Changelog v2.x¶
v2.25.0 (31/5/2022)¶
Highlights¶
Support dedicated
WandbLogger
hookSupport ConvNeXt, DDOD, SOLOv2
Support Mask2Former for instance segmentation
Rename config files of Mask2Former
Backwards incompatible changes¶
Rename config files of Mask2Former (#7571)
before v2.25.0 after v2.25.0 mask2former_xxx_coco.py
represents config files for panoptic segmentation.
mask2former_xxx_coco.py
represents config files for instance segmentation.mask2former_xxx_coco-panoptic.py
represents config files for panoptic segmentation.
New Features¶
Support ConvNeXt (#7281)
Support DDOD (#7279)
Support SOLOv2 (#7441)
Support Mask2Former for instance segmentation (#7571, #8032)
Bug Fixes¶
Enable YOLOX training on different devices (#7912)
Fix the log plot error when evaluation with
interval != 1
(#7784)Fix RuntimeError of HTC (#8083)
Improvements¶
Support dedicated
WandbLogger
hook (#7459)Users can set
cfg.log_config.hooks = [ dict(type='MMDetWandbHook', init_kwargs={'project': 'MMDetection-tutorial'}, interval=10, log_checkpoint=True, log_checkpoint_metadata=True, num_eval_images=10)]
in the config to use
MMDetWandbHook
. Example can be found in this colab tutorialAdd
AvoidOOM
to avoid OOM (#7434, #8091)Try to use
AvoidCUDAOOM
to avoid GPU out of memory. It will first retry after callingtorch.cuda.empty_cache()
. If it still fails, it will then retry by converting the type of inputs to FP16 format. If it still fails, it will try to copy inputs from GPUs to CPUs to continue computing. Try AvoidOOM in code to make the code continue to run when GPU memory runs out:from mmdet.utils import AvoidCUDAOOM output = AvoidCUDAOOM.retry_if_cuda_oom(some_function)(input1, input2)
Users can also try
AvoidCUDAOOM
as a decorator to make the code continue to run when GPU memory runs out:from mmdet.utils import AvoidCUDAOOM @AvoidCUDAOOM.retry_if_cuda_oom def function(*args, **kwargs): ... return xxx
Support reading
gpu_collect
fromcfg.evaluation.gpu_collect
(#7672)Speedup the Video Inference by Accelerating data-loading Stage (#7832)
Support replacing the
${key}
with the value ofcfg.key
(#7492)Accelerate result analysis in
analyze_result.py
. The evaluation time is speedup by 10 ~ 15 times and only tasks 10 ~ 15 minutes now. (#7891)Support to set
block_dilations
inDilatedEncoder
(#7812)Support panoptic segmentation result analysis (#7922)
Release DyHead with Swin-Large backbone (#7733)
Documentations updating and adding
Fix wrong default type of
act_cfg
inSwinTransformer
(#7794)Fix text errors in the tutorials (#7959)
Rewrite the installation guide (#7897)
Useful hooks (#7810)
Fix heading anchor in documentation (#8006)
Replace
markdownlint
withmdformat
for avoiding installing ruby (#8009)
Contributors¶
A total of 20 developers contributed to this release.
Thanks @ZwwWayne, @DarthThomas, @solyaH, @LutingWang, @chenxinfeng4, @Czm369, @Chenastron, @chhluo, @austinmw, @Shanyaliux @hellock, @Y-M-Y, @jbwang1997, @hhaAndroid, @Irvingao, @zhanggefan, @BIGWangYuDong, @Keiku, @PeterVennerstrom, @ayulockin
v2.24.0 (26/4/2022)¶
Highlights¶
Support Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
Support automatically scaling LR according to GPU number and samples per GPU
Support Class Aware Sampler that improves performance on OpenImages Dataset
New Features¶
Support Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation, see example configs (#7501)
Support Class Aware Sampler, users can set
data=dict(train_dataloader=dict(class_aware_sampler=dict(num_sample_class=1))))
in the config to use
ClassAwareSampler
. Examples can be found in the configs of OpenImages Dataset. (#7436)Support automatically scaling LR according to GPU number and samples per GPU. (#7482) In each config, there is a corresponding config of auto-scaling LR as below,
auto_scale_lr = dict(enable=True, base_batch_size=N)
where
N
is the batch size used for the current learning rate in the config (also equals tosamples_per_gpu
* gpu number to train this config). By default, we setenable=False
so that the original usages will not be affected. Users can setenable=True
in each config or add--auto-scale-lr
after the command line to enable this feature and should check the correctness ofbase_batch_size
in customized configs.Support setting dataloader arguments in config and add functions to handle config compatibility. (#7668) The comparison between the old and new usages is as below.
v2.23.0 v2.24.0 data = dict( samples_per_gpu=64, workers_per_gpu=4, train=dict(type='xxx', ...), val=dict(type='xxx', samples_per_gpu=4, ...), test=dict(type='xxx', ...), )
# A recommended config that is clear data = dict( train=dict(type='xxx', ...), val=dict(type='xxx', ...), test=dict(type='xxx', ...), # Use different batch size during inference. train_dataloader=dict(samples_per_gpu=64, workers_per_gpu=4), val_dataloader=dict(samples_per_gpu=8, workers_per_gpu=2), test_dataloader=dict(samples_per_gpu=8, workers_per_gpu=2), ) # Old style still works but allows to set more arguments about data loaders data = dict( samples_per_gpu=64, # only works for train_dataloader workers_per_gpu=4, # only works for train_dataloader train=dict(type='xxx', ...), val=dict(type='xxx', ...), test=dict(type='xxx', ...), # Use different batch size during inference. val_dataloader=dict(samples_per_gpu=8, workers_per_gpu=2), test_dataloader=dict(samples_per_gpu=8, workers_per_gpu=2), )
Support memory profile hook. Users can use it to monitor the memory usages during training as below (#7560)
custom_hooks = [ dict(type='MemoryProfilerHook', interval=50) ]
Support to run on PyTorch with MLU chip (#7578)
Support re-spliting data batch with tag (#7641)
Support the
DiceCost
used by K-Net inMaskHungarianAssigner
(#7716)Support splitting COCO data for Semi-supervised object detection (#7431)
Support Pathlib for Config.fromfile (#7685)
Support to use file client in OpenImages dataset (#7433)
Add a probability parameter to Mosaic transformation (#7371)
Support specifying interpolation mode in
Resize
pipeline (#7585)
Bug Fixes¶
Avoid invalid bbox after deform_sampling (#7567)
Fix the issue that argument color_theme does not take effect when exporting confusion matrix (#7701)
Fix the
end_level
in Necks, which should be the index of the end input backbone level (#7502)Fix the bug that
mix_results
may be None inMultiImageMixDataset
(#7530)Fix the bug in ResNet plugin when two plugins are used (#7797)
Improvements¶
Enhance
load_json_logs
of analyze_logs.py for resumed training logs (#7732)Add argument
out_file
in image_demo.py (#7676)Allow mixed precision training with
SimOTAAssigner
(#7516)Updated INF to 100000.0 to be the same as that in the official YOLOX (#7778)
Add documentations of:
how to get channels of a new backbone (#7642)
how to unfreeze the backbone network (#7570)
how to train fast_rcnn model (#7549)
proposals in Deformable DETR (#7690)
from-scratch install script in get_started.md (#7575)
Release pre-trained models of
Mask2Former (#7595, #7709)
RetinaNet with ResNet-18 and release models (#7387)
RetinaNet with EfficientNet backbone (#7646)
Contributors¶
A total of 27 developers contributed to this release. Thanks @jovialio, @zhangsanfeng2022, @HarryZJ, @jamiechoi1995, @nestiank, @PeterH0323, @RangeKing, @Y-M-Y, @mattcasey02, @weiji14, @Yulv-git, @xiefeifeihu, @FANG-MING, @meng976537406, @nijkah, @sudz123, @CCODING04, @SheffieldCao, @Czm369, @BIGWangYuDong, @zytx121, @jbwang1997, @chhluo, @jshilong, @RangiLyu, @hhaAndroid, @ZwwWayne
v2.23.0 (28/3/2022)¶
Highlights¶
Support Mask2Former: Masked-attention Mask Transformer for Universal Image Segmentation
Support EfficientNet: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Support setting data root through environment variable
MMDET_DATASETS
, users don’t have to modify the corresponding path in config files anymore.Find a good recipe for fine-tuning high precision ResNet backbone pre-trained by Torchvision.
