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student_tinycnn.py
Tiny CNN student model for DLoc knowledge distillation.
Architecture (from professor's spec):
Block 1: Conv(4->16) + DepthwiseSep(16->16) + MaxPool
Block 2: DepthwiseSep(16->32) + DepthwiseSep(32->32) + MaxPool
Block 3: DepthwiseSep(32->64)
Decoder 1: ConvTranspose(64->32, stride=2)
Decoder 2: ConvTranspose(32->16, stride=2)
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):
"""Depthwise separable convolution: depthwise + pointwise."""
def __init__(self, in_ch, out_ch, kernel_size=3, padding=1):
super().__init__()
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)
self.bn = nn.BatchNorm2d(out_ch)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
return self.relu(self.bn(self.pointwise(self.depthwise(x))))
class TinyCNNStudent(nn.Module):
OUTPUT_SIZE = (161, 361)
def __init__(self, input_nc=4):
super().__init__()
# Block 1: 4 -> 16, downsample 2x
self.block1 = nn.Sequential(
nn.Conv2d(input_nc, 16, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(16),
nn.ReLU(inplace=True),
DepthwiseSeparableConv(16, 16),
nn.MaxPool2d(2),
)
# Block 2: 16 -> 32, downsample 2x
self.block2 = nn.Sequential(
DepthwiseSeparableConv(16, 32),
DepthwiseSeparableConv(32, 32),
nn.MaxPool2d(2),
)
# Block 3: 32 -> 64 (no downsample)
self.block3 = nn.Sequential(
DepthwiseSeparableConv(32, 64),
)
# Decoder 1: 64 -> 32, upsample 2x
self.dec1 = nn.Sequential(
nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
)
# Decoder 2: 32 -> 16, upsample 2x
self.dec2 = nn.Sequential(
nn.ConvTranspose2d(32, 16, kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(16),
nn.ReLU(inplace=True),
)
# Output head
self.head = nn.Sequential(
nn.Conv2d(16, 1, kernel_size=1),
nn.Sigmoid(),
)
def forward(self, x):
x = self.block1(x) # [B,16, H/2, W/2]
x = self.block2(x) # [B,32, H/4, W/4]
x = self.block3(x) # [B,64, H/4, W/4]
x = self.dec1(x) # [B,32, H/2, W/2]
x = self.dec2(x) # [B,16, H, W]
x = self.head(x) # [B,1, H, W]
# Ensure exact output size
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")
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