File size: 13,150 Bytes
bd1d180 75afda4 eae357c bd1d180 75afda4 0cdffb9 bd1d180 75afda4 bd1d180 b1fbce9 5e7db30 bd1d180 ca8e8cb bd1d180 c41c5f2 bd1d180 0cdffb9 374865c 75afda4 374865c bd1d180 75afda4 bd1d180 0642f5b bd1d180 e661577 374865c 75afda4 374865c 75afda4 02d04ca 75afda4 02d04ca 75afda4 02d04ca fa4c226 75afda4 02d04ca 75afda4 02d04ca 75afda4 bd1d180 e4d7d13 accdcf1 d7bad8b 3472aef a909331 3472aef a909331 accdcf1 75afda4 02d04ca bd1d180 75afda4 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 |
import json
import os
import zipfile
from io import BytesIO
from tempfile import NamedTemporaryFile
import tempfile
import gradio as gr
import pandas as pd
from PIL import Image
import safetensors.torch
import spaces
import timm
from timm.models import VisionTransformer
import torch
from torchvision.transforms import transforms
from torchvision.transforms import InterpolationMode
import torchvision.transforms.functional as TF
from torch.utils.data import Dataset, DataLoader
torch.set_grad_enabled(False)
class Fit(torch.nn.Module):
def __init__(
self,
bounds: tuple[int, int] | int,
interpolation = InterpolationMode.LANCZOS,
grow: bool = True,
pad: float | None = None
):
super().__init__()
self.bounds = (bounds, bounds) if isinstance(bounds, int) else bounds
self.interpolation = interpolation
self.grow = grow
self.pad = pad
def forward(self, img: Image) -> Image:
wimg, himg = img.size
hbound, wbound = self.bounds
hscale = hbound / himg
wscale = wbound / wimg
if not self.grow:
hscale = min(hscale, 1.0)
wscale = min(wscale, 1.0)
scale = min(hscale, wscale)
if scale == 1.0:
return img
hnew = min(round(himg * scale), hbound)
wnew = min(round(wimg * scale), wbound)
img = TF.resize(img, (hnew, wnew), self.interpolation)
if self.pad is None:
return img
hpad = hbound - hnew
wpad = wbound - wnew
tpad = hpad // 2
bpad = hpad - tpad
lpad = wpad // 2
rpad = wpad - lpad
return TF.pad(img, (lpad, tpad, rpad, bpad), self.pad)
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}(" +
f"bounds={self.bounds}, " +
f"interpolation={self.interpolation.value}, " +
f"grow={self.grow}, " +
f"pad={self.pad})"
)
class CompositeAlpha(torch.nn.Module):
def __init__(
self,
background: tuple[float, float, float] | float,
):
super().__init__()
self.background = (background, background, background) if isinstance(background, float) else background
self.background = torch.tensor(self.background).unsqueeze(1).unsqueeze(2)
def forward(self, img: torch.Tensor) -> torch.Tensor:
if img.shape[-3] == 3:
return img
alpha = img[..., 3, None, :, :]
img[..., :3, :, :] *= alpha
background = self.background.expand(-1, img.shape[-2], img.shape[-1])
if background.ndim == 1:
background = background[:, None, None]
elif background.ndim == 2:
background = background[None, :, :]
img[..., :3, :, :] += (1.0 - alpha) * background
return img[..., :3, :, :]
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}(" +
f"background={self.background})"
)
transform = transforms.Compose([
Fit((384, 384)),
transforms.ToTensor(),
CompositeAlpha(0.5),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
transforms.CenterCrop((384, 384)),
])
model = timm.create_model(
"vit_so400m_patch14_siglip_384.webli",
pretrained=False,
num_classes=9083,
) # type: VisionTransformer
