pytorch加载自己数据集并训练的完整代码

下面是一个使用PyTorch加载自己的数据集并进行训练的完整代码示例：

import torch
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim

# 定义数据集的预处理转换
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

# 定义自己的数据集类
class CustomDataset(torch.utils.data.Dataset):
    def __init__(self, ...):
        # 读取数据集并进行必要的预处理操作

    def __len__(self):
        # 返回数据集的长度

    def __getitem__(self, index):
        # 返回指定索引处的数据样本

# 创建数据集实例
train_dataset = CustomDataset(...)
test_dataset = CustomDataset(...)

# 创建数据加载器
batch_size = 64
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 定义模型
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        # 定义模型结构

    def forward(self, x):
        # 定义前向传播过程

model = MyModel()

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

# 训练模型
num_epochs = 10
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

for epoch in range(num_epochs):
    train_loss = 0.0
    model.train()

    for images, labels in train_loader:
        images = images.to(device)
        labels = labels.to(device)

        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        train_loss += loss.item() * images.size(0)

    train_loss /= len(train_loader.dataset)

    print(f"Epoch {epoch+1}/{num_epochs}, Training Loss: {train_loss}")

# 在测试集上评估模型
model.eval()
test_loss = 0.0
correct = 0

with torch.no_grad():
    for images, labels in test_loader:
        images = images.to(device)
        labels = labels.to(device)

        outputs = model(images)
        loss = criterion(outputs, labels)
        test_loss += loss.item() * images.size(0)

        _, predicted = torch.max(outputs.data, 1)
        correct += (predicted == labels).sum().item()

test_loss /= len(test_loader.dataset)
accuracy = correct / len(test_loader.dataset)

print(f"Test Loss: {test_loss}, Accuracy: {accuracy}")

请注意，上述代码中有一些需要替换为你自己数据集和模型的部分，如`CustomDataset`类的定义、数据集实例的创建、模型的定义和前向传播过程等。你需要根据你自己的数据集和模型来进行相应的修改。