text: # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
  import bisect
  import copy
  import logging
  
  import torch.utils.data
  from maskrcnn_benchmark.utils.comm import get_world_size
  from maskrcnn_benchmark.utils.imports import import_file
  
  from . import datasets as D
  from . import samplers
  
  from .collate_batch import BatchCollator
  from .transforms import build_transforms
  
  
  def build_dataset(dataset_list, transforms, dataset_catalog, is_train=True):
      """
      Arguments:
          dataset_list (list[str]): Contains the names of the datasets, i.e.,
              coco_2014_trian, coco_2014_val, etc
          transforms (callable): transforms to apply to each (image, target) sample
          dataset_catalog (DatasetCatalog): contains the information on how to
              construct a dataset.
          is_train (bool): whether to setup the dataset for training or testing
      """
      if not isinstance(dataset_list, (list, tuple)):
          raise RuntimeError(
              "dataset_list should be a list of strings, got {}".format(dataset_list)
          )
      datasets = []
      for dataset_name in dataset_list:
          data = dataset_catalog.get(dataset_name)
          factory = getattr(D, data["factory"])
          args = data["args"]
          # for COCODataset, we want to remove images without annotations
          # during training
          if data["factory"] == "COCODataset":
              args["remove_images_without_annotations"] = is_train
          if data["factory"] == "PascalVOCDataset":
              args["use_difficult"] = not is_train
          args["transforms"] = transforms
          # make dataset from factory
          dataset = factory(**args)
          datasets.append(dataset)
  
      # for testing, return a list of datasets
      if not is_train:
          return datasets
  
      # for training, concatenate all datasets into a single one
      dataset = datasets[0]
      if len(datasets) > 1:
          dataset = D.ConcatDataset(datasets)
  
      return [dataset]
  
  
  def make_data_sampler(dataset, shuffle, distributed):
      if distributed:
          return samplers.DistributedSampler(dataset, shuffle=shuffle)
      if shuffle:
          sampler = torch.utils.data.sampler.RandomSampler(dataset)
      else:
          sampler = torch.utils.data.sampler.SequentialSampler(dataset)
      return sampler
  
  
  def _quantize(x, bins):
      bins = copy.copy(bins)
      bins = sorted(bins)
      quantized = list(map(lambda y: bisect.bisect_right(bins, y), x))
      return quantized
  
  
  def _compute_aspect_ratios(dataset):
      aspect_ratios = []
      for i in range(len(dataset)):
          img_info = dataset.get_img_info(i)
          aspect_ratio = float(img_info["height"]) / float(img_info["width"])
          aspect_ratios.append(aspect_ratio)
      return aspect_ratios
  
  
  def make_batch_data_sampler(
      dataset, sampler, aspect_grouping, images_per_batch, num_iters=None, start_iter=0
  ):
      if aspect_grouping:
          if not isinstance(aspect_grouping, (list, tuple)):
              aspect_grouping = [aspect_grouping]
          aspect_ratios = _compute_aspect_ratios(dataset)
          group_ids = _quantize(aspect_ratios, aspect_grouping)
          batch_sampler = samplers.GroupedBatchSampler(
              sampler, group_ids, images_per_batch, drop_uneven=False
          )
      else:
          batch_sampler = torch.utils.data.sampler.BatchSampler(
              sampler, images_per_batch, drop_last=False
          )
      if num_iters is not None:
          batch_sampler = samplers.IterationBasedBatchSampler(
              batch_sampler, num_iters, start_iter
          )
      return batch_sampler
  
  
  def make_data_loader(cfg, is_train=True, is_distributed=False, start_iter=0):
      num_gpus = get_world_size()
      if is_train:
          images_per_batch = cfg.SOLVER.IMS_PER_BATCH
          assert (
              images_per_batch % num_gpus == 0
          ), "SOLVER.IMS_PER_BATCH ({}) must be divisible by the number "
          "of GPUs ({}) used.".format(images_per_batch, num_gpus)
          images_per_gpu = images_per_batch // num_gpus
          shuffle = True
          num_iters = cfg.SOLVER.MAX_ITER
      else:
          images_per_batch = cfg.TEST.IMS_PER_BATCH
          assert (
              images_per_batch % num_gpus == 0
          ), "TEST.IMS_PER_BATCH ({}) must be divisible by the number "
          "of GPUs ({}) used.".format(images_per_batch, num_gpus)
          images_per_gpu = images_per_batch // num_gpus
          shuffle = False if not is_distributed else True
          num_iters = None
          start_iter = 0
  
      if images_per_gpu > 1:
          logger = logging.getLogger(__name__)
          logger.warning(
              "When using more than one image per GPU you may encounter "
              "an out-of-memory (OOM) error if your GPU does not have "
              "sufficient memory. If this happens, you can reduce "
              "SOLVER.IMS_PER_BATCH (for training) or "
              "TEST.IMS_PER_BATCH (for inference). For training, you must "
              "also adjust the learning rate and schedule length according "
              "to the linear scaling rule. See for example: "
              "https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14"
          )
  
      # group images which have similar aspect ratio. In this case, we only
      # group in two cases: those with width / height > 1, and the other way around,
      # but the code supports more general grouping strategy
      aspect_grouping = [1] if cfg.DATALOADER.ASPECT_RATIO_GROUPING else []
  
      paths_catalog = import_file(
          "maskrcnn_benchmark.config.paths_catalog", cfg.PATHS_CATALOG, True
      )
      DatasetCatalog = paths_catalog.DatasetCatalog
      dataset_list = cfg.DATASETS.TRAIN if is_train else cfg.DATASETS.TEST
  
      transforms = build_transforms(cfg, is_train)
      datasets = build_dataset(dataset_list, transforms, DatasetCatalog, is_train)
  
      data_loaders = []
      for dataset in datasets:
          sampler = make_data_sampler(dataset, shuffle, is_distributed)
          batch_sampler = make_batch_data_sampler(
              dataset, sampler, aspect_grouping, images_per_gpu, num_iters, start_iter
          )
          collator = BatchCollator(cfg.DATALOADER.SIZE_DIVISIBILITY)
          num_workers = cfg.DATALOADER.NUM_WORKERS
          data_loader = torch.utils.data.DataLoader(
              dataset,
              num_workers=num_workers,
              batch_sampler=batch_sampler,
              collate_fn=collator,
          )
          data_loaders.append(data_loader)
      if is_train:
          # during training, a single (possibly concatenated) data_loader is returned
          assert len(data_loaders) == 1
          return data_loaders[0]
      return data_loaders
  