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Changelog (MMPreTrain)

v1.1.0(12/10/2023)

New Features

  • [Feature] Implement of Zero-Shot CLIP Classifier (#1737)
  • [Feature] Add minigpt4 gradio demo and training script. (#1758)

Improvements

  • [Config] New Version of config Adapting MobileNet Algorithm (#1774)
  • [Config] Support DINO self-supervised learning in project (#1756)
  • [Config] New Version of config Adapting Swin Transformer Algorithm (#1780)
  • [Enhance] Add iTPN Supports for Non-three channel image (#1735)
  • [Docs] Update dataset download script from opendatalab to openXlab (#1765)
  • [Docs] Update COCO-Retrieval dataset docs. (#1806)

Bug Fix

  • Update train.py to compat with new config.
  • Update OFA module to compat with the latest huggingface.
  • Fix pipeline bug in ImageRetrievalInferencer.

v1.0.2(15/08/2023)

New Features

  • Add MFF (#1725)
  • Support training of BLIP2 (#1700)

Improvements

  • New Version of config Adapting MAE Algorithm (#1750)
  • New Version of config Adapting ConvNeXt Algorithm (#1760)
  • New version of config adapting BeitV2 Algorithm (#1755)
  • Update dataset_prepare.md (#1732)
  • New Version of config Adapting Vision Transformer Algorithm (#1727)
  • Support Infographic VQA dataset and ANLS metric. (#1667)
  • Support IconQA dataset. (#1670)
  • Fix typo MIMHIVIT to MAEHiViT (#1749)

v1.0.1(28/07/2023)

Improvements

  • Add init_cfg with type='pretrained' to downstream tasks (#1717
  • Set 'is_init' in some multimodal methods (#1718
  • Adapt test cases on Ascend NPU (#1728
  • Add GPU Acceleration Apple silicon mac (#1699
  • BEiT refactor (#1705

Bug Fixes

  • Fix dict update in minigpt4. (#1709
  • Fix nested predict for multi-task prediction (#1716
  • Fix the issue #1711 "GaussianBlur doesn't work" (#1722
  • Just to correct a typo of 'target' (#1655
  • Fix freeze without cls_token in vit (#1693
  • Fix RandomCrop bug (#1706

Docs Update

v1.0.0(04/07/2023)

Highlights

  • Support inference of more multi-modal algorithms, such as LLaVA, MiniGPT-4, Otter, etc.
  • Support around 10 multi-modal datasets!
  • Add iTPN, SparK self-supervised learning algorithms.
  • Provide examples of New Config and DeepSpeed/FSDP.

New Features

  • Transfer shape-bias tool from mmselfsup (#1658)
  • Download dataset by using MIM&OpenDataLab (#1630)
  • Support New Configs (#1639, #1647, #1665)
  • Support Flickr30k Retrieval dataset (#1625)
  • Support SparK (#1531)
  • Support LLaVA (#1652)
  • Support Otter (#1651)
  • Support MiniGPT-4 (#1642)
  • Add support for VizWiz dataset (#1636)
  • Add support for vsr dataset (#1634)
  • Add InternImage Classification project (#1569)
  • Support OCR-VQA dataset (#1621)
  • Support OK-VQA dataset (#1615)
  • Support TextVQA dataset (#1569)
  • Support iTPN and HiViT (#1584)
  • Add retrieval mAP metric (#1552)
  • Support NoCap dataset based on BLIP. (#1582)
  • Add GQA dataset (#1585)

Improvements

  • Update fsdp vit-huge and vit-large config (#1675)
  • Support deepspeed with flexible runner (#1673)
  • Update Otter and LLaVA docs and config. (#1653)
  • Add image_only param of ScienceQA (#1613)
  • Support to use "split" to specify training set/validation (#1535)

Bug Fixes

  • Refactor _prepare_pos_embed in ViT (#1656， #1679)
  • Freeze pre norm in vision transformer (#1672)
  • Fix bug loading IN1k dataset (#1641)
  • Fix sam bug (#1633)
  • Fixed circular import error for new transform (#1609)
  • Update torchvision transform wrapper (#1595)
  • Set default out_type in CAM visualization (#1586)

Docs Update

v1.0.0rc8(22/05/2023)

Highlights

  • Support multiple multi-modal algorithms and inferencers. You can explore these features by the gradio demo!
  • Add EVA-02, Dino-V2, ViT-SAM and GLIP backbones.
  • Register torchvision transforms into MMPretrain, you can now easily integrate torchvision's data augmentations in MMPretrain.

