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## Motivation
Support depth estimation algorithm [VPD](https://github.com/wl-zhao/VPD)
## Modification
1. add VPD backbone
2. add VPD decoder head for depth estimation
3. add a new segmentor `DepthEstimator` based on `EncoderDecoder` for
depth estimation
4. add an integrated metric that calculate common metrics in depth
estimation
5. add SiLog loss for depth estimation
6. add config for VPD
## 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.
7. The modification is covered by complete unit tests. If not, please
add more unit test to ensure the correctness.
8. If the modification has potential influence on downstream projects,
this PR should be tested with downstream projects, like MMDet or
MMDet3D.
9. The documentation has been modified accordingly, like docstring or
example tutorials.
Thanks for your contribution and we appreciate it a lot. The following
instructions would make your pull request more healthy and more easily
get feedback. If you do not understand some items, don't worry, just
make the pull request and seek help from maintainers.
## Motivation
It's OpenMMLab Codecamp task.
## Modification
Implementd Kullback-Leibler divergence loss and also added tests for it.
## 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.
---------
Co-authored-by: xiexinch <xiexinch@outlook.com>
Added ignore_index param to forward(),
also implemented one hot encoding to ensure the dims of target matches
pred.
Thanks for your contribution and we appreciate it a lot. The following
instructions would make your pull request more healthy and more easily
get feedback. If you do not understand some items, don't worry, just
make the pull request and seek help from maintainers.
## 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.
Thanks for your contribution and we appreciate it a lot. The following
instructions would make your pull request more healthy and more easily
get feedback. If you do not understand some items, don't worry, just
make the pull request and seek help from maintainers.
## Motivation
Add Huasdorff distance loss
---------
Co-authored-by: xiexinch <xiexinch@outlook.com>
* [Fix] Fix the bug in binary_cross_entropy
Fix the bug in binary_cross_entropy
'label.max() <= 1' should mask out ignore_index, since the ignore_index often set as 255.
* [Fix] Fix the bug in binary_cross_entropy, add comments
As the ignore_index often set as 255, so the binary class label check should mask out ignore_index.
Co-authored-by: Miao Zheng <76149310+MeowZheng@users.noreply.github.com>
* [Fix] Fix the bug in binary_cross_entropy
As the ignore_index often set as 255, so the binary class label check should mask out ignore_index.
Co-authored-by: Miao Zheng <76149310+MeowZheng@users.noreply.github.com>
Co-authored-by: MeowZheng <meowzheng@outlook.com>
* [Fix] Add avg_non_ignore in cross entropy loss
* [Fix] Add avg_non_ignore in cross entropy loss
* add docstring
* fix ut
* fix docstring and comments
* fix
* fix bce
* fix avg_factor in BCE and add more ut
* add avg_non_ignore
* add more ut
* fix part of ut
* fix part of ut
* test avg_non_ignore would not affect ce/bce when reduction none/sum
* test avg_non_ignore would not affect ce/bce when reduction none/sum/mean
* re-organize ut
* re-organize ut
* re-organize ut
* re-organize hardcode case
* fix parts of comments
* fix another parts of comments
* fix
* [Fix] Fix the bug that when all pixels in an image is ignored, the accuracy calculation raises ZeroDivisionError
* use eps
* all close
* add ignore test
* add eps
* [Feature] add focal loss
* fix the bug of 'non' reduction type
* refine the implementation
* add class_weight and ignore_index; support different alpha values for different classes
* fixed some bugs
* fix bugs
* add comments
* modify test
* Update mmseg/models/losses/focal_loss.py
Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>
* update test_focal_loss.py
* modified the implementation
* Update mmseg/models/losses/focal_loss.py
Co-authored-by: Jerry Jiarui XU <xvjiarui0826@gmail.com>
* update focal_loss.py
Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>
Co-authored-by: Jerry Jiarui XU <xvjiarui0826@gmail.com>
* support reading class_weight from file in loss function
* add unit test of loss with class_weight from file
* minor fix
* move get_class_weight to utils
