OpenMixup Release V0.2.3

Highlight

• Support the online document of OpenMixup (built on Read the Docs).
• Provide README and update configs for self-supervised and supervised methods.
• Support new contrastive learning method (Barlow Twins) and Masked Image Modeling (MIM) methods (MAE, SimMIM, MaskFeat, CAE, A2MIM).
• Support new backbone networks (ConvMixer, DenseNet, MLPMixer, ResNeSt, PoolFormer, UniFormer, VAN).
• Support new Fine-tuing method (HCR).
• Support new mixup augmentation methods (SmoothMix, GridMix).
• Support more regression losses (Charbonnier loss, Focal Frequency loss, Focal L1/L2 loss, Balanced L1 loss, Balanced MSE loss).
• Support more regression metrics (regression errors and correlations) and the regression dataset.
• Support more reweight classification losses (Gradient Harmonized loss, Varifocal Focal Loss) from MMDetection.
• Model Zoos and lists of Awesome Mixups have been updated.

Bug Fixes

• Refactor code structures of openmixup.models.utils and support more network layers.
• Fix the bug of DropPath (using stochastic depth rule) in ResNet for RSB A1/A2 training settings.
• Fix bugs in self-supervised classification benchmarks (configs and implementations of VisionTransformer).
• Update INSTALL.md. We suggest you install PyTorch 1.8 or higher and mmcv-full for better usage of this repo. Since PyTorch 1.8 has bugs in AdamW optimizer, do not use PyTorch 1.8 to fine-tune ViT-based methods.
• Fix bugs in PreciseBNHook (update all BN stats) and RepeatSampler (set sync_random_seed) for RSB A1/A2.
• Fix bugs in regression metrics, MIM dataset, and benchmark configs. Notice that only l1_loss is supported by FP16 training, other regression losses (e.g., MSE and Smooth_L1 losses) will cause NAN when the target and prediction are not normalized in FP16 training.