[Fix] Added ignore_index and one hot encoding for dice loss

[yeedrag]

Added ignore_index param to forward(),
also implemented one hot encoding to ensure the dims of target matches pred.

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Motivation

Please describe the motivation of this PR and the goal you want to achieve through this PR.

Attempted to solve the problems mentioned by #3172

Modification

Please briefly describe what modification is made in this PR.

Added ignore_index into forward function (although the dice loss itself does not actually take account for it for some reason).
Added _expand_onehot_labels_dice, which takes the target with shape [N, H, W] into [N, num_classes, H, W].

BC-breaking (Optional)

Does the modification introduce changes that break the backward-compatibility of the downstream repos?
If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR.

Use cases (Optional)

If this PR introduces a new feature, it is better to list some use cases here, and update the documentation.

Checklist

1. Pre-commit or other linting tools are used to fix the potential lint issues.
2. The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness.
3. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMDet3D.
4. The documentation has been modified accordingly, like docstring or example tutorials.

This is my first time contributing to open-source code, so I might have made some stupid mistakes. Please don't hesitate to point it out.

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[yeedrag]

Hmm, I'm not entirely sure why the pr_stage_test failed after adding the type hints...
edit: I think its a problem with Circleci, because multiple other prs are facing the same fail.

[xiexinch]

Hmm, I'm not entirely sure why the pr_stage_test failed after adding the type hints... edit: I think its a problem with Circleci, because multiple other prs are facing the same fail.

It's caused by the update of MMEngine...
We're working on fixing it.

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[yeedrag]

Also, with my current pr, I haven't implemented actually using ignore_index to calculate the loss. I think I can implement it, but should I just commit the implementation in this pr or should I make a new one? Thanks.

[xiexinch]

Also, with my current pr, I haven't implemented actually using ignore_index to calculate the loss. I think I can implement it, but should I just commit the implementation in this pr or should I make a new one? Thanks.

Might commit to this pr.

[xiexinch]

Could you help to update some code?

if self.activate:
    if self.use_sigmoid:
        pred = pred.sigmoid()
    else:
        pred = pred.softmax(dim=1)

The current implementation only supports sigmoid, we might refer to the implementation at MONAI(https://github.com/Project-MONAI/MONAI/blob/dev/monai/losses/dice.py#L140).

[yeedrag]

Could you help to update some code?

if self.activate:
    if self.use_sigmoid:
        pred = pred.sigmoid()
    else:
        pred = pred.softmax(dim=1)

The current implementation only supports sigmoid, we might refer to the implementation at MONAI(https://github.com/Project-MONAI/MONAI/blob/dev/monai/losses/dice.py#L140).

So I add softmax and other_act?

[xiexinch]

Could you help to update some code?

if self.activate:
    if self.use_sigmoid:
        pred = pred.sigmoid()
    else:
        pred = pred.softmax(dim=1)

The current implementation only supports sigmoid, we might refer to the implementation at MONAI(https://github.com/Project-MONAI/MONAI/blob/dev/monai/losses/dice.py#L140).

So I add softmax and other_act?

Currently, we only need to add softmax, if you have a better solution, you can support adding other activation methods.

[yeedrag]

Currently, we only need to add softmax, if you have a better solution, you can support adding other activation methods.

Done!

if self.activate:
    if self.use_sigmoid:
        pred = pred.sigmoid()
    elif pred.shape[1] != 1:
        # softmax does not work when there is only 1 class
        pred = pred.softmax(dim=1)

I'm not sure whether to use an assert or a warning when there's only 1 class so I currently just used an elif.