pytorch VGG16 迁移学习服装检索

对于服装检索任务，可以利用迁移学习将预训练的VGG16模型应用于新的数据集。以下是一个基本的迁移学习流程：

1. 导入所需的库和模块：

import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, models, transforms

2. 定义数据预处理和增强的转换：

data_transforms = {
    'train': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}

3. 加载数据集，并将其转换为可用于训练的格式：

data_dir = 'path/to/dataset'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=64, shuffle=True) for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes

4. 加载预训练的VGG16模型：

model = models.vgg16(pretrained=True)

5. 冻结卷积层的参数，只训练全连接层：

for param in model.parameters():
    param.requires_grad = False

num_features = model.classifier[6].in_features
model.classifier[6] = nn.Linear(num_features, len(class_names))

6. 定义损失函数和优化器：

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

7. 进行模型训练：

def train_model(model, criterion, optimizer, num_epochs=25):
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    model = model.to(device)

    for epoch in range(num_epochs):
        for phase in ['train', 'val']:
            if phase == 'train':
                model.train()
            else:
                model.eval()

            running_loss = 0.0
            running_corrects = 0

            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                optimizer.zero_grad()

                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects.double() / dataset_sizes[phase]

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(
                phase, epoch_loss, epoch_acc))

    return model

model = train_model(model, criterion, optimizer, num_epochs=25)

8. 使用训练好的模型进行预测：

def predict(model, image_path):
    image = Image.open(image_path)
    image_tensor = data_transforms['val'](image).unsqueeze(0)
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    image_tensor = image_tensor.to(device)

    model = model.to(device)
    model.eval()

    with torch.no_grad():
        outputs = model(image_tensor)
        _, predicted = torch.max(outputs, 1)

    return class_names[predicted.item()]

以上是一个基本的迁移学习示例，你可以根据自己的需求进行相应的调整和优化。注意，这只是一个示例，并不代表最佳实践。