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array_ops.py
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1252 lines (1011 loc) · 39.9 KB
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# Copyright 2015 Google Inc. 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.
# ==============================================================================
"""## Casting
TensorFlow provides several operations that you can use to cast tensor data
types in your graph.
@@string_to_number
@@to_double
@@to_float
@@to_bfloat16
@@to_int32
@@to_int64
@@cast
## Shapes and Shaping
TensorFlow provides several operations that you can use to determine the shape
of a tensor and change the shape of a tensor.
@@shape
@@size
@@rank
@@reshape
@@squeeze
@@expand_dims
## Slicing and Joining
TensorFlow provides several operations to slice or extract parts of a tensor,
or join multiple tensors together.
@@slice
@@split
@@tile
@@pad
@@concat
@@pack
@@unpack
@@reverse_sequence
@@reverse
@@transpose
@@gather
@@dynamic_partition
@@dynamic_stitch
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import tensorflow.python.platform
import numpy as np
from tensorflow.python.framework import dtypes
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 common_shapes
from tensorflow.python.ops import gen_array_ops
from tensorflow.python.ops import gen_math_ops
# pylint: disable=wildcard-import
# 'Constant' gets imported in the module 'array_ops'.
from tensorflow.python.ops.constant_op import constant
from tensorflow.python.ops.gen_array_ops import *
# We override the 'slice' for the "slice" op, so we keep python's
# existing 'slice' for later use in this module.
_baseslice = slice
# Aliases for some automatically-generated names.
listdiff = gen_array_ops.list_diff
# pylint: disable=undefined-variable,protected-access
def _SliceHelper(tensor, slice_spec):
"""Overload for Tensor.__getitem__.
Currently the size of the slice must be statically known in each dimension,
i.e. the "stop" of the slice must not be omitted.
TODO(mrry): Support slices where the sizes are not specified.
TODO(mrry): Support negative indices in slices with numpy/Python semantics.
Args:
tensor: An ops.Tensor object.
slice_spec: The arguments to Tensor.__getitem__.
Returns:
The appropriate slice of "tensor", based on "slice_spec".
Raises:
ValueError: If a slice range is negative size.
TypeError: If the slice indices aren't int, slice, or Ellipsis.
"""
if not isinstance(slice_spec, (list, tuple)):
slice_spec = [slice_spec]
indices = []
sizes = []
squeeze_dims = []
for dim, s in enumerate(slice_spec):
if isinstance(s, int):
if s < 0:
raise NotImplementedError("Negative indices are currently unsupported")
indices.append(s)
sizes.append(1)
squeeze_dims.append(dim)
elif isinstance(s, _baseslice):
if s.step not in (None, 1):
raise NotImplementedError(
"Steps other than 1 are not currently supported")
start = s.start if s.start is not None else 0
if start < 0:
raise NotImplementedError(
"Negative start indices are not currently supported")
indices.append(start)
if s.stop is not None and s.stop < 0:
raise NotImplementedError(
"Negative stop indices are not currently supported")
# NOTE(mrry): If the stop is not specified, Python substitutes
# sys.maxsize, which is typically (2 ** 63) - 1. Since Slice currently
# supports signed DT_INT32 arguments, we use -1 to specify that all
# elements should be captured.
if s.stop is None or s.stop == sys.maxsize:
sizes.append(-1)
else:
if start > s.stop:
raise ValueError("Stop must be at least start")
sizes.append(s.stop - start)
elif s is Ellipsis:
raise NotImplementedError("Ellipsis is not currently supported")
else:
raise TypeError("Bad slice index %s of type %s" % (s, type(s)))
sliced = slice(tensor, indices, sizes)
if squeeze_dims:
return squeeze(sliced, squeeze_dims=squeeze_dims)
else:
return sliced
def slice(input_, begin, size, name=None):
"""Extracts a slice from a tensor.
