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

See original GitHub issue

Hi,

When running python3 lanenet/train.py --dataset ./data/training_data_example, I’m seeing the following exception:

Traceback (most recent call last):
  File "lanenet/train.py", line 156, in <module>
    main()
  File "lanenet/train.py", line 144, in main
    train_iou = train(train_loader, model, optimizer, epoch)
  File "lanenet/train.py", line 68, in train
    total_loss, binary_loss, instance_loss, out, train_iou = compute_loss(net_output, binary_label, instance_label)
  File "/usr/local/lib/python3.6/dist-packages/lanenet-0.1.0-py3.6.egg/lanenet/model/model.py", line 75, in compute_loss
  File "/home/lashar/.local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/lanenet-0.1.0-py3.6.egg/lanenet/model/loss.py", line 33, in forward
  File "/usr/local/lib/python3.6/dist-packages/lanenet-0.1.0-py3.6.egg/lanenet/model/loss.py", line 71, in _discriminative_loss
  File "/home/lashar/.local/lib/python3.6/site-packages/torch/functional.py", line 1100, in norm
    return _VF.frobenius_norm(input, _dim, keepdim=keepdim)
IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)

Any clues about how I can fix this? Not sure if I’m doing something incorrectly.

Thanks!

Issue Analytics

State:
Created 3 years ago
Reactions:1
Comments:11 (1 by maintainers)

Top GitHub Comments

4reactions

mummy2358commented, May 6, 2021

Managed to fix it. It should be dim=1. The norm should be calculated along the “embedding” axis. Problem comes from

embedding_i = embedding_b[seg_mask_i]

which break the dims to get a single dimensional vector.

Changing it to: embedding_I = embedding_b * seg_mask_i

works for me

2reactions

sharmalakshay93commented, Jan 5, 2021

PS: I just saw issues/12, and its related commit. Changing it back to dim=0 makes it work. However, since the aforementioned issue says that the correct value is dim=1, not sure if this works as intended? Would appreciate it if clarification could be provided!

Thanks!