New Features

  • Support Chinese CLIP. (#1576)
  • Add ScienceQA Metrics (#1577)
  • Support multiple multi-modal algorithms and inferencers. (#1561)
  • add eva02 backbone (#1450)
  • Support dinov2 backbone (#1522)
  • Support some downstream classification datasets. (#1467)
  • Support GLIP (#1308)
  • Register torchvision transforms into mmpretrain (#1265)
  • Add ViT of SAM (#1476)

Improvements

  • [Refactor] Support to freeze channel reduction and add layer decay function (#1490)
  • [Refactor] Support resizing pos_embed while loading ckpt and format output (#1488)

Bug Fixes

  • Fix scienceqa (#1581)
  • Fix config of beit (#1528)
  • Incorrect stage freeze on RIFormer Model (#1573)
  • Fix ddp bugs caused by out_type. (#1570)
  • Fix multi-task-head loss potential bug (#1530)
  • Support bce loss without batch augmentations (#1525)
  • Fix clip generator init bug (#1518)
  • Fix the bug in binary cross entropy loss (#1499)

Docs Update

  • Update PoolFormer citation to CVPR version (#1505)
  • Refine Inference Doc (#1489)
  • Add doc for usage of confusion matrix (#1513)
  • Update MMagic link (#1517)
  • Fix example_project README (#1575)
  • Add NPU support page (#1481)
  • train cfg: Removed old description (#1473)
  • Fix typo in MultiLabelDataset docstring (#1483)

v1.0.0rc7(07/04/2023)

Highlights

  • Integrated Self-supervised learning algorithms from MMSelfSup, such as MAE, BEiT, etc.
  • Support RIFormer, a simple but effective vision backbone by removing token mixer.
  • Support LeViT, XCiT, ViG and ConvNeXt-V2 backbone.
  • Add t-SNE visualization.
  • Refactor dataset pipeline visualization.
  • Support confusion matrix calculation and plot.

New Features

  • Support RIFormer. (#1453)
  • Support XCiT Backbone. (#1305)
  • Support calculate confusion matrix and plot it. (#1287)
  • Support RetrieverRecall metric & Add ArcFace config (#1316)
  • Add ImageClassificationInferencer. (#1261)
  • Support InShop Dataset (Image Retrieval). (#1019)
  • Support LeViT backbone. (#1238)
  • Support VIG Backbone. (#1304)
  • Support ConvNeXt-V2 backbone. (#1294)

Improvements

  • Use PyTorch official scaled_dot_product_attention to accelerate MultiheadAttention. (#1434)
  • Add ln to vit avg_featmap output (#1447)
  • Update analysis tools and documentations. (#1359)
  • Unify the --out and --dump in tools/test.py. (#1307)
  • Enable to toggle whether Gem Pooling is trainable or not. (#1246)
  • Update registries of mmcls. (#1306)
  • Add metafile fill and validation tools. (#1297)
  • Remove useless EfficientnetV2 config files. (#1300)

Bug Fixes

  • Fix precise bn hook (#1466)
  • Fix retrieval multi gpu bug (#1319)
  • Fix error repvgg-deploy base config path. (#1357)
  • Fix bug in test tools. (#1309)

Docs Update

  • Translate some tools tutorials to Chinese. (#1321)
  • Add Chinese translation for runtime.md. (#1313)

Changelog (MMClassification)

v1.0.0rc5(30/12/2022)

Highlights

  • Support EVA, RevViT, EfficientnetV2, CLIP, TinyViT and MixMIM backbones.
  • Reproduce the training accuracy of ConvNeXt and RepVGG.
  • Support multi-task training and testing.
  • Support Test-time Augmentation.

New Features

  • [Feature] Add EfficientnetV2 Backbone. (#1253)
  • [Feature] Support TTA and add --tta in tools/test.py. (#1161)
  • [Feature] Support Multi-task. (#1229)
  • [Feature] Add clip backbone. (#1258)
  • [Feature] Add mixmim backbone with checkpoints. (#1224)
  • [Feature] Add TinyViT for dev-1.x. (#1042)
  • [Feature] Add some scripts for development. (#1257)
  • [Feature] Support EVA. (#1239)
  • [Feature] Implementation of RevViT. (#1127)

Improvements

  • [Reproduce] Reproduce RepVGG Training Accuracy. (#1264)
  • [Enhance] Support ConvNeXt More Weights. (#1240)
  • [Reproduce] Update ConvNeXt config files. (#1256)
  • [CI] Update CI to test PyTorch 1.13.0. (#1260)
  • [Project] Add ACCV workshop 1st Solution. (#1245)
  • [Project] Add Example project. (#1254)

Bug Fixes

  • [Fix] Fix imports in transforms. (#1255)
  • [Fix] Fix CAM visualization. (#1248)
  • [Fix] Fix the requirements and lazy register mmpretrain models. (#1275)

v1.0.0rc4(06/12/2022)

Highlights

  • Upgrade API to get pre-defined models of MMClassification. See #1236 for more details.
  • Refactor BEiT backbone and support v1/v2 inference. See #1144.

