won't work and you will need to write a different data loader. +The data loader is simply a +python iterator that produces [the format](./models.md) that the model accepts. +You can implement it using any tools you like. + +No matter what to implement, it's recommended to +check out [API documentation of detectron2.data](../modules/data) to learn more about the APIs of +these functions. + +## Use a Custom Dataloader + +If you use [DefaultTrainer](../modules/engine.html#detectron2.engine.defaults.DefaultTrainer), +you can overwrite its `build_{train,test}_loader` method to use your own dataloader. +See the [deeplab dataloader](../../projects/DeepLab/train_net.py) +for an example. + +If you write your own training loop, you can plug in your data loader easily. diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/datasets.md b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/datasets.md new file mode 100644 index 0000000000000000000000000000000000000000..91103f64264aa6f3059611c5fe06ecd65bcb986f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/datasets.md @@ -0,0 +1,290 @@ +# Use Custom Datasets + +This document explains how the dataset APIs +([DatasetCatalog](../modules/data.html#detectron2.data.DatasetCatalog), [MetadataCatalog](../modules/data.html#detectron2.data.MetadataCatalog)) +work, and how to use them to add custom datasets. + +Datasets that have builtin support in detectron2 are listed in [builtin datasets](builtin_datasets.md). +If you want to use a custom dataset while also reusing detectron2's data loaders, +you will need to: + +1. __Register__ your dataset (i.e., tell detectron2 how to obtain your dataset). +2. Optionally, __register metadata__ for your dataset. + +Next, we explain the above two concepts in detail. + +The [Colab tutorial](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5) +has a live example of how to register and train on a dataset of custom formats. + +### Register a Dataset + +To let detectron2 know how to obtain a dataset named "my_dataset", users need to implement +a function that returns the items in your dataset and then tell detectron2 about this +function: +```python +def my_dataset_function(): + ... + return list[dict] in the following format + +from detectron2.data import DatasetCatalog +DatasetCatalog.register("my_dataset", my_dataset_function) +# later, to access the data: +data: List[Dict] = DatasetCatalog.get("my_dataset") +``` + +Here, the snippet associates a dataset named "my_dataset" with a function that returns the data. +The function must return the same data (with same order) if called multiple times. +The registration stays effective until the process exits. + +The function can do arbitrary things and should return the data in `list[dict]`, each dict in either +of the following formats: +1. Detectron2's standard dataset dict, described below. This will make it work with many other builtin + features in detectron2, so it's recommended to use it when it's sufficient. +2. Any custom format. You can also return arbitrary dicts in your own format, + such as adding extra keys for new tasks. + Then you will need to handle them properly downstream as well. + See below for more details. + +#### Standard Dataset Dicts + +For standard tasks +(instance detection, instance/semantic/panoptic segmentation, keypoint detection), +we load the original dataset into `list[dict]` with a specification similar to COCO's annotations. +This is our standard representation for a dataset. + +Each dict contains information about one image. +The dict may have the following fields, +and the required fields vary based on what the dataloader or the task needs (see more below). + +```eval_rst +.. list-table:: + :header-rows: 1 + + * - Task + - Fields + * - Common + - file_name, height, width, image_id + + * - Instance detection/segmentation + - annotations + + * - Semantic segmentation + - sem_seg_file_name + + * - Panoptic segmentation + - pan_seg_file_name, segments_info +``` + ++ `file_name`: the full path to the image file. ++ `height`, `width`: integer. The shape of the image. ++ `image_id` (str or int): a unique id that identifies this image. Required by many + evaluators to identify the images, but a dataset may use it for different purposes. ++ `annotations` (list[dict]): Required by __instance detection/segmentation or keypoint detection__ tasks. + Each dict corresponds to annotations of one instance in this image, and + may contain the following keys: + + `bbox` (list[float], required): list of 4 numbers representing the bounding box of the instance. + + `bbox_mode` (int, required): the format of bbox. It must be a member of + [structures.BoxMode](../modules/structures.html#detectron2.structures.BoxMode). + Currently supports: `BoxMode.XYXY_ABS`, `BoxMode.XYWH_ABS`. + + `category_id` (int, required): an integer in the range [0, num_categories-1] representing the category label. + The value num_categories is reserved to represent the "background" category, if applicable. + + `segmentation` (list[list[float]] or dict): the segmentation mask of the instance. + + If `list[list[float]]`, it represents a list of polygons, one for each connected component + of the object. Each `list[float]` is one simple polygon in the format of `[x1, y1, ..., xn, yn]` (n≥3). + The Xs and Ys are absolute coordinates in unit of pixels. + + If `dict`, it represents the per-pixel segmentation mask in COCO's compressed RLE format. + The dict should have keys "size" and "counts". You can convert a uint8 segmentation mask of 0s and + 1s into such dict by `pycocotools.mask.encode(np.asarray(mask, order="F"))`. + `cfg.INPUT.MASK_FORMAT` must be set to `bitmask` if using the default data loader with such format. + + `keypoints` (list[float]): in the format of [x1, y1, v1,..., xn, yn, vn]. + v[i] means the [visibility](http://cocodataset.org/#format-data) of this keypoint. + `n` must be equal to the number of keypoint categories. + The Xs and Ys are absolute real-value coordinates in range [0, W or H]. + + (Note that the keypoint coordinates in COCO format are integers in range [0, W-1 or H-1], which is different + from our standard format. Detectron2 adds 0.5 to COCO keypoint coordinates to convert them from discrete + pixel indices to floating point coordinates.) + + `iscrowd`: 0 (default) or 1. Whether this instance is labeled as COCO's "crowd + region". Don't include this field if you don't know what it means. + + If `annotations` is an empty list, it means the image is labeled to have no objects. + Such images will by default be removed from training, + but can be included using `DATALOADER.FILTER_EMPTY_ANNOTATIONS`. + ++ `sem_seg_file_name` (str): + The full path to the semantic segmentation ground truth file. + It should be a grayscale image whose pixel values are integer labels. ++ `pan_seg_file_name` (str): + The full path to panoptic segmentation ground truth file. + It should be an RGB image whose pixel values are integer ids encoded using the + [panopticapi.utils.id2rgb](https://github.com/cocodataset/panopticapi/) function. + The ids are defined by `segments_info`. + If an id does not appear in `segments_info`, the pixel is considered unlabeled + and is usually ignored in training & evaluation. ++ `segments_info` (list[dict]): defines the meaning of each id in panoptic segmentation ground truth. + Each dict has the following keys: + + `id` (int): integer that appears in the ground truth image. + + `category_id` (int): an integer in the range [0, num_categories-1] representing the category label. + + `iscrowd`: 0 (default) or 1. Whether this instance is labeled as COCO's "crowd region". + + +```eval_rst + +.. note:: + + The PanopticFPN model does not use the panoptic segmentation + format defined here, but a combination of both instance segmentation and semantic segmentation data + format. See :doc:`builtin_datasets` for instructions on COCO. + +``` + +Fast R-CNN (with pre-computed proposals) models are rarely used today. +To train a Fast R-CNN, the following extra keys are needed: + ++ `proposal_boxes` (array): 2D numpy array with shape (K, 4) representing K precomputed proposal boxes for this image. ++ `proposal_objectness_logits` (array): numpy array with shape (K, ), which corresponds to the objectness + logits of proposals in 'proposal_boxes'. ++ `proposal_bbox_mode` (int): the format of the precomputed proposal bbox. + It must be a member of + [structures.BoxMode](../modules/structures.html#detectron2.structures.BoxMode). + Default is `BoxMode.XYXY_ABS`. + + + +#### Custom Dataset Dicts for New Tasks + +In the `list[dict]` that your dataset function returns, the dictionary can also have __arbitrary custom data__. +This will be useful for a new task that needs extra information not covered +by the standard dataset dicts. In this case, you need to make sure the downstream code can handle your data +correctly. Usually this requires writing a new `mapper` for the dataloader (see [Use Custom Dataloaders](./data_loading.md)). + +When designing a custom format, note that all dicts are stored in memory +(sometimes serialized and with multiple copies). +To save memory, each dict is meant to contain __small__ but sufficient information +about each sample, such as file names and annotations. +Loading full samples typically happens in the data loader. + +For attributes shared among the entire dataset, use `Metadata` (see below). +To avoid extra memory, do not save such information inside each sample. + +### "Metadata" for Datasets + +Each dataset is associated with some metadata, accessible through +`MetadataCatalog.get(dataset_name).some_metadata`. +Metadata is a key-value mapping that contains information that's shared among +the entire dataset, and usually is used to interpret what's in the dataset, e.g., +names of classes, colors of classes, root of files, etc. +This information will be useful for augmentation, evaluation, visualization, logging, etc. +The structure of metadata depends on what is needed from the corresponding downstream code. + +If you register a new dataset through `DatasetCatalog.register`, +you may also want to add its corresponding metadata through +`MetadataCatalog.get(dataset_name).some_key = some_value`, to enable any features that need the metadata. +You can do it like this (using the metadata key "thing_classes" as an example): + +```python +from detectron2.data import MetadataCatalog +MetadataCatalog.get("my_dataset").thing_classes = ["person", "dog"] +``` + +Here is a list of metadata keys that are used by builtin features in detectron2. +If you add your own dataset without these metadata, some features may be +unavailable to you: + +* `thing_classes` (list[str]): Used by all instance detection/segmentation tasks. + A list of names for each instance/thing category. + If you load a COCO format dataset, it will be automatically set by the function `load_coco_json`. + +* `thing_colors` (list[tuple(r, g, b)]): Pre-defined color (in [0, 255]) for each thing category. + Used for visualization. If not given, random colors will be used. + +* `stuff_classes` (list[str]): Used by semantic and panoptic segmentation tasks. + A list of names for each stuff category. + +* `stuff_colors` (list[tuple(r, g, b)]): Pre-defined color (in [0, 255]) for each stuff category. + Used for visualization. If not given, random colors are used. + +* `ignore_label` (int): Used by semantic and panoptic segmentation tasks. Pixels in ground-truth + annotations with this category label should be ignored in evaluation. Typically these are "unlabeled" + pixels. + +* `keypoint_names` (list[str]): Used by keypoint detection. A list of names for each keypoint. + +* `keypoint_flip_map` (list[tuple[str]]): Used by keypoint detection. A list of pairs of names, + where each pair are the two keypoints that should be flipped if the image is + flipped horizontally during augmentation. +* `keypoint_connection_rules`: list[tuple(str, str, (r, g, b))]. Each tuple specifies a pair of keypoints + that are connected and the color (in [0, 255]) to use for the line between them when visualized. + +Some additional metadata that are specific to the evaluation of certain datasets (e.g. COCO): + +* `thing_dataset_id_to_contiguous_id` (dict[int->int]): Used by all instance detection/segmentation tasks in the COCO format. + A mapping from instance class ids in the dataset to contiguous ids in range [0, #class). + Will be automatically set by the function `load_coco_json`. + +* `stuff_dataset_id_to_contiguous_id` (dict[int->int]): Used when generating prediction json files for + semantic/panoptic segmentation. + A mapping from semantic segmentation class ids in the dataset + to contiguous ids in [0, num_categories). It is useful for evaluation only. + +* `json_file`: The COCO annotation json file. Used by COCO evaluation for COCO-format datasets. +* `panoptic_root`, `panoptic_json`: Used by COCO-format panoptic evaluation. +* `evaluator_type`: Used by the builtin main training script to select + evaluator. Don't use it in a new training script. + You can just provide the [DatasetEvaluator](../modules/evaluation.html#detectron2.evaluation.DatasetEvaluator) + for your dataset directly in your main script. + +```eval_rst +.. note:: + + In recognition, sometimes we use the term "thing" for instance-level tasks, + and "stuff" for semantic segmentation tasks. + Both are used in panoptic segmentation tasks. + For background on the concept of "thing" and "stuff", see + `On Seeing Stuff: The Perception of Materials by Humans and Machines + `_. +``` + +### Register a COCO Format Dataset + +If your instance-level (detection, segmentation, keypoint) dataset is already a json file in the COCO format, +the dataset and its associated metadata can be registered easily with: +```python +from detectron2.data.datasets import register_coco_instances +register_coco_instances("my_dataset", {}, "json_annotation.json", "path/to/image/dir") +``` + +If your dataset is in COCO format but need to be further processed, or has extra custom per-instance annotations, +the [load_coco_json](../modules/data.html#detectron2.data.datasets.load_coco_json) +function might be useful. + +### Update the Config for New Datasets + +Once you've registered the dataset, you can use the name of the dataset (e.g., "my_dataset" in +example above) in `cfg.DATASETS.{TRAIN,TEST}`. +There are other configs you might want to change to train or evaluate on new datasets: + +* `MODEL.ROI_HEADS.NUM_CLASSES` and `MODEL.RETINANET.NUM_CLASSES` are the number of thing classes + for R-CNN and RetinaNet models, respectively. +* `MODEL.ROI_KEYPOINT_HEAD.NUM_KEYPOINTS` sets the number of keypoints for Keypoint R-CNN. + You'll also need to set [Keypoint OKS](http://cocodataset.org/#keypoints-eval) + with `TEST.KEYPOINT_OKS_SIGMAS` for evaluation. +* `MODEL.SEM_SEG_HEAD.NUM_CLASSES` sets the number of stuff classes for Semantic FPN & Panoptic FPN. +* `TEST.DETECTIONS_PER_IMAGE` controls the maximum number of objects to be detected. + Set it to a larger number if test images may contain >100 objects. +* If you're training Fast R-CNN (with precomputed proposals), `DATASETS.PROPOSAL_FILES_{TRAIN,TEST}` + need to match the datasets. The format of proposal files are documented + [here](../modules/data.html#detectron2.data.load_proposals_into_dataset). + +New models +(e.g. [TensorMask](../../projects/TensorMask), +[PointRend](../../projects/PointRend)) +often have similar configs of their own that need to be changed as well. + +```eval_rst +.. tip:: + + After changing the number of classes, certain layers in a pre-trained model will become incompatible + and therefore cannot be loaded to the new model. + This is expected, and loading such pre-trained models will produce warnings about such layers. +``` diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/deployment.md b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/deployment.md new file mode 100644 index 0000000000000000000000000000000000000000..173b9a0e1ec1e768d1b9dc5744c104578512d638 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/deployment.md @@ -0,0 +1,137 @@ +# Deployment + +Models written in Python need to go through an export process to become a deployable artifact. +A few basic concepts about this process: + +__"Export method"__ is how a Python model is fully serialized to a deployable format. +We support the following export methods: + +* `tracing`: see [pytorch documentation](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html) to learn about it +* `scripting`: see [pytorch documentation](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html) to learn about it +* `caffe2_tracing`: replace parts of the model by caffe2 operators, then use tracing. + +__"Format"__ is how a serialized model is described in a file, e.g. +TorchScript, Caffe2 protobuf, ONNX format. +__"Runtime"__ is an engine that loads a serialized model and executes it, +e.g., PyTorch, Caffe2, TensorFlow, onnxruntime, TensorRT, etc. +A runtime is often tied to a specific format +(e.g. PyTorch needs TorchScript format, Caffe2 needs protobuf format). +We currently support the following combination and each has some limitations: + +```eval_rst ++----------------------------+-------------+-------------+-----------------------------+ +| Export Method | tracing | scripting | caffe2_tracing | ++============================+=============+=============+=============================+ +| **Formats** | TorchScript | TorchScript | Caffe2, TorchScript, ONNX | ++----------------------------+-------------+-------------+-----------------------------+ +| **Runtime** | PyTorch | PyTorch | Caffe2, PyTorch | ++----------------------------+-------------+-------------+-----------------------------+ +| C++/Python inference | ✅ | ✅ | ✅ | ++----------------------------+-------------+-------------+-----------------------------+ +| Dynamic resolution | ✅ | ✅ | ✅ | ++----------------------------+-------------+-------------+-----------------------------+ +| Batch size requirement | Constant | Dynamic | Batch inference unsupported | ++----------------------------+-------------+-------------+-----------------------------+ +| Extra runtime deps | torchvision | torchvision | Caffe2 ops (usually already | +| | | | | +| | | | included in PyTorch) | ++----------------------------+-------------+-------------+-----------------------------+ +| Faster/Mask/Keypoint R-CNN | ✅ | ✅ | ✅ | ++----------------------------+-------------+-------------+-----------------------------+ +| RetinaNet | ✅ | ✅ | ✅ | ++----------------------------+-------------+-------------+-----------------------------+ +| PointRend R-CNN | ✅ | ❌ | ❌ | ++----------------------------+-------------+-------------+-----------------------------+ +| Cascade R-CNN | ✅ | ❌ | ❌ | ++----------------------------+-------------+-------------+-----------------------------+ + +``` + +`caffe2_tracing` is going to be deprecated. +We don't plan to work on additional support for other formats/runtime, but contributions are welcome. + + +## Deployment with Tracing or Scripting + +Models can be exported to TorchScript format, by either +[tracing or scripting](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html). +The output model file can be loaded without detectron2 dependency in either Python or C++. +The exported model often requires torchvision (or its C++ library) dependency for some custom ops. + +This feature requires PyTorch ≥ 1.8. + +### Coverage +Most official models under the meta architectures `GeneralizedRCNN` and `RetinaNet` +are supported in both tracing and scripting mode. +Cascade R-CNN and PointRend are currently supported in tracing. +Users' custom extensions are supported if they are also scriptable or traceable. + +For models exported with tracing, dynamic input resolution is allowed, but batch size +(number of input images) must be fixed. +Scripting can support dynamic batch size. + +### Usage + +The main export APIs for tracing and scripting are [TracingAdapter](../modules/export.html#detectron2.export.TracingAdapter) +and [scripting_with_instances](../modules/export.html#detectron2.export.scripting_with_instances). +Their usage is currently demonstrated in [test_export_torchscript.py](../../tests/test_export_torchscript.py) +(see `TestScripting` and `TestTracing`) +as well as the [deployment example](../../tools/deploy). +Please check that these examples can run, and then modify for your use cases. +The usage now requires some user effort and necessary knowledge for each model to workaround the limitation of scripting and tracing. +In the future we plan to wrap these under simpler APIs to lower the bar to use them. + +## Deployment with Caffe2-tracing +We provide [Caffe2Tracer](../modules/export.html#detectron2.export.Caffe2Tracer) +that performs the export logic. +It replaces parts of the model with Caffe2 operators, +and then export the model into Caffe2, TorchScript or ONNX format. + +The converted model is able to run in either Python or C++ without detectron2/torchvision dependency, on CPU or GPUs. +It has a runtime optimized for CPU & mobile inference, but not optimized for GPU inference. + +This feature requires 1.9 > ONNX ≥ 1.6. + +### Coverage + +Most official models under these 3 common meta architectures: `GeneralizedRCNN`, `RetinaNet`, `PanopticFPN` +are supported. Cascade R-CNN is not supported. Batch inference is not supported. + +Users' custom extensions under these architectures (added through registration) are supported +as long as they do not contain control flow or operators not available in Caffe2 (e.g. deformable convolution). +For example, custom backbones and heads are often supported out of the box. + +### Usage + +The APIs are listed at [the API documentation](../modules/export). +We provide [export_model.py](../../tools/deploy/) as an example that uses +these APIs to convert a standard model. For custom models/datasets, you can add them to this script. + +### Use the model in C++/Python + +The model can be loaded in C++ and deployed with +either Caffe2 or Pytorch runtime.. [C++ examples](../../tools/deploy/) for Mask R-CNN +are given as a reference. Note that: + +* Models exported with `caffe2_tracing` method take a special input format + described in [documentation](../modules/export.html#detectron2.export.Caffe2Tracer). + This was taken care of in the C++ example. + +* The converted models do not contain post-processing operations that + transform raw layer outputs into formatted predictions. + For example, the C++ examples only produce raw outputs (28x28 masks) from the final + layers that are not post-processed, because in actual deployment, an application often needs + its custom lightweight post-processing, so this step is left for users. + +To help use the Caffe2-format model in python, +we provide a python wrapper around the converted model, in the +[Caffe2Model.\_\_call\_\_](../modules/export.html#detectron2.export.Caffe2Model.__call__) method. +This method has an interface that's identical to the [pytorch versions of models](./models.md), +and it internally applies pre/post-processing code to match the formats. +This wrapper can serve as a reference for how to use Caffe2's python API, +or for how to implement pre/post-processing in actual deployment. + +## Conversion to TensorFlow +[tensorpack Faster R-CNN](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN/convert_d2) +provides scripts to convert a few standard detectron2 R-CNN models to TensorFlow's pb format. +It works by translating configs and weights, therefore only support a few models. diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/evaluation.md b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/evaluation.md new file mode 100644 index 0000000000000000000000000000000000000000..bd924a3b1d9bb1e0dacc53306d30f938a724135e --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/evaluation.md @@ -0,0 +1,68 @@ + +# Evaluation + +Evaluation is a process that takes a number of inputs/outputs pairs and aggregate them. +You can always [use the model](./models.md) directly and just parse its inputs/outputs manually to perform +evaluation. +Alternatively, evaluation is implemented in detectron2 using the [DatasetEvaluator](../modules/evaluation.html#detectron2.evaluation.DatasetEvaluator) +interface. + +Detectron2 includes a few `DatasetEvaluator` that computes metrics using standard dataset-specific +APIs (e.g., COCO, LVIS). +You can also implement your own `DatasetEvaluator` that performs some other jobs +using the inputs/outputs pairs. +For example, to count how many instances are detected on the validation set: + +``` +class Counter(DatasetEvaluator): + def reset(self): + self.count = 0 + def process(self, inputs, outputs): + for output in outputs: + self.count += len(output["instances"]) + def evaluate(self): + # save self.count somewhere, or print it, or return it. + return {"count": self.count} +``` + +## Use evaluators + +To evaluate using the methods of evaluators manually: +``` +def get_all_inputs_outputs(): + for data in data_loader: + yield data, model(data) + +evaluator.reset() +for inputs, outputs in get_all_inputs_outputs(): + evaluator.process(inputs, outputs) +eval_results = evaluator.evaluate() +``` + +Evaluators can also be used with [inference_on_dataset](../modules/evaluation.html#detectron2.evaluation.inference_on_dataset). +For example, + +```python +eval_results = inference_on_dataset( + model, + data_loader, + DatasetEvaluators([COCOEvaluator(...), Counter()])) +``` +This will execute `model` on all inputs from `data_loader`, and call evaluator to process them. + +Compared to running the evaluation manually using the model, the benefit of this function is that +evaluators can be merged together using [DatasetEvaluators](../modules/evaluation.html#detectron2.evaluation.DatasetEvaluators), +and all the evaluation can finish in one forward pass over the dataset. +This function also provides accurate speed benchmarks for the given model and dataset. + +## Evaluators for custom dataset + +Many evaluators in detectron2 are made for specific datasets, +in order to obtain scores using each dataset's official API. +In addition to that, two evaluators are able to evaluate any generic dataset +that follows detectron2's [standard dataset format](./datasets.md), so they +can be used to evaluate custom datasets: + +* [COCOEvaluator](../modules/evaluation.html#detectron2.evaluation.COCOEvaluator) is able to evaluate AP (Average Precision) for box detection, + instance segmentation, keypoint detection on any custom dataset. +* [SemSegEvaluator](../modules/evaluation.html#detectron2.evaluation.SemSegEvaluator) is able to evaluate semantic segmentation metrics on any custom dataset. diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/extend.md b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/extend.md new file mode 100644 index 0000000000000000000000000000000000000000..a6af550fdb2aa79c818cef54b009f2fe816d46a9 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/extend.md @@ -0,0 +1,141 @@ +# Extend Detectron2's Defaults + +__Research is about doing things in new ways__. +This brings a tension in how to create abstractions in code, +which is a challenge for any research engineering project of a significant size: + +1. On one hand, it needs to have very thin abstractions to allow for the possibility of doing + everything in new ways. It should be reasonably easy to break existing + abstractions and replace them with new ones. + +2. On the other hand, such a project also needs reasonably high-level + abstractions, so that users can easily do things in standard ways, + without worrying too much about the details that only certain researchers care about. + +In detectron2, there are two types of interfaces that address this tension together: + +1. Functions and classes that take a config (`cfg`) argument + created from a yaml file + (sometimes with few extra arguments). + + Such functions and classes implement + the "standard default" behavior: it will read what it needs from a given + config and do the "standard" thing. + Users only need to load an expert-made config and pass it around, without having to worry about + which arguments are used and what they all mean. + + See [Yacs Configs](configs.md) for a detailed tutorial. + +2. Functions and classes that have well-defined explicit arguments. + + Each of these is a small building block of the entire system. + They require users' expertise to understand what each argument should be, + and require more effort to stitch together to a larger system. + But they can be stitched together in more flexible ways. + + When you need to implement something not supported by the "standard defaults" + included in detectron2, these well-defined components can be reused. + + The [LazyConfig system](lazyconfigs.md) relies on such functions and classes. + +3. A few functions and classes are implemented with the + [@configurable](../modules/config.html#detectron2.config.configurable) + decorator - they can be called with either a config, or with explicit arguments, or a mixture of both. + Their explicit argument interfaces are currently experimental. + + As an example, a Mask R-CNN model can be built in the following ways: + + 1. Config-only: + ```python + # load proper yaml config file, then + model = build_model(cfg) + ``` + + 2. Mixture of config and additional argument overrides: + ```python + model = GeneralizedRCNN( + cfg, + roi_heads=StandardROIHeads(cfg, batch_size_per_image=666), + pixel_std=[57.0, 57.0, 57.0]) + ``` + + 3. Full explicit arguments: +
+ + (click to expand) + + + ```python + model = GeneralizedRCNN( + backbone=FPN( + ResNet( + BasicStem(3, 64, norm="FrozenBN"), + ResNet.make_default_stages(50, stride_in_1x1=True, norm="FrozenBN"), + out_features=["res2", "res3", "res4", "res5"], + ).freeze(2), + ["res2", "res3", "res4", "res5"], + 256, + top_block=LastLevelMaxPool(), + ), + proposal_generator=RPN( + in_features=["p2", "p3", "p4", "p5", "p6"], + head=StandardRPNHead(in_channels=256, num_anchors=3), + anchor_generator=DefaultAnchorGenerator( + sizes=[[32], [64], [128], [256], [512]], + aspect_ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64], + offset=0.0, + ), + anchor_matcher=Matcher([0.3, 0.7], [0, -1, 1], allow_low_quality_matches=True), + box2box_transform=Box2BoxTransform([1.0, 1.0, 1.0, 1.0]), + batch_size_per_image=256, + positive_fraction=0.5, + pre_nms_topk=(2000, 1000), + post_nms_topk=(1000, 1000), + nms_thresh=0.7, + ), + roi_heads=StandardROIHeads( + num_classes=80, + batch_size_per_image=512, + positive_fraction=0.25, + proposal_matcher=Matcher([0.5], [0, 1], allow_low_quality_matches=False), + box_in_features=["p2", "p3", "p4", "p5"], + box_pooler=ROIPooler(7, (1.0 / 4, 1.0 / 8, 1.0 / 16, 1.0 / 32), 0, "ROIAlignV2"), + box_head=FastRCNNConvFCHead( + ShapeSpec(channels=256, height=7, width=7), conv_dims=[], fc_dims=[1024, 1024] + ), + box_predictor=FastRCNNOutputLayers( + ShapeSpec(channels=1024), + test_score_thresh=0.05, + box2box_transform=Box2BoxTransform((10, 10, 5, 5)), + num_classes=80, + ), + mask_in_features=["p2", "p3", "p4", "p5"], + mask_pooler=ROIPooler(14, (1.0 / 4, 1.0 / 8, 1.0 / 16, 1.0 / 32), 0, "ROIAlignV2"), + mask_head=MaskRCNNConvUpsampleHead( + ShapeSpec(channels=256, width=14, height=14), + num_classes=80, + conv_dims=[256, 256, 256, 256, 256], + ), + ), + pixel_mean=[103.530, 116.280, 123.675], + pixel_std=[1.0, 1.0, 1.0], + input_format="BGR", + ) + ``` + +
+ + +If you only need the standard behavior, the [Beginner's Tutorial](./getting_started.md) +should suffice. If you need to extend detectron2 to your own needs, +see the following tutorials for more details: + +* Detectron2 includes a few standard datasets. To use custom ones, see + [Use Custom Datasets](./datasets.md). +* Detectron2 contains the standard logic that creates a data loader for training/testing from a + dataset, but you can write your own as well. See [Use Custom Data Loaders](./data_loading.md). +* Detectron2 implements many standard detection models, and provide ways for you + to overwrite their behaviors. See [Use Models](./models.md) and [Write Models](./write-models.md). +* Detectron2 provides a default training loop that is good for common training tasks. + You can customize it with hooks, or write your own loop instead. See [training](./training.md). diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/getting_started.md b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/getting_started.md new file mode 100644 index 0000000000000000000000000000000000000000..e90bde77a3197b77f4cfdce86ca8f96491650acd --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/getting_started.md @@ -0,0 +1 @@ +../../GETTING_STARTED.md \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/index.rst b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..850b95cfa873ffa0ba2d6f6e4263ad0895c08be8 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/index.rst @@ -0,0 +1,20 @@ +Tutorials +====================================== + +.. toctree:: + :maxdepth: 2 + + install + getting_started + builtin_datasets + extend + datasets + data_loading + augmentation + models + write-models + training + evaluation + configs + lazyconfigs + deployment diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/install.md b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/install.md new file mode 100644 index 0000000000000000000000000000000000000000..5f52b2be3c9650cfc3e16ffb8fa374d3fcbad371 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/install.md @@ -0,0 +1 @@ +../../INSTALL.md \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/lazyconfigs.md b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/lazyconfigs.md new file mode 100644 index 0000000000000000000000000000000000000000..ca9de3052a8065c1c4579499cb8ef7ed9fc2d660 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/lazyconfigs.md @@ -0,0 +1,170 @@ +# Lazy Configs + +The traditional yacs-based config system provides basic, standard functionalities. +However, it does not offer enough flexibility for many new projects. +We develop an alternative, non-intrusive config system that can be used with +detectron2 or potentially any other complex projects. + +## Python Syntax + +Our config objects are still dictionaries. Instead of using Yaml to define dictionaries, +we create dictionaries in Python directly. This gives users the following power that +doesn't exist in Yaml: + +* Easily manipulate the dictionary (addition & deletion) using Python. +* Write simple arithmetics or call simple functions. +* Use more data types / objects. +* Import / compose other config files, using the familiar Python import syntax. + +A Python config file can be loaded like this: +```python +# config.py: +a = dict(x=1, y=2, z=dict(xx=1)) +b = dict(x=3, y=4) + +# my_code.py: +from detectron2.config import LazyConfig +cfg = LazyConfig.load("path/to/config.py") # an omegaconf dictionary +assert cfg.a.z.xx == 1 +``` + +After [LazyConfig.load](../modules/config.html#detectron2.config.LazyConfig.load), `cfg` will be a dictionary that contains all dictionaries +defined in the global scope of the config file. Note that: +* All dictionaries are turned to an [omegaconf](https://omegaconf.readthedocs.io/) + config object during loading. This enables access to omegaconf features, + such as its [access syntax](https://omegaconf.readthedocs.io/en/2.1_branch/usage.html#access-and-manipulation) + and [interpolation](https://omegaconf.readthedocs.io/en/2.1_branch/usage.html#variable-interpolation). +* Absolute imports in `config.py` works the same as in regular Python. +* Relative imports can only import dictionaries from config files. + They are simply a syntax sugar for [LazyConfig.load_rel](../modules/config.html#detectron2.config.LazyConfig.load_rel). + They can load Python files at relative path without requiring `__init__.py`. + +[LazyConfig.save](../modules/config.html#detectron2.config.LazyConfig.save) can save a config object to yaml. +Note that this is not always successful if non-serializable objects appear in the config file (e.g. lambdas). +It is up to users whether to sacrifice the ability to save in exchange for flexibility. + +## Recursive Instantiation + +The LazyConfig system heavily uses recursive instantiation, which is a pattern that +uses a dictionary to describe a +call to a function/class. The dictionary consists of: + +1. A "\_target\_" key which contains path to the callable, such as "module.submodule.class_name". +2. Other keys that represent arguments to pass to the callable. Arguments themselves can be defined + using recursive instantiation. + +We provide a helper function [LazyCall](../modules/config.html#detectron2.config.LazyCall) that helps create such dictionaries. +The following code using `LazyCall` +```python +from detectron2.config import LazyCall as L +from my_app import Trainer, Optimizer +cfg = L(Trainer)( + optimizer=L(Optimizer)( + lr=0.01, + algo="SGD" + ) +) +``` +creates a dictionary like this: +``` +cfg = { + "_target_": "my_app.Trainer", + "optimizer": { + "_target_": "my_app.Optimizer", + "lr": 0.01, "algo": "SGD" + } +} +``` + +By representing objects using such dictionaries, a general +[instantiate](../modules/config.html#detectron2.config.instantiate) +function can turn them into actual objects, i.e.: +```python +from detectron2.config import instantiate +trainer = instantiate(cfg) +# equivalent to: +# from my_app import Trainer, Optimizer +# trainer = Trainer(optimizer=Optimizer(lr=0.01, algo="SGD")) +``` + +This pattern is powerful enough to describe very complex objects, e.g.: + +
+ +A Full Mask R-CNN described in recursive instantiation (click to expand) + + +```eval_rst +.. literalinclude:: ../../configs/common/models/mask_rcnn_fpn.py + :language: python + :linenos: +``` + +
+ +There are also objects or logic that cannot be described simply by a dictionary, +such as reused objects or method calls. They may require some refactoring +to work with recursive instantiation. + +## Using Model Zoo LazyConfigs + +We provide some configs in the model zoo using the LazyConfig system, for example: + +* [common baselines](../../configs/common/). +* [new Mask R-CNN baselines](../../configs/new_baselines/) + +After installing detectron2, they can be loaded by the model zoo API +[model_zoo.get_config](../modules/model_zoo.html#detectron2.model_zoo.get_config). + +Using these as references, you're free to define custom config structure / fields for your own +project, as long as your training script can understand them. +Despite of this, our model zoo configs still follow some simple conventions for consistency, e.g. +`cfg.model` defines a model object, `cfg.dataloader.{train,test}` defines dataloader objects, +and `cfg.train` contains training options in key-value form. +In addition to `print()`, a better way to view the structure of a config is like this: +``` +from detectron2.model_zoo import get_config +from detectron2.config import LazyConfig +print(LazyConfig.to_py(get_config("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.py"))) +``` +From the output it's easier to find relevant options to change, e.g. +`dataloader.train.total_batch_size` for the batch size, or `optimizer.lr` for base learning rate. + +We provide a reference training script +[tools/lazyconfig_train_net.py](../../tools/lazyconfig_train_net.py), +that can train/eval our model zoo configs. +It also shows how to support command line value overrides. + +To demonstrate the power and flexibility of the new system, we show that +[a simple config file](../../configs/Misc/torchvision_imagenet_R_50.py) +can let detectron2 train an ImageNet classification model from torchvision, even though +detectron2 contains no features about ImageNet classification. +This can serve as a reference for using detectron2 in other deep learning tasks. + +## Summary + +By using recursive instantiation to create objects, +we avoid passing a giant config to many places, because `cfg` is only passed to `instantiate`. +This has the following benefits: + +* It's __non-intrusive__: objects to be constructed are config-agnostic, regular Python + functions/classes. + They can even live in other libraries. For example, + `{"_target_": "torch.nn.Conv2d", "in_channels": 10, "out_channels": 10, "kernel_size": 1}` + defines a conv layer. +* __Clarity__ of what function/classes will be called, and what arguments they use. +* `cfg` doesn't need pre-defined keys and structures. It's valid as long as it translates to valid + code. This gives a lot more __flexibility__. +* You can still pass huge dictionaries as arguments, just like the old way. + +Recursive instantiation and Python syntax are orthogonal: you can use one without the other. +But by putting them together, the config file looks a lot like the code that will be executed: + +![img](./lazyconfig.jpg) + +However, the config file just defines dictionaries, which can be easily manipulated further +by composition or overrides. +The corresponding code will only be executed +later when `instantiate` is called. In some way, +in config files we're writing "editable code" that will be "lazily executed" later when needed. +That's why we call this system "LazyConfig". diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/models.md b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/models.md new file mode 100644 index 0000000000000000000000000000000000000000..3cf918e7a145ee326c6cccf8a88835b7e02a7c30 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/models.md @@ -0,0 +1,180 @@ +# Use Models + +## Build Models from Yacs Config +From a yacs config object, +models (and their sub-models) can be built by +functions such as `build_model`, `build_backbone`, `build_roi_heads`: +```python +from detectron2.modeling import build_model +model = build_model(cfg) # returns a torch.nn.Module +``` + +`build_model` only builds the model structure and fills it with random parameters. +See below for how to load an existing checkpoint to the model and how to use the `model` object. + +### Load/Save a Checkpoint +```python +from detectron2.checkpoint import DetectionCheckpointer +DetectionCheckpointer(model).load(file_path_or_url) # load a file, usually from cfg.MODEL.WEIGHTS + +checkpointer = DetectionCheckpointer(model, save_dir="output") +checkpointer.save("model_999") # save to output/model_999.pth +``` + +Detectron2's checkpointer recognizes models in pytorch's `.pth` format, as well as the `.pkl` files +in our model zoo. +See [API doc](../modules/checkpoint.html#detectron2.checkpoint.DetectionCheckpointer) +for more details about its usage. + +The model files can be arbitrarily manipulated using `torch.{load,save}` for `.pth` files or +`pickle.{dump,load}` for `.pkl` files. + +### Use a Model + +A model can be called by `outputs = model(inputs)`, where `inputs` is a `list[dict]`. +Each dict corresponds to one image and the required keys +depend on the type of model, and whether the model is in training or evaluation mode. +For example, in order to do inference, +all existing models expect the "image" key, and optionally "height" and "width". +The detailed format of inputs and outputs of existing models are explained below. + +__Training__: When in training mode, all models are required to be used under an `EventStorage`. +The training statistics will be put into the storage: +```python +from detectron2.utils.events import EventStorage +with EventStorage() as storage: + losses = model(inputs) +``` + +__Inference__: If you only want to do simple inference using an existing model, +[DefaultPredictor](../modules/engine.html#detectron2.engine.defaults.DefaultPredictor) +is a wrapper around model that provides such basic functionality. +It includes default behavior including model loading, preprocessing, +and operates on single image rather than batches. See its documentation for usage. + +You can also run inference directly like this: +``` +model.eval() +with torch.no_grad(): + outputs = model(inputs) +``` + +### Model Input Format + +Users can implement custom models that support any arbitrary input format. +Here we describe the standard input format that all builtin models support in detectron2. +They all take a `list[dict]` as the inputs. Each dict +corresponds to information about one image. + +The dict may contain the following keys: + +* "image": `Tensor` in (C, H, W) format. The meaning of channels are defined by `cfg.INPUT.FORMAT`. + Image normalization, if any, will be performed inside the model using + `cfg.MODEL.PIXEL_{MEAN,STD}`. +* "height", "width": the **desired** output height and width **in inference**, which is not necessarily the same + as the height or width of the `image` field. + For example, the `image` field contains the resized image, if resize is used as a preprocessing step. + But you may want the outputs to be in **original** resolution. + If provided, the model will produce output in this resolution, + rather than in the resolution of the `image` as input into the model. This is more efficient and accurate. +* "instances": an [Instances](../modules/structures.html#detectron2.structures.Instances) + object for training, with the following fields: + + "gt_boxes": a [Boxes](../modules/structures.html#detectron2.structures.Boxes) object storing N boxes, one for each instance. + + "gt_classes": `Tensor` of long type, a vector of N labels, in range [0, num_categories). + + "gt_masks": a [PolygonMasks](../modules/structures.html#detectron2.structures.PolygonMasks) + or [BitMasks](../modules/structures.html#detectron2.structures.BitMasks) object storing N masks, one for each instance. + + "gt_keypoints": a [Keypoints](../modules/structures.html#detectron2.structures.Keypoints) + object storing N keypoint sets, one for each instance. +* "sem_seg": `Tensor[int]` in (H, W) format. The semantic segmentation ground truth for training. + Values represent category labels starting from 0. +* "proposals": an [Instances](../modules/structures.html#detectron2.structures.Instances) + object used only in Fast R-CNN style models, with the following fields: + + "proposal_boxes": a [Boxes](../modules/structures.html#detectron2.structures.Boxes) object storing P proposal boxes. + + "objectness_logits": `Tensor`, a vector of P scores, one for each proposal. + +For inference of builtin models, only "image" key is required, and "width/height" are optional. + +We currently don't define standard input format for panoptic segmentation training, +because models now use custom formats produced by custom data loaders. + +#### How it connects to data loader: + +The output of the default [DatasetMapper]( ../modules/data.html#detectron2.data.DatasetMapper) is a dict +that follows the above format. +After the data loader performs batching, it becomes `list[dict]` which the builtin models support. + + +### Model Output Format + +When in training mode, the builtin models output a `dict[str->ScalarTensor]` with all the losses. + +When in inference mode, the builtin models output a `list[dict]`, one dict for each image. +Based on the tasks the model is doing, each dict may contain the following fields: + +* "instances": [Instances](../modules/structures.html#detectron2.structures.Instances) + object with the following fields: + * "pred_boxes": [Boxes](../modules/structures.html#detectron2.structures.Boxes) object storing N boxes, one for each detected instance. + * "scores": `Tensor`, a vector of N confidence scores. + * "pred_classes": `Tensor`, a vector of N labels in range [0, num_categories). + + "pred_masks": a `Tensor` of shape (N, H, W), masks for each detected instance. + + "pred_keypoints": a `Tensor` of shape (N, num_keypoint, 3). + Each row in the last dimension is (x, y, score). Confidence scores are larger than 0. +* "sem_seg": `Tensor` of (num_categories, H, W), the semantic segmentation prediction. +* "proposals": [Instances](../modules/structures.html#detectron2.structures.Instances) + object with the following fields: + * "proposal_boxes": [Boxes](../modules/structures.html#detectron2.structures.Boxes) + object storing N boxes. + * "objectness_logits": a torch vector of N confidence scores. +* "panoptic_seg": A tuple of `(pred: Tensor, segments_info: Optional[list[dict]])`. + The `pred` tensor has shape (H, W), containing the segment id of each pixel. + + * If `segments_info` exists, each dict describes one segment id in `pred` and has the following fields: + + * "id": the segment id + * "isthing": whether the segment is a thing or stuff + * "category_id": the category id of this segment. + + If a pixel's id does not exist in `segments_info`, it is considered to be void label + defined in [Panoptic Segmentation](https://arxiv.org/abs/1801.00868). + + * If `segments_info` is None, all pixel values in `pred` must be ≥ -1. + Pixels with value -1 are assigned void labels. + Otherwise, the category id of each pixel is obtained by + `category_id = pixel // metadata.label_divisor`. + + +### Partially execute a model: + +Sometimes you may want to obtain an intermediate tensor inside a model, +such as the input of certain layer, the output before post-processing. +Since there are typically hundreds of intermediate tensors, there isn't an API that provides you +the intermediate result you need. +You have the following options: + +1. Write a (sub)model. Following the [tutorial](./write-models.md), you can + rewrite a model component (e.g. a head of a model), such that it + does the same thing as the existing component, but returns the output + you need. +2. Partially execute a model. You can create the model as usual, + but use custom code to execute it instead of its `forward()`. For example, + the following code obtains mask features before mask head. + + ```python + images = ImageList.from_tensors(...) # preprocessed input tensor + model = build_model(cfg) + model.eval() + features = model.backbone(images.tensor) + proposals, _ = model.proposal_generator(images, features) + instances, _ = model.roi_heads(images, features, proposals) + mask_features = [features[f] for f in model.roi_heads.in_features] + mask_features = model.roi_heads.mask_pooler(mask_features, [x.pred_boxes for x in instances]) + ``` + +3. Use [forward hooks](https://pytorch.org/tutorials/beginner/former_torchies/nnft_tutorial.html#forward-and-backward-function-hooks). + Forward hooks can help you obtain inputs or outputs of a certain module. + If they are not exactly what you want, they can at least be used together with partial execution + to obtain other tensors. + +All options require you to read documentation and sometimes code +of the existing models to understand the internal logic, +in order to write code to obtain the internal tensors. diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/training.md b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/training.md new file mode 100644 index 0000000000000000000000000000000000000000..7e2987e4e96c024da24d03b2110f826c0fb64824 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/training.md @@ -0,0 +1,67 @@ +# Training + +From the previous tutorials, you may now have a custom model and a data loader. +To run training, users typically have a preference in one of the following two styles: + +### Custom Training Loop + +With a model and a data loader ready, everything else needed to write a training loop can +be found in PyTorch, and you are free to write the training loop yourself. +This style allows researchers to manage the entire training logic more clearly and have full control. +One such example is provided in [tools/plain_train_net.py](../../tools/plain_train_net.py). + +Any customization on the training logic is then easily controlled by the user. + +### Trainer Abstraction + +We also provide a standardized "trainer" abstraction with a +hook system that helps simplify the standard training behavior. +It includes the following two instantiations: + +* [SimpleTrainer](../modules/engine.html#detectron2.engine.SimpleTrainer) + provides a minimal training loop for single-cost single-optimizer single-data-source training, with nothing else. + Other tasks (checkpointing, logging, etc) can be implemented using + [the hook system](../modules/engine.html#detectron2.engine.HookBase). +* [DefaultTrainer](../modules/engine.html#detectron2.engine.defaults.DefaultTrainer) is a `SimpleTrainer` initialized from a + yacs config, used by + [tools/train_net.py](../../tools/train_net.py) and many scripts. + It includes more standard default behaviors that one might want to opt in, + including default configurations for optimizer, learning rate schedule, + logging, evaluation, checkpointing etc. + +To customize a `DefaultTrainer`: + +1. For simple customizations (e.g. change optimizer, evaluator, LR scheduler, data loader, etc.), overwrite [its methods](../modules/engine.html#detectron2.engine.defaults.DefaultTrainer) in a subclass, just like [tools/train_net.py](../../tools/train_net.py). +2. For extra tasks during training, check the + [hook system](../modules/engine.html#detectron2.engine.HookBase) to see if it's supported. + + As an example, to print hello during training: + ```python + class HelloHook(HookBase): + def after_step(self): + if self.trainer.iter % 100 == 0: + print(f"Hello at iteration {self.trainer.iter}!") + ``` +3. Using a trainer+hook system means there will always be some non-standard behaviors that cannot be supported, especially in research. + For this reason, we intentionally keep the trainer & hook system minimal, rather than powerful. + If anything cannot be achieved by such a system, it's easier to start from [tools/plain_train_net.py](../../tools/plain_train_net.py) to implement custom training logic manually. + +### Logging of Metrics + +During training, detectron2 models and trainer put metrics to a centralized [EventStorage](../modules/utils.html#detectron2.utils.events.EventStorage). +You can use the following code to access it and log metrics to it: +``` +from detectron2.utils.events import get_event_storage + +# inside the model: +if self.training: + value = # compute the value from inputs + storage = get_event_storage() + storage.put_scalar("some_accuracy", value) +``` + +Refer to its documentation for more details. + +Metrics are then written to various destinations with [EventWriter](../modules/utils.html#module-detectron2.utils.events). +DefaultTrainer enables a few `EventWriter` with default configurations. +See above for how to customize them. diff --git a/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/write-models.md b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/write-models.md new file mode 100644 index 0000000000000000000000000000000000000000..967d126503c71b419bca94615cb1090e1a79cb49 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/docs/tutorials/write-models.md @@ -0,0 +1,90 @@ +# Write Models + +If you are trying to do something completely new, you may wish to implement +a model entirely from scratch. However, in many situations you may +be interested in modifying or extending some components of an existing model. +Therefore, we also provide mechanisms that let users override the +behavior of certain internal components of standard models. + + +## Register New Components + +For common concepts that users often want to customize, such as "backbone feature extractor", "box head", +we provide a registration mechanism for users to inject custom implementation that +will be immediately available to use in config files. + +For example, to add a new backbone, import this code in your code: +```python +from detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec + +@BACKBONE_REGISTRY.register() +class ToyBackbone(Backbone): + def __init__(self, cfg, input_shape): + super().__init__() + # create your own backbone + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=16, padding=3) + + def forward(self, image): + return {"conv1": self.conv1(image)} + + def output_shape(self): + return {"conv1": ShapeSpec(channels=64, stride=16)} +``` + +In this code, we implement a new backbone following the interface of the +[Backbone](../modules/modeling.html#detectron2.modeling.Backbone) class, +and register it into the [BACKBONE_REGISTRY](../modules/modeling.html#detectron2.modeling.BACKBONE_REGISTRY) +which requires subclasses of `Backbone`. +After importing this code, detectron2 can link the name of the class to its implementation. Therefore you can write the following code: + +```python +cfg = ... # read a config +cfg.MODEL.BACKBONE.NAME = 'ToyBackbone' # or set it in the config file +model = build_model(cfg) # it will find `ToyBackbone` defined above +``` + +As another example, to add new abilities to the ROI heads in the Generalized R-CNN meta-architecture, +you can implement a new +[ROIHeads](../modules/modeling.html#detectron2.modeling.ROIHeads) subclass and put it in the `ROI_HEADS_REGISTRY`. +[DensePose](../../projects/DensePose) +and [MeshRCNN](https://github.com/facebookresearch/meshrcnn) +are two examples that implement new ROIHeads to perform new tasks. +And [projects/](../../projects/) +contains more examples that implement different architectures. + +A complete list of registries can be found in [API documentation](../modules/modeling.html#model-registries). +You can register components in these registries to customize different parts of a model, or the +entire model. + +## Construct Models with Explicit Arguments + +Registry is a bridge to connect names in config files to the actual code. +They are meant to cover a few main components that users frequently need to replace. +However, the capability of a text-based config file is sometimes limited and +some deeper customization may be available only through writing code. + +Most model components in detectron2 have a clear `__init__` interface that documents +what input arguments it needs. Calling them with custom arguments will give you a custom variant +of the model. + +As an example, to use __custom loss function__ in the box head of a Faster R-CNN, we can do the following: + +1. Losses are currently computed in [FastRCNNOutputLayers](../modules/modeling.html#detectron2.modeling.FastRCNNOutputLayers). + We need to implement a variant or a subclass of it, with custom loss functions, named `MyRCNNOutput`. +2. Call `StandardROIHeads` with `box_predictor=MyRCNNOutput()` argument instead of the builtin `FastRCNNOutputLayers`. + If all other arguments should stay unchanged, this can be easily achieved by using the [configurable `__init__`](../modules/config.html#detectron2.config.configurable) mechanism: + + ```python + roi_heads = StandardROIHeads( + cfg, backbone.output_shape(), + box_predictor=MyRCNNOutput(...) + ) + ``` +3. (optional) If we want to enable this new model from a config file, registration is needed: + ```python + @ROI_HEADS_REGISTRY.register() + class MyStandardROIHeads(StandardROIHeads): + def __init__(self, cfg, input_shape): + super().__init__(cfg, input_shape, + box_predictor=MyRCNNOutput(...)) + ``` diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/.gitignore b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..51c1768851d9842649eacb00a44d24f67509a295 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/.gitignore @@ -0,0 +1,10 @@ +# compilation and distribution +__pycache__ +_ext +*.pyc +*.pyd +*.so +centernet.egg-info/ +build/ +dist/ +wheels/ diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e17db317d9ba31b9f027bc922332c1ca8b039128 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/__init__.py @@ -0,0 +1,14 @@ +from .modeling.meta_arch.centernet_detector import CenterNetDetector +from .modeling.dense_heads.centernet import CenterNet +from .modeling.roi_heads.custom_roi_heads import CustomROIHeads, CustomCascadeROIHeads + +from .modeling.backbone.fpn_p5 import build_p67_resnet_fpn_backbone +from .modeling.backbone.dla import build_dla_backbone +from .modeling.backbone.dlafpn import build_dla_fpn3_backbone +from .modeling.backbone.bifpn import build_resnet_bifpn_backbone +from .modeling.backbone.bifpn_fcos import build_fcos_resnet_bifpn_backbone +from .modeling.backbone.res2net import build_p67_res2net_fpn_backbone + +from .data.datasets.objects365 import categories_v1 +from .data.datasets.coco import _PREDEFINED_SPLITS_COCO +from .data.datasets import nuimages diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/config.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/config.py new file mode 100644 index 0000000000000000000000000000000000000000..36d0d250556686f8dfa69ed2ba6372f9ebb0ec85 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/config.py @@ -0,0 +1,87 @@ +from detectron2.config import CfgNode as CN + +def add_centernet_config(cfg): + _C = cfg + + _C.MODEL.CENTERNET = CN() + _C.MODEL.CENTERNET.NUM_CLASSES = 80 + _C.MODEL.CENTERNET.IN_FEATURES = ["p3", "p4", "p5", "p6", "p7"] + _C.MODEL.CENTERNET.FPN_STRIDES = [8, 16, 32, 64, 128] + _C.MODEL.CENTERNET.PRIOR_PROB = 0.01 + _C.MODEL.CENTERNET.INFERENCE_TH = 0.05 + _C.MODEL.CENTERNET.CENTER_NMS = False + _C.MODEL.CENTERNET.NMS_TH_TRAIN = 0.6 + _C.MODEL.CENTERNET.NMS_TH_TEST = 0.6 + _C.MODEL.CENTERNET.PRE_NMS_TOPK_TRAIN = 1000 + _C.MODEL.CENTERNET.POST_NMS_TOPK_TRAIN = 100 + _C.MODEL.CENTERNET.PRE_NMS_TOPK_TEST = 1000 + _C.MODEL.CENTERNET.POST_NMS_TOPK_TEST = 100 + _C.MODEL.CENTERNET.NORM = "GN" + _C.MODEL.CENTERNET.USE_DEFORMABLE = False + _C.MODEL.CENTERNET.NUM_CLS_CONVS = 4 + _C.MODEL.CENTERNET.NUM_BOX_CONVS = 4 + _C.MODEL.CENTERNET.NUM_SHARE_CONVS = 0 + _C.MODEL.CENTERNET.LOC_LOSS_TYPE = 'giou' + _C.MODEL.CENTERNET.SIGMOID_CLAMP = 1e-4 + _C.MODEL.CENTERNET.HM_MIN_OVERLAP = 0.8 + _C.MODEL.CENTERNET.MIN_RADIUS = 4 + _C.MODEL.CENTERNET.SOI = [[0, 80], [64, 160], [128, 320], [256, 640], [512, 10000000]] + _C.MODEL.CENTERNET.POS_WEIGHT = 1. + _C.MODEL.CENTERNET.NEG_WEIGHT = 1. + _C.MODEL.CENTERNET.REG_WEIGHT = 2. + _C.MODEL.CENTERNET.HM_FOCAL_BETA = 4 + _C.MODEL.CENTERNET.HM_FOCAL_ALPHA = 0.25 + _C.MODEL.CENTERNET.LOSS_GAMMA = 2.0 + _C.MODEL.CENTERNET.WITH_AGN_HM = False + _C.MODEL.CENTERNET.ONLY_PROPOSAL = False + _C.MODEL.CENTERNET.AS_PROPOSAL = False + _C.MODEL.CENTERNET.IGNORE_HIGH_FP = -1. + _C.MODEL.CENTERNET.MORE_POS = False + _C.MODEL.CENTERNET.MORE_POS_THRESH = 0.2 + _C.MODEL.CENTERNET.MORE_POS_TOPK = 9 + _C.MODEL.CENTERNET.NOT_NORM_REG = True + _C.MODEL.CENTERNET.NOT_NMS = False + _C.MODEL.CENTERNET.NO_REDUCE = False + + _C.MODEL.ROI_BOX_HEAD.USE_SIGMOID_CE = False + _C.MODEL.ROI_BOX_HEAD.PRIOR_PROB = 0.01 + _C.MODEL.ROI_BOX_HEAD.USE_EQL_LOSS = False + _C.MODEL.ROI_BOX_HEAD.CAT_FREQ_PATH = \ + 'datasets/lvis/lvis_v1_train_cat_info.json' + _C.MODEL.ROI_BOX_HEAD.EQL_FREQ_CAT = 200 + _C.MODEL.ROI_BOX_HEAD.USE_FED_LOSS = False + _C.MODEL.ROI_BOX_HEAD.FED_LOSS_NUM_CAT = 50 + _C.MODEL.ROI_BOX_HEAD.FED_LOSS_FREQ_WEIGHT = 0.5 + _C.MODEL.ROI_BOX_HEAD.MULT_PROPOSAL_SCORE = False + + _C.MODEL.BIFPN = CN() + _C.MODEL.BIFPN.NUM_LEVELS = 5 + _C.MODEL.BIFPN.NUM_BIFPN = 6 + _C.MODEL.BIFPN.NORM = 'GN' + _C.MODEL.BIFPN.OUT_CHANNELS = 160 + _C.MODEL.BIFPN.SEPARABLE_CONV = False + + _C.MODEL.DLA = CN() + _C.MODEL.DLA.OUT_FEATURES = ['dla2'] + _C.MODEL.DLA.USE_DLA_UP = True + _C.MODEL.DLA.NUM_LAYERS = 34 + _C.MODEL.DLA.MS_OUTPUT = False + _C.MODEL.DLA.NORM = 'BN' + _C.MODEL.DLA.DLAUP_IN_FEATURES = ['dla3', 'dla4', 'dla5'] + _C.MODEL.DLA.DLAUP_NODE = 'conv' + + _C.SOLVER.RESET_ITER = False + _C.SOLVER.TRAIN_ITER = -1 + + _C.INPUT.CUSTOM_AUG = '' + _C.INPUT.TRAIN_SIZE = 640 + _C.INPUT.TEST_SIZE = 640 + _C.INPUT.SCALE_RANGE = (0.1, 2.) + # 'default' for fixed short/ long edge, 'square' for max size=INPUT.SIZE + _C.INPUT.TEST_INPUT_TYPE = 'default' + + _C.DEBUG = False + _C.SAVE_DEBUG = False + _C.SAVE_PTH = False + _C.VIS_THRESH = 0.3 + _C.DEBUG_SHOW_NAME = False diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/custom_build_augmentation.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/custom_build_augmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..7d91f21edb082c079c5a1e85bdf669c7b55bad9a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/custom_build_augmentation.py @@ -0,0 +1,59 @@ +import logging +import numpy as np +import pycocotools.mask as mask_util +import torch +from fvcore.common.file_io import PathManager +from PIL import Image + +from detectron2.structures import ( + BitMasks, + Boxes, + BoxMode, + Instances, + Keypoints, + PolygonMasks, + RotatedBoxes, + polygons_to_bitmask, +) + +from detectron2.data import transforms as T +from .transforms.custom_augmentation_impl import EfficientDetResizeCrop + +def build_custom_augmentation(cfg, is_train): + """ + Create a list of default :class:`Augmentation` from config. + Now it includes resizing and flipping. + + Returns: + list[Augmentation] + """ + if cfg.INPUT.CUSTOM_AUG == 'ResizeShortestEdge': + if is_train: + min_size = cfg.INPUT.MIN_SIZE_TRAIN + max_size = cfg.INPUT.MAX_SIZE_TRAIN + sample_style = cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING + else: + min_size = cfg.INPUT.MIN_SIZE_TEST + max_size = cfg.INPUT.MAX_SIZE_TEST + sample_style = "choice" + augmentation = [T.ResizeShortestEdge(min_size, max_size, sample_style)] + elif cfg.INPUT.CUSTOM_AUG == 'EfficientDetResizeCrop': + if is_train: + scale = cfg.INPUT.SCALE_RANGE + size = cfg.INPUT.TRAIN_SIZE + else: + scale = (1, 1) + size = cfg.INPUT.TEST_SIZE + augmentation = [EfficientDetResizeCrop(size, scale)] + else: + assert 0, cfg.INPUT.CUSTOM_AUG + + if is_train: + augmentation.append(T.RandomFlip()) + return augmentation + + +build_custom_transform_gen = build_custom_augmentation +""" +Alias for backward-compatibility. +""" \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/custom_dataset_dataloader.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/custom_dataset_dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..4e9844c99b4acf318e2935963bbf94b878076591 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/custom_dataset_dataloader.py @@ -0,0 +1,229 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import copy +import logging +import numpy as np +import operator +import torch +import torch.utils.data +import json +from detectron2.utils.comm import get_world_size + +from detectron2.data import samplers +from torch.utils.data.sampler import BatchSampler, Sampler +from detectron2.data.common import DatasetFromList, MapDataset +from detectron2.data.dataset_mapper import DatasetMapper +from detectron2.data.build import get_detection_dataset_dicts, build_batch_data_loader +from detectron2.data.samplers import TrainingSampler, RepeatFactorTrainingSampler +from detectron2.data.build import worker_init_reset_seed, print_instances_class_histogram +from detectron2.data.build import filter_images_with_only_crowd_annotations +from detectron2.data.build import filter_images_with_few_keypoints +from detectron2.data.build import check_metadata_consistency +from detectron2.data.catalog import MetadataCatalog, DatasetCatalog +from detectron2.utils import comm +import itertools +import math +from collections import defaultdict +from typing import Optional + +# from .custom_build_augmentation import build_custom_augmentation + +def build_custom_train_loader(cfg, mapper=None): + """ + Modified from detectron2.data.build.build_custom_train_loader, but supports + different samplers + """ + source_aware = cfg.DATALOADER.SOURCE_AWARE + if source_aware: + dataset_dicts = get_detection_dataset_dicts_with_source( + cfg.DATASETS.TRAIN, + filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS, + min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE + if cfg.MODEL.KEYPOINT_ON + else 0, + proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None, + ) + sizes = [0 for _ in range(len(cfg.DATASETS.TRAIN))] + for d in dataset_dicts: + sizes[d['dataset_source']] += 1 + print('dataset sizes', sizes) + else: + dataset_dicts = get_detection_dataset_dicts( + cfg.DATASETS.TRAIN, + filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS, + min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE + if cfg.MODEL.KEYPOINT_ON + else 0, + proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None, + ) + dataset = DatasetFromList(dataset_dicts, copy=False) + + if mapper is None: + assert 0 + # mapper = DatasetMapper(cfg, True) + dataset = MapDataset(dataset, mapper) + + sampler_name = cfg.DATALOADER.SAMPLER_TRAIN + logger = logging.getLogger(__name__) + logger.info("Using training sampler {}".format(sampler_name)) + # TODO avoid if-else? + if sampler_name == "TrainingSampler": + sampler = TrainingSampler(len(dataset)) + elif sampler_name == "MultiDatasetSampler": + assert source_aware + sampler = MultiDatasetSampler(cfg, sizes, dataset_dicts) + elif sampler_name == "RepeatFactorTrainingSampler": + repeat_factors = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset_dicts, cfg.DATALOADER.REPEAT_THRESHOLD + ) + sampler = RepeatFactorTrainingSampler(repeat_factors) + elif sampler_name == "ClassAwareSampler": + sampler = ClassAwareSampler(dataset_dicts) + else: + raise ValueError("Unknown training sampler: {}".format(sampler_name)) + + return build_batch_data_loader( + dataset, + sampler, + cfg.SOLVER.IMS_PER_BATCH, + aspect_ratio_grouping=cfg.DATALOADER.ASPECT_RATIO_GROUPING, + num_workers=cfg.DATALOADER.NUM_WORKERS, + ) + + +class ClassAwareSampler(Sampler): + def __init__(self, dataset_dicts, seed: Optional[int] = None): + """ + Args: + size (int): the total number of data of the underlying dataset to sample from + seed (int): the initial seed of the shuffle. Must be the same + across all workers. If None, will use a random seed shared + among workers (require synchronization among all workers). + """ + self._size = len(dataset_dicts) + assert self._size > 0 + if seed is None: + seed = comm.shared_random_seed() + self._seed = int(seed) + + self._rank = comm.get_rank() + self._world_size = comm.get_world_size() + self.weights = self._get_class_balance_factor(dataset_dicts) + + + def __iter__(self): + start = self._rank + yield from itertools.islice( + self._infinite_indices(), start, None, self._world_size) + + + def _infinite_indices(self): + g = torch.Generator() + g.manual_seed(self._seed) + while True: + ids = torch.multinomial( + self.weights, self._size, generator=g, + replacement=True) + yield from ids + + + def _get_class_balance_factor(self, dataset_dicts, l=1.): + # 1. For each category c, compute the fraction of images that contain it: f(c) + ret = [] + category_freq = defaultdict(int) + for dataset_dict in dataset_dicts: # For each image (without repeats) + cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]} + for cat_id in cat_ids: + category_freq[cat_id] += 1 + for i, dataset_dict in enumerate(dataset_dicts): + cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]} + ret.append(sum( + [1. / (category_freq[cat_id] ** l) for cat_id in cat_ids])) + return torch.tensor(ret).float() + + +def get_detection_dataset_dicts_with_source( + dataset_names, filter_empty=True, min_keypoints=0, proposal_files=None +): + assert len(dataset_names) + dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in dataset_names] + for dataset_name, dicts in zip(dataset_names, dataset_dicts): + assert len(dicts), "Dataset '{}' is empty!".format(dataset_name) + + for source_id, (dataset_name, dicts) in \ + enumerate(zip(dataset_names, dataset_dicts)): + assert len(dicts), "Dataset '{}' is empty!".format(dataset_name) + for d in dicts: + d['dataset_source'] = source_id + + if "annotations" in dicts[0]: + try: + class_names = MetadataCatalog.get(dataset_name).thing_classes + check_metadata_consistency("thing_classes", dataset_name) + print_instances_class_histogram(dicts, class_names) + except AttributeError: # class names are not available for this dataset + pass + + assert proposal_files is None + + dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts)) + + has_instances = "annotations" in dataset_dicts[0] + if filter_empty and has_instances: + dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts) + if min_keypoints > 0 and has_instances: + dataset_dicts = filter_images_with_few_keypoints(dataset_dicts, min_keypoints) + + return dataset_dicts + +class MultiDatasetSampler(Sampler): + def __init__(self, cfg, sizes, dataset_dicts, seed: Optional[int] = None): + """ + Args: + size (int): the total number of data of the underlying dataset to sample from + seed (int): the initial seed of the shuffle. Must be the same + across all workers. If None, will use a random seed shared + among workers (require synchronization among all workers). + """ + self.sizes = sizes + dataset_ratio = cfg.DATALOADER.DATASET_RATIO + self._batch_size = cfg.SOLVER.IMS_PER_BATCH + assert len(dataset_ratio) == len(sizes), \ + 'length of dataset ratio {} should be equal to number if dataset {}'.format( + len(dataset_ratio), len(sizes) + ) + if seed is None: + seed = comm.shared_random_seed() + self._seed = int(seed) + self._rank = comm.get_rank() + self._world_size = comm.get_world_size() + + self._ims_per_gpu = self._batch_size // self._world_size + self.dataset_ids = torch.tensor( + [d['dataset_source'] for d in dataset_dicts], dtype=torch.long) + + dataset_weight = [torch.ones(s) * max(sizes) / s * r / sum(dataset_ratio) \ + for i, (r, s) in enumerate(zip(dataset_ratio, sizes))] + dataset_weight = torch.cat(dataset_weight) + self.weights = dataset_weight + self.sample_epoch_size = len(self.weights) + + def __iter__(self): + start = self._rank + yield from itertools.islice( + self._infinite_indices(), start, None, self._world_size) + + + def _infinite_indices(self): + g = torch.Generator() + g.manual_seed(self._seed) + while True: + ids = torch.multinomial( + self.weights, self.sample_epoch_size, generator=g, + replacement=True) + nums = [(self.dataset_ids[ids] == i).sum().int().item() \ + for i in range(len(self.sizes))] + print('_rank, len, nums', self._rank, len(ids), nums, flush=True) + # print('_rank, len, nums, self.dataset_ids[ids[:10]], ', + # self._rank, len(ids), nums, self.dataset_ids[ids[:10]], + # flush=True) + yield from ids \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/datasets/coco.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/datasets/coco.py new file mode 100644 index 0000000000000000000000000000000000000000..f8496aacf2eda691e55a8fabfc0f5db496dcc186 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/datasets/coco.py @@ -0,0 +1,49 @@ +import os + +from detectron2.data.datasets.register_coco import register_coco_instances +from detectron2.data.datasets.coco import load_coco_json +from detectron2.data.datasets.builtin_meta import _get_builtin_metadata +from detectron2.data import DatasetCatalog, MetadataCatalog + + +def register_distill_coco_instances(name, metadata, json_file, image_root): + """ + add extra_annotation_keys + """ + assert isinstance(name, str), name + assert isinstance(json_file, (str, os.PathLike)), json_file + assert isinstance(image_root, (str, os.PathLike)), image_root + # 1. register a function which returns dicts + DatasetCatalog.register(name, lambda: load_coco_json( + json_file, image_root, name, extra_annotation_keys=['score'])) + + # 2. Optionally, add metadata about this dataset, + # since they might be useful in evaluation, visualization or logging + MetadataCatalog.get(name).set( + json_file=json_file, image_root=image_root, evaluator_type="coco", **metadata + ) + + +_PREDEFINED_SPLITS_COCO = { + "coco_2017_unlabeled": ("coco/unlabeled2017", "coco/annotations/image_info_unlabeled2017.json"), +} + +for key, (image_root, json_file) in _PREDEFINED_SPLITS_COCO.items(): + register_coco_instances( + key, + _get_builtin_metadata('coco'), + os.path.join("datasets", json_file) if "://" not in json_file else json_file, + os.path.join("datasets", image_root), + ) + +_PREDEFINED_SPLITS_DISTILL_COCO = { + "coco_un_yolov4_55_0.5": ("coco/unlabeled2017", "coco/annotations/yolov4_cocounlabeled_55_ann0.5.json"), +} + +for key, (image_root, json_file) in _PREDEFINED_SPLITS_DISTILL_COCO.items(): + register_distill_coco_instances( + key, + _get_builtin_metadata('coco'), + os.path.join("datasets", json_file) if "://" not in json_file else json_file, + os.path.join("datasets", image_root), + ) \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/datasets/nuimages.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/datasets/nuimages.py new file mode 100644 index 0000000000000000000000000000000000000000..52736e331cc6c95001bc84f2c17a0805789b2450 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/datasets/nuimages.py @@ -0,0 +1,37 @@ +from detectron2.data.datasets.register_coco import register_coco_instances +import os + +categories = [ + {'id': 0, 'name': 'car'}, + {'id': 1, 'name': 'truck'}, + {'id': 2, 'name': 'trailer'}, + {'id': 3, 'name': 'bus'}, + {'id': 4, 'name': 'construction_vehicle'}, + {'id': 5, 'name': 'bicycle'}, + {'id': 6, 'name': 'motorcycle'}, + {'id': 7, 'name': 'pedestrian'}, + {'id': 8, 'name': 'traffic_cone'}, + {'id': 9, 'name': 'barrier'}, +] + +def _get_builtin_metadata(): + id_to_name = {x['id']: x['name'] for x in categories} + thing_dataset_id_to_contiguous_id = {i: i for i in range(len(categories))} + thing_classes = [id_to_name[k] for k in sorted(id_to_name)] + return { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes} + +_PREDEFINED_SPLITS = { + "nuimages_train": ("nuimages", "nuimages/annotations/nuimages_v1.0-train.json"), + "nuimages_val": ("nuimages", "nuimages/annotations/nuimages_v1.0-val.json"), + "nuimages_mini": ("nuimages", "nuimages/annotations/nuimages_v1.0-mini.json"), +} + +for key, (image_root, json_file) in _PREDEFINED_SPLITS.items(): + register_coco_instances( + key, + _get_builtin_metadata(), + os.path.join("datasets", json_file) if "://" not in json_file else json_file, + os.path.join("datasets", image_root), + ) diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/datasets/objects365.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/datasets/objects365.py new file mode 100644 index 0000000000000000000000000000000000000000..41395bdd53b67b7a7111f06564c3a2d2b63a7cdc --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/datasets/objects365.py @@ -0,0 +1,394 @@ +from detectron2.data.datasets.register_coco import register_coco_instances +import os + +categories_v1 = [ +{'id': 164, 'name': 'cutting/chopping board'} , +{'id': 49, 'name': 'tie'} , +{'id': 306, 'name': 'crosswalk sign'} , +{'id': 145, 'name': 'gun'} , +{'id': 14, 'name': 'street lights'} , +{'id': 223, 'name': 'bar soap'} , +{'id': 74, 'name': 'wild bird'} , +{'id': 219, 'name': 'ice cream'} , +{'id': 37, 'name': 'stool'} , +{'id': 25, 'name': 'storage box'} , +{'id': 153, 'name': 'giraffe'} , +{'id': 52, 'name': 'pen/pencil'} , +{'id': 61, 'name': 'high heels'} , +{'id': 340, 'name': 'mangosteen'} , +{'id': 22, 'name': 'bracelet'} , +{'id': 155, 'name': 'piano'} , +{'id': 162, 'name': 'vent'} , +{'id': 75, 'name': 'laptop'} , +{'id': 236, 'name': 'toaster'} , +{'id': 231, 'name': 'fire truck'} , +{'id': 42, 'name': 'basket'} , +{'id': 150, 'name': 'zebra'} , +{'id': 124, 'name': 'head phone'} , +{'id': 90, 'name': 'sheep'} , +{'id': 322, 'name': 'steak'} , +{'id': 39, 'name': 'couch'} , +{'id': 209, 'name': 'toothbrush'} , +{'id': 59, 'name': 'bicycle'} , +{'id': 336, 'name': 'red cabbage'} , +{'id': 228, 'name': 'golf ball'} , +{'id': 120, 'name': 'tomato'} , +{'id': 132, 'name': 'computer box'} , +{'id': 8, 'name': 'cup'} , +{'id': 183, 'name': 'basketball'} , +{'id': 298, 'name': 'butterfly'} , +{'id': 250, 'name': 'garlic'} , +{'id': 12, 'name': 'desk'} , +{'id': 141, 'name': 'microwave'} , +{'id': 171, 'name': 'strawberry'} , +{'id': 200, 'name': 'kettle'} , +{'id': 63, 'name': 'van'} , +{'id': 300, 'name': 'cheese'} , +{'id': 215, 'name': 'marker'} , +{'id': 100, 'name': 'blackboard/whiteboard'} , +{'id': 186, 'name': 'printer'} , +{'id': 333, 'name': 'bread/bun'} , +{'id': 243, 'name': 'penguin'} , +{'id': 364, 'name': 'iron'} , +{'id': 180, 'name': 'ladder'} , +{'id': 34, 'name': 'flag'} , +{'id': 78, 'name': 'cell phone'} , +{'id': 97, 'name': 'fan'} , +{'id': 224, 'name': 'scale'} , +{'id': 151, 'name': 'duck'} , +{'id': 319, 'name': 'flute'} , +{'id': 156, 'name': 'stop sign'} , +{'id': 290, 'name': 'rickshaw'} , +{'id': 128, 'name': 'sailboat'} , +{'id': 165, 'name': 'tennis racket'} , +{'id': 241, 'name': 'cigar'} , +{'id': 101, 'name': 'balloon'} , +{'id': 308, 'name': 'hair drier'} , +{'id': 167, 'name': 'skating and skiing shoes'} , +{'id': 237, 'name': 'helicopter'} , +{'id': 65, 'name': 'sink'} , +{'id': 129, 'name': 'tangerine'} , +{'id': 330, 'name': 'crab'} , +{'id': 320, 'name': 'measuring cup'} , +{'id': 260, 'name': 'fishing rod'} , +{'id': 346, 'name': 'saw'} , +{'id': 216, 'name': 'ship'} , +{'id': 46, 'name': 'coffee table'} , +{'id': 194, 'name': 'facial mask'} , +{'id': 281, 'name': 'stapler'} , +{'id': 118, 'name': 'refrigerator'} , +{'id': 40, 'name': 'belt'} , +{'id': 349, 'name': 'starfish'} , +{'id': 87, 'name': 'hanger'} , +{'id': 116, 'name': 'baseball glove'} , +{'id': 261, 'name': 'cherry'} , +{'id': 334, 'name': 'baozi'} , +{'id': 267, 'name': 'screwdriver'} , +{'id': 158, 'name': 'converter'} , +{'id': 335, 'name': 'lion'} , +{'id': 170, 'name': 'baseball'} , +{'id': 111, 'name': 'skis'} , +{'id': 136, 'name': 'broccoli'} , +{'id': 342, 'name': 'eraser'} , +{'id': 337, 'name': 'polar bear'} , +{'id': 139, 'name': 'shovel'} , +{'id': 193, 'name': 'extension cord'} , +{'id': 284, 'name': 'goldfish'} , +{'id': 174, 'name': 'pepper'} , +{'id': 138, 'name': 'stroller'} , +{'id': 328, 'name': 'yak'} , +{'id': 83, 'name': 'clock'} , +{'id': 235, 'name': 'tricycle'} , +{'id': 248, 'name': 'parking meter'} , +{'id': 274, 'name': 'trophy'} , +{'id': 324, 'name': 'binoculars'} , +{'id': 51, 'name': 'traffic light'} , +{'id': 314, 'name': 'donkey'} , +{'id': 45, 'name': 'barrel/bucket'} , +{'id': 292, 'name': 'pomegranate'} , +{'id': 13, 'name': 'handbag'} , +{'id': 262, 'name': 'tablet'} , +{'id': 68, 'name': 'apple'} , +{'id': 226, 'name': 'cabbage'} , +{'id': 23, 'name': 'flower'} , +{'id': 58, 'name': 'faucet'} , +{'id': 206, 'name': 'tong'} , +{'id': 291, 'name': 'trombone'} , +{'id': 160, 'name': 'carrot'} , +{'id': 172, 'name': 'bow tie'} , +{'id': 122, 'name': 'tent'} , +{'id': 163, 'name': 'cookies'} , +{'id': 115, 'name': 'remote'} , +{'id': 175, 'name': 'coffee machine'} , +{'id': 238, 'name': 'green beans'} , +{'id': 233, 'name': 'cello'} , +{'id': 28, 'name': 'wine glass'} , +{'id': 295, 'name': 'mushroom'} , +{'id': 344, 'name': 'scallop'} , +{'id': 125, 'name': 'lantern'} , +{'id': 123, 'name': 'shampoo/shower gel'} , +{'id': 285, 'name': 'meat balls'} , +{'id': 266, 'name': 'key'} , +{'id': 296, 'name': 'calculator'} , +{'id': 168, 'name': 'scissors'} , +{'id': 103, 'name': 'cymbal'} , +{'id': 6, 'name': 'bottle'} , +{'id': 264, 'name': 'nuts'} , +{'id': 234, 'name': 'notepaper'} , +{'id': 211, 'name': 'mango'} , +{'id': 287, 'name': 'toothpaste'} , +{'id': 196, 'name': 'chopsticks'} , +{'id': 140, 'name': 'baseball bat'} , +{'id': 244, 'name': 'hurdle'} , +{'id': 195, 'name': 'tennis ball'} , +{'id': 144, 'name': 'surveillance camera'} , +{'id': 271, 'name': 'volleyball'} , +{'id': 94, 'name': 'keyboard'} , +{'id': 339, 'name': 'seal'} , +{'id': 11, 'name': 'picture/frame'} , +{'id': 348, 'name': 'okra'} , +{'id': 191, 'name': 'sausage'} , +{'id': 166, 'name': 'candy'} , +{'id': 62, 'name': 'ring'} , +{'id': 311, 'name': 'dolphin'} , +{'id': 273, 'name': 'eggplant'} , +{'id': 84, 'name': 'drum'} , +{'id': 143, 'name': 'surfboard'} , +{'id': 288, 'name': 'antelope'} , +{'id': 204, 'name': 'clutch'} , +{'id': 207, 'name': 'slide'} , +{'id': 43, 'name': 'towel/napkin'} , +{'id': 352, 'name': 'durian'} , +{'id': 276, 'name': 'board eraser'} , +{'id': 315, 'name': 'electric drill'} , +{'id': 312, 'name': 'sushi'} , +{'id': 198, 'name': 'pie'} , +{'id': 106, 'name': 'pickup truck'} , +{'id': 176, 'name': 'bathtub'} , +{'id': 26, 'name': 'vase'} , +{'id': 133, 'name': 'elephant'} , +{'id': 256, 'name': 'sandwich'} , +{'id': 327, 'name': 'noodles'} , +{'id': 10, 'name': 'glasses'} , +{'id': 109, 'name': 'airplane'} , +{'id': 95, 'name': 'tripod'} , +{'id': 247, 'name': 'CD'} , +{'id': 121, 'name': 'machinery vehicle'} , +{'id': 365, 'name': 'flashlight'} , +{'id': 53, 'name': 'microphone'} , +{'id': 270, 'name': 'pliers'} , +{'id': 362, 'name': 'chainsaw'} , +{'id': 259, 'name': 'bear'} , +{'id': 197, 'name': 'electronic stove and gas stove'} , +{'id': 89, 'name': 'pot/pan'} , +{'id': 220, 'name': 'tape'} , +{'id': 338, 'name': 'lighter'} , +{'id': 177, 'name': 'snowboard'} , +{'id': 214, 'name': 'violin'} , +{'id': 217, 'name': 'chicken'} , +{'id': 2, 'name': 'sneakers'} , +{'id': 161, 'name': 'washing machine'} , +{'id': 131, 'name': 'kite'} , +{'id': 354, 'name': 'rabbit'} , +{'id': 86, 'name': 'bus'} , +{'id': 275, 'name': 'dates'} , +{'id': 282, 'name': 'camel'} , +{'id': 88, 'name': 'nightstand'} , +{'id': 179, 'name': 'grapes'} , +{'id': 229, 'name': 'pine apple'} , +{'id': 56, 'name': 'necklace'} , +{'id': 18, 'name': 'leather shoes'} , +{'id': 358, 'name': 'hoverboard'} , +{'id': 345, 'name': 'pencil case'} , +{'id': 359, 'name': 'pasta'} , +{'id': 157, 'name': 'radiator'} , +{'id': 201, 'name': 'hamburger'} , +{'id': 268, 'name': 'globe'} , +{'id': 332, 'name': 'barbell'} , +{'id': 329, 'name': 'mop'} , +{'id': 252, 'name': 'horn'} , +{'id': 350, 'name': 'eagle'} , +{'id': 169, 'name': 'folder'} , +{'id': 137, 'name': 'toilet'} , +{'id': 5, 'name': 'lamp'} , +{'id': 27, 'name': 'bench'} , +{'id': 249, 'name': 'swan'} , +{'id': 76, 'name': 'knife'} , +{'id': 341, 'name': 'comb'} , +{'id': 64, 'name': 'watch'} , +{'id': 105, 'name': 'telephone'} , +{'id': 3, 'name': 'chair'} , +{'id': 33, 'name': 'boat'} , +{'id': 107, 'name': 'orange'} , +{'id': 60, 'name': 'bread'} , +{'id': 147, 'name': 'cat'} , +{'id': 135, 'name': 'gas stove'} , +{'id': 307, 'name': 'papaya'} , +{'id': 227, 'name': 'router/modem'} , +{'id': 357, 'name': 'asparagus'} , +{'id': 73, 'name': 'motorcycle'} , +{'id': 77, 'name': 'traffic sign'} , +{'id': 67, 'name': 'fish'} , +{'id': 326, 'name': 'radish'} , +{'id': 213, 'name': 'egg'} , +{'id': 203, 'name': 'cucumber'} , +{'id': 17, 'name': 'helmet'} , +{'id': 110, 'name': 'luggage'} , +{'id': 80, 'name': 'truck'} , +{'id': 199, 'name': 'frisbee'} , +{'id': 232, 'name': 'peach'} , +{'id': 1, 'name': 'person'} , +{'id': 29, 'name': 'boots'} , +{'id': 310, 'name': 'chips'} , +{'id': 142, 'name': 'skateboard'} , +{'id': 44, 'name': 'slippers'} , +{'id': 4, 'name': 'hat'} , +{'id': 178, 'name': 'suitcase'} , +{'id': 24, 'name': 'tv'} , +{'id': 119, 'name': 'train'} , +{'id': 82, 'name': 'power outlet'} , +{'id': 245, 'name': 'swing'} , +{'id': 15, 'name': 'book'} , +{'id': 294, 'name': 'jellyfish'} , +{'id': 192, 'name': 'fire extinguisher'} , +{'id': 212, 'name': 'deer'} , +{'id': 181, 'name': 'pear'} , +{'id': 347, 'name': 'table tennis paddle'} , +{'id': 113, 'name': 'trolley'} , +{'id': 91, 'name': 'guitar'} , +{'id': 202, 'name': 'golf club'} , +{'id': 221, 'name': 'wheelchair'} , +{'id': 254, 'name': 'saxophone'} , +{'id': 117, 'name': 'paper towel'} , +{'id': 303, 'name': 'race car'} , +{'id': 240, 'name': 'carriage'} , +{'id': 246, 'name': 'radio'} , +{'id': 318, 'name': 'parrot'} , +{'id': 251, 'name': 'french fries'} , +{'id': 98, 'name': 'dog'} , +{'id': 112, 'name': 'soccer'} , +{'id': 355, 'name': 'french horn'} , +{'id': 79, 'name': 'paddle'} , +{'id': 283, 'name': 'lettuce'} , +{'id': 9, 'name': 'car'} , +{'id': 258, 'name': 'kiwi fruit'} , +{'id': 325, 'name': 'llama'} , +{'id': 187, 'name': 'billiards'} , +{'id': 210, 'name': 'facial cleanser'} , +{'id': 81, 'name': 'cow'} , +{'id': 331, 'name': 'microscope'} , +{'id': 148, 'name': 'lemon'} , +{'id': 302, 'name': 'pomelo'} , +{'id': 85, 'name': 'fork'} , +{'id': 154, 'name': 'pumpkin'} , +{'id': 289, 'name': 'shrimp'} , +{'id': 71, 'name': 'teddy bear'} , +{'id': 184, 'name': 'potato'} , +{'id': 102, 'name': 'air conditioner'} , +{'id': 208, 'name': 'hot dog'} , +{'id': 222, 'name': 'plum'} , +{'id': 316, 'name': 'spring rolls'} , +{'id': 230, 'name': 'crane'} , +{'id': 149, 'name': 'liquid soap'} , +{'id': 55, 'name': 'canned'} , +{'id': 35, 'name': 'speaker'} , +{'id': 108, 'name': 'banana'} , +{'id': 297, 'name': 'treadmill'} , +{'id': 99, 'name': 'spoon'} , +{'id': 104, 'name': 'mouse'} , +{'id': 182, 'name': 'american football'} , +{'id': 299, 'name': 'egg tart'} , +{'id': 127, 'name': 'cleaning products'} , +{'id': 313, 'name': 'urinal'} , +{'id': 286, 'name': 'medal'} , +{'id': 239, 'name': 'brush'} , +{'id': 96, 'name': 'hockey'} , +{'id': 279, 'name': 'dumbbell'} , +{'id': 32, 'name': 'umbrella'} , +{'id': 272, 'name': 'hammer'} , +{'id': 16, 'name': 'plate'} , +{'id': 21, 'name': 'potted plant'} , +{'id': 242, 'name': 'earphone'} , +{'id': 70, 'name': 'candle'} , +{'id': 185, 'name': 'paint brush'} , +{'id': 48, 'name': 'toy'} , +{'id': 130, 'name': 'pizza'} , +{'id': 255, 'name': 'trumpet'} , +{'id': 361, 'name': 'hotair balloon'} , +{'id': 188, 'name': 'fire hydrant'} , +{'id': 50, 'name': 'bed'} , +{'id': 253, 'name': 'avocado'} , +{'id': 293, 'name': 'coconut'} , +{'id': 257, 'name': 'cue'} , +{'id': 280, 'name': 'hamimelon'} , +{'id': 66, 'name': 'horse'} , +{'id': 173, 'name': 'pigeon'} , +{'id': 190, 'name': 'projector'} , +{'id': 69, 'name': 'camera'} , +{'id': 30, 'name': 'bowl'} , +{'id': 269, 'name': 'broom'} , +{'id': 343, 'name': 'pitaya'} , +{'id': 305, 'name': 'tuba'} , +{'id': 309, 'name': 'green onion'} , +{'id': 363, 'name': 'lobster'} , +{'id': 225, 'name': 'watermelon'} , +{'id': 47, 'name': 'suv'} , +{'id': 31, 'name': 'dining table'} , +{'id': 54, 'name': 'sandals'} , +{'id': 351, 'name': 'monkey'} , +{'id': 218, 'name': 'onion'} , +{'id': 36, 'name': 'trash bin/can'} , +{'id': 20, 'name': 'glove'} , +{'id': 277, 'name': 'rice'} , +{'id': 152, 'name': 'sports car'} , +{'id': 360, 'name': 'target'} , +{'id': 205, 'name': 'blender'} , +{'id': 19, 'name': 'pillow'} , +{'id': 72, 'name': 'cake'} , +{'id': 93, 'name': 'tea pot'} , +{'id': 353, 'name': 'game board'} , +{'id': 38, 'name': 'backpack'} , +{'id': 356, 'name': 'ambulance'} , +{'id': 146, 'name': 'life saver'} , +{'id': 189, 'name': 'goose'} , +{'id': 278, 'name': 'tape measure/ruler'} , +{'id': 92, 'name': 'traffic cone'} , +{'id': 134, 'name': 'toiletries'} , +{'id': 114, 'name': 'oven'} , +{'id': 317, 'name': 'tortoise/turtle'} , +{'id': 265, 'name': 'corn'} , +{'id': 126, 'name': 'donut'} , +{'id': 57, 'name': 'mirror'} , +{'id': 7, 'name': 'cabinet/shelf'} , +{'id': 263, 'name': 'green vegetables'} , +{'id': 159, 'name': 'tissue '} , +{'id': 321, 'name': 'shark'} , +{'id': 301, 'name': 'pig'} , +{'id': 41, 'name': 'carpet'} , +{'id': 304, 'name': 'rice cooker'} , +{'id': 323, 'name': 'poker card'} , +] + +def _get_builtin_metadata(version): + if version == 'v1': + id_to_name = {x['id']: x['name'] for x in categories_v1} + else: + assert 0, version + thing_dataset_id_to_contiguous_id = {i + 1: i for i in range(365)} + thing_classes = [id_to_name[k] for k in sorted(id_to_name)] + return { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes} + +_PREDEFINED_SPLITS_OBJECTS365 = { + "objects365_train": ("objects365/train", "objects365/annotations/objects365_train.json"), + "objects365_val": ("objects365/val", "objects365/annotations/objects365_val.json"), +} + +for key, (image_root, json_file) in _PREDEFINED_SPLITS_OBJECTS365.items(): + register_coco_instances( + key, + _get_builtin_metadata('v1'), + os.path.join("datasets", json_file) if "://" not in json_file else json_file, + os.path.join("datasets", image_root), + ) diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/transforms/custom_augmentation_impl.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/transforms/custom_augmentation_impl.py new file mode 100644 index 0000000000000000000000000000000000000000..5a69e178a5ac67f69c2eeab667b9c0740a862eee --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/transforms/custom_augmentation_impl.py @@ -0,0 +1,63 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# Modified by Xingyi Zhou +""" +Implement many useful :class:`Augmentation`. +""" +import numpy as np +import sys +from fvcore.transforms.transform import ( + BlendTransform, + CropTransform, + HFlipTransform, + NoOpTransform, + Transform, + VFlipTransform, +) +from PIL import Image + +from detectron2.data.transforms.augmentation import Augmentation +from .custom_transform import EfficientDetResizeCropTransform + +__all__ = [ + "EfficientDetResizeCrop", +] + + +class EfficientDetResizeCrop(Augmentation): + """ + Scale the shorter edge to the given size, with a limit of `max_size` on the longer edge. + If `max_size` is reached, then downscale so that the longer edge does not exceed max_size. + """ + + def __init__( + self, size, scale, interp=Image.BILINEAR + ): + """ + Args: + """ + super().__init__() + self.target_size = (size, size) + self.scale = scale + self.interp = interp + + def get_transform(self, img): + # Select a random scale factor. + scale_factor = np.random.uniform(*self.scale) + scaled_target_height = scale_factor * self.target_size[0] + scaled_target_width = scale_factor * self.target_size[1] + # Recompute the accurate scale_factor using rounded scaled image size. + width, height = img.shape[1], img.shape[0] + img_scale_y = scaled_target_height / height + img_scale_x = scaled_target_width / width + img_scale = min(img_scale_y, img_scale_x) + + # Select non-zero random offset (x, y) if scaled image is larger than target size + scaled_h = int(height * img_scale) + scaled_w = int(width * img_scale) + offset_y = scaled_h - self.target_size[0] + offset_x = scaled_w - self.target_size[1] + offset_y = int(max(0.0, float(offset_y)) * np.random.uniform(0, 1)) + offset_x = int(max(0.0, float(offset_x)) * np.random.uniform(0, 1)) + return EfficientDetResizeCropTransform( + scaled_h, scaled_w, offset_y, offset_x, img_scale, self.target_size, self.interp) diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/transforms/custom_transform.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/transforms/custom_transform.py new file mode 100644 index 0000000000000000000000000000000000000000..654d65d97d90a66e45b414bb878b13ba9f64e70a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/transforms/custom_transform.py @@ -0,0 +1,94 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# Modified by Xingyi Zhou +# File: transform.py + +import numpy as np +import torch +import torch.nn.functional as F +from fvcore.transforms.transform import ( + CropTransform, + HFlipTransform, + NoOpTransform, + Transform, + TransformList, +) +from PIL import Image + +try: + import cv2 # noqa +except ImportError: + # OpenCV is an optional dependency at the moment + pass + +__all__ = [ + "EfficientDetResizeCropTransform", +] + + +class EfficientDetResizeCropTransform(Transform): + """ + """ + + def __init__(self, scaled_h, scaled_w, offset_y, offset_x, img_scale, target_size, interp=None): + """ + Args: + h, w (int): original image size + new_h, new_w (int): new image size + interp: PIL interpolation methods, defaults to bilinear. + """ + # TODO decide on PIL vs opencv + super().__init__() + if interp is None: + interp = Image.BILINEAR + self._set_attributes(locals()) + + def apply_image(self, img, interp=None): + # assert img.shape[:2] == (self.h, self.w) + assert len(img.shape) <= 4 + + if img.dtype == np.uint8: + pil_image = Image.fromarray(img) + interp_method = interp if interp is not None else self.interp + pil_image = pil_image.resize((self.scaled_w, self.scaled_h), interp_method) + ret = np.asarray(pil_image) + right = min(self.scaled_w, self.offset_x + self.target_size[1]) + lower = min(self.scaled_h, self.offset_y + self.target_size[0]) + # img = img.crop((self.offset_x, self.offset_y, right, lower)) + if len(ret.shape) <= 3: + ret = ret[self.offset_y: lower, self.offset_x: right] + else: + ret = ret[..., self.offset_y: lower, self.offset_x: right, :] + else: + # PIL only supports uint8 + img = torch.from_numpy(img) + shape = list(img.shape) + shape_4d = shape[:2] + [1] * (4 - len(shape)) + shape[2:] + img = img.view(shape_4d).permute(2, 3, 0, 1) # hw(c) -> nchw + _PIL_RESIZE_TO_INTERPOLATE_MODE = {Image.BILINEAR: "bilinear", Image.BICUBIC: "bicubic"} + mode = _PIL_RESIZE_TO_INTERPOLATE_MODE[self.interp] + img = F.interpolate(img, (self.scaled_h, self.scaled_w), mode=mode, align_corners=False) + shape[:2] = (self.scaled_h, self.scaled_w) + ret = img.permute(2, 3, 0, 1).view(shape).numpy() # nchw -> hw(c) + right = min(self.scaled_w, self.offset_x + self.target_size[1]) + lower = min(self.scaled_h, self.offset_y + self.target_size[0]) + if len(ret.shape) <= 3: + ret = ret[self.offset_y: lower, self.offset_x: right] + else: + ret = ret[..., self.offset_y: lower, self.offset_x: right, :] + return ret + + def apply_coords(self, coords): + coords[:, 0] = coords[:, 0] * self.img_scale + coords[:, 1] = coords[:, 1] * self.img_scale + coords[:, 0] -= self.offset_x + coords[:, 1] -= self.offset_y + return coords + + def apply_segmentation(self, segmentation): + segmentation = self.apply_image(segmentation, interp=Image.NEAREST) + return segmentation + + def inverse(self): + raise NotImplementedError + # return ResizeTransform(self.new_h, self.new_w, self.h, self.w, self.interp) \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/bifpn.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/bifpn.py new file mode 100644 index 0000000000000000000000000000000000000000..565e2940ad0e4c43ec2172d4a79a9bd72adef09e --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/bifpn.py @@ -0,0 +1,425 @@ +# Modified from https://github.com/rwightman/efficientdet-pytorch/blob/master/effdet/efficientdet.py +# The original file is under Apache-2.0 License +import math +from os.path import join +import numpy as np +from collections import OrderedDict +from typing import List + +import torch +from torch import nn +import torch.utils.model_zoo as model_zoo +import torch.nn.functional as F +import fvcore.nn.weight_init as weight_init + +from detectron2.layers import ShapeSpec, Conv2d +from detectron2.modeling.backbone.resnet import build_resnet_backbone +from detectron2.modeling.backbone.build import BACKBONE_REGISTRY +from detectron2.layers.batch_norm import get_norm +from detectron2.modeling.backbone import Backbone +from .dlafpn import dla34 + +def get_fpn_config(base_reduction=8): + """BiFPN config with sum.""" + p = { + 'nodes': [ + {'reduction': base_reduction << 3, 'inputs_offsets': [3, 4]}, + {'reduction': base_reduction << 2, 'inputs_offsets': [2, 5]}, + {'reduction': base_reduction << 1, 'inputs_offsets': [1, 6]}, + {'reduction': base_reduction, 'inputs_offsets': [0, 7]}, + {'reduction': base_reduction << 1, 'inputs_offsets': [1, 7, 8]}, + {'reduction': base_reduction << 2, 'inputs_offsets': [2, 6, 9]}, + {'reduction': base_reduction << 3, 'inputs_offsets': [3, 5, 10]}, + {'reduction': base_reduction << 4, 'inputs_offsets': [4, 11]}, + ], + 'weight_method': 'fastattn', + } + return p + + +def swish(x, inplace: bool = False): + """Swish - Described in: https://arxiv.org/abs/1710.05941 + """ + return x.mul_(x.sigmoid()) if inplace else x.mul(x.sigmoid()) + + +class Swish(nn.Module): + def __init__(self, inplace: bool = False): + super(Swish, self).__init__() + self.inplace = inplace + + def forward(self, x): + return swish(x, self.inplace) + + +class SequentialAppend(nn.Sequential): + def __init__(self, *args): + super(SequentialAppend, self).__init__(*args) + + def forward(self, x): + for module in self: + x.append(module(x)) + return x + + +class SequentialAppendLast(nn.Sequential): + def __init__(self, *args): + super(SequentialAppendLast, self).__init__(*args) + + # def forward(self, x: List[torch.Tensor]): + def forward(self, x): + for module in self: + x.append(module(x[-1])) + return x + + +class ConvBnAct2d(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, padding='', bias=False, + norm='', act_layer=Swish): + super(ConvBnAct2d, self).__init__() + # self.conv = create_conv2d( + # in_channels, out_channels, kernel_size, stride=stride, dilation=dilation, padding=padding, bias=bias) + self.conv = Conv2d( + in_channels, out_channels, kernel_size=kernel_size, stride=stride, + padding=kernel_size // 2, bias=(norm == '')) + self.bn = get_norm(norm, out_channels) + self.act = None if act_layer is None else act_layer(inplace=True) + + def forward(self, x): + x = self.conv(x) + if self.bn is not None: + x = self.bn(x) + if self.act is not None: + x = self.act(x) + return x + + +class SeparableConv2d(nn.Module): + """ Separable Conv + """ + def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1, padding='', bias=False, + channel_multiplier=1.0, pw_kernel_size=1, act_layer=Swish, + norm=''): + super(SeparableConv2d, self).__init__() + + # self.conv_dw = create_conv2d( + # in_channels, int(in_channels * channel_multiplier), kernel_size, + # stride=stride, dilation=dilation, padding=padding, depthwise=True) + + self.conv_dw = Conv2d( + in_channels, int(in_channels * channel_multiplier), + kernel_size=kernel_size, stride=stride, padding=kernel_size // 2, bias=bias, + groups=out_channels) + # print('conv_dw', kernel_size, stride) + # self.conv_pw = create_conv2d( + # int(in_channels * channel_multiplier), out_channels, pw_kernel_size, padding=padding, bias=bias) + + self.conv_pw = Conv2d( + int(in_channels * channel_multiplier), out_channels, + kernel_size=pw_kernel_size, padding=pw_kernel_size // 2, bias=(norm=='')) + # print('conv_pw', pw_kernel_size) + + self.bn = get_norm(norm, out_channels) + self.act = None if act_layer is None else act_layer(inplace=True) + + def forward(self, x): + x = self.conv_dw(x) + x = self.conv_pw(x) + if self.bn is not None: + x = self.bn(x) + if self.act is not None: + x = self.act(x) + return x + + +class ResampleFeatureMap(nn.Sequential): + def __init__(self, in_channels, out_channels, reduction_ratio=1., pad_type='', pooling_type='max', + norm='', apply_bn=False, conv_after_downsample=False, + redundant_bias=False): + super(ResampleFeatureMap, self).__init__() + pooling_type = pooling_type or 'max' + self.in_channels = in_channels + self.out_channels = out_channels + self.reduction_ratio = reduction_ratio + self.conv_after_downsample = conv_after_downsample + + conv = None + if in_channels != out_channels: + conv = ConvBnAct2d( + in_channels, out_channels, kernel_size=1, padding=pad_type, + norm=norm if apply_bn else '', + bias=not apply_bn or redundant_bias, act_layer=None) + + if reduction_ratio > 1: + stride_size = int(reduction_ratio) + if conv is not None and not self.conv_after_downsample: + self.add_module('conv', conv) + self.add_module( + 'downsample', + # create_pool2d( + # pooling_type, kernel_size=stride_size + 1, stride=stride_size, padding=pad_type) + # nn.MaxPool2d(kernel_size=stride_size + 1, stride=stride_size, padding=pad_type) + nn.MaxPool2d(kernel_size=stride_size, stride=stride_size) + ) + if conv is not None and self.conv_after_downsample: + self.add_module('conv', conv) + else: + if conv is not None: + self.add_module('conv', conv) + if reduction_ratio < 1: + scale = int(1 // reduction_ratio) + self.add_module('upsample', nn.UpsamplingNearest2d(scale_factor=scale)) + + +class FpnCombine(nn.Module): + def __init__(self, feature_info, fpn_config, fpn_channels, inputs_offsets, target_reduction, pad_type='', + pooling_type='max', norm='', apply_bn_for_resampling=False, + conv_after_downsample=False, redundant_bias=False, weight_method='attn'): + super(FpnCombine, self).__init__() + self.inputs_offsets = inputs_offsets + self.weight_method = weight_method + + self.resample = nn.ModuleDict() + for idx, offset in enumerate(inputs_offsets): + in_channels = fpn_channels + if offset < len(feature_info): + in_channels = feature_info[offset]['num_chs'] + input_reduction = feature_info[offset]['reduction'] + else: + node_idx = offset - len(feature_info) + # print('node_idx, len', node_idx, len(fpn_config['nodes'])) + input_reduction = fpn_config['nodes'][node_idx]['reduction'] + reduction_ratio = target_reduction / input_reduction + self.resample[str(offset)] = ResampleFeatureMap( + in_channels, fpn_channels, reduction_ratio=reduction_ratio, pad_type=pad_type, + pooling_type=pooling_type, norm=norm, + apply_bn=apply_bn_for_resampling, conv_after_downsample=conv_after_downsample, + redundant_bias=redundant_bias) + + if weight_method == 'attn' or weight_method == 'fastattn': + # WSM + self.edge_weights = nn.Parameter(torch.ones(len(inputs_offsets)), requires_grad=True) + else: + self.edge_weights = None + + def forward(self, x): + dtype = x[0].dtype + nodes = [] + for offset in self.inputs_offsets: + input_node = x[offset] + input_node = self.resample[str(offset)](input_node) + nodes.append(input_node) + + if self.weight_method == 'attn': + normalized_weights = torch.softmax(self.edge_weights.type(dtype), dim=0) + x = torch.stack(nodes, dim=-1) * normalized_weights + elif self.weight_method == 'fastattn': + edge_weights = nn.functional.relu(self.edge_weights.type(dtype)) + weights_sum = torch.sum(edge_weights) + x = torch.stack( + [(nodes[i] * edge_weights[i]) / (weights_sum + 0.0001) for i in range(len(nodes))], dim=-1) + elif self.weight_method == 'sum': + x = torch.stack(nodes, dim=-1) + else: + raise ValueError('unknown weight_method {}'.format(self.weight_method)) + x = torch.sum(x, dim=-1) + return x + + +class BiFpnLayer(nn.Module): + def __init__(self, feature_info, fpn_config, fpn_channels, num_levels=5, pad_type='', + pooling_type='max', norm='', act_layer=Swish, + apply_bn_for_resampling=False, conv_after_downsample=True, conv_bn_relu_pattern=False, + separable_conv=True, redundant_bias=False): + super(BiFpnLayer, self).__init__() + self.fpn_config = fpn_config + self.num_levels = num_levels + self.conv_bn_relu_pattern = False + + self.feature_info = [] + self.fnode = SequentialAppend() + for i, fnode_cfg in enumerate(fpn_config['nodes']): + # logging.debug('fnode {} : {}'.format(i, fnode_cfg)) + # print('fnode {} : {}'.format(i, fnode_cfg)) + fnode_layers = OrderedDict() + + # combine features + reduction = fnode_cfg['reduction'] + fnode_layers['combine'] = FpnCombine( + feature_info, fpn_config, fpn_channels, fnode_cfg['inputs_offsets'], target_reduction=reduction, + pad_type=pad_type, pooling_type=pooling_type, norm=norm, + apply_bn_for_resampling=apply_bn_for_resampling, conv_after_downsample=conv_after_downsample, + redundant_bias=redundant_bias, weight_method=fpn_config['weight_method']) + self.feature_info.append(dict(num_chs=fpn_channels, reduction=reduction)) + + # after combine ops + after_combine = OrderedDict() + if not conv_bn_relu_pattern: + after_combine['act'] = act_layer(inplace=True) + conv_bias = redundant_bias + conv_act = None + else: + conv_bias = False + conv_act = act_layer + conv_kwargs = dict( + in_channels=fpn_channels, out_channels=fpn_channels, kernel_size=3, padding=pad_type, + bias=conv_bias, norm=norm, act_layer=conv_act) + after_combine['conv'] = SeparableConv2d(**conv_kwargs) if separable_conv else ConvBnAct2d(**conv_kwargs) + fnode_layers['after_combine'] = nn.Sequential(after_combine) + + self.fnode.add_module(str(i), nn.Sequential(fnode_layers)) + + self.feature_info = self.feature_info[-num_levels::] + + def forward(self, x): + x = self.fnode(x) + return x[-self.num_levels::] + + +class BiFPN(Backbone): + def __init__( + self, cfg, bottom_up, in_features, out_channels, norm='', + num_levels=5, num_bifpn=4, separable_conv=False, + ): + super(BiFPN, self).__init__() + assert isinstance(bottom_up, Backbone) + + # Feature map strides and channels from the bottom up network (e.g. ResNet) + input_shapes = bottom_up.output_shape() + in_strides = [input_shapes[f].stride for f in in_features] + in_channels = [input_shapes[f].channels for f in in_features] + + self.num_levels = num_levels + self.num_bifpn = num_bifpn + self.bottom_up = bottom_up + self.in_features = in_features + self._size_divisibility = 128 + levels = [int(math.log2(s)) for s in in_strides] + self._out_feature_strides = { + "p{}".format(int(math.log2(s))): s for s in in_strides} + if len(in_features) < num_levels: + for l in range(num_levels - len(in_features)): + s = l + levels[-1] + self._out_feature_strides["p{}".format(s + 1)] = 2 ** (s + 1) + self._out_features = list(sorted(self._out_feature_strides.keys())) + self._out_feature_channels = {k: out_channels for k in self._out_features} + + # print('self._out_feature_strides', self._out_feature_strides) + # print('self._out_feature_channels', self._out_feature_channels) + + feature_info = [ + {'num_chs': in_channels[level], 'reduction': in_strides[level]} \ + for level in range(len(self.in_features)) + ] + # self.config = config + fpn_config = get_fpn_config() + self.resample = SequentialAppendLast() + for level in range(num_levels): + if level < len(feature_info): + in_chs = in_channels[level] # feature_info[level]['num_chs'] + reduction = in_strides[level] # feature_info[level]['reduction'] + else: + # Adds a coarser level by downsampling the last feature map + reduction_ratio = 2 + self.resample.add_module(str(level), ResampleFeatureMap( + in_channels=in_chs, + out_channels=out_channels, + pad_type='same', + pooling_type=None, + norm=norm, + reduction_ratio=reduction_ratio, + apply_bn=True, + conv_after_downsample=False, + redundant_bias=False, + )) + in_chs = out_channels + reduction = int(reduction * reduction_ratio) + feature_info.append(dict(num_chs=in_chs, reduction=reduction)) + + self.cell = nn.Sequential() + for rep in range(self.num_bifpn): + # logging.debug('building cell {}'.format(rep)) + # print('building cell {}'.format(rep)) + fpn_layer = BiFpnLayer( + feature_info=feature_info, + fpn_config=fpn_config, + fpn_channels=out_channels, + num_levels=self.num_levels, + pad_type='same', + pooling_type=None, + norm=norm, + act_layer=Swish, + separable_conv=separable_conv, + apply_bn_for_resampling=True, + conv_after_downsample=False, + conv_bn_relu_pattern=False, + redundant_bias=False, + ) + self.cell.add_module(str(rep), fpn_layer) + feature_info = fpn_layer.feature_info + # import pdb; pdb.set_trace() + + @property + def size_divisibility(self): + return self._size_divisibility + + def forward(self, x): + # print('input shapes', x.shape) + bottom_up_features = self.bottom_up(x) + x = [bottom_up_features[f] for f in self.in_features] + assert len(self.resample) == self.num_levels - len(x) + x = self.resample(x) + shapes = [xx.shape for xx in x] + # print('resample shapes', shapes) + x = self.cell(x) + out = {f: xx for f, xx in zip(self._out_features, x)} + # import pdb; pdb.set_trace() + return out + + +@BACKBONE_REGISTRY.register() +def build_resnet_bifpn_backbone(cfg, input_shape: ShapeSpec): + """ + Args: + cfg: a detectron2 CfgNode + + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + bottom_up = build_resnet_backbone(cfg, input_shape) + in_features = cfg.MODEL.FPN.IN_FEATURES + backbone = BiFPN( + cfg=cfg, + bottom_up=bottom_up, + in_features=in_features, + out_channels=cfg.MODEL.BIFPN.OUT_CHANNELS, + norm=cfg.MODEL.BIFPN.NORM, + num_levels=cfg.MODEL.BIFPN.NUM_LEVELS, + num_bifpn=cfg.MODEL.BIFPN.NUM_BIFPN, + separable_conv=cfg.MODEL.BIFPN.SEPARABLE_CONV, + ) + return backbone + +@BACKBONE_REGISTRY.register() +def build_p37_dla_bifpn_backbone(cfg, input_shape: ShapeSpec): + """ + Args: + cfg: a detectron2 CfgNode + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + bottom_up = dla34(cfg) + in_features = cfg.MODEL.FPN.IN_FEATURES + assert cfg.MODEL.BIFPN.NUM_LEVELS == 5 + + backbone = BiFPN( + cfg=cfg, + bottom_up=bottom_up, + in_features=in_features, + out_channels=cfg.MODEL.BIFPN.OUT_CHANNELS, + norm=cfg.MODEL.BIFPN.NORM, + num_levels=cfg.MODEL.BIFPN.NUM_LEVELS, + num_bifpn=cfg.MODEL.BIFPN.NUM_BIFPN, + separable_conv=cfg.MODEL.BIFPN.SEPARABLE_CONV, + ) + return backbone diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/bifpn_fcos.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/bifpn_fcos.py new file mode 100644 index 0000000000000000000000000000000000000000..17f2904ccad484f380b64efc668b9090d047d15e --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/bifpn_fcos.py @@ -0,0 +1,469 @@ +# This file is modified from https://github.com/aim-uofa/AdelaiDet/blob/master/adet/modeling/backbone/bifpn.py +# The original file is under 2-clause BSD License for academic use, and *non-commercial use*. +import torch +import torch.nn.functional as F +from torch import nn + +from detectron2.layers import Conv2d, ShapeSpec, get_norm + +from detectron2.modeling.backbone import Backbone, build_resnet_backbone +from detectron2.modeling import BACKBONE_REGISTRY +from .dlafpn import dla34 + +__all__ = [] + + +def swish(x): + return x * x.sigmoid() + + +def split_name(name): + for i, c in enumerate(name): + if not c.isalpha(): + return name[:i], int(name[i:]) + raise ValueError() + + +class FeatureMapResampler(nn.Module): + def __init__(self, in_channels, out_channels, stride, norm=""): + super(FeatureMapResampler, self).__init__() + if in_channels != out_channels: + self.reduction = Conv2d( + in_channels, out_channels, kernel_size=1, + bias=(norm == ""), + norm=get_norm(norm, out_channels), + activation=None + ) + else: + self.reduction = None + + assert stride <= 2 + self.stride = stride + + def forward(self, x): + if self.reduction is not None: + x = self.reduction(x) + + if self.stride == 2: + x = F.max_pool2d( + x, kernel_size=self.stride + 1, + stride=self.stride, padding=1 + ) + elif self.stride == 1: + pass + else: + raise NotImplementedError() + return x + + +class BackboneWithTopLevels(Backbone): + def __init__(self, backbone, out_channels, num_top_levels, norm=""): + super(BackboneWithTopLevels, self).__init__() + self.backbone = backbone + backbone_output_shape = backbone.output_shape() + + self._out_feature_channels = {name: shape.channels for name, shape in backbone_output_shape.items()} + self._out_feature_strides = {name: shape.stride for name, shape in backbone_output_shape.items()} + self._out_features = list(self._out_feature_strides.keys()) + + last_feature_name = max(self._out_feature_strides.keys(), key=lambda x: split_name(x)[1]) + self.last_feature_name = last_feature_name + self.num_top_levels = num_top_levels + + last_channels = self._out_feature_channels[last_feature_name] + last_stride = self._out_feature_strides[last_feature_name] + + prefix, suffix = split_name(last_feature_name) + prev_channels = last_channels + for i in range(num_top_levels): + name = prefix + str(suffix + i + 1) + self.add_module(name, FeatureMapResampler( + prev_channels, out_channels, 2, norm + )) + prev_channels = out_channels + + self._out_feature_channels[name] = out_channels + self._out_feature_strides[name] = last_stride * 2 ** (i + 1) + self._out_features.append(name) + + def forward(self, x): + outputs = self.backbone(x) + last_features = outputs[self.last_feature_name] + prefix, suffix = split_name(self.last_feature_name) + + x = last_features + for i in range(self.num_top_levels): + name = prefix + str(suffix + i + 1) + x = self.__getattr__(name)(x) + outputs[name] = x + + return outputs + + +class SingleBiFPN(Backbone): + """ + This module implements Feature Pyramid Network. + It creates pyramid features built on top of some input feature maps. + """ + + def __init__( + self, in_channels_list, out_channels, norm="" + ): + """ + Args: + bottom_up (Backbone): module representing the bottom up subnetwork. + Must be a subclass of :class:`Backbone`. The multi-scale feature + maps generated by the bottom up network, and listed in `in_features`, + are used to generate FPN levels. + in_features (list[str]): names of the input feature maps coming + from the backbone to which FPN is attached. For example, if the + backbone produces ["res2", "res3", "res4"], any *contiguous* sublist + of these may be used; order must be from high to low resolution. + out_channels (int): number of channels in the output feature maps. + norm (str): the normalization to use. + """ + super(SingleBiFPN, self).__init__() + + self.out_channels = out_channels + # build 5-levels bifpn + if len(in_channels_list) == 5: + self.nodes = [ + {'feat_level': 3, 'inputs_offsets': [3, 4]}, + {'feat_level': 2, 'inputs_offsets': [2, 5]}, + {'feat_level': 1, 'inputs_offsets': [1, 6]}, + {'feat_level': 0, 'inputs_offsets': [0, 7]}, + {'feat_level': 1, 'inputs_offsets': [1, 7, 8]}, + {'feat_level': 2, 'inputs_offsets': [2, 6, 9]}, + {'feat_level': 3, 'inputs_offsets': [3, 5, 10]}, + {'feat_level': 4, 'inputs_offsets': [4, 11]}, + ] + elif len(in_channels_list) == 3: + self.nodes = [ + {'feat_level': 1, 'inputs_offsets': [1, 2]}, + {'feat_level': 0, 'inputs_offsets': [0, 3]}, + {'feat_level': 1, 'inputs_offsets': [1, 3, 4]}, + {'feat_level': 2, 'inputs_offsets': [2, 5]}, + ] + else: + raise NotImplementedError + + node_info = [_ for _ in in_channels_list] + + num_output_connections = [0 for _ in in_channels_list] + for fnode in self.nodes: + feat_level = fnode["feat_level"] + inputs_offsets = fnode["inputs_offsets"] + inputs_offsets_str = "_".join(map(str, inputs_offsets)) + for input_offset in inputs_offsets: + num_output_connections[input_offset] += 1 + + in_channels = node_info[input_offset] + if in_channels != out_channels: + lateral_conv = Conv2d( + in_channels, + out_channels, + kernel_size=1, + norm=get_norm(norm, out_channels) + ) + self.add_module( + "lateral_{}_f{}".format(input_offset, feat_level), lateral_conv + ) + node_info.append(out_channels) + num_output_connections.append(0) + + # generate attention weights + name = "weights_f{}_{}".format(feat_level, inputs_offsets_str) + self.__setattr__(name, nn.Parameter( + torch.ones(len(inputs_offsets), dtype=torch.float32), + requires_grad=True + )) + + # generate convolutions after combination + name = "outputs_f{}_{}".format(feat_level, inputs_offsets_str) + self.add_module(name, Conv2d( + out_channels, + out_channels, + kernel_size=3, + padding=1, + norm=get_norm(norm, out_channels), + bias=(norm == "") + )) + + def forward(self, feats): + """ + Args: + input (dict[str->Tensor]): mapping feature map name (e.g., "p5") to + feature map tensor for each feature level in high to low resolution order. + Returns: + dict[str->Tensor]: + mapping from feature map name to FPN feature map tensor + in high to low resolution order. Returned feature names follow the FPN + paper convention: "p", where stage has stride = 2 ** stage e.g., + ["n2", "n3", ..., "n6"]. + """ + feats = [_ for _ in feats] + num_levels = len(feats) + num_output_connections = [0 for _ in feats] + for fnode in self.nodes: + feat_level = fnode["feat_level"] + inputs_offsets = fnode["inputs_offsets"] + inputs_offsets_str = "_".join(map(str, inputs_offsets)) + input_nodes = [] + _, _, target_h, target_w = feats[feat_level].size() + for input_offset in inputs_offsets: + num_output_connections[input_offset] += 1 + input_node = feats[input_offset] + + # reduction + if input_node.size(1) != self.out_channels: + name = "lateral_{}_f{}".format(input_offset, feat_level) + input_node = self.__getattr__(name)(input_node) + + # maybe downsample + _, _, h, w = input_node.size() + if h > target_h and w > target_w: + height_stride_size = int((h - 1) // target_h + 1) + width_stride_size = int((w - 1) // target_w + 1) + assert height_stride_size == width_stride_size == 2 + input_node = F.max_pool2d( + input_node, kernel_size=(height_stride_size + 1, width_stride_size + 1), + stride=(height_stride_size, width_stride_size), padding=1 + ) + elif h <= target_h and w <= target_w: + if h < target_h or w < target_w: + input_node = F.interpolate( + input_node, + size=(target_h, target_w), + mode="nearest" + ) + else: + raise NotImplementedError() + input_nodes.append(input_node) + + # attention + name = "weights_f{}_{}".format(feat_level, inputs_offsets_str) + weights = F.relu(self.__getattr__(name)) + norm_weights = weights / (weights.sum() + 0.0001) + + new_node = torch.stack(input_nodes, dim=-1) + new_node = (norm_weights * new_node).sum(dim=-1) + new_node = swish(new_node) + + name = "outputs_f{}_{}".format(feat_level, inputs_offsets_str) + feats.append(self.__getattr__(name)(new_node)) + + num_output_connections.append(0) + + output_feats = [] + for idx in range(num_levels): + for i, fnode in enumerate(reversed(self.nodes)): + if fnode['feat_level'] == idx: + output_feats.append(feats[-1 - i]) + break + else: + raise ValueError() + return output_feats + + +class BiFPN(Backbone): + """ + This module implements Feature Pyramid Network. + It creates pyramid features built on top of some input feature maps. + """ + + def __init__( + self, bottom_up, in_features, out_channels, num_top_levels, num_repeats, norm="" + ): + """ + Args: + bottom_up (Backbone): module representing the bottom up subnetwork. + Must be a subclass of :class:`Backbone`. The multi-scale feature + maps generated by the bottom up network, and listed in `in_features`, + are used to generate FPN levels. + in_features (list[str]): names of the input feature maps coming + from the backbone to which FPN is attached. For example, if the + backbone produces ["res2", "res3", "res4"], any *contiguous* sublist + of these may be used; order must be from high to low resolution. + out_channels (int): number of channels in the output feature maps. + num_top_levels (int): the number of the top levels (p6 or p7). + num_repeats (int): the number of repeats of BiFPN. + norm (str): the normalization to use. + """ + super(BiFPN, self).__init__() + assert isinstance(bottom_up, Backbone) + + # add extra feature levels (i.e., 6 and 7) + self.bottom_up = BackboneWithTopLevels( + bottom_up, out_channels, + num_top_levels, norm + ) + bottom_up_output_shapes = self.bottom_up.output_shape() + + in_features = sorted(in_features, key=lambda x: split_name(x)[1]) + self._size_divisibility = 128 #bottom_up_output_shapes[in_features[-1]].stride + self.out_channels = out_channels + self.min_level = split_name(in_features[0])[1] + + # add the names for top blocks + prefix, last_suffix = split_name(in_features[-1]) + for i in range(num_top_levels): + in_features.append(prefix + str(last_suffix + i + 1)) + self.in_features = in_features + + # generate output features + self._out_features = ["p{}".format(split_name(name)[1]) for name in in_features] + self._out_feature_strides = { + out_name: bottom_up_output_shapes[in_name].stride + for out_name, in_name in zip(self._out_features, in_features) + } + self._out_feature_channels = {k: out_channels for k in self._out_features} + + # build bifpn + self.repeated_bifpn = nn.ModuleList() + for i in range(num_repeats): + if i == 0: + in_channels_list = [ + bottom_up_output_shapes[name].channels for name in in_features + ] + else: + in_channels_list = [ + self._out_feature_channels[name] for name in self._out_features + ] + self.repeated_bifpn.append(SingleBiFPN( + in_channels_list, out_channels, norm + )) + + @property + def size_divisibility(self): + return self._size_divisibility + + def forward(self, x): + """ + Args: + input (dict[str->Tensor]): mapping feature map name (e.g., "p5") to + feature map tensor for each feature level in high to low resolution order. + Returns: + dict[str->Tensor]: + mapping from feature map name to FPN feature map tensor + in high to low resolution order. Returned feature names follow the FPN + paper convention: "p", where stage has stride = 2 ** stage e.g., + ["n2", "n3", ..., "n6"]. + """ + bottom_up_features = self.bottom_up(x) + feats = [bottom_up_features[f] for f in self.in_features] + + for bifpn in self.repeated_bifpn: + feats = bifpn(feats) + + return dict(zip(self._out_features, feats)) + + +def _assert_strides_are_log2_contiguous(strides): + """ + Assert that each stride is 2x times its preceding stride, i.e. "contiguous in log2". + """ + for i, stride in enumerate(strides[1:], 1): + assert stride == 2 * strides[i - 1], "Strides {} {} are not log2 contiguous".format( + stride, strides[i - 1] + ) + + +@BACKBONE_REGISTRY.register() +def build_fcos_resnet_bifpn_backbone(cfg, input_shape: ShapeSpec): + """ + Args: + cfg: a detectron2 CfgNode + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + bottom_up = build_resnet_backbone(cfg, input_shape) + in_features = cfg.MODEL.FPN.IN_FEATURES + out_channels = cfg.MODEL.BIFPN.OUT_CHANNELS + num_repeats = cfg.MODEL.BIFPN.NUM_BIFPN + top_levels = 2 + + backbone = BiFPN( + bottom_up=bottom_up, + in_features=in_features, + out_channels=out_channels, + num_top_levels=top_levels, + num_repeats=num_repeats, + norm=cfg.MODEL.BIFPN.NORM + ) + return backbone + + + +@BACKBONE_REGISTRY.register() +def build_p35_fcos_resnet_bifpn_backbone(cfg, input_shape: ShapeSpec): + """ + Args: + cfg: a detectron2 CfgNode + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + bottom_up = build_resnet_backbone(cfg, input_shape) + in_features = cfg.MODEL.FPN.IN_FEATURES + out_channels = cfg.MODEL.BIFPN.OUT_CHANNELS + num_repeats = cfg.MODEL.BIFPN.NUM_BIFPN + top_levels = 0 + + backbone = BiFPN( + bottom_up=bottom_up, + in_features=in_features, + out_channels=out_channels, + num_top_levels=top_levels, + num_repeats=num_repeats, + norm=cfg.MODEL.BIFPN.NORM + ) + return backbone + + +@BACKBONE_REGISTRY.register() +def build_p35_fcos_dla_bifpn_backbone(cfg, input_shape: ShapeSpec): + """ + Args: + cfg: a detectron2 CfgNode + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + bottom_up = dla34(cfg) + in_features = cfg.MODEL.FPN.IN_FEATURES + out_channels = cfg.MODEL.BIFPN.OUT_CHANNELS + num_repeats = cfg.MODEL.BIFPN.NUM_BIFPN + top_levels = 0 + + backbone = BiFPN( + bottom_up=bottom_up, + in_features=in_features, + out_channels=out_channels, + num_top_levels=top_levels, + num_repeats=num_repeats, + norm=cfg.MODEL.BIFPN.NORM + ) + return backbone + +@BACKBONE_REGISTRY.register() +def build_p37_fcos_dla_bifpn_backbone(cfg, input_shape: ShapeSpec): + """ + Args: + cfg: a detectron2 CfgNode + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + bottom_up = dla34(cfg) + in_features = cfg.MODEL.FPN.IN_FEATURES + out_channels = cfg.MODEL.BIFPN.OUT_CHANNELS + num_repeats = cfg.MODEL.BIFPN.NUM_BIFPN + assert cfg.MODEL.BIFPN.NUM_LEVELS == 5 + top_levels = 2 + + backbone = BiFPN( + bottom_up=bottom_up, + in_features=in_features, + out_channels=out_channels, + num_top_levels=top_levels, + num_repeats=num_repeats, + norm=cfg.MODEL.BIFPN.NORM + ) + return backbone \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/dla.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/dla.py new file mode 100644 index 0000000000000000000000000000000000000000..9f15f840355571b6d02d5534fa8a9b6b8cb22c70 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/dla.py @@ -0,0 +1,479 @@ +import numpy as np +import math +from os.path import join +import fvcore.nn.weight_init as weight_init +import torch +import torch.nn.functional as F +from torch import nn +import torch.utils.model_zoo as model_zoo + +from detectron2.modeling.backbone.resnet import ( + BasicStem, BottleneckBlock, DeformBottleneckBlock) +from detectron2.layers import ( + Conv2d, + DeformConv, + FrozenBatchNorm2d, + ModulatedDeformConv, + ShapeSpec, + get_norm, +) + +from detectron2.modeling.backbone.backbone import Backbone +from detectron2.modeling.backbone.build import BACKBONE_REGISTRY +from detectron2.modeling.backbone.fpn import FPN + +__all__ = [ + "BottleneckBlock", + "DeformBottleneckBlock", + "BasicStem", +] + +DCNV1 = False + +HASH = { + 34: 'ba72cf86', + 60: '24839fc4', +} + +def get_model_url(data, name, hash): + return join('http://dl.yf.io/dla/models', data, '{}-{}.pth'.format(name, hash)) + +class BasicBlock(nn.Module): + def __init__(self, inplanes, planes, stride=1, dilation=1, norm='BN'): + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, + stride=stride, padding=dilation, + bias=False, dilation=dilation) + self.bn1 = get_norm(norm, planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, + stride=1, padding=dilation, + bias=False, dilation=dilation) + self.bn2 = get_norm(norm, planes) + self.stride = stride + + def forward(self, x, residual=None): + if residual is None: + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + out += residual + out = self.relu(out) + + return out + +class Bottleneck(nn.Module): + expansion = 2 + + def __init__(self, inplanes, planes, stride=1, dilation=1, norm='BN'): + super(Bottleneck, self).__init__() + expansion = Bottleneck.expansion + bottle_planes = planes // expansion + self.conv1 = nn.Conv2d(inplanes, bottle_planes, + kernel_size=1, bias=False) + self.bn1 = get_norm(norm, bottle_planes) + self.conv2 = nn.Conv2d(bottle_planes, bottle_planes, kernel_size=3, + stride=stride, padding=dilation, + bias=False, dilation=dilation) + self.bn2 = get_norm(norm, bottle_planes) + self.conv3 = nn.Conv2d(bottle_planes, planes, + kernel_size=1, bias=False) + self.bn3 = get_norm(norm, planes) + self.relu = nn.ReLU(inplace=True) + self.stride = stride + + def forward(self, x, residual=None): + if residual is None: + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + out += residual + out = self.relu(out) + + return out + +class Root(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, residual, norm='BN'): + super(Root, self).__init__() + self.conv = nn.Conv2d( + in_channels, out_channels, 1, + stride=1, bias=False, padding=(kernel_size - 1) // 2) + self.bn = get_norm(norm, out_channels) + self.relu = nn.ReLU(inplace=True) + self.residual = residual + + def forward(self, *x): + children = x + x = self.conv(torch.cat(x, 1)) + x = self.bn(x) + if self.residual: + x += children[0] + x = self.relu(x) + + return x + + +class Tree(nn.Module): + def __init__(self, levels, block, in_channels, out_channels, stride=1, + level_root=False, root_dim=0, root_kernel_size=1, + dilation=1, root_residual=False, norm='BN'): + super(Tree, self).__init__() + if root_dim == 0: + root_dim = 2 * out_channels + if level_root: + root_dim += in_channels + if levels == 1: + self.tree1 = block(in_channels, out_channels, stride, + dilation=dilation, norm=norm) + self.tree2 = block(out_channels, out_channels, 1, + dilation=dilation, norm=norm) + else: + self.tree1 = Tree(levels - 1, block, in_channels, out_channels, + stride, root_dim=0, + root_kernel_size=root_kernel_size, + dilation=dilation, root_residual=root_residual, + norm=norm) + self.tree2 = Tree(levels - 1, block, out_channels, out_channels, + root_dim=root_dim + out_channels, + root_kernel_size=root_kernel_size, + dilation=dilation, root_residual=root_residual, + norm=norm) + if levels == 1: + self.root = Root(root_dim, out_channels, root_kernel_size, + root_residual, norm=norm) + self.level_root = level_root + self.root_dim = root_dim + self.downsample = None + self.project = None + self.levels = levels + if stride > 1: + self.downsample = nn.MaxPool2d(stride, stride=stride) + if in_channels != out_channels: + self.project = nn.Sequential( + nn.Conv2d(in_channels, out_channels, + kernel_size=1, stride=1, bias=False), + get_norm(norm, out_channels) + ) + + def forward(self, x, residual=None, children=None): + children = [] if children is None else children + bottom = self.downsample(x) if self.downsample else x + residual = self.project(bottom) if self.project else bottom + if self.level_root: + children.append(bottom) + x1 = self.tree1(x, residual) + if self.levels == 1: + x2 = self.tree2(x1) + x = self.root(x2, x1, *children) + else: + children.append(x1) + x = self.tree2(x1, children=children) + return x + +class DLA(nn.Module): + def __init__(self, num_layers, levels, channels, + block=BasicBlock, residual_root=False, norm='BN'): + """ + Args: + """ + super(DLA, self).__init__() + self.norm = norm + self.channels = channels + self.base_layer = nn.Sequential( + nn.Conv2d(3, channels[0], kernel_size=7, stride=1, + padding=3, bias=False), + get_norm(self.norm, channels[0]), + nn.ReLU(inplace=True)) + self.level0 = self._make_conv_level( + channels[0], channels[0], levels[0]) + self.level1 = self._make_conv_level( + channels[0], channels[1], levels[1], stride=2) + self.level2 = Tree(levels[2], block, channels[1], channels[2], 2, + level_root=False, + root_residual=residual_root, norm=norm) + self.level3 = Tree(levels[3], block, channels[2], channels[3], 2, + level_root=True, root_residual=residual_root, + norm=norm) + self.level4 = Tree(levels[4], block, channels[3], channels[4], 2, + level_root=True, root_residual=residual_root, + norm=norm) + self.level5 = Tree(levels[5], block, channels[4], channels[5], 2, + level_root=True, root_residual=residual_root, + norm=norm) + self.load_pretrained_model( + data='imagenet', name='dla{}'.format(num_layers), + hash=HASH[num_layers]) + + def load_pretrained_model(self, data, name, hash): + model_url = get_model_url(data, name, hash) + model_weights = model_zoo.load_url(model_url) + num_classes = len(model_weights[list(model_weights.keys())[-1]]) + self.fc = nn.Conv2d( + self.channels[-1], num_classes, + kernel_size=1, stride=1, padding=0, bias=True) + print('Loading pretrained') + self.load_state_dict(model_weights, strict=False) + + def _make_conv_level(self, inplanes, planes, convs, stride=1, dilation=1): + modules = [] + for i in range(convs): + modules.extend([ + nn.Conv2d(inplanes, planes, kernel_size=3, + stride=stride if i == 0 else 1, + padding=dilation, bias=False, dilation=dilation), + get_norm(self.norm, planes), + nn.ReLU(inplace=True)]) + inplanes = planes + return nn.Sequential(*modules) + + def forward(self, x): + y = [] + x = self.base_layer(x) + for i in range(6): + x = getattr(self, 'level{}'.format(i))(x) + y.append(x) + return y + + +def fill_up_weights(up): + w = up.weight.data + f = math.ceil(w.size(2) / 2) + c = (2 * f - 1 - f % 2) / (2. * f) + for i in range(w.size(2)): + for j in range(w.size(3)): + w[0, 0, i, j] = \ + (1 - math.fabs(i / f - c)) * (1 - math.fabs(j / f - c)) + for c in range(1, w.size(0)): + w[c, 0, :, :] = w[0, 0, :, :] + + +class _DeformConv(nn.Module): + def __init__(self, chi, cho, norm='BN'): + super(_DeformConv, self).__init__() + self.actf = nn.Sequential( + get_norm(norm, cho), + nn.ReLU(inplace=True) + ) + if DCNV1: + self.offset = Conv2d( + chi, 18, kernel_size=3, stride=1, + padding=1, dilation=1) + self.conv = DeformConv( + chi, cho, kernel_size=(3,3), stride=1, padding=1, + dilation=1, deformable_groups=1) + else: + self.offset = Conv2d( + chi, 27, kernel_size=3, stride=1, + padding=1, dilation=1) + self.conv = ModulatedDeformConv( + chi, cho, kernel_size=3, stride=1, padding=1, + dilation=1, deformable_groups=1) + nn.init.constant_(self.offset.weight, 0) + nn.init.constant_(self.offset.bias, 0) + + def forward(self, x): + if DCNV1: + offset = self.offset(x) + x = self.conv(x, offset) + else: + offset_mask = self.offset(x) + offset_x, offset_y, mask = torch.chunk(offset_mask, 3, dim=1) + offset = torch.cat((offset_x, offset_y), dim=1) + mask = mask.sigmoid() + x = self.conv(x, offset, mask) + x = self.actf(x) + return x + + +class IDAUp(nn.Module): + def __init__(self, o, channels, up_f, norm='BN'): + super(IDAUp, self).__init__() + for i in range(1, len(channels)): + c = channels[i] + f = int(up_f[i]) + proj = _DeformConv(c, o, norm=norm) + node = _DeformConv(o, o, norm=norm) + + up = nn.ConvTranspose2d(o, o, f * 2, stride=f, + padding=f // 2, output_padding=0, + groups=o, bias=False) + fill_up_weights(up) + + setattr(self, 'proj_' + str(i), proj) + setattr(self, 'up_' + str(i), up) + setattr(self, 'node_' + str(i), node) + + + def forward(self, layers, startp, endp): + for i in range(startp + 1, endp): + upsample = getattr(self, 'up_' + str(i - startp)) + project = getattr(self, 'proj_' + str(i - startp)) + layers[i] = upsample(project(layers[i])) + node = getattr(self, 'node_' + str(i - startp)) + layers[i] = node(layers[i] + layers[i - 1]) + + +class DLAUp(nn.Module): + def __init__(self, startp, channels, scales, in_channels=None, norm='BN'): + super(DLAUp, self).__init__() + self.startp = startp + if in_channels is None: + in_channels = channels + self.channels = channels + channels = list(channels) + scales = np.array(scales, dtype=int) + for i in range(len(channels) - 1): + j = -i - 2 + setattr(self, 'ida_{}'.format(i), + IDAUp(channels[j], in_channels[j:], + scales[j:] // scales[j], norm=norm)) + scales[j + 1:] = scales[j] + in_channels[j + 1:] = [channels[j] for _ in channels[j + 1:]] + + def forward(self, layers): + out = [layers[-1]] # start with 32 + for i in range(len(layers) - self.startp - 1): + ida = getattr(self, 'ida_{}'.format(i)) + ida(layers, len(layers) -i - 2, len(layers)) + out.insert(0, layers[-1]) + return out + +DLA_CONFIGS = { + 34: ([1, 1, 1, 2, 2, 1], [16, 32, 64, 128, 256, 512], BasicBlock), + 60: ([1, 1, 1, 2, 3, 1], [16, 32, 128, 256, 512, 1024], Bottleneck) +} + + +class DLASeg(Backbone): + def __init__(self, num_layers, out_features, use_dla_up=True, + ms_output=False, norm='BN'): + super(DLASeg, self).__init__() + # depth = 34 + levels, channels, Block = DLA_CONFIGS[num_layers] + self.base = DLA(num_layers=num_layers, + levels=levels, channels=channels, block=Block, norm=norm) + down_ratio = 4 + self.first_level = int(np.log2(down_ratio)) + self.ms_output = ms_output + self.last_level = 5 if not self.ms_output else 6 + channels = self.base.channels + scales = [2 ** i for i in range(len(channels[self.first_level:]))] + self.use_dla_up = use_dla_up + if self.use_dla_up: + self.dla_up = DLAUp( + self.first_level, channels[self.first_level:], scales, + norm=norm) + out_channel = channels[self.first_level] + if not self.ms_output: # stride 4 DLA + self.ida_up = IDAUp( + out_channel, channels[self.first_level:self.last_level], + [2 ** i for i in range(self.last_level - self.first_level)], + norm=norm) + self._out_features = out_features + self._out_feature_channels = { + 'dla{}'.format(i): channels[i] for i in range(6)} + self._out_feature_strides = { + 'dla{}'.format(i): 2 ** i for i in range(6)} + self._size_divisibility = 32 + + @property + def size_divisibility(self): + return self._size_divisibility + + def forward(self, x): + x = self.base(x) + if self.use_dla_up: + x = self.dla_up(x) + if not self.ms_output: # stride 4 dla + y = [] + for i in range(self.last_level - self.first_level): + y.append(x[i].clone()) + self.ida_up(y, 0, len(y)) + ret = {} + for i in range(self.last_level - self.first_level): + out_feature = 'dla{}'.format(i) + if out_feature in self._out_features: + ret[out_feature] = y[i] + else: + ret = {} + st = self.first_level if self.use_dla_up else 0 + for i in range(self.last_level - st): + out_feature = 'dla{}'.format(i + st) + if out_feature in self._out_features: + ret[out_feature] = x[i] + + return ret + + +@BACKBONE_REGISTRY.register() +def build_dla_backbone(cfg, input_shape): + """ + Create a ResNet instance from config. + + Returns: + ResNet: a :class:`ResNet` instance. + """ + return DLASeg( + out_features=cfg.MODEL.DLA.OUT_FEATURES, + num_layers=cfg.MODEL.DLA.NUM_LAYERS, + use_dla_up=cfg.MODEL.DLA.USE_DLA_UP, + ms_output=cfg.MODEL.DLA.MS_OUTPUT, + norm=cfg.MODEL.DLA.NORM) + +class LastLevelP6P7(nn.Module): + """ + This module is used in RetinaNet to generate extra layers, P6 and P7 from + C5 feature. + """ + + def __init__(self, in_channels, out_channels): + super().__init__() + self.num_levels = 2 + self.in_feature = "dla5" + self.p6 = nn.Conv2d(in_channels, out_channels, 3, 2, 1) + self.p7 = nn.Conv2d(out_channels, out_channels, 3, 2, 1) + for module in [self.p6, self.p7]: + weight_init.c2_xavier_fill(module) + + def forward(self, c5): + p6 = self.p6(c5) + p7 = self.p7(F.relu(p6)) + return [p6, p7] + +@BACKBONE_REGISTRY.register() +def build_retinanet_dla_fpn_backbone(cfg, input_shape: ShapeSpec): + """ + Args: + cfg: a detectron2 CfgNode + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + bottom_up = build_dla_backbone(cfg, input_shape) + in_features = cfg.MODEL.FPN.IN_FEATURES + out_channels = cfg.MODEL.FPN.OUT_CHANNELS + in_channels_p6p7 = bottom_up.output_shape()['dla5'].channels + backbone = FPN( + bottom_up=bottom_up, + in_features=in_features, + out_channels=out_channels, + norm=cfg.MODEL.FPN.NORM, + top_block=LastLevelP6P7(in_channels_p6p7, out_channels), + fuse_type=cfg.MODEL.FPN.FUSE_TYPE, + ) + return backbone diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/dlafpn.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/dlafpn.py new file mode 100644 index 0000000000000000000000000000000000000000..2a33c66bf3d5b97bf882eaf0b80de012151a62b4 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/dlafpn.py @@ -0,0 +1,493 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# this file is from https://github.com/ucbdrive/dla/blob/master/dla.py. + +import math +from os.path import join +import numpy as np + +import torch +from torch import nn +import torch.utils.model_zoo as model_zoo +import torch.nn.functional as F +import fvcore.nn.weight_init as weight_init + +from detectron2.modeling.backbone import FPN +from detectron2.layers import ShapeSpec, ModulatedDeformConv, Conv2d +from detectron2.modeling.backbone.build import BACKBONE_REGISTRY +from detectron2.layers.batch_norm import get_norm +from detectron2.modeling.backbone import Backbone + +WEB_ROOT = 'http://dl.yf.io/dla/models' + + +def get_model_url(data, name, hash): + return join( + 'http://dl.yf.io/dla/models', data, '{}-{}.pth'.format(name, hash)) + + +def conv3x3(in_planes, out_planes, stride=1): + "3x3 convolution with padding" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +class BasicBlock(nn.Module): + def __init__(self, cfg, inplanes, planes, stride=1, dilation=1): + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, + stride=stride, padding=dilation, + bias=False, dilation=dilation) + self.bn1 = get_norm(cfg.MODEL.DLA.NORM, planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, + stride=1, padding=dilation, + bias=False, dilation=dilation) + self.bn2 = get_norm(cfg.MODEL.DLA.NORM, planes) + self.stride = stride + + def forward(self, x, residual=None): + if residual is None: + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + out += residual + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 2 + + def __init__(self, cfg, inplanes, planes, stride=1, dilation=1): + super(Bottleneck, self).__init__() + expansion = Bottleneck.expansion + bottle_planes = planes // expansion + self.conv1 = nn.Conv2d(inplanes, bottle_planes, + kernel_size=1, bias=False) + self.bn1 = get_norm(cfg.MODEL.DLA.NORM, bottle_planes) + self.conv2 = nn.Conv2d(bottle_planes, bottle_planes, kernel_size=3, + stride=stride, padding=dilation, + bias=False, dilation=dilation) + self.bn2 = get_norm(cfg.MODEL.DLA.NORM, bottle_planes) + self.conv3 = nn.Conv2d(bottle_planes, planes, + kernel_size=1, bias=False) + self.bn3 = get_norm(cfg.MODEL.DLA.NORM, planes) + self.relu = nn.ReLU(inplace=True) + self.stride = stride + + def forward(self, x, residual=None): + if residual is None: + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + out += residual + out = self.relu(out) + + return out + + +class Root(nn.Module): + def __init__(self, cfg, in_channels, out_channels, kernel_size, residual): + super(Root, self).__init__() + self.conv = nn.Conv2d( + in_channels, out_channels, kernel_size, + stride=1, bias=False, padding=(kernel_size - 1) // 2) + self.bn = get_norm(cfg.MODEL.DLA.NORM, out_channels) + self.relu = nn.ReLU(inplace=True) + self.residual = residual + + def forward(self, *x): + children = x + x = self.conv(torch.cat(x, 1)) + x = self.bn(x) + if self.residual: + x += children[0] + x = self.relu(x) + + return x + + +class Tree(nn.Module): + def __init__(self, cfg, levels, block, in_channels, out_channels, stride=1, + level_root=False, root_dim=0, root_kernel_size=1, + dilation=1, root_residual=False): + super(Tree, self).__init__() + if root_dim == 0: + root_dim = 2 * out_channels + if level_root: + root_dim += in_channels + if levels == 1: + self.tree1 = block(cfg, in_channels, out_channels, stride, + dilation=dilation) + self.tree2 = block(cfg, out_channels, out_channels, 1, + dilation=dilation) + else: + self.tree1 = Tree(cfg, levels - 1, block, in_channels, out_channels, + stride, root_dim=0, + root_kernel_size=root_kernel_size, + dilation=dilation, root_residual=root_residual) + self.tree2 = Tree(cfg, levels - 1, block, out_channels, out_channels, + root_dim=root_dim + out_channels, + root_kernel_size=root_kernel_size, + dilation=dilation, root_residual=root_residual) + if levels == 1: + self.root = Root(cfg, root_dim, out_channels, root_kernel_size, + root_residual) + self.level_root = level_root + self.root_dim = root_dim + self.downsample = None + self.project = None + self.levels = levels + if stride > 1: + self.downsample = nn.MaxPool2d(stride, stride=stride) + if in_channels != out_channels: + self.project = nn.Sequential( + nn.Conv2d(in_channels, out_channels, + kernel_size=1, stride=1, bias=False), + get_norm(cfg.MODEL.DLA.NORM, out_channels) + ) + + def forward(self, x, residual=None, children=None): + if self.training and residual is not None: + x = x + residual.sum() * 0.0 + children = [] if children is None else children + bottom = self.downsample(x) if self.downsample else x + residual = self.project(bottom) if self.project else bottom + if self.level_root: + children.append(bottom) + x1 = self.tree1(x, residual) + if self.levels == 1: + x2 = self.tree2(x1) + x = self.root(x2, x1, *children) + else: + children.append(x1) + x = self.tree2(x1, children=children) + return x + + +class DLA(Backbone): + def __init__(self, cfg, levels, channels, block=BasicBlock, residual_root=False): + super(DLA, self).__init__() + self.cfg = cfg + self.channels = channels + + self._out_features = ["dla{}".format(i) for i in range(6)] + self._out_feature_channels = {k: channels[i] for i, k in enumerate(self._out_features)} + self._out_feature_strides = {k: 2 ** i for i, k in enumerate(self._out_features)} + + self.base_layer = nn.Sequential( + nn.Conv2d(3, channels[0], kernel_size=7, stride=1, + padding=3, bias=False), + get_norm(cfg.MODEL.DLA.NORM, channels[0]), + nn.ReLU(inplace=True)) + self.level0 = self._make_conv_level( + channels[0], channels[0], levels[0]) + self.level1 = self._make_conv_level( + channels[0], channels[1], levels[1], stride=2) + self.level2 = Tree(cfg, levels[2], block, channels[1], channels[2], 2, + level_root=False, + root_residual=residual_root) + self.level3 = Tree(cfg, levels[3], block, channels[2], channels[3], 2, + level_root=True, root_residual=residual_root) + self.level4 = Tree(cfg, levels[4], block, channels[3], channels[4], 2, + level_root=True, root_residual=residual_root) + self.level5 = Tree(cfg, levels[5], block, channels[4], channels[5], 2, + level_root=True, root_residual=residual_root) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + + self.load_pretrained_model( + data='imagenet', name='dla34', hash='ba72cf86') + + def load_pretrained_model(self, data, name, hash): + model_url = get_model_url(data, name, hash) + model_weights = model_zoo.load_url(model_url) + del model_weights['fc.weight'] + del model_weights['fc.bias'] + print('Loading pretrained DLA!') + self.load_state_dict(model_weights, strict=True) + + def _make_conv_level(self, inplanes, planes, convs, stride=1, dilation=1): + modules = [] + for i in range(convs): + modules.extend([ + nn.Conv2d(inplanes, planes, kernel_size=3, + stride=stride if i == 0 else 1, + padding=dilation, bias=False, dilation=dilation), + get_norm(self.cfg.MODEL.DLA.NORM, planes), + nn.ReLU(inplace=True)]) + inplanes = planes + return nn.Sequential(*modules) + + def forward(self, x): + y = {} + x = self.base_layer(x) + for i in range(6): + name = 'level{}'.format(i) + x = getattr(self, name)(x) + y['dla{}'.format(i)] = x + return y + + +def fill_up_weights(up): + w = up.weight.data + f = math.ceil(w.size(2) / 2) + c = (2 * f - 1 - f % 2) / (2. * f) + for i in range(w.size(2)): + for j in range(w.size(3)): + w[0, 0, i, j] = \ + (1 - math.fabs(i / f - c)) * (1 - math.fabs(j / f - c)) + for c in range(1, w.size(0)): + w[c, 0, :, :] = w[0, 0, :, :] + + +class Conv(nn.Module): + def __init__(self, chi, cho, norm): + super(Conv, self).__init__() + self.conv = nn.Sequential( + nn.Conv2d(chi, cho, kernel_size=1, stride=1, bias=False), + get_norm(norm, cho), + nn.ReLU(inplace=True)) + + def forward(self, x): + return self.conv(x) + + +class DeformConv(nn.Module): + def __init__(self, chi, cho, norm): + super(DeformConv, self).__init__() + self.actf = nn.Sequential( + get_norm(norm, cho), + nn.ReLU(inplace=True) + ) + self.offset = Conv2d( + chi, 27, kernel_size=3, stride=1, + padding=1, dilation=1) + self.conv = ModulatedDeformConv( + chi, cho, kernel_size=3, stride=1, padding=1, + dilation=1, deformable_groups=1) + nn.init.constant_(self.offset.weight, 0) + nn.init.constant_(self.offset.bias, 0) + + def forward(self, x): + offset_mask = self.offset(x) + offset_x, offset_y, mask = torch.chunk(offset_mask, 3, dim=1) + offset = torch.cat((offset_x, offset_y), dim=1) + mask = mask.sigmoid() + x = self.conv(x, offset, mask) + x = self.actf(x) + return x + + +class IDAUp(nn.Module): + def __init__(self, o, channels, up_f, norm='FrozenBN', node_type=Conv): + super(IDAUp, self).__init__() + for i in range(1, len(channels)): + c = channels[i] + f = int(up_f[i]) + proj = node_type(c, o, norm) + node = node_type(o, o, norm) + + up = nn.ConvTranspose2d(o, o, f * 2, stride=f, + padding=f // 2, output_padding=0, + groups=o, bias=False) + fill_up_weights(up) + + setattr(self, 'proj_' + str(i), proj) + setattr(self, 'up_' + str(i), up) + setattr(self, 'node_' + str(i), node) + + + def forward(self, layers, startp, endp): + for i in range(startp + 1, endp): + upsample = getattr(self, 'up_' + str(i - startp)) + project = getattr(self, 'proj_' + str(i - startp)) + layers[i] = upsample(project(layers[i])) + node = getattr(self, 'node_' + str(i - startp)) + layers[i] = node(layers[i] + layers[i - 1]) + + +DLAUP_NODE_MAP = { + 'conv': Conv, + 'dcn': DeformConv, +} + +class DLAUP(Backbone): + def __init__(self, bottom_up, in_features, norm, dlaup_node='conv'): + super(DLAUP, self).__init__() + assert isinstance(bottom_up, Backbone) + self.bottom_up = bottom_up + input_shapes = bottom_up.output_shape() + in_strides = [input_shapes[f].stride for f in in_features] + in_channels = [input_shapes[f].channels for f in in_features] + in_levels = [int(math.log2(input_shapes[f].stride)) for f in in_features] + self.in_features = in_features + out_features = ['dlaup{}'.format(l) for l in in_levels] + self._out_features = out_features + self._out_feature_channels = { + 'dlaup{}'.format(l): in_channels[i] for i, l in enumerate(in_levels)} + self._out_feature_strides = { + 'dlaup{}'.format(l): 2 ** l for l in in_levels} + + print('self._out_features', self._out_features) + print('self._out_feature_channels', self._out_feature_channels) + print('self._out_feature_strides', self._out_feature_strides) + self._size_divisibility = 32 + + node_type = DLAUP_NODE_MAP[dlaup_node] + + self.startp = int(math.log2(in_strides[0])) + self.channels = in_channels + channels = list(in_channels) + scales = np.array([2 ** i for i in range(len(out_features))], dtype=int) + for i in range(len(channels) - 1): + j = -i - 2 + setattr(self, 'ida_{}'.format(i), + IDAUp(channels[j], in_channels[j:], + scales[j:] // scales[j], + norm=norm, + node_type=node_type)) + scales[j + 1:] = scales[j] + in_channels[j + 1:] = [channels[j] for _ in channels[j + 1:]] + + @property + def size_divisibility(self): + return self._size_divisibility + + def forward(self, x): + bottom_up_features = self.bottom_up(x) + layers = [bottom_up_features[f] for f in self.in_features] + out = [layers[-1]] # start with 32 + for i in range(len(layers) - 1): + ida = getattr(self, 'ida_{}'.format(i)) + ida(layers, len(layers) - i - 2, len(layers)) + out.insert(0, layers[-1]) + ret = {} + for k, v in zip(self._out_features, out): + ret[k] = v + # import pdb; pdb.set_trace() + return ret + + +def dla34(cfg, pretrained=None): # DLA-34 + model = DLA(cfg, [1, 1, 1, 2, 2, 1], + [16, 32, 64, 128, 256, 512], + block=BasicBlock) + return model + + +class LastLevelP6P7(nn.Module): + """ + This module is used in RetinaNet to generate extra layers, P6 and P7 from + C5 feature. + """ + + def __init__(self, in_channels, out_channels): + super().__init__() + self.num_levels = 2 + self.in_feature = "dla5" + self.p6 = nn.Conv2d(in_channels, out_channels, 3, 2, 1) + self.p7 = nn.Conv2d(out_channels, out_channels, 3, 2, 1) + for module in [self.p6, self.p7]: + weight_init.c2_xavier_fill(module) + + def forward(self, c5): + p6 = self.p6(c5) + p7 = self.p7(F.relu(p6)) + return [p6, p7] + + +@BACKBONE_REGISTRY.register() +def build_dla_fpn3_backbone(cfg, input_shape: ShapeSpec): + """ + Args: + cfg: a detectron2 CfgNode + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + + depth_to_creator = {"dla34": dla34} + bottom_up = depth_to_creator['dla{}'.format(cfg.MODEL.DLA.NUM_LAYERS)](cfg) + in_features = cfg.MODEL.FPN.IN_FEATURES + out_channels = cfg.MODEL.FPN.OUT_CHANNELS + + backbone = FPN( + bottom_up=bottom_up, + in_features=in_features, + out_channels=out_channels, + norm=cfg.MODEL.FPN.NORM, + top_block=None, + fuse_type=cfg.MODEL.FPN.FUSE_TYPE, + ) + + return backbone + +@BACKBONE_REGISTRY.register() +def build_dla_fpn5_backbone(cfg, input_shape: ShapeSpec): + """ + Args: + cfg: a detectron2 CfgNode + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + + depth_to_creator = {"dla34": dla34} + bottom_up = depth_to_creator['dla{}'.format(cfg.MODEL.DLA.NUM_LAYERS)](cfg) + in_features = cfg.MODEL.FPN.IN_FEATURES + out_channels = cfg.MODEL.FPN.OUT_CHANNELS + in_channels_top = bottom_up.output_shape()['dla5'].channels + + backbone = FPN( + bottom_up=bottom_up, + in_features=in_features, + out_channels=out_channels, + norm=cfg.MODEL.FPN.NORM, + top_block=LastLevelP6P7(in_channels_top, out_channels), + fuse_type=cfg.MODEL.FPN.FUSE_TYPE, + ) + + return backbone + + +@BACKBONE_REGISTRY.register() +def build_dlaup_backbone(cfg, input_shape: ShapeSpec): + """ + Args: + cfg: a detectron2 CfgNode + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + + depth_to_creator = {"dla34": dla34} + bottom_up = depth_to_creator['dla{}'.format(cfg.MODEL.DLA.NUM_LAYERS)](cfg) + + backbone = DLAUP( + bottom_up=bottom_up, + in_features=cfg.MODEL.DLA.DLAUP_IN_FEATURES, + norm=cfg.MODEL.DLA.NORM, + dlaup_node=cfg.MODEL.DLA.DLAUP_NODE, + ) + + return backbone diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/fpn_p5.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/fpn_p5.py new file mode 100644 index 0000000000000000000000000000000000000000..e991f9c7be095e2a40e12c849b35e246cd0344bd --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/fpn_p5.py @@ -0,0 +1,78 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import math +import fvcore.nn.weight_init as weight_init +import torch.nn.functional as F +from torch import nn + +from detectron2.layers import Conv2d, ShapeSpec, get_norm + +from detectron2.modeling.backbone import Backbone +from detectron2.modeling.backbone.fpn import FPN +from detectron2.modeling.backbone.build import BACKBONE_REGISTRY +from detectron2.modeling.backbone.resnet import build_resnet_backbone + + +class LastLevelP6P7_P5(nn.Module): + """ + This module is used in RetinaNet to generate extra layers, P6 and P7 from + C5 feature. + """ + + def __init__(self, in_channels, out_channels): + super().__init__() + self.num_levels = 2 + self.in_feature = "p5" + self.p6 = nn.Conv2d(in_channels, out_channels, 3, 2, 1) + self.p7 = nn.Conv2d(out_channels, out_channels, 3, 2, 1) + for module in [self.p6, self.p7]: + weight_init.c2_xavier_fill(module) + + def forward(self, c5): + p6 = self.p6(c5) + p7 = self.p7(F.relu(p6)) + return [p6, p7] + + +@BACKBONE_REGISTRY.register() +def build_p67_resnet_fpn_backbone(cfg, input_shape: ShapeSpec): + """ + Args: + cfg: a detectron2 CfgNode + + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + bottom_up = build_resnet_backbone(cfg, input_shape) + in_features = cfg.MODEL.FPN.IN_FEATURES + out_channels = cfg.MODEL.FPN.OUT_CHANNELS + backbone = FPN( + bottom_up=bottom_up, + in_features=in_features, + out_channels=out_channels, + norm=cfg.MODEL.FPN.NORM, + top_block=LastLevelP6P7_P5(out_channels, out_channels), + fuse_type=cfg.MODEL.FPN.FUSE_TYPE, + ) + return backbone + +@BACKBONE_REGISTRY.register() +def build_p35_resnet_fpn_backbone(cfg, input_shape: ShapeSpec): + """ + Args: + cfg: a detectron2 CfgNode + + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + bottom_up = build_resnet_backbone(cfg, input_shape) + in_features = cfg.MODEL.FPN.IN_FEATURES + out_channels = cfg.MODEL.FPN.OUT_CHANNELS + backbone = FPN( + bottom_up=bottom_up, + in_features=in_features, + out_channels=out_channels, + norm=cfg.MODEL.FPN.NORM, + top_block=None, + fuse_type=cfg.MODEL.FPN.FUSE_TYPE, + ) + return backbone \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/res2net.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/res2net.py new file mode 100644 index 0000000000000000000000000000000000000000..1d0d40adb4a300d916deecebd20bcaac08936e6d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/res2net.py @@ -0,0 +1,802 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# This file is modified from https://github.com/Res2Net/Res2Net-detectron2/blob/master/detectron2/modeling/backbone/resnet.py +# The original file is under Apache-2.0 License +import numpy as np +import fvcore.nn.weight_init as weight_init +import torch +import torch.nn.functional as F +from torch import nn + +from detectron2.layers import ( + CNNBlockBase, + Conv2d, + DeformConv, + ModulatedDeformConv, + ShapeSpec, + get_norm, +) + +from detectron2.modeling.backbone import Backbone +from detectron2.modeling.backbone.fpn import FPN +from detectron2.modeling.backbone.build import BACKBONE_REGISTRY +from .fpn_p5 import LastLevelP6P7_P5 +from .bifpn import BiFPN + +__all__ = [ + "ResNetBlockBase", + "BasicBlock", + "BottleneckBlock", + "DeformBottleneckBlock", + "BasicStem", + "ResNet", + "make_stage", + "build_res2net_backbone", +] + + +ResNetBlockBase = CNNBlockBase +""" +Alias for backward compatibiltiy. +""" + + +class BasicBlock(CNNBlockBase): + """ + The basic residual block for ResNet-18 and ResNet-34, with two 3x3 conv layers + and a projection shortcut if needed. + """ + + def __init__(self, in_channels, out_channels, *, stride=1, norm="BN"): + """ + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + stride (int): Stride for the first conv. + norm (str or callable): normalization for all conv layers. + See :func:`layers.get_norm` for supported format. + """ + super().__init__(in_channels, out_channels, stride) + + if in_channels != out_channels: + self.shortcut = Conv2d( + in_channels, + out_channels, + kernel_size=1, + stride=stride, + bias=False, + norm=get_norm(norm, out_channels), + ) + else: + self.shortcut = None + + self.conv1 = Conv2d( + in_channels, + out_channels, + kernel_size=3, + stride=stride, + padding=1, + bias=False, + norm=get_norm(norm, out_channels), + ) + + self.conv2 = Conv2d( + out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + bias=False, + norm=get_norm(norm, out_channels), + ) + + for layer in [self.conv1, self.conv2, self.shortcut]: + if layer is not None: # shortcut can be None + weight_init.c2_msra_fill(layer) + + def forward(self, x): + out = self.conv1(x) + out = F.relu_(out) + out = self.conv2(out) + + if self.shortcut is not None: + shortcut = self.shortcut(x) + else: + shortcut = x + + out += shortcut + out = F.relu_(out) + return out + + +class BottleneckBlock(CNNBlockBase): + """ + The standard bottle2neck residual block used by Res2Net-50, 101 and 152. + """ + + def __init__( + self, + in_channels, + out_channels, + *, + bottleneck_channels, + stride=1, + num_groups=1, + norm="BN", + stride_in_1x1=False, + dilation=1, + basewidth=26, + scale=4, + ): + """ + Args: + bottleneck_channels (int): number of output channels for the 3x3 + "bottleneck" conv layers. + num_groups (int): number of groups for the 3x3 conv layer. + norm (str or callable): normalization for all conv layers. + See :func:`layers.get_norm` for supported format. + stride_in_1x1 (bool): when stride>1, whether to put stride in the + first 1x1 convolution or the bottleneck 3x3 convolution. + dilation (int): the dilation rate of the 3x3 conv layer. + """ + super().__init__(in_channels, out_channels, stride) + + if in_channels != out_channels: + self.shortcut = nn.Sequential( + nn.AvgPool2d(kernel_size=stride, stride=stride, + ceil_mode=True, count_include_pad=False), + Conv2d( + in_channels, + out_channels, + kernel_size=1, + stride=1, + bias=False, + norm=get_norm(norm, out_channels), + ) + ) + else: + self.shortcut = None + + # The original MSRA ResNet models have stride in the first 1x1 conv + # The subsequent fb.torch.resnet and Caffe2 ResNe[X]t implementations have + # stride in the 3x3 conv + stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride) + width = bottleneck_channels//scale + + self.conv1 = Conv2d( + in_channels, + bottleneck_channels, + kernel_size=1, + stride=stride_1x1, + bias=False, + norm=get_norm(norm, bottleneck_channels), + ) + if scale == 1: + self.nums = 1 + else: + self.nums = scale -1 + if self.in_channels!=self.out_channels and stride_3x3!=2: + self.pool = nn.AvgPool2d(kernel_size=3, stride = stride_3x3, padding=1) + + convs = [] + bns = [] + for i in range(self.nums): + convs.append(nn.Conv2d( + width, + width, + kernel_size=3, + stride=stride_3x3, + padding=1 * dilation, + bias=False, + groups=num_groups, + dilation=dilation, + )) + bns.append(get_norm(norm, width)) + self.convs = nn.ModuleList(convs) + self.bns = nn.ModuleList(bns) + + self.conv3 = Conv2d( + bottleneck_channels, + out_channels, + kernel_size=1, + bias=False, + norm=get_norm(norm, out_channels), + ) + self.scale = scale + self.width = width + self.in_channels = in_channels + self.out_channels = out_channels + self.stride_3x3 = stride_3x3 + for layer in [self.conv1, self.conv3]: + if layer is not None: # shortcut can be None + weight_init.c2_msra_fill(layer) + if self.shortcut is not None: + for layer in self.shortcut.modules(): + if isinstance(layer, Conv2d): + weight_init.c2_msra_fill(layer) + + for layer in self.convs: + if layer is not None: # shortcut can be None + weight_init.c2_msra_fill(layer) + + # Zero-initialize the last normalization in each residual branch, + # so that at the beginning, the residual branch starts with zeros, + # and each residual block behaves like an identity. + # See Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour": + # "For BN layers, the learnable scaling coefficient γ is initialized + # to be 1, except for each residual block's last BN + # where γ is initialized to be 0." + + # nn.init.constant_(self.conv3.norm.weight, 0) + # TODO this somehow hurts performance when training GN models from scratch. + # Add it as an option when we need to use this code to train a backbone. + + def forward(self, x): + out = self.conv1(x) + out = F.relu_(out) + + spx = torch.split(out, self.width, 1) + for i in range(self.nums): + if i==0 or self.in_channels!=self.out_channels: + sp = spx[i] + else: + sp = sp + spx[i] + sp = self.convs[i](sp) + sp = F.relu_(self.bns[i](sp)) + if i==0: + out = sp + else: + out = torch.cat((out, sp), 1) + if self.scale!=1 and self.stride_3x3==1: + out = torch.cat((out, spx[self.nums]), 1) + elif self.scale != 1 and self.stride_3x3==2: + out = torch.cat((out, self.pool(spx[self.nums])), 1) + + out = self.conv3(out) + + if self.shortcut is not None: + shortcut = self.shortcut(x) + else: + shortcut = x + + out += shortcut + out = F.relu_(out) + return out + + +class DeformBottleneckBlock(ResNetBlockBase): + """ + Not implemented for res2net yet. + Similar to :class:`BottleneckBlock`, but with deformable conv in the 3x3 convolution. + """ + + def __init__( + self, + in_channels, + out_channels, + *, + bottleneck_channels, + stride=1, + num_groups=1, + norm="BN", + stride_in_1x1=False, + dilation=1, + deform_modulated=False, + deform_num_groups=1, + basewidth=26, + scale=4, + ): + super().__init__(in_channels, out_channels, stride) + self.deform_modulated = deform_modulated + + if in_channels != out_channels: + # self.shortcut = Conv2d( + # in_channels, + # out_channels, + # kernel_size=1, + # stride=stride, + # bias=False, + # norm=get_norm(norm, out_channels), + # ) + self.shortcut = nn.Sequential( + nn.AvgPool2d(kernel_size=stride, stride=stride, + ceil_mode=True, count_include_pad=False), + Conv2d( + in_channels, + out_channels, + kernel_size=1, + stride=1, + bias=False, + norm=get_norm(norm, out_channels), + ) + ) + else: + self.shortcut = None + + stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride) + width = bottleneck_channels//scale + + self.conv1 = Conv2d( + in_channels, + bottleneck_channels, + kernel_size=1, + stride=stride_1x1, + bias=False, + norm=get_norm(norm, bottleneck_channels), + ) + + if scale == 1: + self.nums = 1 + else: + self.nums = scale -1 + if self.in_channels!=self.out_channels and stride_3x3!=2: + self.pool = nn.AvgPool2d(kernel_size=3, stride = stride_3x3, padding=1) + + if deform_modulated: + deform_conv_op = ModulatedDeformConv + # offset channels are 2 or 3 (if with modulated) * kernel_size * kernel_size + offset_channels = 27 + else: + deform_conv_op = DeformConv + offset_channels = 18 + + # self.conv2_offset = Conv2d( + # bottleneck_channels, + # offset_channels * deform_num_groups, + # kernel_size=3, + # stride=stride_3x3, + # padding=1 * dilation, + # dilation=dilation, + # ) + # self.conv2 = deform_conv_op( + # bottleneck_channels, + # bottleneck_channels, + # kernel_size=3, + # stride=stride_3x3, + # padding=1 * dilation, + # bias=False, + # groups=num_groups, + # dilation=dilation, + # deformable_groups=deform_num_groups, + # norm=get_norm(norm, bottleneck_channels), + # ) + + conv2_offsets = [] + convs = [] + bns = [] + for i in range(self.nums): + conv2_offsets.append(Conv2d( + width, + offset_channels * deform_num_groups, + kernel_size=3, + stride=stride_3x3, + padding=1 * dilation, + bias=False, + groups=num_groups, + dilation=dilation, + )) + convs.append(deform_conv_op( + width, + width, + kernel_size=3, + stride=stride_3x3, + padding=1 * dilation, + bias=False, + groups=num_groups, + dilation=dilation, + deformable_groups=deform_num_groups, + )) + bns.append(get_norm(norm, width)) + self.conv2_offsets = nn.ModuleList(conv2_offsets) + self.convs = nn.ModuleList(convs) + self.bns = nn.ModuleList(bns) + + self.conv3 = Conv2d( + bottleneck_channels, + out_channels, + kernel_size=1, + bias=False, + norm=get_norm(norm, out_channels), + ) + self.scale = scale + self.width = width + self.in_channels = in_channels + self.out_channels = out_channels + self.stride_3x3 = stride_3x3 + # for layer in [self.conv1, self.conv2, self.conv3, self.shortcut]: + # if layer is not None: # shortcut can be None + # weight_init.c2_msra_fill(layer) + + # nn.init.constant_(self.conv2_offset.weight, 0) + # nn.init.constant_(self.conv2_offset.bias, 0) + for layer in [self.conv1, self.conv3]: + if layer is not None: # shortcut can be None + weight_init.c2_msra_fill(layer) + if self.shortcut is not None: + for layer in self.shortcut.modules(): + if isinstance(layer, Conv2d): + weight_init.c2_msra_fill(layer) + + for layer in self.convs: + if layer is not None: # shortcut can be None + weight_init.c2_msra_fill(layer) + + for layer in self.conv2_offsets: + if layer.weight is not None: + nn.init.constant_(layer.weight, 0) + if layer.bias is not None: + nn.init.constant_(layer.bias, 0) + + def forward(self, x): + out = self.conv1(x) + out = F.relu_(out) + + # if self.deform_modulated: + # offset_mask = self.conv2_offset(out) + # offset_x, offset_y, mask = torch.chunk(offset_mask, 3, dim=1) + # offset = torch.cat((offset_x, offset_y), dim=1) + # mask = mask.sigmoid() + # out = self.conv2(out, offset, mask) + # else: + # offset = self.conv2_offset(out) + # out = self.conv2(out, offset) + # out = F.relu_(out) + + spx = torch.split(out, self.width, 1) + for i in range(self.nums): + if i==0 or self.in_channels!=self.out_channels: + sp = spx[i].contiguous() + else: + sp = sp + spx[i].contiguous() + + # sp = self.convs[i](sp) + if self.deform_modulated: + offset_mask = self.conv2_offsets[i](sp) + offset_x, offset_y, mask = torch.chunk(offset_mask, 3, dim=1) + offset = torch.cat((offset_x, offset_y), dim=1) + mask = mask.sigmoid() + sp = self.convs[i](sp, offset, mask) + else: + offset = self.conv2_offsets[i](sp) + sp = self.convs[i](sp, offset) + sp = F.relu_(self.bns[i](sp)) + if i==0: + out = sp + else: + out = torch.cat((out, sp), 1) + if self.scale!=1 and self.stride_3x3==1: + out = torch.cat((out, spx[self.nums]), 1) + elif self.scale != 1 and self.stride_3x3==2: + out = torch.cat((out, self.pool(spx[self.nums])), 1) + + out = self.conv3(out) + + if self.shortcut is not None: + shortcut = self.shortcut(x) + else: + shortcut = x + + out += shortcut + out = F.relu_(out) + return out + + +def make_stage(block_class, num_blocks, first_stride, *, in_channels, out_channels, **kwargs): + """ + Create a list of blocks just like those in a ResNet stage. + Args: + block_class (type): a subclass of ResNetBlockBase + num_blocks (int): + first_stride (int): the stride of the first block. The other blocks will have stride=1. + in_channels (int): input channels of the entire stage. + out_channels (int): output channels of **every block** in the stage. + kwargs: other arguments passed to the constructor of every block. + Returns: + list[nn.Module]: a list of block module. + """ + assert "stride" not in kwargs, "Stride of blocks in make_stage cannot be changed." + blocks = [] + for i in range(num_blocks): + blocks.append( + block_class( + in_channels=in_channels, + out_channels=out_channels, + stride=first_stride if i == 0 else 1, + **kwargs, + ) + ) + in_channels = out_channels + return blocks + + +class BasicStem(CNNBlockBase): + """ + The standard ResNet stem (layers before the first residual block). + """ + + def __init__(self, in_channels=3, out_channels=64, norm="BN"): + """ + Args: + norm (str or callable): norm after the first conv layer. + See :func:`layers.get_norm` for supported format. + """ + super().__init__(in_channels, out_channels, 4) + self.in_channels = in_channels + self.conv1 = nn.Sequential( + Conv2d( + in_channels, + 32, + kernel_size=3, + stride=2, + padding=1, + bias=False, + ), + get_norm(norm, 32), + nn.ReLU(inplace=True), + Conv2d( + 32, + 32, + kernel_size=3, + stride=1, + padding=1, + bias=False, + ), + get_norm(norm, 32), + nn.ReLU(inplace=True), + Conv2d( + 32, + out_channels, + kernel_size=3, + stride=1, + padding=1, + bias=False, + ), + ) + self.bn1 = get_norm(norm, out_channels) + + for layer in self.conv1: + if isinstance(layer, Conv2d): + weight_init.c2_msra_fill(layer) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = F.relu_(x) + x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) + return x + + +class ResNet(Backbone): + def __init__(self, stem, stages, num_classes=None, out_features=None): + """ + Args: + stem (nn.Module): a stem module + stages (list[list[CNNBlockBase]]): several (typically 4) stages, + each contains multiple :class:`CNNBlockBase`. + num_classes (None or int): if None, will not perform classification. + Otherwise, will create a linear layer. + out_features (list[str]): name of the layers whose outputs should + be returned in forward. Can be anything in "stem", "linear", or "res2" ... + If None, will return the output of the last layer. + """ + super(ResNet, self).__init__() + self.stem = stem + self.num_classes = num_classes + + current_stride = self.stem.stride + self._out_feature_strides = {"stem": current_stride} + self._out_feature_channels = {"stem": self.stem.out_channels} + + self.stages_and_names = [] + for i, blocks in enumerate(stages): + assert len(blocks) > 0, len(blocks) + for block in blocks: + assert isinstance(block, CNNBlockBase), block + + name = "res" + str(i + 2) + stage = nn.Sequential(*blocks) + + self.add_module(name, stage) + self.stages_and_names.append((stage, name)) + + self._out_feature_strides[name] = current_stride = int( + current_stride * np.prod([k.stride for k in blocks]) + ) + self._out_feature_channels[name] = curr_channels = blocks[-1].out_channels + + if num_classes is not None: + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.linear = nn.Linear(curr_channels, num_classes) + + # Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour": + # "The 1000-way fully-connected layer is initialized by + # drawing weights from a zero-mean Gaussian with standard deviation of 0.01." + nn.init.normal_(self.linear.weight, std=0.01) + name = "linear" + + if out_features is None: + out_features = [name] + self._out_features = out_features + assert len(self._out_features) + children = [x[0] for x in self.named_children()] + for out_feature in self._out_features: + assert out_feature in children, "Available children: {}".format(", ".join(children)) + + def forward(self, x): + outputs = {} + x = self.stem(x) + if "stem" in self._out_features: + outputs["stem"] = x + for stage, name in self.stages_and_names: + x = stage(x) + if name in self._out_features: + outputs[name] = x + if self.num_classes is not None: + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.linear(x) + if "linear" in self._out_features: + outputs["linear"] = x + return outputs + + def output_shape(self): + return { + name: ShapeSpec( + channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] + ) + for name in self._out_features + } + + def freeze(self, freeze_at=0): + """ + Freeze the first several stages of the ResNet. Commonly used in + fine-tuning. + Args: + freeze_at (int): number of stem and stages to freeze. + `1` means freezing the stem. `2` means freezing the stem and + the first stage, etc. + Returns: + nn.Module: this ResNet itself + """ + if freeze_at >= 1: + self.stem.freeze() + for idx, (stage, _) in enumerate(self.stages_and_names, start=2): + if freeze_at >= idx: + for block in stage.children(): + block.freeze() + return self + + +@BACKBONE_REGISTRY.register() +def build_res2net_backbone(cfg, input_shape): + """ + Create a Res2Net instance from config. + Returns: + ResNet: a :class:`ResNet` instance. + """ + # need registration of new blocks/stems? + norm = cfg.MODEL.RESNETS.NORM + stem = BasicStem( + in_channels=input_shape.channels, + out_channels=cfg.MODEL.RESNETS.STEM_OUT_CHANNELS, + norm=norm, + ) + + # fmt: off + freeze_at = cfg.MODEL.BACKBONE.FREEZE_AT + out_features = cfg.MODEL.RESNETS.OUT_FEATURES + depth = cfg.MODEL.RESNETS.DEPTH + num_groups = cfg.MODEL.RESNETS.NUM_GROUPS + width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP + scale = 4 + bottleneck_channels = num_groups * width_per_group * scale + in_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS + out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS + stride_in_1x1 = cfg.MODEL.RESNETS.STRIDE_IN_1X1 + res5_dilation = cfg.MODEL.RESNETS.RES5_DILATION + deform_on_per_stage = cfg.MODEL.RESNETS.DEFORM_ON_PER_STAGE + deform_modulated = cfg.MODEL.RESNETS.DEFORM_MODULATED + deform_num_groups = cfg.MODEL.RESNETS.DEFORM_NUM_GROUPS + # fmt: on + assert res5_dilation in {1, 2}, "res5_dilation cannot be {}.".format(res5_dilation) + + num_blocks_per_stage = { + 18: [2, 2, 2, 2], + 34: [3, 4, 6, 3], + 50: [3, 4, 6, 3], + 101: [3, 4, 23, 3], + 152: [3, 8, 36, 3], + }[depth] + + if depth in [18, 34]: + assert out_channels == 64, "Must set MODEL.RESNETS.RES2_OUT_CHANNELS = 64 for R18/R34" + assert not any( + deform_on_per_stage + ), "MODEL.RESNETS.DEFORM_ON_PER_STAGE unsupported for R18/R34" + assert res5_dilation == 1, "Must set MODEL.RESNETS.RES5_DILATION = 1 for R18/R34" + assert num_groups == 1, "Must set MODEL.RESNETS.NUM_GROUPS = 1 for R18/R34" + + stages = [] + + # Avoid creating variables without gradients + # It consumes extra memory and may cause allreduce to fail + out_stage_idx = [{"res2": 2, "res3": 3, "res4": 4, "res5": 5}[f] for f in out_features] + max_stage_idx = max(out_stage_idx) + for idx, stage_idx in enumerate(range(2, max_stage_idx + 1)): + dilation = res5_dilation if stage_idx == 5 else 1 + first_stride = 1 if idx == 0 or (stage_idx == 5 and dilation == 2) else 2 + stage_kargs = { + "num_blocks": num_blocks_per_stage[idx], + "first_stride": first_stride, + "in_channels": in_channels, + "out_channels": out_channels, + "norm": norm, + } + # Use BasicBlock for R18 and R34. + if depth in [18, 34]: + stage_kargs["block_class"] = BasicBlock + else: + stage_kargs["bottleneck_channels"] = bottleneck_channels + stage_kargs["stride_in_1x1"] = stride_in_1x1 + stage_kargs["dilation"] = dilation + stage_kargs["num_groups"] = num_groups + stage_kargs["scale"] = scale + + if deform_on_per_stage[idx]: + stage_kargs["block_class"] = DeformBottleneckBlock + stage_kargs["deform_modulated"] = deform_modulated + stage_kargs["deform_num_groups"] = deform_num_groups + else: + stage_kargs["block_class"] = BottleneckBlock + blocks = make_stage(**stage_kargs) + in_channels = out_channels + out_channels *= 2 + bottleneck_channels *= 2 + stages.append(blocks) + return ResNet(stem, stages, out_features=out_features).freeze(freeze_at) + + +@BACKBONE_REGISTRY.register() +def build_p67_res2net_fpn_backbone(cfg, input_shape: ShapeSpec): + """ + Args: + cfg: a detectron2 CfgNode + + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + bottom_up = build_res2net_backbone(cfg, input_shape) + in_features = cfg.MODEL.FPN.IN_FEATURES + out_channels = cfg.MODEL.FPN.OUT_CHANNELS + backbone = FPN( + bottom_up=bottom_up, + in_features=in_features, + out_channels=out_channels, + norm=cfg.MODEL.FPN.NORM, + top_block=LastLevelP6P7_P5(out_channels, out_channels), + fuse_type=cfg.MODEL.FPN.FUSE_TYPE, + ) + return backbone + + +@BACKBONE_REGISTRY.register() +def build_res2net_bifpn_backbone(cfg, input_shape: ShapeSpec): + """ + Args: + cfg: a detectron2 CfgNode + + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + bottom_up = build_res2net_backbone(cfg, input_shape) + in_features = cfg.MODEL.FPN.IN_FEATURES + backbone = BiFPN( + cfg=cfg, + bottom_up=bottom_up, + in_features=in_features, + out_channels=cfg.MODEL.BIFPN.OUT_CHANNELS, + norm=cfg.MODEL.BIFPN.NORM, + num_levels=cfg.MODEL.BIFPN.NUM_LEVELS, + num_bifpn=cfg.MODEL.BIFPN.NUM_BIFPN, + separable_conv=cfg.MODEL.BIFPN.SEPARABLE_CONV, + ) + return backbone \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/debug.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/debug.py new file mode 100644 index 0000000000000000000000000000000000000000..0a4437fb5ae7522e46ca6c42ba5fd980df250446 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/debug.py @@ -0,0 +1,283 @@ +import cv2 +import numpy as np +import torch +import torch.nn.functional as F + +COLORS = ((np.random.rand(1300, 3) * 0.4 + 0.6) * 255).astype( + np.uint8).reshape(1300, 1, 1, 3) + +def _get_color_image(heatmap): + heatmap = heatmap.reshape( + heatmap.shape[0], heatmap.shape[1], heatmap.shape[2], 1) + if heatmap.shape[0] == 1: + color_map = (heatmap * np.ones((1, 1, 1, 3), np.uint8) * 255).max( + axis=0).astype(np.uint8) # H, W, 3 + else: + color_map = (heatmap * COLORS[:heatmap.shape[0]]).max(axis=0).astype(np.uint8) # H, W, 3 + + return color_map + +def _blend_image(image, color_map, a=0.7): + color_map = cv2.resize(color_map, (image.shape[1], image.shape[0])) + ret = np.clip(image * (1 - a) + color_map * a, 0, 255).astype(np.uint8) + return ret + +def _blend_image_heatmaps(image, color_maps, a=0.7): + merges = np.zeros((image.shape[0], image.shape[1], 3), np.float32) + for color_map in color_maps: + color_map = cv2.resize(color_map, (image.shape[1], image.shape[0])) + merges = np.maximum(merges, color_map) + ret = np.clip(image * (1 - a) + merges * a, 0, 255).astype(np.uint8) + return ret + +def _decompose_level(x, shapes_per_level, N): + ''' + x: LNHiWi x C + ''' + x = x.view(x.shape[0], -1) + ret = [] + st = 0 + for l in range(len(shapes_per_level)): + ret.append([]) + h = shapes_per_level[l][0].int().item() + w = shapes_per_level[l][1].int().item() + for i in range(N): + ret[l].append(x[st + h * w * i:st + h * w * (i + 1)].view( + h, w, -1).permute(2, 0, 1)) + st += h * w * N + return ret + +def _imagelist_to_tensor(images): + images = [x for x in images] + image_sizes = [x.shape[-2:] for x in images] + h = max([size[0] for size in image_sizes]) + w = max([size[1] for size in image_sizes]) + S = 32 + h, w = ((h - 1) // S + 1) * S, ((w - 1) // S + 1) * S + images = [F.pad(x, (0, w - x.shape[2], 0, h - x.shape[1], 0, 0)) \ + for x in images] + images = torch.stack(images) + return images + + +def _ind2il(ind, shapes_per_level, N): + r = ind + l = 0 + S = 0 + while r - S >= N * shapes_per_level[l][0] * shapes_per_level[l][1]: + S += N * shapes_per_level[l][0] * shapes_per_level[l][1] + l += 1 + i = (r - S) // (shapes_per_level[l][0] * shapes_per_level[l][1]) + return i, l + +def debug_train( + images, gt_instances, flattened_hms, reg_targets, labels, pos_inds, + shapes_per_level, locations, strides): + ''' + images: N x 3 x H x W + flattened_hms: LNHiWi x C + shapes_per_level: L x 2 [(H_i, W_i)] + locations: LNHiWi x 2 + ''' + reg_inds = torch.nonzero( + reg_targets.max(dim=1)[0] > 0).squeeze(1) + N = len(images) + images = _imagelist_to_tensor(images) + repeated_locations = [torch.cat([loc] * N, dim=0) \ + for loc in locations] + locations = torch.cat(repeated_locations, dim=0) + gt_hms = _decompose_level(flattened_hms, shapes_per_level, N) + masks = flattened_hms.new_zeros((flattened_hms.shape[0], 1)) + masks[pos_inds] = 1 + masks = _decompose_level(masks, shapes_per_level, N) + for i in range(len(images)): + image = images[i].detach().cpu().numpy().transpose(1, 2, 0) + color_maps = [] + for l in range(len(gt_hms)): + color_map = _get_color_image( + gt_hms[l][i].detach().cpu().numpy()) + color_maps.append(color_map) + cv2.imshow('gthm_{}'.format(l), color_map) + blend = _blend_image_heatmaps(image.copy(), color_maps) + if gt_instances is not None: + bboxes = gt_instances[i].gt_boxes.tensor + for j in range(len(bboxes)): + bbox = bboxes[j] + cv2.rectangle( + blend, + (int(bbox[0]), int(bbox[1])), + (int(bbox[2]), int(bbox[3])), + (0, 0, 255), 3, cv2.LINE_AA) + + for j in range(len(pos_inds)): + image_id, l = _ind2il(pos_inds[j], shapes_per_level, N) + if image_id != i: + continue + loc = locations[pos_inds[j]] + cv2.drawMarker( + blend, (int(loc[0]), int(loc[1])), (0, 255, 255), + markerSize=(l + 1) * 16) + + for j in range(len(reg_inds)): + image_id, l = _ind2il(reg_inds[j], shapes_per_level, N) + if image_id != i: + continue + ltrb = reg_targets[reg_inds[j]] + ltrb *= strides[l] + loc = locations[reg_inds[j]] + bbox = [(loc[0] - ltrb[0]), (loc[1] - ltrb[1]), + (loc[0] + ltrb[2]), (loc[1] + ltrb[3])] + cv2.rectangle( + blend, + (int(bbox[0]), int(bbox[1])), + (int(bbox[2]), int(bbox[3])), + (255, 0, 0), 1, cv2.LINE_AA) + cv2.circle(blend, (int(loc[0]), int(loc[1])), 2, (255, 0, 0), -1) + + cv2.imshow('blend', blend) + cv2.waitKey() + + +def debug_test( + images, logits_pred, reg_pred, agn_hm_pred=[], preds=[], + vis_thresh=0.3, debug_show_name=False, mult_agn=False): + ''' + images: N x 3 x H x W + class_target: LNHiWi x C + cat_agn_heatmap: LNHiWi + shapes_per_level: L x 2 [(H_i, W_i)] + ''' + N = len(images) + for i in range(len(images)): + image = images[i].detach().cpu().numpy().transpose(1, 2, 0) + result = image.copy().astype(np.uint8) + pred_image = image.copy().astype(np.uint8) + color_maps = [] + L = len(logits_pred) + for l in range(L): + if logits_pred[0] is not None: + stride = min(image.shape[0], image.shape[1]) / min( + logits_pred[l][i].shape[1], logits_pred[l][i].shape[2]) + else: + stride = min(image.shape[0], image.shape[1]) / min( + agn_hm_pred[l][i].shape[1], agn_hm_pred[l][i].shape[2]) + stride = stride if stride < 60 else 64 if stride < 100 else 128 + if logits_pred[0] is not None: + if mult_agn: + logits_pred[l][i] = logits_pred[l][i] * agn_hm_pred[l][i] + color_map = _get_color_image( + logits_pred[l][i].detach().cpu().numpy()) + color_maps.append(color_map) + cv2.imshow('predhm_{}'.format(l), color_map) + + if debug_show_name: + from detectron2.data.datasets.lvis_v1_categories import LVIS_CATEGORIES + cat2name = [x['name'] for x in LVIS_CATEGORIES] + for j in range(len(preds[i].scores) if preds is not None else 0): + if preds[i].scores[j] > vis_thresh: + bbox = preds[i].proposal_boxes[j] \ + if preds[i].has('proposal_boxes') else \ + preds[i].pred_boxes[j] + bbox = bbox.tensor[0].detach().cpu().numpy().astype(np.int32) + cat = int(preds[i].pred_classes[j]) \ + if preds[i].has('pred_classes') else 0 + cl = COLORS[cat, 0, 0] + cv2.rectangle( + pred_image, (int(bbox[0]), int(bbox[1])), + (int(bbox[2]), int(bbox[3])), + (int(cl[0]), int(cl[1]), int(cl[2])), 2, cv2.LINE_AA) + if debug_show_name: + txt = '{}{:.1f}'.format( + cat2name[cat] if cat > 0 else '', + preds[i].scores[j]) + font = cv2.FONT_HERSHEY_SIMPLEX + cat_size = cv2.getTextSize(txt, font, 0.5, 2)[0] + cv2.rectangle( + pred_image, + (int(bbox[0]), int(bbox[1] - cat_size[1] - 2)), + (int(bbox[0] + cat_size[0]), int(bbox[1] - 2)), + (int(cl[0]), int(cl[1]), int(cl[2])), -1) + cv2.putText( + pred_image, txt, (int(bbox[0]), int(bbox[1] - 2)), + font, 0.5, (0, 0, 0), thickness=1, lineType=cv2.LINE_AA) + + + if agn_hm_pred[l] is not None: + agn_hm_ = agn_hm_pred[l][i, 0, :, :, None].detach().cpu().numpy() + agn_hm_ = (agn_hm_ * np.array([255, 255, 255]).reshape( + 1, 1, 3)).astype(np.uint8) + cv2.imshow('agn_hm_{}'.format(l), agn_hm_) + blend = _blend_image_heatmaps(image.copy(), color_maps) + cv2.imshow('blend', blend) + cv2.imshow('preds', pred_image) + cv2.waitKey() + +global cnt +cnt = 0 + +def debug_second_stage(images, instances, proposals=None, vis_thresh=0.3, + save_debug=False, debug_show_name=False): + images = _imagelist_to_tensor(images) + if debug_show_name: + from detectron2.data.datasets.lvis_v1_categories import LVIS_CATEGORIES + cat2name = [x['name'] for x in LVIS_CATEGORIES] + for i in range(len(images)): + image = images[i].detach().cpu().numpy().transpose(1, 2, 0).astype(np.uint8).copy() + if instances[i].has('gt_boxes'): + bboxes = instances[i].gt_boxes.tensor.cpu().numpy() + scores = np.ones(bboxes.shape[0]) + cats = instances[i].gt_classes.cpu().numpy() + else: + bboxes = instances[i].pred_boxes.tensor.cpu().numpy() + scores = instances[i].scores.cpu().numpy() + cats = instances[i].pred_classes.cpu().numpy() + for j in range(len(bboxes)): + if scores[j] > vis_thresh: + bbox = bboxes[j] + cl = COLORS[cats[j], 0, 0] + cl = (int(cl[0]), int(cl[1]), int(cl[2])) + cv2.rectangle( + image, + (int(bbox[0]), int(bbox[1])), + (int(bbox[2]), int(bbox[3])), + cl, 2, cv2.LINE_AA) + if debug_show_name: + cat = cats[j] + txt = '{}{:.1f}'.format( + cat2name[cat] if cat > 0 else '', + scores[j]) + font = cv2.FONT_HERSHEY_SIMPLEX + cat_size = cv2.getTextSize(txt, font, 0.5, 2)[0] + cv2.rectangle( + image, + (int(bbox[0]), int(bbox[1] - cat_size[1] - 2)), + (int(bbox[0] + cat_size[0]), int(bbox[1] - 2)), + (int(cl[0]), int(cl[1]), int(cl[2])), -1) + cv2.putText( + image, txt, (int(bbox[0]), int(bbox[1] - 2)), + font, 0.5, (0, 0, 0), thickness=1, lineType=cv2.LINE_AA) + if proposals is not None: + proposal_image = images[i].detach().cpu().numpy().transpose(1, 2, 0).astype(np.uint8).copy() + bboxes = proposals[i].proposal_boxes.tensor.cpu().numpy() + if proposals[i].has('scores'): + scores = proposals[i].scores.cpu().numpy() + else: + scores = proposals[i].objectness_logits.sigmoid().cpu().numpy() + for j in range(len(bboxes)): + if scores[j] > vis_thresh: + bbox = bboxes[j] + cl = (209, 159, 83) + cv2.rectangle( + proposal_image, + (int(bbox[0]), int(bbox[1])), + (int(bbox[2]), int(bbox[3])), + cl, 2, cv2.LINE_AA) + + cv2.imshow('image', image) + if proposals is not None: + cv2.imshow('proposals', proposal_image) + if save_debug: + global cnt + cnt += 1 + cv2.imwrite('output/save_debug/{}.jpg'.format(cnt), proposal_image) + cv2.waitKey() \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py new file mode 100644 index 0000000000000000000000000000000000000000..feb7a8222487756d38482da95183bbbcbbe96ed9 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py @@ -0,0 +1,864 @@ + +import math +import json +import copy +from typing import List, Dict +import numpy as np +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.modeling.proposal_generator.build import PROPOSAL_GENERATOR_REGISTRY +from detectron2.layers import ShapeSpec, cat +from detectron2.structures import Instances, Boxes +from detectron2.modeling import detector_postprocess +from detectron2.utils.comm import get_world_size +from detectron2.config import configurable + +from ..layers.heatmap_focal_loss import heatmap_focal_loss_jit +from ..layers.heatmap_focal_loss import binary_heatmap_focal_loss +from ..layers.iou_loss import IOULoss +from ..layers.ml_nms import ml_nms +from ..debug import debug_train, debug_test +from .utils import reduce_sum, _transpose +from .centernet_head import CenterNetHead + +__all__ = ["CenterNet"] + +INF = 100000000 + +@PROPOSAL_GENERATOR_REGISTRY.register() +class CenterNet(nn.Module): + @configurable + def __init__(self, + # input_shape: Dict[str, ShapeSpec], + in_channels=256, + *, + num_classes=80, + in_features=("p3", "p4", "p5", "p6", "p7"), + strides=(8, 16, 32, 64, 128), + score_thresh=0.05, + hm_min_overlap=0.8, + loc_loss_type='giou', + min_radius=4, + hm_focal_alpha=0.25, + hm_focal_beta=4, + loss_gamma=2.0, + reg_weight=2.0, + not_norm_reg=True, + with_agn_hm=False, + only_proposal=False, + as_proposal=False, + not_nms=False, + pos_weight=1., + neg_weight=1., + sigmoid_clamp=1e-4, + ignore_high_fp=-1., + center_nms=False, + sizes_of_interest=[[0,80],[64,160],[128,320],[256,640],[512,10000000]], + more_pos=False, + more_pos_thresh=0.2, + more_pos_topk=9, + pre_nms_topk_train=1000, + pre_nms_topk_test=1000, + post_nms_topk_train=100, + post_nms_topk_test=100, + nms_thresh_train=0.6, + nms_thresh_test=0.6, + no_reduce=False, + debug=False, + vis_thresh=0.5, + pixel_mean=[103.530,116.280,123.675], + pixel_std=[1.0,1.0,1.0], + device='cuda', + centernet_head=None, + ): + super().__init__() + self.num_classes = num_classes + self.in_features = in_features + self.strides = strides + self.score_thresh = score_thresh + self.min_radius = min_radius + self.hm_focal_alpha = hm_focal_alpha + self.hm_focal_beta = hm_focal_beta + self.loss_gamma = loss_gamma + self.reg_weight = reg_weight + self.not_norm_reg = not_norm_reg + self.with_agn_hm = with_agn_hm + self.only_proposal = only_proposal + self.as_proposal = as_proposal + self.not_nms = not_nms + self.pos_weight = pos_weight + self.neg_weight = neg_weight + self.sigmoid_clamp = sigmoid_clamp + self.ignore_high_fp = ignore_high_fp + self.center_nms = center_nms + self.sizes_of_interest = sizes_of_interest + self.more_pos = more_pos + self.more_pos_thresh = more_pos_thresh + self.more_pos_topk = more_pos_topk + self.pre_nms_topk_train = pre_nms_topk_train + self.pre_nms_topk_test = pre_nms_topk_test + self.post_nms_topk_train = post_nms_topk_train + self.post_nms_topk_test = post_nms_topk_test + self.nms_thresh_train = nms_thresh_train + self.nms_thresh_test = nms_thresh_test + self.no_reduce = no_reduce + self.debug = debug + self.vis_thresh = vis_thresh + if self.center_nms: + self.not_nms = True + self.iou_loss = IOULoss(loc_loss_type) + assert (not self.only_proposal) or self.with_agn_hm + # delta for rendering heatmap + self.delta = (1 - hm_min_overlap) / (1 + hm_min_overlap) + if centernet_head is None: + self.centernet_head = CenterNetHead( + in_channels=in_channels, + num_levels=len(in_features), + with_agn_hm=with_agn_hm, + only_proposal=only_proposal) + else: + self.centernet_head = centernet_head + if self.debug: + pixel_mean = torch.Tensor(pixel_mean).to( + torch.device(device)).view(3, 1, 1) + pixel_std = torch.Tensor(pixel_std).to( + torch.device(device)).view(3, 1, 1) + self.denormalizer = lambda x: x * pixel_std + pixel_mean + + @classmethod + def from_config(cls, cfg, input_shape): + ret = { + # 'input_shape': input_shape, + 'in_channels': input_shape[ + cfg.MODEL.CENTERNET.IN_FEATURES[0]].channels, + 'num_classes': cfg.MODEL.CENTERNET.NUM_CLASSES, + 'in_features': cfg.MODEL.CENTERNET.IN_FEATURES, + 'strides': cfg.MODEL.CENTERNET.FPN_STRIDES, + 'score_thresh': cfg.MODEL.CENTERNET.INFERENCE_TH, + 'loc_loss_type': cfg.MODEL.CENTERNET.LOC_LOSS_TYPE, + 'hm_min_overlap': cfg.MODEL.CENTERNET.HM_MIN_OVERLAP, + 'min_radius': cfg.MODEL.CENTERNET.MIN_RADIUS, + 'hm_focal_alpha': cfg.MODEL.CENTERNET.HM_FOCAL_ALPHA, + 'hm_focal_beta': cfg.MODEL.CENTERNET.HM_FOCAL_BETA, + 'loss_gamma': cfg.MODEL.CENTERNET.LOSS_GAMMA, + 'reg_weight': cfg.MODEL.CENTERNET.REG_WEIGHT, + 'not_norm_reg': cfg.MODEL.CENTERNET.NOT_NORM_REG, + 'with_agn_hm': cfg.MODEL.CENTERNET.WITH_AGN_HM, + 'only_proposal': cfg.MODEL.CENTERNET.ONLY_PROPOSAL, + 'as_proposal': cfg.MODEL.CENTERNET.AS_PROPOSAL, + 'not_nms': cfg.MODEL.CENTERNET.NOT_NMS, + 'pos_weight': cfg.MODEL.CENTERNET.POS_WEIGHT, + 'neg_weight': cfg.MODEL.CENTERNET.NEG_WEIGHT, + 'sigmoid_clamp': cfg.MODEL.CENTERNET.SIGMOID_CLAMP, + 'ignore_high_fp': cfg.MODEL.CENTERNET.IGNORE_HIGH_FP, + 'center_nms': cfg.MODEL.CENTERNET.CENTER_NMS, + 'sizes_of_interest': cfg.MODEL.CENTERNET.SOI, + 'more_pos': cfg.MODEL.CENTERNET.MORE_POS, + 'more_pos_thresh': cfg.MODEL.CENTERNET.MORE_POS_THRESH, + 'more_pos_topk': cfg.MODEL.CENTERNET.MORE_POS_TOPK, + 'pre_nms_topk_train': cfg.MODEL.CENTERNET.PRE_NMS_TOPK_TRAIN, + 'pre_nms_topk_test': cfg.MODEL.CENTERNET.PRE_NMS_TOPK_TEST, + 'post_nms_topk_train': cfg.MODEL.CENTERNET.POST_NMS_TOPK_TRAIN, + 'post_nms_topk_test': cfg.MODEL.CENTERNET.POST_NMS_TOPK_TEST, + 'nms_thresh_train': cfg.MODEL.CENTERNET.NMS_TH_TRAIN, + 'nms_thresh_test': cfg.MODEL.CENTERNET.NMS_TH_TEST, + 'no_reduce': cfg.MODEL.CENTERNET.NO_REDUCE, + 'debug': cfg.DEBUG, + 'vis_thresh': cfg.VIS_THRESH, + 'pixel_mean': cfg.MODEL.PIXEL_MEAN, + 'pixel_std': cfg.MODEL.PIXEL_STD, + 'device': cfg.MODEL.DEVICE, + 'centernet_head': CenterNetHead( + cfg, [input_shape[f] for f in cfg.MODEL.CENTERNET.IN_FEATURES]), + } + return ret + + + def forward(self, images, features_dict, gt_instances): + features = [features_dict[f] for f in self.in_features] + clss_per_level, reg_pred_per_level, agn_hm_pred_per_level = \ + self.centernet_head(features) + grids = self.compute_grids(features) + shapes_per_level = grids[0].new_tensor( + [(x.shape[2], x.shape[3]) for x in reg_pred_per_level]) + + if not self.training: + return self.inference( + images, clss_per_level, reg_pred_per_level, + agn_hm_pred_per_level, grids) + else: + pos_inds, labels, reg_targets, flattened_hms = \ + self._get_ground_truth( + grids, shapes_per_level, gt_instances) + # logits_pred: M x F, reg_pred: M x 4, agn_hm_pred: M + logits_pred, reg_pred, agn_hm_pred = self._flatten_outputs( + clss_per_level, reg_pred_per_level, agn_hm_pred_per_level) + + if self.more_pos: + # add more pixels as positive if \ + # 1. they are within the center3x3 region of an object + # 2. their regression losses are small (= 0).squeeze(1) + reg_pred = reg_pred[reg_inds] + reg_targets_pos = reg_targets[reg_inds] + reg_weight_map = flattened_hms.max(dim=1)[0] + reg_weight_map = reg_weight_map[reg_inds] + reg_weight_map = reg_weight_map * 0 + 1 \ + if self.not_norm_reg else reg_weight_map + if self.no_reduce: + reg_norm = max(reg_weight_map.sum(), 1) + else: + reg_norm = max(reduce_sum(reg_weight_map.sum()).item() / num_gpus, 1) + + reg_loss = self.reg_weight * self.iou_loss( + reg_pred, reg_targets_pos, reg_weight_map, + reduction='sum') / reg_norm + losses['loss_centernet_loc'] = reg_loss + + if self.with_agn_hm: + cat_agn_heatmap = flattened_hms.max(dim=1)[0] # M + agn_pos_loss, agn_neg_loss = binary_heatmap_focal_loss( + agn_hm_pred, cat_agn_heatmap, pos_inds, + alpha=self.hm_focal_alpha, + beta=self.hm_focal_beta, + gamma=self.loss_gamma, + sigmoid_clamp=self.sigmoid_clamp, + ignore_high_fp=self.ignore_high_fp, + ) + agn_pos_loss = self.pos_weight * agn_pos_loss / num_pos_avg + agn_neg_loss = self.neg_weight * agn_neg_loss / num_pos_avg + losses['loss_centernet_agn_pos'] = agn_pos_loss + losses['loss_centernet_agn_neg'] = agn_neg_loss + + if self.debug: + print('losses', losses) + print('total_num_pos', total_num_pos) + return losses + + + def compute_grids(self, features): + grids = [] + for level, feature in enumerate(features): + h, w = feature.size()[-2:] + shifts_x = torch.arange( + 0, w * self.strides[level], + step=self.strides[level], + dtype=torch.float32, device=feature.device) + shifts_y = torch.arange( + 0, h * self.strides[level], + step=self.strides[level], + dtype=torch.float32, device=feature.device) + shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) + shift_x = shift_x.reshape(-1) + shift_y = shift_y.reshape(-1) + grids_per_level = torch.stack((shift_x, shift_y), dim=1) + \ + self.strides[level] // 2 + grids.append(grids_per_level) + return grids + + + def _get_ground_truth(self, grids, shapes_per_level, gt_instances): + ''' + Input: + grids: list of tensors [(hl x wl, 2)]_l + shapes_per_level: list of tuples L x 2: + gt_instances: gt instances + Retuen: + pos_inds: N + labels: N + reg_targets: M x 4 + flattened_hms: M x C or M x 1 + N: number of objects in all images + M: number of pixels from all FPN levels + ''' + + # get positive pixel index + if not self.more_pos: + pos_inds, labels = self._get_label_inds( + gt_instances, shapes_per_level) + else: + pos_inds, labels = None, None + heatmap_channels = self.num_classes + L = len(grids) + num_loc_list = [len(loc) for loc in grids] + strides = torch.cat([ + shapes_per_level.new_ones(num_loc_list[l]) * self.strides[l] \ + for l in range(L)]).float() # M + reg_size_ranges = torch.cat([ + shapes_per_level.new_tensor(self.sizes_of_interest[l]).float().view( + 1, 2).expand(num_loc_list[l], 2) for l in range(L)]) # M x 2 + grids = torch.cat(grids, dim=0) # M x 2 + M = grids.shape[0] + + reg_targets = [] + flattened_hms = [] + for i in range(len(gt_instances)): # images + boxes = gt_instances[i].gt_boxes.tensor # N x 4 + area = gt_instances[i].gt_boxes.area() # N + gt_classes = gt_instances[i].gt_classes # N in [0, self.num_classes] + + N = boxes.shape[0] + if N == 0: + reg_targets.append(grids.new_zeros((M, 4)) - INF) + flattened_hms.append( + grids.new_zeros(( + M, 1 if self.only_proposal else heatmap_channels))) + continue + + l = grids[:, 0].view(M, 1) - boxes[:, 0].view(1, N) # M x N + t = grids[:, 1].view(M, 1) - boxes[:, 1].view(1, N) # M x N + r = boxes[:, 2].view(1, N) - grids[:, 0].view(M, 1) # M x N + b = boxes[:, 3].view(1, N) - grids[:, 1].view(M, 1) # M x N + reg_target = torch.stack([l, t, r, b], dim=2) # M x N x 4 + + centers = ((boxes[:, [0, 1]] + boxes[:, [2, 3]]) / 2) # N x 2 + centers_expanded = centers.view(1, N, 2).expand(M, N, 2) # M x N x 2 + strides_expanded = strides.view(M, 1, 1).expand(M, N, 2) + centers_discret = ((centers_expanded / strides_expanded).int() * \ + strides_expanded).float() + strides_expanded / 2 # M x N x 2 + + is_peak = (((grids.view(M, 1, 2).expand(M, N, 2) - \ + centers_discret) ** 2).sum(dim=2) == 0) # M x N + is_in_boxes = reg_target.min(dim=2)[0] > 0 # M x N + is_center3x3 = self.get_center3x3( + grids, centers, strides) & is_in_boxes # M x N + is_cared_in_the_level = self.assign_reg_fpn( + reg_target, reg_size_ranges) # M x N + reg_mask = is_center3x3 & is_cared_in_the_level # M x N + + dist2 = ((grids.view(M, 1, 2).expand(M, N, 2) - \ + centers_expanded) ** 2).sum(dim=2) # M x N + dist2[is_peak] = 0 + radius2 = self.delta ** 2 * 2 * area # N + radius2 = torch.clamp( + radius2, min=self.min_radius ** 2) + weighted_dist2 = dist2 / radius2.view(1, N).expand(M, N) # M x N + reg_target = self._get_reg_targets( + reg_target, weighted_dist2.clone(), reg_mask, area) # M x 4 + + if self.only_proposal: + flattened_hm = self._create_agn_heatmaps_from_dist( + weighted_dist2.clone()) # M x 1 + else: + flattened_hm = self._create_heatmaps_from_dist( + weighted_dist2.clone(), gt_classes, + channels=heatmap_channels) # M x C + + reg_targets.append(reg_target) + flattened_hms.append(flattened_hm) + + # transpose im first training_targets to level first ones + reg_targets = _transpose(reg_targets, num_loc_list) + flattened_hms = _transpose(flattened_hms, num_loc_list) + for l in range(len(reg_targets)): + reg_targets[l] = reg_targets[l] / float(self.strides[l]) + reg_targets = cat([x for x in reg_targets], dim=0) # MB x 4 + flattened_hms = cat([x for x in flattened_hms], dim=0) # MB x C + + return pos_inds, labels, reg_targets, flattened_hms + + + def _get_label_inds(self, gt_instances, shapes_per_level): + ''' + Inputs: + gt_instances: [n_i], sum n_i = N + shapes_per_level: L x 2 [(h_l, w_l)]_L + Returns: + pos_inds: N' + labels: N' + ''' + pos_inds = [] + labels = [] + L = len(self.strides) + B = len(gt_instances) + shapes_per_level = shapes_per_level.long() + loc_per_level = (shapes_per_level[:, 0] * shapes_per_level[:, 1]).long() # L + level_bases = [] + s = 0 + for l in range(L): + level_bases.append(s) + s = s + B * loc_per_level[l] + level_bases = shapes_per_level.new_tensor(level_bases).long() # L + strides_default = shapes_per_level.new_tensor(self.strides).float() # L + for im_i in range(B): + targets_per_im = gt_instances[im_i] + bboxes = targets_per_im.gt_boxes.tensor # n x 4 + n = bboxes.shape[0] + centers = ((bboxes[:, [0, 1]] + bboxes[:, [2, 3]]) / 2) # n x 2 + centers = centers.view(n, 1, 2).expand(n, L, 2) + strides = strides_default.view(1, L, 1).expand(n, L, 2) + centers_inds = (centers / strides).long() # n x L x 2 + Ws = shapes_per_level[:, 1].view(1, L).expand(n, L) + pos_ind = level_bases.view(1, L).expand(n, L) + \ + im_i * loc_per_level.view(1, L).expand(n, L) + \ + centers_inds[:, :, 1] * Ws + \ + centers_inds[:, :, 0] # n x L + is_cared_in_the_level = self.assign_fpn_level(bboxes) + pos_ind = pos_ind[is_cared_in_the_level].view(-1) + label = targets_per_im.gt_classes.view( + n, 1).expand(n, L)[is_cared_in_the_level].view(-1) + + pos_inds.append(pos_ind) # n' + labels.append(label) # n' + pos_inds = torch.cat(pos_inds, dim=0).long() + labels = torch.cat(labels, dim=0) + return pos_inds, labels # N, N + + + def assign_fpn_level(self, boxes): + ''' + Inputs: + boxes: n x 4 + size_ranges: L x 2 + Return: + is_cared_in_the_level: n x L + ''' + size_ranges = boxes.new_tensor( + self.sizes_of_interest).view(len(self.sizes_of_interest), 2) # L x 2 + crit = ((boxes[:, 2:] - boxes[:, :2]) **2).sum(dim=1) ** 0.5 / 2 # n + n, L = crit.shape[0], size_ranges.shape[0] + crit = crit.view(n, 1).expand(n, L) + size_ranges_expand = size_ranges.view(1, L, 2).expand(n, L, 2) + is_cared_in_the_level = (crit >= size_ranges_expand[:, :, 0]) & \ + (crit <= size_ranges_expand[:, :, 1]) + return is_cared_in_the_level + + + def assign_reg_fpn(self, reg_targets_per_im, size_ranges): + ''' + TODO (Xingyi): merge it with assign_fpn_level + Inputs: + reg_targets_per_im: M x N x 4 + size_ranges: M x 2 + ''' + crit = ((reg_targets_per_im[:, :, :2] + \ + reg_targets_per_im[:, :, 2:])**2).sum(dim=2) ** 0.5 / 2 # M x N + is_cared_in_the_level = (crit >= size_ranges[:, [0]]) & \ + (crit <= size_ranges[:, [1]]) + return is_cared_in_the_level + + + def _get_reg_targets(self, reg_targets, dist, mask, area): + ''' + reg_targets (M x N x 4): long tensor + dist (M x N) + is_*: M x N + ''' + dist[mask == 0] = INF * 1.0 + min_dist, min_inds = dist.min(dim=1) # M + reg_targets_per_im = reg_targets[ + range(len(reg_targets)), min_inds] # M x N x 4 --> M x 4 + reg_targets_per_im[min_dist == INF] = - INF + return reg_targets_per_im + + + def _create_heatmaps_from_dist(self, dist, labels, channels): + ''' + dist: M x N + labels: N + return: + heatmaps: M x C + ''' + heatmaps = dist.new_zeros((dist.shape[0], channels)) + for c in range(channels): + inds = (labels == c) # N + if inds.int().sum() == 0: + continue + heatmaps[:, c] = torch.exp(-dist[:, inds].min(dim=1)[0]) + zeros = heatmaps[:, c] < 1e-4 + heatmaps[zeros, c] = 0 + return heatmaps + + + def _create_agn_heatmaps_from_dist(self, dist): + ''' + TODO (Xingyi): merge it with _create_heatmaps_from_dist + dist: M x N + return: + heatmaps: M x 1 + ''' + heatmaps = dist.new_zeros((dist.shape[0], 1)) + heatmaps[:, 0] = torch.exp(-dist.min(dim=1)[0]) + zeros = heatmaps < 1e-4 + heatmaps[zeros] = 0 + return heatmaps + + + def _flatten_outputs(self, clss, reg_pred, agn_hm_pred): + # Reshape: (N, F, Hl, Wl) -> (N, Hl, Wl, F) -> (sum_l N*Hl*Wl, F) + clss = cat([x.permute(0, 2, 3, 1).reshape(-1, x.shape[1]) \ + for x in clss], dim=0) if clss[0] is not None else None + reg_pred = cat( + [x.permute(0, 2, 3, 1).reshape(-1, 4) for x in reg_pred], dim=0) + agn_hm_pred = cat([x.permute(0, 2, 3, 1).reshape(-1) \ + for x in agn_hm_pred], dim=0) if self.with_agn_hm else None + return clss, reg_pred, agn_hm_pred + + + def get_center3x3(self, locations, centers, strides): + ''' + Inputs: + locations: M x 2 + centers: N x 2 + strides: M + ''' + M, N = locations.shape[0], centers.shape[0] + locations_expanded = locations.view(M, 1, 2).expand(M, N, 2) # M x N x 2 + centers_expanded = centers.view(1, N, 2).expand(M, N, 2) # M x N x 2 + strides_expanded = strides.view(M, 1, 1).expand(M, N, 2) # M x N + centers_discret = ((centers_expanded / strides_expanded).int() * \ + strides_expanded).float() + strides_expanded / 2 # M x N x 2 + dist_x = (locations_expanded[:, :, 0] - centers_discret[:, :, 0]).abs() + dist_y = (locations_expanded[:, :, 1] - centers_discret[:, :, 1]).abs() + return (dist_x <= strides_expanded[:, :, 0]) & \ + (dist_y <= strides_expanded[:, :, 0]) + + + def inference(self, images, clss_per_level, reg_pred_per_level, + agn_hm_pred_per_level, grids): + logits_pred = [x.sigmoid() if x is not None else None \ + for x in clss_per_level] + agn_hm_pred_per_level = [x.sigmoid() if x is not None else None \ + for x in agn_hm_pred_per_level] + + if self.only_proposal: + proposals = self.predict_instances( + grids, agn_hm_pred_per_level, reg_pred_per_level, + images.image_sizes, [None for _ in agn_hm_pred_per_level]) + else: + proposals = self.predict_instances( + grids, logits_pred, reg_pred_per_level, + images.image_sizes, agn_hm_pred_per_level) + if self.as_proposal or self.only_proposal: + for p in range(len(proposals)): + proposals[p].proposal_boxes = proposals[p].get('pred_boxes') + proposals[p].objectness_logits = proposals[p].get('scores') + proposals[p].remove('pred_boxes') + + if self.debug: + debug_test( + [self.denormalizer(x) for x in images], + logits_pred, reg_pred_per_level, + agn_hm_pred_per_level, preds=proposals, + vis_thresh=self.vis_thresh, + debug_show_name=False) + return proposals, {} + + + def predict_instances( + self, grids, logits_pred, reg_pred, image_sizes, agn_hm_pred, + is_proposal=False): + sampled_boxes = [] + for l in range(len(grids)): + sampled_boxes.append(self.predict_single_level( + grids[l], logits_pred[l], reg_pred[l] * self.strides[l], + image_sizes, agn_hm_pred[l], l, is_proposal=is_proposal)) + boxlists = list(zip(*sampled_boxes)) + boxlists = [Instances.cat(boxlist) for boxlist in boxlists] + boxlists = self.nms_and_topK( + boxlists, nms=not self.not_nms) + return boxlists + + + def predict_single_level( + self, grids, heatmap, reg_pred, image_sizes, agn_hm, level, + is_proposal=False): + N, C, H, W = heatmap.shape + # put in the same format as grids + if self.center_nms: + heatmap_nms = nn.functional.max_pool2d( + heatmap, (3, 3), stride=1, padding=1) + heatmap = heatmap * (heatmap_nms == heatmap).float() + heatmap = heatmap.permute(0, 2, 3, 1) # N x H x W x C + heatmap = heatmap.reshape(N, -1, C) # N x HW x C + box_regression = reg_pred.view(N, 4, H, W).permute(0, 2, 3, 1) # N x H x W x 4 + box_regression = box_regression.reshape(N, -1, 4) + + candidate_inds = heatmap > self.score_thresh # 0.05 + pre_nms_top_n = candidate_inds.view(N, -1).sum(1) # N + pre_nms_topk = self.pre_nms_topk_train if self.training else self.pre_nms_topk_test + pre_nms_top_n = pre_nms_top_n.clamp(max=pre_nms_topk) # N + + if agn_hm is not None: + agn_hm = agn_hm.view(N, 1, H, W).permute(0, 2, 3, 1) + agn_hm = agn_hm.reshape(N, -1) + heatmap = heatmap * agn_hm[:, :, None] + + results = [] + for i in range(N): + per_box_cls = heatmap[i] # HW x C + per_candidate_inds = candidate_inds[i] # n + per_box_cls = per_box_cls[per_candidate_inds] # n + + per_candidate_nonzeros = per_candidate_inds.nonzero() # n + per_box_loc = per_candidate_nonzeros[:, 0] # n + per_class = per_candidate_nonzeros[:, 1] # n + + per_box_regression = box_regression[i] # HW x 4 + per_box_regression = per_box_regression[per_box_loc] # n x 4 + per_grids = grids[per_box_loc] # n x 2 + + per_pre_nms_top_n = pre_nms_top_n[i] # 1 + + if per_candidate_inds.sum().item() > per_pre_nms_top_n.item(): + per_box_cls, top_k_indices = \ + per_box_cls.topk(per_pre_nms_top_n, sorted=False) + per_class = per_class[top_k_indices] + per_box_regression = per_box_regression[top_k_indices] + per_grids = per_grids[top_k_indices] + + detections = torch.stack([ + per_grids[:, 0] - per_box_regression[:, 0], + per_grids[:, 1] - per_box_regression[:, 1], + per_grids[:, 0] + per_box_regression[:, 2], + per_grids[:, 1] + per_box_regression[:, 3], + ], dim=1) # n x 4 + + # avoid invalid boxes in RoI heads + detections[:, 2] = torch.max(detections[:, 2], detections[:, 0] + 0.01) + detections[:, 3] = torch.max(detections[:, 3], detections[:, 1] + 0.01) + boxlist = Instances(image_sizes[i]) + boxlist.scores = torch.sqrt(per_box_cls) \ + if self.with_agn_hm else per_box_cls # n + # import pdb; pdb.set_trace() + boxlist.pred_boxes = Boxes(detections) + boxlist.pred_classes = per_class + results.append(boxlist) + return results + + + def nms_and_topK(self, boxlists, nms=True): + num_images = len(boxlists) + results = [] + for i in range(num_images): + nms_thresh = self.nms_thresh_train if self.training else \ + self.nms_thresh_test + result = ml_nms(boxlists[i], nms_thresh) if nms else boxlists[i] + if self.debug: + print('#proposals before nms', len(boxlists[i])) + print('#proposals after nms', len(result)) + num_dets = len(result) + post_nms_topk = self.post_nms_topk_train if self.training else \ + self.post_nms_topk_test + if num_dets > post_nms_topk: + cls_scores = result.scores + image_thresh, _ = torch.kthvalue( + cls_scores.float().cpu(), + num_dets - post_nms_topk + 1 + ) + keep = cls_scores >= image_thresh.item() + keep = torch.nonzero(keep).squeeze(1) + result = result[keep] + if self.debug: + print('#proposals after filter', len(result)) + results.append(result) + return results + + + def _add_more_pos(self, reg_pred, gt_instances, shapes_per_level): + labels, level_masks, c33_inds, c33_masks, c33_regs = \ + self._get_c33_inds(gt_instances, shapes_per_level) + N, L, K = labels.shape[0], len(self.strides), 9 + c33_inds[c33_masks == 0] = 0 + reg_pred_c33 = reg_pred[c33_inds].detach() # N x L x K + invalid_reg = c33_masks == 0 + c33_regs_expand = c33_regs.view(N * L * K, 4).clamp(min=0) + if N > 0: + with torch.no_grad(): + c33_reg_loss = self.iou_loss( + reg_pred_c33.view(N * L * K, 4), + c33_regs_expand, None, + reduction='none').view(N, L, K).detach() # N x L x K + else: + c33_reg_loss = reg_pred_c33.new_zeros((N, L, K)).detach() + c33_reg_loss[invalid_reg] = INF # N x L x K + c33_reg_loss.view(N * L, K)[level_masks.view(N * L), 4] = 0 # real center + c33_reg_loss = c33_reg_loss.view(N, L * K) + if N == 0: + loss_thresh = c33_reg_loss.new_ones((N)).float() + else: + loss_thresh = torch.kthvalue( + c33_reg_loss, self.more_pos_topk, dim=1)[0] # N + loss_thresh[loss_thresh > self.more_pos_thresh] = self.more_pos_thresh # N + new_pos = c33_reg_loss.view(N, L, K) < \ + loss_thresh.view(N, 1, 1).expand(N, L, K) + pos_inds = c33_inds[new_pos].view(-1) # P + labels = labels.view(N, 1, 1).expand(N, L, K)[new_pos].view(-1) + return pos_inds, labels + + + def _get_c33_inds(self, gt_instances, shapes_per_level): + ''' + TODO (Xingyi): The current implementation is ugly. Refactor. + Get the center (and the 3x3 region near center) locations of each objects + Inputs: + gt_instances: [n_i], sum n_i = N + shapes_per_level: L x 2 [(h_l, w_l)]_L + ''' + labels = [] + level_masks = [] + c33_inds = [] + c33_masks = [] + c33_regs = [] + L = len(self.strides) + B = len(gt_instances) + shapes_per_level = shapes_per_level.long() + loc_per_level = (shapes_per_level[:, 0] * shapes_per_level[:, 1]).long() # L + level_bases = [] + s = 0 + for l in range(L): + level_bases.append(s) + s = s + B * loc_per_level[l] + level_bases = shapes_per_level.new_tensor(level_bases).long() # L + strides_default = shapes_per_level.new_tensor(self.strides).float() # L + K = 9 + dx = shapes_per_level.new_tensor([-1, 0, 1, -1, 0, 1, -1, 0, 1]).long() + dy = shapes_per_level.new_tensor([-1, -1, -1, 0, 0, 0, 1, 1, 1]).long() + for im_i in range(B): + targets_per_im = gt_instances[im_i] + bboxes = targets_per_im.gt_boxes.tensor # n x 4 + n = bboxes.shape[0] + if n == 0: + continue + centers = ((bboxes[:, [0, 1]] + bboxes[:, [2, 3]]) / 2) # n x 2 + centers = centers.view(n, 1, 2).expand(n, L, 2) + + strides = strides_default.view(1, L, 1).expand(n, L, 2) # + centers_inds = (centers / strides).long() # n x L x 2 + center_grids = centers_inds * strides + strides // 2# n x L x 2 + l = center_grids[:, :, 0] - bboxes[:, 0].view(n, 1).expand(n, L) + t = center_grids[:, :, 1] - bboxes[:, 1].view(n, 1).expand(n, L) + r = bboxes[:, 2].view(n, 1).expand(n, L) - center_grids[:, :, 0] + b = bboxes[:, 3].view(n, 1).expand(n, L) - center_grids[:, :, 1] # n x L + reg = torch.stack([l, t, r, b], dim=2) # n x L x 4 + reg = reg / strides_default.view(1, L, 1).expand(n, L, 4).float() + + Ws = shapes_per_level[:, 1].view(1, L).expand(n, L) + Hs = shapes_per_level[:, 0].view(1, L).expand(n, L) + expand_Ws = Ws.view(n, L, 1).expand(n, L, K) + expand_Hs = Hs.view(n, L, 1).expand(n, L, K) + label = targets_per_im.gt_classes.view(n).clone() + mask = reg.min(dim=2)[0] >= 0 # n x L + mask = mask & self.assign_fpn_level(bboxes) + labels.append(label) # n + level_masks.append(mask) # n x L + + Dy = dy.view(1, 1, K).expand(n, L, K) + Dx = dx.view(1, 1, K).expand(n, L, K) + c33_ind = level_bases.view(1, L, 1).expand(n, L, K) + \ + im_i * loc_per_level.view(1, L, 1).expand(n, L, K) + \ + (centers_inds[:, :, 1:2].expand(n, L, K) + Dy) * expand_Ws + \ + (centers_inds[:, :, 0:1].expand(n, L, K) + Dx) # n x L x K + + c33_mask = \ + ((centers_inds[:, :, 1:2].expand(n, L, K) + dy) < expand_Hs) & \ + ((centers_inds[:, :, 1:2].expand(n, L, K) + dy) >= 0) & \ + ((centers_inds[:, :, 0:1].expand(n, L, K) + dx) < expand_Ws) & \ + ((centers_inds[:, :, 0:1].expand(n, L, K) + dx) >= 0) + # TODO (Xingyi): think about better way to implement this + # Currently it hard codes the 3x3 region + c33_reg = reg.view(n, L, 1, 4).expand(n, L, K, 4).clone() + c33_reg[:, :, [0, 3, 6], 0] -= 1 + c33_reg[:, :, [0, 3, 6], 2] += 1 + c33_reg[:, :, [2, 5, 8], 0] += 1 + c33_reg[:, :, [2, 5, 8], 2] -= 1 + c33_reg[:, :, [0, 1, 2], 1] -= 1 + c33_reg[:, :, [0, 1, 2], 3] += 1 + c33_reg[:, :, [6, 7, 8], 1] += 1 + c33_reg[:, :, [6, 7, 8], 3] -= 1 + c33_mask = c33_mask & (c33_reg.min(dim=3)[0] >= 0) # n x L x K + c33_inds.append(c33_ind) + c33_masks.append(c33_mask) + c33_regs.append(c33_reg) + + if len(level_masks) > 0: + labels = torch.cat(labels, dim=0) + level_masks = torch.cat(level_masks, dim=0) + c33_inds = torch.cat(c33_inds, dim=0).long() + c33_regs = torch.cat(c33_regs, dim=0) + c33_masks = torch.cat(c33_masks, dim=0) + else: + labels = shapes_per_level.new_zeros((0)).long() + level_masks = shapes_per_level.new_zeros((0, L)).bool() + c33_inds = shapes_per_level.new_zeros((0, L, K)).long() + c33_regs = shapes_per_level.new_zeros((0, L, K, 4)).float() + c33_masks = shapes_per_level.new_zeros((0, L, K)).bool() + return labels, level_masks, c33_inds, c33_masks, c33_regs # N x L, N x L x K \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet_head.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet_head.py new file mode 100644 index 0000000000000000000000000000000000000000..57e0960a57c904c097b6a717391474a4a635dd7d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet_head.py @@ -0,0 +1,162 @@ +import math +from typing import List +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.layers import ShapeSpec, get_norm +from detectron2.config import configurable +from ..layers.deform_conv import DFConv2d + +__all__ = ["CenterNetHead"] + +class Scale(nn.Module): + def __init__(self, init_value=1.0): + super(Scale, self).__init__() + self.scale = nn.Parameter(torch.FloatTensor([init_value])) + + def forward(self, input): + return input * self.scale + +class CenterNetHead(nn.Module): + @configurable + def __init__(self, + # input_shape: List[ShapeSpec], + in_channels, + num_levels, + *, + num_classes=80, + with_agn_hm=False, + only_proposal=False, + norm='GN', + num_cls_convs=4, + num_box_convs=4, + num_share_convs=0, + use_deformable=False, + prior_prob=0.01): + super().__init__() + self.num_classes = num_classes + self.with_agn_hm = with_agn_hm + self.only_proposal = only_proposal + self.out_kernel = 3 + + head_configs = { + "cls": (num_cls_convs if not self.only_proposal else 0, \ + use_deformable), + "bbox": (num_box_convs, use_deformable), + "share": (num_share_convs, use_deformable)} + + # in_channels = [s.channels for s in input_shape] + # assert len(set(in_channels)) == 1, \ + # "Each level must have the same channel!" + # in_channels = in_channels[0] + channels = { + 'cls': in_channels, + 'bbox': in_channels, + 'share': in_channels, + } + for head in head_configs: + tower = [] + num_convs, use_deformable = head_configs[head] + channel = channels[head] + for i in range(num_convs): + if use_deformable and i == num_convs - 1: + conv_func = DFConv2d + else: + conv_func = nn.Conv2d + tower.append(conv_func( + in_channels if i == 0 else channel, + channel, + kernel_size=3, stride=1, + padding=1, bias=True + )) + if norm == 'GN' and channel % 32 != 0: + tower.append(nn.GroupNorm(25, channel)) + elif norm != '': + tower.append(get_norm(norm, channel)) + tower.append(nn.ReLU()) + self.add_module('{}_tower'.format(head), + nn.Sequential(*tower)) + + self.bbox_pred = nn.Conv2d( + in_channels, 4, kernel_size=self.out_kernel, + stride=1, padding=self.out_kernel // 2 + ) + + self.scales = nn.ModuleList( + [Scale(init_value=1.0) for _ in range(num_levels)]) + + for modules in [ + self.cls_tower, self.bbox_tower, + self.share_tower, + self.bbox_pred, + ]: + for l in modules.modules(): + if isinstance(l, nn.Conv2d): + torch.nn.init.normal_(l.weight, std=0.01) + torch.nn.init.constant_(l.bias, 0) + + torch.nn.init.constant_(self.bbox_pred.bias, 8.) + prior_prob = prior_prob + bias_value = -math.log((1 - prior_prob) / prior_prob) + + if self.with_agn_hm: + self.agn_hm = nn.Conv2d( + in_channels, 1, kernel_size=self.out_kernel, + stride=1, padding=self.out_kernel // 2 + ) + torch.nn.init.constant_(self.agn_hm.bias, bias_value) + torch.nn.init.normal_(self.agn_hm.weight, std=0.01) + + if not self.only_proposal: + cls_kernel_size = self.out_kernel + self.cls_logits = nn.Conv2d( + in_channels, self.num_classes, + kernel_size=cls_kernel_size, + stride=1, + padding=cls_kernel_size // 2, + ) + + torch.nn.init.constant_(self.cls_logits.bias, bias_value) + torch.nn.init.normal_(self.cls_logits.weight, std=0.01) + + @classmethod + def from_config(cls, cfg, input_shape): + ret = { + # 'input_shape': input_shape, + 'in_channels': [s.channels for s in input_shape][0], + 'num_levels': len(input_shape), + 'num_classes': cfg.MODEL.CENTERNET.NUM_CLASSES, + 'with_agn_hm': cfg.MODEL.CENTERNET.WITH_AGN_HM, + 'only_proposal': cfg.MODEL.CENTERNET.ONLY_PROPOSAL, + 'norm': cfg.MODEL.CENTERNET.NORM, + 'num_cls_convs': cfg.MODEL.CENTERNET.NUM_CLS_CONVS, + 'num_box_convs': cfg.MODEL.CENTERNET.NUM_BOX_CONVS, + 'num_share_convs': cfg.MODEL.CENTERNET.NUM_SHARE_CONVS, + 'use_deformable': cfg.MODEL.CENTERNET.USE_DEFORMABLE, + 'prior_prob': cfg.MODEL.CENTERNET.PRIOR_PROB, + } + return ret + + def forward(self, x): + clss = [] + bbox_reg = [] + agn_hms = [] + for l, feature in enumerate(x): + feature = self.share_tower(feature) + cls_tower = self.cls_tower(feature) + bbox_tower = self.bbox_tower(feature) + if not self.only_proposal: + clss.append(self.cls_logits(cls_tower)) + else: + clss.append(None) + + if self.with_agn_hm: + agn_hms.append(self.agn_hm(bbox_tower)) + else: + agn_hms.append(None) + reg = self.bbox_pred(bbox_tower) + reg = self.scales[l](reg) + bbox_reg.append(F.relu(reg)) + + return clss, bbox_reg, agn_hms \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/utils.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c9efa287fc71315f633347023b390fe4ce57913a --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/utils.py @@ -0,0 +1,38 @@ +import cv2 +import torch +from torch import nn +from detectron2.utils.comm import get_world_size +from detectron2.structures import pairwise_iou, Boxes +# from .data import CenterNetCrop +import torch.nn.functional as F +import numpy as np +from detectron2.structures import Boxes, ImageList, Instances + +__all__ = ['reduce_sum', '_transpose'] + +INF = 1000000000 + +def _transpose(training_targets, num_loc_list): + ''' + This function is used to transpose image first training targets to + level first ones + :return: level first training targets + ''' + for im_i in range(len(training_targets)): + training_targets[im_i] = torch.split( + training_targets[im_i], num_loc_list, dim=0) + + targets_level_first = [] + for targets_per_level in zip(*training_targets): + targets_level_first.append( + torch.cat(targets_per_level, dim=0)) + return targets_level_first + + +def reduce_sum(tensor): + world_size = get_world_size() + if world_size < 2: + return tensor + tensor = tensor.clone() + torch.distributed.all_reduce(tensor, op=torch.distributed.ReduceOp.SUM) + return tensor \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/layers/deform_conv.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/layers/deform_conv.py new file mode 100644 index 0000000000000000000000000000000000000000..e5650c40673882c9164ddc56fd3ee63af0be730c --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/layers/deform_conv.py @@ -0,0 +1,116 @@ +import torch +from torch import nn + +from detectron2.layers import Conv2d + + +class _NewEmptyTensorOp(torch.autograd.Function): + @staticmethod + def forward(ctx, x, new_shape): + ctx.shape = x.shape + return x.new_empty(new_shape) + + @staticmethod + def backward(ctx, grad): + shape = ctx.shape + return _NewEmptyTensorOp.apply(grad, shape), None + + +class DFConv2d(nn.Module): + """Deformable convolutional layer""" + def __init__( + self, + in_channels, + out_channels, + with_modulated_dcn=True, + kernel_size=3, + stride=1, + groups=1, + dilation=1, + deformable_groups=1, + bias=False, + padding=None + ): + super(DFConv2d, self).__init__() + if isinstance(kernel_size, (list, tuple)): + assert isinstance(stride, (list, tuple)) + assert isinstance(dilation, (list, tuple)) + assert len(kernel_size) == 2 + assert len(stride) == 2 + assert len(dilation) == 2 + padding = ( + dilation[0] * (kernel_size[0] - 1) // 2, + dilation[1] * (kernel_size[1] - 1) // 2 + ) + offset_base_channels = kernel_size[0] * kernel_size[1] + else: + padding = dilation * (kernel_size - 1) // 2 + offset_base_channels = kernel_size * kernel_size + if with_modulated_dcn: + from detectron2.layers.deform_conv import ModulatedDeformConv + offset_channels = offset_base_channels * 3 # default: 27 + conv_block = ModulatedDeformConv + else: + from detectron2.layers.deform_conv import DeformConv + offset_channels = offset_base_channels * 2 # default: 18 + conv_block = DeformConv + self.offset = Conv2d( + in_channels, + deformable_groups * offset_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + groups=1, + dilation=dilation + ) + nn.init.constant_(self.offset.weight, 0) + nn.init.constant_(self.offset.bias, 0) + ''' + for l in [self.offset, ]: + nn.init.kaiming_uniform_(l.weight, a=1) + torch.nn.init.constant_(l.bias, 0.) + ''' + self.conv = conv_block( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + deformable_groups=deformable_groups, + bias=bias + ) + self.with_modulated_dcn = with_modulated_dcn + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.dilation = dilation + self.offset_split = offset_base_channels * deformable_groups * 2 + + def forward(self, x, return_offset=False): + if x.numel() > 0: + if not self.with_modulated_dcn: + offset_mask = self.offset(x) + x = self.conv(x, offset_mask) + else: + offset_mask = self.offset(x) + offset = offset_mask[:, :self.offset_split, :, :] + mask = offset_mask[:, self.offset_split:, :, :].sigmoid() + x = self.conv(x, offset, mask) + if return_offset: + return x, offset_mask + return x + # get output shape + output_shape = [ + (i + 2 * p - (di * (k - 1) + 1)) // d + 1 + for i, p, di, k, d in zip( + x.shape[-2:], + self.padding, + self.dilation, + self.kernel_size, + self.stride + ) + ] + output_shape = [x.shape[0], self.conv.weight.shape[0]] + output_shape + return _NewEmptyTensorOp.apply(x, output_shape) \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/layers/heatmap_focal_loss.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/layers/heatmap_focal_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..d4693b2125217527033727ec9a82959286d180f9 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/layers/heatmap_focal_loss.py @@ -0,0 +1,92 @@ +import torch +from torch.nn import functional as F + +# TODO: merge these two function +def heatmap_focal_loss( + inputs, + targets, + pos_inds, + labels, + alpha: float = -1, + beta: float = 4, + gamma: float = 2, + reduction: str = 'sum', + sigmoid_clamp: float = 1e-4, + ignore_high_fp: float = -1., +): + """ + Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. + Args: + inputs: (sum_l N*Hl*Wl, C) + targets: (sum_l N*Hl*Wl, C) + pos_inds: N + labels: N + Returns: + Loss tensor with the reduction option applied. + """ + pred = torch.clamp(inputs.sigmoid_(), min=sigmoid_clamp, max=1-sigmoid_clamp) + neg_weights = torch.pow(1 - targets, beta) + pos_pred_pix = pred[pos_inds] # N x C + pos_pred = pos_pred_pix.gather(1, labels.unsqueeze(1)) + pos_loss = torch.log(pos_pred) * torch.pow(1 - pos_pred, gamma) + neg_loss = torch.log(1 - pred) * torch.pow(pred, gamma) * neg_weights + + if ignore_high_fp > 0: + not_high_fp = (pred < ignore_high_fp).float() + neg_loss = not_high_fp * neg_loss + + if reduction == "sum": + pos_loss = pos_loss.sum() + neg_loss = neg_loss.sum() + + if alpha >= 0: + pos_loss = alpha * pos_loss + neg_loss = (1 - alpha) * neg_loss + + return - pos_loss, - neg_loss + +heatmap_focal_loss_jit = torch.jit.script(heatmap_focal_loss) +# heatmap_focal_loss_jit = heatmap_focal_loss + +def binary_heatmap_focal_loss( + inputs, + targets, + pos_inds, + alpha: float = -1, + beta: float = 4, + gamma: float = 2, + sigmoid_clamp: float = 1e-4, + ignore_high_fp: float = -1., +): + """ + Args: + inputs: (sum_l N*Hl*Wl,) + targets: (sum_l N*Hl*Wl,) + pos_inds: N + Returns: + Loss tensor with the reduction option applied. + """ + pred = torch.clamp(inputs.sigmoid_(), min=sigmoid_clamp, max=1-sigmoid_clamp) + neg_weights = torch.pow(1 - targets, beta) + for i, ind in enumerate(pos_inds): + if ind >= pred.shape[0]: + print('%'*100) + print(pred.shape, ind, pos_inds) + pos_inds[i] = pred.shape[0] - 1 + pos_pred = pred[pos_inds] # N + pos_loss = torch.log(pos_pred) * torch.pow(1 - pos_pred, gamma) + neg_loss = torch.log(1 - pred) * torch.pow(pred, gamma) * neg_weights + if ignore_high_fp > 0: + not_high_fp = (pred < ignore_high_fp).float() + neg_loss = not_high_fp * neg_loss + + pos_loss = - pos_loss.sum() + neg_loss = - neg_loss.sum() + + if alpha >= 0: + pos_loss = alpha * pos_loss + neg_loss = (1 - alpha) * neg_loss + + return pos_loss, neg_loss + +# binary_heatmap_focal_loss_jit = torch.jit.script(binary_heatmap_focal_loss) \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/layers/iou_loss.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/layers/iou_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..6a02464651dc1a0dcec9f30285a3a4ef74209f89 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/layers/iou_loss.py @@ -0,0 +1,121 @@ +import torch +from torch import nn + + +class IOULoss(nn.Module): + def __init__(self, loc_loss_type='iou'): + super(IOULoss, self).__init__() + self.loc_loss_type = loc_loss_type + + def forward(self, pred, target, weight=None, reduction='sum'): + pred_left = pred[:, 0] + pred_top = pred[:, 1] + pred_right = pred[:, 2] + pred_bottom = pred[:, 3] + + target_left = target[:, 0] + target_top = target[:, 1] + target_right = target[:, 2] + target_bottom = target[:, 3] + + target_aera = (target_left + target_right) * \ + (target_top + target_bottom) + pred_aera = (pred_left + pred_right) * \ + (pred_top + pred_bottom) + + w_intersect = torch.min(pred_left, target_left) + \ + torch.min(pred_right, target_right) + h_intersect = torch.min(pred_bottom, target_bottom) + \ + torch.min(pred_top, target_top) + + g_w_intersect = torch.max(pred_left, target_left) + \ + torch.max(pred_right, target_right) + g_h_intersect = torch.max(pred_bottom, target_bottom) + \ + torch.max(pred_top, target_top) + ac_uion = g_w_intersect * g_h_intersect + + area_intersect = w_intersect * h_intersect + area_union = target_aera + pred_aera - area_intersect + + ious = (area_intersect + 1.0) / (area_union + 1.0) + gious = ious - (ac_uion - area_union) / ac_uion + if self.loc_loss_type == 'iou': + losses = -torch.log(ious) + elif self.loc_loss_type == 'linear_iou': + losses = 1 - ious + elif self.loc_loss_type == 'giou': + losses = 1 - gious + else: + raise NotImplementedError + + if weight is not None: + losses = losses * weight + else: + losses = losses + + if reduction == 'sum': + return losses.sum() + elif reduction == 'batch': + return losses.sum(dim=[1]) + elif reduction == 'none': + return losses + else: + raise NotImplementedError + + +def giou_loss( + boxes1: torch.Tensor, + boxes2: torch.Tensor, + reduction: str = "none", + eps: float = 1e-7, +) -> torch.Tensor: + """ + Generalized Intersection over Union Loss (Hamid Rezatofighi et. al) + https://arxiv.org/abs/1902.09630 + Gradient-friendly IoU loss with an additional penalty that is non-zero when the + boxes do not overlap and scales with the size of their smallest enclosing box. + This loss is symmetric, so the boxes1 and boxes2 arguments are interchangeable. + Args: + boxes1, boxes2 (Tensor): box locations in XYXY format, shape (N, 4) or (4,). + reduction: 'none' | 'mean' | 'sum' + 'none': No reduction will be applied to the output. + 'mean': The output will be averaged. + 'sum': The output will be summed. + eps (float): small number to prevent division by zero + """ + + x1, y1, x2, y2 = boxes1.unbind(dim=-1) + x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1) + + assert (x2 >= x1).all(), "bad box: x1 larger than x2" + assert (y2 >= y1).all(), "bad box: y1 larger than y2" + + # Intersection keypoints + xkis1 = torch.max(x1, x1g) + ykis1 = torch.max(y1, y1g) + xkis2 = torch.min(x2, x2g) + ykis2 = torch.min(y2, y2g) + + intsctk = torch.zeros_like(x1) + mask = (ykis2 > ykis1) & (xkis2 > xkis1) + intsctk[mask] = (xkis2[mask] - xkis1[mask]) * (ykis2[mask] - ykis1[mask]) + unionk = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g) - intsctk + iouk = intsctk / (unionk + eps) + + # smallest enclosing box + xc1 = torch.min(x1, x1g) + yc1 = torch.min(y1, y1g) + xc2 = torch.max(x2, x2g) + yc2 = torch.max(y2, y2g) + + area_c = (xc2 - xc1) * (yc2 - yc1) + miouk = iouk - ((area_c - unionk) / (area_c + eps)) + + loss = 1 - miouk + + if reduction == "mean": + loss = loss.mean() + elif reduction == "sum": + loss = loss.sum() + + return loss \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/layers/ml_nms.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/layers/ml_nms.py new file mode 100644 index 0000000000000000000000000000000000000000..325d709a98422d8a355fc7c7e281179642850968 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/layers/ml_nms.py @@ -0,0 +1,31 @@ +from detectron2.layers import batched_nms + + +def ml_nms(boxlist, nms_thresh, max_proposals=-1, + score_field="scores", label_field="labels"): + """ + Performs non-maximum suppression on a boxlist, with scores specified + in a boxlist field via score_field. + Arguments: + boxlist(BoxList) + nms_thresh (float) + max_proposals (int): if > 0, then only the top max_proposals are kept + after non-maximum suppression + score_field (str) + """ + if nms_thresh <= 0: + return boxlist + if boxlist.has('pred_boxes'): + boxes = boxlist.pred_boxes.tensor + labels = boxlist.pred_classes + else: + boxes = boxlist.proposal_boxes.tensor + labels = boxlist.proposal_boxes.tensor.new_zeros( + len(boxlist.proposal_boxes.tensor)) + scores = boxlist.scores + + keep = batched_nms(boxes, scores, labels, nms_thresh) + if max_proposals > 0: + keep = keep[: max_proposals] + boxlist = boxlist[keep] + return boxlist diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/meta_arch/centernet_detector.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/meta_arch/centernet_detector.py new file mode 100644 index 0000000000000000000000000000000000000000..b7525c7b31cbbca504442e9a0dc8fb5005ea91b3 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/meta_arch/centernet_detector.py @@ -0,0 +1,69 @@ +import math +import json +import numpy as np +import torch +from torch import nn + +from detectron2.modeling.meta_arch.build import META_ARCH_REGISTRY +from detectron2.modeling import build_backbone, build_proposal_generator +from detectron2.modeling import detector_postprocess +from detectron2.structures import ImageList + +@META_ARCH_REGISTRY.register() +class CenterNetDetector(nn.Module): + def __init__(self, cfg): + super().__init__() + self.mean, self.std = cfg.MODEL.PIXEL_MEAN, cfg.MODEL.PIXEL_STD + self.register_buffer("pixel_mean", torch.Tensor(cfg.MODEL.PIXEL_MEAN).view(-1, 1, 1)) + self.register_buffer("pixel_std", torch.Tensor(cfg.MODEL.PIXEL_STD).view(-1, 1, 1)) + + self.backbone = build_backbone(cfg) + self.proposal_generator = build_proposal_generator( + cfg, self.backbone.output_shape()) # TODO: change to a more precise name + + + def forward(self, batched_inputs): + if not self.training: + return self.inference(batched_inputs) + images = self.preprocess_image(batched_inputs) + features = self.backbone(images.tensor) + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + + _, proposal_losses = self.proposal_generator( + images, features, gt_instances) + return proposal_losses + + + @property + def device(self): + return self.pixel_mean.device + + + @torch.no_grad() + def inference(self, batched_inputs, do_postprocess=True): + images = self.preprocess_image(batched_inputs) + inp = images.tensor + features = self.backbone(inp) + proposals, _ = self.proposal_generator(images, features, None) + + processed_results = [] + for results_per_image, input_per_image, image_size in zip( + proposals, batched_inputs, images.image_sizes): + if do_postprocess: + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + r = detector_postprocess(results_per_image, height, width) + processed_results.append({"instances": r}) + else: + r = results_per_image + processed_results.append(r) + return processed_results + + def preprocess_image(self, batched_inputs): + """ + Normalize, pad and batch the input images. + """ + images = [x["image"].to(self.device) for x in batched_inputs] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors(images, self.backbone.size_divisibility) + return images diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/roi_heads/custom_fast_rcnn.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/roi_heads/custom_fast_rcnn.py new file mode 100644 index 0000000000000000000000000000000000000000..b6d95690c381798d6af54087f050105791e94fe3 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/roi_heads/custom_fast_rcnn.py @@ -0,0 +1,124 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# Part of the code is from https://github.com/tztztztztz/eql.detectron2/blob/master/projects/EQL/eql/fast_rcnn.py +import logging +import math +import json +from typing import Dict, Union +import torch +from fvcore.nn import giou_loss, smooth_l1_loss +from torch import nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import Linear, ShapeSpec, batched_nms, cat, nonzero_tuple +from detectron2.modeling.box_regression import Box2BoxTransform +from detectron2.structures import Boxes, Instances +from detectron2.utils.events import get_event_storage +from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers +from detectron2.modeling.roi_heads.fast_rcnn import fast_rcnn_inference +from detectron2.modeling.roi_heads.fast_rcnn import _log_classification_stats +from detectron2.utils.comm import get_world_size +from .fed_loss import load_class_freq, get_fed_loss_inds + +__all__ = ["CustomFastRCNNOutputLayers"] + +class CustomFastRCNNOutputLayers(FastRCNNOutputLayers): + def __init__( + self, + cfg, + input_shape: ShapeSpec, + **kwargs + ): + super().__init__(cfg, input_shape, **kwargs) + + self.cfg = cfg + + def losses(self, predictions, proposals): + """ + enable advanced loss + """ + scores, proposal_deltas = predictions + gt_classes = ( + cat([p.gt_classes for p in proposals], dim=0) if len(proposals) else torch.empty(0) + ) + num_classes = self.num_classes + _log_classification_stats(scores, gt_classes) + + if len(proposals): + proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0) # Nx4 + assert not proposal_boxes.requires_grad, "Proposals should not require gradients!" + gt_boxes = cat( + [(p.gt_boxes if p.has("gt_boxes") else p.proposal_boxes).tensor for p in proposals], + dim=0, + ) + else: + proposal_boxes = gt_boxes = torch.empty((0, 4), device=proposal_deltas.device) + + loss_cls = self.softmax_cross_entropy_loss(scores, gt_classes) + return { + "loss_cls": loss_cls, + "loss_box_reg": self.box_reg_loss( + proposal_boxes, gt_boxes, proposal_deltas, gt_classes) + } + + + def sigmoid_cross_entropy_loss(self, pred_class_logits, gt_classes): + if pred_class_logits.numel() == 0: + return pred_class_logits.new_zeros([1])[0] # This is more robust than .sum() * 0. + + B = pred_class_logits.shape[0] + C = pred_class_logits.shape[1] - 1 + + target = pred_class_logits.new_zeros(B, C + 1) + target[range(len(gt_classes)), gt_classes] = 1 # B x (C + 1) + target = target[:, :C] # B x C + + weight = 1 + + cls_loss = F.binary_cross_entropy_with_logits( + pred_class_logits[:, :-1], target, reduction='none') # B x C + loss = torch.sum(cls_loss * weight) / B + return loss + + + def softmax_cross_entropy_loss(self, pred_class_logits, gt_classes): + """ + change _no_instance handling + """ + if pred_class_logits.numel() == 0: + return pred_class_logits.new_zeros([1])[0] + + loss = F.cross_entropy( + pred_class_logits, gt_classes, reduction="mean") + return loss + + + def inference(self, predictions, proposals): + """ + enable use proposal boxes + """ + boxes = self.predict_boxes(predictions, proposals) + scores = self.predict_probs(predictions, proposals) + if self.cfg.MODEL.ROI_BOX_HEAD.MULT_PROPOSAL_SCORE: + proposal_scores = [p.get('objectness_logits') for p in proposals] + scores = [(s * ps[:, None]) ** 0.5 \ + for s, ps in zip(scores, proposal_scores)] + image_shapes = [x.image_size for x in proposals] + return fast_rcnn_inference( + boxes, + scores, + image_shapes, + self.test_score_thresh, + self.test_nms_thresh, + self.test_topk_per_image, + ) + + + def predict_probs(self, predictions, proposals): + """ + support sigmoid + """ + scores, _ = predictions + num_inst_per_image = [len(p) for p in proposals] + probs = F.softmax(scores, dim=-1) + return probs.split(num_inst_per_image, dim=0) diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/roi_heads/custom_roi_heads.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/roi_heads/custom_roi_heads.py new file mode 100644 index 0000000000000000000000000000000000000000..90fadf1a9667cf836223945b22c5147b89ad98a4 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/roi_heads/custom_roi_heads.py @@ -0,0 +1,185 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import numpy as np +import json +import math +import torch +from torch import nn +from torch.autograd.function import Function +from typing import Dict, List, Optional, Tuple, Union + +from detectron2.layers import ShapeSpec +from detectron2.structures import Boxes, Instances, pairwise_iou +from detectron2.utils.events import get_event_storage + +from detectron2.modeling.box_regression import Box2BoxTransform +from detectron2.modeling.roi_heads.fast_rcnn import fast_rcnn_inference +from detectron2.modeling.roi_heads.roi_heads import ROI_HEADS_REGISTRY, StandardROIHeads +from detectron2.modeling.roi_heads.cascade_rcnn import CascadeROIHeads +from detectron2.modeling.roi_heads.box_head import build_box_head +from .custom_fast_rcnn import CustomFastRCNNOutputLayers + + +@ROI_HEADS_REGISTRY.register() +class CustomROIHeads(StandardROIHeads): + @classmethod + def _init_box_head(self, cfg, input_shape): + ret = super()._init_box_head(cfg, input_shape) + del ret['box_predictor'] + ret['box_predictor'] = CustomFastRCNNOutputLayers( + cfg, ret['box_head'].output_shape) + self.debug = cfg.DEBUG + if self.debug: + self.debug_show_name = cfg.DEBUG_SHOW_NAME + self.save_debug = cfg.SAVE_DEBUG + self.vis_thresh = cfg.VIS_THRESH + self.pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to( + torch.device(cfg.MODEL.DEVICE)).view(3, 1, 1) + self.pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to( + torch.device(cfg.MODEL.DEVICE)).view(3, 1, 1) + return ret + + def forward(self, images, features, proposals, targets=None): + """ + enable debug + """ + if not self.debug: + del images + if self.training: + assert targets + proposals = self.label_and_sample_proposals(proposals, targets) + del targets + + if self.training: + losses = self._forward_box(features, proposals) + losses.update(self._forward_mask(features, proposals)) + losses.update(self._forward_keypoint(features, proposals)) + return proposals, losses + else: + pred_instances = self._forward_box(features, proposals) + pred_instances = self.forward_with_given_boxes(features, pred_instances) + if self.debug: + from ..debug import debug_second_stage + denormalizer = lambda x: x * self.pixel_std + self.pixel_mean + debug_second_stage( + [denormalizer(images[0].clone())], + pred_instances, proposals=proposals, + debug_show_name=self.debug_show_name) + return pred_instances, {} + + +@ROI_HEADS_REGISTRY.register() +class CustomCascadeROIHeads(CascadeROIHeads): + @classmethod + def _init_box_head(self, cfg, input_shape): + self.mult_proposal_score = cfg.MODEL.ROI_BOX_HEAD.MULT_PROPOSAL_SCORE + ret = super()._init_box_head(cfg, input_shape) + del ret['box_predictors'] + cascade_bbox_reg_weights = cfg.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS + box_predictors = [] + for box_head, bbox_reg_weights in zip(ret['box_heads'], cascade_bbox_reg_weights): + box_predictors.append( + CustomFastRCNNOutputLayers( + cfg, box_head.output_shape, + box2box_transform=Box2BoxTransform(weights=bbox_reg_weights) + )) + ret['box_predictors'] = box_predictors + self.debug = cfg.DEBUG + if self.debug: + self.debug_show_name = cfg.DEBUG_SHOW_NAME + self.save_debug = cfg.SAVE_DEBUG + self.vis_thresh = cfg.VIS_THRESH + self.pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to( + torch.device(cfg.MODEL.DEVICE)).view(3, 1, 1) + self.pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to( + torch.device(cfg.MODEL.DEVICE)).view(3, 1, 1) + return ret + + + def _forward_box(self, features, proposals, targets=None): + """ + Add mult proposal scores at testing + """ + if (not self.training) and self.mult_proposal_score: + if len(proposals) > 0 and proposals[0].has('scores'): + proposal_scores = [ + p.get('scores') for p in proposals] + else: + proposal_scores = [ + p.get('objectness_logits') for p in proposals] + + features = [features[f] for f in self.box_in_features] + head_outputs = [] # (predictor, predictions, proposals) + prev_pred_boxes = None + image_sizes = [x.image_size for x in proposals] + for k in range(self.num_cascade_stages): + if k > 0: + proposals = self._create_proposals_from_boxes(prev_pred_boxes, image_sizes) + if self.training: + proposals = self._match_and_label_boxes(proposals, k, targets) + predictions = self._run_stage(features, proposals, k) + prev_pred_boxes = self.box_predictor[k].predict_boxes(predictions, proposals) + head_outputs.append((self.box_predictor[k], predictions, proposals)) + + if self.training: + losses = {} + storage = get_event_storage() + for stage, (predictor, predictions, proposals) in enumerate(head_outputs): + with storage.name_scope("stage{}".format(stage)): + stage_losses = predictor.losses(predictions, proposals) + losses.update({k + "_stage{}".format(stage): v for k, v in stage_losses.items()}) + return losses + else: + # Each is a list[Tensor] of length #image. Each tensor is Ri x (K+1) + scores_per_stage = [h[0].predict_probs(h[1], h[2]) for h in head_outputs] + scores = [ + sum(list(scores_per_image)) * (1.0 / self.num_cascade_stages) + for scores_per_image in zip(*scores_per_stage) + ] + + if self.mult_proposal_score: + scores = [(s * ps[:, None]) ** 0.5 \ + for s, ps in zip(scores, proposal_scores)] + + predictor, predictions, proposals = head_outputs[-1] + boxes = predictor.predict_boxes(predictions, proposals) + pred_instances, _ = fast_rcnn_inference( + boxes, + scores, + image_sizes, + predictor.test_score_thresh, + predictor.test_nms_thresh, + predictor.test_topk_per_image, + ) + + return pred_instances + + def forward(self, images, features, proposals, targets=None): + ''' + enable debug + ''' + if not self.debug: + del images + if self.training: + proposals = self.label_and_sample_proposals(proposals, targets) + + if self.training: + losses = self._forward_box(features, proposals, targets) + losses.update(self._forward_mask(features, proposals)) + losses.update(self._forward_keypoint(features, proposals)) + return proposals, losses + else: + # import pdb; pdb.set_trace() + pred_instances = self._forward_box(features, proposals) + pred_instances = self.forward_with_given_boxes(features, pred_instances) + if self.debug: + from ..debug import debug_second_stage + denormalizer = lambda x: x * self.pixel_std + self.pixel_mean + debug_second_stage( + [denormalizer(x.clone()) for x in images], + pred_instances, proposals=proposals, + save_debug=self.save_debug, + debug_show_name=self.debug_show_name, + vis_thresh=self.vis_thresh) + return pred_instances, {} + + diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/roi_heads/fed_loss.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/roi_heads/fed_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..290f0f07204e78ef2c4ff918aa500b04330279e6 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/roi_heads/fed_loss.py @@ -0,0 +1,31 @@ +import torch +import json +import numpy as np +from torch.nn import functional as F + +def load_class_freq( + path='datasets/lvis/lvis_v1_train_cat_info.json', + freq_weight=0.5): + cat_info = json.load(open(path, 'r')) + cat_info = torch.tensor( + [c['image_count'] for c in sorted(cat_info, key=lambda x: x['id'])]) + freq_weight = cat_info.float() ** freq_weight + return freq_weight + +def get_fed_loss_inds( + gt_classes, num_sample_cats=50, C=1203, \ + weight=None, fed_cls_inds=-1): + appeared = torch.unique(gt_classes) # C' + prob = appeared.new_ones(C + 1).float() + prob[-1] = 0 + if len(appeared) < num_sample_cats: + if weight is not None: + prob[:C] = weight.float().clone() + prob[appeared] = 0 + if fed_cls_inds > 0: + prob[fed_cls_inds:] = 0 + more_appeared = torch.multinomial( + prob, num_sample_cats - len(appeared), + replacement=False) + appeared = torch.cat([appeared, more_appeared]) + return appeared \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet2_docs/MODEL_ZOO.md b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet2_docs/MODEL_ZOO.md new file mode 100644 index 0000000000000000000000000000000000000000..7a2a92b60d0ebf8f6444f24c3bd74b753c80c57f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet2_docs/MODEL_ZOO.md @@ -0,0 +1,73 @@ +# MODEL_ZOO + +### Common settings and notes + +- Multiscale training is used by default in all models. The results are all reported using single-scale testing. +- We report runtime on our local workstation with a TitanXp GPU and a Titan RTX GPU. +- All models are trained on 8-GPU servers by default. The 1280 models are trained on 24G GPUs. Reducing the batchsize with the linear learning rate rule should be fine. +- All models can be downloaded directly from [Google drive](https://drive.google.com/drive/folders/1eae1cTX8tvIaCeof36sBgxrXEXALYlf-?usp=sharing). + + +## COCO + +### CenterNet + +| Model | val mAP | FPS (Titan Xp/ Titan RTX) | links | +|-------------------------------------------|---------|---------|-----------| +| CenterNet-S4_DLA_8x | 42.5 | 50 / 71 |[config](../configs/CenterNet-S4_DLA_8x.yaml)/[model](https://drive.google.com/file/d/1lNBhVHnZAEBRD66MFaHjm5Ij6Z4KYrJq/view?usp=sharing)| +| CenterNet-FPN_R50_1x | 40.2 | 20 / 24 |[config](../configs/CenterNet-FPN_R50_1x.yaml)/[model](https://drive.google.com/file/d/1rVG1YTthMXvutC6jr9KoE2DthT5-jhGj/view?usp=sharing)| + +#### Note + +- `CenterNet-S4_DLA_8x` is a re-implemented version of the original CenterNet (stride 4), with several changes, including + - Using top-left-right-bottom box encoding and GIoU Loss; adding regression loss to the center 3x3 region. + - Adding more positive pixels for the heatmap loss whose regression loss is small and is within the center3x3 region. + - Using more heavy crop augmentation (EfficientDet-style crop ratio 0.1-2), and removing color augmentations. + - Using standard NMS instead of max pooling. + - Using RetinaNet-style optimizer (SGD), learning rate rule (0.01 for each batch size 16), and schedule (8x12 epochs). +- `CenterNet-FPN_R50_1x` is a (new) FPN version of CenterNet. It includes the changes above, and assigns objects to FPN levels based on a fixed size range. The model is trained with standard short edge 640-800 multi-scale training with 12 epochs (1x). + + +### CenterNet2 + +| Model | val mAP | FPS (Titan Xp/ Titan RTX) | links | +|-------------------------------------------|---------|---------|-----------| +| CenterNet2-F_R50_1x | 41.7 | 22 / 27 |[config](../configs/CenterNet2-F_R50_1x.yaml)/[model](X)| +| CenterNet2_R50_1x | 42.9 | 18 / 24 |[config](../configs/CenterNet2_R50_1x.yaml)/[model](https://drive.google.com/file/d/1Osu1J_sskt_1FaGdfJKa4vd2N71TWS9W/view?usp=sharing)| +| CenterNet2_X101-DCN_2x | 49.9 | 6 / 8 |[config](../configs/CenterNet2_X101-DCN_2x.yaml)/[model](https://drive.google.com/file/d/1IHgpUHVJWpvMuFUUetgKWsw27pRNN2oK/view?usp=sharing)| +| CenterNet2_DLA-BiFPN-P3_4x | 43.8 | 40 / 50|[config](../configs/CenterNet2_DLA-BiFPN-P3_4x.yaml)/[model](https://drive.google.com/file/d/12GUNlDW9RmOs40UEMSiiUsk5QK_lpGsE/view?usp=sharing)| +| CenterNet2_DLA-BiFPN-P3_24x | 45.6 | 40 / 50 |[config](../configs/CenterNet2_DLA-BiFPN-P3_24x.yaml)/[model](https://drive.google.com/file/d/15ZES1ySxubDPzKsHPA7pYg8o_Vwmf-Mb/view?usp=sharing)| +| CenterNet2_R2-101-DCN_896_4x | 51.2 | 9 / 13 |[config](../configs/CenterNet2_R2-101-DCN_896_4x.yaml)/[model](https://drive.google.com/file/d/1S7_GE8ZDQBWuLEfKHkxzeF3KBsxsbABg/view?usp=sharing)| +| CenterNet2_R2-101-DCN-BiFPN_1280_4x | 52.9 | 6 / 8 |[config](../configs/CenterNet2_R2-101-DCN-BiFPN_1280_4x.yaml)/[model](https://drive.google.com/file/d/14EBHNMagBCNTQjOXcHoZwLYIi2lFIm7F/view?usp=sharing)| +| CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST | 56.1 | 3 / 5 |[config](../configs/CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST.yaml)/[model](https://drive.google.com/file/d/11ww9VlOi_nhpdsU_vBAecSxBU0dR_JzW/view?usp=sharing)| +| CenterNet2_DLA-BiFPN-P5_640_24x_ST | 49.2 | 33 / 38 |[config](../configs/CenterNet2_DLA-BiFPN-P5_640_24x_ST.yaml)/[model](https://drive.google.com/file/d/1qsHp2HrM1u8WrtBzF5S0oCoLMz-B40wk/view?usp=sharing)| + +#### Note + +- `CenterNet2-F_R50_1x` uses Faster RCNN as the second stage. All other CenterNet2 models use Cascade RCNN as the second stage. +- `CenterNet2_DLA-BiFPN-P3_4x` follows the same training setting as [realtime-FCOS](https://github.com/aim-uofa/AdelaiDet/blob/master/configs/FCOS-Detection/README.md). +- `CenterNet2_DLA-BiFPN-P3_24x` is trained by repeating the `4x` schedule (starting from learning rate 0.01) 6 times. +- R2 means [Res2Net](https://github.com/Res2Net/Res2Net-detectron2) backbone. To train Res2Net models, you need to download the ImageNet pre-trained weight [here](https://github.com/Res2Net/Res2Net-detectron2) and place it in `output/r2_101.pkl`. +- The last 4 models in the table are trained with the EfficientDet-style resize-and-crop augmentation, instead of the default random resizing short edge in detectron2. We found this trains faster (per-iteration) and gives better performance under a long schedule. +- `_ST` means using [self-training](https://arxiv.org/abs/2006.06882) using pseudo-labels produced by [Scaled-YOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4) on COCO unlabeled images, with a hard score threshold 0.5. Our processed pseudo-labels can be downloaded [here](https://drive.google.com/file/d/1LMBjtHhLp6dYf6MjwEQmzCLWQLkmWPpw/view?usp=sharing). +- `CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST` finetunes from `CenterNet2_R2-101-DCN-BiFPN_1280_4x` for an additional `4x` schedule with the self-training data. It is trained under `1280x1280` but tested under `1560x1560`. + +## LVIS v1 + +| Model | val mAP box | links | +|-------------------------------------------|--------------|-----------| +| LVIS_CenterNet2_R50_1x | 26.5 |[config](../configs/LVIS_CenterNet2_R50_1x.yaml)/[model](https://drive.google.com/file/d/1gT9e-tNw8uzEBaCadQuoOOP2TEYa4kKP/view?usp=sharing)| +| LVIS_CenterNet2_R50_Fed_1x | 28.3 |[config](../configs/LVIS_CenterNet2_R50_Fed_1x.yaml)/[model](https://drive.google.com/file/d/1a9UjheMCKax0qAKEwPVpq2ZHN6vpqJv8/view?usp=sharing)| + +- The models are trained with repeat-factor sampling. +- `LVIS_CenterNet2_R50_Fed_1x` is CenterNet2 with our federated loss. Check our Appendix D of our [paper](https://arxiv.org/abs/2103.07461) or our [technical report at LVIS challenge](https://www.lvisdataset.org/assets/challenge_reports/2020/CenterNet2.pdf) for references. + +## Objects365 + +| Model | val mAP| links | +|-------------------------------------------|---------|-----------| +| O365_CenterNet2_R50_1x | 22.6 |[config](../configs/O365_CenterNet2_R50_1x.yaml)/[model](https://drive.google.com/file/d/18fG6xGchAlpNp5sx8RAtwadGkS-gdIBU/view?usp=sharing)| + +#### Note +- Objects365 dataset can be downloaded [here](https://www.objects365.org/overview.html). +- The model is trained with class-aware sampling. diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/Base-CenterNet-FPN.yaml b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/Base-CenterNet-FPN.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bef3dc10dee4aaf0e7158711cc9d088f2b28c940 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/Base-CenterNet-FPN.yaml @@ -0,0 +1,28 @@ +MODEL: + META_ARCHITECTURE: "CenterNetDetector" + PROPOSAL_GENERATOR: + NAME: "CenterNet" + BACKBONE: + NAME: "build_p67_resnet_fpn_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + RESNETS: + DEPTH: 50 + OUT_FEATURES: ["res3", "res4", "res5"] + FPN: + IN_FEATURES: ["res3", "res4", "res5"] +DATASETS: + TRAIN: ("coco_2017_train",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.01 + STEPS: (60000, 80000) + MAX_ITER: 90000 + CHECKPOINT_PERIOD: 1000000000 + WARMUP_ITERS: 4000 + WARMUP_FACTOR: 0.00025 + CLIP_GRADIENTS: + ENABLED: True +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +OUTPUT_DIR: "./output/CenterNet2/auto" diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/Base-CenterNet2.yaml b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/Base-CenterNet2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..689372310149062acd703760d11f83800e12e74f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/Base-CenterNet2.yaml @@ -0,0 +1,56 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedRCNN" + PROPOSAL_GENERATOR: + NAME: "CenterNet" + BACKBONE: + NAME: "build_p67_resnet_fpn_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + RESNETS: + DEPTH: 50 + OUT_FEATURES: ["res3", "res4", "res5"] + FPN: + IN_FEATURES: ["res3", "res4", "res5"] + ROI_HEADS: + NAME: CustomCascadeROIHeads + IN_FEATURES: ["p3", "p4", "p5", "p6", "p7"] + IOU_THRESHOLDS: [0.6] + NMS_THRESH_TEST: 0.7 + ROI_BOX_CASCADE_HEAD: + IOUS: [0.6, 0.7, 0.8] + ROI_BOX_HEAD: + NAME: "FastRCNNConvFCHead" + NUM_FC: 2 + POOLER_RESOLUTION: 7 + CLS_AGNOSTIC_BBOX_REG: True + MULT_PROPOSAL_SCORE: True + CENTERNET: + REG_WEIGHT: 1. + NOT_NORM_REG: True + ONLY_PROPOSAL: True + WITH_AGN_HM: True + INFERENCE_TH: 0.0001 + PRE_NMS_TOPK_TRAIN: 4000 + POST_NMS_TOPK_TRAIN: 2000 + PRE_NMS_TOPK_TEST: 1000 + POST_NMS_TOPK_TEST: 256 + NMS_TH_TRAIN: 0.9 + NMS_TH_TEST: 0.9 + POS_WEIGHT: 0.5 + NEG_WEIGHT: 0.5 + IGNORE_HIGH_FP: 0.85 +DATASETS: + TRAIN: ("coco_2017_train",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.02 + STEPS: (60000, 80000) + MAX_ITER: 90000 + CHECKPOINT_PERIOD: 1000000000 + WARMUP_ITERS: 4000 + WARMUP_FACTOR: 0.00025 + CLIP_GRADIENTS: + ENABLED: True +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +OUTPUT_DIR: "./output/CenterNet2/auto" diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/Base_S4_DLA.yaml b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/Base_S4_DLA.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7e01be7e5503055ebcbbe4aee7e43738f004fde0 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/Base_S4_DLA.yaml @@ -0,0 +1,40 @@ +MODEL: + META_ARCHITECTURE: "CenterNetDetector" + PROPOSAL_GENERATOR: + NAME: "CenterNet" + PIXEL_STD: [57.375, 57.120, 58.395] + BACKBONE: + NAME: "build_dla_backbone" + DLA: + NORM: "BN" + CENTERNET: + IN_FEATURES: ["dla2"] + FPN_STRIDES: [4] + SOI: [[0, 1000000]] + NUM_CLS_CONVS: 1 + NUM_BOX_CONVS: 1 + REG_WEIGHT: 1. + MORE_POS: True + HM_FOCAL_ALPHA: 0.25 +DATASETS: + TRAIN: ("coco_2017_train",) + TEST: ("coco_2017_val",) +SOLVER: + LR_SCHEDULER_NAME: "WarmupCosineLR" + MAX_ITER: 90000 + BASE_LR: 0.04 + IMS_PER_BATCH: 64 + WEIGHT_DECAY: 0.0001 + CHECKPOINT_PERIOD: 1000000 + CLIP_GRADIENTS: + ENABLED: True +INPUT: + CUSTOM_AUG: EfficientDetResizeCrop + TRAIN_SIZE: 640 + MIN_SIZE_TEST: 608 + MAX_SIZE_TEST: 900 +TEST: + EVAL_PERIOD: 7500 +DATALOADER: + NUM_WORKERS: 8 +OUTPUT_DIR: "output/CenterNet2/auto" diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet-FPN_R50_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet-FPN_R50_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6ea7d9b70324d172efbff299f9cff2c60e136e93 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet-FPN_R50_1x.yaml @@ -0,0 +1,4 @@ +_BASE_: "Base-CenterNet-FPN.yaml" +MODEL: + CENTERNET: + MORE_POS: True \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet-S4_DLA_8x.yaml b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet-S4_DLA_8x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b3d88be9f50b53766bd4c4b88130c9ee670a4984 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet-S4_DLA_8x.yaml @@ -0,0 +1,5 @@ +_BASE_: "Base_S4_DLA.yaml" +SOLVER: + MAX_ITER: 90000 + BASE_LR: 0.08 + IMS_PER_BATCH: 128 \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2-F_R50_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2-F_R50_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c40eecc13aaae3757dd1917ca3cfcb3cd7fc467f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2-F_R50_1x.yaml @@ -0,0 +1,4 @@ +_BASE_: "Base-CenterNet2.yaml" +MODEL: + ROI_HEADS: + NAME: CustomROIHeads \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_DLA-BiFPN-P3_24x.yaml b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_DLA-BiFPN-P3_24x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d7491447ebd7e769eec7309b533947c5577d8563 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_DLA-BiFPN-P3_24x.yaml @@ -0,0 +1,36 @@ +_BASE_: "Base-CenterNet2.yaml" +MODEL: + BACKBONE: + NAME: "build_p35_fcos_dla_bifpn_backbone" + BIFPN: + OUT_CHANNELS: 160 + NUM_LEVELS: 3 + NUM_BIFPN: 4 + DLA: + NUM_LAYERS: 34 + NORM: "SyncBN" + FPN: + IN_FEATURES: ["dla3", "dla4", "dla5"] + ROI_HEADS: + IN_FEATURES: ["p3", "p4", "p5"] + CENTERNET: + POST_NMS_TOPK_TEST: 128 + FPN_STRIDES: [8, 16, 32] + IN_FEATURES: ['p3', 'p4', 'p5'] + SOI: [[0, 64], [48, 192], [128, 1000000]] +DATASETS: + TRAIN: ("coco_2017_train",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.02 + STEPS: (300000, 340000) + MAX_ITER: 360000 + CHECKPOINT_PERIOD: 100000 + WARMUP_ITERS: 4000 + WARMUP_FACTOR: 0.00025 +INPUT: + MIN_SIZE_TRAIN: (256, 288, 320, 352, 384, 416, 448, 480, 512, 544, 576, 608) + MAX_SIZE_TRAIN: 900 + MAX_SIZE_TEST: 736 + MIN_SIZE_TEST: 512 \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_DLA-BiFPN-P3_4x.yaml b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_DLA-BiFPN-P3_4x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d7491447ebd7e769eec7309b533947c5577d8563 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_DLA-BiFPN-P3_4x.yaml @@ -0,0 +1,36 @@ +_BASE_: "Base-CenterNet2.yaml" +MODEL: + BACKBONE: + NAME: "build_p35_fcos_dla_bifpn_backbone" + BIFPN: + OUT_CHANNELS: 160 + NUM_LEVELS: 3 + NUM_BIFPN: 4 + DLA: + NUM_LAYERS: 34 + NORM: "SyncBN" + FPN: + IN_FEATURES: ["dla3", "dla4", "dla5"] + ROI_HEADS: + IN_FEATURES: ["p3", "p4", "p5"] + CENTERNET: + POST_NMS_TOPK_TEST: 128 + FPN_STRIDES: [8, 16, 32] + IN_FEATURES: ['p3', 'p4', 'p5'] + SOI: [[0, 64], [48, 192], [128, 1000000]] +DATASETS: + TRAIN: ("coco_2017_train",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.02 + STEPS: (300000, 340000) + MAX_ITER: 360000 + CHECKPOINT_PERIOD: 100000 + WARMUP_ITERS: 4000 + WARMUP_FACTOR: 0.00025 +INPUT: + MIN_SIZE_TRAIN: (256, 288, 320, 352, 384, 416, 448, 480, 512, 544, 576, 608) + MAX_SIZE_TRAIN: 900 + MAX_SIZE_TEST: 736 + MIN_SIZE_TEST: 512 \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_DLA-BiFPN-P5_640_16x.yaml b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_DLA-BiFPN-P5_640_16x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..80413a62d666a3588fec4f5adc3ca5c3af788b45 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_DLA-BiFPN-P5_640_16x.yaml @@ -0,0 +1,29 @@ +_BASE_: "Base-CenterNet2.yaml" +MODEL: + BACKBONE: + NAME: "build_p37_dla_bifpn_backbone" + BIFPN: + OUT_CHANNELS: 160 + NUM_LEVELS: 5 + NUM_BIFPN: 3 + CENTERNET: + POST_NMS_TOPK_TEST: 128 + WEIGHTS: '' + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.12, 57.375] + FPN: + IN_FEATURES: ["dla3", "dla4", "dla5"] +SOLVER: + LR_SCHEDULER_NAME: "WarmupCosineLR" + MAX_ITER: 360000 + BASE_LR: 0.08 + IMS_PER_BATCH: 64 + CHECKPOINT_PERIOD: 90000 +TEST: + EVAL_PERIOD: 7500 +INPUT: + FORMAT: RGB + CUSTOM_AUG: EfficientDetResizeCrop + TRAIN_SIZE: 640 + MIN_SIZE_TEST: 608 + MAX_SIZE_TEST: 900 diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_DLA-BiFPN-P5_640_16x_ST.yaml b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_DLA-BiFPN-P5_640_16x_ST.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8813b39c1c2cf02290e491d7efa75296d9897591 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_DLA-BiFPN-P5_640_16x_ST.yaml @@ -0,0 +1,30 @@ +_BASE_: "Base-CenterNet2.yaml" +MODEL: + BACKBONE: + NAME: "build_p37_dla_bifpn_backbone" + BIFPN: + OUT_CHANNELS: 160 + NUM_LEVELS: 5 + NUM_BIFPN: 3 + CENTERNET: + POST_NMS_TOPK_TEST: 128 + WEIGHTS: '' + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.12, 57.375] + FPN: + IN_FEATURES: ["dla3", "dla4", "dla5"] +SOLVER: + LR_SCHEDULER_NAME: "WarmupCosineLR" + MAX_ITER: 360000 + BASE_LR: 0.08 + IMS_PER_BATCH: 64 +TEST: + EVAL_PERIOD: 7500 +INPUT: + FORMAT: RGB + CUSTOM_AUG: EfficientDetResizeCrop + TRAIN_SIZE: 640 + MIN_SIZE_TEST: 608 + MAX_SIZE_TEST: 900 +DATASETS: + TRAIN: ("coco_2017_train","coco_un_yolov4_55_0.5",) diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_DLA-fcosBiFPN-P5_640_16x_ST.yaml b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_DLA-fcosBiFPN-P5_640_16x_ST.