torch20有transformer模块学习torch20文档用他写一个分类例子。

以下是使用torch2.0中的transformer模块实现分类的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim

# 定义模型
class TransformerClassifier(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim, num_layers, num_heads, dropout):
        super(TransformerClassifier, self).__init__()
        self.embedding = nn.Embedding(input_dim, hidden_dim)
        self.transformer_encoder = nn.TransformerEncoder(nn.TransformerEncoderLayer(hidden_dim, num_heads, hidden_dim, dropout), num_layers)
        self.fc = nn.Linear(hidden_dim, output_dim)
        self.softmax = nn.Softmax(dim=1)

    def forward(self, x):
        x = self.embedding(x)
        x = x.permute(1, 0, 2)
        x = self.transformer_encoder(x)
        x = x.mean(dim=0)
        x = self.fc(x)
        x = self.softmax(x)
        return x

# 定义训练函数
def train(model, train_loader, criterion, optimizer):
    model.train()
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    return running_loss / len(train_loader)

# 定义测试函数
def test(model, test_loader, criterion):
    model.eval()
    correct = 0
    total = 0
    running_loss = 0.0
    with torch.no_grad():
        for data in test_loader:
            inputs, labels = data
            outputs = model(inputs)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
            loss = criterion(outputs, labels)
            running_loss += loss.item()
    accuracy = correct / total
    return running_loss / len(test_loader), accuracy

# 定义数据集
train_data = [("I love this movie", 1), ("This movie is terrible", 0), ("The acting is great", 1), ("The plot is confusing", 0)]
test_data = [("This is a good movie", 1), ("I hate this movie", 0), ("The story is interesting", 1), ("The direction is bad", 0)]

# 定义数据处理函数
def process_data(data, word_to_ix):
    processed_data = []
    for sentence, label in data:
        sentence = sentence.lower().split()
        sentence_ix = [word_to_ix[word] for word in sentence]
        processed_data.append((torch.tensor(sentence_ix), torch.tensor([label])))
    return processed_data

# 定义词汇表和数据集
vocab = set([word for sentence, _ in train_data for word in sentence.lower().split()])
word_to_ix = {word: i for i, word in enumerate(vocab)}
train_data = process_data(train_data, word_to_ix)
test_data = process_data(test_data, word_to_ix)

# 定义超参数
input_dim = len(vocab)
hidden_dim = 32
output_dim = 2
num_layers = 2
num_heads = 2
dropout = 0.2
learning_rate = 0.001
num_epochs = 10
batch_size = 2

# 定义模型、损失函数和优化器
model = TransformerClassifier(input_dim, hidden_dim, output_dim, num_layers, num_heads, dropout)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, shuffle=False)
for epoch in range(num_epochs):
    train_loss = train(model, train_loader, criterion, optimizer)
    test_loss, accuracy = test(model, test_loader, criterion)
    print("Epoch: {}  Train Loss: {:.4f}  Test Loss: {:.4f}  Accuracy: {:.4f}".format(epoch+1, train_loss, test_loss, accuracy))

上述示例代码使用了一个简单的电影评论数据集，其中包含了一些评论文本以及对应的情感标签（1表示积极，0表示消极）。首先将文本数据处理成了词汇表中的索引序列，然后使用torch2.0中的transformer模块搭建了一个分类器，其中embedding层将输入的词汇索引映射成了词向量，然后经过多层transformer编码器后取平均得到了一个文本表示，最后通过全连接层和softmax函数得到了分类结果。训练过程中使用了交叉熵损失函数和Adam优化器。在训练过程中，每个epoch都会输出训练损失、测试损失和准确率