ValueError: Annotation must have data_list and metainfo keys

[690696306]

Prerequisite

• [X] I have searched Issues and Discussions but cannot get the expected help.
• [X] The bug has not been fixed in the latest version(https://github.com/open-mmlab/mmpose).

Environment

py10_cuda118_torch200

Reproduces the problem - code sample

Train config file

base = ['../../../base/default_runtime.py']

runtime

max_epochs = 270 stage2_num_epochs = 30 base_lr = 4e-3

train_cfg = dict(max_epochs=max_epochs, val_interval=10) randomness = dict(seed=21)

optimizer

optim_wrapper = dict( type='OptimWrapper', optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), paramwise_cfg=dict( norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))

learning rate

param_scheduler = [ dict( type='LinearLR', start_factor=1.0e-5, by_epoch=False, begin=0, end=1000), dict( type='CosineAnnealingLR', eta_min=base_lr * 0.05, begin=max_epochs // 2, end=max_epochs, T_max=max_epochs // 2, by_epoch=True, convert_to_iter_based=True), ]

automatically scaling LR based on the actual training batch size

auto_scale_lr = dict(base_batch_size=512)

codec settings

codec = dict( type='SimCCLabel', input_size=(288, 384), sigma=(6., 6.93), simcc_split_ratio=2.0, normalize=False, use_dark=False)

model settings

model = dict( type='TopdownPoseEstimator', data_preprocessor=dict( type='PoseDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], bgr_to_rgb=True), backbone=dict( scope='mmdet', type='CSPNeXt', arch='P5', expand_ratio=0.5, deepen_factor=1., widen_factor=1., out_indices=(4, ), channel_attention=True, norm_cfg=dict(type='SyncBN'), act_cfg=dict(type='SiLU'), init_cfg=dict( type='Pretrained', prefix='backbone.', checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' 'rtmposev1/cspnext-m_udp-aic-coco_210e-256x192-f2f7d6f6_20230130.pth' # noqa: E501 )), head=dict( type='RTMCCHead', in_channels=1024, out_channels=133, input_size=codec['input_size'], in_featuremap_size=tuple([s // 32 for s in codec['input_size']]), simcc_split_ratio=codec['simcc_split_ratio'], final_layer_kernel_size=7, gau_cfg=dict( hidden_dims=256, s=128, expansion_factor=2, dropout_rate=0., drop_path=0., act_fn='SiLU', use_rel_bias=False, pos_enc=False), loss=dict( type='KLDiscretLoss', use_target_weight=True, beta=10., label_softmax=True), decoder=codec), test_cfg=dict(flip_test=True, ))

base dataset settings

dataset_type = 'MyCustomDataset' #data_mode = 'topdown' data_root = 'data/coco/'

backend_args = dict(backend='local')

backend_args = dict(

backend='petrel',

path_mapping=dict({

f'{data_root}': 's3://openmmlab/datasets/detection/coco/',

f'{data_root}': 's3://openmmlab/datasets/detection/coco/'

}))

pipelines

train_pipeline = [ dict(type='LoadImage', backend_args=backend_args), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), dict(type='RandomHalfBody'), dict( type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), dict(type='TopdownAffine', input_size=codec['input_size']), dict(type='mmdet.YOLOXHSVRandomAug'), dict( type='Albumentation', transforms=[ dict(type='Blur', p=0.1), dict(type='MedianBlur', p=0.1), dict( type='CoarseDropout', max_holes=1, max_height=0.4, max_width=0.4, min_holes=1, min_height=0.2, min_width=0.2, p=1.0), ]), dict(type='GenerateTarget', encoder=codec), dict(type='PackPoseInputs') ] val_pipeline = [ dict(type='LoadImage', backend_args=backend_args), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=codec['input_size']), dict(type='PackPoseInputs') ]

train_pipeline_stage2 = [ dict(type='LoadImage', backend_args=backend_args), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), dict(type='RandomHalfBody'), dict( type='RandomBBoxTransform', shift_factor=0., scale_factor=[0.75, 1.25], rotate_factor=60), dict(type='TopdownAffine', input_size=codec['input_size']), dict(type='mmdet.YOLOXHSVRandomAug'), dict( type='Albumentation', transforms=[ dict(type='Blur', p=0.1), dict(type='MedianBlur', p=0.1), dict( type='CoarseDropout', max_holes=1, max_height=0.4, max_width=0.4, min_holes=1, min_height=0.2, min_width=0.2, p=0.5), ]), dict(type='GenerateTarget', encoder=codec), dict(type='PackPoseInputs') ]

data loaders

train_dataloader = dict( batch_size=32, num_workers=10, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type=dataset_type, data_root=data_root, #data_mode=data_mode, ann_file='annotations/coco_wholebody_train_v1.0.json', data_prefix=dict(img='train2017/'), pipeline=train_pipeline, metainfo=dict(from_file='configs/base/datasets/coco_wholebody.py'), )) val_dataloader = dict( batch_size=32, num_workers=10, 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/coco_wholebody_val_v1.0.json', data_prefix=dict(img='val2017/'), test_mode=True, #bbox_file='data/coco/person_detection_results/' #'COCO_val2017_detections_AP_H_56_person.json', pipeline=val_pipeline, metainfo=dict(from_file='configs/base/datasets/coco_wholebody.py'), )) test_dataloader = val_dataloader

