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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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from typing import Callable |
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import gradio as gr |
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import huggingface_hub |
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import numpy as np |
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import PIL.Image |
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import torch |
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import torch.nn as nn |
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import torchvision |
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import torchvision.transforms as T |
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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 = ( |
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"# [ShuhongChen/bizarre-pose-estimator (segmenter)](https://github.com/ShuhongChen/bizarre-pose-estimator)" |
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) |
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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, "images.tar.gz", 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) -> tuple[torch.nn.Module, torch.nn.Module]: |
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path = huggingface_hub.hf_hub_download("public-data/bizarre-pose-estimator-models", "segmenter.pth") |
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ckpt = torch.load(path) |
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model = torchvision.models.segmentation.deeplabv3_resnet101() |
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model.classifier = nn.Sequential( |
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torchvision.models.segmentation.deeplabv3.ASPP(2048, [12, 24, 36]), |
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nn.Conv2d(256, 64, kernel_size=3, stride=1, padding=1), |
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nn.BatchNorm2d(64), |
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nn.LeakyReLU(), |
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nn.Conv2d(64, 16, kernel_size=3, stride=1, padding=1), |
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nn.BatchNorm2d(16), |
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nn.LeakyReLU(), |
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) |
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final_head = nn.Sequential( |
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nn.Conv2d(16 + 3, 16, kernel_size=3, stride=1, padding=1), |
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nn.BatchNorm2d(16), |
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nn.LeakyReLU(), |
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nn.Conv2d(16, 8, kernel_size=3, stride=1, padding=1), |
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nn.BatchNorm2d(8), |
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nn.LeakyReLU(), |
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nn.Conv2d(8, 2, kernel_size=1, stride=1), |
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) |
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model.load_state_dict(ckpt["model"]) |
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final_head.load_state_dict(ckpt["final_head"]) |
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model.to(device) |
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model.eval() |
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final_head.to(device) |
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final_head.eval() |
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return model, final_head |
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@torch.inference_mode() |
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def predict( |
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image: PIL.Image.Image, |
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score_threshold: float, |
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transform: Callable, |
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device: torch.device, |
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model: torch.nn.Module, |
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final_head: torch.nn.Module, |
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) -> np.ndarray: |
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data = ImageWrapper(image).resize_min(256).convert("RGBA").alpha_bg(1).convert("RGB").pil() |
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data = torchvision.transforms.functional.to_tensor(data) |
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data = transform(data) |
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data = data.to(device).unsqueeze(0) |
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out = model(data)["out"] |
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out_fin = final_head( |
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torch.cat( |
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[ |
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out, |
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data, |
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], |
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dim=1, |
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) |
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) |
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probs = torch.softmax(out_fin, dim=1)[0] |
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probs = probs[1] |
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probs = PIL.Image.fromarray(probs.cpu().numpy()).resize(image.size) |
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mask = np.asarray(probs).copy() |
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mask[mask < score_threshold] = 0 |
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mask[mask > 0] = 1 |
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mask = mask.astype(bool) |
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res = np.asarray(image).copy() |
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res[~mask] = 255 |
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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, final_head = load_model(device) |
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transform = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) |
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fn = functools.partial(predict, transform=transform, device=device, model=model, final_head=final_head) |
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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", minimum=0, maximum=1, step=0.05, 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.Image(label="Masked") |
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inputs = [image, threshold] |
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gr.Examples( |
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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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) |
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run_button.click( |
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fn=fn, |
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inputs=inputs, |
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outputs=result, |
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api_name="predict", |
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) |
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if __name__ == "__main__": |
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demo.queue(max_size=15).launch() |
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