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# Copyright 2016 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.
# ==============================================================================
# pylint: disable=g-short-docstring-punctuation
"""Asserts and Boolean Checks.
See the @{$python/check_ops} guide.
@@assert_negative
@@assert_positive
@@assert_non_negative
@@assert_non_positive
@@assert_equal
@@assert_none_equal
@@assert_near
@@assert_less
@@assert_less_equal
@@assert_greater
@@assert_greater_equal
@@assert_rank
@@assert_rank_at_least
@@assert_rank_in
@@assert_type
@@assert_integer
@@assert_proper_iterable
@@assert_same_float_dtype
@@assert_scalar
@@is_non_decreasing
@@is_numeric_tensor
@@is_strictly_increasing
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.eager import context
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.util import compat
from tensorflow.python.util.tf_export import tf_export
NUMERIC_TYPES = frozenset(
[dtypes.float32, dtypes.float64, dtypes.int8, dtypes.int16, dtypes.int32,
dtypes.int64, dtypes.uint8, dtypes.qint8, dtypes.qint32, dtypes.quint8,
dtypes.complex64])
__all__ = [
'assert_negative',
'assert_positive',
'assert_proper_iterable',
'assert_non_negative',
'assert_non_positive',
'assert_equal',
'assert_none_equal',
'assert_near',
'assert_integer',
'assert_less',
'assert_less_equal',
'assert_greater',
'assert_greater_equal',
'assert_rank',
'assert_rank_at_least',
'assert_rank_in',
'assert_same_float_dtype',
'assert_scalar',
'assert_type',
'is_non_decreasing',
'is_numeric_tensor',
'is_strictly_increasing',
]
def _maybe_constant_value_string(t):
if not isinstance(t, ops.Tensor):
return str(t)
const_t = tensor_util.constant_value(t)
if const_t is not None:
return str(const_t)
return t
def _assert_static(condition, data):
"""Raises a InvalidArgumentError with as much information as possible."""
if not condition:
data_static = [_maybe_constant_value_string(x) for x in data]
raise errors.InvalidArgumentError(node_def=None, op=None,
message='\n'.join(data_static))
def _shape_and_dtype_str(tensor):
"""Returns a string containing tensor's shape and dtype."""
return 'shape=%s dtype=%s' % (tensor.shape, tensor.dtype.name)
@tf_export('assert_proper_iterable')
def assert_proper_iterable(values):
"""Static assert that values is a "proper" iterable.
`Ops` that expect iterables of `Tensor` can call this to validate input.
Useful since `Tensor`, `ndarray`, byte/text type are all iterables themselves.
Args:
values: Object to be checked.
Raises:
TypeError: If `values` is not iterable or is one of
`Tensor`, `SparseTensor`, `np.array`, `tf.compat.bytes_or_text_types`.
"""
unintentional_iterables = (
(ops.Tensor, sparse_tensor.SparseTensor, np.ndarray)
+ compat.bytes_or_text_types
)
if isinstance(values, unintentional_iterables):
raise TypeError(
'Expected argument "values" to be a "proper" iterable. Found: %s' %
type(values))
if not hasattr(values, '__iter__'):
raise TypeError(
'Expected argument "values" to be iterable. Found: %s' % type(values))
@tf_export('assert_negative')
def assert_negative(x, data=None, summarize=None, message=None, name=None):
"""Assert the condition `x < 0` holds element-wise.
Example of adding a dependency to an operation:
```python
with tf.control_dependencies([tf.assert_negative(x)]):
output = tf.reduce_sum(x)
```
Negative means, for every element `x[i]` of `x`, we have `x[i] < 0`.
If `x` is empty this is trivially satisfied.
Args:
x: Numeric `Tensor`.
data: The tensors to print out if the condition is False. Defaults to
error message and first few entries of `x`.
summarize: Print this many entries of each tensor.
message: A string to prefix to the default message.
name: A name for this operation (optional). Defaults to "assert_negative".
