Function _batch_join

tensorflow/python/training/input.py:800–832

Helper function for `batch_join` and `maybe_batch_join`.

(tensors_list, batch_size, keep_input, capacity=32,
                enqueue_many=False, shapes=None, dynamic_pad=False,
                allow_smaller_final_batch=False, shared_name=None, name=None)

# read that many files in parallel due to the number of seeks required).
# Once this is done, batch() can be written as a call to batch_join().
def _batch_join(tensors_list, batch_size, keep_input, capacity=32,
                enqueue_many=False, shapes=None, dynamic_pad=False,
                allow_smaller_final_batch=False, shared_name=None, name=None):
  """Helper function for `batch_join` and `maybe_batch_join`."""
  if context.executing_eagerly():
    raise ValueError(
        "Input pipelines based on Queues are not supported when eager execution"
        " is enabled. Please use tf.data to ingest data into your model"
        " instead.")
  tensor_list_list = _as_tensor_list_list(tensors_list)
  with ops.name_scope(name, "batch_join",
                      _flatten(tensor_list_list) + [keep_input]) as name:
    tensor_list_list = _validate_join(tensor_list_list)
    keep_input = _validate_keep_input(keep_input, enqueue_many)
    tensor_list_list, sparse_info = _store_sparse_tensors_join(
        tensor_list_list, enqueue_many, keep_input)
    types = _dtypes(tensor_list_list)
    shapes = _shapes(tensor_list_list, shapes, enqueue_many)
    # TODO(josh11b,mrry): Switch to BatchQueue once it is written.
    queue = _which_queue(dynamic_pad)(
        capacity=capacity, dtypes=types, shapes=shapes, shared_name=shared_name)
    _enqueue_join(queue, tensor_list_list, enqueue_many, keep_input)
    summary.scalar(
        "fraction_of_%d_full" % capacity,
        math_ops.cast(queue.size(), dtypes.float32) * (1. / capacity))

    if allow_smaller_final_batch:
      dequeued = queue.dequeue_up_to(batch_size, name=name)
    else:
      dequeued = queue.dequeue_many(batch_size, name=name)
    dequeued = _restore_sparse_tensors(dequeued, sparse_info)
    # tensors_list was validated to not be empty.
    return _as_original_type(tensors_list[0], dequeued)


def _shuffle_batch(tensors, batch_size, capacity, min_after_dequeue,