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有谁知道这个错误的解决方案？我正在尝试将我的 PyTorch 网络切换到联邦学习网络，但我总是收到此错误。

我在 GPU 上使用 Google Colab 训练。当我打印嵌入的大小时，我得到 0，但我不明白为什么那里不使用数据。

RuntimeError                              Traceback (most recent call last)
<ipython-input-42-fd4a5223524b> in <module>()
----> 1 model, history = train_model(net, dataloaders_dict, criterion, optimizer, num_epochs=10)
      2 #model = train_model(net, dataloaders_dict, criterion, optimizer, num_epochs=10)

6 frames
<ipython-input-41-a386f044d41f> in train_model(model, dataloaders, criterion, optimizer, num_epochs, batch_size)
     68                         # detaching it from its history on the last instance.
     69 
---> 70                         outputs = model(inputs)
     71 
     72                         loss = criterion(outputs, labels)

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    530             result = self._slow_forward(*input, **kwargs)
    531         else:
--> 532             result = self.forward(*input, **kwargs)
    533         for hook in self._forward_hooks.values():
    534             hook_result = hook(self, input, result)

<ipython-input-36-64e9a7d68b11> in forward(self, sentence)
     11     def forward(self, sentence):
     12         embeds = self.word_embeddings(sentence)
---> 13         lstm_out, (h,t) = self.lstm(embeds)
     14         lstm_out = self.dropout(lstm_out)
     15         tag_space = self.output(lstm_out[:,-1,:])

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    530             result = self._slow_forward(*input, **kwargs)
    531         else:
--> 532             result = self.forward(*input, **kwargs)
    533         for hook in self._forward_hooks.values():
    534             hook_result = hook(self, input, result)

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/rnn.py in forward(self, input, hx)
    554             hx = self.permute_hidden(hx, sorted_indices)
    555 
--> 556         self.check_forward_args(input, hx, batch_sizes)
    557         if batch_sizes is None:
    558             result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/rnn.py in check_forward_args(self, input, hidden, batch_sizes)
    506     def check_forward_args(self, input, hidden, batch_sizes):
    507         # type: (Tensor, Tuple[Tensor, Tensor], Optional[Tensor]) -> None
--> 508         self.check_input(input, batch_sizes)
    509         expected_hidden_size = self.get_expected_hidden_size(input, batch_sizes)
    510 

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/rnn.py in check_input(self, input, batch_sizes)
    157             raise RuntimeError(
    158                 'input.size(-1) must be equal to input_size. Expected {}, got {}'.format(
--> 159                     self.input_size, input.size(-1)))
    160 
    161     def get_expected_hidden_size(self, input, batch_sizes):

RuntimeError: input.size(-1) must be equal to input_size. Expected 200, got 0

class LSTM(nn.Module):

def __init__(self, embedding_dim, hidden_layers,vocab_size,num_layers,pretrained_weights):
    super(LSTM, self).__init__()

    self.word_embeddings = nn.Embedding(vocab_size, embedding_dim,_weight=pretrained_weights, padding_idx=0)
    self.lstm = nn.LSTM(embedding_dim, hidden_size=hidden_layers,num_layers=num_layers, batch_first=True)
    self.output = nn.Linear(hidden_layers, vocab_size, bias=False)
    self.dropout = nn.Dropout(0.1)

def forward(self, sentence):
    embeds = self.word_embeddings(sentence)
    lstm_out, (h,t) = self.lstm(embeds)
    lstm_out = self.dropout(lstm_out)
    tag_space = self.output(lstm_out[:,-1,:])

    return tag_space

此行中抛出错误：lstm_out, (h,t) = self.lstm(embeds)

  for epoch in range(num_epochs):
    print('Epoch {}/{}'.format(epoch, num_epochs - 1))
    print('-' * 10)
    train_loss = 0
    acc_score = 0

    valid_loss = 0
    acc_valid_score = 0

    #Variables to store the losses temporary
    train_loss_result = 0
    acc_score_result = 0
    valid_loss_result = 0
    acc_valid_score_result = 0

    valid_loss_not_decreased = 0

    if valid_loss_not_decreased == 5:
      break

    # Each epoch has a training and validation phase
    for phase in ['train', 'val']:
        if phase == 'train':
            model.train()  # Set model to training mode
        else:
            model.eval()   # Set model to evaluate mode
        

        for inputs,labels in dataloaders[phase]:

            # Location of current batch
            worker = inputs.location  # <---- Where will send the model to
            
            #model.to(device)

            model = model.send(worker)   # <---- for Federated Learning

            inputs, labels = inputs.to(device), labels.to(device)
            
            print("--------> INPUT: ",inputs)
            print("--------> LABEL: ",labels)

            # zero the parameter gradients
            optimizer.zero_grad()

            # forward
            # track history if only in train
            with torch.set_grad_enabled(phase == 'train'):
                # Get model outputs and calculate loss

                # backward + optimize only if in training phase
                if phase == 'train':
                    # we need to clear out the hidden state of the LSTM,
                    # detaching it from its history on the last instance.

                    outputs = model(inputs)

                    loss = criterion(outputs, labels)
                    acc = binary_accuracy(outputs,labels)       
                    acc_score = acc_score + acc
                    train_loss = train_loss + loss.item()
                    loss.backward()
                    
                    optimizer.step()
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1 回答 1

0

当我使用 Pysyft 运行 LSTM 模型时，我也有这个错误。除此之外，LSTM 的模型可以在不使用 Pysyft 的情况下运行。实际上，使用 Pysyft 可以成功运行 CNN 模型。所以，我认为 Pysyft 中可能存在一些错误。

于 2021-01-18T05:52:00.917 回答