This operation extracts a slice of size `size` from a tensor `input` starting
at the location specified by `begin`. The slice `size` is represented as a
tensor shape, where `size[i]` is the number of elements of the 'i'th dimension
of `input` that you want to slice. The starting location (`begin`) for the
slice is represented as an offset in each dimension of `input`. In other
words, `begin[i]` is the offset into the 'i'th dimension of `input` that you
want to slice from.
`begin` is zero-based; `size` is one-based. If `size[i]` is -1,
all remaining elements in dimension i are included in the
slice. In other words, this is equivalent to setting:
`size[i] = input.dim_size(i) - begin[i]`
This operation requires that:
`0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n]`
For example:
```
# 'input' is [[[1, 1, 1], [2, 2, 2]],
# [[3, 3, 3], [4, 4, 4]],
# [[5, 5, 5], [6, 6, 6]]]
tf.slice(input, [1, 0, 0], [1, 1, 3]) ==> [[[3, 3, 3]]]
tf.slice(input, [1, 0, 0], [1, 2, 3]) ==> [[[3, 3, 3],
[4, 4, 4]]]
tf.slice(input, [1, 0, 0], [2, 1, 3]) ==> [[[3, 3, 3]],
[[5, 5, 5]]]
```
Args:
input_: A `Tensor`.
begin: An `int32` or `int64` `Tensor`.
size: An `int32` or `int64` `Tensor`.
name: A name for the operation (optional).
Returns:
A `Tensor` the same type as `input`.
"""
return gen_array_ops._slice(input_, begin, size, name=name)
ops.Tensor._override_operator("__getitem__", _SliceHelper)
def pack(values, name="pack"):
"""Packs a list of rank-`R` tensors into one rank-`(R+1)` tensor.
Packs tensors in `values` into a tensor with rank one higher than each tensor
in `values` and shape `[len(values)] + values[0].shape`. The output satisfies
`output[i, ...] = values[i][...]`.
This is the opposite of unpack. The numpy equivalent is
tf.pack([x, y, z]) = np.asarray([x, y, z])
Args:
values: A list of `Tensor` objects with the same shape and type.
name: A name for this operation (optional).
Returns:
output: A packed `Tensor` with the same type as `values`.
"""
return gen_array_ops._pack(values, name=name)
def unpack(value, num=None, name="unpack"):
"""Unpacks the outer dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
Unpacks `num` tensors from `value` along the first dimension.
If `num` is not specified (the default), it is inferred from `value`'s shape.
If `value.shape[0]` is not known, `ValueError` is raised.
The ith tensor in `output` is the slice `value[i, ...]`. Each tensor in
`output` has shape `value.shape[1:]`.
This is the opposite of pack. The numpy equivalent is
tf.unpack(x, n) = list(x)
Args:
value: A rank `R > 0` `Tensor` to be unpacked.
num: An `int`. The first dimension of value. Automatically inferred if
`None` (the default).
name: A name for the operation (optional).
Returns:
The list of `Tensor` objects unpacked from `value`.
Raises:
ValueError: If `num` is unspecified and cannot be inferred.
"""
if num is None:
value = ops.convert_to_tensor(value)
shape = value.get_shape()
num = shape[0].value
if num is None:
raise ValueError("Cannot infer num from shape %s" % shape)
return gen_array_ops._unpack(value, num=num, name=name)
def concat(concat_dim, values, name="concat"):
"""Concatenates tensors along one dimension.
Concatenates the list of tensors `values` along dimension `concat_dim`. If
`values[i].shape = [D0, D1, ... Dconcat_dim(i), ...Dn]`, the concatenated
result has shape
[D0, D1, ... Rconcat_dim, ...Dn]
where
Rconcat_dim = sum(Dconcat_dim(i))
That is, the data from the input tensors is joined along the `concat_dim`
dimension.
The number of dimensions of the input tensors must match, and all dimensions
except `concat_dim` must be equal.
