transformers做cifar10图像分类任务保存loss曲线和准确率曲线并保存模型pytorch

由于`transformers`主要是用于自然语言处理的，因此不能直接用于图像分类任务，但可以使用它的预训练模型进行特征提取。以下是使用`transformers`中的预训练模型进行CIFAR-10图像分类的代码，并保存loss曲线、准确率曲线和模型。

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
from transformers import ViTModel

# 定义超参数
input_size = 32
num_classes = 10
batch_size = 100
num_epochs = 5
learning_rate = 0.001

# 加载数据集并进行预处理
transform = transforms.Compose(
    [transforms.Resize((input_size, input_size)),
     transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                             download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size,
                                           shuffle=True)

test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                            download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size,
                                          shuffle=False)

# 定义ViT模型
model = ViTModel.from_pretrained('google/vit-base-patch16-224').to(device)
model.classifier = nn.Linear(768, num_classes).to(device)

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

# 记录训练过程中的loss和accuracy
train_loss_list = []
test_loss_list = []
train_acc_list = []
test_acc_list = []

# 训练模型
total_step = len(train_loader)
for epoch in range(num_epochs):
    train_loss = 0
    train_total = 0
    train_correct = 0
    for i, (images, labels) in enumerate(train_loader):
        images = images.to(device)
        labels = labels.to(device)

        # 前向传播
        outputs = model(images)
        loss = criterion(outputs, labels)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # 记录loss和accuracy
        train_loss += loss.item() * labels.size(0)
        _, predicted = torch.max(outputs.data, 1)
        train_total += labels.size(0)
        train_correct += (predicted == labels).sum().item()

        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch+1, num_epochs, i+1, total_step, loss.item()))

    # 在测试集上计算loss和accuracy
    test_loss = 0
    test_total = 0
    test_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() * labels.size(0)
            _, predicted = torch.max(outputs.data, 1)
            test_total += labels.size(0)
            test_correct += (predicted == labels).sum().item()

    # 计算平均loss和accuracy
    train_loss = train_loss / train_total
    test_loss = test_loss / test_total
    train_acc = 100 * train_correct / train_total
    test_acc = 100 * test_correct / test_total
    train_loss_list.append(train_loss)
    test_loss_list.append(test_loss)
    train_acc_list.append(train_acc)
    test_acc_list.append(test_acc)

    print('Epoch [{}/{}], Train Loss: {:.4f}, Train Accuracy: {:.2f}%, Test Loss: {:.4f}, Test Accuracy: {:.2f}%'
          .format(epoch+1, num_epochs, train_loss, train_acc, test_loss, test_acc))

# 保存loss曲线和准确率曲线
plt.plot(train_loss_list, label='Train Loss')
plt.plot(test_loss_list, label='Test Loss')
plt.legend()
plt.savefig('loss.png')

plt.clf()

plt.plot(train_acc_list, label='Train Accuracy')
plt.plot(test_acc_list, label='Test Accuracy')
plt.legend()
plt.savefig('accuracy.png')

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

这里我们使用了ViT模型进行特征提取，并通过全连接层进行分类。在训练过程中，我们记录了训练和测试的loss和accuracy，并保存了loss曲线和准确率曲线。在最后，我们保存了训练好的模型。