+
+| + | name | +backbone | +Data | +box AP on COCO | +Checkpoint | +Config | +
|---|---|---|---|---|---|---|
| 1 | +GroundingDINO-T | +Swin-T | +O365,GoldG,Cap4M | +48.4 (zero-shot) / 57.2 (fine-tune) | +GitHub link | HF link | +link | +
| 2 | +GroundingDINO-B | +Swin-B | +COCO,O365,GoldG,Cap4M,OpenImage,ODinW-35,RefCOCO | +56.7 | +GitHub link | HF link + | link | +
+
+
+
+
+
+- **AOT**: Associating Objects with Transformers for Video Object Segmentation (NeurIPS 2021, Score 8/8/7/8) [[OpenReview](https://openreview.net/forum?id=hl3v8io3ZYt)][[PDF](https://arxiv.org/abs/2106.02638)]
+
+
+An extension of AOT, [AOST](https://arxiv.org/abs/2203.11442) (under review), is available now. AOST is a more robust and flexible framework, supporting run-time speed-accuracy trade-offs.
+
+## Examples
+Benchmark examples:
+
+
+
+General examples (Messi and Kobe):
+
+
+
+## Highlights
+- **High performance:** up to **85.5%** ([R50-AOTL](MODEL_ZOO.md#youtube-vos-2018-val)) on YouTube-VOS 2018 and **82.1%** ([SwinB-AOTL]((MODEL_ZOO.md#youtube-vos-2018-val))) on DAVIS-2017 Test-dev under standard settings (without any test-time augmentation and post processing).
+- **High efficiency:** up to **51fps** ([AOTT](MODEL_ZOO.md#davis-2017-test)) on DAVIS-2017 (480p) even with **10** objects and **41fps** on YouTube-VOS (1.3x480p). AOT can process multiple objects (less than a pre-defined number, 10 is the default) as efficiently as processing a single object. This project also supports inferring any number of objects together within a video by automatic separation and aggregation.
+- **Multi-GPU training and inference**
+- **Mixed precision training and inference**
+- **Test-time augmentation:** multi-scale and flipping augmentations are supported.
+
+## Requirements
+ * Python3
+ * pytorch >= 1.7.0 and torchvision
+ * opencv-python
+ * Pillow
+ * Pytorch Correlation (Recommend to install from [source](https://github.com/ClementPinard/Pytorch-Correlation-extension) instead of using `pip`. **The project can also work without this module but will lose some efficiency of the short-term attention**.)
+
+Optional:
+ * scikit-image (if you want to run our **Demo**, please install)
+
+## Model Zoo and Results
+Pre-trained models, benckmark scores, and pre-computed results reproduced by this project can be found in [MODEL_ZOO.md](MODEL_ZOO.md).
+
+## Demo - Panoptic Propagation
+We provide a simple demo to demonstrate AOT's effectiveness. The demo will propagate more than **40** objects, including semantic regions (like sky) and instances (like person), together within a single complex scenario and predict its video panoptic segmentation.
+
+To run the demo, download the [checkpoint](https://drive.google.com/file/d/1qJDYn3Ibpquu4ffYoQmVjg1YCbr2JQep/view?usp=sharing) of R50-AOTL into [pretrain_models](pretrain_models), and then run:
+```bash
+python tools/demo.py
+```
+which will predict the given scenarios in the resolution of 1.3x480p. You can also run this demo with other AOTs ([MODEL_ZOO.md](MODEL_ZOO.md)) by setting `--model` (model type) and `--ckpt_path` (checkpoint path).
+
+Two scenarios from [VSPW](https://www.vspwdataset.com/home) are supplied in [datasets/Demo](datasets/Demo):
+
+- 1001_3iEIq5HBY1s: 44 objects. 1080P.
+- 1007_YCTBBdbKSSg: 43 objects. 1080P.
+
+Results:
+
+
+
+
+## Getting Started
+0. Prepare a valid environment follow the [requirements](#requirements).
+
+1. Prepare datasets:
+
+ Please follow the below instruction to prepare datasets in each corresponding folder.
+ * **Static**
+
+ [datasets/Static](datasets/Static): pre-training dataset with static images. Guidance can be found in [AFB-URR](https://github.com/xmlyqing00/AFB-URR), which we referred to in the implementation of the pre-training.
+ * **YouTube-VOS**
+
+ A commonly-used large-scale VOS dataset.
+
+ [datasets/YTB/2019](datasets/YTB/2019): version 2019, download [link](https://drive.google.com/drive/folders/1BWzrCWyPEmBEKm0lOHe5KLuBuQxUSwqz?usp=sharing). `train` is required for training. `valid` (6fps) and `valid_all_frames` (30fps, optional) are used for evaluation.
+
+ [datasets/YTB/2018](datasets/YTB/2018): version 2018, download [link](https://drive.google.com/drive/folders/1bI5J1H3mxsIGo7Kp-pPZU8i6rnykOw7f?usp=sharing). Only `valid` (6fps) and `valid_all_frames` (30fps, optional) are required for this project and used for evaluation.
+
+ * **DAVIS**
+
+ A commonly-used small-scale VOS dataset.
+
+ [datasets/DAVIS](datasets/DAVIS): [TrainVal](https://data.vision.ee.ethz.ch/csergi/share/davis/DAVIS-2017-trainval-480p.zip) (480p) contains both the training and validation split. [Test-Dev](https://data.vision.ee.ethz.ch/csergi/share/davis/DAVIS-2017-test-dev-480p.zip) (480p) contains the Test-dev split. The [full-resolution version](https://davischallenge.org/davis2017/code.html) is also supported for training and evaluation but not required.
+
+
+2. Prepare ImageNet pre-trained encoders
+
+ Select and download below checkpoints into [pretrain_models](pretrain_models):
+
+ - [MobileNet-V2](https://download.pytorch.org/models/mobilenet_v2-b0353104.pth) (default encoder)
+ - [MobileNet-V3](https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth)
+ - [ResNet-50](https://download.pytorch.org/models/resnet50-0676ba61.pth)
+ - [ResNet-101](https://download.pytorch.org/models/resnet101-63fe2227.pth)
+ - [ResNeSt-50](https://github.com/zhanghang1989/ResNeSt/releases/download/weights_step1/resnest50-528c19ca.pth)
+ - [ResNeSt-101](https://github.com/zhanghang1989/ResNeSt/releases/download/weights_step1/resnest101-22405ba7.pth)
+ - [Swin-Base](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth)
+
+ The current default training configs are not optimized for encoders larger than ResNet-50. If you want to use larger encoders, we recommend early stopping the main-training stage at 80,000 iterations (100,000 in default) to avoid over-fitting on the seen classes of YouTube-VOS.
+
+
+
+3. Training and Evaluation
+
+ The [example script](train_eval.sh) will train AOTT with 2 stages using 4 GPUs and auto-mixed precision (`--amp`). The first stage is a pre-training stage using `Static` dataset, and the second stage is a main-training stage, which uses both `YouTube-VOS 2019 train` and `DAVIS-2017 train` for training, resulting in a model that can generalize to different domains (YouTube-VOS and DAVIS) and different frame rates (6fps, 24fps, and 30fps).
+
+ Notably, you can use only the `YouTube-VOS 2019 train` split in the second stage by changing `pre_ytb_dav` to `pre_ytb`, which leads to better YouTube-VOS performance on unseen classes. Besides, if you don't want to do the first stage, you can start the training from stage `ytb`, but the performance will drop about 1~2% absolutely.
+
+ After the training is finished (about 0.6 days for each stage with 4 Tesla V100 GPUs), the [example script](train_eval.sh) will evaluate the model on YouTube-VOS and DAVIS, and the results will be packed into Zip files. For calculating scores, please use official YouTube-VOS servers ([2018 server](https://competitions.codalab.org/competitions/19544) and [2019 server](https://competitions.codalab.org/competitions/20127)), official [DAVIS toolkit](https://github.com/davisvideochallenge/davis-2017) (for Val), and official [DAVIS server](https://competitions.codalab.org/competitions/20516#learn_the_details) (for Test-dev).
+
+## Adding your own dataset
+Coming
+
+## Troubleshooting
+Waiting
+
+## TODO
+- [ ] Code documentation
+- [ ] Adding your own dataset
+- [ ] Results with test-time augmentations in Model Zoo
+- [ ] Support gradient accumulation
+- [x] Demo tool
+
+## Citations
+Please consider citing the related paper(s) in your publications if it helps your research.
+```
+@inproceedings{yang2022deaot,
+ title={Decoupling Features in Hierarchical Propagation for Video Object Segmentation},
+ author={Yang, Zongxin and Yang, Yi},
+ booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
+ year={2022}
+}
+@article{yang2021aost,
+ title={Scalable Multi-object Identification for Video Object Segmentation},
+ author={Yang, Zongxin and Miao, Jiaxu and Wang, Xiaohan and Wei, Yunchao and Yang, Yi},
+ journal={arXiv preprint arXiv:2203.11442},
+ year={2022}
+}
+@inproceedings{yang2021aot,
+ title={Associating Objects with Transformers for Video Object Segmentation},
+ author={Yang, Zongxin and Wei, Yunchao and Yang, Yi},
+ booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
+ year={2021}
+}
+```
+
+## License
+This project is released under the BSD-3-Clause license. See [LICENSE](LICENSE) for additional details.
diff --git a/aot/__init__.py b/aot/__init__.py
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diff --git a/aot/configs/default.py b/aot/configs/default.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc96c45cb196c50a70d447ba628b26bd5a293e0e
--- /dev/null
+++ b/aot/configs/default.py
@@ -0,0 +1,138 @@
+import os
+import importlib
+
+
+class DefaultEngineConfig():
+ def __init__(self, exp_name='default', model='aott'):
+ model_cfg = importlib.import_module('configs.models.' +
+ model).ModelConfig()
+ self.__dict__.update(model_cfg.__dict__) # add model config
+
+ self.EXP_NAME = exp_name + '_' + self.MODEL_NAME
+
+ self.STAGE_NAME = 'YTB'
+
+ self.DATASETS = ['youtubevos']
+ self.DATA_WORKERS = 8
+ self.DATA_RANDOMCROP = (465,
+ 465) if self.MODEL_ALIGN_CORNERS else (464,
+ 464)
+ self.DATA_RANDOMFLIP = 0.5
+ self.DATA_MAX_CROP_STEPS = 10
+ self.DATA_SHORT_EDGE_LEN = 480
+ self.DATA_MIN_SCALE_FACTOR = 0.7
+ self.DATA_MAX_SCALE_FACTOR = 1.3
+ self.DATA_RANDOM_REVERSE_SEQ = True
+ self.DATA_SEQ_LEN = 5
+ self.DATA_DAVIS_REPEAT = 5
+ self.DATA_RANDOM_GAP_DAVIS = 12 # max frame interval between two sampled frames for DAVIS (24fps)
+ self.DATA_RANDOM_GAP_YTB = 3 # max frame interval between two sampled frames for YouTube-VOS (6fps)
+ self.DATA_DYNAMIC_MERGE_PROB = 0.3
+
+ self.PRETRAIN = True
+ self.PRETRAIN_FULL = False # if False, load encoder only
+ self.PRETRAIN_MODEL = './data_wd/pretrain_model/mobilenet_v2.pth'
+ # self.PRETRAIN_MODEL = './pretrain_models/mobilenet_v2-b0353104.pth'
+
+ self.TRAIN_TOTAL_STEPS = 100000
+ self.TRAIN_START_STEP = 0
+ self.TRAIN_WEIGHT_DECAY = 0.07
+ self.TRAIN_WEIGHT_DECAY_EXCLUSIVE = {
+ # 'encoder.': 0.01
+ }
+ self.TRAIN_WEIGHT_DECAY_EXEMPTION = [
+ 'absolute_pos_embed', 'relative_position_bias_table',
+ 'relative_emb_v', 'conv_out'
+ ]
+ self.TRAIN_LR = 2e-4
+ self.TRAIN_LR_MIN = 2e-5 if 'mobilenetv2' in self.MODEL_ENCODER else 1e-5
+ self.TRAIN_LR_POWER = 0.9
+ self.TRAIN_LR_ENCODER_RATIO = 0.1
+ self.TRAIN_LR_WARM_UP_RATIO = 0.05
+ self.TRAIN_LR_COSINE_DECAY = False
+ self.TRAIN_LR_RESTART = 1
+ self.TRAIN_LR_UPDATE_STEP = 1
+ self.TRAIN_AUX_LOSS_WEIGHT = 1.0
+ self.TRAIN_AUX_LOSS_RATIO = 1.0
+ self.TRAIN_OPT = 'adamw'
+ self.TRAIN_SGD_MOMENTUM = 0.9
+ self.TRAIN_GPUS = 4
+ self.TRAIN_BATCH_SIZE = 16
+ self.TRAIN_TBLOG = False
+ self.TRAIN_TBLOG_STEP = 50
+ self.TRAIN_LOG_STEP = 20
+ self.TRAIN_IMG_LOG = True
+ self.TRAIN_TOP_K_PERCENT_PIXELS = 0.15
+ self.TRAIN_SEQ_TRAINING_FREEZE_PARAMS = ['patch_wise_id_bank']
+ self.TRAIN_SEQ_TRAINING_START_RATIO = 0.5
+ self.TRAIN_HARD_MINING_RATIO = 0.5
+ self.TRAIN_EMA_RATIO = 0.1
+ self.TRAIN_CLIP_GRAD_NORM = 5.
+ self.TRAIN_SAVE_STEP = 5000
+ self.TRAIN_MAX_KEEP_CKPT = 8
+ self.TRAIN_RESUME = False
+ self.TRAIN_RESUME_CKPT = None
+ self.TRAIN_RESUME_STEP = 0
+ self.TRAIN_AUTO_RESUME = True
+ self.TRAIN_DATASET_FULL_RESOLUTION = False
+ self.TRAIN_ENABLE_PREV_FRAME = False
+ self.TRAIN_ENCODER_FREEZE_AT = 2
+ self.TRAIN_LSTT_EMB_DROPOUT = 0.
+ self.TRAIN_LSTT_ID_DROPOUT = 0.
+ self.TRAIN_LSTT_DROPPATH = 0.1
+ self.TRAIN_LSTT_DROPPATH_SCALING = False
+ self.TRAIN_LSTT_DROPPATH_LST = False
+ self.TRAIN_LSTT_LT_DROPOUT = 0.
+ self.TRAIN_LSTT_ST_DROPOUT = 0.
+
+ self.TEST_GPU_ID = 0
+ self.TEST_GPU_NUM = 1
+ self.TEST_FRAME_LOG = False
+ self.TEST_DATASET = 'youtubevos'
+ self.TEST_DATASET_FULL_RESOLUTION = False
+ self.TEST_DATASET_SPLIT = 'val'
+ self.TEST_CKPT_PATH = None
+ # if "None", evaluate the latest checkpoint.
+ self.TEST_CKPT_STEP = None
+ self.TEST_FLIP = False
+ self.TEST_MULTISCALE = [1]
+ self.TEST_MAX_SHORT_EDGE = None
+ self.TEST_MAX_LONG_EDGE = 800 * 1.3
+ self.TEST_WORKERS = 4
+
+ # GPU distribution
+ self.DIST_ENABLE = True
+ self.DIST_BACKEND = "nccl" # "gloo"
+ self.DIST_URL = "tcp://127.0.0.1:13241"
+ self.DIST_START_GPU = 0
+
+ def init_dir(self):
+ self.DIR_DATA = '../VOS02/datasets'#'./datasets'
+ self.DIR_DAVIS = os.path.join(self.DIR_DATA, 'DAVIS')
+ self.DIR_YTB = os.path.join(self.DIR_DATA, 'YTB')
+ self.DIR_STATIC = os.path.join(self.DIR_DATA, 'Static')
+
+ self.DIR_ROOT = './'#'./data_wd/youtube_vos_jobs'
+
+ self.DIR_RESULT = os.path.join(self.DIR_ROOT, 'result', self.EXP_NAME,
+ self.STAGE_NAME)
+ self.DIR_CKPT = os.path.join(self.DIR_RESULT, 'ckpt')
+ self.DIR_EMA_CKPT = os.path.join(self.DIR_RESULT, 'ema_ckpt')
+ self.DIR_LOG = os.path.join(self.DIR_RESULT, 'log')
+ self.DIR_TB_LOG = os.path.join(self.DIR_RESULT, 'log', 'tensorboard')
+ # self.DIR_IMG_LOG = os.path.join(self.DIR_RESULT, 'log', 'img')
+ # self.DIR_EVALUATION = os.path.join(self.DIR_RESULT, 'eval')
+ self.DIR_IMG_LOG = './img_logs'
+ self.DIR_EVALUATION = './results'
+
+ for path in [
+ self.DIR_RESULT, self.DIR_CKPT, self.DIR_EMA_CKPT,
+ self.DIR_LOG, self.DIR_EVALUATION, self.DIR_IMG_LOG,
+ self.DIR_TB_LOG
+ ]:
+ if not os.path.isdir(path):
+ try:
+ os.makedirs(path)
+ except Exception as inst:
+ print(inst)
+ print('Failed to make dir: {}.'.format(path))
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diff --git a/aot/configs/models/aotb.py b/aot/configs/models/aotb.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d0bc396f04e399e5d06f4cb40dd86e8dedd2019
--- /dev/null
+++ b/aot/configs/models/aotb.py
@@ -0,0 +1,9 @@
+import os
+from .default import DefaultModelConfig
+
+class ModelConfig(DefaultModelConfig):
+ def __init__(self):
+ super().__init__()
+ self.MODEL_NAME = 'AOTB'
+
+ self.MODEL_LSTT_NUM = 3
diff --git a/aot/configs/models/aotl.py b/aot/configs/models/aotl.py
new file mode 100644
index 0000000000000000000000000000000000000000..4fcedefa3b39bc3e49e70eae357273757c332778
--- /dev/null
+++ b/aot/configs/models/aotl.py
@@ -0,0 +1,13 @@
+import os
+from .default import DefaultModelConfig
+
+class ModelConfig(DefaultModelConfig):
+ def __init__(self):
+ super().__init__()
+ self.MODEL_NAME = 'AOTL'
+
+ self.MODEL_LSTT_NUM = 3
+
+ self.TRAIN_LONG_TERM_MEM_GAP = 2
+
+ self.TEST_LONG_TERM_MEM_GAP = 5
\ No newline at end of file
diff --git a/aot/configs/models/aots.py b/aot/configs/models/aots.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb5e8458e5747116fc3da7101fc70413452aebd4
--- /dev/null
+++ b/aot/configs/models/aots.py
@@ -0,0 +1,9 @@
+import os
+from .default import DefaultModelConfig
+
+class ModelConfig(DefaultModelConfig):
+ def __init__(self):
+ super().__init__()
+ self.MODEL_NAME = 'AOTS'
+
+ self.MODEL_LSTT_NUM = 2
diff --git a/aot/configs/models/aott.py b/aot/configs/models/aott.py
new file mode 100644
index 0000000000000000000000000000000000000000..587fce66d43c23ddc2eed105e1033650f3ef5080
--- /dev/null
+++ b/aot/configs/models/aott.py
@@ -0,0 +1,7 @@
+import os
+from .default import DefaultModelConfig
+
+class ModelConfig(DefaultModelConfig):
+ def __init__(self):
+ super().__init__()
+ self.MODEL_NAME = 'AOTT'
diff --git a/aot/configs/models/deaotb.py b/aot/configs/models/deaotb.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fcf2c1251f5a7e032375d425b1e979ff68bdaee
--- /dev/null
+++ b/aot/configs/models/deaotb.py
@@ -0,0 +1,9 @@
+from .default_deaot import DefaultModelConfig
+
+
+class ModelConfig(DefaultModelConfig):
+ def __init__(self):
+ super().__init__()
+ self.MODEL_NAME = 'DeAOTB'
+
+ self.MODEL_LSTT_NUM = 3
diff --git a/aot/configs/models/deaotl.py b/aot/configs/models/deaotl.py
new file mode 100644
index 0000000000000000000000000000000000000000..b61601e36a01dc536c29f45efab27dd0f8e857ba
--- /dev/null
+++ b/aot/configs/models/deaotl.py
@@ -0,0 +1,13 @@
+from .default_deaot import DefaultModelConfig
+
+
+class ModelConfig(DefaultModelConfig):
+ def __init__(self):
+ super().__init__()
+ self.MODEL_NAME = 'DeAOTL'
+
+ self.MODEL_LSTT_NUM = 3
+
+ self.TRAIN_LONG_TERM_MEM_GAP = 2
+
+ self.TEST_LONG_TERM_MEM_GAP = 5
diff --git a/aot/configs/models/deaots.py b/aot/configs/models/deaots.py
new file mode 100644
index 0000000000000000000000000000000000000000..632916c59e9c92cf26c6d12c9a0d2aadd2cd07cf
--- /dev/null
+++ b/aot/configs/models/deaots.py
@@ -0,0 +1,9 @@
+from .default_deaot import DefaultModelConfig
+
+
+class ModelConfig(DefaultModelConfig):
+ def __init__(self):
+ super().__init__()
+ self.MODEL_NAME = 'DeAOTS'
+
+ self.MODEL_LSTT_NUM = 2
diff --git a/aot/configs/models/deaott.py b/aot/configs/models/deaott.py
new file mode 100644
index 0000000000000000000000000000000000000000..78a414b74e53572ac34e30d74c0dd91a61cae4a1
--- /dev/null
+++ b/aot/configs/models/deaott.py
@@ -0,0 +1,7 @@
+from .default_deaot import DefaultModelConfig
+
+
+class ModelConfig(DefaultModelConfig):
+ def __init__(self):
+ super().__init__()
+ self.MODEL_NAME = 'DeAOTT'
diff --git a/aot/configs/models/default.py b/aot/configs/models/default.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ec250c637882027824483babaef3618044a347d
--- /dev/null
+++ b/aot/configs/models/default.py
@@ -0,0 +1,27 @@
+class DefaultModelConfig():
+ def __init__(self):
+ self.MODEL_NAME = 'AOTDefault'
+
+ self.MODEL_VOS = 'aot'
+ self.MODEL_ENGINE = 'aotengine'
+ self.MODEL_ALIGN_CORNERS = True
+ self.MODEL_ENCODER = 'mobilenetv2'
+ self.MODEL_ENCODER_PRETRAIN = './pretrain_models/mobilenet_v2-b0353104.pth'
+ self.MODEL_ENCODER_DIM = [24, 32, 96, 1280] # 4x, 8x, 16x, 16x
+ self.MODEL_ENCODER_EMBEDDING_DIM = 256
+ self.MODEL_DECODER_INTERMEDIATE_LSTT = True
+ self.MODEL_FREEZE_BN = True
+ self.MODEL_FREEZE_BACKBONE = False
+ self.MODEL_MAX_OBJ_NUM = 10
+ self.MODEL_SELF_HEADS = 8
+ self.MODEL_ATT_HEADS = 8
+ self.MODEL_LSTT_NUM = 1
+ self.MODEL_EPSILON = 1e-5
+ self.MODEL_USE_PREV_PROB = False
+
+ self.TRAIN_LONG_TERM_MEM_GAP = 9999
+ self.TRAIN_AUG_TYPE = 'v1'
+
+ self.TEST_LONG_TERM_MEM_GAP = 9999
+
+ self.TEST_SHORT_TERM_MEM_SKIP = 1
diff --git a/aot/configs/models/default_deaot.py b/aot/configs/models/default_deaot.py
new file mode 100644
index 0000000000000000000000000000000000000000..f28a52e99ab79c37346848ea9f6329521da91e36
--- /dev/null
+++ b/aot/configs/models/default_deaot.py
@@ -0,0 +1,17 @@
+from .default import DefaultModelConfig as BaseConfig
+
+
+class DefaultModelConfig(BaseConfig):
+ def __init__(self):
+ super().__init__()
+ self.MODEL_NAME = 'DeAOTDefault'
+
+ self.MODEL_VOS = 'deaot'
+ self.MODEL_ENGINE = 'deaotengine'
+
+ self.MODEL_DECODER_INTERMEDIATE_LSTT = False
+
+ self.MODEL_SELF_HEADS = 1
+ self.MODEL_ATT_HEADS = 1
+
+ self.TRAIN_AUG_TYPE = 'v2'
diff --git a/aot/configs/models/r101_aotl.py b/aot/configs/models/r101_aotl.py
new file mode 100644
index 0000000000000000000000000000000000000000..1687165de3f066648aefc985298b0f783a3f4a48
--- /dev/null
+++ b/aot/configs/models/r101_aotl.py
@@ -0,0 +1,16 @@
+from .default import DefaultModelConfig
+
+
+class ModelConfig(DefaultModelConfig):
+ def __init__(self):
+ super().__init__()
+ self.MODEL_NAME = 'R101_AOTL'
+
+ self.MODEL_ENCODER = 'resnet101'
+ self.MODEL_ENCODER_PRETRAIN = './pretrain_models/resnet101-63fe2227.pth' # https://download.pytorch.org/models/resnet101-63fe2227.pth
+ self.MODEL_ENCODER_DIM = [256, 512, 1024, 1024] # 4x, 8x, 16x, 16x
+ self.MODEL_LSTT_NUM = 3
+
+ self.TRAIN_LONG_TERM_MEM_GAP = 2
+
+ self.TEST_LONG_TERM_MEM_GAP = 5
\ No newline at end of file
diff --git a/aot/configs/models/r50_aotl.py b/aot/configs/models/r50_aotl.py
new file mode 100644
index 0000000000000000000000000000000000000000..941b9228f06e7b7fe7ef8fda6596c19120a254c0
--- /dev/null
+++ b/aot/configs/models/r50_aotl.py
@@ -0,0 +1,16 @@
+from .default import DefaultModelConfig
+
+
+class ModelConfig(DefaultModelConfig):
+ def __init__(self):
+ super().__init__()
+ self.MODEL_NAME = 'R50_AOTL'
+
+ self.MODEL_ENCODER = 'resnet50'
+ self.MODEL_ENCODER_PRETRAIN = './pretrain_models/resnet50-0676ba61.pth' # https://download.pytorch.org/models/resnet50-0676ba61.pth
+ self.MODEL_ENCODER_DIM = [256, 512, 1024, 1024] # 4x, 8x, 16x, 16x
+ self.MODEL_LSTT_NUM = 3
+
+ self.TRAIN_LONG_TERM_MEM_GAP = 2
+
+ self.TEST_LONG_TERM_MEM_GAP = 5
\ No newline at end of file
diff --git a/aot/configs/models/r50_deaotl.py b/aot/configs/models/r50_deaotl.py
new file mode 100644
index 0000000000000000000000000000000000000000..216abdb07c20b3fad131f868fa9d5b96cb17e8f9
--- /dev/null
+++ b/aot/configs/models/r50_deaotl.py
@@ -0,0 +1,16 @@
+from .default_deaot import DefaultModelConfig
+
+
+class ModelConfig(DefaultModelConfig):
+ def __init__(self):
+ super().__init__()
+ self.MODEL_NAME = 'R50_DeAOTL'
+
+ self.MODEL_ENCODER = 'resnet50'
+ self.MODEL_ENCODER_DIM = [256, 512, 1024, 1024] # 4x, 8x, 16x, 16x
+
+ self.MODEL_LSTT_NUM = 3
+
+ self.TRAIN_LONG_TERM_MEM_GAP = 2
+
+ self.TEST_LONG_TERM_MEM_GAP = 5
diff --git a/aot/configs/models/rs101_aotl.py b/aot/configs/models/rs101_aotl.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1636ec2d13db08758d1765a71c5acf717ff143a
--- /dev/null
+++ b/aot/configs/models/rs101_aotl.py
@@ -0,0 +1,16 @@
+from .default import DefaultModelConfig
+
+
+class ModelConfig(DefaultModelConfig):
+ def __init__(self):
+ super().__init__()
+ self.MODEL_NAME = 'R101_AOTL'
+
+ self.MODEL_ENCODER = 'resnest101'
+ self.MODEL_ENCODER_PRETRAIN = './pretrain_models/resnest101-22405ba7.pth' # https://github.com/zhanghang1989/ResNeSt/releases/download/weights_step1/resnest101-22405ba7.pth
+ self.MODEL_ENCODER_DIM = [256, 512, 1024, 1024] # 4x, 8x, 16x, 16x
+ self.MODEL_LSTT_NUM = 3
+
+ self.TRAIN_LONG_TERM_MEM_GAP = 2
+
+ self.TEST_LONG_TERM_MEM_GAP = 5
\ No newline at end of file
diff --git a/aot/configs/models/swinb_aotl.py b/aot/configs/models/swinb_aotl.py
new file mode 100644
index 0000000000000000000000000000000000000000..360a16d33184ca6e265bbe5a7315f72ce755b53a
--- /dev/null
+++ b/aot/configs/models/swinb_aotl.py
@@ -0,0 +1,17 @@
+from .default import DefaultModelConfig
+
+
+class ModelConfig(DefaultModelConfig):
+ def __init__(self):
+ super().__init__()
+ self.MODEL_NAME = 'SwinB_AOTL'
+
+ self.MODEL_ENCODER = 'swin_base'
+ self.MODEL_ENCODER_PRETRAIN = './pretrain_models/swin_base_patch4_window7_224_22k.pth' # https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth
+ self.MODEL_ALIGN_CORNERS = False
+ self.MODEL_ENCODER_DIM = [128, 256, 512, 512] # 4x, 8x, 16x, 16x
+ self.MODEL_LSTT_NUM = 3
+
+ self.TRAIN_LONG_TERM_MEM_GAP = 2
+
+ self.TEST_LONG_TERM_MEM_GAP = 5
\ No newline at end of file
diff --git a/aot/configs/models/swinb_deaotl.py b/aot/configs/models/swinb_deaotl.py
new file mode 100644
index 0000000000000000000000000000000000000000..463a3fa61b45740a3f821b7bc4bcbb432950f62b
--- /dev/null
+++ b/aot/configs/models/swinb_deaotl.py
@@ -0,0 +1,17 @@
+from .default_deaot import DefaultModelConfig
+
+
+class ModelConfig(DefaultModelConfig):
+ def __init__(self):
+ super().__init__()
+ self.MODEL_NAME = 'SwinB_DeAOTL'
+
+ self.MODEL_ENCODER = 'swin_base'
+ self.MODEL_ALIGN_CORNERS = False
+ self.MODEL_ENCODER_DIM = [128, 256, 512, 512] # 4x, 8x, 16x, 16x
+
+ self.MODEL_LSTT_NUM = 3
+
+ self.TRAIN_LONG_TERM_MEM_GAP = 2
+
+ self.TEST_LONG_TERM_MEM_GAP = 5
\ No newline at end of file
diff --git a/aot/configs/pre.py b/aot/configs/pre.py
new file mode 100644
index 0000000000000000000000000000000000000000..53b8b0283a59eb3c048e64ce200836a33c5be7ab
--- /dev/null
+++ b/aot/configs/pre.py
@@ -0,0 +1,19 @@
+from .default import DefaultEngineConfig
+
+
+class EngineConfig(DefaultEngineConfig):
+ def __init__(self, exp_name='default', model='AOTT'):
+ super().__init__(exp_name, model)
+ self.STAGE_NAME = 'PRE'
+
+ self.init_dir()
+
+ self.DATASETS = ['static']
+
+ self.DATA_DYNAMIC_MERGE_PROB = 1.0
+
+ self.TRAIN_LR = 4e-4
+ self.TRAIN_LR_MIN = 2e-5
+ self.TRAIN_WEIGHT_DECAY = 0.03
+ self.TRAIN_SEQ_TRAINING_START_RATIO = 1.0
+ self.TRAIN_AUX_LOSS_RATIO = 0.1
diff --git a/aot/configs/pre_dav.py b/aot/configs/pre_dav.py
new file mode 100644
index 0000000000000000000000000000000000000000..2abf75f557815ba2c0499d6c7f68539079b25293
--- /dev/null
+++ b/aot/configs/pre_dav.py
@@ -0,0 +1,21 @@
+import os
+from .default import DefaultEngineConfig
+
+
+class EngineConfig(DefaultEngineConfig):
+ def __init__(self, exp_name='default', model='AOTT'):
+ super().__init__(exp_name, model)
+ self.STAGE_NAME = 'PRE_DAV'
+
+ self.init_dir()
+
+ self.DATASETS = ['davis2017']
+
+ self.TRAIN_TOTAL_STEPS = 50000
+
+ pretrain_stage = 'PRE'
+ pretrain_ckpt = 'save_step_100000.pth'
+ self.PRETRAIN_FULL = True # if False, load encoder only
+ self.PRETRAIN_MODEL = os.path.join(self.DIR_ROOT, 'result',
+ self.EXP_NAME, pretrain_stage,
+ 'ema_ckpt', pretrain_ckpt)
diff --git a/aot/configs/pre_ytb.py b/aot/configs/pre_ytb.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1edbb1103b2d9fcc5606cf41ee03bece7cb2d93
--- /dev/null
+++ b/aot/configs/pre_ytb.py
@@ -0,0 +1,17 @@
+import os
+from .default import DefaultEngineConfig
+
+
+class EngineConfig(DefaultEngineConfig):
+ def __init__(self, exp_name='default', model='AOTT'):
+ super().__init__(exp_name, model)
+ self.STAGE_NAME = 'PRE_YTB'
+
+ self.init_dir()
+
+ pretrain_stage = 'PRE'
+ pretrain_ckpt = 'save_step_100000.pth'
+ self.PRETRAIN_FULL = True # if False, load encoder only
+ self.PRETRAIN_MODEL = os.path.join(self.DIR_ROOT, 'result',
+ self.EXP_NAME, pretrain_stage,
+ 'ema_ckpt', pretrain_ckpt)
diff --git a/aot/configs/pre_ytb_dav.py b/aot/configs/pre_ytb_dav.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d58a5dc20af434394d157750a3f0f2f19095027
--- /dev/null
+++ b/aot/configs/pre_ytb_dav.py
@@ -0,0 +1,19 @@
+import os
+from .default import DefaultEngineConfig
+
+
+class EngineConfig(DefaultEngineConfig):
+ def __init__(self, exp_name='default', model='AOTT'):
+ super().__init__(exp_name, model)
+ self.STAGE_NAME = 'PRE_YTB_DAV'
+
+ self.init_dir()
+
+ self.DATASETS = ['youtubevos', 'davis2017']
+
+ pretrain_stage = 'PRE'
+ pretrain_ckpt = 'save_step_100000.pth'
+ self.PRETRAIN_FULL = True # if False, load encoder only
+ self.PRETRAIN_MODEL = os.path.join(self.DIR_ROOT, 'result',
+ self.EXP_NAME, pretrain_stage,
+ 'ema_ckpt', pretrain_ckpt)
diff --git a/aot/configs/ytb.py b/aot/configs/ytb.py
new file mode 100644
index 0000000000000000000000000000000000000000..f476ee106290fe390cccf2b9e8f116ee1c8fbd61
--- /dev/null
+++ b/aot/configs/ytb.py
@@ -0,0 +1,10 @@
+import os
+from .default import DefaultEngineConfig
+
+
+class EngineConfig(DefaultEngineConfig):
+ def __init__(self, exp_name='default', model='AOTT'):
+ super().__init__(exp_name, model)
+ self.STAGE_NAME = 'YTB'
+
+ self.init_dir()
diff --git a/aot/dataloaders/__init__.py b/aot/dataloaders/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/aot/dataloaders/__pycache__/__init__.cpython-311.pyc b/aot/dataloaders/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2a8bc05014ebc1d58541316fb18529dc8d2b6877
Binary files /dev/null and b/aot/dataloaders/__pycache__/__init__.cpython-311.pyc differ
diff --git a/aot/dataloaders/__pycache__/image_transforms.cpython-311.pyc b/aot/dataloaders/__pycache__/image_transforms.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ec22eda49e7ac30590606cfcc1510b22bd157f26
Binary files /dev/null and b/aot/dataloaders/__pycache__/image_transforms.cpython-311.pyc differ
diff --git a/aot/dataloaders/__pycache__/video_transforms.cpython-311.pyc b/aot/dataloaders/__pycache__/video_transforms.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c8d530e6919e87e5463579eb5eb6b629fb2bd78e
Binary files /dev/null and b/aot/dataloaders/__pycache__/video_transforms.cpython-311.pyc differ
diff --git a/aot/dataloaders/eval_datasets.py b/aot/dataloaders/eval_datasets.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5c5bc22f2c9b14d90d201d0e128fdacb9efc58f
--- /dev/null
+++ b/aot/dataloaders/eval_datasets.py
@@ -0,0 +1,411 @@
+from __future__ import division
+import os
+import shutil
+import json
+import cv2
+from PIL import Image
+
+import numpy as np
+from torch.utils.data import Dataset
+
+from utils.image import _palette
+
+
+class VOSTest(Dataset):
+ def __init__(self,
+ image_root,
+ label_root,
+ seq_name,
+ images,
+ labels,
+ rgb=True,
+ transform=None,
+ single_obj=False,
+ resolution=None):
+ self.image_root = image_root
+ self.label_root = label_root
+ self.seq_name = seq_name
+ self.images = images
+ self.labels = labels
+ self.obj_num = 1
+ self.num_frame = len(self.images)
+ self.transform = transform
+ self.rgb = rgb
+ self.single_obj = single_obj
+ self.resolution = resolution
+
+ self.obj_nums = []
+ self.obj_indices = []
+
+ curr_objs = [0]
+ for img_name in self.images:
+ self.obj_nums.append(len(curr_objs) - 1)
+ current_label_name = img_name.split('.')[0] + '.png'
+ if current_label_name in self.labels:
+ current_label = self.read_label(current_label_name)
+ curr_obj = list(np.unique(current_label))
+ for obj_idx in curr_obj:
+ if obj_idx not in curr_objs:
+ curr_objs.append(obj_idx)
+ self.obj_indices.append(curr_objs.copy())
+
+ self.obj_nums[0] = self.obj_nums[1]
+
+ def __len__(self):
+ return len(self.images)
+
+ def read_image(self, idx):
+ img_name = self.images[idx]
+ img_path = os.path.join(self.image_root, self.seq_name, img_name)
+ img = cv2.imread(img_path)
+ img = np.array(img, dtype=np.float32)
+ if self.rgb:
+ img = img[:, :, [2, 1, 0]]
+ return img
+
+ def read_label(self, label_name, squeeze_idx=None):
+ label_path = os.path.join(self.label_root, self.seq_name, label_name)
+ label = Image.open(label_path)
+ label = np.array(label, dtype=np.uint8)
+ if self.single_obj:
+ label = (label > 0).astype(np.uint8)
+ elif squeeze_idx is not None:
+ squeezed_label = label * 0
+ for idx in range(len(squeeze_idx)):
+ obj_id = squeeze_idx[idx]
+ if obj_id == 0:
+ continue
+ mask = label == obj_id
+ squeezed_label += (mask * idx).astype(np.uint8)
+ label = squeezed_label
+ return label
+
+ def __getitem__(self, idx):
+ img_name = self.images[idx]
+ current_img = self.read_image(idx)
+ height, width, channels = current_img.shape
+ if self.resolution is not None:
+ width = int(np.ceil(
+ float(width) * self.resolution / float(height)))
+ height = int(self.resolution)
+
+ current_label_name = img_name.split('.')[0] + '.png'
+ obj_num = self.obj_nums[idx]
+ obj_idx = self.obj_indices[idx]
+
+ if current_label_name in self.labels:
+ current_label = self.read_label(current_label_name, obj_idx)
+ sample = {
+ 'current_img': current_img,
+ 'current_label': current_label
+ }
+ else:
+ sample = {'current_img': current_img}
+
+ sample['meta'] = {
+ 'seq_name': self.seq_name,
+ 'frame_num': self.num_frame,
+ 'obj_num': obj_num,
+ 'current_name': img_name,
+ 'height': height,
+ 'width': width,
+ 'flip': False,
+ 'obj_idx': obj_idx
+ }
+
+ if self.transform is not None:
+ sample = self.transform(sample)
+ return sample
+
+
+class YOUTUBEVOS_Test(object):
+ def __init__(self,
+ root='./datasets/YTB',
+ year=2018,
+ split='val',
+ transform=None,
+ rgb=True,
+ result_root=None):
+ if split == 'val':
+ split = 'valid'
+ root = os.path.join(root, str(year), split)
+ self.db_root_dir = root
+ self.result_root = result_root
+ self.rgb = rgb
+ self.transform = transform
+ self.seq_list_file = os.path.join(self.db_root_dir, 'meta.json')
+ self._check_preprocess()
+ self.seqs = list(self.ann_f.keys())
+ self.image_root = os.path.join(root, 'JPEGImages')
+ self.label_root = os.path.join(root, 'Annotations')
+
+ def __len__(self):
+ return len(self.seqs)
+
+ def __getitem__(self, idx):
+ seq_name = self.seqs[idx]
+ data = self.ann_f[seq_name]['objects']
+ obj_names = list(data.keys())
+ images = []
+ labels = []
+ for obj_n in obj_names:
+ images += map(lambda x: x + '.jpg', list(data[obj_n]["frames"]))
+ labels.append(data[obj_n]["frames"][0] + '.png')
+ images = np.sort(np.unique(images))
+ labels = np.sort(np.unique(labels))
+
+ try:
+ if not os.path.isfile(
+ os.path.join(self.result_root, seq_name, labels[0])):
+ if not os.path.exists(os.path.join(self.result_root,
+ seq_name)):
+ os.makedirs(os.path.join(self.result_root, seq_name))
+ shutil.copy(
+ os.path.join(self.label_root, seq_name, labels[0]),
+ os.path.join(self.result_root, seq_name, labels[0]))
+ except Exception as inst:
+ print(inst)
+ print('Failed to create a result folder for sequence {}.'.format(
+ seq_name))
+
+ seq_dataset = VOSTest(self.image_root,
+ self.label_root,
+ seq_name,
+ images,
+ labels,
+ transform=self.transform,
+ rgb=self.rgb)
+ return seq_dataset
+
+ def _check_preprocess(self):
+ _seq_list_file = self.seq_list_file
+ if not os.path.isfile(_seq_list_file):
+ print(_seq_list_file)
+ return False
+ else:
+ self.ann_f = json.load(open(self.seq_list_file, 'r'))['videos']
+ return True
+
+
+class YOUTUBEVOS_DenseTest(object):
+ def __init__(self,
+ root='./datasets/YTB',
+ year=2018,
+ split='val',
+ transform=None,
+ rgb=True,
+ result_root=None):
+ if split == 'val':
+ split = 'valid'
+ root_sparse = os.path.join(root, str(year), split)
+ root_dense = root_sparse + '_all_frames'
+ self.db_root_dir = root_dense
+ self.result_root = result_root
+ self.rgb = rgb
+ self.transform = transform
+ self.seq_list_file = os.path.join(root_sparse, 'meta.json')
+ self._check_preprocess()
+ self.seqs = list(self.ann_f.keys())
+ self.image_root = os.path.join(root_dense, 'JPEGImages')
+ self.label_root = os.path.join(root_sparse, 'Annotations')
+
+ def __len__(self):
+ return len(self.seqs)
+
+ def __getitem__(self, idx):
+ seq_name = self.seqs[idx]
+
+ data = self.ann_f[seq_name]['objects']
+ obj_names = list(data.keys())
+ images_sparse = []
+ for obj_n in obj_names:
+ images_sparse += map(lambda x: x + '.jpg',
+ list(data[obj_n]["frames"]))
+ images_sparse = np.sort(np.unique(images_sparse))
+
+ images = np.sort(
+ list(os.listdir(os.path.join(self.image_root, seq_name))))
+ start_img = images_sparse[0]
+ end_img = images_sparse[-1]
+ for start_idx in range(len(images)):
+ if start_img in images[start_idx]:
+ break
+ for end_idx in range(len(images))[::-1]:
+ if end_img in images[end_idx]:
+ break
+ images = images[start_idx:(end_idx + 1)]
+ labels = np.sort(
+ list(os.listdir(os.path.join(self.label_root, seq_name))))
+
+ try:
+ if not os.path.isfile(
+ os.path.join(self.result_root, seq_name, labels[0])):
+ if not os.path.exists(os.path.join(self.result_root,
+ seq_name)):
+ os.makedirs(os.path.join(self.result_root, seq_name))
+ shutil.copy(
+ os.path.join(self.label_root, seq_name, labels[0]),
+ os.path.join(self.result_root, seq_name, labels[0]))
+ except Exception as inst:
+ print(inst)
+ print('Failed to create a result folder for sequence {}.'.format(
+ seq_name))
+
+ seq_dataset = VOSTest(self.image_root,
+ self.label_root,
+ seq_name,
+ images,
+ labels,
+ transform=self.transform,
+ rgb=self.rgb)
+ seq_dataset.images_sparse = images_sparse
+
+ return seq_dataset
+
+ def _check_preprocess(self):
+ _seq_list_file = self.seq_list_file
+ if not os.path.isfile(_seq_list_file):
+ print(_seq_list_file)
+ return False
+ else:
+ self.ann_f = json.load(open(self.seq_list_file, 'r'))['videos']
+ return True
+
+
+class DAVIS_Test(object):
+ def __init__(self,
+ split=['val'],
+ root='./DAVIS',
+ year=2017,
+ transform=None,
+ rgb=True,
+ full_resolution=False,
+ result_root=None):
+ self.transform = transform
+ self.rgb = rgb
+ self.result_root = result_root
+ if year == 2016:
+ self.single_obj = True
+ else:
+ self.single_obj = False
+ if full_resolution:
+ resolution = 'Full-Resolution'
+ else:
+ resolution = '480p'
+ self.image_root = os.path.join(root, 'JPEGImages', resolution)
+ self.label_root = os.path.join(root, 'Annotations', resolution)
+ seq_names = []
+ for spt in split:
+ if spt == 'test':
+ spt = 'test-dev'
+ with open(os.path.join(root, 'ImageSets', str(year),
+ spt + '.txt')) as f:
+ seqs_tmp = f.readlines()
+ seqs_tmp = list(map(lambda elem: elem.strip(), seqs_tmp))
+ seq_names.extend(seqs_tmp)
+ self.seqs = list(np.unique(seq_names))
+
+ def __len__(self):
+ return len(self.seqs)
+
+ def __getitem__(self, idx):
+ seq_name = self.seqs[idx]
+ images = list(
+ np.sort(os.listdir(os.path.join(self.image_root, seq_name))))
+ labels = [images[0].replace('jpg', 'png')]
+
+ if not os.path.isfile(
+ os.path.join(self.result_root, seq_name, labels[0])):
+ seq_result_folder = os.path.join(self.result_root, seq_name)
+ try:
+ if not os.path.exists(seq_result_folder):
+ os.makedirs(seq_result_folder)
+ except Exception as inst:
+ print(inst)
+ print(
+ 'Failed to create a result folder for sequence {}.'.format(
+ seq_name))
+ source_label_path = os.path.join(self.label_root, seq_name,
+ labels[0])
+ result_label_path = os.path.join(self.result_root, seq_name,
+ labels[0])
+ if self.single_obj:
+ label = Image.open(source_label_path)
+ label = np.array(label, dtype=np.uint8)
+ label = (label > 0).astype(np.uint8)
+ label = Image.fromarray(label).convert('P')
+ label.putpalette(_palette)
+ label.save(result_label_path)
+ else:
+ shutil.copy(source_label_path, result_label_path)
+
+ seq_dataset = VOSTest(self.image_root,
+ self.label_root,
+ seq_name,
+ images,
+ labels,
+ transform=self.transform,
+ rgb=self.rgb,
+ single_obj=self.single_obj,
+ resolution=480)
+ return seq_dataset
+
+
+class _EVAL_TEST(Dataset):
+ def __init__(self, transform, seq_name):
+ self.seq_name = seq_name
+ self.num_frame = 10
+ self.transform = transform
+
+ def __len__(self):
+ return self.num_frame
+
+ def __getitem__(self, idx):
+ current_frame_obj_num = 2
+ height = 400
+ width = 400
+ img_name = 'test{}.jpg'.format(idx)
+ current_img = np.zeros((height, width, 3)).astype(np.float32)
+ if idx == 0:
+ current_label = (current_frame_obj_num * np.ones(
+ (height, width))).astype(np.uint8)
+ sample = {
+ 'current_img': current_img,
+ 'current_label': current_label
+ }
+ else:
+ sample = {'current_img': current_img}
+
+ sample['meta'] = {
+ 'seq_name': self.seq_name,
+ 'frame_num': self.num_frame,
+ 'obj_num': current_frame_obj_num,
+ 'current_name': img_name,
+ 'height': height,
+ 'width': width,
+ 'flip': False
+ }
+
+ if self.transform is not None:
+ sample = self.transform(sample)
+ return sample
+
+
+class EVAL_TEST(object):
+ def __init__(self, transform=None, result_root=None):
+ self.transform = transform
+ self.result_root = result_root
+
+ self.seqs = ['test1', 'test2', 'test3']
+
+ def __len__(self):
+ return len(self.seqs)
+
+ def __getitem__(self, idx):
+ seq_name = self.seqs[idx]
+
+ if not os.path.exists(os.path.join(self.result_root, seq_name)):
+ os.makedirs(os.path.join(self.result_root, seq_name))
+
+ seq_dataset = _EVAL_TEST(self.transform, seq_name)
+ return seq_dataset
diff --git a/aot/dataloaders/image_transforms.py b/aot/dataloaders/image_transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c90be41e911f8770820277474b3b782d9be3024
--- /dev/null
+++ b/aot/dataloaders/image_transforms.py
@@ -0,0 +1,530 @@
+import math
+import warnings
+import random
+import numbers
+import numpy as np
+from PIL import Image, ImageFilter
+from collections.abc import Sequence
+
+import torch
+import torchvision.transforms.functional as TF
+
+_pil_interpolation_to_str = {
+ Image.NEAREST: 'PIL.Image.NEAREST',
+ Image.BILINEAR: 'PIL.Image.BILINEAR',
+ Image.BICUBIC: 'PIL.Image.BICUBIC',
+ Image.LANCZOS: 'PIL.Image.LANCZOS',
+ Image.HAMMING: 'PIL.Image.HAMMING',
+ Image.BOX: 'PIL.Image.BOX',
+}
+
+
+def _get_image_size(img):
+ if TF._is_pil_image(img):
+ return img.size
+ elif isinstance(img, torch.Tensor) and img.dim() > 2:
+ return img.shape[-2:][::-1]
+ else:
+ raise TypeError("Unexpected type {}".format(type(img)))
+
+
+class RandomHorizontalFlip(object):
+ """Horizontal flip the given PIL Image randomly with a given probability.
+
+ Args:
+ p (float): probability of the image being flipped. Default value is 0.5
+ """
+ def __init__(self, p=0.5):
+ self.p = p
+
+ def __call__(self, img, mask):
+ """
+ Args:
+ img (PIL Image): Image to be flipped.
+
+ Returns:
+ PIL Image: Randomly flipped image.
+ """
+ if random.random() < self.p:
+ img = TF.hflip(img)
+ mask = TF.hflip(mask)
+ return img, mask
+
+ def __repr__(self):
+ return self.__class__.__name__ + '(p={})'.format(self.p)
+
+
+class RandomVerticalFlip(object):
+ """Vertical flip the given PIL Image randomly with a given probability.
+
+ Args:
+ p (float): probability of the image being flipped. Default value is 0.5
+ """
+ def __init__(self, p=0.5):
+ self.p = p
+
+ def __call__(self, img, mask):
+ """
+ Args:
+ img (PIL Image): Image to be flipped.
+
+ Returns:
+ PIL Image: Randomly flipped image.
+ """
+ if random.random() < self.p:
+ img = TF.vflip(img)
+ mask = TF.vflip(mask)
+ return img, mask
+
+ def __repr__(self):
+ return self.__class__.__name__ + '(p={})'.format(self.p)
+
+
+class GaussianBlur(object):
+ """Gaussian blur augmentation from SimCLR: https://arxiv.org/abs/2002.05709"""
+ def __init__(self, sigma=[.1, 2.]):
+ self.sigma = sigma
+
+ def __call__(self, x):
+ sigma = random.uniform(self.sigma[0], self.sigma[1])
+ x = x.filter(ImageFilter.GaussianBlur(radius=sigma))
+ return x
+
+
+class RandomAffine(object):
+ """Random affine transformation of the image keeping center invariant
+
+ Args:
+ degrees (sequence or float or int): Range of degrees to select from.
+ If degrees is a number instead of sequence like (min, max), the range of degrees
+ will be (-degrees, +degrees). Set to 0 to deactivate rotations.
+ translate (tuple, optional): tuple of maximum absolute fraction for horizontal
+ and vertical translations. For example translate=(a, b), then horizontal shift
+ is randomly sampled in the range -img_width * a < dx < img_width * a and vertical shift is
+ randomly sampled in the range -img_height * b < dy < img_height * b. Will not translate by default.
+ scale (tuple, optional): scaling factor interval, e.g (a, b), then scale is
+ randomly sampled from the range a <= scale <= b. Will keep original scale by default.
+ shear (sequence or float or int, optional): Range of degrees to select from.
+ If shear is a number, a shear parallel to the x axis in the range (-shear, +shear)
+ will be apllied. Else if shear is a tuple or list of 2 values a shear parallel to the x axis in the
+ range (shear[0], shear[1]) will be applied. Else if shear is a tuple or list of 4 values,
+ a x-axis shear in (shear[0], shear[1]) and y-axis shear in (shear[2], shear[3]) will be applied.
+ Will not apply shear by default
+ resample ({PIL.Image.NEAREST, PIL.Image.BILINEAR, PIL.Image.BICUBIC}, optional):
+ An optional resampling filter. See `filters`_ for more information.
+ If omitted, or if the image has mode "1" or "P", it is set to PIL.Image.NEAREST.
+ fillcolor (tuple or int): Optional fill color (Tuple for RGB Image And int for grayscale) for the area
+ outside the transform in the output image.(Pillow>=5.0.0)
+
+ .. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters
+
+ """
+ def __init__(self,
+ degrees,
+ translate=None,
+ scale=None,
+ shear=None,
+ resample=False,
+ fillcolor=0):
+ if isinstance(degrees, numbers.Number):
+ if degrees < 0:
+ raise ValueError(
+ "If degrees is a single number, it must be positive.")
+ self.degrees = (-degrees, degrees)
+ else:
+ assert isinstance(degrees, (tuple, list)) and len(degrees) == 2, \
+ "degrees should be a list or tuple and it must be of length 2."
+ self.degrees = degrees
+
+ if translate is not None:
+ assert isinstance(translate, (tuple, list)) and len(translate) == 2, \
+ "translate should be a list or tuple and it must be of length 2."