For example:
```python
t1 = [[1, 2, 3], [4, 5, 6]]
t2 = [[7, 8, 9], [10, 11, 12]]
tf.concat(0, [t1, t2]) ==> [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]
tf.concat(1, [t1, t2]) ==> [[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]]
# tensor t3 with shape [2, 3]
# tensor t4 with shape [2, 3]
tf.shape(tf.concat(0, [t3, t4])) ==> [4, 3]
tf.shape(tf.concat(1, [t3, t4])) ==> [2, 6]
```
Args:
concat_dim: 0-D `int32` `Tensor`. Dimension along which to concatenate.
values: A list of `Tensor` objects or a single `Tensor`.
name: A name for the operation (optional).
Returns:
A `Tensor` resulting from concatenation of the input tensors.
"""
if not isinstance(values, (list, tuple)):
values = [values]
# TODO(mrry): Change to return values?
if len(values) == 1: # Degenerate case of one tensor.
return identity(values[0], name=name)
return gen_array_ops._concat(concat_dim=concat_dim,
values=values,
name=name)
@ops.RegisterShape("Pack")
def _PackShape(op):
input_shape = op.inputs[0].get_shape()
for inp in op.inputs[1:]:
input_shape = input_shape.merge_with(inp.get_shape())
return [tensor_shape.TensorShape([len(op.inputs)]).concatenate(input_shape)]
@ops.RegisterShape("Unpack")
def _UnpackShape(op):
input_shape = op.inputs[0].get_shape()
return [input_shape[1:]] * op.get_attr("num")
@ops.RegisterShape("Concat")
def _ConcatShape(op):
concat_dim = tensor_util.constant_value(op.inputs[0])
if concat_dim is None:
# Return an unknown shape with the same rank as the inputs, or an
# unknown rank if no input's rank is known.
rank = None
for value in op.inputs[1:]:
if rank is not None:
value.get_shape().assert_has_rank(rank)
else:
rank = value.get_shape().ndims
if rank == 0:
raise ValueError("Can't concatenate scalars (use tf.pack instead)")
return [tensor_shape.unknown_shape(ndims=rank)]
else:
# Merge all the non-concat dims, and sum the concat dim to make an
# output shape.
concat_dim = int(concat_dim)
output_shape = op.inputs[1].get_shape()
for value in op.inputs[2:]:
value_shape = value.get_shape()
if value_shape.ndims is not None and concat_dim >= value_shape.ndims:
raise ValueError("concat_dim is out of range (values rank = %d)" %
value_shape.ndims)
before = output_shape[:concat_dim].merge_with(value_shape[:concat_dim])
at = output_shape[concat_dim] + value_shape[concat_dim]
after = output_shape[
concat_dim + 1:].merge_with(value_shape[concat_dim + 1:])
output_shape = before.concatenate(at).concatenate(after)
return [output_shape]
@ops.RegisterShape("ConcatOffset")
def _ConcatOffsetShape(op):
return [x.get_shape() for x in op.inputs[1:]]
def sparse_mask(a, mask_indices, name=None):
"""Masks elements of `IndexedSlices`.
Given an `IndexedSlices` instance `a`, returns another `IndexedSlices` that
contains a subset of the slices of `a`. Only the slices at indices specified
in `mask_indices` are returned.
This is useful when you need to extract a subset of slices in an
`IndexedSlices` object.
For example:
```python
# `a` contains slices at indices [12, 26, 37, 45] from a large tensor
# with shape [1000, 10]
a.indices => [12, 26, 37, 45]
tf.shape(a.values) => [4, 10]
# `b` will be the subset of `a` slices at its second and third indices, so
# we want to mask of its first and last indices (which are at absolute
# indices 12, 45)
b = tf.sparse_mask(a, [12, 45])
b.indices => [26, 37]
tf.shape(b.values) => [2, 10]
```
Args:
* `a`: An `IndexedSlices` instance.
* `mask_indices`: Indices of elements to mask.
* `name`: A name for the operation (optional).
Returns:
The masked `IndexedSlices` instance.
