Python queue_runner.add_queue_runner方法代碼示例

# 需要導入模塊: from tensorflow.python.training import queue_runner [as 別名]
# 或者: from tensorflow.python.training.queue_runner import add_queue_runner [as 別名]
def _add_remote_queue_runner(self, queue, enq_ops):
    """Adds a remote queue runner to the graph.

    These queue runners differ from the standard in two ways: First,
    they never close their queue. Second, they are added to the
    `Feeder.REMOTE_QUEUE_RUNNERS` collection, rather than
    `ops.GraphKeys.QUEUE_RUNNERS`, so they can be started/stopped
    separately.

    Args:
      queue: The queue.
      enq_ops: A list of ops which perform enqueues (each on its own thread).
    """

    runner = queue_runner.QueueRunner(
        queue,
        enq_ops,
        cancel_op=self._fake_op,
        close_op=self._fake_op)
    queue_runner.add_queue_runner(
        runner, collection=Feeder.REMOTE_QUEUE_RUNNERS) 

# 需要導入模塊: from tensorflow.python.training import queue_runner [as 別名]
# 或者: from tensorflow.python.training.queue_runner import add_queue_runner [as 別名]
def _add_remote_queue_runner(self, queue, enq_ops):
    """Adds a remote queue runner to the graph.

    These queue runners differ from the standard in two ways: First,
    they never close their queue. Second, they are added to the
    `Feeder.REMOTE_QUEUE_RUNNERS` collection, rather than
    `ops.GraphKeys.QUEUE_RUNNERS`, so they can be started/stopped
    seperately.

    Args:
      queue: The queue.
      enq_ops: A list of ops which perform enqueues (each on its own thread).
    """

    runner = queue_runner.QueueRunner(
        queue,
        enq_ops,
        cancel_op=self._fake_op,
        close_op=self._fake_op)
    queue_runner.add_queue_runner(
        runner, collection=Feeder.REMOTE_QUEUE_RUNNERS) 

# 需要導入模塊: from tensorflow.python.training import queue_runner [as 別名]
# 或者: from tensorflow.python.training.queue_runner import add_queue_runner [as 別名]
def _enqueue_join(queue, tensor_list_list):
    enqueue_ops = [queue.enqueue(tl) for tl in tensor_list_list]
    queue_runner.add_queue_runner(queue_runner.QueueRunner(queue, enqueue_ops)) 

# 需要導入模塊: from tensorflow.python.training import queue_runner [as 別名]
# 或者: from tensorflow.python.training.queue_runner import add_queue_runner [as 別名]
def _enqueue_join(queue, tensor_list_list, enqueue_many, keep_input):
  """Enqueue `tensor_list_list` in `queue`."""
  if enqueue_many:
    enqueue_fn = queue.enqueue_many
  else:
    enqueue_fn = queue.enqueue
  if keep_input.get_shape().ndims == 1:
    enqueue_ops = [enqueue_fn(_select_which_to_enqueue(x, keep_input))
                   for x in tensor_list_list]
  else:
    enqueue_ops = [_smart_cond(
        keep_input,
        lambda: enqueue_fn(tl),  # pylint:disable=cell-var-from-loop
        control_flow_ops.no_op) for tl in tensor_list_list]
  queue_runner.add_queue_runner(queue_runner.QueueRunner(queue, enqueue_ops)) 

# 需要導入模塊: from tensorflow.python.training import queue_runner [as 別名]
# 或者: from tensorflow.python.training.queue_runner import add_queue_runner [as 別名]
def _enqueue(queue, tensor_list, threads, enqueue_many, keep_input):
  """Enqueue `tensor_list` in `queue`."""
  if enqueue_many:
    enqueue_fn = queue.enqueue_many
  else:
    enqueue_fn = queue.enqueue
  if keep_input.get_shape().ndims == 1:
    enqueue_ops = [
        enqueue_fn(_select_which_to_enqueue(tensor_list, keep_input))] * threads
  else:
    enqueue_ops = [_smart_cond(
        keep_input,
        lambda: enqueue_fn(tensor_list),
        control_flow_ops.no_op)] * threads
  queue_runner.add_queue_runner(queue_runner.QueueRunner(queue, enqueue_ops)) 

# 需要導入模塊: from tensorflow.python.training import queue_runner [as 別名]
# 或者: from tensorflow.python.training.queue_runner import add_queue_runner [as 別名]
def read(self, queue, name=None):
    """Returns the next record (key, value pair) produced by the reader.

