I am trying to use this to train on my own dataset. I couldn't figure out a way to acc

Masks format about pytorch-unet HOT 4 CLOSED

mehditlili commented on May 21, 2024

Masks format

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mehditlili commented on May 21, 2024 2

I now have two classes + background, my mask images have 3 values, 0 for background, 1 for the first class and 2 for the second class. however when I set n_classes to 2 this happens:

Starting epoch 1/1000.
/home/vision/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/upsampling.py:129: UserWarning: nn.Upsample is deprecated. Use nn.functional.interpolate instead.
  warnings.warn("nn.{} is deprecated. Use nn.functional.interpolate instead.".format(self.name))
/home/vision/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/functional.py:1332: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.
  warnings.warn("nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.")
/home/vision/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/functional.py:2016: UserWarning: Using a target size (torch.Size([921600])) that is different to the input size (torch.Size([1843200])) is deprecated. Please ensure they have the same size.
  "Please ensure they have the same size.".format(target.size(), input.size()))
Traceback (most recent call last):
  File "train.py", line 139, in <module>
    img_scale=args.scale)
  File "train.py", line 80, in train_net
    loss = criterion(masks_probs_flat, true_masks_flat)
  File "/home/vision/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/vision/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/loss.py", line 504, in forward
    return F.binary_cross_entropy(input, target, weight=self.weight, reduction=self.reduction)
  File "/home/vision/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/functional.py", line 2019, in binary_cross_entropy
    "!= input nelement ({})".format(target.numel(), input.numel()))
ValueError: Target and input must have the same number of elements. target nelement (921600) != input nelement (1843200)

Not sure whad I should do

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milesial commented on May 21, 2024 2

You have to separate your target masks into 2 layers with values 0 or 1 (black and white). So you would have a target tensor with 2 channels:

mask == 1 (first class) -> to int values (0 or 1)
mask == 2 (second class) -> to int values (0 or 1)

After this processing your mask tensor should be HxWxC, with C=2 for you.
In the error you can see that pytorch expects twice as many values in the target mask, that should come from these two channels.

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mehditlili commented on May 21, 2024 1

ah I see, so I did understand it correctly at the beginning

it seems that I need a multichannel image with a channel for each class, can you confirm that?

Thank you

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milesial commented on May 21, 2024

As I see it, with only 0 and 1 in the masks, you only have 1 class: the class with the ones. For the kaggle dataset the class was 'car'. The zeros do not correspond to any class. But you could see the zeros as the class 'not a car' as well, so there would be 2 "classes".

It just depends on how you define your classes and your problem. If you would have two types of objects to segment, you might have an additional id that correspond to 'nothing', here that id is 0.

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Masks format about pytorch-unet HOT 4 CLOSED

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