"""
with ops.op_scope([a, mask_indices], name, "sparse_mask") as name:
indices = a.indices
out_indices, to_gather = listdiff(indices, mask_indices)
out_values = gather(a.values, to_gather, name=name)
return ops.IndexedSlices(out_values, out_indices, a.dense_shape)
def split(split_dim, num_split, value, name="split"):
"""Splits a tensor into `num_split` tensors along one dimension.
Splits `value` along dimension `split_dim` into `num_split` smaller tensors.
Requires that `num_split` evenly divide `value.shape[split_dim]`.
For example:
```python
# 'value' is a tensor with shape [5, 30]
# Split 'value' into 3 tensors along dimension 1
split0, split1, split2 = tf.split(1, 3, value)
tf.shape(split0) ==> [5, 10]
```
Args:
split_dim: A 0-D `int32` `Tensor`. The dimension along which to split.
Must be in the range `[0, rank(value))`.
num_split: A Python integer. The number of ways to split.
value: The `Tensor` to split.
name: A name for the operation (optional).
Returns:
`num_split` `Tensor` objects resulting from splitting `value`.
"""
return gen_array_ops._split(split_dim=split_dim,
num_split=num_split,
value=value,
name=name)
@ops.RegisterShape("Reverse")
def _ReverseShape(op):
dims_shape = op.inputs[1].get_shape().with_rank(1)
input_shape = op.inputs[0].get_shape().with_rank(dims_shape[0])
if input_shape.ndims is not None and input_shape.ndims > 8:
raise ValueError(
"tf.reverse() does not work on tensors with more than 8 dimensions")
return [input_shape]
def transpose(a, perm=None, name="transpose"):
"""Transposes `a`. Permutes the dimensions according to `perm`.
The returned tensor's dimension i will correspond to the input dimension
`perm[i]`. If `perm` is not given, it is set to (n-1...0), where n is
the rank of the input tensor. Hence by default, this operation performs a
regular matrix transpose on 2-D input Tensors.
For example:
```python
# 'x' is [[1 2 3]
# [4 5 6]]
tf.transpose(x) ==> [[1 4]
[2 5]
[3 6]]
# Equivalently
tf.transpose(x, perm=[1, 0]) ==> [[1 4]
[2 5]
[3 6]]
# 'perm' is more useful for n-dimensional tensors, for n > 2
# 'x' is [[[1 2 3]
# [4 5 6]]
# [[7 8 9]
# [10 11 12]]]
# Take the transpose of the matrices in dimension-0
tf.transpose(b, perm=[0, 2, 1]) ==> [[[1 4]
[2 5]
[3 6]]
[[7 10]
[8 11]
[9 12]]]
```
Args:
a: A `Tensor`.
perm: A permutation of the dimensions of `a`.
name: A name for the operation (optional).
Returns:
A transposed `Tensor`.
"""
with ops.op_scope([a], name, "transpose") as name:
if perm is None:
dims = gen_math_ops._range(0, gen_array_ops.rank(a), 1)
perm = gen_array_ops.reverse(dims, [True])
ret = gen_array_ops.transpose(a, perm, name=name)
# NOTE(mrry): Setting the shape explicitly because
# reverse is not handled by the shape function.
input_shape = ret.op.inputs[0].get_shape().dims
if input_shape is not None:
ret.set_shape(input_shape[::-1])
else:
ret = gen_array_ops.transpose(a, perm, name=name)
return ret
def zeros(shape, dtype=dtypes.float32, name=None):
"""Creates a tensor with all elements set to zero.
This operation returns a tensor of type `dtype` with shape `shape` and
all elements set to zero.
For example:
```python
tf.zeros([3, 4], int32) ==> [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
```
Args:
shape: Either a list of integers, or a 1-D `Tensor` of type `int32`.
dtype: The type of an element in the resulting `Tensor`.
name: A name for the operation (optional).
Returns:
A `Tensor` with all elements set to zero.
