42_ResNet (深度残差网络)---学习笔记

我不是女神ヾ 2022-10-30 09:24 209阅读 0赞

## 1.39.ResNet (深度残差网络) ##

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Why call Residual  
![在这里插入图片描述][20210218170435330.png]  
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![在这里插入图片描述][20210218170510461.png]

# -*- coding: UTF-8 -*-
    
    import torch.nn as nn
    import torch.nn.functional as F
    
    
    class ResBlk(nn.Module):
        def __init__(self, ch_in, ch_out):
            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):
            out = F.relu(self.bn1(self.conv1(x)))
            out = self.bn2(self.conv2(out))
            out = self.extra(x) + out
            return out

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# -*- coding: UTF-8 -*-
    
    import torch
    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()

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[20210218170435330.png]: /images/20221024/adcdf216bcbb48c0bb7e8750d53437bb.png
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[20210218170510461.png]: /images/20221024/dfd92b6cf7664a28bb2da69555fc2b39.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 5]: /images/20221024/c2b7e89fe40542d0828077393cd9feee.png