Spaces:
Restarting
Restarting
File size: 4,941 Bytes
9bdd97c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 |
#!/usr/bin/env python
from __future__ import annotations
import argparse
import functools
import os
import pathlib
import subprocess
import sys
# workaround for https://github.com/gradio-app/gradio/issues/483
command = 'pip install -U gradio==2.7.0'
subprocess.call(command.split())
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
TOKEN = os.environ['TOKEN']
MODEL_REPO = 'hysts/bizarre-pose-estimator-models'
MODEL_PATH = 'tagger.pth'
LABEL_PATH = 'tags.txt'
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cpu')
parser.add_argument('--score-slider-step', type=float, default=0.05)
parser.add_argument('--score-threshold', type=float, default=0.5)
parser.add_argument('--theme', type=str, default='dark-grass')
parser.add_argument('--live', action='store_true')
parser.add_argument('--share', action='store_true')
parser.add_argument('--port', type=int)
parser.add_argument('--disable-queue',
dest='enable_queue',
action='store_false')
parser.add_argument('--allow-flagging', type=str, default='never')
parser.add_argument('--allow-screenshot', action='store_true')
return parser.parse_args()
def download_sample_images() -> list[pathlib.Path]:
image_dir = pathlib.Path('samples')
image_dir.mkdir(exist_ok=True)
dataset_repo = 'hysts/sample-images-TADNE'
n_images = 36
paths = []
for index in range(n_images):
path = huggingface_hub.hf_hub_download(dataset_repo,
f'{index:02d}.jpg',
repo_type='dataset',
cache_dir=image_dir.as_posix(),
use_auth_token=TOKEN)
paths.append(pathlib.Path(path))
return paths
@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, labels):
if prob < score_threshold:
continue
res[label] = prob
return res
def load_model(device: torch.device) -> torch.nn.Module:
model_path = huggingface_hub.hf_hub_download(MODEL_REPO,
MODEL_PATH,
use_auth_token=TOKEN)
state_dict = torch.load(model_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_PATH,
use_auth_token=TOKEN)
with open(label_path) as f:
labels = [line.strip() for line in f.readlines()]
return labels
def main():
gr.close_all()
args = parse_args()
device = torch.device(args.device)
image_paths = download_sample_images()
examples = [[path.as_posix(), args.score_threshold]
for path in image_paths]
model = load_model(device)
labels = load_labels()
func = functools.partial(predict,
device=device,
model=model,
labels=labels)
func = functools.update_wrapper(func, predict)
repo_url = 'https://github.com/ShuhongChen/bizarre-pose-estimator'
title = 'ShuhongChen/bizarre-pose-estimator (tagger)'
description = f'A demo for {repo_url}'
article = None
gr.Interface(
func,
[
gr.inputs.Image(type='pil', label='Input'),
gr.inputs.Slider(0,
1,
step=args.score_slider_step,
default=args.score_threshold,
label='Score Threshold'),
],
gr.outputs.Label(label='Output'),
theme=args.theme,
title=title,
description=description,
article=article,
examples=examples,
allow_screenshot=args.allow_screenshot,
allow_flagging=args.allow_flagging,
live=args.live,
).launch(
enable_queue=args.enable_queue,
server_port=args.port,
share=args.share,
)
if __name__ == '__main__':
main()
|