Returns:
Op raising `InvalidArgumentError` unless `x` is all negative.
"""
message = message or ''
with ops.name_scope(name, 'assert_negative', [x, data]):
x = ops.convert_to_tensor(x, name='x')
if data is None:
if context.executing_eagerly():
name = _shape_and_dtype_str(x)
else:
name = x.name
data = [
message,
'Condition x < 0 did not hold element-wise:',
'x (%s) = ' % name, x]
zero = ops.convert_to_tensor(0, dtype=x.dtype)
return assert_less(x, zero, data=data, summarize=summarize)
@tf_export('assert_positive')
def assert_positive(x, data=None, summarize=None, message=None, name=None):
"""Assert the condition `x > 0` holds element-wise.
Example of adding a dependency to an operation:
```python
with tf.control_dependencies([tf.assert_positive(x)]):
output = tf.reduce_sum(x)
```
Positive means, for every element `x[i]` of `x`, we have `x[i] > 0`.
If `x` is empty this is trivially satisfied.
Args:
x: Numeric `Tensor`.
data: The tensors to print out if the condition is False. Defaults to
error message and first few entries of `x`.
summarize: Print this many entries of each tensor.
message: A string to prefix to the default message.
name: A name for this operation (optional). Defaults to "assert_positive".
Returns:
Op raising `InvalidArgumentError` unless `x` is all positive.
"""
message = message or ''
with ops.name_scope(name, 'assert_positive', [x, data]):
x = ops.convert_to_tensor(x, name='x')
if data is None:
if context.executing_eagerly():
name = _shape_and_dtype_str(x)
else:
name = x.name
data = [
message, 'Condition x > 0 did not hold element-wise:',
'x (%s) = ' % name, x]
zero = ops.convert_to_tensor(0, dtype=x.dtype)
return assert_less(zero, x, data=data, summarize=summarize)
@tf_export('assert_non_negative')
def assert_non_negative(x, data=None, summarize=None, message=None, name=None):
"""Assert the condition `x >= 0` holds element-wise.
Example of adding a dependency to an operation:
```python
with tf.control_dependencies([tf.assert_non_negative(x)]):
output = tf.reduce_sum(x)
```
Non-negative means, for every element `x[i]` of `x`, we have `x[i] >= 0`.
If `x` is empty this is trivially satisfied.
Args:
x: Numeric `Tensor`.
data: The tensors to print out if the condition is False. Defaults to
error message and first few entries of `x`.
summarize: Print this many entries of each tensor.
message: A string to prefix to the default message.
name: A name for this operation (optional).
Defaults to "assert_non_negative".
Returns:
Op raising `InvalidArgumentError` unless `x` is all non-negative.
"""
message = message or ''
with ops.name_scope(name, 'assert_non_negative', [x, data]):
x = ops.convert_to_tensor(x, name='x')
if data is None:
if context.executing_eagerly():
name = _shape_and_dtype_str(x)
else:
name = x.name
data = [
message,
'Condition x >= 0 did not hold element-wise:',
'x (%s) = ' % name, x]
zero = ops.convert_to_tensor(0, dtype=x.dtype)
return assert_less_equal(zero, x, data=data, summarize=summarize)
@tf_export('assert_non_positive')
def assert_non_positive(x, data=None, summarize=None, message=None, name=None):
"""Assert the condition `x <= 0` holds element-wise.
Example of adding a dependency to an operation:
```python
with tf.control_dependencies([tf.assert_non_positive(x)]):
output = tf.reduce_sum(x)
```
Non-positive means, for every element `x[i]` of `x`, we have `x[i] <= 0`.
If `x` is empty this is trivially satisfied.
Args:
x: Numeric `Tensor`.
data: The tensors to print out if the condition is False. Defaults to
error message and first few entries of `x`.
summarize: Print this many entries of each tensor.
message: A string to prefix to the default message.
name: A name for this operation (optional).
Defaults to "assert_non_positive".
