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.

[mw66]

python trainer_DSN.py --sparse True --density 0.2 --sparse_init remain_sort --fix False --growth random --depth 4 --ch_size 47 --c_size 3 --k_size 39 \ --root /data/UCR_TS_Archive_2015

Traceback (most recent call last): File "/home/./project/contrib/DSN/trainer_DSN.py", line 193, in main(args) File "/home/./project/contrib/DSN/trainer_DSN.py", line 116, in main mask.add_module(model, sparse_init=args.sparse_init, density=args.density) File "/home/./project/contrib/DSN/sparselearning/core_kernel.py", line 241, in add_module self.init(mode=sparse_init, density=density) File "/home/./project/contrib/DSN/sparselearning/core_kernel.py", line 175, in init new_mask.data.view(-1)[:num_remain] = 1.0 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.

[mw66]

https://github.com/cezannec/capsule_net_pytorch/issues/4#issuecomment-629573681

sparselearning/core_kernel.py

change to:

-                        new_mask.data.view(-1)[:num_remain] = 1.0
+                        new_mask.data.contiguous().view(-1)[:num_remain] = 1.0

worked.

[anushiya1]

May I check if you managed to reproduce the paper's results? Thanks.

[houchenyu]

May I check if you managed to reproduce the paper's results? Thanks.

Hi, I run the code with the daily_sport dataset, but I got a very bad performance (Train Accuracy only 0.04=200/4560). How about you?

[Sauce16]

Hi @ houchenyu @mw66 pls help am facing issue 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
    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
    )

    # backward pass

    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) 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.