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Recent Releases of mmpretrain

mmpretrain - MMPreTrain Release v1.0.1

Fix some bugs and enhance the codebase.

What's Changed

  • [Fix] Fix Wrong-paramer Bug of RandomCrop by @Ezra-Yu in https://github.com/open-mmlab/mmpretrain/pull/1706
  • [Refactor] BEiT refactor by @fanqiNO1 in https://github.com/open-mmlab/mmpretrain/pull/1705
  • [Refactor] Fix spelling by @fanqiNO1 in https://github.com/open-mmlab/mmpretrain/pull/1689
  • [Fix] Freezing of cls_token in VisionTransformer by @fabien-merceron in https://github.com/open-mmlab/mmpretrain/pull/1693
  • [Fix] Typo fix of 'target' in vis_cam.py by @bryanbocao in https://github.com/open-mmlab/mmpretrain/pull/1655
  • [Feature] Support LoRA by @fanqiNO1 in https://github.com/open-mmlab/mmpretrain/pull/1687
  • [Fix] Fix the issue #1711 "GaussianBlur doesn't work" by @liyunlong10 in https://github.com/open-mmlab/mmpretrain/pull/1722
  • [Enhance] Add GPU Acceleration Apple silicon mac by @NripeshN in https://github.com/open-mmlab/mmpretrain/pull/1699
  • [Enhance] Adapt test cases on Ascend NPU. by @Ginray in https://github.com/open-mmlab/mmpretrain/pull/1728
  • [Enhance] Nested predict by @marouaneamz in https://github.com/open-mmlab/mmpretrain/pull/1716
  • [Enhance] Set 'is_init' in some multimodal methods by @fangyixiao18 in https://github.com/open-mmlab/mmpretrain/pull/1718
  • [Enhance] Add init_cfg with type='pretrained' to downstream tasks by @fangyixiao18 in https://github.com/open-mmlab/mmpretrain/pull/1717
  • [Fix] Fix dict update in minigpt4 by @fangyixiao18 in https://github.com/open-mmlab/mmpretrain/pull/1709
  • Bump version to 1.0.1 by @fangyixiao18 in https://github.com/open-mmlab/mmpretrain/pull/1731

New Contributors

  • @fabien-merceron made their first contribution in https://github.com/open-mmlab/mmpretrain/pull/1693
  • @bryanbocao made their first contribution in https://github.com/open-mmlab/mmpretrain/pull/1655
  • @liyunlong10 made their first contribution in https://github.com/open-mmlab/mmpretrain/pull/1722
  • @NripeshN made their first contribution in https://github.com/open-mmlab/mmpretrain/pull/1699

Full Changelog: https://github.com/open-mmlab/mmpretrain/compare/1.0.0...v1.0.1

- Python
Published by fangyixiao18 over 2 years ago

mmpretrain - MMPreTrain Release v1.0.0: Backbones, Self-Supervised Learning and Multi-Modalilty

MMPreTrain Release v1.0.0: Backbones, Self-Supervised Learning and Multi-Modalilty

Support more multi-modal algorithms and datasets

We are excited to announce that there are several advanced multi-modal methods suppported! We integrated huggingface/transformers with vision backbones in MMPreTrain to run inference and training(in developing).

| Methods | Datasets | |:---|:---| |BLIP (arxiv'2022) | COCO (caption, retrieval, vqa) | |BLIP-2 (arxiv'2023) | Flickr30k (caption, retrieval) | |OFA (CoRR'2022) | GQA | |Flamingo (NeurIPS'2022) | NLVR2 | |Chinese CLIP (arxiv'2022) | NoCaps | |MiniGPT-4 (arxiv'2023) | OCR VQA | |LLaVA (arxiv'2023) | Text VQA | |Otter (arxiv'2023) | VG VQA| || VisualGenomeQA | || VizWiz | || VSR |

Add iTPN, SparK self-supervised learning algorithms.

image image

Provide examples of New Config and DeepSpeed/FSDP

We test DeepSpeed and FSDP with MMEngine. The following are the memory and training time with ViT-large, ViT-huge and 8B multi-modal models, the left figure is the memory data, and the right figure is the training time data.

