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c65e61c | 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 | import torch
import torch.nn as nn
import torch.nn.functional as F
class CustomCNN(nn.Module):
"""
Custom CNN optimized for CIFAR-10 with ~3-4M parameters.
Designed to compete with ResNet18 while being lightweight.
"""
def __init__(self, num_classes=10, dropout_rate=0.4):
super(CustomCNN, self).__init__()
# Feature extractor with efficient blocks
self.features = nn.Sequential(
# Block 1: 32x32 -> 32x32
self._conv_block(3, 64, stride=1),
self._conv_block(64, 64, stride=1),
# Block 2: 32x32 -> 16x16
self._conv_block(64, 128, stride=2),
self._conv_block(128, 128, stride=1),
# Block 3: 16x16 -> 8x8
self._conv_block(128, 256, stride=2),
self._conv_block(256, 256, stride=1),
# Block 4: 8x8 -> 4x4 (deep feature extraction)
self._conv_block(256, 512, stride=2),
)
# Global pooling and classifier
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.dropout = nn.Dropout(dropout_rate)
self.classifier = nn.Linear(512, num_classes)
# Initialize weights
self._initialize_weights()
def _conv_block(self, in_channels, out_channels, stride=1):
"""Efficient conv block with BatchNorm and ReLU."""
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3,
stride=stride, padding=1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def _initialize_weights(self):
"""Initialize weights using He initialization for ReLU."""
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.constant_(m.bias, 0)
def forward(self, x):
x = self.features(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.dropout(x)
x = self.classifier(x)
return x
def get_model_info(self):
"""Return model architecture info."""
total_params = sum(p.numel() for p in self.parameters())
trainable_params = sum(p.numel() for p in self.parameters() if p.requires_grad)
return {
'total_params': total_params,
'trainable_params': trainable_params,
'model_size_mb': total_params * 4 / (1024 * 1024), # Assuming float32
'architecture': '7-layer CNN with BatchNorm and Dropout'
}
# Factory function for easy instantiation
def create_custom_cnn(num_classes=10, dropout_rate=0.4):
"""Create and return a CustomCNN instance."""
return CustomCNN(num_classes=num_classes, dropout_rate=dropout_rate)
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