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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""TensorArray: a dynamically sized array of Tensors.
@@TensorArray
"""
# Mixture of pep8 and non-pep8 names, so disable pylint bad-name
# pylint: disable=g-bad-name
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_data_flow_ops
from tensorflow.python.ops import math_ops
def _maybe_set_device(handle_op, value_t):
# NOTE(ebrevdo): Do not try this at home, kids
# _______________________________________________
# | I WILL NOT ACCESS PRIVATE METHODS ^^^^^^^^\ |
# | I WILL NOT ACCESS PRIVATE METHODS | | |
# | I WILL NOT ACCESS PRIVATE METHODS |_ __ | |
# | I WILL NOT ACCESS PRIVATE METHODS (.(. ) | |
# | I WILL NOT ACCESS PRIVATE (_ ) |
# | \\ /___/' / |
# | _\\_ \ | |
# | (( ) /====| |
# | \ <.__._- \ |
# |___________________________ <//___. ||
#
if not handle_op.device and value_t.device:
handle_op._set_device(value_t.device) # pylint: disable=protected-access
# TensorArray object accesses many of the hidden generated ops, but is
# in fact built to wrap these methods.
# pylint: disable=protected-access
class TensorArray(object):
"""Class wrapping dynamic-sized, per-time-step, write-once Tensor arrays.
This class is meant to be used with dynamic iteration primitives such as
`while_loop` and `map_fn`. It supports gradient back-propagation via special
"flow" control flow dependencies.
"""
def __init__(self,
dtype,
size=None,
dynamic_size=None,
clear_after_read=None,
tensor_array_name=None,
handle=None,
flow=None,
infer_shape=True,
element_shape=None,
name=None):
"""Construct a new TensorArray or wrap an existing TensorArray handle.
A note about the parameter `name`:
The name of the `TensorArray` (even if passed in) is uniquified: each time
a new `TensorArray` is created at runtime it is assigned its own name for
the duration of the run. This avoids name collisions if a `TensorArray`
is created within a `while_loop`.
Args:
dtype: (required) data type of the TensorArray.
size: (optional) int32 scalar `Tensor`: the size of the TensorArray.
Required if handle is not provided.
dynamic_size: (optional) Python bool: If true, writes to the TensorArray
can grow the TensorArray past its initial size. Default: False.
clear_after_read: Boolean (optional, default: True). If True, clear
TensorArray values after reading them. This disables read-many
semantics, but allows early release of memory.
tensor_array_name: (optional) Python string: the name of the TensorArray.
This is used when creating the TensorArray handle. If this value is
set, handle should be None.
handle: (optional) A `Tensor` handle to an existing TensorArray. If this
is set, tensor_array_name should be None.
flow: (optional) A float `Tensor` scalar coming from an existing
`TensorArray.flow`.
infer_shape: (optional, default: True) If True, shape inference
is enabled. In this case, all elements must have the same shape.
element_shape: (optional, default: None) A `TensorShape` object specifying
the shape constraints of each of the elements of the TensorArray.
Need not be fully defined.
name: A name for the operation (optional).
Raises:
ValueError: if both handle and tensor_array_name are provided.
TypeError: if handle is provided but is not a Tensor.
"""
if handle is not None and tensor_array_name:
raise ValueError(
"Cannot construct with both handle and tensor_array_name")
if handle is not None and not isinstance(handle, ops.Tensor):
raise TypeError("Handle must be a Tensor")
if handle is None and size is None:
raise ValueError("Size must be provided if handle is not provided")
if handle is not None and size is not None:
raise ValueError("Cannot provide both a handle and size "
"at the same time")
if handle is not None and element_shape is not None:
raise ValueError("Cannot provide both a handle and element_shape "
"at the same time")
if handle is not None and dynamic_size is not None:
raise ValueError("Cannot provide both a handle and dynamic_size "
"at the same time")
if handle is not None and clear_after_read is not None:
raise ValueError("Cannot provide both a handle and clear_after_read "
"at the same time")
if clear_after_read is None:
clear_after_read = True
dynamic_size = dynamic_size or False
self._dtype = dtype
# Record the current static shape for the array elements. The element
# shape is defined either by `element_shape` or the shape of the tensor
# of the first write. If `infer_shape` is true, all writes checks for
# shape equality.
if element_shape is None:
self._infer_shape = infer_shape
self._element_shape = []
else:
self._infer_shape = True
self._element_shape = [tensor_shape.TensorShape(element_shape)]
with ops.name_scope(name, "TensorArray", [handle, size, flow]) as scope:
if handle is not None:
self._handle = handle
if flow is None:
raise ValueError("flow must not be None if handle is not None.")
self._flow = flow
else:
# Construct the TensorArray with an empty device. The first
# write into the TensorArray from a Tensor with a set device
# will retroactively set the device value of this op.
with ops.device(None), ops.colocate_with(None, ignore_existing=True):
self._handle, self._flow = gen_data_flow_ops._tensor_array_v3(
dtype=dtype,
size=size,
element_shape=element_shape,
dynamic_size=dynamic_size,
clear_after_read=clear_after_read,
tensor_array_name=tensor_array_name,
name=scope)
@property
def flow(self):
"""The flow `Tensor` forcing ops leading to this TensorArray state."""
return self._flow
@property
def dtype(self):
"""The data type of this TensorArray."""
return self._dtype
@property
def handle(self):
"""The reference to the TensorArray."""
return self._handle
def _merge_element_shape(self, shape):
"""Changes the element shape of the array given a shape to merge with.