safetensors.torch.load_model(model, "JTP_PILOT-e4-vit_so400m_patch14_siglip_384.safetensors")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()
with open("tagger_tags.json", "r") as file:
tags = json.load(file) # type: dict
allowed_tags = list(tags.keys())
for idx, tag in enumerate(allowed_tags):
allowed_tags[idx] = tag.replace("_", " ")
sorted_tag_score = {}
@spaces.GPU(duration=9)
def run_classifier(image, threshold):
global sorted_tag_score
img = image.convert('RGB')
tensor = transform(img).unsqueeze(0).to(device)
with torch.no_grad():
logits = model(tensor)
probabilities = torch.nn.functional.sigmoid(logits[0])
indices = torch.topk(probabilities, 250).indices
values = probabilities[indices]
tag_score = dict()
for i in range(indices.size(0)):
tag_score[allowed_tags[indices[i]]] = values[i].item()
sorted_tag_score = dict(sorted(tag_score.items(), key=lambda item: item[1], reverse=True))
return create_tags(threshold)
def create_tags(threshold):
global sorted_tag_score
filtered_tag_score = {key: value for key, value in sorted_tag_score.items() if value > threshold}
text_no_impl = ", ".join(filtered_tag_score.keys())
return text_no_impl, filtered_tag_score
class ImageDataset(Dataset):
def __init__(self, image_files, transform):
self.image_files = image_files
self.transform = transform
def __len__(self):
return len(self.image_files)
def __getitem__(self, idx):
img_path = self.image_files[idx]
img = Image.open(img_path).convert('RGB')
return self.transform(img), os.path.basename(img_path)
@spaces.GPU(duration=299)
def process_images(images, threshold):
dataset = ImageDataset(images, transform)
dataloader = DataLoader(dataset, batch_size=64, num_workers=0, pin_memory=True, drop_last=False)
all_results = []
with torch.no_grad():
for batch, filenames in dataloader:
batch = batch.to(device)
with torch.no_grad():
logits = model(batch)
probabilities = torch.nn.functional.sigmoid(logits)
for i, prob in enumerate(probabilities):
indices = torch.where(prob > threshold)[0]
values = prob[indices]
temp = []
tag_score = dict()
for j in range(indices.size(0)):
temp.append([allowed_tags[indices[j]], values[j].item()])
tag_score[allowed_tags[indices[j]]] = values[j].item()
tags = ", ".join([t[0] for t in temp])
all_results.append((filenames[i], tags, tag_score))
return all_results
def is_valid_image(file_path):
try:
with Image.open(file_path) as img:
img.verify()
return True
except:
return False
def process_zip(zip_file, threshold):
if zip_file is None:
return None, None
with tempfile.TemporaryDirectory() as temp_dir:
with zipfile.ZipFile(zip_file.name, 'r') as zip_ref:
zip_ref.extractall(temp_dir)
all_files = [os.path.join(temp_dir, f) for f in os.listdir(temp_dir)]
image_files = [f for f in all_files if is_valid_image(f)]
results = process_images(image_files, threshold)
temp_file = NamedTemporaryFile(delete=False, suffix=".zip")
with zipfile.ZipFile(temp_file, "w") as zip_ref:
for image_name, text_no_impl, _ in results:
with zip_ref.open(''.join(image_name.split('.')[:-1]) + ".txt", 'w') as file:
file.write(text_no_impl.encode())
temp_file.seek(0)
df = pd.DataFrame([(os.path.basename(f), t) for f, t, _ in results], columns=['Image', 'Tags'])
return temp_file.name, df
@spaces.GPU(duration=120) # Reduced GPU duration for less wait time...
