对于 Pytorch 中的 Transformer 网络，tgt 和 src 必须具有相同的特征

如何解决对于 Pytorch 中的 Transformer 网络，tgt 和 src 必须具有相同的特征

我正在尝试通过变压器网络训练 EEG 数据。输入尺寸为 50x16684x60（seq x batch x features），输出为 16684x2。现在我只是想运行一个基本的变压器，但我不断收到错误消息

RuntimeError: the feature number of src and tgt must be equal to d_model

为什么源特征数和目标特征数会相等？有没有可能通过transformer来运行这样的数据集？

这是我的基本模型：

input_size = 60 # seq x batch x features
hidden_size = 32
num_classes = 2
learning_rate = 0.001
batch_size = 64
num_epochs = 2
sequence_length = 50
num_layers = 2
dropout = 0.5

class Transformer(nn.Module):
    def __init__(self,input_size,hidden_size,num_layers,num_classes):
        super(Transformer,self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        
        self.transformer = nn.Transformer(60,2)
        self.fc = nn.Linear(hidden_size * sequence_length,num_classes)
    
    def forward(self,x,y):
        
        # Forward Propogation
        out,_ = self.transformer(x,y)
        out = out.reshape(out.shape[0],-1)
        out = self.fc(out)
        return out

model = Transformer(input_size,num_classes)

criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(),lr=learning_rate)

for epoch in range(num_epochs):
    for index in tqdm(range(16684)):
        X,y = (X_train[index],Y_train[index])
        print(X.shape,y.shape)
    
        output = model(X,y)

        loss = criterion(output,y)
        
        model.zero_grad()
        loss.backward()
        
        optimizer.step()
        
        if index % 500 == 0:
            print(f"Epoch {epoch},Batch: {index},Loss: {loss}")