New Features

  • Support getting model from the name defined in the model-index file. (#1236)

Improvements

  • Support evaluate on both EMA and non-EMA models. (#1204)
  • Refactor BEiT backbone and support v1/v2 inference. (#1144)

Bug Fixes

  • Fix reparameterize_model.py doesn't save meta info. (#1221)
  • Fix dict update in BEiT. (#1234)

Docs Update

  • Update install tutorial. (#1223)
  • Update MobileNetv2 & MobileNetv3 readme. (#1222)
  • Add version selection in the banner. (#1217)

v1.0.0rc3(21/11/2022)

Highlights

  • Add Switch Recipe Hook, Now we can modify training pipeline, mixup and loss settings during training, see #1101.
  • Add TIMM and HuggingFace wrappers. Now you can train/use models in TIMM/HuggingFace directly, see #1102.
  • Support retrieval tasks, see #1055.
  • Reproduce mobileone training accuracy. See #1191

New Features

  • Add checkpoints from EfficientNets NoisyStudent & L2. (#1122)
  • Migrate CSRA head to 1.x. (#1177)
  • Support RepLKnet backbone. (#1129)
  • Add Switch Recipe Hook. (#1101)
  • Add adan optimizer. (#1180)
  • Support DaViT. (#1105)
  • Support Activation Checkpointing for ConvNeXt. (#1153)
  • Add TIMM and HuggingFace wrappers to build classifiers from them directly. (#1102)
  • Add reduction for neck (#978)
  • Support HorNet Backbone for dev1.x. (#1094)
  • Add arcface head. (#926)
  • Add Base Retriever and Image2Image Retriever for retrieval tasks. (#1055)
  • Support MobileViT backbone. (#1068)

Improvements

  • [Enhance] Enhance ArcFaceClsHead. (#1181)
  • [Refactor] Refactor to use new fileio API in MMEngine. (#1176)
  • [Enhance] Reproduce mobileone training accuracy. (#1191)
  • [Enhance] add deleting params info in swinv2. (#1142)
  • [Enhance] Add more mobilenetv3 pretrains. (#1154)
  • [Enhancement] RepVGG for YOLOX-PAI for dev-1.x. (#1126)
  • [Improve] Speed up data preprocessor. (#1064)

Bug Fixes

  • Fix the torchserve. (#1143)
  • Fix configs due to api refactor of num_classes. (#1184)
  • Update mmpretrain2torchserve. (#1189)
  • Fix for inference_model cannot get classes information in checkpoint. (#1093)

Docs Update

  • Add not-found page extension. (#1207)
  • update visualization doc. (#1160)
  • Support sort and search the Model Summary table. (#1100)
  • Improve the ResNet model page. (#1118)
  • update the readme of convnext. (#1156)
  • Fix the installation docs link in README. (#1164)
  • Improve ViT and MobileViT model pages. (#1155)
  • Improve Swin Doc and Add Tabs enxtation. (#1145)
  • Add MMEval projects link in README. (#1162)
  • Add runtime configuration docs. (#1128)
  • Add custom evaluation docs (#1130)
  • Add custom pipeline docs. (#1124)
  • Add MMYOLO projects link in MMCLS1.x. (#1117)

v1.0.0rc2(12/10/2022)

New Features

  • [Feature] Support DeiT3. (#1065)

Improvements

  • [Enhance] Update analyze_results.py for dev-1.x. (#1071)
  • [Enhance] Get scores from inference api. (#1070)

Bug Fixes

  • [Fix] Update requirements. (#1083)

Docs Update

  • [Docs] Add 1x docs schedule. (#1015)

v1.0.0rc1(30/9/2022)

New Features

  • Support MViT for MMCLS 1.x (#1023)
  • Add ViT huge architecture. (#1049)
  • Support EdgeNeXt for dev-1.x. (#1037)
  • Support Swin Transformer V2 for MMCLS 1.x. (#1029)
  • Add efficientformer Backbone for MMCls 1.x. (#1031)
  • Add MobileOne Backbone For MMCls 1.x. (#1030)
  • Support BEiT Transformer layer. (#919)

Improvements

  • [Refactor] Fix visualization tools. (#1045)
  • [Improve] Update benchmark scripts (#1028)
  • [Improve] Update tools to enable pin_memory and persistent_workers by default. (#1024)
  • [CI] Update circle-ci and github workflow. (#1018)

Bug Fixes

  • Fix verify dataset tool in 1.x. (#1062)
  • Fix loss_weight in LabelSmoothLoss. (#1058)
  • Fix the output position of Swin-Transformer. (#947)

Docs Update

  • Auto generate model summary table. (#1010)
  • Refactor new modules tutorial. (#998)

v1.0.0rc0(31/8/2022)

MMClassification 1.0.0rc0 is the first version of MMClassification 1.x, a part of the OpenMMLab 2.0 projects.

Built upon the new training engine, MMClassification 1.x unifies the interfaces of dataset, models, evaluation, and visualization.

And there are some BC-breaking changes. Please check the migration tutorial for more details.

v0.23.1(2/6/2022)

New Features

  • Dedicated MMClsWandbHook for MMClassification (Weights and Biases Integration) (#764)

Improvements

  • Use mdformat instead of markdownlint to format markdown. (#844)

Bug Fixes

  • Fix wrong --local_rank.