Args:
shape: A `TensorShape` object to merge with.
Raises:
ValueError: if the provided shape is incompatible with the current
element shape of the `TensorArray`.
"""
if self._element_shape:
if not shape.is_compatible_with(self._element_shape[0]):
raise ValueError(
"Inconsistent shapes: saw %s but expected %s "
"(and infer_shape=True)" % (shape, self._element_shape[0]))
self._element_shape[0] = self._element_shape[0].merge_with(shape)
else:
self._element_shape.append(shape)
def identity(self):
"""Returns a TensorArray with the same content and properties.
Returns:
A new TensorArray object with flow that ensures the control dependencies
from the contexts will become control dependencies for writes, reads, etc.
Use this object all for subsequent operations.
"""
flow = array_ops.identity(self._flow)
ta = TensorArray(dtype=self._dtype, handle=self._handle, flow=flow,
infer_shape=self._infer_shape)
ta._element_shape = self._element_shape
return ta
def grad(self, source, flow=None, name=None):
# tensor_array_grad requires a flow input when forward
# TensorArrays are dynamically sized. This forces the creation
# of the grad TensorArray only once the final forward array's size
# is fixed.
if flow is None:
flow = self.flow
with ops.name_scope(name, "TensorArrayGrad", [self._handle]):
with ops.colocate_with(self._handle):
g_handle, unused_flow = gen_data_flow_ops._tensor_array_grad_v3(
handle=self._handle, source=source, flow_in=flow, name=name)
with ops.control_dependencies([g_handle]):
flow = array_ops.identity(flow, name="gradient_flow")
g = TensorArray(
dtype=self._dtype,
handle=g_handle,
flow=flow,
infer_shape=self._infer_shape)
g._element_shape = self._element_shape
return g
def read(self, index, name=None):
"""Read the value at location `index` in the TensorArray.
Args:
index: 0-D. int32 tensor with the index to read from.
name: A name for the operation (optional).
Returns:
The tensor at index `index`.
"""
with ops.colocate_with(self._handle):
value = gen_data_flow_ops._tensor_array_read_v3(
handle=self._handle,
index=index,
flow_in=self._flow,
dtype=self._dtype,
name=name)
if self._element_shape:
value.set_shape(self._element_shape[0].dims)
return value
def write(self, index, value, name=None):
"""Write `value` into index `index` of the TensorArray.
Args:
index: 0-D. int32 scalar with the index to write to.
value: N-D. Tensor of type `dtype`. The Tensor to write to this index.
name: A name for the operation (optional).
Returns:
A new TensorArray object with flow that ensures the write occurs.
Use this object all for subsequent operations.
Raises:
ValueError: if there are more writers than specified.
"""
with ops.name_scope(name, "TensorArrayWrite", [self._handle, index, value]):
value = ops.convert_to_tensor(value, name="value")
_maybe_set_device(self._handle.op, value)
with ops.colocate_with(self._handle):
flow_out = gen_data_flow_ops._tensor_array_write_v3(
handle=self._handle,
index=index,
value=value,
flow_in=self._flow,
name=name)
ta = TensorArray(dtype=self._dtype, handle=self._handle, flow=flow_out)
ta._infer_shape = self._infer_shape
ta._element_shape = self._element_shape
if ta._infer_shape:
ta._merge_element_shape(value.get_shape())
return ta
def stack(self, name=None):
"""Return the values in the TensorArray as a stacked `Tensor`.
All of the values must have been written and their shapes must all match.
If input shapes have rank-`R`, then output shape will have rank-`(R+1)`.
Args:
name: A name for the operation (optional).
Returns:
All the tensors in the TensorArray stacked into one tensor.
"""
with ops.colocate_with(self._handle):
with ops.name_scope(name, "TensorArrayStack", [self._handle]):
return self.gather(math_ops.range(0, self.size()), name=name)
def gather(self, indices, name=None):
"""Return selected values in the TensorArray as a packed `Tensor`.
All of selected values must have been written and their shapes
must all match.
Args:
indices: A `1-D` `Tensor` taking values in `[0, max_value)`. If
the `TensorArray` is not dynamic, `max_value=size()`.
name: A name for the operation (optional).
Returns:
The in the `TensorArray` selected by `indices`, packed into one tensor.
"""
with ops.colocate_with(self._handle):
if self._element_shape:
element_shape = self._element_shape[0]
else:
element_shape = tensor_shape.TensorShape(None)
value = gen_data_flow_ops._tensor_array_gather_v3(
handle=self._handle,
indices=indices,
flow_in=self._flow,
dtype=self._dtype,
name=name,
element_shape=element_shape)
if self._element_shape and self._element_shape[0].dims is not None:
value.set_shape([None] + self._element_shape[0].dims)
return value
def concat(self, name=None):
"""Return the values in the TensorArray as a concatenated `Tensor`.
