File size: 2,608 Bytes
76ec265 | 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 | import torch
import torch.nn as nn
import torch.nn.functional as F
class TrafficSignNet(nn.Module):
"""
Ultra-lightweight CNN model optimized for Edge / Web CPU inference.
Input size: (batch_size, 3, 32, 32)
Output size: (batch_size, 12)
Number of parameters: ~65k
"""
def __init__(self, num_classes=12):
super().__init__()
# Block 1: Input 3x32x32 -> Output 16x16x16
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(16)
# Block 2: Input 16x16x16 -> Output 32x8x8
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(32)
# Block 3: Input 32x8x8 -> Output 64x4x4
self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
self.bn3 = nn.BatchNorm2d(64)
# Global Average Pooling to collapse spatial dimensions (4x4) to 1x1
self.global_pool = nn.AdaptiveAvgPool2d((1, 1))
# 1x1 Convolution layers replacing Fully Connected layers
# to bypass PyTorch 2.x ONNX Gemm exporter conversion bugs.
self.fc1_conv = nn.Conv2d(64, 64, kernel_size=1)
self.dropout = nn.Dropout(0.3)
self.fc2_conv = nn.Conv2d(64, num_classes, kernel_size=1)
def forward(self, x):
# Layer 1
x = self.conv1(x)
x = self.bn1(x)
x = F.relu(x)
x = F.max_pool2d(x, 2, 2) # Downsample to 16x16
# Layer 2
x = self.conv2(x)
x = self.bn2(x)
x = F.relu(x)
x = F.max_pool2d(x, 2, 2) # Downsample to 8x8
# Layer 3
x = self.conv3(x)
x = self.bn3(x)
x = F.relu(x)
x = F.max_pool2d(x, 2, 2) # Downsample to 4x4
# Global Pooling -> Shape: (batch_size, 64, 1, 1)
x = self.global_pool(x)
# 1x1 Conv classification (acting as FC layers)
x = self.fc1_conv(x)
x = F.relu(x)
x = self.dropout(x)
x = self.fc2_conv(x) # Shape: (batch_size, 12, 1, 1)
# Flatten at the very end
x = torch.flatten(x, 1) # Shape: (batch_size, 12)
return x
if __name__ == "__main__":
# Test model shape and parameters
model = TrafficSignNet(num_classes=12)
x = torch.randn(1, 3, 32, 32)
out = model(x)
print("Input shape:", x.shape)
print("Output shape:", out.shape)
total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f"Total trainable parameters: {total_params:,}")
|