ResNet PyTorch model from scratch: residual connection enables deeper network

ResNet (short for Residual Network) is a popular deep learning architecture that has revolutionized the field of computer vision. It was introduced by researchers at Microsoft Research in 2015 and has since become a standard model for image classification tasks.

One of the key insights behind ResNet is the use of residual connections, which enable the network to be much deeper than traditional architectures without suffering from the vanishing gradient problem. This is achieved by adding skip connections that bypass one or more layers in the network, allowing the gradients to flow more easily during training.

In this article, we will show how to implement a ResNet model from scratch using PyTorch, a popular deep learning framework. We will start by defining the basic building blocks of the network, such as convolutional layers, batch normalization, and activation functions. Then, we will show how to create the residual blocks with skip connections.

By the end of this article, you will have a basic understanding of how ResNet works and how to implement it in PyTorch. This knowledge will be valuable for anyone looking to dive deeper into the world of deep learning and computer vision.

Implementing the ResNet model in PyTorch

Let’s start by importing the necessary libraries and defining the basic building blocks of the ResNet model:

        
            import torch
            import torch.nn as nn
            import torch.nn.functional as F

            class BasicBlock(nn.Module):
                def __init__(self, in_channels, out_channels, stride=1):
                    super(BasicBlock, self).__init()
                    self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
                    self.bn1 = nn.BatchNorm2d(out_channels)
                    self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
                    self.bn2 = nn.BatchNorm2d(out_channels)

                def forward(self, x):
                    identity = x

                    out = F.relu(self.bn1(self.conv1(x)))
                    out = self.bn2(self.conv2(out))

                    out += identity
                    out = F.relu(out)

                    return out
        
    

In the code snippet above, we have defined a basic building block called BasicBlock, which consists of two convolutional layers followed by batch normalization and a skip connection. The skip connection adds the input to the output of the second convolutional layer, allowing the network to learn residual mappings.

Next, we can define the ResNet model by stacking multiple BasicBlocks together:

        
            class ResNet(nn.Module):
                def __init__(self, block, num_blocks, num_classes=10):
                    super(ResNet, self).__init__()
                    self.in_channels = 64
                    self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
                    self.bn1 = nn.BatchNorm2d(64)
                    self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
                    self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
                    self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
                    self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
                    self.linear = nn.Linear(512, num_classes)

                def _make_layer(self, block, out_channels, num_blocks, stride):
                    strides = [stride] + [1] * (num_blocks - 1)
                    layers = []
                    for stride in strides:
                        layers.append(block(self.in_channels, out_channels, stride))
                        self.in_channels = out_channels
                    return nn.Sequential(*layers)

                def forward(self, x):
                    out = F.relu(self.bn1(self.conv1(x)))
                    out = self.layer1(out)
                    out = self.layer2(out)
                    out = self.layer3(out)
                    out = self.layer4(out)
                    out = F.avg_pool2d(out, 4)
                    out = out.view(out.size(0), -1)
                    out = self.linear(out)

                    return out

            def ResNet18():
                return ResNet(BasicBlock, [2, 2, 2, 2])
        
    

In the code snippet above, we have defined the ResNet class, which consists of four main blocks of layers. Each block contains multiple BasicBlocks with increasing number of output channels. The ResNet18 function creates a ResNet model with 18 layers in total.

Conclusion

In this article, we have shown how to implement a ResNet model from scratch using PyTorch. The use of residual connections enables us to build deeper networks that can learn more complex features and achieve better performance on image classification tasks.

By understanding the inner workings of ResNet and how to implement it in PyTorch, you will be better equipped to experiment with different architectures and push the boundaries of deep learning research. We hope this article has inspired you to dive deeper into the world of deep learning and computer vision.