Test environment: 8*A100 (80G) PyTorch 2.0.0 image Remark: Both FSDP and DeepSpeed were tested with default configurations and not tuned, besides manually tuning the FSDP wrap policy can further reduce training time and memory usage.

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 _prepareposembed 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

New Contributors

  • @alexwangxiang made their first contribution in https://github.com/open-mmlab/mmpretrain/pull/1555
  • @InvincibleWyq made their first contribution in https://github.com/open-mmlab/mmpretrain/pull/1615
  • @yyk-wew made their first contribution in https://github.com/open-mmlab/mmpretrain/pull/1634
  • @fanqiNO1 made their first contribution in https://github.com/open-mmlab/mmpretrain/pull/1673
  • @Ben-Louis made their first contribution in https://github.com/open-mmlab/mmpretrain/pull/1679
  • @Lamply made their first contribution in https://github.com/open-mmlab/mmpretrain/pull/1671
  • @minato-ellie made their first contribution in https://github.com/open-mmlab/mmpretrain/pull/1644
  • @liweiwp made their first contribution in https://github.com/open-mmlab/mmpretrain/pull/1629

- Python
Published by fangyixiao18 over 2 years ago

mmpretrain - MMPreTrain Release v1.0.0rc8: Multi-Modality Support

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)

Contributors

A total of 12 developers contributed to this release.

@XiudingCai @Ezra-Yu @KeiChiTse @mzr1996 @bobo0810 @wangbo-zhao @yuweihao @fangyixiao18 @YuanLiuuuuuu @MGAMZ @okotaku @zzc98

- Python
Published by mzr1996 almost 3 years ago

mmpretrain - MMPreTrain Release v1.0.0rc7: Providing powerful backbones with various pre-training strategies

MMPreTrain v1.0.0rc7 Release Notes

  • Highlights
  • New Features
  • Improvements
  • Bug Fixes
  • Docs Update

Highlights

We are excited to announce that MMClassification and MMSelfSup have been merged into ONE codebase, named MMPreTrain, which has the following highlights: - Integrated Self-supervised learning algorithms from MMSelfSup, such as MAE, BEiT, etc. Users could find that in our directory mmpretrain/models, where a new folder selfsup was made, which support 18 recent self-supervised learning algorithms.

| Contrastive leanrning | Masked image modeling | | :----------------------------: | :----------------------------------: | | MoCo series | BEiT series | | SimCLR | MAE | | BYOL | SimMIM | | SwAV | MaskFeat | | DenseCL | CAE | | SimSiam | MILAN | | BarlowTwins | EVA | | DenseCL | MixMIM |

  • Support RIFormer, which is a way to keep a vision backbone effective while removing token mixers in its basic building blocks. Equipped with our proposed optimization strategy, we are able to build an extremely simple vision backbone with encouraging performance, while enjoying high efficiency during inference.

  • Support LeViT, XCiT, ViG, and ConvNeXt-V2 backbone, thus currently we support 68 backbones or algorithms and 472 checkpoints.

  • Add t-SNE visualization, users could visualize t-SNE to analyze the ability of your backbone. An example of visualization: left is from MoCoV2_ResNet50 and the right is from MAE_ViT-base:

  • Refactor dataset pipeline visualization, now we could also visualize the pipeline of mask image modeling, such as BEiT:

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)

Contributors

A total of 13 developers contributed to this release. Thanks to @techmonsterwang , @qingtian5 , @mzr1996 , @okotaku , @zzc98 , @aso538 , @szwlh-c , @fangyixiao18 , @yukkyo , @Ezra-Yu , @csatsurnh , @2546025323 , @GhaSiKey .

New Contributors

  • @csatsurnh made their first contribution in https://github.com/open-mmlab/mmpretrain/pull/1309
  • @szwlh-c made their first contribution in https://github.com/open-mmlab/mmpretrain/pull/1304
  • @aso538 made their first contribution in https://github.com/open-mmlab/mmpretrain/pull/1238
  • @GhaSiKey made their first contribution in https://github.com/open-mmlab/mmpretrain/pull/1313
  • @yukkyo made their first contribution in https://github.com/open-mmlab/mmpretrain/pull/1246
  • @2546025323 made their first contribution in https://github.com/open-mmlab/mmpretrain/pull/1321

Full Changelog: https://github.com/open-mmlab/mmpretrain/compare/v1.0.0rc5...v1.0.0rc7

- Python
Published by fangyixiao18 almost 3 years ago

mmpretrain - MMClassification Release V1.0.0rc5

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 mmcls models. (#1275)

Contributors

A total of 12 developers contributed to this release.

