* Update workflow to add Windows CI
* Add some optional requirements to requirements/optional.txt
* Update setup.py
* Update PyTorch version in Windows CI
* Update CI and requirement
* Replace `tempfile.NamedTemporaryFile` to avoid unit test error on
Windows
* Update tests
* Update CI
* Add OpenCV installation
* Update CI
* Support to use `indices` to specify which samples to evaluate.
* Add KFoldDataset wrapper
* Rename 'K' to 'num_splits' accroding to sklearn
* Add `kfold-cross-valid.py`
* Add unit tests
* Add help doc and docstring
* sampler bugfixes
* sampler bugfixes
* reorganize code
* minor fixes
* reorganize code
* minor fixes
* Use `mmcv.runner.get_dist_info` instead of `dist` package to get rank
and world size.
* Add `build_dataloader` unit tests and fix sampler's unit tests.
* Fix unit tests
* Fix unit tests
Co-authored-by: mzr1996 <mzr1996@163.com>
* fix dataset wrapper bug and add unit tests
* fix dataset wrapper bug and add unit tests
* update unit tests
* fix lint
* align interface to mmdet
* update unit tests
* refactor resize, test tobe done
* resize reimpl according to discussion; add pad
* minor fixes and add tests
* minor fixes on docstring
* add additional unit test
* reformat resize and pad
* revise code and docstr according to the comments
* add imagnet21k
* Update unit test
* update imaenet21k
* use slots
* use slots
* rename Data_item to ImageInfo
* add unit tests
* Update unit tests
* rm some print
* update unit tests
* fix lint
* remove default value of pipeline
* Add hparams argument in `AutoAugment` and `RandAugment`.
And `pad_val` supports sequence instead of tuple only.
* Add unit tests for `AutoAugment` and `hparams` in `RandAugment`.
* Use smaller test image to speed up uni tests.
* Use hparams to simplify RandAugment config in swin-transformer.
* Rename augment config name from `pipeline` to `pipelines`.
* Add some commnet ad docstring.
* Refactor unit tests folder structure.
* Remove label smooth and Vit test in `test_classifiers.py`
* Rename test_utils in dataset to test_dataset_utils
* Split test_models/test_utils/test_utils.py to multiple sub files.
* Add unit tests of classifiers and heads
* Use patch context manager.
* Add unit test of `is_tracing`, and add warning in `is_tracing` if torch
verison is smaller than 1.6.0
