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# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

# http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

# ==============================================================================

"""Input pipeline.

Please see the @{$reading_data$reading data how-to}

for context.

"""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import collections

from six.moves import xrange # pylint: disable=redefined-builtin

from tensorflow.python.eager import context

from tensorflow.python.framework import constant_op

from tensorflow.python.framework import dtypes

from tensorflow.python.framework import ops

from tensorflow.python.framework import sparse_tensor

from tensorflow.python.framework import tensor_shape

from tensorflow.python.layers import utils

from tensorflow.python.ops import array_ops

from tensorflow.python.ops import control_flow_ops

from tensorflow.python.ops import data_flow_ops

from tensorflow.python.ops import io_ops

from tensorflow.python.ops import math_ops

from tensorflow.python.ops import random_ops

from tensorflow.python.ops import sparse_ops

from tensorflow.python.ops import variable_scope as vs

from tensorflow.python.summary import summary

from tensorflow.python.training import queue_runner

from tensorflow.python.util.tf_export import tf_export

# pylint: disable=protected-access

_store_sparse = sparse_ops._add_sparse_to_tensors_map

_store_many_sparse = sparse_ops._add_many_sparse_to_tensors_map

_restore_sparse = sparse_ops._take_many_sparse_from_tensors_map

# pylint: enable=protected-access

@tf_export("train.match_filenames_once")

def match_filenames_once(pattern, name=None):

"""Save the list of files matching pattern, so it is only computed once.

NOTE: The order of the files returned can be non-deterministic.

Args:

pattern: A file pattern (glob), or 1D tensor of file patterns.

Returns:

A variable that is initialized to the list of files matching the pattern(s).

"""

with ops.name_scope(name, "matching_filenames", [pattern]) as name:

return vs.variable(

name=name, initial_value=io_ops.matching_files(pattern),

trainable=False, validate_shape=False,

collections=[ops.GraphKeys.LOCAL_VARIABLES])

@tf_export("train.limit_epochs")

def limit_epochs(tensor, num_epochs=None, name=None):

"""Returns tensor `num_epochs` times and then raises an `OutOfRange` error.

Note: creates local counter `epochs`. Use `local_variables_initializer()` to

initialize local variables.

Args:

tensor: Any `Tensor`.

num_epochs: A positive integer (optional). If specified, limits the number

of steps the output tensor may be evaluated.

Returns:

tensor or `OutOfRange`.

Raises:

ValueError: if `num_epochs` is invalid.

"""

if num_epochs is None:

return tensor

if num_epochs <= 0:

raise ValueError("num_epochs must be > 0 not %d." % num_epochs)

with ops.name_scope(name, "limit_epochs", [tensor]) as name:

zero64 = constant_op.constant(0, dtype=dtypes.int64)

epochs = vs.variable(

zero64, name="epochs", trainable=False,

collections=[ops.GraphKeys.LOCAL_VARIABLES])

counter = epochs.count_up_to(num_epochs)

with ops.control_dependencies([counter]):

return array_ops.identity(tensor, name=name)

@tf_export("train.input_producer")

def input_producer(input_tensor,

element_shape=None,

num_epochs=None,

shuffle=True,

seed=None,

capacity=32,

shared_name=None,

summary_name=None,

name=None,

cancel_op=None):

"""Output the rows of `input_tensor` to a queue for an input pipeline.

Note: if `num_epochs` is not `None`, this function creates local counter

`epochs`. Use `local_variables_initializer()` to initialize local variables.

Args:

input_tensor: A tensor with the rows to produce. Must be at least

one-dimensional. Must either have a fully-defined shape, or

`element_shape` must be defined.

element_shape: (Optional.) A `TensorShape` representing the shape of a

row of `input_tensor`, if it cannot be inferred.

num_epochs: (Optional.) An integer. If specified `input_producer` produces

each row of `input_tensor` `num_epochs` times before generating an

`OutOfRange` error. If not specified, `input_producer` can cycle through

the rows of `input_tensor` an unlimited number of times.

shuffle: (Optional.) A boolean. If true, the rows are randomly shuffled

within each epoch.

seed: (Optional.) An integer. The seed to use if `shuffle` is true.

capacity: (Optional.) The capacity of the queue to be used for buffering

the input.

shared_name: (Optional.) If set, this queue will be shared under the given

name across multiple sessions.

summary_name: (Optional.) If set, a scalar summary for the current queue

size will be generated, using this name as part of the tag.

cancel_op: (Optional.) Cancel op for the queue

Returns:

A queue with the output rows. A `QueueRunner` for the queue is

added to the current `QUEUE_RUNNER` collection of the current

graph.

