运行时错误：视图大小与输入张量的大小和步幅不兼容（至少一维跨越两个连续的子空间）

Question

我正在使用 Pytorch。我在评估模型时遇到了这个运行时错误。知道如何解决这个问题吗？

Answer 1

解决方案： 只需按照错误中的建议将 view() 函数替换为 reshape() 函数即可。

我想这与张量在内存中的存储方式有关。

Answer 2

解决方案2：将

.view(...)

更改为

.contiguous().view(...)

。

实际上，如果需要的话，“reshape()”在幕后的作用与 contigious() 类似。

Answer 3

Hi  I am facing similar both soln did not work.

def train(epoch):
    tr_loss, tr_accuracy = 0, 0
    nb_tr_examples, nb_tr_steps = 0, 0
    tr_preds, tr_labels = [], []
    # put model in training mode
    model.train()

    for idx, batch in enumerate(training_loader):

        ids = batch['ids']
        ids = ids.transpose(0, 1) 
        ids=ids.to(device)
        mask = batch['mask']
        mask=mask.transpose(0, 1) 
        mask=mask.to(device)
        targets = batch['targets']
        targets=targets.transpose(0, 1) 
        targets=targets.to(device)


        optimizer.zero_grad()
        print(ids.shape)
        print(mask.shape)
        outputs= model(input_ids=ids, attention_mask=mask, labels=targets)
        loss, tr_logits = outputs.loss, outputs.logits
        tr_loss += loss.item()

        nb_tr_steps += 1
        nb_tr_examples += targets.size(0)

        if idx % 100==0:
            loss_step = tr_loss/nb_tr_steps
            print(f"Training loss per 100 training steps: {loss_step}")

        # compute training accuracy

> Blockquote

        flattened_targets = targets.view(-1) # shape (batch_size * seq_len,)
        active_logits = tr_logits.view(-1, model.num_labels) # shape (batch_size * seq_len, num_labels)
        flattened_predictions = torch.argmax(active_logits, axis=1) # shape (batch_size * seq_len,)
        # now, use mask to determine where we should compare predictions with targets (includes [CLS] and [SEP] token predictions)
        active_accuracy = mask.view(-1) == 1 # active accuracy is also of shape (batch_size * seq_len,)
        targets = torch.masked_select(flattened_targets, active_accuracy)
        predictions = torch.masked_select(flattened_predictions, active_accuracy)

        tr_preds.extend(predictions)
        tr_labels.extend(targets)

        tmp_tr_accuracy = accuracy_score(targets.cpu().numpy(), predictions.cpu().numpy())
        tr_accuracy += tmp_tr_accuracy

        # gradient clipping
        torch.nn.utils.clip_grad_norm_(
            parameters=model.parameters(), max_norm=MAX_GRAD_NORM
        )

        loss.backward()
        optimizer.step()

    epoch_loss = tr_loss / nb_tr_steps
    tr_accuracy = tr_accuracy / nb_tr_steps
    print(f"Training loss epoch: {epoch_loss}")
    print(f"Training accuracy epoch: {tr_accuracy}")

Training epoch: 1 torch.Size([512, 4]) torch.Size([512, 4])
--------------------------------------------------------------------------- RuntimeError                              Traceback (most recent call last) <ipython-input-50-8d08785e3057> in <cell line: 1>()
      1 for epoch in range(EPOCHS):
      2     print(f"Training epoch: {epoch + 1}")
----> 3     train(epoch)

3 frames /usr/local/lib/python3.10/dist-packages/transformers/models/roberta/modeling_roberta.py in forward(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)    1230             elif self.config.problem_type == "single_label_classification":    1231     loss_fct = CrossEntropyLoss()
-> 1232                 loss = loss_fct(logits.contiguous().view(-1, self.num_labels), labels.contiguous().view(-1))    1233             elif self.config.problem_type == "multi_label_classification":    1234 loss_fct = BCEWithLogitsLoss()

RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.