+ for t in translate:
+ if not (0.0 <= t <= 1.0):
+ raise ValueError(
+ "translation values should be between 0 and 1")
+ self.translate = translate
+
+ if scale is not None:
+ assert isinstance(scale, (tuple, list)) and len(scale) == 2, \
+ "scale should be a list or tuple and it must be of length 2."
+ for s in scale:
+ if s <= 0:
+ raise ValueError("scale values should be positive")
+ self.scale = scale
+
+ if shear is not None:
+ if isinstance(shear, numbers.Number):
+ if shear < 0:
+ raise ValueError(
+ "If shear is a single number, it must be positive.")
+ self.shear = (-shear, shear)
+ else:
+ assert isinstance(shear, (tuple, list)) and \
+ (len(shear) == 2 or len(shear) == 4), \
+ "shear should be a list or tuple and it must be of length 2 or 4."
+ # X-Axis shear with [min, max]
+ if len(shear) == 2:
+ self.shear = [shear[0], shear[1], 0., 0.]
+ elif len(shear) == 4:
+ self.shear = [s for s in shear]
+ else:
+ self.shear = shear
+
+ self.resample = resample
+ self.fillcolor = fillcolor
+
+ @staticmethod
+ def get_params(degrees, translate, scale_ranges, shears, img_size):
+ """Get parameters for affine transformation
+
+ Returns:
+ sequence: params to be passed to the affine transformation
+ """
+ angle = random.uniform(degrees[0], degrees[1])
+ if translate is not None:
+ max_dx = translate[0] * img_size[0]
+ max_dy = translate[1] * img_size[1]
+ translations = (np.round(random.uniform(-max_dx, max_dx)),
+ np.round(random.uniform(-max_dy, max_dy)))
+ else:
+ translations = (0, 0)
+
+ if scale_ranges is not None:
+ scale = random.uniform(scale_ranges[0], scale_ranges[1])
+ else:
+ scale = 1.0
+
+ if shears is not None:
+ if len(shears) == 2:
+ shear = [random.uniform(shears[0], shears[1]), 0.]
+ elif len(shears) == 4:
+ shear = [
+ random.uniform(shears[0], shears[1]),
+ random.uniform(shears[2], shears[3])
+ ]
+ else:
+ shear = 0.0
+
+ return angle, translations, scale, shear
+
+ def __call__(self, img, mask):
+ """
+ img (PIL Image): Image to be transformed.
+
+ Returns:
+ PIL Image: Affine transformed image.
+ """
+ ret = self.get_params(self.degrees, self.translate, self.scale,
+ self.shear, img.size)
+ img = TF.affine(img,
+ *ret,
+ resample=self.resample,
+ fillcolor=self.fillcolor)
+ mask = TF.affine(mask, *ret, resample=Image.NEAREST, fillcolor=0)
+ return img, mask
+
+ def __repr__(self):
+ s = '{name}(degrees={degrees}'
+ if self.translate is not None:
+ s += ', translate={translate}'
+ if self.scale is not None:
+ s += ', scale={scale}'
+ if self.shear is not None:
+ s += ', shear={shear}'
+ if self.resample > 0:
+ s += ', resample={resample}'
+ if self.fillcolor != 0:
+ s += ', fillcolor={fillcolor}'
+ s += ')'
+ d = dict(self.__dict__)
+ d['resample'] = _pil_interpolation_to_str[d['resample']]
+ return s.format(name=self.__class__.__name__, **d)
+
+
+class RandomCrop(object):
+ """Crop the given PIL Image at a random location.
+
+ Args:
+ size (sequence or int): Desired output size of the crop. If size is an
+ int instead of sequence like (h, w), a square crop (size, size) is
+ made.
+ padding (int or sequence, optional): Optional padding on each border
+ of the image. Default is None, i.e no padding. If a sequence of length
+ 4 is provided, it is used to pad left, top, right, bottom borders
+ respectively. If a sequence of length 2 is provided, it is used to
+ pad left/right, top/bottom borders, respectively.
+ pad_if_needed (boolean): It will pad the image if smaller than the
+ desired size to avoid raising an exception. Since cropping is done
+ after padding, the padding seems to be done at a random offset.
+ fill: Pixel fill value for constant fill. Default is 0. If a tuple of
+ length 3, it is used to fill R, G, B channels respectively.
+ This value is only used when the padding_mode is constant
+ padding_mode: Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant.
+
+ - constant: pads with a constant value, this value is specified with fill
+
+ - edge: pads with the last value on the edge of the image
+
+ - reflect: pads with reflection of image (without repeating the last value on the edge)
+
+ padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
+ will result in [3, 2, 1, 2, 3, 4, 3, 2]
+
+ - symmetric: pads with reflection of image (repeating the last value on the edge)
+
+ padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
+ will result in [2, 1, 1, 2, 3, 4, 4, 3]
+
+ """
+ def __init__(self,
+ size,
+ padding=None,
+ pad_if_needed=False,
+ fill=0,
+ padding_mode='constant'):
+ if isinstance(size, numbers.Number):
+ self.size = (int(size), int(size))
+ else:
+ self.size = size
+ self.padding = padding
+ self.pad_if_needed = pad_if_needed
+ self.fill = fill
+ self.padding_mode = padding_mode
+
+ @staticmethod
+ def get_params(img, output_size):
+ """Get parameters for ``crop`` for a random crop.
+
+ Args:
+ img (PIL Image): Image to be cropped.
+ output_size (tuple): Expected output size of the crop.
+
+ Returns:
+ tuple: params (i, j, h, w) to be passed to ``crop`` for random crop.
+ """
+ w, h = _get_image_size(img)
+ th, tw = output_size
+ if w == tw and h == th:
+ return 0, 0, h, w
+
+ i = random.randint(0, h - th)
+ j = random.randint(0, w - tw)
+ return i, j, th, tw
+
+ def __call__(self, img, mask):
+ """
+ Args:
+ img (PIL Image): Image to be cropped.
+
+ Returns:
+ PIL Image: Cropped image.
+ """
+ # if self.padding is not None:
+ # img = TF.pad(img, self.padding, self.fill, self.padding_mode)
+ #
+ # # pad the width if needed
+ # if self.pad_if_needed and img.size[0] < self.size[1]:
+ # img = TF.pad(img, (self.size[1] - img.size[0], 0), self.fill, self.padding_mode)
+ # # pad the height if needed
+ # if self.pad_if_needed and img.size[1] < self.size[0]:
+ # img = TF.pad(img, (0, self.size[0] - img.size[1]), self.fill, self.padding_mode)
+
+ i, j, h, w = self.get_params(img, self.size)
+ img = TF.crop(img, i, j, h, w)
+ mask = TF.crop(mask, i, j, h, w)
+
+ return img, mask
+
+ def __repr__(self):
+ return self.__class__.__name__ + '(size={0}, padding={1})'.format(
+ self.size, self.padding)
+
+
+class RandomResizedCrop(object):
+ """Crop the given PIL Image to random size and aspect ratio.
+
+ A crop of random size (default: of 0.08 to 1.0) of the original size and a random
+ aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This crop
+ is finally resized to given size.
+ This is popularly used to train the Inception networks.
+
+ Args:
+ size: expected output size of each edge
+ scale: range of size of the origin size cropped
+ ratio: range of aspect ratio of the origin aspect ratio cropped
+ interpolation: Default: PIL.Image.BILINEAR
+ """
+ def __init__(self,
+ size,
+ scale=(0.08, 1.0),
+ ratio=(3. / 4., 4. / 3.),
+ interpolation=Image.BILINEAR):
+ if isinstance(size, (tuple, list)):
+ self.size = size
+ else:
+ self.size = (size, size)
+ if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
+ warnings.warn("range should be of kind (min, max)")
+
+ self.interpolation = interpolation
+ self.scale = scale
+ self.ratio = ratio
+
+ @staticmethod
+ def get_params(img, scale, ratio):
+ """Get parameters for ``crop`` for a random sized crop.
+
+ Args:
+ img (PIL Image): Image to be cropped.
+ scale (tuple): range of size of the origin size cropped
+ ratio (tuple): range of aspect ratio of the origin aspect ratio cropped
+
+ Returns:
+ tuple: params (i, j, h, w) to be passed to ``crop`` for a random
+ sized crop.
+ """
+ width, height = _get_image_size(img)
+ area = height * width
+
+ for _ in range(10):
+ target_area = random.uniform(*scale) * area
+ log_ratio = (math.log(ratio[0]), math.log(ratio[1]))
+ aspect_ratio = math.exp(random.uniform(*log_ratio))
+
+ w = int(round(math.sqrt(target_area * aspect_ratio)))
+ h = int(round(math.sqrt(target_area / aspect_ratio)))
+
+ if 0 < w <= width and 0 < h <= height:
+ i = random.randint(0, height - h)
+ j = random.randint(0, width - w)
+ return i, j, h, w
+
+ # Fallback to central crop
+ in_ratio = float(width) / float(height)
+ if (in_ratio < min(ratio)):
+ w = width
+ h = int(round(w / min(ratio)))
+ elif (in_ratio > max(ratio)):
+ h = height
+ w = int(round(h * max(ratio)))
+ else: # whole image
+ w = width
+ h = height
+ i = (height - h) // 2
+ j = (width - w) // 2
+ return i, j, h, w
+
+ def __call__(self, img, mask):
+ """
+ Args:
+ img (PIL Image): Image to be cropped and resized.
+
+ Returns:
+ PIL Image: Randomly cropped and resized image.
+ """
+ i, j, h, w = self.get_params(img, self.scale, self.ratio)
+ # print(i, j, h, w)
+ img = TF.resized_crop(img, i, j, h, w, self.size, self.interpolation)
+ mask = TF.resized_crop(mask, i, j, h, w, self.size, Image.NEAREST)
+ return img, mask
+
+ def __repr__(self):
+ interpolate_str = _pil_interpolation_to_str[self.interpolation]
+ format_string = self.__class__.__name__ + '(size={0}'.format(self.size)
+ format_string += ', scale={0}'.format(
+ tuple(round(s, 4) for s in self.scale))
+ format_string += ', ratio={0}'.format(
+ tuple(round(r, 4) for r in self.ratio))
+ format_string += ', interpolation={0})'.format(interpolate_str)
+ return format_string
+
+
+class ToOnehot(object):
+ """To oneshot tensor
+
+ Args:
+ max_obj_n (float): Maximum number of the objects
+ """
+ def __init__(self, max_obj_n, shuffle):
+ self.max_obj_n = max_obj_n
+ self.shuffle = shuffle
+
+ def __call__(self, mask, obj_list=None):
+ """
+ Args:
+ mask (Mask in Numpy): Mask to be converted.
+
+ Returns:
+ Tensor: Converted mask in onehot format.
+ """
+
+ new_mask = np.zeros((self.max_obj_n + 1, *mask.shape), np.uint8)
+
+ if not obj_list:
+ obj_list = list()
+ obj_max = mask.max() + 1
+ for i in range(1, obj_max):
+ tmp = (mask == i).astype(np.uint8)
+ if tmp.max() > 0:
+ obj_list.append(i)
+
+ if self.shuffle:
+ random.shuffle(obj_list)
+ obj_list = obj_list[:self.max_obj_n]
+
+ for i in range(len(obj_list)):
+ new_mask[i + 1] = (mask == obj_list[i]).astype(np.uint8)
+ new_mask[0] = 1 - np.sum(new_mask, axis=0)
+
+ return torch.from_numpy(new_mask), obj_list
+
+ def __repr__(self):
+ return self.__class__.__name__ + '(max_obj_n={})'.format(
+ self.max_obj_n)
+
+
+class Resize(torch.nn.Module):
+ """Resize the input image to the given size.
+ The image can be a PIL Image or a torch Tensor, in which case it is expected
+ to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
+
+ Args:
+ size (sequence or int): Desired output size. If size is a sequence like
+ (h, w), output size will be matched to this. If size is an int,
+ smaller edge of the image will be matched to this number.
+ i.e, if height > width, then image will be rescaled to
+ (size * height / width, size).
+ In torchscript mode padding as single int is not supported, use a tuple or
+ list of length 1: ``[size, ]``.
+ interpolation (int, optional): Desired interpolation enum defined by `filters`_.
+ Default is ``PIL.Image.BILINEAR``. If input is Tensor, only ``PIL.Image.NEAREST``, ``PIL.Image.BILINEAR``
+ and ``PIL.Image.BICUBIC`` are supported.
+ """
+ def __init__(self, size, interpolation=Image.BILINEAR):
+ super().__init__()
+ if not isinstance(size, (int, Sequence)):
+ raise TypeError("Size should be int or sequence. Got {}".format(
+ type(size)))
+ if isinstance(size, Sequence) and len(size) not in (1, 2):
+ raise ValueError(
+ "If size is a sequence, it should have 1 or 2 values")
+ self.size = size
+ self.interpolation = interpolation
+
+ def forward(self, img, mask):
+ """
+ Args:
+ img (PIL Image or Tensor): Image to be scaled.
+
+ Returns:
+ PIL Image or Tensor: Rescaled image.
+ """
+ img = TF.resize(img, self.size, self.interpolation)
+ mask = TF.resize(mask, self.size, Image.NEAREST)
+ return img, mask
+
+ def __repr__(self):
+ interpolate_str = _pil_interpolation_to_str[self.interpolation]
+ return self.__class__.__name__ + '(size={0}, interpolation={1})'.format(
+ self.size, interpolate_str)
diff --git a/aot/dataloaders/train_datasets.py b/aot/dataloaders/train_datasets.py
new file mode 100644
index 0000000000000000000000000000000000000000..eadc573fa23d6f16233a764bc14ec1dd24671e52
--- /dev/null
+++ b/aot/dataloaders/train_datasets.py
@@ -0,0 +1,682 @@
+from __future__ import division
+import os
+from glob import glob
+import json
+import random
+import cv2
+from PIL import Image
+
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+import torchvision.transforms as TF
+
+import dataloaders.image_transforms as IT
+
+cv2.setNumThreads(0)
+
+
+def _get_images(sample):
+ return [sample['ref_img'], sample['prev_img']] + sample['curr_img']
+
+
+def _get_labels(sample):
+ return [sample['ref_label'], sample['prev_label']] + sample['curr_label']
+
+
+def _merge_sample(sample1, sample2, min_obj_pixels=100, max_obj_n=10):
+
+ sample1_images = _get_images(sample1)
+ sample2_images = _get_images(sample2)
+
+ sample1_labels = _get_labels(sample1)
+ sample2_labels = _get_labels(sample2)
+
+ obj_idx = torch.arange(0, max_obj_n * 2 + 1).view(max_obj_n * 2 + 1, 1, 1)
+ selected_idx = None
+ selected_obj = None
+
+ all_img = []
+ all_mask = []
+ for idx, (s1_img, s2_img, s1_label, s2_label) in enumerate(
+ zip(sample1_images, sample2_images, sample1_labels,
+ sample2_labels)):
+ s2_fg = (s2_label > 0).float()
+ s2_bg = 1 - s2_fg
+ merged_img = s1_img * s2_bg + s2_img * s2_fg
+ merged_mask = s1_label * s2_bg.long() + (
+ (s2_label + max_obj_n) * s2_fg.long())
+ merged_mask = (merged_mask == obj_idx).float()
+ if idx == 0:
+ after_merge_pixels = merged_mask.sum(dim=(1, 2), keepdim=True)
+ selected_idx = after_merge_pixels > min_obj_pixels
+ selected_idx[0] = True
+ obj_num = selected_idx.sum().int().item() - 1
+ selected_idx = selected_idx.expand(-1,
+ s1_label.size()[1],
+ s1_label.size()[2])
+ if obj_num > max_obj_n:
+ selected_obj = list(range(1, obj_num + 1))
+ random.shuffle(selected_obj)
+ selected_obj = [0] + selected_obj[:max_obj_n]
+
+ merged_mask = merged_mask[selected_idx].view(obj_num + 1,
+ s1_label.size()[1],
+ s1_label.size()[2])
+ if obj_num > max_obj_n:
+ merged_mask = merged_mask[selected_obj]
+ merged_mask[0] += 0.1
+ merged_mask = torch.argmax(merged_mask, dim=0, keepdim=True).long()
+
+ all_img.append(merged_img)
+ all_mask.append(merged_mask)
+
+ sample = {
+ 'ref_img': all_img[0],
+ 'prev_img': all_img[1],
+ 'curr_img': all_img[2:],
+ 'ref_label': all_mask[0],
+ 'prev_label': all_mask[1],
+ 'curr_label': all_mask[2:]
+ }
+ sample['meta'] = sample1['meta']
+ sample['meta']['obj_num'] = min(obj_num, max_obj_n)
+ return sample
+
+
+class StaticTrain(Dataset):
+ def __init__(self,
+ root,
+ output_size,
+ seq_len=5,
+ max_obj_n=10,
+ dynamic_merge=True,
+ merge_prob=1.0,
+ aug_type='v1'):
+ self.root = root
+ self.clip_n = seq_len
+ self.output_size = output_size
+ self.max_obj_n = max_obj_n
+
+ self.dynamic_merge = dynamic_merge
+ self.merge_prob = merge_prob
+
+ self.img_list = list()
+ self.mask_list = list()
+
+ dataset_list = list()
+ lines = ['COCO', 'ECSSD', 'MSRA10K', 'PASCAL-S', 'PASCALVOC2012']
+ for line in lines:
+ dataset_name = line.strip()
+
+ img_dir = os.path.join(root, 'JPEGImages', dataset_name)
+ mask_dir = os.path.join(root, 'Annotations', dataset_name)
+
+ img_list = sorted(glob(os.path.join(img_dir, '*.jpg'))) + \
+ sorted(glob(os.path.join(img_dir, '*.png')))
+ mask_list = sorted(glob(os.path.join(mask_dir, '*.png')))
+
+ if len(img_list) > 0:
+ if len(img_list) == len(mask_list):
+ dataset_list.append(dataset_name)
+ self.img_list += img_list
+ self.mask_list += mask_list
+ print(f'\t{dataset_name}: {len(img_list)} imgs.')
+ else:
+ print(
+ f'\tPreTrain dataset {dataset_name} has {len(img_list)} imgs and {len(mask_list)} annots. Not match! Skip.'
+ )
+ else:
+ print(
+ f'\tPreTrain dataset {dataset_name} doesn\'t exist. Skip.')
+
+ print(
+ f'{len(self.img_list)} imgs are used for PreTrain. They are from {dataset_list}.'
+ )
+
+ self.aug_type = aug_type
+
+ self.pre_random_horizontal_flip = IT.RandomHorizontalFlip(0.5)
+
+ self.random_horizontal_flip = IT.RandomHorizontalFlip(0.3)
+
+ if self.aug_type == 'v1':
+ self.color_jitter = TF.ColorJitter(0.1, 0.1, 0.1, 0.03)
+ elif self.aug_type == 'v2':
+ self.color_jitter = TF.RandomApply(
+ [TF.ColorJitter(0.4, 0.4, 0.2, 0.1)], p=0.8)
+ self.gray_scale = TF.RandomGrayscale(p=0.2)
+ self.blur = TF.RandomApply([IT.GaussianBlur([.1, 2.])], p=0.3)
+ else:
+ assert NotImplementedError
+
+ self.random_affine = IT.RandomAffine(degrees=20,
+ translate=(0.1, 0.1),
+ scale=(0.9, 1.1),
+ shear=10,
+ resample=Image.BICUBIC,
+ fillcolor=(124, 116, 104))
+ base_ratio = float(output_size[1]) / output_size[0]
+ self.random_resize_crop = IT.RandomResizedCrop(
+ output_size, (0.8, 1),
+ ratio=(base_ratio * 3. / 4., base_ratio * 4. / 3.),
+ interpolation=Image.BICUBIC)
+ self.to_tensor = TF.ToTensor()
+ self.to_onehot = IT.ToOnehot(max_obj_n, shuffle=True)
+ self.normalize = TF.Normalize((0.485, 0.456, 0.406),
+ (0.229, 0.224, 0.225))
+
+ def __len__(self):
+ return len(self.img_list)
+
+ def load_image_in_PIL(self, path, mode='RGB'):
+ img = Image.open(path)
+ img.load() # Very important for loading large image
+ return img.convert(mode)
+
+ def sample_sequence(self, idx):
+ img_pil = self.load_image_in_PIL(self.img_list[idx], 'RGB')
+ mask_pil = self.load_image_in_PIL(self.mask_list[idx], 'P')
+
+ frames = []
+ masks = []
+
+ img_pil, mask_pil = self.pre_random_horizontal_flip(img_pil, mask_pil)
+ # img_pil, mask_pil = self.pre_random_vertical_flip(img_pil, mask_pil)
+
+ for i in range(self.clip_n):
+ img, mask = img_pil, mask_pil
+
+ if i > 0:
+ img, mask = self.random_horizontal_flip(img, mask)
+ img, mask = self.random_affine(img, mask)
+
+ img = self.color_jitter(img)
+
+ img, mask = self.random_resize_crop(img, mask)
+
+ if self.aug_type == 'v2':
+ img = self.gray_scale(img)
+ img = self.blur(img)
+
+ mask = np.array(mask, np.uint8)
+
+ if i == 0:
+ mask, obj_list = self.to_onehot(mask)
+ obj_num = len(obj_list)
+ else:
+ mask, _ = self.to_onehot(mask, obj_list)
+
+ mask = torch.argmax(mask, dim=0, keepdim=True)
+
+ frames.append(self.normalize(self.to_tensor(img)))
+ masks.append(mask)
+
+ sample = {
+ 'ref_img': frames[0],
+ 'prev_img': frames[1],
+ 'curr_img': frames[2:],
+ 'ref_label': masks[0],
+ 'prev_label': masks[1],
+ 'curr_label': masks[2:]
+ }
+ sample['meta'] = {
+ 'seq_name': self.img_list[idx],
+ 'frame_num': 1,
+ 'obj_num': obj_num
+ }
+
+ return sample
+
+ def __getitem__(self, idx):
+ sample1 = self.sample_sequence(idx)
+
+ if self.dynamic_merge and (sample1['meta']['obj_num'] == 0
+ or random.random() < self.merge_prob):
+ rand_idx = np.random.randint(len(self.img_list))
+ while (rand_idx == idx):
+ rand_idx = np.random.randint(len(self.img_list))
+
+ sample2 = self.sample_sequence(rand_idx)
+
+ sample = self.merge_sample(sample1, sample2)
+ else:
+ sample = sample1
+
+ return sample
+
+ def merge_sample(self, sample1, sample2, min_obj_pixels=100):
+ return _merge_sample(sample1, sample2, min_obj_pixels, self.max_obj_n)
+
+
+class VOSTrain(Dataset):
+ def __init__(self,
+ image_root,
+ label_root,
+ imglistdic,
+ transform=None,
+ rgb=True,
+ repeat_time=1,
+ rand_gap=3,
+ seq_len=5,
+ rand_reverse=True,
+ dynamic_merge=True,
+ enable_prev_frame=False,
+ merge_prob=0.3,
+ max_obj_n=10):
+ self.image_root = image_root
+ self.label_root = label_root
+ self.rand_gap = rand_gap
+ self.seq_len = seq_len
+ self.rand_reverse = rand_reverse
+ self.repeat_time = repeat_time
+ self.transform = transform
+ self.dynamic_merge = dynamic_merge
+ self.merge_prob = merge_prob
+ self.enable_prev_frame = enable_prev_frame
+ self.max_obj_n = max_obj_n
+ self.rgb = rgb
+ self.imglistdic = imglistdic
+ self.seqs = list(self.imglistdic.keys())
+ print('Video Num: {} X {}'.format(len(self.seqs), self.repeat_time))
+
+ def __len__(self):
+ return int(len(self.seqs) * self.repeat_time)
+
+ def reverse_seq(self, imagelist, lablist):
+ if np.random.randint(2) == 1:
+ imagelist = imagelist[::-1]
+ lablist = lablist[::-1]
+ return imagelist, lablist
+
+ def get_ref_index(self,
+ seqname,
+ lablist,
+ objs,
+ min_fg_pixels=200,
+ max_try=5):
+ bad_indices = []
+ for _ in range(max_try):
+ ref_index = np.random.randint(len(lablist))
+ if ref_index in bad_indices:
+ continue
+ ref_label = Image.open(
+ os.path.join(self.label_root, seqname, lablist[ref_index]))
+ ref_label = np.array(ref_label, dtype=np.uint8)
+ ref_objs = list(np.unique(ref_label))
+ is_consistent = True
+ for obj in ref_objs:
+ if obj == 0:
+ continue
+ if obj not in objs:
+ is_consistent = False
+ xs, ys = np.nonzero(ref_label)
+ if len(xs) > min_fg_pixels and is_consistent:
+ break
+ bad_indices.append(ref_index)
+ return ref_index
+
+ def get_ref_index_v2(self,
+ seqname,
+ lablist,
+ min_fg_pixels=200,
+ max_try=20,
+ total_gap=0):
+ search_range = len(lablist) - total_gap
+ if search_range <= 1:
+ return 0
+ bad_indices = []
+ for _ in range(max_try):
+ ref_index = np.random.randint(search_range)
+ if ref_index in bad_indices:
+ continue
+ ref_label = Image.open(
+ os.path.join(self.label_root, seqname, lablist[ref_index]))
+ ref_label = np.array(ref_label, dtype=np.uint8)
+ xs, ys = np.nonzero(ref_label)
+ if len(xs) > min_fg_pixels:
+ break
+ bad_indices.append(ref_index)
+ return ref_index
+
+ def get_curr_gaps(self, seq_len, max_gap=999, max_try=10):
+ for _ in range(max_try):
+ curr_gaps = []
+ total_gap = 0
+ for _ in range(seq_len):
+ gap = int(np.random.randint(self.rand_gap) + 1)
+ total_gap += gap
+ curr_gaps.append(gap)
+ if total_gap <= max_gap:
+ break
+ return curr_gaps, total_gap
+
+ def get_prev_index(self, lablist, total_gap):
+ search_range = len(lablist) - total_gap
+ if search_range > 1:
+ prev_index = np.random.randint(search_range)
+ else:
+ prev_index = 0
+ return prev_index
+
+ def check_index(self, total_len, index, allow_reflect=True):
+ if total_len <= 1:
+ return 0
+
+ if index < 0:
+ if allow_reflect:
+ index = -index
+ index = self.check_index(total_len, index, True)
+ else:
+ index = 0
+ elif index >= total_len:
+ if allow_reflect:
+ index = 2 * (total_len - 1) - index
+ index = self.check_index(total_len, index, True)
+ else:
+ index = total_len - 1
+
+ return index
+
+ def get_curr_indices(self, lablist, prev_index, gaps):
+ total_len = len(lablist)
+ curr_indices = []
+ now_index = prev_index
+ for gap in gaps:
+ now_index += gap
+ curr_indices.append(self.check_index(total_len, now_index))
+ return curr_indices
+
+ def get_image_label(self, seqname, imagelist, lablist, index):
+ image = cv2.imread(
+ os.path.join(self.image_root, seqname, imagelist[index]))
+ image = np.array(image, dtype=np.float32)
+
+ if self.rgb:
+ image = image[:, :, [2, 1, 0]]
+
+ label = Image.open(
+ os.path.join(self.label_root, seqname, lablist[index]))
+ label = np.array(label, dtype=np.uint8)
+
+ return image, label
+
+ def sample_sequence(self, idx):
+ idx = idx % len(self.seqs)
+ seqname = self.seqs[idx]
+ imagelist, lablist = self.imglistdic[seqname]
+ frame_num = len(imagelist)
+ if self.rand_reverse:
+ imagelist, lablist = self.reverse_seq(imagelist, lablist)
+
+ is_consistent = False
+ max_try = 5
+ try_step = 0
+ while (is_consistent is False and try_step < max_try):
+ try_step += 1
+
+ # generate random gaps
+ curr_gaps, total_gap = self.get_curr_gaps(self.seq_len - 1)
+
+ if self.enable_prev_frame: # prev frame is randomly sampled
+ # get prev frame
+ prev_index = self.get_prev_index(lablist, total_gap)
+ prev_image, prev_label = self.get_image_label(
+ seqname, imagelist, lablist, prev_index)
+ prev_objs = list(np.unique(prev_label))
+
+ # get curr frames
+ curr_indices = self.get_curr_indices(lablist, prev_index,
+ curr_gaps)
+ curr_images, curr_labels, curr_objs = [], [], []
+ for curr_index in curr_indices:
+ curr_image, curr_label = self.get_image_label(
+ seqname, imagelist, lablist, curr_index)
+ c_objs = list(np.unique(curr_label))
+ curr_images.append(curr_image)
+ curr_labels.append(curr_label)
+ curr_objs.extend(c_objs)
+
+ objs = list(np.unique(prev_objs + curr_objs))
+
+ start_index = prev_index
+ end_index = max(curr_indices)
+ # get ref frame
+ _try_step = 0
+ ref_index = self.get_ref_index_v2(seqname, lablist)
+ while (ref_index > start_index and ref_index <= end_index
+ and _try_step < max_try):
+ _try_step += 1
+ ref_index = self.get_ref_index_v2(seqname, lablist)
+ ref_image, ref_label = self.get_image_label(
+ seqname, imagelist, lablist, ref_index)
+ ref_objs = list(np.unique(ref_label))
+ else: # prev frame is next to ref frame
+ # get ref frame
+ ref_index = self.get_ref_index_v2(seqname, lablist)
+
+ ref_image, ref_label = self.get_image_label(
+ seqname, imagelist, lablist, ref_index)
+ ref_objs = list(np.unique(ref_label))
+
+ # get curr frames
+ curr_indices = self.get_curr_indices(lablist, ref_index,
+ curr_gaps)
+ curr_images, curr_labels, curr_objs = [], [], []
+ for curr_index in curr_indices:
+ curr_image, curr_label = self.get_image_label(
+ seqname, imagelist, lablist, curr_index)
+ c_objs = list(np.unique(curr_label))
+ curr_images.append(curr_image)
+ curr_labels.append(curr_label)
+ curr_objs.extend(c_objs)
+
+ objs = list(np.unique(curr_objs))
+ prev_image, prev_label = curr_images[0], curr_labels[0]
+ curr_images, curr_labels = curr_images[1:], curr_labels[1:]
+
+ is_consistent = True
+ for obj in objs:
+ if obj == 0:
+ continue
+ if obj not in ref_objs:
+ is_consistent = False
+ break
+
+ # get meta info
+ obj_num = list(np.sort(ref_objs))[-1]
+
+ sample = {
+ 'ref_img': ref_image,
+ 'prev_img': prev_image,
+ 'curr_img': curr_images,
+ 'ref_label': ref_label,
+ 'prev_label': prev_label,
+ 'curr_label': curr_labels
+ }
+ sample['meta'] = {
+ 'seq_name': seqname,
+ 'frame_num': frame_num,
+ 'obj_num': obj_num
+ }
+
+ if self.transform is not None:
+ sample = self.transform(sample)
+
+ return sample
+
+ def __getitem__(self, idx):
+ sample1 = self.sample_sequence(idx)
+
+ if self.dynamic_merge and (sample1['meta']['obj_num'] == 0
+ or random.random() < self.merge_prob):
+ rand_idx = np.random.randint(len(self.seqs))
+ while (rand_idx == (idx % len(self.seqs))):
+ rand_idx = np.random.randint(len(self.seqs))
+
+ sample2 = self.sample_sequence(rand_idx)
+
+ sample = self.merge_sample(sample1, sample2)
+ else:
+ sample = sample1
+
+ return sample
+
+ def merge_sample(self, sample1, sample2, min_obj_pixels=100):
+ return _merge_sample(sample1, sample2, min_obj_pixels, self.max_obj_n)
+
+
+class DAVIS2017_Train(VOSTrain):
+ def __init__(self,
+ split=['train'],
+ root='./DAVIS',
+ transform=None,
+ rgb=True,
+ repeat_time=1,
+ full_resolution=True,
+ year=2017,
+ rand_gap=3,
+ seq_len=5,
+ rand_reverse=True,
+ dynamic_merge=True,
+ enable_prev_frame=False,
+ max_obj_n=10,
+ merge_prob=0.3):
+ if full_resolution:
+ resolution = 'Full-Resolution'
+ if not os.path.exists(os.path.join(root, 'JPEGImages',
+ resolution)):
+ print('No Full-Resolution, use 480p instead.')
+ resolution = '480p'
+ else:
+ resolution = '480p'
+ image_root = os.path.join(root, 'JPEGImages', resolution)
+ label_root = os.path.join(root, 'Annotations', resolution)
+ seq_names = []
+ for spt in split:
+ with open(os.path.join(root, 'ImageSets', str(year),
+ spt + '.txt')) as f:
+ seqs_tmp = f.readlines()
+ seqs_tmp = list(map(lambda elem: elem.strip(), seqs_tmp))
+ seq_names.extend(seqs_tmp)
+ imglistdic = {}
+ for seq_name in seq_names:
+ images = list(
+ np.sort(os.listdir(os.path.join(image_root, seq_name))))
+ labels = list(
+ np.sort(os.listdir(os.path.join(label_root, seq_name))))
+ imglistdic[seq_name] = (images, labels)
+
+ super(DAVIS2017_Train, self).__init__(image_root,
+ label_root,
+ imglistdic,
+ transform,
+ rgb,
+ repeat_time,
+ rand_gap,
+ seq_len,
+ rand_reverse,
+ dynamic_merge,
+ enable_prev_frame,
+ merge_prob=merge_prob,
+ max_obj_n=max_obj_n)
+
+
+class YOUTUBEVOS_Train(VOSTrain):
+ def __init__(self,
+ root='./datasets/YTB',
+ year=2019,
+ transform=None,
+ rgb=True,
+ rand_gap=3,
+ seq_len=3,
+ rand_reverse=True,
+ dynamic_merge=True,
+ enable_prev_frame=False,
+ max_obj_n=10,
+ merge_prob=0.3):
+ root = os.path.join(root, str(year), 'train')
+ image_root = os.path.join(root, 'JPEGImages')
+ label_root = os.path.join(root, 'Annotations')
+ self.seq_list_file = os.path.join(root, 'meta.json')
+ self._check_preprocess()
+ seq_names = list(self.ann_f.keys())
+
+ imglistdic = {}
+ for seq_name in seq_names:
+ data = self.ann_f[seq_name]['objects']
+ obj_names = list(data.keys())
+ images = []
+ labels = []
+ for obj_n in obj_names:
+ if len(data[obj_n]["frames"]) < 2:
+ print("Short object: " + seq_name + '-' + obj_n)
+ continue
+ images += list(
+ map(lambda x: x + '.jpg', list(data[obj_n]["frames"])))
+ labels += list(
+ map(lambda x: x + '.png', list(data[obj_n]["frames"])))
+ images = np.sort(np.unique(images))
+ labels = np.sort(np.unique(labels))
+ if len(images) < 2:
+ print("Short video: " + seq_name)
+ continue
+ imglistdic[seq_name] = (images, labels)
+
+ super(YOUTUBEVOS_Train, self).__init__(image_root,
+ label_root,
+ imglistdic,
+ transform,
+ rgb,
+ 1,
+ rand_gap,
+ seq_len,
+ rand_reverse,
+ dynamic_merge,
+ enable_prev_frame,
+ merge_prob=merge_prob,
+ max_obj_n=max_obj_n)
+
+ def _check_preprocess(self):
+ if not os.path.isfile(self.seq_list_file):
+ print('No such file: {}.'.format(self.seq_list_file))
+ return False
+ else:
+ self.ann_f = json.load(open(self.seq_list_file, 'r'))['videos']
+ return True
+
+
+class TEST(Dataset):
+ def __init__(
+ self,
+ seq_len=3,
+ obj_num=3,
+ transform=None,
+ ):
+ self.seq_len = seq_len
+ self.obj_num = obj_num
+ self.transform = transform
+
+ def __len__(self):
+ return 3000
+
+ def __getitem__(self, idx):
+ img = np.zeros((800, 800, 3)).astype(np.float32)
+ label = np.ones((800, 800)).astype(np.uint8)
+ sample = {
+ 'ref_img': img,
+ 'prev_img': img,
+ 'curr_img': [img] * (self.seq_len - 2),
+ 'ref_label': label,
+ 'prev_label': label,
+ 'curr_label': [label] * (self.seq_len - 2)
+ }
+ sample['meta'] = {
+ 'seq_name': 'test',
+ 'frame_num': 100,
+ 'obj_num': self.obj_num
+ }
+
+ if self.transform is not None:
+ sample = self.transform(sample)
+ return sample
diff --git a/aot/dataloaders/video_transforms.py b/aot/dataloaders/video_transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7c3eaafbff595730b747d36170edee3ff5e16b6
--- /dev/null
+++ b/aot/dataloaders/video_transforms.py
@@ -0,0 +1,715 @@
+import random
+import cv2
+import numpy as np
+from PIL import Image
+
+import torch
+import torchvision.transforms as TF
+import dataloaders.image_transforms as IT
+
+cv2.setNumThreads(0)
+
+
+class Resize(object):
+ """Rescale the image in a sample to a given size.
+
+ Args:
+ output_size (tuple or int): Desired output size. If tuple, output is
+ matched to output_size. If int, smaller of image edges is matched
+ to output_size keeping aspect ratio the same.
+ """
+ def __init__(self, output_size, use_padding=False):
+ assert isinstance(output_size, (int, tuple))
+ if isinstance(output_size, int):
+ self.output_size = (output_size, output_size)
+ else:
+ self.output_size = output_size
+ self.use_padding = use_padding
+
+ def __call__(self, sample):
+ return self.padding(sample) if self.use_padding else self.rescale(
+ sample)
+
+ def rescale(self, sample):
+ prev_img = sample['prev_img']
+ h, w = prev_img.shape[:2]
+ if self.output_size == (h, w):
+ return sample
+ else:
+ new_h, new_w = self.output_size
+
+ for elem in sample.keys():
+ if 'meta' in elem:
+ continue
+ tmp = sample[elem]
+
+ if elem == 'prev_img' or elem == 'curr_img' or elem == 'ref_img':
+ flagval = cv2.INTER_CUBIC
+ else:
+ flagval = cv2.INTER_NEAREST
+
+ if elem == 'curr_img' or elem == 'curr_label':
+ new_tmp = []
+ all_tmp = tmp
+ for tmp in all_tmp:
+ tmp = cv2.resize(tmp,
+ dsize=(new_w, new_h),
+ interpolation=flagval)
+ new_tmp.append(tmp)
+ tmp = new_tmp
+ else:
+ tmp = cv2.resize(tmp,
+ dsize=(new_w, new_h),
+ interpolation=flagval)
+
+ sample[elem] = tmp
+
+ return sample
+
+ def padding(self, sample):
+ prev_img = sample['prev_img']
+ h, w = prev_img.shape[:2]
+ if self.output_size == (h, w):
+ return sample
+ else:
+ new_h, new_w = self.output_size
+
+ def sep_pad(x):
+ x0 = np.random.randint(0, x + 1)
+ x1 = x - x0
+ return x0, x1
+
+ top_pad, bottom_pad = sep_pad(new_h - h)
+ left_pad, right_pad = sep_pad(new_w - w)
+
+ for elem in sample.keys():
+ if 'meta' in elem:
+ continue
+ tmp = sample[elem]
+
+ if elem == 'prev_img' or elem == 'curr_img' or elem == 'ref_img':
+ pad_value = (124, 116, 104)
+ else:
+ pad_value = (0)
+
+ if elem == 'curr_img' or elem == 'curr_label':
+ new_tmp = []
+ all_tmp = tmp
+ for tmp in all_tmp:
+ tmp = cv2.copyMakeBorder(tmp,
+ top_pad,
+ bottom_pad,
+ left_pad,
+ right_pad,
+ cv2.BORDER_CONSTANT,
+ value=pad_value)
+ new_tmp.append(tmp)
+ tmp = new_tmp
+ else:
+ tmp = cv2.copyMakeBorder(tmp,
+ top_pad,
+ bottom_pad,
+ left_pad,
+ right_pad,
+ cv2.BORDER_CONSTANT,
+ value=pad_value)
+
+ sample[elem] = tmp
+
+ return sample
+
+
+class BalancedRandomCrop(object):
+ """Crop randomly the image in a sample.
+
+ Args:
+ output_size (tuple or int): Desired output size. If int, square crop
+ is made.
+ """
+ def __init__(self,
+ output_size,
+ max_step=5,
+ max_obj_num=5,
+ min_obj_pixel_num=100):
+ assert isinstance(output_size, (int, tuple))
+ if isinstance(output_size, int):
+ self.output_size = (output_size, output_size)
+ else:
+ assert len(output_size) == 2
+ self.output_size = output_size
+ self.max_step = max_step
+ self.max_obj_num = max_obj_num
+ self.min_obj_pixel_num = min_obj_pixel_num
+
+ def __call__(self, sample):
+
+ image = sample['prev_img']
+ h, w = image.shape[:2]
+ new_h, new_w = self.output_size
+ new_h = h if new_h >= h else new_h
+ new_w = w if new_w >= w else new_w
+ ref_label = sample["ref_label"]
+ prev_label = sample["prev_label"]
+ curr_label = sample["curr_label"]
+
+ is_contain_obj = False
+ step = 0
+ while (not is_contain_obj) and (step < self.max_step):
+ step += 1
+ top = np.random.randint(0, h - new_h + 1)
+ left = np.random.randint(0, w - new_w + 1)
+ after_crop = []
+ contains = []
+ for elem in ([ref_label, prev_label] + curr_label):
+ tmp = elem[top:top + new_h, left:left + new_w]
+ contains.append(np.unique(tmp))
+ after_crop.append(tmp)
+
+ all_obj = list(np.sort(contains[0]))
+
+ if all_obj[-1] == 0:
+ continue
+
+ # remove background
+ if all_obj[0] == 0:
+ all_obj = all_obj[1:]
+
+ # remove small obj
+ new_all_obj = []
+ for obj_id in all_obj:
+ after_crop_pixels = np.sum(after_crop[0] == obj_id)
+ if after_crop_pixels > self.min_obj_pixel_num:
+ new_all_obj.append(obj_id)
+
+ if len(new_all_obj) == 0:
+ is_contain_obj = False
+ else:
+ is_contain_obj = True
+
+ if len(new_all_obj) > self.max_obj_num:
+ random.shuffle(new_all_obj)
+ new_all_obj = new_all_obj[:self.max_obj_num]
+
+ all_obj = [0] + new_all_obj
+
+ post_process = []
+ for elem in after_crop:
+ new_elem = elem * 0
+ for idx in range(len(all_obj)):
+ obj_id = all_obj[idx]
+ if obj_id == 0:
+ continue
+ mask = elem == obj_id
+
+ new_elem += (mask * idx).astype(np.uint8)
+ post_process.append(new_elem.astype(np.uint8))
+
+ sample["ref_label"] = post_process[0]
+ sample["prev_label"] = post_process[1]
+ curr_len = len(sample["curr_img"])
+ sample["curr_label"] = []
+ for idx in range(curr_len):
+ sample["curr_label"].append(post_process[idx + 2])
+
+ for elem in sample.keys():
+ if 'meta' in elem or 'label' in elem:
+ continue
+ if elem == 'curr_img':
+ new_tmp = []
+ for tmp_ in sample[elem]:
+ tmp_ = tmp_[top:top + new_h, left:left + new_w]
+ new_tmp.append(tmp_)
+ sample[elem] = new_tmp
+ else:
+ tmp = sample[elem]
+ tmp = tmp[top:top + new_h, left:left + new_w]
+ sample[elem] = tmp
+
+ obj_num = len(all_obj) - 1
+
+ sample['meta']['obj_num'] = obj_num
+
+ return sample
+
+
+class RandomScale(object):
+ """Randomly resize the image and the ground truth to specified scales.
+ Args:
+ scales (list): the list of scales
+ """
+ def __init__(self, min_scale=1., max_scale=1.3, short_edge=None):
+ self.min_scale = min_scale
+ self.max_scale = max_scale
+ self.short_edge = short_edge
+
+ def __call__(self, sample):
+ # Fixed range of scales
+ sc = np.random.uniform(self.min_scale, self.max_scale)
+ # Align short edge
+ if self.short_edge is not None:
+ image = sample['prev_img']
+ h, w = image.shape[:2]
+ if h > w:
+ sc *= float(self.short_edge) / w
+ else:
+ sc *= float(self.short_edge) / h
+
+ for elem in sample.keys():
+ if 'meta' in elem:
+ continue
+ tmp = sample[elem]
+
+ if elem == 'prev_img' or elem == 'curr_img' or elem == 'ref_img':
+ flagval = cv2.INTER_CUBIC
+ else:
+ flagval = cv2.INTER_NEAREST
+
+ if elem == 'curr_img' or elem == 'curr_label':
+ new_tmp = []
+ for tmp_ in tmp:
+ tmp_ = cv2.resize(tmp_,
+ None,
+ fx=sc,
+ fy=sc,
+ interpolation=flagval)
+ new_tmp.append(tmp_)
+ tmp = new_tmp
+ else:
+ tmp = cv2.resize(tmp,
+ None,
+ fx=sc,
+ fy=sc,
+ interpolation=flagval)
+
+ sample[elem] = tmp
+
+ return sample
+
+
+class RandomScaleV2(object):
+ """Randomly resize the image and the ground truth to specified scales.
+ Args:
+ scales (list): the list of scales
+ """
+ def __init__(self,
+ min_scale=0.36,
+ max_scale=1.0,
+ short_edge=None,
+ ratio=[3. / 4., 4. / 3.]):
+ self.min_scale = min_scale
+ self.max_scale = max_scale
+ self.short_edge = short_edge
+ self.ratio = ratio
+
+ def __call__(self, sample):
+ image = sample['prev_img']
+ h, w = image.shape[:2]
+
+ new_h, new_w = self.get_params(h, w)
+
+ sc_x = float(new_w) / w
+ sc_y = float(new_h) / h
+
+ # Align short edge
+ if not (self.short_edge is None):
+ if h > w:
+ sc_x *= float(self.short_edge) / w
+ sc_y *= float(self.short_edge) / w
+ else:
+ sc_x *= float(self.short_edge) / h
+ sc_y *= float(self.short_edge) / h
+
+ for elem in sample.keys():
+ if 'meta' in elem:
+ continue
+ tmp = sample[elem]
+
+ if elem == 'prev_img' or elem == 'curr_img' or elem == 'ref_img':
+ flagval = cv2.INTER_CUBIC
+ else:
+ flagval = cv2.INTER_NEAREST
+
+ if elem == 'curr_img' or elem == 'curr_label':
+ new_tmp = []
+ for tmp_ in tmp:
+ tmp_ = cv2.resize(tmp_,
+ None,
+ fx=sc_x,
+ fy=sc_y,
+ interpolation=flagval)
+ new_tmp.append(tmp_)
+ tmp = new_tmp
+ else:
+ tmp = cv2.resize(tmp,
+ None,
+ fx=sc_x,
+ fy=sc_y,
+ interpolation=flagval)
+
+ sample[elem] = tmp
+
+ return sample
+
+ def get_params(self, height, width):
+ area = height * width
+
+ log_ratio = [np.log(item) for item in self.ratio]
+ for _ in range(10):
+ target_area = area * np.random.uniform(self.min_scale**2,
+ self.max_scale**2)
+ aspect_ratio = np.exp(np.random.uniform(log_ratio[0],
+ log_ratio[1]))
+
+ w = int(round(np.sqrt(target_area * aspect_ratio)))
+ h = int(round(np.sqrt(target_area / aspect_ratio)))
+
+ if 0 < w <= width and 0 < h <= height:
+ return h, w
+
+ # Fallback to central crop
+ in_ratio = float(width) / float(height)
+ if in_ratio < min(self.ratio):
+ w = width
+ h = int(round(w / min(self.ratio)))
+ elif in_ratio > max(self.ratio):
+ h = height
+ w = int(round(h * max(self.ratio)))
+ else: # whole image
+ w = width
+ h = height
+
+ return h, w
+
+class RestrictSize(object):
+ """Randomly resize the image and the ground truth to specified scales.
+ Args:
+ scales (list): the list of scales
+ """
+ def __init__(self, max_short_edge=None, max_long_edge=800 * 1.3):
+ self.max_short_edge = max_short_edge
+ self.max_long_edge = max_long_edge
+ assert ((max_short_edge is None)) or ((max_long_edge is None))
+
+ def __call__(self, sample):
+
+ # Fixed range of scales
+ sc = None
+ image = sample['ref_img']
+ h, w = image.shape[:2]
+ # Align short edge
+ if not (self.max_short_edge is None):
+ if h > w:
+ short_edge = w
+ else:
+ short_edge = h
+ if short_edge < self.max_short_edge:
+ sc = float(self.max_short_edge) / short_edge
+ else:
+ if h > w:
+ long_edge = h
+ else:
+ long_edge = w
+ if long_edge > self.max_long_edge:
+ sc = float(self.max_long_edge) / long_edge
+
+ if sc is None:
+ new_h = h
+ new_w = w
+ else:
+ new_h = int(sc * h)
+ new_w = int(sc * w)
+ new_h = new_h - (new_h - 1) % 4
+ new_w = new_w - (new_w - 1) % 4
+ if new_h == h and new_w == w:
+ return sample
+
+ for elem in sample.keys():
+ if 'meta' in elem:
+ continue
+ tmp = sample[elem]
+
+ if 'label' in elem:
+ flagval = cv2.INTER_NEAREST
+ else:
+ flagval = cv2.INTER_CUBIC
+
+ tmp = cv2.resize(tmp, dsize=(new_w, new_h), interpolation=flagval)
+
+ sample[elem] = tmp
+
+ return sample
+
+
+class RandomHorizontalFlip(object):
+ """Horizontally flip the given image and ground truth randomly with a probability of 0.5."""
+ def __init__(self, prob):
+ self.p = prob
+
+ def __call__(self, sample):
+
+ if random.random() < self.p:
+ for elem in sample.keys():
+ if 'meta' in elem:
+ continue
+ if elem == 'curr_img' or elem == 'curr_label':
+ new_tmp = []
+ for tmp_ in sample[elem]:
+ tmp_ = cv2.flip(tmp_, flipCode=1)
+ new_tmp.append(tmp_)
+ sample[elem] = new_tmp
+ else:
+ tmp = sample[elem]
+ tmp = cv2.flip(tmp, flipCode=1)
+ sample[elem] = tmp
+
+ return sample
+
+
+class RandomVerticalFlip(object):
+ """Vertically flip the given image and ground truth randomly with a probability of 0.5."""
+ def __init__(self, prob=0.3):
+ self.p = prob
+
+ def __call__(self, sample):
+
+ if random.random() < self.p:
+ for elem in sample.keys():
+ if 'meta' in elem:
+ continue
+ if elem == 'curr_img' or elem == 'curr_label':
+ new_tmp = []
+ for tmp_ in sample[elem]:
+ tmp_ = cv2.flip(tmp_, flipCode=0)
+ new_tmp.append(tmp_)
+ sample[elem] = new_tmp
+ else:
+ tmp = sample[elem]
+ tmp = cv2.flip(tmp, flipCode=0)
+ sample[elem] = tmp
+
+ return sample
+
+
+class RandomGaussianBlur(object):
+ def __init__(self, prob=0.3, sigma=[0.1, 2.]):
+ self.aug = TF.RandomApply([IT.GaussianBlur(sigma)], p=prob)
+
+ def __call__(self, sample):
+ for elem in sample.keys():
+ if 'meta' in elem or 'label' in elem:
+ continue
+
+ if elem == 'curr_img':
+ new_tmp = []
+ for tmp_ in sample[elem]:
+ tmp_ = self.apply_augmentation(tmp_)
+ new_tmp.append(tmp_)
+ sample[elem] = new_tmp
+ else:
+ tmp = sample[elem]
+ tmp = self.apply_augmentation(tmp)
+ sample[elem] = tmp
+ return sample
+
+ def apply_augmentation(self, x):
+ x = Image.fromarray(np.uint8(x))
+ x = self.aug(x)
+ x = np.array(x, dtype=np.float32)
+ return x
+
+
+class RandomGrayScale(RandomGaussianBlur):
+ def __init__(self, prob=0.2):
+ self.aug = TF.RandomGrayscale(p=prob)
+
+
+class RandomColorJitter(RandomGaussianBlur):
+ def __init__(self,
+ prob=0.8,
+ brightness=0.4,
+ contrast=0.4,
+ saturation=0.2,
+ hue=0.1):
+ self.aug = TF.RandomApply(
+ [TF.ColorJitter(brightness, contrast, saturation, hue)], p=prob)
+
+
+class SubtractMeanImage(object):
+ def __init__(self, mean, change_channels=False):
+ self.mean = mean
+ self.change_channels = change_channels
+
+ def __call__(self, sample):
+ for elem in sample.keys():
+ if 'image' in elem:
+ if self.change_channels:
+ sample[elem] = sample[elem][:, :, [2, 1, 0]]
+ sample[elem] = np.subtract(
+ sample[elem], np.array(self.mean, dtype=np.float32))
+ return sample
+
+ def __str__(self):
+ return 'SubtractMeanImage' + str(self.mean)
+
+
+class ToTensor(object):
+ """Convert ndarrays in sample to Tensors."""
+ def __call__(self, sample):
+
+ for elem in sample.keys():
+ if 'meta' in elem:
+ continue
+ tmp = sample[elem]
+
+ if elem == 'curr_img' or elem == 'curr_label':
+ new_tmp = []
+ for tmp_ in tmp:
+ if tmp_.ndim == 2:
+ tmp_ = tmp_[:, :, np.newaxis]
+ tmp_ = tmp_.transpose((2, 0, 1))
+ new_tmp.append(torch.from_numpy(tmp_).int())
+ else:
+ tmp_ = tmp_ / 255.
+ tmp_ -= (0.485, 0.456, 0.406)
+ tmp_ /= (0.229, 0.224, 0.225)
+ tmp_ = tmp_.transpose((2, 0, 1))
+ new_tmp.append(torch.from_numpy(tmp_))
+ tmp = new_tmp
+ else:
+ if tmp.ndim == 2:
+ tmp = tmp[:, :, np.newaxis]
+ tmp = tmp.transpose((2, 0, 1))
+ tmp = torch.from_numpy(tmp).int()
+ else:
+ tmp = tmp / 255.
+ tmp -= (0.485, 0.456, 0.406)
+ tmp /= (0.229, 0.224, 0.225)
+ tmp = tmp.transpose((2, 0, 1))
+ tmp = torch.from_numpy(tmp)
+ sample[elem] = tmp
+
+ return sample
+
+
+class MultiRestrictSize(object):
+ def __init__(self,
+ max_short_edge=None,
+ max_long_edge=800,
+ flip=False,
+ multi_scale=[1.3],
+ align_corners=True,
+ max_stride=16):
+ self.max_short_edge = max_short_edge
+ self.max_long_edge = max_long_edge
+ self.multi_scale = multi_scale
+ self.flip = flip
+ self.align_corners = align_corners
+ self.max_stride = max_stride
+
+ def __call__(self, sample):
+ samples = []
+ image = sample['current_img']
+ h, w = image.shape[:2]
+ for scale in self.multi_scale:
+ # restrict short edge
+ sc = 1.
