changelog.md

Changelog

v2.1.0 (8/6/2020)

Highlights

• Support new backbones: RegNetX, Res2Net
• Support new methods: NASFCOS, PISA, GRoIE
• 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 handle gt_bboxes_ignore, gt_label_ignore, and gt_masks_ignore in RandomCrop, MinIoURandomCrop and Expand 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 default img_fields for back compatibility (#2809)
• Rename the pretrained model from open-mmlab://resnet50_caffe and open-mmlab://resnet50_caffe_bgr to open-mmlab://detectron/resnet50_caffe and open-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.

1. 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.

2. 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.

3. More documentation and tutorials. We add a bunch of documentation and tutorials to help users get started more smoothly. Read it here.

4. Support PyTorch 1.5. The support for 1.1 and 1.2 is dropped, and we switch to some new APIs.

5. Better configuration system. Inheritance is supported to reduce the redundancy of configs.

6. 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.

7. Support new methods: FSAF and PAFPN (part of PAFPN).

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 and test_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 and ConvModule 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 and rle_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 and valid_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 to ConvFCBBoxHead / SharedFCBBoxHead for consistency.