目录

• 1.经典卷积网络
• • 1.1 ImageNet
  • 1.2 VGG
  • 1.3 GoogLeNet
  • 1,4 Stack more layers?
• 2.深度残差网络
• • 2.1 The residual module
  • 2.2 Deeper residual module
  • 2.3 代码实现

1.经典卷积网络

1.1 ImageNet

1.2 VGG

VGG网络层

1.3 GoogLeNet

1,4 Stack more layers?

2.深度残差网络

2.1 The residual module

2.2 Deeper residual module

2.3 代码实现

import  torch
from    torch import  nn
from    torch.nn import functional as F
from    torch.utils.data import DataLoader
from    torchvision import datasets
from    torchvision import transforms
from    torch import nn, optim

# from    torchvision.models import resnet18

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=1, padding=1),
            nn.BatchNorm2d(16)
        )
        # followed 4 blocks
        # [b, 64, h, w] => [b, 128, h ,w]
        self.blk1 = ResBlk(16, 16)
        # [b, 128, h, w] => [b, 256, h, w]
        self.blk2 = ResBlk(16, 32)
        # # [b, 256, h, w] => [b, 512, h, w]
        # self.blk3 = ResBlk(128, 256)
        # # [b, 512, h, w] => [b, 1024, h, w]
        # self.blk4 = ResBlk(256, 512)

        self.outlayer = nn.Linear(32*32*32, 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():
    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().to(device)

    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()