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#!/usr/bin/env python
from __future__ import annotations
import functools
import os
import pathlib
import sys
import tarfile
import gradio as gr
import huggingface_hub
import PIL.Image
import torch
import torchvision
sys.path.insert(0, "bizarre-pose-estimator")
from _util.twodee_v0 import I as ImageWrapper
DESCRIPTION = "# [ShuhongChen/bizarre-pose-estimator (tagger)](https://github.com/ShuhongChen/bizarre-pose-estimator)"
MODEL_REPO = "public-data/bizarre-pose-estimator-models"
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, "tagger.pth")
state_dict = torch.load(path)
model = torchvision.models.resnet50(num_classes=1062)
model.load_state_dict(state_dict)
model.to(device)
model.eval()
return model
def load_labels() -> list[str]:
label_path = huggingface_hub.hf_hub_download(MODEL_REPO, "tags.txt")
with open(label_path) as f:
labels = [line.strip() for line in f.readlines()]
return labels
@torch.inference_mode()
def predict(
image: PIL.Image.Image, score_threshold: float, device: torch.device, model: torch.nn.Module, labels: list[str]
) -> dict[str, float]:
data = ImageWrapper(image).resize_square(256).alpha_bg(c="w").convert("RGB").tensor()
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(), 0.5] for path in image_paths]
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = load_model(device)
labels = load_labels()
fn = functools.partial(predict, 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=0.5)
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",
)
if __name__ == "__main__":
demo.queue(max_size=15).launch()
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