Raises:

ValueError: If the shape of the input cannot be inferred from the arguments.

RuntimeError: If called with eager execution enabled.

@compatibility(eager)

Input pipelines based on Queues are not supported when eager execution is

enabled. Please use the `tf.data` API to ingest data under eager execution.

@end_compatibility

"""

if context.executing_eagerly():

raise RuntimeError(

"Input pipelines based on Queues are not supported when eager execution"

" is enabled. Please use tf.data to ingest data into your model"

" instead.")

with ops.name_scope(name, "input_producer", [input_tensor]):

input_tensor = ops.convert_to_tensor(input_tensor, name="input_tensor")

element_shape = input_tensor.shape[1:].merge_with(element_shape)

if not element_shape.is_fully_defined():

raise ValueError("Either `input_tensor` must have a fully defined shape "

"or `element_shape` must be specified")

if shuffle:

input_tensor = random_ops.random_shuffle(input_tensor, seed=seed)

input_tensor = limit_epochs(input_tensor, num_epochs)

q = data_flow_ops.FIFOQueue(capacity=capacity,

dtypes=[input_tensor.dtype.base_dtype],

shapes=[element_shape],

shared_name=shared_name, name=name)

enq = q.enqueue_many([input_tensor])

queue_runner.add_queue_runner(

queue_runner.QueueRunner(

q, [enq], cancel_op=cancel_op))

if summary_name is not None:

summary.scalar(summary_name,

math_ops.to_float(q.size()) * (1. / capacity))

return q

@tf_export("train.string_input_producer")

def string_input_producer(string_tensor,

num_epochs=None,

shuffle=True,

seed=None,

capacity=32,

shared_name=None,

name=None,

cancel_op=None):

"""Output strings (e.g. filenames) to a queue for an input pipeline.

Note: if `num_epochs` is not `None`, this function creates local counter

`epochs`. Use `local_variables_initializer()` to initialize local variables.

Args:

string_tensor: A 1-D string tensor with the strings to produce.

num_epochs: An integer (optional). If specified, `string_input_producer`

produces each string from `string_tensor` `num_epochs` times before

generating an `OutOfRange` error. If not specified,

`string_input_producer` can cycle through the strings in `string_tensor`

an unlimited number of times.

shuffle: Boolean. If true, the strings are randomly shuffled within each

epoch.

seed: An integer (optional). Seed used if shuffle == True.

capacity: An integer. Sets the queue capacity.

shared_name: (optional). If set, this queue will be shared under the given

name across multiple sessions. All sessions open to the device which has

this queue will be able to access it via the shared_name. Using this in

a distributed setting means each name will only be seen by one of the

sessions which has access to this operation.

cancel_op: Cancel op for the queue (optional).

Returns:

A queue with the output strings. A `QueueRunner` for the Queue

is added to the current `Graph`'s `QUEUE_RUNNER` collection.

Raises:

ValueError: If the string_tensor is a null Python list. At runtime,

will fail with an assertion if string_tensor becomes a null tensor.

@compatibility(eager)

Input pipelines based on Queues are not supported when eager execution is

enabled. Please use the `tf.data` API to ingest data under eager execution.

@end_compatibility

"""

not_null_err = "string_input_producer requires a non-null input tensor"

if not isinstance(string_tensor, ops.Tensor) and not string_tensor:

raise ValueError(not_null_err)

with ops.name_scope(name, "input_producer", [string_tensor]) as name:

string_tensor = ops.convert_to_tensor(string_tensor, dtype=dtypes.string)

with ops.control_dependencies([

control_flow_ops.Assert(

math_ops.greater(array_ops.size(string_tensor), 0),

[not_null_err])]):

string_tensor = array_ops.identity(string_tensor)

return input_producer(

input_tensor=string_tensor,

element_shape=[],

num_epochs=num_epochs,

shuffle=shuffle,

seed=seed,

capacity=capacity,

shared_name=shared_name,

name=name,

summary_name="fraction_of_%d_full" % capacity,

cancel_op=cancel_op)

@tf_export("train.range_input_producer")

def range_input_producer(limit, num_epochs=None, shuffle=True, seed=None,

capacity=32, shared_name=None, name=None):

"""Produces the integers from 0 to limit-1 in a queue.