Returns:
Op raising `InvalidArgumentError` unless `x` is all non-positive.
"""
message = message or ''
with ops.name_scope(name, 'assert_non_positive', [x, data]):
x = ops.convert_to_tensor(x, name='x')
if data is None:
if context.executing_eagerly():
name = _shape_and_dtype_str(x)
else:
name = x.name
data = [
message,
'Condition x <= 0 did not hold element-wise:'
'x (%s) = ' % name, x]
zero = ops.convert_to_tensor(0, dtype=x.dtype)
return assert_less_equal(x, zero, data=data, summarize=summarize)
@tf_export('assert_equal')
def assert_equal(x, y, data=None, summarize=None, message=None, name=None):
"""Assert the condition `x == y` holds element-wise.
Example of adding a dependency to an operation:
```python
with tf.control_dependencies([tf.assert_equal(x, y)]):
output = tf.reduce_sum(x)
```
This condition holds if for every pair of (possibly broadcast) elements
`x[i]`, `y[i]`, we have `x[i] == y[i]`.
If both `x` and `y` are empty, this is trivially satisfied.
Args:
x: Numeric `Tensor`.
y: Numeric `Tensor`, same dtype as and broadcastable to `x`.
data: The tensors to print out if the condition is False. Defaults to
error message and first few entries of `x`, `y`.
summarize: Print this many entries of each tensor.
message: A string to prefix to the default message.
name: A name for this operation (optional). Defaults to "assert_equal".
Returns:
Op that raises `InvalidArgumentError` if `x == y` is False.
@compatibility{eager} returns None
Raises:
InvalidArgumentError: if the check can be performed immediately and
`x == y` is False. The check can be performed immediately during eager
execution or if `x` and `y` are statically known.
"""
message = message or ''
with ops.name_scope(name, 'assert_equal', [x, y, data]):
x = ops.convert_to_tensor(x, name='x')
y = ops.convert_to_tensor(y, name='y')
if context.executing_eagerly():
eq = math_ops.equal(x, y)
condition = math_ops.reduce_all(eq)
if not condition:
# Prepare a message with first elements of x and y.
summary_msg = ''
# Default to printing 3 elements like control_flow_ops.Assert (used
# by graph mode) does.
summarize = 3 if summarize is None else summarize
if summarize:
# reshape((-1,)) is the fastest way to get a flat array view.
x_np = x.numpy().reshape((-1,))
y_np = y.numpy().reshape((-1,))
x_sum = min(x_np.size, summarize)
y_sum = min(y_np.size, summarize)
summary_msg = ('First %d elements of x:\n%s\n'
'First %d elements of y:\n%s\n' %
(x_sum, x_np[:x_sum],
y_sum, y_np[:y_sum]))
index_and_values_str = ''
if x.shape == y.shape:
# If the shapes of x and y are the same,
# Get the values that actually differed and their indices.
# If shapes are different this information is more confusing
# than useful.
mask = math_ops.logical_not(eq)
indices = array_ops.where(mask)
indices_np = indices.numpy()
x_vals = array_ops.boolean_mask(x, mask)
y_vals = array_ops.boolean_mask(y, mask)
summarize = min(summarize, indices_np.shape[0])
index_and_values_str = (
'Indices of first %s different values:\n%s\n'
'Corresponding x values:\n%s\n'
'Corresponding y values:\n%s\n' %
(summarize, indices_np[:summarize],
x_vals.numpy().reshape((-1,))[:summarize],
y_vals.numpy().reshape((-1,))[:summarize]))
raise errors.InvalidArgumentError(
node_def=None, op=None,
message=('%s\nCondition x == y did not hold.\n%s%s' %
(message or '', index_and_values_str, summary_msg)))
return
if data is None:
data = [
message,
'Condition x == y did not hold element-wise:',
'x (%s) = ' % x.name, x,
'y (%s) = ' % y.name, y
]
condition = math_ops.reduce_all(math_ops.equal(x, y))
x_static = tensor_util.constant_value(x)
y_static = tensor_util.constant_value(y)
if x_static is not None and y_static is not None:
condition_static = (x_static == y_static).all()
_assert_static(condition_static, data)
return control_flow_ops.Assert(condition, data, summarize=summarize)
@tf_export('assert_none_equal')
def assert_none_equal(
x, y, data=None, summarize=None, message=None, name=None):
"""Assert the condition `x != y` holds for all elements.
