content: # Adopted from https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/data/samplers/iteration_based_batch_sampler.py
  from torch.utils.data.sampler import BatchSampler
  
  
  class IterationBasedBatchSampler(BatchSampler):
      """
      Wraps a BatchSampler, resampling from it until
      a specified number of iterations have been sampled
      """
  
      def __init__(self, batch_sampler, num_iterations, start_iter=0):
          self.batch_sampler = batch_sampler
          self.num_iterations = num_iterations
          self.start_iter = start_iter
  
      def __iter__(self):
          iteration = self.start_iter
          while iteration <= self.num_iterations:
              # if the underlying sampler has a set_epoch method, like
              # DistributedSampler, used for making each process see
              # a different split of the dataset, then set it
              if hasattr(self.batch_sampler.sampler, "set_epoch"):
                  self.batch_sampler.sampler.set_epoch(iteration)
              for batch in self.batch_sampler:
                  iteration += 1
                  if iteration > self.num_iterations:
                      break
                  yield batch
  
      def __len__(self):
          return self.num_iterations
  