+ if self.max_short_edge is not None:
+ if h > w:
+ short_edge = w
+ else:
+ short_edge = h
+ if short_edge > self.max_short_edge:
+ sc *= float(self.max_short_edge) / short_edge
+ new_h, new_w = sc * h, sc * w
+
+ # restrict long edge
+ sc = 1.
+ if self.max_long_edge is not None:
+ if new_h > new_w:
+ long_edge = new_h
+ else:
+ long_edge = new_w
+ if long_edge > self.max_long_edge:
+ sc *= float(self.max_long_edge) / long_edge
+
+ new_h, new_w = sc * new_h, sc * new_w
+
+ new_h = int(new_h * scale)
+ new_w = int(new_w * scale)
+
+ if self.align_corners:
+ if (new_h - 1) % self.max_stride != 0:
+ new_h = int(
+ np.around((new_h - 1) / self.max_stride) *
+ self.max_stride + 1)
+ if (new_w - 1) % self.max_stride != 0:
+ new_w = int(
+ np.around((new_w - 1) / self.max_stride) *
+ self.max_stride + 1)
+ else:
+ if new_h % self.max_stride != 0:
+ new_h = int(
+ np.around(new_h / self.max_stride) * self.max_stride)
+ if new_w % self.max_stride != 0:
+ new_w = int(
+ np.around(new_w / self.max_stride) * self.max_stride)
+
+ if new_h == h and new_w == w:
+ samples.append(sample)
+ else:
+ new_sample = {}
+ for elem in sample.keys():
+ if 'meta' in elem:
+ new_sample[elem] = sample[elem]
+ continue
+ tmp = sample[elem]
+ if 'label' in elem:
+ new_sample[elem] = sample[elem]
+ continue
+ else:
+ flagval = cv2.INTER_CUBIC
+ tmp = cv2.resize(tmp,
+ dsize=(new_w, new_h),
+ interpolation=flagval)
+ new_sample[elem] = tmp
+ samples.append(new_sample)
+
+ if self.flip:
+ now_sample = samples[-1]
+ new_sample = {}
+ for elem in now_sample.keys():
+ if 'meta' in elem:
+ new_sample[elem] = now_sample[elem].copy()
+ new_sample[elem]['flip'] = True
+ continue
+ tmp = now_sample[elem]
+ tmp = tmp[:, ::-1].copy()
+ new_sample[elem] = tmp
+ samples.append(new_sample)
+
+ return samples
+
+
+class MultiToTensor(object):
+ def __call__(self, samples):
+ for idx in range(len(samples)):
+ sample = samples[idx]
+ for elem in sample.keys():
+ if 'meta' in elem:
+ continue
+ tmp = sample[elem]
+ if tmp is None:
+ continue
+
+ if tmp.ndim == 2:
+ tmp = tmp[:, :, np.newaxis]
+ tmp = tmp.transpose((2, 0, 1))
+ samples[idx][elem] = torch.from_numpy(tmp).int()
+ else:
+ tmp = tmp / 255.
+ tmp -= (0.485, 0.456, 0.406)
+ tmp /= (0.229, 0.224, 0.225)
+ tmp = tmp.transpose((2, 0, 1))
+ samples[idx][elem] = torch.from_numpy(tmp)
+
+ return samples
diff --git a/aot/datasets/.DS_Store b/aot/datasets/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..567774adc688ea1b1460d745f0a1886870056d26
Binary files /dev/null and b/aot/datasets/.DS_Store differ
diff --git a/aot/datasets/DAVIS/README.md b/aot/datasets/DAVIS/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..d1c18f61dc18ce6fd6d012e983d90253b139762d
--- /dev/null
+++ b/aot/datasets/DAVIS/README.md
@@ -0,0 +1 @@
+Put DAVIS 2017 here.
\ No newline at end of file
diff --git a/aot/datasets/Static/README.md b/aot/datasets/Static/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..084f78a76a0b82ea59555db819e9ca4137e69d0a
--- /dev/null
+++ b/aot/datasets/Static/README.md
@@ -0,0 +1 @@
+Put the static dataset here. Guidance can be found in [AFB-URR](https://github.com/xmlyqing00/AFB-URR), which we referred to in the implementation of the pre-training.
diff --git a/aot/datasets/YTB/2018/train/README.md b/aot/datasets/YTB/2018/train/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..b9bfd5ec65839444fc0536479ad05a93e5839ae6
--- /dev/null
+++ b/aot/datasets/YTB/2018/train/README.md
@@ -0,0 +1 @@
+Put the training split of YouTube-VOS 2018 here.
\ No newline at end of file
diff --git a/aot/datasets/YTB/2018/valid/README.md b/aot/datasets/YTB/2018/valid/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..37036488a307621c617aba2825c60ad3895fe4ba
--- /dev/null
+++ b/aot/datasets/YTB/2018/valid/README.md
@@ -0,0 +1 @@
+Put the validation split of YouTube-VOS 2018 here.
\ No newline at end of file
diff --git a/aot/datasets/YTB/2018/valid_all_frames/README.md b/aot/datasets/YTB/2018/valid_all_frames/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..7fc905e2d328ddba5c00915d2cae1a9f4175fe29
--- /dev/null
+++ b/aot/datasets/YTB/2018/valid_all_frames/README.md
@@ -0,0 +1 @@
+Put the all-frame validation split of YouTube-VOS 2018 here.
\ No newline at end of file
diff --git a/aot/datasets/YTB/2019/train/README.md b/aot/datasets/YTB/2019/train/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..f816ba6895e0c7c3d4b4bb63b83ffb19c21c0ed4
--- /dev/null
+++ b/aot/datasets/YTB/2019/train/README.md
@@ -0,0 +1 @@
+Put the training split of YouTube-VOS 2019 here.
\ No newline at end of file
diff --git a/aot/datasets/YTB/2019/valid/README.md b/aot/datasets/YTB/2019/valid/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..445bb13cf1938869995902f580478daf7d20c364
--- /dev/null
+++ b/aot/datasets/YTB/2019/valid/README.md
@@ -0,0 +1 @@
+Put the validation split of YouTube-VOS 2019 here.
\ No newline at end of file
diff --git a/aot/datasets/YTB/2019/valid_all_frames/README.md b/aot/datasets/YTB/2019/valid_all_frames/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..7fc905e2d328ddba5c00915d2cae1a9f4175fe29
--- /dev/null
+++ b/aot/datasets/YTB/2019/valid_all_frames/README.md
@@ -0,0 +1 @@
+Put the all-frame validation split of YouTube-VOS 2018 here.
\ No newline at end of file
diff --git a/aot/networks/.DS_Store b/aot/networks/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..996bc3ce8680c9ed43d713cdae21fba9989ff7b1
Binary files /dev/null and b/aot/networks/.DS_Store differ
diff --git a/aot/networks/__init__.py b/aot/networks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/aot/networks/__pycache__/__init__.cpython-311.pyc b/aot/networks/__pycache__/__init__.cpython-311.pyc
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diff --git a/aot/networks/decoders/__init__.py b/aot/networks/decoders/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5cd58d425f47367ef77c97e89a5f005cc2d2001
--- /dev/null
+++ b/aot/networks/decoders/__init__.py
@@ -0,0 +1,9 @@
+from networks.decoders.fpn import FPNSegmentationHead
+
+
+def build_decoder(name, **kwargs):
+
+ if name == 'fpn':
+ return FPNSegmentationHead(**kwargs)
+ else:
+ raise NotImplementedError
diff --git a/aot/networks/decoders/__pycache__/__init__.cpython-311.pyc b/aot/networks/decoders/__pycache__/__init__.cpython-311.pyc
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diff --git a/aot/networks/decoders/__pycache__/fpn.cpython-311.pyc b/aot/networks/decoders/__pycache__/fpn.cpython-311.pyc
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index 0000000000000000000000000000000000000000..983cd1a2cb677fcf7f6562ba6629af2e9d756738
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diff --git a/aot/networks/decoders/fpn.py b/aot/networks/decoders/fpn.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ce4d3f0a380078b646cc0b28ffd8e91b9810adb
--- /dev/null
+++ b/aot/networks/decoders/fpn.py
@@ -0,0 +1,63 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from networks.layers.basic import ConvGN
+
+
+class FPNSegmentationHead(nn.Module):
+ def __init__(self,
+ in_dim,
+ out_dim,
+ decode_intermediate_input=True,
+ hidden_dim=256,
+ shortcut_dims=[24, 32, 96, 1280],
+ align_corners=True):
+ super().__init__()
+ self.align_corners = align_corners
+
+ self.decode_intermediate_input = decode_intermediate_input
+
+ self.conv_in = ConvGN(in_dim, hidden_dim, 1)
+
+ self.conv_16x = ConvGN(hidden_dim, hidden_dim, 3)
+ self.conv_8x = ConvGN(hidden_dim, hidden_dim // 2, 3)
+ self.conv_4x = ConvGN(hidden_dim // 2, hidden_dim // 2, 3)
+
+ self.adapter_16x = nn.Conv2d(shortcut_dims[-2], hidden_dim, 1)
+ self.adapter_8x = nn.Conv2d(shortcut_dims[-3], hidden_dim, 1)
+ self.adapter_4x = nn.Conv2d(shortcut_dims[-4], hidden_dim // 2, 1)
+
+ self.conv_out = nn.Conv2d(hidden_dim // 2, out_dim, 1)
+
+ self._init_weight()
+
+ def forward(self, inputs, shortcuts):
+
+ if self.decode_intermediate_input:
+ x = torch.cat(inputs, dim=1)
+ else:
+ x = inputs[-1]
+
+ x = F.relu_(self.conv_in(x))
+ x = F.relu_(self.conv_16x(self.adapter_16x(shortcuts[-2]) + x))
+
+ x = F.interpolate(x,
+ size=shortcuts[-3].size()[-2:],
+ mode="bilinear",
+ align_corners=self.align_corners)
+ x = F.relu_(self.conv_8x(self.adapter_8x(shortcuts[-3]) + x))
+
+ x = F.interpolate(x,
+ size=shortcuts[-4].size()[-2:],
+ mode="bilinear",
+ align_corners=self.align_corners)
+ x = F.relu_(self.conv_4x(self.adapter_4x(shortcuts[-4]) + x))
+
+ x = self.conv_out(x)
+
+ return x
+
+ def _init_weight(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
diff --git a/aot/networks/encoders/.DS_Store b/aot/networks/encoders/.DS_Store
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index 0000000000000000000000000000000000000000..a280bcfdd4d5bc576488337f5b69531bcbb7017d
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diff --git a/aot/networks/encoders/__init__.py b/aot/networks/encoders/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf240be6ec6c2ce2f6e1c81adb86af4196223c83
--- /dev/null
+++ b/aot/networks/encoders/__init__.py
@@ -0,0 +1,35 @@
+from networks.encoders.mobilenetv2 import MobileNetV2
+from networks.encoders.mobilenetv3 import MobileNetV3Large
+from networks.encoders.resnet import ResNet101, ResNet50
+from networks.encoders.resnest import resnest
+from networks.encoders.swin import build_swin_model
+from networks.layers.normalization import FrozenBatchNorm2d
+from torch import nn
+
+
+def build_encoder(name, frozen_bn=True, freeze_at=-1):
+ if frozen_bn:
+ BatchNorm = FrozenBatchNorm2d
+ else:
+ BatchNorm = nn.BatchNorm2d
+
+ if name == 'mobilenetv2':
+ return MobileNetV2(16, BatchNorm, freeze_at=freeze_at)
+ elif name == 'mobilenetv3':
+ return MobileNetV3Large(16, BatchNorm, freeze_at=freeze_at)
+ elif name == 'resnet50':
+ return ResNet50(16, BatchNorm, freeze_at=freeze_at)
+ elif name == 'resnet101':
+ return ResNet101(16, BatchNorm, freeze_at=freeze_at)
+ elif name == 'resnest50':
+ return resnest.resnest50(norm_layer=BatchNorm,
+ dilation=2,
+ freeze_at=freeze_at)
+ elif name == 'resnest101':
+ return resnest.resnest101(norm_layer=BatchNorm,
+ dilation=2,
+ freeze_at=freeze_at)
+ elif 'swin' in name:
+ return build_swin_model(name, freeze_at=freeze_at)
+ else:
+ raise NotImplementedError
diff --git a/aot/networks/encoders/__pycache__/__init__.cpython-311.pyc b/aot/networks/encoders/__pycache__/__init__.cpython-311.pyc
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diff --git a/aot/networks/encoders/__pycache__/mobilenetv2.cpython-311.pyc b/aot/networks/encoders/__pycache__/mobilenetv2.cpython-311.pyc
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diff --git a/aot/networks/encoders/__pycache__/mobilenetv3.cpython-311.pyc b/aot/networks/encoders/__pycache__/mobilenetv3.cpython-311.pyc
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diff --git a/aot/networks/encoders/__pycache__/resnet.cpython-311.pyc b/aot/networks/encoders/__pycache__/resnet.cpython-311.pyc
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diff --git a/aot/networks/encoders/mobilenetv2.py b/aot/networks/encoders/mobilenetv2.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ff40d99f609c2c443110d02dd16875a9209b3b1
--- /dev/null
+++ b/aot/networks/encoders/mobilenetv2.py
@@ -0,0 +1,247 @@
+from torch import nn
+from torch import Tensor
+from typing import Callable, Optional, List
+from utils.learning import freeze_params
+
+__all__ = ['MobileNetV2']
+
+
+def _make_divisible(v: float,
+ divisor: int,
+ min_value: Optional[int] = None) -> int:
+ """
+ This function is taken from the original tf repo.
+ It ensures that all layers have a channel number that is divisible by 8
+ It can be seen here:
+ https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
+ """
+ if min_value is None:
+ min_value = divisor
+ new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+ # Make sure that round down does not go down by more than 10%.
+ if new_v < 0.9 * v:
+ new_v += divisor
+ return new_v
+
+
+class ConvBNActivation(nn.Sequential):
+ def __init__(
+ self,
+ in_planes: int,
+ out_planes: int,
+ kernel_size: int = 3,
+ stride: int = 1,
+ groups: int = 1,
+ padding: int = -1,
+ norm_layer: Optional[Callable[..., nn.Module]] = None,
+ activation_layer: Optional[Callable[..., nn.Module]] = None,
+ dilation: int = 1,
+ ) -> None:
+ if padding == -1:
+ padding = (kernel_size - 1) // 2 * dilation
+ if norm_layer is None:
+ norm_layer = nn.BatchNorm2d
+ if activation_layer is None:
+ activation_layer = nn.ReLU6
+ super().__init__(
+ nn.Conv2d(in_planes,
+ out_planes,
+ kernel_size,
+ stride,
+ padding,
+ dilation=dilation,
+ groups=groups,
+ bias=False), norm_layer(out_planes),
+ activation_layer(inplace=True))
+ self.out_channels = out_planes
+
+
+# necessary for backwards compatibility
+ConvBNReLU = ConvBNActivation
+
+
+class InvertedResidual(nn.Module):
+ def __init__(
+ self,
+ inp: int,
+ oup: int,
+ stride: int,
+ dilation: int,
+ expand_ratio: int,
+ norm_layer: Optional[Callable[..., nn.Module]] = None) -> None:
+ super(InvertedResidual, self).__init__()
+ self.stride = stride
+ assert stride in [1, 2]
+
+ if norm_layer is None:
+ norm_layer = nn.BatchNorm2d
+
+ self.kernel_size = 3
+ self.dilation = dilation
+
+ hidden_dim = int(round(inp * expand_ratio))
+ self.use_res_connect = self.stride == 1 and inp == oup
+
+ layers: List[nn.Module] = []
+ if expand_ratio != 1:
+ # pw
+ layers.append(
+ ConvBNReLU(inp,
+ hidden_dim,
+ kernel_size=1,
+ norm_layer=norm_layer))
+ layers.extend([
+ # dw
+ ConvBNReLU(hidden_dim,
+ hidden_dim,
+ stride=stride,
+ dilation=dilation,
+ groups=hidden_dim,
+ norm_layer=norm_layer),
+ # pw-linear
+ nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
+ norm_layer(oup),
+ ])
+ self.conv = nn.Sequential(*layers)
+ self.out_channels = oup
+ self._is_cn = stride > 1
+
+ def forward(self, x: Tensor) -> Tensor:
+ if self.use_res_connect:
+ return x + self.conv(x)
+ else:
+ return self.conv(x)
+
+
+class MobileNetV2(nn.Module):
+ def __init__(self,
+ output_stride=8,
+ norm_layer: Optional[Callable[..., nn.Module]] = None,
+ width_mult: float = 1.0,
+ inverted_residual_setting: Optional[List[List[int]]] = None,
+ round_nearest: int = 8,
+ block: Optional[Callable[..., nn.Module]] = None,
+ freeze_at=0) -> None:
+ """
+ MobileNet V2 main class
+ Args:
+ num_classes (int): Number of classes
+ width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
+ inverted_residual_setting: Network structure
+ round_nearest (int): Round the number of channels in each layer to be a multiple of this number
+ Set to 1 to turn off rounding
+ block: Module specifying inverted residual building block for mobilenet
+ norm_layer: Module specifying the normalization layer to use
+ """
+ super(MobileNetV2, self).__init__()
+
+ if block is None:
+ block = InvertedResidual
+
+ if norm_layer is None:
+ norm_layer = nn.BatchNorm2d
+
+ last_channel = 1280
+ input_channel = 32
+ current_stride = 1
+ rate = 1
+
+ if inverted_residual_setting is None:
+ inverted_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],
+ ]
+
+ # only check the first element, assuming user knows t,c,n,s are required
+ if len(inverted_residual_setting) == 0 or len(
+ inverted_residual_setting[0]) != 4:
+ raise ValueError("inverted_residual_setting should be non-empty "
+ "or a 4-element list, got {}".format(
+ inverted_residual_setting))
+
+ # building first layer
+ input_channel = _make_divisible(input_channel * width_mult,
+ round_nearest)
+ self.last_channel = _make_divisible(
+ last_channel * max(1.0, width_mult), round_nearest)
+ features: List[nn.Module] = [
+ ConvBNReLU(3, input_channel, stride=2, norm_layer=norm_layer)
+ ]
+ current_stride *= 2
+ # building inverted residual blocks
+ for t, c, n, s in inverted_residual_setting:
+ if current_stride == output_stride:
+ stride = 1
+ dilation = rate
+ rate *= s
+ else:
+ stride = s
+ dilation = 1
+ current_stride *= s
+ output_channel = _make_divisible(c * width_mult, round_nearest)
+ for i in range(n):
+ if i == 0:
+ features.append(
+ block(input_channel, output_channel, stride, dilation,
+ t, norm_layer))
+ else:
+ features.append(
+ block(input_channel, output_channel, 1, rate, t,
+ norm_layer))
+ input_channel = output_channel
+
+ # building last several layers
+ features.append(
+ ConvBNReLU(input_channel,
+ self.last_channel,
+ kernel_size=1,
+ norm_layer=norm_layer))
+ # make it nn.Sequential
+ self.features = nn.Sequential(*features)
+
+ self._initialize_weights()
+
+ feature_4x = self.features[0:4]
+ feautre_8x = self.features[4:7]
+ feature_16x = self.features[7:14]
+ feature_32x = self.features[14:]
+
+ self.stages = [feature_4x, feautre_8x, feature_16x, feature_32x]
+
+ self.freeze(freeze_at)
+
+ def forward(self, x):
+ xs = []
+ for stage in self.stages:
+ x = stage(x)
+ xs.append(x)
+ return xs
+
+ def _initialize_weights(self):
+ # weight initialization
+ for m in self.modules():
+ if isinstance(m, nn.Conv2d):
+ nn.init.kaiming_normal_(m.weight, mode='fan_out')
+ if m.bias is not None:
+ nn.init.zeros_(m.bias)
+ elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
+ nn.init.ones_(m.weight)
+ nn.init.zeros_(m.bias)
+ elif isinstance(m, nn.Linear):
+ nn.init.normal_(m.weight, 0, 0.01)
+ nn.init.zeros_(m.bias)
+
+ def freeze(self, freeze_at):
+ if freeze_at >= 1:
+ for m in self.stages[0][0]:
+ freeze_params(m)
+
+ for idx, stage in enumerate(self.stages, start=2):
+ if freeze_at >= idx:
+ freeze_params(stage)
diff --git a/aot/networks/encoders/mobilenetv3.py b/aot/networks/encoders/mobilenetv3.py
new file mode 100644
index 0000000000000000000000000000000000000000..47bd0db64f62f6c36d17ca7d8f586b86a01d35bb
--- /dev/null
+++ b/aot/networks/encoders/mobilenetv3.py
@@ -0,0 +1,239 @@
+"""
+Creates a MobileNetV3 Model as defined in:
+Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam. (2019).
+Searching for MobileNetV3
+arXiv preprint arXiv:1905.02244.
+"""
+
+import torch.nn as nn
+import math
+from utils.learning import freeze_params
+
+
+def _make_divisible(v, divisor, min_value=None):
+ """
+ This function is taken from the original tf repo.
+ It ensures that all layers have a channel number that is divisible by 8
+ It can be seen here:
+ https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
+ :param v:
+ :param divisor:
+ :param min_value:
+ :return:
+ """
+ if min_value is None:
+ min_value = divisor
+ new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+ # Make sure that round down does not go down by more than 10%.
+ if new_v < 0.9 * v:
+ new_v += divisor
+ return new_v
+
+
+class h_sigmoid(nn.Module):
+ def __init__(self, inplace=True):
+ super(h_sigmoid, self).__init__()
+ self.relu = nn.ReLU6(inplace=inplace)
+
+ def forward(self, x):
+ return self.relu(x + 3) / 6
+
+
+class h_swish(nn.Module):
+ def __init__(self, inplace=True):
+ super(h_swish, self).__init__()
+ self.sigmoid = h_sigmoid(inplace=inplace)
+
+ def forward(self, x):
+ return x * self.sigmoid(x)
+
+
+class SELayer(nn.Module):
+ def __init__(self, channel, reduction=4):
+ super(SELayer, self).__init__()
+ self.avg_pool = nn.AdaptiveAvgPool2d(1)
+ self.fc = nn.Sequential(
+ nn.Linear(channel, _make_divisible(channel // reduction, 8)),
+ nn.ReLU(inplace=True),
+ nn.Linear(_make_divisible(channel // reduction, 8), channel),
+ h_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)
+ return x * y
+
+
+def conv_3x3_bn(inp, oup, stride, norm_layer=nn.BatchNorm2d):
+ return nn.Sequential(nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
+ norm_layer(oup), h_swish())
+
+
+def conv_1x1_bn(inp, oup, norm_layer=nn.BatchNorm2d):
+ return nn.Sequential(nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
+ norm_layer(oup), h_swish())
+
+
+class InvertedResidual(nn.Module):
+ def __init__(self,
+ inp,
+ hidden_dim,
+ oup,
+ kernel_size,
+ stride,
+ use_se,
+ use_hs,
+ dilation=1,
+ norm_layer=nn.BatchNorm2d):
+ super(InvertedResidual, self).__init__()
+ assert stride in [1, 2]
+
+ self.identity = stride == 1 and inp == oup
+
+ if inp == hidden_dim:
+ self.conv = nn.Sequential(
+ # dw
+ nn.Conv2d(hidden_dim,
+ hidden_dim,
+ kernel_size,
+ stride, (kernel_size - 1) // 2 * dilation,
+ dilation=dilation,
+ groups=hidden_dim,
+ bias=False),
+ norm_layer(hidden_dim),
+ h_swish() if use_hs else nn.ReLU(inplace=True),
+ # Squeeze-and-Excite
+ SELayer(hidden_dim) if use_se else nn.Identity(),
+ # pw-linear
+ nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
+ norm_layer(oup),
+ )
+ else:
+ self.conv = nn.Sequential(
+ # pw
+ nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
+ norm_layer(hidden_dim),
+ h_swish() if use_hs else nn.ReLU(inplace=True),
+ # dw
+ nn.Conv2d(hidden_dim,
+ hidden_dim,
+ kernel_size,
+ stride, (kernel_size - 1) // 2 * dilation,
+ dilation=dilation,
+ groups=hidden_dim,
+ bias=False),
+ norm_layer(hidden_dim),
+ # Squeeze-and-Excite
+ SELayer(hidden_dim) if use_se else nn.Identity(),
+ h_swish() if use_hs else nn.ReLU(inplace=True),
+ # pw-linear
+ nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
+ norm_layer(oup),
+ )
+
+ def forward(self, x):
+ if self.identity:
+ return x + self.conv(x)
+ else:
+ return self.conv(x)
+
+
+class MobileNetV3Large(nn.Module):
+ def __init__(self,
+ output_stride=16,
+ norm_layer=nn.BatchNorm2d,
+ width_mult=1.,
+ freeze_at=0):
+ super(MobileNetV3Large, self).__init__()
+ """
+ Constructs a MobileNetV3-Large model
+ """
+ cfgs = [
+ # k, t, c, SE, HS, s
+ [3, 1, 16, 0, 0, 1],
+ [3, 4, 24, 0, 0, 2],
+ [3, 3, 24, 0, 0, 1],
+ [5, 3, 40, 1, 0, 2],
+ [5, 3, 40, 1, 0, 1],
+ [5, 3, 40, 1, 0, 1],
+ [3, 6, 80, 0, 1, 2],
+ [3, 2.5, 80, 0, 1, 1],
+ [3, 2.3, 80, 0, 1, 1],
+ [3, 2.3, 80, 0, 1, 1],
+ [3, 6, 112, 1, 1, 1],
+ [3, 6, 112, 1, 1, 1],
+ [5, 6, 160, 1, 1, 2],
+ [5, 6, 160, 1, 1, 1],
+ [5, 6, 160, 1, 1, 1]
+ ]
+ self.cfgs = cfgs
+
+ # building first layer
+ input_channel = _make_divisible(16 * width_mult, 8)
+ layers = [conv_3x3_bn(3, input_channel, 2, norm_layer)]
+ # building inverted residual blocks
+ block = InvertedResidual
+ now_stride = 2
+ rate = 1
+ for k, t, c, use_se, use_hs, s in self.cfgs:
+ if now_stride == output_stride:
+ dilation = rate
+ rate *= s
+ s = 1
+ else:
+ dilation = 1
+ now_stride *= s
+ output_channel = _make_divisible(c * width_mult, 8)
+ exp_size = _make_divisible(input_channel * t, 8)
+ layers.append(
+ block(input_channel, exp_size, output_channel, k, s, use_se,
+ use_hs, dilation, norm_layer))
+ input_channel = output_channel
+
+ self.features = nn.Sequential(*layers)
+ self.conv = conv_1x1_bn(input_channel, exp_size, norm_layer)
+ # building last several layers
+
+ self._initialize_weights()
+
+ feature_4x = self.features[0:4]
+ feautre_8x = self.features[4:7]
+ feature_16x = self.features[7:13]
+ feature_32x = self.features[13:]
+
+ self.stages = [feature_4x, feautre_8x, feature_16x, feature_32x]
+
+ self.freeze(freeze_at)
+
+ def forward(self, x):
+ xs = []
+ for stage in self.stages:
+ x = stage(x)
+ xs.append(x)
+ xs[-1] = self.conv(xs[-1])
+ return xs
+
+ 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, nn.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 freeze(self, freeze_at):
+ if freeze_at >= 1:
+ for m in self.stages[0][0]:
+ freeze_params(m)
+
+ for idx, stage in enumerate(self.stages, start=2):
+ if freeze_at >= idx:
+ freeze_params(stage)
diff --git a/aot/networks/encoders/resnest/__init__.py b/aot/networks/encoders/resnest/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d46216bfb8fed8ea1f951bf23211d6e9d44b4148
--- /dev/null
+++ b/aot/networks/encoders/resnest/__init__.py
@@ -0,0 +1 @@
+from .resnest import *
diff --git a/aot/networks/encoders/resnest/__pycache__/__init__.cpython-311.pyc b/aot/networks/encoders/resnest/__pycache__/__init__.cpython-311.pyc
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diff --git a/aot/networks/encoders/resnest/__pycache__/resnest.cpython-311.pyc b/aot/networks/encoders/resnest/__pycache__/resnest.cpython-311.pyc
new file mode 100644
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new file mode 100644
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diff --git a/aot/networks/encoders/resnest/__pycache__/splat.cpython-311.pyc b/aot/networks/encoders/resnest/__pycache__/splat.cpython-311.pyc
new file mode 100644
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diff --git a/aot/networks/encoders/resnest/resnest.py b/aot/networks/encoders/resnest/resnest.py
new file mode 100644
index 0000000000000000000000000000000000000000..b22600e8717fedaa8b95077c428eaac3ea300ba1
--- /dev/null
+++ b/aot/networks/encoders/resnest/resnest.py
@@ -0,0 +1,108 @@
+import torch
+from .resnet import ResNet, Bottleneck
+
+__all__ = ['resnest50', 'resnest101', 'resnest200', 'resnest269']
+
+_url_format = 'https://s3.us-west-1.wasabisys.com/resnest/torch/{}-{}.pth'
+
+_model_sha256 = {
+ name: checksum
+ for checksum, name in [
+ ('528c19ca', 'resnest50'),
+ ('22405ba7', 'resnest101'),
+ ('75117900', 'resnest200'),
+ ('0cc87c48', 'resnest269'),
+ ]
+}
+
+
+def short_hash(name):
+ if name not in _model_sha256:
+ raise ValueError(
+ 'Pretrained model for {name} is not available.'.format(name=name))
+ return _model_sha256[name][:8]
+
+
+resnest_model_urls = {
+ name: _url_format.format(name, short_hash(name))
+ for name in _model_sha256.keys()
+}
+
+
+def resnest50(pretrained=False, root='~/.encoding/models', **kwargs):
+ model = ResNet(Bottleneck, [3, 4, 6, 3],
+ radix=2,
+ groups=1,
+ bottleneck_width=64,
+ deep_stem=True,
+ stem_width=32,
+ avg_down=True,
+ avd=True,
+ avd_first=False,
+ **kwargs)
+ if pretrained:
+ model.load_state_dict(
+ torch.hub.load_state_dict_from_url(resnest_model_urls['resnest50'],
+ progress=True,
+ check_hash=True))
+ return model
+
+
+def resnest101(pretrained=False, root='~/.encoding/models', **kwargs):
+ model = ResNet(Bottleneck, [3, 4, 23, 3],
+ radix=2,
+ groups=1,
+ bottleneck_width=64,
+ deep_stem=True,
+ stem_width=64,
+ avg_down=True,
+ avd=True,
+ avd_first=False,
+ **kwargs)
+ if pretrained:
+ model.load_state_dict(
+ torch.hub.load_state_dict_from_url(
+ resnest_model_urls['resnest101'],
+ progress=True,
+ check_hash=True))
+ return model
+
+
+def resnest200(pretrained=False, root='~/.encoding/models', **kwargs):
+ model = ResNet(Bottleneck, [3, 24, 36, 3],
+ radix=2,
+ groups=1,
+ bottleneck_width=64,
+ deep_stem=True,
+ stem_width=64,
+ avg_down=True,
+ avd=True,
+ avd_first=False,
+ **kwargs)
+ if pretrained:
+ model.load_state_dict(
+ torch.hub.load_state_dict_from_url(
+ resnest_model_urls['resnest200'],
+ progress=True,
+ check_hash=True))
+ return model
+
+
+def resnest269(pretrained=False, root='~/.encoding/models', **kwargs):
+ model = ResNet(Bottleneck, [3, 30, 48, 8],
+ radix=2,
+ groups=1,
+ bottleneck_width=64,
+ deep_stem=True,
+ stem_width=64,
+ avg_down=True,
+ avd=True,
+ avd_first=False,
+ **kwargs)
+ if pretrained:
+ model.load_state_dict(
+ torch.hub.load_state_dict_from_url(
+ resnest_model_urls['resnest269'],
+ progress=True,
+ check_hash=True))
+ return model
diff --git a/aot/networks/encoders/resnest/resnet.py b/aot/networks/encoders/resnest/resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce8c99b3c236bfd6934599d30949bb92bb0124d7
--- /dev/null
+++ b/aot/networks/encoders/resnest/resnet.py
@@ -0,0 +1,444 @@
+import math
+import torch.nn as nn
+
+from .splat import SplAtConv2d, DropBlock2D
+from utils.learning import freeze_params
+
+__all__ = ['ResNet', 'Bottleneck']
+
+_url_format = 'https://s3.us-west-1.wasabisys.com/resnest/torch/{}-{}.pth'
+
+_model_sha256 = {name: checksum for checksum, name in []}
+
+
+def short_hash(name):
+ if name not in _model_sha256:
+ raise ValueError(
+ 'Pretrained model for {name} is not available.'.format(name=name))
+ return _model_sha256[name][:8]
+
+
+resnest_model_urls = {
+ name: _url_format.format(name, short_hash(name))
+ for name in _model_sha256.keys()
+}
+
+
+class GlobalAvgPool2d(nn.Module):
+ def __init__(self):
+ """Global average pooling over the input's spatial dimensions"""
+ super(GlobalAvgPool2d, self).__init__()
+
+ def forward(self, inputs):
+ return nn.functional.adaptive_avg_pool2d(inputs,
+ 1).view(inputs.size(0), -1)
+
+
+class Bottleneck(nn.Module):
+ """ResNet Bottleneck
+ """
+ # pylint: disable=unused-argument
+ expansion = 4
+
+ def __init__(self,
+ inplanes,
+ planes,
+ stride=1,
+ downsample=None,
+ radix=1,
+ cardinality=1,
+ bottleneck_width=64,
+ avd=False,
+ avd_first=False,
+ dilation=1,
+ is_first=False,
+ rectified_conv=False,
+ rectify_avg=False,
+ norm_layer=None,
+ dropblock_prob=0.0,
+ last_gamma=False):
+ super(Bottleneck, self).__init__()
+ group_width = int(planes * (bottleneck_width / 64.)) * cardinality
+ self.conv1 = nn.Conv2d(inplanes,
+ group_width,
+ kernel_size=1,
+ bias=False)
+ self.bn1 = norm_layer(group_width)
+ self.dropblock_prob = dropblock_prob
+ self.radix = radix
+ self.avd = avd and (stride > 1 or is_first)
+ self.avd_first = avd_first
+
+ if self.avd:
+ self.avd_layer = nn.AvgPool2d(3, stride, padding=1)
+ stride = 1
+
+ if dropblock_prob > 0.0:
+ self.dropblock1 = DropBlock2D(dropblock_prob, 3)
+ if radix == 1:
+ self.dropblock2 = DropBlock2D(dropblock_prob, 3)
+ self.dropblock3 = DropBlock2D(dropblock_prob, 3)
+
+ if radix >= 1:
+ self.conv2 = SplAtConv2d(group_width,
+ group_width,
+ kernel_size=3,
+ stride=stride,
+ padding=dilation,
+ dilation=dilation,
+ groups=cardinality,
+ bias=False,
+ radix=radix,
+ rectify=rectified_conv,
+ rectify_avg=rectify_avg,
+ norm_layer=norm_layer,
+ dropblock_prob=dropblock_prob)
+ elif rectified_conv:
+ from rfconv import RFConv2d
+ self.conv2 = RFConv2d(group_width,
+ group_width,
+ kernel_size=3,
+ stride=stride,
+ padding=dilation,
+ dilation=dilation,
+ groups=cardinality,
+ bias=False,
+ average_mode=rectify_avg)
+ self.bn2 = norm_layer(group_width)
+ else:
+ self.conv2 = nn.Conv2d(group_width,
+ group_width,
+ kernel_size=3,
+ stride=stride,
+ padding=dilation,
+ dilation=dilation,
+ groups=cardinality,
+ bias=False)
+ self.bn2 = norm_layer(group_width)
+
+ self.conv3 = nn.Conv2d(group_width,
+ planes * 4,
+ kernel_size=1,
+ bias=False)
+ self.bn3 = norm_layer(planes * 4)
+
+ if last_gamma:
+ from torch.nn.init import zeros_
+ zeros_(self.bn3.weight)
+ self.relu = nn.ReLU(inplace=True)
+ self.downsample = downsample
+ self.dilation = dilation
+ self.stride = stride
+
+ def forward(self, x):
+ residual = x
+
+ out = self.conv1(x)
+ out = self.bn1(out)
+ if self.dropblock_prob > 0.0:
+ out = self.dropblock1(out)
+ out = self.relu(out)
+
+ if self.avd and self.avd_first:
+ out = self.avd_layer(out)
+
+ out = self.conv2(out)
+ if self.radix == 0:
+ out = self.bn2(out)
+ if self.dropblock_prob > 0.0:
+ out = self.dropblock2(out)
+ out = self.relu(out)
+
+ if self.avd and not self.avd_first:
+ out = self.avd_layer(out)
+
+ out = self.conv3(out)
+ out = self.bn3(out)
+ if self.dropblock_prob > 0.0:
+ out = self.dropblock3(out)
+
+ if self.downsample is not None:
+ residual = self.downsample(x)
+
+ out += residual
+ out = self.relu(out)
+
+ return out
+
+
+class ResNet(nn.Module):
+ """ResNet Variants
+ Parameters
+ ----------
+ block : Block
+ Class for the residual block. Options are BasicBlockV1, BottleneckV1.
+ layers : list of int
+ Numbers of layers in each block
+ classes : int, default 1000
+ Number of classification classes.
+ dilated : bool, default False
+ Applying dilation strategy to pretrained ResNet yielding a stride-8 model,
+ typically used in Semantic Segmentation.
+ norm_layer : object
+ Normalization layer used in backbone network (default: :class:`mxnet.gluon.nn.BatchNorm`;
+ for Synchronized Cross-GPU BachNormalization).
+ Reference:
+ - He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
+ - Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions."
+ """
+
+ # pylint: disable=unused-variable
+ def __init__(self,
+ block,
+ layers,
+ radix=1,
+ groups=1,
+ bottleneck_width=64,
+ num_classes=1000,
+ dilated=False,
+ dilation=1,
+ deep_stem=False,
+ stem_width=64,
+ avg_down=False,
+ rectified_conv=False,
+ rectify_avg=False,
+ avd=False,
+ avd_first=False,
+ final_drop=0.0,
+ dropblock_prob=0,
+ last_gamma=False,
+ norm_layer=nn.BatchNorm2d,
+ freeze_at=0):
+ self.cardinality = groups
+ self.bottleneck_width = bottleneck_width
+ # ResNet-D params
+ self.inplanes = stem_width * 2 if deep_stem else 64
+ self.avg_down = avg_down
+ self.last_gamma = last_gamma
+ # ResNeSt params
+ self.radix = radix
+ self.avd = avd
+ self.avd_first = avd_first
+
+ super(ResNet, self).__init__()
+ self.rectified_conv = rectified_conv
+ self.rectify_avg = rectify_avg
+ if rectified_conv:
+ from rfconv import RFConv2d
+ conv_layer = RFConv2d
+ else:
+ conv_layer = nn.Conv2d
+ conv_kwargs = {'average_mode': rectify_avg} if rectified_conv else {}
+ if deep_stem:
+ self.conv1 = nn.Sequential(
+ conv_layer(3,
+ stem_width,
+ kernel_size=3,
+ stride=2,
+ padding=1,
+ bias=False,
+ **conv_kwargs),
+ norm_layer(stem_width),
+ nn.ReLU(inplace=True),
+ conv_layer(stem_width,
+ stem_width,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ bias=False,
+ **conv_kwargs),
+ norm_layer(stem_width),
+ nn.ReLU(inplace=True),
+ conv_layer(stem_width,
+ stem_width * 2,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ bias=False,
+ **conv_kwargs),
+ )
+ else:
+ self.conv1 = conv_layer(3,
+ 64,
+ kernel_size=7,
+ stride=2,
+ padding=3,
+ bias=False,
+ **conv_kwargs)
+ self.bn1 = norm_layer(self.inplanes)
+ self.relu = nn.ReLU(inplace=True)
+ self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.layer1 = self._make_layer(block,
+ 64,
+ layers[0],
+ norm_layer=norm_layer,
+ is_first=False)
+ self.layer2 = self._make_layer(block,
+ 128,
+ layers[1],
+ stride=2,
+ norm_layer=norm_layer)
+ if dilated or dilation == 4:
+ self.layer3 = self._make_layer(block,
+ 256,
+ layers[2],
+ stride=1,
+ dilation=2,
+ norm_layer=norm_layer,
+ dropblock_prob=dropblock_prob)
+ elif dilation == 2:
+ self.layer3 = self._make_layer(block,
+ 256,
+ layers[2],
+ stride=2,
+ dilation=1,
+ norm_layer=norm_layer,
+ dropblock_prob=dropblock_prob)
+ else:
+ self.layer3 = self._make_layer(block,
+ 256,
+ layers[2],
+ stride=2,
+ norm_layer=norm_layer,
+ dropblock_prob=dropblock_prob)
+
+ self.stem = [self.conv1, self.bn1]
+ self.stages = [self.layer1, self.layer2, self.layer3]
+
+ 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, norm_layer):
+ m.weight.data.fill_(1)
+ m.bias.data.zero_()
+
+ self.freeze(freeze_at)
+
+ def _make_layer(self,
+ block,
+ planes,
+ blocks,
+ stride=1,
+ dilation=1,
+ norm_layer=None,
+ dropblock_prob=0.0,
+ is_first=True):
+ downsample = None
+ if stride != 1 or self.inplanes != planes * block.expansion:
+ down_layers = []
+ if self.avg_down:
+ if dilation == 1:
+ down_layers.append(
+ nn.AvgPool2d(kernel_size=stride,
+ stride=stride,
+ ceil_mode=True,
+ count_include_pad=False))
+ else:
+ down_layers.append(
+ nn.AvgPool2d(kernel_size=1,
+ stride=1,
+ ceil_mode=True,
+ count_include_pad=False))
+ down_layers.append(
+ nn.Conv2d(self.inplanes,
+ planes * block.expansion,
+ kernel_size=1,
+ stride=1,
+ bias=False))
+ else:
+ down_layers.append(
+ nn.Conv2d(self.inplanes,
+ planes * block.expansion,
+ kernel_size=1,
+ stride=stride,
+ bias=False))
+ down_layers.append(norm_layer(planes * block.expansion))
+ downsample = nn.Sequential(*down_layers)
+
+ layers = []
+ if dilation == 1 or dilation == 2:
+ layers.append(
+ block(self.inplanes,
+ planes,
+ stride,
+ downsample=downsample,
+ radix=self.radix,
+ cardinality=self.cardinality,
+ bottleneck_width=self.bottleneck_width,
+ avd=self.avd,
+ avd_first=self.avd_first,
+ dilation=1,
+ is_first=is_first,
+ rectified_conv=self.rectified_conv,
+ rectify_avg=self.rectify_avg,
+ norm_layer=norm_layer,
+ dropblock_prob=dropblock_prob,
+ last_gamma=self.last_gamma))
+ elif dilation == 4:
+ layers.append(
+ block(self.inplanes,
+ planes,
+ stride,
+ downsample=downsample,
+ radix=self.radix,
+ cardinality=self.cardinality,
+ bottleneck_width=self.bottleneck_width,
+ avd=self.avd,
+ avd_first=self.avd_first,
+ dilation=2,
+ is_first=is_first,
+ rectified_conv=self.rectified_conv,
+ rectify_avg=self.rectify_avg,
+ norm_layer=norm_layer,
+ dropblock_prob=dropblock_prob,
+ last_gamma=self.last_gamma))
+ else:
+ raise RuntimeError("=> unknown dilation size: {}".format(dilation))
+
+ self.inplanes = planes * block.expansion
+ for i in range(1, blocks):
+ layers.append(
+ block(self.inplanes,
+ planes,
+ radix=self.radix,
+ cardinality=self.cardinality,
+ bottleneck_width=self.bottleneck_width,
+ avd=self.avd,
+ avd_first=self.avd_first,
+ dilation=dilation,
+ rectified_conv=self.rectified_conv,
+ rectify_avg=self.rectify_avg,
+ norm_layer=norm_layer,
+ dropblock_prob=dropblock_prob,
+ last_gamma=self.last_gamma))
+
+ return nn.Sequential(*layers)
+
+ def forward(self, x):
+ x = self.conv1(x)
+ x = self.bn1(x)
+ x = self.relu(x)
+ x = self.maxpool(x)
+
+ xs = []
+
+ x = self.layer1(x)
+ xs.append(x) # 4X
+ x = self.layer2(x)
+ xs.append(x) # 8X
+ x = self.layer3(x)
+ xs.append(x) # 16X
+ # Following STMVOS, we drop stage 5.
+ xs.append(x) # 16X
+
+ return xs
+
+ def freeze(self, freeze_at):
+ if freeze_at >= 1:
+ for m in self.stem:
+ freeze_params(m)
+
+ for idx, stage in enumerate(self.stages, start=2):
+ if freeze_at >= idx:
+ freeze_params(stage)
diff --git a/aot/networks/encoders/resnest/splat.py b/aot/networks/encoders/resnest/splat.py
new file mode 100644
index 0000000000000000000000000000000000000000..147d684332e378ac390e2be2cfed2daf9a94ad87
--- /dev/null
+++ b/aot/networks/encoders/resnest/splat.py
@@ -0,0 +1,132 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+from torch.nn import Conv2d, Module, ReLU
+from torch.nn.modules.utils import _pair
+
+__all__ = ['SplAtConv2d', 'DropBlock2D']
+
+
+class DropBlock2D(object):
+ def __init__(self, *args, **kwargs):
+ raise NotImplementedError
+
+
+class SplAtConv2d(Module):
+ """Split-Attention Conv2d
+ """
+ def __init__(self,
+ in_channels,
+ channels,
+ kernel_size,
+ stride=(1, 1),
+ padding=(0, 0),
+ dilation=(1, 1),
+ groups=1,
+ bias=True,
+ radix=2,
+ reduction_factor=4,
+ rectify=False,
+ rectify_avg=False,
+ norm_layer=None,
+ dropblock_prob=0.0,
+ **kwargs):
+ super(SplAtConv2d, self).__init__()
+ padding = _pair(padding)
+ self.rectify = rectify and (padding[0] > 0 or padding[1] > 0)
+ self.rectify_avg = rectify_avg
+ inter_channels = max(in_channels * radix // reduction_factor, 32)
+ self.radix = radix
+ self.cardinality = groups
+ self.channels = channels
+ self.dropblock_prob = dropblock_prob
+ if self.rectify:
+ from rfconv import RFConv2d
+ self.conv = RFConv2d(in_channels,
+ channels * radix,
+ kernel_size,
+ stride,
+ padding,
+ dilation,
+ groups=groups * radix,
+ bias=bias,
+ average_mode=rectify_avg,
+ **kwargs)
+ else:
+ self.conv = Conv2d(in_channels,
+ channels * radix,
+ kernel_size,
+ stride,
+ padding,
+ dilation,
+ groups=groups * radix,
+ bias=bias,
+ **kwargs)
+ self.use_bn = norm_layer is not None
+ if self.use_bn:
+ self.bn0 = norm_layer(channels * radix)
+ self.relu = ReLU(inplace=True)
+ self.fc1 = Conv2d(channels, inter_channels, 1, groups=self.cardinality)
+ if self.use_bn:
+ self.bn1 = norm_layer(inter_channels)
+ self.fc2 = Conv2d(inter_channels,
+ channels * radix,
+ 1,
+ groups=self.cardinality)
+ if dropblock_prob > 0.0:
+ self.dropblock = DropBlock2D(dropblock_prob, 3)
+ self.rsoftmax = rSoftMax(radix, groups)
+
+ def forward(self, x):
+ x = self.conv(x)
+ if self.use_bn:
+ x = self.bn0(x)
+ if self.dropblock_prob > 0.0:
+ x = self.dropblock(x)
+ x = self.relu(x)
+
+ batch, rchannel = x.shape[:2]
+ if self.radix > 1:
+ if torch.__version__ < '1.5':
+ splited = torch.split(x, int(rchannel // self.radix), dim=1)
+ else:
+ splited = torch.split(x, rchannel // self.radix, dim=1)
+ gap = sum(splited)
+ else:
+ gap = x
+ gap = F.adaptive_avg_pool2d(gap, 1)
+ gap = self.fc1(gap)
+
+ if self.use_bn:
+ gap = self.bn1(gap)
+ gap = self.relu(gap)
+
+ atten = self.fc2(gap)
+ atten = self.rsoftmax(atten).view(batch, -1, 1, 1)
+
+ if self.radix > 1:
+ if torch.__version__ < '1.5':
+ attens = torch.split(atten, int(rchannel // self.radix), dim=1)
+ else:
+ attens = torch.split(atten, rchannel // self.radix, dim=1)
+ out = sum([att * split for (att, split) in zip(attens, splited)])
+ else:
+ out = atten * x
+ return out.contiguous()
+
+
+class rSoftMax(nn.Module):
+ def __init__(self, radix, cardinality):
+ super().__init__()
+ self.radix = radix
+ self.cardinality = cardinality
+
+ def forward(self, x):
+ batch = x.size(0)
+ if self.radix > 1:
+ x = x.view(batch, self.cardinality, self.radix, -1).transpose(1, 2)
+ x = F.softmax(x, dim=1)
+ x = x.reshape(batch, -1)
+ else:
+ x = torch.sigmoid(x)
+ return x
diff --git a/aot/networks/encoders/resnet.py b/aot/networks/encoders/resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ba2845e98990b3baadb02dff820f9da6d0e8d37
--- /dev/null
+++ b/aot/networks/encoders/resnet.py
@@ -0,0 +1,208 @@
+import math
+import torch.nn as nn
+from utils.learning import freeze_params
+
+
+class Bottleneck(nn.Module):
+ expansion = 4
+
+ def __init__(self,
+ inplanes,
+ planes,
+ stride=1,
+ dilation=1,
+ downsample=None,
+ BatchNorm=None):
+ super(Bottleneck, self).__init__()
+ self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+ self.bn1 = BatchNorm(planes)
+ self.conv2 = nn.Conv2d(planes,
+ planes,
+ kernel_size=3,
+ stride=stride,
+ dilation=dilation,
+ padding=dilation,
+ bias=False)
+ self.bn2 = BatchNorm(planes)
+ self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
+ self.bn3 = BatchNorm(planes * 4)
+ self.relu = nn.ReLU(inplace=True)
+ self.downsample = downsample
+ self.stride = stride
+ self.dilation = dilation
+
+ 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, output_stride, BatchNorm, freeze_at=0):
+ self.inplanes = 64
+ super(ResNet, self).__init__()
+
+ if output_stride == 16:
+ strides = [1, 2, 2, 1]
+ dilations = [1, 1, 1, 2]
+ elif output_stride == 8:
+ strides = [1, 2, 1, 1]
+ dilations = [1, 1, 2, 4]
+ else:
+ raise NotImplementedError
+
+ # Modules
+ self.conv1 = nn.Conv2d(3,
+ 64,
+ kernel_size=7,
+ stride=2,
+ padding=3,
+ bias=False)
+ self.bn1 = BatchNorm(64)
+ self.relu = nn.ReLU(inplace=True)
+ self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+ self.layer1 = self._make_layer(block,
+ 64,
+ layers[0],
+ stride=strides[0],
+ dilation=dilations[0],
+ BatchNorm=BatchNorm)
+ self.layer2 = self._make_layer(block,
+ 128,
+ layers[1],
+ stride=strides[1],
+ dilation=dilations[1],
+ BatchNorm=BatchNorm)
+ self.layer3 = self._make_layer(block,
+ 256,
+ layers[2],
+ stride=strides[2],
+ dilation=dilations[2],
+ BatchNorm=BatchNorm)
+ # self.layer4 = self._make_layer(block, 512, layers[3], stride=strides[3], dilation=dilations[3], BatchNorm=BatchNorm)
+
+ self.stem = [self.conv1, self.bn1]
+ self.stages = [self.layer1, self.layer2, self.layer3]
+
+ self._init_weight()
+ self.freeze(freeze_at)
+
+ def _make_layer(self,
+ block,
+ planes,
+ blocks,
+ stride=1,
+ dilation=1,
+ BatchNorm=None):
+ 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),
+ BatchNorm(planes * block.expansion),
+ )
+
+ layers = []
+ layers.append(
+ block(self.inplanes, planes, stride, max(dilation // 2, 1),
+ downsample, BatchNorm))
+ self.inplanes = planes * block.expansion
+ for i in range(1, blocks):
+ layers.append(
+ block(self.inplanes,
+ planes,
+ dilation=dilation,
+ BatchNorm=BatchNorm))
+
+ return nn.Sequential(*layers)
+
+ def forward(self, input):
+ x = self.conv1(input)
+ x = self.bn1(x)
+ x = self.relu(x)
+ x = self.maxpool(x)
+
+ xs = []
+
+ x = self.layer1(x)
+ xs.append(x) # 4X
+ x = self.layer2(x)
+ xs.append(x) # 8X
+ x = self.layer3(x)
+ xs.append(x) # 16X
+ # Following STMVOS, we drop stage 5.
+ xs.append(x) # 16X
+
+ return xs
+
+ def _init_weight(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))
+ elif isinstance(m, nn.BatchNorm2d):
+ m.weight.data.fill_(1)
+ m.bias.data.zero_()
+
+ def freeze(self, freeze_at):
+ if freeze_at >= 1:
+ for m in self.stem:
+ freeze_params(m)
+
+ for idx, stage in enumerate(self.stages, start=2):
+ if freeze_at >= idx:
+ freeze_params(stage)
+
+
+def ResNet50(output_stride, BatchNorm, freeze_at=0):
+ """Constructs a ResNet-50 model.
+ Args:
+ pretrained (bool): If True, returns a model pre-trained on ImageNet
+ """
+ model = ResNet(Bottleneck, [3, 4, 6, 3],
+ output_stride,
+ BatchNorm,
+ freeze_at=freeze_at)
+ return model
+
+
+def ResNet101(output_stride, BatchNorm, freeze_at=0):
+ """Constructs a ResNet-101 model.
