train 2D Xnet question

[Ystartff]

Hello! I would like to know how your mean and std are calculated if training other datasets, because I see that in semi-supervised learning, the unlabelled and labelled files contain L and H files.

[Ystartff]

Hello, my problem is that when I do onehot coding the previous batches target.shape is (2,128,128,2) but the later batches have (2,128,128,256). A problem with the code prompts me to
Traceback (most recent call last):
File "train_semi_XNet.py", line 255, in
loss_train_sup1 = criterion(pred_train_sup1, mask_train_sup)
File "/home/panxue/anaconda3/envs/wave/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/mnt/data/panxue/PX/XNet-main/loss/loss_function.py", line 132, in forward
return self._base_forward(output, target_one_hot, valid_mask)
File "/mnt/data/panxue/PX/XNet-main/loss/loss_function.py", line 100, in _base_forward
dice_loss = dice(predict[:, i], target[..., i], valid_mask)
IndexError: index 2 is out of bounds for dimension 1 with size 2
Traceback (most recent call last):
File "train_semi_XNet.py", line 255, in
loss_train_sup1 = criterion(pred_train_sup1, mask_train_sup)
File "/home/panxue/anaconda3/envs/wave/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/mnt/data/panxue/PX/XNet-main/loss/loss_function.py", line 132, in forward
return self._base_forward(output, target_one_hot, valid_mask)
File "/mnt/data/panxue/PX/XNet-main/loss/loss_function.py", line 100, in _base_forward
dice_loss = dice(predict[:, i], target[..., i], valid_mask)
IndexError: index 2 is out of bounds for dimension 1 with size 2

[Yanfeng-Zhou]

Hello!

1. I have written the corresponding code for the calculation of mean and std, but have not uploaded it. I will consider updating the code. In fact, the calculation of mean and std is very simple: calculate the mean and standard deviation of the individual pixel values of all images in the training data.
2. IndexError: index 2 is out of bounds for dimension 1 with size 2. This error most likely comes from you not processing the mask (the foreground pixel in the mask before processing is 255). For binary semantic segmentation problems, the foreground pixel in the mask should be 1.