@marouaneamz @piercus @Ezra-Yu @mzr1996 @bobo0810 @suibe-qingtian @Scarecrow0 @tonysy @WINDSKY45 @wangbo-zhao @Francis777 @okotaku

- Python
Published by mzr1996 about 3 years ago

mmpretrain - MMClassification Release V0.25.0

Highlights

  • Support MLU backend.
  • Add dist_train_arm.sh for ARM device.

New Features

  • Support MLU backend. (#1159)
  • Support Activation Checkpointing for ConvNeXt. (#1152)

Improvements

  • Add dist_train_arm.sh for ARM device and update NPU results. (#1218)

Bug Fixes

  • Fix a bug caused MMClsWandbHook stuck. (#1242)
  • Fix the redundant device_ids in tools/test.py. (#1215)

Docs Update

  • Add version banner and version warning in master docs. (#1216)
  • Update NPU support doc. (#1198)
  • Fixed typo in pytorch2torchscript.md. (#1173)
  • Fix typo in miscellaneous.md. (#1137)
  • further detail for the doc for ClassBalancedDataset. (#901)

Contributors

A total of 7 developers contributed to this release.

@nijkah @xiaoyuan0203 @mzr1996 @Qiza-lyhm @ganghe74 @unseenme @wangjiangben-hw

- Python
Published by mzr1996 about 3 years ago

mmpretrain - MMClassification Release V1.0.0rc4

Highlights

  • New 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 models from the name defined in the model-index file. (#1236)

Improvements

  • Support evaluation 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)

Contributors

A total of 4 developers contributed to this release.

@techmonsterwang @mzr1996 @fangyixiao18 @kitecats

- Python
Published by mzr1996 about 3 years ago

mmpretrain - MMClassification Release V1.0.0rc3

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 mmcls2torchserve. (#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)

Contributors

A total of 14 developers contributed to this release.

@austinmw @Ezra-Yu @nijkah @yingfhu @techmonsterwang @mzr1996 @sanbuphy @tonysy @XingyuXie @gaoyang07 @kitecats @marouaneamz @okotaku @zzc98

- Python
Published by mzr1996 over 3 years ago

mmpretrain - MMClassification Release V0.24.1

New Features

  • [Feature] Support mmcls with NPU backend. (#1072)

Bug Fixes

  • [Fix] Fix performance issue in convnext DDP train. (#1098)

Contributors

A total of 3 developers contributed to this release.

@wangjiangben-hw @790475019 @mzr1996

- Python
Published by mzr1996 over 3 years ago

mmpretrain - MMClassification Release V1.0.0rc2

New Features

Improvements

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

Bug Fixes

  • Update requirements. (#1083)

Docs Update

  • Add 1x docs schedule. (#1015)

Contributors

A total of 3 developers contributed to this release.

@mzr1996 @okotaku @yingfhu

- Python
Published by mzr1996 over 3 years ago

mmpretrain - MMClassification Release V0.24.0

Highlights

  • Support HorNet, EfficientFormerm, SwinTransformer V2, and MViT backbones.
  • Support Standford Cars dataset.

New Features

  • Support HorNet Backbone. (#1013)
  • Support EfficientFormer. (#954)
  • Support Stanford Cars dataset. (#893)
  • Support CSRA head. (#881)
  • Support Swin Transform V2. (#799)
  • Support MViT and add checkpoints. (#924)

Improvements

  • [Improve] replace loop of progressbar in api/test. (#878)
  • [Enhance] RepVGG for YOLOX-PAI. (#1025)
  • [Enhancement] Update VAN. (#1017)
  • [Refactor] Re-write get_sinusoid_encoding from third-party implementation. (#965)
  • [Improve] Upgrade onnxsim to v0.4.0. (#915)
  • [Improve] Fixed typo in RepVGG. (#985)
  • [Imporve] Using train_step instead of forward in PreciseBNHook (#964)
  • [Improve] Use forward_dummy to calculate FLOPS. (#953)

