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student_mobilenet.py
MobileNetV2-based lightweight student model for DLoc knowledge distillation.
Architecture (from professor's Stage 1 spec):
Encoder: MobileNetV2 backbone (first conv modified for 4-channel input)
Decoder: Upsample + Conv (320->128->64->32->1) + Sigmoid
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):
# Target output spatial size
OUTPUT_SIZE = (161, 361)
def __init__(self, input_nc=4):
super().__init__()
# Load MobileNetV2 backbone (features[0:18] = 320-channel output)
mobilenet = mobilenet_v2(weights=MobileNet_V2_Weights.DEFAULT)
self.encoder = mobilenet.features[:-1] # drop final 320->1280 conv
# Replace first conv: 3-channel RGB -> 4-channel AoA
old_conv = self.encoder[0][0]
self.encoder[0][0] = nn.Conv2d(
input_nc, old_conv.out_channels,
kernel_size=old_conv.kernel_size,
stride=old_conv.stride,
padding=old_conv.padding,
bias=False,
)
# Decoder: 320 -> 128 -> 64 -> 32 -> 1
self.decoder = nn.Sequential(
nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
nn.Conv2d(320, 128, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
nn.Conv2d(128, 64, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
nn.Conv2d(64, 32, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.Conv2d(32, 1, kernel_size=1),
nn.Sigmoid(),
)
def forward(self, x):
feat = self.encoder(x)
out = self.decoder(feat)
# Ensure exact output size (MobileNetV2 downsamples aggressively)
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__":
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")
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