* [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
linfangjian.vendor
zhengmiao
limengzhang.vendor
MeowZheng
Miao Zheng
Yuan Liu
FangjianLin
Rockey
jiangyitong
MengzhangLI
Waterman0524
Hyeokjoon Kwon
rstrudel
Kyungmin Lee
Kingdrone
Lxinyang
Junjun2016