Docs Update

  • Update install tutorials. (#854)
  • Fix wrong link in README. (#835)

v0.23.0(1/5/2022)

New Features

  • Support DenseNet. (#750)
  • Support VAN. (#739)

Improvements

  • Support training on IPU and add fine-tuning configs of ViT. (#723)

Docs Update

  • New style API reference, and easier to use! Welcome view it. (#774)

v0.22.1(15/4/2022)

New Features

  • [Feature] Support resize relative position embedding in SwinTransformer. (#749)
  • [Feature] Add PoolFormer backbone and checkpoints. (#746)

Improvements

  • [Enhance] Improve CPE performance by reduce memory copy. (#762)
  • [Enhance] Add extra dataloader settings in configs. (#752)

v0.22.0(30/3/2022)

Highlights

  • Support a series of CSP Network, such as CSP-ResNet, CSP-ResNeXt and CSP-DarkNet.
  • A new CustomDataset class to help you build dataset of yourself!
  • Support ConvMixer, RepMLP and new dataset - CUB dataset.

New Features

  • [Feature] Add CSPNet and backbone and checkpoints (#735)
  • [Feature] Add CustomDataset. (#738)
  • [Feature] Add diff seeds to diff ranks. (#744)
  • [Feature] Support ConvMixer. (#716)
  • [Feature] Our dist_train & dist_test tools support distributed training on multiple machines. (#734)
  • [Feature] Add RepMLP backbone and checkpoints. (#709)
  • [Feature] Support CUB dataset. (#703)
  • [Feature] Support ResizeMix. (#676)

Improvements

  • [Enhance] Use --a-b instead of --a_b in arguments. (#754)
  • [Enhance] Add get_cat_ids and get_gt_labels to KFoldDataset. (#721)
  • [Enhance] Set torch seed in worker_init_fn. (#733)

Bug Fixes

  • [Fix] Fix the discontiguous output feature map of ConvNeXt. (#743)

Docs Update

  • [Docs] Add brief installation steps in README for copy&paste. (#755)
  • [Docs] fix logo url link from mmocr to mmpretrain. (#732)

v0.21.0(04/03/2022)

Highlights

  • Support ResNetV1c and Wide-ResNet, and provide pre-trained models.
  • Support dynamic input shape for ViT-based algorithms. Now our ViT, DeiT, Swin-Transformer and T2T-ViT support forwarding with any input shape.
  • Reproduce training results of DeiT. And our DeiT-T and DeiT-S have higher accuracy comparing with the official weights.

New Features

  • Add ResNetV1c. (#692)
  • Support Wide-ResNet. (#715)
  • Support gem pooling (#677)

Improvements

  • Reproduce training results of DeiT. (#711)
  • Add ConvNeXt pretrain models on ImageNet-1k. (#707)
  • Support dynamic input shape for ViT-based algorithms. (#706)
  • Add evaluate function for ConcatDataset. (#650)
  • Enhance vis-pipeline tool. (#604)
  • Return code 1 if scripts runs failed. (#694)
  • Use PyTorch official one_hot to implement convert_to_one_hot. (#696)
  • Add a new pre-commit-hook to automatically add a copyright. (#710)
  • Add deprecation message for deploy tools. (#697)
  • Upgrade isort pre-commit hooks. (#687)
  • Use --gpu-id instead of --gpu-ids in non-distributed multi-gpu training/testing. (#688)
  • Remove deprecation. (#633)

Bug Fixes

  • Fix Conformer forward with irregular input size. (#686)
  • Add dist.barrier to fix a bug in directory checking. (#666)

v0.20.1(07/02/2022)

Bug Fixes

  • Fix the MMCV dependency version.

v0.20.0(30/01/2022)

Highlights

  • Support K-fold cross-validation. The tutorial will be released later.
  • Support HRNet, ConvNeXt, Twins and EfficientNet.
  • Support model conversion from PyTorch to Core-ML by a tool.

New Features

  • Support K-fold cross-validation. (#563)
  • Support HRNet and add pre-trained models. (#660)
  • Support ConvNeXt and add pre-trained models. (#670)
  • Support Twins and add pre-trained models. (#642)
  • Support EfficientNet and add pre-trained models.(#649)
  • Support features_only option in TIMMBackbone. (#668)
  • Add conversion script from pytorch to Core-ML model. (#597)

Improvements

  • New-style CPU training and inference. (#674)
  • Add setup multi-processing both in train and test. (#671)
  • Rewrite channel split operation in ShufflenetV2. (#632)
  • Deprecate the support for "python setup.py test". (#646)
  • Support single-label, softmax, custom eps by asymmetric loss. (#609)
  • Save class names in best checkpoint created by evaluation hook. (#641)

Bug Fixes

  • Fix potential unexcepted behaviors if metric_options is not specified in multi-label evaluation. (#647)
  • Fix API changes in pytorch-grad-cam>=1.3.7. (#656)
  • Fix bug which breaks cal_train_time in analyze_logs.py. (#662)

Docs Update

  • Update README in configs according to OpenMMLab standard. (#672)
  • Update installation guide and README. (#624)

v0.19.0(31/12/2021)

Highlights

  • The feature extraction function has been enhanced. See #593 for more details.
  • Provide the high-acc ResNet-50 training settings from ResNet strikes back.
  • Reproduce the training accuracy of T2T-ViT & RegNetX, and provide self-training checkpoints.
  • Support DeiT & Conformer backbone and checkpoints.
  • Provide a CAM visualization tool based on pytorch-grad-cam, and detailed user guide!