Example of adding a dependency to an operation:
```python
with tf.control_dependencies([tf.assert_none_equal(x, y)]):
output = tf.reduce_sum(x)
```
This condition holds if for every pair of (possibly broadcast) elements
`x[i]`, `y[i]`, we have `x[i] != y[i]`.
If both `x` and `y` are empty, this is trivially satisfied.
Args:
x: Numeric `Tensor`.
y: Numeric `Tensor`, same dtype as and broadcastable to `x`.
data: The tensors to print out if the condition is False. Defaults to
error message and first few entries of `x`, `y`.
summarize: Print this many entries of each tensor.
message: A string to prefix to the default message.
name: A name for this operation (optional).
Defaults to "assert_none_equal".
Returns:
Op that raises `InvalidArgumentError` if `x != y` is ever False.
"""
message = message or ''
with ops.name_scope(name, 'assert_none_equal', [x, y, data]):
x = ops.convert_to_tensor(x, name='x')
y = ops.convert_to_tensor(y, name='y')
if context.executing_eagerly():
x_name = _shape_and_dtype_str(x)
y_name = _shape_and_dtype_str(y)
else:
x_name = x.name
y_name = y.name
if data is None:
data = [
message,
'Condition x != y did not hold for every single element:',
'x (%s) = ' % x_name, x,
'y (%s) = ' % y_name, y
]
condition = math_ops.reduce_all(math_ops.not_equal(x, y))
return control_flow_ops.Assert(condition, data, summarize=summarize)
@tf_export('assert_near')
def assert_near(
x, y, rtol=None, atol=None, data=None, summarize=None, message=None,
name=None):
"""Assert the condition `x` and `y` are close element-wise.
Example of adding a dependency to an operation:
```python
with tf.control_dependencies([tf.assert_near(x, y)]):
output = tf.reduce_sum(x)
```
This condition holds if for every pair of (possibly broadcast) elements
`x[i]`, `y[i]`, we have
```tf.abs(x[i] - y[i]) <= atol + rtol * tf.abs(y[i])```.
If both `x` and `y` are empty, this is trivially satisfied.
The default `atol` and `rtol` is `10 * eps`, where `eps` is the smallest
representable positive number such that `1 + eps != eps`. This is about
`1.2e-6` in `32bit`, `2.22e-15` in `64bit`, and `0.00977` in `16bit`.
See `numpy.finfo`.
Args:
x: Float or complex `Tensor`.
y: Float or complex `Tensor`, same `dtype` as, and broadcastable to, `x`.
rtol: `Tensor`. Same `dtype` as, and broadcastable to, `x`.
The relative tolerance. Default is `10 * eps`.
atol: `Tensor`. Same `dtype` as, and broadcastable to, `x`.
The absolute tolerance. Default is `10 * eps`.
data: The tensors to print out if the condition is False. Defaults to
error message and first few entries of `x`, `y`.
summarize: Print this many entries of each tensor.
message: A string to prefix to the default message.
name: A name for this operation (optional). Defaults to "assert_near".
Returns:
Op that raises `InvalidArgumentError` if `x` and `y` are not close enough.
@compatibility(numpy)
Similar to `numpy.assert_allclose`, except tolerance depends on data type.
This is due to the fact that `TensorFlow` is often used with `32bit`, `64bit`,
and even `16bit` data.
