基于vit图像识别代码

对于vit图像识别代码，一般分为以下几个步骤：数据准备、模型构建、模型训练和模型评估。具体实现过程可以参考以下代码：

1. 数据准备

from torchvision import transforms, datasets

data_transform = transforms.Compose([
    transforms.Resize(224), # 缩放到指定大小
    transforms.CenterCrop(224), # 居中裁剪
    transforms.ToTensor(), # 转化为张量
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # 标准化
])

train_dataset = datasets.ImageFolder(root='train_path', transform=data_transform)
val_dataset = datasets.ImageFolder(root='val_path', transform=data_transform)

2. 模型构建

import torch.nn as nn

class ViT(nn.Module):
	def __init__(self, img_size, patch_size, num_classes, dim):
		super().__init__()
		self.patch_size = patch_size
		num_patches = (img_size // patch_size) ** 2
		patch_dim = 3 * patch_size ** 2 # 输入的通道数，3表示RGB通道
		self.class_embed = nn.Parameter(torch.randn(1, 1, dim))
		self.patch_embed = nn.Linear(patch_dim, dim)
		self.pos_embed = nn.Parameter(torch.randn(1, num_patches + 1, dim))
		self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
		self.transformer = nn.TransformerEncoderLayer(d_model=dim, nhead=8)
		self.linear = nn.Linear(dim, num_classes)

	def forward(self, x):
		batch_size, _, _, _ = x.shape
		patches = x.unfold(2, self.patch_size, self.patch_size).unfold(3, self.patch_size, self.patch_size)
		patches = patches.flatten(2).transpose(1, 2)
		patch_embed = self.patch_embed(patches)
		pos_embed = self.pos_embed[:, :(patches.size(1) + 1)]
		cls_tokens = self.cls_token.expand(batch_size, -1, -1)
		x = torch.cat([cls_tokens, patch_embed], dim=1)
		x += pos_embed
		x = self.transformer(x)
		x = x.mean(dim=1)
		x = self.linear(x)
		return x

3. 模型训练

import torch.optim as optim

device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = ViT(img_size=224, patch_size=16, num_classes=10, dim=512).to(device)

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)

num_epochs = 50
for epoch in range(num_epochs):
    train_loss = 0.0
    train_acc = 0.0
    model.train()
    for inputs, labels in train_loader:
        inputs = inputs.to(device)
        labels = labels.to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * inputs.size(0)
        _, 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)
    val_loss = 0.0
    val_acc = 0.0
    model.eval()
    with torch.no_grad():
        for inputs, labels in val_loader:
            inputs = inputs.to(device)
            labels = labels.to(device)
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            val_loss += loss.item() * inputs.size(0)
            _, 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)
    scheduler.step()
    print('[Epoch %d/%d] Train Loss: %.4f, Train Acc: %.4f, Val Loss: %.4f, Val Acc: %.4f' \
          % (epoch + 1, num_epochs, train_loss, train_acc, val_loss, val_acc))

4. 模型评估

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

print('Accuracy of the network on the test images: %d %%' % (100 * correct / total))

以上代码仅为参考，实际实现时需要根据具体的需求进行相应的修改和调整。