Note: if `num_epochs` is not `None`, this function creates local counter

`epochs`. Use `local_variables_initializer()` to initialize local variables.

Args:

limit: An int32 scalar tensor.

num_epochs: An integer (optional). If specified, `range_input_producer`

produces each integer `num_epochs` times before generating an

OutOfRange error. If not specified, `range_input_producer` can cycle

through the integers an unlimited number of times.

shuffle: Boolean. If true, the integers are randomly shuffled within each

epoch.

seed: An integer (optional). Seed used if shuffle == True.

capacity: An integer. Sets the queue capacity.

shared_name: (optional). If set, this queue will be shared under the given

name across multiple sessions.

Returns:

A Queue with the output integers. A `QueueRunner` for the Queue

is added to the current `Graph`'s `QUEUE_RUNNER` collection.

@compatibility(eager)

Input pipelines based on Queues are not supported when eager execution is

enabled. Please use the `tf.data` API to ingest data under eager execution.

@end_compatibility

"""

with ops.name_scope(name, "input_producer", [limit]) as name:

range_tensor = math_ops.range(limit)

return input_producer(

range_tensor, [], num_epochs, shuffle, seed, capacity,

shared_name, "fraction_of_%d_full" % capacity, name)

@tf_export("train.slice_input_producer")

def slice_input_producer(tensor_list, num_epochs=None, shuffle=True, seed=None,

capacity=32, shared_name=None, name=None):

"""Produces a slice of each `Tensor` in `tensor_list`.

Implemented using a Queue -- a `QueueRunner` for the Queue

is added to the current `Graph`'s `QUEUE_RUNNER` collection.

Args:

tensor_list: A list of `Tensor` objects. Every `Tensor` in

`tensor_list` must have the same size in the first dimension.

num_epochs: An integer (optional). If specified, `slice_input_producer`

produces each slice `num_epochs` times before generating

an `OutOfRange` error. If not specified, `slice_input_producer` can cycle

through the slices an unlimited number of times.

shuffle: Boolean. If true, the integers are randomly shuffled within each

epoch.

seed: An integer (optional). Seed used if shuffle == True.

capacity: An integer. Sets the queue capacity.

shared_name: (optional). If set, this queue will be shared under the given

name across multiple sessions.

Returns:

A list of tensors, one for each element of `tensor_list`. If the tensor

in `tensor_list` has shape `[N, a, b, .., z]`, then the corresponding output

tensor will have shape `[a, b, ..., z]`.

Raises:

ValueError: if `slice_input_producer` produces nothing from `tensor_list`.

@compatibility(eager)

Input pipelines based on Queues are not supported when eager execution is

enabled. Please use the `tf.data` API to ingest data under eager execution.

@end_compatibility

"""

with ops.name_scope(name, "input_producer", tensor_list):

tensor_list = ops.convert_n_to_tensor_or_indexed_slices(tensor_list)

if not tensor_list:

raise ValueError(

"Expected at least one tensor in slice_input_producer().")

range_size = array_ops.shape(tensor_list[0])[0]

# TODO(josh11b): Add an assertion that the first dimension of

# everything in TensorList matches. Maybe just check the inferred shapes?

queue = range_input_producer(range_size, num_epochs=num_epochs,

shuffle=shuffle, seed=seed, capacity=capacity,

shared_name=shared_name)

index = queue.dequeue()

output = [array_ops.gather(t, index) for t in tensor_list]

return output

# Helpers for the batching functions ------------------------------------------

def _flatten(tensor_list_list):

return [tensor for tensor_list in tensor_list_list for tensor in tensor_list]

class _SparseMetaData(object):

"""Store information about the Tensor: Is it sparse?, map_op, and rank."""

def __init__(self, sparse, map_op, rank):

"""Create the metadata.