hooks

#default_hooks = dict( #checkpoint=dict( #save_best='coco-wholebody/AP', rule='greater', max_keep_ckpts=1))

custom_hooks = [ dict( type='EMAHook', ema_type='ExpMomentumEMA', momentum=0.0002, update_buffers=True, priority=49), dict( type='mmdet.PipelineSwitchHook', switch_epoch=max_epochs - stage2_num_epochs, switch_pipeline=train_pipeline_stage2) ]

evaluators

#val_evaluator = dict( #type='CocoWholeBodyMetric', #ann_file=data_root + 'annotations/coco_wholebody_val_v1.0.json')

val_evaluator = [ dict(type='PCKAccuracy', thr=0.2), dict(type='AUC'), dict(type='EPE'), ]

test_evaluator = val_evaluator

Custom datasets

from mmengine.dataset import BaseDataset from mmpose.registry import DATASETS

import copy import os.path as osp from typing import Optional

import numpy as np

@DATASETS.register_module(name='MyCustomDataset') class MyCustomDataset(BaseDataset):

METAINFO: dict = dict(
    from_file='configs/_base_/datasets/coco_wholebody.py')

Reproduces the problem - command or script

python tools/train.py /home/lab/Anaconda_project/mmpose/configs/wholebody_2d_keypoint/rtmpose/coco-wholebody/rtmpose-l_8xb32-270e_coco-wholebody-384x288.py --work-dir /home/lab/Anaconda_project/mmpose/weights

Reproduces the problem - error message

/home/lab/Anaconda_project/mmpose/mmpose/datasets/transforms/common_transforms.py:656: UserWarning: Blur is not pixel-level transformations. Please use with caution. warnings.warn( /home/lab/Anaconda_project/mmpose/mmpose/datasets/transforms/common_transforms.py:656: UserWarning: MedianBlur is not pixel-level transformations. Please use with caution. warnings.warn( /home/lab/Anaconda_project/mmpose/mmpose/datasets/transforms/common_transforms.py:656: UserWarning: CoarseDropout is not pixel-level transformations. Please use with caution. warnings.warn( Traceback (most recent call last): File "/home/lab/Anaconda_project/mmpose/tools/train.py", line 162, in main() File "/home/lab/Anaconda_project/mmpose/tools/train.py", line 158, in main runner.train() File "/home/lab/.local/lib/python3.10/site-packages/mmengine/runner/runner.py", line 1728, in train self._train_loop = self.build_train_loop( File "/home/lab/.local/lib/python3.10/site-packages/mmengine/runner/runner.py", line 1527, in build_train_loop loop = EpochBasedTrainLoop( File "/home/lab/.local/lib/python3.10/site-packages/mmengine/runner/loops.py", line 44, in init super().init(runner, dataloader) File "/home/lab/.local/lib/python3.10/site-packages/mmengine/runner/base_loop.py", line 26, in init self.dataloader = runner.build_dataloader( File "/home/lab/.local/lib/python3.10/site-packages/mmengine/runner/runner.py", line 1370, in build_dataloader dataset = DATASETS.build(dataset_cfg) File "/home/lab/.local/lib/python3.10/site-packages/mmengine/registry/registry.py", line 570, in build return self.build_func(cfg, *args, **kwargs, registry=self) File "/home/lab/.local/lib/python3.10/site-packages/mmengine/registry/build_functions.py", line 121, in build_from_cfg obj = obj_cls(**args) # type: ignore File "/home/lab/.local/lib/python3.10/site-packages/mmengine/dataset/base_dataset.py", line 247, in init self.full_init() File "/home/lab/.local/lib/python3.10/site-packages/mmengine/dataset/base_dataset.py", line 298, in full_init self.data_list = self.load_data_list() File "/home/lab/.local/lib/python3.10/site-packages/mmengine/dataset/base_dataset.py", line 440, in load_data_list raise ValueError('Annotation must have data_list and metainfo ' ValueError: Annotation must have data_list and metainfo keys

Additional information

Hi,I want to use my customize data to train ,and I follow the doc to build file.But when I run train.py , I keep getting [ValueError: Annotation must have data_list and metainfo keys] error.

I thought it might be the problem with my config file,so I changed all the file path to the original coco_wholebody config but remain with dataset_type = 'MyCustomDataset'

out_channels=133, ann_file='annotations/coco_wholebody_train_v1.0.json', data_prefix=dict(img='train2017/'), ann_file='annotations/coco_wholebody_val_v1.0.json', data_prefix=dict(img='val2017/'), metainfo=dict(from_file='configs/base/datasets/coco_wholebody.py')

but I still get this error.The way to fix this is to change the dataset_type = 'MyCustomDataset' to dataset_type ='CocoWholeBodyDataset'.But this way I can not train the custom dataset.

Does any one have this problem before?