用deepfashion数据集，基本pytorch和resnet算法进行学习的代码示例

好的，以下是一个使用DeepFashion数据集、基本PyTorch和ResNet算法进行学习的代码示例：

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


# 定义ResNet模型
class ResNet(nn.Module):
    def __init__(self, num_classes=50):
        super(ResNet, self).__init__()
        self.resnet = torchvision.models.resnet50(pretrained=False)
        self.resnet.fc = nn.Linear(2048, num_classes)

    def forward(self, x):
        out = self.resnet(x)
        return out


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

transform_val = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 加载数据集
train_dataset = ImageFolder(root='./deepfashion/train', transform=transform_train)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

val_dataset = ImageFolder(root='./deepfashion/validation', transform=transform_val)
val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)

# 定义模型、损失函数和优化器
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = ResNet(num_classes=50).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)

# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
    train_loss = 0.0
    train_acc = 0.0
    val_loss = 0.0
    val_acc = 0.0

    # 训练阶段
    model.train()
    for i, (images, labels) in enumerate(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()
        _, preds = torch.max(outputs, 1)
        train_acc += torch.sum(preds == labels.data)

    train_loss /= len(train_loader.dataset)
    train_acc = train_acc.float() / len(train_loader.dataset)

    # 验证阶段
    model.eval()
    with torch.no_grad():
        for i, (images, labels) in enumerate(val_loader):
            images = images.to(device)
            labels = labels.to(device)

            outputs = model(images)
            loss = criterion(outputs, labels)

            val_loss += loss.item()
            _, preds = torch.max(outputs, 1)
            val_acc += torch.sum(preds == labels.data)

        val_loss /= len(val_loader.dataset)
        val_acc = val_acc.float() / len(val_loader.dataset)

    # 打印结果
    print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Val Loss: {:.4f}, Val Acc: {:.4f}'
          .format(epoch+1, num_epochs, train_loss, train_acc, val_loss, val_acc))

这里我们使用了PyTorch内置的ResNet模型，并在其基础上进行微调以适应DeepFashion数据集。我们还定义了数据预处理、数据加载、模型、损失函数和优化器。在训练过程中，我们使用了交叉熵损失函数和随机梯度下降（SGD）优化器。最后，我们打印了每个epoch的训练和验证损失，以及训练和验证准确率。