通过pytorch使用cnn进行垃圾分类，需完整代码

好的，以下是使用 PyTorch 进行垃圾分类的完整代码：

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

# 定义数据预处理
transform = transforms.Compose(
    [transforms.Resize((32, 32)),
     transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

# 加载训练集和测试集
trainset = torchvision.datasets.ImageFolder(root='./garbage_classify/train', transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32,
                                          shuffle=True, num_workers=2)

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

# 定义网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(16)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(32)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(2, 2)
        self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
        self.bn3 = nn.BatchNorm2d(64)
        self.relu3 = nn.ReLU()
        self.pool3 = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(64 * 4 * 4, 256)
        self.dropout = nn.Dropout(p=0.5)
        self.fc2 = nn.Linear(256, 6)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)
        x = self.pool2(x)
        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu3(x)
        x = self.pool3(x)
        x = x.view(-1, 64 * 4 * 4)
        x = self.fc1(x)
        x = self.dropout(x)
        x = self.fc2(x)
        return x

# 定义训练函数
def train(net, trainloader, optimizer, criterion, epoch):
    net.train()
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        optimizer.zero_grad()

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

        running_loss += loss.item()
        if i % 100 == 99:    # 每 100 个 batch 输出一次损失
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

# 定义测试函数
def test(net, testloader):
    net.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            images, labels = images.to(device), labels.to(device)
            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))

# 初始化网络
net = Net()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net.to(device)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# 训练模型
for epoch in range(10):
    train(net, trainloader, optimizer, criterion, epoch)
    test(net, testloader)

print('Finished Training')

在这个代码中，我们首先定义了数据预处理方式，然后使用 `ImageFolder` 加载了训练集和测试集。接着定义了一个卷积神经网络模型 `Net`，其中包含了三个卷积层和两个全连接层。然后定义了训练函数 `train` 和测试函数 `test`，分别用于训练和测试模型。最后，我们初始化了网络，定义了损失函数和优化器，并进行了训练。