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f94f1358ced6f9ea88e75db668c0afa173215111 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_DLA-fcosBiFPN-P5_640_16x_ST.yaml @@ -0,0 +1,30 @@ +_BASE_: "Base-CenterNet2.yaml" +MODEL: + BACKBONE: + NAME: "build_p37_fcos_dla_bifpn_backbone" + BIFPN: + OUT_CHANNELS: 160 + NUM_LEVELS: 5 + NUM_BIFPN: 3 + CENTERNET: + POST_NMS_TOPK_TEST: 128 + WEIGHTS: '' + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.12, 57.375] + FPN: + IN_FEATURES: ["dla3", "dla4", "dla5"] +TEST: + EVAL_PERIOD: 7500 +SOLVER: + LR_SCHEDULER_NAME: "WarmupCosineLR" + MAX_ITER: 360000 + BASE_LR: 0.08 + IMS_PER_BATCH: 64 +INPUT: + FORMAT: RGB + CUSTOM_AUG: EfficientDetResizeCrop + TRAIN_SIZE: 640 + MIN_SIZE_TEST: 608 + MAX_SIZE_TEST: 900 +DATASETS: + TRAIN: ("coco_2017_train","coco_un_yolov4_55_0.5",) diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_R2-101-DCN-BiFPN_1280_4x.yaml b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_R2-101-DCN-BiFPN_1280_4x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e07574b3511a372ab9e04747e584fdeef37a9700 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_R2-101-DCN-BiFPN_1280_4x.yaml @@ -0,0 +1,32 @@ +_BASE_: "Base-CenterNet2.yaml" +MODEL: + BACKBONE: + NAME: "build_res2net_bifpn_backbone" + BIFPN: + NUM_BIFPN: 7 + OUT_CHANNELS: 288 + WEIGHTS: "output/r2_101.pkl" + RESNETS: + DEPTH: 101 + WIDTH_PER_GROUP: 26 + DEFORM_ON_PER_STAGE: [False, False, True, True] # on Res4, Res5 + DEFORM_MODULATED: True + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.12, 57.375] + CENTERNET: + USE_DEFORMABLE: True + ROI_HEADS: + IN_FEATURES: ["p3", "p4"] +INPUT: + FORMAT: RGB +TEST: + EVAL_PERIOD: 7500 +SOLVER: + MAX_ITER: 180000 + CHECKPOINT_PERIOD: 60000 + LR_SCHEDULER_NAME: "WarmupCosineLR" + BASE_LR: 0.04 + IMS_PER_BATCH: 32 +INPUT: + CUSTOM_AUG: EfficientDetResizeCrop + TRAIN_SIZE: 1280 diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST.yaml b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST.yaml new file mode 100644 index 0000000000000000000000000000000000000000..81fcab0972a943256239705b4edd320c78312532 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST.yaml @@ -0,0 +1,36 @@ +_BASE_: "Base-CenterNet2.yaml" +MODEL: + BACKBONE: + NAME: "build_res2net_bifpn_backbone" + BIFPN: + NUM_BIFPN: 7 + OUT_CHANNELS: 288 + WEIGHTS: "output/r2_101.pkl" + RESNETS: + DEPTH: 101 + WIDTH_PER_GROUP: 26 + DEFORM_ON_PER_STAGE: [False, False, True, True] # on Res4, Res5 + DEFORM_MODULATED: True + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.12, 57.375] + CENTERNET: + USE_DEFORMABLE: True + ROI_HEADS: + IN_FEATURES: ["p3", "p4"] +TEST: + EVAL_PERIOD: 7500 +SOLVER: + MAX_ITER: 180000 + CHECKPOINT_PERIOD: 7500 + LR_SCHEDULER_NAME: "WarmupCosineLR" + BASE_LR: 0.04 + IMS_PER_BATCH: 32 +DATASETS: + TRAIN: "('coco_2017_train', 'coco_un_yolov4_55_0.5')" +INPUT: + FORMAT: RGB + CUSTOM_AUG: EfficientDetResizeCrop + TRAIN_SIZE: 1280 + TEST_SIZE: 1560 + TEST_INPUT_TYPE: 'square' + \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_R2-101-DCN_896_4x.yaml b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_R2-101-DCN_896_4x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fd6c49ee40ca927090e1a9dcd397049e6d42e649 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_R2-101-DCN_896_4x.yaml @@ -0,0 +1,29 @@ +_BASE_: "Base-CenterNet2.yaml" +MODEL: + BACKBONE: + NAME: "build_p67_res2net_fpn_backbone" + WEIGHTS: "output/r2_101.pkl" + RESNETS: + DEPTH: 101 + WIDTH_PER_GROUP: 26 + DEFORM_ON_PER_STAGE: [False, False, True, True] # on Res4, Res5 + DEFORM_MODULATED: True + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.12, 57.375] + CENTERNET: + USE_DEFORMABLE: True + ROI_HEADS: + IN_FEATURES: ["p3", "p4"] +INPUT: + FORMAT: RGB +TEST: + EVAL_PERIOD: 7500 +SOLVER: + MAX_ITER: 180000 + CHECKPOINT_PERIOD: 600000 + LR_SCHEDULER_NAME: "WarmupCosineLR" + BASE_LR: 0.04 + IMS_PER_BATCH: 32 +INPUT: + CUSTOM_AUG: EfficientDetResizeCrop + TRAIN_SIZE: 896 \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_R50_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_R50_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9dcdf5b8b6b8c613a0d4a036dbf9fd662512558c --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_R50_1x.yaml @@ -0,0 +1 @@ +_BASE_: "Base-CenterNet2.yaml" diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_X101-DCN_2x.yaml b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_X101-DCN_2x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..009c68085bdd3340df9e9ef5325bb6ca1c003478 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/CenterNet2_X101-DCN_2x.yaml @@ -0,0 +1,22 @@ +_BASE_: "Base-CenterNet2.yaml" +MODEL: + CENTERNET: + USE_DEFORMABLE: True + WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl" + PIXEL_STD: [57.375, 57.120, 58.395] + RESNETS: + STRIDE_IN_1X1: False + NUM_GROUPS: 32 + WIDTH_PER_GROUP: 8 + DEPTH: 101 + DEFORM_ON_PER_STAGE: [False, False, True, True] # on Res4, Res5 + DEFORM_MODULATED: True + ROI_HEADS: + IN_FEATURES: ["p3", "p4"] +SOLVER: + STEPS: (120000, 160000) + MAX_ITER: 180000 + CHECKPOINT_PERIOD: 40000 +INPUT: + MIN_SIZE_TRAIN: (480, 960) + MIN_SIZE_TRAIN_SAMPLING: "range" diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/LVIS_CenterNet2_R50_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/LVIS_CenterNet2_R50_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..912e8925dcd72cacb1dd7e08b21c97c8acf44ca1 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/LVIS_CenterNet2_R50_1x.yaml @@ -0,0 +1,17 @@ +_BASE_: "Base-CenterNet2.yaml" +MODEL: + ROI_HEADS: + NUM_CLASSES: 1203 + SCORE_THRESH_TEST: 0.02 + NMS_THRESH_TEST: 0.5 + CENTERNET: + NUM_CLASSES: 1203 + +DATASETS: + TRAIN: ("lvis_v1_train",) + TEST: ("lvis_v1_val",) +DATALOADER: + SAMPLER_TRAIN: "RepeatFactorTrainingSampler" + REPEAT_THRESHOLD: 0.001 +TEST: + DETECTIONS_PER_IMAGE: 300 diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/LVIS_CenterNet2_R50_Fed_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/LVIS_CenterNet2_R50_Fed_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d6b6c823f27f3cb1459cfac3abd34dd6166ceb55 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/LVIS_CenterNet2_R50_Fed_1x.yaml @@ -0,0 +1,19 @@ +_BASE_: "Base-CenterNet2.yaml" +MODEL: + ROI_HEADS: + NUM_CLASSES: 1203 + SCORE_THRESH_TEST: 0.02 + NMS_THRESH_TEST: 0.5 + CENTERNET: + NUM_CLASSES: 1203 + ROI_BOX_HEAD: + USE_SIGMOID_CE: True + USE_FED_LOSS: True +DATASETS: + TRAIN: ("lvis_v1_train",) + TEST: ("lvis_v1_val",) +DATALOADER: + SAMPLER_TRAIN: "RepeatFactorTrainingSampler" + REPEAT_THRESHOLD: 0.001 +TEST: + DETECTIONS_PER_IMAGE: 300 diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/O365_CenterNet2_R50_1x.yaml b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/O365_CenterNet2_R50_1x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..514e52cddca8bb42afb578f1a66be71c1e6ddbe8 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/O365_CenterNet2_R50_1x.yaml @@ -0,0 +1,13 @@ +_BASE_: "Base-CenterNet2.yaml" +MODEL: + ROI_HEADS: + NUM_CLASSES: 365 + CENTERNET: + NUM_CLASSES: 365 +DATASETS: + TRAIN: ("objects365_train",) + TEST: ("objects365_val",) +DATALOADER: + SAMPLER_TRAIN: "ClassAwareSampler" +TEST: + DETECTIONS_PER_IMAGE: 300 \ No newline at end of file diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/nuImages_CenterNet2_DLA_640_8x.yaml b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/nuImages_CenterNet2_DLA_640_8x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c400e92ce787bce299306589707295d0cb1ede6f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/configs/nuImages_CenterNet2_DLA_640_8x.yaml @@ -0,0 +1,42 @@ +_BASE_: "Base-CenterNet2.yaml" +MODEL: + MASK_ON: True + ROI_MASK_HEAD: + NAME: "MaskRCNNConvUpsampleHead" + NUM_CONV: 4 + POOLER_RESOLUTION: 14 + ROI_HEADS: + NUM_CLASSES: 10 + IN_FEATURES: ["dla2"] + BACKBONE: + NAME: "build_dla_backbone" + DLA: + NORM: "BN" + CENTERNET: + IN_FEATURES: ["dla2"] + FPN_STRIDES: [4] + SOI: [[0, 1000000]] + NUM_CLS_CONVS: 1 + NUM_BOX_CONVS: 1 + REG_WEIGHT: 1. + MORE_POS: True + HM_FOCAL_ALPHA: 0.25 + POST_NMS_TOPK_TEST: 128 + WEIGHTS: '' + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.12, 57.375] +SOLVER: + MAX_ITER: 180000 + STEPS: (120000, 160000) + BASE_LR: 0.08 + IMS_PER_BATCH: 64 +INPUT: + FORMAT: RGB + CUSTOM_AUG: EfficientDetResizeCrop + TRAIN_SIZE: 640 + MIN_SIZE_TEST: 608 + MAX_SIZE_TEST: 900 + MASK_FORMAT: bitmask +DATASETS: + TRAIN: ("nuimages_train",) + TEST: ("nuimages_val",) diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/demo.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/demo.py new file mode 100644 index 0000000000000000000000000000000000000000..5213faf4d859bb109a03bcd2721a02d63d2f89ce --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/demo.py @@ -0,0 +1,185 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import argparse +import glob +import multiprocessing as mp +import os +import time +import cv2 +import tqdm + +from detectron2.config import get_cfg +from detectron2.data.detection_utils import read_image +from detectron2.utils.logger import setup_logger + +from predictor import VisualizationDemo +from centernet.config import add_centernet_config +# constants +WINDOW_NAME = "CenterNet2 detections" + +from detectron2.utils.video_visualizer import VideoVisualizer +from detectron2.utils.visualizer import ColorMode, Visualizer +from detectron2.data import MetadataCatalog + +def setup_cfg(args): + # load config from file and command-line arguments + cfg = get_cfg() + add_centernet_config(cfg) + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + # Set score_threshold for builtin models + cfg.MODEL.RETINANET.SCORE_THRESH_TEST = args.confidence_threshold + cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold + if cfg.MODEL.META_ARCHITECTURE in ['ProposalNetwork', 'CenterNetDetector']: + cfg.MODEL.CENTERNET.INFERENCE_TH = args.confidence_threshold + cfg.MODEL.CENTERNET.NMS_TH = cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST + cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.confidence_threshold + cfg.freeze() + return cfg + + +def get_parser(): + parser = argparse.ArgumentParser(description="Detectron2 demo for builtin models") + parser.add_argument( + "--config-file", + default="configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml", + metavar="FILE", + help="path to config file", + ) + parser.add_argument("--webcam", action="store_true", help="Take inputs from webcam.") + parser.add_argument("--video-input", help="Path to video file.") + parser.add_argument("--input", nargs="+", help="A list of space separated input images") + parser.add_argument( + "--output", + help="A file or directory to save output visualizations. " + "If not given, will show output in an OpenCV window.", + ) + + parser.add_argument( + "--confidence-threshold", + type=float, + default=0.3, + help="Minimum score for instance predictions to be shown", + ) + parser.add_argument( + "--opts", + help="Modify config options using the command-line 'KEY VALUE' pairs", + default=[], + nargs=argparse.REMAINDER, + ) + return parser + + +if __name__ == "__main__": + mp.set_start_method("spawn", force=True) + args = get_parser().parse_args() + logger = setup_logger() + logger.info("Arguments: " + str(args)) + + cfg = setup_cfg(args) + + demo = VisualizationDemo(cfg) + output_file = None + if args.input: + if len(args.input) == 1: + args.input = glob.glob(os.path.expanduser(args.input[0])) + files = os.listdir(args.input[0]) + args.input = [args.input[0] + x for x in files] + assert args.input, "The input path(s) was not found" + visualizer = VideoVisualizer( + MetadataCatalog.get( + cfg.DATASETS.TEST[0] if len(cfg.DATASETS.TEST) else "__unused" + ), + instance_mode=ColorMode.IMAGE) + for path in tqdm.tqdm(args.input, disable=not args.output): + # use PIL, to be consistent with evaluation + img = read_image(path, format="BGR") + start_time = time.time() + predictions, visualized_output = demo.run_on_image( + img, visualizer=visualizer) + if 'instances' in predictions: + logger.info( + "{}: detected {} instances in {:.2f}s".format( + path, len(predictions["instances"]), time.time() - start_time + ) + ) + else: + logger.info( + "{}: detected {} instances in {:.2f}s".format( + path, len(predictions["proposals"]), time.time() - start_time + ) + ) + + if args.output: + if os.path.isdir(args.output): + assert os.path.isdir(args.output), args.output + out_filename = os.path.join(args.output, os.path.basename(path)) + visualized_output.save(out_filename) + else: + # assert len(args.input) == 1, "Please specify a directory with args.output" + # out_filename = args.output + if output_file is None: + width = visualized_output.get_image().shape[1] + height = visualized_output.get_image().shape[0] + frames_per_second = 15 + output_file = cv2.VideoWriter( + filename=args.output, + # some installation of opencv may not support x264 (due to its license), + # you can try other format (e.g. MPEG) + fourcc=cv2.VideoWriter_fourcc(*"x264"), + fps=float(frames_per_second), + frameSize=(width, height), + isColor=True, + ) + output_file.write(visualized_output.get_image()[:, :, ::-1]) + else: + # cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) + cv2.imshow(WINDOW_NAME, visualized_output.get_image()[:, :, ::-1]) + if cv2.waitKey(1 ) == 27: + break # esc to quit + elif args.webcam: + assert args.input is None, "Cannot have both --input and --webcam!" + cam = cv2.VideoCapture(0) + for vis in tqdm.tqdm(demo.run_on_video(cam)): + cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) + cv2.imshow(WINDOW_NAME, vis) + if cv2.waitKey(1) == 27: + break # esc to quit + cv2.destroyAllWindows() + elif args.video_input: + video = cv2.VideoCapture(args.video_input) + width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) + height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) + frames_per_second = 15 # video.get(cv2.CAP_PROP_FPS) + num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) + basename = os.path.basename(args.video_input) + + if args.output: + if os.path.isdir(args.output): + output_fname = os.path.join(args.output, basename) + output_fname = os.path.splitext(output_fname)[0] + ".mkv" + else: + output_fname = args.output + # assert not os.path.isfile(output_fname), output_fname + output_file = cv2.VideoWriter( + filename=output_fname, + # some installation of opencv may not support x264 (due to its license), + # you can try other format (e.g. MPEG) + fourcc=cv2.VideoWriter_fourcc(*"x264"), + fps=float(frames_per_second), + frameSize=(width, height), + isColor=True, + ) + assert os.path.isfile(args.video_input) + for vis_frame in tqdm.tqdm(demo.run_on_video(video), total=num_frames): + if args.output: + output_file.write(vis_frame) + + cv2.namedWindow(basename, cv2.WINDOW_NORMAL) + cv2.imshow(basename, vis_frame) + if cv2.waitKey(1) == 27: + break # esc to quit + video.release() + if args.output: + output_file.release() + else: + cv2.destroyAllWindows() diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/predictor.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..8a036bde3f0fffd770f9ec6fd04a3505b88b09df --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/predictor.py @@ -0,0 +1,243 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import atexit +import bisect +import multiprocessing as mp +from collections import deque +import cv2 +import torch + +from detectron2.data import MetadataCatalog +from detectron2.engine.defaults import DefaultPredictor +from detectron2.utils.video_visualizer import VideoVisualizer +from detectron2.utils.visualizer import ColorMode, Visualizer + + +class VisualizationDemo(object): + def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False): + """ + Args: + cfg (CfgNode): + instance_mode (ColorMode): + parallel (bool): whether to run the model in different processes from visualization. + Useful since the visualization logic can be slow. + """ + self.metadata = MetadataCatalog.get( + cfg.DATASETS.TRAIN[0] if len(cfg.DATASETS.TRAIN) else "__unused" + ) + self.cpu_device = torch.device("cpu") + self.instance_mode = instance_mode + + self.parallel = parallel + if parallel: + num_gpu = torch.cuda.device_count() + self.predictor = AsyncPredictor(cfg, num_gpus=num_gpu) + else: + self.predictor = DefaultPredictor(cfg) + + def run_on_image(self, image, visualizer=None): + """ + Args: + image (np.ndarray): an image of shape (H, W, C) (in BGR order). + This is the format used by OpenCV. + + Returns: + predictions (dict): the output of the model. + vis_output (VisImage): the visualized image output. + """ + vis_output = None + predictions = self.predictor(image) + # Convert image from OpenCV BGR format to Matplotlib RGB format. + image = image[:, :, ::-1] + use_video_vis = True + if visualizer is None: + use_video_vis = False + visualizer = Visualizer(image, self.metadata, instance_mode=self.instance_mode) + if "panoptic_seg" in predictions: + panoptic_seg, segments_info = predictions["panoptic_seg"] + vis_output = visualizer.draw_panoptic_seg_predictions( + panoptic_seg.to(self.cpu_device), segments_info + ) + else: + if "sem_seg" in predictions: + vis_output = visualizer.draw_sem_seg( + predictions["sem_seg"].argmax(dim=0).to(self.cpu_device) + ) + if "instances" in predictions: + instances = predictions["instances"].to(self.cpu_device) + if use_video_vis: + vis_output = visualizer.draw_instance_predictions( + image, predictions=instances) + else: + vis_output = visualizer.draw_instance_predictions(predictions=instances) + elif "proposals" in predictions: + instances = predictions["proposals"].to(self.cpu_device) + instances.pred_boxes = instances.proposal_boxes + instances.scores = instances.objectness_logits + instances.pred_classes[:] = -1 + if use_video_vis: + vis_output = visualizer.draw_instance_predictions( + image, predictions=instances) + else: + vis_output = visualizer.draw_instance_predictions(predictions=instances) + + return predictions, vis_output + + def _frame_from_video(self, video): + while video.isOpened(): + success, frame = video.read() + if success: + yield frame + else: + break + + def run_on_video(self, video): + """ + Visualizes predictions on frames of the input video. + + Args: + video (cv2.VideoCapture): a :class:`VideoCapture` object, whose source can be + either a webcam or a video file. + + Yields: + ndarray: BGR visualizations of each video frame. + """ + video_visualizer = VideoVisualizer(self.metadata, self.instance_mode) + + def process_predictions(frame, predictions): + frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) + if "panoptic_seg" in predictions: + panoptic_seg, segments_info = predictions["panoptic_seg"] + vis_frame = video_visualizer.draw_panoptic_seg_predictions( + frame, panoptic_seg.to(self.cpu_device), segments_info + ) + elif "instances" in predictions: + predictions = predictions["instances"].to(self.cpu_device) + vis_frame = video_visualizer.draw_instance_predictions(frame, predictions) + elif "sem_seg" in predictions: + vis_frame = video_visualizer.draw_sem_seg( + frame, predictions["sem_seg"].argmax(dim=0).to(self.cpu_device) + ) + elif "proposals" in predictions: + predictions = predictions["proposals"].to(self.cpu_device) + predictions.pred_boxes = predictions.proposal_boxes + predictions.scores = predictions.objectness_logits + predictions.pred_classes[:] = -1 + vis_frame = video_visualizer.draw_instance_predictions(frame, predictions) + + # Converts Matplotlib RGB format to OpenCV BGR format + vis_frame = cv2.cvtColor(vis_frame.get_image(), cv2.COLOR_RGB2BGR) + return vis_frame + + frame_gen = self._frame_from_video(video) + if self.parallel: + buffer_size = self.predictor.default_buffer_size + + frame_data = deque() + + for cnt, frame in enumerate(frame_gen): + frame_data.append(frame) + self.predictor.put(frame) + + if cnt >= buffer_size: + frame = frame_data.popleft() + predictions = self.predictor.get() + yield process_predictions(frame, predictions) + + while len(frame_data): + frame = frame_data.popleft() + predictions = self.predictor.get() + yield process_predictions(frame, predictions) + else: + for frame in frame_gen: + yield process_predictions(frame, self.predictor(frame)) + + +class AsyncPredictor: + """ + A predictor that runs the model asynchronously, possibly on >1 GPUs. + Because rendering the visualization takes considerably amount of time, + this helps improve throughput when rendering videos. + """ + + class _StopToken: + pass + + class _PredictWorker(mp.Process): + def __init__(self, cfg, task_queue, result_queue): + self.cfg = cfg + self.task_queue = task_queue + self.result_queue = result_queue + super().__init__() + + def run(self): + predictor = DefaultPredictor(self.cfg) + + while True: + task = self.task_queue.get() + if isinstance(task, AsyncPredictor._StopToken): + break + idx, data = task + result = predictor(data) + self.result_queue.put((idx, result)) + + def __init__(self, cfg, num_gpus: int = 1): + """ + Args: + cfg (CfgNode): + num_gpus (int): if 0, will run on CPU + """ + num_workers = max(num_gpus, 1) + self.task_queue = mp.Queue(maxsize=num_workers * 3) + self.result_queue = mp.Queue(maxsize=num_workers * 3) + self.procs = [] + for gpuid in range(max(num_gpus, 1)): + cfg = cfg.clone() + cfg.defrost() + cfg.MODEL.DEVICE = "cuda:{}".format(gpuid) if num_gpus > 0 else "cpu" + self.procs.append( + AsyncPredictor._PredictWorker(cfg, self.task_queue, self.result_queue) + ) + + self.put_idx = 0 + self.get_idx = 0 + self.result_rank = [] + self.result_data = [] + + for p in self.procs: + p.start() + atexit.register(self.shutdown) + + def put(self, image): + self.put_idx += 1 + self.task_queue.put((self.put_idx, image)) + + def get(self): + self.get_idx += 1 # the index needed for this request + if len(self.result_rank) and self.result_rank[0] == self.get_idx: + res = self.result_data[0] + del self.result_data[0], self.result_rank[0] + return res + + while True: + # make sure the results are returned in the correct order + idx, res = self.result_queue.get() + if idx == self.get_idx: + return res + insert = bisect.bisect(self.result_rank, idx) + self.result_rank.insert(insert, idx) + self.result_data.insert(insert, res) + + def __len__(self): + return self.put_idx - self.get_idx + + def __call__(self, image): + self.put(image) + return self.get() + + def shutdown(self): + for _ in self.procs: + self.task_queue.put(AsyncPredictor._StopToken()) + + @property + def default_buffer_size(self): + return len(self.procs) * 5 diff --git a/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/train_net.py b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/train_net.py new file mode 100644 index 0000000000000000000000000000000000000000..d903efde074e97e1720f970ea94717ebf105d9af --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/projects/CenterNet2/train_net.py @@ -0,0 +1,228 @@ +import logging +import os +from collections import OrderedDict +import torch +from torch.nn.parallel import DistributedDataParallel +import time +import datetime +import json + +from fvcore.common.timer import Timer +import detectron2.utils.comm as comm +from detectron2.checkpoint import DetectionCheckpointer, PeriodicCheckpointer +from detectron2.config import get_cfg +from detectron2.data import ( + MetadataCatalog, + build_detection_test_loader, +) +from detectron2.engine import default_argument_parser, default_setup, launch + +from detectron2.evaluation import ( + COCOEvaluator, + LVISEvaluator, + inference_on_dataset, + print_csv_format, +) +from detectron2.modeling import build_model +from detectron2.solver import build_lr_scheduler, build_optimizer +from detectron2.utils.events import ( + CommonMetricPrinter, + EventStorage, + JSONWriter, + TensorboardXWriter, +) +from detectron2.modeling.test_time_augmentation import GeneralizedRCNNWithTTA +from detectron2.data.dataset_mapper import DatasetMapper +from detectron2.data.build import build_detection_train_loader + +from centernet.config import add_centernet_config +from centernet.data.custom_build_augmentation import build_custom_augmentation + +logger = logging.getLogger("detectron2") + +def do_test(cfg, model): + results = OrderedDict() + for dataset_name in cfg.DATASETS.TEST: + mapper = None if cfg.INPUT.TEST_INPUT_TYPE == 'default' else \ + DatasetMapper( + cfg, False, augmentations=build_custom_augmentation(cfg, False)) + data_loader = build_detection_test_loader(cfg, dataset_name, mapper=mapper) + output_folder = os.path.join( + cfg.OUTPUT_DIR, "inference_{}".format(dataset_name)) + evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type + + if evaluator_type == "lvis": + evaluator = LVISEvaluator(dataset_name, cfg, True, output_folder) + elif evaluator_type == 'coco': + evaluator = COCOEvaluator(dataset_name, cfg, True, output_folder) + else: + assert 0, evaluator_type + + results[dataset_name] = inference_on_dataset( + model, data_loader, evaluator) + if comm.is_main_process(): + logger.info("Evaluation results for {} in csv format:".format( + dataset_name)) + print_csv_format(results[dataset_name]) + if len(results) == 1: + results = list(results.values())[0] + return results + +def do_train(cfg, model, resume=False): + model.train() + optimizer = build_optimizer(cfg, model) + scheduler = build_lr_scheduler(cfg, optimizer) + + checkpointer = DetectionCheckpointer( + model, cfg.OUTPUT_DIR, optimizer=optimizer, scheduler=scheduler + ) + + start_iter = ( + checkpointer.resume_or_load( + cfg.MODEL.WEIGHTS, resume=resume, + ).get("iteration", -1) + 1 + ) + if cfg.SOLVER.RESET_ITER: + logger.info('Reset loaded iteration. Start training from iteration 0.') + start_iter = 0 + max_iter = cfg.SOLVER.MAX_ITER if cfg.SOLVER.TRAIN_ITER < 0 else cfg.SOLVER.TRAIN_ITER + + periodic_checkpointer = PeriodicCheckpointer( + checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD, max_iter=max_iter + ) + + writers = ( + [ + CommonMetricPrinter(max_iter), + JSONWriter(os.path.join(cfg.OUTPUT_DIR, "metrics.json")), + TensorboardXWriter(cfg.OUTPUT_DIR), + ] + if comm.is_main_process() + else [] + ) + + + mapper = DatasetMapper(cfg, True) if cfg.INPUT.CUSTOM_AUG == '' else \ + DatasetMapper(cfg, True, augmentations=build_custom_augmentation(cfg, True)) + if cfg.DATALOADER.SAMPLER_TRAIN in ['TrainingSampler', 'RepeatFactorTrainingSampler']: + data_loader = build_detection_train_loader(cfg, mapper=mapper) + else: + from centernet.data.custom_dataset_dataloader import build_custom_train_loader + data_loader = build_custom_train_loader(cfg, mapper=mapper) + + + logger.info("Starting training from iteration {}".format(start_iter)) + with EventStorage(start_iter) as storage: + step_timer = Timer() + data_timer = Timer() + start_time = time.perf_counter() + for data, iteration in zip(data_loader, range(start_iter, max_iter)): + data_time = data_timer.seconds() + storage.put_scalars(data_time=data_time) + step_timer.reset() + iteration = iteration + 1 + storage.step() + loss_dict = model(data) + + losses = sum( + loss for k, loss in loss_dict.items()) + assert torch.isfinite(losses).all(), loss_dict + + loss_dict_reduced = {k: v.item() \ + for k, v in comm.reduce_dict(loss_dict).items()} + losses_reduced = sum(loss for loss in loss_dict_reduced.values()) + if comm.is_main_process(): + storage.put_scalars( + total_loss=losses_reduced, **loss_dict_reduced) + + optimizer.zero_grad() + losses.backward() + optimizer.step() + + storage.put_scalar( + "lr", optimizer.param_groups[0]["lr"], smoothing_hint=False) + + step_time = step_timer.seconds() + storage.put_scalars(time=step_time) + data_timer.reset() + scheduler.step() + + if ( + cfg.TEST.EVAL_PERIOD > 0 + and iteration % cfg.TEST.EVAL_PERIOD == 0 + and iteration != max_iter + ): + do_test(cfg, model) + comm.synchronize() + + if iteration - start_iter > 5 and \ + (iteration % 20 == 0 or iteration == max_iter): + for writer in writers: + writer.write() + periodic_checkpointer.step(iteration) + + total_time = time.perf_counter() - start_time + logger.info( + "Total training time: {}".format( + str(datetime.timedelta(seconds=int(total_time))))) + +def setup(args): + """ + Create configs and perform basic setups. + """ + cfg = get_cfg() + add_centernet_config(cfg) + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + if '/auto' in cfg.OUTPUT_DIR: + file_name = os.path.basename(args.config_file)[:-5] + cfg.OUTPUT_DIR = cfg.OUTPUT_DIR.replace('/auto', '/{}'.format(file_name)) + logger.info('OUTPUT_DIR: {}'.format(cfg.OUTPUT_DIR)) + cfg.freeze() + default_setup(cfg, args) + return cfg + + +def main(args): + cfg = setup(args) + + model = build_model(cfg) + logger.info("Model:\n{}".format(model)) + if args.eval_only: + DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load( + cfg.MODEL.WEIGHTS, resume=args.resume + ) + if cfg.TEST.AUG.ENABLED: + logger.info("Running inference with test-time augmentation ...") + model = GeneralizedRCNNWithTTA(cfg, model, batch_size=1) + + return do_test(cfg, model) + + distributed = comm.get_world_size() > 1 + if distributed: + model = DistributedDataParallel( + model, device_ids=[comm.get_local_rank()], broadcast_buffers=False, + find_unused_parameters=True + ) + + do_train(cfg, model, resume=args.resume) + return do_test(cfg, model) + + +if __name__ == "__main__": + args = default_argument_parser() + args.add_argument('--manual_device', default='') + args = args.parse_args() + if args.manual_device != '': + os.environ['CUDA_VISIBLE_DEVICES'] = args.manual_device + args.dist_url = 'tcp://127.0.0.1:{}'.format( + torch.randint(11111, 60000, (1,))[0].item()) + print("Command Line Args:", args) + launch( + main, + args.num_gpus, + num_machines=args.num_machines, + machine_rank=args.machine_rank, + dist_url=args.dist_url, + args=(args,), + ) diff --git a/iChat/models/grit_src/third_party/CenterNet2/setup.cfg b/iChat/models/grit_src/third_party/CenterNet2/setup.cfg new file mode 100644 index 0000000000000000000000000000000000000000..2a1ccd4e54e57367b2c26325ec14fbc8d7b8b972 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/setup.cfg @@ -0,0 +1,26 @@ +[isort] +line_length=100 +multi_line_output=3 +include_trailing_comma=True +known_standard_library=numpy,setuptools,mock +skip=./datasets,docs +skip_glob=*/__init__.py,**/configs/**,tests/config/** +known_myself=detectron2 +known_third_party=fvcore,matplotlib,cv2,torch,torchvision,PIL,pycocotools,yacs,termcolor,cityscapesscripts,tabulate,tqdm,scipy,lvis,psutil,pkg_resources,caffe2,onnx,panopticapi,black,isort,av,iopath,omegaconf,hydra,yaml,pydoc,submitit,cloudpickle +no_lines_before=STDLIB,THIRDPARTY +sections=FUTURE,STDLIB,THIRDPARTY,myself,FIRSTPARTY,LOCALFOLDER +default_section=FIRSTPARTY + +[mypy] +python_version=3.6 +ignore_missing_imports = True +warn_unused_configs = True +disallow_untyped_defs = True +check_untyped_defs = True +warn_unused_ignores = True +warn_redundant_casts = True +show_column_numbers = True +follow_imports = silent +allow_redefinition = True +; Require all functions to be annotated +disallow_incomplete_defs = True diff --git a/iChat/models/grit_src/third_party/CenterNet2/setup.py b/iChat/models/grit_src/third_party/CenterNet2/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..50a5e23e9ba6fab32775ebd16fb2746c7bf6660c --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/setup.py @@ -0,0 +1,206 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. + +import glob +import os +import shutil +from os import path +from setuptools import find_packages, setup +from typing import List +import torch +from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension + +torch_ver = [int(x) for x in torch.__version__.split(".")[:2]] +assert torch_ver >= [1, 8], "Requires PyTorch >= 1.8" + + +def get_version(): + init_py_path = path.join(path.abspath(path.dirname(__file__)), "detectron2", "__init__.py") + init_py = open(init_py_path, "r").readlines() + version_line = [l.strip() for l in init_py if l.startswith("__version__")][0] + version = version_line.split("=")[-1].strip().strip("'\"") + + # The following is used to build release packages. + # Users should never use it. + suffix = os.getenv("D2_VERSION_SUFFIX", "") + version = version + suffix + if os.getenv("BUILD_NIGHTLY", "0") == "1": + from datetime import datetime + + date_str = datetime.today().strftime("%y%m%d") + version = version + ".dev" + date_str + + new_init_py = [l for l in init_py if not l.startswith("__version__")] + new_init_py.append('__version__ = "{}"\n'.format(version)) + with open(init_py_path, "w") as f: + f.write("".join(new_init_py)) + return version + + +def get_extensions(): + this_dir = path.dirname(path.abspath(__file__)) + extensions_dir = path.join(this_dir, "detectron2", "layers", "csrc") + + main_source = path.join(extensions_dir, "vision.cpp") + sources = glob.glob(path.join(extensions_dir, "**", "*.cpp")) + + from torch.utils.cpp_extension import ROCM_HOME + + is_rocm_pytorch = ( + True if ((torch.version.hip is not None) and (ROCM_HOME is not None)) else False + ) + if is_rocm_pytorch: + assert torch_ver >= [1, 8], "ROCM support requires PyTorch >= 1.8!" + + # common code between cuda and rocm platforms, for hipify version [1,0,0] and later. + source_cuda = glob.glob(path.join(extensions_dir, "**", "*.cu")) + glob.glob( + path.join(extensions_dir, "*.cu") + ) + sources = [main_source] + sources + + extension = CppExtension + + extra_compile_args = {"cxx": []} + define_macros = [] + + if (torch.cuda.is_available() and ((CUDA_HOME is not None) or is_rocm_pytorch)) or os.getenv( + "FORCE_CUDA", "0" + ) == "1": + extension = CUDAExtension + sources += source_cuda + + if not is_rocm_pytorch: + define_macros += [("WITH_CUDA", None)] + extra_compile_args["nvcc"] = [ + "-O3", + "-DCUDA_HAS_FP16=1", + "-D__CUDA_NO_HALF_OPERATORS__", + "-D__CUDA_NO_HALF_CONVERSIONS__", + "-D__CUDA_NO_HALF2_OPERATORS__", + ] + else: + define_macros += [("WITH_HIP", None)] + extra_compile_args["nvcc"] = [] + + if torch_ver < [1, 7]: + # supported by https://github.com/pytorch/pytorch/pull/43931 + CC = os.environ.get("CC", None) + if CC is not None: + extra_compile_args["nvcc"].append("-ccbin={}".format(CC)) + + include_dirs = [extensions_dir] + + ext_modules = [ + extension( + "detectron2._C", + sources, + include_dirs=include_dirs, + define_macros=define_macros, + extra_compile_args=extra_compile_args, + ) + ] + + return ext_modules + + +def get_model_zoo_configs() -> List[str]: + """ + Return a list of configs to include in package for model zoo. Copy over these configs inside + detectron2/model_zoo. + """ + + # Use absolute paths while symlinking. + source_configs_dir = path.join(path.dirname(path.realpath(__file__)), "configs") + destination = path.join( + path.dirname(path.realpath(__file__)), "detectron2", "model_zoo", "configs" + ) + # Symlink the config directory inside package to have a cleaner pip install. + + # Remove stale symlink/directory from a previous build. + if path.exists(source_configs_dir): + if path.islink(destination): + os.unlink(destination) + elif path.isdir(destination): + shutil.rmtree(destination) + + if not path.exists(destination): + try: + os.symlink(source_configs_dir, destination) + except OSError: + # Fall back to copying if symlink fails: ex. on Windows. + shutil.copytree(source_configs_dir, destination) + + config_paths = glob.glob("configs/**/*.yaml", recursive=True) + glob.glob( + "configs/**/*.py", recursive=True + ) + return config_paths + + +# For projects that are relative small and provide features that are very close +# to detectron2's core functionalities, we install them under detectron2.projects +PROJECTS = { + +} + +setup( + name="detectron2", + version=get_version(), + author="FAIR", + url="https://github.com/facebookresearch/detectron2", + description="Detectron2 is FAIR's next-generation research " + "platform for object detection and segmentation.", + packages=find_packages(exclude=("configs", "tests*")) + list(PROJECTS.keys()), + package_dir=PROJECTS, + package_data={"detectron2.model_zoo": get_model_zoo_configs()}, + python_requires=">=3.6", + install_requires=[ + # These dependencies are not pure-python. + # In general, avoid adding more dependencies like them because they are not + # guaranteed to be installable by `pip install` on all platforms. + # To tell if a package is pure-python, go to https://pypi.org/project/{name}/#files + "Pillow>=7.1", # or use pillow-simd for better performance + "matplotlib", # TODO move it to optional after we add opencv visualization + "pycocotools>=2.0.2", # corresponds to https://github.com/ppwwyyxx/cocoapi + # Do not add opencv here. Just like pytorch, user should install + # opencv themselves, preferrably by OS's package manager, or by + # choosing the proper pypi package name at https://github.com/skvark/opencv-python + # The following are pure-python dependencies that should be easily installable + "termcolor>=1.1", + "yacs>=0.1.8", + "tabulate", + "cloudpickle", + "tqdm>4.29.0", + "tensorboard", + # Lock version of fvcore/iopath because they may have breaking changes + # NOTE: when updating fvcore/iopath version, make sure fvcore depends + # on compatible version of iopath. + "fvcore>=0.1.5,<0.1.6", # required like this to make it pip installable + "iopath>=0.1.7,<0.1.10", + "future", # used by caffe2 + "pydot", # used to save caffe2 SVGs + "dataclasses; python_version<'3.7'", + "omegaconf>=2.1", + "hydra-core>=1.1", + "black==21.4b2", + # If a new dependency is required at import time (in addition to runtime), it + # probably needs to exist in docs/requirements.txt, or as a mock in docs/conf.py + ], + extras_require={ + # optional dependencies, required by some features + "all": [ + "shapely", + "pygments>=2.2", + "psutil", + "panopticapi @ https://github.com/cocodataset/panopticapi/archive/master.zip", + ], + # dev dependencies. Install them by `pip install 'detectron2[dev]'` + "dev": [ + "flake8==3.8.1", + "isort==4.3.21", + "flake8-bugbear", + "flake8-comprehensions", + ], + }, + ext_modules=get_extensions(), + cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension}, +) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/README.md b/iChat/models/grit_src/third_party/CenterNet2/tests/README.md new file mode 100644 index 0000000000000000000000000000000000000000..f560384045ab4f6bc2beabef1170308fca117eb3 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/README.md @@ -0,0 +1,9 @@ +## Unit Tests + +To run the unittests, do: +``` +cd detectron2 +python -m unittest discover -v -s ./tests +``` + +There are also end-to-end inference & training tests, in [dev/run_*_tests.sh](../dev). diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9020c2df23e2af280b7bb168b996ae9eaf312eb8 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/config/dir1/dir1_a.py b/iChat/models/grit_src/third_party/CenterNet2/tests/config/dir1/dir1_a.py new file mode 100644 index 0000000000000000000000000000000000000000..a939955124556355524f48c0f0c16abb07cfc4c4 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/config/dir1/dir1_a.py @@ -0,0 +1,3 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +dir1a_str = "base_a_1" +dir1a_dict = {"a": 1, "b": 2} diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/config/dir1/dir1_b.py b/iChat/models/grit_src/third_party/CenterNet2/tests/config/dir1/dir1_b.py new file mode 100644 index 0000000000000000000000000000000000000000..2dcb54cb1054c5d80ccc823af21f13b9ebbcf1a3 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/config/dir1/dir1_b.py @@ -0,0 +1,11 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from detectron2.config import LazyConfig + +# equivalent to relative import +dir1a_str, dir1a_dict = LazyConfig.load_rel("dir1_a.py", ("dir1a_str", "dir1a_dict")) + +dir1b_str = dir1a_str + "_from_b" +dir1b_dict = dir1a_dict + +# Every import is a reload: not modified by other config files +assert dir1a_dict.a == 1 diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/config/root_cfg.py b/iChat/models/grit_src/third_party/CenterNet2/tests/config/root_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..33d1d4bd2d9ddf31d55c655c49d13a8b7ac7b376 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/config/root_cfg.py @@ -0,0 +1,14 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from itertools import count + +from detectron2.config import LazyCall as L + +from .dir1.dir1_a import dir1a_dict, dir1a_str + +dir1a_dict.a = "modified" + +# modification above won't affect future imports +from .dir1.dir1_b import dir1b_dict, dir1b_str + + +lazyobj = L(count)(x=dir1a_str, y=dir1b_str) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/config/test_instantiate_config.py b/iChat/models/grit_src/third_party/CenterNet2/tests/config/test_instantiate_config.py new file mode 100644 index 0000000000000000000000000000000000000000..b76f71b9a206cb59006765803c96713cb990d22c --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/config/test_instantiate_config.py @@ -0,0 +1,100 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import os +import tempfile +import unittest +import yaml +from omegaconf import OmegaConf +from omegaconf import __version__ as oc_version +from dataclasses import dataclass + +from detectron2.config import instantiate, LazyCall as L +from detectron2.layers import ShapeSpec + +OC_VERSION = tuple(int(x) for x in oc_version.split(".")[:2]) + + +class TestClass: + def __init__(self, int_arg, list_arg=None, dict_arg=None, extra_arg=None): + self.int_arg = int_arg + self.list_arg = list_arg + self.dict_arg = dict_arg + self.extra_arg = extra_arg + + def __call__(self, call_arg): + return call_arg + self.int_arg + + +@dataclass +class TestDataClass: + x: int + y: str + + +@unittest.skipIf(OC_VERSION < (2, 1), "omegaconf version too old") +class TestConstruction(unittest.TestCase): + def test_basic_construct(self): + objconf = L(TestClass)( + int_arg=3, + list_arg=[10], + dict_arg={}, + extra_arg=L(TestClass)(int_arg=4, list_arg="${..list_arg}"), + ) + + obj = instantiate(objconf) + self.assertIsInstance(obj, TestClass) + self.assertEqual(obj.int_arg, 3) + self.assertEqual(obj.extra_arg.int_arg, 4) + self.assertEqual(obj.extra_arg.list_arg, obj.list_arg) + + objconf.extra_arg.list_arg = [5] + obj = instantiate(objconf) + self.assertIsInstance(obj, TestClass) + self.assertEqual(obj.extra_arg.list_arg, [5]) + + def test_instantiate_other_obj(self): + # do nothing for other obj + self.assertEqual(instantiate(5), 5) + x = [3, 4, 5] + self.assertEqual(instantiate(x), x) + x = TestClass(1) + self.assertIs(instantiate(x), x) + x = {"xx": "yy"} + self.assertIs(instantiate(x), x) + + def test_instantiate_lazy_target(self): + # _target_ is result of instantiate + objconf = L(L(len)(int_arg=3))(call_arg=4) + objconf._target_._target_ = TestClass + self.assertEqual(instantiate(objconf), 7) + + def test_instantiate_lst(self): + lst = [1, 2, L(TestClass)(int_arg=1)] + x = L(TestClass)(int_arg=lst) # list as an argument should be recursively instantiated + x = instantiate(x).int_arg + self.assertEqual(x[:2], [1, 2]) + self.assertIsInstance(x[2], TestClass) + self.assertEqual(x[2].int_arg, 1) + + def test_instantiate_namedtuple(self): + x = L(TestClass)(int_arg=ShapeSpec(channels=1, width=3)) + # test serialization + with tempfile.TemporaryDirectory() as d: + fname = os.path.join(d, "d2_test.yaml") + OmegaConf.save(x, fname) + with open(fname) as f: + x = yaml.unsafe_load(f) + + x = instantiate(x) + self.assertIsInstance(x.int_arg, ShapeSpec) + self.assertEqual(x.int_arg.channels, 1) + + def test_bad_lazycall(self): + with self.assertRaises(Exception): + L(3) + + def test_instantiate_dataclass(self): + a = L(TestDataClass)(x=1, y="s") + a = instantiate(a) + self.assertEqual(a.x, 1) + self.assertEqual(a.y, "s") diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/config/test_lazy_config.py b/iChat/models/grit_src/third_party/CenterNet2/tests/config/test_lazy_config.py new file mode 100644 index 0000000000000000000000000000000000000000..6ff5b6dc117744a9d978e0aff324bddeb496409b --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/config/test_lazy_config.py @@ -0,0 +1,79 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import os +import unittest +import tempfile +from itertools import count + +from detectron2.config import LazyConfig, LazyCall as L +from omegaconf import DictConfig + + +class TestLazyPythonConfig(unittest.TestCase): + def setUp(self): + self.root_filename = os.path.join(os.path.dirname(__file__), "root_cfg.py") + + def test_load(self): + cfg = LazyConfig.load(self.root_filename) + + self.assertEqual(cfg.dir1a_dict.a, "modified") + self.assertEqual(cfg.dir1b_dict.a, 1) + self.assertEqual(cfg.lazyobj.x, "base_a_1") + + cfg.lazyobj.x = "new_x" + # reload + cfg = LazyConfig.load(self.root_filename) + self.assertEqual(cfg.lazyobj.x, "base_a_1") + + def test_save_load(self): + cfg = LazyConfig.load(self.root_filename) + with tempfile.TemporaryDirectory(prefix="detectron2") as d: + fname = os.path.join(d, "test_config.yaml") + LazyConfig.save(cfg, fname) + cfg2 = LazyConfig.load(fname) + + self.assertEqual(cfg2.lazyobj._target_, "itertools.count") + self.assertEqual(cfg.lazyobj._target_, count) + cfg2.lazyobj.pop("_target_") + cfg.lazyobj.pop("_target_") + # the rest are equal + self.assertEqual(cfg, cfg2) + + def test_failed_save(self): + cfg = DictConfig({"x": lambda: 3}, flags={"allow_objects": True}) + with tempfile.TemporaryDirectory(prefix="detectron2") as d: + fname = os.path.join(d, "test_config.yaml") + LazyConfig.save(cfg, fname) + self.assertTrue(os.path.exists(fname)) + self.assertTrue(os.path.exists(fname + ".pkl")) + + def test_overrides(self): + cfg = LazyConfig.load(self.root_filename) + LazyConfig.apply_overrides(cfg, ["lazyobj.x=123", 'dir1b_dict.a="123"']) + self.assertEqual(cfg.dir1b_dict.a, "123") + self.assertEqual(cfg.lazyobj.x, 123) + + def test_invalid_overrides(self): + cfg = LazyConfig.load(self.root_filename) + with self.assertRaises(KeyError): + LazyConfig.apply_overrides(cfg, ["lazyobj.x.xxx=123"]) + + def test_to_py(self): + cfg = LazyConfig.load(self.root_filename) + cfg.lazyobj.x = {"a": 1, "b": 2, "c": L(count)(x={"r": "a", "s": 2.4, "t": [1, 2, 3, "z"]})} + cfg.list = ["a", 1, "b", 3.2] + py_str = LazyConfig.to_py(cfg) + expected = """cfg.dir1a_dict.a = "modified" +cfg.dir1a_dict.b = 2 +cfg.dir1b_dict.a = 1 +cfg.dir1b_dict.b = 2 +cfg.lazyobj = itertools.count( + x={ + "a": 1, + "b": 2, + "c": itertools.count(x={"r": "a", "s": 2.4, "t": [1, 2, 3, "z"]}), + }, + y="base_a_1_from_b", +) +cfg.list = ["a", 1, "b", 3.2] +""" + self.assertEqual(py_str, expected) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/config/test_yacs_config.py b/iChat/models/grit_src/third_party/CenterNet2/tests/config/test_yacs_config.py new file mode 100644 index 0000000000000000000000000000000000000000..01dd6955f78e2700ffc10ed723ab1c95df0e5a18 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/config/test_yacs_config.py @@ -0,0 +1,270 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. + + +import os +import tempfile +import unittest +import torch +from omegaconf import OmegaConf + +from detectron2 import model_zoo +from detectron2.config import configurable, downgrade_config, get_cfg, upgrade_config +from detectron2.layers import ShapeSpec +from detectron2.modeling import build_model + +_V0_CFG = """ +MODEL: + RPN_HEAD: + NAME: "TEST" +VERSION: 0 +""" + +_V1_CFG = """ +MODEL: + WEIGHT: "/path/to/weight" +""" + + +class TestConfigVersioning(unittest.TestCase): + def test_upgrade_downgrade_consistency(self): + cfg = get_cfg() + # check that custom is preserved + cfg.USER_CUSTOM = 1 + + down = downgrade_config(cfg, to_version=0) + up = upgrade_config(down) + self.assertTrue(up == cfg) + + def _merge_cfg_str(self, cfg, merge_str): + f = tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) + try: + f.write(merge_str) + f.close() + cfg.merge_from_file(f.name) + finally: + os.remove(f.name) + return cfg + + def test_auto_upgrade(self): + cfg = get_cfg() + latest_ver = cfg.VERSION + cfg.USER_CUSTOM = 1 + + self._merge_cfg_str(cfg, _V0_CFG) + + self.assertEqual(cfg.MODEL.RPN.HEAD_NAME, "TEST") + self.assertEqual(cfg.VERSION, latest_ver) + + def test_guess_v1(self): + cfg = get_cfg() + latest_ver = cfg.VERSION + self._merge_cfg_str(cfg, _V1_CFG) + self.assertEqual(cfg.VERSION, latest_ver) + + +class _TestClassA(torch.nn.Module): + @configurable + def __init__(self, arg1, arg2, arg3=3): + super().__init__() + self.arg1 = arg1 + self.arg2 = arg2 + self.arg3 = arg3 + assert arg1 == 1 + assert arg2 == 2 + assert arg3 == 3 + + @classmethod + def from_config(cls, cfg): + args = {"arg1": cfg.ARG1, "arg2": cfg.ARG2} + return args + + +class _TestClassB(_TestClassA): + @configurable + def __init__(self, input_shape, arg1, arg2, arg3=3): + """ + Doc of _TestClassB + """ + assert input_shape == "shape" + super().__init__(arg1, arg2, arg3) + + @classmethod + def from_config(cls, cfg, input_shape): # test extra positional arg in from_config + args = {"arg1": cfg.ARG1, "arg2": cfg.ARG2} + args["input_shape"] = input_shape + return args + + +class _LegacySubClass(_TestClassB): + # an old subclass written in cfg style + def __init__(self, cfg, input_shape, arg4=4): + super().__init__(cfg, input_shape) + assert self.arg1 == 1 + assert self.arg2 == 2 + assert self.arg3 == 3 + + +class _NewSubClassNewInit(_TestClassB): + # test new subclass with a new __init__ + @configurable + def __init__(self, input_shape, arg4=4, **kwargs): + super().__init__(input_shape, **kwargs) + assert self.arg1 == 1 + assert self.arg2 == 2 + assert self.arg3 == 3 + + +class _LegacySubClassNotCfg(_TestClassB): + # an old subclass written in cfg style, but argument is not called "cfg" + def __init__(self, config, input_shape): + super().__init__(config, input_shape) + assert self.arg1 == 1 + assert self.arg2 == 2 + assert self.arg3 == 3 + + +class _TestClassC(_TestClassB): + @classmethod + def from_config(cls, cfg, input_shape, **kwargs): # test extra kwarg overwrite + args = {"arg1": cfg.ARG1, "arg2": cfg.ARG2} + args["input_shape"] = input_shape + args.update(kwargs) + return args + + +class _TestClassD(_TestClassA): + @configurable + def __init__(self, input_shape: ShapeSpec, arg1: int, arg2, arg3=3): + assert input_shape == "shape" + super().__init__(arg1, arg2, arg3) + + # _TestClassA.from_config does not have input_shape args. + # Test whether input_shape will be forwarded to __init__ + + +@configurable(from_config=lambda cfg, arg2: {"arg1": cfg.ARG1, "arg2": arg2, "arg3": cfg.ARG3}) +def _test_func(arg1, arg2=2, arg3=3, arg4=4): + return arg1, arg2, arg3, arg4 + + +class TestConfigurable(unittest.TestCase): + def testInitWithArgs(self): + _ = _TestClassA(arg1=1, arg2=2, arg3=3) + _ = _TestClassB("shape", arg1=1, arg2=2) + _ = _TestClassC("shape", arg1=1, arg2=2) + _ = _TestClassD("shape", arg1=1, arg2=2, arg3=3) + + def testPatchedAttr(self): + self.assertTrue("Doc" in _TestClassB.__init__.__doc__) + self.assertEqual(_TestClassD.__init__.__annotations__["arg1"], int) + + def testInitWithCfg(self): + cfg = get_cfg() + cfg.ARG1 = 1 + cfg.ARG2 = 2 + cfg.ARG3 = 3 + _ = _TestClassA(cfg) + _ = _TestClassB(cfg, input_shape="shape") + _ = _TestClassC(cfg, input_shape="shape") + _ = _TestClassD(cfg, input_shape="shape") + _ = _LegacySubClass(cfg, input_shape="shape") + _ = _NewSubClassNewInit(cfg, input_shape="shape") + _ = _LegacySubClassNotCfg(cfg, input_shape="shape") + with self.assertRaises(TypeError): + # disallow forwarding positional args to __init__ since it's prone to errors + _ = _TestClassD(cfg, "shape") + + # call with kwargs instead + _ = _TestClassA(cfg=cfg) + _ = _TestClassB(cfg=cfg, input_shape="shape") + _ = _TestClassC(cfg=cfg, input_shape="shape") + _ = _TestClassD(cfg=cfg, input_shape="shape") + _ = _LegacySubClass(cfg=cfg, input_shape="shape") + _ = _NewSubClassNewInit(cfg=cfg, input_shape="shape") + _ = _LegacySubClassNotCfg(config=cfg, input_shape="shape") + + def testInitWithCfgOverwrite(self): + cfg = get_cfg() + cfg.ARG1 = 1 + cfg.ARG2 = 999 # wrong config + with self.assertRaises(AssertionError): + _ = _TestClassA(cfg, arg3=3) + + # overwrite arg2 with correct config later: + _ = _TestClassA(cfg, arg2=2, arg3=3) + _ = _TestClassB(cfg, input_shape="shape", arg2=2, arg3=3) + _ = _TestClassC(cfg, input_shape="shape", arg2=2, arg3=3) + _ = _TestClassD(cfg, input_shape="shape", arg2=2, arg3=3) + + # call with kwargs cfg=cfg instead + _ = _TestClassA(cfg=cfg, arg2=2, arg3=3) + _ = _TestClassB(cfg=cfg, input_shape="shape", arg2=2, arg3=3) + _ = _TestClassC(cfg=cfg, input_shape="shape", arg2=2, arg3=3) + _ = _TestClassD(cfg=cfg, input_shape="shape", arg2=2, arg3=3) + + def testInitWithCfgWrongArgs(self): + cfg = get_cfg() + cfg.ARG1 = 1 + cfg.ARG2 = 2 + with self.assertRaises(TypeError): + _ = _TestClassB(cfg, "shape", not_exist=1) + with self.assertRaises(TypeError): + _ = _TestClassC(cfg, "shape", not_exist=1) + with self.assertRaises(TypeError): + _ = _TestClassD(cfg, "shape", not_exist=1) + + def testBadClass(self): + class _BadClass1: + @configurable + def __init__(self, a=1, b=2): + pass + + class _BadClass2: + @configurable + def __init__(self, a=1, b=2): + pass + + def from_config(self, cfg): # noqa + pass + + class _BadClass3: + @configurable + def __init__(self, a=1, b=2): + pass + + # bad name: must be cfg + @classmethod + def from_config(cls, config): # noqa + pass + + with self.assertRaises(AttributeError): + _ = _BadClass1(a=1) + + with self.assertRaises(TypeError): + _ = _BadClass2(a=1) + + with self.assertRaises(TypeError): + _ = _BadClass3(get_cfg()) + + def testFuncWithCfg(self): + cfg = get_cfg() + cfg.ARG1 = 10 + cfg.ARG3 = 30 + + self.assertEqual(_test_func(1), (1, 2, 3, 4)) + with self.assertRaises(TypeError): + _test_func(cfg) + self.assertEqual(_test_func(cfg, arg2=2), (10, 2, 30, 4)) + self.assertEqual(_test_func(cfg, arg1=100, arg2=20), (100, 20, 30, 4)) + self.assertEqual(_test_func(cfg, arg1=100, arg2=20, arg4=40), (100, 20, 30, 40)) + + self.assertTrue(callable(_test_func.from_config)) + + def testOmegaConf(self): + cfg = model_zoo.get_config("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml") + cfg = OmegaConf.create(cfg.dump()) + if not torch.cuda.is_available(): + cfg.MODEL.DEVICE = "cpu" + # test that a model can be built with omegaconf config as well + build_model(cfg) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/data/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/tests/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/data/test_coco.py b/iChat/models/grit_src/third_party/CenterNet2/tests/data/test_coco.py new file mode 100644 index 0000000000000000000000000000000000000000..caabead5527639056daeef71027a69c47ee2ebf7 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/data/test_coco.py @@ -0,0 +1,139 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import json +import numpy as np +import os +import tempfile +import unittest +import pycocotools.mask as mask_util + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets.coco import convert_to_coco_dict, load_coco_json +from detectron2.structures import BoxMode + + +def make_mask(): + """ + Makes a donut shaped binary mask. + """ + H = 100 + W = 100 + mask = np.zeros([H, W], dtype=np.uint8) + for x in range(W): + for y in range(H): + d = np.linalg.norm(np.array([W, H]) / 2 - np.array([x, y])) + if d > 10 and d < 20: + mask[y, x] = 1 + return mask + + +def uncompressed_rle(mask): + l = mask.flatten(order="F").tolist() + counts = [] + p = False + cnt = 0 + for i in l: + if i == p: + cnt += 1 + else: + counts.append(cnt) + p = i + cnt = 1 + counts.append(cnt) + return {"counts": counts, "size": [mask.shape[0], mask.shape[1]]} + + +def make_dataset_dicts(mask, compressed: bool = True): + """ + Returns a list of dicts that represents a single COCO data point for + object detection. The single instance given by `mask` is represented by + RLE, either compressed or uncompressed. + """ + record = {} + record["file_name"] = "test" + record["image_id"] = 0 + record["height"] = mask.shape[0] + record["width"] = mask.shape[1] + + y, x = np.nonzero(mask) + if compressed: + segmentation = mask_util.encode(np.asarray(mask, order="F")) + else: + segmentation = uncompressed_rle(mask) + min_x = np.min(x) + max_x = np.max(x) + min_y = np.min(y) + max_y = np.max(y) + obj = { + "bbox": [min_x, min_y, max_x, max_y], + "bbox_mode": BoxMode.XYXY_ABS, + "category_id": 0, + "iscrowd": 0, + "segmentation": segmentation, + } + record["annotations"] = [obj] + return [record] + + +class TestRLEToJson(unittest.TestCase): + def test(self): + # Make a dummy dataset. + mask = make_mask() + DatasetCatalog.register("test_dataset", lambda: make_dataset_dicts(mask)) + MetadataCatalog.get("test_dataset").set(thing_classes=["test_label"]) + + # Dump to json. + json_dict = convert_to_coco_dict("test_dataset") + with tempfile.TemporaryDirectory() as tmpdir: + json_file_name = os.path.join(tmpdir, "test.json") + with open(json_file_name, "w") as f: + json.dump(json_dict, f) + # Load from json. + dicts = load_coco_json(json_file_name, "") + + # Check the loaded mask matches the original. + anno = dicts[0]["annotations"][0] + loaded_mask = mask_util.decode(anno["segmentation"]) + self.assertTrue(np.array_equal(loaded_mask, mask)) + DatasetCatalog.pop("test_dataset") + MetadataCatalog.pop("test_dataset") + + def test_uncompressed_RLE(self): + mask = make_mask() + rle = mask_util.encode(np.asarray(mask, order="F")) + uncompressed = uncompressed_rle(mask) + compressed = mask_util.frPyObjects(uncompressed, *rle["size"]) + self.assertEqual(rle, compressed) + + +class TestConvertCOCO(unittest.TestCase): + @staticmethod + def generate_data(): + record = { + "file_name": "test", + "image_id": 0, + "height": 100, + "width": 100, + "annotations": [ + { + "bbox": [10, 10, 10, 10, 5], + "bbox_mode": BoxMode.XYWHA_ABS, + "category_id": 0, + "iscrowd": 0, + }, + { + "bbox": [15, 15, 3, 3], + "bbox_mode": BoxMode.XYXY_ABS, + "category_id": 0, + "iscrowd": 0, + }, + ], + } + + return [record] + + def test_convert_to_coco(self): + DatasetCatalog.register("test_dataset", lambda: TestConvertCOCO.generate_data()) + MetadataCatalog.get("test_dataset").set(thing_classes=["test_label"]) + convert_to_coco_dict("test_dataset") + DatasetCatalog.pop("test_dataset") + MetadataCatalog.pop("test_dataset") diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/data/test_coco_evaluation.py b/iChat/models/grit_src/third_party/CenterNet2/tests/data/test_coco_evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..964f00284df64d3378ebfe32913c07deb5a1f819 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/data/test_coco_evaluation.py @@ -0,0 +1,138 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import contextlib +import copy +import io +import json +import numpy as np +import os +import tempfile +import unittest +import torch +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval + +from detectron2.data import DatasetCatalog +from detectron2.evaluation import COCOEvaluator +from detectron2.evaluation.fast_eval_api import COCOeval_opt +from detectron2.structures import Boxes, Instances + + +class TestCOCOeval(unittest.TestCase): + def test_fast_eval(self): + # A small set of images/categories from COCO val + # fmt: off + detections = [{"image_id": 139, "category_id": 1, "bbox": [417.3332824707031, 159.27003479003906, 47.66064453125, 143.00193786621094], "score": 0.9949821829795837, "segmentation": {"size": [426, 640], "counts": "Tc`52W=3N0N4aNN^E7]:4XE1g:8kDMT;U100000001O1gE[Nk8h1dFiNY9Z1aFkN]9g2J3NdN`FlN`9S1cFRN07]9g1bFoM6;X9c1cFoM=8R9g1bFQN>3U9Y30O01OO1O001N2O1N1O4L4L5UNoE3V:CVF6Q:@YF9l9@ZF 0 else 0.0 + msg = "%s: comparing COCO APIs, %s differs by %f" % (name, k, abs_diff) + self.assertTrue(abs_diff < 1e-4, msg=msg) + + def test_unknown_category(self): + dataset = "coco_2017_val_100" + evaluator = COCOEvaluator(dataset) + evaluator.reset() + inputs = DatasetCatalog.get(dataset)[:2] + pred = Instances((100, 100)) + pred.pred_boxes = Boxes(torch.rand(2, 4)) + pred.scores = torch.rand(2) + pred.pred_classes = torch.tensor([10, 80]) + output = {"instances": pred} + evaluator.process(inputs, [output, output]) + with self.assertRaises(AssertionError): + evaluator.evaluate() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/data/test_dataset.py b/iChat/models/grit_src/third_party/CenterNet2/tests/data/test_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..7d16ec4c63113e105cd2c2ea7a727a30656fc738 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/data/test_dataset.py @@ -0,0 +1,134 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import os +import pickle +import sys +import unittest +from functools import partial +import torch +from iopath.common.file_io import LazyPath + +from detectron2 import model_zoo +from detectron2.config import instantiate +from detectron2.data import ( + DatasetFromList, + MapDataset, + ToIterableDataset, + build_batch_data_loader, + build_detection_test_loader, + build_detection_train_loader, +) +from detectron2.data.samplers import InferenceSampler, TrainingSampler + + +def _a_slow_func(x): + return "path/{}".format(x) + + +class TestDatasetFromList(unittest.TestCase): + # Failing for py3.6, likely due to pickle + @unittest.skipIf(sys.version_info.minor <= 6, "Not supported in Python 3.6") + def test_using_lazy_path(self): + dataset = [] + for i in range(10): + dataset.append({"file_name": LazyPath(partial(_a_slow_func, i))}) + + dataset = DatasetFromList(dataset) + for i in range(10): + path = dataset[i]["file_name"] + self.assertTrue(isinstance(path, LazyPath)) + self.assertEqual(os.fspath(path), _a_slow_func(i)) + + +class TestMapDataset(unittest.TestCase): + @staticmethod + def map_func(x): + if x == 2: + return None + return x * 2 + + def test_map_style(self): + ds = DatasetFromList([1, 2, 3]) + ds = MapDataset(ds, TestMapDataset.map_func) + self.assertEqual(ds[0], 2) + self.assertEqual(ds[2], 6) + self.assertIn(ds[1], [2, 6]) + + def test_iter_style(self): + class DS(torch.utils.data.IterableDataset): + def __iter__(self): + yield from [1, 2, 3] + + ds = DS() + ds = MapDataset(ds, TestMapDataset.map_func) + self.assertIsInstance(ds, torch.utils.data.IterableDataset) + + data = list(iter(ds)) + self.assertEqual(data, [2, 6]) + + def test_pickleability(self): + ds = DatasetFromList([1, 2, 3]) + ds = MapDataset(ds, lambda x: x * 2) + ds = pickle.loads(pickle.dumps(ds)) + self.assertEqual(ds[0], 2) + + +class TestDataLoader(unittest.TestCase): + def _get_kwargs(self): + # get kwargs of build_detection_train_loader + cfg = model_zoo.get_config("common/data/coco.py").dataloader.train + cfg.dataset.names = "coco_2017_val_100" + cfg.pop("_target_") + kwargs = {k: instantiate(v) for k, v in cfg.items()} + return kwargs + + def test_build_dataloader_train(self): + kwargs = self._get_kwargs() + dl = build_detection_train_loader(**kwargs) + next(iter(dl)) + + def test_build_iterable_dataloader_train(self): + kwargs = self._get_kwargs() + ds = DatasetFromList(kwargs.pop("dataset")) + ds = ToIterableDataset(ds, TrainingSampler(len(ds))) + dl = build_detection_train_loader(dataset=ds, **kwargs) + next(iter(dl)) + + def _check_is_range(self, data_loader, N): + # check that data_loader produces range(N) + data = list(iter(data_loader)) + data = [x for batch in data for x in batch] # flatten the batches + self.assertEqual(len(data), N) + self.assertEqual(set(data), set(range(N))) + + def test_build_batch_dataloader_inference(self): + # Test that build_batch_data_loader can be used for inference + N = 96 + ds = DatasetFromList(list(range(N))) + sampler = InferenceSampler(len(ds)) + dl = build_batch_data_loader(ds, sampler, 8, num_workers=3) + self._check_is_range(dl, N) + + def test_build_dataloader_inference(self): + N = 50 + ds = DatasetFromList(list(range(N))) + sampler = InferenceSampler(len(ds)) + # test that parallel loader works correctly + dl = build_detection_test_loader( + dataset=ds, sampler=sampler, mapper=lambda x: x, num_workers=3 + ) + self._check_is_range(dl, N) + + # test that batch_size works correctly + dl = build_detection_test_loader( + dataset=ds, sampler=sampler, mapper=lambda x: x, batch_size=4, num_workers=0 + ) + self._check_is_range(dl, N) + + def test_build_iterable_dataloader_inference(self): + # Test that build_detection_test_loader supports iterable dataset + N = 50 + ds = DatasetFromList(list(range(N))) + ds = ToIterableDataset(ds, InferenceSampler(len(ds))) + dl = build_detection_test_loader(dataset=ds, mapper=lambda x: x, num_workers=3) + self._check_is_range(dl, N) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/data/test_detection_utils.py b/iChat/models/grit_src/third_party/CenterNet2/tests/data/test_detection_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..aac56c07da2be4e181e3e95de8cee1fc2858286d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/data/test_detection_utils.py @@ -0,0 +1,176 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import copy +import numpy as np +import os +import unittest +import pycocotools.mask as mask_util + +from detectron2.data import MetadataCatalog, detection_utils +from detectron2.data import transforms as T +from detectron2.structures import BitMasks, BoxMode +from detectron2.utils.file_io import PathManager + + +class TestTransformAnnotations(unittest.TestCase): + def test_transform_simple_annotation(self): + transforms = T.TransformList([T.HFlipTransform(400)]) + anno = { + "bbox": np.asarray([10, 10, 200, 300]), + "bbox_mode": BoxMode.XYXY_ABS, + "category_id": 3, + "segmentation": [[10, 10, 100, 100, 100, 10], [150, 150, 200, 150, 200, 200]], + } + + output = detection_utils.transform_instance_annotations(anno, transforms, (400, 400)) + self.assertTrue(np.allclose(output["bbox"], [200, 10, 390, 300])) + self.assertEqual(len(output["segmentation"]), len(anno["segmentation"])) + self.assertTrue(np.allclose(output["segmentation"][0], [390, 10, 300, 100, 300, 10])) + + detection_utils.annotations_to_instances([output, output], (400, 400)) + + def test_transform_empty_annotation(self): + detection_utils.annotations_to_instances([], (400, 400)) + + def test_flip_keypoints(self): + transforms = T.TransformList([T.HFlipTransform(400)]) + anno = { + "bbox": np.asarray([10, 10, 200, 300]), + "bbox_mode": BoxMode.XYXY_ABS, + "keypoints": np.random.rand(17, 3) * 50 + 15, + } + + output = detection_utils.transform_instance_annotations( + copy.deepcopy(anno), + transforms, + (400, 400), + keypoint_hflip_indices=detection_utils.create_keypoint_hflip_indices( + ["keypoints_coco_2017_train"] + ), + ) + # The first keypoint is nose + self.assertTrue(np.allclose(output["keypoints"][0, 0], 400 - anno["keypoints"][0, 0])) + # The last 16 keypoints are 8 left-right pairs + self.assertTrue( + np.allclose( + output["keypoints"][1:, 0].reshape(-1, 2)[:, ::-1], + 400 - anno["keypoints"][1:, 0].reshape(-1, 2), + ) + ) + self.assertTrue( + np.allclose( + output["keypoints"][1:, 1:].reshape(-1, 2, 2)[:, ::-1, :], + anno["keypoints"][1:, 1:].reshape(-1, 2, 2), + ) + ) + + def test_crop(self): + transforms = T.TransformList([T.CropTransform(300, 300, 10, 10)]) + keypoints = np.random.rand(17, 3) * 50 + 15 + keypoints[:, 2] = 2 + anno = { + "bbox": np.asarray([10, 10, 200, 400]), + "bbox_mode": BoxMode.XYXY_ABS, + "keypoints": keypoints, + } + + output = detection_utils.transform_instance_annotations( + copy.deepcopy(anno), transforms, (10, 10) + ) + # box is shifted and cropped + self.assertTrue((output["bbox"] == np.asarray([0, 0, 0, 10])).all()) + # keypoints are no longer visible + self.assertTrue((output["keypoints"][:, 2] == 0).all()) + + def test_transform_RLE(self): + transforms = T.TransformList([T.HFlipTransform(400)]) + mask = np.zeros((300, 400), order="F").astype("uint8") + mask[:, :200] = 1 + + anno = { + "bbox": np.asarray([10, 10, 200, 300]), + "bbox_mode": BoxMode.XYXY_ABS, + "segmentation": mask_util.encode(mask[:, :, None])[0], + "category_id": 3, + } + output = detection_utils.transform_instance_annotations( + copy.deepcopy(anno), transforms, (300, 400) + ) + mask = output["segmentation"] + self.assertTrue((mask[:, 200:] == 1).all()) + self.assertTrue((mask[:, :200] == 0).all()) + + inst = detection_utils.annotations_to_instances( + [output, output], (400, 400), mask_format="bitmask" + ) + self.assertTrue(isinstance(inst.gt_masks, BitMasks)) + + def test_transform_RLE_resize(self): + transforms = T.TransformList( + [T.HFlipTransform(400), T.ScaleTransform(300, 400, 400, 400, "bilinear")] + ) + mask = np.zeros((300, 400), order="F").astype("uint8") + mask[:, :200] = 1 + + anno = { + "bbox": np.asarray([10, 10, 200, 300]), + "bbox_mode": BoxMode.XYXY_ABS, + "segmentation": mask_util.encode(mask[:, :, None])[0], + "category_id": 3, + } + output = detection_utils.transform_instance_annotations( + copy.deepcopy(anno), transforms, (400, 400) + ) + + inst = detection_utils.annotations_to_instances( + [output, output], (400, 400), mask_format="bitmask" + ) + self.assertTrue(isinstance(inst.gt_masks, BitMasks)) + + def test_gen_crop(self): + instance = {"bbox": [10, 10, 100, 100], "bbox_mode": BoxMode.XYXY_ABS} + t = detection_utils.gen_crop_transform_with_instance((10, 10), (150, 150), instance) + # the box center must fall into the cropped region + self.assertTrue(t.x0 <= 55 <= t.x0 + t.w) + + def test_gen_crop_outside_boxes(self): + instance = {"bbox": [10, 10, 100, 100], "bbox_mode": BoxMode.XYXY_ABS} + with self.assertRaises(AssertionError): + detection_utils.gen_crop_transform_with_instance((10, 10), (15, 15), instance) + + def test_read_sem_seg(self): + cityscapes_dir = MetadataCatalog.get("cityscapes_fine_sem_seg_val").gt_dir + sem_seg_gt_path = os.path.join( + cityscapes_dir, "frankfurt", "frankfurt_000001_083852_gtFine_labelIds.png" + ) + if not PathManager.exists(sem_seg_gt_path): + raise unittest.SkipTest( + "Semantic segmentation ground truth {} not found.".format(sem_seg_gt_path) + ) + sem_seg = detection_utils.read_image(sem_seg_gt_path, "L") + self.assertEqual(sem_seg.ndim, 3) + self.assertEqual(sem_seg.shape[2], 1) + self.assertEqual(sem_seg.dtype, np.uint8) + self.assertEqual(sem_seg.max(), 32) + self.assertEqual(sem_seg.min(), 1) + + def test_read_exif_orientation(self): + # https://github.com/recurser/exif-orientation-examples/raw/master/Landscape_5.jpg + URL = "detectron2://assets/Landscape_5.jpg" + img = detection_utils.read_image(URL, "RGB") + self.assertEqual(img.ndim, 3) + self.assertEqual(img.dtype, np.uint8) + self.assertEqual(img.shape, (1200, 1800, 3)) # check that shape is not transposed + + def test_opencv_exif_orientation(self): + import cv2 + + URL = "detectron2://assets/Landscape_5.jpg" + with PathManager.open(URL, "rb") as f: + img = cv2.imdecode(np.frombuffer(f.read(), dtype="uint8"), cv2.IMREAD_COLOR) + self.assertEqual(img.dtype, np.uint8) + self.assertEqual(img.shape, (1200, 1800, 3)) + + +if __name__ == "__main__": + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/data/test_rotation_transform.py b/iChat/models/grit_src/third_party/CenterNet2/tests/data/test_rotation_transform.py new file mode 100644 index 0000000000000000000000000000000000000000..0e8299ed78a425c91fc2e43fede0b26461d1c9ff --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/data/test_rotation_transform.py @@ -0,0 +1,71 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +import unittest + +from detectron2.data.transforms.transform import RotationTransform + + +class TestRotationTransform(unittest.TestCase): + def assertEqualsArrays(self, a1, a2): + self.assertTrue(np.allclose(a1, a2)) + + def randomData(self, h=5, w=5): + image = np.random.rand(h, w) + coords = np.array([[i, j] for j in range(h + 1) for i in range(w + 1)], dtype=float) + return image, coords, h, w + + def test180(self): + image, coords, h, w = self.randomData(6, 6) + rot = RotationTransform(h, w, 180, expand=False, center=None) + self.assertEqualsArrays(rot.apply_image(image), image[::-1, ::-1]) + rotated_coords = [[w - c[0], h - c[1]] for c in coords] + self.assertEqualsArrays(rot.apply_coords(coords), rotated_coords) + + def test45_coords(self): + _, coords, h, w = self.randomData(4, 6) + rot = RotationTransform(h, w, 45, expand=False, center=None) + rotated_coords = [ + [(x + y - (h + w) / 2) / np.sqrt(2) + w / 2, h / 2 + (y + (w - h) / 2 - x) / np.sqrt(2)] + for (x, y) in coords + ] + self.assertEqualsArrays(rot.apply_coords(coords), rotated_coords) + + def test90(self): + image, coords, h, w = self.randomData() + rot = RotationTransform(h, w, 90, expand=False, center=None) + self.assertEqualsArrays(rot.apply_image(image), image.T[::-1]) + rotated_coords = [[c[1], w - c[0]] for c in coords] + self.assertEqualsArrays(rot.apply_coords(coords), rotated_coords) + + def test90_expand(self): # non-square image + image, coords, h, w = self.randomData(h=5, w=8) + rot = RotationTransform(h, w, 90, expand=True, center=None) + self.assertEqualsArrays(rot.apply_image(image), image.T[::-1]) + rotated_coords = [[c[1], w - c[0]] for c in coords] + self.assertEqualsArrays(rot.apply_coords(coords), rotated_coords) + + def test_center_expand(self): + # center has no effect if expand=True because it only affects shifting + image, coords, h, w = self.randomData(h=5, w=8) + angle = np.random.randint(360) + rot1 = RotationTransform(h, w, angle, expand=True, center=None) + rot2 = RotationTransform(h, w, angle, expand=True, center=(0, 0)) + rot3 = RotationTransform(h, w, angle, expand=True, center=(h, w)) + rot4 = RotationTransform(h, w, angle, expand=True, center=(2, 5)) + for r1 in [rot1, rot2, rot3, rot4]: + for r2 in [rot1, rot2, rot3, rot4]: + self.assertEqualsArrays(r1.apply_image(image), r2.apply_image(image)) + self.assertEqualsArrays(r1.apply_coords(coords), r2.apply_coords(coords)) + + def test_inverse_transform(self): + image, coords, h, w = self.randomData(h=5, w=8) + rot = RotationTransform(h, w, 90, expand=True, center=None) + rot_image = rot.apply_image(image) + self.assertEqualsArrays(rot.inverse().apply_image(rot_image), image) + rot = RotationTransform(h, w, 65, expand=True, center=None) + rotated_coords = rot.apply_coords(coords) + self.assertEqualsArrays(rot.inverse().apply_coords(rotated_coords), coords) + + +if __name__ == "__main__": + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/data/test_sampler.py b/iChat/models/grit_src/third_party/CenterNet2/tests/data/test_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..0d2784390801314862524e1b85703535d199e41d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/data/test_sampler.py @@ -0,0 +1,111 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import itertools +import math +import operator +import unittest +import torch +from torch.utils import data +from torch.utils.data.sampler import SequentialSampler + +from detectron2.data.build import worker_init_reset_seed +from detectron2.data.common import DatasetFromList, ToIterableDataset +from detectron2.data.samplers import ( + GroupedBatchSampler, + InferenceSampler, + RepeatFactorTrainingSampler, + TrainingSampler, +) +from detectron2.utils.env import seed_all_rng + + +class TestGroupedBatchSampler(unittest.TestCase): + def test_missing_group_id(self): + sampler = SequentialSampler(list(range(100))) + group_ids = [1] * 100 + samples = GroupedBatchSampler(sampler, group_ids, 2) + + for mini_batch in samples: + self.assertEqual(len(mini_batch), 2) + + def test_groups(self): + sampler = SequentialSampler(list(range(100))) + group_ids = [1, 0] * 50 + samples = GroupedBatchSampler(sampler, group_ids, 2) + + for mini_batch in samples: + self.assertEqual((mini_batch[0] + mini_batch[1]) % 2, 0) + + +class TestSamplerDeterministic(unittest.TestCase): + def test_to_iterable(self): + sampler = TrainingSampler(100, seed=10) + gt_output = list(itertools.islice(sampler, 100)) + self.assertEqual(set(gt_output), set(range(100))) + + dataset = DatasetFromList(list(range(100))) + dataset = ToIterableDataset(dataset, sampler) + data_loader = data.DataLoader(dataset, num_workers=0, collate_fn=operator.itemgetter(0)) + + output = list(itertools.islice(data_loader, 100)) + self.assertEqual(output, gt_output) + + data_loader = data.DataLoader( + dataset, + num_workers=2, + collate_fn=operator.itemgetter(0), + worker_init_fn=worker_init_reset_seed, + # reset seed should not affect behavior of TrainingSampler + ) + output = list(itertools.islice(data_loader, 100)) + # multiple workers should not lead to duplicate or different data + self.assertEqual(output, gt_output) + + def test_training_sampler_seed(self): + seed_all_rng(42) + sampler = TrainingSampler(30) + data = list(itertools.islice(sampler, 65)) + + seed_all_rng(42) + sampler = TrainingSampler(30) + seed_all_rng(999) # should be ineffective + data2 = list(itertools.islice(sampler, 65)) + self.assertEqual(data, data2) + + +class TestRepeatFactorTrainingSampler(unittest.TestCase): + def test_repeat_factors_from_category_frequency(self): + repeat_thresh = 0.5 + + dataset_dicts = [ + {"annotations": [{"category_id": 0}, {"category_id": 1}]}, + {"annotations": [{"category_id": 0}]}, + {"annotations": []}, + ] + + rep_factors = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset_dicts, repeat_thresh + ) + + expected_rep_factors = torch.tensor([math.sqrt(3 / 2), 1.0, 1.0]) + self.assertTrue(torch.allclose(rep_factors, expected_rep_factors)) + + +class TestInferenceSampler(unittest.TestCase): + def test_local_indices(self): + sizes = [0, 16, 2, 42] + world_sizes = [5, 2, 3, 4] + + expected_results = [ + [range(0) for _ in range(5)], + [range(8), range(8, 16)], + [range(1), range(1, 2), range(0)], + [range(11), range(11, 22), range(22, 32), range(32, 42)], + ] + + for size, world_size, expected_result in zip(sizes, world_sizes, expected_results): + with self.subTest(f"size={size}, world_size={world_size}"): + local_indices = [ + InferenceSampler._get_local_indices(size, world_size, r) + for r in range(world_size) + ] + self.assertEqual(local_indices, expected_result) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/data/test_transforms.py b/iChat/models/grit_src/third_party/CenterNet2/tests/data/test_transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..382048e533708dec3fabf89528564ebc2ad4c83f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/data/test_transforms.py @@ -0,0 +1,268 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import logging +import numpy as np +import unittest +from unittest import mock +import torch +from PIL import Image, ImageOps +from torch.nn import functional as F + +from detectron2.config import get_cfg +from detectron2.data import detection_utils +from detectron2.data import transforms as T +from detectron2.utils.logger import setup_logger + +logger = logging.getLogger(__name__) + + +def polygon_allclose(poly1, poly2): + """ + Test whether two polygons are the same. + Both arguments are nx2 numpy arrays. + """ + # ABCD and CDAB are the same polygon. So it's important to check after rolling + for k in range(len(poly1)): + rolled_poly1 = np.roll(poly1, k, axis=0) + if np.allclose(rolled_poly1, poly2): + return True + return False + + +class TestTransforms(unittest.TestCase): + def setUp(self): + setup_logger() + + def test_apply_rotated_boxes(self): + np.random.seed(125) + cfg = get_cfg() + is_train = True + augs = detection_utils.build_augmentation(cfg, is_train) + image = np.random.rand(200, 300) + image, transforms = T.apply_augmentations(augs, image) + image_shape = image.shape[:2] # h, w + assert image_shape == (800, 1200) + annotation = {"bbox": [179, 97, 62, 40, -56]} + + boxes = np.array([annotation["bbox"]], dtype=np.float64) # boxes.shape = (1, 5) + transformed_bbox = transforms.apply_rotated_box(boxes)[0] + + expected_bbox = np.array([484, 388, 248, 160, 56], dtype=np.float64) + err_msg = "transformed_bbox = {}, expected {}".format(transformed_bbox, expected_bbox) + assert np.allclose(transformed_bbox, expected_bbox), err_msg + + def test_resize_and_crop(self): + np.random.seed(125) + min_scale = 0.2 + max_scale = 2.0 + target_height = 1100 + target_width = 1000 + resize_aug = T.ResizeScale(min_scale, max_scale, target_height, target_width) + fixed_size_crop_aug = T.FixedSizeCrop((target_height, target_width)) + hflip_aug = T.RandomFlip() + augs = [resize_aug, fixed_size_crop_aug, hflip_aug] + original_image = np.random.rand(900, 800) + image, transforms = T.apply_augmentations(augs, original_image) + image_shape = image.shape[:2] # h, w + self.assertEqual((1100, 1000), image_shape) + + boxes = np.array( + [[91, 46, 144, 111], [523, 251, 614, 295]], + dtype=np.float64, + ) + transformed_bboxs = transforms.apply_box(boxes) + expected_bboxs = np.array( + [ + [895.42, 33.42666667, 933.91125, 80.66], + [554.0825, 182.39333333, 620.17125, 214.36666667], + ], + dtype=np.float64, + ) + err_msg = "transformed_bbox = {}, expected {}".format(transformed_bboxs, expected_bboxs) + self.assertTrue(np.allclose(transformed_bboxs, expected_bboxs), err_msg) + + polygon = np.array([[91, 46], [144, 46], [144, 111], [91, 111]]) + transformed_polygons = transforms.apply_polygons([polygon]) + expected_polygon = np.array([[934.0, 33.0], [934.0, 80.0], [896.0, 80.0], [896.0, 33.0]]) + self.assertEqual(1, len(transformed_polygons)) + err_msg = "transformed_polygon = {}, expected {}".format( + transformed_polygons[0], expected_polygon + ) + self.assertTrue(polygon_allclose(transformed_polygons[0], expected_polygon), err_msg) + + def test_apply_rotated_boxes_unequal_scaling_factor(self): + np.random.seed(125) + h, w = 400, 200 + newh, neww = 800, 800 + image = np.random.rand(h, w) + augs = [] + augs.append(T.Resize(shape=(newh, neww))) + image, transforms = T.apply_augmentations(augs, image) + image_shape = image.shape[:2] # h, w + assert image_shape == (newh, neww) + + boxes = np.array( + [ + [150, 100, 40, 20, 0], + [150, 100, 40, 20, 30], + [150, 100, 40, 20, 90], + [150, 100, 40, 20, -90], + ], + dtype=np.float64, + ) + transformed_boxes = transforms.apply_rotated_box(boxes) + + expected_bboxes = np.array( + [ + [600, 200, 160, 40, 0], + [600, 200, 144.22205102, 52.91502622, 49.10660535], + [600, 200, 80, 80, 90], + [600, 200, 80, 80, -90], + ], + dtype=np.float64, + ) + err_msg = "transformed_boxes = {}, expected {}".format(transformed_boxes, expected_bboxes) + assert np.allclose(transformed_boxes, expected_bboxes), err_msg + + def test_print_augmentation(self): + t = T.RandomCrop("relative", (100, 100)) + self.assertEqual(str(t), "RandomCrop(crop_type='relative', crop_size=(100, 100))") + + t0 = T.RandomFlip(prob=0.5) + self.assertEqual(str(t0), "RandomFlip(prob=0.5)") + + t1 = T.RandomFlip() + self.assertEqual(str(t1), "RandomFlip()") + + t = T.AugmentationList([t0, t1]) + self.assertEqual(str(t), f"AugmentationList[{t0}, {t1}]") + + def test_random_apply_prob_out_of_range_check(self): + test_probabilities = {0.0: True, 0.5: True, 1.0: True, -0.01: False, 1.01: False} + + for given_probability, is_valid in test_probabilities.items(): + if not is_valid: + self.assertRaises(AssertionError, T.RandomApply, None, prob=given_probability) + else: + T.RandomApply(T.NoOpTransform(), prob=given_probability) + + def test_random_apply_wrapping_aug_probability_occured_evaluation(self): + transform_mock = mock.MagicMock(name="MockTransform", spec=T.Augmentation) + image_mock = mock.MagicMock(name="MockImage") + random_apply = T.RandomApply(transform_mock, prob=0.001) + + with mock.patch.object(random_apply, "_rand_range", return_value=0.0001): + transform = random_apply.get_transform(image_mock) + transform_mock.get_transform.assert_called_once_with(image_mock) + self.assertIsNot(transform, transform_mock) + + def test_random_apply_wrapping_std_transform_probability_occured_evaluation(self): + transform_mock = mock.MagicMock(name="MockTransform", spec=T.Transform) + image_mock = mock.MagicMock(name="MockImage") + random_apply = T.RandomApply(transform_mock, prob=0.001) + + with mock.patch.object(random_apply, "_rand_range", return_value=0.0001): + transform = random_apply.get_transform(image_mock) + self.assertIs(transform, transform_mock) + + def test_random_apply_probability_not_occured_evaluation(self): + transform_mock = mock.MagicMock(name="MockTransform", spec=T.Augmentation) + image_mock = mock.MagicMock(name="MockImage") + random_apply = T.RandomApply(transform_mock, prob=0.001) + + with mock.patch.object(random_apply, "_rand_range", return_value=0.9): + transform = random_apply.get_transform(image_mock) + transform_mock.get_transform.assert_not_called() + self.assertIsInstance(transform, T.NoOpTransform) + + def test_augmentation_input_args(self): + input_shape = (100, 100) + output_shape = (50, 50) + + # define two augmentations with different args + class TG1(T.Augmentation): + def get_transform(self, image, sem_seg): + return T.ResizeTransform( + input_shape[0], input_shape[1], output_shape[0], output_shape[1] + ) + + class TG2(T.Augmentation): + def get_transform(self, image): + assert image.shape[:2] == output_shape # check that TG1 is applied + return T.HFlipTransform(output_shape[1]) + + image = np.random.rand(*input_shape).astype("float32") + sem_seg = (np.random.rand(*input_shape) < 0.5).astype("uint8") + inputs = T.AugInput(image, sem_seg=sem_seg) # provide two args + tfms = inputs.apply_augmentations([TG1(), TG2()]) + self.assertIsInstance(tfms[0], T.ResizeTransform) + self.assertIsInstance(tfms[1], T.HFlipTransform) + self.assertTrue(inputs.image.shape[:2] == output_shape) + self.assertTrue(inputs.sem_seg.shape[:2] == output_shape) + + class TG3(T.Augmentation): + def get_transform(self, image, nonexist): + pass + + with self.assertRaises(AttributeError): + inputs.apply_augmentations([TG3()]) + + def test_augmentation_list(self): + input_shape = (100, 100) + image = np.random.rand(*input_shape).astype("float32") + sem_seg = (np.random.rand(*input_shape) < 0.5).astype("uint8") + inputs = T.AugInput(image, sem_seg=sem_seg) # provide two args + + augs = T.AugmentationList([T.RandomFlip(), T.Resize(20)]) + _ = T.AugmentationList([augs, T.Resize(30)])(inputs) + # 3 in latest fvcore (flattened transformlist), 2 in older + # self.assertEqual(len(tfms), 3) + + def test_color_transforms(self): + rand_img = np.random.random((100, 100, 3)) * 255 + rand_img = rand_img.astype("uint8") + + # Test no-op + noop_transform = T.ColorTransform(lambda img: img) + self.assertTrue(np.array_equal(rand_img, noop_transform.apply_image(rand_img))) + + # Test a ImageOps operation + magnitude = np.random.randint(0, 256) + solarize_transform = T.PILColorTransform(lambda img: ImageOps.solarize(img, magnitude)) + expected_img = ImageOps.solarize(Image.fromarray(rand_img), magnitude) + self.assertTrue(np.array_equal(expected_img, solarize_transform.apply_image(rand_img))) + + def test_resize_transform(self): + input_shapes = [(100, 100), (100, 100, 1), (100, 100, 3)] + output_shapes = [(200, 200), (200, 200, 1), (200, 200, 3)] + for in_shape, out_shape in zip(input_shapes, output_shapes): + in_img = np.random.randint(0, 255, size=in_shape, dtype=np.uint8) + tfm = T.ResizeTransform(in_shape[0], in_shape[1], out_shape[0], out_shape[1]) + out_img = tfm.apply_image(in_img) + self.assertEqual(out_img.shape, out_shape) + + def test_resize_shorted_edge_scriptable(self): + def f(image): + newh, neww = T.ResizeShortestEdge.get_output_shape( + image.shape[-2], image.shape[-1], 80, 133 + ) + return F.interpolate(image.unsqueeze(0), size=(newh, neww)) + + input = torch.randn(3, 10, 10) + script_f = torch.jit.script(f) + self.assertTrue(torch.allclose(f(input), script_f(input))) + + # generalize to new shapes + input = torch.randn(3, 8, 100) + self.assertTrue(torch.allclose(f(input), script_f(input))) + + def test_extent_transform(self): + input_shapes = [(100, 100), (100, 100, 1), (100, 100, 3)] + src_rect = (20, 20, 80, 80) + output_shapes = [(200, 200), (200, 200, 1), (200, 200, 3)] + for in_shape, out_shape in zip(input_shapes, output_shapes): + in_img = np.random.randint(0, 255, size=in_shape, dtype=np.uint8) + tfm = T.ExtentTransform(src_rect, out_shape[:2]) + out_img = tfm.apply_image(in_img) + self.assertTrue(out_img.shape == out_shape) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/layers/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/tests/layers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_blocks.py b/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..5a0488adbfcf0c7eca08616f43ebf695acad4b7e --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_blocks.py @@ -0,0 +1,51 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import unittest +import torch +from torch import nn + +from detectron2.layers import ASPP, DepthwiseSeparableConv2d, FrozenBatchNorm2d +from detectron2.modeling.backbone.resnet import BasicStem, ResNet + + +""" +Test for misc layers. +""" + + +class TestBlocks(unittest.TestCase): + def test_separable_conv(self): + DepthwiseSeparableConv2d(3, 10, norm1="BN", activation1=nn.PReLU()) + + def test_aspp(self): + m = ASPP(3, 10, [2, 3, 4], norm="", activation=nn.PReLU()) + self.assertIsNot(m.convs[0].activation.weight, m.convs[1].activation.weight) + self.assertIsNot(m.convs[0].activation.weight, m.project.activation.weight) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_frozen_batchnorm_fp16(self): + from torch.cuda.amp import autocast + + C = 10 + input = torch.rand(1, C, 10, 10).cuda() + m = FrozenBatchNorm2d(C).cuda() + with autocast(): + output = m(input.half()) + self.assertEqual(output.dtype, torch.float16) + + # requires_grad triggers a different codepath + input.requires_grad_() + with autocast(): + output = m(input.half()) + self.assertEqual(output.dtype, torch.float16) + + def test_resnet_unused_stages(self): + resnet = ResNet(BasicStem(), ResNet.make_default_stages(18), out_features=["res2"]) + self.assertTrue(hasattr(resnet, "res2")) + self.assertFalse(hasattr(resnet, "res3")) + self.assertFalse(hasattr(resnet, "res5")) + + resnet = ResNet(BasicStem(), ResNet.make_default_stages(18), out_features=["res2", "res5"]) + self.assertTrue(hasattr(resnet, "res2")) + self.assertTrue(hasattr(resnet, "res4")) + self.assertTrue(hasattr(resnet, "res5")) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_deformable.py b/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_deformable.py new file mode 100644 index 0000000000000000000000000000000000000000..4aa319fc7e614f6a7a8ece7a45c177211c03012d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_deformable.py @@ -0,0 +1,175 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +import unittest +import torch + +from detectron2.layers import DeformConv, ModulatedDeformConv +from detectron2.utils.env import TORCH_VERSION + + +@unittest.skipIf( + TORCH_VERSION == (1, 8) and torch.cuda.is_available(), + "This test fails under cuda11 + torch1.8.", +) +class DeformableTest(unittest.TestCase): + @unittest.skipIf(not torch.cuda.is_available(), "Deformable not supported for cpu") + def test_forward_output(self): + device = torch.device("cuda") + N, C, H, W = shape = 1, 1, 5, 5 + kernel_size = 3 + padding = 1 + + inputs = torch.arange(np.prod(shape), dtype=torch.float32).reshape(*shape).to(device) + """ + 0 1 2 3 4 + 5 6 7 8 9 + 10 11 12 13 14 + 15 16 17 18 19 + 20 21 22 23 24 + """ + offset_channels = kernel_size * kernel_size * 2 + offset = torch.full((N, offset_channels, H, W), 0.5, dtype=torch.float32).to(device) + + # Test DCN v1 + deform = DeformConv(C, C, kernel_size=kernel_size, padding=padding).to(device) + deform.weight = torch.nn.Parameter(torch.ones_like(deform.weight)) + output = deform(inputs, offset) + output = output.detach().cpu().numpy() + deform_results = np.array( + [ + [30, 41.25, 48.75, 45, 28.75], + [62.25, 81, 90, 80.25, 50.25], + [99.75, 126, 135, 117.75, 72.75], + [105, 131.25, 138.75, 120, 73.75], + [71.75, 89.25, 93.75, 80.75, 49.5], + ] + ) + self.assertTrue(np.allclose(output.flatten(), deform_results.flatten())) + + # Test DCN v2 + mask_channels = kernel_size * kernel_size + mask = torch.full((N, mask_channels, H, W), 0.5, dtype=torch.float32).to(device) + modulate_deform = ModulatedDeformConv(C, C, kernel_size, padding=padding, bias=False).to( + device + ) + modulate_deform.weight = deform.weight + output = modulate_deform(inputs, offset, mask) + output = output.detach().cpu().numpy() + self.assertTrue(np.allclose(output.flatten(), deform_results.flatten() * 0.5)) + + def test_forward_output_on_cpu(self): + device = torch.device("cpu") + N, C, H, W = shape = 1, 1, 5, 5 + kernel_size = 3 + padding = 1 + + inputs = torch.arange(np.prod(shape), dtype=torch.float32).reshape(*shape).to(device) + + offset_channels = kernel_size * kernel_size * 2 + offset = torch.full((N, offset_channels, H, W), 0.5, dtype=torch.float32).to(device) + + # Test DCN v1 on cpu + deform = DeformConv(C, C, kernel_size=kernel_size, padding=padding).to(device) + deform.weight = torch.nn.Parameter(torch.ones_like(deform.weight)) + output = deform(inputs, offset) + output = output.detach().cpu().numpy() + deform_results = np.array( + [ + [30, 41.25, 48.75, 45, 28.75], + [62.25, 81, 90, 80.25, 50.25], + [99.75, 126, 135, 117.75, 72.75], + [105, 131.25, 138.75, 120, 73.75], + [71.75, 89.25, 93.75, 80.75, 49.5], + ] + ) + self.assertTrue(np.allclose(output.flatten(), deform_results.flatten())) + + @unittest.skipIf(not torch.cuda.is_available(), "This test requires gpu access") + def test_forward_output_on_cpu_equals_output_on_gpu(self): + N, C, H, W = shape = 2, 4, 10, 10 + kernel_size = 3 + padding = 1 + + for groups in [1, 2]: + inputs = torch.arange(np.prod(shape), dtype=torch.float32).reshape(*shape) + offset_channels = kernel_size * kernel_size * 2 + offset = torch.full((N, offset_channels, H, W), 0.5, dtype=torch.float32) + + deform_gpu = DeformConv( + C, C, kernel_size=kernel_size, padding=padding, groups=groups + ).to("cuda") + deform_gpu.weight = torch.nn.Parameter(torch.ones_like(deform_gpu.weight)) + output_gpu = deform_gpu(inputs.to("cuda"), offset.to("cuda")).detach().cpu().numpy() + + deform_cpu = DeformConv( + C, C, kernel_size=kernel_size, padding=padding, groups=groups + ).to("cpu") + deform_cpu.weight = torch.nn.Parameter(torch.ones_like(deform_cpu.weight)) + output_cpu = deform_cpu(inputs.to("cpu"), offset.to("cpu")).detach().numpy() + + self.assertTrue(np.allclose(output_gpu.flatten(), output_cpu.flatten())) + + @unittest.skipIf(not torch.cuda.is_available(), "Deformable not supported for cpu") + def test_small_input(self): + device = torch.device("cuda") + for kernel_size in [3, 5]: + padding = kernel_size // 2 + N, C, H, W = shape = (1, 1, kernel_size - 1, kernel_size - 1) + + inputs = torch.rand(shape).to(device) # input size is smaller than kernel size + + offset_channels = kernel_size * kernel_size * 2 + offset = torch.randn((N, offset_channels, H, W), dtype=torch.float32).to(device) + deform = DeformConv(C, C, kernel_size=kernel_size, padding=padding).to(device) + output = deform(inputs, offset) + self.assertTrue(output.shape == inputs.shape) + + mask_channels = kernel_size * kernel_size + mask = torch.ones((N, mask_channels, H, W), dtype=torch.float32).to(device) + modulate_deform = ModulatedDeformConv( + C, C, kernel_size, padding=padding, bias=False + ).to(device) + output = modulate_deform(inputs, offset, mask) + self.assertTrue(output.shape == inputs.shape) + + @unittest.skipIf(not torch.cuda.is_available(), "Deformable not supported for cpu") + def test_raise_exception(self): + device = torch.device("cuda") + N, C, H, W = shape = 1, 1, 3, 3 + kernel_size = 3 + padding = 1 + + inputs = torch.rand(shape, dtype=torch.float32).to(device) + offset_channels = kernel_size * kernel_size # This is wrong channels for offset + offset = torch.randn((N, offset_channels, H, W), dtype=torch.float32).to(device) + deform = DeformConv(C, C, kernel_size=kernel_size, padding=padding).to(device) + self.assertRaises(RuntimeError, deform, inputs, offset) + + offset_channels = kernel_size * kernel_size * 2 + offset = torch.randn((N, offset_channels, H, W), dtype=torch.float32).to(device) + mask_channels = kernel_size * kernel_size * 2 # This is wrong channels for mask + mask = torch.ones((N, mask_channels, H, W), dtype=torch.float32).to(device) + modulate_deform = ModulatedDeformConv(C, C, kernel_size, padding=padding, bias=False).to( + device + ) + self.assertRaises(RuntimeError, modulate_deform, inputs, offset, mask) + + def test_repr(self): + module = DeformConv(3, 10, kernel_size=3, padding=1, deformable_groups=2) + correct_string = ( + "DeformConv(in_channels=3, out_channels=10, kernel_size=(3, 3), " + "stride=(1, 1), padding=(1, 1), dilation=(1, 1), " + "groups=1, deformable_groups=2, bias=False)" + ) + self.assertEqual(repr(module), correct_string) + + module = ModulatedDeformConv(3, 10, kernel_size=3, padding=1, deformable_groups=2) + correct_string = ( + "ModulatedDeformConv(in_channels=3, out_channels=10, kernel_size=(3, 3), " + "stride=1, padding=1, dilation=1, groups=1, deformable_groups=2, bias=True)" + ) + self.assertEqual(repr(module), correct_string) + + +if __name__ == "__main__": + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_losses.py b/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_losses.py new file mode 100644 index 0000000000000000000000000000000000000000..d74920246cbd4a188b3c81cf0c78e982af6da1ac --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_losses.py @@ -0,0 +1,82 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +import unittest +import torch + +from detectron2.layers import ciou_loss, diou_loss + + +class TestLosses(unittest.TestCase): + def test_diou_loss(self): + """ + loss = 1 - iou + d/c + where, + d = (distance between centers of the 2 boxes)^2 + c = (diagonal length of the smallest enclosing box covering the 2 boxes)^2 + """ + # Identical boxes should have loss of 0 + box = torch.tensor([-1, -1, 1, 1], dtype=torch.float32) + loss = diou_loss(box, box) + self.assertTrue(np.allclose(loss, [0.0])) + + # Half size box inside other box + # iou = 0.5, d = 0.25, c = 8 + box2 = torch.tensor([0, -1, 1, 1], dtype=torch.float32) + loss = diou_loss(box, box2) + self.assertTrue(np.allclose(loss, [0.53125])) + + # Two diagonally adjacent boxes + # iou = 0, d = 2, c = 8 + box3 = torch.tensor([0, 0, 1, 1], dtype=torch.float32) + box4 = torch.tensor([1, 1, 2, 2], dtype=torch.float32) + loss = diou_loss(box3, box4) + self.assertTrue(np.allclose(loss, [1.25])) + + # Test batched loss and reductions + box1s = torch.stack([box, box3], dim=0) + box2s = torch.stack([box2, box4], dim=0) + + loss = diou_loss(box1s, box2s, reduction="sum") + self.assertTrue(np.allclose(loss, [1.78125])) + + loss = diou_loss(box1s, box2s, reduction="mean") + self.assertTrue(np.allclose(loss, [0.890625])) + + def test_ciou_loss(self): + """ + loss = 1 - iou + d/c + alpha*v + where, + d = (distance between centers of the 2 boxes)^2 + c = (diagonal length of the smallest enclosing box covering the 2 boxes)^2 + v = (4/pi^2) * (arctan(box1_w/box1_h) - arctan(box2_w/box2_h))^2 + alpha = v/(1 - iou + v) + """ + # Identical boxes should have loss of 0 + box = torch.tensor([-1, -1, 1, 1], dtype=torch.float32) + loss = ciou_loss(box, box) + self.assertTrue(np.allclose(loss, [0.0])) + + # Half size box inside other box + # iou = 0.5, d = 0.25, c = 8 + # v = (4/pi^2) * (arctan(1) - arctan(0.5))^2 = 0.042 + # alpha = 0.0775 + box2 = torch.tensor([0, -1, 1, 1], dtype=torch.float32) + loss = ciou_loss(box, box2) + self.assertTrue(np.allclose(loss, [0.5345])) + + # Two diagonally adjacent boxes + # iou = 0, d = 2, c = 8, v = 0, alpha = 0 + box3 = torch.tensor([0, 0, 1, 1], dtype=torch.float32) + box4 = torch.tensor([1, 1, 2, 2], dtype=torch.float32) + loss = ciou_loss(box3, box4) + self.assertTrue(np.allclose(loss, [1.25])) + + # Test batched loss and reductions + box1s = torch.stack([box, box3], dim=0) + box2s = torch.stack([box2, box4], dim=0) + + loss = ciou_loss(box1s, box2s, reduction="sum") + self.assertTrue(np.allclose(loss, [1.7845])) + + loss = ciou_loss(box1s, box2s, reduction="mean") + self.assertTrue(np.allclose(loss, [0.89225])) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_mask_ops.py b/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_mask_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..162c449c604e05a85d7187518943712b407cb4bb --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_mask_ops.py @@ -0,0 +1,202 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import contextlib +import io +import numpy as np +import unittest +from collections import defaultdict +import torch +import tqdm +from fvcore.common.benchmark import benchmark +from pycocotools.coco import COCO +from tabulate import tabulate +from torch.nn import functional as F + +from detectron2.data import MetadataCatalog +from detectron2.layers.mask_ops import ( + pad_masks, + paste_mask_in_image_old, + paste_masks_in_image, + scale_boxes, +) +from detectron2.structures import BitMasks, Boxes, BoxMode, PolygonMasks +from detectron2.structures.masks import polygons_to_bitmask +from detectron2.utils.file_io import PathManager +from detectron2.utils.testing import random_boxes + + +def iou_between_full_image_bit_masks(a, b): + intersect = (a & b).sum() + union = (a | b).sum() + return intersect / union + + +def rasterize_polygons_with_grid_sample(full_image_bit_mask, box, mask_size, threshold=0.5): + x0, y0, x1, y1 = box[0], box[1], box[2], box[3] + + img_h, img_w = full_image_bit_mask.shape + + mask_y = np.arange(0.0, mask_size) + 0.5 # mask y sample coords in [0.5, mask_size - 0.5] + mask_x = np.arange(0.0, mask_size) + 0.5 # mask x sample coords in [0.5, mask_size - 0.5] + mask_y = mask_y / mask_size * (y1 - y0) + y0 + mask_x = mask_x / mask_size * (x1 - x0) + x0 + + mask_x = (mask_x - 0.5) / (img_w - 1) * 2 + -1 + mask_y = (mask_y - 0.5) / (img_h - 1) * 2 + -1 + gy, gx = torch.meshgrid(torch.from_numpy(mask_y), torch.from_numpy(mask_x)) + ind = torch.stack([gx, gy], dim=-1).to(dtype=torch.float32) + + full_image_bit_mask = torch.from_numpy(full_image_bit_mask) + mask = F.grid_sample( + full_image_bit_mask[None, None, :, :].to(dtype=torch.float32), + ind[None, :, :, :], + align_corners=True, + ) + + return mask[0, 0] >= threshold + + +class TestMaskCropPaste(unittest.TestCase): + def setUp(self): + json_file = MetadataCatalog.get("coco_2017_val_100").json_file + if not PathManager.isfile(json_file): + raise unittest.SkipTest("{} not found".format(json_file)) + with contextlib.redirect_stdout(io.StringIO()): + json_file = PathManager.get_local_path(json_file) + self.coco = COCO(json_file) + + def test_crop_paste_consistency(self): + """ + rasterize_polygons_within_box (used in training) + and + paste_masks_in_image (used in inference) + should be inverse operations to each other. + + This function runs several implementation of the above two operations and prints + the reconstruction error. + """ + + anns = self.coco.loadAnns(self.coco.getAnnIds(iscrowd=False)) # avoid crowd annotations + + selected_anns = anns[:100] + + ious = [] + for ann in tqdm.tqdm(selected_anns): + results = self.process_annotation(ann) + ious.append([k[2] for k in results]) + + ious = np.array(ious) + mean_ious = ious.mean(axis=0) + table = [] + res_dic = defaultdict(dict) + for row, iou in zip(results, mean_ious): + table.append((row[0], row[1], iou)) + res_dic[row[0]][row[1]] = iou + print(tabulate(table, headers=["rasterize", "paste", "iou"], tablefmt="simple")) + # assert that the reconstruction is good: + self.assertTrue(res_dic["polygon"]["aligned"] > 0.94) + self.assertTrue(res_dic["roialign"]["aligned"] > 0.95) + + def process_annotation(self, ann, mask_side_len=28): + # Parse annotation data + img_info = self.coco.loadImgs(ids=[ann["image_id"]])[0] + height, width = img_info["height"], img_info["width"] + gt_polygons = [np.array(p, dtype=np.float64) for p in ann["segmentation"]] + gt_bbox = BoxMode.convert(ann["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) + gt_bit_mask = polygons_to_bitmask(gt_polygons, height, width) + + # Run rasterize .. + torch_gt_bbox = torch.tensor(gt_bbox).to(dtype=torch.float32).reshape(-1, 4) + box_bitmasks = { + "polygon": PolygonMasks([gt_polygons]).crop_and_resize(torch_gt_bbox, mask_side_len)[0], + "gridsample": rasterize_polygons_with_grid_sample(gt_bit_mask, gt_bbox, mask_side_len), + "roialign": BitMasks(torch.from_numpy(gt_bit_mask[None, :, :])).crop_and_resize( + torch_gt_bbox, mask_side_len + )[0], + } + + # Run paste .. + results = defaultdict(dict) + for k, box_bitmask in box_bitmasks.items(): + padded_bitmask, scale = pad_masks(box_bitmask[None, :, :], 1) + scaled_boxes = scale_boxes(torch_gt_bbox, scale) + + r = results[k] + r["old"] = paste_mask_in_image_old( + padded_bitmask[0], scaled_boxes[0], height, width, threshold=0.5 + ) + r["aligned"] = paste_masks_in_image( + box_bitmask[None, :, :], Boxes(torch_gt_bbox), (height, width) + )[0] + + table = [] + for rasterize_method, r in results.items(): + for paste_method, mask in r.items(): + mask = np.asarray(mask) + iou = iou_between_full_image_bit_masks(gt_bit_mask.astype("uint8"), mask) + table.append((rasterize_method, paste_method, iou)) + return table + + def test_polygon_area(self): + # Draw polygon boxes + for d in [5.0, 10.0, 1000.0]: + polygon = PolygonMasks([[[0, 0, 0, d, d, d, d, 0]]]) + area = polygon.area()[0] + target = d ** 2 + self.assertEqual(area, target) + + # Draw polygon triangles + for d in [5.0, 10.0, 1000.0]: + polygon = PolygonMasks([[[0, 0, 0, d, d, d]]]) + area = polygon.area()[0] + target = d ** 2 / 2 + self.assertEqual(area, target) + + def test_paste_mask_scriptable(self): + scripted_f = torch.jit.script(paste_masks_in_image) + N = 10 + masks = torch.rand(N, 28, 28) + boxes = Boxes(random_boxes(N, 100)).tensor + image_shape = (150, 150) + + out = paste_masks_in_image(masks, boxes, image_shape) + scripted_out = scripted_f(masks, boxes, image_shape) + self.assertTrue(torch.equal(out, scripted_out)) + + +def benchmark_paste(): + S = 800 + H, W = image_shape = (S, S) + N = 64 + torch.manual_seed(42) + masks = torch.rand(N, 28, 28) + + center = torch.rand(N, 2) * 600 + 100 + wh = torch.clamp(torch.randn(N, 2) * 40 + 200, min=50) + x0y0 = torch.clamp(center - wh * 0.5, min=0.0) + x1y1 = torch.clamp(center + wh * 0.5, max=S) + boxes = Boxes(torch.cat([x0y0, x1y1], axis=1)) + + def func(device, n=3): + m = masks.to(device=device) + b = boxes.to(device=device) + + def bench(): + for _ in range(n): + paste_masks_in_image(m, b, image_shape) + if device.type == "cuda": + torch.cuda.synchronize() + + return bench + + specs = [{"device": torch.device("cpu"), "n": 3}] + if torch.cuda.is_available(): + specs.append({"device": torch.device("cuda"), "n": 3}) + + benchmark(func, "paste_masks", specs, num_iters=10, warmup_iters=2) + + +if __name__ == "__main__": + benchmark_paste() + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_nms.py b/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_nms.py new file mode 100644 index 0000000000000000000000000000000000000000..a042db6147f110a82597c98f38e6b2221ccad53c --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_nms.py @@ -0,0 +1,33 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from __future__ import absolute_import, division, print_function, unicode_literals +import unittest +import torch + +from detectron2.layers import batched_nms +from detectron2.utils.testing import random_boxes + + +class TestNMS(unittest.TestCase): + def _create_tensors(self, N): + boxes = random_boxes(N, 200) + scores = torch.rand(N) + return boxes, scores + + def test_nms_scriptability(self): + N = 2000 + num_classes = 50 + boxes, scores = self._create_tensors(N) + idxs = torch.randint(0, num_classes, (N,)) + scripted_batched_nms = torch.jit.script(batched_nms) + err_msg = "NMS is incompatible with jit-scripted NMS for IoU={}" + + for iou in [0.2, 0.5, 0.8]: + keep_ref = batched_nms(boxes, scores, idxs, iou) + backup = boxes.clone() + scripted_keep = scripted_batched_nms(boxes, scores, idxs, iou) + assert torch.allclose(boxes, backup), "boxes modified by jit-scripted batched_nms" + self.assertTrue(torch.equal(keep_ref, scripted_keep), err_msg.format(iou)) + + +if __name__ == "__main__": + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_nms_rotated.py b/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_nms_rotated.py new file mode 100644 index 0000000000000000000000000000000000000000..4b45384892ab2a7cb20871cf19374f1bd08907ce --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_nms_rotated.py @@ -0,0 +1,172 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from __future__ import absolute_import, division, print_function, unicode_literals +import numpy as np +import unittest +from copy import deepcopy +import torch +from torchvision import ops + +from detectron2.layers import batched_nms, batched_nms_rotated, nms_rotated +from detectron2.utils.testing import random_boxes + + +def nms_edit_distance(keep1, keep2): + """ + Compare the "keep" result of two nms call. + They are allowed to be different in terms of edit distance + due to floating point precision issues, e.g., + if a box happen to have an IoU of 0.5 with another box, + one implentation may choose to keep it while another may discard it. + """ + keep1, keep2 = keep1.cpu(), keep2.cpu() + if torch.equal(keep1, keep2): + # they should be equal most of the time + return 0 + keep1, keep2 = tuple(keep1), tuple(keep2) + m, n = len(keep1), len(keep2) + + # edit distance with DP + f = [np.arange(n + 1), np.arange(n + 1)] + for i in range(m): + cur_row = i % 2 + other_row = (i + 1) % 2 + f[other_row][0] = i + 1 + for j in range(n): + f[other_row][j + 1] = ( + f[cur_row][j] + if keep1[i] == keep2[j] + else min(min(f[cur_row][j], f[cur_row][j + 1]), f[other_row][j]) + 1 + ) + return f[m % 2][n] + + +class TestNMSRotated(unittest.TestCase): + def reference_horizontal_nms(self, boxes, scores, iou_threshold): + """ + Args: + box_scores (N, 5): boxes in corner-form and probabilities. + (Note here 5 == 4 + 1, i.e., 4-dim horizontal box + 1-dim prob) + iou_threshold: intersection over union threshold. + Returns: + picked: a list of indexes of the kept boxes + """ + picked = [] + _, indexes = scores.sort(descending=True) + while len(indexes) > 0: + current = indexes[0] + picked.append(current.item()) + if len(indexes) == 1: + break + current_box = boxes[current, :] + indexes = indexes[1:] + rest_boxes = boxes[indexes, :] + iou = ops.box_iou(rest_boxes, current_box.unsqueeze(0)).squeeze(1) + indexes = indexes[iou <= iou_threshold] + + return torch.as_tensor(picked) + + def _create_tensors(self, N, device="cpu"): + boxes = random_boxes(N, 200, device=device) + scores = torch.rand(N, device=device) + return boxes, scores + + def test_batched_nms_rotated_0_degree_cpu(self, device="cpu"): + N = 2000 + num_classes = 50 + boxes, scores = self._create_tensors(N, device=device) + idxs = torch.randint(0, num_classes, (N,)) + rotated_boxes = torch.zeros(N, 5, device=device) + rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0 + rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0 + rotated_boxes[:, 2] = boxes[:, 2] - boxes[:, 0] + rotated_boxes[:, 3] = boxes[:, 3] - boxes[:, 1] + err_msg = "Rotated NMS with 0 degree is incompatible with horizontal NMS for IoU={}" + for iou in [0.2, 0.5, 0.8]: + backup = boxes.clone() + keep_ref = batched_nms(boxes, scores, idxs, iou) + assert torch.allclose(boxes, backup), "boxes modified by batched_nms" + backup = rotated_boxes.clone() + keep = batched_nms_rotated(rotated_boxes, scores, idxs, iou) + assert torch.allclose( + rotated_boxes, backup + ), "rotated_boxes modified by batched_nms_rotated" + # Occasionally the gap can be large if there are many IOU on the threshold boundary + self.assertLessEqual(nms_edit_distance(keep, keep_ref), 5, err_msg.format(iou)) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_batched_nms_rotated_0_degree_cuda(self): + self.test_batched_nms_rotated_0_degree_cpu(device="cuda") + + def test_nms_rotated_0_degree_cpu(self, device="cpu"): + N = 1000 + boxes, scores = self._create_tensors(N, device=device) + rotated_boxes = torch.zeros(N, 5, device=device) + rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0 + rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0 + rotated_boxes[:, 2] = boxes[:, 2] - boxes[:, 0] + rotated_boxes[:, 3] = boxes[:, 3] - boxes[:, 1] + err_msg = "Rotated NMS incompatible between CPU and reference implementation for IoU={}" + for iou in [0.2, 0.5, 0.8]: + keep_ref = self.reference_horizontal_nms(boxes, scores, iou) + keep = nms_rotated(rotated_boxes, scores, iou) + self.assertLessEqual(nms_edit_distance(keep, keep_ref), 1, err_msg.format(iou)) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_nms_rotated_0_degree_cuda(self): + self.test_nms_rotated_0_degree_cpu(device="cuda") + + def test_nms_rotated_90_degrees_cpu(self): + N = 1000 + boxes, scores = self._create_tensors(N) + rotated_boxes = torch.zeros(N, 5) + rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0 + rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0 + # Note for rotated_boxes[:, 2] and rotated_boxes[:, 3]: + # widths and heights are intentionally swapped here for 90 degrees case + # so that the reference horizontal nms could be used + rotated_boxes[:, 2] = boxes[:, 3] - boxes[:, 1] + rotated_boxes[:, 3] = boxes[:, 2] - boxes[:, 0] + + rotated_boxes[:, 4] = torch.ones(N) * 90 + err_msg = "Rotated NMS incompatible between CPU and reference implementation for IoU={}" + for iou in [0.2, 0.5, 0.8]: + keep_ref = self.reference_horizontal_nms(boxes, scores, iou) + keep = nms_rotated(rotated_boxes, scores, iou) + self.assertLessEqual(nms_edit_distance(keep, keep_ref), 1, err_msg.format(iou)) + + def test_nms_rotated_180_degrees_cpu(self): + N = 1000 + boxes, scores = self._create_tensors(N) + rotated_boxes = torch.zeros(N, 5) + rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0 + rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0 + rotated_boxes[:, 2] = boxes[:, 2] - boxes[:, 0] + rotated_boxes[:, 3] = boxes[:, 3] - boxes[:, 1] + rotated_boxes[:, 4] = torch.ones(N) * 180 + err_msg = "Rotated NMS incompatible between CPU and reference implementation for IoU={}" + for iou in [0.2, 0.5, 0.8]: + keep_ref = self.reference_horizontal_nms(boxes, scores, iou) + keep = nms_rotated(rotated_boxes, scores, iou) + self.assertLessEqual(nms_edit_distance(keep, keep_ref), 1, err_msg.format(iou)) + + +class TestScriptable(unittest.TestCase): + def setUp(self): + class TestingModule(torch.nn.Module): + def forward(self, boxes, scores, threshold): + return nms_rotated(boxes, scores, threshold) + + self.module = TestingModule() + + def test_scriptable_cpu(self): + m = deepcopy(self.module).cpu() + _ = torch.jit.script(m) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_scriptable_cuda(self): + m = deepcopy(self.module).cuda() + _ = torch.jit.script(m) + + +if __name__ == "__main__": + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_roi_align.py b/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_roi_align.py new file mode 100644 index 0000000000000000000000000000000000000000..b6fd8edefd107b727e3e523f1364fea1f4a20576 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_roi_align.py @@ -0,0 +1,210 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +import unittest +from copy import copy +import cv2 +import torch +from fvcore.common.benchmark import benchmark +from torch.nn import functional as F + +from detectron2.layers.roi_align import ROIAlign, roi_align + + +class ROIAlignTest(unittest.TestCase): + def test_forward_output(self): + input = np.arange(25).reshape(5, 5).astype("float32") + """ + 0 1 2 3 4 + 5 6 7 8 9 + 10 11 12 13 14 + 15 16 17 18 19 + 20 21 22 23 24 + """ + + output = self._simple_roialign(input, [1, 1, 3, 3], (4, 4), aligned=False) + output_correct = self._simple_roialign(input, [1, 1, 3, 3], (4, 4), aligned=True) + + # without correction: + old_results = [ + [7.5, 8, 8.5, 9], + [10, 10.5, 11, 11.5], + [12.5, 13, 13.5, 14], + [15, 15.5, 16, 16.5], + ] + + # with 0.5 correction: + correct_results = [ + [4.5, 5.0, 5.5, 6.0], + [7.0, 7.5, 8.0, 8.5], + [9.5, 10.0, 10.5, 11.0], + [12.0, 12.5, 13.0, 13.5], + ] + # This is an upsampled version of [[6, 7], [11, 12]] + + self.assertTrue(np.allclose(output.flatten(), np.asarray(old_results).flatten())) + self.assertTrue( + np.allclose(output_correct.flatten(), np.asarray(correct_results).flatten()) + ) + + # Also see similar issues in tensorflow at + # https://github.com/tensorflow/tensorflow/issues/26278 + + def test_resize(self): + H, W = 30, 30 + input = np.random.rand(H, W).astype("float32") * 100 + box = [10, 10, 20, 20] + output = self._simple_roialign(input, box, (5, 5), aligned=True) + + input2x = cv2.resize(input, (W // 2, H // 2), interpolation=cv2.INTER_LINEAR) + box2x = [x / 2 for x in box] + output2x = self._simple_roialign(input2x, box2x, (5, 5), aligned=True) + diff = np.abs(output2x - output) + self.assertTrue(diff.max() < 1e-4) + + def test_grid_sample_equivalence(self): + H, W = 30, 30 + input = np.random.rand(H, W).astype("float32") * 100 + box = [10, 10, 20, 20] + for ratio in [1, 2, 3]: + output = self._simple_roialign(input, box, (5, 5), sampling_ratio=ratio) + output_grid_sample = grid_sample_roi_align( + torch.from_numpy(input[None, None, :, :]).float(), + torch.as_tensor(box).float()[None, :], + 5, + 1.0, + ratio, + ) + self.assertTrue(torch.allclose(output, output_grid_sample)) + + def _simple_roialign(self, img, box, resolution, sampling_ratio=0, aligned=True): + """ + RoiAlign with scale 1.0. + """ + if isinstance(resolution, int): + resolution = (resolution, resolution) + op = ROIAlign(resolution, 1.0, sampling_ratio, aligned=aligned) + input = torch.from_numpy(img[None, None, :, :].astype("float32")) + + rois = [0] + list(box) + rois = torch.from_numpy(np.asarray(rois)[None, :].astype("float32")) + output = op.forward(input, rois) + if torch.cuda.is_available(): + output_cuda = op.forward(input.cuda(), rois.cuda()).cpu() + self.assertTrue(torch.allclose(output, output_cuda)) + return output[0, 0] + + def _simple_roialign_with_grad(self, img, box, resolution, device): + if isinstance(resolution, int): + resolution = (resolution, resolution) + + op = ROIAlign(resolution, 1.0, 0, aligned=True) + input = torch.from_numpy(img[None, None, :, :].astype("float32")) + + rois = [0] + list(box) + rois = torch.from_numpy(np.asarray(rois)[None, :].astype("float32")) + input = input.to(device=device) + rois = rois.to(device=device) + input.requires_grad = True + output = op.forward(input, rois) + return input, output + + def test_empty_box(self): + img = np.random.rand(5, 5) + box = [3, 4, 5, 4] + o = self._simple_roialign(img, box, 7) + self.assertTrue(o.shape == (7, 7)) + self.assertTrue((o == 0).all()) + + for dev in ["cpu"] + ["cuda"] if torch.cuda.is_available() else []: + input, output = self._simple_roialign_with_grad(img, box, 7, torch.device(dev)) + output.sum().backward() + self.assertTrue(torch.allclose(input.grad, torch.zeros_like(input))) + + def test_empty_batch(self): + input = torch.zeros(0, 3, 10, 10, dtype=torch.float32) + rois = torch.zeros(0, 5, dtype=torch.float32) + op = ROIAlign((7, 7), 1.0, 0, aligned=True) + output = op.forward(input, rois) + self.assertTrue(output.shape == (0, 3, 7, 7)) + + +def grid_sample_roi_align(input, boxes, output_size, scale, sampling_ratio): + # unlike true roi_align, this does not support different batch_idx + from detectron2.projects.point_rend.point_features import ( + generate_regular_grid_point_coords, + get_point_coords_wrt_image, + point_sample, + ) + + N, _, H, W = input.shape + R = len(boxes) + assert N == 1 + boxes = boxes * scale + grid = generate_regular_grid_point_coords(R, output_size * sampling_ratio, device=boxes.device) + coords = get_point_coords_wrt_image(boxes, grid) + coords = coords / torch.as_tensor([W, H], device=coords.device) # R, s^2, 2 + res = point_sample(input, coords.unsqueeze(0), align_corners=False) # 1,C, R,s^2 + res = ( + res.squeeze(0) + .permute(1, 0, 2) + .reshape(R, -1, output_size * sampling_ratio, output_size * sampling_ratio) + ) + res = F.avg_pool2d(res, sampling_ratio) + return res + + +def benchmark_roi_align(): + def random_boxes(mean_box, stdev, N, maxsize): + ret = torch.rand(N, 4) * stdev + torch.tensor(mean_box, dtype=torch.float) + ret.clamp_(min=0, max=maxsize) + return ret + + def func(shape, nboxes_per_img, sampling_ratio, device, box_size="large"): + N, _, H, _ = shape + input = torch.rand(*shape) + boxes = [] + batch_idx = [] + for k in range(N): + if box_size == "large": + b = random_boxes([80, 80, 130, 130], 24, nboxes_per_img, H) + else: + b = random_boxes([100, 100, 110, 110], 4, nboxes_per_img, H) + boxes.append(b) + batch_idx.append(torch.zeros(nboxes_per_img, 1, dtype=torch.float32) + k) + boxes = torch.cat(boxes, axis=0) + batch_idx = torch.cat(batch_idx, axis=0) + boxes = torch.cat([batch_idx, boxes], axis=1) + + input = input.to(device=device) + boxes = boxes.to(device=device) + + def bench(): + if False and sampling_ratio > 0 and N == 1: + # enable to benchmark grid_sample (slower) + grid_sample_roi_align(input, boxes[:, 1:], 7, 1.0, sampling_ratio) + else: + roi_align(input, boxes, 7, 1.0, sampling_ratio, True) + if device == "cuda": + torch.cuda.synchronize() + + return bench + + def gen_args(arg): + args = [] + for size in ["small", "large"]: + for ratio in [0, 2]: + args.append(copy(arg)) + args[-1]["sampling_ratio"] = ratio + args[-1]["box_size"] = size + return args + + arg = dict(shape=(1, 512, 256, 256), nboxes_per_img=512, device="cuda") + benchmark(func, "cuda_roialign", gen_args(arg), num_iters=20, warmup_iters=1) + arg.update({"device": "cpu", "shape": (1, 256, 128, 128)}) + benchmark(func, "cpu_roialign", gen_args(arg), num_iters=5, warmup_iters=1) + + +if __name__ == "__main__": + if torch.cuda.is_available(): + benchmark_roi_align() + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_roi_align_rotated.py b/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_roi_align_rotated.py new file mode 100644 index 0000000000000000000000000000000000000000..7323d7d5a86816f337571221313c428238c439f4 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/layers/test_roi_align_rotated.py @@ -0,0 +1,176 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import unittest +import cv2 +import torch +from torch.autograd import Variable, gradcheck + +from detectron2.layers.roi_align import ROIAlign +from detectron2.layers.roi_align_rotated import ROIAlignRotated + +logger = logging.getLogger(__name__) + + +class ROIAlignRotatedTest(unittest.TestCase): + def _box_to_rotated_box(self, box, angle): + return [ + (box[0] + box[2]) / 2.0, + (box[1] + box[3]) / 2.0, + box[2] - box[0], + box[3] - box[1], + angle, + ] + + def _rot90(self, img, num): + num = num % 4 # note: -1 % 4 == 3 + for _ in range(num): + img = img.transpose(0, 1).flip(0) + return img + + def test_forward_output_0_90_180_270(self): + for i in range(4): + # i = 0, 1, 2, 3 corresponding to 0, 90, 180, 270 degrees + img = torch.arange(25, dtype=torch.float32).reshape(5, 5) + """ + 0 1 2 3 4 + 5 6 7 8 9 + 10 11 12 13 14 + 15 16 17 18 19 + 20 21 22 23 24 + """ + box = [1, 1, 3, 3] + rotated_box = self._box_to_rotated_box(box=box, angle=90 * i) + + result = self._simple_roi_align_rotated(img=img, box=rotated_box, resolution=(4, 4)) + + # Here's an explanation for 0 degree case: + # point 0 in the original input lies at [0.5, 0.5] + # (the center of bin [0, 1] x [0, 1]) + # point 1 in the original input lies at [1.5, 0.5], etc. + # since the resolution is (4, 4) that divides [1, 3] x [1, 3] + # into 4 x 4 equal bins, + # the top-left bin is [1, 1.5] x [1, 1.5], and its center + # (1.25, 1.25) lies at the 3/4 position + # between point 0 and point 1, point 5 and point 6, + # point 0 and point 5, point 1 and point 6, so it can be calculated as + # 0.25*(0*0.25+1*0.75)+(5*0.25+6*0.75)*0.75 = 4.5 + result_expected = torch.tensor( + [ + [4.5, 5.0, 5.5, 6.0], + [7.0, 7.5, 8.0, 8.5], + [9.5, 10.0, 10.5, 11.0], + [12.0, 12.5, 13.0, 13.5], + ] + ) + # This is also an upsampled version of [[6, 7], [11, 12]] + + # When the box is rotated by 90 degrees CCW, + # the result would be rotated by 90 degrees CW, thus it's -i here + result_expected = self._rot90(result_expected, -i) + + assert torch.allclose(result, result_expected) + + def test_resize(self): + H, W = 30, 30 + input = torch.rand(H, W) * 100 + box = [10, 10, 20, 20] + rotated_box = self._box_to_rotated_box(box, angle=0) + output = self._simple_roi_align_rotated(img=input, box=rotated_box, resolution=(5, 5)) + + input2x = cv2.resize(input.numpy(), (W // 2, H // 2), interpolation=cv2.INTER_LINEAR) + input2x = torch.from_numpy(input2x) + box2x = [x / 2 for x in box] + rotated_box2x = self._box_to_rotated_box(box2x, angle=0) + output2x = self._simple_roi_align_rotated(img=input2x, box=rotated_box2x, resolution=(5, 5)) + assert torch.allclose(output2x, output) + + def _simple_roi_align_rotated(self, img, box, resolution): + """ + RoiAlignRotated with scale 1.0 and 0 sample ratio. + """ + op = ROIAlignRotated(output_size=resolution, spatial_scale=1.0, sampling_ratio=0) + input = img[None, None, :, :] + + rois = [0] + list(box) + rois = torch.tensor(rois, dtype=torch.float32)[None, :] + result_cpu = op.forward(input, rois) + if torch.cuda.is_available(): + result_cuda = op.forward(input.cuda(), rois.cuda()) + assert torch.allclose(result_cpu, result_cuda.cpu()) + return result_cpu[0, 0] + + def test_empty_box(self): + img = torch.rand(5, 5) + out = self._simple_roi_align_rotated(img, [2, 3, 0, 0, 0], (7, 7)) + self.assertTrue((out == 0).all()) + + def test_roi_align_rotated_gradcheck_cpu(self): + dtype = torch.float64 + device = torch.device("cpu") + roi_align_rotated_op = ROIAlignRotated( + output_size=(5, 5), spatial_scale=0.5, sampling_ratio=1 + ).to(dtype=dtype, device=device) + x = torch.rand(1, 1, 10, 10, dtype=dtype, device=device, requires_grad=True) + # roi format is (batch index, x_center, y_center, width, height, angle) + rois = torch.tensor( + [[0, 4.5, 4.5, 9, 9, 0], [0, 2, 7, 4, 4, 0], [0, 7, 7, 4, 4, 0]], + dtype=dtype, + device=device, + ) + + def func(input): + return roi_align_rotated_op(input, rois) + + assert gradcheck(func, (x,)), "gradcheck failed for RoIAlignRotated CPU" + assert gradcheck(func, (x.transpose(2, 3),)), "gradcheck failed for RoIAlignRotated CPU" + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_roi_align_rotated_gradient_cuda(self): + """ + Compute gradients for ROIAlignRotated with multiple bounding boxes on the GPU, + and compare the result with ROIAlign + """ + # torch.manual_seed(123) + dtype = torch.float64 + device = torch.device("cuda") + pool_h, pool_w = (5, 5) + + roi_align = ROIAlign(output_size=(pool_h, pool_w), spatial_scale=1, sampling_ratio=2).to( + device=device + ) + + roi_align_rotated = ROIAlignRotated( + output_size=(pool_h, pool_w), spatial_scale=1, sampling_ratio=2 + ).to(device=device) + + x = torch.rand(1, 1, 10, 10, dtype=dtype, device=device, requires_grad=True) + # x_rotated = x.clone() won't work (will lead to grad_fun=CloneBackward)! + x_rotated = Variable(x.data.clone(), requires_grad=True) + + # roi_rotated format is (batch index, x_center, y_center, width, height, angle) + rois_rotated = torch.tensor( + [[0, 4.5, 4.5, 9, 9, 0], [0, 2, 7, 4, 4, 0], [0, 7, 7, 4, 4, 0]], + dtype=dtype, + device=device, + ) + + y_rotated = roi_align_rotated(x_rotated, rois_rotated) + s_rotated = y_rotated.sum() + s_rotated.backward() + + # roi format is (batch index, x1, y1, x2, y2) + rois = torch.tensor( + [[0, 0, 0, 9, 9], [0, 0, 5, 4, 9], [0, 5, 5, 9, 9]], dtype=dtype, device=device + ) + + y = roi_align(x, rois) + s = y.sum() + s.backward() + + assert torch.allclose( + x.grad, x_rotated.grad + ), "gradients for ROIAlign and ROIAlignRotated mismatch on CUDA" + + +if __name__ == "__main__": + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_anchor_generator.py b/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_anchor_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..13a808e587382216da6fe7ee957603f448172657 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_anchor_generator.py @@ -0,0 +1,120 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import unittest +import torch + +from detectron2.config import get_cfg +from detectron2.layers import ShapeSpec +from detectron2.modeling.anchor_generator import DefaultAnchorGenerator, RotatedAnchorGenerator + +logger = logging.getLogger(__name__) + + +class TestAnchorGenerator(unittest.TestCase): + def test_default_anchor_generator(self): + cfg = get_cfg() + cfg.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64]] + cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.25, 1, 4]] + + anchor_generator = DefaultAnchorGenerator(cfg, [ShapeSpec(stride=4)]) + + # only the last two dimensions of features matter here + num_images = 2 + features = {"stage3": torch.rand(num_images, 96, 1, 2)} + anchors = anchor_generator([features["stage3"]]) + expected_anchor_tensor = torch.tensor( + [ + [-32.0, -8.0, 32.0, 8.0], + [-16.0, -16.0, 16.0, 16.0], + [-8.0, -32.0, 8.0, 32.0], + [-64.0, -16.0, 64.0, 16.0], + [-32.0, -32.0, 32.0, 32.0], + [-16.0, -64.0, 16.0, 64.0], + [-28.0, -8.0, 36.0, 8.0], # -28.0 == -32.0 + STRIDE (4) + [-12.0, -16.0, 20.0, 16.0], + [-4.0, -32.0, 12.0, 32.0], + [-60.0, -16.0, 68.0, 16.0], + [-28.0, -32.0, 36.0, 32.0], + [-12.0, -64.0, 20.0, 64.0], + ] + ) + + self.assertTrue(torch.allclose(anchors[0].tensor, expected_anchor_tensor)) + + def test_default_anchor_generator_centered(self): + # test explicit args + anchor_generator = DefaultAnchorGenerator( + sizes=[32, 64], aspect_ratios=[0.25, 1, 4], strides=[4] + ) + + # only the last two dimensions of features matter here + num_images = 2 + features = {"stage3": torch.rand(num_images, 96, 1, 2)} + expected_anchor_tensor = torch.tensor( + [ + [-30.0, -6.0, 34.0, 10.0], + [-14.0, -14.0, 18.0, 18.0], + [-6.0, -30.0, 10.0, 34.0], + [-62.0, -14.0, 66.0, 18.0], + [-30.0, -30.0, 34.0, 34.0], + [-14.0, -62.0, 18.0, 66.0], + [-26.0, -6.0, 38.0, 10.0], + [-10.0, -14.0, 22.0, 18.0], + [-2.0, -30.0, 14.0, 34.0], + [-58.0, -14.0, 70.0, 18.0], + [-26.0, -30.0, 38.0, 34.0], + [-10.0, -62.0, 22.0, 66.0], + ] + ) + + anchors = anchor_generator([features["stage3"]]) + self.assertTrue(torch.allclose(anchors[0].tensor, expected_anchor_tensor)) + + anchors = torch.jit.script(anchor_generator)([features["stage3"]]) + self.assertTrue(torch.allclose(anchors[0].tensor, expected_anchor_tensor)) + + def test_rrpn_anchor_generator(self): + cfg = get_cfg() + cfg.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64]] + cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.25, 1, 4]] + cfg.MODEL.ANCHOR_GENERATOR.ANGLES = [0, 45] # test single list[float] + anchor_generator = RotatedAnchorGenerator(cfg, [ShapeSpec(stride=4)]) + + # only the last two dimensions of features matter here + num_images = 2 + features = {"stage3": torch.rand(num_images, 96, 1, 2)} + anchors = anchor_generator([features["stage3"]]) + expected_anchor_tensor = torch.tensor( + [ + [0.0, 0.0, 64.0, 16.0, 0.0], + [0.0, 0.0, 64.0, 16.0, 45.0], + [0.0, 0.0, 32.0, 32.0, 0.0], + [0.0, 0.0, 32.0, 32.0, 45.0], + [0.0, 0.0, 16.0, 64.0, 0.0], + [0.0, 0.0, 16.0, 64.0, 45.0], + [0.0, 0.0, 128.0, 32.0, 0.0], + [0.0, 0.0, 128.0, 32.0, 45.0], + [0.0, 0.0, 64.0, 64.0, 0.0], + [0.0, 0.0, 64.0, 64.0, 45.0], + [0.0, 0.0, 32.0, 128.0, 0.0], + [0.0, 0.0, 32.0, 128.0, 45.0], + [4.0, 0.0, 64.0, 16.0, 0.0], # 4.0 == 0.0 + STRIDE (4) + [4.0, 0.0, 64.0, 16.0, 45.0], + [4.0, 0.0, 32.0, 32.0, 0.0], + [4.0, 0.0, 32.0, 32.0, 45.0], + [4.0, 0.0, 16.0, 64.0, 0.0], + [4.0, 0.0, 16.0, 64.0, 45.0], + [4.0, 0.0, 128.0, 32.0, 0.0], + [4.0, 0.0, 128.0, 32.0, 45.0], + [4.0, 0.0, 64.0, 64.0, 0.0], + [4.0, 0.0, 64.0, 64.0, 45.0], + [4.0, 0.0, 32.0, 128.0, 0.0], + [4.0, 0.0, 32.0, 128.0, 45.0], + ] + ) + + self.assertTrue(torch.allclose(anchors[0].tensor, expected_anchor_tensor)) + + +if __name__ == "__main__": + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_backbone.py b/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_backbone.py new file mode 100644 index 0000000000000000000000000000000000000000..3bb100f9bd5b4939e4646821c5a60d51c8ea65fd --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_backbone.py @@ -0,0 +1,34 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import unittest +import torch + +import detectron2.export.torchscript # apply patch # noqa +from detectron2 import model_zoo +from detectron2.config import get_cfg +from detectron2.layers import ShapeSpec +from detectron2.modeling.backbone import build_resnet_backbone +from detectron2.modeling.backbone.fpn import build_resnet_fpn_backbone + + +class TestBackBone(unittest.TestCase): + def test_resnet_scriptability(self): + cfg = get_cfg() + resnet = build_resnet_backbone(cfg, ShapeSpec(channels=3)) + + scripted_resnet = torch.jit.script(resnet) + + inp = torch.rand(2, 3, 100, 100) + out1 = resnet(inp)["res4"] + out2 = scripted_resnet(inp)["res4"] + self.assertTrue(torch.allclose(out1, out2)) + + def test_fpn_scriptability(self): + cfg = model_zoo.get_config("Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml") + bb = build_resnet_fpn_backbone(cfg, ShapeSpec(channels=3)) + bb_s = torch.jit.script(bb) + + inp = torch.rand(2, 3, 128, 128) + out1 = bb(inp)["p5"] + out2 = bb_s(inp)["p5"] + self.assertTrue(torch.allclose(out1, out2)) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_box2box_transform.py b/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_box2box_transform.py new file mode 100644 index 0000000000000000000000000000000000000000..fd3a7b79b6b7a3608ad7cb3918de020a5a600d2f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_box2box_transform.py @@ -0,0 +1,94 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import unittest +import torch + +from detectron2.modeling.box_regression import ( + Box2BoxTransform, + Box2BoxTransformLinear, + Box2BoxTransformRotated, +) +from detectron2.utils.testing import random_boxes + +logger = logging.getLogger(__name__) + + +class TestBox2BoxTransform(unittest.TestCase): + def test_reconstruction(self): + weights = (5, 5, 10, 10) + b2b_tfm = Box2BoxTransform(weights=weights) + src_boxes = random_boxes(10) + dst_boxes = random_boxes(10) + + devices = [torch.device("cpu")] + if torch.cuda.is_available(): + devices.append(torch.device("cuda")) + for device in devices: + src_boxes = src_boxes.to(device=device) + dst_boxes = dst_boxes.to(device=device) + deltas = b2b_tfm.get_deltas(src_boxes, dst_boxes) + dst_boxes_reconstructed = b2b_tfm.apply_deltas(deltas, src_boxes) + self.assertTrue(torch.allclose(dst_boxes, dst_boxes_reconstructed)) + + def test_apply_deltas_tracing(self): + weights = (5, 5, 10, 10) + b2b_tfm = Box2BoxTransform(weights=weights) + + with torch.no_grad(): + func = torch.jit.trace(b2b_tfm.apply_deltas, (torch.randn(10, 20), torch.randn(10, 4))) + + o = func(torch.randn(10, 20), torch.randn(10, 4)) + self.assertEqual(o.shape, (10, 20)) + o = func(torch.randn(5, 20), torch.randn(5, 4)) + self.assertEqual(o.shape, (5, 20)) + + +def random_rotated_boxes(mean_box, std_length, std_angle, N): + return torch.cat( + [torch.rand(N, 4) * std_length, torch.rand(N, 1) * std_angle], dim=1 + ) + torch.tensor(mean_box, dtype=torch.float) + + +class TestBox2BoxTransformRotated(unittest.TestCase): + def test_reconstruction(self): + weights = (5, 5, 10, 10, 1) + b2b_transform = Box2BoxTransformRotated(weights=weights) + src_boxes = random_rotated_boxes([10, 10, 20, 20, -30], 5, 60.0, 10) + dst_boxes = random_rotated_boxes([10, 10, 20, 20, -30], 5, 60.0, 10) + + devices = [torch.device("cpu")] + if torch.cuda.is_available(): + devices.append(torch.device("cuda")) + for device in devices: + src_boxes = src_boxes.to(device=device) + dst_boxes = dst_boxes.to(device=device) + deltas = b2b_transform.get_deltas(src_boxes, dst_boxes) + dst_boxes_reconstructed = b2b_transform.apply_deltas(deltas, src_boxes) + assert torch.allclose(dst_boxes[:, :4], dst_boxes_reconstructed[:, :4], atol=1e-5) + # angle difference has to be normalized + assert torch.allclose( + (dst_boxes[:, 4] - dst_boxes_reconstructed[:, 4] + 180.0) % 360.0 - 180.0, + torch.zeros_like(dst_boxes[:, 4]), + atol=1e-4, + ) + + +class TestBox2BoxTransformLinear(unittest.TestCase): + def test_reconstruction(self): + b2b_tfm = Box2BoxTransformLinear() + src_boxes = random_boxes(10) + dst_boxes = torch.tensor([0, 0, 101, 101] * 10).reshape(10, 4).float() + + devices = [torch.device("cpu")] + if torch.cuda.is_available(): + devices.append(torch.device("cuda")) + for device in devices: + src_boxes = src_boxes.to(device=device) + dst_boxes = dst_boxes.to(device=device) + deltas = b2b_tfm.get_deltas(src_boxes, dst_boxes) + dst_boxes_reconstructed = b2b_tfm.apply_deltas(deltas, src_boxes) + self.assertTrue(torch.allclose(dst_boxes, dst_boxes_reconstructed, atol=1e-3)) + + +if __name__ == "__main__": + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_fast_rcnn.py b/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_fast_rcnn.py new file mode 100644 index 0000000000000000000000000000000000000000..e29b944bffca1ccbf5b02be59a753f3188d90a4f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_fast_rcnn.py @@ -0,0 +1,171 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import unittest +import torch + +from detectron2.layers import ShapeSpec +from detectron2.modeling.box_regression import Box2BoxTransform, Box2BoxTransformRotated +from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers +from detectron2.modeling.roi_heads.rotated_fast_rcnn import RotatedFastRCNNOutputLayers +from detectron2.structures import Boxes, Instances, RotatedBoxes +from detectron2.utils.events import EventStorage + +logger = logging.getLogger(__name__) + + +class FastRCNNTest(unittest.TestCase): + def test_fast_rcnn(self): + torch.manual_seed(132) + + box_head_output_size = 8 + + box_predictor = FastRCNNOutputLayers( + ShapeSpec(channels=box_head_output_size), + box2box_transform=Box2BoxTransform(weights=(10, 10, 5, 5)), + num_classes=5, + ) + feature_pooled = torch.rand(2, box_head_output_size) + predictions = box_predictor(feature_pooled) + + proposal_boxes = torch.tensor([[0.8, 1.1, 3.2, 2.8], [2.3, 2.5, 7, 8]], dtype=torch.float32) + gt_boxes = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6]], dtype=torch.float32) + proposal = Instances((10, 10)) + proposal.proposal_boxes = Boxes(proposal_boxes) + proposal.gt_boxes = Boxes(gt_boxes) + proposal.gt_classes = torch.tensor([1, 2]) + + with EventStorage(): # capture events in a new storage to discard them + losses = box_predictor.losses(predictions, [proposal]) + + expected_losses = { + "loss_cls": torch.tensor(1.7951188087), + "loss_box_reg": torch.tensor(4.0357131958), + } + for name in expected_losses.keys(): + assert torch.allclose(losses[name], expected_losses[name]) + + def test_fast_rcnn_empty_batch(self, device="cpu"): + box_predictor = FastRCNNOutputLayers( + ShapeSpec(channels=10), + box2box_transform=Box2BoxTransform(weights=(10, 10, 5, 5)), + num_classes=8, + ).to(device=device) + + logits = torch.randn(0, 100, requires_grad=True, device=device) + deltas = torch.randn(0, 4, requires_grad=True, device=device) + losses = box_predictor.losses([logits, deltas], []) + for value in losses.values(): + self.assertTrue(torch.allclose(value, torch.zeros_like(value))) + sum(losses.values()).backward() + self.assertTrue(logits.grad is not None) + self.assertTrue(deltas.grad is not None) + + predictions, _ = box_predictor.inference([logits, deltas], []) + self.assertEqual(len(predictions), 0) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_fast_rcnn_empty_batch_cuda(self): + self.test_fast_rcnn_empty_batch(device=torch.device("cuda")) + + def test_fast_rcnn_rotated(self): + torch.manual_seed(132) + box_head_output_size = 8 + + box_predictor = RotatedFastRCNNOutputLayers( + ShapeSpec(channels=box_head_output_size), + box2box_transform=Box2BoxTransformRotated(weights=(10, 10, 5, 5, 1)), + num_classes=5, + ) + feature_pooled = torch.rand(2, box_head_output_size) + predictions = box_predictor(feature_pooled) + proposal_boxes = torch.tensor( + [[2, 1.95, 2.4, 1.7, 0], [4.65, 5.25, 4.7, 5.5, 0]], dtype=torch.float32 + ) + gt_boxes = torch.tensor([[2, 2, 2, 2, 0], [4, 4, 4, 4, 0]], dtype=torch.float32) + proposal = Instances((10, 10)) + proposal.proposal_boxes = RotatedBoxes(proposal_boxes) + proposal.gt_boxes = RotatedBoxes(gt_boxes) + proposal.gt_classes = torch.tensor([1, 2]) + + with EventStorage(): # capture events in a new storage to discard them + losses = box_predictor.losses(predictions, [proposal]) + + # Note: the expected losses are slightly different even if + # the boxes are essentially the same as in the FastRCNNOutput test, because + # bbox_pred in FastRCNNOutputLayers have different Linear layers/initialization + # between the two cases. + expected_losses = { + "loss_cls": torch.tensor(1.7920907736), + "loss_box_reg": torch.tensor(4.0410838127), + } + for name in expected_losses.keys(): + assert torch.allclose(losses[name], expected_losses[name]) + + def test_predict_boxes_tracing(self): + class Model(torch.nn.Module): + def __init__(self, output_layer): + super(Model, self).__init__() + self._output_layer = output_layer + + def forward(self, proposal_deltas, proposal_boxes): + instances = Instances((10, 10)) + instances.proposal_boxes = Boxes(proposal_boxes) + return self._output_layer.predict_boxes((None, proposal_deltas), [instances]) + + box_head_output_size = 8 + + box_predictor = FastRCNNOutputLayers( + ShapeSpec(channels=box_head_output_size), + box2box_transform=Box2BoxTransform(weights=(10, 10, 5, 5)), + num_classes=5, + ) + + model = Model(box_predictor) + + from detectron2.export.torchscript_patch import patch_builtin_len + + with torch.no_grad(), patch_builtin_len(): + func = torch.jit.trace(model, (torch.randn(10, 20), torch.randn(10, 4))) + + o = func(torch.randn(10, 20), torch.randn(10, 4)) + self.assertEqual(o[0].shape, (10, 20)) + o = func(torch.randn(5, 20), torch.randn(5, 4)) + self.assertEqual(o[0].shape, (5, 20)) + o = func(torch.randn(20, 20), torch.randn(20, 4)) + self.assertEqual(o[0].shape, (20, 20)) + + def test_predict_probs_tracing(self): + class Model(torch.nn.Module): + def __init__(self, output_layer): + super(Model, self).__init__() + self._output_layer = output_layer + + def forward(self, scores, proposal_boxes): + instances = Instances((10, 10)) + instances.proposal_boxes = Boxes(proposal_boxes) + return self._output_layer.predict_probs((scores, None), [instances]) + + box_head_output_size = 8 + + box_predictor = FastRCNNOutputLayers( + ShapeSpec(channels=box_head_output_size), + box2box_transform=Box2BoxTransform(weights=(10, 10, 5, 5)), + num_classes=5, + ) + + model = Model(box_predictor) + + from detectron2.export.torchscript_patch import patch_builtin_len + + with torch.no_grad(), patch_builtin_len(): + func = torch.jit.trace(model, (torch.randn(10, 6), torch.rand(10, 4))) + o = func(torch.randn(10, 6), torch.randn(10, 4)) + self.assertEqual(o[0].shape, (10, 6)) + o = func(torch.randn(5, 6), torch.randn(5, 4)) + self.assertEqual(o[0].shape, (5, 6)) + o = func(torch.randn(20, 6), torch.randn(20, 4)) + self.assertEqual(o[0].shape, (20, 6)) + + +if __name__ == "__main__": + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_matcher.py b/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_matcher.py new file mode 100644 index 0000000000000000000000000000000000000000..6eb2db0c24b117337c431e9ef00a85a3bced71b9 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_matcher.py @@ -0,0 +1,42 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import unittest +from typing import List +import torch + +from detectron2.config import get_cfg +from detectron2.modeling.matcher import Matcher + + +class TestMatcher(unittest.TestCase): + def test_scriptability(self): + cfg = get_cfg() + anchor_matcher = Matcher( + cfg.MODEL.RPN.IOU_THRESHOLDS, cfg.MODEL.RPN.IOU_LABELS, allow_low_quality_matches=True + ) + match_quality_matrix = torch.tensor( + [[0.15, 0.45, 0.2, 0.6], [0.3, 0.65, 0.05, 0.1], [0.05, 0.4, 0.25, 0.4]] + ) + expected_matches = torch.tensor([1, 1, 2, 0]) + expected_match_labels = torch.tensor([-1, 1, 0, 1], dtype=torch.int8) + + matches, match_labels = anchor_matcher(match_quality_matrix) + self.assertTrue(torch.allclose(matches, expected_matches)) + self.assertTrue(torch.allclose(match_labels, expected_match_labels)) + + # nonzero_tuple must be import explicitly to let jit know what it is. + # https://github.com/pytorch/pytorch/issues/38964 + from detectron2.layers import nonzero_tuple # noqa F401 + + def f(thresholds: List[float], labels: List[int]): + return Matcher(thresholds, labels, allow_low_quality_matches=True) + + scripted_anchor_matcher = torch.jit.script(f)( + cfg.MODEL.RPN.IOU_THRESHOLDS, cfg.MODEL.RPN.IOU_LABELS + ) + matches, match_labels = scripted_anchor_matcher(match_quality_matrix) + self.assertTrue(torch.allclose(matches, expected_matches)) + self.assertTrue(torch.allclose(match_labels, expected_match_labels)) + + +if __name__ == "__main__": + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_mmdet.py b/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_mmdet.py new file mode 100644 index 0000000000000000000000000000000000000000..a743b0b67d5ab664257040621d28c1b1b4451709 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_mmdet.py @@ -0,0 +1,186 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import unittest + +from detectron2.layers import ShapeSpec +from detectron2.modeling.mmdet_wrapper import MMDetBackbone, MMDetDetector + +try: + import mmdet.models # noqa + + HAS_MMDET = True +except ImportError: + HAS_MMDET = False + + +@unittest.skipIf(not HAS_MMDET, "mmdet not available") +class TestMMDetWrapper(unittest.TestCase): + def test_backbone(self): + MMDetBackbone( + backbone=dict( + type="DetectoRS_ResNet", + conv_cfg=dict(type="ConvAWS"), + sac=dict(type="SAC", use_deform=True), + stage_with_sac=(False, True, True, True), + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type="BN", requires_grad=True), + norm_eval=True, + style="pytorch", + ), + neck=dict( + type="FPN", + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5, + ), + # skip pretrained model for tests + # pretrained_backbone="torchvision://resnet50", + output_shapes=[ShapeSpec(channels=256, stride=s) for s in [4, 8, 16, 32, 64]], + output_names=["p2", "p3", "p4", "p5", "p6"], + ) + + def test_detector(self): + # a basic R50 Mask R-CNN + MMDetDetector( + detector=dict( + type="MaskRCNN", + backbone=dict( + type="ResNet", + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type="BN", requires_grad=True), + norm_eval=True, + style="pytorch", + # skip pretrained model for tests + # init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')) + ), + neck=dict( + type="FPN", in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5 + ), + rpn_head=dict( + type="RPNHead", + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type="AnchorGenerator", + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64], + ), + bbox_coder=dict( + type="DeltaXYWHBBoxCoder", + target_means=[0.0, 0.0, 0.0, 0.0], + target_stds=[1.0, 1.0, 1.0, 1.0], + ), + loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type="L1Loss", loss_weight=1.0), + ), + roi_head=dict( + type="StandardRoIHead", + bbox_roi_extractor=dict( + type="SingleRoIExtractor", + roi_layer=dict(type="RoIAlign", output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + ), + bbox_head=dict( + type="Shared2FCBBoxHead", + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type="DeltaXYWHBBoxCoder", + target_means=[0.0, 0.0, 0.0, 0.0], + target_stds=[0.1, 0.1, 0.2, 0.2], + ), + reg_class_agnostic=False, + loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type="L1Loss", loss_weight=1.0), + ), + mask_roi_extractor=dict( + type="SingleRoIExtractor", + roi_layer=dict(type="RoIAlign", output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + ), + mask_head=dict( + type="FCNMaskHead", + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_mask=dict(type="CrossEntropyLoss", use_mask=True, loss_weight=1.0), + ), + ), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type="MaxIoUAssigner", + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1, + ), + sampler=dict( + type="RandomSampler", + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False, + ), + allowed_border=-1, + pos_weight=-1, + debug=False, + ), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type="nms", iou_threshold=0.7), + min_bbox_size=0, + ), + rcnn=dict( + assigner=dict( + type="MaxIoUAssigner", + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=True, + ignore_iof_thr=-1, + ), + sampler=dict( + type="RandomSampler", + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True, + ), + mask_size=28, + pos_weight=-1, + debug=False, + ), + ), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type="nms", iou_threshold=0.7), + min_bbox_size=0, + ), + rcnn=dict( + score_thr=0.05, + nms=dict(type="nms", iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5, + ), + ), + ), + pixel_mean=[1, 2, 3], + pixel_std=[1, 2, 3], + ) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_model_e2e.py b/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_model_e2e.py new file mode 100644 index 0000000000000000000000000000000000000000..5da35205eba60c739b8a919121f4e9a85a24138b --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_model_e2e.py @@ -0,0 +1,223 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + + +import itertools +import unittest +from contextlib import contextmanager +from copy import deepcopy +import torch + +from detectron2.structures import BitMasks, Boxes, ImageList, Instances +from detectron2.utils.events import EventStorage +from detectron2.utils.testing import get_model_no_weights + + +@contextmanager +def typecheck_hook(model, *, in_dtype=None, out_dtype=None): + """ + Check that the model must be called with the given input/output dtype + """ + if not isinstance(in_dtype, set): + in_dtype = {in_dtype} + if not isinstance(out_dtype, set): + out_dtype = {out_dtype} + + def flatten(x): + if isinstance(x, torch.Tensor): + return [x] + if isinstance(x, (list, tuple)): + return list(itertools.chain(*[flatten(t) for t in x])) + if isinstance(x, dict): + return flatten(list(x.values())) + return [] + + def hook(module, input, output): + if in_dtype is not None: + dtypes = {x.dtype for x in flatten(input)} + assert ( + dtypes == in_dtype + ), f"Expected input dtype of {type(module)} is {in_dtype}. Got {dtypes} instead!" + + if out_dtype is not None: + dtypes = {x.dtype for x in flatten(output)} + assert ( + dtypes == out_dtype + ), f"Expected output dtype of {type(module)} is {out_dtype}. Got {dtypes} instead!" + + with model.register_forward_hook(hook): + yield + + +def create_model_input(img, inst=None): + if inst is not None: + return {"image": img, "instances": inst} + else: + return {"image": img} + + +def get_empty_instance(h, w): + inst = Instances((h, w)) + inst.gt_boxes = Boxes(torch.rand(0, 4)) + inst.gt_classes = torch.tensor([]).to(dtype=torch.int64) + inst.gt_masks = BitMasks(torch.rand(0, h, w)) + return inst + + +def get_regular_bitmask_instances(h, w): + inst = Instances((h, w)) + inst.gt_boxes = Boxes(torch.rand(3, 4)) + inst.gt_boxes.tensor[:, 2:] += inst.gt_boxes.tensor[:, :2] + inst.gt_classes = torch.tensor([3, 4, 5]).to(dtype=torch.int64) + inst.gt_masks = BitMasks((torch.rand(3, h, w) > 0.5)) + return inst + + +class InstanceModelE2ETest: + def setUp(self): + torch.manual_seed(43) + self.model = get_model_no_weights(self.CONFIG_PATH) + + def _test_eval(self, input_sizes): + inputs = [create_model_input(torch.rand(3, s[0], s[1])) for s in input_sizes] + self.model.eval() + self.model(inputs) + + def _test_train(self, input_sizes, instances): + assert len(input_sizes) == len(instances) + inputs = [ + create_model_input(torch.rand(3, s[0], s[1]), inst) + for s, inst in zip(input_sizes, instances) + ] + self.model.train() + with EventStorage(): + losses = self.model(inputs) + sum(losses.values()).backward() + del losses + + def _inf_tensor(self, *shape): + return 1.0 / torch.zeros(*shape, device=self.model.device) + + def _nan_tensor(self, *shape): + return torch.zeros(*shape, device=self.model.device).fill_(float("nan")) + + def test_empty_data(self): + instances = [get_empty_instance(200, 250), get_empty_instance(200, 249)] + self._test_eval([(200, 250), (200, 249)]) + self._test_train([(200, 250), (200, 249)], instances) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA unavailable") + def test_eval_tocpu(self): + model = deepcopy(self.model).cpu() + model.eval() + input_sizes = [(200, 250), (200, 249)] + inputs = [create_model_input(torch.rand(3, s[0], s[1])) for s in input_sizes] + model(inputs) + + +class MaskRCNNE2ETest(InstanceModelE2ETest, unittest.TestCase): + CONFIG_PATH = "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml" + + def test_half_empty_data(self): + instances = [get_empty_instance(200, 250), get_regular_bitmask_instances(200, 249)] + self._test_train([(200, 250), (200, 249)], instances) + + # This test is flaky because in some environment the output features are zero due to relu + # def test_rpn_inf_nan_data(self): + # self.model.eval() + # for tensor in [self._inf_tensor, self._nan_tensor]: + # images = ImageList(tensor(1, 3, 512, 512), [(510, 510)]) + # features = { + # "p2": tensor(1, 256, 256, 256), + # "p3": tensor(1, 256, 128, 128), + # "p4": tensor(1, 256, 64, 64), + # "p5": tensor(1, 256, 32, 32), + # "p6": tensor(1, 256, 16, 16), + # } + # props, _ = self.model.proposal_generator(images, features) + # self.assertEqual(len(props[0]), 0) + + def test_roiheads_inf_nan_data(self): + self.model.eval() + for tensor in [self._inf_tensor, self._nan_tensor]: + images = ImageList(tensor(1, 3, 512, 512), [(510, 510)]) + features = { + "p2": tensor(1, 256, 256, 256), + "p3": tensor(1, 256, 128, 128), + "p4": tensor(1, 256, 64, 64), + "p5": tensor(1, 256, 32, 32), + "p6": tensor(1, 256, 16, 16), + } + props = [Instances((510, 510))] + props[0].proposal_boxes = Boxes([[10, 10, 20, 20]]).to(device=self.model.device) + props[0].objectness_logits = torch.tensor([1.0]).reshape(1, 1) + det, _ = self.model.roi_heads(images, features, props) + self.assertEqual(len(det[0]), 0) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_autocast(self): + from torch.cuda.amp import autocast + + inputs = [{"image": torch.rand(3, 100, 100)}] + self.model.eval() + with autocast(), typecheck_hook( + self.model.backbone, in_dtype=torch.float32, out_dtype=torch.float16 + ), typecheck_hook( + self.model.roi_heads.box_predictor, in_dtype=torch.float16, out_dtype=torch.float16 + ): + out = self.model.inference(inputs, do_postprocess=False)[0] + self.assertEqual(out.pred_boxes.tensor.dtype, torch.float32) + self.assertEqual(out.pred_masks.dtype, torch.float16) + self.assertEqual(out.scores.dtype, torch.float32) # scores comes from softmax + + +class RetinaNetE2ETest(InstanceModelE2ETest, unittest.TestCase): + CONFIG_PATH = "COCO-Detection/retinanet_R_50_FPN_1x.yaml" + + def test_inf_nan_data(self): + self.model.eval() + self.model.score_threshold = -999999999 + for tensor in [self._inf_tensor, self._nan_tensor]: + images = ImageList(tensor(1, 3, 512, 512), [(510, 510)]) + features = [ + tensor(1, 256, 128, 128), + tensor(1, 256, 64, 64), + tensor(1, 256, 32, 32), + tensor(1, 256, 16, 16), + tensor(1, 256, 8, 8), + ] + pred_logits, pred_anchor_deltas = self.model.head(features) + pred_logits = [tensor(*x.shape) for x in pred_logits] + pred_anchor_deltas = [tensor(*x.shape) for x in pred_anchor_deltas] + det = self.model.forward_inference(images, features, [pred_logits, pred_anchor_deltas]) + # all predictions (if any) are infinite or nan + if len(det[0]): + self.assertTrue(torch.isfinite(det[0].pred_boxes.tensor).sum() == 0) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_autocast(self): + from torch.cuda.amp import autocast + + inputs = [{"image": torch.rand(3, 100, 100)}] + self.model.eval() + with autocast(), typecheck_hook( + self.model.backbone, in_dtype=torch.float32, out_dtype=torch.float16 + ), typecheck_hook(self.model.head, in_dtype=torch.float16, out_dtype=torch.float16): + out = self.model(inputs)[0]["instances"] + self.assertEqual(out.pred_boxes.tensor.dtype, torch.float32) + self.assertEqual(out.scores.dtype, torch.float16) + + +class SemSegE2ETest(unittest.TestCase): + CONFIG_PATH = "Misc/semantic_R_50_FPN_1x.yaml" + + def setUp(self): + torch.manual_seed(43) + self.model = get_model_no_weights(self.CONFIG_PATH) + + def _test_eval(self, input_sizes): + inputs = [create_model_input(torch.rand(3, s[0], s[1])) for s in input_sizes] + self.model.eval() + self.model(inputs) + + def test_forward(self): + self._test_eval([(200, 250), (200, 249)]) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_roi_heads.py b/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_roi_heads.py new file mode 100644 index 0000000000000000000000000000000000000000..6af160efeb02e500e5f354fa8107a05a12b735eb --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_roi_heads.py @@ -0,0 +1,323 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import unittest +from copy import deepcopy +import torch +from torch import nn + +from detectron2 import model_zoo +from detectron2.config import get_cfg +from detectron2.export.torchscript_patch import ( + freeze_training_mode, + patch_builtin_len, + patch_instances, +) +from detectron2.layers import ShapeSpec +from detectron2.modeling.proposal_generator.build import build_proposal_generator +from detectron2.modeling.roi_heads import ( + FastRCNNConvFCHead, + KRCNNConvDeconvUpsampleHead, + MaskRCNNConvUpsampleHead, + StandardROIHeads, + build_roi_heads, +) +from detectron2.projects import point_rend +from detectron2.structures import BitMasks, Boxes, ImageList, Instances, RotatedBoxes +from detectron2.utils.events import EventStorage +from detectron2.utils.testing import assert_instances_allclose, random_boxes + +logger = logging.getLogger(__name__) + +""" +Make sure the losses of ROIHeads/RPN do not change, to avoid +breaking the forward logic by mistake. +This relies on assumption that pytorch's RNG is stable. +""" + + +class ROIHeadsTest(unittest.TestCase): + def test_roi_heads(self): + torch.manual_seed(121) + cfg = get_cfg() + cfg.MODEL.ROI_BOX_HEAD.NAME = "FastRCNNConvFCHead" + cfg.MODEL.ROI_BOX_HEAD.NUM_FC = 2 + cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignV2" + cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10, 10, 5, 5) + cfg.MODEL.MASK_ON = True + num_images = 2 + images_tensor = torch.rand(num_images, 20, 30) + image_sizes = [(10, 10), (20, 30)] + images = ImageList(images_tensor, image_sizes) + num_channels = 1024 + features = {"res4": torch.rand(num_images, num_channels, 1, 2)} + feature_shape = {"res4": ShapeSpec(channels=num_channels, stride=16)} + + image_shape = (15, 15) + gt_boxes0 = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6]], dtype=torch.float32) + gt_instance0 = Instances(image_shape) + gt_instance0.gt_boxes = Boxes(gt_boxes0) + gt_instance0.gt_classes = torch.tensor([2, 1]) + gt_instance0.gt_masks = BitMasks(torch.rand((2,) + image_shape) > 0.5) + gt_boxes1 = torch.tensor([[1, 5, 2, 8], [7, 3, 10, 5]], dtype=torch.float32) + gt_instance1 = Instances(image_shape) + gt_instance1.gt_boxes = Boxes(gt_boxes1) + gt_instance1.gt_classes = torch.tensor([1, 2]) + gt_instance1.gt_masks = BitMasks(torch.rand((2,) + image_shape) > 0.5) + gt_instances = [gt_instance0, gt_instance1] + + proposal_generator = build_proposal_generator(cfg, feature_shape) + roi_heads = StandardROIHeads(cfg, feature_shape) + + with EventStorage(): # capture events in a new storage to discard them + proposals, proposal_losses = proposal_generator(images, features, gt_instances) + _, detector_losses = roi_heads(images, features, proposals, gt_instances) + + detector_losses.update(proposal_losses) + expected_losses = { + "loss_cls": 4.5253729820251465, + "loss_box_reg": 0.009785720147192478, + "loss_mask": 0.693184494972229, + "loss_rpn_cls": 0.08186662942171097, + "loss_rpn_loc": 0.1104838103055954, + } + succ = all( + torch.allclose(detector_losses[name], torch.tensor(expected_losses.get(name, 0.0))) + for name in detector_losses.keys() + ) + self.assertTrue( + succ, + "Losses has changed! New losses: {}".format( + {k: v.item() for k, v in detector_losses.items()} + ), + ) + + def test_rroi_heads(self): + torch.manual_seed(121) + cfg = get_cfg() + cfg.MODEL.PROPOSAL_GENERATOR.NAME = "RRPN" + cfg.MODEL.ANCHOR_GENERATOR.NAME = "RotatedAnchorGenerator" + cfg.MODEL.ROI_HEADS.NAME = "RROIHeads" + cfg.MODEL.ROI_BOX_HEAD.NAME = "FastRCNNConvFCHead" + cfg.MODEL.ROI_BOX_HEAD.NUM_FC = 2 + cfg.MODEL.RPN.BBOX_REG_WEIGHTS = (1, 1, 1, 1, 1) + cfg.MODEL.RPN.HEAD_NAME = "StandardRPNHead" + cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignRotated" + cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10, 10, 5, 5, 1) + num_images = 2 + images_tensor = torch.rand(num_images, 20, 30) + image_sizes = [(10, 10), (20, 30)] + images = ImageList(images_tensor, image_sizes) + num_channels = 1024 + features = {"res4": torch.rand(num_images, num_channels, 1, 2)} + feature_shape = {"res4": ShapeSpec(channels=num_channels, stride=16)} + + image_shape = (15, 15) + gt_boxes0 = torch.tensor([[2, 2, 2, 2, 30], [4, 4, 4, 4, 0]], dtype=torch.float32) + gt_instance0 = Instances(image_shape) + gt_instance0.gt_boxes = RotatedBoxes(gt_boxes0) + gt_instance0.gt_classes = torch.tensor([2, 1]) + gt_boxes1 = torch.tensor([[1.5, 5.5, 1, 3, 0], [8.5, 4, 3, 2, -50]], dtype=torch.float32) + gt_instance1 = Instances(image_shape) + gt_instance1.gt_boxes = RotatedBoxes(gt_boxes1) + gt_instance1.gt_classes = torch.tensor([1, 2]) + gt_instances = [gt_instance0, gt_instance1] + + proposal_generator = build_proposal_generator(cfg, feature_shape) + roi_heads = build_roi_heads(cfg, feature_shape) + + with EventStorage(): # capture events in a new storage to discard them + proposals, proposal_losses = proposal_generator(images, features, gt_instances) + _, detector_losses = roi_heads(images, features, proposals, gt_instances) + + detector_losses.update(proposal_losses) + expected_losses = { + "loss_cls": 4.365657806396484, + "loss_box_reg": 0.0015851043863222003, + "loss_rpn_cls": 0.2427729219198227, + "loss_rpn_loc": 0.3646621108055115, + } + succ = all( + torch.allclose(detector_losses[name], torch.tensor(expected_losses.get(name, 0.0))) + for name in detector_losses.keys() + ) + self.assertTrue( + succ, + "Losses has changed! New losses: {}".format( + {k: v.item() for k, v in detector_losses.items()} + ), + ) + + def test_box_head_scriptability(self): + input_shape = ShapeSpec(channels=1024, height=14, width=14) + box_features = torch.randn(4, 1024, 14, 14) + + box_head = FastRCNNConvFCHead( + input_shape, conv_dims=[512, 512], fc_dims=[1024, 1024] + ).eval() + script_box_head = torch.jit.script(box_head) + + origin_output = box_head(box_features) + script_output = script_box_head(box_features) + self.assertTrue(torch.equal(origin_output, script_output)) + + def test_mask_head_scriptability(self): + input_shape = ShapeSpec(channels=1024) + mask_features = torch.randn(4, 1024, 14, 14) + + image_shapes = [(10, 10), (15, 15)] + pred_instance0 = Instances(image_shapes[0]) + pred_classes0 = torch.tensor([1, 2, 3], dtype=torch.int64) + pred_instance0.pred_classes = pred_classes0 + pred_instance1 = Instances(image_shapes[1]) + pred_classes1 = torch.tensor([4], dtype=torch.int64) + pred_instance1.pred_classes = pred_classes1 + + mask_head = MaskRCNNConvUpsampleHead( + input_shape, num_classes=80, conv_dims=[256, 256] + ).eval() + # pred_instance will be in-place changed during the inference + # process of `MaskRCNNConvUpsampleHead` + origin_outputs = mask_head(mask_features, deepcopy([pred_instance0, pred_instance1])) + + fields = {"pred_masks": torch.Tensor, "pred_classes": torch.Tensor} + with freeze_training_mode(mask_head), patch_instances(fields) as NewInstances: + sciript_mask_head = torch.jit.script(mask_head) + pred_instance0 = NewInstances.from_instances(pred_instance0) + pred_instance1 = NewInstances.from_instances(pred_instance1) + script_outputs = sciript_mask_head(mask_features, [pred_instance0, pred_instance1]) + + for origin_ins, script_ins in zip(origin_outputs, script_outputs): + assert_instances_allclose(origin_ins, script_ins, rtol=0) + + def test_keypoint_head_scriptability(self): + input_shape = ShapeSpec(channels=1024, height=14, width=14) + keypoint_features = torch.randn(4, 1024, 14, 14) + + image_shapes = [(10, 10), (15, 15)] + pred_boxes0 = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6], [1, 5, 2, 8]], dtype=torch.float32) + pred_instance0 = Instances(image_shapes[0]) + pred_instance0.pred_boxes = Boxes(pred_boxes0) + pred_boxes1 = torch.tensor([[7, 3, 10, 5]], dtype=torch.float32) + pred_instance1 = Instances(image_shapes[1]) + pred_instance1.pred_boxes = Boxes(pred_boxes1) + + keypoint_head = KRCNNConvDeconvUpsampleHead( + input_shape, num_keypoints=17, conv_dims=[512, 512] + ).eval() + origin_outputs = keypoint_head( + keypoint_features, deepcopy([pred_instance0, pred_instance1]) + ) + + fields = { + "pred_boxes": Boxes, + "pred_keypoints": torch.Tensor, + "pred_keypoint_heatmaps": torch.Tensor, + } + with freeze_training_mode(keypoint_head), patch_instances(fields) as NewInstances: + sciript_keypoint_head = torch.jit.script(keypoint_head) + pred_instance0 = NewInstances.from_instances(pred_instance0) + pred_instance1 = NewInstances.from_instances(pred_instance1) + script_outputs = sciript_keypoint_head( + keypoint_features, [pred_instance0, pred_instance1] + ) + + for origin_ins, script_ins in zip(origin_outputs, script_outputs): + assert_instances_allclose(origin_ins, script_ins, rtol=0) + + def test_StandardROIHeads_scriptability(self): + cfg = get_cfg() + cfg.MODEL.ROI_BOX_HEAD.NAME = "FastRCNNConvFCHead" + cfg.MODEL.ROI_BOX_HEAD.NUM_FC = 2 + cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignV2" + cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10, 10, 5, 5) + cfg.MODEL.MASK_ON = True + cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST = 0.01 + cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.01 + num_images = 2 + images_tensor = torch.rand(num_images, 20, 30) + image_sizes = [(10, 10), (20, 30)] + images = ImageList(images_tensor, image_sizes) + num_channels = 1024 + features = {"res4": torch.rand(num_images, num_channels, 1, 2)} + feature_shape = {"res4": ShapeSpec(channels=num_channels, stride=16)} + + roi_heads = StandardROIHeads(cfg, feature_shape).eval() + + proposal0 = Instances(image_sizes[0]) + proposal_boxes0 = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6]], dtype=torch.float32) + proposal0.proposal_boxes = Boxes(proposal_boxes0) + proposal0.objectness_logits = torch.tensor([0.5, 0.7], dtype=torch.float32) + + proposal1 = Instances(image_sizes[1]) + proposal_boxes1 = torch.tensor([[1, 5, 2, 8], [7, 3, 10, 5]], dtype=torch.float32) + proposal1.proposal_boxes = Boxes(proposal_boxes1) + proposal1.objectness_logits = torch.tensor([0.1, 0.9], dtype=torch.float32) + proposals = [proposal0, proposal1] + + pred_instances, _ = roi_heads(images, features, proposals) + fields = { + "objectness_logits": torch.Tensor, + "proposal_boxes": Boxes, + "pred_classes": torch.Tensor, + "scores": torch.Tensor, + "pred_masks": torch.Tensor, + "pred_boxes": Boxes, + "pred_keypoints": torch.Tensor, + "pred_keypoint_heatmaps": torch.Tensor, + } + with freeze_training_mode(roi_heads), patch_instances(fields) as new_instances: + proposal0 = new_instances.from_instances(proposal0) + proposal1 = new_instances.from_instances(proposal1) + proposals = [proposal0, proposal1] + scripted_rot_heads = torch.jit.script(roi_heads) + scripted_pred_instances, _ = scripted_rot_heads(images, features, proposals) + + for instance, scripted_instance in zip(pred_instances, scripted_pred_instances): + assert_instances_allclose(instance, scripted_instance, rtol=0) + + def test_PointRend_mask_head_tracing(self): + cfg = model_zoo.get_config("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml") + point_rend.add_pointrend_config(cfg) + cfg.MODEL.ROI_HEADS.IN_FEATURES = ["p2", "p3"] + cfg.MODEL.ROI_MASK_HEAD.NAME = "PointRendMaskHead" + cfg.MODEL.ROI_MASK_HEAD.POOLER_TYPE = "" + cfg.MODEL.ROI_MASK_HEAD.POINT_HEAD_ON = True + chan = 256 + head = point_rend.PointRendMaskHead( + cfg, + { + "p2": ShapeSpec(channels=chan, stride=4), + "p3": ShapeSpec(channels=chan, stride=8), + }, + ) + + def gen_inputs(h, w, N): + p2 = torch.rand(1, chan, h, w) + p3 = torch.rand(1, chan, h // 2, w // 2) + boxes = random_boxes(N, max_coord=h) + return p2, p3, boxes + + class Wrap(nn.ModuleDict): + def forward(self, p2, p3, boxes): + features = { + "p2": p2, + "p3": p3, + } + inst = Instances((p2.shape[2] * 4, p2.shape[3] * 4)) + inst.pred_boxes = Boxes(boxes) + inst.pred_classes = torch.zeros(inst.__len__(), dtype=torch.long) + out = self.head(features, [inst])[0] + return out.pred_masks + + model = Wrap({"head": head}) + model.eval() + with torch.no_grad(), patch_builtin_len(): + traced = torch.jit.trace(model, gen_inputs(302, 208, 20)) + inputs = gen_inputs(100, 120, 30) + out_eager = model(*inputs) + out_trace = traced(*inputs) + self.assertTrue(torch.allclose(out_eager, out_trace)) + + +if __name__ == "__main__": + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_roi_pooler.py b/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_roi_pooler.py new file mode 100644 index 0000000000000000000000000000000000000000..b93b7ae6ca633fa8686c86f82e7d55ac5ca62bc7 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_roi_pooler.py @@ -0,0 +1,165 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import unittest +import torch + +from detectron2.modeling.poolers import ROIPooler +from detectron2.structures import Boxes, RotatedBoxes +from detectron2.utils.testing import random_boxes + +logger = logging.getLogger(__name__) + + +class TestROIPooler(unittest.TestCase): + def _test_roialignv2_roialignrotated_match(self, device): + pooler_resolution = 14 + canonical_level = 4 + canonical_scale_factor = 2 ** canonical_level + pooler_scales = (1.0 / canonical_scale_factor,) + sampling_ratio = 0 + + N, C, H, W = 2, 4, 10, 8 + N_rois = 10 + std = 11 + mean = 0 + feature = (torch.rand(N, C, H, W) - 0.5) * 2 * std + mean + + features = [feature.to(device)] + + rois = [] + rois_rotated = [] + for _ in range(N): + boxes = random_boxes(N_rois, W * canonical_scale_factor) + rotated_boxes = torch.zeros(N_rois, 5) + rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0 + rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0 + rotated_boxes[:, 2] = boxes[:, 2] - boxes[:, 0] + rotated_boxes[:, 3] = boxes[:, 3] - boxes[:, 1] + rois.append(Boxes(boxes).to(device)) + rois_rotated.append(RotatedBoxes(rotated_boxes).to(device)) + + roialignv2_pooler = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type="ROIAlignV2", + ) + + roialignv2_out = roialignv2_pooler(features, rois) + + roialignrotated_pooler = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type="ROIAlignRotated", + ) + + roialignrotated_out = roialignrotated_pooler(features, rois_rotated) + + self.assertTrue(torch.allclose(roialignv2_out, roialignrotated_out, atol=1e-4)) + + def test_roialignv2_roialignrotated_match_cpu(self): + self._test_roialignv2_roialignrotated_match(device="cpu") + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_roialignv2_roialignrotated_match_cuda(self): + self._test_roialignv2_roialignrotated_match(device="cuda") + + def _test_scriptability(self, device): + pooler_resolution = 14 + canonical_level = 4 + canonical_scale_factor = 2 ** canonical_level + pooler_scales = (1.0 / canonical_scale_factor,) + sampling_ratio = 0 + + N, C, H, W = 2, 4, 10, 8 + N_rois = 10 + std = 11 + mean = 0 + feature = (torch.rand(N, C, H, W) - 0.5) * 2 * std + mean + + features = [feature.to(device)] + + rois = [] + for _ in range(N): + boxes = random_boxes(N_rois, W * canonical_scale_factor) + + rois.append(Boxes(boxes).to(device)) + + roialignv2_pooler = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type="ROIAlignV2", + ) + + roialignv2_out = roialignv2_pooler(features, rois) + scripted_roialignv2_out = torch.jit.script(roialignv2_pooler)(features, rois) + self.assertTrue(torch.equal(roialignv2_out, scripted_roialignv2_out)) + + def test_scriptability_cpu(self): + self._test_scriptability(device="cpu") + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_scriptability_gpu(self): + self._test_scriptability(device="cuda") + + def test_no_images(self): + N, C, H, W = 0, 32, 32, 32 + feature = torch.rand(N, C, H, W) - 0.5 + features = [feature] + pooler = ROIPooler( + output_size=14, scales=(1.0,), sampling_ratio=0.0, pooler_type="ROIAlignV2" + ) + output = pooler.forward(features, []) + self.assertEqual(output.shape, (0, C, 14, 14)) + + def test_roi_pooler_tracing(self): + class Model(torch.nn.Module): + def __init__(self, roi): + super(Model, self).__init__() + self.roi = roi + + def forward(self, x, boxes): + return self.roi(x, [Boxes(boxes)]) + + pooler_resolution = 14 + canonical_level = 4 + canonical_scale_factor = 2 ** canonical_level + pooler_scales = (1.0 / canonical_scale_factor, 0.5 / canonical_scale_factor) + sampling_ratio = 0 + + N, C, H, W = 1, 4, 10, 8 + N_rois = 10 + std = 11 + mean = 0 + feature = (torch.rand(N, C, H, W) - 0.5) * 2 * std + mean + feature = [feature, feature] + + rois = random_boxes(N_rois, W * canonical_scale_factor) + # Add one larger box so that this level has only one box. + # This may trigger the bug https://github.com/pytorch/pytorch/issues/49852 + # that we shall workaround. + rois = torch.cat([rois, torch.tensor([[0, 0, 448, 448]])]) + + model = Model( + ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type="ROIAlign", + ) + ) + + with torch.no_grad(): + func = torch.jit.trace(model, (feature, rois)) + o = func(feature, rois) + self.assertEqual(o.shape, (11, 4, 14, 14)) + o = func(feature, rois[:5]) + self.assertEqual(o.shape, (5, 4, 14, 14)) + o = func(feature, random_boxes(20, W * canonical_scale_factor)) + self.assertEqual(o.shape, (20, 4, 14, 14)) + + +if __name__ == "__main__": + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_rpn.py b/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_rpn.py new file mode 100644 index 0000000000000000000000000000000000000000..f14faae56e580d3d4762d31273b9f65c5774346b --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/modeling/test_rpn.py @@ -0,0 +1,262 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import unittest +import torch + +from detectron2.config import get_cfg +from detectron2.export import scripting_with_instances +from detectron2.layers import ShapeSpec +from detectron2.modeling.backbone import build_backbone +from detectron2.modeling.proposal_generator import RPN, build_proposal_generator +from detectron2.modeling.proposal_generator.proposal_utils import ( + add_ground_truth_to_proposals, + find_top_rpn_proposals, +) +from detectron2.structures import Boxes, ImageList, Instances, RotatedBoxes +from detectron2.utils.events import EventStorage + +logger = logging.getLogger(__name__) + + +class RPNTest(unittest.TestCase): + def get_gt_and_features(self): + num_images = 2 + images_tensor = torch.rand(num_images, 20, 30) + image_sizes = [(10, 10), (20, 30)] + images = ImageList(images_tensor, image_sizes) + image_shape = (15, 15) + num_channels = 1024 + features = {"res4": torch.rand(num_images, num_channels, 1, 2)} + gt_boxes = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6]], dtype=torch.float32) + gt_instances = Instances(image_shape) + gt_instances.gt_boxes = Boxes(gt_boxes) + return (gt_instances, features, images, image_sizes) + + def test_rpn(self): + torch.manual_seed(121) + cfg = get_cfg() + backbone = build_backbone(cfg) + proposal_generator = RPN(cfg, backbone.output_shape()) + (gt_instances, features, images, image_sizes) = self.get_gt_and_features() + with EventStorage(): # capture events in a new storage to discard them + proposals, proposal_losses = proposal_generator( + images, features, [gt_instances[0], gt_instances[1]] + ) + + expected_losses = { + "loss_rpn_cls": torch.tensor(0.08011703193), + "loss_rpn_loc": torch.tensor(0.101470276), + } + for name in expected_losses.keys(): + err_msg = "proposal_losses[{}] = {}, expected losses = {}".format( + name, proposal_losses[name], expected_losses[name] + ) + self.assertTrue(torch.allclose(proposal_losses[name], expected_losses[name]), err_msg) + + self.assertEqual(len(proposals), len(image_sizes)) + for proposal, im_size in zip(proposals, image_sizes): + self.assertEqual(proposal.image_size, im_size) + + expected_proposal_box = torch.tensor([[0, 0, 10, 10], [7.2702, 0, 10, 10]]) + expected_objectness_logit = torch.tensor([0.1596, -0.0007]) + self.assertTrue( + torch.allclose(proposals[0].proposal_boxes.tensor, expected_proposal_box, atol=1e-4) + ) + self.assertTrue( + torch.allclose(proposals[0].objectness_logits, expected_objectness_logit, atol=1e-4) + ) + + def verify_rpn(self, conv_dims, expected_conv_dims): + torch.manual_seed(121) + cfg = get_cfg() + cfg.MODEL.RPN.CONV_DIMS = conv_dims + backbone = build_backbone(cfg) + proposal_generator = RPN(cfg, backbone.output_shape()) + for k, conv in enumerate(proposal_generator.rpn_head.conv): + self.assertEqual(expected_conv_dims[k], conv.out_channels) + return proposal_generator + + def test_rpn_larger_num_convs(self): + conv_dims = [64, 64, 64, 64, 64] + proposal_generator = self.verify_rpn(conv_dims, conv_dims) + (gt_instances, features, images, image_sizes) = self.get_gt_and_features() + with EventStorage(): # capture events in a new storage to discard them + proposals, proposal_losses = proposal_generator( + images, features, [gt_instances[0], gt_instances[1]] + ) + expected_losses = { + "loss_rpn_cls": torch.tensor(0.08122821152), + "loss_rpn_loc": torch.tensor(0.10064548254), + } + for name in expected_losses.keys(): + err_msg = "proposal_losses[{}] = {}, expected losses = {}".format( + name, proposal_losses[name], expected_losses[name] + ) + self.assertTrue(torch.allclose(proposal_losses[name], expected_losses[name]), err_msg) + + def test_rpn_conv_dims_not_set(self): + conv_dims = [-1, -1, -1] + expected_conv_dims = [1024, 1024, 1024] + self.verify_rpn(conv_dims, expected_conv_dims) + + def test_rpn_scriptability(self): + cfg = get_cfg() + proposal_generator = RPN(cfg, {"res4": ShapeSpec(channels=1024, stride=16)}).eval() + num_images = 2 + images_tensor = torch.rand(num_images, 30, 40) + image_sizes = [(32, 32), (30, 40)] + images = ImageList(images_tensor, image_sizes) + features = {"res4": torch.rand(num_images, 1024, 1, 2)} + + fields = {"proposal_boxes": Boxes, "objectness_logits": torch.Tensor} + proposal_generator_ts = scripting_with_instances(proposal_generator, fields) + + proposals, _ = proposal_generator(images, features) + proposals_ts, _ = proposal_generator_ts(images, features) + + for proposal, proposal_ts in zip(proposals, proposals_ts): + self.assertEqual(proposal.image_size, proposal_ts.image_size) + self.assertTrue( + torch.equal(proposal.proposal_boxes.tensor, proposal_ts.proposal_boxes.tensor) + ) + self.assertTrue(torch.equal(proposal.objectness_logits, proposal_ts.objectness_logits)) + + def test_rrpn(self): + torch.manual_seed(121) + cfg = get_cfg() + cfg.MODEL.PROPOSAL_GENERATOR.NAME = "RRPN" + cfg.MODEL.ANCHOR_GENERATOR.NAME = "RotatedAnchorGenerator" + cfg.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64]] + cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.25, 1]] + cfg.MODEL.ANCHOR_GENERATOR.ANGLES = [[0, 60]] + cfg.MODEL.RPN.BBOX_REG_WEIGHTS = (1, 1, 1, 1, 1) + cfg.MODEL.RPN.HEAD_NAME = "StandardRPNHead" + backbone = build_backbone(cfg) + proposal_generator = build_proposal_generator(cfg, backbone.output_shape()) + num_images = 2 + images_tensor = torch.rand(num_images, 20, 30) + image_sizes = [(10, 10), (20, 30)] + images = ImageList(images_tensor, image_sizes) + image_shape = (15, 15) + num_channels = 1024 + features = {"res4": torch.rand(num_images, num_channels, 1, 2)} + gt_boxes = torch.tensor([[2, 2, 2, 2, 0], [4, 4, 4, 4, 0]], dtype=torch.float32) + gt_instances = Instances(image_shape) + gt_instances.gt_boxes = RotatedBoxes(gt_boxes) + with EventStorage(): # capture events in a new storage to discard them + proposals, proposal_losses = proposal_generator( + images, features, [gt_instances[0], gt_instances[1]] + ) + + expected_losses = { + "loss_rpn_cls": torch.tensor(0.04291602224), + "loss_rpn_loc": torch.tensor(0.145077362), + } + for name in expected_losses.keys(): + err_msg = "proposal_losses[{}] = {}, expected losses = {}".format( + name, proposal_losses[name], expected_losses[name] + ) + self.assertTrue(torch.allclose(proposal_losses[name], expected_losses[name]), err_msg) + + expected_proposal_box = torch.tensor( + [ + [-1.77999556, 0.78155339, 68.04367828, 14.78156471, 60.59333801], + [13.82740974, -1.50282836, 34.67269897, 29.19676590, -3.81942749], + [8.10392570, -0.99071521, 145.39100647, 32.13126373, 3.67242432], + [5.00000000, 4.57370186, 10.00000000, 9.14740372, 0.89196777], + ] + ) + + expected_objectness_logit = torch.tensor([0.10924313, 0.09881870, 0.07649877, 0.05858029]) + + torch.set_printoptions(precision=8, sci_mode=False) + + self.assertEqual(len(proposals), len(image_sizes)) + + proposal = proposals[0] + # It seems that there's some randomness in the result across different machines: + # This test can be run on a local machine for 100 times with exactly the same result, + # However, a different machine might produce slightly different results, + # thus the atol here. + err_msg = "computed proposal boxes = {}, expected {}".format( + proposal.proposal_boxes.tensor, expected_proposal_box + ) + self.assertTrue( + torch.allclose(proposal.proposal_boxes.tensor[:4], expected_proposal_box, atol=1e-5), + err_msg, + ) + + err_msg = "computed objectness logits = {}, expected {}".format( + proposal.objectness_logits, expected_objectness_logit + ) + self.assertTrue( + torch.allclose(proposal.objectness_logits[:4], expected_objectness_logit, atol=1e-5), + err_msg, + ) + + def test_find_rpn_proposals_inf(self): + N, Hi, Wi, A = 3, 3, 3, 3 + proposals = [torch.rand(N, Hi * Wi * A, 4)] + pred_logits = [torch.rand(N, Hi * Wi * A)] + pred_logits[0][1][3:5].fill_(float("inf")) + find_top_rpn_proposals(proposals, pred_logits, [(10, 10)], 0.5, 1000, 1000, 0, False) + + def test_find_rpn_proposals_tracing(self): + N, Hi, Wi, A = 3, 50, 50, 9 + proposal = torch.rand(N, Hi * Wi * A, 4) + pred_logit = torch.rand(N, Hi * Wi * A) + + def func(proposal, logit, image_size): + r = find_top_rpn_proposals( + [proposal], [logit], [image_size], 0.7, 1000, 1000, 0, False + )[0] + size = r.image_size + if not isinstance(size, torch.Tensor): + size = torch.tensor(size) + return (size, r.proposal_boxes.tensor, r.objectness_logits) + + other_inputs = [] + # test that it generalizes to other shapes + for Hi, Wi, shp in [(30, 30, 60), (10, 10, 800)]: + other_inputs.append( + ( + torch.rand(N, Hi * Wi * A, 4), + torch.rand(N, Hi * Wi * A), + torch.tensor([shp, shp]), + ) + ) + torch.jit.trace( + func, (proposal, pred_logit, torch.tensor([100, 100])), check_inputs=other_inputs + ) + + def test_append_gt_to_proposal(self): + proposals = Instances( + (10, 10), + **{ + "proposal_boxes": Boxes(torch.empty((0, 4))), + "objectness_logits": torch.tensor([]), + "custom_attribute": torch.tensor([]), + } + ) + gt_boxes = Boxes(torch.tensor([[0, 0, 1, 1]])) + + self.assertRaises(AssertionError, add_ground_truth_to_proposals, [gt_boxes], [proposals]) + + gt_instances = Instances((10, 10)) + gt_instances.gt_boxes = gt_boxes + + self.assertRaises( + AssertionError, add_ground_truth_to_proposals, [gt_instances], [proposals] + ) + + gt_instances.custom_attribute = torch.tensor([1]) + gt_instances.custom_attribute2 = torch.tensor([1]) + new_proposals = add_ground_truth_to_proposals([gt_instances], [proposals])[0] + + self.assertEqual(new_proposals.custom_attribute[0], 1) + # new proposals should only include the attributes in proposals + self.assertRaises(AttributeError, lambda: new_proposals.custom_attribute2) + + +if __name__ == "__main__": + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/structures/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/tests/structures/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/structures/test_boxes.py b/iChat/models/grit_src/third_party/CenterNet2/tests/structures/test_boxes.py new file mode 100644 index 0000000000000000000000000000000000000000..101191818c511cf90c3c8f2cbc55aa49295697fa --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/structures/test_boxes.py @@ -0,0 +1,223 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import json +import math +import numpy as np +import unittest +import torch + +from detectron2.structures import Boxes, BoxMode, pairwise_ioa, pairwise_iou +from detectron2.utils.testing import reload_script_model + + +class TestBoxMode(unittest.TestCase): + def _convert_xy_to_wh(self, x): + return BoxMode.convert(x, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) + + def _convert_xywha_to_xyxy(self, x): + return BoxMode.convert(x, BoxMode.XYWHA_ABS, BoxMode.XYXY_ABS) + + def _convert_xywh_to_xywha(self, x): + return BoxMode.convert(x, BoxMode.XYWH_ABS, BoxMode.XYWHA_ABS) + + def test_convert_int_mode(self): + BoxMode.convert([1, 2, 3, 4], 0, 1) + + def test_box_convert_list(self): + for tp in [list, tuple]: + box = tp([5.0, 5.0, 10.0, 10.0]) + output = self._convert_xy_to_wh(box) + self.assertIsInstance(output, tp) + self.assertIsInstance(output[0], float) + self.assertEqual(output, tp([5.0, 5.0, 5.0, 5.0])) + + with self.assertRaises(Exception): + self._convert_xy_to_wh([box]) + + def test_box_convert_array(self): + box = np.asarray([[5, 5, 10, 10], [1, 1, 2, 3]]) + output = self._convert_xy_to_wh(box) + self.assertEqual(output.dtype, box.dtype) + self.assertEqual(output.shape, box.shape) + self.assertTrue((output[0] == [5, 5, 5, 5]).all()) + self.assertTrue((output[1] == [1, 1, 1, 2]).all()) + + def test_box_convert_cpu_tensor(self): + box = torch.tensor([[5, 5, 10, 10], [1, 1, 2, 3]]) + output = self._convert_xy_to_wh(box) + self.assertEqual(output.dtype, box.dtype) + self.assertEqual(output.shape, box.shape) + output = output.numpy() + self.assertTrue((output[0] == [5, 5, 5, 5]).all()) + self.assertTrue((output[1] == [1, 1, 1, 2]).all()) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_box_convert_cuda_tensor(self): + box = torch.tensor([[5, 5, 10, 10], [1, 1, 2, 3]]).cuda() + output = self._convert_xy_to_wh(box) + self.assertEqual(output.dtype, box.dtype) + self.assertEqual(output.shape, box.shape) + self.assertEqual(output.device, box.device) + output = output.cpu().numpy() + self.assertTrue((output[0] == [5, 5, 5, 5]).all()) + self.assertTrue((output[1] == [1, 1, 1, 2]).all()) + + def test_box_convert_xywha_to_xyxy_list(self): + for tp in [list, tuple]: + box = tp([50, 50, 30, 20, 0]) + output = self._convert_xywha_to_xyxy(box) + self.assertIsInstance(output, tp) + self.assertEqual(output, tp([35, 40, 65, 60])) + + with self.assertRaises(Exception): + self._convert_xywha_to_xyxy([box]) + + def test_box_convert_xywha_to_xyxy_array(self): + for dtype in [np.float64, np.float32]: + box = np.asarray( + [ + [50, 50, 30, 20, 0], + [50, 50, 30, 20, 90], + [1, 1, math.sqrt(2), math.sqrt(2), -45], + ], + dtype=dtype, + ) + output = self._convert_xywha_to_xyxy(box) + self.assertEqual(output.dtype, box.dtype) + expected = np.asarray([[35, 40, 65, 60], [40, 35, 60, 65], [0, 0, 2, 2]], dtype=dtype) + self.assertTrue(np.allclose(output, expected, atol=1e-6), "output={}".format(output)) + + def test_box_convert_xywha_to_xyxy_tensor(self): + for dtype in [torch.float32, torch.float64]: + box = torch.tensor( + [ + [50, 50, 30, 20, 0], + [50, 50, 30, 20, 90], + [1, 1, math.sqrt(2), math.sqrt(2), -45], + ], + dtype=dtype, + ) + output = self._convert_xywha_to_xyxy(box) + self.assertEqual(output.dtype, box.dtype) + expected = torch.tensor([[35, 40, 65, 60], [40, 35, 60, 65], [0, 0, 2, 2]], dtype=dtype) + + self.assertTrue(torch.allclose(output, expected, atol=1e-6), "output={}".format(output)) + + def test_box_convert_xywh_to_xywha_list(self): + for tp in [list, tuple]: + box = tp([50, 50, 30, 20]) + output = self._convert_xywh_to_xywha(box) + self.assertIsInstance(output, tp) + self.assertEqual(output, tp([65, 60, 30, 20, 0])) + + with self.assertRaises(Exception): + self._convert_xywh_to_xywha([box]) + + def test_box_convert_xywh_to_xywha_array(self): + for dtype in [np.float64, np.float32]: + box = np.asarray([[30, 40, 70, 60], [30, 40, 60, 70], [-1, -1, 2, 2]], dtype=dtype) + output = self._convert_xywh_to_xywha(box) + self.assertEqual(output.dtype, box.dtype) + expected = np.asarray( + [[65, 70, 70, 60, 0], [60, 75, 60, 70, 0], [0, 0, 2, 2, 0]], dtype=dtype + ) + self.assertTrue(np.allclose(output, expected, atol=1e-6), "output={}".format(output)) + + def test_box_convert_xywh_to_xywha_tensor(self): + for dtype in [torch.float32, torch.float64]: + box = torch.tensor([[30, 40, 70, 60], [30, 40, 60, 70], [-1, -1, 2, 2]], dtype=dtype) + output = self._convert_xywh_to_xywha(box) + self.assertEqual(output.dtype, box.dtype) + expected = torch.tensor( + [[65, 70, 70, 60, 0], [60, 75, 60, 70, 0], [0, 0, 2, 2, 0]], dtype=dtype + ) + + self.assertTrue(torch.allclose(output, expected, atol=1e-6), "output={}".format(output)) + + def test_json_serializable(self): + payload = {"box_mode": BoxMode.XYWH_REL} + try: + json.dumps(payload) + except Exception: + self.fail("JSON serialization failed") + + def test_json_deserializable(self): + payload = '{"box_mode": 2}' + obj = json.loads(payload) + try: + obj["box_mode"] = BoxMode(obj["box_mode"]) + except Exception: + self.fail("JSON deserialization failed") + + +class TestBoxIOU(unittest.TestCase): + def create_boxes(self): + boxes1 = torch.tensor([[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]]) + + boxes2 = torch.tensor( + [ + [0.0, 0.0, 1.0, 1.0], + [0.0, 0.0, 0.5, 1.0], + [0.0, 0.0, 1.0, 0.5], + [0.0, 0.0, 0.5, 0.5], + [0.5, 0.5, 1.0, 1.0], + [0.5, 0.5, 1.5, 1.5], + ] + ) + return boxes1, boxes2 + + def test_pairwise_iou(self): + boxes1, boxes2 = self.create_boxes() + expected_ious = torch.tensor( + [ + [1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], + [1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], + ] + ) + + ious = pairwise_iou(Boxes(boxes1), Boxes(boxes2)) + self.assertTrue(torch.allclose(ious, expected_ious)) + + def test_pairwise_ioa(self): + boxes1, boxes2 = self.create_boxes() + expected_ioas = torch.tensor( + [[1.0, 1.0, 1.0, 1.0, 1.0, 0.25], [1.0, 1.0, 1.0, 1.0, 1.0, 0.25]] + ) + ioas = pairwise_ioa(Boxes(boxes1), Boxes(boxes2)) + self.assertTrue(torch.allclose(ioas, expected_ioas)) + + +class TestBoxes(unittest.TestCase): + def test_empty_cat(self): + x = Boxes.cat([]) + self.assertTrue(x.tensor.shape, (0, 4)) + + def test_to(self): + x = Boxes(torch.rand(3, 4)) + self.assertEqual(x.to(device="cpu").tensor.device.type, "cpu") + + def test_scriptability(self): + def func(x): + boxes = Boxes(x) + test = boxes.to(torch.device("cpu")).tensor + return boxes.area(), test + + f = torch.jit.script(func) + f = reload_script_model(f) + f(torch.rand((3, 4))) + + data = torch.rand((3, 4)) + + def func_cat(x: torch.Tensor): + boxes1 = Boxes(x) + boxes2 = Boxes(x) + # boxes3 = Boxes.cat([boxes1, boxes2]) # this is not supported by torchsript for now. + boxes3 = boxes1.cat([boxes1, boxes2]) + return boxes3 + + f = torch.jit.script(func_cat) + script_box = f(data) + self.assertTrue(torch.equal(torch.cat([data, data]), script_box.tensor)) + + +if __name__ == "__main__": + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/structures/test_imagelist.py b/iChat/models/grit_src/third_party/CenterNet2/tests/structures/test_imagelist.py new file mode 100644 index 0000000000000000000000000000000000000000..e446e44a37f5d8f9a68362e4b93a291d314d5d68 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/structures/test_imagelist.py @@ -0,0 +1,75 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import unittest +from typing import List, Sequence, Tuple +import torch + +from detectron2.structures import ImageList + + +class TestImageList(unittest.TestCase): + def test_imagelist_padding_tracing(self): + # test that the trace does not contain hard-coded constant sizes + def to_imagelist(tensors: Sequence[torch.Tensor]): + image_list = ImageList.from_tensors(tensors, 4) + return image_list.tensor, image_list.image_sizes + + def _tensor(*shape): + return torch.ones(shape, dtype=torch.float32) + + # test CHW (inputs needs padding vs. no padding) + for shape in [(3, 10, 10), (3, 12, 12)]: + func = torch.jit.trace(to_imagelist, ([_tensor(*shape)],)) + tensor, image_sizes = func([_tensor(3, 15, 20)]) + self.assertEqual(tensor.shape, (1, 3, 16, 20), tensor.shape) + self.assertEqual(image_sizes[0].tolist(), [15, 20], image_sizes[0]) + + # test HW + func = torch.jit.trace(to_imagelist, ([_tensor(10, 10)],)) + tensor, image_sizes = func([_tensor(15, 20)]) + self.assertEqual(tensor.shape, (1, 16, 20), tensor.shape) + self.assertEqual(image_sizes[0].tolist(), [15, 20], image_sizes[0]) + + # test 2x CHW + func = torch.jit.trace( + to_imagelist, + ([_tensor(3, 16, 10), _tensor(3, 13, 11)],), + ) + tensor, image_sizes = func([_tensor(3, 25, 20), _tensor(3, 10, 10)]) + self.assertEqual(tensor.shape, (2, 3, 28, 20), tensor.shape) + self.assertEqual(image_sizes[0].tolist(), [25, 20], image_sizes[0]) + self.assertEqual(image_sizes[1].tolist(), [10, 10], image_sizes[1]) + # support calling with different spatial sizes, but not with different #images + + def test_imagelist_scriptability(self): + image_nums = 2 + image_tensor = torch.randn((image_nums, 10, 20), dtype=torch.float32) + image_shape = [(10, 20)] * image_nums + + def f(image_tensor, image_shape: List[Tuple[int, int]]): + return ImageList(image_tensor, image_shape) + + ret = f(image_tensor, image_shape) + ret_script = torch.jit.script(f)(image_tensor, image_shape) + + self.assertEqual(len(ret), len(ret_script)) + for i in range(image_nums): + self.assertTrue(torch.equal(ret[i], ret_script[i])) + + def test_imagelist_from_tensors_scriptability(self): + image_tensor_0 = torch.randn(10, 20, dtype=torch.float32) + image_tensor_1 = torch.randn(12, 22, dtype=torch.float32) + inputs = [image_tensor_0, image_tensor_1] + + def f(image_tensor: List[torch.Tensor]): + return ImageList.from_tensors(image_tensor, 10) + + ret = f(inputs) + ret_script = torch.jit.script(f)(inputs) + + self.assertEqual(len(ret), len(ret_script)) + self.assertTrue(torch.equal(ret.tensor, ret_script.tensor)) + + +if __name__ == "__main__": + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/structures/test_instances.py b/iChat/models/grit_src/third_party/CenterNet2/tests/structures/test_instances.py new file mode 100644 index 0000000000000000000000000000000000000000..a352f74313ae9b2b7a42398f0ef4606fcb4a610c --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/structures/test_instances.py @@ -0,0 +1,219 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import unittest +import torch +from torch import Tensor + +from detectron2.export.torchscript import patch_instances +from detectron2.structures import Boxes, Instances +from detectron2.utils.testing import convert_scripted_instances + + +class TestInstances(unittest.TestCase): + def test_int_indexing(self): + attr1 = torch.tensor([[0.0, 0.0, 1.0], [0.0, 0.0, 0.5], [0.0, 0.0, 1.0], [0.0, 0.5, 0.5]]) + attr2 = torch.tensor([0.1, 0.2, 0.3, 0.4]) + instances = Instances((100, 100)) + instances.attr1 = attr1 + instances.attr2 = attr2 + for i in range(-len(instances), len(instances)): + inst = instances[i] + self.assertEqual((inst.attr1 == attr1[i]).all(), True) + self.assertEqual((inst.attr2 == attr2[i]).all(), True) + + self.assertRaises(IndexError, lambda: instances[len(instances)]) + self.assertRaises(IndexError, lambda: instances[-len(instances) - 1]) + + def test_script_new_fields(self): + def get_mask(x: Instances) -> torch.Tensor: + return x.mask + + class f(torch.nn.Module): + def forward(self, x: Instances): + proposal_boxes = x.proposal_boxes # noqa F841 + objectness_logits = x.objectness_logits # noqa F841 + return x + + class g(torch.nn.Module): + def forward(self, x: Instances): + return get_mask(x) + + class g2(torch.nn.Module): + def __init__(self): + super().__init__() + self.g = g() + + def forward(self, x: Instances): + proposal_boxes = x.proposal_boxes # noqa F841 + return x, self.g(x) + + fields = {"proposal_boxes": Boxes, "objectness_logits": Tensor} + with patch_instances(fields): + torch.jit.script(f()) + + # can't script anymore after exiting the context + with self.assertRaises(Exception): + # will create a ConcreteType for g + torch.jit.script(g2()) + + new_fields = {"mask": Tensor} + with patch_instances(new_fields): + # will compile g with a different Instances; this should pass + torch.jit.script(g()) + with self.assertRaises(Exception): + torch.jit.script(g2()) + + new_fields = {"mask": Tensor, "proposal_boxes": Boxes} + with patch_instances(new_fields) as NewInstances: + # get_mask will be compiled with a different Instances; this should pass + scripted_g2 = torch.jit.script(g2()) + x = NewInstances((3, 4)) + x.mask = torch.rand(3) + x.proposal_boxes = Boxes(torch.rand(3, 4)) + scripted_g2(x) # it should accept the new Instances object and run successfully + + def test_script_access_fields(self): + class f(torch.nn.Module): + def forward(self, x: Instances): + proposal_boxes = x.proposal_boxes + objectness_logits = x.objectness_logits + return proposal_boxes.tensor + objectness_logits + + fields = {"proposal_boxes": Boxes, "objectness_logits": Tensor} + with patch_instances(fields): + torch.jit.script(f()) + + def test_script_len(self): + class f(torch.nn.Module): + def forward(self, x: Instances): + return len(x) + + class g(torch.nn.Module): + def forward(self, x: Instances): + return len(x) + + image_shape = (15, 15) + + fields = {"proposal_boxes": Boxes} + with patch_instances(fields) as new_instance: + script_module = torch.jit.script(f()) + x = new_instance(image_shape) + with self.assertRaises(Exception): + script_module(x) + box_tensors = torch.tensor([[5, 5, 10, 10], [1, 1, 2, 3]]) + x.proposal_boxes = Boxes(box_tensors) + length = script_module(x) + self.assertEqual(length, 2) + + fields = {"objectness_logits": Tensor} + with patch_instances(fields) as new_instance: + script_module = torch.jit.script(g()) + x = new_instance(image_shape) + objectness_logits = torch.tensor([1.0]).reshape(1, 1) + x.objectness_logits = objectness_logits + length = script_module(x) + self.assertEqual(length, 1) + + def test_script_has(self): + class f(torch.nn.Module): + def forward(self, x: Instances): + return x.has("proposal_boxes") + + image_shape = (15, 15) + fields = {"proposal_boxes": Boxes} + with patch_instances(fields) as new_instance: + script_module = torch.jit.script(f()) + x = new_instance(image_shape) + self.assertFalse(script_module(x)) + + box_tensors = torch.tensor([[5, 5, 10, 10], [1, 1, 2, 3]]) + x.proposal_boxes = Boxes(box_tensors) + self.assertTrue(script_module(x)) + + def test_script_to(self): + class f(torch.nn.Module): + def forward(self, x: Instances): + return x.to(torch.device("cpu")) + + image_shape = (15, 15) + fields = {"proposal_boxes": Boxes, "a": Tensor} + with patch_instances(fields) as new_instance: + script_module = torch.jit.script(f()) + x = new_instance(image_shape) + script_module(x) + + box_tensors = torch.tensor([[5, 5, 10, 10], [1, 1, 2, 3]]) + x.proposal_boxes = Boxes(box_tensors) + x.a = box_tensors + script_module(x) + + def test_script_getitem(self): + class f(torch.nn.Module): + def forward(self, x: Instances, idx): + return x[idx] + + image_shape = (15, 15) + fields = {"proposal_boxes": Boxes, "a": Tensor} + inst = Instances(image_shape) + inst.proposal_boxes = Boxes(torch.rand(4, 4)) + inst.a = torch.rand(4, 10) + idx = torch.tensor([True, False, True, False]) + with patch_instances(fields) as new_instance: + script_module = torch.jit.script(f()) + + out = f()(inst, idx) + out_scripted = script_module(new_instance.from_instances(inst), idx) + self.assertTrue( + torch.equal(out.proposal_boxes.tensor, out_scripted.proposal_boxes.tensor) + ) + self.assertTrue(torch.equal(out.a, out_scripted.a)) + + def test_from_to_instances(self): + orig = Instances((30, 30)) + orig.proposal_boxes = Boxes(torch.rand(3, 4)) + + fields = {"proposal_boxes": Boxes, "a": Tensor} + with patch_instances(fields) as NewInstances: + # convert to NewInstances and back + new1 = NewInstances.from_instances(orig) + new2 = convert_scripted_instances(new1) + self.assertTrue(torch.equal(orig.proposal_boxes.tensor, new1.proposal_boxes.tensor)) + self.assertTrue(torch.equal(orig.proposal_boxes.tensor, new2.proposal_boxes.tensor)) + + def test_script_init_args(self): + def f(x: Tensor): + image_shape = (15, 15) + # __init__ can take arguments + inst = Instances(image_shape, a=x, proposal_boxes=Boxes(x)) + inst2 = Instances(image_shape, a=x) + return inst.a, inst2.a + + fields = {"proposal_boxes": Boxes, "a": Tensor} + with patch_instances(fields): + script_f = torch.jit.script(f) + x = torch.randn(3, 4) + outputs = script_f(x) + self.assertTrue(torch.equal(outputs[0], x)) + self.assertTrue(torch.equal(outputs[1], x)) + + def test_script_cat(self): + def f(x: Tensor): + image_shape = (15, 15) + # __init__ can take arguments + inst = Instances(image_shape, a=x) + inst2 = Instances(image_shape, a=x) + + inst3 = Instances(image_shape, proposal_boxes=Boxes(x)) + return inst.cat([inst, inst2]), inst3.cat([inst3, inst3]) + + fields = {"proposal_boxes": Boxes, "a": Tensor} + with patch_instances(fields): + script_f = torch.jit.script(f) + x = torch.randn(3, 4) + output, output2 = script_f(x) + self.assertTrue(torch.equal(output.a, torch.cat([x, x]))) + self.assertFalse(output.has("proposal_boxes")) + self.assertTrue(torch.equal(output2.proposal_boxes.tensor, torch.cat([x, x]))) + + +if __name__ == "__main__": + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/structures/test_keypoints.py b/iChat/models/grit_src/third_party/CenterNet2/tests/structures/test_keypoints.py new file mode 100644 index 0000000000000000000000000000000000000000..adc616e42341343e503afcbe181dbfae3f8ea063 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/structures/test_keypoints.py @@ -0,0 +1,19 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import unittest +import torch + +from detectron2.structures.keypoints import Keypoints + + +class TestKeypoints(unittest.TestCase): + def test_cat_keypoints(self): + keypoints1 = Keypoints(torch.rand(2, 21, 3)) + keypoints2 = Keypoints(torch.rand(4, 21, 3)) + + cat_keypoints = keypoints1.cat([keypoints1, keypoints2]) + self.assertTrue(torch.all(cat_keypoints.tensor[:2] == keypoints1.tensor).item()) + self.assertTrue(torch.all(cat_keypoints.tensor[2:] == keypoints2.tensor).item()) + + +if __name__ == "__main__": + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/structures/test_masks.py b/iChat/models/grit_src/third_party/CenterNet2/tests/structures/test_masks.py new file mode 100644 index 0000000000000000000000000000000000000000..7991eb0b35724f2f2f402d788a273d68b7cad5f2 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/structures/test_masks.py @@ -0,0 +1,53 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import unittest +import torch + +from detectron2.structures.masks import BitMasks, PolygonMasks, polygons_to_bitmask + + +class TestBitMask(unittest.TestCase): + def test_get_bounding_box(self): + masks = torch.tensor( + [ + [ + [False, False, False, True], + [False, False, True, True], + [False, True, True, False], + [False, True, True, False], + ], + [ + [False, False, False, False], + [False, False, True, False], + [False, True, True, False], + [False, True, True, False], + ], + torch.zeros(4, 4), + ] + ) + bitmask = BitMasks(masks) + box_true = torch.tensor([[1, 0, 4, 4], [1, 1, 3, 4], [0, 0, 0, 0]], dtype=torch.float32) + box = bitmask.get_bounding_boxes() + self.assertTrue(torch.all(box.tensor == box_true).item()) + + for box in box_true: + poly = box[[0, 1, 2, 1, 2, 3, 0, 3]].numpy() + mask = polygons_to_bitmask([poly], 4, 4) + reconstruct_box = BitMasks(mask[None, :, :]).get_bounding_boxes()[0].tensor + self.assertTrue(torch.all(box == reconstruct_box).item()) + + reconstruct_box = PolygonMasks([[poly]]).get_bounding_boxes()[0].tensor + self.assertTrue(torch.all(box == reconstruct_box).item()) + + def test_from_empty_polygons(self): + masks = BitMasks.from_polygon_masks([], 100, 100) + self.assertEqual(masks.tensor.shape, (0, 100, 100)) + + def test_getitem(self): + masks = BitMasks(torch.ones(3, 10, 10)) + self.assertEqual(masks[1].tensor.shape, (1, 10, 10)) + self.assertEqual(masks[1:3].tensor.shape, (2, 10, 10)) + self.assertEqual(masks[torch.tensor([True, False, False])].tensor.shape, (1, 10, 10)) + + +if __name__ == "__main__": + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/structures/test_rotated_boxes.py b/iChat/models/grit_src/third_party/CenterNet2/tests/structures/test_rotated_boxes.py new file mode 100644 index 0000000000000000000000000000000000000000..2781237427d74c92f082b0a563165174985daa41 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/structures/test_rotated_boxes.py @@ -0,0 +1,437 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from __future__ import absolute_import, division, print_function, unicode_literals +import logging +import math +import random +import unittest +import torch +from fvcore.common.benchmark import benchmark + +from detectron2.layers.rotated_boxes import pairwise_iou_rotated +from detectron2.structures.boxes import Boxes +from detectron2.structures.rotated_boxes import RotatedBoxes, pairwise_iou +from detectron2.utils.testing import reload_script_model + +logger = logging.getLogger(__name__) + + +class TestRotatedBoxesLayer(unittest.TestCase): + def test_iou_0_dim_cpu(self): + boxes1 = torch.rand(0, 5, dtype=torch.float32) + boxes2 = torch.rand(10, 5, dtype=torch.float32) + expected_ious = torch.zeros(0, 10, dtype=torch.float32) + ious = pairwise_iou_rotated(boxes1, boxes2) + self.assertTrue(torch.allclose(ious, expected_ious)) + + boxes1 = torch.rand(10, 5, dtype=torch.float32) + boxes2 = torch.rand(0, 5, dtype=torch.float32) + expected_ious = torch.zeros(10, 0, dtype=torch.float32) + ious = pairwise_iou_rotated(boxes1, boxes2) + self.assertTrue(torch.allclose(ious, expected_ious)) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_iou_0_dim_cuda(self): + boxes1 = torch.rand(0, 5, dtype=torch.float32) + boxes2 = torch.rand(10, 5, dtype=torch.float32) + expected_ious = torch.zeros(0, 10, dtype=torch.float32) + ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda()) + self.assertTrue(torch.allclose(ious_cuda.cpu(), expected_ious)) + + boxes1 = torch.rand(10, 5, dtype=torch.float32) + boxes2 = torch.rand(0, 5, dtype=torch.float32) + expected_ious = torch.zeros(10, 0, dtype=torch.float32) + ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda()) + self.assertTrue(torch.allclose(ious_cuda.cpu(), expected_ious)) + + def test_iou_half_overlap_cpu(self): + boxes1 = torch.tensor([[0.5, 0.5, 1.0, 1.0, 0.0]], dtype=torch.float32) + boxes2 = torch.tensor([[0.25, 0.5, 0.5, 1.0, 0.0]], dtype=torch.float32) + expected_ious = torch.tensor([[0.5]], dtype=torch.float32) + ious = pairwise_iou_rotated(boxes1, boxes2) + self.assertTrue(torch.allclose(ious, expected_ious)) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_iou_half_overlap_cuda(self): + boxes1 = torch.tensor([[0.5, 0.5, 1.0, 1.0, 0.0]], dtype=torch.float32) + boxes2 = torch.tensor([[0.25, 0.5, 0.5, 1.0, 0.0]], dtype=torch.float32) + expected_ious = torch.tensor([[0.5]], dtype=torch.float32) + ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda()) + self.assertTrue(torch.allclose(ious_cuda.cpu(), expected_ious)) + + def test_iou_precision(self): + for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): + boxes1 = torch.tensor([[565, 565, 10, 10.0, 0]], dtype=torch.float32, device=device) + boxes2 = torch.tensor([[565, 565, 10, 8.3, 0]], dtype=torch.float32, device=device) + iou = 8.3 / 10.0 + expected_ious = torch.tensor([[iou]], dtype=torch.float32) + ious = pairwise_iou_rotated(boxes1, boxes2) + self.assertTrue(torch.allclose(ious.cpu(), expected_ious)) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_iou_too_many_boxes_cuda(self): + s1, s2 = 5, 1289035 + boxes1 = torch.zeros(s1, 5) + boxes2 = torch.zeros(s2, 5) + ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda()) + self.assertTupleEqual(tuple(ious_cuda.shape), (s1, s2)) + + def test_iou_extreme(self): + # Cause floating point issues in cuda kernels (#1266) + for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): + boxes1 = torch.tensor([[160.0, 153.0, 230.0, 23.0, -37.0]], device=device) + boxes2 = torch.tensor( + [ + [ + -1.117407639806935e17, + 1.3858420478349148e18, + 1000.0000610351562, + 1000.0000610351562, + 1612.0, + ] + ], + device=device, + ) + ious = pairwise_iou_rotated(boxes1, boxes2) + self.assertTrue(ious.min() >= 0, ious) + + def test_iou_issue_2154(self): + for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): + boxes1 = torch.tensor( + [ + [ + 296.6620178222656, + 458.73883056640625, + 23.515729904174805, + 47.677001953125, + 0.08795166015625, + ] + ], + device=device, + ) + boxes2 = torch.tensor( + [[296.66201, 458.73882000000003, 23.51573, 47.67702, 0.087951]], + device=device, + ) + ious = pairwise_iou_rotated(boxes1, boxes2) + expected_ious = torch.tensor([[1.0]], dtype=torch.float32) + self.assertTrue(torch.allclose(ious.cpu(), expected_ious)) + + def test_iou_issue_2167(self): + for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): + boxes1 = torch.tensor( + [ + [ + 2563.74462890625000000000, + 1436.79016113281250000000, + 2174.70336914062500000000, + 214.09500122070312500000, + 115.11834716796875000000, + ] + ], + device=device, + ) + boxes2 = torch.tensor( + [ + [ + 2563.74462890625000000000, + 1436.79028320312500000000, + 2174.70288085937500000000, + 214.09495544433593750000, + 115.11835479736328125000, + ] + ], + device=device, + ) + ious = pairwise_iou_rotated(boxes1, boxes2) + expected_ious = torch.tensor([[1.0]], dtype=torch.float32) + self.assertTrue(torch.allclose(ious.cpu(), expected_ious)) + + +class TestRotatedBoxesStructure(unittest.TestCase): + def test_clip_area_0_degree(self): + for _ in range(50): + num_boxes = 100 + boxes_5d = torch.zeros(num_boxes, 5) + boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-100, 500) + boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-100, 500) + boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, 500) + boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, 500) + # Convert from (x_ctr, y_ctr, w, h, 0) to (x1, y1, x2, y2) + boxes_4d = torch.zeros(num_boxes, 4) + boxes_4d[:, 0] = boxes_5d[:, 0] - boxes_5d[:, 2] / 2.0 + boxes_4d[:, 1] = boxes_5d[:, 1] - boxes_5d[:, 3] / 2.0 + boxes_4d[:, 2] = boxes_5d[:, 0] + boxes_5d[:, 2] / 2.0 + boxes_4d[:, 3] = boxes_5d[:, 1] + boxes_5d[:, 3] / 2.0 + + image_size = (500, 600) + test_boxes_4d = Boxes(boxes_4d) + test_boxes_5d = RotatedBoxes(boxes_5d) + # Before clip + areas_4d = test_boxes_4d.area() + areas_5d = test_boxes_5d.area() + self.assertTrue(torch.allclose(areas_4d, areas_5d, atol=1e-1, rtol=1e-5)) + # After clip + test_boxes_4d.clip(image_size) + test_boxes_5d.clip(image_size) + areas_4d = test_boxes_4d.area() + areas_5d = test_boxes_5d.area() + self.assertTrue(torch.allclose(areas_4d, areas_5d, atol=1e-1, rtol=1e-5)) + + def test_clip_area_arbitrary_angle(self): + num_boxes = 100 + boxes_5d = torch.zeros(num_boxes, 5) + boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-100, 500) + boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-100, 500) + boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, 500) + boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, 500) + boxes_5d[:, 4] = torch.FloatTensor(num_boxes).uniform_(-1800, 1800) + clip_angle_threshold = random.uniform(0, 180) + + image_size = (500, 600) + test_boxes_5d = RotatedBoxes(boxes_5d) + # Before clip + areas_before = test_boxes_5d.area() + # After clip + test_boxes_5d.clip(image_size, clip_angle_threshold) + areas_diff = test_boxes_5d.area() - areas_before + + # the areas should only decrease after clipping + self.assertTrue(torch.all(areas_diff <= 0)) + # whenever the box is clipped (thus the area shrinks), + # the angle for the box must be within the clip_angle_threshold + # Note that the clip function will normalize the angle range + # to be within (-180, 180] + self.assertTrue( + torch.all(torch.abs(boxes_5d[:, 4][torch.where(areas_diff < 0)]) < clip_angle_threshold) + ) + + def test_normalize_angles(self): + # torch.manual_seed(0) + for _ in range(50): + num_boxes = 100 + boxes_5d = torch.zeros(num_boxes, 5) + boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-100, 500) + boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-100, 500) + boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, 500) + boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, 500) + boxes_5d[:, 4] = torch.FloatTensor(num_boxes).uniform_(-1800, 1800) + rotated_boxes = RotatedBoxes(boxes_5d) + normalized_boxes = rotated_boxes.clone() + normalized_boxes.normalize_angles() + self.assertTrue(torch.all(normalized_boxes.tensor[:, 4] >= -180)) + self.assertTrue(torch.all(normalized_boxes.tensor[:, 4] < 180)) + # x, y, w, h should not change + self.assertTrue(torch.allclose(boxes_5d[:, :4], normalized_boxes.tensor[:, :4])) + # the cos/sin values of the angles should stay the same + + self.assertTrue( + torch.allclose( + torch.cos(boxes_5d[:, 4] * math.pi / 180), + torch.cos(normalized_boxes.tensor[:, 4] * math.pi / 180), + atol=1e-5, + ) + ) + + self.assertTrue( + torch.allclose( + torch.sin(boxes_5d[:, 4] * math.pi / 180), + torch.sin(normalized_boxes.tensor[:, 4] * math.pi / 180), + atol=1e-5, + ) + ) + + def test_pairwise_iou_0_degree(self): + for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): + boxes1 = torch.tensor( + [[0.5, 0.5, 1.0, 1.0, 0.0], [0.5, 0.5, 1.0, 1.0, 0.0]], + dtype=torch.float32, + device=device, + ) + boxes2 = torch.tensor( + [ + [0.5, 0.5, 1.0, 1.0, 0.0], + [0.25, 0.5, 0.5, 1.0, 0.0], + [0.5, 0.25, 1.0, 0.5, 0.0], + [0.25, 0.25, 0.5, 0.5, 0.0], + [0.75, 0.75, 0.5, 0.5, 0.0], + [1.0, 1.0, 1.0, 1.0, 0.0], + ], + dtype=torch.float32, + device=device, + ) + expected_ious = torch.tensor( + [ + [1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], + [1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], + ], + dtype=torch.float32, + device=device, + ) + ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) + self.assertTrue(torch.allclose(ious, expected_ious)) + + def test_pairwise_iou_45_degrees(self): + for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): + boxes1 = torch.tensor( + [ + [1, 1, math.sqrt(2), math.sqrt(2), 45], + [1, 1, 2 * math.sqrt(2), 2 * math.sqrt(2), -45], + ], + dtype=torch.float32, + device=device, + ) + boxes2 = torch.tensor([[1, 1, 2, 2, 0]], dtype=torch.float32, device=device) + expected_ious = torch.tensor([[0.5], [0.5]], dtype=torch.float32, device=device) + ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) + self.assertTrue(torch.allclose(ious, expected_ious)) + + def test_pairwise_iou_orthogonal(self): + for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): + boxes1 = torch.tensor([[5, 5, 10, 6, 55]], dtype=torch.float32, device=device) + boxes2 = torch.tensor([[5, 5, 10, 6, -35]], dtype=torch.float32, device=device) + iou = (6.0 * 6.0) / (6.0 * 6.0 + 4.0 * 6.0 + 4.0 * 6.0) + expected_ious = torch.tensor([[iou]], dtype=torch.float32, device=device) + ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) + self.assertTrue(torch.allclose(ious, expected_ious)) + + def test_pairwise_iou_large_close_boxes(self): + for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): + boxes1 = torch.tensor( + [[299.500000, 417.370422, 600.000000, 364.259186, 27.1828]], + dtype=torch.float32, + device=device, + ) + boxes2 = torch.tensor( + [[299.500000, 417.370422, 600.000000, 364.259155, 27.1828]], + dtype=torch.float32, + device=device, + ) + iou = 364.259155 / 364.259186 + expected_ious = torch.tensor([[iou]], dtype=torch.float32, device=device) + ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) + self.assertTrue(torch.allclose(ious, expected_ious)) + + def test_pairwise_iou_many_boxes(self): + for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): + num_boxes1 = 100 + num_boxes2 = 200 + boxes1 = torch.stack( + [ + torch.tensor( + [5 + 20 * i, 5 + 20 * i, 10, 10, 0], + dtype=torch.float32, + device=device, + ) + for i in range(num_boxes1) + ] + ) + boxes2 = torch.stack( + [ + torch.tensor( + [5 + 20 * i, 5 + 20 * i, 10, 1 + 9 * i / num_boxes2, 0], + dtype=torch.float32, + device=device, + ) + for i in range(num_boxes2) + ] + ) + expected_ious = torch.zeros(num_boxes1, num_boxes2, dtype=torch.float32, device=device) + for i in range(min(num_boxes1, num_boxes2)): + expected_ious[i][i] = (1 + 9 * i / num_boxes2) / 10.0 + ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) + self.assertTrue(torch.allclose(ious, expected_ious)) + + def test_pairwise_iou_issue1207_simplified(self): + for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): + # Simplified test case of D2-issue-1207 + boxes1 = torch.tensor([[3, 3, 8, 2, -45.0]], device=device) + boxes2 = torch.tensor([[6, 0, 8, 2, -45.0]], device=device) + iou = 0.0 + expected_ious = torch.tensor([[iou]], dtype=torch.float32, device=device) + + ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) + self.assertTrue(torch.allclose(ious, expected_ious)) + + def test_pairwise_iou_issue1207(self): + for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): + # The original test case in D2-issue-1207 + boxes1 = torch.tensor([[160.0, 153.0, 230.0, 23.0, -37.0]], device=device) + boxes2 = torch.tensor([[190.0, 127.0, 80.0, 21.0, -46.0]], device=device) + + iou = 0.0 + expected_ious = torch.tensor([[iou]], dtype=torch.float32, device=device) + + ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) + self.assertTrue(torch.allclose(ious, expected_ious)) + + def test_empty_cat(self): + x = RotatedBoxes.cat([]) + self.assertTrue(x.tensor.shape, (0, 5)) + + def test_scriptability(self): + def func(x): + boxes = RotatedBoxes(x) + test = boxes.to(torch.device("cpu")).tensor + return boxes.area(), test + + f = torch.jit.script(func) + f = reload_script_model(f) + f(torch.rand((3, 5))) + + data = torch.rand((3, 5)) + + def func_cat(x: torch.Tensor): + boxes1 = RotatedBoxes(x) + boxes2 = RotatedBoxes(x) + # this is not supported by torchscript for now. + # boxes3 = RotatedBoxes.cat([boxes1, boxes2]) + boxes3 = boxes1.cat([boxes1, boxes2]) + return boxes3 + + f = torch.jit.script(func_cat) + script_box = f(data) + self.assertTrue(torch.equal(torch.cat([data, data]), script_box.tensor)) + + +def benchmark_rotated_iou(): + num_boxes1 = 200 + num_boxes2 = 500 + boxes1 = torch.stack( + [ + torch.tensor([5 + 20 * i, 5 + 20 * i, 10, 10, 0], dtype=torch.float32) + for i in range(num_boxes1) + ] + ) + boxes2 = torch.stack( + [ + torch.tensor( + [5 + 20 * i, 5 + 20 * i, 10, 1 + 9 * i / num_boxes2, 0], + dtype=torch.float32, + ) + for i in range(num_boxes2) + ] + ) + + def func(dev, n=1): + b1 = boxes1.to(device=dev) + b2 = boxes2.to(device=dev) + + def bench(): + for _ in range(n): + pairwise_iou_rotated(b1, b2) + if dev.type == "cuda": + torch.cuda.synchronize() + + return bench + + # only run it once per timed loop, since it's slow + args = [{"dev": torch.device("cpu"), "n": 1}] + if torch.cuda.is_available(): + args.append({"dev": torch.device("cuda"), "n": 10}) + + benchmark(func, "rotated_iou", args, warmup_iters=3) + + +if __name__ == "__main__": + unittest.main() + benchmark_rotated_iou() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/test_checkpoint.py b/iChat/models/grit_src/third_party/CenterNet2/tests/test_checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..ab0bfbd590e0c37e377cc93bf99c7c005fc4bdd6 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/test_checkpoint.py @@ -0,0 +1,49 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import unittest +from collections import OrderedDict +import torch +from torch import nn + +from detectron2.checkpoint.c2_model_loading import align_and_update_state_dicts +from detectron2.utils.logger import setup_logger + + +class TestCheckpointer(unittest.TestCase): + def setUp(self): + setup_logger() + + def create_complex_model(self): + m = nn.Module() + m.block1 = nn.Module() + m.block1.layer1 = nn.Linear(2, 3) + m.layer2 = nn.Linear(3, 2) + m.res = nn.Module() + m.res.layer2 = nn.Linear(3, 2) + + state_dict = OrderedDict() + state_dict["layer1.weight"] = torch.rand(3, 2) + state_dict["layer1.bias"] = torch.rand(3) + state_dict["layer2.weight"] = torch.rand(2, 3) + state_dict["layer2.bias"] = torch.rand(2) + state_dict["res.layer2.weight"] = torch.rand(2, 3) + state_dict["res.layer2.bias"] = torch.rand(2) + return m, state_dict + + def test_complex_model_loaded(self): + for add_data_parallel in [False, True]: + model, state_dict = self.create_complex_model() + if add_data_parallel: + model = nn.DataParallel(model) + model_sd = model.state_dict() + + sd_to_load = align_and_update_state_dicts(model_sd, state_dict) + model.load_state_dict(sd_to_load) + for loaded, stored in zip(model_sd.values(), state_dict.values()): + # different tensor references + self.assertFalse(id(loaded) == id(stored)) + # same content + self.assertTrue(loaded.to(stored).equal(stored)) + + +if __name__ == "__main__": + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/test_engine.py b/iChat/models/grit_src/third_party/CenterNet2/tests/test_engine.py new file mode 100644 index 0000000000000000000000000000000000000000..6f6a0997d2a670e40e26286b258773ae56536a87 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/test_engine.py @@ -0,0 +1,186 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import json +import math +import os +import tempfile +import time +import unittest +from unittest import mock +import torch +from fvcore.common.checkpoint import Checkpointer +from torch import nn + +from detectron2 import model_zoo +from detectron2.config import configurable, get_cfg +from detectron2.engine import DefaultTrainer, SimpleTrainer, default_setup, hooks +from detectron2.modeling.meta_arch import META_ARCH_REGISTRY +from detectron2.utils.events import CommonMetricPrinter, JSONWriter + + +@META_ARCH_REGISTRY.register() +class _SimpleModel(nn.Module): + @configurable + def __init__(self, sleep_sec=0): + super().__init__() + self.mod = nn.Linear(10, 20) + self.sleep_sec = sleep_sec + + @classmethod + def from_config(cls, cfg): + return {} + + def forward(self, x): + if self.sleep_sec > 0: + time.sleep(self.sleep_sec) + return {"loss": x.sum() + sum([x.mean() for x in self.parameters()])} + + +class TestTrainer(unittest.TestCase): + def _data_loader(self, device): + device = torch.device(device) + while True: + yield torch.rand(3, 3).to(device) + + def test_simple_trainer(self, device="cpu"): + model = _SimpleModel().to(device=device) + trainer = SimpleTrainer( + model, self._data_loader(device), torch.optim.SGD(model.parameters(), 0.1) + ) + trainer.train(0, 10) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_simple_trainer_cuda(self): + self.test_simple_trainer(device="cuda") + + def test_writer_hooks(self): + model = _SimpleModel(sleep_sec=0.1) + trainer = SimpleTrainer( + model, self._data_loader("cpu"), torch.optim.SGD(model.parameters(), 0.1) + ) + + max_iter = 50 + + with tempfile.TemporaryDirectory(prefix="detectron2_test") as d: + json_file = os.path.join(d, "metrics.json") + writers = [CommonMetricPrinter(max_iter), JSONWriter(json_file)] + + trainer.register_hooks( + [hooks.EvalHook(0, lambda: {"metric": 100}), hooks.PeriodicWriter(writers)] + ) + with self.assertLogs(writers[0].logger) as logs: + trainer.train(0, max_iter) + + with open(json_file, "r") as f: + data = [json.loads(line.strip()) for line in f] + self.assertEqual([x["iteration"] for x in data], [19, 39, 49, 50]) + # the eval metric is in the last line with iter 50 + self.assertIn("metric", data[-1], "Eval metric must be in last line of JSON!") + + # test logged messages from CommonMetricPrinter + self.assertEqual(len(logs.output), 3) + for log, iter in zip(logs.output, [19, 39, 49]): + self.assertIn(f"iter: {iter}", log) + + self.assertIn("eta: 0:00:00", logs.output[-1], "Last ETA must be 0!") + + def test_default_trainer(self): + # TODO: this test requires manifold access, so changed device to CPU. see: T88318502 + cfg = get_cfg() + cfg.MODEL.DEVICE = "cpu" + cfg.MODEL.META_ARCHITECTURE = "_SimpleModel" + cfg.DATASETS.TRAIN = ("coco_2017_val_100",) + with tempfile.TemporaryDirectory(prefix="detectron2_test") as d: + cfg.OUTPUT_DIR = d + trainer = DefaultTrainer(cfg) + + # test property + self.assertIs(trainer.model, trainer._trainer.model) + trainer.model = _SimpleModel() + self.assertIs(trainer.model, trainer._trainer.model) + + def test_checkpoint_resume(self): + model = _SimpleModel() + dataloader = self._data_loader("cpu") + opt = torch.optim.SGD(model.parameters(), 0.1) + scheduler = torch.optim.lr_scheduler.StepLR(opt, 3) + + with tempfile.TemporaryDirectory(prefix="detectron2_test") as d: + trainer = SimpleTrainer(model, dataloader, opt) + checkpointer = Checkpointer(model, d, opt=opt, trainer=trainer) + + trainer.register_hooks( + [ + hooks.LRScheduler(scheduler=scheduler), + # checkpoint after scheduler to properly save the state of scheduler + hooks.PeriodicCheckpointer(checkpointer, 10), + ] + ) + + trainer.train(0, 12) + self.assertAlmostEqual(opt.param_groups[0]["lr"], 1e-5) + self.assertEqual(scheduler.last_epoch, 12) + del trainer + + opt = torch.optim.SGD(model.parameters(), 999) # lr will be loaded + trainer = SimpleTrainer(model, dataloader, opt) + scheduler = torch.optim.lr_scheduler.StepLR(opt, 3) + trainer.register_hooks( + [ + hooks.LRScheduler(scheduler=scheduler), + ] + ) + checkpointer = Checkpointer(model, d, opt=opt, trainer=trainer) + checkpointer.resume_or_load("non_exist.pth") + self.assertEqual(trainer.iter, 11) # last finished iter number (0-based in Trainer) + # number of times `scheduler.step()` was called (1-based) + self.assertEqual(scheduler.last_epoch, 12) + self.assertAlmostEqual(opt.param_groups[0]["lr"], 1e-5) + + def test_eval_hook(self): + model = _SimpleModel() + dataloader = self._data_loader("cpu") + opt = torch.optim.SGD(model.parameters(), 0.1) + + for total_iter, period, eval_count in [(30, 15, 2), (31, 15, 3), (20, 0, 1)]: + test_func = mock.Mock(return_value={"metric": 3.0}) + trainer = SimpleTrainer(model, dataloader, opt) + trainer.register_hooks([hooks.EvalHook(period, test_func)]) + trainer.train(0, total_iter) + self.assertEqual(test_func.call_count, eval_count) + + def test_best_checkpointer(self): + model = _SimpleModel() + dataloader = self._data_loader("cpu") + opt = torch.optim.SGD(model.parameters(), 0.1) + metric_name = "metric" + total_iter = 40 + test_period = 10 + test_cases = [ + ("max", iter([0.3, 0.4, 0.35, 0.5]), 3), + ("min", iter([1.0, 0.8, 0.9, 0.9]), 2), + ("min", iter([math.nan, 0.8, 0.9, 0.9]), 1), + ] + for mode, metrics, call_count in test_cases: + trainer = SimpleTrainer(model, dataloader, opt) + with tempfile.TemporaryDirectory(prefix="detectron2_test") as d: + checkpointer = Checkpointer(model, d, opt=opt, trainer=trainer) + trainer.register_hooks( + [ + hooks.EvalHook(test_period, lambda: {metric_name: next(metrics)}), + hooks.BestCheckpointer(test_period, checkpointer, metric_name, mode=mode), + ] + ) + with mock.patch.object(checkpointer, "save") as mock_save_method: + trainer.train(0, total_iter) + self.assertEqual(mock_save_method.call_count, call_count) + + def test_setup_config(self): + with tempfile.TemporaryDirectory(prefix="detectron2_test") as d: + cfg = get_cfg() + cfg.OUTPUT_DIR = os.path.join(d, "yacs") + default_setup(cfg, {}) + + cfg = model_zoo.get_config("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.py") + cfg.train.output_dir = os.path.join(d, "omegaconf") + default_setup(cfg, {}) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/test_events.py b/iChat/models/grit_src/third_party/CenterNet2/tests/test_events.py new file mode 100644 index 0000000000000000000000000000000000000000..c1b03e4d1a703a417a83c2805be1ca15a4e458ed --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/test_events.py @@ -0,0 +1,64 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import json +import os +import tempfile +import unittest + +from detectron2.utils.events import CommonMetricPrinter, EventStorage, JSONWriter + + +class TestEventWriter(unittest.TestCase): + def testScalar(self): + with tempfile.TemporaryDirectory( + prefix="detectron2_tests" + ) as dir, EventStorage() as storage: + json_file = os.path.join(dir, "test.json") + writer = JSONWriter(json_file) + for k in range(60): + storage.put_scalar("key", k, smoothing_hint=False) + if (k + 1) % 20 == 0: + writer.write() + storage.step() + writer.close() + with open(json_file) as f: + data = [json.loads(l) for l in f] + self.assertTrue([int(k["key"]) for k in data] == [19, 39, 59]) + + def testScalarMismatchedPeriod(self): + with tempfile.TemporaryDirectory( + prefix="detectron2_tests" + ) as dir, EventStorage() as storage: + json_file = os.path.join(dir, "test.json") + + writer = JSONWriter(json_file) + for k in range(60): + if k % 17 == 0: # write in a differnt period + storage.put_scalar("key2", k, smoothing_hint=False) + storage.put_scalar("key", k, smoothing_hint=False) + if (k + 1) % 20 == 0: + writer.write() + storage.step() + writer.close() + with open(json_file) as f: + data = [json.loads(l) for l in f] + self.assertTrue([int(k.get("key2", 0)) for k in data] == [17, 0, 34, 0, 51, 0]) + self.assertTrue([int(k.get("key", 0)) for k in data] == [0, 19, 0, 39, 0, 59]) + self.assertTrue([int(k["iteration"]) for k in data] == [17, 19, 34, 39, 51, 59]) + + def testPrintETA(self): + with EventStorage() as s: + p1 = CommonMetricPrinter(10) + p2 = CommonMetricPrinter() + + s.put_scalar("time", 1.0) + s.step() + s.put_scalar("time", 1.0) + s.step() + + with self.assertLogs("detectron2.utils.events") as logs: + p1.write() + self.assertIn("eta", logs.output[0]) + + with self.assertLogs("detectron2.utils.events") as logs: + p2.write() + self.assertNotIn("eta", logs.output[0]) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/test_export_caffe2.py b/iChat/models/grit_src/third_party/CenterNet2/tests/test_export_caffe2.py new file mode 100644 index 0000000000000000000000000000000000000000..9a5e155fda907003f60e6bb3d40fd58599c50d59 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/test_export_caffe2.py @@ -0,0 +1,52 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# -*- coding: utf-8 -*- + +import copy +import os +import tempfile +import unittest +import torch + +from detectron2 import model_zoo +from detectron2.export import Caffe2Model, Caffe2Tracer +from detectron2.utils.logger import setup_logger +from detectron2.utils.testing import get_sample_coco_image + + +# TODO: this test requires manifold access, see: T88318502 +# Running it on CircleCI causes crash, not sure why. +@unittest.skipIf(os.environ.get("CIRCLECI"), "Caffe2 tests crash on CircleCI.") +class TestCaffe2Export(unittest.TestCase): + def setUp(self): + setup_logger() + + def _test_model(self, config_path, device="cpu"): + cfg = model_zoo.get_config(config_path) + cfg.MODEL.DEVICE = device + model = model_zoo.get(config_path, trained=True, device=device) + + inputs = [{"image": get_sample_coco_image()}] + tracer = Caffe2Tracer(cfg, model, copy.deepcopy(inputs)) + + with tempfile.TemporaryDirectory(prefix="detectron2_unittest") as d: + if not os.environ.get("CI"): + # This requires onnx, which is not yet available on public CI + c2_model = tracer.export_caffe2() + c2_model.save_protobuf(d) + c2_model.save_graph(os.path.join(d, "test.svg"), inputs=copy.deepcopy(inputs)) + + c2_model = Caffe2Model.load_protobuf(d) + c2_model(inputs)[0]["instances"] + + ts_model = tracer.export_torchscript() + ts_model.save(os.path.join(d, "model.ts")) + + def testMaskRCNN(self): + self._test_model("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml") + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def testMaskRCNNGPU(self): + self._test_model("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml", device="cuda") + + def testRetinaNet(self): + self._test_model("COCO-Detection/retinanet_R_50_FPN_3x.yaml") diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/test_export_torchscript.py b/iChat/models/grit_src/third_party/CenterNet2/tests/test_export_torchscript.py new file mode 100644 index 0000000000000000000000000000000000000000..e9a0ff5851fc1f7aadd6577ab07858b089b4de3f --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/test_export_torchscript.py @@ -0,0 +1,296 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import json +import os +import random +import tempfile +import unittest +import torch +from torch import Tensor, nn + +from detectron2 import model_zoo +from detectron2.config import get_cfg +from detectron2.config.instantiate import dump_dataclass, instantiate +from detectron2.export import dump_torchscript_IR, scripting_with_instances +from detectron2.export.flatten import TracingAdapter, flatten_to_tuple +from detectron2.export.torchscript_patch import patch_builtin_len +from detectron2.layers import ShapeSpec +from detectron2.modeling import build_backbone +from detectron2.modeling.postprocessing import detector_postprocess +from detectron2.modeling.roi_heads import KRCNNConvDeconvUpsampleHead +from detectron2.structures import Boxes, Instances +from detectron2.utils.env import TORCH_VERSION +from detectron2.utils.testing import ( + assert_instances_allclose, + convert_scripted_instances, + get_sample_coco_image, + random_boxes, +) + +""" +https://detectron2.readthedocs.io/tutorials/deployment.html +contains some explanations of this file. +""" + +SLOW_PUBLIC_CPU_TEST = unittest.skipIf( + os.environ.get("CI") and not torch.cuda.is_available(), + "The test is too slow on CPUs and will be executed on CircleCI's GPU jobs.", +) + + +class TestScripting(unittest.TestCase): + def testMaskRCNNFPN(self): + self._test_rcnn_model("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml") + + @SLOW_PUBLIC_CPU_TEST + def testMaskRCNNC4(self): + self._test_rcnn_model("COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml") + + def testRetinaNet(self): + self._test_retinanet_model("COCO-Detection/retinanet_R_50_FPN_3x.yaml") + + def _test_rcnn_model(self, config_path): + model = model_zoo.get(config_path, trained=True) + model.eval() + + fields = { + "proposal_boxes": Boxes, + "objectness_logits": Tensor, + "pred_boxes": Boxes, + "scores": Tensor, + "pred_classes": Tensor, + "pred_masks": Tensor, + } + script_model = scripting_with_instances(model, fields) + + # Test that batch inference with different shapes are supported + image = get_sample_coco_image() + small_image = nn.functional.interpolate(image, scale_factor=0.5) + inputs = [{"image": image}, {"image": small_image}] + with torch.no_grad(): + instance = model.inference(inputs, do_postprocess=False)[0] + scripted_instance = script_model.inference(inputs, do_postprocess=False)[0] + assert_instances_allclose(instance, scripted_instance) + + def _test_retinanet_model(self, config_path): + model = model_zoo.get(config_path, trained=True) + model.eval() + + fields = { + "pred_boxes": Boxes, + "scores": Tensor, + "pred_classes": Tensor, + } + script_model = scripting_with_instances(model, fields) + + img = get_sample_coco_image() + inputs = [{"image": img}] * 2 + with torch.no_grad(): + instance = model(inputs)[0]["instances"] + scripted_instance = convert_scripted_instances(script_model(inputs)[0]) + scripted_instance = detector_postprocess(scripted_instance, img.shape[1], img.shape[2]) + assert_instances_allclose(instance, scripted_instance) + # Note that the model currently cannot be saved and loaded into a new process: + # https://github.com/pytorch/pytorch/issues/46944 + + +# TODO: this test requires manifold access, see: T88318502 +class TestTracing(unittest.TestCase): + def testMaskRCNNFPN(self): + def inference_func(model, image): + inputs = [{"image": image}] + return model.inference(inputs, do_postprocess=False)[0] + + self._test_model("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml", inference_func) + + def testMaskRCNNFPN_with_postproc(self): + def inference_func(model, image): + inputs = [{"image": image, "height": image.shape[1], "width": image.shape[2]}] + return model.inference(inputs, do_postprocess=True)[0]["instances"] + + self._test_model("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml", inference_func) + + @SLOW_PUBLIC_CPU_TEST + def testMaskRCNNC4(self): + def inference_func(model, image): + inputs = [{"image": image}] + return model.inference(inputs, do_postprocess=False)[0] + + self._test_model("COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml", inference_func) + + @SLOW_PUBLIC_CPU_TEST + def testCascadeRCNN(self): + def inference_func(model, image): + inputs = [{"image": image}] + return model.inference(inputs, do_postprocess=False)[0] + + self._test_model("Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml", inference_func) + + # bug fixed by https://github.com/pytorch/pytorch/pull/67734 + @unittest.skipIf(TORCH_VERSION == (1, 10) and os.environ.get("CI"), "1.10 has bugs.") + def testRetinaNet(self): + def inference_func(model, image): + return model.forward([{"image": image}])[0]["instances"] + + self._test_model("COCO-Detection/retinanet_R_50_FPN_3x.yaml", inference_func) + + def _test_model(self, config_path, inference_func, batch=1): + model = model_zoo.get(config_path, trained=True) + image = get_sample_coco_image() + inputs = tuple(image.clone() for _ in range(batch)) + + wrapper = TracingAdapter(model, inputs, inference_func) + wrapper.eval() + with torch.no_grad(): + # trace with smaller images, and the trace must still work + trace_inputs = tuple( + nn.functional.interpolate(image, scale_factor=random.uniform(0.5, 0.7)) + for _ in range(batch) + ) + traced_model = torch.jit.trace(wrapper, trace_inputs) + + outputs = inference_func(model, *inputs) + traced_outputs = wrapper.outputs_schema(traced_model(*inputs)) + if batch > 1: + for output, traced_output in zip(outputs, traced_outputs): + assert_instances_allclose(output, traced_output, size_as_tensor=True) + else: + assert_instances_allclose(outputs, traced_outputs, size_as_tensor=True) + + @SLOW_PUBLIC_CPU_TEST + def testMaskRCNNFPN_batched(self): + def inference_func(model, image1, image2): + inputs = [{"image": image1}, {"image": image2}] + return model.inference(inputs, do_postprocess=False) + + self._test_model( + "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml", inference_func, batch=2 + ) + + def testKeypointHead(self): + class M(nn.Module): + def __init__(self): + super().__init__() + self.model = KRCNNConvDeconvUpsampleHead( + ShapeSpec(channels=4, height=14, width=14), num_keypoints=17, conv_dims=(4,) + ) + + def forward(self, x, predbox1, predbox2): + inst = [ + Instances((100, 100), pred_boxes=Boxes(predbox1)), + Instances((100, 100), pred_boxes=Boxes(predbox2)), + ] + ret = self.model(x, inst) + return tuple(x.pred_keypoints for x in ret) + + model = M() + model.eval() + + def gen_input(num1, num2): + feat = torch.randn((num1 + num2, 4, 14, 14)) + box1 = random_boxes(num1) + box2 = random_boxes(num2) + return feat, box1, box2 + + with torch.no_grad(), patch_builtin_len(): + trace = torch.jit.trace(model, gen_input(15, 15), check_trace=False) + + inputs = gen_input(12, 10) + trace_outputs = trace(*inputs) + true_outputs = model(*inputs) + for trace_output, true_output in zip(trace_outputs, true_outputs): + self.assertTrue(torch.allclose(trace_output, true_output)) + + +class TestTorchscriptUtils(unittest.TestCase): + # TODO: add test to dump scripting + def test_dump_IR_tracing(self): + cfg = get_cfg() + cfg.MODEL.RESNETS.DEPTH = 18 + cfg.MODEL.RESNETS.RES2_OUT_CHANNELS = 64 + + class Mod(nn.Module): + def forward(self, x): + return tuple(self.m(x).values()) + + model = Mod() + model.m = build_backbone(cfg) + model.eval() + + with torch.no_grad(): + ts_model = torch.jit.trace(model, (torch.rand(2, 3, 224, 224),)) + + with tempfile.TemporaryDirectory(prefix="detectron2_test") as d: + dump_torchscript_IR(ts_model, d) + # check that the files are created + for name in ["model_ts_code", "model_ts_IR", "model_ts_IR_inlined", "model"]: + fname = os.path.join(d, name + ".txt") + self.assertTrue(os.stat(fname).st_size > 0, fname) + + def test_dump_IR_function(self): + @torch.jit.script + def gunc(x, y): + return x + y + + def func(x, y): + return x + y + gunc(x, y) + + ts_model = torch.jit.trace(func, (torch.rand(3), torch.rand(3))) + with tempfile.TemporaryDirectory(prefix="detectron2_test") as d: + dump_torchscript_IR(ts_model, d) + for name in ["model_ts_code", "model_ts_IR", "model_ts_IR_inlined"]: + fname = os.path.join(d, name + ".txt") + self.assertTrue(os.stat(fname).st_size > 0, fname) + + def test_flatten_basic(self): + obj = [3, ([5, 6], {"name": [7, 9], "name2": 3})] + res, schema = flatten_to_tuple(obj) + self.assertEqual(res, (3, 5, 6, 7, 9, 3)) + new_obj = schema(res) + self.assertEqual(new_obj, obj) + + _, new_schema = flatten_to_tuple(new_obj) + self.assertEqual(schema, new_schema) # test __eq__ + self._check_schema(schema) + + def _check_schema(self, schema): + dumped_schema = dump_dataclass(schema) + # Check that the schema is json-serializable + # Although in reality you might want to use yaml because it often has many levels + json.dumps(dumped_schema) + + # Check that the schema can be deserialized + new_schema = instantiate(dumped_schema) + self.assertEqual(schema, new_schema) + + def test_flatten_instances_boxes(self): + inst = Instances( + torch.tensor([5, 8]), pred_masks=torch.tensor([3]), pred_boxes=Boxes(torch.ones((1, 4))) + ) + obj = [3, ([5, 6], inst)] + res, schema = flatten_to_tuple(obj) + self.assertEqual(res[:3], (3, 5, 6)) + for r, expected in zip(res[3:], (inst.pred_boxes.tensor, inst.pred_masks, inst.image_size)): + self.assertIs(r, expected) + new_obj = schema(res) + assert_instances_allclose(new_obj[1][1], inst, rtol=0.0, size_as_tensor=True) + + self._check_schema(schema) + + def test_allow_non_tensor(self): + data = (torch.tensor([5, 8]), 3) # contains non-tensor + + class M(nn.Module): + def forward(self, input, number): + return input + + model = M() + with self.assertRaisesRegex(ValueError, "must only contain tensors"): + adap = TracingAdapter(model, data, allow_non_tensor=False) + + adap = TracingAdapter(model, data, allow_non_tensor=True) + _ = adap(*adap.flattened_inputs) + + newdata = (data[0].clone(),) + with self.assertRaisesRegex(ValueError, "cannot generalize"): + _ = adap(*newdata) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/test_model_analysis.py b/iChat/models/grit_src/third_party/CenterNet2/tests/test_model_analysis.py new file mode 100644 index 0000000000000000000000000000000000000000..c01b7af09703c8dad889dee0118d74fcc12ac4b0 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/test_model_analysis.py @@ -0,0 +1,80 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + + +import unittest +import torch +from torch import nn + +from detectron2.utils.analysis import find_unused_parameters, flop_count_operators, parameter_count +from detectron2.utils.testing import get_model_no_weights + + +class RetinaNetTest(unittest.TestCase): + def setUp(self): + self.model = get_model_no_weights("COCO-Detection/retinanet_R_50_FPN_1x.yaml") + + def test_flop(self): + # RetinaNet supports flop-counting with random inputs + inputs = [{"image": torch.rand(3, 800, 800), "test_unused": "abcd"}] + res = flop_count_operators(self.model, inputs) + self.assertEqual(int(res["conv"]), 146) # 146B flops + + def test_param_count(self): + res = parameter_count(self.model) + self.assertEqual(res[""], 37915572) + self.assertEqual(res["backbone"], 31452352) + + +class FasterRCNNTest(unittest.TestCase): + def setUp(self): + self.model = get_model_no_weights("COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml") + + def test_flop(self): + # Faster R-CNN supports flop-counting with random inputs + inputs = [{"image": torch.rand(3, 800, 800)}] + res = flop_count_operators(self.model, inputs) + + # This only checks flops for backbone & proposal generator + # Flops for box head is not conv, and depends on #proposals, which is + # almost 0 for random inputs. + self.assertEqual(int(res["conv"]), 117) + + def test_flop_with_output_shape(self): + inputs = [{"image": torch.rand(3, 800, 800), "height": 700, "width": 700}] + res = flop_count_operators(self.model, inputs) + self.assertEqual(int(res["conv"]), 117) + + def test_param_count(self): + res = parameter_count(self.model) + self.assertEqual(res[""], 41699936) + self.assertEqual(res["backbone"], 26799296) + + +class MaskRCNNTest(unittest.TestCase): + def setUp(self): + self.model = get_model_no_weights("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml") + + def test_flop(self): + inputs1 = [{"image": torch.rand(3, 800, 800)}] + inputs2 = [{"image": torch.rand(3, 800, 800), "height": 700, "width": 700}] + + for inputs in [inputs1, inputs2]: + res = flop_count_operators(self.model, inputs) + # The mask head could have extra conv flops, so total >= 117 + self.assertGreaterEqual(int(res["conv"]), 117) + + +class UnusedParamTest(unittest.TestCase): + def test_unused(self): + class TestMod(nn.Module): + def __init__(self): + super().__init__() + self.fc1 = nn.Linear(10, 10) + self.t = nn.Linear(10, 10) + + def forward(self, x): + return self.fc1(x).mean() + + m = TestMod() + ret = find_unused_parameters(m, torch.randn(10, 10)) + self.assertEqual(set(ret), {"t.weight", "t.bias"}) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/test_model_zoo.py b/iChat/models/grit_src/third_party/CenterNet2/tests/test_model_zoo.py new file mode 100644 index 0000000000000000000000000000000000000000..e3360a74864e0c00ed92ffbc8531c8d36e8be379 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/test_model_zoo.py @@ -0,0 +1,50 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import unittest + +from detectron2 import model_zoo +from detectron2.config import instantiate +from detectron2.modeling import FPN, GeneralizedRCNN + +logger = logging.getLogger(__name__) + + +class TestModelZoo(unittest.TestCase): + def test_get_returns_model(self): + model = model_zoo.get("Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml", trained=False) + self.assertIsInstance(model, GeneralizedRCNN) + self.assertIsInstance(model.backbone, FPN) + + def test_get_invalid_model(self): + self.assertRaises(RuntimeError, model_zoo.get, "Invalid/config.yaml") + + def test_get_url(self): + url = model_zoo.get_checkpoint_url("Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml") + self.assertEqual( + url, + "https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn/138602908/model_final_01ca85.pkl", # noqa + ) + url2 = model_zoo.get_checkpoint_url("Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.py") + self.assertEqual(url, url2) + + def _build_lazy_model(self, name): + cfg = model_zoo.get_config("common/models/" + name) + instantiate(cfg.model) + + def test_mask_rcnn_fpn(self): + self._build_lazy_model("mask_rcnn_fpn.py") + + def test_mask_rcnn_c4(self): + self._build_lazy_model("mask_rcnn_c4.py") + + def test_panoptic_fpn(self): + self._build_lazy_model("panoptic_fpn.py") + + def test_schedule(self): + cfg = model_zoo.get_config("common/coco_schedule.py") + for _, v in cfg.items(): + instantiate(v) + + +if __name__ == "__main__": + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/test_packaging.py b/iChat/models/grit_src/third_party/CenterNet2/tests/test_packaging.py new file mode 100644 index 0000000000000000000000000000000000000000..a5b1661e8f341fe66a6e02c59fe172bce445782b --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/test_packaging.py @@ -0,0 +1,24 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import unittest + +from detectron2.utils.collect_env import collect_env_info + + +class TestProjects(unittest.TestCase): + def test_import(self): + from detectron2.projects import point_rend + + _ = point_rend.add_pointrend_config + + import detectron2.projects.deeplab as deeplab + + _ = deeplab.add_deeplab_config + + # import detectron2.projects.panoptic_deeplab as panoptic_deeplab + + # _ = panoptic_deeplab.add_panoptic_deeplab_config + + +class TestCollectEnv(unittest.TestCase): + def test(self): + _ = collect_env_info() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/test_registry.py b/iChat/models/grit_src/third_party/CenterNet2/tests/test_registry.py new file mode 100644 index 0000000000000000000000000000000000000000..4e425a6ec44c7c47a5a106bfdf5ce8062c2110c9 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/test_registry.py @@ -0,0 +1,45 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import unittest +import torch + +from detectron2.modeling.meta_arch import GeneralizedRCNN +from detectron2.utils.registry import _convert_target_to_string, locate + + +class A: + class B: + pass + + +class TestLocate(unittest.TestCase): + def _test_obj(self, obj): + name = _convert_target_to_string(obj) + newobj = locate(name) + self.assertIs(obj, newobj) + + def test_basic(self): + self._test_obj(GeneralizedRCNN) + + def test_inside_class(self): + # requires using __qualname__ instead of __name__ + self._test_obj(A.B) + + def test_builtin(self): + self._test_obj(len) + self._test_obj(dict) + + def test_pytorch_optim(self): + # pydoc.locate does not work for it + self._test_obj(torch.optim.SGD) + + def test_failure(self): + with self.assertRaises(ImportError): + locate("asdf") + + def test_compress_target(self): + from detectron2.data.transforms import RandomCrop + + name = _convert_target_to_string(RandomCrop) + # name shouldn't contain 'augmentation_impl' + self.assertEqual(name, "detectron2.data.transforms.RandomCrop") + self.assertIs(RandomCrop, locate(name)) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/test_scheduler.py b/iChat/models/grit_src/third_party/CenterNet2/tests/test_scheduler.py new file mode 100644 index 0000000000000000000000000000000000000000..6cccb03f74b594c06add44a134b526e41c2974f0 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/test_scheduler.py @@ -0,0 +1,68 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import math +import numpy as np +from unittest import TestCase +import torch +from fvcore.common.param_scheduler import CosineParamScheduler, MultiStepParamScheduler +from torch import nn + +from detectron2.solver import LRMultiplier, WarmupParamScheduler + + +class TestScheduler(TestCase): + def test_warmup_multistep(self): + p = nn.Parameter(torch.zeros(0)) + opt = torch.optim.SGD([p], lr=5) + + multiplier = WarmupParamScheduler( + MultiStepParamScheduler( + [1, 0.1, 0.01, 0.001], + milestones=[10, 15, 20], + num_updates=30, + ), + 0.001, + 5 / 30, + ) + sched = LRMultiplier(opt, multiplier, 30) + # This is an equivalent of: + # sched = WarmupMultiStepLR( + # opt, milestones=[10, 15, 20], gamma=0.1, warmup_factor=0.001, warmup_iters=5) + + p.sum().backward() + opt.step() + + lrs = [0.005] + for _ in range(30): + sched.step() + lrs.append(opt.param_groups[0]["lr"]) + self.assertTrue(np.allclose(lrs[:5], [0.005, 1.004, 2.003, 3.002, 4.001])) + self.assertTrue(np.allclose(lrs[5:10], 5.0)) + self.assertTrue(np.allclose(lrs[10:15], 0.5)) + self.assertTrue(np.allclose(lrs[15:20], 0.05)) + self.assertTrue(np.allclose(lrs[20:], 0.005)) + + def test_warmup_cosine(self): + p = nn.Parameter(torch.zeros(0)) + opt = torch.optim.SGD([p], lr=5) + multiplier = WarmupParamScheduler( + CosineParamScheduler(1, 0), + 0.001, + 5 / 30, + ) + sched = LRMultiplier(opt, multiplier, 30) + + p.sum().backward() + opt.step() + self.assertEqual(opt.param_groups[0]["lr"], 0.005) + lrs = [0.005] + + for _ in range(30): + sched.step() + lrs.append(opt.param_groups[0]["lr"]) + for idx, lr in enumerate(lrs): + expected_cosine = 2.5 * (1.0 + math.cos(math.pi * idx / 30)) + if idx >= 5: + self.assertAlmostEqual(lr, expected_cosine) + else: + self.assertNotAlmostEqual(lr, expected_cosine) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/test_solver.py b/iChat/models/grit_src/third_party/CenterNet2/tests/test_solver.py new file mode 100644 index 0000000000000000000000000000000000000000..6b3ae84c00b789df071ab5d12bae42d991df1d0b --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/test_solver.py @@ -0,0 +1,66 @@ +import unittest + +from detectron2.solver.build import _expand_param_groups, reduce_param_groups + + +class TestOptimizer(unittest.TestCase): + def testExpandParamsGroups(self): + params = [ + { + "params": ["p1", "p2", "p3", "p4"], + "lr": 1.0, + "weight_decay": 3.0, + }, + { + "params": ["p2", "p3", "p5"], + "lr": 2.0, + "momentum": 2.0, + }, + { + "params": ["p1"], + "weight_decay": 4.0, + }, + ] + out = _expand_param_groups(params) + gt = [ + dict(params=["p1"], lr=1.0, weight_decay=4.0), # noqa + dict(params=["p2"], lr=2.0, weight_decay=3.0, momentum=2.0), # noqa + dict(params=["p3"], lr=2.0, weight_decay=3.0, momentum=2.0), # noqa + dict(params=["p4"], lr=1.0, weight_decay=3.0), # noqa + dict(params=["p5"], lr=2.0, momentum=2.0), # noqa + ] + self.assertEqual(out, gt) + + def testReduceParamGroups(self): + params = [ + dict(params=["p1"], lr=1.0, weight_decay=4.0), # noqa + dict(params=["p2", "p6"], lr=2.0, weight_decay=3.0, momentum=2.0), # noqa + dict(params=["p3"], lr=2.0, weight_decay=3.0, momentum=2.0), # noqa + dict(params=["p4"], lr=1.0, weight_decay=3.0), # noqa + dict(params=["p5"], lr=2.0, momentum=2.0), # noqa + ] + gt_groups = [ + { + "lr": 1.0, + "weight_decay": 4.0, + "params": ["p1"], + }, + { + "lr": 2.0, + "weight_decay": 3.0, + "momentum": 2.0, + "params": ["p2", "p6", "p3"], + }, + { + "lr": 1.0, + "weight_decay": 3.0, + "params": ["p4"], + }, + { + "lr": 2.0, + "momentum": 2.0, + "params": ["p5"], + }, + ] + out = reduce_param_groups(params) + self.assertEqual(out, gt_groups) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tests/test_visualizer.py b/iChat/models/grit_src/third_party/CenterNet2/tests/test_visualizer.py new file mode 100644 index 0000000000000000000000000000000000000000..1005000f525bc876ae32a3421737e3f9fe3bc5f4 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tests/test_visualizer.py @@ -0,0 +1,278 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import numpy as np +import os +import tempfile +import unittest +import cv2 +import torch + +from detectron2.data import MetadataCatalog +from detectron2.structures import BoxMode, Instances, RotatedBoxes +from detectron2.utils.visualizer import ColorMode, Visualizer + + +class TestVisualizer(unittest.TestCase): + def _random_data(self): + H, W = 100, 100 + N = 10 + img = np.random.rand(H, W, 3) * 255 + boxxy = np.random.rand(N, 2) * (H // 2) + boxes = np.concatenate((boxxy, boxxy + H // 2), axis=1) + + def _rand_poly(): + return np.random.rand(3, 2).flatten() * H + + polygons = [[_rand_poly() for _ in range(np.random.randint(1, 5))] for _ in range(N)] + + mask = np.zeros_like(img[:, :, 0], dtype=np.bool) + mask[:40, 10:20] = 1 + + labels = [str(i) for i in range(N)] + return img, boxes, labels, polygons, [mask] * N + + @property + def metadata(self): + return MetadataCatalog.get("coco_2017_train") + + def test_draw_dataset_dict(self): + img = np.random.rand(512, 512, 3) * 255 + dic = { + "annotations": [ + { + "bbox": [ + 368.9946492271106, + 330.891438763377, + 13.148537455410235, + 13.644708680142685, + ], + "bbox_mode": BoxMode.XYWH_ABS, + "category_id": 0, + "iscrowd": 1, + "segmentation": { + "counts": "_jh52m?2N2N2N2O100O10O001N1O2MceP2", + "size": [512, 512], + }, + } + ], + "height": 512, + "image_id": 1, + "width": 512, + } + v = Visualizer(img) + v.draw_dataset_dict(dic) + + v = Visualizer(img, self.metadata) + v.draw_dataset_dict(dic) + + def test_draw_rotated_dataset_dict(self): + img = np.random.rand(512, 512, 3) * 255 + dic = { + "annotations": [ + { + "bbox": [ + 368.9946492271106, + 330.891438763377, + 13.148537455410235, + 13.644708680142685, + 45.0, + ], + "bbox_mode": BoxMode.XYWHA_ABS, + "category_id": 0, + "iscrowd": 1, + } + ], + "height": 512, + "image_id": 1, + "width": 512, + } + v = Visualizer(img, self.metadata) + v.draw_dataset_dict(dic) + + def test_overlay_instances(self): + img, boxes, labels, polygons, masks = self._random_data() + + v = Visualizer(img, self.metadata) + output = v.overlay_instances(masks=polygons, boxes=boxes, labels=labels).get_image() + self.assertEqual(output.shape, img.shape) + + # Test 2x scaling + v = Visualizer(img, self.metadata, scale=2.0) + output = v.overlay_instances(masks=polygons, boxes=boxes, labels=labels).get_image() + self.assertEqual(output.shape[0], img.shape[0] * 2) + + # Test overlay masks + v = Visualizer(img, self.metadata) + output = v.overlay_instances(masks=masks, boxes=boxes, labels=labels).get_image() + self.assertEqual(output.shape, img.shape) + + def test_overlay_instances_no_boxes(self): + img, boxes, labels, polygons, _ = self._random_data() + v = Visualizer(img, self.metadata) + v.overlay_instances(masks=polygons, boxes=None, labels=labels).get_image() + + def test_draw_instance_predictions(self): + img, boxes, _, _, masks = self._random_data() + num_inst = len(boxes) + inst = Instances((img.shape[0], img.shape[1])) + inst.pred_classes = torch.randint(0, 80, size=(num_inst,)) + inst.scores = torch.rand(num_inst) + inst.pred_boxes = torch.from_numpy(boxes) + inst.pred_masks = torch.from_numpy(np.asarray(masks)) + + v = Visualizer(img) + v.draw_instance_predictions(inst) + + v = Visualizer(img, self.metadata) + v.draw_instance_predictions(inst) + + def test_BWmode_nomask(self): + img, boxes, _, _, masks = self._random_data() + num_inst = len(boxes) + inst = Instances((img.shape[0], img.shape[1])) + inst.pred_classes = torch.randint(0, 80, size=(num_inst,)) + inst.scores = torch.rand(num_inst) + inst.pred_boxes = torch.from_numpy(boxes) + + v = Visualizer(img, self.metadata, instance_mode=ColorMode.IMAGE_BW) + v.draw_instance_predictions(inst) + + # check that output is grayscale + inst = inst[:0] + v = Visualizer(img, self.metadata, instance_mode=ColorMode.IMAGE_BW) + output = v.draw_instance_predictions(inst).get_image() + self.assertTrue(np.allclose(output[:, :, 0], output[:, :, 1])) + self.assertTrue(np.allclose(output[:, :, 0], output[:, :, 2])) + + def test_draw_empty_mask_predictions(self): + img, boxes, _, _, masks = self._random_data() + num_inst = len(boxes) + inst = Instances((img.shape[0], img.shape[1])) + inst.pred_classes = torch.randint(0, 80, size=(num_inst,)) + inst.scores = torch.rand(num_inst) + inst.pred_boxes = torch.from_numpy(boxes) + inst.pred_masks = torch.from_numpy(np.zeros_like(np.asarray(masks))) + + v = Visualizer(img, self.metadata) + v.draw_instance_predictions(inst) + + def test_correct_output_shape(self): + img = np.random.rand(928, 928, 3) * 255 + v = Visualizer(img, self.metadata) + out = v.output.get_image() + self.assertEqual(out.shape, img.shape) + + def test_overlay_rotated_instances(self): + H, W = 100, 150 + img = np.random.rand(H, W, 3) * 255 + num_boxes = 50 + boxes_5d = torch.zeros(num_boxes, 5) + boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-0.1 * W, 1.1 * W) + boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-0.1 * H, 1.1 * H) + boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, max(W, H)) + boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, max(W, H)) + boxes_5d[:, 4] = torch.FloatTensor(num_boxes).uniform_(-1800, 1800) + rotated_boxes = RotatedBoxes(boxes_5d) + labels = [str(i) for i in range(num_boxes)] + + v = Visualizer(img, self.metadata) + output = v.overlay_instances(boxes=rotated_boxes, labels=labels).get_image() + self.assertEqual(output.shape, img.shape) + + def test_draw_no_metadata(self): + img, boxes, _, _, masks = self._random_data() + num_inst = len(boxes) + inst = Instances((img.shape[0], img.shape[1])) + inst.pred_classes = torch.randint(0, 80, size=(num_inst,)) + inst.scores = torch.rand(num_inst) + inst.pred_boxes = torch.from_numpy(boxes) + inst.pred_masks = torch.from_numpy(np.asarray(masks)) + + v = Visualizer(img, MetadataCatalog.get("asdfasdf")) + v.draw_instance_predictions(inst) + + def test_draw_binary_mask(self): + img, boxes, _, _, masks = self._random_data() + img[:, :, 0] = 0 # remove red color + mask = masks[0] + mask_with_hole = np.zeros_like(mask).astype("uint8") + mask_with_hole = cv2.rectangle(mask_with_hole, (10, 10), (50, 50), 1, 5) + + for m in [mask, mask_with_hole]: + for save in [True, False]: + v = Visualizer(img) + o = v.draw_binary_mask(m, color="red", text="test") + if save: + with tempfile.TemporaryDirectory(prefix="detectron2_viz") as d: + path = os.path.join(d, "output.png") + o.save(path) + o = cv2.imread(path)[:, :, ::-1] + else: + o = o.get_image().astype("float32") + # red color is drawn on the image + self.assertTrue(o[:, :, 0].sum() > 0) + + def test_draw_soft_mask(self): + img = np.random.rand(100, 100, 3) * 255 + img[:, :, 0] = 0 # remove red color + mask = np.zeros((100, 100), dtype=np.float32) + mask[30:50, 40:50] = 1.0 + cv2.GaussianBlur(mask, (21, 21), 10) + + v = Visualizer(img) + o = v.draw_soft_mask(mask, color="red", text="test") + o = o.get_image().astype("float32") + # red color is drawn on the image + self.assertTrue(o[:, :, 0].sum() > 0) + + # test draw empty mask + v = Visualizer(img) + o = v.draw_soft_mask(np.zeros((100, 100), dtype=np.float32), color="red", text="test") + o = o.get_image().astype("float32") + + def test_border_mask_with_holes(self): + H, W = 200, 200 + img = np.zeros((H, W, 3)) + img[:, :, 0] = 255.0 + v = Visualizer(img, scale=3) + + mask = np.zeros((H, W)) + mask[:, 100:150] = 1 + # create a hole, to trigger imshow + mask = cv2.rectangle(mask, (110, 110), (130, 130), 0, thickness=-1) + output = v.draw_binary_mask(mask, color="blue") + output = output.get_image()[:, :, ::-1] + + first_row = {tuple(x.tolist()) for x in output[0]} + last_row = {tuple(x.tolist()) for x in output[-1]} + # Check quantization / off-by-1 error: the first and last row must have two colors + self.assertEqual(len(last_row), 2) + self.assertEqual(len(first_row), 2) + self.assertIn((0, 0, 255), last_row) + self.assertIn((0, 0, 255), first_row) + + def test_border_polygons(self): + H, W = 200, 200 + img = np.zeros((H, W, 3)) + img[:, :, 0] = 255.0 + v = Visualizer(img, scale=3) + mask = np.zeros((H, W)) + mask[:, 100:150] = 1 + + output = v.draw_binary_mask(mask, color="blue") + output = output.get_image()[:, :, ::-1] + + first_row = {tuple(x.tolist()) for x in output[0]} + last_row = {tuple(x.tolist()) for x in output[-1]} + # Check quantization / off-by-1 error: + # the first and last row must have >=2 colors, because the polygon + # touches both rows + self.assertGreaterEqual(len(last_row), 2) + self.assertGreaterEqual(len(first_row), 2) + self.assertIn((0, 0, 255), last_row) + self.assertIn((0, 0, 255), first_row) + + +if __name__ == "__main__": + unittest.main() diff --git a/iChat/models/grit_src/third_party/CenterNet2/tools/README.md b/iChat/models/grit_src/third_party/CenterNet2/tools/README.md new file mode 100644 index 0000000000000000000000000000000000000000..0b40d5319c0838fdaa22bc6a10ef0d88bc6578ed --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tools/README.md @@ -0,0 +1,49 @@ + +This directory contains a few example scripts that demonstrate features of detectron2. + + +* `train_net.py` + +An example training script that's made to train builtin models of detectron2. + +For usage, see [GETTING_STARTED.md](../GETTING_STARTED.md). + +* `plain_train_net.py` + +Similar to `train_net.py`, but implements a training loop instead of using `Trainer`. +This script includes fewer features but it may be more friendly to hackers. + +* `benchmark.py` + +Benchmark the training speed, inference speed or data loading speed of a given config. + +Usage: +``` +python benchmark.py --config-file config.yaml --task train/eval/data [optional DDP flags] +``` + +* `analyze_model.py` + +Analyze FLOPs, parameters, activations of a detectron2 model. See its `--help` for usage. + +* `visualize_json_results.py` + +Visualize the json instance detection/segmentation results dumped by `COCOEvalutor` or `LVISEvaluator` + +Usage: +``` +python visualize_json_results.py --input x.json --output dir/ --dataset coco_2017_val +``` +If not using a builtin dataset, you'll need your own script or modify this script. + +* `visualize_data.py` + +Visualize ground truth raw annotations or training data (after preprocessing/augmentations). + +Usage: +``` +python visualize_data.py --config-file config.yaml --source annotation/dataloader --output-dir dir/ [--show] +``` + +NOTE: the script does not stop by itself when using `--source dataloader` because a training +dataloader is usually infinite. diff --git a/iChat/models/grit_src/third_party/CenterNet2/tools/__init__.py b/iChat/models/grit_src/third_party/CenterNet2/tools/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/iChat/models/grit_src/third_party/CenterNet2/tools/analyze_model.py b/iChat/models/grit_src/third_party/CenterNet2/tools/analyze_model.py new file mode 100644 index 0000000000000000000000000000000000000000..8e38f8b71eb3b8d1e2b670e7f01a796ec2ea4b7e --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tools/analyze_model.py @@ -0,0 +1,159 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import logging +import numpy as np +from collections import Counter +import tqdm +from fvcore.nn import flop_count_table # can also try flop_count_str + +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate +from detectron2.data import build_detection_test_loader +from detectron2.engine import default_argument_parser +from detectron2.modeling import build_model +from detectron2.utils.analysis import ( + FlopCountAnalysis, + activation_count_operators, + parameter_count_table, +) +from detectron2.utils.logger import setup_logger + +logger = logging.getLogger("detectron2") + + +def setup(args): + if args.config_file.endswith(".yaml"): + cfg = get_cfg() + cfg.merge_from_file(args.config_file) + cfg.DATALOADER.NUM_WORKERS = 0 + cfg.merge_from_list(args.opts) + cfg.freeze() + else: + cfg = LazyConfig.load(args.config_file) + cfg = LazyConfig.apply_overrides(cfg, args.opts) + setup_logger(name="fvcore") + setup_logger() + return cfg + + +def do_flop(cfg): + if isinstance(cfg, CfgNode): + data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0]) + model = build_model(cfg) + DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS) + else: + data_loader = instantiate(cfg.dataloader.test) + model = instantiate(cfg.model) + model.to(cfg.train.device) + DetectionCheckpointer(model).load(cfg.train.init_checkpoint) + model.eval() + + counts = Counter() + total_flops = [] + for idx, data in zip(tqdm.trange(args.num_inputs), data_loader): # noqa + flops = FlopCountAnalysis(model, data) + if idx > 0: + flops.unsupported_ops_warnings(False).uncalled_modules_warnings(False) + counts += flops.by_operator() + total_flops.append(flops.total()) + + logger.info("Flops table computed from only one input sample:\n" + flop_count_table(flops)) + logger.info( + "Average GFlops for each type of operators:\n" + + str([(k, v / (idx + 1) / 1e9) for k, v in counts.items()]) + ) + logger.info( + "Total GFlops: {:.1f}±{:.1f}".format(np.mean(total_flops) / 1e9, np.std(total_flops) / 1e9) + ) + + +def do_activation(cfg): + if isinstance(cfg, CfgNode): + data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0]) + model = build_model(cfg) + DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS) + else: + data_loader = instantiate(cfg.dataloader.test) + model = instantiate(cfg.model) + model.to(cfg.train.device) + DetectionCheckpointer(model).load(cfg.train.init_checkpoint) + model.eval() + + counts = Counter() + total_activations = [] + for idx, data in zip(tqdm.trange(args.num_inputs), data_loader): # noqa + count = activation_count_operators(model, data) + counts += count + total_activations.append(sum(count.values())) + logger.info( + "(Million) Activations for Each Type of Operators:\n" + + str([(k, v / idx) for k, v in counts.items()]) + ) + logger.info( + "Total (Million) Activations: {}±{}".format( + np.mean(total_activations), np.std(total_activations) + ) + ) + + +def do_parameter(cfg): + if isinstance(cfg, CfgNode): + model = build_model(cfg) + else: + model = instantiate(cfg.model) + logger.info("Parameter Count:\n" + parameter_count_table(model, max_depth=5)) + + +def do_structure(cfg): + if isinstance(cfg, CfgNode): + model = build_model(cfg) + else: + model = instantiate(cfg.model) + logger.info("Model Structure:\n" + str(model)) + + +if __name__ == "__main__": + parser = default_argument_parser( + epilog=""" +Examples: + +To show parameters of a model: +$ ./analyze_model.py --tasks parameter \\ + --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml + +Flops and activations are data-dependent, therefore inputs and model weights +are needed to count them: + +$ ./analyze_model.py --num-inputs 100 --tasks flop \\ + --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \\ + MODEL.WEIGHTS /path/to/model.pkl +""" + ) + parser.add_argument( + "--tasks", + choices=["flop", "activation", "parameter", "structure"], + required=True, + nargs="+", + ) + parser.add_argument( + "-n", + "--num-inputs", + default=100, + type=int, + help="number of inputs used to compute statistics for flops/activations, " + "both are data dependent.", + ) + args = parser.parse_args() + assert not args.eval_only + assert args.num_gpus == 1 + + cfg = setup(args) + + for task in args.tasks: + { + "flop": do_flop, + "activation": do_activation, + "parameter": do_parameter, + "structure": do_structure, + }[task](cfg) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tools/benchmark.py b/iChat/models/grit_src/third_party/CenterNet2/tools/benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..aaac56400148f7b140b7c1356bbbc3b4293e5ce3 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tools/benchmark.py @@ -0,0 +1,197 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. +""" +A script to benchmark builtin models. + +Note: this script has an extra dependency of psutil. +""" + +import itertools +import logging +import psutil +import torch +import tqdm +from fvcore.common.timer import Timer +from torch.nn.parallel import DistributedDataParallel + +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import LazyConfig, get_cfg, instantiate +from detectron2.data import ( + DatasetFromList, + build_detection_test_loader, + build_detection_train_loader, +) +from detectron2.data.benchmark import DataLoaderBenchmark +from detectron2.engine import AMPTrainer, SimpleTrainer, default_argument_parser, hooks, launch +from detectron2.modeling import build_model +from detectron2.solver import build_optimizer +from detectron2.utils import comm +from detectron2.utils.collect_env import collect_env_info +from detectron2.utils.events import CommonMetricPrinter +from detectron2.utils.logger import setup_logger + +logger = logging.getLogger("detectron2") + + +def setup(args): + if args.config_file.endswith(".yaml"): + cfg = get_cfg() + cfg.merge_from_file(args.config_file) + cfg.SOLVER.BASE_LR = 0.001 # Avoid NaNs. Not useful in this script anyway. + cfg.merge_from_list(args.opts) + cfg.freeze() + else: + cfg = LazyConfig.load(args.config_file) + cfg = LazyConfig.apply_overrides(cfg, args.opts) + setup_logger(distributed_rank=comm.get_rank()) + return cfg + + +def create_data_benchmark(cfg, args): + if args.config_file.endswith(".py"): + dl_cfg = cfg.dataloader.train + dl_cfg._target_ = DataLoaderBenchmark + return instantiate(dl_cfg) + else: + kwargs = build_detection_train_loader.from_config(cfg) + kwargs.pop("aspect_ratio_grouping", None) + kwargs["_target_"] = DataLoaderBenchmark + return instantiate(kwargs) + + +def RAM_msg(): + vram = psutil.virtual_memory() + return "RAM Usage: {:.2f}/{:.2f} GB".format( + (vram.total - vram.available) / 1024 ** 3, vram.total / 1024 ** 3 + ) + + +def benchmark_data(args): + cfg = setup(args) + logger.info("After spawning " + RAM_msg()) + + benchmark = create_data_benchmark(cfg, args) + benchmark.benchmark_distributed(250, 10) + # test for a few more rounds + for k in range(10): + logger.info(f"Iteration {k} " + RAM_msg()) + benchmark.benchmark_distributed(250, 1) + + +def benchmark_data_advanced(args): + # benchmark dataloader with more details to help analyze performance bottleneck + cfg = setup(args) + benchmark = create_data_benchmark(cfg, args) + + if comm.get_rank() == 0: + benchmark.benchmark_dataset(100) + benchmark.benchmark_mapper(100) + benchmark.benchmark_workers(100, warmup=10) + benchmark.benchmark_IPC(100, warmup=10) + if comm.get_world_size() > 1: + benchmark.benchmark_distributed(100) + logger.info("Rerun ...") + benchmark.benchmark_distributed(100) + + +def benchmark_train(args): + cfg = setup(args) + model = build_model(cfg) + logger.info("Model:\n{}".format(model)) + if comm.get_world_size() > 1: + model = DistributedDataParallel( + model, device_ids=[comm.get_local_rank()], broadcast_buffers=False + ) + optimizer = build_optimizer(cfg, model) + checkpointer = DetectionCheckpointer(model, optimizer=optimizer) + checkpointer.load(cfg.MODEL.WEIGHTS) + + cfg.defrost() + cfg.DATALOADER.NUM_WORKERS = 2 + data_loader = build_detection_train_loader(cfg) + dummy_data = list(itertools.islice(data_loader, 100)) + + def f(): + data = DatasetFromList(dummy_data, copy=False, serialize=False) + while True: + yield from data + + max_iter = 400 + trainer = (AMPTrainer if cfg.SOLVER.AMP.ENABLED else SimpleTrainer)(model, f(), optimizer) + trainer.register_hooks( + [ + hooks.IterationTimer(), + hooks.PeriodicWriter([CommonMetricPrinter(max_iter)]), + hooks.TorchProfiler( + lambda trainer: trainer.iter == max_iter - 1, cfg.OUTPUT_DIR, save_tensorboard=True + ), + ] + ) + trainer.train(1, max_iter) + + +@torch.no_grad() +def benchmark_eval(args): + cfg = setup(args) + if args.config_file.endswith(".yaml"): + model = build_model(cfg) + DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS) + + cfg.defrost() + cfg.DATALOADER.NUM_WORKERS = 0 + data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0]) + else: + model = instantiate(cfg.model) + model.to(cfg.train.device) + DetectionCheckpointer(model).load(cfg.train.init_checkpoint) + + cfg.dataloader.num_workers = 0 + data_loader = instantiate(cfg.dataloader.test) + + model.eval() + logger.info("Model:\n{}".format(model)) + dummy_data = DatasetFromList(list(itertools.islice(data_loader, 100)), copy=False) + + def f(): + while True: + yield from dummy_data + + for k in range(5): # warmup + model(dummy_data[k]) + + max_iter = 300 + timer = Timer() + with tqdm.tqdm(total=max_iter) as pbar: + for idx, d in enumerate(f()): + if idx == max_iter: + break + model(d) + pbar.update() + logger.info("{} iters in {} seconds.".format(max_iter, timer.seconds())) + + +if __name__ == "__main__": + parser = default_argument_parser() + parser.add_argument("--task", choices=["train", "eval", "data", "data_advanced"], required=True) + args = parser.parse_args() + assert not args.eval_only + + logger.info("Environment info:\n" + collect_env_info()) + if "data" in args.task: + print("Initial " + RAM_msg()) + if args.task == "data": + f = benchmark_data + if args.task == "data_advanced": + f = benchmark_data_advanced + elif args.task == "train": + """ + Note: training speed may not be representative. + The training cost of a R-CNN model varies with the content of the data + and the quality of the model. + """ + f = benchmark_train + elif args.task == "eval": + f = benchmark_eval + # only benchmark single-GPU inference. + assert args.num_gpus == 1 and args.num_machines == 1 + launch(f, args.num_gpus, args.num_machines, args.machine_rank, args.dist_url, args=(args,)) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tools/convert-torchvision-to-d2.py b/iChat/models/grit_src/third_party/CenterNet2/tools/convert-torchvision-to-d2.py new file mode 100644 index 0000000000000000000000000000000000000000..4b827d960cca69657e98bd89a9aa5623a847099d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tools/convert-torchvision-to-d2.