The less successful implementation and lessons of RESNET

import  torch
from    torch import  nn
from    torch.nn import functional as F
class ResBlk(nn.Module):
    """ resnet block """

    def __init__(self, ch_in, ch_out):
        """ :param ch_in: :param 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):
        """ :param x: [b, ch, h, w] :return: """
        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:
        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, 16, kernel_size=3, stride=3, padding=0),
            nn.BatchNorm2d(16)
        )
        # followed 4 blocks
        # [b, 64, h, w] => [b, 128, h ,w]
        self.blk1 = ResBlk(16, 32)
        # [b, 128, h, w] => [b, 256, h, w]
        self.blk2 = ResBlk(32, 64)
        # # [b, 256, h, w] => [b, 512, h, w]
        self.blk3 = ResBlk(64, 128)
        # # [b, 512, h, w] => [b, 1024, h, w]
        self.blk4 = ResBlk(128, 256)

        self.outlayer = nn.Linear(256*10*10, 10)

    def forward(self, x):
        """ :param x: :return: """
        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)

        # print(x.shape)
        x = x.view(x.size(0), -1)
        x = self.outlayer(x)


        return x



def main():
    blk = ResBlk(64, 128)
    tmp = torch.randn(2, 64,32, 32)
    out = blk(tmp)
    print('blkk', out.shape)


    model = ResNet18()
    tmp = torch.randn(2, 3, 32, 32)
    out = model(tmp)
    print('resnet:', out.shape)


if __name__ == '__main__':
    main()

This is the code written by the teacher , Don't post your own code if it's a little messy
The biggest feeling is CNN There is stride and padding after ,[b, chn, h,w], It's too messy It's easy to come out x And the next step to accept x Not right .
It mainly affects h,w

And then there was cnn Pick up fc Remember to level the back chn* H * W, namely
hold cnn Of [b, chn ,h ,w] Make it even [b, chnhw]

There is a key short cut

mistake

 out = self.extra(x) + out

It's written in

out = self.extra(out) +x

It took two hours to find this problem , Still not familiar with the dimension transformation in the middle of the network ,
Yes short cut I don't understand well

from    resnet_teacher import ResNet18

def main():
    batchsz = 32

    cifar_train = datasets.CIFAR10('cifar', True, transform=transforms.Compose([
        transforms.Resize((32, 32)),
        transforms.ToTensor()
    ]), download=True)
    cifar_train = DataLoader(cifar_train, batch_size=batchsz, shuffle=True)

    cifar_test = datasets.CIFAR10('cifar', False, transform=transforms.Compose([
        transforms.Resize((32, 32)),
        transforms.ToTensor()
    ]), download=True)
    cifar_test = DataLoader(cifar_test, batch_size=batchsz, shuffle=True)

    x, label = iter(cifar_train).next()
    print('x:', x.shape, 'label:', label.shape)

    device = torch.device('cuda')
    # model = Lenet5().to(device)
    model = ResNet18()

    criteon = nn.CrossEntropyLoss().to(device)
    optimizer = optim.Adam(model.parameters(), lr=1e-3)
    print(model)

    for epoch in range(1000):

        model.train()
        for batchidx, (x, label) in enumerate(cifar_train):
            # [b, 3, 32, 32]
            # [b]
            #x, label = x.to(device), label.to(device)


            logits = model(x)
            # logits: [b, 10]
            # label: [b]
            # loss: tensor scalar
            loss = criteon(logits, label)

            # backprop
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()


        #
        print(epoch, 'loss:', loss.item())


        model.eval()
        with torch.no_grad():
            # test
            total_correct = 0
            total_num = 0
            for x, label in cifar_test:
                # [b, 3, 32, 32]
                # [b]
                #x, label = x.to(device), label.to(device)

                # [b, 10]
                logits = model(x)
                # [b]
                pred = logits.argmax(dim=1)
                # [b] vs [b] => scalar tensor
                correct = torch.eq(pred, label).float().sum().item()
                total_correct += correct
                total_num += x.size(0)
                # print(correct)

            acc = total_correct / total_num
            print(epoch, 'acc:', acc)



if __name__ == '__main__':
    main()