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Intelegentny_Pszczelarz/.venv/Lib/site-packages/tensorboard/compat/tensorflow_stub/tensor_shape.py

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feature: "ANN commit 2"
2023-06-19 00:49:18 +02:00
# 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.
# ==============================================================================
"""Helper classes for tensor shape inference."""
from . import compat, dtypes
from tensorboard.compat.proto import tensor_shape_pb2
# @tf_export("Dimension")
class Dimension:
"""Represents the value of one dimension in a TensorShape."""
def __init__(self, value):
"""Creates a new Dimension with the given value."""
if value is None:
self._value = None
elif isinstance(value, dtypes.DType):
raise TypeError("Cannot convert %s to Dimension" % value)
else:
self._value = int(value)
if (
not isinstance(value, compat.bytes_or_text_types)
and self._value != value
):
raise ValueError("Ambiguous dimension: %s" % value)
if self._value < 0:
raise ValueError("Dimension %d must be >= 0" % self._value)
def __repr__(self):
return "Dimension(%s)" % repr(self._value)
def __str__(self):
value = self._value
return "?" if value is None else str(value)
def __eq__(self, other):
"""Returns true if `other` has the same known value as this
Dimension."""
try:
other = as_dimension(other)
except (TypeError, ValueError):
return NotImplemented
if self._value is None or other.value is None:
return None
return self._value == other.value
def __ne__(self, other):
"""Returns true if `other` has a different known value from `self`."""
try:
other = as_dimension(other)
except (TypeError, ValueError):
return NotImplemented
if self._value is None or other.value is None:
return None
return self._value != other.value
def __int__(self):
return self._value
# This is needed for Windows.
# See https://github.com/tensorflow/tensorflow/pull/9780
def __long__(self):
return self._value
def __index__(self):
# Allow use in Python 3 range
return self._value
@property
def value(self):
"""The value of this dimension, or None if it is unknown."""
return self._value
def is_convertible_with(self, other):
"""Returns true if `other` is convertible with this Dimension.
Two known Dimensions are convertible if they have the same value.
An unknown Dimension is convertible with all other Dimensions.
Args:
other: Another Dimension.
Returns:
True if this Dimension and `other` are convertible.
"""
other = as_dimension(other)
return (
self._value is None
or other.value is None
or self._value == other.value
)
def assert_is_convertible_with(self, other):
"""Raises an exception if `other` is not convertible with this
Dimension.
Args:
other: Another Dimension.
Raises:
ValueError: If `self` and `other` are not convertible (see
is_convertible_with).
"""
if not self.is_convertible_with(other):
raise ValueError(
"Dimensions %s and %s are not convertible" % (self, other)
)
def merge_with(self, other):
"""Returns a Dimension that combines the information in `self` and
`other`.
Dimensions are combined as follows:
```python
tf.Dimension(n) .merge_with(tf.Dimension(n)) == tf.Dimension(n)
tf.Dimension(n) .merge_with(tf.Dimension(None)) == tf.Dimension(n)
tf.Dimension(None).merge_with(tf.Dimension(n)) == tf.Dimension(n)
tf.Dimension(None).merge_with(tf.Dimension(None)) == tf.Dimension(None)
tf.Dimension(n) .merge_with(tf.Dimension(m)) # raises ValueError for n != m
```
Args:
other: Another Dimension.
Returns:
A Dimension containing the combined information of `self` and
`other`.
Raises:
ValueError: If `self` and `other` are not convertible (see
is_convertible_with).
"""
other = as_dimension(other)
self.assert_is_convertible_with(other)
if self._value is None:
return Dimension(other.value)
else:
return Dimension(self._value)
def __add__(self, other):
"""Returns the sum of `self` and `other`.
Dimensions are summed as follows:
```python
tf.Dimension(m) + tf.Dimension(n) == tf.Dimension(m + n)
tf.Dimension(m) + tf.Dimension(None) == tf.Dimension(None)
tf.Dimension(None) + tf.Dimension(n) == tf.Dimension(None)
tf.Dimension(None) + tf.Dimension(None) == tf.Dimension(None)
```
Args:
other: Another Dimension, or a value accepted by `as_dimension`.
Returns:
A Dimension whose value is the sum of `self` and `other`.
"""
other = as_dimension(other)
if self._value is None or other.value is None:
return Dimension(None)
else:
return Dimension(self._value + other.value)
def __radd__(self, other):
"""Returns the sum of `other` and `self`.
Args:
other: Another Dimension, or a value accepted by `as_dimension`.
Returns:
A Dimension whose value is the sum of `self` and `other`.
"""
return self + other
def __sub__(self, other):
"""Returns the subtraction of `other` from `self`.
Dimensions are subtracted as follows:
```python
tf.Dimension(m) - tf.Dimension(n) == tf.Dimension(m - n)
tf.Dimension(m) - tf.Dimension(None) == tf.Dimension(None)
tf.Dimension(None) - tf.Dimension(n) == tf.Dimension(None)
tf.Dimension(None) - tf.Dimension(None) == tf.Dimension(None)
```
Args:
other: Another Dimension, or a value accepted by `as_dimension`.
Returns:
A Dimension whose value is the subtraction of `other` from `self`.
"""
other = as_dimension(other)
if self._value is None or other.value is None:
return Dimension(None)
else:
return Dimension(self._value - other.value)
def __rsub__(self, other):
"""Returns the subtraction of `self` from `other`.
Args:
other: Another Dimension, or a value accepted by `as_dimension`.
Returns:
A Dimension whose value is the subtraction of `self` from `other`.
"""
other = as_dimension(other)
if self._value is None or other.value is None:
return Dimension(None)
else:
return Dimension(other.value - self._value)
def __mul__(self, other):
"""Returns the product of `self` and `other`.
Dimensions are summed as follows:
```python
tf.Dimension(m) * tf.Dimension(n) == tf.Dimension(m * n)
tf.Dimension(m) * tf.Dimension(None) == tf.Dimension(None)
tf.Dimension(None) * tf.Dimension(n) == tf.Dimension(None)
tf.Dimension(None) * tf.Dimension(None) == tf.Dimension(None)
```
Args:
other: Another Dimension, or a value accepted by `as_dimension`.
Returns:
A Dimension whose value is the product of `self` and `other`.
"""
try:
other = as_dimension(other)
except (TypeError, ValueError):
return NotImplemented
if self._value is None or other.value is None:
return Dimension(None)
else:
return Dimension(self._value * other.value)
def __rmul__(self, other):
"""Returns the product of `self` and `other`.
Args:
other: Another Dimension, or a value accepted by `as_dimension`.
Returns:
A Dimension whose value is the product of `self` and `other`.
"""
return self * other
def __floordiv__(self, other):
"""Returns the quotient of `self` and `other` rounded down.
Dimensions are divided as follows:
```python
tf.Dimension(m) // tf.Dimension(n) == tf.Dimension(m // n)
tf.Dimension(m) // tf.Dimension(None) == tf.Dimension(None)
tf.Dimension(None) // tf.Dimension(n) == tf.Dimension(None)
tf.Dimension(None) // tf.Dimension(None) == tf.Dimension(None)
```
Args:
other: Another Dimension, or a value accepted by `as_dimension`.
Returns:
A `Dimension` whose value is the integer quotient of `self` and `other`.
"""
try:
other = as_dimension(other)
except (TypeError, ValueError):
return NotImplemented
if self._value is None or other.value is None:
return Dimension(None)
else:
return Dimension(self._value // other.value)
def __rfloordiv__(self, other):
"""Returns the quotient of `other` and `self` rounded down.
Args:
other: Another Dimension, or a value accepted by `as_dimension`.
Returns:
A `Dimension` whose value is the integer quotient of `self` and `other`.
"""
other = as_dimension(other)
if self._value is None or other.value is None:
return Dimension(None)
else:
return Dimension(other.value // self._value)
def __div__(self, other):
"""DEPRECATED: Use `__floordiv__` via `x // y` instead.
This function exists only for backwards convertibility purposes; new code
should use `__floordiv__` via the syntax `x // y`. Using `x // y`
communicates clearly that the result rounds down, and is forward convertible
to Python 3.
Args:
other: Another `Dimension`.
Returns:
A `Dimension` whose value is the integer quotient of `self` and `other`.
"""
return self // other
def __mod__(self, other):
"""Returns `self` modulo `other`.
Dimension moduli are computed as follows:
```python
tf.Dimension(m) % tf.Dimension(n) == tf.Dimension(m % n)
tf.Dimension(m) % tf.Dimension(None) == tf.Dimension(None)
tf.Dimension(None) % tf.Dimension(n) == tf.Dimension(None)
tf.Dimension(None) % tf.Dimension(None) == tf.Dimension(None)
```
Args:
other: Another Dimension, or a value accepted by `as_dimension`.
Returns:
A Dimension whose value is `self` modulo `other`.
"""
try:
other = as_dimension(other)
except (TypeError, ValueError):
return NotImplemented
if self._value is None or other.value is None:
return Dimension(None)
else:
return Dimension(self._value % other.value)
def __rmod__(self, other):
"""Returns `other` modulo `self`.
Args:
other: Another Dimension, or a value accepted by `as_dimension`.
Returns:
A Dimension whose value is `other` modulo `self`.
"""
try:
other = as_dimension(other)
except (TypeError, ValueError):
return NotImplemented
return other % self
def __lt__(self, other):
"""Returns True if `self` is known to be less than `other`.
Dimensions are compared as follows:
```python
(tf.Dimension(m) < tf.Dimension(n)) == (m < n)
(tf.Dimension(m) < tf.Dimension(None)) == None
(tf.Dimension(None) < tf.Dimension(n)) == None
(tf.Dimension(None) < tf.Dimension(None)) == None
```
Args:
other: Another Dimension.
Returns:
The value of `self.value < other.value` if both are known, otherwise
None.
"""
other = as_dimension(other)
if self._value is None or other.value is None:
return None
else:
return self._value < other.value
def __le__(self, other):
"""Returns True if `self` is known to be less than or equal to `other`.
Dimensions are compared as follows:
```python
(tf.Dimension(m) <= tf.Dimension(n)) == (m <= n)
(tf.Dimension(m) <= tf.Dimension(None)) == None
(tf.Dimension(None) <= tf.Dimension(n)) == None
(tf.Dimension(None) <= tf.Dimension(None)) == None
```
Args:
other: Another Dimension.
Returns:
The value of `self.value <= other.value` if both are known, otherwise
None.
"""
other = as_dimension(other)
if self._value is None or other.value is None:
return None
else:
return self._value <= other.value
def __gt__(self, other):
"""Returns True if `self` is known to be greater than `other`.
Dimensions are compared as follows:
```python
(tf.Dimension(m) > tf.Dimension(n)) == (m > n)
(tf.Dimension(m) > tf.Dimension(None)) == None
(tf.Dimension(None) > tf.Dimension(n)) == None
(tf.Dimension(None) > tf.Dimension(None)) == None
```
Args:
other: Another Dimension.
Returns:
The value of `self.value > other.value` if both are known, otherwise
None.
"""
other = as_dimension(other)
if self._value is None or other.value is None:
return None
else:
return self._value > other.value
def __ge__(self, other):
"""Returns True if `self` is known to be greater than or equal to
`other`.
Dimensions are compared as follows:
```python
(tf.Dimension(m) >= tf.Dimension(n)) == (m >= n)
(tf.Dimension(m) >= tf.Dimension(None)) == None
(tf.Dimension(None) >= tf.Dimension(n)) == None
(tf.Dimension(None) >= tf.Dimension(None)) == None
```
Args:
other: Another Dimension.
Returns:
The value of `self.value >= other.value` if both are known, otherwise
None.
"""
other = as_dimension(other)
if self._value is None or other.value is None:
return None
else:
return self._value >= other.value
def __reduce__(self):
return Dimension, (self._value,)
def as_dimension(value):
"""Converts the given value to a Dimension.
A Dimension input will be returned unmodified.
An input of `None` will be converted to an unknown Dimension.
An integer input will be converted to a Dimension with that value.
Args:
value: The value to be converted.
Returns:
A Dimension corresponding to the given value.
"""
if isinstance(value, Dimension):
return value
else:
return Dimension(value)
# @tf_export("TensorShape")
class TensorShape:
"""Represents the shape of a `Tensor`.
A `TensorShape` represents a possibly-partial shape specification for a
`Tensor`. It may be one of the following:
* *Fully-known shape:* has a known number of dimensions and a known size
for each dimension. e.g. `TensorShape([16, 256])`
* *Partially-known shape:* has a known number of dimensions, and an unknown
size for one or more dimension. e.g. `TensorShape([None, 256])`
* *Unknown shape:* has an unknown number of dimensions, and an unknown
size in all dimensions. e.g. `TensorShape(None)`
If a tensor is produced by an operation of type `"Foo"`, its shape
may be inferred if there is a registered shape function for
`"Foo"`. See @{$adding_an_op#shape-functions-in-c$`Shape functions in C++`}
for details of shape functions and how to register them. Alternatively,
the shape may be set explicitly using @{tf.Tensor.set_shape}.
"""
def __init__(self, dims):
"""Creates a new TensorShape with the given dimensions.
Args:
dims: A list of Dimensions, or None if the shape is unspecified.
DEPRECATED: A single integer is treated as a singleton list.
Raises:
TypeError: If dims cannot be converted to a list of dimensions.
"""
# TODO(irving): Eliminate the single integer special case.
if dims is None:
self._dims = None
elif isinstance(dims, compat.bytes_or_text_types):
raise TypeError(
"A string has ambiguous TensorShape, please wrap in a "
"list or convert to an int: %s" % dims
)
elif isinstance(dims, tensor_shape_pb2.TensorShapeProto):
if dims.unknown_rank:
self._dims = None
else:
self._dims = [
# Protos store variable-size dimensions as -1
as_dimension(dim.size if dim.size != -1 else None)
for dim in dims.dim
]
elif isinstance(dims, TensorShape):
self._dims = dims.dims
else:
try:
dims_iter = iter(dims)
except TypeError:
# Treat as a singleton dimension
self._dims = [as_dimension(dims)]
else:
# Got a list of dimensions
self._dims = [as_dimension(d) for d in dims_iter]
self._ndims = None
def __repr__(self):
return "TensorShape(%r)" % self._dims
def __str__(self):
if self.ndims is None:
return "<unknown>"
elif self.ndims == 1:
return "(%s,)" % self._dims[0]
else:
return "(%s)" % ", ".join(str(d) for d in self._dims)
@property
def dims(self):
"""Returns a list of Dimensions, or None if the shape is
unspecified."""
return self._dims
@dims.setter
def dims(self, dims):
self._dims = dims
self._ndims = None
@property
def ndims(self):
"""Returns the rank of this shape, or None if it is unspecified."""
if self._dims is None:
return None
else:
if self._ndims is None:
self._ndims = len(self._dims)
return self._ndims
def __len__(self):
"""Returns the rank of this shape, or raises ValueError if
unspecified."""
if self._dims is None:
raise ValueError(
"Cannot take the length of Shape with unknown rank."
)
return self.ndims
def __bool__(self):
"""Returns True if this shape contains non-zero information."""
return self._dims is not None
# Python 3 wants __bool__, Python 2.7 wants __nonzero__
__nonzero__ = __bool__
def __iter__(self):
"""Returns `self.dims` if the rank is known, otherwise raises
ValueError."""
if self._dims is None:
raise ValueError("Cannot iterate over a shape with unknown rank.")
else:
return iter(self._dims)
def __getitem__(self, key):
"""Returns the value of a dimension or a shape, depending on the key.
Args:
key: If `key` is an integer, returns the dimension at that index;
otherwise if `key` is a slice, returns a TensorShape whose
dimensions are those selected by the slice from `self`.
Returns:
A dimension if `key` is an integer, or a `TensorShape` if `key` is a
slice.
Raises:
ValueError: If `key` is a slice, and any of its elements are negative, or
if `self` is completely unknown and the step is set.
"""
if self._dims is not None:
if isinstance(key, slice):
return TensorShape(self._dims[key])
else:
return self._dims[key]
else:
if isinstance(key, slice):
start = key.start if key.start is not None else 0
stop = key.stop
if key.step is not None:
# TODO(mrry): Handle these maybe.
raise ValueError("Steps are not yet handled")
if stop is None:
# NOTE(mrry): This implies that TensorShape(None) is convertible with
# TensorShape(None)[1:], which is obviously not true. It would be
# possible to track the number of dimensions symbolically,
# and perhaps we should do that.
return unknown_shape()
elif start < 0 or stop < 0:
# TODO(mrry): Handle this better, as it will be useful for handling
# suffixes of otherwise unknown shapes.
return unknown_shape()
else:
return unknown_shape(ndims=stop - start)
else:
return Dimension(None)
def num_elements(self):
"""Returns the total number of elements, or none for incomplete
shapes."""
if self.is_fully_defined():
size = 1
for dim in self._dims:
size *= dim.value
return size
else:
return None
def merge_with(self, other):
"""Returns a `TensorShape` combining the information in `self` and
`other`.
The dimensions in `self` and `other` are merged elementwise,
according to the rules defined for `Dimension.merge_with()`.
Args:
other: Another `TensorShape`.
Returns:
A `TensorShape` containing the combined information of `self` and
`other`.
Raises:
ValueError: If `self` and `other` are not convertible.
"""
other = as_shape(other)
if self._dims is None:
return other
else:
try:
self.assert_same_rank(other)
new_dims = []
for i, dim in enumerate(self._dims):
new_dims.append(dim.merge_with(other[i]))
return TensorShape(new_dims)
except ValueError:
raise ValueError(
"Shapes %s and %s are not convertible" % (self, other)
)
def concatenate(self, other):
"""Returns the concatenation of the dimension in `self` and `other`.
*N.B.* If either `self` or `other` is completely unknown,
concatenation will discard information about the other shape. In
future, we might support concatenation that preserves this
information for use with slicing.
Args:
other: Another `TensorShape`.
Returns:
A `TensorShape` whose dimensions are the concatenation of the
dimensions in `self` and `other`.
"""
# TODO(mrry): Handle the case where we concatenate a known shape with a
# completely unknown shape, so that we can use the partial information.
other = as_shape(other)
if self._dims is None or other.dims is None:
return unknown_shape()
else:
return TensorShape(self._dims + other.dims)
def assert_same_rank(self, other):
"""Raises an exception if `self` and `other` do not have convertible
ranks.
Args:
other: Another `TensorShape`.
Raises:
ValueError: If `self` and `other` do not represent shapes with the
same rank.
"""
other = as_shape(other)
if self.ndims is not None and other.ndims is not None:
if self.ndims != other.ndims:
raise ValueError(
"Shapes %s and %s must have the same rank" % (self, other)
)
def assert_has_rank(self, rank):
"""Raises an exception if `self` is not convertible with the given
`rank`.
Args:
rank: An integer.
Raises:
ValueError: If `self` does not represent a shape with the given `rank`.
"""
if self.ndims not in (None, rank):
raise ValueError("Shape %s must have rank %d" % (self, rank))
def with_rank(self, rank):
"""Returns a shape based on `self` with the given rank.
This method promotes a completely unknown shape to one with a
known rank.
Args:
rank: An integer.
Returns:
A shape that is at least as specific as `self` with the given rank.
Raises:
ValueError: If `self` does not represent a shape with the given `rank`.
"""
try:
return self.merge_with(unknown_shape(ndims=rank))
except ValueError:
raise ValueError("Shape %s must have rank %d" % (self, rank))
def with_rank_at_least(self, rank):
"""Returns a shape based on `self` with at least the given rank.
Args:
rank: An integer.
Returns:
A shape that is at least as specific as `self` with at least the given
rank.
Raises:
ValueError: If `self` does not represent a shape with at least the given
`rank`.
"""
if self.ndims is not None and self.ndims < rank:
raise ValueError(
"Shape %s must have rank at least %d" % (self, rank)
)
else:
return self
def with_rank_at_most(self, rank):
"""Returns a shape based on `self` with at most the given rank.
Args:
rank: An integer.
Returns:
A shape that is at least as specific as `self` with at most the given
rank.
Raises:
ValueError: If `self` does not represent a shape with at most the given
`rank`.
"""
if self.ndims is not None and self.ndims > rank:
raise ValueError(
"Shape %s must have rank at most %d" % (self, rank)
)
else:
return self
def is_convertible_with(self, other):
"""Returns True iff `self` is convertible with `other`.
Two possibly-partially-defined shapes are convertible if there
exists a fully-defined shape that both shapes can represent. Thus,
convertibility allows the shape inference code to reason about
partially-defined shapes. For example:
* TensorShape(None) is convertible with all shapes.
* TensorShape([None, None]) is convertible with all two-dimensional
shapes, such as TensorShape([32, 784]), and also TensorShape(None). It is
not convertible with, for example, TensorShape([None]) or
TensorShape([None, None, None]).
* TensorShape([32, None]) is convertible with all two-dimensional shapes
with size 32 in the 0th dimension, and also TensorShape([None, None])
and TensorShape(None). It is not convertible with, for example,
TensorShape([32]), TensorShape([32, None, 1]) or TensorShape([64, None]).
* TensorShape([32, 784]) is convertible with itself, and also
TensorShape([32, None]), TensorShape([None, 784]), TensorShape([None,
None]) and TensorShape(None). It is not convertible with, for example,
TensorShape([32, 1, 784]) or TensorShape([None]).
The convertibility relation is reflexive and symmetric, but not
transitive. For example, TensorShape([32, 784]) is convertible with
TensorShape(None), and TensorShape(None) is convertible with
TensorShape([4, 4]), but TensorShape([32, 784]) is not convertible with
TensorShape([4, 4]).
Args:
other: Another TensorShape.
Returns:
True iff `self` is convertible with `other`.
"""
other = as_shape(other)
if self._dims is not None and other.dims is not None:
if self.ndims != other.ndims:
return False
for x_dim, y_dim in zip(self._dims, other.dims):
if not x_dim.is_convertible_with(y_dim):
return False
return True
def assert_is_convertible_with(self, other):
"""Raises exception if `self` and `other` do not represent the same
shape.
This method can be used to assert that there exists a shape that both
`self` and `other` represent.
Args:
other: Another TensorShape.
Raises:
ValueError: If `self` and `other` do not represent the same shape.
"""
if not self.is_convertible_with(other):
raise ValueError(
"Shapes %s and %s are inconvertible" % (self, other)
)
def most_specific_convertible_shape(self, other):
"""Returns the most specific TensorShape convertible with `self` and
`other`.
* TensorShape([None, 1]) is the most specific TensorShape convertible with
both TensorShape([2, 1]) and TensorShape([5, 1]). Note that
TensorShape(None) is also convertible with above mentioned TensorShapes.
* TensorShape([1, 2, 3]) is the most specific TensorShape convertible with
both TensorShape([1, 2, 3]) and TensorShape([1, 2, 3]). There are more
less specific TensorShapes convertible with above mentioned TensorShapes,
e.g. TensorShape([1, 2, None]), TensorShape(None).
Args:
other: Another `TensorShape`.
Returns:
A `TensorShape` which is the most specific convertible shape of `self`
and `other`.
"""
other = as_shape(other)
if (
self._dims is None
or other.dims is None
or self.ndims != other.ndims
):
return unknown_shape()
dims = [(Dimension(None))] * self.ndims
for i, (d1, d2) in enumerate(zip(self._dims, other.dims)):
if d1 is not None and d2 is not None and d1 == d2:
dims[i] = d1
return TensorShape(dims)
def is_fully_defined(self):
"""Returns True iff `self` is fully defined in every dimension."""
return self._dims is not None and all(
dim.value is not None for dim in self._dims
)
def assert_is_fully_defined(self):
"""Raises an exception if `self` is not fully defined in every
dimension.
Raises:
ValueError: If `self` does not have a known value for every dimension.
"""
if not self.is_fully_defined():
raise ValueError("Shape %s is not fully defined" % self)
def as_list(self):
"""Returns a list of integers or `None` for each dimension.
Returns:
A list of integers or `None` for each dimension.
Raises:
ValueError: If `self` is an unknown shape with an unknown rank.
"""
if self._dims is None:
raise ValueError(
"as_list() is not defined on an unknown TensorShape."
)
return [dim.value for dim in self._dims]
def as_proto(self):
"""Returns this shape as a `TensorShapeProto`."""
if self._dims is None:
return tensor_shape_pb2.TensorShapeProto(unknown_rank=True)
else:
return tensor_shape_pb2.TensorShapeProto(
dim=[
tensor_shape_pb2.TensorShapeProto.Dim(
size=-1 if d.value is None else d.value
)
for d in self._dims
]
)
def __eq__(self, other):
"""Returns True if `self` is equivalent to `other`."""
try:
other = as_shape(other)
except TypeError:
return NotImplemented
return self._dims == other.dims
def __ne__(self, other):
"""Returns True if `self` is known to be different from `other`."""
try:
other = as_shape(other)
except TypeError:
return NotImplemented
if self.ndims is None or other.ndims is None:
raise ValueError(
"The inequality of unknown TensorShapes is undefined."
)
if self.ndims != other.ndims:
return True
return self._dims != other.dims
def __reduce__(self):
return TensorShape, (self._dims,)
def as_shape(shape):
"""Converts the given object to a TensorShape."""
if isinstance(shape, TensorShape):
return shape
else:
return TensorShape(shape)
def unknown_shape(ndims=None):
"""Returns an unknown TensorShape, optionally with a known rank.
Args:
ndims: (Optional) If specified, the number of dimensions in the shape.
Returns:
An unknown TensorShape.
"""
if ndims is None:
return TensorShape(None)
else:
return TensorShape([Dimension(None)] * ndims)
_SCALAR_SHAPE = TensorShape([])
def scalar():
"""Returns a shape representing a scalar."""
return _SCALAR_SHAPE
def vector(length):
"""Returns a shape representing a vector.
Args:
length: The length of the vector, which may be None if unknown.
Returns:
A TensorShape representing a vector of the given length.
"""
return TensorShape([length])
def matrix(rows, cols):
"""Returns a shape representing a matrix.
Args:
rows: The number of rows in the matrix, which may be None if unknown.
cols: The number of columns in the matrix, which may be None if unknown.
Returns:
A TensorShape representing a matrix of the given size.
"""
return TensorShape([rows, cols])
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