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import torch |
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print("Installed the dependencies!") |
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import numpy as np |
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from PIL import Image |
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import cv2 |
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import imutils |
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from detectron2.config import get_cfg |
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from detectron2.projects.deeplab import add_deeplab_config |
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from detectron2.data import MetadataCatalog |
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from oneformer import ( |
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add_oneformer_config, |
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add_common_config, |
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add_swin_config, |
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add_dinat_config, |
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) |
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from demo.defaults import DefaultPredictor |
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from demo.visualizer import Visualizer, ColorMode |
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import gradio as gr |
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from huggingface_hub import hf_hub_download |
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KEY_DICT = {"Cityscapes (19 classes)": "cityscapes", |
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"COCO (133 classes)": "coco", |
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"ADE20K (150 classes)": "ade20k",} |
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SWIN_CFG_DICT = {"cityscapes": "configs/cityscapes/oneformer_swin_large_IN21k_384_bs16_90k.yaml", |
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"coco": "configs/coco/oneformer_swin_large_IN21k_384_bs16_100ep.yaml", |
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"ade20k": "configs/ade20k/oneformer_swin_large_IN21k_384_bs16_160k.yaml",} |
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SWIN_MODEL_DICT = {"cityscapes": hf_hub_download(repo_id="shi-labs/oneformer_cityscapes_swin_large", |
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filename="250_16_swin_l_oneformer_cityscapes_90k.pth"), |
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"coco": hf_hub_download(repo_id="shi-labs/oneformer_coco_swin_large", |
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filename="150_16_swin_l_oneformer_coco_100ep.pth"), |
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"ade20k": hf_hub_download(repo_id="shi-labs/oneformer_ade20k_swin_large", |
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filename="250_16_swin_l_oneformer_ade20k_160k.pth") |
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} |
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DINAT_CFG_DICT = {"cityscapes": "configs/cityscapes/oneformer_dinat_large_bs16_90k.yaml", |
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"coco": "configs/coco/oneformer_dinat_large_bs16_100ep.yaml", |
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"ade20k": "configs/ade20k/oneformer_dinat_large_IN21k_384_bs16_160k.yaml",} |
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DINAT_MODEL_DICT = {"cityscapes": hf_hub_download(repo_id="shi-labs/oneformer_cityscapes_dinat_large", |
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filename="250_16_dinat_l_oneformer_cityscapes_90k.pth"), |
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"coco": hf_hub_download(repo_id="shi-labs/oneformer_coco_dinat_large", |
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filename="150_16_dinat_l_oneformer_coco_100ep.pth"), |
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"ade20k": hf_hub_download(repo_id="shi-labs/oneformer_ade20k_dinat_large", |
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filename="250_16_dinat_l_oneformer_ade20k_160k.pth") |
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} |
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MODEL_DICT = {"DiNAT-L": DINAT_MODEL_DICT, |
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"Swin-L": SWIN_MODEL_DICT } |
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CFG_DICT = {"DiNAT-L": DINAT_CFG_DICT, |
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"Swin-L": SWIN_CFG_DICT } |
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WIDTH_DICT = {"cityscapes": 512, |
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"coco": 512, |
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"ade20k": 640} |
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cpu_device = torch.device("cpu") |
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PREDICTORS = { |
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"DiNAT-L": { |
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"Cityscapes (19 classes)": None, |
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"COCO (133 classes)": None, |
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"ADE20K (150 classes)": None |
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}, |
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"Swin-L": { |
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"Cityscapes (19 classes)": None, |
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"COCO (133 classes)": None, |
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"ADE20K (150 classes)": None |
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} |
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} |
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METADATA = { |
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"DiNAT-L": { |
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"Cityscapes (19 classes)": None, |
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"COCO (133 classes)": None, |
