Pytorch学习笔记6——训练分类器

Pytorch Learning Notes

Reference：
Pytorch官方文档——TRAINING A CLASSIFIER

以上是Pytorch官方的文档，本文主要对其进行翻译整理，并加入一些自己的理解，仅作日后复习查阅所用。

TRAINING A NEURAL NETWORK

1 Load Data

import torch
import torchvision
import torchvision.transforms as transforms

#torchvision.transforms是pytorch中的图像预处理包。用Compose把多个变换整合到一起
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

batch_size = 4

#获得训练集和测试集
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
                                         shuffle=False, num_workers=2)

#定义label
classes = ('plane', 'car', 'bird', 'cat','deer', 'dog', 'frog', 'horse', 'ship', 'truck')
           

2 定义神经网络

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


class Net(nn.Module):
    #定义卷积层、池化层、线性映射等
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)
    
    #定义前向运算
    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = torch.flatten(x, 1) # flatten all dimensions except batch
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


net = Net()

3 定义损失函数和优化器

import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

4 训练神经网络

for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

5 保存模型

PATH = './cifar_net.pth'
torch.save(net.state_dict(), PATH)

6 测试模型

correct = 0
total = 0
# since we're not training, we don't need to calculate the gradients for our outputs
with torch.no_grad():
    for data in testloader:
        images, labels = data
        # calculate outputs by running images through the network
        outputs = net(images)
        # the class with the highest energy is what we choose as prediction
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))

7 在GPU上训练

在cpu上训练则无需做如下改动

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#将网络移到gpu上
net.to(device)

#将数据移到gpu上
inputs, labels = data[0].to(device), data[1].to(device)

8 最终代码（GPU版本）

import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim 

transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))])

batch_size = 4

trainset = torchvision.datasets.CIFAR10(root='./data',train=True,download=True,transform=transform)

trainloader = torch.utils.data.DataLoader(trainset,batch_size=batch_size,shuffle=True,num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data',train=False,download=True,transform=transform)

testloader = torch.utils.data.DataLoader(testset,batch_size=batch_size,shuffle=False,num_workers=2)

classes = ('plane', 'car', 'bird', 'cat','deer', 'dog', 'frog', 'horse', 'ship', 'truck')

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)
    
    def forward(self,x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = torch.flatten(x, 1) # flatten all dimensions except batch
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

net = Net()

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net.to(device)

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)



if __name__ == '__main__':
    for epoch in range(2):
        running_loss = 0.0
        for i,data in enumerate(trainloader,0):
            inputs, labels = data[0].to(device), data[1].to(device)
            optimizer.zero_grad()
            outputs = net(inputs)
            loss = criterion(outputs,labels)
            loss.backward()
            optimizer.step()
            running_loss+= loss.item()
            if i%2000 ==1999:
                print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
                running_loss = 0.0

    print('Finished Training')

    PATH = './cifar_net.pth'
    torch.save(net.state_dict(), PATH)

    correct = 0
    total = 0
    # since we're not training, we don't need to calculate the gradients for our outputs
    with torch.no_grad():
        for data in testloader:
            images, labels = data[0].to(device), data[1].to(device)
            # calculate outputs by running images through the network
            outputs = net(images)
            # the class with the highest energy is what we choose as prediction
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

        print('Accuracy of the network on the 10000 test images: %d %%' % (
        100 * correct / total))

9 输出