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import json
from PIL import Image
import gradio as gr
import torch
from torchvision.transforms import transforms
from torchvision.transforms import InterpolationMode
import torchvision.transforms.functional as TF
import spaces
import huggingface_hub
import timm
from timm.models import VisionTransformer
import safetensors.torch
torch.jit.script = lambda f: f
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_file = huggingface_hub.hf_hub_download(
repo_id="RedRocket/JointTaggerProject",
filename="JTP_PILOT-e4-vit_so400m_patch14_siglip_384.safetensors",
subfolder="JTP_PILOT"
)
model = timm.create_model(
"vit_so400m_patch14_siglip_384.webli",
pretrained=False,
num_classes=9083,
) # type: VisionTransformer
safetensors.torch.load_model(model, model_file)
model.eval()
tags_file = huggingface_hub.hf_hub_download(
repo_id="RedRocket/JointTaggerProject",
filename="tags.json",
subfolder="JTP_PILOT"
)
with open(tags_file, "r") as file:
tags = json.load(file) # type: dict
allowed_tags = tags.keys()
@spaces.GPU(duration=5)
def create_tags(image, threshold):
img = image.convert('RGB')
tensor = transform(img).unsqueeze(0)
with torch.no_grad():
logits = model(tensor)
probabilities = torch.nn.functional.sigmoid(logits[0])
indices = torch.where(probabilities > threshold)[0]
values = probabilities[indices]
temp = []
tag_score = dict()
for i in range(indices.size(0)):
temp.append([allowed_tags[indices[i]], values[i].item()])
tag_score[allowed_tags[indices[i]]] = values[i].item()
temp = [t[0] for t in temp]
text_no_impl = ", ".join(temp)
return text_no_impl, tag_score
with gr.Blocks() as demo:
with gr.Tab("Single Image"):
gr.Interface(
create_tags,
inputs=[gr.Image(label="Source", sources=['upload', 'webcam'], type='pil'), gr.Slider(minimum=0.00, maximum=1.00, step=0.01, value=0.30, label="Threshold")],
outputs=[
gr.Textbox(label="Tag String"),
gr.Label(label="Tag Predictions", num_top_classes=200),
],
allow_flagging="never",
)
if __name__ == "__main__":
demo.launch() |