+ Args:
+ pretrained (bool): If True, returns a model pre-trained on ImageNet
+ """
+ model = ResNet(Bottleneck, [3, 4, 23, 3],
+ output_stride,
+ BatchNorm,
+ freeze_at=freeze_at)
+ return model
+
+
+if __name__ == "__main__":
+ import torch
+ model = ResNet101(BatchNorm=nn.BatchNorm2d, output_stride=8)
+ input = torch.rand(1, 3, 512, 512)
+ output, low_level_feat = model(input)
+ print(output.size())
+ print(low_level_feat.size())
diff --git a/aot/networks/encoders/swin/__init__.py b/aot/networks/encoders/swin/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..828b95150cbc9f2cd42ead98a85d0370a62e8b9a
--- /dev/null
+++ b/aot/networks/encoders/swin/__init__.py
@@ -0,0 +1 @@
+from .build import build_swin_model
\ No newline at end of file
diff --git a/aot/networks/encoders/swin/__pycache__/__init__.cpython-311.pyc b/aot/networks/encoders/swin/__pycache__/__init__.cpython-311.pyc
new file mode 100644
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diff --git a/aot/networks/encoders/swin/__pycache__/build.cpython-311.pyc b/aot/networks/encoders/swin/__pycache__/build.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0c918da0a460fc88dc3356def6e0fd453c8b43ee
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diff --git a/aot/networks/encoders/swin/__pycache__/swin_transformer.cpython-311.pyc b/aot/networks/encoders/swin/__pycache__/swin_transformer.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..88ff393c35b63e37a25ef9506fd622d2ee4af73f
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diff --git a/aot/networks/encoders/swin/build.py b/aot/networks/encoders/swin/build.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d832de035be764f7e3caac25ee595c36cacc575
--- /dev/null
+++ b/aot/networks/encoders/swin/build.py
@@ -0,0 +1,27 @@
+# --------------------------------------------------------
+# Swin Transformer
+# Copyright (c) 2021 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ze Liu
+# --------------------------------------------------------
+
+from .swin_transformer import SwinTransformer
+
+
+def build_swin_model(model_type, freeze_at=0):
+ if model_type == 'swin_base':
+ model = SwinTransformer(embed_dim=128,
+ depths=[2, 2, 18, 2],
+ num_heads=[4, 8, 16, 32],
+ window_size=7,
+ drop_path_rate=0.3,
+ out_indices=(0, 1, 2),
+ ape=False,
+ patch_norm=True,
+ frozen_stages=freeze_at,
+ use_checkpoint=False)
+
+ else:
+ raise NotImplementedError(f"Unkown model: {model_type}")
+
+ return model
diff --git a/aot/networks/encoders/swin/swin_transformer.py b/aot/networks/encoders/swin/swin_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..743cfaae8888d919cd131b2e33736bafd0f53991
--- /dev/null
+++ b/aot/networks/encoders/swin/swin_transformer.py
@@ -0,0 +1,716 @@
+# --------------------------------------------------------
+# Swin Transformer
+# Copyright (c) 2021 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ze Liu
+# --------------------------------------------------------
+from itertools import repeat
+import collections.abc
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+
+from networks.layers.basic import DropPath
+
+
+def _ntuple(n):
+ def parse(x):
+ if isinstance(x, collections.abc.Iterable):
+ return x
+ return tuple(repeat(x, n))
+
+ return parse
+
+
+to_2tuple = _ntuple(2)
+
+
+def trunc_normal_(tensor, mean=0, std=1):
+ size = tensor.shape
+ tmp = tensor.new_empty(size + (4, )).normal_()
+ valid = (tmp < 2) & (tmp > -2)
+ ind = valid.max(-1, keepdim=True)[1]
+ tensor.data.copy_(tmp.gather(-1, ind).squeeze(-1))
+ tensor.data.mul_(std).add_(mean)
+ return tensor
+
+
+class Mlp(nn.Module):
+ """ Multilayer perceptron."""
+ 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):
+ """ 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):
+ """ Forward function.
+ 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
+
+
+class SwinTransformerBlock(nn.Module):
+ """ Swin Transformer Block.
+ Args:
+ dim (int): Number of input channels.
+ 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,
+ 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.num_heads = num_heads
+ self.window_size = window_size
+ self.shift_size = shift_size
+ self.mlp_ratio = mlp_ratio
+ 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)
+
+ self.H = None
+ self.W = None
+
+ def forward(self, x, mask_matrix):
+ """ Forward function.
+ Args:
+ x: Input feature, tensor size (B, H*W, C).
+ H, W: Spatial resolution of the input feature.
+ mask_matrix: Attention mask for cyclic shift.
+ """
+ B, L, C = x.shape
+ H, W = self.H, self.W
+ assert L == H * W, "input feature has wrong size"
+
+ shortcut = x
+ x = self.norm1(x)
+ x = x.view(B, H, W, C)
+
+ # pad feature maps to multiples of window size
+ pad_l = pad_t = 0
+ pad_r = (self.window_size - W % self.window_size) % self.window_size
+ pad_b = (self.window_size - H % self.window_size) % self.window_size
+ x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+ _, Hp, Wp, _ = x.shape
+
+ # cyclic shift
+ if self.shift_size > 0:
+ shifted_x = torch.roll(x,
+ shifts=(-self.shift_size, -self.shift_size),
+ dims=(1, 2))
+ attn_mask = mask_matrix
+ else:
+ shifted_x = x
+ attn_mask = None
+
+ # 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=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, Hp,
+ Wp) # 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
+
+ if pad_r > 0 or pad_b > 0:
+ x = x[:, :H, :W, :].contiguous()
+
+ 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
+
+
+class PatchMerging(nn.Module):
+ """ Patch Merging Layer
+ Args:
+ dim (int): Number of input channels.
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ """
+ def __init__(self, dim, norm_layer=nn.LayerNorm):
+ super().__init__()
+ self.dim = dim
+ self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+ self.norm = norm_layer(4 * dim)
+
+ def forward(self, x, H, W):
+ """ Forward function.
+ Args:
+ x: Input feature, tensor size (B, H*W, C).
+ H, W: Spatial resolution of the input feature.
+ """
+ B, L, C = x.shape
+ assert L == H * W, "input feature has wrong size"
+
+ x = x.view(B, H, W, C)
+
+ # padding
+ pad_input = (H % 2 == 1) or (W % 2 == 1)
+ if pad_input:
+ x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
+
+ 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
+
+
+class BasicLayer(nn.Module):
+ """ A basic Swin Transformer layer for one stage.
+ Args:
+ dim (int): Number of feature channels
+ depth (int): Depths of this stage.
+ num_heads (int): Number of attention head.
+ window_size (int): Local window size. Default: 7.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+ 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,
+ depth,
+ num_heads,
+ window_size=7,
+ 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.window_size = window_size
+ self.shift_size = window_size // 2
+ self.depth = depth
+ self.use_checkpoint = use_checkpoint
+
+ # build blocks
+ self.blocks = nn.ModuleList([
+ SwinTransformerBlock(dim=dim,
+ 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(dim=dim, norm_layer=norm_layer)
+ else:
+ self.downsample = None
+
+ def forward(self, x, H, W):
+ """ Forward function.
+ Args:
+ x: Input feature, tensor size (B, H*W, C).
+ H, W: Spatial resolution of the input feature.
+ """
+
+ # calculate attention mask for SW-MSA
+ Hp = int(np.ceil(H / self.window_size)) * self.window_size
+ Wp = int(np.ceil(W / self.window_size)) * self.window_size
+ img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 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))
+
+ for blk in self.blocks:
+ blk.H, blk.W = H, W
+ if self.use_checkpoint:
+ x = checkpoint.checkpoint(blk, x, attn_mask)
+ else:
+ x = blk(x, attn_mask)
+ if self.downsample is not None:
+ x_down = self.downsample(x, H, W)
+ Wh, Ww = (H + 1) // 2, (W + 1) // 2
+ return x, H, W, x_down, Wh, Ww
+ else:
+ return x, H, W, x, H, W
+
+
+class PatchEmbed(nn.Module):
+ """ Image to Patch Embedding
+ Args:
+ 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,
+ patch_size=4,
+ in_chans=3,
+ embed_dim=96,
+ norm_layer=None):
+ super().__init__()
+ patch_size = to_2tuple(patch_size)
+ self.patch_size = patch_size
+
+ 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):
+ """Forward function."""
+ # padding
+ _, _, H, W = x.size()
+ if W % self.patch_size[1] != 0:
+ x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
+ if H % self.patch_size[0] != 0:
+ x = F.pad(x,
+ (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
+
+ x = self.proj(x) # B C Wh Ww
+ if self.norm is not None:
+ Wh, Ww = x.size(2), x.size(3)
+ x = x.flatten(2).transpose(1, 2)
+ x = self.norm(x)
+ x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
+
+ return x
+
+
+class SwinTransformer(nn.Module):
+ """ Swin Transformer backbone.
+ A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
+ https://arxiv.org/pdf/2103.14030
+ Args:
+ pretrain_img_size (int): Input image size for training the pretrained model,
+ used in absolute postion embedding. Default 224.
+ patch_size (int | tuple(int)): Patch size. Default: 4.
+ in_chans (int): Number of input image channels. Default: 3.
+ embed_dim (int): Number of linear projection output channels. Default: 96.
+ depths (tuple[int]): Depths of each Swin Transformer stage.
+ num_heads (tuple[int]): Number of attention head of each stage.
+ 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.
+ drop_rate (float): Dropout rate.
+ attn_drop_rate (float): Attention dropout rate. Default: 0.
+ drop_path_rate (float): Stochastic depth rate. Default: 0.2.
+ 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.
+ out_indices (Sequence[int]): Output from which stages.
+ frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+ -1 means not freezing any parameters.
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+ """
+ def __init__(self,
+ pretrain_img_size=224,
+ patch_size=4,
+ in_chans=3,
+ 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=0.2,
+ norm_layer=nn.LayerNorm,
+ ape=False,
+ patch_norm=True,
+ out_indices=(0, 1, 2),
+ frozen_stages=-1,
+ use_checkpoint=False):
+ super().__init__()
+
+ self.pretrain_img_size = pretrain_img_size
+ self.num_layers = len(depths) - 1 # remove the last stage
+ self.embed_dim = embed_dim
+ self.ape = ape
+ self.patch_norm = patch_norm
+ self.out_indices = out_indices
+ self.frozen_stages = frozen_stages
+
+ # split image into non-overlapping patches
+ self.patch_embed = PatchEmbed(
+ patch_size=patch_size,
+ in_chans=in_chans,
+ embed_dim=embed_dim,
+ norm_layer=norm_layer if self.patch_norm else None)
+
+ # absolute position embedding
+ if self.ape:
+ pretrain_img_size = to_2tuple(pretrain_img_size)
+ patch_size = to_2tuple(patch_size)
+ patches_resolution = [
+ pretrain_img_size[0] // patch_size[0],
+ pretrain_img_size[1] // patch_size[1]
+ ]
+
+ self.absolute_pos_embed = nn.Parameter(
+ torch.zeros(1, embed_dim, patches_resolution[0],
+ patches_resolution[1]))
+ 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),
+ depth=depths[i_layer],
+ num_heads=num_heads[i_layer],
+ window_size=window_size,
+ mlp_ratio=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)
+
+ num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]
+ self.num_features = num_features
+
+ # add a norm layer for each output
+ for i_layer in out_indices:
+ layer = norm_layer(num_features[i_layer])
+ layer_name = f'norm{i_layer}'
+ self.add_module(layer_name, layer)
+
+ self._freeze_stages()
+
+ def _freeze_stages(self):
+ if self.frozen_stages >= 0:
+ self.patch_embed.eval()
+ for param in self.patch_embed.parameters():
+ param.requires_grad = False
+
+ if self.frozen_stages >= 1 and self.ape:
+ self.absolute_pos_embed.requires_grad = False
+
+ if self.frozen_stages >= 2:
+ self.pos_drop = nn.Identity()
+ for i in range(0, self.frozen_stages - 1):
+ m = self.layers[i]
+ for block in m.blocks:
+ block.drop_path = nn.Identity()
+ block.attn.attn_drop = nn.Identity()
+ block.attn.proj_drop = nn.Identity()
+ for param in m.parameters():
+ param.requires_grad = False
+ if m.downsample is not None:
+ for param in m.downsample.parameters():
+ param.requires_grad = True
+
+ def init_weights(self, pretrained=None):
+ """Initialize the weights in backbone.
+ Args:
+ pretrained (str, optional): Path to pre-trained weights.
+ Defaults to None.
+ """
+ def _init_weights(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)
+
+ if isinstance(pretrained, str):
+ self.apply(_init_weights)
+ # logger = get_root_logger()
+ # load_checkpoint(self, pretrained, strict=False, logger=logger)
+ elif pretrained is None:
+ self.apply(_init_weights)
+ else:
+ raise TypeError('pretrained must be a str or None')
+
+ def forward(self, x):
+ """Forward function."""
+ x = self.patch_embed(x)
+
+ Wh, Ww = x.size(2), x.size(3)
+ if self.ape:
+ # interpolate the position embedding to the corresponding size
+ absolute_pos_embed = F.interpolate(self.absolute_pos_embed,
+ size=(Wh, Ww),
+ mode='bicubic')
+ x = (x + absolute_pos_embed).flatten(2).transpose(1,
+ 2) # B Wh*Ww C
+ else:
+ x = x.flatten(2).transpose(1, 2)
+ x = self.pos_drop(x)
+
+ outs = []
+ for i in range(self.num_layers):
+ layer = self.layers[i]
+ x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+
+ if i in self.out_indices:
+ norm_layer = getattr(self, f'norm{i}')
+ x_out = norm_layer(x_out)
+
+ out = x_out.view(-1, H, W,
+ self.num_features[i]).permute(0, 3, 1,
+ 2).contiguous()
+ outs.append(out)
+
+ outs.append(outs[-1])
+
+ return outs
diff --git a/aot/networks/engines/__init__.py b/aot/networks/engines/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcfa80a1ee8121eadcc2763869cab973207b5c41
--- /dev/null
+++ b/aot/networks/engines/__init__.py
@@ -0,0 +1,21 @@
+from networks.engines.aot_engine import AOTEngine, AOTInferEngine
+from networks.engines.deaot_engine import DeAOTEngine, DeAOTInferEngine
+
+
+def build_engine(name, phase='train', **kwargs):
+ if name == 'aotengine':
+ if phase == 'train':
+ return AOTEngine(**kwargs)
+ elif phase == 'eval':
+ return AOTInferEngine(**kwargs)
+ else:
+ raise NotImplementedError
+ elif name == 'deaotengine':
+ if phase == 'train':
+ return DeAOTEngine(**kwargs)
+ elif phase == 'eval':
+ return DeAOTInferEngine(**kwargs)
+ else:
+ raise NotImplementedError
+ else:
+ raise NotImplementedError
diff --git a/aot/networks/engines/__pycache__/__init__.cpython-311.pyc b/aot/networks/engines/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f6a2d4a20ba868a163daaf452846582928b5a6f8
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diff --git a/aot/networks/engines/__pycache__/aot_engine.cpython-311.pyc b/aot/networks/engines/__pycache__/aot_engine.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..669d370b0b86de02482996d66163ec754e8ac601
Binary files /dev/null and b/aot/networks/engines/__pycache__/aot_engine.cpython-311.pyc differ
diff --git a/aot/networks/engines/__pycache__/deaot_engine.cpython-311.pyc b/aot/networks/engines/__pycache__/deaot_engine.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f9d5761685b8dfcb380cd81d3fcc91220dea6bc7
Binary files /dev/null and b/aot/networks/engines/__pycache__/deaot_engine.cpython-311.pyc differ
diff --git a/aot/networks/engines/aot_engine.py b/aot/networks/engines/aot_engine.py
new file mode 100644
index 0000000000000000000000000000000000000000..4474d8cef3828308ac2c42c4b15056bd84247247
--- /dev/null
+++ b/aot/networks/engines/aot_engine.py
@@ -0,0 +1,643 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import numpy as np
+
+from utils.math import generate_permute_matrix
+from utils.image import one_hot_mask
+
+from networks.layers.basic import seq_to_2d
+
+
+class AOTEngine(nn.Module):
+ def __init__(self,
+ aot_model,
+ gpu_id=0,
+ long_term_mem_gap=9999,
+ short_term_mem_skip=1,
+ max_len_long_term=9999):
+ super().__init__()
+
+ self.cfg = aot_model.cfg
+ self.align_corners = aot_model.cfg.MODEL_ALIGN_CORNERS
+ self.AOT = aot_model
+
+ self.max_obj_num = aot_model.max_obj_num
+ self.gpu_id = gpu_id
+ self.long_term_mem_gap = long_term_mem_gap
+ self.short_term_mem_skip = short_term_mem_skip
+ self.max_len_long_term = max_len_long_term
+ self.losses = None
+
+ self.restart_engine()
+
+ def forward(self,
+ all_frames,
+ all_masks,
+ batch_size,
+ obj_nums,
+ step=0,
+ tf_board=False,
+ use_prev_pred=False,
+ enable_prev_frame=False,
+ use_prev_prob=False): # only used for training
+ if self.losses is None:
+ self._init_losses()
+
+ self.freeze_id = True if use_prev_pred else False
+ aux_weight = self.aux_weight * max(self.aux_step - step,
+ 0.) / self.aux_step
+
+ self.offline_encoder(all_frames, all_masks)
+
+ self.add_reference_frame(frame_step=0, obj_nums=obj_nums)
+
+ grad_state = torch.no_grad if aux_weight == 0 else torch.enable_grad
+ with grad_state():
+ ref_aux_loss, ref_aux_mask = self.generate_loss_mask(
+ self.offline_masks[self.frame_step], step)
+
+ aux_losses = [ref_aux_loss]
+ aux_masks = [ref_aux_mask]
+
+ curr_losses, curr_masks = [], []
+ if enable_prev_frame:
+ self.set_prev_frame(frame_step=1)
+ with grad_state():
+ prev_aux_loss, prev_aux_mask = self.generate_loss_mask(
+ self.offline_masks[self.frame_step], step)
+ aux_losses.append(prev_aux_loss)
+ aux_masks.append(prev_aux_mask)
+ else:
+ self.match_propogate_one_frame()
+ curr_loss, curr_mask, curr_prob = self.generate_loss_mask(
+ self.offline_masks[self.frame_step], step, return_prob=True)
+ self.update_short_term_memory(
+ curr_mask if not use_prev_prob else curr_prob,
+ None if use_prev_pred else self.assign_identity(
+ self.offline_one_hot_masks[self.frame_step]))
+ curr_losses.append(curr_loss)
+ curr_masks.append(curr_mask)
+
+ self.match_propogate_one_frame()
+ curr_loss, curr_mask, curr_prob = self.generate_loss_mask(
+ self.offline_masks[self.frame_step], step, return_prob=True)
+ curr_losses.append(curr_loss)
+ curr_masks.append(curr_mask)
+ for _ in range(self.total_offline_frame_num - 3):
+ self.update_short_term_memory(
+ curr_mask if not use_prev_prob else curr_prob,
+ None if use_prev_pred else self.assign_identity(
+ self.offline_one_hot_masks[self.frame_step]))
+ self.match_propogate_one_frame()
+ curr_loss, curr_mask, curr_prob = self.generate_loss_mask(
+ self.offline_masks[self.frame_step], step, return_prob=True)
+ curr_losses.append(curr_loss)
+ curr_masks.append(curr_mask)
+
+ aux_loss = torch.cat(aux_losses, dim=0).mean(dim=0)
+ pred_loss = torch.cat(curr_losses, dim=0).mean(dim=0)
+
+ loss = aux_weight * aux_loss + pred_loss
+
+ all_pred_mask = aux_masks + curr_masks
+
+ all_frame_loss = aux_losses + curr_losses
+
+ boards = {'image': {}, 'scalar': {}}
+
+ return loss, all_pred_mask, all_frame_loss, boards
+
+ def _init_losses(self):
+ cfg = self.cfg
+
+ from networks.layers.loss import CrossEntropyLoss, SoftJaccordLoss
+ bce_loss = CrossEntropyLoss(
+ cfg.TRAIN_TOP_K_PERCENT_PIXELS,
+ cfg.TRAIN_HARD_MINING_RATIO * cfg.TRAIN_TOTAL_STEPS)
+ iou_loss = SoftJaccordLoss()
+
+ losses = [bce_loss, iou_loss]
+ loss_weights = [0.5, 0.5]
+
+ self.losses = nn.ModuleList(losses)
+ self.loss_weights = loss_weights
+ self.aux_weight = cfg.TRAIN_AUX_LOSS_WEIGHT
+ self.aux_step = cfg.TRAIN_TOTAL_STEPS * cfg.TRAIN_AUX_LOSS_RATIO + 1e-5
+
+ def encode_one_img_mask(self, img=None, mask=None, frame_step=-1):
+ if frame_step == -1:
+ frame_step = self.frame_step
+
+ if self.enable_offline_enc:
+ curr_enc_embs = self.offline_enc_embs[frame_step]
+ elif img is None:
+ curr_enc_embs = None
+ else:
+ curr_enc_embs = self.AOT.encode_image(img)
+
+ if mask is not None:
+ curr_one_hot_mask = one_hot_mask(mask, self.max_obj_num)
+ elif self.enable_offline_enc:
+ curr_one_hot_mask = self.offline_one_hot_masks[frame_step]
+ else:
+ curr_one_hot_mask = None
+
+ return curr_enc_embs, curr_one_hot_mask
+
+ def offline_encoder(self, all_frames, all_masks=None):
+ self.enable_offline_enc = True
+ self.offline_frames = all_frames.size(0) // self.batch_size
+
+ # extract backbone features
+ self.offline_enc_embs = self.split_frames(
+ self.AOT.encode_image(all_frames), self.batch_size)
+ self.total_offline_frame_num = len(self.offline_enc_embs)
+
+ if all_masks is not None:
+ # extract mask embeddings
+ offline_one_hot_masks = one_hot_mask(all_masks, self.max_obj_num)
+ self.offline_masks = list(
+ torch.split(all_masks, self.batch_size, dim=0))
+ self.offline_one_hot_masks = list(
+ torch.split(offline_one_hot_masks, self.batch_size, dim=0))
+
+ if self.input_size_2d is None:
+ self.update_size(all_frames.size()[2:],
+ self.offline_enc_embs[0][-1].size()[2:])
+
+ def assign_identity(self, one_hot_mask):
+ if self.enable_id_shuffle:
+ one_hot_mask = torch.einsum('bohw,bot->bthw', one_hot_mask,
+ self.id_shuffle_matrix)
+
+ id_emb = self.AOT.get_id_emb(one_hot_mask).view(
+ self.batch_size, -1, self.enc_hw).permute(2, 0, 1)
+
+ if self.training and self.freeze_id:
+ id_emb = id_emb.detach()
+
+ return id_emb
+
+ def split_frames(self, xs, chunk_size):
+ new_xs = []
+ for x in xs:
+ all_x = list(torch.split(x, chunk_size, dim=0))
+ new_xs.append(all_x)
+ return list(zip(*new_xs))
+
+ def add_reference_frame(self,
+ img=None,
+ mask=None,
+ frame_step=-1,
+ obj_nums=None,
+ img_embs=None):
+ if self.obj_nums is None and obj_nums is None:
+ print('No objects for reference frame!')
+ exit()
+ elif obj_nums is not None:
+ self.obj_nums = obj_nums
+
+ if frame_step == -1:
+ frame_step = self.frame_step
+
+ if img_embs is None:
+ curr_enc_embs, curr_one_hot_mask = self.encode_one_img_mask(
+ img, mask, frame_step)
+ else:
+ _, curr_one_hot_mask = self.encode_one_img_mask(
+ None, mask, frame_step)
+ curr_enc_embs = img_embs
+
+ if curr_enc_embs is None:
+ print('No image for reference frame!')
+ exit()
+
+ if curr_one_hot_mask is None:
+ print('No mask for reference frame!')
+ exit()
+
+ if self.input_size_2d is None:
+ self.update_size(img.size()[2:], curr_enc_embs[-1].size()[2:])
+
+ self.curr_enc_embs = curr_enc_embs
+ self.curr_one_hot_mask = curr_one_hot_mask
+
+ if self.pos_emb is None:
+ self.pos_emb = self.AOT.get_pos_emb(curr_enc_embs[-1]).expand(
+ self.batch_size, -1, -1,
+ -1).view(self.batch_size, -1, self.enc_hw).permute(2, 0, 1)
+
+ curr_id_emb = self.assign_identity(curr_one_hot_mask)
+ self.curr_id_embs = curr_id_emb
+
+ # self matching and propagation
+ self.curr_lstt_output = self.AOT.LSTT_forward(curr_enc_embs,
+ None,
+ None,
+ curr_id_emb,
+ pos_emb=self.pos_emb,
+ size_2d=self.enc_size_2d)
+
+ lstt_embs, lstt_curr_memories, lstt_long_memories, lstt_short_memories = self.curr_lstt_output
+
+ if self.long_term_memories is None:
+ self.long_term_memories = lstt_long_memories
+ else:
+ self.update_long_term_memory(lstt_long_memories)
+
+ self.last_mem_step = self.frame_step
+
+ self.short_term_memories_list = [lstt_short_memories]
+ self.short_term_memories = lstt_short_memories
+
+ def set_prev_frame(self, img=None, mask=None, frame_step=1):
+ self.frame_step = frame_step
+ curr_enc_embs, curr_one_hot_mask = self.encode_one_img_mask(
+ img, mask, frame_step)
+
+ if curr_enc_embs is None:
+ print('No image for previous frame!')
+ exit()
+
+ if curr_one_hot_mask is None:
+ print('No mask for previous frame!')
+ exit()
+
+ self.curr_enc_embs = curr_enc_embs
+ self.curr_one_hot_mask = curr_one_hot_mask
+
+ curr_id_emb = self.assign_identity(curr_one_hot_mask)
+ self.curr_id_embs = curr_id_emb
+
+ # self matching and propagation
+ self.curr_lstt_output = self.AOT.LSTT_forward(curr_enc_embs,
+ None,
+ None,
+ curr_id_emb,
+ pos_emb=self.pos_emb,
+ size_2d=self.enc_size_2d)
+
+ lstt_embs, lstt_curr_memories, lstt_long_memories, lstt_short_memories = self.curr_lstt_output
+
+ if self.long_term_memories is None:
+ self.long_term_memories = lstt_long_memories
+ else:
+ self.update_long_term_memory(lstt_long_memories)
+ self.last_mem_step = frame_step
+
+ self.short_term_memories_list = [lstt_short_memories]
+ self.short_term_memories = lstt_short_memories
+
+ def update_long_term_memory(self, new_long_term_memories):
+ TOKEN_NUM = new_long_term_memories[0][0].shape[0]
+ if self.long_term_memories is None:
+ self.long_term_memories = new_long_term_memories
+ updated_long_term_memories = []
+ for new_long_term_memory, last_long_term_memory in zip(
+ new_long_term_memories, self.long_term_memories):
+ updated_e = []
+ for new_e, last_e in zip(new_long_term_memory,
+ last_long_term_memory):
+ if new_e is None or last_e is None:
+ updated_e.append(None)
+ else:
+ if last_e.shape[0] >= self.max_len_long_term * TOKEN_NUM:
+ last_e = last_e[:(self.max_len_long_term - 1) * TOKEN_NUM]
+ updated_e.append(torch.cat([new_e, last_e], dim=0))
+ updated_long_term_memories.append(updated_e)
+ self.long_term_memories = updated_long_term_memories
+
+ def update_short_term_memory(self, curr_mask, curr_id_emb=None, skip_long_term_update=False):
+ if curr_id_emb is None:
+ if len(curr_mask.size()) == 3 or curr_mask.size()[0] == 1:
+ curr_one_hot_mask = one_hot_mask(curr_mask, self.max_obj_num)
+ else:
+ curr_one_hot_mask = curr_mask
+ curr_id_emb = self.assign_identity(curr_one_hot_mask)
+
+ lstt_curr_memories = self.curr_lstt_output[1]
+ lstt_curr_memories_2d = []
+ for layer_idx in range(len(lstt_curr_memories)):
+ curr_k, curr_v = lstt_curr_memories[layer_idx][
+ 0], lstt_curr_memories[layer_idx][1]
+ curr_k, curr_v = self.AOT.LSTT.layers[layer_idx].fuse_key_value_id(
+ curr_k, curr_v, curr_id_emb)
+ lstt_curr_memories[layer_idx][0], lstt_curr_memories[layer_idx][
+ 1] = curr_k, curr_v
+ lstt_curr_memories_2d.append([
+ seq_to_2d(lstt_curr_memories[layer_idx][0], self.enc_size_2d),
+ seq_to_2d(lstt_curr_memories[layer_idx][1], self.enc_size_2d)
+ ])
+
+ self.short_term_memories_list.append(lstt_curr_memories_2d)
+ self.short_term_memories_list = self.short_term_memories_list[
+ -self.short_term_mem_skip:]
+ self.short_term_memories = self.short_term_memories_list[0]
+
+ if self.frame_step - self.last_mem_step >= self.long_term_mem_gap:
+ # skip the update of long-term memory or not
+ if not skip_long_term_update:
+ self.update_long_term_memory(lstt_curr_memories)
+ self.last_mem_step = self.frame_step
+
+ def match_propogate_one_frame(self, img=None, img_embs=None):
+ self.frame_step += 1
+ if img_embs is None:
+ curr_enc_embs, _ = self.encode_one_img_mask(
+ img, None, self.frame_step)
+ else:
+ curr_enc_embs = img_embs
+ self.curr_enc_embs = curr_enc_embs
+
+ self.curr_lstt_output = self.AOT.LSTT_forward(curr_enc_embs,
+ self.long_term_memories,
+ self.short_term_memories,
+ None,
+ pos_emb=self.pos_emb,
+ size_2d=self.enc_size_2d)
+
+ def decode_current_logits(self, output_size=None):
+ curr_enc_embs = self.curr_enc_embs
+ curr_lstt_embs = self.curr_lstt_output[0]
+
+ pred_id_logits = self.AOT.decode_id_logits(curr_lstt_embs,
+ curr_enc_embs)
+
+ if self.enable_id_shuffle: # reverse shuffle
+ pred_id_logits = torch.einsum('bohw,bto->bthw', pred_id_logits,
+ self.id_shuffle_matrix)
+
+ # remove unused identities
+ for batch_idx, obj_num in enumerate(self.obj_nums):
+ pred_id_logits[batch_idx, (obj_num+1):] = - \
+ 1e+10 if pred_id_logits.dtype == torch.float32 else -1e+4
+
+ self.pred_id_logits = pred_id_logits
+
+ if output_size is not None:
+ pred_id_logits = F.interpolate(pred_id_logits,
+ size=output_size,
+ mode="bilinear",
+ align_corners=self.align_corners)
+
+ return pred_id_logits
+
+ def predict_current_mask(self, output_size=None, return_prob=False):
+ if output_size is None:
+ output_size = self.input_size_2d
+
+ pred_id_logits = F.interpolate(self.pred_id_logits,
+ size=output_size,
+ mode="bilinear",
+ align_corners=self.align_corners)
+ pred_mask = torch.argmax(pred_id_logits, dim=1)
+
+ if not return_prob:
+ return pred_mask
+ else:
+ pred_prob = torch.softmax(pred_id_logits, dim=1)
+ return pred_mask, pred_prob
+
+ def calculate_current_loss(self, gt_mask, step):
+ pred_id_logits = self.pred_id_logits
+
+ pred_id_logits = F.interpolate(pred_id_logits,
+ size=gt_mask.size()[-2:],
+ mode="bilinear",
+ align_corners=self.align_corners)
+
+ label_list = []
+ logit_list = []
+ for batch_idx, obj_num in enumerate(self.obj_nums):
+ now_label = gt_mask[batch_idx].long()
+ now_logit = pred_id_logits[batch_idx, :(obj_num + 1)].unsqueeze(0)
+ label_list.append(now_label.long())
+ logit_list.append(now_logit)
+
+ total_loss = 0
+ for loss, loss_weight in zip(self.losses, self.loss_weights):
+ total_loss = total_loss + loss_weight * \
+ loss(logit_list, label_list, step)
+
+ return total_loss
+
+ def generate_loss_mask(self, gt_mask, step, return_prob=False):
+ self.decode_current_logits()
+ loss = self.calculate_current_loss(gt_mask, step)
+ if return_prob:
+ mask, prob = self.predict_current_mask(return_prob=True)
+ return loss, mask, prob
+ else:
+ mask = self.predict_current_mask()
+ return loss, mask
+
+ def keep_gt_mask(self, pred_mask, keep_prob=0.2):
+ pred_mask = pred_mask.float()
+ gt_mask = self.offline_masks[self.frame_step].float().squeeze(1)
+
+ shape = [1 for _ in range(pred_mask.ndim)]
+ shape[0] = self.batch_size
+ random_tensor = keep_prob + torch.rand(
+ shape, dtype=pred_mask.dtype, device=pred_mask.device)
+ random_tensor.floor_() # binarize
+
+ pred_mask = pred_mask * (1 - random_tensor) + gt_mask * random_tensor
+
+ return pred_mask
+
+ def restart_engine(self, batch_size=1, enable_id_shuffle=False):
+
+ self.batch_size = batch_size
+ self.frame_step = 0
+ self.last_mem_step = -1
+ self.enable_id_shuffle = enable_id_shuffle
+ self.freeze_id = False
+
+ self.obj_nums = None
+ self.pos_emb = None
+ self.enc_size_2d = None
+ self.enc_hw = None
+ self.input_size_2d = None
+
+ self.long_term_memories = None
+ self.short_term_memories_list = []
+ self.short_term_memories = None
+
+ self.enable_offline_enc = False
+ self.offline_enc_embs = None
+ self.offline_one_hot_masks = None
+ self.offline_frames = -1
+ self.total_offline_frame_num = 0
+
+ self.curr_enc_embs = None
+ self.curr_memories = None
+ self.curr_id_embs = None
+
+ if enable_id_shuffle:
+ self.id_shuffle_matrix = generate_permute_matrix(
+ self.max_obj_num + 1, batch_size, gpu_id=self.gpu_id)
+ else:
+ self.id_shuffle_matrix = None
+
+ def update_size(self, input_size, enc_size):
+ self.input_size_2d = input_size
+ self.enc_size_2d = enc_size
+ self.enc_hw = self.enc_size_2d[0] * self.enc_size_2d[1]
+
+
+class AOTInferEngine(nn.Module):
+ def __init__(self,
+ aot_model,
+ gpu_id=0,
+ long_term_mem_gap=9999,
+ short_term_mem_skip=1,
+ max_aot_obj_num=None,
+ max_len_long_term=9999,):
+ super().__init__()
+
+ self.cfg = aot_model.cfg
+ self.AOT = aot_model
+
+ if max_aot_obj_num is None or max_aot_obj_num > aot_model.max_obj_num:
+ self.max_aot_obj_num = aot_model.max_obj_num
+ else:
+ self.max_aot_obj_num = max_aot_obj_num
+
+ self.gpu_id = gpu_id
+ self.long_term_mem_gap = long_term_mem_gap
+ self.short_term_mem_skip = short_term_mem_skip
+ self.max_len_long_term = max_len_long_term
+ self.aot_engines = []
+
+ self.restart_engine()
+ def restart_engine(self):
+ del (self.aot_engines)
+ self.aot_engines = []
+ self.obj_nums = None
+
+ def separate_mask(self, mask, obj_nums):
+ if mask is None:
+ return [None] * len(self.aot_engines)
+ if len(self.aot_engines) == 1:
+ return [mask], [obj_nums]
+
+ separated_obj_nums = [
+ self.max_aot_obj_num for _ in range(len(self.aot_engines))
+ ]
+ if obj_nums % self.max_aot_obj_num > 0:
+ separated_obj_nums[-1] = obj_nums % self.max_aot_obj_num
+
+ if len(mask.size()) == 3 or mask.size()[0] == 1:
+ separated_masks = []
+ for idx in range(len(self.aot_engines)):
+ start_id = idx * self.max_aot_obj_num + 1
+ end_id = (idx + 1) * self.max_aot_obj_num
+ fg_mask = ((mask >= start_id) & (mask <= end_id)).float()
+ separated_mask = (fg_mask * mask - start_id + 1) * fg_mask
+ separated_masks.append(separated_mask)
+ return separated_masks, separated_obj_nums
+ else:
+ prob = mask
+ separated_probs = []
+ for idx in range(len(self.aot_engines)):
+ start_id = idx * self.max_aot_obj_num + 1
+ end_id = (idx + 1) * self.max_aot_obj_num
+ fg_prob = prob[start_id:(end_id + 1)]
+ bg_prob = 1. - torch.sum(fg_prob, dim=1, keepdim=True)
+ separated_probs.append(torch.cat([bg_prob, fg_prob], dim=1))
+ return separated_probs, separated_obj_nums
+
+ def min_logit_aggregation(self, all_logits):
+ if len(all_logits) == 1:
+ return all_logits[0]
+
+ fg_logits = []
+ bg_logits = []
+
+ for logit in all_logits:
+ bg_logits.append(logit[:, 0:1])
+ fg_logits.append(logit[:, 1:1 + self.max_aot_obj_num])
+
+ bg_logit, _ = torch.min(torch.cat(bg_logits, dim=1),
+ dim=1,
+ keepdim=True)
+ merged_logit = torch.cat([bg_logit] + fg_logits, dim=1)
+
+ return merged_logit
+
+ def soft_logit_aggregation(self, all_logits):
+ if len(all_logits) == 1:
+ return all_logits[0]
+
+ fg_probs = []
+ bg_probs = []
+
+ for logit in all_logits:
+ prob = torch.softmax(logit, dim=1)
+ bg_probs.append(prob[:, 0:1])
+ fg_probs.append(prob[:, 1:1 + self.max_aot_obj_num])
+
+ bg_prob = torch.prod(torch.cat(bg_probs, dim=1), dim=1, keepdim=True)
+ merged_prob = torch.cat([bg_prob] + fg_probs,
+ dim=1).clamp(1e-5, 1 - 1e-5)
+ merged_logit = torch.logit(merged_prob)
+
+ return merged_logit
+
+ def add_reference_frame(self, img, mask, obj_nums, frame_step=-1):
+ if isinstance(obj_nums, list):
+ obj_nums = obj_nums[0]
+ self.obj_nums = obj_nums
+ aot_num = max(np.ceil(obj_nums / self.max_aot_obj_num), 1)
+ while (aot_num > len(self.aot_engines)):
+ new_engine = AOTEngine(self.AOT, self.gpu_id,
+ self.long_term_mem_gap,
+ self.short_term_mem_skip,
+ self.max_len_long_term,)
+ new_engine.eval()
+ self.aot_engines.append(new_engine)
+
+ separated_masks, separated_obj_nums = self.separate_mask(
+ mask, obj_nums)
+ img_embs = None
+ for aot_engine, separated_mask, separated_obj_num in zip(
+ self.aot_engines, separated_masks, separated_obj_nums):
+ aot_engine.add_reference_frame(img,
+ separated_mask,
+ obj_nums=[separated_obj_num],
+ frame_step=frame_step,
+ img_embs=img_embs)
+
+ if img_embs is None: # reuse image embeddings
+ img_embs = aot_engine.curr_enc_embs
+
+ self.update_size()
+
+ def match_propogate_one_frame(self, img=None):
+ img_embs = None
+ for aot_engine in self.aot_engines:
+ aot_engine.match_propogate_one_frame(img, img_embs=img_embs)
+ if img_embs is None: # reuse image embeddings
+ img_embs = aot_engine.curr_enc_embs
+
+ def decode_current_logits(self, output_size=None):
+ all_logits = []
+ for aot_engine in self.aot_engines:
+ all_logits.append(aot_engine.decode_current_logits(output_size))
+ pred_id_logits = self.soft_logit_aggregation(all_logits)
+ return pred_id_logits
+
+ def update_memory(self, curr_mask, skip_long_term_update=False):
+ _curr_mask = F.interpolate(curr_mask,self.input_size_2d)
+ separated_masks, _ = self.separate_mask(_curr_mask, self.obj_nums)
+ for aot_engine, separated_mask in zip(self.aot_engines,
+ separated_masks):
+ aot_engine.update_short_term_memory(separated_mask,
+ skip_long_term_update=skip_long_term_update)
+
+ def update_size(self):
+ self.input_size_2d = self.aot_engines[0].input_size_2d
+ self.enc_size_2d = self.aot_engines[0].enc_size_2d
+ self.enc_hw = self.aot_engines[0].enc_hw
diff --git a/aot/networks/engines/deaot_engine.py b/aot/networks/engines/deaot_engine.py
new file mode 100644
index 0000000000000000000000000000000000000000..b27be6fba82b31f0ff5542ce703f31e5efb9e2d7
--- /dev/null
+++ b/aot/networks/engines/deaot_engine.py
@@ -0,0 +1,98 @@
+import numpy as np
+
+from utils.image import one_hot_mask
+
+from networks.layers.basic import seq_to_2d
+from networks.engines.aot_engine import AOTEngine, AOTInferEngine
+
+
+class DeAOTEngine(AOTEngine):
+ def __init__(self,
+ aot_model,
+ gpu_id=0,
+ long_term_mem_gap=9999,
+ short_term_mem_skip=1,
+ layer_loss_scaling_ratio=2.,
+ max_len_long_term=9999):
+ super().__init__(aot_model, gpu_id, long_term_mem_gap,
+ short_term_mem_skip, max_len_long_term)
+ self.layer_loss_scaling_ratio = layer_loss_scaling_ratio
+ def update_short_term_memory(self, curr_mask, curr_id_emb=None, skip_long_term_update=False):
+
+ if curr_id_emb is None:
+ if len(curr_mask.size()) == 3 or curr_mask.size()[0] == 1:
+ curr_one_hot_mask = one_hot_mask(curr_mask, self.max_obj_num)
+ else:
+ curr_one_hot_mask = curr_mask
+ curr_id_emb = self.assign_identity(curr_one_hot_mask)
+
+ lstt_curr_memories = self.curr_lstt_output[1]
+ lstt_curr_memories_2d = []
+ for layer_idx in range(len(lstt_curr_memories)):
+ curr_k, curr_v, curr_id_k, curr_id_v = lstt_curr_memories[
+ layer_idx]
+ curr_id_k, curr_id_v = self.AOT.LSTT.layers[
+ layer_idx].fuse_key_value_id(curr_id_k, curr_id_v, curr_id_emb)
+ lstt_curr_memories[layer_idx][2], lstt_curr_memories[layer_idx][
+ 3] = curr_id_k, curr_id_v
+ local_curr_id_k = seq_to_2d(
+ curr_id_k, self.enc_size_2d) if curr_id_k is not None else None
+ local_curr_id_v = seq_to_2d(curr_id_v, self.enc_size_2d)
+ lstt_curr_memories_2d.append([
+ seq_to_2d(curr_k, self.enc_size_2d),
+ seq_to_2d(curr_v, self.enc_size_2d), local_curr_id_k,
+ local_curr_id_v
+ ])
+
+ self.short_term_memories_list.append(lstt_curr_memories_2d)
+ self.short_term_memories_list = self.short_term_memories_list[
+ -self.short_term_mem_skip:]
+ self.short_term_memories = self.short_term_memories_list[0]
+
+ if self.frame_step - self.last_mem_step >= self.long_term_mem_gap:
+ # skip the update of long-term memory or not
+ if not skip_long_term_update:
+ self.update_long_term_memory(lstt_curr_memories)
+ self.last_mem_step = self.frame_step
+
+
+class DeAOTInferEngine(AOTInferEngine):
+ def __init__(self,
+ aot_model,
+ gpu_id=0,
+ long_term_mem_gap=9999,
+ short_term_mem_skip=1,
+ max_aot_obj_num=None,
+ max_len_long_term=9999):
+ super().__init__(aot_model, gpu_id, long_term_mem_gap,
+ short_term_mem_skip, max_aot_obj_num, max_len_long_term)
+ def add_reference_frame(self, img, mask, obj_nums, frame_step=-1):
+ if isinstance(obj_nums, list):
+ obj_nums = obj_nums[0]
+ self.obj_nums = obj_nums
+ aot_num = max(np.ceil(obj_nums / self.max_aot_obj_num), 1)
+ while (aot_num > len(self.aot_engines)):
+ new_engine = DeAOTEngine(self.AOT, self.gpu_id,
+ self.long_term_mem_gap,
+ self.short_term_mem_skip,
+ max_len_long_term = self.max_len_long_term)
+ new_engine.eval()
+ self.aot_engines.append(new_engine)
+
+ separated_masks, separated_obj_nums = self.separate_mask(
+ mask, obj_nums)
+ img_embs = None
+ for aot_engine, separated_mask, separated_obj_num in zip(
+ self.aot_engines, separated_masks, separated_obj_nums):
+ if aot_engine.obj_nums is None or aot_engine.obj_nums[0] < separated_obj_num:
+ aot_engine.add_reference_frame(img,
+ separated_mask,
+ obj_nums=[separated_obj_num],
+ frame_step=frame_step,
+ img_embs=img_embs)
+ else:
+ aot_engine.update_short_term_memory(separated_mask)
+ if img_embs is None: # reuse image embeddings
+ img_embs = aot_engine.curr_enc_embs
+
+ self.update_size()
diff --git a/aot/networks/layers/__init__.py b/aot/networks/layers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/aot/networks/layers/__pycache__/__init__.cpython-311.pyc b/aot/networks/layers/__pycache__/__init__.cpython-311.pyc
new file mode 100644
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diff --git a/aot/networks/layers/__pycache__/basic.cpython-311.pyc b/aot/networks/layers/__pycache__/basic.cpython-311.pyc
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new file mode 100644
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--- /dev/null
+++ b/aot/networks/layers/attention.py
@@ -0,0 +1,905 @@
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from networks.layers.basic import DropOutLogit, ScaleOffset, DWConv2d
+
+
+def multiply_by_ychunks(x, y, chunks=1):
+ if chunks <= 1:
+ return x @ y
+ else:
+ return torch.cat([x @ _y for _y in y.chunk(chunks, dim=-1)], dim=-1)
+
+
+def multiply_by_xchunks(x, y, chunks=1):
+ if chunks <= 1:
+ return x @ y
+ else:
+ return torch.cat([_x @ y for _x in x.chunk(chunks, dim=-2)], dim=-2)
+
+
+# Long-term attention
+class MultiheadAttention(nn.Module):
+ def __init__(self,
+ d_model,
+ num_head=8,
+ dropout=0.,
+ use_linear=True,
+ d_att=None,
+ use_dis=False,
+ qk_chunks=1,
+ max_mem_len_ratio=-1,
+ top_k=-1):
+ super().__init__()
+ self.d_model = d_model
+ self.num_head = num_head
+ self.use_dis = use_dis
+ self.qk_chunks = qk_chunks
+ self.max_mem_len_ratio = float(max_mem_len_ratio)
+ self.top_k = top_k
+
+ self.hidden_dim = d_model // num_head
+ self.d_att = self.hidden_dim if d_att is None else d_att
+ self.T = self.d_att**0.5
+ self.use_linear = use_linear
+
+ if use_linear:
+ self.linear_Q = nn.Linear(d_model, d_model)
+ self.linear_K = nn.Linear(d_model, d_model)
+ self.linear_V = nn.Linear(d_model, d_model)
+
+ self.dropout = nn.Dropout(dropout)
+ self.drop_prob = dropout
+ self.projection = nn.Linear(d_model, d_model)
+ self._init_weight()
+
+ def forward(self, Q, K, V):
+ """
+ :param Q: A 3d tensor with shape of [T_q, bs, C_q]
+ :param K: A 3d tensor with shape of [T_k, bs, C_k]
+ :param V: A 3d tensor with shape of [T_v, bs, C_v]
+ """
+ num_head = self.num_head
+ hidden_dim = self.hidden_dim
+
+ bs = Q.size()[1]
+
+ # Linear projections
+ if self.use_linear:
+ Q = self.linear_Q(Q)
+ K = self.linear_K(K)
+ V = self.linear_V(V)
+
+ # Scale
+ Q = Q / self.T
+
+ if not self.training and self.max_mem_len_ratio > 0:
+ mem_len_ratio = float(K.size(0)) / Q.size(0)
+ if mem_len_ratio > self.max_mem_len_ratio:
+ scaling_ratio = math.log(mem_len_ratio) / math.log(
+ self.max_mem_len_ratio)
+ Q = Q * scaling_ratio
+
+ # Multi-head
+ Q = Q.view(-1, bs, num_head, self.d_att).permute(1, 2, 0, 3)
+ K = K.view(-1, bs, num_head, self.d_att).permute(1, 2, 3, 0)
+ V = V.view(-1, bs, num_head, hidden_dim).permute(1, 2, 0, 3)
+
+ # Multiplication
+ QK = multiply_by_ychunks(Q, K, self.qk_chunks)
+ if self.use_dis:
+ QK = 2 * QK - K.pow(2).sum(dim=-2, keepdim=True)
+
+ # Activation
+ if not self.training and self.top_k > 0 and self.top_k < QK.size()[-1]:
+ top_QK, indices = torch.topk(QK, k=self.top_k, dim=-1)
+ top_attn = torch.softmax(top_QK, dim=-1)
+ attn = torch.zeros_like(QK).scatter_(-1, indices, top_attn)
+ else:
+ attn = torch.softmax(QK, dim=-1)
+
+ # Dropouts
+ attn = self.dropout(attn)
+
+ # Weighted sum
+ outputs = multiply_by_xchunks(attn, V,
+ self.qk_chunks).permute(2, 0, 1, 3)
+
+ # Restore shape
+ outputs = outputs.reshape(-1, bs, self.d_model)
+
+ outputs = self.projection(outputs)
+
+ return outputs, attn
+
+ def _init_weight(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+
+
+# Short-term attention
+class MultiheadLocalAttentionV1(nn.Module):
+ def __init__(self,
+ d_model,
+ num_head,
+ dropout=0.,
+ max_dis=7,
+ dilation=1,
+ use_linear=True,
+ enable_corr=True):
+ super().__init__()
+ self.dilation = dilation
+ self.window_size = 2 * max_dis + 1
+ self.max_dis = max_dis
+ self.num_head = num_head
+ self.T = ((d_model / num_head)**0.5)
+
+ self.use_linear = use_linear
+ if use_linear:
+ self.linear_Q = nn.Conv2d(d_model, d_model, kernel_size=1)
+ self.linear_K = nn.Conv2d(d_model, d_model, kernel_size=1)
+ self.linear_V = nn.Conv2d(d_model, d_model, kernel_size=1)
+
+ self.relative_emb_k = nn.Conv2d(d_model,
+ num_head * self.window_size *
+ self.window_size,
+ kernel_size=1,
+ groups=num_head)
+ self.relative_emb_v = nn.Parameter(
+ torch.zeros([
+ self.num_head, d_model // self.num_head,
+ self.window_size * self.window_size
+ ]))
+
+ self.enable_corr = enable_corr
+
+ if enable_corr:
+ from spatial_correlation_sampler import SpatialCorrelationSampler
+ self.correlation_sampler = SpatialCorrelationSampler(
+ kernel_size=1,
+ patch_size=self.window_size,
+ stride=1,
+ padding=0,
+ dilation=1,
+ dilation_patch=self.dilation)
+
+ self.projection = nn.Linear(d_model, d_model)
+
+ self.dropout = nn.Dropout(dropout)
+ self.drop_prob = dropout
+
+ def forward(self, q, k, v):
+ n, c, h, w = v.size()
+
+ if self.use_linear:
+ q = self.linear_Q(q)
+ k = self.linear_K(k)
+ v = self.linear_V(v)
+
+ hidden_dim = c // self.num_head
+
+ relative_emb = self.relative_emb_k(q)
+ memory_mask = torch.ones((1, 1, h, w), device=v.device).float()
+
+ # Scale
+ q = q / self.T
+
+ q = q.view(-1, hidden_dim, h, w)
+ k = k.reshape(-1, hidden_dim, h, w).contiguous()
+ unfolded_vu = self.pad_and_unfold(v).view(
+ n, self.num_head, hidden_dim, self.window_size * self.window_size,