All of the values must have been written, their ranks must match, and
and their shapes must all match for all dimensions except the first.
Args:
name: A name for the operation (optional).
Returns:
All the tensors in the TensorArray concatenated into one tensor.
"""
if self._element_shape and self._element_shape[0].dims is not None:
element_shape_except0 = (
tensor_shape.TensorShape(self._element_shape[0].dims[1:]))
else:
element_shape_except0 = tensor_shape.TensorShape(None)
with ops.colocate_with(self._handle):
value, _ = gen_data_flow_ops._tensor_array_concat_v3(
handle=self._handle,
flow_in=self._flow,
dtype=self._dtype,
name=name,
element_shape_except0=element_shape_except0)
if self._element_shape and self._element_shape[0].dims is not None:
value.set_shape([None] + self._element_shape[0].dims[1:])
return value
def unstack(self, value, name=None):
"""Unstack the values of a `Tensor` in the TensorArray.
If input value shapes have rank-`R`, then the output TensorArray will
contain elements whose shapes are rank-`(R-1)`.
Args:
value: (N+1)-D. Tensor of type `dtype`. The Tensor to unstack.
name: A name for the operation (optional).
Returns:
A new TensorArray object with flow that ensures the unstack occurs.
Use this object all for subsequent operations.
Raises:
ValueError: if the shape inference fails.
"""
with ops.name_scope(name, "TensorArrayUnstack", [self._handle, value]):
num_elements = array_ops.shape(value)[0]
return self.scatter(
indices=math_ops.range(0, num_elements), value=value, name=name)
def scatter(self, indices, value, name=None):
"""Scatter the values of a `Tensor` in specific indices of a `TensorArray`.
Args:
indices: A `1-D` `Tensor` taking values in `[0, max_value)`. If
the `TensorArray` is not dynamic, `max_value=size()`.
value: (N+1)-D. Tensor of type `dtype`. The Tensor to unpack.
name: A name for the operation (optional).
Returns:
A new TensorArray object with flow that ensures the scatter occurs.
Use this object all for subsequent operations.
Raises:
ValueError: if the shape inference fails.
"""
with ops.name_scope(name, "TensorArrayScatter",
[self._handle, value, indices]):
value = ops.convert_to_tensor(value, name="value")
_maybe_set_device(self._handle.op, value)
with ops.colocate_with(self._handle):
flow_out = gen_data_flow_ops._tensor_array_scatter_v3(
handle=self._handle,
indices=indices,
value=value,
flow_in=self._flow,
name=name)
ta = TensorArray(dtype=self._dtype, handle=self._handle, flow=flow_out)
ta._infer_shape = self._infer_shape
ta._element_shape = self._element_shape
if ta._infer_shape:
val_shape = flow_out.op.inputs[2].get_shape()
element_shape = tensor_shape.unknown_shape()
if val_shape.dims is not None:
element_shape = tensor_shape.TensorShape(val_shape.dims[1:])
ta._merge_element_shape(element_shape)
return ta
def split(self, value, lengths, name=None):
"""Split the values of a `Tensor` into the TensorArray.
Args:
value: (N+1)-D. Tensor of type `dtype`. The Tensor to split.
lengths: 1-D. int32 vector with the lengths to use when splitting
`value` along its first dimension.
name: A name for the operation (optional).
Returns:
A new TensorArray object with flow that ensures the split occurs.
Use this object all for subsequent operations.
Raises:
ValueError: if the shape inference fails.
"""
with ops.name_scope(name, "TensorArraySplit",
[self._handle, value, lengths]):
value = ops.convert_to_tensor(value, name="value")
_maybe_set_device(self._handle.op, value)
lengths_64 = math_ops.to_int64(lengths)
with ops.colocate_with(self._handle):
flow_out = gen_data_flow_ops._tensor_array_split_v3(
handle=self._handle,
value=value,
lengths=lengths_64,
flow_in=self._flow,
name=name)
ta = TensorArray(dtype=self._dtype, handle=self._handle, flow=flow_out)
ta._infer_shape = self._infer_shape
ta._element_shape = self._element_shape
if ta._infer_shape:
val_shape = flow_out.op.inputs[1].get_shape()
clengths = tensor_util.constant_value(flow_out.op.inputs[2])
element_shape = tensor_shape.unknown_shape()
if val_shape.dims is not None:
if clengths is not None and clengths.max() == clengths.min():
element_shape = tensor_shape.TensorShape([clengths[0]] +
val_shape.dims[1:])
ta._merge_element_shape(element_shape)
return ta
def size(self, name=None):
"""Return the size of the TensorArray."""
with ops.colocate_with(self._handle):
return gen_data_flow_ops._tensor_array_size_v3(
handle=self._handle, flow_in=self.flow, name=name)
def close(self, name=None):
"""Close the current TensorArray."""
with ops.colocate_with(self._handle):
return gen_data_flow_ops._tensor_array_close_v3(
handle=self._handle, name=name)
# pylint: enable=protected-access