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. + +import pickle as pkl +import sys +import torch + +""" +Usage: + # download one of the ResNet{18,34,50,101,152} models from torchvision: + wget https://download.pytorch.org/models/resnet50-19c8e357.pth -O r50.pth + # run the conversion + ./convert-torchvision-to-d2.py r50.pth r50.pkl + + # Then, use r50.pkl with the following changes in config: + +MODEL: + WEIGHTS: "/path/to/r50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + RESNETS: + DEPTH: 50 + STRIDE_IN_1X1: False +INPUT: + FORMAT: "RGB" + + These models typically produce slightly worse results than the + pre-trained ResNets we use in official configs, which are the + original ResNet models released by MSRA. +""" + +if __name__ == "__main__": + input = sys.argv[1] + + obj = torch.load(input, map_location="cpu") + + newmodel = {} + for k in list(obj.keys()): + old_k = k + if "layer" not in k: + k = "stem." + k + for t in [1, 2, 3, 4]: + k = k.replace("layer{}".format(t), "res{}".format(t + 1)) + for t in [1, 2, 3]: + k = k.replace("bn{}".format(t), "conv{}.norm".format(t)) + k = k.replace("downsample.0", "shortcut") + k = k.replace("downsample.1", "shortcut.norm") + print(old_k, "->", k) + newmodel[k] = obj.pop(old_k).detach().numpy() + + res = {"model": newmodel, "__author__": "torchvision", "matching_heuristics": True} + + with open(sys.argv[2], "wb") as f: + pkl.dump(res, f) + if obj: + print("Unconverted keys:", obj.keys()) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tools/deploy/CMakeLists.txt b/iChat/models/grit_src/third_party/CenterNet2/tools/deploy/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..80dae12500af4c7e7e6cfc5b7b3a5800782956c3 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tools/deploy/CMakeLists.txt @@ -0,0 +1,15 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# See https://pytorch.org/tutorials/advanced/cpp_frontend.html +cmake_minimum_required(VERSION 3.12 FATAL_ERROR) +project(torchscript_mask_rcnn) + +find_package(Torch REQUIRED) +find_package(OpenCV REQUIRED) +find_package(TorchVision REQUIRED) # needed by export-method=tracing/scripting + +add_executable(torchscript_mask_rcnn torchscript_mask_rcnn.cpp) +target_link_libraries( + torchscript_mask_rcnn + -Wl,--no-as-needed TorchVision::TorchVision -Wl,--as-needed + "${TORCH_LIBRARIES}" ${OpenCV_LIBS}) +set_property(TARGET torchscript_mask_rcnn PROPERTY CXX_STANDARD 14) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tools/deploy/README.md b/iChat/models/grit_src/third_party/CenterNet2/tools/deploy/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e33cbeb54c003a5738da68c838fdaa4e0d218501 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tools/deploy/README.md @@ -0,0 +1,66 @@ +See [deployment tutorial](https://detectron2.readthedocs.io/tutorials/deployment.html) +for some high-level background about deployment. + +This directory contains the following examples: + +1. An example script `export_model.py` + that exports a detectron2 model for deployment using different methods and formats. + +2. A C++ example that runs inference with Mask R-CNN model in TorchScript format. + +## Build +Deployment depends on libtorch and OpenCV. Some require more dependencies: + +* Running TorchScript-format models produced by `--export-method=caffe2_tracing` requires libtorch + to be built with caffe2 enabled. +* Running TorchScript-format models produced by `--export-method=tracing/scripting` requires libtorchvision (C++ library of torchvision). + +All methods are supported in one C++ file that requires all the above dependencies. +Adjust it and remove code you don't need. +As a reference, we provide a [Dockerfile](../../docker/deploy.Dockerfile) that installs all the above dependencies and builds the C++ example. + +## Use + +We show a few example commands to export and execute a Mask R-CNN model in C++. + +* `export-method=tracing, format=torchscript`: +``` +./export_model.py --config-file ../../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \ + --output ./output --export-method tracing --format torchscript \ + MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl \ + MODEL.DEVICE cuda + +./build/torchscript_mask_rcnn output/model.ts input.jpg tracing +``` + +* `export-method=scripting, format=torchscript`: +``` +./export_model.py --config-file ../../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \ + --output ./output --export-method scripting --format torchscript \ + MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl \ + +./build/torchscript_mask_rcnn output/model.ts input.jpg scripting +``` + +* `export-method=caffe2_tracing, format=torchscript`: + +``` +./export_model.py --config-file ../../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \ + --output ./output --export-method caffe2_tracing --format torchscript \ + MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl \ + +./build/torchscript_mask_rcnn output/model.ts input.jpg caffe2_tracing +``` + + +## Notes: + +1. Tracing/Caffe2-tracing requires valid weights & sample inputs. + Therefore the above commands require pre-trained models and [COCO dataset](https://detectron2.readthedocs.io/tutorials/builtin_datasets.html). + You can modify the script to obtain sample inputs in other ways instead of from COCO. + +2. `--run-eval` is implemented only for tracing mode + to evaluate the exported model using the dataset in the config. + It's recommended to always verify the accuracy in case the conversion is not successful. + Evaluation can be slow if model is exported to CPU or dataset is too large ("coco_2017_val_100" is a small subset of COCO useful for evaluation). + `caffe2_tracing` accuracy may be slightly different (within 0.1 AP) from original model due to numerical precisions between different runtime. diff --git a/iChat/models/grit_src/third_party/CenterNet2/tools/deploy/export_model.py b/iChat/models/grit_src/third_party/CenterNet2/tools/deploy/export_model.py new file mode 100644 index 0000000000000000000000000000000000000000..bb1bcee6323372e80e42e3217b055d0c3a902954 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tools/deploy/export_model.py @@ -0,0 +1,235 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. +import argparse +import os +from typing import Dict, List, Tuple +import torch +from torch import Tensor, nn + +import detectron2.data.transforms as T +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import get_cfg +from detectron2.data import build_detection_test_loader, detection_utils +from detectron2.evaluation import COCOEvaluator, inference_on_dataset, print_csv_format +from detectron2.export import TracingAdapter, dump_torchscript_IR, scripting_with_instances +from detectron2.modeling import GeneralizedRCNN, RetinaNet, build_model +from detectron2.modeling.postprocessing import detector_postprocess +from detectron2.projects.point_rend import add_pointrend_config +from detectron2.structures import Boxes +from detectron2.utils.env import TORCH_VERSION +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import setup_logger + + +def setup_cfg(args): + cfg = get_cfg() + # cuda context is initialized before creating dataloader, so we don't fork anymore + cfg.DATALOADER.NUM_WORKERS = 0 + add_pointrend_config(cfg) + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + cfg.freeze() + return cfg + + +def export_caffe2_tracing(cfg, torch_model, inputs): + from detectron2.export import Caffe2Tracer + + tracer = Caffe2Tracer(cfg, torch_model, inputs) + if args.format == "caffe2": + caffe2_model = tracer.export_caffe2() + caffe2_model.save_protobuf(args.output) + # draw the caffe2 graph + caffe2_model.save_graph(os.path.join(args.output, "model.svg"), inputs=inputs) + return caffe2_model + elif args.format == "onnx": + import onnx + + onnx_model = tracer.export_onnx() + onnx.save(onnx_model, os.path.join(args.output, "model.onnx")) + elif args.format == "torchscript": + ts_model = tracer.export_torchscript() + with PathManager.open(os.path.join(args.output, "model.ts"), "wb") as f: + torch.jit.save(ts_model, f) + dump_torchscript_IR(ts_model, args.output) + + +# experimental. API not yet final +def export_scripting(torch_model): + assert TORCH_VERSION >= (1, 8) + fields = { + "proposal_boxes": Boxes, + "objectness_logits": Tensor, + "pred_boxes": Boxes, + "scores": Tensor, + "pred_classes": Tensor, + "pred_masks": Tensor, + "pred_keypoints": torch.Tensor, + "pred_keypoint_heatmaps": torch.Tensor, + } + assert args.format == "torchscript", "Scripting only supports torchscript format." + + class ScriptableAdapterBase(nn.Module): + # Use this adapter to workaround https://github.com/pytorch/pytorch/issues/46944 + # by not retuning instances but dicts. Otherwise the exported model is not deployable + def __init__(self): + super().__init__() + self.model = torch_model + self.eval() + + if isinstance(torch_model, GeneralizedRCNN): + + class ScriptableAdapter(ScriptableAdapterBase): + def forward(self, inputs: Tuple[Dict[str, torch.Tensor]]) -> List[Dict[str, Tensor]]: + instances = self.model.inference(inputs, do_postprocess=False) + return [i.get_fields() for i in instances] + + else: + + class ScriptableAdapter(ScriptableAdapterBase): + def forward(self, inputs: Tuple[Dict[str, torch.Tensor]]) -> List[Dict[str, Tensor]]: + instances = self.model(inputs) + return [i.get_fields() for i in instances] + + ts_model = scripting_with_instances(ScriptableAdapter(), fields) + with PathManager.open(os.path.join(args.output, "model.ts"), "wb") as f: + torch.jit.save(ts_model, f) + dump_torchscript_IR(ts_model, args.output) + # TODO inference in Python now missing postprocessing glue code + return None + + +# experimental. API not yet final +def export_tracing(torch_model, inputs): + assert TORCH_VERSION >= (1, 8) + image = inputs[0]["image"] + inputs = [{"image": image}] # remove other unused keys + + if isinstance(torch_model, GeneralizedRCNN): + + def inference(model, inputs): + # use do_postprocess=False so it returns ROI mask + inst = model.inference(inputs, do_postprocess=False)[0] + return [{"instances": inst}] + + else: + inference = None # assume that we just call the model directly + + traceable_model = TracingAdapter(torch_model, inputs, inference) + + if args.format == "torchscript": + ts_model = torch.jit.trace(traceable_model, (image,)) + with PathManager.open(os.path.join(args.output, "model.ts"), "wb") as f: + torch.jit.save(ts_model, f) + dump_torchscript_IR(ts_model, args.output) + elif args.format == "onnx": + with PathManager.open(os.path.join(args.output, "model.onnx"), "wb") as f: + torch.onnx.export(traceable_model, (image,), f, opset_version=11) + logger.info("Inputs schema: " + str(traceable_model.inputs_schema)) + logger.info("Outputs schema: " + str(traceable_model.outputs_schema)) + + if args.format != "torchscript": + return None + if not isinstance(torch_model, (GeneralizedRCNN, RetinaNet)): + return None + + def eval_wrapper(inputs): + """ + The exported model does not contain the final resize step, which is typically + unused in deployment but needed for evaluation. We add it manually here. + """ + input = inputs[0] + instances = traceable_model.outputs_schema(ts_model(input["image"]))[0]["instances"] + postprocessed = detector_postprocess(instances, input["height"], input["width"]) + return [{"instances": postprocessed}] + + return eval_wrapper + + +def get_sample_inputs(args): + + if args.sample_image is None: + # get a first batch from dataset + data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0]) + first_batch = next(iter(data_loader)) + return first_batch + else: + # get a sample data + original_image = detection_utils.read_image(args.sample_image, format=cfg.INPUT.FORMAT) + # Do same preprocessing as DefaultPredictor + aug = T.ResizeShortestEdge( + [cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST + ) + height, width = original_image.shape[:2] + image = aug.get_transform(original_image).apply_image(original_image) + image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1)) + + inputs = {"image": image, "height": height, "width": width} + + # Sample ready + sample_inputs = [inputs] + return sample_inputs + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Export a model for deployment.") + parser.add_argument( + "--format", + choices=["caffe2", "onnx", "torchscript"], + help="output format", + default="torchscript", + ) + parser.add_argument( + "--export-method", + choices=["caffe2_tracing", "tracing", "scripting"], + help="Method to export models", + default="tracing", + ) + parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file") + parser.add_argument("--sample-image", default=None, type=str, help="sample image for input") + parser.add_argument("--run-eval", action="store_true") + parser.add_argument("--output", help="output directory for the converted model") + parser.add_argument( + "opts", + help="Modify config options using the command-line", + default=None, + nargs=argparse.REMAINDER, + ) + args = parser.parse_args() + logger = setup_logger() + logger.info("Command line arguments: " + str(args)) + PathManager.mkdirs(args.output) + # Disable respecialization on new shapes. Otherwise --run-eval will be slow + torch._C._jit_set_bailout_depth(1) + + cfg = setup_cfg(args) + + # create a torch model + torch_model = build_model(cfg) + DetectionCheckpointer(torch_model).resume_or_load(cfg.MODEL.WEIGHTS) + torch_model.eval() + + # get sample data + sample_inputs = get_sample_inputs(args) + + # convert and save model + if args.export_method == "caffe2_tracing": + exported_model = export_caffe2_tracing(cfg, torch_model, sample_inputs) + elif args.export_method == "scripting": + exported_model = export_scripting(torch_model) + elif args.export_method == "tracing": + exported_model = export_tracing(torch_model, sample_inputs) + + # run evaluation with the converted model + if args.run_eval: + assert exported_model is not None, ( + "Python inference is not yet implemented for " + f"export_method={args.export_method}, format={args.format}." + ) + logger.info("Running evaluation ... this takes a long time if you export to CPU.") + dataset = cfg.DATASETS.TEST[0] + data_loader = build_detection_test_loader(cfg, dataset) + # NOTE: hard-coded evaluator. change to the evaluator for your dataset + evaluator = COCOEvaluator(dataset, output_dir=args.output) + metrics = inference_on_dataset(exported_model, data_loader, evaluator) + print_csv_format(metrics) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tools/deploy/torchscript_mask_rcnn.cpp b/iChat/models/grit_src/third_party/CenterNet2/tools/deploy/torchscript_mask_rcnn.cpp new file mode 100644 index 0000000000000000000000000000000000000000..b40f13b81f601788847992e6627b448d62a287e2 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tools/deploy/torchscript_mask_rcnn.cpp @@ -0,0 +1,187 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// @lint-ignore-every CLANGTIDY +// This is an example code that demonstrates how to run inference +// with a torchscript format Mask R-CNN model exported by ./export_model.py +// using export method=tracing, caffe2_tracing & scripting. + +#include +#include +#include + +#include +#include +#include +#include + +// only needed for export_method=tracing +#include // @oss-only +// @fb-only: #include + +using namespace std; + +c10::IValue get_caffe2_tracing_inputs(cv::Mat& img, c10::Device device) { + const int height = img.rows; + const int width = img.cols; + // FPN models require divisibility of 32. + // Tracing mode does padding inside the graph, but caffe2_tracing does not. + assert(height % 32 == 0 && width % 32 == 0); + const int channels = 3; + + auto input = + torch::from_blob(img.data, {1, height, width, channels}, torch::kUInt8); + // NHWC to NCHW + input = input.to(device, torch::kFloat).permute({0, 3, 1, 2}).contiguous(); + + std::array im_info_data{height * 1.0f, width * 1.0f, 1.0f}; + auto im_info = + torch::from_blob(im_info_data.data(), {1, 3}).clone().to(device); + return std::make_tuple(input, im_info); +} + +c10::IValue get_tracing_inputs(cv::Mat& img, c10::Device device) { + const int height = img.rows; + const int width = img.cols; + const int channels = 3; + + auto input = + torch::from_blob(img.data, {height, width, channels}, torch::kUInt8); + // HWC to CHW + input = input.to(device, torch::kFloat).permute({2, 0, 1}).contiguous(); + return input; +} + +// create a Tuple[Dict[str, Tensor]] which is the input type of scripted model +c10::IValue get_scripting_inputs(cv::Mat& img, c10::Device device) { + const int height = img.rows; + const int width = img.cols; + const int channels = 3; + + auto img_tensor = + torch::from_blob(img.data, {height, width, channels}, torch::kUInt8); + // HWC to CHW + img_tensor = + img_tensor.to(device, torch::kFloat).permute({2, 0, 1}).contiguous(); + auto dic = c10::Dict(); + dic.insert("image", img_tensor); + return std::make_tuple(dic); +} + +c10::IValue +get_inputs(std::string export_method, cv::Mat& img, c10::Device device) { + // Given an image, create inputs in the format required by the model. + if (export_method == "tracing") + return get_tracing_inputs(img, device); + if (export_method == "caffe2_tracing") + return get_caffe2_tracing_inputs(img, device); + if (export_method == "scripting") + return get_scripting_inputs(img, device); + abort(); +} + +struct MaskRCNNOutputs { + at::Tensor pred_boxes, pred_classes, pred_masks, scores; + int num_instances() const { + return pred_boxes.sizes()[0]; + } +}; + +MaskRCNNOutputs get_outputs(std::string export_method, c10::IValue outputs) { + // Given outputs of the model, extract tensors from it to turn into a + // common MaskRCNNOutputs format. + if (export_method == "tracing") { + auto out_tuple = outputs.toTuple()->elements(); + // They are ordered alphabetically by their field name in Instances + return MaskRCNNOutputs{ + out_tuple[0].toTensor(), + out_tuple[1].toTensor(), + out_tuple[2].toTensor(), + out_tuple[3].toTensor()}; + } + if (export_method == "caffe2_tracing") { + auto out_tuple = outputs.toTuple()->elements(); + // A legacy order used by caffe2 models + return MaskRCNNOutputs{ + out_tuple[0].toTensor(), + out_tuple[2].toTensor(), + out_tuple[3].toTensor(), + out_tuple[1].toTensor()}; + } + if (export_method == "scripting") { + // With the ScriptableAdapter defined in export_model.py, the output is + // List[Dict[str, Any]]. + auto out_dict = outputs.toList().get(0).toGenericDict(); + return MaskRCNNOutputs{ + out_dict.at("pred_boxes").toTensor(), + out_dict.at("pred_classes").toTensor(), + out_dict.at("pred_masks").toTensor(), + out_dict.at("scores").toTensor()}; + } + abort(); +} + +int main(int argc, const char* argv[]) { + if (argc != 4) { + cerr << R"xx( +Usage: + ./torchscript_mask_rcnn model.ts input.jpg EXPORT_METHOD + + EXPORT_METHOD can be "tracing", "caffe2_tracing" or "scripting". +)xx"; + return 1; + } + std::string image_file = argv[2]; + std::string export_method = argv[3]; + assert( + export_method == "caffe2_tracing" || export_method == "tracing" || + export_method == "scripting"); + + torch::jit::getBailoutDepth() = 1; + torch::autograd::AutoGradMode guard(false); + auto module = torch::jit::load(argv[1]); + + assert(module.buffers().size() > 0); + // Assume that the entire model is on the same device. + // We just put input to this device. + auto device = (*begin(module.buffers())).device(); + + cv::Mat input_img = cv::imread(image_file, cv::IMREAD_COLOR); + auto inputs = get_inputs(export_method, input_img, device); + + // Run the network + auto output = module.forward({inputs}); + if (device.is_cuda()) + c10::cuda::getCurrentCUDAStream().synchronize(); + + // run 3 more times to benchmark + int N_benchmark = 3, N_warmup = 1; + auto start_time = chrono::high_resolution_clock::now(); + for (int i = 0; i < N_benchmark + N_warmup; ++i) { + if (i == N_warmup) + start_time = chrono::high_resolution_clock::now(); + output = module.forward({inputs}); + if (device.is_cuda()) + c10::cuda::getCurrentCUDAStream().synchronize(); + } + auto end_time = chrono::high_resolution_clock::now(); + auto ms = chrono::duration_cast(end_time - start_time) + .count(); + cout << "Latency (should vary with different inputs): " + << ms * 1.0 / 1e6 / N_benchmark << " seconds" << endl; + + // Parse Mask R-CNN outputs + auto rcnn_outputs = get_outputs(export_method, output); + cout << "Number of detected objects: " << rcnn_outputs.num_instances() + << endl; + + cout << "pred_boxes: " << rcnn_outputs.pred_boxes.toString() << " " + << rcnn_outputs.pred_boxes.sizes() << endl; + cout << "scores: " << rcnn_outputs.scores.toString() << " " + << rcnn_outputs.scores.sizes() << endl; + cout << "pred_classes: " << rcnn_outputs.pred_classes.toString() << " " + << rcnn_outputs.pred_classes.sizes() << endl; + cout << "pred_masks: " << rcnn_outputs.pred_masks.toString() << " " + << rcnn_outputs.pred_masks.sizes() << endl; + + cout << rcnn_outputs.pred_boxes << endl; + return 0; +} diff --git a/iChat/models/grit_src/third_party/CenterNet2/tools/lazyconfig_train_net.py b/iChat/models/grit_src/third_party/CenterNet2/tools/lazyconfig_train_net.py new file mode 100644 index 0000000000000000000000000000000000000000..bb62d36c0c171b0391453afafc2828ebab1b0da1 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tools/lazyconfig_train_net.py @@ -0,0 +1,131 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. +""" +Training script using the new "LazyConfig" python config files. + +This scripts reads a given python config file and runs the training or evaluation. +It can be used to train any models or dataset as long as they can be +instantiated by the recursive construction defined in the given config file. + +Besides lazy construction of models, dataloader, etc., this scripts expects a +few common configuration parameters currently defined in "configs/common/train.py". +To add more complicated training logic, you can easily add other configs +in the config file and implement a new train_net.py to handle them. +""" +import logging + +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import LazyConfig, instantiate +from detectron2.engine import ( + AMPTrainer, + SimpleTrainer, + default_argument_parser, + default_setup, + default_writers, + hooks, + launch, +) +from detectron2.engine.defaults import create_ddp_model +from detectron2.evaluation import inference_on_dataset, print_csv_format +from detectron2.utils import comm + +logger = logging.getLogger("detectron2") + + +def do_test(cfg, model): + if "evaluator" in cfg.dataloader: + ret = inference_on_dataset( + model, instantiate(cfg.dataloader.test), instantiate(cfg.dataloader.evaluator) + ) + print_csv_format(ret) + return ret + + +def do_train(args, cfg): + """ + Args: + cfg: an object with the following attributes: + model: instantiate to a module + dataloader.{train,test}: instantiate to dataloaders + dataloader.evaluator: instantiate to evaluator for test set + optimizer: instantaite to an optimizer + lr_multiplier: instantiate to a fvcore scheduler + train: other misc config defined in `configs/common/train.py`, including: + output_dir (str) + init_checkpoint (str) + amp.enabled (bool) + max_iter (int) + eval_period, log_period (int) + device (str) + checkpointer (dict) + ddp (dict) + """ + model = instantiate(cfg.model) + logger = logging.getLogger("detectron2") + logger.info("Model:\n{}".format(model)) + model.to(cfg.train.device) + + cfg.optimizer.params.model = model + optim = instantiate(cfg.optimizer) + + train_loader = instantiate(cfg.dataloader.train) + + model = create_ddp_model(model, **cfg.train.ddp) + trainer = (AMPTrainer if cfg.train.amp.enabled else SimpleTrainer)(model, train_loader, optim) + checkpointer = DetectionCheckpointer( + model, + cfg.train.output_dir, + trainer=trainer, + ) + trainer.register_hooks( + [ + hooks.IterationTimer(), + hooks.LRScheduler(scheduler=instantiate(cfg.lr_multiplier)), + hooks.PeriodicCheckpointer(checkpointer, **cfg.train.checkpointer) + if comm.is_main_process() + else None, + hooks.EvalHook(cfg.train.eval_period, lambda: do_test(cfg, model)), + hooks.PeriodicWriter( + default_writers(cfg.train.output_dir, cfg.train.max_iter), + period=cfg.train.log_period, + ) + if comm.is_main_process() + else None, + ] + ) + + checkpointer.resume_or_load(cfg.train.init_checkpoint, resume=args.resume) + if args.resume and checkpointer.has_checkpoint(): + # The checkpoint stores the training iteration that just finished, thus we start + # at the next iteration + start_iter = trainer.iter + 1 + else: + start_iter = 0 + trainer.train(start_iter, cfg.train.max_iter) + + +def main(args): + cfg = LazyConfig.load(args.config_file) + cfg = LazyConfig.apply_overrides(cfg, args.opts) + default_setup(cfg, args) + + if args.eval_only: + model = instantiate(cfg.model) + model.to(cfg.train.device) + model = create_ddp_model(model) + DetectionCheckpointer(model).load(cfg.train.init_checkpoint) + print(do_test(cfg, model)) + else: + do_train(args, cfg) + + +if __name__ == "__main__": + args = default_argument_parser().parse_args() + launch( + main, + args.num_gpus, + num_machines=args.num_machines, + machine_rank=args.machine_rank, + dist_url=args.dist_url, + args=(args,), + ) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tools/lightning_train_net.py b/iChat/models/grit_src/third_party/CenterNet2/tools/lightning_train_net.py new file mode 100644 index 0000000000000000000000000000000000000000..f6734b566b6764ee54dd2af1b7310fedb34bb40d --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tools/lightning_train_net.py @@ -0,0 +1,239 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# Lightning Trainer should be considered beta at this point +# We have confirmed that training and validation run correctly and produce correct results +# Depending on how you launch the trainer, there are issues with processes terminating correctly +# This module is still dependent on D2 logging, but could be transferred to use Lightning logging + +import logging +import os +import time +import weakref +from collections import OrderedDict +from typing import Any, Dict, List + +import detectron2.utils.comm as comm +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import get_cfg +from detectron2.data import build_detection_test_loader, build_detection_train_loader +from detectron2.engine import ( + DefaultTrainer, + SimpleTrainer, + default_argument_parser, + default_setup, + default_writers, + hooks, +) +from detectron2.evaluation import print_csv_format +from detectron2.evaluation.testing import flatten_results_dict +from detectron2.modeling import build_model +from detectron2.solver import build_lr_scheduler, build_optimizer +from detectron2.utils.events import EventStorage +from detectron2.utils.logger import setup_logger + +import pytorch_lightning as pl # type: ignore +from pytorch_lightning import LightningDataModule, LightningModule +from train_net import build_evaluator + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger("detectron2") + + +class TrainingModule(LightningModule): + def __init__(self, cfg): + super().__init__() + if not logger.isEnabledFor(logging.INFO): # setup_logger is not called for d2 + setup_logger() + self.cfg = DefaultTrainer.auto_scale_workers(cfg, comm.get_world_size()) + self.storage: EventStorage = None + self.model = build_model(self.cfg) + + self.start_iter = 0 + self.max_iter = cfg.SOLVER.MAX_ITER + + def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None: + checkpoint["iteration"] = self.storage.iter + + def on_load_checkpoint(self, checkpointed_state: Dict[str, Any]) -> None: + self.start_iter = checkpointed_state["iteration"] + self.storage.iter = self.start_iter + + def setup(self, stage: str): + if self.cfg.MODEL.WEIGHTS: + self.checkpointer = DetectionCheckpointer( + # Assume you want to save checkpoints together with logs/statistics + self.model, + self.cfg.OUTPUT_DIR, + ) + logger.info(f"Load model weights from checkpoint: {self.cfg.MODEL.WEIGHTS}.") + # Only load weights, use lightning checkpointing if you want to resume + self.checkpointer.load(self.cfg.MODEL.WEIGHTS) + + self.iteration_timer = hooks.IterationTimer() + self.iteration_timer.before_train() + self.data_start = time.perf_counter() + self.writers = None + + def training_step(self, batch, batch_idx): + data_time = time.perf_counter() - self.data_start + # Need to manually enter/exit since trainer may launch processes + # This ideally belongs in setup, but setup seems to run before processes are spawned + if self.storage is None: + self.storage = EventStorage(0) + self.storage.__enter__() + self.iteration_timer.trainer = weakref.proxy(self) + self.iteration_timer.before_step() + self.writers = ( + default_writers(self.cfg.OUTPUT_DIR, self.max_iter) + if comm.is_main_process() + else {} + ) + + loss_dict = self.model(batch) + SimpleTrainer.write_metrics(loss_dict, data_time) + + opt = self.optimizers() + self.storage.put_scalar( + "lr", opt.param_groups[self._best_param_group_id]["lr"], smoothing_hint=False + ) + self.iteration_timer.after_step() + self.storage.step() + # A little odd to put before step here, but it's the best way to get a proper timing + self.iteration_timer.before_step() + + if self.storage.iter % 20 == 0: + for writer in self.writers: + writer.write() + return sum(loss_dict.values()) + + def training_step_end(self, training_step_outpus): + self.data_start = time.perf_counter() + return training_step_outpus + + def training_epoch_end(self, training_step_outputs): + self.iteration_timer.after_train() + if comm.is_main_process(): + self.checkpointer.save("model_final") + for writer in self.writers: + writer.write() + writer.close() + self.storage.__exit__(None, None, None) + + def _process_dataset_evaluation_results(self) -> OrderedDict: + results = OrderedDict() + for idx, dataset_name in enumerate(self.cfg.DATASETS.TEST): + results[dataset_name] = self._evaluators[idx].evaluate() + if comm.is_main_process(): + print_csv_format(results[dataset_name]) + + if len(results) == 1: + results = list(results.values())[0] + return results + + def _reset_dataset_evaluators(self): + self._evaluators = [] + for dataset_name in self.cfg.DATASETS.TEST: + evaluator = build_evaluator(self.cfg, dataset_name) + evaluator.reset() + self._evaluators.append(evaluator) + + def on_validation_epoch_start(self, _outputs): + self._reset_dataset_evaluators() + + def validation_epoch_end(self, _outputs): + results = self._process_dataset_evaluation_results(_outputs) + + flattened_results = flatten_results_dict(results) + for k, v in flattened_results.items(): + try: + v = float(v) + except Exception as e: + raise ValueError( + "[EvalHook] eval_function should return a nested dict of float. " + "Got '{}: {}' instead.".format(k, v) + ) from e + self.storage.put_scalars(**flattened_results, smoothing_hint=False) + + def validation_step(self, batch, batch_idx: int, dataloader_idx: int = 0) -> None: + if not isinstance(batch, List): + batch = [batch] + outputs = self.model(batch) + self._evaluators[dataloader_idx].process(batch, outputs) + + def configure_optimizers(self): + optimizer = build_optimizer(self.cfg, self.model) + self._best_param_group_id = hooks.LRScheduler.get_best_param_group_id(optimizer) + scheduler = build_lr_scheduler(self.cfg, optimizer) + return [optimizer], [{"scheduler": scheduler, "interval": "step"}] + + +class DataModule(LightningDataModule): + def __init__(self, cfg): + super().__init__() + self.cfg = DefaultTrainer.auto_scale_workers(cfg, comm.get_world_size()) + + def train_dataloader(self): + return build_detection_train_loader(self.cfg) + + def val_dataloader(self): + dataloaders = [] + for dataset_name in self.cfg.DATASETS.TEST: + dataloaders.append(build_detection_test_loader(self.cfg, dataset_name)) + return dataloaders + + +def main(args): + cfg = setup(args) + train(cfg, args) + + +def train(cfg, args): + trainer_params = { + # training loop is bounded by max steps, use a large max_epochs to make + # sure max_steps is met first + "max_epochs": 10 ** 8, + "max_steps": cfg.SOLVER.MAX_ITER, + "val_check_interval": cfg.TEST.EVAL_PERIOD if cfg.TEST.EVAL_PERIOD > 0 else 10 ** 8, + "num_nodes": args.num_machines, + "gpus": args.num_gpus, + "num_sanity_val_steps": 0, + } + if cfg.SOLVER.AMP.ENABLED: + trainer_params["precision"] = 16 + + last_checkpoint = os.path.join(cfg.OUTPUT_DIR, "last.ckpt") + if args.resume: + # resume training from checkpoint + trainer_params["resume_from_checkpoint"] = last_checkpoint + logger.info(f"Resuming training from checkpoint: {last_checkpoint}.") + + trainer = pl.Trainer(**trainer_params) + logger.info(f"start to train with {args.num_machines} nodes and {args.num_gpus} GPUs") + + module = TrainingModule(cfg) + data_module = DataModule(cfg) + if args.eval_only: + logger.info("Running inference") + trainer.validate(module, data_module) + else: + logger.info("Running training") + trainer.fit(module, data_module) + + +def setup(args): + """ + Create configs and perform basic setups. + """ + cfg = get_cfg() + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + cfg.freeze() + default_setup(cfg, args) + return cfg + + +if __name__ == "__main__": + parser = default_argument_parser() + args = parser.parse_args() + logger.info("Command Line Args:", args) + main(args) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tools/plain_train_net.py b/iChat/models/grit_src/third_party/CenterNet2/tools/plain_train_net.py new file mode 100644 index 0000000000000000000000000000000000000000..4851a8398e128bdce1986feccf0f1cca4a12f704 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tools/plain_train_net.py @@ -0,0 +1,223 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. +""" +Detectron2 training script with a plain training loop. + +This script reads a given config file and runs the training or evaluation. +It is an entry point that is able to train standard models in detectron2. + +In order to let one script support training of many models, +this script contains logic that are specific to these built-in models and therefore +may not be suitable for your own project. +For example, your research project perhaps only needs a single "evaluator". + +Therefore, we recommend you to use detectron2 as a library and take +this file as an example of how to use the library. +You may want to write your own script with your datasets and other customizations. + +Compared to "train_net.py", this script supports fewer default features. +It also includes fewer abstraction, therefore is easier to add custom logic. +""" + +import logging +import os +from collections import OrderedDict +import torch +from torch.nn.parallel import DistributedDataParallel + +import detectron2.utils.comm as comm +from detectron2.checkpoint import DetectionCheckpointer, PeriodicCheckpointer +from detectron2.config import get_cfg +from detectron2.data import ( + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, +) +from detectron2.engine import default_argument_parser, default_setup, default_writers, launch +from detectron2.evaluation import ( + CityscapesInstanceEvaluator, + CityscapesSemSegEvaluator, + COCOEvaluator, + COCOPanopticEvaluator, + DatasetEvaluators, + LVISEvaluator, + PascalVOCDetectionEvaluator, + SemSegEvaluator, + inference_on_dataset, + print_csv_format, +) +from detectron2.modeling import build_model +from detectron2.solver import build_lr_scheduler, build_optimizer +from detectron2.utils.events import EventStorage + +logger = logging.getLogger("detectron2") + + +def get_evaluator(cfg, dataset_name, output_folder=None): + """ + Create evaluator(s) for a given dataset. + This uses the special metadata "evaluator_type" associated with each builtin dataset. + For your own dataset, you can simply create an evaluator manually in your + script and do not have to worry about the hacky if-else logic here. + """ + if output_folder is None: + output_folder = os.path.join(cfg.OUTPUT_DIR, "inference") + evaluator_list = [] + evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type + if evaluator_type in ["sem_seg", "coco_panoptic_seg"]: + evaluator_list.append( + SemSegEvaluator( + dataset_name, + distributed=True, + output_dir=output_folder, + ) + ) + if evaluator_type in ["coco", "coco_panoptic_seg"]: + evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder)) + if evaluator_type == "coco_panoptic_seg": + evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder)) + if evaluator_type == "cityscapes_instance": + assert ( + torch.cuda.device_count() > comm.get_rank() + ), "CityscapesEvaluator currently do not work with multiple machines." + return CityscapesInstanceEvaluator(dataset_name) + if evaluator_type == "cityscapes_sem_seg": + assert ( + torch.cuda.device_count() > comm.get_rank() + ), "CityscapesEvaluator currently do not work with multiple machines." + return CityscapesSemSegEvaluator(dataset_name) + if evaluator_type == "pascal_voc": + return PascalVOCDetectionEvaluator(dataset_name) + if evaluator_type == "lvis": + return LVISEvaluator(dataset_name, cfg, True, output_folder) + if len(evaluator_list) == 0: + raise NotImplementedError( + "no Evaluator for the dataset {} with the type {}".format(dataset_name, evaluator_type) + ) + if len(evaluator_list) == 1: + return evaluator_list[0] + return DatasetEvaluators(evaluator_list) + + +def do_test(cfg, model): + results = OrderedDict() + for dataset_name in cfg.DATASETS.TEST: + data_loader = build_detection_test_loader(cfg, dataset_name) + evaluator = get_evaluator( + cfg, dataset_name, os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name) + ) + results_i = inference_on_dataset(model, data_loader, evaluator) + results[dataset_name] = results_i + if comm.is_main_process(): + logger.info("Evaluation results for {} in csv format:".format(dataset_name)) + print_csv_format(results_i) + if len(results) == 1: + results = list(results.values())[0] + return results + + +def do_train(cfg, model, resume=False): + model.train() + optimizer = build_optimizer(cfg, model) + scheduler = build_lr_scheduler(cfg, optimizer) + + checkpointer = DetectionCheckpointer( + model, cfg.OUTPUT_DIR, optimizer=optimizer, scheduler=scheduler + ) + start_iter = ( + checkpointer.resume_or_load(cfg.MODEL.WEIGHTS, resume=resume).get("iteration", -1) + 1 + ) + max_iter = cfg.SOLVER.MAX_ITER + + periodic_checkpointer = PeriodicCheckpointer( + checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD, max_iter=max_iter + ) + + writers = default_writers(cfg.OUTPUT_DIR, max_iter) if comm.is_main_process() else [] + + # compared to "train_net.py", we do not support accurate timing and + # precise BN here, because they are not trivial to implement in a small training loop + data_loader = build_detection_train_loader(cfg) + logger.info("Starting training from iteration {}".format(start_iter)) + with EventStorage(start_iter) as storage: + for data, iteration in zip(data_loader, range(start_iter, max_iter)): + storage.iter = iteration + + loss_dict = model(data) + losses = sum(loss_dict.values()) + assert torch.isfinite(losses).all(), loss_dict + + loss_dict_reduced = {k: v.item() for k, v in comm.reduce_dict(loss_dict).items()} + losses_reduced = sum(loss for loss in loss_dict_reduced.values()) + if comm.is_main_process(): + storage.put_scalars(total_loss=losses_reduced, **loss_dict_reduced) + + optimizer.zero_grad() + losses.backward() + optimizer.step() + storage.put_scalar("lr", optimizer.param_groups[0]["lr"], smoothing_hint=False) + scheduler.step() + + if ( + cfg.TEST.EVAL_PERIOD > 0 + and (iteration + 1) % cfg.TEST.EVAL_PERIOD == 0 + and iteration != max_iter - 1 + ): + do_test(cfg, model) + # Compared to "train_net.py", the test results are not dumped to EventStorage + comm.synchronize() + + if iteration - start_iter > 5 and ( + (iteration + 1) % 20 == 0 or iteration == max_iter - 1 + ): + for writer in writers: + writer.write() + periodic_checkpointer.step(iteration) + + +def setup(args): + """ + Create configs and perform basic setups. + """ + cfg = get_cfg() + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + cfg.freeze() + default_setup( + cfg, args + ) # if you don't like any of the default setup, write your own setup code + return cfg + + +def main(args): + cfg = setup(args) + + model = build_model(cfg) + logger.info("Model:\n{}".format(model)) + if args.eval_only: + DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load( + cfg.MODEL.WEIGHTS, resume=args.resume + ) + return do_test(cfg, model) + + distributed = comm.get_world_size() > 1 + if distributed: + model = DistributedDataParallel( + model, device_ids=[comm.get_local_rank()], broadcast_buffers=False + ) + + do_train(cfg, model, resume=args.resume) + return do_test(cfg, model) + + +if __name__ == "__main__": + args = default_argument_parser().parse_args() + print("Command Line Args:", args) + launch( + main, + args.num_gpus, + num_machines=args.num_machines, + machine_rank=args.machine_rank, + dist_url=args.dist_url, + args=(args,), + ) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tools/train_net.py b/iChat/models/grit_src/third_party/CenterNet2/tools/train_net.py new file mode 100644 index 0000000000000000000000000000000000000000..6ebf5f60a2197633fa418e83aadfedb824537b8e --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tools/train_net.py @@ -0,0 +1,170 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. +""" +A main training script. + +This scripts reads a given config file and runs the training or evaluation. +It is an entry point that is made to train standard models in detectron2. + +In order to let one script support training of many models, +this script contains logic that are specific to these built-in models and therefore +may not be suitable for your own project. +For example, your research project perhaps only needs a single "evaluator". + +Therefore, we recommend you to use detectron2 as an library and take +this file as an example of how to use the library. +You may want to write your own script with your datasets and other customizations. +""" + +import logging +import os +from collections import OrderedDict +import torch + +import detectron2.utils.comm as comm +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import get_cfg +from detectron2.data import MetadataCatalog +from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, hooks, launch +from detectron2.evaluation import ( + CityscapesInstanceEvaluator, + CityscapesSemSegEvaluator, + COCOEvaluator, + COCOPanopticEvaluator, + DatasetEvaluators, + LVISEvaluator, + PascalVOCDetectionEvaluator, + SemSegEvaluator, + verify_results, +) +from detectron2.modeling import GeneralizedRCNNWithTTA + + +def build_evaluator(cfg, dataset_name, output_folder=None): + """ + Create evaluator(s) for a given dataset. + This uses the special metadata "evaluator_type" associated with each builtin dataset. + For your own dataset, you can simply create an evaluator manually in your + script and do not have to worry about the hacky if-else logic here. + """ + if output_folder is None: + output_folder = os.path.join(cfg.OUTPUT_DIR, "inference") + evaluator_list = [] + evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type + if evaluator_type in ["sem_seg", "coco_panoptic_seg"]: + evaluator_list.append( + SemSegEvaluator( + dataset_name, + distributed=True, + output_dir=output_folder, + ) + ) + if evaluator_type in ["coco", "coco_panoptic_seg"]: + evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder)) + if evaluator_type == "coco_panoptic_seg": + evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder)) + if evaluator_type == "cityscapes_instance": + assert ( + torch.cuda.device_count() > comm.get_rank() + ), "CityscapesEvaluator currently do not work with multiple machines." + return CityscapesInstanceEvaluator(dataset_name) + if evaluator_type == "cityscapes_sem_seg": + assert ( + torch.cuda.device_count() > comm.get_rank() + ), "CityscapesEvaluator currently do not work with multiple machines." + return CityscapesSemSegEvaluator(dataset_name) + elif evaluator_type == "pascal_voc": + return PascalVOCDetectionEvaluator(dataset_name) + elif evaluator_type == "lvis": + return LVISEvaluator(dataset_name, output_dir=output_folder) + if len(evaluator_list) == 0: + raise NotImplementedError( + "no Evaluator for the dataset {} with the type {}".format(dataset_name, evaluator_type) + ) + elif len(evaluator_list) == 1: + return evaluator_list[0] + return DatasetEvaluators(evaluator_list) + + +class Trainer(DefaultTrainer): + """ + We use the "DefaultTrainer" which contains pre-defined default logic for + standard training workflow. They may not work for you, especially if you + are working on a new research project. In that case you can write your + own training loop. You can use "tools/plain_train_net.py" as an example. + """ + + @classmethod + def build_evaluator(cls, cfg, dataset_name, output_folder=None): + return build_evaluator(cfg, dataset_name, output_folder) + + @classmethod + def test_with_TTA(cls, cfg, model): + logger = logging.getLogger("detectron2.trainer") + # In the end of training, run an evaluation with TTA + # Only support some R-CNN models. + logger.info("Running inference with test-time augmentation ...") + model = GeneralizedRCNNWithTTA(cfg, model) + evaluators = [ + cls.build_evaluator( + cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, "inference_TTA") + ) + for name in cfg.DATASETS.TEST + ] + res = cls.test(cfg, model, evaluators) + res = OrderedDict({k + "_TTA": v for k, v in res.items()}) + return res + + +def setup(args): + """ + Create configs and perform basic setups. + """ + cfg = get_cfg() + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + cfg.freeze() + default_setup(cfg, args) + return cfg + + +def main(args): + cfg = setup(args) + + if args.eval_only: + model = Trainer.build_model(cfg) + DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load( + cfg.MODEL.WEIGHTS, resume=args.resume + ) + res = Trainer.test(cfg, model) + if cfg.TEST.AUG.ENABLED: + res.update(Trainer.test_with_TTA(cfg, model)) + if comm.is_main_process(): + verify_results(cfg, res) + return res + + """ + If you'd like to do anything fancier than the standard training logic, + consider writing your own training loop (see plain_train_net.py) or + subclassing the trainer. + """ + trainer = Trainer(cfg) + trainer.resume_or_load(resume=args.resume) + if cfg.TEST.AUG.ENABLED: + trainer.register_hooks( + [hooks.EvalHook(0, lambda: trainer.test_with_TTA(cfg, trainer.model))] + ) + return trainer.train() + + +if __name__ == "__main__": + args = default_argument_parser().parse_args() + print("Command Line Args:", args) + launch( + main, + args.num_gpus, + num_machines=args.num_machines, + machine_rank=args.machine_rank, + dist_url=args.dist_url, + args=(args,), + ) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tools/visualize_data.py b/iChat/models/grit_src/third_party/CenterNet2/tools/visualize_data.py new file mode 100644 index 0000000000000000000000000000000000000000..fd0ba8347bfd34fc8fac5ffef9aee10915ad1820 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tools/visualize_data.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. +import argparse +import os +from itertools import chain +import cv2 +import tqdm + +from detectron2.config import get_cfg +from detectron2.data import DatasetCatalog, MetadataCatalog, build_detection_train_loader +from detectron2.data import detection_utils as utils +from detectron2.data.build import filter_images_with_few_keypoints +from detectron2.utils.logger import setup_logger +from detectron2.utils.visualizer import Visualizer + + +def setup(args): + cfg = get_cfg() + if args.config_file: + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + cfg.DATALOADER.NUM_WORKERS = 0 + cfg.freeze() + return cfg + + +def parse_args(in_args=None): + parser = argparse.ArgumentParser(description="Visualize ground-truth data") + parser.add_argument( + "--source", + choices=["annotation", "dataloader"], + required=True, + help="visualize the annotations or the data loader (with pre-processing)", + ) + parser.add_argument("--config-file", metavar="FILE", help="path to config file") + parser.add_argument("--output-dir", default="./", help="path to output directory") + parser.add_argument("--show", action="store_true", help="show output in a window") + parser.add_argument( + "opts", + help="Modify config options using the command-line", + default=None, + nargs=argparse.REMAINDER, + ) + return parser.parse_args(in_args) + + +if __name__ == "__main__": + args = parse_args() + logger = setup_logger() + logger.info("Arguments: " + str(args)) + cfg = setup(args) + + dirname = args.output_dir + os.makedirs(dirname, exist_ok=True) + metadata = MetadataCatalog.get(cfg.DATASETS.TRAIN[0]) + + def output(vis, fname): + if args.show: + print(fname) + cv2.imshow("window", vis.get_image()[:, :, ::-1]) + cv2.waitKey() + else: + filepath = os.path.join(dirname, fname) + print("Saving to {} ...".format(filepath)) + vis.save(filepath) + + scale = 1.0 + if args.source == "dataloader": + train_data_loader = build_detection_train_loader(cfg) + for batch in train_data_loader: + for per_image in batch: + # Pytorch tensor is in (C, H, W) format + img = per_image["image"].permute(1, 2, 0).cpu().detach().numpy() + img = utils.convert_image_to_rgb(img, cfg.INPUT.FORMAT) + + visualizer = Visualizer(img, metadata=metadata, scale=scale) + target_fields = per_image["instances"].get_fields() + labels = [metadata.thing_classes[i] for i in target_fields["gt_classes"]] + vis = visualizer.overlay_instances( + labels=labels, + boxes=target_fields.get("gt_boxes", None), + masks=target_fields.get("gt_masks", None), + keypoints=target_fields.get("gt_keypoints", None), + ) + output(vis, str(per_image["image_id"]) + ".jpg") + else: + dicts = list(chain.from_iterable([DatasetCatalog.get(k) for k in cfg.DATASETS.TRAIN])) + if cfg.MODEL.KEYPOINT_ON: + dicts = filter_images_with_few_keypoints(dicts, 1) + for dic in tqdm.tqdm(dicts): + img = utils.read_image(dic["file_name"], "RGB") + visualizer = Visualizer(img, metadata=metadata, scale=scale) + vis = visualizer.draw_dataset_dict(dic) + output(vis, os.path.basename(dic["file_name"])) diff --git a/iChat/models/grit_src/third_party/CenterNet2/tools/visualize_json_results.py b/iChat/models/grit_src/third_party/CenterNet2/tools/visualize_json_results.py new file mode 100644 index 0000000000000000000000000000000000000000..472190e0b3b38b55773795915badbb5bc4599d42 --- /dev/null +++ b/iChat/models/grit_src/third_party/CenterNet2/tools/visualize_json_results.py @@ -0,0 +1,90 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. + +import argparse +import json +import numpy as np +import os +from collections import defaultdict +import cv2 +import tqdm + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.structures import Boxes, BoxMode, Instances +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import setup_logger +from detectron2.utils.visualizer import Visualizer + + +def create_instances(predictions, image_size): + ret = Instances(image_size) + + score = np.asarray([x["score"] for x in predictions]) + chosen = (score > args.conf_threshold).nonzero()[0] + score = score[chosen] + bbox = np.asarray([predictions[i]["bbox"] for i in chosen]).reshape(-1, 4) + bbox = BoxMode.convert(bbox, BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) + + labels = np.asarray([dataset_id_map(predictions[i]["category_id"]) for i in chosen]) + + ret.scores = score + ret.pred_boxes = Boxes(bbox) + ret.pred_classes = labels + + try: + ret.pred_masks = [predictions[i]["segmentation"] for i in chosen] + except KeyError: + pass + return ret + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="A script that visualizes the json predictions from COCO or LVIS dataset." + ) + parser.add_argument("--input", required=True, help="JSON file produced by the model") + parser.add_argument("--output", required=True, help="output directory") + parser.add_argument("--dataset", help="name of the dataset", default="coco_2017_val") + parser.add_argument("--conf-threshold", default=0.5, type=float, help="confidence threshold") + args = parser.parse_args() + + logger = setup_logger() + + with PathManager.open(args.input, "r") as f: + predictions = json.load(f) + + pred_by_image = defaultdict(list) + for p in predictions: + pred_by_image[p["image_id"]].append(p) + + dicts = list(DatasetCatalog.get(args.dataset)) + metadata = MetadataCatalog.get(args.dataset) + if hasattr(metadata, "thing_dataset_id_to_contiguous_id"): + + def dataset_id_map(ds_id): + return metadata.thing_dataset_id_to_contiguous_id[ds_id] + + elif "lvis" in args.dataset: + # LVIS results are in the same format as COCO results, but have a different + # mapping from dataset category id to contiguous category id in [0, #categories - 1] + def dataset_id_map(ds_id): + return ds_id - 1 + + else: + raise ValueError("Unsupported dataset: {}".format(args.dataset)) + + os.makedirs(args.output, exist_ok=True) + + for dic in tqdm.tqdm(dicts): + img = cv2.imread(dic["file_name"], cv2.IMREAD_COLOR)[:, :, ::-1] + basename = os.path.basename(dic["file_name"]) + + predictions = create_instances(pred_by_image[dic["image_id"]], img.shape[:2]) + vis = Visualizer(img, metadata) + vis_pred = vis.draw_instance_predictions(predictions).get_image() + + vis = Visualizer(img, metadata) + vis_gt = vis.draw_dataset_dict(dic).get_image() + + concat = np.concatenate((vis_pred, vis_gt), axis=1) + cv2.imwrite(os.path.join(args.output, basename), concat[:, :, ::-1]) diff --git a/iChat/models/husky.py b/iChat/models/husky.py new file mode 100644 index 0000000000000000000000000000000000000000..642d6f52cc1260438f1660c82b1208a935e72a05 --- /dev/null +++ b/iChat/models/husky.py @@ -0,0 +1,423 @@ +"""Inference for FastChat models.""" +import abc +from typing import Optional + +import os +import requests +from PIL import Image +from io import BytesIO +import numpy as np + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision.transforms as T +from torchvision.transforms.functional import InterpolationMode + +from transformers import ( + AutoTokenizer, + GenerationConfig, + StoppingCriteria, + StoppingCriteriaList, + Blip2VisionConfig +) +from .husky_src.husky_chat import Blip2LlaMAForConditionalGeneration + +from .husky_src.conversation import ( + conv_templates, + get_default_conv_template, +) + +from .husky_src.compression import compress_module +from .utils import prompts, gen_new_name + +DEFAULT_UNK_TOKEN = "" +DEFAULT_IMAGE_TOKEN = "" +DEFAULT_IMG_START_TOKEN = "" +DEFAULT_IMG_END_TOKEN = "" +IGNORE_INDEX = -100 + + +def get_gpu_memory(max_gpus=None): + gpu_memory = [] + num_gpus = ( + torch.cuda.device_count() + if max_gpus is None + else min(max_gpus, torch.cuda.device_count()) + ) + + for gpu_id in range(num_gpus): + with torch.cuda.device(gpu_id): + device = torch.cuda.current_device() + gpu_properties = torch.cuda.get_device_properties(device) + total_memory = gpu_properties.total_memory / (1024 ** 3) + allocated_memory = torch.cuda.memory_allocated() / (1024 ** 3) + available_memory = total_memory - allocated_memory + gpu_memory.append(available_memory) + return gpu_memory + + +def load_model( + model_path, device, num_gpus, max_gpu_memory=None, load_8bit=False, debug=False +): + kwargs = {"torch_dtype": torch.float16} + + tokenizer = AutoTokenizer.from_pretrained( + model_path, use_fast=False) + model = Blip2LlaMAForConditionalGeneration.from_pretrained( + model_path, low_cpu_mem_usage=True, **kwargs + ) + + if load_8bit: + compress_module(model, device) + + if (device == "cuda" and num_gpus == 1) or device == "mps": + model.to(device) + + if debug: + print(model) + + model = model.eval() + return model, tokenizer + + +def load_image(image_file): + if image_file.startswith('http') or image_file.startswith('https'): + response = requests.get(image_file) + image = Image.open(BytesIO(response.content)).convert('RGB') + else: + image = Image.open(image_file).convert('RGB') + return image + + +def build_transform(input_size): + crop_pct = 224 / 256 + size = int(input_size / crop_pct) + transform = T.Compose([ + T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img), + T.Resize(size, interpolation=InterpolationMode.BICUBIC), + T.CenterCrop(input_size), + T.ToTensor(), + T.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)) + ]) + return transform + + +class StoppingCriteriaSub(StoppingCriteria): + + def __init__(self, stops, encounters=1): + super().__init__() + self.stops = stops + + def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs): + for stop in self.stops: + if torch.all((stop == input_ids[0][-len(stop):])).item(): + return True + + return False + + +@torch.inference_mode() +def generate_stream( + model, tokenizer, image_processor, params, device +): + prompt = params["prompt"] + images = params.get("images", None) + temperature = float(params.get("temperature", 0.7)) + max_new_tokens = int(params.get("max_new_tokens", 1024)) + + num_queries = model.config.num_query_tokens + + stop_words = ["Human: ", "Assistant: ", "###", "\n\n"] + stop_words_ids = [tokenizer(stop_word, return_tensors='pt')[ + 'input_ids'].squeeze() for stop_word in stop_words] + stopping_criteria = StoppingCriteriaList( + [StoppingCriteriaSub(stops=stop_words_ids)]) + + if images is not None: + pixel_values = image_processor(load_image(images)).to( + device) # only support one image + image_query = DEFAULT_IMG_START_TOKEN + \ + DEFAULT_IMAGE_TOKEN * num_queries + DEFAULT_IMG_END_TOKEN + prompt = prompt.replace(DEFAULT_IMAGE_TOKEN, image_query) + model_inputs = tokenizer([prompt], return_tensors="pt") + model_inputs["pixel_values"] = pixel_values + model_inputs.pop("token_type_ids", None) + else: + raise NotImplementedError + + generation_config = GenerationConfig( + bos_token_id=1, + do_sample=True, + temperature=temperature, + max_new_tokens=max_new_tokens, + stopping_criteria=stopping_criteria + ) + + generation_output = model.generate( + **model_inputs, + generation_config=generation_config, + return_dict_in_generate=True, + output_scores=True + ) + + preds = generation_output.sequences + outputs = tokenizer.batch_decode(preds, skip_special_tokens=True) + return outputs + + +def resize_pos_embed(posemb, posemb_new, num_prefix_tokens=1, gs_new=()): + # Rescale the grid of position embeddings when loading from state_dict. + ntok_new = posemb_new.shape[1] + if num_prefix_tokens: + posemb_prefix, posemb_grid = posemb[:, + :num_prefix_tokens], posemb[0, num_prefix_tokens:] + ntok_new -= num_prefix_tokens + else: + posemb_prefix, posemb_grid = posemb[:, :0], posemb[0] + gs_old = int(math.sqrt(len(posemb_grid))) + + if not len(gs_new): # backwards compatibility + gs_new = [int(math.sqrt(ntok_new))] * 2 + assert len(gs_new) >= 2 + posemb_grid = posemb_grid.reshape( + 1, gs_old, gs_old, -1).permute(0, 3, 1, 2) + posemb_grid = F.interpolate( + posemb_grid, size=gs_new, mode='bicubic', align_corners=False) + posemb_grid = posemb_grid.permute( + 0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1) + posemb = torch.cat([posemb_prefix, posemb_grid], dim=1) + return posemb + + +class Blip2VisionEmbeddings(nn.Module): + def __init__(self, config: Blip2VisionConfig): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.image_size = config.image_size + self.patch_size = config.patch_size + + self.num_frames = getattr(self.config, "num_frames", 16) + self.frame_stride = 4 + + self.patch_embedding = nn.Conv3d( + in_channels=3, out_channels=self.embed_dim, + kernel_size=(self.frame_stride, self.patch_size, self.patch_size), + stride=(self.frame_stride, self.patch_size, self.patch_size) + ) + + self.num_patches = int(self.num_frames // self.frame_stride) * \ + (self.image_size // self.patch_size) ** 2 + + self.class_embedding = nn.Parameter( + torch.randn(1, 1, self.embed_dim), ) + self.num_positions = self.num_patches + 1 + + self.position_embedding = nn.Parameter( + torch.randn(1, self.num_positions, self.embed_dim)) + + def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: + batch_size = pixel_values.shape[0] + target_dtype = self.patch_embedding.weight.dtype + patch_embeds = self.patch_embedding(pixel_values).squeeze( + 1) # shape = [*, width, grid, grid] + patch_embeds = patch_embeds.flatten(2).transpose(1, 2) + + class_embeds = self.class_embedding.expand( + batch_size, 1, -1).to(target_dtype) + embeddings = torch.cat([class_embeds, patch_embeds], dim=1) + embeddings = embeddings + \ + self.position_embedding[:, : embeddings.size( + 1), :].to(target_dtype) + return embeddings + + +class Chat: + def __init__( + self, + model_path, + device, + num_gpus=1, + load_8bit=False, + conv_template="multi_model", + temperature=0.7, + max_new_tokens=512, + ): + model, tokenizer = load_model( + model_path, device, num_gpus, load_8bit=load_8bit + ) + self.conv_template = conv_template + self.model = model.to(device) + self.tokenizer = tokenizer + num_queries = model.config.num_query_tokens + self.image_processor = build_transform(input_size=224) + + self.device = device + self.dtype = model.dtype + + stop_words = ["Human: ", "Assistant: ", "###", "\n\n"] + stop_words_ids = [tokenizer(stop_word, return_tensors='pt')[ + 'input_ids'].squeeze() for stop_word in stop_words] + stopping_criteria = StoppingCriteriaList( + [StoppingCriteriaSub(stops=stop_words_ids)]) + + if conv_template: + conv = conv_templates[conv_template].copy() + else: + conv = get_default_conv_template(model_path).copy() + + self.conv = conv + self.image_query = DEFAULT_IMG_START_TOKEN + \ + DEFAULT_IMAGE_TOKEN * num_queries + DEFAULT_IMG_END_TOKEN + + self.generation_config = GenerationConfig( + bos_token_id=1, + do_sample=True, + top_k=20, + temperature=temperature, + max_new_tokens=max_new_tokens, + stopping_criteria=stopping_criteria + ) + + def ask(self, text, conv): + conversations = [] + if len(conv.messages) > 0: + conv.append_message(conv.roles[0], text) + else: + conv.append_message(conv.roles[0], self.image_query + "\n" + text) + + conv.append_message(conv.roles[1], None) + conversations.append(conv.get_prompt()) + return conversations + + @torch.no_grad() + def get_image_embedding(self, image_file): + image = load_image(image_file) + pixel_values = self.image_processor(image) + pixel_values = pixel_values.unsqueeze( + 0).to(self.device, dtype=self.dtype) + language_model_inputs = self.model.extract_feature(pixel_values) + return language_model_inputs + + @torch.no_grad() + def answer(self, conversations, language_model_inputs): + model_inputs = self.tokenizer( + conversations, + return_tensors="pt", + ) + model_inputs.pop("token_type_ids", None) + input_ids = model_inputs["input_ids"].to(self.device) + attention_mask = model_inputs["attention_mask"].to(self.device) + generation_output = self.model.generate( + pixel_values=None, + input_ids=input_ids, + attention_mask=attention_mask, + language_model_inputs=language_model_inputs, + generation_config=self.generation_config, + return_dict_in_generate=True, + output_scores=True + ) + + preds = generation_output.sequences + outputs = self.tokenizer.batch_decode( + preds, skip_special_tokens=True)[0] + return outputs + + def reset(self): + if self.conv_template: + self.conv = conv_templates[self.conv_template].copy() + else: + self.conv = get_default_conv_template(self.model_path).copy() + +class HuskyVQA: + def __init__( + self, + device + ): + model_path="model_zoo/husky-7b-v0_01" + load_8bit=True + max_new_tokens=512 + self.chat = Chat( + model_path=model_path, + device=device, + load_8bit=load_8bit, + max_new_tokens=max_new_tokens, + num_gpus=1, + ) + + # @prompts(name="Visual Question Answering or Image Caption", + # description="useful when you want to ask some questions about this image or generate a caption for it. " + # "like: describe this image in details, or what can you see in this image? " + # "The input to this tool should be a string like \"{image_path},{query}\", containing the image_path and user query.") + @prompts(name="Answer Question About The Image", + description="useful when you need an answer for a question based on an image. " + "like: what is the background color of this image, or how many cats in this figure " + "The input to this tool should be a comma separated string of two, representing the image_path and the question") + def inference(self, inputs): + print(f'inputs: {inputs}') + image_file = inputs.split(',')[0] + query = ','.join(inputs.split(',')[1:]) + + vision_feature = self.chat.get_image_embedding(image_file) + conversations = self.chat.ask(text=query, conv=self.chat.conv) + outputs = self.chat.answer(conversations, vision_feature) + # NOTE: strip is important to align with the training data. + self.chat.conv.messages[-1][1] = outputs.strip() + # print(f'HuskyVQA: {outputs}') + self.reset() + print( + f"\nProcessed HuskyVQA, Inputs: {inputs}. " + f"Output: {outputs}") + return outputs + + @prompts(name="Get Photo Description", + description="useful when you want to know what is inside the photo. " + "like: describe this image in detail, what is it in this figure, " + "or introduce this image." + "The input to this tool should be a string, representing the image_path. ") + def inference_captioning(self, inputs): + print(f'inputs: {inputs}') + image_file = inputs.strip() + query = 'please describe this image in details' + + vision_feature = self.chat.get_image_embedding(image_file) + + conversations = self.chat.ask(text=query, conv=self.chat.conv) + outputs = self.chat.answer(conversations, vision_feature) + # NOTE: strip is important to align with the training data. + self.chat.conv.messages[-1][1] = outputs.strip() + self.reset() + print( + f"\nProcessed HuskyVQA captioning, Inputs: {inputs}. " + f"Output: {outputs}") + + return outputs + + @prompts(name="Answer Question About The Masked Image", + description="useful when you need an answer for a question based on a masked image. " + "like: what is the background color in the masked region, " + "how many cats in this masked figure or what is in this masked figure. " + "The input to this tool should be a comma separated string of three, " + "representing the image_path, mask_path and the question") + def inference_by_mask(self, inputs): + print(f'inputs: {inputs}') + image_path, mask_path = inputs.split(",")[0], inputs.split(",")[1] + question = ','.join(inputs.split(',')[2:]) + # mask_path = self.SegmentAnything.inference_by_mask(image_path) + raw_image = Image.open(image_path).convert('RGB') + mask_image = Image.open(mask_path).convert('RGB') + new_image_arr = np.array(raw_image, dtype=np.uint8) // 255 * np.array(mask_image) + new_image = Image.fromarray(new_image_arr) + new_image_path = gen_new_name(image_path, '') + new_image.save(new_image_path, 'PNG') + answer = self.inference(f'{new_image_path},{question}') + self.reset() + print(f"\nProcessed HuskyVQA, Inputs: {inputs}, Input Question: {question}, " + f"Output Answer: {answer}") + return answer + + def reset(self): + self.chat.reset() diff --git a/iChat/models/husky_src/compression.py b/iChat/models/husky_src/compression.py new file mode 100644 index 0000000000000000000000000000000000000000..1f3aa61aec8ca0554341bbf174896ed7cc7f0418 --- /dev/null +++ b/iChat/models/husky_src/compression.py @@ -0,0 +1,138 @@ +import dataclasses + +import torch +from torch import Tensor +import torch.nn as nn +from torch.nn import functional as F + + +@dataclasses.dataclass +class CompressionConfig: + """Group-wise quantization.""" + + num_bits: int + group_size: int + group_dim: int + symmetric: bool + enabled: bool = True + + +default_compression_config = CompressionConfig( + num_bits=8, group_size=256, group_dim=1, symmetric=True, enabled=True +) + + +class CLinear(nn.Module): + """Compressed Linear Layer.""" + + def __init__(self, weight, bias, device): + super().__init__() + + self.weight = compress(weight.data.to(device), default_compression_config) + self.bias = bias + + def forward(self, input: Tensor) -> Tensor: + weight = decompress(self.weight, default_compression_config) + return F.linear(input, weight, self.bias) + + +def compress_module(module, target_device): + for attr_str in dir(module): + target_attr = getattr(module, attr_str) + if type(target_attr) == torch.nn.Linear: + setattr( + module, + attr_str, + CLinear(target_attr.weight, target_attr.bias, target_device), + ) + for name, child in module.named_children(): + compress_module(child, target_device) + + +def compress(tensor, config): + """Simulate group-wise quantization.""" + if not config.enabled: + return tensor + + group_size, num_bits, group_dim, symmetric = ( + config.group_size, + config.num_bits, + config.group_dim, + config.symmetric, + ) + assert num_bits <= 8 + + original_shape = tensor.shape + num_groups = (original_shape[group_dim] + group_size - 1) // group_size + new_shape = ( + original_shape[:group_dim] + + (num_groups, group_size) + + original_shape[group_dim + 1 :] + ) + + # Pad + pad_len = (group_size - original_shape[group_dim] % group_size) % group_size + if pad_len != 0: + pad_shape = ( + original_shape[:group_dim] + (pad_len,) + original_shape[group_dim + 1 :] + ) + tensor = torch.cat( + [tensor, torch.zeros(pad_shape, dtype=tensor.dtype, device=tensor.device)], + dim=group_dim, + ) + data = tensor.view(new_shape) + + # Quantize + if symmetric: + B = 2 ** (num_bits - 1) - 1 + scale = B / torch.max(data.abs(), dim=group_dim + 1, keepdim=True)[0] + data = data * scale + data = data.clamp_(-B, B).round_().to(torch.int8) + return data, scale, original_shape + else: + B = 2**num_bits - 1 + mn = torch.min(data, dim=group_dim + 1, keepdim=True)[0] + mx = torch.max(data, dim=group_dim + 1, keepdim=True)[0] + + scale = B / (mx - mn) + data = data - mn + data.mul_(scale) + + data = data.clamp_(0, B).round_().to(torch.uint8) + return data, mn, scale, original_shape + + +def decompress(packed_data, config): + """Simulate group-wise dequantization.""" + if not config.enabled: + return packed_data + + group_size, num_bits, group_dim, symmetric = ( + config.group_size, + config.num_bits, + config.group_dim, + config.symmetric, + ) + + # Dequantize + if symmetric: + data, scale, original_shape = packed_data + data = data / scale + else: + data, mn, scale, original_shape = packed_data + data = data / scale + data.add_(mn) + + # Unpad + pad_len = (group_size - original_shape[group_dim] % group_size) % group_size + if pad_len: + padded_original_shape = ( + original_shape[:group_dim] + + (original_shape[group_dim] + pad_len,) + + original_shape[group_dim + 1 :] + ) + data = data.reshape(padded_original_shape) + indices = [slice(0, x) for x in original_shape] + return data[indices].contiguous() + else: + return data.view(original_shape) diff --git a/iChat/models/husky_src/conversation.py b/iChat/models/husky_src/conversation.py new file mode 100644 index 0000000000000000000000000000000000000000..6c1e2e062bd4d84514f490d65bafdeff16ee582d --- /dev/null +++ b/iChat/models/husky_src/conversation.py @@ -0,0 +1,201 @@ +""" +Conversation prompt template. +""" + +import dataclasses +from enum import auto, Enum +from typing import List, Tuple, Any + +class SeparatorStyle(Enum): + """Different separator style.""" + + SINGLE = auto() + TWO = auto() + DOLLY = auto() + OASST_PYTHIA = auto() + BAIZE = auto() + +@dataclasses.dataclass +class Conversation: + """A class that keeps all conversation history.""" + + system: str + roles: List[str] + messages: List[List[str]] + offset: int + sep_style: SeparatorStyle = SeparatorStyle.SINGLE + sep: str = "###" + sep2: str = None + + # Used for the state in the gradio servers. + conv_id: Any = None + skip_next: bool = False + model_name: str = None + + def get_prompt(self): + if self.sep_style == SeparatorStyle.SINGLE: + ret = self.system + for role, message in self.messages: + if message: + ret += self.sep + " " + role + ": " + message + else: + ret += self.sep + " " + role + ":" + return ret + elif self.sep_style == SeparatorStyle.TWO: + seps = [self.sep, self.sep2] + ret = self.system + seps[0] + for i, (role, message) in enumerate(self.messages): + if message: + ret += role + ": " + message + seps[i % 2] + else: + ret += role + ":" + return ret + elif self.sep_style == SeparatorStyle.DOLLY: + seps = [self.sep, self.sep2] + ret = self.system + for i, (role, message) in enumerate(self.messages): + if message: + ret += role + ":\n" + message + seps[i % 2] + if i % 2 == 1: + ret += "\n\n" + else: + ret += role + ":\n" + return ret + elif self.sep_style == SeparatorStyle.OASST_PYTHIA: + ret = self.system + for role, message in self.messages: + if message: + ret += role + message + self.sep + else: + ret += role + return ret + elif self.sep_style == SeparatorStyle.BAIZE: + ret = self.system + for role, message in self.messages: + if message: + ret += "\n" + role + message + else: + ret += "\n" + role + return ret + else: + raise ValueError(f"Invalid style: {self.sep_style}") + + def append_message(self, role, message): + self.messages.append([role, message]) + + def to_gradio_chatbot(self): + ret = [] + for i, (role, msg) in enumerate(self.messages[self.offset:]): + if i % 2 == 0: + ret.append([msg, None]) + else: + ret[-1][-1] = msg + return ret + + def copy(self): + return Conversation( + system=self.system, + roles=self.roles, + messages=[[x, y] for x, y in self.messages], + offset=self.offset, + sep_style=self.sep_style, + sep=self.sep, + sep2=self.sep2, + conv_id=self.conv_id, + model_name=self.model_name, + ) + + def dict(self): + return { + "system": self.system, + "roles": self.roles, + "messages": self.messages, + "offset": self.offset, + "sep": self.sep, + "sep2": self.sep2, + "conv_id": self.conv_id, + "model_name": self.model_name, + } + +conv_one_shot = Conversation( + system="A chat between a curious human and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the human's questions.", + roles=("Human", "Assistant"), + messages=( + ( + "Human", + "What are the key differences between renewable and non-renewable energy sources?", + ), + ( + "Assistant", + "Renewable energy sources are those that can be replenished naturally in a relatively " + "short amount of time, such as solar, wind, hydro, geothermal, and biomass. " + "Non-renewable energy sources, on the other hand, are finite and will eventually be " + "depleted, such as coal, oil, and natural gas. Here are some key differences between " + "renewable and non-renewable energy sources:\n" + "1. Availability: Renewable energy sources are virtually inexhaustible, while non-renewable " + "energy sources are finite and will eventually run out.\n" + "2. Environmental impact: Renewable energy sources have a much lower environmental impact " + "than non-renewable sources, which can lead to air and water pollution, greenhouse gas emissions, " + "and other negative effects.\n" + "3. Cost: Renewable energy sources can be more expensive to initially set up, but they typically " + "have lower operational costs than non-renewable sources.\n" + "4. Reliability: Renewable energy sources are often more reliable and can be used in more remote " + "locations than non-renewable sources.\n" + "5. Flexibility: Renewable energy sources are often more flexible and can be adapted to different " + "situations and needs, while non-renewable sources are more rigid and inflexible.\n" + "6. Sustainability: Renewable energy sources are more sustainable over the long term, while " + "non-renewable sources are not, and their depletion can lead to economic and social instability.", + ), + ), + offset=2, + sep_style=SeparatorStyle.SINGLE, + sep="###", +) + +conv_husky = Conversation( + system="", + roles=("Human", "Assistant"), + messages=(), + offset=0, + sep_style=SeparatorStyle.TWO, + sep=" ", + sep2="", +) + +conv_multi_model = Conversation( + system="", + roles=("Human", "Assistant"), + messages=(), + offset=0, + sep_style=SeparatorStyle.TWO, + sep=" ", + sep2="", +) + + +conv_templates = { + "conv_one_shot": conv_one_shot, + "husky": conv_husky, + "multi_model": conv_multi_model, +} + +def get_default_conv_template(model_name): + model_name = model_name.lower() + if "husky" in model_name or "llama" in model_name: + return conv_husky + elif "multi_model" in model_name or "vision" in model_name: + return conv_multi_model + return conv_one_shot + +def compute_skip_echo_len(model_name, conv, prompt): + model_name = model_name.lower() + if "husky" in model_name: + skip_echo_len = len(prompt) - prompt.count("") * 3 + else: + skip_echo_len = len(prompt) + 1 - prompt.count("") * 3 + return skip_echo_len + +if __name__ == "__main__": + default_conversation = conv_templates["husky"] + print(default_conversation.get_prompt()) diff --git a/iChat/models/husky_src/husky_chat.py b/iChat/models/husky_src/husky_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..178cc76193073f016190a451b297732765f9361c --- /dev/null +++ b/iChat/models/husky_src/husky_chat.py @@ -0,0 +1,297 @@ +import logging +import warnings +import torch +import torch.nn as nn + +from dataclasses import dataclass, field +from typing import Optional, Dict, Sequence, Union, List, Tuple, Any + +from transformers import ( + LlamaForCausalLM, + Blip2PreTrainedModel, + Blip2VisionModel, + Blip2Config, + Blip2QFormerModel, + GenerationConfig, +) + +from transformers.utils import ModelOutput + +warnings.filterwarnings('ignore') +logger = logging.getLogger(__name__) + +@dataclass +class Blip2ForConditionalGenerationModelOutput(ModelOutput): + """ + Class defining the outputs of [`Blip2ForConditionalGeneration`]. + + Args: + loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`): + Language modeling loss from the language model. + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head of the language model. + vision_outputs (`BaseModelOutputWithPooling`): + Outputs of the vision encoder. + qformer_outputs (`BaseModelOutputWithPoolingAndCrossAttentions`): + Outputs of the Q-Former (Querying Transformer). + language_model_outputs (`CausalLMOutputWithPast` or `Seq2SeqLMOutput`): + Outputs of the language model. + """ + + loss: Optional[Tuple[torch.FloatTensor]] = None + logits: Optional[Tuple[torch.FloatTensor]] = None + vision_outputs: Optional[torch.FloatTensor] = None + qformer_outputs: Optional[Tuple[torch.FloatTensor]] = None + language_model_outputs: Optional[Tuple[torch.FloatTensor]] = None + + def to_tuple(self) -> Tuple[Any]: + return tuple( + self[k] + if k not in ["vision_outputs", "qformer_outputs", "language_model_outputs"] + else getattr(self, k).to_tuple() + for k in self.keys() + ) + +class Blip2LlaMAForConditionalGeneration(Blip2PreTrainedModel): + config_class = Blip2Config + main_input_name = "pixel_values" + + def __init__(self, config: Blip2Config): + super().__init__(config) + + self.vision_model = Blip2VisionModel(config.vision_config) + + self.query_tokens = nn.Parameter(torch.zeros(1, config.num_query_tokens, config.qformer_config.hidden_size)) + self.qformer = Blip2QFormerModel(config.qformer_config) + + language_model = LlamaForCausalLM(config.text_config) + self.language_model = language_model + + self.language_projection = nn.Linear(config.qformer_config.hidden_size, language_model.config.hidden_size) + + self.config.hidden_size = config.text_config.hidden_size + self.num_queries = config.num_query_tokens + self.offset = 5 + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.language_model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.language_model.set_input_embeddings(value) + + def set_output_embeddings(self, new_embeddings): + self.language_model.set_output_embeddings(new_embeddings) + + def get_output_embeddings(self) -> nn.Module: + return self.language_model.get_output_embeddings() + + def get_encoder(self): + return self.language_model.get_encoder() + + def get_decoder(self): + return self.language_model.get_decoder() + + def extract_feature( + self, + pixel_values: torch.FloatTensor, + ): + image_embeds = self.vision_model(pixel_values, return_dict=True).last_hidden_state + image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device) + + query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1) + query_outputs = self.qformer( + query_embeds=query_tokens, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_attention_mask, + return_dict=True, + ) + query_output = query_outputs.last_hidden_state + + language_model_inputs = self.language_projection(query_output) + return language_model_inputs + + def _tie_weights(self): + if not self.config.use_decoder_only_language_model: + self.language_model.encoder.embed_tokens = self.language_model.shared + self.language_model.decoder.embed_tokens = self.language_model.shared + + def _preprocess_accelerate(self): + r""" + Some pre-processing hacks to make the model `accelerate` compatible. Check + https://github.com/huggingface/transformers/pull/21707 for more details. + """ + hf_device_map = self.hf_device_map + + if len(hf_device_map) > 1 and "language_model" not in hf_device_map and torch.cuda.device_count() > 1: + # warn users about unexpected behavior when using multi-GPU + BLIP-2 + `accelerate`. + logger.warning( + "The `language_model` is not in the `hf_device_map` dictionary and you are running your script" + " in a multi-GPU environment. this may lead to unexpected behavior when using `accelerate`." + " Please pass a `device_map` that contains `language_model` to remove this warning." + " Please refer to https://github.com/huggingface/blog/blob/main/accelerate-large-models.md for", + " more details on creating a `device_map` for large models.", + ) + + if hasattr(self.language_model, "_hf_hook"): + self.language_model._hf_hook.io_same_device = True # For `generate` compatibility + + def forward( + self, + pixel_values: torch.FloatTensor, + input_ids: torch.FloatTensor, + attention_mask: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + labels: Optional[torch.LongTensor] = None, + return_dict: Optional[bool] = None, + + ) -> Union[Tuple, Blip2ForConditionalGenerationModelOutput]: + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # step 1: forward the images through the vision encoder, + # to get image embeddings of shape (batch_size, seq_len, hidden_size) + vision_outputs = self.vision_model( + pixel_values=pixel_values, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + image_embeds = vision_outputs[0] + + # step 2: forward the query tokens through the QFormer, using the image embeddings for cross-attention + image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device) + + query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1) + query_outputs = self.qformer( + query_embeds=query_tokens, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + query_output = query_outputs[0] + + # step 3: use the language model, conditioned on the query outputs and the prompt + language_model_inputs = self.language_projection(query_output) + assert language_model_inputs.shape[1] == self.num_queries + + inputs_embeds = self.language_model.get_input_embeddings()(input_ids) + # Human: . Give the describe Assistant: + # position of : [offset: offset+num_queries] + + inputs_embeds[:, self.offset:self.offset + self.num_queries, :] = language_model_inputs + if attention_mask is None: + attention_mask = torch.ones_like(input_ids) + + outputs = self.language_model( + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + logits = outputs.logits if return_dict else outputs[0] + loss = None + + # we compute the loss here since we need to take into account the sequence length of the query embeds + if labels is not None: + logits = logits[:, -labels.size(1):, :] + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous().to(logits.device).to(torch.long) + + # Flatten the tokens + loss_fct = nn.CrossEntropyLoss(reduction="mean") + loss = loss_fct(shift_logits.view(-1, self.config.text_config.vocab_size), shift_labels.view(-1)) + + if not return_dict: + output = (logits, vision_outputs, query_outputs, outputs) + return ((loss,) + output) if loss is not None else output + + return Blip2ForConditionalGenerationModelOutput( + loss=loss, + logits=logits, + vision_outputs=vision_outputs, + qformer_outputs=query_outputs, + language_model_outputs=outputs, + ) + + @torch.no_grad() + def generate( + self, + pixel_values: torch.FloatTensor, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.LongTensor] = None, + language_model_inputs: Optional[torch.FloatTensor] = None, + generation_config: Optional[GenerationConfig] = None, + **generate_kwargs, + ) -> torch.LongTensor: + """ + Overrides `generate` function to be able to use the model as a conditional generator. + + Args: + pixel_values (`torch.FloatTensor` of shape (batch_size, num_channels, height, width)): + Input images to be processed. + input_ids (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*): + The sequence used as a prompt for the generation. + attention_mask (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*): + Mask to avoid performing attention on padding token indices + generation_config (`~generation.GenerationConfig`, *optional*): + The generation configuration to be used as base parametrization for the generation call. `**kwargs` + passed to generate matching the attributes of `generation_config` will override them. If + `generation_config` is not provided, the default will be used, which had the following loading + priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model + configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s + default values, whose documentation should be checked to parameterize generation. + + Returns: + captions (list): A list of strings of length batch_size * num_captions. + """ + if hasattr(self, "hf_device_map"): + # preprocess for `accelerate` + self._preprocess_accelerate() + if language_model_inputs is None: + batch_size = pixel_values.shape[0] + image_embeds = self.vision_model(pixel_values, return_dict=True).last_hidden_state + image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device) + + query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1) + query_outputs = self.qformer( + query_embeds=query_tokens, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_attention_mask, + return_dict=True, + ) + query_output = query_outputs.last_hidden_state + + language_model_inputs = self.language_projection(query_output) + assert language_model_inputs.shape[1] == self.num_queries + + if input_ids is None: + input_ids = ( + torch.LongTensor([[self.config.text_config.bos_token_id]]) + .repeat(batch_size, 1) + .to(image_embeds.device) + ) + + if attention_mask is None: + attention_mask = torch.ones_like(input_ids) + + inputs_embeds = self.language_model.get_input_embeddings()(input_ids) + + # position of : [offset: offset+num_queries] + inputs_embeds[:, self.offset:self.offset + self.num_queries, :] = language_model_inputs + + outputs = self.language_model.generate( + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + generation_config=generation_config, + **generate_kwargs, + ) + + return outputs diff --git a/iChat/models/image.py b/iChat/models/image.py new file mode 100644 index 0000000000000000000000000000000000000000..7e72da458eb55e612f41a08793de8d348abd3cc7 --- /dev/null +++ b/iChat/models/image.py @@ -0,0 +1,1527 @@ +import os +import torch +from PIL import Image, ImageOps +import math +import time +import numpy as np +import uuid +import cv2 + +from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation +from transformers import pipeline, BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering + +from .utils import seed_everything, gen_new_name, prompts, GLOBAL_SEED +from diffusers import StableDiffusionPipeline, StableDiffusionInpaintPipeline, StableDiffusionInstructPix2PixPipeline +from diffusers import EulerAncestralDiscreteScheduler +from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler +from controlnet_aux import OpenposeDetector, MLSDdetector, HEDdetector + +from segment_anything.utils.amg import remove_small_regions +from segment_anything import build_sam, sam_model_registry, SamAutomaticMaskGenerator, SamPredictor +from torchvision.utils import save_image +import matplotlib.pyplot as plt + +from omegaconf import OmegaConf +import yaml + +import matplotlib.pyplot as plt +# Please DO NOT MOVE THE IMPORT ORDER FOR easyocr. +import easyocr + +from saicinpainting.evaluation.utils import move_to_device +from saicinpainting.training.trainers import load_checkpoint +from saicinpainting.evaluation.data import pad_tensor_to_modulo + +# from .utils import prompts + + +# GLOBAL_SEED=19120623 + + +def HWC3(x): + assert x.dtype == np.uint8 + if x.ndim == 2: + x = x[:, :, None] + assert x.ndim == 3 + H, W, C = x.shape + assert C == 1 or C == 3 or C == 4 + if C == 3: + return x + if C == 1: + return np.concatenate([x, x, x], axis=2) + if C == 4: + color = x[:, :, 0:3].astype(np.float32) + alpha = x[:, :, 3:4].astype(np.float32) / 255.0 + y = color * alpha + 255.0 * (1.0 - alpha) + y = y.clip(0, 255).astype(np.uint8) + return y + + +class MaskFormer: + def __init__(self, device): + print(f"Initializing MaskFormer to {device}") + self.device = device + self.processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") + self.model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined").to(device) + + def inference(self, image_path, text): + threshold = 0.5 + min_area = 0.02 + padding = 20 + original_image = Image.open(image_path) + image = original_image.resize((512, 512)) + inputs = self.processor(text=text, images=image, padding="max_length", return_tensors="pt").to(self.device) + with torch.no_grad(): + outputs = self.model(**inputs) + mask = torch.sigmoid(outputs[0]).squeeze().cpu().numpy() > threshold + area_ratio = len(np.argwhere(mask)) / (mask.shape[0] * mask.shape[1]) + if area_ratio < min_area: + return None + true_indices = np.argwhere(mask) + mask_array = np.zeros_like(mask, dtype=bool) + for idx in true_indices: + padded_slice = tuple(slice(max(0, i - padding), i + padding + 1) for i in idx) + mask_array[padded_slice] = True + visual_mask = (mask_array * 255).astype(np.uint8) + image_mask = Image.fromarray(visual_mask) + return image_mask.resize(original_image.size) + + +class ImageEditing: + def __init__(self, device): + print(f"Initializing ImageEditing to {device}") + self.device = device + self.mask_former = MaskFormer(device=self.device) + self.revision = 'fp16' if 'cuda' in device else None + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.inpaint = StableDiffusionInpaintPipeline.from_pretrained( + "runwayml/stable-diffusion-inpainting", revision=self.revision, torch_dtype=self.torch_dtype).to(device) + + @prompts(name="Remove Something From The Photo", + description="useful when you want to remove and object or something from the photo " + "from its description or location. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the object need to be removed. ") + def inference_remove(self, inputs): + image_path, to_be_removed_txt = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) + return self.inference_replace(f"{image_path},{to_be_removed_txt},background") + + @prompts(name="Replace Something From The Photo", + description="useful when you want to replace an object from the object description or " + "location with another object from its description. " + "The input to this tool should be a comma separated string of three, " + "representing the image_path, the object to be replaced, the object to be replaced with ") + def inference_replace(self, inputs): + image_path, to_be_replaced_txt, replace_with_txt = inputs.split(",") + original_image = Image.open(image_path) + original_size = original_image.size + mask_image = self.mask_former.inference(image_path, to_be_replaced_txt) + updated_image = self.inpaint(prompt=replace_with_txt, image=original_image.resize((512, 512)), + mask_image=mask_image.resize((512, 512))).images[0] + # updated_image_path = get_new_image_name(image_path, func_name="replace-something") + updated_image_path = gen_new_name(image_path, f'{type(self).__name__}') + updated_image = updated_image.resize(original_size) + updated_image.save(updated_image_path) + print( + f"\nProcessed ImageEditing, Input Image: {image_path}, Replace {to_be_replaced_txt} to {replace_with_txt}, " + f"Output Image: {updated_image_path}") + return updated_image_path + + +class InstructPix2Pix: + def __init__(self, device): + print(f"Initializing InstructPix2Pix to {device}") + self.device = device + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained("timbrooks/instruct-pix2pix", + safety_checker=None, + torch_dtype=self.torch_dtype).to(device) + self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config) + + @prompts(name="Instruct Image Using Text", + description="useful when you want to the style of the image to be like the text. " + "like: make it look like a painting. or make it like a robot. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the text. ") + def inference(self, inputs): + """Change style of image.""" + print("===>Starting InstructPix2Pix Inference") + image_path, text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) + original_image = Image.open(image_path) + image = self.pipe(text, image=original_image, num_inference_steps=40, image_guidance_scale=1.2).images[0] + updated_image_path = get_new_image_name(image_path, func_name="pix2pix") + updated_image_path = gen_new_name(image_path, f'{type(self).__name__}') + image.save(updated_image_path) + print(f"\nProcessed InstructPix2Pix, Input Image: {image_path}, Instruct Text: {text}, " + f"Output Image: {updated_image_path}") + return updated_image_path + + +class Text2Image: + def __init__(self, device): + print(f"Initializing Text2Image to {device}") + self.device = device + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", + torch_dtype=self.torch_dtype) + self.pipe.to(device) + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \ + 'fewer digits, cropped, worst quality, low quality' + + @prompts(name="Generate Image From User Input Text", + description="useful when you want to generate an image from a user input text and save it to a file. " + "like: generate an image of an object or something, or generate an image that includes some objects. " + "The input to this tool should be a string, representing the text used to generate image. ") + def inference(self, text): + image_filename = os.path.join('image', f"{str(uuid.uuid4())[:8]}.png") + prompt = text + ', ' + self.a_prompt + image = self.pipe(prompt, negative_prompt=self.n_prompt).images[0] + image.save(image_filename) + print( + f"\nProcessed Text2Image, Input Text: {text}, Output Image: {image_filename}") + return image_filename + + +class ImageCaptioning: + def __init__(self, device): + print(f"Initializing ImageCaptioning to {device}") + self.device = device + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") + self.model = BlipForConditionalGeneration.from_pretrained( + "Salesforce/blip-image-captioning-base", torch_dtype=self.torch_dtype).to(self.device) + + @prompts(name="Get Photo Description", + description="useful when you want to know what is inside the photo. receives image_path as input. " + "The input to this tool should be a string, representing the image_path. ") + def inference(self, image_path): + inputs = self.processor(Image.open(image_path), return_tensors="pt").to(self.device, self.torch_dtype) + out = self.model.generate(**inputs) + captions = self.processor.decode(out[0], skip_special_tokens=True) + print(f"\nProcessed ImageCaptioning, Input Image: {image_path}, Output Text: {captions}") + return captions + + +class Image2Canny: + def __init__(self, device): + print("Initializing Image2Canny") + self.low_threshold = 100 + self.high_threshold = 200 + + @prompts(name="Edge Detection On Image", + description="useful when you want to detect the edge of the image. " + "like: detect the edges of this image, or canny detection on image, " + "or perform edge detection on this image, or detect the canny image of this image. " + "The input to this tool should be a string, representing the image_path") + def inference(self, inputs): + image = Image.open(inputs) + image = np.array(image) + canny = cv2.Canny(image, self.low_threshold, self.high_threshold) + canny = canny[:, :, None] + canny = np.concatenate([canny, canny, canny], axis=2) + canny = Image.fromarray(canny) + # updated_image_path = get_new_image_name(inputs, func_name="edge") + updated_image_path = gen_new_name(inputs, f'{type(self).__name__}') + canny.save(updated_image_path) + print(f"\nProcessed Image2Canny, Input Image: {inputs}, Output Text: {updated_image_path}") + return updated_image_path + + +class CannyText2Image: + def __init__(self, device): + print(f"Initializing CannyText2Image to {device}") + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-canny", + torch_dtype=self.torch_dtype) + self.pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None, + torch_dtype=self.torch_dtype) + self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config) + self.pipe.to(device) + self.seed = -1 + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \ + 'fewer digits, cropped, worst quality, low quality' + + @prompts(name="Generate Image Condition On Canny Image", + description="useful when you want to generate a new real image from both the user description and a canny image." + " like: generate a real image of a object or something from this canny image," + " or generate a new real image of a object or something from this edge image. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the user description. ") + def inference(self, inputs): + image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) + image = Image.open(image_path) + self.seed = random.randint(0, 65535) + seed_everything(self.seed) + prompt = f'{instruct_text}, {self.a_prompt}' + image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt, + guidance_scale=9.0).images[0] + updated_image_path = get_new_image_name(image_path, func_name="canny2image") + updated_image_path = gen_new_name(image_path, f'{type(self).__name__}') + image.save(updated_image_path) + print(f"\nProcessed CannyText2Image, Input Canny: {image_path}, Input Text: {instruct_text}, " + f"Output Text: {updated_image_path}") + return updated_image_path + + +class Image2Line: + def __init__(self, device): + print("Initializing Image2Line") + self.detector = MLSDdetector.from_pretrained('lllyasviel/ControlNet') + + @prompts(name="Line Detection On Image", + description="useful when you want to detect the straight line of the image. " + "like: detect the straight lines of this image, or straight line detection on image, " + "or perform straight line detection on this image, or detect the straight line image of this image. " + "The input to this tool should be a string, representing the image_path") + def inference(self, inputs): + image = Image.open(inputs) + mlsd = self.detector(image) + # updated_image_path = get_new_image_name(inputs, func_name="line-of") + updated_image_path = gen_new_name(inputs, f'{type(self).__name__}') + mlsd.save(updated_image_path) + print(f"\nProcessed Image2Line, Input Image: {inputs}, Output Line: {updated_image_path}") + return updated_image_path + + +class LineText2Image: + def __init__(self, device): + print(f"Initializing LineText2Image to {device}") + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-mlsd", + torch_dtype=self.torch_dtype) + self.pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None, + torch_dtype=self.torch_dtype + ) + self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config) + self.pipe.to(device) + self.seed = -1 + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \ + 'fewer digits, cropped, worst quality, low quality' + + @prompts(name="Generate Image Condition On Line Image", + description="useful when you want to generate a new real image from both the user description " + "and a straight line image. " + "like: generate a real image of a object or something from this straight line image, " + "or generate a new real image of a object or something from this straight lines. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the user description. ") + def inference(self, inputs): + image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) + image = Image.open(image_path) + self.seed = random.randint(0, 65535) + seed_everything(self.seed) + prompt = f'{instruct_text}, {self.a_prompt}' + image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt, + guidance_scale=9.0).images[0] + # updated_image_path = get_new_image_name(image_path, func_name="line2image") + updated_image_path = gen_new_name(image_path, f'{type(self).__name__}') + image.save(updated_image_path) + print(f"\nProcessed LineText2Image, Input Line: {image_path}, Input Text: {instruct_text}, " + f"Output Text: {updated_image_path}") + return updated_image_path + + +class Image2Hed: + def __init__(self, device): + print("Initializing Image2Hed") + self.detector = HEDdetector.from_pretrained('lllyasviel/ControlNet') + + @prompts(name="Hed Detection On Image", + description="useful when you want to detect the soft hed boundary of the image. " + "like: detect the soft hed boundary of this image, or hed boundary detection on image, " + "or perform hed boundary detection on this image, or detect soft hed boundary image of this image. " + "The input to this tool should be a string, representing the image_path") + def inference(self, inputs): + image = Image.open(inputs) + hed = self.detector(image) + # updated_image_path = get_new_image_name(inputs, func_name="hed-boundary") + updated_image_path = gen_new_name(inputs, f'{type(self).__name__}') + hed.save(updated_image_path) + print(f"\nProcessed Image2Hed, Input Image: {inputs}, Output Hed: {updated_image_path}") + return updated_image_path + + +class HedText2Image: + def __init__(self, device): + print(f"Initializing HedText2Image to {device}") + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-hed", + torch_dtype=self.torch_dtype) + self.pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None, + torch_dtype=self.torch_dtype + ) + self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config) + self.pipe.to(device) + self.seed = -1 + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \ + 'fewer digits, cropped, worst quality, low quality' + + @prompts(name="Generate Image Condition On Soft Hed Boundary Image", + description="useful when you want to generate a new real image from both the user description " + "and a soft hed boundary image. " + "like: generate a real image of a object or something from this soft hed boundary image, " + "or generate a new real image of a object or something from this hed boundary. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the user description") + def inference(self, inputs): + image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) + image = Image.open(image_path) + self.seed = random.randint(0, 65535) + seed_everything(self.seed) + prompt = f'{instruct_text}, {self.a_prompt}' + image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt, + guidance_scale=9.0).images[0] + updated_image_path = get_new_image_name(image_path, func_name="hed2image") + updated_image_path = gen_new_name(image_path, f'{type(self).__name__}') + image.save(updated_image_path) + print(f"\nProcessed HedText2Image, Input Hed: {image_path}, Input Text: {instruct_text}, " + f"Output Image: {updated_image_path}") + return updated_image_path + + +class Image2Scribble: + def __init__(self, device): + print("Initializing Image2Scribble") + self.detector = HEDdetector.from_pretrained('lllyasviel/ControlNet') + + @prompts(name="Sketch Detection On Image", + description="useful when you want to generate a scribble of the image. " + "like: generate a scribble of this image, or generate a sketch from this image, " + "detect the sketch from this image. " + "The input to this tool should be a string, representing the image_path") + def inference(self, inputs): + image = Image.open(inputs) + scribble = self.detector(image, scribble=True) + # updated_image_path = get_new_image_name(inputs, func_name="scribble") + updated_image_path = gen_new_name(inputs, f'{type(self).__name__}') + scribble.save(updated_image_path) + print(f"\nProcessed Image2Scribble, Input Image: {inputs}, Output Scribble: {updated_image_path}") + return updated_image_path + + +class ScribbleText2Image: + def __init__(self, device): + print(f"Initializing ScribbleText2Image to {device}") + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-scribble", + torch_dtype=self.torch_dtype) + self.pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None, + torch_dtype=self.torch_dtype + ) + self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config) + self.pipe.to(device) + self.seed = -1 + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \ + 'fewer digits, cropped, worst quality, low quality' + + @prompts(name="Generate Image Condition On Sketch Image", + description="useful when you want to generate a new real image from both the user description and " + "a scribble image or a sketch image. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the user description") + def inference(self, inputs): + image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) + image = Image.open(image_path) + self.seed = random.randint(0, 65535) + seed_everything(self.seed) + prompt = f'{instruct_text}, {self.a_prompt}' + image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt, + guidance_scale=9.0).images[0] + # updated_image_path = get_new_image_name(image_path, func_name="scribble2image") + updated_image_path = gen_new_name(image_path, f'{type(self).__name__}') + image.save(updated_image_path) + print(f"\nProcessed ScribbleText2Image, Input Scribble: {image_path}, Input Text: {instruct_text}, " + f"Output Image: {updated_image_path}") + return updated_image_path + + +class Image2Pose: + def __init__(self, device): + print("Initializing Image2Pose") + self.detector = OpenposeDetector.from_pretrained('lllyasviel/ControlNet') + + @prompts(name="Pose Detection On Image", + description="useful when you want to detect the human pose of the image. " + "like: generate human poses of this image, or generate a pose image from this image. " + "The input to this tool should be a string, representing the image_path") + def inference(self, inputs): + image = Image.open(inputs) + pose = self.detector(image) + # updated_image_path = get_new_image_name(inputs, func_name="human-pose") + updated_image_path = gen_new_name(inputs, f'{type(self).__name__}') + pose.save(updated_image_path) + print(f"\nProcessed Image2Pose, Input Image: {inputs}, Output Pose: {updated_image_path}") + return updated_image_path + + +class PoseText2Image: + def __init__(self, device): + print(f"Initializing PoseText2Image to {device}") + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-openpose", + torch_dtype=self.torch_dtype) + self.pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None, + torch_dtype=self.torch_dtype) + self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config) + self.pipe.to(device) + self.num_inference_steps = 20 + self.seed = -1 + self.unconditional_guidance_scale = 9.0 + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \ + ' fewer digits, cropped, worst quality, low quality' + + @prompts(name="Generate Image Condition On Pose Image", + description="useful when you want to generate a new real image from both the user description " + "and a human pose image. " + "like: generate a real image of a human from this human pose image, " + "or generate a new real image of a human from this pose. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the user description") + def inference(self, inputs): + image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) + image = Image.open(image_path) + self.seed = random.randint(0, 65535) + seed_everything(self.seed) + prompt = f'{instruct_text}, {self.a_prompt}' + image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt, + guidance_scale=9.0).images[0] + # updated_image_path = get_new_image_name(image_path, func_name="pose2image") + updated_image_path = gen_new_name(image_path, f'{type(self).__name__}') + image.save(updated_image_path) + print(f"\nProcessed PoseText2Image, Input Pose: {image_path}, Input Text: {instruct_text}, " + f"Output Image: {updated_image_path}") + return updated_image_path + + +class SegText2Image: + def __init__(self, device): + print(f"Initializing SegText2Image to {device}") + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-seg", + torch_dtype=self.torch_dtype) + self.pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None, + torch_dtype=self.torch_dtype) + self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config) + self.pipe.to(device) + self.seed = -1 + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \ + ' fewer digits, cropped, worst quality, low quality' + + @prompts(name="Generate Image Condition On Segmentations", + description="useful when you want to generate a new real image from both the user description and segmentations. " + "like: generate a real image of a object or something from this segmentation image, " + "or generate a new real image of a object or something from these segmentations. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the user description") + def a_inference(self, inputs): + image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) + image = Image.open(image_path) + seed_everything(GLOBAL_SEED) + prompt = f'{instruct_text}, {self.a_prompt}' + image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt, + guidance_scale=9.0).images[0] + # updated_image_path = get_new_image_name(image_path, func_name="segment2image") + updated_image_path = gen_new_name(image_path, f'{type(self).__name__}') + image.save(updated_image_path) + print(f"\nProcessed SegText2Image, Input Seg: {image_path}, Input Text: {instruct_text}, " + f"Output Image: {updated_image_path}") + return updated_image_path + + +''' +class ConditionalText2Image: + template_model = True + def __init__(self, models): + self.models = models + self.seed = -1 + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \ + ' fewer digits, cropped, worst quality, low quality' + + @prompts(name="Beautify the Image with other Conditions", + description="useful when you want to generate a new real image from both the user description and condition. " + "Condition can be one of segmentation, canny, scribble" + "like: Beautify a image from its segmentation and description " + "or generate a image from its canny and description. " + "or generate a real image from scribble and description. " + "The input to this tool should be a comma separated string of three, " + "representing the image_path, condition, instruction") + def inference(self, inputs): + image_path, condition, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) + # image = Image.open(image_path) + if 'segmentation' in condition.lower(): + seg_path = self.models['SegmentAnything'].inference(image_path) + updated_image_path = self.models['SegText2Image'].a_inference(seg_path+","+instruct_text) + else: + raise NotImplementedError(condition) + + print(f"\nProcessed SegText2Image, Input Seg: {image_path}, Input Text: {instruct_text}, " + f"Output Image: {updated_image_path}") + return updated_image_path +''' + +class Image2Depth: + def __init__(self, device): + print("Initializing Image2Depth") + self.depth_estimator = pipeline('depth-estimation') + + @prompts(name="Predict Depth On Image", + description="useful when you want to detect depth of the image. like: generate the depth from this image, " + "or detect the depth map on this image, or predict the depth for this image. " + "The input to this tool should be a string, representing the image_path") + def inference(self, inputs): + image = Image.open(inputs) + depth = self.depth_estimator(image)['depth'] + depth = np.array(depth) + depth = depth[:, :, None] + depth = np.concatenate([depth, depth, depth], axis=2) + depth = Image.fromarray(depth) + # updated_image_path = get_new_image_name(inputs, func_name="depth") + updated_image_path = gen_new_name(inputs, f'{type(self).__name__}') + depth.save(updated_image_path) + print(f"\nProcessed Image2Depth, Input Image: {inputs}, Output Depth: {updated_image_path}") + return updated_image_path + + +class DepthText2Image: + def __init__(self, device): + print(f"Initializing DepthText2Image to {device}") + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.controlnet = ControlNetModel.from_pretrained( + "fusing/stable-diffusion-v1-5-controlnet-depth", torch_dtype=self.torch_dtype) + self.pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None, + torch_dtype=self.torch_dtype) + self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config) + self.pipe.to(device) + self.seed = -1 + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \ + ' fewer digits, cropped, worst quality, low quality' + + @prompts(name="Generate Image Condition On Depth", + description="useful when you want to generate a new real image from both the user description and depth image. " + "like: generate a real image of a object or something from this depth image, " + "or generate a new real image of a object or something from the depth map. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the user description") + def inference(self, inputs): + image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) + image = Image.open(image_path) + self.seed = random.randint(0, 65535) + seed_everything(self.seed) + prompt = f'{instruct_text}, {self.a_prompt}' + image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt, + guidance_scale=9.0).images[0] + # updated_image_path = get_new_image_name(image_path, func_name="depth2image") + updated_image_path = gen_new_name(image_path, f'{type(self).__name__}') + image.save(updated_image_path) + print(f"\nProcessed DepthText2Image, Input Depth: {image_path}, Input Text: {instruct_text}, " + f"Output Image: {updated_image_path}") + return updated_image_path + + +class Image2Normal: + def __init__(self, device): + print("Initializing Image2Normal") + self.depth_estimator = pipeline("depth-estimation", model="Intel/dpt-hybrid-midas") + self.bg_threhold = 0.4 + + @prompts(name="Predict Normal Map On Image", + description="useful when you want to detect norm map of the image. " + "like: generate normal map from this image, or predict normal map of this image. " + "The input to this tool should be a string, representing the image_path") + def inference(self, inputs): + image = Image.open(inputs) + original_size = image.size + image = self.depth_estimator(image)['predicted_depth'][0] + image = image.numpy() + image_depth = image.copy() + image_depth -= np.min(image_depth) + image_depth /= np.max(image_depth) + x = cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=3) + x[image_depth < self.bg_threhold] = 0 + y = cv2.Sobel(image, cv2.CV_32F, 0, 1, ksize=3) + y[image_depth < self.bg_threhold] = 0 + z = np.ones_like(x) * np.pi * 2.0 + image = np.stack([x, y, z], axis=2) + image /= np.sum(image ** 2.0, axis=2, keepdims=True) ** 0.5 + image = (image * 127.5 + 127.5).clip(0, 255).astype(np.uint8) + image = Image.fromarray(image) + image = image.resize(original_size) + updated_image_path = get_new_image_name(inputs, func_name="normal-map") + updated_image_path = gen_new_name(inputs, f'{type(self).__name__}') + image.save(updated_image_path) + print(f"\nProcessed Image2Normal, Input Image: {inputs}, Output Depth: {updated_image_path}") + return updated_image_path + + +class NormalText2Image: + def __init__(self, device): + print(f"Initializing NormalText2Image to {device}") + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.controlnet = ControlNetModel.from_pretrained( + "fusing/stable-diffusion-v1-5-controlnet-normal", torch_dtype=self.torch_dtype) + self.pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None, + torch_dtype=self.torch_dtype) + self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config) + self.pipe.to(device) + self.seed = -1 + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \ + ' fewer digits, cropped, worst quality, low quality' + + @prompts(name="Generate Image Condition On Normal Map", + description="useful when you want to generate a new real image from both the user description and normal map. " + "like: generate a real image of a object or something from this normal map, " + "or generate a new real image of a object or something from the normal map. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the user description") + def inference(self, inputs): + image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) + image = Image.open(image_path) + self.seed = random.randint(0, 65535) + seed_everything(self.seed) + prompt = f'{instruct_text}, {self.a_prompt}' + image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt, + guidance_scale=9.0).images[0] + updated_image_path = get_new_image_name(image_path, func_name="normal2image") + updated_image_path = gen_new_name(image_path, f'{type(self).__name__}') + image.save(updated_image_path) + print(f"\nProcessed NormalText2Image, Input Normal: {image_path}, Input Text: {instruct_text}, " + f"Output Image: {updated_image_path}") + return updated_image_path + + +class VisualQuestionAnswering: + def __init__(self, device): + print(f"Initializing VisualQuestionAnswering to {device}") + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.device = device + self.processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base") + self.model = BlipForQuestionAnswering.from_pretrained( + "Salesforce/blip-vqa-base", torch_dtype=self.torch_dtype).to(self.device) + + @prompts(name="Answer Question About The Image", + description="useful when you need an answer for a question based on an image. " + "like: what is the background color of the last image, how many cats in this figure, what is in this figure. " + "The input to this tool should be a comma separated string of two, representing the image_path and the question") + def inference(self, inputs): + image_path, question = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) + raw_image = Image.open(image_path).convert('RGB') + inputs = self.processor(raw_image, question, return_tensors="pt").to(self.device, self.torch_dtype) + out = self.model.generate(**inputs) + answer = self.processor.decode(out[0], skip_special_tokens=True) + print(f"\nProcessed VisualQuestionAnswering, Input Image: {image_path}, Input Question: {question}, " + f"Output Answer: {answer}") + return answer + + +class InfinityOutPainting: + template_model = True # Add this line to show this is a template model. + def __init__(self, ImageCaptioning, ImageEditing, VisualQuestionAnswering): + self.llm = OpenAI(temperature=0) + self.ImageCaption = ImageCaptioning + self.ImageEditing = ImageEditing + self.ImageVQA = VisualQuestionAnswering + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \ + 'fewer digits, cropped, worst quality, low quality' + + def get_BLIP_vqa(self, image, question): + inputs = self.ImageVQA.processor(image, question, return_tensors="pt").to(self.ImageVQA.device, + self.ImageVQA.torch_dtype) + out = self.ImageVQA.model.generate(**inputs) + answer = self.ImageVQA.processor.decode(out[0], skip_special_tokens=True) + print(f"\nProcessed VisualQuestionAnswering, Input Question: {question}, Output Answer: {answer}") + return answer + + def get_BLIP_caption(self, image): + inputs = self.ImageCaption.processor(image, return_tensors="pt").to(self.ImageCaption.device, + self.ImageCaption.torch_dtype) + out = self.ImageCaption.model.generate(**inputs) + BLIP_caption = self.ImageCaption.processor.decode(out[0], skip_special_tokens=True) + return BLIP_caption + + def check_prompt(self, prompt): + check = f"Here is a paragraph with adjectives. " \ + f"{prompt} " \ + f"Please change all plural forms in the adjectives to singular forms. " + return self.llm(check) + + def get_imagine_caption(self, image, imagine): + BLIP_caption = self.get_BLIP_caption(image) + background_color = self.get_BLIP_vqa(image, 'what is the background color of this image') + style = self.get_BLIP_vqa(image, 'what is the style of this image') + imagine_prompt = f"let's pretend you are an excellent painter and now " \ + f"there is an incomplete painting with {BLIP_caption} in the center, " \ + f"please imagine the complete painting and describe it" \ + f"you should consider the background color is {background_color}, the style is {style}" \ + f"You should make the painting as vivid and realistic as possible" \ + f"You can not use words like painting or picture" \ + f"and you should use no more than 50 words to describe it" + caption = self.llm(imagine_prompt) if imagine else BLIP_caption + caption = self.check_prompt(caption) + print(f'BLIP observation: {BLIP_caption}, ChatGPT imagine to {caption}') if imagine else print( + f'Prompt: {caption}') + return caption + + def resize_image(self, image, max_size=1000000, multiple=8): + aspect_ratio = image.size[0] / image.size[1] + new_width = int(math.sqrt(max_size * aspect_ratio)) + new_height = int(new_width / aspect_ratio) + new_width, new_height = new_width - (new_width % multiple), new_height - (new_height % multiple) + return image.resize((new_width, new_height)) + + def dowhile(self, original_img, tosize, expand_ratio, imagine, usr_prompt): + old_img = original_img + while (old_img.size != tosize): + prompt = self.check_prompt(usr_prompt) if usr_prompt else self.get_imagine_caption(old_img, imagine) + crop_w = 15 if old_img.size[0] != tosize[0] else 0 + crop_h = 15 if old_img.size[1] != tosize[1] else 0 + old_img = ImageOps.crop(old_img, (crop_w, crop_h, crop_w, crop_h)) + temp_canvas_size = (expand_ratio * old_img.width if expand_ratio * old_img.width < tosize[0] else tosize[0], + expand_ratio * old_img.height if expand_ratio * old_img.height < tosize[1] else tosize[ + 1]) + temp_canvas, temp_mask = Image.new("RGB", temp_canvas_size, color="white"), Image.new("L", temp_canvas_size, + color="white") + x, y = (temp_canvas.width - old_img.width) // 2, (temp_canvas.height - old_img.height) // 2 + temp_canvas.paste(old_img, (x, y)) + temp_mask.paste(0, (x, y, x + old_img.width, y + old_img.height)) + resized_temp_canvas, resized_temp_mask = self.resize_image(temp_canvas), self.resize_image(temp_mask) + image = self.ImageEditing.inpaint(prompt=prompt, image=resized_temp_canvas, mask_image=resized_temp_mask, + height=resized_temp_canvas.height, width=resized_temp_canvas.width, + num_inference_steps=50).images[0].resize( + (temp_canvas.width, temp_canvas.height), Image.ANTIALIAS) + image = blend_gt2pt(old_img, image) + old_img = image + return old_img + + @prompts(name="Extend An Image", + description="useful when you need to extend an image into a larger image." + "like: extend the image into a resolution of 2048x1024, extend the image into 2048x1024. " + "The input to this tool should be a comma separated string of two, representing the image_path and the resolution of widthxheight") + def inference(self, inputs): + image_path, resolution = inputs.split(',') + width, height = resolution.split('x') + tosize = (int(width), int(height)) + image = Image.open(image_path) + image = ImageOps.crop(image, (10, 10, 10, 10)) + out_painted_image = self.dowhile(image, tosize, 4, True, False) + # updated_image_path = get_new_image_name(image_path, func_name="outpainting") + updated_image_path = gen_new_name(image_path, f'{type(self).__name__}') + out_painted_image.save(updated_image_path) + print(f"\nProcessed InfinityOutPainting, Input Image: {image_path}, Input Resolution: {resolution}, " + f"Output Image: {updated_image_path}") + return updated_image_path + + +##################### New Models ##################### +class SegmentAnything: + def __init__(self, device): + print(f"Initializing SegmentAnything to {device}") + + sam_checkpoint = "sam_vit_h_4b8939.pth" + model_type = "vit_h" + self.sam = sam_model_registry[model_type](checkpoint=sam_checkpoint) + self.predictor = SamPredictor(self.sam) + self.sam.to(device=device) + self.clicked_region = None + self.img_path = None + + @prompts(name="Segment Anything on Image", + description="useful when you want to segment anything in the image. " + "like: segment anything from this image, " + "The input to this tool should be a string, representing the image_path") + def inference(self, inputs): + print("Inputs: ", inputs) + img_path = inputs.strip() + self.img_path = img_path + annos = self.segment_anything(img_path) + full_img, _ = self.show_annos(annos) + # full_img = Image.fromarray(full_img) + # res = Image.fromarray(res) + # print(os.path.splitext(img_path)) + seg_all_image_path = gen_new_name(img_path, 'SegmentAnything') + full_img.save(seg_all_image_path, "PNG") + + print(f"\nProcessed SegmentAnything, Input Image: {inputs}, Output Depth: {seg_all_image_path}") + return seg_all_image_path + + + @prompts(name="Segment the Clicked Region", + description="useful when you want to segment the masked region or block in the image. " + "like: segment the masked region in this image, " + "or segment the clicked region in this image, " + "The input to this tool should be None.") + def inference_by_mask(self, inputs=None): + # mask = np.array(Image.open(mask_path).convert('L')) + res_mask = self.segment_by_mask(self.clicked_region) + filaname = gen_new_name(self.img_path, 'SegmentAnything') + mask_img = Image.fromarray(res_mask.astype(np.uint8)*255) + mask_img.save(filaname, "PNG") + return filaname + + def segment_by_mask(self, mask=None): + import random + random.seed(GLOBAL_SEED) + if mask is None: + mask = self.clicked_region + idxs = np.nonzero(mask) + # print(idxs) + num_points = min(max(1, int(len(idxs[0]) * 0.01)), 16) + sampled_idx = random.sample(range(0, len(idxs[0])), num_points) + new_mask = [] + for i in range(len(idxs)): + new_mask.append(idxs[i][sampled_idx]) + points = np.array(new_mask).reshape(2, -1).transpose(1, 0)[:, ::-1] + labels = np.array([1] * num_points) + + res_masks, scores, _ = self.predictor.predict( + point_coords=points, + point_labels=labels, + multimask_output=True, + ) + + return res_masks[np.argmax(scores), :, :] + + + def segment_anything(self, img_path): + img = cv2.imread(img_path) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + + mask_generator = SamAutomaticMaskGenerator(self.sam) + annos = mask_generator.generate(img) + return annos + + def get_detection_map(self, img_path): + annos = self.segment_anything(img_path) + _, detection_map = self.show_anns(annos) + + return detection_map + + def preprocess(self, img, img_path): + self.predictor.set_image(img) + self.img_path = img_path + + def reset(self): + self.predictor.reset_image() + self.clicked_region = None + self.img_path = None + + def show_annos(self, anns): + # From https://github.com/sail-sg/EditAnything/blob/main/sam2image.py#L91 + if len(anns) == 0: + return + sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True) + full_img = None + + # for ann in sorted_anns: + for i in range(len(sorted_anns)): + ann = anns[i] + m = ann['segmentation'] + if full_img is None: + full_img = np.zeros((m.shape[0], m.shape[1], 3)) + map = np.zeros((m.shape[0], m.shape[1]), dtype=np.uint16) + map[m != 0] = i + 1 + color_mask = np.random.random((1, 3)).tolist()[0] + full_img[m != 0] = color_mask + full_img = full_img * 255 + # anno encoding from https://github.com/LUSSeg/ImageNet-S + res = np.zeros((map.shape[0], map.shape[1], 3)) + res[:, :, 0] = map % 256 + res[:, :, 1] = map // 256 + res.astype(np.float32) + full_img = Image.fromarray(np.uint8(full_img)) + return full_img, res + + def segment_by_points(self, img, points, lables): + # TODO + # masks, _, _ = self.predictor.predict( + # point_coords=np.array(points[-1]), + # point_labels=np.array(lables[-1]), + # # mask_input=mask_input[-1], + # multimask_output=True, # SAM outputs 3 masks, we choose the one with highest score + # ) + # # return masks_[np.argmax(scores_), :, :] + # return masks + pass + + +class RemoveClickedAnything: + """ + prepare: + ``` + curl -L $(yadisk-direct https://disk.yandex.ru/d/ouP6l8VJ0HpMZg) -o big-lama.zip + unzip big-lama.zip + ``` + """ + def __init__(self, device): + print(f"Initializing RemoveClickedAnything to {device}") + self.device=device + sam_checkpoint = "sam_vit_h_4b8939.pth" + model_type = "vit_h" + self.sam = sam_model_registry[model_type](checkpoint=sam_checkpoint) + self.sam.to(device=device) + self.click_point_state = None + + @prompts(name="Remove the clicked object", + description="useful when you want to remove an object by clicking in the image. " + "like: remove object by clicking, " + "The input to this tool should be a string, representing the image_path") + def inference_replace(self, inputs): + if self.click_point_state is None: + print(f"The current point state is None, return the input path {inputs}") + return inputs + + print("Inputs: ", inputs) + image_path = inputs + img = np.array(Image.open(image_path)) + + predictor = SamPredictor(self.sam) + predictor.set_image(img) + point_coords = [self.click_point_state] + point_labels = [1] + point_coords = np.array(point_coords) + point_labels = np.array(point_labels) + + masks, scores, logits = predictor.predict( + point_coords=point_coords, + point_labels=point_labels, + multimask_output=True, + ) + # ''' + for i, (mask, score) in enumerate(zip(masks, scores)): + plt.figure(figsize=(10,10)) + plt.imshow(img) + show_mask(mask, plt.gca()) + show_points(point_coords, point_labels, plt.gca()) + plt.title(f"Mask {i+1}, Score: {score:.3f}", fontsize=18) + plt.axis('off') + plt.savefig("show_mask{}.jpg".format(i)) + # ''' + + masks = masks.astype(np.uint8) * 255 + masks = [self.dilate_mask(mask) for mask in masks] + + imgs = [] + for idx, mask in enumerate(masks): + img_inpainted = self.inpaint_img_with_lama( + img, mask, "./lama/configs/prediction/default.yaml", "big-lama") + img_inpainted = img_inpainted.astype(np.uint8) + each = resize_image(img_inpainted, 512) + imgs.append(torch.DoubleTensor(each)) + image_list = torch.stack(imgs).permute(0, 3, 1, 2) + + updated_image_path = gen_new_name(inputs, f'{type(self).__name__}') + save_image(image_list, updated_image_path, nrow=3, # requires data type: torch.float64 + normalize=True, value_range=(0, 255)) + print(f"\nProcessed SegmentAnything, Input Image: {inputs}, Output Depth: {updated_image_path}") + return updated_image_path + + + def inpaint_img_with_lama(self, img, mask, config_p, + ckpt_p: str="./lama/configs/prediction/default.yaml", + mod=8): + assert len(mask.shape) == 2 + if np.max(mask) == 1: + mask = mask * 255 + img = torch.from_numpy(img).float().div(255.) + mask = torch.from_numpy(mask).float() + predict_config = OmegaConf.load(config_p) + predict_config.model.path = ckpt_p + device = torch.device(predict_config.device) + + train_config_path = os.path.join( + predict_config.model.path, 'config.yaml') + + with open(train_config_path, 'r') as f: + train_config = OmegaConf.create(yaml.safe_load(f)) + + train_config.training_model.predict_only = True + train_config.visualizer.kind = 'noop' + + checkpoint_path = os.path.join( + predict_config.model.path, 'models', + predict_config.model.checkpoint + ) + model = load_checkpoint( + train_config, checkpoint_path, strict=False, map_location='cpu') + model.freeze() + if not predict_config.get('refine', False): + model.to(device) + + batch = {} + batch['image'] = img.permute(2, 0, 1).unsqueeze(0) + batch['mask'] = mask[None, None] + unpad_to_size = [batch['image'].shape[2], batch['image'].shape[3]] + batch['image'] = pad_tensor_to_modulo(batch['image'], mod) + batch['mask'] = pad_tensor_to_modulo(batch['mask'], mod) + batch = move_to_device(batch, device) + batch['mask'] = (batch['mask'] > 0) * 1 + + batch = model(batch) + cur_res = batch[predict_config.out_key][0].permute(1, 2, 0) + cur_res = cur_res.detach().cpu().numpy() + + if unpad_to_size is not None: + orig_height, orig_width = unpad_to_size + cur_res = cur_res[:orig_height, :orig_width] + + cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8') + return cur_res + + def dilate_mask(self, mask, dilate_factor=15): + # dilate mask + mask = mask.astype(np.uint8) + mask = cv2.dilate(mask, np.ones((15, 15), np.uint8), iterations=1) + + return mask + + +class RemovesMaskedAnything: + """ + prepare: + ``` + curl -L $(yadisk-direct https://disk.yandex.ru/d/ouP6l8VJ0HpMZg) -o big-lama.zip + unzip big-lama.zip + ``` + """ + template_model=True # Add this line to show this is a template model. + def __init__(self, SegmentAnything): + self.SegmentAnything = SegmentAnything + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \ + 'fewer digits, cropped, worst quality, low quality' + + @prompts(name="Remove the masked object", + description="useful when you want to remove an object by masking the region in the image. " + "like: remove object by the masked region" + "The input to this tool should be a comma separated string of two, " + "representing the image_path and seg_path") + def inference_replace(self, inputs): + print("Inputs: ", inputs) + image_path, seg_mask_path = inputs.split(',') + image_path = image_path.strip() + seg_mask_path = seg_mask_path.strip() + img = np.array(Image.open(image_path)) + seg_mask = Image.open(seg_mask_path).convert('L') + seg_mask = np.array(seg_mask) + + seg_mask = self.dilate_mask(seg_mask) + inpainted_img = self.inpaint_img_with_lama( + img, seg_mask, "./lama/configs/prediction/default.yaml", "big-lama") + + inpainted_img_path = gen_new_name(image_path, "RemovesMaskedAnything") + Image.fromarray(inpainted_img).save(inpainted_img_path, 'PNG') + + print(f"\nProcessed SegmentAnything, Input Image: {inputs}, Output Image: {inpainted_img_path}") + return inpainted_img_path + + + def preprocess_img(self, img): + self.SegmentAnything.set_image(img) + + def inpaint_img_with_lama(self, img, mask, config_p, + ckpt_p: str="./lama/configs/prediction/default.yaml", + mod=8): + assert len(mask.shape) == 2 + if np.max(mask) == 1: + mask = mask * 255 + img = torch.from_numpy(img).float().div(255.) + mask = torch.from_numpy(mask).float() + predict_config = OmegaConf.load(config_p) + predict_config.model.path = ckpt_p + device = torch.device(predict_config.device) + + train_config_path = os.path.join( + predict_config.model.path, 'config.yaml') + + with open(train_config_path, 'r') as f: + train_config = OmegaConf.create(yaml.safe_load(f)) + + train_config.training_model.predict_only = True + train_config.visualizer.kind = 'noop' + + checkpoint_path = os.path.join( + predict_config.model.path, 'models', + predict_config.model.checkpoint + ) + model = load_checkpoint( + train_config, checkpoint_path, strict=False, map_location='cpu') + model.freeze() + if not predict_config.get('refine', False): + model.to(device) + + batch = {} + batch['image'] = img.permute(2, 0, 1).unsqueeze(0) + batch['mask'] = mask[None, None] + unpad_to_size = [batch['image'].shape[2], batch['image'].shape[3]] + batch['image'] = pad_tensor_to_modulo(batch['image'], mod) + batch['mask'] = pad_tensor_to_modulo(batch['mask'], mod) + batch = move_to_device(batch, device) + batch['mask'] = (batch['mask'] > 0) * 1 + + batch = model(batch) + cur_res = batch[predict_config.out_key][0].permute(1, 2, 0) + cur_res = cur_res.detach().cpu().numpy() + + if unpad_to_size is not None: + orig_height, orig_width = unpad_to_size + cur_res = cur_res[:orig_height, :orig_width] + + cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8') + return cur_res + + def dilate_mask(self, mask, dilate_factor=15): + # dilate mask + mask = mask.astype(np.uint8) + mask = cv2.dilate(mask, np.ones((15, 15), np.uint8), iterations=1) + + return mask + + +class InpaintMaskedAnything: + """ + prepare: + ``` + curl -L $(yadisk-direct https://disk.yandex.ru/d/ouP6l8VJ0HpMZg) -o big-lama.zip + unzip big-lama.zip + ``` + """ + template_model=True # Add this line to show this is a template model. + def __init__(self, SegmentAnything): + self.SegmentAnything = SegmentAnything + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \ + 'fewer digits, cropped, worst quality, low quality' + + @prompts(name="Inpaint the Masked Object", + description="useful when you want to remove an object by masking the region in the image. " + "like: inpaint the masked region. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and seg_path") + def inference_replace(self, inputs): + print("Inputs: ", inputs) + image_path, seg_mask_path = inputs.split(',') + image_path = image_path.strip() + seg_mask_path = seg_mask_path.strip() + img = np.array(Image.open(image_path)) + seg_mask = Image.open(seg_mask_path).convert('L') + seg_mask = np.array(seg_mask) + + seg_mask = self.dilate_mask(seg_mask) + inpainted_img = self.inpaint_img_with_lama( + img, seg_mask, "./lama/configs/prediction/default.yaml", "big-lama") + + time_stamp = int(time.time()) + inpainted_img_path = gen_new_name(image_path, "InpaintMaskedAnything") + Image.fromarray(inpainted_img).save(inpainted_img_path, 'PNG') + + print(f"\nProcessed InpaintMaskedAnything, Input Image: {inputs}, Output Image: {inpainted_img_path}") + return inpainted_img_path + + + def preprocess_img(self, img): + self.SegmentAnything.set_image(img) + + def inpaint_img_with_lama(self, img, mask, config_p, + ckpt_p: str="./lama/configs/prediction/default.yaml", + mod=8): + assert len(mask.shape) == 2 + if np.max(mask) == 1: + mask = mask * 255 + img = torch.from_numpy(img).float().div(255.) + mask = torch.from_numpy(mask).float() + predict_config = OmegaConf.load(config_p) + predict_config.model.path = ckpt_p + device = torch.device(predict_config.device) + + train_config_path = os.path.join( + predict_config.model.path, 'config.yaml') + + with open(train_config_path, 'r') as f: + train_config = OmegaConf.create(yaml.safe_load(f)) + + train_config.training_model.predict_only = True + train_config.visualizer.kind = 'noop' + + checkpoint_path = os.path.join( + predict_config.model.path, 'models', + predict_config.model.checkpoint + ) + model = load_checkpoint( + train_config, checkpoint_path, strict=False, map_location='cpu') + model.freeze() + if not predict_config.get('refine', False): + model.to(device) + + batch = {} + batch['image'] = img.permute(2, 0, 1).unsqueeze(0) + batch['mask'] = mask[None, None] + unpad_to_size = [batch['image'].shape[2], batch['image'].shape[3]] + batch['image'] = pad_tensor_to_modulo(batch['image'], mod) + batch['mask'] = pad_tensor_to_modulo(batch['mask'], mod) + batch = move_to_device(batch, device) + batch['mask'] = (batch['mask'] > 0) * 1 + + batch = model(batch) + cur_res = batch[predict_config.out_key][0].permute(1, 2, 0) + cur_res = cur_res.detach().cpu().numpy() + + if unpad_to_size is not None: + orig_height, orig_width = unpad_to_size + cur_res = cur_res[:orig_height, :orig_width] + + cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8') + return cur_res + + def dilate_mask(self, mask, dilate_factor=15): + # dilate mask + mask = mask.astype(np.uint8) + mask = cv2.dilate(mask, np.ones((15, 15), np.uint8), iterations=1) + + return mask + + +class ExtractMaskedAnything: + template_model=True # Add this line to show this is a template model. + def __init__(self, SegmentAnything): + self.SegmentAnything = SegmentAnything + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \ + 'fewer digits, cropped, worst quality, low quality' + + # @prompts(name="Extract the masked anything", + # description="useful when you want to extract the masked region in the image. " + # "like: extract the masked region or keep the masked region in the image. " + # "The input to this tool should be a comma separated string of two, " + # "representing the image_path and mask_path") + @prompts(name="Extract the Masked anything", + description="useful when you want to extract the masked region in the image. " + "like: extract the masked region in the image or " + "keep the masked region in the image. " + "The input to this tool should be a string, " + "representing the image_path") + def inference(self, inputs): + print("Inputs: ", inputs) + image_path = inputs.strip() + seg_mask_path = self.SegmentAnything.inference_by_mask() + # if len(inputs.split(',')) == 1: + # seg_mask_path = self.SegmentAnything.inference_by_mask() + # image_path = inputs.strip() + # else: + # image_path, seg_mask_path = inputs.split(',') + # image_path = image_path.strip() + seg_mask_path = seg_mask_path.strip() + img = np.array(Image.open(image_path).convert("RGB")) + seg_mask = Image.open(seg_mask_path).convert('RGB') + seg_mask = np.array(seg_mask, dtype=np.uint8) + new_img = img * (seg_mask // 255) + print(new_img.shape) + print(seg_mask.shape) + rgba_img = np.concatenate((new_img, seg_mask[:, :, :1]), axis=-1).astype(np.uint8) + rgba_img = Image.fromarray(rgba_img).convert("RGBA") + new_name = gen_new_name(image_path, "ExtractMaskedAnything") + rgba_img.save(new_name, 'PNG') + + print(f"\nProcessed ExtractMaskedAnything, Input Image: {inputs}, Output Image: {new_name}") + return new_name + + +class ReplaceClickedAnything: + def __init__(self, device): + print(f"Initializing ReplaceClickedAnything to {device}") + self.device=device + sam_checkpoint = "sam_vit_h_4b8939.pth" + model_type = "vit_h" + self.sam = sam_model_registry[model_type](checkpoint=sam_checkpoint) + self.sam.to(device=device) + self.click_point_state = None + self.revision = 'fp16' if 'cuda' in device else None + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.inpaint = StableDiffusionInpaintPipeline.from_pretrained( + "runwayml/stable-diffusion-inpainting", revision=self.revision, torch_dtype=self.torch_dtype).to(device) + + + @prompts(name="Replace the clicked object", + description="useful when you want to replace an object by clicking in the image. " + "like: replace object by clicking, " + "The input to this tool should be a string, representing the image_path") + def inference_replace(self, inputs): + if self.click_point_state is None: + print(f"The current point state is None, return the input path {inputs}") + return inputs + + print("Inputs: ", inputs) + image_path = inputs + img = np.array(Image.open(image_path)) + + predictor = SamPredictor(self.sam) + predictor.set_image(img) + point_coords = [self.click_point_state] + point_labels = [1] + point_coords = np.array(point_coords) + point_labels = np.array(point_labels) + + masks, scores, logits = predictor.predict( + point_coords=point_coords, + point_labels=point_labels, + multimask_output=True, + ) + # ''' + for i, (mask, score) in enumerate(zip(masks, scores)): + plt.figure(figsize=(10,10)) + plt.imshow(img) + show_mask(mask, plt.gca()) + show_points(point_coords, point_labels, plt.gca()) + plt.title(f"Mask {i+1}, Score: {score:.3f}", fontsize=18) + plt.axis('off') + plt.savefig("show_mask{}.jpg".format(i)) + # ''' + + imgs = [] + for mask in masks: + mask_pil = Image.fromarray(mask) + image_pil = Image.fromarray(img) + + image_pil = image_pil.resize((512, 512)) + mask_pil = mask_pil.resize((512, 512)) + each = self.inpaint(prompt='cute dog', image=image_pil, mask_image=mask_pil).images[0] + each = resize_image(np.array(each), 512) + imgs.append(torch.DoubleTensor(each)) + image_list = torch.stack(imgs).permute(0, 3, 1, 2) + + # updated_image_path = get_new_image_name(inputs, func_name="clickreplace") + updated_image_path = gen_new_name(inputs, f'{type(self).__name__}') + save_image(image_list, updated_image_path, nrow=3, # requires data type: torch.float64 + normalize=True, value_range=(0, 255)) + print(f"\nProcessed ReplaceClickedAnything, Input Image: {inputs}, Output Depth: {updated_image_path}") + return updated_image_path + + +class ReplaceMaskedAnything: + def __init__(self, device): + print(f"Initializing ReplaceClickedAnything to {device}") + self.device=device + self.revision = 'fp16' if 'cuda' in device else None + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.inpaint = StableDiffusionInpaintPipeline.from_pretrained( + "runwayml/stable-diffusion-inpainting", revision=self.revision, torch_dtype=self.torch_dtype).to(device) + + + @prompts(name="Replace the masked object", + description="useful when you want to replace an object by clicking in the image. " + "like: replace the masked object" + "The input to this tool should be a comma separated string of Three, " + "representing the image_path and the seg_path and the prompt") + def inference_replace(self, inputs): + print("Inputs: ", inputs) + image_path, seg_path = inputs.split(',')[:2] + image_path = image_path.strip() + seg_path = seg_path.strip() + prompt = ','.join(inputs.split(',')[2:]).strip() + img = Image.open(image_path) + original_shape = img.size + img = img.resize((512, 512)) + seg_img = Image.open(seg_path).convert("L").resize((512, 512)) + + gen_img = self.inpaint(prompt=prompt, image=img, mask_image=seg_img).images[0] + # gen_img = resize_image(np.array(gen_img), 512) + gen_img = gen_img.resize(original_shape) + gen_img_path = gen_new_name(image_path, 'ReplaceMaskedAnything') + gen_img.save(gen_img_path, 'PNG') + print(f"\nProcessed ReplaceMaskedAnything, Input Image: {inputs}, Output Depth: {gen_img_path}.") + return gen_img_path + + +class ImageOCRRecognition: + def __init__(self, device): + print(f"Initializing ImageOCRRecognition to {device}") + self.device = device + self.reader = easyocr.Reader(['ch_sim', 'en'], gpu=device) # this needs to run only once to load the model into memory + self.result = None + self.image_path=None + self.clicked_region = None + + + @prompts(name="recognize the optical characters in the image", + description="useful when you want to recognize the characters or words in the clicked region of image. " + "like: recognize the characters or words in the clicked region." + "The input to this tool should be a comma separated string of two, " + "The input to this tool should be None.") + def inference_by_mask(self, inputs=None): + mask = self.clicked_region + inds =np.where(mask != 0) + coord = [] + for ind_per_dim in inds: + coord.append(int(ind_per_dim.mean())) + + if self.image_path is None or len(inds[0]) == 0: + # self.result = self.reader.readtext(image_path) + return 'No characters in the image' + + # stat = [100, 595] # todo + + # reader = easyocr.Reader(['ch_sim', 'en', 'fr', 'it', 'ja', 'ko', 'ru', 'de', 'pt']) # this needs to run only once to load the model into memory + orc_text = self.search((coord[1], coord[0])) + if orc_text is None or len(orc_text) == 0: + orc_text = 'No characters in the image' + + print( + f"\nProcessed ImageOCRRecognition, Input Image: {self.image_path}, " + f"Output Text: {orc_text}.") + return orc_text + + @prompts(name="recognize all optical characters in the image", + description="useful when you want to recognize all characters or words in the image. " + "like: recognize all characters and words in the image." + "The input to this tool should be a string, " + "representing the image_path.") + def inference(self, inputs): + image_path = inputs.strip() + if self.image_path != image_path: + self.result = self.reader.readtext(image_path) + self.image_path = image_path + # print(self.result) + res_text = [] + for item in self.result: + # ([[x, y], [x, y], [x, y], [x, y]], text, confidence) + res_text.append(item[1]) + print( + f"\nProcessed ImageOCRRecognition, Input Image: {self.image_path}, " + f"Output Text: {res_text}") + return res_text + + def preprocess(self, img, img_path): + self.image_path = img_path + self.result = self.reader.readtext(self.image_path) + + def search(self, coord): + for item in self.result: + left_top = item[0][0] + right_bottom=item[0][-2] + if (coord[0] >= left_top[0] and coord[1] >= left_top[1]) and \ + (coord[0] <= right_bottom[0] and coord[1] <= right_bottom[1]): + return item[1] + + return None + + def reset(self): + self.image_path = None + self.result = None + self.mask = None diff --git a/iChat/models/inpainting.py b/iChat/models/inpainting.py new file mode 100644 index 0000000000000000000000000000000000000000..98e7609a7d95b7ca12edf8c63bcf5fc003f771fb --- /dev/null +++ b/iChat/models/inpainting.py @@ -0,0 +1,115 @@ +import os +import sys +import cv2 +import numpy as np +import torch +from PIL import Image +from .utils import gen_new_name, prompts +import torch +from omegaconf import OmegaConf +import numpy as np +from .inpainting_src.ldm_inpainting.ldm.models.diffusion.ddim import DDIMSampler +from .inpainting_src.ldm_inpainting.ldm.util import instantiate_from_config +from .utils import cal_dilate_factor, dilate_mask + + +def make_batch(image, mask, device): + image = image.astype(np.float32) / 255.0 + image = image[None].transpose(0, 3, 1, 2) + image = torch.from_numpy(image) + + mask = mask.astype(np.float32) / 255.0 + mask = mask[None, None] + mask[mask < 0.5] = 0 + mask[mask >= 0.5] = 1 + mask = torch.from_numpy(mask) + + masked_image = (1 - mask) * image + + batch = {"image": image, "mask": mask, "masked_image": masked_image} + for k in batch: + batch[k] = batch[k].to(device=device) + batch[k] = batch[k] * 2.0 - 1.0 + return batch + + +class LDMInpainting: + def __init__(self, device): + ckpt_path = 'model_zoo/ldm_inpainting_big.ckpt' + config = './iChat/models/inpainting_src/ldm_inpainting/config.yaml' + self.ddim_steps = 50 + self.device = device + config = OmegaConf.load(config) + model = instantiate_from_config(config.model) + model.load_state_dict(torch.load(ckpt_path)["state_dict"], strict=False) + self.model = model.to(device=device) + self.sampler = DDIMSampler(model) + + @prompts(name="Remove the Masked Object", + description="useful when you want to remove an object by masking the region in the image. " + "like: remove masked object or inpaint the masked region.. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and mask_path") + @torch.no_grad() + def inference(self, inputs): + print(f'inputs: {inputs}') + # image, mask, device + img_path, mask_path = inputs.split(',')[0], inputs.split(',')[1] + img_path = img_path.strip() + mask_path = mask_path.strip() + image = Image.open(img_path) + mask = Image.open(mask_path).convert('L') + w, h = image.size + image = image.resize((512, 512)) + mask = mask.resize((512, 512)) + image = np.array(image) + mask = np.array(mask) + dilate_factor = cal_dilate_factor(mask.astype(np.uint8)) + mask = dilate_mask(mask, dilate_factor) + + with self.model.ema_scope(): + batch = make_batch(image, mask, device=self.device) + # encode masked image and concat downsampled mask + c = self.model.cond_stage_model.encode(batch["masked_image"]) + cc = torch.nn.functional.interpolate(batch["mask"], + size=c.shape[-2:]) + c = torch.cat((c, cc), dim=1) + + shape = (c.shape[1] - 1,) + c.shape[2:] + samples_ddim, _ = self.sampler.sample(S=self.ddim_steps, + conditioning=c, + batch_size=c.shape[0], + shape=shape, + verbose=False) + x_samples_ddim = self.model.decode_first_stage(samples_ddim) + + image = torch.clamp((batch["image"] + 1.0) / 2.0, + min=0.0, max=1.0) + mask = torch.clamp((batch["mask"] + 1.0) / 2.0, + min=0.0, max=1.0) + predicted_image = torch.clamp((x_samples_ddim + 1.0) / 2.0, + min=0.0, max=1.0) + + inpainted = (1 - mask) * image + mask * predicted_image + inpainted = inpainted.cpu().numpy().transpose(0, 2, 3, 1)[0] * 255 + + # print(type(inpainted)) + inpainted = inpainted.astype(np.uint8) + new_img_name = gen_new_name(img_path, 'LDMInpainter') + new_img = Image.fromarray(inpainted) + new_img = new_img.resize((w, h)) + new_img.save(new_img_name) + print( + f"\nProcessed LDMInpainting, Inputs: {inputs}, " + f"Output Image: {new_img_name}") + return new_img_name + # return inpainted + +''' +if __name__ == '__main__': + painting = LDMInpainting('cuda:0') + res = painting.inference(f'image/82e612_fe54ca_raw.png,image/04a785_fe54ca_mask.png.') + print(res) +''' + + diff --git a/iChat/models/inpainting_src/__init__.py b/iChat/models/inpainting_src/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..124d14f95ff910c5ad933907e449d3a68a76dff6 --- /dev/null +++ b/iChat/models/inpainting_src/__init__.py @@ -0,0 +1 @@ +from .ldm_inpainting import LDMInpainter \ No newline at end of file diff --git a/iChat/models/inpainting_src/ldm_inpainting/__init__.py b/iChat/models/inpainting_src/ldm_inpainting/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..718792553c8df8b26014ef79d13abe6796fb3736 --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/__init__.py @@ -0,0 +1 @@ +from .wrapper import LDMInpainter \ No newline at end of file diff --git a/iChat/models/inpainting_src/ldm_inpainting/config.yaml b/iChat/models/inpainting_src/ldm_inpainting/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..52a3d1e5a14c6ab2ad4495249e24738316160eb2 --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/config.yaml @@ -0,0 +1,67 @@ +model: + base_learning_rate: 1.0e-06 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.0015 + linear_end: 0.0205 + log_every_t: 100 + timesteps: 1000 + loss_type: l1 + first_stage_key: image + cond_stage_key: masked_image + image_size: 64 + channels: 3 + concat_mode: true + monitor: val/loss + scheduler_config: + target: ldm.lr_scheduler.LambdaWarmUpCosineScheduler + params: + verbosity_interval: 0 + warm_up_steps: 1000 + max_decay_steps: 50000 + lr_start: 0.001 + lr_max: 0.1 + lr_min: 0.0001 + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 64 + in_channels: 7 + out_channels: 3 + model_channels: 256 + attention_resolutions: + - 8 + - 4 + - 2 + num_res_blocks: 2 + channel_mult: + - 1 + - 2 + - 3 + - 4 + num_heads: 8 + resblock_updown: true + first_stage_config: + target: ldm.models.autoencoder.VQModelInterface + params: + embed_dim: 3 + n_embed: 8192 + monitor: val/rec_loss + ddconfig: + attn_type: none + double_z: false + z_channels: 3 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: ldm.modules.losses.DummyLoss + cond_stage_config: __is_first_stage__ diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/__init__.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/lr_scheduler.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/lr_scheduler.py new file mode 100644 index 0000000000000000000000000000000000000000..be39da9ca6dacc22bf3df9c7389bbb403a4a3ade --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/lr_scheduler.py @@ -0,0 +1,98 @@ +import numpy as np + + +class LambdaWarmUpCosineScheduler: + """ + note: use with a base_lr of 1.0 + """ + def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0): + self.lr_warm_up_steps = warm_up_steps + self.lr_start = lr_start + self.lr_min = lr_min + self.lr_max = lr_max + self.lr_max_decay_steps = max_decay_steps + self.last_lr = 0. + self.verbosity_interval = verbosity_interval + + def schedule(self, n, **kwargs): + if self.verbosity_interval > 0: + if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}") + if n < self.lr_warm_up_steps: + lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start + self.last_lr = lr + return lr + else: + t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps) + t = min(t, 1.0) + lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * ( + 1 + np.cos(t * np.pi)) + self.last_lr = lr + return lr + + def __call__(self, n, **kwargs): + return self.schedule(n,**kwargs) + + +class LambdaWarmUpCosineScheduler2: + """ + supports repeated iterations, configurable via lists + note: use with a base_lr of 1.0. + """ + def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0): + assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths) + self.lr_warm_up_steps = warm_up_steps + self.f_start = f_start + self.f_min = f_min + self.f_max = f_max + self.cycle_lengths = cycle_lengths + self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths)) + self.last_f = 0. + self.verbosity_interval = verbosity_interval + + def find_in_interval(self, n): + interval = 0 + for cl in self.cum_cycles[1:]: + if n <= cl: + return interval + interval += 1 + + def schedule(self, n, **kwargs): + cycle = self.find_in_interval(n) + n = n - self.cum_cycles[cycle] + if self.verbosity_interval > 0: + if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, " + f"current cycle {cycle}") + if n < self.lr_warm_up_steps[cycle]: + f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle] + self.last_f = f + return f + else: + t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle]) + t = min(t, 1.0) + f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * ( + 1 + np.cos(t * np.pi)) + self.last_f = f + return f + + def __call__(self, n, **kwargs): + return self.schedule(n, **kwargs) + + +class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2): + + def schedule(self, n, **kwargs): + cycle = self.find_in_interval(n) + n = n - self.cum_cycles[cycle] + if self.verbosity_interval > 0: + if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, " + f"current cycle {cycle}") + + if n < self.lr_warm_up_steps[cycle]: + f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle] + self.last_f = f + return f + else: + f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle]) + self.last_f = f + return f + diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/models/__init__.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/models/autoencoder.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/models/autoencoder.py new file mode 100644 index 0000000000000000000000000000000000000000..f1a89b63e4259e89ced73762d8f9713eaaf7a6d6 --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/models/autoencoder.py @@ -0,0 +1,443 @@ +import torch +import pytorch_lightning as pl +import torch.nn.functional as F +from contextlib import contextmanager + + +from ldm.modules.diffusionmodules.model import Encoder, Decoder +from ldm.modules.distributions.distributions import DiagonalGaussianDistribution +from .quantize import VectorQuantizer2 as VectorQuantizer + +from ldm.util import instantiate_from_config + + +class VQModel(pl.LightningModule): + def __init__(self, + ddconfig, + lossconfig, + n_embed, + embed_dim, + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + batch_resize_range=None, + scheduler_config=None, + lr_g_factor=1.0, + remap=None, + sane_index_shape=False, # tell vector quantizer to return indices as bhw + use_ema=False + ): + super().__init__() + self.embed_dim = embed_dim + self.n_embed = n_embed + self.image_key = image_key + self.encoder = Encoder(**ddconfig) + self.decoder = Decoder(**ddconfig) + self.loss = instantiate_from_config(lossconfig) + self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25, + remap=remap, + sane_index_shape=sane_index_shape) + self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) + if colorize_nlabels is not None: + assert type(colorize_nlabels)==int + self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) + if monitor is not None: + self.monitor = monitor + self.batch_resize_range = batch_resize_range + if self.batch_resize_range is not None: + print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.") + + self.use_ema = use_ema + if self.use_ema: + self.model_ema = LitEma(self) + print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") + + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + self.scheduler_config = scheduler_config + self.lr_g_factor = lr_g_factor + + @contextmanager + def ema_scope(self, context=None): + if self.use_ema: + self.model_ema.store(self.parameters()) + self.model_ema.copy_to(self) + if context is not None: + print(f"{context}: Switched to EMA weights") + try: + yield None + finally: + if self.use_ema: + self.model_ema.restore(self.parameters()) + if context is not None: + print(f"{context}: Restored training weights") + + def init_from_ckpt(self, path, ignore_keys=list()): + sd = torch.load(path, map_location="cpu")["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + missing, unexpected = self.load_state_dict(sd, strict=False) + print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + if len(missing) > 0: + print(f"Missing Keys: {missing}") + print(f"Unexpected Keys: {unexpected}") + + def on_train_batch_end(self, *args, **kwargs): + if self.use_ema: + self.model_ema(self) + + def encode(self, x): + h = self.encoder(x) + h = self.quant_conv(h) + quant, emb_loss, info = self.quantize(h) + return quant, emb_loss, info + + def encode_to_prequant(self, x): + h = self.encoder(x) + h = self.quant_conv(h) + return h + + def decode(self, quant): + quant = self.post_quant_conv(quant) + dec = self.decoder(quant) + return dec + + def decode_code(self, code_b): + quant_b = self.quantize.embed_code(code_b) + dec = self.decode(quant_b) + return dec + + def forward(self, input, return_pred_indices=False): + quant, diff, (_,_,ind) = self.encode(input) + dec = self.decode(quant) + if return_pred_indices: + return dec, diff, ind + return dec, diff + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() + if self.batch_resize_range is not None: + lower_size = self.batch_resize_range[0] + upper_size = self.batch_resize_range[1] + if self.global_step <= 4: + # do the first few batches with max size to avoid later oom + new_resize = upper_size + else: + new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16)) + if new_resize != x.shape[2]: + x = F.interpolate(x, size=new_resize, mode="bicubic") + x = x.detach() + return x + + def training_step(self, batch, batch_idx, optimizer_idx): + # https://github.com/pytorch/pytorch/issues/37142 + # try not to fool the heuristics + x = self.get_input(batch, self.image_key) + xrec, qloss, ind = self(x, return_pred_indices=True) + + if optimizer_idx == 0: + # autoencode + aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train", + predicted_indices=ind) + + self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) + return aeloss + + if optimizer_idx == 1: + # discriminator + discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True) + return discloss + + def validation_step(self, batch, batch_idx): + log_dict = self._validation_step(batch, batch_idx) + with self.ema_scope(): + log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema") + return log_dict + + def _validation_step(self, batch, batch_idx, suffix=""): + x = self.get_input(batch, self.image_key) + xrec, qloss, ind = self(x, return_pred_indices=True) + aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0, + self.global_step, + last_layer=self.get_last_layer(), + split="val"+suffix, + predicted_indices=ind + ) + + discloss, log_dict_disc = self.loss(qloss, x, xrec, 1, + self.global_step, + last_layer=self.get_last_layer(), + split="val"+suffix, + predicted_indices=ind + ) + rec_loss = log_dict_ae[f"val{suffix}/rec_loss"] + self.log(f"val{suffix}/rec_loss", rec_loss, + prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) + self.log(f"val{suffix}/aeloss", aeloss, + prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) + if version.parse(pl.__version__) >= version.parse('1.4.0'): + del log_dict_ae[f"val{suffix}/rec_loss"] + self.log_dict(log_dict_ae) + self.log_dict(log_dict_disc) + return self.log_dict + + def configure_optimizers(self): + lr_d = self.learning_rate + lr_g = self.lr_g_factor*self.learning_rate + print("lr_d", lr_d) + print("lr_g", lr_g) + opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ + list(self.decoder.parameters())+ + list(self.quantize.parameters())+ + list(self.quant_conv.parameters())+ + list(self.post_quant_conv.parameters()), + lr=lr_g, betas=(0.5, 0.9)) + opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), + lr=lr_d, betas=(0.5, 0.9)) + + if self.scheduler_config is not None: + scheduler = instantiate_from_config(self.scheduler_config) + + print("Setting up LambdaLR scheduler...") + scheduler = [ + { + 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule), + 'interval': 'step', + 'frequency': 1 + }, + { + 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule), + 'interval': 'step', + 'frequency': 1 + }, + ] + return [opt_ae, opt_disc], scheduler + return [opt_ae, opt_disc], [] + + def get_last_layer(self): + return self.decoder.conv_out.weight + + def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs): + log = dict() + x = self.get_input(batch, self.image_key) + x = x.to(self.device) + if only_inputs: + log["inputs"] = x + return log + xrec, _ = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec.shape[1] > 3 + x = self.to_rgb(x) + xrec = self.to_rgb(xrec) + log["inputs"] = x + log["reconstructions"] = xrec + if plot_ema: + with self.ema_scope(): + xrec_ema, _ = self(x) + if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema) + log["reconstructions_ema"] = xrec_ema + return log + + def to_rgb(self, x): + assert self.image_key == "segmentation" + if not hasattr(self, "colorize"): + self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) + x = F.conv2d(x, weight=self.colorize) + x = 2.*(x-x.min())/(x.max()-x.min()) - 1. + return x + + +class VQModelInterface(VQModel): + def __init__(self, embed_dim, *args, **kwargs): + super().__init__(embed_dim=embed_dim, *args, **kwargs) + self.embed_dim = embed_dim + + def encode(self, x): + h = self.encoder(x) + h = self.quant_conv(h) + return h + + def decode(self, h, force_not_quantize=False): + # also go through quantization layer + if not force_not_quantize: + quant, emb_loss, info = self.quantize(h) + else: + quant = h + quant = self.post_quant_conv(quant) + dec = self.decoder(quant) + return dec + + +class AutoencoderKL(pl.LightningModule): + def __init__(self, + ddconfig, + lossconfig, + embed_dim, + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + ): + super().__init__() + self.image_key = image_key + self.encoder = Encoder(**ddconfig) + self.decoder = Decoder(**ddconfig) + self.loss = instantiate_from_config(lossconfig) + assert ddconfig["double_z"] + self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) + self.embed_dim = embed_dim + if colorize_nlabels is not None: + assert type(colorize_nlabels)==int + self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) + if monitor is not None: + self.monitor = monitor + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + + def init_from_ckpt(self, path, ignore_keys=list()): + sd = torch.load(path, map_location="cpu")["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + self.load_state_dict(sd, strict=False) + print(f"Restored from {path}") + + def encode(self, x): + h = self.encoder(x) + moments = self.quant_conv(h) + posterior = DiagonalGaussianDistribution(moments) + return posterior + + def decode(self, z): + z = self.post_quant_conv(z) + dec = self.decoder(z) + return dec + + def forward(self, input, sample_posterior=True): + posterior = self.encode(input) + if sample_posterior: + z = posterior.sample() + else: + z = posterior.mode() + dec = self.decode(z) + return dec, posterior + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() + return x + + def training_step(self, batch, batch_idx, optimizer_idx): + inputs = self.get_input(batch, self.image_key) + reconstructions, posterior = self(inputs) + + if optimizer_idx == 0: + # train encoder+decoder+logvar + aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False) + return aeloss + + if optimizer_idx == 1: + # train the discriminator + discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + + self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False) + return discloss + + def validation_step(self, batch, batch_idx): + inputs = self.get_input(batch, self.image_key) + reconstructions, posterior = self(inputs) + aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step, + last_layer=self.get_last_layer(), split="val") + + discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step, + last_layer=self.get_last_layer(), split="val") + + self.log("val/rec_loss", log_dict_ae["val/rec_loss"]) + self.log_dict(log_dict_ae) + self.log_dict(log_dict_disc) + return self.log_dict + + def configure_optimizers(self): + lr = self.learning_rate + opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ + list(self.decoder.parameters())+ + list(self.quant_conv.parameters())+ + list(self.post_quant_conv.parameters()), + lr=lr, betas=(0.5, 0.9)) + opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), + lr=lr, betas=(0.5, 0.9)) + return [opt_ae, opt_disc], [] + + def get_last_layer(self): + return self.decoder.conv_out.weight + + @torch.no_grad() + def log_images(self, batch, only_inputs=False, **kwargs): + log = dict() + x = self.get_input(batch, self.image_key) + x = x.to(self.device) + if not only_inputs: + xrec, posterior = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec.shape[1] > 3 + x = self.to_rgb(x) + xrec = self.to_rgb(xrec) + log["samples"] = self.decode(torch.randn_like(posterior.sample())) + log["reconstructions"] = xrec + log["inputs"] = x + return log + + def to_rgb(self, x): + assert self.image_key == "segmentation" + if not hasattr(self, "colorize"): + self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) + x = F.conv2d(x, weight=self.colorize) + x = 2.*(x-x.min())/(x.max()-x.min()) - 1. + return x + + +class IdentityFirstStage(torch.nn.Module): + def __init__(self, *args, vq_interface=False, **kwargs): + self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff + super().__init__() + + def encode(self, x, *args, **kwargs): + return x + + def decode(self, x, *args, **kwargs): + return x + + def quantize(self, x, *args, **kwargs): + if self.vq_interface: + return x, None, [None, None, None] + return x + + def forward(self, x, *args, **kwargs): + return x diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/models/diffusion/__init__.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/models/diffusion/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/models/diffusion/classifier.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/models/diffusion/classifier.py new file mode 100644 index 0000000000000000000000000000000000000000..67e98b9d8ffb96a150b517497ace0a242d7163ef --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/models/diffusion/classifier.py @@ -0,0 +1,267 @@ +import os +import torch +import pytorch_lightning as pl +from omegaconf import OmegaConf +from torch.nn import functional as F +from torch.optim import AdamW +from torch.optim.lr_scheduler import LambdaLR +from copy import deepcopy +from einops import rearrange +from glob import glob +from natsort import natsorted + +from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel +from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config + +__models__ = { + 'class_label': EncoderUNetModel, + 'segmentation': UNetModel +} + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + + +class NoisyLatentImageClassifier(pl.LightningModule): + + def __init__(self, + diffusion_path, + num_classes, + ckpt_path=None, + pool='attention', + label_key=None, + diffusion_ckpt_path=None, + scheduler_config=None, + weight_decay=1.e-2, + log_steps=10, + monitor='val/loss', + *args, + **kwargs): + super().__init__(*args, **kwargs) + self.num_classes = num_classes + # get latest config of diffusion model + diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1] + self.diffusion_config = OmegaConf.load(diffusion_config).model + self.diffusion_config.params.ckpt_path = diffusion_ckpt_path + self.load_diffusion() + + self.monitor = monitor + self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1 + self.log_time_interval = self.diffusion_model.num_timesteps // log_steps + self.log_steps = log_steps + + self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \ + else self.diffusion_model.cond_stage_key + + assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params' + + if self.label_key not in __models__: + raise NotImplementedError() + + self.load_classifier(ckpt_path, pool) + + self.scheduler_config = scheduler_config + self.use_scheduler = self.scheduler_config is not None + self.weight_decay = weight_decay + + def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): + sd = torch.load(path, map_location="cpu") + if "state_dict" in list(sd.keys()): + sd = sd["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( + sd, strict=False) + print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + if len(missing) > 0: + print(f"Missing Keys: {missing}") + if len(unexpected) > 0: + print(f"Unexpected Keys: {unexpected}") + + def load_diffusion(self): + model = instantiate_from_config(self.diffusion_config) + self.diffusion_model = model.eval() + self.diffusion_model.train = disabled_train + for param in self.diffusion_model.parameters(): + param.requires_grad = False + + def load_classifier(self, ckpt_path, pool): + model_config = deepcopy(self.diffusion_config.params.unet_config.params) + model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels + model_config.out_channels = self.num_classes + if self.label_key == 'class_label': + model_config.pool = pool + + self.model = __models__[self.label_key](**model_config) + if ckpt_path is not None: + print('#####################################################################') + print(f'load from ckpt "{ckpt_path}"') + print('#####################################################################') + self.init_from_ckpt(ckpt_path) + + @torch.no_grad() + def get_x_noisy(self, x, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x)) + continuous_sqrt_alpha_cumprod = None + if self.diffusion_model.use_continuous_noise: + continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1) + # todo: make sure t+1 is correct here + + return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise, + continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod) + + def forward(self, x_noisy, t, *args, **kwargs): + return self.model(x_noisy, t) + + @torch.no_grad() + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = rearrange(x, 'b h w c -> b c h w') + x = x.to(memory_format=torch.contiguous_format).float() + return x + + @torch.no_grad() + def get_conditioning(self, batch, k=None): + if k is None: + k = self.label_key + assert k is not None, 'Needs to provide label key' + + targets = batch[k].to(self.device) + + if self.label_key == 'segmentation': + targets = rearrange(targets, 'b h w c -> b c h w') + for down in range(self.numd): + h, w = targets.shape[-2:] + targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest') + + # targets = rearrange(targets,'b c h w -> b h w c') + + return targets + + def compute_top_k(self, logits, labels, k, reduction="mean"): + _, top_ks = torch.topk(logits, k, dim=1) + if reduction == "mean": + return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item() + elif reduction == "none": + return (top_ks == labels[:, None]).float().sum(dim=-1) + + def on_train_epoch_start(self): + # save some memory + self.diffusion_model.model.to('cpu') + + @torch.no_grad() + def write_logs(self, loss, logits, targets): + log_prefix = 'train' if self.training else 'val' + log = {} + log[f"{log_prefix}/loss"] = loss.mean() + log[f"{log_prefix}/acc@1"] = self.compute_top_k( + logits, targets, k=1, reduction="mean" + ) + log[f"{log_prefix}/acc@5"] = self.compute_top_k( + logits, targets, k=5, reduction="mean" + ) + + self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True) + self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False) + self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True) + lr = self.optimizers().param_groups[0]['lr'] + self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True) + + def shared_step(self, batch, t=None): + x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key) + targets = self.get_conditioning(batch) + if targets.dim() == 4: + targets = targets.argmax(dim=1) + if t is None: + t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long() + else: + t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long() + x_noisy = self.get_x_noisy(x, t) + logits = self(x_noisy, t) + + loss = F.cross_entropy(logits, targets, reduction='none') + + self.write_logs(loss.detach(), logits.detach(), targets.detach()) + + loss = loss.mean() + return loss, logits, x_noisy, targets + + def training_step(self, batch, batch_idx): + loss, *_ = self.shared_step(batch) + return loss + + def reset_noise_accs(self): + self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in + range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)} + + def on_validation_start(self): + self.reset_noise_accs() + + @torch.no_grad() + def validation_step(self, batch, batch_idx): + loss, *_ = self.shared_step(batch) + + for t in self.noisy_acc: + _, logits, _, targets = self.shared_step(batch, t) + self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean')) + self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean')) + + return loss + + def configure_optimizers(self): + optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay) + + if self.use_scheduler: + scheduler = instantiate_from_config(self.scheduler_config) + + print("Setting up LambdaLR scheduler...") + scheduler = [ + { + 'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule), + 'interval': 'step', + 'frequency': 1 + }] + return [optimizer], scheduler + + return optimizer + + @torch.no_grad() + def log_images(self, batch, N=8, *args, **kwargs): + log = dict() + x = self.get_input(batch, self.diffusion_model.first_stage_key) + log['inputs'] = x + + y = self.get_conditioning(batch) + + if self.label_key == 'class_label': + y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) + log['labels'] = y + + if ismap(y): + log['labels'] = self.diffusion_model.to_rgb(y) + + for step in range(self.log_steps): + current_time = step * self.log_time_interval + + _, logits, x_noisy, _ = self.shared_step(batch, t=current_time) + + log[f'inputs@t{current_time}'] = x_noisy + + pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes) + pred = rearrange(pred, 'b h w c -> b c h w') + + log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred) + + for key in log: + log[key] = log[key][:N] + + return log diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/models/diffusion/ddim.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/models/diffusion/ddim.py new file mode 100644 index 0000000000000000000000000000000000000000..7a0429759fae780f515770bc5e58e8d0852d4347 --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/models/diffusion/ddim.py @@ -0,0 +1,203 @@ +"""SAMPLING ONLY.""" + +import torch +import numpy as np +from tqdm import tqdm +from functools import partial + +from ...modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like + + +class DDIMSampler(object): + def __init__(self, model, schedule="linear", **kwargs): + super().__init__() + self.model = model + self.ddpm_num_timesteps = model.num_timesteps + self.schedule = schedule + + def register_buffer(self, name, attr): + if type(attr) == torch.Tensor: + if attr.device != torch.device("cuda"): + attr = attr.to(torch.device("cuda")) + setattr(self, name, attr) + + def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): + self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, + num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) + alphas_cumprod = self.model.alphas_cumprod + assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' + to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) + + self.register_buffer('betas', to_torch(self.model.betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) + + # ddim sampling parameters + ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), + ddim_timesteps=self.ddim_timesteps, + eta=ddim_eta,verbose=verbose) + self.register_buffer('ddim_sigmas', ddim_sigmas) + self.register_buffer('ddim_alphas', ddim_alphas) + self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) + self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) + sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( + (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( + 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) + self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) + + @torch.no_grad() + def sample(self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, + # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + **kwargs + ): + if conditioning is not None: + if isinstance(conditioning, dict): + cbs = conditioning[list(conditioning.keys())[0]].shape[0] + if cbs != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + else: + if conditioning.shape[0] != batch_size: + print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") + + self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) + # sampling + C, H, W = shape + size = (batch_size, C, H, W) + print(f'Data shape for DDIM sampling is {size}, eta {eta}') + + samples, intermediates = self.ddim_sampling(conditioning, size, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + ) + return samples, intermediates + + @torch.no_grad() + def ddim_sampling(self, cond, shape, + x_T=None, ddim_use_original_steps=False, + callback=None, timesteps=None, quantize_denoised=False, + mask=None, x0=None, img_callback=None, log_every_t=100, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None,): + device = self.model.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + + if timesteps is None: + timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps + elif timesteps is not None and not ddim_use_original_steps: + subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 + timesteps = self.ddim_timesteps[:subset_end] + + intermediates = {'x_inter': [img], 'pred_x0': [img]} + time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps) + total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] + print(f"Running DDIM Sampling with {total_steps} timesteps") + + iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) + + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((b,), step, device=device, dtype=torch.long) + + if mask is not None: + assert x0 is not None + img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? + img = img_orig * mask + (1. - mask) * img + + outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, + quantize_denoised=quantize_denoised, temperature=temperature, + noise_dropout=noise_dropout, score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning) + img, pred_x0 = outs + if callback: callback(i) + if img_callback: img_callback(pred_x0, i) + + if index % log_every_t == 0 or index == total_steps - 1: + intermediates['x_inter'].append(img) + intermediates['pred_x0'].append(pred_x0) + + return img, intermediates + + @torch.no_grad() + def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None): + b, *_, device = *x.shape, x.device + + if unconditional_conditioning is None or unconditional_guidance_scale == 1.: + e_t = self.model.apply_model(x, t, c) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t] * 2) + c_in = torch.cat([unconditional_conditioning, c]) + e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) + e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) + + if score_corrector is not None: + assert self.model.parameterization == "eps" + e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) + + alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas + alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev + sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas + sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas + # select parameters corresponding to the currently considered timestep + a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) + a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) + sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) + sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) + + # current prediction for x_0 + pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() + if quantize_denoised: + pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + # direction pointing to x_t + dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t + noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + return x_prev, pred_x0 diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/models/diffusion/ddpm.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/models/diffusion/ddpm.py new file mode 100644 index 0000000000000000000000000000000000000000..bbedd04cfd6f736ac066434a75618b9ba5125be7 --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/models/diffusion/ddpm.py @@ -0,0 +1,1445 @@ +""" +wild mixture of +https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py +https://github.com/CompVis/taming-transformers +-- merci +""" + +import torch +import torch.nn as nn +import numpy as np +import pytorch_lightning as pl +from torch.optim.lr_scheduler import LambdaLR +from einops import rearrange, repeat +from contextlib import contextmanager +from functools import partial +from tqdm import tqdm +from torchvision.utils import make_grid +from pytorch_lightning.utilities.distributed import rank_zero_only + +from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config +from ldm.modules.ema import LitEma +from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution +from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL +from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like +from ldm.models.diffusion.ddim import DDIMSampler + + +__conditioning_keys__ = {'concat': 'c_concat', + 'crossattn': 'c_crossattn', + 'adm': 'y'} + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + + +def uniform_on_device(r1, r2, shape, device): + return (r1 - r2) * torch.rand(*shape, device=device) + r2 + + +class DDPM(pl.LightningModule): + # classic DDPM with Gaussian diffusion, in image space + def __init__(self, + unet_config, + timesteps=1000, + beta_schedule="linear", + loss_type="l2", + ckpt_path=None, + ignore_keys=[], + load_only_unet=False, + monitor="val/loss", + use_ema=True, + first_stage_key="image", + image_size=256, + channels=3, + log_every_t=100, + clip_denoised=True, + linear_start=1e-4, + linear_end=2e-2, + cosine_s=8e-3, + given_betas=None, + original_elbo_weight=0., + v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta + l_simple_weight=1., + conditioning_key=None, + parameterization="eps", # all assuming fixed variance schedules + scheduler_config=None, + use_positional_encodings=False, + learn_logvar=False, + logvar_init=0., + ): + super().__init__() + assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"' + self.parameterization = parameterization + print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode") + self.cond_stage_model = None + self.clip_denoised = clip_denoised + self.log_every_t = log_every_t + self.first_stage_key = first_stage_key + self.image_size = image_size # try conv? + self.channels = channels + self.use_positional_encodings = use_positional_encodings + self.model = DiffusionWrapper(unet_config, conditioning_key) + count_params(self.model, verbose=True) + self.use_ema = use_ema + if self.use_ema: + self.model_ema = LitEma(self.model) + print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") + + self.use_scheduler = scheduler_config is not None + if self.use_scheduler: + self.scheduler_config = scheduler_config + + self.v_posterior = v_posterior + self.original_elbo_weight = original_elbo_weight + self.l_simple_weight = l_simple_weight + + if monitor is not None: + self.monitor = monitor + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet) + + self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps, + linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) + + self.loss_type = loss_type + + self.learn_logvar = learn_logvar + self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,)) + if self.learn_logvar: + self.logvar = nn.Parameter(self.logvar, requires_grad=True) + + + def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if exists(given_betas): + betas = given_betas + else: + betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, + cosine_s=cosine_s) + alphas = 1. - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) + + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer('betas', to_torch(betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) + + # calculations for posterior q(x_{t-1} | x_t, x_0) + posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / ( + 1. - alphas_cumprod) + self.v_posterior * betas + # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) + self.register_buffer('posterior_variance', to_torch(posterior_variance)) + # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain + self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) + self.register_buffer('posterior_mean_coef1', to_torch( + betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) + self.register_buffer('posterior_mean_coef2', to_torch( + (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) + + if self.parameterization == "eps": + lvlb_weights = self.betas ** 2 / ( + 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)) + elif self.parameterization == "x0": + lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod)) + else: + raise NotImplementedError("mu not supported") + # TODO how to choose this term + lvlb_weights[0] = lvlb_weights[1] + self.register_buffer('lvlb_weights', lvlb_weights, persistent=False) + assert not torch.isnan(self.lvlb_weights).all() + + @contextmanager + def ema_scope(self, context=None): + if self.use_ema: + self.model_ema.store(self.model.parameters()) + self.model_ema.copy_to(self.model) + if context is not None: + print(f"{context}: Switched to EMA weights") + try: + yield None + finally: + if self.use_ema: + self.model_ema.restore(self.model.parameters()) + if context is not None: + print(f"{context}: Restored training weights") + + def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): + sd = torch.load(path, map_location="cpu") + if "state_dict" in list(sd.keys()): + sd = sd["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( + sd, strict=False) + print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + if len(missing) > 0: + print(f"Missing Keys: {missing}") + if len(unexpected) > 0: + print(f"Unexpected Keys: {unexpected}") + + def q_mean_variance(self, x_start, t): + """ + Get the distribution q(x_t | x_0). + :param x_start: the [N x C x ...] tensor of noiseless inputs. + :param t: the number of diffusion steps (minus 1). Here, 0 means one step. + :return: A tuple (mean, variance, log_variance), all of x_start's shape. + """ + mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start) + variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape) + log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape) + return mean, variance, log_variance + + def predict_start_from_noise(self, x_t, t, noise): + return ( + extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - + extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise + ) + + def q_posterior(self, x_start, x_t, t): + posterior_mean = ( + extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start + + extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t + ) + posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape) + posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape) + return posterior_mean, posterior_variance, posterior_log_variance_clipped + + def p_mean_variance(self, x, t, clip_denoised: bool): + model_out = self.model(x, t) + if self.parameterization == "eps": + x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) + elif self.parameterization == "x0": + x_recon = model_out + if clip_denoised: + x_recon.clamp_(-1., 1.) + + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, t, clip_denoised=True, repeat_noise=False): + b, *_, device = *x.shape, x.device + model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised) + noise = noise_like(x.shape, device, repeat_noise) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def p_sample_loop(self, shape, return_intermediates=False): + device = self.betas.device + b = shape[0] + img = torch.randn(shape, device=device) + intermediates = [img] + for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps): + img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long), + clip_denoised=self.clip_denoised) + if i % self.log_every_t == 0 or i == self.num_timesteps - 1: + intermediates.append(img) + if return_intermediates: + return img, intermediates + return img + + @torch.no_grad() + def sample(self, batch_size=16, return_intermediates=False): + image_size = self.image_size + channels = self.channels + return self.p_sample_loop((batch_size, channels, image_size, image_size), + return_intermediates=return_intermediates) + + def q_sample(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) + + def get_loss(self, pred, target, mean=True): + if self.loss_type == 'l1': + loss = (target - pred).abs() + if mean: + loss = loss.mean() + elif self.loss_type == 'l2': + if mean: + loss = torch.nn.functional.mse_loss(target, pred) + else: + loss = torch.nn.functional.mse_loss(target, pred, reduction='none') + else: + raise NotImplementedError("unknown loss type '{loss_type}'") + + return loss + + def p_losses(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + model_out = self.model(x_noisy, t) + + loss_dict = {} + if self.parameterization == "eps": + target = noise + elif self.parameterization == "x0": + target = x_start + else: + raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported") + + loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3]) + + log_prefix = 'train' if self.training else 'val' + + loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()}) + loss_simple = loss.mean() * self.l_simple_weight + + loss_vlb = (self.lvlb_weights[t] * loss).mean() + loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb}) + + loss = loss_simple + self.original_elbo_weight * loss_vlb + + loss_dict.update({f'{log_prefix}/loss': loss}) + + return loss, loss_dict + + def forward(self, x, *args, **kwargs): + # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size + # assert h == img_size and w == img_size, f'height and width of image must be {img_size}' + t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() + return self.p_losses(x, t, *args, **kwargs) + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = rearrange(x, 'b h w c -> b c h w') + x = x.to(memory_format=torch.contiguous_format).float() + return x + + def shared_step(self, batch): + x = self.get_input(batch, self.first_stage_key) + loss, loss_dict = self(x) + return loss, loss_dict + + def training_step(self, batch, batch_idx): + loss, loss_dict = self.shared_step(batch) + + self.log_dict(loss_dict, prog_bar=True, + logger=True, on_step=True, on_epoch=True) + + self.log("global_step", self.global_step, + prog_bar=True, logger=True, on_step=True, on_epoch=False) + + if self.use_scheduler: + lr = self.optimizers().param_groups[0]['lr'] + self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False) + + return loss + + @torch.no_grad() + def validation_step(self, batch, batch_idx): + _, loss_dict_no_ema = self.shared_step(batch) + with self.ema_scope(): + _, loss_dict_ema = self.shared_step(batch) + loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema} + self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) + self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) + + def on_train_batch_end(self, *args, **kwargs): + if self.use_ema: + self.model_ema(self.model) + + def _get_rows_from_list(self, samples): + n_imgs_per_row = len(samples) + denoise_grid = rearrange(samples, 'n b c h w -> b n c h w') + denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') + denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) + return denoise_grid + + @torch.no_grad() + def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs): + log = dict() + x = self.get_input(batch, self.first_stage_key) + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + x = x.to(self.device)[:N] + log["inputs"] = x + + # get diffusion row + diffusion_row = list() + x_start = x[:n_row] + + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), '1 -> b', b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(x_start) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + diffusion_row.append(x_noisy) + + log["diffusion_row"] = self._get_rows_from_list(diffusion_row) + + if sample: + # get denoise row + with self.ema_scope("Plotting"): + samples, denoise_row = self.sample(batch_size=N, return_intermediates=True) + + log["samples"] = samples + log["denoise_row"] = self._get_rows_from_list(denoise_row) + + if return_keys: + if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: + return log + else: + return {key: log[key] for key in return_keys} + return log + + def configure_optimizers(self): + lr = self.learning_rate + params = list(self.model.parameters()) + if self.learn_logvar: + params = params + [self.logvar] + opt = torch.optim.AdamW(params, lr=lr) + return opt + + +class LatentDiffusion(DDPM): + """main class""" + def __init__(self, + first_stage_config, + cond_stage_config, + num_timesteps_cond=None, + cond_stage_key="image", + cond_stage_trainable=False, + concat_mode=True, + cond_stage_forward=None, + conditioning_key=None, + scale_factor=1.0, + scale_by_std=False, + *args, **kwargs): + self.num_timesteps_cond = default(num_timesteps_cond, 1) + self.scale_by_std = scale_by_std + assert self.num_timesteps_cond <= kwargs['timesteps'] + # for backwards compatibility after implementation of DiffusionWrapper + if conditioning_key is None: + conditioning_key = 'concat' if concat_mode else 'crossattn' + if cond_stage_config == '__is_unconditional__': + conditioning_key = None + ckpt_path = kwargs.pop("ckpt_path", None) + ignore_keys = kwargs.pop("ignore_keys", []) + super().__init__(conditioning_key=conditioning_key, *args, **kwargs) + self.concat_mode = concat_mode + self.cond_stage_trainable = cond_stage_trainable + self.cond_stage_key = cond_stage_key + try: + self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 + except: + self.num_downs = 0 + if not scale_by_std: + self.scale_factor = scale_factor + else: + self.register_buffer('scale_factor', torch.tensor(scale_factor)) + self.instantiate_first_stage(first_stage_config) + self.instantiate_cond_stage(cond_stage_config) + self.cond_stage_forward = cond_stage_forward + self.clip_denoised = False + self.bbox_tokenizer = None + + self.restarted_from_ckpt = False + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys) + self.restarted_from_ckpt = True + + def make_cond_schedule(self, ): + self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) + ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() + self.cond_ids[:self.num_timesteps_cond] = ids + + @rank_zero_only + @torch.no_grad() + def on_train_batch_start(self, batch, batch_idx, dataloader_idx): + # only for very first batch + if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt: + assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously' + # set rescale weight to 1./std of encodings + print("### USING STD-RESCALING ###") + x = super().get_input(batch, self.first_stage_key) + x = x.to(self.device) + encoder_posterior = self.encode_first_stage(x) + z = self.get_first_stage_encoding(encoder_posterior).detach() + del self.scale_factor + self.register_buffer('scale_factor', 1. / z.flatten().std()) + print(f"setting self.scale_factor to {self.scale_factor}") + print("### USING STD-RESCALING ###") + + def register_schedule(self, + given_betas=None, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s) + + self.shorten_cond_schedule = self.num_timesteps_cond > 1 + if self.shorten_cond_schedule: + self.make_cond_schedule() + + def instantiate_first_stage(self, config): + model = instantiate_from_config(config) + self.first_stage_model = model.eval() + self.first_stage_model.train = disabled_train + for param in self.first_stage_model.parameters(): + param.requires_grad = False + + def instantiate_cond_stage(self, config): + if not self.cond_stage_trainable: + if config == "__is_first_stage__": + print("Using first stage also as cond stage.") + self.cond_stage_model = self.first_stage_model + elif config == "__is_unconditional__": + print(f"Training {self.__class__.__name__} as an unconditional model.") + self.cond_stage_model = None + # self.be_unconditional = True + else: + model = instantiate_from_config(config) + self.cond_stage_model = model.eval() + self.cond_stage_model.train = disabled_train + for param in self.cond_stage_model.parameters(): + param.requires_grad = False + else: + assert config != '__is_first_stage__' + assert config != '__is_unconditional__' + model = instantiate_from_config(config) + self.cond_stage_model = model + + def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False): + denoise_row = [] + for zd in tqdm(samples, desc=desc): + denoise_row.append(self.decode_first_stage(zd.to(self.device), + force_not_quantize=force_no_decoder_quantization)) + n_imgs_per_row = len(denoise_row) + denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W + denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w') + denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') + denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) + return denoise_grid + + def get_first_stage_encoding(self, encoder_posterior): + if isinstance(encoder_posterior, DiagonalGaussianDistribution): + z = encoder_posterior.sample() + elif isinstance(encoder_posterior, torch.Tensor): + z = encoder_posterior + else: + raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") + return self.scale_factor * z + + def get_learned_conditioning(self, c): + if self.cond_stage_forward is None: + if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode): + c = self.cond_stage_model.encode(c) + if isinstance(c, DiagonalGaussianDistribution): + c = c.mode() + else: + c = self.cond_stage_model(c) + else: + assert hasattr(self.cond_stage_model, self.cond_stage_forward) + c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) + return c + + def meshgrid(self, h, w): + y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1) + x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1) + + arr = torch.cat([y, x], dim=-1) + return arr + + def delta_border(self, h, w): + """ + :param h: height + :param w: width + :return: normalized distance to image border, + wtith min distance = 0 at border and max dist = 0.5 at image center + """ + lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2) + arr = self.meshgrid(h, w) / lower_right_corner + dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0] + dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0] + edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0] + return edge_dist + + def get_weighting(self, h, w, Ly, Lx, device): + weighting = self.delta_border(h, w) + weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"], + self.split_input_params["clip_max_weight"], ) + weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device) + + if self.split_input_params["tie_braker"]: + L_weighting = self.delta_border(Ly, Lx) + L_weighting = torch.clip(L_weighting, + self.split_input_params["clip_min_tie_weight"], + self.split_input_params["clip_max_tie_weight"]) + + L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device) + weighting = weighting * L_weighting + return weighting + + def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code + """ + :param x: img of size (bs, c, h, w) + :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1]) + """ + bs, nc, h, w = x.shape + + # number of crops in image + Ly = (h - kernel_size[0]) // stride[0] + 1 + Lx = (w - kernel_size[1]) // stride[1] + 1 + + if uf == 1 and df == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params) + + weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx)) + + elif uf > 1 and df == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf), + dilation=1, padding=0, + stride=(stride[0] * uf, stride[1] * uf)) + fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2) + + weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)) + + elif df > 1 and uf == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df), + dilation=1, padding=0, + stride=(stride[0] // df, stride[1] // df)) + fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2) + + weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)) + + else: + raise NotImplementedError + + return fold, unfold, normalization, weighting + + @torch.no_grad() + def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False, + cond_key=None, return_original_cond=False, bs=None): + x = super().get_input(batch, k) + if bs is not None: + x = x[:bs] + x = x.to(self.device) + encoder_posterior = self.encode_first_stage(x) + z = self.get_first_stage_encoding(encoder_posterior).detach() + + if self.model.conditioning_key is not None: + if cond_key is None: + cond_key = self.cond_stage_key + if cond_key != self.first_stage_key: + if cond_key in ['caption', 'coordinates_bbox']: + xc = batch[cond_key] + elif cond_key == 'class_label': + xc = batch + else: + xc = super().get_input(batch, cond_key).to(self.device) + else: + xc = x + if not self.cond_stage_trainable or force_c_encode: + if isinstance(xc, dict) or isinstance(xc, list): + # import pudb; pudb.set_trace() + c = self.get_learned_conditioning(xc) + else: + c = self.get_learned_conditioning(xc.to(self.device)) + else: + c = xc + if bs is not None: + c = c[:bs] + + if self.use_positional_encodings: + pos_x, pos_y = self.compute_latent_shifts(batch) + ckey = __conditioning_keys__[self.model.conditioning_key] + c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y} + + else: + c = None + xc = None + if self.use_positional_encodings: + pos_x, pos_y = self.compute_latent_shifts(batch) + c = {'pos_x': pos_x, 'pos_y': pos_y} + out = [z, c] + if return_first_stage_outputs: + xrec = self.decode_first_stage(z) + out.extend([x, xrec]) + if return_original_cond: + out.append(xc) + return out + + @torch.no_grad() + def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): + if predict_cids: + if z.dim() == 4: + z = torch.argmax(z.exp(), dim=1).long() + z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) + z = rearrange(z, 'b h w c -> b c h w').contiguous() + + z = 1. / self.scale_factor * z + + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + uf = self.split_input_params["vqf"] + bs, nc, h, w = z.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + print("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + print("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) + + z = unfold(z) # (bn, nc * prod(**ks), L) + # 1. Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + # 2. apply model loop over last dim + if isinstance(self.first_stage_model, VQModelInterface): + output_list = [self.first_stage_model.decode(z[:, :, :, :, i], + force_not_quantize=predict_cids or force_not_quantize) + for i in range(z.shape[-1])] + else: + + output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) + for i in range(z.shape[-1])] + + o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) + o = o * weighting + # Reverse 1. reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization # norm is shape (1, 1, h, w) + return decoded + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + # same as above but without decorator + def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): + if predict_cids: + if z.dim() == 4: + z = torch.argmax(z.exp(), dim=1).long() + z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) + z = rearrange(z, 'b h w c -> b c h w').contiguous() + + z = 1. / self.scale_factor * z + + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + uf = self.split_input_params["vqf"] + bs, nc, h, w = z.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + print("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + print("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) + + z = unfold(z) # (bn, nc * prod(**ks), L) + # 1. Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + # 2. apply model loop over last dim + if isinstance(self.first_stage_model, VQModelInterface): + output_list = [self.first_stage_model.decode(z[:, :, :, :, i], + force_not_quantize=predict_cids or force_not_quantize) + for i in range(z.shape[-1])] + else: + + output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) + for i in range(z.shape[-1])] + + o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) + o = o * weighting + # Reverse 1. reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization # norm is shape (1, 1, h, w) + return decoded + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + @torch.no_grad() + def encode_first_stage(self, x): + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + df = self.split_input_params["vqf"] + self.split_input_params['original_image_size'] = x.shape[-2:] + bs, nc, h, w = x.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + print("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + print("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df) + z = unfold(x) # (bn, nc * prod(**ks), L) + # Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + output_list = [self.first_stage_model.encode(z[:, :, :, :, i]) + for i in range(z.shape[-1])] + + o = torch.stack(output_list, axis=-1) + o = o * weighting + + # Reverse reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization + return decoded + + else: + return self.first_stage_model.encode(x) + else: + return self.first_stage_model.encode(x) + + def shared_step(self, batch, **kwargs): + x, c = self.get_input(batch, self.first_stage_key) + loss = self(x, c) + return loss + + def forward(self, x, c, *args, **kwargs): + t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() + if self.model.conditioning_key is not None: + assert c is not None + if self.cond_stage_trainable: + c = self.get_learned_conditioning(c) + if self.shorten_cond_schedule: # TODO: drop this option + tc = self.cond_ids[t].to(self.device) + c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float())) + return self.p_losses(x, c, t, *args, **kwargs) + + def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset + def rescale_bbox(bbox): + x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2]) + y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3]) + w = min(bbox[2] / crop_coordinates[2], 1 - x0) + h = min(bbox[3] / crop_coordinates[3], 1 - y0) + return x0, y0, w, h + + return [rescale_bbox(b) for b in bboxes] + + def apply_model(self, x_noisy, t, cond, return_ids=False): + + if isinstance(cond, dict): + # hybrid case, cond is exptected to be a dict + pass + else: + if not isinstance(cond, list): + cond = [cond] + key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn' + cond = {key: cond} + + if hasattr(self, "split_input_params"): + assert len(cond) == 1 # todo can only deal with one conditioning atm + assert not return_ids + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + + h, w = x_noisy.shape[-2:] + + fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride) + + z = unfold(x_noisy) # (bn, nc * prod(**ks), L) + # Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])] + + if self.cond_stage_key in ["image", "LR_image", "segmentation", + 'bbox_img'] and self.model.conditioning_key: # todo check for completeness + c_key = next(iter(cond.keys())) # get key + c = next(iter(cond.values())) # get value + assert (len(c) == 1) # todo extend to list with more than one elem + c = c[0] # get element + + c = unfold(c) + c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])] + + elif self.cond_stage_key == 'coordinates_bbox': + assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size' + + # assuming padding of unfold is always 0 and its dilation is always 1 + n_patches_per_row = int((w - ks[0]) / stride[0] + 1) + full_img_h, full_img_w = self.split_input_params['original_image_size'] + # as we are operating on latents, we need the factor from the original image size to the + # spatial latent size to properly rescale the crops for regenerating the bbox annotations + num_downs = self.first_stage_model.encoder.num_resolutions - 1 + rescale_latent = 2 ** (num_downs) + + # get top left postions of patches as conforming for the bbbox tokenizer, therefore we + # need to rescale the tl patch coordinates to be in between (0,1) + tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w, + rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h) + for patch_nr in range(z.shape[-1])] + + # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w) + patch_limits = [(x_tl, y_tl, + rescale_latent * ks[0] / full_img_w, + rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates] + # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates] + + # tokenize crop coordinates for the bounding boxes of the respective patches + patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device) + for bbox in patch_limits] # list of length l with tensors of shape (1, 2) + print(patch_limits_tknzd[0].shape) + # cut tknzd crop position from conditioning + assert isinstance(cond, dict), 'cond must be dict to be fed into model' + cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device) + print(cut_cond.shape) + + adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd]) + adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n') + print(adapted_cond.shape) + adapted_cond = self.get_learned_conditioning(adapted_cond) + print(adapted_cond.shape) + adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1]) + print(adapted_cond.shape) + + cond_list = [{'c_crossattn': [e]} for e in adapted_cond] + + else: + cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient + + # apply model by loop over crops + output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])] + assert not isinstance(output_list[0], + tuple) # todo cant deal with multiple model outputs check this never happens + + o = torch.stack(output_list, axis=-1) + o = o * weighting + # Reverse reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + x_recon = fold(o) / normalization + + else: + x_recon = self.model(x_noisy, t, **cond) + + if isinstance(x_recon, tuple) and not return_ids: + return x_recon[0] + else: + return x_recon + + def _predict_eps_from_xstart(self, x_t, t, pred_xstart): + return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \ + extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) + + def _prior_bpd(self, x_start): + """ + Get the prior KL term for the variational lower-bound, measured in + bits-per-dim. + This term can't be optimized, as it only depends on the encoder. + :param x_start: the [N x C x ...] tensor of inputs. + :return: a batch of [N] KL values (in bits), one per batch element. + """ + batch_size = x_start.shape[0] + t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) + qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) + kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0) + return mean_flat(kl_prior) / np.log(2.0) + + def p_losses(self, x_start, cond, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + model_output = self.apply_model(x_noisy, t, cond) + + loss_dict = {} + prefix = 'train' if self.training else 'val' + + if self.parameterization == "x0": + target = x_start + elif self.parameterization == "eps": + target = noise + else: + raise NotImplementedError() + + loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3]) + loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()}) + + logvar_t = self.logvar[t].to(self.device) + loss = loss_simple / torch.exp(logvar_t) + logvar_t + # loss = loss_simple / torch.exp(self.logvar) + self.logvar + if self.learn_logvar: + loss_dict.update({f'{prefix}/loss_gamma': loss.mean()}) + loss_dict.update({'logvar': self.logvar.data.mean()}) + + loss = self.l_simple_weight * loss.mean() + + loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3)) + loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() + loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) + loss += (self.original_elbo_weight * loss_vlb) + loss_dict.update({f'{prefix}/loss': loss}) + + return loss, loss_dict + + def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False, + return_x0=False, score_corrector=None, corrector_kwargs=None): + t_in = t + model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids) + + if score_corrector is not None: + assert self.parameterization == "eps" + model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs) + + if return_codebook_ids: + model_out, logits = model_out + + if self.parameterization == "eps": + x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) + elif self.parameterization == "x0": + x_recon = model_out + else: + raise NotImplementedError() + + if clip_denoised: + x_recon.clamp_(-1., 1.) + if quantize_denoised: + x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon) + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + if return_codebook_ids: + return model_mean, posterior_variance, posterior_log_variance, logits + elif return_x0: + return model_mean, posterior_variance, posterior_log_variance, x_recon + else: + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, + return_codebook_ids=False, quantize_denoised=False, return_x0=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None): + b, *_, device = *x.shape, x.device + outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, + return_codebook_ids=return_codebook_ids, + quantize_denoised=quantize_denoised, + return_x0=return_x0, + score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) + if return_codebook_ids: + raise DeprecationWarning("Support dropped.") + model_mean, _, model_log_variance, logits = outputs + elif return_x0: + model_mean, _, model_log_variance, x0 = outputs + else: + model_mean, _, model_log_variance = outputs + + noise = noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + + if return_codebook_ids: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1) + if return_x0: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 + else: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False, + img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0., + score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None, + log_every_t=None): + if not log_every_t: + log_every_t = self.log_every_t + timesteps = self.num_timesteps + if batch_size is not None: + b = batch_size if batch_size is not None else shape[0] + shape = [batch_size] + list(shape) + else: + b = batch_size = shape[0] + if x_T is None: + img = torch.randn(shape, device=self.device) + else: + img = x_T + intermediates = [] + if cond is not None: + if isinstance(cond, dict): + cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else + list(map(lambda x: x[:batch_size], cond[key])) for key in cond} + else: + cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] + + if start_T is not None: + timesteps = min(timesteps, start_T) + iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation', + total=timesteps) if verbose else reversed( + range(0, timesteps)) + if type(temperature) == float: + temperature = [temperature] * timesteps + + for i in iterator: + ts = torch.full((b,), i, device=self.device, dtype=torch.long) + if self.shorten_cond_schedule: + assert self.model.conditioning_key != 'hybrid' + tc = self.cond_ids[ts].to(cond.device) + cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) + + img, x0_partial = self.p_sample(img, cond, ts, + clip_denoised=self.clip_denoised, + quantize_denoised=quantize_denoised, return_x0=True, + temperature=temperature[i], noise_dropout=noise_dropout, + score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) + if mask is not None: + assert x0 is not None + img_orig = self.q_sample(x0, ts) + img = img_orig * mask + (1. - mask) * img + + if i % log_every_t == 0 or i == timesteps - 1: + intermediates.append(x0_partial) + if callback: callback(i) + if img_callback: img_callback(img, i) + return img, intermediates + + @torch.no_grad() + def p_sample_loop(self, cond, shape, return_intermediates=False, + x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False, + mask=None, x0=None, img_callback=None, start_T=None, + log_every_t=None): + + if not log_every_t: + log_every_t = self.log_every_t + device = self.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + + intermediates = [img] + if timesteps is None: + timesteps = self.num_timesteps + + if start_T is not None: + timesteps = min(timesteps, start_T) + iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed( + range(0, timesteps)) + + if mask is not None: + assert x0 is not None + assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match + + for i in iterator: + ts = torch.full((b,), i, device=device, dtype=torch.long) + if self.shorten_cond_schedule: + assert self.model.conditioning_key != 'hybrid' + tc = self.cond_ids[ts].to(cond.device) + cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) + + img = self.p_sample(img, cond, ts, + clip_denoised=self.clip_denoised, + quantize_denoised=quantize_denoised) + if mask is not None: + img_orig = self.q_sample(x0, ts) + img = img_orig * mask + (1. - mask) * img + + if i % log_every_t == 0 or i == timesteps - 1: + intermediates.append(img) + if callback: callback(i) + if img_callback: img_callback(img, i) + + if return_intermediates: + return img, intermediates + return img + + @torch.no_grad() + def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None, + verbose=True, timesteps=None, quantize_denoised=False, + mask=None, x0=None, shape=None,**kwargs): + if shape is None: + shape = (batch_size, self.channels, self.image_size, self.image_size) + if cond is not None: + if isinstance(cond, dict): + cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else + list(map(lambda x: x[:batch_size], cond[key])) for key in cond} + else: + cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] + return self.p_sample_loop(cond, + shape, + return_intermediates=return_intermediates, x_T=x_T, + verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised, + mask=mask, x0=x0) + + @torch.no_grad() + def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs): + + if ddim: + ddim_sampler = DDIMSampler(self) + shape = (self.channels, self.image_size, self.image_size) + samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size, + shape,cond,verbose=False,**kwargs) + + else: + samples, intermediates = self.sample(cond=cond, batch_size=batch_size, + return_intermediates=True,**kwargs) + + return samples, intermediates + + + @torch.no_grad() + def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, + quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, + plot_diffusion_rows=True, **kwargs): + + use_ddim = ddim_steps is not None + + log = dict() + z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, + return_first_stage_outputs=True, + force_c_encode=True, + return_original_cond=True, + bs=N) + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + log["inputs"] = x + log["reconstruction"] = xrec + if self.model.conditioning_key is not None: + if hasattr(self.cond_stage_model, "decode"): + xc = self.cond_stage_model.decode(c) + log["conditioning"] = xc + elif self.cond_stage_key in ["caption"]: + xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"]) + log["conditioning"] = xc + elif self.cond_stage_key == 'class_label': + xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) + log['conditioning'] = xc + elif isimage(xc): + log["conditioning"] = xc + if ismap(xc): + log["original_conditioning"] = self.to_rgb(xc) + + if plot_diffusion_rows: + # get diffusion row + diffusion_row = list() + z_start = z[:n_row] + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), '1 -> b', b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(z_start) + z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) + diffusion_row.append(self.decode_first_stage(z_noisy)) + + diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W + diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') + diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') + diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) + log["diffusion_row"] = diffusion_grid + + if sample: + # get denoise row + with self.ema_scope("Plotting"): + samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, + ddim_steps=ddim_steps,eta=ddim_eta) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) + x_samples = self.decode_first_stage(samples) + log["samples"] = x_samples + if plot_denoise_rows: + denoise_grid = self._get_denoise_row_from_list(z_denoise_row) + log["denoise_row"] = denoise_grid + + if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance( + self.first_stage_model, IdentityFirstStage): + # also display when quantizing x0 while sampling + with self.ema_scope("Plotting Quantized Denoised"): + samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, + ddim_steps=ddim_steps,eta=ddim_eta, + quantize_denoised=True) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True, + # quantize_denoised=True) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_x0_quantized"] = x_samples + + if inpaint: + # make a simple center square + b, h, w = z.shape[0], z.shape[2], z.shape[3] + mask = torch.ones(N, h, w).to(self.device) + # zeros will be filled in + mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0. + mask = mask[:, None, ...] + with self.ema_scope("Plotting Inpaint"): + + samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta, + ddim_steps=ddim_steps, x0=z[:N], mask=mask) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_inpainting"] = x_samples + log["mask"] = mask + + # outpaint + with self.ema_scope("Plotting Outpaint"): + samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta, + ddim_steps=ddim_steps, x0=z[:N], mask=mask) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_outpainting"] = x_samples + + if plot_progressive_rows: + with self.ema_scope("Plotting Progressives"): + img, progressives = self.progressive_denoising(c, + shape=(self.channels, self.image_size, self.image_size), + batch_size=N) + prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") + log["progressive_row"] = prog_row + + if return_keys: + if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: + return log + else: + return {key: log[key] for key in return_keys} + return log + + def configure_optimizers(self): + lr = self.learning_rate + params = list(self.model.parameters()) + if self.cond_stage_trainable: + print(f"{self.__class__.__name__}: Also optimizing conditioner params!") + params = params + list(self.cond_stage_model.parameters()) + if self.learn_logvar: + print('Diffusion model optimizing logvar') + params.append(self.logvar) + opt = torch.optim.AdamW(params, lr=lr) + if self.use_scheduler: + assert 'target' in self.scheduler_config + scheduler = instantiate_from_config(self.scheduler_config) + + print("Setting up LambdaLR scheduler...") + scheduler = [ + { + 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule), + 'interval': 'step', + 'frequency': 1 + }] + return [opt], scheduler + return opt + + @torch.no_grad() + def to_rgb(self, x): + x = x.float() + if not hasattr(self, "colorize"): + self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x) + x = nn.functional.conv2d(x, weight=self.colorize) + x = 2. * (x - x.min()) / (x.max() - x.min()) - 1. + return x + + +class DiffusionWrapper(pl.LightningModule): + def __init__(self, diff_model_config, conditioning_key): + super().__init__() + self.diffusion_model = instantiate_from_config(diff_model_config) + self.conditioning_key = conditioning_key + assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm'] + + def forward(self, x, t, c_concat: list = None, c_crossattn: list = None): + if self.conditioning_key is None: + out = self.diffusion_model(x, t) + elif self.conditioning_key == 'concat': + xc = torch.cat([x] + c_concat, dim=1) + out = self.diffusion_model(xc, t) + elif self.conditioning_key == 'crossattn': + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(x, t, context=cc) + elif self.conditioning_key == 'hybrid': + xc = torch.cat([x] + c_concat, dim=1) + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(xc, t, context=cc) + elif self.conditioning_key == 'adm': + cc = c_crossattn[0] + out = self.diffusion_model(x, t, y=cc) + else: + raise NotImplementedError() + + return out + + +class Layout2ImgDiffusion(LatentDiffusion): + # TODO: move all layout-specific hacks to this class + def __init__(self, cond_stage_key, *args, **kwargs): + assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"' + super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs) + + def log_images(self, batch, N=8, *args, **kwargs): + logs = super().log_images(batch=batch, N=N, *args, **kwargs) + + key = 'train' if self.training else 'validation' + dset = self.trainer.datamodule.datasets[key] + mapper = dset.conditional_builders[self.cond_stage_key] + + bbox_imgs = [] + map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno)) + for tknzd_bbox in batch[self.cond_stage_key][:N]: + bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256)) + bbox_imgs.append(bboximg) + + cond_img = torch.stack(bbox_imgs, dim=0) + logs['bbox_image'] = cond_img + return logs diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/models/diffusion/plms.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/models/diffusion/plms.py new file mode 100644 index 0000000000000000000000000000000000000000..78eeb1003aa45d27bdbfc6b4a1d7ccbff57cd2e3 --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/models/diffusion/plms.py @@ -0,0 +1,236 @@ +"""SAMPLING ONLY.""" + +import torch +import numpy as np +from tqdm import tqdm +from functools import partial + +from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like + + +class PLMSSampler(object): + def __init__(self, model, schedule="linear", **kwargs): + super().__init__() + self.model = model + self.ddpm_num_timesteps = model.num_timesteps + self.schedule = schedule + + def register_buffer(self, name, attr): + if type(attr) == torch.Tensor: + if attr.device != torch.device("cuda"): + attr = attr.to(torch.device("cuda")) + setattr(self, name, attr) + + def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): + if ddim_eta != 0: + raise ValueError('ddim_eta must be 0 for PLMS') + self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, + num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) + alphas_cumprod = self.model.alphas_cumprod + assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' + to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) + + self.register_buffer('betas', to_torch(self.model.betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) + + # ddim sampling parameters + ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), + ddim_timesteps=self.ddim_timesteps, + eta=ddim_eta,verbose=verbose) + self.register_buffer('ddim_sigmas', ddim_sigmas) + self.register_buffer('ddim_alphas', ddim_alphas) + self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) + self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) + sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( + (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( + 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) + self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) + + @torch.no_grad() + def sample(self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, + # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + **kwargs + ): + if conditioning is not None: + if isinstance(conditioning, dict): + cbs = conditioning[list(conditioning.keys())[0]].shape[0] + if cbs != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + else: + if conditioning.shape[0] != batch_size: + print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") + + self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) + # sampling + C, H, W = shape + size = (batch_size, C, H, W) + print(f'Data shape for PLMS sampling is {size}') + + samples, intermediates = self.plms_sampling(conditioning, size, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + ) + return samples, intermediates + + @torch.no_grad() + def plms_sampling(self, cond, shape, + x_T=None, ddim_use_original_steps=False, + callback=None, timesteps=None, quantize_denoised=False, + mask=None, x0=None, img_callback=None, log_every_t=100, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None,): + device = self.model.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + + if timesteps is None: + timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps + elif timesteps is not None and not ddim_use_original_steps: + subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 + timesteps = self.ddim_timesteps[:subset_end] + + intermediates = {'x_inter': [img], 'pred_x0': [img]} + time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps) + total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] + print(f"Running PLMS Sampling with {total_steps} timesteps") + + iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps) + old_eps = [] + + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((b,), step, device=device, dtype=torch.long) + ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long) + + if mask is not None: + assert x0 is not None + img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? + img = img_orig * mask + (1. - mask) * img + + outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, + quantize_denoised=quantize_denoised, temperature=temperature, + noise_dropout=noise_dropout, score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + old_eps=old_eps, t_next=ts_next) + img, pred_x0, e_t = outs + old_eps.append(e_t) + if len(old_eps) >= 4: + old_eps.pop(0) + if callback: callback(i) + if img_callback: img_callback(pred_x0, i) + + if index % log_every_t == 0 or index == total_steps - 1: + intermediates['x_inter'].append(img) + intermediates['pred_x0'].append(pred_x0) + + return img, intermediates + + @torch.no_grad() + def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None): + b, *_, device = *x.shape, x.device + + def get_model_output(x, t): + if unconditional_conditioning is None or unconditional_guidance_scale == 1.: + e_t = self.model.apply_model(x, t, c) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t] * 2) + c_in = torch.cat([unconditional_conditioning, c]) + e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) + e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) + + if score_corrector is not None: + assert self.model.parameterization == "eps" + e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) + + return e_t + + alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas + alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev + sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas + sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas + + def get_x_prev_and_pred_x0(e_t, index): + # select parameters corresponding to the currently considered timestep + a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) + a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) + sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) + sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) + + # current prediction for x_0 + pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() + if quantize_denoised: + pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + # direction pointing to x_t + dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t + noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + return x_prev, pred_x0 + + e_t = get_model_output(x, t) + if len(old_eps) == 0: + # Pseudo Improved Euler (2nd order) + x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index) + e_t_next = get_model_output(x_prev, t_next) + e_t_prime = (e_t + e_t_next) / 2 + elif len(old_eps) == 1: + # 2nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (3 * e_t - old_eps[-1]) / 2 + elif len(old_eps) == 2: + # 3nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12 + elif len(old_eps) >= 3: + # 4nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24 + + x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index) + + return x_prev, pred_x0, e_t diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/models/quantize.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/models/quantize.py new file mode 100644 index 0000000000000000000000000000000000000000..81800337f30b993df1ebd46f536cfe7ecf88737b --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/models/quantize.py @@ -0,0 +1,445 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from torch import einsum +from einops import rearrange + + +class VectorQuantizer(nn.Module): + """ + see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py + ____________________________________________ + Discretization bottleneck part of the VQ-VAE. + Inputs: + - n_e : number of embeddings + - e_dim : dimension of embedding + - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 + _____________________________________________ + """ + + # NOTE: this class contains a bug regarding beta; see VectorQuantizer2 for + # a fix and use legacy=False to apply that fix. VectorQuantizer2 can be + # used wherever VectorQuantizer has been used before and is additionally + # more efficient. + def __init__(self, n_e, e_dim, beta): + super(VectorQuantizer, self).__init__() + self.n_e = n_e + self.e_dim = e_dim + self.beta = beta + + self.embedding = nn.Embedding(self.n_e, self.e_dim) + self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) + + def forward(self, z): + """ + Inputs the output of the encoder network z and maps it to a discrete + one-hot vector that is the index of the closest embedding vector e_j + z (continuous) -> z_q (discrete) + z.shape = (batch, channel, height, width) + quantization pipeline: + 1. get encoder input (B,C,H,W) + 2. flatten input to (B*H*W,C) + """ + # reshape z -> (batch, height, width, channel) and flatten + z = z.permute(0, 2, 3, 1).contiguous() + z_flattened = z.view(-1, self.e_dim) + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + + d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \ + torch.sum(self.embedding.weight**2, dim=1) - 2 * \ + torch.matmul(z_flattened, self.embedding.weight.t()) + + ## could possible replace this here + # #\start... + # find closest encodings + min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1) + + min_encodings = torch.zeros( + min_encoding_indices.shape[0], self.n_e).to(z) + min_encodings.scatter_(1, min_encoding_indices, 1) + + # dtype min encodings: torch.float32 + # min_encodings shape: torch.Size([2048, 512]) + # min_encoding_indices.shape: torch.Size([2048, 1]) + + # get quantized latent vectors + z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) + #.........\end + + # with: + # .........\start + #min_encoding_indices = torch.argmin(d, dim=1) + #z_q = self.embedding(min_encoding_indices) + # ......\end......... (TODO) + + # compute loss for embedding + loss = torch.mean((z_q.detach()-z)**2) + self.beta * \ + torch.mean((z_q - z.detach()) ** 2) + + # preserve gradients + z_q = z + (z_q - z).detach() + + # perplexity + e_mean = torch.mean(min_encodings, dim=0) + perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) + + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q, loss, (perplexity, min_encodings, min_encoding_indices) + + def get_codebook_entry(self, indices, shape): + # shape specifying (batch, height, width, channel) + # TODO: check for more easy handling with nn.Embedding + min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices) + min_encodings.scatter_(1, indices[:,None], 1) + + # get quantized latent vectors + z_q = torch.matmul(min_encodings.float(), self.embedding.weight) + + if shape is not None: + z_q = z_q.view(shape) + + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q + + +class GumbelQuantize(nn.Module): + """ + credit to @karpathy: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py (thanks!) + Gumbel Softmax trick quantizer + Categorical Reparameterization with Gumbel-Softmax, Jang et al. 2016 + https://arxiv.org/abs/1611.01144 + """ + def __init__(self, num_hiddens, embedding_dim, n_embed, straight_through=True, + kl_weight=5e-4, temp_init=1.0, use_vqinterface=True, + remap=None, unknown_index="random"): + super().__init__() + + self.embedding_dim = embedding_dim + self.n_embed = n_embed + + self.straight_through = straight_through + self.temperature = temp_init + self.kl_weight = kl_weight + + self.proj = nn.Conv2d(num_hiddens, n_embed, 1) + self.embed = nn.Embedding(n_embed, embedding_dim) + + self.use_vqinterface = use_vqinterface + + self.remap = remap + if self.remap is not None: + self.register_buffer("used", torch.tensor(np.load(self.remap))) + self.re_embed = self.used.shape[0] + self.unknown_index = unknown_index # "random" or "extra" or integer + if self.unknown_index == "extra": + self.unknown_index = self.re_embed + self.re_embed = self.re_embed+1 + print(f"Remapping {self.n_embed} indices to {self.re_embed} indices. " + f"Using {self.unknown_index} for unknown indices.") + else: + self.re_embed = n_embed + + def remap_to_used(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + match = (inds[:,:,None]==used[None,None,...]).long() + new = match.argmax(-1) + unknown = match.sum(2)<1 + if self.unknown_index == "random": + new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device) + else: + new[unknown] = self.unknown_index + return new.reshape(ishape) + + def unmap_to_all(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + if self.re_embed > self.used.shape[0]: # extra token + inds[inds>=self.used.shape[0]] = 0 # simply set to zero + back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds) + return back.reshape(ishape) + + def forward(self, z, temp=None, return_logits=False): + # force hard = True when we are in eval mode, as we must quantize. actually, always true seems to work + hard = self.straight_through if self.training else True + temp = self.temperature if temp is None else temp + + logits = self.proj(z) + if self.remap is not None: + # continue only with used logits + full_zeros = torch.zeros_like(logits) + logits = logits[:,self.used,...] + + soft_one_hot = F.gumbel_softmax(logits, tau=temp, dim=1, hard=hard) + if self.remap is not None: + # go back to all entries but unused set to zero + full_zeros[:,self.used,...] = soft_one_hot + soft_one_hot = full_zeros + z_q = einsum('b n h w, n d -> b d h w', soft_one_hot, self.embed.weight) + + # + kl divergence to the prior loss + qy = F.softmax(logits, dim=1) + diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.n_embed + 1e-10), dim=1).mean() + + ind = soft_one_hot.argmax(dim=1) + if self.remap is not None: + ind = self.remap_to_used(ind) + if self.use_vqinterface: + if return_logits: + return z_q, diff, (None, None, ind), logits + return z_q, diff, (None, None, ind) + return z_q, diff, ind + + def get_codebook_entry(self, indices, shape): + b, h, w, c = shape + assert b*h*w == indices.shape[0] + indices = rearrange(indices, '(b h w) -> b h w', b=b, h=h, w=w) + if self.remap is not None: + indices = self.unmap_to_all(indices) + one_hot = F.one_hot(indices, num_classes=self.n_embed).permute(0, 3, 1, 2).float() + z_q = einsum('b n h w, n d -> b d h w', one_hot, self.embed.weight) + return z_q + + +class VectorQuantizer2(nn.Module): + """ + Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly + avoids costly matrix multiplications and allows for post-hoc remapping of indices. + """ + # NOTE: due to a bug the beta term was applied to the wrong term. for + # backwards compatibility we use the buggy version by default, but you can + # specify legacy=False to fix it. + def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random", + sane_index_shape=False, legacy=True): + super().__init__() + self.n_e = n_e + self.e_dim = e_dim + self.beta = beta + self.legacy = legacy + + self.embedding = nn.Embedding(self.n_e, self.e_dim) + self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) + + self.remap = remap + if self.remap is not None: + self.register_buffer("used", torch.tensor(np.load(self.remap))) + self.re_embed = self.used.shape[0] + self.unknown_index = unknown_index # "random" or "extra" or integer + if self.unknown_index == "extra": + self.unknown_index = self.re_embed + self.re_embed = self.re_embed+1 + print(f"Remapping {self.n_e} indices to {self.re_embed} indices. " + f"Using {self.unknown_index} for unknown indices.") + else: + self.re_embed = n_e + + self.sane_index_shape = sane_index_shape + + def remap_to_used(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + match = (inds[:,:,None]==used[None,None,...]).long() + new = match.argmax(-1) + unknown = match.sum(2)<1 + if self.unknown_index == "random": + new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device) + else: + new[unknown] = self.unknown_index + return new.reshape(ishape) + + def unmap_to_all(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + if self.re_embed > self.used.shape[0]: # extra token + inds[inds>=self.used.shape[0]] = 0 # simply set to zero + back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds) + return back.reshape(ishape) + + def forward(self, z, temp=None, rescale_logits=False, return_logits=False): + assert temp is None or temp==1.0, "Only for interface compatible with Gumbel" + assert rescale_logits==False, "Only for interface compatible with Gumbel" + assert return_logits==False, "Only for interface compatible with Gumbel" + # reshape z -> (batch, height, width, channel) and flatten + z = rearrange(z, 'b c h w -> b h w c').contiguous() + z_flattened = z.view(-1, self.e_dim) + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + + d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \ + torch.sum(self.embedding.weight**2, dim=1) - 2 * \ + torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n')) + + min_encoding_indices = torch.argmin(d, dim=1) + z_q = self.embedding(min_encoding_indices).view(z.shape) + perplexity = None + min_encodings = None + + # compute loss for embedding + if not self.legacy: + loss = self.beta * torch.mean((z_q.detach()-z)**2) + \ + torch.mean((z_q - z.detach()) ** 2) + else: + loss = torch.mean((z_q.detach()-z)**2) + self.beta * \ + torch.mean((z_q - z.detach()) ** 2) + + # preserve gradients + z_q = z + (z_q - z).detach() + + # reshape back to match original input shape + z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous() + + if self.remap is not None: + min_encoding_indices = min_encoding_indices.reshape(z.shape[0],-1) # add batch axis + min_encoding_indices = self.remap_to_used(min_encoding_indices) + min_encoding_indices = min_encoding_indices.reshape(-1,1) # flatten + + if self.sane_index_shape: + min_encoding_indices = min_encoding_indices.reshape( + z_q.shape[0], z_q.shape[2], z_q.shape[3]) + + return z_q, loss, (perplexity, min_encodings, min_encoding_indices) + + def get_codebook_entry(self, indices, shape): + # shape specifying (batch, height, width, channel) + if self.remap is not None: + indices = indices.reshape(shape[0],-1) # add batch axis + indices = self.unmap_to_all(indices) + indices = indices.reshape(-1) # flatten again + + # get quantized latent vectors + z_q = self.embedding(indices) + + if shape is not None: + z_q = z_q.view(shape) + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q + +class EmbeddingEMA(nn.Module): + def __init__(self, num_tokens, codebook_dim, decay=0.99, eps=1e-5): + super().__init__() + self.decay = decay + self.eps = eps + weight = torch.randn(num_tokens, codebook_dim) + self.weight = nn.Parameter(weight, requires_grad = False) + self.cluster_size = nn.Parameter(torch.zeros(num_tokens), requires_grad = False) + self.embed_avg = nn.Parameter(weight.clone(), requires_grad = False) + self.update = True + + def forward(self, embed_id): + return F.embedding(embed_id, self.weight) + + def cluster_size_ema_update(self, new_cluster_size): + self.cluster_size.data.mul_(self.decay).add_(new_cluster_size, alpha=1 - self.decay) + + def embed_avg_ema_update(self, new_embed_avg): + self.embed_avg.data.mul_(self.decay).add_(new_embed_avg, alpha=1 - self.decay) + + def weight_update(self, num_tokens): + n = self.cluster_size.sum() + smoothed_cluster_size = ( + (self.cluster_size + self.eps) / (n + num_tokens * self.eps) * n + ) + #normalize embedding average with smoothed cluster size + embed_normalized = self.embed_avg / smoothed_cluster_size.unsqueeze(1) + self.weight.data.copy_(embed_normalized) + + +class EMAVectorQuantizer(nn.Module): + def __init__(self, n_embed, embedding_dim, beta, decay=0.99, eps=1e-5, + remap=None, unknown_index="random"): + super().__init__() + self.codebook_dim = codebook_dim + self.num_tokens = num_tokens + self.beta = beta + self.embedding = EmbeddingEMA(self.num_tokens, self.codebook_dim, decay, eps) + + self.remap = remap + if self.remap is not None: + self.register_buffer("used", torch.tensor(np.load(self.remap))) + self.re_embed = self.used.shape[0] + self.unknown_index = unknown_index # "random" or "extra" or integer + if self.unknown_index == "extra": + self.unknown_index = self.re_embed + self.re_embed = self.re_embed+1 + print(f"Remapping {self.n_embed} indices to {self.re_embed} indices. " + f"Using {self.unknown_index} for unknown indices.") + else: + self.re_embed = n_embed + + def remap_to_used(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + match = (inds[:,:,None]==used[None,None,...]).long() + new = match.argmax(-1) + unknown = match.sum(2)<1 + if self.unknown_index == "random": + new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device) + else: + new[unknown] = self.unknown_index + return new.reshape(ishape) + + def unmap_to_all(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + if self.re_embed > self.used.shape[0]: # extra token + inds[inds>=self.used.shape[0]] = 0 # simply set to zero + back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds) + return back.reshape(ishape) + + def forward(self, z): + # reshape z -> (batch, height, width, channel) and flatten + #z, 'b c h w -> b h w c' + z = rearrange(z, 'b c h w -> b h w c') + z_flattened = z.reshape(-1, self.codebook_dim) + + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + d = z_flattened.pow(2).sum(dim=1, keepdim=True) + \ + self.embedding.weight.pow(2).sum(dim=1) - 2 * \ + torch.einsum('bd,nd->bn', z_flattened, self.embedding.weight) # 'n d -> d n' + + + encoding_indices = torch.argmin(d, dim=1) + + z_q = self.embedding(encoding_indices).view(z.shape) + encodings = F.one_hot(encoding_indices, self.num_tokens).type(z.dtype) + avg_probs = torch.mean(encodings, dim=0) + perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10))) + + if self.training and self.embedding.update: + #EMA cluster size + encodings_sum = encodings.sum(0) + self.embedding.cluster_size_ema_update(encodings_sum) + #EMA embedding average + embed_sum = encodings.transpose(0,1) @ z_flattened + self.embedding.embed_avg_ema_update(embed_sum) + #normalize embed_avg and update weight + self.embedding.weight_update(self.num_tokens) + + # compute loss for embedding + loss = self.beta * F.mse_loss(z_q.detach(), z) + + # preserve gradients + z_q = z + (z_q - z).detach() + + # reshape back to match original input shape + #z_q, 'b h w c -> b c h w' + z_q = rearrange(z_q, 'b h w c -> b c h w') + return z_q, loss, (perplexity, encodings, encoding_indices) \ No newline at end of file diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/__init__.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/attention.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..f4eff39ccb6d75daa764f6eb70a7cef024fb5a3f --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/attention.py @@ -0,0 +1,261 @@ +from inspect import isfunction +import math +import torch +import torch.nn.functional as F +from torch import nn, einsum +from einops import rearrange, repeat + +from ldm.modules.diffusionmodules.util import checkpoint + + +def exists(val): + return val is not None + + +def uniq(arr): + return{el: True for el in arr}.keys() + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def max_neg_value(t): + return -torch.finfo(t.dtype).max + + +def init_(tensor): + dim = tensor.shape[-1] + std = 1 / math.sqrt(dim) + tensor.uniform_(-std, std) + return tensor + + +# feedforward +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential( + nn.Linear(dim, inner_dim), + nn.GELU() + ) if not glu else GEGLU(dim, inner_dim) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def Normalize(in_channels): + return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + + +class LinearAttention(nn.Module): + def __init__(self, dim, heads=4, dim_head=32): + super().__init__() + self.heads = heads + hidden_dim = dim_head * heads + self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False) + self.to_out = nn.Conv2d(hidden_dim, dim, 1) + + def forward(self, x): + b, c, h, w = x.shape + qkv = self.to_qkv(x) + q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3) + k = k.softmax(dim=-1) + context = torch.einsum('bhdn,bhen->bhde', k, v) + out = torch.einsum('bhde,bhdn->bhen', context, q) + out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w) + return self.to_out(out) + + +class SpatialSelfAttention(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b,c,h,w = q.shape + q = rearrange(q, 'b c h w -> b (h w) c') + k = rearrange(k, 'b c h w -> b c (h w)') + w_ = torch.einsum('bij,bjk->bik', q, k) + + w_ = w_ * (int(c)**(-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = rearrange(v, 'b c h w -> b c (h w)') + w_ = rearrange(w_, 'b i j -> b j i') + h_ = torch.einsum('bij,bjk->bik', v, w_) + h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h) + h_ = self.proj_out(h_) + + return x+h_ + + +class CrossAttention(nn.Module): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.scale = dim_head ** -0.5 + self.heads = heads + + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, query_dim), + nn.Dropout(dropout) + ) + + def forward(self, x, context=None, mask=None): + h = self.heads + + q = self.to_q(x) + context = default(context, x) + k = self.to_k(context) + v = self.to_v(context) + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) + + sim = einsum('b i d, b j d -> b i j', q, k) * self.scale + + if exists(mask): + mask = rearrange(mask, 'b ... -> b (...)') + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, 'b j -> (b h) () j', h=h) + sim.masked_fill_(~mask, max_neg_value) + + # attention, what we cannot get enough of + attn = sim.softmax(dim=-1) + + out = einsum('b i j, b j d -> b i d', attn, v) + out = rearrange(out, '(b h) n d -> b n (h d)', h=h) + return self.to_out(out) + + +class BasicTransformerBlock(nn.Module): + def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True): + super().__init__() + self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout) # is a self-attention + self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) + self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim, + heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + self.norm3 = nn.LayerNorm(dim) + self.checkpoint = checkpoint + + def forward(self, x, context=None): + return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint) + + def _forward(self, x, context=None): + x = self.attn1(self.norm1(x)) + x + x = self.attn2(self.norm2(x), context=context) + x + x = self.ff(self.norm3(x)) + x + return x + + +class SpatialTransformer(nn.Module): + """ + Transformer block for image-like data. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + """ + def __init__(self, in_channels, n_heads, d_head, + depth=1, dropout=0., context_dim=None): + super().__init__() + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = Normalize(in_channels) + + self.proj_in = nn.Conv2d(in_channels, + inner_dim, + kernel_size=1, + stride=1, + padding=0) + + self.transformer_blocks = nn.ModuleList( + [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim) + for d in range(depth)] + ) + + self.proj_out = zero_module(nn.Conv2d(inner_dim, + in_channels, + kernel_size=1, + stride=1, + padding=0)) + + def forward(self, x, context=None): + # note: if no context is given, cross-attention defaults to self-attention + b, c, h, w = x.shape + x_in = x + x = self.norm(x) + x = self.proj_in(x) + x = rearrange(x, 'b c h w -> b (h w) c') + for block in self.transformer_blocks: + x = block(x, context=context) + x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w) + x = self.proj_out(x) + return x + x_in \ No newline at end of file diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/diffusionmodules/__init__.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/diffusionmodules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/diffusionmodules/model.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/diffusionmodules/model.py new file mode 100644 index 0000000000000000000000000000000000000000..533e589a2024f1d7c52093d8c472c3b1b6617e26 --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/diffusionmodules/model.py @@ -0,0 +1,835 @@ +# pytorch_diffusion + derived encoder decoder +import math +import torch +import torch.nn as nn +import numpy as np +from einops import rearrange + +from ldm.util import instantiate_from_config +from ldm.modules.attention import LinearAttention + + +def get_timestep_embedding(timesteps, embedding_dim): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: + From Fairseq. + Build sinusoidal embeddings. + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + assert len(timesteps.shape) == 1 + + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) + emb = emb.to(device=timesteps.device) + emb = timesteps.float()[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0,1,0,0)) + return emb + + +def nonlinearity(x): + # swish + return x*torch.sigmoid(x) + + +def Normalize(in_channels, num_groups=32): + return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) + + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=2, + padding=0) + + def forward(self, x): + if self.with_conv: + pad = (0,1,0,1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + + +class ResnetBlock(nn.Module): + def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, + dropout, temb_channels=512): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.norm1 = Normalize(in_channels) + self.conv1 = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if temb_channels > 0: + self.temb_proj = torch.nn.Linear(temb_channels, + out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = torch.nn.Conv2d(out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + else: + self.nin_shortcut = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None] + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x+h + + +class LinAttnBlock(LinearAttention): + """to match AttnBlock usage""" + def __init__(self, in_channels): + super().__init__(dim=in_channels, heads=1, dim_head=in_channels) + + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b,c,h,w = q.shape + q = q.reshape(b,c,h*w) + q = q.permute(0,2,1) # b,hw,c + k = k.reshape(b,c,h*w) # b,c,hw + w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] + w_ = w_ * (int(c)**(-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = v.reshape(b,c,h*w) + w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q) + h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] + h_ = h_.reshape(b,c,h,w) + + h_ = self.proj_out(h_) + + return x+h_ + + +def make_attn(in_channels, attn_type="vanilla"): + assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown' + print(f"making attention of type '{attn_type}' with {in_channels} in_channels") + if attn_type == "vanilla": + return AttnBlock(in_channels) + elif attn_type == "none": + return nn.Identity(in_channels) + else: + return LinAttnBlock(in_channels) + + +class Model(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = self.ch*4 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + self.use_timestep = use_timestep + if self.use_timestep: + # timestep embedding + self.temb = nn.Module() + self.temb.dense = nn.ModuleList([ + torch.nn.Linear(self.ch, + self.temb_ch), + torch.nn.Linear(self.temb_ch, + self.temb_ch), + ]) + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + skip_in = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + if i_block == self.num_res_blocks: + skip_in = ch*in_ch_mult[i_level] + block.append(ResnetBlock(in_channels=block_in+skip_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x, t=None, context=None): + #assert x.shape[2] == x.shape[3] == self.resolution + if context is not None: + # assume aligned context, cat along channel axis + x = torch.cat((x, context), dim=1) + if self.use_timestep: + # timestep embedding + assert t is not None + temb = get_timestep_embedding(t, self.ch) + temb = self.temb.dense[0](temb) + temb = nonlinearity(temb) + temb = self.temb.dense[1](temb) + else: + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block]( + torch.cat([h, hs.pop()], dim=1), temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + def get_last_layer(self): + return self.conv_out.weight + + +class Encoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla", + **ignore_kwargs): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + 2*z_channels if double_z else z_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + # timestep embedding + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class Decoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False, + attn_type="vanilla", **ignorekwargs): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.tanh_out = tanh_out + + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,)+tuple(ch_mult) + block_in = ch*ch_mult[self.num_resolutions-1] + curr_res = resolution // 2**(self.num_resolutions-1) + self.z_shape = (1,z_channels,curr_res,curr_res) + print("Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape))) + + # z to block_in + self.conv_in = torch.nn.Conv2d(z_channels, + block_in, + kernel_size=3, + stride=1, + padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, z): + #assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + if self.tanh_out: + h = torch.tanh(h) + return h + + +class SimpleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, *args, **kwargs): + super().__init__() + self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1), + ResnetBlock(in_channels=in_channels, + out_channels=2 * in_channels, + temb_channels=0, dropout=0.0), + ResnetBlock(in_channels=2 * in_channels, + out_channels=4 * in_channels, + temb_channels=0, dropout=0.0), + ResnetBlock(in_channels=4 * in_channels, + out_channels=2 * in_channels, + temb_channels=0, dropout=0.0), + nn.Conv2d(2*in_channels, in_channels, 1), + Upsample(in_channels, with_conv=True)]) + # end + self.norm_out = Normalize(in_channels) + self.conv_out = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + for i, layer in enumerate(self.model): + if i in [1,2,3]: + x = layer(x, None) + else: + x = layer(x) + + h = self.norm_out(x) + h = nonlinearity(h) + x = self.conv_out(h) + return x + + +class UpsampleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution, + ch_mult=(2,2), dropout=0.0): + super().