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CIFAR10 and SimpleCNN training: IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)

See original GitHub issue

🐛 Describe the bug Trying to modify notebook 4_training to fit SplitCIFAR10 with EWC in tutorials results in the following error

🐜 To Reproduce Steps / minimal snipped of code to reproduce the issue.

from avalanche.benchmarks.classic import SplitCIFAR10
from torch.nn import CrossEntropyLoss
from avalanche.models import SimpleCNN
from torch.optim import SGD

scenario = SplitCIFAR10(n_experiences=2, return_task_id=True)

criterion = CrossEntropyLoss()
model = SimpleCNN(num_classes=5)
optimizer = SGD(model.parameters(), lr=0.001, momentum=0.9)

strategy = EWC(model, optimizer = optimizer, criterion = criterion, ewc_lambda = 0.1)

print('Starting experiment...')
results_ewc = []
for experience in scenario.train_stream:
    print("Start of experience: ", experience.current_experience)
    print("shape dataset ", len(experience.dataset))
    print("Current Classes: ", experience.classes_in_this_experience)

    print('Training completed')

    print('Computing accuracy on the whole test set')

🐝 Expected behavior A clear and concise description of what you expected to happen.

Training proceeds through the experiences

🐞 What happens

Starting experiment...
<class 'avalanche.benchmarks.utils.avalanche_dataset.AvalancheSubset'>
Start of experience:  0
shape dataset  25000
Current Classes:  [0, 1, 2, 3, 4]
-- >> Start of training phase << --
-- Starting training on experience 0 (Task 0) from train stream --
IndexError                                Traceback (most recent call last)
<ipython-input-60-4c8d2621329b> in <module>
     12     print("Current Classes: ", experience.classes_in_this_experience)
---> 14     strategy.train(experience)
     15     print('Training completed')

~/miniconda3/envs/torch/lib/python3.8/site-packages/avalanche/training/strategies/ in train(self, experiences, eval_streams, **kwargs)
    247         self.before_training(**kwargs)
    248         for exp in experiences:
--> 249             self.train_exp(exp, eval_streams, **kwargs)
    250         self.after_training(**kwargs)

~/miniconda3/envs/torch/lib/python3.8/site-packages/avalanche/training/strategies/ in train_exp(self, experience, eval_streams, **kwargs)
    277         for self.epoch in range(self.train_epochs):
    278             self.before_training_epoch(**kwargs)
--> 279             self.training_epoch(**kwargs)
    280             self._periodic_eval(eval_streams, do_final=False)
    281             self.after_training_epoch(**kwargs)

~/miniconda3/envs/torch/lib/python3.8/site-packages/avalanche/training/strategies/ in training_epoch(self, **kwargs)
    432                 # Loss & Backward
--> 433                 self.loss += self.criterion(self.logits, self.mb_y)
    435             self.before_backward(**kwargs)

~/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/modules/ in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--> 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

~/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/modules/ in forward(self, input, target)
   1045     def forward(self, input: Tensor, target: Tensor) -> Tensor:
   1046         assert self.weight is None or isinstance(self.weight, Tensor)
-> 1047         return F.cross_entropy(input, target, weight=self.weight,
   1048                                ignore_index=self.ignore_index, reduction=self.reduction)

~/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/ in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction)
   2691     if size_average is not None or reduce is not None:
   2692         reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 2693     return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)

~/miniconda3/envs/torch/lib/python3.8/site-packages/torch/nn/ in log_softmax(input, dim, _stacklevel, dtype)
   1670         dim = _get_softmax_dim("log_softmax", input.dim(), _stacklevel)
   1671     if dtype is None:
-> 1672         ret = input.log_softmax(dim)
   1673     else:
   1674         ret = input.log_softmax(dim, dtype=dtype)

IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)

Issue Analytics

  • State:closed
  • Created 2 years ago
  • Comments:5 (1 by maintainers)

github_iconTop GitHub Comments

vlomonacocommented, Apr 12, 2021

Thanks @RgMoller for reporting the issue, I’ll look into it ASAP.

AndreaCossucommented, Apr 12, 2021

Mmmm… maybe it is because now the training progresses and reaches the second experience where you have labels in [5-9]. You should set the num_classes in SimpleCNN to 10 if you are training on CIFAR-10.

Let me know if this was the problem, otherwise I can look into this more carefully.

Read more comments on GitHub >

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