Args:

sparse: Python boolean.

map_op: The `Operation` that created the `SparseTensorsMap` in question.

This Op contains information about the underlying Map object and the

dtype of the original data.

rank: The statically known rank of the `SparseTensor`.

"""

self._sparse = sparse

self._map_op = map_op

self._rank = rank

def __eq__(self, other):

if self.sparse != other.sparse:

return False

if not self.sparse:

return True

# If map_ops are not the same, the data source is not the same.

if (self.map_op is not None) != (other.map_op is not None):

return False

if self.map_op != other.map_op:

return False

if not self.rank.is_compatible_with(other.rank):

return False

return True

def __ne__(self, other):

return not self.__eq__(other)

def __str__(self):

return "[SparseMetaData(%s, %s, %s)]" % (self.sparse, self.map_op.name,

self.rank)

def merge_with(self, other):

if self != other:

raise ValueError("SparseMetaData objects are incompatible: %s vs. %s"

% (self, other))

if self.sparse:

self.rank.merge_with(other.rank)

return self

@property

def map_op(self):

return self._map_op

@property

def sparse(self):

return self._sparse

@property

def rank(self):

return self._rank

def _as_tensor_list(tensors):

if isinstance(tensors, dict):

return [tensors[k] for k in sorted(tensors, key=str)]

else:

return tensors

def _as_tensor_list_list(tensors_list):

if not tensors_list:

raise ValueError("Expected at least one set of tensors")

if isinstance(tensors_list[0], dict):

expected_keys = set(tensors_list[0].keys())

for tensors in tensors_list[1:]:

if set(tensors.keys()) != expected_keys:

raise ValueError("All dictionaries in tensors_list must have "

"the same keys")

return [_as_tensor_list(tensors) for tensors in tensors_list]

else:

return tensors_list

def _as_original_type(original_tensors, tensor_list):

if isinstance(original_tensors, dict):

if len(original_tensors) == 1:

# tensor_list is bogusly returned as a single tensor if only one tensor

# was enqueued. Make it a list again. See b/28117485.

tensor_list = [tensor_list]

return {k: tensor_list[i]

for i, k in enumerate(sorted(original_tensors, key=str))}

else:

return tensor_list

def _store_sparse_tensors(tensor_list, enqueue_many, keep_input,

shared_map_ops=None):

"""Store SparseTensors for feeding into batch, etc.

If `shared_map_ops` is provided, the underlying `SparseTensorsMap` objects

are reused (shared). This argument is useful for, e.g., `batch_join`

where multiple enqueue operations write to the same Queue component,

and another (dequeue) thread reads from that same location and must then

restore the associated `SparseTensor` objects. In this case, the sparse

restore must have a single `SparseTensorMap` from which to read out the

handles; so a single `SparseTensorMap` must be shared for storing

across the multiple enqueue operations. This sharing is performed by

calling `_store_sparse_tensors` the first time with `shared_map_ops=None`,

and then in subsequent times with this value set to the list of `Operation`

objects created in the first call.

Args:

tensor_list: List of `Tensor` and `SparseTensor` objects.

enqueue_many: Python `Boolean`.

keep_input: Must be a scalar bool Tensor (not a Python bool). If False,

don't store.

shared_map_ops: (optional) List of `Operation` objects from a previous

call to `_store_sparse_tensors`. If not `None`, the op types should be

one of `AddSparseToTensorsMap` or `AddManySparseToTensorsMap` in the

locations corresponding to `SparseTensors` in `tensor_list`.

Returns:

A tuple `(stored_list, sparse_info_list)` where `stored_list` is a list

of `Tensor` objects (same length as `tensor_list`) and `sparse_info_list`

is a list of the same length of `_SparseMetaData` objects.