__init__() + # upsampling + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + block_in = in_channels + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.res_blocks = nn.ModuleList() + self.upsample_blocks = nn.ModuleList() + for i_level in range(self.num_resolutions): + res_block = [] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + res_block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + self.res_blocks.append(nn.ModuleList(res_block)) + if i_level != self.num_resolutions - 1: + self.upsample_blocks.append(Upsample(block_in, True)) + curr_res = curr_res * 2 + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + # upsampling + h = x + for k, i_level in enumerate(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.res_blocks[i_level][i_block](h, None) + if i_level != self.num_resolutions - 1: + h = self.upsample_blocks[k](h) + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class LatentRescaler(nn.Module): + def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2): + super().__init__() + # residual block, interpolate, residual block + self.factor = factor + self.conv_in = nn.Conv2d(in_channels, + mid_channels, + kernel_size=3, + stride=1, + padding=1) + self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, + out_channels=mid_channels, + temb_channels=0, + dropout=0.0) for _ in range(depth)]) + self.attn = AttnBlock(mid_channels) + self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, + out_channels=mid_channels, + temb_channels=0, + dropout=0.0) for _ in range(depth)]) + + self.conv_out = nn.Conv2d(mid_channels, + out_channels, + kernel_size=1, + ) + + def forward(self, x): + x = self.conv_in(x) + for block in self.res_block1: + x = block(x, None) + x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor)))) + x = self.attn(x) + for block in self.res_block2: + x = block(x, None) + x = self.conv_out(x) + return x + + +class MergedRescaleEncoder(nn.Module): + def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, + ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1): + super().__init__() + intermediate_chn = ch * ch_mult[-1] + self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult, + z_channels=intermediate_chn, double_z=False, resolution=resolution, + attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, + out_ch=None) + self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn, + mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth) + + def forward(self, x): + x = self.encoder(x) + x = self.rescaler(x) + return x + + +class MergedRescaleDecoder(nn.Module): + def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8), + dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1): + super().__init__() + tmp_chn = z_channels*ch_mult[-1] + self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout, + resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks, + ch_mult=ch_mult, resolution=resolution, ch=ch) + self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn, + out_channels=tmp_chn, depth=rescale_module_depth) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Upsampler(nn.Module): + def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2): + super().__init__() + assert out_size >= in_size + num_blocks = int(np.log2(out_size//in_size))+1 + factor_up = 1.+ (out_size % in_size) + print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}") + self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels, + out_channels=in_channels) + self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2, + attn_resolutions=[], in_channels=None, ch=in_channels, + ch_mult=[ch_mult for _ in range(num_blocks)]) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Resize(nn.Module): + def __init__(self, in_channels=None, learned=False, mode="bilinear"): + super().__init__() + self.with_conv = learned + self.mode = mode + if self.with_conv: + print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode") + raise NotImplementedError() + assert in_channels is not None + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=4, + stride=2, + padding=1) + + def forward(self, x, scale_factor=1.0): + if scale_factor==1.0: + return x + else: + x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor) + return x + +class FirstStagePostProcessor(nn.Module): + + def __init__(self, ch_mult:list, in_channels, + pretrained_model:nn.Module=None, + reshape=False, + n_channels=None, + dropout=0., + pretrained_config=None): + super().__init__() + if pretrained_config is None: + assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' + self.pretrained_model = pretrained_model + else: + assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' + self.instantiate_pretrained(pretrained_config) + + self.do_reshape = reshape + + if n_channels is None: + n_channels = self.pretrained_model.encoder.ch + + self.proj_norm = Normalize(in_channels,num_groups=in_channels//2) + self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3, + stride=1,padding=1) + + blocks = [] + downs = [] + ch_in = n_channels + for m in ch_mult: + blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout)) + ch_in = m * n_channels + downs.append(Downsample(ch_in, with_conv=False)) + + self.model = nn.ModuleList(blocks) + self.downsampler = nn.ModuleList(downs) + + + def instantiate_pretrained(self, config): + model = instantiate_from_config(config) + self.pretrained_model = model.eval() + # self.pretrained_model.train = False + for param in self.pretrained_model.parameters(): + param.requires_grad = False + + + @torch.no_grad() + def encode_with_pretrained(self,x): + c = self.pretrained_model.encode(x) + if isinstance(c, DiagonalGaussianDistribution): + c = c.mode() + return c + + def forward(self,x): + z_fs = self.encode_with_pretrained(x) + z = self.proj_norm(z_fs) + z = self.proj(z) + z = nonlinearity(z) + + for submodel, downmodel in zip(self.model,self.downsampler): + z = submodel(z,temb=None) + z = downmodel(z) + + if self.do_reshape: + z = rearrange(z,'b c h w -> b (h w) c') + return z + diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/diffusionmodules/openaimodel.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/diffusionmodules/openaimodel.py new file mode 100644 index 0000000000000000000000000000000000000000..fcf95d1ea8a078dd259915109203789f78f0643a --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/diffusionmodules/openaimodel.py @@ -0,0 +1,961 @@ +from abc import abstractmethod +from functools import partial +import math +from typing import Iterable + +import numpy as np +import torch as th +import torch.nn as nn +import torch.nn.functional as F + +from ldm.modules.diffusionmodules.util import ( + checkpoint, + conv_nd, + linear, + avg_pool_nd, + zero_module, + normalization, + timestep_embedding, +) +from ldm.modules.attention import SpatialTransformer + + +# dummy replace +def convert_module_to_f16(x): + pass + +def convert_module_to_f32(x): + pass + + +## go +class AttentionPool2d(nn.Module): + """ + Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py + """ + + def __init__( + self, + spacial_dim: int, + embed_dim: int, + num_heads_channels: int, + output_dim: int = None, + ): + super().__init__() + self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5) + self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1) + self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1) + self.num_heads = embed_dim // num_heads_channels + self.attention = QKVAttention(self.num_heads) + + def forward(self, x): + b, c, *_spatial = x.shape + x = x.reshape(b, c, -1) # NC(HW) + x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1) + x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1) + x = self.qkv_proj(x) + x = self.attention(x) + x = self.c_proj(x) + return x[:, :, 0] + + +class TimestepBlock(nn.Module): + """ + Any module where forward() takes timestep embeddings as a second argument. + """ + + @abstractmethod + def forward(self, x, emb): + """ + Apply the module to `x` given `emb` timestep embeddings. + """ + + +class TimestepEmbedSequential(nn.Sequential, TimestepBlock): + """ + A sequential module that passes timestep embeddings to the children that + support it as an extra input. + """ + + def forward(self, x, emb, context=None): + for layer in self: + if isinstance(layer, TimestepBlock): + x = layer(x, emb) + elif isinstance(layer, SpatialTransformer): + x = layer(x, context) + else: + x = layer(x) + return x + + +class Upsample(nn.Module): + """ + An upsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + upsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + if use_conv: + self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding) + + def forward(self, x): + assert x.shape[1] == self.channels + if self.dims == 3: + x = F.interpolate( + x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" + ) + else: + x = F.interpolate(x, scale_factor=2, mode="nearest") + if self.use_conv: + x = self.conv(x) + return x + +class TransposedUpsample(nn.Module): + 'Learned 2x upsampling without padding' + def __init__(self, channels, out_channels=None, ks=5): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + + self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2) + + def forward(self,x): + return self.up(x) + + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = conv_nd( + dims, self.channels, self.out_channels, 3, stride=stride, padding=padding + ) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) + + +class ResBlock(TimestepBlock): + """ + A residual block that can optionally change the number of channels. + :param channels: the number of input channels. + :param emb_channels: the number of timestep embedding channels. + :param dropout: the rate of dropout. + :param out_channels: if specified, the number of out channels. + :param use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param use_checkpoint: if True, use gradient checkpointing on this module. + :param up: if True, use this block for upsampling. + :param down: if True, use this block for downsampling. + """ + + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + use_conv=False, + use_scale_shift_norm=False, + dims=2, + use_checkpoint=False, + up=False, + down=False, + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_checkpoint = use_checkpoint + self.use_scale_shift_norm = use_scale_shift_norm + + self.in_layers = nn.Sequential( + normalization(channels), + nn.SiLU(), + conv_nd(dims, channels, self.out_channels, 3, padding=1), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False, dims) + self.x_upd = Upsample(channels, False, dims) + elif down: + self.h_upd = Downsample(channels, False, dims) + self.x_upd = Downsample(channels, False, dims) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.emb_layers = nn.Sequential( + nn.SiLU(), + linear( + emb_channels, + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, + ), + ) + self.out_layers = nn.Sequential( + normalization(self.out_channels), + nn.SiLU(), + nn.Dropout(p=dropout), + zero_module( + conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1) + ), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = conv_nd( + dims, channels, self.out_channels, 3, padding=1 + ) + else: + self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) + + def forward(self, x, emb): + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + :param x: an [N x C x ...] Tensor of features. + :param emb: an [N x emb_channels] Tensor of timestep embeddings. + :return: an [N x C x ...] Tensor of outputs. + """ + return checkpoint( + self._forward, (x, emb), self.parameters(), self.use_checkpoint + ) + + + def _forward(self, x, emb): + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = th.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + + +class AttentionBlock(nn.Module): + """ + An attention block that allows spatial positions to attend to each other. + Originally ported from here, but adapted to the N-d case. + https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66. + """ + + def __init__( + self, + channels, + num_heads=1, + num_head_channels=-1, + use_checkpoint=False, + use_new_attention_order=False, + ): + super().__init__() + self.channels = channels + if num_head_channels == -1: + self.num_heads = num_heads + else: + assert ( + channels % num_head_channels == 0 + ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" + self.num_heads = channels // num_head_channels + self.use_checkpoint = use_checkpoint + self.norm = normalization(channels) + self.qkv = conv_nd(1, channels, channels * 3, 1) + if use_new_attention_order: + # split qkv before split heads + self.attention = QKVAttention(self.num_heads) + else: + # split heads before split qkv + self.attention = QKVAttentionLegacy(self.num_heads) + + self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) + + def forward(self, x): + return checkpoint(self._forward, (x,), self.parameters(), True) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!! + #return pt_checkpoint(self._forward, x) # pytorch + + def _forward(self, x): + b, c, *spatial = x.shape + x = x.reshape(b, c, -1) + qkv = self.qkv(self.norm(x)) + h = self.attention(qkv) + h = self.proj_out(h) + return (x + h).reshape(b, c, *spatial) + + +def count_flops_attn(model, _x, y): + """ + A counter for the `thop` package to count the operations in an + attention operation. + Meant to be used like: + macs, params = thop.profile( + model, + inputs=(inputs, timestamps), + custom_ops={QKVAttention: QKVAttention.count_flops}, + ) + """ + b, c, *spatial = y[0].shape + num_spatial = int(np.prod(spatial)) + # We perform two matmuls with the same number of ops. + # The first computes the weight matrix, the second computes + # the combination of the value vectors. + matmul_ops = 2 * b * (num_spatial ** 2) * c + model.total_ops += th.DoubleTensor([matmul_ops]) + + +class QKVAttentionLegacy(nn.Module): + """ + A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, qkv): + """ + Apply QKV attention. + :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. + :return: an [N x (H * C) x T] tensor after attention. + """ + bs, width, length = qkv.shape + assert width % (3 * self.n_heads) == 0 + ch = width // (3 * self.n_heads) + q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1) + scale = 1 / math.sqrt(math.sqrt(ch)) + weight = th.einsum( + "bct,bcs->bts", q * scale, k * scale + ) # More stable with f16 than dividing afterwards + weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) + a = th.einsum("bts,bcs->bct", weight, v) + return a.reshape(bs, -1, length) + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class QKVAttention(nn.Module): + """ + A module which performs QKV attention and splits in a different order. + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, qkv): + """ + Apply QKV attention. + :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. + :return: an [N x (H * C) x T] tensor after attention. + """ + bs, width, length = qkv.shape + assert width % (3 * self.n_heads) == 0 + ch = width // (3 * self.n_heads) + q, k, v = qkv.chunk(3, dim=1) + scale = 1 / math.sqrt(math.sqrt(ch)) + weight = th.einsum( + "bct,bcs->bts", + (q * scale).view(bs * self.n_heads, ch, length), + (k * scale).view(bs * self.n_heads, ch, length), + ) # More stable with f16 than dividing afterwards + weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) + a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)) + return a.reshape(bs, -1, length) + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class UNetModel(nn.Module): + """ + The full UNet model with attention and timestep embedding. + :param in_channels: channels in the input Tensor. + :param model_channels: base channel count for the model. + :param out_channels: channels in the output Tensor. + :param num_res_blocks: number of residual blocks per downsample. + :param attention_resolutions: a collection of downsample rates at which + attention will take place. May be a set, list, or tuple. + For example, if this contains 4, then at 4x downsampling, attention + will be used. + :param dropout: the dropout probability. + :param channel_mult: channel multiplier for each level of the UNet. + :param conv_resample: if True, use learned convolutions for upsampling and + downsampling. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param num_classes: if specified (as an int), then this model will be + class-conditional with `num_classes` classes. + :param use_checkpoint: use gradient checkpointing to reduce memory usage. + :param num_heads: the number of attention heads in each attention layer. + :param num_heads_channels: if specified, ignore num_heads and instead use + a fixed channel width per attention head. + :param num_heads_upsample: works with num_heads to set a different number + of heads for upsampling. Deprecated. + :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. + :param resblock_updown: use residual blocks for up/downsampling. + :param use_new_attention_order: use a different attention pattern for potentially + increased efficiency. + """ + + def __init__( + self, + image_size, + in_channels, + model_channels, + out_channels, + num_res_blocks, + attention_resolutions, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + num_classes=None, + use_checkpoint=False, + use_fp16=False, + num_heads=-1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + use_spatial_transformer=False, # custom transformer support + transformer_depth=1, # custom transformer support + context_dim=None, # custom transformer support + n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model + legacy=True, + ): + super().__init__() + if use_spatial_transformer: + assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' + + if context_dim is not None: + assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' + from omegaconf.listconfig import ListConfig + if type(context_dim) == ListConfig: + context_dim = list(context_dim) + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + if num_heads == -1: + assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' + + if num_head_channels == -1: + assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' + + self.image_size = image_size + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + self.num_res_blocks = num_res_blocks + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.num_classes = num_classes + self.use_checkpoint = use_checkpoint + self.dtype = th.float16 if use_fp16 else th.float32 + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.predict_codebook_ids = n_embed is not None + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + linear(model_channels, time_embed_dim), + nn.SiLU(), + linear(time_embed_dim, time_embed_dim), + ) + + if self.num_classes is not None: + self.label_emb = nn.Embedding(num_classes, time_embed_dim) + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + conv_nd(dims, in_channels, model_channels, 3, padding=1) + ) + ] + ) + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for _ in range(num_res_blocks): + layers = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = mult * model_channels + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + layers.append( + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) if not use_spatial_transformer else SpatialTransformer( + ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + ) + if resblock_updown + else Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + self.middle_block = TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) if not use_spatial_transformer else SpatialTransformer( + ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + ) + self._feature_size += ch + + self.output_blocks = nn.ModuleList([]) + for level, mult in list(enumerate(channel_mult))[::-1]: + for i in range(num_res_blocks + 1): + ich = input_block_chans.pop() + layers = [ + ResBlock( + ch + ich, + time_embed_dim, + dropout, + out_channels=model_channels * mult, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = model_channels * mult + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + layers.append( + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads_upsample, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) if not use_spatial_transformer else SpatialTransformer( + ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim + ) + ) + if level and i == num_res_blocks: + out_ch = ch + layers.append( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + up=True, + ) + if resblock_updown + else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) + ) + ds //= 2 + self.output_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), + ) + if self.predict_codebook_ids: + self.id_predictor = nn.Sequential( + normalization(ch), + conv_nd(dims, model_channels, n_embed, 1), + #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits + ) + + def convert_to_fp16(self): + """ + Convert the torso of the model to float16. + """ + self.input_blocks.apply(convert_module_to_f16) + self.middle_block.apply(convert_module_to_f16) + self.output_blocks.apply(convert_module_to_f16) + + def convert_to_fp32(self): + """ + Convert the torso of the model to float32. + """ + self.input_blocks.apply(convert_module_to_f32) + self.middle_block.apply(convert_module_to_f32) + self.output_blocks.apply(convert_module_to_f32) + + def forward(self, x, timesteps=None, context=None, y=None,**kwargs): + """ + Apply the model to an input batch. + :param x: an [N x C x ...] Tensor of inputs. + :param timesteps: a 1-D batch of timesteps. + :param context: conditioning plugged in via crossattn + :param y: an [N] Tensor of labels, if class-conditional. + :return: an [N x C x ...] Tensor of outputs. + """ + assert (y is not None) == ( + self.num_classes is not None + ), "must specify y if and only if the model is class-conditional" + hs = [] + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) + emb = self.time_embed(t_emb) + + if self.num_classes is not None: + assert y.shape == (x.shape[0],) + emb = emb + self.label_emb(y) + + h = x.type(self.dtype) + for module in self.input_blocks: + h = module(h, emb, context) + hs.append(h) + h = self.middle_block(h, emb, context) + for module in self.output_blocks: + h = th.cat([h, hs.pop()], dim=1) + h = module(h, emb, context) + h = h.type(x.dtype) + if self.predict_codebook_ids: + return self.id_predictor(h) + else: + return self.out(h) + + +class EncoderUNetModel(nn.Module): + """ + The half UNet model with attention and timestep embedding. + For usage, see UNet. + """ + + def __init__( + self, + image_size, + in_channels, + model_channels, + out_channels, + num_res_blocks, + attention_resolutions, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + use_checkpoint=False, + use_fp16=False, + num_heads=1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + pool="adaptive", + *args, + **kwargs + ): + super().__init__() + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + self.num_res_blocks = num_res_blocks + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.use_checkpoint = use_checkpoint + self.dtype = th.float16 if use_fp16 else th.float32 + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + linear(model_channels, time_embed_dim), + nn.SiLU(), + linear(time_embed_dim, time_embed_dim), + ) + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + conv_nd(dims, in_channels, model_channels, 3, padding=1) + ) + ] + ) + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for _ in range(num_res_blocks): + layers = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = mult * model_channels + if ds in attention_resolutions: + layers.append( + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=num_head_channels, + use_new_attention_order=use_new_attention_order, + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + ) + if resblock_updown + else Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + self.middle_block = TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=num_head_channels, + use_new_attention_order=use_new_attention_order, + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + ) + self._feature_size += ch + self.pool = pool + if pool == "adaptive": + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + nn.AdaptiveAvgPool2d((1, 1)), + zero_module(conv_nd(dims, ch, out_channels, 1)), + nn.Flatten(), + ) + elif pool == "attention": + assert num_head_channels != -1 + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + AttentionPool2d( + (image_size // ds), ch, num_head_channels, out_channels + ), + ) + elif pool == "spatial": + self.out = nn.Sequential( + nn.Linear(self._feature_size, 2048), + nn.ReLU(), + nn.Linear(2048, self.out_channels), + ) + elif pool == "spatial_v2": + self.out = nn.Sequential( + nn.Linear(self._feature_size, 2048), + normalization(2048), + nn.SiLU(), + nn.Linear(2048, self.out_channels), + ) + else: + raise NotImplementedError(f"Unexpected {pool} pooling") + + def convert_to_fp16(self): + """ + Convert the torso of the model to float16. + """ + self.input_blocks.apply(convert_module_to_f16) + self.middle_block.apply(convert_module_to_f16) + + def convert_to_fp32(self): + """ + Convert the torso of the model to float32. + """ + self.input_blocks.apply(convert_module_to_f32) + self.middle_block.apply(convert_module_to_f32) + + def forward(self, x, timesteps): + """ + Apply the model to an input batch. + :param x: an [N x C x ...] Tensor of inputs. + :param timesteps: a 1-D batch of timesteps. + :return: an [N x K] Tensor of outputs. + """ + emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) + + results = [] + h = x.type(self.dtype) + for module in self.input_blocks: + h = module(h, emb) + if self.pool.startswith("spatial"): + results.append(h.type(x.dtype).mean(dim=(2, 3))) + h = self.middle_block(h, emb) + if self.pool.startswith("spatial"): + results.append(h.type(x.dtype).mean(dim=(2, 3))) + h = th.cat(results, axis=-1) + return self.out(h) + else: + h = h.type(x.dtype) + return self.out(h) + diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/diffusionmodules/util.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/diffusionmodules/util.py new file mode 100644 index 0000000000000000000000000000000000000000..8ff2c070b8d30e7687e24cce8425d0227c17b893 --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/diffusionmodules/util.py @@ -0,0 +1,267 @@ +# adopted from +# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py +# and +# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +# and +# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py +# +# thanks! + + +import os +import math +import torch +import torch.nn as nn +import numpy as np +from einops import repeat + +from ...util import instantiate_from_config + + +def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if schedule == "linear": + betas = ( + torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 + ) + + elif schedule == "cosine": + timesteps = ( + torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s + ) + alphas = timesteps / (1 + cosine_s) * np.pi / 2 + alphas = torch.cos(alphas).pow(2) + alphas = alphas / alphas[0] + betas = 1 - alphas[1:] / alphas[:-1] + betas = np.clip(betas, a_min=0, a_max=0.999) + + elif schedule == "sqrt_linear": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) + elif schedule == "sqrt": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 + else: + raise ValueError(f"schedule '{schedule}' unknown.") + return betas.numpy() + + +def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): + if ddim_discr_method == 'uniform': + c = num_ddpm_timesteps // num_ddim_timesteps + ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) + elif ddim_discr_method == 'quad': + ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) + else: + raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') + + # assert ddim_timesteps.shape[0] == num_ddim_timesteps + # add one to get the final alpha values right (the ones from first scale to data during sampling) + steps_out = ddim_timesteps + 1 + if verbose: + print(f'Selected timesteps for ddim sampler: {steps_out}') + return steps_out + + +def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): + # select alphas for computing the variance schedule + alphas = alphacums[ddim_timesteps] + alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) + + # according the the formula provided in https://arxiv.org/abs/2010.02502 + sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) + if verbose: + print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') + print(f'For the chosen value of eta, which is {eta}, ' + f'this results in the following sigma_t schedule for ddim sampler {sigmas}') + return sigmas, alphas, alphas_prev + + +def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): + """ + Create a beta schedule that discretizes the given alpha_t_bar function, + which defines the cumulative product of (1-beta) over time from t = [0,1]. + :param num_diffusion_timesteps: the number of betas to produce. + :param alpha_bar: a lambda that takes an argument t from 0 to 1 and + produces the cumulative product of (1-beta) up to that + part of the diffusion process. + :param max_beta: the maximum beta to use; use values lower than 1 to + prevent singularities. + """ + betas = [] + for i in range(num_diffusion_timesteps): + t1 = i / num_diffusion_timesteps + t2 = (i + 1) / num_diffusion_timesteps + betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) + return np.array(betas) + + +def extract_into_tensor(a, t, x_shape): + b, *_ = t.shape + out = a.gather(-1, t) + return out.reshape(b, *((1,) * (len(x_shape) - 1))) + + +def checkpoint(func, inputs, params, flag): + """ + Evaluate a function without caching intermediate activations, allowing for + reduced memory at the expense of extra compute in the backward pass. + :param func: the function to evaluate. + :param inputs: the argument sequence to pass to `func`. + :param params: a sequence of parameters `func` depends on but does not + explicitly take as arguments. + :param flag: if False, disable gradient checkpointing. + """ + if flag: + args = tuple(inputs) + tuple(params) + return CheckpointFunction.apply(func, len(inputs), *args) + else: + return func(*inputs) + + +class CheckpointFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, run_function, length, *args): + ctx.run_function = run_function + ctx.input_tensors = list(args[:length]) + ctx.input_params = list(args[length:]) + + with torch.no_grad(): + output_tensors = ctx.run_function(*ctx.input_tensors) + return output_tensors + + @staticmethod + def backward(ctx, *output_grads): + ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] + with torch.enable_grad(): + # Fixes a bug where the first op in run_function modifies the + # Tensor storage in place, which is not allowed for detach()'d + # Tensors. + shallow_copies = [x.view_as(x) for x in ctx.input_tensors] + output_tensors = ctx.run_function(*shallow_copies) + input_grads = torch.autograd.grad( + output_tensors, + ctx.input_tensors + ctx.input_params, + output_grads, + allow_unused=True, + ) + del ctx.input_tensors + del ctx.input_params + del output_tensors + return (None, None) + input_grads + + +def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): + """ + Create sinusoidal timestep embeddings. + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + if not repeat_only: + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=timesteps.device) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + else: + embedding = repeat(timesteps, 'b -> b d', d=dim) + return embedding + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def scale_module(module, scale): + """ + Scale the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().mul_(scale) + return module + + +def mean_flat(tensor): + """ + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def normalization(channels): + """ + Make a standard normalization layer. + :param channels: number of input channels. + :return: an nn.Module for normalization. + """ + return GroupNorm32(32, channels) + + +# PyTorch 1.7 has SiLU, but we support PyTorch 1.5. +class SiLU(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + + +class GroupNorm32(nn.GroupNorm): + def forward(self, x): + return super().forward(x.float()).type(x.dtype) + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return nn.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def linear(*args, **kwargs): + """ + Create a linear module. + """ + return nn.Linear(*args, **kwargs) + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +class HybridConditioner(nn.Module): + + def __init__(self, c_concat_config, c_crossattn_config): + super().__init__() + self.concat_conditioner = instantiate_from_config(c_concat_config) + self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) + + def forward(self, c_concat, c_crossattn): + c_concat = self.concat_conditioner(c_concat) + c_crossattn = self.crossattn_conditioner(c_crossattn) + return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} + + +def noise_like(shape, device, repeat=False): + repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) + noise = lambda: torch.randn(shape, device=device) + return repeat_noise() if repeat else noise() \ No newline at end of file diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/distributions/__init__.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/distributions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/distributions/distributions.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/distributions/distributions.py new file mode 100644 index 0000000000000000000000000000000000000000..f2b8ef901130efc171aa69742ca0244d94d3f2e9 --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/distributions/distributions.py @@ -0,0 +1,92 @@ +import torch +import numpy as np + + +class AbstractDistribution: + def sample(self): + raise NotImplementedError() + + def mode(self): + raise NotImplementedError() + + +class DiracDistribution(AbstractDistribution): + def __init__(self, value): + self.value = value + + def sample(self): + return self.value + + def mode(self): + return self.value + + +class DiagonalGaussianDistribution(object): + def __init__(self, parameters, deterministic=False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) + + def sample(self): + x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device) + return x + + def kl(self, other=None): + if self.deterministic: + return torch.Tensor([0.]) + else: + if other is None: + return 0.5 * torch.sum(torch.pow(self.mean, 2) + + self.var - 1.0 - self.logvar, + dim=[1, 2, 3]) + else: + return 0.5 * torch.sum( + torch.pow(self.mean - other.mean, 2) / other.var + + self.var / other.var - 1.0 - self.logvar + other.logvar, + dim=[1, 2, 3]) + + def nll(self, sample, dims=[1,2,3]): + if self.deterministic: + return torch.Tensor([0.]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum( + logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, + dim=dims) + + def mode(self): + return self.mean + + +def normal_kl(mean1, logvar1, mean2, logvar2): + """ + source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 + Compute the KL divergence between two gaussians. + Shapes are automatically broadcasted, so batches can be compared to + scalars, among other use cases. + """ + tensor = None + for obj in (mean1, logvar1, mean2, logvar2): + if isinstance(obj, torch.Tensor): + tensor = obj + break + assert tensor is not None, "at least one argument must be a Tensor" + + # Force variances to be Tensors. Broadcasting helps convert scalars to + # Tensors, but it does not work for torch.exp(). + logvar1, logvar2 = [ + x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) + for x in (logvar1, logvar2) + ] + + return 0.5 * ( + -1.0 + + logvar2 + - logvar1 + + torch.exp(logvar1 - logvar2) + + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) + ) diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/ema.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/ema.py new file mode 100644 index 0000000000000000000000000000000000000000..c8c75af43565f6e140287644aaaefa97dd6e67c5 --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/ema.py @@ -0,0 +1,76 @@ +import torch +from torch import nn + + +class LitEma(nn.Module): + def __init__(self, model, decay=0.9999, use_num_upates=True): + super().__init__() + if decay < 0.0 or decay > 1.0: + raise ValueError('Decay must be between 0 and 1') + + self.m_name2s_name = {} + self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32)) + self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates + else torch.tensor(-1,dtype=torch.int)) + + for name, p in model.named_parameters(): + if p.requires_grad: + #remove as '.'-character is not allowed in buffers + s_name = name.replace('.','') + self.m_name2s_name.update({name:s_name}) + self.register_buffer(s_name,p.clone().detach().data) + + self.collected_params = [] + + def forward(self,model): + decay = self.decay + + if self.num_updates >= 0: + self.num_updates += 1 + decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates)) + + one_minus_decay = 1.0 - decay + + with torch.no_grad(): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + + for key in m_param: + if m_param[key].requires_grad: + sname = self.m_name2s_name[key] + shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) + shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) + else: + assert not key in self.m_name2s_name + + def copy_to(self, model): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + for key in m_param: + if m_param[key].requires_grad: + m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) + else: + assert not key in self.m_name2s_name + + def store(self, parameters): + """ + Save the current parameters for restoring later. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + temporarily stored. + """ + self.collected_params = [param.clone() for param in parameters] + + def restore(self, parameters): + """ + Restore the parameters stored with the `store` method. + Useful to validate the model with EMA parameters without affecting the + original optimization process. Store the parameters before the + `copy_to` method. After validation (or model saving), use this to + restore the former parameters. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + updated with the stored parameters. + """ + for c_param, param in zip(self.collected_params, parameters): + param.data.copy_(c_param.data) diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/encoders/__init__.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/encoders/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/encoders/modules.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/encoders/modules.py new file mode 100644 index 0000000000000000000000000000000000000000..aa3031df683a1ab60f8ebecd1d219b7cabc51dff --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/encoders/modules.py @@ -0,0 +1,202 @@ +import torch +import torch.nn as nn +from functools import partial +import clip +from einops import rearrange, repeat +import kornia + + +from ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test + + +class AbstractEncoder(nn.Module): + def __init__(self): + super().__init__() + + def encode(self, *args, **kwargs): + raise NotImplementedError + + + +class ClassEmbedder(nn.Module): + def __init__(self, embed_dim, n_classes=1000, key='class'): + super().__init__() + self.key = key + self.embedding = nn.Embedding(n_classes, embed_dim) + + def forward(self, batch, key=None): + if key is None: + key = self.key + # this is for use in crossattn + c = batch[key][:, None] + c = self.embedding(c) + return c + + +class TransformerEmbedder(AbstractEncoder): + """Some transformer encoder layers""" + def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"): + super().__init__() + self.device = device + self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, + attn_layers=Encoder(dim=n_embed, depth=n_layer)) + + def forward(self, tokens): + tokens = tokens.to(self.device) # meh + z = self.transformer(tokens, return_embeddings=True) + return z + + def encode(self, x): + return self(x) + + +class BERTTokenizer(AbstractEncoder): + """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)""" + def __init__(self, device="cuda", vq_interface=True, max_length=77): + super().__init__() + from transformers import BertTokenizerFast # TODO: add to reuquirements + self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased") + self.device = device + self.vq_interface = vq_interface + self.max_length = max_length + + def forward(self, text): + batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, + return_overflowing_tokens=False, padding="max_length", return_tensors="pt") + tokens = batch_encoding["input_ids"].to(self.device) + return tokens + + @torch.no_grad() + def encode(self, text): + tokens = self(text) + if not self.vq_interface: + return tokens + return None, None, [None, None, tokens] + + def decode(self, text): + return text + + +class BERTEmbedder(AbstractEncoder): + """Uses the BERT tokenizr model and add some transformer encoder layers""" + def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77, + device="cuda",use_tokenizer=True, embedding_dropout=0.0): + super().__init__() + self.use_tknz_fn = use_tokenizer + if self.use_tknz_fn: + self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len) + self.device = device + self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, + attn_layers=Encoder(dim=n_embed, depth=n_layer), + emb_dropout=embedding_dropout) + + def forward(self, text): + if self.use_tknz_fn: + tokens = self.tknz_fn(text)#.to(self.device) + else: + tokens = text + z = self.transformer(tokens, return_embeddings=True) + return z + + def encode(self, text): + # output of length 77 + return self(text) + + +class SpatialRescaler(nn.Module): + def __init__(self, + n_stages=1, + method='bilinear', + multiplier=0.5, + in_channels=3, + out_channels=None, + bias=False): + super().__init__() + self.n_stages = n_stages + assert self.n_stages >= 0 + assert method in ['nearest','linear','bilinear','trilinear','bicubic','area'] + self.multiplier = multiplier + self.interpolator = partial(torch.nn.functional.interpolate, mode=method) + self.remap_output = out_channels is not None + if self.remap_output: + print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.') + self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias) + + def forward(self,x): + for stage in range(self.n_stages): + x = self.interpolator(x, scale_factor=self.multiplier) + + + if self.remap_output: + x = self.channel_mapper(x) + return x + + def encode(self, x): + return self(x) + + +class FrozenCLIPTextEmbedder(nn.Module): + """ + Uses the CLIP transformer encoder for text. + """ + def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True): + super().__init__() + self.model, _ = clip.load(version, jit=False, device="cpu") + self.device = device + self.max_length = max_length + self.n_repeat = n_repeat + self.normalize = normalize + + def freeze(self): + self.model = self.model.eval() + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + tokens = clip.tokenize(text).to(self.device) + z = self.model.encode_text(tokens) + if self.normalize: + z = z / torch.linalg.norm(z, dim=1, keepdim=True) + return z + + def encode(self, text): + z = self(text) + if z.ndim==2: + z = z[:, None, :] + z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat) + return z + + +class FrozenClipImageEmbedder(nn.Module): + """ + Uses the CLIP image encoder. + """ + def __init__( + self, + model, + jit=False, + device='cuda' if torch.cuda.is_available() else 'cpu', + antialias=False, + ): + super().__init__() + self.model, _ = clip.load(name=model, device=device, jit=jit) + + self.antialias = antialias + + self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False) + self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False) + + def preprocess(self, x): + # normalize to [0,1] + x = kornia.geometry.resize(x, (224, 224), + interpolation='bicubic',align_corners=True, + antialias=self.antialias) + x = (x + 1.) / 2. + # renormalize according to clip + x = kornia.enhance.normalize(x, self.mean, self.std) + return x + + def forward(self, x): + # x is assumed to be in range [-1,1] + return self.model.encode_image(self.preprocess(x)) + diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/image_degradation/__init__.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/image_degradation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7836cada81f90ded99c58d5942eea4c3477f58fc --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/image_degradation/__init__.py @@ -0,0 +1,2 @@ +from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr +from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/image_degradation/bsrgan.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/image_degradation/bsrgan.py new file mode 100644 index 0000000000000000000000000000000000000000..32ef56169978e550090261cddbcf5eb611a6173b --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/image_degradation/bsrgan.py @@ -0,0 +1,730 @@ +# -*- coding: utf-8 -*- +""" +# -------------------------------------------- +# Super-Resolution +# -------------------------------------------- +# +# Kai Zhang (cskaizhang@gmail.com) +# https://github.com/cszn +# From 2019/03--2021/08 +# -------------------------------------------- +""" + +import numpy as np +import cv2 +import torch + +from functools import partial +import random +from scipy import ndimage +import scipy +import scipy.stats as ss +from scipy.interpolate import interp2d +from scipy.linalg import orth +import albumentations + +import ldm.modules.image_degradation.utils_image as util + + +def modcrop_np(img, sf): + ''' + Args: + img: numpy image, WxH or WxHxC + sf: scale factor + Return: + cropped image + ''' + w, h = img.shape[:2] + im = np.copy(img) + return im[:w - w % sf, :h - h % sf, ...] + + +""" +# -------------------------------------------- +# anisotropic Gaussian kernels +# -------------------------------------------- +""" + + +def analytic_kernel(k): + """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)""" + k_size = k.shape[0] + # Calculate the big kernels size + big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2)) + # Loop over the small kernel to fill the big one + for r in range(k_size): + for c in range(k_size): + big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k + # Crop the edges of the big kernel to ignore very small values and increase run time of SR + crop = k_size // 2 + cropped_big_k = big_k[crop:-crop, crop:-crop] + # Normalize to 1 + return cropped_big_k / cropped_big_k.sum() + + +def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): + """ generate an anisotropic Gaussian kernel + Args: + ksize : e.g., 15, kernel size + theta : [0, pi], rotation angle range + l1 : [0.1,50], scaling of eigenvalues + l2 : [0.1,l1], scaling of eigenvalues + If l1 = l2, will get an isotropic Gaussian kernel. + Returns: + k : kernel + """ + + v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.])) + V = np.array([[v[0], v[1]], [v[1], -v[0]]]) + D = np.array([[l1, 0], [0, l2]]) + Sigma = np.dot(np.dot(V, D), np.linalg.inv(V)) + k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize) + + return k + + +def gm_blur_kernel(mean, cov, size=15): + center = size / 2.0 + 0.5 + k = np.zeros([size, size]) + for y in range(size): + for x in range(size): + cy = y - center + 1 + cx = x - center + 1 + k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov) + + k = k / np.sum(k) + return k + + +def shift_pixel(x, sf, upper_left=True): + """shift pixel for super-resolution with different scale factors + Args: + x: WxHxC or WxH + sf: scale factor + upper_left: shift direction + """ + h, w = x.shape[:2] + shift = (sf - 1) * 0.5 + xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0) + if upper_left: + x1 = xv + shift + y1 = yv + shift + else: + x1 = xv - shift + y1 = yv - shift + + x1 = np.clip(x1, 0, w - 1) + y1 = np.clip(y1, 0, h - 1) + + if x.ndim == 2: + x = interp2d(xv, yv, x)(x1, y1) + if x.ndim == 3: + for i in range(x.shape[-1]): + x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1) + + return x + + +def blur(x, k): + ''' + x: image, NxcxHxW + k: kernel, Nx1xhxw + ''' + n, c = x.shape[:2] + p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2 + x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate') + k = k.repeat(1, c, 1, 1) + k = k.view(-1, 1, k.shape[2], k.shape[3]) + x = x.view(1, -1, x.shape[2], x.shape[3]) + x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c) + x = x.view(n, c, x.shape[2], x.shape[3]) + + return x + + +def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0): + """" + # modified version of https://github.com/assafshocher/BlindSR_dataset_generator + # Kai Zhang + # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var + # max_var = 2.5 * sf + """ + # Set random eigen-vals (lambdas) and angle (theta) for COV matrix + lambda_1 = min_var + np.random.rand() * (max_var - min_var) + lambda_2 = min_var + np.random.rand() * (max_var - min_var) + theta = np.random.rand() * np.pi # random theta + noise = -noise_level + np.random.rand(*k_size) * noise_level * 2 + + # Set COV matrix using Lambdas and Theta + LAMBDA = np.diag([lambda_1, lambda_2]) + Q = np.array([[np.cos(theta), -np.sin(theta)], + [np.sin(theta), np.cos(theta)]]) + SIGMA = Q @ LAMBDA @ Q.T + INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :] + + # Set expectation position (shifting kernel for aligned image) + MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2) + MU = MU[None, None, :, None] + + # Create meshgrid for Gaussian + [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1])) + Z = np.stack([X, Y], 2)[:, :, :, None] + + # Calcualte Gaussian for every pixel of the kernel + ZZ = Z - MU + ZZ_t = ZZ.transpose(0, 1, 3, 2) + raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise) + + # shift the kernel so it will be centered + # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor) + + # Normalize the kernel and return + # kernel = raw_kernel_centered / np.sum(raw_kernel_centered) + kernel = raw_kernel / np.sum(raw_kernel) + return kernel + + +def fspecial_gaussian(hsize, sigma): + hsize = [hsize, hsize] + siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0] + std = sigma + [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)) + arg = -(x * x + y * y) / (2 * std * std) + h = np.exp(arg) + h[h < scipy.finfo(float).eps * h.max()] = 0 + sumh = h.sum() + if sumh != 0: + h = h / sumh + return h + + +def fspecial_laplacian(alpha): + alpha = max([0, min([alpha, 1])]) + h1 = alpha / (alpha + 1) + h2 = (1 - alpha) / (alpha + 1) + h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]] + h = np.array(h) + return h + + +def fspecial(filter_type, *args, **kwargs): + ''' + python code from: + https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py + ''' + if filter_type == 'gaussian': + return fspecial_gaussian(*args, **kwargs) + if filter_type == 'laplacian': + return fspecial_laplacian(*args, **kwargs) + + +""" +# -------------------------------------------- +# degradation models +# -------------------------------------------- +""" + + +def bicubic_degradation(x, sf=3): + ''' + Args: + x: HxWxC image, [0, 1] + sf: down-scale factor + Return: + bicubicly downsampled LR image + ''' + x = util.imresize_np(x, scale=1 / sf) + return x + + +def srmd_degradation(x, k, sf=3): + ''' blur + bicubic downsampling + Args: + x: HxWxC image, [0, 1] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + Reference: + @inproceedings{zhang2018learning, + title={Learning a single convolutional super-resolution network for multiple degradations}, + author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + pages={3262--3271}, + year={2018} + } + ''' + x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror' + x = bicubic_degradation(x, sf=sf) + return x + + +def dpsr_degradation(x, k, sf=3): + ''' bicubic downsampling + blur + Args: + x: HxWxC image, [0, 1] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + Reference: + @inproceedings{zhang2019deep, + title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, + author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + pages={1671--1681}, + year={2019} + } + ''' + x = bicubic_degradation(x, sf=sf) + x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') + return x + + +def classical_degradation(x, k, sf=3): + ''' blur + downsampling + Args: + x: HxWxC image, [0, 1]/[0, 255] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + ''' + x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') + # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2)) + st = 0 + return x[st::sf, st::sf, ...] + + +def add_sharpening(img, weight=0.5, radius=50, threshold=10): + """USM sharpening. borrowed from real-ESRGAN + Input image: I; Blurry image: B. + 1. K = I + weight * (I - B) + 2. Mask = 1 if abs(I - B) > threshold, else: 0 + 3. Blur mask: + 4. Out = Mask * K + (1 - Mask) * I + Args: + img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. + weight (float): Sharp weight. Default: 1. + radius (float): Kernel size of Gaussian blur. Default: 50. + threshold (int): + """ + if radius % 2 == 0: + radius += 1 + blur = cv2.GaussianBlur(img, (radius, radius), 0) + residual = img - blur + mask = np.abs(residual) * 255 > threshold + mask = mask.astype('float32') + soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) + + K = img + weight * residual + K = np.clip(K, 0, 1) + return soft_mask * K + (1 - soft_mask) * img + + +def add_blur(img, sf=4): + wd2 = 4.0 + sf + wd = 2.0 + 0.2 * sf + if random.random() < 0.5: + l1 = wd2 * random.random() + l2 = wd2 * random.random() + k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2) + else: + k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random()) + img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror') + + return img + + +def add_resize(img, sf=4): + rnum = np.random.rand() + if rnum > 0.8: # up + sf1 = random.uniform(1, 2) + elif rnum < 0.7: # down + sf1 = random.uniform(0.5 / sf, 1) + else: + sf1 = 1.0 + img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) + img = np.clip(img, 0.0, 1.0) + + return img + + +# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): +# noise_level = random.randint(noise_level1, noise_level2) +# rnum = np.random.rand() +# if rnum > 0.6: # add color Gaussian noise +# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) +# elif rnum < 0.4: # add grayscale Gaussian noise +# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) +# else: # add noise +# L = noise_level2 / 255. +# D = np.diag(np.random.rand(3)) +# U = orth(np.random.rand(3, 3)) +# conv = np.dot(np.dot(np.transpose(U), D), U) +# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) +# img = np.clip(img, 0.0, 1.0) +# return img + +def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): + noise_level = random.randint(noise_level1, noise_level2) + rnum = np.random.rand() + if rnum > 0.6: # add color Gaussian noise + img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) + elif rnum < 0.4: # add grayscale Gaussian noise + img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) + else: # add noise + L = noise_level2 / 255. + D = np.diag(np.random.rand(3)) + U = orth(np.random.rand(3, 3)) + conv = np.dot(np.dot(np.transpose(U), D), U) + img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) + img = np.clip(img, 0.0, 1.0) + return img + + +def add_speckle_noise(img, noise_level1=2, noise_level2=25): + noise_level = random.randint(noise_level1, noise_level2) + img = np.clip(img, 0.0, 1.0) + rnum = random.random() + if rnum > 0.6: + img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) + elif rnum < 0.4: + img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) + else: + L = noise_level2 / 255. + D = np.diag(np.random.rand(3)) + U = orth(np.random.rand(3, 3)) + conv = np.dot(np.dot(np.transpose(U), D), U) + img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) + img = np.clip(img, 0.0, 1.0) + return img + + +def add_Poisson_noise(img): + img = np.clip((img * 255.0).round(), 0, 255) / 255. + vals = 10 ** (2 * random.random() + 2.0) # [2, 4] + if random.random() < 0.5: + img = np.random.poisson(img * vals).astype(np.float32) / vals + else: + img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114]) + img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255. + noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray + img += noise_gray[:, :, np.newaxis] + img = np.clip(img, 0.0, 1.0) + return img + + +def add_JPEG_noise(img): + quality_factor = random.randint(30, 95) + img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR) + result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]) + img = cv2.imdecode(encimg, 1) + img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB) + return img + + +def random_crop(lq, hq, sf=4, lq_patchsize=64): + h, w = lq.shape[:2] + rnd_h = random.randint(0, h - lq_patchsize) + rnd_w = random.randint(0, w - lq_patchsize) + lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :] + + rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf) + hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :] + return lq, hq + + +def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): + """ + This is the degradation model of BSRGAN from the paper + "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" + ---------- + img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) + sf: scale factor + isp_model: camera ISP model + Returns + ------- + img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] + hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] + """ + isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 + sf_ori = sf + + h1, w1 = img.shape[:2] + img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop + h, w = img.shape[:2] + + if h < lq_patchsize * sf or w < lq_patchsize * sf: + raise ValueError(f'img size ({h1}X{w1}) is too small!') + + hq = img.copy() + + if sf == 4 and random.random() < scale2_prob: # downsample1 + if np.random.rand() < 0.5: + img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + img = util.imresize_np(img, 1 / 2, True) + img = np.clip(img, 0.0, 1.0) + sf = 2 + + shuffle_order = random.sample(range(7), 7) + idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) + if idx1 > idx2: # keep downsample3 last + shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] + + for i in shuffle_order: + + if i == 0: + img = add_blur(img, sf=sf) + + elif i == 1: + img = add_blur(img, sf=sf) + + elif i == 2: + a, b = img.shape[1], img.shape[0] + # downsample2 + if random.random() < 0.75: + sf1 = random.uniform(1, 2 * sf) + img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) + k_shifted = shift_pixel(k, sf) + k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel + img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror') + img = img[0::sf, 0::sf, ...] # nearest downsampling + img = np.clip(img, 0.0, 1.0) + + elif i == 3: + # downsample3 + img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) + img = np.clip(img, 0.0, 1.0) + + elif i == 4: + # add Gaussian noise + img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) + + elif i == 5: + # add JPEG noise + if random.random() < jpeg_prob: + img = add_JPEG_noise(img) + + elif i == 6: + # add processed camera sensor noise + if random.random() < isp_prob and isp_model is not None: + with torch.no_grad(): + img, hq = isp_model.forward(img.copy(), hq) + + # add final JPEG compression noise + img = add_JPEG_noise(img) + + # random crop + img, hq = random_crop(img, hq, sf_ori, lq_patchsize) + + return img, hq + + +# todo no isp_model? +def degradation_bsrgan_variant(image, sf=4, isp_model=None): + """ + This is the degradation model of BSRGAN from the paper + "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" + ---------- + sf: scale factor + isp_model: camera ISP model + Returns + ------- + img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] + hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] + """ + image = util.uint2single(image) + isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 + sf_ori = sf + + h1, w1 = image.shape[:2] + image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop + h, w = image.shape[:2] + + hq = image.copy() + + if sf == 4 and random.random() < scale2_prob: # downsample1 + if np.random.rand() < 0.5: + image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + image = util.imresize_np(image, 1 / 2, True) + image = np.clip(image, 0.0, 1.0) + sf = 2 + + shuffle_order = random.sample(range(7), 7) + idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) + if idx1 > idx2: # keep downsample3 last + shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] + + for i in shuffle_order: + + if i == 0: + image = add_blur(image, sf=sf) + + elif i == 1: + image = add_blur(image, sf=sf) + + elif i == 2: + a, b = image.shape[1], image.shape[0] + # downsample2 + if random.random() < 0.75: + sf1 = random.uniform(1, 2 * sf) + image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) + k_shifted = shift_pixel(k, sf) + k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel + image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror') + image = image[0::sf, 0::sf, ...] # nearest downsampling + image = np.clip(image, 0.0, 1.0) + + elif i == 3: + # downsample3 + image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) + image = np.clip(image, 0.0, 1.0) + + elif i == 4: + # add Gaussian noise + image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25) + + elif i == 5: + # add JPEG noise + if random.random() < jpeg_prob: + image = add_JPEG_noise(image) + + # elif i == 6: + # # add processed camera sensor noise + # if random.random() < isp_prob and isp_model is not None: + # with torch.no_grad(): + # img, hq = isp_model.forward(img.copy(), hq) + + # add final JPEG compression noise + image = add_JPEG_noise(image) + image = util.single2uint(image) + example = {"image":image} + return example + + +# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc... +def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None): + """ + This is an extended degradation model by combining + the degradation models of BSRGAN and Real-ESRGAN + ---------- + img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) + sf: scale factor + use_shuffle: the degradation shuffle + use_sharp: sharpening the img + Returns + ------- + img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] + hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] + """ + + h1, w1 = img.shape[:2] + img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop + h, w = img.shape[:2] + + if h < lq_patchsize * sf or w < lq_patchsize * sf: + raise ValueError(f'img size ({h1}X{w1}) is too small!') + + if use_sharp: + img = add_sharpening(img) + hq = img.copy() + + if random.random() < shuffle_prob: + shuffle_order = random.sample(range(13), 13) + else: + shuffle_order = list(range(13)) + # local shuffle for noise, JPEG is always the last one + shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6))) + shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13))) + + poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1 + + for i in shuffle_order: + if i == 0: + img = add_blur(img, sf=sf) + elif i == 1: + img = add_resize(img, sf=sf) + elif i == 2: + img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) + elif i == 3: + if random.random() < poisson_prob: + img = add_Poisson_noise(img) + elif i == 4: + if random.random() < speckle_prob: + img = add_speckle_noise(img) + elif i == 5: + if random.random() < isp_prob and isp_model is not None: + with torch.no_grad(): + img, hq = isp_model.forward(img.copy(), hq) + elif i == 6: + img = add_JPEG_noise(img) + elif i == 7: + img = add_blur(img, sf=sf) + elif i == 8: + img = add_resize(img, sf=sf) + elif i == 9: + img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) + elif i == 10: + if random.random() < poisson_prob: + img = add_Poisson_noise(img) + elif i == 11: + if random.random() < speckle_prob: + img = add_speckle_noise(img) + elif i == 12: + if random.random() < isp_prob and isp_model is not None: + with torch.no_grad(): + img, hq = isp_model.forward(img.copy(), hq) + else: + print('check the shuffle!') + + # resize to desired size + img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])), + interpolation=random.choice([1, 2, 3])) + + # add final JPEG compression noise + img = add_JPEG_noise(img) + + # random crop + img, hq = random_crop(img, hq, sf, lq_patchsize) + + return img, hq + + +if __name__ == '__main__': + print("hey") + img = util.imread_uint('utils/test.png', 3) + print(img) + img = util.uint2single(img) + print(img) + img = img[:448, :448] + h = img.shape[0] // 4 + print("resizing to", h) + sf = 4 + deg_fn = partial(degradation_bsrgan_variant, sf=sf) + for i in range(20): + print(i) + img_lq = deg_fn(img) + print(img_lq) + img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"] + print(img_lq.shape) + print("bicubic", img_lq_bicubic.shape) + print(img_hq.shape) + lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), + interpolation=0) + lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), + interpolation=0) + img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1) + util.imsave(img_concat, str(i) + '.png') + + diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/image_degradation/bsrgan_light.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/image_degradation/bsrgan_light.py new file mode 100644 index 0000000000000000000000000000000000000000..9e1f823996bf559e9b015ea9aa2b3cd38dd13af1 --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/image_degradation/bsrgan_light.py @@ -0,0 +1,650 @@ +# -*- coding: utf-8 -*- +import numpy as np +import cv2 +import torch + +from functools import partial +import random +from scipy import ndimage +import scipy +import scipy.stats as ss +from scipy.interpolate import interp2d +from scipy.linalg import orth +import albumentations + +import ldm.modules.image_degradation.utils_image as util + +""" +# -------------------------------------------- +# Super-Resolution +# -------------------------------------------- +# +# Kai Zhang (cskaizhang@gmail.com) +# https://github.com/cszn +# From 2019/03--2021/08 +# -------------------------------------------- +""" + + +def modcrop_np(img, sf): + ''' + Args: + img: numpy image, WxH or WxHxC + sf: scale factor + Return: + cropped image + ''' + w, h = img.shape[:2] + im = np.copy(img) + return im[:w - w % sf, :h - h % sf, ...] + + +""" +# -------------------------------------------- +# anisotropic Gaussian kernels +# -------------------------------------------- +""" + + +def analytic_kernel(k): + """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)""" + k_size = k.shape[0] + # Calculate the big kernels size + big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2)) + # Loop over the small kernel to fill the big one + for r in range(k_size): + for c in range(k_size): + big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k + # Crop the edges of the big kernel to ignore very small values and increase run time of SR + crop = k_size // 2 + cropped_big_k = big_k[crop:-crop, crop:-crop] + # Normalize to 1 + return cropped_big_k / cropped_big_k.sum() + + +def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): + """ generate an anisotropic Gaussian kernel + Args: + ksize : e.g., 15, kernel size + theta : [0, pi], rotation angle range + l1 : [0.1,50], scaling of eigenvalues + l2 : [0.1,l1], scaling of eigenvalues + If l1 = l2, will get an isotropic Gaussian kernel. + Returns: + k : kernel + """ + + v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.])) + V = np.array([[v[0], v[1]], [v[1], -v[0]]]) + D = np.array([[l1, 0], [0, l2]]) + Sigma = np.dot(np.dot(V, D), np.linalg.inv(V)) + k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize) + + return k + + +def gm_blur_kernel(mean, cov, size=15): + center = size / 2.0 + 0.5 + k = np.zeros([size, size]) + for y in range(size): + for x in range(size): + cy = y - center + 1 + cx = x - center + 1 + k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov) + + k = k / np.sum(k) + return k + + +def shift_pixel(x, sf, upper_left=True): + """shift pixel for super-resolution with different scale factors + Args: + x: WxHxC or WxH + sf: scale factor + upper_left: shift direction + """ + h, w = x.shape[:2] + shift = (sf - 1) * 0.5 + xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0) + if upper_left: + x1 = xv + shift + y1 = yv + shift + else: + x1 = xv - shift + y1 = yv - shift + + x1 = np.clip(x1, 0, w - 1) + y1 = np.clip(y1, 0, h - 1) + + if x.ndim == 2: + x = interp2d(xv, yv, x)(x1, y1) + if x.ndim == 3: + for i in range(x.shape[-1]): + x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1) + + return x + + +def blur(x, k): + ''' + x: image, NxcxHxW + k: kernel, Nx1xhxw + ''' + n, c = x.shape[:2] + p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2 + x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate') + k = k.repeat(1, c, 1, 1) + k = k.view(-1, 1, k.shape[2], k.shape[3]) + x = x.view(1, -1, x.shape[2], x.shape[3]) + x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c) + x = x.view(n, c, x.shape[2], x.shape[3]) + + return x + + +def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0): + """" + # modified version of https://github.com/assafshocher/BlindSR_dataset_generator + # Kai Zhang + # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var + # max_var = 2.5 * sf + """ + # Set random eigen-vals (lambdas) and angle (theta) for COV matrix + lambda_1 = min_var + np.random.rand() * (max_var - min_var) + lambda_2 = min_var + np.random.rand() * (max_var - min_var) + theta = np.random.rand() * np.pi # random theta + noise = -noise_level + np.random.rand(*k_size) * noise_level * 2 + + # Set COV matrix using Lambdas and Theta + LAMBDA = np.diag([lambda_1, lambda_2]) + Q = np.array([[np.cos(theta), -np.sin(theta)], + [np.sin(theta), np.cos(theta)]]) + SIGMA = Q @ LAMBDA @ Q.T + INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :] + + # Set expectation position (shifting kernel for aligned image) + MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2) + MU = MU[None, None, :, None] + + # Create meshgrid for Gaussian + [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1])) + Z = np.stack([X, Y], 2)[:, :, :, None] + + # Calcualte Gaussian for every pixel of the kernel + ZZ = Z - MU + ZZ_t = ZZ.transpose(0, 1, 3, 2) + raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise) + + # shift the kernel so it will be centered + # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor) + + # Normalize the kernel and return + # kernel = raw_kernel_centered / np.sum(raw_kernel_centered) + kernel = raw_kernel / np.sum(raw_kernel) + return kernel + + +def fspecial_gaussian(hsize, sigma): + hsize = [hsize, hsize] + siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0] + std = sigma + [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)) + arg = -(x * x + y * y) / (2 * std * std) + h = np.exp(arg) + h[h < scipy.finfo(float).eps * h.max()] = 0 + sumh = h.sum() + if sumh != 0: + h = h / sumh + return h + + +def fspecial_laplacian(alpha): + alpha = max([0, min([alpha, 1])]) + h1 = alpha / (alpha + 1) + h2 = (1 - alpha) / (alpha + 1) + h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]] + h = np.array(h) + return h + + +def fspecial(filter_type, *args, **kwargs): + ''' + python code from: + https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py + ''' + if filter_type == 'gaussian': + return fspecial_gaussian(*args, **kwargs) + if filter_type == 'laplacian': + return fspecial_laplacian(*args, **kwargs) + + +""" +# -------------------------------------------- +# degradation models +# -------------------------------------------- +""" + + +def bicubic_degradation(x, sf=3): + ''' + Args: + x: HxWxC image, [0, 1] + sf: down-scale factor + Return: + bicubicly downsampled LR image + ''' + x = util.imresize_np(x, scale=1 / sf) + return x + + +def srmd_degradation(x, k, sf=3): + ''' blur + bicubic downsampling + Args: + x: HxWxC image, [0, 1] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + Reference: + @inproceedings{zhang2018learning, + title={Learning a single convolutional super-resolution network for multiple degradations}, + author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + pages={3262--3271}, + year={2018} + } + ''' + x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror' + x = bicubic_degradation(x, sf=sf) + return x + + +def dpsr_degradation(x, k, sf=3): + ''' bicubic downsampling + blur + Args: + x: HxWxC image, [0, 1] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + Reference: + @inproceedings{zhang2019deep, + title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, + author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + pages={1671--1681}, + year={2019} + } + ''' + x = bicubic_degradation(x, sf=sf) + x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') + return x + + +def classical_degradation(x, k, sf=3): + ''' blur + downsampling + Args: + x: HxWxC image, [0, 1]/[0, 255] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + ''' + x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') + # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2)) + st = 0 + return x[st::sf, st::sf, ...] + + +def add_sharpening(img, weight=0.5, radius=50, threshold=10): + """USM sharpening. borrowed from real-ESRGAN + Input image: I; Blurry image: B. + 1. K = I + weight * (I - B) + 2. Mask = 1 if abs(I - B) > threshold, else: 0 + 3. Blur mask: + 4. Out = Mask * K + (1 - Mask) * I + Args: + img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. + weight (float): Sharp weight. Default: 1. + radius (float): Kernel size of Gaussian blur. Default: 50. + threshold (int): + """ + if radius % 2 == 0: + radius += 1 + blur = cv2.GaussianBlur(img, (radius, radius), 0) + residual = img - blur + mask = np.abs(residual) * 255 > threshold + mask = mask.astype('float32') + soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) + + K = img + weight * residual + K = np.clip(K, 0, 1) + return soft_mask * K + (1 - soft_mask) * img + + +def add_blur(img, sf=4): + wd2 = 4.0 + sf + wd = 2.0 + 0.2 * sf + + wd2 = wd2/4 + wd = wd/4 + + if random.random() < 0.5: + l1 = wd2 * random.random() + l2 = wd2 * random.random() + k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2) + else: + k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random()) + img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror') + + return img + + +def add_resize(img, sf=4): + rnum = np.random.rand() + if rnum > 0.8: # up + sf1 = random.uniform(1, 2) + elif rnum < 0.7: # down + sf1 = random.uniform(0.5 / sf, 1) + else: + sf1 = 1.0 + img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) + img = np.clip(img, 0.0, 1.0) + + return img + + +# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): +# noise_level = random.randint(noise_level1, noise_level2) +# rnum = np.random.rand() +# if rnum > 0.6: # add color Gaussian noise +# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) +# elif rnum < 0.4: # add grayscale Gaussian noise +# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) +# else: # add noise +# L = noise_level2 / 255. +# D = np.diag(np.random.rand(3)) +# U = orth(np.random.rand(3, 3)) +# conv = np.dot(np.dot(np.transpose(U), D), U) +# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) +# img = np.clip(img, 0.0, 1.0) +# return img + +def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): + noise_level = random.randint(noise_level1, noise_level2) + rnum = np.random.rand() + if rnum > 0.6: # add color Gaussian noise + img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) + elif rnum < 0.4: # add grayscale Gaussian noise + img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) + else: # add noise + L = noise_level2 / 255. + D = np.diag(np.random.rand(3)) + U = orth(np.random.rand(3, 3)) + conv = np.dot(np.dot(np.transpose(U), D), U) + img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) + img = np.clip(img, 0.0, 1.0) + return img + + +def add_speckle_noise(img, noise_level1=2, noise_level2=25): + noise_level = random.randint(noise_level1, noise_level2) + img = np.clip(img, 0.0, 1.0) + rnum = random.random() + if rnum > 0.6: + img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) + elif rnum < 0.4: + img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) + else: + L = noise_level2 / 255. + D = np.diag(np.random.rand(3)) + U = orth(np.random.rand(3, 3)) + conv = np.dot(np.dot(np.transpose(U), D), U) + img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) + img = np.clip(img, 0.0, 1.0) + return img + + +def add_Poisson_noise(img): + img = np.clip((img * 255.0).round(), 0, 255) / 255. + vals = 10 ** (2 * random.random() + 2.0) # [2, 4] + if random.random() < 0.5: + img = np.random.poisson(img * vals).astype(np.float32) / vals + else: + img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114]) + img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255. + noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray + img += noise_gray[:, :, np.newaxis] + img = np.clip(img, 0.0, 1.0) + return img + + +def add_JPEG_noise(img): + quality_factor = random.randint(80, 95) + img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR) + result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]) + img = cv2.imdecode(encimg, 1) + img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB) + return img + + +def random_crop(lq, hq, sf=4, lq_patchsize=64): + h, w = lq.shape[:2] + rnd_h = random.randint(0, h - lq_patchsize) + rnd_w = random.randint(0, w - lq_patchsize) + lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :] + + rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf) + hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :] + return lq, hq + + +def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): + """ + This is the degradation model of BSRGAN from the paper + "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" + ---------- + img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) + sf: scale factor + isp_model: camera ISP model + Returns + ------- + img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] + hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] + """ + isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 + sf_ori = sf + + h1, w1 = img.shape[:2] + img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop + h, w = img.shape[:2] + + if h < lq_patchsize * sf or w < lq_patchsize * sf: + raise ValueError(f'img size ({h1}X{w1}) is too small!') + + hq = img.copy() + + if sf == 4 and random.random() < scale2_prob: # downsample1 + if np.random.rand() < 0.5: + img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + img = util.imresize_np(img, 1 / 2, True) + img = np.clip(img, 0.0, 1.0) + sf = 2 + + shuffle_order = random.sample(range(7), 7) + idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) + if idx1 > idx2: # keep downsample3 last + shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] + + for i in shuffle_order: + + if i == 0: + img = add_blur(img, sf=sf) + + elif i == 1: + img = add_blur(img, sf=sf) + + elif i == 2: + a, b = img.shape[1], img.shape[0] + # downsample2 + if random.random() < 0.75: + sf1 = random.uniform(1, 2 * sf) + img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) + k_shifted = shift_pixel(k, sf) + k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel + img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror') + img = img[0::sf, 0::sf, ...] # nearest downsampling + img = np.clip(img, 0.0, 1.0) + + elif i == 3: + # downsample3 + img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) + img = np.clip(img, 0.0, 1.0) + + elif i == 4: + # add Gaussian noise + img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8) + + elif i == 5: + # add JPEG noise + if random.random() < jpeg_prob: + img = add_JPEG_noise(img) + + elif i == 6: + # add processed camera sensor noise + if random.random() < isp_prob and isp_model is not None: + with torch.no_grad(): + img, hq = isp_model.forward(img.copy(), hq) + + # add final JPEG compression noise + img = add_JPEG_noise(img) + + # random crop + img, hq = random_crop(img, hq, sf_ori, lq_patchsize) + + return img, hq + + +# todo no isp_model? +def degradation_bsrgan_variant(image, sf=4, isp_model=None): + """ + This is the degradation model of BSRGAN from the paper + "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" + ---------- + sf: scale factor + isp_model: camera ISP model + Returns + ------- + img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] + hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] + """ + image = util.uint2single(image) + isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 + sf_ori = sf + + h1, w1 = image.shape[:2] + image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop + h, w = image.shape[:2] + + hq = image.copy() + + if sf == 4 and random.random() < scale2_prob: # downsample1 + if np.random.rand() < 0.5: + image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + image = util.imresize_np(image, 1 / 2, True) + image = np.clip(image, 0.0, 1.0) + sf = 2 + + shuffle_order = random.sample(range(7), 7) + idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) + if idx1 > idx2: # keep downsample3 last + shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] + + for i in shuffle_order: + + if i == 0: + image = add_blur(image, sf=sf) + + # elif i == 1: + # image = add_blur(image, sf=sf) + + if i == 0: + pass + + elif i == 2: + a, b = image.shape[1], image.shape[0] + # downsample2 + if random.random() < 0.8: + sf1 = random.uniform(1, 2 * sf) + image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) + k_shifted = shift_pixel(k, sf) + k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel + image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror') + image = image[0::sf, 0::sf, ...] # nearest downsampling + + image = np.clip(image, 0.0, 1.0) + + elif i == 3: + # downsample3 + image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) + image = np.clip(image, 0.0, 1.0) + + elif i == 4: + # add Gaussian noise + image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2) + + elif i == 5: + # add JPEG noise + if random.random() < jpeg_prob: + image = add_JPEG_noise(image) + # + # elif i == 6: + # # add processed camera sensor noise + # if random.random() < isp_prob and isp_model is not None: + # with torch.no_grad(): + # img, hq = isp_model.forward(img.copy(), hq) + + # add final JPEG compression noise + image = add_JPEG_noise(image) + image = util.single2uint(image) + example = {"image": image} + return example + + + + +if __name__ == '__main__': + print("hey") + img = util.imread_uint('utils/test.png', 3) + img = img[:448, :448] + h = img.shape[0] // 4 + print("resizing to", h) + sf = 4 + deg_fn = partial(degradation_bsrgan_variant, sf=sf) + for i in range(20): + print(i) + img_hq = img + img_lq = deg_fn(img)["image"] + img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq) + print(img_lq) + img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"] + print(img_lq.shape) + print("bicubic", img_lq_bicubic.shape) + print(img_hq.shape) + lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), + interpolation=0) + lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), + (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), + interpolation=0) + img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1) + util.imsave(img_concat, str(i) + '.png') diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/image_degradation/utils/test.png b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/image_degradation/utils/test.png new file mode 100644 index 0000000000000000000000000000000000000000..e720ed04ac7e1e7938d367e692fb6a742c54a24c --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/image_degradation/utils/test.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:92e516278f0d3e85e84cfb55b43338e12d5896a0ee3833aafdf378025457d753 +size 441072 diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/image_degradation/utils_image.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/image_degradation/utils_image.py new file mode 100644 index 0000000000000000000000000000000000000000..0175f155ad900ae33c3c46ed87f49b352e3faf98 --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/image_degradation/utils_image.py @@ -0,0 +1,916 @@ +import os +import math +import random +import numpy as np +import torch +import cv2 +from torchvision.utils import make_grid +from datetime import datetime +#import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py + + +os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" + + +''' +# -------------------------------------------- +# Kai Zhang (github: https://github.com/cszn) +# 03/Mar/2019 +# -------------------------------------------- +# https://github.com/twhui/SRGAN-pyTorch +# https://github.com/xinntao/BasicSR +# -------------------------------------------- +''' + + +IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif'] + + +def is_image_file(filename): + return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) + + +def get_timestamp(): + return datetime.now().strftime('%y%m%d-%H%M%S') + + +def imshow(x, title=None, cbar=False, figsize=None): + plt.figure(figsize=figsize) + plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray') + if title: + plt.title(title) + if cbar: + plt.colorbar() + plt.show() + + +def surf(Z, cmap='rainbow', figsize=None): + plt.figure(figsize=figsize) + ax3 = plt.axes(projection='3d') + + w, h = Z.shape[:2] + xx = np.arange(0,w,1) + yy = np.arange(0,h,1) + X, Y = np.meshgrid(xx, yy) + ax3.plot_surface(X,Y,Z,cmap=cmap) + #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap) + plt.show() + + +''' +# -------------------------------------------- +# get image pathes +# -------------------------------------------- +''' + + +def get_image_paths(dataroot): + paths = None # return None if dataroot is None + if dataroot is not None: + paths = sorted(_get_paths_from_images(dataroot)) + return paths + + +def _get_paths_from_images(path): + assert os.path.isdir(path), '{:s} is not a valid directory'.format(path) + images = [] + for dirpath, _, fnames in sorted(os.walk(path)): + for fname in sorted(fnames): + if is_image_file(fname): + img_path = os.path.join(dirpath, fname) + images.append(img_path) + assert images, '{:s} has no valid image file'.format(path) + return images + + +''' +# -------------------------------------------- +# split large images into small images +# -------------------------------------------- +''' + + +def patches_from_image(img, p_size=512, p_overlap=64, p_max=800): + w, h = img.shape[:2] + patches = [] + if w > p_max and h > p_max: + w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int)) + h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int)) + w1.append(w-p_size) + h1.append(h-p_size) +# print(w1) +# print(h1) + for i in w1: + for j in h1: + patches.append(img[i:i+p_size, j:j+p_size,:]) + else: + patches.append(img) + + return patches + + +def imssave(imgs, img_path): + """ + imgs: list, N images of size WxHxC + """ + img_name, ext = os.path.splitext(os.path.basename(img_path)) + + for i, img in enumerate(imgs): + if img.ndim == 3: + img = img[:, :, [2, 1, 0]] + new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png') + cv2.imwrite(new_path, img) + + +def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000): + """ + split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size), + and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max) + will be splitted. + Args: + original_dataroot: + taget_dataroot: + p_size: size of small images + p_overlap: patch size in training is a good choice + p_max: images with smaller size than (p_max)x(p_max) keep unchanged. + """ + paths = get_image_paths(original_dataroot) + for img_path in paths: + # img_name, ext = os.path.splitext(os.path.basename(img_path)) + img = imread_uint(img_path, n_channels=n_channels) + patches = patches_from_image(img, p_size, p_overlap, p_max) + imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path))) + #if original_dataroot == taget_dataroot: + #del img_path + +''' +# -------------------------------------------- +# makedir +# -------------------------------------------- +''' + + +def mkdir(path): + if not os.path.exists(path): + os.makedirs(path) + + +def mkdirs(paths): + if isinstance(paths, str): + mkdir(paths) + else: + for path in paths: + mkdir(path) + + +def mkdir_and_rename(path): + if os.path.exists(path): + new_name = path + '_archived_' + get_timestamp() + print('Path already exists. Rename it to [{:s}]'.format(new_name)) + os.rename(path, new_name) + os.makedirs(path) + + +''' +# -------------------------------------------- +# read image from path +# opencv is fast, but read BGR numpy image +# -------------------------------------------- +''' + + +# -------------------------------------------- +# get uint8 image of size HxWxn_channles (RGB) +# -------------------------------------------- +def imread_uint(path, n_channels=3): + # input: path + # output: HxWx3(RGB or GGG), or HxWx1 (G) + if n_channels == 1: + img = cv2.imread(path, 0) # cv2.IMREAD_GRAYSCALE + img = np.expand_dims(img, axis=2) # HxWx1 + elif n_channels == 3: + img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # BGR or G + if img.ndim == 2: + img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # GGG + else: + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB + return img + + +# -------------------------------------------- +# matlab's imwrite +# -------------------------------------------- +def imsave(img, img_path): + img = np.squeeze(img) + if img.ndim == 3: + img = img[:, :, [2, 1, 0]] + cv2.imwrite(img_path, img) + +def imwrite(img, img_path): + img = np.squeeze(img) + if img.ndim == 3: + img = img[:, :, [2, 1, 0]] + cv2.imwrite(img_path, img) + + + +# -------------------------------------------- +# get single image of size HxWxn_channles (BGR) +# -------------------------------------------- +def read_img(path): + # read image by cv2 + # return: Numpy float32, HWC, BGR, [0,1] + img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # cv2.IMREAD_GRAYSCALE + img = img.astype(np.float32) / 255. + if img.ndim == 2: + img = np.expand_dims(img, axis=2) + # some images have 4 channels + if img.shape[2] > 3: + img = img[:, :, :3] + return img + + +''' +# -------------------------------------------- +# image format conversion +# -------------------------------------------- +# numpy(single) <---> numpy(unit) +# numpy(single) <---> tensor +# numpy(unit) <---> tensor +# -------------------------------------------- +''' + + +# -------------------------------------------- +# numpy(single) [0, 1] <---> numpy(unit) +# -------------------------------------------- + + +def uint2single(img): + + return np.float32(img/255.) + + +def single2uint(img): + + return np.uint8((img.clip(0, 1)*255.).round()) + + +def uint162single(img): + + return np.float32(img/65535.) + + +def single2uint16(img): + + return np.uint16((img.clip(0, 1)*65535.).round()) + + +# -------------------------------------------- +# numpy(unit) (HxWxC or HxW) <---> tensor +# -------------------------------------------- + + +# convert uint to 4-dimensional torch tensor +def uint2tensor4(img): + if img.ndim == 2: + img = np.expand_dims(img, axis=2) + return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0) + + +# convert uint to 3-dimensional torch tensor +def uint2tensor3(img): + if img.ndim == 2: + img = np.expand_dims(img, axis=2) + return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.) + + +# convert 2/3/4-dimensional torch tensor to uint +def tensor2uint(img): + img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy() + if img.ndim == 3: + img = np.transpose(img, (1, 2, 0)) + return np.uint8((img*255.0).round()) + + +# -------------------------------------------- +# numpy(single) (HxWxC) <---> tensor +# -------------------------------------------- + + +# convert single (HxWxC) to 3-dimensional torch tensor +def single2tensor3(img): + return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float() + + +# convert single (HxWxC) to 4-dimensional torch tensor +def single2tensor4(img): + return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0) + + +# convert torch tensor to single +def tensor2single(img): + img = img.data.squeeze().float().cpu().numpy() + if img.ndim == 3: + img = np.transpose(img, (1, 2, 0)) + + return img + +# convert torch tensor to single +def tensor2single3(img): + img = img.data.squeeze().float().cpu().numpy() + if img.ndim == 3: + img = np.transpose(img, (1, 2, 0)) + elif img.ndim == 2: + img = np.expand_dims(img, axis=2) + return img + + +def single2tensor5(img): + return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0) + + +def single32tensor5(img): + return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0) + + +def single42tensor4(img): + return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float() + + +# from skimage.io import imread, imsave +def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)): + ''' + Converts a torch Tensor into an image Numpy array of BGR channel order + Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order + Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default) + ''' + tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp + tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1] + n_dim = tensor.dim() + if n_dim == 4: + n_img = len(tensor) + img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy() + img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR + elif n_dim == 3: + img_np = tensor.numpy() + img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR + elif n_dim == 2: + img_np = tensor.numpy() + else: + raise TypeError( + 'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim)) + if out_type == np.uint8: + img_np = (img_np * 255.0).round() + # Important. Unlike matlab, numpy.unit8() WILL NOT round by default. + return img_np.astype(out_type) + + +''' +# -------------------------------------------- +# Augmentation, flipe and/or rotate +# -------------------------------------------- +# The following two are enough. +# (1) augmet_img: numpy image of WxHxC or WxH +# (2) augment_img_tensor4: tensor image 1xCxWxH +# -------------------------------------------- +''' + + +def augment_img(img, mode=0): + '''Kai Zhang (github: https://github.com/cszn) + ''' + if mode == 0: + return img + elif mode == 1: + return np.flipud(np.rot90(img)) + elif mode == 2: + return np.flipud(img) + elif mode == 3: + return np.rot90(img, k=3) + elif mode == 4: + return np.flipud(np.rot90(img, k=2)) + elif mode == 5: + return np.rot90(img) + elif mode == 6: + return np.rot90(img, k=2) + elif mode == 7: + return np.flipud(np.rot90(img, k=3)) + + +def augment_img_tensor4(img, mode=0): + '''Kai Zhang (github: https://github.com/cszn) + ''' + if mode == 0: + return img + elif mode == 1: + return img.rot90(1, [2, 3]).flip([2]) + elif mode == 2: + return img.flip([2]) + elif mode == 3: + return img.rot90(3, [2, 3]) + elif mode == 4: + return img.rot90(2, [2, 3]).flip([2]) + elif mode == 5: + return img.rot90(1, [2, 3]) + elif mode == 6: + return img.rot90(2, [2, 3]) + elif mode == 7: + return img.rot90(3, [2, 3]).flip([2]) + + +def augment_img_tensor(img, mode=0): + '''Kai Zhang (github: https://github.com/cszn) + ''' + img_size = img.size() + img_np = img.data.cpu().numpy() + if len(img_size) == 3: + img_np = np.transpose(img_np, (1, 2, 0)) + elif len(img_size) == 4: + img_np = np.transpose(img_np, (2, 3, 1, 0)) + img_np = augment_img(img_np, mode=mode) + img_tensor = torch.from_numpy(np.ascontiguousarray(img_np)) + if len(img_size) == 3: + img_tensor = img_tensor.permute(2, 0, 1) + elif len(img_size) == 4: + img_tensor = img_tensor.permute(3, 2, 0, 1) + + return img_tensor.type_as(img) + + +def augment_img_np3(img, mode=0): + if mode == 0: + return img + elif mode == 1: + return img.transpose(1, 0, 2) + elif mode == 2: + return img[::-1, :, :] + elif mode == 3: + img = img[::-1, :, :] + img = img.transpose(1, 0, 2) + return img + elif mode == 4: + return img[:, ::-1, :] + elif mode == 5: + img = img[:, ::-1, :] + img = img.transpose(1, 0, 2) + return img + elif mode == 6: + img = img[:, ::-1, :] + img = img[::-1, :, :] + return img + elif mode == 7: + img = img[:, ::-1, :] + img = img[::-1, :, :] + img = img.transpose(1, 0, 2) + return img + + +def augment_imgs(img_list, hflip=True, rot=True): + # horizontal flip OR rotate + hflip = hflip and random.random() < 0.5 + vflip = rot and random.random() < 0.5 + rot90 = rot and random.random() < 0.5 + + def _augment(img): + if hflip: + img = img[:, ::-1, :] + if vflip: + img = img[::-1, :, :] + if rot90: + img = img.transpose(1, 0, 2) + return img + + return [_augment(img) for img in img_list] + + +''' +# -------------------------------------------- +# modcrop and shave +# -------------------------------------------- +''' + + +def modcrop(img_in, scale): + # img_in: Numpy, HWC or HW + img = np.copy(img_in) + if img.ndim == 2: + H, W = img.shape + H_r, W_r = H % scale, W % scale + img = img[:H - H_r, :W - W_r] + elif img.ndim == 3: + H, W, C = img.shape + H_r, W_r = H % scale, W % scale + img = img[:H - H_r, :W - W_r, :] + else: + raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim)) + return img + + +def shave(img_in, border=0): + # img_in: Numpy, HWC or HW + img = np.copy(img_in) + h, w = img.shape[:2] + img = img[border:h-border, border:w-border] + return img + + +''' +# -------------------------------------------- +# image processing process on numpy image +# channel_convert(in_c, tar_type, img_list): +# rgb2ycbcr(img, only_y=True): +# bgr2ycbcr(img, only_y=True): +# ycbcr2rgb(img): +# -------------------------------------------- +''' + + +def rgb2ycbcr(img, only_y=True): + '''same as matlab rgb2ycbcr + only_y: only return Y channel + Input: + uint8, [0, 255] + float, [0, 1] + ''' + in_img_type = img.dtype + img.astype(np.float32) + if in_img_type != np.uint8: + img *= 255. + # convert + if only_y: + rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0 + else: + rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], + [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128] + if in_img_type == np.uint8: + rlt = rlt.round() + else: + rlt /= 255. + return rlt.astype(in_img_type) + + +def ycbcr2rgb(img): + '''same as matlab ycbcr2rgb + Input: + uint8, [0, 255] + float, [0, 1] + ''' + in_img_type = img.dtype + img.astype(np.float32) + if in_img_type != np.uint8: + img *= 255. + # convert + rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071], + [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836] + if in_img_type == np.uint8: + rlt = rlt.round() + else: + rlt /= 255. + return rlt.astype(in_img_type) + + +def bgr2ycbcr(img, only_y=True): + '''bgr version of rgb2ycbcr + only_y: only return Y channel + Input: + uint8, [0, 255] + float, [0, 1] + ''' + in_img_type = img.dtype + img.astype(np.float32) + if in_img_type != np.uint8: + img *= 255. + # convert + if only_y: + rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0 + else: + rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], + [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128] + if in_img_type == np.uint8: + rlt = rlt.round() + else: + rlt /= 255. + return rlt.astype(in_img_type) + + +def channel_convert(in_c, tar_type, img_list): + # conversion among BGR, gray and y + if in_c == 3 and tar_type == 'gray': # BGR to gray + gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list] + return [np.expand_dims(img, axis=2) for img in gray_list] + elif in_c == 3 and tar_type == 'y': # BGR to y + y_list = [bgr2ycbcr(img, only_y=True) for img in img_list] + return [np.expand_dims(img, axis=2) for img in y_list] + elif in_c == 1 and tar_type == 'RGB': # gray/y to BGR + return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list] + else: + return img_list + + +''' +# -------------------------------------------- +# metric, PSNR and SSIM +# -------------------------------------------- +''' + + +# -------------------------------------------- +# PSNR +# -------------------------------------------- +def calculate_psnr(img1, img2, border=0): + # img1 and img2 have range [0, 255] + #img1 = img1.squeeze() + #img2 = img2.squeeze() + if not img1.shape == img2.shape: + raise ValueError('Input images must have the same dimensions.') + h, w = img1.shape[:2] + img1 = img1[border:h-border, border:w-border] + img2 = img2[border:h-border, border:w-border] + + img1 = img1.astype(np.float64) + img2 = img2.astype(np.float64) + mse = np.mean((img1 - img2)**2) + if mse == 0: + return float('inf') + return 20 * math.log10(255.0 / math.sqrt(mse)) + + +# -------------------------------------------- +# SSIM +# -------------------------------------------- +def calculate_ssim(img1, img2, border=0): + '''calculate SSIM + the same outputs as MATLAB's + img1, img2: [0, 255] + ''' + #img1 = img1.squeeze() + #img2 = img2.squeeze() + if not img1.shape == img2.shape: + raise ValueError('Input images must have the same dimensions.') + h, w = img1.shape[:2] + img1 = img1[border:h-border, border:w-border] + img2 = img2[border:h-border, border:w-border] + + if img1.ndim == 2: + return ssim(img1, img2) + elif img1.ndim == 3: + if img1.shape[2] == 3: + ssims = [] + for i in range(3): + ssims.append(ssim(img1[:,:,i], img2[:,:,i])) + return np.array(ssims).mean() + elif img1.shape[2] == 1: + return ssim(np.squeeze(img1), np.squeeze(img2)) + else: + raise ValueError('Wrong input image dimensions.') + + +def ssim(img1, img2): + C1 = (0.01 * 255)**2 + C2 = (0.03 * 255)**2 + + img1 = img1.astype(np.float64) + img2 = img2.astype(np.float64) + kernel = cv2.getGaussianKernel(11, 1.5) + window = np.outer(kernel, kernel.transpose()) + + mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid + mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5] + mu1_sq = mu1**2 + mu2_sq = mu2**2 + mu1_mu2 = mu1 * mu2 + sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq + sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq + sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2 + + ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * + (sigma1_sq + sigma2_sq + C2)) + return ssim_map.mean() + + +''' +# -------------------------------------------- +# matlab's bicubic imresize (numpy and torch) [0, 1] +# -------------------------------------------- +''' + + +# matlab 'imresize' function, now only support 'bicubic' +def cubic(x): + absx = torch.abs(x) + absx2 = absx**2 + absx3 = absx**3 + return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \ + (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx)) + + +def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing): + if (scale < 1) and (antialiasing): + # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width + kernel_width = kernel_width / scale + + # Output-space coordinates + x = torch.linspace(1, out_length, out_length) + + # Input-space coordinates. Calculate the inverse mapping such that 0.5 + # in output space maps to 0.5 in input space, and 0.5+scale in output + # space maps to 1.5 in input space. + u = x / scale + 0.5 * (1 - 1 / scale) + + # What is the left-most pixel that can be involved in the computation? + left = torch.floor(u - kernel_width / 2) + + # What is the maximum number of pixels that can be involved in the + # computation? Note: it's OK to use an extra pixel here; if the + # corresponding weights are all zero, it will be eliminated at the end + # of this function. + P = math.ceil(kernel_width) + 2 + + # The indices of the input pixels involved in computing the k-th output + # pixel are in row k of the indices matrix. + indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view( + 1, P).expand(out_length, P) + + # The weights used to compute the k-th output pixel are in row k of the + # weights matrix. + distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices + # apply cubic kernel + if (scale < 1) and (antialiasing): + weights = scale * cubic(distance_to_center * scale) + else: + weights = cubic(distance_to_center) + # Normalize the weights matrix so that each row sums to 1. + weights_sum = torch.sum(weights, 1).view(out_length, 1) + weights = weights / weights_sum.expand(out_length, P) + + # If a column in weights is all zero, get rid of it. only consider the first and last column. + weights_zero_tmp = torch.sum((weights == 0), 0) + if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6): + indices = indices.narrow(1, 1, P - 2) + weights = weights.narrow(1, 1, P - 2) + if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6): + indices = indices.narrow(1, 0, P - 2) + weights = weights.narrow(1, 0, P - 2) + weights = weights.contiguous() + indices = indices.contiguous() + sym_len_s = -indices.min() + 1 + sym_len_e = indices.max() - in_length + indices = indices + sym_len_s - 1 + return weights, indices, int(sym_len_s), int(sym_len_e) + + +# -------------------------------------------- +# imresize for tensor image [0, 1] +# -------------------------------------------- +def imresize(img, scale, antialiasing=True): + # Now the scale should be the same for H and W + # input: img: pytorch tensor, CHW or HW [0,1] + # output: CHW or HW [0,1] w/o round + need_squeeze = True if img.dim() == 2 else False + if need_squeeze: + img.unsqueeze_(0) + in_C, in_H, in_W = img.size() + out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale) + kernel_width = 4 + kernel = 'cubic' + + # Return the desired dimension order for performing the resize. The + # strategy is to perform the resize first along the dimension with the + # smallest scale factor. + # Now we do not support this. + + # get weights and indices + weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices( + in_H, out_H, scale, kernel, kernel_width, antialiasing) + weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices( + in_W, out_W, scale, kernel, kernel_width, antialiasing) + # process H dimension + # symmetric copying + img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W) + img_aug.narrow(1, sym_len_Hs, in_H).copy_(img) + + sym_patch = img[:, :sym_len_Hs, :] + inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(1, inv_idx) + img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv) + + sym_patch = img[:, -sym_len_He:, :] + inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(1, inv_idx) + img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv) + + out_1 = torch.FloatTensor(in_C, out_H, in_W) + kernel_width = weights_H.size(1) + for i in range(out_H): + idx = int(indices_H[i][0]) + for j in range(out_C): + out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i]) + + # process W dimension + # symmetric copying + out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We) + out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1) + + sym_patch = out_1[:, :, :sym_len_Ws] + inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(2, inv_idx) + out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv) + + sym_patch = out_1[:, :, -sym_len_We:] + inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(2, inv_idx) + out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv) + + out_2 = torch.FloatTensor(in_C, out_H, out_W) + kernel_width = weights_W.size(1) + for i in range(out_W): + idx = int(indices_W[i][0]) + for j in range(out_C): + out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i]) + if need_squeeze: + out_2.squeeze_() + return out_2 + + +# -------------------------------------------- +# imresize for numpy image [0, 1] +# -------------------------------------------- +def imresize_np(img, scale, antialiasing=True): + # Now the scale should be the same for H and W + # input: img: Numpy, HWC or HW [0,1] + # output: HWC or HW [0,1] w/o round + img = torch.from_numpy(img) + need_squeeze = True if img.dim() == 2 else False + if need_squeeze: + img.unsqueeze_(2) + + in_H, in_W, in_C = img.size() + out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale) + kernel_width = 4 + kernel = 'cubic' + + # Return the desired dimension order for performing the resize. The + # strategy is to perform the resize first along the dimension with the + # smallest scale factor. + # Now we do not support this. + + # get weights and indices + weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices( + in_H, out_H, scale, kernel, kernel_width, antialiasing) + weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices( + in_W, out_W, scale, kernel, kernel_width, antialiasing) + # process H dimension + # symmetric copying + img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C) + img_aug.narrow(0, sym_len_Hs, in_H).copy_(img) + + sym_patch = img[:sym_len_Hs, :, :] + inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(0, inv_idx) + img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv) + + sym_patch = img[-sym_len_He:, :, :] + inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(0, inv_idx) + img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv) + + out_1 = torch.FloatTensor(out_H, in_W, in_C) + kernel_width = weights_H.size(1) + for i in range(out_H): + idx = int(indices_H[i][0]) + for j in range(out_C): + out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i]) + + # process W dimension + # symmetric copying + out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C) + out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1) + + sym_patch = out_1[:, :sym_len_Ws, :] + inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(1, inv_idx) + out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv) + + sym_patch = out_1[:, -sym_len_We:, :] + inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(1, inv_idx) + out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv) + + out_2 = torch.FloatTensor(out_H, out_W, in_C) + kernel_width = weights_W.size(1) + for i in range(out_W): + idx = int(indices_W[i][0]) + for j in range(out_C): + out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i]) + if need_squeeze: + out_2.squeeze_() + + return out_2.numpy() + + +if __name__ == '__main__': + print('---') +# img = imread_uint('test.bmp', 3) +# img = uint2single(img) +# img_bicubic = imresize_np(img, 1/4) \ No newline at end of file diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/losses/__init__.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/losses/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..57ad9121b66026e330e6b0305cfb0e87df6f9021 --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/losses/__init__.py @@ -0,0 +1,5 @@ +import torch.nn as nn + +class DummyLoss(nn.Module): + def __init__(self): + super().__init__() \ No newline at end of file diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/x_transformer.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/x_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..5fc15bf9cfe0111a910e7de33d04ffdec3877576 --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/modules/x_transformer.py @@ -0,0 +1,641 @@ +"""shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers""" +import torch +from torch import nn, einsum +import torch.nn.functional as F +from functools import partial +from inspect import isfunction +from collections import namedtuple +from einops import rearrange, repeat, reduce + +# constants + +DEFAULT_DIM_HEAD = 64 + +Intermediates = namedtuple('Intermediates', [ + 'pre_softmax_attn', + 'post_softmax_attn' +]) + +LayerIntermediates = namedtuple('Intermediates', [ + 'hiddens', + 'attn_intermediates' +]) + + +class AbsolutePositionalEmbedding(nn.Module): + def __init__(self, dim, max_seq_len): + super().__init__() + self.emb = nn.Embedding(max_seq_len, dim) + self.init_() + + def init_(self): + nn.init.normal_(self.emb.weight, std=0.02) + + def forward(self, x): + n = torch.arange(x.shape[1], device=x.device) + return self.emb(n)[None, :, :] + + +class FixedPositionalEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim)) + self.register_buffer('inv_freq', inv_freq) + + def forward(self, x, seq_dim=1, offset=0): + t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset + sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq) + emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1) + return emb[None, :, :] + + +# helpers + +def exists(val): + return val is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def always(val): + def inner(*args, **kwargs): + return val + return inner + + +def not_equals(val): + def inner(x): + return x != val + return inner + + +def equals(val): + def inner(x): + return x == val + return inner + + +def max_neg_value(tensor): + return -torch.finfo(tensor.dtype).max + + +# keyword argument helpers + +def pick_and_pop(keys, d): + values = list(map(lambda key: d.pop(key), keys)) + return dict(zip(keys, values)) + + +def group_dict_by_key(cond, d): + return_val = [dict(), dict()] + for key in d.keys(): + match = bool(cond(key)) + ind = int(not match) + return_val[ind][key] = d[key] + return (*return_val,) + + +def string_begins_with(prefix, str): + return str.startswith(prefix) + + +def group_by_key_prefix(prefix, d): + return group_dict_by_key(partial(string_begins_with, prefix), d) + + +def groupby_prefix_and_trim(prefix, d): + kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d) + kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items()))) + return kwargs_without_prefix, kwargs + + +# classes +class Scale(nn.Module): + def __init__(self, value, fn): + super().__init__() + self.value = value + self.fn = fn + + def forward(self, x, **kwargs): + x, *rest = self.fn(x, **kwargs) + return (x * self.value, *rest) + + +class Rezero(nn.Module): + def __init__(self, fn): + super().__init__() + self.fn = fn + self.g = nn.Parameter(torch.zeros(1)) + + def forward(self, x, **kwargs): + x, *rest = self.fn(x, **kwargs) + return (x * self.g, *rest) + + +class ScaleNorm(nn.Module): + def __init__(self, dim, eps=1e-5): + super().__init__() + self.scale = dim ** -0.5 + self.eps = eps + self.g = nn.Parameter(torch.ones(1)) + + def forward(self, x): + norm = torch.norm(x, dim=-1, keepdim=True) * self.scale + return x / norm.clamp(min=self.eps) * self.g + + +class RMSNorm(nn.Module): + def __init__(self, dim, eps=1e-8): + super().__init__() + self.scale = dim ** -0.5 + self.eps = eps + self.g = nn.Parameter(torch.ones(dim)) + + def forward(self, x): + norm = torch.norm(x, dim=-1, keepdim=True) * self.scale + return x / norm.clamp(min=self.eps) * self.g + + +class Residual(nn.Module): + def forward(self, x, residual): + return x + residual + + +class GRUGating(nn.Module): + def __init__(self, dim): + super().__init__() + self.gru = nn.GRUCell(dim, dim) + + def forward(self, x, residual): + gated_output = self.gru( + rearrange(x, 'b n d -> (b n) d'), + rearrange(residual, 'b n d -> (b n) d') + ) + + return gated_output.reshape_as(x) + + +# feedforward + +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential( + nn.Linear(dim, inner_dim), + nn.GELU() + ) if not glu else GEGLU(dim, inner_dim) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +# attention. +class Attention(nn.Module): + def __init__( + self, + dim, + dim_head=DEFAULT_DIM_HEAD, + heads=8, + causal=False, + mask=None, + talking_heads=False, + sparse_topk=None, + use_entmax15=False, + num_mem_kv=0, + dropout=0., + on_attn=False + ): + super().__init__() + if use_entmax15: + raise NotImplementedError("Check out entmax activation instead of softmax activation!") + self.scale = dim_head ** -0.5 + self.heads = heads + self.causal = causal + self.mask = mask + + inner_dim = dim_head * heads + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_k = nn.Linear(dim, inner_dim, bias=False) + self.to_v = nn.Linear(dim, inner_dim, bias=False) + self.dropout = nn.Dropout(dropout) + + # talking heads + self.talking_heads = talking_heads + if talking_heads: + self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads)) + self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads)) + + # explicit topk sparse attention + self.sparse_topk = sparse_topk + + # entmax + #self.attn_fn = entmax15 if use_entmax15 else F.softmax + self.attn_fn = F.softmax + + # add memory key / values + self.num_mem_kv = num_mem_kv + if num_mem_kv > 0: + self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) + self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) + + # attention on attention + self.attn_on_attn = on_attn + self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim) + + def forward( + self, + x, + context=None, + mask=None, + context_mask=None, + rel_pos=None, + sinusoidal_emb=None, + prev_attn=None, + mem=None + ): + b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device + kv_input = default(context, x) + + q_input = x + k_input = kv_input + v_input = kv_input + + if exists(mem): + k_input = torch.cat((mem, k_input), dim=-2) + v_input = torch.cat((mem, v_input), dim=-2) + + if exists(sinusoidal_emb): + # in shortformer, the query would start at a position offset depending on the past cached memory + offset = k_input.shape[-2] - q_input.shape[-2] + q_input = q_input + sinusoidal_emb(q_input, offset=offset) + k_input = k_input + sinusoidal_emb(k_input) + + q = self.to_q(q_input) + k = self.to_k(k_input) + v = self.to_v(v_input) + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v)) + + input_mask = None + if any(map(exists, (mask, context_mask))): + q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool()) + k_mask = q_mask if not exists(context) else context_mask + k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool()) + q_mask = rearrange(q_mask, 'b i -> b () i ()') + k_mask = rearrange(k_mask, 'b j -> b () () j') + input_mask = q_mask * k_mask + + if self.num_mem_kv > 0: + mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v)) + k = torch.cat((mem_k, k), dim=-2) + v = torch.cat((mem_v, v), dim=-2) + if exists(input_mask): + input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True) + + dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale + mask_value = max_neg_value(dots) + + if exists(prev_attn): + dots = dots + prev_attn + + pre_softmax_attn = dots + + if talking_heads: + dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous() + + if exists(rel_pos): + dots = rel_pos(dots) + + if exists(input_mask): + dots.masked_fill_(~input_mask, mask_value) + del input_mask + + if self.causal: + i, j = dots.shape[-2:] + r = torch.arange(i, device=device) + mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j') + mask = F.pad(mask, (j - i, 0), value=False) + dots.masked_fill_(mask, mask_value) + del mask + + if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]: + top, _ = dots.topk(self.sparse_topk, dim=-1) + vk = top[..., -1].unsqueeze(-1).expand_as(dots) + mask = dots < vk + dots.masked_fill_(mask, mask_value) + del mask + + attn = self.attn_fn(dots, dim=-1) + post_softmax_attn = attn + + attn = self.dropout(attn) + + if talking_heads: + attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous() + + out = einsum('b h i j, b h j d -> b h i d', attn, v) + out = rearrange(out, 'b h n d -> b n (h d)') + + intermediates = Intermediates( + pre_softmax_attn=pre_softmax_attn, + post_softmax_attn=post_softmax_attn + ) + + return self.to_out(out), intermediates + + +class AttentionLayers(nn.Module): + def __init__( + self, + dim, + depth, + heads=8, + causal=False, + cross_attend=False, + only_cross=False, + use_scalenorm=False, + use_rmsnorm=False, + use_rezero=False, + rel_pos_num_buckets=32, + rel_pos_max_distance=128, + position_infused_attn=False, + custom_layers=None, + sandwich_coef=None, + par_ratio=None, + residual_attn=False, + cross_residual_attn=False, + macaron=False, + pre_norm=True, + gate_residual=False, + **kwargs + ): + super().__init__() + ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs) + attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs) + + dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD) + + self.dim = dim + self.depth = depth + self.layers = nn.ModuleList([]) + + self.has_pos_emb = position_infused_attn + self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None + self.rotary_pos_emb = always(None) + + assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance' + self.rel_pos = None + + self.pre_norm = pre_norm + + self.residual_attn = residual_attn + self.cross_residual_attn = cross_residual_attn + + norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm + norm_class = RMSNorm if use_rmsnorm else norm_class + norm_fn = partial(norm_class, dim) + + norm_fn = nn.Identity if use_rezero else norm_fn + branch_fn = Rezero if use_rezero else None + + if cross_attend and not only_cross: + default_block = ('a', 'c', 'f') + elif cross_attend and only_cross: + default_block = ('c', 'f') + else: + default_block = ('a', 'f') + + if macaron: + default_block = ('f',) + default_block + + if exists(custom_layers): + layer_types = custom_layers + elif exists(par_ratio): + par_depth = depth * len(default_block) + assert 1 < par_ratio <= par_depth, 'par ratio out of range' + default_block = tuple(filter(not_equals('f'), default_block)) + par_attn = par_depth // par_ratio + depth_cut = par_depth * 2 // 3 # 2 / 3 attention layer cutoff suggested by PAR paper + par_width = (depth_cut + depth_cut // par_attn) // par_attn + assert len(default_block) <= par_width, 'default block is too large for par_ratio' + par_block = default_block + ('f',) * (par_width - len(default_block)) + par_head = par_block * par_attn + layer_types = par_head + ('f',) * (par_depth - len(par_head)) + elif exists(sandwich_coef): + assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth' + layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef + else: + layer_types = default_block * depth + + self.layer_types = layer_types + self.num_attn_layers = len(list(filter(equals('a'), layer_types))) + + for layer_type in self.layer_types: + if layer_type == 'a': + layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs) + elif layer_type == 'c': + layer = Attention(dim, heads=heads, **attn_kwargs) + elif layer_type == 'f': + layer = FeedForward(dim, **ff_kwargs) + layer = layer if not macaron else Scale(0.5, layer) + else: + raise Exception(f'invalid layer type {layer_type}') + + if isinstance(layer, Attention) and exists(branch_fn): + layer = branch_fn(layer) + + if gate_residual: + residual_fn = GRUGating(dim) + else: + residual_fn = Residual() + + self.layers.append(nn.ModuleList([ + norm_fn(), + layer, + residual_fn + ])) + + def forward( + self, + x, + context=None, + mask=None, + context_mask=None, + mems=None, + return_hiddens=False + ): + hiddens = [] + intermediates = [] + prev_attn = None + prev_cross_attn = None + + mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers + + for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)): + is_last = ind == (len(self.layers) - 1) + + if layer_type == 'a': + hiddens.append(x) + layer_mem = mems.pop(0) + + residual = x + + if self.pre_norm: + x = norm(x) + + if layer_type == 'a': + out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos, + prev_attn=prev_attn, mem=layer_mem) + elif layer_type == 'c': + out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn) + elif layer_type == 'f': + out = block(x) + + x = residual_fn(out, residual) + + if layer_type in ('a', 'c'): + intermediates.append(inter) + + if layer_type == 'a' and self.residual_attn: + prev_attn = inter.pre_softmax_attn + elif layer_type == 'c' and self.cross_residual_attn: + prev_cross_attn = inter.pre_softmax_attn + + if not self.pre_norm and not is_last: + x = norm(x) + + if return_hiddens: + intermediates = LayerIntermediates( + hiddens=hiddens, + attn_intermediates=intermediates + ) + + return x, intermediates + + return x + + +class Encoder(AttentionLayers): + def __init__(self, **kwargs): + assert 'causal' not in kwargs, 'cannot set causality on encoder' + super().__init__(causal=False, **kwargs) + + + +class TransformerWrapper(nn.Module): + def __init__( + self, + *, + num_tokens, + max_seq_len, + attn_layers, + emb_dim=None, + max_mem_len=0., + emb_dropout=0., + num_memory_tokens=None, + tie_embedding=False, + use_pos_emb=True + ): + super().__init__() + assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder' + + dim = attn_layers.dim + emb_dim = default(emb_dim, dim) + + self.max_seq_len = max_seq_len + self.max_mem_len = max_mem_len + self.num_tokens = num_tokens + + self.token_emb = nn.Embedding(num_tokens, emb_dim) + self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if ( + use_pos_emb and not attn_layers.has_pos_emb) else always(0) + self.emb_dropout = nn.Dropout(emb_dropout) + + self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity() + self.attn_layers = attn_layers + self.norm = nn.LayerNorm(dim) + + self.init_() + + self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t() + + # memory tokens (like [cls]) from Memory Transformers paper + num_memory_tokens = default(num_memory_tokens, 0) + self.num_memory_tokens = num_memory_tokens + if num_memory_tokens > 0: + self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim)) + + # let funnel encoder know number of memory tokens, if specified + if hasattr(attn_layers, 'num_memory_tokens'): + attn_layers.num_memory_tokens = num_memory_tokens + + def init_(self): + nn.init.normal_(self.token_emb.weight, std=0.02) + + def forward( + self, + x, + return_embeddings=False, + mask=None, + return_mems=False, + return_attn=False, + mems=None, + **kwargs + ): + b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens + x = self.token_emb(x) + x += self.pos_emb(x) + x = self.emb_dropout(x) + + x = self.project_emb(x) + + if num_mem > 0: + mem = repeat(self.memory_tokens, 'n d -> b n d', b=b) + x = torch.cat((mem, x), dim=1) + + # auto-handle masking after appending memory tokens + if exists(mask): + mask = F.pad(mask, (num_mem, 0), value=True) + + x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs) + x = self.norm(x) + + mem, x = x[:, :num_mem], x[:, num_mem:] + + out = self.to_logits(x) if not return_embeddings else x + + if return_mems: + hiddens = intermediates.hiddens + new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens + new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems)) + return out, new_mems + + if return_attn: + attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates)) + return out, attn_maps + + return out + diff --git a/iChat/models/inpainting_src/ldm_inpainting/ldm/util.py b/iChat/models/inpainting_src/ldm_inpainting/ldm/util.py new file mode 100644 index 0000000000000000000000000000000000000000..8ba38853e7a07228cc2c187742b5c45d7359b3f9 --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/ldm/util.py @@ -0,0 +1,203 @@ +import importlib + +import torch +import numpy as np +from collections import abc +from einops import rearrange +from functools import partial + +import multiprocessing as mp +from threading import Thread +from queue import Queue + +from inspect import isfunction +from PIL import Image, ImageDraw, ImageFont + + +def log_txt_as_img(wh, xc, size=10): + # wh a tuple of (width, height) + # xc a list of captions to plot + b = len(xc) + txts = list() + for bi in range(b): + txt = Image.new("RGB", wh, color="white") + draw = ImageDraw.Draw(txt) + font = ImageFont.truetype('data/DejaVuSans.ttf', size=size) + nc = int(40 * (wh[0] / 256)) + lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) + + try: + draw.text((0, 0), lines, fill="black", font=font) + except UnicodeEncodeError: + print("Cant encode string for logging. Skipping.") + + txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 + txts.append(txt) + txts = np.stack(txts) + txts = torch.tensor(txts) + return txts + + +def ismap(x): + if not isinstance(x, torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] > 3) + + +def isimage(x): + if not isinstance(x, torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) + + +def exists(x): + return x is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def mean_flat(tensor): + """ + https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def count_params(model, verbose=False): + total_params = sum(p.numel() for p in model.parameters()) + if verbose: + print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.") + return total_params + + +def instantiate_from_config(config): + if not "target" in config: + if config == '__is_first_stage__': + return None + elif config == "__is_unconditional__": + return None + raise KeyError("Expected key `target` to instantiate.") + return get_obj_from_str(config["target"])(**config.get("params", dict())) + + +def get_obj_from_str(string, reload=False): + module, cls = string.rsplit(".", 1) + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + + +def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False): + # create dummy dataset instance + + # run prefetching + if idx_to_fn: + res = func(data, worker_id=idx) + else: + res = func(data) + Q.put([idx, res]) + Q.put("Done") + + +def parallel_data_prefetch( + func: callable, data, n_proc, target_data_type="ndarray", cpu_intensive=True, use_worker_id=False +): + # if target_data_type not in ["ndarray", "list"]: + # raise ValueError( + # "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray." + # ) + if isinstance(data, np.ndarray) and target_data_type == "list": + raise ValueError("list expected but function got ndarray.") + elif isinstance(data, abc.Iterable): + if isinstance(data, dict): + print( + f'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.' + ) + data = list(data.values()) + if target_data_type == "ndarray": + data = np.asarray(data) + else: + data = list(data) + else: + raise TypeError( + f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}." + ) + + if cpu_intensive: + Q = mp.Queue(1000) + proc = mp.Process + else: + Q = Queue(1000) + proc = Thread + # spawn processes + if target_data_type == "ndarray": + arguments = [ + [func, Q, part, i, use_worker_id] + for i, part in enumerate(np.array_split(data, n_proc)) + ] + else: + step = ( + int(len(data) / n_proc + 1) + if len(data) % n_proc != 0 + else int(len(data) / n_proc) + ) + arguments = [ + [func, Q, part, i, use_worker_id] + for i, part in enumerate( + [data[i: i + step] for i in range(0, len(data), step)] + ) + ] + processes = [] + for i in range(n_proc): + p = proc(target=_do_parallel_data_prefetch, args=arguments[i]) + processes += [p] + + # start processes + print(f"Start prefetching...") + import time + + start = time.time() + gather_res = [[] for _ in range(n_proc)] + try: + for p in processes: + p.start() + + k = 0 + while k < n_proc: + # get result + res = Q.get() + if res == "Done": + k += 1 + else: + gather_res[res[0]] = res[1] + + except Exception as e: + print("Exception: ", e) + for p in processes: + p.terminate() + + raise e + finally: + for p in processes: + p.join() + print(f"Prefetching complete. [{time.time() - start} sec.]") + + if target_data_type == 'ndarray': + if not isinstance(gather_res[0], np.ndarray): + return np.concatenate([np.asarray(r) for r in gather_res], axis=0) + + # order outputs + return np.concatenate(gather_res, axis=0) + elif target_data_type == 'list': + out = [] + for r in gather_res: + out.extend(r) + return out + else: + return gather_res diff --git a/iChat/models/inpainting_src/ldm_inpainting/wrapper.py b/iChat/models/inpainting_src/ldm_inpainting/wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..01c6d02ae065975221997e7c3eded17792e066cc --- /dev/null +++ b/iChat/models/inpainting_src/ldm_inpainting/wrapper.py @@ -0,0 +1,80 @@ +import os +import sys + +import torch +from omegaconf import OmegaConf +import numpy as np + +from .ldm.models.diffusion.ddim import DDIMSampler +from .ldm.util import instantiate_from_config + +CURRENT_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(CURRENT_DIR) + + +def make_batch(image, mask, device): + image = image.astype(np.float32) / 255.0 + image = image[None].transpose(0, 3, 1, 2) + image = torch.from_numpy(image) + + mask = mask.astype(np.float32) / 255.0 + mask = mask[None, None] + mask[mask < 0.5] = 0 + mask[mask >= 0.5] = 1 + mask = torch.from_numpy(mask) + + masked_image = (1 - mask) * image + + batch = {"image": image, "mask": mask, "masked_image": masked_image} + for k in batch: + batch[k] = batch[k].to(device=device) + batch[k] = batch[k] * 2.0 - 1.0 + return batch + + +class LDMInpainter: + def __init__(self, ckpt_path, ddim_steps=50): + config = OmegaConf.load(os.path.join(CURRENT_DIR, "config.yaml")) + model = instantiate_from_config(config.model) + model.load_state_dict(torch.load(ckpt_path)["state_dict"], strict=False) + self.model = model + self.sampler = DDIMSampler(model) + self.ddim_steps = ddim_steps + + @torch.no_grad() + def __call__(self, image, mask, device): + self.model.to(device) + + model = self.model + sampler = self.sampler + + with self.model.ema_scope(): + batch = make_batch(image, mask, device=device) + + # encode masked image and concat downsampled mask + c = model.cond_stage_model.encode(batch["masked_image"]) + cc = torch.nn.functional.interpolate(batch["mask"], + size=c.shape[-2:]) + c = torch.cat((c, cc), dim=1) + + shape = (c.shape[1] - 1,) + c.shape[2:] + samples_ddim, _ = sampler.sample(S=self.ddim_steps, + conditioning=c, + batch_size=c.shape[0], + shape=shape, + verbose=False) + x_samples_ddim = model.decode_first_stage(samples_ddim) + + image = torch.clamp((batch["image"] + 1.0) / 2.0, + min=0.0, max=1.0) + mask = torch.clamp((batch["mask"] + 1.0) / 2.0, + min=0.0, max=1.0) + predicted_image = torch.clamp((x_samples_ddim + 1.0) / 2.0, + min=0.0, max=1.0) + + inpainted = (1 - mask) * image + mask * predicted_image + inpainted = inpainted.cpu().numpy().transpose(0, 2, 3, 1)[0] * 255 + + # offload to cpu to save memory + self.model.to(torch.device('cpu')) + return inpainted.astype(np.uint8) diff --git a/iChat/models/intern_action.py b/iChat/models/intern_action.py new file mode 100644 index 0000000000000000000000000000000000000000..f60f6df1748bce94c4ebaed85619c403b2ec33ab --- /dev/null +++ b/iChat/models/intern_action.py @@ -0,0 +1,510 @@ +#!/usr/bin/env python +import os +from collections import OrderedDict + +from timm.models.layers import DropPath +import torch +from torch import nn +from torch.nn import MultiheadAttention +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint + + +MODEL_PATH = './' +_MODELS = { + "ViT-B/16": os.path.join(MODEL_PATH, "vit_b16.pth"), + "ViT-L/14": os.path.join(MODEL_PATH, "vit_l14.pth"), + "ViT-L/14_336": os.path.join(MODEL_PATH, "vit_l14_336.pth"), +} + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16.""" + + def forward(self, x): + orig_type = x.dtype + ret = super().forward(x.type(torch.float32)) + return ret.type(orig_type) + + +class QuickGELU(nn.Module): + def forward(self, x): + return x * torch.sigmoid(1.702 * x) + + +class Local_MHRA(nn.Module): + def __init__(self, d_model, dw_reduction=1.5, pos_kernel_size=3): + super().__init__() + + padding = pos_kernel_size // 2 + re_d_model = int(d_model // dw_reduction) + self.pos_embed = nn.Sequential( + nn.BatchNorm3d(d_model), + nn.Conv3d(d_model, re_d_model, kernel_size=1, stride=1, padding=0), + nn.Conv3d(re_d_model, re_d_model, kernel_size=(pos_kernel_size, 1, 1), stride=(1, 1, 1), padding=(padding, 0, 0), groups=re_d_model), + nn.Conv3d(re_d_model, d_model, kernel_size=1, stride=1, padding=0), + ) + + # init zero + print('Init zero for Conv in pos_emb') + nn.init.constant_(self.pos_embed[3].weight, 0) + nn.init.constant_(self.pos_embed[3].bias, 0) + + def forward(self, x): + return self.pos_embed(x) + + +class ResidualAttentionBlock(nn.Module): + def __init__( + self, d_model, n_head, attn_mask=None, drop_path=0.0, + dw_reduction=1.5, no_lmhra=False, double_lmhra=True + ): + super().__init__() + + self.n_head = n_head + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + print(f'Drop path rate: {drop_path}') + + self.no_lmhra = no_lmhra + self.double_lmhra = double_lmhra + print(f'No L_MHRA: {no_lmhra}') + print(f'Double L_MHRA: {double_lmhra}') + if not no_lmhra: + self.lmhra1 = Local_MHRA(d_model, dw_reduction=dw_reduction) + if double_lmhra: + self.lmhra2 = Local_MHRA(d_model, dw_reduction=dw_reduction) + + # spatial + self.attn = MultiheadAttention(d_model, n_head) + self.ln_1 = LayerNorm(d_model) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, d_model * 4)), + ("gelu", QuickGELU()), + ("c_proj", nn.Linear(d_model * 4, d_model)) + ])) + self.ln_2 = LayerNorm(d_model) + self.attn_mask = attn_mask + + def attention(self, x): + self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None + return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] + + def forward(self, x, T=8, use_checkpoint=False): + # x: 1+HW, NT, C + if not self.no_lmhra: + # Local MHRA + tmp_x = x[1:, :, :] + L, NT, C = tmp_x.shape + N = NT // T + H = W = int(L ** 0.5) + tmp_x = tmp_x.view(H, W, N, T, C).permute(2, 4, 3, 0, 1).contiguous() + tmp_x = tmp_x + self.drop_path(self.lmhra1(tmp_x)) + tmp_x = tmp_x.view(N, C, T, L).permute(3, 0, 2, 1).contiguous().view(L, NT, C) + x = torch.cat([x[:1, :, :], tmp_x], dim=0) + # MHSA + if use_checkpoint: + attn_out = checkpoint.checkpoint(self.attention, self.ln_1(x)) + x = x + self.drop_path(attn_out) + else: + x = x + self.drop_path(self.attention(self.ln_1(x))) + # Local MHRA + if not self.no_lmhra and self.double_lmhra: + tmp_x = x[1:, :, :] + tmp_x = tmp_x.view(H, W, N, T, C).permute(2, 4, 3, 0, 1).contiguous() + tmp_x = tmp_x + self.drop_path(self.lmhra2(tmp_x)) + tmp_x = tmp_x.view(N, C, T, L).permute(3, 0, 2, 1).contiguous().view(L, NT, C) + x = torch.cat([x[:1, :, :], tmp_x], dim=0) + # FFN + if use_checkpoint: + mlp_out = checkpoint.checkpoint(self.mlp, self.ln_2(x)) + x = x + self.drop_path(mlp_out) + else: + x = x + self.drop_path(self.mlp(self.ln_2(x))) + return x + + +class Extractor(nn.Module): + def __init__( + self, d_model, n_head, attn_mask=None, + mlp_factor=4.0, dropout=0.0, drop_path=0.0, + ): + super().__init__() + + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + print(f'Drop path rate: {drop_path}') + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ln_1 = nn.LayerNorm(d_model) + d_mlp = round(mlp_factor * d_model) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, d_mlp)), + ("gelu", QuickGELU()), + ("dropout", nn.Dropout(dropout)), + ("c_proj", nn.Linear(d_mlp, d_model)) + ])) + self.ln_2 = nn.LayerNorm(d_model) + self.ln_3 = nn.LayerNorm(d_model) + self.attn_mask = attn_mask + + # zero init + nn.init.xavier_uniform_(self.attn.in_proj_weight) + nn.init.constant_(self.attn.out_proj.weight, 0.) + nn.init.constant_(self.attn.out_proj.bias, 0.) + nn.init.xavier_uniform_(self.mlp[0].weight) + nn.init.constant_(self.mlp[-1].weight, 0.) + nn.init.constant_(self.mlp[-1].bias, 0.) + + def attention(self, x, y): + d_model = self.ln_1.weight.size(0) + q = (x @ self.attn.in_proj_weight[:d_model].T) + self.attn.in_proj_bias[:d_model] + + k = (y @ self.attn.in_proj_weight[d_model:-d_model].T) + self.attn.in_proj_bias[d_model:-d_model] + v = (y @ self.attn.in_proj_weight[-d_model:].T) + self.attn.in_proj_bias[-d_model:] + Tx, Ty, N = q.size(0), k.size(0), q.size(1) + q = q.view(Tx, N, self.attn.num_heads, self.attn.head_dim).permute(1, 2, 0, 3) + k = k.view(Ty, N, self.attn.num_heads, self.attn.head_dim).permute(1, 2, 0, 3) + v = v.view(Ty, N, self.attn.num_heads, self.attn.head_dim).permute(1, 2, 0, 3) + aff = (q @ k.transpose(-2, -1) / (self.attn.head_dim ** 0.5)) + + aff = aff.softmax(dim=-1) + out = aff @ v + out = out.permute(2, 0, 1, 3).flatten(2) + out = self.attn.out_proj(out) + return out + + def forward(self, x, y): + x = x + self.drop_path(self.attention(self.ln_1(x), self.ln_3(y))) + x = x + self.drop_path(self.mlp(self.ln_2(x))) + return x + + +class Transformer(nn.Module): + def __init__( + self, width, layers, heads, attn_mask=None, backbone_drop_path_rate=0., + use_checkpoint=False, checkpoint_num=[0], t_size=8, dw_reduction=2, + no_lmhra=False, double_lmhra=True, + return_list=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], + n_layers=12, n_dim=768, n_head=12, mlp_factor=4.0, drop_path_rate=0., + mlp_dropout=[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], + cls_dropout=0.5, num_classes=400, + ): + super().__init__() + self.T = t_size + self.return_list = return_list + # backbone + b_dpr = [x.item() for x in torch.linspace(0, backbone_drop_path_rate, layers)] + self.resblocks = nn.ModuleList([ + ResidualAttentionBlock( + width, heads, attn_mask, + drop_path=b_dpr[i], + dw_reduction=dw_reduction, + no_lmhra=no_lmhra, + double_lmhra=double_lmhra, + ) for i in range(layers) + ]) + # checkpoint + self.use_checkpoint = use_checkpoint + self.checkpoint_num = checkpoint_num + self.n_layers = n_layers + print(f'Use checkpoint: {self.use_checkpoint}') + print(f'Checkpoint number: {self.checkpoint_num}') + + # global block + assert n_layers == len(return_list) + if n_layers > 0: + self.temporal_cls_token = nn.Parameter(torch.zeros(1, 1, n_dim)) + self.dpe = nn.ModuleList([ + nn.Conv3d(n_dim, n_dim, kernel_size=3, stride=1, padding=1, bias=True, groups=n_dim) + for i in range(n_layers) + ]) + for m in self.dpe: + nn.init.constant_(m.bias, 0.) + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, n_layers)] + self.dec = nn.ModuleList([ + Extractor( + n_dim, n_head, mlp_factor=mlp_factor, + dropout=mlp_dropout[i], drop_path=dpr[i], + ) for i in range(n_layers) + ]) + self.balance = nn.Parameter(torch.zeros((n_dim))) + self.sigmoid = nn.Sigmoid() + # projection + self.proj = nn.Sequential( + nn.LayerNorm(n_dim), + nn.Dropout(cls_dropout), + nn.Linear(n_dim, num_classes), + ) + + def forward(self, x): + T_down = self.T + L, NT, C = x.shape + N = NT // T_down + H = W = int((L - 1) ** 0.5) + + if self.n_layers > 0: + cls_token = self.temporal_cls_token.repeat(1, N, 1) + + j = -1 + for i, resblock in enumerate(self.resblocks): + if self.use_checkpoint and i < self.checkpoint_num[0]: + x = resblock(x, self.T, use_checkpoint=True) + else: + x = resblock(x, T_down) + if i in self.return_list: + j += 1 + tmp_x = x.clone() + tmp_x = tmp_x.view(L, N, T_down, C) + # dpe + _, tmp_feats = tmp_x[:1], tmp_x[1:] + tmp_feats = tmp_feats.permute(1, 3, 2, 0).reshape(N, C, T_down, H, W) + tmp_feats = self.dpe[j](tmp_feats).view(N, C, T_down, L - 1).permute(3, 0, 2, 1).contiguous() + tmp_x[1:] = tmp_x[1:] + tmp_feats + # global block + tmp_x = tmp_x.permute(2, 0, 1, 3).flatten(0, 1) # T * L, N, C + cls_token = self.dec[j](cls_token, tmp_x) + + if self.n_layers > 0: + weight = self.sigmoid(self.balance) + residual = x.view(L, N, T_down, C)[0].mean(1) # L, N, T, C + return self.proj((1 - weight) * cls_token[0, :, :] + weight * residual) + else: + residual = x.view(L, N, T_down, C)[0].mean(1) # L, N, T, C + return self.proj(residual) + + +class VisionTransformer(nn.Module): + def __init__( + self, + # backbone + input_resolution, patch_size, width, layers, heads, output_dim, backbone_drop_path_rate=0., + use_checkpoint=False, checkpoint_num=[0], t_size=8, kernel_size=3, dw_reduction=1.5, + temporal_downsample=True, + no_lmhra=-False, double_lmhra=True, + # global block + return_list=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], + n_layers=12, n_dim=768, n_head=12, mlp_factor=4.0, drop_path_rate=0., + mlp_dropout=[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], + cls_dropout=0.5, num_classes=400, + ): + super().__init__() + self.input_resolution = input_resolution + self.output_dim = output_dim + padding = (kernel_size - 1) // 2 + if temporal_downsample: + self.conv1 = nn.Conv3d(3, width, (kernel_size, patch_size, patch_size), (2, patch_size, patch_size), (padding, 0, 0), bias=False) + t_size = t_size // 2 + else: + self.conv1 = nn.Conv3d(3, width, (1, patch_size, patch_size), (1, patch_size, patch_size), (0, 0, 0), bias=False) + + scale = width ** -0.5 + self.class_embedding = nn.Parameter(scale * torch.randn(width)) + self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width)) + self.ln_pre = LayerNorm(width) + + self.transformer = Transformer( + width, layers, heads, dw_reduction=dw_reduction, + backbone_drop_path_rate=backbone_drop_path_rate, + use_checkpoint=use_checkpoint, checkpoint_num=checkpoint_num, t_size=t_size, + no_lmhra=no_lmhra, double_lmhra=double_lmhra, + return_list=return_list, n_layers=n_layers, n_dim=n_dim, n_head=n_head, + mlp_factor=mlp_factor, drop_path_rate=drop_path_rate, mlp_dropout=mlp_dropout, + cls_dropout=cls_dropout, num_classes=num_classes, + ) + + def forward(self, x): + x = self.conv1(x) # shape = [*, width, grid, grid] + N, C, T, H, W = x.shape + x = x.permute(0, 2, 3, 4, 1).reshape(N * T, H * W, C) + + x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width] + x = x + self.positional_embedding.to(x.dtype) + x = self.ln_pre(x) + + x = x.permute(1, 0, 2) # NLD -> LND + out = self.transformer(x) + return out + + +def inflate_weight(weight_2d, time_dim, center=True): + print(f'Init center: {center}') + if center: + weight_3d = torch.zeros(*weight_2d.shape) + weight_3d = weight_3d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1) + middle_idx = time_dim // 2 + weight_3d[:, :, middle_idx, :, :] = weight_2d + else: + weight_3d = weight_2d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1) + weight_3d = weight_3d / time_dim + return weight_3d + + +def load_state_dict(model, state_dict): + state_dict_3d = model.state_dict() + for k in state_dict.keys(): + if state_dict[k].shape != state_dict_3d[k].shape: + if len(state_dict_3d[k].shape) <= 2: + print(f'Ignore: {k}') + continue + print(f'Inflate: {k}, {state_dict[k].shape} => {state_dict_3d[k].shape}') + time_dim = state_dict_3d[k].shape[2] + state_dict[k] = inflate_weight(state_dict[k], time_dim) + model.load_state_dict(state_dict, strict=False) + + +def intern_action_b16( + pretrained=True, use_checkpoint=False, checkpoint_num=[0], + t_size=16, dw_reduction=1.5, backbone_drop_path_rate=0., + temporal_downsample=True, + no_lmhra=False, double_lmhra=True, + return_list=[8, 9, 10, 11], + n_layers=4, n_dim=768, n_head=12, mlp_factor=4.0, drop_path_rate=0., + mlp_dropout=[0.5, 0.5, 0.5, 0.5], + cls_dropout=0.5, num_classes=400, +): + model = VisionTransformer( + input_resolution=224, + patch_size=16, + width=768, + layers=12, + heads=12, + output_dim=512, + use_checkpoint=use_checkpoint, + checkpoint_num=checkpoint_num, + t_size=t_size, + dw_reduction=dw_reduction, + backbone_drop_path_rate=backbone_drop_path_rate, + temporal_downsample=temporal_downsample, + no_lmhra=no_lmhra, + double_lmhra=double_lmhra, + return_list=return_list, + n_layers=n_layers, + n_dim=n_dim, + n_head=n_head, + mlp_factor=mlp_factor, + drop_path_rate=drop_path_rate, + mlp_dropout=mlp_dropout, + cls_dropout=cls_dropout, + num_classes=num_classes, + ) + + if pretrained: + print('load pretrained weights') + state_dict = torch.load(_MODELS["ViT-B/16"], map_location='cpu') + load_state_dict(model, state_dict) + return model.eval() + + +def intern_action_l14( + pretrained=True, use_checkpoint=False, checkpoint_num=[0], + t_size=16, dw_reduction=1.5, backbone_drop_path_rate=0., + temporal_downsample=True, + no_lmhra=False, double_lmhra=True, + return_list=[20, 21, 22, 23], + n_layers=4, n_dim=1024, n_head=16, mlp_factor=4.0, drop_path_rate=0., + mlp_dropout=[0.5, 0.5, 0.5, 0.5], + cls_dropout=0.5, num_classes=400, +): + model = VisionTransformer( + input_resolution=224, + patch_size=14, + width=1024, + layers=24, + heads=16, + output_dim=768, + use_checkpoint=use_checkpoint, + checkpoint_num=checkpoint_num, + t_size=t_size, + dw_reduction=dw_reduction, + backbone_drop_path_rate=backbone_drop_path_rate, + temporal_downsample=temporal_downsample, + no_lmhra=no_lmhra, + double_lmhra=double_lmhra, + return_list=return_list, + n_layers=n_layers, + n_dim=n_dim, + n_head=n_head, + mlp_factor=mlp_factor, + drop_path_rate=drop_path_rate, + mlp_dropout=mlp_dropout, + cls_dropout=cls_dropout, + num_classes=num_classes, + ) + + if pretrained: + print('load pretrained weights') + state_dict = torch.load(_MODELS["ViT-L/14"], map_location='cpu') + load_state_dict(model, state_dict) + return model.eval() + + +def intern_action_l14_336( + pretrained=True, use_checkpoint=False, checkpoint_num=[0], + t_size=16, dw_reduction=1.5, backbone_drop_path_rate=0., + no_temporal_downsample=True, + no_lmhra=False, double_lmhra=True, + return_list=[20, 21, 22, 23], + n_layers=4, n_dim=1024, n_head=16, mlp_factor=4.0, drop_path_rate=0., + mlp_dropout=[0.5, 0.5, 0.5, 0.5], + cls_dropout=0.5, num_classes=400, +): + model = VisionTransformer( + input_resolution=336, + patch_size=14, + width=1024, + layers=24, + heads=16, + output_dim=768, + use_checkpoint=use_checkpoint, + checkpoint_num=checkpoint_num, + t_size=t_size, + dw_reduction=dw_reduction, + backbone_drop_path_rate=backbone_drop_path_rate, + no_temporal_downsample=no_temporal_downsample, + no_lmhra=no_lmhra, + double_lmhra=double_lmhra, + return_list=return_list, + n_layers=n_layers, + n_dim=n_dim, + n_head=n_head, + mlp_factor=mlp_factor, + drop_path_rate=drop_path_rate, + mlp_dropout=mlp_dropout, + cls_dropout=cls_dropout, + num_classes=num_classes, + ) + + if pretrained: + print('load pretrained weights') + state_dict = torch.load(_MODELS["ViT-L/14_336"], map_location='cpu') + load_state_dict(model, state_dict) + return model.eval() + + +if __name__ == '__main__': + import time + from fvcore.nn import FlopCountAnalysis + from fvcore.nn import flop_count_table + import numpy as np + + seed = 4217 + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + num_frames = 16 + + model = intern_action_l14( + pretrained=False, + t_size=num_frames, backbone_drop_path_rate=0., drop_path_rate=0., + dw_reduction=1.5, + no_lmhra=False, + temporal_downsample=True, + return_list=[8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], + mlp_dropout=[0.5]*16, + n_layers=16 + ) + print(model) + + flops = FlopCountAnalysis(model, torch.rand(1, 3, num_frames, 224, 224)) + s = time.time() + print(flop_count_table(flops, max_depth=1)) + print(time.time()-s) diff --git a/iChat/models/lang.py b/iChat/models/lang.py new file mode 100644 index 0000000000000000000000000000000000000000..81b0415482a25985aed58e410efcddd28a59b242 --- /dev/null +++ b/iChat/models/lang.py @@ -0,0 +1,137 @@ +from langchain.llms.openai import OpenAI +from langchain.agents.initialize import initialize_agent +from langchain.agents.tools import Tool +from langchain.chains.conversation.memory import ConversationBufferMemory + +VISUAL_CHATGPT_PREFIX = """iGPT is designed to be able to assist with a wide range of text and visual related tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. iGPT is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand. + +iGPT is able to process and understand large amounts of text and visual signals that include images and videos. As a language model, iGPT can not directly read images or videos, but it has a list of tools to finish different visual tasks. Each image will have a file name formed as "tmp/xxx.png" and each video have a file name formed as "tmp/xxx.mp4". iGPT can invoke different tools to indirectly understand pictures and videos. When talking about images or videos, iGPT is very strict to the file name and will never fabricate nonexistent files. When using tools to generate new image or video files, iGPT is also known that the image or video may not be the same as the user's demand, and will use other visual question answering tools or description tools to observe the real image. iGPT is able to use tools in a sequence, and is loyal to the tool observation outputs rather than faking the image content, video content and their file name. It will remember to provide the file name from the last tool observation, if a new image or video is generated. + +Human may provide new figures or videos to iGPT with a description. The description helps iGPT to understand this image or video, but iGPT should use tools to finish following tasks, rather than directly imagine from the description. + +Be careful, iGPT should distinguish the image tasks and video tasks when invoking the tools to answer the question. + +Overall, iGPT is a powerful visual dialogue assistant tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. + +TOOLS: +------ + +iGPT has access to the following tools:""" + +VISUAL_CHATGPT_FORMAT_INSTRUCTIONS = """To use a tool, please use the following format: + +``` +Thought: Do I need to use a tool? Yes +Action: the action to take, should be one of [{tool_names}] +Action Input: the input to the action +Observation: the result of the action +``` + +When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format: + +``` +Thought: Do I need to use a tool? No +{ai_prefix}: [your response here] +``` +""" + +VISUAL_CHATGPT_SUFFIX = """You are very strict to the filename correctness and will never fake a file name if it does not exist. +You will remember to provide the image file name loyally if it's provided in the last tool observation. + +Begin! + +Previous conversation history: +{chat_history} + +New input: {input} +Since iGPT is a text language model, iGPT must use tools to observe images and videos rather than imagination. +The thoughts and observations are only visible for iGPT, iGPT should remember to repeat important information in the final response for Human. +Thought: Do I need to use a tool? {agent_scratchpad} Let's think step by step. +""" + +VISUAL_CHATGPT_PREFIX_CN = """iGPT 旨在能够协助完成范围广泛的文本和视觉相关任务，从回答简单的问题到提供对广泛主题的深入解释和讨论。 iGPT 能够根据收到的输入生成类似人类的文本，使其能够进行听起来自然的对话，并提供连贯且与手头主题相关的响应。 + +iGPT 能够处理和理解大量文本、视觉信息，其中视觉信息包括图像和视频。作为一种语言模型，iGPT 不能直接读取图像或者视频，但它有一系列工具来完成不同的视觉任务。每张图片或者视频都会有一个文件名，图片的格式为“tmp/xxx.png”，视频的格式为“tmp/xxx.mp4”。iGPT可以调用不同的工具来间接理解图片和视频。在谈论图片和视频时，iGPT 对文件名的要求非常严格，绝不会伪造不存在的文件。在使用工具生成新的图像或者视频文件时，iGPT也知道图像或者视频可能与用户需求不一样，会使用其他视觉问答工具或描述工具来观察真实图像和视频。 iGPT 能够按顺序使用工具，并且忠于工具观察输出，而不是伪造视觉内容和文件名。如果生成新图像或者视频，它将记得提供上次工具观察的文件名。 + +Human 可能会向 iGPT 提供带有描述的新图形。描述帮助 iGPT 理解这个图像或者视频，但 iGPT 应该使用工具来完成以下任务，而不是直接从描述中想象。有些工具将会返回英文描述，但你对用户的聊天应当采用中文。 + +注意，在调用接口时，请正确区分图片任务和视频任务，避免将图片传入到视频工具中或者视频传入到图片工具中。 + +总的来说，iGPT 是一个强大的可视化对话辅助工具，可以帮助处理范围广泛的任务，并提供关于范围广泛的主题的有价值的见解和信息。 + + +工具列表: +------ + +iGPT 可以使用这些工具:""" + +VISUAL_CHATGPT_FORMAT_INSTRUCTIONS_CN = """用户使用中文和你进行聊天，但是工具的参数应当使用英文。如果要调用工具，你必须遵循如下格式: + +``` +Thought: Do I need to use a tool? Yes +Action: the action to take, should be one of [{tool_names}] +Action Input: the input to the action +Observation: the result of the action +``` + +当你不再需要继续调用工具，而是对观察结果进行总结回复时，你必须使用如下格式： + + +``` +Thought: Do I need to use a tool? No +{ai_prefix}: [your response here] +``` +""" + +VISUAL_CHATGPT_SUFFIX_CN = """你对文件名的正确性非常严格，而且永远不会伪造不存在的文件。 + +开始! + +因为iGPT是一个文本语言模型，必须使用工具去观察图片或者视频而不是依靠想象。 +推理想法和观察结果只对iGPT可见，需要记得在最终回复时把重要的信息重复给用户，你只能给用户返回中文句子。我们一步一步思考。在你使用工具时，工具的参数只能是英文。 + +聊天历史: +{chat_history} + +新输入: {input} +Thought: Do I need to use a tool? {agent_scratchpad} +""" + + +class SimpleLanguageModel: + def __init__(self, device=None): + self.device = device + self.llm = OpenAI(temperature=0) + + def inference(self, inputs): + text = inputs.strip() + return self.llm(text) + + def __call__(self, inputs): + return self.inference(inputs) + + +class LanguageModelWithMemory: + def __init__(self, device): + self.device = device + self.memory = ConversationBufferMemory(memory_key="chat_history", output_key='output') + self.llm = OpenAI(temperature=0) + self.agent = None + + def init(self, tools): + self.agent = initialize_agent( + tools, + self.llm, + agent="conversational-react-description", + verbose=True, + memory=self.memory, + return_intermediate_steps=True, + agent_kwargs={'prefix': VISUAL_CHATGPT_PREFIX, 'format_instructions': VISUAL_CHATGPT_FORMAT_INSTRUCTIONS, + 'suffix': VISUAL_CHATGPT_SUFFIX}, ) + + def __call__(self, inputs): + text = inputs.strip() + return self.agent({"input": text.strip()}) + + + diff --git a/iChat/models/load_internvideo.py b/iChat/models/load_internvideo.py new file mode 100644 index 0000000000000000000000000000000000000000..da24068b9ac20aa85195c0b2a0e36bf509e2371b --- /dev/null +++ b/iChat/models/load_internvideo.py @@ -0,0 +1,469 @@ +from .intern_action import intern_action_b16 +from huggingface_hub import hf_hub_download +# from kinetics_class_index import kinetics_classnames +import torch +import torch.nn as nn +import torchvision.transforms as T +import torch.nn.functional as F +import numpy as np + +from .processing import ( + GroupNormalize, GroupScale, GroupCenterCrop, + Stack, ToTorchFormatTensor +) + +class Intern_Action(nn.Module): + def __init__(self, model): + super().__init__() + self.backbone = model + + def forward(self, x): + return self.backbone(x) + +def get_index(num_frames, num_segments=8): + seg_size = float(num_frames - 1) / num_segments + start = int(seg_size / 2) + offsets = np.array([ + start + int(np.round(seg_size * idx)) for idx in range(num_segments) + ]) + return offsets + +def transform_action(): + # transform + crop_size = 224 + scale_size = 256 + input_mean = [0.485, 0.456, 0.406] + input_std = [0.229, 0.224, 0.225] + + return T.Compose([ + # T.ToPILImage(), + GroupScale(int(scale_size)), + GroupCenterCrop(crop_size), + Stack(), + ToTorchFormatTensor(), + GroupNormalize(input_mean, input_std) + ]) + +def load_intern_action(device): + # Create an id to label name mapping + kinetics_id_to_classname = {} + for k, v in kinetics_classnames.items(): + kinetics_id_to_classname[k] = v + + model_path = hf_hub_download(repo_id="Andy1621/uniformerv2", filename="k400+k710_uniformerv2_b16_8x224.pyth") + # Pick a pretrained model + model = Intern_Action(intern_action_b16(pretrained=False, t_size=8, no_lmhra=True, temporal_downsample=False)) + state_dict = torch.load(model_path, map_location=device) + model.load_state_dict(state_dict) + # Set to eval mode and move to desired device + model = model.to(device) + model = model.eval() + return model + +def cut_frame_to_8(data): + index = np.linspace(0, len(data)-1, 8).astype(int) + return data[index] + +kinetics_classnames = { + "0": "riding a bike", + "1": "marching", + "2": "dodgeball", + "3": "playing cymbals", + "4": "checking tires", + "5": "roller skating", + "6": "tasting beer", + "7": "clapping", + "8": "drawing", + "9": "juggling fire", + "10": "bobsledding", + "11": "petting animal (not cat)", + "12": "spray painting", + "13": "training dog", + "14": "eating watermelon", + "15": "building cabinet", + "16": "applauding", + "17": "playing harp", + "18": "balloon blowing", + "19": "sled dog racing", + "20": "wrestling", + "21": "pole vault", + "22": "hurling (sport)", + "23": "riding scooter", + "24": "shearing sheep", + "25": "sweeping floor", + "26": "eating carrots", + "27": "skateboarding", + "28": "dunking basketball", + "29": "disc golfing", + "30": "eating spaghetti", + "31": "playing flute", + "32": "riding mechanical bull", + "33": "making sushi", + "34": "trapezing", + "35": "picking fruit", + "36": "stretching leg", + "37": "playing ukulele", + "38": "tying tie", + "39": "skydiving", + "40": "playing cello", + "41": "jumping into pool", + "42": "shooting goal (soccer)", + "43": "trimming trees", + "44": "bookbinding", + "45": "ski jumping", + "46": "walking the dog", + "47": "riding unicycle", + "48": "shaving head", + "49": "hopscotch", + "50": "playing piano", + "51": "parasailing", + "52": "bartending", + "53": "kicking field goal", + "54": "finger snapping", + "55": "dining", + "56": "yawning", + "57": "peeling potatoes", + "58": "canoeing or kayaking", + "59": "front raises", + "60": "laughing", + "61": "dancing macarena", + "62": "digging", + "63": "reading newspaper", + "64": "hitting baseball", + "65": "clay pottery making", + "66": "exercising with an exercise ball", + "67": "playing saxophone", + "68": "shooting basketball", + "69": "washing hair", + "70": "lunge", + "71": "brushing hair", + "72": "curling hair", + "73": "kitesurfing", + "74": "tapping guitar", + "75": "bending back", + "76": "skipping rope", + "77": "situp", + "78": "folding paper", + "79": "cracking neck", + "80": "assembling computer", + "81": "cleaning gutters", + "82": "blowing out candles", + "83": "shaking hands", + "84": "dancing gangnam style", + "85": "windsurfing", + "86": "tap dancing", + "87": "skiing (not slalom or crosscountry)", + "88": "bandaging", + "89": "push up", + "90": "doing nails", + "91": "punching person (boxing)", + "92": "bouncing on trampoline", + "93": "scrambling eggs", + "94": "singing", + "95": "cleaning floor", + "96": "krumping", + "97": "drumming fingers", + "98": "snowmobiling", + "99": "gymnastics tumbling", + "100": "headbanging", + "101": "catching or throwing frisbee", + "102": "riding elephant", + "103": "bee keeping", + "104": "feeding birds", + "105": "snatch weight lifting", + "106": "mowing lawn", + "107": "fixing hair", + "108": "playing trumpet", + "109": "flying kite", + "110": "crossing river", + "111": "swinging legs", + "112": "sanding floor", + "113": "belly dancing", + "114": "sneezing", + "115": "clean and jerk", + "116": "side kick", + "117": "filling eyebrows", + "118": "shuffling cards", + "119": "recording music", + "120": "cartwheeling", + "121": "feeding fish", + "122": "folding clothes", + "123": "water skiing", + "124": "tobogganing", + "125": "blowing leaves", + "126": "smoking", + "127": "unboxing", + "128": "tai chi", + "129": "waxing legs", + "130": "riding camel", + "131": "slapping", + "132": "tossing salad", + "133": "capoeira", + "134": "playing cards", + "135": "playing organ", + "136": "playing violin", + "137": "playing drums", + "138": "tapping pen", + "139": "vault", + "140": "shoveling snow", + "141": "playing tennis", + "142": "getting a tattoo", + "143": "making a sandwich", + "144": "making tea", + "145": "grinding meat", + "146": "squat", + "147": "eating doughnuts", + "148": "ice fishing", + "149": "snowkiting", + "150": "kicking soccer ball", + "151": "playing controller", + "152": "giving or receiving award", + "153": "welding", + "154": "throwing discus", + "155": "throwing axe", + "156": "ripping paper", + "157": "swimming butterfly stroke", + "158": "air drumming", + "159": "blowing nose", + "160": "hockey stop", + "161": "taking a shower", + "162": "bench pressing", + "163": "planting trees", + "164": "pumping fist", + "165": "climbing tree", + "166": "tickling", + "167": "high kick", + "168": "waiting in line", + "169": "slacklining", + "170": "tango dancing", + "171": "hurdling", + "172": "carrying baby", + "173": "celebrating", + "174": "sharpening knives", + "175": "passing American football (in game)", + "176": "headbutting", + "177": "playing recorder", + "178": "brush painting", + "179": "garbage collecting", + "180": "robot dancing", + "181": "shredding paper", + "182": "pumping gas", + "183": "rock climbing", + "184": "hula hooping", + "185": "braiding hair", + "186": "opening present", + "187": "texting", + "188": "decorating the christmas tree", + "189": "answering questions", + "190": "playing keyboard", + "191": "writing", + "192": "bungee jumping", + "193": "sniffing", + "194": "eating burger", + "195": "playing accordion", + "196": "making pizza", + "197": "playing volleyball", + "198": "tasting food", + "199": "pushing cart", + "200": "spinning poi", + "201": "cleaning windows", + "202": "arm wrestling", + "203": "changing oil", + "204": "swimming breast stroke", + "205": "tossing coin", + "206": "deadlifting", + "207": "hoverboarding", + "208": "cutting watermelon", + "209": "cheerleading", + "210": "snorkeling", + "211": "washing hands", + "212": "eating cake", + "213": "pull ups", + "214": "surfing water", + "215": "eating hotdog", + "216": "holding snake", + "217": "playing harmonica", + "218": "ironing", + "219": "cutting nails", + "220": "golf chipping", + "221": "shot put", + "222": "hugging", + "223": "playing clarinet", + "224": "faceplanting", + "225": "trimming or shaving beard", + "226": "drinking shots", + "227": "riding mountain bike", + "228": "tying bow tie", + "229": "swinging on something", + "230": "skiing crosscountry", + "231": "unloading truck", + "232": "cleaning pool", + "233": "jogging", + "234": "ice climbing", + "235": "mopping floor", + "236": "making bed", + "237": "diving cliff", + "238": "washing dishes", + "239": "grooming dog", + "240": "weaving basket", + "241": "frying vegetables", + "242": "stomping grapes", + "243": "moving furniture", + "244": "cooking sausages", + "245": "doing laundry", + "246": "dying hair", + "247": "knitting", + "248": "reading book", + "249": "baby waking up", + "250": "punching bag", + "251": "surfing crowd", + "252": "cooking chicken", + "253": "pushing car", + "254": "springboard diving", + "255": "swing dancing", + "256": "massaging legs", + "257": "beatboxing", + "258": "breading or breadcrumbing", + "259": "somersaulting", + "260": "brushing teeth", + "261": "stretching arm", + "262": "juggling balls", + "263": "massaging person's head", + "264": "eating ice cream", + "265": "extinguishing fire", + "266": "hammer throw", + "267": "whistling", + "268": "crawling baby", + "269": "using remote controller (not gaming)", + "270": "playing cricket", + "271": "opening bottle", + "272": "playing xylophone", + "273": "motorcycling", + "274": "driving car", + "275": "exercising arm", + "276": "passing American football (not in game)", + "277": "playing kickball", + "278": "sticking tongue out", + "279": "flipping pancake", + "280": "catching fish", + "281": "eating chips", + "282": "shaking head", + "283": "sword fighting", + "284": "playing poker", + "285": "cooking on campfire", + "286": "doing aerobics", + "287": "paragliding", + "288": "using segway", + "289": "folding napkins", + "290": "playing bagpipes", + "291": "gargling", + "292": "skiing slalom", + "293": "strumming guitar", + "294": "javelin throw", + "295": "waxing back", + "296": "riding or walking with horse", + "297": "plastering", + "298": "long jump", + "299": "parkour", + "300": "wrapping present", + "301": "egg hunting", + "302": "archery", + "303": "cleaning toilet", + "304": "swimming backstroke", + "305": "snowboarding", + "306": "catching or throwing baseball", + "307": "massaging back", + "308": "blowing glass", + "309": "playing guitar", + "310": "playing chess", + "311": "golf driving", + "312": "presenting weather forecast", + "313": "rock scissors paper", + "314": "high jump", + "315": "baking cookies", + "316": "using computer", + "317": "washing feet", + "318": "arranging flowers", + "319": "playing bass guitar", + "320": "spraying", + "321": "cutting pineapple", + "322": "waxing chest", + "323": "auctioning", + "324": "jetskiing", + "325": "drinking", + "326": "busking", + "327": "playing monopoly", + "328": "salsa dancing", + "329": "waxing eyebrows", + "330": "watering plants", + "331": "zumba", + "332": "chopping wood", + "333": "pushing wheelchair", + "334": "carving pumpkin", + "335": "building shed", + "336": "making jewelry", + "337": "catching or throwing softball", + "338": "bending metal", + "339": "ice skating", + "340": "dancing charleston", + "341": "abseiling", + "342": "climbing a rope", + "343": "crying", + "344": "cleaning shoes", + "345": "dancing ballet", + "346": "driving tractor", + "347": "triple jump", + "348": "throwing ball", + "349": "getting a haircut", + "350": "running on treadmill", + "351": "climbing ladder", + "352": "blasting sand", + "353": "playing trombone", + "354": "drop kicking", + "355": "country line dancing", + "356": "changing wheel", + "357": "feeding goats", + "358": "tying knot (not on a tie)", + "359": "setting table", + "360": "shaving legs", + "361": "kissing", + "362": "riding mule", + "363": "counting money", + "364": "laying bricks", + "365": "barbequing", + "366": "news anchoring", + "367": "smoking hookah", + "368": "cooking egg", + "369": "peeling apples", + "370": "yoga", + "371": "sharpening pencil", + "372": "dribbling basketball", + "373": "petting cat", + "374": "playing ice hockey", + "375": "milking cow", + "376": "shining shoes", + "377": "juggling soccer ball", + "378": "scuba diving", + "379": "playing squash or racquetball", + "380": "drinking beer", + "381": "sign language interpreting", + "382": "playing basketball", + "383": "breakdancing", + "384": "testifying", + "385": "making snowman", + "386": "golf putting", + "387": "playing didgeridoo", + "388": "biking through snow", + "389": "sailing", + "390": "jumpstyle dancing", + "391": "water sliding", + "392": "grooming horse", + "393": "massaging feet", + "394": "playing paintball", + "395": "making a cake", + "396": "bowling", + "397": "contact juggling", + "398": "applying cream", + "399": "playing badminton" +} + diff --git a/iChat/models/med.py b/iChat/models/med.py new file mode 100644 index 0000000000000000000000000000000000000000..6f9709c38e81fe4e9713e0d68d260bce44391167 --- /dev/null +++ b/iChat/models/med.py @@ -0,0 +1,1037 @@ +''' + * Copyright (c) 2022, salesforce.com, inc. + * All rights reserved. + * SPDX-License-Identifier: BSD-3-Clause + * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause + * By Junnan Li + * Based on huggingface code base + * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert +''' + +import math +import os +import warnings +from dataclasses import dataclass +from typing import Optional, Tuple + +import torch +from torch import Tensor, device, dtype, nn +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss +import torch.nn.functional as F + +from transformers.activations import ACT2FN +from transformers.file_utils import ( + ModelOutput, +) +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + MaskedLMOutput, + MultipleChoiceModelOutput, + NextSentencePredictorOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from transformers.modeling_utils import ( + PreTrainedModel, + apply_chunking_to_forward, + find_pruneable_heads_and_indices, + prune_linear_layer, +) +from transformers.utils import logging +from transformers.models.bert.configuration_bert import BertConfig + + +logger = logging.get_logger(__name__) + + +class BertEmbeddings_nopos(nn.Module): + """Construct the embeddings from word and position embeddings.""" + + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + # self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + # self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) + # self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + + self.config = config + + def forward( + self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 + ): + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # if position_ids is None: + # position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + embeddings = inputs_embeds + + # if self.position_embedding_type == "absolute": + # position_embeddings = self.position_embeddings(position_ids) + # # print('add position_embeddings!!!!') + # embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + + + +class BertEmbeddings(nn.Module): + """Construct the embeddings from word and position embeddings.""" + + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + + self.config = config + + def forward( + self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 + ): + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + if position_ids is None: + position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + embeddings = inputs_embeds + + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + # print('add position_embeddings!!!!') + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class BertSelfAttention(nn.Module): + def __init__(self, config, is_cross_attention): + super().__init__() + self.config = config + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + "The hidden size (%d) is not a multiple of the number of attention " + "heads (%d)" % (config.hidden_size, config.num_attention_heads) + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + if is_cross_attention: + self.key = nn.Linear(config.encoder_width, self.all_head_size) + self.value = nn.Linear(config.encoder_width, self.all_head_size) + else: + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) + self.save_attention = False + + def save_attn_gradients(self, attn_gradients): + self.attn_gradients = attn_gradients + + def get_attn_gradients(self): + return self.attn_gradients + + def save_attention_map(self, attention_map): + self.attention_map = attention_map + + def get_attention_map(self): + return self.attention_map + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention: + if hidden_states.shape[0]!= encoder_hidden_states.shape[0]: + encoder_hidden_states = encoder_hidden_states[:hidden_states.shape[0]] + attention_mask = attention_mask[:hidden_states.shape[0]] + encoder_attention_mask = encoder_attention_mask[:hidden_states.shape[0]] + # past_key_value = past_key_value[:hidden_states.shape[0]] + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + past_key_value = (key_layer, value_layer) + + + # + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) + position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) + positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(dim=-1)(attention_scores) + + if is_cross_attention and self.save_attention: + self.save_attention_map(attention_probs) + attention_probs.register_hook(self.save_attn_gradients) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs_dropped = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs_dropped = attention_probs_dropped * head_mask + + context_layer = torch.matmul(attention_probs_dropped, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + outputs = outputs + (past_key_value,) + return outputs + + +class BertSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertAttention(nn.Module): + def __init__(self, config, is_cross_attention=False): + super().__init__() + self.self = BertSelfAttention(config, is_cross_attention) + self.output = BertSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +class BertIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +class BertOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertLayer(nn.Module): + def __init__(self, config, layer_num): + super().__init__() + self.config = config + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = BertAttention(config) + self.layer_num = layer_num + if self.config.add_cross_attention: + self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention) + self.intermediate = BertIntermediate(config) + self.output = BertOutput(config) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + mode=None, + ): + + if mode == 'mlr': + + assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers" + + # print('attention_output.shape',attention_output.shape) + # print('encoder_hidden_states.shape',encoder_hidden_states.shape) + cross_attention_outputs = self.crossattention( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + output_attentions=output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = cross_attention_outputs[1:-1] # add cross attentions if we output attention weights + + present_key_value = cross_attention_outputs[-1] + + else: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + ) + attention_output = self_attention_outputs[0] + + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + + if mode=='multimodal': + assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers" + + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + output_attentions=output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + outputs = (layer_output,) + outputs + + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +class BertEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + mode='multimodal', + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None + + next_decoder_cache = () if use_cache else None + + for i in range(self.config.num_hidden_layers): + layer_module = self.layer[i] + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warn( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + mode=mode, + ) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + mode=mode, + ) + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +class BertPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class BertPredictionHeadTransform(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if isinstance(config.hidden_act, str): + self.transform_act_fn = ACT2FN[config.hidden_act] + else: + self.transform_act_fn = config.hidden_act + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +class BertLMPredictionHead(nn.Module): + def __init__(self, config): + super().__init__() + self.transform = BertPredictionHeadTransform(config) + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` + self.decoder.bias = self.bias + + def forward(self, hidden_states): + hidden_states = self.transform(hidden_states) + hidden_states = self.decoder(hidden_states) + return hidden_states + + +class BertOnlyMLMHead(nn.Module): + def __init__(self, config): + super().__init__() + self.predictions = BertLMPredictionHead(config) + + def forward(self, sequence_output): + prediction_scores = self.predictions(sequence_output) + return prediction_scores + + +class BertPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = BertConfig + base_model_prefix = "bert" + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def _init_weights(self, module): + """ Initialize the weights """ + if isinstance(module, (nn.Linear, nn.Embedding)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + + +class BertModel(BertPreTrainedModel): + """ + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in `Attention is + all you need `__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, + Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. + argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an + input to the forward pass. + """ + + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = BertEmbeddings(config) + + self.encoder = BertEncoder(config) + + self.pooler = BertPooler(config) if add_pooling_layer else None + + self.init_weights() + + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + + def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor: + """ + Makes broadcastable attention and causal masks so that future and masked tokens are ignored. + + Arguments: + attention_mask (:obj:`torch.Tensor`): + Mask with ones indicating tokens to attend to, zeros for tokens to ignore. + input_shape (:obj:`Tuple[int]`): + The shape of the input to the model. + device: (:obj:`torch.device`): + The device of the input to the model. + + Returns: + :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`. + """ + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + if attention_mask.dim() == 3: + extended_attention_mask = attention_mask[:, None, :, :] + elif attention_mask.dim() == 2: + # Provided a padding mask of dimensions [batch_size, seq_length] + # - if the model is a decoder, apply a causal mask in addition to the padding mask + # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] + if is_decoder: + batch_size, seq_length = input_shape + + seq_ids = torch.arange(seq_length, device=device) + causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None] + # in case past_key_values are used we need to add a prefix ones mask to the causal mask + # causal and attention masks must have same type with pytorch version < 1.3 + causal_mask = causal_mask.to(attention_mask.dtype) + + if causal_mask.shape[1] < attention_mask.shape[1]: + prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1] + causal_mask = torch.cat( + [ + torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype), + causal_mask, + ], + axis=-1, + ) + + extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :] + else: + extended_attention_mask = attention_mask[:, None, None, :] + else: + raise ValueError( + "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format( + input_shape, attention_mask.shape + ) + ) + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and -10000.0 for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility + extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 + return extended_attention_mask + + def forward( + self, + input_ids=None, + attention_mask=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + is_decoder=False, + mode='multimodal', + ): + r""" + encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` + (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` + instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. + use_cache (:obj:`bool`, `optional`): + If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up + decoding (see :obj:`past_key_values`). + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + batch_size, seq_length = input_shape + device = input_ids.device + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + batch_size, seq_length = input_shape + device = inputs_embeds.device + elif encoder_embeds is not None: + input_shape = encoder_embeds.size()[:-1] + batch_size, seq_length = input_shape + device = encoder_embeds.device + else: + raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds") + + # past_key_values_length + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + + if attention_mask is None: + attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, + device, is_decoder) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if encoder_hidden_states is not None: + if type(encoder_hidden_states) == list: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size() + else: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + + if type(encoder_attention_mask) == list: + encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask] + elif encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + if encoder_embeds is None: + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + else: + embedding_output = encoder_embeds + + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + mode=mode, + ) + sequence_output = encoder_outputs[0] + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class BertLMHeadModel(BertPreTrainedModel): + + _keys_to_ignore_on_load_unexpected = [r"pooler"] + _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] + + def __init__(self, config): + super().__init__(config) + + self.bert = BertModel(config, add_pooling_layer=False) + self.cls = BertOnlyMLMHead(config) + + self.init_weights() + + def get_output_embeddings(self): + return self.cls.predictions.decoder + + def set_output_embeddings(self, new_embeddings): + self.cls.predictions.decoder = new_embeddings + + def forward( + self, + input_ids=None, + attention_mask=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + labels=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + return_logits=False, + is_decoder=True, + reduction='mean', + mode='multimodal', + ): + r""" + encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in + ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are + ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]`` + past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` + (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` + instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. + use_cache (:obj:`bool`, `optional`): + If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up + decoding (see :obj:`past_key_values`). + Returns: + Example:: + >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig + >>> import torch + >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') + >>> config = BertConfig.from_pretrained("bert-base-cased") + >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config) + >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") + >>> outputs = model(**inputs) + >>> prediction_logits = outputs.logits + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + if labels is not None: + use_cache = False + + outputs = self.bert( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + is_decoder=is_decoder, + mode=mode, + ) + + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output) + # sequence_output.shape torch.Size([85, 30, 768]) + # prediction_scores.shape torch.Size([85, 30, 30524]) + # labels.shape torch.Size([85, 30]) + + + if return_logits: + return prediction_scores[:, :-1, :].contiguous() + + lm_loss = None + if labels is not None: + # we are doing next-token prediction; shift prediction scores and input ids by one + shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() + labels = labels[:, 1:].contiguous() + loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) + lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + if reduction=='none': + lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ((lm_loss,) + output) if lm_loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=lm_loss, + logits=prediction_scores, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs): + input_shape = input_ids.shape + # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly + if attention_mask is None: + attention_mask = input_ids.new_ones(input_shape) + + # cut decoder_input_ids if past is used + if past is not None: + input_ids = input_ids[:, -1:] + + return { + "input_ids": input_ids, + "attention_mask": attention_mask, + "past_key_values": past, + "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None), + "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None), + "is_decoder": True, + } + + def _reorder_cache(self, past, beam_idx): + reordered_past = () + for layer_past in past: + reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) + return reordered_past + + diff --git a/iChat/models/processing.py b/iChat/models/processing.py new file mode 100644 index 0000000000000000000000000000000000000000..b926e2141b8aa6d9075e309c59f569880cf0eb5a --- /dev/null +++ b/iChat/models/processing.py @@ -0,0 +1,443 @@ +import torch +import torchvision +import random +from PIL import Image, ImageOps +import numpy as np +import numbers +import math + + +class GroupRandomCrop(object): + def __init__(self, size): + if isinstance(size, numbers.Number): + self.size = (int(size), int(size)) + else: + self.size = size + + def __call__(self, img_group): + + w, h = img_group[0].size + th, tw = self.size + + out_images = list() + + x1 = random.randint(0, w - tw) + y1 = random.randint(0, h - th) + + for img in img_group: + assert(img.size[0] == w and img.size[1] == h) + if w == tw and h == th: + out_images.append(img) + else: + out_images.append(img.crop((x1, y1, x1 + tw, y1 + th))) + + return out_images + + +class MultiGroupRandomCrop(object): + def __init__(self, size, groups=1): + if isinstance(size, numbers.Number): + self.size = (int(size), int(size)) + else: + self.size = size + self.groups = groups + + def __call__(self, img_group): + + w, h = img_group[0].size + th, tw = self.size + + out_images = list() + + for i in range(self.groups): + x1 = random.randint(0, w - tw) + y1 = random.randint(0, h - th) + + for img in img_group: + assert(img.size[0] == w and img.size[1] == h) + if w == tw and h == th: + out_images.append(img) + else: + out_images.append(img.crop((x1, y1, x1 + tw, y1 + th))) + + return out_images + + +class GroupCenterCrop(object): + def __init__(self, size): + self.worker = torchvision.transforms.CenterCrop(size) + + def __call__(self, img_group): + return [self.worker(img) for img in img_group] + + +class GroupRandomHorizontalFlip(object): + """Randomly horizontally flips the given PIL.Image with a probability of 0.5 + """ + + def __init__(self, is_flow=False): + self.is_flow = is_flow + + def __call__(self, img_group, is_flow=False): + v = random.random() + if v < 0.5: + ret = [img.transpose(Image.FLIP_LEFT_RIGHT) for img in img_group] + if self.is_flow: + for i in range(0, len(ret), 2): + # invert flow pixel values when flipping + ret[i] = ImageOps.invert(ret[i]) + return ret + else: + return img_group + + +class GroupNormalize(object): + def __init__(self, mean, std): + self.mean = mean + self.std = std + + def __call__(self, tensor): + rep_mean = self.mean * (tensor.size()[0] // len(self.mean)) + rep_std = self.std * (tensor.size()[0] // len(self.std)) + + # TODO: make efficient + for t, m, s in zip(tensor, rep_mean, rep_std): + t.sub_(m).div_(s) + + return tensor + + +class GroupScale(object): + """ Rescales the input PIL.Image to the given 'size'. + 'size' will be the size of the smaller edge. + For example, if height > width, then image will be + rescaled to (size * height / width, size) + size: size of the smaller edge + interpolation: Default: PIL.Image.BILINEAR + """ + + def __init__(self, size, interpolation=Image.BILINEAR): + self.worker = torchvision.transforms.Resize(size, interpolation) + + def __call__(self, img_group): + return [self.worker(img) for img in img_group] + + +class GroupOverSample(object): + def __init__(self, crop_size, scale_size=None, flip=True): + self.crop_size = crop_size if not isinstance( + crop_size, int) else (crop_size, crop_size) + + if scale_size is not None: + self.scale_worker = GroupScale(scale_size) + else: + self.scale_worker = None + self.flip = flip + + def __call__(self, img_group): + + if self.scale_worker is not None: + img_group = self.scale_worker(img_group) + + image_w, image_h = img_group[0].size + crop_w, crop_h = self.crop_size + + offsets = GroupMultiScaleCrop.fill_fix_offset( + False, image_w, image_h, crop_w, crop_h) + oversample_group = list() + for o_w, o_h in offsets: + normal_group = list() + flip_group = list() + for i, img in enumerate(img_group): + crop = img.crop((o_w, o_h, o_w + crop_w, o_h + crop_h)) + normal_group.append(crop) + flip_crop = crop.copy().transpose(Image.FLIP_LEFT_RIGHT) + + if img.mode == 'L' and i % 2 == 0: + flip_group.append(ImageOps.invert(flip_crop)) + else: + flip_group.append(flip_crop) + + oversample_group.extend(normal_group) + if self.flip: + oversample_group.extend(flip_group) + return oversample_group + + +class GroupFullResSample(object): + def __init__(self, crop_size, scale_size=None, flip=True): + self.crop_size = crop_size if not isinstance( + crop_size, int) else (crop_size, crop_size) + + if scale_size is not None: + self.scale_worker = GroupScale(scale_size) + else: + self.scale_worker = None + self.flip = flip + + def __call__(self, img_group): + + if self.scale_worker is not None: + img_group = self.scale_worker(img_group) + + image_w, image_h = img_group[0].size + crop_w, crop_h = self.crop_size + + w_step = (image_w - crop_w) // 4 + h_step = (image_h - crop_h) // 4 + + offsets = list() + offsets.append((0 * w_step, 2 * h_step)) # left + offsets.append((4 * w_step, 2 * h_step)) # right + offsets.append((2 * w_step, 2 * h_step)) # center + + oversample_group = list() + for o_w, o_h in offsets: + normal_group = list() + flip_group = list() + for i, img in enumerate(img_group): + crop = img.crop((o_w, o_h, o_w + crop_w, o_h + crop_h)) + normal_group.append(crop) + if self.flip: + flip_crop = crop.copy().transpose(Image.FLIP_LEFT_RIGHT) + + if img.mode == 'L' and i % 2 == 0: + flip_group.append(ImageOps.invert(flip_crop)) + else: + flip_group.append(flip_crop) + + oversample_group.extend(normal_group) + oversample_group.extend(flip_group) + return oversample_group + + +class GroupMultiScaleCrop(object): + + def __init__(self, input_size, scales=None, max_distort=1, + fix_crop=True, more_fix_crop=True): + self.scales = scales if scales is not None else [1, .875, .75, .66] + self.max_distort = max_distort + self.fix_crop = fix_crop + self.more_fix_crop = more_fix_crop + self.input_size = input_size if not isinstance(input_size, int) else [ + input_size, input_size] + self.interpolation = Image.BILINEAR + + def __call__(self, img_group): + + im_size = img_group[0].size + + crop_w, crop_h, offset_w, offset_h = self._sample_crop_size(im_size) + crop_img_group = [ + img.crop( + (offset_w, + offset_h, + offset_w + + crop_w, + offset_h + + crop_h)) for img in img_group] + ret_img_group = [img.resize((self.input_size[0], self.input_size[1]), self.interpolation) + for img in crop_img_group] + return ret_img_group + + def _sample_crop_size(self, im_size): + image_w, image_h = im_size[0], im_size[1] + + # find a crop size + base_size = min(image_w, image_h) + crop_sizes = [int(base_size * x) for x in self.scales] + crop_h = [ + self.input_size[1] if abs( + x - self.input_size[1]) < 3 else x for x in crop_sizes] + crop_w = [ + self.input_size[0] if abs( + x - self.input_size[0]) < 3 else x for x in crop_sizes] + + pairs = [] + for i, h in enumerate(crop_h): + for j, w in enumerate(crop_w): + if abs(i - j) <= self.max_distort: + pairs.append((w, h)) + + crop_pair = random.choice(pairs) + if not self.fix_crop: + w_offset = random.randint(0, image_w - crop_pair[0]) + h_offset = random.randint(0, image_h - crop_pair[1]) + else: + w_offset, h_offset = self._sample_fix_offset( + image_w, image_h, crop_pair[0], crop_pair[1]) + + return crop_pair[0], crop_pair[1], w_offset, h_offset + + def _sample_fix_offset(self, image_w, image_h, crop_w, crop_h): + offsets = self.fill_fix_offset( + self.more_fix_crop, image_w, image_h, crop_w, crop_h) + return random.choice(offsets) + + @staticmethod + def fill_fix_offset(more_fix_crop, image_w, image_h, crop_w, crop_h): + w_step = (image_w - crop_w) // 4 + h_step = (image_h - crop_h) // 4 + + ret = list() + ret.append((0, 0)) # upper left + ret.append((4 * w_step, 0)) # upper right + ret.append((0, 4 * h_step)) # lower left + ret.append((4 * w_step, 4 * h_step)) # lower right + ret.append((2 * w_step, 2 * h_step)) # center + + if more_fix_crop: + ret.append((0, 2 * h_step)) # center left + ret.append((4 * w_step, 2 * h_step)) # center right + ret.append((2 * w_step, 4 * h_step)) # lower center + ret.append((2 * w_step, 0 * h_step)) # upper center + + ret.append((1 * w_step, 1 * h_step)) # upper left quarter + ret.append((3 * w_step, 1 * h_step)) # upper right quarter + ret.append((1 * w_step, 3 * h_step)) # lower left quarter + ret.append((3 * w_step, 3 * h_step)) # lower righ quarter + + return ret + + +class GroupRandomSizedCrop(object): + """Random crop the given PIL.Image to a random size of (0.08 to 1.0) of the original size + and and a random aspect ratio of 3/4 to 4/3 of the original aspect ratio + This is popularly used to train the Inception networks + size: size of the smaller edge + interpolation: Default: PIL.Image.BILINEAR + """ + + def __init__(self, size, interpolation=Image.BILINEAR): + self.size = size + self.interpolation = interpolation + + def __call__(self, img_group): + for attempt in range(10): + area = img_group[0].size[0] * img_group[0].size[1] + target_area = random.uniform(0.08, 1.0) * area + aspect_ratio = random.uniform(3. / 4, 4. / 3) + + w = int(round(math.sqrt(target_area * aspect_ratio))) + h = int(round(math.sqrt(target_area / aspect_ratio))) + + if random.random() < 0.5: + w, h = h, w + + if w <= img_group[0].size[0] and h <= img_group[0].size[1]: + x1 = random.randint(0, img_group[0].size[0] - w) + y1 = random.randint(0, img_group[0].size[1] - h) + found = True + break + else: + found = False + x1 = 0 + y1 = 0 + + if found: + out_group = list() + for img in img_group: + img = img.crop((x1, y1, x1 + w, y1 + h)) + assert(img.size == (w, h)) + out_group.append( + img.resize( + (self.size, self.size), self.interpolation)) + return out_group + else: + # Fallback + scale = GroupScale(self.size, interpolation=self.interpolation) + crop = GroupRandomCrop(self.size) + return crop(scale(img_group)) + + +class ConvertDataFormat(object): + def __init__(self, model_type): + self.model_type = model_type + + def __call__(self, images): + if self.model_type == '2D': + return images + tc, h, w = images.size() + t = tc // 3 + images = images.view(t, 3, h, w) + images = images.permute(1, 0, 2, 3) + return images + + +class Stack(object): + + def __init__(self, roll=False): + self.roll = roll + + def __call__(self, img_group): + if img_group[0].mode == 'L': + return np.concatenate([np.expand_dims(x, 2) + for x in img_group], axis=2) + elif img_group[0].mode == 'RGB': + if self.roll: + return np.concatenate([np.array(x)[:, :, ::-1] + for x in img_group], axis=2) + else: + #print(np.concatenate(img_group, axis=2).shape) + # print(img_group[0].shape) + return np.concatenate(img_group, axis=2) + + +class ToTorchFormatTensor(object): + """ Converts a PIL.Image (RGB) or numpy.ndarray (H x W x C) in the range [0, 255] + to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] """ + + def __init__(self, div=True): + self.div = div + + def __call__(self, pic): + if isinstance(pic, np.ndarray): + # handle numpy array + img = torch.from_numpy(pic).permute(2, 0, 1).contiguous() + else: + # handle PIL Image + img = torch.ByteTensor( + torch.ByteStorage.from_buffer( + pic.tobytes())) + img = img.view(pic.size[1], pic.size[0], len(pic.mode)) + # put it from HWC to CHW format + # yikes, this transpose takes 80% of the loading time/CPU + img = img.transpose(0, 1).transpose(0, 2).contiguous() + return img.float().div(255) if self.div else img.float() + + +class IdentityTransform(object): + + def __call__(self, data): + return data + + +if __name__ == "__main__": + trans = torchvision.transforms.Compose([ + GroupScale(256), + GroupRandomCrop(224), + Stack(), + ToTorchFormatTensor(), + GroupNormalize( + mean=[.485, .456, .406], + std=[.229, .224, .225] + )] + ) + + im = Image.open('../tensorflow-model-zoo.torch/lena_299.png') + + color_group = [im] * 3 + rst = trans(color_group) + + gray_group = [im.convert('L')] * 9 + gray_rst = trans(gray_group) + + trans2 = torchvision.transforms.Compose([ + GroupRandomSizedCrop(256), + Stack(), + ToTorchFormatTensor(), + GroupNormalize( + mean=[.485, .456, .406], + std=[.229, .224, .225]) + ]) + print(trans2(color_group)) \ No newline at end of file diff --git a/iChat/models/sam_preditor.py b/iChat/models/sam_preditor.py new file mode 100644 index 0000000000000000000000000000000000000000..e73f351c3f4d336a8bc6dc919e953453fca66613 --- /dev/null +++ b/iChat/models/sam_preditor.py @@ -0,0 +1,258 @@ +from segment_anything.modeling import Sam +# from segment_anything import SamPredictor as SamPredictorBase +import numpy as np +import torch + +from typing import Optional, Tuple + +from segment_anything.utils.transforms import ResizeLongestSide + +class SamPredictor: + def __init__( + self, + sam_model: Sam, + ) -> None: + """ + Uses SAM to calculate the image embedding for an image, and then + allow repeated, efficient mask prediction given prompts. + + Arguments: + sam_model (Sam): The model to use for mask prediction. + """ + super().__init__() + self.model = sam_model + self.transform = ResizeLongestSide(sam_model.image_encoder.img_size) + # self.reset_image() + + + def set_image( + self, + image: np.ndarray, + image_format: str = "RGB", + ) -> None: + """ + Calculates the image embeddings for the provided image, allowing + masks to be predicted with the 'predict' method. + + Arguments: + image (np.ndarray): The image for calculating masks. Expects an + image in HWC uint8 format, with pixel values in [0, 255]. + image_format (str): The color format of the image, in ['RGB', 'BGR']. + """ + assert image_format in [ + "RGB", + "BGR", + ], f"image_format must be in ['RGB', 'BGR'], is {image_format}." + if image_format != self.model.image_format: + image = image[..., ::-1] + + # Transform the image to the form expected by the model + input_image = self.transform.apply_image(image) + input_image_torch = torch.as_tensor(input_image, device=self.device) + input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :] + + return self.set_torch_image(input_image_torch, image.shape[:2]) + + @torch.no_grad() + def set_torch_image( + self, + transformed_image: torch.Tensor, + original_image_size: Tuple[int, ...], + ) -> None: + """ + Calculates the image embeddings for the provided image, allowing + masks to be predicted with the 'predict' method. Expects the input + image to be already transformed to the format expected by the model. + + Arguments: + transformed_image (torch.Tensor): The input image, with shape + 1x3xHxW, which has been transformed with ResizeLongestSide. + original_image_size (tuple(int, int)): The size of the image + before transformation, in (H, W) format. + """ + assert ( + len(transformed_image.shape) == 4 + and transformed_image.shape[1] == 3 + and max(*transformed_image.shape[2:]) == self.model.image_encoder.img_size + ), f"set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}." + # self.reset_image() + + original_size = original_image_size + input_size = tuple(transformed_image.shape[-2:]) + input_image = self.model.preprocess(transformed_image) + features = self.model.image_encoder(input_image) + # self.is_image_set = True + res = {'features': features, 'original_size': original_size, 'input_size': input_size} + return res + + def predict( + self, + features, + point_coords: Optional[np.ndarray] = None, + point_labels: Optional[np.ndarray] = None, + box: Optional[np.ndarray] = None, + mask_input: Optional[np.ndarray] = None, + multimask_output: bool = True, + return_logits: bool = False, + ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """ + Predict masks for the given input prompts, using the currently set image. + + Arguments: + point_coords (np.ndarray or None): A Nx2 array of point prompts to the + model. Each point is in (X,Y) in pixels. + point_labels (np.ndarray or None): A length N array of labels for the + point prompts. 1 indicates a foreground point and 0 indicates a + background point. + box (np.ndarray or None): A length 4 array given a box prompt to the + model, in XYXY format. + mask_input (np.ndarray): A low resolution mask input to the model, typically + coming from a previous prediction iteration. Has form 1xHxW, where + for SAM, H=W=256. + multimask_output (bool): If true, the model will return three masks. + For ambiguous input prompts (such as a single click), this will often + produce better masks than a single prediction. If only a single + mask is needed, the model's predicted quality score can be used + to select the best mask. For non-ambiguous prompts, such as multiple + input prompts, multimask_output=False can give better results. + return_logits (bool): If true, returns un-thresholded masks logits + instead of a binary mask. + + Returns: + (np.ndarray): The output masks in CxHxW format, where C is the + number of masks, and (H, W) is the original image size. + (np.ndarray): An array of length C containing the model's + predictions for the quality of each mask. + (np.ndarray): An array of shape CxHxW, where C is the number + of masks and H=W=256. These low resolution logits can be passed to + a subsequent iteration as mask input. + """ + if features.get('features', None) is None: + raise RuntimeError("An image must be set with .set_image(...) before mask prediction.") + + # Transform input prompts + coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None + if point_coords is not None: + assert ( + point_labels is not None + ), "point_labels must be supplied if point_coords is supplied." + point_coords = self.transform.apply_coords(point_coords, features['original_size']) + coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.device) + labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.device) + coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :] + if box is not None: + box = self.transform.apply_boxes(box, features['original_size']) + box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device) + box_torch = box_torch[None, :] + if mask_input is not None: + mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.device) + mask_input_torch = mask_input_torch[None, :, :, :] + + masks, iou_predictions, low_res_masks = self.predict_torch( + features, + coords_torch, + labels_torch, + box_torch, + mask_input_torch, + multimask_output, + return_logits=return_logits, + ) + + masks_np = masks[0].detach().cpu().numpy() + iou_predictions_np = iou_predictions[0].detach().cpu().numpy() + low_res_masks_np = low_res_masks[0].detach().cpu().numpy() + return masks_np, iou_predictions_np, low_res_masks_np + + @torch.no_grad() + def predict_torch( + self, + features, + point_coords: Optional[torch.Tensor], + point_labels: Optional[torch.Tensor], + boxes: Optional[torch.Tensor] = None, + mask_input: Optional[torch.Tensor] = None, + multimask_output: bool = True, + return_logits: bool = False, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Predict masks for the given input prompts, using the currently set image. + Input prompts are batched torch tensors and are expected to already be + transformed to the input frame using ResizeLongestSide. + + Arguments: + point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the + model. Each point is in (X,Y) in pixels. + point_labels (torch.Tensor or None): A BxN array of labels for the + point prompts. 1 indicates a foreground point and 0 indicates a + background point. + boxes (np.ndarray or None): A Bx4 array given a box prompt to the + model, in XYXY format. + mask_input (np.ndarray): A low resolution mask input to the model, typically + coming from a previous prediction iteration. Has form Bx1xHxW, where + for SAM, H=W=256. Masks returned by a previous iteration of the + predict method do not need further transformation. + multimask_output (bool): If true, the model will return three masks. + For ambiguous input prompts (such as a single click), this will often + produce better masks than a single prediction. If only a single + mask is needed, the model's predicted quality score can be used + to select the best mask. For non-ambiguous prompts, such as multiple + input prompts, multimask_output=False can give better results. + return_logits (bool): If true, returns un-thresholded masks logits + instead of a binary mask. + + Returns: + (torch.Tensor): The output masks in BxCxHxW format, where C is the + number of masks, and (H, W) is the original image size. + (torch.Tensor): An array of shape BxC containing the model's + predictions for the quality of each mask. + (torch.Tensor): An array of shape BxCxHxW, where C is the number + of masks and H=W=256. These low res logits can be passed to + a subsequent iteration as mask input. + """ + if features.get('features', None) is None: + raise RuntimeError("An image must be set with .set_image(...) before mask prediction.") + + if point_coords is not None: + points = (point_coords, point_labels) + else: + points = None + + # Embed prompts + sparse_embeddings, dense_embeddings = self.model.prompt_encoder( + points=points, + boxes=boxes, + masks=mask_input, + ) + + # Predict masks + low_res_masks, iou_predictions = self.model.mask_decoder( + image_embeddings=features['features'], + image_pe=self.model.prompt_encoder.get_dense_pe(), + sparse_prompt_embeddings=sparse_embeddings, + dense_prompt_embeddings=dense_embeddings, + multimask_output=multimask_output, + ) + + # Upscale the masks to the original image resolution + masks = self.model.postprocess_masks(low_res_masks, features['input_size'], features['original_size']) + + if not return_logits: + masks = masks > self.model.mask_threshold + + return masks, iou_predictions, low_res_masks + + def get_image_embedding(self, image) -> torch.Tensor: + return self.set_image(image) + + @property + def device(self) -> torch.device: + return self.model.device + + # def reset_image(self) -> None: + # """Resets the currently set image.""" + # self.is_image_set = False + # self.features = None + # self.orig_h = None + # self.orig_w = None + # self.input_h = None + # self.input_w = None \ No newline at end of file diff --git a/iChat/models/swin_transformer.py b/iChat/models/swin_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..c1affc9a8695474e831ad060343c1988d750dc5f --- /dev/null +++ b/iChat/models/swin_transformer.py @@ -0,0 +1,654 @@ +# -------------------------------------------------------- +# Swin Transformer +# Copyright (c) 2021 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ze Liu +# -------------------------------------------------------- + +import numpy as np +from scipy import interpolate + +import torch +import torch.nn as nn +import torch.utils.checkpoint as checkpoint +from timm.models.layers import DropPath, to_2tuple, trunc_normal_ + + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + r""" Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """ + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + + relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + def extra_repr(self) -> str: + return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}' + + def flops(self, N): + # calculate flops for 1 window with token length of N + flops = 0 + # qkv = self.qkv(x) + flops += N * self.dim * 3 * self.dim + # attn = (q @ k.transpose(-2, -1)) + flops += self.num_heads * N * (self.dim // self.num_heads) * N + # x = (attn @ v) + flops += self.num_heads * N * N * (self.dim // self.num_heads) + # x = self.proj(x) + flops += N * self.dim * self.dim + return flops + + +class SwinTransformerBlock(nn.Module): + r""" Swin Transformer Block. + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resulotion. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, + mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., + act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + if min(self.input_resolution) <= self.window_size: + # if window size is larger than input resolution, we don't partition windows + self.shift_size = 0 + self.window_size = min(self.input_resolution) + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, + qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + if self.shift_size > 0: + # calculate attention mask for SW-MSA + H, W = self.input_resolution + img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 + h_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + w_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + else: + attn_mask = None + + self.register_buffer("attn_mask", attn_mask) + + def forward(self, x): + H, W = self.input_resolution + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + else: + shifted_x = x + + # partition windows + x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C + x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \ + f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" + + def flops(self): + flops = 0 + H, W = self.input_resolution + # norm1 + flops += self.dim * H * W + # W-MSA/SW-MSA + nW = H * W / self.window_size / self.window_size + flops += nW * self.attn.flops(self.window_size * self.window_size) + # mlp + flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio + # norm2 + flops += self.dim * H * W + return flops + + +class PatchMerging(nn.Module): + r""" Patch Merging Layer. + + Args: + input_resolution (tuple[int]): Resolution of input feature. + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = input_resolution + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x): + """ + x: B, H*W, C + """ + H, W = self.input_resolution + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." + + x = x.view(B, H, W, C) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + def extra_repr(self) -> str: + return f"input_resolution={self.input_resolution}, dim={self.dim}" + + def flops(self): + H, W = self.input_resolution + flops = H * W * self.dim + flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim + return flops + + +class BasicLayer(nn.Module): + """ A basic Swin Transformer layer for one stage. + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__(self, dim, input_resolution, depth, num_heads, window_size, + mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False): + + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList([ + SwinTransformerBlock(dim=dim, input_resolution=input_resolution, + num_heads=num_heads, window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop, attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer) + for i in range(depth)]) + + # patch merging layer + if downsample is not None: + self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) + else: + self.downsample = None + + def forward(self, x): + for blk in self.blocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x) + else: + x = blk(x) + if self.downsample is not None: + x = self.downsample(x) + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" + + def flops(self): + flops = 0 + for blk in self.blocks: + flops += blk.flops() + if self.downsample is not None: + flops += self.downsample.flops() + return flops + + +class PatchEmbed(nn.Module): + r""" Image to Patch Embedding + + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + B, C, H, W = x.shape + # FIXME look at relaxing size constraints + assert H == self.img_size[0] and W == self.img_size[1], \ + f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C + if self.norm is not None: + x = self.norm(x) + return x + + def flops(self): + Ho, Wo = self.patches_resolution + flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) + if self.norm is not None: + flops += Ho * Wo * self.embed_dim + return flops + + +class SwinTransformer(nn.Module): + r""" Swin Transformer + A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - + https://arxiv.org/pdf/2103.14030 + + Args: + img_size (int | tuple(int)): Input image size. Default 224 + patch_size (int | tuple(int)): Patch size. Default: 4 + in_chans (int): Number of input image channels. Default: 3 + num_classes (int): Number of classes for classification head. Default: 1000 + embed_dim (int): Patch embedding dimension. Default: 96 + depths (tuple(int)): Depth of each Swin Transformer layer. + num_heads (tuple(int)): Number of attention heads in different layers. + window_size (int): Window size. Default: 7 + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False + patch_norm (bool): If True, add normalization after patch embedding. Default: True + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False + """ + + def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000, + embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], + window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None, + drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, + norm_layer=nn.LayerNorm, ape=False, patch_norm=True, + use_checkpoint=False, **kwargs): + super().__init__() + + self.num_classes = num_classes + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.num_features = int(embed_dim * 2 ** (self.num_layers - 1)) + self.mlp_ratio = mlp_ratio + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None) + num_patches = self.patch_embed.num_patches + patches_resolution = self.patch_embed.patches_resolution + self.patches_resolution = patches_resolution + + # absolute position embedding + if self.ape: + self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) + trunc_normal_(self.absolute_pos_embed, std=.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + + # build layers + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer), + input_resolution=(patches_resolution[0] // (2 ** i_layer), + patches_resolution[1] // (2 ** i_layer)), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], + norm_layer=norm_layer, + downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, + use_checkpoint=use_checkpoint) + self.layers.append(layer) + + self.norm = norm_layer(self.num_features) + self.avgpool = nn.AdaptiveAvgPool1d(1) + # self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity() + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'absolute_pos_embed'} + + @torch.jit.ignore + def no_weight_decay_keywords(self): + return {'relative_position_bias_table'} + + def forward(self, x, idx_to_group_img=None, image_atts=None, **kwargs): + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers: + x = layer(x) + + x = self.norm(x) # B L C + + x_cls = self.avgpool(x.transpose(1, 2)) # B C 1 + + if idx_to_group_img is None: + return torch.cat([x_cls.transpose(1, 2), x], dim=1) + else: + x_bs = torch.gather(x, dim=0, index=idx_to_group_img.view(-1, 1, 1).expand(-1, x.shape[1], x.shape[2])) + weights = image_atts[:, 1:].unsqueeze(2) # B L 1 + x_bs_cls = torch.sum((weights * x_bs).transpose(1, 2), dim=-1, keepdim=True) # B C 1 + x_bs_cls = x_bs_cls / torch.sum(weights.transpose(1, 2), dim=-1, keepdim=True) # avgpool + + return torch.cat([x_bs_cls.transpose(1, 2), x_bs], dim=1), \ + torch.cat([x_cls.transpose(1, 2), x], dim=1) + + def flops(self): + flops = 0 + flops += self.patch_embed.flops() + for i, layer in enumerate(self.layers): + flops += layer.flops() + flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers) + flops += self.num_features * self.num_classes + return flops + + +def interpolate_relative_pos_embed(rel_pos_bias, dst_num_pos, param_name=''): + # from: https://github.com/microsoft/unilm/blob/8a0a1c1f4e7326938ea7580a00d56d7f17d65612/beit/run_class_finetuning.py#L348 + + # rel_pos_bias: relative_position_bias_table + src_num_pos, num_attn_heads = rel_pos_bias.size() + + num_extra_tokens = 0 + src_size = int((src_num_pos - num_extra_tokens) ** 0.5) + dst_size = int((dst_num_pos - num_extra_tokens) ** 0.5) + if src_size != dst_size: + print("Position interpolate %s from %dx%d to %dx%d" % (param_name, src_size, src_size, dst_size, dst_size)) + + # extra_tokens = rel_pos_bias[-num_extra_tokens:, :] + # rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :] + + def geometric_progression(a, r, n): + return a * (1.0 - r ** n) / (1.0 - r) + + left, right = 1.01, 1.5 + while right - left > 1e-6: + q = (left + right) / 2.0 + gp = geometric_progression(1, q, src_size // 2) + if gp > dst_size // 2: + right = q + else: + left = q + + # if q > 1.090307: + # q = 1.090307 + + dis = [] + cur = 1 + for i in range(src_size // 2): + dis.append(cur) + cur += q ** (i + 1) + + r_ids = [-_ for _ in reversed(dis)] + + x = r_ids + [0] + dis + y = r_ids + [0] + dis + + t = dst_size // 2.0 + dx = np.arange(-t, t + 0.1, 1.0) + dy = np.arange(-t, t + 0.1, 1.0) + + # print("Original positions = %s" % str(x)) + # print("Target positions = %s" % str(dx)) + + all_rel_pos_bias = [] + + for i in range(num_attn_heads): + z = rel_pos_bias[:, i].view(src_size, src_size).float().numpy() + f = interpolate.interp2d(x, y, z, kind='cubic') + all_rel_pos_bias.append( + torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(rel_pos_bias.device)) + + rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1) + + return rel_pos_bias \ No newline at end of file diff --git a/iChat/models/tag2text.py b/iChat/models/tag2text.py new file mode 100644 index 0000000000000000000000000000000000000000..c521d6a587d1cca70db4e16ce7897ec69d06c338 --- /dev/null +++ b/iChat/models/tag2text.py @@ -0,0 +1,430 @@ +''' + * Tag2Text + * Written by Xinyu Huang +''' +import warnings +warnings.filterwarnings("ignore") + +from .vit import VisionTransformer, interpolate_pos_embed +from .swin_transformer import SwinTransformer, interpolate_relative_pos_embed +from .med import BertConfig, BertModel, BertLMHeadModel +from .utils import tra_array +from transformers import BertTokenizer + +import torch +from torch import nn +import torch.nn.functional as F + +import os +from urllib.parse import urlparse +from timm.models.hub import download_cached_file +import json +import math +import numpy as np + +def read_json(rpath): + with open(rpath, 'r') as f: + return json.load(f) + +# delete some tags that may disturb captioning +# 127: "quarter"; 2961: "back"; 3351: "two"; 3265: "three"; 3338: "four"; 3355: "five"; 3359: "one" +delete_tag_index = [127,2961, 3351, 3265, 3338, 3355, 3359] + +# adjust thresholds for some tags +# default threshold: 0.68 +# 2701: "person"; 2828: "man"; 1167: "woman"; +tag_thrshold = {2701:0.7, 2828: 0.7, 1167: 0.7} + +class Tag2Text_Caption(nn.Module): + def __init__(self, + med_config = 'configs/med_config.json', + image_size = 384, + vit = 'base', + vit_grad_ckpt = False, + vit_ckpt_layer = 0, + prompt = 'a picture of ', + threshold = 0.68, + ): + """ + Args: + med_config (str): path for the mixture of encoder-decoder model's configuration file + image_size (int): input image size + vit (str): model size of vision transformer + """ + super().__init__() + + if vit=='swin_b': + if image_size == 224: + vision_config_path = 'configs/swin/config_swinB_224.json' + elif image_size == 384: + vision_config_path = 'configs/swin/config_swinB_384.json' + vision_config = read_json(vision_config_path) + assert image_size == vision_config['image_res'] + # assert config['patch_size'] == 32 + vision_width = vision_config['vision_width'] + + self.visual_encoder = SwinTransformer(img_size=vision_config['image_res'], + patch_size=4, + in_chans=3, + embed_dim=vision_config['embed_dim'], + depths=vision_config['depths'], + num_heads=vision_config['num_heads'], + window_size=vision_config['window_size'], + mlp_ratio=4., + qkv_bias=True, + drop_rate=0.0, + drop_path_rate=0.1, + ape=False, + patch_norm=True, + use_checkpoint=False) + + else: + self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer) + + + self.tokenizer = init_tokenizer() + + # create the decoder + decoder_config = BertConfig.from_json_file(med_config) + decoder_config.encoder_width = 768 + self.text_decoder = BertLMHeadModel(config=decoder_config) + + # create encoder + encoder_config = BertConfig.from_json_file(med_config) + encoder_config.encoder_width = vision_width + self.tag_encoder = BertModel(config=encoder_config, add_pooling_layer=False) + + self.prompt = prompt + self.prompt_length = len(self.tokenizer(self.prompt).input_ids)-1 + + self.threshold = threshold + num_features = 768 + self.num_class = 3429 + + q2l_config = BertConfig.from_json_file('configs/q2l_config.json') + q2l_config.encoder_width = vision_width + self.vision_multi = BertModel(config=q2l_config, add_pooling_layer=False) + self.vision_multi.resize_token_embeddings(len(self.tokenizer)) + self.label_embed = nn.Embedding(self.num_class, q2l_config.hidden_size) + self.fc = GroupWiseLinear(self.num_class, num_features, bias=True) + self.del_selfattention() + + tie_encoder_decoder_weights(self.tag_encoder,self.vision_multi,'',' ') + self.tag_array = tra_array + + self.class_threshold = torch.ones(self.num_class) * self.threshold + for key,value in tag_thrshold.items(): + self.class_threshold[key] = value + + def del_selfattention(self): + del self.vision_multi.embeddings + for layer in self.vision_multi.encoder.layer: + del layer.attention + + def generate(self, image, sample=False, num_beams=3, max_length=30, min_length=10, top_p=0.9, repetition_penalty=1.0, tag_input = None, return_tag_predict = False): + image_embeds = self.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) + + #==============generate tag==============# + if tag_input == None: + image_spatial_embeds = image_embeds[:,1:,:] + image_cls_embeds = image_embeds[:,0,:] + + bs = image_spatial_embeds.shape[0] + label_embed = self.label_embed.weight.unsqueeze(0).repeat(bs,1,1) + mlr_tagembedding = self.vision_multi(encoder_embeds = label_embed, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = False, + mode = 'mlr', + ) + + logits = self.fc(mlr_tagembedding[0]) + + # targets = torch.where(torch.sigmoid(logits) > self.threshold , torch.tensor(1.0).to(image.device), torch.zeros(self.num_class).to(image.device)) + targets = torch.where(torch.sigmoid(logits) > self.class_threshold.to(image.device) , torch.tensor(1.0).to(image.device), torch.zeros(self.num_class).to(image.device)) + + tag = targets.cpu().numpy() + tag[:,delete_tag_index] = 0 + bs = image.size(0) + tag_input = [] + for b in range(bs): + index = np.argwhere(tag[b] == 1) + token = self.tag_array[index].squeeze(axis = 1) + tag_input.append(' | '.join(token)) + #========================================# + + if not sample: + image_embeds = image_embeds.repeat_interleave(num_beams,dim=0) + image_atts = image_atts.repeat_interleave(num_beams,dim=0) + tag_input_temp = [] + for tag in tag_input: + for i in range(num_beams): + tag_input_temp.append(tag) + tag_input = tag_input_temp + + + tag_input_tokenzier = self.tokenizer(tag_input, padding='max_length', truncation=True, max_length=40, + return_tensors="pt").to(image.device) + encoder_input_ids = tag_input_tokenzier.input_ids + encoder_input_ids[:,0] = self.tokenizer.enc_token_id + + output_tagembedding = self.tag_encoder(encoder_input_ids, + attention_mask = tag_input_tokenzier.attention_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = True, + ) + + prompt = [self.prompt] * image.size(0) + input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(image.device) + input_ids[:,0] = self.tokenizer.bos_token_id + input_ids = input_ids[:, :-1] + + if sample: + #nucleus sampling + model_kwargs = {"encoder_hidden_states": output_tagembedding.last_hidden_state, "encoder_attention_mask":None} + outputs = self.text_decoder.generate(input_ids=input_ids, + max_length=max_length, + min_length=min_length, + do_sample=True, + top_p=top_p, + num_return_sequences=1, + eos_token_id=self.tokenizer.sep_token_id, + pad_token_id=self.tokenizer.pad_token_id, + repetition_penalty=1.1, + **model_kwargs) + else: + #beam search + model_kwargs = {"encoder_hidden_states": output_tagembedding.last_hidden_state, "encoder_attention_mask":None} + outputs = self.text_decoder.generate(input_ids=input_ids, + max_length=max_length, + min_length=min_length, + num_beams=num_beams, + eos_token_id=self.tokenizer.sep_token_id, + pad_token_id=self.tokenizer.pad_token_id, + repetition_penalty=repetition_penalty, + **model_kwargs) + + captions = [] + for output in outputs: + caption = self.tokenizer.decode(output, skip_special_tokens=True) + captions.append(caption[len(self.prompt):]) + if return_tag_predict == True: + if sample: + return captions, tag_input + else: + return captions, tag_input[0:int(len(tag_input)/num_beams)] + return captions + + +def tag2text_caption(pretrained='',**kwargs): + model = Tag2Text_Caption(**kwargs) + if pretrained: + if kwargs['vit'] == 'swin_b': + model,msg = load_checkpoint_swinbase(model,pretrained,kwargs) + else: + model,msg = load_checkpoint(model,pretrained) + # print('vit:',kwargs['vit']) + # print('msg_v2',msg) + return model + + +from typing import List +def tie_encoder_decoder_weights(encoder: nn.Module, decoder: nn.Module, base_model_prefix: str, skip_key:str): + uninitialized_encoder_weights: List[str] = [] + if decoder.__class__ != encoder.__class__: + logger.info( + f"{decoder.__class__} and {encoder.__class__} are not equal. In this case make sure that all encoder weights are correctly initialized." + ) + + def tie_encoder_to_decoder_recursively( + decoder_pointer: nn.Module, + encoder_pointer: nn.Module, + module_name: str, + uninitialized_encoder_weights: List[str], + skip_key: str, + depth=0, + ): + assert isinstance(decoder_pointer, nn.Module) and isinstance( + encoder_pointer, nn.Module + ), f"{decoder_pointer} and {encoder_pointer} have to be of type torch.nn.Module" + if hasattr(decoder_pointer, "weight") and skip_key not in module_name: + assert hasattr(encoder_pointer, "weight") + encoder_pointer.weight = decoder_pointer.weight + if hasattr(decoder_pointer, "bias"): + assert hasattr(encoder_pointer, "bias") + encoder_pointer.bias = decoder_pointer.bias + # print(module_name+' is tied') + return + + encoder_modules = encoder_pointer._modules + decoder_modules = decoder_pointer._modules + if len(decoder_modules) > 0: + assert ( + len(encoder_modules) > 0 + ), f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}" + + all_encoder_weights = set([module_name + "/" + sub_name for sub_name in encoder_modules.keys()]) + encoder_layer_pos = 0 + for name, module in decoder_modules.items(): + if name.isdigit(): + encoder_name = str(int(name) + encoder_layer_pos) + decoder_name = name + if not isinstance(decoder_modules[decoder_name], type(encoder_modules[encoder_name])) and len( + encoder_modules + ) != len(decoder_modules): + # this can happen if the name corresponds to the position in a list module list of layers + # in this case the decoder has added a cross-attention that the encoder does not have + # thus skip this step and subtract one layer pos from encoder + encoder_layer_pos -= 1 + continue + elif name not in encoder_modules: + continue + elif depth > 500: + raise ValueError( + "Max depth of recursive function `tie_encoder_to_decoder` reached. It seems that there is a circular dependency between two or more `nn.Modules` of your model." + ) + else: + decoder_name = encoder_name = name + tie_encoder_to_decoder_recursively( + decoder_modules[decoder_name], + encoder_modules[encoder_name], + module_name + "/" + name, + uninitialized_encoder_weights, + skip_key, + depth=depth + 1, + ) + all_encoder_weights.remove(module_name + "/" + encoder_name) + + uninitialized_encoder_weights += list(all_encoder_weights) + + # tie weights recursively + tie_encoder_to_decoder_recursively(decoder, encoder, base_model_prefix, uninitialized_encoder_weights, skip_key) + + +class GroupWiseLinear(nn.Module): + # could be changed to: + # output = torch.einsum('ijk,zjk->ij', x, self.W) + # or output = torch.einsum('ijk,jk->ij', x, self.W[0]) + def __init__(self, num_class, hidden_dim, bias=True): + super().__init__() + self.num_class = num_class + self.hidden_dim = hidden_dim + self.bias = bias + + self.W = nn.Parameter(torch.Tensor(1, num_class, hidden_dim)) + if bias: + self.b = nn.Parameter(torch.Tensor(1, num_class)) + self.reset_parameters() + + def reset_parameters(self): + stdv = 1. / math.sqrt(self.W.size(2)) + for i in range(self.num_class): + self.W[0][i].data.uniform_(-stdv, stdv) + if self.bias: + for i in range(self.num_class): + self.b[0][i].data.uniform_(-stdv, stdv) + + def forward(self, x): + # x: B,K,d + x = (self.W * x).sum(-1) + if self.bias: + x = x + self.b + return x + + +def init_tokenizer(): + tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') + tokenizer.add_special_tokens({'bos_token':'[DEC]'}) + tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']}) + tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0] + return tokenizer + + +def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0): + + assert vit in ['base', 'large'], "vit parameter must be base or large" + if vit=='base': + vision_width = 768 + visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12, + num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, + drop_path_rate=0 or drop_path_rate + ) + elif vit=='large': + vision_width = 1024 + visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24, + num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, + drop_path_rate=0.1 or drop_path_rate + ) + return visual_encoder, vision_width + +def is_url(url_or_filename): + parsed = urlparse(url_or_filename) + return parsed.scheme in ("http", "https") + +def load_checkpoint(model,url_or_filename): + if is_url(url_or_filename): + cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True) + checkpoint = torch.load(cached_file, map_location='cpu') + elif os.path.isfile(url_or_filename): + checkpoint = torch.load(url_or_filename, map_location='cpu') + else: + raise RuntimeError('checkpoint url or path is invalid') + + state_dict = checkpoint['model'] + + state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) + if 'visual_encoder_m.pos_embed' in model.state_dict().keys(): + state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'], + model.visual_encoder_m) + for key in model.state_dict().keys(): + if key in state_dict.keys(): + if state_dict[key].shape!=model.state_dict()[key].shape: + del state_dict[key] + + msg = model.load_state_dict(state_dict,strict=False) + print('load checkpoint from %s'%url_or_filename) + return model,msg + + +def load_checkpoint_swinbase(model,url_or_filename,kwargs): + if kwargs['image_size'] == 224: + vision_config_path = 'configs/swin/config_swinB_224.json' + elif kwargs['image_size'] == 384: + vision_config_path = 'configs/swin/config_swinB_384.json' + elif kwargs['image_size'] == 480: + vision_config_path = 'configs/swin/config_swinB_480.json' + elif kwargs['image_size'] == 576: + vision_config_path = 'configs/swin/config_swinB_576.json' + elif kwargs['image_size'] == 608: + vision_config_path = 'configs/swin/config_swinB_608.json' + window_size = read_json(vision_config_path)['window_size'] + # print('--------------') + # print(url_or_filename) + # print('--------------') + if is_url(url_or_filename): + cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True) + checkpoint = torch.load(cached_file, map_location='cpu') + elif os.path.isfile(url_or_filename): + checkpoint = torch.load(url_or_filename, map_location='cpu') + else: + raise RuntimeError('checkpoint url or path is invalid') + + state_dict = checkpoint['model'] + + for k in list(state_dict.keys()): + if 'relative_position_bias_table' in k: + dst_num_pos = (2 * window_size - 1) ** 2 + state_dict[k] = interpolate_relative_pos_embed(state_dict[k], dst_num_pos, param_name=k) + elif ('relative_position_index' in k) or ('attn_mask' in k): + del state_dict[k] + + msg = model.load_state_dict(state_dict,strict=False) + print('load checkpoint from %s'%url_or_filename) + return model,msg + + + + + diff --git a/iChat/models/utils.py b/iChat/models/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a9d6619b44ca49c54fea2106fb992d016c906269 --- /dev/null +++ b/iChat/models/utils.py @@ -0,0 +1,3645 @@ +import torch +import numpy as np + +from decord import VideoReader +from decord import cpu + +import uuid +import os + +import torchvision.transforms as transforms +import math +import time +import cv2 +import random + + +GLOBAL_SEED=1912 + +def seed_everything(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + return seed + + +def prompts(name, description): + def decorator(func): + func.name = name + func.description = description + return func + + return decorator + + +def gen_new_name(orginal_name, suffix="update", ext="png"): + root_path, filename = os.path.split(orginal_name) + name_split = os.path.splitext(filename)[0].split('_') + this_new_uuid = str(uuid.uuid4())[:3] + timestamp = int(math.modf(time.time())[0] * 1000) + if len(name_split) == 1: + prev_file_name = name_split[0] + else: + # assert len(name_split) == 3 + prev_file_name = name_split[1] + if len(suffix.strip()) == 0: + new_file_name = f'{this_new_uuid}{timestamp:03d}_{prev_file_name}.{ext}' + else: + new_file_name = f'{this_new_uuid}{timestamp:03d}_{prev_file_name}_{suffix}.{ext}' + return os.path.join(root_path, new_file_name) + + +def dilate_mask(mask, dilate_factor=9): + # dilate mask + mask = mask.astype(np.uint8) + dilated_mask = cv2.dilate(mask, np.ones((dilate_factor, dilate_factor), np.uint8), iterations=1) + + return dilated_mask + + +def cal_dilate_factor(mask): + area = mask[mask != 0].sum() + edge = cv2.Canny(mask, 30, 226) + perimeter = edge.sum() + ratio = 0 + if perimeter > 0: + ratio = int(area * 0.55 / perimeter) + if ratio % 2 == 0: + ratio += 1 + return ratio + + +def blend_gt2pt(old_image, new_image, sigma=0.15, steps=100): + new_size = new_image.size + old_size = old_image.size + easy_img = np.array(new_image) + gt_img_array = np.array(old_image) + pos_w = (new_size[0] - old_size[0]) // 2 + pos_h = (new_size[1] - old_size[1]) // 2 + + kernel_h = cv2.getGaussianKernel(old_size[1], old_size[1] * sigma) + kernel_w = cv2.getGaussianKernel(old_size[0], old_size[0] * sigma) + kernel = np.multiply(kernel_h, np.transpose(kernel_w)) + + kernel[steps:-steps, steps:-steps] = 1 + kernel[:steps, :steps] = kernel[:steps, :steps] / kernel[steps - 1, steps - 1] + kernel[:steps, -steps:] = kernel[:steps, -steps:] / kernel[steps - 1, -(steps)] + kernel[-steps:, :steps] = kernel[-steps:, :steps] / kernel[-steps, steps - 1] + kernel[-steps:, -steps:] = kernel[-steps:, -steps:] / kernel[-steps, -steps] + kernel = np.expand_dims(kernel, 2) + kernel = np.repeat(kernel, 3, 2) + + weight = np.linspace(0, 1, steps) + top = np.expand_dims(weight, 1) + top = np.repeat(top, old_size[0] - 2 * steps, 1) + top = np.expand_dims(top, 2) + top = np.repeat(top, 3, 2) + + weight = np.linspace(1, 0, steps) + down = np.expand_dims(weight, 1) + down = np.repeat(down, old_size[0] - 2 * steps, 1) + down = np.expand_dims(down, 2) + down = np.repeat(down, 3, 2) + + weight = np.linspace(0, 1, steps) + left = np.expand_dims(weight, 0) + left = np.repeat(left, old_size[1] - 2 * steps, 0) + left = np.expand_dims(left, 2) + left = np.repeat(left, 3, 2) + + weight = np.linspace(1, 0, steps) + right = np.expand_dims(weight, 0) + right = np.repeat(right, old_size[1] - 2 * steps, 0) + right = np.expand_dims(right, 2) + right = np.repeat(right, 3, 2) + + kernel[:steps, steps:-steps] = top + kernel[-steps:, steps:-steps] = down + kernel[steps:-steps, :steps] = left + kernel[steps:-steps, -steps:] = right + + pt_gt_img = easy_img[pos_h:pos_h + old_size[1], pos_w:pos_w + old_size[0]] + gaussian_gt_img = kernel * gt_img_array + (1 - kernel) * pt_gt_img # gt img with blur img + gaussian_gt_img = gaussian_gt_img.astype(np.int64) + easy_img[pos_h:pos_h + old_size[1], pos_w:pos_w + old_size[0]] = gaussian_gt_img + gaussian_img = Image.fromarray(easy_img) + return gaussian_img + + +def loadvideo_decord(sample, sample_rate_scale=1,new_width=384, new_height=384, clip_len=8, frame_sample_rate=2,num_segment=1): + fname = sample + vr = VideoReader(fname, width=new_width, height=new_height, + num_threads=1, ctx=cpu(0)) + # handle temporal segments + converted_len = int(clip_len * frame_sample_rate) + seg_len = len(vr) //num_segment + duration = max(len(vr) // vr.get_avg_fps(),8) + + all_index = [] + for i in range(num_segment): + index = np.linspace(0, seg_len, num=int(duration)) + index = np.clip(index, 0, seg_len - 1).astype(np.int64) + index = index + i*seg_len + all_index.extend(list(index)) + + all_index = all_index[::int(sample_rate_scale)] + vr.seek(0) + buffer = vr.get_batch(all_index).asnumpy() + return buffer + + +class LoadVideo: + def __init__(self): + self.video_path = None + self.data = None + + def __call__(self, video_path): + if self.video_path == video_path: + return self.data + self.data = self.load_original_video_decord(video_path) + self.video_path = video_path + return self.data + + def load_original_video_decord(self, sample, sample_rate_scale=1, clip_len=8, frame_sample_rate=2,num_segment=1): + fname = sample + vr = VideoReader(fname, + num_threads=1, + ctx=cpu(0) + ) + # handle temporal segments + converted_len = int(clip_len * frame_sample_rate) + seg_len = len(vr) //num_segment + duration = max(len(vr) // vr.get_avg_fps(),8) + + all_index = [] + for i in range(num_segment): + index = np.linspace(0, seg_len, num=int(duration)) + index = np.clip(index, 0, seg_len - 1).astype(np.int64) + index = index + i*seg_len + all_index.extend(list(index)) + + all_index = all_index[::int(sample_rate_scale)] + vr.seek(0) + buffer = vr.get_batch(all_index).asnumpy() + return buffer + + +def loadvideo_decord_origin(self, sample, sample_rate_scale=1,new_width=384, new_height=384, clip_len=8, frame_sample_rate=2,num_segment=1): + fname = sample + vr = VideoReader(fname, + num_threads=1, ctx=cpu(0)) + # handle temporal segments + converted_len = int(clip_len * frame_sample_rate) + seg_len = len(vr) //num_segment + duration = max(len(vr) // vr.get_avg_fps(),8) + + all_index = [] + for i in range(num_segment): + index = np.linspace(0, seg_len, num=int(duration)) + index = np.clip(index, 0, seg_len - 1).astype(np.int64) + index = index + i*seg_len + all_index.extend(list(index)) + + all_index = all_index[::int(sample_rate_scale)] + vr.seek(0) + buffer = vr.get_batch(all_index).asnumpy() + return buffer + + + + + +tra_array = ['tennis', +'bear cub', +'observatory', +'bicycle', +'hillside', +'judge', +'watercolor illustration', +'granite', +'lobster', +'livery', +'stone', +'ceramic', +'ranch', +'cloth', +'smile', +'building', +'tattoo', +'cricketer', +'cheek', +'pear', +'source', +'winter', +'surface', +'spray', +'ceremony', +'magic', +'curve', +'container', +'fair', +'medicine', +'baby', +'tennis racquet', +'ornament', +'bamboo', +'duckling', +'song', +'safari', +'team presentation', +'daffodil', +'cross', +'toothpaste', +'shield', +'fashion model', +'capsule', +'map', +'creek', +'glass house', +'glass plate', +'siding', +'corner', +'water buffalo', +'bison', +'figure skater', +'diploma', +'tire', +'race', +'cable car', +'brain', +'gas stove', +'soap bubble', +'palette', +'snowboard', +'school child', +'trench coat', +'monk', +'fiber', +'kitchen window', +'sunglass', +'coffee', +'security', +'strawberry', +'penguin', +'tree root', +'loaf', +'engagement ring', +'lamb', +'vector cartoon illustration', +'sandwich', +'mountain village', +'shape', +'charm', +'fiction', +'knot', +'greenhouse', +'sushi', +'text', +'disaster', +'trophy', +'gang', +'strap', +'soccer game', +'cardinal', +'tee', +'turtle', +'water surface', +'grassland', +'dolphin', +'store', +'dirt', +'iceberg', +'pergola', +'farmer market', +'publicity portrait', +'tote bag', +'teenage girl', +'view mirror', +'session', +'commuter', +'dressing room', +'tricycle', +'christmas ball', +'headlight', +'police', +'armchair', +'chart', +'yacht', +'saw', +'printer', +'rock band', +'gingerbread house', +'tag', +'table lamp', +'hockey game', +'slope', +'font', +'wicker basket', +'jewelry', +'quarter', +'software', +'weapon', +'pin', +'worship', +'painter', +'goal', +'morning light', +'bike', +'baseball bat', +'elevator', +'cuisine', +'sausage', +'stunt', +'wrestler', +'statue', +'landing', +'pillar', +'willow tree', +'sea wave', +'chicken', +'peanut', +'muscle', +'bob', +'tv genre', +'bathroom window', +'radish', +'textile', +'pelican', +'marketplace', +'crest', +'elevation map', +'gift', +'parish', +'traffic light', +'campfire', +'fog', +'award winner', +'beach ball', +'mat', +'white house', +'plaster', +'moped', +'football team', +'solution', +'bicyclist', +'bit', +'playground', +'darkness', +'cake', +'maple leave', +'mold', +'cracker', +'blueberry', +'rubble', +'container ship', +'pedestrian bridge', +'snail', +'parrot', +'form', +'circuit', +'highlight', +'pickup truck', +'koala', +'rain', +'system', +'weather', +'raincoat', +'soccer team', +'windshield', +'thunderstorm', +'mike', +'bird house', +'bridge', +'grandfather', +'restroom', +'animation', +'wilderness', +'clown', +'banana', +'brown', +'braid', +'dining room', +'kindergarten', +'launch event', +'purple', +'school', +'stairwell', +'brooch', +'movie poster image', +'mountain river', +'shelf', +'wicket', +'headboard', +'buddha', +'flower field', +'dugout', +'cd', +'bald eagle', +'lagoon', +'seaweed', +'agriculture', +'emergency service', +'maple tree', +'parachute', +'continent', +'amusement park', +'remote', +'bun', +'tackle', +'hospital', +'garage door', +'birthday party', +'friendship', +'go', +'mausoleum', +'jeep', +'raccoon', +'step', +'ice hockey team', +'cigarette', +'lace dress', +'forest floor', +'mall', +'captain', +'milk', +'golf course', +'meal', +'picnic table', +'sail', +'volleyball', +'canal', +'terrace', +'computer desk', +'caravan', +'hotel', +'cheerleader', +'nurse', +'museum', +'marsh', +'fox', +'plateau', +'night', +'twin', +'letter logo', +'autumn tree', +'powder', +'convention', +'creature', +'lighthouse', +'shop window', +'jacket', +'stork', +'taxi', +'trade', +'blackboard', +'olive', +'road sign', +'resort', +'snowflake', +'cemetery', +'travel', +'evening dress', +'picnic', +'drink', +'winter morning', +'football player', +'snack', +'boxing glove', +'dinner party', +'airline', +'swing', +'port', +'wheelbarrow', +'bathroom sink', +'sweater', +'ambulance', +'gear', +'oil', +'wii controller', +'array', +'home office', +'car show', +'mixture', +'profession', +'tree frog', +'square', +'facility', +'coral reef', +'sea wall', +'pizza', +'exhibit', +'demolition', +'trout', +'ring', +'coffee shop', +'bracelet', +'bean', +'lip', +'fencing', +'landscape', +'sitting', +'package', +'metal', +'bust', +'king', +'hair', +'window seat', +'wildlife', +'trunk', +'greenery', +'stencil', +'fire hydrant', +'bridesmaid', +'plaza', +'alps', +'tower bridge', +'crop top', +'crossing', +'cinema', +'pedestrian crossing', +'family', +'shopping cart', +'stomach', +'church building', +'screen door', +'skater', +'soccer field', +'kettle', +'mussel', +'raindrop', +'candy cane', +'water lily', +'flower girl', +'desert', +'enclosure', +'christmas light', +'kitchen', +'caterpillar', +'plaid', +'bath', +'bush', +'mud', +'ballet', +'knee', +'adult', +'raft', +'sea view', +'cactus', +'office chair', +'overall', +'rim', +'scaffolding', +'pig', +'cover', +'poster page', +'sprinkle', +'chandelier', +'algae', +'traffic', +'surfboard', +'book', +'filming', +'flash', +'mansion', +'camouflage', +'trouser', +'ticket', +'weed', +'cab', +'trench', +'elephant', +'huddle', +'sphere', +'christmas decoration', +'city', +'launch', +'doll', +'christmas ornament', +'fabric', +'bikini', +'biplane', +'breakfast', +'neighbourhood', +'race track', +'foliage', +'avocado', +'school bus', +'footwear', +'highway', +'ocean view', +'art vector illustration', +'wall clock', +'curtain', +'teenager', +'kitchen area', +'robot', +'tusk', +'lounge chair', +'beam', +'paddle', +'camel', +'lid', +'world map', +'city view', +'newlywed', +'cargo ship', +'yellow', +'exhibition', +'bend', +'novel', +'wool', +'ontario', +'bread', +'campus', +'coastline', +'cutting board', +'booth', +'table top', +'carpet', +'beach chair', +'workout', +'street food', +'fun', +'costumer film designer', +'gadget', +'artist', +'fishing village', +'builder', +'violinist', +'iphone', +'spider web', +'traffic sign', +'ruin', +'rescue', +'clipboard', +'seal', +'film director', +'paw', +'nursery', +'intersection', +'tomato sauce', +'taste', +'paddy field', +'christmas tree', +'wave', +'stool', +'watering can', +'rug', +'daytime', +'subway station', +'craft', +'pine forest', +'black', +'planet', +'motif', +'christmas market', +'glass window', +'college', +'wheat', +'damage', +'rectangle', +'picture frame', +'chess', +'guest room', +'street corner', +'religion', +'seed', +'puzzle', +'freeway', +'beauty', +'ocean', +'watch', +'mother', +'garage', +'quote', +'dj', +'supporter', +'hip hop artist', +'muffin', +'eiffel tower', +'cash', +'firefighter', +'cauliflower', +'bunker', +'sled', +'manicure', +'shark', +'stall', +'jungle', +'family home', +'tour bus', +'chimney', +'touchdown', +'roundabout', +'coyote', +'street scene', +'tank', +'wedding dress', +'mantle', +'bedroom window', +'coconut', +'chapel', +'goat', +'living space', +'rock wall', +'polka dot', +'railway', +'mandala', +'mango', +'lesson', +'mountain landscape', +'team photo', +'bookshelf', +'meter', +'bulldog', +'evening sun', +'stick', +'card', +'pink', +'fish pond', +'paint', +'pill', +'cart', +'pea', +'van', +'album', +'football college game', +'mountain pass', +'doughnut', +'ski slope', +'match', +'official', +'shadow', +'organ', +'celebration', +'coin', +'log cabin', +'firework display', +'present', +'twig', +'chef', +'confetti', +'footpath', +'tour', +'ponytail', +'artwork', +'race car', +'club', +'season', +'hose', +'pencil', +'aircraft', +'rock formation', +'wardrobe', +'participant', +'politician', +'engineer', +'peace', +'filter', +'sailing boat', +'water bottle', +'service dog', +'poodle', +'loki', +'statesman', +'sleeping bag', +'outskirt', +'clock', +'factory', +'oak tree', +'physician', +'color', +'room', +'stairway', +'company', +'lady', +'graph', +'faucet', +'tablecloth', +'subway train', +'chocolate chip cookie', +'headquarters', +'screw', +'goggle', +'halloween', +'city street', +'swirl', +'cord', +'forward', +'bone', +'bedding', +'archway', +'wig', +'lobby', +'mask', +'attic', +'kitchen table', +'skylight', +'fire', +'exit', +'oil painting', +'passenger', +'meditation', +'salmon', +'fedora', +'rubber stamp', +'orange juice', +'arch', +'scientist', +'stroll', +'manhattan', +'float', +'baseball uniform', +'circle', +'church', +'decker bus', +'competitor', +'zoo', +'basketball team', +'tourist', +'daughter', +'silverware', +'ceiling fan', +'birth', +'vase', +'jack', +'mushroom', +'spiral', +'cage', +'limb', +'salad', +'ad', +'control', +'earth', +'party', +'bolt', +'tractor', +'barley', +'wedding photo', +'hawk', +'warehouse', +'vegetable garden', +'chocolate cake', +'cabbage', +'floor window', +'baby shower', +'magnifying glass', +'table', +'stethoscope', +'reading', +'mission', +'croissant', +'gift box', +'rocket', +'forest road', +'cooking', +'suite', +'hill country', +'motorcycle', +'baseball player', +'angle', +'drug', +'sport association', +'championship', +'family portrait', +'florist', +'softball', +'egret', +'office', +'plywood', +'jockey', +'mosque', +'brunch', +'beanie', +'office building', +'pattern', +'calendar', +'indoor', +'pepper', +'ledge', +'trail', +'fuel', +'laptop computer', +'tennis shoe', +'deck chair', +'guitarist', +'barn', +'surgery', +'cartoon illustration', +'nebula', +'railroad', +'mountain goat', +'goose', +'car door', +'cheer', +'liquid', +'hardwood floor', +'pathway', +'acorn', +'gull', +'airliner', +'couch', +'lake house', +'spaghetti', +'promenade', +'collection', +'garden', +'bank', +'robin', +'tennis ball', +'peony', +'gymnast', +'lavender', +'deck', +'test', +'riverside', +'rapper', +'domino', +'bride', +'mouse', +'basil', +'wedding couple', +'ocean wave', +'arm', +'kitchen floor', +'grove', +'family member', +'backyard', +'raspberry', +'forest fire', +'officer', +'hibiscus', +'canyon', +'composer', +'signature', +'olive oil', +'hibiscus flower', +'rose', +'vector icon', +'sunrise', +'horseback', +'motor scooter', +'office worker', +'tradition', +'ingredient', +'washing machine', +'lighting', +'bagel', +'sailboat', +'policeman', +'mare', +'graphic', +'halloween pumpkin', +'stock', +'pilot', +'education', +'team', +'body', +'horse', +'kimono', +'bazaar', +'bag', +'recording studio', +'parsley', +'entrance', +'denim', +'vet', +'horse farm', +'charcoal', +'architecture', +'glass vase', +'puppy', +'estuary', +'television show host', +'city bus', +'shoulder', +'beast', +'balance', +'golfer', +'roadside', +'denim jacket', +'stone wall', +'counter top', +'app icon', +'toast', +'head coach', +'ham', +'warrior', +'gem', +'refrigerator', +'snowman', +'construction worker', +'coal', +'website', +'morning fog', +'mustard', +'human', +'owl', +'puppy dog', +'piggy bank', +'vegetation', +'pirate', +'action film', +'marshmallow', +'thanksgiving', +'business', +'disease', +'signage', +'greeting', +'skate park', +'tile', +'mouth', +'spinach', +'vacation', +'leader', +'shrine', +'walker', +'science fiction film', +'bill', +'rabbit', +'motor boat', +'bar', +'radio', +'barge', +'tail', +'chainsaw', +'gallery', +'rainbow', +'pasta', +'padlock', +'web', +'pastry', +'ink', +'reef', +'school uniform', +'shawl', +'treasure', +'peach', +'dinner table', +'injury', +'harbor', +'witch', +'car dealership', +'litter', +'gesture', +'documentary', +'marriage', +'sea shell', +'priest', +'dome', +'kit', +'icon', +'seaside', +'bucket', +'entertainment', +'stable', +'hat', +'puddle', +'sock', +'shopper', +'technology', +'harbour', +'orbit', +'antler', +'tube', +'flag waving', +'cook', +'tight', +'commander', +'farmland', +'switch', +'hiker', +'wedding ceremony', +'award ceremony', +'champion', +'chopstick', +'farmhouse', +'performer', +'spike', +'accident', +'cruise ship', +'passenger train', +'attraction', +'entertainer', +'rear view', +'sidewalk', +'parade', +'racing', +'plane', +'ritual', +'peacock', +'pocket', +'plum', +'drop', +'carrot', +'floor', +'sunset', +'troop', +'architect', +'coffee table', +'dust', +'outline', +'leather', +'charity event', +'heat', +'whale', +'laundry', +'coconut tree', +'crosswalk', +'pony', +'ant', +'pipe', +'string', +'coat', +'angel', +'beef', +'church tower', +'dish', +'pitch', +'cupboard', +'thermometer', +'dirt field', +'fireworks', +'minute', +'cane', +'pajama', +'flower garden', +'autumn', +'trash can', +'dachshund', +'banana tree', +'tray', +'moose', +'roadway', +'carnival', +'antenna', +'pole', +'castle wall', +'ram', +'cattle', +'hay', +'cookie', +'swimmer', +'baseball team', +'strait', +'hedge', +'jet', +'fire pit', +'octopus', +'calf', +'cube', +'opera', +'cardboard box', +'tiara', +'kitchen sink', +'prairie', +'bowl', +'galaxy', +'straw hat', +'linen', +'ski resort', +'stitch', +'street lamp', +'motorist', +'icicle', +'stain', +'flora', +'drain', +'kitchen cabinet', +'decor', +'bouquet', +'pound', +'interior design', +'nail polish', +'figurine', +'tomb', +'disc', +'twist', +'blouse', +'ribbon', +'figure', +'burger', +'cork', +'soccer goalkeeper', +'train bridge', +'drinking water', +'dew', +'baker', +'storm cloud', +'tarmac', +'tv drama', +'sponge', +'magnet', +'sailor', +'entry', +'swan', +'exercise', +'sloth', +'jewel', +'scuba diver', +'bite', +'cat tree', +'tent', +'can', +'tennis match', +'ecosystem', +'picket fence', +'palm', +'train car', +'frying pan', +'rally', +'tablet pc', +'reindeer', +'image', +'wolf', +'chin', +'conservatory', +'flood water', +'cityscape', +'beach sand', +'car park', +'pavement', +'farm field', +'swimming', +'winter storm', +'stem', +'pillow', +'inning', +'gorilla', +'desk', +'avenue', +'fern', +'money', +'pearl', +'train station', +'skillet', +'nap', +'barber', +'library', +'freezer', +'label', +'rainforest', +'parking sign', +'mirror', +'wing', +'noodle', +'press room', +'sculpture', +'tablet', +'viewer', +'prayer', +'mini', +'mechanic', +'laugh', +'rice field', +'hand', +'mustache', +'mountain road', +'catwalk', +'conference', +'cape', +'installation', +'musician', +'stream', +'machine', +'speech', +'crocodile', +'soccer match', +'town square', +'passport', +'post box', +'point', +'stone building', +'motorway', +'mix', +'dentist', +'businessperson', +'happiness', +'boat', +'vineyard', +'treadmill', +'glass wall', +'water droplet', +'coffee mug', +'graduate', +'sunflower', +'parliament', +'shepherd', +'movie', +'wine', +'orchard', +'tulip', +'motherboard', +'cup', +'broom', +'spot', +'drawing', +'polo shirt', +'graduation', +'film producer', +'moonlight', +'glow', +'film format', +'t shirt', +'rock face', +'sword', +'clinic', +'festival day', +'meadow', +'staple', +'pupil', +'training ground', +'rider', +'flower', +'foal', +'wharf', +'foot bridge', +'shooting', +'top', +'mast', +'police car', +'robe', +'wedding bouquet', +'stop sign', +'birthday cake', +'glitter', +'butter', +'scooter', +'tundra', +'superhero', +'pocket watch', +'inscription', +'youngster', +'fruit tree', +'movie poster', +'engine', +'foundation', +'motorcyclist', +'take', +'woman', +'antelope', +'country artist', +'road trip', +'typewriter', +'tuxedo', +'brand', +'pine', +'bathroom', +'paradise', +'texture', +'balloon', +'dining table', +'home', +'computer screen', +'actor', +'clip', +'tv tower', +'panorama', +'summit', +'cat', +'plot', +'eagle', +'dancer', +'pup', +'studio shot', +'tear', +'bird bath', +'classroom', +'bookstore', +'city wall', +'tv programme', +'blade', +'easel', +'buttercream', +'sweet', +'designer', +'diamond', +'handshake', +'herb', +'corn field', +'seafront', +'concrete', +'street artist', +'gas', +'stamp', +'window display', +'paper', +'note', +'pint', +'quarry', +'research', +'fixture', +'manager', +'soil', +'leopard', +'board game', +'ladder', +'stop light', +'island', +'ramp', +'football match', +'icing', +'drill', +'currency', +'summer evening', +'topping', +'pyramid', +'pomegranate', +'cell', +'ivy', +'squad', +'scenery', +'computer', +'locomotive', +'surf', +'mascot', +'dune', +'path', +'duck', +'twilight', +'wire', +'bow tie', +'strike', +'cormorant', +'car wash', +'crane', +'market', +'philosopher', +'alarm clock', +'camera', +'birch', +'greeting card', +'plain', +'clay', +'donut', +'lock', +'moth', +'laboratory', +'fan', +'violin', +'jazz fusion artist', +'mountain biker', +'terrain', +'magazine', +'pickup', +'comedy film', +'smartphone', +'film', +'bed', +'microwave oven', +'tournament', +'lawn', +'car window', +'alligator', +'screen', +'jetty', +'shopping bag', +'landscape view', +'cabinetry', +'friendly match', +'thing', +'petal', +'shopping center', +'transport', +'ballet dancer', +'shoreline', +'princess', +'car seat', +'parking meter', +'green', +'vodka', +'band', +'rock', +'costume', +'warning sign', +'strip', +'plaque', +'wheelchair', +'headband', +'ginger', +'dice', +'media', +'hairdresser', +'press', +'living room', +'stove', +'player', +'cherry', +'workshop', +'carving', +'embroidery', +'doodle', +'adventure', +'rugby player', +'monument', +'brush', +'marker', +'loft', +'postcard', +'collage', +'ball', +'professor', +'dresser', +'gig', +'festival', +'blackbird', +'makeup artist', +'video camera', +'sticker', +'peak', +'wildflower', +'santa hat', +'rodeo', +'wedding photographer', +'guy', +'staff', +'waterfall', +'operation', +'defender', +'falcon', +'haze', +'individual', +'gentleman', +'greyhound', +'rocking chair', +'rice', +'garbage', +'platter', +'chocolate', +'splash', +'business suit', +'cheetah', +'valley', +'maze', +'trampoline', +'garland', +'slalom', +'unicorn', +'tree stump', +'painting', +'romance', +'fight', +'alcohol', +'ghost', +'fondant', +'spa', +'shutter', +'death', +'demonstration', +'cotton', +'pier', +'flea market', +'history', +'savannah', +'fist', +'aisle', +'crew', +'jug', +'pose', +'anchor', +'teapot', +'boat house', +'business team', +'tripod', +'bee', +'pebble', +'mattress', +'canvas', +'hallway', +'campaign', +'pod', +'lake district', +'article', +'white', +'sofa', +'honey', +'marathon', +'pancake', +'tourist attraction', +'wedding gown', +'battle', +'shelving', +'sea', +'sheet music', +'pie', +'yarn', +'construction site', +'flyer', +'tie', +'star', +'lettuce', +'martial artist', +'dart', +'straw', +'reflection', +'conference room', +'temperature', +'rugby', +'mosquito', +'physicist', +'rock climber', +'crash', +'backdrop', +'toilet seat', +'sand castle', +'water park', +'toy car', +'waste', +'luxury', +'hangar', +'rv', +'tree trunk', +'board', +'gold', +'project picture', +'cap', +'cottage', +'relief', +'attire', +'microscope', +'battery', +'roll', +'line', +'parking garage', +'crystal', +'broadcasting', +'brick wall', +'lab', +'flooring', +'meeting', +'3d cg rendering', +'desktop computer', +'cowboy', +'sailing ship', +'junction', +'hairstyle', +'homework', +'profile', +'model', +'flower pot', +'street light', +'salt lake', +'maple', +'space', +'blizzard', +'throw', +'zebras', +'brochure', +'constellation', +'beak', +'kilt', +'pond', +'blue sky', +'sneaker', +'sand dune', +'morning sun', +'almond', +'grill', +'curl', +'basketball girl game', +'chameleon', +'toilet bowl', +'prince', +'keyboard', +'queen', +'computer monitor', +'writing', +'crown', +'basilica', +'kiss', +'house', +'parking', +'football competition', +'shell', +'sport equipment', +'comedy', +'baboon', +'vendor', +'rise building', +'wrap', +'food truck', +'cat bed', +'rickshaw', +'flare', +'teal', +'nectar', +'eclipse', +'vehicle', +'steam locomotive', +'gorge', +'cow', +'christmas card', +'demonstrator', +'memorial', +'towel', +'jewellery', +'train', +'frisbee', +'baseball game', +'fur', +'afternoon sun', +'community', +'sparkler', +'bandage', +'firework', +'dollar', +'pasture', +'video', +'bus', +'tree house', +'seashore', +'field', +'hamburger', +'souvenir', +'hedgehog', +'worm', +'pine cone', +'osprey', +'dinosaur', +'vegetable', +'junk', +'poster', +'army', +'winger', +'bundle', +'stage', +'growth', +'wedding party', +'service', +'blanket', +'ruler', +'eye', +'credit card', +'castle', +'diner', +'hut', +'elk', +'hard rock artist', +'nun', +'dog breed', +'nest', +'drama film', +'number icon', +'water tank', +'giraffe', +'altar', +'pavilion', +'tv personality', +'suv', +'street vendor', +'street sign', +'ditch', +'debris', +'foam', +'takeoff', +'spice', +'mountain lake', +'tea', +'orchestra', +'spacecraft', +'counter', +'abbey', +'mountain', +'hydrangea', +'racer', +'orange tree', +'tide', +'cowboy hat', +'rapid', +'town', +'wild', +'herd', +'vein', +'driveway', +'jar', +'bark', +'illustration', +'horror film', +'corn', +'stroller', +'industry', +'mountain stream', +'gym', +'neckline', +'pan', +'client', +'spectator', +'eggplant', +'camper', +'fawn', +'hoodie', +'meat', +'lemonade', +'food market', +'slum', +'comic book character', +'flower market', +'love', +'palace', +'gun', +'heel', +'shopping street', +'shooting basketball guard', +'family photo', +'rooftop', +'laundry basket', +'airport runway', +'horn', +'face mask', +'flight', +'appetizer', +'violet', +'country lane', +'cement', +'instrument', +'tv actor', +'spark', +'celebrity', +'award', +'country house', +'standing', +'auction', +'date', +'engagement', +'puck', +'advertisement', +'chair', +'zebra', +'driftwood', +'bumblebee', +'maple leaf', +'bonnet', +'orange', +'water tower', +'door', +'singer', +'floor plan', +'discussion', +'theatre', +'pilgrim', +'mug', +'branch', +'window sill', +'baseball pitcher', +'bakery', +'lollipop', +'basketball player', +'toilet paper', +'chalkboard', +'cabin', +'sign', +'night sky', +'cannon', +'fishing net', +'submarine', +'suit', +'fur coat', +'wine bottle', +'folder', +'street art', +'suspension bridge', +'evening sky', +'billboard', +'postage stamp', +'newspaper', +'transportation', +'surgeon', +'light', +'park', +'horizon', +'road', +'sand bar', +'trumpet', +'lounge', +'cloud forest', +'birthday celebration', +'balcony', +'anime', +'beehive', +'umbrella', +'goldfish', +'baseball cap', +'waterhole', +'ceiling', +'carousel', +'backpack', +'plant pot', +'atmosphere', +'sunflower field', +'spire', +'vision', +'woodpecker', +'chip', +'pool table', +'lotus flower', +'cone', +'humpback whale', +'reservoir', +'hunt', +'piano', +'plate', +'dining area', +'luggage', +'skier', +'dance floor', +'crow', +'stair', +'overpass', +'opera house', +'bear', +'jazz artist', +'water', +'vessel', +'cast', +'yard', +'cathedral', +'basketball hoop', +'graveyard', +'sound', +'berry', +'onlooker', +'fauna', +'birch tree', +'retail', +'hill', +'skeleton', +'journalist', +'frost', +'basket', +'nail', +'dusk', +'trash', +'dawn', +'clover', +'hen', +'volcano', +'basketball coach', +'home decor', +'charge', +'haircut', +'sense', +'university', +'lizard', +'daisy', +'tablet computer', +'grass field', +'prison', +'metal artist', +'bathroom mirror', +'window frame', +'chest', +'flavor', +'pop country artist', +'market square', +'monkey', +'blog', +'deer', +'speech bubble', +'dog', +'independence day', +'girl', +'boy', +'tartan', +'furniture', +'appliance', +'office window', +'fish boat', +'sand box', +'tv sitcom', +'drama', +'sleigh', +'depression', +'paper towel', +'baseball', +'protestor', +'grape', +'wedding cake', +'invitation', +'accessory', +'pick', +'grandparent', +'racket', +'tea plantation', +'outdoors', +'egg', +'glass bowl', +'sun', +'organization', +'lion', +'panel', +'station', +'wallpaper', +'helicopter', +'salt', +'vanity', +'patio', +'lunch', +'street performer', +'mountain range', +'soup', +'bacon', +'power station', +'cantilever bridge', +'hummingbird', +'shirt', +'rope', +'hip', +'chalk', +'pendant', +'choir', +'tv', +'lichen', +'railway bridge', +'art gallery', +'bartender', +'wagon', +'baby elephant', +'accordion', +'horseshoe', +'building site', +'clutch', +'harvest', +'savanna', +'geranium', +'business woman', +'paddock', +'patch', +'beech tree', +'war', +'suburbs', +'hospital bed', +'motorcycle racer', +'moss', +'gravel', +'government agency', +'dollar bill', +'father', +'fjord', +'concert', +'nut', +'wedding photography', +'finish line', +'home plate', +'food', +'nose', +'thumb', +'village', +'dining room table', +'bumper', +'monster', +'blackberry', +'lime', +'conflict', +'gala', +'wallet', +'wrist', +'hug', +'mermaid', +'lava', +'lawyer', +'folk rock artist', +'arena', +'onion', +'toothbrush', +'fashion', +'perfume', +'flip', +'triangle', +'woodland', +'mail', +'grasshopper', +'studio', +'wood floor', +'den', +'racquet', +'cello', +'lemur', +'astronaut', +'glass table', +'blood', +'dvd', +'planter', +'silver', +'leash', +'master bedroom', +'forest', +'batter', +'shoe', +'engraving', +'opening', +'product', +'toe', +'cocktail', +'mallard duck', +'bike ride', +'oasis', +'wedding ring', +'cinematographer', +'holly', +'autograph', +'fence', +'ice cube', +'cove', +'pineapple', +'aurora', +'glass bead', +'produce', +'apartment building', +'cob', +'miniature', +'cockpit', +'flashlight', +'frog', +'sheep', +'groom', +'steel', +'watermelon', +'clip art', +'paper plate', +'ostrich', +'contour', +'mural', +'cub', +'paisley bandanna', +'winery', +'turn', +'handle', +'satellite', +'post', +'pork', +'child', +'asphalt', +'grocery store', +'vulture', +'trolley', +'nightclub', +'brick', +'trailer', +'compass', +'cereal', +'cafe', +'cartoon character', +'sugar', +'fiction book', +'glass floor', +'umpire', +'guitar', +'hamster', +'protester', +'airplane', +'garment', +'blazer', +'railway line', +'wedding', +'shoe box', +'parking lot', +'construction', +'graduation ceremony', +'tram', +'telescope', +'copper', +'pain', +'autumn forest', +'guest house', +'partner', +'crayon', +'dip', +'boot', +'corridor', +'computer keyboard', +'hockey player', +'chicken coop', +'bus station', +'gathering', +'ankle', +'bunk bed', +'wood table', +'football coach', +'monarch', +'pharmacy', +'legging', +'mannequin', +'female', +'train track', +'stack', +'canopy', +'design element', +'grandmother', +'symbol', +'beach hut', +'zucchini', +'bomb', +'businessman', +'skyscraper', +'tongue', +'case', +'sparkle', +'highland', +'ballroom', +'prom', +'estate', +'customer', +'archipelago', +'cheese', +'debate', +'carriage', +'bulldozer', +'pumpkin', +'sitting room', +'gas station', +'wedding reception', +'camp', +'dog bed', +'tower', +'property', +'river bed', +'pop latin artist', +'fridge', +'wine glass', +'coast', +'beer', +'tow truck', +'fire truck', +'mountain bike', +'thigh', +'heron', +'boat ride', +'gondola', +'turquoise', +'lake', +'llama', +'kitty', +'tin', +'waiting room', +'coffee cup', +'socialite', +'guard', +'tap', +'waterway', +'forehead', +'list', +'erosion', +'box', +'sea lion', +'pollen', +'dam', +'wasp', +'salon', +'tennis tournament', +'flower box', +'aquarium', +'rain cloud', +'clothing store', +'lead singer', +'cupcake', +'tortoise', +'lettering', +'sport facility', +'dance', +'dog house', +'nature', +'football', +'rooster', +'footballer', +'railway track', +'crowd', +'fishing rod', +'silhouette', +'wind turbine', +'sari', +'bus window', +'cloud', +'charity', +'medal', +'yoga', +'event', +'veil', +'fashion menswear milan week', +'news', +'knife', +'print', +'screen tv', +'walnut', +'fungus', +'ice cream', +'computer mouse', +'play', +'tribe', +'picture', +'video game', +'business card', +'music festival', +'rack', +'envelope', +'shower', +'dirt road', +'mine', +'oyster', +'monarch butterfly', +'dude', +'fruit salad', +'podium', +'fork', +'lace', +'test match', +'boulder', +'cricket player', +'staircase', +'peninsula', +'shopping', +'popcorn', +'oak', +'market stall', +'pine tree', +'mountaineer', +'student', +'closet', +'hood', +'handstand', +'centerpiece', +'insect', +'patient', +'makeover', +'tennis player', +'sheet', +'park bench', +'apple', +'organism', +'hook', +'turkey', +'tangerine', +'sibling', +'shopping mall', +'bird', +'scarf', +'smoothie', +'net', +'grass', +'napkin', +'ray', +'eyebrow', +'laptop keyboard', +'motorbike', +'woman hand', +'oven', +'book cover', +'easter egg', +'microwave', +'sand', +'snapshot', +'soccer ball', +'makeup', +'knight', +'bowling ball', +'shower curtain', +'flame', +'lightning', +'running', +'power plant', +'crib', +'cartoon', +'moat', +'fashion girl', +'wedding invitation', +'bottle', +'cliff', +'monastery', +'file photo', +'apartment', +'casino', +'cream', +'sweatshirt', +'storm', +'cruise', +'teddy bear', +'shovel', +'wind farm', +'writer', +'dock', +'professional', +'hotel room', +'job', +'monitor', +'donkey', +'pass', +'interview', +'duchess', +'mark', +'plank', +'beard', +'zombie', +'trio', +'channel', +'cricket team', +'windmill', +'vest', +'diagram', +'cable', +'winter scene', +'golden gate bridge', +'buffalo', +'studio portrait', +'pagoda', +'whiskey', +'freight train', +'kite', +'future', +'steam train', +'phone box', +'headset', +'wood', +'snowboarder', +'paper bag', +'slide', +'grapefruit', +'seating', +'morning', +'bronze sculpture', +'theatre actor', +'stump', +'jean', +'landmark', +'jam', +'waist', +'watercolor', +'hammock', +'light fixture', +'ice', +'basin', +'beverage', +'shelter', +'premiere', +'mound', +'ear', +'bronze', +'sunlight', +'street', +'energy', +'barn door', +'hike', +'fleet', +'claw', +'beach', +'pepperoni', +'bin', +'trainer', +'buffet', +'archive', +'toddler', +'referee', +'bay window', +'dove', +'production company', +'evening light', +'gate', +'farm', +'reed', +'fruit stand', +'explorer', +'snow storm', +'throw pillow', +'button', +'display case', +'bookcase', +'lead', +'lipstick', +'basketball court', +'cargo', +'ensemble', +'pope', +'clock tower', +'teen', +'speaker', +'rat', +'laptop', +'ski', +'mess', +'stadium', +'ferry boat', +'bunny', +'waterfront', +'downtown', +'sink', +'press conference', +'dinner', +'condiment', +'thread', +'audience', +'grid', +'car', +'plastic', +'people', +'barbecue', +'pigeon', +'urinal', +'seagull', +'volunteer', +'hockey', +'fir tree', +'pollution', +'trial', +'collar', +'area', +'meeting room', +'circus', +'yogurt', +'orangutan', +'viaduct', +'comedian', +'drone', +'scissor', +'pop rock artist', +'biscuit', +'panda', +'water feature', +'air balloon', +'remote control', +'watercolor painting', +'show', +'walk', +'post office', +'bike path', +'rap gangsta artist', +'microphone', +'crack', +'sunset sky', +'glass', +'tv show', +'cartoon style', +'stripe', +'foyer', +'signal', +'calligraphy', +'bulb', +'gardener', +'coffee bean', +'spider', +'tapestry', +'city skyline', +'necklace', +'kitten', +'traveler', +'veteran', +'frosting', +'fry', +'tennis court', +'tank top', +'butterfly house', +'mist', +'drummer', +'water level', +'scale', +'baseball glove', +'music video performer', +'champagne', +'camping', +'clothing', +'water drop', +'telephone box', +'pen', +'morning mist', +'fire engine', +'porch', +'opening ceremony', +'style', +'palm tree', +'fashion show', +'universe', +'scratch', +'axe', +'ottoman', +'explosion', +'rib', +'boutique', +'game', +'cucumber', +'fruit', +'stone bridge', +'nature reserve', +'track', +'train window', +'punch', +'telephone pole', +'velvet', +'sauce', +'moon', +'contrast', +'flamingo', +'bat', +'vending machine', +'ship', +'equestrian', +'shade', +'comforter', +'pallet', +'sparrow', +'wii', +'glaze', +'grocery', +'steeple', +'soccer player', +'contract', +'advertising', +'runner', +'chimpanzee', +'world', +'seat', +'project', +'chihuahua', +'bubble', +'willow', +'pedestal', +'soul hip hop artist', +'curb', +'drawer', +'leaf', +'banner', +'launch party', +'coach', +'government', +'snowball', +'toy', +'portrait', +'doctor', +'whiteboard', +'electronic', +'tiger', +'graffiti', +'column', +'nightstand', +'whistle', +'maxi dress', +'bench', +'wetsuit', +'bird feeder', +'football game', +'basketball', +'class', +'bathroom door', +'store window', +'text message', +'wreath', +'street view', +'binocular', +'pet', +'facade', +'drought', +'lemon', +'new year', +'night view', +'airplane window', +'specie', +'rule', +'jaw', +'wheat field', +'diet', +'pop artist', +'habitat', +'screenshot', +'scoreboard', +'shore', +'mane', +'quilt', +'ski lift', +'orchid', +'turban', +'christmas', +'airport', +'marina', +'glass door', +'glass bottle', +'restaurant', +'conductor', +'logo', +'sleep', +'tape', +'tomato', +'river bank', +'lilac', +'tooth', +'training', +'pottery', +'shop', +'steam engine', +'mason jar', +'base', +'procession', +'border', +'shoot', +'footprint', +'hotdog', +'bull', +'stocking', +'recreation', +'automobile model', +'design', +'country pop artist', +'river', +'retriever', +'department store', +'auditorium', +'sport car', +'supermarket', +'belt', +'cricket', +'window box', +'dress shirt', +'letter', +'residence', +'megaphone', +'pant', +'wildfire', +'bird nest', +'crab', +'swimsuit', +'candle', +'funeral', +'mill', +'national park', +'plant', +'cop', +'power line', +'perch', +'blue', +'finger', +'ferris wheel', +'globe', +'skateboard', +'helmet', +'movie theater', +'uniform', +'hammer', +'material', +'kid', +'well', +'butterfly', +'sideline', +'fashion fall show', +'planet earth', +'lift', +'male', +'sauna', +'gray', +'flour', +'sand sculpture', +'program', +'cabinet', +'infant', +'wheel', +'aircraft model', +'dough', +'garlic', +'skate', +'arrow', +'wrapping paper', +'ripple', +'lamp', +'iron', +'banknote', +'beaver', +'ferry', +'courtyard', +'bassist', +'countryside', +'steak', +'comfort', +'boxer', +'laundry room', +'campsite', +'brick building', +'golf', +'subway', +'headphone', +'fort', +'handbag', +'drum', +'flood', +'saddle', +'bass', +'labyrinth', +'needle', +'sun ray', +'app', +'menu', +'president', +'cardigan', +'dandelion', +'wetland', +'ice hockey player', +'number', +'city hall', +'fishing', +'portrait session', +'pug', +'key', +'art print', +'minister', +'hurdle', +'emergency', +'painting artist', +'flag pole', +'evening', +'purse', +'recipe', +'golf ball', +'coloring book', +'mountain peak', +'senior', +'holiday', +'bud', +'cousin', +'pantry', +'lap', +'skin', +'flag', +'tissue paper', +'ridge', +'wire fence', +'surfer', +'climber', +'photograph', +'sewing machine', +'cooler', +'actress', +'apple tree', +'cancer', +'starfish', +'automobile make', +'dumbbell', +'brace', +'tunnel', +'window', +'paint artist', +'composition', +'school student', +'condo', +'convertible', +'cushion', +'selfie', +'territory', +'guide', +'tree', +'court', +'shrimp', +'stone house', +'dress', +'eyelash', +'juice', +'broccoli', +'chain', +'tourism', +'mountain top', +'concept car', +'film premiere', +'light bulb', +'cafeteria', +'badge', +'flower bed', +'theater', +'root', +'racecar driver', +'basketball boy game', +'glove', +'skyline', +'wall', +'glacier', +'airport terminal', +'bug', +'trim', +'railway station', +'briefcase', +'flat', +'fountain', +'person', +'lane', +'asparagus', +'art', +'lantern', +'dishwasher', +'director', +'snake', +'lecture', +'game controller', +'tree branch', +'pub', +'bathing suit', +'queue', +'belly', +'poppy', +'bow', +'pitcher', +'ice cream cone', +'cave', +'candy', +'road bridge', +'host', +'traffic jam', +'earring', +'file', +'foot', +'watermark overlay stamp', +'mailbox', +'supercar', +'railing', +'bedroom', +'seafood', +'waffle', +'bronze statue', +'plan', +'flow', +'marble', +'basketball game', +'automobile', +'scene', +'cypress tree', +'soldier', +'skateboarder', +'glass building', +'cherry tree', +'pump', +'grain', +'wildebeest', +'loop', +'frame', +'bathtub', +'saxophone', +'diver', +'stalk', +'lily', +'bead', +'alley', +'flock', +'family room', +'manufacturing', +'pointer', +'worker', +'navy', +'potato', +'teacher', +'photography', +'dolly', +'boardwalk', +'water fountain', +'athlete', +'side dish', +'bay', +'ice hockey', +'phone', +'hero', +'face', +'gold medal', +'blind', +'swamp', +'researcher', +'swim', +'meatball', +'iguana', +'leather jacket', +'jellyfish', +'site', +'smoke', +'traffic signal', +'melon', +'beetle', +'calculator', +'skirt', +'plantation', +'sculptor', +'barrier', +'catcher', +'security guard', +'sketch', +'awning', +'steering wheel', +'mountain view', +'bus stop', +'pool', +'leg', +'spotlight', +'apron', +'mineral', +'inlet', +'sleeve', +'torch', +'emotion', +'march', +'police officer', +'performance', +'lamp post', +'fishing boat', +'summer', +'presentation', +'saucer', +'suitcase', +'supermodel', +'goalkeeper', +'shrub', +'rock artist', +'document', +'beach house', +'man', +'blue artist', +'cigar', +'railroad track', +'gown', +'mosaic', +'bungalow', +'alphabet', +'baseball field', +'shed', +'pedestrian', +'rail', +'soap', +'kitchen counter', +'dessert', +'dunk', +'blossom', +'conversation', +'fruit market', +'glass jar', +'military', +'beer bottle', +'photographer', +'tennis racket', +'competition', +'escalator', +'bell tower', +'stilt', +'ballerina', +'television', +'feather', +'fence post', +'rear', +'dahlia', +'red carpet', +'tub', +'hole', +'fortress', +'pack', +'telephone', +'cardboard', +'city park', +'platform', +'college student', +'arch bridge', +'wind', +'blender', +'bloom', +'ice rink', +'birthday', +'raven', +'fairy', +'embankment', +'hall', +'flower shop', +'suburb', +'barrel', +'biker', +'steam', +'dragonfly', +'formation', +'electricity', +'business people', +'symmetry', +'walkway', +'fisherman', +'gas mask', +'loch', +'youth', +'hanger', +'dot', +'fish', +'street market', +'animation film', +'crime fiction film', +'boar', +'emblem', +'halloween costume', +'kangaroo', +'couple', +'spoon', +'squirrel', +'neon sign', +'sky', +'office desk', +'beauty salon', +'breakwater', +'fashion look', +'toaster', +'author', +'news conference', +'outdoor', +'canoe', +'dragon', +'tool', +'shopping centre', +'ladybug', +'swimming pool', +'landscaping', +'ski pole', +'red', +'truck', +'fly', +'temple', +'level', +'sunday', +'railroad bridge', +'car mirror', +'lawn mower', +'flute', +'aircraft carrier', +'fashion menswear london week', +'sunshine', +'tile floor', +'skull', +'fossil', +'flower arrangement', +'diaper', +'sea turtle', +'cherry blossom', +'fireman', +'shack', +'lens', +'waiter', +'animal', +'basement', +'snow', +'autumn park', +'glass box', +'kick', +'head', +'anniversary', +'vine', +'back', +'paper lantern', +'fish tank', +'cellphone', +'silk', +'coral', +'notebook', +'photo', +'gazebo', +'ketchup', +'driver', +'farmer', +'bonfire', +'chestnut', +'photoshoot', +'football field', +'olive tree', +'pheasant', +'sandal', +'toilet', +'fireplace', +'music', +'deity', +'fish market', +'fig', +'bell', +'neck', +'grave', +'villa', +'cyclist', +'crate', +'grey', +'asphalt road', +'soccer', +'hostel', +'municipality', +'courthouse', +'roof', +'end table', +'pot', +'sedan', +'structure', +'folk artist', +'sport', +'sport team', +'protest', +'syringe', +'fashion designer', +'jersey', +'heart shape', +'kayak', +'stare', +'sit with', +'direct', +'read', +'photograph', +'spin', +'teach', +'laugh', +'carve', +'grow on', +'warm', +'watch', +'stretch', +'smell', +'decorate', +'shine', +'light', +'dance', +'send', +'park', +'chase', +'collect', +'lead', +'kiss', +'lead to', +'lick', +'smile', +'cheer', +'sit', +'point', +'block', +'rock', +'drop', +'cut', +'ski', +'wrap', +'lose', +'serve', +'provide', +'sleep', +'dress', +'embrace', +'burn', +'pack', +'stir', +'create', +'touch', +'wash', +'stick', +'reveal', +'shop', +'train', +'paint', +'groom', +'hunt', +'bloom', +'play', +'pay', +'brush', +'shoot', +'hold', +'picture', +'carry', +'sip', +'contain', +'turn', +'pour', +'pitch', +'give', +'add', +'blow', +'look in', +'show', +'walk', +'illuminate', +'kneel', +'cover', +'drag', +'post', +'present', +'fit', +'operate', +'fish', +'race', +'write', +'deliver', +'peel', +'push', +'run', +'sit around', +'buy', +'jump', +'walk on', +'attend', +'clean', +'sell', +'ride on', +'mount', +'host', +'dry', +'plant', +'sing', +'row', +'shake', +'perch', +'ride', +'fight', +'skateboard', +'live', +'call', +'surround', +'practice', +'play on', +'work on', +'step', +'relax', +'hit', +'fall in', +'flow', +'greet', +'launch', +'wear', +'hang on', +'drive', +'sit in', +'break', +'learn', +'fly', +'connect', +'display', +'locate', +'compete', +'go for', +'sail', +'lift', +'toast', +'help', +'run on', +'reflect', +'pose', +'scratch', +'frame', +'dribble', +'herd', +'enter', +'exit', +'place', +'inspect', +'build', +'pick', +'fill', +'grind', +'skate', +'offer', +'float', +'sit by', +'stand', +'release', +'rest', +'singe', +'climb', +'tie', +'mark', +'lay', +'stand around', +'capture', +'set', +'land', +'swinge', +'run in', +'kick', +'lean', +'head', +'sign', +'approach', +'swim', +'close', +'crash', +'control', +'fall', +'remove', +'repair', +'open', +'appear', +'travel', +'load', +'miss', +'check', +'surf', +'moor', +'smoke', +'drink', +'board', +'seat', +'feed', +'rise', +'sit on', +'swing', +'grow', +'strike', +'date', +'slide', +'share', +'graze', +'jump in', +'lie', +'extrude', +'roll', +'move', +'gather', +'eat', +'pull', +'run through', +'squeeze', +'lay on', +'draw', +'play with', +'wave', +'assemble', +'perform', +'march', +'score', +'attach', +'adjust', +'hang', +'hug', +'sleep on', +'throw', +'live in', +'talk', +'pet', +'work', +'run with', +'see', +'flip', +'catch', +'cook', +'receive', +'celebrate', +'look', +'classic', +'bridal', +'indoor', +'industrial', +'teenage', +'mini', +'grassy', +'aged', +'long', +'warm', +'light', +'handsome', +'happy', +'three', +'pregnant', +'circular', +'urban', +'silver', +'ceramic', +'3d', +'green', +'blonde', +'golden', +'dark', +'tropical', +'ripe', +'deep', +'fat', +'musical', +'giant', +'medical', +'medieval', +'bare', +'stunning', +'bold', +'geographical', +'huge', +'plastic', +'foggy', +'stormy', +'gothic', +'biological', +'empty', +'clear', +'antique', +'pink', +'steep', +'brown', +'striped', +'aerial', +'rainy', +'cool', +'flying', +'commercial', +'purple', +'trendy', +'blank', +'haired', +'dead', +'wooden', +'flat', +'high', +'beige', +'panoramic', +'angry', +'dozen', +'rural', +'solar', +'big', +'small', +'stained', +'thick', +'many', +'fresh', +'clean', +'strong', +'abstract', +'crowded', +'retro', +'dry', +'gorgeous', +'martial', +'modern', +'blue', +'cloudy', +'low', +'four', +'outdoor', +'single', +'much', +'beautiful', +'snowy', +'pretty', +'new', +'short', +'sunny', +'closed', +'rocky', +'red', +'two', +'double', +'male', +'gray', +'five', +'colorful', +'automotive', +'various', +'one', +'old', +'rusty', +'tall', +'wild', +'narrow', +'natural', +'several', +'frozen', +'textured', +'lush', +'young', +'hot', +'mixed', +'white', +'float', +'quiet', +'round', +'bright', +'religious', +'female', +'historical', +'shiny', +'traditional', +'tourist', +'yellow', +'bald', +'coastal', +'lovely', +'little', +'broken', +'romantic', +'wide', +'royal', +'rich', +'open', +'cute', +'ancient', +'cold', +'political', +'elderly', +'gold', +'full', +'rustic', +'metallic', +'floral', +'sad', +'wet', +'fancy', +'senior', +'tiny', +'stylish', +'large', +'frosty', +'orange', +'transparent', +'electronic', +'shallow', +'scared', +'armed', +'dirty', +'historic', +'black', +'few', +'windy', +'some', +'square', +'ornamental', +'sandy', +'thin'] + +tra_array = np.array(tra_array) + + diff --git a/iChat/models/video.py b/iChat/models/video.py new file mode 100644 index 0000000000000000000000000000000000000000..4aeb371dc545b3ec17f861d79b07de2f612262c8 --- /dev/null +++ b/iChat/models/video.py @@ -0,0 +1,339 @@ +import os +os.environ['CURL_CA_BUNDLE'] = '' + +import torch +# from simplet5 import SimpleT5 +import torchvision.transforms as transforms +import openai +import ffmpeg +from .tag2text import tag2text_caption +from .utils import * + +from .load_internvideo import * + +from .grit_model import DenseCaptioning +from .lang import SimpleLanguageModel +from scipy.io.wavfile import write as write_wav +from bark import SAMPLE_RATE, generate_audio + + +class VideoCaption: + def __init__(self, device): + self.device = device + self.image_size = 384 + # self.threshold = 0.68 + self.video_path = None + self.result = None + self.tags = None + self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + self.transform = transforms.Compose([transforms.ToPILImage(),transforms.Resize((self.image_size, self.image_size)), transforms.ToTensor(),self.normalize]) + self.model = tag2text_caption(pretrained="model_zoo/tag2text_swin_14m.pth", image_size=self.image_size, vit='swin_b').eval().to(device) + self.load_video = LoadVideo() + print("[INFO] initialize Caption model success!") + + def framewise_details(self, inputs): + video_path = inputs.strip() + caption = self.inference(video_path) + frame_caption = "" + prev_caption = "" + start_time = 0 + end_time = 0 + for i, j in enumerate(caption): + current_caption = f"{j}." + current_dcs = f"{i+1}" + if len(current_dcs) > 0: + last_valid_dcs = current_dcs + if current_caption == prev_caption: + end_time = i+1 + else: + if prev_caption: + frame_caption += f"Second {start_time} - {end_time}: {prev_caption}{last_valid_dcs}\n" + start_time = i+1 + end_time = i+1 + prev_caption = current_caption + if prev_caption: + frame_caption += f"Second {start_time} - {end_time}: {prev_caption}{current_dcs}\n" + total_dur = end_time + frame_caption += f"| Total Duration: {total_dur} seconds.