# Copyright (c) OpenMMLab. All rights reserved. from mmcv import Config from mmpose.datasets.builder import build_dataset def test_concat_dataset(): # build COCO-like dataset config dataset_info = Config.fromfile( 'configs/_base_/datasets/coco.py').dataset_info channel_cfg = dict( num_output_channels=17, dataset_joints=17, dataset_channel=[ [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], ], inference_channel=[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 ]) data_cfg = dict( image_size=[192, 256], heatmap_size=[48, 64], num_output_channels=channel_cfg['num_output_channels'], num_joints=channel_cfg['dataset_joints'], dataset_channel=channel_cfg['dataset_channel'], inference_channel=channel_cfg['inference_channel'], soft_nms=False, nms_thr=1.0, oks_thr=0.9, vis_thr=0.2, use_gt_bbox=True, det_bbox_thr=0.0, bbox_file='tests/data/coco/test_coco_det_AP_H_56.json', ) dataset_cfg = dict( type='TopDownCocoDataset', ann_file='tests/data/coco/test_coco.json', img_prefix='tests/data/coco/', data_cfg=data_cfg, pipeline=[], dataset_info=dataset_info) dataset = build_dataset(dataset_cfg) # Case 1: build ConcatDataset explicitly concat_dataset_cfg = dict( type='ConcatDataset', datasets=[dataset_cfg, dataset_cfg]) concat_dataset = build_dataset(concat_dataset_cfg) assert len(concat_dataset) == 2 * len(dataset) # Case 2: build ConcatDataset from cfg sequence concat_dataset = build_dataset([dataset_cfg, dataset_cfg]) assert len(concat_dataset) == 2 * len(dataset) # Case 3: build ConcatDataset from ann_file sequence concat_dataset_cfg = dataset_cfg.copy() for key in ['ann_file', 'type', 'img_prefix', 'dataset_info']: val = concat_dataset_cfg[key] concat_dataset_cfg[key] = [val] * 2 for key in ['num_joints', 'dataset_channel']: val = concat_dataset_cfg['data_cfg'][key] concat_dataset_cfg['data_cfg'][key] = [val] * 2 concat_dataset = build_dataset(concat_dataset_cfg) assert len(concat_dataset) == 2 * len(dataset)