Add network

network.py

@@ -0,0 +1,129 @@
+import torch
+import torch.nn as nn
+import torchvision.models as models
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        
+        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(out_channels)
+        
+        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, 
+                              stride=stride, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(out_channels)
+        
+        self.conv3 = nn.Conv2d(out_channels, out_channels * self.expansion, 
+                              kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
+        
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+class ResNet50(nn.Module):
+    def __init__(self, num_classes=1000):
+        super(ResNet50, self).__init__()
+        
+        self.in_channels = 64
+        
+        # Initial layers
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        
+        # Residual layers
+        self.layer1 = self._make_layer(64, 3)
+        self.layer2 = self._make_layer(128, 4, stride=2)
+        self.layer3 = self._make_layer(256, 6, stride=2)
+        self.layer4 = self._make_layer(512, 3, stride=2)
+        
+        # Classification head
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = nn.Linear(512 * Bottleneck.expansion, num_classes)
+        
+        # Weight initialization
+        self._initialize_weights()
+
+    def _make_layer(self, out_channels, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.in_channels != out_channels * Bottleneck.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.in_channels, out_channels * Bottleneck.expansion,
+                         kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(out_channels * Bottleneck.expansion),
+            )
+
+        layers = []
+        layers.append(Bottleneck(self.in_channels, out_channels, stride, downsample))
+        
+        self.in_channels = out_channels * Bottleneck.expansion
+        for _ in range(1, blocks):
+            layers.append(Bottleneck(self.in_channels, out_channels))
+
+        return nn.Sequential(*layers)
+
+    def _initialize_weights(self):
+        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)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = torch.flatten(x, 1)
+        x = self.fc(x)
+
+        return x
+
+def create_model(num_classes, pretrained=False):
+    """
+    Create a ResNet-50 model
+    Args:
+        num_classes: Number of output classes
+        pretrained: Whether to use pretrained weights from ImageNet
+    """
+    # Load model with or without pretrained weights
+    model = models.resnet50(pretrained=pretrained)
+    
+    # Modify the final layer for our number of classes
+    num_ftrs = model.fc.in_features
+    model.fc = nn.Linear(num_ftrs, num_classes)
+    
+    return model