[Feature] Liu yi/surpport edpose

LICENSES.md

@@ -0,0 +1,7 @@
+# Licenses for special algorithms
+
+In this file, we list the algorithms with other licenses instead of Apache 2.0. Users should be careful about adopting these algorithms in any commercial matters.
+
+| Algorithm |                                                                            Files                                                                            |     License      |
+| :-------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------: |
+|  EDPose   | [mmpose/models/heads/transformer_heads/edpose_head.py](https://github.com/open-mmlab/mmpose/blob/main/mmpose/models/heads/transformer_heads/edpose_head.py) | IDEA License 1.0 |

configs/body_2d_keypoint/edpose/coco/edpose_coco.md

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+<!-- [ALGORITHM] -->
+
+<details>
+<summary align="right"><a href="https://arxiv.org/pdf/2302.01593.pdf">ED-Pose (ICLR'2023)</a></summary>
+
+```bibtex
+@inproceedings{
+yang2023explicit,
+title={Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation},
+author={Jie Yang and Ailing Zeng and Shilong Liu and Feng Li and Ruimao Zhang and Lei Zhang},
+booktitle={International Conference on Learning Representations},
+year={2023},
+url={https://openreview.net/forum?id=s4WVupnJjmX}
+}
+```
+
+</details>
+
+<!-- [BACKBONE] -->
+
+<details>
+<summary align="right"><a href="http://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html">ResNet (CVPR'2016)</a></summary>
+
+```bibtex
+@inproceedings{he2016deep,
+  title={Deep residual learning for image recognition},
+  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={770--778},
+  year={2016}
+}
+```
+
+</details>
+
+<!-- [DATASET] -->
+
+<details>
+<summary align="right"><a href="https://link.springer.com/chapter/10.1007/978-3-319-10602-1_48">COCO (ECCV'2014)</a></summary>
+
+```bibtex
+@inproceedings{lin2014microsoft,
+  title={Microsoft coco: Common objects in context},
+  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
+  booktitle={European conference on computer vision},
+  pages={740--755},
+  year={2014},
+  organization={Springer}
+}
+```
+
+</details>
+
+Results on COCO val2017.
+
+| Arch                                          | BackBone  |  AP   | AP<sup>50</sup> | AP<sup>75</sup> |  AR   | AR<sup>50</sup> |                      ckpt                      |                      log                      |
+| :-------------------------------------------- | :-------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :--------------------------------------------: | :-------------------------------------------: |
+| [edpose_res50_coco](/configs/body_2d_keypoint/edpose/coco/edpose_res50_8xb2-50e_coco-800x1333.py) | ResNet-50 | 0.716 |      0.898      |      0.783      | 0.793 |      0.944      | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/edpose/coco/edpose_res50_coco_3rdparty.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/edpose/coco/edpose_res50_coco_3rdparty.json) |
+
+The checkpoint is converted from the official repo. The training of EDPose is not supported yet. It will be supported in the future updates.
+
+The above config follows [Pure Python style](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta). Please install `mmengine>=0.8.2` to use this config.

configs/body_2d_keypoint/edpose/coco/edpose_coco.yml

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+Collections:
+- Name: ED-Pose
+  Paper:
+    Title: Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation
+    URL: https://arxiv.org/pdf/2302.01593.pdf
+  README: https://github.com/open-mmlab/mmpose/blob/main/docs/src/papers/algorithms/edpose.md
+Models:
+- Config: configs/body_2d_keypoint/edpose/coco/edpose_res50_8xb2-50e_coco-800x1333.py
+  In Collection: ED-Pose
+  Alias: edpose
+  Metadata:
+    Architecture: &id001
+    - ED-Pose
+    - ResNet
+    Training Data: COCO
+  Name: edpose_res50_8xb2-50e_coco-800x1333
+  Results:
+  - Dataset: COCO
+    Metrics:
+      AP: 0.716
+      [email protected]: 0.898
+      [email protected]: 0.783
+      AR: 0.793
+      [email protected]: 0.944
+    Task: Body 2D Keypoint
+  Weights: https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/edpose/coco/edpose_res50_coco_3rdparty.pth

