code: # 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 torch.utils.data import DataLoader
  
  from .samplers import DistributedGroupSampler, DistributedSampler, GroupSampler
  
  DATA_ROOT = {
      'BIT': './data/BIT',
      'UT': './data/ut120',
      'highfive': './data/highfive'
  }
  
  FRAMES_ROOT = {
      'BIT': 'Bit-frames',
  }
  
  ANNO_ROOT = {
      'BIT': 'BIT-anno/tidy_anno'
  }
  
  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))
  
  HID_DATASETS = Registry('hid_dataset')
  HID_PIPELINES = Registry('hid_pipeline')
  
  
  def build_dataset(cfg, default_args=None):
      dataset = build_from_cfg(cfg, HID_DATASETS, default_args)
      return dataset
  
  
  def build_dataloader(dataset,
                       samples_per_gpu,
                       workers_per_gpu,
                       num_gpus=1,
                       dist=True,
                       shuffle=True,
                       seed=None,
                       **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.
          kwargs: any keyword argument to be used to initialize DataLoader
  
      Returns:
          DataLoader: A PyTorch dataloader.
      """
      rank, world_size = get_dist_info()
      if dist:
          # DistributedGroupSampler will definitely shuffle the data to satisfy
          # that images on each GPU are in the same group
          if shuffle:
              sampler = DistributedGroupSampler(
                  dataset, samples_per_gpu, world_size, rank, seed=seed)
          else:
              sampler = DistributedSampler(
                  dataset, world_size, rank, shuffle=False, seed=seed)
          batch_size = samples_per_gpu
          num_workers = workers_per_gpu
      else:
          sampler = GroupSampler(dataset, samples_per_gpu) if shuffle else 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
  
      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=False,
          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)
  
apis: numpy.random.seed