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configs/body_2d_keypoint/edpose/coco/edpose_coco.md

@@ -58,3 +58,5 @@ Results on COCO val2017.
 | [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_res50_8xb2-50e_coco-800x1333.py

@@ -1,21 +1,40 @@
-_base_ = ['../../../_base_/default_runtime.py']
+# 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 = dict(max_epochs=50, val_interval=10)
+train_cfg.update(max_epochs=50, val_interval=10)  # noqa
 
 # optimizer
 optim_wrapper = dict(optimizer=dict(
-    type='Adam',
+    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='LinearLR', begin=0, end=500, start_factor=0.001,
-        by_epoch=False),  # warm-up
-    dict(
-        type='MultiStepLR',
+        type=MultiStepLR,
         begin=0,
         end=140,
         milestones=[33, 45],
@@ -27,40 +46,42 @@
 auto_scale_lr = dict(base_batch_size=80)
 
 # hooks
-default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater'))
+default_hooks.update(  # noqa
+    checkpoint=dict(save_best='coco/AP', rule='greater'))
 
 # codec settings
-codec = dict(type='EDPoseLabel', num_select=50, num_keypoints=17)
+codec = dict(type=EDPoseLabel, num_select=50, num_keypoints=17)
 
 # model settings
 model = dict(
-    type='BottomupPoseEstimator',
+    type=BottomupPoseEstimator,
     data_preprocessor=dict(
-        type='PoseDataPreprocessor',
+        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',
+        type=ResNet,
         depth=50,
         num_stages=4,
         out_indices=(1, 2, 3),
         frozen_stages=1,
-        norm_cfg=dict(type='FrozenBatchNorm2d', requires_grad=False),
+        norm_cfg=dict(type=FrozenBatchNorm2d, requires_grad=False),
         norm_eval=True,
         style='pytorch',
-        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+        init_cfg=dict(
+            type=PretrainedInit, checkpoint='torchvision://resnet50')),
     neck=dict(
-        type='ChannelMapper',
+        type=ChannelMapper,
         in_channels=[512, 1024, 2048],
         kernel_size=1,
         out_channels=256,
         act_cfg=None,
-        norm_cfg=dict(type='GN', num_groups=32),
+        norm_cfg=dict(type=GroupNorm, num_groups=32),
         num_outs=4),
     head=dict(
-        type='EDPoseHead',
+        type=EDPoseHead,
         num_queries=900,
         num_feature_levels=4,
         num_keypoints=17,
@@ -117,57 +138,57 @@
 find_unused_parameters = True
 
 # base dataset settings
-dataset_type = 'CocoDataset'
+dataset_type = CocoDataset
 data_mode = 'bottomup'
 data_root = 'data/coco/'
 
 # pipelines
 train_pipeline = [
-    dict(type='LoadImage'),
-    dict(type='RandomFlip', direction='horizontal'),
+    dict(type=LoadImage),
+    dict(type=RandomFlip, direction='horizontal'),
     dict(
-        type='RandomChoice',
+        type=RandomChoice,
         transforms=[
             [
                 dict(
-                    type='RandomChoiceResize',
+                    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',
+                    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',
+                    type=BottomupRandomCrop,
                     crop_type='absolute_range',
                     crop_size=(384, 600),
                     allow_negative_crop=True),
                 dict(
-                    type='BottomupRandomChoiceResize',
+                    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'),
+    dict(type=PackPoseInputs),
 ]
 
 val_pipeline = [
-    dict(type='LoadImage'),
+    dict(type=LoadImage),
     dict(
-        type='BottomupRandomChoiceResize',
+        type=BottomupRandomChoiceResize,
         scales=[(800, 1333)],
         keep_ratio=True,
         backend='pillow'),
     dict(
-        type='PackPoseInputs',
+        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',
@@ -179,7 +200,7 @@
     batch_size=1,
     num_workers=1,
     persistent_workers=True,
-    sampler=dict(type='DefaultSampler', shuffle=False),
+    sampler=dict(type=DefaultSampler, shuffle=False),
     dataset=dict(
         type=dataset_type,
         data_root=data_root,
@@ -194,7 +215,7 @@
     num_workers=8,
     persistent_workers=True,
     drop_last=False,
-    sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
+    sampler=dict(type=DefaultSampler, shuffle=False, round_up=False),
     dataset=dict(
         type=dataset_type,
         data_root=data_root,
@@ -208,8 +229,7 @@
 
 # evaluators
 val_evaluator = dict(
-    type='CocoMetric',
-    ann_file=data_root + 'annotations/person_keypoints_val2017.json',
+    type=CocoMetric,
     nms_mode='none',
     score_mode='keypoint',
 )

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)