+ h * w) + self.relative_emb_v.unsqueeze(0).unsqueeze(-1)
+
+ relative_emb = relative_emb.view(n, self.num_head,
+ self.window_size * self.window_size,
+ h * w)
+ unfolded_k_mask = self.pad_and_unfold(memory_mask).bool().view(
+ 1, 1, self.window_size * self.window_size,
+ h * w).expand(n, self.num_head, -1, -1)
+
+ if self.enable_corr:
+ qk = self.correlation_sampler(q, k).view(
+ n, self.num_head, self.window_size * self.window_size,
+ h * w) + relative_emb
+ else:
+ unfolded_k = self.pad_and_unfold(k).view(
+ n * self.num_head, hidden_dim,
+ self.window_size * self.window_size, h, w)
+ qk = (q.unsqueeze(2) * unfolded_k).sum(dim=1).view(
+ n, self.num_head, self.window_size * self.window_size,
+ h * w) + relative_emb
+
+ qk_mask = 1 - unfolded_k_mask
+
+ qk -= qk_mask * 1e+8 if qk.dtype == torch.float32 else qk_mask * 1e+4
+
+ local_attn = torch.softmax(qk, dim=2)
+
+ local_attn = self.dropout(local_attn)
+
+ output = (local_attn.unsqueeze(2) * unfolded_vu).sum(dim=3).permute(
+ 3, 0, 1, 2).view(h * w, n, c)
+
+ output = self.projection(output)
+
+ return output, local_attn
+
+ def pad_and_unfold(self, x):
+ pad_pixel = self.max_dis * self.dilation
+ x = F.pad(x, (pad_pixel, pad_pixel, pad_pixel, pad_pixel),
+ mode='constant',
+ value=0)
+ x = F.unfold(x,
+ kernel_size=(self.window_size, self.window_size),
+ stride=(1, 1),
+ dilation=self.dilation)
+ return x
+
+
+class MultiheadLocalAttentionV2(nn.Module):
+ def __init__(self,
+ d_model,
+ num_head,
+ dropout=0.,
+ max_dis=7,
+ dilation=1,
+ use_linear=True,
+ enable_corr=True,
+ d_att=None,
+ use_dis=False):
+ super().__init__()
+ self.dilation = dilation
+ self.window_size = 2 * max_dis + 1
+ self.max_dis = max_dis
+ self.num_head = num_head
+ self.hidden_dim = d_model // num_head
+ self.d_att = self.hidden_dim if d_att is None else d_att
+ self.T = self.d_att**0.5
+ self.use_dis = use_dis
+
+ self.use_linear = use_linear
+ if use_linear:
+ self.linear_Q = nn.Conv2d(d_model, d_model, kernel_size=1)
+ self.linear_K = nn.Conv2d(d_model, d_model, kernel_size=1)
+ self.linear_V = nn.Conv2d(d_model, d_model, kernel_size=1)
+
+ self.relative_emb_k = nn.Conv2d(self.d_att * self.num_head,
+ num_head * self.window_size *
+ self.window_size,
+ kernel_size=1,
+ groups=num_head)
+ self.relative_emb_v = nn.Parameter(
+ torch.zeros([
+ self.num_head, d_model // self.num_head,
+ self.window_size * self.window_size
+ ]))
+
+ self.enable_corr = enable_corr
+
+ if enable_corr:
+ from spatial_correlation_sampler import SpatialCorrelationSampler
+ self.correlation_sampler = SpatialCorrelationSampler(
+ kernel_size=1,
+ patch_size=self.window_size,
+ stride=1,
+ padding=0,
+ dilation=1,
+ dilation_patch=self.dilation)
+
+ self.projection = nn.Linear(d_model, d_model)
+
+ self.dropout = nn.Dropout(dropout)
+
+ self.drop_prob = dropout
+
+ self.local_mask = None
+ self.last_size_2d = None
+ self.qk_mask = None
+
+ def forward(self, q, k, v):
+ n, c, h, w = v.size()
+
+ if self.use_linear:
+ q = self.linear_Q(q)
+ k = self.linear_K(k)
+ v = self.linear_V(v)
+
+ hidden_dim = self.hidden_dim
+
+ if self.qk_mask is not None and (h, w) == self.last_size_2d:
+ qk_mask = self.qk_mask
+ else:
+ memory_mask = torch.ones((1, 1, h, w), device=v.device).float()
+ unfolded_k_mask = self.pad_and_unfold(memory_mask).view(
+ 1, 1, self.window_size * self.window_size, h * w)
+ qk_mask = 1 - unfolded_k_mask
+ self.qk_mask = qk_mask
+
+ relative_emb = self.relative_emb_k(q)
+
+ # Scale
+ q = q / self.T
+
+ q = q.view(-1, self.d_att, h, w)
+ k = k.view(-1, self.d_att, h, w)
+ v = v.view(-1, self.num_head, hidden_dim, h * w)
+
+ relative_emb = relative_emb.view(n, self.num_head,
+ self.window_size * self.window_size,
+ h * w)
+
+ if self.enable_corr:
+ qk = self.correlation_sampler(q, k).view(
+ n, self.num_head, self.window_size * self.window_size, h * w)
+ else:
+ unfolded_k = self.pad_and_unfold(k).view(
+ n * self.num_head, hidden_dim,
+ self.window_size * self.window_size, h, w)
+ qk = (q.unsqueeze(2) * unfolded_k).sum(dim=1).view(
+ n, self.num_head, self.window_size * self.window_size, h * w)
+ if self.use_dis:
+ qk = 2 * qk - self.pad_and_unfold(
+ k.pow(2).sum(dim=1, keepdim=True)).view(
+ n, self.num_head, self.window_size * self.window_size,
+ h * w)
+
+ qk = qk + relative_emb
+
+ qk -= qk_mask * 1e+8 if qk.dtype == torch.float32 else qk_mask * 1e+4
+
+ local_attn = torch.softmax(qk, dim=2)
+
+ local_attn = self.dropout(local_attn)
+
+ agg_bias = torch.einsum('bhwn,hcw->bhnc', local_attn,
+ self.relative_emb_v)
+
+ global_attn = self.local2global(local_attn, h, w)
+
+ agg_value = (global_attn @ v.transpose(-2, -1))
+
+ output = (agg_value + agg_bias).permute(2, 0, 1,
+ 3).reshape(h * w, n, c)
+
+ output = self.projection(output)
+
+ self.last_size_2d = (h, w)
+ return output, local_attn
+
+ def local2global(self, local_attn, height, width):
+ batch_size = local_attn.size()[0]
+
+ pad_height = height + 2 * self.max_dis
+ pad_width = width + 2 * self.max_dis
+
+ if self.local_mask is not None and (height,
+ width) == self.last_size_2d:
+ local_mask = self.local_mask
+ else:
+ ky, kx = torch.meshgrid([
+ torch.arange(0, pad_height, device=local_attn.device),
+ torch.arange(0, pad_width, device=local_attn.device)
+ ])
+ qy, qx = torch.meshgrid([
+ torch.arange(0, height, device=local_attn.device),
+ torch.arange(0, width, device=local_attn.device)
+ ])
+
+ offset_y = qy.reshape(-1, 1) - ky.reshape(1, -1) + self.max_dis
+ offset_x = qx.reshape(-1, 1) - kx.reshape(1, -1) + self.max_dis
+
+ local_mask = (offset_y.abs() <= self.max_dis) & (offset_x.abs() <=
+ self.max_dis)
+ local_mask = local_mask.view(1, 1, height * width, pad_height,
+ pad_width)
+ self.local_mask = local_mask
+
+ global_attn = torch.zeros(
+ (batch_size, self.num_head, height * width, pad_height, pad_width),
+ device=local_attn.device)
+ global_attn[local_mask.expand(batch_size, self.num_head,
+ -1, -1, -1)] = local_attn.transpose(
+ -1, -2).reshape(-1)
+ global_attn = global_attn[:, :, :, self.max_dis:-self.max_dis,
+ self.max_dis:-self.max_dis].reshape(
+ batch_size, self.num_head,
+ height * width, height * width)
+
+ return global_attn
+
+ def pad_and_unfold(self, x):
+ pad_pixel = self.max_dis * self.dilation
+ x = F.pad(x, (pad_pixel, pad_pixel, pad_pixel, pad_pixel),
+ mode='constant',
+ value=0)
+ x = F.unfold(x,
+ kernel_size=(self.window_size, self.window_size),
+ stride=(1, 1),
+ dilation=self.dilation)
+ return x
+
+
+class MultiheadLocalAttentionV3(nn.Module):
+ def __init__(self,
+ d_model,
+ num_head,
+ dropout=0.,
+ max_dis=7,
+ dilation=1,
+ use_linear=True):
+ super().__init__()
+ self.dilation = dilation
+ self.window_size = 2 * max_dis + 1
+ self.max_dis = max_dis
+ self.num_head = num_head
+ self.T = ((d_model / num_head)**0.5)
+
+ self.use_linear = use_linear
+ if use_linear:
+ self.linear_Q = nn.Conv2d(d_model, d_model, kernel_size=1)
+ self.linear_K = nn.Conv2d(d_model, d_model, kernel_size=1)
+ self.linear_V = nn.Conv2d(d_model, d_model, kernel_size=1)
+
+ self.relative_emb_k = nn.Conv2d(d_model,
+ num_head * self.window_size *
+ self.window_size,
+ kernel_size=1,
+ groups=num_head)
+ self.relative_emb_v = nn.Parameter(
+ torch.zeros([
+ self.num_head, d_model // self.num_head,
+ self.window_size * self.window_size
+ ]))
+
+ self.projection = nn.Linear(d_model, d_model)
+ self.dropout = DropOutLogit(dropout)
+
+ self.padded_local_mask = None
+ self.local_mask = None
+ self.last_size_2d = None
+ self.qk_mask = None
+
+ def forward(self, q, k, v):
+ n, c, h, w = q.size()
+
+ if self.use_linear:
+ q = self.linear_Q(q)
+ k = self.linear_K(k)
+ v = self.linear_V(v)
+
+ hidden_dim = c // self.num_head
+
+ relative_emb = self.relative_emb_k(q)
+ relative_emb = relative_emb.view(n, self.num_head,
+ self.window_size * self.window_size,
+ h * w)
+ padded_local_mask, local_mask = self.compute_mask(h,
+ w,
+ device=q.device)
+ qk_mask = (~padded_local_mask).float()
+
+ # Scale
+ q = q / self.T
+
+ q = q.view(-1, self.num_head, hidden_dim, h * w)
+ k = k.view(-1, self.num_head, hidden_dim, h * w)
+ v = v.view(-1, self.num_head, hidden_dim, h * w)
+
+ qk = q.transpose(-1, -2) @ k # [B, nH, kL, qL]
+
+ pad_pixel = self.max_dis * self.dilation
+
+ padded_qk = F.pad(qk.view(-1, self.num_head, h * w, h, w),
+ (pad_pixel, pad_pixel, pad_pixel, pad_pixel),
+ mode='constant',
+ value=-1e+8 if qk.dtype == torch.float32 else -1e+4)
+
+ qk_mask = qk_mask * 1e+8 if (padded_qk.dtype
+ == torch.float32) else qk_mask * 1e+4
+ padded_qk = padded_qk - qk_mask
+
+ padded_qk[padded_local_mask.expand(n, self.num_head, -1, -1,
+ -1)] += relative_emb.transpose(
+ -1, -2).reshape(-1)
+ padded_qk = self.dropout(padded_qk)
+
+ local_qk = padded_qk[padded_local_mask.expand(n, self.num_head, -1, -1,
+ -1)]
+
+ global_qk = padded_qk[:, :, :, self.max_dis:-self.max_dis,
+ self.max_dis:-self.max_dis].reshape(
+ n, self.num_head, h * w, h * w)
+
+ local_attn = torch.softmax(local_qk.reshape(
+ n, self.num_head, h * w, self.window_size * self.window_size),
+ dim=3)
+ global_attn = torch.softmax(global_qk, dim=3)
+
+ agg_bias = torch.einsum('bhnw,hcw->nbhc', local_attn,
+ self.relative_emb_v).reshape(h * w, n, c)
+
+ agg_value = (global_attn @ v.transpose(-2, -1))
+
+ output = agg_value + agg_bias
+
+ output = self.projection(output)
+
+ self.last_size_2d = (h, w)
+ return output, local_attn
+
+ def compute_mask(self, height, width, device=None):
+ pad_height = height + 2 * self.max_dis
+ pad_width = width + 2 * self.max_dis
+
+ if self.padded_local_mask is not None and (height,
+ width) == self.last_size_2d:
+ padded_local_mask = self.padded_local_mask
+ local_mask = self.local_mask
+
+ else:
+ ky, kx = torch.meshgrid([
+ torch.arange(0, pad_height, device=device),
+ torch.arange(0, pad_width, device=device)
+ ])
+ qy, qx = torch.meshgrid([
+ torch.arange(0, height, device=device),
+ torch.arange(0, width, device=device)
+ ])
+
+ qy = qy.reshape(-1, 1)
+ qx = qx.reshape(-1, 1)
+ offset_y = qy - ky.reshape(1, -1) + self.max_dis
+ offset_x = qx - kx.reshape(1, -1) + self.max_dis
+ padded_local_mask = (offset_y.abs() <= self.max_dis) & (
+ offset_x.abs() <= self.max_dis)
+ padded_local_mask = padded_local_mask.view(1, 1, height * width,
+ pad_height, pad_width)
+ local_mask = padded_local_mask[:, :, :, self.max_dis:-self.max_dis,
+ self.max_dis:-self.max_dis]
+ pad_pixel = self.max_dis * self.dilation
+ local_mask = F.pad(local_mask.float(),
+ (pad_pixel, pad_pixel, pad_pixel, pad_pixel),
+ mode='constant',
+ value=0).view(1, 1, height * width, pad_height,
+ pad_width)
+ self.padded_local_mask = padded_local_mask
+ self.local_mask = local_mask
+
+ return padded_local_mask, local_mask
+
+
+def linear_gate(x, dim=-1):
+ # return F.relu_(x).pow(2.) / x.size()[dim]
+ return torch.softmax(x, dim=dim)
+
+
+def silu(x):
+ return x * torch.sigmoid(x)
+
+
+class GatedPropagation(nn.Module):
+ def __init__(self,
+ d_qk,
+ d_vu,
+ num_head=8,
+ dropout=0.,
+ use_linear=True,
+ d_att=None,
+ use_dis=False,
+ qk_chunks=1,
+ max_mem_len_ratio=-1,
+ top_k=-1,
+ expand_ratio=2.):
+ super().__init__()
+ expand_ratio = expand_ratio
+ self.expand_d_vu = int(d_vu * expand_ratio)
+ self.d_vu = d_vu
+ self.d_qk = d_qk
+ self.num_head = num_head
+ self.use_dis = use_dis
+ self.qk_chunks = qk_chunks
+ self.max_mem_len_ratio = float(max_mem_len_ratio)
+ self.top_k = top_k
+
+ self.hidden_dim = self.expand_d_vu // num_head
+ self.d_att = d_qk // num_head if d_att is None else d_att
+ self.T = self.d_att**0.5
+ self.use_linear = use_linear
+ self.d_middle = self.d_att * self.num_head
+
+ if use_linear:
+ self.linear_QK = nn.Linear(d_qk, self.d_middle)
+ half_d_vu = self.hidden_dim * num_head // 2
+ self.linear_V1 = nn.Linear(d_vu // 2, half_d_vu)
+ self.linear_V2 = nn.Linear(d_vu // 2, half_d_vu)
+ self.linear_U1 = nn.Linear(d_vu // 2, half_d_vu)
+ self.linear_U2 = nn.Linear(d_vu // 2, half_d_vu)
+
+ self.dropout = nn.Dropout(dropout)
+ self.drop_prob = dropout
+
+ self.dw_conv = DWConv2d(self.expand_d_vu)
+ self.projection = nn.Linear(self.expand_d_vu, d_vu)
+
+ self._init_weight()
+
+ def forward(self, Q, K, V, U, size_2d):
+ """
+ :param Q: A 3d tensor with shape of [T_q, bs, C_q]
+ :param K: A 3d tensor with shape of [T_k, bs, C_k]
+ :param V: A 3d tensor with shape of [T_v, bs, C_v]
+ """
+ num_head = self.num_head
+ hidden_dim = self.hidden_dim
+
+ l, bs, _ = Q.size()
+
+ # Linear projections
+ if self.use_linear:
+ Q = K = self.linear_QK(Q)
+
+ def cat(X1, X2):
+ if num_head > 1:
+ X1 = X1.view(-1, bs, num_head, hidden_dim // 2)
+ X2 = X2.view(-1, bs, num_head, hidden_dim // 2)
+ X = torch.cat([X1, X2],
+ dim=-1).view(-1, bs, num_head * hidden_dim)
+ else:
+ X = torch.cat([X1, X2], dim=-1)
+ return X
+
+ V1, V2 = torch.split(V, self.d_vu // 2, dim=-1)
+ V1 = self.linear_V1(V1)
+ V2 = self.linear_V2(V2)
+ V = silu(cat(V1, V2))
+
+ U1, U2 = torch.split(U, self.d_vu // 2, dim=-1)
+ U1 = self.linear_U1(U1)
+ U2 = self.linear_U2(U2)
+ U = silu(cat(U1, U2))
+
+ # Scale
+ Q = Q / self.T
+
+ if not self.training and self.max_mem_len_ratio > 0:
+ mem_len_ratio = float(K.size(0)) / Q.size(0)
+ if mem_len_ratio > self.max_mem_len_ratio:
+ scaling_ratio = math.log(mem_len_ratio) / math.log(
+ self.max_mem_len_ratio)
+ Q = Q * scaling_ratio
+
+ # Multi-head
+ Q = Q.view(-1, bs, num_head, self.d_att).permute(1, 2, 0, 3)
+ K = K.view(-1, bs, num_head, self.d_att).permute(1, 2, 3, 0)
+ V = V.view(-1, bs, num_head, hidden_dim).permute(1, 2, 0, 3)
+
+ # Multiplication
+ QK = multiply_by_ychunks(Q, K, self.qk_chunks)
+ if self.use_dis:
+ QK = 2 * QK - K.pow(2).sum(dim=-2, keepdim=True)
+
+ # Activation
+ if not self.training and self.top_k > 0 and self.top_k < QK.size()[-1]:
+ top_QK, indices = torch.topk(QK, k=self.top_k, dim=-1)
+ top_attn = linear_gate(top_QK, dim=-1)
+ attn = torch.zeros_like(QK).scatter_(-1, indices, top_attn)
+ else:
+ attn = linear_gate(QK, dim=-1)
+
+ # Dropouts
+ attn = self.dropout(attn)
+
+ # Weighted sum
+ outputs = multiply_by_xchunks(attn, V,
+ self.qk_chunks).permute(2, 0, 1, 3)
+
+ # Restore shape
+ outputs = outputs.reshape(l, bs, -1) * U
+
+ outputs = self.dw_conv(outputs, size_2d)
+ outputs = self.projection(outputs)
+
+ return outputs, attn
+
+ def _init_weight(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+
+
+class LocalGatedPropagation(nn.Module):
+ def __init__(self,
+ d_qk,
+ d_vu,
+ num_head,
+ dropout=0.,
+ max_dis=7,
+ dilation=1,
+ use_linear=True,
+ enable_corr=True,
+ d_att=None,
+ use_dis=False,
+ expand_ratio=2.):
+ super().__init__()
+ expand_ratio = expand_ratio
+ self.expand_d_vu = int(d_vu * expand_ratio)
+ self.d_qk = d_qk
+ self.d_vu = d_vu
+ self.dilation = dilation
+ self.window_size = 2 * max_dis + 1
+ self.max_dis = max_dis
+ self.num_head = num_head
+ self.hidden_dim = self.expand_d_vu // num_head
+ self.d_att = d_qk // num_head if d_att is None else d_att
+ self.T = self.d_att**0.5
+ self.use_dis = use_dis
+
+ self.d_middle = self.d_att * self.num_head
+ self.use_linear = use_linear
+ if use_linear:
+ self.linear_QK = nn.Conv2d(d_qk, self.d_middle, kernel_size=1)
+ self.linear_V = nn.Conv2d(d_vu,
+ self.expand_d_vu,
+ kernel_size=1,
+ groups=2)
+ self.linear_U = nn.Conv2d(d_vu,
+ self.expand_d_vu,
+ kernel_size=1,
+ groups=2)
+
+ self.relative_emb_k = nn.Conv2d(self.d_middle,
+ num_head * self.window_size *
+ self.window_size,
+ kernel_size=1,
+ groups=num_head)
+
+ self.enable_corr = enable_corr
+
+ if enable_corr:
+ from spatial_correlation_sampler import SpatialCorrelationSampler
+ self.correlation_sampler = SpatialCorrelationSampler(
+ kernel_size=1,
+ patch_size=self.window_size,
+ stride=1,
+ padding=0,
+ dilation=1,
+ dilation_patch=self.dilation)
+
+ self.dw_conv = DWConv2d(self.expand_d_vu)
+ self.projection = nn.Linear(self.expand_d_vu, d_vu)
+
+ self.dropout = nn.Dropout(dropout)
+
+ self.drop_prob = dropout
+
+ self.local_mask = None
+ self.last_size_2d = None
+ self.qk_mask = None
+
+ def forward(self, q, k, v, u, size_2d):
+ n, c, h, w = v.size()
+ hidden_dim = self.hidden_dim
+
+ if self.use_linear:
+ q = k = self.linear_QK(q)
+ v = silu(self.linear_V(v))
+ u = silu(self.linear_U(u))
+ if self.num_head > 1:
+ v = v.view(-1, 2, self.num_head, hidden_dim // 2,
+ h * w).permute(0, 2, 1, 3, 4).reshape(n, -1, h, w)
+ u = u.view(-1, 2, self.num_head, hidden_dim // 2,
+ h * w).permute(4, 0, 2, 1, 3).reshape(h * w, n, -1)
+ else:
+ u = u.permute(2, 3, 0, 1).reshape(h * w, n, -1)
+
+ if self.qk_mask is not None and (h, w) == self.last_size_2d:
+ qk_mask = self.qk_mask
+ else:
+ memory_mask = torch.ones((1, 1, h, w), device=v.device).float()
+ unfolded_k_mask = self.pad_and_unfold(memory_mask).view(
+ 1, 1, self.window_size * self.window_size, h * w)
+ qk_mask = 1 - unfolded_k_mask
+ self.qk_mask = qk_mask
+
+ relative_emb = self.relative_emb_k(q)
+
+ # Scale
+ q = q / self.T
+
+ q = q.view(-1, self.d_att, h, w)
+ k = k.view(-1, self.d_att, h, w)
+ v = v.view(-1, self.num_head, hidden_dim, h * w)
+
+ relative_emb = relative_emb.view(n, self.num_head,
+ self.window_size * self.window_size,
+ h * w)
+
+ if self.enable_corr:
+ qk = self.correlation_sampler(q, k).view(
+ n, self.num_head, self.window_size * self.window_size, h * w)
+ else:
+ unfolded_k = self.pad_and_unfold(k).view(
+ n * self.num_head, self.d_att,
+ self.window_size * self.window_size, h, w)
+ qk = (q.unsqueeze(2) * unfolded_k).sum(dim=1).view(
+ n, self.num_head, self.window_size * self.window_size, h * w)
+ if self.use_dis:
+ qk = 2 * qk - self.pad_and_unfold(
+ k.pow(2).sum(dim=1, keepdim=True)).view(
+ n, self.num_head, self.window_size * self.window_size,
+ h * w)
+
+ qk = qk + relative_emb
+
+ qk -= qk_mask * 1e+8 if qk.dtype == torch.float32 else qk_mask * 1e+4
+
+ local_attn = linear_gate(qk, dim=2)
+
+ local_attn = self.dropout(local_attn)
+
+ global_attn = self.local2global(local_attn, h, w)
+
+ agg_value = (global_attn @ v.transpose(-2, -1)).permute(
+ 2, 0, 1, 3).reshape(h * w, n, -1)
+
+ output = agg_value * u
+
+ output = self.dw_conv(output, size_2d)
+ output = self.projection(output)
+
+ self.last_size_2d = (h, w)
+ return output, local_attn
+
+ def local2global(self, local_attn, height, width):
+ batch_size = local_attn.size()[0]
+
+ pad_height = height + 2 * self.max_dis
+ pad_width = width + 2 * self.max_dis
+
+ if self.local_mask is not None and (height,
+ width) == self.last_size_2d:
+ local_mask = self.local_mask
+ else:
+ ky, kx = torch.meshgrid([
+ torch.arange(0, pad_height, device=local_attn.device),
+ torch.arange(0, pad_width, device=local_attn.device)
+ ])
+ qy, qx = torch.meshgrid([
+ torch.arange(0, height, device=local_attn.device),
+ torch.arange(0, width, device=local_attn.device)
+ ])
+
+ offset_y = qy.reshape(-1, 1) - ky.reshape(1, -1) + self.max_dis
+ offset_x = qx.reshape(-1, 1) - kx.reshape(1, -1) + self.max_dis
+
+ local_mask = (offset_y.abs() <= self.max_dis) & (offset_x.abs() <=
+ self.max_dis)
+ local_mask = local_mask.view(1, 1, height * width, pad_height,
+ pad_width)
+ self.local_mask = local_mask
+
+ global_attn = torch.zeros(
+ (batch_size, self.num_head, height * width, pad_height, pad_width),
+ device=local_attn.device)
+ global_attn[local_mask.expand(batch_size, self.num_head,
+ -1, -1, -1)] = local_attn.transpose(
+ -1, -2).reshape(-1)
+ global_attn = global_attn[:, :, :, self.max_dis:-self.max_dis,
+ self.max_dis:-self.max_dis].reshape(
+ batch_size, self.num_head,
+ height * width, height * width)
+
+ return global_attn
+
+ def pad_and_unfold(self, x):
+ pad_pixel = self.max_dis * self.dilation
+ x = F.pad(x, (pad_pixel, pad_pixel, pad_pixel, pad_pixel),
+ mode='constant',
+ value=0)
+ x = F.unfold(x,
+ kernel_size=(self.window_size, self.window_size),
+ stride=(1, 1),
+ dilation=self.dilation)
+ return x
diff --git a/aot/networks/layers/basic.py b/aot/networks/layers/basic.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c9137c1c99c04f33658fc3cf0442ec5d23c50fa
--- /dev/null
+++ b/aot/networks/layers/basic.py
@@ -0,0 +1,168 @@
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+
+class GroupNorm1D(nn.Module):
+ def __init__(self, indim, groups=8):
+ super().__init__()
+ self.gn = nn.GroupNorm(groups, indim)
+
+ def forward(self, x):
+ return self.gn(x.permute(1, 2, 0)).permute(2, 0, 1)
+
+
+class GNActDWConv2d(nn.Module):
+ def __init__(self, indim, gn_groups=32):
+ super().__init__()
+ self.gn = nn.GroupNorm(gn_groups, indim)
+ self.conv = nn.Conv2d(indim,
+ indim,
+ 5,
+ dilation=1,
+ padding=2,
+ groups=indim,
+ bias=False)
+
+ def forward(self, x, size_2d):
+ h, w = size_2d
+ _, bs, c = x.size()
+ x = x.view(h, w, bs, c).permute(2, 3, 0, 1)
+ x = self.gn(x)
+ x = F.gelu(x)
+ x = self.conv(x)
+ x = x.view(bs, c, h * w).permute(2, 0, 1)
+ return x
+
+
+class DWConv2d(nn.Module):
+ def __init__(self, indim, dropout=0.1):
+ super().__init__()
+ self.conv = nn.Conv2d(indim,
+ indim,
+ 5,
+ dilation=1,
+ padding=2,
+ groups=indim,
+ bias=False)
+ self.dropout = nn.Dropout2d(p=dropout, inplace=True)
+
+ def forward(self, x, size_2d):
+ h, w = size_2d
+ _, bs, c = x.size()
+ x = x.view(h, w, bs, c).permute(2, 3, 0, 1)
+ x = self.conv(x)
+ x = self.dropout(x)
+ x = x.view(bs, c, h * w).permute(2, 0, 1)
+ return x
+
+
+class ScaleOffset(nn.Module):
+ def __init__(self, indim):
+ super().__init__()
+ self.gamma = nn.Parameter(torch.ones(indim))
+ # torch.nn.init.normal_(self.gamma, std=0.02)
+ self.beta = nn.Parameter(torch.zeros(indim))
+
+ def forward(self, x):
+ if len(x.size()) == 3:
+ return x * self.gamma + self.beta
+ else:
+ return x * self.gamma.view(1, -1, 1, 1) + self.beta.view(
+ 1, -1, 1, 1)
+
+
+class ConvGN(nn.Module):
+ def __init__(self, indim, outdim, kernel_size, gn_groups=8):
+ super().__init__()
+ self.conv = nn.Conv2d(indim,
+ outdim,
+ kernel_size,
+ padding=kernel_size // 2)
+ self.gn = nn.GroupNorm(gn_groups, outdim)
+
+ def forward(self, x):
+ return self.gn(self.conv(x))
+
+
+def seq_to_2d(tensor, size_2d):
+ h, w = size_2d
+ _, n, c = tensor.size()
+ tensor = tensor.view(h, w, n, c).permute(2, 3, 0, 1).contiguous()
+ return tensor
+
+
+def drop_path(x, drop_prob: float = 0., training: bool = False):
+ if drop_prob == 0. or not training:
+ return x
+ keep_prob = 1 - drop_prob
+ shape = (
+ x.shape[0],
+ x.shape[1],
+ ) + (1, ) * (x.ndim - 2
+ ) # work with diff dim tensors, not just 2D ConvNets
+ random_tensor = keep_prob + torch.rand(
+ shape, dtype=x.dtype, device=x.device)
+ random_tensor.floor_() # binarize
+ output = x.div(keep_prob) * random_tensor
+ return output
+
+
+def mask_out(x, y, mask_rate=0.15, training=False):
+ if mask_rate == 0. or not training:
+ return x
+
+ keep_prob = 1 - mask_rate
+ shape = (
+ x.shape[0],
+ x.shape[1],
+ ) + (1, ) * (x.ndim - 2
+ ) # work with diff dim tensors, not just 2D ConvNets
+ random_tensor = keep_prob + torch.rand(
+ shape, dtype=x.dtype, device=x.device)
+ random_tensor.floor_() # binarize
+ output = x * random_tensor + y * (1 - random_tensor)
+
+ return output
+
+
+class DropPath(nn.Module):
+ def __init__(self, drop_prob=None, batch_dim=0):
+ super(DropPath, self).__init__()
+ self.drop_prob = drop_prob
+ self.batch_dim = batch_dim
+
+ def forward(self, x):
+ return self.drop_path(x, self.drop_prob)
+
+ def drop_path(self, x, drop_prob):
+ if drop_prob == 0. or not self.training:
+ return x
+ keep_prob = 1 - drop_prob
+ shape = [1 for _ in range(x.ndim)]
+ shape[self.batch_dim] = x.shape[self.batch_dim]
+ random_tensor = keep_prob + torch.rand(
+ shape, dtype=x.dtype, device=x.device)
+ random_tensor.floor_() # binarize
+ output = x.div(keep_prob) * random_tensor
+ return output
+
+
+class DropOutLogit(nn.Module):
+ def __init__(self, drop_prob=None):
+ super(DropOutLogit, self).__init__()
+ self.drop_prob = drop_prob
+
+ def forward(self, x):
+ return self.drop_logit(x, self.drop_prob)
+
+ def drop_logit(self, x, drop_prob):
+ if drop_prob == 0. or not self.training:
+ return x
+ random_tensor = drop_prob + torch.rand(
+ x.shape, dtype=x.dtype, device=x.device)
+ random_tensor.floor_() # binarize
+ mask = random_tensor * 1e+8 if (
+ x.dtype == torch.float32) else random_tensor * 1e+4
+ output = x - mask
+ return output
diff --git a/aot/networks/layers/loss.py b/aot/networks/layers/loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..acdd4daef65eb768f5696a11c07615a2fd2d5d8e
--- /dev/null
+++ b/aot/networks/layers/loss.py
@@ -0,0 +1,188 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+try:
+ from itertools import ifilterfalse
+except ImportError: # py3k
+ from itertools import filterfalse as ifilterfalse
+
+
+def dice_loss(probas, labels, smooth=1):
+
+ C = probas.size(1)
+ losses = []
+ for c in list(range(C)):
+ fg = (labels == c).float()
+ if fg.sum() == 0:
+ continue
+ class_pred = probas[:, c]
+ p0 = class_pred
+ g0 = fg
+ numerator = 2 * torch.sum(p0 * g0) + smooth
+ denominator = torch.sum(p0) + torch.sum(g0) + smooth
+ losses.append(1 - ((numerator) / (denominator)))
+ return mean(losses)
+
+
+def tversky_loss(probas, labels, alpha=0.5, beta=0.5, epsilon=1e-6):
+ '''
+ Tversky loss function.
+ probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
+ labels: [P] Tensor, ground truth labels (between 0 and C - 1)
+
+ Same as soft dice loss when alpha=beta=0.5.
+ Same as Jaccord loss when alpha=beta=1.0.
+ See `Tversky loss function for image segmentation using 3D fully convolutional deep networks`
+ https://arxiv.org/pdf/1706.05721.pdf
+ '''
+ C = probas.size(1)
+ losses = []
+ for c in list(range(C)):
+ fg = (labels == c).float()
+ if fg.sum() == 0:
+ continue
+ class_pred = probas[:, c]
+ p0 = class_pred
+ p1 = 1 - class_pred
+ g0 = fg
+ g1 = 1 - fg
+ numerator = torch.sum(p0 * g0)
+ denominator = numerator + alpha * \
+ torch.sum(p0*g1) + beta*torch.sum(p1*g0)
+ losses.append(1 - ((numerator) / (denominator + epsilon)))
+ return mean(losses)
+
+
+def flatten_probas(probas, labels, ignore=255):
+ """
+ Flattens predictions in the batch
+ """
+ B, C, H, W = probas.size()
+ probas = probas.permute(0, 2, 3,
+ 1).contiguous().view(-1, C) # B * H * W, C = P, C
+ labels = labels.view(-1)
+ if ignore is None:
+ return probas, labels
+ valid = (labels != ignore)
+ vprobas = probas[valid.view(-1, 1).expand(-1, C)].reshape(-1, C)
+ # vprobas = probas[torch.nonzero(valid).squeeze()]
+ vlabels = labels[valid]
+ return vprobas, vlabels
+
+
+def isnan(x):
+ return x != x
+
+
+def mean(l, ignore_nan=False, empty=0):
+ """
+ nanmean compatible with generators.
+ """
+ l = iter(l)
+ if ignore_nan:
+ l = ifilterfalse(isnan, l)
+ try:
+ n = 1
+ acc = next(l)
+ except StopIteration:
+ if empty == 'raise':
+ raise ValueError('Empty mean')
+ return empty
+ for n, v in enumerate(l, 2):
+ acc += v
+ if n == 1:
+ return acc
+ return acc / n
+
+
+class DiceLoss(nn.Module):
+ def __init__(self, ignore_index=255):
+ super(DiceLoss, self).__init__()
+ self.ignore_index = ignore_index
+
+ def forward(self, tmp_dic, label_dic, step=None):
+ total_loss = []
+ for idx in range(len(tmp_dic)):
+ pred = tmp_dic[idx]
+ label = label_dic[idx]
+ pred = F.softmax(pred, dim=1)
+ label = label.view(1, 1, pred.size()[2], pred.size()[3])
+ loss = dice_loss(
+ *flatten_probas(pred, label, ignore=self.ignore_index))
+ total_loss.append(loss.unsqueeze(0))
+ total_loss = torch.cat(total_loss, dim=0)
+ return total_loss
+
+
+class SoftJaccordLoss(nn.Module):
+ def __init__(self, ignore_index=255):
+ super(SoftJaccordLoss, self).__init__()
+ self.ignore_index = ignore_index
+
+ def forward(self, tmp_dic, label_dic, step=None):
+ total_loss = []
+ for idx in range(len(tmp_dic)):
+ pred = tmp_dic[idx]
+ label = label_dic[idx]
+ pred = F.softmax(pred, dim=1)
+ label = label.view(1, 1, pred.size()[2], pred.size()[3])
+ loss = tversky_loss(*flatten_probas(pred,
+ label,
+ ignore=self.ignore_index),
+ alpha=1.0,
+ beta=1.0)
+ total_loss.append(loss.unsqueeze(0))
+ total_loss = torch.cat(total_loss, dim=0)
+ return total_loss
+
+
+class CrossEntropyLoss(nn.Module):
+ def __init__(self,
+ top_k_percent_pixels=None,
+ hard_example_mining_step=100000):
+ super(CrossEntropyLoss, self).__init__()
+ self.top_k_percent_pixels = top_k_percent_pixels
+ if top_k_percent_pixels is not None:
+ assert (top_k_percent_pixels > 0 and top_k_percent_pixels < 1)
+ self.hard_example_mining_step = hard_example_mining_step + 1e-5
+ if self.top_k_percent_pixels is None:
+ self.celoss = nn.CrossEntropyLoss(ignore_index=255,
+ reduction='mean')
+ else:
+ self.celoss = nn.CrossEntropyLoss(ignore_index=255,
+ reduction='none')
+
+ def forward(self, dic_tmp, y, step):
+ total_loss = []
+ for i in range(len(dic_tmp)):
+ pred_logits = dic_tmp[i]
+ gts = y[i]
+ if self.top_k_percent_pixels is None:
+ final_loss = self.celoss(pred_logits, gts)
+ else:
+ # Only compute the loss for top k percent pixels.
+ # First, compute the loss for all pixels. Note we do not put the loss
+ # to loss_collection and set reduction = None to keep the shape.
+ num_pixels = float(pred_logits.size(2) * pred_logits.size(3))
+ pred_logits = pred_logits.view(
+ -1, pred_logits.size(1),
+ pred_logits.size(2) * pred_logits.size(3))
+ gts = gts.view(-1, gts.size(1) * gts.size(2))
+ pixel_losses = self.celoss(pred_logits, gts)
+ if self.hard_example_mining_step == 0:
+ top_k_pixels = int(self.top_k_percent_pixels * num_pixels)
+ else:
+ ratio = min(1.0,
+ step / float(self.hard_example_mining_step))
+ top_k_pixels = int((ratio * self.top_k_percent_pixels +
+ (1.0 - ratio)) * num_pixels)
+ top_k_loss, top_k_indices = torch.topk(pixel_losses,
+ k=top_k_pixels,
+ dim=1)
+
+ final_loss = torch.mean(top_k_loss)
+ final_loss = final_loss.unsqueeze(0)
+ total_loss.append(final_loss)
+ total_loss = torch.cat(total_loss, dim=0)
+ return total_loss
diff --git a/aot/networks/layers/normalization.py b/aot/networks/layers/normalization.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d89d9c43e5273c4141983fa0654ef5b912f2b92
--- /dev/null
+++ b/aot/networks/layers/normalization.py
@@ -0,0 +1,43 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class FrozenBatchNorm2d(nn.Module):
+ """
+ BatchNorm2d where the batch statistics and the affine parameters
+ are fixed
+ """
+ def __init__(self, n, epsilon=1e-5):
+ super(FrozenBatchNorm2d, self).__init__()
+ self.register_buffer("weight", torch.ones(n))
+ self.register_buffer("bias", torch.zeros(n))
+ self.register_buffer("running_mean", torch.zeros(n))
+ self.register_buffer("running_var", torch.ones(n) - epsilon)
+ self.epsilon = epsilon
+
+ def forward(self, x):
+ """
+ Refer to Detectron2 (https://github.com/facebookresearch/detectron2/blob/cbbc1ce26473cb2a5cc8f58e8ada9ae14cb41052/detectron2/layers/batch_norm.py)
+ """
+ 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.epsilon).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.epsilon,
+ )
diff --git a/aot/networks/layers/position.py b/aot/networks/layers/position.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d37f7273bcf509dd90f67d6ced7534339611ed0
--- /dev/null
+++ b/aot/networks/layers/position.py
@@ -0,0 +1,90 @@
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from utils.math import truncated_normal_
+
+
+class Downsample2D(nn.Module):
+ def __init__(self, mode='nearest', scale=4):
+ super().__init__()
+ self.mode = mode
+ self.scale = scale
+
+ def forward(self, x):
+ n, c, h, w = x.size()
+ x = F.interpolate(x,
+ size=(h // self.scale + 1, w // self.scale + 1),
+ mode=self.mode)
+ return x
+
+
+def generate_coord(x):
+ _, _, h, w = x.size()
+ device = x.device
+ col = torch.arange(0, h, device=device)
+ row = torch.arange(0, w, device=device)
+ grid_h, grid_w = torch.meshgrid(col, row)
+ return grid_h, grid_w
+
+
+class PositionEmbeddingSine(nn.Module):
+ def __init__(self,
+ num_pos_feats=64,
+ temperature=10000,
+ normalize=False,
+ scale=None):
+ super().__init__()
+ self.num_pos_feats = num_pos_feats
+ self.temperature = temperature
+ self.normalize = normalize
+ if scale is not None and normalize is False:
+ raise ValueError("normalize should be True if scale is passed")
+ if scale is None:
+ scale = 2 * math.pi
+ self.scale = scale
+
+ def forward(self, x):
+ grid_y, grid_x = generate_coord(x)
+
+ y_embed = grid_y.unsqueeze(0).float()
+ x_embed = grid_x.unsqueeze(0).float()
+
+ if self.normalize:
+ eps = 1e-6
+ y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+ x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+ dim_t = torch.arange(self.num_pos_feats,
+ dtype=torch.float32,
+ device=x.device)
+ dim_t = self.temperature**(2 * (dim_t // 2) / self.num_pos_feats)
+
+ pos_x = x_embed[:, :, :, None] / dim_t
+ pos_y = y_embed[:, :, :, None] / dim_t
+ pos_x = torch.stack(
+ (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()),
+ dim=4).flatten(3)
+ pos_y = torch.stack(
+ (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()),
+ dim=4).flatten(3)
+ pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+ return pos
+
+
+class PositionEmbeddingLearned(nn.Module):
+ def __init__(self, num_pos_feats=64, H=30, W=30):
+ super().__init__()
+ self.H = H
+ self.W = W
+ self.pos_emb = nn.Parameter(
+ truncated_normal_(torch.zeros(1, num_pos_feats, H, W)))
+
+ def forward(self, x):
+ bs, _, h, w = x.size()
+ pos_emb = self.pos_emb
+ if h != self.H or w != self.W:
+ pos_emb = F.interpolate(pos_emb, size=(h, w), mode="bilinear")
+ return pos_emb
diff --git a/aot/networks/layers/transformer.py b/aot/networks/layers/transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f20ab82112214101b9481b793e2b1931539b34b
--- /dev/null
+++ b/aot/networks/layers/transformer.py
@@ -0,0 +1,690 @@
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from networks.layers.basic import DropPath, GroupNorm1D, GNActDWConv2d, seq_to_2d, ScaleOffset, mask_out
+from networks.layers.attention import silu, MultiheadAttention, MultiheadLocalAttentionV2, MultiheadLocalAttentionV3, GatedPropagation, LocalGatedPropagation
+
+
+def _get_norm(indim, type='ln', groups=8):
+ if type == 'gn':
+ return GroupNorm1D(indim, groups)
+ else:
+ return nn.LayerNorm(indim)
+
+
+def _get_activation_fn(activation):
+ """Return an activation function given a string"""
+ if activation == "relu":
+ return F.relu
+ if activation == "gelu":
+ return F.gelu
+ if activation == "glu":
+ return F.glu
+ raise RuntimeError(
+ F"activation should be relu/gele/glu, not {activation}.")
+
+
+class LongShortTermTransformer(nn.Module):
+ def __init__(self,
+ num_layers=2,
+ d_model=256,
+ self_nhead=8,
+ att_nhead=8,
+ dim_feedforward=1024,
+ emb_dropout=0.,
+ droppath=0.1,
+ lt_dropout=0.,
+ st_dropout=0.,
+ droppath_lst=False,
+ droppath_scaling=False,
+ activation="gelu",
+ return_intermediate=False,
+ intermediate_norm=True,
+ final_norm=True,
+ block_version="v1"):
+
+ super().__init__()
+ self.intermediate_norm = intermediate_norm
+ self.final_norm = final_norm
+ self.num_layers = num_layers
+ self.return_intermediate = return_intermediate
+
+ self.emb_dropout = nn.Dropout(emb_dropout, True)
+ self.mask_token = nn.Parameter(torch.randn([1, 1, d_model]))
+
+ if block_version == "v1":
+ block = LongShortTermTransformerBlock
+ elif block_version == "v2":
+ block = LongShortTermTransformerBlockV2
+ elif block_version == "v3":
+ block = LongShortTermTransformerBlockV3
+ else:
+ raise NotImplementedError
+
+ layers = []
+ for idx in range(num_layers):
+ if droppath_scaling:
+ if num_layers == 1:
+ droppath_rate = 0
+ else:
+ droppath_rate = droppath * idx / (num_layers - 1)
+ else:
+ droppath_rate = droppath
+ layers.append(
+ block(d_model, self_nhead, att_nhead, dim_feedforward,
+ droppath_rate, lt_dropout, st_dropout, droppath_lst,
+ activation))
+ self.layers = nn.ModuleList(layers)
+
+ num_norms = num_layers - 1 if intermediate_norm else 0
+ if final_norm:
+ num_norms += 1
+ self.decoder_norms = [
+ _get_norm(d_model, type='ln') for _ in range(num_norms)
+ ] if num_norms > 0 else None
+
+ if self.decoder_norms is not None:
+ self.decoder_norms = nn.ModuleList(self.decoder_norms)
+
+ def forward(self,
+ tgt,
+ long_term_memories,
+ short_term_memories,
+ curr_id_emb=None,
+ self_pos=None,
+ size_2d=None):
+
+ output = self.emb_dropout(tgt)
+
+ # output = mask_out(output, self.mask_token, 0.15, self.training)
+
+ intermediate = []
+ intermediate_memories = []
+
+ for idx, layer in enumerate(self.layers):
+ output, memories = layer(output,
+ long_term_memories[idx] if
+ long_term_memories is not None else None,
+ short_term_memories[idx] if
+ short_term_memories is not None else None,
+ curr_id_emb=curr_id_emb,
+ self_pos=self_pos,
+ size_2d=size_2d)
+
+ if self.return_intermediate:
+ intermediate.append(output)
+ intermediate_memories.append(memories)
+
+ if self.decoder_norms is not None:
+ if self.final_norm:
+ output = self.decoder_norms[-1](output)
+
+ if self.return_intermediate:
+ intermediate.pop()
+ intermediate.append(output)
+
+ if self.intermediate_norm:
+ for idx in range(len(intermediate) - 1):
+ intermediate[idx] = self.decoder_norms[idx](
+ intermediate[idx])
+
+ if self.return_intermediate:
+ return intermediate, intermediate_memories
+
+ return output, memories
+
+
+class DualBranchGPM(nn.Module):
+ def __init__(self,
+ num_layers=2,
+ d_model=256,
+ self_nhead=8,
+ att_nhead=8,
+ dim_feedforward=1024,
+ emb_dropout=0.,
+ droppath=0.1,
+ lt_dropout=0.,
+ st_dropout=0.,
+ droppath_lst=False,
+ droppath_scaling=False,
+ activation="gelu",
+ return_intermediate=False,
+ intermediate_norm=True,
+ final_norm=True):
+
+ super().__init__()
+ self.intermediate_norm = intermediate_norm
+ self.final_norm = final_norm
+ self.num_layers = num_layers
+ self.return_intermediate = return_intermediate
+
+ self.emb_dropout = nn.Dropout(emb_dropout, True)
+ # self.mask_token = nn.Parameter(torch.randn([1, 1, d_model]))
+
+ block = GatedPropagationModule
+
+ layers = []
+ for idx in range(num_layers):
+ if droppath_scaling:
+ if num_layers == 1:
+ droppath_rate = 0
+ else:
+ droppath_rate = droppath * idx / (num_layers - 1)
+ else:
+ droppath_rate = droppath
+ layers.append(
+ block(d_model,
+ self_nhead,
+ att_nhead,
+ dim_feedforward,
+ droppath_rate,
+ lt_dropout,
+ st_dropout,
+ droppath_lst,
+ activation,
+ layer_idx=idx))
+ self.layers = nn.ModuleList(layers)
+
+ num_norms = num_layers - 1 if intermediate_norm else 0
+ if final_norm:
+ num_norms += 1
+ self.decoder_norms = [
+ _get_norm(d_model * 2, type='gn', groups=2)
+ for _ in range(num_norms)
+ ] if num_norms > 0 else None
+
+ if self.decoder_norms is not None:
+ self.decoder_norms = nn.ModuleList(self.decoder_norms)
+
+ def forward(self,
+ tgt,
+ long_term_memories,
+ short_term_memories,
+ curr_id_emb=None,
+ self_pos=None,
+ size_2d=None):
+
+ output = self.emb_dropout(tgt)
+
+ # output = mask_out(output, self.mask_token, 0.15, self.training)
+
+ intermediate = []
+ intermediate_memories = []
+ output_id = None
+
+ for idx, layer in enumerate(self.layers):
+ output, output_id, memories = layer(
+ output,
+ output_id,
+ long_term_memories[idx]
+ if long_term_memories is not None else None,
+ short_term_memories[idx]
+ if short_term_memories is not None else None,
+ curr_id_emb=curr_id_emb,
+ self_pos=self_pos,
+ size_2d=size_2d)
+
+ cat_output = torch.cat([output, output_id], dim=2)
+
+ if self.return_intermediate:
+ intermediate.append(cat_output)
+ intermediate_memories.append(memories)
+
+ if self.decoder_norms is not None:
+ if self.final_norm:
+ cat_output = self.decoder_norms[-1](cat_output)
+
+ if self.return_intermediate:
+ intermediate.pop()
+ intermediate.append(cat_output)
+
+ if self.intermediate_norm:
+ for idx in range(len(intermediate) - 1):
+ intermediate[idx] = self.decoder_norms[idx](
+ intermediate[idx])
+
+ if self.return_intermediate:
+ return intermediate, intermediate_memories
+
+ return cat_output, memories
+
+
+class LongShortTermTransformerBlock(nn.Module):
+ def __init__(self,
+ d_model,
+ self_nhead,
+ att_nhead,
+ dim_feedforward=1024,
+ droppath=0.1,
+ lt_dropout=0.,
+ st_dropout=0.,
+ droppath_lst=False,
+ activation="gelu",
+ local_dilation=1,
+ enable_corr=True):
+ super().__init__()
+
+ # Long Short-Term Attention
+ self.norm1 = _get_norm(d_model)
+ self.linear_Q = nn.Linear(d_model, d_model)
+ self.linear_V = nn.Linear(d_model, d_model)
+
+ self.long_term_attn = MultiheadAttention(d_model,
+ att_nhead,
+ use_linear=False,
+ dropout=lt_dropout)
+
+ # MultiheadLocalAttention = MultiheadLocalAttentionV2 if enable_corr else MultiheadLocalAttentionV3
+ if enable_corr:
+ try:
+ import spatial_correlation_sampler
+ MultiheadLocalAttention = MultiheadLocalAttentionV2
+ except Exception as inst:
+ print(inst)
+ print("Failed to import PyTorch Correlation, For better efficiency, please install it.")
+ MultiheadLocalAttention = MultiheadLocalAttentionV3
+ else:
+ MultiheadLocalAttention = MultiheadLocalAttentionV3
+ self.short_term_attn = MultiheadLocalAttention(d_model,
+ att_nhead,
+ dilation=local_dilation,
+ use_linear=False,
+ dropout=st_dropout)
+ self.lst_dropout = nn.Dropout(max(lt_dropout, st_dropout), True)
+ self.droppath_lst = droppath_lst
+
+ # Self-attention
+ self.norm2 = _get_norm(d_model)
+ self.self_attn = MultiheadAttention(d_model, self_nhead)
+
+ # Feed-forward
+ self.norm3 = _get_norm(d_model)
+ self.linear1 = nn.Linear(d_model, dim_feedforward)
+ self.activation = GNActDWConv2d(dim_feedforward)
+ self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+ self.droppath = DropPath(droppath, batch_dim=1)
+ self._init_weight()
+
+ def with_pos_embed(self, tensor, pos=None):
+ size = tensor.size()
+ if len(size) == 4 and pos is not None:
+ n, c, h, w = size
+ pos = pos.view(h, w, n, c).permute(2, 3, 0, 1)
+ return tensor if pos is None else tensor + pos
+
+ def forward(self,
+ tgt,
+ long_term_memory=None,
+ short_term_memory=None,
+ curr_id_emb=None,
+ self_pos=None,
+ size_2d=(30, 30)):
+
+ # Self-attention
+ _tgt = self.norm1(tgt)
+ q = k = self.with_pos_embed(_tgt, self_pos)
+ v = _tgt
+ tgt2 = self.self_attn(q, k, v)[0]
+
+ tgt = tgt + self.droppath(tgt2)
+
+ # Long Short-Term Attention
+ _tgt = self.norm2(tgt)
+
+ curr_Q = self.linear_Q(_tgt)
+ curr_K = curr_Q
+ curr_V = _tgt
+
+ local_Q = seq_to_2d(curr_Q, size_2d)
+
+ if curr_id_emb is not None:
+ global_K, global_V = self.fuse_key_value_id(
+ curr_K, curr_V, curr_id_emb)
+ local_K = seq_to_2d(global_K, size_2d)
+ local_V = seq_to_2d(global_V, size_2d)
+ else:
+ global_K, global_V = long_term_memory
+ local_K, local_V = short_term_memory
+
+ tgt2 = self.long_term_attn(curr_Q, global_K, global_V)[0]
+ tgt3 = self.short_term_attn(local_Q, local_K, local_V)[0]
+
+ if self.droppath_lst:
+ tgt = tgt + self.droppath(tgt2 + tgt3)
+ else:
+ tgt = tgt + self.lst_dropout(tgt2 + tgt3)
+
+ # Feed-forward
+ _tgt = self.norm3(tgt)
+
+ tgt2 = self.linear2(self.activation(self.linear1(_tgt), size_2d))
+
+ tgt = tgt + self.droppath(tgt2)
+
+ return tgt, [[curr_K, curr_V], [global_K, global_V],
+ [local_K, local_V]]
+
+ def fuse_key_value_id(self, key, value, id_emb):
+ K = key
+ V = self.linear_V(value + id_emb)
+ return K, V
+
+ def _init_weight(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+
+
+class LongShortTermTransformerBlockV2(nn.Module):
+ def __init__(self,
+ d_model,
+ self_nhead,
+ att_nhead,
+ dim_feedforward=1024,
+ droppath=0.1,
+ lt_dropout=0.,
+ st_dropout=0.,
+ droppath_lst=False,
+ activation="gelu",
+ local_dilation=1,
+ enable_corr=True):
+ super().__init__()
+ self.d_model = d_model
+ self.att_nhead = att_nhead
+
+ # Self-attention
+ self.norm1 = _get_norm(d_model)
+ self.self_attn = MultiheadAttention(d_model, self_nhead)
+
+ # Long Short-Term Attention
+ self.norm2 = _get_norm(d_model)
+ self.linear_QV = nn.Linear(d_model, 2 * d_model)
+ self.linear_ID_KV = nn.Linear(d_model, d_model + att_nhead)
+
+ self.long_term_attn = MultiheadAttention(d_model,
+ att_nhead,
+ use_linear=False,
+ dropout=lt_dropout)
+
+ # MultiheadLocalAttention = MultiheadLocalAttentionV2 if enable_corr else MultiheadLocalAttentionV3
+ if enable_corr:
+ try:
+ import spatial_correlation_sampler
+ MultiheadLocalAttention = MultiheadLocalAttentionV2
+ except Exception as inst:
+ print(inst)
+ print("Failed to import PyTorch Correlation, For better efficiency, please install it.")