New Features¶
Support Mask2Former(#6938)(#7466)(#7471)
Support EfficientNet (#7514)
Support setting data root through environment variable
MMDET_DATASETS
, users don’t have to modify the corresponding path in config files anymore. (#7386)Support setting different seeds to different ranks (#7432)
Update the
dist_train.sh
so that the script can be used to support launching multi-node training on machines without slurm (#7415)Find a good recipe for fine-tuning high precision ResNet backbone pre-trained by Torchvision (#7489)
Bug Fixes¶
Fix bug in VOC unit test which removes the data directory (#7270)
Adjust the order of
get_classes
andFileClient
(#7276)Force the inputs of
get_bboxes
in yolox_head to float32 (#7324)Fix misplaced arguments in LoadPanopticAnnotations (#7388)
Fix reduction=mean in CELoss. (#7449)
Update unit test of CrossEntropyCost (#7537)
Fix memory leaking in panpotic segmentation evaluation (#7538)
Fix the bug of shape broadcast in YOLOv3 (#7551)
Improvements¶
Add Chinese version of onnx2tensorrt.md (#7219)
Update colab tutorials (#7310)
Update information about Localization Distillation (#7350)
Add Chinese version of
finetune.md
(#7178)Update YOLOX log for non square input (#7235)
Add
nproc
incoco_panoptic.py
for panoptic quality computing (#7315)Allow to set channel_order in LoadImageFromFile (#7258)
Take point sample related functions out of mask_point_head (#7353)
Add instance evaluation for coco_panoptic (#7313)
Enhance the robustness of analyze_logs.py (#7407)
Supplementary notes of sync_random_seed (#7440)
Update docstring of cross entropy loss (#7472)
Update pascal voc result (#7503)
We create How-to documentation to record any questions about How to xxx. In this version, we added
How to use Mosaic augmentation (#7507)
How to use backbone in mmcls (#7438)
How to produce and submit the prediction results of panoptic segmentation models on COCO test-dev set (#7430))
Contributors¶
A total of 27 developers contributed to this release. Thanks @ZwwWayne, @haofanwang, @shinya7y, @chhluo, @yangrisheng, @triple-Mu, @jbwang1997, @HikariTJU, @imflash217, @274869388, @zytx121, @matrixgame2018, @jamiechoi1995, @BIGWangYuDong, @JingweiZhang12, @Xiangxu-0103, @hhaAndroid, @jshilong, @osbm, @ceroytres, @bunge-bedstraw-herb, @Youth-Got, @daavoo, @jiangyitong, @RangiLyu, @CCODING04, @yarkable
v2.22.0 (24/2/2022)¶
Highlights¶
Support MaskFormer: Per-Pixel Classification is Not All You Need for Semantic Segmentation (#7212)
Support DyHead: Dynamic Head: Unifying Object Detection Heads with Attentions (#6823)
Release a good recipe of using ResNet in object detectors pre-trained by ResNet Strikes Back, which consistently brings about 3~4 mAP improvements over RetinaNet, Faster/Mask/Cascade Mask R-CNN (#7001)
Support Open Images Dataset (#6331)
Support TIMM backbone: PyTorch Image Models (#7020)
New Features¶
Support MaskFormer (#7212)
Support DyHead (#6823)
Support ResNet Strikes Back (#7001)
Support OpenImages Dataset (#6331)
Support TIMM backbone (#7020)
Support visualization for Panoptic Segmentation (#7041)
Breaking Changes¶
In order to support the visualization for Panoptic Segmentation, the num_classes
can not be None
when using the get_palette
function to determine whether to use the panoptic palette.
Bug Fixes¶
Fix bug for the best checkpoints can not be saved when the
key_score
is None (#7101)Fix MixUp transform filter boxes failing case (#7080)
Add missing properties in SABLHead (#7091)
Fix bug when NaNs exist in confusion matrix (#7147)
Fix PALETTE AttributeError in downstream task (#7230)
Improvements¶
Speed up SimOTA matching (#7098)
Add Chinese translation of
docs_zh-CN/tutorials/init_cfg.md
(#7188)
Contributors¶
A total of 20 developers contributed to this release. Thanks @ZwwWayne, @hhaAndroid, @RangiLyu, @AronLin, @BIGWangYuDong, @jbwang1997, @zytx121, @chhluo, @shinya7y, @LuooChen, @dvansa, @siatwangmin, @del-zhenwu, @vikashranjan26, @haofanwang, @jamiechoi1995, @HJoonKwon, @yarkable, @zhijian-liu, @RangeKing
v2.21.0 (8/2/2022)¶
Breaking Changes¶
To standardize the contents in config READMEs and meta files of OpenMMLab projects, the READMEs and meta files in each config directory have been significantly changed. The template will be released in the future, for now, you can refer to the examples of README for algorithm, dataset and backbone. To align with the standard, the configs in dcn are put into to two directories named dcn
and dcnv2
.
New Features¶
Allow to customize colors of different classes during visualization (#6716)
Support CPU training (#7016)
Add download script of COCO, LVIS, and VOC dataset (#7015)
Bug Fixes¶
Fix weight conversion issue of RetinaNet with Swin-S (#6973)
Update
__repr__
ofCompose
(#6951)Fix BadZipFile Error when build docker (#6966)
Fix bug in non-distributed multi-gpu training/testing (#7019)
Fix bbox clamp in PyTorch 1.10 (#7074)
Relax the requirement of PALETTE in dataset wrappers (#7085)
Keep the same weights before reassign in the PAA head (#7032)
Update code demo in doc (#7092)
Improvements¶
Speed-up training by allow to set variables of multi-processing (#6974, #7036)
Add links of Chinese tutorials in readme (#6897)
Disable cv2 multiprocessing by default for acceleration (#6867)
Deprecate the support for “python setup.py test” (#6998)
Re-organize metafiles and config readmes (#7051)
Fix None grad problem during training TOOD by adding
SigmoidGeometricMean
(#7090)
Contributors¶
A total of 26 developers contributed to this release. Thanks @del-zhenwu, @zimoqingfeng, @srishilesh, @imyhxy, @jenhaoyang, @jliu-ac, @kimnamu, @ShengliLiu, @garvan2021, @ciusji, @DIYer22, @kimnamu, @q3394101, @zhouzaida, @gaotongxiao, @topsy404, @AntoAndGar, @jbwang1997, @nijkah, @ZwwWayne, @Czm369, @jshilong, @RangiLyu, @BIGWangYuDong, @hhaAndroid, @AronLin
v2.20.0 (27/12/2021)¶
New Features¶
Support TOOD: Task-aligned One-stage Object Detection (ICCV 2021 Oral) (#6746)
Support resuming from the latest checkpoint automatically (#6727)
Bug Fixes¶
Fix wrong bbox
loss_weight
of the PAA head (#6744)Fix the padding value of
gt_semantic_seg
in batch collating (#6837)Fix test error of lvis when using
classwise
(#6845)Avoid BC-breaking of
get_local_path
(#6719)Fix bug in
sync_norm_hook
when the BN layer does not exist (#6852)Use pycocotools directly no matter what platform it is (#6838)
Improvements¶
Add unit test for SimOTA with no valid bbox (#6770)
Use precommit to check readme (#6802)
Support selecting GPU-ids in non-distributed testing time (#6781)
Contributors¶
A total of 16 developers contributed to this release. Thanks @ZwwWayne, @Czm369, @jshilong, @RangiLyu, @BIGWangYuDong, @hhaAndroid, @jamiechoi1995, @AronLin, @Keiku, @gkagkos, @fcakyon, @www516717402, @vansin, @zactodd, @kimnamu, @jenhaoyang
v2.19.1 (14/12/2021)¶
New Features¶
Release YOLOX COCO pretrained models (#6698)
Bug Fixes¶
Fix DCN initialization in DenseHead (#6625)
Fix initialization of ConvFCHead (#6624)
Fix PseudoSampler in RCNN (#6622)
Fix weight initialization in Swin and PVT (#6663)
Fix dtype bug in BaseDenseHead (#6767)
Fix SimOTA with no valid bbox (#6733)
Improvements¶
Add an example of combining swin and one-stage models (#6621)
Add
get_ann_info
to dataset_wrappers (#6526)Support keeping image ratio in the multi-scale training of YOLOX (#6732)
Support
bbox_clip_border
for the augmentations of YOLOX (#6730)
Documents¶
Update metafile (#6717)
Add mmhuman3d in readme (#6699)
Update FAQ docs (#6587)
Add doc for
detect_anomalous_params
(#6697)
Contributors¶
A total of 11 developers contributed to this release. Thanks @ZwwWayne, @LJoson, @Czm369, @jshilong, @ZCMax, @RangiLyu, @BIGWangYuDong, @hhaAndroid, @zhaoxin111, @GT9505, @shinya7y
v2.19.0 (29/11/2021)¶
Highlights¶
Support Label Assignment Distillation
Support
persistent_workers
for Pytorch >= 1.7Align accuracy to the updated official YOLOX
New Features¶
Support Label Assignment Distillation (#6342)
Support
persistent_workers
for Pytorch >= 1.7 (#6435)
Bug Fixes¶
Fix repeatedly output warning message (#6584)
Avoid infinite GPU waiting in dist training (#6501)
Fix SSD512 config error (#6574)
Fix MMDetection model to ONNX command (#6558)
Improvements¶
Refactor configs of FP16 models (#6592)
Align accuracy to the updated official YOLOX (#6443)
Speed up training and reduce memory cost when using PhotoMetricDistortion. (#6442)
Make OHEM work with seesaw loss (#6514)
Documents¶
Update README.md (#6567)
Contributors¶
A total of 11 developers contributed to this release. Thanks @FloydHsiu, @RangiLyu, @ZwwWayne, @AndreaPi, @st9007a, @hachreak, @BIGWangYuDong, @hhaAndroid, @AronLin, @chhluo, @vealocia, @HarborYuan, @st9007a, @jshilong
v2.18.1 (15/11/2021)¶
Bug Fixes¶
Fix aug test error when the number of prediction bboxes is 0 (#6398)
Fix SpatialReductionAttention in PVT (#6488)
Fix wrong use of
trunc_normal_init
in PVT and Swin-Transformer (#6432)
Improvements¶