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"ADE20K (150 classes)": None |
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}, |
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"Swin-L": { |
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"Cityscapes (19 classes)": None, |
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"COCO (133 classes)": None, |
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"ADE20K (150 classes)": None |
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} |
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} |
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def setup_modules(): |
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for dataset in ["Cityscapes (19 classes)", "COCO (133 classes)", "ADE20K (150 classes)"]: |
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for backbone in ["DiNAT-L", "Swin-L"]: |
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cfg = setup_cfg(dataset, backbone) |
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metadata = MetadataCatalog.get( |
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cfg.DATASETS.TEST_PANOPTIC[0] if len(cfg.DATASETS.TEST_PANOPTIC) else "__unused" |
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) |
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if 'cityscapes_fine_sem_seg_val' in cfg.DATASETS.TEST_PANOPTIC[0]: |
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from cityscapesscripts.helpers.labels import labels |
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stuff_colors = [k.color for k in labels if k.trainId != 255] |
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metadata = metadata.set(stuff_colors=stuff_colors) |
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PREDICTORS[backbone][dataset] = DefaultPredictor(cfg) |
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METADATA[backbone][dataset] = metadata |
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def setup_cfg(dataset, backbone): |
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cfg = get_cfg() |
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add_deeplab_config(cfg) |
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add_common_config(cfg) |
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add_swin_config(cfg) |
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add_oneformer_config(cfg) |
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add_dinat_config(cfg) |
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dataset = KEY_DICT[dataset] |
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cfg_path = CFG_DICT[backbone][dataset] |
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cfg.merge_from_file(cfg_path) |
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if torch.cuda.is_available(): |
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cfg.MODEL.DEVICE = 'cuda' |
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else: |
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cfg.MODEL.DEVICE = 'cpu' |
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cfg.MODEL.WEIGHTS = MODEL_DICT[backbone][dataset] |
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cfg.freeze() |
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return cfg |
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def panoptic_run(img, predictor, metadata): |
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visualizer = Visualizer(img[:, :, ::-1], metadata=metadata, instance_mode=ColorMode.IMAGE) |
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predictions = predictor(img, "panoptic") |
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panoptic_seg, segments_info = predictions["panoptic_seg"] |
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out = visualizer.draw_panoptic_seg_predictions( |
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panoptic_seg.to(cpu_device), segments_info, alpha=0.5 |
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) |
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visualizer_map = Visualizer(img[:, :, ::-1], is_img=False, metadata=metadata, instance_mode=ColorMode.IMAGE) |
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out_map = visualizer_map.draw_panoptic_seg_predictions( |
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panoptic_seg.to(cpu_device), segments_info, alpha=1, is_text=False |
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) |
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return out, out_map |
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def instance_run(img, predictor, metadata): |
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visualizer = Visualizer(img[:, :, ::-1], metadata=metadata, instance_mode=ColorMode.IMAGE) |
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predictions = predictor(img, "instance") |
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instances = predictions["instances"].to(cpu_device) |
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out = visualizer.draw_instance_predictions(predictions=instances, alpha=0.5) |
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visualizer_map = Visualizer(img[:, :, ::-1], is_img=False, metadata=metadata, instance_mode=ColorMode.IMAGE) |
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out_map = visualizer_map.draw_instance_predictions(predictions=instances, alpha=1, is_text=False) |
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return out, out_map |
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def semantic_run(img, predictor, metadata): |
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visualizer = Visualizer(img[:, :, ::-1], metadata=metadata, instance_mode=ColorMode.IMAGE) |
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predictions = predictor(img, "semantic") |
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out = visualizer.draw_sem_seg( |
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predictions["sem_seg"].argmax(dim=0).to(cpu_device), alpha=0.5 |
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) |
