pytorch读取图像文件并转化给神经网络模型训练的代码

以下是一个基于PyTorch的读取图像文件并转换为神经网络模型训练的代码示例：

import torch
from torch.utils.data import DataLoader, Dataset
from torchvision import datasets, transforms

# 数据预处理
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 定义数据集，将图像和标签一一对应
class MyDataset(Dataset):
    def __init__(self, file_path):
        self.data = datasets.ImageFolder(root=file_path, transform=transform)

    def __getitem__(self, index):
        x, y = self.data[index]
        return x, y

    def __len__(self):
        return len(self.data)

# 加载数据集并分批次训练
train_data = MyDataset('train')
train_loader = DataLoader(dataset=train_data, batch_size=32, shuffle=True)

# 定义神经网络模型和优化器
model = MyModel()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 开始训练
for epoch in range(10):
    for i, (images, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # 每迭代100个批次输出一次训练结果
        if (i + 1) % 100 == 0:
            print('Epoch [{}/{}], Iteration [{}/{}], Loss: {:.4f}'
                  .format(epoch + 1, 10, i + 1, len(train_data) // 32, loss.item()))