"""
with ops.op_scope([shape], name, "zeros") as name:
if isinstance(shape, list):
output = constant(0, shape=shape, dtype=dtype, name=name)
else:
shape = ops.convert_to_tensor(shape, name="shape")
output = fill(shape, constant(0, dtype=dtype), name=name)
assert output.dtype.base_dtype == dtypes.as_dtype(dtype).base_dtype
return output
def zeros_like(tensor, dtype=None, name=None):
"""Creates a tensor with all elements set to zero.
Given a single tensor (`tensor`), this operation returns a tensor of the
same type and shape as `tensor` with all elements set to zero. Optionally,
you can use `dtype` to specify a new type for the returned tensor.
For example:
```python
# 'tensor' is [[1, 2, 3], [4, 5, 6]]
tf.zeros_like(tensor) ==> [[0, 0, 0], [0, 0, 0]]
```
Args:
tensor: A `Tensor`.
dtype: A type for the returned `Tensor`. Must be `float32`, `float64`,
`int8`, `int16`, `int32`, `int64`, `uint8`, or `complex64`.
name: A name for the operation (optional).
Returns:
A `Tensor` with all elements set to zero.
"""
with ops.op_scope([tensor], name, "zeros_like") as name:
tensor = ops.convert_to_tensor(tensor, name="tensor")
zeros_shape = shape(tensor)
if dtype is None:
dtype = tensor.dtype
ret = zeros(zeros_shape, dtype=dtype, name=name)
ret.set_shape(tensor.get_shape())
return ret
def ones_like(tensor, dtype=None, name=None):
"""Creates a tensor with all elements set to 1.
Given a single tensor (`tensor`), this operation returns a tensor of the same
type and shape as `tensor` with all elements set to 1. Optionally, you can
specify a new type (`dtype`) for the returned tensor.
For example:
```python
# 'tensor' is [[1, 2, 3], [4, 5, 6]]
tf.ones_like(tensor) ==> [[1, 1, 1], [1, 1, 1]]
```
Args:
tensor: A `Tensor`.
dtype: A type for the returned `Tensor`. Must be `float32`, `float64`,
`int8`, `int16`, `int32`, `int64`, `uint8`, or `complex64`.
name: A name for the operation (optional).
Returns:
A `Tensor` with all elements set to 1.
"""
with ops.op_scope([tensor], name, "ones_like") as name:
tensor = ops.convert_to_tensor(tensor, name="tensor")
ones_shape = shape(tensor)
if dtype is None:
dtype = tensor.dtype
ret = ones(ones_shape, dtype=dtype, name=name)
ret.set_shape(tensor.get_shape())
return ret
def zeros_initializer(shape, dtype=dtypes.float32):
"""An adaptor for zeros() to match the Initializer spec."""
return zeros(shape, dtype)
def ones(shape, dtype=dtypes.float32, name=None):
"""Creates a tensor with all elements set to 1.
This operation returns a tensor of type `dtype` with shape `shape` and all
elements set to 1.
For example:
```python
tf.ones([2, 3], int32) ==> [[1, 1, 1], [1, 1, 1]]
```
Args:
shape: Either a list of integers, or a 1-D `Tensor` of type `int32`.
dtype: The type of an element in the resulting `Tensor`.
name: A name for the operation (optional).
Returns:
A `Tensor` with all elements set to 1.
"""
with ops.op_scope([shape], name, "ones") as name:
if isinstance(shape, list):
output = constant(1, shape=shape, dtype=dtype, name=name)
else:
shape = ops.convert_to_tensor(shape, name="shape")
output = fill(shape, constant(1, dtype=dtype), name=name)
assert output.dtype.base_dtype == dtypes.as_dtype(dtype).base_dtype
return output
def placeholder(dtype, shape=None, name=None):
"""Inserts a placeholder for a tensor that will be always fed.
**Important**: This tensor will produce an error if evaluated. Its value must
be fed using the `feed_dict` optional argument to `Session.run()`,
`Tensor.eval()`, or `Operation.run()`.
For example:
```python
x = tf.placeholder(tf.float32, shape=(1024, 1024))
y = tf.matmul(x, x)
with tf.Session() as sess:
print(sess.run(y)) # ERROR: will fail because x was not fed.
rand_array = np.random.rand(1024, 1024)
print(sess.run(y, feed_dict={x: rand_array})) # Will succeed.
