| """
|
| student_mobilenet.py
|
| MobileNetV2-based lightweight student model for DLoc knowledge distillation.
|
|
|
| Architecture (from professor's Stage 1 spec):
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| Encoder: MobileNetV2 backbone (first conv modified for 4-channel input)
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| Decoder: Upsample + Conv (320->128->64->32->1) + Sigmoid
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| Output: [B, 1, 161, 361] location heatmap
|
|
|
| ~2.3M params vs DLoc's ~15M params.
|
| """
|
| import torch
|
| import torch.nn as nn
|
| from torchvision.models import mobilenet_v2, MobileNet_V2_Weights
|
|
|
|
|
| class MobileNetStudent(nn.Module):
|
|
|
| OUTPUT_SIZE = (161, 361)
|
|
|
| def __init__(self, input_nc=4):
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| super().__init__()
|
|
|
|
|
| mobilenet = mobilenet_v2(weights=MobileNet_V2_Weights.DEFAULT)
|
| self.encoder = mobilenet.features[:-1]
|
|
|
|
|
| old_conv = self.encoder[0][0]
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| self.encoder[0][0] = nn.Conv2d(
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| input_nc, old_conv.out_channels,
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| kernel_size=old_conv.kernel_size,
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| stride=old_conv.stride,
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| padding=old_conv.padding,
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| bias=False,
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| )
|
|
|
|
|
| self.decoder = nn.Sequential(
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| nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
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| nn.Conv2d(320, 128, kernel_size=3, padding=1, bias=False),
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| nn.BatchNorm2d(128),
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| nn.ReLU(inplace=True),
|
|
|
| nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
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| nn.Conv2d(128, 64, kernel_size=3, padding=1, bias=False),
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| nn.BatchNorm2d(64),
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| nn.ReLU(inplace=True),
|
|
|
| nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
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| nn.Conv2d(64, 32, kernel_size=3, padding=1, bias=False),
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| nn.BatchNorm2d(32),
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| nn.ReLU(inplace=True),
|
|
|
| nn.Conv2d(32, 1, kernel_size=1),
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| nn.Sigmoid(),
|
| )
|
|
|
| def forward(self, x):
|
| feat = self.encoder(x)
|
| out = self.decoder(feat)
|
|
|
| if out.shape[2:] != self.OUTPUT_SIZE:
|
| out = nn.functional.interpolate(out, size=self.OUTPUT_SIZE, mode='bilinear', align_corners=False)
|
| return out
|
|
|
|
|
| if __name__ == "__main__":
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| model = MobileNetStudent(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")
|
|
|