@end_compatibility
"""
message = message or ''
with ops.name_scope(name, 'assert_near', [x, y, rtol, atol, data]):
x = ops.convert_to_tensor(x, name='x')
y = ops.convert_to_tensor(y, name='y', dtype=x.dtype)
eps = np.finfo(x.dtype.as_numpy_dtype).eps
rtol = 10 * eps if rtol is None else rtol
atol = 10 * eps if atol is None else atol
rtol = ops.convert_to_tensor(rtol, name='rtol', dtype=x.dtype)
atol = ops.convert_to_tensor(atol, name='atol', dtype=x.dtype)
if context.executing_eagerly():
x_name = _shape_and_dtype_str(x)
y_name = _shape_and_dtype_str(y)
else:
x_name = x.name
y_name = y.name
if data is None:
data = [
message,
'x and y not equal to tolerance rtol = %s, atol = %s' % (rtol, atol),
'x (%s) = ' % x_name, x, 'y (%s) = ' % y_name, y
]
tol = atol + rtol * math_ops.abs(y)
diff = math_ops.abs(x - y)
condition = math_ops.reduce_all(math_ops.less(diff, tol))
return control_flow_ops.Assert(condition, data, summarize=summarize)
@tf_export('assert_less')
def assert_less(x, y, data=None, summarize=None, message=None, name=None):
"""Assert the condition `x < y` holds element-wise.
Example of adding a dependency to an operation:
```python
with tf.control_dependencies([tf.assert_less(x, y)]):
output = tf.reduce_sum(x)
```
This condition holds if for every pair of (possibly broadcast) elements
`x[i]`, `y[i]`, we have `x[i] < y[i]`.
If both `x` and `y` are empty, this is trivially satisfied.
Args:
x: Numeric `Tensor`.
y: Numeric `Tensor`, same dtype as and broadcastable to `x`.
data: The tensors to print out if the condition is False. Defaults to
error message and first few entries of `x`, `y`.
summarize: Print this many entries of each tensor.
message: A string to prefix to the default message.
name: A name for this operation (optional). Defaults to "assert_less".
Returns:
Op that raises `InvalidArgumentError` if `x < y` is False.
"""
message = message or ''
with ops.name_scope(name, 'assert_less', [x, y, data]):
x = ops.convert_to_tensor(x, name='x')
y = ops.convert_to_tensor(y, name='y')
if context.executing_eagerly():
x_name = _shape_and_dtype_str(x)
y_name = _shape_and_dtype_str(y)
else:
x_name = x.name
y_name = y.name
if data is None:
data = [
message,
'Condition x < y did not hold element-wise:',
'x (%s) = ' % x_name, x, 'y (%s) = ' % y_name, y
]
condition = math_ops.reduce_all(math_ops.less(x, y))
return control_flow_ops.Assert(condition, data, summarize=summarize)
@tf_export('assert_less_equal')
def assert_less_equal(x, y, data=None, summarize=None, message=None, name=None):
"""Assert the condition `x <= y` holds element-wise.
Example of adding a dependency to an operation:
```python
with tf.control_dependencies([tf.assert_less_equal(x, y)]):
output = tf.reduce_sum(x)
```
This condition holds if for every pair of (possibly broadcast) elements
`x[i]`, `y[i]`, we have `x[i] <= y[i]`.
If both `x` and `y` are empty, this is trivially satisfied.
Args:
x: Numeric `Tensor`.
y: Numeric `Tensor`, same dtype as and broadcastable to `x`.
data: The tensors to print out if the condition is False. Defaults to
error message and first few entries of `x`, `y`.
summarize: Print this many entries of each tensor.
message: A string to prefix to the default message.
name: A name for this operation (optional). Defaults to "assert_less_equal"
Returns:
Op that raises `InvalidArgumentError` if `x <= y` is False.