def process_images_light(images, threshold):
dataset = ImageDataset(images, transform)
dataloader = DataLoader(dataset, batch_size=32, num_workers=0, pin_memory=True, drop_last=False)
all_results = []
with torch.no_grad():
for batch, filenames in dataloader:
batch = batch.to(device)
with torch.no_grad():
logits = model(batch)
probabilities = torch.nn.functional.sigmoid(logits)
for i, prob in enumerate(probabilities):
indices = torch.where(prob > threshold)[0]
values = prob[indices]
temp = []
tag_score = dict()
for j in range(indices.size(0)):
temp.append([allowed_tags[indices[j]], values[j].item()])
tag_score[allowed_tags[indices[j]]] = values[j].item()
tags = ", ".join([t[0] for t in temp])
all_results.append((filenames[i], tags, tag_score))
return all_results
def process_zip_light(zip_file, threshold):
if zip_file is None:
return None, None
with tempfile.TemporaryDirectory() as temp_dir:
with zipfile.ZipFile(zip_file.name, 'r') as zip_ref:
zip_ref.extractall(temp_dir)
all_files = [os.path.join(temp_dir, f) for f in os.listdir(temp_dir)]
image_files = [f for f in all_files if is_valid_image(f)]
results = process_images_light(image_files, threshold)
temp_file = NamedTemporaryFile(delete=False, suffix=".zip")
with zipfile.ZipFile(temp_file, "w") as zip_ref:
for image_name, text_no_impl, _ in results:
with zip_ref.open(''.join(image_name.split('.')[:-1]) + ".txt", 'w') as file:
file.write(text_no_impl.encode())
temp_file.seek(0)
df = pd.DataFrame([(os.path.basename(f), t) for f, t, _ in results], columns=['Image', 'Tags'])
return temp_file.name, df
with gr.Blocks(css=".output-class { display: none; }") as demo:
gr.Markdown("""
## Joint Tagger Project: PILOT Demo
This tagger is designed for use on furry images (though may very well work on out-of-distribution images, potentially with funny results). A threshold of 0.2 is recommended. Lower thresholds often turn up more valid tags, but can also result in some amount of hallucinated tags.
This tagger is the result of joint efforts between members of the RedRocket team. Special thanks to Minotoro at frosting.ai for providing the compute power for this project.
Usage Note for batch tagging:
the normal version is limited to 300s and uses batch size 64
the light version is limited to 120s with batch size 32
if your image count is low use the light version for lower gpu wait time (most of the time you instantly get a gpu anyway)
""")
with gr.Tabs():
with gr.TabItem("Single Image"):
with gr.Row():
with gr.Column():
image_input = gr.Image(label="Source", sources=['upload'], type='pil', height=512, show_label=False)
threshold_slider = gr.Slider(minimum=0.00, maximum=1.00, step=0.01, value=0.20, label="Threshold")
with gr.Column():
tag_string = gr.Textbox(label="Tag String")
label_box = gr.Label(label="Tag Predictions", num_top_classes=250, show_label=False)
image_input.upload(
fn=run_classifier,
inputs=[image_input, threshold_slider],
outputs=[tag_string, label_box]
)
threshold_slider.input(
fn=create_tags,
inputs=[threshold_slider],
outputs=[tag_string, label_box]
)
with gr.TabItem("Multiple Images"):
with gr.Row():
with gr.Column():
zip_input = gr.File(label="Upload ZIP file", file_types=['.zip'])
multi_threshold_slider = gr.Slider(minimum=0.00, maximum=1.00, step=0.01, value=0.20, label="Threshold")
process_button = gr.Button("Process Images")
with gr.Column():
zip_output = gr.File(label="Download Tagged Text Files (ZIP)")
dataframe_output = gr.Dataframe(label="Image Tags Summary")
process_button.click(
fn=process_zip,
inputs=[zip_input, multi_threshold_slider],
outputs=[zip_output, dataframe_output]
)
with gr.TabItem("Multiple Images (Light)"):
with gr.Row():
with gr.Column():
zip_input_light = gr.File(label="Upload ZIP file", file_types=['.zip'])
multi_threshold_slider_light = gr.Slider(minimum=0.00, maximum=1.00, step=0.01, value=0.20, label="Threshold")
process_button_light = gr.Button("Process Images (Light)")
with gr.Column():
zip_output_light = gr.File(label="Download Tagged Text Files (ZIP)")
dataframe_output_light = gr.Dataframe(label="Image Tags Summary")
process_button_light.click(
fn=process_zip_light,
inputs=[zip_input_light, multi_threshold_slider_light],
outputs=[zip_output_light, dataframe_output_light]
)
if __name__ == "__main__":
demo.queue().launch() |