【Pytorch学习笔记】5.卷积神经网络

柔情只为你懂 2022-09-04 05:56 173阅读 0赞

### 文章目录 ###

*  32.卷积神经网络
 *  33.池化层&上/下采样
 *  34.批量正则化
 *  35.经典卷积网络
 *  36.残差网络
 *  37.nn.Module
 *  38.数据增强
 *  39.实战

根据龙良曲Pytorch学习视频整理，视频链接：  
[【计算机-AI】PyTorch学这个就够了！][-AI_PyTorch]  
[(好课推荐)深度学习与PyTorch入门实战——主讲人龙良曲][PyTorch]

# 32.卷积神经网络 #

基础知识还是得看ng

import torch
    import torch.nn as nn
    import torch.nn.functional as F
    
    # 卷积层
    x = torch.rand(1, 1, 28, 28)
    
    layer = nn.Conv2d(1, 3, kernel_size=3, stride=1, padding=0)
    out = layer.forward(x)
    print(out.size())   # torch.Size([1, 3, 26, 26])
    
    layer = nn.Conv2d(1, 3, kernel_size=3, stride=1, padding=1)
    out = layer.forward(x)
    print(out.size())   # torch.Size([1, 3, 28, 28])
    
    layer = nn.Conv2d(1, 3, kernel_size=3, stride=2, padding=1)
    out = layer.forward(x)
    print(out.size())   # torch.Size([1, 3, 14, 14])
    
    out = layer(x)  # __call__ 推荐使用，不推荐使用forward
    print(out.size())   # torch.Size([1, 3, 14, 14])
    
    # print(layer.weight)
    print(layer.weight.shape)   # torch.Size([3, 1, 3, 3])
    print(layer.bias.shape)     # torch.Size([3])
    
    w = torch.rand(16, 3, 5, 5)
    b = torch.rand(16)
    x = torch.randn(1, 3, 28, 28)
    out = F.conv2d(x, w, b, stride=2, padding=2)
    print(out.shape)    # torch.Size([1, 16, 14, 14])

# 33.池化层&上/下采样 #

Pooling：feature map变小，与隔行采样（Down sample）不同

*  Max pooling
 *  Avg pooling

# 池化层
    x = out
    print(x.shape)      # torch.Size([1, 16, 14, 14])
    
    layer = nn.MaxPool2d(2, stride=2)
    out = layer(x)
    print(out.shape)    # torch.Size([1, 16, 7, 7])
    
    out = F.avg_pool2d(x, 2, stride=2)
    print(out.shape)    # torch.Size([1, 16, 7, 7])

Up sample

# 上采样
    x = out
    out = F.interpolate(x, scale_factor=2, mode='nearest')
    print(out.shape)    # torch.Size([1, 16, 14, 14])

ReLU

# ReLU
    print(x.shape)      # torch.Size([1, 16, 7, 7])
    layer = nn.ReLU(inplace=True)
    out = layer(x)
    print(out.shape)    # torch.Size([1, 16, 7, 7])
    
    out = F.relu(x)
    print(out.shape)    # torch.Size([1, 16, 7, 7])

# 34.批量正则化 #

import torch
    import torch.nn as nn
    
    x = torch.randn(100, 16, 784)
    
    layer = nn.BatchNorm1d(16, momentum=0.1, affine=True)   # affine自动更新beta、gama
    # layer.eval() # test时加上
    out = layer(x)
    print(layer.running_mean)
    print(layer.running_var)
    
    for i in range(100):
        out = layer(x)
    print(layer.running_mean)
    print(layer.running_var)
    """ tensor([-3.3207e-04, -2.9735e-04, 8.1316e-04, 9.3234e-05, 1.9049e-04, 6.9931e-04, 2.9378e-04, 3.1153e-05, 3.4325e-04, 3.2283e-04, -2.0425e-04, -4.0346e-04, 1.7246e-04, -1.9482e-04, -1.2086e-04, -8.4132e-04]) tensor([0.9999, 0.9999, 1.0002, 1.0006, 0.9997, 1.0003, 1.0000, 1.0002, 1.0003, 1.0000, 0.9998, 1.0000, 1.0005, 0.9996, 0.9997, 0.9999]) tensor([-0.0033, -0.0030, 0.0081, 0.0009, 0.0019, 0.0070, 0.0029, 0.0003, 0.0034, 0.0032, -0.0020, -0.0040, 0.0017, -0.0019, -0.0012, -0.0084]) tensor([0.9986, 0.9987, 1.0023, 1.0065, 0.9969, 1.0033, 0.9997, 1.0022, 1.0029, 1.0004, 0.9981, 0.9999, 1.0053, 0.9957, 0.9972, 0.9985]) """