```
Args:
dtype: The type of elements in the tensor to be fed.
shape: The shape of the tensor to be fed (optional). If the shape is not
specified, you can feed a tensor of any shape.
name: A name for the operation (optional).
Returns:
A `Tensor` that may be used as a handle for feeding a value, but not
evaluated directly.
"""
shape = tensor_shape.as_shape(shape)
if shape.is_fully_defined():
dim_list = shape.as_list()
else:
dim_list = []
ret = gen_array_ops._placeholder(
dtype=dtype,
shape=dim_list,
name=name)
ret.set_shape(shape)
return ret
@ops.RegisterShape("Placeholder")
def _PlaceholderShape(op):
given_shape = tensor_util.TensorShapeProtoToList(op.get_attr("shape"))
if given_shape:
return [tensor_shape.TensorShape(given_shape)]
else:
return [tensor_shape.unknown_shape()]
@ops.RegisterShape("CheckNumerics")
@ops.RegisterShape("Identity")
@ops.RegisterShape("RefIdentity")
@ops.RegisterShape("StopGradient")
def _UnchangedShape(op):
return [op.inputs[0].get_shape()]
@ops.RegisterShape("Rank")
@ops.RegisterShape("Size")
def _ScalarShape(unused_op):
return [tensor_shape.scalar()]
@ops.RegisterShape("Slice")
def _SliceShape(op):
"""Shape function for array_ops.slice."""
input_shape = op.inputs[0].get_shape()
begin_shape = op.inputs[1].get_shape().with_rank_at_most(1)
sizes_shape = op.inputs[2].get_shape().with_rank_at_most(1)
rank_vector_shape = begin_shape.merge_with(sizes_shape)
ndims = rank_vector_shape.num_elements()
if ndims is not None:
input_shape.assert_has_rank(ndims)
begin_value = tensor_util.constant_value(op.inputs[1])
sizes_value = tensor_util.constant_value(op.inputs[2])
if sizes_value is not None:
returned_dims = []
for i, slice_size in enumerate(sizes_value.ravel()):
if slice_size != -1:
returned_dims.append(slice_size)
elif begin_value is not None:
returned_dims.append(input_shape[i] - begin_value[i])
else:
returned_dims.append(None)
return [tensor_shape.TensorShape(returned_dims)]
else:
if input_shape.ndims is not None:
return [tensor_shape.unknown_shape(ndims=input_shape.ndims)]
elif ndims is not None:
return [tensor_shape.unknown_shape(ndims=ndims)]
else:
return [tensor_shape.unknown_shape()]
@ops.RegisterShape("Gather")
def _GatherShape(op):
"""Shape function for array_ops.gather."""
params_shape = op.inputs[0].get_shape()
indices_shape = op.inputs[1].get_shape()
return [indices_shape.concatenate(params_shape[1:])]
@ops.RegisterShape("Unique")
def _UniqueShape(op):
"""Shape function for array_ops.Unique."""
# The output is a vector with data-dependent length.
input_shape = op.inputs[0].get_shape()
input_shape.assert_has_rank(1)
return [tensor_shape.vector(None), input_shape]
@ops.RegisterShape("UniqueWithCounts")
def _UniqueWithCountsShape(op):
"""Shape function for array_ops.Unique."""
# The output is a vector with data-dependent length.
input_shape = op.inputs[0].get_shape()
input_shape.assert_has_rank(1)
return [tensor_shape.vector(None), input_shape, tensor_shape.vector(None)]
@ops.RegisterShape("Diag")
def _DiagShape(op):
"""Shape function for array_ops.diag.
This op has one input (of rank k <= 3), and one output (of rank 2k),
where the shape of the output is the concatenation of the input
shape with itself.
Args:
op: A Diag Operation.
Returns:
A single-element list containing the shape of the output.
