No folder named "Labels", Tuple Index out of range, size mismatch.

[NicWickman]

First I think there's a small error in the README tutorial: Should the correct directory should be data/labels instead of data/classes ?

Anyways, I get the following traceback:

/home/nic/.conda/envs/piwise/lib/python3.6/site-packages/torch/nn/functional.py:1423: UserWarning: nn.functional.upsample_bilinear is deprecated. Use nn.functional.upsample instead.
  warnings.warn("nn.functional.upsample_bilinear is deprecated. Use nn.functional.upsample instead.")
/home/nic/machineLearning/Automatter/machine-learning/piwise/piwise/criterion.py:13: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.
  return self.loss(F.log_softmax(outputs), targets)
Traceback (most recent call last):
  File "main.py", line 163, in <module>
    main(parser.parse_args())
  File "main.py", line 138, in main
    train(args, model)
  File "main.py", line 85, in train
    board.image(color_transform(outputs[0].cpu().max(0)[1].data),
  File "/home/nic/machineLearning/Automatter/machine-learning/piwise/piwise/transform.py", line 48, in __call__
    color_image = torch.ByteTensor(3, size[1], size[2]).fill_(0)
IndexError: tuple index out of range

My first attempt at an obvious solution was to change it to

color_image = torch.ByteTensor(3, size[0], size[1]).fill_(0)

But I still have a size mismatch error. Tensor is 256 x 256 and Mask is 256.

[bodokaiser]

First I think there's a small error in the README tutorial: Should the correct directory should be data/labels instead of data/classes ?

Yes, I think it should be 'data/labels'. The README was not updated a while.

My first attempt at an obvious solution was to change it to
color_image = torch.ByteTensor(3, size[0], size[1]).fill_(0)
But I still have a size mismatch error. Tensor is 256 x 256 and Mask is 256.

The last time I used piwise which was more then a year ago. It worked straight out of the box, however memories are prone to failures. Some things I would suggest:

1. Did any pytorch api change?
2. Try different indices (debug the program)?
3. You used the Pascal VOC 2012 data?

I am not sure if this has any relevance but the color_transform takes an image labeled 0 to 17, 255 and converts it into a color images where each label has distinct colors. The implementation was more or less copy and paste from the Matlab script Pascal VOC 2012 provides.

[SleyAI]

I'm having the same issue. Did you find a solution?

[NicWickman]

No, I have not. Sorry I'm not of more help.

…

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[fantasyzl]

The problem should be in transform.py. You can try deleting the lines 101 to 111 in main.py and try again.

[guizilaile23]

board.image(color_transform(targets[0].cpu()[0].data) will fix

[swg209]

This is caused by the gray_image 's axis, after add this line code
gray_image = gray_image.view(1,256,256)
in transform.py line 47,
the issue is solved.
def __call__(self, gray_image):
gray_image = gray_image.view(1,256,256)
size = gray_image.size()
color_image = torch.ByteTensor(3, size[1], size[2]).fill_(0)