> `layer.running_mean`和`layer.running_var`得到的是全局的均值和方差，不是当前Batch上的，第一次只跑了一个Batch，现在还没有办法直接查看某个Batch上的这两个统计量的值。第一次只进行一次前向传播，在前向传播中来更新均值和方差的值： μ ′ = ( 1 − m ) μ + m μ t = ( 1 − 0.1 ) × 0 + 0.1 × 0.5 = 0.05 \\mu '= (1-m) \\mu + m \\mu \_t= ( 1 − 0.1 ) × 0 + 0.1 × 0.5 = 0.05 μ′=(1−m)μ\+mμt=(1−0.1)×0\+0.1×0.5=0.05  
> 默认动量m = 0.1， μ \\mu μ是更新前的均值（初始值为0）， μ t \\mu\_t μt是当前batch的平均值。进行多次前向传播，均值和方差就会趋于数据真实分布

> 注：test时要加上`layer.eval()`，test不进行反向传播，即 β \\beta β和 γ \\gamma γ不更新

# 接卷积神经网络实例
    print(x.shape)              # torch.Size([1, 16, 7, 7])
    
    layer = nn.BatchNorm2d(16)
    out = layer(x)
    print(out.shape)            # torch.Size([1, 16, 7, 7])
    print(layer.weight.shape)   # torch.Size([16])
    print(layer.bias.shape)     # torch.Size([16])
    print(vars(layer))          # layer的参数

Advantages

*  Converge faster
 *  Better performance
 *  Robust  
    stable  
    larger learning rate

# 35.经典卷积网络 #

ImageNet: LeNet-5  → \\rightarrow → AlexNet  → \\rightarrow → VGG  → \\rightarrow → GoogLeNet  → \\rightarrow → ResNet  → \\rightarrow → Inception

# 36.残差网络 #

import torch
    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_in, 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

# 37.nn.Module #

*  Every Layer is nn.Module
 *  nn.Module nested in nn.Module

1.  embed current layers  
    Linear ReLU Sigmoid Conv2d ConvTransposed2d Dropout etc.
2.  Container  
    net(x)
3.  parameters
4.  modules  
    modules: all nodes  
    children: direct childrend
5.  to(device)
6.  save and load  
    `net.load_state_dict(torch.load('ckpt.mdl'))`  
    `torch.save(net.state_dict(), 'ckpt.mdl')`
7.  train / test  
    `net.train()`  
    `net.eval()`
8.  implement own layer

class Flatten(nn.Module):
        def __init__(self):
            super(Flatten, self).__init__()
    
        def forward(self, input):
            return input.view(input.size(0), -1)
    
    class TestNet(nn.Module):
        def __init__(self):
            super(TestNet, self).__init__()
            self.net = nn.Sequential(nn.Conv2d(1, 16, stride=1, padding=1),
                                     nn.MaxPool2d(2, 2),
                                     Flatten(),
                                     nn.Linear(1*14*14, 10))
            
        def forward(self, x):
            return self.net(x)

# 38.数据增强 #

Limited Data

*  Small network capacity
 *  Regularization
 *  Data argumentation（will help but not much）

Data argumentation

*  Flip  
    `transforms.RandomHorizontalFlip()` 随机水平翻转  
    `transforms.RandomVertialFlip()` 随机垂直翻转
 *  Rotate  
    `transforms.RandomRotation(15)` 随机旋转，不超过15度  
    `transforms.RandomRotation([90, 180, 270])` 随机在指定角度中旋转
 *  Scale  
    `transforms.Resize([32, 32])`
 *  Random Move & Crop  
    `transforms.RandomCrop([28, 28])`
 *  Noise
 *  GAN

# 39.实战 #

main.py

import torch
    import torch.nn as nn
    import torch.optim as optim
    from torch.utils.data import DataLoader
    from torchvision import transforms, datasets
    from Pytorch21_7_29.conv.lenet5 import Lenet5
    from Pytorch21_7_29.conv.resnet import ResNet18
    
    def main():
        batch_size = 32
    
        cifar_train = datasets.CIFAR10('cifar', train=True, transform=transforms.Compose([
            transforms.Resize([32, 32]),
            transforms.ToTensor()
        ]), download=True)
        cifar_train = DataLoader(cifar_train, batch_size=batch_size, shuffle=True)
    
        cifar_test = datasets.CIFAR10('cifar', train=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=batch_size, shuffle=True)
    
        x, label = iter(cifar_train).next()
        print("x:", x.shape, 'label:', label.shape) # x: torch.Size([32, 3, 32, 32]) label: torch.Size([32])
    
        device = torch.device('cuda')
        # model = Lenet5().to(device)
        model = ResNet18().to(device)
        criteon = nn.CrossEntropyLoss()
        optimizer = optim.Adam(model.parameters(), lr=1e-3)
        print(model)
    
        for epoch in range(1000):
    
            model.train()
            for batch_idx, (x, label) in enumerate(cifar_train):
                x, label = x.to(device), label.to(device)
    
                logits = model(x)
                # logits: [b, 10] label: [b]
                loss = criteon(logits, label)   # tensor scalar
    
