address comments

torch/utils/data/dataloader.py

@@ -95,17 +95,12 @@ class DataLoader(object):
  :ref:`multiprocessing-best-practices` on more details related
  to multiprocessing in PyTorch.
 
- .. note:: ``len(dataloader)`` is determined by length of the sampler used.
- When :attr:`dataset` is an :class:`~torch.utils.data.IterableDataset`,
+ .. note:: ``len(dataloader)`` heuristic based on the length of the sampler used.
+ When :attr:`dataset` is a subclass of :class:`~torch.utils.data.IterableDataset`,
  an infinite sampler is used, whose :meth:`__len__` is not
- implemented. With :class:`~torch.utils.data.IterableDataset` the
- actual iterator size depends on :attr:`num_workers`.
- If :attr:`num_workers > 0` (i.e., multi-process loading), each
- worker gets a copy of the same iterable dataset object and can
- return duplicate data, unless the dataset copies and/or the
- workers are configured differently (e.g., in :meth:`__iter__`).
- See :class:`~torch.utils.data.IterableDataset` for more details
- and examples.
+ implemented. So one should not query this method unless they work
+ with a map-style dataset. See `Dataset Types`_ for more details on
+ these two types of dataset.
  """
 
  __initialized = False
@@ -612,9 +607,9 @@ def __init__(self, loader):
 
  self.index_queues = []
  self.workers = []
- # A list of booleans representing whether each worker still has word to
+ # A list of booleans representing whether each worker still has work to
  # do, i.e., not having exhausted its iterable dataset object. It always
- # contains all `True`s if not using an iterable dataset
+ # contains all `True`s if not using an iterable-style dataset
  # (i.e., if kind != Iterable).
  self.workers_status = []
  for i in range(self.num_workers):