New Features

  • Support Precise BN. (#401)
  • Add CAM visualization tool. (#577)
  • Repeated Aug and Sampler Registry. (#588)
  • Add DeiT backbone and checkpoints. (#576)
  • Support LAMB optimizer. (#591)
  • Implement the conformer backbone. (#494)
  • Add the frozen function for Swin Transformer model. (#574)
  • Support using checkpoint in Swin Transformer to save memory. (#557)

Improvements

  • [Reproduction] Reproduce RegNetX training accuracy. (#587)
  • [Reproduction] Reproduce training results of T2T-ViT. (#610)
  • [Enhance] Provide high-acc training settings of ResNet. (#572)
  • [Enhance] Set a random seed when the user does not set a seed. (#554)
  • [Enhance] Added NumClassCheckHook and unit tests. (#559)
  • [Enhance] Enhance feature extraction function. (#593)
  • [Enhance] Improve efficiency of precision, recall, f1_score and support. (#595)
  • [Enhance] Improve accuracy calculation performance. (#592)
  • [Refactor] Refactor analysis_log.py. (#529)
  • [Refactor] Use new API of matplotlib to handle blocking input in visualization. (#568)
  • [CI] Cancel previous runs that are not completed. (#583)
  • [CI] Skip build CI if only configs or docs modification. (#575)

Bug Fixes

  • Fix test sampler bug. (#611)
  • Try to create a symbolic link, otherwise copy. (#580)
  • Fix a bug for multiple output in swin transformer. (#571)

Docs Update

  • Update mmcv, torch, cuda version in Dockerfile and docs. (#594)
  • Add analysis&misc docs. (#525)
  • Fix docs build dependency. (#584)

v0.18.0(30/11/2021)

Highlights

  • Support MLP-Mixer backbone and provide pre-trained checkpoints.
  • Add a tool to visualize the learning rate curve of the training phase. Welcome to use with the tutorial!

New Features

  • Add MLP Mixer Backbone. (#528, #539)
  • Support positive weights in BCE. (#516)
  • Add a tool to visualize learning rate in each iterations. (#498)

Improvements

  • Use CircleCI to do unit tests. (#567)
  • Focal loss for single label tasks. (#548)
  • Remove useless import_modules_from_string. (#544)
  • Rename config files according to the config name standard. (#508)
  • Use reset_classifier to remove head of timm backbones. (#534)
  • Support passing arguments to loss from head. (#523)
  • Refactor Resize transform and add Pad transform. (#506)
  • Update mmcv dependency version. (#509)

Bug Fixes

  • Fix bug when using ClassBalancedDataset. (#555)
  • Fix a bug when using iter-based runner with 'val' workflow. (#542)
  • Fix interpolation method checking in Resize. (#547)
  • Fix a bug when load checkpoints in mulit-GPUs environment. (#527)
  • Fix an error on indexing scalar metrics in analyze_result.py. (#518)
  • Fix wrong condition judgment in analyze_logs.py and prevent empty curve. (#510)

Docs Update

  • Fix vit config and model broken links. (#564)
  • Add abstract and image for every paper. (#546)
  • Add mmflow and mim in banner and readme. (#543)
  • Add schedule and runtime tutorial docs. (#499)
  • Add the top-5 acc in ResNet-CIFAR README. (#531)
  • Fix TOC of visualization.md and add example images. (#513)
  • Use docs link of other projects and add MMCV docs. (#511)

v0.17.0(29/10/2021)

Highlights

  • Support Tokens-to-Token ViT backbone and Res2Net backbone. Welcome to use!
  • Support ImageNet21k dataset.
  • Add a pipeline visualization tool. Try it with the tutorials!

New Features

  • Add Tokens-to-Token ViT backbone and converted checkpoints. (#467)
  • Add Res2Net backbone and converted weights. (#465)
  • Support ImageNet21k dataset. (#461)
  • Support seesaw loss. (#500)
  • Add a pipeline visualization tool. (#406)
  • Add a tool to find broken files. (#482)
  • Add a tool to test TorchServe. (#468)

Improvements

  • Refator Vision Transformer. (#395)
  • Use context manager to reuse matplotlib figures. (#432)

Bug Fixes

  • Remove DistSamplerSeedHook if use IterBasedRunner. (#501)
  • Set the priority of EvalHook to "LOW" to avoid a bug when using IterBasedRunner. (#488)
  • Fix a wrong parameter of get_root_logger in apis/train.py. (#486)
  • Fix version check in dataset builder. (#474)

Docs Update

  • Add English Colab tutorials and update Chinese Colab tutorials. (#483, #497)
  • Add tutuorial for config files. (#487)
  • Add model-pages in Model Zoo. (#480)
  • Add code-spell pre-commit hook and fix a large mount of typos. (#470)

v0.16.0(30/9/2021)

Highlights

  • We have improved compatibility with downstream repositories like MMDetection and MMSegmentation. We will add some examples about how to use our backbones in MMDetection.
  • Add RepVGG backbone and checkpoints. Welcome to use it!
  • Add timm backbones wrapper, now you can simply use backbones of pytorch-image-models in MMClassification!