Save the printed AP information of COCO API to logger (#6505)
Always map location to cpu when load checkpoint (#6405)
Set a random seed when the user does not set a seed (#6457)
Documents¶
Chinese version of Corruption Benchmarking (#6375)
Fix config path in docs (#6396)
Update GRoIE readme (#6401)
Contributors¶
A total of 11 developers contributed to this release. Thanks @st9007a, @hachreak, @HarborYuan, @vealocia, @chhluo, @AndreaPi, @AronLin, @BIGWangYuDong, @hhaAndroid, @RangiLyu, @ZwwWayne
v2.18.0 (27/10/2021)¶
Highlights¶
Support QueryInst (#6050)
Refactor dense heads to decouple onnx export logics from
get_bboxes
and speed up inference (#5317, #6003, #6369, #6268, #6315)
Bug Fixes¶
Fix init_weight in fcn_mask_head (#6378)
Fix type error in imshow_bboxes of RPN (#6386)
Fix broken colab link in MMDetection Tutorial (#6382)
Make sure the device and dtype of scale_factor are the same as bboxes (#6374)
Remove sampling hardcode (#6317)
Fix RandomAffine bbox coordinate recorrection (#6293)
Fix init bug of final cls/reg layer in convfc head (#6279)
Fix img_shape broken in auto_augment (#6259)
Fix kwargs parameter missing error in two_stage (#6256)
Improvements¶
Unify the interface of stuff head and panoptic head (#6308)
Polish readme (#6243)
Add code-spell pre-commit hook and fix a typo (#6306)
Fix typo (#6245, #6190)
Fix sampler unit test (#6284)
Fix
forward_dummy
of YOLACT to enableget_flops
(#6079)Fix link error in the config documentation (#6252)
Adjust the order to beautify the document (#6195)
Refactors¶
Refactor one-stage get_bboxes logic (#5317)
Refactor ONNX export of One-Stage models (#6003, #6369)
Refactor dense_head and speedup (#6268)
Migrate to use prior_generator in training of dense heads (#6315)
Contributors¶
A total of 18 developers contributed to this release. Thanks @Boyden, @onnkeat, @st9007a, @vealocia, @yhcao6, @DapangpangX, @yellowdolphin, @cclauss, @kennymckormick, @pingguokiller, @collinzrj, @AndreaPi, @AronLin, @BIGWangYuDong, @hhaAndroid, @jshilong, @RangiLyu, @ZwwWayne
v2.17.0 (28/9/2021)¶
Highlights¶
New Features¶
Support SOLO (#5832)
Support large scale jittering and New Mask R-CNN baselines (#6132)
Add a general data structure for the results of models (#5508)
Added a base class for one-stage instance segmentation (#5904)
Speed up
YOLOv3
inference (#5991)Release Swin Transformer pre-trained models (#6100)
Support mixed precision training in
YOLOX
(#5983)Support
val
workflow inYOLACT
(#5986)Add script to test
torchserve
(#5936)Support
onnxsim
with dynamic input shape (#6117)
Bug Fixes¶
Fix the function naming errors in
model_wrappers
(#5975)Fix regression loss bug when the input is an empty tensor (#5976)
Fix scores not contiguous error in
centernet_head
(#6016)Fix missing parameters bug in
imshow_bboxes
(#6034)Fix bug in
aug_test
ofHTC
when the length ofdet_bboxes
is 0 (#6088)Fix empty proposal errors in the training of some two-stage models (#5941)
Fix
dynamic_axes
parameter error inONNX
dynamic shape export (#6104)Fix
dynamic_shape
bug ofSyncRandomSizeHook
(#6144)Fix the Swin Transformer config link error in the configuration (#6172)
Improvements¶
Add filter rules in
Mosaic
transform (#5897)Add size divisor in get flops to avoid some potential bugs (#6076)
Add Chinese translation of
docs_zh-CN/tutorials/customize_dataset.md
(#5915)Add Chinese translation of
conventions.md
(#5825)Add description of the output of data pipeline (#5886)
Add dataset information in the README file for
PanopticFPN
(#5996)Add
extra_repr
forDropBlock
layer to get details in the model printing (#6140)Fix CI out of memory and add PyTorch1.9 Python3.9 unit tests (#5862)
Fix download links error of some model (#6069)
Improve the generalization of XML dataset (#5943)
Polish assertion error messages (#6017)
Remove
opencv-python-headless
dependency byalbumentations
(#5868)Check dtype in transform unit tests (#5969)
Replace the default theme of documentation with PyTorch Sphinx Theme (#6146)
Update the paper and code fields in the metafile (#6043)
Support to customize padding value of segmentation map (#6152)
Support to resize multiple segmentation maps (#5747)
Contributors¶
A total of 24 developers contributed to this release. Thanks @morkovka1337, @HarborYuan, @guillaumefrd, @guigarfr, @www516717402, @gaotongxiao, @ypwhs, @MartaYang, @shinya7y, @justiceeem, @zhaojinjian0000, @VVsssssk, @aravind-anantha, @wangbo-zhao, @czczup, @whai362, @czczup, @marijnl, @AronLin, @BIGWangYuDong, @hhaAndroid, @jshilong, @RangiLyu, @ZwwWayne
v2.16.0 (30/8/2021)¶
Highlights¶
Support Panoptic FPN and Swin Transformer
New Features¶
Support Panoptic FPN and release models (#5577, #5902)
Support Swin Transformer backbone (#5748)
Release RetinaNet models pre-trained with multi-scale 3x schedule (#5636)
Add script to convert unlabeled image list to coco format (#5643)
Add hook to check whether the loss value is valid (#5674)
Add YOLO anchor optimizing tool (#5644)
Support export onnx models without post process. (#5851)
Support classwise evaluation in CocoPanopticDataset (#5896)
Adapt browse_dataset for concatenated datasets. (#5935)
Add
PatchEmbed
andPatchMerging
withAdaptivePadding
(#5952)
Bug Fixes¶
Fix unit tests of YOLOX (#5859)
Fix lose randomness in
imshow_det_bboxes
(#5845)Make output result of
ImageToTensor
contiguous (#5756)Fix inference bug when calling
regress_by_class
in RoIHead in some cases (#5884)Fix bug in CIoU loss where alpha should not have gradient. (#5835)
Fix the bug that
multiscale_output
is defined but not used in HRNet (#5887)Set the priority of EvalHook to LOW. (#5882)
Fix a YOLOX bug when applying bbox rescaling in test mode (#5899)
Fix mosaic coordinate error (#5947)
Fix dtype of bbox in RandomAffine. (#5930)
Improvements¶
Add Chinese version of
data_pipeline
and (#5662)Support to remove state dicts of EMA when publishing models. (#5858)
Refactor the loss function in HTC and SCNet (#5881)
Use warnings instead of logger.warning (#5540)
Use legacy coordinate in metric of VOC (#5627)
Add Chinese version of customize_losses (#5826)
Add Chinese version of model_zoo (#5827)
Contributors¶
A total of 19 developers contributed to this release. Thanks @ypwhs, @zywvvd, @collinzrj, @OceanPang, @ddonatien, @@haotian-liu, @viibridges, @Muyun99, @guigarfr, @zhaojinjian0000, @jbwang1997,@wangbo-zhao, @xvjiarui, @RangiLyu, @jshilong, @AronLin, @BIGWangYuDong, @hhaAndroid, @ZwwWayne
v2.15.1 (11/8/2021)¶
Bug Fixes¶
Update correct SSD models. (#5789)
Fix casting error in mask structure (#5820)
Fix MMCV deployment documentation links. (#5790)
Improvements¶
Use dynamic MMCV download link in TorchServe dockerfile (#5779)
Rename the function
upsample_like
tointerpolate_as
for more general usage (#5788)
Contributors¶
A total of 14 developers contributed to this release. Thanks @HAOCHENYE, @xiaohu2015, @HsLOL, @zhiqwang, @Adamdad, @shinya7y, @Johnson-Wang, @RangiLyu, @jshilong, @mmeendez8, @AronLin, @BIGWangYuDong, @hhaAndroid, @ZwwWayne
v2.15.0 (02/8/2021)¶
Highlights¶
Support adding MIM dependencies during pip installation
Support MobileNetV2 for SSD-Lite and YOLOv3
Support Chinese Documentation
New Features¶
Add function
upsample_like
(#5732)Support to output pdf and epub format documentation (#5738)
Support and release Cascade Mask R-CNN 3x pre-trained models (#5645)
Add
ignore_index
to CrossEntropyLoss (#5646)Support adding MIM dependencies during pip installation (#5676)
Add MobileNetV2 config and models for YOLOv3 (#5510)
Support COCO Panoptic Dataset (#5231)
Support ONNX export of cascade models (#5486)
Support DropBlock with RetinaNet (#5544)
Support MobileNetV2 SSD-Lite (#5526)
Bug Fixes¶
Fix the device of label in multiclass_nms (#5673)
Fix error of backbone initialization from pre-trained checkpoint in config file (#5603, #5550)
Fix download links of RegNet pretrained weights (#5655)
Fix two-stage runtime error given empty proposal (#5559)
Fix flops count error in DETR (#5654)
Fix unittest for
NumClassCheckHook
when it is not used. (#5626)Fix description bug of using custom dataset (#5546)
Fix bug of
multiclass_nms
that returns the global indices (#5592)Fix
valid_mask
logic error in RPNHead (#5562)Fix unit test error of pretrained configs (#5561)
Fix typo error in anchor_head.py (#5555)
Fix bug when using dataset wrappers (#5552)
Fix a typo error in demo/MMDet_Tutorial.ipynb (#5511)
Fixing crash in
get_root_logger
whencfg.log_level
is not None (#5521)Fix docker version (#5502)
Fix optimizer parameter error when using
IterBasedRunner
(#5490)
Improvements¶
Add unit tests for MMTracking (#5620)
Add Chinese translation of documentation (#5718, #5618, #5558, #5423, #5593, #5421, #5408. #5369, #5419, #5530, #5531)
Update resource limit (#5697)
Update docstring for InstaBoost (#5640)
Support key
reduction_override
in all loss functions (#5515)Use repeatdataset to accelerate CenterNet training (#5509)
Remove unnecessary code in autoassign (#5519)
