Pytorch实现ResNet

from torch import nn

__all__ = ["ResNet50", "ResNet101", "ResNet152"]

class Bottleneck(nn.Module):
    def __init__(self, in_planes, planes, stride=1, downsampling=False, expansion=4):
        super(Bottleneck, self).__init__()
        self.expansion = expansion
        self.downsampling = downsampling

        self.bottleneck = nn.Sequential(
            nn.Conv2d(in_channels=in_planes, out_channels=planes, kernel_size=1, stride=1, bias=False),
            nn.BatchNorm2d(planes),
            nn.ReLU(inplace=True),
            nn.Conv2d(in_channels=planes, out_channels=planes, kernel_size=3, stride=stride, padding=1, bias=False),
            nn.BatchNorm2d(planes),
            nn.ReLU(inplace=True),
            nn.Conv2d(in_channels=planes, out_channels=planes*self.expansion, kernel_size=1, stride=1, bias=False),
            nn.BatchNorm2d(planes*self.expansion)
        )

        if self.downsampling:
            self.downsample = nn.Sequential(
                nn.Conv2d(in_channels=in_planes, out_channels=planes * self.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes*self.expansion)
            )
        self.relu = nn.ReLU(inplace=True)
        
    def forward(self, x):
        residual = x
        out = self.bottleneck(x)

        if self.downsampling:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)
        return out

class ResNet(nn.Module):
    def __init__(self,blocks, num_classes=1000, expansion = 4):
        super(ResNet,self).__init__()
        self.expansion = expansion

        self.pre_process = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=64, kernel_size=7, stride=2, padding=3, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        )

        self.layer1 = self.make_layer(in_places = 64, places= 64, block=blocks[0], stride=1)
        self.layer2 = self.make_layer(in_places = 256,places=128, block=blocks[1], stride=2)
        self.layer3 = self.make_layer(in_places=512,places=256, block=blocks[2], stride=2)
        self.layer4 = self.make_layer(in_places=1024,places=512, block=blocks[3], stride=2)

        self.avgpool = nn.AvgPool2d(7, stride=1)
        self.fc = nn.Linear(2048,num_classes)

        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 make_layer(self, in_places, places, block, stride):
        layers = []
        layers.append(Bottleneck(in_places, places,stride, downsampling =True))
        for i in range(1, block):
            layers.append(Bottleneck(places*self.expansion, places))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.pre_process(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

def ResNet50():
    return ResNet([3, 4, 6, 3])

def ResNet101():
    return ResNet([3, 4, 23, 3])

def ResNet152():
    return ResNet([3, 8, 36, 3])

if __name__ == "__main__":
    model = ResNet50().cuda()
    x = torch.randn(2, 3, 224, 224).cuda()
    out = model(x)
    print(out.shape)