+ MultiheadLocalAttention = MultiheadLocalAttentionV3
+ else:
+ MultiheadLocalAttention = MultiheadLocalAttentionV3
+ self.short_term_attn = MultiheadLocalAttention(d_model,
+ att_nhead,
+ dilation=local_dilation,
+ use_linear=False,
+ dropout=st_dropout)
+ self.lst_dropout = nn.Dropout(max(lt_dropout, st_dropout), True)
+ self.droppath_lst = droppath_lst
+
+ # Feed-forward
+ self.norm3 = _get_norm(d_model)
+ self.linear1 = nn.Linear(d_model, dim_feedforward)
+ self.activation = GNActDWConv2d(dim_feedforward)
+ self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+ self.droppath = DropPath(droppath, batch_dim=1)
+ self._init_weight()
+
+ def with_pos_embed(self, tensor, pos=None):
+ size = tensor.size()
+ if len(size) == 4 and pos is not None:
+ n, c, h, w = size
+ pos = pos.view(h, w, n, c).permute(2, 3, 0, 1)
+ return tensor if pos is None else tensor + pos
+
+ def forward(self,
+ tgt,
+ long_term_memory=None,
+ short_term_memory=None,
+ curr_id_emb=None,
+ self_pos=None,
+ size_2d=(30, 30)):
+
+ # Self-attention
+ _tgt = self.norm1(tgt)
+ q = k = self.with_pos_embed(_tgt, self_pos)
+ v = _tgt
+ tgt2 = self.self_attn(q, k, v)[0]
+
+ tgt = tgt + self.droppath(tgt2)
+
+ # Long Short-Term Attention
+ _tgt = self.norm2(tgt)
+
+ curr_QV = self.linear_QV(_tgt)
+ curr_QV = torch.split(curr_QV, self.d_model, dim=2)
+ curr_Q = curr_K = curr_QV[0]
+ curr_V = curr_QV[1]
+
+ local_Q = seq_to_2d(curr_Q, size_2d)
+
+ if curr_id_emb is not None:
+ global_K, global_V = self.fuse_key_value_id(
+ curr_K, curr_V, curr_id_emb)
+
+ local_K = seq_to_2d(global_K, size_2d)
+ local_V = seq_to_2d(global_V, size_2d)
+ else:
+ global_K, global_V = long_term_memory
+ local_K, local_V = short_term_memory
+
+ tgt2 = self.long_term_attn(curr_Q, global_K, global_V)[0]
+ tgt3 = self.short_term_attn(local_Q, local_K, local_V)[0]
+
+ if self.droppath_lst:
+ tgt = tgt + self.droppath(tgt2 + tgt3)
+ else:
+ tgt = tgt + self.lst_dropout(tgt2 + tgt3)
+
+ # Feed-forward
+ _tgt = self.norm3(tgt)
+
+ tgt2 = self.linear2(self.activation(self.linear1(_tgt), size_2d))
+
+ tgt = tgt + self.droppath(tgt2)
+
+ return tgt, [[curr_K, curr_V], [global_K, global_V],
+ [local_K, local_V]]
+
+ def fuse_key_value_id(self, key, value, id_emb):
+ ID_KV = self.linear_ID_KV(id_emb)
+ ID_K, ID_V = torch.split(ID_KV, [self.att_nhead, self.d_model], dim=2)
+ bs = key.size(1)
+ K = key.view(-1, bs, self.att_nhead, self.d_model //
+ self.att_nhead) * (1 + torch.tanh(ID_K)).unsqueeze(-1)
+ K = K.view(-1, bs, self.d_model)
+ V = value + ID_V
+ return K, V
+
+ def _init_weight(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+
+class GatedPropagationModule(nn.Module):
+ def __init__(self,
+ d_model,
+ self_nhead,
+ att_nhead,
+ dim_feedforward=1024,
+ droppath=0.1,
+ lt_dropout=0.,
+ st_dropout=0.,
+ droppath_lst=False,
+ activation="gelu",
+ local_dilation=1,
+ enable_corr=True,
+ max_local_dis=7,
+ layer_idx=0,
+ expand_ratio=2.):
+ super().__init__()
+ expand_ratio = expand_ratio
+ expand_d_model = int(d_model * expand_ratio)
+ self.expand_d_model = expand_d_model
+ self.d_model = d_model
+ self.att_nhead = att_nhead
+
+ d_att = d_model // 2 if att_nhead == 1 else d_model // att_nhead
+ self.d_att = d_att
+ self.layer_idx = layer_idx
+
+ # Long Short-Term Attention
+ self.norm1 = _get_norm(d_model)
+ self.linear_QV = nn.Linear(d_model, d_att * att_nhead + expand_d_model)
+ self.linear_U = nn.Linear(d_model, expand_d_model)
+
+ if layer_idx == 0:
+ self.linear_ID_V = nn.Linear(d_model, expand_d_model)
+ else:
+ self.id_norm1 = _get_norm(d_model)
+ self.linear_ID_V = nn.Linear(d_model * 2, expand_d_model)
+ self.linear_ID_U = nn.Linear(d_model, expand_d_model)
+
+ self.long_term_attn = GatedPropagation(d_qk=self.d_model,
+ d_vu=self.d_model * 2,
+ num_head=att_nhead,
+ use_linear=False,
+ dropout=lt_dropout,
+ d_att=d_att,
+ top_k=-1,
+ expand_ratio=expand_ratio)
+
+ if enable_corr:
+ try:
+ import spatial_correlation_sampler
+ except Exception as inst:
+ print(inst)
+ print("Failed to import PyTorch Correlation, For better efficiency, please install it.")
+ enable_corr = False
+ self.short_term_attn = LocalGatedPropagation(d_qk=self.d_model,
+ d_vu=self.d_model * 2,
+ num_head=att_nhead,
+ dilation=local_dilation,
+ use_linear=False,
+ enable_corr=enable_corr,
+ dropout=st_dropout,
+ d_att=d_att,
+ max_dis=max_local_dis,
+ expand_ratio=expand_ratio)
+
+ self.lst_dropout = nn.Dropout(max(lt_dropout, st_dropout), True)
+ self.droppath_lst = droppath_lst
+
+ # Self-attention
+ self.norm2 = _get_norm(d_model)
+ self.id_norm2 = _get_norm(d_model)
+ self.self_attn = GatedPropagation(d_model * 2,
+ d_model * 2,
+ self_nhead,
+ d_att=d_att)
+
+ self.droppath = DropPath(droppath, batch_dim=1)
+ self._init_weight()
+
+ def with_pos_embed(self, tensor, pos=None):
+ size = tensor.size()
+ if len(size) == 4 and pos is not None:
+ n, c, h, w = size
+ pos = pos.view(h, w, n, c).permute(2, 3, 0, 1)
+ return tensor if pos is None else tensor + pos
+
+ def forward(self,
+ tgt,
+ tgt_id=None,
+ long_term_memory=None,
+ short_term_memory=None,
+ curr_id_emb=None,
+ self_pos=None,
+ size_2d=(30, 30)):
+
+ # Long Short-Term Attention
+ _tgt = self.norm1(tgt)
+
+ curr_QV = self.linear_QV(_tgt)
+ curr_QV = torch.split(
+ curr_QV, [self.d_att * self.att_nhead, self.expand_d_model], dim=2)
+ curr_Q = curr_K = curr_QV[0]
+ local_Q = seq_to_2d(curr_Q, size_2d)
+ curr_V = silu(curr_QV[1])
+ curr_U = self.linear_U(_tgt)
+
+ if tgt_id is None:
+ tgt_id = 0
+ cat_curr_U = torch.cat(
+ [silu(curr_U), torch.ones_like(curr_U)], dim=-1)
+ curr_ID_V = None
+ else:
+ _tgt_id = self.id_norm1(tgt_id)
+ curr_ID_V = _tgt_id
+ curr_ID_U = self.linear_ID_U(_tgt_id)
+ cat_curr_U = silu(torch.cat([curr_U, curr_ID_U], dim=-1))
+
+ if curr_id_emb is not None:
+ global_K, global_V = curr_K, curr_V
+ local_K = seq_to_2d(global_K, size_2d)
+ local_V = seq_to_2d(global_V, size_2d)
+
+ _, global_ID_V = self.fuse_key_value_id(None, curr_ID_V,
+ curr_id_emb)
+ local_ID_V = seq_to_2d(global_ID_V, size_2d)
+ else:
+ global_K, global_V, _, global_ID_V = long_term_memory
+ local_K, local_V, _, local_ID_V = short_term_memory
+
+ cat_global_V = torch.cat([global_V, global_ID_V], dim=-1)
+ cat_local_V = torch.cat([local_V, local_ID_V], dim=1)
+
+ cat_tgt2, _ = self.long_term_attn(curr_Q, global_K, cat_global_V,
+ cat_curr_U, size_2d)
+ cat_tgt3, _ = self.short_term_attn(local_Q, local_K, cat_local_V,
+ cat_curr_U, size_2d)
+
+ tgt2, tgt_id2 = torch.split(cat_tgt2, self.d_model, dim=-1)
+ tgt3, tgt_id3 = torch.split(cat_tgt3, self.d_model, dim=-1)
+
+ if self.droppath_lst:
+ tgt = tgt + self.droppath(tgt2 + tgt3)
+ tgt_id = tgt_id + self.droppath(tgt_id2 + tgt_id3)
+ else:
+ tgt = tgt + self.lst_dropout(tgt2 + tgt3)
+ tgt_id = tgt_id + self.lst_dropout(tgt_id2 + tgt_id3)
+
+ # Self-attention
+ _tgt = self.norm2(tgt)
+ _tgt_id = self.id_norm2(tgt_id)
+ q = k = v = u = torch.cat([_tgt, _tgt_id], dim=-1)
+
+ cat_tgt2, _ = self.self_attn(q, k, v, u, size_2d)
+
+ tgt2, tgt_id2 = torch.split(cat_tgt2, self.d_model, dim=-1)
+
+ tgt = tgt + self.droppath(tgt2)
+ tgt_id = tgt_id + self.droppath(tgt_id2)
+
+ return tgt, tgt_id, [[curr_K, curr_V, None, curr_ID_V],
+ [global_K, global_V, None, global_ID_V],
+ [local_K, local_V, None, local_ID_V]]
+
+ def fuse_key_value_id(self, key, value, id_emb):
+ ID_K = None
+ if value is not None:
+ ID_V = silu(self.linear_ID_V(torch.cat([value, id_emb], dim=2)))
+ else:
+ ID_V = silu(self.linear_ID_V(id_emb))
+ return ID_K, ID_V
+
+ def _init_weight(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
diff --git a/aot/networks/managers/evaluator.py b/aot/networks/managers/evaluator.py
new file mode 100644
index 0000000000000000000000000000000000000000..7414519d6f878a4552847fa76b45f2fff12bff21
--- /dev/null
+++ b/aot/networks/managers/evaluator.py
@@ -0,0 +1,552 @@
+import os
+import time
+import datetime as datetime
+import json
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch.utils.data import DataLoader
+from torchvision import transforms
+
+from dataloaders.eval_datasets import YOUTUBEVOS_Test, YOUTUBEVOS_DenseTest, DAVIS_Test, EVAL_TEST
+import dataloaders.video_transforms as tr
+
+from utils.image import flip_tensor, save_mask
+from utils.checkpoint import load_network
+from utils.eval import zip_folder
+
+from networks.models import build_vos_model
+from networks.engines import build_engine
+
+
+class Evaluator(object):
+ def __init__(self, cfg, rank=0, seq_queue=None, info_queue=None):
+ self.gpu = cfg.TEST_GPU_ID + rank
+ self.gpu_num = cfg.TEST_GPU_NUM
+ self.rank = rank
+ self.cfg = cfg
+ self.seq_queue = seq_queue
+ self.info_queue = info_queue
+
+ self.print_log("Exp {}:".format(cfg.EXP_NAME))
+ self.print_log(json.dumps(cfg.__dict__, indent=4, sort_keys=True))
+
+ print("Use GPU {} for evaluating.".format(self.gpu))
+ torch.cuda.set_device(self.gpu)
+
+ self.print_log('Build VOS model.')
+ self.model = build_vos_model(cfg.MODEL_VOS, cfg).cuda(self.gpu)
+
+ self.process_pretrained_model()
+
+ self.prepare_dataset()
+
+ def process_pretrained_model(self):
+ cfg = self.cfg
+
+ if cfg.TEST_CKPT_PATH == 'test':
+ self.ckpt = 'test'
+ self.print_log('Test evaluation.')
+ return
+
+ if cfg.TEST_CKPT_PATH is None:
+ if cfg.TEST_CKPT_STEP is not None:
+ ckpt = str(cfg.TEST_CKPT_STEP)
+ else:
+ ckpts = os.listdir(cfg.DIR_CKPT)
+ if len(ckpts) > 0:
+ ckpts = list(
+ map(lambda x: int(x.split('_')[-1].split('.')[0]),
+ ckpts))
+ ckpt = np.sort(ckpts)[-1]
+ else:
+ self.print_log('No checkpoint in {}.'.format(cfg.DIR_CKPT))
+ exit()
+ self.ckpt = ckpt
+ if cfg.TEST_EMA:
+ cfg.DIR_CKPT = os.path.join(cfg.DIR_RESULT, 'ema_ckpt')
+ cfg.TEST_CKPT_PATH = os.path.join(cfg.DIR_CKPT,
+ 'save_step_%s.pth' % ckpt)
+ try:
+ self.model, removed_dict = load_network(
+ self.model, cfg.TEST_CKPT_PATH, self.gpu)
+ except Exception as inst:
+ self.print_log(inst)
+ self.print_log('Try to use backup checkpoint.')
+ DIR_RESULT = './backup/{}/{}'.format(cfg.EXP_NAME,
+ cfg.STAGE_NAME)
+ DIR_CKPT = os.path.join(DIR_RESULT, 'ema_ckpt')
+ TEST_CKPT_PATH = os.path.join(DIR_CKPT,
+ 'save_step_%s.pth' % ckpt)
+ self.model, removed_dict = load_network(
+ self.model, TEST_CKPT_PATH, self.gpu)
+
+ if len(removed_dict) > 0:
+ self.print_log(
+ 'Remove {} from pretrained model.'.format(removed_dict))
+ self.print_log('Load latest checkpoint from {}'.format(
+ cfg.TEST_CKPT_PATH))
+ else:
+ self.ckpt = 'unknown'
+ self.model, removed_dict = load_network(self.model,
+ cfg.TEST_CKPT_PATH,
+ self.gpu)
+ if len(removed_dict) > 0:
+ self.print_log(
+ 'Remove {} from pretrained model.'.format(removed_dict))
+ self.print_log('Load checkpoint from {}'.format(
+ cfg.TEST_CKPT_PATH))
+
+ def prepare_dataset(self):
+ cfg = self.cfg
+ self.print_log('Process dataset...')
+ eval_transforms = transforms.Compose([
+ tr.MultiRestrictSize(cfg.TEST_MAX_SHORT_EDGE,
+ cfg.TEST_MAX_LONG_EDGE, cfg.TEST_FLIP,
+ cfg.TEST_MULTISCALE, cfg.MODEL_ALIGN_CORNERS),
+ tr.MultiToTensor()
+ ])
+
+ exp_name = cfg.EXP_NAME
+ if 'aost' in cfg.MODEL_VOS:
+ exp_name += '_L{}'.format(int(cfg.MODEL_LSTT_NUM))
+
+ eval_name = '{}_{}_{}_{}_ckpt_{}'.format(cfg.TEST_DATASET,
+ cfg.TEST_DATASET_SPLIT,
+ exp_name, cfg.STAGE_NAME,
+ self.ckpt)
+
+ if cfg.TEST_EMA:
+ eval_name += '_ema'
+ if cfg.TEST_FLIP:
+ eval_name += '_flip'
+ if len(cfg.TEST_MULTISCALE) > 1:
+ eval_name += '_ms_' + str(cfg.TEST_MULTISCALE).replace(
+ '.', 'dot').replace('[', '').replace(']', '').replace(
+ ', ', '_')
+
+ if 'youtubevos' in cfg.TEST_DATASET:
+ year = int(cfg.TEST_DATASET[-4:])
+ self.result_root = os.path.join(cfg.DIR_EVALUATION,
+ cfg.TEST_DATASET, eval_name,
+ 'Annotations')
+ if '_all_frames' in cfg.TEST_DATASET_SPLIT:
+ split = cfg.TEST_DATASET_SPLIT.split('_')[0]
+ youtubevos_test = YOUTUBEVOS_DenseTest
+
+ self.result_root_sparse = os.path.join(cfg.DIR_EVALUATION,
+ cfg.TEST_DATASET,
+ eval_name + '_sparse',
+ 'Annotations')
+ self.zip_dir_sparse = os.path.join(
+ cfg.DIR_EVALUATION, cfg.TEST_DATASET,
+ '{}_sparse.zip'.format(eval_name))
+ else:
+ split = cfg.TEST_DATASET_SPLIT
+ youtubevos_test = YOUTUBEVOS_Test
+
+ self.dataset = youtubevos_test(root=cfg.DIR_YTB,
+ year=year,
+ split=split,
+ transform=eval_transforms,
+ result_root=self.result_root)
+
+ elif cfg.TEST_DATASET == 'davis2017':
+ resolution = 'Full-Resolution' if cfg.TEST_DATASET_FULL_RESOLUTION else '480p'
+ self.result_root = os.path.join(cfg.DIR_EVALUATION,
+ cfg.TEST_DATASET, eval_name,
+ 'Annotations', resolution)
+ self.dataset = DAVIS_Test(
+ split=[cfg.TEST_DATASET_SPLIT],
+ root=cfg.DIR_DAVIS,
+ year=2017,
+ transform=eval_transforms,
+ full_resolution=cfg.TEST_DATASET_FULL_RESOLUTION,
+ result_root=self.result_root)
+
+ elif cfg.TEST_DATASET == 'davis2016':
+ resolution = 'Full-Resolution' if cfg.TEST_DATASET_FULL_RESOLUTION else '480p'
+ self.result_root = os.path.join(cfg.DIR_EVALUATION,
+ cfg.TEST_DATASET, eval_name,
+ 'Annotations', resolution)
+ self.dataset = DAVIS_Test(
+ split=[cfg.TEST_DATASET_SPLIT],
+ root=cfg.DIR_DAVIS,
+ year=2016,
+ transform=eval_transforms,
+ full_resolution=cfg.TEST_DATASET_FULL_RESOLUTION,
+ result_root=self.result_root)
+
+ elif cfg.TEST_DATASET == 'test':
+ self.result_root = os.path.join(cfg.DIR_EVALUATION,
+ cfg.TEST_DATASET, eval_name,
+ 'Annotations')
+ self.dataset = EVAL_TEST(eval_transforms, self.result_root)
+ else:
+ self.print_log('Unknown dataset!')
+ exit()
+
+ self.print_log('Eval {} on {} {}:'.format(cfg.EXP_NAME,
+ cfg.TEST_DATASET,
+ cfg.TEST_DATASET_SPLIT))
+ self.source_folder = os.path.join(cfg.DIR_EVALUATION, cfg.TEST_DATASET,
+ eval_name, 'Annotations')
+ self.zip_dir = os.path.join(cfg.DIR_EVALUATION, cfg.TEST_DATASET,
+ '{}.zip'.format(eval_name))
+ if not os.path.exists(self.result_root):
+ try:
+ os.makedirs(self.result_root)
+ except Exception as inst:
+ self.print_log(inst)
+ self.print_log('Failed to mask dir: {}.'.format(
+ self.result_root))
+ self.print_log('Done!')
+
+ def evaluating(self):
+ cfg = self.cfg
+ self.model.eval()
+ video_num = 0
+ processed_video_num = 0
+ total_time = 0
+ total_frame = 0
+ total_sfps = 0
+ total_video_num = len(self.dataset)
+ start_eval_time = time.time()
+
+ if self.seq_queue is not None:
+ if self.rank == 0:
+ for seq_idx in range(total_video_num):
+ self.seq_queue.put(seq_idx)
+ for _ in range(self.gpu_num):
+ self.seq_queue.put('END')
+ coming_seq_idx = self.seq_queue.get()
+
+ all_engines = []
+ with torch.no_grad():
+ for seq_idx, seq_dataset in enumerate(self.dataset):
+ video_num += 1
+
+ if self.seq_queue is not None:
+ if coming_seq_idx == 'END':
+ break
+ elif coming_seq_idx != seq_idx:
+ continue
+ else:
+ coming_seq_idx = self.seq_queue.get()
+
+ processed_video_num += 1
+
+ for engine in all_engines:
+ engine.restart_engine()
+
+ seq_name = seq_dataset.seq_name
+ print('GPU {} - Processing Seq {} [{}/{}]:'.format(
+ self.gpu, seq_name, video_num, total_video_num))
+ torch.cuda.empty_cache()
+
+ seq_dataloader = DataLoader(seq_dataset,
+ batch_size=1,
+ shuffle=False,
+ num_workers=cfg.TEST_WORKERS,
+ pin_memory=True)
+
+ if 'all_frames' in cfg.TEST_DATASET_SPLIT:
+ images_sparse = seq_dataset.images_sparse
+ seq_dir_sparse = os.path.join(self.result_root_sparse,
+ seq_name)
+ if not os.path.exists(seq_dir_sparse):
+ os.makedirs(seq_dir_sparse)
+
+ seq_total_time = 0
+ seq_total_frame = 0
+ seq_pred_masks = {'dense': [], 'sparse': []}
+ seq_timers = []
+
+ for frame_idx, samples in enumerate(seq_dataloader):
+
+ all_preds = []
+ new_obj_label = None
+ aug_num = len(samples)
+
+ for aug_idx in range(aug_num):
+ if len(all_engines) <= aug_idx:
+ all_engines.append(
+ build_engine(cfg.MODEL_ENGINE,
+ phase='eval',
+ aot_model=self.model,
+ gpu_id=self.gpu,
+ long_term_mem_gap=self.cfg.
+ TEST_LONG_TERM_MEM_GAP,
+ short_term_mem_skip=self.cfg.
+ TEST_SHORT_TERM_MEM_SKIP))
+ all_engines[-1].eval()
+
+ if aug_num > 1: # if use test-time augmentation
+ torch.cuda.empty_cache() # release GPU memory
+
+ engine = all_engines[aug_idx]
+
+ sample = samples[aug_idx]
+
+ is_flipped = sample['meta']['flip']
+
+ obj_nums = sample['meta']['obj_num']
+ imgname = sample['meta']['current_name']
+ ori_height = sample['meta']['height']
+ ori_width = sample['meta']['width']
+ obj_idx = sample['meta']['obj_idx']
+
+ obj_nums = [int(obj_num) for obj_num in obj_nums]
+ obj_idx = [int(_obj_idx) for _obj_idx in obj_idx]
+
+ current_img = sample['current_img']
+ current_img = current_img.cuda(self.gpu,
+ non_blocking=True)
+ sample['current_img'] = current_img
+
+ if 'current_label' in sample.keys():
+ current_label = sample['current_label'].cuda(
+ self.gpu, non_blocking=True).float()
+ else:
+ current_label = None
+
+ #############################################################
+
+ if frame_idx == 0:
+ _current_label = F.interpolate(
+ current_label,
+ size=current_img.size()[2:],
+ mode="nearest")
+ engine.add_reference_frame(current_img,
+ _current_label,
+ frame_step=0,
+ obj_nums=obj_nums)
+ else:
+ if aug_idx == 0:
+ seq_timers.append([])
+ now_timer = torch.cuda.Event(
+ enable_timing=True)
+ now_timer.record()
+ seq_timers[-1].append(now_timer)
+
+ engine.match_propogate_one_frame(current_img)
+ pred_logit = engine.decode_current_logits(
+ (ori_height, ori_width))
+
+ if is_flipped:
+ pred_logit = flip_tensor(pred_logit, 3)
+
+ pred_prob = torch.softmax(pred_logit, dim=1)
+ all_preds.append(pred_prob)
+
+ if not is_flipped and current_label is not None and new_obj_label is None:
+ new_obj_label = current_label
+
+ if frame_idx > 0:
+ all_pred_probs = [
+ torch.mean(pred, dim=0, keepdim=True)
+ for pred in all_preds
+ ]
+ all_pred_labels = [
+ torch.argmax(prob, dim=1, keepdim=True).float()
+ for prob in all_pred_probs
+ ]
+
+ cat_all_preds = torch.cat(all_preds, dim=0)
+ pred_prob = torch.mean(cat_all_preds,
+ dim=0,
+ keepdim=True)
+ pred_label = torch.argmax(pred_prob,
+ dim=1,
+ keepdim=True).float()
+
+ if new_obj_label is not None:
+ keep = (new_obj_label == 0).float()
+ all_pred_labels = [label * \
+ keep + new_obj_label * (1 - keep) for label in all_pred_labels]
+
+ pred_label = pred_label * \
+ keep + new_obj_label * (1 - keep)
+ new_obj_nums = [int(pred_label.max().item())]
+
+ if cfg.TEST_FLIP:
+ all_flip_pred_labels = [
+ flip_tensor(label, 3)
+ for label in all_pred_labels
+ ]
+ flip_pred_label = flip_tensor(pred_label, 3)
+
+ for aug_idx in range(len(samples)):
+ engine = all_engines[aug_idx]
+ current_img = samples[aug_idx]['current_img']
+
+ # current_label = flip_pred_label if samples[
+ # aug_idx]['meta']['flip'] else pred_label
+ current_label = all_flip_pred_labels[
+ aug_idx] if samples[aug_idx]['meta'][
+ 'flip'] else all_pred_labels[aug_idx]
+ current_label = F.interpolate(
+ current_label,
+ size=engine.input_size_2d,
+ mode="nearest")
+ engine.add_reference_frame(
+ current_img,
+ current_label,
+ obj_nums=new_obj_nums,
+ frame_step=frame_idx)
+ engine.decode_current_logits(
+ (ori_height, ori_width))
+ engine.update_memory(current_label)
+ else:
+ if not cfg.MODEL_USE_PREV_PROB:
+ if cfg.TEST_FLIP:
+ all_flip_pred_labels = [
+ flip_tensor(label, 3)
+ for label in all_pred_labels
+ ]
+ flip_pred_label = flip_tensor(
+ pred_label, 3)
+
+ for aug_idx in range(len(samples)):
+ engine = all_engines[aug_idx]
+ # current_label = flip_pred_label if samples[
+ # aug_idx]['meta']['flip'] else pred_label
+ current_label = all_flip_pred_labels[
+ aug_idx] if samples[aug_idx]['meta'][
+ 'flip'] else all_pred_labels[
+ aug_idx]
+ current_label = F.interpolate(
+ current_label,
+ size=engine.input_size_2d,
+ mode="nearest")
+ engine.update_memory(current_label)
+ else:
+ if cfg.TEST_FLIP:
+ all_flip_pred_probs = [
+ flip_tensor(prob, 3)
+ for prob in all_pred_probs
+ ]
+ flip_pred_prob = flip_tensor(pred_prob, 3)
+
+ for aug_idx in range(len(samples)):
+ engine = all_engines[aug_idx]
+ # current_prob = flip_pred_prob if samples[
+ # aug_idx]['meta']['flip'] else pred_prob
+ current_label = all_flip_pred_probs[
+ aug_idx] if samples[aug_idx]['meta'][
+ 'flip'] else all_pred_probs[aug_idx]
+ current_prob = F.interpolate(
+ current_prob,
+ size=engine.input_size_2d,
+ mode="nearest")
+ engine.update_memory(current_prob)
+
+ now_timer = torch.cuda.Event(enable_timing=True)
+ now_timer.record()
+ seq_timers[-1].append((now_timer))
+
+ if cfg.TEST_FRAME_LOG:
+ torch.cuda.synchronize()
+ one_frametime = seq_timers[-1][0].elapsed_time(
+ seq_timers[-1][1]) / 1e3
+ obj_num = obj_nums[0]
+ print(
+ 'GPU {} - Frame: {} - Obj Num: {}, Time: {}ms'.
+ format(self.gpu, imgname[0].split('.')[0],
+ obj_num, int(one_frametime * 1e3)))
+ # Save result
+ seq_pred_masks['dense'].append({
+ 'path':
+ os.path.join(self.result_root, seq_name,
+ imgname[0].split('.')[0] + '.png'),
+ 'mask':
+ pred_label,
+ 'obj_idx':
+ obj_idx
+ })
+ if 'all_frames' in cfg.TEST_DATASET_SPLIT and imgname in images_sparse:
+ seq_pred_masks['sparse'].append({
+ 'path':
+ os.path.join(self.result_root_sparse, seq_name,
+ imgname[0].split('.')[0] +
+ '.png'),
+ 'mask':
+ pred_label,
+ 'obj_idx':
+ obj_idx
+ })
+
+ # Save result
+ for mask_result in seq_pred_masks['dense'] + seq_pred_masks[
+ 'sparse']:
+ save_mask(mask_result['mask'].squeeze(0).squeeze(0),
+ mask_result['path'], mask_result['obj_idx'])
+ del (seq_pred_masks)
+
+ for timer in seq_timers:
+ torch.cuda.synchronize()
+ one_frametime = timer[0].elapsed_time(timer[1]) / 1e3
+ seq_total_time += one_frametime
+ seq_total_frame += 1
+ del (seq_timers)
+
+ seq_avg_time_per_frame = seq_total_time / seq_total_frame
+ total_time += seq_total_time
+ total_frame += seq_total_frame
+ total_avg_time_per_frame = total_time / total_frame
+ total_sfps += seq_avg_time_per_frame
+ avg_sfps = total_sfps / processed_video_num
+ max_mem = torch.cuda.max_memory_allocated(
+ device=self.gpu) / (1024.**3)
+ print(
+ "GPU {} - Seq {} - FPS: {:.2f}. All-Frame FPS: {:.2f}, All-Seq FPS: {:.2f}, Max Mem: {:.2f}G"
+ .format(self.gpu, seq_name, 1. / seq_avg_time_per_frame,
+ 1. / total_avg_time_per_frame, 1. / avg_sfps,
+ max_mem))
+
+ if self.seq_queue is not None:
+ if self.rank != 0:
+ self.info_queue.put({
+ 'total_time': total_time,
+ 'total_frame': total_frame,
+ 'total_sfps': total_sfps,
+ 'processed_video_num': processed_video_num,
+ 'max_mem': max_mem
+ })
+ print('Finished the evaluation on GPU {}.'.format(self.gpu))
+ if self.rank == 0:
+ for _ in range(self.gpu_num - 1):
+ info_dict = self.info_queue.get()
+ total_time += info_dict['total_time']
+ total_frame += info_dict['total_frame']
+ total_sfps += info_dict['total_sfps']
+ processed_video_num += info_dict['processed_video_num']
+ max_mem = max(max_mem, info_dict['max_mem'])
+ all_reduced_total_avg_time_per_frame = total_time / total_frame
+ all_reduced_avg_sfps = total_sfps / processed_video_num
+ print(
+ "GPU {} - All-Frame FPS: {:.2f}, All-Seq FPS: {:.2f}, Max Mem: {:.2f}G"
+ .format(list(range(self.gpu_num)),
+ 1. / all_reduced_total_avg_time_per_frame,
+ 1. / all_reduced_avg_sfps, max_mem))
+ else:
+ print(
+ "GPU {} - All-Frame FPS: {:.2f}, All-Seq FPS: {:.2f}, Max Mem: {:.2f}G"
+ .format(self.gpu, 1. / total_avg_time_per_frame, 1. / avg_sfps,
+ max_mem))
+
+ if self.rank == 0:
+ zip_folder(self.source_folder, self.zip_dir)
+ self.print_log('Saving result to {}.'.format(self.zip_dir))
+ if 'all_frames' in cfg.TEST_DATASET_SPLIT:
+ zip_folder(self.result_root_sparse, self.zip_dir_sparse)
+ end_eval_time = time.time()
+ total_eval_time = str(
+ datetime.timedelta(seconds=int(end_eval_time -
+ start_eval_time)))
+ self.print_log("Total evaluation time: {}".format(total_eval_time))
+
+ def print_log(self, string):
+ if self.rank == 0:
+ print(string)
diff --git a/aot/networks/managers/trainer.py b/aot/networks/managers/trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fe4f42494720c0702e0dc871d2fa6aed83b85b9
--- /dev/null
+++ b/aot/networks/managers/trainer.py
@@ -0,0 +1,686 @@
+import os
+import time
+import json
+import datetime as datetime
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.distributed as dist
+from torch.utils.data import DataLoader
+from torchvision import transforms
+
+from dataloaders.train_datasets import DAVIS2017_Train, YOUTUBEVOS_Train, StaticTrain, TEST
+import dataloaders.video_transforms as tr
+
+from utils.meters import AverageMeter
+from utils.image import label2colormap, masked_image, save_image
+from utils.checkpoint import load_network_and_optimizer, load_network, save_network
+from utils.learning import adjust_learning_rate, get_trainable_params
+from utils.metric import pytorch_iou
+from utils.ema import ExponentialMovingAverage, get_param_buffer_for_ema
+
+from networks.models import build_vos_model
+from networks.engines import build_engine
+
+
+class Trainer(object):
+ def __init__(self, rank, cfg, enable_amp=True):
+ self.gpu = rank + cfg.DIST_START_GPU
+ self.gpu_num = cfg.TRAIN_GPUS
+ self.rank = rank
+ self.cfg = cfg
+
+ self.print_log("Exp {}:".format(cfg.EXP_NAME))
+ self.print_log(json.dumps(cfg.__dict__, indent=4, sort_keys=True))
+
+ print("Use GPU {} for training VOS.".format(self.gpu))
+ torch.cuda.set_device(self.gpu)
+ torch.backends.cudnn.benchmark = True if cfg.DATA_RANDOMCROP[
+ 0] == cfg.DATA_RANDOMCROP[
+ 1] and 'swin' not in cfg.MODEL_ENCODER else False
+
+ self.print_log('Build VOS model.')
+
+ self.model = build_vos_model(cfg.MODEL_VOS, cfg).cuda(self.gpu)
+ self.model_encoder = self.model.encoder
+ self.engine = build_engine(
+ cfg.MODEL_ENGINE,
+ 'train',
+ aot_model=self.model,
+ gpu_id=self.gpu,
+ long_term_mem_gap=cfg.TRAIN_LONG_TERM_MEM_GAP)
+
+ if cfg.MODEL_FREEZE_BACKBONE:
+ for param in self.model_encoder.parameters():
+ param.requires_grad = False
+
+ if cfg.DIST_ENABLE:
+ dist.init_process_group(backend=cfg.DIST_BACKEND,
+ init_method=cfg.DIST_URL,
+ world_size=cfg.TRAIN_GPUS,
+ rank=rank,
+ timeout=datetime.timedelta(seconds=300))
+
+ self.model.encoder = nn.SyncBatchNorm.convert_sync_batchnorm(
+ self.model.encoder).cuda(self.gpu)
+
+ self.dist_engine = torch.nn.parallel.DistributedDataParallel(
+ self.engine,
+ device_ids=[self.gpu],
+ output_device=self.gpu,
+ find_unused_parameters=True,
+ broadcast_buffers=False)
+ else:
+ self.dist_engine = self.engine
+
+ self.use_frozen_bn = False
+ if 'swin' in cfg.MODEL_ENCODER:
+ self.print_log('Use LN in Encoder!')
+ elif not cfg.MODEL_FREEZE_BN:
+ if cfg.DIST_ENABLE:
+ self.print_log('Use Sync BN in Encoder!')
+ else:
+ self.print_log('Use BN in Encoder!')
+ else:
+ self.use_frozen_bn = True
+ self.print_log('Use Frozen BN in Encoder!')
+
+ if self.rank == 0:
+ try:
+ total_steps = float(cfg.TRAIN_TOTAL_STEPS)
+ ema_decay = 1. - 1. / (total_steps * cfg.TRAIN_EMA_RATIO)
+ self.ema_params = get_param_buffer_for_ema(
+ self.model, update_buffer=(not cfg.MODEL_FREEZE_BN))
+ self.ema = ExponentialMovingAverage(self.ema_params,
+ decay=ema_decay)
+ self.ema_dir = cfg.DIR_EMA_CKPT
+ except Exception as inst:
+ self.print_log(inst)
+ self.print_log('Error: failed to create EMA model!')
+
+ self.print_log('Build optimizer.')
+
+ trainable_params = get_trainable_params(
+ model=self.dist_engine,
+ base_lr=cfg.TRAIN_LR,
+ use_frozen_bn=self.use_frozen_bn,
+ weight_decay=cfg.TRAIN_WEIGHT_DECAY,
+ exclusive_wd_dict=cfg.TRAIN_WEIGHT_DECAY_EXCLUSIVE,
+ no_wd_keys=cfg.TRAIN_WEIGHT_DECAY_EXEMPTION)
+
+ if cfg.TRAIN_OPT == 'sgd':
+ self.optimizer = optim.SGD(trainable_params,
+ lr=cfg.TRAIN_LR,
+ momentum=cfg.TRAIN_SGD_MOMENTUM,
+ nesterov=True)
+ else:
+ self.optimizer = optim.AdamW(trainable_params,
+ lr=cfg.TRAIN_LR,
+ weight_decay=cfg.TRAIN_WEIGHT_DECAY)
+
+ self.enable_amp = enable_amp
+ if enable_amp:
+ self.scaler = torch.cuda.amp.GradScaler()
+ else:
+ self.scaler = None
+
+ self.prepare_dataset()
+ self.process_pretrained_model()
+
+ if cfg.TRAIN_TBLOG and self.rank == 0:
+ from tensorboardX import SummaryWriter
+ self.tblogger = SummaryWriter(cfg.DIR_TB_LOG)
+
+ def process_pretrained_model(self):
+ cfg = self.cfg
+
+ self.step = cfg.TRAIN_START_STEP
+ self.epoch = 0
+
+ if cfg.TRAIN_AUTO_RESUME:
+ ckpts = os.listdir(cfg.DIR_CKPT)
+ if len(ckpts) > 0:
+ ckpts = list(
+ map(lambda x: int(x.split('_')[-1].split('.')[0]), ckpts))
+ ckpt = np.sort(ckpts)[-1]
+ cfg.TRAIN_RESUME = True
+ cfg.TRAIN_RESUME_CKPT = ckpt
+ cfg.TRAIN_RESUME_STEP = ckpt
+ else:
+ cfg.TRAIN_RESUME = False
+
+ if cfg.TRAIN_RESUME:
+ if self.rank == 0:
+ try:
+ try:
+ ema_ckpt_dir = os.path.join(
+ self.ema_dir,
+ 'save_step_%s.pth' % (cfg.TRAIN_RESUME_CKPT))
+ ema_model, removed_dict = load_network(
+ self.model, ema_ckpt_dir, self.gpu)
+ except Exception as inst:
+ self.print_log(inst)
+ self.print_log('Try to use backup EMA checkpoint.')
+ DIR_RESULT = './backup/{}/{}'.format(
+ cfg.EXP_NAME, cfg.STAGE_NAME)
+ DIR_EMA_CKPT = os.path.join(DIR_RESULT, 'ema_ckpt')
+ ema_ckpt_dir = os.path.join(
+ DIR_EMA_CKPT,
+ 'save_step_%s.pth' % (cfg.TRAIN_RESUME_CKPT))
+ ema_model, removed_dict = load_network(
+ self.model, ema_ckpt_dir, self.gpu)
+
+ if len(removed_dict) > 0:
+ self.print_log(
+ 'Remove {} from EMA model.'.format(removed_dict))
+ ema_decay = self.ema.decay
+ del (self.ema)
+
+ ema_params = get_param_buffer_for_ema(
+ ema_model, update_buffer=(not cfg.MODEL_FREEZE_BN))
+ self.ema = ExponentialMovingAverage(ema_params,
+ decay=ema_decay)
+ self.ema.num_updates = cfg.TRAIN_RESUME_CKPT
+ except Exception as inst:
+ self.print_log(inst)
+ self.print_log('Error: EMA model not found!')
+
+ try:
+ resume_ckpt = os.path.join(
+ cfg.DIR_CKPT, 'save_step_%s.pth' % (cfg.TRAIN_RESUME_CKPT))
+ self.model, self.optimizer, removed_dict = load_network_and_optimizer(
+ self.model,
+ self.optimizer,
+ resume_ckpt,
+ self.gpu,
+ scaler=self.scaler)
+ except Exception as inst:
+ self.print_log(inst)
+ self.print_log('Try to use backup checkpoint.')
+ DIR_RESULT = './backup/{}/{}'.format(cfg.EXP_NAME,
+ cfg.STAGE_NAME)
+ DIR_CKPT = os.path.join(DIR_RESULT, 'ckpt')
+ resume_ckpt = os.path.join(
+ DIR_CKPT, 'save_step_%s.pth' % (cfg.TRAIN_RESUME_CKPT))
+ self.model, self.optimizer, removed_dict = load_network_and_optimizer(
+ self.model,
+ self.optimizer,
+ resume_ckpt,
+ self.gpu,
+ scaler=self.scaler)
+
+ if len(removed_dict) > 0:
+ self.print_log(
+ 'Remove {} from checkpoint.'.format(removed_dict))
+
+ self.step = cfg.TRAIN_RESUME_STEP
+ if cfg.TRAIN_TOTAL_STEPS <= self.step:
+ self.print_log("Your training has finished!")
+ exit()
+ self.epoch = int(np.ceil(self.step / len(self.train_loader)))
+
+ self.print_log('Resume from step {}'.format(self.step))
+
+ elif cfg.PRETRAIN:
+ if cfg.PRETRAIN_FULL:
+ try:
+ self.model, removed_dict = load_network(
+ self.model, cfg.PRETRAIN_MODEL, self.gpu)
+ except Exception as inst:
+ self.print_log(inst)
+ self.print_log('Try to use backup EMA checkpoint.')
+ DIR_RESULT = './backup/{}/{}'.format(
+ cfg.EXP_NAME, cfg.STAGE_NAME)
+ DIR_EMA_CKPT = os.path.join(DIR_RESULT, 'ema_ckpt')
+ PRETRAIN_MODEL = os.path.join(
+ DIR_EMA_CKPT,
+ cfg.PRETRAIN_MODEL.split('/')[-1])
+ self.model, removed_dict = load_network(
+ self.model, PRETRAIN_MODEL, self.gpu)
+
+ if len(removed_dict) > 0:
+ self.print_log('Remove {} from pretrained model.'.format(
+ removed_dict))
+ self.print_log('Load pretrained VOS model from {}.'.format(
+ cfg.PRETRAIN_MODEL))
+ else:
+ model_encoder, removed_dict = load_network(
+ self.model_encoder, cfg.PRETRAIN_MODEL, self.gpu)
+ if len(removed_dict) > 0:
+ self.print_log('Remove {} from pretrained model.'.format(
+ removed_dict))
+ self.print_log(
+ 'Load pretrained backbone model from {}.'.format(
+ cfg.PRETRAIN_MODEL))
+
+ def prepare_dataset(self):
+ cfg = self.cfg
+ self.enable_prev_frame = cfg.TRAIN_ENABLE_PREV_FRAME
+
+ self.print_log('Process dataset...')
+ if cfg.TRAIN_AUG_TYPE == 'v1':
+ composed_transforms = transforms.Compose([
+ tr.RandomScale(cfg.DATA_MIN_SCALE_FACTOR,
+ cfg.DATA_MAX_SCALE_FACTOR,
+ cfg.DATA_SHORT_EDGE_LEN),
+ tr.BalancedRandomCrop(cfg.DATA_RANDOMCROP,
+ max_obj_num=cfg.MODEL_MAX_OBJ_NUM),
+ tr.RandomHorizontalFlip(cfg.DATA_RANDOMFLIP),
+ tr.Resize(cfg.DATA_RANDOMCROP, use_padding=True),
+ tr.ToTensor()
+ ])
+ elif cfg.TRAIN_AUG_TYPE == 'v2':
+ composed_transforms = transforms.Compose([
+ tr.RandomScale(cfg.DATA_MIN_SCALE_FACTOR,
+ cfg.DATA_MAX_SCALE_FACTOR,
+ cfg.DATA_SHORT_EDGE_LEN),
+ tr.BalancedRandomCrop(cfg.DATA_RANDOMCROP,
+ max_obj_num=cfg.MODEL_MAX_OBJ_NUM),
+ tr.RandomColorJitter(),
+ tr.RandomGrayScale(),
+ tr.RandomGaussianBlur(),
+ tr.RandomHorizontalFlip(cfg.DATA_RANDOMFLIP),
+ tr.Resize(cfg.DATA_RANDOMCROP, use_padding=True),
+ tr.ToTensor()
+ ])
+ else:
+ assert NotImplementedError
+
+ train_datasets = []
+ if 'static' in cfg.DATASETS:
+ pretrain_vos_dataset = StaticTrain(
+ cfg.DIR_STATIC,
+ cfg.DATA_RANDOMCROP,
+ seq_len=cfg.DATA_SEQ_LEN,
+ merge_prob=cfg.DATA_DYNAMIC_MERGE_PROB,
+ max_obj_n=cfg.MODEL_MAX_OBJ_NUM,
+ aug_type=cfg.TRAIN_AUG_TYPE)
+ train_datasets.append(pretrain_vos_dataset)
+ self.enable_prev_frame = False
+
+ if 'davis2017' in cfg.DATASETS:
+ train_davis_dataset = DAVIS2017_Train(
+ root=cfg.DIR_DAVIS,
+ full_resolution=cfg.TRAIN_DATASET_FULL_RESOLUTION,
+ transform=composed_transforms,
+ repeat_time=cfg.DATA_DAVIS_REPEAT,
+ seq_len=cfg.DATA_SEQ_LEN,
+ rand_gap=cfg.DATA_RANDOM_GAP_DAVIS,
+ rand_reverse=cfg.DATA_RANDOM_REVERSE_SEQ,
+ merge_prob=cfg.DATA_DYNAMIC_MERGE_PROB,
+ enable_prev_frame=self.enable_prev_frame,
+ max_obj_n=cfg.MODEL_MAX_OBJ_NUM)
+ train_datasets.append(train_davis_dataset)
+
+ if 'youtubevos' in cfg.DATASETS:
+ train_ytb_dataset = YOUTUBEVOS_Train(
+ root=cfg.DIR_YTB,
+ transform=composed_transforms,
+ seq_len=cfg.DATA_SEQ_LEN,
+ rand_gap=cfg.DATA_RANDOM_GAP_YTB,
+ rand_reverse=cfg.DATA_RANDOM_REVERSE_SEQ,
+ merge_prob=cfg.DATA_DYNAMIC_MERGE_PROB,
+ enable_prev_frame=self.enable_prev_frame,
+ max_obj_n=cfg.MODEL_MAX_OBJ_NUM)
+ train_datasets.append(train_ytb_dataset)
+
+ if 'test' in cfg.DATASETS:
+ test_dataset = TEST(transform=composed_transforms,
+ seq_len=cfg.DATA_SEQ_LEN)
+ train_datasets.append(test_dataset)
+
+ if len(train_datasets) > 1:
+ train_dataset = torch.utils.data.ConcatDataset(train_datasets)
+ elif len(train_datasets) == 1:
+ train_dataset = train_datasets[0]
+ else:
+ self.print_log('No dataset!')
+ exit(0)
+
+ self.train_sampler = torch.utils.data.distributed.DistributedSampler(
+ train_dataset) if self.cfg.DIST_ENABLE else None
+ self.train_loader = DataLoader(train_dataset,
+ batch_size=int(cfg.TRAIN_BATCH_SIZE /
+ cfg.TRAIN_GPUS),
+ shuffle=False if self.cfg.DIST_ENABLE else True,
+ num_workers=cfg.DATA_WORKERS,
+ pin_memory=True,
+ sampler=self.train_sampler,
+ drop_last=True,
+ prefetch_factor=4)
+
+ self.print_log('Done!')
+
+ def sequential_training(self):
+
+ cfg = self.cfg
+
+ if self.enable_prev_frame:
+ frame_names = ['Ref', 'Prev']
+ else:
+ frame_names = ['Ref(Prev)']
+
+ for i in range(cfg.DATA_SEQ_LEN - 1):
+ frame_names.append('Curr{}'.format(i + 1))
+
+ seq_len = len(frame_names)
+
+ running_losses = []
+ running_ious = []
+ for _ in range(seq_len):
+ running_losses.append(AverageMeter())
+ running_ious.append(AverageMeter())
+ batch_time = AverageMeter()
+ avg_obj = AverageMeter()
+
+ optimizer = self.optimizer
+ model = self.dist_engine
+ train_sampler = self.train_sampler
+ train_loader = self.train_loader
+ step = self.step
+ epoch = self.epoch
+ max_itr = cfg.TRAIN_TOTAL_STEPS
+ start_seq_training_step = int(cfg.TRAIN_SEQ_TRAINING_START_RATIO *
+ max_itr)
+ use_prev_prob = cfg.MODEL_USE_PREV_PROB
+
+ self.print_log('Start training:')
+ model.train()
+ while step < cfg.TRAIN_TOTAL_STEPS:
+ if self.cfg.DIST_ENABLE:
+ train_sampler.set_epoch(epoch)
+ epoch += 1
+ last_time = time.time()
+ for frame_idx, sample in enumerate(train_loader):
+ if step > cfg.TRAIN_TOTAL_STEPS:
+ break
+
+ if step % cfg.TRAIN_TBLOG_STEP == 0 and self.rank == 0 and cfg.TRAIN_TBLOG:
+ tf_board = True
+ else:
+ tf_board = False
+
+ if step >= start_seq_training_step:
+ use_prev_pred = True
+ freeze_params = cfg.TRAIN_SEQ_TRAINING_FREEZE_PARAMS
+ else:
+ use_prev_pred = False
+ freeze_params = []
+
+ if step % cfg.TRAIN_LR_UPDATE_STEP == 0:
+ now_lr = adjust_learning_rate(
+ optimizer=optimizer,
+ base_lr=cfg.TRAIN_LR,
+ p=cfg.TRAIN_LR_POWER,
+ itr=step,
+ max_itr=max_itr,
+ restart=cfg.TRAIN_LR_RESTART,
+ warm_up_steps=cfg.TRAIN_LR_WARM_UP_RATIO * max_itr,
+ is_cosine_decay=cfg.TRAIN_LR_COSINE_DECAY,
+ min_lr=cfg.TRAIN_LR_MIN,
+ encoder_lr_ratio=cfg.TRAIN_LR_ENCODER_RATIO,
+ freeze_params=freeze_params)
+
+ ref_imgs = sample['ref_img'] # batch_size * 3 * h * w
+ prev_imgs = sample['prev_img']
+ curr_imgs = sample['curr_img']
+ ref_labels = sample['ref_label'] # batch_size * 1 * h * w
+ prev_labels = sample['prev_label']
+ curr_labels = sample['curr_label']
+ obj_nums = sample['meta']['obj_num']
+ bs, _, h, w = curr_imgs[0].size()
+
+ ref_imgs = ref_imgs.cuda(self.gpu, non_blocking=True)
+ prev_imgs = prev_imgs.cuda(self.gpu, non_blocking=True)
+ curr_imgs = [
+ curr_img.cuda(self.gpu, non_blocking=True)
+ for curr_img in curr_imgs
+ ]
+ ref_labels = ref_labels.cuda(self.gpu, non_blocking=True)
+ prev_labels = prev_labels.cuda(self.gpu, non_blocking=True)
+ curr_labels = [
+ curr_label.cuda(self.gpu, non_blocking=True)
+ for curr_label in curr_labels
+ ]
+ obj_nums = list(obj_nums)
+ obj_nums = [int(obj_num) for obj_num in obj_nums]
+
+ batch_size = ref_imgs.size(0)
+
+ all_frames = torch.cat([ref_imgs, prev_imgs] + curr_imgs,
+ dim=0)
+ all_labels = torch.cat([ref_labels, prev_labels] + curr_labels,
+ dim=0)
+
+ self.engine.restart_engine(batch_size, True)
+ optimizer.zero_grad(set_to_none=True)
+
+ if self.enable_amp:
+ with torch.cuda.amp.autocast(enabled=True):
+
+ loss, all_pred, all_loss, boards = model(
+ all_frames,
+ all_labels,
+ batch_size,
+ use_prev_pred=use_prev_pred,
+ obj_nums=obj_nums,
+ step=step,
+ tf_board=tf_board,
+ enable_prev_frame=self.enable_prev_frame,
+ use_prev_prob=use_prev_prob)
+ loss = torch.mean(loss)
+
+ start = time.time()
+ self.scaler.scale(loss).backward()
+ end = time.time()
+ print(end-start)
+ self.scaler.unscale_(optimizer)
+ torch.nn.utils.clip_grad_norm_(model.parameters(),
+ cfg.TRAIN_CLIP_GRAD_NORM)
+ self.scaler.step(optimizer)
+ self.scaler.update()
+
+ else:
+ loss, all_pred, all_loss, boards = model(
+ all_frames,
+ all_labels,
+ ref_imgs.size(0),
+ use_prev_pred=use_prev_pred,
+ obj_nums=obj_nums,
+ step=step,
+ tf_board=tf_board,
+ enable_prev_frame=self.enable_prev_frame,
+ use_prev_prob=use_prev_prob)
+ loss = torch.mean(loss)
+
+ torch.nn.utils.clip_grad_norm_(model.parameters(),
+ cfg.TRAIN_CLIP_GRAD_NORM)
+ loss.backward()
+ optimizer.step()
+
+ for idx in range(seq_len):
+ now_pred = all_pred[idx].detach()
+ now_label = all_labels[idx * bs:(idx + 1) * bs].detach()
+ now_loss = torch.mean(all_loss[idx].detach())
+ now_iou = pytorch_iou(now_pred.unsqueeze(1), now_label,
+ obj_nums) * 100
+ if self.cfg.DIST_ENABLE:
+ dist.all_reduce(now_loss)
+ dist.all_reduce(now_iou)
+ now_loss /= self.gpu_num
+ now_iou /= self.gpu_num
+ if self.rank == 0:
+ running_losses[idx].update(now_loss.item())
+ running_ious[idx].update(now_iou.item())
+
+ if self.rank == 0:
+ self.ema.update(self.ema_params)
+
+ avg_obj.update(sum(obj_nums) / float(len(obj_nums)))
+ curr_time = time.time()
+ batch_time.update(curr_time - last_time)
+ last_time = curr_time
+
+ if step % cfg.TRAIN_TBLOG_STEP == 0:
+ all_f = [ref_imgs, prev_imgs] + curr_imgs
+ self.process_log(ref_imgs, all_f[-2], all_f[-1],
+ ref_labels, all_pred[-2], now_label,
+ now_pred, boards, running_losses,
+ running_ious, now_lr, step)
+
+ if step % cfg.TRAIN_LOG_STEP == 0:
+ strs = 'I:{}, LR:{:.5f}, T:{:.1f}({:.1f})s, Obj:{:.1f}({:.1f})'.format(
+ step, now_lr, batch_time.val,
+ batch_time.moving_avg, avg_obj.val,
+ avg_obj.moving_avg)
+ batch_time.reset()
+ avg_obj.reset()
+ for idx in range(seq_len):
+ strs += ', {}: L {:.3f}({:.3f}) IoU {:.1f}({:.1f})%'.format(
+ frame_names[idx], running_losses[idx].val,
+ running_losses[idx].moving_avg,
+ running_ious[idx].val,
+ running_ious[idx].moving_avg)
+ running_losses[idx].reset()
+ running_ious[idx].reset()
+
+ self.print_log(strs)
+
+ step += 1
+
+ if step % cfg.TRAIN_SAVE_STEP == 0 and self.rank == 0:
+ max_mem = torch.cuda.max_memory_allocated(
+ device=self.gpu) / (1024.**3)
+ ETA = str(
+ datetime.timedelta(
+ seconds=int(batch_time.moving_avg *
+ (cfg.TRAIN_TOTAL_STEPS - step))))
+ self.print_log('ETA: {}, Max Mem: {:.2f}G.'.format(
+ ETA, max_mem))
+ self.print_log('Save CKPT (Step {}).'.format(step))
+ save_network(self.model,
+ optimizer,
+ step,
+ cfg.DIR_CKPT,
+ cfg.TRAIN_MAX_KEEP_CKPT,
+ backup_dir='./backup/{}/{}/ckpt'.format(
+ cfg.EXP_NAME, cfg.STAGE_NAME),
+ scaler=self.scaler)
+ try:
+ torch.cuda.empty_cache()
+ # First save original parameters before replacing with EMA version
+ self.ema.store(self.ema_params)
+ # Copy EMA parameters to model
+ self.ema.copy_to(self.ema_params)
+ # Save EMA model
+ save_network(
+ self.model,
+ optimizer,
+ step,
+ self.ema_dir,
+ cfg.TRAIN_MAX_KEEP_CKPT,
+ backup_dir='./backup/{}/{}/ema_ckpt'.format(
+ cfg.EXP_NAME, cfg.STAGE_NAME),
+ scaler=self.scaler)
+ # Restore original parameters to resume training later
+ self.ema.restore(self.ema_params)
+ except Exception as inst:
+ self.print_log(inst)
+ self.print_log('Error: failed to save EMA model!')
