Pytorch-卷积神经网络CNN之ResNet的Pytorch代码实现

先说一个小知识，助于理解代码中各个层之间维度是怎么变换的。

卷积函数：一般只用来改变输入数据的维度，例如3维到16维。

Conv2d()

Conv2d(in_channels:int,out_channels:int,kernel_size:Union[int,tuple],stride=1,padding=o):
"""
:param in_channels: 输入的维度
:param out_channels: 通过卷积核之后，要输出的维度
:param kernel_size: 卷积核大小
:param stride: 移动步长
:param padding: 四周添多少个零
"""

一个小例子：

import torch
import torch.nn
# 定义一个16张照片，每个照片3个通道，大小是28*28
x= torch.randn(16,3,32,32)
# 改变照片的维度，从3维升到16维，卷积核大小是5
conv= torch.nn.Conv2d(3,16,kernel_size=5,stride=1,padding=0)
res=conv(x)

print(res.shape)
# torch.Size([16, 16, 28, 28])
# 维度升到16维，因为卷积核大小是5，步长是1，所以照片的大小缩小了，变成28

卷积神经网络实战之ResNet18：

下面放一个ResNet18的一个示意图，

ResNet18主要是在层与层之间，加入了一个短接层，可以每隔k个层，进行一次短接。网络层的层数不是 越深就越好。

ResNet18就是，如果在原先的基础上再加上k层，如果有小优化，则保留，如果比原先结果还差，那就利用短接层，直接跳过。

ResNet18的构造如下：

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)
)

程序运行前，先启动visdom,如果没有配置好visdom环境的，先百度安装好visdom环境

• 1.使用快捷键win+r,在输入框输出cmd,然后在命令行窗口里输入python -m visdom.server，启动visdom

代码实战

定义一个名为resnet.py的文件，代码如下

import torch
from    torch import  nn
from torch.nn import functional as F

# 定义两个卷积层 + 一个短接层
class ResBlk(nn.Module):
    def __init__(self,ch_in,ch_out,stride=1):
        super(ResBlk, self).__init__()

        # 两个卷积层
        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)

        # 短接层
        self.extra=nn.Sequential()
        if ch_out != ch_in:
            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))

        # 短接层
        # element-wise add: [b,ch_in,h,w]=>[b,ch_out,h,w]
        out=self.extra(x)+out
        return out

class ResNet18(nn.Module):
    def __init__(self):
        super(ResNet18, self).__init__()

        # 定义预处理层
        self.conv1=nn.Sequential(
            nn.Conv2d(3,64,kernel_size=3,stride=3,padding=0),
            nn.BatchNorm2d(64)
        )

        # 定义堆叠ResBlock层
        # 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,512,h,w]
        self.blk4=ResBlk(512,512,stride=2)

        # 定义全连接层
        self.outlayer=nn.Linear(512,10)

    def forward(self,x):
        """
        :param x:
        :return:
        """
        # 1.预处理层
        x=F.relu(self.conv1(x))

        # 2. 堆叠ResBlock层：channel会慢慢的增加，  长和宽会慢慢的减少
        # [b,64,h,w]-->[b,512,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]
        # 不管原先什么后面两个维度是多少，都化成[1,1]，
        # [b,512,1,1]
        x=F.adaptive_avg_pool2d(x,[1,1])
        # print("after pool2d:",x.shape) # [b,512,1,1]

        # 将[b,512,1,1]打平成[b,512*1*1]
        x=x.view(x.size(0),-1)

        # 3.放到全连接层，进行打平
        # [b,512]-->[b,10]
        x=self.outlayer(x)

        return x
def main():
    blk=ResBlk(64,128,stride=2)
    temp=torch.randn(2,64,32,32)
    out=blk(temp)
    # print('block:',out.shape)

    x=torch.randn(2,3,32,32)
    model=ResNet()
    out=model(x)
    # print("resnet:",out.shape)

if __name__ == '__main__':
    main()

定义一个名为main.py的文件，代码如下

import torch
from torchvision import datasets
from torchvision import transforms
from torch.utils.data import DataLoader
from torch import nn,optim
from visdom import Visdom
# from lenet5 import  Lenet5
from resnet import ResNet18
import time

def main():
    batch_siz=32
    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=batch_siz,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=batch_siz,shuffle=True)

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

    # 指定运行到cpu //GPU
    device=torch.device('cpu')
    # model = Lenet5().to(device)
    model = ResNet18().to(device)

    # 调用损失函数use Cross Entropy loss交叉熵
    # 分类问题使用CrossEntropyLoss比MSELoss更合适
    criteon = nn.CrossEntropyLoss().to(device)
    # 定义一个优化器
    optimizer=optim.Adam(model.parameters(),lr=1e-3)
    print(model)

    viz=Visdom()
    viz.line([0.],[0.],win="loss",opts=dict(title='Lenet5 Loss'))
    viz.line([0.],[0.],win="acc",opts=dict(title='Lenet5 Acc'))

    # 训练train
    for epoch in range(1000):
        # 变成train模式
        model.train()
        # barchidx:下标，x:[b,3,32,32],label:[b]
        str_time=time.time()
        for barchidx,(x,label) in enumerate(cifar_train):
            # 将x，label放在gpu上
            x,label=x.to(device),label.to(device)
            # logits:[b,10]
            # label:[b]
            logits = model(x)
            loss = criteon(logits,label)

            # viz.line([loss.item()],[barchidx],win='loss',update='append')
            # backprop
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            # print(barchidx)
        end_time=time.time()
        print('第 {} 次训练用时: {}'.format(epoch,(end_time-str_time)))
        viz.line([loss.item()],[epoch],win='loss',update='append')
        print(epoch,'loss:',loss.item())

        # 变成测试模式
        model.eval()
        with torch.no_grad():
            #  测试test
            # 正确的数目
            total_correct=0
            total_num=0
            for x,label in cifar_test:
                # 将x，label放在gpu上
                x,label=x.to(device),label.to(device)
                # [b,10]
                logits=model(x)
                # [b]
                pred=logits.argmax(dim=1)
                # [b] = [b'] 统计相等个数
                total_correct+=pred.eq(label).float().sum().item()
                total_num+=x.size(0)
            acc=total_correct/total_num
            print(epoch,'acc:',acc)
            print("------------------------------")

            viz.line([acc],[epoch],win='acc',update='append')
            # viz.images(x.view(-1, 3, 32, 32), win='x')

if __name__ == '__main__':
    main()

测试结果

ResNet跑起来太费劲了，需要用GPU跑，但是我的电脑不支持GPU，头都大了，用cpu跑二十多分钟学习一次，头都大了。