"""

maybe_shared_map_ops = shared_map_ops or [None] * len(tensor_list)

def _sparse_meta_data(t, storing_op, map_op):

if not isinstance(t, sparse_tensor.SparseTensor):

return _SparseMetaData(False, None, None)

rank = t.dense_shape.shape.with_rank(1)[0]

if enqueue_many:

rank -= 1

# If a shared map_op was provided, use that. Otherwise use the name of

# the operation used to store the SparseTensor.

return _SparseMetaData(

sparse=True, map_op=map_op or storing_op, rank=rank)

def _maybe_store(t, shared_map_op):

"""Store Sparse tensor, if necessary."""

if not isinstance(t, sparse_tensor.SparseTensor):

return t

map_op_name = shared_map_op.name if shared_map_op else None

def _maybe_store_sparse(t, map_op_name, keep_input):

"""Conditionally store a single sparse Tensor."""

return utils.smart_cond(

keep_input,

lambda: _store_sparse(t, shared_name=map_op_name),

lambda: constant_op.constant(-1, dtypes.int64))

def _maybe_store_many_sparse(t, map_op_name, keep_input):

"""Conditionally store multiple sparse Tensors."""

out_tensor = utils.smart_cond(

keep_input,

lambda: _store_many_sparse(t, shared_name=map_op_name),

lambda: -1 * array_ops.ones(array_ops.shape(t)[0:1], dtypes.int64))

out_tensor.set_shape([None]) # necessary when t.ndims is unknown

return out_tensor

def _sparse_values_to_keep(t, keep_input):

"""Convert a per-row `keep_input` vector to a per-value one."""

# Get the rows of every value in the sparse Tensor.

row_values = t.indices[:, 0]

# The value should be kept iff the row should be kept.

return array_ops.gather(keep_input, row_values)

if keep_input.shape.ndims == 1:

t = sparse_ops.sparse_retain(t, _sparse_values_to_keep(t, keep_input))

store_f = lambda t, name, _: _store_many_sparse(t, shared_name=name)

elif enqueue_many:

store_f = _maybe_store_many_sparse

else:

store_f = _maybe_store_sparse

return store_f(t, map_op_name, keep_input)

stored_list = [

_maybe_store(t, shared_map_op) for t, shared_map_op

in zip(tensor_list, maybe_shared_map_ops)]

# Since the output of `_store{_many}_sparse is wrapped in a tf.cond `Merge`,

# we can't just get the Op of the resulting tensor.

def _sparse_op(stored):

for input_tensor in stored.op.inputs:

if input_tensor.op.type in ("AddSparseToTensorsMap",

"AddManySparseToTensorsMap"):

return input_tensor.op

# If there was no sparse input, then the original stored Tensor wasn't

# sparse and we can just return the original Tensor's Op.

return stored.op

sparse_info_list = [

_sparse_meta_data(t, _sparse_op(stored), shared_map_op)

for t, stored, shared_map_op

in zip(tensor_list, stored_list, maybe_shared_map_ops)]

# Expand dims of stored tensors by 1 for proper enqueue shape

stored_list = [

array_ops.expand_dims(s, [-1]) if s_info.sparse else s

for s, s_info in zip(stored_list, sparse_info_list)]

return stored_list, sparse_info_list

def _store_sparse_tensors_join(tensor_list_list, enqueue_many, keep_input):

"""Store SparseTensors for feeding into batch_join, etc."""

(s0, sparse_info_list) = _store_sparse_tensors(

tensor_list_list[0], enqueue_many, keep_input)

stored_list_list = [s0]

for tensor_list in tensor_list_list[1:]:

s, sparse_info_candidate = _store_sparse_tensors(

tensor_list, enqueue_many, keep_input,

[st.map_op for st in sparse_info_list])

if sparse_info_list != sparse_info_candidate:

raise ValueError("Inconsistent SparseTensors list: %s vs. %s"

% (tensor_list_list[0], tensor_list))

sparse_info_list = [

info.merge_with(candidate)

for (info, candidate) in zip(sparse_info_list, sparse_info_candidate)]

stored_list_list.append(s)

return (stored_list_list, sparse_info_list)

def _restore_sparse_tensors(stored_list, sparse_info_list):

"""Restore SparseTensors after dequeue in batch, batch_join, etc."""

