基于pytorch的图像五分类代码

以下是一个基于PyTorch的图像五分类代码示例：

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

# 定义数据增强
transform = transforms.Compose(
    [transforms.RandomHorizontalFlip(),
     transforms.RandomRotation(10),
     transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

# 加载数据集
trainset = torchvision.datasets.ImageFolder(root='path/to/training_data', transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True, num_workers=2)

testset = torchvision.datasets.ImageFolder(root='path/to/testing_data', transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=False, num_workers=2)

# 定义模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(128 * 28 * 28, 512)
        self.fc2 = nn.Linear(512, 5)

    def forward(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = self.pool(nn.functional.relu(self.conv3(x)))
        x = x.view(-1, 128 * 28 * 28)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

net = Net()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data

        optimizer.zero_grad()

        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

# 评估模型
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

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

这个示例使用了一个包含三个卷积层和三个池化层的卷积神经网络，以及一个全连接层和一个输出层。它使用了数据增强来增加数据量，使用了Adam优化器和交叉熵损失函数进行训练。你需要将`'path/to/training_data'`和`'path/to/testing_data'`替换为你自己的训练和测试数据的路径。