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v0.2.8 (25/05/2023)

Bump version to V0.2.8 with new features in MMPreTrain.

New Features

Bug Fixes

  • Fix the by_epoch setting in CustomSchedulerHook and update DecoupleMix in soft_mix_cross_entropy to support label smoothing settings.

v0.2.7 (16/12/2022)

Bump version to V0.2.7 with new features as #35. Update new features of OpenMixup v0.2.7 as issue #36.

Code Refactoring

  • Refactor openmixup.core (instead of openmixup.hooks) and openmixup.models.augments (contains mixup augmentation methods which are originally implemented in openmixup.models.utils). After code refactoring, the macro design of OpenMixup is similar to most projects of MMLab.

  • Support deployment of ONNX and TorchScript in openmixup.core.export and tools/deployment. We refactored the abstract class BaseModel (implemented in openmixup/models/classifiers/base_model.py) to support forward_inference (for custom inference and visualization). We also refactored openmixup.models.heads and openmixup.models.losses to support forward_inference. You can deploy the classification models in OpenMixup according to deployment tutorials.

  • Support testing API methods in openmixup/apis/test.py for evaluation and deployment of classification models.

  • Refactor openmixup.core.optimizers to separate optimizers and builders and support the latest Adan optimizer.

  • Refactor mixup_classification.py to support label mixup methods, add return_mask for mixup methods in augments and add return_attn in ViT backbone.

  • Refactor ValidateHook to support new features as EvalHook in mmcv, e.g., save_best="auto" during training.

  • Refactor ClsHead with BaseClsHead to support MLP classification head variants in modern network architectures.

New Features

  • Support detailed usage instructions in README of config files for image classification methods in configs/classification, e.g., mixups on ImageNet. READMEs of other methods in configs/selfsup and configs/semisup will also be updated.

  • Refine the origianzation of README files according to README-Template.

  • Support the new mixup augmentation method (AlignMix) and provide the relevant config files in various datasets.

  • Refine the setup for the local installation and PyPi release in setup.py and setup.cfg. View PyPi project of OpenMixup.

  • Support a new mixup method TransMix and provide config files in mixups/deit.

  • Update config files. Provide full config files of mixup methods based on ViT-T/S/B on ImageNet and update RSB A3 config files for popular backbones.

  • Update target_generators to support the latest MIM pre-training methods (fixed requirements).

  • Update config files and scripts for SSL downstream tasks benchmarks (classification, detection, and segmentation).

  • Update and fix bugs in visualization tools (vis_loss_landscape). Fix model converters tools.

  • Support Semantic-Softmax loss and ImageNet-21K-P (Winter) pre-training.

  • Support more backbone architectures, including BEiT, MetaFormer, ConvNeXtV2, VanillaNet, and CoC.

Update Documents

  • Update documents of mixup benchmarks on ImageNet in Model_Zoo_sup.md. Update config files for supported mixup methods.

  • Update formats (figures, introductions and content tables) of awesome lists in Awesome Mixups and Awesome MIM and provide the latest methods (updated to 18/03/2023).

  • Update api that describes the overall code structures in docs/en/api for the readthedocs page.

  • Reorganize and update tutorials for SSL downstream tasks benchmarks (classification, detection, and segmentation).

v0.2.6 (41/09/2022)

Bump version to V0.2.6 with new features as #20. Update new features and documents of OpenMixup v0.2.6 as issue #24, fix relevant issue #25, issue #26, issue #27, issue #31, and issue #33.

New Features

  • Support new backbone architectures (EdgeNeXt, EfficientFormer, HorNet, (MogaNet, MViT.V2, ShuffleNet.V1, DeiT-3), and provide relevant network modules in models/utils/layers. Config files and README.md are updated.

  • Support new self-supervised method BEiT with ViT-Base on ImageNet-1K, and fix bugs of CAE, MaskFeat, and SimMIM in Dataset, Model, and Head. Note that we added HOG feature implementation borrowed from the original repo for MaskFeat. Update pre-training and fine-tuning config files, and documents for the relevant masked image modeling (MIM) methods (BEiT, MaskFeat, CAE, and A2MIM). Support more fine-tuning setting on ImageNet for MIM pre-training based on various backbones (e.g., ViTs, ResNets, ConvNeXts).

  • Fix the updated arXiv.V2 version of VAN by adding architecture configurations.

  • Support ArcFace loss for metric learning and the relevant NormLinearClsHead. And support SeeSaw loss for long-tail classification tasks.

  • Update the issue template with more relevant links and emojis.

  • Support Grad-CAM visualization tools vis_cam.py of supported architectures.

Update Documents

  • Update our OpenMixup tech report on arXiv, which provides more technical details and benchmark results.

  • Update self-supervised learning Model_Zoo_selfsup.md. And update documents of the new backbone and self-supervised methods.

  • Update supervised learning Model_Zoo_sup.md as provided in AutoMix and support more mixup benchmark results.

  • Update the template and add the latest paper lists of mixup and MIM methods in Awesome Mixups and Awesome MIM. We provide teaser figures of most papers as illustrations.

  • Update documents of tools.

Bug Fixes

  • Fix raising error notification of torch.fft for PyTorch 1.6 or lower versions in backbones and heads.

  • Fix README.md (new icons, fixing typos) and support pytest in tests.

  • Fix the classification heads and update implementations and config files of AlexNet and InceptionV3.

v0.2.5 (21/07/2022)

Bump version to V0.2.5 with new features and updating documents as #10. Update features and fix bugs in V0.2.5 as #17. Update features and documents in V0.2.5 as #18 and #19.

New Features

Update Documents

  • Recognize README and README for various methods.

  • Update Awesome Mixups and Awesome MIM.

