Deep-Learning/Resnet.py at main · AgatheZ/Deep-Learning · GitHub

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## define resnet building blocks

class ResidualBlock(nn.Module):
    def __init__(self, inchannel, outchannel, stride=1):

        super(ResidualBlock, self).__init__()

        self.left = nn.Sequential(Conv2d(inchannel, outchannel, kernel_size=3,
                                         stride=stride, padding=1, bias=False),
                                  nn.BatchNorm2d(outchannel),
                                  nn.ReLU(inplace=True),
                                  Conv2d(outchannel, outchannel, kernel_size=3,
                                         stride=1, padding=1, bias=False),
                                  nn.BatchNorm2d(outchannel))

        self.shortcut = nn.Sequential()

        if stride != 1 or inchannel != outchannel:

            self.shortcut = nn.Sequential(Conv2d(inchannel, outchannel,
                                                 kernel_size=1, stride=stride,
                                                 padding = 0, bias=False),
                                          nn.BatchNorm2d(outchannel) )

    def forward(self, x):

        out = self.left(x)

        out += self.shortcut(x)

        out = F.relu(out)


        return out


# define resnet

class ResNet(nn.Module):

    def __init__(self, ResidualBlock, num_classes = 20):

        super(ResNet, self).__init__()

        self.inchannel = 20
        self.conv1 = nn.Sequential(Conv2d(3, 20, kernel_size = 3, stride = 1,
                                            padding = 1, bias = False),
                                  nn.BatchNorm2d(20),
                                  nn.ReLU(),
                                  )

        self.layer0 = self.make_layer(ResidualBlock, 16, 2, stride = 2)
        self.layer1 = self.make_layer(ResidualBlock, 32, 2, stride = 2)
        self.layer2 = self.make_layer(ResidualBlock, 64, 2, stride = 2)
        self.layer3 = self.make_layer(ResidualBlock, 128, 2, stride = 2)
        self.layer4 = self.make_layer(ResidualBlock, 256, 2, stride = 2)
        self.layer5 = self.make_layer(ResidualBlock, 512, 2, stride = 2)
        self.maxpool = MaxPool2d(4)
        self.fc = nn.Linear(512, num_classes)


    def make_layer(self, block, channels, num_blocks, stride):

        strides = [stride] + [1] * (num_blocks - 1)

        layers = []

        for stride in strides:

            layers.append(block(self.inchannel, channels, stride))

            self.inchannel = channels

        return nn.Sequential(*layers)


    def forward(self, x):

        x = self.conv1(x)
        x = self.layer0(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.layer5(x)
        x = self.maxpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

# please do not change the name of this class
def MyResNet():
    return ResNet(ResidualBlock)