    The multiple reader instances are all configured to `read()` from the
    filenames listed in `queue` and enqueue their output into the `common_queue`
    passed to the constructor, and this method returns the next record dequeued
    from that `common_queue`.


    Readers dequeue a work unit from `queue` if necessary (e.g. when a
    reader needs to start reading from a new file since it has finished with
    the previous file).

    A queue runner for enqueing in the `common_queue` is automatically added to
    the TF QueueRunners collection.

    Args:
      queue: A Queue or a mutable string Tensor representing a handle
        to a Queue, with string work items.
      name: A name for the operation (optional).

    Returns:
      The next record (i.e. (key, value pair)) from the common_queue.
    """

    enqueue_ops = []
    for reader in self._readers:
      enqueue_ops.append(self._common_queue.enqueue(reader.read(queue)))

    queue_runner.add_queue_runner(
        queue_runner.QueueRunner(self._common_queue, enqueue_ops))

    return self._common_queue.dequeue(name=name) 

# 需要導入模塊: from tensorflow.python.training import queue_runner [as 別名]
# 或者: from tensorflow.python.training.queue_runner import add_queue_runner [as 別名]
def set_fed_tensors(self, tensors):
    """Sets fed tensors."""
    enq_op = self._local_q.enqueue(tensors)
    queue_runner.add_queue_runner(queue_runner.QueueRunner(
        self._local_q, [enq_op])) 

# 需要導入模塊: from tensorflow.python.training import queue_runner [as 別名]
# 或者: from tensorflow.python.training.queue_runner import add_queue_runner [as 別名]
def _enqueue_join(queue, tensor_list_list, enqueue_many, keep_input):
  """Enqueue `tensor_list_list` in `queue`."""
  if enqueue_many:
    enqueue_fn = queue.enqueue_many
  else:
    enqueue_fn = queue.enqueue
  if keep_input is None:
    enqueue_ops = [enqueue_fn(tl) for tl in tensor_list_list]
  else:
    enqueue_ops = [control_flow_ops.cond(
        keep_input,
        lambda: enqueue_fn(tl),
        control_flow_ops.no_op) for tl in tensor_list_list]
  queue_runner.add_queue_runner(queue_runner.QueueRunner(queue, enqueue_ops)) 

# 需要導入模塊: from tensorflow.python.training import queue_runner [as 別名]
# 或者: from tensorflow.python.training.queue_runner import add_queue_runner [as 別名]
def _enqueue(queue, tensor_list, threads, enqueue_many, keep_input):
  """Enqueue `tensor_list` in `queue`."""
  if enqueue_many:
    enqueue_fn = queue.enqueue_many
  else:
    enqueue_fn = queue.enqueue
  if keep_input is None:
    enqueue_ops = [enqueue_fn(tensor_list)] * threads
  else:
    enqueue_ops = [control_flow_ops.cond(
        keep_input,
        lambda: enqueue_fn(tensor_list),
        control_flow_ops.no_op)] * threads
  queue_runner.add_queue_runner(queue_runner.QueueRunner(queue, enqueue_ops)) 

# 需要導入模塊: from tensorflow.python.training import queue_runner [as 別名]
# 或者: from tensorflow.python.training.queue_runner import add_queue_runner [as 別名]
def _configure_readers_by(self, queue):
    enqueue_ops = []
    for reader in self._readers:
      enqueue_ops.append(self._common_queue.enqueue(reader.read(queue)))

    queue_runner.add_queue_runner(
        queue_runner.QueueRunner(self._common_queue, enqueue_ops)) 

