text: """
  Copyright 2020 Nvidia Corporation
  
  Redistribution and use in source and binary forms, with or without
  modification, are permitted provided that the following conditions are met:
  
  1. Redistributions of source code must retain the above copyright notice, this
     list of conditions and the following disclaimer.
  
  2. Redistributions in binary form must reproduce the above copyright notice,
     this list of conditions and the following disclaimer in the documentation
     and/or other materials provided with the distribution.
  
  3. Neither the name of the copyright holder nor the names of its contributors
     may be used to endorse or promote products derived from this software
     without specific prior written permission.
  
  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
  POSSIBILITY OF SUCH DAMAGE.
  
  Dataset setup and loaders
  """
  
  import importlib
  import torchvision.transforms as standard_transforms
  
  import transforms.joint_transforms as joint_transforms
  import transforms.transforms as extended_transforms
  from torch.utils.data import DataLoader
  
  from config import cfg, update_dataset_cfg, update_dataset_inst
  from runx.logx import logx
  from datasets.randaugment import RandAugment
  
  
  def setup_loaders(args):
      """
      Setup Data Loaders[Currently supports Cityscapes, Mapillary and ADE20kin]
      input: argument passed by the user
      return:  training data loader, validation data loader loader,  train_set
      """
  
      # TODO add error checking to make sure class exists
      logx.msg(f'dataset = {args.dataset}')
  
      mod = importlib.import_module('datasets.{}'.format(args.dataset))
      dataset_cls = getattr(mod, 'Loader')
  
      logx.msg(f'ignore_label = {dataset_cls.ignore_label}')
  
      update_dataset_cfg(num_classes=dataset_cls.num_classes,
                         ignore_label=dataset_cls.ignore_label)
  
      ######################################################################
      # Define transformations, augmentations
      ######################################################################
  
      # Joint transformations that must happen on both image and mask
      if ',' in args.crop_size:
          args.crop_size = [int(x) for x in args.crop_size.split(',')]
      else:
          args.crop_size = int(args.crop_size)
      train_joint_transform_list = [
          # TODO FIXME: move these hparams into cfg
          joint_transforms.RandomSizeAndCrop(args.crop_size,
                                             False,
                                             scale_min=args.scale_min,
                                             scale_max=args.scale_max,
                                             full_size=args.full_crop_training,
                                             pre_size=args.pre_size)]
      train_joint_transform_list.append(
          joint_transforms.RandomHorizontallyFlip())
  
      if args.rand_augment is not None:
          N, M = [int(i) for i in args.rand_augment.split(',')]
          assert isinstance(N, int) and isinstance(M, int), \
              f'Either N {N} or M {M} not integer'
          train_joint_transform_list.append(RandAugment(N, M))
  
      ######################################################################
      # Image only augmentations
      ######################################################################
      train_input_transform = []
  
      if args.color_aug:
          train_input_transform += [extended_transforms.ColorJitter(
              brightness=args.color_aug,
              contrast=args.color_aug,
              saturation=args.color_aug,
              hue=args.color_aug)]
      if args.bblur:
          train_input_transform += [extended_transforms.RandomBilateralBlur()]
      elif args.gblur:
          train_input_transform += [extended_transforms.RandomGaussianBlur()]
  
      mean_std = (cfg.DATASET.MEAN, cfg.DATASET.STD)
      train_input_transform += [standard_transforms.ToTensor(),
                                standard_transforms.Normalize(*mean_std)]
      train_input_transform = standard_transforms.Compose(train_input_transform)
  
      val_input_transform = standard_transforms.Compose([
          standard_transforms.ToTensor(),
          standard_transforms.Normalize(*mean_std)
      ])
  
      target_transform = extended_transforms.MaskToTensor()
  
      if args.jointwtborder:
          target_train_transform = \
              extended_transforms.RelaxedBoundaryLossToTensor(cfg.DATASET.NUM_CLASSES,cfg.DATASET.IGNORE_LABEL)
      else:
          target_train_transform = extended_transforms.MaskToTensor()
  
      if args.eval == 'folder':
          val_joint_transform_list = None
      elif 'mapillary' in args.dataset:
          if args.pre_size is None:
              eval_size = 2177
          else:
              eval_size = args.pre_size
          if cfg.DATASET.MAPILLARY_CROP_VAL:
              val_joint_transform_list = [
                  joint_transforms.ResizeHeight(eval_size),
                  joint_transforms.CenterCropPad(eval_size)]
          else:
              val_joint_transform_list = [
                  joint_transforms.Scale(eval_size)]
      else:
          val_joint_transform_list = None
  
      if args.eval is None or args.eval == 'val':
          val_name = 'val'
      elif args.eval == 'trn':
          val_name = 'train'
      elif args.eval == 'folder':
          val_name = 'folder'
      elif args.eval == 'test':
          val_name = 'test'
      else:
          raise 'unknown eval mode {}'.format(args.eval)
  
      ######################################################################
      # Create loaders
      ######################################################################
      val_set = dataset_cls(
          mode=val_name,
          joint_transform_list=val_joint_transform_list,
          img_transform=val_input_transform,
          label_transform=target_transform,
          eval_folder=args.eval_folder)
  
      update_dataset_inst(dataset_inst=val_set)
  
      if args.apex:
          from datasets.sampler import DistributedSampler
          val_sampler = DistributedSampler(val_set, pad=False, permutation=False,
                                           consecutive_sample=False)
      else:
          val_sampler = None
  
      val_loader = DataLoader(val_set, batch_size=args.bs_val,
                              num_workers=args.num_workers // 2,
                              shuffle=False, drop_last=False,
                              sampler=val_sampler)
  
      if args.eval is not None:
          # Don't create train dataloader if eval
          train_set = None
          train_loader = None
      else:
          train_set = dataset_cls(
              mode='train',
              joint_transform_list=train_joint_transform_list,
              img_transform=train_input_transform,
              label_transform=target_train_transform)
  
          if args.apex:
              from datasets.sampler import DistributedSampler
              train_sampler = DistributedSampler(train_set, pad=True,
                                                 permutation=True,
                                                 consecutive_sample=False)
              train_batch_size = args.bs_trn
          else:
              train_sampler = None
              train_batch_size = args.bs_trn * args.ngpu
  
          train_loader = DataLoader(train_set, batch_size=train_batch_size,
                                    num_workers=args.num_workers,
                                    shuffle=(train_sampler is None),
                                    drop_last=True, sampler=train_sampler)
  
      return train_loader, val_loader, train_set
  