* [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>
* Fix typo in usage example
* [Feature] Add CutOut transform
* CutOut repr covered by unittests
* Cutout ignore index, test
* ignore_index -> seg_fill_in, defualt is None
* seg_fill_in is added to repr
* test is modified for seg_fill_in is None
* seg_fill_in (int), 0-255
* add seg_fill_in test
* doc string for seg_fill_in
* rename CutOut to RandomCutOut, add prob
* Add unittest when cutout is False
* Skip CI on ignoring given paths
Signed-off-by: del-zhenwu <dele.zhenwu@gmail.com>
* Fix ignoring path with using
Signed-off-by: del-zhenwu <dele.zhenwu@gmail.com>
* update LoveDA dataset api
* revised lint errors in dataset_prepare.md
* revised lint errors in loveda.py
* revised lint errors in loveda.py
* revised lint errors in dataset_prepare.md
* revised lint errors in dataset_prepare.md
* checked with isort and yapf
* checked with isort and yapf
* checked with isort and yapf
* Revert "checked with isort and yapf"
This reverts commit 686a51d9
* Revert "checked with isort and yapf"
This reverts commit b877e121bb.
* Revert "revised lint errors in dataset_prepare.md"
This reverts commit 2289e27c
* Revert "checked with isort and yapf"
This reverts commit 159db2f8
* Revert "checked with isort and yapf"
This reverts commit 159db2f8
* add configs & fix bugs
* update new branch
* upload models&logs and add format-only
* change pretraied model path of HRNet
* fix the errors in dataset_prepare.md
* fix the errors in dataset_prepare.md and configs in loveda.py
* change the description in docs_zh-CN/dataset_prepare.md
* use init_cfg
* fix test converage
* adding pseudo loveda dataset
* adding pseudo loveda dataset
* adding pseudo loveda dataset
* adding pseudo loveda dataset
* adding pseudo loveda dataset
* adding pseudo loveda dataset
* Update docs/dataset_prepare.md
Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>
* Update docs_zh-CN/dataset_prepare.md
Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>
* Update docs_zh-CN/dataset_prepare.md
Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>
* Delete unused lines of unittest and Add docs
* add convert .py file
* add downloading links from zenodo
* move place of LoveDA and Cityscapes in doc
* move place of LoveDA and Cityscapes in doc
Co-authored-by: MengzhangLI <mcmong@pku.edu.cn>
Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>
* [Fix] Fix the bug that vit cannot load pretrain properly when using init_cfg to specify the pretrain scheme
* [Fix] fix the coverage problem
* Update mmseg/models/backbones/vit.py
Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>
* [Fix] make the predicate more concise and clearer
* [Fix] Modified the judgement logic
* Update tests/test_models/test_backbones/test_vit.py
Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>
* add comments
Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>
* add TIMMBackbone and unittests
* add timm to tests requirements
* deprecate pt1.3.1
* reduce the unittests input of timm backbone
* fix ci
* fix ci
* fix ci
* fix ci
* fix ci
* fix ci
* fix ci
* fix ci
* fix ci
* remove unittests of large models of timm backbone
* generate coverage report for all unittests env
* reduce the unittests input of timm backbone
* reduce the unittests input of timm backbone
* fix single loss type
* fix error in ohem & point_head
* fix coverage miss
* fix uncoverage error of PointHead loss
* fix coverage miss
* fix uncoverage error of PointHead loss
* nn.modules.container.ModuleList to nn.ModuleList
* more simple format
* merge unittest def
MengzhangLI
Junjun2016
Rockey
irvingzhang0512
Han Zhang
Zhaolun Zou
KangBK0120
Kyungmin Lee
del-zhenwu
Ebby
Kingdrone
gszh
Yuan Haobo
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