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visualizer_map = Visualizer(img[:, :, ::-1], is_img=False, metadata=metadata, instance_mode=ColorMode.IMAGE) |
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out_map = visualizer_map.draw_sem_seg( |
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predictions["sem_seg"].argmax(dim=0).to(cpu_device), alpha=1, is_text=False |
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) |
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return out, out_map |
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TASK_INFER = {"the task is panoptic": panoptic_run, "the task is instance": instance_run, "the task is semantic": semantic_run} |
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def segment(path, task, dataset, backbone): |
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predictor = PREDICTORS[backbone][dataset] |
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metadata = METADATA[backbone][dataset] |
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img = cv2.imread(path) |
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width = WIDTH_DICT[KEY_DICT[dataset]] |
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img = imutils.resize(img, width=width) |
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out, out_map = TASK_INFER[task](img, predictor, metadata) |
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out = Image.fromarray(out.get_image()) |
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out_map = Image.fromarray(out_map.get_image()) |
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return out, out_map |
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title = "<h1 style='margin-bottom: -10px; text-align: center'>OneFormer: One Transformer to Rule Universal Image Segmentation</h1>" |
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description = "<p style='font-size: 14px; margin: 5px; font-weight: w300; text-align: center'> <a href='https://praeclarumjj3.github.io/' style='text-decoration:none' target='_blank'>Jitesh Jain, </a> <a href='https://chrisjuniorli.github.io/' style='text-decoration:none' target='_blank'>Jiachen Li<sup>*</sup>, </a> <a href='https://www.linkedin.com/in/mtchiu/' style='text-decoration:none' target='_blank'>MangTik Chiu<sup>*</sup>, </a> <a href='https://alihassanijr.com/' style='text-decoration:none' target='_blank'>Ali Hassani, </a> <a href='https://www.linkedin.com/in/nukich74/' style='text-decoration:none' target='_blank'>Nikita Orlov, </a> <a href='https://www.humphreyshi.com/home' style='text-decoration:none' target='_blank'>Humphrey Shi</a></p>" \ |
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+ "<p style='font-size: 16px; margin: 5px; font-weight: w600; text-align: center'> <a href='https://praeclarumjj3.github.io/oneformer/' target='_blank'>Project Page</a> | <a href='https://arxiv.org/abs/2211.06220' target='_blank'>ArXiv Paper</a> | <a href='https://github.com/SHI-Labs/OneFormer' target='_blank'>Github Repo</a></p>" \ |
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+ "<p style='text-align: center; margin: 5px; font-size: 14px; font-weight: w300;'> \ |
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OneFormer is the first multi-task universal image segmentation framework based on transformers. Our single OneFormer model achieves state-of-the-art performance across all three segmentation tasks with a single task-conditioned joint training process. OneFormer uses a task token to condition the model on the task in focus, making our architecture task-guided for training, and task-dynamic for inference, all with a single model. We believe OneFormer is a significant step towards making image segmentation more universal and accessible.\ |
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</p>" \ |
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+ "<p style='text-align: center; font-size: 14px; margin: 5px; font-weight: w300;'> [Note: Inference on CPU may take upto 2 minutes. On a single RTX A6000 GPU, OneFormer is able to inference at more than 15 FPS.]</p>" |
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setup_modules() |
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gradio_inputs = [gr.Image(source="upload", tool=None, label="Input Image",type="filepath"), |
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gr.Radio(choices=["the task is panoptic" ,"the task is instance", "the task is semantic"], type="value", value="the task is panoptic", label="Task Token Input"), |
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gr.Radio(choices=["COCO (133 classes)" ,"Cityscapes (19 classes)", "ADE20K (150 classes)"], type="value", value="COCO (133 classes)", label="Model"), |
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gr.Radio(choices=["DiNAT-L" ,"Swin-L"], type="value", value="DiNAT-L", label="Backbone"), |
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] |
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gradio_outputs = [gr.Image(type="pil", label="Segmentation Overlay"), gr.Image(type="pil", label="Segmentation Map")] |
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examples = [["examples/coco.jpeg", "the task is panoptic", "COCO (133 classes)", "DiNAT-L"], |
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["examples/cityscapes.png", "the task is panoptic", "Cityscapes (19 classes)", "DiNAT-L"], |
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["examples/ade20k.jpeg", "the task is panoptic", "ADE20K (150 classes)", "DiNAT-L"]] |
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iface = gr.Interface(fn=segment, inputs=gradio_inputs, |
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outputs=gradio_outputs, |
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examples_per_page=5, |
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allow_flagging="never", |
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examples=examples, title=title, |
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description=description) |
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iface.launch(enable_queue=True, server_name="0.0.0.0") |