  • Update get_started.md and Tutorials for better usage of OpenMixup.

  • Update mixup benchmarks in model_zoos: providing configs, weights, and more details.

  • Update latest methods in Awesome Mixups and Awesome MIM.

  • Update README.md and fix auto_train_mixups.py for various datasets.

Bug Fixes

  • Fix visualization of the reconstruction results in MAE.

  • Fix the normalization bug in config files and plot_torch.py as mentioned in #16.

  • Fix the random seeds in tools/train.py as mentioned in #14.

v0.2.4 (07/07/2022)

Update new features and fix bugs as #7.

New Features

  • Support new backbone architectures (LITv2).

  • Refactor code structures weight initialization in various network modules (using BaseModule in mmcv).

  • Refactor code structures of openmixup.models.utils.layers to support more network structures.

Bug Fixes

  • Fix bugs that cause degenerate performances of pure Transformer backbones (DeiT and Swin) in OpenMixup. The main reason might be the old version of auto_fp16 and DistOptimizerHook implementations, since PyTorch=>1.6.0 has better support of fp16 training than mmcv.

  • Fix the bug of ViT fine-tuning for MIM methods (e.g., MAE, SimMIM). The original MIMVisionTransformer in openmixup.models.mim_vit has frozen all the backbone parameters during fine-tuning.

  • Fix the initialization of Transformer-based architectures (e.g., ViT, Swin) to reproduce the train-from-scratch performances.

  • Fix the weight initialization of Transformer-based architectures (e.g., ViT, Swin) to reproduce the train-from-scratch performance. Update weight initialization, parameter-wise weight decay, and fp16 settings in relevant config files.

v0.2.3 (17/06/2022)

Support new features as #6.

New Features

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.

v0.2.2 (24/05/2022)

Support new features and finish code refactoring as #5.

Highlight

Bug Fixes

  • 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. 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).

v0.2.1 (19/04/2022)

Support new features and finish code refactoring as #4.

New Features

  • Support masked image modeling (MIM) self-supervised methods (MAE, SimMIM, MaskFeat).

  • Support visualization of reconstruction results in MIM methods.

  • Support basic regression losses and metrics.

Bug Fixes

  • 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.

  • We suggest you install PyTorch 1.8 or higher (required by some self-supervised methods) and mmcv-full for better usage of this repo. Do not use PyTorch 1.8 to fine-tune ViT-based methods, and you can still use PyTorch 1.6 for supervised classification methods.

v0.2.0 (31/03/2022)

Support new features and finish code refactoring as #3.

New Features

  • Support various popular backbones (ConvNets and ViTs), various image datasets, popular mixup methods, and benchmarks for supervised learning. Config files are available.

  • Support popular self-supervised methods (e.g., BYOL, MoCo.V3, MAE) on both large-scale and small-scale datasets, and self-supervised benchmarks (merged from MMSelfSup). Config files are available.

  • Support analyzing tools for self-supervised learning (kNN/SVM/linear metrics and t-SNE/UMAP visualization).

  • Convenient usage of configs: fast configs generation by ‘auto_train.py’ and configs inheriting (MMCV).

  • Support mixed-precision training (NVIDIA Apex or MMCV Apex) for all methods.

  • Model Zoos and lists of Awesome Mixups have been released.

Bug Fixes

  • Done code refactoring follows MMSelfSup and MMClassification.

v0.1.3 (25/03/2022)

  • Refactor code structures for vision transformers and self-supervised methods (e.g., MoCo.V3 and MAE).

  • Provide online analysis of self-supervised methods (knn metric and t-SNE/UMAP visualization).

  • More results are provided in Model Zoos.

Bug Fixes

  • Fix bugs of reusing of configs, ViTs, visualization tools, etc. It requires rebuilding of OpenMixup (install mmcv-full).

v0.1.2 (20/03/2022)

New Features

  • Refactor code structures according to MMSelfsup to fit high version of mmcv and PyTorch.

  • Support self-supervised methods and optimizes config structures.

v0.1.1 (15/03/2022)

New Features

  • Support various popular backbones (ConvNets and ViTs) and update config files.

  • Support various handcrafted methods and optimization-based methods (e.g., PuzzleMix, AutoMix, SAMix, DecoupleMix, etc.). Config files generation of mixup methods are supported.

  • Provide supervised image classification benchmarks in model_zoo and results (on updating).

Bug Fixes

  • Fix bugs of new mixup methods (e.g., gco for Puzzlemix, etc.).

v0.1.0 (22/01/2022)

New Features

  • Support various popular backbones (popular ConvNets and ViTs).

  • Support mixed precision training (NVIDIA Apex or MMCV Apex).

  • Support supervised, self- & semi-supervised learning methods and benchmarks.

  • Support fast configs generation from a basic config file by auto_train.py.

Bug Fixes

  • Fix bugs of code refactoring (backbones, fp16 training, etc.).

OpenSelfSup (v0.3.0, 14/10/2020) Supported Features

This repo is originally built on OpenSelfSup (the old version of MMSelfSup) and borrows some implementations from MMClassification.

  • Mixed Precision Training (based on NVIDIA Apex for PyTorch 1.6).

  • Improvement of GaussianBlur doubles the training speed of MoCo V2, SimCLR, and BYOL.

  • More benchmarking results, including benchmarks on Places, VOC, COCO, and linear/semi-supervised benchmarks.

  • Fix bugs in moco v2 and BYOL so that the reported results are reproducible.

  • Provide benchmarking results and model download links.

  • Support updating the network every several iterations (accumulation).

  • Support LARS and LAMB optimizer with Nesterov (LAMB from MMClassification).

  • Support excluding specific parameter-wise settings from the optimizer updating.