                # backward
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()
    
            #
            print(epoch, loss.item())
    
            # 测试不需要构建计算图
            model.eval()
            with torch.no_grad():
                # test
                total_correct = 0
                total_num = 0
                for x, label in cifar_test:
                    x, label = x.to(device), label.to(device)
                    logits = model(x)
                    pred = logits.argmax(dim=1)
                    total_correct += torch.eq(pred, label).float().sum().item()
                    total_num += x.size(0)
    
                acc = total_correct / total_num
                print(epoch, acc)
    
    if __name__ == '__main__':
        main()
    
    """ Lenet5( (conv_unit): Sequential( (0): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1)) (1): AvgPool2d(kernel_size=2, stride=2, padding=0) (2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1)) (3): AvgPool2d(kernel_size=2, stride=2, padding=0) ) (fc_unit): Sequential( (0): Linear(in_features=400, out_features=120, bias=True) (1): ReLU() (2): Linear(in_features=120, out_features=84, bias=True) (3): ReLU() (4): Linear(in_features=84, out_features=10, bias=True) ) (criteon): CrossEntropyLoss() ) ResNet18( (conv1): Sequential( (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(3, 3)) (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (blk1): ResBlk( (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (extra): Sequential( (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2)) (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (blk2): ResBlk( (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (extra): Sequential( (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2)) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (blk3): ResBlk( (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (extra): Sequential( (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2)) (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (blk4): ResBlk( (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (extra): Sequential() ) (outlayer): Linear(in_features=512, out_features=10, bias=True) ) """

lenet5.py

import torch
    import torch.nn as nn
    import torch.nn.functional as F
    
    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, 6, 28, 28]
                nn.Conv2d(3, 6, kernel_size=5, stride=1, padding=0),
                nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
                # x:[b, 6, 14, 14] => [b, 16, 10, 10]
                nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0),
                nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
                # x:[b, 16, 5, 5] => [b, 400]
            )
            # flatten
            # fc unit
            self.fc_unit = nn.Sequential(
                nn.Linear(16*5*5, 120),
                nn.ReLU(),
                nn.Linear(120, 84),
                nn.ReLU(),
                nn.Linear(84, 10)
            )
    
            # self.criteon = nn.MSELoss() # logistics问题使用
            self.criteon = nn.CrossEntropyLoss()    # 一般分类问题使用
    
        def forward(self, x):
            """ :param x: [b, 3, 32, 32] :return: """
            batch_size = x.size(0)
            x = self.conv_unit(x)
            x = x.view(batch_size, -1)  # -1指16*5*5
            logits = self.fc_unit(x)
    
            # pred = F.softmax(logits, dim=1) 交叉熵函数内包含了softmax
            # 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)  # lenet_out: torch.Size([2, 10])
    
    if __name__ == '__main__':
        main()

resnet.py

import torch
    import torch.nn as nn
    import torch.nn.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 resblk, 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
            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))
    
            x = self.blk1(x)
            x = self.blk2(x)
            x = self.blk3(x)
            x = self.blk4(x)
            # print("after_conv:", x.shape) # torch.Size([32, 512, 2, 2])
            # [b, 512, h, w] => [b, 512, 1, 1]
            x = F.adaptive_avg_pool2d(x, [1, 1])
            # print("after_pool:", x.shape) # torch.Size([32, 512, 1, 1])
            x = x.view(x.size(0), -1)
    
            x = self.outlayer(x)
    
            return x
    
    def main():
        blk = ResBlk(64, 128, stride=2)
        tmp = torch.randn(2, 64, 32, 32)
        out = blk(tmp)
        print("block:", out.shape)      # torch.Size([2, 128, 16, 16])
    
        x = torch.randn(2, 3, 32, 32)
        model = ResNet18()
        out = model(x)
        print("resnet:", out.shape)     # torch.Size([2, 10])
    
    if __name__ == '__main__':
        main()

[-AI_PyTorch]: https://www.bilibili.com/video/BV1Rv411y7oE?p=1
[PyTorch]: https://www.bilibili.com/video/BV1mh41167e3?p=1