Add documentation about
init_cfg
(#5273)
Contributors¶
A total of 18 developers contributed to this release. Thanks @OceanPang, @AronLin, @hellock, @Outsider565, @RangiLyu, @ElectronicElephant, @likyoo, @BIGWangYuDong, @hhaAndroid, @noobying, @yyz561, @likyoo, @zeakey, @ZwwWayne, @ChenyangLiu, @johnson-magic, @qingswu, @BuxianChen
v2.14.0 (29/6/2021)¶
Highlights¶
Add
simple_test
to dense heads to improve the consistency of single-stage and two-stage detectorsRevert the
test_mixins
to single image test to improve efficiency and readabilityAdd Faster R-CNN and Mask R-CNN config using multi-scale training with 3x schedule
New Features¶
Support pretrained models from MoCo v2 and SwAV (#5286)
Add Faster R-CNN and Mask R-CNN config using multi-scale training with 3x schedule (#5179, #5233)
Add
reduction_override
in MSELoss (#5437)Stable support of exporting DETR to ONNX with dynamic shapes and batch inference (#5168)
Stable support of exporting PointRend to ONNX with dynamic shapes and batch inference (#5440)
Bug Fixes¶
Fix size mismatch bug in
multiclass_nms
(#4980)Fix the import path of
MultiScaleDeformableAttention
(#5338)Fix errors in config of GCNet ResNext101 models (#5360)
Fix Grid-RCNN error when there is no bbox result (#5357)
Fix errors in
onnx_export
of bbox_head when setting reg_class_agnostic (#5468)Fix type error of AutoAssign in the document (#5478)
Fix web links ending with
.md
(#5315)
Improvements¶
Add
simple_test
to dense heads to improve the consistency of single-stage and two-stage detectors (#5264)Add support for mask diagonal flip in TTA (#5403)
Revert the
test_mixins
to single image test to improve efficiency and readability (#5249)Make YOLOv3 Neck more flexible (#5218)
Refactor SSD to make it more general (#5291)
Refactor
anchor_generator
andpoint_generator
(#5349)Allow to configure out the
mask_head
of the HTC algorithm (#5389)Delete deprecated warning in FPN (#5311)
Move
model.pretrained
tomodel.backbone.init_cfg
(#5370)Make deployment tools more friendly to use (#5280)
Clarify installation documentation (#5316)
Add ImageNet Pretrained Models docs (#5268)
Add FAQ about training loss=nan solution and COCO AP or AR =-1 (# 5312, #5313)
Change all weight links of http to https (#5328)
v2.13.0 (01/6/2021)¶
Highlights¶
Support new methods: CenterNet, Seesaw Loss, MobileNetV2
New Features¶
Support paper Objects as Points (#4602)
Support paper Seesaw Loss for Long-Tailed Instance Segmentation (CVPR 2021) (#5128)
Support MobileNetV2 backbone and inverted residual block (#5122)
Support MIM (#5143)
ONNX exportation with dynamic shapes of CornerNet (#5136)
Add
mask_soft
config option to allow non-binary masks (#4615)Add PWC metafile (#5135)
Bug Fixes¶
Fix YOLOv3 FP16 training error (#5172)
Fix Cacscade R-CNN TTA test error when
det_bboxes
length is 0 (#5221)Fix
iou_thr
variable naming errors in VOC recall calculation function (#5195)Fix Faster R-CNN performance dropped in ONNX Runtime (#5197)
Fix DETR dict changed error when using python 3.8 during iteration (#5226)
Improvements¶
Refactor ONNX export of two stage detector (#5205)
Replace MMDetection’s EvalHook with MMCV’s EvalHook for consistency (#4806)
Update RoI extractor for ONNX (#5194)
Use better parameter initialization in YOLOv3 head for higher performance (#5181)
Release new DCN models of Mask R-CNN by mixed-precision training (#5201)
Update YOLOv3 model weights (#5229)
Add DetectoRS ResNet-101 model weights (#4960)
Discard bboxes with sizes equals to
min_bbox_size
(#5011)Remove duplicated code in DETR head (#5129)
Remove unnecessary object in class definition (#5180)
Fix doc link (#5192)
v2.12.0 (01/5/2021)¶
Highlights¶
Support new methods: AutoAssign, YOLOF, and Deformable DETR
Stable support of exporting models to ONNX with batched images and dynamic shape (#5039)
Backwards Incompatible Changes¶
MMDetection is going through big refactoring for more general and convenient usages during the releases from v2.12.0 to v2.15.0 (maybe longer). In v2.12.0 MMDetection inevitably brings some BC-breakings, including the MMCV dependency, model initialization, model registry, and mask AP evaluation.
MMCV version. MMDetection v2.12.0 relies on the newest features in MMCV 1.3.3, including
BaseModule
for unified parameter initialization, model registry, and the CUDA operatorMultiScaleDeformableAttn
for Deformable DETR. Note that MMCV 1.3.2 already contains all the features used by MMDet but has known issues. Therefore, we recommend users skip MMCV v1.3.2 and use v1.3.3, though v1.3.2 might work for most cases.Unified model initialization (#4750). To unify the parameter initialization in OpenMMLab projects, MMCV supports
BaseModule
that acceptsinit_cfg
to allow the modules’ parameters initialized in a flexible and unified manner. Now the users need to explicitly callmodel.init_weights()
in the training script to initialize the model (as in here, previously this was handled by the detector. The models in MMDetection have been re-benchmarked to ensure accuracy based on PR #4750. The downstream projects should update their code accordingly to use MMDetection v2.12.0.Unified model registry (#5059). To easily use backbones implemented in other OpenMMLab projects, MMDetection migrates to inherit the model registry created in MMCV (#760). In this way, as long as the backbone is supported in an OpenMMLab project and that project also uses the registry in MMCV, users can use that backbone in MMDetection by simply modifying the config without copying the code of that backbone into MMDetection.
Mask AP evaluation (#4898). Previous versions calculate the areas of masks through the bounding boxes when calculating the mask AP of small, medium, and large instances. To indeed use the areas of masks, we pop the key
bbox
during mask AP calculation. This change does not affect the overall mask AP evaluation and aligns the mask AP of similar models in other projects like Detectron2.
New Features¶
Support paper AutoAssign: Differentiable Label Assignment for Dense Object Detection (#4295)
Support paper You Only Look One-level Feature (#4295)
Support paper Deformable DETR: Deformable Transformers for End-to-End Object Detection (#4778)
Support calculating IoU with FP16 tensor in
bbox_overlaps
to save memory and keep speed (#4889)Add
__repr__
in custom dataset to count the number of instances (#4756)Add windows support by updating requirements.txt (#5052)
Stable support of exporting models to ONNX with batched images and dynamic shape, including SSD, FSAF,FCOS, YOLOv3, RetinaNet, Faster R-CNN, and Mask R-CNN (#5039)
Improvements¶
Use MMCV
MODEL_REGISTRY
(#5059)Unified parameter initialization for more flexible usage (#4750)
Rename variable names and fix docstring in anchor head (#4883)
Support training with empty GT in Cascade RPN (#4928)
Add more details of usage of
test_robustness
in documentation (#4917)Changing to use
pycocotools
instead ofmmpycocotools
to fully support Detectron2 and MMDetection in one environment (#4939)Update torch serve dockerfile to support dockers of more versions (#4954)
Add check for training with single class dataset (#4973)
Refactor transformer and DETR Head (#4763)
Update FPG model zoo (#5079)
More accurate mask AP of small/medium/large instances (#4898)
Bug Fixes¶
Fix bug in mean_ap.py when calculating mAP by 11 points (#4875)
Fix error when key
meta
is not in old checkpoints (#4936)Fix hanging bug when training with empty GT in VFNet, GFL, and FCOS by changing the place of
reduce_mean
(#4923, #4978, #5058)Fix asyncronized inference error and provide related demo (#4941)
Fix IoU losses dimensionality unmatch error (#4982)
Fix torch.randperm whtn using PyTorch 1.8 (#5014)
Fix empty bbox error in
mask_head
when using CARAFE (#5062)Fix
supplement_mask
bug when there are zero-size RoIs (#5065)Fix testing with empty rois in RoI Heads (#5081)
v2.11.0 (01/4/2021)¶
Highlights
Support new method: Localization Distillation for Object Detection
Support Pytorch2ONNX with batch inference and dynamic shape
New Features
Support Localization Distillation for Object Detection (#4758)
Support Pytorch2ONNX with batch inference and dynamic shape for Faster-RCNN and mainstream one-stage detectors (#4796)
Improvements
Support batch inference in head of RetinaNet (#4699)
Add batch dimension in second stage of Faster-RCNN (#4785)
Support batch inference in bbox coder (#4721)
Add check for
ann_ids
inCOCODataset
to ensure it is unique (#4789)support for showing the FPN results (#4716)
support dynamic shape for grid_anchor (#4684)
Move pycocotools version check to when it is used (#4880)
Bug Fixes
Fix a bug of TridentNet when doing the batch inference (#4717)
Fix a bug of Pytorch2ONNX in FASF (#4735)
Fix a bug when show the image with float type (#4732)
v2.10.0 (01/03/2021)¶
Highlights¶
Support new methods: FPG
Support ONNX2TensorRT for SSD, FSAF, FCOS, YOLOv3, and Faster R-CNN.