+
+ self.print_log('Stop training!')
+
+ def print_log(self, string):
+ if self.rank == 0:
+ print(string)
+
+ def process_log(self, ref_imgs, prev_imgs, curr_imgs, ref_labels,
+ prev_labels, curr_labels, curr_pred, boards,
+ running_losses, running_ious, now_lr, step):
+ cfg = self.cfg
+
+ mean = np.array([[[0.485]], [[0.456]], [[0.406]]])
+ sigma = np.array([[[0.229]], [[0.224]], [[0.225]]])
+
+ show_ref_img, show_prev_img, show_curr_img = [
+ img.cpu().numpy()[0] * sigma + mean
+ for img in [ref_imgs, prev_imgs, curr_imgs]
+ ]
+
+ show_gt, show_prev_gt, show_ref_gt, show_preds_s = [
+ label.cpu()[0].squeeze(0).numpy()
+ for label in [curr_labels, prev_labels, ref_labels, curr_pred]
+ ]
+
+ show_gtf, show_prev_gtf, show_ref_gtf, show_preds_sf = [
+ label2colormap(label).transpose((2, 0, 1))
+ for label in [show_gt, show_prev_gt, show_ref_gt, show_preds_s]
+ ]
+
+ if cfg.TRAIN_IMG_LOG or cfg.TRAIN_TBLOG:
+
+ show_ref_img = masked_image(show_ref_img, show_ref_gtf,
+ show_ref_gt)
+ if cfg.TRAIN_IMG_LOG:
+ save_image(
+ show_ref_img,
+ os.path.join(cfg.DIR_IMG_LOG,
+ '%06d_ref_img.jpeg' % (step)))
+
+ show_prev_img = masked_image(show_prev_img, show_prev_gtf,
+ show_prev_gt)
+ if cfg.TRAIN_IMG_LOG:
+ save_image(
+ show_prev_img,
+ os.path.join(cfg.DIR_IMG_LOG,
+ '%06d_prev_img.jpeg' % (step)))
+
+ show_img_pred = masked_image(show_curr_img, show_preds_sf,
+ show_preds_s)
+ if cfg.TRAIN_IMG_LOG:
+ save_image(
+ show_img_pred,
+ os.path.join(cfg.DIR_IMG_LOG,
+ '%06d_prediction.jpeg' % (step)))
+
+ show_curr_img = masked_image(show_curr_img, show_gtf, show_gt)
+ if cfg.TRAIN_IMG_LOG:
+ save_image(
+ show_curr_img,
+ os.path.join(cfg.DIR_IMG_LOG,
+ '%06d_groundtruth.jpeg' % (step)))
+
+ if cfg.TRAIN_TBLOG:
+ for seq_step, running_loss, running_iou in zip(
+ range(len(running_losses)), running_losses,
+ running_ious):
+ self.tblogger.add_scalar('S{}/Loss'.format(seq_step),
+ running_loss.avg, step)
+ self.tblogger.add_scalar('S{}/IoU'.format(seq_step),
+ running_iou.avg, step)
+
+ self.tblogger.add_scalar('LR', now_lr, step)
+ self.tblogger.add_image('Ref/Image', show_ref_img, step)
+ self.tblogger.add_image('Ref/GT', show_ref_gtf, step)
+
+ self.tblogger.add_image('Prev/Image', show_prev_img, step)
+ self.tblogger.add_image('Prev/GT', show_prev_gtf, step)
+
+ self.tblogger.add_image('Curr/Image_GT', show_curr_img, step)
+ self.tblogger.add_image('Curr/Image_Pred', show_img_pred, step)
+
+ self.tblogger.add_image('Curr/Mask_GT', show_gtf, step)
+ self.tblogger.add_image('Curr/Mask_Pred', show_preds_sf, step)
+
+ for key in boards['image'].keys():
+ tmp = boards['image'][key].cpu().numpy()
+ self.tblogger.add_image('S{}/' + key, tmp, step)
+ for key in boards['scalar'].keys():
+ tmp = boards['scalar'][key].cpu().numpy()
+ self.tblogger.add_scalar('S{}/' + key, tmp, step)
+
+ self.tblogger.flush()
+
+ del (boards)
diff --git a/aot/networks/models/__init__.py b/aot/networks/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..63995d437a912b4c2e7d44f7dc9ea9ec8dc3b546
--- /dev/null
+++ b/aot/networks/models/__init__.py
@@ -0,0 +1,11 @@
+from networks.models.aot import AOT
+from networks.models.deaot import DeAOT
+
+
+def build_vos_model(name, cfg, **kwargs):
+ if name == 'aot':
+ return AOT(cfg, encoder=cfg.MODEL_ENCODER, **kwargs)
+ elif name == 'deaot':
+ return DeAOT(cfg, encoder=cfg.MODEL_ENCODER, **kwargs)
+ else:
+ raise NotImplementedError
diff --git a/aot/networks/models/__pycache__/__init__.cpython-311.pyc b/aot/networks/models/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2bda82b047983e1d24efd62ef24363b4d48095e0
Binary files /dev/null and b/aot/networks/models/__pycache__/__init__.cpython-311.pyc differ
diff --git a/aot/networks/models/__pycache__/aot.cpython-311.pyc b/aot/networks/models/__pycache__/aot.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d17bf3fe3eb601663a67f92a74322f2f57360e0d
Binary files /dev/null and b/aot/networks/models/__pycache__/aot.cpython-311.pyc differ
diff --git a/aot/networks/models/__pycache__/deaot.cpython-311.pyc b/aot/networks/models/__pycache__/deaot.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ce988eb3763655bf003524fccd00315d115068dc
Binary files /dev/null and b/aot/networks/models/__pycache__/deaot.cpython-311.pyc differ
diff --git a/aot/networks/models/aot.py b/aot/networks/models/aot.py
new file mode 100644
index 0000000000000000000000000000000000000000..813ee04be9028fe342310574d6f73793c4d506a8
--- /dev/null
+++ b/aot/networks/models/aot.py
@@ -0,0 +1,115 @@
+import torch.nn as nn
+
+from networks.encoders import build_encoder
+from networks.layers.transformer import LongShortTermTransformer
+from networks.decoders import build_decoder
+from networks.layers.position import PositionEmbeddingSine
+
+
+class AOT(nn.Module):
+ def __init__(self, cfg, encoder='mobilenetv2', decoder='fpn'):
+ super().__init__()
+ self.cfg = cfg
+ self.max_obj_num = cfg.MODEL_MAX_OBJ_NUM
+ self.epsilon = cfg.MODEL_EPSILON
+
+ self.encoder = build_encoder(encoder,
+ frozen_bn=cfg.MODEL_FREEZE_BN,
+ freeze_at=cfg.TRAIN_ENCODER_FREEZE_AT)
+ self.encoder_projector = nn.Conv2d(cfg.MODEL_ENCODER_DIM[-1],
+ cfg.MODEL_ENCODER_EMBEDDING_DIM,
+ kernel_size=1)
+
+ self.LSTT = LongShortTermTransformer(
+ cfg.MODEL_LSTT_NUM,
+ cfg.MODEL_ENCODER_EMBEDDING_DIM,
+ cfg.MODEL_SELF_HEADS,
+ cfg.MODEL_ATT_HEADS,
+ emb_dropout=cfg.TRAIN_LSTT_EMB_DROPOUT,
+ droppath=cfg.TRAIN_LSTT_DROPPATH,
+ lt_dropout=cfg.TRAIN_LSTT_LT_DROPOUT,
+ st_dropout=cfg.TRAIN_LSTT_ST_DROPOUT,
+ droppath_lst=cfg.TRAIN_LSTT_DROPPATH_LST,
+ droppath_scaling=cfg.TRAIN_LSTT_DROPPATH_SCALING,
+ intermediate_norm=cfg.MODEL_DECODER_INTERMEDIATE_LSTT,
+ return_intermediate=True)
+
+ decoder_indim = cfg.MODEL_ENCODER_EMBEDDING_DIM * \
+ (cfg.MODEL_LSTT_NUM +
+ 1) if cfg.MODEL_DECODER_INTERMEDIATE_LSTT else cfg.MODEL_ENCODER_EMBEDDING_DIM
+
+ self.decoder = build_decoder(
+ decoder,
+ in_dim=decoder_indim,
+ out_dim=cfg.MODEL_MAX_OBJ_NUM + 1,
+ decode_intermediate_input=cfg.MODEL_DECODER_INTERMEDIATE_LSTT,
+ hidden_dim=cfg.MODEL_ENCODER_EMBEDDING_DIM,
+ shortcut_dims=cfg.MODEL_ENCODER_DIM,
+ align_corners=cfg.MODEL_ALIGN_CORNERS)
+
+ if cfg.MODEL_ALIGN_CORNERS:
+ self.patch_wise_id_bank = nn.Conv2d(
+ cfg.MODEL_MAX_OBJ_NUM + 1,
+ cfg.MODEL_ENCODER_EMBEDDING_DIM,
+ kernel_size=17,
+ stride=16,
+ padding=8)
+ else:
+ self.patch_wise_id_bank = nn.Conv2d(
+ cfg.MODEL_MAX_OBJ_NUM + 1,
+ cfg.MODEL_ENCODER_EMBEDDING_DIM,
+ kernel_size=16,
+ stride=16,
+ padding=0)
+
+ self.id_dropout = nn.Dropout(cfg.TRAIN_LSTT_ID_DROPOUT, True)
+
+ self.pos_generator = PositionEmbeddingSine(
+ cfg.MODEL_ENCODER_EMBEDDING_DIM // 2, normalize=True)
+
+ self._init_weight()
+
+ def get_pos_emb(self, x):
+ pos_emb = self.pos_generator(x)
+ return pos_emb
+
+ def get_id_emb(self, x):
+ id_emb = self.patch_wise_id_bank(x)
+ id_emb = self.id_dropout(id_emb)
+ return id_emb
+
+ def encode_image(self, img):
+ xs = self.encoder(img)
+ xs[-1] = self.encoder_projector(xs[-1])
+ return xs
+
+ def decode_id_logits(self, lstt_emb, shortcuts):
+ n, c, h, w = shortcuts[-1].size()
+ decoder_inputs = [shortcuts[-1]]
+ for emb in lstt_emb:
+ decoder_inputs.append(emb.view(h, w, n, c).permute(2, 3, 0, 1))
+ pred_logit = self.decoder(decoder_inputs, shortcuts)
+ return pred_logit
+
+ def LSTT_forward(self,
+ curr_embs,
+ long_term_memories,
+ short_term_memories,
+ curr_id_emb=None,
+ pos_emb=None,
+ size_2d=(30, 30)):
+ n, c, h, w = curr_embs[-1].size()
+ curr_emb = curr_embs[-1].view(n, c, h * w).permute(2, 0, 1)
+ lstt_embs, lstt_memories = self.LSTT(curr_emb, long_term_memories,
+ short_term_memories, curr_id_emb,
+ pos_emb, size_2d)
+ lstt_curr_memories, lstt_long_memories, lstt_short_memories = zip(
+ *lstt_memories)
+ return lstt_embs, lstt_curr_memories, lstt_long_memories, lstt_short_memories
+
+ def _init_weight(self):
+ nn.init.xavier_uniform_(self.encoder_projector.weight)
+ nn.init.orthogonal_(
+ self.patch_wise_id_bank.weight.view(
+ self.cfg.MODEL_ENCODER_EMBEDDING_DIM, -1).permute(0, 1),
+ gain=17**-2 if self.cfg.MODEL_ALIGN_CORNERS else 16**-2)
diff --git a/aot/networks/models/deaot.py b/aot/networks/models/deaot.py
new file mode 100644
index 0000000000000000000000000000000000000000..008dd43c75911d056843582bd073c0c226ddf37d
--- /dev/null
+++ b/aot/networks/models/deaot.py
@@ -0,0 +1,55 @@
+import torch.nn as nn
+
+from networks.layers.transformer import DualBranchGPM
+from networks.models.aot import AOT
+from networks.decoders import build_decoder
+
+
+class DeAOT(AOT):
+ def __init__(self, cfg, encoder='mobilenetv2', decoder='fpn'):
+ super().__init__(cfg, encoder, decoder)
+
+ self.LSTT = DualBranchGPM(
+ cfg.MODEL_LSTT_NUM,
+ cfg.MODEL_ENCODER_EMBEDDING_DIM,
+ cfg.MODEL_SELF_HEADS,
+ cfg.MODEL_ATT_HEADS,
+ emb_dropout=cfg.TRAIN_LSTT_EMB_DROPOUT,
+ droppath=cfg.TRAIN_LSTT_DROPPATH,
+ lt_dropout=cfg.TRAIN_LSTT_LT_DROPOUT,
+ st_dropout=cfg.TRAIN_LSTT_ST_DROPOUT,
+ droppath_lst=cfg.TRAIN_LSTT_DROPPATH_LST,
+ droppath_scaling=cfg.TRAIN_LSTT_DROPPATH_SCALING,
+ intermediate_norm=cfg.MODEL_DECODER_INTERMEDIATE_LSTT,
+ return_intermediate=True)
+
+ decoder_indim = cfg.MODEL_ENCODER_EMBEDDING_DIM * \
+ (cfg.MODEL_LSTT_NUM * 2 +
+ 1) if cfg.MODEL_DECODER_INTERMEDIATE_LSTT else cfg.MODEL_ENCODER_EMBEDDING_DIM * 2
+
+ self.decoder = build_decoder(
+ decoder,
+ in_dim=decoder_indim,
+ out_dim=cfg.MODEL_MAX_OBJ_NUM + 1,
+ decode_intermediate_input=cfg.MODEL_DECODER_INTERMEDIATE_LSTT,
+ hidden_dim=cfg.MODEL_ENCODER_EMBEDDING_DIM,
+ shortcut_dims=cfg.MODEL_ENCODER_DIM,
+ align_corners=cfg.MODEL_ALIGN_CORNERS)
+
+ self.id_norm = nn.LayerNorm(cfg.MODEL_ENCODER_EMBEDDING_DIM)
+
+ self._init_weight()
+
+ def decode_id_logits(self, lstt_emb, shortcuts):
+ n, c, h, w = shortcuts[-1].size()
+ decoder_inputs = [shortcuts[-1]]
+ for emb in lstt_emb:
+ decoder_inputs.append(emb.view(h, w, n, -1).permute(2, 3, 0, 1))
+ pred_logit = self.decoder(decoder_inputs, shortcuts)
+ return pred_logit
+
+ def get_id_emb(self, x):
+ id_emb = self.patch_wise_id_bank(x)
+ id_emb = self.id_norm(id_emb.permute(2, 3, 0, 1)).permute(2, 3, 0, 1)
+ id_emb = self.id_dropout(id_emb)
+ return id_emb
diff --git a/aot/pretrain_models/README.md b/aot/pretrain_models/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..6c72d93391cd314e85690ab2514b2d22642c4d97
--- /dev/null
+++ b/aot/pretrain_models/README.md
@@ -0,0 +1 @@
+Put pretrained models here.
\ No newline at end of file
diff --git a/aot/source/.DS_Store b/aot/source/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6
Binary files /dev/null and b/aot/source/.DS_Store differ
diff --git a/aot/source/overview.png b/aot/source/overview.png
new file mode 100644
index 0000000000000000000000000000000000000000..0b3308870114399f601900ca7369a5904ed5de72
Binary files /dev/null and b/aot/source/overview.png differ
diff --git a/aot/source/overview_deaot.png b/aot/source/overview_deaot.png
new file mode 100644
index 0000000000000000000000000000000000000000..bdb15a162c7557aa62a1439d8cc6e922c7567db4
Binary files /dev/null and b/aot/source/overview_deaot.png differ
diff --git a/aot/tools/demo.py b/aot/tools/demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..64c30c0dd0afc9b335d286a0af7165738c24e3dd
--- /dev/null
+++ b/aot/tools/demo.py
@@ -0,0 +1,286 @@
+import importlib
+import sys
+import os
+
+sys.path.append('.')
+sys.path.append('..')
+
+import cv2
+from PIL import Image
+from skimage.morphology.binary import binary_dilation
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch.utils.data import DataLoader
+from torchvision import transforms
+
+from networks.models import build_vos_model
+from networks.engines import build_engine
+from utils.checkpoint import load_network
+
+from dataloaders.eval_datasets import VOSTest
+import dataloaders.video_transforms as tr
+from utils.image import save_mask
+
+_palette = [
+ 255, 0, 0, 0, 0, 139, 255, 255, 84, 0, 255, 0, 139, 0, 139, 0, 128, 128,
+ 128, 128, 128, 139, 0, 0, 218, 165, 32, 144, 238, 144, 160, 82, 45, 148, 0,
+ 211, 255, 0, 255, 30, 144, 255, 255, 218, 185, 85, 107, 47, 255, 140, 0,
+ 50, 205, 50, 123, 104, 238, 240, 230, 140, 72, 61, 139, 128, 128, 0, 0, 0,
+ 205, 221, 160, 221, 143, 188, 143, 127, 255, 212, 176, 224, 230, 244, 164,
+ 96, 250, 128, 114, 70, 130, 180, 0, 128, 0, 173, 255, 47, 255, 105, 180,
+ 238, 130, 238, 154, 205, 50, 220, 20, 60, 176, 48, 96, 0, 206, 209, 0, 191,
+ 255, 40, 40, 40, 41, 41, 41, 42, 42, 42, 43, 43, 43, 44, 44, 44, 45, 45,
+ 45, 46, 46, 46, 47, 47, 47, 48, 48, 48, 49, 49, 49, 50, 50, 50, 51, 51, 51,
+ 52, 52, 52, 53, 53, 53, 54, 54, 54, 55, 55, 55, 56, 56, 56, 57, 57, 57, 58,
+ 58, 58, 59, 59, 59, 60, 60, 60, 61, 61, 61, 62, 62, 62, 63, 63, 63, 64, 64,
+ 64, 65, 65, 65, 66, 66, 66, 67, 67, 67, 68, 68, 68, 69, 69, 69, 70, 70, 70,
+ 71, 71, 71, 72, 72, 72, 73, 73, 73, 74, 74, 74, 75, 75, 75, 76, 76, 76, 77,
+ 77, 77, 78, 78, 78, 79, 79, 79, 80, 80, 80, 81, 81, 81, 82, 82, 82, 83, 83,
+ 83, 84, 84, 84, 85, 85, 85, 86, 86, 86, 87, 87, 87, 88, 88, 88, 89, 89, 89,
+ 90, 90, 90, 91, 91, 91, 92, 92, 92, 93, 93, 93, 94, 94, 94, 95, 95, 95, 96,
+ 96, 96, 97, 97, 97, 98, 98, 98, 99, 99, 99, 100, 100, 100, 101, 101, 101,
+ 102, 102, 102, 103, 103, 103, 104, 104, 104, 105, 105, 105, 106, 106, 106,
+ 107, 107, 107, 108, 108, 108, 109, 109, 109, 110, 110, 110, 111, 111, 111,
+ 112, 112, 112, 113, 113, 113, 114, 114, 114, 115, 115, 115, 116, 116, 116,
+ 117, 117, 117, 118, 118, 118, 119, 119, 119, 120, 120, 120, 121, 121, 121,
+ 122, 122, 122, 123, 123, 123, 124, 124, 124, 125, 125, 125, 126, 126, 126,
+ 127, 127, 127, 128, 128, 128, 129, 129, 129, 130, 130, 130, 131, 131, 131,
+ 132, 132, 132, 133, 133, 133, 134, 134, 134, 135, 135, 135, 136, 136, 136,
+ 137, 137, 137, 138, 138, 138, 139, 139, 139, 140, 140, 140, 141, 141, 141,
+ 142, 142, 142, 143, 143, 143, 144, 144, 144, 145, 145, 145, 146, 146, 146,
+ 147, 147, 147, 148, 148, 148, 149, 149, 149, 150, 150, 150, 151, 151, 151,
+ 152, 152, 152, 153, 153, 153, 154, 154, 154, 155, 155, 155, 156, 156, 156,
+ 157, 157, 157, 158, 158, 158, 159, 159, 159, 160, 160, 160, 161, 161, 161,
+ 162, 162, 162, 163, 163, 163, 164, 164, 164, 165, 165, 165, 166, 166, 166,
+ 167, 167, 167, 168, 168, 168, 169, 169, 169, 170, 170, 170, 171, 171, 171,
+ 172, 172, 172, 173, 173, 173, 174, 174, 174, 175, 175, 175, 176, 176, 176,
+ 177, 177, 177, 178, 178, 178, 179, 179, 179, 180, 180, 180, 181, 181, 181,
+ 182, 182, 182, 183, 183, 183, 184, 184, 184, 185, 185, 185, 186, 186, 186,
+ 187, 187, 187, 188, 188, 188, 189, 189, 189, 190, 190, 190, 191, 191, 191,
+ 192, 192, 192, 193, 193, 193, 194, 194, 194, 195, 195, 195, 196, 196, 196,
+ 197, 197, 197, 198, 198, 198, 199, 199, 199, 200, 200, 200, 201, 201, 201,
+ 202, 202, 202, 203, 203, 203, 204, 204, 204, 205, 205, 205, 206, 206, 206,
+ 207, 207, 207, 208, 208, 208, 209, 209, 209, 210, 210, 210, 211, 211, 211,
+ 212, 212, 212, 213, 213, 213, 214, 214, 214, 215, 215, 215, 216, 216, 216,
+ 217, 217, 217, 218, 218, 218, 219, 219, 219, 220, 220, 220, 221, 221, 221,
+ 222, 222, 222, 223, 223, 223, 224, 224, 224, 225, 225, 225, 226, 226, 226,
+ 227, 227, 227, 228, 228, 228, 229, 229, 229, 230, 230, 230, 231, 231, 231,
+ 232, 232, 232, 233, 233, 233, 234, 234, 234, 235, 235, 235, 236, 236, 236,
+ 237, 237, 237, 238, 238, 238, 239, 239, 239, 240, 240, 240, 241, 241, 241,
+ 242, 242, 242, 243, 243, 243, 244, 244, 244, 245, 245, 245, 246, 246, 246,
+ 247, 247, 247, 248, 248, 248, 249, 249, 249, 250, 250, 250, 251, 251, 251,
+ 252, 252, 252, 253, 253, 253, 254, 254, 254, 255, 255, 255, 0, 0, 0
+]
+color_palette = np.array(_palette).reshape(-1, 3)
+
+
+def overlay(image, mask, colors=[255, 0, 0], cscale=1, alpha=0.4):
+ colors = np.atleast_2d(colors) * cscale
+
+ im_overlay = image.copy()
+ object_ids = np.unique(mask)
+
+ for object_id in object_ids[1:]:
+ # Overlay color on binary mask
+
+ foreground = image * alpha + np.ones(
+ image.shape) * (1 - alpha) * np.array(colors[object_id])
+ binary_mask = mask == object_id
+
+ # Compose image
+ im_overlay[binary_mask] = foreground[binary_mask]
+
+ countours = binary_dilation(binary_mask) ^ binary_mask
+ im_overlay[countours, :] = 0
+
+ return im_overlay.astype(image.dtype)
+
+
+def demo(cfg):
+ video_fps = 15
+ gpu_id = cfg.TEST_GPU_ID
+
+ # Load pre-trained model
+ print('Build AOT model.')
+ model = build_vos_model(cfg.MODEL_VOS, cfg).cuda(gpu_id)
+
+ print('Load checkpoint from {}'.format(cfg.TEST_CKPT_PATH))
+ model, _ = load_network(model, cfg.TEST_CKPT_PATH, gpu_id)
+
+ print('Build AOT engine.')
+ engine = build_engine(cfg.MODEL_ENGINE,
+ phase='eval',
+ aot_model=model,
+ gpu_id=gpu_id,
+ long_term_mem_gap=cfg.TEST_LONG_TERM_MEM_GAP)
+
+ # Prepare datasets for each sequence
+ transform = transforms.Compose([
+ tr.MultiRestrictSize(cfg.TEST_MIN_SIZE, cfg.TEST_MAX_SIZE,
+ cfg.TEST_FLIP, cfg.TEST_MULTISCALE,
+ cfg.MODEL_ALIGN_CORNERS),
+ tr.MultiToTensor()
+ ])
+ image_root = os.path.join(cfg.TEST_DATA_PATH, 'images')
+ label_root = os.path.join(cfg.TEST_DATA_PATH, 'masks')
+
+ sequences = os.listdir(image_root)
+ seq_datasets = []
+ for seq_name in sequences:
+ print('Build a dataset for sequence {}.'.format(seq_name))
+ seq_images = np.sort(os.listdir(os.path.join(image_root, seq_name)))
+ seq_labels = [seq_images[0].replace('jpg', 'png')]
+ seq_dataset = VOSTest(image_root,
+ label_root,
+ seq_name,
+ seq_images,
+ seq_labels,
+ transform=transform)
+ seq_datasets.append(seq_dataset)
+
+ # Infer
+ output_root = cfg.TEST_OUTPUT_PATH
+ output_mask_root = os.path.join(output_root, 'pred_masks')
+ if not os.path.exists(output_mask_root):
+ os.makedirs(output_mask_root)
+
+ for seq_dataset in seq_datasets:
+ seq_name = seq_dataset.seq_name
+ image_seq_root = os.path.join(image_root, seq_name)
+ output_mask_seq_root = os.path.join(output_mask_root, seq_name)
+ if not os.path.exists(output_mask_seq_root):
+ os.makedirs(output_mask_seq_root)
+ print('Build a dataloader for sequence {}.'.format(seq_name))
+ seq_dataloader = DataLoader(seq_dataset,
+ batch_size=1,
+ shuffle=False,
+ num_workers=cfg.TEST_WORKERS,
+ pin_memory=True)
+
+ fourcc = cv2.VideoWriter_fourcc(*'XVID')
+ output_video_path = os.path.join(
+ output_root, '{}_{}fps.avi'.format(seq_name, video_fps))
+
+ print('Start the inference of sequence {}:'.format(seq_name))
+ model.eval()
+ engine.restart_engine()
+ with torch.no_grad():
+ for frame_idx, samples in enumerate(seq_dataloader):
+ sample = samples[0]
+ img_name = sample['meta']['current_name'][0]
+
+ obj_nums = sample['meta']['obj_num']
+ output_height = sample['meta']['height']
+ output_width = sample['meta']['width']
+ obj_idx = sample['meta']['obj_idx']
+
+ obj_nums = [int(obj_num) for obj_num in obj_nums]
+ obj_idx = [int(_obj_idx) for _obj_idx in obj_idx]
+
+ current_img = sample['current_img']
+ current_img = current_img.cuda(gpu_id, non_blocking=True)
+
+ if frame_idx == 0:
+ videoWriter = cv2.VideoWriter(
+ output_video_path, fourcc, video_fps,
+ (int(output_width), int(output_height)))
+ print(
+ 'Object number: {}. Inference size: {}x{}. Output size: {}x{}.'
+ .format(obj_nums[0],
+ current_img.size()[2],
+ current_img.size()[3], int(output_height),
+ int(output_width)))
+ current_label = sample['current_label'].cuda(
+ gpu_id, non_blocking=True).float()
+ current_label = F.interpolate(current_label,
+ size=current_img.size()[2:],
+ mode="nearest")
+ # add reference frame
+ engine.add_reference_frame(current_img,
+ current_label,
+ frame_step=0,
+ obj_nums=obj_nums)
+ else:
+ print('Processing image {}...'.format(img_name))
+ # predict segmentation
+ engine.match_propogate_one_frame(current_img)
+ pred_logit = engine.decode_current_logits(
+ (output_height, output_width))
+ pred_prob = torch.softmax(pred_logit, dim=1)
+ pred_label = torch.argmax(pred_prob, dim=1,
+ keepdim=True).float()
+ _pred_label = F.interpolate(pred_label,
+ size=engine.input_size_2d,
+ mode="nearest")
+ # update memory
+ engine.update_memory(_pred_label)
+
+ # save results
+ input_image_path = os.path.join(image_seq_root, img_name)
+ output_mask_path = os.path.join(
+ output_mask_seq_root,
+ img_name.split('.')[0] + '.png')
+
+ pred_label = Image.fromarray(
+ pred_label.squeeze(0).squeeze(0).cpu().numpy().astype(
+ 'uint8')).convert('P')
+ pred_label.putpalette(_palette)
+ pred_label.save(output_mask_path)
+
+ input_image = Image.open(input_image_path)
+
+ overlayed_image = overlay(
+ np.array(input_image, dtype=np.uint8),
+ np.array(pred_label, dtype=np.uint8), color_palette)
+ videoWriter.write(overlayed_image[..., [2, 1, 0]])
+
+ print('Save a visualization video to {}.'.format(output_video_path))
+ videoWriter.release()
+
+
+def main():
+ import argparse
+ parser = argparse.ArgumentParser(description="AOT Demo")
+ parser.add_argument('--exp_name', type=str, default='default')
+
+ parser.add_argument('--stage', type=str, default='pre_ytb_dav')
+ parser.add_argument('--model', type=str, default='r50_aotl')
+
+ parser.add_argument('--gpu_id', type=int, default=0)
+
+ parser.add_argument('--data_path', type=str, default='./datasets/Demo')
+ parser.add_argument('--output_path', type=str, default='./demo_output')
+ parser.add_argument('--ckpt_path',
+ type=str,
+ default='./pretrain_models/R50_AOTL_PRE_YTB_DAV.pth')
+
+ parser.add_argument('--max_resolution', type=float, default=480 * 1.3)
+
+ parser.add_argument('--amp', action='store_true')
+ parser.set_defaults(amp=False)
+
+ args = parser.parse_args()
+
+ engine_config = importlib.import_module('configs.' + args.stage)
+ cfg = engine_config.EngineConfig(args.exp_name, args.model)
+
+ cfg.TEST_GPU_ID = args.gpu_id
+
+ cfg.TEST_CKPT_PATH = args.ckpt_path
+ cfg.TEST_DATA_PATH = args.data_path
+ cfg.TEST_OUTPUT_PATH = args.output_path
+
+ cfg.TEST_MIN_SIZE = None
+ cfg.TEST_MAX_SIZE = args.max_resolution * 800. / 480.
+
+ if args.amp:
+ with torch.cuda.amp.autocast(enabled=True):
+ demo(cfg)
+ else:
+ demo(cfg)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/aot/tools/eval.py b/aot/tools/eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3eef634c9ba60ad896be9cde9e20783a3679cae
--- /dev/null
+++ b/aot/tools/eval.py
@@ -0,0 +1,112 @@
+import importlib
+import sys
+
+sys.path.append('.')
+sys.path.append('..')
+
+import torch
+import torch.multiprocessing as mp
+
+from networks.managers.evaluator import Evaluator
+
+
+def main_worker(gpu, cfg, seq_queue=None, info_queue=None, enable_amp=False):
+ # Initiate a evaluating manager
+ evaluator = Evaluator(rank=gpu,
+ cfg=cfg,
+ seq_queue=seq_queue,
+ info_queue=info_queue)
+ # Start evaluation
+ if enable_amp:
+ with torch.cuda.amp.autocast(enabled=True):
+ evaluator.evaluating()
+ else:
+ evaluator.evaluating()
+
+
+def main():
+ import argparse
+ parser = argparse.ArgumentParser(description="Eval VOS")
+ parser.add_argument('--exp_name', type=str, default='default')
+
+ parser.add_argument('--stage', type=str, default='pre')
+ parser.add_argument('--model', type=str, default='aott')
+ parser.add_argument('--lstt_num', type=int, default=-1)
+ parser.add_argument('--lt_gap', type=int, default=-1)
+ parser.add_argument('--st_skip', type=int, default=-1)
+ parser.add_argument('--max_id_num', type=int, default='-1')
+
+ parser.add_argument('--gpu_id', type=int, default=0)
+ parser.add_argument('--gpu_num', type=int, default=1)
+
+ parser.add_argument('--ckpt_path', type=str, default='')
+ parser.add_argument('--ckpt_step', type=int, default=-1)
+
+ parser.add_argument('--dataset', type=str, default='')
+ parser.add_argument('--split', type=str, default='')
+
+ parser.add_argument('--ema', action='store_true')
+ parser.set_defaults(ema=False)
+
+ parser.add_argument('--flip', action='store_true')
+ parser.set_defaults(flip=False)
+ parser.add_argument('--ms', nargs='+', type=float, default=[1.])
+
+ parser.add_argument('--max_resolution', type=float, default=480 * 1.3)
+
+ parser.add_argument('--amp', action='store_true')
+ parser.set_defaults(amp=False)
+
+ args = parser.parse_args()
+
+ engine_config = importlib.import_module('configs.' + args.stage)
+ cfg = engine_config.EngineConfig(args.exp_name, args.model)
+
+ cfg.TEST_EMA = args.ema
+
+ cfg.TEST_GPU_ID = args.gpu_id
+ cfg.TEST_GPU_NUM = args.gpu_num
+
+ if args.lstt_num > 0:
+ cfg.MODEL_LSTT_NUM = args.lstt_num
+ if args.lt_gap > 0:
+ cfg.TEST_LONG_TERM_MEM_GAP = args.lt_gap
+ if args.st_skip > 0:
+ cfg.TEST_SHORT_TERM_MEM_SKIP = args.st_skip
+
+ if args.max_id_num > 0:
+ cfg.MODEL_MAX_OBJ_NUM = args.max_id_num
+
+ if args.ckpt_path != '':
+ cfg.TEST_CKPT_PATH = args.ckpt_path
+ if args.ckpt_step > 0:
+ cfg.TEST_CKPT_STEP = args.ckpt_step
+
+ if args.dataset != '':
+ cfg.TEST_DATASET = args.dataset
+
+ if args.split != '':
+ cfg.TEST_DATASET_SPLIT = args.split
+
+ cfg.TEST_FLIP = args.flip
+ cfg.TEST_MULTISCALE = args.ms
+
+ if cfg.TEST_MULTISCALE != [1.]:
+ cfg.TEST_MAX_SHORT_EDGE = args.max_resolution # for preventing OOM
+ else:
+ cfg.TEST_MAX_SHORT_EDGE = None # the default resolution setting of CFBI and AOT
+ cfg.TEST_MAX_LONG_EDGE = args.max_resolution * 800. / 480.
+
+ if args.gpu_num > 1:
+ mp.set_start_method('spawn')
+ seq_queue = mp.Queue()
+ info_queue = mp.Queue()
+ mp.spawn(main_worker,
+ nprocs=cfg.TEST_GPU_NUM,
+ args=(cfg, seq_queue, info_queue, args.amp))
+ else:
+ main_worker(0, cfg, enable_amp=args.amp)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/aot/tools/train.py b/aot/tools/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..177dd36a2d5341730672d59933f5bfe16cc9322f
--- /dev/null
+++ b/aot/tools/train.py
@@ -0,0 +1,87 @@
+import importlib
+import random
+import sys
+
+sys.setrecursionlimit(10000)
+sys.path.append('.')
+sys.path.append('..')
+
+import torch.multiprocessing as mp
+
+from networks.managers.trainer import Trainer
+
+
+def main_worker(gpu, cfg, enable_amp=True):
+ # Initiate a training manager
+ trainer = Trainer(rank=gpu, cfg=cfg, enable_amp=enable_amp)
+ # Start Training
+ trainer.sequential_training()
+
+
+def main():
+ import argparse
+ parser = argparse.ArgumentParser(description="Train VOS")
+ parser.add_argument('--exp_name', type=str, default='')
+ parser.add_argument('--stage', type=str, default='pre')
+ parser.add_argument('--model', type=str, default='aott')
+ parser.add_argument('--max_id_num', type=int, default='-1')
+
+ parser.add_argument('--start_gpu', type=int, default=0)
+ parser.add_argument('--gpu_num', type=int, default=-1)
+ parser.add_argument('--batch_size', type=int, default=-1)
+ parser.add_argument('--dist_url', type=str, default='')
+ parser.add_argument('--amp', action='store_true')
+ parser.set_defaults(amp=False)
+
+ parser.add_argument('--pretrained_path', type=str, default='')
+
+ parser.add_argument('--datasets', nargs='+', type=str, default=[])
+ parser.add_argument('--lr', type=float, default=-1.)
+ parser.add_argument('--total_step', type=int, default=-1.)
+ parser.add_argument('--start_step', type=int, default=-1.)
+
+ args = parser.parse_args()
+
+ engine_config = importlib.import_module('configs.' + args.stage)
+
+ cfg = engine_config.EngineConfig(args.exp_name, args.model)
+
+ if len(args.datasets) > 0:
+ cfg.DATASETS = args.datasets
+
+ cfg.DIST_START_GPU = args.start_gpu
+ if args.gpu_num > 0:
+ cfg.TRAIN_GPUS = args.gpu_num
+ if args.batch_size > 0:
+ cfg.TRAIN_BATCH_SIZE = args.batch_size
+
+ if args.pretrained_path != '':
+ cfg.PRETRAIN_MODEL = args.pretrained_path
+
+ if args.max_id_num > 0:
+ cfg.MODEL_MAX_OBJ_NUM = args.max_id_num
+
+ if args.lr > 0:
+ cfg.TRAIN_LR = args.lr
+
+ if args.total_step > 0:
+ cfg.TRAIN_TOTAL_STEPS = args.total_step
+
+ if args.start_step > 0:
+ cfg.TRAIN_START_STEP = args.start_step
+
+ if args.dist_url == '':
+ cfg.DIST_URL = 'tcp://127.0.0.1:123' + str(random.randint(0, 9)) + str(
+ random.randint(0, 9))
+ else:
+ cfg.DIST_URL = args.dist_url
+
+ if cfg.TRAIN_GPUS > 1:
+ # Use torch.multiprocessing.spawn to launch distributed processes
+ mp.spawn(main_worker, nprocs=cfg.TRAIN_GPUS, args=(cfg, args.amp))
+ else:
+ cfg.TRAIN_GPUS = 1
+ main_worker(0, cfg, args.amp)
+
+if __name__ == '__main__':
+ main()
diff --git a/aot/train_eval.sh b/aot/train_eval.sh
new file mode 100644
index 0000000000000000000000000000000000000000..2f15c85e65e603186b9f581aa14ac8ba39cefac2
--- /dev/null
+++ b/aot/train_eval.sh
@@ -0,0 +1,50 @@
+exp="default"
+gpu_num="4"
+
+model="aott"
+# model="aots"
+# model="aotb"
+# model="aotl"
+# model="r50_deaotl"
+# model="swinb_aotl"
+
+## Training ##
+stage="pre"
+python tools/train.py --amp \
+ --exp_name ${exp} \
+ --stage ${stage} \
+ --model ${model} \
+ --gpu_num ${gpu_num}
+
+stage="pre_ytb_dav"
+python tools/train.py --amp \
+ --exp_name ${exp} \
+ --stage ${stage} \
+ --model ${model} \
+ --gpu_num ${gpu_num}
+
+## Evaluation ##
+dataset="davis2017"
+split="test"
+python tools/eval.py --exp_name ${exp} --stage ${stage} --model ${model} \
+ --dataset ${dataset} --split ${split} --gpu_num ${gpu_num}
+
+dataset="davis2017"
+split="val"
+python tools/eval.py --exp_name ${exp} --stage ${stage} --model ${model} \
+ --dataset ${dataset} --split ${split} --gpu_num ${gpu_num}
+
+dataset="davis2016"
+split="val"
+python tools/eval.py --exp_name ${exp} --stage ${stage} --model ${model} \
+ --dataset ${dataset} --split ${split} --gpu_num ${gpu_num}
+
+dataset="youtubevos2018"
+split="val" # or "val_all_frames"
+python tools/eval.py --exp_name ${exp} --stage ${stage} --model ${model} \
+ --dataset ${dataset} --split ${split} --gpu_num ${gpu_num}
+
+dataset="youtubevos2019"
+split="val" # or "val_all_frames"
+python tools/eval.py --exp_name ${exp} --stage ${stage} --model ${model} \
+ --dataset ${dataset} --split ${split} --gpu_num ${gpu_num}
\ No newline at end of file
diff --git a/aot/utils/__init__.py b/aot/utils/__init__.py
new file mode 100644
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diff --git a/aot/utils/__pycache__/math.cpython-311.pyc b/aot/utils/__pycache__/math.cpython-311.pyc
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diff --git a/aot/utils/checkpoint.py b/aot/utils/checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbee512afa972f2d61678b6b5752c72dd41023c7
--- /dev/null
+++ b/aot/utils/checkpoint.py
@@ -0,0 +1,163 @@
+import torch
+import os
+import shutil
+import numpy as np
+
+
+def load_network_and_optimizer(net, opt, pretrained_dir, gpu, scaler=None):
+ pretrained = torch.load(pretrained_dir,
+ map_location=torch.device("cuda:" + str(gpu)))
+ pretrained_dict = pretrained['state_dict']
+ model_dict = net.state_dict()
+ pretrained_dict_update = {}
+ pretrained_dict_remove = []
+ for k, v in pretrained_dict.items():
+ if k in model_dict:
+ pretrained_dict_update[k] = v
+ elif k[:7] == 'module.':
+ if k[7:] in model_dict:
+ pretrained_dict_update[k[7:]] = v
+ else:
+ pretrained_dict_remove.append(k)
+ model_dict.update(pretrained_dict_update)
+ net.load_state_dict(model_dict)
+ opt.load_state_dict(pretrained['optimizer'])
+ if scaler is not None and 'scaler' in pretrained.keys():
+ scaler.load_state_dict(pretrained['scaler'])
+ del (pretrained)
+ return net.cuda(gpu), opt, pretrained_dict_remove
+
+
+def load_network_and_optimizer_v2(net, opt, pretrained_dir, gpu, scaler=None):
+ pretrained = torch.load(pretrained_dir,
+ map_location=torch.device("cuda:" + str(gpu)))
+ # load model
+ pretrained_dict = pretrained['state_dict']
+ model_dict = net.state_dict()
+ pretrained_dict_update = {}
+ pretrained_dict_remove = []
+ for k, v in pretrained_dict.items():
+ if k in model_dict:
+ pretrained_dict_update[k] = v
+ elif k[:7] == 'module.':
+ if k[7:] in model_dict:
+ pretrained_dict_update[k[7:]] = v
+ else:
+ pretrained_dict_remove.append(k)
+ model_dict.update(pretrained_dict_update)
+ net.load_state_dict(model_dict)
+
+ # load optimizer
+ opt_dict = opt.state_dict()
+ all_params = {
+ param_group['name']: param_group['params'][0]
+ for param_group in opt_dict['param_groups']
+ }
+ pretrained_opt_dict = {'state': {}, 'param_groups': []}
+ for idx in range(len(pretrained['optimizer']['param_groups'])):
+ param_group = pretrained['optimizer']['param_groups'][idx]
+ if param_group['name'] in all_params.keys():
+ pretrained_opt_dict['state'][all_params[
+ param_group['name']]] = pretrained['optimizer']['state'][
+ param_group['params'][0]]
+ param_group['params'][0] = all_params[param_group['name']]
+ pretrained_opt_dict['param_groups'].append(param_group)
+
+ opt_dict.update(pretrained_opt_dict)
+ opt.load_state_dict(opt_dict)
+
+ # load scaler
+ if scaler is not None and 'scaler' in pretrained.keys():
+ scaler.load_state_dict(pretrained['scaler'])
+ del (pretrained)
+ return net.cuda(gpu), opt, pretrained_dict_remove
+
+
+def load_network(net, pretrained_dir, gpu):
+ pretrained = torch.load(pretrained_dir,
+ map_location=torch.device("cuda:" + str(gpu)))
+ if 'state_dict' in pretrained.keys():
+ pretrained_dict = pretrained['state_dict']
+ elif 'model' in pretrained.keys():
+ pretrained_dict = pretrained['model']
+ else:
+ pretrained_dict = pretrained
+ model_dict = net.state_dict()
+ pretrained_dict_update = {}
+ pretrained_dict_remove = []
+ for k, v in pretrained_dict.items():
+ if k in model_dict:
+ pretrained_dict_update[k] = v
+ elif k[:7] == 'module.':
+ if k[7:] in model_dict:
+ pretrained_dict_update[k[7:]] = v
+ else:
+ pretrained_dict_remove.append(k)
+ model_dict.update(pretrained_dict_update)
+ net.load_state_dict(model_dict)
+ del (pretrained)
+ return net.cuda(gpu), pretrained_dict_remove
+
+
+def save_network(net,
+ opt,
+ step,
+ save_path,
+ max_keep=8,
+ backup_dir='./saved_models',
+ scaler=None):
+ ckpt = {'state_dict': net.state_dict(), 'optimizer': opt.state_dict()}
+ if scaler is not None:
+ ckpt['scaler'] = scaler.state_dict()
+
+ try:
+ if not os.path.exists(save_path):
+ os.makedirs(save_path)
+ save_file = 'save_step_%s.pth' % (step)
+ save_dir = os.path.join(save_path, save_file)
+ torch.save(ckpt, save_dir)
+ except:
+ save_path = backup_dir
+ if not os.path.exists(save_path):
+ os.makedirs(save_path)
+ save_file = 'save_step_%s.pth' % (step)
+ save_dir = os.path.join(save_path, save_file)
+ torch.save(ckpt, save_dir)
+
+ all_ckpt = os.listdir(save_path)
+ if len(all_ckpt) > max_keep:
+ all_step = []
+ for ckpt_name in all_ckpt:
+ step = int(ckpt_name.split('_')[-1].split('.')[0])
+ all_step.append(step)
+ all_step = list(np.sort(all_step))[:-max_keep]
+ for step in all_step:
+ ckpt_path = os.path.join(save_path, 'save_step_%s.pth' % (step))
+ os.system('rm {}'.format(ckpt_path))
+
+
+def cp_ckpt(remote_dir="data_wd/youtube_vos_jobs/result", curr_dir="backup"):
+ exps = os.listdir(curr_dir)
+ for exp in exps:
+ exp_dir = os.path.join(curr_dir, exp)
+ stages = os.listdir(exp_dir)
+ for stage in stages:
+ stage_dir = os.path.join(exp_dir, stage)
+ finals = ["ema_ckpt", "ckpt"]
+ for final in finals:
+ final_dir = os.path.join(stage_dir, final)
+ ckpts = os.listdir(final_dir)
+ for ckpt in ckpts:
+ if '.pth' not in ckpt:
+ continue
+ curr_ckpt_path = os.path.join(final_dir, ckpt)
+ remote_ckpt_path = os.path.join(remote_dir, exp, stage,
+ final, ckpt)
+ if os.path.exists(remote_ckpt_path):
+ os.system('rm {}'.format(remote_ckpt_path))
+ try:
+ shutil.copy(curr_ckpt_path, remote_ckpt_path)
+ print("Copy {} to {}.".format(curr_ckpt_path,
+ remote_ckpt_path))
+ except OSError as Inst:
+ return
diff --git a/aot/utils/cp_ckpt.py b/aot/utils/cp_ckpt.py
new file mode 100644
index 0000000000000000000000000000000000000000..22cb96e2291676b4c913deb8e4e47a99d5b0ee16
--- /dev/null
+++ b/aot/utils/cp_ckpt.py
@@ -0,0 +1,36 @@
+import os
+import shutil
+
+
+def cp_ckpt(remote_dir="data_wd/youtube_vos_jobs/result", curr_dir="backup"):
+ exps = os.listdir(curr_dir)
+ for exp in exps:
+ print("Exp: ", exp)
+ exp_dir = os.path.join(curr_dir, exp)
+ stages = os.listdir(exp_dir)
+ for stage in stages:
+ print("Stage: ", stage)
+ stage_dir = os.path.join(exp_dir, stage)
+ finals = ["ema_ckpt", "ckpt"]
+ for final in finals:
+ print("Final: ", final)
+ final_dir = os.path.join(stage_dir, final)
+ ckpts = os.listdir(final_dir)
+ for ckpt in ckpts:
+ if '.pth' not in ckpt:
+ continue
+ curr_ckpt_path = os.path.join(final_dir, ckpt)
+ remote_ckpt_path = os.path.join(remote_dir, exp, stage,
+ final, ckpt)
+ if os.path.exists(remote_ckpt_path):
+ os.system('rm {}'.format(remote_ckpt_path))
+ try:
+ shutil.copy(curr_ckpt_path, remote_ckpt_path)
+ print(ckpt, ': OK')
+ except OSError as Inst:
+ print(Inst)
+ print(ckpt, ': Fail')
+
+
+if __name__ == "__main__":
+ cp_ckpt()
diff --git a/aot/utils/ema.py b/aot/utils/ema.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba1bc69511b65c04c7658d6693f9e403279f44a4
--- /dev/null
+++ b/aot/utils/ema.py
@@ -0,0 +1,93 @@
+from __future__ import division
+from __future__ import unicode_literals
+
+import torch
+
+
+def get_param_buffer_for_ema(model,
+ update_buffer=False,
+ required_buffers=['running_mean', 'running_var']):
+ params = model.parameters()
+ all_param_buffer = [p for p in params if p.requires_grad]
+ if update_buffer:
+ named_buffers = model.named_buffers()
+ for key, value in named_buffers:
+ for buffer_name in required_buffers:
+ if buffer_name in key:
+ all_param_buffer.append(value)
+ break
+ return all_param_buffer
+
+
+class ExponentialMovingAverage:
+ """
+ Maintains (exponential) moving average of a set of parameters.
+ """
+ def __init__(self, parameters, decay, use_num_updates=True):
+ """
+ Args:
+ parameters: Iterable of `torch.nn.Parameter`; usually the result of
+ `model.parameters()`.
+ decay: The exponential decay.
+ use_num_updates: Whether to use number of updates when computing
+ averages.
+ """
+ if decay < 0.0 or decay > 1.0:
+ raise ValueError('Decay must be between 0 and 1')
+ self.decay = decay
+ self.num_updates = 0 if use_num_updates else None
+ self.shadow_params = [p.clone().detach() for p in parameters]
+ self.collected_params = []
+
+ def update(self, parameters):
+ """
+ Update currently maintained parameters.
+ Call this every time the parameters are updated, such as the result of
+ the `optimizer.step()` call.
+ Args:
+ parameters: Iterable of `torch.nn.Parameter`; usually the same set of
+ parameters used to initialize this object.
+ """
+ decay = self.decay
+ if self.num_updates is not None:
+ self.num_updates += 1
+ decay = min(decay,
+ (1 + self.num_updates) / (10 + self.num_updates))
+ one_minus_decay = 1.0 - decay
+ with torch.no_grad():
+ for s_param, param in zip(self.shadow_params, parameters):
+ s_param.sub_(one_minus_decay * (s_param - param))
+
+ def copy_to(self, parameters):
+ """
+ Copy current parameters into given collection of parameters.
+ Args:
+ parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+ updated with the stored moving averages.
