| """
|
| student_tinycnn.py
|
| Tiny CNN student model for DLoc knowledge distillation.
|
|
|
| Architecture (from professor's spec):
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| Block 1: Conv(4->16) + DepthwiseSep(16->16) + MaxPool
|
| Block 2: DepthwiseSep(16->32) + DepthwiseSep(32->32) + MaxPool
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| Block 3: DepthwiseSep(32->64)
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| Decoder 1: ConvTranspose(64->32, stride=2)
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| Decoder 2: ConvTranspose(32->16, stride=2)
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| Output: Conv(16->1, 1x1) + Sigmoid
|
|
|
| Adapted for 161x361 input (professor's spec used 64x64).
|
| ~30K params — extremely lightweight.
|
| """
|
| import torch
|
| import torch.nn as nn
|
|
|
|
|
| class DepthwiseSeparableConv(nn.Module):
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| """Depthwise separable convolution: depthwise + pointwise."""
|
| def __init__(self, in_ch, out_ch, kernel_size=3, padding=1):
|
| super().__init__()
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| self.depthwise = nn.Conv2d(in_ch, in_ch, kernel_size, padding=padding, groups=in_ch, bias=False)
|
| self.pointwise = nn.Conv2d(in_ch, out_ch, 1, bias=False)
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| self.bn = nn.BatchNorm2d(out_ch)
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| self.relu = nn.ReLU(inplace=True)
|
|
|
| def forward(self, x):
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| return self.relu(self.bn(self.pointwise(self.depthwise(x))))
|
|
|
|
|
| class TinyCNNStudent(nn.Module):
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| OUTPUT_SIZE = (161, 361)
|
|
|
| def __init__(self, input_nc=4):
|
| super().__init__()
|
|
|
|
|
| self.block1 = nn.Sequential(
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| nn.Conv2d(input_nc, 16, kernel_size=3, padding=1, bias=False),
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| nn.BatchNorm2d(16),
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| nn.ReLU(inplace=True),
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| DepthwiseSeparableConv(16, 16),
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| nn.MaxPool2d(2),
|
| )
|
|
|
|
|
| self.block2 = nn.Sequential(
|
| DepthwiseSeparableConv(16, 32),
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| DepthwiseSeparableConv(32, 32),
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| nn.MaxPool2d(2),
|
| )
|
|
|
|
|
| self.block3 = nn.Sequential(
|
| DepthwiseSeparableConv(32, 64),
|
| )
|
|
|
|
|
| self.dec1 = nn.Sequential(
|
| nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1, bias=False),
|
| nn.BatchNorm2d(32),
|
| nn.ReLU(inplace=True),
|
| )
|
|
|
|
|
| self.dec2 = nn.Sequential(
|
| nn.ConvTranspose2d(32, 16, kernel_size=4, stride=2, padding=1, bias=False),
|
| nn.BatchNorm2d(16),
|
| nn.ReLU(inplace=True),
|
| )
|
|
|
|
|
| self.head = nn.Sequential(
|
| nn.Conv2d(16, 1, kernel_size=1),
|
| nn.Sigmoid(),
|
| )
|
|
|
| def forward(self, x):
|
| x = self.block1(x)
|
| x = self.block2(x)
|
| x = self.block3(x)
|
| x = self.dec1(x)
|
| x = self.dec2(x)
|
| x = self.head(x)
|
|
|
| if x.shape[2:] != self.OUTPUT_SIZE:
|
| x = nn.functional.interpolate(x, size=self.OUTPUT_SIZE, mode='bilinear', align_corners=False)
|
| return x
|
|
|
|
|
| if __name__ == "__main__":
|
| model = TinyCNNStudent(input_nc=4)
|
| n = sum(p.numel() for p in model.parameters())
|
| print(f"Parameters: {n:,} ({n/1e6:.2f}M)")
|
| x = torch.randn(2, 4, 161, 361)
|
| with torch.no_grad():
|
| y = model(x)
|
| print(f"Input: {x.shape}")
|
| print(f"Output: {y.shape}")
|
| assert y.shape == (2, 1, 161, 361), f"Wrong shape: {y.shape}"
|
| print("OK")
|
|
|
