Source code for mmpose.datasets.builder
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import platform
import random
import numpy as np
import torch
from mmengine import build_from_cfg, is_seq_of
from mmengine.dataset import ConcatDataset, RepeatDataset
from mmpose.registry import DATASETS
if platform.system() != 'Windows':
# https://github.com/pytorch/pytorch/issues/973
import resource
rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
base_soft_limit = rlimit[0]
hard_limit = rlimit[1]
soft_limit = min(max(4096, base_soft_limit), hard_limit)
resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit))
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)
[docs]def build_dataset(cfg, default_args=None):
"""Build a dataset from config dict.
Args:
cfg (dict): Config dict. It should at least contain the key "type".
default_args (dict, optional): Default initialization arguments.
Default: None.
Returns:
Dataset: The constructed dataset.
"""
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
def worker_init_fn(worker_id, num_workers, rank, seed):
"""Init the random seed for various workers."""
# The seed of each worker equals to
# num_worker * rank + worker_id + user_seed
worker_seed = num_workers * rank + worker_id + seed
np.random.seed(worker_seed)
random.seed(worker_seed)
torch.manual_seed(worker_seed)