# 需要導入模塊: from tensorflow.python.training import queue_runner [as 別名]
# 或者: from tensorflow.python.training.queue_runner import add_queue_runner [as 別名]
def _enqueue_join(queue, tensor_list_list, enqueue_many):
  if enqueue_many:
    enqueue_ops = [queue.enqueue_many(tl) for tl in tensor_list_list]
  else:
    enqueue_ops = [queue.enqueue(tl) for tl in tensor_list_list]
  queue_runner.add_queue_runner(queue_runner.QueueRunner(queue, enqueue_ops)) 

# 需要導入模塊: from tensorflow.python.training import queue_runner [as 別名]
# 或者: from tensorflow.python.training.queue_runner import add_queue_runner [as 別名]
def _enqueue(queue, tensor_list, threads, enqueue_many):
  if enqueue_many:
    enqueue_ops = [queue.enqueue_many(tensor_list)] * threads
  else:
    enqueue_ops = [queue.enqueue(tensor_list)] * threads
  queue_runner.add_queue_runner(queue_runner.QueueRunner(queue, enqueue_ops)) 

# 需要導入模塊: from tensorflow.python.training import queue_runner [as 別名]
# 或者: from tensorflow.python.training.queue_runner import add_queue_runner [as 別名]
def read(self, queue, name=None):
    """Returns the next record (key, value pair) produced by the reader.

    The multiple reader instances are all configured to `read()` from the
    filenames listed in `queue` and enqueue their output into the `common_queue`
    passed to the constructor, and this method returns the next record dequeued
    from that `common_queue`.


    Readers dequeue a work unit from `queue` if necessary (e.g. when a
    reader needs to start reading from a new file since it has finished with
    the previous file).

    A queue runner for enqueing in the `common_queue` is automatically added to
    the TF QueueRunners collection.

    Args:
      queue: A Queue or a mutable string Tensor representing a handle
        to a Queue, with string work items.
      name: A name for the operation (optional).

    Returns:
      The next record (i.e. (key, value pair)) from the common_queue.
    """

    enqueue_ops = []
    for reader in self._readers:
      enqueue_ops.append(self._common_queue.enqueue(reader.read(queue)))

    queue_runner.add_queue_runner(queue_runner.QueueRunner(
        self._common_queue, enqueue_ops))

    return self._common_queue.dequeue(name=name) 

# 需要導入模塊: from tensorflow.python.training import queue_runner [as 別名]
# 或者: from tensorflow.python.training.queue_runner import add_queue_runner [as 別名]
def _make_batch_queue(input, capacity, num_threads=1):
    queue = tf.PaddingFIFOQueue(capacity=capacity, dtypes=[s.dtype for s in input], shapes=[s.get_shape() for s in input])
    tf.summary.scalar("fraction_of_%d_full" % capacity,
           tf.cast(queue.size(), tf.float32) *
           (1. / capacity))
    enqueue_ops = [queue.enqueue(input)]*num_threads
    queue_runner.add_queue_runner(queue_runner.QueueRunner(queue, enqueue_ops))
    return queue

# This class is responsible for setting up and running experiments
# Also provides helper functions related to experiments (e.g. get accuracy) 

# 需要導入模塊: from tensorflow.python.training import queue_runner [as 別名]
# 或者: from tensorflow.python.training.queue_runner import add_queue_runner [as 別名]
def _enqueue_join(queue, tensor_list_list, enqueue_many, keep_input):
  """Enqueue `tensor_list_list` in `queue`."""
  if enqueue_many:
    enqueue_fn = queue.enqueue_many
  else:
    enqueue_fn = queue.enqueue
  if keep_input.shape.ndims == 1:
    enqueue_ops = [enqueue_fn(_select_which_to_enqueue(x, keep_input))
                   for x in tensor_list_list]
  else:
    enqueue_ops = [_smart_cond(
        keep_input,
        lambda: enqueue_fn(tl),  # pylint:disable=cell-var-from-loop
        control_flow_ops.no_op) for tl in tensor_list_list]
  queue_runner.add_queue_runner(queue_runner.QueueRunner(queue, enqueue_ops))