\n" + + print(frame_caption) + # self.result = frame_caption + self.video_path = video_path + # video_prompt = f"""The tags for this vieo are: {prediction}, {','.join(tag_1)}; + # The temporal description of the video is: {frame_caption} + # The dense caption of the video is: {dense_caption} + # The general description of the video is: {synth_caption[0]}""" + return frame_caption + + @prompts(name="Video Caption", + description="useful when you want to generate a description for video. " + "like: generate a description or caption for this video. " + "The input to this tool should be a string, " + "representing the video_path") + def inference(self, inputs): + video_path = inputs.strip() + data = self.load_video(video_path) + # progress(0.2, desc="Loading Videos") + tmp = [] + for _, img in enumerate(data): + tmp.append(self.transform(img).to(self.device).unsqueeze(0)) + + # Video Caption + image = torch.cat(tmp).to(self.device) + # self.threshold = 0.68 + + input_tag_list = None + with torch.no_grad(): + caption, tags = self.model.generate(image,tag_input = input_tag_list, max_length = 50, return_tag_predict = True) + # print(frame_caption, dense_caption, synth_caption) + # print(caption) + del data, image, tmp + torch.cuda.empty_cache() + torch.cuda.ipc_collect() + self.result = caption + self.tags = tags + # return '. '.join(caption) + return caption + + +class Summarization: + def __init__(self, device): + self.device = device + self.model = SimpleT5() + self.model.load_model( + "t5", "./model_zoo/flan-t5-large-finetuned-openai-summarize_from_feedback", use_gpu=False) + self.model.model = self.model.model.to(self.device) + self.model.device = device + + print("[INFO] initialize Summarize model success!") + + @prompts(name="Video Summarization", + description="useful when you want to Summarize video content for input video. " + "like: summarize this video. " + "The input to this tool should be a string, " + "representing the video_path") + def inference(self, inputs): + caption = inputs.strip() + sum_res = self.model.predict(caption) + return sum_res + + +class ActionRecognition: + def __init__(self, device): + self.device = device + self.video_path = None + # self.result = None + self.model = load_intern_action(device) + self.transform = transform_action() + self.toPIL = T.ToPILImage() + self.load_video = LoadVideo() + print("[INFO] initialize InternVideo model success!") + + @prompts(name="Action Recognition", + description="useful when you want to recognize the action category in this video. " + "like: recognize the action or classify this video" + "The input to this tool should be a string, " + "representing the video_path") + def inference(self, inputs): + video_path = inputs.strip() + # if self.video_path == video_path: + # return self.result + # self.video_path = video_path + # data = loadvideo_decord_origin(video_path) + data = self.load_video(video_path) + + # InternVideo + action_index = np.linspace(0, len(data)-1, 8).astype(int) + tmp_pred = [] + for i,img in enumerate(data): + if i in action_index: + tmp_pred.append(self.toPIL(img)) + action_tensor = self.transform(tmp_pred) + TC, H, W = action_tensor.shape + action_tensor = action_tensor.reshape(1, TC//3, 3, H, W).permute(0, 2, 1, 3, 4).to(self.device) + with torch.no_grad(): + prediction = self.model(action_tensor) + prediction = F.softmax(prediction, dim=1).flatten() + prediction = kinetics_classnames[str(int(prediction.argmax()))] + # self.result = prediction + return prediction + + +class DenseCaption: + def __init__(self, device): + self.device = device + self.model = DenseCaptioning(device) + self.model.initialize_model() + # self.model = self.model.to(device) + self.load_video = LoadVideo() + print("[INFO] initialize DenseCaptioe model success!") + + @prompts(name="Video Dense Caption", + description="useful when you want to generate a dense caption for video. " + "like: generate a dense caption or description for this video. " + "The input to this tool should be a string, " + "representing the video_path") + def inference(self, inputs): + video_path = inputs.strip() + # data = loadvideo_decord_origin(video_path) + data = self.load_video(video_path) + dense_caption = [] + dense_index = np.arange(0, len(data)-1, 5) + original_images = data[dense_index,:,:,::-1] + with torch.no_grad(): + for original_image in original_images: + dense_caption.append(self.model.run_caption_tensor(original_image)) + dense_caption = ' '.join([f"Second {i+1} : {j}.\n" for i,j in zip(dense_index,dense_caption)]) + + return dense_caption + + +class GenerateTikTokVideo: + template_model = True + def __init__(self, ActionRecognition, VideoCaption, DenseCaption): + self.ActionRecognition = ActionRecognition + self.VideoCaption = VideoCaption + # self.Summarization = Summarization + self.DenseCaption = DenseCaption + self.SimpleLanguageModel = None + + @prompts(name="Generate TikTok Video", + description="useful when you want to generate a video with TikTok style based on prompt." + "like: cut this video to a TikTok video based on prompt." + "The input to this tool should be a comma separated string of two, " + "representing the video_path and prompt") + def inference(self, inputs): + video_path = inputs.split(',')[0].strip() + text = ', '.join(inputs.split(',')[1: ]) + if self.SimpleLanguageModel == None: + self.SimpleLanguageModel = SimpleLanguageModel() + action_classes = self.ActionRecognition.inference(video_path) + print(f'action_classes = {action_classes}') + dense_caption = self.DenseCaption.inference(video_path) + print(f'dense_caption = {dense_caption}') + caption = self.VideoCaption.inference(video_path) + caption = '. '.join(caption) + print(f'caption = {caption}') + tags = self.VideoCaption.tags + print(f'tags = {tags}') + framewise_caption = self.VideoCaption.framewise_details(video_path) + print(f'framewise_caption = {framewise_caption}') + video_prompt = f"""The tags for this video are: {action_classes}, {','.join(tags)}; + The temporal description of the video is: {framewise_caption} + The dense caption of the video is: {dense_caption}""" + timestamp = self.run_text_with_time(video_prompt, text) + print(f'timestamp = {timestamp}') + if not timestamp: + return 'Error! Please try it again.' + start_time, end_time = min(timestamp), max(timestamp) + print(f'start_time, end_time = = {start_time}, {end_time}') + video_during = end_time - start_time + 1 + + + # prompt=f"忘记之前的回答模板，请使用中文回答这个问题。如果情节里遇到男生就叫小帅，女生就叫小美，请以’注意看，这个人叫’开始写一段的视频营销文案。尽量根据第{start_time}秒到第{end_time}秒左右的视频内容生成文案，不要生成重复句子。" + # prompt=f"忘记之前的回答模板，请使用中文回答这个问题。如果情节里遇到男生就叫小帅，女生就叫小美，请以’注意看，这个人叫’为开头，根据第{start_time}秒到第{end_time}秒左右的视频内容生成一段视频营销文案。" + prompt=f"忘记之前的回答模板，请使用中文回答这个问题。视频里如果出现男生就叫小帅，出现女生就叫小美，如果不确定性别，就叫大聪明。请以’注意看，这个人叫’为开头生成一段视频营销文案。" + texts = self.run_text_with_tiktok(video_prompt, prompt).strip() + # if texts.endswith('') + texts += '。' + print(f"before polishing: {texts}") + print('*' * 40) + # texts = openai.ChatCompletion.create(model="gpt-3.5-turbo",messages=[{"role":"user","content":f"请用润色下面的句子，去除重复的片段，但尽量保持原文内容且不许更改人物名字，并且以“注意看，这个人叫”作为开头：{texts}"}]).choices[0].message['content'] + texts = openai.ChatCompletion.create(model="gpt-3.5-turbo",messages=[{"role":"user","content":f"使用中文回答这个问题，请用润色下面的句子，去除重复的片段，并且仍以’注意看，这个人叫’为开头：{texts}"}]).choices[0].message['content'] + print(f"after polishing: {texts}") + clipped_video_path = gen_new_name(video_path, 'tmp', 'mp4') + wav_file = clipped_video_path.replace('.mp4', '.wav') + audio_path = self.gen_audio(texts, wav_file) + audio_duration = int(float(ffmpeg.probe(audio_path)['streams'][0]['duration']))+1 + os.system(f"ffmpeg -y -v quiet -ss {start_time} -t {video_during} -i {video_path} -c:v libx264 -c:a copy -movflags +faststart {clipped_video_path}") + # output_path = self.image_filename.replace('.mp4','_tiktok.mp4') + new_video_path = gen_new_name(video_path, 'GenerateTickTokVideo', 'mp4') + if video_during < audio_duration: + # 鬼畜hou + # video_concat = os.path.join(os.path.dirname(clipped_video_path), 'concat.info') + # video_concat = gen_new_name(clipped_video_path, '', 'info') + video_concat = os.path.join(os.path.dirname(clipped_video_path), 'concat.info') + video_concat = gen_new_name(video_concat, '', 'info') + with open(video_concat,'w') as f: + for _ in range(audio_duration//video_during+1): + f.write(f"file \'{os.path.basename(clipped_video_path)}\'\n") + tmp_path = gen_new_name(video_path, 'tmp', 'mp4') + os.system(f"ffmpeg -y -f concat -i {video_concat} {tmp_path}") + print(f"ffmpeg -y -i {tmp_path} -i {wav_file} {new_video_path}") + os.system(f"ffmpeg -y -i {tmp_path} -i {wav_file} {new_video_path}") + else: + print(f"ffmpeg -y -i {clipped_video_path} -i {wav_file} {new_video_path}") + os.system(f"ffmpeg -y -i {clipped_video_path} -i {wav_file} {new_video_path}") + if not os.path.exists(new_video_path): + import pdb + pdb.set_trace() + # state = state + [(text, f"Here is the video in *{new_file_path}*")] +[("show me the video.", (new_file_path,))] + # print(f"\nProcessed run_video, Input video: {new_file_path}\nCurrent state: {state}\n" + # f"Current Memory: {self.agent.memory.buffer}") + return (new_video_path, ) + + def run_text_with_time(self, video_caption, text): + # self.agent.memory.buffer = cut_dialogue_history(self.agent.memory.buffer, keep_last_n_words=500) + prompt = "Only in this conversation, \ + You must find the text-related start time \ + and end time based on video caption. Your answer \ + must end with the format {answer} [start time: end time]." + response = self.SimpleLanguageModel(f"Video content: {video_caption}. Text: {text.strip()}." + prompt) + # res['output'] = res['output'].replace("\\", "/") + # print(response) + import re + pattern = r"\d+" + # response = res['output']#rsplit(']')[-1] + try: + # matches = re.findall(pattern, res['output']) + matches = re.findall(pattern, response) + start_idx , end_idx = matches[-2:] + start_idx , end_idx = int(start_idx), int(end_idx) + except: + return None + import pdb + pdb.set_trace() + # state = state + [(text, response)] + print(f"\nProcessed run_text_with_time, Input text: {text}\n") + return (start_idx, end_idx) + + def run_text_with_tiktok(self, video_content, prompt): + # self.agent.memory.buffer = cut_dialogue_history(self.agent.memory.buffer, keep_last_n_words=500) + inputs = f"Video description: {video_content}. {prompt}" + + response = self.SimpleLanguageModel(inputs) + response = response.replace("\\", "/") + # res = self.agent({"input":text}) + # res['output'] = res['output'].replace("\\", "/") + # response = res['output'] + # state = state + [(prompt, response)] + print(f"\nProcessed run_text_with_tiktok, Input text: {prompt}\n, Response: {response}") + return response + + def gen_audio(self, text, save_path): + audio_array = generate_audio(text) + write_wav(save_path, SAMPLE_RATE, audio_array) + return save_path + + +if __name__ == '__main__': + # model = VideoCaption('cuda:0') + # print(model.inference('./assets/f4236666.mp4')) + # model = ActionRecognition('cuda:0') + # print(model.inference('./assets/f4236666.mp4')) + video_path = './tmp_files/f4236666.mp4' + device = 'cuda:0' + # caption_model = VideoCaption('cuda:0') + # caption = caption_model.inference('./assets/f4236666.mp4') + # sum_model = Summarize('cuda:0') + # res = sum_model.inference(caption) + # ds = DenseCaption(device) + # res = ds.inference(video_path) + from lang import SimpleLanguageModel + model = GenerateTikTokVideo(ActionRecognition(device), + VideoCaption(device), + DenseCaption(device) + ) + out = model.inference(video_path+",帮我剪辑出最精彩的片段") + print(out) \ No newline at end of file diff --git a/iChat/models/vit.py b/iChat/models/vit.py new file mode 100644 index 0000000000000000000000000000000000000000..9d910a687ef6e74a6d7541e81c93b06aea7fda60 --- /dev/null +++ b/iChat/models/vit.py @@ -0,0 +1,300 @@ +''' + * Copyright (c) 2022, salesforce.com, inc. + * All rights reserved. + * SPDX-License-Identifier: BSD-3-Clause + * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause + * By Junnan Li + * Based on timm code base + * https://github.com/rwightman/pytorch-image-models/tree/master/timm +''' + +import torch +import torch.nn as nn +import torch.nn.functional as F +from functools import partial + +from timm.models.vision_transformer import _cfg, PatchEmbed +from timm.models.registry import register_model +from timm.models.layers import trunc_normal_, DropPath +from timm.models.helpers import named_apply, adapt_input_conv + +from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper + +class Mlp(nn.Module): + """ MLP as used in Vision Transformer, MLP-Mixer and related networks + """ + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights + self.scale = qk_scale or head_dim ** -0.5 + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.attn_gradients = None + self.attention_map = None + + def save_attn_gradients(self, attn_gradients): + self.attn_gradients = attn_gradients + + def get_attn_gradients(self): + return self.attn_gradients + + def save_attention_map(self, attention_map): + self.attention_map = attention_map + + def get_attention_map(self): + return self.attention_map + + def forward(self, x, register_hook=False): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + if register_hook: + self.save_attention_map(attn) + attn.register_hook(self.save_attn_gradients) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_grad_checkpointing=False): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + if use_grad_checkpointing: + self.attn = checkpoint_wrapper(self.attn) + self.mlp = checkpoint_wrapper(self.mlp) + + def forward(self, x, register_hook=False): + x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook)) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class VisionTransformer(nn.Module): + """ Vision Transformer + A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - + https://arxiv.org/abs/2010.11929 + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None, + drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None, + use_grad_checkpointing=False, ckpt_layer=0): + """ + Args: + img_size (int, tuple): input image size + patch_size (int, tuple): patch size + in_chans (int): number of input channels + num_classes (int): number of classes for classification head + embed_dim (int): embedding dimension + depth (int): depth of transformer + num_heads (int): number of attention heads + mlp_ratio (int): ratio of mlp hidden dim to embedding dim + qkv_bias (bool): enable bias for qkv if True + qk_scale (float): override default qk scale of head_dim ** -0.5 if set + representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set + drop_rate (float): dropout rate + attn_drop_rate (float): attention dropout rate + drop_path_rate (float): stochastic depth rate + norm_layer: (nn.Module): normalization layer + """ + super().__init__() + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) + + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, + use_grad_checkpointing=(use_grad_checkpointing and i>=depth-ckpt_layer) + ) + for i in range(depth)]) + self.norm = norm_layer(embed_dim) + + trunc_normal_(self.pos_embed, std=.02) + trunc_normal_(self.cls_token, std=.02) + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def forward(self, x, register_blk=-1): + B = x.shape[0] + x = self.patch_embed(x) + + cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks + x = torch.cat((cls_tokens, x), dim=1) + + x = x + self.pos_embed[:,:x.size(1),:] + x = self.pos_drop(x) + + for i,blk in enumerate(self.blocks): + x = blk(x, register_blk==i) + x = self.norm(x) + + return x + + @torch.jit.ignore() + def load_pretrained(self, checkpoint_path, prefix=''): + _load_weights(self, checkpoint_path, prefix) + + +@torch.no_grad() +def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''): + """ Load weights from .npz checkpoints for official Google Brain Flax implementation + """ + import numpy as np + + def _n2p(w, t=True): + if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1: + w = w.flatten() + if t: + if w.ndim == 4: + w = w.transpose([3, 2, 0, 1]) + elif w.ndim == 3: + w = w.transpose([2, 0, 1]) + elif w.ndim == 2: + w = w.transpose([1, 0]) + return torch.from_numpy(w) + + w = np.load(checkpoint_path) + if not prefix and 'opt/target/embedding/kernel' in w: + prefix = 'opt/target/' + + if hasattr(model.patch_embed, 'backbone'): + # hybrid + backbone = model.patch_embed.backbone + stem_only = not hasattr(backbone, 'stem') + stem = backbone if stem_only else backbone.stem + stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel']))) + stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale'])) + stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias'])) + if not stem_only: + for i, stage in enumerate(backbone.stages): + for j, block in enumerate(stage.blocks): + bp = f'{prefix}block{i + 1}/unit{j + 1}/' + for r in range(3): + getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel'])) + getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale'])) + getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias'])) + if block.downsample is not None: + block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel'])) + block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale'])) + block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias'])) + embed_conv_w = _n2p(w[f'{prefix}embedding/kernel']) + else: + embed_conv_w = adapt_input_conv( + model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel'])) + model.patch_embed.proj.weight.copy_(embed_conv_w) + model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias'])) + model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False)) + pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False) + if pos_embed_w.shape != model.pos_embed.shape: + pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights + pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size) + model.pos_embed.copy_(pos_embed_w) + model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale'])) + model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias'])) + + for i, block in enumerate(model.blocks.children()): + block_prefix = f'{prefix}Transformer/encoderblock_{i}/' + mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/' + block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale'])) + block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias'])) + block.attn.qkv.weight.copy_(torch.cat([ + _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')])) + block.attn.qkv.bias.copy_(torch.cat([ + _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')])) + block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1)) + block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias'])) + for r in range(2): + getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel'])) + getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias'])) + block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale'])) + block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias'])) + + +def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder): + # interpolate position embedding + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = visual_encoder.patch_embed.num_patches + num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + + if orig_size!=new_size: + # class_token and dist_token are kept unchanged + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + print('reshape position embedding from %d to %d'%(orig_size ** 2,new_size ** 2)) + + return new_pos_embed + else: + return pos_embed_checkpoint \ No newline at end of file diff --git a/iChatApp.py b/iChatApp.py new file mode 100644 index 0000000000000000000000000000000000000000..79e483ee487ccac10645411c49f562ce75822242 --- /dev/null +++ b/iChatApp.py @@ -0,0 +1,1908 @@ +# coding: utf-8 +import os +os.environ['CURL_CA_BUNDLE'] = '' + +from pathlib import Path +import sys +sys.path.insert(0, str(Path(__file__).resolve().parent / "third-party" / "lama")) + +import random +import torch +import cv2 +import re +import uuid +from PIL import Image, ImageOps +import math +import numpy as np +import argparse +import inspect +from functools import partial +import shutil +import whisper + +import gradio as gr +import gradio.themes.base as ThemeBase +from gradio.themes.utils import colors, fonts, sizes + +from transformers import pipeline, BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering + +from diffusers import StableDiffusionPipeline, StableDiffusionInpaintPipeline, StableDiffusionInstructPix2PixPipeline +from diffusers import EulerAncestralDiscreteScheduler +from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler +from controlnet_aux import OpenposeDetector, MLSDdetector, HEDdetector + +from langchain.agents.initialize import initialize_agent +from langchain.agents.tools import Tool +from langchain.chains.conversation.memory import ConversationBufferMemory +from langchain.llms.openai import OpenAI + +from iChat.models import VideoCaption, ActionRecognition, DenseCaption, GenerateTikTokVideo +from iChat.models import HuskyVQA, LDMInpainting +from iChat.models.utils import (cal_dilate_factor, dilate_mask, gen_new_name, + seed_everything, prompts, blend_gt2pt) + +from segment_anything.utils.amg import remove_small_regions +from segment_anything import build_sam, sam_model_registry, SamAutomaticMaskGenerator, SamPredictor +from bark import SAMPLE_RATE, generate_audio + +import matplotlib.pyplot as plt +# Please DO NOT MOVE THE IMPORT ORDER FOR easyocr. +import easyocr + +from saicinpainting.evaluation.utils import move_to_device +from saicinpainting.training.trainers import load_checkpoint +from saicinpainting.evaluation.data import pad_tensor_to_modulo + +# openai.api_base = 'https://closeai.deno.dev/v1' + +GLOBAL_SEED=1912 + +INTERN_CHAT_PREFIX = """InternChat is designed to be able to assist with a wide range of text and visual related tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. InternChat is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand. + +InternChat is able to process and understand large amounts of text and images. As a language model, InternChat can not directly read images, but it has a list of tools to finish different visual tasks. Each image will have a file name formed as "image/xxx.png", and InternChat can invoke different tools to indirectly understand pictures. When talking about images, InternChat is very strict to the file name and will never fabricate nonexistent files. When using tools to generate new image files, InternChat is also known that the image may not be the same as the user's demand, and will use other visual question answering tools or description tools to observe the real image. InternChat is able to use tools in a sequence, and is loyal to the tool observation outputs rather than faking the image content and image file name. It will remember to provide the file name from the last tool observation, if a new image is generated. + +Human may provide new figures to InternChat with a description. The description helps InternChat to understand this image, but InternChat should use tools to finish following tasks, rather than directly imagine from the description. + +Overall, InternChat is a powerful visual dialogue assistant tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. + + +TOOLS: +------ + +InternChat has access to the following tools:""" + +INTERN_CHAT_FORMAT_INSTRUCTIONS = """To use a tool, please use the following format: + +``` +Thought: Do I need to use a tool? Yes +Action: the action to take, should be one of [{tool_names}] +Action Input: the input to the action, you can find all input paths in the history but can not feed the tool's description into the tool. +Observation: the result of the action +``` + +When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format: + +``` +Thought: Do I need to use a tool? No +{ai_prefix}: [your response here] +``` +""" + +INTERN_CHAT_SUFFIX = """You are very strict to the filename correctness and will never fake a file name if it does not exist. +You will remember to provide the image file name loyally if it's provided in the last tool observation. + +Begin! + +Previous conversation history: +{chat_history} + +New input: {input} +Since InternChat is a text language model, InternChat must use tools to observe images rather than imagination. +The thoughts and observations are only visible for InternChat, InternChat should remember to repeat important information in the final response for Human. +Thought: Do I need to use a tool? {agent_scratchpad} Let's think step by step. +""" + +INTERN_CHAT_PREFIX_CN = """InternChat 旨在能够协助完成范围广泛的文本和视觉相关任务，从回答简单的问题到提供对广泛主题的深入解释和讨论。 InternChat 能够根据收到的输入生成类似人类的文本，使其能够进行听起来自然的对话，并提供连贯且与手头主题相关的响应。 + +InternChat 能够处理和理解大量文本和图像。作为一种语言模型，InternChat 不能直接读取图像，但它有一系列工具来完成不同的视觉任务。每张图片都会有一个文件名，格式为“image/xxx.png”，InternChat可以调用不同的工具来间接理解图片。在谈论图片时，InternChat 对文件名的要求非常严格，绝不会伪造不存在的文件。在使用工具生成新的图像文件时，InternChat也知道图像可能与用户需求不一样，会使用其他视觉问答工具或描述工具来观察真实图像。 InternChat 能够按顺序使用工具，并且忠于工具观察输出，而不是伪造图像内容和图像文件名。如果生成新图像，它将记得提供上次工具观察的文件名。 + +Human 可能会向 InternChat 提供带有描述的新图形。描述帮助 InternChat 理解这个图像，但 InternChat 应该使用工具来完成以下任务，而不是直接从描述中想象。有些工具将会返回英文描述，但你对用户的聊天应当采用中文。 + +总的来说，InternChat 是一个强大的可视化对话辅助工具，可以帮助处理范围广泛的任务，并提供关于范围广泛的主题的有价值的见解和信息。 + +工具列表: +------ + +InternChat 可以使用这些工具:""" + +INTERN_CHAT_FORMAT_INSTRUCTIONS_CN = """用户使用中文和你进行聊天，但是工具的参数应当使用英文。如果要调用工具，你必须遵循如下格式: + +``` +Thought: Do I need to use a tool? Yes +Action: the action to take, should be one of [{tool_names}] +Action Input: the input to the action +Observation: the result of the action +``` + +当你不再需要继续调用工具，而是对观察结果进行总结回复时，你必须使用如下格式： + + +``` +Thought: Do I need to use a tool? No +{ai_prefix}: [your response here] +``` +""" + +INTERN_CHAT_SUFFIX_CN = """你对文件名的正确性非常严格，而且永远不会伪造不存在的文件。 + +开始! + +因为InternChat是一个文本语言模型，必须使用工具去观察图片而不是依靠想象。 +推理想法和观察结果只对InternChat可见，需要记得在最终回复时把重要的信息重复给用户，你只能给用户返回中文句子。我们一步一步思考。在你使用工具时，工具的参数只能是英文。 + +聊天历史: +{chat_history} + +新输入: {input} +Thought: Do I need to use a tool? {agent_scratchpad} +""" + +os.makedirs('image', exist_ok=True) + + +def cut_dialogue_history(history_memory, keep_last_n_words=500): + if history_memory is None or len(history_memory) == 0: + return history_memory + tokens = history_memory.split() + n_tokens = len(tokens) + print(f"history_memory:{history_memory}, n_tokens: {n_tokens}") + if n_tokens < keep_last_n_words: + return history_memory + paragraphs = history_memory.split('\n') + last_n_tokens = n_tokens + while last_n_tokens >= keep_last_n_words: + last_n_tokens -= len(paragraphs[0].split(' ')) + paragraphs = paragraphs[1:] + return '\n' + '\n'.join(paragraphs) + + +def login_with_key(bot, debug, api_key): + # Just for debug + print('===>logging in') + if debug: + bot.init_agent() + return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False, value='') + else: + import openai + from langchain.llms.openai import OpenAI + if api_key and len(api_key) > 30: + os.environ["OPENAI_API_KEY"] = api_key + openai.api_key = api_key + try: + llm = OpenAI(temperature=0) + llm('Hi!') + response = 'Success!' + is_error = False + bot.init_agent() + except: + # gr.update(visible=True) + response = 'Incorrect key, please input again' + is_error = True + else: + is_error = True + response = 'Incorrect key, please input again' + + return gr.update(visible=not is_error), gr.update(visible=is_error), gr.update(visible=is_error, value=response) + + +class InstructPix2Pix: + def __init__(self, device): + print(f"Initializing InstructPix2Pix to {device}") + self.device = device + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained("timbrooks/instruct-pix2pix", + safety_checker=None, + torch_dtype=self.torch_dtype).to(device) + self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config) + + @prompts(name="Instruct Image Using Text", + description="useful when you want to the style of the image to be like the text. " + "like: make it look like a painting. or make it like a robot. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the text. ") + def inference(self, inputs): + """Change style of image.""" + print("===>Starting InstructPix2Pix Inference") + image_path, text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) + original_image = Image.open(image_path) + image = self.pipe(text, image=original_image, num_inference_steps=40, image_guidance_scale=1.2).images[0] + # updated_image_path = get_new_image_name(image_path, func_name="pix2pix") + updated_image_path = gen_new_name(image_path, f'{type(self).__name__}') + image.save(updated_image_path) + print(f"\nProcessed InstructPix2Pix, Input Image: {image_path}, Instruct Text: {text}, " + f"Output Image: {updated_image_path}") + return updated_image_path + + +class Text2Image: + def __init__(self, device): + print(f"Initializing Text2Image to {device}") + self.device = device + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", + torch_dtype=self.torch_dtype) + self.pipe.to(device) + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \ + 'fewer digits, cropped, worst quality, low quality' + + @prompts(name="Generate Image From User Input Text", + description="useful when you want to generate an image from a user input text and save it to a file. " + "like: generate an image of an object or something, or generate an image that includes some objects. " + "The input to this tool should be a string, representing the text used to generate image. ") + def inference(self, text): + image_filename = os.path.join('image', f"{str(uuid.uuid4())[:6]}.png") + image_filename = gen_new_name(image_filename) + prompt = text + ', ' + self.a_prompt + image = self.pipe(prompt, negative_prompt=self.n_prompt).images[0] + image.save(image_filename) + print( + f"\nProcessed Text2Image, Input Text: {text}, Output Image: {image_filename}") + return image_filename + + +class Image2Canny: + def __init__(self, device): + print("Initializing Image2Canny") + self.low_threshold = 100 + self.high_threshold = 200 + + @prompts(name="Edge Detection On Image", + description="useful when you want to detect the edge of the image. " + "like: detect the edges of this image, or canny detection on image, " + "or perform edge detection on this image, or detect the canny image of this image. " + "The input to this tool should be a string, representing the image_path") + def inference(self, inputs): + image = Image.open(inputs) + image = np.array(image) + canny = cv2.Canny(image, self.low_threshold, self.high_threshold) + canny = canny[:, :, None] + canny = np.concatenate([canny, canny, canny], axis=2) + canny = Image.fromarray(canny) + # updated_image_path = get_new_image_name(inputs, func_name="edge") + updated_image_path = gen_new_name(inputs, f'{type(self).__name__}') + canny.save(updated_image_path) + print(f"\nProcessed Image2Canny, Input Image: {inputs}, Output Text: {updated_image_path}") + return updated_image_path + + +class CannyText2Image: + def __init__(self, device): + print(f"Initializing CannyText2Image to {device}") + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-canny", + torch_dtype=self.torch_dtype) + self.pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None, + torch_dtype=self.torch_dtype) + self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config) + self.pipe.to(device) + self.seed = -1 + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \ + 'fewer digits, cropped, worst quality, low quality' + + @prompts(name="Generate Image Condition On Canny Image", + description="useful when you want to generate a new real image from both the user description and a canny image." + " like: generate a real image of a object or something from this canny image," + " or generate a new real image of a object or something from this edge image. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the user description. ") + def inference(self, inputs): + image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) + image = Image.open(image_path) + self.seed = random.randint(0, 65535) + seed_everything(self.seed) + prompt = f'{instruct_text}, {self.a_prompt}' + image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt, + guidance_scale=9.0).images[0] + # updated_image_path = get_new_image_name(image_path, func_name="canny2image") + updated_image_path = gen_new_name(image_path, f'{type(self).__name__}') + image.save(updated_image_path) + print(f"\nProcessed CannyText2Image, Input Canny: {image_path}, Input Text: {instruct_text}, " + f"Output Text: {updated_image_path}") + return updated_image_path + + +class Image2Line: + def __init__(self, device): + print("Initializing Image2Line") + self.detector = MLSDdetector.from_pretrained('lllyasviel/ControlNet') + + @prompts(name="Line Detection On Image", + description="useful when you want to detect the straight line of the image. " + "like: detect the straight lines of this image, or straight line detection on image, " + "or perform straight line detection on this image, or detect the straight line image of this image. " + "The input to this tool should be a string, representing the image_path") + def inference(self, inputs): + image = Image.open(inputs) + mlsd = self.detector(image) + # updated_image_path = get_new_image_name(inputs, func_name="line-of") + updated_image_path = gen_new_name(inputs, f'{type(self).__name__}') + mlsd.save(updated_image_path) + print(f"\nProcessed Image2Line, Input Image: {inputs}, Output Line: {updated_image_path}") + return updated_image_path + + +class LineText2Image: + def __init__(self, device): + print(f"Initializing LineText2Image to {device}") + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-mlsd", + torch_dtype=self.torch_dtype) + self.pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None, + torch_dtype=self.torch_dtype + ) + self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config) + self.pipe.to(device) + self.seed = -1 + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \ + 'fewer digits, cropped, worst quality, low quality' + + @prompts(name="Generate Image Condition On Line Image", + description="useful when you want to generate a new real image from both the user description " + "and a straight line image. " + "like: generate a real image of a object or something from this straight line image, " + "or generate a new real image of a object or something from this straight lines. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the user description. ") + def inference(self, inputs): + image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) + image = Image.open(image_path) + self.seed = random.randint(0, 65535) + seed_everything(self.seed) + prompt = f'{instruct_text}, {self.a_prompt}' + image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt, + guidance_scale=9.0).images[0] + # updated_image_path = get_new_image_name(image_path, func_name="line2image") + updated_image_path = gen_new_name(image_path, f'{type(self).__name__}') + image.save(updated_image_path) + print(f"\nProcessed LineText2Image, Input Line: {image_path}, Input Text: {instruct_text}, " + f"Output Text: {updated_image_path}") + return updated_image_path + + +class Image2Hed: + def __init__(self, device): + print("Initializing Image2Hed") + self.detector = HEDdetector.from_pretrained('lllyasviel/ControlNet') + + @prompts(name="Hed Detection On Image", + description="useful when you want to detect the soft hed boundary of the image. " + "like: detect the soft hed boundary of this image, or hed boundary detection on image, " + "or perform hed boundary detection on this image, or detect soft hed boundary image of this image. " + "The input to this tool should be a string, representing the image_path") + def inference(self, inputs): + image = Image.open(inputs) + hed = self.detector(image) + # updated_image_path = get_new_image_name(inputs, func_name="hed-boundary") + updated_image_path = gen_new_name(inputs, f'{type(self).__name__}') + hed.save(updated_image_path) + print(f"\nProcessed Image2Hed, Input Image: {inputs}, Output Hed: {updated_image_path}") + return updated_image_path + + +class HedText2Image: + def __init__(self, device): + print(f"Initializing HedText2Image to {device}") + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-hed", + torch_dtype=self.torch_dtype) + self.pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None, + torch_dtype=self.torch_dtype + ) + self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config) + self.pipe.to(device) + self.seed = -1 + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \ + 'fewer digits, cropped, worst quality, low quality' + + @prompts(name="Generate Image Condition On Soft Hed Boundary Image", + description="useful when you want to generate a new real image from both the user description " + "and a soft hed boundary image. " + "like: generate a real image of a object or something from this soft hed boundary image, " + "or generate a new real image of a object or something from this hed boundary. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the user description") + def inference(self, inputs): + image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) + image = Image.open(image_path) + self.seed = random.randint(0, 65535) + seed_everything(self.seed) + prompt = f'{instruct_text}, {self.a_prompt}' + image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt, + guidance_scale=9.0).images[0] + # updated_image_path = get_new_image_name(image_path, func_name="hed2image") + updated_image_path = gen_new_name(image_path, f'{type(self).__name__}') + image.save(updated_image_path) + print(f"\nProcessed HedText2Image, Input Hed: {image_path}, Input Text: {instruct_text}, " + f"Output Image: {updated_image_path}") + return updated_image_path + + +class Image2Scribble: + def __init__(self, device): + print("Initializing Image2Scribble") + self.detector = HEDdetector.from_pretrained('lllyasviel/ControlNet') + + @prompts(name="Sketch Detection On Image", + description="useful when you want to generate a scribble of the image. " + "like: generate a scribble of this image, or generate a sketch from this image, " + "detect the sketch from this image. " + "The input to this tool should be a string, representing the image_path") + def inference(self, inputs): + image = Image.open(inputs) + scribble = self.detector(image, scribble=True) + # updated_image_path = get_new_image_name(inputs, func_name="scribble") + updated_image_path = gen_new_name(inputs, f'{type(self).__name__}') + scribble.save(updated_image_path) + print(f"\nProcessed Image2Scribble, Input Image: {inputs}, Output Scribble: {updated_image_path}") + return updated_image_path + + +class ScribbleText2Image: + def __init__(self, device): + print(f"Initializing ScribbleText2Image to {device}") + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-scribble", + torch_dtype=self.torch_dtype) + self.pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None, + torch_dtype=self.torch_dtype + ) + self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config) + self.pipe.to(device) + self.seed = -1 + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \ + 'fewer digits, cropped, worst quality, low quality' + + @prompts(name="Generate Image Condition On Sketch Image", + description="useful when you want to generate a new real image from both the user description and " + "a scribble image or a sketch image. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the user description") + def inference(self, inputs): + image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) + image = Image.open(image_path) + seed_everything(GLOBAL_SEED) + prompt = f'{instruct_text}, {self.a_prompt}' + image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt, + guidance_scale=9.0).images[0] + # updated_image_path = get_new_image_name(image_path, func_name="scribble2image") + updated_image_path = gen_new_name(image_path, f'{type(self).__name__}') + image.save(updated_image_path) + print(f"\nProcessed ScribbleText2Image, Input Scribble: {image_path}, Input Text: {instruct_text}, " + f"Output Image: {updated_image_path}") + return updated_image_path + + +class Image2Pose: + def __init__(self, device): + print("Initializing Image2Pose") + self.detector = OpenposeDetector.from_pretrained('lllyasviel/ControlNet') + + @prompts(name="Pose Detection On Image", + description="useful when you want to detect the human pose of the image. " + "like: generate human poses of this image, or generate a pose image from this image. " + "The input to this tool should be a string, representing the image_path") + def inference(self, inputs): + image = Image.open(inputs) + pose = self.detector(image) + # updated_image_path = get_new_image_name(inputs, func_name="human-pose") + updated_image_path = gen_new_name(inputs, f'{type(self).__name__}') + pose.save(updated_image_path) + print(f"\nProcessed Image2Pose, Input Image: {inputs}, Output Pose: {updated_image_path}") + return updated_image_path + + +class PoseText2Image: + def __init__(self, device): + print(f"Initializing PoseText2Image to {device}") + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-openpose", + torch_dtype=self.torch_dtype) + self.pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None, + torch_dtype=self.torch_dtype) + self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config) + self.pipe.to(device) + self.num_inference_steps = 20 + self.seed = -1 + self.unconditional_guidance_scale = 9.0 + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \ + ' fewer digits, cropped, worst quality, low quality' + + @prompts(name="Generate Image Condition On Pose Image", + description="useful when you want to generate a new real image from both the user description " + "and a human pose image. " + "like: generate a real image of a human from this human pose image, " + "or generate a new real image of a human from this pose. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the user description") + def inference(self, inputs): + image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) + image = Image.open(image_path) + self.seed = random.randint(0, 65535) + seed_everything(self.seed) + prompt = f'{instruct_text}, {self.a_prompt}' + image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt, + guidance_scale=9.0).images[0] + # updated_image_path = get_new_image_name(image_path, func_name="pose2image") + updated_image_path = gen_new_name(image_path, f'{type(self).__name__}') + image.save(updated_image_path) + print(f"\nProcessed PoseText2Image, Input Pose: {image_path}, Input Text: {instruct_text}, " + f"Output Image: {updated_image_path}") + return updated_image_path + + +class SegText2Image: + def __init__(self, device): + print(f"Initializing SegText2Image to {device}") + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-seg", + torch_dtype=self.torch_dtype) + self.pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None, + torch_dtype=self.torch_dtype) + self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config) + self.pipe.to(device) + self.seed = -1 + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \ + ' fewer digits, cropped, worst quality, low quality' + + @prompts(name="Generate Image Condition On Segmentations", + description="useful when you want to generate a new real image from both the user description and segmentations. " + "like: generate a real image of a object or something from this segmentation image, " + "or generate a new real image of a object or something from these segmentations. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the user description") + def inference(self, inputs): + image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) + image = Image.open(image_path) + self.seed = random.randint(0, 65535) + seed_everything(self.seed) + prompt = f'{instruct_text}, {self.a_prompt}' + image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt, + guidance_scale=9.0).images[0] + # updated_image_path = get_new_image_name(image_path, func_name="segment2image") + updated_image_path = gen_new_name(image_path, f'{type(self).__name__}') + image.save(updated_image_path) + print(f"\nProcessed SegText2Image, Input Seg: {image_path}, Input Text: {instruct_text}, " + f"Output Image: {updated_image_path}") + return updated_image_path + + +# ''' +class ImageText2Image: + template_model=True + def __init__(self, SegText2Image, SegmentAnything): + # print(f"Initializing SegText2Image to {device}") + # self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.SegText2Image = SegText2Image + self.SegmentAnything = SegmentAnything + self.seed = -1 + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \ + ' fewer digits, cropped, worst quality, low quality' + + @prompts(name="Beautify The Image", + description="useful when you want to beatify or create a new real image from both the user description and segmentations. " + "like: generate a real image from its segmentation image, " + "beautify this image with it's segmentations, " + "or beautify this image by user description. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the user description") + def inference(self, inputs): + img_path, prompt = inputs.split(',')[0], inputs.split(',')[1] + seg_path = self.SegmentAnything.inference(img_path) + res_path = self.SegText2Image.inference(f'{seg_path},{prompt}') + print(f"\nProcessed SegText2Image, Input Seg: {img_path}, Input Text: {res_path}, " + f"Output Image: {res_path}") + return res_path +# ''' + + +class Image2Depth: + def __init__(self, device): + print("Initializing Image2Depth") + self.depth_estimator = pipeline('depth-estimation') + + @prompts(name="Predict Depth On Image", + description="useful when you want to detect depth of the image. like: generate the depth from this image, " + "or detect the depth map on this image, or predict the depth for this image. " + "The input to this tool should be a string, representing the image_path") + def inference(self, inputs): + image = Image.open(inputs) + depth = self.depth_estimator(image)['depth'] + depth = np.array(depth) + depth = depth[:, :, None] + depth = np.concatenate([depth, depth, depth], axis=2) + depth = Image.fromarray(depth) + # updated_image_path = get_new_image_name(inputs, func_name="depth") + updated_image_path = gen_new_name(inputs, f'{type(self).__name__}') + depth.save(updated_image_path) + print(f"\nProcessed Image2Depth, Input Image: {inputs}, Output Depth: {updated_image_path}") + return updated_image_path + + +class DepthText2Image: + def __init__(self, device): + print(f"Initializing DepthText2Image to {device}") + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.controlnet = ControlNetModel.from_pretrained( + "fusing/stable-diffusion-v1-5-controlnet-depth", torch_dtype=self.torch_dtype) + self.pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None, + torch_dtype=self.torch_dtype) + self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config) + self.pipe.to(device) + self.seed = -1 + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \ + ' fewer digits, cropped, worst quality, low quality' + + @prompts(name="Generate Image Condition On Depth", + description="useful when you want to generate a new real image from both the user description and depth image. " + "like: generate a real image of a object or something from this depth image, " + "or generate a new real image of a object or something from the depth map. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the user description") + def inference(self, inputs): + image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) + image = Image.open(image_path) + self.seed = random.randint(0, 65535) + seed_everything(self.seed) + prompt = f'{instruct_text}, {self.a_prompt}' + image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt, + guidance_scale=9.0).images[0] + # updated_image_path = get_new_image_name(image_path, func_name="depth2image") + updated_image_path = gen_new_name(image_path, f'{type(self).__name__}') + image.save(updated_image_path) + print(f"\nProcessed DepthText2Image, Input Depth: {image_path}, Input Text: {instruct_text}, " + f"Output Image: {updated_image_path}") + return updated_image_path + + +class Image2Normal: + def __init__(self, device): + print("Initializing Image2Normal") + self.depth_estimator = pipeline("depth-estimation", model="Intel/dpt-hybrid-midas") + self.bg_threhold = 0.4 + + @prompts(name="Predict Normal Map On Image", + description="useful when you want to detect norm map of the image. " + "like: generate normal map from this image, or predict normal map of this image. " + "The input to this tool should be a string, representing the image_path") + def inference(self, inputs): + image = Image.open(inputs) + original_size = image.size + image = self.depth_estimator(image)['predicted_depth'][0] + image = image.numpy() + image_depth = image.copy() + image_depth -= np.min(image_depth) + image_depth /= np.max(image_depth) + x = cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=3) + x[image_depth < self.bg_threhold] = 0 + y = cv2.Sobel(image, cv2.CV_32F, 0, 1, ksize=3) + y[image_depth < self.bg_threhold] = 0 + z = np.ones_like(x) * np.pi * 2.0 + image = np.stack([x, y, z], axis=2) + image /= np.sum(image ** 2.0, axis=2, keepdims=True) ** 0.5 + image = (image * 127.5 + 127.5).clip(0, 255).astype(np.uint8) + image = Image.fromarray(image) + image = image.resize(original_size) + updated_image_path = get_new_image_name(inputs, func_name="normal-map") + updated_image_path = gen_new_name(inputs, f'{type(self).__name__}') + image.save(updated_image_path) + print(f"\nProcessed Image2Normal, Input Image: {inputs}, Output Depth: {updated_image_path}") + return updated_image_path + + +class NormalText2Image: + def __init__(self, device): + print(f"Initializing NormalText2Image to {device}") + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.controlnet = ControlNetModel.from_pretrained( + "fusing/stable-diffusion-v1-5-controlnet-normal", torch_dtype=self.torch_dtype) + self.pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None, + torch_dtype=self.torch_dtype) + self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config) + self.pipe.to(device) + self.seed = -1 + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \ + ' fewer digits, cropped, worst quality, low quality' + + @prompts(name="Generate Image Condition On Normal Map", + description="useful when you want to generate a new real image from both the user description and normal map. " + "like: generate a real image of a object or something from this normal map, " + "or generate a new real image of a object or something from the normal map. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the user description") + def inference(self, inputs): + image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) + image = Image.open(image_path) + self.seed = random.randint(0, 65535) + seed_everything(self.seed) + prompt = f'{instruct_text}, {self.a_prompt}' + image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt, + guidance_scale=9.0).images[0] + updated_image_path = get_new_image_name(image_path, func_name="normal2image") + updated_image_path = gen_new_name(image_path, f'{type(self).__name__}') + image.save(updated_image_path) + print(f"\nProcessed NormalText2Image, Input Normal: {image_path}, Input Text: {instruct_text}, " + f"Output Image: {updated_image_path}") + return updated_image_path + + +class InfinityOutPainting: + template_model = True # Add this line to show this is a template model. + def __init__(self, ImageCaptioning, ImageEditing, VisualQuestionAnswering): + self.llm = OpenAI(temperature=0) + self.ImageCaption = ImageCaptioning + self.ImageEditing = ImageEditing + self.ImageVQA = VisualQuestionAnswering + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \ + 'fewer digits, cropped, worst quality, low quality' + + def get_BLIP_vqa(self, image, question): + inputs = self.ImageVQA.processor(image, question, return_tensors="pt").to(self.ImageVQA.device, + self.ImageVQA.torch_dtype) + out = self.ImageVQA.model.generate(**inputs) + answer = self.ImageVQA.processor.decode(out[0], skip_special_tokens=True) + print(f"\nProcessed VisualQuestionAnswering, Input Question: {question}, Output Answer: {answer}") + return answer + + def get_BLIP_caption(self, image): + inputs = self.ImageCaption.processor(image, return_tensors="pt").to(self.ImageCaption.device, + self.ImageCaption.torch_dtype) + out = self.ImageCaption.model.generate(**inputs) + BLIP_caption = self.ImageCaption.processor.decode(out[0], skip_special_tokens=True) + return BLIP_caption + + def check_prompt(self, prompt): + check = f"Here is a paragraph with adjectives. " \ + f"{prompt} " \ + f"Please change all plural forms in the adjectives to singular forms. " + return self.llm(check) + + def get_imagine_caption(self, image, imagine): + BLIP_caption = self.get_BLIP_caption(image) + background_color = self.get_BLIP_vqa(image, 'what is the background color of this image') + style = self.get_BLIP_vqa(image, 'what is the style of this image') + imagine_prompt = f"let's pretend you are an excellent painter and now " \ + f"there is an incomplete painting with {BLIP_caption} in the center, " \ + f"please imagine the complete painting and describe it" \ + f"you should consider the background color is {background_color}, the style is {style}" \ + f"You should make the painting as vivid and realistic as possible" \ + f"You can not use words like painting or picture" \ + f"and you should use no more than 50 words to describe it" + caption = self.llm(imagine_prompt) if imagine else BLIP_caption + caption = self.check_prompt(caption) + print(f'BLIP observation: {BLIP_caption}, ChatGPT imagine to {caption}') if imagine else print( + f'Prompt: {caption}') + return caption + + def resize_image(self, image, max_size=1000000, multiple=8): + aspect_ratio = image.size[0] / image.size[1] + new_width = int(math.sqrt(max_size * aspect_ratio)) + new_height = int(new_width / aspect_ratio) + new_width, new_height = new_width - (new_width % multiple), new_height - (new_height % multiple) + return image.resize((new_width, new_height)) + + def dowhile(self, original_img, tosize, expand_ratio, imagine, usr_prompt): + old_img = original_img + while (old_img.size != tosize): + prompt = self.check_prompt(usr_prompt) if usr_prompt else self.get_imagine_caption(old_img, imagine) + crop_w = 15 if old_img.size[0] != tosize[0] else 0 + crop_h = 15 if old_img.size[1] != tosize[1] else 0 + old_img = ImageOps.crop(old_img, (crop_w, crop_h, crop_w, crop_h)) + temp_canvas_size = (expand_ratio * old_img.width if expand_ratio * old_img.width < tosize[0] else tosize[0], + expand_ratio * old_img.height if expand_ratio * old_img.height < tosize[1] else tosize[ + 1]) + temp_canvas, temp_mask = Image.new("RGB", temp_canvas_size, color="white"), Image.new("L", temp_canvas_size, + color="white") + x, y = (temp_canvas.width - old_img.width) // 2, (temp_canvas.height - old_img.height) // 2 + temp_canvas.paste(old_img, (x, y)) + temp_mask.paste(0, (x, y, x + old_img.width, y + old_img.height)) + resized_temp_canvas, resized_temp_mask = self.resize_image(temp_canvas), self.resize_image(temp_mask) + image = self.ImageEditing.inpaint(prompt=prompt, image=resized_temp_canvas, mask_image=resized_temp_mask, + height=resized_temp_canvas.height, width=resized_temp_canvas.width, + num_inference_steps=50).images[0].resize( + (temp_canvas.width, temp_canvas.height), Image.ANTIALIAS) + image = blend_gt2pt(old_img, image) + old_img = image + return old_img + + @prompts(name="Extend An Image", + description="useful when you need to extend an image into a larger image." + "like: extend the image into a resolution of 2048x1024, extend the image into 2048x1024. " + "The input to this tool should be a comma separated string of two, representing the image_path and the resolution of widthxheight") + def inference(self, inputs): + image_path, resolution = inputs.split(',') + width, height = resolution.split('x') + tosize = (int(width), int(height)) + image = Image.open(image_path) + image = ImageOps.crop(image, (10, 10, 10, 10)) + out_painted_image = self.dowhile(image, tosize, 4, True, False) + # updated_image_path = get_new_image_name(image_path, func_name="outpainting") + updated_image_path = gen_new_name(image_path, f'{type(self).__name__}') + out_painted_image.save(updated_image_path) + print(f"\nProcessed InfinityOutPainting, Input Image: {image_path}, Input Resolution: {resolution}, " + f"Output Image: {updated_image_path}") + return updated_image_path + + +##################### New Models ##################### +class SegmentAnything: + def __init__(self, device): + print(f"Initializing SegmentAnything to {device}") + + self.device = device + sam_checkpoint = "model_zoo/sam_vit_h_4b8939.pth" + model_type = "vit_h" + self.sam = sam_model_registry[model_type](checkpoint=sam_checkpoint) + self.predictor = SamPredictor(self.sam) + self.sam.to(device=device) + self.clicked_region = None + self.img_path = None + self.history_mask_res = None + + @prompts(name="Segment Anything on Image", + description="useful when you want to segment anything in the image. " + "like: segment anything from this image, " + "The input to this tool should be a string, representing the image_path") + def inference(self, inputs): + print("Inputs: ", inputs) + img_path = inputs.strip() + self.img_path = img_path + annos = self.segment_anything(img_path) + full_img, _ = self.show_annos(annos) + # full_img = Image.fromarray(full_img) + # res = Image.fromarray(res) + # print(os.path.splitext(img_path)) + seg_all_image_path = gen_new_name(img_path, 'seg') + full_img.save(seg_all_image_path, "PNG") + + print(f"\nProcessed SegmentAnything, Input Image: {inputs}, Output Depth: {seg_all_image_path}") + return seg_all_image_path + + @prompts(name="Segment the Clicked Region in the Image", + description="useful when you want to segment the masked region or block in the image. " + "like: segment the masked region in this image, " + "The input to this tool should be None.") + def inference_by_mask(self, inputs=None): + # mask = np.array(Image.open(mask_path).convert('L')) + res_mask = self.segment_by_mask(self.clicked_region) + + if self.history_mask_res is None: + self.history_mask_res = res_mask + else: + self.history_mask_res = np.logical_or(self.history_mask_res, res_mask) + + res_mask = self.history_mask_res.astype(np.uint8)*255 + # res_mask = self.dilate_mask(res_mask) + filaname = gen_new_name(self.img_path, 'mask') + mask_img = Image.fromarray(res_mask) + mask_img.save(filaname, "PNG") + return filaname + + def segment_by_mask(self, mask=None): + random.seed(GLOBAL_SEED) + if mask is None: + mask = self.clicked_region + idxs = np.nonzero(mask) + num_points = min(max(1, int(len(idxs[0]) * 0.01)), 16) + sampled_idx = random.sample(range(0, len(idxs[0])), num_points) + new_mask = [] + for i in range(len(idxs)): + new_mask.append(idxs[i][sampled_idx]) + points = np.array(new_mask).reshape(2, -1).transpose(1, 0)[:, ::-1] + labels = np.array([1] * num_points) + + res_masks, scores, _ = self.predictor.predict( + point_coords=points, + point_labels=labels, + multimask_output=True, + ) + + return res_masks[np.argmax(scores), :, :] + + + def segment_anything(self, img_path): + img = cv2.imread(img_path) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + + mask_generator = SamAutomaticMaskGenerator(self.sam) + annos = mask_generator.generate(img) + return annos + + def get_detection_map(self, img_path): + annos = self.segment_anything(img_path) + _, detection_map = self.show_anns(annos) + + return detection_map + + def preprocess(self, img, img_path): + self.predictor.set_image(img) + self.img_path = img_path + + def reset(self): + self.predictor.reset_image() + self.clicked_region = None + self.img_path = None + self.history_mask_res = None + + def show_annos(self, anns): + # From https://github.com/sail-sg/EditAnything/blob/main/sam2image.py#L91 + if len(anns) == 0: + return + sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True) + full_img = None + + # for ann in sorted_anns: + for i in range(len(sorted_anns)): + ann = anns[i] + m = ann['segmentation'] + if full_img is None: + full_img = np.zeros((m.shape[0], m.shape[1], 3)) + map = np.zeros((m.shape[0], m.shape[1]), dtype=np.uint16) + map[m != 0] = i + 1 + color_mask = np.random.random((1, 3)).tolist()[0] + full_img[m != 0] = color_mask + full_img = full_img * 255 + # anno encoding from https://github.com/LUSSeg/ImageNet-S + res = np.zeros((map.shape[0], map.shape[1], 3)) + res[:, :, 0] = map % 256 + res[:, :, 1] = map // 256 + res.astype(np.float32) + full_img = Image.fromarray(np.uint8(full_img)) + return full_img, res + + def segment_by_points(self, img, points, lables): + # TODO + # masks, _, _ = self.predictor.predict( + # point_coords=np.array(points[-1]), + # point_labels=np.array(lables[-1]), + # # mask_input=mask_input[-1], + # multimask_output=True, # SAM outputs 3 masks, we choose the one with highest score + # ) + # # return masks_[np.argmax(scores_), :, :] + # return masks + pass + + +class ExtractMaskedAnything: + """ + prepare: + ``` + curl -L $(yadisk-direct https://disk.yandex.ru/d/ouP6l8VJ0HpMZg) -o big-lama.zip + unzip big-lama.zip + ``` + """ + template_model=True # Add this line to show this is a template model. + def __init__(self, SegmentAnything): + self.SegmentAnything = SegmentAnything + self.a_prompt = 'best quality, extremely detailed' + self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \ + 'fewer digits, cropped, worst quality, low quality' + + @prompts(name="Extract the masked anything", + description="useful when you want to extract the masked region in the image. " + "like: extract the masked region or keep the masked region in the image" + "The input to this tool should be a comma separated string of two, " + "representing the image_path and mask_path") + def inference(self, inputs): + print("Inputs: ", inputs) + image_path, seg_mask_path = inputs.split(',') + image_path = image_path.strip() + seg_mask_path = seg_mask_path.strip() + img = np.array(Image.open(image_path).convert("RGB")) + seg_mask = Image.open(seg_mask_path).convert('RGB') + seg_mask = np.array(seg_mask, dtype=np.uint8) + new_img = img * (seg_mask // 255) + rgba_img = np.concatenate((new_img, seg_mask[:, :, :1]), axis=-1).astype(np.uint8) + rgba_img = Image.fromarray(rgba_img).convert("RGBA") + new_name = gen_new_name(image_path, "ExtractMaskedAnything") + rgba_img.save(new_name, 'PNG') + + print(f"\nProcessed ExtractMaskedAnything, Input Image: {inputs}, Output Image: {new_name}") + return new_name + + +class ReplaceMaskedAnything: + def __init__(self, device): + print(f"Initializing ReplaceMaskedAnything to {device}") + self.device=device + self.revision = 'fp16' if 'cuda' in device else None + self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 + self.inpaint = StableDiffusionInpaintPipeline.from_pretrained( + "runwayml/stable-diffusion-inpainting", revision=self.revision, torch_dtype=self.torch_dtype).to(device) + + + @prompts(name="Replace the Masked Object", + description="useful when you want to replace an object by clicking in the image " + "with other object or something. " + "like: replace the masked object with a new object or something. " + "The input to this tool should be a comma separated string of three, " + "representing the image_path and the mask_path and the prompt") + def inference(self, inputs): + print("Inputs: ", inputs) + image_path, mask_path = inputs.split(',')[:2] + image_path = image_path.strip() + mask_path = mask_path.strip() + prompt = ','.join(inputs.split(',')[2:]).strip() + img = Image.open(image_path) + original_shape = img.size + img = img.resize((512, 512)) + mask_img = Image.open(mask_path).convert("L").resize((512, 512)) + mask = np.array(mask_img, dtype=np.uint8) + dilate_factor = cal_dilate_factor(mask) + mask = dilate_mask(mask, dilate_factor) + + gen_img = self.inpaint(prompt=prompt, image=img, mask_image=mask_img).images[0] + # gen_img = resize_image(np.array(gen_img), 512) + gen_img = gen_img.resize(original_shape) + gen_img_path = gen_new_name(image_path, 'ReplaceMaskedAnything') + gen_img.save(gen_img_path, 'PNG') + print(f"\nProcessed ReplaceMaskedAnything, Input Image: {inputs}, Output Depth: {gen_img_path}.") + return gen_img_path + + +class ImageOCRRecognition: + def __init__(self, device): + print(f"Initializing ImageOCRRecognition to {device}") + self.device = device + self.reader = easyocr.Reader(['ch_sim', 'en'], gpu=device) # this needs to run only once to load the model into memory + self.result = None + self.image_path=None + self.clicked_region = None + + @prompts(name="recognize the optical characters in the image", + description="useful when you want to recognize the characters or words in the clicked region of image. " + "like: recognize the characters or words in the clicked region." + "The input to this tool should be a comma separated string of two, " + "The input to this tool should be None.") + def inference_by_mask(self, inputs=None): + mask = self.clicked_region + inds =np.where(mask != 0) + inds = (inds[0][::8], inds[1][::8]) + + if self.image_path is None or len(inds[0]) == 0: + # self.result = self.reader.readtext(image_path) + return 'No characters in the image' + + # stat = [100, 595] # todo + + # reader = easyocr.Reader(['ch_sim', 'en', 'fr', 'it', 'ja', 'ko', 'ru', 'de', 'pt']) # this needs to run only once to load the model into memory + ocr_text_list = [] + for i in range(len(inds[0])): + res = self.search((inds[1][i], inds[0][i])) + if res is not None and len(res) > 0: + ocr_text_list.append(res) + ocr_text_list = list(dict.fromkeys(ocr_text_list)) + ocr_text = '\n'.join(ocr_text_list) + if ocr_text is None or len(ocr_text.strip()) == 0: + ocr_text = 'No characters in the image' + else: + ocr_text = '\n' + ocr_text + + print( + f"\nProcessed ImageOCRRecognition, Input Image: {self.image_path}, " + f"Output Text: {ocr_text}.") + return ocr_text + + @prompts(name="recognize all optical characters in the image", + description="useful when you want to recognize all characters or words in the image. " + "like: recognize all characters and words in the image." + "The input to this tool should be a string, " + "representing the image_path.") + def inference(self, inputs): + image_path = inputs.strip() + if self.image_path != image_path: + self.result = self.reader.readtext(image_path) + self.image_path = image_path + # print(self.result) + res_text = [] + for item in self.result: + # ([[x, y], [x, y], [x, y], [x, y]], text, confidence) + res_text.append(item[1]) + print( + f"\nProcessed ImageOCRRecognition, Input Image: {self.image_path}, " + f"Output Text: {res_text}") + return res_text + + def preprocess(self, img, img_path): + self.image_path = img_path + self.result = self.reader.readtext(self.image_path) + + def search(self, coord): + for item in self.result: + left_top = item[0][0] + right_bottom=item[0][-2] + if (coord[0] >= left_top[0] and coord[1] >= left_top[1]) and \ + (coord[0] <= right_bottom[0] and coord[1] <= right_bottom[1]): + return item[1] + + return '' + + def reset(self): + self.image_path = None + self.result = None + self.mask = None + + +class ConversationBot: + def __init__(self, load_dict): + # load_dict = {'VisualQuestionAnswering':'cuda:0', 'ImageCaptioning':'cuda:1',...} + print(f"Initializing VisualChatGPT, load_dict={load_dict}") + if 'HuskyVQA' not in load_dict: + raise ValueError("You have to load ImageCaptioning as a basic function for i-GPT") + if 'SegmentAnything' not in load_dict: + raise ValueError("You have to load SegmentAnything as a basic function for i-GPT") + + self.models = {} + self.uploaded_image_filename = None + # self.segmented_image_filename = None + self.history_mask = None + # Load Basic Foundation Models + for class_name, device in load_dict.items(): + self.models[class_name] = globals()[class_name](device=device) + + # Load Template Foundation Models + for class_name, module in globals().items(): + if getattr(module, 'template_model', False): + template_required_names = {k for k in inspect.signature(module.__init__).parameters.keys() if k!='self'} + loaded_names = set([type(e).__name__ for e in self.models.values()]) + if template_required_names.issubset(loaded_names): + self.models[class_name] = globals()[class_name]( + **{name: self.models[name] for name in template_required_names}) + self.tools = [] + for instance in self.models.values(): + for e in dir(instance): + if e.startswith('inference'): + func = getattr(instance, e) + self.tools.append(Tool(name=func.name, description=func.description, func=func)) + self.llm = None + self.memory = None + self.audio_model = None + + def init_agent(self): + if self.memory is not None: + self.memory.clear() #clear previous history + else: + self.memory = ConversationBufferMemory(memory_key="chat_history", output_key='output') + + self.reset() + self.llm = OpenAI(temperature=0) + self.agent = initialize_agent( + self.tools, + self.llm, + agent="conversational-react-description", + verbose=True, + memory=self.memory, + return_intermediate_steps=True, + agent_kwargs={'prefix': INTERN_CHAT_PREFIX, 'format_instructions': INTERN_CHAT_FORMAT_INSTRUCTIONS, + 'suffix': INTERN_CHAT_SUFFIX}, ) + + def find_latest_image(self, file_list): + res = None + prev_mtime = None + for file_item in file_list: + file_path = os.path.basename(file_item[0]) + if not os.path.exists(file_item[0]): + continue + + if res is None: + res = file_item[0] + ms = int(file_path.split('_')[0][3:]) * 0.001 + prev_mtime = int(os.path.getmtime(file_item[0])) + ms + else: + + ms = int(file_path.split('_')[0][3:]) * 0.001 + cur_mtime = int(os.path.getmtime(file_item[0])) + ms + # cur_mtime = cur_mtime + ms + if cur_mtime > prev_mtime: + prev_mtime = cur_mtime + res = file_item[0] + return res + + def run_task(self, use_voice, text, audio_path, state): + if use_voice: + state, _ = self.run_audio(audio_path, state) + else: + state, _ = self.run_text(text, state) + return state, state, None + + def find_param(self, msg, keyword, excluded=False): + p1 = re.compile(f'(image/[-\\w]*.(png|mp4))') + p2 = re.compile(f'(image/[-\\w]*_{keyword}.(png|mp4))') + if keyword == None or len(keyword) == 0: + out_filenames = p1.findall(msg) + elif not excluded: + out_filenames = p2.findall(msg) + elif excluded: + all_files = p1.findall(msg) + excluded_files = p2.findall(msg) + out_filenames = set(all_files) - set(excluded_files) + + res = self.find_latest_image(out_filenames) + return res + + def rectify_action(self, inputs, history_msg): + # history_msg = self.agent.memory.buffer.copy() + print('Rectify the action.') + print(inputs) + func = None + func_name = None + func_inputs = None + if 'remove' in inputs.lower() or 'erase' in inputs.lower(): + # func = self.models['RemoveMaskedAnything'] + # cls = self.models.get('RemoveMaskedAnything', None) + cls = self.models.get('LDMInpainting', None) + if cls is not None: + func = cls.inference + mask_path = self.find_param(history_msg+inputs, 'mask') + img_path = self.find_param(history_msg+inputs, 'mask', excluded=True) + func_inputs = f'{img_path},{mask_path}' + func_name = 'RemoveMaskedAnything' + elif 'replace' in inputs.lower(): + cls = self.models.get('ReplaceMaskedAnything', None) + if cls is not None: + func = cls.inference + mask_path = self.find_param(history_msg+inputs, 'mask') + # img_path = self.find_param(history_msg, 'raw') + img_path = self.find_param(history_msg+inputs, 'mask', excluded=True) + prompt = inputs.strip() + func_inputs = f'{img_path},{mask_path},{prompt}' + func_name = 'ReplaceMaskedAnything' + elif 'generate' in inputs.lower() or 'beautify' in inputs.lower(): + # print('*' * 40) + cls = self.models.get('ImageText2Image', None) + if cls is not None: + func = cls.inference + img_path = self.find_param(history_msg+inputs, '') + # img_path = self.find_param(history_msg, 'raw') + prompt = inputs.strip() + func_inputs = f'{img_path},{prompt}' + func_name = 'ImageText2Image' + elif 'describe' in inputs.lower() or 'introduce' in inputs.lower(): + cls = self.models.get('HuskyVQA', None) + func_name = 'HuskyVQA' + if cls is not None and 'mask' in inputs.lower(): + prompt = inputs.strip() + func = cls.inference_by_mask + img_path = self.find_param(history_msg+inputs, 'mask', excluded=True) + mask_path = self.find_param(history_msg+inputs, 'mask') + func_inputs = f'{img_path},{mask_path},{prompt}' + elif cls is not None: + prompt = inputs.strip() + func = cls.inference + img_path = self.find_param(history_msg+inputs, 'mask', excluded=True) + func_inputs = f'{img_path}' + + elif 'image' in inputs.lower() or 'figure' in inputs.lower() or 'picture' in inputs.lower(): + cls = self.models.get('HuskyVQA', None) + func_name = 'HuskyVQA' + if cls is not None: + func = cls.inference + img_path = self.find_param(history_msg+inputs, 'mask', excluded=True) + # img_path = self.find_param(history_msg, 'raw') + prompt = inputs.strip() + func_inputs = f'{img_path},{prompt}' + else: + # raise NotImplementedError('Can not find the matched function.') + res = self.agent(f"You can use history message to sanswer this question without using any tools. {inputs}") + res = res['output'].replace("\\", "/") + + print(f'{func_name}: {func_inputs}') + return_res = None + if func is None: + res = f"I have tried to use the tool: \"{func_name}\" to acquire the results, but it is not sucessfully loaded." + else: + return_res = func(func_inputs) + if os.path.exists(return_res): + res = f"I have used the tool: \"{func_name}\" to obtain the results. The output image is named {return_res}." + else: + res = f"I have used the tool: \"{func_name}\" to obtain the results. {return_res}" + print(f"I have used the tool: \"{func_name}\" to obtain the results. The Inputs: {func_inputs}. Result: {return_res}.") + return res + + def check_illegal_files(self, file_list): + illegal_files = [] + for file_item in file_list: + if not os.path.exists(file_item[0]): + illegal_files.append(file_item[0]) + + return illegal_files + + def run_text(self, text, state): + if text is None or len(text) == 0: + state += [(None, 'Please input text.')] + return state, state + self.agent.memory.buffer = cut_dialogue_history(self.agent.memory.buffer, keep_last_n_words=500) + pattern = re.compile('(image/[-\\w]*.(png|mp4))') + try: + # new_text = text.strip() + 'You can find all input paths in the history.' + # res = self.agent({"input": new_text}) + response = self.agent({"input": text.strip()})['output'] + # print(f'*******response*********: {response}') + response = response.replace("\\", "/") + out_filenames = pattern.findall(response) + illegal_files = self.check_illegal_files(out_filenames) + if len(illegal_files) > 0: + raise FileNotFoundError(f'{illegal_files} do (does) not exist.') + res = self.find_latest_image(out_filenames) + except Exception as err1: + # state += [(text, 'Sorry, I failed to understand your instruction. You can try it again or turn to more powerful language model.')] + print(f'Error: {err1}') + try: + response = self.rectify_action(text, self.agent.memory.buffer[:]) + # print('response = ', response) + out_filenames = pattern.findall(response) + res = self.find_latest_image(out_filenames) + # print(out_filenames) + self.agent.memory.buffer += f'\nHuman: {text.strip()}\n' + f'AI:{response})' + + except Exception as err2: + print(f'Error: {err2}') + state += [(text, 'Sorry, I failed to understand your instruction. You can try it again or turn to more powerful language model.')] + return state, state + + if res is not None and self.agent.memory.buffer.count(res) <= 1: + state = state + [(text, response + f' `{res}` is as follows: ')] + state = state + [(None, (res, ))] + else: + state = state + [(text, response)] + + print(f"\nProcessed run_text, Input text: {text}\nCurrent state: {state}\n" + f"Current Memory: {self.agent.memory.buffer}") + return state, state + + def run_audio(self, audio_path, state): + print(f'audio_path = {audio_path}') + if audio_path is None or not os.path.exists(audio_path): + state += [(None, 'No audio input. Please stop recording first and then send the audio.')] + return state, state + if self.audio_model is None: + self.audio_model = whisper.load_model("small").to('cuda:0') + text = self.audio_model.transcribe(audio_path)["text"] + res = self.run_text(text, state) + print(f"\nProcessed run_audio, Input transcribed audio: {text}\nCurrent state: {state}\n" + f"Current Memory: {self.agent.memory.buffer}") + return res[0], res[1] + + def upload_image(self, image, state, txt): + self.reset() + print('upload an image') + img = image['image'] + image_filename = os.path.join('image', f"{str(uuid.uuid4())[:6]}.png") + image_filename = gen_new_name(image_filename, 'raw') + self.uploaded_image_filename = image_filename + img = img.convert('RGB') + img.save(image_filename, "PNG") + # print(f"Resize image form {width}x{height} to {width_new}x{height_new}") + # let some foundation models preprocess image + NEED_PREPROCESSING_LIST = ["SegmentAnything", "ImageOCRRecognition"] + for model_name in NEED_PREPROCESSING_LIST: + if model_name in self.models.keys(): + self.models[model_name].preprocess(np.array(img), image_filename) + # description = self.models['ImageCaptioning'].inference(image_filename) + description = self.models['HuskyVQA'].inference_captioning(image_filename) + # description = 'Debug' + + ocr_text = None + if 'ImageOCRRecognition' in self.models.keys(): + ocr_text = self.models['ImageOCRRecognition'].inference(image_filename) + + if ocr_text is not None and len(ocr_text) > 0: + Human_prompt = f'\nHuman: provide a image named {image_filename}. The description is: {description}. OCR result is: {ocr_text}. This information helps you to understand this image, but you should use tools to finish following tasks, rather than directly imagine from my description. If you understand, say \"Received\". \n' + else: + Human_prompt = f'\nHuman: provide a image named {image_filename}. The description is: {description}. This information helps you to understand this image, but you should use tools to finish following tasks, rather than directly imagine from my description. If you understand, say \"Received\". \n' + AI_prompt = "Received. " + self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + ' AI: ' + AI_prompt + state = state + [(f"![](file={image_filename})*{image_filename}*", AI_prompt)] + print(f"\nProcessed upload_image, Input image: {image_filename}\nCurrent state: {state}\n" + f"Current Memory: {self.agent.memory.buffer}") + return state, state, f'{txt}' + + def upload_video(self, video_path, state, txt): + self.reset() + print('upload a video') + vid_name = os.path.basename(video_path) + # vid_name = gen_new_name(vid_name, '', vid_name.split('.')[-1]) + new_video_path = os.path.join('./image/', vid_name) + new_video_path = gen_new_name(new_video_path, 'raw', vid_name.split('.')[-1]) + shutil.copy(video_path, new_video_path) + + if "VideoCaption" in self.models.keys(): + description = self.models['VideoCaption'].inference(new_video_path) + else: + description = 'A video.' + Human_prompt = f'\nHuman: provide a video named {new_video_path}. The description is: {description}. This information helps you to understand this video, but you should use tools to finish following tasks, rather than directly imagine from my description. If you understand, say \"Received\". \n' + AI_prompt = f"Received video: {new_video_path} " + self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + 'AI: ' + AI_prompt + # state = state + [(f"![](file={new_video_path})*{new_video_path}*", AI_prompt)] + # state = state + [(f"![](file={video_path})*{new_video_path}*", AI_prompt)] + state = state + [((new_video_path, ), AI_prompt)] + # print('exists = ', os.path.exists("./tmp_files/1e7f_f4236666_tmp.mp4")) + print(f"\nProcessed upload_video, Input video: `{new_video_path}`\nCurrent state: {state}\n" + f"Current Memory: {self.agent.memory.buffer}") + return state, state, f'{txt}' + + def blend_mask(self, img, mask): + mask = mask.astype(np.uint8) + transparency_ratio = mask.astype(np.float32) / 255 / 3 + transparency_ratio = transparency_ratio[:, :, np.newaxis] + mask = mask[:, :, np.newaxis] + mask[mask != 0] = 255 + mask= mask.repeat(3, axis=2) + mask[:,:,0] = 0 + mask[:,:,2] = 0 + new_img_arr = img * (1 - transparency_ratio) + mask * transparency_ratio + new_img_arr = np.clip(new_img_arr, 0, 255).astype(np.uint8) + # print(new_img_arr.shape) + return new_img_arr + + def process_seg(self, image, state): + if self.uploaded_image_filename is None or \ + not os.path.exists(self.uploaded_image_filename) or \ + image is None: + return state, state, None + + if 'SegmentAnything' not in self.models.keys(): + state += [(None, 'Please load the segmentation tool.')] + return state, state, image + + img = Image.open(self.uploaded_image_filename).convert('RGB') + # img = image['image'].convert('RGB') + mask = image['mask'].convert('L') + mask = np.array(mask, dtype=np.uint8) + + Human_prompt="Please process this image based on given mask." + if self.uploaded_image_filename is None: + AI_prompt = "Please upload an image for processing." + state += [(Human_prompt, AI_prompt)] + return state, state, None + if mask.sum() == 0: + AI_prompt = "You can click the image in the right and ask me some questions." + state += [(Human_prompt, AI_prompt)] + return state, state, image['image'] + + if 'SegmentAnything' in self.models.keys(): + self.models['SegmentAnything'].clicked_region = mask + + res_mask_path = self.models['SegmentAnything'].inference_by_mask() + res_mask = Image.open(res_mask_path) + res_mask_arr = np.array(res_mask, dtype=np.uint8) + # dilate_factor = self.models['SegmentAnything'].cal_dilate_factor(res_mask_arr) + # res_mask_arr = self.models['SegmentAnything'].dilate_mask(res_mask_arr, dilate_factor) + new_img_arr = self.blend_mask(img, res_mask_arr) + new_img = Image.fromarray(new_img_arr) + new_img_name = gen_new_name(self.uploaded_image_filename, 'blended') + print(new_img_name) + new_img.save(new_img_name) + # AI_prompt = f"I have finished processing. Now, you can ask me some questions." + # state = state + [(Human_prompt, AI_prompt)] + AI_prompt = f"Received. The mask_path is named {res_mask_path}." + self.agent.memory.buffer = self.agent.memory.buffer + '\nHuman: ' + Human_prompt + ' AI: ' + AI_prompt + # state = state + [(Human_prompt, f"![](file={seg_filename})*{AI_prompt}*")] + state = state + [(Human_prompt, f'Received. The sgemented figure named `{res_mask_path}` is as follows: ')] + state = state + [(None, (res_mask_path, ))] + + print(f"\nProcessed run_image, Input image: `{self.uploaded_image_filename}`\nCurrent state: {state}\n" + f"Current Memory: {self.agent.memory.buffer}") + return state, state, new_img + + def process_ocr(self, image, state): + if self.uploaded_image_filename is None or \ + not os.path.exists(self.uploaded_image_filename) or \ + image is None: + return state, state, None + img = Image.open(self.uploaded_image_filename).convert('RGB') + img = np.array(img) + # img[:100+int(time.time() % 50),:100, :] = 0 + img = Image.fromarray(img) + # img = image['image'].convert('RGB') + mask = image['mask'].convert('L') + # mask.save(f'test_{int(time.time()) % 1000}.png') + mask = np.array(mask, dtype=np.uint8) + + Human_prompt="Please process this image based on given mask." + if self.uploaded_image_filename is None: + AI_prompt = "Please upload an image for processing." + state += [(Human_prompt, AI_prompt)] + return state, state, None + if mask.sum() == 0: + AI_prompt = "You can click the image in the right and ask me some questions." + state += [(Human_prompt, AI_prompt)] + return state, state, image['image'] + + ocr_text = None + if 'ImageOCRRecognition' in self.models.keys(): + self.models['ImageOCRRecognition'].clicked_region = mask + ocr_text = self.models['ImageOCRRecognition'].inference_by_mask() + else: + state += [Human_prompt, f'ImageOCRRecognition is not loaded.'] + + if ocr_text is not None and len(ocr_text) > 0: + AI_prompt = f'OCR result: {ocr_text}' + # self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + ' AI: ' + AI_prompt + else: + AI_prompt = 'I didn\'t find any optical characters at given location.' + + state = state + [(Human_prompt, AI_prompt)] + self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + ' AI: ' + AI_prompt + print(f"\nProcessed process_ocr, Input image: {self.uploaded_image_filename}\nCurrent state: {state}\n" + f"Current Memory: {self.agent.memory.buffer}") + return state, state, image['image'] + + def process_save(self, image, state): + if image is None: + return state, state, None + + mask_image = image['mask'].convert('RGB') + # mask = np.array(mask, dtype=np.uint8) + # mask_image = Image.fromarray(mask).convert('RGB') + random_name = os.path.join('image', f"{str(uuid.uuid4())[:6]}.png") + mask_image_name = gen_new_name(random_name, 'rawmask') + mask_image.save(mask_image_name, "PNG") + Human_prompt="Please save the given mask." + if np.array(mask_image, dtype=np.uint8).sum() == 0: + AI_prompt = "I can not find the mask. Please operate on the image at first." + state += [(Human_prompt, AI_prompt)] + return state, state, image['image'] + + AI_prompt = f'The saved mask is named {mask_image_name}: ' + state = state + [(Human_prompt, AI_prompt)] + state = state + [(None, (mask_image_name, ))] + self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + ' AI: ' + AI_prompt + print(f"\nProcessed process_ocr, Input image: {self.uploaded_image_filename}\nCurrent state: {state}\n" + f"Current Memory: {self.agent.memory.buffer}") + return state, state, image['image'] + + def process_image(self, image, state): + img = Image.open(self.uploaded_image_filename).convert('RGB') + # img = image['image'].convert('RGB') + mask = image['mask'].convert('L') + mask = np.array(mask, dtype=np.uint8) + + Human_prompt="Please process this image based on given mask." + if self.uploaded_image_filename is None: + AI_prompt = "Please upload an image for processing." + state += [(Human_prompt, AI_prompt)] + return state, state, None + if mask.sum() == 0: + AI_prompt = "You can click the image in the right and ask me some questions." + state += [(Human_prompt, AI_prompt)] + return state, state, image['image'] + + if self.history_mask is None: + self.history_mask = mask + else: + self.history_mask = np.logical_or(self.history_mask, mask) + + ocr_text = None + if 'SegmentAnything' in self.models.keys(): + self.models['SegmentAnything'].clicked_region = self.history_mask + if 'ImageOCRRecognition' in self.models.keys(): + self.models['ImageOCRRecognition'].clicked_region = mask + inds = np.where(mask != 0) + coord = (int(np.mean(inds[1])), int(np.mean(inds[0]))) + ocr_text = self.models['ImageOCRRecognition'].search(coord) + + # description = self.models['ImageCaptioning'].inference(image_filename) + res_mask = self.models['SegmentAnything'].segment_by_mask(self.history_mask) + mask_image = Image.fromarray(res_mask.astype(np.uint8)*255) + img = self.blend_mask(img, res_mask) + seg_filename = gen_new_name(self.uploaded_image_filename, 'mask') + mask_image.save(seg_filename, "PNG") + + AI_prompt = f"Received. The mask_path is named {seg_filename}:" + self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + ' AI: ' + AI_prompt + # state = state + [(Human_prompt, f"![](file={seg_filename})*{AI_prompt}*")] + state = state + [(Human_prompt, f"Received. The segmented image is named {seg_filename}:")] + state = state + [(None, (seg_filename, ))] + if ocr_text is not None and len(ocr_text) > 0: + state = state + [(None, f'OCR result: {ocr_text}')] + + print(f"\nProcessed process_image, Input image: {self.uploaded_image_filename}\nCurrent state: {state}\n" + f"Current Memory: {self.agent.memory.buffer}") + return state, state, img + + def reset(self, clear_history_memory=False): + print('reset the model cache.') + NEED_RESET_LIST = ['SegmentAnything', 'HuskyVQA'] + for model_name in NEED_RESET_LIST: + if model_name in self.models.keys(): + self.models[model_name].reset() + + self.history_mask = None + self.uploaded_image_filename = None + if clear_history_memory and bot.memory is not None: + self.memory.clear() + return None + + +class ImageSketcher(gr.Image): + """ + Code is from https://github.com/ttengwang/Caption-Anything/blob/main/app.py#L32. + Fix the bug of gradio.Image that cannot upload with tool == 'sketch'. + """ + + is_template = True # Magic to make this work with gradio.Block, don't remove unless you know what you're doing. + + def __init__(self, **kwargs): + super().__init__(tool="sketch", **kwargs) + + def preprocess(self, x): + if x is None: + return x + if self.tool == 'sketch' and self.source in ["upload", "webcam"]: + # assert isinstance(x, dict) + if isinstance(x, dict) and x['mask'] is None: + decode_image = gr.processing_utils.decode_base64_to_image(x['image']) + width, height = decode_image.size + mask = np.zeros((height, width, 4), dtype=np.uint8) + mask[..., -1] = 255 + mask = self.postprocess(mask) + # print(type(mask)) + # print(mask.shape) + x['mask'] = mask + elif not isinstance(x, dict): + # print(x) + print(f'type(x) = {type(x)}') + decode_image = gr.processing_utils.decode_base64_to_image(x) + width, height = decode_image.size + decode_image.save('sketch_test.png') + # print(width, height) + mask = np.zeros((height, width, 4), dtype=np.uint8) + mask[..., -1] = 255 + # print(mask.shape) + mask = self.postprocess(mask) + # print(type(mask)) + x = {'image': x, 'mask': mask} + x = super().preprocess(x) + return x + + +class Seafoam(ThemeBase.Base): + def __init__( + self, + *, + primary_hue=colors.emerald, + secondary_hue=colors.blue, + neutral_hue=colors.gray, + spacing_size=sizes.spacing_md, + radius_size=sizes.radius_md, + text_size=sizes.text_lg, + font=( + fonts.GoogleFont("Quicksand"), + "ui-sans-serif", + "sans-serif", + ), + font_mono=( + fonts.GoogleFont("IBM Plex Mono"), + "ui-monospace", + "monospace", + ), + ): + super().__init__( + primary_hue=primary_hue, + secondary_hue=secondary_hue, + neutral_hue=neutral_hue, + spacing_size=spacing_size, + radius_size=radius_size, + text_size=text_size, + font=font, + font_mono=font_mono, + ) + super().set( + # body_background_fill="#D8E9EB", + body_background_fill_dark="AAAAAA", + button_primary_background_fill="*primary_300", + button_primary_background_fill_hover="*primary_200", + button_primary_text_color="black", + button_secondary_background_fill="*secondary_300", + button_secondary_background_fill_hover="*secondary_200", + border_color_primary="#0BB9BF", + slider_color="*secondary_300", + slider_color_dark="*secondary_600", + block_title_text_weight="600", + block_border_width="3px", + block_shadow="*shadow_drop_lg", + button_shadow="*shadow_drop_lg", + button_large_padding="10px", + ) + + +css=''' +#chatbot{min-height: 480px} +#image_upload:{align-items: center; min-width: 640px} +''' + +def change_input_type(flag): + if flag: + print('Using voice input.') + else: + print('Using text input.') + return gr.update(visible=not flag), gr.update(visible=flag) + +def ramdom_image(): + root_path = './assets/images' + img_list = os.listdir(root_path) + img_item = random.sample(img_list, 1)[0] + return Image.open(os.path.join(root_path, img_item)) + +def ramdom_video(): + root_path = './assets/videos' + img_list = os.listdir(root_path) + img_item = random.sample(img_list, 1)[0] + return os.path.join(root_path, img_item) + +def process_video_tab(): + return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True) + +def process_image_tab(): + return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False) + +def add_whiteboard(): + # wb = np.ones((1080, 1920, 3), dtype=np.uint8) * 255 + # wb = np.ones((540, 960, 3), dtype=np.uint8) * 255 + wb = np.ones((720, 1280, 3), dtype=np.uint8) * 255 + # wb[0, 0, 0] = int(time.time() % 100) + return Image.fromarray(wb) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('-p', '--port', type=int, default=7862) + parser.add_argument('-d', '--debug', action='store_true') + parser.add_argument('--https', action='store_true') + parser.add_argument('--load', type=str, default="HuskyVQA_cuda:0,Text2Image_cuda:0") + args = parser.parse_args() + load_dict = {e.split('_')[0].strip(): e.split('_')[1].strip() for e in args.load.split(',')} + bot = ConversationBot(load_dict=load_dict) + # bot.init_agent() + with gr.Blocks(theme=Seafoam(), css=css) as demo: + state = gr.State([]) + gr.HTML("
") + + with gr.Row(visible=True, elem_id='login') as login: + with gr.Column(scale=0.6, min_width=0) : + openai_api_key_text = gr.Textbox( + placeholder="Input openAI API key", + show_label=False, + label="OpenAI API Key", + lines=1, + type="password").style(container=False) + with gr.Column(scale=0.4, min_width=0) : + key_submit_button = gr.Button(value="Please log in with your OpenAI API Key", interactive=True, variant='primary').style(container=False) + + with gr.Row(visible=False) as user_interface: + with gr.Column(scale=0.5, elem_id="text_input") as chat_part: + chatbot = gr.Chatbot(elem_id="chatbot", label="InternChat").style(height=360) + with gr.Row(visible=True) as input_row: + with gr.Column(scale=0.8, min_width=0) as text_col: + txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter, or upload an image").style( + container=False) + audio_input = gr.Audio(source="microphone", type="filepath", visible=False) + with gr.Column(scale=0.2, min_width=20): + # clear = gr.Button("Clear") + send_btn = gr.Button("📤 Send", variant="primary", visible=True) + + with gr.Column(elem_id="visual_input", scale=0.5) as img_part: + with gr.Row(visible=True): + with gr.Column(scale=0.3, min_width=0): + audio_switch = gr.Checkbox(label="Voice Assistant", elem_id='audio_switch', info=None) + with gr.Column(scale=0.3, min_width=0): + whiteboard_mode = gr.Button("⬜️ Whiteboard", variant="primary", visible=True) + # whiteboard_mode = gr.Checkbox(label="Whiteboard", elem_id='whiteboard', info=None) + with gr.Column(scale=0.4, min_width=0, visible=True)as img_example: + add_img_example = gr.Button("🖼️ Give an Example", variant="primary") + with gr.Column(scale=0.4, min_width=0, visible=False) as vid_example: + add_vid_example = gr.Button("🖼️ Give an Example", variant="primary") + with gr.Tab("Image", elem_id='image_tab') as img_tab: + click_img = ImageSketcher(type="pil", interactive=True, brush_radius=15, elem_id="image_upload").style(height=360) + with gr.Row() as vis_btn: + with gr.Column(scale=0.25, min_width=0): + process_seg_btn = gr.Button(value="👆 Pick", variant="primary", elem_id="process_seg_btn") + with gr.Column(scale=0.25, min_width=0): + process_ocr_btn = gr.Button(value="🔍 OCR", variant="primary", elem_id="process_ocr_btn") + with gr.Column(scale=0.25, min_width=0): + process_save_btn = gr.Button(value="📁 Save", variant="primary", elem_id="process_save_btn") + with gr.Column(scale=0.25, min_width=0): + clear_btn = gr.Button(value="🗑️ Clear All", elem_id="clear_btn") + with gr.Tab("Video", elem_id='video_tab') as video_tab: + video_input = gr.Video(interactive=True, include_audio=True, elem_id="video_upload").style(height=360) + + login_func = partial(login_with_key, bot, args.debug) + openai_api_key_text.submit(login_func, [openai_api_key_text], [user_interface, openai_api_key_text, key_submit_button]) + key_submit_button.click(login_func, [openai_api_key_text, ], [user_interface, openai_api_key_text, key_submit_button]) + # txt.submit(bot.run_text, [txt, state], [chatbot, state]) + # txt.submit(lambda: "", None, txt) + txt.submit( + lambda: gr.update(visible=False), [], [send_btn]).then( + lambda: gr.update(visible=False), [], [txt]).then( + bot.run_text, [txt, state], [chatbot, state]).then( + lambda: gr.update(visible=True), [], [send_btn] + ).then(lambda: "", None, [txt, ]).then( + lambda: gr.update(visible=True), [], [txt]) + + # send_audio_btn.click(bot.run_audio, [audio_input, state], [chatbot, state]) + send_btn.click( + lambda: gr.update(visible=False), [], [send_btn]).then( + lambda: gr.update(visible=False), [], [txt]).then( + bot.run_task, [audio_switch, txt, audio_input, state], [chatbot, state, txt]).then( + lambda: gr.update(visible=True), [], [send_btn]).then( + lambda: gr.update(visible=True), [], [txt] + ) + + audio_switch.change(change_input_type, [audio_switch, ], [txt, audio_input]) + add_img_example.click(ramdom_image, [], [click_img,]).then( + bot.upload_image, [click_img, state, txt], [chatbot, state, txt]) + + add_vid_example.click(ramdom_video, [], [video_input,]).then( + bot.upload_video, [video_input, state, txt], [chatbot, state, txt]) + + whiteboard_mode.click(add_whiteboard, [], [click_img,]) + + # click_img.upload(bot.upload_image, [click_img, state, txt], [chatbot, state, txt]) + click_img.upload(lambda: gr.update(visible=False), [], [send_btn]).then(bot.upload_image, [click_img, state, txt], [chatbot, state, txt]).then(lambda: gr.update(visible=True), [], [send_btn]) + + # process_btn.click(bot.process_image, [click_img, state], [chatbot, state, click_img]) + # process_ocr_btn.click(bot.process_ocr, [click_img, state], [chatbot, state, click_img]) + process_ocr_btn.click( + lambda: gr.update(visible=False), [], [vis_btn]).then( + bot.process_ocr, [click_img, state], [chatbot, state, click_img]).then( + lambda: gr.update(visible=True), [], [vis_btn] + ) + # process_seg_btn.click(bot.process_seg, [click_img, state], [chatbot, state, click_img]) + process_seg_btn.click( + lambda: gr.update(visible=False), [], [vis_btn]).then( + bot.process_seg, [click_img, state], [chatbot, state, click_img]).then( + lambda: gr.update(visible=True), [], [vis_btn] + ) + # process_save_btn.click(bot.process_save, [click_img, state], [chatbot, state, click_img]) + process_save_btn.click( + lambda: gr.update(visible=False), [], [vis_btn]).then( + bot.process_save, [click_img, state], [chatbot, state, click_img]).then( + lambda: gr.update(visible=True), [], [vis_btn] + ) + video_tab.select(process_video_tab, [], [whiteboard_mode, img_example, vid_example]) + img_tab.select(process_image_tab, [], [whiteboard_mode, img_example, vid_example]) + # clear_img_btn.click(bot.reset, [], [click_img]) + clear_func = partial(bot.reset, clear_history_memory=True) + clear_btn.click(clear_func, [], [click_img]).then( + lambda: [], None, state).then( + lambda: None, None, chatbot + ) + click_img.upload(bot.reset, None, None) + + video_input.upload(bot.upload_video, [video_input, state, txt], [chatbot, state, txt]) + video_input.clear(bot.reset, [], [video_input]) + + if args.https: + demo.queue().launch(server_name="0.0.0.0", ssl_certfile="./cert.pem", ssl_keyfile="./key.pem", ssl_verify=False, server_port=args.port) + else: + demo.queue().launch(server_name="0.0.0.0", server_port=args.port) + diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..caccdfd333fa4fecccafef259c0432506dbf0e94 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,264 @@ +absl-py==1.4.0 +accelerate==0.18.0 +addict==2.4.0 +aiofiles==23.1.0 +aiohttp==3.8.4 +aiosignal==1.3.1 +albumentations==0.5.2 +altair==4.2.2 +antlr4-python3-runtime==4.8 +anyio==3.6.2 +appdirs==1.4.4 +astunparse==1.6.3 +async-timeout==4.0.2 +attrs==23.1.0 +backports.zoneinfo==0.2.1 +basicsr==1.4.2 +beautifulsoup4==4.12.2 +black==21.4b2 +blinker==1.6.2 +boto3==1.26.123 +botocore==1.29.123 +braceexpand==0.1.7 +brotlipy==0.7.0 +bs4==0.0.1 +cachetools==5.3.0 +certifi==2022.12.7 +cffi==1.15.1 +charset-normalizer==2.0.4 +click==8.1.3 +cloudpickle==2.2.1 +cmake==3.26.3 +coloredlogs==15.0.1 +contourpy==1.0.7 +controlnet-aux==0.0.3 +cryptography==39.0.1 +cycler==0.11.0 +Cython==0.29.34 +dataclasses-json==0.5.7 +decorator==5.1.1 +decord==0.6.0 +-e git+https://github.com/facebookresearch/detectron2.git@d1e04565d3bec8719335b88be9e9b961bf3ec464#egg=detectron2 +diffusers==0.15.1 +easydict==1.9 +easyocr==1.6.2 +einops==0.6.1 +encodec==0.1.1 +entrypoints==0.4 +fairscale==0.4.3 +fastapi==0.95.1 +ffmpeg-python==0.2.0 +ffmpy==0.3.0 +filelock==3.12.0 +flatbuffers==23.3.3 +fonttools==4.39.3 +frozenlist==1.3.3 +fsspec==2023.4.0 +ftfy==6.1.1 +funcy==2.0 +future==0.18.3 +fvcore==0.1.5.post20221221 +gast==0.4.0 +gitdb==4.0.10 +GitPython==3.1.31 +google-auth==2.17.3 +google-auth-oauthlib==1.0.0 +google-pasta==0.2.0 +gradio==3.28.1 +gradio-client==0.1.3 +graphviz==0.8.4 +greenlet==2.0.2 +groundingdino==0.1.0 +grpcio==1.54.0 +h11==0.14.0 +h5py==3.8.0 +httpcore==0.17.0 +httpx==0.24.0 +huggingface-hub==0.13.4 +humanfriendly==10.0 +hydra-core==1.1.0 +idna==3.4 +imageio==2.27.0 +imageio-ffmpeg==0.4.8 +imgaug==0.4.0 +importlib-metadata==6.5.0 +importlib-resources==5.12.0 +invisible-watermark==0.1.5 +iopath==0.1.9 +jax==0.4.6 +Jinja2==3.1.2 +jmespath==1.0.1 +joblib==1.2.0 +jsonschema==4.17.3 +keras==2.12.0 +kiwisolver==1.4.4 +kornia==0.5.0 +langchain==0.0.101 +lazy-loader==0.2 +libclang==16.0.0 +linkify-it-py==2.0.0 +lit==16.0.2 +llvmlite==0.39.1 +lmdb==1.4.1 +lvis==0.5.3 +Markdown==3.4.3 +markdown-it-py==2.2.0 +MarkupSafe==2.1.2 +marshmallow==3.19.0 +marshmallow-enum==1.5.1 +matplotlib==3.7.1 +mdit-py-plugins==0.3.3 +mdurl==0.1.2 +mkl-fft==1.3.1 +mkl-random==1.2.2 +mkl-service==2.4.0 +more-itertools==9.1.0 +mpmath==1.3.0 +multidict==6.0.4 +mxnet-mkl==1.6.0 +mypy-extensions==1.0.0 +netaddr==0.8.0 +networkx==3.1 +ninja==1.11.1 +numba==0.56.4 +numpy==1.23.1 +nvidia-cublas-cu11==11.10.3.66 +nvidia-cublas-cu12==12.1.3.1 +nvidia-cuda-cupti-cu11==11.7.101 +nvidia-cuda-nvrtc-cu11==11.7.99 +nvidia-cuda-runtime-cu11==11.7.99 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cudnn-cu11==8.5.0.96 +nvidia-cudnn-cu12==8.9.0.131 +nvidia-cufft-cu11==10.9.0.58 +nvidia-curand-cu11==10.2.10.91 +nvidia-cusolver-cu11==11.4.0.1 +nvidia-cusparse-cu11==11.7.4.91 +nvidia-nccl-cu11==2.14.3 +nvidia-nvtx-cu11==11.7.91 +oauthlib==3.2.2 +omegaconf==2.1.2 +onnx==1.13.1 +onnxruntime==1.14.1 +open-clip-torch==2.17.1 +openai==0.27.4 +openai-whisper==20230314 +opencv-python==4.7.0.72 +opencv-python-headless==4.5.4.60 +opt-einsum==3.3.0 +orjson==3.8.10 +packaging==23.1 +pandas==1.5.3 +pathspec==0.11.1 +Pillow==9.5.0 +pip-search==0.0.12 +pkgutil-resolve-name==1.3.10 +portalocker==2.7.0 +prettytable==3.7.0 +protobuf==3.20.3 +psutil==5.9.5 +pyarrow==11.0.0 +pyasn1==0.5.0 +pyasn1-modules==0.3.0 +pyclipper==1.3.0.post4 +pycocotools==2.0.2 +pycparser==2.21 +pycryptodome==3.17 +pydantic==1.10.7 +pydeck==0.8.1b0 +pyDeprecate==0.3.1 +pydot==1.4.2 +pydub==0.25.1 +pyee==9.0.4 +Pygments==2.15.1 +Pympler==1.0.1 +pyOpenSSL==23.0.0 +pyparsing==3.0.9 +pyre-extensions==0.0.23 +pyrsistent==0.19.3 +PySocks==1.7.1 +python-bidi==0.4.2 +python-dateutil==2.8.2 +python-multipart==0.0.6 +pytorch-lightning==1.5.10 +pytz==2023.3 +pytz-deprecation-shim==0.1.0.post0 +PyWavelets==1.4.1 +PyYAML==6.0 +qudida==0.0.4 +regex==2023.3.23 +requests==2.29.0 +requests-oauthlib==1.3.1 +rich==13.3.4 +rsa==4.9 +s3transfer==0.6.0 +sacremoses==0.0.53 +safetensors==0.3.0 +scikit-image==0.17.2 +scikit-learn==0.24.2 +scipy==1.9.1 +segment-anything==1.0 +semantic-version==2.10.0 +sentencepiece==0.1.98 +setuptools-rust==1.6.0 +shapely==2.0.1 +simplet5==0.1.4 +six==1.16.0 +smmap==5.0.0 +sniffio==1.3.0 +soupsieve==2.4.1 +SQLAlchemy==1.4.47 +starlette==0.26.1 +streamlit==1.21.0 +suno-bark==0.0.1a0 +supervision==0.4.0 +sympy==1.11.1 +tabulate==0.9.0 +tb-nightly==2.13.0a20230420 +tenacity==8.2.2 +tensorboard==2.12.2 +tensorboard-data-server==0.7.0 +tensorboard-plugin-wit==1.8.1 +tensorflow==2.12.0 +tensorflow-estimator==2.12.0 +tensorflow-io-gcs-filesystem==0.32.0 +tensorrt==8.6.0 +termcolor==2.2.0 +test-tube==0.7.5 +threadpoolctl==3.1.0 +tifffile==2023.4.12 +tiktoken==0.3.1 +timm==0.6.13 +tokenizers==0.11.4 +toml==0.10.2 +tomli==2.0.1 +toolz==0.12.0 +torch==1.13.0 +torchaudio==0.13.0 +torchmetrics==0.11.4 +torchvision==0.14.0 +tornado==6.3 +tqdm==4.65.0 +transformers==4.28.0 +triton==2.0.0 +typing-extensions==4.5.0 +typing-inspect==0.8.0 +tzdata==2023.3 +tzlocal==4.3 +uc-micro-py==1.0.1 +urllib3==1.26.15 +uvicorn==0.21.1 +validators==0.20.0 +watchdog==3.0.0 +wcwidth==0.2.6 +webdataset==0.2.48 +WebOb==1.8.7 +websockets==11.0.2 +Werkzeug==2.2.3 +wget==3.2 +wldhx.yadisk-direct==0.0.6 +wrapt==1.14.1 +yacs==0.1.8 +yapf==0.33.0 +yarl==1.8.2 +zipp==3.15.0 diff --git a/third-party/lama/LICENSE b/third-party/lama/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..ca822bb5f62a37a5a73f56a2d563b16dab46c03f --- /dev/null +++ b/third-party/lama/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [2021] Samsung Research + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/third-party/lama/README.md b/third-party/lama/README.md new file mode 100644 index 0000000000000000000000000000000000000000..390e111ca1de77832210aa2c7ffe5ccd890973b3 --- /dev/null +++ b/third-party/lama/README.md @@ -0,0 +1,459 @@ +# 🦙 LaMa: Resolution-robust Large Mask Inpainting with Fourier Convolutions + +by Roman Suvorov, Elizaveta Logacheva, Anton Mashikhin, +Anastasia Remizova, Arsenii Ashukha, Aleksei Silvestrov, Naejin Kong, Harshith Goka, Kiwoong Park, Victor Lempitsky. + +

+ 🔥🔥🔥 +
+ +LaMa generalizes surprisingly well to much higher resolutions (~2k❗️) than it saw during training (256x256), and achieves the excellent performance even in challenging scenarios, e.g. completion of periodic structures. +

+ +[[Project page](https://advimman.github.io/lama-project/)] [[arXiv](https://arxiv.org/abs/2109.07161)] [[Supplementary](https://ashukha.com/projects/lama_21/lama_supmat_2021.pdf)] [[BibTeX](https://senya-ashukha.github.io/projects/lama_21/paper.txt)] [[Casual GAN Papers Summary](https://www.casualganpapers.com/large-masks-fourier-convolutions-inpainting/LaMa-explained.html)] + +

+ + + +
+ Try out in Google Colab +

+ +

+ +

+ + +

+ +

+ +# LaMa development +(Feel free to share your paper by creating an issue) +- Amazing results [paper](https://arxiv.org/abs/2206.13644) / [video](https://www.youtube.com/watch?v=gEukhOheWgE) / code https://github.com/advimman/lama/pull/112 / by Geomagical Labs ([geomagical.com](geomagical.com)) +

+ +

+ +# Non-official 3rd party apps: +(Feel free to share your app/implementation/demo by creating an issue) +- [https://cleanup.pictures](https://cleanup.pictures/) - a simple interactive object removal tool by [@cyrildiagne](https://twitter.com/cyrildiagne) + - [lama-cleaner](https://github.com/Sanster/lama-cleaner) by [@Sanster](https://github.com/Sanster/lama-cleaner) is a self-host version of [https://cleanup.pictures](https://cleanup.pictures/) +- Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/lama) by [@AK391](https://github.com/AK391) +- Telegram bot [@MagicEraserBot](https://t.me/MagicEraserBot) by [@Moldoteck](https://github.com/Moldoteck), [code](https://github.com/Moldoteck/MagicEraser) +- [Auto-LaMa](https://github.com/andy971022/auto-lama) = DE:TR object detection + LaMa inpainting by [@andy971022](https://github.com/andy971022) +- [LAMA-Magic-Eraser-Local](https://github.com/zhaoyun0071/LAMA-Magic-Eraser-Local) = a standalone inpainting application built with PyQt5 by [@zhaoyun0071](https://github.com/zhaoyun0071) +- [Hama](https://www.hama.app/) - object removal with a smart brush which simplifies mask drawing. +- [ModelScope](https://www.modelscope.cn/models/damo/cv_fft_inpainting_lama/summary) = the largest Model Community in Chinese by [@chenbinghui1](https://github.com/chenbinghui1). +- [LaMa with MaskDINO](https://github.com/qwopqwop200/lama-with-maskdino) = MaskDINO object detection + LaMa inpainting with refinement by [@qwopqwop200](https://github.com/qwopqwop200). + +# Environment setup + +Clone the repo: +`git clone https://github.com/advimman/lama.git` + +There are three options of an environment: + +1. Python virtualenv: + + ``` + virtualenv inpenv --python=/usr/bin/python3 + source inpenv/bin/activate + pip install torch==1.8.0 torchvision==0.9.0 + + cd lama + pip install -r requirements.txt + ``` + +2. Conda + + ``` + % Install conda for Linux, for other OS download miniconda at https://docs.conda.io/en/latest/miniconda.html + wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh + bash Miniconda3-latest-Linux-x86_64.sh -b -p $HOME/miniconda + $HOME/miniconda/bin/conda init bash + + cd lama + conda env create -f conda_env.yml + conda activate lama + conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch -y + pip install pytorch-lightning==1.2.9 + ``` + +3. Docker: No actions are needed 🎉. + +# Inference + +Run +``` +cd lama +export TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd) +``` + +**1. Download pre-trained models** + +Install tool for yandex disk link extraction: + +``` +pip3 install wldhx.yadisk-direct +``` + +The best model (Places2, Places Challenge): + +``` +curl -L $(yadisk-direct https://disk.yandex.ru/d/ouP6l8VJ0HpMZg) -o big-lama.zip +unzip big-lama.zip +``` + +All models (Places & CelebA-HQ): + +``` +curl -L $(yadisk-direct https://disk.yandex.ru/d/EgqaSnLohjuzAg) -o lama-models.zip +unzip lama-models.zip +``` + +**2. Prepare images and masks** + +Download test images: + +``` +curl -L $(yadisk-direct https://disk.yandex.ru/d/xKQJZeVRk5vLlQ) -o LaMa_test_images.zip +unzip LaMa_test_images.zip +``` +
+ OR prepare your data: +1) Create masks named as `[images_name]_maskXXX[image_suffix]`, put images and masks in the same folder. + +- You can use the [script](https://github.com/advimman/lama/blob/main/bin/gen_mask_dataset.py) for random masks generation. +- Check the format of the files: + ``` + image1_mask001.png + image1.png + image2_mask001.png + image2.png + ``` + +2) Specify `image_suffix`, e.g. `.png` or `.jpg` or `_input.jpg` in `configs/prediction/default.yaml`. + +
+ + +**3. Predict** + +On the host machine: + + python3 bin/predict.py model.path=$(pwd)/big-lama indir=$(pwd)/LaMa_test_images outdir=$(pwd)/output + +**OR** in the docker + +The following command will pull the docker image from Docker Hub and execute the prediction script +``` +bash docker/2_predict.sh $(pwd)/big-lama $(pwd)/LaMa_test_images $(pwd)/output device=cpu +``` +Docker cuda: TODO + +**4. Predict with Refinement** + +On the host machine: + + python3 bin/predict.py refine=True model.path=$(pwd)/big-lama indir=$(pwd)/LaMa_test_images outdir=$(pwd)/output + +# Train and Eval + +Make sure you run: + +``` +cd lama +export TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd) +``` + +Then download models for _perceptual loss_: + + mkdir -p ade20k/ade20k-resnet50dilated-ppm_deepsup/ + wget -P ade20k/ade20k-resnet50dilated-ppm_deepsup/ http://sceneparsing.csail.mit.edu/model/pytorch/ade20k-resnet50dilated-ppm_deepsup/encoder_epoch_20.pth + + +## Places + +⚠️ NB: FID/SSIM/LPIPS metric values for Places that we see in LaMa paper are computed on 30000 images that we produce in evaluation section below. +For more details on evaluation data check [[Section 3. Dataset splits in Supplementary](https://ashukha.com/projects/lama_21/lama_supmat_2021.pdf#subsection.3.1)] ⚠️ + +On the host machine: + + # Download data from http://places2.csail.mit.edu/download.html + # Places365-Standard: Train(105GB)/Test(19GB)/Val(2.1GB) from High-resolution images section + wget http://data.csail.mit.edu/places/places365/train_large_places365standard.tar + wget http://data.csail.mit.edu/places/places365/val_large.tar + wget http://data.csail.mit.edu/places/places365/test_large.tar + + # Unpack train/test/val data and create .yaml config for it + bash fetch_data/places_standard_train_prepare.sh + bash fetch_data/places_standard_test_val_prepare.sh + + # Sample images for test and viz at the end of epoch + bash fetch_data/places_standard_test_val_sample.sh + bash fetch_data/places_standard_test_val_gen_masks.sh + + # Run training + python3 bin/train.py -cn lama-fourier location=places_standard + + # To evaluate trained model and report metrics as in our paper + # we need to sample previously unseen 30k images and generate masks for them + bash fetch_data/places_standard_evaluation_prepare_data.sh + + # Infer model on thick/thin/medium masks in 256 and 512 and run evaluation + # like this: + python3 bin/predict.py \ + model.path=$(pwd)/experiments/__lama-fourier_/ \ + indir=$(pwd)/places_standard_dataset/evaluation/random_thick_512/ \ + outdir=$(pwd)/inference/random_thick_512 model.checkpoint=last.ckpt + + python3 bin/evaluate_predicts.py \ + $(pwd)/configs/eval2_gpu.yaml \ + $(pwd)/places_standard_dataset/evaluation/random_thick_512/ \ + $(pwd)/inference/random_thick_512 \ + $(pwd)/inference/random_thick_512_metrics.csv + + + +Docker: TODO + +## CelebA +On the host machine: + + # Make shure you are in lama folder + cd lama + export TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd) + + # Download CelebA-HQ dataset + # Download data256x256.zip from https://drive.google.com/drive/folders/11Vz0fqHS2rXDb5pprgTjpD7S2BAJhi1P + + # unzip & split into train/test/visualization & create config for it + bash fetch_data/celebahq_dataset_prepare.sh + + # generate masks for test and visual_test at the end of epoch + bash fetch_data/celebahq_gen_masks.sh + + # Run training + python3 bin/train.py -cn lama-fourier-celeba data.batch_size=10 + + # Infer model on thick/thin/medium masks in 256 and run evaluation + # like this: + python3 bin/predict.py \ + model.path=$(pwd)/experiments/__lama-fourier-celeba_/ \ + indir=$(pwd)/celeba-hq-dataset/visual_test_256/random_thick_256/ \ + outdir=$(pwd)/inference/celeba_random_thick_256 model.checkpoint=last.ckpt + + +Docker: TODO + +## Places Challenge + +On the host machine: + + # This script downloads multiple .tar files in parallel and unpacks them + # Places365-Challenge: Train(476GB) from High-resolution images (to train Big-Lama) + bash places_challenge_train_download.sh + + TODO: prepare + TODO: train + TODO: eval + +Docker: TODO + +## Create your data + +Please check bash scripts for data preparation and mask generation from CelebaHQ section, +if you stuck at one of the following steps. + + +On the host machine: + + # Make shure you are in lama folder + cd lama + export TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd) + + # You need to prepare following image folders: + $ ls my_dataset + train + val_source # 2000 or more images + visual_test_source # 100 or more images + eval_source # 2000 or more images + + # LaMa generates random masks for the train data on the flight, + # but needs fixed masks for test and visual_test for consistency of evaluation. + + # Suppose, we want to evaluate and pick best models + # on 512x512 val dataset with thick/thin/medium masks + # And your images have .jpg extention: + + python3 bin/gen_mask_dataset.py \ + $(pwd)/configs/data_gen/random__512.yaml \ # thick, thin, medium + my_dataset/val_source/ \ + my_dataset/val/random__512.yaml \# thick, thin, medium + --ext jpg + + # So the mask generator will: + # 1. resize and crop val images and save them as .png + # 2. generate masks + + ls my_dataset/val/random_medium_512/ + image1_crop000_mask000.png + image1_crop000.png + image2_crop000_mask000.png + image2_crop000.png + ... + + # Generate thick, thin, medium masks for visual_test folder: + + python3 bin/gen_mask_dataset.py \ + $(pwd)/configs/data_gen/random__512.yaml \ #thick, thin, medium + my_dataset/visual_test_source/ \ + my_dataset/visual_test/random__512/ \ #thick, thin, medium + --ext jpg + + + ls my_dataset/visual_test/random_thick_512/ + image1_crop000_mask000.png + image1_crop000.png + image2_crop000_mask000.png + image2_crop000.png + ... + + # Same process for eval_source image folder: + + python3 bin/gen_mask_dataset.py \ + $(pwd)/configs/data_gen/random__512.yaml \ #thick, thin, medium + my_dataset/eval_source/ \ + my_dataset/eval/random__512/ \ #thick, thin, medium + --ext jpg + + + + # Generate location config file which locate these folders: + + touch my_dataset.yaml + echo "data_root_dir: $(pwd)/my_dataset/" >> my_dataset.yaml + echo "out_root_dir: $(pwd)/experiments/" >> my_dataset.yaml + echo "tb_dir: $(pwd)/tb_logs/" >> my_dataset.yaml + mv my_dataset.yaml ${PWD}/configs/training/location/ + + + # Check data config for consistency with my_dataset folder structure: + $ cat ${PWD}/configs/training/data/abl-04-256-mh-dist + ... + train: + indir: ${location.data_root_dir}/train + ... + val: + indir: ${location.data_root_dir}/val + img_suffix: .png + visual_test: + indir: ${location.data_root_dir}/visual_test + img_suffix: .png + + + # Run training + python3 bin/train.py -cn lama-fourier location=my_dataset data.batch_size=10 + + # Evaluation: LaMa training procedure picks best few models according to + # scores on my_dataset/val/ + + # To evaluate one of your best models (i.e. at epoch=32) + # on previously unseen my_dataset/eval do the following + # for thin, thick and medium: + + # infer: + python3 bin/predict.py \ + model.path=$(pwd)/experiments/__lama-fourier_/ \ + indir=$(pwd)/my_dataset/eval/random__512/ \ + outdir=$(pwd)/inference/my_dataset/random__512 \ + model.checkpoint=epoch32.ckpt + + # metrics calculation: + python3 bin/evaluate_predicts.py \ + $(pwd)/configs/eval2_gpu.yaml \ + $(pwd)/my_dataset/eval/random__512/ \ + $(pwd)/inference/my_dataset/random__512 \ + $(pwd)/inference/my_dataset/random__512_metrics.csv + + +**OR** in the docker: + + TODO: train + TODO: eval + +# Hints + +### Generate different kinds of masks +The following command will execute a script that generates random masks. + + bash docker/1_generate_masks_from_raw_images.sh \ + configs/data_gen/random_medium_512.yaml \ + /directory_with_input_images \ + /directory_where_to_store_images_and_masks \ + --ext png + +The test data generation command stores images in the format, +which is suitable for [prediction](#prediction). + +The table below describes which configs we used to generate different test sets from the paper. +Note that we *do not fix a random seed*, so the results will be slightly different each time. + +| | Places 512x512 | CelebA 256x256 | +|--------|------------------------|------------------------| +| Narrow | random_thin_512.yaml | random_thin_256.yaml | +| Medium | random_medium_512.yaml | random_medium_256.yaml | +| Wide | random_thick_512.yaml | random_thick_256.yaml | + +Feel free to change the config path (argument #1) to any other config in `configs/data_gen` +or adjust config files themselves. + +### Override parameters in configs +Also you can override parameters in config like this: + + python3 bin/train.py -cn data.batch_size=10 run_title=my-title + +Where .yaml file extension is omitted + +### Models options +Config names for models from paper (substitude into the training command): + + * big-lama + * big-lama-regular + * lama-fourier + * lama-regular + * lama_small_train_masks + +Which are seated in configs/training/folder + +### Links +- All the data (models, test images, etc.) https://disk.yandex.ru/d/AmdeG-bIjmvSug +- Test images from the paper https://disk.yandex.ru/d/xKQJZeVRk5vLlQ +- The pre-trained models https://disk.yandex.ru/d/EgqaSnLohjuzAg +- The models for perceptual loss https://disk.yandex.ru/d/ncVmQlmT_kTemQ +- Our training logs are available at https://disk.yandex.ru/d/9Bt1wNSDS4jDkQ + + +### Training time & resources + +TODO + +## Acknowledgments + +* Segmentation code and models if form [CSAILVision](https://github.com/CSAILVision/semantic-segmentation-pytorch). +* LPIPS metric is from [richzhang](https://github.com/richzhang/PerceptualSimilarity) +* SSIM is from [Po-Hsun-Su](https://github.com/Po-Hsun-Su/pytorch-ssim) +* FID is from [mseitzer](https://github.com/mseitzer/pytorch-fid) + +## Citation +If you found this code helpful, please consider citing: +``` +@article{suvorov2021resolution, + title={Resolution-robust Large Mask Inpainting with Fourier Convolutions}, + author={Suvorov, Roman and Logacheva, Elizaveta and Mashikhin, Anton and Remizova, Anastasia and Ashukha, Arsenii and Silvestrov, Aleksei and Kong, Naejin and Goka, Harshith and Park, Kiwoong and Lempitsky, Victor}, + journal={arXiv preprint arXiv:2109.07161}, + year={2021} +} +``` diff --git a/third-party/lama/bin/analyze_errors.py b/third-party/lama/bin/analyze_errors.py new file mode 100755 index 0000000000000000000000000000000000000000..a11f9478de76ede162f5511449ac98e549ff4b6e --- /dev/null +++ b/third-party/lama/bin/analyze_errors.py @@ -0,0 +1,316 @@ +#!/usr/bin/env python3 +import cv2 +import numpy as np +import sklearn +import torch +import os +import pickle +import pandas as pd +import matplotlib.pyplot as plt +from joblib import Parallel, delayed + +from saicinpainting.evaluation.data import PrecomputedInpaintingResultsDataset, load_image +from saicinpainting.evaluation.losses.fid.inception import InceptionV3 +from saicinpainting.evaluation.utils import load_yaml +from saicinpainting.training.visualizers.base import visualize_mask_and_images + + +def draw_score(img, score): + img = np.transpose(img, (1, 2, 0)) + cv2.putText(img, f'{score:.2f}', + (40, 40), + cv2.FONT_HERSHEY_SIMPLEX, + 1, + (0, 1, 0), + thickness=3) + img = np.transpose(img, (2, 0, 1)) + return img + + +def save_global_samples(global_mask_fnames, mask2real_fname, mask2fake_fname, out_dir, real_scores_by_fname, fake_scores_by_fname): + for cur_mask_fname in global_mask_fnames: + cur_real_fname = mask2real_fname[cur_mask_fname] + orig_img = load_image(cur_real_fname, mode='RGB') + fake_img = load_image(mask2fake_fname[cur_mask_fname], mode='RGB')[:, :orig_img.shape[1], :orig_img.shape[2]] + mask = load_image(cur_mask_fname, mode='L')[None, ...] + + draw_score(orig_img, real_scores_by_fname.loc[cur_real_fname, 'real_score']) + draw_score(fake_img, fake_scores_by_fname.loc[cur_mask_fname, 'fake_score']) + + cur_grid = visualize_mask_and_images(dict(image=orig_img, mask=mask, fake=fake_img), + keys=['image', 'fake'], + last_without_mask=True) + cur_grid = np.clip(cur_grid * 255, 0, 255).astype('uint8') + cur_grid = cv2.cvtColor(cur_grid, cv2.COLOR_RGB2BGR) + cv2.imwrite(os.path.join(out_dir, os.path.splitext(os.path.basename(cur_mask_fname))[0] + '.jpg'), + cur_grid) + + +def save_samples_by_real(worst_best_by_real, mask2fake_fname, fake_info, out_dir): + for real_fname in worst_best_by_real.index: + worst_mask_path = worst_best_by_real.loc[real_fname, 'worst'] + best_mask_path = worst_best_by_real.loc[real_fname, 'best'] + orig_img = load_image(real_fname, mode='RGB') + worst_mask_img = load_image(worst_mask_path, mode='L')[None, ...] + worst_fake_img = load_image(mask2fake_fname[worst_mask_path], mode='RGB')[:, :orig_img.shape[1], :orig_img.shape[2]] + best_mask_img = load_image(best_mask_path, mode='L')[None, ...] + best_fake_img = load_image(mask2fake_fname[best_mask_path], mode='RGB')[:, :orig_img.shape[1], :orig_img.shape[2]] + + draw_score(orig_img, worst_best_by_real.loc[real_fname, 'real_score']) + draw_score(worst_fake_img, worst_best_by_real.loc[real_fname, 'worst_score']) + draw_score(best_fake_img, worst_best_by_real.loc[real_fname, 'best_score']) + + cur_grid = visualize_mask_and_images(dict(image=orig_img, mask=np.zeros_like(worst_mask_img), + worst_mask=worst_mask_img, worst_img=worst_fake_img, + best_mask=best_mask_img, best_img=best_fake_img), + keys=['image', 'worst_mask', 'worst_img', 'best_mask', 'best_img'], + rescale_keys=['worst_mask', 'best_mask'], + last_without_mask=True) + cur_grid = np.clip(cur_grid * 255, 0, 255).astype('uint8') + cur_grid = cv2.cvtColor(cur_grid, cv2.COLOR_RGB2BGR) + cv2.imwrite(os.path.join(out_dir, + os.path.splitext(os.path.basename(real_fname))[0] + '.jpg'), + cur_grid) + + fig, (ax1, ax2) = plt.subplots(1, 2) + cur_stat = fake_info[fake_info['real_fname'] == real_fname] + cur_stat['fake_score'].hist(ax=ax1) + cur_stat['real_score'].hist(ax=ax2) + fig.tight_layout() + fig.savefig(os.path.join(out_dir, + os.path.splitext(os.path.basename(real_fname))[0] + '_scores.png')) + plt.close(fig) + + +def extract_overlapping_masks(mask_fnames, cur_i, fake_scores_table, max_overlaps_n=2): + result_pairs = [] + result_scores = [] + mask_fname_a = mask_fnames[cur_i] + mask_a = load_image(mask_fname_a, mode='L')[None, ...] > 0.5 + cur_score_a = fake_scores_table.loc[mask_fname_a, 'fake_score'] + for mask_fname_b in mask_fnames[cur_i + 1:]: + mask_b = load_image(mask_fname_b, mode='L')[None, ...] > 0.5 + if not np.any(mask_a & mask_b): + continue + cur_score_b = fake_scores_table.loc[mask_fname_b, 'fake_score'] + result_pairs.append((mask_fname_a, mask_fname_b)) + result_scores.append(cur_score_b - cur_score_a) + if len(result_pairs) >= max_overlaps_n: + break + return result_pairs, result_scores + + +def main(args): + config = load_yaml(args.config) + + latents_dir = os.path.join(args.outpath, 'latents') + os.makedirs(latents_dir, exist_ok=True) + global_worst_dir = os.path.join(args.outpath, 'global_worst') + os.makedirs(global_worst_dir, exist_ok=True) + global_best_dir = os.path.join(args.outpath, 'global_best') + os.makedirs(global_best_dir, exist_ok=True) + worst_best_by_best_worst_score_diff_max_dir = os.path.join(args.outpath, 'worst_best_by_real', 'best_worst_score_diff_max') + os.makedirs(worst_best_by_best_worst_score_diff_max_dir, exist_ok=True) + worst_best_by_best_worst_score_diff_min_dir = os.path.join(args.outpath, 'worst_best_by_real', 'best_worst_score_diff_min') + os.makedirs(worst_best_by_best_worst_score_diff_min_dir, exist_ok=True) + worst_best_by_real_best_score_diff_max_dir = os.path.join(args.outpath, 'worst_best_by_real', 'real_best_score_diff_max') + os.makedirs(worst_best_by_real_best_score_diff_max_dir, exist_ok=True) + worst_best_by_real_best_score_diff_min_dir = os.path.join(args.outpath, 'worst_best_by_real', 'real_best_score_diff_min') + os.makedirs(worst_best_by_real_best_score_diff_min_dir, exist_ok=True) + worst_best_by_real_worst_score_diff_max_dir = os.path.join(args.outpath, 'worst_best_by_real', 'real_worst_score_diff_max') + os.makedirs(worst_best_by_real_worst_score_diff_max_dir, exist_ok=True) + worst_best_by_real_worst_score_diff_min_dir = os.path.join(args.outpath, 'worst_best_by_real', 'real_worst_score_diff_min') + os.makedirs(worst_best_by_real_worst_score_diff_min_dir, exist_ok=True) + + if not args.only_report: + block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[2048] + inception_model = InceptionV3([block_idx]).eval().cuda() + + dataset = PrecomputedInpaintingResultsDataset(args.datadir, args.predictdir, **config.dataset_kwargs) + + real2vector_cache = {} + + real_features = [] + fake_features = [] + + orig_fnames = [] + mask_fnames = [] + mask2real_fname = {} + mask2fake_fname = {} + + for batch_i, batch in enumerate(dataset): + orig_img_fname = dataset.img_filenames[batch_i] + mask_fname = dataset.mask_filenames[batch_i] + fake_fname = dataset.pred_filenames[batch_i] + mask2real_fname[mask_fname] = orig_img_fname + mask2fake_fname[mask_fname] = fake_fname + + cur_real_vector = real2vector_cache.get(orig_img_fname, None) + if cur_real_vector is None: + with torch.no_grad(): + in_img = torch.from_numpy(batch['image'][None, ...]).cuda() + cur_real_vector = inception_model(in_img)[0].squeeze(-1).squeeze(-1).cpu().numpy() + real2vector_cache[orig_img_fname] = cur_real_vector + + pred_img = torch.from_numpy(batch['inpainted'][None, ...]).cuda() + cur_fake_vector = inception_model(pred_img)[0].squeeze(-1).squeeze(-1).cpu().numpy() + + real_features.append(cur_real_vector) + fake_features.append(cur_fake_vector) + + orig_fnames.append(orig_img_fname) + mask_fnames.append(mask_fname) + + ids_features = np.concatenate(real_features + fake_features, axis=0) + ids_labels = np.array(([1] * len(real_features)) + ([0] * len(fake_features))) + + with open(os.path.join(latents_dir, 'featues.pkl'), 'wb') as f: + pickle.dump(ids_features, f, protocol=3) + with open(os.path.join(latents_dir, 'labels.pkl'), 'wb') as f: + pickle.dump(ids_labels, f, protocol=3) + with open(os.path.join(latents_dir, 'orig_fnames.pkl'), 'wb') as f: + pickle.dump(orig_fnames, f, protocol=3) + with open(os.path.join(latents_dir, 'mask_fnames.pkl'), 'wb') as f: + pickle.dump(mask_fnames, f, protocol=3) + with open(os.path.join(latents_dir, 'mask2real_fname.pkl'), 'wb') as f: + pickle.dump(mask2real_fname, f, protocol=3) + with open(os.path.join(latents_dir, 'mask2fake_fname.pkl'), 'wb') as f: + pickle.dump(mask2fake_fname, f, protocol=3) + + svm = sklearn.svm.LinearSVC(dual=False) + svm.fit(ids_features, ids_labels) + + pred_scores = svm.decision_function(ids_features) + real_scores = pred_scores[:len(real_features)] + fake_scores = pred_scores[len(real_features):] + + with open(os.path.join(latents_dir, 'pred_scores.pkl'), 'wb') as f: + pickle.dump(pred_scores, f, protocol=3) + with open(os.path.join(latents_dir, 'real_scores.pkl'), 'wb') as f: + pickle.dump(real_scores, f, protocol=3) + with open(os.path.join(latents_dir, 'fake_scores.pkl'), 'wb') as f: + pickle.dump(fake_scores, f, protocol=3) + else: + with open(os.path.join(latents_dir, 'orig_fnames.pkl'), 'rb') as f: + orig_fnames = pickle.load(f) + with open(os.path.join(latents_dir, 'mask_fnames.pkl'), 'rb') as f: + mask_fnames = pickle.load(f) + with open(os.path.join(latents_dir, 'mask2real_fname.pkl'), 'rb') as f: + mask2real_fname = pickle.load(f) + with open(os.path.join(latents_dir, 'mask2fake_fname.pkl'), 'rb') as f: + mask2fake_fname = pickle.load(f) + with open(os.path.join(latents_dir, 'real_scores.pkl'), 'rb') as f: + real_scores = pickle.load(f) + with open(os.path.join(latents_dir, 'fake_scores.pkl'), 'rb') as f: + fake_scores = pickle.load(f) + + real_info = pd.DataFrame(data=[dict(real_fname=fname, + real_score=score) + for fname, score + in zip(orig_fnames, real_scores)]) + real_info.set_index('real_fname', drop=True, inplace=True) + + fake_info = pd.DataFrame(data=[dict(mask_fname=fname, + fake_fname=mask2fake_fname[fname], + real_fname=mask2real_fname[fname], + fake_score=score) + for fname, score + in zip(mask_fnames, fake_scores)]) + fake_info = fake_info.join(real_info, on='real_fname', how='left') + fake_info.drop_duplicates(['fake_fname', 'real_fname'], inplace=True) + + fake_stats_by_real = fake_info.groupby('real_fname')['fake_score'].describe()[['mean', 'std']].rename( + {'mean': 'mean_fake_by_real', 'std': 'std_fake_by_real'}, axis=1) + fake_info = fake_info.join(fake_stats_by_real, on='real_fname', rsuffix='stat_by_real') + fake_info.drop_duplicates(['fake_fname', 'real_fname'], inplace=True) + fake_info.to_csv(os.path.join(latents_dir, 'join_scores_table.csv'), sep='\t', index=False) + + fake_scores_table = fake_info.set_index('mask_fname')['fake_score'].to_frame() + real_scores_table = fake_info.set_index('real_fname')['real_score'].drop_duplicates().to_frame() + + fig, (ax1, ax2) = plt.subplots(1, 2) + ax1.hist(fake_scores) + ax2.hist(real_scores) + fig.tight_layout() + fig.savefig(os.path.join(args.outpath, 'global_scores_hist.png')) + plt.close(fig) + + global_worst_masks = fake_info.sort_values('fake_score', ascending=True)['mask_fname'].iloc[:config.take_global_top].to_list() + global_best_masks = fake_info.sort_values('fake_score', ascending=False)['mask_fname'].iloc[:config.take_global_top].to_list() + save_global_samples(global_worst_masks, mask2real_fname, mask2fake_fname, global_worst_dir, real_scores_table, fake_scores_table) + save_global_samples(global_best_masks, mask2real_fname, mask2fake_fname, global_best_dir, real_scores_table, fake_scores_table) + + # grouped by real + worst_samples_by_real = fake_info.groupby('real_fname').apply( + lambda d: d.set_index('mask_fname')['fake_score'].idxmin()).to_frame().rename({0: 'worst'}, axis=1) + best_samples_by_real = fake_info.groupby('real_fname').apply( + lambda d: d.set_index('mask_fname')['fake_score'].idxmax()).to_frame().rename({0: 'best'}, axis=1) + worst_best_by_real = pd.concat([worst_samples_by_real, best_samples_by_real], axis=1) + + worst_best_by_real = worst_best_by_real.join(fake_scores_table.rename({'fake_score': 'worst_score'}, axis=1), + on='worst') + worst_best_by_real = worst_best_by_real.join(fake_scores_table.rename({'fake_score': 'best_score'}, axis=1), + on='best') + worst_best_by_real = worst_best_by_real.join(real_scores_table) + + worst_best_by_real['best_worst_score_diff'] = worst_best_by_real['best_score'] - worst_best_by_real['worst_score'] + worst_best_by_real['real_best_score_diff'] = worst_best_by_real['real_score'] - worst_best_by_real['best_score'] + worst_best_by_real['real_worst_score_diff'] = worst_best_by_real['real_score'] - worst_best_by_real['worst_score'] + + worst_best_by_best_worst_score_diff_min = worst_best_by_real.sort_values('best_worst_score_diff', ascending=True).iloc[:config.take_worst_best_top] + worst_best_by_best_worst_score_diff_max = worst_best_by_real.sort_values('best_worst_score_diff', ascending=False).iloc[:config.take_worst_best_top] + save_samples_by_real(worst_best_by_best_worst_score_diff_min, mask2fake_fname, fake_info, worst_best_by_best_worst_score_diff_min_dir) + save_samples_by_real(worst_best_by_best_worst_score_diff_max, mask2fake_fname, fake_info, worst_best_by_best_worst_score_diff_max_dir) + + worst_best_by_real_best_score_diff_min = worst_best_by_real.sort_values('real_best_score_diff', ascending=True).iloc[:config.take_worst_best_top] + worst_best_by_real_best_score_diff_max = worst_best_by_real.sort_values('real_best_score_diff', ascending=False).iloc[:config.take_worst_best_top] + save_samples_by_real(worst_best_by_real_best_score_diff_min, mask2fake_fname, fake_info, worst_best_by_real_best_score_diff_min_dir) + save_samples_by_real(worst_best_by_real_best_score_diff_max, mask2fake_fname, fake_info, worst_best_by_real_best_score_diff_max_dir) + + worst_best_by_real_worst_score_diff_min = worst_best_by_real.sort_values('real_worst_score_diff', ascending=True).iloc[:config.take_worst_best_top] + worst_best_by_real_worst_score_diff_max = worst_best_by_real.sort_values('real_worst_score_diff', ascending=False).iloc[:config.take_worst_best_top] + save_samples_by_real(worst_best_by_real_worst_score_diff_min, mask2fake_fname, fake_info, worst_best_by_real_worst_score_diff_min_dir) + save_samples_by_real(worst_best_by_real_worst_score_diff_max, mask2fake_fname, fake_info, worst_best_by_real_worst_score_diff_max_dir) + + # analyze what change of mask causes bigger change of score + overlapping_mask_fname_pairs = [] + overlapping_mask_fname_score_diffs = [] + for cur_real_fname in orig_fnames: + cur_fakes_info = fake_info[fake_info['real_fname'] == cur_real_fname] + cur_mask_fnames = sorted(cur_fakes_info['mask_fname'].unique()) + + cur_mask_pairs_and_scores = Parallel(args.n_jobs)( + delayed(extract_overlapping_masks)(cur_mask_fnames, i, fake_scores_table) + for i in range(len(cur_mask_fnames) - 1) + ) + for cur_pairs, cur_scores in cur_mask_pairs_and_scores: + overlapping_mask_fname_pairs.extend(cur_pairs) + overlapping_mask_fname_score_diffs.extend(cur_scores) + + overlapping_mask_fname_pairs = np.asarray(overlapping_mask_fname_pairs) + overlapping_mask_fname_score_diffs = np.asarray(overlapping_mask_fname_score_diffs) + overlapping_sort_idx = np.argsort(overlapping_mask_fname_score_diffs) + overlapping_mask_fname_pairs = overlapping_mask_fname_pairs[overlapping_sort_idx] + overlapping_mask_fname_score_diffs = overlapping_mask_fname_score_diffs[overlapping_sort_idx] + + + + + + +if __name__ == '__main__': + import argparse + + aparser = argparse.ArgumentParser() + aparser.add_argument('config', type=str, help='Path to config for dataset generation') + aparser.add_argument('datadir', type=str, + help='Path to folder with images and masks (output of gen_mask_dataset.py)') + aparser.add_argument('predictdir', type=str, + help='Path to folder with predicts (e.g. predict_hifill_baseline.py)') + aparser.add_argument('outpath', type=str, help='Where to put results') + aparser.add_argument('--only-report', action='store_true', + help='Whether to skip prediction and feature extraction, ' + 'load all the possible latents and proceed with report only') + aparser.add_argument('--n-jobs', type=int, default=8, help='how many processes to use for pair mask mining') + + main(aparser.parse_args()) diff --git a/third-party/lama/bin/blur_predicts.py b/third-party/lama/bin/blur_predicts.py new file mode 100755 index 0000000000000000000000000000000000000000..a14fcc28d5a906ad3a21ab4ba482f38b4fc411cb --- /dev/null +++ b/third-party/lama/bin/blur_predicts.py @@ -0,0 +1,57 @@ +#!/usr/bin/env python3 + +import os + +import cv2 +import numpy as np +import tqdm + +from saicinpainting.evaluation.data import PrecomputedInpaintingResultsDataset +from saicinpainting.evaluation.utils import load_yaml + + +def main(args): + config = load_yaml(args.config) + + if not args.predictdir.endswith('/'): + args.predictdir += '/' + + dataset = PrecomputedInpaintingResultsDataset(args.datadir, args.predictdir, **config.dataset_kwargs) + + os.makedirs(os.path.dirname(args.outpath), exist_ok=True) + + for img_i in tqdm.trange(len(dataset)): + pred_fname = dataset.pred_filenames[img_i] + cur_out_fname = os.path.join(args.outpath, pred_fname[len(args.predictdir):]) + os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True) + + sample = dataset[img_i] + img = sample['image'] + mask = sample['mask'] + inpainted = sample['inpainted'] + + inpainted_blurred = cv2.GaussianBlur(np.transpose(inpainted, (1, 2, 0)), + ksize=(args.k, args.k), + sigmaX=args.s, sigmaY=args.s, + borderType=cv2.BORDER_REFLECT) + + cur_res = (1 - mask) * np.transpose(img, (1, 2, 0)) + mask * inpainted_blurred + cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8') + cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR) + cv2.imwrite(cur_out_fname, cur_res) + + +if __name__ == '__main__': + import argparse + + aparser = argparse.ArgumentParser() + aparser.add_argument('config', type=str, help='Path to evaluation config') + aparser.add_argument('datadir', type=str, + help='Path to folder with images and masks (output of gen_mask_dataset.py)') + aparser.add_argument('predictdir', type=str, + help='Path to folder with predicts (e.g. predict_hifill_baseline.py)') + aparser.add_argument('outpath', type=str, help='Where to put results') + aparser.add_argument('-s', type=float, default=0.1, help='Gaussian blur sigma') + aparser.add_argument('-k', type=int, default=5, help='Kernel size in gaussian blur') + + main(aparser.parse_args()) diff --git a/third-party/lama/bin/calc_dataset_stats.py b/third-party/lama/bin/calc_dataset_stats.py new file mode 100755 index 0000000000000000000000000000000000000000..5086fea1bab691892f2e52e3c59e5ef048bcfac0 --- /dev/null +++ b/third-party/lama/bin/calc_dataset_stats.py @@ -0,0 +1,88 @@ +#!/usr/bin/env python3 + +import os + +import numpy as np +import tqdm +from scipy.ndimage.morphology import distance_transform_edt + +from saicinpainting.evaluation.data import InpaintingDataset +from saicinpainting.evaluation.vis import save_item_for_vis + + +def main(args): + dataset = InpaintingDataset(args.datadir, img_suffix='.png') + + area_bins = np.linspace(0, 1, args.area_bins + 1) + + heights = [] + widths = [] + image_areas = [] + hole_areas = [] + hole_area_percents = [] + known_pixel_distances = [] + + area_bins_count = np.zeros(args.area_bins) + area_bin_titles = [f'{area_bins[i] * 100:.0f}-{area_bins[i + 1] * 100:.0f}' for i in range(args.area_bins)] + + bin2i = [[] for _ in range(args.area_bins)] + + for i, item in enumerate(tqdm.tqdm(dataset)): + h, w = item['image'].shape[1:] + heights.append(h) + widths.append(w) + full_area = h * w + image_areas.append(full_area) + bin_mask = item['mask'] > 0.5 + hole_area = bin_mask.sum() + hole_areas.append(hole_area) + hole_percent = hole_area / full_area + hole_area_percents.append(hole_percent) + bin_i = np.clip(np.searchsorted(area_bins, hole_percent) - 1, 0, len(area_bins_count) - 1) + area_bins_count[bin_i] += 1 + bin2i[bin_i].append(i) + + cur_dist = distance_transform_edt(bin_mask) + cur_dist_inside_mask = cur_dist[bin_mask] + known_pixel_distances.append(cur_dist_inside_mask.mean()) + + os.makedirs(args.outdir, exist_ok=True) + with open(os.path.join(args.outdir, 'summary.txt'), 'w') as f: + f.write(f'''Location: {args.datadir} + +Number of samples: {len(dataset)} + +Image height: min {min(heights):5d} max {max(heights):5d} mean {np.mean(heights):.2f} +Image width: min {min(widths):5d} max {max(widths):5d} mean {np.mean(widths):.2f} +Image area: min {min(image_areas):7d} max {max(image_areas):7d} mean {np.mean(image_areas):.2f} +Hole area: min {min(hole_areas):7d} max {max(hole_areas):7d} mean {np.mean(hole_areas):.2f} +Hole area %: min {min(hole_area_percents) * 100:2.2f} max {max(hole_area_percents) * 100:2.2f} mean {np.mean(hole_area_percents) * 100:2.2f} +Dist 2known: min {min(known_pixel_distances):2.2f} max {max(known_pixel_distances):2.2f} mean {np.mean(known_pixel_distances):2.2f} median {np.median(known_pixel_distances):2.2f} + +Stats by hole area %: +''') + for bin_i in range(args.area_bins): + f.write(f'{area_bin_titles[bin_i]}%: ' + f'samples number {area_bins_count[bin_i]}, ' + f'{area_bins_count[bin_i] / len(dataset) * 100:.1f}%\n') + + for bin_i in range(args.area_bins): + bindir = os.path.join(args.outdir, 'samples', area_bin_titles[bin_i]) + os.makedirs(bindir, exist_ok=True) + bin_idx = bin2i[bin_i] + for sample_i in np.random.choice(bin_idx, size=min(len(bin_idx), args.samples_n), replace=False): + save_item_for_vis(dataset[sample_i], os.path.join(bindir, f'{sample_i}.png')) + + +if __name__ == '__main__': + import argparse + + aparser = argparse.ArgumentParser() + aparser.add_argument('datadir', type=str, + help='Path to folder with images and masks (output of gen_mask_dataset.py)') + aparser.add_argument('outdir', type=str, help='Where to put results') + aparser.add_argument('--samples-n', type=int, default=10, + help='Number of sample images with masks to copy for visualization for each area bin') + aparser.add_argument('--area-bins', type=int, default=10, help='How many area bins to have') + + main(aparser.parse_args()) diff --git a/third-party/lama/bin/debug/analyze_overlapping_masks.sh b/third-party/lama/bin/debug/analyze_overlapping_masks.sh new file mode 100755 index 0000000000000000000000000000000000000000..4a4727b0129007d9b0eed3fc25780adb565965a2 --- /dev/null +++ b/third-party/lama/bin/debug/analyze_overlapping_masks.sh @@ -0,0 +1,31 @@ +#!/bin/bash + +BASEDIR="$(dirname $0)" + +# paths are valid for mml7 + +# select images +#ls /data/inpainting/work/data/train | shuf | head -2000 | xargs -n1 -I{} cp {} /data/inpainting/mask_analysis/src + +# generate masks +#"$BASEDIR/../gen_debug_mask_dataset.py" \ +# "$BASEDIR/../../configs/debug_mask_gen.yaml" \ +# "/data/inpainting/mask_analysis/src" \ +# "/data/inpainting/mask_analysis/generated" + +# predict +#"$BASEDIR/../predict.py" \ +# model.path="simple_pix2pix2_gap_sdpl_novgg_large_b18_ffc075_batch8x15/saved_checkpoint/r.suvorov_2021-04-30_14-41-12_train_simple_pix2pix2_gap_sdpl_novgg_large_b18_ffc075_batch8x15_epoch22-step-574999" \ +# indir="/data/inpainting/mask_analysis/generated" \ +# outdir="/data/inpainting/mask_analysis/predicted" \ +# dataset.img_suffix=.jpg \ +# +out_ext=.jpg + +# analyze good and bad samples +"$BASEDIR/../analyze_errors.py" \ + --only-report \ + --n-jobs 8 \ + "$BASEDIR/../../configs/analyze_mask_errors.yaml" \ + "/data/inpainting/mask_analysis/small/generated" \ + "/data/inpainting/mask_analysis/small/predicted" \ + "/data/inpainting/mask_analysis/small/report" diff --git a/third-party/lama/bin/evaluate_predicts.py b/third-party/lama/bin/evaluate_predicts.py new file mode 100755 index 0000000000000000000000000000000000000000..a4c182a50bc0cc3e2e03c713c2c0be2a804b04b8 --- /dev/null +++ b/third-party/lama/bin/evaluate_predicts.py @@ -0,0 +1,79 @@ +#!/usr/bin/env python3 + +import os + +import pandas as pd + +from saicinpainting.evaluation.data import PrecomputedInpaintingResultsDataset +from saicinpainting.evaluation.evaluator import InpaintingEvaluator, lpips_fid100_f1 +from saicinpainting.evaluation.losses.base_loss import SegmentationAwareSSIM, \ + SegmentationClassStats, SSIMScore, LPIPSScore, FIDScore, SegmentationAwareLPIPS, SegmentationAwareFID +from saicinpainting.evaluation.utils import load_yaml + + +def main(args): + config = load_yaml(args.config) + + dataset = PrecomputedInpaintingResultsDataset(args.datadir, args.predictdir, **config.dataset_kwargs) + + metrics = { + 'ssim': SSIMScore(), + 'lpips': LPIPSScore(), + 'fid': FIDScore() + } + enable_segm = config.get('segmentation', dict(enable=False)).get('enable', False) + if enable_segm: + weights_path = os.path.expandvars(config.segmentation.weights_path) + metrics.update(dict( + segm_stats=SegmentationClassStats(weights_path=weights_path), + segm_ssim=SegmentationAwareSSIM(weights_path=weights_path), + segm_lpips=SegmentationAwareLPIPS(weights_path=weights_path), + segm_fid=SegmentationAwareFID(weights_path=weights_path) + )) + evaluator = InpaintingEvaluator(dataset, scores=metrics, + integral_title='lpips_fid100_f1', integral_func=lpips_fid100_f1, + **config.evaluator_kwargs) + + os.makedirs(os.path.dirname(args.outpath), exist_ok=True) + + results = evaluator.evaluate() + + results = pd.DataFrame(results).stack(1).unstack(0) + results.dropna(axis=1, how='all', inplace=True) + results.to_csv(args.outpath, sep='\t', float_format='%.4f') + + if enable_segm: + only_short_results = results[[c for c in results.columns if not c[0].startswith('segm_')]].dropna(axis=1, how='all') + only_short_results.to_csv(args.outpath + '_short', sep='\t', float_format='%.4f') + + print(only_short_results) + + segm_metrics_results = results[['segm_ssim', 'segm_lpips', 'segm_fid']].dropna(axis=1, how='all').transpose().unstack(0).reorder_levels([1, 0], axis=1) + segm_metrics_results.drop(['mean', 'std'], axis=0, inplace=True) + + segm_stats_results = results['segm_stats'].dropna(axis=1, how='all').transpose() + segm_stats_results.index = pd.MultiIndex.from_tuples(n.split('/') for n in segm_stats_results.index) + segm_stats_results = segm_stats_results.unstack(0).reorder_levels([1, 0], axis=1) + segm_stats_results.sort_index(axis=1, inplace=True) + segm_stats_results.dropna(axis=0, how='all', inplace=True) + + segm_results = pd.concat([segm_metrics_results, segm_stats_results], axis=1, sort=True) + segm_results.sort_values(('mask_freq', 'total'), ascending=False, inplace=True) + + segm_results.to_csv(args.outpath + '_segm', sep='\t', float_format='%.4f') + else: + print(results) + + +if __name__ == '__main__': + import argparse + + aparser = argparse.ArgumentParser() + aparser.add_argument('config', type=str, help='Path to evaluation config') + aparser.add_argument('datadir', type=str, + help='Path to folder with images and masks (output of gen_mask_dataset.py)') + aparser.add_argument('predictdir', type=str, + help='Path to folder with predicts (e.g. predict_hifill_baseline.py)') + aparser.add_argument('outpath', type=str, help='Where to put results') + + main(aparser.parse_args()) diff --git a/third-party/lama/bin/evaluator_example.py b/third-party/lama/bin/evaluator_example.py new file mode 100644 index 0000000000000000000000000000000000000000..669e3c53c1218444a880dc78f19a565a406ff6dc --- /dev/null +++ b/third-party/lama/bin/evaluator_example.py @@ -0,0 +1,76 @@ +import os + +import cv2 +import numpy as np +import torch +from skimage import io +from skimage.transform import resize +from torch.utils.data import Dataset + +from saicinpainting.evaluation.evaluator import InpaintingEvaluator +from saicinpainting.evaluation.losses.base_loss import SSIMScore, LPIPSScore, FIDScore + + +class SimpleImageDataset(Dataset): + def __init__(self, root_dir, image_size=(400, 600)): + self.root_dir = root_dir + self.files = sorted(os.listdir(root_dir)) + self.image_size = image_size + + def __getitem__(self, index): + img_name = os.path.join(self.root_dir, self.files[index]) + image = io.imread(img_name) + image = resize(image, self.image_size, anti_aliasing=True) + image = torch.FloatTensor(image).permute(2, 0, 1) + return image + + def __len__(self): + return len(self.files) + + +def create_rectangle_mask(height, width): + mask = np.ones((height, width)) + up_left_corner = width // 4, height // 4 + down_right_corner = (width - up_left_corner[0] - 1, height - up_left_corner[1] - 1) + cv2.rectangle(mask, up_left_corner, down_right_corner, (0, 0, 0), thickness=cv2.FILLED) + return mask + + +class Model(): + def __call__(self, img_batch, mask_batch): + mean = (img_batch * mask_batch[:, None, :, :]).sum(dim=(2, 3)) / mask_batch.sum(dim=(1, 2))[:, None] + inpainted = mean[:, :, None, None] * (1 - mask_batch[:, None, :, :]) + img_batch * mask_batch[:, None, :, :] + return inpainted + + +class SimpleImageSquareMaskDataset(Dataset): + def __init__(self, dataset): + self.dataset = dataset + self.mask = torch.FloatTensor(create_rectangle_mask(*self.dataset.image_size)) + self.model = Model() + + def __getitem__(self, index): + img = self.dataset[index] + mask = self.mask.clone() + inpainted = self.model(img[None, ...], mask[None, ...]) + return dict(image=img, mask=mask, inpainted=inpainted) + + def __len__(self): + return len(self.dataset) + + +dataset = SimpleImageDataset('imgs') +mask_dataset = SimpleImageSquareMaskDataset(dataset) +model = Model() +metrics = { + 'ssim': SSIMScore(), + 'lpips': LPIPSScore(), + 'fid': FIDScore() +} + +evaluator = InpaintingEvaluator( + mask_dataset, scores=metrics, batch_size=3, area_grouping=True +) + +results = evaluator.evaluate(model) +print(results) diff --git a/third-party/lama/bin/extract_masks.py b/third-party/lama/bin/extract_masks.py new file mode 100755 index 0000000000000000000000000000000000000000..d114e0fe470595f1d2aaeeeb84b36352f65b121e --- /dev/null +++ b/third-party/lama/bin/extract_masks.py @@ -0,0 +1,63 @@ +import PIL.Image as Image +import numpy as np +import os + + +def main(args): + if not args.indir.endswith('/'): + args.indir += '/' + os.makedirs(args.outdir, exist_ok=True) + + src_images = [ + args.indir+fname for fname in os.listdir(args.indir)] + + tgt_masks = [ + args.outdir+fname[:-4] + f'_mask000.png' + for fname in os.listdir(args.indir)] + + for img_name, msk_name in zip(src_images, tgt_masks): + #print(img) + #print(msk) + + image = Image.open(img_name).convert('RGB') + image = np.transpose(np.array(image), (2, 0, 1)) + + mask = (image == 255).astype(int) + + print(mask.dtype, mask.shape) + + + Image.fromarray( + np.clip(mask[0,:,:] * 255, 0, 255).astype('uint8'),mode='L' + ).save(msk_name) + + + + + ''' + for infile in src_images: + try: + file_relpath = infile[len(indir):] + img_outpath = os.path.join(outdir, file_relpath) + os.makedirs(os.path.dirname(img_outpath), exist_ok=True) + + image = Image.open(infile).convert('RGB') + + mask = + + Image.fromarray( + np.clip( + cur_mask * 255, 0, 255).astype('uint8'), + mode='L' + ).save(cur_basename + f'_mask{i:03d}.png') + ''' + + + +if __name__ == '__main__': + import argparse + aparser = argparse.ArgumentParser() + aparser.add_argument('--indir', type=str, help='Path to folder with images') + aparser.add_argument('--outdir', type=str, help='Path to folder to store aligned images and masks to') + + main(aparser.parse_args()) diff --git a/third-party/lama/bin/filter_sharded_dataset.py b/third-party/lama/bin/filter_sharded_dataset.py new file mode 100755 index 0000000000000000000000000000000000000000..b3c2b490e88bb3b55c6bb717e08f97f7a396d5fa --- /dev/null +++ b/third-party/lama/bin/filter_sharded_dataset.py @@ -0,0 +1,69 @@ +#!/usr/bin/env python3 + + +import math +import os +import random + +import braceexpand +import webdataset as wds + +DEFAULT_CATS_FILE = os.path.join(os.path.dirname(__file__), '..', 'configs', 'places2-categories_157.txt') + +def is_good_key(key, cats): + return any(c in key for c in cats) + + +def main(args): + if args.categories == 'nofilter': + good_categories = None + else: + with open(args.categories, 'r') as f: + good_categories = set(line.strip().split(' ')[0] for line in f if line.strip()) + + all_input_files = list(braceexpand.braceexpand(args.infile)) + chunk_size = int(math.ceil(len(all_input_files) / args.n_read_streams)) + + input_iterators = [iter(wds.Dataset(all_input_files[start : start + chunk_size]).shuffle(args.shuffle_buffer)) + for start in range(0, len(all_input_files), chunk_size)] + output_datasets = [wds.ShardWriter(args.outpattern.format(i)) for i in range(args.n_write_streams)] + + good_readers = list(range(len(input_iterators))) + step_i = 0 + good_samples = 0 + bad_samples = 0 + while len(good_readers) > 0: + if step_i % args.print_freq == 0: + print(f'Iterations done {step_i}; readers alive {good_readers}; good samples {good_samples}; bad samples {bad_samples}') + + step_i += 1 + + ri = random.choice(good_readers) + try: + sample = next(input_iterators[ri]) + except StopIteration: + good_readers = list(set(good_readers) - {ri}) + continue + + if good_categories is not None and not is_good_key(sample['__key__'], good_categories): + bad_samples += 1 + continue + + wi = random.randint(0, args.n_write_streams - 1) + output_datasets[wi].write(sample) + good_samples += 1 + + +if __name__ == '__main__': + import argparse + + aparser = argparse.ArgumentParser() + aparser.add_argument('--categories', type=str, default=DEFAULT_CATS_FILE) + aparser.add_argument('--shuffle-buffer', type=int, default=10000) + aparser.add_argument('--n-read-streams', type=int, default=10) + aparser.add_argument('--n-write-streams', type=int, default=10) + aparser.add_argument('--print-freq', type=int, default=1000) + aparser.add_argument('infile', type=str) + aparser.add_argument('outpattern', type=str) + + main(aparser.parse_args()) diff --git a/third-party/lama/bin/gen_debug_mask_dataset.py b/third-party/lama/bin/gen_debug_mask_dataset.py new file mode 100755 index 0000000000000000000000000000000000000000..738f76875c82aa412063bb5bff15e69c46f20362 --- /dev/null +++ b/third-party/lama/bin/gen_debug_mask_dataset.py @@ -0,0 +1,61 @@ +#!/usr/bin/env python3 + +import glob +import os + +import PIL.Image as Image +import cv2 +import numpy as np +import tqdm +import shutil + + +from saicinpainting.evaluation.utils import load_yaml + + +def generate_masks_for_img(infile, outmask_pattern, mask_size=200, step=0.5): + inimg = Image.open(infile) + width, height = inimg.size + step_abs = int(mask_size * step) + + mask = np.zeros((height, width), dtype='uint8') + mask_i = 0 + + for start_vertical in range(0, height - step_abs, step_abs): + for start_horizontal in range(0, width - step_abs, step_abs): + mask[start_vertical:start_vertical + mask_size, start_horizontal:start_horizontal + mask_size] = 255 + + cv2.imwrite(outmask_pattern.format(mask_i), mask) + + mask[start_vertical:start_vertical + mask_size, start_horizontal:start_horizontal + mask_size] = 0 + mask_i += 1 + + +def main(args): + if not args.indir.endswith('/'): + args.indir += '/' + if not args.outdir.endswith('/'): + args.outdir += '/' + + config = load_yaml(args.config) + + in_files = list(glob.glob(os.path.join(args.indir, '**', f'*{config.img_ext}'), recursive=True)) + for infile in tqdm.tqdm(in_files): + outimg = args.outdir + infile[len(args.indir):] + outmask_pattern = outimg[:-len(config.img_ext)] + '_mask{:04d}.png' + + os.makedirs(os.path.dirname(outimg), exist_ok=True) + shutil.copy2(infile, outimg) + + generate_masks_for_img(infile, outmask_pattern, **config.gen_kwargs) + + +if __name__ == '__main__': + import argparse + + aparser = argparse.ArgumentParser() + aparser.add_argument('config', type=str, help='Path to config for dataset generation') + aparser.add_argument('indir', type=str, help='Path to folder with images') + aparser.add_argument('outdir', type=str, help='Path to folder to store aligned images and masks to') + + main(aparser.parse_args()) diff --git a/third-party/lama/bin/gen_mask_dataset.py b/third-party/lama/bin/gen_mask_dataset.py new file mode 100755 index 0000000000000000000000000000000000000000..6e2ce3a9bc9708fd46641cab815113508af32d02 --- /dev/null +++ b/third-party/lama/bin/gen_mask_dataset.py @@ -0,0 +1,130 @@ +#!/usr/bin/env python3 + +import glob +import os +import shutil +import traceback + +import PIL.Image as Image +import numpy as np +from joblib import Parallel, delayed + +from saicinpainting.evaluation.masks.mask import SegmentationMask, propose_random_square_crop +from saicinpainting.evaluation.utils import load_yaml, SmallMode +from saicinpainting.training.data.masks import MixedMaskGenerator + + +class MakeManyMasksWrapper: + def __init__(self, impl, variants_n=2): + self.impl = impl + self.variants_n = variants_n + + def get_masks(self, img): + img = np.transpose(np.array(img), (2, 0, 1)) + return [self.impl(img)[0] for _ in range(self.variants_n)] + + +def process_images(src_images, indir, outdir, config): + if config.generator_kind == 'segmentation': + mask_generator = SegmentationMask(**config.mask_generator_kwargs) + elif config.generator_kind == 'random': + variants_n = config.mask_generator_kwargs.pop('variants_n', 2) + mask_generator = MakeManyMasksWrapper(MixedMaskGenerator(**config.mask_generator_kwargs), + variants_n=variants_n) + else: + raise ValueError(f'Unexpected generator kind: {config.generator_kind}') + + max_tamper_area = config.get('max_tamper_area', 1) + + for infile in src_images: + try: + file_relpath = infile[len(indir):] + img_outpath = os.path.join(outdir, file_relpath) + os.makedirs(os.path.dirname(img_outpath), exist_ok=True) + + image = Image.open(infile).convert('RGB') + + # scale input image to output resolution and filter smaller images + if min(image.size) < config.cropping.out_min_size: + handle_small_mode = SmallMode(config.cropping.handle_small_mode) + if handle_small_mode == SmallMode.DROP: + continue + elif handle_small_mode == SmallMode.UPSCALE: + factor = config.cropping.out_min_size / min(image.size) + out_size = (np.array(image.size) * factor).round().astype('uint32') + image = image.resize(out_size, resample=Image.BICUBIC) + else: + factor = config.cropping.out_min_size / min(image.size) + out_size = (np.array(image.size) * factor).round().astype('uint32') + image = image.resize(out_size, resample=Image.BICUBIC) + + # generate and select masks + src_masks = mask_generator.get_masks(image) + + filtered_image_mask_pairs = [] + for cur_mask in src_masks: + if config.cropping.out_square_crop: + (crop_left, + crop_top, + crop_right, + crop_bottom) = propose_random_square_crop(cur_mask, + min_overlap=config.cropping.crop_min_overlap) + cur_mask = cur_mask[crop_top:crop_bottom, crop_left:crop_right] + cur_image = image.copy().crop((crop_left, crop_top, crop_right, crop_bottom)) + else: + cur_image = image + + if len(np.unique(cur_mask)) == 0 or cur_mask.mean() > max_tamper_area: + continue + + filtered_image_mask_pairs.append((cur_image, cur_mask)) + + mask_indices = np.random.choice(len(filtered_image_mask_pairs), + size=min(len(filtered_image_mask_pairs), config.max_masks_per_image), + replace=False) + + # crop masks; save masks together with input image + mask_basename = os.path.join(outdir, os.path.splitext(file_relpath)[0]) + for i, idx in enumerate(mask_indices): + cur_image, cur_mask = filtered_image_mask_pairs[idx] + cur_basename = mask_basename + f'_crop{i:03d}' + Image.fromarray(np.clip(cur_mask * 255, 0, 255).astype('uint8'), + mode='L').save(cur_basename + f'_mask{i:03d}.png') + cur_image.save(cur_basename + '.png') + except KeyboardInterrupt: + return + except Exception as ex: + print(f'Could not make masks for {infile} due to {ex}:\n{traceback.format_exc()}') + + +def main(args): + if not args.indir.endswith('/'): + args.indir += '/' + + os.makedirs(args.outdir, exist_ok=True) + + config = load_yaml(args.config) + + in_files = list(glob.glob(os.path.join(args.indir, '**', f'*.{args.ext}'), recursive=True)) + if args.n_jobs == 0: + process_images(in_files, args.indir, args.outdir, config) + else: + in_files_n = len(in_files) + chunk_size = in_files_n // args.n_jobs + (1 if in_files_n % args.n_jobs > 0 else 0) + Parallel(n_jobs=args.n_jobs)( + delayed(process_images)(in_files[start:start+chunk_size], args.indir, args.outdir, config) + for start in range(0, len(in_files), chunk_size) + ) + + +if __name__ == '__main__': + import argparse + + aparser = argparse.ArgumentParser() + aparser.add_argument('config', type=str, help='Path to config for dataset generation') + aparser.add_argument('indir', type=str, help='Path to folder with images') + aparser.add_argument('outdir', type=str, help='Path to folder to store aligned images and masks to') + aparser.add_argument('--n-jobs', type=int, default=0, help='How many processes to use') + aparser.add_argument('--ext', type=str, default='jpg', help='Input image extension') + + main(aparser.parse_args()) diff --git a/third-party/lama/bin/gen_mask_dataset_hydra.py b/third-party/lama/bin/gen_mask_dataset_hydra.py new file mode 100755 index 0000000000000000000000000000000000000000..4f4fdea52315f24f83fbd802e51a1815097d0fcb --- /dev/null +++ b/third-party/lama/bin/gen_mask_dataset_hydra.py @@ -0,0 +1,124 @@ +#!/usr/bin/env python3 + +import glob +import os +import shutil +import traceback +import hydra +from omegaconf import OmegaConf + +import PIL.Image as Image +import numpy as np +from joblib import Parallel, delayed + +from saicinpainting.evaluation.masks.mask import SegmentationMask, propose_random_square_crop +from saicinpainting.evaluation.utils import load_yaml, SmallMode +from saicinpainting.training.data.masks import MixedMaskGenerator + + +class MakeManyMasksWrapper: + def __init__(self, impl, variants_n=2): + self.impl = impl + self.variants_n = variants_n + + def get_masks(self, img): + img = np.transpose(np.array(img), (2, 0, 1)) + return [self.impl(img)[0] for _ in range(self.variants_n)] + + +def process_images(src_images, indir, outdir, config): + if config.generator_kind == 'segmentation': + mask_generator = SegmentationMask(**config.mask_generator_kwargs) + elif config.generator_kind == 'random': + mask_generator_kwargs = OmegaConf.to_container(config.mask_generator_kwargs, resolve=True) + variants_n = mask_generator_kwargs.pop('variants_n', 2) + mask_generator = MakeManyMasksWrapper(MixedMaskGenerator(**mask_generator_kwargs), + variants_n=variants_n) + else: + raise ValueError(f'Unexpected generator kind: {config.generator_kind}') + + max_tamper_area = config.get('max_tamper_area', 1) + + for infile in src_images: + try: + file_relpath = infile[len(indir):] + img_outpath = os.path.join(outdir, file_relpath) + os.makedirs(os.path.dirname(img_outpath), exist_ok=True) + + image = Image.open(infile).convert('RGB') + + # scale input image to output resolution and filter smaller images + if min(image.size) < config.cropping.out_min_size: + handle_small_mode = SmallMode(config.cropping.handle_small_mode) + if handle_small_mode == SmallMode.DROP: + continue + elif handle_small_mode == SmallMode.UPSCALE: + factor = config.cropping.out_min_size / min(image.size) + out_size = (np.array(image.size) * factor).round().astype('uint32') + image = image.resize(out_size, resample=Image.BICUBIC) + else: + factor = config.cropping.out_min_size / min(image.size) + out_size = (np.array(image.size) * factor).round().astype('uint32') + image = image.resize(out_size, resample=Image.BICUBIC) + + # generate and select masks + src_masks = mask_generator.get_masks(image) + + filtered_image_mask_pairs = [] + for cur_mask in src_masks: + if config.cropping.out_square_crop: + (crop_left, + crop_top, + crop_right, + crop_bottom) = propose_random_square_crop(cur_mask, + min_overlap=config.cropping.crop_min_overlap) + cur_mask = cur_mask[crop_top:crop_bottom, crop_left:crop_right] + cur_image = image.copy().crop((crop_left, crop_top, crop_right, crop_bottom)) + else: + cur_image = image + + if len(np.unique(cur_mask)) == 0 or cur_mask.mean() > max_tamper_area: + continue + + filtered_image_mask_pairs.append((cur_image, cur_mask)) + + mask_indices = np.random.choice(len(filtered_image_mask_pairs), + size=min(len(filtered_image_mask_pairs), config.max_masks_per_image), + replace=False) + + # crop masks; save masks together with input image + mask_basename = os.path.join(outdir, os.path.splitext(file_relpath)[0]) + for i, idx in enumerate(mask_indices): + cur_image, cur_mask = filtered_image_mask_pairs[idx] + cur_basename = mask_basename + f'_crop{i:03d}' + Image.fromarray(np.clip(cur_mask * 255, 0, 255).astype('uint8'), + mode='L').save(cur_basename + f'_mask{i:03d}.png') + cur_image.save(cur_basename + '.png') + except KeyboardInterrupt: + return + except Exception as ex: + print(f'Could not make masks for {infile} due to {ex}:\n{traceback.format_exc()}') + + +@hydra.main(config_path='../configs/data_gen/whydra', config_name='random_medium_256.yaml') +def main(config: OmegaConf): + if not config.indir.endswith('/'): + config.indir += '/' + + os.makedirs(config.outdir, exist_ok=True) + + in_files = list(glob.glob(os.path.join(config.indir, '**', f'*.{config.location.extension}'), + recursive=True)) + if config.n_jobs == 0: + process_images(in_files, config.indir, config.outdir, config) + else: + in_files_n = len(in_files) + chunk_size = in_files_n // config.n_jobs + (1 if in_files_n % config.n_jobs > 0 else 0) + Parallel(n_jobs=config.n_jobs)( + delayed(process_images)(in_files[start:start+chunk_size], config.indir, config.outdir, config) + for start in range(0, len(in_files), chunk_size) + ) + + +if __name__ == '__main__': + main() diff --git a/third-party/lama/bin/gen_outpainting_dataset.py b/third-party/lama/bin/gen_outpainting_dataset.py new file mode 100755 index 0000000000000000000000000000000000000000..72f6fc16c372fbc0aec9643c7be1c44ce5efeba4 --- /dev/null +++ b/third-party/lama/bin/gen_outpainting_dataset.py @@ -0,0 +1,88 @@ +#!/usr/bin/env python3 +import glob +import logging +import os +import shutil +import sys +import traceback + +from saicinpainting.evaluation.data import load_image +from saicinpainting.evaluation.utils import move_to_device + +os.environ['OMP_NUM_THREADS'] = '1' +os.environ['OPENBLAS_NUM_THREADS'] = '1' +os.environ['MKL_NUM_THREADS'] = '1' +os.environ['VECLIB_MAXIMUM_THREADS'] = '1' +os.environ['NUMEXPR_NUM_THREADS'] = '1' + +import cv2 +import hydra +import numpy as np +import torch +import tqdm +import yaml +from omegaconf import OmegaConf +from torch.utils.data._utils.collate import default_collate + +from saicinpainting.training.data.datasets import make_default_val_dataset +from saicinpainting.training.trainers import load_checkpoint +from saicinpainting.utils import register_debug_signal_handlers + +LOGGER = logging.getLogger(__name__) + + +def main(args): + try: + if not args.indir.endswith('/'): + args.indir += '/' + + for in_img in glob.glob(os.path.join(args.indir, '**', '*' + args.img_suffix), recursive=True): + if 'mask' in os.path.basename(in_img): + continue + + out_img_path = os.path.join(args.outdir, os.path.splitext(in_img[len(args.indir):])[0] + '.png') + out_mask_path = f'{os.path.splitext(out_img_path)[0]}_mask.png' + + os.makedirs(os.path.dirname(out_img_path), exist_ok=True) + + img = load_image(in_img) + height, width = img.shape[1:] + pad_h, pad_w = int(height * args.coef / 2), int(width * args.coef / 2) + + mask = np.zeros((height, width), dtype='uint8') + + if args.expand: + img = np.pad(img, ((0, 0), (pad_h, pad_h), (pad_w, pad_w))) + mask = np.pad(mask, ((pad_h, pad_h), (pad_w, pad_w)), mode='constant', constant_values=255) + else: + mask[:pad_h] = 255 + mask[-pad_h:] = 255 + mask[:, :pad_w] = 255 + mask[:, -pad_w:] = 255 + + # img = np.pad(img, ((0, 0), (pad_h * 2, pad_h * 2), (pad_w * 2, pad_w * 2)), mode='symmetric') + # mask = np.pad(mask, ((pad_h * 2, pad_h * 2), (pad_w * 2, pad_w * 2)), mode = 'symmetric') + + img = np.clip(np.transpose(img, (1, 2, 0)) * 255, 0, 255).astype('uint8') + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + cv2.imwrite(out_img_path, img) + + cv2.imwrite(out_mask_path, mask) + except KeyboardInterrupt: + LOGGER.warning('Interrupted by user') + except Exception as ex: + LOGGER.critical(f'Prediction failed due to {ex}:\n{traceback.format_exc()}') + sys.exit(1) + + +if __name__ == '__main__': + import argparse + + aparser = argparse.ArgumentParser() + aparser.add_argument('indir', type=str, help='Root directory with images') + aparser.add_argument('outdir', type=str, help='Where to store results') + aparser.add_argument('--img-suffix', type=str, default='.png', help='Input image extension') + aparser.add_argument('--expand', action='store_true', help='Generate mask by padding (true) or by cropping (false)') + aparser.add_argument('--coef', type=float, default=0.2, help='How much to crop/expand in order to get masks') + + main(aparser.parse_args()) diff --git a/third-party/lama/bin/make_checkpoint.py b/third-party/lama/bin/make_checkpoint.py new file mode 100755 index 0000000000000000000000000000000000000000..322147483915bef758770ae931e705e56083fa8d --- /dev/null +++ b/third-party/lama/bin/make_checkpoint.py @@ -0,0 +1,79 @@ +#!/usr/bin/env python3 + +import os +import shutil + +import torch + + +def get_checkpoint_files(s): + s = s.strip() + if ',' in s: + return [get_checkpoint_files(chunk) for chunk in s.split(',')] + return 'last.ckpt' if s == 'last' else f'{s}.ckpt' + + +def main(args): + checkpoint_fnames = get_checkpoint_files(args.epochs) + if isinstance(checkpoint_fnames, str): + checkpoint_fnames = [checkpoint_fnames] + assert len(checkpoint_fnames) >= 1 + + checkpoint_path = os.path.join(args.indir, 'models', checkpoint_fnames[0]) + checkpoint = torch.load(checkpoint_path, map_location='cpu') + del checkpoint['optimizer_states'] + + if len(checkpoint_fnames) > 1: + for fname in checkpoint_fnames[1:]: + print('sum', fname) + sum_tensors_cnt = 0 + other_cp = torch.load(os.path.join(args.indir, 'models', fname), map_location='cpu') + for k in checkpoint['state_dict'].keys(): + if checkpoint['state_dict'][k].dtype is torch.float: + checkpoint['state_dict'][k].data.add_(other_cp['state_dict'][k].data) + sum_tensors_cnt += 1 + print('summed', sum_tensors_cnt, 'tensors') + + for k in checkpoint['state_dict'].keys(): + if checkpoint['state_dict'][k].dtype is torch.float: + checkpoint['state_dict'][k].data.mul_(1 / float(len(checkpoint_fnames))) + + state_dict = checkpoint['state_dict'] + + if not args.leave_discriminators: + for k in list(state_dict.keys()): + if k.startswith('discriminator.'): + del state_dict[k] + + if not args.leave_losses: + for k in list(state_dict.keys()): + if k.startswith('loss_'): + del state_dict[k] + + out_checkpoint_path = os.path.join(args.outdir, 'models', 'best.ckpt') + os.makedirs(os.path.dirname(out_checkpoint_path), exist_ok=True) + + torch.save(checkpoint, out_checkpoint_path) + + shutil.copy2(os.path.join(args.indir, 'config.yaml'), + os.path.join(args.outdir, 'config.yaml')) + + +if __name__ == '__main__': + import argparse + + aparser = argparse.ArgumentParser() + aparser.add_argument('indir', + help='Path to directory with output of training ' + '(i.e. directory, which has samples, modules, config.yaml and train.log') + aparser.add_argument('outdir', + help='Where to put minimal checkpoint, which can be consumed by "bin/predict.py"') + aparser.add_argument('--epochs', type=str, default='last', + help='Which checkpoint to take. ' + 'Can be "last" or integer - number of epoch') + aparser.add_argument('--leave-discriminators', action='store_true', + help='If enabled, the state of discriminators will not be removed from the checkpoint') + aparser.add_argument('--leave-losses', action='store_true', + help='If enabled, weights of nn-based losses (e.g. perceptual) will not be removed') + + main(aparser.parse_args()) diff --git a/third-party/lama/bin/mask_example.py b/third-party/lama/bin/mask_example.py new file mode 100644 index 0000000000000000000000000000000000000000..59e25ca8eb3ed4141851c3af284fc66285444de0 --- /dev/null +++ b/third-party/lama/bin/mask_example.py @@ -0,0 +1,14 @@ +import matplotlib.pyplot as plt +from skimage import io +from skimage.transform import resize + +from saicinpainting.evaluation.masks.mask import SegmentationMask + +im = io.imread('imgs/ex4.jpg') +im = resize(im, (512, 1024), anti_aliasing=True) +mask_seg = SegmentationMask(num_variants_per_mask=10) +mask_examples = mask_seg.get_masks(im) +for i, example in enumerate(mask_examples): + plt.imshow(example) + plt.show() + plt.imsave(f'tmp/img_masks/{i}.png', example) diff --git a/third-party/lama/bin/models/ade20k/__init__.py b/third-party/lama/bin/models/ade20k/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..773cfc4664eef45a4f6fe05bd3fe2aa2143fdb5c --- /dev/null +++ b/third-party/lama/bin/models/ade20k/__init__.py @@ -0,0 +1 @@ +from .base import * \ No newline at end of file diff --git a/third-party/lama/bin/models/ade20k/base.py b/third-party/lama/bin/models/ade20k/base.py new file mode 100644 index 0000000000000000000000000000000000000000..8cdbe2d3e7dbadf4ed5e5a7cf2d248761ef25d9c --- /dev/null +++ b/third-party/lama/bin/models/ade20k/base.py @@ -0,0 +1,627 @@ +"""Modified from https://github.com/CSAILVision/semantic-segmentation-pytorch""" + +import os + +import pandas as pd +import torch +import torch.nn as nn +import torch.nn.functional as F +from scipy.io import loadmat +from torch.nn.modules import BatchNorm2d + +from . import resnet +from . import mobilenet + + +NUM_CLASS = 150 +base_path = os.path.dirname(os.path.abspath(__file__)) # current file path +colors_path = os.path.join(base_path, 'color150.mat') +classes_path = os.path.join(base_path, 'object150_info.csv') + +segm_options = dict(colors=loadmat(colors_path)['colors'], + classes=pd.read_csv(classes_path),) + + +class NormalizeTensor: + def __init__(self, mean, std, inplace=False): + """Normalize a tensor image with mean and standard deviation. + .. note:: + This transform acts out of place by default, i.e., it does not mutates the input tensor. + See :class:`~torchvision.transforms.Normalize` for more details. + Args: + tensor (Tensor): Tensor image of size (C, H, W) to be normalized. + mean (sequence): Sequence of means for each channel. + std (sequence): Sequence of standard deviations for each channel. + inplace(bool,optional): Bool to make this operation inplace. + Returns: + Tensor: Normalized Tensor image. + """ + + self.mean = mean + self.std = std + self.inplace = inplace + + def __call__(self, tensor): + if not self.inplace: + tensor = tensor.clone() + + dtype = tensor.dtype + mean = torch.as_tensor(self.mean, dtype=dtype, device=tensor.device) + std = torch.as_tensor(self.std, dtype=dtype, device=tensor.device) + tensor.sub_(mean[None, :, None, None]).div_(std[None, :, None, None]) + return tensor + + +# Model Builder +class ModelBuilder: + # custom weights initialization + @staticmethod + def weights_init(m): + classname = m.__class__.__name__ + if classname.find('Conv') != -1: + nn.init.kaiming_normal_(m.weight.data) + elif classname.find('BatchNorm') != -1: + m.weight.data.fill_(1.) + m.bias.data.fill_(1e-4) + + @staticmethod + def build_encoder(arch='resnet50dilated', fc_dim=512, weights=''): + pretrained = True if len(weights) == 0 else False + arch = arch.lower() + if arch == 'mobilenetv2dilated': + orig_mobilenet = mobilenet.__dict__['mobilenetv2'](pretrained=pretrained) + net_encoder = MobileNetV2Dilated(orig_mobilenet, dilate_scale=8) + elif arch == 'resnet18': + orig_resnet = resnet.__dict__['resnet18'](pretrained=pretrained) + net_encoder = Resnet(orig_resnet) + elif arch == 'resnet18dilated': + orig_resnet = resnet.__dict__['resnet18'](pretrained=pretrained) + net_encoder = ResnetDilated(orig_resnet, dilate_scale=8) + elif arch == 'resnet50dilated': + orig_resnet = resnet.__dict__['resnet50'](pretrained=pretrained) + net_encoder = ResnetDilated(orig_resnet, dilate_scale=8) + elif arch == 'resnet50': + orig_resnet = resnet.__dict__['resnet50'](pretrained=pretrained) + net_encoder = Resnet(orig_resnet) + else: + raise Exception('Architecture undefined!') + + # encoders are usually pretrained + # net_encoder.apply(ModelBuilder.weights_init) + if len(weights) > 0: + print('Loading weights for net_encoder') + net_encoder.load_state_dict( + torch.load(weights, map_location=lambda storage, loc: storage), strict=False) + return net_encoder + + @staticmethod + def build_decoder(arch='ppm_deepsup', + fc_dim=512, num_class=NUM_CLASS, + weights='', use_softmax=False, drop_last_conv=False): + arch = arch.lower() + if arch == 'ppm_deepsup': + net_decoder = PPMDeepsup( + num_class=num_class, + fc_dim=fc_dim, + use_softmax=use_softmax, + drop_last_conv=drop_last_conv) + elif arch == 'c1_deepsup': + net_decoder = C1DeepSup( + num_class=num_class, + fc_dim=fc_dim, + use_softmax=use_softmax, + drop_last_conv=drop_last_conv) + else: + raise Exception('Architecture undefined!') + + net_decoder.apply(ModelBuilder.weights_init) + if len(weights) > 0: + print('Loading weights for net_decoder') + net_decoder.load_state_dict( + torch.load(weights, map_location=lambda storage, loc: storage), strict=False) + return net_decoder + + @staticmethod + def get_decoder(weights_path, arch_encoder, arch_decoder, fc_dim, drop_last_conv, *arts, **kwargs): + path = os.path.join(weights_path, 'ade20k', f'ade20k-{arch_encoder}-{arch_decoder}/decoder_epoch_20.pth') + return ModelBuilder.build_decoder(arch=arch_decoder, fc_dim=fc_dim, weights=path, use_softmax=True, drop_last_conv=drop_last_conv) + + @staticmethod + def get_encoder(weights_path, arch_encoder, arch_decoder, fc_dim, segmentation, + *arts, **kwargs): + if segmentation: + path = os.path.join(weights_path, 'ade20k', f'ade20k-{arch_encoder}-{arch_decoder}/encoder_epoch_20.pth') + else: + path = '' + return ModelBuilder.build_encoder(arch=arch_encoder, fc_dim=fc_dim, weights=path) + + +def conv3x3_bn_relu(in_planes, out_planes, stride=1): + return nn.Sequential( + nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False), + BatchNorm2d(out_planes), + nn.ReLU(inplace=True), + ) + + +class SegmentationModule(nn.Module): + def __init__(self, + weights_path, + num_classes=150, + arch_encoder="resnet50dilated", + drop_last_conv=False, + net_enc=None, # None for Default encoder + net_dec=None, # None for Default decoder + encode=None, # {None, 'binary', 'color', 'sky'} + use_default_normalization=False, + return_feature_maps=False, + return_feature_maps_level=3, # {0, 1, 2, 3} + return_feature_maps_only=True, + **kwargs, + ): + super().__init__() + self.weights_path = weights_path + self.drop_last_conv = drop_last_conv + self.arch_encoder = arch_encoder + if self.arch_encoder == "resnet50dilated": + self.arch_decoder = "ppm_deepsup" + self.fc_dim = 2048 + elif self.arch_encoder == "mobilenetv2dilated": + self.arch_decoder = "c1_deepsup" + self.fc_dim = 320 + else: + raise NotImplementedError(f"No such arch_encoder={self.arch_encoder}") + model_builder_kwargs = dict(arch_encoder=self.arch_encoder, + arch_decoder=self.arch_decoder, + fc_dim=self.fc_dim, + drop_last_conv=drop_last_conv, + weights_path=self.weights_path) + + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + self.encoder = ModelBuilder.get_encoder(**model_builder_kwargs) if net_enc is None else net_enc + self.decoder = ModelBuilder.get_decoder(**model_builder_kwargs) if net_dec is None else net_dec + self.use_default_normalization = use_default_normalization + self.default_normalization = NormalizeTensor(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + + self.encode = encode + + self.return_feature_maps = return_feature_maps + + assert 0 <= return_feature_maps_level <= 3 + self.return_feature_maps_level = return_feature_maps_level + + def normalize_input(self, tensor): + if tensor.min() < 0 or tensor.max() > 1: + raise ValueError("Tensor should be 0..1 before using normalize_input") + return self.default_normalization(tensor) + + @property + def feature_maps_channels(self): + return 256 * 2**(self.return_feature_maps_level) # 256, 512, 1024, 2048 + + def forward(self, img_data, segSize=None): + if segSize is None: + raise NotImplementedError("Please pass segSize param. By default: (300, 300)") + + fmaps = self.encoder(img_data, return_feature_maps=True) + pred = self.decoder(fmaps, segSize=segSize) + + if self.return_feature_maps: + return pred, fmaps + # print("BINARY", img_data.shape, pred.shape) + return pred + + def multi_mask_from_multiclass(self, pred, classes): + def isin(ar1, ar2): + return (ar1[..., None] == ar2).any(-1).float() + return isin(pred, torch.LongTensor(classes).to(self.device)) + + @staticmethod + def multi_mask_from_multiclass_probs(scores, classes): + res = None + for c in classes: + if res is None: + res = scores[:, c] + else: + res += scores[:, c] + return res + + def predict(self, tensor, imgSizes=(-1,), # (300, 375, 450, 525, 600) + segSize=None): + """Entry-point for segmentation. Use this methods instead of forward + Arguments: + tensor {torch.Tensor} -- BCHW + Keyword Arguments: + imgSizes {tuple or list} -- imgSizes for segmentation input. + default: (300, 450) + original implementation: (300, 375, 450, 525, 600) + + """ + if segSize is None: + segSize = tensor.shape[-2:] + segSize = (tensor.shape[2], tensor.shape[3]) + with torch.no_grad(): + if self.use_default_normalization: + tensor = self.normalize_input(tensor) + scores = torch.zeros(1, NUM_CLASS, segSize[0], segSize[1]).to(self.device) + features = torch.zeros(1, self.feature_maps_channels, segSize[0], segSize[1]).to(self.device) + + result = [] + for img_size in imgSizes: + if img_size != -1: + img_data = F.interpolate(tensor.clone(), size=img_size) + else: + img_data = tensor.clone() + + if self.return_feature_maps: + pred_current, fmaps = self.forward(img_data, segSize=segSize) + else: + pred_current = self.forward(img_data, segSize=segSize) + + + result.append(pred_current) + scores = scores + pred_current / len(imgSizes) + + # Disclaimer: We use and aggregate only last fmaps: fmaps[3] + if self.return_feature_maps: + features = features + F.interpolate(fmaps[self.return_feature_maps_level], size=segSize) / len(imgSizes) + + _, pred = torch.max(scores, dim=1) + + if self.return_feature_maps: + return features + + return pred, result + + def get_edges(self, t): + edge = torch.cuda.ByteTensor(t.size()).zero_() + edge[:, :, :, 1:] = edge[:, :, :, 1:] | (t[:, :, :, 1:] != t[:, :, :, :-1]) + edge[:, :, :, :-1] = edge[:, :, :, :-1] | (t[:, :, :, 1:] != t[:, :, :, :-1]) + edge[:, :, 1:, :] = edge[:, :, 1:, :] | (t[:, :, 1:, :] != t[:, :, :-1, :]) + edge[:, :, :-1, :] = edge[:, :, :-1, :] | (t[:, :, 1:, :] != t[:, :, :-1, :]) + + if True: + return edge.half() + return edge.float() + + +# pyramid pooling, deep supervision +class PPMDeepsup(nn.Module): + def __init__(self, num_class=NUM_CLASS, fc_dim=4096, + use_softmax=False, pool_scales=(1, 2, 3, 6), + drop_last_conv=False): + super().__init__() + self.use_softmax = use_softmax + self.drop_last_conv = drop_last_conv + + self.ppm = [] + for scale in pool_scales: + self.ppm.append(nn.Sequential( + nn.AdaptiveAvgPool2d(scale), + nn.Conv2d(fc_dim, 512, kernel_size=1, bias=False), + BatchNorm2d(512), + nn.ReLU(inplace=True) + )) + self.ppm = nn.ModuleList(self.ppm) + self.cbr_deepsup = conv3x3_bn_relu(fc_dim // 2, fc_dim // 4, 1) + + self.conv_last = nn.Sequential( + nn.Conv2d(fc_dim + len(pool_scales) * 512, 512, + kernel_size=3, padding=1, bias=False), + BatchNorm2d(512), + nn.ReLU(inplace=True), + nn.Dropout2d(0.1), + nn.Conv2d(512, num_class, kernel_size=1) + ) + self.conv_last_deepsup = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0) + self.dropout_deepsup = nn.Dropout2d(0.1) + + def forward(self, conv_out, segSize=None): + conv5 = conv_out[-1] + + input_size = conv5.size() + ppm_out = [conv5] + for pool_scale in self.ppm: + ppm_out.append(nn.functional.interpolate( + pool_scale(conv5), + (input_size[2], input_size[3]), + mode='bilinear', align_corners=False)) + ppm_out = torch.cat(ppm_out, 1) + + if self.drop_last_conv: + return ppm_out + else: + x = self.conv_last(ppm_out) + + if self.use_softmax: # is True during inference + x = nn.functional.interpolate( + x, size=segSize, mode='bilinear', align_corners=False) + x = nn.functional.softmax(x, dim=1) + return x + + # deep sup + conv4 = conv_out[-2] + _ = self.cbr_deepsup(conv4) + _ = self.dropout_deepsup(_) + _ = self.conv_last_deepsup(_) + + x = nn.functional.log_softmax(x, dim=1) + _ = nn.functional.log_softmax(_, dim=1) + + return (x, _) + + +class Resnet(nn.Module): + def __init__(self, orig_resnet): + super(Resnet, self).__init__() + + # take pretrained resnet, except AvgPool and FC + self.conv1 = orig_resnet.conv1 + self.bn1 = orig_resnet.bn1 + self.relu1 = orig_resnet.relu1 + self.conv2 = orig_resnet.conv2 + self.bn2 = orig_resnet.bn2 + self.relu2 = orig_resnet.relu2 + self.conv3 = orig_resnet.conv3 + self.bn3 = orig_resnet.bn3 + self.relu3 = orig_resnet.relu3 + self.maxpool = orig_resnet.maxpool + self.layer1 = orig_resnet.layer1 + self.layer2 = orig_resnet.layer2 + self.layer3 = orig_resnet.layer3 + self.layer4 = orig_resnet.layer4 + + def forward(self, x, return_feature_maps=False): + conv_out = [] + + x = self.relu1(self.bn1(self.conv1(x))) + x = self.relu2(self.bn2(self.conv2(x))) + x = self.relu3(self.bn3(self.conv3(x))) + x = self.maxpool(x) + + x = self.layer1(x); conv_out.append(x); + x = self.layer2(x); conv_out.append(x); + x = self.layer3(x); conv_out.append(x); + x = self.layer4(x); conv_out.append(x); + + if return_feature_maps: + return conv_out + return [x] + +# Resnet Dilated +class ResnetDilated(nn.Module): + def __init__(self, orig_resnet, dilate_scale=8): + super().__init__() + from functools import partial + + if dilate_scale == 8: + orig_resnet.layer3.apply( + partial(self._nostride_dilate, dilate=2)) + orig_resnet.layer4.apply( + partial(self._nostride_dilate, dilate=4)) + elif dilate_scale == 16: + orig_resnet.layer4.apply( + partial(self._nostride_dilate, dilate=2)) + + # take pretrained resnet, except AvgPool and FC + self.conv1 = orig_resnet.conv1 + self.bn1 = orig_resnet.bn1 + self.relu1 = orig_resnet.relu1 + self.conv2 = orig_resnet.conv2 + self.bn2 = orig_resnet.bn2 + self.relu2 = orig_resnet.relu2 + self.conv3 = orig_resnet.conv3 + self.bn3 = orig_resnet.bn3 + self.relu3 = orig_resnet.relu3 + self.maxpool = orig_resnet.maxpool + self.layer1 = orig_resnet.layer1 + self.layer2 = orig_resnet.layer2 + self.layer3 = orig_resnet.layer3 + self.layer4 = orig_resnet.layer4 + + def _nostride_dilate(self, m, dilate): + classname = m.__class__.__name__ + if classname.find('Conv') != -1: + # the convolution with stride + if m.stride == (2, 2): + m.stride = (1, 1) + if m.kernel_size == (3, 3): + m.dilation = (dilate // 2, dilate // 2) + m.padding = (dilate // 2, dilate // 2) + # other convoluions + else: + if m.kernel_size == (3, 3): + m.dilation = (dilate, dilate) + m.padding = (dilate, dilate) + + def forward(self, x, return_feature_maps=False): + conv_out = [] + + x = self.relu1(self.bn1(self.conv1(x))) + x = self.relu2(self.bn2(self.conv2(x))) + x = self.relu3(self.bn3(self.conv3(x))) + x = self.maxpool(x) + + x = self.layer1(x) + conv_out.append(x) + x = self.layer2(x) + conv_out.append(x) + x = self.layer3(x) + conv_out.append(x) + x = self.layer4(x) + conv_out.append(x) + + if return_feature_maps: + return conv_out + return [x] + +class MobileNetV2Dilated(nn.Module): + def __init__(self, orig_net, dilate_scale=8): + super(MobileNetV2Dilated, self).__init__() + from functools import partial + + # take pretrained mobilenet features + self.features = orig_net.features[:-1] + + self.total_idx = len(self.features) + self.down_idx = [2, 4, 7, 14] + + if dilate_scale == 8: + for i in range(self.down_idx[-2], self.down_idx[-1]): + self.features[i].apply( + partial(self._nostride_dilate, dilate=2) + ) + for i in range(self.down_idx[-1], self.total_idx): + self.features[i].apply( + partial(self._nostride_dilate, dilate=4) + ) + elif dilate_scale == 16: + for i in range(self.down_idx[-1], self.total_idx): + self.features[i].apply( + partial(self._nostride_dilate, dilate=2) + ) + + def _nostride_dilate(self, m, dilate): + classname = m.__class__.__name__ + if classname.find('Conv') != -1: + # the convolution with stride + if m.stride == (2, 2): + m.stride = (1, 1) + if m.kernel_size == (3, 3): + m.dilation = (dilate//2, dilate//2) + m.padding = (dilate//2, dilate//2) + # other convoluions + else: + if m.kernel_size == (3, 3): + m.dilation = (dilate, dilate) + m.padding = (dilate, dilate) + + def forward(self, x, return_feature_maps=False): + if return_feature_maps: + conv_out = [] + for i in range(self.total_idx): + x = self.features[i](x) + if i in self.down_idx: + conv_out.append(x) + conv_out.append(x) + return conv_out + + else: + return [self.features(x)] + + +# last conv, deep supervision +class C1DeepSup(nn.Module): + def __init__(self, num_class=150, fc_dim=2048, use_softmax=False, drop_last_conv=False): + super(C1DeepSup, self).__init__() + self.use_softmax = use_softmax + self.drop_last_conv = drop_last_conv + + self.cbr = conv3x3_bn_relu(fc_dim, fc_dim // 4, 1) + self.cbr_deepsup = conv3x3_bn_relu(fc_dim // 2, fc_dim // 4, 1) + + # last conv + self.conv_last = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0) + self.conv_last_deepsup = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0) + + def forward(self, conv_out, segSize=None): + conv5 = conv_out[-1] + + x = self.cbr(conv5) + + if self.drop_last_conv: + return x + else: + x = self.conv_last(x) + + if self.use_softmax: # is True during inference + x = nn.functional.interpolate( + x, size=segSize, mode='bilinear', align_corners=False) + x = nn.functional.softmax(x, dim=1) + return x + + # deep sup + conv4 = conv_out[-2] + _ = self.cbr_deepsup(conv4) + _ = self.conv_last_deepsup(_) + + x = nn.functional.log_softmax(x, dim=1) + _ = nn.functional.log_softmax(_, dim=1) + + return (x, _) + + +# last conv +class C1(nn.Module): + def __init__(self, num_class=150, fc_dim=2048, use_softmax=False): + super(C1, self).__init__() + self.use_softmax = use_softmax + + self.cbr = conv3x3_bn_relu(fc_dim, fc_dim // 4, 1) + + # last conv + self.conv_last = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0) + + def forward(self, conv_out, segSize=None): + conv5 = conv_out[-1] + x = self.cbr(conv5) + x = self.conv_last(x) + + if self.use_softmax: # is True during inference + x = nn.functional.interpolate( + x, size=segSize, mode='bilinear', align_corners=False) + x = nn.functional.softmax(x, dim=1) + else: + x = nn.functional.log_softmax(x, dim=1) + + return x + + +# pyramid pooling +class PPM(nn.Module): + def __init__(self, num_class=150, fc_dim=4096, + use_softmax=False, pool_scales=(1, 2, 3, 6)): + super(PPM, self).__init__() + self.use_softmax = use_softmax + + self.ppm = [] + for scale in pool_scales: + self.ppm.append(nn.Sequential( + nn.AdaptiveAvgPool2d(scale), + nn.Conv2d(fc_dim, 512, kernel_size=1, bias=False), + BatchNorm2d(512), + nn.ReLU(inplace=True) + )) + self.ppm = nn.ModuleList(self.ppm) + + self.conv_last = nn.Sequential( + nn.Conv2d(fc_dim+len(pool_scales)*512, 512, + kernel_size=3, padding=1, bias=False), + BatchNorm2d(512), + nn.ReLU(inplace=True), + nn.Dropout2d(0.1), + nn.Conv2d(512, num_class, kernel_size=1) + ) + + def forward(self, conv_out, segSize=None): + conv5 = conv_out[-1] + + input_size = conv5.size() + ppm_out = [conv5] + for pool_scale in self.ppm: + ppm_out.append(nn.functional.interpolate( + pool_scale(conv5), + (input_size[2], input_size[3]), + mode='bilinear', align_corners=False)) + ppm_out = torch.cat(ppm_out, 1) + + x = self.conv_last(ppm_out) + + if self.use_softmax: # is True during inference + x = nn.functional.interpolate( + x, size=segSize, mode='bilinear', align_corners=False) + x = nn.functional.softmax(x, dim=1) + else: + x = nn.functional.log_softmax(x, dim=1) + return x diff --git a/third-party/lama/bin/models/ade20k/color150.mat b/third-party/lama/bin/models/ade20k/color150.mat new file mode 100644 index 0000000000000000000000000000000000000000..c518b64fbbe899d4a8b2705f012eeba795339892 Binary files /dev/null and b/third-party/lama/bin/models/ade20k/color150.mat differ diff --git a/third-party/lama/bin/models/ade20k/mobilenet.py b/third-party/lama/bin/models/ade20k/mobilenet.py new file mode 100644 index 0000000000000000000000000000000000000000..f501266e56ee71cdf455744020f8fc1a58ec9fff --- /dev/null +++ b/third-party/lama/bin/models/ade20k/mobilenet.py @@ -0,0 +1,154 @@ +""" +This MobileNetV2 implementation is modified from the following repository: +https://github.com/tonylins/pytorch-mobilenet-v2 +""" + +import torch.nn as nn +import math +from .utils import load_url +from .segm_lib.nn import SynchronizedBatchNorm2d + +BatchNorm2d = SynchronizedBatchNorm2d + + +__all__ = ['mobilenetv2'] + + +model_urls = { + 'mobilenetv2': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/mobilenet_v2.pth.tar', +} + + +def conv_bn(inp, oup, stride): + return nn.Sequential( + nn.Conv2d(inp, oup, 3, stride, 1, bias=False), + BatchNorm2d(oup), + nn.ReLU6(inplace=True) + ) + + +def conv_1x1_bn(inp, oup): + return nn.Sequential( + nn.Conv2d(inp, oup, 1, 1, 0, bias=False), + BatchNorm2d(oup), + nn.ReLU6(inplace=True) + ) + + +class InvertedResidual(nn.Module): + def __init__(self, inp, oup, stride, expand_ratio): + super(InvertedResidual, self).__init__() + self.stride = stride + assert stride in [1, 2] + + hidden_dim = round(inp * expand_ratio) + self.use_res_connect = self.stride == 1 and inp == oup + + if expand_ratio == 1: + self.conv = nn.Sequential( + # dw + nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False), + BatchNorm2d(hidden_dim), + nn.ReLU6(inplace=True), + # pw-linear + nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), + BatchNorm2d(oup), + ) + else: + self.conv = nn.Sequential( + # pw + nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False), + BatchNorm2d(hidden_dim), + nn.ReLU6(inplace=True), + # dw + nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False), + BatchNorm2d(hidden_dim), + nn.ReLU6(inplace=True), + # pw-linear + nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), + BatchNorm2d(oup), + ) + + def forward(self, x): + if self.use_res_connect: + return x + self.conv(x) + else: + return self.conv(x) + + +class MobileNetV2(nn.Module): + def __init__(self, n_class=1000, input_size=224, width_mult=1.): + super(MobileNetV2, self).__init__() + block = InvertedResidual + input_channel = 32 + last_channel = 1280 + interverted_residual_setting = [ + # t, c, n, s + [1, 16, 1, 1], + [6, 24, 2, 2], + [6, 32, 3, 2], + [6, 64, 4, 2], + [6, 96, 3, 1], + [6, 160, 3, 2], + [6, 320, 1, 1], + ] + + # building first layer + assert input_size % 32 == 0 + input_channel = int(input_channel * width_mult) + self.last_channel = int(last_channel * width_mult) if width_mult > 1.0 else last_channel + self.features = [conv_bn(3, input_channel, 2)] + # building inverted residual blocks + for t, c, n, s in interverted_residual_setting: + output_channel = int(c * width_mult) + for i in range(n): + if i == 0: + self.features.append(block(input_channel, output_channel, s, expand_ratio=t)) + else: + self.features.append(block(input_channel, output_channel, 1, expand_ratio=t)) + input_channel = output_channel + # building last several layers + self.features.append(conv_1x1_bn(input_channel, self.last_channel)) + # make it nn.Sequential + self.features = nn.Sequential(*self.features) + + # building classifier + self.classifier = nn.Sequential( + nn.Dropout(0.2), + nn.Linear(self.last_channel, n_class), + ) + + self._initialize_weights() + + def forward(self, x): + x = self.features(x) + x = x.mean(3).mean(2) + x = self.classifier(x) + return x + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + n = m.weight.size(1) + m.weight.data.normal_(0, 0.01) + m.bias.data.zero_() + + +def mobilenetv2(pretrained=False, **kwargs): + """Constructs a MobileNet_V2 model. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = MobileNetV2(n_class=1000, **kwargs) + if pretrained: + model.load_state_dict(load_url(model_urls['mobilenetv2']), strict=False) + return model \ No newline at end of file diff --git a/third-party/lama/bin/models/ade20k/object150_info.csv b/third-party/lama/bin/models/ade20k/object150_info.csv new file mode 100644 index 0000000000000000000000000000000000000000..8b34d8f3874a38b96894863c5458a7c3c2b0e2e6 --- /dev/null +++ b/third-party/lama/bin/models/ade20k/object150_info.csv 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+149,0.0002,775,96,0,clock +150,0.0002,421,56,0,flag diff --git a/third-party/lama/bin/models/ade20k/resnet.py b/third-party/lama/bin/models/ade20k/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..3e1d521f171c984cf6a7ff3dcebd96f8c5faf908 --- /dev/null +++ b/third-party/lama/bin/models/ade20k/resnet.py @@ -0,0 +1,181 @@ +"""Modified from https://github.com/CSAILVision/semantic-segmentation-pytorch""" + +import math + +import torch.nn as nn +from torch.nn import BatchNorm2d + +from .utils import load_url + +__all__ = ['ResNet', 'resnet50'] + + +model_urls = { + 'resnet50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet50-imagenet.pth', +} + + +def conv3x3(in_planes, out_planes, stride=1): + "3x3 convolution with padding" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = BatchNorm2d(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) + self.bn3 = BatchNorm2d(planes * 4) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class ResNet(nn.Module): + + def __init__(self, block, layers, num_classes=1000): + self.inplanes = 128 + super(ResNet, self).__init__() + self.conv1 = conv3x3(3, 64, stride=2) + self.bn1 = BatchNorm2d(64) + self.relu1 = nn.ReLU(inplace=True) + self.conv2 = conv3x3(64, 64) + self.bn2 = BatchNorm2d(64) + self.relu2 = nn.ReLU(inplace=True) + self.conv3 = conv3x3(64, 128) + self.bn3 = BatchNorm2d(128) + self.relu3 = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.avgpool = nn.AvgPool2d(7, stride=1) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.relu1(self.bn1(self.conv1(x))) + x = self.relu2(self.bn2(self.conv2(x))) + x = self.relu3(self.bn3(self.conv3(x))) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = x.view(x.size(0), -1) + x = self.fc(x) + + return x + + +def resnet50(pretrained=False, **kwargs): + """Constructs a ResNet-50 model. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) + if pretrained: + model.load_state_dict(load_url(model_urls['resnet50']), strict=False) + return model + + +def resnet18(pretrained=False, **kwargs): + """Constructs a ResNet-18 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) + if pretrained: + model.load_state_dict(load_url(model_urls['resnet18'])) + return model \ No newline at end of file diff --git a/third-party/lama/bin/models/ade20k/segm_lib/nn/__init__.py b/third-party/lama/bin/models/ade20k/segm_lib/nn/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..98a96370ef04570f516052bb73f568d0ebc346c3 --- /dev/null +++ b/third-party/lama/bin/models/ade20k/segm_lib/nn/__init__.py @@ -0,0 +1,2 @@ +from .modules import * +from .parallel import UserScatteredDataParallel, user_scattered_collate, async_copy_to diff --git a/third-party/lama/bin/models/ade20k/segm_lib/nn/modules/__init__.py b/third-party/lama/bin/models/ade20k/segm_lib/nn/modules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bc8709d92c610b36e0bcbd7da20c1eb41dc8cfcf --- /dev/null +++ b/third-party/lama/bin/models/ade20k/segm_lib/nn/modules/__init__.py @@ -0,0 +1,12 @@ +# -*- coding: utf-8 -*- +# File : __init__.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d +from .replicate import DataParallelWithCallback, patch_replication_callback diff --git a/third-party/lama/bin/models/ade20k/segm_lib/nn/modules/batchnorm.py b/third-party/lama/bin/models/ade20k/segm_lib/nn/modules/batchnorm.py new file mode 100644 index 0000000000000000000000000000000000000000..18318965335b37cc671004a6aceda3229dc7b477 --- /dev/null +++ b/third-party/lama/bin/models/ade20k/segm_lib/nn/modules/batchnorm.py @@ -0,0 +1,329 @@ +# -*- coding: utf-8 -*- +# File : batchnorm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import collections + +import torch +import torch.nn.functional as F + +from torch.nn.modules.batchnorm import _BatchNorm +from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast + +from .comm import SyncMaster + +__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d'] + + +def _sum_ft(tensor): + """sum over the first and last dimention""" + return tensor.sum(dim=0).sum(dim=-1) + + +def _unsqueeze_ft(tensor): + """add new dementions at the front and the tail""" + return tensor.unsqueeze(0).unsqueeze(-1) + + +_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size']) +_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std']) + + +class _SynchronizedBatchNorm(_BatchNorm): + def __init__(self, num_features, eps=1e-5, momentum=0.001, affine=True): + super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine) + + self._sync_master = SyncMaster(self._data_parallel_master) + + self._is_parallel = False + self._parallel_id = None + self._slave_pipe = None + + # customed batch norm statistics + self._moving_average_fraction = 1. - momentum + self.register_buffer('_tmp_running_mean', torch.zeros(self.num_features)) + self.register_buffer('_tmp_running_var', torch.ones(self.num_features)) + self.register_buffer('_running_iter', torch.ones(1)) + self._tmp_running_mean = self.running_mean.clone() * self._running_iter + self._tmp_running_var = self.running_var.clone() * self._running_iter + + def forward(self, input): + # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation. + if not (self._is_parallel and self.training): + return F.batch_norm( + input, self.running_mean, self.running_var, self.weight, self.bias, + self.training, self.momentum, self.eps) + + # Resize the input to (B, C, -1). + input_shape = input.size() + input = input.view(input.size(0), self.num_features, -1) + + # Compute the sum and square-sum. + sum_size = input.size(0) * input.size(2) + input_sum = _sum_ft(input) + input_ssum = _sum_ft(input ** 2) + + # Reduce-and-broadcast the statistics. + if self._parallel_id == 0: + mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size)) + else: + mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size)) + + # Compute the output. + if self.affine: + # MJY:: Fuse the multiplication for speed. + output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias) + else: + output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std) + + # Reshape it. + return output.view(input_shape) + + def __data_parallel_replicate__(self, ctx, copy_id): + self._is_parallel = True + self._parallel_id = copy_id + + # parallel_id == 0 means master device. + if self._parallel_id == 0: + ctx.sync_master = self._sync_master + else: + self._slave_pipe = ctx.sync_master.register_slave(copy_id) + + def _data_parallel_master(self, intermediates): + """Reduce the sum and square-sum, compute the statistics, and broadcast it.""" + intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device()) + + to_reduce = [i[1][:2] for i in intermediates] + to_reduce = [j for i in to_reduce for j in i] # flatten + target_gpus = [i[1].sum.get_device() for i in intermediates] + + sum_size = sum([i[1].sum_size for i in intermediates]) + sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce) + + mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size) + + broadcasted = Broadcast.apply(target_gpus, mean, inv_std) + + outputs = [] + for i, rec in enumerate(intermediates): + outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2]))) + + return outputs + + def _add_weighted(self, dest, delta, alpha=1, beta=1, bias=0): + """return *dest* by `dest := dest*alpha + delta*beta + bias`""" + return dest * alpha + delta * beta + bias + + def _compute_mean_std(self, sum_, ssum, size): + """Compute the mean and standard-deviation with sum and square-sum. This method + also maintains the moving average on the master device.""" + assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.' + mean = sum_ / size + sumvar = ssum - sum_ * mean + unbias_var = sumvar / (size - 1) + bias_var = sumvar / size + + self._tmp_running_mean = self._add_weighted(self._tmp_running_mean, mean.data, alpha=self._moving_average_fraction) + self._tmp_running_var = self._add_weighted(self._tmp_running_var, unbias_var.data, alpha=self._moving_average_fraction) + self._running_iter = self._add_weighted(self._running_iter, 1, alpha=self._moving_average_fraction) + + self.running_mean = self._tmp_running_mean / self._running_iter + self.running_var = self._tmp_running_var / self._running_iter + + return mean, bias_var.clamp(self.eps) ** -0.5 + + +class SynchronizedBatchNorm1d(_SynchronizedBatchNorm): + r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a + mini-batch. + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm1d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm + + Args: + num_features: num_features from an expected input of size + `batch_size x num_features [x width]` + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape: + - Input: :math:`(N, C)` or :math:`(N, C, L)` + - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm1d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm1d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 2 and input.dim() != 3: + raise ValueError('expected 2D or 3D input (got {}D input)' + .format(input.dim())) + super(SynchronizedBatchNorm1d, self)._check_input_dim(input) + + +class SynchronizedBatchNorm2d(_SynchronizedBatchNorm): + r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch + of 3d inputs + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm2d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm + + Args: + num_features: num_features from an expected input of + size batch_size x num_features x height x width + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape: + - Input: :math:`(N, C, H, W)` + - Output: :math:`(N, C, H, W)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm2d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm2d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 4: + raise ValueError('expected 4D input (got {}D input)' + .format(input.dim())) + super(SynchronizedBatchNorm2d, self)._check_input_dim(input) + + +class SynchronizedBatchNorm3d(_SynchronizedBatchNorm): + r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch + of 4d inputs + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm3d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm + or Spatio-temporal BatchNorm + + Args: + num_features: num_features from an expected input of + size batch_size x num_features x depth x height x width + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape: + - Input: :math:`(N, C, D, H, W)` + - Output: :math:`(N, C, D, H, W)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm3d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm3d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 5: + raise ValueError('expected 5D input (got {}D input)' + .format(input.dim())) + super(SynchronizedBatchNorm3d, self)._check_input_dim(input) diff --git a/third-party/lama/bin/models/ade20k/segm_lib/nn/modules/comm.py b/third-party/lama/bin/models/ade20k/segm_lib/nn/modules/comm.py new file mode 100644 index 0000000000000000000000000000000000000000..b64bf6ba3b3e7abbab375c6dd4a87d8239e62138 --- /dev/null +++ b/third-party/lama/bin/models/ade20k/segm_lib/nn/modules/comm.py @@ -0,0 +1,131 @@ +# -*- coding: utf-8 -*- +# File : comm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import queue +import collections +import threading + +__all__ = ['FutureResult', 'SlavePipe', 'SyncMaster'] + + +class FutureResult(object): + """A thread-safe future implementation. Used only as one-to-one pipe.""" + + def __init__(self): + self._result = None + self._lock = threading.Lock() + self._cond = threading.Condition(self._lock) + + def put(self, result): + with self._lock: + assert self._result is None, 'Previous result has\'t been fetched.' + self._result = result + self._cond.notify() + + def get(self): + with self._lock: + if self._result is None: + self._cond.wait() + + res = self._result + self._result = None + return res + + +_MasterRegistry = collections.namedtuple('MasterRegistry', ['result']) +_SlavePipeBase = collections.namedtuple('_SlavePipeBase', ['identifier', 'queue', 'result']) + + +class SlavePipe(_SlavePipeBase): + """Pipe for master-slave communication.""" + + def run_slave(self, msg): + self.queue.put((self.identifier, msg)) + ret = self.result.get() + self.queue.put(True) + return ret + + +class SyncMaster(object): + """An abstract `SyncMaster` object. + + - During the replication, as the data parallel will trigger an callback of each module, all slave devices should + call `register(id)` and obtain an `SlavePipe` to communicate with the master. + - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected, + and passed to a registered callback. + - After receiving the messages, the master device should gather the information and determine to message passed + back to each slave devices. + """ + + def __init__(self, master_callback): + """ + + Args: + master_callback: a callback to be invoked after having collected messages from slave devices. + """ + self._master_callback = master_callback + self._queue = queue.Queue() + self._registry = collections.OrderedDict() + self._activated = False + + def register_slave(self, identifier): + """ + Register an slave device. + + Args: + identifier: an identifier, usually is the device id. + + Returns: a `SlavePipe` object which can be used to communicate with the master device. + + """ + if self._activated: + assert self._queue.empty(), 'Queue is not clean before next initialization.' + self._activated = False + self._registry.clear() + future = FutureResult() + self._registry[identifier] = _MasterRegistry(future) + return SlavePipe(identifier, self._queue, future) + + def run_master(self, master_msg): + """ + Main entry for the master device in each forward pass. + The messages were first collected from each devices (including the master device), and then + an callback will be invoked to compute the message to be sent back to each devices + (including the master device). + + Args: + master_msg: the message that the master want to send to itself. This will be placed as the first + message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example. + + Returns: the message to be sent back to the master device. + + """ + self._activated = True + + intermediates = [(0, master_msg)] + for i in range(self.nr_slaves): + intermediates.append(self._queue.get()) + + results = self._master_callback(intermediates) + assert results[0][0] == 0, 'The first result should belongs to the master.' + + for i, res in results: + if i == 0: + continue + self._registry[i].result.put(res) + + for i in range(self.nr_slaves): + assert self._queue.get() is True + + return results[0][1] + + @property + def nr_slaves(self): + return len(self._registry) diff --git a/third-party/lama/bin/models/ade20k/segm_lib/nn/modules/replicate.py b/third-party/lama/bin/models/ade20k/segm_lib/nn/modules/replicate.py new file mode 100644 index 0000000000000000000000000000000000000000..b71c7b8ed51a1d6c55b1f753bdd8d90bad79bd06 --- /dev/null +++ b/third-party/lama/bin/models/ade20k/segm_lib/nn/modules/replicate.py @@ -0,0 +1,94 @@ +# -*- coding: utf-8 -*- +# File : replicate.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import functools + +from torch.nn.parallel.data_parallel import DataParallel + +__all__ = [ + 'CallbackContext', + 'execute_replication_callbacks', + 'DataParallelWithCallback', + 'patch_replication_callback' +] + + +class CallbackContext(object): + pass + + +def execute_replication_callbacks(modules): + """ + Execute an replication callback `__data_parallel_replicate__` on each module created by original replication. + + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + + Note that, as all modules are isomorphism, we assign each sub-module with a context + (shared among multiple copies of this module on different devices). + Through this context, different copies can share some information. + + We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback + of any slave copies. + """ + master_copy = modules[0] + nr_modules = len(list(master_copy.modules())) + ctxs = [CallbackContext() for _ in range(nr_modules)] + + for i, module in enumerate(modules): + for j, m in enumerate(module.modules()): + if hasattr(m, '__data_parallel_replicate__'): + m.__data_parallel_replicate__(ctxs[j], i) + + +class DataParallelWithCallback(DataParallel): + """ + Data Parallel with a replication callback. + + An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by + original `replicate` function. + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + # sync_bn.__data_parallel_replicate__ will be invoked. + """ + + def replicate(self, module, device_ids): + modules = super(DataParallelWithCallback, self).replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + +def patch_replication_callback(data_parallel): + """ + Monkey-patch an existing `DataParallel` object. Add the replication callback. + Useful when you have customized `DataParallel` implementation. + + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallel(sync_bn, device_ids=[0, 1]) + > patch_replication_callback(sync_bn) + # this is equivalent to + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + """ + + assert isinstance(data_parallel, DataParallel) + + old_replicate = data_parallel.replicate + + @functools.wraps(old_replicate) + def new_replicate(module, device_ids): + modules = old_replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + data_parallel.replicate = new_replicate diff --git a/third-party/lama/bin/models/ade20k/segm_lib/nn/modules/tests/test_numeric_batchnorm.py b/third-party/lama/bin/models/ade20k/segm_lib/nn/modules/tests/test_numeric_batchnorm.py new file mode 100644 index 0000000000000000000000000000000000000000..8bd45a930d3dc84912e58659ee575be08e9038f0 --- /dev/null +++ b/third-party/lama/bin/models/ade20k/segm_lib/nn/modules/tests/test_numeric_batchnorm.py @@ -0,0 +1,56 @@ +# -*- coding: utf-8 -*- +# File : test_numeric_batchnorm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. + +import unittest + +import torch +import torch.nn as nn +from torch.autograd import Variable + +from sync_batchnorm.unittest import TorchTestCase + + +def handy_var(a, unbias=True): + n = a.size(0) + asum = a.sum(dim=0) + as_sum = (a ** 2).sum(dim=0) # a square sum + sumvar = as_sum - asum * asum / n + if unbias: + return sumvar / (n - 1) + else: + return sumvar / n + + +class NumericTestCase(TorchTestCase): + def testNumericBatchNorm(self): + a = torch.rand(16, 10) + bn = nn.BatchNorm2d(10, momentum=1, eps=1e-5, affine=False) + bn.train() + + a_var1 = Variable(a, requires_grad=True) + b_var1 = bn(a_var1) + loss1 = b_var1.sum() + loss1.backward() + + a_var2 = Variable(a, requires_grad=True) + a_mean2 = a_var2.mean(dim=0, keepdim=True) + a_std2 = torch.sqrt(handy_var(a_var2, unbias=False).clamp(min=1e-5)) + # a_std2 = torch.sqrt(a_var2.var(dim=0, keepdim=True, unbiased=False) + 1e-5) + b_var2 = (a_var2 - a_mean2) / a_std2 + loss2 = b_var2.sum() + loss2.backward() + + self.assertTensorClose(bn.running_mean, a.mean(dim=0)) + self.assertTensorClose(bn.running_var, handy_var(a)) + self.assertTensorClose(a_var1.data, a_var2.data) + self.assertTensorClose(b_var1.data, b_var2.data) + self.assertTensorClose(a_var1.grad, a_var2.grad) + + +if __name__ == '__main__': + unittest.main() diff --git a/third-party/lama/bin/models/ade20k/segm_lib/nn/modules/tests/test_sync_batchnorm.py b/third-party/lama/bin/models/ade20k/segm_lib/nn/modules/tests/test_sync_batchnorm.py new file mode 100644 index 0000000000000000000000000000000000000000..45bb3c8cfd36d8f668e6fde756b17587eab72082 --- /dev/null +++ b/third-party/lama/bin/models/ade20k/segm_lib/nn/modules/tests/test_sync_batchnorm.py @@ -0,0 +1,111 @@ +# -*- coding: utf-8 -*- +# File : test_sync_batchnorm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. + +import unittest + +import torch +import torch.nn as nn +from torch.autograd import Variable + +from sync_batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, DataParallelWithCallback +from sync_batchnorm.unittest import TorchTestCase + + +def handy_var(a, unbias=True): + n = a.size(0) + asum = a.sum(dim=0) + as_sum = (a ** 2).sum(dim=0) # a square sum + sumvar = as_sum - asum * asum / n + if unbias: + return sumvar / (n - 1) + else: + return sumvar / n + + +def _find_bn(module): + for m in module.modules(): + if isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, SynchronizedBatchNorm1d, SynchronizedBatchNorm2d)): + return m + + +class SyncTestCase(TorchTestCase): + def _syncParameters(self, bn1, bn2): + bn1.reset_parameters() + bn2.reset_parameters() + if bn1.affine and bn2.affine: + bn2.weight.data.copy_(bn1.weight.data) + bn2.bias.data.copy_(bn1.bias.data) + + def _checkBatchNormResult(self, bn1, bn2, input, is_train, cuda=False): + """Check the forward and backward for the customized batch normalization.""" + bn1.train(mode=is_train) + bn2.train(mode=is_train) + + if cuda: + input = input.cuda() + + self._syncParameters(_find_bn(bn1), _find_bn(bn2)) + + input1 = Variable(input, requires_grad=True) + output1 = bn1(input1) + output1.sum().backward() + input2 = Variable(input, requires_grad=True) + output2 = bn2(input2) + output2.sum().backward() + + self.assertTensorClose(input1.data, input2.data) + self.assertTensorClose(output1.data, output2.data) + self.assertTensorClose(input1.grad, input2.grad) + self.assertTensorClose(_find_bn(bn1).running_mean, _find_bn(bn2).running_mean) + self.assertTensorClose(_find_bn(bn1).running_var, _find_bn(bn2).running_var) + + def testSyncBatchNormNormalTrain(self): + bn = nn.BatchNorm1d(10) + sync_bn = SynchronizedBatchNorm1d(10) + + self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), True) + + def testSyncBatchNormNormalEval(self): + bn = nn.BatchNorm1d(10) + sync_bn = SynchronizedBatchNorm1d(10) + + self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), False) + + def testSyncBatchNormSyncTrain(self): + bn = nn.BatchNorm1d(10, eps=1e-5, affine=False) + sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + + bn.cuda() + sync_bn.cuda() + + self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), True, cuda=True) + + def testSyncBatchNormSyncEval(self): + bn = nn.BatchNorm1d(10, eps=1e-5, affine=False) + sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + + bn.cuda() + sync_bn.cuda() + + self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), False, cuda=True) + + def testSyncBatchNorm2DSyncTrain(self): + bn = nn.BatchNorm2d(10) + sync_bn = SynchronizedBatchNorm2d(10) + sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + + bn.cuda() + sync_bn.cuda() + + self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10, 16, 16), True, cuda=True) + + +if __name__ == '__main__': + unittest.main() diff --git a/third-party/lama/bin/models/ade20k/segm_lib/nn/modules/unittest.py b/third-party/lama/bin/models/ade20k/segm_lib/nn/modules/unittest.py new file mode 100644 index 0000000000000000000000000000000000000000..0675c022e4ba85d38d1f813490f6740150909524 --- /dev/null +++ b/third-party/lama/bin/models/ade20k/segm_lib/nn/modules/unittest.py @@ -0,0 +1,29 @@ +# -*- coding: utf-8 -*- +# File : unittest.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import unittest + +import numpy as np +from torch.autograd import Variable + + +def as_numpy(v): + if isinstance(v, Variable): + v = v.data + return v.cpu().numpy() + + +class TorchTestCase(unittest.TestCase): + def assertTensorClose(self, a, b, atol=1e-3, rtol=1e-3): + npa, npb = as_numpy(a), as_numpy(b) + self.assertTrue( + np.allclose(npa, npb, atol=atol), + 'Tensor close check failed\n{}\n{}\nadiff={}, rdiff={}'.format(a, b, np.abs(npa - npb).max(), np.abs((npa - npb) / np.fmax(npa, 1e-5)).max()) + ) diff --git a/third-party/lama/bin/models/ade20k/segm_lib/nn/parallel/__init__.py b/third-party/lama/bin/models/ade20k/segm_lib/nn/parallel/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9b52f49cc0755562218a460483cbf02514ddd773 --- /dev/null +++ b/third-party/lama/bin/models/ade20k/segm_lib/nn/parallel/__init__.py @@ -0,0 +1 @@ +from .data_parallel import UserScatteredDataParallel, user_scattered_collate, async_copy_to diff --git a/third-party/lama/bin/models/ade20k/segm_lib/nn/parallel/data_parallel.py b/third-party/lama/bin/models/ade20k/segm_lib/nn/parallel/data_parallel.py new file mode 100644 index 0000000000000000000000000000000000000000..376fc038919aa2a5bd696141e7bb6025d4981306 --- /dev/null +++ b/third-party/lama/bin/models/ade20k/segm_lib/nn/parallel/data_parallel.py @@ -0,0 +1,112 @@ +# -*- coding: utf8 -*- + +import torch.cuda as cuda +import torch.nn as nn +import torch +import collections +from torch.nn.parallel._functions import Gather + + +__all__ = ['UserScatteredDataParallel', 'user_scattered_collate', 'async_copy_to'] + + +def async_copy_to(obj, dev, main_stream=None): + if torch.is_tensor(obj): + v = obj.cuda(dev, non_blocking=True) + if main_stream is not None: + v.data.record_stream(main_stream) + return v + elif isinstance(obj, collections.Mapping): + return {k: async_copy_to(o, dev, main_stream) for k, o in obj.items()} + elif isinstance(obj, collections.Sequence): + return [async_copy_to(o, dev, main_stream) for o in obj] + else: + return obj + + +def dict_gather(outputs, target_device, dim=0): + """ + Gathers variables from different GPUs on a specified device + (-1 means the CPU), with dictionary support. + """ + def gather_map(outputs): + out = outputs[0] + if torch.is_tensor(out): + # MJY(20180330) HACK:: force nr_dims > 0 + if out.dim() == 0: + outputs = [o.unsqueeze(0) for o in outputs] + return Gather.apply(target_device, dim, *outputs) + elif out is None: + return None + elif isinstance(out, collections.Mapping): + return {k: gather_map([o[k] for o in outputs]) for k in out} + elif isinstance(out, collections.Sequence): + return type(out)(map(gather_map, zip(*outputs))) + return gather_map(outputs) + + +class DictGatherDataParallel(nn.DataParallel): + def gather(self, outputs, output_device): + return dict_gather(outputs, output_device, dim=self.dim) + + +class UserScatteredDataParallel(DictGatherDataParallel): + def scatter(self, inputs, kwargs, device_ids): + assert len(inputs) == 1 + inputs = inputs[0] + inputs = _async_copy_stream(inputs, device_ids) + inputs = [[i] for i in inputs] + assert len(kwargs) == 0 + kwargs = [{} for _ in range(len(inputs))] + + return inputs, kwargs + + +def user_scattered_collate(batch): + return batch + + +def _async_copy(inputs, device_ids): + nr_devs = len(device_ids) + assert type(inputs) in (tuple, list) + assert len(inputs) == nr_devs + + outputs = [] + for i, dev in zip(inputs, device_ids): + with cuda.device(dev): + outputs.append(async_copy_to(i, dev)) + + return tuple(outputs) + + +def _async_copy_stream(inputs, device_ids): + nr_devs = len(device_ids) + assert type(inputs) in (tuple, list) + assert len(inputs) == nr_devs + + outputs = [] + streams = [_get_stream(d) for d in device_ids] + for i, dev, stream in zip(inputs, device_ids, streams): + with cuda.device(dev): + main_stream = cuda.current_stream() + with cuda.stream(stream): + outputs.append(async_copy_to(i, dev, main_stream=main_stream)) + main_stream.wait_stream(stream) + + return outputs + + +"""Adapted from: torch/nn/parallel/_functions.py""" +# background streams used for copying +_streams = None + + +def _get_stream(device): + """Gets a background stream for copying between CPU and GPU""" + global _streams + if device == -1: + return None + if _streams is None: + _streams = [None] * cuda.device_count() + if _streams[device] is None: _streams[device] = cuda.Stream(device) + return _streams[device] diff --git a/third-party/lama/bin/models/ade20k/segm_lib/utils/__init__.py b/third-party/lama/bin/models/ade20k/segm_lib/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..abe3cbe49477fe37d4fc16249de8a10f4fb4a013 --- /dev/null +++ b/third-party/lama/bin/models/ade20k/segm_lib/utils/__init__.py @@ -0,0 +1 @@ +from .th import * diff --git a/third-party/lama/bin/models/ade20k/segm_lib/utils/data/__init__.py b/third-party/lama/bin/models/ade20k/segm_lib/utils/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f3b008fb13c5e8a84b1b785056e8c4f5226dc976 --- /dev/null +++ b/third-party/lama/bin/models/ade20k/segm_lib/utils/data/__init__.py @@ -0,0 +1,3 @@ + +from .dataset import Dataset, TensorDataset, ConcatDataset +from .dataloader import DataLoader diff --git a/third-party/lama/bin/models/ade20k/segm_lib/utils/data/dataloader.py b/third-party/lama/bin/models/ade20k/segm_lib/utils/data/dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..039b9ec3645b2a4626ff47c221e372f32a6ad339 --- /dev/null +++ b/third-party/lama/bin/models/ade20k/segm_lib/utils/data/dataloader.py @@ -0,0 +1,425 @@ +import torch +import torch.multiprocessing as multiprocessing +from torch._C import _set_worker_signal_handlers, \ + _remove_worker_pids, _error_if_any_worker_fails +try: + from torch._C import _set_worker_pids +except: + from torch._C import _update_worker_pids as _set_worker_pids +from .sampler import SequentialSampler, RandomSampler, BatchSampler +import signal +import collections +import re +import sys +import threading +import traceback +from torch._six import string_classes, int_classes +import numpy as np + +if sys.version_info[0] == 2: + import Queue as queue +else: + import queue + + +class ExceptionWrapper(object): + r"Wraps an exception plus traceback to communicate across threads" + + def __init__(self, exc_info): + self.exc_type = exc_info[0] + self.exc_msg = "".join(traceback.format_exception(*exc_info)) + + +_use_shared_memory = False +"""Whether to use shared memory in default_collate""" + + +def _worker_loop(dataset, index_queue, data_queue, collate_fn, seed, init_fn, worker_id): + global _use_shared_memory + _use_shared_memory = True + + # Intialize C side signal handlers for SIGBUS and SIGSEGV. Python signal + # module's handlers are executed after Python returns from C low-level + # handlers, likely when the same fatal signal happened again already. + # https://docs.python.org/3/library/signal.html Sec. 18.8.1.1 + _set_worker_signal_handlers() + + torch.set_num_threads(1) + torch.manual_seed(seed) + np.random.seed(seed) + + if init_fn is not None: + init_fn(worker_id) + + while True: + r = index_queue.get() + if r is None: + break + idx, batch_indices = r + try: + samples = collate_fn([dataset[i] for i in batch_indices]) + except Exception: + data_queue.put((idx, ExceptionWrapper(sys.exc_info()))) + else: + data_queue.put((idx, samples)) + + +def _worker_manager_loop(in_queue, out_queue, done_event, pin_memory, device_id): + if pin_memory: + torch.cuda.set_device(device_id) + + while True: + try: + r = in_queue.get() + except Exception: + if done_event.is_set(): + return + raise + if r is None: + break + if isinstance(r[1], ExceptionWrapper): + out_queue.put(r) + continue + idx, batch = r + try: + if pin_memory: + batch = pin_memory_batch(batch) + except Exception: + out_queue.put((idx, ExceptionWrapper(sys.exc_info()))) + else: + out_queue.put((idx, batch)) + +numpy_type_map = { + 'float64': torch.DoubleTensor, + 'float32': torch.FloatTensor, + 'float16': torch.HalfTensor, + 'int64': torch.LongTensor, + 'int32': torch.IntTensor, + 'int16': torch.ShortTensor, + 'int8': torch.CharTensor, + 'uint8': torch.ByteTensor, +} + + +def default_collate(batch): + "Puts each data field into a tensor with outer dimension batch size" + + error_msg = "batch must contain tensors, numbers, dicts or lists; found {}" + elem_type = type(batch[0]) + if torch.is_tensor(batch[0]): + out = None + if _use_shared_memory: + # If we're in a background process, concatenate directly into a + # shared memory tensor to avoid an extra copy + numel = sum([x.numel() for x in batch]) + storage = batch[0].storage()._new_shared(numel) + out = batch[0].new(storage) + return torch.stack(batch, 0, out=out) + elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ + and elem_type.__name__ != 'string_': + elem = batch[0] + if elem_type.__name__ == 'ndarray': + # array of string classes and object + if re.search('[SaUO]', elem.dtype.str) is not None: + raise TypeError(error_msg.format(elem.dtype)) + + return torch.stack([torch.from_numpy(b) for b in batch], 0) + if elem.shape == (): # scalars + py_type = float if elem.dtype.name.startswith('float') else int + return numpy_type_map[elem.dtype.name](list(map(py_type, batch))) + elif isinstance(batch[0], int_classes): + return torch.LongTensor(batch) + elif isinstance(batch[0], float): + return torch.DoubleTensor(batch) + elif isinstance(batch[0], string_classes): + return batch + elif isinstance(batch[0], collections.Mapping): + return {key: default_collate([d[key] for d in batch]) for key in batch[0]} + elif isinstance(batch[0], collections.Sequence): + transposed = zip(*batch) + return [default_collate(samples) for samples in transposed] + + raise TypeError((error_msg.format(type(batch[0])))) + + +def pin_memory_batch(batch): + if torch.is_tensor(batch): + return batch.pin_memory() + elif isinstance(batch, string_classes): + return batch + elif isinstance(batch, collections.Mapping): + return {k: pin_memory_batch(sample) for k, sample in batch.items()} + elif isinstance(batch, collections.Sequence): + return [pin_memory_batch(sample) for sample in batch] + else: + return batch + + +_SIGCHLD_handler_set = False +"""Whether SIGCHLD handler is set for DataLoader worker failures. Only one +handler needs to be set for all DataLoaders in a process.""" + + +def _set_SIGCHLD_handler(): + # Windows doesn't support SIGCHLD handler + if sys.platform == 'win32': + return + # can't set signal in child threads + if not isinstance(threading.current_thread(), threading._MainThread): + return + global _SIGCHLD_handler_set + if _SIGCHLD_handler_set: + return + previous_handler = signal.getsignal(signal.SIGCHLD) + if not callable(previous_handler): + previous_handler = None + + def handler(signum, frame): + # This following call uses `waitid` with WNOHANG from C side. Therefore, + # Python can still get and update the process status successfully. + _error_if_any_worker_fails() + if previous_handler is not None: + previous_handler(signum, frame) + + signal.signal(signal.SIGCHLD, handler) + _SIGCHLD_handler_set = True + + +class DataLoaderIter(object): + "Iterates once over the DataLoader's dataset, as specified by the sampler" + + def __init__(self, loader): + self.dataset = loader.dataset + self.collate_fn = loader.collate_fn + self.batch_sampler = loader.batch_sampler + self.num_workers = loader.num_workers + self.pin_memory = loader.pin_memory and torch.cuda.is_available() + self.timeout = loader.timeout + self.done_event = threading.Event() + + self.sample_iter = iter(self.batch_sampler) + + if self.num_workers > 0: + self.worker_init_fn = loader.worker_init_fn + self.index_queue = multiprocessing.SimpleQueue() + self.worker_result_queue = multiprocessing.SimpleQueue() + self.batches_outstanding = 0 + self.worker_pids_set = False + self.shutdown = False + self.send_idx = 0 + self.rcvd_idx = 0 + self.reorder_dict = {} + + base_seed = torch.LongTensor(1).random_(0, 2**31-1)[0] + self.workers = [ + multiprocessing.Process( + target=_worker_loop, + args=(self.dataset, self.index_queue, self.worker_result_queue, self.collate_fn, + base_seed + i, self.worker_init_fn, i)) + for i in range(self.num_workers)] + + if self.pin_memory or self.timeout > 0: + self.data_queue = queue.Queue() + if self.pin_memory: + maybe_device_id = torch.cuda.current_device() + else: + # do not initialize cuda context if not necessary + maybe_device_id = None + self.worker_manager_thread = threading.Thread( + target=_worker_manager_loop, + args=(self.worker_result_queue, self.data_queue, self.done_event, self.pin_memory, + maybe_device_id)) + self.worker_manager_thread.daemon = True + self.worker_manager_thread.start() + else: + self.data_queue = self.worker_result_queue + + for w in self.workers: + w.daemon = True # ensure that the worker exits on process exit + w.start() + + _set_worker_pids(id(self), tuple(w.pid for w in self.workers)) + _set_SIGCHLD_handler() + self.worker_pids_set = True + + # prime the prefetch loop + for _ in range(2 * self.num_workers): + self._put_indices() + + def __len__(self): + return len(self.batch_sampler) + + def _get_batch(self): + if self.timeout > 0: + try: + return self.data_queue.get(timeout=self.timeout) + except queue.Empty: + raise RuntimeError('DataLoader timed out after {} seconds'.format(self.timeout)) + else: + return self.data_queue.get() + + def __next__(self): + if self.num_workers == 0: # same-process loading + indices = next(self.sample_iter) # may raise StopIteration + batch = self.collate_fn([self.dataset[i] for i in indices]) + if self.pin_memory: + batch = pin_memory_batch(batch) + return batch + + # check if the next sample has already been generated + if self.rcvd_idx in self.reorder_dict: + batch = self.reorder_dict.pop(self.rcvd_idx) + return self._process_next_batch(batch) + + if self.batches_outstanding == 0: + self._shutdown_workers() + raise StopIteration + + while True: + assert (not self.shutdown and self.batches_outstanding > 0) + idx, batch = self._get_batch() + self.batches_outstanding -= 1 + if idx != self.rcvd_idx: + # store out-of-order samples + self.reorder_dict[idx] = batch + continue + return self._process_next_batch(batch) + + next = __next__ # Python 2 compatibility + + def __iter__(self): + return self + + def _put_indices(self): + assert self.batches_outstanding < 2 * self.num_workers + indices = next(self.sample_iter, None) + if indices is None: + return + self.index_queue.put((self.send_idx, indices)) + self.batches_outstanding += 1 + self.send_idx += 1 + + def _process_next_batch(self, batch): + self.rcvd_idx += 1 + self._put_indices() + if isinstance(batch, ExceptionWrapper): + raise batch.exc_type(batch.exc_msg) + return batch + + def __getstate__(self): + # TODO: add limited pickling support for sharing an iterator + # across multiple threads for HOGWILD. + # Probably the best way to do this is by moving the sample pushing + # to a separate thread and then just sharing the data queue + # but signalling the end is tricky without a non-blocking API + raise NotImplementedError("DataLoaderIterator cannot be pickled") + + def _shutdown_workers(self): + try: + if not self.shutdown: + self.shutdown = True + self.done_event.set() + # if worker_manager_thread is waiting to put + while not self.data_queue.empty(): + self.data_queue.get() + for _ in self.workers: + self.index_queue.put(None) + # done_event should be sufficient to exit worker_manager_thread, + # but be safe here and put another None + self.worker_result_queue.put(None) + finally: + # removes pids no matter what + if self.worker_pids_set: + _remove_worker_pids(id(self)) + self.worker_pids_set = False + + def __del__(self): + if self.num_workers > 0: + self._shutdown_workers() + + +class DataLoader(object): + """ + Data loader. Combines a dataset and a sampler, and provides + single- or multi-process iterators over the dataset. + + Arguments: + dataset (Dataset): dataset from which to load the data. + batch_size (int, optional): how many samples per batch to load + (default: 1). + shuffle (bool, optional): set to ``True`` to have the data reshuffled + at every epoch (default: False). + sampler (Sampler, optional): defines the strategy to draw samples from + the dataset. If specified, ``shuffle`` must be False. + batch_sampler (Sampler, optional): like sampler, but returns a batch of + indices at a time. Mutually exclusive with batch_size, shuffle, + sampler, and drop_last. + num_workers (int, optional): how many subprocesses to use for data + loading. 0 means that the data will be loaded in the main process. + (default: 0) + collate_fn (callable, optional): merges a list of samples to form a mini-batch. + pin_memory (bool, optional): If ``True``, the data loader will copy tensors + into CUDA pinned memory before returning them. + drop_last (bool, optional): set to ``True`` to drop the last incomplete batch, + if the dataset size is not divisible by the batch size. If ``False`` and + the size of dataset is not divisible by the batch size, then the last batch + will be smaller. (default: False) + timeout (numeric, optional): if positive, the timeout value for collecting a batch + from workers. Should always be non-negative. (default: 0) + worker_init_fn (callable, optional): If not None, this will be called on each + worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as + input, after seeding and before data loading. (default: None) + + .. note:: By default, each worker will have its PyTorch seed set to + ``base_seed + worker_id``, where ``base_seed`` is a long generated + by main process using its RNG. You may use ``torch.initial_seed()`` to access + this value in :attr:`worker_init_fn`, which can be used to set other seeds + (e.g. NumPy) before data loading. + + .. warning:: If ``spawn'' start method is used, :attr:`worker_init_fn` cannot be an + unpicklable object, e.g., a lambda function. + """ + + def __init__(self, dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, + num_workers=0, collate_fn=default_collate, pin_memory=False, drop_last=False, + timeout=0, worker_init_fn=None): + self.dataset = dataset + self.batch_size = batch_size + self.num_workers = num_workers + self.collate_fn = collate_fn + self.pin_memory = pin_memory + self.drop_last = drop_last + self.timeout = timeout + self.worker_init_fn = worker_init_fn + + if timeout < 0: + raise ValueError('timeout option should be non-negative') + + if batch_sampler is not None: + if batch_size > 1 or shuffle or sampler is not None or drop_last: + raise ValueError('batch_sampler is mutually exclusive with ' + 'batch_size, shuffle, sampler, and drop_last') + + if sampler is not None and shuffle: + raise ValueError('sampler is mutually exclusive with shuffle') + + if self.num_workers < 0: + raise ValueError('num_workers cannot be negative; ' + 'use num_workers=0 to disable multiprocessing.') + + if batch_sampler is None: + if sampler is None: + if shuffle: + sampler = RandomSampler(dataset) + else: + sampler = SequentialSampler(dataset) + batch_sampler = BatchSampler(sampler, batch_size, drop_last) + + self.sampler = sampler + self.batch_sampler = batch_sampler + + def __iter__(self): + return DataLoaderIter(self) + + def __len__(self): + return len(self.batch_sampler) diff --git a/third-party/lama/bin/models/ade20k/segm_lib/utils/data/dataset.py b/third-party/lama/bin/models/ade20k/segm_lib/utils/data/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..605aa877f7031a5cd2b98c0f831410aa80fddefa --- /dev/null +++ b/third-party/lama/bin/models/ade20k/segm_lib/utils/data/dataset.py @@ -0,0 +1,118 @@ +import bisect +import warnings + +from torch._utils import _accumulate +from torch import randperm + + +class Dataset(object): + """An abstract class representing a Dataset. + + All other datasets should subclass it. All subclasses should override + ``__len__``, that provides the size of the dataset, and ``__getitem__``, + supporting integer indexing in range from 0 to len(self) exclusive. + """ + + def __getitem__(self, index): + raise NotImplementedError + + def __len__(self): + raise NotImplementedError + + def __add__(self, other): + return ConcatDataset([self, other]) + + +class TensorDataset(Dataset): + """Dataset wrapping data and target tensors. + + Each sample will be retrieved by indexing both tensors along the first + dimension. + + Arguments: + data_tensor (Tensor): contains sample data. + target_tensor (Tensor): contains sample targets (labels). + """ + + def __init__(self, data_tensor, target_tensor): + assert data_tensor.size(0) == target_tensor.size(0) + self.data_tensor = data_tensor + self.target_tensor = target_tensor + + def __getitem__(self, index): + return self.data_tensor[index], self.target_tensor[index] + + def __len__(self): + return self.data_tensor.size(0) + + +class ConcatDataset(Dataset): + """ + Dataset to concatenate multiple datasets. + Purpose: useful to assemble different existing datasets, possibly + large-scale datasets as the concatenation operation is done in an + on-the-fly manner. + + Arguments: + datasets (iterable): List of datasets to be concatenated + """ + + @staticmethod + def cumsum(sequence): + r, s = [], 0 + for e in sequence: + l = len(e) + r.append(l + s) + s += l + return r + + def __init__(self, datasets): + super(ConcatDataset, self).__init__() + assert len(datasets) > 0, 'datasets should not be an empty iterable' + self.datasets = list(datasets) + self.cumulative_sizes = self.cumsum(self.datasets) + + def __len__(self): + return self.cumulative_sizes[-1] + + def __getitem__(self, idx): + dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx) + if dataset_idx == 0: + sample_idx = idx + else: + sample_idx = idx - self.cumulative_sizes[dataset_idx - 1] + return self.datasets[dataset_idx][sample_idx] + + @property + def cummulative_sizes(self): + warnings.warn("cummulative_sizes attribute is renamed to " + "cumulative_sizes", DeprecationWarning, stacklevel=2) + return self.cumulative_sizes + + +class Subset(Dataset): + def __init__(self, dataset, indices): + self.dataset = dataset + self.indices = indices + + def __getitem__(self, idx): + return self.dataset[self.indices[idx]] + + def __len__(self): + return len(self.indices) + + +def random_split(dataset, lengths): + """ + Randomly split a dataset into non-overlapping new datasets of given lengths + ds + + Arguments: + dataset (Dataset): Dataset to be split + lengths (iterable): lengths of splits to be produced + """ + if sum(lengths) != len(dataset): + raise ValueError("Sum of input lengths does not equal the length of the input dataset!") + + indices = randperm(sum(lengths)) + return [Subset(dataset, indices[offset - length:offset]) for offset, length in zip(_accumulate(lengths), lengths)] diff --git a/third-party/lama/bin/models/ade20k/segm_lib/utils/data/distributed.py b/third-party/lama/bin/models/ade20k/segm_lib/utils/data/distributed.py new file mode 100644 index 0000000000000000000000000000000000000000..c3d890e28fd2b9e044bdd9494de4a43ad2471eed --- /dev/null +++ b/third-party/lama/bin/models/ade20k/segm_lib/utils/data/distributed.py @@ -0,0 +1,58 @@ +import math +import torch +from .sampler import Sampler +from torch.distributed import get_world_size, get_rank + + +class DistributedSampler(Sampler): + """Sampler that restricts data loading to a subset of the dataset. + + It is especially useful in conjunction with + :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each + process can pass a DistributedSampler instance as a DataLoader sampler, + and load a subset of the original dataset that is exclusive to it. + + .. note:: + Dataset is assumed to be of constant size. + + Arguments: + dataset: Dataset used for sampling. + num_replicas (optional): Number of processes participating in + distributed training. + rank (optional): Rank of the current process within num_replicas. + """ + + def __init__(self, dataset, num_replicas=None, rank=None): + if num_replicas is None: + num_replicas = get_world_size() + if rank is None: + rank = get_rank() + self.dataset = dataset + self.num_replicas = num_replicas + self.rank = rank + self.epoch = 0 + self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas)) + self.total_size = self.num_samples * self.num_replicas + + def __iter__(self): + # deterministically shuffle based on epoch + g = torch.Generator() + g.manual_seed(self.epoch) + indices = list(torch.randperm(len(self.dataset), generator=g)) + + # add extra samples to make it evenly divisible + indices += indices[:(self.total_size - len(indices))] + assert len(indices) == self.total_size + + # subsample + offset = self.num_samples * self.rank + indices = indices[offset:offset + self.num_samples] + assert len(indices) == self.num_samples + + return iter(indices) + + def __len__(self): + return self.num_samples + + def set_epoch(self, epoch): + self.epoch = epoch diff --git a/third-party/lama/bin/models/ade20k/segm_lib/utils/data/sampler.py b/third-party/lama/bin/models/ade20k/segm_lib/utils/data/sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..62a9a43bd1d4c21fbdcb262db7da8d4fe27b26de --- /dev/null +++ b/third-party/lama/bin/models/ade20k/segm_lib/utils/data/sampler.py @@ -0,0 +1,131 @@ +import torch + + +class Sampler(object): + """Base class for all Samplers. + + Every Sampler subclass has to provide an __iter__ method, providing a way + to iterate over indices of dataset elements, and a __len__ method that + returns the length of the returned iterators. + """ + + def __init__(self, data_source): + pass + + def __iter__(self): + raise NotImplementedError + + def __len__(self): + raise NotImplementedError + + +class SequentialSampler(Sampler): + """Samples elements sequentially, always in the same order. + + Arguments: + data_source (Dataset): dataset to sample from + """ + + def __init__(self, data_source): + self.data_source = data_source + + def __iter__(self): + return iter(range(len(self.data_source))) + + def __len__(self): + return len(self.data_source) + + +class RandomSampler(Sampler): + """Samples elements randomly, without replacement. + + Arguments: + data_source (Dataset): dataset to sample from + """ + + def __init__(self, data_source): + self.data_source = data_source + + def __iter__(self): + return iter(torch.randperm(len(self.data_source)).long()) + + def __len__(self): + return len(self.data_source) + + +class SubsetRandomSampler(Sampler): + """Samples elements randomly from a given list of indices, without replacement. + + Arguments: + indices (list): a list of indices + """ + + def __init__(self, indices): + self.indices = indices + + def __iter__(self): + return (self.indices[i] for i in torch.randperm(len(self.indices))) + + def __len__(self): + return len(self.indices) + + +class WeightedRandomSampler(Sampler): + """Samples elements from [0,..,len(weights)-1] with given probabilities (weights). + + Arguments: + weights (list) : a list of weights, not necessary summing up to one + num_samples (int): number of samples to draw + replacement (bool): if ``True``, samples are drawn with replacement. + If not, they are drawn without replacement, which means that when a + sample index is drawn for a row, it cannot be drawn again for that row. + """ + + def __init__(self, weights, num_samples, replacement=True): + self.weights = torch.DoubleTensor(weights) + self.num_samples = num_samples + self.replacement = replacement + + def __iter__(self): + return iter(torch.multinomial(self.weights, self.num_samples, self.replacement)) + + def __len__(self): + return self.num_samples + + +class BatchSampler(object): + """Wraps another sampler to yield a mini-batch of indices. + + Args: + sampler (Sampler): Base sampler. + batch_size (int): Size of mini-batch. + drop_last (bool): If ``True``, the sampler will drop the last batch if + its size would be less than ``batch_size`` + + Example: + >>> list(BatchSampler(range(10), batch_size=3, drop_last=False)) + [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] + >>> list(BatchSampler(range(10), batch_size=3, drop_last=True)) + [[0, 1, 2], [3, 4, 5], [6, 7, 8]] + """ + + def __init__(self, sampler, batch_size, drop_last): + self.sampler = sampler + self.batch_size = batch_size + self.drop_last = drop_last + + def __iter__(self): + batch = [] + for idx in self.sampler: + batch.append(idx) + if len(batch) == self.batch_size: + yield batch + batch = [] + if len(batch) > 0 and not self.drop_last: + yield batch + + def __len__(self): + if self.drop_last: + return len(self.sampler) // self.batch_size + else: + return (len(self.sampler) + self.batch_size - 1) // self.batch_size diff --git a/third-party/lama/bin/models/ade20k/segm_lib/utils/th.py b/third-party/lama/bin/models/ade20k/segm_lib/utils/th.py new file mode 100644 index 0000000000000000000000000000000000000000..ca6ef9385e3b5c0a439579d3fd7aa73b5dc62758 --- /dev/null +++ b/third-party/lama/bin/models/ade20k/segm_lib/utils/th.py @@ -0,0 +1,41 @@ +import torch +from torch.autograd import Variable +import numpy as np +import collections + +__all__ = ['as_variable', 'as_numpy', 'mark_volatile'] + +def as_variable(obj): + if isinstance(obj, Variable): + return obj + if isinstance(obj, collections.Sequence): + return [as_variable(v) for v in obj] + elif isinstance(obj, collections.Mapping): + return {k: as_variable(v) for k, v in obj.items()} + else: + return Variable(obj) + +def as_numpy(obj): + if isinstance(obj, collections.Sequence): + return [as_numpy(v) for v in obj] + elif isinstance(obj, collections.Mapping): + return {k: as_numpy(v) for k, v in obj.items()} + elif isinstance(obj, Variable): + return obj.data.cpu().numpy() + elif torch.is_tensor(obj): + return obj.cpu().numpy() + else: + return np.array(obj) + +def mark_volatile(obj): + if torch.is_tensor(obj): + obj = Variable(obj) + if isinstance(obj, Variable): + obj.no_grad = True + return obj + elif isinstance(obj, collections.Mapping): + return {k: mark_volatile(o) for k, o in obj.items()} + elif isinstance(obj, collections.Sequence): + return [mark_volatile(o) for o in obj] + else: + return obj diff --git a/third-party/lama/bin/models/ade20k/utils.py b/third-party/lama/bin/models/ade20k/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f337db7db54c82be041698d694e1403e8918c4c0 --- /dev/null +++ b/third-party/lama/bin/models/ade20k/utils.py @@ -0,0 +1,40 @@ +"""Modified from https://github.com/CSAILVision/semantic-segmentation-pytorch""" + +import os +import sys + +import numpy as np +import torch + +try: + from urllib import urlretrieve +except ImportError: + from urllib.request import urlretrieve + + +def load_url(url, model_dir='./pretrained', map_location=None): + if not os.path.exists(model_dir): + os.makedirs(model_dir) + filename = url.split('/')[-1] + cached_file = os.path.join(model_dir, filename) + if not os.path.exists(cached_file): + sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file)) + urlretrieve(url, cached_file) + return torch.load(cached_file, map_location=map_location) + + +def color_encode(labelmap, colors, mode='RGB'): + labelmap = labelmap.astype('int') + labelmap_rgb = np.zeros((labelmap.shape[0], labelmap.shape[1], 3), + dtype=np.uint8) + for label in np.unique(labelmap): + if label < 0: + continue + labelmap_rgb += (labelmap == label)[:, :, np.newaxis] * \ + np.tile(colors[label], + (labelmap.shape[0], labelmap.shape[1], 1)) + + if mode == 'BGR': + return labelmap_rgb[:, :, ::-1] + else: + return labelmap_rgb diff --git a/third-party/lama/bin/models/lpips_models/alex.pth b/third-party/lama/bin/models/lpips_models/alex.pth new file mode 100644 index 0000000000000000000000000000000000000000..fa4067abc5d4da16a7204fd94776506e4868030e --- /dev/null +++ b/third-party/lama/bin/models/lpips_models/alex.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:df73285e35b22355a2df87cdb6b70b343713b667eddbda73e1977e0c860835c0 +size 6009 diff --git a/third-party/lama/bin/models/lpips_models/squeeze.pth b/third-party/lama/bin/models/lpips_models/squeeze.pth new file mode 100644 index 0000000000000000000000000000000000000000..f892a84a130828b1c9e2e8156e84fc5a962c665d --- /dev/null +++ b/third-party/lama/bin/models/lpips_models/squeeze.