configs/body_2d_keypoint/edpose/coco/edpose_res50_8xb2-50e_coco-800x1333.py

@@ -0,0 +1,236 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+
+with read_base():
+    from mmpose.configs._base_.default_runtime import *  # noqa
+
+from mmcv.transforms import RandomChoice, RandomChoiceResize
+from mmengine.dataset import DefaultSampler
+from mmengine.model import PretrainedInit
+from mmengine.optim import LinearLR, MultiStepLR
+from torch.nn import GroupNorm
+from torch.optim import Adam
+
+from mmpose.codecs import EDPoseLabel
+from mmpose.datasets import (BottomupRandomChoiceResize, BottomupRandomCrop,
+                             CocoDataset, LoadImage, PackPoseInputs,
+                             RandomFlip)
+from mmpose.evaluation import CocoMetric
+from mmpose.models import (BottomupPoseEstimator, ChannelMapper, EDPoseHead,
+                           PoseDataPreprocessor, ResNet)
+from mmpose.models.utils import FrozenBatchNorm2d
+
+# runtime
+train_cfg.update(max_epochs=50, val_interval=10)  # noqa
+
+# optimizer
+optim_wrapper = dict(optimizer=dict(
+    type=Adam,
+    lr=1e-3,
+))
+
+# learning policy
+param_scheduler = [
+    dict(type=LinearLR, begin=0, end=500, start_factor=0.001,
+         by_epoch=False),  # warm-up
+    dict(
+        type=MultiStepLR,
+        begin=0,
+        end=140,
+        milestones=[33, 45],
+        gamma=0.1,
+        by_epoch=True)
+]
+
+# automatically scaling LR based on the actual training batch size
+auto_scale_lr = dict(base_batch_size=80)
+
+# hooks
+default_hooks.update(  # noqa
+    checkpoint=dict(save_best='coco/AP', rule='greater'))
+
+# codec settings
+codec = dict(type=EDPoseLabel, num_select=50, num_keypoints=17)
+
+# model settings
+model = dict(
+    type=BottomupPoseEstimator,
+    data_preprocessor=dict(
+        type=PoseDataPreprocessor,
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        bgr_to_rgb=True,
+        pad_size_divisor=1),
+    backbone=dict(
+        type=ResNet,
+        depth=50,
+        num_stages=4,
+        out_indices=(1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type=FrozenBatchNorm2d, requires_grad=False),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type=PretrainedInit, checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type=ChannelMapper,
+        in_channels=[512, 1024, 2048],
+        kernel_size=1,
+        out_channels=256,
+        act_cfg=None,
+        norm_cfg=dict(type=GroupNorm, num_groups=32),
+        num_outs=4),
+    head=dict(
+        type=EDPoseHead,
+        num_queries=900,
+        num_feature_levels=4,
+        num_keypoints=17,
+        as_two_stage=True,
+        encoder=dict(
+            num_layers=6,
+            layer_cfg=dict(  # DeformableDetrTransformerEncoderLayer
+                self_attn_cfg=dict(  # MultiScaleDeformableAttention
+                    embed_dims=256,
+                    num_heads=8,
+                    num_levels=4,
+                    num_points=4,
+                    batch_first=True),
+                ffn_cfg=dict(
+                    embed_dims=256,
+                    feedforward_channels=2048,
+                    num_fcs=2,
+                    ffn_drop=0.0))),
+        decoder=dict(
+            num_layers=6,
+            embed_dims=256,
+            layer_cfg=dict(  # DeformableDetrTransformerDecoderLayer
+                self_attn_cfg=dict(  # MultiheadAttention
+                    embed_dims=256,
+                    num_heads=8,
+                    batch_first=True),
+                cross_attn_cfg=dict(  # MultiScaleDeformableAttention
+                    embed_dims=256,
+                    batch_first=True),
+                ffn_cfg=dict(
+                    embed_dims=256, feedforward_channels=2048, ffn_drop=0.1)),
+            query_dim=4,
+            num_feature_levels=4,
+            num_group=100,
+            num_dn=100,
+            num_box_decoder_layers=2,
+            return_intermediate=True),
+        out_head=dict(num_classes=2),
+        positional_encoding=dict(
+            num_pos_feats=128,
+            temperatureH=20,
+            temperatureW=20,
+            normalize=True),
+        denosing_cfg=dict(
+            dn_box_noise_scale=0.4,
+            dn_label_noise_ratio=0.5,
+            dn_labelbook_size=100,
+            dn_attn_mask_type_list=['match2dn', 'dn2dn', 'group2group']),
+        data_decoder=codec),
+    test_cfg=dict(Pmultiscale_test=False, flip_test=False, num_select=50),
+    train_cfg=dict())
+
+# enable DDP training when rescore net is used
+find_unused_parameters = True
+
+# base dataset settings
+dataset_type = CocoDataset
+data_mode = 'bottomup'
+data_root = 'data/coco/'
+
+# pipelines
+train_pipeline = [
+    dict(type=LoadImage),
+    dict(type=RandomFlip, direction='horizontal'),
+    dict(
+        type=RandomChoice,
+        transforms=[
+            [
+                dict(
+                    type=RandomChoiceResize,
+                    scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+                            (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+                            (736, 1333), (768, 1333), (800, 1333)],
+                    keep_ratio=True)
+            ],
+            [
+                dict(
+                    type=BottomupRandomChoiceResize,
+                    # The radio of all image in train dataset < 7
+                    # follow the original implement
+                    scales=[(400, 4200), (500, 4200), (600, 4200)],
+                    keep_ratio=True),
+                dict(
+                    type=BottomupRandomCrop,
+                    crop_type='absolute_range',
+                    crop_size=(384, 600),
+                    allow_negative_crop=True),
+                dict(
+                    type=BottomupRandomChoiceResize,
+                    scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+                            (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+                            (736, 1333), (768, 1333), (800, 1333)],
+                    keep_ratio=True)
+            ]
+        ]),
+    dict(type=PackPoseInputs),
+]
+
+val_pipeline = [
+    dict(type=LoadImage),
+    dict(
+        type=BottomupRandomChoiceResize,
+        scales=[(800, 1333)],
+        keep_ratio=True,
+        backend='pillow'),
+    dict(
+        type=PackPoseInputs,
+        meta_keys=('id', 'img_id', 'img_path', 'crowd_index', 'ori_shape',
+                   'img_shape', 'input_size', 'input_center', 'input_scale',
+                   'flip', 'flip_direction', 'flip_indices', 'raw_ann_info',
+                   'skeleton_links'))
+]
+
+# data loaders
+train_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    sampler=dict(type=DefaultSampler, shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_mode=data_mode,
+        ann_file='annotations/person_keypoints_train2017.json',
+        data_prefix=dict(img='train2017/'),
+        pipeline=train_pipeline,
+    ))
+
+val_dataloader = dict(
+    batch_size=4,
+    num_workers=8,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type=DefaultSampler, shuffle=False, round_up=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_mode=data_mode,
+        ann_file='annotations/person_keypoints_val2017.json',
+        data_prefix=dict(img='val2017/'),
+        test_mode=True,
+        pipeline=val_pipeline,
+    ))
+test_dataloader = val_dataloader
+
+# evaluators
+val_evaluator = dict(
+    type=CocoMetric,
+    nms_mode='none',
+    score_mode='keypoint',
+)
+test_evaluator = val_evaluator

