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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
TITLE = 'ShuhongChen/bizarre-pose-estimator (tagger)'
DESCRIPTION = 'This is an unofficial demo for https://github.com/ShuhongChen/bizarre-pose-estimator.'
HF_TOKEN = os.getenv('HF_TOKEN')
MODEL_REPO = 'hysts/bizarre-pose-estimator-models'
MODEL_FILENAME = 'tagger.pth'
LABEL_FILENAME = 'tags.txt'
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',
use_auth_token=HF_TOKEN)
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,
MODEL_FILENAME,
use_auth_token=HF_TOKEN)
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,
LABEL_FILENAME,
use_auth_token=HF_TOKEN)
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()
func = functools.partial(predict, device=device, model=model, labels=labels)
gr.Interface(
fn=func,
inputs=[
gr.Image(label='Input', type='pil'),
gr.Slider(label='Score Threshold',
minimum=0,
maximum=1,
step=0.05,
value=0.5),
],
outputs=gr.Label(label='Output'),
examples=examples,
title=TITLE,
description=DESCRIPTION,
).queue().launch(show_api=False)
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