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from __future__ import annotations |
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import argparse |
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import functools |
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import os |
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import pathlib |
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import subprocess |
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import sys |
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command = 'pip install -U gradio==2.7.0' |
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subprocess.call(command.split()) |
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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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TOKEN = os.environ['TOKEN'] |
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MODEL_REPO = 'hysts/bizarre-pose-estimator-models' |
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MODEL_PATH = 'tagger.pth' |
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LABEL_PATH = 'tags.txt' |
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def parse_args() -> argparse.Namespace: |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--device', type=str, default='cpu') |
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parser.add_argument('--score-slider-step', type=float, default=0.05) |
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parser.add_argument('--score-threshold', type=float, default=0.5) |
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parser.add_argument('--theme', type=str, default='dark-grass') |
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parser.add_argument('--live', action='store_true') |
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parser.add_argument('--share', action='store_true') |
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parser.add_argument('--port', type=int) |
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parser.add_argument('--disable-queue', |
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dest='enable_queue', |
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action='store_false') |
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parser.add_argument('--allow-flagging', type=str, default='never') |
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parser.add_argument('--allow-screenshot', action='store_true') |
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return parser.parse_args() |
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def download_sample_images() -> list[pathlib.Path]: |
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image_dir = pathlib.Path('samples') |
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image_dir.mkdir(exist_ok=True) |
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dataset_repo = 'hysts/sample-images-TADNE' |
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n_images = 36 |
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paths = [] |
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for index in range(n_images): |
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path = huggingface_hub.hf_hub_download(dataset_repo, |
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f'{index:02d}.jpg', |
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repo_type='dataset', |
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cache_dir=image_dir.as_posix(), |
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use_auth_token=TOKEN) |
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paths.append(pathlib.Path(path)) |
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return paths |
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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, 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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def load_model(device: torch.device) -> torch.nn.Module: |
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model_path = huggingface_hub.hf_hub_download(MODEL_REPO, |
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MODEL_PATH, |
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use_auth_token=TOKEN) |
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state_dict = torch.load(model_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, |
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LABEL_PATH, |
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use_auth_token=TOKEN) |
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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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def main(): |
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gr.close_all() |
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args = parse_args() |
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device = torch.device(args.device) |
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image_paths = download_sample_images() |
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examples = [[path.as_posix(), args.score_threshold] |
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for path in image_paths] |
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model = load_model(device) |
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labels = load_labels() |
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func = functools.partial(predict, |
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device=device, |
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model=model, |
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labels=labels) |
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func = functools.update_wrapper(func, predict) |
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repo_url = 'https://github.com/ShuhongChen/bizarre-pose-estimator' |
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title = 'ShuhongChen/bizarre-pose-estimator (tagger)' |
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description = f'A demo for {repo_url}' |
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article = None |
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gr.Interface( |
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func, |
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[ |
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gr.inputs.Image(type='pil', label='Input'), |
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gr.inputs.Slider(0, |
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1, |
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step=args.score_slider_step, |
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default=args.score_threshold, |
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label='Score Threshold'), |
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], |
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gr.outputs.Label(label='Output'), |
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theme=args.theme, |
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title=title, |
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description=description, |
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article=article, |
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examples=examples, |
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allow_screenshot=args.allow_screenshot, |
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allow_flagging=args.allow_flagging, |
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live=args.live, |
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).launch( |
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enable_queue=args.enable_queue, |
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server_port=args.port, |
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share=args.share, |
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) |
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if __name__ == '__main__': |
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main() |
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