New Features¶
Support ONNX2TensorRT for SSD, FSAF, FCOS, YOLOv3, and Faster R-CNN (#4569)
Support Feature Pyramid Grids (FPG) (#4645)
Support video demo (#4420)
Add seed option for sampler (#4665)
Support to customize type of runner (#4570, #4669)
Support synchronizing BN buffer in
EvalHook
(#4582)Add script for GIF demo (#4573)
Bug Fixes¶
Fix ConfigDict AttributeError and add Colab link (#4643)
Avoid crash in empty gt training of GFL head (#4631)
Fix
iou_thrs
bug in RPN evaluation (#4581)Fix syntax error of config when upgrading model version (#4584)
Improvements¶
Refactor unit test file structures (#4600)
Refactor nms config (#4636)
Get loading pipeline by checking the class directly rather than through config strings (#4619)
Add doctests for mask target generation and mask structures (#4614)
Use deep copy when copying pipeline arguments (#4621)
Update documentations (#4642, #4650, #4620, #4630)
Remove redundant code calling
import_modules_from_strings
(#4601)Clean deprecated FP16 API (#4571)
Check whether
CLASSES
is correctly initialized in the initialization ofXMLDataset
(#4555)Support batch inference in the inference API (#4462, #4526)
Clean deprecated warning and fix ‘meta’ error (#4695)
v2.9.0 (01/02/2021)¶
Highlights¶
Support new methods: SCNet, Sparse R-CNN
Move
train_cfg
andtest_cfg
into model in configsSupport to visualize results based on prediction quality
New Features¶
Support SCNet (#4356)
Support Sparse R-CNN (#4219)
Support evaluate mAP by multiple IoUs (#4398)
Support concatenate dataset for testing (#4452)
Support to visualize results based on prediction quality (#4441)
Add ONNX simplify option to Pytorch2ONNX script (#4468)
Add hook for checking compatibility of class numbers in heads and datasets (#4508)
Bug Fixes¶
Fix CPU inference bug of Cascade RPN (#4410)
Fix NMS error of CornerNet when there is no prediction box (#4409)
Fix TypeError in CornerNet inference (#4411)
Fix bug of PAA when training with background images (#4391)
Fix the error that the window data is not destroyed when
out_file is not None
andshow==False
(#4442)Fix order of NMS
score_factor
that will decrease the performance of YOLOv3 (#4473)Fix bug in HTC TTA when the number of detection boxes is 0 (#4516)
Fix resize error in mask data structures (#4520)
Improvements¶
Allow to customize classes in LVIS dataset (#4382)
Add tutorials for building new models with existing datasets (#4396)
Add CPU compatibility information in documentation (#4405)
Add documentation of deprecated
ImageToTensor
for batch inference (#4408)Add more details in documentation for customizing dataset (#4430)
Switch
imshow_det_bboxes
visualization backend from OpenCV to Matplotlib (#4389)Deprecate
ImageToTensor
inimage_demo.py
(#4400)Move train_cfg/test_cfg into model (#4347, #4489)
Update docstring for
reg_decoded_bbox
option in bbox heads (#4467)Update dataset information in documentation (#4525)
Release pre-trained R50 and R101 PAA detectors with multi-scale 3x training schedules (#4495)
Add guidance for speed benchmark (#4537)
v2.8.0 (04/01/2021)¶
Highlights¶
Support new methods: Cascade RPN, TridentNet
New Features¶
Support Cascade RPN (#1900)
Support TridentNet (#3313)
Bug Fixes¶
Fix bug of show result in async_benchmark (#4367)
Fix scale factor in MaskTestMixin (#4366)
Fix but when returning indices in
multiclass_nms
(#4362)Fix bug of empirical attention in resnext backbone error (#4300)
Fix bug of
img_norm_cfg
in FCOS-HRNet models with updated performance and models (#4250)Fix invalid checkpoint and log in Mask R-CNN models on Cityscapes dataset (#4287)
Fix bug in distributed sampler when dataset is too small (#4257)
Fix bug of ‘PAFPN has no attribute extra_convs_on_inputs’ (#4235)
Improvements¶
Update model url from aws to aliyun (#4349)
Update ATSS for PyTorch 1.6+ (#4359)
Update script to install ruby in pre-commit installation (#4360)
Delete deprecated
mmdet.ops
(#4325)Refactor hungarian assigner for more general usage in Sparse R-CNN (#4259)
Handle scipy import in DETR to reduce package dependencies (#4339)
Update documentation of usages for config options after MMCV (1.2.3) supports overriding list in config (#4326)
Update pre-train models of faster rcnn trained on COCO subsets (#4307)
Avoid zero or too small value for beta in Dynamic R-CNN (#4303)
Add doccumentation for Pytorch2ONNX (#4271)
Add deprecated warning FPN arguments (#4264)
Support returning indices of kept bboxes when using nms (#4251)
Update type and device requirements when creating tensors
GFLHead
(#4210)Update device requirements when creating tensors in
CrossEntropyLoss
(#4224)
v2.7.0 (30/11/2020)¶
Support YOLO, Mask R-CNN, and Cascade R-CNN models exportable to ONNX.
New Features¶
Support DETR (#4201, #4206)
Support to link the best checkpoint in training (#3773)
Support to override config through options in inference.py (#4175)
Support YOLO, Mask R-CNN, and Cascade R-CNN models exportable to ONNX (#4087, #4083)
Support ResNeSt backbone (#2959)
Support unclip border bbox regression (#4076)
Add tpfp func in evaluating AP (#4069)
Support mixed precision training of SSD detector with other backbones (#4081)
Add Faster R-CNN DC5 models (#4043)
Bug Fixes¶
Fix bug of
gpu_id
in distributed training mode (#4163)Support Albumentations with version higher than 0.5 (#4032)
Fix num_classes bug in faster rcnn config (#4088)
Update code in docs/2_new_data_model.md (#4041)
Improvements¶
Ensure DCN offset to have similar type as features in VFNet (#4198)
Add config links in README files of models (#4190)
Add tutorials for loss conventions (#3818)
Add solution to installation issues in 30-series GPUs (#4176)
Update docker version in get_started.md (#4145)
Add model statistics and polish some titles in configs README (#4140)
Clamp neg probability in FreeAnchor (#4082)
Speed up expanding large images (#4089)
Fix Pytorch 1.7 incompatibility issues (#4103)
Update trouble shooting page to resolve segmentation fault (#4055)
Update aLRP-Loss in project page (#4078)
Clean duplicated
reduce_mean
function (#4056)Refactor Q&A (#4045)
v2.6.0 (1/11/2020)¶
Support new method: VarifocalNet.
Refactored documentation with more tutorials.
New Features¶
Support GIoU calculation in
BboxOverlaps2D
, and re-implementgiou_loss
usingbbox_overlaps
(#3936)Support random sampling in CPU mode (#3948)
Support VarifocalNet (#3666, #4024)
Bug Fixes¶
Fix SABL validating bug in Cascade R-CNN (#3913)
Avoid division by zero in PAA head when num_pos=0 (#3938)
Fix temporary directory bug of multi-node testing error (#4034, #4017)
Fix
--show-dir
option in test script (#4025)Fix GA-RetinaNet r50 model url (#3983)
Update code in docs and fix broken urls (#3947)
Improvements¶
Refactor pytorch2onnx API into
mmdet.core.export
and usegenerate_inputs_and_wrap_model
for pytorch2onnx (#3857, #3912)Update RPN upgrade scripts for v2.5.0 compatibility (#3986)
Use mmcv
tensor2imgs
(#4010)Update test robustness (#4000)
Update trouble shooting page (#3994)
Accelerate PAA training speed (#3985)
Support batch_size > 1 in validation (#3966)
Use RoIAlign implemented in MMCV for inference in CPU mode (#3930)
Documentation refactoring (#4031)
v2.5.0 (5/10/2020)¶
Highlights¶
Support new methods: YOLACT, CentripetalNet.