received_sequence = isinstance(stored_list, collections.Sequence)

if not received_sequence:

stored_list = (stored_list,)

tensors = [

_restore_sparse(sparse_map_op=info.map_op,

sparse_handles=array_ops.squeeze(s, [1]),

rank=(info.rank + 1).value)

if info.sparse else s

for (s, info) in zip(stored_list, sparse_info_list)]

has_st = any(isinstance(x, sparse_tensor.SparseTensor) for x in tensors)

if has_st:

t_values = [

x.values if isinstance(x, sparse_tensor.SparseTensor)

else x

for x in tensors]

with_deps = lambda x: control_flow_ops.with_dependencies(t_values, x)

ensure_restore_tensors = [

sparse_tensor.SparseTensor(indices=with_deps(x.indices),

values=with_deps(x.values),

dense_shape=with_deps(x.dense_shape))

if isinstance(x, sparse_tensor.SparseTensor)

else with_deps(x)

for x in tensors]

else:

ensure_restore_tensors = tensors

return ensure_restore_tensors if received_sequence else tensors[0]

def _validate(tensor_list):

tensor_list = ops.convert_n_to_tensor_or_indexed_slices(tensor_list)

if not tensor_list:

raise ValueError("Expected at least one tensor in batch().")

return tensor_list

def _validate_join(tensor_list_list):

tensor_list_list = [ops.convert_n_to_tensor_or_indexed_slices(tl)

for tl in tensor_list_list]

if not tensor_list_list:

raise ValueError("Expected at least one input in batch_join().")

return tensor_list_list

def _validate_keep_input(keep_input, enqueue_many):

"""Validate `keep_input` argument to conditional batching functions."""

keep_input = ops.convert_to_tensor(keep_input)

if keep_input.shape.ndims is None:

raise ValueError(

"`keep_input` dimensions must be known at graph construction.")

if not enqueue_many and keep_input.shape.ndims == 1:

raise ValueError(

"`keep_input` cannot be a vector when `enqueue_many=False`.")

if keep_input.shape.ndims > 1:

raise ValueError("`keep_input` must be 0 or 1 dimensions.")

return keep_input

def _dtypes(tensor_list_list):

all_types = [[t.dtype for t in tl] for tl in tensor_list_list]

types = all_types[0]

for other_types in all_types[1:]:

if other_types != types:

raise TypeError("Expected types to be consistent: %s vs. %s." %

(", ".join(x.name for x in types),

", ".join(x.name for x in other_types)))

return types

def _merge_shapes(shape_list, enqueue_many):

shape_list = [tensor_shape.as_shape(s) for s in shape_list]

if enqueue_many:

# We want the shapes without the leading batch dimension.

shape_list = [s.with_rank_at_least(1)[1:] for s in shape_list]

merged_shape = shape_list[0]

for s in shape_list[1:]:

merged_shape.merge_with(s)

return merged_shape.as_list()

def _shapes(tensor_list_list, shapes, enqueue_many):

"""Calculate and merge the shapes of incoming tensors.

Args:

tensor_list_list: List of tensor lists.

shapes: List of shape tuples corresponding to tensors within the lists.

enqueue_many: Boolean describing whether shapes will be enqueued as

batches or individual entries.

Returns:

A list of shapes aggregating shape inference info from `tensor_list_list`,

or returning `shapes` if it is not `None`.

Raises:

ValueError: If any of the inferred shapes in `tensor_list_list` lack a

well defined rank.

"""

if shapes is None:

len0 = len(tensor_list_list[0])

for tl in tensor_list_list:

for i in xrange(len0):

if tl[i].shape.ndims is None:

raise ValueError("Cannot infer Tensor's rank: %s" % tl[i])

shapes = [_merge_shapes(

[tl[i].shape.as_list() for tl in tensor_list_list], enqueue_many)

for i in xrange(len0)]

return shapes

def _select_which_to_enqueue(tensor_list, keep_input):

"""Select which examples to enqueue based on vector `keep_input`."""

select_i = math_ops.to_int32(keep_input)

tensor_list = [

data_flow_ops.dynamic_partition(x, select_i, num_partitions=2)[1]

for x in tensor_list]

return tensor_list

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 = [utils.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))

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.shape.ndims == 1:

enqueue_ops = [

enqueue_fn(_select_which_to_enqueue(tensor_list, keep_input))] * threads

else:

enqueue_ops = [utils.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))

def _which_queue(dynamic_pad):

return (data_flow_ops.PaddingFIFOQueue if dynamic_pad

else data_flow_ops.FIFOQueue)

def _batch(tensors, batch_size, keep_input, num_threads=1, capacity=32,

enqueue_many=False, shapes=None, dynamic_pad=False,

allow_smaller_final_batch=False, shared_name=None,

name=None):

"""Helper function for `batch` and `maybe_batch`."""

