text: # 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, is_seq_of
  from mmcv.utils.parrots_wrapper import _get_dataloader
  from torch.utils.data.dataset import ConcatDataset
  
  from .samplers import DistributedSampler
  
  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))
  
  DATASETS = Registry('dataset')
  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.
  
      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.
      """
      from .dataset_wrappers import 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
  
  
  def build_dataloader(dataset,
                       samples_per_gpu,
                       workers_per_gpu,
                       num_gpus=1,
                       dist=True,
                       shuffle=True,
                       seed=None,
                       drop_last=True,
                       pin_memory=True,
                       **kwargs):
      """Build PyTorch DataLoader.
  
      In distributed training, each GPU/process has a dataloader.
      In non-distributed training, there is only one dataloader for all GPUs.
  
      Args:
          dataset (Dataset): A PyTorch dataset.
          samples_per_gpu (int): Number of training samples on each GPU, i.e.,
              batch size of each GPU.
          workers_per_gpu (int): How many subprocesses to use for data loading
              for each GPU.
          num_gpus (int): Number of GPUs. Only used in non-distributed training.
          dist (bool): Distributed training/test or not. Default: True.
          shuffle (bool): Whether to shuffle the data at every epoch.
              Default: True.
          drop_last (bool): Whether to drop the last incomplete batch in epoch.
              Default: True
          pin_memory (bool): Whether to use pin_memory in DataLoader.
              Default: True
          kwargs: any keyword argument to be used to initialize DataLoader
  
      Returns:
          DataLoader: A PyTorch dataloader.
      """
      rank, world_size = get_dist_info()
      if dist:
          sampler = DistributedSampler(
              dataset, world_size, rank, shuffle=shuffle, seed=seed)
          shuffle = False
          batch_size = samples_per_gpu
          num_workers = workers_per_gpu
      else:
          sampler = None
          batch_size = num_gpus * samples_per_gpu
          num_workers = num_gpus * workers_per_gpu
  
      init_fn = partial(
          worker_init_fn, num_workers=num_workers, rank=rank,
          seed=seed) if seed is not None else None
  
      _, DataLoader = _get_dataloader()
      data_loader = DataLoader(
          dataset,
          batch_size=batch_size,
          sampler=sampler,
          num_workers=num_workers,
          collate_fn=partial(collate, samples_per_gpu=samples_per_gpu),
          pin_memory=pin_memory,
          shuffle=shuffle,
          worker_init_fn=init_fn,
          drop_last=drop_last,
          **kwargs)
  
      return data_loader
  
  
  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)
  