New Features

  • Add RepVGG backbone and checkpoints. (#414)
  • Add timm backbones wrapper. (#427)

Improvements

  • Fix TnT compatibility and verbose warning. (#436)
  • Support setting --out-items in tools/test.py. (#437)
  • Add datetime info and saving model using torch<1.6 format. (#439)
  • Improve downstream repositories compatibility. (#421)
  • Rename the option --options to --cfg-options in some tools. (#425)
  • Add PyTorch 1.9 and Python 3.9 build workflow, and remove some CI. (#422)

Bug Fixes

  • Fix format error in test.py when metric returns np.ndarray. (#441)
  • Fix publish_model bug if no parent of out_file. (#463)
  • Fix num_classes bug in pytorch2onnx.py. (#458)
  • Fix missing runtime requirement packaging. (#459)
  • Fix saving simplified model bug in ONNX export tool. (#438)

Docs Update

  • Update getting_started.md and install.md. And rewrite finetune.md. (#466)
  • Use PyTorch style docs theme. (#457)
  • Update metafile and Readme. (#435)
  • Add CITATION.cff. (#428)

v0.15.0(31/8/2021)

Highlights

  • Support hparams argument in AutoAugment and RandAugment to provide hyperparameters for sub-policies.
  • Support custom squeeze channels in SELayer.
  • Support classwise weight in losses.

New Features

  • Add hparams argument in AutoAugment and RandAugment and some other improvement. (#398)
  • Support classwise weight in losses. (#388)
  • Enhance SELayer to support custom squeeze channels. (#417)

Code Refactor

  • Better result visualization. (#419)
  • Use post_process function to handle pred result processing. (#390)
  • Update digit_version function. (#402)
  • Avoid albumentations to install both opencv and opencv-headless. (#397)
  • Avoid unnecessary listdir when building ImageNet. (#396)
  • Use dynamic mmcv download link in TorchServe dockerfile. (#387)

Docs Improvement

  • Add readme of some algorithms and update meta yml. (#418)
  • Add Copyright information. (#413)
  • Fix typo 'metirc'. (#411)
  • Update QQ group QR code. (#393)
  • Add PR template and modify issue template. (#380)

v0.14.0(4/8/2021)

Highlights

  • Add transformer-in-transformer backbone and pretrain checkpoints, refers to the paper.
  • Add Chinese colab tutorial.
  • Provide dockerfile to build mmpretrain dev docker image.

New Features

  • Add transformer in transformer backbone and pretrain checkpoints. (#339)
  • Support mim, welcome to use mim to manage your mmpretrain project. (#376)
  • Add Dockerfile. (#365)
  • Add ResNeSt configs. (#332)

Improvements

  • Use the presistent_works option if available, to accelerate training. (#349)
  • Add Chinese ipynb tutorial. (#306)
  • Refactor unit tests. (#321)
  • Support to test mmdet inference with mmpretrain backbone. (#343)
  • Use zero as default value of thrs in metrics. (#341)

Bug Fixes

  • Fix ImageNet dataset annotation file parse bug. (#370)
  • Fix docstring typo and init bug in ShuffleNetV1. (#374)
  • Use local ATTENTION registry to avoid conflict with other repositories. (#376)
  • Fix swin transformer config bug. (#355)
  • Fix patch_cfg argument bug in SwinTransformer. (#368)
  • Fix duplicate init_weights call in ViT init function. (#373)
  • Fix broken _base_ link in a resnet config. (#361)
  • Fix vgg-19 model link missing. (#363)

v0.13.0(3/7/2021)

  • Support Swin-Transformer backbone and add training configs for Swin-Transformer on ImageNet.

New Features

  • Support Swin-Transformer backbone and add training configs for Swin-Transformer on ImageNet. (#271)
  • Add pretained model of RegNetX. (#269)
  • Support adding custom hooks in config file. (#305)
  • Improve and add Chinese translation of CONTRIBUTING.md and all tools tutorials. (#320)
  • Dump config before training. (#282)
  • Add torchscript and torchserve deployment tools. (#279, #284)

Improvements

  • Improve test tools and add some new tools. (#322)
  • Correct MobilenetV3 backbone structure and add pretained models. (#291)
  • Refactor PatchEmbed and HybridEmbed as independent components. (#330)
  • Refactor mixup and cutmix as Augments to support more functions. (#278)
  • Refactor weights initialization method. (#270, #318, #319)
  • Refactor LabelSmoothLoss to support multiple calculation formulas. (#285)

Bug Fixes

  • Fix bug for CPU training. (#286)
  • Fix missing test data when num_imgs can not be evenly divided by num_gpus. (#299)
  • Fix build compatible with pytorch v1.3-1.5. (#301)
  • Fix magnitude_std bug in RandAugment. (#309)
  • Fix bug when samples_per_gpu is 1. (#311)

v0.12.0(3/6/2021)

  • Finish adding Chinese tutorials and build Chinese documentation on readthedocs.
  • Update ResNeXt checkpoints and ResNet checkpoints on CIFAR.

