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switch to new config
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‎configs/body_2d_keypoint/edpose/coco/edpose_coco.md‎

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@@ -58,3 +58,5 @@ Results on COCO val2017.
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| [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) |
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The checkpoint is converted from the official repo. The training of EDPose is not supported yet. It will be supported in the future updates.
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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_res50_8xb2-50e_coco-800x1333.py‎

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@@ -1,21 +1,40 @@
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_base_ = ['../../../_base_/default_runtime.py']
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# Copyright (c) OpenMMLab. All rights reserved.
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from mmengine.config import read_base
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with read_base():
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from mmpose.configs._base_.default_runtime import * # noqa
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from mmcv.transforms import RandomChoice, RandomChoiceResize
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from mmengine.dataset import DefaultSampler
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from mmengine.model import PretrainedInit
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from mmengine.optim import LinearLR, MultiStepLR
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from torch.nn import GroupNorm
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from torch.optim import Adam
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from mmpose.codecs import EDPoseLabel
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from mmpose.datasets import (BottomupRandomChoiceResize, BottomupRandomCrop,
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CocoDataset, LoadImage, PackPoseInputs,
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RandomFlip)
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from mmpose.evaluation import CocoMetric
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from mmpose.models import (BottomupPoseEstimator, ChannelMapper, EDPoseHead,
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PoseDataPreprocessor, ResNet)
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from mmpose.models.utils import FrozenBatchNorm2d
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# runtime
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train_cfg = dict(max_epochs=50, val_interval=10)
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train_cfg.update(max_epochs=50, val_interval=10) # noqa
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# optimizer
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optim_wrapper = dict(optimizer=dict(
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type='Adam',
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type=Adam,
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lr=1e-3,
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))
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# learning policy
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param_scheduler = [
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dict(type=LinearLR, begin=0, end=500, start_factor=0.001,
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by_epoch=False), # warm-up
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dict(
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type='LinearLR', begin=0, end=500, start_factor=0.001,
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by_epoch=False), # warm-up
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dict(
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type='MultiStepLR',
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type=MultiStepLR,
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begin=0,
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end=140,
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milestones=[33, 45],
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auto_scale_lr = dict(base_batch_size=80)
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# hooks
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default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater'))
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default_hooks.update( # noqa
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checkpoint=dict(save_best='coco/AP', rule='greater'))
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# codec settings
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codec = dict(type='EDPoseLabel', num_select=50, num_keypoints=17)
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codec = dict(type=EDPoseLabel, num_select=50, num_keypoints=17)
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# model settings
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model = dict(
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type='BottomupPoseEstimator',
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type=BottomupPoseEstimator,
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data_preprocessor=dict(
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type='PoseDataPreprocessor',
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type=PoseDataPreprocessor,
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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bgr_to_rgb=True,
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pad_size_divisor=1),
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backbone=dict(
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type='ResNet',
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type=ResNet,
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depth=50,
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num_stages=4,
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out_indices=(1, 2, 3),
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frozen_stages=1,
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norm_cfg=dict(type='FrozenBatchNorm2d', requires_grad=False),
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norm_cfg=dict(type=FrozenBatchNorm2d, requires_grad=False),
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norm_eval=True,
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style='pytorch',
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init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
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init_cfg=dict(
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type=PretrainedInit, checkpoint='torchvision://resnet50')),
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neck=dict(
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type='ChannelMapper',
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type=ChannelMapper,
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in_channels=[512, 1024, 2048],
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kernel_size=1,
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out_channels=256,
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act_cfg=None,
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norm_cfg=dict(type='GN', num_groups=32),
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norm_cfg=dict(type=GroupNorm, num_groups=32),
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num_outs=4),
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head=dict(
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type='EDPoseHead',
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type=EDPoseHead,
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num_queries=900,
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num_feature_levels=4,
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num_keypoints=17,
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find_unused_parameters = True
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# base dataset settings
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dataset_type = 'CocoDataset'
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dataset_type = CocoDataset
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data_mode = 'bottomup'
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data_root = 'data/coco/'
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# pipelines
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train_pipeline = [
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dict(type='LoadImage'),
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dict(type='RandomFlip', direction='horizontal'),
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dict(type=LoadImage),
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dict(type=RandomFlip, direction='horizontal'),
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dict(
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type='RandomChoice',
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type=RandomChoice,
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transforms=[
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[
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dict(
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type='RandomChoiceResize',
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type=RandomChoiceResize,
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scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
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(608, 1333), (640, 1333), (672, 1333), (704, 1333),
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(736, 1333), (768, 1333), (800, 1333)],
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keep_ratio=True)
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],
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[
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dict(
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type='BottomupRandomChoiceResize',
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type=BottomupRandomChoiceResize,
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# The radio of all image in train dataset < 7
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# follow the original implement
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scales=[(400, 4200), (500, 4200), (600, 4200)],
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keep_ratio=True),
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dict(
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type='BottomupRandomCrop',
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type=BottomupRandomCrop,
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crop_type='absolute_range',
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crop_size=(384, 600),
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allow_negative_crop=True),
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dict(
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type='BottomupRandomChoiceResize',
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type=BottomupRandomChoiceResize,
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scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
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(608, 1333), (640, 1333), (672, 1333), (704, 1333),
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(736, 1333), (768, 1333), (800, 1333)],
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keep_ratio=True)
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]
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]),
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dict(type='PackPoseInputs'),
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dict(type=PackPoseInputs),
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]
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val_pipeline = [
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dict(type='LoadImage'),
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dict(type=LoadImage),
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dict(
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type='BottomupRandomChoiceResize',
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type=BottomupRandomChoiceResize,
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scales=[(800, 1333)],
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keep_ratio=True,
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backend='pillow'),
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dict(
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type='PackPoseInputs',
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type=PackPoseInputs,
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meta_keys=('id', 'img_id', 'img_path', 'crowd_index', 'ori_shape',
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'img_shape', 'input_size', 'input_center', 'input_scale',
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'flip', 'flip_direction', 'flip_indices', 'raw_ann_info',
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batch_size=1,
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num_workers=1,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=False),
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sampler=dict(type=DefaultSampler, shuffle=False),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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num_workers=8,
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persistent_workers=True,
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drop_last=False,
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sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
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sampler=dict(type=DefaultSampler, shuffle=False, round_up=False),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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# evaluators
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val_evaluator = dict(
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type='CocoMetric',
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ann_file=data_root + 'annotations/person_keypoints_val2017.json',
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type=CocoMetric,
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nms_mode='none',
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score_mode='keypoint',
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)

‎mmpose/apis/inference.py‎

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

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