Bug Fixes

  • Fix warning with torch.meshgrid. (#860)
  • Add matplotlib minimum version requirements. (#909)
  • val loader should not drop last by default. (#857)
  • Fix config.device bug in toturial. (#1059)
  • Fix attenstion clamp max params (#1034)
  • Fix device mismatch in Swin-v2. (#976)
  • Fix the output position of Swin-Transformer. (#947)

Docs Update

  • Add version for torchvision to avoid error. (#903)
  • Fix typo for --out-dir option of analyze_results.py. (#898)
  • Refine the docstring of RegNet. (#935)

Contributors

A total of 19 developers contributed to this release.

@a-mos @Ezra-Yu @Fei-Wang @nijkah @PeterH0323 @yingfhu @techmonsterwang @JiayuXu0 @jlim262 @hukkai @mzr1996 @liu-mengyang @twmht @pallgeuer @timothylimyl @daquexian @okotaku @tpoisonooo @zzc98

- Python
Published by mzr1996 over 3 years ago

mmpretrain - MMClassification Release V1.0.0rc1

Highlights

  • Support MViT, EdgeNeXt, Swin-Transformer V2, EfficientFormer and MobileOne.
  • Support BEiT type transformer layer.

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)
  • [Imporve] 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

  • Fix typo in migration document. (#1063)
  • Auto generate model summary table. (#1010)
  • Refactor new modules tutorial. (#998)

Contributors

A total of 8 developers contributed to this release.

@Ezra-Yu @yingfhu @mzr1996 @tonysy @fangyixiao18 @YuanLiuuuuuu @HIT-cwh @techmonsterwang

- Python
Published by mzr1996 over 3 years ago

mmpretrain - MMClassification Release V1.0.0rc0

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.

- Python
Published by mzr1996 over 3 years ago

mmpretrain - MMClassification Release V0.23.2

New Features

  • Support MPS device. (#894)

Improvements

  • Add test mim CI. (#879)

Bug Fixes

  • [Fix] Fix Albu crash bug. (#918)
  • [Fix] Add mim to extras_require in setup.py. (#872)

Contributors

A total of 2 developers contributed to this release.

@mzr1996 @PeterH0323

- Python
Published by mzr1996 over 3 years ago

mmpretrain - MMClassification Release V0.23.1

Highlights

  • New WandbHook to store your training log and visualize validation results!

New Features

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

Improvements

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

Bug Fixes

  • [Fix] Fix wrong --local_rank.

Docs Update

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

Contributors

A total of 3 developers contributed to this release.

@ayulockin @mzr1996 @timothylimyl

- Python
Published by mzr1996 over 3 years ago

mmpretrain - MMClassification Release V0.23.0

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)

Contributors

A total of 4 developers contributed to this release.

@mzr1996 @okotaku @yingfhu @HuDi2018

- Python
Published by mzr1996 almost 4 years ago

mmpretrain - MMClassification Release V0.22.1

New Features

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

Improvements

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

Contributors

A total of 4 developers contributed to this release.

@mzr1996 @yuweihao @XiaobingSuper @YuanLiuuuuuu

- Python
Published by mzr1996 almost 4 years ago

mmpretrain - MMClassification Release V0.22.0

Considering more and more codebase depends on new features of MMClassification, we will release a minor version at the middle of every month. 😉

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

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

Improvements

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

Bug Fixes

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

Docs Update

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

Contributors

A total of 6 developers contributed to this release.

@Ezra-Yu @yingfhu @Hydrion-Qlz @mzr1996 @huyu398 @okotaku

- Python
Published by mzr1996 almost 4 years ago

mmpretrain - MMClassification Release V0.21.0

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 supports 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)

Contributors

A total of 8 developers contributed to this release.

@Ezra-Yu @HumberMe @mzr1996 @twmht @RunningLeon @yasu0001 @okotaku @yingfhu

- Python
Published by mzr1996 almost 4 years ago

mmpretrain - MMClassification Release V0.20.1

Bug Fixes

  • Fix the MMCV dependency version.