New Features

  • Improve and add Chinese translation of data_pipeline.md and new_modules.md. (#265)
  • Build Chinese translation on readthedocs. (#267)
  • Add an argument efficientnet_style to RandomResizedCrop and CenterCrop. (#268)

Improvements

  • Only allow directory operation when rank==0 when testing. (#258)
  • Fix typo in base_head. (#274)
  • Update ResNeXt checkpoints. (#283)

Bug Fixes

  • Add attribute data.test in MNIST configs. (#264)
  • Download CIFAR/MNIST dataset only on rank 0. (#273)
  • Fix MMCV version compatibility. (#276)
  • Fix CIFAR color channels bug and update checkpoints in model zoo. (#280)

v0.11.1(21/5/2021)

  • Refine new_dataset.md and add Chinese translation of finture.md, new_dataset.md.

New Features

  • Add dim argument for GlobalAveragePooling. (#236)
  • Add random noise to RandAugment magnitude. (#240)
  • Refine new_dataset.md and add Chinese translation of finture.md, new_dataset.md. (#243)

Improvements

  • Refactor arguments passing for Heads. (#239)
  • Allow more flexible magnitude_range in RandAugment. (#249)
  • Inherits MMCV registry so that in the future OpenMMLab repos like MMDet and MMSeg could directly use the backbones supported in MMCls. (#252)

Bug Fixes

  • Fix typo in analyze_results.py. (#237)
  • Fix typo in unittests. (#238)
  • Check if specified tmpdir exists when testing to avoid deleting existing data. (#242 & #258)
  • Add missing config files in MANIFEST.in. (#250 & #255)
  • Use temporary directory under shared directory to collect results to avoid unavailability of temporary directory for multi-node testing. (#251)

v0.11.0(1/5/2021)

  • Support cutmix trick.
  • Support random augmentation.
  • Add tools/deployment/test.py as a ONNX runtime test tool.
  • Support ViT backbone and add training configs for ViT on ImageNet.
  • Add Chinese README.md and some Chinese tutorials.

New Features

  • Support cutmix trick. (#198)
  • Add simplify option in pytorch2onnx.py. (#200)
  • Support random augmentation. (#201)
  • Add config and checkpoint for training ResNet on CIFAR-100. (#208)
  • Add tools/deployment/test.py as a ONNX runtime test tool. (#212)
  • Support ViT backbone and add training configs for ViT on ImageNet. (#214)
  • Add finetuning configs for ViT on ImageNet. (#217)
  • Add device option to support training on CPU. (#219)
  • Add Chinese README.md and some Chinese tutorials. (#221)
  • Add metafile.yml in configs to support interaction with paper with code(PWC) and MMCLI. (#225)
  • Upload configs and converted checkpoints for ViT fintuning on ImageNet. (#230)

Improvements

  • Fix LabelSmoothLoss so that label smoothing and mixup could be enabled at the same time. (#203)
  • Add cal_acc option in ClsHead. (#206)
  • Check CLASSES in checkpoint to avoid unexpected key error. (#207)
  • Check mmcv version when importing mmpretrain to ensure compatibility. (#209)
  • Update CONTRIBUTING.md to align with that in MMCV. (#210)
  • Change tags to html comments in configs README.md. (#226)
  • Clean codes in ViT backbone. (#227)
  • Reformat pytorch2onnx.md tutorial. (#229)
  • Update setup.py to support MMCLI. (#232)

Bug Fixes

  • Fix missing cutmix_prob in ViT configs. (#220)
  • Fix backend for resize in ResNeXt configs. (#222)

v0.10.0(1/4/2021)

  • Support AutoAugmentation
  • Add tutorials for installation and usage.

New Features

  • Add Rotate pipeline for data augmentation. (#167)
  • Add Invert pipeline for data augmentation. (#168)
  • Add Color pipeline for data augmentation. (#171)
  • Add Solarize and Posterize pipeline for data augmentation. (#172)
  • Support fp16 training. (#178)
  • Add tutorials for installation and basic usage of MMClassification.(#176)
  • Support AutoAugmentation, AutoContrast, Equalize, Contrast, Brightness and Sharpness pipelines for data augmentation. (#179)

Improvements

  • Support dynamic shape export to onnx. (#175)
  • Release training configs and update model zoo for fp16 (#184)
  • Use MMCV's EvalHook in MMClassification (#182)

Bug Fixes

  • Fix wrong naming in vgg config (#181)

v0.9.0(1/3/2021)

  • Implement mixup trick.
  • Add a new tool to create TensorRT engine from ONNX, run inference and verify outputs in Python.

