File size: 2,795 Bytes
a16f583 | 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 87 88 | import numpy as np
import torch
import torch.nn as nn
class SmallCNN(nn.Module):
def __init__(self, input_channels=6, dropout_rate=0.3):
super(SmallCNN, self).__init__()
# Convolutional layers
self.conv1 = nn.Conv2d(input_channels, 16, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
# Additional convolutional layers
self.conv4 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
self.conv5 = nn.Conv2d(64, 32, kernel_size=3, padding=1)
# Skip connection layers (1x1 convolutions for dimension matching)
self.skip1 = nn.Conv2d(16, 32, kernel_size=1)
self.skip2 = nn.Conv2d(32, 64, kernel_size=1)
self.skip3 = nn.Conv2d(64, 32, kernel_size=1)
# Pooling layer
self.pool = nn.MaxPool2d(2, 2)
# Dropout layers
self.dropout_conv = nn.Dropout2d(p=dropout_rate)
self.dropout_fc = nn.Dropout(p=dropout_rate)
# Fully connected layers
self.fc1 = nn.Linear(128, 64)
self.fc2 = nn.Linear(64, 1)
# Activation
self.relu = nn.ReLU()
def forward(self, x):
# First convolutional block
x1 = self.conv1(x)
x1 = self.relu(x1)
x1 = self.dropout_conv(x1)
x1_pooled = self.pool(x1)
# Second convolutional block with skip connection
x2 = self.conv2(x1_pooled)
x2 = self.relu(x2)
x2 = self.dropout_conv(x2)
# Skip connection from first block (with dimension matching)
skip_x1 = self.skip1(self.pool(x1))
x2 = x2 + skip_x1
x2_pooled = self.pool(x2)
# Third convolutional block with skip connection
x3 = self.conv3(x2_pooled)
x3 = self.relu(x3)
x3 = self.dropout_conv(x3)
# Skip connection from second block
skip_x2 = self.skip2(self.pool(x2))
x3 = x3 + skip_x2
x3_pooled = self.pool(x3)
# Fourth convolutional block (additional depth)
x4 = self.conv4(x3_pooled)
x4 = self.relu(x4)
x4 = self.dropout_conv(x4)
x4 = x4 + x3_pooled # Residual connection
# Fifth convolutional block (additional depth)
x5 = self.conv5(x4)
x5 = self.relu(x5)
x5 = self.dropout_conv(x5)
# Skip connection from third block
skip_x3 = self.skip3(x3_pooled)
x5 = x5 + skip_x3
# Flatten and fully connected layers
x = x5.view(x5.size(0), -1)
x = self.fc1(x)
x = self.relu(x)
x = self.dropout_fc(x)
x = self.fc2(x)
return x.squeeze()
def count_parameters(self):
return sum(p.numel() for p in self.parameters() if p.requires_grad)
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