"""
message = message or ''
with ops.name_scope(name, 'assert_less_equal', [x, y, data]):
x = ops.convert_to_tensor(x, name='x')
y = ops.convert_to_tensor(y, name='y')
if context.executing_eagerly():
x_name = _shape_and_dtype_str(x)
y_name = _shape_and_dtype_str(y)
else:
x_name = x.name
y_name = y.name
if data is None:
data = [
message,
'Condition x <= y did not hold element-wise:'
'x (%s) = ' % x_name, x, 'y (%s) = ' % y_name, y
]
condition = math_ops.reduce_all(math_ops.less_equal(x, y))
return control_flow_ops.Assert(condition, data, summarize=summarize)
@tf_export('assert_greater')
def assert_greater(x, y, data=None, summarize=None, message=None, name=None):
"""Assert the condition `x > y` holds element-wise.
Example of adding a dependency to an operation:
```python
with tf.control_dependencies([tf.assert_greater(x, y)]):
output = tf.reduce_sum(x)
```
This condition holds if for every pair of (possibly broadcast) elements
`x[i]`, `y[i]`, we have `x[i] > y[i]`.
If both `x` and `y` are empty, this is trivially satisfied.
Args:
x: Numeric `Tensor`.
y: Numeric `Tensor`, same dtype as and broadcastable to `x`.
data: The tensors to print out if the condition is False. Defaults to
error message and first few entries of `x`, `y`.
summarize: Print this many entries of each tensor.
message: A string to prefix to the default message.
name: A name for this operation (optional). Defaults to "assert_greater".
Returns:
Op that raises `InvalidArgumentError` if `x > y` is False.
"""
message = message or ''
with ops.name_scope(name, 'assert_greater', [x, y, data]):
x = ops.convert_to_tensor(x, name='x')
y = ops.convert_to_tensor(y, name='y')
if context.executing_eagerly():
x_name = _shape_and_dtype_str(x)
y_name = _shape_and_dtype_str(y)
else:
x_name = x.name
y_name = y.name
if data is None:
data = [
message,
'Condition x > y did not hold element-wise:'
'x (%s) = ' % x_name, x, 'y (%s) = ' % y_name, y
]
condition = math_ops.reduce_all(math_ops.greater(x, y))
return control_flow_ops.Assert(condition, data, summarize=summarize)
@tf_export('assert_greater_equal')
def assert_greater_equal(x, y, data=None, summarize=None, message=None,
name=None):
"""Assert the condition `x >= y` holds element-wise.
Example of adding a dependency to an operation:
```python
with tf.control_dependencies([tf.assert_greater_equal(x, y)]):
output = tf.reduce_sum(x)
```
This condition holds if for every pair of (possibly broadcast) elements
`x[i]`, `y[i]`, we have `x[i] >= y[i]`.
If both `x` and `y` are empty, this is trivially satisfied.
Args:
x: Numeric `Tensor`.
y: Numeric `Tensor`, same dtype as and broadcastable to `x`.
data: The tensors to print out if the condition is False. Defaults to
error message and first few entries of `x`, `y`.
summarize: Print this many entries of each tensor.
message: A string to prefix to the default message.
name: A name for this operation (optional). Defaults to
"assert_greater_equal"
Returns:
Op that raises `InvalidArgumentError` if `x >= y` is False.
"""
message = message or ''
with ops.name_scope(name, 'assert_greater_equal', [x, y, data]):
x = ops.convert_to_tensor(x, name='x')
y = ops.convert_to_tensor(y, name='y')
if context.executing_eagerly():
x_name = _shape_and_dtype_str(x)
y_name = _shape_and_dtype_str(y)
else:
x_name = x.name
y_name = y.name
if data is None:
data = [
message,
'Condition x >= y did not hold element-wise:'
'x (%s) = ' % x_name, x, 'y (%s) = ' % y_name, y
]
condition = math_ops.reduce_all(math_ops.greater_equal(x, y))
return control_flow_ops.Assert(condition, data, summarize=summarize)
def _assert_rank_condition(
x, rank, static_condition, dynamic_condition, data, summarize):
"""Assert `x` has a rank that satisfies a given condition.