docs/src/papers/algorithms/edpose.md

@@ -0,0 +1,31 @@
+# Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation
+
+<!-- [ALGORITHM] -->
+
+<details>
+<summary align="right"><a href="https://arxiv.org/pdf/2302.01593.pdf">ED-Pose (ICLR'2023)</a></summary>
+
+```bibtex
+@inproceedings{
+yang2023explicit,
+title={Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation},
+author={Jie Yang and Ailing Zeng and Shilong Liu and Feng Li and Ruimao Zhang and Lei Zhang},
+booktitle={International Conference on Learning Representations},
+year={2023},
+url={https://openreview.net/forum?id=s4WVupnJjmX}
+}
+```
+
+</details>
+
+## Abstract
+
+<!-- [ABSTRACT] -->
+
+This paper presents a novel end-to-end framework with Explicit box Detection for multi-person Pose estimation, called ED-Pose, where it unifies the contextual learning between human-level (global) and keypoint-level (local) information. Different from previous one-stage methods, ED-Pose re-considers this task as two explicit box detection processes with a unified representation and regression supervision. First, we introduce a human detection decoder from encoded tokens to extract global features. It can provide a good initialization for the latter keypoint detection, making the training process converge fast. Second, to bring in contextual information near keypoints, we regard pose estimation as a keypoint box detection problem to learn both box positions and contents for each keypoint. A human-to-keypoint detection decoder adopts an interactive learning strategy between human and keypoint features to further enhance global and local feature aggregation. In general, ED-Pose is conceptually simple without post-processing and dense heatmap supervision. It demonstrates its effectiveness and efficiency compared with both two-stage and one-stage methods. Notably, explicit box detection boosts the pose estimation performance by 4.5 AP on COCO and 9.9 AP on CrowdPose. For the first time, as a fully end-to-end framework with a L1 regression loss, ED-Pose surpasses heatmap-based Top-down methods under the same backbone by 1.2 AP on COCO and achieves the state-of-the-art with 76.6 AP on CrowdPose without bells and whistles. Code is available at https://github.com/IDEA-Research/ED-Pose.
+
+<!-- [IMAGE] -->
+
+<div align=center>
+<img src="https://github.com/IDEA-Research/ED-Pose/raw/master/figs/edpose_git.jpg">
+</div>

mmpose/apis/inference.py

@@ -53,7 +53,8 @@ def dataset_meta_from_config(config: Config,
             import mmpose.datasets.datasets  # noqa: F401, F403
             from mmpose.registry import DATASETS
 
-            dataset_class = DATASETS.get(dataset_cfg.type)
+            dataset_class = dataset_cfg.type if isinstance(
+                dataset_cfg.type, type) else DATASETS.get(dataset_cfg.type)
             metainfo = dataset_class.METAINFO
 
         metainfo = parse_pose_metainfo(metainfo)

mmpose/codecs/__init__.py

@@ -2,6 +2,7 @@
 from .annotation_processors import YOLOXPoseAnnotationProcessor
 from .associative_embedding import AssociativeEmbedding
 from .decoupled_heatmap import DecoupledHeatmap
+from .edpose_label import EDPoseLabel
 from .image_pose_lifting import ImagePoseLifting
 from .integral_regression_label import IntegralRegressionLabel
 from .megvii_heatmap import MegviiHeatmap
@@ -17,5 +18,5 @@
     'MSRAHeatmap', 'MegviiHeatmap', 'UDPHeatmap', 'RegressionLabel',
     'SimCCLabel', 'IntegralRegressionLabel', 'AssociativeEmbedding', 'SPR',
     'DecoupledHeatmap', 'VideoPoseLifting', 'ImagePoseLifting',
-    'MotionBERTLabel', 'YOLOXPoseAnnotationProcessor'
+    'MotionBERTLabel', 'YOLOXPoseAnnotationProcessor', 'EDPoseLabel'
 ]