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from __future__ import annotations |
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import functools |
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import os |
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import pathlib |
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import sys |
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import tarfile |
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import gradio as gr |
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import huggingface_hub |
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import PIL.Image |
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import torch |
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import torchvision |
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sys.path.insert(0, 'bizarre-pose-estimator') |
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from _util.twodee_v0 import I as ImageWrapper |
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DESCRIPTION = '# [ShuhongChen/bizarre-pose-estimator (tagger)](https://github.com/ShuhongChen/bizarre-pose-estimator)' |
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MODEL_REPO = 'public-data/bizarre-pose-estimator-models' |
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def load_sample_image_paths() -> list[pathlib.Path]: |
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image_dir = pathlib.Path('images') |
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if not image_dir.exists(): |
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dataset_repo = 'hysts/sample-images-TADNE' |
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path = huggingface_hub.hf_hub_download(dataset_repo, |
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'images.tar.gz', |
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repo_type='dataset') |
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with tarfile.open(path) as f: |
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f.extractall() |
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return sorted(image_dir.glob('*')) |
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def load_model(device: torch.device) -> torch.nn.Module: |
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path = huggingface_hub.hf_hub_download(MODEL_REPO, 'tagger.pth') |
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state_dict = torch.load(path) |
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model = torchvision.models.resnet50(num_classes=1062) |
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model.load_state_dict(state_dict) |
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model.to(device) |
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model.eval() |
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return model |
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def load_labels() -> list[str]: |
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label_path = huggingface_hub.hf_hub_download(MODEL_REPO, 'tags.txt') |
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with open(label_path) as f: |
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labels = [line.strip() for line in f.readlines()] |
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return labels |
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@torch.inference_mode() |
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def predict(image: PIL.Image.Image, score_threshold: float, |
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device: torch.device, model: torch.nn.Module, |
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labels: list[str]) -> dict[str, float]: |
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data = ImageWrapper(image).resize_square(256).alpha_bg( |
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c='w').convert('RGB').tensor() |
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data = data.to(device).unsqueeze(0) |
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preds = model(data)[0] |
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preds = torch.sigmoid(preds) |
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preds = preds.cpu().numpy().astype(float) |
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res = dict() |
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for prob, label in zip(preds.tolist(), labels): |
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if prob < score_threshold: |
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continue |
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res[label] = prob |
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return res |
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image_paths = load_sample_image_paths() |
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examples = [[path.as_posix(), 0.5] for path in image_paths] |
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device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') |
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model = load_model(device) |
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labels = load_labels() |
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fn = functools.partial(predict, device=device, model=model, labels=labels) |
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with gr.Blocks(css='style.css') as demo: |
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gr.Markdown(DESCRIPTION) |
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with gr.Row(): |
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with gr.Column(): |
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image = gr.Image(label='Input', type='pil') |
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threshold = gr.Slider(label='Score Threshold', |
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minimum=0, |
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maximum=1, |
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step=0.05, |
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value=0.5) |
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run_button = gr.Button('Run') |
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with gr.Column(): |
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result = gr.Label(label='Output') |
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inputs = [image, threshold] |
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gr.Examples(examples=examples, |
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inputs=inputs, |
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outputs=result, |
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fn=fn, |
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cache_examples=os.getenv('CACHE_EXAMPLES') == '1') |
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run_button.click(fn=fn, inputs=inputs, outputs=result, api_name='predict') |
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demo.queue(max_size=15).launch() |
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