MahdiHasan/Image_Classifier

1import torch
2import torch.nn as nn
3import torch.optim as optim
4
5class ResidualBlock(nn.Module):
6    def __init__(self, in_channels, out_channels, stride=1):
7        super(ResidualBlock, self).__init__()
8        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
9        self.bn1 = nn.BatchNorm2d(out_channels)
10        self.relu = nn.ReLU(inplace=True)
11        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
12        self.bn2 = nn.BatchNorm2d(out_channels)
13        
14        self.shortcut = nn.Sequential()
15        if stride != 1 or in_channels != out_channels:
16            self.shortcut = nn.Sequential(
17                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
18                nn.BatchNorm2d(out_channels)
19            )
20    
21    def forward(self, x):
22        residual = x
23        out = self.conv1(x)
24        out = self.bn1(out)
25        out = self.relu(out)
26        out = self.conv2(out)
27        out = self.bn2(out)
28        out += self.shortcut(residual)
29        out = self.relu(out)
30        return out
31
32# Define the ResNet model (same as before)
33
34class ResNet(nn.Module):
35    def __init__(self, num_classes=4):
36        super(ResNet, self).__init__()
37        self.in_channels = 64
38        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
39        self.bn1 = nn.BatchNorm2d(64)
40        self.relu = nn.ReLU(inplace=True)
41        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
42        
43        self.layer1 = self._make_layer(64, 2)
44        self.layer2 = self._make_layer(128, 2, stride=2)
45        self.layer3 = self._make_layer(256, 2, stride=2)
46        self.layer4 = self._make_layer(512, 2, stride=2)
47        
48        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
49        self.fc = nn.Linear(512, num_classes)
50    
51    def _make_layer(self, out_channels, num_blocks, stride=1):
52        layers = []
53        layers.append(ResidualBlock(self.in_channels, out_channels, stride))
54        self.in_channels = out_channels
55        for _ in range(1, num_blocks):
56            layers.append(ResidualBlock(out_channels, out_channels))
57        return nn.Sequential(*layers)
58    
59    def forward(self, x):
60        out = self.conv1(x)
61        out = self.bn1(out)
62        out = self.relu(out)
63        out = self.maxpool(out)
64        
65        out = self.layer1(out)
66        out = self.layer2(out)
67        out = self.layer3(out)
68        out = self.layer4(out)
69        
70        out = self.avgpool(out)
71        out = out.view(out.size(0), -1)  # Flatten before FC
72        out = self.fc(out)
73        return out