"""
input_shape = op.inputs[0].get_shape().with_rank_at_most(3)
return [input_shape.concatenate(input_shape)]
@ops.RegisterShape("ExpandDims")
def _ExpandDimsShape(op):
"""Determine shape for expand op's output tensor.
Args:
op: Operation for which to determine shape.
op.inputs[0] is the input tensor.
op.inputs[1] is the dimension in which to expand.
Returns:
Shape of op's output tensor.
Raises:
ValueError: If dim is outside of [-rank - 1, rank], where rank is the number
of dimensions in the input tensor.
"""
input_shape = op.inputs[0].get_shape()
if input_shape.dims is None:
return [tensor_shape.unknown_shape()]
dim = tensor_util.constant_value(op.inputs[1])
input_ndims = input_shape.ndims
if dim < -input_ndims - 1 or dim > input_ndims:
raise ValueError(
"dim %d not in [%d, %d]." % (dim, -input_ndims, input_ndims))
if dim < 0:
dim += (input_ndims + 1)
result_shape = list(input_shape.dims)
result_shape.insert(dim, 1)
return [tensor_shape.TensorShape(result_shape)]
@ops.RegisterShape("Squeeze")
def _SqueezeShape(op):
"""Determine shape for squeeze op's output tensor.
Args:
op: Operation for which to determine shape.
Returns:
Shape of op's output tensor.
Raises:
ValueError: if squeeze_dims includes a dimension outside of [-rank, rank),
where rank is the number of dimensions in the input tensor. Or, if
squeeze_dims includes a dimension for which input shape has a value
not equal to 1.
"""
input_shape = op.inputs[0].get_shape()
if input_shape.dims is None:
return [tensor_shape.unknown_shape()]
squeeze_dims = op.get_attr("squeeze_dims") or []
wrapped_squeeze_dims = []
input_ndims = input_shape.ndims
for i, squeeze_dim in enumerate(squeeze_dims):
if squeeze_dim < -input_ndims or squeeze_dim >= input_ndims:
raise ValueError(
"squeeze_dims[%d]=%d not in [%d, %d)." % (
i, squeeze_dim, -input_ndims, input_ndims))
if squeeze_dim < 0:
squeeze_dim += input_ndims
wrapped_squeeze_dims.append(squeeze_dim)
result_shape = []
for i, dim in enumerate([d.value for d in input_shape.dims]):
is_explicit_match = i in wrapped_squeeze_dims
if is_explicit_match or not wrapped_squeeze_dims:
if dim is None:
return [tensor_shape.unknown_shape()]
if dim != 1:
if is_explicit_match:
raise ValueError(
"Can not squeeze dim[%d], expected a dimension of 1, got %d." % (
i, dim))
result_shape.append(dim)
else:
result_shape.append(dim)
return [tensor_shape.TensorShape(result_shape)]
@ops.RegisterShape("Reshape")
def _ReshapeShape(op):
"""Shape function for Reshape op."""
input_shape = op.inputs[0].get_shape()
if input_shape.ndims is not None:
num_elements = tensor_shape.Dimension(1)
for dim in input_shape.dims:
num_elements *= dim
else:
num_elements = tensor_shape.Dimension(None)
new_shape_shape = op.inputs[1].get_shape().with_rank_at_most(1)
new_shape = tensor_util.constant_value(op.inputs[1])
if new_shape is None:
# Attempt to infer the rank of the output from the length of
# new_shape.
return [tensor_shape.unknown_shape(ndims=new_shape_shape.num_elements())]
new_shape = np.reshape(new_shape, -1).tolist()
if -1 not in new_shape:
# The new shape is fully defined.
if (num_elements.value is not None
and num_elements.value != np.prod(new_shape)):
raise ValueError(
"Cannot reshape a tensor with %d elements to shape %s (%d elements)"
% (num_elements.value, new_shape, np.prod(new_shape)))
return [tensor_shape.TensorShape(new_shape)]
elif num_elements.value is not None:
