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import torch
import torch.nn as nn
from torchvision import transforms
from PIL import Image
from huggingface_hub import hf_hub_download
import io
import base64
import numpy as np
# --- Basic UNet Components ---
class DoubleConv(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.double_conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.double_conv(x)
class Down(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.maxpool_conv = nn.Sequential(
nn.MaxPool2d(2),
DoubleConv(in_channels, out_channels)
)
def forward(self, x):
return self.maxpool_conv(x)
class Up(nn.Module):
def __init__(self, in_channels, out_channels, bilinear=True):
super().__init__()
if bilinear:
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
else:
self.up = nn.ConvTranspose2d(in_channels // 2, in_channels // 2, kernel_size=2, stride=2)
self.conv = DoubleConv(in_channels, out_channels)
def forward(self, x1, x2):
x1 = self.up(x1)
diffY = x2.size()[2] - x1.size()[2]
diffX = x2.size()[3] - x1.size()[3]
x1 = nn.functional.pad(x1, [diffX // 2, diffX - diffX // 2,
diffY // 2, diffY - diffY // 2])
x = torch.cat([x2, x1], dim=1)
return self.conv(x)
class OutConv(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
def forward(self, x):
return self.conv(x)
# --- Full UNet ---
class UNet(nn.Module):
def __init__(self, n_channels=3, n_classes=1, bilinear=True):
super().__init__()
self.n_channels = n_channels
self.n_classes = n_classes
self.bilinear = bilinear
self.inc = DoubleConv(n_channels, 64)
self.down1 = Down(64, 128)
self.down2 = Down(128, 256)
self.down3 = Down(256, 512)
factor = 2 if bilinear else 1
self.down4 = Down(512, 1024 // factor)
self.up1 = Up(1024, 512 // factor, bilinear)
self.up2 = Up(512, 256 // factor, bilinear)
self.up3 = Up(256, 128 // factor, bilinear)
self.up4 = Up(128, 64, bilinear)
self.outc = OutConv(64, n_classes)
def forward(self, x):
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
x = self.up1(x5, x4)
x = self.up2(x, x3)
x = self.up3(x, x2)
x = self.up4(x, x1)
logits = self.outc(x)
return torch.sigmoid(logits)
# --- EndpointHandler for Hugging Face Inference Endpoint ---
class EndpointHandler:
def __init__(self, path=""):
model_path = hf_hub_download(repo_id="whitney0507/unet-model", filename="UNet_Model.pth")
self.model = UNet()
state_dict = torch.load(model_path, map_location=torch.device("cpu"))
self.model.load_state_dict(state_dict)
self.model.eval()
self.transform = transforms.Compose([
transforms.Resize((256, 256)),
transforms.ToTensor()
])
def __call__(self, data):
image_bytes = base64.b64decode(data["inputs"])
image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
input_tensor = self.transform(image).unsqueeze(0)
with torch.no_grad():
output = self.model(input_tensor)
mask = (output > 0.5).int().squeeze().cpu().numpy()
# Ensure mask is in uint8 format for image encoding
result_img = Image.fromarray((mask * 255).astype(np.uint8))
buffer = io.BytesIO()
result_img.save(buffer, format="PNG")
encoded_output = base64.b64encode(buffer.getvalue()).decode("utf-8")
return {"prediction": encoded_output}
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