+ """
+ for s_param, param in zip(self.shadow_params, parameters):
+ param.data.copy_(s_param.data)
+
+ 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)
+ del (self.collected_params)
diff --git a/aot/utils/eval.py b/aot/utils/eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..eff31ab65491c10c248061b2b102e26fb9ffacbc
--- /dev/null
+++ b/aot/utils/eval.py
@@ -0,0 +1,13 @@
+import zipfile
+import os
+
+
+def zip_folder(source_folder, zip_dir):
+ f = zipfile.ZipFile(zip_dir, 'w', zipfile.ZIP_DEFLATED)
+ pre_len = len(os.path.dirname(source_folder))
+ for dirpath, dirnames, filenames in os.walk(source_folder):
+ for filename in filenames:
+ pathfile = os.path.join(dirpath, filename)
+ arcname = pathfile[pre_len:].strip(os.path.sep)
+ f.write(pathfile, arcname)
+ f.close()
\ No newline at end of file
diff --git a/aot/utils/image.py b/aot/utils/image.py
new file mode 100644
index 0000000000000000000000000000000000000000..062a810c2e3399c4cb624582c200badb5e7731de
--- /dev/null
+++ b/aot/utils/image.py
@@ -0,0 +1,127 @@
+import numpy as np
+from PIL import Image
+import torch
+import threading
+
+_palette = [
+ 0, 0, 0, 128, 0, 0, 0, 128, 0, 128, 128, 0, 0, 0, 128, 128, 0, 128, 0, 128,
+ 128, 128, 128, 128, 64, 0, 0, 191, 0, 0, 64, 128, 0, 191, 128, 0, 64, 0,
+ 128, 191, 0, 128, 64, 128, 128, 191, 128, 128, 0, 64, 0, 128, 64, 0, 0,
+ 191, 0, 128, 191, 0, 0, 64, 128, 128, 64, 128, 22, 22, 22, 23, 23, 23, 24,
+ 24, 24, 25, 25, 25, 26, 26, 26, 27, 27, 27, 28, 28, 28, 29, 29, 29, 30, 30,
+ 30, 31, 31, 31, 32, 32, 32, 33, 33, 33, 34, 34, 34, 35, 35, 35, 36, 36, 36,
+ 37, 37, 37, 38, 38, 38, 39, 39, 39, 40, 40, 40, 41, 41, 41, 42, 42, 42, 43,
+ 43, 43, 44, 44, 44, 45, 45, 45, 46, 46, 46, 47, 47, 47, 48, 48, 48, 49, 49,
+ 49, 50, 50, 50, 51, 51, 51, 52, 52, 52, 53, 53, 53, 54, 54, 54, 55, 55, 55,
+ 56, 56, 56, 57, 57, 57, 58, 58, 58, 59, 59, 59, 60, 60, 60, 61, 61, 61, 62,
+ 62, 62, 63, 63, 63, 64, 64, 64, 65, 65, 65, 66, 66, 66, 67, 67, 67, 68, 68,
+ 68, 69, 69, 69, 70, 70, 70, 71, 71, 71, 72, 72, 72, 73, 73, 73, 74, 74, 74,
+ 75, 75, 75, 76, 76, 76, 77, 77, 77, 78, 78, 78, 79, 79, 79, 80, 80, 80, 81,
+ 81, 81, 82, 82, 82, 83, 83, 83, 84, 84, 84, 85, 85, 85, 86, 86, 86, 87, 87,
+ 87, 88, 88, 88, 89, 89, 89, 90, 90, 90, 91, 91, 91, 92, 92, 92, 93, 93, 93,
+ 94, 94, 94, 95, 95, 95, 96, 96, 96, 97, 97, 97, 98, 98, 98, 99, 99, 99,
+ 100, 100, 100, 101, 101, 101, 102, 102, 102, 103, 103, 103, 104, 104, 104,
+ 105, 105, 105, 106, 106, 106, 107, 107, 107, 108, 108, 108, 109, 109, 109,
+ 110, 110, 110, 111, 111, 111, 112, 112, 112, 113, 113, 113, 114, 114, 114,
+ 115, 115, 115, 116, 116, 116, 117, 117, 117, 118, 118, 118, 119, 119, 119,
+ 120, 120, 120, 121, 121, 121, 122, 122, 122, 123, 123, 123, 124, 124, 124,
+ 125, 125, 125, 126, 126, 126, 127, 127, 127, 128, 128, 128, 129, 129, 129,
+ 130, 130, 130, 131, 131, 131, 132, 132, 132, 133, 133, 133, 134, 134, 134,
+ 135, 135, 135, 136, 136, 136, 137, 137, 137, 138, 138, 138, 139, 139, 139,
+ 140, 140, 140, 141, 141, 141, 142, 142, 142, 143, 143, 143, 144, 144, 144,
+ 145, 145, 145, 146, 146, 146, 147, 147, 147, 148, 148, 148, 149, 149, 149,
+ 150, 150, 150, 151, 151, 151, 152, 152, 152, 153, 153, 153, 154, 154, 154,
+ 155, 155, 155, 156, 156, 156, 157, 157, 157, 158, 158, 158, 159, 159, 159,
+ 160, 160, 160, 161, 161, 161, 162, 162, 162, 163, 163, 163, 164, 164, 164,
+ 165, 165, 165, 166, 166, 166, 167, 167, 167, 168, 168, 168, 169, 169, 169,
+ 170, 170, 170, 171, 171, 171, 172, 172, 172, 173, 173, 173, 174, 174, 174,
+ 175, 175, 175, 176, 176, 176, 177, 177, 177, 178, 178, 178, 179, 179, 179,
+ 180, 180, 180, 181, 181, 181, 182, 182, 182, 183, 183, 183, 184, 184, 184,
+ 185, 185, 185, 186, 186, 186, 187, 187, 187, 188, 188, 188, 189, 189, 189,
+ 190, 190, 190, 191, 191, 191, 192, 192, 192, 193, 193, 193, 194, 194, 194,
+ 195, 195, 195, 196, 196, 196, 197, 197, 197, 198, 198, 198, 199, 199, 199,
+ 200, 200, 200, 201, 201, 201, 202, 202, 202, 203, 203, 203, 204, 204, 204,
+ 205, 205, 205, 206, 206, 206, 207, 207, 207, 208, 208, 208, 209, 209, 209,
+ 210, 210, 210, 211, 211, 211, 212, 212, 212, 213, 213, 213, 214, 214, 214,
+ 215, 215, 215, 216, 216, 216, 217, 217, 217, 218, 218, 218, 219, 219, 219,
+ 220, 220, 220, 221, 221, 221, 222, 222, 222, 223, 223, 223, 224, 224, 224,
+ 225, 225, 225, 226, 226, 226, 227, 227, 227, 228, 228, 228, 229, 229, 229,
+ 230, 230, 230, 231, 231, 231, 232, 232, 232, 233, 233, 233, 234, 234, 234,
+ 235, 235, 235, 236, 236, 236, 237, 237, 237, 238, 238, 238, 239, 239, 239,
+ 240, 240, 240, 241, 241, 241, 242, 242, 242, 243, 243, 243, 244, 244, 244,
+ 245, 245, 245, 246, 246, 246, 247, 247, 247, 248, 248, 248, 249, 249, 249,
+ 250, 250, 250, 251, 251, 251, 252, 252, 252, 253, 253, 253, 254, 254, 254,
+ 255, 255, 255
+]
+
+
+def label2colormap(label):
+
+ m = label.astype(np.uint8)
+ r, c = m.shape
+ cmap = np.zeros((r, c, 3), dtype=np.uint8)
+ cmap[:, :, 0] = (m & 1) << 7 | (m & 8) << 3 | (m & 64) >> 1
+ cmap[:, :, 1] = (m & 2) << 6 | (m & 16) << 2 | (m & 128) >> 2
+ cmap[:, :, 2] = (m & 4) << 5 | (m & 32) << 1
+ return cmap
+
+
+def one_hot_mask(mask, cls_num):
+ if len(mask.size()) == 3:
+ mask = mask.unsqueeze(1)
+ indices = torch.arange(0, cls_num + 1,
+ device=mask.device).view(1, -1, 1, 1)
+ return (mask == indices).float()
+
+
+def masked_image(image, colored_mask, mask, alpha=0.7):
+ mask = np.expand_dims(mask > 0, axis=0)
+ mask = np.repeat(mask, 3, axis=0)
+ show_img = (image * alpha + colored_mask *
+ (1 - alpha)) * mask + image * (1 - mask)
+ return show_img
+
+
+def save_image(image, path):
+ im = Image.fromarray(np.uint8(image * 255.).transpose((1, 2, 0)))
+ im.save(path)
+
+
+def _save_mask(mask, path, squeeze_idx=None):
+ if squeeze_idx is not None:
+ unsqueezed_mask = mask * 0
+ for idx in range(1, len(squeeze_idx)):
+ obj_id = squeeze_idx[idx]
+ mask_i = mask == idx
+ unsqueezed_mask += (mask_i * obj_id).astype(np.uint8)
+ mask = unsqueezed_mask
+ mask = Image.fromarray(mask).convert('P')
+ mask.putpalette(_palette)
+ mask.save(path)
+
+
+def save_mask(mask_tensor, path, squeeze_idx=None):
+ mask = mask_tensor.cpu().numpy().astype('uint8')
+ threading.Thread(target=_save_mask, args=[mask, path, squeeze_idx]).start()
+
+
+def flip_tensor(tensor, dim=0):
+ inv_idx = torch.arange(tensor.size(dim) - 1, -1, -1,
+ device=tensor.device).long()
+ tensor = tensor.index_select(dim, inv_idx)
+ return tensor
+
+
+def shuffle_obj_mask(mask):
+
+ bs, obj_num, _, _ = mask.size()
+ new_masks = []
+ for idx in range(bs):
+ now_mask = mask[idx]
+ random_matrix = torch.eye(obj_num, device=mask.device)
+ fg = random_matrix[1:][torch.randperm(obj_num - 1)]
+ random_matrix = torch.cat([random_matrix[0:1], fg], dim=0)
+ now_mask = torch.einsum('nm,nhw->mhw', random_matrix, now_mask)
+ new_masks.append(now_mask)
+
+ return torch.stack(new_masks, dim=0)
diff --git a/aot/utils/learning.py b/aot/utils/learning.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a2147c26d041b7c73491c219ec6f767c74a7511
--- /dev/null
+++ b/aot/utils/learning.py
@@ -0,0 +1,106 @@
+import math
+
+
+def adjust_learning_rate(optimizer,
+ base_lr,
+ p,
+ itr,
+ max_itr,
+ restart=1,
+ warm_up_steps=1000,
+ is_cosine_decay=False,
+ min_lr=1e-5,
+ encoder_lr_ratio=1.0,
+ freeze_params=[]):
+
+ if restart > 1:
+ each_max_itr = int(math.ceil(float(max_itr) / restart))
+ itr = itr % each_max_itr
+ warm_up_steps /= restart
+ max_itr = each_max_itr
+
+ if itr < warm_up_steps:
+ now_lr = min_lr + (base_lr - min_lr) * itr / warm_up_steps
+ else:
+ itr = itr - warm_up_steps
+ max_itr = max_itr - warm_up_steps
+ if is_cosine_decay:
+ now_lr = min_lr + (base_lr - min_lr) * (math.cos(math.pi * itr /
+ (max_itr + 1)) +
+ 1.) * 0.5
+ else:
+ now_lr = min_lr + (base_lr - min_lr) * (1 - itr / (max_itr + 1))**p
+
+ for param_group in optimizer.param_groups:
+ if encoder_lr_ratio != 1.0 and "encoder." in param_group["name"]:
+ param_group['lr'] = (now_lr - min_lr) * encoder_lr_ratio + min_lr
+ else:
+ param_group['lr'] = now_lr
+
+ for freeze_param in freeze_params:
+ if freeze_param in param_group["name"]:
+ param_group['lr'] = 0
+ param_group['weight_decay'] = 0
+ break
+
+ return now_lr
+
+
+def get_trainable_params(model,
+ base_lr,
+ weight_decay,
+ use_frozen_bn=False,
+ exclusive_wd_dict={},
+ no_wd_keys=[]):
+ params = []
+ memo = set()
+ total_param = 0
+ for key, value in model.named_parameters():
+ if value in memo:
+ continue
+ total_param += value.numel()
+ if not value.requires_grad:
+ continue
+ memo.add(value)
+ wd = weight_decay
+ for exclusive_key in exclusive_wd_dict.keys():
+ if exclusive_key in key:
+ wd = exclusive_wd_dict[exclusive_key]
+ break
+ if len(value.shape) == 1: # normalization layers
+ if 'bias' in key: # bias requires no weight decay
+ wd = 0.
+ elif not use_frozen_bn: # if not use frozen BN, apply zero weight decay
+ wd = 0.
+ elif 'encoder.' not in key: # if use frozen BN, apply weight decay to all frozen BNs in the encoder
+ wd = 0.
+ else:
+ for no_wd_key in no_wd_keys:
+ if no_wd_key in key:
+ wd = 0.
+ break
+ params += [{
+ "params": [value],
+ "lr": base_lr,
+ "weight_decay": wd,
+ "name": key
+ }]
+
+ print('Total Param: {:.2f}M'.format(total_param / 1e6))
+ return params
+
+
+def freeze_params(module):
+ for p in module.parameters():
+ p.requires_grad = False
+
+
+def calculate_params(state_dict):
+ memo = set()
+ total_param = 0
+ for key, value in state_dict.items():
+ if value in memo:
+ continue
+ memo.add(value)
+ total_param += value.numel()
+ print('Total Param: {:.2f}M'.format(total_param / 1e6))
diff --git a/aot/utils/math.py b/aot/utils/math.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f9ddc106892632f355d8dc0ce3cc46089c98e36
--- /dev/null
+++ b/aot/utils/math.py
@@ -0,0 +1,24 @@
+import torch
+
+
+def generate_permute_matrix(dim, num, keep_first=True, gpu_id=0):
+ all_matrix = []
+ for idx in range(num):
+ random_matrix = torch.eye(dim, device=torch.device('cuda', gpu_id))
+ if keep_first:
+ fg = random_matrix[1:][torch.randperm(dim - 1)]
+ random_matrix = torch.cat([random_matrix[0:1], fg], dim=0)
+ else:
+ random_matrix = random_matrix[torch.randperm(dim)]
+ all_matrix.append(random_matrix)
+ return torch.stack(all_matrix, dim=0)
+
+
+def truncated_normal_(tensor, mean=0, std=.02):
+ size = tensor.shape
+ tmp = tensor.new_empty(size + (4, )).normal_()
+ valid = (tmp < 2) & (tmp > -2)
+ ind = valid.max(-1, keepdim=True)[1]
+ tensor.data.copy_(tmp.gather(-1, ind).squeeze(-1))
+ tensor.data.mul_(std).add_(mean)
+ return tensor
diff --git a/aot/utils/meters.py b/aot/utils/meters.py
new file mode 100644
index 0000000000000000000000000000000000000000..00f48d871f8088cb59710105a462679d344d4b0f
--- /dev/null
+++ b/aot/utils/meters.py
@@ -0,0 +1,31 @@
+from __future__ import absolute_import
+
+
+class AverageMeter(object):
+ """Computes and stores the average and current value"""
+ def __init__(self, momentum=0.999):
+ self.val = 0
+ self.avg = 0
+ self.sum = 0
+ self.count = 0
+ self.long_count = 0
+ self.momentum = momentum
+ self.moving_avg = 0
+
+ def reset(self):
+ self.val = 0
+ self.avg = 0
+ self.sum = 0
+ self.count = 0
+
+ def update(self, val, n=1):
+ if self.long_count == 0:
+ self.moving_avg = val
+ else:
+ momentum = min(self.momentum, 1. - 1. / self.long_count)
+ self.moving_avg = self.moving_avg * momentum + val * (1 - momentum)
+ self.val = val
+ self.sum += val * n
+ self.count += n
+ self.long_count += n
+ self.avg = self.sum / self.count
diff --git a/aot/utils/metric.py b/aot/utils/metric.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad474f825fd33dac8bb5d3d34a61092c48470996
--- /dev/null
+++ b/aot/utils/metric.py
@@ -0,0 +1,36 @@
+import torch
+
+
+def pytorch_iou(pred, target, obj_num, epsilon=1e-6):
+ '''
+ pred: [bs, h, w]
+ target: [bs, h, w]
+ obj_num: [bs]
+ '''
+ bs = pred.size(0)
+ all_iou = []
+ for idx in range(bs):
+ now_pred = pred[idx].unsqueeze(0)
+ now_target = target[idx].unsqueeze(0)
+ now_obj_num = obj_num[idx]
+
+ obj_ids = torch.arange(0, now_obj_num + 1,
+ device=now_pred.device).int().view(-1, 1, 1)
+ if obj_ids.size(0) == 1: # only contain background
+ continue
+ else:
+ obj_ids = obj_ids[1:]
+ now_pred = (now_pred == obj_ids).float()
+ now_target = (now_target == obj_ids).float()
+
+ intersection = (now_pred * now_target).sum((1, 2))
+ union = ((now_pred + now_target) > 0).float().sum((1, 2))
+
+ now_iou = (intersection + epsilon) / (union + epsilon)
+
+ all_iou.append(now_iou.mean())
+ if len(all_iou) > 0:
+ all_iou = torch.stack(all_iou).mean()
+ else:
+ all_iou = torch.ones((1), device=pred.device)
+ return all_iou
diff --git a/aot_tracker.py b/aot_tracker.py
new file mode 100644
index 0000000000000000000000000000000000000000..3073d3e8f481b0331463682aa108a900b13e784b
--- /dev/null
+++ b/aot_tracker.py
@@ -0,0 +1,186 @@
+from statistics import mode
+import torch
+import torch.nn.functional as F
+import os
+import sys
+sys.path.append("./aot")
+from aot.networks.engines.aot_engine import AOTEngine,AOTInferEngine
+from aot.networks.engines.deaot_engine import DeAOTEngine,DeAOTInferEngine
+import importlib
+import numpy as np
+from PIL import Image
+from skimage.morphology.binary import binary_dilation
+
+
+np.random.seed(200)
+_palette = ((np.random.random((3*255))*0.7+0.3)*255).astype(np.uint8).tolist()
+_palette = [0,0,0]+_palette
+
+import aot.dataloaders.video_transforms as tr
+from aot.utils.checkpoint import load_network
+from aot.networks.models import build_vos_model
+from aot.networks.engines import build_engine
+from torchvision import transforms
+
+class AOTTracker(object):
+ def __init__(self, cfg, gpu_id=0):
+ self.gpu_id = gpu_id
+ self.model = build_vos_model(cfg.MODEL_VOS, cfg).cuda(gpu_id)
+ self.model, _ = load_network(self.model, cfg.TEST_CKPT_PATH, gpu_id)
+ # self.engine = self.build_tracker_engine(cfg.MODEL_ENGINE,
+ # aot_model=self.model,
+ # gpu_id=gpu_id,
+ # short_term_mem_skip=4,
+ # long_term_mem_gap=cfg.TEST_LONG_TERM_MEM_GAP)
+ self.engine = build_engine(cfg.MODEL_ENGINE,
+ phase='eval',
+ aot_model=self.model,
+ gpu_id=gpu_id,
+ short_term_mem_skip=1,
+ long_term_mem_gap=cfg.TEST_LONG_TERM_MEM_GAP,
+ max_len_long_term=cfg.MAX_LEN_LONG_TERM)
+
+ self.transform = transforms.Compose([
+ tr.MultiRestrictSize(cfg.TEST_MAX_SHORT_EDGE,
+ cfg.TEST_MAX_LONG_EDGE, cfg.TEST_FLIP,
+ cfg.TEST_MULTISCALE, cfg.MODEL_ALIGN_CORNERS),
+ tr.MultiToTensor()
+ ])
+
+ self.model.eval()
+
+ @torch.no_grad()
+ def add_reference_frame(self, frame, mask, obj_nums, frame_step, incremental=False):
+ # mask = cv2.resize(mask, frame.shape[:2][::-1], interpolation = cv2.INTER_NEAREST)
+
+ sample = {
+ 'current_img': frame,
+ 'current_label': mask,
+ }
+
+ sample = self.transform(sample)
+ frame = sample[0]['current_img'].unsqueeze(0).float().cuda(self.gpu_id)
+ mask = sample[0]['current_label'].unsqueeze(0).float().cuda(self.gpu_id)
+ _mask = F.interpolate(mask,size=frame.shape[-2:],mode='nearest')
+
+ if incremental:
+ self.engine.add_reference_frame_incremental(frame, _mask, obj_nums=obj_nums, frame_step=frame_step)
+ else:
+ self.engine.add_reference_frame(frame, _mask, obj_nums=obj_nums, frame_step=frame_step)
+
+
+
+ @torch.no_grad()
+ def track(self, image):
+ output_height, output_width = image.shape[0], image.shape[1]
+ sample = {'current_img': image}
+ sample = self.transform(sample)
+ image = sample[0]['current_img'].unsqueeze(0).float().cuda(self.gpu_id)
+ self.engine.match_propogate_one_frame(image)
+ pred_logit = self.engine.decode_current_logits((output_height, output_width))
+
+ # pred_prob = torch.softmax(pred_logit, dim=1)
+ pred_label = torch.argmax(pred_logit, dim=1,
+ keepdim=True).float()
+
+ return pred_label
+
+ @torch.no_grad()
+ def update_memory(self, pred_label):
+ self.engine.update_memory(pred_label)
+
+ @torch.no_grad()
+ def restart(self):
+ self.engine.restart_engine()
+
+ @torch.no_grad()
+ def build_tracker_engine(self, name, **kwargs):
+ if name == 'aotengine':
+ return AOTTrackerInferEngine(**kwargs)
+ elif name == 'deaotengine':
+ return DeAOTTrackerInferEngine(**kwargs)
+ else:
+ raise NotImplementedError
+
+
+class AOTTrackerInferEngine(AOTInferEngine):
+ def __init__(self, aot_model, gpu_id=0, long_term_mem_gap=9999, short_term_mem_skip=1, max_aot_obj_num=None):
+ super().__init__(aot_model, gpu_id, long_term_mem_gap, short_term_mem_skip, max_aot_obj_num)
+ def add_reference_frame_incremental(self, img, mask, obj_nums, frame_step=-1):
+ if isinstance(obj_nums, list):
+ obj_nums = obj_nums[0]
+ self.obj_nums = obj_nums
+ aot_num = max(np.ceil(obj_nums / self.max_aot_obj_num), 1)
+ while (aot_num > len(self.aot_engines)):
+ new_engine = AOTEngine(self.AOT, self.gpu_id,
+ self.long_term_mem_gap,
+ self.short_term_mem_skip)
+ new_engine.eval()
+ self.aot_engines.append(new_engine)
+
+ separated_masks, separated_obj_nums = self.separate_mask(
+ mask, obj_nums)
+ img_embs = None
+ for aot_engine, separated_mask, separated_obj_num in zip(
+ self.aot_engines, separated_masks, separated_obj_nums):
+ if aot_engine.obj_nums is None or aot_engine.obj_nums[0] < separated_obj_num:
+ aot_engine.add_reference_frame(img,
+ separated_mask,
+ obj_nums=[separated_obj_num],
+ frame_step=frame_step,
+ img_embs=img_embs)
+ else:
+ aot_engine.update_short_term_memory(separated_mask)
+
+ if img_embs is None: # reuse image embeddings
+ img_embs = aot_engine.curr_enc_embs
+
+ self.update_size()
+
+
+
+class DeAOTTrackerInferEngine(DeAOTInferEngine):
+ def __init__(self, aot_model, gpu_id=0, long_term_mem_gap=9999, short_term_mem_skip=1, max_aot_obj_num=None):
+ super().__init__(aot_model, gpu_id, long_term_mem_gap, short_term_mem_skip, max_aot_obj_num)
+ def add_reference_frame_incremental(self, img, mask, obj_nums, frame_step=-1):
+ if isinstance(obj_nums, list):
+ obj_nums = obj_nums[0]
+ self.obj_nums = obj_nums
+ aot_num = max(np.ceil(obj_nums / self.max_aot_obj_num), 1)
+ while (aot_num > len(self.aot_engines)):
+ new_engine = DeAOTEngine(self.AOT, self.gpu_id,
+ self.long_term_mem_gap,
+ self.short_term_mem_skip)
+ new_engine.eval()
+ self.aot_engines.append(new_engine)
+
+ separated_masks, separated_obj_nums = self.separate_mask(
+ mask, obj_nums)
+ img_embs = None
+ for aot_engine, separated_mask, separated_obj_num in zip(
+ self.aot_engines, separated_masks, separated_obj_nums):
+ if aot_engine.obj_nums is None or aot_engine.obj_nums[0] < separated_obj_num:
+ aot_engine.add_reference_frame(img,
+ separated_mask,
+ obj_nums=[separated_obj_num],
+ frame_step=frame_step,
+ img_embs=img_embs)
+ else:
+ aot_engine.update_short_term_memory(separated_mask)
+
+ if img_embs is None: # reuse image embeddings
+ img_embs = aot_engine.curr_enc_embs
+
+ self.update_size()
+
+
+def get_aot(args):
+ # build vos engine
+ engine_config = importlib.import_module('configs.' + 'pre_ytb_dav')
+ cfg = engine_config.EngineConfig(args['phase'], args['model'])
+ cfg.TEST_CKPT_PATH = args['model_path']
+ cfg.TEST_LONG_TERM_MEM_GAP = args['long_term_mem_gap']
+ cfg.MAX_LEN_LONG_TERM = args['max_len_long_term']
+ # init AOTTracker
+ tracker = AOTTracker(cfg, args['gpu_id'])
+ return tracker
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c665b9f4af82fa9d69a548519ddf10ea974ace1
--- /dev/null
+++ b/app.py
@@ -0,0 +1,782 @@
+from PIL.ImageOps import colorize, scale
+import gradio as gr
+import importlib
+import sys
+import os
+
+from matplotlib.pyplot import step
+
+from model_args import segtracker_args,sam_args,aot_args
+from SegTracker import SegTracker
+
+# sys.path.append('.')
+# sys.path.append('..')
+
+import cv2
+from PIL import Image
+from skimage.morphology.binary import binary_dilation
+import argparse
+import torch
+import time
+from seg_track_anything import aot_model2ckpt, tracking_objects_in_video, draw_mask
+import gc
+import numpy as np
+import json
+from tool.transfer_tools import mask2bbox
+
+def clean():
+ return None, None, None, None, None, None, [[], []]
+
+def get_click_prompt(click_stack, point):
+
+ click_stack[0].append(point["coord"])
+ click_stack[1].append(point["mode"]
+ )
+
+ prompt = {
+ "points_coord":click_stack[0],
+ "points_mode":click_stack[1],
+ "multimask":"True",
+ }
+
+ return prompt
+
+def get_meta_from_video(input_video):
+ if input_video is None:
+ return None, None, None, ""
+
+ print("get meta information of input video")
+ cap = cv2.VideoCapture(input_video)
+
+ _, first_frame = cap.read()
+ cap.release()
+
+ first_frame = cv2.cvtColor(first_frame, cv2.COLOR_BGR2RGB)
+
+ return first_frame, first_frame, first_frame, ""
+
+def get_meta_from_img_seq(input_img_seq):
+ if input_img_seq is None:
+ return None, None, None, ""
+
+ print("get meta information of img seq")
+ # Create dir
+ file_name = input_img_seq.name.split('/')[-1].split('.')[0]
+ file_path = f'./assets/{file_name}'
+ if os.path.isdir(file_path):
+ os.system(f'rm -r {file_path}')
+ os.makedirs(file_path)
+ # Unzip file
+ os.system(f'unzip {input_img_seq.name} -d ./assets ')
+
+ imgs_path = sorted([os.path.join(file_path, img_name) for img_name in os.listdir(file_path)])
+ first_frame = imgs_path[0]
+ first_frame = cv2.imread(first_frame)
+ first_frame = cv2.cvtColor(first_frame, cv2.COLOR_BGR2RGB)
+
+ return first_frame, first_frame, first_frame
+
+def SegTracker_add_first_frame(Seg_Tracker, origin_frame, predicted_mask):
+ with torch.cuda.amp.autocast():
+ # Reset the first frame's mask
+ frame_idx = 0
+ Seg_Tracker.restart_tracker()
+ Seg_Tracker.add_reference(origin_frame, predicted_mask, frame_idx)
+ Seg_Tracker.first_frame_mask = predicted_mask
+
+ return Seg_Tracker
+
+def init_SegTracker(aot_model, long_term_mem, max_len_long_term, sam_gap, max_obj_num, points_per_side, origin_frame):
+
+ if origin_frame is None:
+ return None, origin_frame, [[], []], ""
+
+ # reset aot args
+ aot_args["model"] = aot_model
+ aot_args["model_path"] = aot_model2ckpt[aot_model]
+ aot_args["long_term_mem_gap"] = long_term_mem
+ aot_args["max_len_long_term"] = max_len_long_term
+ # reset sam args
+ segtracker_args["sam_gap"] = sam_gap
+ segtracker_args["max_obj_num"] = max_obj_num
+ sam_args["generator_args"]["points_per_side"] = points_per_side
+
+ Seg_Tracker = SegTracker(segtracker_args, sam_args, aot_args)
+ Seg_Tracker.restart_tracker()
+
+ return Seg_Tracker, origin_frame, [[], []], ""
+
+def init_SegTracker_Stroke(aot_model, long_term_mem, max_len_long_term, sam_gap, max_obj_num, points_per_side, origin_frame):
+
+ if origin_frame is None:
+ return None, origin_frame, [[], []], origin_frame
+
+ # reset aot args
+ aot_args["model"] = aot_model
+ aot_args["model_path"] = aot_model2ckpt[aot_model]
+ aot_args["long_term_mem_gap"] = long_term_mem
+ aot_args["max_len_long_term"] = max_len_long_term
+
+ # reset sam args
+ segtracker_args["sam_gap"] = sam_gap
+ segtracker_args["max_obj_num"] = max_obj_num
+ sam_args["generator_args"]["points_per_side"] = points_per_side
+
+ Seg_Tracker = SegTracker(segtracker_args, sam_args, aot_args)
+ Seg_Tracker.restart_tracker()
+ return Seg_Tracker, origin_frame, [[], []], origin_frame
+
+def undo_click_stack_and_refine_seg(Seg_Tracker, origin_frame, click_stack, aot_model, long_term_mem, max_len_long_term, sam_gap, max_obj_num, points_per_side):
+
+ if Seg_Tracker is None:
+ return Seg_Tracker, origin_frame, [[], []]
+
+ print("Undo!")
+ if len(click_stack[0]) > 0:
+ click_stack[0] = click_stack[0][: -1]
+ click_stack[1] = click_stack[1][: -1]
+
+ if len(click_stack[0]) > 0:
+ prompt = {
+ "points_coord":click_stack[0],
+ "points_mode":click_stack[1],
+ "multimask":"True",
+ }
+
+ masked_frame = seg_acc_click(Seg_Tracker, prompt, origin_frame)
+ return Seg_Tracker, masked_frame, click_stack
+ else:
+ return Seg_Tracker, origin_frame, [[], []]
+
+
+def seg_acc_click(Seg_Tracker, prompt, origin_frame):
+ # seg acc to click
+ predicted_mask, masked_frame = Seg_Tracker.seg_acc_click(
+ origin_frame=origin_frame,
+ coords=np.array(prompt["points_coord"]),
+ modes=np.array(prompt["points_mode"]),
+ multimask=prompt["multimask"],
+ )
+
+ Seg_Tracker = SegTracker_add_first_frame(Seg_Tracker, origin_frame, predicted_mask)
+
+ return masked_frame
+
+def sam_click(Seg_Tracker, origin_frame, point_mode, click_stack, aot_model, long_term_mem, max_len_long_term, sam_gap, max_obj_num, points_per_side, evt:gr.SelectData):
+ """
+ Args:
+ origin_frame: nd.array
+ click_stack: [[coordinate], [point_mode]]
+ """
+
+ print("Click")
+
+ if point_mode == "Positive":
+ point = {"coord": [evt.index[0], evt.index[1]], "mode": 1}
+ else:
+ # TODO：add everything positive points
+ point = {"coord": [evt.index[0], evt.index[1]], "mode": 0}
+
+ if Seg_Tracker is None:
+ Seg_Tracker, _, _, _ = init_SegTracker(aot_model, long_term_mem, max_len_long_term, sam_gap, max_obj_num, points_per_side, origin_frame)
+
+ # get click prompts for sam to predict mask
+ click_prompt = get_click_prompt(click_stack, point)
+
+ # Refine acc to prompt
+ masked_frame = seg_acc_click(Seg_Tracker, click_prompt, origin_frame)
+
+ return Seg_Tracker, masked_frame, click_stack
+
+def sam_stroke(Seg_Tracker, origin_frame, drawing_board, aot_model, long_term_mem, max_len_long_term, sam_gap, max_obj_num, points_per_side):
+
+ if Seg_Tracker is None:
+ Seg_Tracker, _ , _, _ = init_SegTracker(aot_model, long_term_mem, max_len_long_term, sam_gap, max_obj_num, points_per_side, origin_frame)
+
+ print("Stroke")
+ mask = drawing_board["mask"]
+ bbox = mask2bbox(mask[:, :, 0]) # bbox: [[x0, y0], [x1, y1]]
+ predicted_mask, masked_frame = Seg_Tracker.seg_acc_bbox(origin_frame, bbox)
+
+ Seg_Tracker = SegTracker_add_first_frame(Seg_Tracker, origin_frame, predicted_mask)
+
+ return Seg_Tracker, masked_frame, origin_frame
+
+def gd_detect(Seg_Tracker, origin_frame, grounding_caption, box_threshold, text_threshold, aot_model, long_term_mem, max_len_long_term, sam_gap, max_obj_num, points_per_side):
+ if Seg_Tracker is None:
+ Seg_Tracker, _ , _, _ = init_SegTracker(aot_model, long_term_mem, max_len_long_term, sam_gap, max_obj_num, points_per_side, origin_frame)
+
+ print("Detect")
+ predicted_mask, annotated_frame= Seg_Tracker.detect_and_seg(origin_frame, grounding_caption, box_threshold, text_threshold)
+
+ Seg_Tracker = SegTracker_add_first_frame(Seg_Tracker, origin_frame, predicted_mask)
+
+
+ masked_frame = draw_mask(annotated_frame, predicted_mask)
+
+ return Seg_Tracker, masked_frame, origin_frame
+
+def segment_everything(Seg_Tracker, aot_model, long_term_mem, max_len_long_term, origin_frame, sam_gap, max_obj_num, points_per_side):
+
+ if Seg_Tracker is None:
+ Seg_Tracker, _ , _, _ = init_SegTracker(aot_model, long_term_mem, max_len_long_term, sam_gap, max_obj_num, points_per_side, origin_frame)
+
+ print("Everything")
+
+ frame_idx = 0
+
+ with torch.cuda.amp.autocast():
+ pred_mask = Seg_Tracker.seg(origin_frame)
+ torch.cuda.empty_cache()
+ gc.collect()
+ Seg_Tracker.add_reference(origin_frame, pred_mask, frame_idx)
+ Seg_Tracker.first_frame_mask = pred_mask
+
+ masked_frame = draw_mask(origin_frame.copy(), pred_mask)
+
+ return Seg_Tracker, masked_frame
+
+def add_new_object(Seg_Tracker):
+
+ prev_mask = Seg_Tracker.first_frame_mask
+ Seg_Tracker.update_origin_merged_mask(prev_mask)
+ Seg_Tracker.curr_idx += 1
+
+ print("Ready to add new object!")
+
+ return Seg_Tracker, [[], []]
+## fps change to duration 4 argument
+def tracking_objects(Seg_Tracker, input_video, input_img_seq, duration):
+ print("Start tracking !")
+ return tracking_objects_in_video(Seg_Tracker, input_video, input_img_seq, duration)
+
+def seg_track_app():
+
+ ##########################################################
+ ###################### Front-end ########################
+ ##########################################################
+ app = gr.Blocks()
+
+ with app:
+ gr.Markdown(
+ '''
+
+ 
+ 
+
+ 
+ 
+
![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\n",
+ "\n",
+ "\"\"\"\n",
+ "))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c0b71431",
+ "metadata": {},
+ "source": [
+ "## Environment Set-up"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "47e5a78f",
+ "metadata": {},
+ "source": [
+ "If running locally using jupyter, first install `segment_anything` in your environment using the [installation instructions](https://github.com/facebookresearch/segment-anything#installation) in the repository. If running from Google Colab, set `using_collab=True` below and run the cell. In Colab, be sure to select 'GPU' under 'Edit'->'Notebook Settings'->'Hardware accelerator'."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "4fe300fb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "using_colab = False"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "0685a2f5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if using_colab:\n",
+ " import torch\n",
+ " import torchvision\n",
+ " print(\"PyTorch version:\", torch.__version__)\n",
+ " print(\"Torchvision version:\", torchvision.__version__)\n",
+ " print(\"CUDA is available:\", torch.cuda.is_available())\n",
+ " import sys\n",
+ " !{sys.executable} -m pip install opencv-python matplotlib\n",
+ " !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/segment-anything.git'\n",
+ " \n",
+ " !mkdir images\n",
+ " !wget -P images https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/dog.jpg\n",
+ " \n",
+ " !wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fd2bc687",
+ "metadata": {},
+ "source": [
+ "## Set-up"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "560725a2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import torch\n",
+ "import matplotlib.pyplot as plt\n",
+ "import cv2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "74b6e5f0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def show_anns(anns):\n",
+ " if len(anns) == 0:\n",
+ " return\n",
+ " sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)\n",
+ " ax = plt.gca()\n",
+ " ax.set_autoscale_on(False)\n",
+ " polygons = []\n",
+ " color = []\n",
+ " for ann in sorted_anns:\n",
+ " m = ann['segmentation']\n",
+ " img = np.ones((m.shape[0], m.shape[1], 3))\n",
+ " color_mask = np.random.random((1, 3)).tolist()[0]\n",
+ " for i in range(3):\n",
+ " img[:,:,i] = color_mask[i]\n",
+ " ax.imshow(np.dstack((img, m*0.35)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "27c41445",
+ "metadata": {},
+ "source": [
+ "## Example image"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "ad354922",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "image = cv2.imread('images/dog.jpg')\n",
+ "image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "e0ac8c67",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\n",
+ "\n",
+ "\"\"\"\n",
+ "))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "55ae4e00",
+ "metadata": {},
+ "source": [
+ "## Environment Set-up"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "109a5cc2",
+ "metadata": {},
+ "source": [
+ "If running locally using jupyter, first install `segment_anything` in your environment using the [installation instructions](https://github.com/facebookresearch/segment-anything#installation) in the repository. The latest stable versions of PyTorch and ONNX are recommended for this notebook. If running from Google Colab, set `using_collab=True` below and run the cell. In Colab, be sure to select 'GPU' under 'Edit'->'Notebook Settings'->'Hardware accelerator'."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "39b99fc4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "using_colab = False"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "296a69be",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if using_colab:\n",
+ " import torch\n",
+ " import torchvision\n",
+ " print(\"PyTorch version:\", torch.__version__)\n",
+ " print(\"Torchvision version:\", torchvision.__version__)\n",
+ " print(\"CUDA is available:\", torch.cuda.is_available())\n",
+ " import sys\n",
+ " !{sys.executable} -m pip install opencv-python matplotlib onnx onnxruntime\n",
+ " !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/segment-anything.git'\n",
+ " \n",
+ " !mkdir images\n",
+ " !wget -P images https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/truck.jpg\n",
+ " \n",
+ " !wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dc4a58be",
+ "metadata": {},
+ "source": [
+ "## Set-up"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "42396e8d",
+ "metadata": {},
+ "source": [
+ "Note that this notebook requires both the `onnx` and `onnxruntime` optional dependencies, in addition to `opencv-python` and `matplotlib` for visualization."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2c712610",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import numpy as np\n",
+ "import cv2\n",
+ "import matplotlib.pyplot as plt\n",
+ "from segment_anything import sam_model_registry, SamPredictor\n",
+ "from segment_anything.utils.onnx import SamOnnxModel\n",
+ "\n",
+ "import onnxruntime\n",
+ "from onnxruntime.quantization import QuantType\n",
+ "from onnxruntime.quantization.quantize import quantize_dynamic"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f29441b9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def show_mask(mask, ax):\n",
+ " color = np.array([30/255, 144/255, 255/255, 0.6])\n",
+ " h, w = mask.shape[-2:]\n",
+ " mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\n",
+ " ax.imshow(mask_image)\n",
+ " \n",
+ "def show_points(coords, labels, ax, marker_size=375):\n",
+ " pos_points = coords[labels==1]\n",
+ " neg_points = coords[labels==0]\n",
+ " ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)\n",
+ " ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25) \n",
+ " \n",
+ "def show_box(box, ax):\n",
+ " x0, y0 = box[0], box[1]\n",
+ " w, h = box[2] - box[0], box[3] - box[1]\n",
+ " ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2)) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bd0f6b2b",
+ "metadata": {},
+ "source": [
+ "## Export an ONNX model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1540f719",
+ "metadata": {},
+ "source": [
+ "Set the path below to a SAM model checkpoint, then load the model. This will be needed to both export the model and to calculate embeddings for the model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "76fc53f4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "checkpoint = \"sam_vit_h_4b8939.pth\"\n",
+ "model_type = \"vit_h\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "11bfc8aa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sam = sam_model_registry[model_type](checkpoint=checkpoint)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "450c089c",
+ "metadata": {},
+ "source": [
+ "The script `segment-anything/scripts/export_onnx_model.py` can be used to export the necessary portion of SAM. Alternatively, run the following code to export an ONNX model. If you have already exported a model, set the path below and skip to the next section. Assure that the exported ONNX model aligns with the checkpoint and model type set above. This notebook expects the model was exported with the parameter `return_single_mask=True`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "38a8add8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "onnx_model_path = None # Set to use an already exported model, then skip to the next section."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7da638ba",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "import warnings\n",
+ "\n",
+ "onnx_model_path = \"sam_onnx_example.onnx\"\n",
+ "\n",
+ "onnx_model = SamOnnxModel(sam, return_single_mask=True)\n",
+ "\n",
+ "dynamic_axes = {\n",
+ " \"point_coords\": {1: \"num_points\"},\n",
+ " \"point_labels\": {1: \"num_points\"},\n",
+ "}\n",
+ "\n",
+ "embed_dim = sam.prompt_encoder.embed_dim\n",
+ "embed_size = sam.prompt_encoder.image_embedding_size\n",
+ "mask_input_size = [4 * x for x in embed_size]\n",
+ "dummy_inputs = {\n",
+ " \"image_embeddings\": torch.randn(1, embed_dim, *embed_size, dtype=torch.float),\n",
+ " \"point_coords\": torch.randint(low=0, high=1024, size=(1, 5, 2), dtype=torch.float),\n",
+ " \"point_labels\": torch.randint(low=0, high=4, size=(1, 5), dtype=torch.float),\n",
+ " \"mask_input\": torch.randn(1, 1, *mask_input_size, dtype=torch.float),\n",
+ " \"has_mask_input\": torch.tensor([1], dtype=torch.float),\n",
+ " \"orig_im_size\": torch.tensor([1500, 2250], dtype=torch.float),\n",
+ "}\n",
+ "output_names = [\"masks\", \"iou_predictions\", \"low_res_masks\"]\n",
+ "\n",
+ "with warnings.catch_warnings():\n",
+ " warnings.filterwarnings(\"ignore\", category=torch.jit.TracerWarning)\n",
+ " warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
+ " with open(onnx_model_path, \"wb\") as f:\n",
+ " torch.onnx.export(\n",
+ " onnx_model,\n",
+ " tuple(dummy_inputs.values()),\n",
+ " f,\n",
+ " export_params=True,\n",
+ " verbose=False,\n",
+ " opset_version=17,\n",
+ " do_constant_folding=True,\n",
+ " input_names=list(dummy_inputs.keys()),\n",
+ " output_names=output_names,\n",
+ " dynamic_axes=dynamic_axes,\n",
+ " ) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c450cf1a",
+ "metadata": {},
+ "source": [
+ "If desired, the model can additionally be quantized and optimized. We find this improves web runtime significantly for negligible change in qualitative performance. Run the next cell to quantize the model, or skip to the next section otherwise."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "235d39fe",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "onnx_model_quantized_path = \"sam_onnx_quantized_example.onnx\"\n",
+ "quantize_dynamic(\n",
+ " model_input=onnx_model_path,\n",
+ " model_output=onnx_model_quantized_path,\n",
+ " optimize_model=True,\n",
+ " per_channel=False,\n",
+ " reduce_range=False,\n",
+ " weight_type=QuantType.QUInt8,\n",
+ ")\n",
+ "onnx_model_path = onnx_model_quantized_path"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "927a928b",
+ "metadata": {},
+ "source": [
+ "## Example Image"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6be6eb55",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "image = cv2.imread('images/truck.jpg')\n",
+ "image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b7e9a27a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(10,10))\n",
+ "plt.imshow(image)\n",
+ "plt.axis('on')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "027b177b",
+ "metadata": {},
+ "source": [
+ "## Using an ONNX model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "778d4593",
+ "metadata": {},
+ "source": [
+ "Here as an example, we use `onnxruntime` in python on CPU to execute the ONNX model. However, any platform that supports an ONNX runtime could be used in principle. Launch the runtime session below:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9689b1bf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ort_session = onnxruntime.InferenceSession(onnx_model_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7708ead6",
+ "metadata": {},
+ "source": [
+ "To use the ONNX model, the image must first be pre-processed using the SAM image encoder. This is a heavier weight process best performed on GPU. SamPredictor can be used as normal, then `.get_image_embedding()` will retreive the intermediate features."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "26e067b4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sam.to(device='cuda')\n",
+ "predictor = SamPredictor(sam)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7ad3f0d6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "predictor.set_image(image)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8a6f0f07",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "image_embedding = predictor.get_image_embedding().cpu().numpy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5e112f33",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "image_embedding.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6337b654",
+ "metadata": {},
+ "source": [
+ "The ONNX model has a different input signature than `SamPredictor.predict`. The following inputs must all be supplied. Note the special cases for both point and mask inputs. All inputs are `np.float32`.\n",
+ "* `image_embeddings`: The image embedding from `predictor.get_image_embedding()`. Has a batch index of length 1.\n",
+ "* `point_coords`: Coordinates of sparse input prompts, corresponding to both point inputs and box inputs. Boxes are encoded using two points, one for the top-left corner and one for the bottom-right corner. *Coordinates must already be transformed to long-side 1024.* Has a batch index of length 1.\n",
+ "* `point_labels`: Labels for the sparse input prompts. 0 is a negative input point, 1 is a positive input point, 2 is a top-left box corner, 3 is a bottom-right box corner, and -1 is a padding point. *If there is no box input, a single padding point with label -1 and coordinates (0.0, 0.0) should be concatenated.*\n",
+ "* `mask_input`: A mask input to the model with shape 1x1x256x256. This must be supplied even if there is no mask input. In this case, it can just be zeros.\n",
+ "* `has_mask_input`: An indicator for the mask input. 1 indicates a mask input, 0 indicates no mask input.\n",
+ "* `orig_im_size`: The size of the input image in (H,W) format, before any transformation. \n",
+ "\n",
+ "Additionally, the ONNX model does not threshold the output mask logits. To obtain a binary mask, threshold at `sam.mask_threshold` (equal to 0.0)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bf5a9f55",
+ "metadata": {},
+ "source": [
+ "### Example point input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1c0deef0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "input_point = np.array([[500, 375]])\n",
+ "input_label = np.array([1])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7256394c",
+ "metadata": {},
+ "source": [
+ "Add a batch index, concatenate a padding point, and transform."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4f69903e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "onnx_coord = np.concatenate([input_point, np.array([[0.0, 0.0]])], axis=0)[None, :, :]\n",
+ "onnx_label = np.concatenate([input_label, np.array([-1])], axis=0)[None, :].astype(np.float32)\n",
+ "\n",
+ "onnx_coord = predictor.transform.apply_coords(onnx_coord, image.shape[:2]).astype(np.float32)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b188dc53",
+ "metadata": {},
+ "source": [
+ "Create an empty mask input and an indicator for no mask."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5cb52bcf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "onnx_mask_input = np.zeros((1, 1, 256, 256), dtype=np.float32)\n",
+ "onnx_has_mask_input = np.zeros(1, dtype=np.float32)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a99c2cc5",
+ "metadata": {},
+ "source": [
+ "Package the inputs to run in the onnx model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b1d7ea11",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ort_inputs = {\n",
+ " \"image_embeddings\": image_embedding,\n",
+ " \"point_coords\": onnx_coord,\n",
+ " \"point_labels\": onnx_label,\n",
+ " \"mask_input\": onnx_mask_input,\n",
+ " \"has_mask_input\": onnx_has_mask_input,\n",
+ " \"orig_im_size\": np.array(image.shape[:2], dtype=np.float32)\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4b6409c9",
+ "metadata": {},
+ "source": [
+ "Predict a mask and threshold it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dc4cc082",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "masks, _, low_res_logits = ort_session.run(None, ort_inputs)\n",
+ "masks = masks > predictor.model.mask_threshold"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d778a8fb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "masks.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "badb1175",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(10,10))\n",
+ "plt.imshow(image)\n",
+ "show_mask(masks, plt.gca())\n",
+ "show_points(input_point, input_label, plt.gca())\n",
+ "plt.axis('off')\n",
+ "plt.show() "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1f1d4d15",
+ "metadata": {},
+ "source": [
+ "### Example mask input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b319da82",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "input_point = np.array([[500, 375], [1125, 625]])\n",
+ "input_label = np.array([1, 1])\n",
+ "\n",
+ "# Use the mask output from the previous run. It is already in the correct form for input to the ONNX model.\n",
+ "onnx_mask_input = low_res_logits"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b1823b37",
+ "metadata": {},
+ "source": [
+ "Transform the points as in the previous example."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8885130f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "onnx_coord = np.concatenate([input_point, np.array([[0.0, 0.0]])], axis=0)[None, :, :]\n",
+ "onnx_label = np.concatenate([input_label, np.array([-1])], axis=0)[None, :].astype(np.float32)\n",
+ "\n",
+ "onnx_coord = predictor.transform.apply_coords(onnx_coord, image.shape[:2]).astype(np.float32)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "28e47b69",
+ "metadata": {},
+ "source": [
+ "The `has_mask_input` indicator is now 1."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3ab4483a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "onnx_has_mask_input = np.ones(1, dtype=np.float32)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d3781955",
+ "metadata": {},
+ "source": [
+ "Package inputs, then predict and threshold the mask."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c1ec096",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ort_inputs = {\n",
+ " \"image_embeddings\": image_embedding,\n",
+ " \"point_coords\": onnx_coord,\n",
+ " \"point_labels\": onnx_label,\n",
+ " \"mask_input\": onnx_mask_input,\n",
+ " \"has_mask_input\": onnx_has_mask_input,\n",
+ " \"orig_im_size\": np.array(image.shape[:2], dtype=np.float32)\n",
+ "}\n",
+ "\n",
+ "masks, _, _ = ort_session.run(None, ort_inputs)\n",
+ "masks = masks > predictor.model.mask_threshold"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1e36554b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(10,10))\n",
+ "plt.imshow(image)\n",
+ "show_mask(masks, plt.gca())\n",
+ "show_points(input_point, input_label, plt.gca())\n",
+ "plt.axis('off')\n",
+ "plt.show() "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2ef211d0",
+ "metadata": {},
+ "source": [
+ "### Example box and point input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "51e58d2e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "input_box = np.array([425, 600, 700, 875])\n",
+ "input_point = np.array([[575, 750]])\n",
+ "input_label = np.array([0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6e119dcb",
+ "metadata": {},
+ "source": [
+ "Add a batch index, concatenate a box and point inputs, add the appropriate labels for the box corners, and transform. There is no padding point since the input includes a box input."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bfbe4911",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "onnx_box_coords = input_box.reshape(2, 2)\n",
+ "onnx_box_labels = np.array([2,3])\n",
+ "\n",
+ "onnx_coord = np.concatenate([input_point, onnx_box_coords], axis=0)[None, :, :]\n",
+ "onnx_label = np.concatenate([input_label, onnx_box_labels], axis=0)[None, :].astype(np.float32)\n",
+ "\n",
+ "onnx_coord = predictor.transform.apply_coords(onnx_coord, image.shape[:2]).astype(np.float32)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "65edabd2",
+ "metadata": {},
+ "source": [
+ "Package inputs, then predict and threshold the mask."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2abfba56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "onnx_mask_input = np.zeros((1, 1, 256, 256), dtype=np.float32)\n",
+ "onnx_has_mask_input = np.zeros(1, dtype=np.float32)\n",
+ "\n",
+ "ort_inputs = {\n",
+ " \"image_embeddings\": image_embedding,\n",
+ " \"point_coords\": onnx_coord,\n",
+ " \"point_labels\": onnx_label,\n",
+ " \"mask_input\": onnx_mask_input,\n",
+ " \"has_mask_input\": onnx_has_mask_input,\n",
+ " \"orig_im_size\": np.array(image.shape[:2], dtype=np.float32)\n",
+ "}\n",
+ "\n",
+ "masks, _, _ = ort_session.run(None, ort_inputs)\n",
+ "masks = masks > predictor.model.mask_threshold"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8301bf33",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.imshow(image)\n",
+ "show_mask(masks[0], plt.gca())\n",
+ "show_box(input_box, plt.gca())\n",
+ "show_points(input_point, input_label, plt.gca())\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "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": "3.8.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/sam/notebooks/predictor_example.ipynb b/sam/notebooks/predictor_example.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..239d3369c505af52ecb1697058b828696e374197
--- /dev/null
+++ b/sam/notebooks/predictor_example.ipynb
@@ -0,0 +1,1016 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "f400486b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Copyright (c) Meta Platforms, Inc. and affiliates."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a1ae39ff",
+ "metadata": {},
+ "source": [
+ "# Object masks from prompts with SAM"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b4a4b25c",
+ "metadata": {},
+ "source": [
+ "The Segment Anything Model (SAM) predicts object masks given prompts that indicate the desired object. The model first converts the image into an image embedding that allows high quality masks to be efficiently produced from a prompt. \n",
+ "\n",
+ "The `SamPredictor` class provides an easy interface to the model for prompting the model. It allows the user to first set an image using the `set_image` method, which calculates the necessary image embeddings. Then, prompts can be provided via the `predict` method to efficiently predict masks from those prompts. The model can take as input both point and box prompts, as well as masks from the previous iteration of prediction."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "18ab8c70",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ " \n",
+ "\n"
+ ],
+ "text/plain": [
+ "\n",
+ "\n",
+ "\"\"\"\n",
+ "))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "644532a8",
+ "metadata": {},
+ "source": [
+ "## Environment Set-up"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "07fabfee",
+ "metadata": {},
+ "source": [
+ "If running locally using jupyter, first install `segment_anything` in your environment using the [installation instructions](https://github.com/facebookresearch/segment-anything#installation) in the repository. If running from Google Colab, set `using_collab=True` below and run the cell. In Colab, be sure to select 'GPU' under 'Edit'->'Notebook Settings'->'Hardware accelerator'."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "5ea65efc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "using_colab = False"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "91dd9a89",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if using_colab:\n",
+ " import torch\n",
+ " import torchvision\n",
+ " print(\"PyTorch version:\", torch.__version__)\n",
+ " print(\"Torchvision version:\", torchvision.__version__)\n",
+ " print(\"CUDA is available:\", torch.cuda.is_available())\n",
+ " import sys\n",
+ " !{sys.executable} -m pip install opencv-python matplotlib\n",
+ " !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/segment-anything.git'\n",
+ " \n",
+ " !mkdir images\n",
+ " !wget -P images https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/truck.jpg\n",
+ " !wget -P images https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/groceries.jpg\n",
+ " \n",
+ " !wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0be845da",
+ "metadata": {},
+ "source": [
+ "## Set-up"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "33681dd1",
+ "metadata": {},
+ "source": [
+ "Necessary imports and helper functions for displaying points, boxes, and masks."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "69b28288",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import torch\n",
+ "import matplotlib.pyplot as plt\n",
+ "import cv2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "29bc90d5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def show_mask(mask, ax, random_color=False):\n",
+ " if random_color:\n",
+ " color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)\n",
+ " else:\n",
+ " color = np.array([30/255, 144/255, 255/255, 0.6])\n",
+ " h, w = mask.shape[-2:]\n",
+ " mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\n",
+ " ax.imshow(mask_image)\n",
+ " \n",
+ "def show_points(coords, labels, ax, marker_size=375):\n",
+ " pos_points = coords[labels==1]\n",
+ " neg_points = coords[labels==0]\n",
+ " ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)\n",
+ " ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25) \n",
+ " \n",
+ "def show_box(box, ax):\n",
+ " x0, y0 = box[0], box[1]\n",
+ " w, h = box[2] - box[0], box[3] - box[1]\n",
+ " ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2)) \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "23842fb2",
+ "metadata": {},
+ "source": [
+ "## Example image"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "3c2e4f6b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "image = cv2.imread('images/truck.jpg')\n",
+ "image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "e30125fd",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
+| + | name | +backbone | +Data | +box AP on COCO | +Checkpoint | +Config | +
|---|---|---|---|---|---|---|
| 1 | +GroundingDINO-T | +Swin-T | +O365,GoldG,Cap4M | +48.4 (zero-shot) / 57.2 (fine-tune) | +GitHub link | HF link | +link | +
| 2 | +GroundingDINO-B | +Swin-B | +COCO,O365,GoldG,Cap4M,OpenImage,ODinW-35,RefCOCO | +56.7 | +GitHub link | HF link + | link | +
+
+
+
+
+Marrying 
+