Pytorch构建ResNet

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学了几天Pytorch，大致明白代码在干什么了，贴一下。。

import torch

from torch.utils.data import DataLoader

from torchvision import datasets

from torchvision import transforms

from torch import nn, optim

from torch.nn import functional as F

class ResBlk(nn.Module):

    """

    resnet block

    """

    def __init__(self, ch_in, ch_out):

        super(ResBlk, self).__init__()

        self.conv1 = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1)

        self.bn1 = nn.BatchNorm2d(ch_out)

        self.conv2 = nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1)

        self.bn2 = nn.BatchNorm2d(ch_out)

        self.extra = nn.Sequential()

        if ch_out != ch_in:

            # [b, ch_in, h, w] => [b, ch_out, h, w]

            self.extra = nn.Sequential(

                nn.Conv2d(ch_in, ch_out, kernel_size=1, stride=1),

                nn.BatchNorm2d(ch_out)

            )

    def forward(self,x):

        """

        x:[b, ch, h, w]

        """

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

        out = self.bn2(self.conv2(out))

        # short cut

        # extra module: [b, ch_in, h, w] => [b, ch_out, h, w]

        # element-wise add: [b, ch_in, h, w] with [b, ch_out, h, w]

        out = self.extra(x) + out

        return out

class ResNet18(nn.Module):

    def __init(self):

        super(ResNet18, self).__init__()

        self.conv1 = nn.Sequential(

            nn.Conv2d(3,64,kernel_size=3, stride=1, padding=1),

            nn.BatchNorm2d(64)

        )

        # followd 4 blocks

        # [b, 64, h, w] => [b, 128, h, w]

        self.blk1 = ResBlk(64,128)

        # [b, 128, h, w] => [b, 256, h, w]

        self.blk2 = ResBlk(128,256)

        # [b, 256, h, w] => [b, 512, h, w]

        self.blk3 = ResBlk(256,512)

        # [b, 512, h, w] => [b, 1024, h, w]

        self.blk4 = ResBlk(512,1024)

        self.outlayer = nn.Linear(1024, 10)

    def forward(self, x):

        x = F.relu(self.conv1(x))

        # [b, 64, h, w] => [b, 1024, h, w]

        x = self.blk1(x)

        x = self.blk2(x)

        x = self.blk3(x)

        x = self.blk4(x)

        x = self.outlayer(x)

        return x

def main():

    blk = ResBlk(64, 128)

    tmp = torch.randn(2, 64, 32, 32)

    out = blk(tmp)

    print(out.shape)

if __name__ == '__main__':

    main()

#