* fix binary
* add ut
* fix ut
* restore metric computation
* remove metric ut update
* set out_channels by num_classes
* replace num_classes in encoder_decoder
* update props setting and fix ut
* update ut
* minor change
* update warning
* [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
* knet first commit
* fix import error in knet
* remove kernel update head from decoder head
* [Feature] Add kenerl updation for some decoder heads.
* [Feature] Add kenerl updation for some decoder heads.
* directly use forward_feature && modify other 3 decoder heads
* remover kernel_update attr
* delete unnecessary variables in forward function
* delete kernel update function
* delete kernel update function
* delete kernel_generate_head
* add unit test & comments in knet.py
* add copyright to fix lint error
* modify config names of knet
* rename swin-l 640
* upload models&logs and refactor knet_head.py
* modify docstrings and add some ut
* add url, modify docstring and add loss ut
* modify docstrings
* [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
* upload original backbone and configs
* ConvNext Refactor
* ConvNext Refactor
* convnext customization refactor with mmseg style
* convnext customization refactor with mmseg style
* add ade20k_640x640.py
* upload files for training
* delete dist_optimizer_hook and remove layer_decay_optimizer_constructor
* check max(out_indices) < num_stages
* add unittest
* fix lint error
* use MMClassification backbone
* fix bugs in base_1k
* add mmcls in requirements/mminstall.txt
* add mmcls in requirements/mminstall.txt
* fix drop_path_rate and layer_scale_init_value
* use logger.info instead of print
* add mmcls in runtime.txt
* fix f string && delete
* add doctring in LearningRateDecayOptimizerConstructor and fix mmcls version in requirements
* fix typo in LearningRateDecayOptimizerConstructor
* use ConvNext models in unit test for LearningRateDecayOptimizerConstructor
* add unit test
* fix typo
* fix typo
* add layer_wise and fix redundant backbone.downsample_norm in it
* fix unit test
* give a ground truth lr_scale and weight_decay
* upload models and readme
* delete 'backbone.stem_norm' and 'backbone.downsample_norm' in get_num_layer()
* fix unit test and use mmcls url
* update md2yml.py and metafile
* fix typo
* support iSAID aerial dataset
* Update and rename docs/dataset_prepare.md to 博士/dataset_prepare.md
* Update dataset_prepare.md
* fix typo
* fix typo
* fix typo
* remove imgviz
* fix wrong order in annotation name
* upload models&logs
* upload models&logs
* add load_annotations
* fix unittest coverage
* fix unittest coverage
* fix correct crop size in config
* fix iSAID unit test
* fix iSAID unit test
* fix typos
* fix wrong crop size in readme
* use smaller figure as test data
* add smaller dataset in test data
* add blank in docs
* use 0 bytes pseudo data
* add footnote and comments for crop size
* change iSAID to isaid and add default value in it
* change iSAID to isaid in _base_
Co-authored-by: MengzhangLI <mcmong@pku.edu.cn>
* assert original HardSwish when PyTorch > 1.6 in unit test
* assert original HardSwish when PyTorch > 1.6 in unit test
* assert original HardSwish when PyTorch > 1.6 in unit test
* assert original HardSwish when PyTorch > 1.6 in unit test
* assert original HardSwish when PyTorch > 1.6 in unit test
* assert original HardSwish when PyTorch > 1.6 in unit test
* Fix typo in usage example
* original mosaic code in mmdet
* Adjust mosaic to the semantic segmentation
* Remove bbox test in test_mosaic
* Add unittests
* Fix resize mode for seg_fields
* Fix repr error
* modify Mosaic docs
* modify from Mosaic to RandomMosaic
* Add docstring
* modify Mosaic docstring
* [Docs] Add a blank line before Returns:
* add blank lines
Co-authored-by: MeowZheng <meowzheng@outlook.com>
* [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
* 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
* [Fix] #916 expection string type classes
* add unittests for string path classes
* fix double quote string in test_dataset.py
* move the import to the top of the file
* fix isort lint error
fix isort lint error when move the import to the top of the file
Miao Zheng
谢昕辰
MengzhangLI
FreyWang
Aston.He
Yuan Liu
FangjianLin
Rockey
jiangyitong
Waterman0524
Hyeokjoon Kwon
rstrudel
Kyungmin Lee
Kingdrone
Lxinyang
Junjun2016
Shouping Shan
Jerry Jiarui XU