New Features

  • Implement mixup and provide configs of training ResNet50 using mixup. (#160)
  • Add Shear pipeline for data augmentation. (#163)
  • Add Translate pipeline for data augmentation. (#165)
  • Add tools/onnx2tensorrt.py as a tool to create TensorRT engine from ONNX, run inference and verify outputs in Python. (#153)

Improvements

  • Add --eval-options in tools/test.py to support eval options override, matching the behavior of other open-mmlab projects. (#158)
  • Support showing and saving painted results in mmpretrain.apis.test and tools/test.py, matching the behavior of other open-mmlab projects. (#162)

Bug Fixes

  • Fix configs for VGG, replace checkpoints converted from other repos with the ones trained by ourselves and upload the missing logs in the model zoo. (#161)

v0.8.0(31/1/2021)

  • Support multi-label task.
  • Support more flexible metrics settings.
  • Fix bugs.

New Features

  • Add evaluation metrics: mAP, CP, CR, CF1, OP, OR, OF1 for multi-label task. (#123)
  • Add BCE loss for multi-label task. (#130)
  • Add focal loss for multi-label task. (#131)
  • Support PASCAL VOC 2007 dataset for multi-label task. (#134)
  • Add asymmetric loss for multi-label task. (#132)
  • Add analyze_results.py to select images for success/fail demonstration. (#142)
  • Support new metric that calculates the total number of occurrences of each label. (#143)
  • Support class-wise evaluation results. (#143)
  • Add thresholds in eval_metrics. (#146)
  • Add heads and a baseline config for multilabel task. (#145)

Improvements

  • Remove the models with 0 checkpoint and ignore the repeated papers when counting papers to gain more accurate model statistics. (#135)
  • Add tags in README.md. (#137)
  • Fix optional issues in docstring. (#138)
  • Update stat.py to classify papers. (#139)
  • Fix mismatched columns in README.md. (#150)
  • Fix test.py to support more evaluation metrics. (#155)

Bug Fixes

  • Fix bug in VGG weight_init. (#140)
  • Fix bug in 2 ResNet configs in which outdated heads were used. (#147)
  • Fix bug of misordered height and width in RandomCrop and RandomResizedCrop. (#151)
  • Fix missing meta_keys in Collect. (#149 & #152)

v0.7.0(31/12/2020)

  • Add more evaluation metrics.
  • Fix bugs.

New Features

  • Remove installation of MMCV from requirements. (#90)
  • Add 3 evaluation metrics: precision, recall and F-1 score. (#93)
  • Allow config override during testing and inference with --options. (#91 & #96)

Improvements

  • Use build_runner to make runners more flexible. (#54)
  • Support to get category ids in BaseDataset. (#72)
  • Allow CLASSES override during BaseDateset initialization. (#85)
  • Allow input image as ndarray during inference. (#87)
  • Optimize MNIST config. (#98)
  • Add config links in model zoo documentation. (#99)
  • Use functions from MMCV to collect environment. (#103)
  • Refactor config files so that they are now categorized by methods. (#116)
  • Add README in config directory. (#117)
  • Add model statistics. (#119)
  • Refactor documentation in consistency with other MM repositories. (#126)

Bug Fixes

  • Add missing CLASSES argument to dataset wrappers. (#66)
  • Fix slurm evaluation error during training. (#69)
  • Resolve error caused by shape in Accuracy. (#104)
  • Fix bug caused by extremely insufficient data in distributed sampler.(#108)
  • Fix bug in gpu_ids in distributed training. (#107)
  • Fix bug caused by extremely insufficient data in collect results during testing (#114)

v0.6.0(11/10/2020)

  • Support new method: ResNeSt and VGG.
  • Support new dataset: CIFAR10.
  • Provide new tools to do model inference, model conversion from pytorch to onnx.

New Features

  • Add model inference. (#16)
  • Add pytorch2onnx. (#20)
  • Add PIL backend for transform Resize. (#21)
  • Add ResNeSt. (#25)
  • Add VGG and its pretained models. (#27)
  • Add CIFAR10 configs and models. (#38)
  • Add albumentations transforms. (#45)
  • Visualize results on image demo. (#58)

Improvements

  • Replace urlretrieve with urlopen in dataset.utils. (#13)
  • Resize image according to its short edge. (#22)
  • Update ShuffleNet config. (#31)
  • Update pre-trained models for shufflenet_v2, shufflenet_v1, se-resnet50, se-resnet101. (#33)

Bug Fixes

  • Fix init_weights in shufflenet_v2.py. (#29)
  • Fix the parameter size in test_pipeline. (#30)
  • Fix the parameter in cosine lr schedule. (#32)
  • Fix the convert tools for mobilenet_v2. (#34)
  • Fix crash in CenterCrop transform when image is greyscale (#40)
  • Fix outdated configs. (#53)