open-mmlab/mmdetection v2.24.0 on GitHub

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 to samples_per_gpu * gpu number to train this config).
By default, we set enable=False so that the original usages will not be affected. Users can set enable=True in each config or add --auto-scale-lr after the command line to enable this feature and should check the correctness of base_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.

Before v2.24.0	Since 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), )

Before v2.24.0

Since 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 in MaskHungarianAssigner (#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 in MultiImageMixDataset (#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

New Contributors

@nestiank made their first contribution in #7591
@PeterH0323 made their first contribution in #7482
@mattcasey02 made their first contribution in #7610
@weiji14 made their first contribution in #7516
@Yulv-git made their first contribution in #7679
@xiefeifeihu made their first contribution in #7701
@SheffieldCao made their first contribution in #7732
@jovialio made their first contribution in #7778
@zhangsanfeng2022 made their first contribution in #7578

Full Changelog: v2.23.0...v2.24.0

open-mmlab/mmdetection v2.24.0 MMDetection V2.24.0 Release on GitHub