Add more documentations for easier and more clear usage.
Backwards Incompatible Changes¶
FP16 related methods are imported from mmcv instead of mmdet. (#3766, #3822)
Mixed precision training utils in mmdet.core.fp16
are moved to mmcv.runner
, including force_fp32
, auto_fp16
, wrap_fp16_model
, and Fp16OptimizerHook
. A deprecation warning will be raised if users attempt to import those methods from mmdet.core.fp16
, and will be finally removed in V2.10.0.
[0, N-1] represents foreground classes and N indicates background classes for all models. (#3221)
Before v2.5.0, the background label for RPN is 0, and N for other heads. Now the behavior is consistent for all models. Thus self.background_labels
in dense_heads
is removed and all heads use self.num_classes
to indicate the class index of background labels.
This change has no effect on the pre-trained models in the v2.x model zoo, but will affect the training of all models with RPN heads. Two-stage detectors whose RPN head uses softmax will be affected because the order of categories is changed.
Only call get_subset_by_classes
when test_mode=True
and self.filter_empty_gt=True
(#3695)
Function get_subset_by_classes
in dataset is refactored and only filters out images when test_mode=True
and self.filter_empty_gt=True
.
In the original implementation, get_subset_by_classes
is not related to the flag self.filter_empty_gt
and will only be called when the classes is set during initialization no matter test_mode
is True
or False
. This brings ambiguous behavior and potential bugs in many cases. After v2.5.0, if filter_empty_gt=False
, no matter whether the classes are specified in a dataset, the dataset will use all the images in the annotations. If filter_empty_gt=True
and test_mode=True
, no matter whether the classes are specified, the dataset will call ``get_subset_by_classes` to check the images and filter out images containing no GT boxes. Therefore, the users should be responsible for the data filtering/cleaning process for the test dataset.
New Features¶
Test time augmentation for single stage detectors (#3844, #3638)
Support to show the name of experiments during training (#3764)
Add
Shear
,Rotate
,Translate
Augmentation (#3656, #3619, #3687)Add image-only transformations including
Constrast
,Equalize
,Color
, andBrightness
. (#3643)Support YOLACT (#3456)
Support CentripetalNet (#3390)
Support PyTorch 1.6 in docker (#3905)
Bug Fixes¶
Fix the bug of training ATSS when there is no ground truth boxes (#3702)
Fix the bug of using Focal Loss when there is
num_pos
is 0 (#3702)Fix the label index mapping in dataset browser (#3708)
Fix Mask R-CNN training stuck problem when their is no positive rois (#3713)
Fix the bug of
self.rpn_head.test_cfg
inRPNTestMixin
by usingself.rpn_head
in rpn head (#3808)Fix deprecated
Conv2d
from mmcv.ops (#3791)Fix device bug in RepPoints (#3836)
Fix SABL validating bug (#3849)
Use
https://download.openmmlab.com/mmcv/dist/index.html
for installing MMCV (#3840)Fix nonzero in NMS for PyTorch 1.6.0 (#3867)
Fix the API change bug of PAA (#3883)
Fix typo in bbox_flip (#3886)
Fix cv2 import error of ligGL.so.1 in Dockerfile (#3891)
Improvements¶
Change to use
mmcv.utils.collect_env
for collecting environment information to avoid duplicate codes (#3779)Update checkpoint file names to v2.0 models in documentation (#3795)
Update tutorials for changing runtime settings (#3778), modifying loss (#3777)
Improve the function of
simple_test_bboxes
in SABL (#3853)Convert mask to bool before using it as img’s index for robustness and speedup (#3870)
Improve documentation of modules and dataset customization (#3821)
v2.4.0 (5/9/2020)¶
Highlights
Fix lots of issues/bugs and reorganize the trouble shooting page
Support new methods SABL, YOLOv3, and PAA Assign
Support Batch Inference
Start to publish
mmdet
package to PyPI since v2.3.0Switch model zoo to download.openmmlab.com
Backwards Incompatible Changes
Support Batch Inference (#3564, #3686, #3705): Since v2.4.0, MMDetection could inference model with multiple images in a single GPU. This change influences all the test APIs in MMDetection and downstream codebases. To help the users migrate their code, we use
replace_ImageToTensor
(#3686) to convert legacy test data pipelines during dataset initialization.Support RandomFlip with horizontal/vertical/diagonal direction (#3608): Since v2.4.0, MMDetection supports horizontal/vertical/diagonal flip in the data augmentation. This influences bounding box, mask, and image transformations in data augmentation process and the process that will map those data back to the original format.
Migrate to use
mmlvis
andmmpycocotools
for COCO and LVIS dataset (#3727). The APIs are fully compatible with the originallvis
andpycocotools
. Users need to uninstall the existing pycocotools and lvis packages in their environment first and installmmlvis
&mmpycocotools
.
Bug Fixes
Fix default mean/std for onnx (#3491)
Fix coco evaluation and add metric items (#3497)
Fix typo for install.md (#3516)
Fix atss when sampler per gpu is 1 (#3528)
Fix import of fuse_conv_bn (#3529)
Fix bug of gaussian_target, update unittest of heatmap (#3543)
Fixed VOC2012 evaluate (#3553)
Fix scale factor bug of rescale (#3566)
Fix with_xxx_attributes in base detector (#3567)
Fix boxes scaling when number is 0 (#3575)
Fix rfp check when neck config is a list (#3591)
Fix import of fuse conv bn in benchmark.py (#3606)
Fix webcam demo (#3634)
Fix typo and itemize issues in tutorial (#3658)
Fix error in distributed training when some levels of FPN are not assigned with bounding boxes (#3670)
Fix the width and height orders of stride in valid flag generation (#3685)
Fix weight initialization bug in Res2Net DCN (#3714)
Fix bug in OHEMSampler (#3677)
New Features
Support Cutout augmentation (#3521)
Support evaluation on multiple datasets through ConcatDataset (#3522)
Support PAA assign #(3547)
Support eval metric with pickle results (#3607)
Support YOLOv3 (#3083)
Support SABL (#3603)
Support to publish to Pypi in github-action (#3510)
Support custom imports (#3641)
Improvements
Refactor common issues in documentation (#3530)
Add pytorch 1.6 to CI config (#3532)
Add config to runner meta (#3534)
Add eval-option flag for testing (#3537)
Add init_eval to evaluation hook (#3550)
Add include_bkg in ClassBalancedDataset (#3577)
Using config’s loading in inference_detector (#3611)
Add ATSS ResNet-101 models in model zoo (#3639)
Update urls to download.openmmlab.com (#3665)
Support non-mask training for CocoDataset (#3711)
v2.3.0 (5/8/2020)¶
Highlights
The CUDA/C++ operators have been moved to
mmcv.ops
. For backward compatibilitymmdet.ops
is kept as warppers ofmmcv.ops
.Support new methods CornerNet, DIOU/CIOU loss, and new dataset: LVIS V1
Provide more detailed colab training tutorials and more complete documentation.
Support to convert RetinaNet from Pytorch to ONNX.