Args:
x: Numeric `Tensor`.
rank: Scalar `Tensor`.
static_condition: A python function that takes `[actual_rank, given_rank]`
and returns `True` if the condition is satisfied, `False` otherwise.
dynamic_condition: An `op` that takes [actual_rank, given_rank]
and return `True` if the condition is satisfied, `False` otherwise.
data: The tensors to print out if the condition is false. Defaults to
error message and first few entries of `x`.
summarize: Print this many entries of each tensor.
Returns:
Op raising `InvalidArgumentError` if `x` fails dynamic_condition.
Raises:
ValueError: If static checks determine `x` fails static_condition.
"""
assert_type(rank, dtypes.int32)
# Attempt to statically defined rank.
rank_static = tensor_util.constant_value(rank)
if rank_static is not None:
if rank_static.ndim != 0:
raise ValueError('Rank must be a scalar.')
x_rank_static = x.get_shape().ndims
if x_rank_static is not None:
if not static_condition(x_rank_static, rank_static):
raise ValueError(
'Static rank condition failed', x_rank_static, rank_static)
return control_flow_ops.no_op(name='static_checks_determined_all_ok')
condition = dynamic_condition(array_ops.rank(x), rank)
# Add the condition that `rank` must have rank zero. Prevents the bug where
# someone does assert_rank(x, [n]), rather than assert_rank(x, n).
if rank_static is None:
this_data = ['Rank must be a scalar. Received rank: ', rank]
rank_check = assert_rank(rank, 0, data=this_data)
condition = control_flow_ops.with_dependencies([rank_check], condition)
return control_flow_ops.Assert(condition, data, summarize=summarize)
@tf_export('assert_rank')
def assert_rank(x, rank, data=None, summarize=None, message=None, name=None):
"""Assert `x` has rank equal to `rank`.
Example of adding a dependency to an operation:
```python
with tf.control_dependencies([tf.assert_rank(x, 2)]):
output = tf.reduce_sum(x)
```
Args:
x: Numeric `Tensor`.
rank: Scalar integer `Tensor`.
data: The tensors to print out if the condition is False. Defaults to
error message and first few entries of `x`.
summarize: Print this many entries of each tensor.
message: A string to prefix to the default message.
name: A name for this operation (optional). Defaults to "assert_rank".
Returns:
Op raising `InvalidArgumentError` unless `x` has specified rank.
If static checks determine `x` has correct rank, a `no_op` is returned.
Raises:
ValueError: If static checks determine `x` has wrong rank.
"""
with ops.name_scope(name, 'assert_rank', (x, rank) + tuple(data or [])):
x = ops.convert_to_tensor(x, name='x')
rank = ops.convert_to_tensor(rank, name='rank')
message = message or ''
static_condition = lambda actual_rank, given_rank: actual_rank == given_rank
dynamic_condition = math_ops.equal
if context.executing_eagerly():
name = ''
else:
name = x.name
if data is None:
data = [
message,
'Tensor %s must have rank' % name, rank, 'Received shape: ',
array_ops.shape(x)
]
try:
assert_op = _assert_rank_condition(x, rank, static_condition,
dynamic_condition, data, summarize)
except ValueError as e:
if e.args[0] == 'Static rank condition failed':
raise ValueError(
'%s. Tensor %s must have rank %d. Received rank %d, shape %s' %
(message, name, e.args[2], e.args[1], x.get_shape()))
else:
raise
return assert_op
@tf_export('assert_rank_at_least')
def assert_rank_at_least(
x, rank, data=None, summarize=None, message=None, name=None):
"""Assert `x` has rank equal to `rank` or higher.
Example of adding a dependency to an operation:
```python
with tf.control_dependencies([tf.assert_rank_at_least(x, 2)]):
output = tf.reduce_sum(x)
```
Args:
x: Numeric `Tensor`.
rank: Scalar `Tensor`.
data: The tensors to print out if the condition is False. Defaults to
error message and first few entries of `x`.
summarize: Print this many entries of each tensor.
message: A string to prefix to the default message.
name: A name for this operation (optional).
