Highlights

• Support RLE (Residual Log-likelihood Estimation), ICCV'2021 (#1259) @Indigo6, @ly015
• Support Swin Transformer, ICCV'2021 (#1300) @yumendecc, @ly015
• Support PVT, ICCV'2021 and PVTv2, CVMJ'2022 (#1343) @zengwang430521
• Speed up inference and reduce CPU usage by optimizing the pre-processing pipeline (#1320) @chenxinfeng4, @liqikai9

New Features

• Support RLE (Residual Log-likelihood Estimation), ICCV'2021 (#1259) @Indigo6, @ly015
• Support Swin Transformer, ICCV'2021 (#1300) @yumendecc, @ly015
• Support PVT, ICCV'2021 and PVTv2, CVMJ'2022 (#1343) @zengwang430521
• Support FPN, CVPR'2017 (#1300) @yumendecc, @ly015

Improvements

• Speed up inference and reduce CPU usage by optimizing the pre-processing pipeline (#1320) @chenxinfeng4, @liqikai9
• Video demo supports models that requires multi-frame inputs (#1300) @liqikai9, @jin-s13
• Update benchmark regression list (#1328) @ly015, @liqikai9
• Remove unnecessary warnings in TopDownPoseTrack18VideoDataset (#1335) @liqikai9
• Improve documentation quality (#1313, #1305) @Ben-Louis, @ly015
• Update deprecating settings in configs (#1317) @ly015

Bug Fixes

• Fix a bug in human skeleton grouping that may skip the matching process unexpectedly when ignore_to_much is True (#1341) @daixinghome
• Fix a GPG key error that leads to CI failure (#1354) @ly015
• Fix bugs in distributed training script (#1338, #1298) @ly015
• Fix an upstream bug in xtoccotools that causes incorrect AP(M) results (#1308) @jin-s13, @ly015
• Fix indentiation errors in the colab tutorial (#1298) @YuanZi1501040205
• Fix incompatible model weight initialization with other OpenMMLab codebases (#1329) @274869388
• Fix HRNet FP16 checkpoints download URL (#1309) @YinAoXiong
• Fix typos in body3d_two_stage_video_demo.py (#1295) @mucozcan

Breaking Changes

• Refactor bbox processing in datasets and pipelines (#1311) @ly015, @Ben-Louis The bbox format conversion (xywh to center-scale) and random translation are moved from the dataset to the pipeline. The comparison between new and old version is as below:

...
# Data sample only contains bbox
rec.append({
    'bbox': obj['clean_bbox][:4],
    ...
})

...
# Convert bbox from xywh to center-scale
center, scale = self._xywh2cs(*obj['clean_bbox'][:4])
# Data sample contains center and scale
rec.append({
    'bbox': obj['clean_bbox][:4],
    'center': center,
    'scale': scale,
    ...
})

...
train_pipeline = [
    dict(type='LoadImageFromFile'),
    # Convert bbox from xywh to center-scale
    dict(type='TopDownGetBboxCenterScale', padding=1.25),
    # Randomly shift bbox center
    dict(type='TopDownRandomShiftBboxCenter', shift_factor=0.16, prob=0.3),
    ...
]

...
train_pipeline = [
    dict(type='LoadImageFromFile'),
    ...
]