ResNet.py

import torch
import torch.nn as nn

class block(nn.Module):
    def __init__(self, in_channels, out_channels, identity_downsample=None, stride=1):
        super(block, self).__init__()
        self.expansion = 4
        self.cov1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.cov2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.cov3 = nn.Conv2d(out_channels, out_channels * self.expansion, kernel_size=1, stride=1, padding=0)
        self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
        self.relu = nn.ReLU()
        self.identity_downsample = identity_downsample

    def forward(self, x):
        identity = x

        x = self.relu(self.bn1(self.cov1(x)))
        x = self.relu(self.bn2(self.cov2(x)))
        x = self.bn3(self.cov3(x))

        if self.identity_downsample is not None:
            identity = self.identity_downsample(identity)
        
        x += identity
        x = self.relu(x)
        return x
    
    
class ResNet(nn.Module):
    def __init__(self, block, layers, image_channels, num_classes):
        super(ResNet, self).__init__()
        self.in_channels = 64
        self.conv1 = nn.Conv2d(image_channels, 64, kernel_size=7, stride=2, padding=3)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU()
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self._make_layer(block, layers[0], out_channels=64, stride=1)
        self.layer2 = self._make_layer(block, layers[1], out_channels=128, stride=2)
        self.layer3 = self._make_layer(block, layers[2], out_channels=256, stride=2)
        self.layer4 = self._make_layer(block, layers[3], out_channels=512, stride=2)

        self.avgpool = nn.AdaptiveAvgPool2d((1,1))
        self.fc = nn.Linear(512*4, num_classes)

    def forward(self, x):
        x = self.maxpool(self.relu(self.bn1(self.conv1(x))))
        x = self.layer4(self.layer3(self.layer2(self.layer1(x))))
        x = self.avgpool(x)
        x = x.reshape(x.shape[0], -1)
        x = self.fc(x)
        return x

    def _make_layer(self, block, num_residual_blocks, out_channels, stride):
        identity_downsample = None
        layers = []

        if stride != 1 or self.in_channels != out_channels * 4:
            identity_downsample = nn.Sequential(nn.Conv2d(self.in_channels, out_channels * 4, kernel_size=1,
                                                          stride=stride),
                                                nn.BatchNorm2d(out_channels * 4))
            
        layers.append(block(self.in_channels, out_channels, identity_downsample, stride))
        self.in_channels = out_channels * 4

        for i in range(num_residual_blocks - 1):
            layers.append(block(self.in_channels, out_channels))

        return nn.Sequential(*layers)
    

def ResNet50(img_channels=3, num_classes=1000):
    return ResNet(block, [3, 4, 6, 3], img_channels, num_classes)


def ResNet101(img_channels=3, num_classes=1000):
    return ResNet(block, [3, 4, 23, 3], img_channels, num_classes)


def ResNet152(img_channels=3, num_classes=1000):
    return ResNet(block, [3, 8, 36, 3], img_channels, num_classes)

def test():
    net = ResNet152()
    x = torch.randn(2, 3, 224, 224)
    y = net(x)
    print(y.shape)

test()