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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import functools | |
| import json | |
| import os | |
| import pathlib | |
| import tarfile | |
| from typing import Callable | |
| import gradio as gr | |
| import huggingface_hub | |
| import PIL.Image | |
| import torch | |
| import torchvision.transforms as T | |
| DESCRIPTION = '# [RF5/danbooru-pretrained](https://github.com/RF5/danbooru-pretrained)' | |
| MODEL_REPO = 'public-data/danbooru-pretrained' | |
| def load_sample_image_paths() -> list[pathlib.Path]: | |
| image_dir = pathlib.Path('images') | |
| if not image_dir.exists(): | |
| dataset_repo = 'hysts/sample-images-TADNE' | |
| path = huggingface_hub.hf_hub_download(dataset_repo, | |
| 'images.tar.gz', | |
| repo_type='dataset') | |
| with tarfile.open(path) as f: | |
| f.extractall() | |
| return sorted(image_dir.glob('*')) | |
| def load_model(device: torch.device) -> torch.nn.Module: | |
| path = huggingface_hub.hf_hub_download(MODEL_REPO, 'resnet50-13306192.pth') | |
| state_dict = torch.load(path) | |
| model = torch.hub.load('RF5/danbooru-pretrained', | |
| 'resnet50', | |
| pretrained=False) | |
| model.load_state_dict(state_dict) | |
| model.to(device) | |
| model.eval() | |
| return model | |
| def load_labels() -> list[str]: | |
| path = huggingface_hub.hf_hub_download(MODEL_REPO, 'class_names_6000.json') | |
| with open(path) as f: | |
| labels = json.load(f) | |
| return labels | |
| def predict(image: PIL.Image.Image, score_threshold: float, | |
| transform: Callable, device: torch.device, model: torch.nn.Module, | |
| labels: list[str]) -> dict[str, float]: | |
| data = transform(image) | |
| data = data.to(device).unsqueeze(0) | |
| preds = model(data)[0] | |
| preds = torch.sigmoid(preds) | |
| preds = preds.cpu().numpy().astype(float) | |
| res = dict() | |
| for prob, label in zip(preds.tolist(), labels): | |
| if prob < score_threshold: | |
| continue | |
| res[label] = prob | |
| return res | |
| image_paths = load_sample_image_paths() | |
| examples = [[path.as_posix(), 0.4] for path in image_paths] | |
| device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') | |
| model = load_model(device) | |
| labels = load_labels() | |
| transform = T.Compose([ | |
| T.Resize(360), | |
| T.ToTensor(), | |
| T.Normalize(mean=[0.7137, 0.6628, 0.6519], std=[0.2970, 0.3017, 0.2979]), | |
| ]) | |
| fn = functools.partial(predict, | |
| transform=transform, | |
| device=device, | |
| model=model, | |
| labels=labels) | |
| with gr.Blocks(css='style.css') as demo: | |
| gr.Markdown(DESCRIPTION) | |
| with gr.Row(): | |
| with gr.Column(): | |
| image = gr.Image(label='Input', type='pil') | |
| threshold = gr.Slider(label='Score Threshold', | |
| minimum=0, | |
| maximum=1, | |
| step=0.05, | |
| value=0.4) | |
| run_button = gr.Button('Run') | |
| with gr.Column(): | |
| result = gr.Label(label='Output') | |
| inputs = [image, threshold] | |
| gr.Examples(examples=examples, | |
| inputs=inputs, | |
| outputs=result, | |
| fn=fn, | |
| cache_examples=os.getenv('CACHE_EXAMPLES') == '1') | |
| run_button.click(fn=fn, inputs=inputs, outputs=result, api_name='predict') | |
| demo.queue(max_size=15).launch() | |