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4a5350f23600cb79923ce65bb07cbf57dca461329894153e05a1346bd531cf76 +size 10811 diff --git a/third-party/lama/bin/models/lpips_models/vgg.pth b/third-party/lama/bin/models/lpips_models/vgg.pth new file mode 100644 index 0000000000000000000000000000000000000000..f57dcf5cc764d61c8a460365847fb2137ff0a62d --- /dev/null +++ b/third-party/lama/bin/models/lpips_models/vgg.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a78928a0af1e5f0fcb1f3b9e8f8c3a2a5a3de244d830ad5c1feddc79b8432868 +size 7289 diff --git a/third-party/lama/bin/paper_runfiles/blur_tests.sh b/third-party/lama/bin/paper_runfiles/blur_tests.sh new file mode 100755 index 0000000000000000000000000000000000000000..8f204a4c643d08935e5561ed27a286536643958d --- /dev/null +++ b/third-party/lama/bin/paper_runfiles/blur_tests.sh @@ -0,0 +1,37 @@ +##!/usr/bin/env bash +# +## !!! file set to make test_large_30k from the vanilla test_large: configs/test_large_30k.lst +# +## paths to data are valid for mml7 +#PLACES_ROOT="/data/inpainting/Places365" +#OUT_DIR="/data/inpainting/paper_data/Places365_val_test" +# +#source "$(dirname $0)/env.sh" +# +#for datadir in test_large_30k # val_large +#do +# for conf in random_thin_256 random_medium_256 random_thick_256 random_thin_512 random_medium_512 random_thick_512 +# do +# "$BINDIR/gen_mask_dataset.py" "$CONFIGDIR/data_gen/${conf}.yaml" \ +# "$PLACES_ROOT/$datadir" "$OUT_DIR/$datadir/$conf" --n-jobs 8 +# +# "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats" +# done +# +# for conf in segm_256 segm_512 +# do +# "$BINDIR/gen_mask_dataset.py" "$CONFIGDIR/data_gen/${conf}.yaml" \ +# "$PLACES_ROOT/$datadir" "$OUT_DIR/$datadir/$conf" --n-jobs 2 +# +# "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats" +# done +#done +# +#IN_DIR="/data/inpainting/paper_data/Places365_val_test/test_large_30k/random_medium_512" +#PRED_DIR="/data/inpainting/predictions/final/images/r.suvorov_2021-03-05_17-08-35_train_ablv2_work_resume_epoch37/random_medium_512" +#BLUR_OUT_DIR="/data/inpainting/predictions/final/blur/images" +# +#for b in 0.1 +# +#"$BINDIR/blur_predicts.py" "$BASEDIR/../../configs/eval2.yaml" "$CUR_IN_DIR" "$CUR_OUT_DIR" "$CUR_EVAL_DIR" +# diff --git a/third-party/lama/bin/paper_runfiles/env.sh b/third-party/lama/bin/paper_runfiles/env.sh new file mode 100644 index 0000000000000000000000000000000000000000..f3052f0ea1672a569e7775f8c54967d730a7b5ec --- /dev/null +++ b/third-party/lama/bin/paper_runfiles/env.sh @@ -0,0 +1,8 @@ +DIRNAME="$(dirname $0)" +DIRNAME="$(realpath ""$DIRNAME"")" + +BINDIR="$DIRNAME/.." +SRCDIR="$BINDIR/.." +CONFIGDIR="$SRCDIR/configs" + +export PYTHONPATH="$SRCDIR:$PYTHONPATH" diff --git a/third-party/lama/bin/paper_runfiles/find_best_checkpoint.py b/third-party/lama/bin/paper_runfiles/find_best_checkpoint.py new file mode 100755 index 0000000000000000000000000000000000000000..42f5e0f9bb1a2ea25dd9a97a58cf318e6de19532 --- /dev/null +++ b/third-party/lama/bin/paper_runfiles/find_best_checkpoint.py @@ -0,0 +1,54 @@ +#!/usr/bin/env python3 + + +import os +from argparse import ArgumentParser + + +def ssim_fid100_f1(metrics, fid_scale=100): + ssim = metrics.loc['total', 'ssim']['mean'] + fid = metrics.loc['total', 'fid']['mean'] + fid_rel = max(0, fid_scale - fid) / fid_scale + f1 = 2 * ssim * fid_rel / (ssim + fid_rel + 1e-3) + return f1 + + +def find_best_checkpoint(model_list, models_dir): + with open(model_list) as f: + models = [m.strip() for m in f.readlines()] + with open(f'{model_list}_best', 'w') as f: + for model in models: + print(model) + best_f1 = 0 + best_epoch = 0 + best_step = 0 + with open(os.path.join(models_dir, model, 'train.log')) as fm: + lines = fm.readlines() + for line_index in range(len(lines)): + line = lines[line_index] + if 'Validation metrics after epoch' in line: + sharp_index = line.index('#') + cur_ep = line[sharp_index + 1:] + comma_index = cur_ep.index(',') + cur_ep = int(cur_ep[:comma_index]) + total_index = line.index('total ') + step = int(line[total_index:].split()[1].strip()) + total_line = lines[line_index + 5] + if not total_line.startswith('total'): + continue + words = total_line.strip().split() + f1 = float(words[-1]) + print(f'\tEpoch: {cur_ep}, f1={f1}') + if f1 > best_f1: + best_f1 = f1 + best_epoch = cur_ep + best_step = step + f.write(f'{model}\t{best_epoch}\t{best_step}\t{best_f1}\n') + + +if __name__ == '__main__': + parser = ArgumentParser() + parser.add_argument('model_list') + parser.add_argument('models_dir') + args = parser.parse_args() + find_best_checkpoint(args.model_list, args.models_dir) diff --git a/third-party/lama/bin/paper_runfiles/generate_test_celeba-hq.sh b/third-party/lama/bin/paper_runfiles/generate_test_celeba-hq.sh new file mode 100755 index 0000000000000000000000000000000000000000..7e04bba426f1c6c0528d88a0e28a5da0dde7ca3e --- /dev/null +++ b/third-party/lama/bin/paper_runfiles/generate_test_celeba-hq.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash + +# paths to data are valid for mml-ws01 +OUT_DIR="/media/inpainting/paper_data/CelebA-HQ_val_test" + +source "$(dirname $0)/env.sh" + +for datadir in "val" "test" +do + for conf in random_thin_256 random_medium_256 random_thick_256 random_thin_512 random_medium_512 random_thick_512 + do + "$BINDIR/gen_mask_dataset_hydra.py" -cn $conf datadir=$datadir location=mml-ws01-celeba-hq \ + location.out_dir=$OUT_DIR cropping.out_square_crop=False + + "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats" + done +done diff --git a/third-party/lama/bin/paper_runfiles/generate_test_ffhq.sh b/third-party/lama/bin/paper_runfiles/generate_test_ffhq.sh new file mode 100755 index 0000000000000000000000000000000000000000..a1b79cb0f3f710eed21a978c3a1489ca830bb7f8 --- /dev/null +++ b/third-party/lama/bin/paper_runfiles/generate_test_ffhq.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash + +# paths to data are valid for mml-ws01 +OUT_DIR="/media/inpainting/paper_data/FFHQ_val" + +source "$(dirname $0)/env.sh" + +for datadir in test +do + for conf in random_thin_256 random_medium_256 random_thick_256 random_thin_512 random_medium_512 random_thick_512 + do + "$BINDIR/gen_mask_dataset_hydra.py" -cn $conf datadir=$datadir location=mml-ws01-ffhq \ + location.out_dir=$OUT_DIR cropping.out_square_crop=False + + "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats" + done +done diff --git a/third-party/lama/bin/paper_runfiles/generate_test_paris.sh b/third-party/lama/bin/paper_runfiles/generate_test_paris.sh new file mode 100755 index 0000000000000000000000000000000000000000..66056017c3aa376ef0767a59583ab25a321b559b --- /dev/null +++ b/third-party/lama/bin/paper_runfiles/generate_test_paris.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash + +# paths to data are valid for mml-ws01 +OUT_DIR="/media/inpainting/paper_data/Paris_StreetView_Dataset_val" + +source "$(dirname $0)/env.sh" + +for datadir in paris_eval_gt +do + for conf in random_thin_256 random_medium_256 random_thick_256 segm_256 + do + "$BINDIR/gen_mask_dataset_hydra.py" -cn $conf datadir=$datadir location=mml-ws01-paris \ + location.out_dir=OUT_DIR cropping.out_square_crop=False cropping.out_min_size=227 + + "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats" + done +done diff --git a/third-party/lama/bin/paper_runfiles/generate_test_paris_256.sh b/third-party/lama/bin/paper_runfiles/generate_test_paris_256.sh new file mode 100755 index 0000000000000000000000000000000000000000..67061298b601ce4e1c37966852421f2153a0d686 --- /dev/null +++ b/third-party/lama/bin/paper_runfiles/generate_test_paris_256.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash + +# paths to data are valid for mml-ws01 +OUT_DIR="/media/inpainting/paper_data/Paris_StreetView_Dataset_val_256" + +source "$(dirname $0)/env.sh" + +for datadir in paris_eval_gt +do + for conf in random_thin_256 random_medium_256 random_thick_256 segm_256 + do + "$BINDIR/gen_mask_dataset_hydra.py" -cn $conf datadir=$datadir location=mml-ws01-paris \ + location.out_dir=$OUT_DIR cropping.out_square_crop=False cropping.out_min_size=256 + + "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats" + done +done diff --git a/third-party/lama/bin/paper_runfiles/generate_val_test.sh b/third-party/lama/bin/paper_runfiles/generate_val_test.sh new file mode 100755 index 0000000000000000000000000000000000000000..d9b2a370ceeeb8f401706f4303298db13e5fad91 --- /dev/null +++ b/third-party/lama/bin/paper_runfiles/generate_val_test.sh @@ -0,0 +1,28 @@ +#!/usr/bin/env bash + +# !!! file set to make test_large_30k from the vanilla test_large: configs/test_large_30k.lst + +# paths to data are valid for mml7 +PLACES_ROOT="/data/inpainting/Places365" +OUT_DIR="/data/inpainting/paper_data/Places365_val_test" + +source "$(dirname $0)/env.sh" + +for datadir in test_large_30k # val_large +do + for conf in random_thin_256 random_medium_256 random_thick_256 random_thin_512 random_medium_512 random_thick_512 + do + "$BINDIR/gen_mask_dataset.py" "$CONFIGDIR/data_gen/${conf}.yaml" \ + "$PLACES_ROOT/$datadir" "$OUT_DIR/$datadir/$conf" --n-jobs 8 + + "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats" + done + + for conf in segm_256 segm_512 + do + "$BINDIR/gen_mask_dataset.py" "$CONFIGDIR/data_gen/${conf}.yaml" \ + "$PLACES_ROOT/$datadir" "$OUT_DIR/$datadir/$conf" --n-jobs 2 + + "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats" + done +done diff --git a/third-party/lama/bin/paper_runfiles/predict_inner_features.sh b/third-party/lama/bin/paper_runfiles/predict_inner_features.sh new file mode 100755 index 0000000000000000000000000000000000000000..864c1a0fca8b93b2a193656e45ff55f6a051eb8c --- /dev/null +++ b/third-party/lama/bin/paper_runfiles/predict_inner_features.sh @@ -0,0 +1,20 @@ +#!/usr/bin/env bash + +# paths to data are valid for mml7 + +source "$(dirname $0)/env.sh" + +"$BINDIR/predict_inner_features.py" \ + -cn default_inner_features_ffc \ + model.path="/data/inpainting/paper_data/final_models/ours/r.suvorov_2021-03-05_17-34-05_train_ablv2_work_ffc075_resume_epoch39" \ + indir="/data/inpainting/paper_data/inner_features_vis/input/" \ + outdir="/data/inpainting/paper_data/inner_features_vis/output/ffc" \ + dataset.img_suffix=.png + + +"$BINDIR/predict_inner_features.py" \ + -cn default_inner_features_work \ + model.path="/data/inpainting/paper_data/final_models/ours/r.suvorov_2021-03-05_17-08-35_train_ablv2_work_resume_epoch37" \ + indir="/data/inpainting/paper_data/inner_features_vis/input/" \ + outdir="/data/inpainting/paper_data/inner_features_vis/output/work" \ + dataset.img_suffix=.png diff --git a/third-party/lama/bin/paper_runfiles/update_test_data_stats.sh b/third-party/lama/bin/paper_runfiles/update_test_data_stats.sh new file mode 100755 index 0000000000000000000000000000000000000000..ff77d586f308202fbd019d8cc4be641f0d6aa1a5 --- /dev/null +++ b/third-party/lama/bin/paper_runfiles/update_test_data_stats.sh @@ -0,0 +1,30 @@ +#!/usr/bin/env bash + +# paths to data are valid for mml7 + +source "$(dirname $0)/env.sh" + +#INDIR="/data/inpainting/paper_data/Places365_val_test/test_large_30k" +# +#for dataset in random_medium_256 random_medium_512 random_thick_256 random_thick_512 random_thin_256 random_thin_512 +#do +# "$BINDIR/calc_dataset_stats.py" "$INDIR/$dataset" "$INDIR/${dataset}_stats2" +#done +# +#"$BINDIR/calc_dataset_stats.py" "/data/inpainting/evalset2" "/data/inpainting/evalset2_stats2" + + +INDIR="/data/inpainting/paper_data/CelebA-HQ_val_test/test" + +for dataset in random_medium_256 random_thick_256 random_thin_256 +do + "$BINDIR/calc_dataset_stats.py" "$INDIR/$dataset" "$INDIR/${dataset}_stats2" +done + + +INDIR="/data/inpainting/paper_data/Paris_StreetView_Dataset_val_256/paris_eval_gt" + +for dataset in random_medium_256 random_thick_256 random_thin_256 +do + "$BINDIR/calc_dataset_stats.py" "$INDIR/$dataset" "$INDIR/${dataset}_stats2" +done \ No newline at end of file diff --git a/third-party/lama/bin/predict.py b/third-party/lama/bin/predict.py new file mode 100755 index 0000000000000000000000000000000000000000..0f704d4043a28b1bc6d80a6862be4830fe4c51ef --- /dev/null +++ b/third-party/lama/bin/predict.py @@ -0,0 +1,103 @@ +#!/usr/bin/env python3 + +# Example command: +# ./bin/predict.py \ +# model.path= \ +# indir= \ +# outdir= + +import logging +import os +import sys +import traceback + +from saicinpainting.evaluation.utils import move_to_device +from saicinpainting.evaluation.refinement import refine_predict +os.environ['OMP_NUM_THREADS'] = '1' +os.environ['OPENBLAS_NUM_THREADS'] = '1' +os.environ['MKL_NUM_THREADS'] = '1' +os.environ['VECLIB_MAXIMUM_THREADS'] = '1' +os.environ['NUMEXPR_NUM_THREADS'] = '1' + +import cv2 +import hydra +import numpy as np +import torch +import tqdm +import yaml +from omegaconf import OmegaConf +from torch.utils.data._utils.collate import default_collate + +from saicinpainting.training.data.datasets import make_default_val_dataset +from saicinpainting.training.trainers import load_checkpoint +from saicinpainting.utils import register_debug_signal_handlers + +LOGGER = logging.getLogger(__name__) + + +@hydra.main(config_path='../configs/prediction', config_name='default.yaml') +def main(predict_config: OmegaConf): + try: + register_debug_signal_handlers() # kill -10 will result in traceback dumped into log + + device = torch.device(predict_config.device) + + train_config_path = os.path.join(predict_config.model.path, 'config.yaml') + with open(train_config_path, 'r') as f: + train_config = OmegaConf.create(yaml.safe_load(f)) + + train_config.training_model.predict_only = True + train_config.visualizer.kind = 'noop' + + out_ext = predict_config.get('out_ext', '.png') + + checkpoint_path = os.path.join(predict_config.model.path, + 'models', + predict_config.model.checkpoint) + model = load_checkpoint(train_config, checkpoint_path, strict=False, map_location='cpu') + model.freeze() + if not predict_config.get('refine', False): + model.to(device) + + if not predict_config.indir.endswith('/'): + predict_config.indir += '/' + + dataset = make_default_val_dataset(predict_config.indir, **predict_config.dataset) + for img_i in tqdm.trange(len(dataset)): + mask_fname = dataset.mask_filenames[img_i] + cur_out_fname = os.path.join( + predict_config.outdir, + os.path.splitext(mask_fname[len(predict_config.indir):])[0] + out_ext + ) + os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True) + batch = default_collate([dataset[img_i]]) + if predict_config.get('refine', False): + assert 'unpad_to_size' in batch, "Unpadded size is required for the refinement" + # image unpadding is taken care of in the refiner, so that output image + # is same size as the input image + cur_res = refine_predict(batch, model, **predict_config.refiner) + cur_res = cur_res[0].permute(1,2,0).detach().cpu().numpy() + else: + with torch.no_grad(): + batch = move_to_device(batch, device) + batch['mask'] = (batch['mask'] > 0) * 1 + batch = model(batch) + cur_res = batch[predict_config.out_key][0].permute(1, 2, 0).detach().cpu().numpy() + unpad_to_size = batch.get('unpad_to_size', None) + if unpad_to_size is not None: + orig_height, orig_width = unpad_to_size + cur_res = cur_res[:orig_height, :orig_width] + + cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8') + cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR) + cv2.imwrite(cur_out_fname, cur_res) + + except KeyboardInterrupt: + LOGGER.warning('Interrupted by user') + except Exception as ex: + LOGGER.critical(f'Prediction failed due to {ex}:\n{traceback.format_exc()}') + sys.exit(1) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/third-party/lama/bin/predict_inner_features.py b/third-party/lama/bin/predict_inner_features.py new file mode 100755 index 0000000000000000000000000000000000000000..4f9f7a11a6c4757a4eaa05cf1ac648d372f7e02f --- /dev/null +++ b/third-party/lama/bin/predict_inner_features.py @@ -0,0 +1,119 @@ +#!/usr/bin/env python3 + +# Example command: +# ./bin/predict.py \ +# model.path= \ +# indir= \ +# outdir= + +import logging +import os +import sys +import traceback + +from saicinpainting.evaluation.utils import move_to_device + +os.environ['OMP_NUM_THREADS'] = '1' +os.environ['OPENBLAS_NUM_THREADS'] = '1' +os.environ['MKL_NUM_THREADS'] = '1' +os.environ['VECLIB_MAXIMUM_THREADS'] = '1' +os.environ['NUMEXPR_NUM_THREADS'] = '1' + +import cv2 +import hydra +import numpy as np +import torch +import tqdm +import yaml +from omegaconf import OmegaConf +from torch.utils.data._utils.collate import default_collate + +from saicinpainting.training.data.datasets import make_default_val_dataset +from saicinpainting.training.trainers import load_checkpoint, DefaultInpaintingTrainingModule +from saicinpainting.utils import register_debug_signal_handlers, get_shape + +LOGGER = logging.getLogger(__name__) + + +@hydra.main(config_path='../configs/prediction', config_name='default_inner_features.yaml') +def main(predict_config: OmegaConf): + try: + register_debug_signal_handlers() # kill -10 will result in traceback dumped into log + + device = torch.device(predict_config.device) + + train_config_path = os.path.join(predict_config.model.path, 'config.yaml') + with open(train_config_path, 'r') as f: + train_config = OmegaConf.create(yaml.safe_load(f)) + + checkpoint_path = os.path.join(predict_config.model.path, 'models', predict_config.model.checkpoint) + model = load_checkpoint(train_config, checkpoint_path, strict=False) + model.freeze() + model.to(device) + + assert isinstance(model, DefaultInpaintingTrainingModule), 'Only DefaultInpaintingTrainingModule is supported' + assert isinstance(getattr(model.generator, 'model', None), torch.nn.Sequential) + + if not predict_config.indir.endswith('/'): + predict_config.indir += '/' + + dataset = make_default_val_dataset(predict_config.indir, **predict_config.dataset) + + max_level = max(predict_config.levels) + + with torch.no_grad(): + for img_i in tqdm.trange(len(dataset)): + mask_fname = dataset.mask_filenames[img_i] + cur_out_fname = os.path.join(predict_config.outdir, os.path.splitext(mask_fname[len(predict_config.indir):])[0]) + os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True) + + batch = move_to_device(default_collate([dataset[img_i]]), device) + + img = batch['image'] + mask = batch['mask'] + mask[:] = 0 + mask_h, mask_w = mask.shape[-2:] + mask[:, :, + mask_h // 2 - predict_config.hole_radius : mask_h // 2 + predict_config.hole_radius, + mask_w // 2 - predict_config.hole_radius : mask_w // 2 + predict_config.hole_radius] = 1 + + masked_img = torch.cat([img * (1 - mask), mask], dim=1) + + feats = masked_img + for level_i, level in enumerate(model.generator.model): + feats = level(feats) + if level_i in predict_config.levels: + cur_feats = torch.cat([f for f in feats if torch.is_tensor(f)], dim=1) \ + if isinstance(feats, tuple) else feats + + if predict_config.slice_channels: + cur_feats = cur_feats[:, slice(*predict_config.slice_channels)] + + cur_feat = cur_feats.pow(2).mean(1).pow(0.5).clone() + cur_feat -= cur_feat.min() + cur_feat /= cur_feat.std() + cur_feat = cur_feat.clamp(0, 1) / 1 + cur_feat = cur_feat.cpu().numpy()[0] + cur_feat *= 255 + cur_feat = np.clip(cur_feat, 0, 255).astype('uint8') + cv2.imwrite(cur_out_fname + f'_lev{level_i:02d}_norm.png', cur_feat) + + # for channel_i in predict_config.channels: + # + # cur_feat = cur_feats[0, channel_i].clone().detach().cpu().numpy() + # cur_feat -= cur_feat.min() + # cur_feat /= cur_feat.max() + # cur_feat *= 255 + # cur_feat = np.clip(cur_feat, 0, 255).astype('uint8') + # cv2.imwrite(cur_out_fname + f'_lev{level_i}_ch{channel_i}.png', cur_feat) + elif level_i >= max_level: + break + except KeyboardInterrupt: + LOGGER.warning('Interrupted by user') + except Exception as ex: + LOGGER.critical(f'Prediction failed due to {ex}:\n{traceback.format_exc()}') + sys.exit(1) + + +if __name__ == '__main__': + main() diff --git a/third-party/lama/bin/report_from_tb.py b/third-party/lama/bin/report_from_tb.py new file mode 100755 index 0000000000000000000000000000000000000000..9a444e6cd8027f88bd34adfc0b1dd000bbb4b2be --- /dev/null +++ b/third-party/lama/bin/report_from_tb.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python3 + +import glob +import os +import re + +import tensorflow as tf +from torch.utils.tensorboard import SummaryWriter + + +GROUPING_RULES = [ + re.compile(r'^(?Ptrain|test|val|extra_val_.*?(256|512))_(?P.*)', re.I) +] + + +DROP_RULES = [ + re.compile(r'_std$', re.I) +] + + +def need_drop(tag): + for rule in DROP_RULES: + if rule.search(tag): + return True + return False + + +def get_group_and_title(tag): + for rule in GROUPING_RULES: + match = rule.search(tag) + if match is None: + continue + return match.group('group'), match.group('title') + return None, None + + +def main(args): + os.makedirs(args.outdir, exist_ok=True) + + ignored_events = set() + + for orig_fname in glob.glob(args.inglob): + cur_dirpath = os.path.dirname(orig_fname) # remove filename, this should point to "version_0" directory + subdirname = os.path.basename(cur_dirpath) # == "version_0" most of time + exp_root_path = os.path.dirname(cur_dirpath) # remove "version_0" + exp_name = os.path.basename(exp_root_path) + + writers_by_group = {} + + for e in tf.compat.v1.train.summary_iterator(orig_fname): + for v in e.summary.value: + if need_drop(v.tag): + continue + + cur_group, cur_title = get_group_and_title(v.tag) + if cur_group is None: + if v.tag not in ignored_events: + print(f'WARNING: Could not detect group for {v.tag}, ignoring it') + ignored_events.add(v.tag) + continue + + cur_writer = writers_by_group.get(cur_group, None) + if cur_writer is None: + if args.include_version: + cur_outdir = os.path.join(args.outdir, exp_name, f'{subdirname}_{cur_group}') + else: + cur_outdir = os.path.join(args.outdir, exp_name, cur_group) + cur_writer = SummaryWriter(cur_outdir) + writers_by_group[cur_group] = cur_writer + + cur_writer.add_scalar(cur_title, v.simple_value, global_step=e.step, walltime=e.wall_time) + + +if __name__ == '__main__': + import argparse + + aparser = argparse.ArgumentParser() + aparser.add_argument('inglob', type=str) + aparser.add_argument('outdir', type=str) + aparser.add_argument('--include-version', action='store_true', + help='Include subdirectory name e.g. "version_0" into output path') + + main(aparser.parse_args()) diff --git a/third-party/lama/bin/saicinpainting/__init__.py b/third-party/lama/bin/saicinpainting/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third-party/lama/bin/saicinpainting/evaluation/__init__.py b/third-party/lama/bin/saicinpainting/evaluation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e9c8117565b252ca069a808b31b8c52aaddd2289 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/evaluation/__init__.py @@ -0,0 +1,33 @@ +import logging + +import torch + +from saicinpainting.evaluation.evaluator import InpaintingEvaluatorOnline, ssim_fid100_f1, lpips_fid100_f1 +from saicinpainting.evaluation.losses.base_loss import SSIMScore, LPIPSScore, FIDScore + + +def make_evaluator(kind='default', ssim=True, lpips=True, fid=True, integral_kind=None, **kwargs): + logging.info(f'Make evaluator {kind}') + device = "cuda" if torch.cuda.is_available() else "cpu" + metrics = {} + if ssim: + metrics['ssim'] = SSIMScore() + if lpips: + metrics['lpips'] = LPIPSScore() + if fid: + metrics['fid'] = FIDScore().to(device) + + if integral_kind is None: + integral_func = None + elif integral_kind == 'ssim_fid100_f1': + integral_func = ssim_fid100_f1 + elif integral_kind == 'lpips_fid100_f1': + integral_func = lpips_fid100_f1 + else: + raise ValueError(f'Unexpected integral_kind={integral_kind}') + + if kind == 'default': + return InpaintingEvaluatorOnline(scores=metrics, + integral_func=integral_func, + integral_title=integral_kind, + **kwargs) diff --git a/third-party/lama/bin/saicinpainting/evaluation/data.py b/third-party/lama/bin/saicinpainting/evaluation/data.py new file mode 100644 index 0000000000000000000000000000000000000000..89a4ea4c9577e6131731444f149eec76978ec260 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/evaluation/data.py @@ -0,0 +1,168 @@ +import glob +import os + +import cv2 +import PIL.Image as Image +import numpy as np + +from torch.utils.data import Dataset +import torch.nn.functional as F + + +def load_image(fname, mode='RGB', return_orig=False): + img = np.array(Image.open(fname).convert(mode)) + if img.ndim == 3: + img = np.transpose(img, (2, 0, 1)) + out_img = img.astype('float32') / 255 + if return_orig: + return out_img, img + else: + return out_img + + +def ceil_modulo(x, mod): + if x % mod == 0: + return x + return (x // mod + 1) * mod + + +def pad_img_to_modulo(img, mod): + channels, height, width = img.shape + out_height = ceil_modulo(height, mod) + out_width = ceil_modulo(width, mod) + return np.pad(img, ((0, 0), (0, out_height - height), (0, out_width - width)), mode='symmetric') + + +def pad_tensor_to_modulo(img, mod): + batch_size, channels, height, width = img.shape + out_height = ceil_modulo(height, mod) + out_width = ceil_modulo(width, mod) + return F.pad(img, pad=(0, out_width - width, 0, out_height - height), mode='reflect') + + +def scale_image(img, factor, interpolation=cv2.INTER_AREA): + if img.shape[0] == 1: + img = img[0] + else: + img = np.transpose(img, (1, 2, 0)) + + img = cv2.resize(img, dsize=None, fx=factor, fy=factor, interpolation=interpolation) + + if img.ndim == 2: + img = img[None, ...] + else: + img = np.transpose(img, (2, 0, 1)) + return img + + +class InpaintingDataset(Dataset): + def __init__(self, datadir, img_suffix='.jpg', pad_out_to_modulo=None, scale_factor=None): + self.datadir = datadir + self.mask_filenames = sorted(list(glob.glob(os.path.join(self.datadir, '**', '*mask*.png'), recursive=True))) + self.img_filenames = [fname.rsplit('_mask', 1)[0] + img_suffix for fname in self.mask_filenames] + self.pad_out_to_modulo = pad_out_to_modulo + self.scale_factor = scale_factor + + def __len__(self): + return len(self.mask_filenames) + + def __getitem__(self, i): + image = load_image(self.img_filenames[i], mode='RGB') + mask = load_image(self.mask_filenames[i], mode='L') + result = dict(image=image, mask=mask[None, ...]) + + if self.scale_factor is not None: + result['image'] = scale_image(result['image'], self.scale_factor) + result['mask'] = scale_image(result['mask'], self.scale_factor, interpolation=cv2.INTER_NEAREST) + + if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1: + result['unpad_to_size'] = result['image'].shape[1:] + result['image'] = pad_img_to_modulo(result['image'], self.pad_out_to_modulo) + result['mask'] = pad_img_to_modulo(result['mask'], self.pad_out_to_modulo) + + return result + +class OurInpaintingDataset(Dataset): + def __init__(self, datadir, img_suffix='.jpg', pad_out_to_modulo=None, scale_factor=None): + self.datadir = datadir + self.mask_filenames = sorted(list(glob.glob(os.path.join(self.datadir, 'mask', '**', '*mask*.png'), recursive=True))) + self.img_filenames = [os.path.join(self.datadir, 'img', os.path.basename(fname.rsplit('-', 1)[0].rsplit('_', 1)[0]) + '.png') for fname in self.mask_filenames] + self.pad_out_to_modulo = pad_out_to_modulo + self.scale_factor = scale_factor + + def __len__(self): + return len(self.mask_filenames) + + def __getitem__(self, i): + result = dict(image=load_image(self.img_filenames[i], mode='RGB'), + mask=load_image(self.mask_filenames[i], mode='L')[None, ...]) + + if self.scale_factor is not None: + result['image'] = scale_image(result['image'], self.scale_factor) + result['mask'] = scale_image(result['mask'], self.scale_factor) + + if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1: + result['image'] = pad_img_to_modulo(result['image'], self.pad_out_to_modulo) + result['mask'] = pad_img_to_modulo(result['mask'], self.pad_out_to_modulo) + + return result + +class PrecomputedInpaintingResultsDataset(InpaintingDataset): + def __init__(self, datadir, predictdir, inpainted_suffix='_inpainted.jpg', **kwargs): + super().__init__(datadir, **kwargs) + if not datadir.endswith('/'): + datadir += '/' + self.predictdir = predictdir + self.pred_filenames = [os.path.join(predictdir, os.path.splitext(fname[len(datadir):])[0] + inpainted_suffix) + for fname in self.mask_filenames] + + def __getitem__(self, i): + result = super().__getitem__(i) + result['inpainted'] = load_image(self.pred_filenames[i]) + if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1: + result['inpainted'] = pad_img_to_modulo(result['inpainted'], self.pad_out_to_modulo) + return result + +class OurPrecomputedInpaintingResultsDataset(OurInpaintingDataset): + def __init__(self, datadir, predictdir, inpainted_suffix="png", **kwargs): + super().__init__(datadir, **kwargs) + if not datadir.endswith('/'): + datadir += '/' + self.predictdir = predictdir + self.pred_filenames = [os.path.join(predictdir, os.path.basename(os.path.splitext(fname)[0]) + f'_inpainted.{inpainted_suffix}') + for fname in self.mask_filenames] + # self.pred_filenames = [os.path.join(predictdir, os.path.splitext(fname[len(datadir):])[0] + inpainted_suffix) + # for fname in self.mask_filenames] + + def __getitem__(self, i): + result = super().__getitem__(i) + result['inpainted'] = self.file_loader(self.pred_filenames[i]) + + if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1: + result['inpainted'] = pad_img_to_modulo(result['inpainted'], self.pad_out_to_modulo) + return result + +class InpaintingEvalOnlineDataset(Dataset): + def __init__(self, indir, mask_generator, img_suffix='.jpg', pad_out_to_modulo=None, scale_factor=None, **kwargs): + self.indir = indir + self.mask_generator = mask_generator + self.img_filenames = sorted(list(glob.glob(os.path.join(self.indir, '**', f'*{img_suffix}' ), recursive=True))) + self.pad_out_to_modulo = pad_out_to_modulo + self.scale_factor = scale_factor + + def __len__(self): + return len(self.img_filenames) + + def __getitem__(self, i): + img, raw_image = load_image(self.img_filenames[i], mode='RGB', return_orig=True) + mask = self.mask_generator(img, raw_image=raw_image) + result = dict(image=img, mask=mask) + + if self.scale_factor is not None: + result['image'] = scale_image(result['image'], self.scale_factor) + result['mask'] = scale_image(result['mask'], self.scale_factor, interpolation=cv2.INTER_NEAREST) + + if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1: + result['image'] = pad_img_to_modulo(result['image'], self.pad_out_to_modulo) + result['mask'] = pad_img_to_modulo(result['mask'], self.pad_out_to_modulo) + return result \ No newline at end of file diff --git a/third-party/lama/bin/saicinpainting/evaluation/evaluator.py b/third-party/lama/bin/saicinpainting/evaluation/evaluator.py new file mode 100644 index 0000000000000000000000000000000000000000..aa9e80402633c08a580929b38a5cb695cb7171d8 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/evaluation/evaluator.py @@ -0,0 +1,220 @@ +import logging +import math +from typing import Dict + +import numpy as np +import torch +import torch.nn as nn +import tqdm +from torch.utils.data import DataLoader + +from saicinpainting.evaluation.utils import move_to_device + +LOGGER = logging.getLogger(__name__) + + +class InpaintingEvaluator(): + def __init__(self, dataset, scores, area_grouping=True, bins=10, batch_size=32, device='cuda', + integral_func=None, integral_title=None, clamp_image_range=None): + """ + :param dataset: torch.utils.data.Dataset which contains images and masks + :param scores: dict {score_name: EvaluatorScore object} + :param area_grouping: in addition to the overall scores, allows to compute score for the groups of samples + which are defined by share of area occluded by mask + :param bins: number of groups, partition is generated by np.linspace(0., 1., bins + 1) + :param batch_size: batch_size for the dataloader + :param device: device to use + """ + self.scores = scores + self.dataset = dataset + + self.area_grouping = area_grouping + self.bins = bins + + self.device = torch.device(device) + + self.dataloader = DataLoader(self.dataset, shuffle=False, batch_size=batch_size) + + self.integral_func = integral_func + self.integral_title = integral_title + self.clamp_image_range = clamp_image_range + + def _get_bin_edges(self): + bin_edges = np.linspace(0, 1, self.bins + 1) + + num_digits = max(0, math.ceil(math.log10(self.bins)) - 1) + interval_names = [] + for idx_bin in range(self.bins): + start_percent, end_percent = round(100 * bin_edges[idx_bin], num_digits), \ + round(100 * bin_edges[idx_bin + 1], num_digits) + start_percent = '{:.{n}f}'.format(start_percent, n=num_digits) + end_percent = '{:.{n}f}'.format(end_percent, n=num_digits) + interval_names.append("{0}-{1}%".format(start_percent, end_percent)) + + groups = [] + for batch in self.dataloader: + mask = batch['mask'] + batch_size = mask.shape[0] + area = mask.to(self.device).reshape(batch_size, -1).mean(dim=-1) + bin_indices = np.searchsorted(bin_edges, area.detach().cpu().numpy(), side='right') - 1 + # corner case: when area is equal to 1, bin_indices should return bins - 1, not bins for that element + bin_indices[bin_indices == self.bins] = self.bins - 1 + groups.append(bin_indices) + groups = np.hstack(groups) + + return groups, interval_names + + def evaluate(self, model=None): + """ + :param model: callable with signature (image_batch, mask_batch); should return inpainted_batch + :return: dict with (score_name, group_type) as keys, where group_type can be either 'overall' or + name of the particular group arranged by area of mask (e.g. '10-20%') + and score statistics for the group as values. + """ + results = dict() + if self.area_grouping: + groups, interval_names = self._get_bin_edges() + else: + groups = None + + for score_name, score in tqdm.auto.tqdm(self.scores.items(), desc='scores'): + score.to(self.device) + with torch.no_grad(): + score.reset() + for batch in tqdm.auto.tqdm(self.dataloader, desc=score_name, leave=False): + batch = move_to_device(batch, self.device) + image_batch, mask_batch = batch['image'], batch['mask'] + if self.clamp_image_range is not None: + image_batch = torch.clamp(image_batch, + min=self.clamp_image_range[0], + max=self.clamp_image_range[1]) + if model is None: + assert 'inpainted' in batch, \ + 'Model is None, so we expected precomputed inpainting results at key "inpainted"' + inpainted_batch = batch['inpainted'] + else: + inpainted_batch = model(image_batch, mask_batch) + score(inpainted_batch, image_batch, mask_batch) + total_results, group_results = score.get_value(groups=groups) + + results[(score_name, 'total')] = total_results + if groups is not None: + for group_index, group_values in group_results.items(): + group_name = interval_names[group_index] + results[(score_name, group_name)] = group_values + + if self.integral_func is not None: + results[(self.integral_title, 'total')] = dict(mean=self.integral_func(results)) + + return results + + +def ssim_fid100_f1(metrics, fid_scale=100): + ssim = metrics[('ssim', 'total')]['mean'] + fid = metrics[('fid', 'total')]['mean'] + fid_rel = max(0, fid_scale - fid) / fid_scale + f1 = 2 * ssim * fid_rel / (ssim + fid_rel + 1e-3) + return f1 + + +def lpips_fid100_f1(metrics, fid_scale=100): + neg_lpips = 1 - metrics[('lpips', 'total')]['mean'] # invert, so bigger is better + fid = metrics[('fid', 'total')]['mean'] + fid_rel = max(0, fid_scale - fid) / fid_scale + f1 = 2 * neg_lpips * fid_rel / (neg_lpips + fid_rel + 1e-3) + return f1 + + + +class InpaintingEvaluatorOnline(nn.Module): + def __init__(self, scores, bins=10, image_key='image', inpainted_key='inpainted', + integral_func=None, integral_title=None, clamp_image_range=None): + """ + :param scores: dict {score_name: EvaluatorScore object} + :param bins: number of groups, partition is generated by np.linspace(0., 1., bins + 1) + :param device: device to use + """ + super().__init__() + LOGGER.info(f'{type(self)} init called') + self.scores = nn.ModuleDict(scores) + self.image_key = image_key + self.inpainted_key = inpainted_key + self.bins_num = bins + self.bin_edges = np.linspace(0, 1, self.bins_num + 1) + + num_digits = max(0, math.ceil(math.log10(self.bins_num)) - 1) + self.interval_names = [] + for idx_bin in range(self.bins_num): + start_percent, end_percent = round(100 * self.bin_edges[idx_bin], num_digits), \ + round(100 * self.bin_edges[idx_bin + 1], num_digits) + start_percent = '{:.{n}f}'.format(start_percent, n=num_digits) + end_percent = '{:.{n}f}'.format(end_percent, n=num_digits) + self.interval_names.append("{0}-{1}%".format(start_percent, end_percent)) + + self.groups = [] + + self.integral_func = integral_func + self.integral_title = integral_title + self.clamp_image_range = clamp_image_range + + LOGGER.info(f'{type(self)} init done') + + def _get_bins(self, mask_batch): + batch_size = mask_batch.shape[0] + area = mask_batch.view(batch_size, -1).mean(dim=-1).detach().cpu().numpy() + bin_indices = np.clip(np.searchsorted(self.bin_edges, area) - 1, 0, self.bins_num - 1) + return bin_indices + + def forward(self, batch: Dict[str, torch.Tensor]): + """ + Calculate and accumulate metrics for batch. To finalize evaluation and obtain final metrics, call evaluation_end + :param batch: batch dict with mandatory fields mask, image, inpainted (can be overriden by self.inpainted_key) + """ + result = {} + with torch.no_grad(): + image_batch, mask_batch, inpainted_batch = batch[self.image_key], batch['mask'], batch[self.inpainted_key] + if self.clamp_image_range is not None: + image_batch = torch.clamp(image_batch, + min=self.clamp_image_range[0], + max=self.clamp_image_range[1]) + self.groups.extend(self._get_bins(mask_batch)) + + for score_name, score in self.scores.items(): + result[score_name] = score(inpainted_batch, image_batch, mask_batch) + return result + + def process_batch(self, batch: Dict[str, torch.Tensor]): + return self(batch) + + def evaluation_end(self, states=None): + """:return: dict with (score_name, group_type) as keys, where group_type can be either 'overall' or + name of the particular group arranged by area of mask (e.g. '10-20%') + and score statistics for the group as values. + """ + LOGGER.info(f'{type(self)}: evaluation_end called') + + self.groups = np.array(self.groups) + + results = {} + for score_name, score in self.scores.items(): + LOGGER.info(f'Getting value of {score_name}') + cur_states = [s[score_name] for s in states] if states is not None else None + total_results, group_results = score.get_value(groups=self.groups, states=cur_states) + LOGGER.info(f'Getting value of {score_name} done') + results[(score_name, 'total')] = total_results + + for group_index, group_values in group_results.items(): + group_name = self.interval_names[group_index] + results[(score_name, group_name)] = group_values + + if self.integral_func is not None: + results[(self.integral_title, 'total')] = dict(mean=self.integral_func(results)) + + LOGGER.info(f'{type(self)}: reset scores') + self.groups = [] + for sc in self.scores.values(): + sc.reset() + LOGGER.info(f'{type(self)}: reset scores done') + + LOGGER.info(f'{type(self)}: evaluation_end done') + return results diff --git a/third-party/lama/bin/saicinpainting/evaluation/losses/__init__.py b/third-party/lama/bin/saicinpainting/evaluation/losses/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third-party/lama/bin/saicinpainting/evaluation/losses/base_loss.py b/third-party/lama/bin/saicinpainting/evaluation/losses/base_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..e5cd5fa8d571b2da829b87f0784bd38978158ce7 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/evaluation/losses/base_loss.py @@ -0,0 +1,528 @@ +import logging +from abc import abstractmethod, ABC + +import numpy as np +import sklearn +import sklearn.svm +import torch +import torch.nn as nn +import torch.nn.functional as F +from joblib import Parallel, delayed +from scipy import linalg + +from models.ade20k import SegmentationModule, NUM_CLASS, segm_options +from .fid.inception import InceptionV3 +from .lpips import PerceptualLoss +from .ssim import SSIM + +LOGGER = logging.getLogger(__name__) + + +def get_groupings(groups): + """ + :param groups: group numbers for respective elements + :return: dict of kind {group_idx: indices of the corresponding group elements} + """ + label_groups, count_groups = np.unique(groups, return_counts=True) + + indices = np.argsort(groups) + + grouping = dict() + cur_start = 0 + for label, count in zip(label_groups, count_groups): + cur_end = cur_start + count + cur_indices = indices[cur_start:cur_end] + grouping[label] = cur_indices + cur_start = cur_end + return grouping + + +class EvaluatorScore(nn.Module): + @abstractmethod + def forward(self, pred_batch, target_batch, mask): + pass + + @abstractmethod + def get_value(self, groups=None, states=None): + pass + + @abstractmethod + def reset(self): + pass + + +class PairwiseScore(EvaluatorScore, ABC): + def __init__(self): + super().__init__() + self.individual_values = None + + def get_value(self, groups=None, states=None): + """ + :param groups: + :return: + total_results: dict of kind {'mean': score mean, 'std': score std} + group_results: None, if groups is None; + else dict {group_idx: {'mean': score mean among group, 'std': score std among group}} + """ + individual_values = torch.cat(states, dim=-1).reshape(-1).cpu().numpy() if states is not None \ + else self.individual_values + + total_results = { + 'mean': individual_values.mean(), + 'std': individual_values.std() + } + + if groups is None: + return total_results, None + + group_results = dict() + grouping = get_groupings(groups) + for label, index in grouping.items(): + group_scores = individual_values[index] + group_results[label] = { + 'mean': group_scores.mean(), + 'std': group_scores.std() + } + return total_results, group_results + + def reset(self): + self.individual_values = [] + + +class SSIMScore(PairwiseScore): + def __init__(self, window_size=11): + super().__init__() + self.score = SSIM(window_size=window_size, size_average=False).eval() + self.reset() + + def forward(self, pred_batch, target_batch, mask=None): + batch_values = self.score(pred_batch, target_batch) + self.individual_values = np.hstack([ + self.individual_values, batch_values.detach().cpu().numpy() + ]) + return batch_values + + +class LPIPSScore(PairwiseScore): + def __init__(self, model='net-lin', net='vgg', model_path=None, use_gpu=True): + super().__init__() + self.score = PerceptualLoss(model=model, net=net, model_path=model_path, + use_gpu=use_gpu, spatial=False).eval() + self.reset() + + def forward(self, pred_batch, target_batch, mask=None): + batch_values = self.score(pred_batch, target_batch).flatten() + self.individual_values = np.hstack([ + self.individual_values, batch_values.detach().cpu().numpy() + ]) + return batch_values + + +def fid_calculate_activation_statistics(act): + mu = np.mean(act, axis=0) + sigma = np.cov(act, rowvar=False) + return mu, sigma + + +def calculate_frechet_distance(activations_pred, activations_target, eps=1e-6): + mu1, sigma1 = fid_calculate_activation_statistics(activations_pred) + mu2, sigma2 = fid_calculate_activation_statistics(activations_target) + + diff = mu1 - mu2 + + # Product might be almost singular + covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False) + if not np.isfinite(covmean).all(): + msg = ('fid calculation produces singular product; ' + 'adding %s to diagonal of cov estimates') % eps + LOGGER.warning(msg) + offset = np.eye(sigma1.shape[0]) * eps + covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset)) + + # Numerical error might give slight imaginary component + if np.iscomplexobj(covmean): + # if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3): + if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-2): + m = np.max(np.abs(covmean.imag)) + raise ValueError('Imaginary component {}'.format(m)) + covmean = covmean.real + + tr_covmean = np.trace(covmean) + + return (diff.dot(diff) + np.trace(sigma1) + + np.trace(sigma2) - 2 * tr_covmean) + + +class FIDScore(EvaluatorScore): + def __init__(self, dims=2048, eps=1e-6): + LOGGER.info("FIDscore init called") + super().__init__() + if getattr(FIDScore, '_MODEL', None) is None: + block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] + FIDScore._MODEL = InceptionV3([block_idx]).eval() + self.model = FIDScore._MODEL + self.eps = eps + self.reset() + LOGGER.info("FIDscore init done") + + def forward(self, pred_batch, target_batch, mask=None): + activations_pred = self._get_activations(pred_batch) + activations_target = self._get_activations(target_batch) + + self.activations_pred.append(activations_pred.detach().cpu()) + self.activations_target.append(activations_target.detach().cpu()) + + return activations_pred, activations_target + + def get_value(self, groups=None, states=None): + LOGGER.info("FIDscore get_value called") + activations_pred, activations_target = zip(*states) if states is not None \ + else (self.activations_pred, self.activations_target) + activations_pred = torch.cat(activations_pred).cpu().numpy() + activations_target = torch.cat(activations_target).cpu().numpy() + + total_distance = calculate_frechet_distance(activations_pred, activations_target, eps=self.eps) + total_results = dict(mean=total_distance) + + if groups is None: + group_results = None + else: + group_results = dict() + grouping = get_groupings(groups) + for label, index in grouping.items(): + if len(index) > 1: + group_distance = calculate_frechet_distance(activations_pred[index], activations_target[index], + eps=self.eps) + group_results[label] = dict(mean=group_distance) + + else: + group_results[label] = dict(mean=float('nan')) + + self.reset() + + LOGGER.info("FIDscore get_value done") + + return total_results, group_results + + def reset(self): + self.activations_pred = [] + self.activations_target = [] + + def _get_activations(self, batch): + activations = self.model(batch)[0] + if activations.shape[2] != 1 or activations.shape[3] != 1: + assert False, \ + 'We should not have got here, because Inception always scales inputs to 299x299' + # activations = F.adaptive_avg_pool2d(activations, output_size=(1, 1)) + activations = activations.squeeze(-1).squeeze(-1) + return activations + + +class SegmentationAwareScore(EvaluatorScore): + def __init__(self, weights_path): + super().__init__() + self.segm_network = SegmentationModule(weights_path=weights_path, use_default_normalization=True).eval() + self.target_class_freq_by_image_total = [] + self.target_class_freq_by_image_mask = [] + self.pred_class_freq_by_image_mask = [] + + def forward(self, pred_batch, target_batch, mask): + pred_segm_flat = self.segm_network.predict(pred_batch)[0].view(pred_batch.shape[0], -1).long().detach().cpu().numpy() + target_segm_flat = self.segm_network.predict(target_batch)[0].view(pred_batch.shape[0], -1).long().detach().cpu().numpy() + mask_flat = (mask.view(mask.shape[0], -1) > 0.5).detach().cpu().numpy() + + batch_target_class_freq_total = [] + batch_target_class_freq_mask = [] + batch_pred_class_freq_mask = [] + + for cur_pred_segm, cur_target_segm, cur_mask in zip(pred_segm_flat, target_segm_flat, mask_flat): + cur_target_class_freq_total = np.bincount(cur_target_segm, minlength=NUM_CLASS)[None, ...] + cur_target_class_freq_mask = np.bincount(cur_target_segm[cur_mask], minlength=NUM_CLASS)[None, ...] + cur_pred_class_freq_mask = np.bincount(cur_pred_segm[cur_mask], minlength=NUM_CLASS)[None, ...] + + self.target_class_freq_by_image_total.append(cur_target_class_freq_total) + self.target_class_freq_by_image_mask.append(cur_target_class_freq_mask) + self.pred_class_freq_by_image_mask.append(cur_pred_class_freq_mask) + + batch_target_class_freq_total.append(cur_target_class_freq_total) + batch_target_class_freq_mask.append(cur_target_class_freq_mask) + batch_pred_class_freq_mask.append(cur_pred_class_freq_mask) + + batch_target_class_freq_total = np.concatenate(batch_target_class_freq_total, axis=0) + batch_target_class_freq_mask = np.concatenate(batch_target_class_freq_mask, axis=0) + batch_pred_class_freq_mask = np.concatenate(batch_pred_class_freq_mask, axis=0) + return batch_target_class_freq_total, batch_target_class_freq_mask, batch_pred_class_freq_mask + + def reset(self): + super().reset() + self.target_class_freq_by_image_total = [] + self.target_class_freq_by_image_mask = [] + self.pred_class_freq_by_image_mask = [] + + +def distribute_values_to_classes(target_class_freq_by_image_mask, values, idx2name): + assert target_class_freq_by_image_mask.ndim == 2 and target_class_freq_by_image_mask.shape[0] == values.shape[0] + total_class_freq = target_class_freq_by_image_mask.sum(0) + distr_values = (target_class_freq_by_image_mask * values[..., None]).sum(0) + result = distr_values / (total_class_freq + 1e-3) + return {idx2name[i]: val for i, val in enumerate(result) if total_class_freq[i] > 0} + + +def get_segmentation_idx2name(): + return {i - 1: name for i, name in segm_options['classes'].set_index('Idx', drop=True)['Name'].to_dict().items()} + + +class SegmentationAwarePairwiseScore(SegmentationAwareScore): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.individual_values = [] + self.segm_idx2name = get_segmentation_idx2name() + + def forward(self, pred_batch, target_batch, mask): + cur_class_stats = super().forward(pred_batch, target_batch, mask) + score_values = self.calc_score(pred_batch, target_batch, mask) + self.individual_values.append(score_values) + return cur_class_stats + (score_values,) + + @abstractmethod + def calc_score(self, pred_batch, target_batch, mask): + raise NotImplementedError() + + def get_value(self, groups=None, states=None): + """ + :param groups: + :return: + total_results: dict of kind {'mean': score mean, 'std': score std} + group_results: None, if groups is None; + else dict {group_idx: {'mean': score mean among group, 'std': score std among group}} + """ + if states is not None: + (target_class_freq_by_image_total, + target_class_freq_by_image_mask, + pred_class_freq_by_image_mask, + individual_values) = states + else: + target_class_freq_by_image_total = self.target_class_freq_by_image_total + target_class_freq_by_image_mask = self.target_class_freq_by_image_mask + pred_class_freq_by_image_mask = self.pred_class_freq_by_image_mask + individual_values = self.individual_values + + target_class_freq_by_image_total = np.concatenate(target_class_freq_by_image_total, axis=0) + target_class_freq_by_image_mask = np.concatenate(target_class_freq_by_image_mask, axis=0) + pred_class_freq_by_image_mask = np.concatenate(pred_class_freq_by_image_mask, axis=0) + individual_values = np.concatenate(individual_values, axis=0) + + total_results = { + 'mean': individual_values.mean(), + 'std': individual_values.std(), + **distribute_values_to_classes(target_class_freq_by_image_mask, individual_values, self.segm_idx2name) + } + + if groups is None: + return total_results, None + + group_results = dict() + grouping = get_groupings(groups) + for label, index in grouping.items(): + group_class_freq = target_class_freq_by_image_mask[index] + group_scores = individual_values[index] + group_results[label] = { + 'mean': group_scores.mean(), + 'std': group_scores.std(), + ** distribute_values_to_classes(group_class_freq, group_scores, self.segm_idx2name) + } + return total_results, group_results + + def reset(self): + super().reset() + self.individual_values = [] + + +class SegmentationClassStats(SegmentationAwarePairwiseScore): + def calc_score(self, pred_batch, target_batch, mask): + return 0 + + def get_value(self, groups=None, states=None): + """ + :param groups: + :return: + total_results: dict of kind {'mean': score mean, 'std': score std} + group_results: None, if groups is None; + else dict {group_idx: {'mean': score mean among group, 'std': score std among group}} + """ + if states is not None: + (target_class_freq_by_image_total, + target_class_freq_by_image_mask, + pred_class_freq_by_image_mask, + _) = states + else: + target_class_freq_by_image_total = self.target_class_freq_by_image_total + target_class_freq_by_image_mask = self.target_class_freq_by_image_mask + pred_class_freq_by_image_mask = self.pred_class_freq_by_image_mask + + target_class_freq_by_image_total = np.concatenate(target_class_freq_by_image_total, axis=0) + target_class_freq_by_image_mask = np.concatenate(target_class_freq_by_image_mask, axis=0) + pred_class_freq_by_image_mask = np.concatenate(pred_class_freq_by_image_mask, axis=0) + + target_class_freq_by_image_total_marginal = target_class_freq_by_image_total.sum(0).astype('float32') + target_class_freq_by_image_total_marginal /= target_class_freq_by_image_total_marginal.sum() + + target_class_freq_by_image_mask_marginal = target_class_freq_by_image_mask.sum(0).astype('float32') + target_class_freq_by_image_mask_marginal /= target_class_freq_by_image_mask_marginal.sum() + + pred_class_freq_diff = (pred_class_freq_by_image_mask - target_class_freq_by_image_mask).sum(0) / (target_class_freq_by_image_mask.sum(0) + 1e-3) + + total_results = dict() + total_results.update({f'total_freq/{self.segm_idx2name[i]}': v + for i, v in enumerate(target_class_freq_by_image_total_marginal) + if v > 0}) + total_results.update({f'mask_freq/{self.segm_idx2name[i]}': v + for i, v in enumerate(target_class_freq_by_image_mask_marginal) + if v > 0}) + total_results.update({f'mask_freq_diff/{self.segm_idx2name[i]}': v + for i, v in enumerate(pred_class_freq_diff) + if target_class_freq_by_image_total_marginal[i] > 0}) + + if groups is None: + return total_results, None + + group_results = dict() + grouping = get_groupings(groups) + for label, index in grouping.items(): + group_target_class_freq_by_image_total = target_class_freq_by_image_total[index] + group_target_class_freq_by_image_mask = target_class_freq_by_image_mask[index] + group_pred_class_freq_by_image_mask = pred_class_freq_by_image_mask[index] + + group_target_class_freq_by_image_total_marginal = group_target_class_freq_by_image_total.sum(0).astype('float32') + group_target_class_freq_by_image_total_marginal /= group_target_class_freq_by_image_total_marginal.sum() + + group_target_class_freq_by_image_mask_marginal = group_target_class_freq_by_image_mask.sum(0).astype('float32') + group_target_class_freq_by_image_mask_marginal /= group_target_class_freq_by_image_mask_marginal.sum() + + group_pred_class_freq_diff = (group_pred_class_freq_by_image_mask - group_target_class_freq_by_image_mask).sum(0) / ( + group_target_class_freq_by_image_mask.sum(0) + 1e-3) + + cur_group_results = dict() + cur_group_results.update({f'total_freq/{self.segm_idx2name[i]}': v + for i, v in enumerate(group_target_class_freq_by_image_total_marginal) + if v > 0}) + cur_group_results.update({f'mask_freq/{self.segm_idx2name[i]}': v + for i, v in enumerate(group_target_class_freq_by_image_mask_marginal) + if v > 0}) + cur_group_results.update({f'mask_freq_diff/{self.segm_idx2name[i]}': v + for i, v in enumerate(group_pred_class_freq_diff) + if group_target_class_freq_by_image_total_marginal[i] > 0}) + + group_results[label] = cur_group_results + return total_results, group_results + + +class SegmentationAwareSSIM(SegmentationAwarePairwiseScore): + def __init__(self, *args, window_size=11, **kwargs): + super().__init__(*args, **kwargs) + self.score_impl = SSIM(window_size=window_size, size_average=False).eval() + + def calc_score(self, pred_batch, target_batch, mask): + return self.score_impl(pred_batch, target_batch).detach().cpu().numpy() + + +class SegmentationAwareLPIPS(SegmentationAwarePairwiseScore): + def __init__(self, *args, model='net-lin', net='vgg', model_path=None, use_gpu=True, **kwargs): + super().__init__(*args, **kwargs) + self.score_impl = PerceptualLoss(model=model, net=net, model_path=model_path, + use_gpu=use_gpu, spatial=False).eval() + + def calc_score(self, pred_batch, target_batch, mask): + return self.score_impl(pred_batch, target_batch).flatten().detach().cpu().numpy() + + +def calculade_fid_no_img(img_i, activations_pred, activations_target, eps=1e-6): + activations_pred = activations_pred.copy() + activations_pred[img_i] = activations_target[img_i] + return calculate_frechet_distance(activations_pred, activations_target, eps=eps) + + +class SegmentationAwareFID(SegmentationAwarePairwiseScore): + def __init__(self, *args, dims=2048, eps=1e-6, n_jobs=-1, **kwargs): + super().__init__(*args, **kwargs) + if getattr(FIDScore, '_MODEL', None) is None: + block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] + FIDScore._MODEL = InceptionV3([block_idx]).eval() + self.model = FIDScore._MODEL + self.eps = eps + self.n_jobs = n_jobs + + def calc_score(self, pred_batch, target_batch, mask): + activations_pred = self._get_activations(pred_batch) + activations_target = self._get_activations(target_batch) + return activations_pred, activations_target + + def get_value(self, groups=None, states=None): + """ + :param groups: + :return: + total_results: dict of kind {'mean': score mean, 'std': score std} + group_results: None, if groups is None; + else dict {group_idx: {'mean': score mean among group, 'std': score std among group}} + """ + if states is not None: + (target_class_freq_by_image_total, + target_class_freq_by_image_mask, + pred_class_freq_by_image_mask, + activation_pairs) = states + else: + target_class_freq_by_image_total = self.target_class_freq_by_image_total + target_class_freq_by_image_mask = self.target_class_freq_by_image_mask + pred_class_freq_by_image_mask = self.pred_class_freq_by_image_mask + activation_pairs = self.individual_values + + target_class_freq_by_image_total = np.concatenate(target_class_freq_by_image_total, axis=0) + target_class_freq_by_image_mask = np.concatenate(target_class_freq_by_image_mask, axis=0) + pred_class_freq_by_image_mask = np.concatenate(pred_class_freq_by_image_mask, axis=0) + activations_pred, activations_target = zip(*activation_pairs) + activations_pred = np.concatenate(activations_pred, axis=0) + activations_target = np.concatenate(activations_target, axis=0) + + total_results = { + 'mean': calculate_frechet_distance(activations_pred, activations_target, eps=self.eps), + 'std': 0, + **self.distribute_fid_to_classes(target_class_freq_by_image_mask, activations_pred, activations_target) + } + + if groups is None: + return total_results, None + + group_results = dict() + grouping = get_groupings(groups) + for label, index in grouping.items(): + if len(index) > 1: + group_activations_pred = activations_pred[index] + group_activations_target = activations_target[index] + group_class_freq = target_class_freq_by_image_mask[index] + group_results[label] = { + 'mean': calculate_frechet_distance(group_activations_pred, group_activations_target, eps=self.eps), + 'std': 0, + **self.distribute_fid_to_classes(group_class_freq, + group_activations_pred, + group_activations_target) + } + else: + group_results[label] = dict(mean=float('nan'), std=0) + return total_results, group_results + + def distribute_fid_to_classes(self, class_freq, activations_pred, activations_target): + real_fid = calculate_frechet_distance(activations_pred, activations_target, eps=self.eps) + + fid_no_images = Parallel(n_jobs=self.n_jobs)( + delayed(calculade_fid_no_img)(img_i, activations_pred, activations_target, eps=self.eps) + for img_i in range(activations_pred.shape[0]) + ) + errors = real_fid - fid_no_images + return distribute_values_to_classes(class_freq, errors, self.segm_idx2name) + + def _get_activations(self, batch): + activations = self.model(batch)[0] + if activations.shape[2] != 1 or activations.shape[3] != 1: + activations = F.adaptive_avg_pool2d(activations, output_size=(1, 1)) + activations = activations.squeeze(-1).squeeze(-1).detach().cpu().numpy() + return activations diff --git a/third-party/lama/bin/saicinpainting/evaluation/losses/fid/__init__.py b/third-party/lama/bin/saicinpainting/evaluation/losses/fid/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third-party/lama/bin/saicinpainting/evaluation/losses/fid/fid_score.py b/third-party/lama/bin/saicinpainting/evaluation/losses/fid/fid_score.py new file mode 100644 index 0000000000000000000000000000000000000000..6ca8e602c21bb6a624d646da3f6479aea033b0ac --- /dev/null +++ b/third-party/lama/bin/saicinpainting/evaluation/losses/fid/fid_score.py @@ -0,0 +1,328 @@ +#!/usr/bin/env python3 +"""Calculates the Frechet Inception Distance (FID) to evalulate GANs + +The FID metric calculates the distance between two distributions of images. +Typically, we have summary statistics (mean & covariance matrix) of one +of these distributions, while the 2nd distribution is given by a GAN. + +When run as a stand-alone program, it compares the distribution of +images that are stored as PNG/JPEG at a specified location with a +distribution given by summary statistics (in pickle format). + +The FID is calculated by assuming that X_1 and X_2 are the activations of +the pool_3 layer of the inception net for generated samples and real world +samples respectively. + +See --help to see further details. + +Code apapted from https://github.com/bioinf-jku/TTUR to use PyTorch instead +of Tensorflow + +Copyright 2018 Institute of Bioinformatics, JKU Linz + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import os +import pathlib +from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser + +import numpy as np +import torch +# from scipy.misc import imread +from imageio import imread +from PIL import Image, JpegImagePlugin +from scipy import linalg +from torch.nn.functional import adaptive_avg_pool2d +from torchvision.transforms import CenterCrop, Compose, Resize, ToTensor + +try: + from tqdm import tqdm +except ImportError: + # If not tqdm is not available, provide a mock version of it + def tqdm(x): return x + +try: + from .inception import InceptionV3 +except ModuleNotFoundError: + from inception import InceptionV3 + +parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter) +parser.add_argument('path', type=str, nargs=2, + help=('Path to the generated images or ' + 'to .npz statistic files')) +parser.add_argument('--batch-size', type=int, default=50, + help='Batch size to use') +parser.add_argument('--dims', type=int, default=2048, + choices=list(InceptionV3.BLOCK_INDEX_BY_DIM), + help=('Dimensionality of Inception features to use. ' + 'By default, uses pool3 features')) +parser.add_argument('-c', '--gpu', default='', type=str, + help='GPU to use (leave blank for CPU only)') +parser.add_argument('--resize', default=256) + +transform = Compose([Resize(256), CenterCrop(256), ToTensor()]) + + +def get_activations(files, model, batch_size=50, dims=2048, + cuda=False, verbose=False, keep_size=False): + """Calculates the activations of the pool_3 layer for all images. + + Params: + -- files : List of image files paths + -- model : Instance of inception model + -- batch_size : Batch size of images for the model to process at once. + Make sure that the number of samples is a multiple of + the batch size, otherwise some samples are ignored. This + behavior is retained to match the original FID score + implementation. + -- dims : Dimensionality of features returned by Inception + -- cuda : If set to True, use GPU + -- verbose : If set to True and parameter out_step is given, the number + of calculated batches is reported. + Returns: + -- A numpy array of dimension (num images, dims) that contains the + activations of the given tensor when feeding inception with the + query tensor. + """ + model.eval() + + if len(files) % batch_size != 0: + print(('Warning: number of images is not a multiple of the ' + 'batch size. Some samples are going to be ignored.')) + if batch_size > len(files): + print(('Warning: batch size is bigger than the data size. ' + 'Setting batch size to data size')) + batch_size = len(files) + + n_batches = len(files) // batch_size + n_used_imgs = n_batches * batch_size + + pred_arr = np.empty((n_used_imgs, dims)) + + for i in tqdm(range(n_batches)): + if verbose: + print('\rPropagating batch %d/%d' % (i + 1, n_batches), + end='', flush=True) + start = i * batch_size + end = start + batch_size + + # # Official code goes below + # images = np.array([imread(str(f)).astype(np.float32) + # for f in files[start:end]]) + + # # Reshape to (n_images, 3, height, width) + # images = images.transpose((0, 3, 1, 2)) + # images /= 255 + # batch = torch.from_numpy(images).type(torch.FloatTensor) + # # + + t = transform if not keep_size else ToTensor() + + if isinstance(files[0], pathlib.PosixPath): + images = [t(Image.open(str(f))) for f in files[start:end]] + + elif isinstance(files[0], Image.Image): + images = [t(f) for f in files[start:end]] + + else: + raise ValueError(f"Unknown data type for image: {type(files[0])}") + + batch = torch.stack(images) + + if cuda: + batch = batch.cuda() + + pred = model(batch)[0] + + # If model output is not scalar, apply global spatial average pooling. + # This happens if you choose a dimensionality not equal 2048. + if pred.shape[2] != 1 or pred.shape[3] != 1: + pred = adaptive_avg_pool2d(pred, output_size=(1, 1)) + + pred_arr[start:end] = pred.cpu().data.numpy().reshape(batch_size, -1) + + if verbose: + print(' done') + + return pred_arr + + +def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): + """Numpy implementation of the Frechet Distance. + The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) + and X_2 ~ N(mu_2, C_2) is + d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)). + + Stable version by Dougal J. Sutherland. + + Params: + -- mu1 : Numpy array containing the activations of a layer of the + inception net (like returned by the function 'get_predictions') + for generated samples. + -- mu2 : The sample mean over activations, precalculated on an + representative data set. + -- sigma1: The covariance matrix over activations for generated samples. + -- sigma2: The covariance matrix over activations, precalculated on an + representative data set. + + Returns: + -- : The Frechet Distance. + """ + + mu1 = np.atleast_1d(mu1) + mu2 = np.atleast_1d(mu2) + + sigma1 = np.atleast_2d(sigma1) + sigma2 = np.atleast_2d(sigma2) + + assert mu1.shape == mu2.shape, \ + 'Training and test mean vectors have different lengths' + assert sigma1.shape == sigma2.shape, \ + 'Training and test covariances have different dimensions' + + diff = mu1 - mu2 + + # Product might be almost singular + covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False) + if not np.isfinite(covmean).all(): + msg = ('fid calculation produces singular product; ' + 'adding %s to diagonal of cov estimates') % eps + print(msg) + offset = np.eye(sigma1.shape[0]) * eps + covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset)) + + # Numerical error might give slight imaginary component + if np.iscomplexobj(covmean): + # if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3): + if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-2): + m = np.max(np.abs(covmean.imag)) + raise ValueError('Imaginary component {}'.format(m)) + covmean = covmean.real + + tr_covmean = np.trace(covmean) + + return (diff.dot(diff) + np.trace(sigma1) + + np.trace(sigma2) - 2 * tr_covmean) + + +def calculate_activation_statistics(files, model, batch_size=50, + dims=2048, cuda=False, verbose=False, keep_size=False): + """Calculation of the statistics used by the FID. + Params: + -- files : List of image files paths + -- model : Instance of inception model + -- batch_size : The images numpy array is split into batches with + batch size batch_size. A reasonable batch size + depends on the hardware. + -- dims : Dimensionality of features returned by Inception + -- cuda : If set to True, use GPU + -- verbose : If set to True and parameter out_step is given, the + number of calculated batches is reported. + Returns: + -- mu : The mean over samples of the activations of the pool_3 layer of + the inception model. + -- sigma : The covariance matrix of the activations of the pool_3 layer of + the inception model. + """ + act = get_activations(files, model, batch_size, dims, cuda, verbose, keep_size=keep_size) + mu = np.mean(act, axis=0) + sigma = np.cov(act, rowvar=False) + return mu, sigma + + +def _compute_statistics_of_path(path, model, batch_size, dims, cuda): + if path.endswith('.npz'): + f = np.load(path) + m, s = f['mu'][:], f['sigma'][:] + f.close() + else: + path = pathlib.Path(path) + files = list(path.glob('*.jpg')) + list(path.glob('*.png')) + m, s = calculate_activation_statistics(files, model, batch_size, + dims, cuda) + + return m, s + + +def _compute_statistics_of_images(images, model, batch_size, dims, cuda, keep_size=False): + if isinstance(images, list): # exact paths to files are provided + m, s = calculate_activation_statistics(images, model, batch_size, + dims, cuda, keep_size=keep_size) + + return m, s + + else: + raise ValueError + + +def calculate_fid_given_paths(paths, batch_size, cuda, dims): + """Calculates the FID of two paths""" + for p in paths: + if not os.path.exists(p): + raise RuntimeError('Invalid path: %s' % p) + + block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] + + model = InceptionV3([block_idx]) + if cuda: + model.cuda() + + m1, s1 = _compute_statistics_of_path(paths[0], model, batch_size, + dims, cuda) + m2, s2 = _compute_statistics_of_path(paths[1], model, batch_size, + dims, cuda) + fid_value = calculate_frechet_distance(m1, s1, m2, s2) + + return fid_value + + +def calculate_fid_given_images(images, batch_size, cuda, dims, use_globals=False, keep_size=False): + if use_globals: + global FID_MODEL # for multiprocessing + + for imgs in images: + if isinstance(imgs, list) and isinstance(imgs[0], (Image.Image, JpegImagePlugin.JpegImageFile)): + pass + else: + raise RuntimeError('Invalid images') + + block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] + + if 'FID_MODEL' not in globals() or not use_globals: + model = InceptionV3([block_idx]) + if cuda: + model.cuda() + + if use_globals: + FID_MODEL = model + + else: + model = FID_MODEL + + m1, s1 = _compute_statistics_of_images(images[0], model, batch_size, + dims, cuda, keep_size=False) + m2, s2 = _compute_statistics_of_images(images[1], model, batch_size, + dims, cuda, keep_size=False) + fid_value = calculate_frechet_distance(m1, s1, m2, s2) + return fid_value + + +if __name__ == '__main__': + args = parser.parse_args() + os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu + + fid_value = calculate_fid_given_paths(args.path, + args.batch_size, + args.gpu != '', + args.dims) + print('FID: ', fid_value) diff --git a/third-party/lama/bin/saicinpainting/evaluation/losses/fid/inception.py b/third-party/lama/bin/saicinpainting/evaluation/losses/fid/inception.py new file mode 100644 index 0000000000000000000000000000000000000000..e9bd0863b457aaa40c770eaa4acbb142b18fc18b --- /dev/null +++ b/third-party/lama/bin/saicinpainting/evaluation/losses/fid/inception.py @@ -0,0 +1,323 @@ +import logging + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchvision import models + +try: + from torchvision.models.utils import load_state_dict_from_url +except ImportError: + from torch.utils.model_zoo import load_url as load_state_dict_from_url + +# Inception weights ported to Pytorch from +# http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz +FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth' + + +LOGGER = logging.getLogger(__name__) + + +class InceptionV3(nn.Module): + """Pretrained InceptionV3 network returning feature maps""" + + # Index of default block of inception to return, + # corresponds to output of final average pooling + DEFAULT_BLOCK_INDEX = 3 + + # Maps feature dimensionality to their output blocks indices + BLOCK_INDEX_BY_DIM = { + 64: 0, # First max pooling features + 192: 1, # Second max pooling featurs + 768: 2, # Pre-aux classifier features + 2048: 3 # Final average pooling features + } + + def __init__(self, + output_blocks=[DEFAULT_BLOCK_INDEX], + resize_input=True, + normalize_input=True, + requires_grad=False, + use_fid_inception=True): + """Build pretrained InceptionV3 + + Parameters + ---------- + output_blocks : list of int + Indices of blocks to return features of. Possible values are: + - 0: corresponds to output of first max pooling + - 1: corresponds to output of second max pooling + - 2: corresponds to output which is fed to aux classifier + - 3: corresponds to output of final average pooling + resize_input : bool + If true, bilinearly resizes input to width and height 299 before + feeding input to model. As the network without fully connected + layers is fully convolutional, it should be able to handle inputs + of arbitrary size, so resizing might not be strictly needed + normalize_input : bool + If true, scales the input from range (0, 1) to the range the + pretrained Inception network expects, namely (-1, 1) + requires_grad : bool + If true, parameters of the model require gradients. Possibly useful + for finetuning the network + use_fid_inception : bool + If true, uses the pretrained Inception model used in Tensorflow's + FID implementation. If false, uses the pretrained Inception model + available in torchvision. The FID Inception model has different + weights and a slightly different structure from torchvision's + Inception model. If you want to compute FID scores, you are + strongly advised to set this parameter to true to get comparable + results. + """ + super(InceptionV3, self).__init__() + + self.resize_input = resize_input + self.normalize_input = normalize_input + self.output_blocks = sorted(output_blocks) + self.last_needed_block = max(output_blocks) + + assert self.last_needed_block <= 3, \ + 'Last possible output block index is 3' + + self.blocks = nn.ModuleList() + + if use_fid_inception: + inception = fid_inception_v3() + else: + inception = models.inception_v3(pretrained=True) + + # Block 0: input to maxpool1 + block0 = [ + inception.Conv2d_1a_3x3, + inception.Conv2d_2a_3x3, + inception.Conv2d_2b_3x3, + nn.MaxPool2d(kernel_size=3, stride=2) + ] + self.blocks.append(nn.Sequential(*block0)) + + # Block 1: maxpool1 to maxpool2 + if self.last_needed_block >= 1: + block1 = [ + inception.Conv2d_3b_1x1, + inception.Conv2d_4a_3x3, + nn.MaxPool2d(kernel_size=3, stride=2) + ] + self.blocks.append(nn.Sequential(*block1)) + + # Block 2: maxpool2 to aux classifier + if self.last_needed_block >= 2: + block2 = [ + inception.Mixed_5b, + inception.Mixed_5c, + inception.Mixed_5d, + inception.Mixed_6a, + inception.Mixed_6b, + inception.Mixed_6c, + inception.Mixed_6d, + inception.Mixed_6e, + ] + self.blocks.append(nn.Sequential(*block2)) + + # Block 3: aux classifier to final avgpool + if self.last_needed_block >= 3: + block3 = [ + inception.Mixed_7a, + inception.Mixed_7b, + inception.Mixed_7c, + nn.AdaptiveAvgPool2d(output_size=(1, 1)) + ] + self.blocks.append(nn.Sequential(*block3)) + + for param in self.parameters(): + param.requires_grad = requires_grad + + def forward(self, inp): + """Get Inception feature maps + + Parameters + ---------- + inp : torch.autograd.Variable + Input tensor of shape Bx3xHxW. Values are expected to be in + range (0, 1) + + Returns + ------- + List of torch.autograd.Variable, corresponding to the selected output + block, sorted ascending by index + """ + outp = [] + x = inp + + if self.resize_input: + x = F.interpolate(x, + size=(299, 299), + mode='bilinear', + align_corners=False) + + if self.normalize_input: + x = 2 * x - 1 # Scale from range (0, 1) to range (-1, 1) + + for idx, block in enumerate(self.blocks): + x = block(x) + if idx in self.output_blocks: + outp.append(x) + + if idx == self.last_needed_block: + break + + return outp + + +def fid_inception_v3(): + """Build pretrained Inception model for FID computation + + The Inception model for FID computation uses a different set of weights + and has a slightly different structure than torchvision's Inception. + + This method first constructs torchvision's Inception and then patches the + necessary parts that are different in the FID Inception model. + """ + LOGGER.info('fid_inception_v3 called') + inception = models.inception_v3(num_classes=1008, + aux_logits=False, + pretrained=False) + LOGGER.info('models.inception_v3 done') + inception.Mixed_5b = FIDInceptionA(192, pool_features=32) + inception.Mixed_5c = FIDInceptionA(256, pool_features=64) + inception.Mixed_5d = FIDInceptionA(288, pool_features=64) + inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128) + inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160) + inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160) + inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192) + inception.Mixed_7b = FIDInceptionE_1(1280) + inception.Mixed_7c = FIDInceptionE_2(2048) + + LOGGER.info('fid_inception_v3 patching done') + + state_dict = load_state_dict_from_url(FID_WEIGHTS_URL, progress=True) + LOGGER.info('fid_inception_v3 weights downloaded') + + inception.load_state_dict(state_dict) + LOGGER.info('fid_inception_v3 weights loaded into model') + + return inception + + +class FIDInceptionA(models.inception.InceptionA): + """InceptionA block patched for FID computation""" + def __init__(self, in_channels, pool_features): + super(FIDInceptionA, self).__init__(in_channels, pool_features) + + def forward(self, x): + branch1x1 = self.branch1x1(x) + + branch5x5 = self.branch5x5_1(x) + branch5x5 = self.branch5x5_2(branch5x5) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) + + # Patch: Tensorflow's average pool does not use the padded zero's in + # its average calculation + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, + count_include_pad=False) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool] + return torch.cat(outputs, 1) + + +class FIDInceptionC(models.inception.InceptionC): + """InceptionC block patched for FID computation""" + def __init__(self, in_channels, channels_7x7): + super(FIDInceptionC, self).__init__(in_channels, channels_7x7) + + def forward(self, x): + branch1x1 = self.branch1x1(x) + + branch7x7 = self.branch7x7_1(x) + branch7x7 = self.branch7x7_2(branch7x7) + branch7x7 = self.branch7x7_3(branch7x7) + + branch7x7dbl = self.branch7x7dbl_1(x) + branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl) + branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl) + branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl) + branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl) + + # Patch: Tensorflow's average pool does not use the padded zero's in + # its average calculation + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, + count_include_pad=False) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool] + return torch.cat(outputs, 1) + + +class FIDInceptionE_1(models.inception.InceptionE): + """First InceptionE block patched for FID computation""" + def __init__(self, in_channels): + super(FIDInceptionE_1, self).__init__(in_channels) + + def forward(self, x): + branch1x1 = self.branch1x1(x) + + branch3x3 = self.branch3x3_1(x) + branch3x3 = [ + self.branch3x3_2a(branch3x3), + self.branch3x3_2b(branch3x3), + ] + branch3x3 = torch.cat(branch3x3, 1) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = [ + self.branch3x3dbl_3a(branch3x3dbl), + self.branch3x3dbl_3b(branch3x3dbl), + ] + branch3x3dbl = torch.cat(branch3x3dbl, 1) + + # Patch: Tensorflow's average pool does not use the padded zero's in + # its average calculation + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, + count_include_pad=False) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] + return torch.cat(outputs, 1) + + +class FIDInceptionE_2(models.inception.InceptionE): + """Second InceptionE block patched for FID computation""" + def __init__(self, in_channels): + super(FIDInceptionE_2, self).__init__(in_channels) + + def forward(self, x): + branch1x1 = self.branch1x1(x) + + branch3x3 = self.branch3x3_1(x) + branch3x3 = [ + self.branch3x3_2a(branch3x3), + self.branch3x3_2b(branch3x3), + ] + branch3x3 = torch.cat(branch3x3, 1) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = [ + self.branch3x3dbl_3a(branch3x3dbl), + self.branch3x3dbl_3b(branch3x3dbl), + ] + branch3x3dbl = torch.cat(branch3x3dbl, 1) + + # Patch: The FID Inception model uses max pooling instead of average + # pooling. This is likely an error in this specific Inception + # implementation, as other Inception models use average pooling here + # (which matches the description in the paper). + branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] + return torch.cat(outputs, 1) diff --git a/third-party/lama/bin/saicinpainting/evaluation/losses/lpips.py b/third-party/lama/bin/saicinpainting/evaluation/losses/lpips.py new file mode 100644 index 0000000000000000000000000000000000000000..b5f19b747f2457902695213f7efcde4fdc306c1f --- /dev/null +++ b/third-party/lama/bin/saicinpainting/evaluation/losses/lpips.py @@ -0,0 +1,891 @@ +############################################################ +# The contents below have been combined using files in the # +# following repository: # +# https://github.com/richzhang/PerceptualSimilarity # +############################################################ + +############################################################ +# __init__.py # +############################################################ + +import numpy as np +from skimage.metrics import structural_similarity +import torch + +from saicinpainting.utils import get_shape + + +class PerceptualLoss(torch.nn.Module): + def __init__(self, model='net-lin', net='alex', colorspace='rgb', model_path=None, spatial=False, use_gpu=True): + # VGG using our perceptually-learned weights (LPIPS metric) + # def __init__(self, model='net', net='vgg', use_gpu=True): # "default" way of using VGG as a perceptual loss + super(PerceptualLoss, self).__init__() + self.use_gpu = use_gpu + self.spatial = spatial + self.model = DistModel() + self.model.initialize(model=model, net=net, use_gpu=use_gpu, colorspace=colorspace, + model_path=model_path, spatial=self.spatial) + + def forward(self, pred, target, normalize=True): + """ + Pred and target are Variables. + If normalize is True, assumes the images are between [0,1] and then scales them between [-1,+1] + If normalize is False, assumes the images are already between [-1,+1] + Inputs pred and target are Nx3xHxW + Output pytorch Variable N long + """ + + if normalize: + target = 2 * target - 1 + pred = 2 * pred - 1 + + return self.model(target, pred) + + +def normalize_tensor(in_feat, eps=1e-10): + norm_factor = torch.sqrt(torch.sum(in_feat ** 2, dim=1, keepdim=True)) + return in_feat / (norm_factor + eps) + + +def l2(p0, p1, range=255.): + return .5 * np.mean((p0 / range - p1 / range) ** 2) + + +def psnr(p0, p1, peak=255.): + return 10 * np.log10(peak ** 2 / np.mean((1. * p0 - 1. * p1) ** 2)) + + +def dssim(p0, p1, range=255.): + return (1 - compare_ssim(p0, p1, data_range=range, multichannel=True)) / 2. + + +def rgb2lab(in_img, mean_cent=False): + from skimage import color + img_lab = color.rgb2lab(in_img) + if (mean_cent): + img_lab[:, :, 0] = img_lab[:, :, 0] - 50 + return img_lab + + +def tensor2np(tensor_obj): + # change dimension of a tensor object into a numpy array + return tensor_obj[0].cpu().float().numpy().transpose((1, 2, 0)) + + +def np2tensor(np_obj): + # change dimenion of np array into tensor array + return torch.Tensor(np_obj[:, :, :, np.newaxis].transpose((3, 2, 0, 1))) + + +def tensor2tensorlab(image_tensor, to_norm=True, mc_only=False): + # image tensor to lab tensor + from skimage import color + + img = tensor2im(image_tensor) + img_lab = color.rgb2lab(img) + if (mc_only): + img_lab[:, :, 0] = img_lab[:, :, 0] - 50 + if (to_norm and not mc_only): + img_lab[:, :, 0] = img_lab[:, :, 0] - 50 + img_lab = img_lab / 100. + + return np2tensor(img_lab) + + +def tensorlab2tensor(lab_tensor, return_inbnd=False): + from skimage import color + import warnings + warnings.filterwarnings("ignore") + + lab = tensor2np(lab_tensor) * 100. + lab[:, :, 0] = lab[:, :, 0] + 50 + + rgb_back = 255. * np.clip(color.lab2rgb(lab.astype('float')), 0, 1) + if (return_inbnd): + # convert back to lab, see if we match + lab_back = color.rgb2lab(rgb_back.astype('uint8')) + mask = 1. * np.isclose(lab_back, lab, atol=2.) + mask = np2tensor(np.prod(mask, axis=2)[:, :, np.newaxis]) + return (im2tensor(rgb_back), mask) + else: + return im2tensor(rgb_back) + + +def rgb2lab(input): + from skimage import color + return color.rgb2lab(input / 255.) + + +def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255. / 2.): + image_numpy = image_tensor[0].cpu().float().numpy() + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor + return image_numpy.astype(imtype) + + +def im2tensor(image, imtype=np.uint8, cent=1., factor=255. / 2.): + return torch.Tensor((image / factor - cent) + [:, :, :, np.newaxis].transpose((3, 2, 0, 1))) + + +def tensor2vec(vector_tensor): + return vector_tensor.data.cpu().numpy()[:, :, 0, 0] + + +def voc_ap(rec, prec, use_07_metric=False): + """ ap = voc_ap(rec, prec, [use_07_metric]) + Compute VOC AP given precision and recall. + If use_07_metric is true, uses the + VOC 07 11 point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0. + for t in np.arange(0., 1.1, 0.1): + if np.sum(rec >= t) == 0: + p = 0 + else: + p = np.max(prec[rec >= t]) + ap = ap + p / 11. + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], rec, [1.])) + mpre = np.concatenate(([0.], prec, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + + +def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255. / 2.): + # def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=1.): + image_numpy = image_tensor[0].cpu().float().numpy() + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor + return image_numpy.astype(imtype) + + +def im2tensor(image, imtype=np.uint8, cent=1., factor=255. / 2.): + # def im2tensor(image, imtype=np.uint8, cent=1., factor=1.): + return torch.Tensor((image / factor - cent) + [:, :, :, np.newaxis].transpose((3, 2, 0, 1))) + + +############################################################ +# base_model.py # +############################################################ + + +class BaseModel(torch.nn.Module): + def __init__(self): + super().__init__() + + def name(self): + return 'BaseModel' + + def initialize(self, use_gpu=True): + self.use_gpu = use_gpu + + def forward(self): + pass + + def get_image_paths(self): + pass + + def optimize_parameters(self): + pass + + def get_current_visuals(self): + return self.input + + def get_current_errors(self): + return {} + + def save(self, label): + pass + + # helper saving function that can be used by subclasses + def save_network(self, network, path, network_label, epoch_label): + save_filename = '%s_net_%s.pth' % (epoch_label, network_label) + save_path = os.path.join(path, save_filename) + torch.save(network.state_dict(), save_path) + + # helper loading function that can be used by subclasses + def load_network(self, network, network_label, epoch_label): + save_filename = '%s_net_%s.pth' % (epoch_label, network_label) + save_path = os.path.join(self.save_dir, save_filename) + print('Loading network from %s' % save_path) + network.load_state_dict(torch.load(save_path, map_location='cpu')) + + def update_learning_rate(): + pass + + def get_image_paths(self): + return self.image_paths + + def save_done(self, flag=False): + np.save(os.path.join(self.save_dir, 'done_flag'), flag) + np.savetxt(os.path.join(self.save_dir, 'done_flag'), [flag, ], fmt='%i') + + +############################################################ +# dist_model.py # +############################################################ + +import os +from collections import OrderedDict +from scipy.ndimage import zoom +from tqdm import tqdm + + +class DistModel(BaseModel): + def name(self): + return self.model_name + + def initialize(self, model='net-lin', net='alex', colorspace='Lab', pnet_rand=False, pnet_tune=False, + model_path=None, + use_gpu=True, printNet=False, spatial=False, + is_train=False, lr=.0001, beta1=0.5, version='0.1'): + ''' + INPUTS + model - ['net-lin'] for linearly calibrated network + ['net'] for off-the-shelf network + ['L2'] for L2 distance in Lab colorspace + ['SSIM'] for ssim in RGB colorspace + net - ['squeeze','alex','vgg'] + model_path - if None, will look in weights/[NET_NAME].pth + colorspace - ['Lab','RGB'] colorspace to use for L2 and SSIM + use_gpu - bool - whether or not to use a GPU + printNet - bool - whether or not to print network architecture out + spatial - bool - whether to output an array containing varying distances across spatial dimensions + spatial_shape - if given, output spatial shape. if None then spatial shape is determined automatically via spatial_factor (see below). + spatial_factor - if given, specifies upsampling factor relative to the largest spatial extent of a convolutional layer. if None then resized to size of input images. + spatial_order - spline order of filter for upsampling in spatial mode, by default 1 (bilinear). + is_train - bool - [True] for training mode + lr - float - initial learning rate + beta1 - float - initial momentum term for adam + version - 0.1 for latest, 0.0 was original (with a bug) + ''' + BaseModel.initialize(self, use_gpu=use_gpu) + + self.model = model + self.net = net + self.is_train = is_train + self.spatial = spatial + self.model_name = '%s [%s]' % (model, net) + + if (self.model == 'net-lin'): # pretrained net + linear layer + self.net = PNetLin(pnet_rand=pnet_rand, pnet_tune=pnet_tune, pnet_type=net, + use_dropout=True, spatial=spatial, version=version, lpips=True) + kw = dict(map_location='cpu') + if (model_path is None): + import inspect + model_path = os.path.abspath( + os.path.join(os.path.dirname(__file__), '..', '..', '..', 'models', 'lpips_models', f'{net}.pth')) + + if (not is_train): + self.net.load_state_dict(torch.load(model_path, **kw), strict=False) + + elif (self.model == 'net'): # pretrained network + self.net = PNetLin(pnet_rand=pnet_rand, pnet_type=net, lpips=False) + elif (self.model in ['L2', 'l2']): + self.net = L2(use_gpu=use_gpu, colorspace=colorspace) # not really a network, only for testing + self.model_name = 'L2' + elif (self.model in ['DSSIM', 'dssim', 'SSIM', 'ssim']): + self.net = DSSIM(use_gpu=use_gpu, colorspace=colorspace) + self.model_name = 'SSIM' + else: + raise ValueError("Model [%s] not recognized." % self.model) + + self.trainable_parameters = list(self.net.parameters()) + + if self.is_train: # training mode + # extra network on top to go from distances (d0,d1) => predicted human judgment (h*) + self.rankLoss = BCERankingLoss() + self.trainable_parameters += list(self.rankLoss.net.parameters()) + self.lr = lr + self.old_lr = lr + self.optimizer_net = torch.optim.Adam(self.trainable_parameters, lr=lr, betas=(beta1, 0.999)) + else: # test mode + self.net.eval() + + # if (use_gpu): + # self.net.to(gpu_ids[0]) + # self.net = torch.nn.DataParallel(self.net, device_ids=gpu_ids) + # if (self.is_train): + # self.rankLoss = self.rankLoss.to(device=gpu_ids[0]) # just put this on GPU0 + + if (printNet): + print('---------- Networks initialized -------------') + print_network(self.net) + print('-----------------------------------------------') + + def forward(self, in0, in1, retPerLayer=False): + ''' Function computes the distance between image patches in0 and in1 + INPUTS + in0, in1 - torch.Tensor object of shape Nx3xXxY - image patch scaled to [-1,1] + OUTPUT + computed distances between in0 and in1 + ''' + + return self.net(in0, in1, retPerLayer=retPerLayer) + + # ***** TRAINING FUNCTIONS ***** + def optimize_parameters(self): + self.forward_train() + self.optimizer_net.zero_grad() + self.backward_train() + self.optimizer_net.step() + self.clamp_weights() + + def clamp_weights(self): + for module in self.net.modules(): + if (hasattr(module, 'weight') and module.kernel_size == (1, 1)): + module.weight.data = torch.clamp(module.weight.data, min=0) + + def set_input(self, data): + self.input_ref = data['ref'] + self.input_p0 = data['p0'] + self.input_p1 = data['p1'] + self.input_judge = data['judge'] + + # if (self.use_gpu): + # self.input_ref = self.input_ref.to(device=self.gpu_ids[0]) + # self.input_p0 = self.input_p0.to(device=self.gpu_ids[0]) + # self.input_p1 = self.input_p1.to(device=self.gpu_ids[0]) + # self.input_judge = self.input_judge.to(device=self.gpu_ids[0]) + + # self.var_ref = Variable(self.input_ref, requires_grad=True) + # self.var_p0 = Variable(self.input_p0, requires_grad=True) + # self.var_p1 = Variable(self.input_p1, requires_grad=True) + + def forward_train(self): # run forward pass + # print(self.net.module.scaling_layer.shift) + # print(torch.norm(self.net.module.net.slice1[0].weight).item(), torch.norm(self.net.module.lin0.model[1].weight).item()) + + assert False, "We shoud've not get here when using LPIPS as a metric" + + self.d0 = self(self.var_ref, self.var_p0) + self.d1 = self(self.var_ref, self.var_p1) + self.acc_r = self.compute_accuracy(self.d0, self.d1, self.input_judge) + + self.var_judge = Variable(1. * self.input_judge).view(self.d0.size()) + + self.loss_total = self.rankLoss(self.d0, self.d1, self.var_judge * 2. - 1.) + + return self.loss_total + + def backward_train(self): + torch.mean(self.loss_total).backward() + + def compute_accuracy(self, d0, d1, judge): + ''' d0, d1 are Variables, judge is a Tensor ''' + d1_lt_d0 = (d1 < d0).cpu().data.numpy().flatten() + judge_per = judge.cpu().numpy().flatten() + return d1_lt_d0 * judge_per + (1 - d1_lt_d0) * (1 - judge_per) + + def get_current_errors(self): + retDict = OrderedDict([('loss_total', self.loss_total.data.cpu().numpy()), + ('acc_r', self.acc_r)]) + + for key in retDict.keys(): + retDict[key] = np.mean(retDict[key]) + + return retDict + + def get_current_visuals(self): + zoom_factor = 256 / self.var_ref.data.size()[2] + + ref_img = tensor2im(self.var_ref.data) + p0_img = tensor2im(self.var_p0.data) + p1_img = tensor2im(self.var_p1.data) + + ref_img_vis = zoom(ref_img, [zoom_factor, zoom_factor, 1], order=0) + p0_img_vis = zoom(p0_img, [zoom_factor, zoom_factor, 1], order=0) + p1_img_vis = zoom(p1_img, [zoom_factor, zoom_factor, 1], order=0) + + return OrderedDict([('ref', ref_img_vis), + ('p0', p0_img_vis), + ('p1', p1_img_vis)]) + + def save(self, path, label): + if (self.use_gpu): + self.save_network(self.net.module, path, '', label) + else: + self.save_network(self.net, path, '', label) + self.save_network(self.rankLoss.net, path, 'rank', label) + + def update_learning_rate(self, nepoch_decay): + lrd = self.lr / nepoch_decay + lr = self.old_lr - lrd + + for param_group in self.optimizer_net.param_groups: + param_group['lr'] = lr + + print('update lr [%s] decay: %f -> %f' % (type, self.old_lr, lr)) + self.old_lr = lr + + +def score_2afc_dataset(data_loader, func, name=''): + ''' Function computes Two Alternative Forced Choice (2AFC) score using + distance function 'func' in dataset 'data_loader' + INPUTS + data_loader - CustomDatasetDataLoader object - contains a TwoAFCDataset inside + func - callable distance function - calling d=func(in0,in1) should take 2 + pytorch tensors with shape Nx3xXxY, and return numpy array of length N + OUTPUTS + [0] - 2AFC score in [0,1], fraction of time func agrees with human evaluators + [1] - dictionary with following elements + d0s,d1s - N arrays containing distances between reference patch to perturbed patches + gts - N array in [0,1], preferred patch selected by human evaluators + (closer to "0" for left patch p0, "1" for right patch p1, + "0.6" means 60pct people preferred right patch, 40pct preferred left) + scores - N array in [0,1], corresponding to what percentage function agreed with humans + CONSTS + N - number of test triplets in data_loader + ''' + + d0s = [] + d1s = [] + gts = [] + + for data in tqdm(data_loader.load_data(), desc=name): + d0s += func(data['ref'], data['p0']).data.cpu().numpy().flatten().tolist() + d1s += func(data['ref'], data['p1']).data.cpu().numpy().flatten().tolist() + gts += data['judge'].cpu().numpy().flatten().tolist() + + d0s = np.array(d0s) + d1s = np.array(d1s) + gts = np.array(gts) + scores = (d0s < d1s) * (1. - gts) + (d1s < d0s) * gts + (d1s == d0s) * .5 + + return (np.mean(scores), dict(d0s=d0s, d1s=d1s, gts=gts, scores=scores)) + + +def score_jnd_dataset(data_loader, func, name=''): + ''' Function computes JND score using distance function 'func' in dataset 'data_loader' + INPUTS + data_loader - CustomDatasetDataLoader object - contains a JNDDataset inside + func - callable distance function - calling d=func(in0,in1) should take 2 + pytorch tensors with shape Nx3xXxY, and return pytorch array of length N + OUTPUTS + [0] - JND score in [0,1], mAP score (area under precision-recall curve) + [1] - dictionary with following elements + ds - N array containing distances between two patches shown to human evaluator + sames - N array containing fraction of people who thought the two patches were identical + CONSTS + N - number of test triplets in data_loader + ''' + + ds = [] + gts = [] + + for data in tqdm(data_loader.load_data(), desc=name): + ds += func(data['p0'], data['p1']).data.cpu().numpy().tolist() + gts += data['same'].cpu().numpy().flatten().tolist() + + sames = np.array(gts) + ds = np.array(ds) + + sorted_inds = np.argsort(ds) + ds_sorted = ds[sorted_inds] + sames_sorted = sames[sorted_inds] + + TPs = np.cumsum(sames_sorted) + FPs = np.cumsum(1 - sames_sorted) + FNs = np.sum(sames_sorted) - TPs + + precs = TPs / (TPs + FPs) + recs = TPs / (TPs + FNs) + score = voc_ap(recs, precs) + + return (score, dict(ds=ds, sames=sames)) + + +############################################################ +# networks_basic.py # +############################################################ + +import torch.nn as nn +from torch.autograd import Variable +import numpy as np + + +def spatial_average(in_tens, keepdim=True): + return in_tens.mean([2, 3], keepdim=keepdim) + + +def upsample(in_tens, out_H=64): # assumes scale factor is same for H and W + in_H = in_tens.shape[2] + scale_factor = 1. * out_H / in_H + + return nn.Upsample(scale_factor=scale_factor, mode='bilinear', align_corners=False)(in_tens) + + +# Learned perceptual metric +class PNetLin(nn.Module): + def __init__(self, pnet_type='vgg', pnet_rand=False, pnet_tune=False, use_dropout=True, spatial=False, + version='0.1', lpips=True): + super(PNetLin, self).__init__() + + self.pnet_type = pnet_type + self.pnet_tune = pnet_tune + self.pnet_rand = pnet_rand + self.spatial = spatial + self.lpips = lpips + self.version = version + self.scaling_layer = ScalingLayer() + + if (self.pnet_type in ['vgg', 'vgg16']): + net_type = vgg16 + self.chns = [64, 128, 256, 512, 512] + elif (self.pnet_type == 'alex'): + net_type = alexnet + self.chns = [64, 192, 384, 256, 256] + elif (self.pnet_type == 'squeeze'): + net_type = squeezenet + self.chns = [64, 128, 256, 384, 384, 512, 512] + self.L = len(self.chns) + + self.net = net_type(pretrained=not self.pnet_rand, requires_grad=self.pnet_tune) + + if (lpips): + self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout) + self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout) + self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout) + self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout) + self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout) + self.lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4] + if (self.pnet_type == 'squeeze'): # 7 layers for squeezenet + self.lin5 = NetLinLayer(self.chns[5], use_dropout=use_dropout) + self.lin6 = NetLinLayer(self.chns[6], use_dropout=use_dropout) + self.lins += [self.lin5, self.lin6] + + def forward(self, in0, in1, retPerLayer=False): + # v0.0 - original release had a bug, where input was not scaled + in0_input, in1_input = (self.scaling_layer(in0), self.scaling_layer(in1)) if self.version == '0.1' else ( + in0, in1) + outs0, outs1 = self.net(in0_input), self.net(in1_input) + feats0, feats1, diffs = {}, {}, {} + + for kk in range(self.L): + feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk]) + diffs[kk] = (feats0[kk] - feats1[kk]) ** 2 + + if (self.lpips): + if (self.spatial): + res = [upsample(self.lins[kk].model(diffs[kk]), out_H=in0.shape[2]) for kk in range(self.L)] + else: + res = [spatial_average(self.lins[kk].model(diffs[kk]), keepdim=True) for kk in range(self.L)] + else: + if (self.spatial): + res = [upsample(diffs[kk].sum(dim=1, keepdim=True), out_H=in0.shape[2]) for kk in range(self.L)] + else: + res = [spatial_average(diffs[kk].sum(dim=1, keepdim=True), keepdim=True) for kk in range(self.L)] + + val = res[0] + for l in range(1, self.L): + val += res[l] + + if (retPerLayer): + return (val, res) + else: + return val + + +class ScalingLayer(nn.Module): + def __init__(self): + super(ScalingLayer, self).__init__() + self.register_buffer('shift', torch.Tensor([-.030, -.088, -.188])[None, :, None, None]) + self.register_buffer('scale', torch.Tensor([.458, .448, .450])[None, :, None, None]) + + def forward(self, inp): + return (inp - self.shift) / self.scale + + +class NetLinLayer(nn.Module): + ''' A single linear layer which does a 1x1 conv ''' + + def __init__(self, chn_in, chn_out=1, use_dropout=False): + super(NetLinLayer, self).__init__() + + layers = [nn.Dropout(), ] if (use_dropout) else [] + layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ] + self.model = nn.Sequential(*layers) + + +class Dist2LogitLayer(nn.Module): + ''' takes 2 distances, puts through fc layers, spits out value between [0,1] (if use_sigmoid is True) ''' + + def __init__(self, chn_mid=32, use_sigmoid=True): + super(Dist2LogitLayer, self).__init__() + + layers = [nn.Conv2d(5, chn_mid, 1, stride=1, padding=0, bias=True), ] + layers += [nn.LeakyReLU(0.2, True), ] + layers += [nn.Conv2d(chn_mid, chn_mid, 1, stride=1, padding=0, bias=True), ] + layers += [nn.LeakyReLU(0.2, True), ] + layers += [nn.Conv2d(chn_mid, 1, 1, stride=1, padding=0, bias=True), ] + if (use_sigmoid): + layers += [nn.Sigmoid(), ] + self.model = nn.Sequential(*layers) + + def forward(self, d0, d1, eps=0.1): + return self.model(torch.cat((d0, d1, d0 - d1, d0 / (d1 + eps), d1 / (d0 + eps)), dim=1)) + + +class BCERankingLoss(nn.Module): + def __init__(self, chn_mid=32): + super(BCERankingLoss, self).__init__() + self.net = Dist2LogitLayer(chn_mid=chn_mid) + # self.parameters = list(self.net.parameters()) + self.loss = torch.nn.BCELoss() + + def forward(self, d0, d1, judge): + per = (judge + 1.) / 2. + self.logit = self.net(d0, d1) + return self.loss(self.logit, per) + + +# L2, DSSIM metrics +class FakeNet(nn.Module): + def __init__(self, use_gpu=True, colorspace='Lab'): + super(FakeNet, self).__init__() + self.use_gpu = use_gpu + self.colorspace = colorspace + + +class L2(FakeNet): + + def forward(self, in0, in1, retPerLayer=None): + assert (in0.size()[0] == 1) # currently only supports batchSize 1 + + if (self.colorspace == 'RGB'): + (N, C, X, Y) = in0.size() + value = torch.mean(torch.mean(torch.mean((in0 - in1) ** 2, dim=1).view(N, 1, X, Y), dim=2).view(N, 1, 1, Y), + dim=3).view(N) + return value + elif (self.colorspace == 'Lab'): + value = l2(tensor2np(tensor2tensorlab(in0.data, to_norm=False)), + tensor2np(tensor2tensorlab(in1.data, to_norm=False)), range=100.).astype('float') + ret_var = Variable(torch.Tensor((value,))) + # if (self.use_gpu): + # ret_var = ret_var.cuda() + return ret_var + + +class DSSIM(FakeNet): + + def forward(self, in0, in1, retPerLayer=None): + assert (in0.size()[0] == 1) # currently only supports batchSize 1 + + if (self.colorspace == 'RGB'): + value = dssim(1. * tensor2im(in0.data), 1. * tensor2im(in1.data), range=255.).astype('float') + elif (self.colorspace == 'Lab'): + value = dssim(tensor2np(tensor2tensorlab(in0.data, to_norm=False)), + tensor2np(tensor2tensorlab(in1.data, to_norm=False)), range=100.).astype('float') + ret_var = Variable(torch.Tensor((value,))) + # if (self.use_gpu): + # ret_var = ret_var.cuda() + return ret_var + + +def print_network(net): + num_params = 0 + for param in net.parameters(): + num_params += param.numel() + print('Network', net) + print('Total number of parameters: %d' % num_params) + + +############################################################ +# pretrained_networks.py # +############################################################ + +from collections import namedtuple +import torch +from torchvision import models as tv + + +class squeezenet(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True): + super(squeezenet, self).__init__() + pretrained_features = tv.squeezenet1_1(pretrained=pretrained).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + self.slice6 = torch.nn.Sequential() + self.slice7 = torch.nn.Sequential() + self.N_slices = 7 + for x in range(2): + self.slice1.add_module(str(x), pretrained_features[x]) + for x in range(2, 5): + self.slice2.add_module(str(x), pretrained_features[x]) + for x in range(5, 8): + self.slice3.add_module(str(x), pretrained_features[x]) + for x in range(8, 10): + self.slice4.add_module(str(x), pretrained_features[x]) + for x in range(10, 11): + self.slice5.add_module(str(x), pretrained_features[x]) + for x in range(11, 12): + self.slice6.add_module(str(x), pretrained_features[x]) + for x in range(12, 13): + self.slice7.add_module(str(x), pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h = self.slice1(X) + h_relu1 = h + h = self.slice2(h) + h_relu2 = h + h = self.slice3(h) + h_relu3 = h + h = self.slice4(h) + h_relu4 = h + h = self.slice5(h) + h_relu5 = h + h = self.slice6(h) + h_relu6 = h + h = self.slice7(h) + h_relu7 = h + vgg_outputs = namedtuple("SqueezeOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5', 'relu6', 'relu7']) + out = vgg_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5, h_relu6, h_relu7) + + return out + + +class alexnet(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True): + super(alexnet, self).__init__() + alexnet_pretrained_features = tv.alexnet(pretrained=pretrained).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + self.N_slices = 5 + for x in range(2): + self.slice1.add_module(str(x), alexnet_pretrained_features[x]) + for x in range(2, 5): + self.slice2.add_module(str(x), alexnet_pretrained_features[x]) + for x in range(5, 8): + self.slice3.add_module(str(x), alexnet_pretrained_features[x]) + for x in range(8, 10): + self.slice4.add_module(str(x), alexnet_pretrained_features[x]) + for x in range(10, 12): + self.slice5.add_module(str(x), alexnet_pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h = self.slice1(X) + h_relu1 = h + h = self.slice2(h) + h_relu2 = h + h = self.slice3(h) + h_relu3 = h + h = self.slice4(h) + h_relu4 = h + h = self.slice5(h) + h_relu5 = h + alexnet_outputs = namedtuple("AlexnetOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5']) + out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5) + + return out + + +class vgg16(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True): + super(vgg16, self).__init__() + vgg_pretrained_features = tv.vgg16(pretrained=pretrained).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + self.N_slices = 5 + for x in range(4): + self.slice1.add_module(str(x), vgg_pretrained_features[x]) + for x in range(4, 9): + self.slice2.add_module(str(x), vgg_pretrained_features[x]) + for x in range(9, 16): + self.slice3.add_module(str(x), vgg_pretrained_features[x]) + for x in range(16, 23): + self.slice4.add_module(str(x), vgg_pretrained_features[x]) + for x in range(23, 30): + self.slice5.add_module(str(x), vgg_pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h = self.slice1(X) + h_relu1_2 = h + h = self.slice2(h) + h_relu2_2 = h + h = self.slice3(h) + h_relu3_3 = h + h = self.slice4(h) + h_relu4_3 = h + h = self.slice5(h) + h_relu5_3 = h + vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3']) + out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3) + + return out + + +class resnet(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True, num=18): + super(resnet, self).__init__() + if (num == 18): + self.net = tv.resnet18(pretrained=pretrained) + elif (num == 34): + self.net = tv.resnet34(pretrained=pretrained) + elif (num == 50): + self.net = tv.resnet50(pretrained=pretrained) + elif (num == 101): + self.net = tv.resnet101(pretrained=pretrained) + elif (num == 152): + self.net = tv.resnet152(pretrained=pretrained) + self.N_slices = 5 + + self.conv1 = self.net.conv1 + self.bn1 = self.net.bn1 + self.relu = self.net.relu + self.maxpool = self.net.maxpool + self.layer1 = self.net.layer1 + self.layer2 = self.net.layer2 + self.layer3 = self.net.layer3 + self.layer4 = self.net.layer4 + + def forward(self, X): + h = self.conv1(X) + h = self.bn1(h) + h = self.relu(h) + h_relu1 = h + h = self.maxpool(h) + h = self.layer1(h) + h_conv2 = h + h = self.layer2(h) + h_conv3 = h + h = self.layer3(h) + h_conv4 = h + h = self.layer4(h) + h_conv5 = h + + outputs = namedtuple("Outputs", ['relu1', 'conv2', 'conv3', 'conv4', 'conv5']) + out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5) + + return out diff --git a/third-party/lama/bin/saicinpainting/evaluation/losses/ssim.py b/third-party/lama/bin/saicinpainting/evaluation/losses/ssim.py new file mode 100644 index 0000000000000000000000000000000000000000..ee43a0095408eca98e253dea194db788446f9c0a --- /dev/null +++ b/third-party/lama/bin/saicinpainting/evaluation/losses/ssim.py @@ -0,0 +1,74 @@ +import numpy as np +import torch +import torch.nn.functional as F + + +class SSIM(torch.nn.Module): + """SSIM. Modified from: + https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py + """ + + def __init__(self, window_size=11, size_average=True): + super().__init__() + self.window_size = window_size + self.size_average = size_average + self.channel = 1 + self.register_buffer('window', self._create_window(window_size, self.channel)) + + def forward(self, img1, img2): + assert len(img1.shape) == 4 + + channel = img1.size()[1] + + if channel == self.channel and self.window.data.type() == img1.data.type(): + window = self.window + else: + window = self._create_window(self.window_size, channel) + + # window = window.to(img1.get_device()) + window = window.type_as(img1) + + self.window = window + self.channel = channel + + return self._ssim(img1, img2, window, self.window_size, channel, self.size_average) + + def _gaussian(self, window_size, sigma): + gauss = torch.Tensor([ + np.exp(-(x - (window_size // 2)) ** 2 / float(2 * sigma ** 2)) for x in range(window_size) + ]) + return gauss / gauss.sum() + + def _create_window(self, window_size, channel): + _1D_window = self._gaussian(window_size, 1.5).unsqueeze(1) + _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) + return _2D_window.expand(channel, 1, window_size, window_size).contiguous() + + def _ssim(self, img1, img2, window, window_size, channel, size_average=True): + mu1 = F.conv2d(img1, window, padding=(window_size // 2), groups=channel) + mu2 = F.conv2d(img2, window, padding=(window_size // 2), groups=channel) + + mu1_sq = mu1.pow(2) + mu2_sq = mu2.pow(2) + mu1_mu2 = mu1 * mu2 + + sigma1_sq = F.conv2d( + img1 * img1, window, padding=(window_size // 2), groups=channel) - mu1_sq + sigma2_sq = F.conv2d( + img2 * img2, window, padding=(window_size // 2), groups=channel) - mu2_sq + sigma12 = F.conv2d( + img1 * img2, window, padding=(window_size // 2), groups=channel) - mu1_mu2 + + C1 = 0.01 ** 2 + C2 = 0.03 ** 2 + + ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / \ + ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)) + + if size_average: + return ssim_map.mean() + + return ssim_map.mean(1).mean(1).mean(1) + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): + return diff --git a/third-party/lama/bin/saicinpainting/evaluation/masks/README.md b/third-party/lama/bin/saicinpainting/evaluation/masks/README.md new file mode 100644 index 0000000000000000000000000000000000000000..cf176bc10fae3b03f139727147c220f2a735c806 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/evaluation/masks/README.md @@ -0,0 +1,27 @@ +# Current algorithm + +## Choice of mask objects + +For identification of the objects which are suitable for mask obtaining, panoptic segmentation model +from [detectron2](https://github.com/facebookresearch/detectron2) trained on COCO. Categories of the detected instances +belong either to "stuff" or "things" types. We consider that instances of objects should have category belong +to "things". Besides, we set upper bound on area which is taken by the object — we consider that too big +area indicates either of the instance being a background or a main object which should not be removed. + +## Choice of position for mask + +We consider that input image has size 2^n x 2^m. We downsample it using +[COUNTLESS](https://github.com/william-silversmith/countless) algorithm so the width is equal to +64 = 2^8 = 2^{downsample_levels}. + +### Augmentation + +There are several parameters for augmentation: +- Scaling factor. We limit scaling to the case when a mask after scaling with pivot point in its center fits inside the + image completely. +- + +### Shift + + +## Select diff --git a/third-party/lama/bin/saicinpainting/evaluation/masks/__init__.py b/third-party/lama/bin/saicinpainting/evaluation/masks/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third-party/lama/bin/saicinpainting/evaluation/masks/countless/.gitignore b/third-party/lama/bin/saicinpainting/evaluation/masks/countless/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..872aa273a4e3ec99d362cefa1c67550f21f3c366 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/evaluation/masks/countless/.gitignore @@ -0,0 +1 @@ +results \ No newline at end of file diff --git a/third-party/lama/bin/saicinpainting/evaluation/masks/countless/README.md b/third-party/lama/bin/saicinpainting/evaluation/masks/countless/README.md new file mode 100644 index 0000000000000000000000000000000000000000..67335464d794776140fd0308f408608f2231309b --- /dev/null +++ b/third-party/lama/bin/saicinpainting/evaluation/masks/countless/README.md @@ -0,0 +1,25 @@ +[![Build Status](https://travis-ci.org/william-silversmith/countless.svg?branch=master)](https://travis-ci.org/william-silversmith/countless) + +Python COUNTLESS Downsampling +============================= + +To install: + +`pip install -r requirements.txt` + +To test: + +`python test.py` + +To benchmark countless2d: + +`python python/countless2d.py python/images/gray_segmentation.png` + +To benchmark countless3d: + +`python python/countless3d.py` + +Adjust N and the list of algorithms inside each script to modify the run parameters. + + +Python3 is slightly faster than Python2. \ No newline at end of file diff --git a/third-party/lama/bin/saicinpainting/evaluation/masks/countless/__init__.py b/third-party/lama/bin/saicinpainting/evaluation/masks/countless/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third-party/lama/bin/saicinpainting/evaluation/masks/countless/countless2d.py b/third-party/lama/bin/saicinpainting/evaluation/masks/countless/countless2d.py new file mode 100644 index 0000000000000000000000000000000000000000..dc27b73affa20ab1a8a199542469a10aaf1f555a --- /dev/null +++ b/third-party/lama/bin/saicinpainting/evaluation/masks/countless/countless2d.py @@ -0,0 +1,529 @@ +from __future__ import print_function, division + +""" +COUNTLESS performance test in Python. + +python countless2d.py ./images/NAMEOFIMAGE +""" + +import six +from six.moves import range +from collections import defaultdict +from functools import reduce +import operator +import io +import os +from PIL import Image +import math +import numpy as np +import random +import sys +import time +from tqdm import tqdm +from scipy import ndimage + +def simplest_countless(data): + """ + Vectorized implementation of downsampling a 2D + image by 2 on each side using the COUNTLESS algorithm. + + data is a 2D numpy array with even dimensions. + """ + sections = [] + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + factor = (2,2) + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + a, b, c, d = sections + + ab = a * (a == b) # PICK(A,B) + ac = a * (a == c) # PICK(A,C) + bc = b * (b == c) # PICK(B,C) + + a = ab | ac | bc # Bitwise OR, safe b/c non-matches are zeroed + + return a + (a == 0) * d # AB || AC || BC || D + +def quick_countless(data): + """ + Vectorized implementation of downsampling a 2D + image by 2 on each side using the COUNTLESS algorithm. + + data is a 2D numpy array with even dimensions. + """ + sections = [] + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + factor = (2,2) + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + a, b, c, d = sections + + ab_ac = a * ((a == b) | (a == c)) # PICK(A,B) || PICK(A,C) w/ optimization + bc = b * (b == c) # PICK(B,C) + + a = ab_ac | bc # (PICK(A,B) || PICK(A,C)) or PICK(B,C) + return a + (a == 0) * d # AB || AC || BC || D + +def quickest_countless(data): + """ + Vectorized implementation of downsampling a 2D + image by 2 on each side using the COUNTLESS algorithm. + + data is a 2D numpy array with even dimensions. + """ + sections = [] + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + factor = (2,2) + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + a, b, c, d = sections + + ab_ac = a * ((a == b) | (a == c)) # PICK(A,B) || PICK(A,C) w/ optimization + ab_ac |= b * (b == c) # PICK(B,C) + return ab_ac + (ab_ac == 0) * d # AB || AC || BC || D + +def quick_countless_xor(data): + """ + Vectorized implementation of downsampling a 2D + image by 2 on each side using the COUNTLESS algorithm. + + data is a 2D numpy array with even dimensions. + """ + sections = [] + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + factor = (2,2) + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + a, b, c, d = sections + + ab = a ^ (a ^ b) # a or b + ab += (ab != a) * ((ab ^ (ab ^ c)) - b) # b or c + ab += (ab == c) * ((ab ^ (ab ^ d)) - c) # c or d + return ab + +def stippled_countless(data): + """ + Vectorized implementation of downsampling a 2D + image by 2 on each side using the COUNTLESS algorithm + that treats zero as "background" and inflates lone + pixels. + + data is a 2D numpy array with even dimensions. + """ + sections = [] + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + factor = (2,2) + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + a, b, c, d = sections + + ab_ac = a * ((a == b) | (a == c)) # PICK(A,B) || PICK(A,C) w/ optimization + ab_ac |= b * (b == c) # PICK(B,C) + + nonzero = a + (a == 0) * (b + (b == 0) * c) + return ab_ac + (ab_ac == 0) * (d + (d == 0) * nonzero) # AB || AC || BC || D + +def zero_corrected_countless(data): + """ + Vectorized implementation of downsampling a 2D + image by 2 on each side using the COUNTLESS algorithm. + + data is a 2D numpy array with even dimensions. + """ + # allows us to prevent losing 1/2 a bit of information + # at the top end by using a bigger type. Without this 255 is handled incorrectly. + data, upgraded = upgrade_type(data) + + # offset from zero, raw countless doesn't handle 0 correctly + # we'll remove the extra 1 at the end. + data += 1 + + sections = [] + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + factor = (2,2) + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + a, b, c, d = sections + + ab = a * (a == b) # PICK(A,B) + ac = a * (a == c) # PICK(A,C) + bc = b * (b == c) # PICK(B,C) + + a = ab | ac | bc # Bitwise OR, safe b/c non-matches are zeroed + + result = a + (a == 0) * d - 1 # a or d - 1 + + if upgraded: + return downgrade_type(result) + + # only need to reset data if we weren't upgraded + # b/c no copy was made in that case + data -= 1 + + return result + +def countless_extreme(data): + nonzeros = np.count_nonzero(data) + # print("nonzeros", nonzeros) + + N = reduce(operator.mul, data.shape) + + if nonzeros == N: + print("quick") + return quick_countless(data) + elif np.count_nonzero(data + 1) == N: + print("quick") + # print("upper", nonzeros) + return quick_countless(data) + else: + return countless(data) + + +def countless(data): + """ + Vectorized implementation of downsampling a 2D + image by 2 on each side using the COUNTLESS algorithm. + + data is a 2D numpy array with even dimensions. + """ + # allows us to prevent losing 1/2 a bit of information + # at the top end by using a bigger type. Without this 255 is handled incorrectly. + data, upgraded = upgrade_type(data) + + # offset from zero, raw countless doesn't handle 0 correctly + # we'll remove the extra 1 at the end. + data += 1 + + sections = [] + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + factor = (2,2) + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + a, b, c, d = sections + + ab_ac = a * ((a == b) | (a == c)) # PICK(A,B) || PICK(A,C) w/ optimization + ab_ac |= b * (b == c) # PICK(B,C) + result = ab_ac + (ab_ac == 0) * d - 1 # (matches or d) - 1 + + if upgraded: + return downgrade_type(result) + + # only need to reset data if we weren't upgraded + # b/c no copy was made in that case + data -= 1 + + return result + +def upgrade_type(arr): + dtype = arr.dtype + + if dtype == np.uint8: + return arr.astype(np.uint16), True + elif dtype == np.uint16: + return arr.astype(np.uint32), True + elif dtype == np.uint32: + return arr.astype(np.uint64), True + + return arr, False + +def downgrade_type(arr): + dtype = arr.dtype + + if dtype == np.uint64: + return arr.astype(np.uint32) + elif dtype == np.uint32: + return arr.astype(np.uint16) + elif dtype == np.uint16: + return arr.astype(np.uint8) + + return arr + +def odd_to_even(image): + """ + To facilitate 2x2 downsampling segmentation, change an odd sized image into an even sized one. + Works by mirroring the starting 1 pixel edge of the image on odd shaped sides. + + e.g. turn a 3x3x5 image into a 4x4x5 (the x and y are what are getting downsampled) + + For example: [ 3, 2, 4 ] => [ 3, 3, 2, 4 ] which is now easy to downsample. + + """ + shape = np.array(image.shape) + + offset = (shape % 2)[:2] # x,y offset + + # detect if we're dealing with an even + # image. if so it's fine, just return. + if not np.any(offset): + return image + + oddshape = image.shape[:2] + offset + oddshape = np.append(oddshape, shape[2:]) + oddshape = oddshape.astype(int) + + newimg = np.empty(shape=oddshape, dtype=image.dtype) + + ox,oy = offset + sx,sy = oddshape + + newimg[0,0] = image[0,0] # corner + newimg[ox:sx,0] = image[:,0] # x axis line + newimg[0,oy:sy] = image[0,:] # y axis line + + return newimg + +def counting(array): + factor = (2, 2, 1) + shape = array.shape + + while len(shape) < 4: + array = np.expand_dims(array, axis=-1) + shape = array.shape + + output_shape = tuple(int(math.ceil(s / f)) for s, f in zip(shape, factor)) + output = np.zeros(output_shape, dtype=array.dtype) + + for chan in range(0, shape[3]): + for z in range(0, shape[2]): + for x in range(0, shape[0], 2): + for y in range(0, shape[1], 2): + block = array[ x:x+2, y:y+2, z, chan ] # 2x2 block + + hashtable = defaultdict(int) + for subx, suby in np.ndindex(block.shape[0], block.shape[1]): + hashtable[block[subx, suby]] += 1 + + best = (0, 0) + for segid, val in six.iteritems(hashtable): + if best[1] < val: + best = (segid, val) + + output[ x // 2, y // 2, chan ] = best[0] + + return output + +def ndzoom(array): + if len(array.shape) == 3: + ratio = ( 1 / 2.0, 1 / 2.0, 1.0 ) + else: + ratio = ( 1 / 2.0, 1 / 2.0) + return ndimage.interpolation.zoom(array, ratio, order=1) + +def countless_if(array): + factor = (2, 2, 1) + shape = array.shape + + if len(shape) < 3: + array = array[ :,:, np.newaxis ] + shape = array.shape + + output_shape = tuple(int(math.ceil(s / f)) for s, f in zip(shape, factor)) + output = np.zeros(output_shape, dtype=array.dtype) + + for chan in range(0, shape[2]): + for x in range(0, shape[0], 2): + for y in range(0, shape[1], 2): + block = array[ x:x+2, y:y+2, chan ] # 2x2 block + + if block[0,0] == block[1,0]: + pick = block[0,0] + elif block[0,0] == block[0,1]: + pick = block[0,0] + elif block[1,0] == block[0,1]: + pick = block[1,0] + else: + pick = block[1,1] + + output[ x // 2, y // 2, chan ] = pick + + return np.squeeze(output) + +def downsample_with_averaging(array): + """ + Downsample x by factor using averaging. + + @return: The downsampled array, of the same type as x. + """ + + if len(array.shape) == 3: + factor = (2,2,1) + else: + factor = (2,2) + + if np.array_equal(factor[:3], np.array([1,1,1])): + return array + + output_shape = tuple(int(math.ceil(s / f)) for s, f in zip(array.shape, factor)) + temp = np.zeros(output_shape, float) + counts = np.zeros(output_shape, np.int) + for offset in np.ndindex(factor): + part = array[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + indexing_expr = tuple(np.s_[:s] for s in part.shape) + temp[indexing_expr] += part + counts[indexing_expr] += 1 + return np.cast[array.dtype](temp / counts) + +def downsample_with_max_pooling(array): + + factor = (2,2) + + if np.all(np.array(factor, int) == 1): + return array + + sections = [] + + for offset in np.ndindex(factor): + part = array[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + output = sections[0].copy() + + for section in sections[1:]: + np.maximum(output, section, output) + + return output + +def striding(array): + """Downsample x by factor using striding. + + @return: The downsampled array, of the same type as x. + """ + factor = (2,2) + if np.all(np.array(factor, int) == 1): + return array + return array[tuple(np.s_[::f] for f in factor)] + +def benchmark(): + filename = sys.argv[1] + img = Image.open(filename) + data = np.array(img.getdata(), dtype=np.uint8) + + if len(data.shape) == 1: + n_channels = 1 + reshape = (img.height, img.width) + else: + n_channels = min(data.shape[1], 3) + data = data[:, :n_channels] + reshape = (img.height, img.width, n_channels) + + data = data.reshape(reshape).astype(np.uint8) + + methods = [ + simplest_countless, + quick_countless, + quick_countless_xor, + quickest_countless, + stippled_countless, + zero_corrected_countless, + countless, + downsample_with_averaging, + downsample_with_max_pooling, + ndzoom, + striding, + # countless_if, + # counting, + ] + + formats = { + 1: 'L', + 3: 'RGB', + 4: 'RGBA' + } + + if not os.path.exists('./results'): + os.mkdir('./results') + + N = 500 + img_size = float(img.width * img.height) / 1024.0 / 1024.0 + print("N = %d, %dx%d (%.2f MPx) %d chan, %s" % (N, img.width, img.height, img_size, n_channels, filename)) + print("Algorithm\tMPx/sec\tMB/sec\tSec") + for fn in methods: + print(fn.__name__, end='') + sys.stdout.flush() + + start = time.time() + # tqdm is here to show you what's going on the first time you run it. + # Feel free to remove it to get slightly more accurate timing results. + for _ in tqdm(range(N), desc=fn.__name__, disable=True): + result = fn(data) + end = time.time() + print("\r", end='') + + total_time = (end - start) + mpx = N * img_size / total_time + mbytes = N * img_size * n_channels / total_time + # Output in tab separated format to enable copy-paste into excel/numbers + print("%s\t%.3f\t%.3f\t%.2f" % (fn.__name__, mpx, mbytes, total_time)) + outimg = Image.fromarray(np.squeeze(result), formats[n_channels]) + outimg.save('./results/{}.png'.format(fn.__name__, "PNG")) + +if __name__ == '__main__': + benchmark() + + +# Example results: +# N = 5, 1024x1024 (1.00 MPx) 1 chan, images/gray_segmentation.png +# Function MPx/sec MB/sec Sec +# simplest_countless 752.855 752.855 0.01 +# quick_countless 920.328 920.328 0.01 +# zero_corrected_countless 534.143 534.143 0.01 +# countless 644.247 644.247 0.01 +# downsample_with_averaging 372.575 372.575 0.01 +# downsample_with_max_pooling 974.060 974.060 0.01 +# ndzoom 137.517 137.517 0.04 +# striding 38550.588 38550.588 0.00 +# countless_if 4.377 4.377 1.14 +# counting 0.117 0.117 42.85 + +# Run without non-numpy implementations: +# N = 2000, 1024x1024 (1.00 MPx) 1 chan, images/gray_segmentation.png +# Algorithm MPx/sec MB/sec Sec +# simplest_countless 800.522 800.522 2.50 +# quick_countless 945.420 945.420 2.12 +# quickest_countless 947.256 947.256 2.11 +# stippled_countless 544.049 544.049 3.68 +# zero_corrected_countless 575.310 575.310 3.48 +# countless 646.684 646.684 3.09 +# downsample_with_averaging 385.132 385.132 5.19 +# downsample_with_max_poolin 988.361 988.361 2.02 +# ndzoom 163.104 163.104 12.26 +# striding 81589.340 81589.340 0.02 + + + + diff --git a/third-party/lama/bin/saicinpainting/evaluation/masks/countless/countless3d.py b/third-party/lama/bin/saicinpainting/evaluation/masks/countless/countless3d.py new file mode 100644 index 0000000000000000000000000000000000000000..810a71e4b1fa344dd2d731186516dbfa96c9cd03 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/evaluation/masks/countless/countless3d.py @@ -0,0 +1,356 @@ +from six.moves import range +from PIL import Image +import numpy as np +import io +import time +import math +import random +import sys +from collections import defaultdict +from copy import deepcopy +from itertools import combinations +from functools import reduce +from tqdm import tqdm + +from memory_profiler import profile + +def countless5(a,b,c,d,e): + """First stage of generalizing from countless2d. + + You have five slots: A, B, C, D, E + + You can decide if something is the winner by first checking for + matches of three, then matches of two, then picking just one if + the other two tries fail. In countless2d, you just check for matches + of two and then pick one of them otherwise. + + Unfortunately, you need to check ABC, ABD, ABE, BCD, BDE, & CDE. + Then you need to check AB, AC, AD, BC, BD + We skip checking E because if none of these match, we pick E. We can + skip checking AE, BE, CE, DE since if any of those match, E is our boy + so it's redundant. + + So countless grows cominatorially in complexity. + """ + sections = [ a,b,c,d,e ] + + p2 = lambda q,r: q * (q == r) # q if p == q else 0 + p3 = lambda q,r,s: q * ( (q == r) & (r == s) ) # q if q == r == s else 0 + + lor = lambda x,y: x + (x == 0) * y + + results3 = ( p3(x,y,z) for x,y,z in combinations(sections, 3) ) + results3 = reduce(lor, results3) + + results2 = ( p2(x,y) for x,y in combinations(sections[:-1], 2) ) + results2 = reduce(lor, results2) + + return reduce(lor, (results3, results2, e)) + +def countless8(a,b,c,d,e,f,g,h): + """Extend countless5 to countless8. Same deal, except we also + need to check for matches of length 4.""" + sections = [ a, b, c, d, e, f, g, h ] + + p2 = lambda q,r: q * (q == r) + p3 = lambda q,r,s: q * ( (q == r) & (r == s) ) + p4 = lambda p,q,r,s: p * ( (p == q) & (q == r) & (r == s) ) + + lor = lambda x,y: x + (x == 0) * y + + results4 = ( p4(x,y,z,w) for x,y,z,w in combinations(sections, 4) ) + results4 = reduce(lor, results4) + + results3 = ( p3(x,y,z) for x,y,z in combinations(sections, 3) ) + results3 = reduce(lor, results3) + + # We can always use our shortcut of omitting the last element + # for N choose 2 + results2 = ( p2(x,y) for x,y in combinations(sections[:-1], 2) ) + results2 = reduce(lor, results2) + + return reduce(lor, [ results4, results3, results2, h ]) + +def dynamic_countless3d(data): + """countless8 + dynamic programming. ~2x faster""" + sections = [] + + # shift zeros up one so they don't interfere with bitwise operators + # we'll shift down at the end + data += 1 + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + factor = (2,2,2) + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + pick = lambda a,b: a * (a == b) + lor = lambda x,y: x + (x == 0) * y + + subproblems2 = {} + + results2 = None + for x,y in combinations(range(7), 2): + res = pick(sections[x], sections[y]) + subproblems2[(x,y)] = res + if results2 is not None: + results2 += (results2 == 0) * res + else: + results2 = res + + subproblems3 = {} + + results3 = None + for x,y,z in combinations(range(8), 3): + res = pick(subproblems2[(x,y)], sections[z]) + + if z != 7: + subproblems3[(x,y,z)] = res + + if results3 is not None: + results3 += (results3 == 0) * res + else: + results3 = res + + results3 = reduce(lor, (results3, results2, sections[-1])) + + # free memory + results2 = None + subproblems2 = None + res = None + + results4 = ( pick(subproblems3[(x,y,z)], sections[w]) for x,y,z,w in combinations(range(8), 4) ) + results4 = reduce(lor, results4) + subproblems3 = None # free memory + + final_result = lor(results4, results3) - 1 + data -= 1 + return final_result + +def countless3d(data): + """Now write countless8 in such a way that it could be used + to process an image.""" + sections = [] + + # shift zeros up one so they don't interfere with bitwise operators + # we'll shift down at the end + data += 1 + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + factor = (2,2,2) + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + p2 = lambda q,r: q * (q == r) + p3 = lambda q,r,s: q * ( (q == r) & (r == s) ) + p4 = lambda p,q,r,s: p * ( (p == q) & (q == r) & (r == s) ) + + lor = lambda x,y: x + (x == 0) * y + + results4 = ( p4(x,y,z,w) for x,y,z,w in combinations(sections, 4) ) + results4 = reduce(lor, results4) + + results3 = ( p3(x,y,z) for x,y,z in combinations(sections, 3) ) + results3 = reduce(lor, results3) + + results2 = ( p2(x,y) for x,y in combinations(sections[:-1], 2) ) + results2 = reduce(lor, results2) + + final_result = reduce(lor, (results4, results3, results2, sections[-1])) - 1 + data -= 1 + return final_result + +def countless_generalized(data, factor): + assert len(data.shape) == len(factor) + + sections = [] + + mode_of = reduce(lambda x,y: x * y, factor) + majority = int(math.ceil(float(mode_of) / 2)) + + data += 1 + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + def pick(elements): + eq = ( elements[i] == elements[i+1] for i in range(len(elements) - 1) ) + anded = reduce(lambda p,q: p & q, eq) + return elements[0] * anded + + def logical_or(x,y): + return x + (x == 0) * y + + result = ( pick(combo) for combo in combinations(sections, majority) ) + result = reduce(logical_or, result) + for i in range(majority - 1, 3-1, -1): # 3-1 b/c of exclusive bounds + partial_result = ( pick(combo) for combo in combinations(sections, i) ) + partial_result = reduce(logical_or, partial_result) + result = logical_or(result, partial_result) + + partial_result = ( pick(combo) for combo in combinations(sections[:-1], 2) ) + partial_result = reduce(logical_or, partial_result) + result = logical_or(result, partial_result) + + result = logical_or(result, sections[-1]) - 1 + data -= 1 + return result + +def dynamic_countless_generalized(data, factor): + assert len(data.shape) == len(factor) + + sections = [] + + mode_of = reduce(lambda x,y: x * y, factor) + majority = int(math.ceil(float(mode_of) / 2)) + + data += 1 # offset from zero + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + pick = lambda a,b: a * (a == b) + lor = lambda x,y: x + (x == 0) * y # logical or + + subproblems = [ {}, {} ] + results2 = None + for x,y in combinations(range(len(sections) - 1), 2): + res = pick(sections[x], sections[y]) + subproblems[0][(x,y)] = res + if results2 is not None: + results2 = lor(results2, res) + else: + results2 = res + + results = [ results2 ] + for r in range(3, majority+1): + r_results = None + for combo in combinations(range(len(sections)), r): + res = pick(subproblems[0][combo[:-1]], sections[combo[-1]]) + + if combo[-1] != len(sections) - 1: + subproblems[1][combo] = res + + if r_results is not None: + r_results = lor(r_results, res) + else: + r_results = res + results.append(r_results) + subproblems[0] = subproblems[1] + subproblems[1] = {} + + results.reverse() + final_result = lor(reduce(lor, results), sections[-1]) - 1 + data -= 1 + return final_result + +def downsample_with_averaging(array): + """ + Downsample x by factor using averaging. + + @return: The downsampled array, of the same type as x. + """ + factor = (2,2,2) + + if np.array_equal(factor[:3], np.array([1,1,1])): + return array + + output_shape = tuple(int(math.ceil(s / f)) for s, f in zip(array.shape, factor)) + temp = np.zeros(output_shape, float) + counts = np.zeros(output_shape, np.int) + for offset in np.ndindex(factor): + part = array[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + indexing_expr = tuple(np.s_[:s] for s in part.shape) + temp[indexing_expr] += part + counts[indexing_expr] += 1 + return np.cast[array.dtype](temp / counts) + +def downsample_with_max_pooling(array): + + factor = (2,2,2) + + sections = [] + + for offset in np.ndindex(factor): + part = array[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + output = sections[0].copy() + + for section in sections[1:]: + np.maximum(output, section, output) + + return output + +def striding(array): + """Downsample x by factor using striding. + + @return: The downsampled array, of the same type as x. + """ + factor = (2,2,2) + if np.all(np.array(factor, int) == 1): + return array + return array[tuple(np.s_[::f] for f in factor)] + +def benchmark(): + def countless3d_generalized(img): + return countless_generalized(img, (2,8,1)) + def countless3d_dynamic_generalized(img): + return dynamic_countless_generalized(img, (8,8,1)) + + methods = [ + # countless3d, + # dynamic_countless3d, + countless3d_generalized, + # countless3d_dynamic_generalized, + # striding, + # downsample_with_averaging, + # downsample_with_max_pooling + ] + + data = np.zeros(shape=(16**2, 16**2, 16**2), dtype=np.uint8) + 1 + + N = 5 + + print('Algorithm\tMPx\tMB/sec\tSec\tN=%d' % N) + + for fn in methods: + start = time.time() + for _ in range(N): + result = fn(data) + end = time.time() + + total_time = (end - start) + mpx = N * float(data.shape[0] * data.shape[1] * data.shape[2]) / total_time / 1024.0 / 1024.0 + mbytes = mpx * np.dtype(data.dtype).itemsize + # Output in tab separated format to enable copy-paste into excel/numbers + print("%s\t%.3f\t%.3f\t%.2f" % (fn.__name__, mpx, mbytes, total_time)) + +if __name__ == '__main__': + benchmark() + +# Algorithm MPx MB/sec Sec N=5 +# countless3d 10.564 10.564 60.58 +# dynamic_countless3d 22.717 22.717 28.17 +# countless3d_generalized 9.702 9.702 65.96 +# countless3d_dynamic_generalized 22.720 22.720 28.17 +# striding 253360.506 253360.506 0.00 +# downsample_with_averaging 224.098 224.098 2.86 +# downsample_with_max_pooling 690.474 690.474 0.93 + + + diff 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a/third-party/lama/bin/saicinpainting/evaluation/masks/countless/requirements.txt b/third-party/lama/bin/saicinpainting/evaluation/masks/countless/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..cbf8c87bf9b4c9fe54cb39d722253c0ab59e63ad --- /dev/null +++ b/third-party/lama/bin/saicinpainting/evaluation/masks/countless/requirements.txt @@ -0,0 +1,7 @@ +Pillow>=6.2.0 +numpy>=1.16 +scipy +tqdm +memory_profiler +six +pytest \ No newline at end of file diff --git a/third-party/lama/bin/saicinpainting/evaluation/masks/countless/test.py b/third-party/lama/bin/saicinpainting/evaluation/masks/countless/test.py new file mode 100644 index 0000000000000000000000000000000000000000..7809beb7aeeb3bcb10d03093a564917b1f2b4786 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/evaluation/masks/countless/test.py @@ -0,0 +1,195 @@ +from copy import deepcopy + +import numpy as np + +import countless2d +import countless3d + +def test_countless2d(): + def test_all_cases(fn, test_zero): + case1 = np.array([ [ 1, 2 ], [ 3, 4 ] ]).reshape((2,2,1,1)) # all different + case2 = np.array([ [ 1, 1 ], [ 2, 3 ] ]).reshape((2,2,1,1)) # two are same + case1z = np.array([ [ 0, 1 ], [ 2, 3 ] ]).reshape((2,2,1,1)) # all different + case2z = np.array([ [ 0, 0 ], [ 2, 3 ] ]).reshape((2,2,1,1)) # two are same + case3 = np.array([ [ 1, 1 ], [ 2, 2 ] ]).reshape((2,2,1,1)) # two groups are same + case4 = np.array([ [ 1, 2 ], [ 2, 2 ] ]).reshape((2,2,1,1)) # 3 are the same + case5 = np.array([ [ 5, 5 ], [ 5, 5 ] ]).reshape((2,2,1,1)) # all are the same + + is_255_handled = np.array([ [ 255, 255 ], [ 1, 2 ] ], dtype=np.uint8).reshape((2,2,1,1)) + + test = lambda case: fn(case) + + if test_zero: + assert test(case1z) == [[[[3]]]] # d + assert test(case2z) == [[[[0]]]] # a==b + else: + assert test(case1) == [[[[4]]]] # d + assert test(case2) == [[[[1]]]] # a==b + + assert test(case3) == [[[[1]]]] # a==b + assert test(case4) == [[[[2]]]] # b==c + assert test(case5) == [[[[5]]]] # a==b + + assert test(is_255_handled) == [[[[255]]]] + + assert fn(case1).dtype == case1.dtype + + test_all_cases(countless2d.simplest_countless, False) + test_all_cases(countless2d.quick_countless, False) + test_all_cases(countless2d.quickest_countless, False) + test_all_cases(countless2d.stippled_countless, False) + + + + methods = [ + countless2d.zero_corrected_countless, + countless2d.countless, + countless2d.countless_if, + # countless2d.counting, # counting doesn't respect order so harder to write a test + ] + + for fn in methods: + print(fn.__name__) + test_all_cases(fn, True) + +def test_stippled_countless2d(): + a = np.array([ [ 1, 2 ], [ 3, 4 ] ]).reshape((2,2,1,1)) + b = np.array([ [ 0, 2 ], [ 3, 4 ] ]).reshape((2,2,1,1)) + c = np.array([ [ 1, 0 ], [ 3, 4 ] ]).reshape((2,2,1,1)) + d = np.array([ [ 1, 2 ], [ 0, 4 ] ]).reshape((2,2,1,1)) + e = np.array([ [ 1, 2 ], [ 3, 0 ] ]).reshape((2,2,1,1)) + f = np.array([ [ 0, 0 ], [ 3, 4 ] ]).reshape((2,2,1,1)) + g = np.array([ [ 0, 2 ], [ 0, 4 ] ]).reshape((2,2,1,1)) + h = np.array([ [ 0, 2 ], [ 3, 0 ] ]).reshape((2,2,1,1)) + i = np.array([ [ 1, 0 ], [ 0, 4 ] ]).reshape((2,2,1,1)) + j = np.array([ [ 1, 2 ], [ 0, 0 ] ]).reshape((2,2,1,1)) + k = np.array([ [ 1, 0 ], [ 3, 0 ] ]).reshape((2,2,1,1)) + l = np.array([ [ 1, 0 ], [ 0, 0 ] ]).reshape((2,2,1,1)) + m = np.array([ [ 0, 2 ], [ 0, 0 ] ]).reshape((2,2,1,1)) + n = np.array([ [ 0, 0 ], [ 3, 0 ] ]).reshape((2,2,1,1)) + o = np.array([ [ 0, 0 ], [ 0, 4 ] ]).reshape((2,2,1,1)) + z = np.array([ [ 0, 0 ], [ 0, 0 ] ]).reshape((2,2,1,1)) + + test = countless2d.stippled_countless + + # Note: We only tested non-matching cases above, + # cases f,g,h,i,j,k prove their duals work as well + # b/c if two pixels are black, either one can be chosen + # if they are different or the same. + + assert test(a) == [[[[4]]]] + assert test(b) == [[[[4]]]] + assert test(c) == [[[[4]]]] + assert test(d) == [[[[4]]]] + assert test(e) == [[[[1]]]] + assert test(f) == [[[[4]]]] + assert test(g) == [[[[4]]]] + assert test(h) == [[[[2]]]] + assert test(i) == [[[[4]]]] + assert test(j) == [[[[1]]]] + assert test(k) == [[[[1]]]] + assert test(l) == [[[[1]]]] + assert test(m) == [[[[2]]]] + assert test(n) == [[[[3]]]] + assert test(o) == [[[[4]]]] + assert test(z) == [[[[0]]]] + + bc = np.array([ [ 0, 2 ], [ 2, 4 ] ]).reshape((2,2,1,1)) + bd = np.array([ [ 0, 2 ], [ 3, 2 ] ]).reshape((2,2,1,1)) + cd = np.array([ [ 0, 2 ], [ 3, 3 ] ]).reshape((2,2,1,1)) + + assert test(bc) == [[[[2]]]] + assert test(bd) == [[[[2]]]] + assert test(cd) == [[[[3]]]] + + ab = np.array([ [ 1, 1 ], [ 0, 4 ] ]).reshape((2,2,1,1)) + ac = np.array([ [ 1, 2 ], [ 1, 0 ] ]).reshape((2,2,1,1)) + ad = np.array([ [ 1, 0 ], [ 3, 1 ] ]).reshape((2,2,1,1)) + + assert test(ab) == [[[[1]]]] + assert test(ac) == [[[[1]]]] + assert test(ad) == [[[[1]]]] + +def test_countless3d(): + def test_all_cases(fn): + alldifferent = [ + [ + [1,2], + [3,4], + ], + [ + [5,6], + [7,8] + ] + ] + allsame = [ + [ + [1,1], + [1,1], + ], + [ + [1,1], + [1,1] + ] + ] + + assert fn(np.array(alldifferent)) == [[[8]]] + assert fn(np.array(allsame)) == [[[1]]] + + twosame = deepcopy(alldifferent) + twosame[1][1][0] = 2 + + assert fn(np.array(twosame)) == [[[2]]] + + threemixed = [ + [ + [3,3], + [1,2], + ], + [ + [2,4], + [4,3] + ] + ] + assert fn(np.array(threemixed)) == [[[3]]] + + foursame = [ + [ + [4,4], + [1,2], + ], + [ + [2,4], + [4,3] + ] + ] + + assert fn(np.array(foursame)) == [[[4]]] + + fivesame = [ + [ + [5,4], + [5,5], + ], + [ + [2,4], + [5,5] + ] + ] + + assert fn(np.array(fivesame)) == [[[5]]] + + def countless3d_generalized(img): + return countless3d.countless_generalized(img, (2,2,2)) + def countless3d_dynamic_generalized(img): + return countless3d.dynamic_countless_generalized(img, (2,2,2)) + + methods = [ + countless3d.countless3d, + countless3d.dynamic_countless3d, + countless3d_generalized, + countless3d_dynamic_generalized, + ] + + for fn in methods: + test_all_cases(fn) \ No newline at end of file diff --git a/third-party/lama/bin/saicinpainting/evaluation/masks/mask.py b/third-party/lama/bin/saicinpainting/evaluation/masks/mask.py new file mode 100644 index 0000000000000000000000000000000000000000..3e34d0675a781fba983cb542f18390255aaf2609 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/evaluation/masks/mask.py @@ -0,0 +1,429 @@ +import enum +from copy import deepcopy + +import numpy as np +from skimage import img_as_ubyte +from skimage.transform import rescale, resize +try: + from detectron2 import model_zoo + from detectron2.config import get_cfg + from detectron2.engine import DefaultPredictor + DETECTRON_INSTALLED = True +except: + print("Detectron v2 is not installed") + DETECTRON_INSTALLED = False + +from .countless.countless2d import zero_corrected_countless + + +class ObjectMask(): + def __init__(self, mask): + self.height, self.width = mask.shape + (self.up, self.down), (self.left, self.right) = self._get_limits(mask) + self.mask = mask[self.up:self.down, self.left:self.right].copy() + + @staticmethod + def _get_limits(mask): + def indicator_limits(indicator): + lower = indicator.argmax() + upper = len(indicator) - indicator[::-1].argmax() + return lower, upper + + vertical_indicator = mask.any(axis=1) + vertical_limits = indicator_limits(vertical_indicator) + + horizontal_indicator = mask.any(axis=0) + horizontal_limits = indicator_limits(horizontal_indicator) + + return vertical_limits, horizontal_limits + + def _clean(self): + self.up, self.down, self.left, self.right = 0, 0, 0, 0 + self.mask = np.empty((0, 0)) + + def horizontal_flip(self, inplace=False): + if not inplace: + flipped = deepcopy(self) + return flipped.horizontal_flip(inplace=True) + + self.mask = self.mask[:, ::-1] + return self + + def vertical_flip(self, inplace=False): + if not inplace: + flipped = deepcopy(self) + return flipped.vertical_flip(inplace=True) + + self.mask = self.mask[::-1, :] + return self + + def image_center(self): + y_center = self.up + (self.down - self.up) / 2 + x_center = self.left + (self.right - self.left) / 2 + return y_center, x_center + + def rescale(self, scaling_factor, inplace=False): + if not inplace: + scaled = deepcopy(self) + return scaled.rescale(scaling_factor, inplace=True) + + scaled_mask = rescale(self.mask.astype(float), scaling_factor, order=0) > 0.5 + (up, down), (left, right) = self._get_limits(scaled_mask) + self.mask = scaled_mask[up:down, left:right] + + y_center, x_center = self.image_center() + mask_height, mask_width = self.mask.shape + self.up = int(round(y_center - mask_height / 2)) + self.down = self.up + mask_height + self.left = int(round(x_center - mask_width / 2)) + self.right = self.left + mask_width + return self + + def crop_to_canvas(self, vertical=True, horizontal=True, inplace=False): + if not inplace: + cropped = deepcopy(self) + cropped.crop_to_canvas(vertical=vertical, horizontal=horizontal, inplace=True) + return cropped + + if vertical: + if self.up >= self.height or self.down <= 0: + self._clean() + else: + cut_up, cut_down = max(-self.up, 0), max(self.down - self.height, 0) + if cut_up != 0: + self.mask = self.mask[cut_up:] + self.up = 0 + if cut_down != 0: + self.mask = self.mask[:-cut_down] + self.down = self.height + + if horizontal: + if self.left >= self.width or self.right <= 0: + self._clean() + else: + cut_left, cut_right = max(-self.left, 0), max(self.right - self.width, 0) + if cut_left != 0: + self.mask = self.mask[:, cut_left:] + self.left = 0 + if cut_right != 0: + self.mask = self.mask[:, :-cut_right] + self.right = self.width + + return self + + def restore_full_mask(self, allow_crop=False): + cropped = self.crop_to_canvas(inplace=allow_crop) + mask = np.zeros((cropped.height, cropped.width), dtype=bool) + mask[cropped.up:cropped.down, cropped.left:cropped.right] = cropped.mask + return mask + + def shift(self, vertical=0, horizontal=0, inplace=False): + if not inplace: + shifted = deepcopy(self) + return shifted.shift(vertical=vertical, horizontal=horizontal, inplace=True) + + self.up += vertical + self.down += vertical + self.left += horizontal + self.right += horizontal + return self + + def area(self): + return self.mask.sum() + + +class RigidnessMode(enum.Enum): + soft = 0 + rigid = 1 + + +class SegmentationMask: + def __init__(self, confidence_threshold=0.5, rigidness_mode=RigidnessMode.rigid, + max_object_area=0.3, min_mask_area=0.02, downsample_levels=6, num_variants_per_mask=4, + max_mask_intersection=0.5, max_foreground_coverage=0.5, max_foreground_intersection=0.5, + max_hidden_area=0.2, max_scale_change=0.25, horizontal_flip=True, + max_vertical_shift=0.1, position_shuffle=True): + """ + :param confidence_threshold: float; threshold for confidence of the panoptic segmentator to allow for + the instance. + :param rigidness_mode: RigidnessMode object + when soft, checks intersection only with the object from which the mask_object was produced + when rigid, checks intersection with any foreground class object + :param max_object_area: float; allowed upper bound for to be considered as mask_object. + :param min_mask_area: float; lower bound for mask to be considered valid + :param downsample_levels: int; defines width of the resized segmentation to obtain shifted masks; + :param num_variants_per_mask: int; maximal number of the masks for the same object; + :param max_mask_intersection: float; maximum allowed area fraction of intersection for 2 masks + produced by horizontal shift of the same mask_object; higher value -> more diversity + :param max_foreground_coverage: float; maximum allowed area fraction of intersection for foreground object to be + covered by mask; lower value -> less the objects are covered + :param max_foreground_intersection: float; maximum allowed area of intersection for the mask with foreground + object; lower value -> mask is more on the background than on the objects + :param max_hidden_area: upper bound on part of the object hidden by shifting object outside the screen area; + :param max_scale_change: allowed scale change for the mask_object; + :param horizontal_flip: if horizontal flips are allowed; + :param max_vertical_shift: amount of vertical movement allowed; + :param position_shuffle: shuffle + """ + + assert DETECTRON_INSTALLED, 'Cannot use SegmentationMask without detectron2' + self.cfg = get_cfg() + self.cfg.merge_from_file(model_zoo.get_config_file("COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml")) + self.cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml") + self.cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = confidence_threshold + self.predictor = DefaultPredictor(self.cfg) + + self.rigidness_mode = RigidnessMode(rigidness_mode) + self.max_object_area = max_object_area + self.min_mask_area = min_mask_area + self.downsample_levels = downsample_levels + self.num_variants_per_mask = num_variants_per_mask + self.max_mask_intersection = max_mask_intersection + self.max_foreground_coverage = max_foreground_coverage + self.max_foreground_intersection = max_foreground_intersection + self.max_hidden_area = max_hidden_area + self.position_shuffle = position_shuffle + + self.max_scale_change = max_scale_change + self.horizontal_flip = horizontal_flip + self.max_vertical_shift = max_vertical_shift + + def get_segmentation(self, img): + im = img_as_ubyte(img) + panoptic_seg, segment_info = self.predictor(im)["panoptic_seg"] + return panoptic_seg, segment_info + + @staticmethod + def _is_power_of_two(n): + return (n != 0) and (n & (n-1) == 0) + + def identify_candidates(self, panoptic_seg, segments_info): + potential_mask_ids = [] + for segment in segments_info: + if not segment["isthing"]: + continue + mask = (panoptic_seg == segment["id"]).int().detach().cpu().numpy() + area = mask.sum().item() / np.prod(panoptic_seg.shape) + if area >= self.max_object_area: + continue + potential_mask_ids.append(segment["id"]) + return potential_mask_ids + + def downsample_mask(self, mask): + height, width = mask.shape + if not (self._is_power_of_two(height) and self._is_power_of_two(width)): + raise ValueError("Image sides are not power of 2.") + + num_iterations = width.bit_length() - 1 - self.downsample_levels + if num_iterations < 0: + raise ValueError(f"Width is lower than 2^{self.downsample_levels}.") + + if height.bit_length() - 1 < num_iterations: + raise ValueError("Height is too low to perform downsampling") + + downsampled = mask + for _ in range(num_iterations): + downsampled = zero_corrected_countless(downsampled) + + return downsampled + + def _augmentation_params(self): + scaling_factor = np.random.uniform(1 - self.max_scale_change, 1 + self.max_scale_change) + if self.horizontal_flip: + horizontal_flip = bool(np.random.choice(2)) + else: + horizontal_flip = False + vertical_shift = np.random.uniform(-self.max_vertical_shift, self.max_vertical_shift) + + return { + "scaling_factor": scaling_factor, + "horizontal_flip": horizontal_flip, + "vertical_shift": vertical_shift + } + + def _get_intersection(self, mask_array, mask_object): + intersection = mask_array[ + mask_object.up:mask_object.down, mask_object.left:mask_object.right + ] & mask_object.mask + return intersection + + def _check_masks_intersection(self, aug_mask, total_mask_area, prev_masks): + for existing_mask in prev_masks: + intersection_area = self._get_intersection(existing_mask, aug_mask).sum() + intersection_existing = intersection_area / existing_mask.sum() + intersection_current = 1 - (aug_mask.area() - intersection_area) / total_mask_area + if (intersection_existing > self.max_mask_intersection) or \ + (intersection_current > self.max_mask_intersection): + return False + return True + + def _check_foreground_intersection(self, aug_mask, foreground): + for existing_mask in foreground: + intersection_area = self._get_intersection(existing_mask, aug_mask).sum() + intersection_existing = intersection_area / existing_mask.sum() + if intersection_existing > self.max_foreground_coverage: + return False + intersection_mask = intersection_area / aug_mask.area() + if intersection_mask > self.max_foreground_intersection: + return False + return True + + def _move_mask(self, mask, foreground): + # Obtaining properties of the original mask_object: + orig_mask = ObjectMask(mask) + + chosen_masks = [] + chosen_parameters = [] + # to fix the case when resizing gives mask_object consisting only of False + scaling_factor_lower_bound = 0. + + for var_idx in range(self.num_variants_per_mask): + # Obtaining augmentation parameters and applying them to the downscaled mask_object + augmentation_params = self._augmentation_params() + augmentation_params["scaling_factor"] = min([ + augmentation_params["scaling_factor"], + 2 * min(orig_mask.up, orig_mask.height - orig_mask.down) / orig_mask.height + 1., + 2 * min(orig_mask.left, orig_mask.width - orig_mask.right) / orig_mask.width + 1. + ]) + augmentation_params["scaling_factor"] = max([ + augmentation_params["scaling_factor"], scaling_factor_lower_bound + ]) + + aug_mask = deepcopy(orig_mask) + aug_mask.rescale(augmentation_params["scaling_factor"], inplace=True) + if augmentation_params["horizontal_flip"]: + aug_mask.horizontal_flip(inplace=True) + total_aug_area = aug_mask.area() + if total_aug_area == 0: + scaling_factor_lower_bound = 1. + continue + + # Fix if the element vertical shift is too strong and shown area is too small: + vertical_area = aug_mask.mask.sum(axis=1) / total_aug_area # share of area taken by rows + # number of rows which are allowed to be hidden from upper and lower parts of image respectively + max_hidden_up = np.searchsorted(vertical_area.cumsum(), self.max_hidden_area) + max_hidden_down = np.searchsorted(vertical_area[::-1].cumsum(), self.max_hidden_area) + # correcting vertical shift, so not too much area will be hidden + augmentation_params["vertical_shift"] = np.clip( + augmentation_params["vertical_shift"], + -(aug_mask.up + max_hidden_up) / aug_mask.height, + (aug_mask.height - aug_mask.down + max_hidden_down) / aug_mask.height + ) + # Applying vertical shift: + vertical_shift = int(round(aug_mask.height * augmentation_params["vertical_shift"])) + aug_mask.shift(vertical=vertical_shift, inplace=True) + aug_mask.crop_to_canvas(vertical=True, horizontal=False, inplace=True) + + # Choosing horizontal shift: + max_hidden_area = self.max_hidden_area - (1 - aug_mask.area() / total_aug_area) + horizontal_area = aug_mask.mask.sum(axis=0) / total_aug_area + max_hidden_left = np.searchsorted(horizontal_area.cumsum(), max_hidden_area) + max_hidden_right = np.searchsorted(horizontal_area[::-1].cumsum(), max_hidden_area) + allowed_shifts = np.arange(-max_hidden_left, aug_mask.width - + (aug_mask.right - aug_mask.left) + max_hidden_right + 1) + allowed_shifts = - (aug_mask.left - allowed_shifts) + + if self.position_shuffle: + np.random.shuffle(allowed_shifts) + + mask_is_found = False + for horizontal_shift in allowed_shifts: + aug_mask_left = deepcopy(aug_mask) + aug_mask_left.shift(horizontal=horizontal_shift, inplace=True) + aug_mask_left.crop_to_canvas(inplace=True) + + prev_masks = [mask] + chosen_masks + is_mask_suitable = self._check_masks_intersection(aug_mask_left, total_aug_area, prev_masks) & \ + self._check_foreground_intersection(aug_mask_left, foreground) + if is_mask_suitable: + aug_draw = aug_mask_left.restore_full_mask() + chosen_masks.append(aug_draw) + augmentation_params["horizontal_shift"] = horizontal_shift / aug_mask_left.width + chosen_parameters.append(augmentation_params) + mask_is_found = True + break + + if not mask_is_found: + break + + return chosen_parameters + + def _prepare_mask(self, mask): + height, width = mask.shape + target_width = width if self._is_power_of_two(width) else (1 << width.bit_length()) + target_height = height if self._is_power_of_two(height) else (1 << height.bit_length()) + + return resize(mask.astype('float32'), (target_height, target_width), order=0, mode='edge').round().astype('int32') + + def get_masks(self, im, return_panoptic=False): + panoptic_seg, segments_info = self.get_segmentation(im) + potential_mask_ids = self.identify_candidates(panoptic_seg, segments_info) + + panoptic_seg_scaled = self._prepare_mask(panoptic_seg.detach().cpu().numpy()) + downsampled = self.downsample_mask(panoptic_seg_scaled) + scene_objects = [] + for segment in segments_info: + if not segment["isthing"]: + continue + mask = downsampled == segment["id"] + if not np.any(mask): + continue + scene_objects.append(mask) + + mask_set = [] + for mask_id in potential_mask_ids: + mask = downsampled == mask_id + if not np.any(mask): + continue + + if self.rigidness_mode is RigidnessMode.soft: + foreground = [mask] + elif self.rigidness_mode is RigidnessMode.rigid: + foreground = scene_objects + else: + raise ValueError(f'Unexpected rigidness_mode: {rigidness_mode}') + + masks_params = self._move_mask(mask, foreground) + + full_mask = ObjectMask((panoptic_seg == mask_id).detach().cpu().numpy()) + + for params in masks_params: + aug_mask = deepcopy(full_mask) + aug_mask.rescale(params["scaling_factor"], inplace=True) + if params["horizontal_flip"]: + aug_mask.horizontal_flip(inplace=True) + + vertical_shift = int(round(aug_mask.height * params["vertical_shift"])) + horizontal_shift = int(round(aug_mask.width * params["horizontal_shift"])) + aug_mask.shift(vertical=vertical_shift, horizontal=horizontal_shift, inplace=True) + aug_mask = aug_mask.restore_full_mask().astype('uint8') + if aug_mask.mean() <= self.min_mask_area: + continue + mask_set.append(aug_mask) + + if return_panoptic: + return mask_set, panoptic_seg.detach().cpu().numpy() + else: + return mask_set + + +def propose_random_square_crop(mask, min_overlap=0.5): + height, width = mask.shape + mask_ys, mask_xs = np.where(mask > 0.5) # mask==0 is known fragment and mask==1 is missing + + if height < width: + crop_size = height + obj_left, obj_right = mask_xs.min(), mask_xs.max() + obj_width = obj_right - obj_left + left_border = max(0, min(width - crop_size - 1, obj_left + obj_width * min_overlap - crop_size)) + right_border = max(left_border + 1, min(width - crop_size, obj_left + obj_width * min_overlap)) + start_x = np.random.randint(left_border, right_border) + return start_x, 0, start_x + crop_size, height + else: + crop_size = width + obj_top, obj_bottom = mask_ys.min(), mask_ys.max() + obj_height = obj_bottom - obj_top + top_border = max(0, min(height - crop_size - 1, obj_top + obj_height * min_overlap - crop_size)) + bottom_border = max(top_border + 1, min(height - crop_size, obj_top + obj_height * min_overlap)) + start_y = np.random.randint(top_border, bottom_border) + return 0, start_y, width, start_y + crop_size diff --git a/third-party/lama/bin/saicinpainting/evaluation/refinement.py b/third-party/lama/bin/saicinpainting/evaluation/refinement.py new file mode 100644 index 0000000000000000000000000000000000000000..d9d3cbac689d99ab20e71daf6f42bb8ca3c9feb8 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/evaluation/refinement.py @@ -0,0 +1,314 @@ +import torch +import torch.nn as nn +from torch.optim import Adam, SGD +from kornia.filters import gaussian_blur2d +from kornia.geometry.transform import resize +from kornia.morphology import erosion +from torch.nn import functional as F +import numpy as np +import cv2 + +from saicinpainting.evaluation.data import pad_tensor_to_modulo +from saicinpainting.evaluation.utils import move_to_device +from saicinpainting.training.modules.ffc import FFCResnetBlock +from saicinpainting.training.modules.pix2pixhd import ResnetBlock + +from tqdm import tqdm + + +def _pyrdown(im : torch.Tensor, downsize : tuple=None): + """downscale the image""" + if downsize is None: + downsize = (im.shape[2]//2, im.shape[3]//2) + assert im.shape[1] == 3, "Expected shape for the input to be (n,3,height,width)" + im = gaussian_blur2d(im, kernel_size=(5,5), sigma=(1.0,1.0)) + im = F.interpolate(im, size=downsize, mode='bilinear', align_corners=False) + return im + +def _pyrdown_mask(mask : torch.Tensor, downsize : tuple=None, eps : float=1e-8, blur_mask : bool=True, round_up : bool=True): + """downscale the mask tensor + + Parameters + ---------- + mask : torch.Tensor + mask of size (B, 1, H, W) + downsize : tuple, optional + size to downscale to. If None, image is downscaled to half, by default None + eps : float, optional + threshold value for binarizing the mask, by default 1e-8 + blur_mask : bool, optional + if True, apply gaussian filter before downscaling, by default True + round_up : bool, optional + if True, values above eps are marked 1, else, values below 1-eps are marked 0, by default True + + Returns + ------- + torch.Tensor + downscaled mask + """ + + if downsize is None: + downsize = (mask.shape[2]//2, mask.shape[3]//2) + assert mask.shape[1] == 1, "Expected shape for the input to be (n,1,height,width)" + if blur_mask == True: + mask = gaussian_blur2d(mask, kernel_size=(5,5), sigma=(1.0,1.0)) + mask = F.interpolate(mask, size=downsize, mode='bilinear', align_corners=False) + else: + mask = F.interpolate(mask, size=downsize, mode='bilinear', align_corners=False) + if round_up: + mask[mask>=eps] = 1 + mask[mask<eps] = 0 + else: + mask[mask>=1.0-eps] = 1 + mask[mask<1.0-eps] = 0 + return mask + +def _erode_mask(mask : torch.Tensor, ekernel : torch.Tensor=None, eps : float=1e-8): + """erode the mask, and set gray pixels to 0""" + if ekernel is not None: + mask = erosion(mask, ekernel) + mask[mask>=1.0-eps] = 1 + mask[mask<1.0-eps] = 0 + return mask + + +def _l1_loss( + pred : torch.Tensor, pred_downscaled : torch.Tensor, ref : torch.Tensor, + mask : torch.Tensor, mask_downscaled : torch.Tensor, + image : torch.Tensor, on_pred : bool=True + ): + """l1 loss on src pixels, and downscaled predictions if on_pred=True""" + loss = torch.mean(torch.abs(pred[mask<1e-8] - image[mask<1e-8])) + if on_pred: + loss += torch.mean(torch.abs(pred_downscaled[mask_downscaled>=1e-8] - ref[mask_downscaled>=1e-8])) + return loss + +def _infer( + image : torch.Tensor, mask : torch.Tensor, + forward_front : nn.Module, forward_rears : nn.Module, + ref_lower_res : torch.Tensor, orig_shape : tuple, devices : list, + scale_ind : int, n_iters : int=15, lr : float=0.002): + """Performs inference with refinement at a given scale. + + Parameters + ---------- + image : torch.Tensor + input image to be inpainted, of size (1,3,H,W) + mask : torch.Tensor + input inpainting mask, of size (1,1,H,W) + forward_front : nn.Module + the front part of the inpainting network + forward_rears : nn.Module + the rear part of the inpainting network + ref_lower_res : torch.Tensor + the inpainting at previous scale, used as reference image + orig_shape : tuple + shape of the original input image before padding + devices : list + list of available devices + scale_ind : int + the scale index + n_iters : int, optional + number of iterations of refinement, by default 15 + lr : float, optional + learning rate, by default 0.002 + + Returns + ------- + torch.Tensor + inpainted image + """ + masked_image = image * (1 - mask) + masked_image = torch.cat([masked_image, mask], dim=1) + + mask = mask.repeat(1,3,1,1) + if ref_lower_res is not None: + ref_lower_res = ref_lower_res.detach() + with torch.no_grad(): + z1,z2 = forward_front(masked_image) + # Inference + mask = mask.to(devices[-1]) + ekernel = torch.from_numpy(cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(15,15)).astype(bool)).float() + ekernel = ekernel.to(devices[-1]) + image = image.to(devices[-1]) + z1, z2 = z1.detach().to(devices[0]), z2.detach().to(devices[0]) + z1.requires_grad, z2.requires_grad = True, True + + optimizer = Adam([z1,z2], lr=lr) + + pbar = tqdm(range(n_iters), leave=False) + for idi in pbar: + optimizer.zero_grad() + input_feat = (z1,z2) + for idd, forward_rear in enumerate(forward_rears): + output_feat = forward_rear(input_feat) + if idd < len(devices) - 1: + midz1, midz2 = output_feat + midz1, midz2 = midz1.to(devices[idd+1]), midz2.to(devices[idd+1]) + input_feat = (midz1, midz2) + else: + pred = output_feat + + if ref_lower_res is None: + break + losses = {} + ######################### multi-scale ############################# + # scaled loss with downsampler + pred_downscaled = _pyrdown(pred[:,:,:orig_shape[0],:orig_shape[1]]) + mask_downscaled = _pyrdown_mask(mask[:,:1,:orig_shape[0],:orig_shape[1]], blur_mask=False, round_up=False) + mask_downscaled = _erode_mask(mask_downscaled, ekernel=ekernel) + mask_downscaled = mask_downscaled.repeat(1,3,1,1) + losses["ms_l1"] = _l1_loss(pred, pred_downscaled, ref_lower_res, mask, mask_downscaled, image, on_pred=True) + + loss = sum(losses.values()) + pbar.set_description("Refining scale {} using scale {} ...current loss: {:.4f}".format(scale_ind+1, scale_ind, loss.item())) + if idi < n_iters - 1: + loss.backward() + optimizer.step() + del pred_downscaled + del loss + del pred + # "pred" is the prediction after Plug-n-Play module + inpainted = mask * pred + (1 - mask) * image + inpainted = inpainted.detach().cpu() + return inpainted + +def _get_image_mask_pyramid(batch : dict, min_side : int, max_scales : int, px_budget : int): + """Build the image mask pyramid + + Parameters + ---------- + batch : dict + batch containing image, mask, etc + min_side : int + minimum side length to limit the number of scales of the pyramid + max_scales : int + maximum number of scales allowed + px_budget : int + the product H*W cannot exceed this budget, because of resource constraints + + Returns + ------- + tuple + image-mask pyramid in the form of list of images and list of masks + """ + + assert batch['image'].shape[0] == 1, "refiner works on only batches of size 1!" + + h, w = batch['unpad_to_size'] + h, w = h[0].item(), w[0].item() + + image = batch['image'][...,:h,:w] + mask = batch['mask'][...,:h,:w] + if h*w > px_budget: + #resize + ratio = np.sqrt(px_budget / float(h*w)) + h_orig, w_orig = h, w + h,w = int(h*ratio), int(w*ratio) + print(f"Original image too large for refinement! Resizing {(h_orig,w_orig)} to {(h,w)}...") + image = resize(image, (h,w),interpolation='bilinear', align_corners=False) + mask = resize(mask, (h,w),interpolation='bilinear', align_corners=False) + mask[mask>1e-8] = 1 + breadth = min(h,w) + n_scales = min(1 + int(round(max(0,np.log2(breadth / min_side)))), max_scales) + ls_images = [] + ls_masks = [] + + ls_images.append(image) + ls_masks.append(mask) + + for _ in range(n_scales - 1): + image_p = _pyrdown(ls_images[-1]) + mask_p = _pyrdown_mask(ls_masks[-1]) + ls_images.append(image_p) + ls_masks.append(mask_p) + # reverse the lists because we want the lowest resolution image as index 0 + return ls_images[::-1], ls_masks[::-1] + +def refine_predict( + batch : dict, inpainter : nn.Module, gpu_ids : str, + modulo : int, n_iters : int, lr : float, min_side : int, + max_scales : int, px_budget : int + ): + """Refines the inpainting of the network + + Parameters + ---------- + batch : dict + image-mask batch, currently we assume the batchsize to be 1 + inpainter : nn.Module + the inpainting neural network + gpu_ids : str + the GPU ids of the machine to use. If only single GPU, use: "0," + modulo : int + pad the image to ensure dimension % modulo == 0 + n_iters : int + number of iterations of refinement for each scale + lr : float + learning rate + min_side : int + all sides of image on all scales should be >= min_side / sqrt(2) + max_scales : int + max number of downscaling scales for the image-mask pyramid + px_budget : int + pixels budget. Any image will be resized to satisfy height*width <= px_budget + + Returns + ------- + torch.Tensor + inpainted image of size (1,3,H,W) + """ + + assert not inpainter.training + assert not inpainter.add_noise_kwargs + assert inpainter.concat_mask + + gpu_ids = [f'cuda:{gpuid}' for gpuid in gpu_ids.replace(" ","").split(",") if gpuid.isdigit()] + n_resnet_blocks = 0 + first_resblock_ind = 0 + found_first_resblock = False + for idl in range(len(inpainter.generator.model)): + if isinstance(inpainter.generator.model[idl], FFCResnetBlock) or isinstance(inpainter.generator.model[idl], ResnetBlock): + n_resnet_blocks += 1 + found_first_resblock = True + elif not found_first_resblock: + first_resblock_ind += 1 + resblocks_per_gpu = n_resnet_blocks // len(gpu_ids) + + devices = [torch.device(gpu_id) for gpu_id in gpu_ids] + + # split the model into front, and rear parts + forward_front = inpainter.generator.model[0:first_resblock_ind] + forward_front.to(devices[0]) + forward_rears = [] + for idd in range(len(gpu_ids)): + if idd < len(gpu_ids) - 1: + forward_rears.append(inpainter.generator.model[first_resblock_ind + resblocks_per_gpu*(idd):first_resblock_ind+resblocks_per_gpu*(idd+1)]) + else: + forward_rears.append(inpainter.generator.model[first_resblock_ind + resblocks_per_gpu*(idd):]) + forward_rears[idd].to(devices[idd]) + + ls_images, ls_masks = _get_image_mask_pyramid( + batch, + min_side, + max_scales, + px_budget + ) + image_inpainted = None + + for ids, (image, mask) in enumerate(zip(ls_images, ls_masks)): + orig_shape = image.shape[2:] + image = pad_tensor_to_modulo(image, modulo) + mask = pad_tensor_to_modulo(mask, modulo) + mask[mask >= 1e-8] = 1.0 + mask[mask < 1e-8] = 0.0 + image, mask = move_to_device(image, devices[0]), move_to_device(mask, devices[0]) + if image_inpainted is not None: + image_inpainted = move_to_device(image_inpainted, devices[-1]) + image_inpainted = _infer(image, mask, forward_front, forward_rears, image_inpainted, orig_shape, devices, ids, n_iters, lr) + image_inpainted = image_inpainted[:,:,:orig_shape[0], :orig_shape[1]] + # detach everything to save resources + image = image.detach().cpu() + mask = mask.detach().cpu() + + return image_inpainted diff --git a/third-party/lama/bin/saicinpainting/evaluation/utils.py b/third-party/lama/bin/saicinpainting/evaluation/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..6d7c15c9242ed8a9bc59fbb3b450cca394720bb8 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/evaluation/utils.py @@ -0,0 +1,28 @@ +from enum import Enum + +import yaml +from easydict import EasyDict as edict +import torch.nn as nn +import torch + + +def load_yaml(path): + with open(path, 'r') as f: + return edict(yaml.safe_load(f)) + + +def move_to_device(obj, device): + if isinstance(obj, nn.Module): + return obj.to(device) + if torch.is_tensor(obj): + return obj.to(device) + if isinstance(obj, (tuple, list)): + return [move_to_device(el, device) for el in obj] + if isinstance(obj, dict): + return {name: move_to_device(val, device) for name, val in obj.items()} + raise ValueError(f'Unexpected type {type(obj)}') + + +class SmallMode(Enum): + DROP = "drop" + UPSCALE = "upscale" diff --git a/third-party/lama/bin/saicinpainting/evaluation/vis.py b/third-party/lama/bin/saicinpainting/evaluation/vis.py new file mode 100644 index 0000000000000000000000000000000000000000..c2910b4ef8c61efee72dabd0531a9b669ec8bf98 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/evaluation/vis.py @@ -0,0 +1,37 @@ +import numpy as np +from skimage import io +from skimage.segmentation import mark_boundaries + + +def save_item_for_vis(item, out_file): + mask = item['mask'] > 0.5 + if mask.ndim == 3: + mask = mask[0] + img = mark_boundaries(np.transpose(item['image'], (1, 2, 0)), + mask, + color=(1., 0., 0.), + outline_color=(1., 1., 1.), + mode='thick') + + if 'inpainted' in item: + inp_img = mark_boundaries(np.transpose(item['inpainted'], (1, 2, 0)), + mask, + color=(1., 0., 0.), + mode='outer') + img = np.concatenate((img, inp_img), axis=1) + + img = np.clip(img * 255, 0, 255).astype('uint8') + io.imsave(out_file, img) + + +def save_mask_for_sidebyside(item, out_file): + mask = item['mask']# > 0.5 + if mask.ndim == 3: + mask = mask[0] + mask = np.clip(mask * 255, 0, 255).astype('uint8') + io.imsave(out_file, mask) + +def save_img_for_sidebyside(item, out_file): + img = np.transpose(item['image'], (1, 2, 0)) + img = np.clip(img * 255, 0, 255).astype('uint8') + io.imsave(out_file, img) \ No newline at end of file diff --git a/third-party/lama/bin/saicinpainting/training/__init__.py b/third-party/lama/bin/saicinpainting/training/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third-party/lama/bin/saicinpainting/training/data/__init__.py b/third-party/lama/bin/saicinpainting/training/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third-party/lama/bin/saicinpainting/training/data/aug.py b/third-party/lama/bin/saicinpainting/training/data/aug.py new file mode 100644 index 0000000000000000000000000000000000000000..b1246250924e79511b58cd3d7ab79de8012f8949 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/data/aug.py @@ -0,0 +1,84 @@ +from albumentations import DualIAATransform, to_tuple +import imgaug.augmenters as iaa + +class IAAAffine2(DualIAATransform): + """Place a regular grid of points on the input and randomly move the neighbourhood of these point around + via affine transformations. + + Note: This class introduce interpolation artifacts to mask if it has values other than {0;1} + + Args: + p (float): probability of applying the transform. Default: 0.5. + + Targets: + image, mask + """ + + def __init__( + self, + scale=(0.7, 1.3), + translate_percent=None, + translate_px=None, + rotate=0.0, + shear=(-0.1, 0.1), + order=1, + cval=0, + mode="reflect", + always_apply=False, + p=0.5, + ): + super(IAAAffine2, self).__init__(always_apply, p) + self.scale = dict(x=scale, y=scale) + self.translate_percent = to_tuple(translate_percent, 0) + self.translate_px = to_tuple(translate_px, 0) + self.rotate = to_tuple(rotate) + self.shear = dict(x=shear, y=shear) + self.order = order + self.cval = cval + self.mode = mode + + @property + def processor(self): + return iaa.Affine( + self.scale, + self.translate_percent, + self.translate_px, + self.rotate, + self.shear, + self.order, + self.cval, + self.mode, + ) + + def get_transform_init_args_names(self): + return ("scale", "translate_percent", "translate_px", "rotate", "shear", "order", "cval", "mode") + + +class IAAPerspective2(DualIAATransform): + """Perform a random four point perspective transform of the input. + + Note: This class introduce interpolation artifacts to mask if it has values other than {0;1} + + Args: + scale ((float, float): standard deviation of the normal distributions. These are used to sample + the random distances of the subimage's corners from the full image's corners. Default: (0.05, 0.1). + p (float): probability of applying the transform. Default: 0.5. + + Targets: + image, mask + """ + + def __init__(self, scale=(0.05, 0.1), keep_size=True, always_apply=False, p=0.5, + order=1, cval=0, mode="replicate"): + super(IAAPerspective2, self).__init__(always_apply, p) + self.scale = to_tuple(scale, 1.0) + self.keep_size = keep_size + self.cval = cval + self.mode = mode + + @property + def processor(self): + return iaa.PerspectiveTransform(self.scale, keep_size=self.keep_size, mode=self.mode, cval=self.cval) + + def get_transform_init_args_names(self): + return ("scale", "keep_size") diff --git a/third-party/lama/bin/saicinpainting/training/data/datasets.py b/third-party/lama/bin/saicinpainting/training/data/datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..c4f503dafffb970d8dbaca33934da417036d1e55 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/data/datasets.py @@ -0,0 +1,304 @@ +import glob +import logging +import os +import random + +import albumentations as A +import cv2 +import numpy as np +import torch +import torch.nn.functional as F +import webdataset +from omegaconf import open_dict, OmegaConf +from skimage.feature import canny +from skimage.transform import rescale, resize +from torch.utils.data import Dataset, IterableDataset, DataLoader, DistributedSampler, ConcatDataset + +from saicinpainting.evaluation.data import InpaintingDataset as InpaintingEvaluationDataset, \ + OurInpaintingDataset as OurInpaintingEvaluationDataset, ceil_modulo, InpaintingEvalOnlineDataset +from saicinpainting.training.data.aug import IAAAffine2, IAAPerspective2 +from saicinpainting.training.data.masks import get_mask_generator + +LOGGER = logging.getLogger(__name__) + + +class InpaintingTrainDataset(Dataset): + def __init__(self, indir, mask_generator, transform): + self.in_files = list(glob.glob(os.path.join(indir, '**', '*.jpg'), recursive=True)) + self.mask_generator = mask_generator + self.transform = transform + self.iter_i = 0 + + def __len__(self): + return len(self.in_files) + + def __getitem__(self, item): + path = self.in_files[item] + img = cv2.imread(path) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = self.transform(image=img)['image'] + img = np.transpose(img, (2, 0, 1)) + # TODO: maybe generate mask before augmentations? slower, but better for segmentation-based masks + mask = self.mask_generator(img, iter_i=self.iter_i) + self.iter_i += 1 + return dict(image=img, + mask=mask) + + +class InpaintingTrainWebDataset(IterableDataset): + def __init__(self, indir, mask_generator, transform, shuffle_buffer=200): + self.impl = webdataset.Dataset(indir).shuffle(shuffle_buffer).decode('rgb').to_tuple('jpg') + self.mask_generator = mask_generator + self.transform = transform + + def __iter__(self): + for iter_i, (img,) in enumerate(self.impl): + img = np.clip(img * 255, 0, 255).astype('uint8') + img = self.transform(image=img)['image'] + img = np.transpose(img, (2, 0, 1)) + mask = self.mask_generator(img, iter_i=iter_i) + yield dict(image=img, + mask=mask) + + +class ImgSegmentationDataset(Dataset): + def __init__(self, indir, mask_generator, transform, out_size, segm_indir, semantic_seg_n_classes): + self.indir = indir + self.segm_indir = segm_indir + self.mask_generator = mask_generator + self.transform = transform + self.out_size = out_size + self.semantic_seg_n_classes = semantic_seg_n_classes + self.in_files = list(glob.glob(os.path.join(indir, '**', '*.jpg'), recursive=True)) + + def __len__(self): + return len(self.in_files) + + def __getitem__(self, item): + path = self.in_files[item] + img = cv2.imread(path) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = cv2.resize(img, (self.out_size, self.out_size)) + img = self.transform(image=img)['image'] + img = np.transpose(img, (2, 0, 1)) + mask = self.mask_generator(img) + segm, segm_classes= self.load_semantic_segm(path) + result = dict(image=img, + mask=mask, + segm=segm, + segm_classes=segm_classes) + return result + + def load_semantic_segm(self, img_path): + segm_path = img_path.replace(self.indir, self.segm_indir).replace(".jpg", ".png") + mask = cv2.imread(segm_path, cv2.IMREAD_GRAYSCALE) + mask = cv2.resize(mask, (self.out_size, self.out_size)) + tensor = torch.from_numpy(np.clip(mask.astype(int)-1, 0, None)) + ohe = F.one_hot(tensor.long(), num_classes=self.semantic_seg_n_classes) # w x h x n_classes + return ohe.permute(2, 0, 1).float(), tensor.unsqueeze(0) + + +def get_transforms(transform_variant, out_size): + if transform_variant == 'default': + transform = A.Compose([ + A.RandomScale(scale_limit=0.2), # +/- 20% + A.PadIfNeeded(min_height=out_size, min_width=out_size), + A.RandomCrop(height=out_size, width=out_size), + A.HorizontalFlip(), + A.CLAHE(), + A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2), + A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5), + A.ToFloat() + ]) + elif transform_variant == 'distortions': + transform = A.Compose([ + IAAPerspective2(scale=(0.0, 0.06)), + IAAAffine2(scale=(0.7, 1.3), + rotate=(-40, 40), + shear=(-0.1, 0.1)), + A.PadIfNeeded(min_height=out_size, min_width=out_size), + A.OpticalDistortion(), + A.RandomCrop(height=out_size, width=out_size), + A.HorizontalFlip(), + A.CLAHE(), + A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2), + A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5), + A.ToFloat() + ]) + elif transform_variant == 'distortions_scale05_1': + transform = A.Compose([ + IAAPerspective2(scale=(0.0, 0.06)), + IAAAffine2(scale=(0.5, 1.0), + rotate=(-40, 40), + shear=(-0.1, 0.1), + p=1), + A.PadIfNeeded(min_height=out_size, min_width=out_size), + A.OpticalDistortion(), + A.RandomCrop(height=out_size, width=out_size), + A.HorizontalFlip(), + A.CLAHE(), + A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2), + A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5), + A.ToFloat() + ]) + elif transform_variant == 'distortions_scale03_12': + transform = A.Compose([ + IAAPerspective2(scale=(0.0, 0.06)), + IAAAffine2(scale=(0.3, 1.2), + rotate=(-40, 40), + shear=(-0.1, 0.1), + p=1), + A.PadIfNeeded(min_height=out_size, min_width=out_size), + A.OpticalDistortion(), + A.RandomCrop(height=out_size, width=out_size), + A.HorizontalFlip(), + A.CLAHE(), + A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2), + A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5), + A.ToFloat() + ]) + elif transform_variant == 'distortions_scale03_07': + transform = A.Compose([ + IAAPerspective2(scale=(0.0, 0.06)), + IAAAffine2(scale=(0.3, 0.7), # scale 512 to 256 in average + rotate=(-40, 40), + shear=(-0.1, 0.1), + p=1), + A.PadIfNeeded(min_height=out_size, min_width=out_size), + A.OpticalDistortion(), + A.RandomCrop(height=out_size, width=out_size), + A.HorizontalFlip(), + A.CLAHE(), + A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2), + A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5), + A.ToFloat() + ]) + elif transform_variant == 'distortions_light': + transform = A.Compose([ + IAAPerspective2(scale=(0.0, 0.02)), + IAAAffine2(scale=(0.8, 1.8), + rotate=(-20, 20), + shear=(-0.03, 0.03)), + A.PadIfNeeded(min_height=out_size, min_width=out_size), + A.RandomCrop(height=out_size, width=out_size), + A.HorizontalFlip(), + A.CLAHE(), + A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2), + A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5), + A.ToFloat() + ]) + elif transform_variant == 'non_space_transform': + transform = A.Compose([ + A.CLAHE(), + A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2), + A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5), + A.ToFloat() + ]) + elif transform_variant == 'no_augs': + transform = A.Compose([ + A.ToFloat() + ]) + else: + raise ValueError(f'Unexpected transform_variant {transform_variant}') + return transform + + +def make_default_train_dataloader(indir, kind='default', out_size=512, mask_gen_kwargs=None, transform_variant='default', + mask_generator_kind="mixed", dataloader_kwargs=None, ddp_kwargs=None, **kwargs): + LOGGER.info(f'Make train dataloader {kind} from {indir}. Using mask generator={mask_generator_kind}') + + mask_generator = get_mask_generator(kind=mask_generator_kind, kwargs=mask_gen_kwargs) + transform = get_transforms(transform_variant, out_size) + + if kind == 'default': + dataset = InpaintingTrainDataset(indir=indir, + mask_generator=mask_generator, + transform=transform, + **kwargs) + elif kind == 'default_web': + dataset = InpaintingTrainWebDataset(indir=indir, + mask_generator=mask_generator, + transform=transform, + **kwargs) + elif kind == 'img_with_segm': + dataset = ImgSegmentationDataset(indir=indir, + mask_generator=mask_generator, + transform=transform, + out_size=out_size, + **kwargs) + else: + raise ValueError(f'Unknown train dataset kind {kind}') + + if dataloader_kwargs is None: + dataloader_kwargs = {} + + is_dataset_only_iterable = kind in ('default_web',) + + if ddp_kwargs is not None and not is_dataset_only_iterable: + dataloader_kwargs['shuffle'] = False + dataloader_kwargs['sampler'] = DistributedSampler(dataset, **ddp_kwargs) + + if is_dataset_only_iterable and 'shuffle' in dataloader_kwargs: + with open_dict(dataloader_kwargs): + del dataloader_kwargs['shuffle'] + + dataloader = DataLoader(dataset, **dataloader_kwargs) + return dataloader + + +def make_default_val_dataset(indir, kind='default', out_size=512, transform_variant='default', **kwargs): + if OmegaConf.is_list(indir) or isinstance(indir, (tuple, list)): + return ConcatDataset([ + make_default_val_dataset(idir, kind=kind, out_size=out_size, transform_variant=transform_variant, **kwargs) for idir in indir + ]) + + LOGGER.info(f'Make val dataloader {kind} from {indir}') + mask_generator = get_mask_generator(kind=kwargs.get("mask_generator_kind"), kwargs=kwargs.get("mask_gen_kwargs")) + + if transform_variant is not None: + transform = get_transforms(transform_variant, out_size) + + if kind == 'default': + dataset = InpaintingEvaluationDataset(indir, **kwargs) + elif kind == 'our_eval': + dataset = OurInpaintingEvaluationDataset(indir, **kwargs) + elif kind == 'img_with_segm': + dataset = ImgSegmentationDataset(indir=indir, + mask_generator=mask_generator, + transform=transform, + out_size=out_size, + **kwargs) + elif kind == 'online': + dataset = InpaintingEvalOnlineDataset(indir=indir, + mask_generator=mask_generator, + transform=transform, + out_size=out_size, + **kwargs) + else: + raise ValueError(f'Unknown val dataset kind {kind}') + + return dataset + + +def make_default_val_dataloader(*args, dataloader_kwargs=None, **kwargs): + dataset = make_default_val_dataset(*args, **kwargs) + + if dataloader_kwargs is None: + dataloader_kwargs = {} + dataloader = DataLoader(dataset, **dataloader_kwargs) + return dataloader + + +def make_constant_area_crop_params(img_height, img_width, min_size=128, max_size=512, area=256*256, round_to_mod=16): + min_size = min(img_height, img_width, min_size) + max_size = min(img_height, img_width, max_size) + if random.random() < 0.5: + out_height = min(max_size, ceil_modulo(random.randint(min_size, max_size), round_to_mod)) + out_width = min(max_size, ceil_modulo(area // out_height, round_to_mod)) + else: + out_width = min(max_size, ceil_modulo(random.randint(min_size, max_size), round_to_mod)) + out_height = min(max_size, ceil_modulo(area // out_width, round_to_mod)) + + start_y = random.randint(0, img_height - out_height) + start_x = random.randint(0, img_width - out_width) + return (start_y, start_x, out_height, out_width) diff --git a/third-party/lama/bin/saicinpainting/training/data/masks.py b/third-party/lama/bin/saicinpainting/training/data/masks.py new file mode 100644 index 0000000000000000000000000000000000000000..e91fc74913356481065c5f5906acd50fb05f521c --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/data/masks.py @@ -0,0 +1,332 @@ +import math +import random +import hashlib +import logging +from enum import Enum + +import cv2 +import numpy as np + +from saicinpainting.evaluation.masks.mask import SegmentationMask +from saicinpainting.utils import LinearRamp + +LOGGER = logging.getLogger(__name__) + + +class DrawMethod(Enum): + LINE = 'line' + CIRCLE = 'circle' + SQUARE = 'square' + + +def make_random_irregular_mask(shape, max_angle=4, max_len=60, max_width=20, min_times=0, max_times=10, + draw_method=DrawMethod.LINE): + draw_method = DrawMethod(draw_method) + + height, width = shape + mask = np.zeros((height, width), np.float32) + times = np.random.randint(min_times, max_times + 1) + for i in range(times): + start_x = np.random.randint(width) + start_y = np.random.randint(height) + for j in range(1 + np.random.randint(5)): + angle = 0.01 + np.random.randint(max_angle) + if i % 2 == 0: + angle = 2 * 3.1415926 - angle + length = 10 + np.random.randint(max_len) + brush_w = 5 + np.random.randint(max_width) + end_x = np.clip((start_x + length * np.sin(angle)).astype(np.int32), 0, width) + end_y = np.clip((start_y + length * np.cos(angle)).astype(np.int32), 0, height) + if draw_method == DrawMethod.LINE: + cv2.line(mask, (start_x, start_y), (end_x, end_y), 1.0, brush_w) + elif draw_method == DrawMethod.CIRCLE: + cv2.circle(mask, (start_x, start_y), radius=brush_w, color=1., thickness=-1) + elif draw_method == DrawMethod.SQUARE: + radius = brush_w // 2 + mask[start_y - radius:start_y + radius, start_x - radius:start_x + radius] = 1 + start_x, start_y = end_x, end_y + return mask[None, ...] + + +class RandomIrregularMaskGenerator: + def __init__(self, max_angle=4, max_len=60, max_width=20, min_times=0, max_times=10, ramp_kwargs=None, + draw_method=DrawMethod.LINE): + self.max_angle = max_angle + self.max_len = max_len + self.max_width = max_width + self.min_times = min_times + self.max_times = max_times + self.draw_method = draw_method + self.ramp = LinearRamp(**ramp_kwargs) if ramp_kwargs is not None else None + + def __call__(self, img, iter_i=None, raw_image=None): + coef = self.ramp(iter_i) if (self.ramp is not None) and (iter_i is not None) else 1 + cur_max_len = int(max(1, self.max_len * coef)) + cur_max_width = int(max(1, self.max_width * coef)) + cur_max_times = int(self.min_times + 1 + (self.max_times - self.min_times) * coef) + return make_random_irregular_mask(img.shape[1:], max_angle=self.max_angle, max_len=cur_max_len, + max_width=cur_max_width, min_times=self.min_times, max_times=cur_max_times, + draw_method=self.draw_method) + + +def make_random_rectangle_mask(shape, margin=10, bbox_min_size=30, bbox_max_size=100, min_times=0, max_times=3): + height, width = shape + mask = np.zeros((height, width), np.float32) + bbox_max_size = min(bbox_max_size, height - margin * 2, width - margin * 2) + times = np.random.randint(min_times, max_times + 1) + for i in range(times): + box_width = np.random.randint(bbox_min_size, bbox_max_size) + box_height = np.random.randint(bbox_min_size, bbox_max_size) + start_x = np.random.randint(margin, width - margin - box_width + 1) + start_y = np.random.randint(margin, height - margin - box_height + 1) + mask[start_y:start_y + box_height, start_x:start_x + box_width] = 1 + return mask[None, ...] + + +class RandomRectangleMaskGenerator: + def __init__(self, margin=10, bbox_min_size=30, bbox_max_size=100, min_times=0, max_times=3, ramp_kwargs=None): + self.margin = margin + self.bbox_min_size = bbox_min_size + self.bbox_max_size = bbox_max_size + self.min_times = min_times + self.max_times = max_times + self.ramp = LinearRamp(**ramp_kwargs) if ramp_kwargs is not None else None + + def __call__(self, img, iter_i=None, raw_image=None): + coef = self.ramp(iter_i) if (self.ramp is not None) and (iter_i is not None) else 1 + cur_bbox_max_size = int(self.bbox_min_size + 1 + (self.bbox_max_size - self.bbox_min_size) * coef) + cur_max_times = int(self.min_times + (self.max_times - self.min_times) * coef) + return make_random_rectangle_mask(img.shape[1:], margin=self.margin, bbox_min_size=self.bbox_min_size, + bbox_max_size=cur_bbox_max_size, min_times=self.min_times, + max_times=cur_max_times) + + +class RandomSegmentationMaskGenerator: + def __init__(self, **kwargs): + self.impl = None # will be instantiated in first call (effectively in subprocess) + self.kwargs = kwargs + + def __call__(self, img, iter_i=None, raw_image=None): + if self.impl is None: + self.impl = SegmentationMask(**self.kwargs) + + masks = self.impl.get_masks(np.transpose(img, (1, 2, 0))) + masks = [m for m in masks if len(np.unique(m)) > 1] + return np.random.choice(masks) + + +def make_random_superres_mask(shape, min_step=2, max_step=4, min_width=1, max_width=3): + height, width = shape + mask = np.zeros((height, width), np.float32) + step_x = np.random.randint(min_step, max_step + 1) + width_x = np.random.randint(min_width, min(step_x, max_width + 1)) + offset_x = np.random.randint(0, step_x) + + step_y = np.random.randint(min_step, max_step + 1) + width_y = np.random.randint(min_width, min(step_y, max_width + 1)) + offset_y = np.random.randint(0, step_y) + + for dy in range(width_y): + mask[offset_y + dy::step_y] = 1 + for dx in range(width_x): + mask[:, offset_x + dx::step_x] = 1 + return mask[None, ...] + + +class RandomSuperresMaskGenerator: + def __init__(self, **kwargs): + self.kwargs = kwargs + + def __call__(self, img, iter_i=None): + return make_random_superres_mask(img.shape[1:], **self.kwargs) + + +class DumbAreaMaskGenerator: + min_ratio = 0.1 + max_ratio = 0.35 + default_ratio = 0.225 + + def __init__(self, is_training): + #Parameters: + # is_training(bool): If true - random rectangular mask, if false - central square mask + self.is_training = is_training + + def _random_vector(self, dimension): + if self.is_training: + lower_limit = math.sqrt(self.min_ratio) + upper_limit = math.sqrt(self.max_ratio) + mask_side = round((random.random() * (upper_limit - lower_limit) + lower_limit) * dimension) + u = random.randint(0, dimension-mask_side-1) + v = u+mask_side + else: + margin = (math.sqrt(self.default_ratio) / 2) * dimension + u = round(dimension/2 - margin) + v = round(dimension/2 + margin) + return u, v + + def __call__(self, img, iter_i=None, raw_image=None): + c, height, width = img.shape + mask = np.zeros((height, width), np.float32) + x1, x2 = self._random_vector(width) + y1, y2 = self._random_vector(height) + mask[x1:x2, y1:y2] = 1 + return mask[None, ...] + + +class OutpaintingMaskGenerator: + def __init__(self, min_padding_percent:float=0.04, max_padding_percent:int=0.25, left_padding_prob:float=0.5, top_padding_prob:float=0.5, + right_padding_prob:float=0.5, bottom_padding_prob:float=0.5, is_fixed_randomness:bool=False): + """ + is_fixed_randomness - get identical paddings for the same image if args are the same + """ + self.min_padding_percent = min_padding_percent + self.max_padding_percent = max_padding_percent + self.probs = [left_padding_prob, top_padding_prob, right_padding_prob, bottom_padding_prob] + self.is_fixed_randomness = is_fixed_randomness + + assert self.min_padding_percent <= self.max_padding_percent + assert self.max_padding_percent > 0 + assert len([x for x in [self.min_padding_percent, self.max_padding_percent] if (x>=0 and x<=1)]) == 2, f"Padding percentage should be in [0,1]" + assert sum(self.probs) > 0, f"At least one of the padding probs should be greater than 0 - {self.probs}" + assert len([x for x in self.probs if (x >= 0) and (x <= 1)]) == 4, f"At least one of padding probs is not in [0,1] - {self.probs}" + if len([x for x in self.probs if x > 0]) == 1: + LOGGER.warning(f"Only one padding prob is greater than zero - {self.probs}. That means that the outpainting masks will be always on the same side") + + def apply_padding(self, mask, coord): + mask[int(coord[0][0]*self.img_h):int(coord[1][0]*self.img_h), + int(coord[0][1]*self.img_w):int(coord[1][1]*self.img_w)] = 1 + return mask + + def get_padding(self, size): + n1 = int(self.min_padding_percent*size) + n2 = int(self.max_padding_percent*size) + return self.rnd.randint(n1, n2) / size + + @staticmethod + def _img2rs(img): + arr = np.ascontiguousarray(img.astype(np.uint8)) + str_hash = hashlib.sha1(arr).hexdigest() + res = hash(str_hash)%(2**32) + return res + + def __call__(self, img, iter_i=None, raw_image=None): + c, self.img_h, self.img_w = img.shape + mask = np.zeros((self.img_h, self.img_w), np.float32) + at_least_one_mask_applied = False + + if self.is_fixed_randomness: + assert raw_image is not None, f"Cant calculate hash on raw_image=None" + rs = self._img2rs(raw_image) + self.rnd = np.random.RandomState(rs) + else: + self.rnd = np.random + + coords = [[ + (0,0), + (1,self.get_padding(size=self.img_h)) + ], + [ + (0,0), + (self.get_padding(size=self.img_w),1) + ], + [ + (0,1-self.get_padding(size=self.img_h)), + (1,1) + ], + [ + (1-self.get_padding(size=self.img_w),0), + (1,1) + ]] + + for pp, coord in zip(self.probs, coords): + if self.rnd.random() < pp: + at_least_one_mask_applied = True + mask = self.apply_padding(mask=mask, coord=coord) + + if not at_least_one_mask_applied: + idx = self.rnd.choice(range(len(coords)), p=np.array(self.probs)/sum(self.probs)) + mask = self.apply_padding(mask=mask, coord=coords[idx]) + return mask[None, ...] + + +class MixedMaskGenerator: + def __init__(self, irregular_proba=1/3, irregular_kwargs=None, + box_proba=1/3, box_kwargs=None, + segm_proba=1/3, segm_kwargs=None, + squares_proba=0, squares_kwargs=None, + superres_proba=0, superres_kwargs=None, + outpainting_proba=0, outpainting_kwargs=None, + invert_proba=0): + self.probas = [] + self.gens = [] + + if irregular_proba > 0: + self.probas.append(irregular_proba) + if irregular_kwargs is None: + irregular_kwargs = {} + else: + irregular_kwargs = dict(irregular_kwargs) + irregular_kwargs['draw_method'] = DrawMethod.LINE + self.gens.append(RandomIrregularMaskGenerator(**irregular_kwargs)) + + if box_proba > 0: + self.probas.append(box_proba) + if box_kwargs is None: + box_kwargs = {} + self.gens.append(RandomRectangleMaskGenerator(**box_kwargs)) + + if segm_proba > 0: + self.probas.append(segm_proba) + if segm_kwargs is None: + segm_kwargs = {} + self.gens.append(RandomSegmentationMaskGenerator(**segm_kwargs)) + + if squares_proba > 0: + self.probas.append(squares_proba) + if squares_kwargs is None: + squares_kwargs = {} + else: + squares_kwargs = dict(squares_kwargs) + squares_kwargs['draw_method'] = DrawMethod.SQUARE + self.gens.append(RandomIrregularMaskGenerator(**squares_kwargs)) + + if superres_proba > 0: + self.probas.append(superres_proba) + if superres_kwargs is None: + superres_kwargs = {} + self.gens.append(RandomSuperresMaskGenerator(**superres_kwargs)) + + if outpainting_proba > 0: + self.probas.append(outpainting_proba) + if outpainting_kwargs is None: + outpainting_kwargs = {} + self.gens.append(OutpaintingMaskGenerator(**outpainting_kwargs)) + + self.probas = np.array(self.probas, dtype='float32') + self.probas /= self.probas.sum() + self.invert_proba = invert_proba + + def __call__(self, img, iter_i=None, raw_image=None): + kind = np.random.choice(len(self.probas), p=self.probas) + gen = self.gens[kind] + result = gen(img, iter_i=iter_i, raw_image=raw_image) + if self.invert_proba > 0 and random.random() < self.invert_proba: + result = 1 - result + return result + + +def get_mask_generator(kind, kwargs): + if kind is None: + kind = "mixed" + if kwargs is None: + kwargs = {} + + if kind == "mixed": + cl = MixedMaskGenerator + elif kind == "outpainting": + cl = OutpaintingMaskGenerator + elif kind == "dumb": + cl = DumbAreaMaskGenerator + else: + raise NotImplementedError(f"No such generator kind = {kind}") + return cl(**kwargs) diff --git a/third-party/lama/bin/saicinpainting/training/losses/__init__.py b/third-party/lama/bin/saicinpainting/training/losses/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third-party/lama/bin/saicinpainting/training/losses/adversarial.py b/third-party/lama/bin/saicinpainting/training/losses/adversarial.py new file mode 100644 index 0000000000000000000000000000000000000000..d6db2967ce5074d94ed3b4c51fc743ff2f7831b1 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/losses/adversarial.py @@ -0,0 +1,177 @@ +from typing import Tuple, Dict, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class BaseAdversarialLoss: + def pre_generator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor, + generator: nn.Module, discriminator: nn.Module): + """ + Prepare for generator step + :param real_batch: Tensor, a batch of real samples + :param fake_batch: Tensor, a batch of samples produced by generator + :param generator: + :param discriminator: + :return: None + """ + + def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor, + generator: nn.Module, discriminator: nn.Module): + """ + Prepare for discriminator step + :param real_batch: Tensor, a batch of real samples + :param fake_batch: Tensor, a batch of samples produced by generator + :param generator: + :param discriminator: + :return: None + """ + + def generator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor, + discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor, + mask: Optional[torch.Tensor] = None) \ + -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: + """ + Calculate generator loss + :param real_batch: Tensor, a batch of real samples + :param fake_batch: Tensor, a batch of samples produced by generator + :param discr_real_pred: Tensor, discriminator output for real_batch + :param discr_fake_pred: Tensor, discriminator output for fake_batch + :param mask: Tensor, actual mask, which was at input of generator when making fake_batch + :return: total generator loss along with some values that might be interesting to log + """ + raise NotImplemented() + + def discriminator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor, + discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor, + mask: Optional[torch.Tensor] = None) \ + -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: + """ + Calculate discriminator loss and call .backward() on it + :param real_batch: Tensor, a batch of real samples + :param fake_batch: Tensor, a batch of samples produced by generator + :param discr_real_pred: Tensor, discriminator output for real_batch + :param discr_fake_pred: Tensor, discriminator output for fake_batch + :param mask: Tensor, actual mask, which was at input of generator when making fake_batch + :return: total discriminator loss along with some values that might be interesting to log + """ + raise NotImplemented() + + def interpolate_mask(self, mask, shape): + assert mask is not None + assert self.allow_scale_mask or shape == mask.shape[-2:] + if shape != mask.shape[-2:] and self.allow_scale_mask: + if self.mask_scale_mode == 'maxpool': + mask = F.adaptive_max_pool2d(mask, shape) + else: + mask = F.interpolate(mask, size=shape, mode=self.mask_scale_mode) + return mask + +def make_r1_gp(discr_real_pred, real_batch): + if torch.is_grad_enabled(): + grad_real = torch.autograd.grad(outputs=discr_real_pred.sum(), inputs=real_batch, create_graph=True)[0] + grad_penalty = (grad_real.view(grad_real.shape[0], -1).norm(2, dim=1) ** 2).mean() + else: + grad_penalty = 0 + real_batch.requires_grad = False + + return grad_penalty + +class NonSaturatingWithR1(BaseAdversarialLoss): + def __init__(self, gp_coef=5, weight=1, mask_as_fake_target=False, allow_scale_mask=False, + mask_scale_mode='nearest', extra_mask_weight_for_gen=0, + use_unmasked_for_gen=True, use_unmasked_for_discr=True): + self.gp_coef = gp_coef + self.weight = weight + # use for discr => use for gen; + # otherwise we teach only the discr to pay attention to very small difference + assert use_unmasked_for_gen or (not use_unmasked_for_discr) + # mask as target => use unmasked for discr: + # if we don't care about unmasked regions at all + # then it doesn't matter if the value of mask_as_fake_target is true or false + assert use_unmasked_for_discr or (not mask_as_fake_target) + self.use_unmasked_for_gen = use_unmasked_for_gen + self.use_unmasked_for_discr = use_unmasked_for_discr + self.mask_as_fake_target = mask_as_fake_target + self.allow_scale_mask = allow_scale_mask + self.mask_scale_mode = mask_scale_mode + self.extra_mask_weight_for_gen = extra_mask_weight_for_gen + + def generator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor, + discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor, + mask=None) \ + -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: + fake_loss = F.softplus(-discr_fake_pred) + if (self.mask_as_fake_target and self.extra_mask_weight_for_gen > 0) or \ + not self.use_unmasked_for_gen: # == if masked region should be treated differently + mask = self.interpolate_mask(mask, discr_fake_pred.shape[-2:]) + if not self.use_unmasked_for_gen: + fake_loss = fake_loss * mask + else: + pixel_weights = 1 + mask * self.extra_mask_weight_for_gen + fake_loss = fake_loss * pixel_weights + + return fake_loss.mean() * self.weight, dict() + + def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor, + generator: nn.Module, discriminator: nn.Module): + real_batch.requires_grad = True + + def discriminator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor, + discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor, + mask=None) \ + -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: + + real_loss = F.softplus(-discr_real_pred) + grad_penalty = make_r1_gp(discr_real_pred, real_batch) * self.gp_coef + fake_loss = F.softplus(discr_fake_pred) + + if not self.use_unmasked_for_discr or self.mask_as_fake_target: + # == if masked region should be treated differently + mask = self.interpolate_mask(mask, discr_fake_pred.shape[-2:]) + # use_unmasked_for_discr=False only makes sense for fakes; + # for reals there is no difference beetween two regions + fake_loss = fake_loss * mask + if self.mask_as_fake_target: + fake_loss = fake_loss + (1 - mask) * F.softplus(-discr_fake_pred) + + sum_discr_loss = real_loss + grad_penalty + fake_loss + metrics = dict(discr_real_out=discr_real_pred.mean(), + discr_fake_out=discr_fake_pred.mean(), + discr_real_gp=grad_penalty) + return sum_discr_loss.mean(), metrics + +class BCELoss(BaseAdversarialLoss): + def __init__(self, weight): + self.weight = weight + self.bce_loss = nn.BCEWithLogitsLoss() + + def generator_loss(self, discr_fake_pred: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: + real_mask_gt = torch.zeros(discr_fake_pred.shape).to(discr_fake_pred.device) + fake_loss = self.bce_loss(discr_fake_pred, real_mask_gt) * self.weight + return fake_loss, dict() + + def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor, + generator: nn.Module, discriminator: nn.Module): + real_batch.requires_grad = True + + def discriminator_loss(self, + mask: torch.Tensor, + discr_real_pred: torch.Tensor, + discr_fake_pred: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: + + real_mask_gt = torch.zeros(discr_real_pred.shape).to(discr_real_pred.device) + sum_discr_loss = (self.bce_loss(discr_real_pred, real_mask_gt) + self.bce_loss(discr_fake_pred, mask)) / 2 + metrics = dict(discr_real_out=discr_real_pred.mean(), + discr_fake_out=discr_fake_pred.mean(), + discr_real_gp=0) + return sum_discr_loss, metrics + + +def make_discrim_loss(kind, **kwargs): + if kind == 'r1': + return NonSaturatingWithR1(**kwargs) + elif kind == 'bce': + return BCELoss(**kwargs) + raise ValueError(f'Unknown adversarial loss kind {kind}') diff --git a/third-party/lama/bin/saicinpainting/training/losses/constants.py b/third-party/lama/bin/saicinpainting/training/losses/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..ae3e5e151342232be8e2c2a77fe6fd5798dc2a8c --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/losses/constants.py @@ -0,0 +1,152 @@ +weights = {"ade20k": + [6.34517766497462, + 9.328358208955224, + 11.389521640091116, + 16.10305958132045, + 20.833333333333332, + 22.22222222222222, + 25.125628140703515, + 43.29004329004329, + 50.5050505050505, + 54.6448087431694, + 55.24861878453038, + 60.24096385542168, + 62.5, + 66.2251655629139, + 84.74576271186442, + 90.90909090909092, + 91.74311926605505, + 96.15384615384616, + 96.15384615384616, + 97.08737864077669, + 102.04081632653062, + 135.13513513513513, + 149.2537313432836, + 153.84615384615384, + 163.93442622950818, + 166.66666666666666, + 188.67924528301887, + 192.30769230769232, + 217.3913043478261, + 227.27272727272725, + 227.27272727272725, + 227.27272727272725, + 303.03030303030306, + 322.5806451612903, + 333.3333333333333, + 370.3703703703703, + 384.61538461538464, + 416.6666666666667, + 416.6666666666667, + 434.7826086956522, + 434.7826086956522, + 454.5454545454545, + 454.5454545454545, + 500.0, + 526.3157894736842, + 526.3157894736842, + 555.5555555555555, + 555.5555555555555, + 555.5555555555555, + 555.5555555555555, + 555.5555555555555, + 555.5555555555555, + 555.5555555555555, + 588.2352941176471, + 588.2352941176471, + 588.2352941176471, + 588.2352941176471, + 588.2352941176471, + 666.6666666666666, + 666.6666666666666, + 666.6666666666666, + 666.6666666666666, + 714.2857142857143, + 714.2857142857143, + 714.2857142857143, + 714.2857142857143, + 714.2857142857143, + 769.2307692307693, + 769.2307692307693, + 769.2307692307693, + 833.3333333333334, + 833.3333333333334, + 833.3333333333334, + 833.3333333333334, + 909.090909090909, + 1000.0, + 1111.111111111111, + 1111.111111111111, + 1111.111111111111, + 1111.111111111111, + 1111.111111111111, + 1250.0, + 1250.0, + 1250.0, + 1250.0, + 1250.0, + 1428.5714285714287, + 1428.5714285714287, + 1428.5714285714287, + 1428.5714285714287, + 1428.5714285714287, + 1428.5714285714287, + 1428.5714285714287, + 1666.6666666666667, + 1666.6666666666667, + 1666.6666666666667, + 1666.6666666666667, + 1666.6666666666667, + 1666.6666666666667, + 1666.6666666666667, + 1666.6666666666667, + 1666.6666666666667, + 1666.6666666666667, + 1666.6666666666667, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2500.0, + 2500.0, + 2500.0, + 2500.0, + 2500.0, + 2500.0, + 2500.0, + 2500.0, + 2500.0, + 2500.0, + 2500.0, + 2500.0, + 2500.0, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 5000.0, + 5000.0, + 5000.0] +} \ No newline at end of file diff --git a/third-party/lama/bin/saicinpainting/training/losses/distance_weighting.py b/third-party/lama/bin/saicinpainting/training/losses/distance_weighting.py new file mode 100644 index 0000000000000000000000000000000000000000..93052003b1e47fd663c70aedcecd144171f49204 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/losses/distance_weighting.py @@ -0,0 +1,126 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision + +from saicinpainting.training.losses.perceptual import IMAGENET_STD, IMAGENET_MEAN + + +def dummy_distance_weighter(real_img, pred_img, mask): + return mask + + +def get_gauss_kernel(kernel_size, width_factor=1): + coords = torch.stack(torch.meshgrid(torch.arange(kernel_size), + torch.arange(kernel_size)), + dim=0).float() + diff = torch.exp(-((coords - kernel_size // 2) ** 2).sum(0) / kernel_size / width_factor) + diff /= diff.sum() + return diff + + +class BlurMask(nn.Module): + def __init__(self, kernel_size=5, width_factor=1): + super().__init__() + self.filter = nn.Conv2d(1, 1, kernel_size, padding=kernel_size // 2, padding_mode='replicate', bias=False) + self.filter.weight.data.copy_(get_gauss_kernel(kernel_size, width_factor=width_factor)) + + def forward(self, real_img, pred_img, mask): + with torch.no_grad(): + result = self.filter(mask) * mask + return result + + +class EmulatedEDTMask(nn.Module): + def __init__(self, dilate_kernel_size=5, blur_kernel_size=5, width_factor=1): + super().__init__() + self.dilate_filter = nn.Conv2d(1, 1, dilate_kernel_size, padding=dilate_kernel_size// 2, padding_mode='replicate', + bias=False) + self.dilate_filter.weight.data.copy_(torch.ones(1, 1, dilate_kernel_size, dilate_kernel_size, dtype=torch.float)) + self.blur_filter = nn.Conv2d(1, 1, blur_kernel_size, padding=blur_kernel_size // 2, padding_mode='replicate', bias=False) + self.blur_filter.weight.data.copy_(get_gauss_kernel(blur_kernel_size, width_factor=width_factor)) + + def forward(self, real_img, pred_img, mask): + with torch.no_grad(): + known_mask = 1 - mask + dilated_known_mask = (self.dilate_filter(known_mask) > 1).float() + result = self.blur_filter(1 - dilated_known_mask) * mask + return result + + +class PropagatePerceptualSim(nn.Module): + def __init__(self, level=2, max_iters=10, temperature=500, erode_mask_size=3): + super().__init__() + vgg = torchvision.models.vgg19(pretrained=True).features + vgg_avg_pooling = [] + + for weights in vgg.parameters(): + weights.requires_grad = False + + cur_level_i = 0 + for module in vgg.modules(): + if module.__class__.__name__ == 'Sequential': + continue + elif module.__class__.__name__ == 'MaxPool2d': + vgg_avg_pooling.append(nn.AvgPool2d(kernel_size=2, stride=2, padding=0)) + else: + vgg_avg_pooling.append(module) + if module.__class__.__name__ == 'ReLU': + cur_level_i += 1 + if cur_level_i == level: + break + + self.features = nn.Sequential(*vgg_avg_pooling) + + self.max_iters = max_iters + self.temperature = temperature + self.do_erode = erode_mask_size > 0 + if self.do_erode: + self.erode_mask = nn.Conv2d(1, 1, erode_mask_size, padding=erode_mask_size // 2, bias=False) + self.erode_mask.weight.data.fill_(1) + + def forward(self, real_img, pred_img, mask): + with torch.no_grad(): + real_img = (real_img - IMAGENET_MEAN.to(real_img)) / IMAGENET_STD.to(real_img) + real_feats = self.features(real_img) + + vertical_sim = torch.exp(-(real_feats[:, :, 1:] - real_feats[:, :, :-1]).pow(2).sum(1, keepdim=True) + / self.temperature) + horizontal_sim = torch.exp(-(real_feats[:, :, :, 1:] - real_feats[:, :, :, :-1]).pow(2).sum(1, keepdim=True) + / self.temperature) + + mask_scaled = F.interpolate(mask, size=real_feats.shape[-2:], mode='bilinear', align_corners=False) + if self.do_erode: + mask_scaled = (self.erode_mask(mask_scaled) > 1).float() + + cur_knowness = 1 - mask_scaled + + for iter_i in range(self.max_iters): + new_top_knowness = F.pad(cur_knowness[:, :, :-1] * vertical_sim, (0, 0, 1, 0), mode='replicate') + new_bottom_knowness = F.pad(cur_knowness[:, :, 1:] * vertical_sim, (0, 0, 0, 1), mode='replicate') + + new_left_knowness = F.pad(cur_knowness[:, :, :, :-1] * horizontal_sim, (1, 0, 0, 0), mode='replicate') + new_right_knowness = F.pad(cur_knowness[:, :, :, 1:] * horizontal_sim, (0, 1, 0, 0), mode='replicate') + + new_knowness = torch.stack([new_top_knowness, new_bottom_knowness, + new_left_knowness, new_right_knowness], + dim=0).max(0).values + + cur_knowness = torch.max(cur_knowness, new_knowness) + + cur_knowness = F.interpolate(cur_knowness, size=mask.shape[-2:], mode='bilinear') + result = torch.min(mask, 1 - cur_knowness) + + return result + + +def make_mask_distance_weighter(kind='none', **kwargs): + if kind == 'none': + return dummy_distance_weighter + if kind == 'blur': + return BlurMask(**kwargs) + if kind == 'edt': + return EmulatedEDTMask(**kwargs) + if kind == 'pps': + return PropagatePerceptualSim(**kwargs) + raise ValueError(f'Unknown mask distance weighter kind {kind}') diff --git a/third-party/lama/bin/saicinpainting/training/losses/feature_matching.py b/third-party/lama/bin/saicinpainting/training/losses/feature_matching.py new file mode 100644 index 0000000000000000000000000000000000000000..c019895c9178817837d1a6773367b178a861dc61 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/losses/feature_matching.py @@ -0,0 +1,33 @@ +from typing import List + +import torch +import torch.nn.functional as F + + +def masked_l2_loss(pred, target, mask, weight_known, weight_missing): + per_pixel_l2 = F.mse_loss(pred, target, reduction='none') + pixel_weights = mask * weight_missing + (1 - mask) * weight_known + return (pixel_weights * per_pixel_l2).mean() + + +def masked_l1_loss(pred, target, mask, weight_known, weight_missing): + per_pixel_l1 = F.l1_loss(pred, target, reduction='none') + pixel_weights = mask * weight_missing + (1 - mask) * weight_known + return (pixel_weights * per_pixel_l1).mean() + + +def feature_matching_loss(fake_features: List[torch.Tensor], target_features: List[torch.Tensor], mask=None): + if mask is None: + res = torch.stack([F.mse_loss(fake_feat, target_feat) + for fake_feat, target_feat in zip(fake_features, target_features)]).mean() + else: + res = 0 + norm = 0 + for fake_feat, target_feat in zip(fake_features, target_features): + cur_mask = F.interpolate(mask, size=fake_feat.shape[-2:], mode='bilinear', align_corners=False) + error_weights = 1 - cur_mask + cur_val = ((fake_feat - target_feat).pow(2) * error_weights).mean() + res = res + cur_val + norm += 1 + res = res / norm + return res diff --git a/third-party/lama/bin/saicinpainting/training/losses/perceptual.py b/third-party/lama/bin/saicinpainting/training/losses/perceptual.py new file mode 100644 index 0000000000000000000000000000000000000000..8c055c2b327ce7943682af5c5f9394b9fcbec506 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/losses/perceptual.py @@ -0,0 +1,113 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision + +from models.ade20k import ModelBuilder +from saicinpainting.utils import check_and_warn_input_range + + +IMAGENET_MEAN = torch.FloatTensor([0.485, 0.456, 0.406])[None, :, None, None] +IMAGENET_STD = torch.FloatTensor([0.229, 0.224, 0.225])[None, :, None, None] + + +class PerceptualLoss(nn.Module): + def __init__(self, normalize_inputs=True): + super(PerceptualLoss, self).__init__() + + self.normalize_inputs = normalize_inputs + self.mean_ = IMAGENET_MEAN + self.std_ = IMAGENET_STD + + vgg = torchvision.models.vgg19(pretrained=True).features + vgg_avg_pooling = [] + + for weights in vgg.parameters(): + weights.requires_grad = False + + for module in vgg.modules(): + if module.__class__.__name__ == 'Sequential': + continue + elif module.__class__.__name__ == 'MaxPool2d': + vgg_avg_pooling.append(nn.AvgPool2d(kernel_size=2, stride=2, padding=0)) + else: + vgg_avg_pooling.append(module) + + self.vgg = nn.Sequential(*vgg_avg_pooling) + + def do_normalize_inputs(self, x): + return (x - self.mean_.to(x.device)) / self.std_.to(x.device) + + def partial_losses(self, input, target, mask=None): + check_and_warn_input_range(target, 0, 1, 'PerceptualLoss target in partial_losses') + + # we expect input and target to be in [0, 1] range + losses = [] + + if self.normalize_inputs: + features_input = self.do_normalize_inputs(input) + features_target = self.do_normalize_inputs(target) + else: + features_input = input + features_target = target + + for layer in self.vgg[:30]: + + features_input = layer(features_input) + features_target = layer(features_target) + + if layer.__class__.__name__ == 'ReLU': + loss = F.mse_loss(features_input, features_target, reduction='none') + + if mask is not None: + cur_mask = F.interpolate(mask, size=features_input.shape[-2:], + mode='bilinear', align_corners=False) + loss = loss * (1 - cur_mask) + + loss = loss.mean(dim=tuple(range(1, len(loss.shape)))) + losses.append(loss) + + return losses + + def forward(self, input, target, mask=None): + losses = self.partial_losses(input, target, mask=mask) + return torch.stack(losses).sum(dim=0) + + def get_global_features(self, input): + check_and_warn_input_range(input, 0, 1, 'PerceptualLoss input in get_global_features') + + if self.normalize_inputs: + features_input = self.do_normalize_inputs(input) + else: + features_input = input + + features_input = self.vgg(features_input) + return features_input + + +class ResNetPL(nn.Module): + def __init__(self, weight=1, + weights_path=None, arch_encoder='resnet50dilated', segmentation=True): + super().__init__() + self.impl = ModelBuilder.get_encoder(weights_path=weights_path, + arch_encoder=arch_encoder, + arch_decoder='ppm_deepsup', + fc_dim=2048, + segmentation=segmentation) + self.impl.eval() + for w in self.impl.parameters(): + w.requires_grad_(False) + + self.weight = weight + + def forward(self, pred, target): + pred = (pred - IMAGENET_MEAN.to(pred)) / IMAGENET_STD.to(pred) + target = (target - IMAGENET_MEAN.to(target)) / IMAGENET_STD.to(target) + + pred_feats = self.impl(pred, return_feature_maps=True) + target_feats = self.impl(target, return_feature_maps=True) + + result = torch.stack([F.mse_loss(cur_pred, cur_target) + for cur_pred, cur_target + in zip(pred_feats, target_feats)]).sum() * self.weight + return result diff --git a/third-party/lama/bin/saicinpainting/training/losses/segmentation.py b/third-party/lama/bin/saicinpainting/training/losses/segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..3d4a9f94eaae84722db584277dbbf9bc41ede357 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/losses/segmentation.py @@ -0,0 +1,43 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .constants import weights as constant_weights + + +class CrossEntropy2d(nn.Module): + def __init__(self, reduction="mean", ignore_label=255, weights=None, *args, **kwargs): + """ + weight (Tensor, optional): a manual rescaling weight given to each class. + If given, has to be a Tensor of size "nclasses" + """ + super(CrossEntropy2d, self).__init__() + self.reduction = reduction + self.ignore_label = ignore_label + self.weights = weights + if self.weights is not None: + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + self.weights = torch.FloatTensor(constant_weights[weights]).to(device) + + def forward(self, predict, target): + """ + Args: + predict:(n, c, h, w) + target:(n, 1, h, w) + """ + target = target.long() + assert not target.requires_grad + assert predict.dim() == 4, "{0}".format(predict.size()) + assert target.dim() == 4, "{0}".format(target.size()) + assert predict.size(0) == target.size(0), "{0} vs {1} ".format(predict.size(0), target.size(0)) + assert target.size(1) == 1, "{0}".format(target.size(1)) + assert predict.size(2) == target.size(2), "{0} vs {1} ".format(predict.size(2), target.size(2)) + assert predict.size(3) == target.size(3), "{0} vs {1} ".format(predict.size(3), target.size(3)) + target = target.squeeze(1) + n, c, h, w = predict.size() + target_mask = (target >= 0) * (target != self.ignore_label) + target = target[target_mask] + predict = predict.transpose(1, 2).transpose(2, 3).contiguous() + predict = predict[target_mask.view(n, h, w, 1).repeat(1, 1, 1, c)].view(-1, c) + loss = F.cross_entropy(predict, target, weight=self.weights, reduction=self.reduction) + return loss diff --git a/third-party/lama/bin/saicinpainting/training/losses/style_loss.py b/third-party/lama/bin/saicinpainting/training/losses/style_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..0bb42d7fbc5d17a47bec7365889868505f5fdfb5 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/losses/style_loss.py @@ -0,0 +1,155 @@ +import torch +import torch.nn as nn +import torchvision.models as models + + +class PerceptualLoss(nn.Module): + r""" + Perceptual loss, VGG-based + https://arxiv.org/abs/1603.08155 + https://github.com/dxyang/StyleTransfer/blob/master/utils.py + """ + + def __init__(self, weights=[1.0, 1.0, 1.0, 1.0, 1.0]): + super(PerceptualLoss, self).__init__() + self.add_module('vgg', VGG19()) + self.criterion = torch.nn.L1Loss() + self.weights = weights + + def __call__(self, x, y): + # Compute features + x_vgg, y_vgg = self.vgg(x), self.vgg(y) + + content_loss = 0.0 + content_loss += self.weights[0] * self.criterion(x_vgg['relu1_1'], y_vgg['relu1_1']) + content_loss += self.weights[1] * self.criterion(x_vgg['relu2_1'], y_vgg['relu2_1']) + content_loss += self.weights[2] * self.criterion(x_vgg['relu3_1'], y_vgg['relu3_1']) + content_loss += self.weights[3] * self.criterion(x_vgg['relu4_1'], y_vgg['relu4_1']) + content_loss += self.weights[4] * self.criterion(x_vgg['relu5_1'], y_vgg['relu5_1']) + + + return content_loss + + +class VGG19(torch.nn.Module): + def __init__(self): + super(VGG19, self).__init__() + features = models.vgg19(pretrained=True).features + self.relu1_1 = torch.nn.Sequential() + self.relu1_2 = torch.nn.Sequential() + + self.relu2_1 = torch.nn.Sequential() + self.relu2_2 = torch.nn.Sequential() + + self.relu3_1 = torch.nn.Sequential() + self.relu3_2 = torch.nn.Sequential() + self.relu3_3 = torch.nn.Sequential() + self.relu3_4 = torch.nn.Sequential() + + self.relu4_1 = torch.nn.Sequential() + self.relu4_2 = torch.nn.Sequential() + self.relu4_3 = torch.nn.Sequential() + self.relu4_4 = torch.nn.Sequential() + + self.relu5_1 = torch.nn.Sequential() + self.relu5_2 = torch.nn.Sequential() + self.relu5_3 = torch.nn.Sequential() + self.relu5_4 = torch.nn.Sequential() + + for x in range(2): + self.relu1_1.add_module(str(x), features[x]) + + for x in range(2, 4): + self.relu1_2.add_module(str(x), features[x]) + + for x in range(4, 7): + self.relu2_1.add_module(str(x), features[x]) + + for x in range(7, 9): + self.relu2_2.add_module(str(x), features[x]) + + for x in range(9, 12): + self.relu3_1.add_module(str(x), features[x]) + + for x in range(12, 14): + self.relu3_2.add_module(str(x), features[x]) + + for x in range(14, 16): + self.relu3_2.add_module(str(x), features[x]) + + for x in range(16, 18): + self.relu3_4.add_module(str(x), features[x]) + + for x in range(18, 21): + self.relu4_1.add_module(str(x), features[x]) + + for x in range(21, 23): + self.relu4_2.add_module(str(x), features[x]) + + for x in range(23, 25): + self.relu4_3.add_module(str(x), features[x]) + + for x in range(25, 27): + self.relu4_4.add_module(str(x), features[x]) + + for x in range(27, 30): + self.relu5_1.add_module(str(x), features[x]) + + for x in range(30, 32): + self.relu5_2.add_module(str(x), features[x]) + + for x in range(32, 34): + self.relu5_3.add_module(str(x), features[x]) + + for x in range(34, 36): + self.relu5_4.add_module(str(x), features[x]) + + # don't need the gradients, just want the features + for param in self.parameters(): + param.requires_grad = False + + def forward(self, x): + relu1_1 = self.relu1_1(x) + relu1_2 = self.relu1_2(relu1_1) + + relu2_1 = self.relu2_1(relu1_2) + relu2_2 = self.relu2_2(relu2_1) + + relu3_1 = self.relu3_1(relu2_2) + relu3_2 = self.relu3_2(relu3_1) + relu3_3 = self.relu3_3(relu3_2) + relu3_4 = self.relu3_4(relu3_3) + + relu4_1 = self.relu4_1(relu3_4) + relu4_2 = self.relu4_2(relu4_1) + relu4_3 = self.relu4_3(relu4_2) + relu4_4 = self.relu4_4(relu4_3) + + relu5_1 = self.relu5_1(relu4_4) + relu5_2 = self.relu5_2(relu5_1) + relu5_3 = self.relu5_3(relu5_2) + relu5_4 = self.relu5_4(relu5_3) + + out = { + 'relu1_1': relu1_1, + 'relu1_2': relu1_2, + + 'relu2_1': relu2_1, + 'relu2_2': relu2_2, + + 'relu3_1': relu3_1, + 'relu3_2': relu3_2, + 'relu3_3': relu3_3, + 'relu3_4': relu3_4, + + 'relu4_1': relu4_1, + 'relu4_2': relu4_2, + 'relu4_3': relu4_3, + 'relu4_4': relu4_4, + + 'relu5_1': relu5_1, + 'relu5_2': relu5_2, + 'relu5_3': relu5_3, + 'relu5_4': relu5_4, + } + return out diff --git a/third-party/lama/bin/saicinpainting/training/modules/__init__.py b/third-party/lama/bin/saicinpainting/training/modules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..82e1a9096a5bd8f3fb00e899d0239b078246cad4 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/modules/__init__.py @@ -0,0 +1,31 @@ +import logging + +from saicinpainting.training.modules.ffc import FFCResNetGenerator +from saicinpainting.training.modules.pix2pixhd import GlobalGenerator, MultiDilatedGlobalGenerator, \ + NLayerDiscriminator, MultidilatedNLayerDiscriminator + +def make_generator(config, kind, **kwargs): + logging.info(f'Make generator {kind}') + + if kind == 'pix2pixhd_multidilated': + return MultiDilatedGlobalGenerator(**kwargs) + + if kind == 'pix2pixhd_global': + return GlobalGenerator(**kwargs) + + if kind == 'ffc_resnet': + return FFCResNetGenerator(**kwargs) + + raise ValueError(f'Unknown generator kind {kind}') + + +def make_discriminator(kind, **kwargs): + logging.info(f'Make discriminator {kind}') + + if kind == 'pix2pixhd_nlayer_multidilated': + return MultidilatedNLayerDiscriminator(**kwargs) + + if kind == 'pix2pixhd_nlayer': + return NLayerDiscriminator(**kwargs) + + raise ValueError(f'Unknown discriminator kind {kind}') diff --git a/third-party/lama/bin/saicinpainting/training/modules/base.py b/third-party/lama/bin/saicinpainting/training/modules/base.py new file mode 100644 index 0000000000000000000000000000000000000000..a50c3fc7753a0bba64a5ab8c1ed64ff97e62313f --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/modules/base.py @@ -0,0 +1,80 @@ +import abc +from typing import Tuple, List + +import torch +import torch.nn as nn + +from saicinpainting.training.modules.depthwise_sep_conv import DepthWiseSeperableConv +from saicinpainting.training.modules.multidilated_conv import MultidilatedConv + + +class BaseDiscriminator(nn.Module): + @abc.abstractmethod + def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """ + Predict scores and get intermediate activations. Useful for feature matching loss + :return tuple (scores, list of intermediate activations) + """ + raise NotImplemented() + + +def get_conv_block_ctor(kind='default'): + if not isinstance(kind, str): + return kind + if kind == 'default': + return nn.Conv2d + if kind == 'depthwise': + return DepthWiseSeperableConv + if kind == 'multidilated': + return MultidilatedConv + raise ValueError(f'Unknown convolutional block kind {kind}') + + +def get_norm_layer(kind='bn'): + if not isinstance(kind, str): + return kind + if kind == 'bn': + return nn.BatchNorm2d + if kind == 'in': + return nn.InstanceNorm2d + raise ValueError(f'Unknown norm block kind {kind}') + + +def get_activation(kind='tanh'): + if kind == 'tanh': + return nn.Tanh() + if kind == 'sigmoid': + return nn.Sigmoid() + if kind is False: + return nn.Identity() + raise ValueError(f'Unknown activation kind {kind}') + + +class SimpleMultiStepGenerator(nn.Module): + def __init__(self, steps: List[nn.Module]): + super().__init__() + self.steps = nn.ModuleList(steps) + + def forward(self, x): + cur_in = x + outs = [] + for step in self.steps: + cur_out = step(cur_in) + outs.append(cur_out) + cur_in = torch.cat((cur_in, cur_out), dim=1) + return torch.cat(outs[::-1], dim=1) + +def deconv_factory(kind, ngf, mult, norm_layer, activation, max_features): + if kind == 'convtranspose': + return [nn.ConvTranspose2d(min(max_features, ngf * mult), + min(max_features, int(ngf * mult / 2)), + kernel_size=3, stride=2, padding=1, output_padding=1), + norm_layer(min(max_features, int(ngf * mult / 2))), activation] + elif kind == 'bilinear': + return [nn.Upsample(scale_factor=2, mode='bilinear'), + DepthWiseSeperableConv(min(max_features, ngf * mult), + min(max_features, int(ngf * mult / 2)), + kernel_size=3, stride=1, padding=1), + norm_layer(min(max_features, int(ngf * mult / 2))), activation] + else: + raise Exception(f"Invalid deconv kind: {kind}") \ No newline at end of file diff --git a/third-party/lama/bin/saicinpainting/training/modules/depthwise_sep_conv.py b/third-party/lama/bin/saicinpainting/training/modules/depthwise_sep_conv.py new file mode 100644 index 0000000000000000000000000000000000000000..83dd15c3df1d9f40baf0091a373fa224532c9ddd --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/modules/depthwise_sep_conv.py @@ -0,0 +1,17 @@ +import torch +import torch.nn as nn + +class DepthWiseSeperableConv(nn.Module): + def __init__(self, in_dim, out_dim, *args, **kwargs): + super().__init__() + if 'groups' in kwargs: + # ignoring groups for Depthwise Sep Conv + del kwargs['groups'] + + self.depthwise = nn.Conv2d(in_dim, in_dim, *args, groups=in_dim, **kwargs) + self.pointwise = nn.Conv2d(in_dim, out_dim, kernel_size=1) + + def forward(self, x): + out = self.depthwise(x) + out = self.pointwise(out) + return out \ No newline at end of file diff --git a/third-party/lama/bin/saicinpainting/training/modules/fake_fakes.py b/third-party/lama/bin/saicinpainting/training/modules/fake_fakes.py new file mode 100644 index 0000000000000000000000000000000000000000..45c4ad559cef2730b771a709197e00ae1c87683c --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/modules/fake_fakes.py @@ -0,0 +1,47 @@ +import torch +from kornia import SamplePadding +from kornia.augmentation import RandomAffine, CenterCrop + + +class FakeFakesGenerator: + def __init__(self, aug_proba=0.5, img_aug_degree=30, img_aug_translate=0.2): + self.grad_aug = RandomAffine(degrees=360, + translate=0.2, + padding_mode=SamplePadding.REFLECTION, + keepdim=False, + p=1) + self.img_aug = RandomAffine(degrees=img_aug_degree, + translate=img_aug_translate, + padding_mode=SamplePadding.REFLECTION, + keepdim=True, + p=1) + self.aug_proba = aug_proba + + def __call__(self, input_images, masks): + blend_masks = self._fill_masks_with_gradient(masks) + blend_target = self._make_blend_target(input_images) + result = input_images * (1 - blend_masks) + blend_target * blend_masks + return result, blend_masks + + def _make_blend_target(self, input_images): + batch_size = input_images.shape[0] + permuted = input_images[torch.randperm(batch_size)] + augmented = self.img_aug(input_images) + is_aug = (torch.rand(batch_size, device=input_images.device)[:, None, None, None] < self.aug_proba).float() + result = augmented * is_aug + permuted * (1 - is_aug) + return result + + def _fill_masks_with_gradient(self, masks): + batch_size, _, height, width = masks.shape + grad = torch.linspace(0, 1, steps=width * 2, device=masks.device, dtype=masks.dtype) \ + .view(1, 1, 1, -1).expand(batch_size, 1, height * 2, width * 2) + grad = self.grad_aug(grad) + grad = CenterCrop((height, width))(grad) + grad *= masks + + grad_for_min = grad + (1 - masks) * 10 + grad -= grad_for_min.view(batch_size, -1).min(-1).values[:, None, None, None] + grad /= grad.view(batch_size, -1).max(-1).values[:, None, None, None] + 1e-6 + grad.clamp_(min=0, max=1) + + return grad diff --git a/third-party/lama/bin/saicinpainting/training/modules/ffc.py b/third-party/lama/bin/saicinpainting/training/modules/ffc.py new file mode 100644 index 0000000000000000000000000000000000000000..2f8aeb1411fc1537916275fd3243706cc74b8d3c --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/modules/ffc.py @@ -0,0 +1,433 @@ +# Fast Fourier Convolution NeurIPS 2020 +# original implementation https://github.com/pkumivision/FFC/blob/main/model_zoo/ffc.py +# paper https://proceedings.neurips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +from saicinpainting.training.modules.base import get_activation, BaseDiscriminator +from saicinpainting.training.modules.spatial_transform import LearnableSpatialTransformWrapper +from saicinpainting.training.modules.squeeze_excitation import SELayer +from saicinpainting.utils import get_shape + + +class FFCSE_block(nn.Module): + + def __init__(self, channels, ratio_g): + super(FFCSE_block, self).__init__() + in_cg = int(channels * ratio_g) + in_cl = channels - in_cg + r = 16 + + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.conv1 = nn.Conv2d(channels, channels // r, + kernel_size=1, bias=True) + self.relu1 = nn.ReLU(inplace=True) + self.conv_a2l = None if in_cl == 0 else nn.Conv2d( + channels // r, in_cl, kernel_size=1, bias=True) + self.conv_a2g = None if in_cg == 0 else nn.Conv2d( + channels // r, in_cg, kernel_size=1, bias=True) + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + x = x if type(x) is tuple else (x, 0) + id_l, id_g = x + + x = id_l if type(id_g) is int else torch.cat([id_l, id_g], dim=1) + x = self.avgpool(x) + x = self.relu1(self.conv1(x)) + + x_l = 0 if self.conv_a2l is None else id_l * \ + self.sigmoid(self.conv_a2l(x)) + x_g = 0 if self.conv_a2g is None else id_g * \ + self.sigmoid(self.conv_a2g(x)) + return x_l, x_g + + +class FourierUnit(nn.Module): + + def __init__(self, in_channels, out_channels, groups=1, spatial_scale_factor=None, spatial_scale_mode='bilinear', + spectral_pos_encoding=False, use_se=False, se_kwargs=None, ffc3d=False, fft_norm='ortho'): + # bn_layer not used + super(FourierUnit, self).__init__() + self.groups = groups + + self.conv_layer = torch.nn.Conv2d(in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0), + out_channels=out_channels * 2, + kernel_size=1, stride=1, padding=0, groups=self.groups, bias=False) + self.bn = torch.nn.BatchNorm2d(out_channels * 2) + self.relu = torch.nn.ReLU(inplace=True) + + # squeeze and excitation block + self.use_se = use_se + if use_se: + if se_kwargs is None: + se_kwargs = {} + self.se = SELayer(self.conv_layer.in_channels, **se_kwargs) + + self.spatial_scale_factor = spatial_scale_factor + self.spatial_scale_mode = spatial_scale_mode + self.spectral_pos_encoding = spectral_pos_encoding + self.ffc3d = ffc3d + self.fft_norm = fft_norm + + def forward(self, x): + batch = x.shape[0] + + if self.spatial_scale_factor is not None: + orig_size = x.shape[-2:] + x = F.interpolate(x, scale_factor=self.spatial_scale_factor, mode=self.spatial_scale_mode, align_corners=False) + + r_size = x.size() + # (batch, c, h, w/2+1, 2) + fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1) + ffted = torch.fft.rfftn(x, dim=fft_dim, norm=self.fft_norm) + ffted = torch.stack((ffted.real, ffted.imag), dim=-1) + ffted = ffted.permute(0, 1, 4, 2, 3).contiguous() # (batch, c, 2, h, w/2+1) + ffted = ffted.view((batch, -1,) + ffted.size()[3:]) + + if self.spectral_pos_encoding: + height, width = ffted.shape[-2:] + coords_vert = torch.linspace(0, 1, height)[None, None, :, None].expand(batch, 1, height, width).to(ffted) + coords_hor = torch.linspace(0, 1, width)[None, None, None, :].expand(batch, 1, height, width).to(ffted) + ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1) + + if self.use_se: + ffted = self.se(ffted) + + ffted = self.conv_layer(ffted) # (batch, c*2, h, w/2+1) + ffted = self.relu(self.bn(ffted)) + + ffted = ffted.view((batch, -1, 2,) + ffted.size()[2:]).permute( + 0, 1, 3, 4, 2).contiguous() # (batch,c, t, h, w/2+1, 2) + ffted = torch.complex(ffted[..., 0], ffted[..., 1]) + + ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:] + output = torch.fft.irfftn(ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm) + + if self.spatial_scale_factor is not None: + output = F.interpolate(output, size=orig_size, mode=self.spatial_scale_mode, align_corners=False) + + return output + + +class SpectralTransform(nn.Module): + + def __init__(self, in_channels, out_channels, stride=1, groups=1, enable_lfu=True, **fu_kwargs): + # bn_layer not used + super(SpectralTransform, self).__init__() + self.enable_lfu = enable_lfu + if stride == 2: + self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2) + else: + self.downsample = nn.Identity() + + self.stride = stride + self.conv1 = nn.Sequential( + nn.Conv2d(in_channels, out_channels // + 2, kernel_size=1, groups=groups, bias=False), + nn.BatchNorm2d(out_channels // 2), + nn.ReLU(inplace=True) + ) + self.fu = FourierUnit( + out_channels // 2, out_channels // 2, groups, **fu_kwargs) + if self.enable_lfu: + self.lfu = FourierUnit( + out_channels // 2, out_channels // 2, groups) + self.conv2 = torch.nn.Conv2d( + out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False) + + def forward(self, x): + + x = self.downsample(x) + x = self.conv1(x) + output = self.fu(x) + + if self.enable_lfu: + n, c, h, w = x.shape + split_no = 2 + split_s = h // split_no + xs = torch.cat(torch.split( + x[:, :c // 4], split_s, dim=-2), dim=1).contiguous() + xs = torch.cat(torch.split(xs, split_s, dim=-1), + dim=1).contiguous() + xs = self.lfu(xs) + xs = xs.repeat(1, 1, split_no, split_no).contiguous() + else: + xs = 0 + + output = self.conv2(x + output + xs) + + return output + + +class FFC(nn.Module): + + def __init__(self, in_channels, out_channels, kernel_size, + ratio_gin, ratio_gout, stride=1, padding=0, + dilation=1, groups=1, bias=False, enable_lfu=True, + padding_type='reflect', gated=False, **spectral_kwargs): + super(FFC, self).__init__() + + assert stride == 1 or stride == 2, "Stride should be 1 or 2." + self.stride = stride + + in_cg = int(in_channels * ratio_gin) + in_cl = in_channels - in_cg + out_cg = int(out_channels * ratio_gout) + out_cl = out_channels - out_cg + #groups_g = 1 if groups == 1 else int(groups * ratio_gout) + #groups_l = 1 if groups == 1 else groups - groups_g + + self.ratio_gin = ratio_gin + self.ratio_gout = ratio_gout + self.global_in_num = in_cg + + module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d + self.convl2l = module(in_cl, out_cl, kernel_size, + stride, padding, dilation, groups, bias, padding_mode=padding_type) + module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d + self.convl2g = module(in_cl, out_cg, kernel_size, + stride, padding, dilation, groups, bias, padding_mode=padding_type) + module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d + self.convg2l = module(in_cg, out_cl, kernel_size, + stride, padding, dilation, groups, bias, padding_mode=padding_type) + module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform + self.convg2g = module( + in_cg, out_cg, stride, 1 if groups == 1 else groups // 2, enable_lfu, **spectral_kwargs) + + self.gated = gated + module = nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d + self.gate = module(in_channels, 2, 1) + + def forward(self, x): + x_l, x_g = x if type(x) is tuple else (x, 0) + out_xl, out_xg = 0, 0 + + if self.gated: + total_input_parts = [x_l] + if torch.is_tensor(x_g): + total_input_parts.append(x_g) + total_input = torch.cat(total_input_parts, dim=1) + + gates = torch.sigmoid(self.gate(total_input)) + g2l_gate, l2g_gate = gates.chunk(2, dim=1) + else: + g2l_gate, l2g_gate = 1, 1 + + if self.ratio_gout != 1: + out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate + if self.ratio_gout != 0: + out_xg = self.convl2g(x_l) * l2g_gate + self.convg2g(x_g) + + return out_xl, out_xg + + +class FFC_BN_ACT(nn.Module): + + def __init__(self, in_channels, out_channels, + kernel_size, ratio_gin, ratio_gout, + stride=1, padding=0, dilation=1, groups=1, bias=False, + norm_layer=nn.BatchNorm2d, activation_layer=nn.Identity, + padding_type='reflect', + enable_lfu=True, **kwargs): + super(FFC_BN_ACT, self).__init__() + self.ffc = FFC(in_channels, out_channels, kernel_size, + ratio_gin, ratio_gout, stride, padding, dilation, + groups, bias, enable_lfu, padding_type=padding_type, **kwargs) + lnorm = nn.Identity if ratio_gout == 1 else norm_layer + gnorm = nn.Identity if ratio_gout == 0 else norm_layer + global_channels = int(out_channels * ratio_gout) + self.bn_l = lnorm(out_channels - global_channels) + self.bn_g = gnorm(global_channels) + + lact = nn.Identity if ratio_gout == 1 else activation_layer + gact = nn.Identity if ratio_gout == 0 else activation_layer + self.act_l = lact(inplace=True) + self.act_g = gact(inplace=True) + + def forward(self, x): + x_l, x_g = self.ffc(x) + x_l = self.act_l(self.bn_l(x_l)) + x_g = self.act_g(self.bn_g(x_g)) + return x_l, x_g + + +class FFCResnetBlock(nn.Module): + def __init__(self, dim, padding_type, norm_layer, activation_layer=nn.ReLU, dilation=1, + spatial_transform_kwargs=None, inline=False, **conv_kwargs): + super().__init__() + self.conv1 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation, + norm_layer=norm_layer, + activation_layer=activation_layer, + padding_type=padding_type, + **conv_kwargs) + self.conv2 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation, + norm_layer=norm_layer, + activation_layer=activation_layer, + padding_type=padding_type, + **conv_kwargs) + if spatial_transform_kwargs is not None: + self.conv1 = LearnableSpatialTransformWrapper(self.conv1, **spatial_transform_kwargs) + self.conv2 = LearnableSpatialTransformWrapper(self.conv2, **spatial_transform_kwargs) + self.inline = inline + + def forward(self, x): + if self.inline: + x_l, x_g = x[:, :-self.conv1.ffc.global_in_num], x[:, -self.conv1.ffc.global_in_num:] + else: + x_l, x_g = x if type(x) is tuple else (x, 0) + + id_l, id_g = x_l, x_g + + x_l, x_g = self.conv1((x_l, x_g)) + x_l, x_g = self.conv2((x_l, x_g)) + + x_l, x_g = id_l + x_l, id_g + x_g + out = x_l, x_g + if self.inline: + out = torch.cat(out, dim=1) + return out + + +class ConcatTupleLayer(nn.Module): + def forward(self, x): + assert isinstance(x, tuple) + x_l, x_g = x + assert torch.is_tensor(x_l) or torch.is_tensor(x_g) + if not torch.is_tensor(x_g): + return x_l + return torch.cat(x, dim=1) + + +class FFCResNetGenerator(nn.Module): + def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d, + padding_type='reflect', activation_layer=nn.ReLU, + up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), + init_conv_kwargs={}, downsample_conv_kwargs={}, resnet_conv_kwargs={}, + spatial_transform_layers=None, spatial_transform_kwargs={}, + add_out_act=True, max_features=1024, out_ffc=False, out_ffc_kwargs={}): + assert (n_blocks >= 0) + super().__init__() + + model = [nn.ReflectionPad2d(3), + FFC_BN_ACT(input_nc, ngf, kernel_size=7, padding=0, norm_layer=norm_layer, + activation_layer=activation_layer, **init_conv_kwargs)] + + ### downsample + for i in range(n_downsampling): + mult = 2 ** i + if i == n_downsampling - 1: + cur_conv_kwargs = dict(downsample_conv_kwargs) + cur_conv_kwargs['ratio_gout'] = resnet_conv_kwargs.get('ratio_gin', 0) + else: + cur_conv_kwargs = downsample_conv_kwargs + model += [FFC_BN_ACT(min(max_features, ngf * mult), + min(max_features, ngf * mult * 2), + kernel_size=3, stride=2, padding=1, + norm_layer=norm_layer, + activation_layer=activation_layer, + **cur_conv_kwargs)] + + mult = 2 ** n_downsampling + feats_num_bottleneck = min(max_features, ngf * mult) + + ### resnet blocks + for i in range(n_blocks): + cur_resblock = FFCResnetBlock(feats_num_bottleneck, padding_type=padding_type, activation_layer=activation_layer, + norm_layer=norm_layer, **resnet_conv_kwargs) + if spatial_transform_layers is not None and i in spatial_transform_layers: + cur_resblock = LearnableSpatialTransformWrapper(cur_resblock, **spatial_transform_kwargs) + model += [cur_resblock] + + model += [ConcatTupleLayer()] + + ### upsample + for i in range(n_downsampling): + mult = 2 ** (n_downsampling - i) + model += [nn.ConvTranspose2d(min(max_features, ngf * mult), + min(max_features, int(ngf * mult / 2)), + kernel_size=3, stride=2, padding=1, output_padding=1), + up_norm_layer(min(max_features, int(ngf * mult / 2))), + up_activation] + + if out_ffc: + model += [FFCResnetBlock(ngf, padding_type=padding_type, activation_layer=activation_layer, + norm_layer=norm_layer, inline=True, **out_ffc_kwargs)] + + model += [nn.ReflectionPad2d(3), + nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] + if add_out_act: + model.append(get_activation('tanh' if add_out_act is True else add_out_act)) + self.model = nn.Sequential(*model) + + def forward(self, input): + return self.model(input) + + +class FFCNLayerDiscriminator(BaseDiscriminator): + def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, max_features=512, + init_conv_kwargs={}, conv_kwargs={}): + super().__init__() + self.n_layers = n_layers + + def _act_ctor(inplace=True): + return nn.LeakyReLU(negative_slope=0.2, inplace=inplace) + + kw = 3 + padw = int(np.ceil((kw-1.0)/2)) + sequence = [[FFC_BN_ACT(input_nc, ndf, kernel_size=kw, padding=padw, norm_layer=norm_layer, + activation_layer=_act_ctor, **init_conv_kwargs)]] + + nf = ndf + for n in range(1, n_layers): + nf_prev = nf + nf = min(nf * 2, max_features) + + cur_model = [ + FFC_BN_ACT(nf_prev, nf, + kernel_size=kw, stride=2, padding=padw, + norm_layer=norm_layer, + activation_layer=_act_ctor, + **conv_kwargs) + ] + sequence.append(cur_model) + + nf_prev = nf + nf = min(nf * 2, 512) + + cur_model = [ + FFC_BN_ACT(nf_prev, nf, + kernel_size=kw, stride=1, padding=padw, + norm_layer=norm_layer, + activation_layer=lambda *args, **kwargs: nn.LeakyReLU(*args, negative_slope=0.2, **kwargs), + **conv_kwargs), + ConcatTupleLayer() + ] + sequence.append(cur_model) + + sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]] + + for n in range(len(sequence)): + setattr(self, 'model'+str(n), nn.Sequential(*sequence[n])) + + def get_all_activations(self, x): + res = [x] + for n in range(self.n_layers + 2): + model = getattr(self, 'model' + str(n)) + res.append(model(res[-1])) + return res[1:] + + def forward(self, x): + act = self.get_all_activations(x) + feats = [] + for out in act[:-1]: + if isinstance(out, tuple): + if torch.is_tensor(out[1]): + out = torch.cat(out, dim=1) + else: + out = out[0] + feats.append(out) + return act[-1], feats diff --git a/third-party/lama/bin/saicinpainting/training/modules/multidilated_conv.py b/third-party/lama/bin/saicinpainting/training/modules/multidilated_conv.py new file mode 100644 index 0000000000000000000000000000000000000000..d267ee2aa5eb84b6a9291d0eaaff322c6c2802d0 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/modules/multidilated_conv.py @@ -0,0 +1,98 @@ +import torch +import torch.nn as nn +import random +from saicinpainting.training.modules.depthwise_sep_conv import DepthWiseSeperableConv + +class MultidilatedConv(nn.Module): + def __init__(self, in_dim, out_dim, kernel_size, dilation_num=3, comb_mode='sum', equal_dim=True, + shared_weights=False, padding=1, min_dilation=1, shuffle_in_channels=False, use_depthwise=False, **kwargs): + super().__init__() + convs = [] + self.equal_dim = equal_dim + assert comb_mode in ('cat_out', 'sum', 'cat_in', 'cat_both'), comb_mode + if comb_mode in ('cat_out', 'cat_both'): + self.cat_out = True + if equal_dim: + assert out_dim % dilation_num == 0 + out_dims = [out_dim // dilation_num] * dilation_num + self.index = sum([[i + j * (out_dims[0]) for j in range(dilation_num)] for i in range(out_dims[0])], []) + else: + out_dims = [out_dim // 2 ** (i + 1) for i in range(dilation_num - 1)] + out_dims.append(out_dim - sum(out_dims)) + index = [] + starts = [0] + out_dims[:-1] + lengths = [out_dims[i] // out_dims[-1] for i in range(dilation_num)] + for i in range(out_dims[-1]): + for j in range(dilation_num): + index += list(range(starts[j], starts[j] + lengths[j])) + starts[j] += lengths[j] + self.index = index + assert(len(index) == out_dim) + self.out_dims = out_dims + else: + self.cat_out = False + self.out_dims = [out_dim] * dilation_num + + if comb_mode in ('cat_in', 'cat_both'): + if equal_dim: + assert in_dim % dilation_num == 0 + in_dims = [in_dim // dilation_num] * dilation_num + else: + in_dims = [in_dim // 2 ** (i + 1) for i in range(dilation_num - 1)] + in_dims.append(in_dim - sum(in_dims)) + self.in_dims = in_dims + self.cat_in = True + else: + self.cat_in = False + self.in_dims = [in_dim] * dilation_num + + conv_type = DepthWiseSeperableConv if use_depthwise else nn.Conv2d + dilation = min_dilation + for i in range(dilation_num): + if isinstance(padding, int): + cur_padding = padding * dilation + else: + cur_padding = padding[i] + convs.append(conv_type( + self.in_dims[i], self.out_dims[i], kernel_size, padding=cur_padding, dilation=dilation, **kwargs + )) + if i > 0 and shared_weights: + convs[-1].weight = convs[0].weight + convs[-1].bias = convs[0].bias + dilation *= 2 + self.convs = nn.ModuleList(convs) + + self.shuffle_in_channels = shuffle_in_channels + if self.shuffle_in_channels: + # shuffle list as shuffling of tensors is nondeterministic + in_channels_permute = list(range(in_dim)) + random.shuffle(in_channels_permute) + # save as buffer so it is saved and loaded with checkpoint + self.register_buffer('in_channels_permute', torch.tensor(in_channels_permute)) + + def forward(self, x): + if self.shuffle_in_channels: + x = x[:, self.in_channels_permute] + + outs = [] + if self.cat_in: + if self.equal_dim: + x = x.chunk(len(self.convs), dim=1) + else: + new_x = [] + start = 0 + for dim in self.in_dims: + new_x.append(x[:, start:start+dim]) + start += dim + x = new_x + for i, conv in enumerate(self.convs): + if self.cat_in: + input = x[i] + else: + input = x + outs.append(conv(input)) + if self.cat_out: + out = torch.cat(outs, dim=1)[:, self.index] + else: + out = sum(outs) + return out diff --git a/third-party/lama/bin/saicinpainting/training/modules/multiscale.py b/third-party/lama/bin/saicinpainting/training/modules/multiscale.py new file mode 100644 index 0000000000000000000000000000000000000000..65f0a54925593e9da8106bfc6d65a4098ce001d7 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/modules/multiscale.py @@ -0,0 +1,244 @@ +from typing import List, Tuple, Union, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from saicinpainting.training.modules.base import get_conv_block_ctor, get_activation +from saicinpainting.training.modules.pix2pixhd import ResnetBlock + + +class ResNetHead(nn.Module): + def __init__(self, input_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d, + padding_type='reflect', conv_kind='default', activation=nn.ReLU(True)): + assert (n_blocks >= 0) + super(ResNetHead, self).__init__() + + conv_layer = get_conv_block_ctor(conv_kind) + + model = [nn.ReflectionPad2d(3), + conv_layer(input_nc, ngf, kernel_size=7, padding=0), + norm_layer(ngf), + activation] + + ### downsample + for i in range(n_downsampling): + mult = 2 ** i + model += [conv_layer(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1), + norm_layer(ngf * mult * 2), + activation] + + mult = 2 ** n_downsampling + + ### resnet blocks + for i in range(n_blocks): + model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer, + conv_kind=conv_kind)] + + self.model = nn.Sequential(*model) + + def forward(self, input): + return self.model(input) + + +class ResNetTail(nn.Module): + def __init__(self, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d, + padding_type='reflect', conv_kind='default', activation=nn.ReLU(True), + up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), add_out_act=False, out_extra_layers_n=0, + add_in_proj=None): + assert (n_blocks >= 0) + super(ResNetTail, self).__init__() + + mult = 2 ** n_downsampling + + model = [] + + if add_in_proj is not None: + model.append(nn.Conv2d(add_in_proj, ngf * mult, kernel_size=1)) + + ### resnet blocks + for i in range(n_blocks): + model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer, + conv_kind=conv_kind)] + + ### upsample + for i in range(n_downsampling): + mult = 2 ** (n_downsampling - i) + model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1, + output_padding=1), + up_norm_layer(int(ngf * mult / 2)), + up_activation] + self.model = nn.Sequential(*model) + + out_layers = [] + for _ in range(out_extra_layers_n): + out_layers += [nn.Conv2d(ngf, ngf, kernel_size=1, padding=0), + up_norm_layer(ngf), + up_activation] + out_layers += [nn.ReflectionPad2d(3), + nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] + + if add_out_act: + out_layers.append(get_activation('tanh' if add_out_act is True else add_out_act)) + + self.out_proj = nn.Sequential(*out_layers) + + def forward(self, input, return_last_act=False): + features = self.model(input) + out = self.out_proj(features) + if return_last_act: + return out, features + else: + return out + + +class MultiscaleResNet(nn.Module): + def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=2, n_blocks_head=2, n_blocks_tail=6, n_scales=3, + norm_layer=nn.BatchNorm2d, padding_type='reflect', conv_kind='default', activation=nn.ReLU(True), + up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), add_out_act=False, out_extra_layers_n=0, + out_cumulative=False, return_only_hr=False): + super().__init__() + + self.heads = nn.ModuleList([ResNetHead(input_nc, ngf=ngf, n_downsampling=n_downsampling, + n_blocks=n_blocks_head, norm_layer=norm_layer, padding_type=padding_type, + conv_kind=conv_kind, activation=activation) + for i in range(n_scales)]) + tail_in_feats = ngf * (2 ** n_downsampling) + ngf + self.tails = nn.ModuleList([ResNetTail(output_nc, + ngf=ngf, n_downsampling=n_downsampling, + n_blocks=n_blocks_tail, norm_layer=norm_layer, padding_type=padding_type, + conv_kind=conv_kind, activation=activation, up_norm_layer=up_norm_layer, + up_activation=up_activation, add_out_act=add_out_act, + out_extra_layers_n=out_extra_layers_n, + add_in_proj=None if (i == n_scales - 1) else tail_in_feats) + for i in range(n_scales)]) + + self.out_cumulative = out_cumulative + self.return_only_hr = return_only_hr + + @property + def num_scales(self): + return len(self.heads) + + def forward(self, ms_inputs: List[torch.Tensor], smallest_scales_num: Optional[int] = None) \ + -> Union[torch.Tensor, List[torch.Tensor]]: + """ + :param ms_inputs: List of inputs of different resolutions from HR to LR + :param smallest_scales_num: int or None, number of smallest scales to take at input + :return: Depending on return_only_hr: + True: Only the most HR output + False: List of outputs of different resolutions from HR to LR + """ + if smallest_scales_num is None: + assert len(self.heads) == len(ms_inputs), (len(self.heads), len(ms_inputs), smallest_scales_num) + smallest_scales_num = len(self.heads) + else: + assert smallest_scales_num == len(ms_inputs) <= len(self.heads), (len(self.heads), len(ms_inputs), smallest_scales_num) + + cur_heads = self.heads[-smallest_scales_num:] + ms_features = [cur_head(cur_inp) for cur_head, cur_inp in zip(cur_heads, ms_inputs)] + + all_outputs = [] + prev_tail_features = None + for i in range(len(ms_features)): + scale_i = -i - 1 + + cur_tail_input = ms_features[-i - 1] + if prev_tail_features is not None: + if prev_tail_features.shape != cur_tail_input.shape: + prev_tail_features = F.interpolate(prev_tail_features, size=cur_tail_input.shape[2:], + mode='bilinear', align_corners=False) + cur_tail_input = torch.cat((cur_tail_input, prev_tail_features), dim=1) + + cur_out, cur_tail_feats = self.tails[scale_i](cur_tail_input, return_last_act=True) + + prev_tail_features = cur_tail_feats + all_outputs.append(cur_out) + + if self.out_cumulative: + all_outputs_cum = [all_outputs[0]] + for i in range(1, len(ms_features)): + cur_out = all_outputs[i] + cur_out_cum = cur_out + F.interpolate(all_outputs_cum[-1], size=cur_out.shape[2:], + mode='bilinear', align_corners=False) + all_outputs_cum.append(cur_out_cum) + all_outputs = all_outputs_cum + + if self.return_only_hr: + return all_outputs[-1] + else: + return all_outputs[::-1] + + +class MultiscaleDiscriminatorSimple(nn.Module): + def __init__(self, ms_impl): + super().__init__() + self.ms_impl = nn.ModuleList(ms_impl) + + @property + def num_scales(self): + return len(self.ms_impl) + + def forward(self, ms_inputs: List[torch.Tensor], smallest_scales_num: Optional[int] = None) \ + -> List[Tuple[torch.Tensor, List[torch.Tensor]]]: + """ + :param ms_inputs: List of inputs of different resolutions from HR to LR + :param smallest_scales_num: int or None, number of smallest scales to take at input + :return: List of pairs (prediction, features) for different resolutions from HR to LR + """ + if smallest_scales_num is None: + assert len(self.ms_impl) == len(ms_inputs), (len(self.ms_impl), len(ms_inputs), smallest_scales_num) + smallest_scales_num = len(self.heads) + else: + assert smallest_scales_num == len(ms_inputs) <= len(self.ms_impl), \ + (len(self.ms_impl), len(ms_inputs), smallest_scales_num) + + return [cur_discr(cur_input) for cur_discr, cur_input in zip(self.ms_impl[-smallest_scales_num:], ms_inputs)] + + +class SingleToMultiScaleInputMixin: + def forward(self, x: torch.Tensor) -> List: + orig_height, orig_width = x.shape[2:] + factors = [2 ** i for i in range(self.num_scales)] + ms_inputs = [F.interpolate(x, size=(orig_height // f, orig_width // f), mode='bilinear', align_corners=False) + for f in factors] + return super().forward(ms_inputs) + + +class GeneratorMultiToSingleOutputMixin: + def forward(self, x): + return super().forward(x)[0] + + +class DiscriminatorMultiToSingleOutputMixin: + def forward(self, x): + out_feat_tuples = super().forward(x) + return out_feat_tuples[0][0], [f for _, flist in out_feat_tuples for f in flist] + + +class DiscriminatorMultiToSingleOutputStackedMixin: + def __init__(self, *args, return_feats_only_levels=None, **kwargs): + super().__init__(*args, **kwargs) + self.return_feats_only_levels = return_feats_only_levels + + def forward(self, x): + out_feat_tuples = super().forward(x) + outs = [out for out, _ in out_feat_tuples] + scaled_outs = [outs[0]] + [F.interpolate(cur_out, size=outs[0].shape[-2:], + mode='bilinear', align_corners=False) + for cur_out in outs[1:]] + out = torch.cat(scaled_outs, dim=1) + if self.return_feats_only_levels is not None: + feat_lists = [out_feat_tuples[i][1] for i in self.return_feats_only_levels] + else: + feat_lists = [flist for _, flist in out_feat_tuples] + feats = [f for flist in feat_lists for f in flist] + return out, feats + + +class MultiscaleDiscrSingleInput(SingleToMultiScaleInputMixin, DiscriminatorMultiToSingleOutputStackedMixin, MultiscaleDiscriminatorSimple): + pass + + +class MultiscaleResNetSingle(GeneratorMultiToSingleOutputMixin, SingleToMultiScaleInputMixin, MultiscaleResNet): + pass diff --git a/third-party/lama/bin/saicinpainting/training/modules/pix2pixhd.py b/third-party/lama/bin/saicinpainting/training/modules/pix2pixhd.py new file mode 100644 index 0000000000000000000000000000000000000000..08c6afd777a88cd232592acbbf0ef25db8d43217 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/modules/pix2pixhd.py @@ -0,0 +1,669 @@ +# original: https://github.com/NVIDIA/pix2pixHD/blob/master/models/networks.py +import collections +from functools import partial +import functools +import logging +from collections import defaultdict + +import numpy as np +import torch.nn as nn + +from saicinpainting.training.modules.base import BaseDiscriminator, deconv_factory, get_conv_block_ctor, get_norm_layer, get_activation +from saicinpainting.training.modules.ffc import FFCResnetBlock +from saicinpainting.training.modules.multidilated_conv import MultidilatedConv + +class DotDict(defaultdict): + # https://stackoverflow.com/questions/2352181/how-to-use-a-dot-to-access-members-of-dictionary + """dot.notation access to dictionary attributes""" + __getattr__ = defaultdict.get + __setattr__ = defaultdict.__setitem__ + __delattr__ = defaultdict.__delitem__ + +class Identity(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x): + return x + + +class ResnetBlock(nn.Module): + def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False, conv_kind='default', + dilation=1, in_dim=None, groups=1, second_dilation=None): + super(ResnetBlock, self).__init__() + self.in_dim = in_dim + self.dim = dim + if second_dilation is None: + second_dilation = dilation + self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout, + conv_kind=conv_kind, dilation=dilation, in_dim=in_dim, groups=groups, + second_dilation=second_dilation) + + if self.in_dim is not None: + self.input_conv = nn.Conv2d(in_dim, dim, 1) + + self.out_channnels = dim + + def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout, conv_kind='default', + dilation=1, in_dim=None, groups=1, second_dilation=1): + conv_layer = get_conv_block_ctor(conv_kind) + + conv_block = [] + p = 0 + if padding_type == 'reflect': + conv_block += [nn.ReflectionPad2d(dilation)] + elif padding_type == 'replicate': + conv_block += [nn.ReplicationPad2d(dilation)] + elif padding_type == 'zero': + p = dilation + else: + raise NotImplementedError('padding [%s] is not implemented' % padding_type) + + if in_dim is None: + in_dim = dim + + conv_block += [conv_layer(in_dim, dim, kernel_size=3, padding=p, dilation=dilation), + norm_layer(dim), + activation] + if use_dropout: + conv_block += [nn.Dropout(0.5)] + + p = 0 + if padding_type == 'reflect': + conv_block += [nn.ReflectionPad2d(second_dilation)] + elif padding_type == 'replicate': + conv_block += [nn.ReplicationPad2d(second_dilation)] + elif padding_type == 'zero': + p = second_dilation + else: + raise NotImplementedError('padding [%s] is not implemented' % padding_type) + conv_block += [conv_layer(dim, dim, kernel_size=3, padding=p, dilation=second_dilation, groups=groups), + norm_layer(dim)] + + return nn.Sequential(*conv_block) + + def forward(self, x): + x_before = x + if self.in_dim is not None: + x = self.input_conv(x) + out = x + self.conv_block(x_before) + return out + +class ResnetBlock5x5(nn.Module): + def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False, conv_kind='default', + dilation=1, in_dim=None, groups=1, second_dilation=None): + super(ResnetBlock5x5, self).__init__() + self.in_dim = in_dim + self.dim = dim + if second_dilation is None: + second_dilation = dilation + self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout, + conv_kind=conv_kind, dilation=dilation, in_dim=in_dim, groups=groups, + second_dilation=second_dilation) + + if self.in_dim is not None: + self.input_conv = nn.Conv2d(in_dim, dim, 1) + + self.out_channnels = dim + + def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout, conv_kind='default', + dilation=1, in_dim=None, groups=1, second_dilation=1): + conv_layer = get_conv_block_ctor(conv_kind) + + conv_block = [] + p = 0 + if padding_type == 'reflect': + conv_block += [nn.ReflectionPad2d(dilation * 2)] + elif padding_type == 'replicate': + conv_block += [nn.ReplicationPad2d(dilation * 2)] + elif padding_type == 'zero': + p = dilation * 2 + else: + raise NotImplementedError('padding [%s] is not implemented' % padding_type) + + if in_dim is None: + in_dim = dim + + conv_block += [conv_layer(in_dim, dim, kernel_size=5, padding=p, dilation=dilation), + norm_layer(dim), + activation] + if use_dropout: + conv_block += [nn.Dropout(0.5)] + + p = 0 + if padding_type == 'reflect': + conv_block += [nn.ReflectionPad2d(second_dilation * 2)] + elif padding_type == 'replicate': + conv_block += [nn.ReplicationPad2d(second_dilation * 2)] + elif padding_type == 'zero': + p = second_dilation * 2 + else: + raise NotImplementedError('padding [%s] is not implemented' % padding_type) + conv_block += [conv_layer(dim, dim, kernel_size=5, padding=p, dilation=second_dilation, groups=groups), + norm_layer(dim)] + + return nn.Sequential(*conv_block) + + def forward(self, x): + x_before = x + if self.in_dim is not None: + x = self.input_conv(x) + out = x + self.conv_block(x_before) + return out + + +class MultidilatedResnetBlock(nn.Module): + def __init__(self, dim, padding_type, conv_layer, norm_layer, activation=nn.ReLU(True), use_dropout=False): + super().__init__() + self.conv_block = self.build_conv_block(dim, padding_type, conv_layer, norm_layer, activation, use_dropout) + + def build_conv_block(self, dim, padding_type, conv_layer, norm_layer, activation, use_dropout, dilation=1): + conv_block = [] + conv_block += [conv_layer(dim, dim, kernel_size=3, padding_mode=padding_type), + norm_layer(dim), + activation] + if use_dropout: + conv_block += [nn.Dropout(0.5)] + + conv_block += [conv_layer(dim, dim, kernel_size=3, padding_mode=padding_type), + norm_layer(dim)] + + return nn.Sequential(*conv_block) + + def forward(self, x): + out = x + self.conv_block(x) + return out + + +class MultiDilatedGlobalGenerator(nn.Module): + def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, + n_blocks=3, norm_layer=nn.BatchNorm2d, + padding_type='reflect', conv_kind='default', + deconv_kind='convtranspose', activation=nn.ReLU(True), + up_norm_layer=nn.BatchNorm2d, affine=None, up_activation=nn.ReLU(True), + add_out_act=True, max_features=1024, multidilation_kwargs={}, + ffc_positions=None, ffc_kwargs={}): + assert (n_blocks >= 0) + super().