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Add UNet encoder and decoder blocks - #8
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edatosun
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Jul 16, 2026
edatosun
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- Implemented UNet model architecture
- Added encoder and decoder blocks
- Added forward pass
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Pull request overview
Adds an initial PyTorch UNet implementation under services/ml/models/ to support image-to-image training/inference in the ML service package.
Changes:
- Introduces a
DoubleConvbuilding block (Conv-BN-ReLU ×2). - Implements a 4-level UNet with encoder/decoder blocks, transposed-conv upsampling, and skip connections.
- Adds a forward pass that produces a single-channel output tensor.
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| x = self.up1(x5) | ||
| x = torch.cat([x, x4], dim=1) | ||
| x = self.dec1(x) | ||
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| x = self.up2(x) | ||
| x = torch.cat([x, x3], dim=1) | ||
| x = self.dec2(x) | ||
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| x = self.up3(x) | ||
| x = torch.cat([x, x2], dim=1) | ||
| x = self.dec3(x) | ||
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| x = self.up4(x) | ||
| x = torch.cat([x, x1], dim=1) | ||
| x = self.dec4(x) |
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