text: # Copyright (c) OpenMMLab. All rights reserved.
  import platform
  import random
  from functools import partial
  
  import numpy as np
  import torch
  from mmcv.parallel import collate
  from mmcv.runner import get_dist_info
  from mmcv.utils import Registry, build_from_cfg, digit_version
  from torch.utils.data import DataLoader
  
  from .samplers import DistributedSampler
  
  if platform.system() != 'Windows':
      # https://github.com/pytorch/pytorch/issues/973
      import resource
      rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
      hard_limit = rlimit[1]
      soft_limit = min(4096, hard_limit)
      resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit))
  
  DATASETS = Registry('dataset')
  PIPELINES = Registry('pipeline')
  
  
  def build_dataset(cfg, default_args=None):
      from .dataset_wrappers import (ConcatDataset, RepeatDataset,
                                     ClassBalancedDataset)
      if isinstance(cfg, (list, tuple)):
          dataset = ConcatDataset([build_dataset(c, default_args) for c in cfg])
      elif cfg['type'] == 'RepeatDataset':
          dataset = RepeatDataset(
              build_dataset(cfg['dataset'], default_args), cfg['times'])
      elif cfg['type'] == 'ClassBalancedDataset':
          dataset = ClassBalancedDataset(
              build_dataset(cfg['dataset'], default_args), cfg['oversample_thr'])
      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,
                       round_up=True,
                       seed=None,
                       pin_memory=True,
                       persistent_workers=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.
          round_up (bool): Whether to round up the length of dataset by adding
              extra samples to make it evenly divisible. Default: True.
          pin_memory (bool): Whether to use pin_memory in DataLoader.
              Default: True
          persistent_workers (bool): If True, the data loader will not shutdown
              the worker processes after a dataset has been consumed once.
              This allows to maintain the workers Dataset instances alive.
              The argument also has effect in PyTorch>=1.7.0.
              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, round_up=round_up)
          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
  
      if digit_version(torch.__version__) >= digit_version('1.8.0'):
          kwargs['persistent_workers'] = persistent_workers
  
      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,
          **kwargs)
  
      return data_loader
  
  
  def worker_init_fn(worker_id, num_workers, rank, seed):
      # 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)
  