__init__() + + conv_layer = get_conv_block_ctor(conv_kind) + resnet_conv_layer = functools.partial(get_conv_block_ctor('multidilated'), **multidilation_kwargs) + norm_layer = get_norm_layer(norm_layer) + if affine is not None: + norm_layer = partial(norm_layer, affine=affine) + up_norm_layer = get_norm_layer(up_norm_layer) + if affine is not None: + up_norm_layer = partial(up_norm_layer, affine=affine) + + model = [nn.ReflectionPad2d(3), + conv_layer(input_nc, ngf, kernel_size=7, padding=0), + norm_layer(ngf), + activation] + + identity = Identity() + ### downsample + for i in range(n_downsampling): + mult = 2 ** i + + model += [conv_layer(min(max_features, ngf * mult), + min(max_features, ngf * mult * 2), + kernel_size=3, stride=2, padding=1), + norm_layer(min(max_features, ngf * mult * 2)), + activation] + + mult = 2 ** n_downsampling + feats_num_bottleneck = min(max_features, ngf * mult) + + ### resnet blocks + for i in range(n_blocks): + if ffc_positions is not None and i in ffc_positions: + model += [FFCResnetBlock(feats_num_bottleneck, padding_type, norm_layer, activation_layer=nn.ReLU, + inline=True, **ffc_kwargs)] + model += [MultidilatedResnetBlock(feats_num_bottleneck, padding_type=padding_type, + conv_layer=resnet_conv_layer, activation=activation, + norm_layer=norm_layer)] + + ### upsample + for i in range(n_downsampling): + mult = 2 ** (n_downsampling - i) + model += deconv_factory(deconv_kind, ngf, mult, up_norm_layer, up_activation, max_features) + model += [nn.ReflectionPad2d(3), + nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] + if add_out_act: + model.append(get_activation('tanh' if add_out_act is True else add_out_act)) + self.model = nn.Sequential(*model) + + def forward(self, input): + return self.model(input) + +class ConfigGlobalGenerator(nn.Module): + def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, + n_blocks=3, norm_layer=nn.BatchNorm2d, + padding_type='reflect', conv_kind='default', + deconv_kind='convtranspose', activation=nn.ReLU(True), + up_norm_layer=nn.BatchNorm2d, affine=None, up_activation=nn.ReLU(True), + add_out_act=True, max_features=1024, + manual_block_spec=[], + resnet_block_kind='multidilatedresnetblock', + resnet_conv_kind='multidilated', + resnet_dilation=1, + multidilation_kwargs={}): + assert (n_blocks >= 0) + super().__init__() + + conv_layer = get_conv_block_ctor(conv_kind) + resnet_conv_layer = functools.partial(get_conv_block_ctor(resnet_conv_kind), **multidilation_kwargs) + norm_layer = get_norm_layer(norm_layer) + if affine is not None: + norm_layer = partial(norm_layer, affine=affine) + up_norm_layer = get_norm_layer(up_norm_layer) + if affine is not None: + up_norm_layer = partial(up_norm_layer, affine=affine) + + model = [nn.ReflectionPad2d(3), + conv_layer(input_nc, ngf, kernel_size=7, padding=0), + norm_layer(ngf), + activation] + + identity = Identity() + + ### downsample + for i in range(n_downsampling): + mult = 2 ** i + model += [conv_layer(min(max_features, ngf * mult), + min(max_features, ngf * mult * 2), + kernel_size=3, stride=2, padding=1), + norm_layer(min(max_features, ngf * mult * 2)), + activation] + + mult = 2 ** n_downsampling + feats_num_bottleneck = min(max_features, ngf * mult) + + if len(manual_block_spec) == 0: + manual_block_spec = [ + DotDict(lambda : None, { + 'n_blocks': n_blocks, + 'use_default': True}) + ] + + ### resnet blocks + for block_spec in manual_block_spec: + def make_and_add_blocks(model, block_spec): + block_spec = DotDict(lambda : None, block_spec) + if not block_spec.use_default: + resnet_conv_layer = functools.partial(get_conv_block_ctor(block_spec.resnet_conv_kind), **block_spec.multidilation_kwargs) + resnet_conv_kind = block_spec.resnet_conv_kind + resnet_block_kind = block_spec.resnet_block_kind + if block_spec.resnet_dilation is not None: + resnet_dilation = block_spec.resnet_dilation + for i in range(block_spec.n_blocks): + if resnet_block_kind == "multidilatedresnetblock": + model += [MultidilatedResnetBlock(feats_num_bottleneck, padding_type=padding_type, + conv_layer=resnet_conv_layer, activation=activation, + norm_layer=norm_layer)] + if resnet_block_kind == "resnetblock": + model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer, + conv_kind=resnet_conv_kind)] + if resnet_block_kind == "resnetblock5x5": + model += [ResnetBlock5x5(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer, + conv_kind=resnet_conv_kind)] + if resnet_block_kind == "resnetblockdwdil": + model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer, + conv_kind=resnet_conv_kind, dilation=resnet_dilation, second_dilation=resnet_dilation)] + make_and_add_blocks(model, block_spec) + + ### upsample + for i in range(n_downsampling): + mult = 2 ** (n_downsampling - i) + model += deconv_factory(deconv_kind, ngf, mult, up_norm_layer, up_activation, max_features) + model += [nn.ReflectionPad2d(3), + nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] + if add_out_act: + model.append(get_activation('tanh' if add_out_act is True else add_out_act)) + self.model = nn.Sequential(*model) + + def forward(self, input): + return self.model(input) + + +def make_dil_blocks(dilated_blocks_n, dilation_block_kind, dilated_block_kwargs): + blocks = [] + for i in range(dilated_blocks_n): + if dilation_block_kind == 'simple': + blocks.append(ResnetBlock(**dilated_block_kwargs, dilation=2 ** (i + 1))) + elif dilation_block_kind == 'multi': + blocks.append(MultidilatedResnetBlock(**dilated_block_kwargs)) + else: + raise ValueError(f'dilation_block_kind could not be "{dilation_block_kind}"') + return blocks + + +class GlobalGenerator(nn.Module): + def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d, + padding_type='reflect', conv_kind='default', activation=nn.ReLU(True), + up_norm_layer=nn.BatchNorm2d, affine=None, + up_activation=nn.ReLU(True), dilated_blocks_n=0, dilated_blocks_n_start=0, + dilated_blocks_n_middle=0, + add_out_act=True, + max_features=1024, is_resblock_depthwise=False, + ffc_positions=None, ffc_kwargs={}, dilation=1, second_dilation=None, + dilation_block_kind='simple', multidilation_kwargs={}): + assert (n_blocks >= 0) + super().__init__() + + conv_layer = get_conv_block_ctor(conv_kind) + norm_layer = get_norm_layer(norm_layer) + if affine is not None: + norm_layer = partial(norm_layer, affine=affine) + up_norm_layer = get_norm_layer(up_norm_layer) + if affine is not None: + up_norm_layer = partial(up_norm_layer, affine=affine) + + if ffc_positions is not None: + ffc_positions = collections.Counter(ffc_positions) + + model = [nn.ReflectionPad2d(3), + conv_layer(input_nc, ngf, kernel_size=7, padding=0), + norm_layer(ngf), + activation] + + identity = Identity() + ### downsample + for i in range(n_downsampling): + mult = 2 ** i + + model += [conv_layer(min(max_features, ngf * mult), + min(max_features, ngf * mult * 2), + kernel_size=3, stride=2, padding=1), + norm_layer(min(max_features, ngf * mult * 2)), + activation] + + mult = 2 ** n_downsampling + feats_num_bottleneck = min(max_features, ngf * mult) + + dilated_block_kwargs = dict(dim=feats_num_bottleneck, padding_type=padding_type, + activation=activation, norm_layer=norm_layer) + if dilation_block_kind == 'simple': + dilated_block_kwargs['conv_kind'] = conv_kind + elif dilation_block_kind == 'multi': + dilated_block_kwargs['conv_layer'] = functools.partial( + get_conv_block_ctor('multidilated'), **multidilation_kwargs) + + # dilated blocks at the start of the bottleneck sausage + if dilated_blocks_n_start is not None and dilated_blocks_n_start > 0: + model += make_dil_blocks(dilated_blocks_n_start, dilation_block_kind, dilated_block_kwargs) + + # resnet blocks + for i in range(n_blocks): + # dilated blocks at the middle of the bottleneck sausage + if i == n_blocks // 2 and dilated_blocks_n_middle is not None and dilated_blocks_n_middle > 0: + model += make_dil_blocks(dilated_blocks_n_middle, dilation_block_kind, dilated_block_kwargs) + + if ffc_positions is not None and i in ffc_positions: + for _ in range(ffc_positions[i]): # same position can occur more than once + model += [FFCResnetBlock(feats_num_bottleneck, padding_type, norm_layer, activation_layer=nn.ReLU, + inline=True, **ffc_kwargs)] + + if is_resblock_depthwise: + resblock_groups = feats_num_bottleneck + else: + resblock_groups = 1 + + model += [ResnetBlock(feats_num_bottleneck, padding_type=padding_type, activation=activation, + norm_layer=norm_layer, conv_kind=conv_kind, groups=resblock_groups, + dilation=dilation, second_dilation=second_dilation)] + + + # dilated blocks at the end of the bottleneck sausage + if dilated_blocks_n is not None and dilated_blocks_n > 0: + model += make_dil_blocks(dilated_blocks_n, dilation_block_kind, dilated_block_kwargs) + + # upsample + for i in range(n_downsampling): + mult = 2 ** (n_downsampling - i) + model += [nn.ConvTranspose2d(min(max_features, ngf * mult), + min(max_features, int(ngf * mult / 2)), + kernel_size=3, stride=2, padding=1, output_padding=1), + up_norm_layer(min(max_features, int(ngf * mult / 2))), + up_activation] + model += [nn.ReflectionPad2d(3), + nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] + if add_out_act: + model.append(get_activation('tanh' if add_out_act is True else add_out_act)) + self.model = nn.Sequential(*model) + + def forward(self, input): + return self.model(input) + + +class GlobalGeneratorGated(GlobalGenerator): + def __init__(self, *args, **kwargs): + real_kwargs=dict( + conv_kind='gated_bn_relu', + activation=nn.Identity(), + norm_layer=nn.Identity + ) + real_kwargs.update(kwargs) + super().__init__(*args, **real_kwargs) + + +class GlobalGeneratorFromSuperChannels(nn.Module): + def __init__(self, input_nc, output_nc, n_downsampling, n_blocks, super_channels, norm_layer="bn", padding_type='reflect', add_out_act=True): + super().__init__() + self.n_downsampling = n_downsampling + norm_layer = get_norm_layer(norm_layer) + if type(norm_layer) == functools.partial: + use_bias = (norm_layer.func == nn.InstanceNorm2d) + else: + use_bias = (norm_layer == nn.InstanceNorm2d) + + channels = self.convert_super_channels(super_channels) + self.channels = channels + + model = [nn.ReflectionPad2d(3), + nn.Conv2d(input_nc, channels[0], kernel_size=7, padding=0, bias=use_bias), + norm_layer(channels[0]), + nn.ReLU(True)] + + for i in range(n_downsampling): # add downsampling layers + mult = 2 ** i + model += [nn.Conv2d(channels[0+i], channels[1+i], kernel_size=3, stride=2, padding=1, bias=use_bias), + norm_layer(channels[1+i]), + nn.ReLU(True)] + + mult = 2 ** n_downsampling + + n_blocks1 = n_blocks // 3 + n_blocks2 = n_blocks1 + n_blocks3 = n_blocks - n_blocks1 - n_blocks2 + + for i in range(n_blocks1): + c = n_downsampling + dim = channels[c] + model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer)] + + for i in range(n_blocks2): + c = n_downsampling+1 + dim = channels[c] + kwargs = {} + if i == 0: + kwargs = {"in_dim": channels[c-1]} + model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer, **kwargs)] + + for i in range(n_blocks3): + c = n_downsampling+2 + dim = channels[c] + kwargs = {} + if i == 0: + kwargs = {"in_dim": channels[c-1]} + model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer, **kwargs)] + + for i in range(n_downsampling): # add upsampling layers + mult = 2 ** (n_downsampling - i) + model += [nn.ConvTranspose2d(channels[n_downsampling+3+i], + channels[n_downsampling+3+i+1], + kernel_size=3, stride=2, + padding=1, output_padding=1, + bias=use_bias), + norm_layer(channels[n_downsampling+3+i+1]), + nn.ReLU(True)] + model += [nn.ReflectionPad2d(3)] + model += [nn.Conv2d(channels[2*n_downsampling+3], output_nc, kernel_size=7, padding=0)] + + if add_out_act: + model.append(get_activation('tanh' if add_out_act is True else add_out_act)) + self.model = nn.Sequential(*model) + + def convert_super_channels(self, super_channels): + n_downsampling = self.n_downsampling + result = [] + cnt = 0 + + if n_downsampling == 2: + N1 = 10 + elif n_downsampling == 3: + N1 = 13 + else: + raise NotImplementedError + + for i in range(0, N1): + if i in [1,4,7,10]: + channel = super_channels[cnt] * (2 ** cnt) + config = {'channel': channel} + result.append(channel) + logging.info(f"Downsample channels {result[-1]}") + cnt += 1 + + for i in range(3): + for counter, j in enumerate(range(N1 + i * 3, N1 + 3 + i * 3)): + if len(super_channels) == 6: + channel = super_channels[3] * 4 + else: + channel = super_channels[i + 3] * 4 + config = {'channel': channel} + if counter == 0: + result.append(channel) + logging.info(f"Bottleneck channels {result[-1]}") + cnt = 2 + + for i in range(N1+9, N1+21): + if i in [22, 25,28]: + cnt -= 1 + if len(super_channels) == 6: + channel = super_channels[5 - cnt] * (2 ** cnt) + else: + channel = super_channels[7 - cnt] * (2 ** cnt) + result.append(int(channel)) + logging.info(f"Upsample channels {result[-1]}") + return result + + def forward(self, input): + return self.model(input) + + +# Defines the PatchGAN discriminator with the specified arguments. +class NLayerDiscriminator(BaseDiscriminator): + def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d,): + super().__init__() + self.n_layers = n_layers + + kw = 4 + padw = int(np.ceil((kw-1.0)/2)) + sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), + nn.LeakyReLU(0.2, True)]] + + nf = ndf + for n in range(1, n_layers): + nf_prev = nf + nf = min(nf * 2, 512) + + cur_model = [] + cur_model += [ + nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=2, padding=padw), + norm_layer(nf), + nn.LeakyReLU(0.2, True) + ] + sequence.append(cur_model) + + nf_prev = nf + nf = min(nf * 2, 512) + + cur_model = [] + cur_model += [ + nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw), + norm_layer(nf), + nn.LeakyReLU(0.2, True) + ] + sequence.append(cur_model) + + sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]] + + for n in range(len(sequence)): + setattr(self, 'model'+str(n), nn.Sequential(*sequence[n])) + + def get_all_activations(self, x): + res = [x] + for n in range(self.n_layers + 2): + model = getattr(self, 'model' + str(n)) + res.append(model(res[-1])) + return res[1:] + + def forward(self, x): + act = self.get_all_activations(x) + return act[-1], act[:-1] + + +class MultidilatedNLayerDiscriminator(BaseDiscriminator): + def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, multidilation_kwargs={}): + super().__init__() + self.n_layers = n_layers + + kw = 4 + padw = int(np.ceil((kw-1.0)/2)) + sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), + nn.LeakyReLU(0.2, True)]] + + nf = ndf + for n in range(1, n_layers): + nf_prev = nf + nf = min(nf * 2, 512) + + cur_model = [] + cur_model += [ + MultidilatedConv(nf_prev, nf, kernel_size=kw, stride=2, padding=[2, 3], **multidilation_kwargs), + norm_layer(nf), + nn.LeakyReLU(0.2, True) + ] + sequence.append(cur_model) + + nf_prev = nf + nf = min(nf * 2, 512) + + cur_model = [] + cur_model += [ + nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw), + norm_layer(nf), + nn.LeakyReLU(0.2, True) + ] + sequence.append(cur_model) + + sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]] + + for n in range(len(sequence)): + setattr(self, 'model'+str(n), nn.Sequential(*sequence[n])) + + def get_all_activations(self, x): + res = [x] + for n in range(self.n_layers + 2): + model = getattr(self, 'model' + str(n)) + res.append(model(res[-1])) + return res[1:] + + def forward(self, x): + act = self.get_all_activations(x) + return act[-1], act[:-1] + + +class NLayerDiscriminatorAsGen(NLayerDiscriminator): + def forward(self, x): + return super().forward(x)[0] diff --git a/third-party/lama/bin/saicinpainting/training/modules/spatial_transform.py b/third-party/lama/bin/saicinpainting/training/modules/spatial_transform.py new file mode 100644 index 0000000000000000000000000000000000000000..2de024ba08c549605a08b64d096f1f0db7b7722a --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/modules/spatial_transform.py @@ -0,0 +1,49 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from kornia.geometry.transform import rotate + + +class LearnableSpatialTransformWrapper(nn.Module): + def __init__(self, impl, pad_coef=0.5, angle_init_range=80, train_angle=True): + super().__init__() + self.impl = impl + self.angle = torch.rand(1) * angle_init_range + if train_angle: + self.angle = nn.Parameter(self.angle, requires_grad=True) + self.pad_coef = pad_coef + + def forward(self, x): + if torch.is_tensor(x): + return self.inverse_transform(self.impl(self.transform(x)), x) + elif isinstance(x, tuple): + x_trans = tuple(self.transform(elem) for elem in x) + y_trans = self.impl(x_trans) + return tuple(self.inverse_transform(elem, orig_x) for elem, orig_x in zip(y_trans, x)) + else: + raise ValueError(f'Unexpected input type {type(x)}') + + def transform(self, x): + height, width = x.shape[2:] + pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef) + x_padded = F.pad(x, [pad_w, pad_w, pad_h, pad_h], mode='reflect') + x_padded_rotated = rotate(x_padded, angle=self.angle.to(x_padded)) + return x_padded_rotated + + def inverse_transform(self, y_padded_rotated, orig_x): + height, width = orig_x.shape[2:] + pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef) + + y_padded = rotate(y_padded_rotated, angle=-self.angle.to(y_padded_rotated)) + y_height, y_width = y_padded.shape[2:] + y = y_padded[:, :, pad_h : y_height - pad_h, pad_w : y_width - pad_w] + return y + + +if __name__ == '__main__': + layer = LearnableSpatialTransformWrapper(nn.Identity()) + x = torch.arange(2* 3 * 15 * 15).view(2, 3, 15, 15).float() + y = layer(x) + assert x.shape == y.shape + assert torch.allclose(x[:, :, 1:, 1:][:, :, :-1, :-1], y[:, :, 1:, 1:][:, :, :-1, :-1]) + print('all ok') diff --git a/third-party/lama/bin/saicinpainting/training/modules/squeeze_excitation.py b/third-party/lama/bin/saicinpainting/training/modules/squeeze_excitation.py new file mode 100644 index 0000000000000000000000000000000000000000..d1d902bb30c071acbc0fa919a134c80fed86bd6c --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/modules/squeeze_excitation.py @@ -0,0 +1,20 @@ +import torch.nn as nn + + +class SELayer(nn.Module): + def __init__(self, channel, reduction=16): + super(SELayer, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channel, channel // reduction, bias=False), + nn.ReLU(inplace=True), + nn.Linear(channel // reduction, channel, bias=False), + nn.Sigmoid() + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + res = x * y.expand_as(x) + return res diff --git a/third-party/lama/bin/saicinpainting/training/trainers/__init__.py b/third-party/lama/bin/saicinpainting/training/trainers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c59241f553efe4e2dd6b198e2e5656a2b1488857 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/trainers/__init__.py @@ -0,0 +1,30 @@ +import logging +import torch +from saicinpainting.training.trainers.default import DefaultInpaintingTrainingModule + + +def get_training_model_class(kind): + if kind == 'default': + return DefaultInpaintingTrainingModule + + raise ValueError(f'Unknown trainer module {kind}') + + +def make_training_model(config): + kind = config.training_model.kind + kwargs = dict(config.training_model) + kwargs.pop('kind') + kwargs['use_ddp'] = config.trainer.kwargs.get('accelerator', None) == 'ddp' + + logging.info(f'Make training model {kind}') + + cls = get_training_model_class(kind) + return cls(config, **kwargs) + + +def load_checkpoint(train_config, path, map_location='cuda', strict=True): + model: torch.nn.Module = make_training_model(train_config) + state = torch.load(path, map_location=map_location) + model.load_state_dict(state['state_dict'], strict=strict) + model.on_load_checkpoint(state) + return model diff --git a/third-party/lama/bin/saicinpainting/training/trainers/base.py b/third-party/lama/bin/saicinpainting/training/trainers/base.py new file mode 100644 index 0000000000000000000000000000000000000000..f1b1c66fc96e7edfba7b1ee193272f92b5db7438 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/trainers/base.py @@ -0,0 +1,291 @@ +import copy +import logging +from typing import Dict, Tuple + +import pandas as pd +import pytorch_lightning as ptl +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import DistributedSampler + +from saicinpainting.evaluation import make_evaluator +from saicinpainting.training.data.datasets import make_default_train_dataloader, make_default_val_dataloader +from saicinpainting.training.losses.adversarial import make_discrim_loss +from saicinpainting.training.losses.perceptual import PerceptualLoss, ResNetPL +from saicinpainting.training.modules import make_generator, make_discriminator +from saicinpainting.training.visualizers import make_visualizer +from saicinpainting.utils import add_prefix_to_keys, average_dicts, set_requires_grad, flatten_dict, \ + get_has_ddp_rank + +LOGGER = logging.getLogger(__name__) + + +def make_optimizer(parameters, kind='adamw', **kwargs): + if kind == 'adam': + optimizer_class = torch.optim.Adam + elif kind == 'adamw': + optimizer_class = torch.optim.AdamW + else: + raise ValueError(f'Unknown optimizer kind {kind}') + return optimizer_class(parameters, **kwargs) + + +def update_running_average(result: nn.Module, new_iterate_model: nn.Module, decay=0.999): + with torch.no_grad(): + res_params = dict(result.named_parameters()) + new_params = dict(new_iterate_model.named_parameters()) + + for k in res_params.keys(): + res_params[k].data.mul_(decay).add_(new_params[k].data, alpha=1 - decay) + + +def make_multiscale_noise(base_tensor, scales=6, scale_mode='bilinear'): + batch_size, _, height, width = base_tensor.shape + cur_height, cur_width = height, width + result = [] + align_corners = False if scale_mode in ('bilinear', 'bicubic') else None + for _ in range(scales): + cur_sample = torch.randn(batch_size, 1, cur_height, cur_width, device=base_tensor.device) + cur_sample_scaled = F.interpolate(cur_sample, size=(height, width), mode=scale_mode, align_corners=align_corners) + result.append(cur_sample_scaled) + cur_height //= 2 + cur_width //= 2 + return torch.cat(result, dim=1) + + +class BaseInpaintingTrainingModule(ptl.LightningModule): + def __init__(self, config, use_ddp, *args, predict_only=False, visualize_each_iters=100, + average_generator=False, generator_avg_beta=0.999, average_generator_start_step=30000, + average_generator_period=10, store_discr_outputs_for_vis=False, + **kwargs): + super().__init__(*args, **kwargs) + LOGGER.info('BaseInpaintingTrainingModule init called') + + self.config = config + + self.generator = make_generator(config, **self.config.generator) + self.use_ddp = use_ddp + + if not get_has_ddp_rank(): + LOGGER.info(f'Generator\n{self.generator}') + + if not predict_only: + self.save_hyperparameters(self.config) + self.discriminator = make_discriminator(**self.config.discriminator) + self.adversarial_loss = make_discrim_loss(**self.config.losses.adversarial) + self.visualizer = make_visualizer(**self.config.visualizer) + self.val_evaluator = make_evaluator(**self.config.evaluator) + self.test_evaluator = make_evaluator(**self.config.evaluator) + + if not get_has_ddp_rank(): + LOGGER.info(f'Discriminator\n{self.discriminator}') + + extra_val = self.config.data.get('extra_val', ()) + if extra_val: + self.extra_val_titles = list(extra_val) + self.extra_evaluators = nn.ModuleDict({k: make_evaluator(**self.config.evaluator) + for k in extra_val}) + else: + self.extra_evaluators = {} + + self.average_generator = average_generator + self.generator_avg_beta = generator_avg_beta + self.average_generator_start_step = average_generator_start_step + self.average_generator_period = average_generator_period + self.generator_average = None + self.last_generator_averaging_step = -1 + self.store_discr_outputs_for_vis = store_discr_outputs_for_vis + + if self.config.losses.get("l1", {"weight_known": 0})['weight_known'] > 0: + self.loss_l1 = nn.L1Loss(reduction='none') + + if self.config.losses.get("mse", {"weight": 0})['weight'] > 0: + self.loss_mse = nn.MSELoss(reduction='none') + + if self.config.losses.perceptual.weight > 0: + self.loss_pl = PerceptualLoss() + + if self.config.losses.get("resnet_pl", {"weight": 0})['weight'] > 0: + self.loss_resnet_pl = ResNetPL(**self.config.losses.resnet_pl) + else: + self.loss_resnet_pl = None + + self.visualize_each_iters = visualize_each_iters + LOGGER.info('BaseInpaintingTrainingModule init done') + + def configure_optimizers(self): + discriminator_params = list(self.discriminator.parameters()) + return [ + dict(optimizer=make_optimizer(self.generator.parameters(), **self.config.optimizers.generator)), + dict(optimizer=make_optimizer(discriminator_params, **self.config.optimizers.discriminator)), + ] + + def train_dataloader(self): + kwargs = dict(self.config.data.train) + if self.use_ddp: + kwargs['ddp_kwargs'] = dict(num_replicas=self.trainer.num_nodes * self.trainer.num_processes, + rank=self.trainer.global_rank, + shuffle=True) + dataloader = make_default_train_dataloader(**self.config.data.train) + return dataloader + + def val_dataloader(self): + res = [make_default_val_dataloader(**self.config.data.val)] + + if self.config.data.visual_test is not None: + res = res + [make_default_val_dataloader(**self.config.data.visual_test)] + else: + res = res + res + + extra_val = self.config.data.get('extra_val', ()) + if extra_val: + res += [make_default_val_dataloader(**extra_val[k]) for k in self.extra_val_titles] + + return res + + def training_step(self, batch, batch_idx, optimizer_idx=None): + self._is_training_step = True + return self._do_step(batch, batch_idx, mode='train', optimizer_idx=optimizer_idx) + + def validation_step(self, batch, batch_idx, dataloader_idx): + extra_val_key = None + if dataloader_idx == 0: + mode = 'val' + elif dataloader_idx == 1: + mode = 'test' + else: + mode = 'extra_val' + extra_val_key = self.extra_val_titles[dataloader_idx - 2] + self._is_training_step = False + return self._do_step(batch, batch_idx, mode=mode, extra_val_key=extra_val_key) + + def training_step_end(self, batch_parts_outputs): + if self.training and self.average_generator \ + and self.global_step >= self.average_generator_start_step \ + and self.global_step >= self.last_generator_averaging_step + self.average_generator_period: + if self.generator_average is None: + self.generator_average = copy.deepcopy(self.generator) + else: + update_running_average(self.generator_average, self.generator, decay=self.generator_avg_beta) + self.last_generator_averaging_step = self.global_step + + full_loss = (batch_parts_outputs['loss'].mean() + if torch.is_tensor(batch_parts_outputs['loss']) # loss is not tensor when no discriminator used + else torch.tensor(batch_parts_outputs['loss']).float().requires_grad_(True)) + log_info = {k: v.mean() for k, v in batch_parts_outputs['log_info'].items()} + self.log_dict(log_info, on_step=True, on_epoch=False) + return full_loss + + def validation_epoch_end(self, outputs): + outputs = [step_out for out_group in outputs for step_out in out_group] + averaged_logs = average_dicts(step_out['log_info'] for step_out in outputs) + self.log_dict({k: v.mean() for k, v in averaged_logs.items()}) + + pd.set_option('display.max_columns', 500) + pd.set_option('display.width', 1000) + + # standard validation + val_evaluator_states = [s['val_evaluator_state'] for s in outputs if 'val_evaluator_state' in s] + val_evaluator_res = self.val_evaluator.evaluation_end(states=val_evaluator_states) + val_evaluator_res_df = pd.DataFrame(val_evaluator_res).stack(1).unstack(0) + val_evaluator_res_df.dropna(axis=1, how='all', inplace=True) + LOGGER.info(f'Validation metrics after epoch #{self.current_epoch}, ' + f'total {self.global_step} iterations:\n{val_evaluator_res_df}') + + for k, v in flatten_dict(val_evaluator_res).items(): + self.log(f'val_{k}', v) + + # standard visual test + test_evaluator_states = [s['test_evaluator_state'] for s in outputs + if 'test_evaluator_state' in s] + test_evaluator_res = self.test_evaluator.evaluation_end(states=test_evaluator_states) + test_evaluator_res_df = pd.DataFrame(test_evaluator_res).stack(1).unstack(0) + test_evaluator_res_df.dropna(axis=1, how='all', inplace=True) + LOGGER.info(f'Test metrics after epoch #{self.current_epoch}, ' + f'total {self.global_step} iterations:\n{test_evaluator_res_df}') + + for k, v in flatten_dict(test_evaluator_res).items(): + self.log(f'test_{k}', v) + + # extra validations + if self.extra_evaluators: + for cur_eval_title, cur_evaluator in self.extra_evaluators.items(): + cur_state_key = f'extra_val_{cur_eval_title}_evaluator_state' + cur_states = [s[cur_state_key] for s in outputs if cur_state_key in s] + cur_evaluator_res = cur_evaluator.evaluation_end(states=cur_states) + cur_evaluator_res_df = pd.DataFrame(cur_evaluator_res).stack(1).unstack(0) + cur_evaluator_res_df.dropna(axis=1, how='all', inplace=True) + LOGGER.info(f'Extra val {cur_eval_title} metrics after epoch #{self.current_epoch}, ' + f'total {self.global_step} iterations:\n{cur_evaluator_res_df}') + for k, v in flatten_dict(cur_evaluator_res).items(): + self.log(f'extra_val_{cur_eval_title}_{k}', v) + + def _do_step(self, batch, batch_idx, mode='train', optimizer_idx=None, extra_val_key=None): + if optimizer_idx == 0: # step for generator + set_requires_grad(self.generator, True) + set_requires_grad(self.discriminator, False) + elif optimizer_idx == 1: # step for discriminator + set_requires_grad(self.generator, False) + set_requires_grad(self.discriminator, True) + + batch = self(batch) + + total_loss = 0 + metrics = {} + + if optimizer_idx is None or optimizer_idx == 0: # step for generator + total_loss, metrics = self.generator_loss(batch) + + elif optimizer_idx is None or optimizer_idx == 1: # step for discriminator + if self.config.losses.adversarial.weight > 0: + total_loss, metrics = self.discriminator_loss(batch) + + if self.get_ddp_rank() in (None, 0) and (batch_idx % self.visualize_each_iters == 0 or mode == 'test'): + if self.config.losses.adversarial.weight > 0: + if self.store_discr_outputs_for_vis: + with torch.no_grad(): + self.store_discr_outputs(batch) + vis_suffix = f'_{mode}' + if mode == 'extra_val': + vis_suffix += f'_{extra_val_key}' + self.visualizer(self.current_epoch, batch_idx, batch, suffix=vis_suffix) + + metrics_prefix = f'{mode}_' + if mode == 'extra_val': + metrics_prefix += f'{extra_val_key}_' + result = dict(loss=total_loss, log_info=add_prefix_to_keys(metrics, metrics_prefix)) + if mode == 'val': + result['val_evaluator_state'] = self.val_evaluator.process_batch(batch) + elif mode == 'test': + result['test_evaluator_state'] = self.test_evaluator.process_batch(batch) + elif mode == 'extra_val': + result[f'extra_val_{extra_val_key}_evaluator_state'] = self.extra_evaluators[extra_val_key].process_batch(batch) + + return result + + def get_current_generator(self, no_average=False): + if not no_average and not self.training and self.average_generator and self.generator_average is not None: + return self.generator_average + return self.generator + + def forward(self, batch: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: + """Pass data through generator and obtain at leas 'predicted_image' and 'inpainted' keys""" + raise NotImplementedError() + + def generator_loss(self, batch) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: + raise NotImplementedError() + + def discriminator_loss(self, batch) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: + raise NotImplementedError() + + def store_discr_outputs(self, batch): + out_size = batch['image'].shape[2:] + discr_real_out, _ = self.discriminator(batch['image']) + discr_fake_out, _ = self.discriminator(batch['predicted_image']) + batch['discr_output_real'] = F.interpolate(discr_real_out, size=out_size, mode='nearest') + batch['discr_output_fake'] = F.interpolate(discr_fake_out, size=out_size, mode='nearest') + batch['discr_output_diff'] = batch['discr_output_real'] - batch['discr_output_fake'] + + def get_ddp_rank(self): + return self.trainer.global_rank if (self.trainer.num_nodes * self.trainer.num_processes) > 1 else None diff --git a/third-party/lama/bin/saicinpainting/training/trainers/default.py b/third-party/lama/bin/saicinpainting/training/trainers/default.py new file mode 100644 index 0000000000000000000000000000000000000000..86c7f0fab42924bfc93a031e851117634c70f593 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/trainers/default.py @@ -0,0 +1,175 @@ +import logging + +import torch +import torch.nn.functional as F +from omegaconf import OmegaConf + +from saicinpainting.training.data.datasets import make_constant_area_crop_params +from saicinpainting.training.losses.distance_weighting import make_mask_distance_weighter +from saicinpainting.training.losses.feature_matching import feature_matching_loss, masked_l1_loss +from saicinpainting.training.modules.fake_fakes import FakeFakesGenerator +from saicinpainting.training.trainers.base import BaseInpaintingTrainingModule, make_multiscale_noise +from saicinpainting.utils import add_prefix_to_keys, get_ramp + +LOGGER = logging.getLogger(__name__) + + +def make_constant_area_crop_batch(batch, **kwargs): + crop_y, crop_x, crop_height, crop_width = make_constant_area_crop_params(img_height=batch['image'].shape[2], + img_width=batch['image'].shape[3], + **kwargs) + batch['image'] = batch['image'][:, :, crop_y : crop_y + crop_height, crop_x : crop_x + crop_width] + batch['mask'] = batch['mask'][:, :, crop_y: crop_y + crop_height, crop_x: crop_x + crop_width] + return batch + + +class DefaultInpaintingTrainingModule(BaseInpaintingTrainingModule): + def __init__(self, *args, concat_mask=True, rescale_scheduler_kwargs=None, image_to_discriminator='predicted_image', + add_noise_kwargs=None, noise_fill_hole=False, const_area_crop_kwargs=None, + distance_weighter_kwargs=None, distance_weighted_mask_for_discr=False, + fake_fakes_proba=0, fake_fakes_generator_kwargs=None, + **kwargs): + super().__init__(*args, **kwargs) + self.concat_mask = concat_mask + self.rescale_size_getter = get_ramp(**rescale_scheduler_kwargs) if rescale_scheduler_kwargs is not None else None + self.image_to_discriminator = image_to_discriminator + self.add_noise_kwargs = add_noise_kwargs + self.noise_fill_hole = noise_fill_hole + self.const_area_crop_kwargs = const_area_crop_kwargs + self.refine_mask_for_losses = make_mask_distance_weighter(**distance_weighter_kwargs) \ + if distance_weighter_kwargs is not None else None + self.distance_weighted_mask_for_discr = distance_weighted_mask_for_discr + + self.fake_fakes_proba = fake_fakes_proba + if self.fake_fakes_proba > 1e-3: + self.fake_fakes_gen = FakeFakesGenerator(**(fake_fakes_generator_kwargs or {})) + + def forward(self, batch): + if self.training and self.rescale_size_getter is not None: + cur_size = self.rescale_size_getter(self.global_step) + batch['image'] = F.interpolate(batch['image'], size=cur_size, mode='bilinear', align_corners=False) + batch['mask'] = F.interpolate(batch['mask'], size=cur_size, mode='nearest') + + if self.training and self.const_area_crop_kwargs is not None: + batch = make_constant_area_crop_batch(batch, **self.const_area_crop_kwargs) + + img = batch['image'] + mask = batch['mask'] + + masked_img = img * (1 - mask) + + if self.add_noise_kwargs is not None: + noise = make_multiscale_noise(masked_img, **self.add_noise_kwargs) + if self.noise_fill_hole: + masked_img = masked_img + mask * noise[:, :masked_img.shape[1]] + masked_img = torch.cat([masked_img, noise], dim=1) + + if self.concat_mask: + masked_img = torch.cat([masked_img, mask], dim=1) + + batch['predicted_image'] = self.generator(masked_img) + batch['inpainted'] = mask * batch['predicted_image'] + (1 - mask) * batch['image'] + + if self.fake_fakes_proba > 1e-3: + if self.training and torch.rand(1).item() < self.fake_fakes_proba: + batch['fake_fakes'], batch['fake_fakes_masks'] = self.fake_fakes_gen(img, mask) + batch['use_fake_fakes'] = True + else: + batch['fake_fakes'] = torch.zeros_like(img) + batch['fake_fakes_masks'] = torch.zeros_like(mask) + batch['use_fake_fakes'] = False + + batch['mask_for_losses'] = self.refine_mask_for_losses(img, batch['predicted_image'], mask) \ + if self.refine_mask_for_losses is not None and self.training \ + else mask + + return batch + + def generator_loss(self, batch): + img = batch['image'] + predicted_img = batch[self.image_to_discriminator] + original_mask = batch['mask'] + supervised_mask = batch['mask_for_losses'] + + # L1 + l1_value = masked_l1_loss(predicted_img, img, supervised_mask, + self.config.losses.l1.weight_known, + self.config.losses.l1.weight_missing) + + total_loss = l1_value + metrics = dict(gen_l1=l1_value) + + # vgg-based perceptual loss + if self.config.losses.perceptual.weight > 0: + pl_value = self.loss_pl(predicted_img, img, mask=supervised_mask).sum() * self.config.losses.perceptual.weight + total_loss = total_loss + pl_value + metrics['gen_pl'] = pl_value + + # discriminator + # adversarial_loss calls backward by itself + mask_for_discr = supervised_mask if self.distance_weighted_mask_for_discr else original_mask + self.adversarial_loss.pre_generator_step(real_batch=img, fake_batch=predicted_img, + generator=self.generator, discriminator=self.discriminator) + discr_real_pred, discr_real_features = self.discriminator(img) + discr_fake_pred, discr_fake_features = self.discriminator(predicted_img) + adv_gen_loss, adv_metrics = self.adversarial_loss.generator_loss(real_batch=img, + fake_batch=predicted_img, + discr_real_pred=discr_real_pred, + discr_fake_pred=discr_fake_pred, + mask=mask_for_discr) + total_loss = total_loss + adv_gen_loss + metrics['gen_adv'] = adv_gen_loss + metrics.update(add_prefix_to_keys(adv_metrics, 'adv_')) + + # feature matching + if self.config.losses.feature_matching.weight > 0: + need_mask_in_fm = OmegaConf.to_container(self.config.losses.feature_matching).get('pass_mask', False) + mask_for_fm = supervised_mask if need_mask_in_fm else None + fm_value = feature_matching_loss(discr_fake_features, discr_real_features, + mask=mask_for_fm) * self.config.losses.feature_matching.weight + total_loss = total_loss + fm_value + metrics['gen_fm'] = fm_value + + if self.loss_resnet_pl is not None: + resnet_pl_value = self.loss_resnet_pl(predicted_img, img) + total_loss = total_loss + resnet_pl_value + metrics['gen_resnet_pl'] = resnet_pl_value + + return total_loss, metrics + + def discriminator_loss(self, batch): + total_loss = 0 + metrics = {} + + predicted_img = batch[self.image_to_discriminator].detach() + self.adversarial_loss.pre_discriminator_step(real_batch=batch['image'], fake_batch=predicted_img, + generator=self.generator, discriminator=self.discriminator) + discr_real_pred, discr_real_features = self.discriminator(batch['image']) + discr_fake_pred, discr_fake_features = self.discriminator(predicted_img) + adv_discr_loss, adv_metrics = self.adversarial_loss.discriminator_loss(real_batch=batch['image'], + fake_batch=predicted_img, + discr_real_pred=discr_real_pred, + discr_fake_pred=discr_fake_pred, + mask=batch['mask']) + total_loss = total_loss + adv_discr_loss + metrics['discr_adv'] = adv_discr_loss + metrics.update(add_prefix_to_keys(adv_metrics, 'adv_')) + + + if batch.get('use_fake_fakes', False): + fake_fakes = batch['fake_fakes'] + self.adversarial_loss.pre_discriminator_step(real_batch=batch['image'], fake_batch=fake_fakes, + generator=self.generator, discriminator=self.discriminator) + discr_fake_fakes_pred, _ = self.discriminator(fake_fakes) + fake_fakes_adv_discr_loss, fake_fakes_adv_metrics = self.adversarial_loss.discriminator_loss( + real_batch=batch['image'], + fake_batch=fake_fakes, + discr_real_pred=discr_real_pred, + discr_fake_pred=discr_fake_fakes_pred, + mask=batch['mask'] + ) + total_loss = total_loss + fake_fakes_adv_discr_loss + metrics['discr_adv_fake_fakes'] = fake_fakes_adv_discr_loss + metrics.update(add_prefix_to_keys(fake_fakes_adv_metrics, 'adv_')) + + return total_loss, metrics diff --git a/third-party/lama/bin/saicinpainting/training/visualizers/__init__.py b/third-party/lama/bin/saicinpainting/training/visualizers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4770d1f15a6790ab9606c7b9881f798c8e2d9545 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/visualizers/__init__.py @@ -0,0 +1,15 @@ +import logging + +from saicinpainting.training.visualizers.directory import DirectoryVisualizer +from saicinpainting.training.visualizers.noop import NoopVisualizer + + +def make_visualizer(kind, **kwargs): + logging.info(f'Make visualizer {kind}') + + if kind == 'directory': + return DirectoryVisualizer(**kwargs) + if kind == 'noop': + return NoopVisualizer() + + raise ValueError(f'Unknown visualizer kind {kind}') diff --git a/third-party/lama/bin/saicinpainting/training/visualizers/base.py b/third-party/lama/bin/saicinpainting/training/visualizers/base.py new file mode 100644 index 0000000000000000000000000000000000000000..675f01682ddf5e31b6cc341735378c6f3b242e49 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/visualizers/base.py @@ -0,0 +1,73 @@ +import abc +from typing import Dict, List + +import numpy as np +import torch +from skimage import color +from skimage.segmentation import mark_boundaries + +from . import colors + +COLORS, _ = colors.generate_colors(151) # 151 - max classes for semantic segmentation + + +class BaseVisualizer: + @abc.abstractmethod + def __call__(self, epoch_i, batch_i, batch, suffix='', rank=None): + """ + Take a batch, make an image from it and visualize + """ + raise NotImplementedError() + + +def visualize_mask_and_images(images_dict: Dict[str, np.ndarray], keys: List[str], + last_without_mask=True, rescale_keys=None, mask_only_first=None, + black_mask=False) -> np.ndarray: + mask = images_dict['mask'] > 0.5 + result = [] + for i, k in enumerate(keys): + img = images_dict[k] + img = np.transpose(img, (1, 2, 0)) + + if rescale_keys is not None and k in rescale_keys: + img = img - img.min() + img /= img.max() + 1e-5 + if len(img.shape) == 2: + img = np.expand_dims(img, 2) + + if img.shape[2] == 1: + img = np.repeat(img, 3, axis=2) + elif (img.shape[2] > 3): + img_classes = img.argmax(2) + img = color.label2rgb(img_classes, colors=COLORS) + + if mask_only_first: + need_mark_boundaries = i == 0 + else: + need_mark_boundaries = i < len(keys) - 1 or not last_without_mask + + if need_mark_boundaries: + if black_mask: + img = img * (1 - mask[0][..., None]) + img = mark_boundaries(img, + mask[0], + color=(1., 0., 0.), + outline_color=(1., 1., 1.), + mode='thick') + result.append(img) + return np.concatenate(result, axis=1) + + +def visualize_mask_and_images_batch(batch: Dict[str, torch.Tensor], keys: List[str], max_items=10, + last_without_mask=True, rescale_keys=None) -> np.ndarray: + batch = {k: tens.detach().cpu().numpy() for k, tens in batch.items() + if k in keys or k == 'mask'} + + batch_size = next(iter(batch.values())).shape[0] + items_to_vis = min(batch_size, max_items) + result = [] + for i in range(items_to_vis): + cur_dct = {k: tens[i] for k, tens in batch.items()} + result.append(visualize_mask_and_images(cur_dct, keys, last_without_mask=last_without_mask, + rescale_keys=rescale_keys)) + return np.concatenate(result, axis=0) diff --git a/third-party/lama/bin/saicinpainting/training/visualizers/colors.py b/third-party/lama/bin/saicinpainting/training/visualizers/colors.py new file mode 100644 index 0000000000000000000000000000000000000000..9e9e39182c58cb06a1c5e97a7e6c497cc3388ebe --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/visualizers/colors.py @@ -0,0 +1,76 @@ +import random +import colorsys + +import numpy as np +import matplotlib +matplotlib.use('agg') +import matplotlib.pyplot as plt +from matplotlib.colors import LinearSegmentedColormap + + +def generate_colors(nlabels, type='bright', first_color_black=False, last_color_black=True, verbose=False): + # https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib + """ + Creates a random colormap to be used together with matplotlib. Useful for segmentation tasks + :param nlabels: Number of labels (size of colormap) + :param type: 'bright' for strong colors, 'soft' for pastel colors + :param first_color_black: Option to use first color as black, True or False + :param last_color_black: Option to use last color as black, True or False + :param verbose: Prints the number of labels and shows the colormap. True or False + :return: colormap for matplotlib + """ + if type not in ('bright', 'soft'): + print ('Please choose "bright" or "soft" for type') + return + + if verbose: + print('Number of labels: ' + str(nlabels)) + + # Generate color map for bright colors, based on hsv + if type == 'bright': + randHSVcolors = [(np.random.uniform(low=0.0, high=1), + np.random.uniform(low=0.2, high=1), + np.random.uniform(low=0.9, high=1)) for i in range(nlabels)] + + # Convert HSV list to RGB + randRGBcolors = [] + for HSVcolor in randHSVcolors: + randRGBcolors.append(colorsys.hsv_to_rgb(HSVcolor[0], HSVcolor[1], HSVcolor[2])) + + if first_color_black: + randRGBcolors[0] = [0, 0, 0] + + if last_color_black: + randRGBcolors[-1] = [0, 0, 0] + + random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels) + + # Generate soft pastel colors, by limiting the RGB spectrum + if type == 'soft': + low = 0.6 + high = 0.95 + randRGBcolors = [(np.random.uniform(low=low, high=high), + np.random.uniform(low=low, high=high), + np.random.uniform(low=low, high=high)) for i in range(nlabels)] + + if first_color_black: + randRGBcolors[0] = [0, 0, 0] + + if last_color_black: + randRGBcolors[-1] = [0, 0, 0] + random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels) + + # Display colorbar + if verbose: + from matplotlib import colors, colorbar + from matplotlib import pyplot as plt + fig, ax = plt.subplots(1, 1, figsize=(15, 0.5)) + + bounds = np.linspace(0, nlabels, nlabels + 1) + norm = colors.BoundaryNorm(bounds, nlabels) + + cb = colorbar.ColorbarBase(ax, cmap=random_colormap, norm=norm, spacing='proportional', ticks=None, + boundaries=bounds, format='%1i', orientation=u'horizontal') + + return randRGBcolors, random_colormap + diff --git a/third-party/lama/bin/saicinpainting/training/visualizers/directory.py b/third-party/lama/bin/saicinpainting/training/visualizers/directory.py new file mode 100644 index 0000000000000000000000000000000000000000..bc42e00500c7a5b70b2cef83b03e45b5bb471ff8 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/visualizers/directory.py @@ -0,0 +1,36 @@ +import os + +import cv2 +import numpy as np + +from saicinpainting.training.visualizers.base import BaseVisualizer, visualize_mask_and_images_batch +from saicinpainting.utils import check_and_warn_input_range + + +class DirectoryVisualizer(BaseVisualizer): + DEFAULT_KEY_ORDER = 'image predicted_image inpainted'.split(' ') + + def __init__(self, outdir, key_order=DEFAULT_KEY_ORDER, max_items_in_batch=10, + last_without_mask=True, rescale_keys=None): + self.outdir = outdir + os.makedirs(self.outdir, exist_ok=True) + self.key_order = key_order + self.max_items_in_batch = max_items_in_batch + self.last_without_mask = last_without_mask + self.rescale_keys = rescale_keys + + def __call__(self, epoch_i, batch_i, batch, suffix='', rank=None): + check_and_warn_input_range(batch['image'], 0, 1, 'DirectoryVisualizer target image') + vis_img = visualize_mask_and_images_batch(batch, self.key_order, max_items=self.max_items_in_batch, + last_without_mask=self.last_without_mask, + rescale_keys=self.rescale_keys) + + vis_img = np.clip(vis_img * 255, 0, 255).astype('uint8') + + curoutdir = os.path.join(self.outdir, f'epoch{epoch_i:04d}{suffix}') + os.makedirs(curoutdir, exist_ok=True) + rank_suffix = f'_r{rank}' if rank is not None else '' + out_fname = os.path.join(curoutdir, f'batch{batch_i:07d}{rank_suffix}.jpg') + + vis_img = cv2.cvtColor(vis_img, cv2.COLOR_RGB2BGR) + cv2.imwrite(out_fname, vis_img) diff --git a/third-party/lama/bin/saicinpainting/training/visualizers/noop.py b/third-party/lama/bin/saicinpainting/training/visualizers/noop.py new file mode 100644 index 0000000000000000000000000000000000000000..4175089a54a8484d51e6c879c1a99c4e4d961d15 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/training/visualizers/noop.py @@ -0,0 +1,9 @@ +from saicinpainting.training.visualizers.base import BaseVisualizer + + +class NoopVisualizer(BaseVisualizer): + def __init__(self, *args, **kwargs): + pass + + def __call__(self, epoch_i, batch_i, batch, suffix='', rank=None): + pass diff --git a/third-party/lama/bin/saicinpainting/utils.py b/third-party/lama/bin/saicinpainting/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c2d67ed8bc793dd5113224fa322adb88f3ed9b22 --- /dev/null +++ b/third-party/lama/bin/saicinpainting/utils.py @@ -0,0 +1,177 @@ +import bisect +import functools +import logging +import numbers +import os +import signal +import sys +import traceback +import warnings + +import torch +from pytorch_lightning import seed_everything + +LOGGER = logging.getLogger(__name__) + +import platform +if platform.system() != 'Linux': + signal.SIGUSR1 = 1 + +def check_and_warn_input_range(tensor, min_value, max_value, name): + actual_min = tensor.min() + actual_max = tensor.max() + if actual_min < min_value or actual_max > max_value: + warnings.warn(f"{name} must be in {min_value}..{max_value} range, but it ranges {actual_min}..{actual_max}") + + +def sum_dict_with_prefix(target, cur_dict, prefix, default=0): + for k, v in cur_dict.items(): + target_key = prefix + k + target[target_key] = target.get(target_key, default) + v + + +def average_dicts(dict_list): + result = {} + norm = 1e-3 + for dct in dict_list: + sum_dict_with_prefix(result, dct, '') + norm += 1 + for k in list(result): + result[k] /= norm + return result + + +def add_prefix_to_keys(dct, prefix): + return {prefix + k: v for k, v in dct.items()} + + +def set_requires_grad(module, value): + for param in module.parameters(): + param.requires_grad = value + + +def flatten_dict(dct): + result = {} + for k, v in dct.items(): + if isinstance(k, tuple): + k = '_'.join(k) + if isinstance(v, dict): + for sub_k, sub_v in flatten_dict(v).items(): + result[f'{k}_{sub_k}'] = sub_v + else: + result[k] = v + return result + + +class LinearRamp: + def __init__(self, start_value=0, end_value=1, start_iter=-1, end_iter=0): + self.start_value = start_value + self.end_value = end_value + self.start_iter = start_iter + self.end_iter = end_iter + + def __call__(self, i): + if i < self.start_iter: + return self.start_value + if i >= self.end_iter: + return self.end_value + part = (i - self.start_iter) / (self.end_iter - self.start_iter) + return self.start_value * (1 - part) + self.end_value * part + + +class LadderRamp: + def __init__(self, start_iters, values): + self.start_iters = start_iters + self.values = values + assert len(values) == len(start_iters) + 1, (len(values), len(start_iters)) + + def __call__(self, i): + segment_i = bisect.bisect_right(self.start_iters, i) + return self.values[segment_i] + + +def get_ramp(kind='ladder', **kwargs): + if kind == 'linear': + return LinearRamp(**kwargs) + if kind == 'ladder': + return LadderRamp(**kwargs) + raise ValueError(f'Unexpected ramp kind: {kind}') + + +def print_traceback_handler(sig, frame): + LOGGER.warning(f'Received signal {sig}') + bt = ''.join(traceback.format_stack()) + LOGGER.warning(f'Requested stack trace:\n{bt}') + + +def register_debug_signal_handlers(sig=signal.SIGUSR1, handler=print_traceback_handler): + LOGGER.warning(f'Setting signal {sig} handler {handler}') + signal.signal(sig, handler) + + +def handle_deterministic_config(config): + seed = dict(config).get('seed', None) + if seed is None: + return False + + seed_everything(seed) + return True + + +def get_shape(t): + if torch.is_tensor(t): + return tuple(t.shape) + elif isinstance(t, dict): + return {n: get_shape(q) for n, q in t.items()} + elif isinstance(t, (list, tuple)): + return [get_shape(q) for q in t] + elif isinstance(t, numbers.Number): + return type(t) + else: + raise ValueError('unexpected type {}'.format(type(t))) + + +def get_has_ddp_rank(): + master_port = os.environ.get('MASTER_PORT', None) + node_rank = os.environ.get('NODE_RANK', None) + local_rank = os.environ.get('LOCAL_RANK', None) + world_size = os.environ.get('WORLD_SIZE', None) + has_rank = master_port is not None or node_rank is not None or local_rank is not None or world_size is not None + return has_rank + + +def handle_ddp_subprocess(): + def main_decorator(main_func): + @functools.wraps(main_func) + def new_main(*args, **kwargs): + # Trainer sets MASTER_PORT, NODE_RANK, LOCAL_RANK, WORLD_SIZE + parent_cwd = os.environ.get('TRAINING_PARENT_WORK_DIR', None) + has_parent = parent_cwd is not None + has_rank = get_has_ddp_rank() + assert has_parent == has_rank, f'Inconsistent state: has_parent={has_parent}, has_rank={has_rank}' + + if has_parent: + # we are in the worker + sys.argv.extend([ + f'hydra.run.dir={parent_cwd}', + # 'hydra/hydra_logging=disabled', + # 'hydra/job_logging=disabled' + ]) + # do nothing if this is a top-level process + # TRAINING_PARENT_WORK_DIR is set in handle_ddp_parent_process after hydra initialization + + main_func(*args, **kwargs) + return new_main + return main_decorator + + +def handle_ddp_parent_process(): + parent_cwd = os.environ.get('TRAINING_PARENT_WORK_DIR', None) + has_parent = parent_cwd is not None + has_rank = get_has_ddp_rank() + assert has_parent == has_rank, f'Inconsistent state: has_parent={has_parent}, has_rank={has_rank}' + + if parent_cwd is None: + os.environ['TRAINING_PARENT_WORK_DIR'] = os.getcwd() + + return has_parent diff --git a/third-party/lama/bin/sample_from_dataset.py b/third-party/lama/bin/sample_from_dataset.py new file mode 100755 index 0000000000000000000000000000000000000000..31593b3212454dd0b6f74a39195a34b489df20a1 --- /dev/null +++ b/third-party/lama/bin/sample_from_dataset.py @@ -0,0 +1,87 @@ +#!/usr/bin/env python3 + +import os + +import numpy as np +import tqdm +from skimage import io +from skimage.segmentation import mark_boundaries + +from saicinpainting.evaluation.data import InpaintingDataset +from saicinpainting.evaluation.vis import save_item_for_vis + +def save_mask_for_sidebyside(item, out_file): + mask = item['mask']# > 0.5 + if mask.ndim == 3: + mask = mask[0] + mask = np.clip(mask * 255, 0, 255).astype('uint8') + io.imsave(out_file, mask) + +def save_img_for_sidebyside(item, out_file): + img = np.transpose(item['image'], (1, 2, 0)) + img = np.clip(img * 255, 0, 255).astype('uint8') + io.imsave(out_file, img) + +def save_masked_img_for_sidebyside(item, out_file): + mask = item['mask'] + img = item['image'] + + img = (1-mask) * img + mask + img = np.transpose(img, (1, 2, 0)) + + img = np.clip(img * 255, 0, 255).astype('uint8') + io.imsave(out_file, img) + +def main(args): + dataset = InpaintingDataset(args.datadir, img_suffix='.png') + + area_bins = np.linspace(0, 1, args.area_bins + 1) + + heights = [] + widths = [] + image_areas = [] + hole_areas = [] + hole_area_percents = [] + area_bins_count = np.zeros(args.area_bins) + area_bin_titles = [f'{area_bins[i] * 100:.0f}-{area_bins[i + 1] * 100:.0f}' for i in range(args.area_bins)] + + bin2i = [[] for _ in range(args.area_bins)] + + for i, item in enumerate(tqdm.tqdm(dataset)): + h, w = item['image'].shape[1:] + heights.append(h) + widths.append(w) + full_area = h * w + image_areas.append(full_area) + hole_area = (item['mask'] == 1).sum() + hole_areas.append(hole_area) + hole_percent = hole_area / full_area + hole_area_percents.append(hole_percent) + bin_i = np.clip(np.searchsorted(area_bins, hole_percent) - 1, 0, len(area_bins_count) - 1) + area_bins_count[bin_i] += 1 + bin2i[bin_i].append(i) + + os.makedirs(args.outdir, exist_ok=True) + + for bin_i in range(args.area_bins): + bindir = os.path.join(args.outdir, area_bin_titles[bin_i]) + os.makedirs(bindir, exist_ok=True) + bin_idx = bin2i[bin_i] + for sample_i in np.random.choice(bin_idx, size=min(len(bin_idx), args.samples_n), replace=False): + item = dataset[sample_i] + path = os.path.join(bindir, dataset.img_filenames[sample_i].split('/')[-1]) + save_masked_img_for_sidebyside(item, path) + + +if __name__ == '__main__': + import argparse + + aparser = argparse.ArgumentParser() + aparser.add_argument('--datadir', type=str, + help='Path to folder with images and masks (output of gen_mask_dataset.py)') + aparser.add_argument('--outdir', type=str, help='Where to put results') + aparser.add_argument('--samples-n', type=int, default=10, + help='Number of sample images with masks to copy for visualization for each area bin') + aparser.add_argument('--area-bins', type=int, default=10, help='How many area bins to have') + + main(aparser.parse_args()) diff --git a/third-party/lama/bin/side_by_side.py b/third-party/lama/bin/side_by_side.py new file mode 100755 index 0000000000000000000000000000000000000000..8ba7a42a3b8597552b8002d1eb245d5776aff7f7 --- /dev/null +++ b/third-party/lama/bin/side_by_side.py @@ -0,0 +1,76 @@ +#!/usr/bin/env python3 +import os +import random + +import cv2 +import numpy as np + +from saicinpainting.evaluation.data import PrecomputedInpaintingResultsDataset +from saicinpainting.evaluation.utils import load_yaml +from saicinpainting.training.visualizers.base import visualize_mask_and_images + + +def main(args): + config = load_yaml(args.config) + + datasets = [PrecomputedInpaintingResultsDataset(args.datadir, cur_predictdir, **config.dataset_kwargs) + for cur_predictdir in args.predictdirs] + assert len({len(ds) for ds in datasets}) == 1 + len_first = len(datasets[0]) + + indices = list(range(len_first)) + if len_first > args.max_n: + indices = sorted(random.sample(indices, args.max_n)) + + os.makedirs(args.outpath, exist_ok=True) + + filename2i = {} + + keys = ['image'] + [i for i in range(len(datasets))] + for img_i in indices: + try: + mask_fname = os.path.basename(datasets[0].mask_filenames[img_i]) + if mask_fname in filename2i: + filename2i[mask_fname] += 1 + idx = filename2i[mask_fname] + mask_fname_only, ext = os.path.split(mask_fname) + mask_fname = f'{mask_fname_only}_{idx}{ext}' + else: + filename2i[mask_fname] = 1 + + cur_vis_dict = datasets[0][img_i] + for ds_i, ds in enumerate(datasets): + cur_vis_dict[ds_i] = ds[img_i]['inpainted'] + + vis_img = visualize_mask_and_images(cur_vis_dict, keys, + last_without_mask=False, + mask_only_first=True, + black_mask=args.black) + vis_img = np.clip(vis_img * 255, 0, 255).astype('uint8') + + out_fname = os.path.join(args.outpath, mask_fname) + + + + vis_img = cv2.cvtColor(vis_img, cv2.COLOR_RGB2BGR) + cv2.imwrite(out_fname, vis_img) + except Exception as ex: + print(f'Could not process {img_i} due to {ex}') + + +if __name__ == '__main__': + import argparse + + aparser = argparse.ArgumentParser() + aparser.add_argument('--max-n', type=int, default=100, help='Maximum number of images to print') + aparser.add_argument('--black', action='store_true', help='Whether to fill mask on GT with black') + aparser.add_argument('config', type=str, help='Path to evaluation config (e.g. configs/eval1.yaml)') + aparser.add_argument('outpath', type=str, help='Where to put results') + aparser.add_argument('datadir', type=str, + help='Path to folder with images and masks') + aparser.add_argument('predictdirs', type=str, + nargs='+', + help='Path to folders with predicts') + + + main(aparser.parse_args()) diff --git a/third-party/lama/bin/split_tar.py b/third-party/lama/bin/split_tar.py new file mode 100755 index 0000000000000000000000000000000000000000..ac1692addbb4191200c8c871fe356bb80d534c44 --- /dev/null +++ b/third-party/lama/bin/split_tar.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python3 + + +import tqdm +import webdataset as wds + + +def main(args): + input_dataset = wds.Dataset(args.infile) + output_dataset = wds.ShardWriter(args.outpattern) + for rec in tqdm.tqdm(input_dataset): + output_dataset.write(rec) + + +if __name__ == '__main__': + import argparse + + aparser = argparse.ArgumentParser() + aparser.add_argument('infile', type=str) + aparser.add_argument('outpattern', type=str) + + main(aparser.parse_args()) diff --git a/third-party/lama/bin/to_jit.py b/third-party/lama/bin/to_jit.py new file mode 100644 index 0000000000000000000000000000000000000000..8acea396545cdadbc004618a23c78d60c0ed6e95 --- /dev/null +++ b/third-party/lama/bin/to_jit.py @@ -0,0 +1,75 @@ +import os +from pathlib import Path + +import hydra +import torch +import yaml +from omegaconf import OmegaConf +from torch import nn + +from saicinpainting.training.trainers import load_checkpoint +from saicinpainting.utils import register_debug_signal_handlers + + +class JITWrapper(nn.Module): + def __init__(self, model): + super().__init__() + self.model = model + + def forward(self, image, mask): + batch = { + "image": image, + "mask": mask + } + out = self.model(batch) + return out["inpainted"] + + +@hydra.main(config_path="../configs/prediction", config_name="default.yaml") +def main(predict_config: OmegaConf): + register_debug_signal_handlers() # kill -10 <pid> will result in traceback dumped into log + + train_config_path = os.path.join(predict_config.model.path, "config.yaml") + with open(train_config_path, "r") as f: + train_config = OmegaConf.create(yaml.safe_load(f)) + + train_config.training_model.predict_only = True + train_config.visualizer.kind = "noop" + + checkpoint_path = os.path.join( + predict_config.model.path, "models", predict_config.model.checkpoint + ) + model = load_checkpoint( + train_config, checkpoint_path, strict=False, map_location="cpu" + ) + model.eval() + jit_model_wrapper = JITWrapper(model) + + image = torch.rand(1, 3, 120, 120) + mask = torch.rand(1, 1, 120, 120) + output = jit_model_wrapper(image, mask) + + if torch.cuda.is_available(): + device = torch.device("cuda") + else: + device = torch.device("cpu") + + image = image.to(device) + mask = mask.to(device) + traced_model = torch.jit.trace(jit_model_wrapper, (image, mask), strict=False).to(device) + + save_path = Path(predict_config.save_path) + save_path.parent.mkdir(parents=True, exist_ok=True) + + print(f"Saving big-lama.pt model to {save_path}") + traced_model.save(save_path) + + print(f"Checking jit model output...") + jit_model = torch.jit.load(str(save_path)) + jit_output = jit_model(image, mask) + diff = (output - jit_output).abs().sum() + print(f"diff: {diff}") + + +if __name__ == "__main__": + main() diff --git a/third-party/lama/bin/train.py b/third-party/lama/bin/train.py new file mode 100755 index 0000000000000000000000000000000000000000..be9ca8c6ef2a0cb9143ab6a0f4d91f571b691a95 --- /dev/null +++ b/third-party/lama/bin/train.py @@ -0,0 +1,72 @@ +#!/usr/bin/env python3 + +import logging +import os +import sys +import traceback + +os.environ['OMP_NUM_THREADS'] = '1' +os.environ['OPENBLAS_NUM_THREADS'] = '1' +os.environ['MKL_NUM_THREADS'] = '1' +os.environ['VECLIB_MAXIMUM_THREADS'] = '1' +os.environ['NUMEXPR_NUM_THREADS'] = '1' + +import hydra +from omegaconf import OmegaConf +from pytorch_lightning import Trainer +from pytorch_lightning.callbacks import ModelCheckpoint +from pytorch_lightning.loggers import TensorBoardLogger +from pytorch_lightning.plugins import DDPPlugin + +from saicinpainting.training.trainers import make_training_model +from saicinpainting.utils import register_debug_signal_handlers, handle_ddp_subprocess, handle_ddp_parent_process, \ + handle_deterministic_config + +LOGGER = logging.getLogger(__name__) + + +@handle_ddp_subprocess() +@hydra.main(config_path='../configs/training', config_name='tiny_test.yaml') +def main(config: OmegaConf): + try: + need_set_deterministic = handle_deterministic_config(config) + + register_debug_signal_handlers() # kill -10 <pid> will result in traceback dumped into log + + is_in_ddp_subprocess = handle_ddp_parent_process() + + config.visualizer.outdir = os.path.join(os.getcwd(), config.visualizer.outdir) + if not is_in_ddp_subprocess: + LOGGER.info(OmegaConf.to_yaml(config)) + OmegaConf.save(config, os.path.join(os.getcwd(), 'config.yaml')) + + checkpoints_dir = os.path.join(os.getcwd(), 'models') + os.makedirs(checkpoints_dir, exist_ok=True) + + # there is no need to suppress this logger in ddp, because it handles rank on its own + metrics_logger = TensorBoardLogger(config.location.tb_dir, name=os.path.basename(os.getcwd())) + metrics_logger.log_hyperparams(config) + + training_model = make_training_model(config) + + trainer_kwargs = OmegaConf.to_container(config.trainer.kwargs, resolve=True) + if need_set_deterministic: + trainer_kwargs['deterministic'] = True + + trainer = Trainer( + # there is no need to suppress checkpointing in ddp, because it handles rank on its own + callbacks=ModelCheckpoint(dirpath=checkpoints_dir, **config.trainer.checkpoint_kwargs), + logger=metrics_logger, + default_root_dir=os.getcwd(), + **trainer_kwargs + ) + trainer.fit(training_model) + except KeyboardInterrupt: + LOGGER.warning('Interrupted by user') + except Exception as ex: + LOGGER.critical(f'Training failed due to {ex}:\n{traceback.format_exc()}') + sys.exit(1) + + +if __name__ == '__main__': + main() diff --git a/third-party/lama/colab/LaMa_inpainting.ipynb b/third-party/lama/colab/LaMa_inpainting.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d905b5a30e9aff0021d093f4f944e57592f04208 --- /dev/null +++ b/third-party/lama/colab/LaMa_inpainting.ipynb @@ -0,0 +1,409 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "_pRpIwnaOnb3" + }, + "source": [ + "# 🦙 **LaMa: Resolution-robust Large Mask Inpainting with Fourier Convolutions**\n", + "\n", + "[[Project page](https://advimman.github.io/lama-project/)] [[GitHub](https://github.com/advimman/lama)] [[arXiv](https://arxiv.org/abs/2109.07161)] [[Supplementary](https://ashukha.com/projects/lama_21/lama_supmat_2021.pdf)] [[BibTeX](https://senya-ashukha.github.io/projects/lama_21/paper.txt)]\n", + "\n", + "<p align=\"center\" \"font-size:30px;\">\n", + "Our model generalizes surprisingly well to much higher resolutions (~2k❗️) than it saw during training (256x256), and achieves the excellent performance even in challenging scenarios, e.g. completion of periodic structures.\n", + "</p>\n", + "\n", + "# Try it yourself!👇\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "id": "RwXRMaNHW4r5" + }, + "outputs": [], + "source": [ + "#@title Run this sell to set everything up\n", + "print('\\n> Cloning the repo')\n", + "!git clone https://github.com/advimman/lama.git\n", + "\n", + "print('\\n> Install dependencies')\n", + "!pip install torch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 torchtext==0.9\n", + "!pip install -r lama/requirements.txt --quiet\n", + "!pip install wget --quiet\n", + "!pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html --quiet\n", + "\n", + "\n", + "print('\\n> Changing the dir to:')\n", + "%cd /content/lama\n", + "\n", + "print('\\n> Download the model')\n", + "!curl -L $(yadisk-direct https://disk.yandex.ru/d/ouP6l8VJ0HpMZg) -o big-lama.zip\n", + "!unzip big-lama.zip\n", + "\n", + "print('>fixing opencv')\n", + "!pip uninstall opencv-python-headless -y --quiet\n", + "!pip install opencv-python-headless==4.1.2.30 --quiet\n", + "\n", + "\n", + "print('\\n> Init mask-drawing code')\n", + "import base64, os\n", + "from IPython.display import HTML, Image\n", + "from google.colab.output import eval_js\n", + "from base64 import b64decode\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import wget\n", + "from shutil import copyfile\n", + "import shutil\n", + "\n", + "\n", + "\n", + "canvas_html = \"\"\"\n", + "<style>\n", + ".button {\n", + " background-color: #4CAF50;\n", + " border: none;\n", + " color: white;\n", + " padding: 15px 32px;\n", + " text-align: center;\n", + " text-decoration: none;\n", + " display: inline-block;\n", + " font-size: 16px;\n", + " margin: 4px 2px;\n", + " cursor: pointer;\n", + "}\n", + "</style>\n", + "<canvas1 width=%d height=%d>\n", + "</canvas1>\n", + "<canvas width=%d height=%d>\n", + "</canvas>\n", + "\n", + "<button class=\"button\">Finish</button>\n", + "<script>\n", + "var canvas = document.querySelector('canvas')\n", + "var ctx = canvas.getContext('2d')\n", + "\n", + "var canvas1 = document.querySelector('canvas1')\n", + "var ctx1 = canvas.getContext('2d')\n", + "\n", + "\n", + "ctx.strokeStyle = 'red';\n", + "\n", + "var img = new Image();\n", + "img.src = \"data:image/%s;charset=utf-8;base64,%s\";\n", + "console.log(img)\n", + "img.onload = function() {\n", + " ctx1.drawImage(img, 0, 0);\n", + "};\n", + "img.crossOrigin = 'Anonymous';\n", + "\n", + "ctx.clearRect(0, 0, canvas.width, canvas.height);\n", + "\n", + "ctx.lineWidth = %d\n", + "var button = document.querySelector('button')\n", + "var mouse = {x: 0, y: 0}\n", + "\n", + "canvas.addEventListener('mousemove', function(e) {\n", + " mouse.x = e.pageX - this.offsetLeft\n", + " mouse.y = e.pageY - this.offsetTop\n", + "})\n", + "canvas.onmousedown = ()=>{\n", + " ctx.beginPath()\n", + " ctx.moveTo(mouse.x, mouse.y)\n", + " canvas.addEventListener('mousemove', onPaint)\n", + "}\n", + "canvas.onmouseup = ()=>{\n", + " canvas.removeEventListener('mousemove', onPaint)\n", + "}\n", + "var onPaint = ()=>{\n", + " ctx.lineTo(mouse.x, mouse.y)\n", + " ctx.stroke()\n", + "}\n", + "\n", + "var data = new Promise(resolve=>{\n", + " button.onclick = ()=>{\n", + " resolve(canvas.toDataURL('image/png'))\n", + " }\n", + "})\n", + "</script>\n", + "\"\"\"\n", + "\n", + "def draw(imgm, filename='drawing.png', w=400, h=200, line_width=1):\n", + " display(HTML(canvas_html % (w, h, w,h, filename.split('.')[-1], imgm, line_width)))\n", + " data = eval_js(\"data\")\n", + " binary = b64decode(data.split(',')[1])\n", + " with open(filename, 'wb') as f:\n", + " f.write(binary)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "23WaUHiJeyBO" + }, + "source": [ + "<center>\n", + "<h1 style=\"font-size:10vw\"><b>Predefined photo</b>: uncomment any line\n", + "<br>\n", + "<b>Local file</b>: leave the <tt>fname = None</tt></h1>\n", + "</center>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IFIDDD4IhPXd" + }, + "outputs": [], + "source": [ + "fname = None\n", + "# fname = 'https://ic.pics.livejournal.com/mostovoy/28566193/1224276/1224276_original.jpg' # <-in the example\n", + "# fname = 'https://raw.githubusercontent.com/senya-ashukha/senya-ashukha.github.io/master/images/1010286.jpeg'\n", + "# fname = 'https://raw.githubusercontent.com/senya-ashukha/senya-ashukha.github.io/master/images/1010287.jpeg'\n", + "# fname = \"https://raw.githubusercontent.com/senya-ashukha/senya-ashukha.github.io/master/images/alex.jpg\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "-VZWySTMeGDM", + "outputId": "c42a411e-e84a-415c-ea2c-182abf34324c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Will use ./data_for_prediction/1224276_original.jpg for inpainting\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "<style>\n", + ".button {\n", + " background-color: #4CAF50;\n", + " border: none;\n", + " color: white;\n", + " padding: 15px 32px;\n", + " text-align: center;\n", + " text-decoration: none;\n", + " display: inline-block;\n", + " font-size: 16px;\n", + " margin: 4px 2px;\n", + " cursor: pointer;\n", + "}\n", + "</style>\n", + "<canvas1 width=1200 height=800>\n", + "</canvas1>\n", + "<canvas width=1200 height=800>\n", + "</canvas>\n", + "\n", + "<button class=\"button\">Finish</button>\n", + "<script>\n", + "var canvas = document.querySelector('canvas')\n", + "var ctx = canvas.getContext('2d')\n", + "\n", + "var canvas1 = document.querySelector('canvas1')\n", + "var ctx1 = canvas.getContext('2d')\n", + "\n", + "\n", + "ctx.strokeStyle = 'red';\n", + "\n", + "var img = new Image();\n", + "img.src = 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\";\n", + "console.log(img)\n", + "img.onload = function() {\n", + " ctx1.drawImage(img, 0, 0);\n", + "};\n", + "img.crossOrigin = 'Anonymous';\n", + "\n", + "ctx.clearRect(0, 0, canvas.width, canvas.height);\n", + "\n", + "ctx.lineWidth = 48\n", + "var button = document.querySelector('button')\n", + "var mouse = {x: 0, y: 0}\n", + "\n", + "canvas.addEventListener('mousemove', function(e) {\n", + " mouse.x = e.pageX - this.offsetLeft\n", + " mouse.y = e.pageY - this.offsetTop\n", + "})\n", + "canvas.onmousedown = ()=>{\n", + " ctx.beginPath()\n", + " ctx.moveTo(mouse.x, mouse.y)\n", + " canvas.addEventListener('mousemove', onPaint)\n", + "}\n", + "canvas.onmouseup = ()=>{\n", + " canvas.removeEventListener('mousemove', onPaint)\n", + "}\n", + "var onPaint = ()=>{\n", + " ctx.lineTo(mouse.x, mouse.y)\n", + " ctx.stroke()\n", + "}\n", + "\n", + "var data = new Promise(resolve=>{\n", + " button.onclick = ()=>{\n", + " resolve(canvas.toDataURL('image/png'))\n", + " }\n", + "})\n", + "</script>\n" + ], + "text/plain": [ + "<IPython.core.display.HTML object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 3000x1000 with 3 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run inpainting\n", + "100% 1/1 [00:01<00:00, 1.73s/it]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 3000x1000 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#@title Draw a Mask, Press Finish, Wait for Inpainting\n", + "\n", + "if fname is None:\n", + " from google.colab import files\n", + " files = files.upload()\n", + " fname = list(files.keys())[0]\n", + "else:\n", + " fname = wget.download(fname)\n", + "\n", + "shutil.rmtree('./data_for_prediction', ignore_errors=True)\n", + "!mkdir data_for_prediction\n", + "\n", + "copyfile(fname, f'./data_for_prediction/{fname}')\n", + "os.remove(fname)\n", + "fname = f'./data_for_prediction/{fname}'\n", + "\n", + "image64 = base64.b64encode(open(fname, 'rb').read())\n", + "image64 = image64.decode('utf-8')\n", + "\n", + "print(f'Will use {fname} for inpainting')\n", + "img = np.array(plt.imread(f'{fname}')[:,:,:3])\n", + "\n", + "draw(image64, filename=f\"./{fname.split('.')[1]}_mask.png\", w=img.shape[1], h=img.shape[0], line_width=0.04*img.shape[1])\n", + "#@title Show a masked image and save a mask\n", + "import matplotlib.pyplot as plt\n", + "plt.rcParams[\"figure.figsize\"] = (15,5)\n", + "plt.rcParams['figure.dpi'] = 200\n", + "plt.subplot(131)\n", + "with_mask = np.array(plt.imread(f\"./{fname.split('.')[1]}_mask.png\")[:,:,:3])\n", + "mask = (with_mask[:,:,0]==1)*(with_mask[:,:,1]==0)*(with_mask[:,:,2]==0)\n", + "plt.imshow(mask, cmap='gray')\n", + "plt.axis('off')\n", + "plt.title('mask')\n", + "plt.imsave(f\"./{fname.split('.')[1]}_mask.png\",mask, cmap='gray')\n", + "\n", + "plt.subplot(132)\n", + "img = np.array(plt.imread(f'{fname}')[:,:,:3])\n", + "plt.imshow(img)\n", + "plt.axis('off')\n", + "plt.title('img')\n", + "\n", + "plt.subplot(133)\n", + "img = np.array((1-mask.reshape(mask.shape[0], mask.shape[1], -1))*plt.imread(fname)[:,:,:3])\n", + "_=plt.imshow(img)\n", + "_=plt.axis('off')\n", + "_=plt.title('img * mask')\n", + "plt.show()\n", + "\n", + "print('Run inpainting')\n", + "if '.jpeg' in fname:\n", + " !PYTHONPATH=. TORCH_HOME=$(pwd) python3 bin/predict.py model.path=$(pwd)/big-lama indir=$(pwd)/data_for_prediction outdir=/content/output dataset.img_suffix=.jpeg > /dev/null\n", + "elif '.jpg' in fname:\n", + " !PYTHONPATH=. TORCH_HOME=$(pwd) python3 bin/predict.py model.path=$(pwd)/big-lama indir=$(pwd)/data_for_prediction outdir=/content/output dataset.img_suffix=.jpg > /dev/null\n", + "elif '.png' in fname:\n", + " !PYTHONPATH=. TORCH_HOME=$(pwd) python3 bin/predict.py model.path=$(pwd)/big-lama indir=$(pwd)/data_for_prediction outdir=/content/output dataset.img_suffix=.png > /dev/null\n", + "else:\n", + " print(f'Error: unknown suffix .{fname.split(\".\")[-1]} use [.png, .jpeg, .jpg]')\n", + "\n", + "plt.rcParams['figure.dpi'] = 200\n", + "plt.imshow(plt.imread(f\"/content/output/{fname.split('.')[1].split('/')[2]}_mask.png\"))\n", + "_=plt.axis('off')\n", + "_=plt.title('inpainting result')\n", + "plt.show()\n", + "fname = None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Ug9vfkBHqxzZ" + }, + "outputs": [], + "source": [ + "" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "LaMa-inpainting.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/third-party/lama/conda_env.yml b/third-party/lama/conda_env.yml new file mode 100644 index 0000000000000000000000000000000000000000..818f5405527b5f1b276d4d21a51fa8fbe3ffb2ae --- /dev/null +++ b/third-party/lama/conda_env.yml @@ -0,0 +1,165 @@ +name: lama +channels: + - defaults + - conda-forge +dependencies: + - _libgcc_mutex=0.1=main + - _openmp_mutex=4.5=1_gnu + - absl-py=0.13.0=py36h06a4308_0 + - aiohttp=3.7.4.post0=py36h7f8727e_2 + - antlr-python-runtime=4.8=py36h9f0ad1d_2 + - async-timeout=3.0.1=py36h06a4308_0 + - attrs=21.2.0=pyhd3eb1b0_0 + - blas=1.0=mkl + - blinker=1.4=py36h06a4308_0 + - brotlipy=0.7.0=py36h27cfd23_1003 + - bzip2=1.0.8=h7b6447c_0 + - c-ares=1.17.1=h27cfd23_0 + - ca-certificates=2021.7.5=h06a4308_1 + - cachetools=4.2.2=pyhd3eb1b0_0 + - certifi=2021.5.30=py36h06a4308_0 + - cffi=1.14.6=py36h400218f_0 + - chardet=4.0.0=py36h06a4308_1003 + - charset-normalizer=2.0.4=pyhd3eb1b0_0 + - click=8.0.1=pyhd3eb1b0_0 + - cloudpickle=2.0.0=pyhd3eb1b0_0 + - coverage=5.5=py36h27cfd23_2 + - cryptography=3.4.7=py36hd23ed53_0 + - cudatoolkit=10.2.89=hfd86e86_1 + - cycler=0.10.0=py36_0 + - cython=0.29.24=py36h295c915_0 + - cytoolz=0.11.0=py36h7b6447c_0 + - dask-core=1.1.4=py36_1 + - dataclasses=0.8=pyh4f3eec9_6 + - dbus=1.13.18=hb2f20db_0 + - decorator=5.0.9=pyhd3eb1b0_0 + - easydict=1.9=py_0 + - expat=2.4.1=h2531618_2 + - ffmpeg=4.2.2=h20bf706_0 + - fontconfig=2.13.1=h6c09931_0 + - freetype=2.10.4=h5ab3b9f_0 + - fsspec=2021.8.1=pyhd3eb1b0_0 + - future=0.18.2=py36_1 + - glib=2.69.1=h5202010_0 + - gmp=6.2.1=h2531618_2 + - gnutls=3.6.15=he1e5248_0 + - google-auth=1.33.0=pyhd3eb1b0_0 + - google-auth-oauthlib=0.4.4=pyhd3eb1b0_0 + - grpcio=1.36.1=py36h2157cd5_1 + - gst-plugins-base=1.14.0=h8213a91_2 + - gstreamer=1.14.0=h28cd5cc_2 + - hydra-core=1.1.0=pyhd8ed1ab_0 + - icu=58.2=he6710b0_3 + - idna=3.2=pyhd3eb1b0_0 + - idna_ssl=1.1.0=py36h06a4308_0 + - imageio=2.9.0=pyhd3eb1b0_0 + - importlib-metadata=4.8.1=py36h06a4308_0 + - importlib_resources=5.2.0=pyhd3eb1b0_1 + - intel-openmp=2021.3.0=h06a4308_3350 + - joblib=1.0.1=pyhd3eb1b0_0 + - jpeg=9b=h024ee3a_2 + - kiwisolver=1.3.1=py36h2531618_0 + - lame=3.100=h7b6447c_0 + - lcms2=2.12=h3be6417_0 + - ld_impl_linux-64=2.35.1=h7274673_9 + - libblas=3.9.0=11_linux64_mkl + - libcblas=3.9.0=11_linux64_mkl + - libffi=3.3=he6710b0_2 + - libgcc-ng=9.3.0=h5101ec6_17 + - libgfortran-ng=9.3.0=ha5ec8a7_17 + - libgfortran5=9.3.0=ha5ec8a7_17 + - libgomp=9.3.0=h5101ec6_17 + - libidn2=2.3.2=h7f8727e_0 + - liblapack=3.9.0=11_linux64_mkl + - libopus=1.3.1=h7b6447c_0 + - libpng=1.6.37=hbc83047_0 + - libprotobuf=3.17.2=h4ff587b_1 + - libstdcxx-ng=9.3.0=hd4cf53a_17 + - libtasn1=4.16.0=h27cfd23_0 + - libtiff=4.2.0=h85742a9_0 + - libunistring=0.9.10=h27cfd23_0 + - libuuid=1.0.3=h1bed415_2 + - libuv=1.40.0=h7b6447c_0 + - libvpx=1.7.0=h439df22_0 + - libwebp-base=1.2.0=h27cfd23_0 + - libxcb=1.14=h7b6447c_0 + - libxml2=2.9.12=h03d6c58_0 + - lz4-c=1.9.3=h295c915_1 + - markdown=3.3.4=py36h06a4308_0 + - matplotlib=3.3.4=py36h06a4308_0 + - matplotlib-base=3.3.4=py36h62a2d02_0 + - mkl=2021.3.0=h06a4308_520 + - multidict=5.1.0=py36h27cfd23_2 + - ncurses=6.2=he6710b0_1 + - nettle=3.7.3=hbbd107a_1 + - networkx=2.2=py36_1 + - ninja=1.10.2=hff7bd54_1 + - numpy=1.19.5=py36hfc0c790_2 + - oauthlib=3.1.1=pyhd3eb1b0_0 + - olefile=0.46=py36_0 + - omegaconf=2.1.1=py36h5fab9bb_0 + - openh264=2.1.0=hd408876_0 + - openjpeg=2.4.0=h3ad879b_0 + - openssl=1.1.1l=h7f8727e_0 + - packaging=21.0=pyhd3eb1b0_0 + - pandas=1.1.5=py36h284efc9_0 + - pcre=8.45=h295c915_0 + - pillow=8.3.1=py36h2c7a002_0 + - pip=21.0.1=py36h06a4308_0 + - protobuf=3.17.2=py36h295c915_0 + - pyasn1=0.4.8=pyhd3eb1b0_0 + - pyasn1-modules=0.2.8=py_0 + - pycparser=2.20=py_2 + - pyjwt=2.1.0=py36h06a4308_0 + - pyopenssl=20.0.1=pyhd3eb1b0_1 + - pyparsing=2.4.7=pyhd3eb1b0_0 + - pyqt=5.9.2=py36h05f1152_2 + - pysocks=1.7.1=py36h06a4308_0 + - python=3.6.13=h12debd9_1 + - python-dateutil=2.8.2=pyhd3eb1b0_0 + - python_abi=3.6=2_cp36m + - pytz=2021.1=pyhd3eb1b0_0 + - pywavelets=1.1.1=py36h7b6447c_2 + - pyyaml=5.4.1=py36h27cfd23_1 + - qt=5.9.7=h5867ecd_1 + - readline=8.1=h27cfd23_0 + - requests=2.26.0=pyhd3eb1b0_0 + - requests-oauthlib=1.3.0=py_0 + - rsa=4.7.2=pyhd3eb1b0_1 + - scikit-image=0.17.2=py36h284efc9_4 + - scikit-learn=0.24.2=py36ha9443f7_0 + - scipy=1.5.3=py36h9e8f40b_0 + - setuptools=58.0.4=py36h06a4308_0 + - sip=4.19.8=py36hf484d3e_0 + - six=1.16.0=pyhd3eb1b0_0 + - sqlite=3.36.0=hc218d9a_0 + - tabulate=0.8.9=py36h06a4308_0 + - tensorboard=2.4.0=pyhc547734_0 + - tensorboard-plugin-wit=1.6.0=py_0 + - threadpoolctl=2.2.0=pyh0d69192_0 + - tifffile=2020.10.1=py36hdd07704_2 + - tk=8.6.11=h1ccaba5_0 + - toolz=0.11.1=pyhd3eb1b0_0 + - tqdm=4.62.2=pyhd3eb1b0_1 + - typing-extensions=3.10.0.2=hd3eb1b0_0 + - typing_extensions=3.10.0.2=pyh06a4308_0 + - urllib3=1.26.6=pyhd3eb1b0_1 + - werkzeug=2.0.1=pyhd3eb1b0_0 + - wheel=0.37.0=pyhd3eb1b0_1 + - x264=1!157.20191217=h7b6447c_0 + - xz=5.2.5=h7b6447c_0 + - yaml=0.2.5=h7b6447c_0 + - yarl=1.6.3=py36h27cfd23_0 + - zipp=3.5.0=pyhd3eb1b0_0 + - zlib=1.2.11=h7b6447c_3 + - zstd=1.4.9=haebb681_0 + - pip: + - albumentations==0.5.2 + - braceexpand==0.1.7 + - imgaug==0.4.0 + - kornia==0.5.0 + - opencv-python==4.5.3.56 + - opencv-python-headless==4.5.3.56 + - shapely==1.7.1 + - webdataset==0.1.76 + - wldhx-yadisk-direct==0.0.6 diff --git a/third-party/lama/configs/analyze_mask_errors.yaml b/third-party/lama/configs/analyze_mask_errors.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3ed3924290c05369db65afd2380df9af48bf5d69 --- /dev/null +++ b/third-party/lama/configs/analyze_mask_errors.yaml @@ -0,0 +1,7 @@ +dataset_kwargs: + img_suffix: .jpg + inpainted_suffix: .jpg + +take_global_top: 30 +take_worst_best_top: 30 +take_overlapping_top: 30 \ No newline at end of file diff --git a/third-party/lama/configs/data_gen/random_medium_256.yaml b/third-party/lama/configs/data_gen/random_medium_256.yaml new file mode 100644 index 0000000000000000000000000000000000000000..34c7f9e7802bdaa7422034560c6a6a2238f8531b --- /dev/null +++ b/third-party/lama/configs/data_gen/random_medium_256.yaml @@ -0,0 +1,33 @@ +generator_kind: random + +mask_generator_kwargs: + irregular_proba: 1 + irregular_kwargs: + min_times: 4 + max_times: 5 + max_width: 50 + max_angle: 4 + max_len: 100 + + box_proba: 0.3 + box_kwargs: + margin: 0 + bbox_min_size: 10 + bbox_max_size: 50 + max_times: 5 + min_times: 1 + + segm_proba: 0 + squares_proba: 0 + + variants_n: 5 + +max_masks_per_image: 1 + +cropping: + out_min_size: 256 + handle_small_mode: upscale + out_square_crop: True + crop_min_overlap: 1 + +max_tamper_area: 0.5 diff --git a/third-party/lama/configs/data_gen/random_medium_512.yaml b/third-party/lama/configs/data_gen/random_medium_512.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2ea33832aa379f5a61c5810736184a50c36faac0 --- /dev/null +++ b/third-party/lama/configs/data_gen/random_medium_512.yaml @@ -0,0 +1,33 @@ +generator_kind: random + +mask_generator_kwargs: + irregular_proba: 1 + irregular_kwargs: + min_times: 4 + max_times: 10 + max_width: 100 + max_angle: 4 + max_len: 200 + + box_proba: 0.3 + box_kwargs: + margin: 0 + bbox_min_size: 30 + bbox_max_size: 150 + max_times: 5 + min_times: 1 + + segm_proba: 0 + squares_proba: 0 + + variants_n: 5 + +max_masks_per_image: 1 + +cropping: + out_min_size: 512 + handle_small_mode: upscale + out_square_crop: True + crop_min_overlap: 1 + +max_tamper_area: 0.5 diff --git a/third-party/lama/configs/data_gen/random_thick_256.yaml b/third-party/lama/configs/data_gen/random_thick_256.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9ade0cfc5e2c56916da8c642968618617687f6db --- /dev/null +++ b/third-party/lama/configs/data_gen/random_thick_256.yaml @@ -0,0 +1,33 @@ +generator_kind: random + +mask_generator_kwargs: + irregular_proba: 1 + irregular_kwargs: + min_times: 1 + max_times: 5 + max_width: 100 + max_angle: 4 + max_len: 200 + + box_proba: 0.3 + box_kwargs: + margin: 10 + bbox_min_size: 30 + bbox_max_size: 150 + max_times: 3 + min_times: 1 + + segm_proba: 0 + squares_proba: 0 + + variants_n: 5 + +max_masks_per_image: 1 + +cropping: + out_min_size: 256 + handle_small_mode: upscale + out_square_crop: True + crop_min_overlap: 1 + +max_tamper_area: 0.5 diff --git a/third-party/lama/configs/data_gen/random_thick_512.yaml b/third-party/lama/configs/data_gen/random_thick_512.yaml new file mode 100644 index 0000000000000000000000000000000000000000..17b7a7689cf02d094b958393aa397c49649e36f6 --- /dev/null +++ b/third-party/lama/configs/data_gen/random_thick_512.yaml @@ -0,0 +1,33 @@ +generator_kind: random + +mask_generator_kwargs: + irregular_proba: 1 + irregular_kwargs: + min_times: 1 + max_times: 5 + max_width: 250 + max_angle: 4 + max_len: 450 + + box_proba: 0.3 + box_kwargs: + margin: 10 + bbox_min_size: 30 + bbox_max_size: 300 + max_times: 4 + min_times: 1 + + segm_proba: 0 + squares_proba: 0 + + variants_n: 5 + +max_masks_per_image: 1 + +cropping: + out_min_size: 512 + handle_small_mode: upscale + out_square_crop: True + crop_min_overlap: 1 + +max_tamper_area: 0.5 diff --git a/third-party/lama/configs/data_gen/random_thin_256.yaml b/third-party/lama/configs/data_gen/random_thin_256.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0bc05cc7b60b522d915c0c08f8fa81a673535f34 --- /dev/null +++ b/third-party/lama/configs/data_gen/random_thin_256.yaml @@ -0,0 +1,25 @@ +generator_kind: random + +mask_generator_kwargs: + irregular_proba: 1 + irregular_kwargs: + min_times: 4 + max_times: 50 + max_width: 10 + max_angle: 4 + max_len: 40 + box_proba: 0 + segm_proba: 0 + squares_proba: 0 + + variants_n: 5 + +max_masks_per_image: 1 + +cropping: + out_min_size: 256 + handle_small_mode: upscale + out_square_crop: True + crop_min_overlap: 1 + +max_tamper_area: 0.5 diff --git a/third-party/lama/configs/data_gen/random_thin_512.yaml b/third-party/lama/configs/data_gen/random_thin_512.yaml new file mode 100644 index 0000000000000000000000000000000000000000..159fb64b540922b04f257cfdf433a2c9ff60dcf3 --- /dev/null +++ b/third-party/lama/configs/data_gen/random_thin_512.yaml @@ -0,0 +1,25 @@ +generator_kind: random + +mask_generator_kwargs: + irregular_proba: 1 + irregular_kwargs: + min_times: 4 + max_times: 70 + max_width: 20 + max_angle: 4 + max_len: 100 + box_proba: 0 + segm_proba: 0 + squares_proba: 0 + + variants_n: 5 + +max_masks_per_image: 1 + +cropping: + out_min_size: 512 + handle_small_mode: upscale + out_square_crop: True + crop_min_overlap: 1 + +max_tamper_area: 0.5 diff --git a/third-party/lama/configs/debug_mask_gen.yaml b/third-party/lama/configs/debug_mask_gen.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c4258157fffeea1d8416e71ef19defcd4184e98e --- /dev/null +++ b/third-party/lama/configs/debug_mask_gen.yaml @@ -0,0 +1,5 @@ +img_ext: .jpg + +gen_kwargs: + mask_size: 200 + step: 0.5 diff --git a/third-party/lama/configs/eval1.yaml b/third-party/lama/configs/eval1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9ebe4e05fbe2b37ac5b60237e21a6dcadba011b8 --- /dev/null +++ b/third-party/lama/configs/eval1.yaml @@ -0,0 +1,6 @@ +evaluator_kwargs: + batch_size: 8 + +dataset_kwargs: + img_suffix: .png + inpainted_suffix: .jpg \ No newline at end of file diff --git a/third-party/lama/configs/eval2.yaml b/third-party/lama/configs/eval2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6dfb35faeb7b0a96e6e69d4bc32857f0e308456e --- /dev/null +++ b/third-party/lama/configs/eval2.yaml @@ -0,0 +1,7 @@ +evaluator_kwargs: + batch_size: 8 + device: cuda + +dataset_kwargs: + img_suffix: .png + inpainted_suffix: .png \ No newline at end of file diff --git a/third-party/lama/configs/eval2_cpu.yaml b/third-party/lama/configs/eval2_cpu.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ba152eb7825e90b8a05b99b968380522e0d6ebee --- /dev/null +++ b/third-party/lama/configs/eval2_cpu.yaml @@ -0,0 +1,7 @@ +evaluator_kwargs: + batch_size: 8 + device: cpu + +dataset_kwargs: + img_suffix: .png + inpainted_suffix: .png \ No newline at end of file diff --git a/third-party/lama/configs/eval2_gpu.yaml b/third-party/lama/configs/eval2_gpu.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6ffab909ec215d6a0bc7056dabca89b44fee416c --- /dev/null +++ b/third-party/lama/configs/eval2_gpu.yaml @@ -0,0 +1,6 @@ +evaluator_kwargs: + batch_size: 8 + +dataset_kwargs: + img_suffix: .png + inpainted_suffix: .png \ No newline at end of file diff --git a/third-party/lama/configs/eval2_jpg.yaml b/third-party/lama/configs/eval2_jpg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9ebe4e05fbe2b37ac5b60237e21a6dcadba011b8 --- /dev/null +++ b/third-party/lama/configs/eval2_jpg.yaml @@ -0,0 +1,6 @@ +evaluator_kwargs: + batch_size: 8 + +dataset_kwargs: + img_suffix: .png + inpainted_suffix: .jpg \ No newline at end of file diff --git a/third-party/lama/configs/eval2_segm.yaml b/third-party/lama/configs/eval2_segm.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a01e7b0f1eff00bf57d8096dad148dd155798798 --- /dev/null +++ b/third-party/lama/configs/eval2_segm.yaml @@ -0,0 +1,10 @@ +evaluator_kwargs: + batch_size: 8 + +dataset_kwargs: + img_suffix: .png + inpainted_suffix: .png + +segmentation: + enable: True + weights_path: ${TORCH_HOME} diff --git a/third-party/lama/configs/eval2_segm_test.yaml b/third-party/lama/configs/eval2_segm_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..11ac85cbbe032002c5927fcc150e1f679d9d273a --- /dev/null +++ b/third-party/lama/configs/eval2_segm_test.yaml @@ -0,0 +1,11 @@ +evaluator_kwargs: + batch_size: 1 + +dataset_kwargs: + img_suffix: _input.png + inpainted_suffix: .png + pad_out_to_modulo: 8 + +segmentation: + enable: True + weights_path: ${TORCH_HOME} diff --git a/third-party/lama/configs/eval2_test.yaml b/third-party/lama/configs/eval2_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..970e35c81df97a4c59d04eb237127719dbbce475 --- /dev/null +++ b/third-party/lama/configs/eval2_test.yaml @@ -0,0 +1,7 @@ +evaluator_kwargs: + batch_size: 1 + +dataset_kwargs: + img_suffix: _input.png + inpainted_suffix: .png + pad_out_to_modulo: 8 diff --git a/third-party/lama/configs/places2-categories_157.txt b/third-party/lama/configs/places2-categories_157.txt new file mode 100644 index 0000000000000000000000000000000000000000..b717681993346d6f776c624019e5cfe46376147b --- /dev/null +++ b/third-party/lama/configs/places2-categories_157.txt @@ -0,0 +1,157 @@ +/a/airplane_cabin 1 +/a/airport_terminal 2 +/a/alcove 3 +/a/alley 4 +/a/amphitheater 5 +/a/amusement_park 7 +/a/apartment_building/outdoor 8 +/a/aqueduct 10 +/a/arcade 11 +/a/arch 12 +/a/archive 14 +/a/art_gallery 19 +/a/artists_loft 22 +/a/assembly_line 23 +/a/atrium/public 25 +/a/attic 26 +/a/auditorium 27 +/b/bakery/shop 31 +/b/balcony/exterior 32 +/b/balcony/interior 33 +/b/ballroom 35 +/b/banquet_hall 38 +/b/barndoor 41 +/b/basement 43 +/b/basketball_court/indoor 44 +/b/bathroom 45 +/b/bazaar/indoor 46 +/b/bazaar/outdoor 47 +/b/beach_house 49 +/b/bedchamber 51 +/b/bedroom 52 +/b/berth 55 +/b/boardwalk 57 +/b/boathouse 59 +/b/bookstore 60 +/b/booth/indoor 61 +/b/bow_window/indoor 63 +/b/bowling_alley 64 +/b/bridge 66 +/b/building_facade 67 +/b/bus_interior 70 +/b/bus_station/indoor 71 +/c/cabin/outdoor 74 +/c/campus 77 +/c/canal/urban 79 +/c/candy_store 80 +/c/carrousel 83 +/c/castle 84 +/c/chalet 87 +/c/childs_room 89 +/c/church/indoor 90 +/c/church/outdoor 91 +/c/closet 95 +/c/conference_center 101 +/c/conference_room 102 +/c/construction_site 103 +/c/corridor 106 +/c/cottage 107 +/c/courthouse 108 +/c/courtyard 109 +/d/delicatessen 114 +/d/department_store 115 +/d/diner/outdoor 119 +/d/dining_hall 120 +/d/dining_room 121 +/d/doorway/outdoor 123 +/d/dorm_room 124 +/d/downtown 125 +/d/driveway 127 +/e/elevator/door 129 +/e/elevator_lobby 130 +/e/elevator_shaft 131 +/e/embassy 132 +/e/entrance_hall 134 +/e/escalator/indoor 135 +/f/fastfood_restaurant 139 +/f/fire_escape 143 +/f/fire_station 144 +/f/food_court 148 +/g/galley 155 +/g/garage/outdoor 157 +/g/gas_station 158 +/g/gazebo/exterior 159 +/g/general_store/indoor 160 +/g/general_store/outdoor 161 +/g/greenhouse/outdoor 166 +/g/gymnasium/indoor 168 +/h/hangar/outdoor 170 +/h/hardware_store 172 +/h/home_office 176 +/h/home_theater 177 +/h/hospital 178 +/h/hotel/outdoor 181 +/h/hotel_room 182 +/h/house 183 +/h/hunting_lodge/outdoor 184 +/i/industrial_area 192 +/i/inn/outdoor 193 +/j/jacuzzi/indoor 195 +/j/jail_cell 196 +/k/kasbah 200 +/k/kitchen 203 +/l/laundromat 208 +/l/library/indoor 212 +/l/library/outdoor 213 +/l/lighthouse 214 +/l/living_room 215 +/l/loading_dock 216 +/l/lobby 217 +/l/lock_chamber 218 +/m/mansion 220 +/m/manufactured_home 221 +/m/mausoleum 226 +/m/medina 227 +/m/mezzanine 228 +/m/mosque/outdoor 230 +/m/movie_theater/indoor 235 +/m/museum/outdoor 237 +/n/nursery 240 +/o/oast_house 242 +/o/office 244 +/o/office_building 245 +/o/office_cubicles 246 +/p/pagoda 251 +/p/palace 252 +/p/pantry 253 +/p/parking_garage/indoor 255 +/p/parking_garage/outdoor 256 +/p/pavilion 260 +/p/pet_shop 261 +/p/porch 272 +/r/reception 280 +/r/recreation_room 281 +/r/restaurant_patio 286 +/r/rope_bridge 291 +/r/ruin 292 +/s/sauna 295 +/s/schoolhouse 296 +/s/server_room 298 +/s/shed 299 +/s/shopfront 301 +/s/shopping_mall/indoor 302 +/s/shower 303 +/s/skyscraper 307 +/s/staircase 317 +/s/storage_room 318 +/s/subway_station/platform 320 +/s/synagogue/outdoor 327 +/t/television_room 328 +/t/temple/asia 330 +/t/throne_room 331 +/t/tower 334 +/t/train_station/platform 337 +/u/utility_room 343 +/w/waiting_room 352 +/w/wet_bar 358 +/y/youth_hostel 363 \ No newline at end of file diff --git a/third-party/lama/configs/prediction/default.yaml b/third-party/lama/configs/prediction/default.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c536f0a79dfcc31b3313fa0f8e4d4c20eb2d0561 --- /dev/null +++ b/third-party/lama/configs/prediction/default.yaml @@ -0,0 +1,24 @@ +indir: no # to be overriden in CLI +outdir: no # to be overriden in CLI + +model: + path: no # to be overriden in CLI + checkpoint: model_zoo/big_lama.ckpt + +dataset: + kind: default + img_suffix: .png + pad_out_to_modulo: 8 + +device: cuda +out_key: inpainted + +refine: False # refiner will only run if this is True +refiner: + gpu_ids: 0,1 # the GPU ids of the machine to use. If only single GPU, use: "0," + modulo: ${dataset.pad_out_to_modulo} + n_iters: 15 # number of iterations of refinement for each scale + lr: 0.002 # learning rate + min_side: 512 # all sides of image on all scales should be >= min_side / sqrt(2) + max_scales: 3 # max number of downscaling scales for the image-mask pyramid + px_budget: 1800000 # pixels budget. 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b/third-party/lama/configs/training/ablv2_work.yaml @@ -0,0 +1,36 @@ +run_title: '' + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: True + store_discr_outputs_for_vis: True + +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: True + allow_scale_mask: True + feature_matching: + weight: 100 + resnet_pl: + weight: 30 + weights_path: ${env:TORCH_HOME} + +defaults: + - location: docker + - data: abl-04-256-mh-dist + - generator: pix2pixhd_global_sigmoid + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final + - hydra: overrides diff --git a/third-party/lama/configs/training/ablv2_work_ffc075.yaml b/third-party/lama/configs/training/ablv2_work_ffc075.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8bea26c5d024d70d8e59c565612e15fe05b8673b --- /dev/null +++ b/third-party/lama/configs/training/ablv2_work_ffc075.yaml @@ -0,0 +1,36 @@ +run_title: '' + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: True + store_discr_outputs_for_vis: True + +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: True + allow_scale_mask: True + feature_matching: + weight: 100 + resnet_pl: + weight: 30 + weights_path: ${env:TORCH_HOME} + +defaults: + - location: docker + - data: abl-04-256-mh-dist + - generator: ffc_resnet_075 + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final + - hydra: overrides diff --git a/third-party/lama/configs/training/ablv2_work_md.yaml b/third-party/lama/configs/training/ablv2_work_md.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b8b86f093d40d45586f450515acbe06371e437b2 --- /dev/null +++ b/third-party/lama/configs/training/ablv2_work_md.yaml @@ -0,0 +1,36 @@ +run_title: '' + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: True + store_discr_outputs_for_vis: True + +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: True + allow_scale_mask: True + feature_matching: + weight: 100 + resnet_pl: + weight: 30 + weights_path: ${env:TORCH_HOME} + +defaults: + - location: docker + - data: abl-04-256-mh-dist + - generator: pix2pixhd_multidilated_catin_4dil_9b + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final_benchmark + - hydra: overrides diff --git a/third-party/lama/configs/training/ablv2_work_no_fm.yaml b/third-party/lama/configs/training/ablv2_work_no_fm.yaml new file mode 100644 index 0000000000000000000000000000000000000000..aac1c0cfc23c211816133745c5628ace775feba6 --- /dev/null +++ b/third-party/lama/configs/training/ablv2_work_no_fm.yaml @@ -0,0 +1,36 @@ +run_title: '' + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: True + store_discr_outputs_for_vis: True + +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: True + allow_scale_mask: True + feature_matching: + weight: 0 + resnet_pl: + weight: 30 + weights_path: ${env:TORCH_HOME} + +defaults: + - location: mlp-mow-final + - data: abl-04-256-mh-dist + - generator: pix2pixhd_global_sigmoid + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final + - hydra: overrides diff --git a/third-party/lama/configs/training/ablv2_work_no_segmpl.yaml b/third-party/lama/configs/training/ablv2_work_no_segmpl.yaml new file mode 100644 index 0000000000000000000000000000000000000000..18b73e7f202686fbb5d01044bb840ae3e6f69724 --- /dev/null +++ b/third-party/lama/configs/training/ablv2_work_no_segmpl.yaml @@ -0,0 +1,36 @@ +run_title: '' + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: True + store_discr_outputs_for_vis: True + +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: True + allow_scale_mask: True + feature_matching: + weight: 100 + resnet_pl: + weight: 0 +# weights_path: ${env:TORCH_HOME} + +defaults: + - location: docker + - data: abl-04-256-mh-dist + - generator: pix2pixhd_global_sigmoid + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final + - hydra: overrides diff --git a/third-party/lama/configs/training/ablv2_work_no_segmpl_csdilirpl.yaml b/third-party/lama/configs/training/ablv2_work_no_segmpl_csdilirpl.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cb8075cfdddd97a288f0630d8611e8d68cec2573 --- /dev/null +++ b/third-party/lama/configs/training/ablv2_work_no_segmpl_csdilirpl.yaml @@ -0,0 +1,36 @@ +run_title: '' + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: True + store_discr_outputs_for_vis: True + +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: True + allow_scale_mask: True + feature_matching: + weight: 100 + resnet_pl: + weight: 1 + segmentation: false + +defaults: + - location: docker + - data: abl-04-256-mh-dist + - generator: pix2pixhd_global_sigmoid + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final_benchmark + - hydra: overrides diff --git a/third-party/lama/configs/training/ablv2_work_no_segmpl_csdilirpl_celeba_csdilirpl1_new.yaml b/third-party/lama/configs/training/ablv2_work_no_segmpl_csdilirpl_celeba_csdilirpl1_new.yaml new file mode 100644 index 0000000000000000000000000000000000000000..26c6cac7a905b30d713b77aec72b14e3d2b1b3a2 --- /dev/null +++ b/third-party/lama/configs/training/ablv2_work_no_segmpl_csdilirpl_celeba_csdilirpl1_new.yaml @@ -0,0 +1,35 @@ +run_title: '' + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: true + store_discr_outputs_for_vis: true +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: true + allow_scale_mask: true + feature_matching: + weight: 100 + segm_pl: + weight: 1 + imagenet_weights: true + +defaults: + - location: celeba + - data: abl-04-256-mh-dist-celeba + - generator: pix2pixhd_global_sigmoid + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final_celeba + - hydra: overrides \ No newline at end of file diff --git a/third-party/lama/configs/training/ablv2_work_no_segmpl_csirpl.yaml b/third-party/lama/configs/training/ablv2_work_no_segmpl_csirpl.yaml new file mode 100644 index 0000000000000000000000000000000000000000..da15b179cfbaf8465042bbbe2f4b1878d6bb69f8 --- /dev/null +++ b/third-party/lama/configs/training/ablv2_work_no_segmpl_csirpl.yaml @@ -0,0 +1,37 @@ +run_title: '' + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: True + store_discr_outputs_for_vis: True + +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: True + allow_scale_mask: True + feature_matching: + weight: 100 + resnet_pl: + weight: 0.3 + arch_encoder: 'resnet50' + segmentation: false + +defaults: + - location: docker + - data: abl-04-256-mh-dist + - generator: pix2pixhd_global_sigmoid + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final + - hydra: overrides diff --git a/third-party/lama/configs/training/ablv2_work_no_segmpl_csirpl_celeba_csirpl03_new.yaml b/third-party/lama/configs/training/ablv2_work_no_segmpl_csirpl_celeba_csirpl03_new.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9061907ca975ef3bf8e6e36e6ad84e57e0f14d52 --- /dev/null +++ b/third-party/lama/configs/training/ablv2_work_no_segmpl_csirpl_celeba_csirpl03_new.yaml @@ -0,0 +1,36 @@ +run_title: '' + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: true + store_discr_outputs_for_vis: true +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: true + allow_scale_mask: true + feature_matching: + weight: 100 + segm_pl: + weight: 0.3 + arch_encoder: resnet50 + imagenet_weights: true + +defaults: + - location: celeba + - data: abl-04-256-mh-dist-celeba + - generator: pix2pixhd_global_sigmoid + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final_celeba + - hydra: overrides diff --git a/third-party/lama/configs/training/ablv2_work_no_segmpl_vgg.yaml b/third-party/lama/configs/training/ablv2_work_no_segmpl_vgg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2019ce11110ecad1559ae317f60b13cffea20762 --- /dev/null +++ b/third-party/lama/configs/training/ablv2_work_no_segmpl_vgg.yaml @@ -0,0 +1,36 @@ +run_title: '' + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: True + store_discr_outputs_for_vis: True + +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0.03 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: True + allow_scale_mask: True + feature_matching: + weight: 100 + resnet_pl: + weight: 0 +# weights_path: ${env:TORCH_HOME} + +defaults: + - location: docker + - data: abl-04-256-mh-dist + - generator: pix2pixhd_global_sigmoid + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final + - hydra: overrides diff --git a/third-party/lama/configs/training/ablv2_work_no_segmpl_vgg_celeba_l2_vgg003_new.yaml b/third-party/lama/configs/training/ablv2_work_no_segmpl_vgg_celeba_l2_vgg003_new.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8ef6b5f8345290e6392a5ae395b9d26e55fc2031 --- /dev/null +++ b/third-party/lama/configs/training/ablv2_work_no_segmpl_vgg_celeba_l2_vgg003_new.yaml @@ -0,0 +1,36 @@ +run_title: '' + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: true + store_discr_outputs_for_vis: true +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0.03 + kwargs: + metric: l2 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: true + allow_scale_mask: true + feature_matching: + weight: 100 + segm_pl: + weight: 0 + +defaults: + - location: celeba + - data: abl-04-256-mh-dist-celeba + - generator: pix2pixhd_global_sigmoid + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final_celeba + - hydra: overrides diff --git a/third-party/lama/configs/training/ablv2_work_nodil_segmpl.yaml b/third-party/lama/configs/training/ablv2_work_nodil_segmpl.yaml new file mode 100644 index 0000000000000000000000000000000000000000..481e7bcee4d44a360b65c99f4749202e96ea83ac --- /dev/null +++ b/third-party/lama/configs/training/ablv2_work_nodil_segmpl.yaml @@ -0,0 +1,37 @@ +run_title: '' + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: True + store_discr_outputs_for_vis: True + +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: True + allow_scale_mask: True + feature_matching: + weight: 100 + resnet_pl: + arch_encoder: resnet50 + weight: 30 + weights_path: ${env:TORCH_HOME} + +defaults: + - location: docker + - data: abl-04-256-mh-dist + - generator: pix2pixhd_global_sigmoid + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final + - hydra: overrides diff --git a/third-party/lama/configs/training/ablv2_work_small_holes.yaml b/third-party/lama/configs/training/ablv2_work_small_holes.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8aa138bf4fc441c72c4cef70e3c834bdc2b18485 --- /dev/null +++ b/third-party/lama/configs/training/ablv2_work_small_holes.yaml @@ -0,0 +1,36 @@ +run_title: '' + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: True + store_discr_outputs_for_vis: True + +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: True + allow_scale_mask: True + feature_matching: + weight: 100 + resnet_pl: + weight: 30 + weights_path: ${env:TORCH_HOME} + +defaults: + - location: docker + - data: abl-02-thin-bb + - generator: pix2pixhd_global_sigmoid + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final + - hydra: overrides diff --git a/third-party/lama/configs/training/big-lama-celeba.yaml b/third-party/lama/configs/training/big-lama-celeba.yaml new file mode 100644 index 0000000000000000000000000000000000000000..08320febb545396eeb8f5e96efb5ed0e7f9ca458 --- /dev/null +++ b/third-party/lama/configs/training/big-lama-celeba.yaml @@ -0,0 +1,55 @@ +run_title: '' + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: true + store_discr_outputs_for_vis: true +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: true + allow_scale_mask: true + feature_matching: + weight: 100 + resnet_pl: + weight: 30 + weights_path: ${env:TORCH_HOME} + +generator: + kind: ffc_resnet + input_nc: 4 + output_nc: 3 + ngf: 64 + n_downsampling: 3 + n_blocks: 18 + add_out_act: sigmoid + init_conv_kwargs: + ratio_gin: 0 + ratio_gout: 0 + enable_lfu: false + downsample_conv_kwargs: + ratio_gin: ${generator.init_conv_kwargs.ratio_gout} + ratio_gout: ${generator.downsample_conv_kwargs.ratio_gin} + enable_lfu: false + resnet_conv_kwargs: + ratio_gin: 0.75 + ratio_gout: ${generator.resnet_conv_kwargs.ratio_gin} + enable_lfu: false + +defaults: + - location: celeba + - data: abl-04-256-mh-dist-celeba + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final_celeba + - hydra: overrides diff --git a/third-party/lama/configs/training/big-lama-regular-celeba.yaml b/third-party/lama/configs/training/big-lama-regular-celeba.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8f6c6de07d83be1e0d9b92d5d523b96124a380af --- /dev/null +++ b/third-party/lama/configs/training/big-lama-regular-celeba.yaml @@ -0,0 +1,45 @@ +run_title: '' + +generator: + kind: pix2pixhd_global + input_nc: 4 + output_nc: 3 + ngf: 64 + n_downsampling: 3 + n_blocks: 15 + conv_kind: default + add_out_act: sigmoid + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: true + store_discr_outputs_for_vis: true + +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: true + allow_scale_mask: true + feature_matching: + weight: 100 + resnet_pl: + weight: 30 + weights_path: ${env:TORCH_HOME} + +defaults: + - location: celeba + - data: abl-04-256-mh-dist-celeba + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final_celeba + - hydra: overrides \ No newline at end of file diff --git a/third-party/lama/configs/training/big-lama-regular.yaml b/third-party/lama/configs/training/big-lama-regular.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d23c280ff70a766ec5cfc4f1a6f9d37d8afa9f64 --- /dev/null +++ b/third-party/lama/configs/training/big-lama-regular.yaml @@ -0,0 +1,45 @@ +run_title: '' + +generator: + kind: pix2pixhd_global + input_nc: 4 + output_nc: 3 + ngf: 64 + n_downsampling: 3 + n_blocks: 15 + conv_kind: default + add_out_act: sigmoid + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: true + store_discr_outputs_for_vis: true + +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: true + allow_scale_mask: true + feature_matching: + weight: 100 + resnet_pl: + weight: 30 + weights_path: ${env:TORCH_HOME} + +defaults: + - location: docker + - data: abl-04-256-mh-dist + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final + - hydra: overrides \ No newline at end of file diff --git a/third-party/lama/configs/training/big-lama.yaml b/third-party/lama/configs/training/big-lama.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b04f809b17b8f2aa4ea111bd34d38d881e665d42 --- /dev/null +++ b/third-party/lama/configs/training/big-lama.yaml @@ -0,0 +1,55 @@ +run_title: '' + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: true + store_discr_outputs_for_vis: true +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: true + allow_scale_mask: true + feature_matching: + weight: 100 + resnet_pl: + weight: 30 + weights_path: ${env:TORCH_HOME} + +generator: + kind: ffc_resnet + input_nc: 4 + output_nc: 3 + ngf: 64 + n_downsampling: 3 + n_blocks: 18 + add_out_act: sigmoid + init_conv_kwargs: + ratio_gin: 0 + ratio_gout: 0 + enable_lfu: false + downsample_conv_kwargs: + ratio_gin: ${generator.init_conv_kwargs.ratio_gout} + ratio_gout: ${generator.downsample_conv_kwargs.ratio_gin} + enable_lfu: false + resnet_conv_kwargs: + ratio_gin: 0.75 + ratio_gout: ${generator.resnet_conv_kwargs.ratio_gin} + enable_lfu: false + +defaults: + - location: docker + - data: abl-04-256-mh-dist + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final + - hydra: overrides diff --git a/third-party/lama/configs/training/data/abl-02-thin-bb.yaml b/third-party/lama/configs/training/data/abl-02-thin-bb.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4506f7bf96bca905c7af09d20bfa297998f92d94 --- /dev/null +++ b/third-party/lama/configs/training/data/abl-02-thin-bb.yaml @@ -0,0 +1,115 @@ +# @package _group_ + +# try to resemble mask generation of DeepFill v2 +# official tf version: https://github.com/JiahuiYu/generative_inpainting/blob/master/inpaint_ops.py#L168 +# pytorch version: https://github.com/zhaoyuzhi/deepfillv2/blob/62dad2c601400e14d79f4d1e090c2effcb9bf3eb/deepfillv2/dataset.py#L40 +# another unofficial pytorch version: https://github.com/avalonstrel/GatedConvolution/blob/master/config/inpaint.yml +# they are a bit different, official version has slightly larger masks + +batch_size: 10 +val_batch_size: 2 +num_workers: 3 + +train: + indir: ${location.data_root_dir}/train + out_size: 256 + + mask_gen_kwargs: # probabilities do not need to sum to 1, they are re-normalized in mask generator + irregular_proba: 1 + irregular_kwargs: + max_angle: 4 + max_len: 80 # math.sqrt(H*H+W*W) / 8 + math.sqrt(H*H+W*W) / 16 https://github.com/JiahuiYu/generative_inpainting/blob/master/inpaint_ops.py#L189 + max_width: 40 + max_times: 12 + min_times: 4 + + box_proba: 1 + box_kwargs: + margin: 0 + bbox_min_size: 30 + bbox_max_size: 128 + max_times: 1 + min_times: 1 + + segm_proba: 0 # not working yet due to RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method + + transform_variant: default + dataloader_kwargs: + batch_size: ${data.batch_size} + shuffle: True + num_workers: ${data.num_workers} + +val: + indir: ${location.data_root_dir}/val + img_suffix: .png + dataloader_kwargs: + batch_size: ${data.val_batch_size} + shuffle: False + num_workers: ${data.num_workers} + +#extra_val: +# random_thin_256: +# indir: ${location.data_root_dir}/extra_val/random_thin_256 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} +# random_medium_256: +# indir: ${location.data_root_dir}/extra_val/random_medium_256 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} +# random_thick_256: +# indir: ${location.data_root_dir}/extra_val/random_thick_256 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} +# random_thin_512: +# indir: ${location.data_root_dir}/extra_val/random_thin_512 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} +# random_medium_512: +# indir: ${location.data_root_dir}/extra_val/random_medium_512 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} +# random_thick_512: +# indir: ${location.data_root_dir}/extra_val/random_thick_512 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} +# segm_256: +# indir: ${location.data_root_dir}/extra_val/segm_256 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} +# segm_512: +# indir: ${location.data_root_dir}/extra_val/segm_512 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} + +visual_test: + indir: ${location.data_root_dir}/visual_test + img_suffix: _input.png + pad_out_to_modulo: 32 + dataloader_kwargs: + batch_size: 1 + shuffle: False + num_workers: ${data.num_workers} diff --git a/third-party/lama/configs/training/data/abl-04-256-mh-dist-celeba.yaml b/third-party/lama/configs/training/data/abl-04-256-mh-dist-celeba.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d19cc76b97a035a94ce8ea5095a17f218d796c0a --- /dev/null +++ b/third-party/lama/configs/training/data/abl-04-256-mh-dist-celeba.yaml @@ -0,0 +1,43 @@ +# @package _group_ + +batch_size: 5 +val_batch_size: 3 +num_workers: 3 + +train: + indir: ${location.data_root_dir}/train_256 + out_size: 256 + mask_gen_kwargs: # probabilities do not need to sum to 1, they are re-normalized in mask generator + irregular_proba: 1 + irregular_kwargs: + max_angle: 4 + max_len: 200 + max_width: 100 + max_times: 5 + min_times: 1 + + box_proba: 1 + box_kwargs: + margin: 10 + bbox_min_size: 30 + bbox_max_size: 150 + max_times: 4 + min_times: 1 + + segm_proba: 0 + + transform_variant: no_augs + dataloader_kwargs: + batch_size: ${data.batch_size} + shuffle: True + num_workers: ${data.num_workers} + +val: + indir: ${location.data_root_dir}/val_256 + img_suffix: .png + dataloader_kwargs: + batch_size: ${data.val_batch_size} + shuffle: False + num_workers: ${data.num_workers} + +visual_test: null diff --git a/third-party/lama/configs/training/data/abl-04-256-mh-dist-web.yaml b/third-party/lama/configs/training/data/abl-04-256-mh-dist-web.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e8e29b3e5fbfd6b9f18a1553a12720d76fd317f4 --- /dev/null +++ b/third-party/lama/configs/training/data/abl-04-256-mh-dist-web.yaml @@ -0,0 +1,110 @@ +# @package _group_ + +batch_size: 10 +val_batch_size: 2 +num_workers: 3 + +train: + kind: default_web + shuffle_buffer: 200 + indir: ${location.data_root_dir}/train_standard/part{00000..00039}.tar + out_size: 256 + mask_gen_kwargs: # probabilities do not need to sum to 1, they are re-normalized in mask generator + irregular_proba: 1 + irregular_kwargs: + max_angle: 4 + max_len: 200 + max_width: 100 + max_times: 5 + min_times: 1 + + box_proba: 1 + box_kwargs: + margin: 10 + bbox_min_size: 30 + bbox_max_size: 150 + max_times: 4 + min_times: 1 + + segm_proba: 0 + + transform_variant: distortions + dataloader_kwargs: + batch_size: ${data.batch_size} + shuffle: True + num_workers: ${data.num_workers} + +val: + indir: ${location.data_root_dir}/val + img_suffix: .png + dataloader_kwargs: + batch_size: ${data.val_batch_size} + shuffle: False + num_workers: ${data.num_workers} + +#extra_val: +# random_thin_256: +# indir: ${location.data_root_dir}/final_extra_val/random_thin_256 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} +# random_medium_256: +# indir: ${location.data_root_dir}/final_extra_val/random_medium_256 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} +# random_thick_256: +# indir: ${location.data_root_dir}/final_extra_val/random_thick_256 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} +# random_thin_512: +# indir: ${location.data_root_dir}/final_extra_val/random_thin_512 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} +# random_medium_512: +# indir: ${location.data_root_dir}/final_extra_val/random_medium_512 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} +# random_thick_512: +# indir: ${location.data_root_dir}/final_extra_val/random_thick_512 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} +# segm_256: +# indir: ${location.data_root_dir}/final_extra_val/segm_256 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} +# segm_512: +# indir: ${location.data_root_dir}/final_extra_val/segm_512 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} + +visual_test: + indir: ${location.data_root_dir}/visual_test + img_suffix: _input.png + pad_out_to_modulo: 32 + dataloader_kwargs: + batch_size: 1 + shuffle: False + num_workers: ${data.num_workers} diff --git a/third-party/lama/configs/training/data/abl-04-256-mh-dist.yaml b/third-party/lama/configs/training/data/abl-04-256-mh-dist.yaml new file mode 100644 index 0000000000000000000000000000000000000000..203e6aa07980f54aa412f930283d32d3acda6c2d --- /dev/null +++ b/third-party/lama/configs/training/data/abl-04-256-mh-dist.yaml @@ -0,0 +1,108 @@ +# @package _group_ + +batch_size: 10 +val_batch_size: 2 +num_workers: 3 + +train: + indir: ${location.data_root_dir}/train + out_size: 256 + mask_gen_kwargs: # probabilities do not need to sum to 1, they are re-normalized in mask generator + irregular_proba: 1 + irregular_kwargs: + max_angle: 4 + max_len: 200 + max_width: 100 + max_times: 5 + min_times: 1 + + box_proba: 1 + box_kwargs: + margin: 10 + bbox_min_size: 30 + bbox_max_size: 150 + max_times: 4 + min_times: 1 + + segm_proba: 0 + + transform_variant: distortions + dataloader_kwargs: + batch_size: ${data.batch_size} + shuffle: True + num_workers: ${data.num_workers} + +val: + indir: ${location.data_root_dir}/val + img_suffix: .png + dataloader_kwargs: + batch_size: ${data.val_batch_size} + shuffle: False + num_workers: ${data.num_workers} + +#extra_val: +# random_thin_256: +# indir: ${location.data_root_dir}/extra_val/random_thin_256 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} +# random_medium_256: +# indir: ${location.data_root_dir}/extra_val/random_medium_256 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} +# random_thick_256: +# indir: ${location.data_root_dir}/extra_val/random_thick_256 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} +# random_thin_512: +# indir: ${location.data_root_dir}/extra_val/random_thin_512 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} +# random_medium_512: +# indir: ${location.data_root_dir}/extra_val/random_medium_512 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} +# random_thick_512: +# indir: ${location.data_root_dir}/extra_val/random_thick_512 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} +# segm_256: +# indir: ${location.data_root_dir}/extra_val/segm_256 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} +# segm_512: +# indir: ${location.data_root_dir}/extra_val/segm_512 +# img_suffix: .png +# dataloader_kwargs: +# batch_size: ${data.val_batch_size} +# shuffle: False +# num_workers: ${data.num_workers} + +visual_test: + indir: ${location.data_root_dir}/visual_test + img_suffix: .png + pad_out_to_modulo: 32 + dataloader_kwargs: + batch_size: 1 + shuffle: False + num_workers: ${data.num_workers} diff --git a/third-party/lama/configs/training/discriminator/pix2pixhd_nlayer.yaml b/third-party/lama/configs/training/discriminator/pix2pixhd_nlayer.yaml new file mode 100644 index 0000000000000000000000000000000000000000..df20421c579e51d3582c89d3871e6ccba79fc572 --- /dev/null +++ b/third-party/lama/configs/training/discriminator/pix2pixhd_nlayer.yaml @@ -0,0 +1,5 @@ +# @package _group_ +kind: pix2pixhd_nlayer +input_nc: 3 +ndf: 64 +n_layers: 4 diff --git a/third-party/lama/configs/training/evaluator/default_inpainted.yaml b/third-party/lama/configs/training/evaluator/default_inpainted.yaml new file mode 100644 index 0000000000000000000000000000000000000000..33bede1b85c08376c10d00ea8f15c7df8733fb09 --- /dev/null +++ b/third-party/lama/configs/training/evaluator/default_inpainted.yaml @@ -0,0 +1,4 @@ +# @package _group_ +kind: default +inpainted_key: inpainted # if you want to evaluate before blending with original image by mask, set predicted_image +integral_kind: ssim_fid100_f1 diff --git a/third-party/lama/configs/training/generator/ffc_resnet_075.yaml b/third-party/lama/configs/training/generator/ffc_resnet_075.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0bac88f9ece517900938c489a7e36643d55c5e62 --- /dev/null +++ b/third-party/lama/configs/training/generator/ffc_resnet_075.yaml @@ -0,0 +1,23 @@ +# @package _group_ +kind: ffc_resnet +input_nc: 4 +output_nc: 3 +ngf: 64 +n_downsampling: 3 +n_blocks: 9 +add_out_act: sigmoid + +init_conv_kwargs: + ratio_gin: 0 + ratio_gout: 0 + enable_lfu: False + +downsample_conv_kwargs: + ratio_gin: ${generator.init_conv_kwargs.ratio_gout} + ratio_gout: ${generator.downsample_conv_kwargs.ratio_gin} + enable_lfu: False + +resnet_conv_kwargs: + ratio_gin: 0.75 + ratio_gout: ${generator.resnet_conv_kwargs.ratio_gin} + enable_lfu: False diff --git a/third-party/lama/configs/training/generator/pix2pixhd_global.yaml b/third-party/lama/configs/training/generator/pix2pixhd_global.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fc3deb98c48151a37fcd3c6cffce764743be69f1 --- /dev/null +++ b/third-party/lama/configs/training/generator/pix2pixhd_global.yaml @@ -0,0 +1,8 @@ +# @package _group_ +kind: pix2pixhd_global +input_nc: 4 +output_nc: 3 +ngf: 64 +n_downsampling: 3 +n_blocks: 9 +conv_kind: default \ No newline at end of file diff --git a/third-party/lama/configs/training/generator/pix2pixhd_global_sigmoid.yaml b/third-party/lama/configs/training/generator/pix2pixhd_global_sigmoid.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5a4f007e6c47d48f1d3eb4a83307f48c94c2c08b --- /dev/null +++ b/third-party/lama/configs/training/generator/pix2pixhd_global_sigmoid.yaml @@ -0,0 +1,9 @@ +# @package _group_ +kind: pix2pixhd_global +input_nc: 4 +output_nc: 3 +ngf: 64 +n_downsampling: 3 +n_blocks: 9 +conv_kind: default +add_out_act: sigmoid diff --git a/third-party/lama/configs/training/generator/pix2pixhd_multidilated_catin_4dil_9b.yaml b/third-party/lama/configs/training/generator/pix2pixhd_multidilated_catin_4dil_9b.yaml new file mode 100644 index 0000000000000000000000000000000000000000..28d10a567db87aacc2d9a0898383b383919880c9 --- /dev/null +++ b/third-party/lama/configs/training/generator/pix2pixhd_multidilated_catin_4dil_9b.yaml @@ -0,0 +1,12 @@ +# @package _group_ +kind: pix2pixhd_multidilated +input_nc: 4 +output_nc: 3 +ngf: 64 +n_downsampling: 3 +n_blocks: 9 +conv_kind: default +add_out_act: sigmoid +multidilation_kwargs: + comb_mode: cat_in + dilation_num: 4 diff --git a/third-party/lama/configs/training/hydra/no_time.yaml b/third-party/lama/configs/training/hydra/no_time.yaml new file mode 100644 index 0000000000000000000000000000000000000000..37ed30d351f4367b46b96463ea9dfee46bb11c19 --- /dev/null +++ b/third-party/lama/configs/training/hydra/no_time.yaml @@ -0,0 +1,6 @@ +# @package _group_ +run: + dir: ${location.out_root_dir}/${env:USER}_${hydra:job.name}_${hydra:job.config_name}_${run_title} +sweep: + dir: ${hydra:run.dir}_sweep + subdir: ${hydra.job.num} diff --git a/third-party/lama/configs/training/hydra/overrides.yaml b/third-party/lama/configs/training/hydra/overrides.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3e9e53f2ca81fde6b9584e4aa4583b23464914c9 --- /dev/null +++ b/third-party/lama/configs/training/hydra/overrides.yaml @@ -0,0 +1,6 @@ +# @package _group_ +run: + dir: ${location.out_root_dir}/${env:USER}_${now:%Y-%m-%d_%H-%M-%S}_${hydra:job.name}_${hydra:job.config_name}_${run_title} +sweep: + dir: ${hydra:run.dir}_sweep + subdir: ${hydra.job.num} diff --git a/third-party/lama/configs/training/lama-fourier-celeba.yaml b/third-party/lama/configs/training/lama-fourier-celeba.yaml new file mode 100644 index 0000000000000000000000000000000000000000..63e562487be733afa2ae37b034f738635dffa4ad --- /dev/null +++ b/third-party/lama/configs/training/lama-fourier-celeba.yaml @@ -0,0 +1,35 @@ +run_title: '' + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: true + store_discr_outputs_for_vis: true +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: true + allow_scale_mask: true + feature_matching: + weight: 100 + resnet_pl: + weight: 30 + weights_path: ${env:TORCH_HOME} + +defaults: + - location: celeba + - data: abl-04-256-mh-dist-celeba + - generator: ffc_resnet_075 + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final_celeba + - hydra: overrides \ No newline at end of file diff --git a/third-party/lama/configs/training/lama-fourier.yaml b/third-party/lama/configs/training/lama-fourier.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0c8d3a92ebc8906d25df735f1438f04d6efc48fb --- /dev/null +++ b/third-party/lama/configs/training/lama-fourier.yaml @@ -0,0 +1,35 @@ +run_title: '' + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: true + store_discr_outputs_for_vis: true +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: true + allow_scale_mask: true + feature_matching: + weight: 100 + resnet_pl: + weight: 30 + weights_path: ${env:TORCH_HOME} + +defaults: + - location: docker + - data: abl-04-256-mh-dist + - generator: ffc_resnet_075 + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final + - hydra: overrides \ No newline at end of file diff --git a/third-party/lama/configs/training/lama-regular-celeba.yaml b/third-party/lama/configs/training/lama-regular-celeba.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dd13ecc9cd4d4271cf62c85cc87b18a8bf9e6129 --- /dev/null +++ b/third-party/lama/configs/training/lama-regular-celeba.yaml @@ -0,0 +1,35 @@ +run_title: '' + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: true + store_discr_outputs_for_vis: true +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: true + allow_scale_mask: true + feature_matching: + weight: 100 + resnet_pl: + weight: 30 + weights_path: ${env:TORCH_HOME} + +defaults: + - location: celeba + - data: abl-04-256-mh-dist-celeba + - generator: pix2pixhd_global_sigmoid + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final_celeba + - hydra: overrides diff --git a/third-party/lama/configs/training/lama-regular.yaml b/third-party/lama/configs/training/lama-regular.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0412c4aafe292f7ec6c8ce284c6803d679620293 --- /dev/null +++ b/third-party/lama/configs/training/lama-regular.yaml @@ -0,0 +1,35 @@ +run_title: '' + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: true + store_discr_outputs_for_vis: true +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: true + allow_scale_mask: true + feature_matching: + weight: 100 + resnet_pl: + weight: 30 + weights_path: ${env:TORCH_HOME} + +defaults: + - location: docker + - data: abl-04-256-mh-dist + - generator: pix2pixhd_global_sigmoid + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final + - hydra: overrides diff --git a/third-party/lama/configs/training/lama_small_train_masks.yaml b/third-party/lama/configs/training/lama_small_train_masks.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bf15faa5d8fca8c442dcc652ed6348cd1c4e5ca4 --- /dev/null +++ b/third-party/lama/configs/training/lama_small_train_masks.yaml @@ -0,0 +1,36 @@ +run_title: '' + +training_model: + kind: default + visualize_each_iters: 1000 + concat_mask: true + store_discr_outputs_for_vis: true + +losses: + l1: + weight_missing: 0 + weight_known: 10 + perceptual: + weight: 0 + adversarial: + kind: r1 + weight: 10 + gp_coef: 0.001 + mask_as_fake_target: true + allow_scale_mask: true + feature_matching: + weight: 100 + resnet_pl: + weight: 30 + weights_path: ${env:TORCH_HOME} + +defaults: + - location: docker + - data: abl-02-thin-bb + - generator: pix2pixhd_sigmoid + - discriminator: pix2pixhd_nlayer + - optimizers: default_optimizers + - visualizer: directory + - evaluator: default_inpainted + - trainer: any_gpu_large_ssim_ddp_final + - hydra: overrides \ No newline at end of file diff --git a/third-party/lama/configs/training/location/celeba_example.yaml b/third-party/lama/configs/training/location/celeba_example.yaml new file mode 100644 index 0000000000000000000000000000000000000000..117fe8a9324d1de5908bdac175985837d10abc75 --- /dev/null +++ b/third-party/lama/configs/training/location/celeba_example.yaml @@ -0,0 +1,5 @@ +# @package _group_ +data_root_dir: /home/user/lama/celeba-hq-dataset/ +out_root_dir: /home/user/lama/experiments/ +tb_dir: /home/user/lama/tb_logs/ +pretrained_models: /home/user/lama/ diff --git a/third-party/lama/configs/training/location/docker.yaml b/third-party/lama/configs/training/location/docker.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5da6a4a452c5e63f6548b60f18861cf66ab738ff --- /dev/null +++ b/third-party/lama/configs/training/location/docker.yaml @@ -0,0 +1,5 @@ +# @package _group_ +data_root_dir: /data/data +out_root_dir: /data/experiments +tb_dir: /data/tb_logs +pretrained_models: /some_path diff --git a/third-party/lama/configs/training/location/places_example.yaml b/third-party/lama/configs/training/location/places_example.yaml new file mode 100644 index 0000000000000000000000000000000000000000..97a9f9b58c7f2fd5ee0b6b50da6a366d91227a15 --- /dev/null +++ b/third-party/lama/configs/training/location/places_example.yaml @@ -0,0 +1,5 @@ +# @package _group_ +data_root_dir: /home/user/inpainting-lama/places_standard_dataset/ +out_root_dir: /home/user/inpainting-lama/experiments +tb_dir: /home/user/inpainting-lama/tb_logs +pretrained_models: /home/user/inpainting-lama/ diff --git a/third-party/lama/configs/training/optimizers/default_optimizers.yaml b/third-party/lama/configs/training/optimizers/default_optimizers.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6d85d1da74c2e30ea3378ed8e0ae43407767c02d --- /dev/null +++ b/third-party/lama/configs/training/optimizers/default_optimizers.yaml @@ -0,0 +1,7 @@ +# @package _group_ +generator: + kind: adam + lr: 0.001 +discriminator: + kind: adam + lr: 0.0001 diff --git a/third-party/lama/configs/training/trainer/any_gpu_large_ssim_ddp_final.yaml b/third-party/lama/configs/training/trainer/any_gpu_large_ssim_ddp_final.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5da9ed3fb8946cb82d5c4e6dba6f882a929619bc --- /dev/null +++ b/third-party/lama/configs/training/trainer/any_gpu_large_ssim_ddp_final.yaml @@ -0,0 +1,31 @@ +# @package _group_ +kwargs: + gpus: -1 + accelerator: ddp + max_epochs: 40 + gradient_clip_val: 1 + log_gpu_memory: None # set to min_max or all for debug + limit_train_batches: 25000 + val_check_interval: ${trainer.kwargs.limit_train_batches} + # fast_dev_run: True # uncomment for faster debug + # track_grad_norm: 2 # uncomment to track L2 gradients norm + log_every_n_steps: 250 + precision: 32 +# precision: 16 +# amp_backend: native +# amp_level: O1 + # resume_from_checkpoint: path # override via command line trainer.resume_from_checkpoint=path_to_checkpoint + terminate_on_nan: False + # auto_scale_batch_size: True # uncomment to find largest batch size + check_val_every_n_epoch: 1 + num_sanity_val_steps: 8 +# limit_val_batches: 1000000 + replace_sampler_ddp: False + +checkpoint_kwargs: + verbose: True + save_top_k: 5 + save_last: True + period: 1 + monitor: val_ssim_fid100_f1_total_mean + mode: max \ No newline at end of file diff --git a/third-party/lama/configs/training/trainer/any_gpu_large_ssim_ddp_final_benchmark.yaml b/third-party/lama/configs/training/trainer/any_gpu_large_ssim_ddp_final_benchmark.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cd72e41fe97b2233bd54be5de2973b6030459b29 --- /dev/null +++ b/third-party/lama/configs/training/trainer/any_gpu_large_ssim_ddp_final_benchmark.yaml @@ -0,0 +1,32 @@ +# @package _group_ +kwargs: + gpus: -1 + accelerator: ddp + max_epochs: 40 + gradient_clip_val: 1 + log_gpu_memory: None # set to min_max or all for debug + limit_train_batches: 25000 + val_check_interval: ${trainer.kwargs.limit_train_batches} + # fast_dev_run: True # uncomment for faster debug + # track_grad_norm: 2 # uncomment to track L2 gradients norm + log_every_n_steps: 250 + precision: 32 +# precision: 16 +# amp_backend: native +# amp_level: O1 + # resume_from_checkpoint: path # override via command line trainer.resume_from_checkpoint=path_to_checkpoint + terminate_on_nan: False + # auto_scale_batch_size: True # uncomment to find largest batch size + check_val_every_n_epoch: 1 + num_sanity_val_steps: 8 +# limit_val_batches: 1000000 + replace_sampler_ddp: False + benchmark: True + +checkpoint_kwargs: + verbose: True + save_top_k: 5 + save_last: True + period: 1 + monitor: val_ssim_fid100_f1_total_mean + mode: max diff --git a/third-party/lama/configs/training/trainer/any_gpu_large_ssim_ddp_final_celeba.yaml b/third-party/lama/configs/training/trainer/any_gpu_large_ssim_ddp_final_celeba.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c175d006e9206449170ab0aed727bbb9a1034c08 --- /dev/null +++ b/third-party/lama/configs/training/trainer/any_gpu_large_ssim_ddp_final_celeba.yaml @@ -0,0 +1,22 @@ +# @package _group_ +kwargs: + gpus: -1 + accelerator: ddp + max_epochs: 40 + gradient_clip_val: 1 + log_gpu_memory: None + limit_train_batches: 25000 + val_check_interval: 2600 + log_every_n_steps: 250 + precision: 32 + terminate_on_nan: False + check_val_every_n_epoch: 1 + num_sanity_val_steps: 8 + replace_sampler_ddp: False +checkpoint_kwargs: + verbose: True + save_top_k: 5 + save_last: True + period: 1 + monitor: val_ssim_fid100_f1_total_mean + mode: max \ No newline at end of file diff --git a/third-party/lama/configs/training/visualizer/directory.yaml b/third-party/lama/configs/training/visualizer/directory.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ff1880d4e033bd6a5709d837f84e60b33b18254c --- /dev/null +++ b/third-party/lama/configs/training/visualizer/directory.yaml @@ -0,0 +1,12 @@ +# @package _group_ +kind: directory +outdir: samples +key_order: + - image + - predicted_image + - discr_output_fake + - discr_output_real + - inpainted +rescale_keys: + - discr_output_fake + - discr_output_real diff --git a/third-party/lama/docker/1_generate_masks_from_raw_images.sh b/third-party/lama/docker/1_generate_masks_from_raw_images.sh new file mode 100755 index 0000000000000000000000000000000000000000..04b780e6d6a3bf46f32b76df12b4a35ea679501d --- /dev/null +++ b/third-party/lama/docker/1_generate_masks_from_raw_images.sh @@ -0,0 +1,31 @@ +#!/usr/bin/env bash + + +if (( $# < 3 )) +then + echo "Usage: $0 config_name input_images_dir image_mask_dataset_out_dir [other args to gen_mask_dataset.py]" + exit 1 +fi + +CURDIR="$(dirname $0)" +SRCDIR="$CURDIR/.." +SRCDIR="$(realpath $SRCDIR)" + +CONFIG_LOCAL_PATH="$(realpath $1)" +INPUT_LOCAL_DIR="$(realpath $2)" +OUTPUT_LOCAL_DIR="$(realpath $3)" +shift 3 + +mkdir -p "$OUTPUT_LOCAL_DIR" + +docker run \ + -v "$SRCDIR":/home/user/project \ + -v "$CONFIG_LOCAL_PATH":/data/config.yaml \ + -v "$INPUT_LOCAL_DIR":/data/input \ + -v "$OUTPUT_LOCAL_DIR":/data/output \ + -u $(id -u):$(id -g) \ + --name="lama-mask-gen" \ + --rm \ + windj007/lama \ + /home/user/project/bin/gen_mask_dataset.py \ + /data/config.yaml /data/input /data/output $@ diff --git a/third-party/lama/docker/2_predict.sh b/third-party/lama/docker/2_predict.sh new file mode 100755 index 0000000000000000000000000000000000000000..8af4ac04ec0c1586933be424d4f7a5a4522521dc --- /dev/null +++ b/third-party/lama/docker/2_predict.sh @@ -0,0 +1,35 @@ +#!/usr/bin/env bash + + +if (( $# < 3 )) +then + echo "Usage: $0 model_dir input_dir output_dir [other arguments to predict.py]" + exit 1 +fi + +CURDIR="$(dirname $0)" +SRCDIR="$CURDIR/.." +SRCDIR="$(realpath $SRCDIR)" + +MODEL_LOCAL_DIR="$(realpath $1)" +INPUT_LOCAL_DIR="$(realpath $2)" +OUTPUT_LOCAL_DIR="$(realpath $3)" +shift 3 + +mkdir -p "$OUTPUT_LOCAL_DIR" + +docker run \ + -v "$SRCDIR":/home/user/project \ + -v "$MODEL_LOCAL_DIR":/data/checkpoint \ + -v "$INPUT_LOCAL_DIR":/data/input \ + -v "$OUTPUT_LOCAL_DIR":/data/output \ + -u $(id -u):$(id -g) \ + --name="lama-predict" \ + --rm \ + windj007/lama \ + /home/user/project/bin/predict.py \ + model.path=/data/checkpoint \ + indir=/data/input \ + outdir=/data/output \ + dataset.img_suffix=.png \ + $@ diff --git a/third-party/lama/docker/3_evaluate.sh b/third-party/lama/docker/3_evaluate.sh new file mode 100755 index 0000000000000000000000000000000000000000..d01e0a39da620e38c1ebf28beead59f286437321 --- /dev/null +++ b/third-party/lama/docker/3_evaluate.sh @@ -0,0 +1,35 @@ +#!/usr/bin/env bash + + +if (( $# < 3 )) +then + echo "Usage: $0 original_dataset_dir predictions_dir output_dir [other arguments to evaluate_predicts.py]" + exit 1 +fi + +CURDIR="$(dirname $0)" +SRCDIR="$CURDIR/.." +SRCDIR="$(realpath $SRCDIR)" + +ORIG_DATASET_LOCAL_DIR="$(realpath $1)" +PREDICTIONS_LOCAL_DIR="$(realpath $2)" +OUTPUT_LOCAL_DIR="$(realpath $3)" +shift 3 + +mkdir -p "$OUTPUT_LOCAL_DIR" + +docker run \ + -v "$SRCDIR":/home/user/project \ + -v "$ORIG_DATASET_LOCAL_DIR":/data/orig_dataset \ + -v "$PREDICTIONS_LOCAL_DIR":/data/predictions \ + -v "$OUTPUT_LOCAL_DIR":/data/output \ + -u $(id -u):$(id -g) \ + --name="lama-eval" \ + --rm \ + windj007/lama \ + /home/user/project/bin/evaluate_predicts.py \ + /home/user/project/configs/eval2_cpu.yaml \ + /data/orig_dataset \ + /data/predictions \ + /data/output/metrics.yaml \ + $@ diff --git a/third-party/lama/docker/Dockerfile b/third-party/lama/docker/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..a53787f6828f360ca44e9ded9d0a7068c705e401 --- /dev/null +++ b/third-party/lama/docker/Dockerfile @@ -0,0 +1,38 @@ +FROM nvidia/cuda:10.2-runtime-ubuntu18.04 + +RUN apt-get update && \ + apt-get upgrade -y && \ + apt-get install -y wget mc tmux nano build-essential rsync libgl1 + +ARG USERNAME=user +RUN apt-get install -y sudo && \ + addgroup --gid 1000 $USERNAME && \ + adduser --uid 1000 --gid 1000 --disabled-password --gecos '' $USERNAME && \ + adduser $USERNAME sudo && \ + echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers && \ + USER=$USERNAME && \ + GROUP=$USERNAME + +USER $USERNAME:$USERNAME +WORKDIR "/home/$USERNAME" +ENV PATH="/home/$USERNAME/miniconda3/bin:/home/$USERNAME/.local/bin:${PATH}" +ENV PYTHONPATH="/home/$USERNAME/project" + +RUN wget -O /tmp/miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-py39_4.9.2-Linux-x86_64.sh && \ + echo "536817d1b14cb1ada88900f5be51ce0a5e042bae178b5550e62f61e223deae7c /tmp/miniconda.sh" > /tmp/miniconda.sh.sha256 && \ + sha256sum --check --status < /tmp/miniconda.sh.sha256 && \ + bash /tmp/miniconda.sh -bt -p "/home/$USERNAME/miniconda3" && \ + rm /tmp/miniconda.sh && \ + conda build purge && \ + conda init + +RUN pip install -U pip +RUN pip install numpy scipy torch==1.8.1 torchvision opencv-python tensorflow joblib matplotlib pandas \ + albumentations==0.5.2 pytorch-lightning==1.2.9 tabulate easydict==1.9.0 kornia==0.5.0 webdataset \ + packaging gpustat tqdm pyyaml hydra-core==1.1.0.dev6 scikit-learn==0.24.2 tabulate +RUN pip install scikit-image==0.17.2 + +ENV TORCH_HOME="/home/$USERNAME/.torch" + +ADD entrypoint.sh /home/$USERNAME/.local/bin/entrypoint.sh +ENTRYPOINT [ "entrypoint.sh" ] diff --git a/third-party/lama/docker/Dockerfile-cuda111 b/third-party/lama/docker/Dockerfile-cuda111 new file mode 100644 index 0000000000000000000000000000000000000000..5b5e7565a4cb2333ab93a68b3ecfb936d9831b1a --- /dev/null +++ b/third-party/lama/docker/Dockerfile-cuda111 @@ -0,0 +1,39 @@ +FROM nvidia/cuda:11.1-runtime-ubuntu18.04 + +RUN apt-get update && \ + apt-get upgrade -y && \ + apt-get install -y wget mc tmux nano build-essential rsync libgl1 + +ARG USERNAME=user +RUN apt-get install -y sudo && \ + addgroup --gid 1000 $USERNAME && \ + adduser --uid 1000 --gid 1000 --disabled-password --gecos '' $USERNAME && \ + adduser $USERNAME sudo && \ + echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers && \ + USER=$USERNAME && \ + GROUP=$USERNAME + +USER $USERNAME:$USERNAME +WORKDIR "/home/$USERNAME" +ENV PATH="/home/$USERNAME/miniconda3/bin:/home/$USERNAME/.local/bin:${PATH}" +ENV PYTHONPATH="/home/$USERNAME/project" + +RUN wget -O /tmp/miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-py39_4.9.2-Linux-x86_64.sh && \ + echo "536817d1b14cb1ada88900f5be51ce0a5e042bae178b5550e62f61e223deae7c /tmp/miniconda.sh" > /tmp/miniconda.sh.sha256 && \ + sha256sum --check --status < /tmp/miniconda.sh.sha256 && \ + bash /tmp/miniconda.sh -bt -p "/home/$USERNAME/miniconda3" && \ + rm /tmp/miniconda.sh && \ + conda build purge && \ + conda init + +RUN pip install -U pip +RUN pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html +RUN pip install numpy scipy opencv-python tensorflow joblib matplotlib pandas \ + albumentations==0.5.2 pytorch-lightning==1.2.9 tabulate easydict==1.9.0 kornia==0.5.0 webdataset \ + packaging gpustat tqdm pyyaml hydra-core==1.1.0.dev6 scikit-learn==0.24.2 tabulate +RUN pip install scikit-image==0.17.2 + +ENV TORCH_HOME="/home/$USERNAME/.torch" + +ADD entrypoint.sh /home/$USERNAME/.local/bin/entrypoint.sh +ENTRYPOINT [ "entrypoint.sh" ] diff --git a/third-party/lama/docker/build-cuda111.sh b/third-party/lama/docker/build-cuda111.sh new file mode 100755 index 0000000000000000000000000000000000000000..b0824f5d536f548fde0b1c8e07cc95217d91310d --- /dev/null +++ b/third-party/lama/docker/build-cuda111.sh @@ -0,0 +1,5 @@ +#!/bin/bash + +BASEDIR="$(dirname $0)" + +docker build -t windj007/lama:cuda111 -f "$BASEDIR/Dockerfile-cuda111" "$BASEDIR" diff --git a/third-party/lama/docker/build.sh b/third-party/lama/docker/build.sh new file mode 100755 index 0000000000000000000000000000000000000000..d70b747ff1282b9c60cf8b8b352b6b7701e1a254 --- /dev/null +++ b/third-party/lama/docker/build.sh @@ -0,0 +1,5 @@ +#!/bin/bash + +BASEDIR="$(dirname $0)" + +docker build -t windj007/lama -f "$BASEDIR/Dockerfile" "$BASEDIR" diff --git a/third-party/lama/docker/entrypoint.sh b/third-party/lama/docker/entrypoint.sh new file mode 100755 index 0000000000000000000000000000000000000000..1b565af1c6702857af1c11bdbb567cba6804cfb8 --- /dev/null +++ b/third-party/lama/docker/entrypoint.sh @@ -0,0 +1,3 @@ +#!/bin/bash + +exec $@ diff --git a/third-party/lama/fetch_data/celebahq_dataset_prepare.sh b/third-party/lama/fetch_data/celebahq_dataset_prepare.sh new file mode 100644 index 0000000000000000000000000000000000000000..6d2ba9a6265c0d5fa580035952a1f568dd8d9e44 --- /dev/null +++ b/third-party/lama/fetch_data/celebahq_dataset_prepare.sh @@ -0,0 +1,37 @@ +mkdir celeba-hq-dataset + +unzip data256x256.zip -d celeba-hq-dataset/ + +# Reindex +for i in `echo {00001..30000}` +do + mv 'celeba-hq-dataset/data256x256/'$i'.jpg' 'celeba-hq-dataset/data256x256/'$[10#$i - 1]'.jpg' +done + + +# Split: split train -> train & val +cat fetch_data/train_shuffled.flist | shuf > celeba-hq-dataset/temp_train_shuffled.flist +cat celeba-hq-dataset/temp_train_shuffled.flist | head -n 2000 > celeba-hq-dataset/val_shuffled.flist +cat celeba-hq-dataset/temp_train_shuffled.flist | tail -n +2001 > celeba-hq-dataset/train_shuffled.flist +cat fetch_data/val_shuffled.flist > celeba-hq-dataset/visual_test_shuffled.flist + +mkdir celeba-hq-dataset/train_256/ +mkdir celeba-hq-dataset/val_source_256/ +mkdir celeba-hq-dataset/visual_test_source_256/ + +cat celeba-hq-dataset/train_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/train_256/ +cat celeba-hq-dataset/val_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/val_source_256/ +cat celeba-hq-dataset/visual_test_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/visual_test_source_256/ + + +# create location config celeba.yaml +PWD=$(pwd) +DATASET=${PWD}/celeba-hq-dataset +CELEBA=${PWD}/configs/training/location/celeba.yaml + +touch $CELEBA +echo "# @package _group_" >> $CELEBA +echo "data_root_dir: ${DATASET}/" >> $CELEBA +echo "out_root_dir: ${PWD}/experiments/" >> $CELEBA +echo "tb_dir: ${PWD}/tb_logs/" >> $CELEBA +echo "pretrained_models: ${PWD}/" >> $CELEBA diff --git a/third-party/lama/fetch_data/celebahq_gen_masks.sh b/third-party/lama/fetch_data/celebahq_gen_masks.sh new file mode 100644 index 0000000000000000000000000000000000000000..190ccfd53038711df34d402ecf1ee729a7c1e254 --- /dev/null +++ b/third-party/lama/fetch_data/celebahq_gen_masks.sh @@ -0,0 +1,29 @@ +python3 bin/gen_mask_dataset.py \ +$(pwd)/configs/data_gen/random_thick_256.yaml \ +celeba-hq-dataset/val_source_256/ \ +celeba-hq-dataset/val_256/random_thick_256/ + +python3 bin/gen_mask_dataset.py \ +$(pwd)/configs/data_gen/random_thin_256.yaml \ +celeba-hq-dataset/val_source_256/ \ +celeba-hq-dataset/val_256/random_thin_256/ + +python3 bin/gen_mask_dataset.py \ +$(pwd)/configs/data_gen/random_medium_256.yaml \ +celeba-hq-dataset/val_source_256/ \ +celeba-hq-dataset/val_256/random_medium_256/ + +python3 bin/gen_mask_dataset.py \ +$(pwd)/configs/data_gen/random_thick_256.yaml \ +celeba-hq-dataset/visual_test_source_256/ \ +celeba-hq-dataset/visual_test_256/random_thick_256/ + +python3 bin/gen_mask_dataset.py \ +$(pwd)/configs/data_gen/random_thin_256.yaml \ +celeba-hq-dataset/visual_test_source_256/ \ +celeba-hq-dataset/visual_test_256/random_thin_256/ + +python3 bin/gen_mask_dataset.py \ +$(pwd)/configs/data_gen/random_medium_256.yaml \ +celeba-hq-dataset/visual_test_source_256/ \ +celeba-hq-dataset/visual_test_256/random_medium_256/ diff --git a/third-party/lama/fetch_data/eval_sampler.py b/third-party/lama/fetch_data/eval_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..7cffdbc969e3f5d5f18f589c29f70abd240f3986 --- /dev/null +++ b/third-party/lama/fetch_data/eval_sampler.py @@ -0,0 +1,20 @@ +import os +import random + +val_files_path = os.path.abspath('.') + '/places_standard_dataset/original/val/' +list_of_random_val_files = os.path.abspath('.') + '/places_standard_dataset/original/eval_random_files.txt' +val_files = [val_files_path + image for image in os.listdir(val_files_path)] + +print(f'Sampling 30000 images out of {len(val_files)} images in {val_files_path}' + \ + f'and put their paths to {list_of_random_val_files}') + +print('In our paper we evaluate trained models on these 30k sampled (mask,image) pairs in our paper (check Sup. mat.)') + +random.shuffle(val_files) +val_files_random = val_files[0:30000] + +with open(list_of_random_val_files, 'w') as fw: + for filename in val_files_random: + fw.write(filename+'\n') +print('...done') + diff --git a/third-party/lama/fetch_data/places_challenge_train_download.sh b/third-party/lama/fetch_data/places_challenge_train_download.sh new file mode 100755 index 0000000000000000000000000000000000000000..f5317b44d16a2f295a56a52d1ce005605a137be7 --- /dev/null +++ b/third-party/lama/fetch_data/places_challenge_train_download.sh @@ -0,0 +1,14 @@ +mkdir places_challenge_dataset + + +declare -a TARPARTS +for i in {a..z} +do + TARPARTS[${#TARPARTS[@]}]="http://data.csail.mit.edu/places/places365/train_large_split/${i}.tar" +done +ls +printf "%s\n" "${TARPARTS[@]}" > places_challenge_dataset/places365_train.txt + +cd places_challenge_dataset/ +xargs -a places365_train.txt -n 1 -P 8 wget [...] +ls *.tar | xargs -i tar xvf {} diff --git a/third-party/lama/fetch_data/places_standard_evaluation_prepare_data.sh b/third-party/lama/fetch_data/places_standard_evaluation_prepare_data.sh new file mode 100755 index 0000000000000000000000000000000000000000..2962ac8c843c84a467679887cb4aab60bd73917a --- /dev/null +++ b/third-party/lama/fetch_data/places_standard_evaluation_prepare_data.sh @@ -0,0 +1,52 @@ +# 0. folder preparation +mkdir -p places_standard_dataset/evaluation/hires/ +mkdir -p places_standard_dataset/evaluation/random_thick_512/ +mkdir -p places_standard_dataset/evaluation/random_thin_512/ +mkdir -p places_standard_dataset/evaluation/random_medium_512/ +mkdir -p places_standard_dataset/evaluation/random_thick_256/ +mkdir -p places_standard_dataset/evaluation/random_thin_256/ +mkdir -p places_standard_dataset/evaluation/random_medium_256/ + +# 1. sample 30000 new images +OUT=$(python3 fetch_data/eval_sampler.py) +echo ${OUT} + +FILELIST=$(cat places_standard_dataset/original/eval_random_files.txt) +for i in $FILELIST +do + $(cp ${i} places_standard_dataset/evaluation/hires/) +done + + +# 2. generate all kinds of masks + +# all 512 +python3 bin/gen_mask_dataset.py \ +$(pwd)/configs/data_gen/random_thick_512.yaml \ +places_standard_dataset/evaluation/hires \ +places_standard_dataset/evaluation/random_thick_512/ + +python3 bin/gen_mask_dataset.py \ +$(pwd)/configs/data_gen/random_thin_512.yaml \ +places_standard_dataset/evaluation/hires \ +places_standard_dataset/evaluation/random_thin_512/ + +python3 bin/gen_mask_dataset.py \ +$(pwd)/configs/data_gen/random_medium_512.yaml \ +places_standard_dataset/evaluation/hires \ +places_standard_dataset/evaluation/random_medium_512/ + +python3 bin/gen_mask_dataset.py \ +$(pwd)/configs/data_gen/random_thick_256.yaml \ +places_standard_dataset/evaluation/hires \ +places_standard_dataset/evaluation/random_thick_256/ + +python3 bin/gen_mask_dataset.py \ +$(pwd)/configs/data_gen/random_thin_256.yaml \ +places_standard_dataset/evaluation/hires \ +places_standard_dataset/evaluation/random_thin_256/ + +python3 bin/gen_mask_dataset.py \ +$(pwd)/configs/data_gen/random_medium_256.yaml \ +places_standard_dataset/evaluation/hires \ +places_standard_dataset/evaluation/random_medium_256/ diff --git a/third-party/lama/fetch_data/places_standard_test_val_gen_masks.sh b/third-party/lama/fetch_data/places_standard_test_val_gen_masks.sh new file mode 100755 index 0000000000000000000000000000000000000000..4654779790564f4aba73fa1629ca6899697ad150 --- /dev/null +++ b/third-party/lama/fetch_data/places_standard_test_val_gen_masks.sh @@ -0,0 +1,13 @@ +mkdir -p places_standard_dataset/val/ +mkdir -p places_standard_dataset/visual_test/ + + +python3 bin/gen_mask_dataset.py \ +$(pwd)/configs/data_gen/random_thick_512.yaml \ +places_standard_dataset/val_hires/ \ +places_standard_dataset/val/ + +python3 bin/gen_mask_dataset.py \ +$(pwd)/configs/data_gen/random_thick_512.yaml \ +places_standard_dataset/visual_test_hires/ \ +places_standard_dataset/visual_test/ \ No newline at end of file diff --git a/third-party/lama/fetch_data/places_standard_test_val_prepare.sh b/third-party/lama/fetch_data/places_standard_test_val_prepare.sh new file mode 100755 index 0000000000000000000000000000000000000000..c0aa15008463c9fb881e0255c45994394a515806 --- /dev/null +++ b/third-party/lama/fetch_data/places_standard_test_val_prepare.sh @@ -0,0 +1,5 @@ +mkdir -p places_standard_dataset/original/test/ +tar -xvf test_large.tar -C places_standard_dataset/original/test/ + +mkdir -p places_standard_dataset/original/val/ +tar -xvf val_large.tar -C places_standard_dataset/original/val/ diff --git a/third-party/lama/fetch_data/places_standard_test_val_sample.sh b/third-party/lama/fetch_data/places_standard_test_val_sample.sh new file mode 100755 index 0000000000000000000000000000000000000000..7b581f457e32e339d7a480845de27d37d0171322 --- /dev/null +++ b/third-party/lama/fetch_data/places_standard_test_val_sample.sh @@ -0,0 +1,22 @@ +mkdir -p places_standard_dataset/val_hires/ +mkdir -p places_standard_dataset/visual_test_hires/ + + +# randomly sample images for test and vis +OUT=$(python3 fetch_data/sampler.py) +echo ${OUT} + +FILELIST=$(cat places_standard_dataset/original/test_random_files.txt) + +for i in $FILELIST +do + $(cp ${i} places_standard_dataset/val_hires/) +done + +FILELIST=$(cat places_standard_dataset/original/val_random_files.txt) + +for i in $FILELIST +do + $(cp ${i} places_standard_dataset/visual_test_hires/) +done + diff --git a/third-party/lama/fetch_data/places_standard_train_prepare.sh b/third-party/lama/fetch_data/places_standard_train_prepare.sh new file mode 100644 index 0000000000000000000000000000000000000000..aaf429243c5b05c9e3319b01842992cb2ab4c06c --- /dev/null +++ b/third-party/lama/fetch_data/places_standard_train_prepare.sh @@ -0,0 +1,16 @@ +mkdir -p places_standard_dataset/train + +# untar without folder structure +tar -xvf train_large_places365standard.tar -C places_standard_dataset/train + +# create location config places.yaml +PWD=$(pwd) +DATASET=${PWD}/places_standard_dataset +PLACES=${PWD}/configs/training/location/places_standard.yaml + +touch $PLACES +echo "# @package _group_" >> $PLACES +echo "data_root_dir: ${DATASET}/" >> $PLACES +echo "out_root_dir: ${PWD}/experiments/" >> $PLACES +echo "tb_dir: ${PWD}/tb_logs/" >> $PLACES +echo "pretrained_models: ${PWD}/" >> $PLACES diff --git a/third-party/lama/fetch_data/sampler.py b/third-party/lama/fetch_data/sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..9bf48b7fab615d404be55cda5c645bf91ad65d18 --- /dev/null +++ b/third-party/lama/fetch_data/sampler.py @@ -0,0 +1,42 @@ +import os +import random + +test_files_path = os.path.abspath('.') + '/places_standard_dataset/original/test/' +list_of_random_test_files = os.path.abspath('.') + '/places_standard_dataset/original/test_random_files.txt' + +test_files = [ + test_files_path + image for image in os.listdir(test_files_path) +] + +print(f'Sampling 2000 images out of {len(test_files)} images in {test_files_path}' + \ + f'and put their paths to {list_of_random_test_files}') +print('Our training procedure will pick best checkpoints according to metrics, computed on these images.') + +random.shuffle(test_files) +test_files_random = test_files[0:2000] +with open(list_of_random_test_files, 'w') as fw: + for filename in test_files_random: + fw.write(filename+'\n') +print('...done') + + +# -------------------------------- + +val_files_path = os.path.abspath('.') + '/places_standard_dataset/original/val/' +list_of_random_val_files = os.path.abspath('.') + '/places_standard_dataset/original/val_random_files.txt' + +val_files = [ + val_files_path + image for image in os.listdir(val_files_path) +] + +print(f'Sampling 100 images out of {len(val_files)} in {val_files_path} ' + \ + f'and put their paths to {list_of_random_val_files}') +print('We use these images for visual check up of evolution of inpainting algorithm epoch to epoch' ) + +random.shuffle(val_files) +val_files_random = val_files[0:100] +with open(list_of_random_val_files, 'w') as fw: + for filename in val_files_random: + fw.write(filename+'\n') +print('...done') + diff --git a/third-party/lama/fetch_data/train_shuffled.flist b/third-party/lama/fetch_data/train_shuffled.flist new file mode 100644 index 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newline at end of file diff --git a/third-party/lama/models/ade20k/base.py b/third-party/lama/models/ade20k/base.py new file mode 100644 index 0000000000000000000000000000000000000000..8cdbe2d3e7dbadf4ed5e5a7cf2d248761ef25d9c --- /dev/null +++ b/third-party/lama/models/ade20k/base.py @@ -0,0 +1,627 @@ +"""Modified from https://github.com/CSAILVision/semantic-segmentation-pytorch""" + +import os + +import pandas as pd +import torch +import torch.nn as nn +import torch.nn.functional as F +from scipy.io import loadmat +from torch.nn.modules import BatchNorm2d + +from . import resnet +from . import mobilenet + + +NUM_CLASS = 150 +base_path = os.path.dirname(os.path.abspath(__file__)) # current file path +colors_path = os.path.join(base_path, 'color150.mat') +classes_path = os.path.join(base_path, 'object150_info.csv') + +segm_options = dict(colors=loadmat(colors_path)['colors'], + classes=pd.read_csv(classes_path),) + + +class NormalizeTensor: + def __init__(self, mean, std, inplace=False): + """Normalize a tensor image with mean and standard deviation. + .. note:: + This transform acts out of place by default, i.e., it does not mutates the input tensor. + See :class:`~torchvision.transforms.Normalize` for more details. + Args: + tensor (Tensor): Tensor image of size (C, H, W) to be normalized. + mean (sequence): Sequence of means for each channel. + std (sequence): Sequence of standard deviations for each channel. + inplace(bool,optional): Bool to make this operation inplace. + Returns: + Tensor: Normalized Tensor image. + """ + + self.mean = mean + self.std = std + self.inplace = inplace + + def __call__(self, tensor): + if not self.inplace: + tensor = tensor.clone() + + dtype = tensor.dtype + mean = torch.as_tensor(self.mean, dtype=dtype, device=tensor.device) + std = torch.as_tensor(self.std, dtype=dtype, device=tensor.device) + tensor.sub_(mean[None, :, None, None]).div_(std[None, :, None, None]) + return tensor + + +# Model Builder +class ModelBuilder: + # custom weights initialization + @staticmethod + def weights_init(m): + classname = m.__class__.__name__ + if classname.find('Conv') != -1: + nn.init.kaiming_normal_(m.weight.data) + elif classname.find('BatchNorm') != -1: + m.weight.data.fill_(1.) + m.bias.data.fill_(1e-4) + + @staticmethod + def build_encoder(arch='resnet50dilated', fc_dim=512, weights=''): + pretrained = True if len(weights) == 0 else False + arch = arch.lower() + if arch == 'mobilenetv2dilated': + orig_mobilenet = mobilenet.__dict__['mobilenetv2'](pretrained=pretrained) + net_encoder = MobileNetV2Dilated(orig_mobilenet, dilate_scale=8) + elif arch == 'resnet18': + orig_resnet = resnet.__dict__['resnet18'](pretrained=pretrained) + net_encoder = Resnet(orig_resnet) + elif arch == 'resnet18dilated': + orig_resnet = resnet.__dict__['resnet18'](pretrained=pretrained) + net_encoder = ResnetDilated(orig_resnet, dilate_scale=8) + elif arch == 'resnet50dilated': + orig_resnet = resnet.__dict__['resnet50'](pretrained=pretrained) + net_encoder = ResnetDilated(orig_resnet, dilate_scale=8) + elif arch == 'resnet50': + orig_resnet = resnet.__dict__['resnet50'](pretrained=pretrained) + net_encoder = Resnet(orig_resnet) + else: + raise Exception('Architecture undefined!') + + # encoders are usually pretrained + # net_encoder.apply(ModelBuilder.weights_init) + if len(weights) > 0: + print('Loading weights for net_encoder') + net_encoder.load_state_dict( + torch.load(weights, map_location=lambda storage, loc: storage), strict=False) + return net_encoder + + @staticmethod + def build_decoder(arch='ppm_deepsup', + fc_dim=512, num_class=NUM_CLASS, + weights='', use_softmax=False, drop_last_conv=False): + arch = arch.lower() + if arch == 'ppm_deepsup': + net_decoder = PPMDeepsup( + num_class=num_class, + fc_dim=fc_dim, + use_softmax=use_softmax, + drop_last_conv=drop_last_conv) + elif arch == 'c1_deepsup': + net_decoder = C1DeepSup( + num_class=num_class, + fc_dim=fc_dim, + use_softmax=use_softmax, + drop_last_conv=drop_last_conv) + else: + raise Exception('Architecture undefined!') + + net_decoder.apply(ModelBuilder.weights_init) + if len(weights) > 0: + print('Loading weights for net_decoder') + net_decoder.load_state_dict( + torch.load(weights, map_location=lambda storage, loc: storage), strict=False) + return net_decoder + + @staticmethod + def get_decoder(weights_path, arch_encoder, arch_decoder, fc_dim, drop_last_conv, *arts, **kwargs): + path = os.path.join(weights_path, 'ade20k', f'ade20k-{arch_encoder}-{arch_decoder}/decoder_epoch_20.pth') + return ModelBuilder.build_decoder(arch=arch_decoder, fc_dim=fc_dim, weights=path, use_softmax=True, drop_last_conv=drop_last_conv) + + @staticmethod + def get_encoder(weights_path, arch_encoder, arch_decoder, fc_dim, segmentation, + *arts, **kwargs): + if segmentation: + path = os.path.join(weights_path, 'ade20k', f'ade20k-{arch_encoder}-{arch_decoder}/encoder_epoch_20.pth') + else: + path = '' + return ModelBuilder.build_encoder(arch=arch_encoder, fc_dim=fc_dim, weights=path) + + +def conv3x3_bn_relu(in_planes, out_planes, stride=1): + return nn.Sequential( + nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False), + BatchNorm2d(out_planes), + nn.ReLU(inplace=True), + ) + + +class SegmentationModule(nn.Module): + def __init__(self, + weights_path, + num_classes=150, + arch_encoder="resnet50dilated", + drop_last_conv=False, + net_enc=None, # None for Default encoder + net_dec=None, # None for Default decoder + encode=None, # {None, 'binary', 'color', 'sky'} + use_default_normalization=False, + return_feature_maps=False, + return_feature_maps_level=3, # {0, 1, 2, 3} + return_feature_maps_only=True, + **kwargs, + ): + super().__init__() + self.weights_path = weights_path + self.drop_last_conv = drop_last_conv + self.arch_encoder = arch_encoder + if self.arch_encoder == "resnet50dilated": + self.arch_decoder = "ppm_deepsup" + self.fc_dim = 2048 + elif self.arch_encoder == "mobilenetv2dilated": + self.arch_decoder = "c1_deepsup" + self.fc_dim = 320 + else: + raise NotImplementedError(f"No such arch_encoder={self.arch_encoder}") + model_builder_kwargs = dict(arch_encoder=self.arch_encoder, + arch_decoder=self.arch_decoder, + fc_dim=self.fc_dim, + drop_last_conv=drop_last_conv, + weights_path=self.weights_path) + + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + self.encoder = ModelBuilder.get_encoder(**model_builder_kwargs) if net_enc is None else net_enc + self.decoder = ModelBuilder.get_decoder(**model_builder_kwargs) if net_dec is None else net_dec + self.use_default_normalization = use_default_normalization + self.default_normalization = NormalizeTensor(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + + self.encode = encode + + self.return_feature_maps = return_feature_maps + + assert 0 <= return_feature_maps_level <= 3 + self.return_feature_maps_level = return_feature_maps_level + + def normalize_input(self, tensor): + if tensor.min() < 0 or tensor.max() > 1: + raise ValueError("Tensor should be 0..1 before using normalize_input") + return self.default_normalization(tensor) + + @property + def feature_maps_channels(self): + return 256 * 2**(self.return_feature_maps_level) # 256, 512, 1024, 2048 + + def forward(self, img_data, segSize=None): + if segSize is None: + raise NotImplementedError("Please pass segSize param. By default: (300, 300)") + + fmaps = self.encoder(img_data, return_feature_maps=True) + pred = self.decoder(fmaps, segSize=segSize) + + if self.return_feature_maps: + return pred, fmaps + # print("BINARY", img_data.shape, pred.shape) + return pred + + def multi_mask_from_multiclass(self, pred, classes): + def isin(ar1, ar2): + return (ar1[..., None] == ar2).any(-1).float() + return isin(pred, torch.LongTensor(classes).to(self.device)) + + @staticmethod + def multi_mask_from_multiclass_probs(scores, classes): + res = None + for c in classes: + if res is None: + res = scores[:, c] + else: + res += scores[:, c] + return res + + def predict(self, tensor, imgSizes=(-1,), # (300, 375, 450, 525, 600) + segSize=None): + """Entry-point for segmentation. Use this methods instead of forward + Arguments: + tensor {torch.Tensor} -- BCHW + Keyword Arguments: + imgSizes {tuple or list} -- imgSizes for segmentation input. + default: (300, 450) + original implementation: (300, 375, 450, 525, 600) + + """ + if segSize is None: + segSize = tensor.shape[-2:] + segSize = (tensor.shape[2], tensor.shape[3]) + with torch.no_grad(): + if self.use_default_normalization: + tensor = self.normalize_input(tensor) + scores = torch.zeros(1, NUM_CLASS, segSize[0], segSize[1]).to(self.device) + features = torch.zeros(1, self.feature_maps_channels, segSize[0], segSize[1]).to(self.device) + + result = [] + for img_size in imgSizes: + if img_size != -1: + img_data = F.interpolate(tensor.clone(), size=img_size) + else: + img_data = tensor.clone() + + if self.return_feature_maps: + pred_current, fmaps = self.forward(img_data, segSize=segSize) + else: + pred_current = self.forward(img_data, segSize=segSize) + + + result.append(pred_current) + scores = scores + pred_current / len(imgSizes) + + # Disclaimer: We use and aggregate only last fmaps: fmaps[3] + if self.return_feature_maps: + features = features + F.interpolate(fmaps[self.return_feature_maps_level], size=segSize) / len(imgSizes) + + _, pred = torch.max(scores, dim=1) + + if self.return_feature_maps: + return features + + return pred, result + + def get_edges(self, t): + edge = torch.cuda.ByteTensor(t.size()).zero_() + edge[:, :, :, 1:] = edge[:, :, :, 1:] | (t[:, :, :, 1:] != t[:, :, :, :-1]) + edge[:, :, :, :-1] = edge[:, :, :, :-1] | (t[:, :, :, 1:] != t[:, :, :, :-1]) + edge[:, :, 1:, :] = edge[:, :, 1:, :] | (t[:, :, 1:, :] != t[:, :, :-1, :]) + edge[:, :, :-1, :] = edge[:, :, :-1, :] | (t[:, :, 1:, :] != t[:, :, :-1, :]) + + if True: + return edge.half() + return edge.float() + + +# pyramid pooling, deep supervision +class PPMDeepsup(nn.Module): + def __init__(self, num_class=NUM_CLASS, fc_dim=4096, + use_softmax=False, pool_scales=(1, 2, 3, 6), + drop_last_conv=False): + super().__init__() + self.use_softmax = use_softmax + self.drop_last_conv = drop_last_conv + + self.ppm = [] + for scale in pool_scales: + self.ppm.append(nn.Sequential( + nn.AdaptiveAvgPool2d(scale), + nn.Conv2d(fc_dim, 512, kernel_size=1, bias=False), + BatchNorm2d(512), + nn.ReLU(inplace=True) + )) + self.ppm = nn.ModuleList(self.ppm) + self.cbr_deepsup = conv3x3_bn_relu(fc_dim // 2, fc_dim // 4, 1) + + self.conv_last = nn.Sequential( + nn.Conv2d(fc_dim + len(pool_scales) * 512, 512, + kernel_size=3, padding=1, bias=False), + BatchNorm2d(512), + nn.ReLU(inplace=True), + nn.Dropout2d(0.1), + nn.Conv2d(512, num_class, kernel_size=1) + ) + self.conv_last_deepsup = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0) + self.dropout_deepsup = nn.Dropout2d(0.1) + + def forward(self, conv_out, segSize=None): + conv5 = conv_out[-1] + + input_size = conv5.size() + ppm_out = [conv5] + for pool_scale in self.ppm: + ppm_out.append(nn.functional.interpolate( + pool_scale(conv5), + (input_size[2], input_size[3]), + mode='bilinear', align_corners=False)) + ppm_out = torch.cat(ppm_out, 1) + + if self.drop_last_conv: + return ppm_out + else: + x = self.conv_last(ppm_out) + + if self.use_softmax: # is True during inference + x = nn.functional.interpolate( + x, size=segSize, mode='bilinear', align_corners=False) + x = nn.functional.softmax(x, dim=1) + return x + + # deep sup + conv4 = conv_out[-2] + _ = self.cbr_deepsup(conv4) + _ = self.dropout_deepsup(_) + _ = self.conv_last_deepsup(_) + + x = nn.functional.log_softmax(x, dim=1) + _ = nn.functional.log_softmax(_, dim=1) + + return (x, _) + + +class Resnet(nn.Module): + def __init__(self, orig_resnet): + super(Resnet, self).__init__() + + # take pretrained resnet, except AvgPool and FC + self.conv1 = orig_resnet.conv1 + self.bn1 = orig_resnet.bn1 + self.relu1 = orig_resnet.relu1 + self.conv2 = orig_resnet.conv2 + self.bn2 = orig_resnet.bn2 + self.relu2 = orig_resnet.relu2 + self.conv3 = orig_resnet.conv3 + self.bn3 = orig_resnet.bn3 + self.relu3 = orig_resnet.relu3 + self.maxpool = orig_resnet.maxpool + self.layer1 = orig_resnet.layer1 + self.layer2 = orig_resnet.layer2 + self.layer3 = orig_resnet.layer3 + self.layer4 = orig_resnet.layer4 + + def forward(self, x, return_feature_maps=False): + conv_out = [] + + x = self.relu1(self.bn1(self.conv1(x))) + x = self.relu2(self.bn2(self.conv2(x))) + x = self.relu3(self.bn3(self.conv3(x))) + x = self.maxpool(x) + + x = self.layer1(x); conv_out.append(x); + x = self.layer2(x); conv_out.append(x); + x = self.layer3(x); conv_out.append(x); + x = self.layer4(x); conv_out.append(x); + + if return_feature_maps: + return conv_out + return [x] + +# Resnet Dilated +class ResnetDilated(nn.Module): + def __init__(self, orig_resnet, dilate_scale=8): + super().__init__() + from functools import partial + + if dilate_scale == 8: + orig_resnet.layer3.apply( + partial(self._nostride_dilate, dilate=2)) + orig_resnet.layer4.apply( + partial(self._nostride_dilate, dilate=4)) + elif dilate_scale == 16: + orig_resnet.layer4.apply( + partial(self._nostride_dilate, dilate=2)) + + # take pretrained resnet, except AvgPool and FC + self.conv1 = orig_resnet.conv1 + self.bn1 = orig_resnet.bn1 + self.relu1 = orig_resnet.relu1 + self.conv2 = orig_resnet.conv2 + self.bn2 = orig_resnet.bn2 + self.relu2 = orig_resnet.relu2 + self.conv3 = orig_resnet.conv3 + self.bn3 = orig_resnet.bn3 + self.relu3 = orig_resnet.relu3 + self.maxpool = orig_resnet.maxpool + self.layer1 = orig_resnet.layer1 + self.layer2 = orig_resnet.layer2 + self.layer3 = orig_resnet.layer3 + self.layer4 = orig_resnet.layer4 + + def _nostride_dilate(self, m, dilate): + classname = m.__class__.__name__ + if classname.find('Conv') != -1: + # the convolution with stride + if m.stride == (2, 2): + m.stride = (1, 1) + if m.kernel_size == (3, 3): + m.dilation = (dilate // 2, dilate // 2) + m.padding = (dilate // 2, dilate // 2) + # other convoluions + else: + if m.kernel_size == (3, 3): + m.dilation = (dilate, dilate) + m.padding = (dilate, dilate) + + def forward(self, x, return_feature_maps=False): + conv_out = [] + + x = self.relu1(self.bn1(self.conv1(x))) + x = self.relu2(self.bn2(self.conv2(x))) + x = self.relu3(self.bn3(self.conv3(x))) + x = self.maxpool(x) + + x = self.layer1(x) + conv_out.append(x) + x = self.layer2(x) + conv_out.append(x) + x = self.layer3(x) + conv_out.append(x) + x = self.layer4(x) + conv_out.append(x) + + if return_feature_maps: + return conv_out + return [x] + +class MobileNetV2Dilated(nn.Module): + def __init__(self, orig_net, dilate_scale=8): + super(MobileNetV2Dilated, self).__init__() + from functools import partial + + # take pretrained mobilenet features + self.features = orig_net.features[:-1] + + self.total_idx = len(self.features) + self.down_idx = [2, 4, 7, 14] + + if dilate_scale == 8: + for i in range(self.down_idx[-2], self.down_idx[-1]): + self.features[i].apply( + partial(self._nostride_dilate, dilate=2) + ) + for i in range(self.down_idx[-1], self.total_idx): + self.features[i].apply( + partial(self._nostride_dilate, dilate=4) + ) + elif dilate_scale == 16: + for i in range(self.down_idx[-1], self.total_idx): + self.features[i].apply( + partial(self._nostride_dilate, dilate=2) + ) + + def _nostride_dilate(self, m, dilate): + classname = m.__class__.__name__ + if classname.find('Conv') != -1: + # the convolution with stride + if m.stride == (2, 2): + m.stride = (1, 1) + if m.kernel_size == (3, 3): + m.dilation = (dilate//2, dilate//2) + m.padding = (dilate//2, dilate//2) + # other convoluions + else: + if m.kernel_size == (3, 3): + m.dilation = (dilate, dilate) + m.padding = (dilate, dilate) + + def forward(self, x, return_feature_maps=False): + if return_feature_maps: + conv_out = [] + for i in range(self.total_idx): + x = self.features[i](x) + if i in self.down_idx: + conv_out.append(x) + conv_out.append(x) + return conv_out + + else: + return [self.features(x)] + + +# last conv, deep supervision +class C1DeepSup(nn.Module): + def __init__(self, num_class=150, fc_dim=2048, use_softmax=False, drop_last_conv=False): + super(C1DeepSup, self).__init__() + self.use_softmax = use_softmax + self.drop_last_conv = drop_last_conv + + self.cbr = conv3x3_bn_relu(fc_dim, fc_dim // 4, 1) + self.cbr_deepsup = conv3x3_bn_relu(fc_dim // 2, fc_dim // 4, 1) + + # last conv + self.conv_last = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0) + self.conv_last_deepsup = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0) + + def forward(self, conv_out, segSize=None): + conv5 = conv_out[-1] + + x = self.cbr(conv5) + + if self.drop_last_conv: + return x + else: + x = self.conv_last(x) + + if self.use_softmax: # is True during inference + x = nn.functional.interpolate( + x, size=segSize, mode='bilinear', align_corners=False) + x = nn.functional.softmax(x, dim=1) + return x + + # deep sup + conv4 = conv_out[-2] + _ = self.cbr_deepsup(conv4) + _ = self.conv_last_deepsup(_) + + x = nn.functional.log_softmax(x, dim=1) + _ = nn.functional.log_softmax(_, dim=1) + + return (x, _) + + +# last conv +class C1(nn.Module): + def __init__(self, num_class=150, fc_dim=2048, use_softmax=False): + super(C1, self).__init__() + self.use_softmax = use_softmax + + self.cbr = conv3x3_bn_relu(fc_dim, fc_dim // 4, 1) + + # last conv + self.conv_last = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0) + + def forward(self, conv_out, segSize=None): + conv5 = conv_out[-1] + x = self.cbr(conv5) + x = self.conv_last(x) + + if self.use_softmax: # is True during inference + x = nn.functional.interpolate( + x, size=segSize, mode='bilinear', align_corners=False) + x = nn.functional.softmax(x, dim=1) + else: + x = nn.functional.log_softmax(x, dim=1) + + return x + + +# pyramid pooling +class PPM(nn.Module): + def __init__(self, num_class=150, fc_dim=4096, + use_softmax=False, pool_scales=(1, 2, 3, 6)): + super(PPM, self).__init__() + self.use_softmax = use_softmax + + self.ppm = [] + for scale in pool_scales: + self.ppm.append(nn.Sequential( + nn.AdaptiveAvgPool2d(scale), + nn.Conv2d(fc_dim, 512, kernel_size=1, bias=False), + BatchNorm2d(512), + nn.ReLU(inplace=True) + )) + self.ppm = nn.ModuleList(self.ppm) + + self.conv_last = nn.Sequential( + nn.Conv2d(fc_dim+len(pool_scales)*512, 512, + kernel_size=3, padding=1, bias=False), + BatchNorm2d(512), + nn.ReLU(inplace=True), + nn.Dropout2d(0.1), + nn.Conv2d(512, num_class, kernel_size=1) + ) + + def forward(self, conv_out, segSize=None): + conv5 = conv_out[-1] + + input_size = conv5.size() + ppm_out = [conv5] + for pool_scale in self.ppm: + ppm_out.append(nn.functional.interpolate( + pool_scale(conv5), + (input_size[2], input_size[3]), + mode='bilinear', align_corners=False)) + ppm_out = torch.cat(ppm_out, 1) + + x = self.conv_last(ppm_out) + + if self.use_softmax: # is True during inference + x = nn.functional.interpolate( + x, size=segSize, mode='bilinear', align_corners=False) + x = nn.functional.softmax(x, dim=1) + else: + x = nn.functional.log_softmax(x, dim=1) + return x diff --git a/third-party/lama/models/ade20k/color150.mat b/third-party/lama/models/ade20k/color150.mat new file mode 100644 index 0000000000000000000000000000000000000000..c518b64fbbe899d4a8b2705f012eeba795339892 Binary files /dev/null and b/third-party/lama/models/ade20k/color150.mat differ diff --git a/third-party/lama/models/ade20k/mobilenet.py b/third-party/lama/models/ade20k/mobilenet.py new file mode 100644 index 0000000000000000000000000000000000000000..f501266e56ee71cdf455744020f8fc1a58ec9fff --- /dev/null +++ b/third-party/lama/models/ade20k/mobilenet.py @@ -0,0 +1,154 @@ +""" +This MobileNetV2 implementation is modified from the following repository: +https://github.com/tonylins/pytorch-mobilenet-v2 +""" + +import torch.nn as nn +import math +from .utils import load_url +from .segm_lib.nn import SynchronizedBatchNorm2d + +BatchNorm2d = SynchronizedBatchNorm2d + + +__all__ = ['mobilenetv2'] + + +model_urls = { + 'mobilenetv2': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/mobilenet_v2.pth.tar', +} + + +def conv_bn(inp, oup, stride): + return nn.Sequential( + nn.Conv2d(inp, oup, 3, stride, 1, bias=False), + BatchNorm2d(oup), + nn.ReLU6(inplace=True) + ) + + +def conv_1x1_bn(inp, oup): + return nn.Sequential( + nn.Conv2d(inp, oup, 1, 1, 0, bias=False), + BatchNorm2d(oup), + nn.ReLU6(inplace=True) + ) + + +class InvertedResidual(nn.Module): + def __init__(self, inp, oup, stride, expand_ratio): + super(InvertedResidual, self).__init__() + self.stride = stride + assert stride in [1, 2] + + hidden_dim = round(inp * expand_ratio) + self.use_res_connect = self.stride == 1 and inp == oup + + if expand_ratio == 1: + self.conv = nn.Sequential( + # dw + nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False), + BatchNorm2d(hidden_dim), + nn.ReLU6(inplace=True), + # pw-linear + nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), + BatchNorm2d(oup), + ) + else: + self.conv = nn.Sequential( + # pw + nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False), + BatchNorm2d(hidden_dim), + nn.ReLU6(inplace=True), + # dw + nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False), + BatchNorm2d(hidden_dim), + nn.ReLU6(inplace=True), + # pw-linear + nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), + BatchNorm2d(oup), + ) + + def forward(self, x): + if self.use_res_connect: + return x + self.conv(x) + else: + return self.conv(x) + + +class MobileNetV2(nn.Module): + def __init__(self, n_class=1000, input_size=224, width_mult=1.): + super(MobileNetV2, self).__init__() + block = InvertedResidual + input_channel = 32 + last_channel = 1280 + interverted_residual_setting = [ + # t, c, n, s + [1, 16, 1, 1], + [6, 24, 2, 2], + [6, 32, 3, 2], + [6, 64, 4, 2], + [6, 96, 3, 1], + [6, 160, 3, 2], + [6, 320, 1, 1], + ] + + # building first layer + assert input_size % 32 == 0 + input_channel = int(input_channel * width_mult) + self.last_channel = int(last_channel * width_mult) if width_mult > 1.0 else last_channel + self.features = [conv_bn(3, input_channel, 2)] + # building inverted residual blocks + for t, c, n, s in interverted_residual_setting: + output_channel = int(c * width_mult) + for i in range(n): + if i == 0: + self.features.append(block(input_channel, output_channel, s, expand_ratio=t)) + else: + self.features.append(block(input_channel, output_channel, 1, expand_ratio=t)) + input_channel = output_channel + # building last several layers + self.features.append(conv_1x1_bn(input_channel, self.last_channel)) + # make it nn.Sequential + self.features = nn.Sequential(*self.features) + + # building classifier + self.classifier = nn.Sequential( + nn.Dropout(0.2), + nn.Linear(self.last_channel, n_class), + ) + + self._initialize_weights() + + def forward(self, x): + x = self.features(x) + x = x.mean(3).mean(2) + x = self.classifier(x) + return x + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + n = m.weight.size(1) + m.weight.data.normal_(0, 0.01) + m.bias.data.zero_() + + +def mobilenetv2(pretrained=False, **kwargs): + """Constructs a MobileNet_V2 model. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = MobileNetV2(n_class=1000, **kwargs) + if pretrained: + model.load_state_dict(load_url(model_urls['mobilenetv2']), strict=False) + return model \ No newline at end of file diff --git a/third-party/lama/models/ade20k/object150_info.csv b/third-party/lama/models/ade20k/object150_info.csv new file mode 100644 index 0000000000000000000000000000000000000000..8b34d8f3874a38b96894863c5458a7c3c2b0e2e6 --- /dev/null +++ b/third-party/lama/models/ade20k/object150_info.csv @@ -0,0 +1,151 @@ +Idx,Ratio,Train,Val,Stuff,Name +1,0.1576,11664,1172,1,wall +2,0.1072,6046,612,1,building;edifice +3,0.0878,8265,796,1,sky +4,0.0621,9336,917,1,floor;flooring +5,0.0480,6678,641,0,tree +6,0.0450,6604,643,1,ceiling +7,0.0398,4023,408,1,road;route +8,0.0231,1906,199,0,bed +9,0.0198,4688,460,0,windowpane;window +10,0.0183,2423,225,1,grass +11,0.0181,2874,294,0,cabinet +12,0.0166,3068,310,1,sidewalk;pavement +13,0.0160,5075,526,0,person;individual;someone;somebody;mortal;soul +14,0.0151,1804,190,1,earth;ground +15,0.0118,6666,796,0,door;double;door +16,0.0110,4269,411,0,table +17,0.0109,1691,160,1,mountain;mount +18,0.0104,3999,441,0,plant;flora;plant;life +19,0.0104,2149,217,0,curtain;drape;drapery;mantle;pall +20,0.0103,3261,318,0,chair +21,0.0098,3164,306,0,car;auto;automobile;machine;motorcar +22,0.0074,709,75,1,water +23,0.0067,3296,315,0,painting;picture +24,0.0065,1191,106,0,sofa;couch;lounge +25,0.0061,1516,162,0,shelf +26,0.0060,667,69,1,house +27,0.0053,651,57,1,sea +28,0.0052,1847,224,0,mirror +29,0.0046,1158,128,1,rug;carpet;carpeting +30,0.0044,480,44,1,field +31,0.0044,1172,98,0,armchair +32,0.0044,1292,184,0,seat +33,0.0033,1386,138,0,fence;fencing +34,0.0031,698,61,0,desk +35,0.0030,781,73,0,rock;stone +36,0.0027,380,43,0,wardrobe;closet;press +37,0.0026,3089,302,0,lamp +38,0.0024,404,37,0,bathtub;bathing;tub;bath;tub +39,0.0024,804,99,0,railing;rail +40,0.0023,1453,153,0,cushion +41,0.0023,411,37,0,base;pedestal;stand +42,0.0022,1440,162,0,box +43,0.0022,800,77,0,column;pillar +44,0.0020,2650,298,0,signboard;sign +45,0.0019,549,46,0,chest;of;drawers;chest;bureau;dresser +46,0.0019,367,36,0,counter +47,0.0018,311,30,1,sand +48,0.0018,1181,122,0,sink +49,0.0018,287,23,1,skyscraper +50,0.0018,468,38,0,fireplace;hearth;open;fireplace +51,0.0018,402,43,0,refrigerator;icebox +52,0.0018,130,12,1,grandstand;covered;stand +53,0.0018,561,64,1,path +54,0.0017,880,102,0,stairs;steps +55,0.0017,86,12,1,runway +56,0.0017,172,11,0,case;display;case;showcase;vitrine +57,0.0017,198,18,0,pool;table;billiard;table;snooker;table +58,0.0017,930,109,0,pillow +59,0.0015,139,18,0,screen;door;screen +60,0.0015,564,52,1,stairway;staircase +61,0.0015,320,26,1,river +62,0.0015,261,29,1,bridge;span +63,0.0014,275,22,0,bookcase +64,0.0014,335,60,0,blind;screen +65,0.0014,792,75,0,coffee;table;cocktail;table +66,0.0014,395,49,0,toilet;can;commode;crapper;pot;potty;stool;throne +67,0.0014,1309,138,0,flower +68,0.0013,1112,113,0,book +69,0.0013,266,27,1,hill +70,0.0013,659,66,0,bench +71,0.0012,331,31,0,countertop +72,0.0012,531,56,0,stove;kitchen;stove;range;kitchen;range;cooking;stove +73,0.0012,369,36,0,palm;palm;tree +74,0.0012,144,9,0,kitchen;island +75,0.0011,265,29,0,computer;computing;machine;computing;device;data;processor;electronic;computer;information;processing;system +76,0.0010,324,33,0,swivel;chair +77,0.0009,304,27,0,boat +78,0.0009,170,20,0,bar +79,0.0009,68,6,0,arcade;machine +80,0.0009,65,8,1,hovel;hut;hutch;shack;shanty +81,0.0009,248,25,0,bus;autobus;coach;charabanc;double-decker;jitney;motorbus;motorcoach;omnibus;passenger;vehicle +82,0.0008,492,49,0,towel +83,0.0008,2510,269,0,light;light;source +84,0.0008,440,39,0,truck;motortruck +85,0.0008,147,18,1,tower +86,0.0008,583,56,0,chandelier;pendant;pendent +87,0.0007,533,61,0,awning;sunshade;sunblind +88,0.0007,1989,239,0,streetlight;street;lamp +89,0.0007,71,5,0,booth;cubicle;stall;kiosk +90,0.0007,618,53,0,television;television;receiver;television;set;tv;tv;set;idiot;box;boob;tube;telly;goggle;box +91,0.0007,135,12,0,airplane;aeroplane;plane +92,0.0007,83,5,1,dirt;track +93,0.0007,178,17,0,apparel;wearing;apparel;dress;clothes +94,0.0006,1003,104,0,pole +95,0.0006,182,12,1,land;ground;soil +96,0.0006,452,50,0,bannister;banister;balustrade;balusters;handrail +97,0.0006,42,6,1,escalator;moving;staircase;moving;stairway +98,0.0006,307,31,0,ottoman;pouf;pouffe;puff;hassock +99,0.0006,965,114,0,bottle 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+149,0.0002,775,96,0,clock +150,0.0002,421,56,0,flag diff --git a/third-party/lama/models/ade20k/resnet.py b/third-party/lama/models/ade20k/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..3e1d521f171c984cf6a7ff3dcebd96f8c5faf908 --- /dev/null +++ b/third-party/lama/models/ade20k/resnet.py @@ -0,0 +1,181 @@ +"""Modified from https://github.com/CSAILVision/semantic-segmentation-pytorch""" + +import math + +import torch.nn as nn +from torch.nn import BatchNorm2d + +from .utils import load_url + +__all__ = ['ResNet', 'resnet50'] + + +model_urls = { + 'resnet50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet50-imagenet.pth', +} + + +def conv3x3(in_planes, out_planes, stride=1): + "3x3 convolution with padding" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = BatchNorm2d(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) + self.bn3 = BatchNorm2d(planes * 4) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class ResNet(nn.Module): + + def __init__(self, block, layers, num_classes=1000): + self.inplanes = 128 + super(ResNet, self).__init__() + self.conv1 = conv3x3(3, 64, stride=2) + self.bn1 = BatchNorm2d(64) + self.relu1 = nn.ReLU(inplace=True) + self.conv2 = conv3x3(64, 64) + self.bn2 = BatchNorm2d(64) + self.relu2 = nn.ReLU(inplace=True) + self.conv3 = conv3x3(64, 128) + self.bn3 = BatchNorm2d(128) + self.relu3 = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.avgpool = nn.AvgPool2d(7, stride=1) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.relu1(self.bn1(self.conv1(x))) + x = self.relu2(self.bn2(self.conv2(x))) + x = self.relu3(self.bn3(self.conv3(x))) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = x.view(x.size(0), -1) + x = self.fc(x) + + return x + + +def resnet50(pretrained=False, **kwargs): + """Constructs a ResNet-50 model. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) + if pretrained: + model.load_state_dict(load_url(model_urls['resnet50']), strict=False) + return model + + +def resnet18(pretrained=False, **kwargs): + """Constructs a ResNet-18 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) + if pretrained: + model.load_state_dict(load_url(model_urls['resnet18'])) + return model \ No newline at end of file diff --git a/third-party/lama/models/ade20k/segm_lib/nn/__init__.py b/third-party/lama/models/ade20k/segm_lib/nn/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..98a96370ef04570f516052bb73f568d0ebc346c3 --- /dev/null +++ b/third-party/lama/models/ade20k/segm_lib/nn/__init__.py @@ -0,0 +1,2 @@ +from .modules import * +from .parallel import UserScatteredDataParallel, user_scattered_collate, async_copy_to diff --git a/third-party/lama/models/ade20k/segm_lib/nn/modules/__init__.py b/third-party/lama/models/ade20k/segm_lib/nn/modules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bc8709d92c610b36e0bcbd7da20c1eb41dc8cfcf --- /dev/null +++ b/third-party/lama/models/ade20k/segm_lib/nn/modules/__init__.py @@ -0,0 +1,12 @@ +# -*- coding: utf-8 -*- +# File : __init__.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d +from .replicate import DataParallelWithCallback, patch_replication_callback diff --git a/third-party/lama/models/ade20k/segm_lib/nn/modules/batchnorm.py b/third-party/lama/models/ade20k/segm_lib/nn/modules/batchnorm.py new file mode 100644 index 0000000000000000000000000000000000000000..18318965335b37cc671004a6aceda3229dc7b477 --- /dev/null +++ b/third-party/lama/models/ade20k/segm_lib/nn/modules/batchnorm.py @@ -0,0 +1,329 @@ +# -*- coding: utf-8 -*- +# File : batchnorm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import collections + +import torch +import torch.nn.functional as F + +from torch.nn.modules.batchnorm import _BatchNorm +from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast + +from .comm import SyncMaster + +__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d'] + + +def _sum_ft(tensor): + """sum over the first and last dimention""" + return tensor.sum(dim=0).sum(dim=-1) + + +def _unsqueeze_ft(tensor): + """add new dementions at the front and the tail""" + return tensor.unsqueeze(0).unsqueeze(-1) + + +_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size']) +_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std']) + + +class _SynchronizedBatchNorm(_BatchNorm): + def __init__(self, num_features, eps=1e-5, momentum=0.001, affine=True): + super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine) + + self._sync_master = SyncMaster(self._data_parallel_master) + + self._is_parallel = False + self._parallel_id = None + self._slave_pipe = None + + # customed batch norm statistics + self._moving_average_fraction = 1. - momentum + self.register_buffer('_tmp_running_mean', torch.zeros(self.num_features)) + self.register_buffer('_tmp_running_var', torch.ones(self.num_features)) + self.register_buffer('_running_iter', torch.ones(1)) + self._tmp_running_mean = self.running_mean.clone() * self._running_iter + self._tmp_running_var = self.running_var.clone() * self._running_iter + + def forward(self, input): + # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation. + if not (self._is_parallel and self.training): + return F.batch_norm( + input, self.running_mean, self.running_var, self.weight, self.bias, + self.training, self.momentum, self.eps) + + # Resize the input to (B, C, -1). + input_shape = input.size() + input = input.view(input.size(0), self.num_features, -1) + + # Compute the sum and square-sum. + sum_size = input.size(0) * input.size(2) + input_sum = _sum_ft(input) + input_ssum = _sum_ft(input ** 2) + + # Reduce-and-broadcast the statistics. + if self._parallel_id == 0: + mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size)) + else: + mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size)) + + # Compute the output. + if self.affine: + # MJY:: Fuse the multiplication for speed. + output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias) + else: + output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std) + + # Reshape it. + return output.view(input_shape) + + def __data_parallel_replicate__(self, ctx, copy_id): + self._is_parallel = True + self._parallel_id = copy_id + + # parallel_id == 0 means master device. + if self._parallel_id == 0: + ctx.sync_master = self._sync_master + else: + self._slave_pipe = ctx.sync_master.register_slave(copy_id) + + def _data_parallel_master(self, intermediates): + """Reduce the sum and square-sum, compute the statistics, and broadcast it.""" + intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device()) + + to_reduce = [i[1][:2] for i in intermediates] + to_reduce = [j for i in to_reduce for j in i] # flatten + target_gpus = [i[1].sum.get_device() for i in intermediates] + + sum_size = sum([i[1].sum_size for i in intermediates]) + sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce) + + mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size) + + broadcasted = Broadcast.apply(target_gpus, mean, inv_std) + + outputs = [] + for i, rec in enumerate(intermediates): + outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2]))) + + return outputs + + def _add_weighted(self, dest, delta, alpha=1, beta=1, bias=0): + """return *dest* by `dest := dest*alpha + delta*beta + bias`""" + return dest * alpha + delta * beta + bias + + def _compute_mean_std(self, sum_, ssum, size): + """Compute the mean and standard-deviation with sum and square-sum. This method + also maintains the moving average on the master device.""" + assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.' + mean = sum_ / size + sumvar = ssum - sum_ * mean + unbias_var = sumvar / (size - 1) + bias_var = sumvar / size + + self._tmp_running_mean = self._add_weighted(self._tmp_running_mean, mean.data, alpha=self._moving_average_fraction) + self._tmp_running_var = self._add_weighted(self._tmp_running_var, unbias_var.data, alpha=self._moving_average_fraction) + self._running_iter = self._add_weighted(self._running_iter, 1, alpha=self._moving_average_fraction) + + self.running_mean = self._tmp_running_mean / self._running_iter + self.running_var = self._tmp_running_var / self._running_iter + + return mean, bias_var.clamp(self.eps) ** -0.5 + + +class SynchronizedBatchNorm1d(_SynchronizedBatchNorm): + r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a + mini-batch. + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm1d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm + + Args: + num_features: num_features from an expected input of size + `batch_size x num_features [x width]` + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape: + - Input: :math:`(N, C)` or :math:`(N, C, L)` + - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm1d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm1d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 2 and input.dim() != 3: + raise ValueError('expected 2D or 3D input (got {}D input)' + .format(input.dim())) + super(SynchronizedBatchNorm1d, self)._check_input_dim(input) + + +class SynchronizedBatchNorm2d(_SynchronizedBatchNorm): + r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch + of 3d inputs + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm2d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm + + Args: + num_features: num_features from an expected input of + size batch_size x num_features x height x width + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape: + - Input: :math:`(N, C, H, W)` + - Output: :math:`(N, C, H, W)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm2d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm2d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 4: + raise ValueError('expected 4D input (got {}D input)' + .format(input.dim())) + super(SynchronizedBatchNorm2d, self)._check_input_dim(input) + + +class SynchronizedBatchNorm3d(_SynchronizedBatchNorm): + r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch + of 4d inputs + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm3d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm + or Spatio-temporal BatchNorm + + Args: + num_features: num_features from an expected input of + size batch_size x num_features x depth x height x width + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape: + - Input: :math:`(N, C, D, H, W)` + - Output: :math:`(N, C, D, H, W)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm3d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm3d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 5: + raise ValueError('expected 5D input (got {}D input)' + .format(input.dim())) + super(SynchronizedBatchNorm3d, self)._check_input_dim(input) diff --git a/third-party/lama/models/ade20k/segm_lib/nn/modules/comm.py b/third-party/lama/models/ade20k/segm_lib/nn/modules/comm.py new file mode 100644 index 0000000000000000000000000000000000000000..b64bf6ba3b3e7abbab375c6dd4a87d8239e62138 --- /dev/null +++ b/third-party/lama/models/ade20k/segm_lib/nn/modules/comm.py @@ -0,0 +1,131 @@ +# -*- coding: utf-8 -*- +# File : comm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import queue +import collections +import threading + +__all__ = ['FutureResult', 'SlavePipe', 'SyncMaster'] + + +class FutureResult(object): + """A thread-safe future implementation. Used only as one-to-one pipe.""" + + def __init__(self): + self._result = None + self._lock = threading.Lock() + self._cond = threading.Condition(self._lock) + + def put(self, result): + with self._lock: + assert self._result is None, 'Previous result has\'t been fetched.' + self._result = result + self._cond.notify() + + def get(self): + with self._lock: + if self._result is None: + self._cond.wait() + + res = self._result + self._result = None + return res + + +_MasterRegistry = collections.namedtuple('MasterRegistry', ['result']) +_SlavePipeBase = collections.namedtuple('_SlavePipeBase', ['identifier', 'queue', 'result']) + + +class SlavePipe(_SlavePipeBase): + """Pipe for master-slave communication.""" + + def run_slave(self, msg): + self.queue.put((self.identifier, msg)) + ret = self.result.get() + self.queue.put(True) + return ret + + +class SyncMaster(object): + """An abstract `SyncMaster` object. + + - During the replication, as the data parallel will trigger an callback of each module, all slave devices should + call `register(id)` and obtain an `SlavePipe` to communicate with the master. + - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected, + and passed to a registered callback. + - After receiving the messages, the master device should gather the information and determine to message passed + back to each slave devices. + """ + + def __init__(self, master_callback): + """ + + Args: + master_callback: a callback to be invoked after having collected messages from slave devices. + """ + self._master_callback = master_callback + self._queue = queue.Queue() + self._registry = collections.OrderedDict() + self._activated = False + + def register_slave(self, identifier): + """ + Register an slave device. + + Args: + identifier: an identifier, usually is the device id. + + Returns: a `SlavePipe` object which can be used to communicate with the master device. + + """ + if self._activated: + assert self._queue.empty(), 'Queue is not clean before next initialization.' + self._activated = False + self._registry.clear() + future = FutureResult() + self._registry[identifier] = _MasterRegistry(future) + return SlavePipe(identifier, self._queue, future) + + def run_master(self, master_msg): + """ + Main entry for the master device in each forward pass. + The messages were first collected from each devices (including the master device), and then + an callback will be invoked to compute the message to be sent back to each devices + (including the master device). + + Args: + master_msg: the message that the master want to send to itself. This will be placed as the first + message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example. + + Returns: the message to be sent back to the master device. + + """ + self._activated = True + + intermediates = [(0, master_msg)] + for i in range(self.nr_slaves): + intermediates.append(self._queue.get()) + + results = self._master_callback(intermediates) + assert results[0][0] == 0, 'The first result should belongs to the master.' + + for i, res in results: + if i == 0: + continue + self._registry[i].result.put(res) + + for i in range(self.nr_slaves): + assert self._queue.get() is True + + return results[0][1] + + @property + def nr_slaves(self): + return len(self._registry) diff --git a/third-party/lama/models/ade20k/segm_lib/nn/modules/replicate.py b/third-party/lama/models/ade20k/segm_lib/nn/modules/replicate.py new file mode 100644 index 0000000000000000000000000000000000000000..b71c7b8ed51a1d6c55b1f753bdd8d90bad79bd06 --- /dev/null +++ b/third-party/lama/models/ade20k/segm_lib/nn/modules/replicate.py @@ -0,0 +1,94 @@ +# -*- coding: utf-8 -*- +# File : replicate.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import functools + +from torch.nn.parallel.data_parallel import DataParallel + +__all__ = [ + 'CallbackContext', + 'execute_replication_callbacks', + 'DataParallelWithCallback', + 'patch_replication_callback' +] + + +class CallbackContext(object): + pass + + +def execute_replication_callbacks(modules): + """ + Execute an replication callback `__data_parallel_replicate__` on each module created by original replication. + + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + + Note that, as all modules are isomorphism, we assign each sub-module with a context + (shared among multiple copies of this module on different devices). + Through this context, different copies can share some information. + + We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback + of any slave copies. + """ + master_copy = modules[0] + nr_modules = len(list(master_copy.modules())) + ctxs = [CallbackContext() for _ in range(nr_modules)] + + for i, module in enumerate(modules): + for j, m in enumerate(module.modules()): + if hasattr(m, '__data_parallel_replicate__'): + m.__data_parallel_replicate__(ctxs[j], i) + + +class DataParallelWithCallback(DataParallel): + """ + Data Parallel with a replication callback. + + An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by + original `replicate` function. + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + # sync_bn.__data_parallel_replicate__ will be invoked. + """ + + def replicate(self, module, device_ids): + modules = super(DataParallelWithCallback, self).replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + +def patch_replication_callback(data_parallel): + """ + Monkey-patch an existing `DataParallel` object. Add the replication callback. + Useful when you have customized `DataParallel` implementation. + + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallel(sync_bn, device_ids=[0, 1]) + > patch_replication_callback(sync_bn) + # this is equivalent to + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + """ + + assert isinstance(data_parallel, DataParallel) + + old_replicate = data_parallel.replicate + + @functools.wraps(old_replicate) + def new_replicate(module, device_ids): + modules = old_replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + data_parallel.replicate = new_replicate diff --git a/third-party/lama/models/ade20k/segm_lib/nn/modules/tests/test_numeric_batchnorm.py b/third-party/lama/models/ade20k/segm_lib/nn/modules/tests/test_numeric_batchnorm.py new file mode 100644 index 0000000000000000000000000000000000000000..8bd45a930d3dc84912e58659ee575be08e9038f0 --- /dev/null +++ b/third-party/lama/models/ade20k/segm_lib/nn/modules/tests/test_numeric_batchnorm.py @@ -0,0 +1,56 @@ +# -*- coding: utf-8 -*- +# File : test_numeric_batchnorm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. + +import unittest + +import torch +import torch.nn as nn +from torch.autograd import Variable + +from sync_batchnorm.unittest import TorchTestCase + + +def handy_var(a, unbias=True): + n = a.size(0) + asum = a.sum(dim=0) + as_sum = (a ** 2).sum(dim=0) # a square sum + sumvar = as_sum - asum * asum / n + if unbias: + return sumvar / (n - 1) + else: + return sumvar / n + + +class NumericTestCase(TorchTestCase): + def testNumericBatchNorm(self): + a = torch.rand(16, 10) + bn = nn.BatchNorm2d(10, momentum=1, eps=1e-5, affine=False) + bn.train() + + a_var1 = Variable(a, requires_grad=True) + b_var1 = bn(a_var1) + loss1 = b_var1.sum() + loss1.backward() + + a_var2 = Variable(a, requires_grad=True) + a_mean2 = a_var2.mean(dim=0, keepdim=True) + a_std2 = torch.sqrt(handy_var(a_var2, unbias=False).clamp(min=1e-5)) + # a_std2 = torch.sqrt(a_var2.var(dim=0, keepdim=True, unbiased=False) + 1e-5) + b_var2 = (a_var2 - a_mean2) / a_std2 + loss2 = b_var2.sum() + loss2.backward() + + self.assertTensorClose(bn.running_mean, a.mean(dim=0)) + self.assertTensorClose(bn.running_var, handy_var(a)) + self.assertTensorClose(a_var1.data, a_var2.data) + self.assertTensorClose(b_var1.data, b_var2.data) + self.assertTensorClose(a_var1.grad, a_var2.grad) + + +if __name__ == '__main__': + unittest.main() diff --git a/third-party/lama/models/ade20k/segm_lib/nn/modules/tests/test_sync_batchnorm.py b/third-party/lama/models/ade20k/segm_lib/nn/modules/tests/test_sync_batchnorm.py new file mode 100644 index 0000000000000000000000000000000000000000..45bb3c8cfd36d8f668e6fde756b17587eab72082 --- /dev/null +++ b/third-party/lama/models/ade20k/segm_lib/nn/modules/tests/test_sync_batchnorm.py @@ -0,0 +1,111 @@ +# -*- coding: utf-8 -*- +# File : test_sync_batchnorm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. + +import unittest + +import torch +import torch.nn as nn +from torch.autograd import Variable + +from sync_batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, DataParallelWithCallback +from sync_batchnorm.unittest import TorchTestCase + + +def handy_var(a, unbias=True): + n = a.size(0) + asum = a.sum(dim=0) + as_sum = (a ** 2).sum(dim=0) # a square sum + sumvar = as_sum - asum * asum / n + if unbias: + return sumvar / (n - 1) + else: + return sumvar / n + + +def _find_bn(module): + for m in module.modules(): + if isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, SynchronizedBatchNorm1d, SynchronizedBatchNorm2d)): + return m + + +class SyncTestCase(TorchTestCase): + def _syncParameters(self, bn1, bn2): + bn1.reset_parameters() + bn2.reset_parameters() + if bn1.affine and bn2.affine: + bn2.weight.data.copy_(bn1.weight.data) + bn2.bias.data.copy_(bn1.bias.data) + + def _checkBatchNormResult(self, bn1, bn2, input, is_train, cuda=False): + """Check the forward and backward for the customized batch normalization.""" + bn1.train(mode=is_train) + bn2.train(mode=is_train) + + if cuda: + input = input.cuda() + + self._syncParameters(_find_bn(bn1), _find_bn(bn2)) + + input1 = Variable(input, requires_grad=True) + output1 = bn1(input1) + output1.sum().backward() + input2 = Variable(input, requires_grad=True) + output2 = bn2(input2) + output2.sum().backward() + + self.assertTensorClose(input1.data, input2.data) + self.assertTensorClose(output1.data, output2.data) + self.assertTensorClose(input1.grad, input2.grad) + self.assertTensorClose(_find_bn(bn1).running_mean, _find_bn(bn2).running_mean) + self.assertTensorClose(_find_bn(bn1).running_var, _find_bn(bn2).running_var) + + def testSyncBatchNormNormalTrain(self): + bn = nn.BatchNorm1d(10) + sync_bn = SynchronizedBatchNorm1d(10) + + self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), True) + + def testSyncBatchNormNormalEval(self): + bn = nn.BatchNorm1d(10) + sync_bn = SynchronizedBatchNorm1d(10) + + self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), False) + + def testSyncBatchNormSyncTrain(self): + bn = nn.BatchNorm1d(10, eps=1e-5, affine=False) + sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + + bn.cuda() + sync_bn.cuda() + + self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), True, cuda=True) + + def testSyncBatchNormSyncEval(self): + bn = nn.BatchNorm1d(10, eps=1e-5, affine=False) + sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + + bn.cuda() + sync_bn.cuda() + + self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), False, cuda=True) + + def testSyncBatchNorm2DSyncTrain(self): + bn = nn.BatchNorm2d(10) + sync_bn = SynchronizedBatchNorm2d(10) + sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + + bn.cuda() + sync_bn.cuda() + + self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10, 16, 16), True, cuda=True) + + +if __name__ == '__main__': + unittest.main() diff --git a/third-party/lama/models/ade20k/segm_lib/nn/modules/unittest.py b/third-party/lama/models/ade20k/segm_lib/nn/modules/unittest.py new file mode 100644 index 0000000000000000000000000000000000000000..0675c022e4ba85d38d1f813490f6740150909524 --- /dev/null +++ b/third-party/lama/models/ade20k/segm_lib/nn/modules/unittest.py @@ -0,0 +1,29 @@ +# -*- coding: utf-8 -*- +# File : unittest.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import unittest + +import numpy as np +from torch.autograd import Variable + + +def as_numpy(v): + if isinstance(v, Variable): + v = v.data + return v.cpu().numpy() + + +class TorchTestCase(unittest.TestCase): + def assertTensorClose(self, a, b, atol=1e-3, rtol=1e-3): + npa, npb = as_numpy(a), as_numpy(b) + self.assertTrue( + np.allclose(npa, npb, atol=atol), + 'Tensor close check failed\n{}\n{}\nadiff={}, rdiff={}'.format(a, b, np.abs(npa - npb).max(), np.abs((npa - npb) / np.fmax(npa, 1e-5)).max()) + ) diff --git a/third-party/lama/models/ade20k/segm_lib/nn/parallel/__init__.py b/third-party/lama/models/ade20k/segm_lib/nn/parallel/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9b52f49cc0755562218a460483cbf02514ddd773 --- /dev/null +++ b/third-party/lama/models/ade20k/segm_lib/nn/parallel/__init__.py @@ -0,0 +1 @@ +from .data_parallel import UserScatteredDataParallel, user_scattered_collate, async_copy_to diff --git a/third-party/lama/models/ade20k/segm_lib/nn/parallel/data_parallel.py b/third-party/lama/models/ade20k/segm_lib/nn/parallel/data_parallel.py new file mode 100644 index 0000000000000000000000000000000000000000..376fc038919aa2a5bd696141e7bb6025d4981306 --- /dev/null +++ b/third-party/lama/models/ade20k/segm_lib/nn/parallel/data_parallel.py @@ -0,0 +1,112 @@ +# -*- coding: utf8 -*- + +import torch.cuda as cuda +import torch.nn as nn +import torch +import collections +from torch.nn.parallel._functions import Gather + + +__all__ = ['UserScatteredDataParallel', 'user_scattered_collate', 'async_copy_to'] + + +def async_copy_to(obj, dev, main_stream=None): + if torch.is_tensor(obj): + v = obj.cuda(dev, non_blocking=True) + if main_stream is not None: + v.data.record_stream(main_stream) + return v + elif isinstance(obj, collections.Mapping): + return {k: async_copy_to(o, dev, main_stream) for k, o in obj.items()} + elif isinstance(obj, collections.Sequence): + return [async_copy_to(o, dev, main_stream) for o in obj] + else: + return obj + + +def dict_gather(outputs, target_device, dim=0): + """ + Gathers variables from different GPUs on a specified device + (-1 means the CPU), with dictionary support. + """ + def gather_map(outputs): + out = outputs[0] + if torch.is_tensor(out): + # MJY(20180330) HACK:: force nr_dims > 0 + if out.dim() == 0: + outputs = [o.unsqueeze(0) for o in outputs] + return Gather.apply(target_device, dim, *outputs) + elif out is None: + return None + elif isinstance(out, collections.Mapping): + return {k: gather_map([o[k] for o in outputs]) for k in out} + elif isinstance(out, collections.Sequence): + return type(out)(map(gather_map, zip(*outputs))) + return gather_map(outputs) + + +class DictGatherDataParallel(nn.DataParallel): + def gather(self, outputs, output_device): + return dict_gather(outputs, output_device, dim=self.dim) + + +class UserScatteredDataParallel(DictGatherDataParallel): + def scatter(self, inputs, kwargs, device_ids): + assert len(inputs) == 1 + inputs = inputs[0] + inputs = _async_copy_stream(inputs, device_ids) + inputs = [[i] for i in inputs] + assert len(kwargs) == 0 + kwargs = [{} for _ in range(len(inputs))] + + return inputs, kwargs + + +def user_scattered_collate(batch): + return batch + + +def _async_copy(inputs, device_ids): + nr_devs = len(device_ids) + assert type(inputs) in (tuple, list) + assert len(inputs) == nr_devs + + outputs = [] + for i, dev in zip(inputs, device_ids): + with cuda.device(dev): + outputs.append(async_copy_to(i, dev)) + + return tuple(outputs) + + +def _async_copy_stream(inputs, device_ids): + nr_devs = len(device_ids) + assert type(inputs) in (tuple, list) + assert len(inputs) == nr_devs + + outputs = [] + streams = [_get_stream(d) for d in device_ids] + for i, dev, stream in zip(inputs, device_ids, streams): + with cuda.device(dev): + main_stream = cuda.current_stream() + with cuda.stream(stream): + outputs.append(async_copy_to(i, dev, main_stream=main_stream)) + main_stream.wait_stream(stream) + + return outputs + + +"""Adapted from: torch/nn/parallel/_functions.py""" +# background streams used for copying +_streams = None + + +def _get_stream(device): + """Gets a background stream for copying between CPU and GPU""" + global _streams + if device == -1: + return None + if _streams is None: + _streams = [None] * cuda.device_count() + if _streams[device] is None: _streams[device] = cuda.Stream(device) + return _streams[device] diff --git a/third-party/lama/models/ade20k/segm_lib/utils/__init__.py b/third-party/lama/models/ade20k/segm_lib/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..abe3cbe49477fe37d4fc16249de8a10f4fb4a013 --- /dev/null +++ b/third-party/lama/models/ade20k/segm_lib/utils/__init__.py @@ -0,0 +1 @@ +from .th import * diff --git a/third-party/lama/models/ade20k/segm_lib/utils/data/__init__.py b/third-party/lama/models/ade20k/segm_lib/utils/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f3b008fb13c5e8a84b1b785056e8c4f5226dc976 --- /dev/null +++ b/third-party/lama/models/ade20k/segm_lib/utils/data/__init__.py @@ -0,0 +1,3 @@ + +from .dataset import Dataset, TensorDataset, ConcatDataset +from .dataloader import DataLoader diff --git a/third-party/lama/models/ade20k/segm_lib/utils/data/dataloader.py b/third-party/lama/models/ade20k/segm_lib/utils/data/dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..039b9ec3645b2a4626ff47c221e372f32a6ad339 --- /dev/null +++ b/third-party/lama/models/ade20k/segm_lib/utils/data/dataloader.py @@ -0,0 +1,425 @@ +import torch +import torch.multiprocessing as multiprocessing +from torch._C import _set_worker_signal_handlers, \ + _remove_worker_pids, _error_if_any_worker_fails +try: + from torch._C import _set_worker_pids +except: + from torch._C import _update_worker_pids as _set_worker_pids +from .sampler import SequentialSampler, RandomSampler, BatchSampler +import signal +import collections +import re +import sys +import threading +import traceback +from torch._six import string_classes, int_classes +import numpy as np + +if sys.version_info[0] == 2: + import Queue as queue +else: + import queue + + +class ExceptionWrapper(object): + r"Wraps an exception plus traceback to communicate across threads" + + def __init__(self, exc_info): + self.exc_type = exc_info[0] + self.exc_msg = "".join(traceback.format_exception(*exc_info)) + + +_use_shared_memory = False +"""Whether to use shared memory in default_collate""" + + +def _worker_loop(dataset, index_queue, data_queue, collate_fn, seed, init_fn, worker_id): + global _use_shared_memory + _use_shared_memory = True + + # Intialize C side signal handlers for SIGBUS and SIGSEGV. Python signal + # module's handlers are executed after Python returns from C low-level + # handlers, likely when the same fatal signal happened again already. + # https://docs.python.org/3/library/signal.html Sec. 18.8.1.1 + _set_worker_signal_handlers() + + torch.set_num_threads(1) + torch.manual_seed(seed) + np.random.seed(seed) + + if init_fn is not None: + init_fn(worker_id) + + while True: + r = index_queue.get() + if r is None: + break + idx, batch_indices = r + try: + samples = collate_fn([dataset[i] for i in batch_indices]) + except Exception: + data_queue.put((idx, ExceptionWrapper(sys.exc_info()))) + else: + data_queue.put((idx, samples)) + + +def _worker_manager_loop(in_queue, out_queue, done_event, pin_memory, device_id): + if pin_memory: + torch.cuda.set_device(device_id) + + while True: + try: + r = in_queue.get() + except Exception: + if done_event.is_set(): + return + raise + if r is None: + break + if isinstance(r[1], ExceptionWrapper): + out_queue.put(r) + continue + idx, batch = r + try: + if pin_memory: + batch = pin_memory_batch(batch) + except Exception: + out_queue.put((idx, ExceptionWrapper(sys.exc_info()))) + else: + out_queue.put((idx, batch)) + +numpy_type_map = { + 'float64': torch.DoubleTensor, + 'float32': torch.FloatTensor, + 'float16': torch.HalfTensor, + 'int64': torch.LongTensor, + 'int32': torch.IntTensor, + 'int16': torch.ShortTensor, + 'int8': torch.CharTensor, + 'uint8': torch.ByteTensor, +} + + +def default_collate(batch): + "Puts each data field into a tensor with outer dimension batch size" + + error_msg = "batch must contain tensors, numbers, dicts or lists; found {}" + elem_type = type(batch[0]) + if torch.is_tensor(batch[0]): + out = None + if _use_shared_memory: + # If we're in a background process, concatenate directly into a + # shared memory tensor to avoid an extra copy + numel = sum([x.numel() for x in batch]) + storage = batch[0].storage()._new_shared(numel) + out = batch[0].new(storage) + return torch.stack(batch, 0, out=out) + elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ + and elem_type.__name__ != 'string_': + elem = batch[0] + if elem_type.__name__ == 'ndarray': + # array of string classes and object + if re.search('[SaUO]', elem.dtype.str) is not None: + raise TypeError(error_msg.format(elem.dtype)) + + return torch.stack([torch.from_numpy(b) for b in batch], 0) + if elem.shape == (): # scalars + py_type = float if elem.dtype.name.startswith('float') else int + return numpy_type_map[elem.dtype.name](list(map(py_type, batch))) + elif isinstance(batch[0], int_classes): + return torch.LongTensor(batch) + elif isinstance(batch[0], float): + return torch.DoubleTensor(batch) + elif isinstance(batch[0], string_classes): + return batch + elif isinstance(batch[0], collections.Mapping): + return {key: default_collate([d[key] for d in batch]) for key in batch[0]} + elif isinstance(batch[0], collections.Sequence): + transposed = zip(*batch) + return [default_collate(samples) for samples in transposed] + + raise TypeError((error_msg.format(type(batch[0])))) + + +def pin_memory_batch(batch): + if torch.is_tensor(batch): + return batch.pin_memory() + elif isinstance(batch, string_classes): + return batch + elif isinstance(batch, collections.Mapping): + return {k: pin_memory_batch(sample) for k, sample in batch.items()} + elif isinstance(batch, collections.Sequence): + return [pin_memory_batch(sample) for sample in batch] + else: + return batch + + +_SIGCHLD_handler_set = False +"""Whether SIGCHLD handler is set for DataLoader worker failures. Only one +handler needs to be set for all DataLoaders in a process.""" + + +def _set_SIGCHLD_handler(): + # Windows doesn't support SIGCHLD handler + if sys.platform == 'win32': + return + # can't set signal in child threads + if not isinstance(threading.current_thread(), threading._MainThread): + return + global _SIGCHLD_handler_set + if _SIGCHLD_handler_set: + return + previous_handler = signal.getsignal(signal.SIGCHLD) + if not callable(previous_handler): + previous_handler = None + + def handler(signum, frame): + # This following call uses `waitid` with WNOHANG from C side. Therefore, + # Python can still get and update the process status successfully. + _error_if_any_worker_fails() + if previous_handler is not None: + previous_handler(signum, frame) + + signal.signal(signal.SIGCHLD, handler) + _SIGCHLD_handler_set = True + + +class DataLoaderIter(object): + "Iterates once over the DataLoader's dataset, as specified by the sampler" + + def __init__(self, loader): + self.dataset = loader.dataset + self.collate_fn = loader.collate_fn + self.batch_sampler = loader.batch_sampler + self.num_workers = loader.num_workers + self.pin_memory = loader.pin_memory and torch.cuda.is_available() + self.timeout = loader.timeout + self.done_event = threading.Event() + + self.sample_iter = iter(self.batch_sampler) + + if self.num_workers > 0: + self.worker_init_fn = loader.worker_init_fn + self.index_queue = multiprocessing.SimpleQueue() + self.worker_result_queue = multiprocessing.SimpleQueue() + self.batches_outstanding = 0 + self.worker_pids_set = False + self.shutdown = False + self.send_idx = 0 + self.rcvd_idx = 0 + self.reorder_dict = {} + + base_seed = torch.LongTensor(1).random_(0, 2**31-1)[0] + self.workers = [ + multiprocessing.Process( + target=_worker_loop, + args=(self.dataset, self.index_queue, self.worker_result_queue, self.collate_fn, + base_seed + i, self.worker_init_fn, i)) + for i in range(self.num_workers)] + + if self.pin_memory or self.timeout > 0: + self.data_queue = queue.Queue() + if self.pin_memory: + maybe_device_id = torch.cuda.current_device() + else: + # do not initialize cuda context if not necessary + maybe_device_id = None + self.worker_manager_thread = threading.Thread( + target=_worker_manager_loop, + args=(self.worker_result_queue, self.data_queue, self.done_event, self.pin_memory, + maybe_device_id)) + self.worker_manager_thread.daemon = True + self.worker_manager_thread.start() + else: + self.data_queue = self.worker_result_queue + + for w in self.workers: + w.daemon = True # ensure that the worker exits on process exit + w.start() + + _set_worker_pids(id(self), tuple(w.pid for w in self.workers)) + _set_SIGCHLD_handler() + self.worker_pids_set = True + + # prime the prefetch loop + for _ in range(2 * self.num_workers): + self._put_indices() + + def __len__(self): + return len(self.batch_sampler) + + def _get_batch(self): + if self.timeout > 0: + try: + return self.data_queue.get(timeout=self.timeout) + except queue.Empty: + raise RuntimeError('DataLoader timed out after {} seconds'.format(self.timeout)) + else: + return self.data_queue.get() + + def __next__(self): + if self.num_workers == 0: # same-process loading + indices = next(self.sample_iter) # may raise StopIteration + batch = self.collate_fn([self.dataset[i] for i in indices]) + if self.pin_memory: + batch = pin_memory_batch(batch) + return batch + + # check if the next sample has already been generated + if self.rcvd_idx in self.reorder_dict: + batch = self.reorder_dict.pop(self.rcvd_idx) + return self._process_next_batch(batch) + + if self.batches_outstanding == 0: + self._shutdown_workers() + raise StopIteration + + while True: + assert (not self.shutdown and self.batches_outstanding > 0) + idx, batch = self._get_batch() + self.batches_outstanding -= 1 + if idx != self.rcvd_idx: + # store out-of-order samples + self.reorder_dict[idx] = batch + continue + return self._process_next_batch(batch) + + next = __next__ # Python 2 compatibility + + def __iter__(self): + return self + + def _put_indices(self): + assert self.batches_outstanding < 2 * self.num_workers + indices = next(self.sample_iter, None) + if indices is None: + return + self.index_queue.put((self.send_idx, indices)) + self.batches_outstanding += 1 + self.send_idx += 1 + + def _process_next_batch(self, batch): + self.rcvd_idx += 1 + self._put_indices() + if isinstance(batch, ExceptionWrapper): + raise batch.exc_type(batch.exc_msg) + return batch + + def __getstate__(self): + # TODO: add limited pickling support for sharing an iterator + # across multiple threads for HOGWILD. + # Probably the best way to do this is by moving the sample pushing + # to a separate thread and then just sharing the data queue + # but signalling the end is tricky without a non-blocking API + raise NotImplementedError("DataLoaderIterator cannot be pickled") + + def _shutdown_workers(self): + try: + if not self.shutdown: + self.shutdown = True + self.done_event.set() + # if worker_manager_thread is waiting to put + while not self.data_queue.empty(): + self.data_queue.get() + for _ in self.workers: + self.index_queue.put(None) + # done_event should be sufficient to exit worker_manager_thread, + # but be safe here and put another None + self.worker_result_queue.put(None) + finally: + # removes pids no matter what + if self.worker_pids_set: + _remove_worker_pids(id(self)) + self.worker_pids_set = False + + def __del__(self): + if self.num_workers > 0: + self._shutdown_workers() + + +class DataLoader(object): + """ + Data loader. Combines a dataset and a sampler, and provides + single- or multi-process iterators over the dataset. + + Arguments: + dataset (Dataset): dataset from which to load the data. + batch_size (int, optional): how many samples per batch to load + (default: 1). + shuffle (bool, optional): set to ``True`` to have the data reshuffled + at every epoch (default: False). + sampler (Sampler, optional): defines the strategy to draw samples from + the dataset. If specified, ``shuffle`` must be False. + batch_sampler (Sampler, optional): like sampler, but returns a batch of + indices at a time. Mutually exclusive with batch_size, shuffle, + sampler, and drop_last. + num_workers (int, optional): how many subprocesses to use for data + loading. 0 means that the data will be loaded in the main process. + (default: 0) + collate_fn (callable, optional): merges a list of samples to form a mini-batch. + pin_memory (bool, optional): If ``True``, the data loader will copy tensors + into CUDA pinned memory before returning them. + drop_last (bool, optional): set to ``True`` to drop the last incomplete batch, + if the dataset size is not divisible by the batch size. If ``False`` and + the size of dataset is not divisible by the batch size, then the last batch + will be smaller. (default: False) + timeout (numeric, optional): if positive, the timeout value for collecting a batch + from workers. Should always be non-negative. (default: 0) + worker_init_fn (callable, optional): If not None, this will be called on each + worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as + input, after seeding and before data loading. (default: None) + + .. note:: By default, each worker will have its PyTorch seed set to + ``base_seed + worker_id``, where ``base_seed`` is a long generated + by main process using its RNG. You may use ``torch.initial_seed()`` to access + this value in :attr:`worker_init_fn`, which can be used to set other seeds + (e.g. NumPy) before data loading. + + .. warning:: If ``spawn'' start method is used, :attr:`worker_init_fn` cannot be an + unpicklable object, e.g., a lambda function. + """ + + def __init__(self, dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, + num_workers=0, collate_fn=default_collate, pin_memory=False, drop_last=False, + timeout=0, worker_init_fn=None): + self.dataset = dataset + self.batch_size = batch_size + self.num_workers = num_workers + self.collate_fn = collate_fn + self.pin_memory = pin_memory + self.drop_last = drop_last + self.timeout = timeout + self.worker_init_fn = worker_init_fn + + if timeout < 0: + raise ValueError('timeout option should be non-negative') + + if batch_sampler is not None: + if batch_size > 1 or shuffle or sampler is not None or drop_last: + raise ValueError('batch_sampler is mutually exclusive with ' + 'batch_size, shuffle, sampler, and drop_last') + + if sampler is not None and shuffle: + raise ValueError('sampler is mutually exclusive with shuffle') + + if self.num_workers < 0: + raise ValueError('num_workers cannot be negative; ' + 'use num_workers=0 to disable multiprocessing.') + + if batch_sampler is None: + if sampler is None: + if shuffle: + sampler = RandomSampler(dataset) + else: + sampler = SequentialSampler(dataset) + batch_sampler = BatchSampler(sampler, batch_size, drop_last) + + self.sampler = sampler + self.batch_sampler = batch_sampler + + def __iter__(self): + return DataLoaderIter(self) + + def __len__(self): + return len(self.batch_sampler) diff --git a/third-party/lama/models/ade20k/segm_lib/utils/data/dataset.py b/third-party/lama/models/ade20k/segm_lib/utils/data/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..605aa877f7031a5cd2b98c0f831410aa80fddefa --- /dev/null +++ b/third-party/lama/models/ade20k/segm_lib/utils/data/dataset.py @@ -0,0 +1,118 @@ +import bisect +import warnings + +from torch._utils import _accumulate +from torch import randperm + + +class Dataset(object): + """An abstract class representing a Dataset. + + All other datasets should subclass it. All subclasses should override + ``__len__``, that provides the size of the dataset, and ``__getitem__``, + supporting integer indexing in range from 0 to len(self) exclusive. + """ + + def __getitem__(self, index): + raise NotImplementedError + + def __len__(self): + raise NotImplementedError + + def __add__(self, other): + return ConcatDataset([self, other]) + + +class TensorDataset(Dataset): + """Dataset wrapping data and target tensors. + + Each sample will be retrieved by indexing both tensors along the first + dimension. + + Arguments: + data_tensor (Tensor): contains sample data. + target_tensor (Tensor): contains sample targets (labels). + """ + + def __init__(self, data_tensor, target_tensor): + assert data_tensor.size(0) == target_tensor.size(0) + self.data_tensor = data_tensor + self.target_tensor = target_tensor + + def __getitem__(self, index): + return self.data_tensor[index], self.target_tensor[index] + + def __len__(self): + return self.data_tensor.size(0) + + +class ConcatDataset(Dataset): + """ + Dataset to concatenate multiple datasets. + Purpose: useful to assemble different existing datasets, possibly + large-scale datasets as the concatenation operation is done in an + on-the-fly manner. + + Arguments: + datasets (iterable): List of datasets to be concatenated + """ + + @staticmethod + def cumsum(sequence): + r, s = [], 0 + for e in sequence: + l = len(e) + r.append(l + s) + s += l + return r + + def __init__(self, datasets): + super(ConcatDataset, self).__init__() + assert len(datasets) > 0, 'datasets should not be an empty iterable' + self.datasets = list(datasets) + self.cumulative_sizes = self.cumsum(self.datasets) + + def __len__(self): + return self.cumulative_sizes[-1] + + def __getitem__(self, idx): + dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx) + if dataset_idx == 0: + sample_idx = idx + else: + sample_idx = idx - self.cumulative_sizes[dataset_idx - 1] + return self.datasets[dataset_idx][sample_idx] + + @property + def cummulative_sizes(self): + warnings.warn("cummulative_sizes attribute is renamed to " + "cumulative_sizes", DeprecationWarning, stacklevel=2) + return self.cumulative_sizes + + +class Subset(Dataset): + def __init__(self, dataset, indices): + self.dataset = dataset + self.indices = indices + + def __getitem__(self, idx): + return self.dataset[self.indices[idx]] + + def __len__(self): + return len(self.indices) + + +def random_split(dataset, lengths): + """ + Randomly split a dataset into non-overlapping new datasets of given lengths + ds + + Arguments: + dataset (Dataset): Dataset to be split + lengths (iterable): lengths of splits to be produced + """ + if sum(lengths) != len(dataset): + raise ValueError("Sum of input lengths does not equal the length of the input dataset!") + + indices = randperm(sum(lengths)) + return [Subset(dataset, indices[offset - length:offset]) for offset, length in zip(_accumulate(lengths), lengths)] diff --git a/third-party/lama/models/ade20k/segm_lib/utils/data/distributed.py b/third-party/lama/models/ade20k/segm_lib/utils/data/distributed.py new file mode 100644 index 0000000000000000000000000000000000000000..c3d890e28fd2b9e044bdd9494de4a43ad2471eed --- /dev/null +++ b/third-party/lama/models/ade20k/segm_lib/utils/data/distributed.py @@ -0,0 +1,58 @@ +import math +import torch +from .sampler import Sampler +from torch.distributed import get_world_size, get_rank + + +class DistributedSampler(Sampler): + """Sampler that restricts data loading to a subset of the dataset. + + It is especially useful in conjunction with + :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each + process can pass a DistributedSampler instance as a DataLoader sampler, + and load a subset of the original dataset that is exclusive to it. + + .. note:: + Dataset is assumed to be of constant size. + + Arguments: + dataset: Dataset used for sampling. + num_replicas (optional): Number of processes participating in + distributed training. + rank (optional): Rank of the current process within num_replicas. + """ + + def __init__(self, dataset, num_replicas=None, rank=None): + if num_replicas is None: + num_replicas = get_world_size() + if rank is None: + rank = get_rank() + self.dataset = dataset + self.num_replicas = num_replicas + self.rank = rank + self.epoch = 0 + self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas)) + self.total_size = self.num_samples * self.num_replicas + + def __iter__(self): + # deterministically shuffle based on epoch + g = torch.Generator() + g.manual_seed(self.epoch) + indices = list(torch.randperm(len(self.dataset), generator=g)) + + # add extra samples to make it evenly divisible + indices += indices[:(self.total_size - len(indices))] + assert len(indices) == self.total_size + + # subsample + offset = self.num_samples * self.rank + indices = indices[offset:offset + self.num_samples] + assert len(indices) == self.num_samples + + return iter(indices) + + def __len__(self): + return self.num_samples + + def set_epoch(self, epoch): + self.epoch = epoch diff --git a/third-party/lama/models/ade20k/segm_lib/utils/data/sampler.py b/third-party/lama/models/ade20k/segm_lib/utils/data/sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..62a9a43bd1d4c21fbdcb262db7da8d4fe27b26de --- /dev/null +++ b/third-party/lama/models/ade20k/segm_lib/utils/data/sampler.py @@ -0,0 +1,131 @@ +import torch + + +class Sampler(object): + """Base class for all Samplers. + + Every Sampler subclass has to provide an __iter__ method, providing a way + to iterate over indices of dataset elements, and a __len__ method that + returns the length of the returned iterators. + """ + + def __init__(self, data_source): + pass + + def __iter__(self): + raise NotImplementedError + + def __len__(self): + raise NotImplementedError + + +class SequentialSampler(Sampler): + """Samples elements sequentially, always in the same order. + + Arguments: + data_source (Dataset): dataset to sample from + """ + + def __init__(self, data_source): + self.data_source = data_source + + def __iter__(self): + return iter(range(len(self.data_source))) + + def __len__(self): + return len(self.data_source) + + +class RandomSampler(Sampler): + """Samples elements randomly, without replacement. + + Arguments: + data_source (Dataset): dataset to sample from + """ + + def __init__(self, data_source): + self.data_source = data_source + + def __iter__(self): + return iter(torch.randperm(len(self.data_source)).long()) + + def __len__(self): + return len(self.data_source) + + +class SubsetRandomSampler(Sampler): + """Samples elements randomly from a given list of indices, without replacement. + + Arguments: + indices (list): a list of indices + """ + + def __init__(self, indices): + self.indices = indices + + def __iter__(self): + return (self.indices[i] for i in torch.randperm(len(self.indices))) + + def __len__(self): + return len(self.indices) + + +class WeightedRandomSampler(Sampler): + """Samples elements from [0,..,len(weights)-1] with given probabilities (weights). + + Arguments: + weights (list) : a list of weights, not necessary summing up to one + num_samples (int): number of samples to draw + replacement (bool): if ``True``, samples are drawn with replacement. + If not, they are drawn without replacement, which means that when a + sample index is drawn for a row, it cannot be drawn again for that row. + """ + + def __init__(self, weights, num_samples, replacement=True): + self.weights = torch.DoubleTensor(weights) + self.num_samples = num_samples + self.replacement = replacement + + def __iter__(self): + return iter(torch.multinomial(self.weights, self.num_samples, self.replacement)) + + def __len__(self): + return self.num_samples + + +class BatchSampler(object): + """Wraps another sampler to yield a mini-batch of indices. + + Args: + sampler (Sampler): Base sampler. + batch_size (int): Size of mini-batch. + drop_last (bool): If ``True``, the sampler will drop the last batch if + its size would be less than ``batch_size`` + + Example: + >>> list(BatchSampler(range(10), batch_size=3, drop_last=False)) + [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] + >>> list(BatchSampler(range(10), batch_size=3, drop_last=True)) + [[0, 1, 2], [3, 4, 5], [6, 7, 8]] + """ + + def __init__(self, sampler, batch_size, drop_last): + self.sampler = sampler + self.batch_size = batch_size + self.drop_last = drop_last + + def __iter__(self): + batch = [] + for idx in self.sampler: + batch.append(idx) + if len(batch) == self.batch_size: + yield batch + batch = [] + if len(batch) > 0 and not self.drop_last: + yield batch + + def __len__(self): + if self.drop_last: + return len(self.sampler) // self.batch_size + else: + return (len(self.sampler) + self.batch_size - 1) // self.batch_size diff --git a/third-party/lama/models/ade20k/segm_lib/utils/th.py b/third-party/lama/models/ade20k/segm_lib/utils/th.py new file mode 100644 index 0000000000000000000000000000000000000000..ca6ef9385e3b5c0a439579d3fd7aa73b5dc62758 --- /dev/null +++ b/third-party/lama/models/ade20k/segm_lib/utils/th.py @@ -0,0 +1,41 @@ +import torch +from torch.autograd import Variable +import numpy as np +import collections + +__all__ = ['as_variable', 'as_numpy', 'mark_volatile'] + +def as_variable(obj): + if isinstance(obj, Variable): + return obj + if isinstance(obj, collections.Sequence): + return [as_variable(v) for v in obj] + elif isinstance(obj, collections.Mapping): + return {k: as_variable(v) for k, v in obj.items()} + else: + return Variable(obj) + +def as_numpy(obj): + if isinstance(obj, collections.Sequence): + return [as_numpy(v) for v in obj] + elif isinstance(obj, collections.Mapping): + return {k: as_numpy(v) for k, v in obj.items()} + elif isinstance(obj, Variable): + return obj.data.cpu().numpy() + elif torch.is_tensor(obj): + return obj.cpu().numpy() + else: + return np.array(obj) + +def mark_volatile(obj): + if torch.is_tensor(obj): + obj = Variable(obj) + if isinstance(obj, Variable): + obj.no_grad = True + return obj + elif isinstance(obj, collections.Mapping): + return {k: mark_volatile(o) for k, o in obj.items()} + elif isinstance(obj, collections.Sequence): + return [mark_volatile(o) for o in obj] + else: + return obj diff --git a/third-party/lama/models/ade20k/utils.py b/third-party/lama/models/ade20k/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f337db7db54c82be041698d694e1403e8918c4c0 --- /dev/null +++ b/third-party/lama/models/ade20k/utils.py @@ -0,0 +1,40 @@ +"""Modified from https://github.com/CSAILVision/semantic-segmentation-pytorch""" + +import os +import sys + +import numpy as np +import torch + +try: + from urllib import urlretrieve +except ImportError: + from urllib.request import urlretrieve + + +def load_url(url, model_dir='./pretrained', map_location=None): + if not os.path.exists(model_dir): + os.makedirs(model_dir) + filename = url.split('/')[-1] + cached_file = os.path.join(model_dir, filename) + if not os.path.exists(cached_file): + sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file)) + urlretrieve(url, cached_file) + return torch.load(cached_file, map_location=map_location) + + +def color_encode(labelmap, colors, mode='RGB'): + labelmap = labelmap.astype('int') + labelmap_rgb = np.zeros((labelmap.shape[0], labelmap.shape[1], 3), + dtype=np.uint8) + for label in np.unique(labelmap): + if label < 0: + continue + labelmap_rgb += (labelmap == label)[:, :, np.newaxis] * \ + np.tile(colors[label], + (labelmap.shape[0], labelmap.shape[1], 1)) + + if mode == 'BGR': + return labelmap_rgb[:, :, ::-1] + else: + return labelmap_rgb diff --git a/third-party/lama/models/lpips_models/alex.pth b/third-party/lama/models/lpips_models/alex.pth new file mode 100644 index 0000000000000000000000000000000000000000..fa4067abc5d4da16a7204fd94776506e4868030e --- /dev/null +++ b/third-party/lama/models/lpips_models/alex.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:df73285e35b22355a2df87cdb6b70b343713b667eddbda73e1977e0c860835c0 +size 6009 diff --git a/third-party/lama/models/lpips_models/squeeze.pth b/third-party/lama/models/lpips_models/squeeze.pth new file mode 100644 index 0000000000000000000000000000000000000000..f892a84a130828b1c9e2e8156e84fc5a962c665d --- /dev/null +++ b/third-party/lama/models/lpips_models/squeeze.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4a5350f23600cb79923ce65bb07cbf57dca461329894153e05a1346bd531cf76 +size 10811 diff --git a/third-party/lama/models/lpips_models/vgg.pth b/third-party/lama/models/lpips_models/vgg.pth new file mode 100644 index 0000000000000000000000000000000000000000..f57dcf5cc764d61c8a460365847fb2137ff0a62d --- /dev/null +++ b/third-party/lama/models/lpips_models/vgg.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a78928a0af1e5f0fcb1f3b9e8f8c3a2a5a3de244d830ad5c1feddc79b8432868 +size 7289 diff --git a/third-party/lama/requirements.txt b/third-party/lama/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..d412392cc3e08187e33741551ca416342373f500 --- /dev/null +++ b/third-party/lama/requirements.txt @@ -0,0 +1,20 @@ +pyyaml +tqdm +numpy +easydict==1.9.0 +scikit-image==0.17.2 +scikit-learn==0.24.2 +opencv-python +tensorflow +joblib +matplotlib +pandas +albumentations==0.5.2 +hydra-core==1.1.0 +pytorch-lightning==1.2.9 +tabulate +kornia==0.5.0 +webdataset +packaging +scikit-learn==0.24.2 +wldhx.yadisk-direct diff --git a/third-party/lama/saicinpainting/__init__.py b/third-party/lama/saicinpainting/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third-party/lama/saicinpainting/evaluation/__init__.py b/third-party/lama/saicinpainting/evaluation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e9c8117565b252ca069a808b31b8c52aaddd2289 --- /dev/null +++ b/third-party/lama/saicinpainting/evaluation/__init__.py @@ -0,0 +1,33 @@ +import logging + +import torch + +from saicinpainting.evaluation.evaluator import InpaintingEvaluatorOnline, ssim_fid100_f1, lpips_fid100_f1 +from saicinpainting.evaluation.losses.base_loss import SSIMScore, LPIPSScore, FIDScore + + +def make_evaluator(kind='default', ssim=True, lpips=True, fid=True, integral_kind=None, **kwargs): + logging.info(f'Make evaluator {kind}') + device = "cuda" if torch.cuda.is_available() else "cpu" + metrics = {} + if ssim: + metrics['ssim'] = SSIMScore() + if lpips: + metrics['lpips'] = LPIPSScore() + if fid: + metrics['fid'] = FIDScore().to(device) + + if integral_kind is None: + integral_func = None + elif integral_kind == 'ssim_fid100_f1': + integral_func = ssim_fid100_f1 + elif integral_kind == 'lpips_fid100_f1': + integral_func = lpips_fid100_f1 + else: + raise ValueError(f'Unexpected integral_kind={integral_kind}') + + if kind == 'default': + return InpaintingEvaluatorOnline(scores=metrics, + integral_func=integral_func, + integral_title=integral_kind, + **kwargs) diff --git a/third-party/lama/saicinpainting/evaluation/data.py b/third-party/lama/saicinpainting/evaluation/data.py new file mode 100644 index 0000000000000000000000000000000000000000..89a4ea4c9577e6131731444f149eec76978ec260 --- /dev/null +++ b/third-party/lama/saicinpainting/evaluation/data.py @@ -0,0 +1,168 @@ +import glob +import os + +import cv2 +import PIL.Image as Image +import numpy as np + +from torch.utils.data import Dataset +import torch.nn.functional as F + + +def load_image(fname, mode='RGB', return_orig=False): + img = np.array(Image.open(fname).convert(mode)) + if img.ndim == 3: + img = np.transpose(img, (2, 0, 1)) + out_img = img.astype('float32') / 255 + if return_orig: + return out_img, img + else: + return out_img + + +def ceil_modulo(x, mod): + if x % mod == 0: + return x + return (x // mod + 1) * mod + + +def pad_img_to_modulo(img, mod): + channels, height, width = img.shape + out_height = ceil_modulo(height, mod) + out_width = ceil_modulo(width, mod) + return np.pad(img, ((0, 0), (0, out_height - height), (0, out_width - width)), mode='symmetric') + + +def pad_tensor_to_modulo(img, mod): + batch_size, channels, height, width = img.shape + out_height = ceil_modulo(height, mod) + out_width = ceil_modulo(width, mod) + return F.pad(img, pad=(0, out_width - width, 0, out_height - height), mode='reflect') + + +def scale_image(img, factor, interpolation=cv2.INTER_AREA): + if img.shape[0] == 1: + img = img[0] + else: + img = np.transpose(img, (1, 2, 0)) + + img = cv2.resize(img, dsize=None, fx=factor, fy=factor, interpolation=interpolation) + + if img.ndim == 2: + img = img[None, ...] + else: + img = np.transpose(img, (2, 0, 1)) + return img + + +class InpaintingDataset(Dataset): + def __init__(self, datadir, img_suffix='.jpg', pad_out_to_modulo=None, scale_factor=None): + self.datadir = datadir + self.mask_filenames = sorted(list(glob.glob(os.path.join(self.datadir, '**', '*mask*.png'), recursive=True))) + self.img_filenames = [fname.rsplit('_mask', 1)[0] + img_suffix for fname in self.mask_filenames] + self.pad_out_to_modulo = pad_out_to_modulo + self.scale_factor = scale_factor + + def __len__(self): + return len(self.mask_filenames) + + def __getitem__(self, i): + image = load_image(self.img_filenames[i], mode='RGB') + mask = load_image(self.mask_filenames[i], mode='L') + result = dict(image=image, mask=mask[None, ...]) + + if self.scale_factor is not None: + result['image'] = scale_image(result['image'], self.scale_factor) + result['mask'] = scale_image(result['mask'], self.scale_factor, interpolation=cv2.INTER_NEAREST) + + if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1: + result['unpad_to_size'] = result['image'].shape[1:] + result['image'] = pad_img_to_modulo(result['image'], self.pad_out_to_modulo) + result['mask'] = pad_img_to_modulo(result['mask'], self.pad_out_to_modulo) + + return result + +class OurInpaintingDataset(Dataset): + def __init__(self, datadir, img_suffix='.jpg', pad_out_to_modulo=None, scale_factor=None): + self.datadir = datadir + self.mask_filenames = sorted(list(glob.glob(os.path.join(self.datadir, 'mask', '**', '*mask*.png'), recursive=True))) + self.img_filenames = [os.path.join(self.datadir, 'img', os.path.basename(fname.rsplit('-', 1)[0].rsplit('_', 1)[0]) + '.png') for fname in self.mask_filenames] + self.pad_out_to_modulo = pad_out_to_modulo + self.scale_factor = scale_factor + + def __len__(self): + return len(self.mask_filenames) + + def __getitem__(self, i): + result = dict(image=load_image(self.img_filenames[i], mode='RGB'), + mask=load_image(self.mask_filenames[i], mode='L')[None, ...]) + + if self.scale_factor is not None: + result['image'] = scale_image(result['image'], self.scale_factor) + result['mask'] = scale_image(result['mask'], self.scale_factor) + + if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1: + result['image'] = pad_img_to_modulo(result['image'], self.pad_out_to_modulo) + result['mask'] = pad_img_to_modulo(result['mask'], self.pad_out_to_modulo) + + return result + +class PrecomputedInpaintingResultsDataset(InpaintingDataset): + def __init__(self, datadir, predictdir, inpainted_suffix='_inpainted.jpg', **kwargs): + super().__init__(datadir, **kwargs) + if not datadir.endswith('/'): + datadir += '/' + self.predictdir = predictdir + self.pred_filenames = [os.path.join(predictdir, os.path.splitext(fname[len(datadir):])[0] + inpainted_suffix) + for fname in self.mask_filenames] + + def __getitem__(self, i): + result = super().__getitem__(i) + result['inpainted'] = load_image(self.pred_filenames[i]) + if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1: + result['inpainted'] = pad_img_to_modulo(result['inpainted'], self.pad_out_to_modulo) + return result + +class OurPrecomputedInpaintingResultsDataset(OurInpaintingDataset): + def __init__(self, datadir, predictdir, inpainted_suffix="png", **kwargs): + super().__init__(datadir, **kwargs) + if not datadir.endswith('/'): + datadir += '/' + self.predictdir = predictdir + self.pred_filenames = [os.path.join(predictdir, os.path.basename(os.path.splitext(fname)[0]) + f'_inpainted.{inpainted_suffix}') + for fname in self.mask_filenames] + # self.pred_filenames = [os.path.join(predictdir, os.path.splitext(fname[len(datadir):])[0] + inpainted_suffix) + # for fname in self.mask_filenames] + + def __getitem__(self, i): + result = super().__getitem__(i) + result['inpainted'] = self.file_loader(self.pred_filenames[i]) + + if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1: + result['inpainted'] = pad_img_to_modulo(result['inpainted'], self.pad_out_to_modulo) + return result + +class InpaintingEvalOnlineDataset(Dataset): + def __init__(self, indir, mask_generator, img_suffix='.jpg', pad_out_to_modulo=None, scale_factor=None, **kwargs): + self.indir = indir + self.mask_generator = mask_generator + self.img_filenames = sorted(list(glob.glob(os.path.join(self.indir, '**', f'*{img_suffix}' ), recursive=True))) + self.pad_out_to_modulo = pad_out_to_modulo + self.scale_factor = scale_factor + + def __len__(self): + return len(self.img_filenames) + + def __getitem__(self, i): + img, raw_image = load_image(self.img_filenames[i], mode='RGB', return_orig=True) + mask = self.mask_generator(img, raw_image=raw_image) + result = dict(image=img, mask=mask) + + if self.scale_factor is not None: + result['image'] = scale_image(result['image'], self.scale_factor) + result['mask'] = scale_image(result['mask'], self.scale_factor, interpolation=cv2.INTER_NEAREST) + + if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1: + result['image'] = pad_img_to_modulo(result['image'], self.pad_out_to_modulo) + result['mask'] = pad_img_to_modulo(result['mask'], self.pad_out_to_modulo) + return result \ No newline at end of file diff --git a/third-party/lama/saicinpainting/evaluation/evaluator.py b/third-party/lama/saicinpainting/evaluation/evaluator.py new file mode 100644 index 0000000000000000000000000000000000000000..aa9e80402633c08a580929b38a5cb695cb7171d8 --- /dev/null +++ b/third-party/lama/saicinpainting/evaluation/evaluator.py @@ -0,0 +1,220 @@ +import logging +import math +from typing import Dict + +import numpy as np +import torch +import torch.nn as nn +import tqdm +from torch.utils.data import DataLoader + +from saicinpainting.evaluation.utils import move_to_device + +LOGGER = logging.getLogger(__name__) + + +class InpaintingEvaluator(): + def __init__(self, dataset, scores, area_grouping=True, bins=10, batch_size=32, device='cuda', + integral_func=None, integral_title=None, clamp_image_range=None): + """ + :param dataset: torch.utils.data.Dataset which contains images and masks + :param scores: dict {score_name: EvaluatorScore object} + :param area_grouping: in addition to the overall scores, allows to compute score for the groups of samples + which are defined by share of area occluded by mask + :param bins: number of groups, partition is generated by np.linspace(0., 1., bins + 1) + :param batch_size: batch_size for the dataloader + :param device: device to use + """ + self.scores = scores + self.dataset = dataset + + self.area_grouping = area_grouping + self.bins = bins + + self.device = torch.device(device) + + self.dataloader = DataLoader(self.dataset, shuffle=False, batch_size=batch_size) + + self.integral_func = integral_func + self.integral_title = integral_title + self.clamp_image_range = clamp_image_range + + def _get_bin_edges(self): + bin_edges = np.linspace(0, 1, self.bins + 1) + + num_digits = max(0, math.ceil(math.log10(self.bins)) - 1) + interval_names = [] + for idx_bin in range(self.bins): + start_percent, end_percent = round(100 * bin_edges[idx_bin], num_digits), \ + round(100 * bin_edges[idx_bin + 1], num_digits) + start_percent = '{:.{n}f}'.format(start_percent, n=num_digits) + end_percent = '{:.{n}f}'.format(end_percent, n=num_digits) + interval_names.append("{0}-{1}%".format(start_percent, end_percent)) + + groups = [] + for batch in self.dataloader: + mask = batch['mask'] + batch_size = mask.shape[0] + area = mask.to(self.device).reshape(batch_size, -1).mean(dim=-1) + bin_indices = np.searchsorted(bin_edges, area.detach().cpu().numpy(), side='right') - 1 + # corner case: when area is equal to 1, bin_indices should return bins - 1, not bins for that element + bin_indices[bin_indices == self.bins] = self.bins - 1 + groups.append(bin_indices) + groups = np.hstack(groups) + + return groups, interval_names + + def evaluate(self, model=None): + """ + :param model: callable with signature (image_batch, mask_batch); should return inpainted_batch + :return: dict with (score_name, group_type) as keys, where group_type can be either 'overall' or + name of the particular group arranged by area of mask (e.g. '10-20%') + and score statistics for the group as values. + """ + results = dict() + if self.area_grouping: + groups, interval_names = self._get_bin_edges() + else: + groups = None + + for score_name, score in tqdm.auto.tqdm(self.scores.items(), desc='scores'): + score.to(self.device) + with torch.no_grad(): + score.reset() + for batch in tqdm.auto.tqdm(self.dataloader, desc=score_name, leave=False): + batch = move_to_device(batch, self.device) + image_batch, mask_batch = batch['image'], batch['mask'] + if self.clamp_image_range is not None: + image_batch = torch.clamp(image_batch, + min=self.clamp_image_range[0], + max=self.clamp_image_range[1]) + if model is None: + assert 'inpainted' in batch, \ + 'Model is None, so we expected precomputed inpainting results at key "inpainted"' + inpainted_batch = batch['inpainted'] + else: + inpainted_batch = model(image_batch, mask_batch) + score(inpainted_batch, image_batch, mask_batch) + total_results, group_results = score.get_value(groups=groups) + + results[(score_name, 'total')] = total_results + if groups is not None: + for group_index, group_values in group_results.items(): + group_name = interval_names[group_index] + results[(score_name, group_name)] = group_values + + if self.integral_func is not None: + results[(self.integral_title, 'total')] = dict(mean=self.integral_func(results)) + + return results + + +def ssim_fid100_f1(metrics, fid_scale=100): + ssim = metrics[('ssim', 'total')]['mean'] + fid = metrics[('fid', 'total')]['mean'] + fid_rel = max(0, fid_scale - fid) / fid_scale + f1 = 2 * ssim * fid_rel / (ssim + fid_rel + 1e-3) + return f1 + + +def lpips_fid100_f1(metrics, fid_scale=100): + neg_lpips = 1 - metrics[('lpips', 'total')]['mean'] # invert, so bigger is better + fid = metrics[('fid', 'total')]['mean'] + fid_rel = max(0, fid_scale - fid) / fid_scale + f1 = 2 * neg_lpips * fid_rel / (neg_lpips + fid_rel + 1e-3) + return f1 + + + +class InpaintingEvaluatorOnline(nn.Module): + def __init__(self, scores, bins=10, image_key='image', inpainted_key='inpainted', + integral_func=None, integral_title=None, clamp_image_range=None): + """ + :param scores: dict {score_name: EvaluatorScore object} + :param bins: number of groups, partition is generated by np.linspace(0., 1., bins + 1) + :param device: device to use + """ + super().__init__() + LOGGER.info(f'{type(self)} init called') + self.scores = nn.ModuleDict(scores) + self.image_key = image_key + self.inpainted_key = inpainted_key + self.bins_num = bins + self.bin_edges = np.linspace(0, 1, self.bins_num + 1) + + num_digits = max(0, math.ceil(math.log10(self.bins_num)) - 1) + self.interval_names = [] + for idx_bin in range(self.bins_num): + start_percent, end_percent = round(100 * self.bin_edges[idx_bin], num_digits), \ + round(100 * self.bin_edges[idx_bin + 1], num_digits) + start_percent = '{:.{n}f}'.format(start_percent, n=num_digits) + end_percent = '{:.{n}f}'.format(end_percent, n=num_digits) + self.interval_names.append("{0}-{1}%".format(start_percent, end_percent)) + + self.groups = [] + + self.integral_func = integral_func + self.integral_title = integral_title + self.clamp_image_range = clamp_image_range + + LOGGER.info(f'{type(self)} init done') + + def _get_bins(self, mask_batch): + batch_size = mask_batch.shape[0] + area = mask_batch.view(batch_size, -1).mean(dim=-1).detach().cpu().numpy() + bin_indices = np.clip(np.searchsorted(self.bin_edges, area) - 1, 0, self.bins_num - 1) + return bin_indices + + def forward(self, batch: Dict[str, torch.Tensor]): + """ + Calculate and accumulate metrics for batch. To finalize evaluation and obtain final metrics, call evaluation_end + :param batch: batch dict with mandatory fields mask, image, inpainted (can be overriden by self.inpainted_key) + """ + result = {} + with torch.no_grad(): + image_batch, mask_batch, inpainted_batch = batch[self.image_key], batch['mask'], batch[self.inpainted_key] + if self.clamp_image_range is not None: + image_batch = torch.clamp(image_batch, + min=self.clamp_image_range[0], + max=self.clamp_image_range[1]) + self.groups.extend(self._get_bins(mask_batch)) + + for score_name, score in self.scores.items(): + result[score_name] = score(inpainted_batch, image_batch, mask_batch) + return result + + def process_batch(self, batch: Dict[str, torch.Tensor]): + return self(batch) + + def evaluation_end(self, states=None): + """:return: dict with (score_name, group_type) as keys, where group_type can be either 'overall' or + name of the particular group arranged by area of mask (e.g. '10-20%') + and score statistics for the group as values. + """ + LOGGER.info(f'{type(self)}: evaluation_end called') + + self.groups = np.array(self.groups) + + results = {} + for score_name, score in self.scores.items(): + LOGGER.info(f'Getting value of {score_name}') + cur_states = [s[score_name] for s in states] if states is not None else None + total_results, group_results = score.get_value(groups=self.groups, states=cur_states) + LOGGER.info(f'Getting value of {score_name} done') + results[(score_name, 'total')] = total_results + + for group_index, group_values in group_results.items(): + group_name = self.interval_names[group_index] + results[(score_name, group_name)] = group_values + + if self.integral_func is not None: + results[(self.integral_title, 'total')] = dict(mean=self.integral_func(results)) + + LOGGER.info(f'{type(self)}: reset scores') + self.groups = [] + for sc in self.scores.values(): + sc.reset() + LOGGER.info(f'{type(self)}: reset scores done') + + LOGGER.info(f'{type(self)}: evaluation_end done') + return results diff --git a/third-party/lama/saicinpainting/evaluation/losses/__init__.py b/third-party/lama/saicinpainting/evaluation/losses/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third-party/lama/saicinpainting/evaluation/losses/base_loss.py b/third-party/lama/saicinpainting/evaluation/losses/base_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..e5cd5fa8d571b2da829b87f0784bd38978158ce7 --- /dev/null +++ b/third-party/lama/saicinpainting/evaluation/losses/base_loss.py @@ -0,0 +1,528 @@ +import logging +from abc import abstractmethod, ABC + +import numpy as np +import sklearn +import sklearn.svm +import torch +import torch.nn as nn +import torch.nn.functional as F +from joblib import Parallel, delayed +from scipy import linalg + +from models.ade20k import SegmentationModule, NUM_CLASS, segm_options +from .fid.inception import InceptionV3 +from .lpips import PerceptualLoss +from .ssim import SSIM + +LOGGER = logging.getLogger(__name__) + + +def get_groupings(groups): + """ + :param groups: group numbers for respective elements + :return: dict of kind {group_idx: indices of the corresponding group elements} + """ + label_groups, count_groups = np.unique(groups, return_counts=True) + + indices = np.argsort(groups) + + grouping = dict() + cur_start = 0 + for label, count in zip(label_groups, count_groups): + cur_end = cur_start + count + cur_indices = indices[cur_start:cur_end] + grouping[label] = cur_indices + cur_start = cur_end + return grouping + + +class EvaluatorScore(nn.Module): + @abstractmethod + def forward(self, pred_batch, target_batch, mask): + pass + + @abstractmethod + def get_value(self, groups=None, states=None): + pass + + @abstractmethod + def reset(self): + pass + + +class PairwiseScore(EvaluatorScore, ABC): + def __init__(self): + super().__init__() + self.individual_values = None + + def get_value(self, groups=None, states=None): + """ + :param groups: + :return: + total_results: dict of kind {'mean': score mean, 'std': score std} + group_results: None, if groups is None; + else dict {group_idx: {'mean': score mean among group, 'std': score std among group}} + """ + individual_values = torch.cat(states, dim=-1).reshape(-1).cpu().numpy() if states is not None \ + else self.individual_values + + total_results = { + 'mean': individual_values.mean(), + 'std': individual_values.std() + } + + if groups is None: + return total_results, None + + group_results = dict() + grouping = get_groupings(groups) + for label, index in grouping.items(): + group_scores = individual_values[index] + group_results[label] = { + 'mean': group_scores.mean(), + 'std': group_scores.std() + } + return total_results, group_results + + def reset(self): + self.individual_values = [] + + +class SSIMScore(PairwiseScore): + def __init__(self, window_size=11): + super().__init__() + self.score = SSIM(window_size=window_size, size_average=False).eval() + self.reset() + + def forward(self, pred_batch, target_batch, mask=None): + batch_values = self.score(pred_batch, target_batch) + self.individual_values = np.hstack([ + self.individual_values, batch_values.detach().cpu().numpy() + ]) + return batch_values + + +class LPIPSScore(PairwiseScore): + def __init__(self, model='net-lin', net='vgg', model_path=None, use_gpu=True): + super().__init__() + self.score = PerceptualLoss(model=model, net=net, model_path=model_path, + use_gpu=use_gpu, spatial=False).eval() + self.reset() + + def forward(self, pred_batch, target_batch, mask=None): + batch_values = self.score(pred_batch, target_batch).flatten() + self.individual_values = np.hstack([ + self.individual_values, batch_values.detach().cpu().numpy() + ]) + return batch_values + + +def fid_calculate_activation_statistics(act): + mu = np.mean(act, axis=0) + sigma = np.cov(act, rowvar=False) + return mu, sigma + + +def calculate_frechet_distance(activations_pred, activations_target, eps=1e-6): + mu1, sigma1 = fid_calculate_activation_statistics(activations_pred) + mu2, sigma2 = fid_calculate_activation_statistics(activations_target) + + diff = mu1 - mu2 + + # Product might be almost singular + covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False) + if not np.isfinite(covmean).all(): + msg = ('fid calculation produces singular product; ' + 'adding %s to diagonal of cov estimates') % eps + LOGGER.warning(msg) + offset = np.eye(sigma1.shape[0]) * eps + covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset)) + + # Numerical error might give slight imaginary component + if np.iscomplexobj(covmean): + # if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3): + if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-2): + m = np.max(np.abs(covmean.imag)) + raise ValueError('Imaginary component {}'.format(m)) + covmean = covmean.real + + tr_covmean = np.trace(covmean) + + return (diff.dot(diff) + np.trace(sigma1) + + np.trace(sigma2) - 2 * tr_covmean) + + +class FIDScore(EvaluatorScore): + def __init__(self, dims=2048, eps=1e-6): + LOGGER.info("FIDscore init called") + super().__init__() + if getattr(FIDScore, '_MODEL', None) is None: + block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] + FIDScore._MODEL = InceptionV3([block_idx]).eval() + self.model = FIDScore._MODEL + self.eps = eps + self.reset() + LOGGER.info("FIDscore init done") + + def forward(self, pred_batch, target_batch, mask=None): + activations_pred = self._get_activations(pred_batch) + activations_target = self._get_activations(target_batch) + + self.activations_pred.append(activations_pred.detach().cpu()) + self.activations_target.append(activations_target.detach().cpu()) + + return activations_pred, activations_target + + def get_value(self, groups=None, states=None): + LOGGER.info("FIDscore get_value called") + activations_pred, activations_target = zip(*states) if states is not None \ + else (self.activations_pred, self.activations_target) + activations_pred = torch.cat(activations_pred).cpu().numpy() + activations_target = torch.cat(activations_target).cpu().numpy() + + total_distance = calculate_frechet_distance(activations_pred, activations_target, eps=self.eps) + total_results = dict(mean=total_distance) + + if groups is None: + group_results = None + else: + group_results = dict() + grouping = get_groupings(groups) + for label, index in grouping.items(): + if len(index) > 1: + group_distance = calculate_frechet_distance(activations_pred[index], activations_target[index], + eps=self.eps) + group_results[label] = dict(mean=group_distance) + + else: + group_results[label] = dict(mean=float('nan')) + + self.reset() + + LOGGER.info("FIDscore get_value done") + + return total_results, group_results + + def reset(self): + self.activations_pred = [] + self.activations_target = [] + + def _get_activations(self, batch): + activations = self.model(batch)[0] + if activations.shape[2] != 1 or activations.shape[3] != 1: + assert False, \ + 'We should not have got here, because Inception always scales inputs to 299x299' + # activations = F.adaptive_avg_pool2d(activations, output_size=(1, 1)) + activations = activations.squeeze(-1).squeeze(-1) + return activations + + +class SegmentationAwareScore(EvaluatorScore): + def __init__(self, weights_path): + super().__init__() + self.segm_network = SegmentationModule(weights_path=weights_path, use_default_normalization=True).eval() + self.target_class_freq_by_image_total = [] + self.target_class_freq_by_image_mask = [] + self.pred_class_freq_by_image_mask = [] + + def forward(self, pred_batch, target_batch, mask): + pred_segm_flat = self.segm_network.predict(pred_batch)[0].view(pred_batch.shape[0], -1).long().detach().cpu().numpy() + target_segm_flat = self.segm_network.predict(target_batch)[0].view(pred_batch.shape[0], -1).long().detach().cpu().numpy() + mask_flat = (mask.view(mask.shape[0], -1) > 0.5).detach().cpu().numpy() + + batch_target_class_freq_total = [] + batch_target_class_freq_mask = [] + batch_pred_class_freq_mask = [] + + for cur_pred_segm, cur_target_segm, cur_mask in zip(pred_segm_flat, target_segm_flat, mask_flat): + cur_target_class_freq_total = np.bincount(cur_target_segm, minlength=NUM_CLASS)[None, ...] + cur_target_class_freq_mask = np.bincount(cur_target_segm[cur_mask], minlength=NUM_CLASS)[None, ...] + cur_pred_class_freq_mask = np.bincount(cur_pred_segm[cur_mask], minlength=NUM_CLASS)[None, ...] + + self.target_class_freq_by_image_total.append(cur_target_class_freq_total) + self.target_class_freq_by_image_mask.append(cur_target_class_freq_mask) + self.pred_class_freq_by_image_mask.append(cur_pred_class_freq_mask) + + batch_target_class_freq_total.append(cur_target_class_freq_total) + batch_target_class_freq_mask.append(cur_target_class_freq_mask) + batch_pred_class_freq_mask.append(cur_pred_class_freq_mask) + + batch_target_class_freq_total = np.concatenate(batch_target_class_freq_total, axis=0) + batch_target_class_freq_mask = np.concatenate(batch_target_class_freq_mask, axis=0) + batch_pred_class_freq_mask = np.concatenate(batch_pred_class_freq_mask, axis=0) + return batch_target_class_freq_total, batch_target_class_freq_mask, batch_pred_class_freq_mask + + def reset(self): + super().reset() + self.target_class_freq_by_image_total = [] + self.target_class_freq_by_image_mask = [] + self.pred_class_freq_by_image_mask = [] + + +def distribute_values_to_classes(target_class_freq_by_image_mask, values, idx2name): + assert target_class_freq_by_image_mask.ndim == 2 and target_class_freq_by_image_mask.shape[0] == values.shape[0] + total_class_freq = target_class_freq_by_image_mask.sum(0) + distr_values = (target_class_freq_by_image_mask * values[..., None]).sum(0) + result = distr_values / (total_class_freq + 1e-3) + return {idx2name[i]: val for i, val in enumerate(result) if total_class_freq[i] > 0} + + +def get_segmentation_idx2name(): + return {i - 1: name for i, name in segm_options['classes'].set_index('Idx', drop=True)['Name'].to_dict().items()} + + +class SegmentationAwarePairwiseScore(SegmentationAwareScore): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.individual_values = [] + self.segm_idx2name = get_segmentation_idx2name() + + def forward(self, pred_batch, target_batch, mask): + cur_class_stats = super().forward(pred_batch, target_batch, mask) + score_values = self.calc_score(pred_batch, target_batch, mask) + self.individual_values.append(score_values) + return cur_class_stats + (score_values,) + + @abstractmethod + def calc_score(self, pred_batch, target_batch, mask): + raise NotImplementedError() + + def get_value(self, groups=None, states=None): + """ + :param groups: + :return: + total_results: dict of kind {'mean': score mean, 'std': score std} + group_results: None, if groups is None; + else dict {group_idx: {'mean': score mean among group, 'std': score std among group}} + """ + if states is not None: + (target_class_freq_by_image_total, + target_class_freq_by_image_mask, + pred_class_freq_by_image_mask, + individual_values) = states + else: + target_class_freq_by_image_total = self.target_class_freq_by_image_total + target_class_freq_by_image_mask = self.target_class_freq_by_image_mask + pred_class_freq_by_image_mask = self.pred_class_freq_by_image_mask + individual_values = self.individual_values + + target_class_freq_by_image_total = np.concatenate(target_class_freq_by_image_total, axis=0) + target_class_freq_by_image_mask = np.concatenate(target_class_freq_by_image_mask, axis=0) + pred_class_freq_by_image_mask = np.concatenate(pred_class_freq_by_image_mask, axis=0) + individual_values = np.concatenate(individual_values, axis=0) + + total_results = { + 'mean': individual_values.mean(), + 'std': individual_values.std(), + **distribute_values_to_classes(target_class_freq_by_image_mask, individual_values, self.segm_idx2name) + } + + if groups is None: + return total_results, None + + group_results = dict() + grouping = get_groupings(groups) + for label, index in grouping.items(): + group_class_freq = target_class_freq_by_image_mask[index] + group_scores = individual_values[index] + group_results[label] = { + 'mean': group_scores.mean(), + 'std': group_scores.std(), + ** distribute_values_to_classes(group_class_freq, group_scores, self.segm_idx2name) + } + return total_results, group_results + + def reset(self): + super().reset() + self.individual_values = [] + + +class SegmentationClassStats(SegmentationAwarePairwiseScore): + def calc_score(self, pred_batch, target_batch, mask): + return 0 + + def get_value(self, groups=None, states=None): + """ + :param groups: + :return: + total_results: dict of kind {'mean': score mean, 'std': score std} + group_results: None, if groups is None; + else dict {group_idx: {'mean': score mean among group, 'std': score std among group}} + """ + if states is not None: + (target_class_freq_by_image_total, + target_class_freq_by_image_mask, + pred_class_freq_by_image_mask, + _) = states + else: + target_class_freq_by_image_total = self.target_class_freq_by_image_total + target_class_freq_by_image_mask = self.target_class_freq_by_image_mask + pred_class_freq_by_image_mask = self.pred_class_freq_by_image_mask + + target_class_freq_by_image_total = np.concatenate(target_class_freq_by_image_total, axis=0) + target_class_freq_by_image_mask = np.concatenate(target_class_freq_by_image_mask, axis=0) + pred_class_freq_by_image_mask = np.concatenate(pred_class_freq_by_image_mask, axis=0) + + target_class_freq_by_image_total_marginal = target_class_freq_by_image_total.sum(0).astype('float32') + target_class_freq_by_image_total_marginal /= target_class_freq_by_image_total_marginal.sum() + + target_class_freq_by_image_mask_marginal = target_class_freq_by_image_mask.sum(0).astype('float32') + target_class_freq_by_image_mask_marginal /= target_class_freq_by_image_mask_marginal.sum() + + pred_class_freq_diff = (pred_class_freq_by_image_mask - target_class_freq_by_image_mask).sum(0) / (target_class_freq_by_image_mask.sum(0) + 1e-3) + + total_results = dict() + total_results.update({f'total_freq/{self.segm_idx2name[i]}': v + for i, v in enumerate(target_class_freq_by_image_total_marginal) + if v > 0}) + total_results.update({f'mask_freq/{self.segm_idx2name[i]}': v + for i, v in enumerate(target_class_freq_by_image_mask_marginal) + if v > 0}) + total_results.update({f'mask_freq_diff/{self.segm_idx2name[i]}': v + for i, v in enumerate(pred_class_freq_diff) + if target_class_freq_by_image_total_marginal[i] > 0}) + + if groups is None: + return total_results, None + + group_results = dict() + grouping = get_groupings(groups) + for label, index in grouping.items(): + group_target_class_freq_by_image_total = target_class_freq_by_image_total[index] + group_target_class_freq_by_image_mask = target_class_freq_by_image_mask[index] + group_pred_class_freq_by_image_mask = pred_class_freq_by_image_mask[index] + + group_target_class_freq_by_image_total_marginal = group_target_class_freq_by_image_total.sum(0).astype('float32') + group_target_class_freq_by_image_total_marginal /= group_target_class_freq_by_image_total_marginal.sum() + + group_target_class_freq_by_image_mask_marginal = group_target_class_freq_by_image_mask.sum(0).astype('float32') + group_target_class_freq_by_image_mask_marginal /= group_target_class_freq_by_image_mask_marginal.sum() + + group_pred_class_freq_diff = (group_pred_class_freq_by_image_mask - group_target_class_freq_by_image_mask).sum(0) / ( + group_target_class_freq_by_image_mask.sum(0) + 1e-3) + + cur_group_results = dict() + cur_group_results.update({f'total_freq/{self.segm_idx2name[i]}': v + for i, v in enumerate(group_target_class_freq_by_image_total_marginal) + if v > 0}) + cur_group_results.update({f'mask_freq/{self.segm_idx2name[i]}': v + for i, v in enumerate(group_target_class_freq_by_image_mask_marginal) + if v > 0}) + cur_group_results.update({f'mask_freq_diff/{self.segm_idx2name[i]}': v + for i, v in enumerate(group_pred_class_freq_diff) + if group_target_class_freq_by_image_total_marginal[i] > 0}) + + group_results[label] = cur_group_results + return total_results, group_results + + +class SegmentationAwareSSIM(SegmentationAwarePairwiseScore): + def __init__(self, *args, window_size=11, **kwargs): + super().