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#!/usr/bin/env python
from __future__ import annotations
import argparse
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
TOKEN = os.environ['TOKEN']
MODEL_REPO = 'hysts/danbooru-pretrained'
MODEL_FILENAME = 'resnet50-13306192.pth'
LABEL_FILENAME = 'class_names_6000.json'
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=0.4)
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 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=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=TOKEN)
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,
LABEL_FILENAME,
use_auth_token=TOKEN)
with open(path) as f:
labels = json.load(f)
return labels
@torch.inference_mode()
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
def main():
gr.close_all()
args = parse_args()
device = torch.device(args.device)
image_paths = load_sample_image_paths()
examples = [[path.as_posix(), args.score_threshold]
for path in image_paths]
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]),
])
func = functools.partial(predict,
transform=transform,
device=device,
model=model,
labels=labels)
func = functools.update_wrapper(func, predict)
repo_url = 'https://github.com/RF5/danbooru-pretrained'
title = 'RF5/danbooru-pretrained'
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()
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