Differences between MMSelfSup and OpenSelfSup

This file records differences between the newest version of MMSelfSup with older versions and OpenSelfSup.

MMSelfSup goes through a refactoring and addresses many legacy issues. It is not compatitible with OpenSelfSup, i.e. the old config files are supposed to be updated as some arguments of the class or names of the components have been modified.

The major differences are in two folds: codebase conventions, modular design.

Modular Design

In order to build more clear directory structure, MMSelfSup redesigns some of the modules.

Datasets

• MMSelfSup merges some datasets to reduce some redundant codes.

  • Classification, Extraction, NPID -> OneViewDataset

  • BYOL, Contrastive -> MultiViewDataset

• The data_sources folder has been refactored, thus the loading function is more robust.

In addition, this part is still under refactoring, it will be released in following version.

Models

• The registry mechanism is updated. Currently, the parts under the models folder are built with a parent called MMCV_MODELS that is imported from MMCV. Please check mmselfsup/models/builder.py and refer to mmcv/utils/registry.py for more details.

• The models folder includes algorithms, backbones, necks, heads, memories and some required utils. The algorithms integrates the other main components to build the self-supervised learning algorithms, which is like classifiers in MMCls or detectors in MMDet.

• In OpenSelfSup, the names of necks are kind of confused and all in one file. Now, the necks are refactored, managed with one folder and renamed for easier understanding. Please check mmselfsup/models/necks for more details.

Codebase Conventions

MMSelfSup renews codebase conventions as OpenSelfSup has not been updated for some time.

Configs

• MMSelfSup renames all config files to use new name convention. Please refer to 0_config for more details.

• In the configs, some arguments of the class or names of the components have been modified.

  • One algorithm name has been modified: MOCO -> MoCo

  • As all models' components inherit BaseModule from MMCV, the models are initialized with init_cfg. Please use it to set your initialization. Besides, init_weights can also be used.

  • Please use new neck names to compose your algorithms, check it before write your own configs.

  • The normalization layers are all built with arguments norm_cfg.

Scripts

• The directory of tools is modified, thus it has more clear structure. It has several folders to manage different scripts. For example, it has two converter folders for models and data format. Besides, the benchmark related scripts are all in benchmarks folder, which has the same structure as configs/benchmarks.

• The arguments in train.py has been updated. Two major modifications are listed below.

  • Add --cfg-options to modify the config from cmd arguments.

  • Remove --pretrained and use --cfg-options to set the pretrained models.