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