[Feature] Support Concat dataset

mmpose/datasets/builder.py

@@ -1,13 +1,15 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import copy
 import platform
 import random
 from functools import partial
 
 import numpy as np
 from mmcv.parallel import collate
 from mmcv.runner import get_dist_info
-from mmcv.utils import Registry, build_from_cfg
+from mmcv.utils import Registry, build_from_cfg, is_seq_of
 from mmcv.utils.parrots_wrapper import _get_dataloader
+from torch.utils.data.dataset import ConcatDataset
 
 from .samplers import DistributedSampler
 
@@ -24,6 +26,39 @@
 PIPELINES = Registry('pipeline')
 
 
+def _concat_dataset(cfg, default_args=None):
+    types = cfg['type']
+    ann_files = cfg['ann_file']
+    img_prefixes = cfg.get('img_prefix', None)
+    dataset_infos = cfg.get('dataset_info', None)
+
+    num_joints = cfg['data_cfg'].get('num_joints', None)
+    dataset_channel = cfg['data_cfg'].get('dataset_channel', None)
+
+    datasets = []
+    num_dset = len(ann_files)
+    for i in range(num_dset):
+        cfg_copy = copy.deepcopy(cfg)
+        cfg_copy['ann_file'] = ann_files[i]
+
+        if isinstance(types, (list, tuple)):
+            cfg_copy['type'] = types[i]
+        if isinstance(img_prefixes, (list, tuple)):
+            cfg_copy['img_prefix'] = img_prefixes[i]
+        if isinstance(dataset_infos, (list, tuple)):
+            cfg_copy['dataset_info'] = dataset_infos[i]
+
+        if isinstance(num_joints, (list, tuple)):
+            cfg_copy['data_cfg']['num_joints'] = num_joints[i]
+
+        if is_seq_of(dataset_channel, list):
+            cfg_copy['data_cfg']['dataset_channel'] = dataset_channel[i]
+
+        datasets.append(build_dataset(cfg_copy, default_args))
+
+    return ConcatDataset(datasets)
+
+
 def build_dataset(cfg, default_args=None):
     """Build a dataset from config dict.
 
@@ -37,9 +72,16 @@ def build_dataset(cfg, default_args=None):
     """
     from .dataset_wrappers import RepeatDataset
 
-    if cfg['type'] == 'RepeatDataset':
+    if isinstance(cfg, (list, tuple)):
+        dataset = ConcatDataset([build_dataset(c, default_args) for c in cfg])
+    elif cfg['type'] == 'ConcatDataset':
+        dataset = ConcatDataset(
+            [build_dataset(c, default_args) for c in cfg['datasets']])
+    elif cfg['type'] == 'RepeatDataset':
         dataset = RepeatDataset(
             build_dataset(cfg['dataset'], default_args), cfg['times'])
+    elif isinstance(cfg.get('ann_file'), (list, tuple)):
+        dataset = _concat_dataset(cfg, default_args)
     else:
         dataset = build_from_cfg(cfg, DATASETS, default_args)
     return dataset

tests/test_datasets/test_dataset_wrapper.py

@@ -0,0 +1,67 @@
+# 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)