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https://github.com/zebrajr/tensorflow.git
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TFE: Add compatibility errors and doc strings to queues, input pipelines and Supervisor
PiperOrigin-RevId: 173712330
This commit is contained in:
Shanqing Cai
TensorFlower Gardener
parent
b31b08bb0f
commit
0bc432a443
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@ -149,14 +149,15 @@ def input_producer(input_tensor,
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RuntimeError: If called with eager execution enabled.
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@compatibility(eager)
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Queue-using input pipelines are not supported when eager execution is enabled.
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Please use tf.data to ingest data into your model instead.
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Input pipelines based on Queues are not supported when eager execution is
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enabled. Please use the `tf.data` API to ingest data under eager execution.
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@end_compatibility
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"""
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if context.in_eager_mode():
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raise RuntimeError(
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"Queue-using input pipelines are not supported when eager execution is"
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" enabled. Please use tf.data to ingest data into your model instead.")
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"Input pipelines based on Queues are not supported when eager execution"
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" is enabled. Please use tf.data to ingest data into your model"
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" instead.")
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with ops.name_scope(name, "input_producer", [input_tensor]):
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input_tensor = ops.convert_to_tensor(input_tensor, name="input_tensor")
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element_shape = input_tensor.shape[1:].merge_with(element_shape)
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@ -222,6 +223,11 @@ def string_input_producer(string_tensor,
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Raises:
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ValueError: If the string_tensor is a null Python list. At runtime,
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will fail with an assertion if string_tensor becomes a null tensor.
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@compatibility(eager)
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Input pipelines based on Queues are not supported when eager execution is
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enabled. Please use the `tf.data` API to ingest data under eager execution.
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@end_compatibility
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"""
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not_null_err = "string_input_producer requires a non-null input tensor"
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if not isinstance(string_tensor, ops.Tensor) and not string_tensor:
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@ -271,6 +277,11 @@ def range_input_producer(limit, num_epochs=None, shuffle=True, seed=None,
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Returns:
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A Queue with the output integers. A `QueueRunner` for the Queue
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is added to the current `Graph`'s `QUEUE_RUNNER` collection.
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@compatibility(eager)
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Input pipelines based on Queues are not supported when eager execution is
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enabled. Please use the `tf.data` API to ingest data under eager execution.
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@end_compatibility
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"""
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with ops.name_scope(name, "input_producer", [limit]) as name:
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range_tensor = math_ops.range(limit)
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@ -308,6 +319,11 @@ def slice_input_producer(tensor_list, num_epochs=None, shuffle=True, seed=None,
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Raises:
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ValueError: if `slice_input_producer` produces nothing from `tensor_list`.
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@compatibility(eager)
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Input pipelines based on Queues are not supported when eager execution is
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enabled. Please use the `tf.data` API to ingest data under eager execution.
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@end_compatibility
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"""
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with ops.name_scope(name, "input_producer", tensor_list):
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tensor_list = ops.convert_n_to_tensor_or_indexed_slices(tensor_list)
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@ -698,8 +714,9 @@ def _batch(tensors, batch_size, keep_input, num_threads=1, capacity=32,
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"""Helper function for `batch` and `maybe_batch`."""
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if context.in_eager_mode():
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raise ValueError(
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"Queue-using input pipelines are not supported when eager execution is"
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" enabled. Please use tf.data to ingest data into your model instead.")
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"Input pipelines based on Queues are not supported when eager execution"
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" is enabled. Please use tf.data to ingest data into your model"
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" instead.")
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tensor_list = _as_tensor_list(tensors)
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with ops.name_scope(name, "batch", list(tensor_list) + [keep_input]) as name:
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tensor_list = _validate(tensor_list)
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@ -735,8 +752,9 @@ def _batch_join(tensors_list, batch_size, keep_input, capacity=32,
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"""Helper function for `batch_join` and `maybe_batch_join`."""
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if context.in_eager_mode():
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raise ValueError(
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"Queue-using input pipelines are not supported when eager execution is"
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" enabled. Please use tf.data to ingest data into your model instead.")
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"Input pipelines based on Queues are not supported when eager execution"
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" is enabled. Please use tf.data to ingest data into your model"
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" instead.")
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tensor_list_list = _as_tensor_list_list(tensors_list)
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with ops.name_scope(name, "batch_join",
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_flatten(tensor_list_list) + [keep_input]) as name:
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@ -769,8 +787,9 @@ def _shuffle_batch(tensors, batch_size, capacity, min_after_dequeue,
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"""Helper function for `shuffle_batch` and `maybe_shuffle_batch`."""
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if context.in_eager_mode():
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raise ValueError(
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"Queue-using input pipelines are not supported when eager execution is"
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" enabled. Please use tf.data to ingest data into your model instead.")
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"Input pipelines based on Queues are not supported when eager execution"
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" is enabled. Please use tf.data to ingest data into your model"
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" instead.")
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tensor_list = _as_tensor_list(tensors)
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with ops.name_scope(name, "shuffle_batch",
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list(tensor_list) + [keep_input]) as name:
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@ -813,8 +832,9 @@ def _shuffle_batch_join(tensors_list, batch_size, capacity,
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"""Helper function for `shuffle_batch_join` and `maybe_shuffle_batch_join`."""
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if context.in_eager_mode():
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raise ValueError(
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"Queue-using input pipelines are not supported when eager execution is"
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" enabled. Please use tf.data to ingest data into your model instead.")
