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b25c087 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | """Siamese CNN baseline for change detection.
Architecture: Shared-weight ResNet18 backbone extracts features from both
images. Feature difference is decoded via transposed convolutions to produce
a binary change mask.
"""
import torch
import torch.nn as nn
import torchvision.models as models
class SiameseCNN(nn.Module):
"""Siamese CNN with shared ResNet18 encoder and transposed-conv decoder.
Args:
backbone: Backbone architecture name (default: 'resnet18').
pretrained: Whether to use ImageNet-pretrained weights.
"""
def __init__(self, backbone: str = "resnet18", pretrained: bool = True) -> None:
super().__init__()
# Shared encoder
resnet = getattr(models, backbone)(
weights=models.ResNet18_Weights.DEFAULT if pretrained else None
)
# Remove avgpool and fc — keep feature extraction layers
self.encoder = nn.Sequential(
resnet.conv1,
resnet.bn1,
resnet.relu,
resnet.maxpool,
resnet.layer1, # 64 channels
resnet.layer2, # 128 channels
resnet.layer3, # 256 channels
resnet.layer4, # 512 channels
)
# Decoder: upsample difference features back to input resolution
self.decoder = nn.Sequential(
nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(32, 1, kernel_size=4, stride=2, padding=1),
)
def forward(self, x1: torch.Tensor, x2: torch.Tensor) -> torch.Tensor:
"""Forward pass.
Args:
x1: Before image [B, 3, 256, 256].
x2: After image [B, 3, 256, 256].
Returns:
Raw logits [B, 1, 256, 256].
"""
f1 = self.encoder(x1)
f2 = self.encoder(x2)
# Feature difference
diff = torch.abs(f1 - f2)
# Decode to change mask
out = self.decoder(diff)
return out
if __name__ == "__main__":
# Quick sanity check
model = SiameseCNN(pretrained=False)
x1 = torch.randn(2, 3, 256, 256)
x2 = torch.randn(2, 3, 256, 256)
out = model(x1, x2)
print(f"Input: {x1.shape}, Output: {out.shape}")
assert out.shape == (2, 1, 256, 256), f"Unexpected shape: {out.shape}"
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