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 = _as_tensor_list(tensors)

with ops.name_scope(name, "batch", list(tensor_list) + [keep_input]) as name:

tensor_list = _validate(tensor_list)

keep_input = _validate_keep_input(keep_input, enqueue_many)

(tensor_list, sparse_info) = _store_sparse_tensors(

tensor_list, enqueue_many, keep_input)

types = _dtypes([tensor_list])

shapes = _shapes([tensor_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(queue, tensor_list, num_threads, enqueue_many, keep_input)

summary.scalar("fraction_of_%d_full" % capacity,

math_ops.to_float(queue.size()) * (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)

return _as_original_type(tensors, dequeued)

# TODO(josh11b): Add a thread_multiplier or num_threads (that has to be

# a multiple of len(tensor_list_list)?) parameter, to address the use

# case where you want more parallelism than you can support different

# readers (either because you don't have that many files or can't

# 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.to_float(queue.size()) * (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,

keep_input, num_threads=1, seed=None, enqueue_many=False,

shapes=None, allow_smaller_final_batch=False,

shared_name=None, name=None):

"""Helper function for `shuffle_batch` and `maybe_shuffle_batch`."""

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 = _as_tensor_list(tensors)

with ops.name_scope(name, "shuffle_batch",

list(tensor_list) + [keep_input]) as name:

if capacity <= min_after_dequeue:

raise ValueError("capacity %d must be bigger than min_after_dequeue %d."

% (capacity, min_after_dequeue))

tensor_list = _validate(tensor_list)

keep_input = _validate_keep_input(keep_input, enqueue_many)

tensor_list, sparse_info = _store_sparse_tensors(

tensor_list, enqueue_many, keep_input)

types = _dtypes([tensor_list])

shapes = _shapes([tensor_list], shapes, enqueue_many)

queue = data_flow_ops.RandomShuffleQueue(

capacity=capacity, min_after_dequeue=min_after_dequeue, seed=seed,

dtypes=types, shapes=shapes, shared_name=shared_name)

_enqueue(queue, tensor_list, num_threads, enqueue_many, keep_input)

full = (math_ops.to_float(

math_ops.maximum(0, queue.size() - min_after_dequeue)) *

(1. / (capacity - min_after_dequeue)))

# Note that name contains a '/' at the end so we intentionally do not place

# a '/' after %s below.

summary_name = (

"fraction_over_%d_of_%d_full" %

(min_after_dequeue, capacity - min_after_dequeue))

summary.scalar(summary_name, full)

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)

return _as_original_type(tensors, dequeued)

def _shuffle_batch_join(tensors_list, batch_size, capacity,

min_after_dequeue, keep_input, seed=None,

enqueue_many=False, shapes=None,

allow_smaller_final_batch=False, shared_name=None,

name=None):

"""Helper function for `shuffle_batch_join` and `maybe_shuffle_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, "shuffle_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)

queue = data_flow_ops.RandomShuffleQueue(

capacity=capacity, min_after_dequeue=min_after_dequeue, seed=seed,

dtypes=types, shapes=shapes, shared_name=shared_name)

_enqueue_join(queue, tensor_list_list, enqueue_many, keep_input)

full = (math_ops.to_float(

math_ops.maximum(0, queue.size() - min_after_dequeue)) *

(1. / (capacity - min_after_dequeue)))

# Note that name contains a '/' at the end so we intentionally do not place

# a '/' after %s below.

summary_name = (

"fraction_over_%d_of_%d_full" %

(min_after_dequeue, capacity - min_after_dequeue))

summary.scalar(summary_name, full)

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)

# Batching functions ----------------------------------------------------------

@tf_export("train.batch")

def batch(tensors, batch_size, num_threads=1, capacity=32,

enqueue_many=False, shapes=None, dynamic_pad=False,

allow_smaller_final_batch=False, shared_name=None, name=None):

"""Creates batches of tensors in `tensors`.