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"Input pipelines based on Queues are not supported when eager execution"
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" is enabled. Please use tf.data to ingest data into your model"
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" instead.")
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tensor_list_list = _as_tensor_list_list(tensors_list)
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with ops.name_scope(name, "shuffle_batch_join",
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_flatten(tensor_list_list) + [keep_input]) as name:
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@ -923,6 +943,11 @@ def batch(tensors, batch_size, num_threads=1, capacity=32,
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Raises:
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ValueError: If the `shapes` are not specified, and cannot be
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inferred from the elements of `tensors`.
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@compatibility(eager)
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Input pipelines based on Queues are not supported when eager execution is
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enabled. Please use the `tf.data` API to ingest data under eager execution.
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@end_compatibility
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"""
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return _batch(
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tensors,
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@ -1076,6 +1101,11 @@ def batch_join(tensors_list, batch_size, capacity=32, enqueue_many=False,
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Raises:
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ValueError: If the `shapes` are not specified, and cannot be
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inferred from the elements of `tensor_list_list`.
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@compatibility(eager)
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Input pipelines based on Queues are not supported when eager execution is
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enabled. Please use the `tf.data` API to ingest data under eager execution.
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@end_compatibility
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"""
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return _batch_join(
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tensors_list,
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@ -1220,6 +1250,11 @@ def shuffle_batch(tensors, batch_size, capacity, min_after_dequeue,
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Raises:
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ValueError: If the `shapes` are not specified, and cannot be
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inferred from the elements of `tensors`.
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@compatibility(eager)
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Input pipelines based on Queues are not supported when eager execution is
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enabled. Please use the `tf.data` API to ingest data under eager execution.
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@end_compatibility
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"""
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return _shuffle_batch(
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tensors,
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@ -1274,6 +1309,11 @@ def maybe_shuffle_batch(tensors, batch_size, capacity, min_after_dequeue,
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Raises:
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ValueError: If the `shapes` are not specified, and cannot be
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inferred from the elements of `tensors`.
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@compatibility(eager)
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Input pipelines based on Queues are not supported when eager execution is
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enabled. Please use the `tf.data` API to ingest data under eager execution.
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@end_compatibility
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"""
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return _shuffle_batch(
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tensors,
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@ -1363,6 +1403,11 @@ def shuffle_batch_join(tensors_list, batch_size, capacity,
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Raises:
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ValueError: If the `shapes` are not specified, and cannot be
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inferred from the elements of `tensors_list`.
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@compatibility(eager)
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Input pipelines based on Queues are not supported when eager execution is
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enabled. Please use the `tf.data` API to ingest data under eager execution.
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@end_compatibility
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"""
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return _shuffle_batch_join(
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tensors_list,
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@ -1417,6 +1462,11 @@ def maybe_shuffle_batch_join(tensors_list, batch_size, capacity,
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Raises:
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ValueError: If the `shapes` are not specified, and cannot be
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inferred from the elements of `tensors_list`.
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@compatibility(eager)
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Input pipelines based on Queues are not supported when eager execution is
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enabled. Please use the `tf.data` API to ingest data under eager execution.
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@end_compatibility
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"""
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return _shuffle_batch_join(
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tensors_list,
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@ -23,6 +23,7 @@ import weakref
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from tensorflow.core.protobuf import queue_runner_pb2
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from tensorflow.python.client import session
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from tensorflow.python.eager import context
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from tensorflow.python.framework import errors
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from tensorflow.python.framework import ops
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from tensorflow.python.platform import tf_logging as logging
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@ -414,7 +415,18 @@ def start_queue_runners(sess=None, coord=None, daemon=True, start=True,
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Returns:
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A list of threads.
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Raises:
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RuntimeError: If called with eager execution enabled.
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ValueError: If called without a default `tf.Session` registered.
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@compatibility(eager)
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Not compatible with eager execution. To ingest data under eager execution,
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use the `tf.data` API instead.
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@end_compatibility
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"""
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if context.in_eager_mode():
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raise RuntimeError("Queues are not compatible with eager execution.")
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if sess is None:
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sess = ops.get_default_session()
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if not sess:
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@ -23,6 +23,7 @@ import time
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from tensorflow.core.framework.summary_pb2 import Summary
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from tensorflow.core.util.event_pb2 import SessionLog
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from tensorflow.python.eager import context
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import meta_graph
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from tensorflow.python.framework import ops
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@ -288,7 +289,16 @@ class Supervisor(object):
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Returns:
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A `Supervisor`.
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Raises:
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RuntimeError: If called with eager execution enabled.
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@compatibility(eager)
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`Supervisor`s are not supported when eager execution is enabled.
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@end_compatibility
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"""
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if context.in_eager_mode():
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raise RuntimeError("Supervisors are compatible with eager execution.")
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# Set default values of arguments.
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if graph is None:
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graph = ops.get_default_graph()
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@ -735,7 +745,17 @@ class Supervisor(object):
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Returns:
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The list of threads started for the `QueueRunners`.
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Raises:
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RuntimeError: If called with eager execution enabled.
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@compatibility(eager)
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Queues are not compatible with eager execution. To ingest data when eager
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execution is enabled, use the `tf.data` API.
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@end_compatibility
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"""
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if context.in_eager_mode():
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raise RuntimeError("Queues are not compatible with eager execution.")
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if queue_runners is None:
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queue_runners = self._graph.get_collection(ops.GraphKeys.QUEUE_RUNNERS)
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threads = []
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