用python写一个基于transformer的图像分类

要使用Transformer进行图像分类，需要将图像转换为序列数据。一种常见的方法是使用卷积神经网络（CNN）提取图像特征，然后将这些特征转换为序列数据。以下是一个基于PyTorch的示例代码：

首先，导入必要的库和模块：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR

import math

然后，定义一个CNN来提取图像特征：

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()

        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)

    def forward(self, x):
        x = self.conv1(x)
        x = nn.functional.relu(x)
        x = self.pool(x)

        x = self.conv2(x)
        x = nn.functional.relu(x)
        x = self.pool(x)

        x = self.conv3(x)
        x = nn.functional.relu(x)
        x = self.pool(x)

        x = x.view(x.size(0), -1)

        return x

接下来，定义一个Transformer模型来处理序列数据：

class Transformer(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_heads, dropout):
        super(Transformer, self).__init__()

        self.embedding = nn.Embedding(input_size, hidden_size)
        self.position_embedding = nn.Embedding(1000, hidden_size)

        encoder_layer = nn.TransformerEncoderLayer(hidden_size, num_heads, hidden_size * 4, dropout)
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers)

    def forward(self, x):
        seq_length = x.size(1)

        positions = torch.arange(seq_length, device=x.device).unsqueeze(0)
        positions = self.position_embedding(positions)

        x = self.embedding(x)
        x = x + positions

        x = x.transpose(0, 1)
        x = self.transformer(x)
        x = x.transpose(0, 1)

        return x[:, -1, :]

最后，定义一个分类器来输出图像的分类：

class Classifier(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_heads, dropout, output_size):
        super(Classifier, self).__init__()

        self.cnn = CNN()
        self.transformer = Transformer(input_size, hidden_size, num_layers, num_heads, dropout)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = self.cnn(x)
        x = x.unsqueeze(1)

        x = self.transformer(x)

        x = self.fc(x)

        return x

最后，训练模型：

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

transform = transforms.Compose([transforms.Resize(224),
                                transforms.RandomHorizontalFlip(),
                                transforms.ToTensor(),
                                transforms.Normalize((0.5,), (0.5,))])

train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)

test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

model = Classifier(input_size=1000, hidden_size=256, num_layers=4, num_heads=8, dropout=0.1, output_size=10).to(device)

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
scheduler = StepLR(optimizer, step_size=1, gamma=0.7)

num_epochs = 10

for epoch in range(num_epochs):
    running_loss = 0.0
    for i, (inputs, labels) in enumerate(train_loader):
        inputs = inputs.to(device)
        labels = labels.to(device)

        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

    scheduler.step()

    correct = 0
    total = 0
    with torch.no_grad():
        for inputs, labels in test_loader:
            inputs = inputs.to(device)
            labels = labels.to(device)

            outputs = model(inputs)
            _, predicted = torch.max(outputs.data, 1)

            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    print('Epoch: %d, Loss: %.3f, Test Accuracy: %.3f%%' % (epoch + 1, running_loss / len(train_loader), 100 * correct / total))