Pytoch residual network RESNET

Pytorch Residual network ResNet

See for complete training code >>Github link

ResNet structure

class Residual(nn.Module):
    def __init__(self,input_channels,num_channels,use_1x1conv=False,strides=1):
        super().__init__()
        self.conv1 = nn.Conv2d(input_channels,num_channels,
                               kernel_size=3,padding=1,stride=strides)
        self.conv2 = nn.Conv2d(num_channels,num_channels,
                               kernel_size=3,padding=1)
        if use_1x1conv:
            self.conv3 = nn.Conv2d(input_channels,num_channels,
                                   kernel_size=1,stride = strides)
        else:
            self.conv3 = None
        self.bn1 = nn.BatchNorm2d(num_channels)
        self.bn2 = nn.BatchNorm2d(num_channels)
        self.relu = nn.ReLU()
    
    def forward(self,x):
        Y = self.relu(self.bn1(self.conv1(x)))
        Y = self.bn2(self.conv2(Y))
        if self.conv3:
            x = self.conv3(x)
        Y = Y + x
        return self.relu(Y)

def resnet_block(input_channels,num_channels,num_residuals,first_block=False):
    blk = []
    for i in range(num_residuals):
        if i==0 and not first_block:
            blk.append(Residual(input_channels,num_channels,use_1x1conv=True,strides=2))
        else:
            blk.append(Residual(num_channels,num_channels))
    return blk

class ResNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.b1 = nn.Sequential(nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3),
                   nn.BatchNorm2d(64), nn.ReLU(),
                   nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
        self.b2 = nn.Sequential(*resnet_block(64,64,2,first_block=True))
        self.b3 = nn.Sequential(*resnet_block(64,128,2))
        self.b4 = nn.Sequential(*resnet_block(128,256,2))
        self.b5 = nn.Sequential(*resnet_block(256,512,2))
        self.net = nn.Sequential(self.b1,self.b2,self.b3,self.b4,self.b5,
                                 nn.AdaptiveAvgPool2d((1,1)),
                                 nn.Flatten(), nn.Linear(512, 10))
    
    def forward(self,x):
        return self.net(x)