transformer模型图像分类任务

Transformer模型在图像分类任务中也有广泛的应用。其中，Swin Transformer是一种新型的Transformer模型，它在图像分类任务中表现出色。下面是使用Swin Transformer进行图像分类任务的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
from timm.models import create_model
from timm.loss import LabelSmoothingCrossEntropy
from timm.optim import create_optimizer
from timm.scheduler import create_scheduler

# 定义超参数
batch_size = 64
num_epochs = 20
learning_rate = 0.001
num_classes = 12

# 定义数据增强和预处理
train_transforms = transforms.Compose([
    transforms.RandomResizedCrop(224),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

val_transforms = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# 加载数据集
train_dataset = datasets.ImageFolder('train', transform=train_transforms)
val_dataset = datasets.ImageFolder('val', transform=val_transforms)

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=4)

# 定义模型
model = create_model('swin_base_patch4_window12_384', pretrained=True, num_classes=num_classes)

# 定义损失函数和优化器
criterion = LabelSmoothingCrossEntropy()
optimizer = create_optimizer(learning_rate, model)
scheduler = create_scheduler(optimizer, num_epochs, len(train_loader))

# 训练模型
for epoch in range(num_epochs):
    model.train()
    for images, labels in train_loader:
        images = images.cuda()
        labels = labels.cuda()
        outputs = model(images)
        loss = criterion(outputs, labels)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        scheduler.step()

    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for images, labels in val_loader:
            images = images.cuda()
            labels = labels.cuda()
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    print('Epoch [{}/{}], Loss: {:.4f}, Accuracy: {:.2f}%'
          .format(epoch+1, num_epochs, loss.item(), 100 * correct / total))

# 保存模型
torch.save(model.state_dict(), 'swin_transformer.pth')

该示例代码使用Swin Transformer模型对一个包含12个类别的图像分类数据集进行训练和验证，并将训练好的模型保存到本地。在训练过程中，使用了Label Smoothing Cross Entropy作为损失函数，并使用了学习率调度器来动态调整学习率。在验证过程中，计算了模型的准确率。