- Python
Published by mzr1996 about 4 years ago

mmpretrain - MMClassification Release V0.20.0

Tomorrow is the Chinese new year. Happy new year!

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)

Contributors

A total of 10 developers contributed to this release.

@Ezra-Yu @mzr1996 @rlleshi @WINDSKY45 @shinya7y @Minyus @0x4f5da2 @imyhxy @dreamer121121 @xiefeifeihu

- Python
Published by mzr1996 about 4 years ago

mmpretrain - MMClassification Release V0.19.0

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] Imporve 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)

Contributors

A total of 6 developers contributed to this release.

@elopezz @Ezra-Yu @mzr1996 @0x4f5da2 @fangxu622 @okotaku

- Python
Published by mzr1996 about 4 years ago

mmpretrain - MMClassification Release V0.18.0

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)

Contributors

A total of 9 developers contributed to this release.

@Ezra-Yu @LeoXing1996 @mzr1996 @0x4f5da2 @huoshuai-dot @imyhxy @juanjompz @okotaku @xcnick

- Python
Published by mzr1996 about 4 years ago

mmpretrain - MMClassification Release V0.17.0

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)

Contributors

A total of 6 developers contributed to this release.

@mzr1996 @Ezra-Yu @tansor @youqingxiaozhua @0x4f5da2 @okotaku

- Python
Published by mzr1996 over 4 years ago

mmpretrain - MMClassification Release V0.16.0

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)

Contributors

A total of 8 developers contributed to this release. @Charlyo @Ezra-Yu @mzr1996 @amirassov @RangiLyu @zhaoxin111 @uniyushu @zhangrui-wolf

- Python
Published by mzr1996 over 4 years ago

mmpretrain - MMClassification Release V0.15.0

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)
  • Enhence 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)
  • Add PR template and modify issue template (#380)

Contributors

A total of 5 developers contributed to this release. @azad96 @Ezra-Yu @mzr1996 @mmeendez8 @sovrasov

- Python
Published by mzr1996 over 4 years ago

mmpretrain - MMClassification Release V0.14.0

Highlights

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

New Features

  • Add transformer in transformer backbone and pretrain checkpoints. (#339)
  • Support mim, welcome to use mim to manage your mmcls 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 mmcls 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)

Contributors

A total of 8 developers contributed to this release.

@Ezra-Yu, @HIT-cwh, @Junjun2016, @LXXXXR, @mzr1996, @pvys, @wangruohui, @ZwwWayne

- Python
Published by mzr1996 over 4 years ago

mmpretrain - MMClassification Release V0.13.0

New Features

  • Support Swin-Transformer backbone and add training configs for Swin-Transformer on ImageNet. (#271)
  • Add pretrained model of RegNetX. (#269)
  • Support adding custom hooks in the 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 funtions. (#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)

- Python
Published by mzr1996 over 4 years ago

mmpretrain - MMClassification Release V0.12.0

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)

- Python
Published by ycxioooong over 4 years ago

mmpretrain - MMClassification Release V0.11.1

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)

- Python
Published by ycxioooong almost 5 years ago

mmpretrain - MMClassification Release V0.11.0

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 mmcls 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)

- Python
Published by ycxioooong almost 5 years ago

mmpretrain - MMClassification Release V0.10.0

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)

- Python
Published by ycxioooong almost 5 years ago

mmpretrain - MMClassification Release V0.9.0

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 mmcls.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)

- Python
Published by ycxioooong almost 5 years ago

mmpretrain - MMClassification Release V0.8.0

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)

- Python
Published by ycxioooong about 5 years ago

mmpretrain - MMClassification Release V0.7.0

New Features

  • Add evaluation metrics: precision, recall, and F1 score. (#93)
  • Allow config override during testing and inference with --options. (#91 & #96)

Improvements

  • Remove installation of MMCV from requirements. (#90)
  • 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 numpy.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 structures. (#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)

- Python
Published by ycxioooong about 5 years ago

mmpretrain - MMClassification Release V0.6.0

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 shufflenetv2, shufflenetv1, se-resnet50, se-resnet101. (#33)

Bug Fixes

  • Fix initweights in ``shufflenetv2.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)

- Python
Published by ycxioooong over 5 years ago