Bug Fixes
Fix the model initialization bug of DetectoRS (#3187)
Fix the bug of module names in NASFCOSHead (#3205)
Fix the filename bug in publish_model.py (#3237)
Fix the dimensionality bug when
inside_flags.any()
isFalse
in dense heads (#3242)Fix the bug of forgetting to pass flip directions in
MultiScaleFlipAug
(#3262)Fixed the bug caused by default value of
stem_channels
(#3333)Fix the bug of model checkpoint loading for CPU inference (#3318, #3316)
Fix topk bug when box number is smaller than the expected topk number in ATSSAssigner (#3361)
Fix the gt priority bug in center_region_assigner.py (#3208)
Fix NaN issue of iou calculation in iou_loss.py (#3394)
Fix the bug that
iou_thrs
is not actually used during evaluation in coco.py (#3407)Fix test-time augmentation of RepPoints (#3435)
Fix runtimeError caused by incontiguous tensor in Res2Net+DCN (#3412)
New Features
Support CornerNet (#3036)
Support LVIS V1 dataset (#)
Support customized hooks in training (#3395)
Support fp16 training of generalized focal loss (#3410)
Support to convert RetinaNet from Pytorch to ONNX (#3075)
Improvements
Support to process ignore boxes in ATSS assigner (#3082)
Allow to crop images without ground truth in
RandomCrop
(#3153)Enable the the
Accuracy
module to set threshold (#3155)Refactoring unit tests (#3206)
Unify the training settings of
to_float32
andnorm_cfg
in RegNets configs (#3210)Add colab training tutorials for beginners (#3213, #3273)
Move CUDA/C++ operators into
mmcv.ops
and keepmmdet.ops
as warppers for backward compatibility (#3232)(#3457)Update installation scripts in documentation (#3290) and dockerfile (#3320)
Support to set image resize backend (#3392)
Remove git hash in version file (#3466)
Check mmcv version to force version compatibility (#3460)
v2.2.0 (1/7/2020)¶
Highlights
Support new methods: DetectoRS, PointRend, Generalized Focal Loss, Dynamic R-CNN
Bug Fixes
Fix FreeAnchor when no gt in image (#3176)
Clean up deprecated usage of
register_module()
(#3092, #3161)Fix pretrain bug in NAS FCOS (#3145)
Fix
num_classes
in SSD (#3142)Fix FCOS warmup (#3119)
Fix
rstrip
intools/publish_model.py
Fix
flip_ratio
default value in RandomFLip pipeline (#3106)Fix cityscapes eval with ms_rcnn (#3112)
Fix RPN softmax (#3056)
Fix filename of LVIS@v0.5 (#2998)
Fix nan loss by filtering out-of-frame gt_bboxes in COCO (#2999)
Fix bug in FSAF (#3018)
Add FocalLoss
num_classes
check (#2964)Fix PISA Loss when there are no gts (#2992)
Avoid nan in
iou_calculator
(#2975)Prevent possible bugs in loading and transforms caused by shallow copy (#2967)
New Features
Add DetectoRS (#3064)
Support Generalize Focal Loss (#3097)
Support PointRend (#2752)
Support Dynamic R-CNN (#3040)
Add DeepFashion dataset (#2968)
Implement FCOS training tricks (#2935)
Use BaseDenseHead as base class for anchor-base heads (#2963)
Add
with_cp
for BasicBlock (#2891)Add
stem_channels
argument for ResNet (#2954)
Improvements
Add anchor free base head (#2867)
Migrate to github action (#3137)
Add docstring for datasets, pipelines, core modules and methods (#3130, #3125, #3120)
Add VOC benchmark (#3060)
Add
concat
mode in GRoI (#3098)Remove cmd arg
autorescale-lr
(#3080)Use
len(data['img_metas'])
to indicatenum_samples
(#3073, #3053)Switch to EpochBasedRunner (#2976)
v2.1.0 (8/6/2020)¶
Highlights
Support new dataset: LVIS
Bug Fixes
Change the CLI argument
--validate
to--no-validate
to enable validation after training epochs by default. (#2651)Add missing cython to docker file (#2713)
Fix bug in nms cpu implementation (#2754)
Fix bug when showing mask results (#2763)
Fix gcc requirement (#2806)
Fix bug in async test (#2820)
Fix mask encoding-decoding bugs in test API (#2824)
Fix bug in test time augmentation (#2858, #2921, #2944)
Fix a typo in comment of apis/train (#2877)
Fix the bug of returning None when no gt bboxes are in the original image in
RandomCrop
. Fix the bug that misses to handlegt_bboxes_ignore
,gt_label_ignore
, andgt_masks_ignore
inRandomCrop
,MinIoURandomCrop
andExpand
modules. (#2810)Fix bug of
base_channels
of regnet (#2917)Fix the bug of logger when loading pre-trained weights in base detector (#2936)
New Features
Add IoU models (#2666)
Add colab demo for inference
Support class agnostic nms (#2553)
Add benchmark gathering scripts for development only (#2676)
Add mmdet-based project links (#2736, #2767, #2895)
Add config dump in training (#2779)
Add ClassBalancedDataset (#2721)
Add res2net backbone (#2237)
Support RegNetX models (#2710)
Use
mmcv.FileClient
to support different storage backends (#2712)Add ClassBalancedDataset (#2721)
Code Release: Prime Sample Attention in Object Detection (CVPR 2020) (#2626)
Implement NASFCOS (#2682)
Add class weight in CrossEntropyLoss (#2797)
Support LVIS dataset (#2088)
Support GRoIE (#2584)
Improvements
Allow different x and y strides in anchor heads. (#2629)
Make FSAF loss more robust to no gt (#2680)
Compute pure inference time instead (#2657) and update inference speed (#2730)
Avoided the possibility that a patch with 0 area is cropped. (#2704)
Add warnings when deprecated
imgs_per_gpu
is used. (#2700)Add a mask rcnn example for config (#2645)
Update model zoo (#2762, #2866, #2876, #2879, #2831)
Add
ori_filename
to img_metas and use it in test show-dir (#2612)Use
img_fields
to handle multiple images during image transform (#2800)Add upsample_cfg support in FPN (#2787)
Add
['img']
as defaultimg_fields
for back compatibility (#2809)Rename the pretrained model from
open-mmlab://resnet50_caffe
andopen-mmlab://resnet50_caffe_bgr
toopen-mmlab://detectron/resnet50_caffe
andopen-mmlab://detectron2/resnet50_caffe
. (#2832)Added sleep(2) in test.py to reduce hanging problem (#2847)
Support
c10::half
in CARAFE (#2890)Improve documentations (#2918, #2714)
Use optimizer constructor in mmcv and clean the original implementation in
mmdet.core.optimizer
(#2947)
v2.0.0 (6/5/2020)¶
In this release, we made lots of major refactoring and modifications.
Faster speed. We optimize the training and inference speed for common models, achieving up to 30% speedup for training and 25% for inference. Please refer to model zoo for details.
Higher performance. We change some default hyperparameters with no additional cost, which leads to a gain of performance for most models. Please refer to compatibility for details.
More documentation and tutorials. We add a bunch of documentation and tutorials to help users get started more smoothly. Read it here.
Support PyTorch 1.5. The support for 1.1 and 1.2 is dropped, and we switch to some new APIs.
Better configuration system. Inheritance is supported to reduce the redundancy of configs.
Better modular design. Towards the goal of simplicity and flexibility, we simplify some encapsulation while add more other configurable modules like BBoxCoder, IoUCalculator, OptimizerConstructor, RoIHead. Target computation is also included in heads and the call hierarchy is simpler.
Breaking Changes Models training with MMDetection 1.x are not fully compatible with 2.0, please refer to the compatibility doc for the details and how to migrate to the new version.
Improvements
Unify cuda and cpp API for custom ops. (#2277)
New config files with inheritance. (#2216)
Encapsulate the second stage into RoI heads. (#1999)
Refactor GCNet/EmpericalAttention into plugins. (#2345)
Set low quality match as an option in IoU-based bbox assigners. (#2375)
Change the codebase’s coordinate system. (#2380)
Refactor the category order in heads. 0 means the first positive class instead of background now. (#2374)
Add bbox sampler and assigner registry. (#2419)
Speed up the inference of RPN. (#2420)
Add
train_cfg
andtest_cfg
as class members in all anchor heads. (#2422)Merge target computation methods into heads. (#2429)
Add bbox coder to support different bbox encoding and losses. (#2480)
Unify the API for regression loss. (#2156)
Refactor Anchor Generator. (#2474)
Make
lr
an optional argument for optimizers. (#2509)Migrate to modules and methods in MMCV. (#2502, #2511, #2569, #2572)
Support PyTorch 1.5. (#2524)
Drop the support for Python 3.5 and use F-string in the codebase. (#2531)
Bug Fixes
Fix the scale factors for resized images without keep the aspect ratio. (#2039)
Check if max_num > 0 before slicing in NMS. (#2486)
Fix Deformable RoIPool when there is no instance. (#2490)
Fix the default value of assigned labels. (#2536)
Fix the evaluation of Cityscapes. (#2578)
New Features
Add deep_stem and avg_down option to ResNet, i.e., support ResNetV1d. (#2252)
Add L1 loss. (#2376)
Support both polygon and bitmap for instance masks. (#2353, #2540)
Support CPU mode for inference. (#2385)
Add optimizer constructor for complicated configuration of optimizers. (#2397, #2488)
Implement PAFPN. (#2392)
Support empty tensor input for some modules. (#2280)
Support for custom dataset classes without overriding it. (#2408, #2443)
Support to train subsets of coco dataset. (#2340)
Add iou_calculator to potentially support more IoU calculation methods. (2405)
Support class wise mean AP (was removed in the last version). (#2459)
Add option to save the testing result images. (#2414)
Support MomentumUpdaterHook. (#2571)
Add a demo to inference a single image. (#2605)
v1.1.0 (24/2/2020)¶
Highlights
Dataset evaluation is rewritten with a unified api, which is used by both evaluation hooks and test scripts.
Support new methods: CARAFE.
Breaking Changes
The new MMDDP inherits from the official DDP, thus the
__init__
api is changed to be the same as official DDP.The
mask_head
field in HTC config files is modified.The evaluation and testing script is updated.
In all transforms, instance masks are stored as a numpy array shaped (n, h, w) instead of a list of (h, w) arrays, where n is the number of instances.
