Cifar10与ResNet18实战、lenet5、resnet（学习笔记）

￡神魔★判官ぃ 2022-10-30 14:28 228阅读 0赞

## 1.44.Cifar10与ResNet18实战 ##

Pytorch工程中建立pytorch，在pytorch里面创建lenet5.py、main.py、resnet.py。

### 1.44.1.lenet5.py ###

# -*- coding: UTF-8 -*-
    
    import torch
    from torch import nn
    
    
    class Lenet5(nn.Module):
        """ for cifar10 dataset. """
        def __init__(self):
            super(Lenet5, self).__init__()
    
            self.conv_unit = nn.Sequential(
                # x: [b, 3, 32, 32] => [b, 16, ]
                nn.Conv2d(3, 16, kernel_size=5, stride=1, padding=0),
                nn.MaxPool2d(kernel_size=2, stride=2, padding=0),
    
                nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=0),
                nn.MaxPool2d(kernel_size=2, stride=2, padding=0),
            )
            # flatten
            # fc unit
            self.fc_unit = nn.Sequential(
                nn.Linear(32 * 5 * 5, 32),
                nn.ReLU(),
                # nn.Linear(120, 84),
                # nn.ReLU(),
                nn.Linear(32, 10)
            )
    
            # [b, 3, 32, 32]
            tmp = torch.randn(2, 3, 32, 32)
            out = self.conv_unit(tmp)
            # [b, 16, 5, 5]
            print('conv out:', out.shape)
    
            # # use Cross Entropy Loss
            # self.criteon = nn.CrossEntropyLoss()
    
        def forward(self, x):
            """ :param x: [b, 3, 32, 32] :return: """
            batchsz = x.size(0)
            # [b, 3, 32, 32] => [b, 16, 5, 5]
            x = self.conv_unit(x)
            # [b, 16, 5, 5] => [b, 16*5*5]
            x = x.view(batchsz, 32 * 5 * 5)
            # [b, 16*5*5] => [b, 10]
            logits = self.fc_unit(x)
    
            # # [b, 10]
            # pred = F.softmax(logits, dim=1)
            # loss = self.criteon(logits, y)
    
            return logits
    
    
    def main():
        net = Lenet5()
    
        tmp = torch.randn(2, 3, 32, 32)
        out = net(tmp)
        print('lenet out:', out.shape)
    
    
    if __name__ == '__main__':
        main()

### 1.44.2.Resnet.py ###

# -*- coding: UTF-8 -*-
    
    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, stride=1):
            """ :param ch_in: :param ch_out: """
            super(ResBlk, self).__init__()
    
            # we add stride support for resbok, which is distinct from tutorials.
            self.conv1 = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=stride, 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=stride),
                    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
            out = F.relu(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=3, padding=0),
                nn.BatchNorm2d(64)
            )
            # followed 4 blocks
            # [b, 64, h, w] => [b, 128, h ,w]
            self.blk1 = ResBlk(64, 128, stride=2)
            # [b, 128, h, w] => [b, 256, h, w]
            self.blk2 = ResBlk(128, 256, stride=2)
            # # [b, 256, h, w] => [b, 512, h, w]
            self.blk3 = ResBlk(256, 512, stride=2)
            # # [b, 512, h, w] => [b, 1024, h, w]
            self.blk4 = ResBlk(512, 512, stride=2)
    
            self.outlayer = nn.Linear(512 * 1 * 1, 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('after conv:', x.shape) #[b, 512, 2, 2]
            # [b, 512, h, w] => [b, 512, 1, 1]
            x = F.adaptive_avg_pool2d(x, [1, 1])
            # print('after pool:', x.shape)
            x = x.view(x.size(0), -1)
            x = self.outlayer(x)
    
            return x
    
    
    def main():
        blk = ResBlk(64, 128, stride=4)
        tmp = torch.randn(2, 64, 32, 32)
        out = blk(tmp)
        print('block:', out.shape)
    
        x = torch.randn(2, 3, 32, 32)
        model = ResNet18()
        out = model(x)
        print('resnet:', out.shape)
    
    
    if __name__ == '__main__':
        main()

### 1.44.3.main.py ###

# -*- coding: UTF-8 -*-
    
    import torch
    from torch.utils.data import DataLoader
    from torchvision import datasets
    from torchvision import transforms
    from torch import nn, optim
    
    from pytorch.lenet5 import Lenet5
    from pytorch.resnet import ResNet18
    
    
    def main():
        batchsz = 128
    
        cifar_train = datasets.CIFAR10('cifar', True, transform=transforms.Compose([
            transforms.Resize((32, 32)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[0.229, 0.224, 0.225])
        ]), 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(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[0.229, 0.224, 0.225])
        ]), 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, 'test acc:', acc)
    
    
    if __name__ == '__main__':
        main()