__init__(*args, **kwargs) + self.score_impl = SSIM(window_size=window_size, size_average=False).eval() + + def calc_score(self, pred_batch, target_batch, mask): + return self.score_impl(pred_batch, target_batch).detach().cpu().numpy() + + +class SegmentationAwareLPIPS(SegmentationAwarePairwiseScore): + def __init__(self, *args, model='net-lin', net='vgg', model_path=None, use_gpu=True, **kwargs): + super().__init__(*args, **kwargs) + self.score_impl = PerceptualLoss(model=model, net=net, model_path=model_path, + use_gpu=use_gpu, spatial=False).eval() + + def calc_score(self, pred_batch, target_batch, mask): + return self.score_impl(pred_batch, target_batch).flatten().detach().cpu().numpy() + + +def calculade_fid_no_img(img_i, activations_pred, activations_target, eps=1e-6): + activations_pred = activations_pred.copy() + activations_pred[img_i] = activations_target[img_i] + return calculate_frechet_distance(activations_pred, activations_target, eps=eps) + + +class SegmentationAwareFID(SegmentationAwarePairwiseScore): + def __init__(self, *args, dims=2048, eps=1e-6, n_jobs=-1, **kwargs): + super().__init__(*args, **kwargs) + if getattr(FIDScore, '_MODEL', None) is None: + block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] + FIDScore._MODEL = InceptionV3([block_idx]).eval() + self.model = FIDScore._MODEL + self.eps = eps + self.n_jobs = n_jobs + + def calc_score(self, pred_batch, target_batch, mask): + activations_pred = self._get_activations(pred_batch) + activations_target = self._get_activations(target_batch) + return activations_pred, activations_target + + def get_value(self, groups=None, states=None): + """ + :param groups: + :return: + total_results: dict of kind {'mean': score mean, 'std': score std} + group_results: None, if groups is None; + else dict {group_idx: {'mean': score mean among group, 'std': score std among group}} + """ + if states is not None: + (target_class_freq_by_image_total, + target_class_freq_by_image_mask, + pred_class_freq_by_image_mask, + activation_pairs) = states + else: + target_class_freq_by_image_total = self.target_class_freq_by_image_total + target_class_freq_by_image_mask = self.target_class_freq_by_image_mask + pred_class_freq_by_image_mask = self.pred_class_freq_by_image_mask + activation_pairs = self.individual_values + + target_class_freq_by_image_total = np.concatenate(target_class_freq_by_image_total, axis=0) + target_class_freq_by_image_mask = np.concatenate(target_class_freq_by_image_mask, axis=0) + pred_class_freq_by_image_mask = np.concatenate(pred_class_freq_by_image_mask, axis=0) + activations_pred, activations_target = zip(*activation_pairs) + activations_pred = np.concatenate(activations_pred, axis=0) + activations_target = np.concatenate(activations_target, axis=0) + + total_results = { + 'mean': calculate_frechet_distance(activations_pred, activations_target, eps=self.eps), + 'std': 0, + **self.distribute_fid_to_classes(target_class_freq_by_image_mask, activations_pred, activations_target) + } + + if groups is None: + return total_results, None + + group_results = dict() + grouping = get_groupings(groups) + for label, index in grouping.items(): + if len(index) > 1: + group_activations_pred = activations_pred[index] + group_activations_target = activations_target[index] + group_class_freq = target_class_freq_by_image_mask[index] + group_results[label] = { + 'mean': calculate_frechet_distance(group_activations_pred, group_activations_target, eps=self.eps), + 'std': 0, + **self.distribute_fid_to_classes(group_class_freq, + group_activations_pred, + group_activations_target) + } + else: + group_results[label] = dict(mean=float('nan'), std=0) + return total_results, group_results + + def distribute_fid_to_classes(self, class_freq, activations_pred, activations_target): + real_fid = calculate_frechet_distance(activations_pred, activations_target, eps=self.eps) + + fid_no_images = Parallel(n_jobs=self.n_jobs)( + delayed(calculade_fid_no_img)(img_i, activations_pred, activations_target, eps=self.eps) + for img_i in range(activations_pred.shape[0]) + ) + errors = real_fid - fid_no_images + return distribute_values_to_classes(class_freq, errors, self.segm_idx2name) + + def _get_activations(self, batch): + activations = self.model(batch)[0] + if activations.shape[2] != 1 or activations.shape[3] != 1: + activations = F.adaptive_avg_pool2d(activations, output_size=(1, 1)) + activations = activations.squeeze(-1).squeeze(-1).detach().cpu().numpy() + return activations diff --git a/third-party/lama/saicinpainting/evaluation/losses/fid/__init__.py b/third-party/lama/saicinpainting/evaluation/losses/fid/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third-party/lama/saicinpainting/evaluation/losses/fid/fid_score.py b/third-party/lama/saicinpainting/evaluation/losses/fid/fid_score.py new file mode 100644 index 0000000000000000000000000000000000000000..6ca8e602c21bb6a624d646da3f6479aea033b0ac --- /dev/null +++ b/third-party/lama/saicinpainting/evaluation/losses/fid/fid_score.py @@ -0,0 +1,328 @@ +#!/usr/bin/env python3 +"""Calculates the Frechet Inception Distance (FID) to evalulate GANs + +The FID metric calculates the distance between two distributions of images. +Typically, we have summary statistics (mean & covariance matrix) of one +of these distributions, while the 2nd distribution is given by a GAN. + +When run as a stand-alone program, it compares the distribution of +images that are stored as PNG/JPEG at a specified location with a +distribution given by summary statistics (in pickle format). + +The FID is calculated by assuming that X_1 and X_2 are the activations of +the pool_3 layer of the inception net for generated samples and real world +samples respectively. + +See --help to see further details. + +Code apapted from https://github.com/bioinf-jku/TTUR to use PyTorch instead +of Tensorflow + +Copyright 2018 Institute of Bioinformatics, JKU Linz + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import os +import pathlib +from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser + +import numpy as np +import torch +# from scipy.misc import imread +from imageio import imread +from PIL import Image, JpegImagePlugin +from scipy import linalg +from torch.nn.functional import adaptive_avg_pool2d +from torchvision.transforms import CenterCrop, Compose, Resize, ToTensor + +try: + from tqdm import tqdm +except ImportError: + # If not tqdm is not available, provide a mock version of it + def tqdm(x): return x + +try: + from .inception import InceptionV3 +except ModuleNotFoundError: + from inception import InceptionV3 + +parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter) +parser.add_argument('path', type=str, nargs=2, + help=('Path to the generated images or ' + 'to .npz statistic files')) +parser.add_argument('--batch-size', type=int, default=50, + help='Batch size to use') +parser.add_argument('--dims', type=int, default=2048, + choices=list(InceptionV3.BLOCK_INDEX_BY_DIM), + help=('Dimensionality of Inception features to use. ' + 'By default, uses pool3 features')) +parser.add_argument('-c', '--gpu', default='', type=str, + help='GPU to use (leave blank for CPU only)') +parser.add_argument('--resize', default=256) + +transform = Compose([Resize(256), CenterCrop(256), ToTensor()]) + + +def get_activations(files, model, batch_size=50, dims=2048, + cuda=False, verbose=False, keep_size=False): + """Calculates the activations of the pool_3 layer for all images. + + Params: + -- files : List of image files paths + -- model : Instance of inception model + -- batch_size : Batch size of images for the model to process at once. + Make sure that the number of samples is a multiple of + the batch size, otherwise some samples are ignored. This + behavior is retained to match the original FID score + implementation. + -- dims : Dimensionality of features returned by Inception + -- cuda : If set to True, use GPU + -- verbose : If set to True and parameter out_step is given, the number + of calculated batches is reported. + Returns: + -- A numpy array of dimension (num images, dims) that contains the + activations of the given tensor when feeding inception with the + query tensor. + """ + model.eval() + + if len(files) % batch_size != 0: + print(('Warning: number of images is not a multiple of the ' + 'batch size. Some samples are going to be ignored.')) + if batch_size > len(files): + print(('Warning: batch size is bigger than the data size. ' + 'Setting batch size to data size')) + batch_size = len(files) + + n_batches = len(files) // batch_size + n_used_imgs = n_batches * batch_size + + pred_arr = np.empty((n_used_imgs, dims)) + + for i in tqdm(range(n_batches)): + if verbose: + print('\rPropagating batch %d/%d' % (i + 1, n_batches), + end='', flush=True) + start = i * batch_size + end = start + batch_size + + # # Official code goes below + # images = np.array([imread(str(f)).astype(np.float32) + # for f in files[start:end]]) + + # # Reshape to (n_images, 3, height, width) + # images = images.transpose((0, 3, 1, 2)) + # images /= 255 + # batch = torch.from_numpy(images).type(torch.FloatTensor) + # # + + t = transform if not keep_size else ToTensor() + + if isinstance(files[0], pathlib.PosixPath): + images = [t(Image.open(str(f))) for f in files[start:end]] + + elif isinstance(files[0], Image.Image): + images = [t(f) for f in files[start:end]] + + else: + raise ValueError(f"Unknown data type for image: {type(files[0])}") + + batch = torch.stack(images) + + if cuda: + batch = batch.cuda() + + pred = model(batch)[0] + + # If model output is not scalar, apply global spatial average pooling. + # This happens if you choose a dimensionality not equal 2048. + if pred.shape[2] != 1 or pred.shape[3] != 1: + pred = adaptive_avg_pool2d(pred, output_size=(1, 1)) + + pred_arr[start:end] = pred.cpu().data.numpy().reshape(batch_size, -1) + + if verbose: + print(' done') + + return pred_arr + + +def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): + """Numpy implementation of the Frechet Distance. + The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) + and X_2 ~ N(mu_2, C_2) is + d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)). + + Stable version by Dougal J. Sutherland. + + Params: + -- mu1 : Numpy array containing the activations of a layer of the + inception net (like returned by the function 'get_predictions') + for generated samples. + -- mu2 : The sample mean over activations, precalculated on an + representative data set. + -- sigma1: The covariance matrix over activations for generated samples. + -- sigma2: The covariance matrix over activations, precalculated on an + representative data set. + + Returns: + -- : The Frechet Distance. + """ + + mu1 = np.atleast_1d(mu1) + mu2 = np.atleast_1d(mu2) + + sigma1 = np.atleast_2d(sigma1) + sigma2 = np.atleast_2d(sigma2) + + assert mu1.shape == mu2.shape, \ + 'Training and test mean vectors have different lengths' + assert sigma1.shape == sigma2.shape, \ + 'Training and test covariances have different dimensions' + + diff = mu1 - mu2 + + # Product might be almost singular + covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False) + if not np.isfinite(covmean).all(): + msg = ('fid calculation produces singular product; ' + 'adding %s to diagonal of cov estimates') % eps + print(msg) + offset = np.eye(sigma1.shape[0]) * eps + covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset)) + + # Numerical error might give slight imaginary component + if np.iscomplexobj(covmean): + # if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3): + if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-2): + m = np.max(np.abs(covmean.imag)) + raise ValueError('Imaginary component {}'.format(m)) + covmean = covmean.real + + tr_covmean = np.trace(covmean) + + return (diff.dot(diff) + np.trace(sigma1) + + np.trace(sigma2) - 2 * tr_covmean) + + +def calculate_activation_statistics(files, model, batch_size=50, + dims=2048, cuda=False, verbose=False, keep_size=False): + """Calculation of the statistics used by the FID. + Params: + -- files : List of image files paths + -- model : Instance of inception model + -- batch_size : The images numpy array is split into batches with + batch size batch_size. A reasonable batch size + depends on the hardware. + -- dims : Dimensionality of features returned by Inception + -- cuda : If set to True, use GPU + -- verbose : If set to True and parameter out_step is given, the + number of calculated batches is reported. + Returns: + -- mu : The mean over samples of the activations of the pool_3 layer of + the inception model. + -- sigma : The covariance matrix of the activations of the pool_3 layer of + the inception model. + """ + act = get_activations(files, model, batch_size, dims, cuda, verbose, keep_size=keep_size) + mu = np.mean(act, axis=0) + sigma = np.cov(act, rowvar=False) + return mu, sigma + + +def _compute_statistics_of_path(path, model, batch_size, dims, cuda): + if path.endswith('.npz'): + f = np.load(path) + m, s = f['mu'][:], f['sigma'][:] + f.close() + else: + path = pathlib.Path(path) + files = list(path.glob('*.jpg')) + list(path.glob('*.png')) + m, s = calculate_activation_statistics(files, model, batch_size, + dims, cuda) + + return m, s + + +def _compute_statistics_of_images(images, model, batch_size, dims, cuda, keep_size=False): + if isinstance(images, list): # exact paths to files are provided + m, s = calculate_activation_statistics(images, model, batch_size, + dims, cuda, keep_size=keep_size) + + return m, s + + else: + raise ValueError + + +def calculate_fid_given_paths(paths, batch_size, cuda, dims): + """Calculates the FID of two paths""" + for p in paths: + if not os.path.exists(p): + raise RuntimeError('Invalid path: %s' % p) + + block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] + + model = InceptionV3([block_idx]) + if cuda: + model.cuda() + + m1, s1 = _compute_statistics_of_path(paths[0], model, batch_size, + dims, cuda) + m2, s2 = _compute_statistics_of_path(paths[1], model, batch_size, + dims, cuda) + fid_value = calculate_frechet_distance(m1, s1, m2, s2) + + return fid_value + + +def calculate_fid_given_images(images, batch_size, cuda, dims, use_globals=False, keep_size=False): + if use_globals: + global FID_MODEL # for multiprocessing + + for imgs in images: + if isinstance(imgs, list) and isinstance(imgs[0], (Image.Image, JpegImagePlugin.JpegImageFile)): + pass + else: + raise RuntimeError('Invalid images') + + block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] + + if 'FID_MODEL' not in globals() or not use_globals: + model = InceptionV3([block_idx]) + if cuda: + model.cuda() + + if use_globals: + FID_MODEL = model + + else: + model = FID_MODEL + + m1, s1 = _compute_statistics_of_images(images[0], model, batch_size, + dims, cuda, keep_size=False) + m2, s2 = _compute_statistics_of_images(images[1], model, batch_size, + dims, cuda, keep_size=False) + fid_value = calculate_frechet_distance(m1, s1, m2, s2) + return fid_value + + +if __name__ == '__main__': + args = parser.parse_args() + os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu + + fid_value = calculate_fid_given_paths(args.path, + args.batch_size, + args.gpu != '', + args.dims) + print('FID: ', fid_value) diff --git a/third-party/lama/saicinpainting/evaluation/losses/fid/inception.py b/third-party/lama/saicinpainting/evaluation/losses/fid/inception.py new file mode 100644 index 0000000000000000000000000000000000000000..e9bd0863b457aaa40c770eaa4acbb142b18fc18b --- /dev/null +++ b/third-party/lama/saicinpainting/evaluation/losses/fid/inception.py @@ -0,0 +1,323 @@ +import logging + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchvision import models + +try: + from torchvision.models.utils import load_state_dict_from_url +except ImportError: + from torch.utils.model_zoo import load_url as load_state_dict_from_url + +# Inception weights ported to Pytorch from +# http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz +FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth' + + +LOGGER = logging.getLogger(__name__) + + +class InceptionV3(nn.Module): + """Pretrained InceptionV3 network returning feature maps""" + + # Index of default block of inception to return, + # corresponds to output of final average pooling + DEFAULT_BLOCK_INDEX = 3 + + # Maps feature dimensionality to their output blocks indices + BLOCK_INDEX_BY_DIM = { + 64: 0, # First max pooling features + 192: 1, # Second max pooling featurs + 768: 2, # Pre-aux classifier features + 2048: 3 # Final average pooling features + } + + def __init__(self, + output_blocks=[DEFAULT_BLOCK_INDEX], + resize_input=True, + normalize_input=True, + requires_grad=False, + use_fid_inception=True): + """Build pretrained InceptionV3 + + Parameters + ---------- + output_blocks : list of int + Indices of blocks to return features of. Possible values are: + - 0: corresponds to output of first max pooling + - 1: corresponds to output of second max pooling + - 2: corresponds to output which is fed to aux classifier + - 3: corresponds to output of final average pooling + resize_input : bool + If true, bilinearly resizes input to width and height 299 before + feeding input to model. As the network without fully connected + layers is fully convolutional, it should be able to handle inputs + of arbitrary size, so resizing might not be strictly needed + normalize_input : bool + If true, scales the input from range (0, 1) to the range the + pretrained Inception network expects, namely (-1, 1) + requires_grad : bool + If true, parameters of the model require gradients. Possibly useful + for finetuning the network + use_fid_inception : bool + If true, uses the pretrained Inception model used in Tensorflow's + FID implementation. If false, uses the pretrained Inception model + available in torchvision. The FID Inception model has different + weights and a slightly different structure from torchvision's + Inception model. If you want to compute FID scores, you are + strongly advised to set this parameter to true to get comparable + results. + """ + super(InceptionV3, self).__init__() + + self.resize_input = resize_input + self.normalize_input = normalize_input + self.output_blocks = sorted(output_blocks) + self.last_needed_block = max(output_blocks) + + assert self.last_needed_block <= 3, \ + 'Last possible output block index is 3' + + self.blocks = nn.ModuleList() + + if use_fid_inception: + inception = fid_inception_v3() + else: + inception = models.inception_v3(pretrained=True) + + # Block 0: input to maxpool1 + block0 = [ + inception.Conv2d_1a_3x3, + inception.Conv2d_2a_3x3, + inception.Conv2d_2b_3x3, + nn.MaxPool2d(kernel_size=3, stride=2) + ] + self.blocks.append(nn.Sequential(*block0)) + + # Block 1: maxpool1 to maxpool2 + if self.last_needed_block >= 1: + block1 = [ + inception.Conv2d_3b_1x1, + inception.Conv2d_4a_3x3, + nn.MaxPool2d(kernel_size=3, stride=2) + ] + self.blocks.append(nn.Sequential(*block1)) + + # Block 2: maxpool2 to aux classifier + if self.last_needed_block >= 2: + block2 = [ + inception.Mixed_5b, + inception.Mixed_5c, + inception.Mixed_5d, + inception.Mixed_6a, + inception.Mixed_6b, + inception.Mixed_6c, + inception.Mixed_6d, + inception.Mixed_6e, + ] + self.blocks.append(nn.Sequential(*block2)) + + # Block 3: aux classifier to final avgpool + if self.last_needed_block >= 3: + block3 = [ + inception.Mixed_7a, + inception.Mixed_7b, + inception.Mixed_7c, + nn.AdaptiveAvgPool2d(output_size=(1, 1)) + ] + self.blocks.append(nn.Sequential(*block3)) + + for param in self.parameters(): + param.requires_grad = requires_grad + + def forward(self, inp): + """Get Inception feature maps + + Parameters + ---------- + inp : torch.autograd.Variable + Input tensor of shape Bx3xHxW. Values are expected to be in + range (0, 1) + + Returns + ------- + List of torch.autograd.Variable, corresponding to the selected output + block, sorted ascending by index + """ + outp = [] + x = inp + + if self.resize_input: + x = F.interpolate(x, + size=(299, 299), + mode='bilinear', + align_corners=False) + + if self.normalize_input: + x = 2 * x - 1 # Scale from range (0, 1) to range (-1, 1) + + for idx, block in enumerate(self.blocks): + x = block(x) + if idx in self.output_blocks: + outp.append(x) + + if idx == self.last_needed_block: + break + + return outp + + +def fid_inception_v3(): + """Build pretrained Inception model for FID computation + + The Inception model for FID computation uses a different set of weights + and has a slightly different structure than torchvision's Inception. + + This method first constructs torchvision's Inception and then patches the + necessary parts that are different in the FID Inception model. + """ + LOGGER.info('fid_inception_v3 called') + inception = models.inception_v3(num_classes=1008, + aux_logits=False, + pretrained=False) + LOGGER.info('models.inception_v3 done') + inception.Mixed_5b = FIDInceptionA(192, pool_features=32) + inception.Mixed_5c = FIDInceptionA(256, pool_features=64) + inception.Mixed_5d = FIDInceptionA(288, pool_features=64) + inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128) + inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160) + inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160) + inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192) + inception.Mixed_7b = FIDInceptionE_1(1280) + inception.Mixed_7c = FIDInceptionE_2(2048) + + LOGGER.info('fid_inception_v3 patching done') + + state_dict = load_state_dict_from_url(FID_WEIGHTS_URL, progress=True) + LOGGER.info('fid_inception_v3 weights downloaded') + + inception.load_state_dict(state_dict) + LOGGER.info('fid_inception_v3 weights loaded into model') + + return inception + + +class FIDInceptionA(models.inception.InceptionA): + """InceptionA block patched for FID computation""" + def __init__(self, in_channels, pool_features): + super(FIDInceptionA, self).__init__(in_channels, pool_features) + + def forward(self, x): + branch1x1 = self.branch1x1(x) + + branch5x5 = self.branch5x5_1(x) + branch5x5 = self.branch5x5_2(branch5x5) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) + + # Patch: Tensorflow's average pool does not use the padded zero's in + # its average calculation + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, + count_include_pad=False) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool] + return torch.cat(outputs, 1) + + +class FIDInceptionC(models.inception.InceptionC): + """InceptionC block patched for FID computation""" + def __init__(self, in_channels, channels_7x7): + super(FIDInceptionC, self).__init__(in_channels, channels_7x7) + + def forward(self, x): + branch1x1 = self.branch1x1(x) + + branch7x7 = self.branch7x7_1(x) + branch7x7 = self.branch7x7_2(branch7x7) + branch7x7 = self.branch7x7_3(branch7x7) + + branch7x7dbl = self.branch7x7dbl_1(x) + branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl) + branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl) + branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl) + branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl) + + # Patch: Tensorflow's average pool does not use the padded zero's in + # its average calculation + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, + count_include_pad=False) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool] + return torch.cat(outputs, 1) + + +class FIDInceptionE_1(models.inception.InceptionE): + """First InceptionE block patched for FID computation""" + def __init__(self, in_channels): + super(FIDInceptionE_1, self).__init__(in_channels) + + def forward(self, x): + branch1x1 = self.branch1x1(x) + + branch3x3 = self.branch3x3_1(x) + branch3x3 = [ + self.branch3x3_2a(branch3x3), + self.branch3x3_2b(branch3x3), + ] + branch3x3 = torch.cat(branch3x3, 1) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = [ + self.branch3x3dbl_3a(branch3x3dbl), + self.branch3x3dbl_3b(branch3x3dbl), + ] + branch3x3dbl = torch.cat(branch3x3dbl, 1) + + # Patch: Tensorflow's average pool does not use the padded zero's in + # its average calculation + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, + count_include_pad=False) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] + return torch.cat(outputs, 1) + + +class FIDInceptionE_2(models.inception.InceptionE): + """Second InceptionE block patched for FID computation""" + def __init__(self, in_channels): + super(FIDInceptionE_2, self).__init__(in_channels) + + def forward(self, x): + branch1x1 = self.branch1x1(x) + + branch3x3 = self.branch3x3_1(x) + branch3x3 = [ + self.branch3x3_2a(branch3x3), + self.branch3x3_2b(branch3x3), + ] + branch3x3 = torch.cat(branch3x3, 1) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = [ + self.branch3x3dbl_3a(branch3x3dbl), + self.branch3x3dbl_3b(branch3x3dbl), + ] + branch3x3dbl = torch.cat(branch3x3dbl, 1) + + # Patch: The FID Inception model uses max pooling instead of average + # pooling. This is likely an error in this specific Inception + # implementation, as other Inception models use average pooling here + # (which matches the description in the paper). + branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] + return torch.cat(outputs, 1) diff --git a/third-party/lama/saicinpainting/evaluation/losses/lpips.py b/third-party/lama/saicinpainting/evaluation/losses/lpips.py new file mode 100644 index 0000000000000000000000000000000000000000..b5f19b747f2457902695213f7efcde4fdc306c1f --- /dev/null +++ b/third-party/lama/saicinpainting/evaluation/losses/lpips.py @@ -0,0 +1,891 @@ +############################################################ +# The contents below have been combined using files in the # +# following repository: # +# https://github.com/richzhang/PerceptualSimilarity # +############################################################ + +############################################################ +# __init__.py # +############################################################ + +import numpy as np +from skimage.metrics import structural_similarity +import torch + +from saicinpainting.utils import get_shape + + +class PerceptualLoss(torch.nn.Module): + def __init__(self, model='net-lin', net='alex', colorspace='rgb', model_path=None, spatial=False, use_gpu=True): + # VGG using our perceptually-learned weights (LPIPS metric) + # def __init__(self, model='net', net='vgg', use_gpu=True): # "default" way of using VGG as a perceptual loss + super(PerceptualLoss, self).__init__() + self.use_gpu = use_gpu + self.spatial = spatial + self.model = DistModel() + self.model.initialize(model=model, net=net, use_gpu=use_gpu, colorspace=colorspace, + model_path=model_path, spatial=self.spatial) + + def forward(self, pred, target, normalize=True): + """ + Pred and target are Variables. + If normalize is True, assumes the images are between [0,1] and then scales them between [-1,+1] + If normalize is False, assumes the images are already between [-1,+1] + Inputs pred and target are Nx3xHxW + Output pytorch Variable N long + """ + + if normalize: + target = 2 * target - 1 + pred = 2 * pred - 1 + + return self.model(target, pred) + + +def normalize_tensor(in_feat, eps=1e-10): + norm_factor = torch.sqrt(torch.sum(in_feat ** 2, dim=1, keepdim=True)) + return in_feat / (norm_factor + eps) + + +def l2(p0, p1, range=255.): + return .5 * np.mean((p0 / range - p1 / range) ** 2) + + +def psnr(p0, p1, peak=255.): + return 10 * np.log10(peak ** 2 / np.mean((1. * p0 - 1. * p1) ** 2)) + + +def dssim(p0, p1, range=255.): + return (1 - compare_ssim(p0, p1, data_range=range, multichannel=True)) / 2. + + +def rgb2lab(in_img, mean_cent=False): + from skimage import color + img_lab = color.rgb2lab(in_img) + if (mean_cent): + img_lab[:, :, 0] = img_lab[:, :, 0] - 50 + return img_lab + + +def tensor2np(tensor_obj): + # change dimension of a tensor object into a numpy array + return tensor_obj[0].cpu().float().numpy().transpose((1, 2, 0)) + + +def np2tensor(np_obj): + # change dimenion of np array into tensor array + return torch.Tensor(np_obj[:, :, :, np.newaxis].transpose((3, 2, 0, 1))) + + +def tensor2tensorlab(image_tensor, to_norm=True, mc_only=False): + # image tensor to lab tensor + from skimage import color + + img = tensor2im(image_tensor) + img_lab = color.rgb2lab(img) + if (mc_only): + img_lab[:, :, 0] = img_lab[:, :, 0] - 50 + if (to_norm and not mc_only): + img_lab[:, :, 0] = img_lab[:, :, 0] - 50 + img_lab = img_lab / 100. + + return np2tensor(img_lab) + + +def tensorlab2tensor(lab_tensor, return_inbnd=False): + from skimage import color + import warnings + warnings.filterwarnings("ignore") + + lab = tensor2np(lab_tensor) * 100. + lab[:, :, 0] = lab[:, :, 0] + 50 + + rgb_back = 255. * np.clip(color.lab2rgb(lab.astype('float')), 0, 1) + if (return_inbnd): + # convert back to lab, see if we match + lab_back = color.rgb2lab(rgb_back.astype('uint8')) + mask = 1. * np.isclose(lab_back, lab, atol=2.) + mask = np2tensor(np.prod(mask, axis=2)[:, :, np.newaxis]) + return (im2tensor(rgb_back), mask) + else: + return im2tensor(rgb_back) + + +def rgb2lab(input): + from skimage import color + return color.rgb2lab(input / 255.) + + +def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255. / 2.): + image_numpy = image_tensor[0].cpu().float().numpy() + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor + return image_numpy.astype(imtype) + + +def im2tensor(image, imtype=np.uint8, cent=1., factor=255. / 2.): + return torch.Tensor((image / factor - cent) + [:, :, :, np.newaxis].transpose((3, 2, 0, 1))) + + +def tensor2vec(vector_tensor): + return vector_tensor.data.cpu().numpy()[:, :, 0, 0] + + +def voc_ap(rec, prec, use_07_metric=False): + """ ap = voc_ap(rec, prec, [use_07_metric]) + Compute VOC AP given precision and recall. + If use_07_metric is true, uses the + VOC 07 11 point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0. + for t in np.arange(0., 1.1, 0.1): + if np.sum(rec >= t) == 0: + p = 0 + else: + p = np.max(prec[rec >= t]) + ap = ap + p / 11. + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], rec, [1.])) + mpre = np.concatenate(([0.], prec, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + + +def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255. / 2.): + # def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=1.): + image_numpy = image_tensor[0].cpu().float().numpy() + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor + return image_numpy.astype(imtype) + + +def im2tensor(image, imtype=np.uint8, cent=1., factor=255. / 2.): + # def im2tensor(image, imtype=np.uint8, cent=1., factor=1.): + return torch.Tensor((image / factor - cent) + [:, :, :, np.newaxis].transpose((3, 2, 0, 1))) + + +############################################################ +# base_model.py # +############################################################ + + +class BaseModel(torch.nn.Module): + def __init__(self): + super().__init__() + + def name(self): + return 'BaseModel' + + def initialize(self, use_gpu=True): + self.use_gpu = use_gpu + + def forward(self): + pass + + def get_image_paths(self): + pass + + def optimize_parameters(self): + pass + + def get_current_visuals(self): + return self.input + + def get_current_errors(self): + return {} + + def save(self, label): + pass + + # helper saving function that can be used by subclasses + def save_network(self, network, path, network_label, epoch_label): + save_filename = '%s_net_%s.pth' % (epoch_label, network_label) + save_path = os.path.join(path, save_filename) + torch.save(network.state_dict(), save_path) + + # helper loading function that can be used by subclasses + def load_network(self, network, network_label, epoch_label): + save_filename = '%s_net_%s.pth' % (epoch_label, network_label) + save_path = os.path.join(self.save_dir, save_filename) + print('Loading network from %s' % save_path) + network.load_state_dict(torch.load(save_path, map_location='cpu')) + + def update_learning_rate(): + pass + + def get_image_paths(self): + return self.image_paths + + def save_done(self, flag=False): + np.save(os.path.join(self.save_dir, 'done_flag'), flag) + np.savetxt(os.path.join(self.save_dir, 'done_flag'), [flag, ], fmt='%i') + + +############################################################ +# dist_model.py # +############################################################ + +import os +from collections import OrderedDict +from scipy.ndimage import zoom +from tqdm import tqdm + + +class DistModel(BaseModel): + def name(self): + return self.model_name + + def initialize(self, model='net-lin', net='alex', colorspace='Lab', pnet_rand=False, pnet_tune=False, + model_path=None, + use_gpu=True, printNet=False, spatial=False, + is_train=False, lr=.0001, beta1=0.5, version='0.1'): + ''' + INPUTS + model - ['net-lin'] for linearly calibrated network + ['net'] for off-the-shelf network + ['L2'] for L2 distance in Lab colorspace + ['SSIM'] for ssim in RGB colorspace + net - ['squeeze','alex','vgg'] + model_path - if None, will look in weights/[NET_NAME].pth + colorspace - ['Lab','RGB'] colorspace to use for L2 and SSIM + use_gpu - bool - whether or not to use a GPU + printNet - bool - whether or not to print network architecture out + spatial - bool - whether to output an array containing varying distances across spatial dimensions + spatial_shape - if given, output spatial shape. if None then spatial shape is determined automatically via spatial_factor (see below). + spatial_factor - if given, specifies upsampling factor relative to the largest spatial extent of a convolutional layer. if None then resized to size of input images. + spatial_order - spline order of filter for upsampling in spatial mode, by default 1 (bilinear). + is_train - bool - [True] for training mode + lr - float - initial learning rate + beta1 - float - initial momentum term for adam + version - 0.1 for latest, 0.0 was original (with a bug) + ''' + BaseModel.initialize(self, use_gpu=use_gpu) + + self.model = model + self.net = net + self.is_train = is_train + self.spatial = spatial + self.model_name = '%s [%s]' % (model, net) + + if (self.model == 'net-lin'): # pretrained net + linear layer + self.net = PNetLin(pnet_rand=pnet_rand, pnet_tune=pnet_tune, pnet_type=net, + use_dropout=True, spatial=spatial, version=version, lpips=True) + kw = dict(map_location='cpu') + if (model_path is None): + import inspect + model_path = os.path.abspath( + os.path.join(os.path.dirname(__file__), '..', '..', '..', 'models', 'lpips_models', f'{net}.pth')) + + if (not is_train): + self.net.load_state_dict(torch.load(model_path, **kw), strict=False) + + elif (self.model == 'net'): # pretrained network + self.net = PNetLin(pnet_rand=pnet_rand, pnet_type=net, lpips=False) + elif (self.model in ['L2', 'l2']): + self.net = L2(use_gpu=use_gpu, colorspace=colorspace) # not really a network, only for testing + self.model_name = 'L2' + elif (self.model in ['DSSIM', 'dssim', 'SSIM', 'ssim']): + self.net = DSSIM(use_gpu=use_gpu, colorspace=colorspace) + self.model_name = 'SSIM' + else: + raise ValueError("Model [%s] not recognized." % self.model) + + self.trainable_parameters = list(self.net.parameters()) + + if self.is_train: # training mode + # extra network on top to go from distances (d0,d1) => predicted human judgment (h*) + self.rankLoss = BCERankingLoss() + self.trainable_parameters += list(self.rankLoss.net.parameters()) + self.lr = lr + self.old_lr = lr + self.optimizer_net = torch.optim.Adam(self.trainable_parameters, lr=lr, betas=(beta1, 0.999)) + else: # test mode + self.net.eval() + + # if (use_gpu): + # self.net.to(gpu_ids[0]) + # self.net = torch.nn.DataParallel(self.net, device_ids=gpu_ids) + # if (self.is_train): + # self.rankLoss = self.rankLoss.to(device=gpu_ids[0]) # just put this on GPU0 + + if (printNet): + print('---------- Networks initialized -------------') + print_network(self.net) + print('-----------------------------------------------') + + def forward(self, in0, in1, retPerLayer=False): + ''' Function computes the distance between image patches in0 and in1 + INPUTS + in0, in1 - torch.Tensor object of shape Nx3xXxY - image patch scaled to [-1,1] + OUTPUT + computed distances between in0 and in1 + ''' + + return self.net(in0, in1, retPerLayer=retPerLayer) + + # ***** TRAINING FUNCTIONS ***** + def optimize_parameters(self): + self.forward_train() + self.optimizer_net.zero_grad() + self.backward_train() + self.optimizer_net.step() + self.clamp_weights() + + def clamp_weights(self): + for module in self.net.modules(): + if (hasattr(module, 'weight') and module.kernel_size == (1, 1)): + module.weight.data = torch.clamp(module.weight.data, min=0) + + def set_input(self, data): + self.input_ref = data['ref'] + self.input_p0 = data['p0'] + self.input_p1 = data['p1'] + self.input_judge = data['judge'] + + # if (self.use_gpu): + # self.input_ref = self.input_ref.to(device=self.gpu_ids[0]) + # self.input_p0 = self.input_p0.to(device=self.gpu_ids[0]) + # self.input_p1 = self.input_p1.to(device=self.gpu_ids[0]) + # self.input_judge = self.input_judge.to(device=self.gpu_ids[0]) + + # self.var_ref = Variable(self.input_ref, requires_grad=True) + # self.var_p0 = Variable(self.input_p0, requires_grad=True) + # self.var_p1 = Variable(self.input_p1, requires_grad=True) + + def forward_train(self): # run forward pass + # print(self.net.module.scaling_layer.shift) + # print(torch.norm(self.net.module.net.slice1[0].weight).item(), torch.norm(self.net.module.lin0.model[1].weight).item()) + + assert False, "We shoud've not get here when using LPIPS as a metric" + + self.d0 = self(self.var_ref, self.var_p0) + self.d1 = self(self.var_ref, self.var_p1) + self.acc_r = self.compute_accuracy(self.d0, self.d1, self.input_judge) + + self.var_judge = Variable(1. * self.input_judge).view(self.d0.size()) + + self.loss_total = self.rankLoss(self.d0, self.d1, self.var_judge * 2. - 1.) + + return self.loss_total + + def backward_train(self): + torch.mean(self.loss_total).backward() + + def compute_accuracy(self, d0, d1, judge): + ''' d0, d1 are Variables, judge is a Tensor ''' + d1_lt_d0 = (d1 < d0).cpu().data.numpy().flatten() + judge_per = judge.cpu().numpy().flatten() + return d1_lt_d0 * judge_per + (1 - d1_lt_d0) * (1 - judge_per) + + def get_current_errors(self): + retDict = OrderedDict([('loss_total', self.loss_total.data.cpu().numpy()), + ('acc_r', self.acc_r)]) + + for key in retDict.keys(): + retDict[key] = np.mean(retDict[key]) + + return retDict + + def get_current_visuals(self): + zoom_factor = 256 / self.var_ref.data.size()[2] + + ref_img = tensor2im(self.var_ref.data) + p0_img = tensor2im(self.var_p0.data) + p1_img = tensor2im(self.var_p1.data) + + ref_img_vis = zoom(ref_img, [zoom_factor, zoom_factor, 1], order=0) + p0_img_vis = zoom(p0_img, [zoom_factor, zoom_factor, 1], order=0) + p1_img_vis = zoom(p1_img, [zoom_factor, zoom_factor, 1], order=0) + + return OrderedDict([('ref', ref_img_vis), + ('p0', p0_img_vis), + ('p1', p1_img_vis)]) + + def save(self, path, label): + if (self.use_gpu): + self.save_network(self.net.module, path, '', label) + else: + self.save_network(self.net, path, '', label) + self.save_network(self.rankLoss.net, path, 'rank', label) + + def update_learning_rate(self, nepoch_decay): + lrd = self.lr / nepoch_decay + lr = self.old_lr - lrd + + for param_group in self.optimizer_net.param_groups: + param_group['lr'] = lr + + print('update lr [%s] decay: %f -> %f' % (type, self.old_lr, lr)) + self.old_lr = lr + + +def score_2afc_dataset(data_loader, func, name=''): + ''' Function computes Two Alternative Forced Choice (2AFC) score using + distance function 'func' in dataset 'data_loader' + INPUTS + data_loader - CustomDatasetDataLoader object - contains a TwoAFCDataset inside + func - callable distance function - calling d=func(in0,in1) should take 2 + pytorch tensors with shape Nx3xXxY, and return numpy array of length N + OUTPUTS + [0] - 2AFC score in [0,1], fraction of time func agrees with human evaluators + [1] - dictionary with following elements + d0s,d1s - N arrays containing distances between reference patch to perturbed patches + gts - N array in [0,1], preferred patch selected by human evaluators + (closer to "0" for left patch p0, "1" for right patch p1, + "0.6" means 60pct people preferred right patch, 40pct preferred left) + scores - N array in [0,1], corresponding to what percentage function agreed with humans + CONSTS + N - number of test triplets in data_loader + ''' + + d0s = [] + d1s = [] + gts = [] + + for data in tqdm(data_loader.load_data(), desc=name): + d0s += func(data['ref'], data['p0']).data.cpu().numpy().flatten().tolist() + d1s += func(data['ref'], data['p1']).data.cpu().numpy().flatten().tolist() + gts += data['judge'].cpu().numpy().flatten().tolist() + + d0s = np.array(d0s) + d1s = np.array(d1s) + gts = np.array(gts) + scores = (d0s < d1s) * (1. - gts) + (d1s < d0s) * gts + (d1s == d0s) * .5 + + return (np.mean(scores), dict(d0s=d0s, d1s=d1s, gts=gts, scores=scores)) + + +def score_jnd_dataset(data_loader, func, name=''): + ''' Function computes JND score using distance function 'func' in dataset 'data_loader' + INPUTS + data_loader - CustomDatasetDataLoader object - contains a JNDDataset inside + func - callable distance function - calling d=func(in0,in1) should take 2 + pytorch tensors with shape Nx3xXxY, and return pytorch array of length N + OUTPUTS + [0] - JND score in [0,1], mAP score (area under precision-recall curve) + [1] - dictionary with following elements + ds - N array containing distances between two patches shown to human evaluator + sames - N array containing fraction of people who thought the two patches were identical + CONSTS + N - number of test triplets in data_loader + ''' + + ds = [] + gts = [] + + for data in tqdm(data_loader.load_data(), desc=name): + ds += func(data['p0'], data['p1']).data.cpu().numpy().tolist() + gts += data['same'].cpu().numpy().flatten().tolist() + + sames = np.array(gts) + ds = np.array(ds) + + sorted_inds = np.argsort(ds) + ds_sorted = ds[sorted_inds] + sames_sorted = sames[sorted_inds] + + TPs = np.cumsum(sames_sorted) + FPs = np.cumsum(1 - sames_sorted) + FNs = np.sum(sames_sorted) - TPs + + precs = TPs / (TPs + FPs) + recs = TPs / (TPs + FNs) + score = voc_ap(recs, precs) + + return (score, dict(ds=ds, sames=sames)) + + +############################################################ +# networks_basic.py # +############################################################ + +import torch.nn as nn +from torch.autograd import Variable +import numpy as np + + +def spatial_average(in_tens, keepdim=True): + return in_tens.mean([2, 3], keepdim=keepdim) + + +def upsample(in_tens, out_H=64): # assumes scale factor is same for H and W + in_H = in_tens.shape[2] + scale_factor = 1. * out_H / in_H + + return nn.Upsample(scale_factor=scale_factor, mode='bilinear', align_corners=False)(in_tens) + + +# Learned perceptual metric +class PNetLin(nn.Module): + def __init__(self, pnet_type='vgg', pnet_rand=False, pnet_tune=False, use_dropout=True, spatial=False, + version='0.1', lpips=True): + super(PNetLin, self).__init__() + + self.pnet_type = pnet_type + self.pnet_tune = pnet_tune + self.pnet_rand = pnet_rand + self.spatial = spatial + self.lpips = lpips + self.version = version + self.scaling_layer = ScalingLayer() + + if (self.pnet_type in ['vgg', 'vgg16']): + net_type = vgg16 + self.chns = [64, 128, 256, 512, 512] + elif (self.pnet_type == 'alex'): + net_type = alexnet + self.chns = [64, 192, 384, 256, 256] + elif (self.pnet_type == 'squeeze'): + net_type = squeezenet + self.chns = [64, 128, 256, 384, 384, 512, 512] + self.L = len(self.chns) + + self.net = net_type(pretrained=not self.pnet_rand, requires_grad=self.pnet_tune) + + if (lpips): + self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout) + self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout) + self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout) + self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout) + self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout) + self.lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4] + if (self.pnet_type == 'squeeze'): # 7 layers for squeezenet + self.lin5 = NetLinLayer(self.chns[5], use_dropout=use_dropout) + self.lin6 = NetLinLayer(self.chns[6], use_dropout=use_dropout) + self.lins += [self.lin5, self.lin6] + + def forward(self, in0, in1, retPerLayer=False): + # v0.0 - original release had a bug, where input was not scaled + in0_input, in1_input = (self.scaling_layer(in0), self.scaling_layer(in1)) if self.version == '0.1' else ( + in0, in1) + outs0, outs1 = self.net(in0_input), self.net(in1_input) + feats0, feats1, diffs = {}, {}, {} + + for kk in range(self.L): + feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk]) + diffs[kk] = (feats0[kk] - feats1[kk]) ** 2 + + if (self.lpips): + if (self.spatial): + res = [upsample(self.lins[kk].model(diffs[kk]), out_H=in0.shape[2]) for kk in range(self.L)] + else: + res = [spatial_average(self.lins[kk].model(diffs[kk]), keepdim=True) for kk in range(self.L)] + else: + if (self.spatial): + res = [upsample(diffs[kk].sum(dim=1, keepdim=True), out_H=in0.shape[2]) for kk in range(self.L)] + else: + res = [spatial_average(diffs[kk].sum(dim=1, keepdim=True), keepdim=True) for kk in range(self.L)] + + val = res[0] + for l in range(1, self.L): + val += res[l] + + if (retPerLayer): + return (val, res) + else: + return val + + +class ScalingLayer(nn.Module): + def __init__(self): + super(ScalingLayer, self).__init__() + self.register_buffer('shift', torch.Tensor([-.030, -.088, -.188])[None, :, None, None]) + self.register_buffer('scale', torch.Tensor([.458, .448, .450])[None, :, None, None]) + + def forward(self, inp): + return (inp - self.shift) / self.scale + + +class NetLinLayer(nn.Module): + ''' A single linear layer which does a 1x1 conv ''' + + def __init__(self, chn_in, chn_out=1, use_dropout=False): + super(NetLinLayer, self).__init__() + + layers = [nn.Dropout(), ] if (use_dropout) else [] + layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ] + self.model = nn.Sequential(*layers) + + +class Dist2LogitLayer(nn.Module): + ''' takes 2 distances, puts through fc layers, spits out value between [0,1] (if use_sigmoid is True) ''' + + def __init__(self, chn_mid=32, use_sigmoid=True): + super(Dist2LogitLayer, self).__init__() + + layers = [nn.Conv2d(5, chn_mid, 1, stride=1, padding=0, bias=True), ] + layers += [nn.LeakyReLU(0.2, True), ] + layers += [nn.Conv2d(chn_mid, chn_mid, 1, stride=1, padding=0, bias=True), ] + layers += [nn.LeakyReLU(0.2, True), ] + layers += [nn.Conv2d(chn_mid, 1, 1, stride=1, padding=0, bias=True), ] + if (use_sigmoid): + layers += [nn.Sigmoid(), ] + self.model = nn.Sequential(*layers) + + def forward(self, d0, d1, eps=0.1): + return self.model(torch.cat((d0, d1, d0 - d1, d0 / (d1 + eps), d1 / (d0 + eps)), dim=1)) + + +class BCERankingLoss(nn.Module): + def __init__(self, chn_mid=32): + super(BCERankingLoss, self).__init__() + self.net = Dist2LogitLayer(chn_mid=chn_mid) + # self.parameters = list(self.net.parameters()) + self.loss = torch.nn.BCELoss() + + def forward(self, d0, d1, judge): + per = (judge + 1.) / 2. + self.logit = self.net(d0, d1) + return self.loss(self.logit, per) + + +# L2, DSSIM metrics +class FakeNet(nn.Module): + def __init__(self, use_gpu=True, colorspace='Lab'): + super(FakeNet, self).__init__() + self.use_gpu = use_gpu + self.colorspace = colorspace + + +class L2(FakeNet): + + def forward(self, in0, in1, retPerLayer=None): + assert (in0.size()[0] == 1) # currently only supports batchSize 1 + + if (self.colorspace == 'RGB'): + (N, C, X, Y) = in0.size() + value = torch.mean(torch.mean(torch.mean((in0 - in1) ** 2, dim=1).view(N, 1, X, Y), dim=2).view(N, 1, 1, Y), + dim=3).view(N) + return value + elif (self.colorspace == 'Lab'): + value = l2(tensor2np(tensor2tensorlab(in0.data, to_norm=False)), + tensor2np(tensor2tensorlab(in1.data, to_norm=False)), range=100.).astype('float') + ret_var = Variable(torch.Tensor((value,))) + # if (self.use_gpu): + # ret_var = ret_var.cuda() + return ret_var + + +class DSSIM(FakeNet): + + def forward(self, in0, in1, retPerLayer=None): + assert (in0.size()[0] == 1) # currently only supports batchSize 1 + + if (self.colorspace == 'RGB'): + value = dssim(1. * tensor2im(in0.data), 1. * tensor2im(in1.data), range=255.).astype('float') + elif (self.colorspace == 'Lab'): + value = dssim(tensor2np(tensor2tensorlab(in0.data, to_norm=False)), + tensor2np(tensor2tensorlab(in1.data, to_norm=False)), range=100.).astype('float') + ret_var = Variable(torch.Tensor((value,))) + # if (self.use_gpu): + # ret_var = ret_var.cuda() + return ret_var + + +def print_network(net): + num_params = 0 + for param in net.parameters(): + num_params += param.numel() + print('Network', net) + print('Total number of parameters: %d' % num_params) + + +############################################################ +# pretrained_networks.py # +############################################################ + +from collections import namedtuple +import torch +from torchvision import models as tv + + +class squeezenet(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True): + super(squeezenet, self).__init__() + pretrained_features = tv.squeezenet1_1(pretrained=pretrained).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + self.slice6 = torch.nn.Sequential() + self.slice7 = torch.nn.Sequential() + self.N_slices = 7 + for x in range(2): + self.slice1.add_module(str(x), pretrained_features[x]) + for x in range(2, 5): + self.slice2.add_module(str(x), pretrained_features[x]) + for x in range(5, 8): + self.slice3.add_module(str(x), pretrained_features[x]) + for x in range(8, 10): + self.slice4.add_module(str(x), pretrained_features[x]) + for x in range(10, 11): + self.slice5.add_module(str(x), pretrained_features[x]) + for x in range(11, 12): + self.slice6.add_module(str(x), pretrained_features[x]) + for x in range(12, 13): + self.slice7.add_module(str(x), pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h = self.slice1(X) + h_relu1 = h + h = self.slice2(h) + h_relu2 = h + h = self.slice3(h) + h_relu3 = h + h = self.slice4(h) + h_relu4 = h + h = self.slice5(h) + h_relu5 = h + h = self.slice6(h) + h_relu6 = h + h = self.slice7(h) + h_relu7 = h + vgg_outputs = namedtuple("SqueezeOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5', 'relu6', 'relu7']) + out = vgg_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5, h_relu6, h_relu7) + + return out + + +class alexnet(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True): + super(alexnet, self).__init__() + alexnet_pretrained_features = tv.alexnet(pretrained=pretrained).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + self.N_slices = 5 + for x in range(2): + self.slice1.add_module(str(x), alexnet_pretrained_features[x]) + for x in range(2, 5): + self.slice2.add_module(str(x), alexnet_pretrained_features[x]) + for x in range(5, 8): + self.slice3.add_module(str(x), alexnet_pretrained_features[x]) + for x in range(8, 10): + self.slice4.add_module(str(x), alexnet_pretrained_features[x]) + for x in range(10, 12): + self.slice5.add_module(str(x), alexnet_pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h = self.slice1(X) + h_relu1 = h + h = self.slice2(h) + h_relu2 = h + h = self.slice3(h) + h_relu3 = h + h = self.slice4(h) + h_relu4 = h + h = self.slice5(h) + h_relu5 = h + alexnet_outputs = namedtuple("AlexnetOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5']) + out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5) + + return out + + +class vgg16(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True): + super(vgg16, self).__init__() + vgg_pretrained_features = tv.vgg16(pretrained=pretrained).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + self.N_slices = 5 + for x in range(4): + self.slice1.add_module(str(x), vgg_pretrained_features[x]) + for x in range(4, 9): + self.slice2.add_module(str(x), vgg_pretrained_features[x]) + for x in range(9, 16): + self.slice3.add_module(str(x), vgg_pretrained_features[x]) + for x in range(16, 23): + self.slice4.add_module(str(x), vgg_pretrained_features[x]) + for x in range(23, 30): + self.slice5.add_module(str(x), vgg_pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h = self.slice1(X) + h_relu1_2 = h + h = self.slice2(h) + h_relu2_2 = h + h = self.slice3(h) + h_relu3_3 = h + h = self.slice4(h) + h_relu4_3 = h + h = self.slice5(h) + h_relu5_3 = h + vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3']) + out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3) + + return out + + +class resnet(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True, num=18): + super(resnet, self).__init__() + if (num == 18): + self.net = tv.resnet18(pretrained=pretrained) + elif (num == 34): + self.net = tv.resnet34(pretrained=pretrained) + elif (num == 50): + self.net = tv.resnet50(pretrained=pretrained) + elif (num == 101): + self.net = tv.resnet101(pretrained=pretrained) + elif (num == 152): + self.net = tv.resnet152(pretrained=pretrained) + self.N_slices = 5 + + self.conv1 = self.net.conv1 + self.bn1 = self.net.bn1 + self.relu = self.net.relu + self.maxpool = self.net.maxpool + self.layer1 = self.net.layer1 + self.layer2 = self.net.layer2 + self.layer3 = self.net.layer3 + self.layer4 = self.net.layer4 + + def forward(self, X): + h = self.conv1(X) + h = self.bn1(h) + h = self.relu(h) + h_relu1 = h + h = self.maxpool(h) + h = self.layer1(h) + h_conv2 = h + h = self.layer2(h) + h_conv3 = h + h = self.layer3(h) + h_conv4 = h + h = self.layer4(h) + h_conv5 = h + + outputs = namedtuple("Outputs", ['relu1', 'conv2', 'conv3', 'conv4', 'conv5']) + out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5) + + return out diff --git a/third-party/lama/saicinpainting/evaluation/losses/ssim.py b/third-party/lama/saicinpainting/evaluation/losses/ssim.py new file mode 100644 index 0000000000000000000000000000000000000000..ee43a0095408eca98e253dea194db788446f9c0a --- /dev/null +++ b/third-party/lama/saicinpainting/evaluation/losses/ssim.py @@ -0,0 +1,74 @@ +import numpy as np +import torch +import torch.nn.functional as F + + +class SSIM(torch.nn.Module): + """SSIM. Modified from: + https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py + """ + + def __init__(self, window_size=11, size_average=True): + super().__init__() + self.window_size = window_size + self.size_average = size_average + self.channel = 1 + self.register_buffer('window', self._create_window(window_size, self.channel)) + + def forward(self, img1, img2): + assert len(img1.shape) == 4 + + channel = img1.size()[1] + + if channel == self.channel and self.window.data.type() == img1.data.type(): + window = self.window + else: + window = self._create_window(self.window_size, channel) + + # window = window.to(img1.get_device()) + window = window.type_as(img1) + + self.window = window + self.channel = channel + + return self._ssim(img1, img2, window, self.window_size, channel, self.size_average) + + def _gaussian(self, window_size, sigma): + gauss = torch.Tensor([ + np.exp(-(x - (window_size // 2)) ** 2 / float(2 * sigma ** 2)) for x in range(window_size) + ]) + return gauss / gauss.sum() + + def _create_window(self, window_size, channel): + _1D_window = self._gaussian(window_size, 1.5).unsqueeze(1) + _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) + return _2D_window.expand(channel, 1, window_size, window_size).contiguous() + + def _ssim(self, img1, img2, window, window_size, channel, size_average=True): + mu1 = F.conv2d(img1, window, padding=(window_size // 2), groups=channel) + mu2 = F.conv2d(img2, window, padding=(window_size // 2), groups=channel) + + mu1_sq = mu1.pow(2) + mu2_sq = mu2.pow(2) + mu1_mu2 = mu1 * mu2 + + sigma1_sq = F.conv2d( + img1 * img1, window, padding=(window_size // 2), groups=channel) - mu1_sq + sigma2_sq = F.conv2d( + img2 * img2, window, padding=(window_size // 2), groups=channel) - mu2_sq + sigma12 = F.conv2d( + img1 * img2, window, padding=(window_size // 2), groups=channel) - mu1_mu2 + + C1 = 0.01 ** 2 + C2 = 0.03 ** 2 + + ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / \ + ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)) + + if size_average: + return ssim_map.mean() + + return ssim_map.mean(1).mean(1).mean(1) + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): + return diff --git a/third-party/lama/saicinpainting/evaluation/masks/README.md b/third-party/lama/saicinpainting/evaluation/masks/README.md new file mode 100644 index 0000000000000000000000000000000000000000..cf176bc10fae3b03f139727147c220f2a735c806 --- /dev/null +++ b/third-party/lama/saicinpainting/evaluation/masks/README.md @@ -0,0 +1,27 @@ +# Current algorithm + +## Choice of mask objects + +For identification of the objects which are suitable for mask obtaining, panoptic segmentation model +from [detectron2](https://github.com/facebookresearch/detectron2) trained on COCO. Categories of the detected instances +belong either to "stuff" or "things" types. We consider that instances of objects should have category belong +to "things". Besides, we set upper bound on area which is taken by the object — we consider that too big +area indicates either of the instance being a background or a main object which should not be removed. + +## Choice of position for mask + +We consider that input image has size 2^n x 2^m. We downsample it using +[COUNTLESS](https://github.com/william-silversmith/countless) algorithm so the width is equal to +64 = 2^8 = 2^{downsample_levels}. + +### Augmentation + +There are several parameters for augmentation: +- Scaling factor. We limit scaling to the case when a mask after scaling with pivot point in its center fits inside the + image completely. +- + +### Shift + + +## Select diff --git a/third-party/lama/saicinpainting/evaluation/masks/__init__.py b/third-party/lama/saicinpainting/evaluation/masks/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third-party/lama/saicinpainting/evaluation/masks/countless/.gitignore b/third-party/lama/saicinpainting/evaluation/masks/countless/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..872aa273a4e3ec99d362cefa1c67550f21f3c366 --- /dev/null +++ b/third-party/lama/saicinpainting/evaluation/masks/countless/.gitignore @@ -0,0 +1 @@ +results \ No newline at end of file diff --git a/third-party/lama/saicinpainting/evaluation/masks/countless/README.md b/third-party/lama/saicinpainting/evaluation/masks/countless/README.md new file mode 100644 index 0000000000000000000000000000000000000000..67335464d794776140fd0308f408608f2231309b --- /dev/null +++ b/third-party/lama/saicinpainting/evaluation/masks/countless/README.md @@ -0,0 +1,25 @@ +[![Build Status](https://travis-ci.org/william-silversmith/countless.svg?branch=master)](https://travis-ci.org/william-silversmith/countless) + +Python COUNTLESS Downsampling +============================= + +To install: + +`pip install -r requirements.txt` + +To test: + +`python test.py` + +To benchmark countless2d: + +`python python/countless2d.py python/images/gray_segmentation.png` + +To benchmark countless3d: + +`python python/countless3d.py` + +Adjust N and the list of algorithms inside each script to modify the run parameters. + + +Python3 is slightly faster than Python2. \ No newline at end of file diff --git a/third-party/lama/saicinpainting/evaluation/masks/countless/__init__.py b/third-party/lama/saicinpainting/evaluation/masks/countless/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third-party/lama/saicinpainting/evaluation/masks/countless/countless2d.py b/third-party/lama/saicinpainting/evaluation/masks/countless/countless2d.py new file mode 100644 index 0000000000000000000000000000000000000000..dc27b73affa20ab1a8a199542469a10aaf1f555a --- /dev/null +++ b/third-party/lama/saicinpainting/evaluation/masks/countless/countless2d.py @@ -0,0 +1,529 @@ +from __future__ import print_function, division + +""" +COUNTLESS performance test in Python. + +python countless2d.py ./images/NAMEOFIMAGE +""" + +import six +from six.moves import range +from collections import defaultdict +from functools import reduce +import operator +import io +import os +from PIL import Image +import math +import numpy as np +import random +import sys +import time +from tqdm import tqdm +from scipy import ndimage + +def simplest_countless(data): + """ + Vectorized implementation of downsampling a 2D + image by 2 on each side using the COUNTLESS algorithm. + + data is a 2D numpy array with even dimensions. + """ + sections = [] + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + factor = (2,2) + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + a, b, c, d = sections + + ab = a * (a == b) # PICK(A,B) + ac = a * (a == c) # PICK(A,C) + bc = b * (b == c) # PICK(B,C) + + a = ab | ac | bc # Bitwise OR, safe b/c non-matches are zeroed + + return a + (a == 0) * d # AB || AC || BC || D + +def quick_countless(data): + """ + Vectorized implementation of downsampling a 2D + image by 2 on each side using the COUNTLESS algorithm. + + data is a 2D numpy array with even dimensions. + """ + sections = [] + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + factor = (2,2) + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + a, b, c, d = sections + + ab_ac = a * ((a == b) | (a == c)) # PICK(A,B) || PICK(A,C) w/ optimization + bc = b * (b == c) # PICK(B,C) + + a = ab_ac | bc # (PICK(A,B) || PICK(A,C)) or PICK(B,C) + return a + (a == 0) * d # AB || AC || BC || D + +def quickest_countless(data): + """ + Vectorized implementation of downsampling a 2D + image by 2 on each side using the COUNTLESS algorithm. + + data is a 2D numpy array with even dimensions. + """ + sections = [] + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + factor = (2,2) + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + a, b, c, d = sections + + ab_ac = a * ((a == b) | (a == c)) # PICK(A,B) || PICK(A,C) w/ optimization + ab_ac |= b * (b == c) # PICK(B,C) + return ab_ac + (ab_ac == 0) * d # AB || AC || BC || D + +def quick_countless_xor(data): + """ + Vectorized implementation of downsampling a 2D + image by 2 on each side using the COUNTLESS algorithm. + + data is a 2D numpy array with even dimensions. + """ + sections = [] + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + factor = (2,2) + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + a, b, c, d = sections + + ab = a ^ (a ^ b) # a or b + ab += (ab != a) * ((ab ^ (ab ^ c)) - b) # b or c + ab += (ab == c) * ((ab ^ (ab ^ d)) - c) # c or d + return ab + +def stippled_countless(data): + """ + Vectorized implementation of downsampling a 2D + image by 2 on each side using the COUNTLESS algorithm + that treats zero as "background" and inflates lone + pixels. + + data is a 2D numpy array with even dimensions. + """ + sections = [] + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + factor = (2,2) + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + a, b, c, d = sections + + ab_ac = a * ((a == b) | (a == c)) # PICK(A,B) || PICK(A,C) w/ optimization + ab_ac |= b * (b == c) # PICK(B,C) + + nonzero = a + (a == 0) * (b + (b == 0) * c) + return ab_ac + (ab_ac == 0) * (d + (d == 0) * nonzero) # AB || AC || BC || D + +def zero_corrected_countless(data): + """ + Vectorized implementation of downsampling a 2D + image by 2 on each side using the COUNTLESS algorithm. + + data is a 2D numpy array with even dimensions. + """ + # allows us to prevent losing 1/2 a bit of information + # at the top end by using a bigger type. Without this 255 is handled incorrectly. + data, upgraded = upgrade_type(data) + + # offset from zero, raw countless doesn't handle 0 correctly + # we'll remove the extra 1 at the end. + data += 1 + + sections = [] + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + factor = (2,2) + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + a, b, c, d = sections + + ab = a * (a == b) # PICK(A,B) + ac = a * (a == c) # PICK(A,C) + bc = b * (b == c) # PICK(B,C) + + a = ab | ac | bc # Bitwise OR, safe b/c non-matches are zeroed + + result = a + (a == 0) * d - 1 # a or d - 1 + + if upgraded: + return downgrade_type(result) + + # only need to reset data if we weren't upgraded + # b/c no copy was made in that case + data -= 1 + + return result + +def countless_extreme(data): + nonzeros = np.count_nonzero(data) + # print("nonzeros", nonzeros) + + N = reduce(operator.mul, data.shape) + + if nonzeros == N: + print("quick") + return quick_countless(data) + elif np.count_nonzero(data + 1) == N: + print("quick") + # print("upper", nonzeros) + return quick_countless(data) + else: + return countless(data) + + +def countless(data): + """ + Vectorized implementation of downsampling a 2D + image by 2 on each side using the COUNTLESS algorithm. + + data is a 2D numpy array with even dimensions. + """ + # allows us to prevent losing 1/2 a bit of information + # at the top end by using a bigger type. Without this 255 is handled incorrectly. + data, upgraded = upgrade_type(data) + + # offset from zero, raw countless doesn't handle 0 correctly + # we'll remove the extra 1 at the end. + data += 1 + + sections = [] + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + factor = (2,2) + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + a, b, c, d = sections + + ab_ac = a * ((a == b) | (a == c)) # PICK(A,B) || PICK(A,C) w/ optimization + ab_ac |= b * (b == c) # PICK(B,C) + result = ab_ac + (ab_ac == 0) * d - 1 # (matches or d) - 1 + + if upgraded: + return downgrade_type(result) + + # only need to reset data if we weren't upgraded + # b/c no copy was made in that case + data -= 1 + + return result + +def upgrade_type(arr): + dtype = arr.dtype + + if dtype == np.uint8: + return arr.astype(np.uint16), True + elif dtype == np.uint16: + return arr.astype(np.uint32), True + elif dtype == np.uint32: + return arr.astype(np.uint64), True + + return arr, False + +def downgrade_type(arr): + dtype = arr.dtype + + if dtype == np.uint64: + return arr.astype(np.uint32) + elif dtype == np.uint32: + return arr.astype(np.uint16) + elif dtype == np.uint16: + return arr.astype(np.uint8) + + return arr + +def odd_to_even(image): + """ + To facilitate 2x2 downsampling segmentation, change an odd sized image into an even sized one. + Works by mirroring the starting 1 pixel edge of the image on odd shaped sides. + + e.g. turn a 3x3x5 image into a 4x4x5 (the x and y are what are getting downsampled) + + For example: [ 3, 2, 4 ] => [ 3, 3, 2, 4 ] which is now easy to downsample. + + """ + shape = np.array(image.shape) + + offset = (shape % 2)[:2] # x,y offset + + # detect if we're dealing with an even + # image. if so it's fine, just return. + if not np.any(offset): + return image + + oddshape = image.shape[:2] + offset + oddshape = np.append(oddshape, shape[2:]) + oddshape = oddshape.astype(int) + + newimg = np.empty(shape=oddshape, dtype=image.dtype) + + ox,oy = offset + sx,sy = oddshape + + newimg[0,0] = image[0,0] # corner + newimg[ox:sx,0] = image[:,0] # x axis line + newimg[0,oy:sy] = image[0,:] # y axis line + + return newimg + +def counting(array): + factor = (2, 2, 1) + shape = array.shape + + while len(shape) < 4: + array = np.expand_dims(array, axis=-1) + shape = array.shape + + output_shape = tuple(int(math.ceil(s / f)) for s, f in zip(shape, factor)) + output = np.zeros(output_shape, dtype=array.dtype) + + for chan in range(0, shape[3]): + for z in range(0, shape[2]): + for x in range(0, shape[0], 2): + for y in range(0, shape[1], 2): + block = array[ x:x+2, y:y+2, z, chan ] # 2x2 block + + hashtable = defaultdict(int) + for subx, suby in np.ndindex(block.shape[0], block.shape[1]): + hashtable[block[subx, suby]] += 1 + + best = (0, 0) + for segid, val in six.iteritems(hashtable): + if best[1] < val: + best = (segid, val) + + output[ x // 2, y // 2, chan ] = best[0] + + return output + +def ndzoom(array): + if len(array.shape) == 3: + ratio = ( 1 / 2.0, 1 / 2.0, 1.0 ) + else: + ratio = ( 1 / 2.0, 1 / 2.0) + return ndimage.interpolation.zoom(array, ratio, order=1) + +def countless_if(array): + factor = (2, 2, 1) + shape = array.shape + + if len(shape) < 3: + array = array[ :,:, np.newaxis ] + shape = array.shape + + output_shape = tuple(int(math.ceil(s / f)) for s, f in zip(shape, factor)) + output = np.zeros(output_shape, dtype=array.dtype) + + for chan in range(0, shape[2]): + for x in range(0, shape[0], 2): + for y in range(0, shape[1], 2): + block = array[ x:x+2, y:y+2, chan ] # 2x2 block + + if block[0,0] == block[1,0]: + pick = block[0,0] + elif block[0,0] == block[0,1]: + pick = block[0,0] + elif block[1,0] == block[0,1]: + pick = block[1,0] + else: + pick = block[1,1] + + output[ x // 2, y // 2, chan ] = pick + + return np.squeeze(output) + +def downsample_with_averaging(array): + """ + Downsample x by factor using averaging. + + @return: The downsampled array, of the same type as x. + """ + + if len(array.shape) == 3: + factor = (2,2,1) + else: + factor = (2,2) + + if np.array_equal(factor[:3], np.array([1,1,1])): + return array + + output_shape = tuple(int(math.ceil(s / f)) for s, f in zip(array.shape, factor)) + temp = np.zeros(output_shape, float) + counts = np.zeros(output_shape, np.int) + for offset in np.ndindex(factor): + part = array[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + indexing_expr = tuple(np.s_[:s] for s in part.shape) + temp[indexing_expr] += part + counts[indexing_expr] += 1 + return np.cast[array.dtype](temp / counts) + +def downsample_with_max_pooling(array): + + factor = (2,2) + + if np.all(np.array(factor, int) == 1): + return array + + sections = [] + + for offset in np.ndindex(factor): + part = array[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + output = sections[0].copy() + + for section in sections[1:]: + np.maximum(output, section, output) + + return output + +def striding(array): + """Downsample x by factor using striding. + + @return: The downsampled array, of the same type as x. + """ + factor = (2,2) + if np.all(np.array(factor, int) == 1): + return array + return array[tuple(np.s_[::f] for f in factor)] + +def benchmark(): + filename = sys.argv[1] + img = Image.open(filename) + data = np.array(img.getdata(), dtype=np.uint8) + + if len(data.shape) == 1: + n_channels = 1 + reshape = (img.height, img.width) + else: + n_channels = min(data.shape[1], 3) + data = data[:, :n_channels] + reshape = (img.height, img.width, n_channels) + + data = data.reshape(reshape).astype(np.uint8) + + methods = [ + simplest_countless, + quick_countless, + quick_countless_xor, + quickest_countless, + stippled_countless, + zero_corrected_countless, + countless, + downsample_with_averaging, + downsample_with_max_pooling, + ndzoom, + striding, + # countless_if, + # counting, + ] + + formats = { + 1: 'L', + 3: 'RGB', + 4: 'RGBA' + } + + if not os.path.exists('./results'): + os.mkdir('./results') + + N = 500 + img_size = float(img.width * img.height) / 1024.0 / 1024.0 + print("N = %d, %dx%d (%.2f MPx) %d chan, %s" % (N, img.width, img.height, img_size, n_channels, filename)) + print("Algorithm\tMPx/sec\tMB/sec\tSec") + for fn in methods: + print(fn.__name__, end='') + sys.stdout.flush() + + start = time.time() + # tqdm is here to show you what's going on the first time you run it. + # Feel free to remove it to get slightly more accurate timing results. + for _ in tqdm(range(N), desc=fn.__name__, disable=True): + result = fn(data) + end = time.time() + print("\r", end='') + + total_time = (end - start) + mpx = N * img_size / total_time + mbytes = N * img_size * n_channels / total_time + # Output in tab separated format to enable copy-paste into excel/numbers + print("%s\t%.3f\t%.3f\t%.2f" % (fn.__name__, mpx, mbytes, total_time)) + outimg = Image.fromarray(np.squeeze(result), formats[n_channels]) + outimg.save('./results/{}.png'.format(fn.__name__, "PNG")) + +if __name__ == '__main__': + benchmark() + + +# Example results: +# N = 5, 1024x1024 (1.00 MPx) 1 chan, images/gray_segmentation.png +# Function MPx/sec MB/sec Sec +# simplest_countless 752.855 752.855 0.01 +# quick_countless 920.328 920.328 0.01 +# zero_corrected_countless 534.143 534.143 0.01 +# countless 644.247 644.247 0.01 +# downsample_with_averaging 372.575 372.575 0.01 +# downsample_with_max_pooling 974.060 974.060 0.01 +# ndzoom 137.517 137.517 0.04 +# striding 38550.588 38550.588 0.00 +# countless_if 4.377 4.377 1.14 +# counting 0.117 0.117 42.85 + +# Run without non-numpy implementations: +# N = 2000, 1024x1024 (1.00 MPx) 1 chan, images/gray_segmentation.png +# Algorithm MPx/sec MB/sec Sec +# simplest_countless 800.522 800.522 2.50 +# quick_countless 945.420 945.420 2.12 +# quickest_countless 947.256 947.256 2.11 +# stippled_countless 544.049 544.049 3.68 +# zero_corrected_countless 575.310 575.310 3.48 +# countless 646.684 646.684 3.09 +# downsample_with_averaging 385.132 385.132 5.19 +# downsample_with_max_poolin 988.361 988.361 2.02 +# ndzoom 163.104 163.104 12.26 +# striding 81589.340 81589.340 0.02 + + + + diff --git a/third-party/lama/saicinpainting/evaluation/masks/countless/countless3d.py b/third-party/lama/saicinpainting/evaluation/masks/countless/countless3d.py new file mode 100644 index 0000000000000000000000000000000000000000..810a71e4b1fa344dd2d731186516dbfa96c9cd03 --- /dev/null +++ b/third-party/lama/saicinpainting/evaluation/masks/countless/countless3d.py @@ -0,0 +1,356 @@ +from six.moves import range +from PIL import Image +import numpy as np +import io +import time +import math +import random +import sys +from collections import defaultdict +from copy import deepcopy +from itertools import combinations +from functools import reduce +from tqdm import tqdm + +from memory_profiler import profile + +def countless5(a,b,c,d,e): + """First stage of generalizing from countless2d. + + You have five slots: A, B, C, D, E + + You can decide if something is the winner by first checking for + matches of three, then matches of two, then picking just one if + the other two tries fail. In countless2d, you just check for matches + of two and then pick one of them otherwise. + + Unfortunately, you need to check ABC, ABD, ABE, BCD, BDE, & CDE. + Then you need to check AB, AC, AD, BC, BD + We skip checking E because if none of these match, we pick E. We can + skip checking AE, BE, CE, DE since if any of those match, E is our boy + so it's redundant. + + So countless grows cominatorially in complexity. + """ + sections = [ a,b,c,d,e ] + + p2 = lambda q,r: q * (q == r) # q if p == q else 0 + p3 = lambda q,r,s: q * ( (q == r) & (r == s) ) # q if q == r == s else 0 + + lor = lambda x,y: x + (x == 0) * y + + results3 = ( p3(x,y,z) for x,y,z in combinations(sections, 3) ) + results3 = reduce(lor, results3) + + results2 = ( p2(x,y) for x,y in combinations(sections[:-1], 2) ) + results2 = reduce(lor, results2) + + return reduce(lor, (results3, results2, e)) + +def countless8(a,b,c,d,e,f,g,h): + """Extend countless5 to countless8. Same deal, except we also + need to check for matches of length 4.""" + sections = [ a, b, c, d, e, f, g, h ] + + p2 = lambda q,r: q * (q == r) + p3 = lambda q,r,s: q * ( (q == r) & (r == s) ) + p4 = lambda p,q,r,s: p * ( (p == q) & (q == r) & (r == s) ) + + lor = lambda x,y: x + (x == 0) * y + + results4 = ( p4(x,y,z,w) for x,y,z,w in combinations(sections, 4) ) + results4 = reduce(lor, results4) + + results3 = ( p3(x,y,z) for x,y,z in combinations(sections, 3) ) + results3 = reduce(lor, results3) + + # We can always use our shortcut of omitting the last element + # for N choose 2 + results2 = ( p2(x,y) for x,y in combinations(sections[:-1], 2) ) + results2 = reduce(lor, results2) + + return reduce(lor, [ results4, results3, results2, h ]) + +def dynamic_countless3d(data): + """countless8 + dynamic programming. ~2x faster""" + sections = [] + + # shift zeros up one so they don't interfere with bitwise operators + # we'll shift down at the end + data += 1 + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + factor = (2,2,2) + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + pick = lambda a,b: a * (a == b) + lor = lambda x,y: x + (x == 0) * y + + subproblems2 = {} + + results2 = None + for x,y in combinations(range(7), 2): + res = pick(sections[x], sections[y]) + subproblems2[(x,y)] = res + if results2 is not None: + results2 += (results2 == 0) * res + else: + results2 = res + + subproblems3 = {} + + results3 = None + for x,y,z in combinations(range(8), 3): + res = pick(subproblems2[(x,y)], sections[z]) + + if z != 7: + subproblems3[(x,y,z)] = res + + if results3 is not None: + results3 += (results3 == 0) * res + else: + results3 = res + + results3 = reduce(lor, (results3, results2, sections[-1])) + + # free memory + results2 = None + subproblems2 = None + res = None + + results4 = ( pick(subproblems3[(x,y,z)], sections[w]) for x,y,z,w in combinations(range(8), 4) ) + results4 = reduce(lor, results4) + subproblems3 = None # free memory + + final_result = lor(results4, results3) - 1 + data -= 1 + return final_result + +def countless3d(data): + """Now write countless8 in such a way that it could be used + to process an image.""" + sections = [] + + # shift zeros up one so they don't interfere with bitwise operators + # we'll shift down at the end + data += 1 + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + factor = (2,2,2) + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + p2 = lambda q,r: q * (q == r) + p3 = lambda q,r,s: q * ( (q == r) & (r == s) ) + p4 = lambda p,q,r,s: p * ( (p == q) & (q == r) & (r == s) ) + + lor = lambda x,y: x + (x == 0) * y + + results4 = ( p4(x,y,z,w) for x,y,z,w in combinations(sections, 4) ) + results4 = reduce(lor, results4) + + results3 = ( p3(x,y,z) for x,y,z in combinations(sections, 3) ) + results3 = reduce(lor, results3) + + results2 = ( p2(x,y) for x,y in combinations(sections[:-1], 2) ) + results2 = reduce(lor, results2) + + final_result = reduce(lor, (results4, results3, results2, sections[-1])) - 1 + data -= 1 + return final_result + +def countless_generalized(data, factor): + assert len(data.shape) == len(factor) + + sections = [] + + mode_of = reduce(lambda x,y: x * y, factor) + majority = int(math.ceil(float(mode_of) / 2)) + + data += 1 + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + def pick(elements): + eq = ( elements[i] == elements[i+1] for i in range(len(elements) - 1) ) + anded = reduce(lambda p,q: p & q, eq) + return elements[0] * anded + + def logical_or(x,y): + return x + (x == 0) * y + + result = ( pick(combo) for combo in combinations(sections, majority) ) + result = reduce(logical_or, result) + for i in range(majority - 1, 3-1, -1): # 3-1 b/c of exclusive bounds + partial_result = ( pick(combo) for combo in combinations(sections, i) ) + partial_result = reduce(logical_or, partial_result) + result = logical_or(result, partial_result) + + partial_result = ( pick(combo) for combo in combinations(sections[:-1], 2) ) + partial_result = reduce(logical_or, partial_result) + result = logical_or(result, partial_result) + + result = logical_or(result, sections[-1]) - 1 + data -= 1 + return result + +def dynamic_countless_generalized(data, factor): + assert len(data.shape) == len(factor) + + sections = [] + + mode_of = reduce(lambda x,y: x * y, factor) + majority = int(math.ceil(float(mode_of) / 2)) + + data += 1 # offset from zero + + # This loop splits the 2D array apart into four arrays that are + # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), + # and (1,1) representing the A, B, C, and D positions from Figure 1. + for offset in np.ndindex(factor): + part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + pick = lambda a,b: a * (a == b) + lor = lambda x,y: x + (x == 0) * y # logical or + + subproblems = [ {}, {} ] + results2 = None + for x,y in combinations(range(len(sections) - 1), 2): + res = pick(sections[x], sections[y]) + subproblems[0][(x,y)] = res + if results2 is not None: + results2 = lor(results2, res) + else: + results2 = res + + results = [ results2 ] + for r in range(3, majority+1): + r_results = None + for combo in combinations(range(len(sections)), r): + res = pick(subproblems[0][combo[:-1]], sections[combo[-1]]) + + if combo[-1] != len(sections) - 1: + subproblems[1][combo] = res + + if r_results is not None: + r_results = lor(r_results, res) + else: + r_results = res + results.append(r_results) + subproblems[0] = subproblems[1] + subproblems[1] = {} + + results.reverse() + final_result = lor(reduce(lor, results), sections[-1]) - 1 + data -= 1 + return final_result + +def downsample_with_averaging(array): + """ + Downsample x by factor using averaging. + + @return: The downsampled array, of the same type as x. + """ + factor = (2,2,2) + + if np.array_equal(factor[:3], np.array([1,1,1])): + return array + + output_shape = tuple(int(math.ceil(s / f)) for s, f in zip(array.shape, factor)) + temp = np.zeros(output_shape, float) + counts = np.zeros(output_shape, np.int) + for offset in np.ndindex(factor): + part = array[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + indexing_expr = tuple(np.s_[:s] for s in part.shape) + temp[indexing_expr] += part + counts[indexing_expr] += 1 + return np.cast[array.dtype](temp / counts) + +def downsample_with_max_pooling(array): + + factor = (2,2,2) + + sections = [] + + for offset in np.ndindex(factor): + part = array[tuple(np.s_[o::f] for o, f in zip(offset, factor))] + sections.append(part) + + output = sections[0].copy() + + for section in sections[1:]: + np.maximum(output, section, output) + + return output + +def striding(array): + """Downsample x by factor using striding. + + @return: The downsampled array, of the same type as x. + """ + factor = (2,2,2) + if np.all(np.array(factor, int) == 1): + return array + return array[tuple(np.s_[::f] for f in factor)] + +def benchmark(): + def countless3d_generalized(img): + return countless_generalized(img, (2,8,1)) + def countless3d_dynamic_generalized(img): + return dynamic_countless_generalized(img, (8,8,1)) + + methods = [ + # countless3d, + # dynamic_countless3d, + countless3d_generalized, + # countless3d_dynamic_generalized, + # striding, + # downsample_with_averaging, + # downsample_with_max_pooling + ] + + data = np.zeros(shape=(16**2, 16**2, 16**2), dtype=np.uint8) + 1 + + N = 5 + + print('Algorithm\tMPx\tMB/sec\tSec\tN=%d' % N) + + for fn in methods: + start = time.time() + for _ in range(N): + result = fn(data) + end = time.time() + + total_time = (end - start) + mpx = N * float(data.shape[0] * data.shape[1] * data.shape[2]) / total_time / 1024.0 / 1024.0 + mbytes = mpx * np.dtype(data.dtype).itemsize + # Output in tab separated format to enable copy-paste into excel/numbers + print("%s\t%.3f\t%.3f\t%.2f" % (fn.__name__, mpx, mbytes, total_time)) + +if __name__ == '__main__': + benchmark() + +# Algorithm MPx MB/sec Sec N=5 +# countless3d 10.564 10.564 60.58 +# dynamic_countless3d 22.717 22.717 28.17 +# countless3d_generalized 9.702 9.702 65.96 +# countless3d_dynamic_generalized 22.720 22.720 28.17 +# striding 253360.506 253360.506 0.00 +# downsample_with_averaging 224.098 224.098 2.86 +# downsample_with_max_pooling 690.474 690.474 0.93 + + + diff 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new file mode 100644 index 0000000000000000000000000000000000000000..cbf8c87bf9b4c9fe54cb39d722253c0ab59e63ad --- /dev/null +++ b/third-party/lama/saicinpainting/evaluation/masks/countless/requirements.txt @@ -0,0 +1,7 @@ +Pillow>=6.2.0 +numpy>=1.16 +scipy +tqdm +memory_profiler +six +pytest \ No newline at end of file diff --git a/third-party/lama/saicinpainting/evaluation/masks/countless/test.py b/third-party/lama/saicinpainting/evaluation/masks/countless/test.py new file mode 100644 index 0000000000000000000000000000000000000000..7809beb7aeeb3bcb10d03093a564917b1f2b4786 --- /dev/null +++ b/third-party/lama/saicinpainting/evaluation/masks/countless/test.py @@ -0,0 +1,195 @@ +from copy import deepcopy + +import numpy as np + +import countless2d +import countless3d + +def test_countless2d(): + def test_all_cases(fn, test_zero): + case1 = np.array([ [ 1, 2 ], [ 3, 4 ] ]).reshape((2,2,1,1)) # all different + case2 = np.array([ [ 1, 1 ], [ 2, 3 ] ]).reshape((2,2,1,1)) # two are same + case1z = np.array([ [ 0, 1 ], [ 2, 3 ] ]).reshape((2,2,1,1)) # all different + case2z = np.array([ [ 0, 0 ], [ 2, 3 ] ]).reshape((2,2,1,1)) # two are same + case3 = np.array([ [ 1, 1 ], [ 2, 2 ] ]).reshape((2,2,1,1)) # two groups are same + case4 = np.array([ [ 1, 2 ], [ 2, 2 ] ]).reshape((2,2,1,1)) # 3 are the same + case5 = np.array([ [ 5, 5 ], [ 5, 5 ] ]).reshape((2,2,1,1)) # all are the same + + is_255_handled = np.array([ [ 255, 255 ], [ 1, 2 ] ], dtype=np.uint8).reshape((2,2,1,1)) + + test = lambda case: fn(case) + + if test_zero: + assert test(case1z) == [[[[3]]]] # d + assert test(case2z) == [[[[0]]]] # a==b + else: + assert test(case1) == [[[[4]]]] # d + assert test(case2) == [[[[1]]]] # a==b + + assert test(case3) == [[[[1]]]] # a==b + assert test(case4) == [[[[2]]]] # b==c + assert test(case5) == [[[[5]]]] # a==b + + assert test(is_255_handled) == [[[[255]]]] + + assert fn(case1).dtype == case1.dtype + + test_all_cases(countless2d.simplest_countless, False) + test_all_cases(countless2d.quick_countless, False) + test_all_cases(countless2d.quickest_countless, False) + test_all_cases(countless2d.stippled_countless, False) + + + + methods = [ + countless2d.zero_corrected_countless, + countless2d.countless, + countless2d.countless_if, + # countless2d.counting, # counting doesn't respect order so harder to write a test + ] + + for fn in methods: + print(fn.__name__) + test_all_cases(fn, True) + +def test_stippled_countless2d(): + a = np.array([ [ 1, 2 ], [ 3, 4 ] ]).reshape((2,2,1,1)) + b = np.array([ [ 0, 2 ], [ 3, 4 ] ]).reshape((2,2,1,1)) + c = np.array([ [ 1, 0 ], [ 3, 4 ] ]).reshape((2,2,1,1)) + d = np.array([ [ 1, 2 ], [ 0, 4 ] ]).reshape((2,2,1,1)) + e = np.array([ [ 1, 2 ], [ 3, 0 ] ]).reshape((2,2,1,1)) + f = np.array([ [ 0, 0 ], [ 3, 4 ] ]).reshape((2,2,1,1)) + g = np.array([ [ 0, 2 ], [ 0, 4 ] ]).reshape((2,2,1,1)) + h = np.array([ [ 0, 2 ], [ 3, 0 ] ]).reshape((2,2,1,1)) + i = np.array([ [ 1, 0 ], [ 0, 4 ] ]).reshape((2,2,1,1)) + j = np.array([ [ 1, 2 ], [ 0, 0 ] ]).reshape((2,2,1,1)) + k = np.array([ [ 1, 0 ], [ 3, 0 ] ]).reshape((2,2,1,1)) + l = np.array([ [ 1, 0 ], [ 0, 0 ] ]).reshape((2,2,1,1)) + m = np.array([ [ 0, 2 ], [ 0, 0 ] ]).reshape((2,2,1,1)) + n = np.array([ [ 0, 0 ], [ 3, 0 ] ]).reshape((2,2,1,1)) + o = np.array([ [ 0, 0 ], [ 0, 4 ] ]).reshape((2,2,1,1)) + z = np.array([ [ 0, 0 ], [ 0, 0 ] ]).reshape((2,2,1,1)) + + test = countless2d.stippled_countless + + # Note: We only tested non-matching cases above, + # cases f,g,h,i,j,k prove their duals work as well + # b/c if two pixels are black, either one can be chosen + # if they are different or the same. + + assert test(a) == [[[[4]]]] + assert test(b) == [[[[4]]]] + assert test(c) == [[[[4]]]] + assert test(d) == [[[[4]]]] + assert test(e) == [[[[1]]]] + assert test(f) == [[[[4]]]] + assert test(g) == [[[[4]]]] + assert test(h) == [[[[2]]]] + assert test(i) == [[[[4]]]] + assert test(j) == [[[[1]]]] + assert test(k) == [[[[1]]]] + assert test(l) == [[[[1]]]] + assert test(m) == [[[[2]]]] + assert test(n) == [[[[3]]]] + assert test(o) == [[[[4]]]] + assert test(z) == [[[[0]]]] + + bc = np.array([ [ 0, 2 ], [ 2, 4 ] ]).reshape((2,2,1,1)) + bd = np.array([ [ 0, 2 ], [ 3, 2 ] ]).reshape((2,2,1,1)) + cd = np.array([ [ 0, 2 ], [ 3, 3 ] ]).reshape((2,2,1,1)) + + assert test(bc) == [[[[2]]]] + assert test(bd) == [[[[2]]]] + assert test(cd) == [[[[3]]]] + + ab = np.array([ [ 1, 1 ], [ 0, 4 ] ]).reshape((2,2,1,1)) + ac = np.array([ [ 1, 2 ], [ 1, 0 ] ]).reshape((2,2,1,1)) + ad = np.array([ [ 1, 0 ], [ 3, 1 ] ]).reshape((2,2,1,1)) + + assert test(ab) == [[[[1]]]] + assert test(ac) == [[[[1]]]] + assert test(ad) == [[[[1]]]] + +def test_countless3d(): + def test_all_cases(fn): + alldifferent = [ + [ + [1,2], + [3,4], + ], + [ + [5,6], + [7,8] + ] + ] + allsame = [ + [ + [1,1], + [1,1], + ], + [ + [1,1], + [1,1] + ] + ] + + assert fn(np.array(alldifferent)) == [[[8]]] + assert fn(np.array(allsame)) == [[[1]]] + + twosame = deepcopy(alldifferent) + twosame[1][1][0] = 2 + + assert fn(np.array(twosame)) == [[[2]]] + + threemixed = [ + [ + [3,3], + [1,2], + ], + [ + [2,4], + [4,3] + ] + ] + assert fn(np.array(threemixed)) == [[[3]]] + + foursame = [ + [ + [4,4], + [1,2], + ], + [ + [2,4], + [4,3] + ] + ] + + assert fn(np.array(foursame)) == [[[4]]] + + fivesame = [ + [ + [5,4], + [5,5], + ], + [ + [2,4], + [5,5] + ] + ] + + assert fn(np.array(fivesame)) == [[[5]]] + + def countless3d_generalized(img): + return countless3d.countless_generalized(img, (2,2,2)) + def countless3d_dynamic_generalized(img): + return countless3d.dynamic_countless_generalized(img, (2,2,2)) + + methods = [ + countless3d.countless3d, + countless3d.dynamic_countless3d, + countless3d_generalized, + countless3d_dynamic_generalized, + ] + + for fn in methods: + test_all_cases(fn) \ No newline at end of file diff --git a/third-party/lama/saicinpainting/evaluation/masks/mask.py b/third-party/lama/saicinpainting/evaluation/masks/mask.py new file mode 100644 index 0000000000000000000000000000000000000000..3e34d0675a781fba983cb542f18390255aaf2609 --- /dev/null +++ b/third-party/lama/saicinpainting/evaluation/masks/mask.py @@ -0,0 +1,429 @@ +import enum +from copy import deepcopy + +import numpy as np +from skimage import img_as_ubyte +from skimage.transform import rescale, resize +try: + from detectron2 import model_zoo + from detectron2.config import get_cfg + from detectron2.engine import DefaultPredictor + DETECTRON_INSTALLED = True +except: + print("Detectron v2 is not installed") + DETECTRON_INSTALLED = False + +from .countless.countless2d import zero_corrected_countless + + +class ObjectMask(): + def __init__(self, mask): + self.height, self.width = mask.shape + (self.up, self.down), (self.left, self.right) = self._get_limits(mask) + self.mask = mask[self.up:self.down, self.left:self.right].copy() + + @staticmethod + def _get_limits(mask): + def indicator_limits(indicator): + lower = indicator.argmax() + upper = len(indicator) - indicator[::-1].argmax() + return lower, upper + + vertical_indicator = mask.any(axis=1) + vertical_limits = indicator_limits(vertical_indicator) + + horizontal_indicator = mask.any(axis=0) + horizontal_limits = indicator_limits(horizontal_indicator) + + return vertical_limits, horizontal_limits + + def _clean(self): + self.up, self.down, self.left, self.right = 0, 0, 0, 0 + self.mask = np.empty((0, 0)) + + def horizontal_flip(self, inplace=False): + if not inplace: + flipped = deepcopy(self) + return flipped.horizontal_flip(inplace=True) + + self.mask = self.mask[:, ::-1] + return self + + def vertical_flip(self, inplace=False): + if not inplace: + flipped = deepcopy(self) + return flipped.vertical_flip(inplace=True) + + self.mask = self.mask[::-1, :] + return self + + def image_center(self): + y_center = self.up + (self.down - self.up) / 2 + x_center = self.left + (self.right - self.left) / 2 + return y_center, x_center + + def rescale(self, scaling_factor, inplace=False): + if not inplace: + scaled = deepcopy(self) + return scaled.rescale(scaling_factor, inplace=True) + + scaled_mask = rescale(self.mask.astype(float), scaling_factor, order=0) > 0.5 + (up, down), (left, right) = self._get_limits(scaled_mask) + self.mask = scaled_mask[up:down, left:right] + + y_center, x_center = self.image_center() + mask_height, mask_width = self.mask.shape + self.up = int(round(y_center - mask_height / 2)) + self.down = self.up + mask_height + self.left = int(round(x_center - mask_width / 2)) + self.right = self.left + mask_width + return self + + def crop_to_canvas(self, vertical=True, horizontal=True, inplace=False): + if not inplace: + cropped = deepcopy(self) + cropped.crop_to_canvas(vertical=vertical, horizontal=horizontal, inplace=True) + return cropped + + if vertical: + if self.up >= self.height or self.down <= 0: + self._clean() + else: + cut_up, cut_down = max(-self.up, 0), max(self.down - self.height, 0) + if cut_up != 0: + self.mask = self.mask[cut_up:] + self.up = 0 + if cut_down != 0: + self.mask = self.mask[:-cut_down] + self.down = self.height + + if horizontal: + if self.left >= self.width or self.right <= 0: + self._clean() + else: + cut_left, cut_right = max(-self.left, 0), max(self.right - self.width, 0) + if cut_left != 0: + self.mask = self.mask[:, cut_left:] + self.left = 0 + if cut_right != 0: + self.mask = self.mask[:, :-cut_right] + self.right = self.width + + return self + + def restore_full_mask(self, allow_crop=False): + cropped = self.crop_to_canvas(inplace=allow_crop) + mask = np.zeros((cropped.height, cropped.width), dtype=bool) + mask[cropped.up:cropped.down, cropped.left:cropped.right] = cropped.mask + return mask + + def shift(self, vertical=0, horizontal=0, inplace=False): + if not inplace: + shifted = deepcopy(self) + return shifted.shift(vertical=vertical, horizontal=horizontal, inplace=True) + + self.up += vertical + self.down += vertical + self.left += horizontal + self.right += horizontal + return self + + def area(self): + return self.mask.sum() + + +class RigidnessMode(enum.Enum): + soft = 0 + rigid = 1 + + +class SegmentationMask: + def __init__(self, confidence_threshold=0.5, rigidness_mode=RigidnessMode.rigid, + max_object_area=0.3, min_mask_area=0.02, downsample_levels=6, num_variants_per_mask=4, + max_mask_intersection=0.5, max_foreground_coverage=0.5, max_foreground_intersection=0.5, + max_hidden_area=0.2, max_scale_change=0.25, horizontal_flip=True, + max_vertical_shift=0.1, position_shuffle=True): + """ + :param confidence_threshold: float; threshold for confidence of the panoptic segmentator to allow for + the instance. + :param rigidness_mode: RigidnessMode object + when soft, checks intersection only with the object from which the mask_object was produced + when rigid, checks intersection with any foreground class object + :param max_object_area: float; allowed upper bound for to be considered as mask_object. + :param min_mask_area: float; lower bound for mask to be considered valid + :param downsample_levels: int; defines width of the resized segmentation to obtain shifted masks; + :param num_variants_per_mask: int; maximal number of the masks for the same object; + :param max_mask_intersection: float; maximum allowed area fraction of intersection for 2 masks + produced by horizontal shift of the same mask_object; higher value -> more diversity + :param max_foreground_coverage: float; maximum allowed area fraction of intersection for foreground object to be + covered by mask; lower value -> less the objects are covered + :param max_foreground_intersection: float; maximum allowed area of intersection for the mask with foreground + object; lower value -> mask is more on the background than on the objects + :param max_hidden_area: upper bound on part of the object hidden by shifting object outside the screen area; + :param max_scale_change: allowed scale change for the mask_object; + :param horizontal_flip: if horizontal flips are allowed; + :param max_vertical_shift: amount of vertical movement allowed; + :param position_shuffle: shuffle + """ + + assert DETECTRON_INSTALLED, 'Cannot use SegmentationMask without detectron2' + self.cfg = get_cfg() + self.cfg.merge_from_file(model_zoo.get_config_file("COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml")) + self.cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml") + self.cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = confidence_threshold + self.predictor = DefaultPredictor(self.cfg) + + self.rigidness_mode = RigidnessMode(rigidness_mode) + self.max_object_area = max_object_area + self.min_mask_area = min_mask_area + self.downsample_levels = downsample_levels + self.num_variants_per_mask = num_variants_per_mask + self.max_mask_intersection = max_mask_intersection + self.max_foreground_coverage = max_foreground_coverage + self.max_foreground_intersection = max_foreground_intersection + self.max_hidden_area = max_hidden_area + self.position_shuffle = position_shuffle + + self.max_scale_change = max_scale_change + self.horizontal_flip = horizontal_flip + self.max_vertical_shift = max_vertical_shift + + def get_segmentation(self, img): + im = img_as_ubyte(img) + panoptic_seg, segment_info = self.predictor(im)["panoptic_seg"] + return panoptic_seg, segment_info + + @staticmethod + def _is_power_of_two(n): + return (n != 0) and (n & (n-1) == 0) + + def identify_candidates(self, panoptic_seg, segments_info): + potential_mask_ids = [] + for segment in segments_info: + if not segment["isthing"]: + continue + mask = (panoptic_seg == segment["id"]).int().detach().cpu().numpy() + area = mask.sum().item() / np.prod(panoptic_seg.shape) + if area >= self.max_object_area: + continue + potential_mask_ids.append(segment["id"]) + return potential_mask_ids + + def downsample_mask(self, mask): + height, width = mask.shape + if not (self._is_power_of_two(height) and self._is_power_of_two(width)): + raise ValueError("Image sides are not power of 2.") + + num_iterations = width.bit_length() - 1 - self.downsample_levels + if num_iterations < 0: + raise ValueError(f"Width is lower than 2^{self.downsample_levels}.") + + if height.bit_length() - 1 < num_iterations: + raise ValueError("Height is too low to perform downsampling") + + downsampled = mask + for _ in range(num_iterations): + downsampled = zero_corrected_countless(downsampled) + + return downsampled + + def _augmentation_params(self): + scaling_factor = np.random.uniform(1 - self.max_scale_change, 1 + self.max_scale_change) + if self.horizontal_flip: + horizontal_flip = bool(np.random.choice(2)) + else: + horizontal_flip = False + vertical_shift = np.random.uniform(-self.max_vertical_shift, self.max_vertical_shift) + + return { + "scaling_factor": scaling_factor, + "horizontal_flip": horizontal_flip, + "vertical_shift": vertical_shift + } + + def _get_intersection(self, mask_array, mask_object): + intersection = mask_array[ + mask_object.up:mask_object.down, mask_object.left:mask_object.right + ] & mask_object.mask + return intersection + + def _check_masks_intersection(self, aug_mask, total_mask_area, prev_masks): + for existing_mask in prev_masks: + intersection_area = self._get_intersection(existing_mask, aug_mask).sum() + intersection_existing = intersection_area / existing_mask.sum() + intersection_current = 1 - (aug_mask.area() - intersection_area) / total_mask_area + if (intersection_existing > self.max_mask_intersection) or \ + (intersection_current > self.max_mask_intersection): + return False + return True + + def _check_foreground_intersection(self, aug_mask, foreground): + for existing_mask in foreground: + intersection_area = self._get_intersection(existing_mask, aug_mask).sum() + intersection_existing = intersection_area / existing_mask.sum() + if intersection_existing > self.max_foreground_coverage: + return False + intersection_mask = intersection_area / aug_mask.area() + if intersection_mask > self.max_foreground_intersection: + return False + return True + + def _move_mask(self, mask, foreground): + # Obtaining properties of the original mask_object: + orig_mask = ObjectMask(mask) + + chosen_masks = [] + chosen_parameters = [] + # to fix the case when resizing gives mask_object consisting only of False + scaling_factor_lower_bound = 0. + + for var_idx in range(self.num_variants_per_mask): + # Obtaining augmentation parameters and applying them to the downscaled mask_object + augmentation_params = self._augmentation_params() + augmentation_params["scaling_factor"] = min([ + augmentation_params["scaling_factor"], + 2 * min(orig_mask.up, orig_mask.height - orig_mask.down) / orig_mask.height + 1., + 2 * min(orig_mask.left, orig_mask.width - orig_mask.right) / orig_mask.width + 1. + ]) + augmentation_params["scaling_factor"] = max([ + augmentation_params["scaling_factor"], scaling_factor_lower_bound + ]) + + aug_mask = deepcopy(orig_mask) + aug_mask.rescale(augmentation_params["scaling_factor"], inplace=True) + if augmentation_params["horizontal_flip"]: + aug_mask.horizontal_flip(inplace=True) + total_aug_area = aug_mask.area() + if total_aug_area == 0: + scaling_factor_lower_bound = 1. + continue + + # Fix if the element vertical shift is too strong and shown area is too small: + vertical_area = aug_mask.mask.sum(axis=1) / total_aug_area # share of area taken by rows + # number of rows which are allowed to be hidden from upper and lower parts of image respectively + max_hidden_up = np.searchsorted(vertical_area.cumsum(), self.max_hidden_area) + max_hidden_down = np.searchsorted(vertical_area[::-1].cumsum(), self.max_hidden_area) + # correcting vertical shift, so not too much area will be hidden + augmentation_params["vertical_shift"] = np.clip( + augmentation_params["vertical_shift"], + -(aug_mask.up + max_hidden_up) / aug_mask.height, + (aug_mask.height - aug_mask.down + max_hidden_down) / aug_mask.height + ) + # Applying vertical shift: + vertical_shift = int(round(aug_mask.height * augmentation_params["vertical_shift"])) + aug_mask.shift(vertical=vertical_shift, inplace=True) + aug_mask.crop_to_canvas(vertical=True, horizontal=False, inplace=True) + + # Choosing horizontal shift: + max_hidden_area = self.max_hidden_area - (1 - aug_mask.area() / total_aug_area) + horizontal_area = aug_mask.mask.sum(axis=0) / total_aug_area + max_hidden_left = np.searchsorted(horizontal_area.cumsum(), max_hidden_area) + max_hidden_right = np.searchsorted(horizontal_area[::-1].cumsum(), max_hidden_area) + allowed_shifts = np.arange(-max_hidden_left, aug_mask.width - + (aug_mask.right - aug_mask.left) + max_hidden_right + 1) + allowed_shifts = - (aug_mask.left - allowed_shifts) + + if self.position_shuffle: + np.random.shuffle(allowed_shifts) + + mask_is_found = False + for horizontal_shift in allowed_shifts: + aug_mask_left = deepcopy(aug_mask) + aug_mask_left.shift(horizontal=horizontal_shift, inplace=True) + aug_mask_left.crop_to_canvas(inplace=True) + + prev_masks = [mask] + chosen_masks + is_mask_suitable = self._check_masks_intersection(aug_mask_left, total_aug_area, prev_masks) & \ + self._check_foreground_intersection(aug_mask_left, foreground) + if is_mask_suitable: + aug_draw = aug_mask_left.restore_full_mask() + chosen_masks.append(aug_draw) + augmentation_params["horizontal_shift"] = horizontal_shift / aug_mask_left.width + chosen_parameters.append(augmentation_params) + mask_is_found = True + break + + if not mask_is_found: + break + + return chosen_parameters + + def _prepare_mask(self, mask): + height, width = mask.shape + target_width = width if self._is_power_of_two(width) else (1 << width.bit_length()) + target_height = height if self._is_power_of_two(height) else (1 << height.bit_length()) + + return resize(mask.astype('float32'), (target_height, target_width), order=0, mode='edge').round().astype('int32') + + def get_masks(self, im, return_panoptic=False): + panoptic_seg, segments_info = self.get_segmentation(im) + potential_mask_ids = self.identify_candidates(panoptic_seg, segments_info) + + panoptic_seg_scaled = self._prepare_mask(panoptic_seg.detach().cpu().numpy()) + downsampled = self.downsample_mask(panoptic_seg_scaled) + scene_objects = [] + for segment in segments_info: + if not segment["isthing"]: + continue + mask = downsampled == segment["id"] + if not np.any(mask): + continue + scene_objects.append(mask) + + mask_set = [] + for mask_id in potential_mask_ids: + mask = downsampled == mask_id + if not np.any(mask): + continue + + if self.rigidness_mode is RigidnessMode.soft: + foreground = [mask] + elif self.rigidness_mode is RigidnessMode.rigid: + foreground = scene_objects + else: + raise ValueError(f'Unexpected rigidness_mode: {rigidness_mode}') + + masks_params = self._move_mask(mask, foreground) + + full_mask = ObjectMask((panoptic_seg == mask_id).detach().cpu().numpy()) + + for params in masks_params: + aug_mask = deepcopy(full_mask) + aug_mask.rescale(params["scaling_factor"], inplace=True) + if params["horizontal_flip"]: + aug_mask.horizontal_flip(inplace=True) + + vertical_shift = int(round(aug_mask.height * params["vertical_shift"])) + horizontal_shift = int(round(aug_mask.width * params["horizontal_shift"])) + aug_mask.shift(vertical=vertical_shift, horizontal=horizontal_shift, inplace=True) + aug_mask = aug_mask.restore_full_mask().astype('uint8') + if aug_mask.mean() <= self.min_mask_area: + continue + mask_set.append(aug_mask) + + if return_panoptic: + return mask_set, panoptic_seg.detach().cpu().numpy() + else: + return mask_set + + +def propose_random_square_crop(mask, min_overlap=0.5): + height, width = mask.shape + mask_ys, mask_xs = np.where(mask > 0.5) # mask==0 is known fragment and mask==1 is missing + + if height < width: + crop_size = height + obj_left, obj_right = mask_xs.min(), mask_xs.max() + obj_width = obj_right - obj_left + left_border = max(0, min(width - crop_size - 1, obj_left + obj_width * min_overlap - crop_size)) + right_border = max(left_border + 1, min(width - crop_size, obj_left + obj_width * min_overlap)) + start_x = np.random.randint(left_border, right_border) + return start_x, 0, start_x + crop_size, height + else: + crop_size = width + obj_top, obj_bottom = mask_ys.min(), mask_ys.max() + obj_height = obj_bottom - obj_top + top_border = max(0, min(height - crop_size - 1, obj_top + obj_height * min_overlap - crop_size)) + bottom_border = max(top_border + 1, min(height - crop_size, obj_top + obj_height * min_overlap)) + start_y = np.random.randint(top_border, bottom_border) + return 0, start_y, width, start_y + crop_size diff --git a/third-party/lama/saicinpainting/evaluation/refinement.py b/third-party/lama/saicinpainting/evaluation/refinement.py new file mode 100644 index 0000000000000000000000000000000000000000..d9d3cbac689d99ab20e71daf6f42bb8ca3c9feb8 --- /dev/null +++ b/third-party/lama/saicinpainting/evaluation/refinement.py @@ -0,0 +1,314 @@ +import torch +import torch.nn as nn +from torch.optim import Adam, SGD +from kornia.filters import gaussian_blur2d +from kornia.geometry.transform import resize +from kornia.morphology import erosion +from torch.nn import functional as F +import numpy as np +import cv2 + +from saicinpainting.evaluation.data import pad_tensor_to_modulo +from saicinpainting.evaluation.utils import move_to_device +from saicinpainting.training.modules.ffc import FFCResnetBlock +from saicinpainting.training.modules.pix2pixhd import ResnetBlock + +from tqdm import tqdm + + +def _pyrdown(im : torch.Tensor, downsize : tuple=None): + """downscale the image""" + if downsize is None: + downsize = (im.shape[2]//2, im.shape[3]//2) + assert im.shape[1] == 3, "Expected shape for the input to be (n,3,height,width)" + im = gaussian_blur2d(im, kernel_size=(5,5), sigma=(1.0,1.0)) + im = F.interpolate(im, size=downsize, mode='bilinear', align_corners=False) + return im + +def _pyrdown_mask(mask : torch.Tensor, downsize : tuple=None, eps : float=1e-8, blur_mask : bool=True, round_up : bool=True): + """downscale the mask tensor + + Parameters + ---------- + mask : torch.Tensor + mask of size (B, 1, H, W) + downsize : tuple, optional + size to downscale to. If None, image is downscaled to half, by default None + eps : float, optional + threshold value for binarizing the mask, by default 1e-8 + blur_mask : bool, optional + if True, apply gaussian filter before downscaling, by default True + round_up : bool, optional + if True, values above eps are marked 1, else, values below 1-eps are marked 0, by default True + + Returns + ------- + torch.Tensor + downscaled mask + """ + + if downsize is None: + downsize = (mask.shape[2]//2, mask.shape[3]//2) + assert mask.shape[1] == 1, "Expected shape for the input to be (n,1,height,width)" + if blur_mask == True: + mask = gaussian_blur2d(mask, kernel_size=(5,5), sigma=(1.0,1.0)) + mask = F.interpolate(mask, size=downsize, mode='bilinear', align_corners=False) + else: + mask = F.interpolate(mask, size=downsize, mode='bilinear', align_corners=False) + if round_up: + mask[mask>=eps] = 1 + mask[mask<eps] = 0 + else: + mask[mask>=1.0-eps] = 1 + mask[mask<1.0-eps] = 0 + return mask + +def _erode_mask(mask : torch.Tensor, ekernel : torch.Tensor=None, eps : float=1e-8): + """erode the mask, and set gray pixels to 0""" + if ekernel is not None: + mask = erosion(mask, ekernel) + mask[mask>=1.0-eps] = 1 + mask[mask<1.0-eps] = 0 + return mask + + +def _l1_loss( + pred : torch.Tensor, pred_downscaled : torch.Tensor, ref : torch.Tensor, + mask : torch.Tensor, mask_downscaled : torch.Tensor, + image : torch.Tensor, on_pred : bool=True + ): + """l1 loss on src pixels, and downscaled predictions if on_pred=True""" + loss = torch.mean(torch.abs(pred[mask<1e-8] - image[mask<1e-8])) + if on_pred: + loss += torch.mean(torch.abs(pred_downscaled[mask_downscaled>=1e-8] - ref[mask_downscaled>=1e-8])) + return loss + +def _infer( + image : torch.Tensor, mask : torch.Tensor, + forward_front : nn.Module, forward_rears : nn.Module, + ref_lower_res : torch.Tensor, orig_shape : tuple, devices : list, + scale_ind : int, n_iters : int=15, lr : float=0.002): + """Performs inference with refinement at a given scale. + + Parameters + ---------- + image : torch.Tensor + input image to be inpainted, of size (1,3,H,W) + mask : torch.Tensor + input inpainting mask, of size (1,1,H,W) + forward_front : nn.Module + the front part of the inpainting network + forward_rears : nn.Module + the rear part of the inpainting network + ref_lower_res : torch.Tensor + the inpainting at previous scale, used as reference image + orig_shape : tuple + shape of the original input image before padding + devices : list + list of available devices + scale_ind : int + the scale index + n_iters : int, optional + number of iterations of refinement, by default 15 + lr : float, optional + learning rate, by default 0.002 + + Returns + ------- + torch.Tensor + inpainted image + """ + masked_image = image * (1 - mask) + masked_image = torch.cat([masked_image, mask], dim=1) + + mask = mask.repeat(1,3,1,1) + if ref_lower_res is not None: + ref_lower_res = ref_lower_res.detach() + with torch.no_grad(): + z1,z2 = forward_front(masked_image) + # Inference + mask = mask.to(devices[-1]) + ekernel = torch.from_numpy(cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(15,15)).astype(bool)).float() + ekernel = ekernel.to(devices[-1]) + image = image.to(devices[-1]) + z1, z2 = z1.detach().to(devices[0]), z2.detach().to(devices[0]) + z1.requires_grad, z2.requires_grad = True, True + + optimizer = Adam([z1,z2], lr=lr) + + pbar = tqdm(range(n_iters), leave=False) + for idi in pbar: + optimizer.zero_grad() + input_feat = (z1,z2) + for idd, forward_rear in enumerate(forward_rears): + output_feat = forward_rear(input_feat) + if idd < len(devices) - 1: + midz1, midz2 = output_feat + midz1, midz2 = midz1.to(devices[idd+1]), midz2.to(devices[idd+1]) + input_feat = (midz1, midz2) + else: + pred = output_feat + + if ref_lower_res is None: + break + losses = {} + ######################### multi-scale ############################# + # scaled loss with downsampler + pred_downscaled = _pyrdown(pred[:,:,:orig_shape[0],:orig_shape[1]]) + mask_downscaled = _pyrdown_mask(mask[:,:1,:orig_shape[0],:orig_shape[1]], blur_mask=False, round_up=False) + mask_downscaled = _erode_mask(mask_downscaled, ekernel=ekernel) + mask_downscaled = mask_downscaled.repeat(1,3,1,1) + losses["ms_l1"] = _l1_loss(pred, pred_downscaled, ref_lower_res, mask, mask_downscaled, image, on_pred=True) + + loss = sum(losses.values()) + pbar.set_description("Refining scale {} using scale {} ...current loss: {:.4f}".format(scale_ind+1, scale_ind, loss.item())) + if idi < n_iters - 1: + loss.backward() + optimizer.step() + del pred_downscaled + del loss + del pred + # "pred" is the prediction after Plug-n-Play module + inpainted = mask * pred + (1 - mask) * image + inpainted = inpainted.detach().cpu() + return inpainted + +def _get_image_mask_pyramid(batch : dict, min_side : int, max_scales : int, px_budget : int): + """Build the image mask pyramid + + Parameters + ---------- + batch : dict + batch containing image, mask, etc + min_side : int + minimum side length to limit the number of scales of the pyramid + max_scales : int + maximum number of scales allowed + px_budget : int + the product H*W cannot exceed this budget, because of resource constraints + + Returns + ------- + tuple + image-mask pyramid in the form of list of images and list of masks + """ + + assert batch['image'].shape[0] == 1, "refiner works on only batches of size 1!" + + h, w = batch['unpad_to_size'] + h, w = h[0].item(), w[0].item() + + image = batch['image'][...,:h,:w] + mask = batch['mask'][...,:h,:w] + if h*w > px_budget: + #resize + ratio = np.sqrt(px_budget / float(h*w)) + h_orig, w_orig = h, w + h,w = int(h*ratio), int(w*ratio) + print(f"Original image too large for refinement! Resizing {(h_orig,w_orig)} to {(h,w)}...") + image = resize(image, (h,w),interpolation='bilinear', align_corners=False) + mask = resize(mask, (h,w),interpolation='bilinear', align_corners=False) + mask[mask>1e-8] = 1 + breadth = min(h,w) + n_scales = min(1 + int(round(max(0,np.log2(breadth / min_side)))), max_scales) + ls_images = [] + ls_masks = [] + + ls_images.append(image) + ls_masks.append(mask) + + for _ in range(n_scales - 1): + image_p = _pyrdown(ls_images[-1]) + mask_p = _pyrdown_mask(ls_masks[-1]) + ls_images.append(image_p) + ls_masks.append(mask_p) + # reverse the lists because we want the lowest resolution image as index 0 + return ls_images[::-1], ls_masks[::-1] + +def refine_predict( + batch : dict, inpainter : nn.Module, gpu_ids : str, + modulo : int, n_iters : int, lr : float, min_side : int, + max_scales : int, px_budget : int + ): + """Refines the inpainting of the network + + Parameters + ---------- + batch : dict + image-mask batch, currently we assume the batchsize to be 1 + inpainter : nn.Module + the inpainting neural network + gpu_ids : str + the GPU ids of the machine to use. If only single GPU, use: "0," + modulo : int + pad the image to ensure dimension % modulo == 0 + n_iters : int + number of iterations of refinement for each scale + lr : float + learning rate + min_side : int + all sides of image on all scales should be >= min_side / sqrt(2) + max_scales : int + max number of downscaling scales for the image-mask pyramid + px_budget : int + pixels budget. Any image will be resized to satisfy height*width <= px_budget + + Returns + ------- + torch.Tensor + inpainted image of size (1,3,H,W) + """ + + assert not inpainter.training + assert not inpainter.add_noise_kwargs + assert inpainter.concat_mask + + gpu_ids = [f'cuda:{gpuid}' for gpuid in gpu_ids.replace(" ","").split(",") if gpuid.isdigit()] + n_resnet_blocks = 0 + first_resblock_ind = 0 + found_first_resblock = False + for idl in range(len(inpainter.generator.model)): + if isinstance(inpainter.generator.model[idl], FFCResnetBlock) or isinstance(inpainter.generator.model[idl], ResnetBlock): + n_resnet_blocks += 1 + found_first_resblock = True + elif not found_first_resblock: + first_resblock_ind += 1 + resblocks_per_gpu = n_resnet_blocks // len(gpu_ids) + + devices = [torch.device(gpu_id) for gpu_id in gpu_ids] + + # split the model into front, and rear parts + forward_front = inpainter.generator.model[0:first_resblock_ind] + forward_front.to(devices[0]) + forward_rears = [] + for idd in range(len(gpu_ids)): + if idd < len(gpu_ids) - 1: + forward_rears.append(inpainter.generator.model[first_resblock_ind + resblocks_per_gpu*(idd):first_resblock_ind+resblocks_per_gpu*(idd+1)]) + else: + forward_rears.append(inpainter.generator.model[first_resblock_ind + resblocks_per_gpu*(idd):]) + forward_rears[idd].to(devices[idd]) + + ls_images, ls_masks = _get_image_mask_pyramid( + batch, + min_side, + max_scales, + px_budget + ) + image_inpainted = None + + for ids, (image, mask) in enumerate(zip(ls_images, ls_masks)): + orig_shape = image.shape[2:] + image = pad_tensor_to_modulo(image, modulo) + mask = pad_tensor_to_modulo(mask, modulo) + mask[mask >= 1e-8] = 1.0 + mask[mask < 1e-8] = 0.0 + image, mask = move_to_device(image, devices[0]), move_to_device(mask, devices[0]) + if image_inpainted is not None: + image_inpainted = move_to_device(image_inpainted, devices[-1]) + image_inpainted = _infer(image, mask, forward_front, forward_rears, image_inpainted, orig_shape, devices, ids, n_iters, lr) + image_inpainted = image_inpainted[:,:,:orig_shape[0], :orig_shape[1]] + # detach everything to save resources + image = image.detach().cpu() + mask = mask.detach().cpu() + + return image_inpainted diff --git a/third-party/lama/saicinpainting/evaluation/utils.py b/third-party/lama/saicinpainting/evaluation/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..6d7c15c9242ed8a9bc59fbb3b450cca394720bb8 --- /dev/null +++ b/third-party/lama/saicinpainting/evaluation/utils.py @@ -0,0 +1,28 @@ +from enum import Enum + +import yaml +from easydict import EasyDict as edict +import torch.nn as nn +import torch + + +def load_yaml(path): + with open(path, 'r') as f: + return edict(yaml.safe_load(f)) + + +def move_to_device(obj, device): + if isinstance(obj, nn.Module): + return obj.to(device) + if torch.is_tensor(obj): + return obj.to(device) + if isinstance(obj, (tuple, list)): + return [move_to_device(el, device) for el in obj] + if isinstance(obj, dict): + return {name: move_to_device(val, device) for name, val in obj.items()} + raise ValueError(f'Unexpected type {type(obj)}') + + +class SmallMode(Enum): + DROP = "drop" + UPSCALE = "upscale" diff --git a/third-party/lama/saicinpainting/evaluation/vis.py b/third-party/lama/saicinpainting/evaluation/vis.py new file mode 100644 index 0000000000000000000000000000000000000000..c2910b4ef8c61efee72dabd0531a9b669ec8bf98 --- /dev/null +++ b/third-party/lama/saicinpainting/evaluation/vis.py @@ -0,0 +1,37 @@ +import numpy as np +from skimage import io +from skimage.segmentation import mark_boundaries + + +def save_item_for_vis(item, out_file): + mask = item['mask'] > 0.5 + if mask.ndim == 3: + mask = mask[0] + img = mark_boundaries(np.transpose(item['image'], (1, 2, 0)), + mask, + color=(1., 0., 0.), + outline_color=(1., 1., 1.), + mode='thick') + + if 'inpainted' in item: + inp_img = mark_boundaries(np.transpose(item['inpainted'], (1, 2, 0)), + mask, + color=(1., 0., 0.), + mode='outer') + img = np.concatenate((img, inp_img), axis=1) + + img = np.clip(img * 255, 0, 255).astype('uint8') + io.imsave(out_file, img) + + +def save_mask_for_sidebyside(item, out_file): + mask = item['mask']# > 0.5 + if mask.ndim == 3: + mask = mask[0] + mask = np.clip(mask * 255, 0, 255).astype('uint8') + io.imsave(out_file, mask) + +def save_img_for_sidebyside(item, out_file): + img = np.transpose(item['image'], (1, 2, 0)) + img = np.clip(img * 255, 0, 255).astype('uint8') + io.imsave(out_file, img) \ No newline at end of file diff --git a/third-party/lama/saicinpainting/training/__init__.py b/third-party/lama/saicinpainting/training/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third-party/lama/saicinpainting/training/data/__init__.py b/third-party/lama/saicinpainting/training/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third-party/lama/saicinpainting/training/data/aug.py b/third-party/lama/saicinpainting/training/data/aug.py new file mode 100644 index 0000000000000000000000000000000000000000..b1246250924e79511b58cd3d7ab79de8012f8949 --- /dev/null +++ b/third-party/lama/saicinpainting/training/data/aug.py @@ -0,0 +1,84 @@ +from albumentations import DualIAATransform, to_tuple +import imgaug.augmenters as iaa + +class IAAAffine2(DualIAATransform): + """Place a regular grid of points on the input and randomly move the neighbourhood of these point around + via affine transformations. + + Note: This class introduce interpolation artifacts to mask if it has values other than {0;1} + + Args: + p (float): probability of applying the transform. Default: 0.5. + + Targets: + image, mask + """ + + def __init__( + self, + scale=(0.7, 1.3), + translate_percent=None, + translate_px=None, + rotate=0.0, + shear=(-0.1, 0.1), + order=1, + cval=0, + mode="reflect", + always_apply=False, + p=0.5, + ): + super(IAAAffine2, self).__init__(always_apply, p) + self.scale = dict(x=scale, y=scale) + self.translate_percent = to_tuple(translate_percent, 0) + self.translate_px = to_tuple(translate_px, 0) + self.rotate = to_tuple(rotate) + self.shear = dict(x=shear, y=shear) + self.order = order + self.cval = cval + self.mode = mode + + @property + def processor(self): + return iaa.Affine( + self.scale, + self.translate_percent, + self.translate_px, + self.rotate, + self.shear, + self.order, + self.cval, + self.mode, + ) + + def get_transform_init_args_names(self): + return ("scale", "translate_percent", "translate_px", "rotate", "shear", "order", "cval", "mode") + + +class IAAPerspective2(DualIAATransform): + """Perform a random four point perspective transform of the input. + + Note: This class introduce interpolation artifacts to mask if it has values other than {0;1} + + Args: + scale ((float, float): standard deviation of the normal distributions. These are used to sample + the random distances of the subimage's corners from the full image's corners. Default: (0.05, 0.1). + p (float): probability of applying the transform. Default: 0.5. + + Targets: + image, mask + """ + + def __init__(self, scale=(0.05, 0.1), keep_size=True, always_apply=False, p=0.5, + order=1, cval=0, mode="replicate"): + super(IAAPerspective2, self).__init__(always_apply, p) + self.scale = to_tuple(scale, 1.0) + self.keep_size = keep_size + self.cval = cval + self.mode = mode + + @property + def processor(self): + return iaa.PerspectiveTransform(self.scale, keep_size=self.keep_size, mode=self.mode, cval=self.cval) + + def get_transform_init_args_names(self): + return ("scale", "keep_size") diff --git a/third-party/lama/saicinpainting/training/data/datasets.py b/third-party/lama/saicinpainting/training/data/datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..c4f503dafffb970d8dbaca33934da417036d1e55 --- /dev/null +++ b/third-party/lama/saicinpainting/training/data/datasets.py @@ -0,0 +1,304 @@ +import glob +import logging +import os +import random + +import albumentations as A +import cv2 +import numpy as np +import torch +import torch.nn.functional as F +import webdataset +from omegaconf import open_dict, OmegaConf +from skimage.feature import canny +from skimage.transform import rescale, resize +from torch.utils.data import Dataset, IterableDataset, DataLoader, DistributedSampler, ConcatDataset + +from saicinpainting.evaluation.data import InpaintingDataset as InpaintingEvaluationDataset, \ + OurInpaintingDataset as OurInpaintingEvaluationDataset, ceil_modulo, InpaintingEvalOnlineDataset +from saicinpainting.training.data.aug import IAAAffine2, IAAPerspective2 +from saicinpainting.training.data.masks import get_mask_generator + +LOGGER = logging.getLogger(__name__) + + +class InpaintingTrainDataset(Dataset): + def __init__(self, indir, mask_generator, transform): + self.in_files = list(glob.glob(os.path.join(indir, '**', '*.jpg'), recursive=True)) + self.mask_generator = mask_generator + self.transform = transform + self.iter_i = 0 + + def __len__(self): + return len(self.in_files) + + def __getitem__(self, item): + path = self.in_files[item] + img = cv2.imread(path) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = self.transform(image=img)['image'] + img = np.transpose(img, (2, 0, 1)) + # TODO: maybe generate mask before augmentations? slower, but better for segmentation-based masks + mask = self.mask_generator(img, iter_i=self.iter_i) + self.iter_i += 1 + return dict(image=img, + mask=mask) + + +class InpaintingTrainWebDataset(IterableDataset): + def __init__(self, indir, mask_generator, transform, shuffle_buffer=200): + self.impl = webdataset.Dataset(indir).shuffle(shuffle_buffer).decode('rgb').to_tuple('jpg') + self.mask_generator = mask_generator + self.transform = transform + + def __iter__(self): + for iter_i, (img,) in enumerate(self.impl): + img = np.clip(img * 255, 0, 255).astype('uint8') + img = self.transform(image=img)['image'] + img = np.transpose(img, (2, 0, 1)) + mask = self.mask_generator(img, iter_i=iter_i) + yield dict(image=img, + mask=mask) + + +class ImgSegmentationDataset(Dataset): + def __init__(self, indir, mask_generator, transform, out_size, segm_indir, semantic_seg_n_classes): + self.indir = indir + self.segm_indir = segm_indir + self.mask_generator = mask_generator + self.transform = transform + self.out_size = out_size + self.semantic_seg_n_classes = semantic_seg_n_classes + self.in_files = list(glob.glob(os.path.join(indir, '**', '*.jpg'), recursive=True)) + + def __len__(self): + return len(self.in_files) + + def __getitem__(self, item): + path = self.in_files[item] + img = cv2.imread(path) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = cv2.resize(img, (self.out_size, self.out_size)) + img = self.transform(image=img)['image'] + img = np.transpose(img, (2, 0, 1)) + mask = self.mask_generator(img) + segm, segm_classes= self.load_semantic_segm(path) + result = dict(image=img, + mask=mask, + segm=segm, + segm_classes=segm_classes) + return result + + def load_semantic_segm(self, img_path): + segm_path = img_path.replace(self.indir, self.segm_indir).replace(".jpg", ".png") + mask = cv2.imread(segm_path, cv2.IMREAD_GRAYSCALE) + mask = cv2.resize(mask, (self.out_size, self.out_size)) + tensor = torch.from_numpy(np.clip(mask.astype(int)-1, 0, None)) + ohe = F.one_hot(tensor.long(), num_classes=self.semantic_seg_n_classes) # w x h x n_classes + return ohe.permute(2, 0, 1).float(), tensor.unsqueeze(0) + + +def get_transforms(transform_variant, out_size): + if transform_variant == 'default': + transform = A.Compose([ + A.RandomScale(scale_limit=0.2), # +/- 20% + A.PadIfNeeded(min_height=out_size, min_width=out_size), + A.RandomCrop(height=out_size, width=out_size), + A.HorizontalFlip(), + A.CLAHE(), + A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2), + A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5), + A.ToFloat() + ]) + elif transform_variant == 'distortions': + transform = A.Compose([ + IAAPerspective2(scale=(0.0, 0.06)), + IAAAffine2(scale=(0.7, 1.3), + rotate=(-40, 40), + shear=(-0.1, 0.1)), + A.PadIfNeeded(min_height=out_size, min_width=out_size), + A.OpticalDistortion(), + A.RandomCrop(height=out_size, width=out_size), + A.HorizontalFlip(), + A.CLAHE(), + A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2), + A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5), + A.ToFloat() + ]) + elif transform_variant == 'distortions_scale05_1': + transform = A.Compose([ + IAAPerspective2(scale=(0.0, 0.06)), + IAAAffine2(scale=(0.5, 1.0), + rotate=(-40, 40), + shear=(-0.1, 0.1), + p=1), + A.PadIfNeeded(min_height=out_size, min_width=out_size), + A.OpticalDistortion(), + A.RandomCrop(height=out_size, width=out_size), + A.HorizontalFlip(), + A.CLAHE(), + A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2), + A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5), + A.ToFloat() + ]) + elif transform_variant == 'distortions_scale03_12': + transform = A.Compose([ + IAAPerspective2(scale=(0.0, 0.06)), + IAAAffine2(scale=(0.3, 1.2), + rotate=(-40, 40), + shear=(-0.1, 0.1), + p=1), + A.PadIfNeeded(min_height=out_size, min_width=out_size), + A.OpticalDistortion(), + A.RandomCrop(height=out_size, width=out_size), + A.HorizontalFlip(), + A.CLAHE(), + A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2), + A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5), + A.ToFloat() + ]) + elif transform_variant == 'distortions_scale03_07': + transform = A.Compose([ + IAAPerspective2(scale=(0.0, 0.06)), + IAAAffine2(scale=(0.3, 0.7), # scale 512 to 256 in average + rotate=(-40, 40), + shear=(-0.1, 0.1), + p=1), + A.PadIfNeeded(min_height=out_size, min_width=out_size), + A.OpticalDistortion(), + A.RandomCrop(height=out_size, width=out_size), + A.HorizontalFlip(), + A.CLAHE(), + A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2), + A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5), + A.ToFloat() + ]) + elif transform_variant == 'distortions_light': + transform = A.Compose([ + IAAPerspective2(scale=(0.0, 0.02)), + IAAAffine2(scale=(0.8, 1.8), + rotate=(-20, 20), + shear=(-0.03, 0.03)), + A.PadIfNeeded(min_height=out_size, min_width=out_size), + A.RandomCrop(height=out_size, width=out_size), + A.HorizontalFlip(), + A.CLAHE(), + A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2), + A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5), + A.ToFloat() + ]) + elif transform_variant == 'non_space_transform': + transform = A.Compose([ + A.CLAHE(), + A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2), + A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5), + A.ToFloat() + ]) + elif transform_variant == 'no_augs': + transform = A.Compose([ + A.ToFloat() + ]) + else: + raise ValueError(f'Unexpected transform_variant {transform_variant}') + return transform + + +def make_default_train_dataloader(indir, kind='default', out_size=512, mask_gen_kwargs=None, transform_variant='default', + mask_generator_kind="mixed", dataloader_kwargs=None, ddp_kwargs=None, **kwargs): + LOGGER.info(f'Make train dataloader {kind} from {indir}. Using mask generator={mask_generator_kind}') + + mask_generator = get_mask_generator(kind=mask_generator_kind, kwargs=mask_gen_kwargs) + transform = get_transforms(transform_variant, out_size) + + if kind == 'default': + dataset = InpaintingTrainDataset(indir=indir, + mask_generator=mask_generator, + transform=transform, + **kwargs) + elif kind == 'default_web': + dataset = InpaintingTrainWebDataset(indir=indir, + mask_generator=mask_generator, + transform=transform, + **kwargs) + elif kind == 'img_with_segm': + dataset = ImgSegmentationDataset(indir=indir, + mask_generator=mask_generator, + transform=transform, + out_size=out_size, + **kwargs) + else: + raise ValueError(f'Unknown train dataset kind {kind}') + + if dataloader_kwargs is None: + dataloader_kwargs = {} + + is_dataset_only_iterable = kind in ('default_web',) + + if ddp_kwargs is not None and not is_dataset_only_iterable: + dataloader_kwargs['shuffle'] = False + dataloader_kwargs['sampler'] = DistributedSampler(dataset, **ddp_kwargs) + + if is_dataset_only_iterable and 'shuffle' in dataloader_kwargs: + with open_dict(dataloader_kwargs): + del dataloader_kwargs['shuffle'] + + dataloader = DataLoader(dataset, **dataloader_kwargs) + return dataloader + + +def make_default_val_dataset(indir, kind='default', out_size=512, transform_variant='default', **kwargs): + if OmegaConf.is_list(indir) or isinstance(indir, (tuple, list)): + return ConcatDataset([ + make_default_val_dataset(idir, kind=kind, out_size=out_size, transform_variant=transform_variant, **kwargs) for idir in indir + ]) + + LOGGER.info(f'Make val dataloader {kind} from {indir}') + mask_generator = get_mask_generator(kind=kwargs.get("mask_generator_kind"), kwargs=kwargs.get("mask_gen_kwargs")) + + if transform_variant is not None: + transform = get_transforms(transform_variant, out_size) + + if kind == 'default': + dataset = InpaintingEvaluationDataset(indir, **kwargs) + elif kind == 'our_eval': + dataset = OurInpaintingEvaluationDataset(indir, **kwargs) + elif kind == 'img_with_segm': + dataset = ImgSegmentationDataset(indir=indir, + mask_generator=mask_generator, + transform=transform, + out_size=out_size, + **kwargs) + elif kind == 'online': + dataset = InpaintingEvalOnlineDataset(indir=indir, + mask_generator=mask_generator, + transform=transform, + out_size=out_size, + **kwargs) + else: + raise ValueError(f'Unknown val dataset kind {kind}') + + return dataset + + +def make_default_val_dataloader(*args, dataloader_kwargs=None, **kwargs): + dataset = make_default_val_dataset(*args, **kwargs) + + if dataloader_kwargs is None: + dataloader_kwargs = {} + dataloader = DataLoader(dataset, **dataloader_kwargs) + return dataloader + + +def make_constant_area_crop_params(img_height, img_width, min_size=128, max_size=512, area=256*256, round_to_mod=16): + min_size = min(img_height, img_width, min_size) + max_size = min(img_height, img_width, max_size) + if random.random() < 0.5: + out_height = min(max_size, ceil_modulo(random.randint(min_size, max_size), round_to_mod)) + out_width = min(max_size, ceil_modulo(area // out_height, round_to_mod)) + else: + out_width = min(max_size, ceil_modulo(random.randint(min_size, max_size), round_to_mod)) + out_height = min(max_size, ceil_modulo(area // out_width, round_to_mod)) + + start_y = random.randint(0, img_height - out_height) + start_x = random.randint(0, img_width - out_width) + return (start_y, start_x, out_height, out_width) diff --git a/third-party/lama/saicinpainting/training/data/masks.py b/third-party/lama/saicinpainting/training/data/masks.py new file mode 100644 index 0000000000000000000000000000000000000000..e91fc74913356481065c5f5906acd50fb05f521c --- /dev/null +++ b/third-party/lama/saicinpainting/training/data/masks.py @@ -0,0 +1,332 @@ +import math +import random +import hashlib +import logging +from enum import Enum + +import cv2 +import numpy as np + +from saicinpainting.evaluation.masks.mask import SegmentationMask +from saicinpainting.utils import LinearRamp + +LOGGER = logging.getLogger(__name__) + + +class DrawMethod(Enum): + LINE = 'line' + CIRCLE = 'circle' + SQUARE = 'square' + + +def make_random_irregular_mask(shape, max_angle=4, max_len=60, max_width=20, min_times=0, max_times=10, + draw_method=DrawMethod.LINE): + draw_method = DrawMethod(draw_method) + + height, width = shape + mask = np.zeros((height, width), np.float32) + times = np.random.randint(min_times, max_times + 1) + for i in range(times): + start_x = np.random.randint(width) + start_y = np.random.randint(height) + for j in range(1 + np.random.randint(5)): + angle = 0.01 + np.random.randint(max_angle) + if i % 2 == 0: + angle = 2 * 3.1415926 - angle + length = 10 + np.random.randint(max_len) + brush_w = 5 + np.random.randint(max_width) + end_x = np.clip((start_x + length * np.sin(angle)).astype(np.int32), 0, width) + end_y = np.clip((start_y + length * np.cos(angle)).astype(np.int32), 0, height) + if draw_method == DrawMethod.LINE: + cv2.line(mask, (start_x, start_y), (end_x, end_y), 1.0, brush_w) + elif draw_method == DrawMethod.CIRCLE: + cv2.circle(mask, (start_x, start_y), radius=brush_w, color=1., thickness=-1) + elif draw_method == DrawMethod.SQUARE: + radius = brush_w // 2 + mask[start_y - radius:start_y + radius, start_x - radius:start_x + radius] = 1 + start_x, start_y = end_x, end_y + return mask[None, ...] + + +class RandomIrregularMaskGenerator: + def __init__(self, max_angle=4, max_len=60, max_width=20, min_times=0, max_times=10, ramp_kwargs=None, + draw_method=DrawMethod.LINE): + self.max_angle = max_angle + self.max_len = max_len + self.max_width = max_width + self.min_times = min_times + self.max_times = max_times + self.draw_method = draw_method + self.ramp = LinearRamp(**ramp_kwargs) if ramp_kwargs is not None else None + + def __call__(self, img, iter_i=None, raw_image=None): + coef = self.ramp(iter_i) if (self.ramp is not None) and (iter_i is not None) else 1 + cur_max_len = int(max(1, self.max_len * coef)) + cur_max_width = int(max(1, self.max_width * coef)) + cur_max_times = int(self.min_times + 1 + (self.max_times - self.min_times) * coef) + return make_random_irregular_mask(img.shape[1:], max_angle=self.max_angle, max_len=cur_max_len, + max_width=cur_max_width, min_times=self.min_times, max_times=cur_max_times, + draw_method=self.draw_method) + + +def make_random_rectangle_mask(shape, margin=10, bbox_min_size=30, bbox_max_size=100, min_times=0, max_times=3): + height, width = shape + mask = np.zeros((height, width), np.float32) + bbox_max_size = min(bbox_max_size, height - margin * 2, width - margin * 2) + times = np.random.randint(min_times, max_times + 1) + for i in range(times): + box_width = np.random.randint(bbox_min_size, bbox_max_size) + box_height = np.random.randint(bbox_min_size, bbox_max_size) + start_x = np.random.randint(margin, width - margin - box_width + 1) + start_y = np.random.randint(margin, height - margin - box_height + 1) + mask[start_y:start_y + box_height, start_x:start_x + box_width] = 1 + return mask[None, ...] + + +class RandomRectangleMaskGenerator: + def __init__(self, margin=10, bbox_min_size=30, bbox_max_size=100, min_times=0, max_times=3, ramp_kwargs=None): + self.margin = margin + self.bbox_min_size = bbox_min_size + self.bbox_max_size = bbox_max_size + self.min_times = min_times + self.max_times = max_times + self.ramp = LinearRamp(**ramp_kwargs) if ramp_kwargs is not None else None + + def __call__(self, img, iter_i=None, raw_image=None): + coef = self.ramp(iter_i) if (self.ramp is not None) and (iter_i is not None) else 1 + cur_bbox_max_size = int(self.bbox_min_size + 1 + (self.bbox_max_size - self.bbox_min_size) * coef) + cur_max_times = int(self.min_times + (self.max_times - self.min_times) * coef) + return make_random_rectangle_mask(img.shape[1:], margin=self.margin, bbox_min_size=self.bbox_min_size, + bbox_max_size=cur_bbox_max_size, min_times=self.min_times, + max_times=cur_max_times) + + +class RandomSegmentationMaskGenerator: + def __init__(self, **kwargs): + self.impl = None # will be instantiated in first call (effectively in subprocess) + self.kwargs = kwargs + + def __call__(self, img, iter_i=None, raw_image=None): + if self.impl is None: + self.impl = SegmentationMask(**self.kwargs) + + masks = self.impl.get_masks(np.transpose(img, (1, 2, 0))) + masks = [m for m in masks if len(np.unique(m)) > 1] + return np.random.choice(masks) + + +def make_random_superres_mask(shape, min_step=2, max_step=4, min_width=1, max_width=3): + height, width = shape + mask = np.zeros((height, width), np.float32) + step_x = np.random.randint(min_step, max_step + 1) + width_x = np.random.randint(min_width, min(step_x, max_width + 1)) + offset_x = np.random.randint(0, step_x) + + step_y = np.random.randint(min_step, max_step + 1) + width_y = np.random.randint(min_width, min(step_y, max_width + 1)) + offset_y = np.random.randint(0, step_y) + + for dy in range(width_y): + mask[offset_y + dy::step_y] = 1 + for dx in range(width_x): + mask[:, offset_x + dx::step_x] = 1 + return mask[None, ...] + + +class RandomSuperresMaskGenerator: + def __init__(self, **kwargs): + self.kwargs = kwargs + + def __call__(self, img, iter_i=None): + return make_random_superres_mask(img.shape[1:], **self.kwargs) + + +class DumbAreaMaskGenerator: + min_ratio = 0.1 + max_ratio = 0.35 + default_ratio = 0.225 + + def __init__(self, is_training): + #Parameters: + # is_training(bool): If true - random rectangular mask, if false - central square mask + self.is_training = is_training + + def _random_vector(self, dimension): + if self.is_training: + lower_limit = math.sqrt(self.min_ratio) + upper_limit = math.sqrt(self.max_ratio) + mask_side = round((random.random() * (upper_limit - lower_limit) + lower_limit) * dimension) + u = random.randint(0, dimension-mask_side-1) + v = u+mask_side + else: + margin = (math.sqrt(self.default_ratio) / 2) * dimension + u = round(dimension/2 - margin) + v = round(dimension/2 + margin) + return u, v + + def __call__(self, img, iter_i=None, raw_image=None): + c, height, width = img.shape + mask = np.zeros((height, width), np.float32) + x1, x2 = self._random_vector(width) + y1, y2 = self._random_vector(height) + mask[x1:x2, y1:y2] = 1 + return mask[None, ...] + + +class OutpaintingMaskGenerator: + def __init__(self, min_padding_percent:float=0.04, max_padding_percent:int=0.25, left_padding_prob:float=0.5, top_padding_prob:float=0.5, + right_padding_prob:float=0.5, bottom_padding_prob:float=0.5, is_fixed_randomness:bool=False): + """ + is_fixed_randomness - get identical paddings for the same image if args are the same + """ + self.min_padding_percent = min_padding_percent + self.max_padding_percent = max_padding_percent + self.probs = [left_padding_prob, top_padding_prob, right_padding_prob, bottom_padding_prob] + self.is_fixed_randomness = is_fixed_randomness + + assert self.min_padding_percent <= self.max_padding_percent + assert self.max_padding_percent > 0 + assert len([x for x in [self.min_padding_percent, self.max_padding_percent] if (x>=0 and x<=1)]) == 2, f"Padding percentage should be in [0,1]" + assert sum(self.probs) > 0, f"At least one of the padding probs should be greater than 0 - {self.probs}" + assert len([x for x in self.probs if (x >= 0) and (x <= 1)]) == 4, f"At least one of padding probs is not in [0,1] - {self.probs}" + if len([x for x in self.probs if x > 0]) == 1: + LOGGER.warning(f"Only one padding prob is greater than zero - {self.probs}. That means that the outpainting masks will be always on the same side") + + def apply_padding(self, mask, coord): + mask[int(coord[0][0]*self.img_h):int(coord[1][0]*self.img_h), + int(coord[0][1]*self.img_w):int(coord[1][1]*self.img_w)] = 1 + return mask + + def get_padding(self, size): + n1 = int(self.min_padding_percent*size) + n2 = int(self.max_padding_percent*size) + return self.rnd.randint(n1, n2) / size + + @staticmethod + def _img2rs(img): + arr = np.ascontiguousarray(img.astype(np.uint8)) + str_hash = hashlib.sha1(arr).hexdigest() + res = hash(str_hash)%(2**32) + return res + + def __call__(self, img, iter_i=None, raw_image=None): + c, self.img_h, self.img_w = img.shape + mask = np.zeros((self.img_h, self.img_w), np.float32) + at_least_one_mask_applied = False + + if self.is_fixed_randomness: + assert raw_image is not None, f"Cant calculate hash on raw_image=None" + rs = self._img2rs(raw_image) + self.rnd = np.random.RandomState(rs) + else: + self.rnd = np.random + + coords = [[ + (0,0), + (1,self.get_padding(size=self.img_h)) + ], + [ + (0,0), + (self.get_padding(size=self.img_w),1) + ], + [ + (0,1-self.get_padding(size=self.img_h)), + (1,1) + ], + [ + (1-self.get_padding(size=self.img_w),0), + (1,1) + ]] + + for pp, coord in zip(self.probs, coords): + if self.rnd.random() < pp: + at_least_one_mask_applied = True + mask = self.apply_padding(mask=mask, coord=coord) + + if not at_least_one_mask_applied: + idx = self.rnd.choice(range(len(coords)), p=np.array(self.probs)/sum(self.probs)) + mask = self.apply_padding(mask=mask, coord=coords[idx]) + return mask[None, ...] + + +class MixedMaskGenerator: + def __init__(self, irregular_proba=1/3, irregular_kwargs=None, + box_proba=1/3, box_kwargs=None, + segm_proba=1/3, segm_kwargs=None, + squares_proba=0, squares_kwargs=None, + superres_proba=0, superres_kwargs=None, + outpainting_proba=0, outpainting_kwargs=None, + invert_proba=0): + self.probas = [] + self.gens = [] + + if irregular_proba > 0: + self.probas.append(irregular_proba) + if irregular_kwargs is None: + irregular_kwargs = {} + else: + irregular_kwargs = dict(irregular_kwargs) + irregular_kwargs['draw_method'] = DrawMethod.LINE + self.gens.append(RandomIrregularMaskGenerator(**irregular_kwargs)) + + if box_proba > 0: + self.probas.append(box_proba) + if box_kwargs is None: + box_kwargs = {} + self.gens.append(RandomRectangleMaskGenerator(**box_kwargs)) + + if segm_proba > 0: + self.probas.append(segm_proba) + if segm_kwargs is None: + segm_kwargs = {} + self.gens.append(RandomSegmentationMaskGenerator(**segm_kwargs)) + + if squares_proba > 0: + self.probas.append(squares_proba) + if squares_kwargs is None: + squares_kwargs = {} + else: + squares_kwargs = dict(squares_kwargs) + squares_kwargs['draw_method'] = DrawMethod.SQUARE + self.gens.append(RandomIrregularMaskGenerator(**squares_kwargs)) + + if superres_proba > 0: + self.probas.append(superres_proba) + if superres_kwargs is None: + superres_kwargs = {} + self.gens.append(RandomSuperresMaskGenerator(**superres_kwargs)) + + if outpainting_proba > 0: + self.probas.append(outpainting_proba) + if outpainting_kwargs is None: + outpainting_kwargs = {} + self.gens.append(OutpaintingMaskGenerator(**outpainting_kwargs)) + + self.probas = np.array(self.probas, dtype='float32') + self.probas /= self.probas.sum() + self.invert_proba = invert_proba + + def __call__(self, img, iter_i=None, raw_image=None): + kind = np.random.choice(len(self.probas), p=self.probas) + gen = self.gens[kind] + result = gen(img, iter_i=iter_i, raw_image=raw_image) + if self.invert_proba > 0 and random.random() < self.invert_proba: + result = 1 - result + return result + + +def get_mask_generator(kind, kwargs): + if kind is None: + kind = "mixed" + if kwargs is None: + kwargs = {} + + if kind == "mixed": + cl = MixedMaskGenerator + elif kind == "outpainting": + cl = OutpaintingMaskGenerator + elif kind == "dumb": + cl = DumbAreaMaskGenerator + else: + raise NotImplementedError(f"No such generator kind = {kind}") + return cl(**kwargs) diff --git a/third-party/lama/saicinpainting/training/losses/__init__.py b/third-party/lama/saicinpainting/training/losses/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third-party/lama/saicinpainting/training/losses/adversarial.py b/third-party/lama/saicinpainting/training/losses/adversarial.py new file mode 100644 index 0000000000000000000000000000000000000000..d6db2967ce5074d94ed3b4c51fc743ff2f7831b1 --- /dev/null +++ b/third-party/lama/saicinpainting/training/losses/adversarial.py @@ -0,0 +1,177 @@ +from typing import Tuple, Dict, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class BaseAdversarialLoss: + def pre_generator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor, + generator: nn.Module, discriminator: nn.Module): + """ + Prepare for generator step + :param real_batch: Tensor, a batch of real samples + :param fake_batch: Tensor, a batch of samples produced by generator + :param generator: + :param discriminator: + :return: None + """ + + def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor, + generator: nn.Module, discriminator: nn.Module): + """ + Prepare for discriminator step + :param real_batch: Tensor, a batch of real samples + :param fake_batch: Tensor, a batch of samples produced by generator + :param generator: + :param discriminator: + :return: None + """ + + def generator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor, + discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor, + mask: Optional[torch.Tensor] = None) \ + -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: + """ + Calculate generator loss + :param real_batch: Tensor, a batch of real samples + :param fake_batch: Tensor, a batch of samples produced by generator + :param discr_real_pred: Tensor, discriminator output for real_batch + :param discr_fake_pred: Tensor, discriminator output for fake_batch + :param mask: Tensor, actual mask, which was at input of generator when making fake_batch + :return: total generator loss along with some values that might be interesting to log + """ + raise NotImplemented() + + def discriminator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor, + discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor, + mask: Optional[torch.Tensor] = None) \ + -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: + """ + Calculate discriminator loss and call .backward() on it + :param real_batch: Tensor, a batch of real samples + :param fake_batch: Tensor, a batch of samples produced by generator + :param discr_real_pred: Tensor, discriminator output for real_batch + :param discr_fake_pred: Tensor, discriminator output for fake_batch + :param mask: Tensor, actual mask, which was at input of generator when making fake_batch + :return: total discriminator loss along with some values that might be interesting to log + """ + raise NotImplemented() + + def interpolate_mask(self, mask, shape): + assert mask is not None + assert self.allow_scale_mask or shape == mask.shape[-2:] + if shape != mask.shape[-2:] and self.allow_scale_mask: + if self.mask_scale_mode == 'maxpool': + mask = F.adaptive_max_pool2d(mask, shape) + else: + mask = F.interpolate(mask, size=shape, mode=self.mask_scale_mode) + return mask + +def make_r1_gp(discr_real_pred, real_batch): + if torch.is_grad_enabled(): + grad_real = torch.autograd.grad(outputs=discr_real_pred.sum(), inputs=real_batch, create_graph=True)[0] + grad_penalty = (grad_real.view(grad_real.shape[0], -1).norm(2, dim=1) ** 2).mean() + else: + grad_penalty = 0 + real_batch.requires_grad = False + + return grad_penalty + +class NonSaturatingWithR1(BaseAdversarialLoss): + def __init__(self, gp_coef=5, weight=1, mask_as_fake_target=False, allow_scale_mask=False, + mask_scale_mode='nearest', extra_mask_weight_for_gen=0, + use_unmasked_for_gen=True, use_unmasked_for_discr=True): + self.gp_coef = gp_coef + self.weight = weight + # use for discr => use for gen; + # otherwise we teach only the discr to pay attention to very small difference + assert use_unmasked_for_gen or (not use_unmasked_for_discr) + # mask as target => use unmasked for discr: + # if we don't care about unmasked regions at all + # then it doesn't matter if the value of mask_as_fake_target is true or false + assert use_unmasked_for_discr or (not mask_as_fake_target) + self.use_unmasked_for_gen = use_unmasked_for_gen + self.use_unmasked_for_discr = use_unmasked_for_discr + self.mask_as_fake_target = mask_as_fake_target + self.allow_scale_mask = allow_scale_mask + self.mask_scale_mode = mask_scale_mode + self.extra_mask_weight_for_gen = extra_mask_weight_for_gen + + def generator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor, + discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor, + mask=None) \ + -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: + fake_loss = F.softplus(-discr_fake_pred) + if (self.mask_as_fake_target and self.extra_mask_weight_for_gen > 0) or \ + not self.use_unmasked_for_gen: # == if masked region should be treated differently + mask = self.interpolate_mask(mask, discr_fake_pred.shape[-2:]) + if not self.use_unmasked_for_gen: + fake_loss = fake_loss * mask + else: + pixel_weights = 1 + mask * self.extra_mask_weight_for_gen + fake_loss = fake_loss * pixel_weights + + return fake_loss.mean() * self.weight, dict() + + def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor, + generator: nn.Module, discriminator: nn.Module): + real_batch.requires_grad = True + + def discriminator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor, + discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor, + mask=None) \ + -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: + + real_loss = F.softplus(-discr_real_pred) + grad_penalty = make_r1_gp(discr_real_pred, real_batch) * self.gp_coef + fake_loss = F.softplus(discr_fake_pred) + + if not self.use_unmasked_for_discr or self.mask_as_fake_target: + # == if masked region should be treated differently + mask = self.interpolate_mask(mask, discr_fake_pred.shape[-2:]) + # use_unmasked_for_discr=False only makes sense for fakes; + # for reals there is no difference beetween two regions + fake_loss = fake_loss * mask + if self.mask_as_fake_target: + fake_loss = fake_loss + (1 - mask) * F.softplus(-discr_fake_pred) + + sum_discr_loss = real_loss + grad_penalty + fake_loss + metrics = dict(discr_real_out=discr_real_pred.mean(), + discr_fake_out=discr_fake_pred.mean(), + discr_real_gp=grad_penalty) + return sum_discr_loss.mean(), metrics + +class BCELoss(BaseAdversarialLoss): + def __init__(self, weight): + self.weight = weight + self.bce_loss = nn.BCEWithLogitsLoss() + + def generator_loss(self, discr_fake_pred: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: + real_mask_gt = torch.zeros(discr_fake_pred.shape).to(discr_fake_pred.device) + fake_loss = self.bce_loss(discr_fake_pred, real_mask_gt) * self.weight + return fake_loss, dict() + + def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor, + generator: nn.Module, discriminator: nn.Module): + real_batch.requires_grad = True + + def discriminator_loss(self, + mask: torch.Tensor, + discr_real_pred: torch.Tensor, + discr_fake_pred: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: + + real_mask_gt = torch.zeros(discr_real_pred.shape).to(discr_real_pred.device) + sum_discr_loss = (self.bce_loss(discr_real_pred, real_mask_gt) + self.bce_loss(discr_fake_pred, mask)) / 2 + metrics = dict(discr_real_out=discr_real_pred.mean(), + discr_fake_out=discr_fake_pred.mean(), + discr_real_gp=0) + return sum_discr_loss, metrics + + +def make_discrim_loss(kind, **kwargs): + if kind == 'r1': + return NonSaturatingWithR1(**kwargs) + elif kind == 'bce': + return BCELoss(**kwargs) + raise ValueError(f'Unknown adversarial loss kind {kind}') diff --git a/third-party/lama/saicinpainting/training/losses/constants.py b/third-party/lama/saicinpainting/training/losses/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..ae3e5e151342232be8e2c2a77fe6fd5798dc2a8c --- /dev/null +++ b/third-party/lama/saicinpainting/training/losses/constants.py @@ -0,0 +1,152 @@ +weights = {"ade20k": + [6.34517766497462, + 9.328358208955224, + 11.389521640091116, + 16.10305958132045, + 20.833333333333332, + 22.22222222222222, + 25.125628140703515, + 43.29004329004329, + 50.5050505050505, + 54.6448087431694, + 55.24861878453038, + 60.24096385542168, + 62.5, + 66.2251655629139, + 84.74576271186442, + 90.90909090909092, + 91.74311926605505, + 96.15384615384616, + 96.15384615384616, + 97.08737864077669, + 102.04081632653062, + 135.13513513513513, + 149.2537313432836, + 153.84615384615384, + 163.93442622950818, + 166.66666666666666, + 188.67924528301887, + 192.30769230769232, + 217.3913043478261, + 227.27272727272725, + 227.27272727272725, + 227.27272727272725, + 303.03030303030306, + 322.5806451612903, + 333.3333333333333, + 370.3703703703703, + 384.61538461538464, + 416.6666666666667, + 416.6666666666667, + 434.7826086956522, + 434.7826086956522, + 454.5454545454545, + 454.5454545454545, + 500.0, + 526.3157894736842, + 526.3157894736842, + 555.5555555555555, + 555.5555555555555, + 555.5555555555555, + 555.5555555555555, + 555.5555555555555, + 555.5555555555555, + 555.5555555555555, + 588.2352941176471, + 588.2352941176471, + 588.2352941176471, + 588.2352941176471, + 588.2352941176471, + 666.6666666666666, + 666.6666666666666, + 666.6666666666666, + 666.6666666666666, + 714.2857142857143, + 714.2857142857143, + 714.2857142857143, + 714.2857142857143, + 714.2857142857143, + 769.2307692307693, + 769.2307692307693, + 769.2307692307693, + 833.3333333333334, + 833.3333333333334, + 833.3333333333334, + 833.3333333333334, + 909.090909090909, + 1000.0, + 1111.111111111111, + 1111.111111111111, + 1111.111111111111, + 1111.111111111111, + 1111.111111111111, + 1250.0, + 1250.0, + 1250.0, + 1250.0, + 1250.0, + 1428.5714285714287, + 1428.5714285714287, + 1428.5714285714287, + 1428.5714285714287, + 1428.5714285714287, + 1428.5714285714287, + 1428.5714285714287, + 1666.6666666666667, + 1666.6666666666667, + 1666.6666666666667, + 1666.6666666666667, + 1666.6666666666667, + 1666.6666666666667, + 1666.6666666666667, + 1666.6666666666667, + 1666.6666666666667, + 1666.6666666666667, + 1666.6666666666667, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2000.0, + 2500.0, + 2500.0, + 2500.0, + 2500.0, + 2500.0, + 2500.0, + 2500.0, + 2500.0, + 2500.0, + 2500.0, + 2500.0, + 2500.0, + 2500.0, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 3333.3333333333335, + 5000.0, + 5000.0, + 5000.0] +} \ No newline at end of file diff --git a/third-party/lama/saicinpainting/training/losses/distance_weighting.py b/third-party/lama/saicinpainting/training/losses/distance_weighting.py new file mode 100644 index 0000000000000000000000000000000000000000..93052003b1e47fd663c70aedcecd144171f49204 --- /dev/null +++ b/third-party/lama/saicinpainting/training/losses/distance_weighting.py @@ -0,0 +1,126 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision + +from saicinpainting.training.losses.perceptual import IMAGENET_STD, IMAGENET_MEAN + + +def dummy_distance_weighter(real_img, pred_img, mask): + return mask + + +def get_gauss_kernel(kernel_size, width_factor=1): + coords = torch.stack(torch.meshgrid(torch.arange(kernel_size), + torch.arange(kernel_size)), + dim=0).float() + diff = torch.exp(-((coords - kernel_size // 2) ** 2).sum(0) / kernel_size / width_factor) + diff /= diff.sum() + return diff + + +class BlurMask(nn.Module): + def __init__(self, kernel_size=5, width_factor=1): + super().__init__() + self.filter = nn.Conv2d(1, 1, kernel_size, padding=kernel_size // 2, padding_mode='replicate', bias=False) + self.filter.weight.data.copy_(get_gauss_kernel(kernel_size, width_factor=width_factor)) + + def forward(self, real_img, pred_img, mask): + with torch.no_grad(): + result = self.filter(mask) * mask + return result + + +class EmulatedEDTMask(nn.Module): + def __init__(self, dilate_kernel_size=5, blur_kernel_size=5, width_factor=1): + super().__init__() + self.dilate_filter = nn.Conv2d(1, 1, dilate_kernel_size, padding=dilate_kernel_size// 2, padding_mode='replicate', + bias=False) + self.dilate_filter.weight.data.copy_(torch.ones(1, 1, dilate_kernel_size, dilate_kernel_size, dtype=torch.float)) + self.blur_filter = nn.Conv2d(1, 1, blur_kernel_size, padding=blur_kernel_size // 2, padding_mode='replicate', bias=False) + self.blur_filter.weight.data.copy_(get_gauss_kernel(blur_kernel_size, width_factor=width_factor)) + + def forward(self, real_img, pred_img, mask): + with torch.no_grad(): + known_mask = 1 - mask + dilated_known_mask = (self.dilate_filter(known_mask) > 1).float() + result = self.blur_filter(1 - dilated_known_mask) * mask + return result + + +class PropagatePerceptualSim(nn.Module): + def __init__(self, level=2, max_iters=10, temperature=500, erode_mask_size=3): + super().__init__() + vgg = torchvision.models.vgg19(pretrained=True).features + vgg_avg_pooling = [] + + for weights in vgg.parameters(): + weights.requires_grad = False + + cur_level_i = 0 + for module in vgg.modules(): + if module.__class__.__name__ == 'Sequential': + continue + elif module.__class__.__name__ == 'MaxPool2d': + vgg_avg_pooling.append(nn.AvgPool2d(kernel_size=2, stride=2, padding=0)) + else: + vgg_avg_pooling.append(module) + if module.__class__.__name__ == 'ReLU': + cur_level_i += 1 + if cur_level_i == level: + break + + self.features = nn.Sequential(*vgg_avg_pooling) + + self.max_iters = max_iters + self.temperature = temperature + self.do_erode = erode_mask_size > 0 + if self.do_erode: + self.erode_mask = nn.Conv2d(1, 1, erode_mask_size, padding=erode_mask_size // 2, bias=False) + self.erode_mask.weight.data.fill_(1) + + def forward(self, real_img, pred_img, mask): + with torch.no_grad(): + real_img = (real_img - IMAGENET_MEAN.to(real_img)) / IMAGENET_STD.to(real_img) + real_feats = self.features(real_img) + + vertical_sim = torch.exp(-(real_feats[:, :, 1:] - real_feats[:, :, :-1]).pow(2).sum(1, keepdim=True) + / self.temperature) + horizontal_sim = torch.exp(-(real_feats[:, :, :, 1:] - real_feats[:, :, :, :-1]).pow(2).sum(1, keepdim=True) + / self.temperature) + + mask_scaled = F.interpolate(mask, size=real_feats.shape[-2:], mode='bilinear', align_corners=False) + if self.do_erode: + mask_scaled = (self.erode_mask(mask_scaled) > 1).float() + + cur_knowness = 1 - mask_scaled + + for iter_i in range(self.max_iters): + new_top_knowness = F.pad(cur_knowness[:, :, :-1] * vertical_sim, (0, 0, 1, 0), mode='replicate') + new_bottom_knowness = F.pad(cur_knowness[:, :, 1:] * vertical_sim, (0, 0, 0, 1), mode='replicate') + + new_left_knowness = F.pad(cur_knowness[:, :, :, :-1] * horizontal_sim, (1, 0, 0, 0), mode='replicate') + new_right_knowness = F.pad(cur_knowness[:, :, :, 1:] * horizontal_sim, (0, 1, 0, 0), mode='replicate') + + new_knowness = torch.stack([new_top_knowness, new_bottom_knowness, + new_left_knowness, new_right_knowness], + dim=0).max(0).values + + cur_knowness = torch.max(cur_knowness, new_knowness) + + cur_knowness = F.interpolate(cur_knowness, size=mask.shape[-2:], mode='bilinear') + result = torch.min(mask, 1 - cur_knowness) + + return result + + +def make_mask_distance_weighter(kind='none', **kwargs): + if kind == 'none': + return dummy_distance_weighter + if kind == 'blur': + return BlurMask(**kwargs) + if kind == 'edt': + return EmulatedEDTMask(**kwargs) + if kind == 'pps': + return PropagatePerceptualSim(**kwargs) + raise ValueError(f'Unknown mask distance weighter kind {kind}') diff --git a/third-party/lama/saicinpainting/training/losses/feature_matching.py b/third-party/lama/saicinpainting/training/losses/feature_matching.py new file mode 100644 index 0000000000000000000000000000000000000000..c019895c9178817837d1a6773367b178a861dc61 --- /dev/null +++ b/third-party/lama/saicinpainting/training/losses/feature_matching.py @@ -0,0 +1,33 @@ +from typing import List + +import torch +import torch.nn.functional as F + + +def masked_l2_loss(pred, target, mask, weight_known, weight_missing): + per_pixel_l2 = F.mse_loss(pred, target, reduction='none') + pixel_weights = mask * weight_missing + (1 - mask) * weight_known + return (pixel_weights * per_pixel_l2).mean() + + +def masked_l1_loss(pred, target, mask, weight_known, weight_missing): + per_pixel_l1 = F.l1_loss(pred, target, reduction='none') + pixel_weights = mask * weight_missing + (1 - mask) * weight_known + return (pixel_weights * per_pixel_l1).mean() + + +def feature_matching_loss(fake_features: List[torch.Tensor], target_features: List[torch.Tensor], mask=None): + if mask is None: + res = torch.stack([F.mse_loss(fake_feat, target_feat) + for fake_feat, target_feat in zip(fake_features, target_features)]).mean() + else: + res = 0 + norm = 0 + for fake_feat, target_feat in zip(fake_features, target_features): + cur_mask = F.interpolate(mask, size=fake_feat.shape[-2:], mode='bilinear', align_corners=False) + error_weights = 1 - cur_mask + cur_val = ((fake_feat - target_feat).pow(2) * error_weights).mean() + res = res + cur_val + norm += 1 + res = res / norm + return res diff --git a/third-party/lama/saicinpainting/training/losses/perceptual.py b/third-party/lama/saicinpainting/training/losses/perceptual.py new file mode 100644 index 0000000000000000000000000000000000000000..8c055c2b327ce7943682af5c5f9394b9fcbec506 --- /dev/null +++ b/third-party/lama/saicinpainting/training/losses/perceptual.py @@ -0,0 +1,113 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision + +from models.ade20k import ModelBuilder +from saicinpainting.utils import check_and_warn_input_range + + +IMAGENET_MEAN = torch.FloatTensor([0.485, 0.456, 0.406])[None, :, None, None] +IMAGENET_STD = torch.FloatTensor([0.229, 0.224, 0.225])[None, :, None, None] + + +class PerceptualLoss(nn.Module): + def __init__(self, normalize_inputs=True): + super(PerceptualLoss, self).__init__() + + self.normalize_inputs = normalize_inputs + self.mean_ = IMAGENET_MEAN + self.std_ = IMAGENET_STD + + vgg = torchvision.models.vgg19(pretrained=True).features + vgg_avg_pooling = [] + + for weights in vgg.parameters(): + weights.requires_grad = False + + for module in vgg.modules(): + if module.__class__.__name__ == 'Sequential': + continue + elif module.__class__.__name__ == 'MaxPool2d': + vgg_avg_pooling.append(nn.AvgPool2d(kernel_size=2, stride=2, padding=0)) + else: + vgg_avg_pooling.append(module) + + self.vgg = nn.Sequential(*vgg_avg_pooling) + + def do_normalize_inputs(self, x): + return (x - self.mean_.to(x.device)) / self.std_.to(x.device) + + def partial_losses(self, input, target, mask=None): + check_and_warn_input_range(target, 0, 1, 'PerceptualLoss target in partial_losses') + + # we expect input and target to be in [0, 1] range + losses = [] + + if self.normalize_inputs: + features_input = self.do_normalize_inputs(input) + features_target = self.do_normalize_inputs(target) + else: + features_input = input + features_target = target + + for layer in self.vgg[:30]: + + features_input = layer(features_input) + features_target = layer(features_target) + + if layer.__class__.__name__ == 'ReLU': + loss = F.mse_loss(features_input, features_target, reduction='none') + + if mask is not None: + cur_mask = F.interpolate(mask, size=features_input.shape[-2:], + mode='bilinear', align_corners=False) + loss = loss * (1 - cur_mask) + + loss = loss.mean(dim=tuple(range(1, len(loss.shape)))) + losses.append(loss) + + return losses + + def forward(self, input, target, mask=None): + losses = self.partial_losses(input, target, mask=mask) + return torch.stack(losses).sum(dim=0) + + def get_global_features(self, input): + check_and_warn_input_range(input, 0, 1, 'PerceptualLoss input in get_global_features') + + if self.normalize_inputs: + features_input = self.do_normalize_inputs(input) + else: + features_input = input + + features_input = self.vgg(features_input) + return features_input + + +class ResNetPL(nn.Module): + def __init__(self, weight=1, + weights_path=None, arch_encoder='resnet50dilated', segmentation=True): + super().__init__() + self.impl = ModelBuilder.get_encoder(weights_path=weights_path, + arch_encoder=arch_encoder, + arch_decoder='ppm_deepsup', + fc_dim=2048, + segmentation=segmentation) + self.impl.eval() + for w in self.impl.parameters(): + w.requires_grad_(False) + + self.weight = weight + + def forward(self, pred, target): + pred = (pred - IMAGENET_MEAN.to(pred)) / IMAGENET_STD.to(pred) + target = (target - IMAGENET_MEAN.to(target)) / IMAGENET_STD.to(target) + + pred_feats = self.impl(pred, return_feature_maps=True) + target_feats = self.impl(target, return_feature_maps=True) + + result = torch.stack([F.mse_loss(cur_pred, cur_target) + for cur_pred, cur_target + in zip(pred_feats, target_feats)]).sum() * self.weight + return result diff --git a/third-party/lama/saicinpainting/training/losses/segmentation.py b/third-party/lama/saicinpainting/training/losses/segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..3d4a9f94eaae84722db584277dbbf9bc41ede357 --- /dev/null +++ b/third-party/lama/saicinpainting/training/losses/segmentation.py @@ -0,0 +1,43 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .constants import weights as constant_weights + + +class CrossEntropy2d(nn.Module): + def __init__(self, reduction="mean", ignore_label=255, weights=None, *args, **kwargs): + """ + weight (Tensor, optional): a manual rescaling weight given to each class. + If given, has to be a Tensor of size "nclasses" + """ + super(CrossEntropy2d, self).__init__() + self.reduction = reduction + self.ignore_label = ignore_label + self.weights = weights + if self.weights is not None: + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + self.weights = torch.FloatTensor(constant_weights[weights]).to(device) + + def forward(self, predict, target): + """ + Args: + predict:(n, c, h, w) + target:(n, 1, h, w) + """ + target = target.long() + assert not target.requires_grad + assert predict.dim() == 4, "{0}".format(predict.size()) + assert target.dim() == 4, "{0}".format(target.size()) + assert predict.size(0) == target.size(0), "{0} vs {1} ".format(predict.size(0), target.size(0)) + assert target.size(1) == 1, "{0}".format(target.size(1)) + assert predict.size(2) == target.size(2), "{0} vs {1} ".format(predict.size(2), target.size(2)) + assert predict.size(3) == target.size(3), "{0} vs {1} ".format(predict.size(3), target.size(3)) + target = target.squeeze(1) + n, c, h, w = predict.size() + target_mask = (target >= 0) * (target != self.ignore_label) + target = target[target_mask] + predict = predict.transpose(1, 2).transpose(2, 3).contiguous() + predict = predict[target_mask.view(n, h, w, 1).repeat(1, 1, 1, c)].view(-1, c) + loss = F.cross_entropy(predict, target, weight=self.weights, reduction=self.reduction) + return loss diff --git a/third-party/lama/saicinpainting/training/losses/style_loss.py b/third-party/lama/saicinpainting/training/losses/style_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..0bb42d7fbc5d17a47bec7365889868505f5fdfb5 --- /dev/null +++ b/third-party/lama/saicinpainting/training/losses/style_loss.py @@ -0,0 +1,155 @@ +import torch +import torch.nn as nn +import torchvision.models as models + + +class PerceptualLoss(nn.Module): + r""" + Perceptual loss, VGG-based + https://arxiv.org/abs/1603.08155 + https://github.com/dxyang/StyleTransfer/blob/master/utils.py + """ + + def __init__(self, weights=[1.0, 1.0, 1.0, 1.0, 1.0]): + super(PerceptualLoss, self).__init__() + self.add_module('vgg', VGG19()) + self.criterion = torch.nn.L1Loss() + self.weights = weights + + def __call__(self, x, y): + # Compute features + x_vgg, y_vgg = self.vgg(x), self.vgg(y) + + content_loss = 0.0 + content_loss += self.weights[0] * self.criterion(x_vgg['relu1_1'], y_vgg['relu1_1']) + content_loss += self.weights[1] * self.criterion(x_vgg['relu2_1'], y_vgg['relu2_1']) + content_loss += self.weights[2] * self.criterion(x_vgg['relu3_1'], y_vgg['relu3_1']) + content_loss += self.weights[3] * self.criterion(x_vgg['relu4_1'], y_vgg['relu4_1']) + content_loss += self.weights[4] * self.criterion(x_vgg['relu5_1'], y_vgg['relu5_1']) + + + return content_loss + + +class VGG19(torch.nn.Module): + def __init__(self): + super(VGG19, self).__init__() + features = models.vgg19(pretrained=True).features + self.relu1_1 = torch.nn.Sequential() + self.relu1_2 = torch.nn.Sequential() + + self.relu2_1 = torch.nn.Sequential() + self.relu2_2 = torch.nn.Sequential() + + self.relu3_1 = torch.nn.Sequential() + self.relu3_2 = torch.nn.Sequential() + self.relu3_3 = torch.nn.Sequential() + self.relu3_4 = torch.nn.Sequential() + + self.relu4_1 = torch.nn.Sequential() + self.relu4_2 = torch.nn.Sequential() + self.relu4_3 = torch.nn.Sequential() + self.relu4_4 = torch.nn.Sequential() + + self.relu5_1 = torch.nn.Sequential() + self.relu5_2 = torch.nn.Sequential() + self.relu5_3 = torch.nn.Sequential() + self.relu5_4 = torch.nn.Sequential() + + for x in range(2): + self.relu1_1.add_module(str(x), features[x]) + + for x in range(2, 4): + self.relu1_2.add_module(str(x), features[x]) + + for x in range(4, 7): + self.relu2_1.add_module(str(x), features[x]) + + for x in range(7, 9): + self.relu2_2.add_module(str(x), features[x]) + + for x in range(9, 12): + self.relu3_1.add_module(str(x), features[x]) + + for x in range(12, 14): + self.relu3_2.add_module(str(x), features[x]) + + for x in range(14, 16): + self.relu3_2.add_module(str(x), features[x]) + + for x in range(16, 18): + self.relu3_4.add_module(str(x), features[x]) + + for x in range(18, 21): + self.relu4_1.add_module(str(x), features[x]) + + for x in range(21, 23): + self.relu4_2.add_module(str(x), features[x]) + + for x in range(23, 25): + self.relu4_3.add_module(str(x), features[x]) + + for x in range(25, 27): + self.relu4_4.add_module(str(x), features[x]) + + for x in range(27, 30): + self.relu5_1.add_module(str(x), features[x]) + + for x in range(30, 32): + self.relu5_2.add_module(str(x), features[x]) + + for x in range(32, 34): + self.relu5_3.add_module(str(x), features[x]) + + for x in range(34, 36): + self.relu5_4.add_module(str(x), features[x]) + + # don't need the gradients, just want the features + for param in self.parameters(): + param.requires_grad = False + + def forward(self, x): + relu1_1 = self.relu1_1(x) + relu1_2 = self.relu1_2(relu1_1) + + relu2_1 = self.relu2_1(relu1_2) + relu2_2 = self.relu2_2(relu2_1) + + relu3_1 = self.relu3_1(relu2_2) + relu3_2 = self.relu3_2(relu3_1) + relu3_3 = self.relu3_3(relu3_2) + relu3_4 = self.relu3_4(relu3_3) + + relu4_1 = self.relu4_1(relu3_4) + relu4_2 = self.relu4_2(relu4_1) + relu4_3 = self.relu4_3(relu4_2) + relu4_4 = self.relu4_4(relu4_3) + + relu5_1 = self.relu5_1(relu4_4) + relu5_2 = self.relu5_2(relu5_1) + relu5_3 = self.relu5_3(relu5_2) + relu5_4 = self.relu5_4(relu5_3) + + out = { + 'relu1_1': relu1_1, + 'relu1_2': relu1_2, + + 'relu2_1': relu2_1, + 'relu2_2': relu2_2, + + 'relu3_1': relu3_1, + 'relu3_2': relu3_2, + 'relu3_3': relu3_3, + 'relu3_4': relu3_4, + + 'relu4_1': relu4_1, + 'relu4_2': relu4_2, + 'relu4_3': relu4_3, + 'relu4_4': relu4_4, + + 'relu5_1': relu5_1, + 'relu5_2': relu5_2, + 'relu5_3': relu5_3, + 'relu5_4': relu5_4, + } + return out diff --git a/third-party/lama/saicinpainting/training/modules/__init__.py b/third-party/lama/saicinpainting/training/modules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..82e1a9096a5bd8f3fb00e899d0239b078246cad4 --- /dev/null +++ b/third-party/lama/saicinpainting/training/modules/__init__.py @@ -0,0 +1,31 @@ +import logging + +from saicinpainting.training.modules.ffc import FFCResNetGenerator +from saicinpainting.training.modules.pix2pixhd import GlobalGenerator, MultiDilatedGlobalGenerator, \ + NLayerDiscriminator, MultidilatedNLayerDiscriminator + +def make_generator(config, kind, **kwargs): + logging.info(f'Make generator {kind}') + + if kind == 'pix2pixhd_multidilated': + return MultiDilatedGlobalGenerator(**kwargs) + + if kind == 'pix2pixhd_global': + return GlobalGenerator(**kwargs) + + if kind == 'ffc_resnet': + return FFCResNetGenerator(**kwargs) + + raise ValueError(f'Unknown generator kind {kind}') + + +def make_discriminator(kind, **kwargs): + logging.info(f'Make discriminator {kind}') + + if kind == 'pix2pixhd_nlayer_multidilated': + return MultidilatedNLayerDiscriminator(**kwargs) + + if kind == 'pix2pixhd_nlayer': + return NLayerDiscriminator(**kwargs) + + raise ValueError(f'Unknown discriminator kind {kind}') diff --git a/third-party/lama/saicinpainting/training/modules/base.py b/third-party/lama/saicinpainting/training/modules/base.py new file mode 100644 index 0000000000000000000000000000000000000000..a50c3fc7753a0bba64a5ab8c1ed64ff97e62313f --- /dev/null +++ b/third-party/lama/saicinpainting/training/modules/base.py @@ -0,0 +1,80 @@ +import abc +from typing import Tuple, List + +import torch +import torch.nn as nn + +from saicinpainting.training.modules.depthwise_sep_conv import DepthWiseSeperableConv +from saicinpainting.training.modules.multidilated_conv import MultidilatedConv + + +class BaseDiscriminator(nn.Module): + @abc.abstractmethod + def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """ + Predict scores and get intermediate activations. Useful for feature matching loss + :return tuple (scores, list of intermediate activations) + """ + raise NotImplemented() + + +def get_conv_block_ctor(kind='default'): + if not isinstance(kind, str): + return kind + if kind == 'default': + return nn.Conv2d + if kind == 'depthwise': + return DepthWiseSeperableConv + if kind == 'multidilated': + return MultidilatedConv + raise ValueError(f'Unknown convolutional block kind {kind}') + + +def get_norm_layer(kind='bn'): + if not isinstance(kind, str): + return kind + if kind == 'bn': + return nn.BatchNorm2d + if kind == 'in': + return nn.InstanceNorm2d + raise ValueError(f'Unknown norm block kind {kind}') + + +def get_activation(kind='tanh'): + if kind == 'tanh': + return nn.Tanh() + if kind == 'sigmoid': + return nn.Sigmoid() + if kind is False: + return nn.Identity() + raise ValueError(f'Unknown activation kind {kind}') + + +class SimpleMultiStepGenerator(nn.Module): + def __init__(self, steps: List[nn.Module]): + super().__init__() + self.steps = nn.ModuleList(steps) + + def forward(self, x): + cur_in = x + outs = [] + for step in self.steps: + cur_out = step(cur_in) + outs.append(cur_out) + cur_in = torch.cat((cur_in, cur_out), dim=1) + return torch.cat(outs[::-1], dim=1) + +def deconv_factory(kind, ngf, mult, norm_layer, activation, max_features): + if kind == 'convtranspose': + return [nn.ConvTranspose2d(min(max_features, ngf * mult), + min(max_features, int(ngf * mult / 2)), + kernel_size=3, stride=2, padding=1, output_padding=1), + norm_layer(min(max_features, int(ngf * mult / 2))), activation] + elif kind == 'bilinear': + return [nn.Upsample(scale_factor=2, mode='bilinear'), + DepthWiseSeperableConv(min(max_features, ngf * mult), + min(max_features, int(ngf * mult / 2)), + kernel_size=3, stride=1, padding=1), + norm_layer(min(max_features, int(ngf * mult / 2))), activation] + else: + raise Exception(f"Invalid deconv kind: {kind}") \ No newline at end of file diff --git a/third-party/lama/saicinpainting/training/modules/depthwise_sep_conv.py b/third-party/lama/saicinpainting/training/modules/depthwise_sep_conv.py new file mode 100644 index 0000000000000000000000000000000000000000..83dd15c3df1d9f40baf0091a373fa224532c9ddd --- /dev/null +++ b/third-party/lama/saicinpainting/training/modules/depthwise_sep_conv.py @@ -0,0 +1,17 @@ +import torch +import torch.nn as nn + +class DepthWiseSeperableConv(nn.Module): + def __init__(self, in_dim, out_dim, *args, **kwargs): + super().__init__() + if 'groups' in kwargs: + # ignoring groups for Depthwise Sep Conv + del kwargs['groups'] + + self.depthwise = nn.Conv2d(in_dim, in_dim, *args, groups=in_dim, **kwargs) + self.pointwise = nn.Conv2d(in_dim, out_dim, kernel_size=1) + + def forward(self, x): + out = self.depthwise(x) + out = self.pointwise(out) + return out \ No newline at end of file diff --git a/third-party/lama/saicinpainting/training/modules/fake_fakes.py b/third-party/lama/saicinpainting/training/modules/fake_fakes.py new file mode 100644 index 0000000000000000000000000000000000000000..45c4ad559cef2730b771a709197e00ae1c87683c --- /dev/null +++ b/third-party/lama/saicinpainting/training/modules/fake_fakes.py @@ -0,0 +1,47 @@ +import torch +from kornia import SamplePadding +from kornia.augmentation import RandomAffine, CenterCrop + + +class FakeFakesGenerator: + def __init__(self, aug_proba=0.5, img_aug_degree=30, img_aug_translate=0.2): + self.grad_aug = RandomAffine(degrees=360, + translate=0.2, + padding_mode=SamplePadding.REFLECTION, + keepdim=False, + p=1) + self.img_aug = RandomAffine(degrees=img_aug_degree, + translate=img_aug_translate, + padding_mode=SamplePadding.REFLECTION, + keepdim=True, + p=1) + self.aug_proba = aug_proba + + def __call__(self, input_images, masks): + blend_masks = self._fill_masks_with_gradient(masks) + blend_target = self._make_blend_target(input_images) + result = input_images * (1 - blend_masks) + blend_target * blend_masks + return result, blend_masks + + def _make_blend_target(self, input_images): + batch_size = input_images.shape[0] + permuted = input_images[torch.randperm(batch_size)] + augmented = self.img_aug(input_images) + is_aug = (torch.rand(batch_size, device=input_images.device)[:, None, None, None] < self.aug_proba).float() + result = augmented * is_aug + permuted * (1 - is_aug) + return result + + def _fill_masks_with_gradient(self, masks): + batch_size, _, height, width = masks.shape + grad = torch.linspace(0, 1, steps=width * 2, device=masks.device, dtype=masks.dtype) \ + .view(1, 1, 1, -1).expand(batch_size, 1, height * 2, width * 2) + grad = self.grad_aug(grad) + grad = CenterCrop((height, width))(grad) + grad *= masks + + grad_for_min = grad + (1 - masks) * 10 + grad -= grad_for_min.view(batch_size, -1).min(-1).values[:, None, None, None] + grad /= grad.view(batch_size, -1).max(-1).values[:, None, None, None] + 1e-6 + grad.clamp_(min=0, max=1) + + return grad diff --git a/third-party/lama/saicinpainting/training/modules/ffc.py b/third-party/lama/saicinpainting/training/modules/ffc.py new file mode 100644 index 0000000000000000000000000000000000000000..2f8aeb1411fc1537916275fd3243706cc74b8d3c --- /dev/null +++ b/third-party/lama/saicinpainting/training/modules/ffc.py @@ -0,0 +1,433 @@ +# Fast Fourier Convolution NeurIPS 2020 +# original implementation https://github.com/pkumivision/FFC/blob/main/model_zoo/ffc.py +# paper https://proceedings.neurips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +from saicinpainting.training.modules.base import get_activation, BaseDiscriminator +from saicinpainting.training.modules.spatial_transform import LearnableSpatialTransformWrapper +from saicinpainting.training.modules.squeeze_excitation import SELayer +from saicinpainting.utils import get_shape + + +class FFCSE_block(nn.Module): + + def __init__(self, channels, ratio_g): + super(FFCSE_block, self).__init__() + in_cg = int(channels * ratio_g) + in_cl = channels - in_cg + r = 16 + + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.conv1 = nn.Conv2d(channels, channels // r, + kernel_size=1, bias=True) + self.relu1 = nn.ReLU(inplace=True) + self.conv_a2l = None if in_cl == 0 else nn.Conv2d( + channels // r, in_cl, kernel_size=1, bias=True) + self.conv_a2g = None if in_cg == 0 else nn.Conv2d( + channels // r, in_cg, kernel_size=1, bias=True) + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + x = x if type(x) is tuple else (x, 0) + id_l, id_g = x + + x = id_l if type(id_g) is int else torch.cat([id_l, id_g], dim=1) + x = self.avgpool(x) + x = self.relu1(self.conv1(x)) + + x_l = 0 if self.conv_a2l is None else id_l * \ + self.sigmoid(self.conv_a2l(x)) + x_g = 0 if self.conv_a2g is None else id_g * \ + self.sigmoid(self.conv_a2g(x)) + return x_l, x_g + + +class FourierUnit(nn.Module): + + def __init__(self, in_channels, out_channels, groups=1, spatial_scale_factor=None, spatial_scale_mode='bilinear', + spectral_pos_encoding=False, use_se=False, se_kwargs=None, ffc3d=False, fft_norm='ortho'): + # bn_layer not used + super(FourierUnit, self).__init__() + self.groups = groups + + self.conv_layer = torch.nn.Conv2d(in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0), + out_channels=out_channels * 2, + kernel_size=1, stride=1, padding=0, groups=self.groups, bias=False) + self.bn = torch.nn.BatchNorm2d(out_channels * 2) + self.relu = torch.nn.ReLU(inplace=True) + + # squeeze and excitation block + self.use_se = use_se + if use_se: + if se_kwargs is None: + se_kwargs = {} + self.se = SELayer(self.conv_layer.in_channels, **se_kwargs) + + self.spatial_scale_factor = spatial_scale_factor + self.spatial_scale_mode = spatial_scale_mode + self.spectral_pos_encoding = spectral_pos_encoding + self.ffc3d = ffc3d + self.fft_norm = fft_norm + + def forward(self, x): + batch = x.shape[0] + + if self.spatial_scale_factor is not None: + orig_size = x.shape[-2:] + x = F.interpolate(x, scale_factor=self.spatial_scale_factor, mode=self.spatial_scale_mode, align_corners=False) + + r_size = x.size() + # (batch, c, h, w/2+1, 2) + fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1) + ffted = torch.fft.rfftn(x, dim=fft_dim, norm=self.fft_norm) + ffted = torch.stack((ffted.real, ffted.imag), dim=-1) + ffted = ffted.permute(0, 1, 4, 2, 3).contiguous() # (batch, c, 2, h, w/2+1) + ffted = ffted.view((batch, -1,) + ffted.size()[3:]) + + if self.spectral_pos_encoding: + height, width = ffted.shape[-2:] + coords_vert = torch.linspace(0, 1, height)[None, None, :, None].expand(batch, 1, height, width).to(ffted) + coords_hor = torch.linspace(0, 1, width)[None, None, None, :].expand(batch, 1, height, width).to(ffted) + ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1) + + if self.use_se: + ffted = self.se(ffted) + + ffted = self.conv_layer(ffted) # (batch, c*2, h, w/2+1) + ffted = self.relu(self.bn(ffted)) + + ffted = ffted.view((batch, -1, 2,) + ffted.size()[2:]).permute( + 0, 1, 3, 4, 2).contiguous() # (batch,c, t, h, w/2+1, 2) + ffted = torch.complex(ffted[..., 0], ffted[..., 1]) + + ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:] + output = torch.fft.irfftn(ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm) + + if self.spatial_scale_factor is not None: + output = F.interpolate(output, size=orig_size, mode=self.spatial_scale_mode, align_corners=False) + + return output + + +class SpectralTransform(nn.Module): + + def __init__(self, in_channels, out_channels, stride=1, groups=1, enable_lfu=True, **fu_kwargs): + # bn_layer not used + super(SpectralTransform, self).__init__() + self.enable_lfu = enable_lfu + if stride == 2: + self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2) + else: + self.downsample = nn.Identity() + + self.stride = stride + self.conv1 = nn.Sequential( + nn.Conv2d(in_channels, out_channels // + 2, kernel_size=1, groups=groups, bias=False), + nn.BatchNorm2d(out_channels // 2), + nn.ReLU(inplace=True) + ) + self.fu = FourierUnit( + out_channels // 2, out_channels // 2, groups, **fu_kwargs) + if self.enable_lfu: + self.lfu = FourierUnit( + out_channels // 2, out_channels // 2, groups) + self.conv2 = torch.nn.Conv2d( + out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False) + + def forward(self, x): + + x = self.downsample(x) + x = self.conv1(x) + output = self.fu(x) + + if self.enable_lfu: + n, c, h, w = x.shape + split_no = 2 + split_s = h // split_no + xs = torch.cat(torch.split( + x[:, :c // 4], split_s, dim=-2), dim=1).contiguous() + xs = torch.cat(torch.split(xs, split_s, dim=-1), + dim=1).contiguous() + xs = self.lfu(xs) + xs = xs.repeat(1, 1, split_no, split_no).contiguous() + else: + xs = 0 + + output = self.conv2(x + output + xs) + + return output + + +class FFC(nn.Module): + + def __init__(self, in_channels, out_channels, kernel_size, + ratio_gin, ratio_gout, stride=1, padding=0, + dilation=1, groups=1, bias=False, enable_lfu=True, + padding_type='reflect', gated=False, **spectral_kwargs): + super(FFC, self).__init__() + + assert stride == 1 or stride == 2, "Stride should be 1 or 2." + self.stride = stride + + in_cg = int(in_channels * ratio_gin) + in_cl = in_channels - in_cg + out_cg = int(out_channels * ratio_gout) + out_cl = out_channels - out_cg + #groups_g = 1 if groups == 1 else int(groups * ratio_gout) + #groups_l = 1 if groups == 1 else groups - groups_g + + self.ratio_gin = ratio_gin + self.ratio_gout = ratio_gout + self.global_in_num = in_cg + + module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d + self.convl2l = module(in_cl, out_cl, kernel_size, + stride, padding, dilation, groups, bias, padding_mode=padding_type) + module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d + self.convl2g = module(in_cl, out_cg, kernel_size, + stride, padding, dilation, groups, bias, padding_mode=padding_type) + module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d + self.convg2l = module(in_cg, out_cl, kernel_size, + stride, padding, dilation, groups, bias, padding_mode=padding_type) + module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform + self.convg2g = module( + in_cg, out_cg, stride, 1 if groups == 1 else groups // 2, enable_lfu, **spectral_kwargs) + + self.gated = gated + module = nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d + self.gate = module(in_channels, 2, 1) + + def forward(self, x): + x_l, x_g = x if type(x) is tuple else (x, 0) + out_xl, out_xg = 0, 0 + + if self.gated: + total_input_parts = [x_l] + if torch.is_tensor(x_g): + total_input_parts.append(x_g) + total_input = torch.cat(total_input_parts, dim=1) + + gates = torch.sigmoid(self.gate(total_input)) + g2l_gate, l2g_gate = gates.chunk(2, dim=1) + else: + g2l_gate, l2g_gate = 1, 1 + + if self.ratio_gout != 1: + out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate + if self.ratio_gout != 0: + out_xg = self.convl2g(x_l) * l2g_gate + self.convg2g(x_g) + + return out_xl, out_xg + + +class FFC_BN_ACT(nn.Module): + + def __init__(self, in_channels, out_channels, + kernel_size, ratio_gin, ratio_gout, + stride=1, padding=0, dilation=1, groups=1, bias=False, + norm_layer=nn.BatchNorm2d, activation_layer=nn.Identity, + padding_type='reflect', + enable_lfu=True, **kwargs): + super(FFC_BN_ACT, self).__init__() + self.ffc = FFC(in_channels, out_channels, kernel_size, + ratio_gin, ratio_gout, stride, padding, dilation, + groups, bias, enable_lfu, padding_type=padding_type, **kwargs) + lnorm = nn.Identity if ratio_gout == 1 else norm_layer + gnorm = nn.Identity if ratio_gout == 0 else norm_layer + global_channels = int(out_channels * ratio_gout) + self.bn_l = lnorm(out_channels - global_channels) + self.bn_g = gnorm(global_channels) + + lact = nn.Identity if ratio_gout == 1 else activation_layer + gact = nn.Identity if ratio_gout == 0 else activation_layer + self.act_l = lact(inplace=True) + self.act_g = gact(inplace=True) + + def forward(self, x): + x_l, x_g = self.ffc(x) + x_l = self.act_l(self.bn_l(x_l)) + x_g = self.act_g(self.bn_g(x_g)) + return x_l, x_g + + +class FFCResnetBlock(nn.Module): + def __init__(self, dim, padding_type, norm_layer, activation_layer=nn.ReLU, dilation=1, + spatial_transform_kwargs=None, inline=False, **conv_kwargs): + super().__init__() + self.conv1 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation, + norm_layer=norm_layer, + activation_layer=activation_layer, + padding_type=padding_type, + **conv_kwargs) + self.conv2 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation, + norm_layer=norm_layer, + activation_layer=activation_layer, + padding_type=padding_type, + **conv_kwargs) + if spatial_transform_kwargs is not None: + self.conv1 = LearnableSpatialTransformWrapper(self.conv1, **spatial_transform_kwargs) + self.conv2 = LearnableSpatialTransformWrapper(self.conv2, **spatial_transform_kwargs) + self.inline = inline + + def forward(self, x): + if self.inline: + x_l, x_g = x[:, :-self.conv1.ffc.global_in_num], x[:, -self.conv1.ffc.global_in_num:] + else: + x_l, x_g = x if type(x) is tuple else (x, 0) + + id_l, id_g = x_l, x_g + + x_l, x_g = self.conv1((x_l, x_g)) + x_l, x_g = self.conv2((x_l, x_g)) + + x_l, x_g = id_l + x_l, id_g + x_g + out = x_l, x_g + if self.inline: + out = torch.cat(out, dim=1) + return out + + +class ConcatTupleLayer(nn.Module): + def forward(self, x): + assert isinstance(x, tuple) + x_l, x_g = x + assert torch.is_tensor(x_l) or torch.is_tensor(x_g) + if not torch.is_tensor(x_g): + return x_l + return torch.cat(x, dim=1) + + +class FFCResNetGenerator(nn.Module): + def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d, + padding_type='reflect', activation_layer=nn.ReLU, + up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), + init_conv_kwargs={}, downsample_conv_kwargs={}, resnet_conv_kwargs={}, + spatial_transform_layers=None, spatial_transform_kwargs={}, + add_out_act=True, max_features=1024, out_ffc=False, out_ffc_kwargs={}): + assert (n_blocks >= 0) + super().__init__() + + model = [nn.ReflectionPad2d(3), + FFC_BN_ACT(input_nc, ngf, kernel_size=7, padding=0, norm_layer=norm_layer, + activation_layer=activation_layer, **init_conv_kwargs)] + + ### downsample + for i in range(n_downsampling): + mult = 2 ** i + if i == n_downsampling - 1: + cur_conv_kwargs = dict(downsample_conv_kwargs) + cur_conv_kwargs['ratio_gout'] = resnet_conv_kwargs.get('ratio_gin', 0) + else: + cur_conv_kwargs = downsample_conv_kwargs + model += [FFC_BN_ACT(min(max_features, ngf * mult), + min(max_features, ngf * mult * 2), + kernel_size=3, stride=2, padding=1, + norm_layer=norm_layer, + activation_layer=activation_layer, + **cur_conv_kwargs)] + + mult = 2 ** n_downsampling + feats_num_bottleneck = min(max_features, ngf * mult) + + ### resnet blocks + for i in range(n_blocks): + cur_resblock = FFCResnetBlock(feats_num_bottleneck, padding_type=padding_type, activation_layer=activation_layer, + norm_layer=norm_layer, **resnet_conv_kwargs) + if spatial_transform_layers is not None and i in spatial_transform_layers: + cur_resblock = LearnableSpatialTransformWrapper(cur_resblock, **spatial_transform_kwargs) + model += [cur_resblock] + + model += [ConcatTupleLayer()] + + ### upsample + for i in range(n_downsampling): + mult = 2 ** (n_downsampling - i) + model += [nn.ConvTranspose2d(min(max_features, ngf * mult), + min(max_features, int(ngf * mult / 2)), + kernel_size=3, stride=2, padding=1, output_padding=1), + up_norm_layer(min(max_features, int(ngf * mult / 2))), + up_activation] + + if out_ffc: + model += [FFCResnetBlock(ngf, padding_type=padding_type, activation_layer=activation_layer, + norm_layer=norm_layer, inline=True, **out_ffc_kwargs)] + + model += [nn.ReflectionPad2d(3), + nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] + if add_out_act: + model.append(get_activation('tanh' if add_out_act is True else add_out_act)) + self.model = nn.Sequential(*model) + + def forward(self, input): + return self.model(input) + + +class FFCNLayerDiscriminator(BaseDiscriminator): + def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, max_features=512, + init_conv_kwargs={}, conv_kwargs={}): + super().__init__() + self.n_layers = n_layers + + def _act_ctor(inplace=True): + return nn.LeakyReLU(negative_slope=0.2, inplace=inplace) + + kw = 3 + padw = int(np.ceil((kw-1.0)/2)) + sequence = [[FFC_BN_ACT(input_nc, ndf, kernel_size=kw, padding=padw, norm_layer=norm_layer, + activation_layer=_act_ctor, **init_conv_kwargs)]] + + nf = ndf + for n in range(1, n_layers): + nf_prev = nf + nf = min(nf * 2, max_features) + + cur_model = [ + FFC_BN_ACT(nf_prev, nf, + kernel_size=kw, stride=2, padding=padw, + norm_layer=norm_layer, + activation_layer=_act_ctor, + **conv_kwargs) + ] + sequence.append(cur_model) + + nf_prev = nf + nf = min(nf * 2, 512) + + cur_model = [ + FFC_BN_ACT(nf_prev, nf, + kernel_size=kw, stride=1, padding=padw, + norm_layer=norm_layer, + activation_layer=lambda *args, **kwargs: nn.LeakyReLU(*args, negative_slope=0.2, **kwargs), + **conv_kwargs), + ConcatTupleLayer() + ] + sequence.append(cur_model) + + sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]] + + for n in range(len(sequence)): + setattr(self, 'model'+str(n), nn.Sequential(*sequence[n])) + + def get_all_activations(self, x): + res = [x] + for n in range(self.n_layers + 2): + model = getattr(self, 'model' + str(n)) + res.append(model(res[-1])) + return res[1:] + + def forward(self, x): + act = self.get_all_activations(x) + feats = [] + for out in act[:-1]: + if isinstance(out, tuple): + if torch.is_tensor(out[1]): + out = torch.cat(out, dim=1) + else: + out = out[0] + feats.append(out) + return act[-1], feats diff --git a/third-party/lama/saicinpainting/training/modules/multidilated_conv.py b/third-party/lama/saicinpainting/training/modules/multidilated_conv.py new file mode 100644 index 0000000000000000000000000000000000000000..d267ee2aa5eb84b6a9291d0eaaff322c6c2802d0 --- /dev/null +++ b/third-party/lama/saicinpainting/training/modules/multidilated_conv.py @@ -0,0 +1,98 @@ +import torch +import torch.nn as nn +import random +from saicinpainting.training.modules.depthwise_sep_conv import DepthWiseSeperableConv + +class MultidilatedConv(nn.Module): + def __init__(self, in_dim, out_dim, kernel_size, dilation_num=3, comb_mode='sum', equal_dim=True, + shared_weights=False, padding=1, min_dilation=1, shuffle_in_channels=False, use_depthwise=False, **kwargs): + super().__init__() + convs = [] + self.equal_dim = equal_dim + assert comb_mode in ('cat_out', 'sum', 'cat_in', 'cat_both'), comb_mode + if comb_mode in ('cat_out', 'cat_both'): + self.cat_out = True + if equal_dim: + assert out_dim % dilation_num == 0 + out_dims = [out_dim // dilation_num] * dilation_num + self.index = sum([[i + j * (out_dims[0]) for j in range(dilation_num)] for i in range(out_dims[0])], []) + else: + out_dims = [out_dim // 2 ** (i + 1) for i in range(dilation_num - 1)] + out_dims.append(out_dim - sum(out_dims)) + index = [] + starts = [0] + out_dims[:-1] + lengths = [out_dims[i] // out_dims[-1] for i in range(dilation_num)] + for i in range(out_dims[-1]): + for j in range(dilation_num): + index += list(range(starts[j], starts[j] + lengths[j])) + starts[j] += lengths[j] + self.index = index + assert(len(index) == out_dim) + self.out_dims = out_dims + else: + self.cat_out = False + self.out_dims = [out_dim] * dilation_num + + if comb_mode in ('cat_in', 'cat_both'): + if equal_dim: + assert in_dim % dilation_num == 0 + in_dims = [in_dim // dilation_num] * dilation_num + else: + in_dims = [in_dim // 2 ** (i + 1) for i in range(dilation_num - 1)] + in_dims.append(in_dim - sum(in_dims)) + self.in_dims = in_dims + self.cat_in = True + else: + self.cat_in = False + self.in_dims = [in_dim] * dilation_num + + conv_type = DepthWiseSeperableConv if use_depthwise else nn.Conv2d + dilation = min_dilation + for i in range(dilation_num): + if isinstance(padding, int): + cur_padding = padding * dilation + else: + cur_padding = padding[i] + convs.append(conv_type( + self.in_dims[i], self.out_dims[i], kernel_size, padding=cur_padding, dilation=dilation, **kwargs + )) + if i > 0 and shared_weights: + convs[-1].weight = convs[0].weight + convs[-1].bias = convs[0].bias + dilation *= 2 + self.convs = nn.ModuleList(convs) + + self.shuffle_in_channels = shuffle_in_channels + if self.shuffle_in_channels: + # shuffle list as shuffling of tensors is nondeterministic + in_channels_permute = list(range(in_dim)) + random.shuffle(in_channels_permute) + # save as buffer so it is saved and loaded with checkpoint + self.register_buffer('in_channels_permute', torch.tensor(in_channels_permute)) + + def forward(self, x): + if self.shuffle_in_channels: + x = x[:, self.in_channels_permute] + + outs = [] + if self.cat_in: + if self.equal_dim: + x = x.chunk(len(self.convs), dim=1) + else: + new_x = [] + start = 0 + for dim in self.in_dims: + new_x.append(x[:, start:start+dim]) + start += dim + x = new_x + for i, conv in enumerate(self.convs): + if self.cat_in: + input = x[i] + else: + input = x + outs.append(conv(input)) + if self.cat_out: + out = torch.cat(outs, dim=1)[:, self.index] + else: + out = sum(outs) + return out diff --git a/third-party/lama/saicinpainting/training/modules/multiscale.py b/third-party/lama/saicinpainting/training/modules/multiscale.py new file mode 100644 index 0000000000000000000000000000000000000000..65f0a54925593e9da8106bfc6d65a4098ce001d7 --- /dev/null +++ b/third-party/lama/saicinpainting/training/modules/multiscale.py @@ -0,0 +1,244 @@ +from typing import List, Tuple, Union, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from saicinpainting.training.modules.base import get_conv_block_ctor, get_activation +from saicinpainting.training.modules.pix2pixhd import ResnetBlock + + +class ResNetHead(nn.Module): + def __init__(self, input_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d, + padding_type='reflect', conv_kind='default', activation=nn.ReLU(True)): + assert (n_blocks >= 0) + super(ResNetHead, self).__init__() + + conv_layer = get_conv_block_ctor(conv_kind) + + model = [nn.ReflectionPad2d(3), + conv_layer(input_nc, ngf, kernel_size=7, padding=0), + norm_layer(ngf), + activation] + + ### downsample + for i in range(n_downsampling): + mult = 2 ** i + model += [conv_layer(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1), + norm_layer(ngf * mult * 2), + activation] + + mult = 2 ** n_downsampling + + ### resnet blocks + for i in range(n_blocks): + model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer, + conv_kind=conv_kind)] + + self.model = nn.Sequential(*model) + + def forward(self, input): + return self.model(input) + + +class ResNetTail(nn.Module): + def __init__(self, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d, + padding_type='reflect', conv_kind='default', activation=nn.ReLU(True), + up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), add_out_act=False, out_extra_layers_n=0, + add_in_proj=None): + assert (n_blocks >= 0) + super(ResNetTail, self).__init__() + + mult = 2 ** n_downsampling + + model = [] + + if add_in_proj is not None: + model.append(nn.Conv2d(add_in_proj, ngf * mult, kernel_size=1)) + + ### resnet blocks + for i in range(n_blocks): + model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer, + conv_kind=conv_kind)] + + ### upsample + for i in range(n_downsampling): + mult = 2 ** (n_downsampling - i) + model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1, + output_padding=1), + up_norm_layer(int(ngf * mult / 2)), + up_activation] + self.model = nn.Sequential(*model) + + out_layers = [] + for _ in range(out_extra_layers_n): + out_layers += [nn.Conv2d(ngf, ngf, kernel_size=1, padding=0), + up_norm_layer(ngf), + up_activation] + out_layers += [nn.ReflectionPad2d(3), + nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] + + if add_out_act: + out_layers.append(get_activation('tanh' if add_out_act is True else add_out_act)) + + self.out_proj = nn.Sequential(*out_layers) + + def forward(self, input, return_last_act=False): + features = self.model(input) + out = self.out_proj(features) + if return_last_act: + return out, features + else: + return out + + +class MultiscaleResNet(nn.Module): + def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=2, n_blocks_head=2, n_blocks_tail=6, n_scales=3, + norm_layer=nn.BatchNorm2d, padding_type='reflect', conv_kind='default', activation=nn.ReLU(True), + up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), add_out_act=False, out_extra_layers_n=0, + out_cumulative=False, return_only_hr=False): + super().__init__() + + self.heads = nn.ModuleList([ResNetHead(input_nc, ngf=ngf, n_downsampling=n_downsampling, + n_blocks=n_blocks_head, norm_layer=norm_layer, padding_type=padding_type, + conv_kind=conv_kind, activation=activation) + for i in range(n_scales)]) + tail_in_feats = ngf * (2 ** n_downsampling) + ngf + self.tails = nn.ModuleList([ResNetTail(output_nc, + ngf=ngf, n_downsampling=n_downsampling, + n_blocks=n_blocks_tail, norm_layer=norm_layer, padding_type=padding_type, + conv_kind=conv_kind, activation=activation, up_norm_layer=up_norm_layer, + up_activation=up_activation, add_out_act=add_out_act, + out_extra_layers_n=out_extra_layers_n, + add_in_proj=None if (i == n_scales - 1) else tail_in_feats) + for i in range(n_scales)]) + + self.out_cumulative = out_cumulative + self.return_only_hr = return_only_hr + + @property + def num_scales(self): + return len(self.heads) + + def forward(self, ms_inputs: List[torch.Tensor], smallest_scales_num: Optional[int] = None) \ + -> Union[torch.Tensor, List[torch.Tensor]]: + """ + :param ms_inputs: List of inputs of different resolutions from HR to LR + :param smallest_scales_num: int or None, number of smallest scales to take at input + :return: Depending on return_only_hr: + True: Only the most HR output + False: List of outputs of different resolutions from HR to LR + """ + if smallest_scales_num is None: + assert len(self.heads) == len(ms_inputs), (len(self.heads), len(ms_inputs), smallest_scales_num) + smallest_scales_num = len(self.heads) + else: + assert smallest_scales_num == len(ms_inputs) <= len(self.heads), (len(self.heads), len(ms_inputs), smallest_scales_num) + + cur_heads = self.heads[-smallest_scales_num:] + ms_features = [cur_head(cur_inp) for cur_head, cur_inp in zip(cur_heads, ms_inputs)] + + all_outputs = [] + prev_tail_features = None + for i in range(len(ms_features)): + scale_i = -i - 1 + + cur_tail_input = ms_features[-i - 1] + if prev_tail_features is not None: + if prev_tail_features.shape != cur_tail_input.shape: + prev_tail_features = F.interpolate(prev_tail_features, size=cur_tail_input.shape[2:], + mode='bilinear', align_corners=False) + cur_tail_input = torch.cat((cur_tail_input, prev_tail_features), dim=1) + + cur_out, cur_tail_feats = self.tails[scale_i](cur_tail_input, return_last_act=True) + + prev_tail_features = cur_tail_feats + all_outputs.append(cur_out) + + if self.out_cumulative: + all_outputs_cum = [all_outputs[0]] + for i in range(1, len(ms_features)): + cur_out = all_outputs[i] + cur_out_cum = cur_out + F.interpolate(all_outputs_cum[-1], size=cur_out.shape[2:], + mode='bilinear', align_corners=False) + all_outputs_cum.append(cur_out_cum) + all_outputs = all_outputs_cum + + if self.return_only_hr: + return all_outputs[-1] + else: + return all_outputs[::-1] + + +class MultiscaleDiscriminatorSimple(nn.Module): + def __init__(self, ms_impl): + super().__init__() + self.ms_impl = nn.ModuleList(ms_impl) + + @property + def num_scales(self): + return len(self.ms_impl) + + def forward(self, ms_inputs: List[torch.Tensor], smallest_scales_num: Optional[int] = None) \ + -> List[Tuple[torch.Tensor, List[torch.Tensor]]]: + """ + :param ms_inputs: List of inputs of different resolutions from HR to LR + :param smallest_scales_num: int or None, number of smallest scales to take at input + :return: List of pairs (prediction, features) for different resolutions from HR to LR + """ + if smallest_scales_num is None: + assert len(self.ms_impl) == len(ms_inputs), (len(self.ms_impl), len(ms_inputs), smallest_scales_num) + smallest_scales_num = len(self.heads) + else: + assert smallest_scales_num == len(ms_inputs) <= len(self.ms_impl), \ + (len(self.ms_impl), len(ms_inputs), smallest_scales_num) + + return [cur_discr(cur_input) for cur_discr, cur_input in zip(self.ms_impl[-smallest_scales_num:], ms_inputs)] + + +class SingleToMultiScaleInputMixin: + def forward(self, x: torch.Tensor) -> List: + orig_height, orig_width = x.shape[2:] + factors = [2 ** i for i in range(self.num_scales)] + ms_inputs = [F.interpolate(x, size=(orig_height // f, orig_width // f), mode='bilinear', align_corners=False) + for f in factors] + return super().forward(ms_inputs) + + +class GeneratorMultiToSingleOutputMixin: + def forward(self, x): + return super().forward(x)[0] + + +class DiscriminatorMultiToSingleOutputMixin: + def forward(self, x): + out_feat_tuples = super().forward(x) + return out_feat_tuples[0][0], [f for _, flist in out_feat_tuples for f in flist] + + +class DiscriminatorMultiToSingleOutputStackedMixin: + def __init__(self, *args, return_feats_only_levels=None, **kwargs): + super().__init__(*args, **kwargs) + self.return_feats_only_levels = return_feats_only_levels + + def forward(self, x): + out_feat_tuples = super().forward(x) + outs = [out for out, _ in out_feat_tuples] + scaled_outs = [outs[0]] + [F.interpolate(cur_out, size=outs[0].shape[-2:], + mode='bilinear', align_corners=False) + for cur_out in outs[1:]] + out = torch.cat(scaled_outs, dim=1) + if self.return_feats_only_levels is not None: + feat_lists = [out_feat_tuples[i][1] for i in self.return_feats_only_levels] + else: + feat_lists = [flist for _, flist in out_feat_tuples] + feats = [f for flist in feat_lists for f in flist] + return out, feats + + +class MultiscaleDiscrSingleInput(SingleToMultiScaleInputMixin, DiscriminatorMultiToSingleOutputStackedMixin, MultiscaleDiscriminatorSimple): + pass + + +class MultiscaleResNetSingle(GeneratorMultiToSingleOutputMixin, SingleToMultiScaleInputMixin, MultiscaleResNet): + pass diff --git a/third-party/lama/saicinpainting/training/modules/pix2pixhd.py b/third-party/lama/saicinpainting/training/modules/pix2pixhd.py new file mode 100644 index 0000000000000000000000000000000000000000..08c6afd777a88cd232592acbbf0ef25db8d43217 --- /dev/null +++ b/third-party/lama/saicinpainting/training/modules/pix2pixhd.py @@ -0,0 +1,669 @@ +# original: https://github.com/NVIDIA/pix2pixHD/blob/master/models/networks.py +import collections +from functools import partial +import functools +import logging +from collections import defaultdict + +import numpy as np +import torch.nn as nn + +from saicinpainting.training.modules.base import BaseDiscriminator, deconv_factory, get_conv_block_ctor, get_norm_layer, get_activation +from saicinpainting.training.modules.ffc import FFCResnetBlock +from saicinpainting.training.modules.multidilated_conv import MultidilatedConv + +class DotDict(defaultdict): + # https://stackoverflow.com/questions/2352181/how-to-use-a-dot-to-access-members-of-dictionary + """dot.notation access to dictionary attributes""" + __getattr__ = defaultdict.get + __setattr__ = defaultdict.__setitem__ + __delattr__ = defaultdict.__delitem__ + +class Identity(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x): + return x + + +class ResnetBlock(nn.Module): + def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False, conv_kind='default', + dilation=1, in_dim=None, groups=1, second_dilation=None): + super(ResnetBlock, self).__init__() + self.in_dim = in_dim + self.dim = dim + if second_dilation is None: + second_dilation = dilation + self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout, + conv_kind=conv_kind, dilation=dilation, in_dim=in_dim, groups=groups, + second_dilation=second_dilation) + + if self.in_dim is not None: + self.input_conv = nn.Conv2d(in_dim, dim, 1) + + self.out_channnels = dim + + def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout, conv_kind='default', + dilation=1, in_dim=None, groups=1, second_dilation=1): + conv_layer = get_conv_block_ctor(conv_kind) + + conv_block = [] + p = 0 + if padding_type == 'reflect': + conv_block += [nn.ReflectionPad2d(dilation)] + elif padding_type == 'replicate': + conv_block += [nn.ReplicationPad2d(dilation)] + elif padding_type == 'zero': + p = dilation + else: + raise NotImplementedError('padding [%s] is not implemented' % padding_type) + + if in_dim is None: + in_dim = dim + + conv_block += [conv_layer(in_dim, dim, kernel_size=3, padding=p, dilation=dilation), + norm_layer(dim), + activation] + if use_dropout: + conv_block += [nn.Dropout(0.5)] + + p = 0 + if padding_type == 'reflect': + conv_block += [nn.ReflectionPad2d(second_dilation)] + elif padding_type == 'replicate': + conv_block += [nn.ReplicationPad2d(second_dilation)] + elif padding_type == 'zero': + p = second_dilation + else: + raise NotImplementedError('padding [%s] is not implemented' % padding_type) + conv_block += [conv_layer(dim, dim, kernel_size=3, padding=p, dilation=second_dilation, groups=groups), + norm_layer(dim)] + + return nn.Sequential(*conv_block) + + def forward(self, x): + x_before = x + if self.in_dim is not None: + x = self.input_conv(x) + out = x + self.conv_block(x_before) + return out + +class ResnetBlock5x5(nn.Module): + def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False, conv_kind='default', + dilation=1, in_dim=None, groups=1, second_dilation=None): + super(ResnetBlock5x5, self).__init__() + self.in_dim = in_dim + self.dim = dim + if second_dilation is None: + second_dilation = dilation + self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout, + conv_kind=conv_kind, dilation=dilation, in_dim=in_dim, groups=groups, + second_dilation=second_dilation) + + if self.in_dim is not None: + self.input_conv = nn.Conv2d(in_dim, dim, 1) + + self.out_channnels = dim + + def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout, conv_kind='default', + dilation=1, in_dim=None, groups=1, second_dilation=1): + conv_layer = get_conv_block_ctor(conv_kind) + + conv_block = [] + p = 0 + if padding_type == 'reflect': + conv_block += [nn.ReflectionPad2d(dilation * 2)] + elif padding_type == 'replicate': + conv_block += [nn.ReplicationPad2d(dilation * 2)] + elif padding_type == 'zero': + p = dilation * 2 + else: + raise NotImplementedError('padding [%s] is not implemented' % padding_type) + + if in_dim is None: + in_dim = dim + + conv_block += [conv_layer(in_dim, dim, kernel_size=5, padding=p, dilation=dilation), + norm_layer(dim), + activation] + if use_dropout: + conv_block += [nn.Dropout(0.5)] + + p = 0 + if padding_type == 'reflect': + conv_block += [nn.ReflectionPad2d(second_dilation * 2)] + elif padding_type == 'replicate': + conv_block += [nn.ReplicationPad2d(second_dilation * 2)] + elif padding_type == 'zero': + p = second_dilation * 2 + else: + raise NotImplementedError('padding [%s] is not implemented' % padding_type) + conv_block += [conv_layer(dim, dim, kernel_size=5, padding=p, dilation=second_dilation, groups=groups), + norm_layer(dim)] + + return nn.Sequential(*conv_block) + + def forward(self, x): + x_before = x + if self.in_dim is not None: + x = self.input_conv(x) + out = x + self.conv_block(x_before) + return out + + +class MultidilatedResnetBlock(nn.Module): + def __init__(self, dim, padding_type, conv_layer, norm_layer, activation=nn.ReLU(True), use_dropout=False): + super().__init__() + self.conv_block = self.build_conv_block(dim, padding_type, conv_layer, norm_layer, activation, use_dropout) + + def build_conv_block(self, dim, padding_type, conv_layer, norm_layer, activation, use_dropout, dilation=1): + conv_block = [] + conv_block += [conv_layer(dim, dim, kernel_size=3, padding_mode=padding_type), + norm_layer(dim), + activation] + if use_dropout: + conv_block += [nn.Dropout(0.5)] + + conv_block += [conv_layer(dim, dim, kernel_size=3, padding_mode=padding_type), + norm_layer(dim)] + + return nn.Sequential(*conv_block) + + def forward(self, x): + out = x + self.conv_block(x) + return out + + +class MultiDilatedGlobalGenerator(nn.Module): + def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, + n_blocks=3, norm_layer=nn.BatchNorm2d, + padding_type='reflect', conv_kind='default', + deconv_kind='convtranspose', activation=nn.ReLU(True), + up_norm_layer=nn.BatchNorm2d, affine=None, up_activation=nn.ReLU(True), + add_out_act=True, max_features=1024, multidilation_kwargs={}, + ffc_positions=None, ffc_kwargs={}): + assert (n_blocks >= 0) + super().__init__() + + conv_layer = get_conv_block_ctor(conv_kind) + resnet_conv_layer = functools.partial(get_conv_block_ctor('multidilated'), **multidilation_kwargs) + norm_layer = get_norm_layer(norm_layer) + if affine is not None: + norm_layer = partial(norm_layer, affine=affine) + up_norm_layer = get_norm_layer(up_norm_layer) + if affine is not None: + up_norm_layer = partial(up_norm_layer, affine=affine) + + model = [nn.ReflectionPad2d(3), + conv_layer(input_nc, ngf, kernel_size=7, padding=0), + norm_layer(ngf), + activation] + + identity = Identity() + ### downsample + for i in range(n_downsampling): + mult = 2 ** i + + model += [conv_layer(min(max_features, ngf * mult), + min(max_features, ngf * mult * 2), + kernel_size=3, stride=2, padding=1), + norm_layer(min(max_features, ngf * mult * 2)), + activation] + + mult = 2 ** n_downsampling + feats_num_bottleneck = min(max_features, ngf * mult) + + ### resnet blocks + for i in range(n_blocks): + if ffc_positions is not None and i in ffc_positions: + model += [FFCResnetBlock(feats_num_bottleneck, padding_type, norm_layer, activation_layer=nn.ReLU, + inline=True, **ffc_kwargs)] + model += [MultidilatedResnetBlock(feats_num_bottleneck, padding_type=padding_type, + conv_layer=resnet_conv_layer, activation=activation, + norm_layer=norm_layer)] + + ### upsample + for i in range(n_downsampling): + mult = 2 ** (n_downsampling - i) + model += deconv_factory(deconv_kind, ngf, mult, up_norm_layer, up_activation, max_features) + model += [nn.ReflectionPad2d(3), + nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] + if add_out_act: + model.append(get_activation('tanh' if add_out_act is True else add_out_act)) + self.model = nn.Sequential(*model) + + def forward(self, input): + return self.model(input) + +class ConfigGlobalGenerator(nn.Module): + def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, + n_blocks=3, norm_layer=nn.BatchNorm2d, + padding_type='reflect', conv_kind='default', + deconv_kind='convtranspose', activation=nn.ReLU(True), + up_norm_layer=nn.BatchNorm2d, affine=None, up_activation=nn.ReLU(True), + add_out_act=True, max_features=1024, + manual_block_spec=[], + resnet_block_kind='multidilatedresnetblock', + resnet_conv_kind='multidilated', + resnet_dilation=1, + multidilation_kwargs={}): + assert (n_blocks >= 0) + super().__init__() + + conv_layer = get_conv_block_ctor(conv_kind) + resnet_conv_layer = functools.partial(get_conv_block_ctor(resnet_conv_kind), **multidilation_kwargs) + norm_layer = get_norm_layer(norm_layer) + if affine is not None: + norm_layer = partial(norm_layer, affine=affine) + up_norm_layer = get_norm_layer(up_norm_layer) + if affine is not None: + up_norm_layer = partial(up_norm_layer, affine=affine) + + model = [nn.ReflectionPad2d(3), + conv_layer(input_nc, ngf, kernel_size=7, padding=0), + norm_layer(ngf), + activation] + + identity = Identity() + + ### downsample + for i in range(n_downsampling): + mult = 2 ** i + model += [conv_layer(min(max_features, ngf * mult), + min(max_features, ngf * mult * 2), + kernel_size=3, stride=2, padding=1), + norm_layer(min(max_features, ngf * mult * 2)), + activation] + + mult = 2 ** n_downsampling + feats_num_bottleneck = min(max_features, ngf * mult) + + if len(manual_block_spec) == 0: + manual_block_spec = [ + DotDict(lambda : None, { + 'n_blocks': n_blocks, + 'use_default': True}) + ] + + ### resnet blocks + for block_spec in manual_block_spec: + def make_and_add_blocks(model, block_spec): + block_spec = DotDict(lambda : None, block_spec) + if not block_spec.use_default: + resnet_conv_layer = functools.partial(get_conv_block_ctor(block_spec.resnet_conv_kind), **block_spec.multidilation_kwargs) + resnet_conv_kind = block_spec.resnet_conv_kind + resnet_block_kind = block_spec.resnet_block_kind + if block_spec.resnet_dilation is not None: + resnet_dilation = block_spec.resnet_dilation + for i in range(block_spec.n_blocks): + if resnet_block_kind == "multidilatedresnetblock": + model += [MultidilatedResnetBlock(feats_num_bottleneck, padding_type=padding_type, + conv_layer=resnet_conv_layer, activation=activation, + norm_layer=norm_layer)] + if resnet_block_kind == "resnetblock": + model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer, + conv_kind=resnet_conv_kind)] + if resnet_block_kind == "resnetblock5x5": + model += [ResnetBlock5x5(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer, + conv_kind=resnet_conv_kind)] + if resnet_block_kind == "resnetblockdwdil": + model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer, + conv_kind=resnet_conv_kind, dilation=resnet_dilation, second_dilation=resnet_dilation)] + make_and_add_blocks(model, block_spec) + + ### upsample + for i in range(n_downsampling): + mult = 2 ** (n_downsampling - i) + model += deconv_factory(deconv_kind, ngf, mult, up_norm_layer, up_activation, max_features) + model += [nn.ReflectionPad2d(3), + nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] + if add_out_act: + model.append(get_activation('tanh' if add_out_act is True else add_out_act)) + self.model = nn.Sequential(*model) + + def forward(self, input): + return self.model(input) + + +def make_dil_blocks(dilated_blocks_n, dilation_block_kind, dilated_block_kwargs): + blocks = [] + for i in range(dilated_blocks_n): + if dilation_block_kind == 'simple': + blocks.append(ResnetBlock(**dilated_block_kwargs, dilation=2 ** (i + 1))) + elif dilation_block_kind == 'multi': + blocks.append(MultidilatedResnetBlock(**dilated_block_kwargs)) + else: + raise ValueError(f'dilation_block_kind could not be "{dilation_block_kind}"') + return blocks + + +class GlobalGenerator(nn.Module): + def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d, + padding_type='reflect', conv_kind='default', activation=nn.ReLU(True), + up_norm_layer=nn.BatchNorm2d, affine=None, + up_activation=nn.ReLU(True), dilated_blocks_n=0, dilated_blocks_n_start=0, + dilated_blocks_n_middle=0, + add_out_act=True, + max_features=1024, is_resblock_depthwise=False, + ffc_positions=None, ffc_kwargs={}, dilation=1, second_dilation=None, + dilation_block_kind='simple', multidilation_kwargs={}): + assert (n_blocks >= 0) + super().__init__() + + conv_layer = get_conv_block_ctor(conv_kind) + norm_layer = get_norm_layer(norm_layer) + if affine is not None: + norm_layer = partial(norm_layer, affine=affine) + up_norm_layer = get_norm_layer(up_norm_layer) + if affine is not None: + up_norm_layer = partial(up_norm_layer, affine=affine) + + if ffc_positions is not None: + ffc_positions = collections.Counter(ffc_positions) + + model = [nn.ReflectionPad2d(3), + conv_layer(input_nc, ngf, kernel_size=7, padding=0), + norm_layer(ngf), + activation] + + identity = Identity() + ### downsample + for i in range(n_downsampling): + mult = 2 ** i + + model += [conv_layer(min(max_features, ngf * mult), + min(max_features, ngf * mult * 2), + kernel_size=3, stride=2, padding=1), + norm_layer(min(max_features, ngf * mult * 2)), + activation] + + mult = 2 ** n_downsampling + feats_num_bottleneck = min(max_features, ngf * mult) + + dilated_block_kwargs = dict(dim=feats_num_bottleneck, padding_type=padding_type, + activation=activation, norm_layer=norm_layer) + if dilation_block_kind == 'simple': + dilated_block_kwargs['conv_kind'] = conv_kind + elif dilation_block_kind == 'multi': + dilated_block_kwargs['conv_layer'] = functools.partial( + get_conv_block_ctor('multidilated'), **multidilation_kwargs) + + # dilated blocks at the start of the bottleneck sausage + if dilated_blocks_n_start is not None and dilated_blocks_n_start > 0: + model += make_dil_blocks(dilated_blocks_n_start, dilation_block_kind, dilated_block_kwargs) + + # resnet blocks + for i in range(n_blocks): + # dilated blocks at the middle of the bottleneck sausage + if i == n_blocks // 2 and dilated_blocks_n_middle is not None and dilated_blocks_n_middle > 0: + model += make_dil_blocks(dilated_blocks_n_middle, dilation_block_kind, dilated_block_kwargs) + + if ffc_positions is not None and i in ffc_positions: + for _ in range(ffc_positions[i]): # same position can occur more than once + model += [FFCResnetBlock(feats_num_bottleneck, padding_type, norm_layer, activation_layer=nn.ReLU, + inline=True, **ffc_kwargs)] + + if is_resblock_depthwise: + resblock_groups = feats_num_bottleneck + else: + resblock_groups = 1 + + model += [ResnetBlock(feats_num_bottleneck, padding_type=padding_type, activation=activation, + norm_layer=norm_layer, conv_kind=conv_kind, groups=resblock_groups, + dilation=dilation, second_dilation=second_dilation)] + + + # dilated blocks at the end of the bottleneck sausage + if dilated_blocks_n is not None and dilated_blocks_n > 0: + model += make_dil_blocks(dilated_blocks_n, dilation_block_kind, dilated_block_kwargs) + + # upsample + for i in range(n_downsampling): + mult = 2 ** (n_downsampling - i) + model += [nn.ConvTranspose2d(min(max_features, ngf * mult), + min(max_features, int(ngf * mult / 2)), + kernel_size=3, stride=2, padding=1, output_padding=1), + up_norm_layer(min(max_features, int(ngf * mult / 2))), + up_activation] + model += [nn.ReflectionPad2d(3), + nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] + if add_out_act: + model.append(get_activation('tanh' if add_out_act is True else add_out_act)) + self.model = nn.Sequential(*model) + + def forward(self, input): + return self.model(input) + + +class GlobalGeneratorGated(GlobalGenerator): + def __init__(self, *args, **kwargs): + real_kwargs=dict( + conv_kind='gated_bn_relu', + activation=nn.Identity(), + norm_layer=nn.Identity + ) + real_kwargs.update(kwargs) + super().__init__(*args, **real_kwargs) + + +class GlobalGeneratorFromSuperChannels(nn.Module): + def __init__(self, input_nc, output_nc, n_downsampling, n_blocks, super_channels, norm_layer="bn", padding_type='reflect', add_out_act=True): + super().__init__() + self.n_downsampling = n_downsampling + norm_layer = get_norm_layer(norm_layer) + if type(norm_layer) == functools.partial: + use_bias = (norm_layer.func == nn.InstanceNorm2d) + else: + use_bias = (norm_layer == nn.InstanceNorm2d) + + channels = self.convert_super_channels(super_channels) + self.channels = channels + + model = [nn.ReflectionPad2d(3), + nn.Conv2d(input_nc, channels[0], kernel_size=7, padding=0, bias=use_bias), + norm_layer(channels[0]), + nn.ReLU(True)] + + for i in range(n_downsampling): # add downsampling layers + mult = 2 ** i + model += [nn.Conv2d(channels[0+i], channels[1+i], kernel_size=3, stride=2, padding=1, bias=use_bias), + norm_layer(channels[1+i]), + nn.ReLU(True)] + + mult = 2 ** n_downsampling + + n_blocks1 = n_blocks // 3 + n_blocks2 = n_blocks1 + n_blocks3 = n_blocks - n_blocks1 - n_blocks2 + + for i in range(n_blocks1): + c = n_downsampling + dim = channels[c] + model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer)] + + for i in range(n_blocks2): + c = n_downsampling+1 + dim = channels[c] + kwargs = {} + if i == 0: + kwargs = {"in_dim": channels[c-1]} + model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer, **kwargs)] + + for i in range(n_blocks3): + c = n_downsampling+2 + dim = channels[c] + kwargs = {} + if i == 0: + kwargs = {"in_dim": channels[c-1]} + model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer, **kwargs)] + + for i in range(n_downsampling): # add upsampling layers + mult = 2 ** (n_downsampling - i) + model += [nn.ConvTranspose2d(channels[n_downsampling+3+i], + channels[n_downsampling+3+i+1], + kernel_size=3, stride=2, + padding=1, output_padding=1, + bias=use_bias), + norm_layer(channels[n_downsampling+3+i+1]), + nn.ReLU(True)] + model += [nn.ReflectionPad2d(3)] + model += [nn.Conv2d(channels[2*n_downsampling+3], output_nc, kernel_size=7, padding=0)] + + if add_out_act: + model.append(get_activation('tanh' if add_out_act is True else add_out_act)) + self.model = nn.Sequential(*model) + + def convert_super_channels(self, super_channels): + n_downsampling = self.n_downsampling + result = [] + cnt = 0 + + if n_downsampling == 2: + N1 = 10 + elif n_downsampling == 3: + N1 = 13 + else: + raise NotImplementedError + + for i in range(0, N1): + if i in [1,4,7,10]: + channel = super_channels[cnt] * (2 ** cnt) + config = {'channel': channel} + result.append(channel) + logging.info(f"Downsample channels {result[-1]}") + cnt += 1 + + for i in range(3): + for counter, j in enumerate(range(N1 + i * 3, N1 + 3 + i * 3)): + if len(super_channels) == 6: + channel = super_channels[3] * 4 + else: + channel = super_channels[i + 3] * 4 + config = {'channel': channel} + if counter == 0: + result.append(channel) + logging.info(f"Bottleneck channels {result[-1]}") + cnt = 2 + + for i in range(N1+9, N1+21): + if i in [22, 25,28]: + cnt -= 1 + if len(super_channels) == 6: + channel = super_channels[5 - cnt] * (2 ** cnt) + else: + channel = super_channels[7 - cnt] * (2 ** cnt) + result.append(int(channel)) + logging.info(f"Upsample channels {result[-1]}") + return result + + def forward(self, input): + return self.model(input) + + +# Defines the PatchGAN discriminator with the specified arguments. +class NLayerDiscriminator(BaseDiscriminator): + def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d,): + super().__init__() + self.n_layers = n_layers + + kw = 4 + padw = int(np.ceil((kw-1.0)/2)) + sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), + nn.LeakyReLU(0.2, True)]] + + nf = ndf + for n in range(1, n_layers): + nf_prev = nf + nf = min(nf * 2, 512) + + cur_model = [] + cur_model += [ + nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=2, padding=padw), + norm_layer(nf), + nn.LeakyReLU(0.2, True) + ] + sequence.append(cur_model) + + nf_prev = nf + nf = min(nf * 2, 512) + + cur_model = [] + cur_model += [ + nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw), + norm_layer(nf), + nn.LeakyReLU(0.2, True) + ] + sequence.append(cur_model) + + sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]] + + for n in range(len(sequence)): + setattr(self, 'model'+str(n), nn.Sequential(*sequence[n])) + + def get_all_activations(self, x): + res = [x] + for n in range(self.n_layers + 2): + model = getattr(self, 'model' + str(n)) + res.append(model(res[-1])) + return res[1:] + + def forward(self, x): + act = self.get_all_activations(x) + return act[-1], act[:-1] + + +class MultidilatedNLayerDiscriminator(BaseDiscriminator): + def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, multidilation_kwargs={}): + super().__init__() + self.n_layers = n_layers + + kw = 4 + padw = int(np.ceil((kw-1.0)/2)) + sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), + nn.LeakyReLU(0.2, True)]] + + nf = ndf + for n in range(1, n_layers): + nf_prev = nf + nf = min(nf * 2, 512) + + cur_model = [] + cur_model += [ + MultidilatedConv(nf_prev, nf, kernel_size=kw, stride=2, padding=[2, 3], **multidilation_kwargs), + norm_layer(nf), + nn.LeakyReLU(0.2, True) + ] + sequence.append(cur_model) + + nf_prev = nf + nf = min(nf * 2, 512) + + cur_model = [] + cur_model += [ + nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw), + norm_layer(nf), + nn.LeakyReLU(0.2, True) + ] + sequence.append(cur_model) + + sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]] + + for n in range(len(sequence)): + setattr(self, 'model'+str(n), nn.Sequential(*sequence[n])) + + def get_all_activations(self, x): + res = [x] + for n in range(self.n_layers + 2): + model = getattr(self, 'model' + str(n)) + res.append(model(res[-1])) + return res[1:] + + def forward(self, x): + act = self.get_all_activations(x) + return act[-1], act[:-1] + + +class NLayerDiscriminatorAsGen(NLayerDiscriminator): + def forward(self, x): + return super().forward(x)[0] diff --git a/third-party/lama/saicinpainting/training/modules/spatial_transform.py b/third-party/lama/saicinpainting/training/modules/spatial_transform.py new file mode 100644 index 0000000000000000000000000000000000000000..2de024ba08c549605a08b64d096f1f0db7b7722a --- /dev/null +++ b/third-party/lama/saicinpainting/training/modules/spatial_transform.py @@ -0,0 +1,49 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from kornia.geometry.transform import rotate + + +class LearnableSpatialTransformWrapper(nn.Module): + def __init__(self, impl, pad_coef=0.5, angle_init_range=80, train_angle=True): + super().__init__() + self.impl = impl + self.angle = torch.rand(1) * angle_init_range + if train_angle: + self.angle = nn.Parameter(self.angle, requires_grad=True) + self.pad_coef = pad_coef + + def forward(self, x): + if torch.is_tensor(x): + return self.inverse_transform(self.impl(self.transform(x)), x) + elif isinstance(x, tuple): + x_trans = tuple(self.transform(elem) for elem in x) + y_trans = self.impl(x_trans) + return tuple(self.inverse_transform(elem, orig_x) for elem, orig_x in zip(y_trans, x)) + else: + raise ValueError(f'Unexpected input type {type(x)}') + + def transform(self, x): + height, width = x.shape[2:] + pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef) + x_padded = F.pad(x, [pad_w, pad_w, pad_h, pad_h], mode='reflect') + x_padded_rotated = rotate(x_padded, angle=self.angle.to(x_padded)) + return x_padded_rotated + + def inverse_transform(self, y_padded_rotated, orig_x): + height, width = orig_x.shape[2:] + pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef) + + y_padded = rotate(y_padded_rotated, angle=-self.angle.to(y_padded_rotated)) + y_height, y_width = y_padded.shape[2:] + y = y_padded[:, :, pad_h : y_height - pad_h, pad_w : y_width - pad_w] + return y + + +if __name__ == '__main__': + layer = LearnableSpatialTransformWrapper(nn.Identity()) + x = torch.arange(2* 3 * 15 * 15).view(2, 3, 15, 15).float() + y = layer(x) + assert x.shape == y.shape + assert torch.allclose(x[:, :, 1:, 1:][:, :, :-1, :-1], y[:, :, 1:, 1:][:, :, :-1, :-1]) + print('all ok') diff --git a/third-party/lama/saicinpainting/training/modules/squeeze_excitation.py b/third-party/lama/saicinpainting/training/modules/squeeze_excitation.py new file mode 100644 index 0000000000000000000000000000000000000000..d1d902bb30c071acbc0fa919a134c80fed86bd6c --- /dev/null +++ b/third-party/lama/saicinpainting/training/modules/squeeze_excitation.py @@ -0,0 +1,20 @@ +import torch.nn as nn + + +class SELayer(nn.Module): + def __init__(self, channel, reduction=16): + super(SELayer, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channel, channel // reduction, bias=False), + nn.ReLU(inplace=True), + nn.Linear(channel // reduction, channel, bias=False), + nn.Sigmoid() + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + res = x * y.expand_as(x) + return res diff --git a/third-party/lama/saicinpainting/training/trainers/__init__.py b/third-party/lama/saicinpainting/training/trainers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c59241f553efe4e2dd6b198e2e5656a2b1488857 --- /dev/null +++ b/third-party/lama/saicinpainting/training/trainers/__init__.py @@ -0,0 +1,30 @@ +import logging +import torch +from saicinpainting.training.trainers.default import DefaultInpaintingTrainingModule + + +def get_training_model_class(kind): + if kind == 'default': + return DefaultInpaintingTrainingModule + + raise ValueError(f'Unknown trainer module {kind}') + + +def make_training_model(config): + kind = config.training_model.kind + kwargs = dict(config.training_model) + kwargs.pop('kind') + kwargs['use_ddp'] = config.trainer.kwargs.get('accelerator', None) == 'ddp' + + logging.info(f'Make training model {kind}') + + cls = get_training_model_class(kind) + return cls(config, **kwargs) + + +def load_checkpoint(train_config, path, map_location='cuda', strict=True): + model: torch.nn.Module = make_training_model(train_config) + state = torch.load(path, map_location=map_location) + model.load_state_dict(state['state_dict'], strict=strict) + model.on_load_checkpoint(state) + return model diff --git a/third-party/lama/saicinpainting/training/trainers/base.py b/third-party/lama/saicinpainting/training/trainers/base.py new file mode 100644 index 0000000000000000000000000000000000000000..f1b1c66fc96e7edfba7b1ee193272f92b5db7438 --- /dev/null +++ b/third-party/lama/saicinpainting/training/trainers/base.py @@ -0,0 +1,291 @@ +import copy +import logging +from typing import Dict, Tuple + +import pandas as pd +import pytorch_lightning as ptl +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import DistributedSampler + +from saicinpainting.evaluation import make_evaluator +from saicinpainting.training.data.datasets import make_default_train_dataloader, make_default_val_dataloader +from saicinpainting.training.losses.adversarial import make_discrim_loss +from saicinpainting.training.losses.perceptual import PerceptualLoss, ResNetPL +from saicinpainting.training.modules import make_generator, make_discriminator +from saicinpainting.training.visualizers import make_visualizer +from saicinpainting.utils import add_prefix_to_keys, average_dicts, set_requires_grad, flatten_dict, \ + get_has_ddp_rank + +LOGGER = logging.getLogger(__name__) + + +def make_optimizer(parameters, kind='adamw', **kwargs): + if kind == 'adam': + optimizer_class = torch.optim.Adam + elif kind == 'adamw': + optimizer_class = torch.optim.AdamW + else: + raise ValueError(f'Unknown optimizer kind {kind}') + return optimizer_class(parameters, **kwargs) + + +def update_running_average(result: nn.Module, new_iterate_model: nn.Module, decay=0.999): + with torch.no_grad(): + res_params = dict(result.named_parameters()) + new_params = dict(new_iterate_model.named_parameters()) + + for k in res_params.keys(): + res_params[k].data.mul_(decay).add_(new_params[k].data, alpha=1 - decay) + + +def make_multiscale_noise(base_tensor, scales=6, scale_mode='bilinear'): + batch_size, _, height, width = base_tensor.shape + cur_height, cur_width = height, width + result = [] + align_corners = False if scale_mode in ('bilinear', 'bicubic') else None + for _ in range(scales): + cur_sample = torch.randn(batch_size, 1, cur_height, cur_width, device=base_tensor.device) + cur_sample_scaled = F.interpolate(cur_sample, size=(height, width), mode=scale_mode, align_corners=align_corners) + result.append(cur_sample_scaled) + cur_height //= 2 + cur_width //= 2 + return torch.cat(result, dim=1) + + +class BaseInpaintingTrainingModule(ptl.LightningModule): + def __init__(self, config, use_ddp, *args, predict_only=False, visualize_each_iters=100, + average_generator=False, generator_avg_beta=0.999, average_generator_start_step=30000, + average_generator_period=10, store_discr_outputs_for_vis=False, + **kwargs): + super().__init__(*args, **kwargs) + LOGGER.info('BaseInpaintingTrainingModule init called') + + self.config = config + + self.generator = make_generator(config, **self.config.generator) + self.use_ddp = use_ddp + + if not get_has_ddp_rank(): + LOGGER.info(f'Generator\n{self.generator}') + + if not predict_only: + self.save_hyperparameters(self.config) + self.discriminator = make_discriminator(**self.config.discriminator) + self.adversarial_loss = make_discrim_loss(**self.config.losses.adversarial) + self.visualizer = make_visualizer(**self.config.visualizer) + self.val_evaluator = make_evaluator(**self.config.evaluator) + self.test_evaluator = make_evaluator(**self.config.evaluator) + + if not get_has_ddp_rank(): + LOGGER.info(f'Discriminator\n{self.discriminator}') + + extra_val = self.config.data.get('extra_val', ()) + if extra_val: + self.extra_val_titles = list(extra_val) + self.extra_evaluators = nn.ModuleDict({k: make_evaluator(**self.config.evaluator) + for k in extra_val}) + else: + self.extra_evaluators = {} + + self.average_generator = average_generator + self.generator_avg_beta = generator_avg_beta + self.average_generator_start_step = average_generator_start_step + self.average_generator_period = average_generator_period + self.generator_average = None + self.last_generator_averaging_step = -1 + self.store_discr_outputs_for_vis = store_discr_outputs_for_vis + + if self.config.losses.get("l1", {"weight_known": 0})['weight_known'] > 0: + self.loss_l1 = nn.L1Loss(reduction='none') + + if self.config.losses.get("mse", {"weight": 0})['weight'] > 0: + self.loss_mse = nn.MSELoss(reduction='none') + + if self.config.losses.perceptual.weight > 0: + self.loss_pl = PerceptualLoss() + + if self.config.losses.get("resnet_pl", {"weight": 0})['weight'] > 0: + self.loss_resnet_pl = ResNetPL(**self.config.losses.resnet_pl) + else: + self.loss_resnet_pl = None + + self.visualize_each_iters = visualize_each_iters + LOGGER.info('BaseInpaintingTrainingModule init done') + + def configure_optimizers(self): + discriminator_params = list(self.discriminator.parameters()) + return [ + dict(optimizer=make_optimizer(self.generator.parameters(), **self.config.optimizers.generator)), + dict(optimizer=make_optimizer(discriminator_params, **self.config.optimizers.discriminator)), + ] + + def train_dataloader(self): + kwargs = dict(self.config.data.train) + if self.use_ddp: + kwargs['ddp_kwargs'] = dict(num_replicas=self.trainer.num_nodes * self.trainer.num_processes, + rank=self.trainer.global_rank, + shuffle=True) + dataloader = make_default_train_dataloader(**self.config.data.train) + return dataloader + + def val_dataloader(self): + res = [make_default_val_dataloader(**self.config.data.val)] + + if self.config.data.visual_test is not None: + res = res + [make_default_val_dataloader(**self.config.data.visual_test)] + else: + res = res + res + + extra_val = self.config.data.get('extra_val', ()) + if extra_val: + res += [make_default_val_dataloader(**extra_val[k]) for k in self.extra_val_titles] + + return res + + def training_step(self, batch, batch_idx, optimizer_idx=None): + self._is_training_step = True + return self._do_step(batch, batch_idx, mode='train', optimizer_idx=optimizer_idx) + + def validation_step(self, batch, batch_idx, dataloader_idx): + extra_val_key = None + if dataloader_idx == 0: + mode = 'val' + elif dataloader_idx == 1: + mode = 'test' + else: + mode = 'extra_val' + extra_val_key = self.extra_val_titles[dataloader_idx - 2] + self._is_training_step = False + return self._do_step(batch, batch_idx, mode=mode, extra_val_key=extra_val_key) + + def training_step_end(self, batch_parts_outputs): + if self.training and self.average_generator \ + and self.global_step >= self.average_generator_start_step \ + and self.global_step >= self.last_generator_averaging_step + self.average_generator_period: + if self.generator_average is None: + self.generator_average = copy.deepcopy(self.generator) + else: + update_running_average(self.generator_average, self.generator, decay=self.generator_avg_beta) + self.last_generator_averaging_step = self.global_step + + full_loss = (batch_parts_outputs['loss'].mean() + if torch.is_tensor(batch_parts_outputs['loss']) # loss is not tensor when no discriminator used + else torch.tensor(batch_parts_outputs['loss']).float().requires_grad_(True)) + log_info = {k: v.mean() for k, v in batch_parts_outputs['log_info'].items()} + self.log_dict(log_info, on_step=True, on_epoch=False) + return full_loss + + def validation_epoch_end(self, outputs): + outputs = [step_out for out_group in outputs for step_out in out_group] + averaged_logs = average_dicts(step_out['log_info'] for step_out in outputs) + self.log_dict({k: v.mean() for k, v in averaged_logs.items()}) + + pd.set_option('display.max_columns', 500) + pd.set_option('display.width', 1000) + + # standard validation + val_evaluator_states = [s['val_evaluator_state'] for s in outputs if 'val_evaluator_state' in s] + val_evaluator_res = self.val_evaluator.evaluation_end(states=val_evaluator_states) + val_evaluator_res_df = pd.DataFrame(val_evaluator_res).stack(1).unstack(0) + val_evaluator_res_df.dropna(axis=1, how='all', inplace=True) + LOGGER.info(f'Validation metrics after epoch #{self.current_epoch}, ' + f'total {self.global_step} iterations:\n{val_evaluator_res_df}') + + for k, v in flatten_dict(val_evaluator_res).items(): + self.log(f'val_{k}', v) + + # standard visual test + test_evaluator_states = [s['test_evaluator_state'] for s in outputs + if 'test_evaluator_state' in s] + test_evaluator_res = self.test_evaluator.evaluation_end(states=test_evaluator_states) + test_evaluator_res_df = pd.DataFrame(test_evaluator_res).stack(1).unstack(0) + test_evaluator_res_df.dropna(axis=1, how='all', inplace=True) + LOGGER.info(f'Test metrics after epoch #{self.current_epoch}, ' + f'total {self.global_step} iterations:\n{test_evaluator_res_df}') + + for k, v in flatten_dict(test_evaluator_res).items(): + self.log(f'test_{k}', v) + + # extra validations + if self.extra_evaluators: + for cur_eval_title, cur_evaluator in self.extra_evaluators.items(): + cur_state_key = f'extra_val_{cur_eval_title}_evaluator_state' + cur_states = [s[cur_state_key] for s in outputs if cur_state_key in s] + cur_evaluator_res = cur_evaluator.evaluation_end(states=cur_states) + cur_evaluator_res_df = pd.DataFrame(cur_evaluator_res).stack(1).unstack(0) + cur_evaluator_res_df.dropna(axis=1, how='all', inplace=True) + LOGGER.info(f'Extra val {cur_eval_title} metrics after epoch #{self.current_epoch}, ' + f'total {self.global_step} iterations:\n{cur_evaluator_res_df}') + for k, v in flatten_dict(cur_evaluator_res).items(): + self.log(f'extra_val_{cur_eval_title}_{k}', v) + + def _do_step(self, batch, batch_idx, mode='train', optimizer_idx=None, extra_val_key=None): + if optimizer_idx == 0: # step for generator + set_requires_grad(self.generator, True) + set_requires_grad(self.discriminator, False) + elif optimizer_idx == 1: # step for discriminator + set_requires_grad(self.generator, False) + set_requires_grad(self.discriminator, True) + + batch = self(batch) + + total_loss = 0 + metrics = {} + + if optimizer_idx is None or optimizer_idx == 0: # step for generator + total_loss, metrics = self.generator_loss(batch) + + elif optimizer_idx is None or optimizer_idx == 1: # step for discriminator + if self.config.losses.adversarial.weight > 0: + total_loss, metrics = self.discriminator_loss(batch) + + if self.get_ddp_rank() in (None, 0) and (batch_idx % self.visualize_each_iters == 0 or mode == 'test'): + if self.config.losses.adversarial.weight > 0: + if self.store_discr_outputs_for_vis: + with torch.no_grad(): + self.store_discr_outputs(batch) + vis_suffix = f'_{mode}' + if mode == 'extra_val': + vis_suffix += f'_{extra_val_key}' + self.visualizer(self.current_epoch, batch_idx, batch, suffix=vis_suffix) + + metrics_prefix = f'{mode}_' + if mode == 'extra_val': + metrics_prefix += f'{extra_val_key}_' + result = dict(loss=total_loss, log_info=add_prefix_to_keys(metrics, metrics_prefix)) + if mode == 'val': + result['val_evaluator_state'] = self.val_evaluator.process_batch(batch) + elif mode == 'test': + result['test_evaluator_state'] = self.test_evaluator.process_batch(batch) + elif mode == 'extra_val': + result[f'extra_val_{extra_val_key}_evaluator_state'] = self.extra_evaluators[extra_val_key].process_batch(batch) + + return result + + def get_current_generator(self, no_average=False): + if not no_average and not self.training and self.average_generator and self.generator_average is not None: + return self.generator_average + return self.generator + + def forward(self, batch: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: + """Pass data through generator and obtain at leas 'predicted_image' and 'inpainted' keys""" + raise NotImplementedError() + + def generator_loss(self, batch) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: + raise NotImplementedError() + + def discriminator_loss(self, batch) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: + raise NotImplementedError() + + def store_discr_outputs(self, batch): + out_size = batch['image'].shape[2:] + discr_real_out, _ = self.discriminator(batch['image']) + discr_fake_out, _ = self.discriminator(batch['predicted_image']) + batch['discr_output_real'] = F.interpolate(discr_real_out, size=out_size, mode='nearest') + batch['discr_output_fake'] = F.interpolate(discr_fake_out, size=out_size, mode='nearest') + batch['discr_output_diff'] = batch['discr_output_real'] - batch['discr_output_fake'] + + def get_ddp_rank(self): + return self.trainer.global_rank if (self.trainer.num_nodes * self.trainer.num_processes) > 1 else None diff --git a/third-party/lama/saicinpainting/training/trainers/default.py b/third-party/lama/saicinpainting/training/trainers/default.py new file mode 100644 index 0000000000000000000000000000000000000000..86c7f0fab42924bfc93a031e851117634c70f593 --- /dev/null +++ b/third-party/lama/saicinpainting/training/trainers/default.py @@ -0,0 +1,175 @@ +import logging + +import torch +import torch.nn.functional as F +from omegaconf import OmegaConf + +from saicinpainting.training.data.datasets import make_constant_area_crop_params +from saicinpainting.training.losses.distance_weighting import make_mask_distance_weighter +from saicinpainting.training.losses.feature_matching import feature_matching_loss, masked_l1_loss +from saicinpainting.training.modules.fake_fakes import FakeFakesGenerator +from saicinpainting.training.trainers.base import BaseInpaintingTrainingModule, make_multiscale_noise +from saicinpainting.utils import add_prefix_to_keys, get_ramp + +LOGGER = logging.getLogger(__name__) + + +def make_constant_area_crop_batch(batch, **kwargs): + crop_y, crop_x, crop_height, crop_width = make_constant_area_crop_params(img_height=batch['image'].shape[2], + img_width=batch['image'].shape[3], + **kwargs) + batch['image'] = batch['image'][:, :, crop_y : crop_y + crop_height, crop_x : crop_x + crop_width] + batch['mask'] = batch['mask'][:, :, crop_y: crop_y + crop_height, crop_x: crop_x + crop_width] + return batch + + +class DefaultInpaintingTrainingModule(BaseInpaintingTrainingModule): + def __init__(self, *args, concat_mask=True, rescale_scheduler_kwargs=None, image_to_discriminator='predicted_image', + add_noise_kwargs=None, noise_fill_hole=False, const_area_crop_kwargs=None, + distance_weighter_kwargs=None, distance_weighted_mask_for_discr=False, + fake_fakes_proba=0, fake_fakes_generator_kwargs=None, + **kwargs): + super().__init__(*args, **kwargs) + self.concat_mask = concat_mask + self.rescale_size_getter = get_ramp(**rescale_scheduler_kwargs) if rescale_scheduler_kwargs is not None else None + self.image_to_discriminator = image_to_discriminator + self.add_noise_kwargs = add_noise_kwargs + self.noise_fill_hole = noise_fill_hole + self.const_area_crop_kwargs = const_area_crop_kwargs + self.refine_mask_for_losses = make_mask_distance_weighter(**distance_weighter_kwargs) \ + if distance_weighter_kwargs is not None else None + self.distance_weighted_mask_for_discr = distance_weighted_mask_for_discr + + self.fake_fakes_proba = fake_fakes_proba + if self.fake_fakes_proba > 1e-3: + self.fake_fakes_gen = FakeFakesGenerator(**(fake_fakes_generator_kwargs or {})) + + def forward(self, batch): + if self.training and self.rescale_size_getter is not None: + cur_size = self.rescale_size_getter(self.global_step) + batch['image'] = F.interpolate(batch['image'], size=cur_size, mode='bilinear', align_corners=False) + batch['mask'] = F.interpolate(batch['mask'], size=cur_size, mode='nearest') + + if self.training and self.const_area_crop_kwargs is not None: + batch = make_constant_area_crop_batch(batch, **self.const_area_crop_kwargs) + + img = batch['image'] + mask = batch['mask'] + + masked_img = img * (1 - mask) + + if self.add_noise_kwargs is not None: + noise = make_multiscale_noise(masked_img, **self.add_noise_kwargs) + if self.noise_fill_hole: + masked_img = masked_img + mask * noise[:, :masked_img.shape[1]] + masked_img = torch.cat([masked_img, noise], dim=1) + + if self.concat_mask: + masked_img = torch.cat([masked_img, mask], dim=1) + + batch['predicted_image'] = self.generator(masked_img) + batch['inpainted'] = mask * batch['predicted_image'] + (1 - mask) * batch['image'] + + if self.fake_fakes_proba > 1e-3: + if self.training and torch.rand(1).item() < self.fake_fakes_proba: + batch['fake_fakes'], batch['fake_fakes_masks'] = self.fake_fakes_gen(img, mask) + batch['use_fake_fakes'] = True + else: + batch['fake_fakes'] = torch.zeros_like(img) + batch['fake_fakes_masks'] = torch.zeros_like(mask) + batch['use_fake_fakes'] = False + + batch['mask_for_losses'] = self.refine_mask_for_losses(img, batch['predicted_image'], mask) \ + if self.refine_mask_for_losses is not None and self.training \ + else mask + + return batch + + def generator_loss(self, batch): + img = batch['image'] + predicted_img = batch[self.image_to_discriminator] + original_mask = batch['mask'] + supervised_mask = batch['mask_for_losses'] + + # L1 + l1_value = masked_l1_loss(predicted_img, img, supervised_mask, + self.config.losses.l1.weight_known, + self.config.losses.l1.weight_missing) + + total_loss = l1_value + metrics = dict(gen_l1=l1_value) + + # vgg-based perceptual loss + if self.config.losses.perceptual.weight > 0: + pl_value = self.loss_pl(predicted_img, img, mask=supervised_mask).sum() * self.config.losses.perceptual.weight + total_loss = total_loss + pl_value + metrics['gen_pl'] = pl_value + + # discriminator + # adversarial_loss calls backward by itself + mask_for_discr = supervised_mask if self.distance_weighted_mask_for_discr else original_mask + self.adversarial_loss.pre_generator_step(real_batch=img, fake_batch=predicted_img, + generator=self.generator, discriminator=self.discriminator) + discr_real_pred, discr_real_features = self.discriminator(img) + discr_fake_pred, discr_fake_features = self.discriminator(predicted_img) + adv_gen_loss, adv_metrics = self.adversarial_loss.generator_loss(real_batch=img, + fake_batch=predicted_img, + discr_real_pred=discr_real_pred, + discr_fake_pred=discr_fake_pred, + mask=mask_for_discr) + total_loss = total_loss + adv_gen_loss + metrics['gen_adv'] = adv_gen_loss + metrics.update(add_prefix_to_keys(adv_metrics, 'adv_')) + + # feature matching + if self.config.losses.feature_matching.weight > 0: + need_mask_in_fm = OmegaConf.to_container(self.config.losses.feature_matching).get('pass_mask', False) + mask_for_fm = supervised_mask if need_mask_in_fm else None + fm_value = feature_matching_loss(discr_fake_features, discr_real_features, + mask=mask_for_fm) * self.config.losses.feature_matching.weight + total_loss = total_loss + fm_value + metrics['gen_fm'] = fm_value + + if self.loss_resnet_pl is not None: + resnet_pl_value = self.loss_resnet_pl(predicted_img, img) + total_loss = total_loss + resnet_pl_value + metrics['gen_resnet_pl'] = resnet_pl_value + + return total_loss, metrics + + def discriminator_loss(self, batch): + total_loss = 0 + metrics = {} + + predicted_img = batch[self.image_to_discriminator].detach() + self.adversarial_loss.pre_discriminator_step(real_batch=batch['image'], fake_batch=predicted_img, + generator=self.generator, discriminator=self.discriminator) + discr_real_pred, discr_real_features = self.discriminator(batch['image']) + discr_fake_pred, discr_fake_features = self.discriminator(predicted_img) + adv_discr_loss, adv_metrics = self.adversarial_loss.discriminator_loss(real_batch=batch['image'], + fake_batch=predicted_img, + discr_real_pred=discr_real_pred, + discr_fake_pred=discr_fake_pred, + mask=batch['mask']) + total_loss = total_loss + adv_discr_loss + metrics['discr_adv'] = adv_discr_loss + metrics.update(add_prefix_to_keys(adv_metrics, 'adv_')) + + + if batch.get('use_fake_fakes', False): + fake_fakes = batch['fake_fakes'] + self.adversarial_loss.pre_discriminator_step(real_batch=batch['image'], fake_batch=fake_fakes, + generator=self.generator, discriminator=self.discriminator) + discr_fake_fakes_pred, _ = self.discriminator(fake_fakes) + fake_fakes_adv_discr_loss, fake_fakes_adv_metrics = self.adversarial_loss.discriminator_loss( + real_batch=batch['image'], + fake_batch=fake_fakes, + discr_real_pred=discr_real_pred, + discr_fake_pred=discr_fake_fakes_pred, + mask=batch['mask'] + ) + total_loss = total_loss + fake_fakes_adv_discr_loss + metrics['discr_adv_fake_fakes'] = fake_fakes_adv_discr_loss + metrics.update(add_prefix_to_keys(fake_fakes_adv_metrics, 'adv_')) + + return total_loss, metrics diff --git a/third-party/lama/saicinpainting/training/visualizers/__init__.py b/third-party/lama/saicinpainting/training/visualizers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4770d1f15a6790ab9606c7b9881f798c8e2d9545 --- /dev/null +++ b/third-party/lama/saicinpainting/training/visualizers/__init__.py @@ -0,0 +1,15 @@ +import logging + +from saicinpainting.training.visualizers.directory import DirectoryVisualizer +from saicinpainting.training.visualizers.noop import NoopVisualizer + + +def make_visualizer(kind, **kwargs): + logging.info(f'Make visualizer {kind}') + + if kind == 'directory': + return DirectoryVisualizer(**kwargs) + if kind == 'noop': + return NoopVisualizer() + + raise ValueError(f'Unknown visualizer kind {kind}') diff --git a/third-party/lama/saicinpainting/training/visualizers/base.py b/third-party/lama/saicinpainting/training/visualizers/base.py new file mode 100644 index 0000000000000000000000000000000000000000..675f01682ddf5e31b6cc341735378c6f3b242e49 --- /dev/null +++ b/third-party/lama/saicinpainting/training/visualizers/base.py @@ -0,0 +1,73 @@ +import abc +from typing import Dict, List + +import numpy as np +import torch +from skimage import color +from skimage.segmentation import mark_boundaries + +from . import colors + +COLORS, _ = colors.generate_colors(151) # 151 - max classes for semantic segmentation + + +class BaseVisualizer: + @abc.abstractmethod + def __call__(self, epoch_i, batch_i, batch, suffix='', rank=None): + """ + Take a batch, make an image from it and visualize + """ + raise NotImplementedError() + + +def visualize_mask_and_images(images_dict: Dict[str, np.ndarray], keys: List[str], + last_without_mask=True, rescale_keys=None, mask_only_first=None, + black_mask=False) -> np.ndarray: + mask = images_dict['mask'] > 0.5 + result = [] + for i, k in enumerate(keys): + img = images_dict[k] + img = np.transpose(img, (1, 2, 0)) + + if rescale_keys is not None and k in rescale_keys: + img = img - img.min() + img /= img.max() + 1e-5 + if len(img.shape) == 2: + img = np.expand_dims(img, 2) + + if img.shape[2] == 1: + img = np.repeat(img, 3, axis=2) + elif (img.shape[2] > 3): + img_classes = img.argmax(2) + img = color.label2rgb(img_classes, colors=COLORS) + + if mask_only_first: + need_mark_boundaries = i == 0 + else: + need_mark_boundaries = i < len(keys) - 1 or not last_without_mask + + if need_mark_boundaries: + if black_mask: + img = img * (1 - mask[0][..., None]) + img = mark_boundaries(img, + mask[0], + color=(1., 0., 0.), + outline_color=(1., 1., 1.), + mode='thick') + result.append(img) + return np.concatenate(result, axis=1) + + +def visualize_mask_and_images_batch(batch: Dict[str, torch.Tensor], keys: List[str], max_items=10, + last_without_mask=True, rescale_keys=None) -> np.ndarray: + batch = {k: tens.detach().cpu().numpy() for k, tens in batch.items() + if k in keys or k == 'mask'} + + batch_size = next(iter(batch.values())).shape[0] + items_to_vis = min(batch_size, max_items) + result = [] + for i in range(items_to_vis): + cur_dct = {k: tens[i] for k, tens in batch.items()} + result.append(visualize_mask_and_images(cur_dct, keys, last_without_mask=last_without_mask, + rescale_keys=rescale_keys)) + return np.concatenate(result, axis=0) diff --git a/third-party/lama/saicinpainting/training/visualizers/colors.py b/third-party/lama/saicinpainting/training/visualizers/colors.py new file mode 100644 index 0000000000000000000000000000000000000000..9e9e39182c58cb06a1c5e97a7e6c497cc3388ebe --- /dev/null +++ b/third-party/lama/saicinpainting/training/visualizers/colors.py @@ -0,0 +1,76 @@ +import random +import colorsys + +import numpy as np +import matplotlib +matplotlib.use('agg') +import matplotlib.pyplot as plt +from matplotlib.colors import LinearSegmentedColormap + + +def generate_colors(nlabels, type='bright', first_color_black=False, last_color_black=True, verbose=False): + # https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib + """ + Creates a random colormap to be used together with matplotlib. Useful for segmentation tasks + :param nlabels: Number of labels (size of colormap) + :param type: 'bright' for strong colors, 'soft' for pastel colors + :param first_color_black: Option to use first color as black, True or False + :param last_color_black: Option to use last color as black, True or False + :param verbose: Prints the number of labels and shows the colormap. True or False + :return: colormap for matplotlib + """ + if type not in ('bright', 'soft'): + print ('Please choose "bright" or "soft" for type') + return + + if verbose: + print('Number of labels: ' + str(nlabels)) + + # Generate color map for bright colors, based on hsv + if type == 'bright': + randHSVcolors = [(np.random.uniform(low=0.0, high=1), + np.random.uniform(low=0.2, high=1), + np.random.uniform(low=0.9, high=1)) for i in range(nlabels)] + + # Convert HSV list to RGB + randRGBcolors = [] + for HSVcolor in randHSVcolors: + randRGBcolors.append(colorsys.hsv_to_rgb(HSVcolor[0], HSVcolor[1], HSVcolor[2])) + + if first_color_black: + randRGBcolors[0] = [0, 0, 0] + + if last_color_black: + randRGBcolors[-1] = [0, 0, 0] + + random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels) + + # Generate soft pastel colors, by limiting the RGB spectrum + if type == 'soft': + low = 0.6 + high = 0.95 + randRGBcolors = [(np.random.uniform(low=low, high=high), + np.random.uniform(low=low, high=high), + np.random.uniform(low=low, high=high)) for i in range(nlabels)] + + if first_color_black: + randRGBcolors[0] = [0, 0, 0] + + if last_color_black: + randRGBcolors[-1] = [0, 0, 0] + random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels) + + # Display colorbar + if verbose: + from matplotlib import colors, colorbar + from matplotlib import pyplot as plt + fig, ax = plt.subplots(1, 1, figsize=(15, 0.5)) + + bounds = np.linspace(0, nlabels, nlabels + 1) + norm = colors.BoundaryNorm(bounds, nlabels) + + cb = colorbar.ColorbarBase(ax, cmap=random_colormap, norm=norm, spacing='proportional', ticks=None, + boundaries=bounds, format='%1i', orientation=u'horizontal') + + return randRGBcolors, random_colormap + diff --git a/third-party/lama/saicinpainting/training/visualizers/directory.py b/third-party/lama/saicinpainting/training/visualizers/directory.py new file mode 100644 index 0000000000000000000000000000000000000000..bc42e00500c7a5b70b2cef83b03e45b5bb471ff8 --- /dev/null +++ b/third-party/lama/saicinpainting/training/visualizers/directory.py @@ -0,0 +1,36 @@ +import os + +import cv2 +import numpy as np + +from saicinpainting.training.visualizers.base import BaseVisualizer, visualize_mask_and_images_batch +from saicinpainting.utils import check_and_warn_input_range + + +class DirectoryVisualizer(BaseVisualizer): + DEFAULT_KEY_ORDER = 'image predicted_image inpainted'.split(' ') + + def __init__(self, outdir, key_order=DEFAULT_KEY_ORDER, max_items_in_batch=10, + last_without_mask=True, rescale_keys=None): + self.outdir = outdir + os.makedirs(self.outdir, exist_ok=True) + self.key_order = key_order + self.max_items_in_batch = max_items_in_batch + self.last_without_mask = last_without_mask + self.rescale_keys = rescale_keys + + def __call__(self, epoch_i, batch_i, batch, suffix='', rank=None): + check_and_warn_input_range(batch['image'], 0, 1, 'DirectoryVisualizer target image') + vis_img = visualize_mask_and_images_batch(batch, self.key_order, max_items=self.max_items_in_batch, + last_without_mask=self.last_without_mask, + rescale_keys=self.rescale_keys) + + vis_img = np.clip(vis_img * 255, 0, 255).astype('uint8') + + curoutdir = os.path.join(self.outdir, f'epoch{epoch_i:04d}{suffix}') + os.makedirs(curoutdir, exist_ok=True) + rank_suffix = f'_r{rank}' if rank is not None else '' + out_fname = os.path.join(curoutdir, f'batch{batch_i:07d}{rank_suffix}.jpg') + + vis_img = cv2.cvtColor(vis_img, cv2.COLOR_RGB2BGR) + cv2.imwrite(out_fname, vis_img) diff --git a/third-party/lama/saicinpainting/training/visualizers/noop.py b/third-party/lama/saicinpainting/training/visualizers/noop.py new file mode 100644 index 0000000000000000000000000000000000000000..4175089a54a8484d51e6c879c1a99c4e4d961d15 --- /dev/null +++ b/third-party/lama/saicinpainting/training/visualizers/noop.py @@ -0,0 +1,9 @@ +from saicinpainting.training.visualizers.base import BaseVisualizer + + +class NoopVisualizer(BaseVisualizer): + def __init__(self, *args, **kwargs): + pass + + def __call__(self, epoch_i, batch_i, batch, suffix='', rank=None): + pass diff --git a/third-party/lama/saicinpainting/utils.py b/third-party/lama/saicinpainting/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c2d67ed8bc793dd5113224fa322adb88f3ed9b22 --- /dev/null +++ b/third-party/lama/saicinpainting/utils.py @@ -0,0 +1,177 @@ +import bisect +import functools +import logging +import numbers +import os +import signal +import sys +import traceback +import warnings + +import torch +from pytorch_lightning import seed_everything + +LOGGER = logging.getLogger(__name__) + +import platform +if platform.system() != 'Linux': + signal.SIGUSR1 = 1 + +def check_and_warn_input_range(tensor, min_value, max_value, name): + actual_min = tensor.min() + actual_max = tensor.max() + if actual_min < min_value or actual_max > max_value: + warnings.warn(f"{name} must be in {min_value}..{max_value} range, but it ranges {actual_min}..{actual_max}") + + +def sum_dict_with_prefix(target, cur_dict, prefix, default=0): + for k, v in cur_dict.items(): + target_key = prefix + k + target[target_key] = target.get(target_key, default) + v + + +def average_dicts(dict_list): + result = {} + norm = 1e-3 + for dct in dict_list: + sum_dict_with_prefix(result, dct, '') + norm += 1 + for k in list(result): + result[k] /= norm + return result + + +def add_prefix_to_keys(dct, prefix): + return {prefix + k: v for k, v in dct.items()} + + +def set_requires_grad(module, value): + for param in module.parameters(): + param.requires_grad = value + + +def flatten_dict(dct): + result = {} + for k, v in dct.items(): + if isinstance(k, tuple): + k = '_'.join(k) + if isinstance(v, dict): + for sub_k, sub_v in flatten_dict(v).items(): + result[f'{k}_{sub_k}'] = sub_v + else: + result[k] = v + return result + + +class LinearRamp: + def __init__(self, start_value=0, end_value=1, start_iter=-1, end_iter=0): + self.start_value = start_value + self.end_value = end_value + self.start_iter = start_iter + self.end_iter = end_iter + + def __call__(self, i): + if i < self.start_iter: + return self.start_value + if i >= self.end_iter: + return self.end_value + part = (i - self.start_iter) / (self.end_iter - self.start_iter) + return self.start_value * (1 - part) + self.end_value * part + + +class LadderRamp: + def __init__(self, start_iters, values): + self.start_iters = start_iters + self.values = values + assert len(values) == len(start_iters) + 1, (len(values), len(start_iters)) + + def __call__(self, i): + segment_i = bisect.bisect_right(self.start_iters, i) + return self.values[segment_i] + + +def get_ramp(kind='ladder', **kwargs): + if kind == 'linear': + return LinearRamp(**kwargs) + if kind == 'ladder': + return LadderRamp(**kwargs) + raise ValueError(f'Unexpected ramp kind: {kind}') + + +def print_traceback_handler(sig, frame): + LOGGER.warning(f'Received signal {sig}') + bt = ''.join(traceback.format_stack()) + LOGGER.warning(f'Requested stack trace:\n{bt}') + + +def register_debug_signal_handlers(sig=signal.SIGUSR1, handler=print_traceback_handler): + LOGGER.warning(f'Setting signal {sig} handler {handler}') + signal.signal(sig, handler) + + +def handle_deterministic_config(config): + seed = dict(config).get('seed', None) + if seed is None: + return False + + seed_everything(seed) + return True + + +def get_shape(t): + if torch.is_tensor(t): + return tuple(t.shape) + elif isinstance(t, dict): + return {n: get_shape(q) for n, q in t.items()} + elif isinstance(t, (list, tuple)): + return [get_shape(q) for q in t] + elif isinstance(t, numbers.Number): + return type(t) + else: + raise ValueError('unexpected type {}'.format(type(t))) + + +def get_has_ddp_rank(): + master_port = os.environ.get('MASTER_PORT', None) + node_rank = os.environ.get('NODE_RANK', None) + local_rank = os.environ.get('LOCAL_RANK', None) + world_size = os.environ.get('WORLD_SIZE', None) + has_rank = master_port is not None or node_rank is not None or local_rank is not None or world_size is not None + return has_rank + + +def handle_ddp_subprocess(): + def main_decorator(main_func): + @functools.wraps(main_func) + def new_main(*args, **kwargs): + # Trainer sets MASTER_PORT, NODE_RANK, LOCAL_RANK, WORLD_SIZE + parent_cwd = os.environ.get('TRAINING_PARENT_WORK_DIR', None) + has_parent = parent_cwd is not None + has_rank = get_has_ddp_rank() + assert has_parent == has_rank, f'Inconsistent state: has_parent={has_parent}, has_rank={has_rank}' + + if has_parent: + # we are in the worker + sys.argv.extend([ + f'hydra.run.dir={parent_cwd}', + # 'hydra/hydra_logging=disabled', + # 'hydra/job_logging=disabled' + ]) + # do nothing if this is a top-level process + # TRAINING_PARENT_WORK_DIR is set in handle_ddp_parent_process after hydra initialization + + main_func(*args, **kwargs) + return new_main + return main_decorator + + +def handle_ddp_parent_process(): + parent_cwd = os.environ.get('TRAINING_PARENT_WORK_DIR', None) + has_parent = parent_cwd is not None + has_rank = get_has_ddp_rank() + assert has_parent == has_rank, f'Inconsistent state: has_parent={has_parent}, has_rank={has_rank}' + + if parent_cwd is None: + os.environ['TRAINING_PARENT_WORK_DIR'] = os.getcwd() + + return has_parent