Bug Fixes
Fix IOU assigners when ignore_iof_thr > 0 and there is no pred boxes. (#2135)
Fix mAP evaluation when there are no ignored boxes. (#2116)
Fix the empty RoI input for Deformable RoI Pooling. (#2099)
Fix the dataset settings for multiple workflows. (#2103)
Fix the warning related to
torch.uint8
in PyTorch 1.4. (#2105)Fix the inference demo on devices other than gpu:0. (#2098)
Fix Dockerfile. (#2097)
Fix the bug that
pad_val
is unused in Pad transform. (#2093)Fix the albumentation transform when there is no ground truth bbox. (#2032)
Improvements
Use torch instead of numpy for random sampling. (#2094)
Migrate to the new MMDDP implementation in MMCV v0.3. (#2090)
Add meta information in logs. (#2086)
Rewrite Soft NMS with pytorch extension and remove cython as a dependency. (#2056)
Rewrite dataset evaluation. (#2042, #2087, #2114, #2128)
Use numpy array for masks in transforms. (#2030)
New Features
Implement “CARAFE: Content-Aware ReAssembly of FEatures”. (#1583)
Add
worker_init_fn()
in data_loader when seed is set. (#2066, #2111)Add logging utils. (#2035)
v1.0.0 (30/1/2020)¶
This release mainly improves the code quality and add more docstrings.
Highlights
Documentation is online now: https://mmdetection.readthedocs.io.
Support new models: ATSS.
DCN is now available with the api
build_conv_layer
andConvModule
like the normal conv layer.A tool to collect environment information is available for trouble shooting.
Bug Fixes
Fix the incompatibility of the latest numpy and pycocotools. (#2024)
Fix the case when distributed package is unavailable, e.g., on Windows. (#1985)
Fix the dimension issue for
refine_bboxes()
. (#1962)Fix the typo when
seg_prefix
is a list. (#1906)Add segmentation map cropping to RandomCrop. (#1880)
Fix the return value of
ga_shape_target_single()
. (#1853)Fix the loaded shape of empty proposals. (#1819)
Fix the mask data type when using albumentation. (#1818)
Improvements
Enhance AssignResult and SamplingResult. (#1995)
Add ability to overwrite existing module in Registry. (#1982)
Reorganize requirements and make albumentations and imagecorruptions optional. (#1969)
Check NaN in
SSDHead
. (#1935)Encapsulate the DCN in ResNe(X)t into a ConvModule & Conv_layers. (#1894)
Refactoring for mAP evaluation and support multiprocessing and logging. (#1889)
Init the root logger before constructing Runner to log more information. (#1865)
Split
SegResizeFlipPadRescale
into different existing transforms. (#1852)Move
init_dist()
to MMCV. (#1851)Documentation and docstring improvements. (#1971, #1938, #1869, #1838)
Fix the color of the same class for mask visualization. (#1834)
Remove the option
keep_all_stages
in HTC and Cascade R-CNN. (#1806)
New Features
Add two test-time options
crop_mask
andrle_mask_encode
for mask heads. (#2013)Support loading grayscale images as single channel. (#1975)
Implement “Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection”. (#1872)
Add sphinx generated docs. (#1859, #1864)
Add GN support for flops computation. (#1850)
Collect env info for trouble shooting. (#1812)
v1.0rc1 (13/12/2019)¶
The RC1 release mainly focuses on improving the user experience, and fixing bugs.
Highlights
Support new models: FoveaBox, RepPoints and FreeAnchor.
Add a Dockerfile.
Add a jupyter notebook demo and a webcam demo.
Setup the code style and CI.
Add lots of docstrings and unit tests.
Fix lots of bugs.
Breaking Changes
There was a bug for computing COCO-style mAP w.r.t different scales (AP_s, AP_m, AP_l), introduced by #621. (#1679)
Bug Fixes
Fix a sampling interval bug in Libra R-CNN. (#1800)
Fix the learning rate in SSD300 WIDER FACE. (#1781)
Fix the scaling issue when
keep_ratio=False
. (#1730)Fix typos. (#1721, #1492, #1242, #1108, #1107)
Fix the shuffle argument in
build_dataloader
. (#1693)Clip the proposal when computing mask targets. (#1688)
Fix the “index out of range” bug for samplers in some corner cases. (#1610, #1404)
Fix the NMS issue on devices other than GPU:0. (#1603)
Fix SSD Head and GHM Loss on CPU. (#1578)
Fix the OOM error when there are too many gt bboxes. (#1575)
Fix the wrong keyword argument
nms_cfg
in HTC. (#1573)Process masks and semantic segmentation in Expand and MinIoUCrop transforms. (#1550, #1361)
Fix a scale bug in the Non Local op. (#1528)
Fix a bug in transforms when
gt_bboxes_ignore
is None. (#1498)Fix a bug when
img_prefix
is None. (#1497)Pass the device argument to
grid_anchors
andvalid_flags
. (#1478)Fix the data pipeline for test_robustness. (#1476)
Fix the argument type of deformable pooling. (#1390)
Fix the coco_eval when there are only two classes. (#1376)
Fix a bug in Modulated DeformableConv when deformable_group>1. (#1359)
Fix the mask cropping in RandomCrop. (#1333)
Fix zero outputs in DeformConv when not running on cuda:0. (#1326)
Fix the type issue in Expand. (#1288)
Fix the inference API. (#1255)
Fix the inplace operation in Expand. (#1249)
Fix the from-scratch training config. (#1196)
Fix inplace add in RoIExtractor which cause an error in PyTorch 1.2. (#1160)
Fix FCOS when input images has no positive sample. (#1136)
Fix recursive imports. (#1099)
Improvements
Print the config file and mmdet version in the log. (#1721)
Lint the code before compiling in travis CI. (#1715)
Add a probability argument for the
Expand
transform. (#1651)Update the PyTorch and CUDA version in the docker file. (#1615)
Raise a warning when specifying
--validate
in non-distributed training. (#1624, #1651)Beautify the mAP printing. (#1614)
Add pre-commit hook. (#1536)
Add the argument
in_channels
to backbones. (#1475)Add lots of docstrings and unit tests, thanks to @Erotemic. (#1603, #1517, #1506, #1505, #1491, #1479, #1477, #1475, #1474)
Add support for multi-node distributed test when there is no shared storage. (#1399)
Optimize Dockerfile to reduce the image size. (#1306)
Update new results of HRNet. (#1284, #1182)
Add an argument
no_norm_on_lateral
in FPN. (#1240)Test the compiling in CI. (#1235)
Move docs to a separate folder. (#1233)
Add a jupyter notebook demo. (#1158)
Support different type of dataset for training. (#1133)
Use int64_t instead of long in cuda kernels. (#1131)
Support unsquare RoIs for bbox and mask heads. (#1128)
Manually add type promotion to make compatible to PyTorch 1.2. (#1114)
Allowing validation dataset for computing validation loss. (#1093)
Use
.scalar_type()
instead of.type()
to suppress some warnings. (#1070)
New Features
Add an option
--with_ap
to compute the AP for each class. (#1549)Implement “FreeAnchor: Learning to Match Anchors for Visual Object Detection”. (#1391)
Support Albumentations for augmentations in the data pipeline. (#1354)
Implement “FoveaBox: Beyond Anchor-based Object Detector”. (#1339)
Support horizontal and vertical flipping. (#1273, #1115)
Implement “RepPoints: Point Set Representation for Object Detection”. (#1265)
Add test-time augmentation to HTC and Cascade R-CNN. (#1251)
Add a COCO result analysis tool. (#1228)
Add Dockerfile. (#1168)
Add a webcam demo. (#1155, #1150)
Add FLOPs counter. (#1127)
Allow arbitrary layer order for ConvModule. (#1078)
v1.0rc0 (27/07/2019)¶
Implement lots of new methods and components (Mixed Precision Training, HTC, Libra R-CNN, Guided Anchoring, Empirical Attention, Mask Scoring R-CNN, Grid R-CNN (Plus), GHM, GCNet, FCOS, HRNet, Weight Standardization, etc.). Thank all collaborators!
Support two additional datasets: WIDER FACE and Cityscapes.
Refactoring for loss APIs and make it more flexible to adopt different losses and related hyper-parameters.
Speed up multi-gpu testing.
Integrate all compiling and installing in a single script.
v0.6.0 (14/04/2019)¶
Up to 30% speedup compared to the model zoo.
Support both PyTorch stable and nightly version.
Replace NMS and SigmoidFocalLoss with Pytorch CUDA extensions.
v0.6rc0(06/02/2019)¶
Migrate to PyTorch 1.0.
v0.5.7 (06/02/2019)¶
Add support for Deformable ConvNet v2. (Many thanks to the authors and @chengdazhi)
This is the last release based on PyTorch 0.4.1.
v0.5.6 (17/01/2019)¶
Add support for Group Normalization.
Unify RPNHead and single stage heads (RetinaHead, SSDHead) with AnchorHead.
v0.5.5 (22/12/2018)¶
Add SSD for COCO and PASCAL VOC.
Add ResNeXt backbones and detection models.
Refactoring for Samplers/Assigners and add OHEM.
Add VOC dataset and evaluation scripts.
v0.5.4 (27/11/2018)¶
Add SingleStageDetector and RetinaNet.
v0.5.3 (26/11/2018)¶
Add Cascade R-CNN and Cascade Mask R-CNN.
Add support for Soft-NMS in config files.
v0.5.2 (21/10/2018)¶
Add support for custom datasets.
Add a script to convert PASCAL VOC annotations to the expected format.
v0.5.1 (20/10/2018)¶
Add BBoxAssigner and BBoxSampler, the
train_cfg
field in config files are restructured.ConvFCRoIHead
/SharedFCRoIHead
are renamed toConvFCBBoxHead
/SharedFCBBoxHead
for consistency.