# We know the number of elements, so we can calculate the missing
# dimension in the new_shape.
known_elements = 1
unknown_index = None
for i, dim in enumerate(new_shape):
if dim == -1:
unknown_index = i
else:
known_elements *= dim
if known_elements == 0:
raise ValueError("cannot infer the missing input size for "
"an empty tensor unless all specified "
"input sizes are non-zero")
if num_elements % known_elements != 0:
raise ValueError("input has %s elements, which isn't divisible by %d" %
(num_elements, known_elements))
new_shape[unknown_index] = num_elements // known_elements
return [tensor_shape.TensorShape(new_shape)]
else:
# We don't know the input shape, but we know n-1 of the dimensions
# in the new shape.
new_shape[new_shape.index(-1)] = None
return [tensor_shape.TensorShape(new_shape)]
@ops.RegisterShape("BroadcastGradientArgs")
def _BroadcastGradientArgsShape(op):
"""Shape function for the BroadcastGradientArgs op."""
# TODO(mrry): Implement constant_value for BroadcastGradientArgs?
op.inputs[0].get_shape().assert_has_rank(1)
op.inputs[1].get_shape().assert_has_rank(1)
return [tensor_shape.vector(None), tensor_shape.vector(None)]
@ops.RegisterShape("Fill")
def _FillShape(op):
"""Shape function for the Fill op.
This op takes a vector of dimensions and a scalar, and produces a
tensor with the given dimensions.
Args:
op: A Fill Operation.
Returns:
A single-element list containing the shape of the output.
"""
dimensions_shape = op.inputs[0].get_shape().with_rank_at_most(1)
op.inputs[1].get_shape().assert_is_compatible_with(tensor_shape.scalar())
fill_dims = tensor_util.constant_value(op.inputs[0])
if fill_dims is None:
# Attempt to infer the rank of the output from the length of
# dimensions.
return [tensor_shape.unknown_shape(ndims=dimensions_shape.num_elements())]
else:
return [tensor_shape.TensorShape(fill_dims.tolist())]
@ops.RegisterShape("InvertPermutation")
def _InvertPermutationShape(op):
"""Shape function for the InvertPermutation op."""
return [op.inputs[0].get_shape().with_rank(1)]
@ops.RegisterShape("ListDiff")
def _ListDiffShape(op):
"""Shape function for the ListDiff op."""
op.inputs[0].get_shape().assert_has_rank(1)
op.inputs[1].get_shape().assert_has_rank(1)
# TODO(mrry): Indicate that the length falls within an interval?
return [tensor_shape.vector(None)] * 2
@ops.RegisterShape("Pad")
def _PadShape(op):
"""Shape function for the Pad op.
This op has two inputs:
* input: A rank-N tensor.
* paddings: An N-by-2 matrix, in which the i^th row contains the
number of padding elements to add before and after `input` in the
i^th dimension.
It has one output, which has the same rank as input, and additional
elements according to the values in paddings.
Args:
op: A Pad Operation.
Returns:
A single-element list containing the shape of the output.
Raises:
ValueError: If the input shapes are incompatible.
"""
paddings_shape = op.inputs[1].get_shape().with_rank(2)
input_shape = op.inputs[0].get_shape()
input_shape = input_shape.with_rank(paddings_shape[0].value)
paddings_shape = paddings_shape.merge_with(
tensor_shape.matrix(input_shape.ndims, 2))
paddings = tensor_util.constant_value(op.inputs[1])
if paddings is None:
return [tensor_shape.unknown_shape(ndims=input_shape.ndims)]
else:
output_dims = []
for i, dim in enumerate(input_shape.dims):
if paddings[i, 0] < 0 or paddings[i, 1] < 0:
raise ValueError("paddings must be non-negative")
output_dims.append(dim + paddings[i, 0] + paddings[i, 1])
return [tensor_shape.TensorShape(output_dims)]
@ops.RegisterShape("ReverseSequence")
def _ReverseSequenceShape(op):
"""Shape function for the ReverseSequence op.
This op has two inputs: