# 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."""
import functools
import operator
from typing import Optional, Sequence
import six
from tensorflow.core.framework import tensor_shape_pb2
from tensorflow.python import tf2
from tensorflow.python.eager import monitoring
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.types import trace
from tensorflow.python.util.tf_export import tf_export
_TENSORSHAPE_V2_OVERRIDE = None
_api_usage_gauge = monitoring.BoolGauge(
"/tensorflow/api/v2_tensorshape",
"Whether tensor_shape.enable_v2_tensorshape() is called.")
@tf_export(v1=["enable_v2_tensorshape"])
def enable_v2_tensorshape():
"""In TensorFlow 2.0, iterating over a TensorShape instance returns values.
This enables the new behavior.
Concretely, `tensor_shape[i]` returned a Dimension instance in V1, but
it V2 it returns either an integer, or None.
Examples:
```
#######################
# If you had this in V1:
value = tensor_shape[i].value
# Do this in V2 instead:
value = tensor_shape[i]
#######################
# If you had this in V1:
for dim in tensor_shape:
value = dim.value
print(value)
# Do this in V2 instead:
for value in tensor_shape:
print(value)
#######################
# If you had this in V1:
dim = tensor_shape[i]
dim.assert_is_compatible_with(other_shape) # or using any other shape method
# Do this in V2 instead:
if tensor_shape.rank is None:
dim = Dimension(None)
else:
dim = tensor_shape.dims[i]
dim.assert_is_compatible_with(other_shape) # or using any other shape method
# The V2 suggestion above is more explicit, which will save you from
# the following trap (present in V1):
# you might do in-place modifications to `dim` and expect them to be reflected
# in `tensor_shape[i]`, but they would not be.
```
"""
global _TENSORSHAPE_V2_OVERRIDE # pylint: disable=invalid-name
_TENSORSHAPE_V2_OVERRIDE = True
logging.vlog(1, "Enabling v2 tensorshape")
_api_usage_gauge.get_cell().set(True)
@tf_export(v1=["disable_v2_tensorshape"])
def disable_v2_tensorshape():
"""Disables the V2 TensorShape behavior and reverts to V1 behavior.
See docstring for `enable_v2_tensorshape` for details about the new behavior.
"""
global _TENSORSHAPE_V2_OVERRIDE # pylint: disable=invalid-name
_TENSORSHAPE_V2_OVERRIDE = False
logging.vlog(1, "Disabling v2 tensorshape")
_api_usage_gauge.get_cell().set(False)
@tf_export(
"compat.dimension_value", v1=["dimension_value", "compat.dimension_value"])
def dimension_value(dimension):
"""Compatibility utility required to allow for both V1 and V2 behavior in TF.
Until the release of TF 2.0, we need the legacy behavior of `TensorShape` to
coexist with the new behavior. This utility is a bridge between the two.
When accessing the value of a TensorShape dimension,
use this utility, like this:
```
# If you had this in your V1 code:
value = tensor_shape[i].value
# Use `dimension_value` as direct replacement compatible with both V1 & V2:
value = dimension_value(tensor_shape[i])
# This would be the V2 equivalent:
value = tensor_shape[i] # Warning: this will return the dim value in V2!
```
Args:
dimension: Either a `Dimension` instance, an integer, or None.
Returns:
A plain value, i.e. an integer or None.
"""
if isinstance(dimension, Dimension):
return dimension.value
return dimension
@tf_export(
"compat.dimension_at_index",
v1=["dimension_at_index", "compat.dimension_at_index"])
def dimension_at_index(shape, index):
"""Compatibility utility required to allow for both V1 and V2 behavior in TF.
Until the release of TF 2.0, we need the legacy behavior of `TensorShape` to
coexist with the new behavior. This utility is a bridge between the two.
If you want to retrieve the Dimension instance corresponding to a certain
index in a TensorShape instance, use this utility, like this:
```
# If you had this in your V1 code:
dim = tensor_shape[i]
# Use `dimension_at_index` as direct replacement compatible with both V1 & V2:
dim = dimension_at_index(tensor_shape, i)
# Another possibility would be this, but WARNING: it only works if the
# tensor_shape instance has a defined rank.
dim = tensor_shape.dims[i] # `dims` may be None if the rank is undefined!
# In native V2 code, we recommend instead being more explicit:
if tensor_shape.rank is None:
dim = Dimension(None)
else:
dim = tensor_shape.dims[i]
# Being more explicit will save you from the following trap (present in V1):
# you might do in-place modifications to `dim` and expect them to be reflected
# in `tensor_shape[i]`, but they would not be (as the Dimension object was
# instantiated on the fly.
```
Args:
shape: A TensorShape instance.
index: An integer index.
Returns:
A dimension object.
"""
assert isinstance(shape, TensorShape)
if shape.rank is None:
return Dimension(None)
else:
return shape.dims[index]
@tf_export(v1=["Dimension"])
class Dimension(object):
"""Represents the value of one dimension in a TensorShape.
@compatibility(TF2)
In TF2, members of a `TensorShape` object are integers. The `Dimension` class
is not part of TF2's data model.
Please refer to the [TensorShape section of the migration guide]
(https://www.tensorflow.org/guide/migrate/index#tensorshape) on common code
patterns adapting Dimension objects to a TF2 syntax.
@end_compatibility
"""
__slots__ = ["_value"]
def __init__(self, value):
"""Creates a new Dimension with the given value."""
if isinstance(value, int): # Most common case.
if value < 0:
raise ValueError("Dimension %d must be >= 0" % value)
self._value = value
elif value is None:
self._value = None
elif isinstance(value, Dimension):
self._value = value._value
else:
try:
# int(...) compensates for the int/long dichotomy on Python 2.X.
# TODO(b/143206389): Remove once we fully migrate to 3.X.
self._value = int(value.__index__())
except AttributeError:
six.raise_from(
TypeError("Dimension value must be integer or None or have "
"an __index__ method, got value '{0!r}' with type '{1!r}'"
.format(value, type(value))), None)
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 __bool__(self):
"""Equivalent to `bool(self.value)`."""
return bool(self._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
# TODO(b/225058047): Reconsider semantics.
def is_compatible_with(self, other):
"""Returns true if `other` is compatible with this Dimension.
Two known Dimensions are compatible if they have the same value.
An unknown Dimension is compatible with all other Dimensions.
Args:
other: Another Dimension.
Returns:
True if this Dimension and `other` are compatible.
"""
other = as_dimension(other)
return (self._value is None or other.value is None or
self._value == other.value)
def assert_is_compatible_with(self, other):
"""Raises an exception if `other` is not compatible with this Dimension.
Args:
other: Another Dimension.
Raises:
ValueError: If `self` and `other` are not compatible (see
is_compatible_with).
"""
if not self.is_compatible_with(other):
raise ValueError("Dimensions %s and %s are not compatible" %
(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.compat.v1.Dimension(n) .merge_with(tf.compat.v1.Dimension(n)) ==
tf.compat.v1.Dimension(n)
tf.compat.v1.Dimension(n) .merge_with(tf.compat.v1.Dimension(None)) ==
tf.compat.v1.Dimension(n)
tf.compat.v1.Dimension(None).merge_with(tf.compat.v1.Dimension(n)) ==
tf.compat.v1.Dimension(n)
# equivalent to tf.compat.v1.Dimension(None)
tf.compat.v1.Dimension(None).merge_with(tf.compat.v1.Dimension(None))
# raises ValueError for n != m
tf.compat.v1.Dimension(n) .merge_with(tf.compat.v1.Dimension(m))
```
Args:
other: Another Dimension.
Returns:
A Dimension containing the combined information of `self` and
`other`.
Raises:
ValueError: If `self` and `other` are not compatible (see
is_compatible_with).
"""
other = as_dimension(other)
self.assert_is_compatible_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.compat.v1.Dimension(m) + tf.compat.v1.Dimension(n) ==
tf.compat.v1.Dimension(m + n)
tf.compat.v1.Dimension(m) + tf.compat.v1.Dimension(None) # equiv. to
tf.compat.v1.Dimension(None)
tf.compat.v1.Dimension(None) + tf.compat.v1.Dimension(n) # equiv. to
tf.compat.v1.Dimension(None)
tf.compat.v1.Dimension(None) + tf.compat.v1.Dimension(None) # equiv. to
tf.compat.v1.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`.
"""
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 __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.compat.v1.Dimension(m) - tf.compat.v1.Dimension(n) ==
tf.compat.v1.Dimension(m - n)
tf.compat.v1.Dimension(m) - tf.compat.v1.Dimension(None) # equiv. to
tf.compat.v1.Dimension(None)
tf.compat.v1.Dimension(None) - tf.compat.v1.Dimension(n) # equiv. to
tf.compat.v1.Dimension(None)
tf.compat.v1.Dimension(None) - tf.compat.v1.Dimension(None) # equiv. to
tf.compat.v1.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`.
"""
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 __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.compat.v1.Dimension(m) * tf.compat.v1.Dimension(n) ==
tf.compat.v1.Dimension(m * n)
tf.compat.v1.Dimension(m) * tf.compat.v1.Dimension(None) # equiv. to
tf.compat.v1.Dimension(None)
tf.compat.v1.Dimension(None) * tf.compat.v1.Dimension(n) # equiv. to
tf.compat.v1.Dimension(None)
tf.compat.v1.Dimension(None) * tf.compat.v1.Dimension(None) # equiv. to
tf.compat.v1.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.compat.v1.Dimension(m) // tf.compat.v1.Dimension(n) ==
tf.compat.v1.Dimension(m // n)
tf.compat.v1.Dimension(m) // tf.compat.v1.Dimension(None) # equiv. to
tf.compat.v1.Dimension(None)
tf.compat.v1.Dimension(None) // tf.compat.v1.Dimension(n) # equiv. to
tf.compat.v1.Dimension(None)
tf.compat.v1.Dimension(None) // tf.compat.v1.Dimension(None) # equiv. to
tf.compat.v1.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 compatibility purposes; new code
should use `__floordiv__` via the syntax `x // y`. Using `x // y`
communicates clearly that the result rounds down, and is forward compatible
to Python 3.
Args:
other: Another `Dimension`.
Returns:
A `Dimension` whose value is the integer quotient of `self` and `other`.
"""
return self // other
def __rdiv__(self, other):
"""Use `__floordiv__` via `x // y` instead.
This function exists only to have a better error message. Instead of:
`TypeError: unsupported operand type(s) for /: 'int' and 'Dimension'`,
this function will explicitly call for usage of `//` instead.
Args:
other: Another `Dimension`.
Raises:
TypeError.
"""
raise TypeError("unsupported operand type(s) for /: '{}' and 'Dimension', "
"please use // instead".format(type(other).__name__))
def __truediv__(self, other):
"""Use `__floordiv__` via `x // y` instead.
This function exists only to have a better error message. Instead of:
`TypeError: unsupported operand type(s) for /: 'Dimension' and 'int'`,
this function will explicitly call for usage of `//` instead.
Args:
other: Another `Dimension`.
Raises:
TypeError.
"""
raise TypeError("unsupported operand type(s) for /: 'Dimension' and '{}', "
"please use // instead".format(type(other).__name__))
def __rtruediv__(self, other):
"""Use `__floordiv__` via `x // y` instead.
This function exists only to have a better error message. Instead of:
`TypeError: unsupported operand type(s) for /: 'int' and 'Dimension'`,
this function will explicitly call for usage of `//` instead.
Args:
other: Another `Dimension`.
Raises:
TypeError.
"""
raise TypeError("unsupported operand type(s) for /: '{}' and 'Dimension', "
"please use // instead".format(type(other).__name__))
def __mod__(self, other):
"""Returns `self` modulo `other`.
Dimension modulo are computed as follows:
```python
tf.compat.v1.Dimension(m) % tf.compat.v1.Dimension(n) ==
tf.compat.v1.Dimension(m % n)
tf.compat.v1.Dimension(m) % tf.compat.v1.Dimension(None) # equiv. to
tf.compat.v1.Dimension(None)
tf.compat.v1.Dimension(None) % tf.compat.v1.Dimension(n) # equiv. to
tf.compat.v1.Dimension(None)
tf.compat.v1.Dimension(None) % tf.compat.v1.Dimension(None) # equiv. to
tf.compat.v1.Dimension(None)
```
Args:
other: Another Dimension, or a value accepted by `as_dimension`.
Returns:
A Dimension whose value is `self` modulo `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 __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`.
"""
other = as_dimension(other)
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.compat.v1.Dimension(m) < tf.compat.v1.Dimension(n)) == (m < n)
(tf.compat.v1.Dimension(m) < tf.compat.v1.Dimension(None)) == None
(tf.compat.v1.Dimension(None) < tf.compat.v1.Dimension(n)) == None
(tf.compat.v1.Dimension(None) < tf.compat.v1.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.compat.v1.Dimension(m) <= tf.compat.v1.Dimension(n)) == (m <= n)
(tf.compat.v1.Dimension(m) <= tf.compat.v1.Dimension(None)) == None
(tf.compat.v1.Dimension(None) <= tf.compat.v1.Dimension(n)) == None
(tf.compat.v1.Dimension(None) <= tf.compat.v1.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.compat.v1.Dimension(m) > tf.compat.v1.Dimension(n)) == (m > n)
(tf.compat.v1.Dimension(m) > tf.compat.v1.Dimension(None)) == None
(tf.compat.v1.Dimension(None) > tf.compat.v1.Dimension(n)) == None
(tf.compat.v1.Dimension(None) > tf.compat.v1.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.compat.v1.Dimension(m) >= tf.compat.v1.Dimension(n)) == (m >= n)
(tf.compat.v1.Dimension(m) >= tf.compat.v1.Dimension(None)) == None
(tf.compat.v1.Dimension(None) >= tf.compat.v1.Dimension(n)) == None
(tf.compat.v1.Dimension(None) >= tf.compat.v1.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)
[文档]@tf_export("TensorShape")
class TensorShape(trace.TraceType):
"""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 [Shape
functions](https://www.tensorflow.org/guide/create_op#shape_functions_in_c)
for details of shape functions and how to register them. Alternatively,
you may set the shape explicitly using `tf.Tensor.set_shape`.
"""
__slots__ = ["_dims"]
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.
Raises:
TypeError: If dims cannot be converted to a list of dimensions.
"""
if isinstance(dims, (tuple, list)): # Most common case.
self._dims = tuple(as_dimension(d).value for d in dims)
elif dims is None:
self._dims = None
elif isinstance(dims, tensor_shape_pb2.TensorShapeProto):
if dims.unknown_rank:
self._dims = None
else:
self._dims = tuple(
# Protos store variable-size dimensions as -1
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).value,)
else:
self._dims = []
for d in dims_iter:
try:
self._dims.append(as_dimension(d).value)
except TypeError as e:
six.raise_from(
TypeError(
"Failed to convert '{0!r}' to a shape: '{1!r}'"
"could not be converted to a dimension. A shape should "
"either be single dimension (e.g. 10), or an iterable of "
"dimensions (e.g. [1, 10, None])."
.format(dims, d)), e)
self._dims = tuple(self._dims)
@property
def _v2_behavior(self):
if _TENSORSHAPE_V2_OVERRIDE is None:
return tf2.enabled()
return _TENSORSHAPE_V2_OVERRIDE
def __repr__(self):
if self._v2_behavior:
if self._dims is not None:
return f"TensorShape({list(self._dims)})"
else:
return "TensorShape(None)"
else:
return f"TensorShape({self.dims})"
def __str__(self):
if self.rank is None:
return "<unknown>"
elif self.rank == 1:
if self._v2_behavior:
return "(%s,)" % self._dims[0]
else:
return "(%s,)" % self.dims[0]
else:
if self._v2_behavior:
return "(%s)" % ", ".join(str(d) for d in self._dims)
else:
return "(%s)" % ", ".join(str(d) for d in self.dims)
@property
def rank(self):
"""Returns the rank of this shape, or None if it is unspecified."""
if self._dims is not None:
return len(self._dims)
return None
@property
def dims(self):
"""Deprecated. Returns list of dimensions for this shape.
Suggest `TensorShape.as_list` instead.
Returns:
A list containing `tf.compat.v1.Dimension`s, or None if the shape is
unspecified.
"""
if self._dims is None:
return None
return [as_dimension(d) for d in self._dims]
@property
def ndims(self):
"""Deprecated accessor for `rank`."""
return self.rank
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 len(self._dims)
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:
if self._v2_behavior:
return iter(d for d in self._dims)
else:
return iter(d for d in 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:
An integer if `key` is an integer, or a `TensorShape` if `key` is a
slice.
Raises:
ValueError: If `key` is a slice and `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:
if self._v2_behavior:
return 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 compatible 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(rank=stop - start)
else:
if self._v2_behavior:
return None
else:
return Dimension(None)
[文档] def num_elements(self):
"""Returns the total number of elements, or none for incomplete shapes."""
if self.is_fully_defined():
return functools.reduce(operator.mul, self.as_list(), 1)
else:
return None
[文档] def merge_with(self, other):
"""Returns a `TensorShape` combining the information in `self` and `other`.
The dimensions in `self` and `other` are merged element-wise,
according to the rules below:
```python
Dimension(n).merge_with(Dimension(None)) == Dimension(n)
Dimension(None).merge_with(Dimension(n)) == Dimension(n)
Dimension(None).merge_with(Dimension(None)) == Dimension(None)
# raises ValueError for n != m
Dimension(n).merge_with(Dimension(m))
```
>> ts = tf.TensorShape([1,2])
>> ot1 = tf.TensorShape([1,2])
>> ts.merge_with(ot).as_list()
[1,2]
>> ot2 = tf.TensorShape([1,None])
>> ts.merge_with(ot2).as_list()
[1,2]
>> ot3 = tf.TensorShape([None, None])
>> ot3.merge_with(ot2).as_list()
[1, None]
Args:
other: Another `TensorShape`.
Returns:
A `TensorShape` containing the combined information of `self` and
`other`.
Raises:
ValueError: If `self` and `other` are not compatible.
"""
other = as_shape(other)
if self.dims is None:
return other
if other.dims is None:
return self
else:
try:
self.assert_same_rank(other)
new_dims = [
dim.merge_with(other_dim)
for dim, other_dim in zip(self.dims, other.dims)
]
return TensorShape(new_dims)
except ValueError:
raise ValueError("Shapes %s and %s are not compatible" % (self, other))
def __add__(self, other):
return self.concatenate(other)
def __radd__(self, other):
if not isinstance(other, TensorShape):
other = TensorShape(other)
return other.concatenate(self)
[文档] 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)
[文档] def assert_same_rank(self, other):
"""Raises an exception if `self` and `other` do not have compatible 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.rank is not None and other.rank is not None:
if self.rank != other.rank:
raise ValueError("Shapes %s and %s must have the same rank" %
(self, other))
[文档] def assert_has_rank(self, rank):
"""Raises an exception if `self` is not compatible with the given `rank`.
Args:
rank: An integer.
Raises:
ValueError: If `self` does not represent a shape with the given `rank`.
"""
if self.rank not in (None, rank):
raise ValueError("Shape %s must have rank %d" % (self, rank))
[文档] 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(rank=rank))
except ValueError:
raise ValueError("Shape %s must have rank %d" % (self, rank))
[文档] 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.rank is not None and self.rank < rank:
raise ValueError("Shape %s must have rank at least %d" % (self, rank))
else:
return self
[文档] 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.rank is not None and self.rank > rank:
raise ValueError("Shape %s must have rank at most %d" % (self, rank))
else:
return self
[文档] def is_subtype_of(self, other: trace.TraceType) -> bool:
"""Returns True iff `self` is subtype of `other`.
Shape A is a subtype of shape B if shape B can successfully represent it:
* A `TensorShape` of any rank is a subtype of `TensorShape(None)`.
* TensorShapes of equal ranks are covariant, i.e.
`TensorShape([A1, A2, ..])` is a subtype of
`TensorShape([B1, B2, ..])` iff An is a subtype of Bn.
An is subtype of Bn iff An == Bn or Bn is None.
* TensorShapes of different defined ranks have no subtyping relation.
The subtyping relation is reflexive and transitive, but not symmetric.
Some examples:
* `TensorShape([32, 784])` is a subtype of `TensorShape(None)`, and
`TensorShape([4, 4])` is also a subtype of `TensorShape(None)` but
`TensorShape([32, 784])` and `TensorShape([4, 4])` are not subtypes of
each other.
* All two-dimensional shapes are subtypes of `TensorShape([None, None])`,
such as `TensorShape([32, 784])`. There is no subtype relationship with,
for example, `TensorShape([None])` or `TensorShape([None, None, None])`.
* `TensorShape([32, None])` is also a subtype of `TensorShape([None, None])`
and `TensorShape(None)`. It is not a subtype of, for example,
`TensorShape([32])`, `TensorShape([32, None, 1])`,
`TensorShape([64, None])` or `TensorShape([None, 32])`.
* `TensorShape([32, 784])` is a subtype of itself, and also
`TensorShape([32, None])`, `TensorShape([None, 784])`,
`TensorShape([None, None])` and `TensorShape(None)`.
It has no subtype relation with, for example, `TensorShape([32, 1, 784])`
or `TensorShape([None])`.
Args:
other: Another `TensorShape`.
Returns:
True iff `self` is subtype of `other`.
"""
if not isinstance(other, TensorShape):
return False
# All Tensors are subtypes of a Tensor with no shape.
if other.rank is None:
return True
# Tensor with a defined shape can only be subtype of another with a defined
# shape if they have the same number of dimensions.
if self.rank != other.rank:
return False
# A Tensor is a subtype if each corresponding dimension is a subtype.
return all(o is None or s == o for s, o in zip(self._dims, other._dims)) # pylint: disable=protected-access
[文档] def most_specific_common_supertype(
self, others: Sequence[trace.TraceType]) -> Optional["TensorShape"]:
"""Returns the most specific supertype `TensorShape` of self and others.
* `TensorShape([None, 1])` is the most specific `TensorShape` supertyping
both `TensorShape([2, 1])` and `TensorShape([5, 1])`. Note that
`TensorShape(None)` is also a supertype but it is not "most specific".
* `TensorShape([1, 2, 3])` is the most specific `TensorShape` supertyping
both `TensorShape([1, 2, 3])` and `TensorShape([1, 2, 3]`). There are
other less specific TensorShapes that supertype above mentioned
TensorShapes, e.g. `TensorShape([1, 2, None])`, `TensorShape(None)`.
* `TensorShape([None, None])` is the most specific `TensorShape`
supertyping both `TensorShape([2, None])` and `TensorShape([None, 3])`.
As always, `TensorShape(None)` is also a supertype but not the most
specific one.
* `TensorShape(None`) is the only `TensorShape` supertyping both
`TensorShape([1, 2, 3])` and `TensorShape([1, 2])`. In general, any two
shapes that have different ranks will only have `TensorShape(None)`
as a common supertype.
* `TensorShape(None)` is the only `TensorShape` supertyping both
`TensorShape([1, 2, 3])` and `TensorShape(None)`. In general, the common
supertype of any shape with `TensorShape(None)` is `TensorShape(None)`.
Args:
others: Sequence of `TensorShape`.
Returns:
A `TensorShape` which is the most specific supertype shape of `self`
and `others`. None if it does not exist.
"""
if any(not isinstance(other, TensorShape) for other in others):
return None
# A Rankless TensorShape is already a global supertype so we return another
# instance of it.
if self.rank is None:
return unknown_shape()
# A Rankless TensorShape is the most specific supertype for shapes whose
# ranks do not match.
if any(other.dims is None or self.rank != other.rank for other in others):
return unknown_shape()
# Retain the integer dimension if it is the same across all others, else
# use an undefined dimension.
dims = [
dim if all(dim == other._dims[i]
for other in others) else None
for i, dim in enumerate(self._dims)
]
return TensorShape(dims)
# TODO(b/216206374): Consider deprecation at TraceType release.
[文档] def is_compatible_with(self, other):
"""Returns True iff `self` is compatible with `other`.
Two possibly-partially-defined shapes are compatible if there
exists a fully-defined shape that both shapes can represent. Thus,
compatibility allows the shape inference code to reason about
partially-defined shapes. For example:
* TensorShape(None) is compatible with all shapes.
* TensorShape([None, None]) is compatible with all two-dimensional
shapes, such as TensorShape([32, 784]), and also TensorShape(None). It is
not compatible with, for example, TensorShape([None]) or
TensorShape([None, None, None]).
* TensorShape([32, None]) is compatible with all two-dimensional shapes
with size 32 in the 0th dimension, and also TensorShape([None, None])
and TensorShape(None). It is not compatible with, for example,
TensorShape([32]), TensorShape([32, None, 1]) or TensorShape([64, None]).
* TensorShape([32, 784]) is compatible with itself, and also
TensorShape([32, None]), TensorShape([None, 784]), TensorShape([None,
None]) and TensorShape(None). It is not compatible with, for example,
TensorShape([32, 1, 784]) or TensorShape([None]).
The compatibility relation is reflexive and symmetric, but not
transitive. For example, TensorShape([32, 784]) is compatible with
TensorShape(None), and TensorShape(None) is compatible with
TensorShape([4, 4]), but TensorShape([32, 784]) is not compatible with
TensorShape([4, 4]).
Args:
other: Another TensorShape.
Returns:
True iff `self` is compatible with `other`.
"""
other = as_shape(other)
if self.dims is not None and other.dims is not None:
if self.rank != other.rank:
return False
for x_dim, y_dim in zip(self.dims, other.dims):
if not x_dim.is_compatible_with(y_dim):
return False
return True
[文档] def assert_is_compatible_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_compatible_with(other):
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
[文档] def most_specific_compatible_shape(self, other):
"""Returns the most specific TensorShape compatible with `self` and `other`.
* TensorShape([None, 1]) is the most specific TensorShape compatible with
both TensorShape([2, 1]) and TensorShape([5, 1]). Note that
TensorShape(None) is also compatible with above mentioned TensorShapes.
* TensorShape([1, 2, 3]) is the most specific TensorShape compatible with
both TensorShape([1, 2, 3]) and TensorShape([1, 2, 3]). There are more
less specific TensorShapes compatible with above mentioned TensorShapes,
e.g. TensorShape([1, 2, None]), TensorShape(None).
Args:
other: Another `TensorShape`.
Returns:
A `TensorShape` which is the most specific compatible shape of `self`
and `other`.
"""
other = as_shape(other)
if self.dims is None or other.dims is None or self.rank != other.rank:
return unknown_shape()
dims = [
d1 if d1 is not None and d2 is not None and d1 == d2 else None
for d1, d2 in zip(self.dims, other.dims)
]
return TensorShape(dims)
[文档] def is_fully_defined(self):
"""Returns True iff `self` is fully defined in every dimension."""
return (self._dims is not None and
all(dim is not None for dim in self._dims))
[文档] 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)
[文档] 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 list(self._dims)
[文档] 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 is None else d) for d in self._dims
])
def __eq__(self, other):
"""Returns True if `self` is equivalent to `other`.
It first tries to convert `other` to `TensorShape`. `TypeError` is thrown
when the conversion fails. Otherwise, it compares each element in the
TensorShape dimensions.
* Two *Fully known* shapes, return True iff each element is equal.
>>> t_a = tf.TensorShape([1,2])
>>> a = [1, 2]
>>> t_b = tf.TensorShape([1,2])
>>> t_c = tf.TensorShape([1,2,3])
>>> t_a.__eq__(a)
True
>>> t_a.__eq__(t_b)
True
>>> t_a.__eq__(t_c)
False
* Two *Partially-known* shapes, return True iff each element is equal.
>>> p_a = tf.TensorShape([1,None])
>>> p_b = tf.TensorShape([1,None])
>>> p_c = tf.TensorShape([2,None])
>>> p_a.__eq__(p_b)
True
>>> t_a.__eq__(p_a)
False
>>> p_a.__eq__(p_c)
False
* Two *Unknown shape*, return True.
>>> unk_a = tf.TensorShape(None)
>>> unk_b = tf.TensorShape(None)
>>> unk_a.__eq__(unk_b)
True
>>> unk_a.__eq__(t_a)
False
Args:
other: A `TensorShape` or type that can be converted to `TensorShape`.
Returns:
True if the dimensions are all equal.
Raises:
TypeError if `other` can not be converted to `TensorShape`.
"""
try:
other = as_shape(other)
except TypeError:
return NotImplemented
return self._dims == other._dims
def __hash__(self):
return hash(self._dims)
def __reduce__(self):
return TensorShape, (self.dims,)
def __concat__(self, other):
return self.concatenate(other)
def as_shape(shape):
"""Converts the given object to a TensorShape."""
if isinstance(shape, TensorShape):
return shape
else:
return TensorShape(shape)
def unknown_shape(rank=None, **kwargs):
"""Returns an unknown TensorShape, optionally with a known rank.
Args:
rank: (Optional) If specified, the number of dimensions in the shape.
**kwargs: For backwards compatibility.
Returns:
An unknown TensorShape.
Raises:
TypeError: In case of invalid arguments.
"""
if rank is None and "ndims" in kwargs:
rank = kwargs.pop("ndims")
if kwargs:
raise TypeError("Unknown argument: %s" % kwargs)
if rank is None:
return TensorShape(None)
else:
return TensorShape([Dimension(None)] * rank)