Query about Tent on semantic segmentation

[2018302345]

The tent processing classification task requires the entropy of the predicted probability as a loss, how is this loss calculated in the segmentation task.

[qinenergy]

Hi, it is similar to the classification case, except that it is calculated for every pixel. You can find an example here:
Use: https://github.com/qinenergy/corda/blob/main/trainUDA_gta.py#L529
Loss: https://github.com/qinenergy/corda/blob/main/utils/loss.py#L35
You can also find our code at #6

[2018302345]

Thank you very much for your serious answer, in the code acdc-submission\mmseg\apis\test.py of #6 in single_gpu_tent, line 227 predicts result directly as gt_semantic_seg, and I didn't find the replacement of the loss function. In acdc-submission\local_configs\segformer\B5\segformer.b5.1024x1024.acdc.160k.py the loss_decode is 'CrossEntropyLoss', in which case it just calculates the CrossEntropyLoss of two identical tensors, which I don't quite understand.

[qinenergy]

1. In our case, they are not two identical tensors because of the teacher-student model~(pseudo-label is from teacher) and masking.
2. Even in the simplified identical case, crossEntropy of the same probability~(two identical tensor) gives you entropy by definition, which I don't see any problem nor difference to the classification use case.

If you are not sure, please refer to https://en.wikipedia.org/wiki/Cross_entropy
A similar question that was asked is #18

[2018302345]

Thank you for your answer and good luck with your research!