The argument `tensors` can be a list or a dictionary of tensors.

The value returned by the function will be of the same type

as `tensors`.

This function is implemented using a queue. A `QueueRunner` for the

queue is added to the current `Graph`'s `QUEUE_RUNNER` collection.

If `enqueue_many` is `False`, `tensors` is assumed to represent a single

example. An input tensor with shape `[x, y, z]` will be output as a tensor

with shape `[batch_size, x, y, z]`.

If `enqueue_many` is `True`, `tensors` is assumed to represent a batch of

examples, where the first dimension is indexed by example, and all members of

`tensors` should have the same size in the first dimension. If an input

tensor has shape `[*, x, y, z]`, the output will have shape `[batch_size, x,

y, z]`. The `capacity` argument controls the how long the prefetching is

allowed to grow the queues.

The returned operation is a dequeue operation and will throw

`tf.errors.OutOfRangeError` if the input queue is exhausted. If this

operation is feeding another input queue, its queue runner will catch

this exception, however, if this operation is used in your main thread

you are responsible for catching this yourself.

*N.B.:* If `dynamic_pad` is `False`, you must ensure that either

(i) the `shapes` argument is passed, or (ii) all of the tensors in

`tensors` must have fully-defined shapes. `ValueError` will be

raised if neither of these conditions holds.

If `dynamic_pad` is `True`, it is sufficient that the *rank* of the

tensors is known, but individual dimensions may have shape `None`.

In this case, for each enqueue the dimensions with value `None`

may have a variable length; upon dequeue, the output tensors will be padded

on the right to the maximum shape of the tensors in the current minibatch.

For numbers, this padding takes value 0. For strings, this padding is

the empty string. See `PaddingFIFOQueue` for more info.

If `allow_smaller_final_batch` is `True`, a smaller batch value than

`batch_size` is returned when the queue is closed and there are not enough

elements to fill the batch, otherwise the pending elements are discarded.

In addition, all output tensors' static shapes, as accessed via the

`shape` property will have a first `Dimension` value of `None`, and

operations that depend on fixed batch_size would fail.

Args:

tensors: The list or dictionary of tensors to enqueue.

batch_size: The new batch size pulled from the queue.

num_threads: The number of threads enqueuing `tensors`. The batching will

be nondeterministic if `num_threads > 1`.

capacity: An integer. The maximum number of elements in the queue.

enqueue_many: Whether each tensor in `tensors` is a single example.

shapes: (Optional) The shapes for each example. Defaults to the

inferred shapes for `tensors`.

dynamic_pad: Boolean. Allow variable dimensions in input shapes.

The given dimensions are padded upon dequeue so that tensors within a

batch have the same shapes.

allow_smaller_final_batch: (Optional) Boolean. If `True`, allow the final

batch to be smaller if there are insufficient items left in the queue.

shared_name: (Optional). If set, this queue will be shared under the given

name across multiple sessions.

Returns:

A list or dictionary of tensors with the same types as `tensors` (except if

the input is a list of one element, then it returns a tensor, not a list).

Raises:

ValueError: If the `shapes` are not specified, and cannot be

inferred from the elements of `tensors`.

@compatibility(eager)

Input pipelines based on Queues are not supported when eager execution is

enabled. Please use the `tf.data` API to ingest data under eager execution.

@end_compatibility

"""

return _batch(

tensors,

batch_size,

keep_input=True,

num_threads=num_threads,

capacity=capacity,

enqueue_many=enqueue_many,

shapes=shapes,

dynamic_pad=dynamic_pad,

allow_smaller_final_batch=allow_smaller_final_batch,

shared_name=shared_name,

name=name)

@tf_export("train.maybe_batch")

def maybe_batch(tensors, keep_input, batch_size, num_threads=1, capacity=32,

enqueue_many=False, shapes=None, dynamic_pad=False,

allow_smaller_final_batch=False, shared_name=None, name=None):

"""Conditionally creates batches of tensors based on `keep_input`.

See docstring in `batch` for more details.

Args:

tensors: The list or dictionary of tensors to enqueue.

View remainder of file in raw view

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