Defaults to "assert_rank_at_least".
Returns:
Op raising `InvalidArgumentError` unless `x` has specified rank or higher.
If static checks determine `x` has correct rank, a `no_op` is returned.
Raises:
ValueError: If static checks determine `x` has wrong rank.
"""
with ops.name_scope(
name, 'assert_rank_at_least', (x, rank) + tuple(data or [])):
x = ops.convert_to_tensor(x, name='x')
rank = ops.convert_to_tensor(rank, name='rank')
message = message or ''
static_condition = lambda actual_rank, given_rank: actual_rank >= given_rank
dynamic_condition = math_ops.greater_equal
if context.executing_eagerly():
name = ''
else:
name = x.name
if data is None:
data = [
message,
'Tensor %s must have rank at least' % name, rank,
'Received shape: ', array_ops.shape(x)
]
try:
assert_op = _assert_rank_condition(x, rank, static_condition,
dynamic_condition, data, summarize)
except ValueError as e:
if e.args[0] == 'Static rank condition failed':
raise ValueError(
'%s. Tensor %s must have rank at least %d. Received rank %d, '
'shape %s' % (message, name, e.args[2], e.args[1], x.get_shape()))
else:
raise
return assert_op
def _static_rank_in(actual_rank, given_ranks):
return actual_rank in given_ranks
def _dynamic_rank_in(actual_rank, given_ranks):
if len(given_ranks) < 1:
return ops.convert_to_tensor(False)
result = math_ops.equal(given_ranks[0], actual_rank)
for given_rank in given_ranks[1:]:
result = math_ops.logical_or(
result, math_ops.equal(given_rank, actual_rank))
return result
def _assert_ranks_condition(
x, ranks, static_condition, dynamic_condition, data, summarize):
"""Assert `x` has a rank that satisfies a given condition.
Args:
x: Numeric `Tensor`.
ranks: Scalar `Tensor`.
static_condition: A python function that takes
`[actual_rank, given_ranks]` and returns `True` if the condition is
satisfied, `False` otherwise.
dynamic_condition: An `op` that takes [actual_rank, given_ranks]
and return `True` if the condition is satisfied, `False` otherwise.
data: The tensors to print out if the condition is false. Defaults to
error message and first few entries of `x`.
summarize: Print this many entries of each tensor.
Returns:
Op raising `InvalidArgumentError` if `x` fails dynamic_condition.
Raises:
ValueError: If static checks determine `x` fails static_condition.
"""
for rank in ranks:
assert_type(rank, dtypes.int32)
# Attempt to statically defined rank.
ranks_static = tuple([tensor_util.constant_value(rank) for rank in ranks])
if not any(r is None for r in ranks_static):
for rank_static in ranks_static:
if rank_static.ndim != 0:
raise ValueError('Rank must be a scalar.')
x_rank_static = x.get_shape().ndims
if x_rank_static is not None:
if not static_condition(x_rank_static, ranks_static):
raise ValueError(
'Static rank condition failed', x_rank_static, ranks_static)
return control_flow_ops.no_op(name='static_checks_determined_all_ok')
condition = dynamic_condition(array_ops.rank(x), ranks)
# Add the condition that `rank` must have rank zero. Prevents the bug where
# someone does assert_rank(x, [n]), rather than assert_rank(x, n).
for rank, rank_static in zip(ranks, ranks_static):
if rank_static is None:
this_data = ['Rank must be a scalar. Received rank: ', rank]
rank_check = assert_rank(rank, 0, data=this_data)
condition = control_flow_ops.with_dependencies([rank_check], condition)
return control_flow_ops.Assert(condition, data, summarize=summarize)
@tf_export('assert_rank_in')
def assert_rank_in(
x, ranks, data=None, summarize=None, message=None, name=None):
"""Assert `x` has rank in `ranks`.
Example of adding a dependency to an operation:
```python
with tf.control_dependencies([tf.assert_rank_in(x, (2, 4))]):
output = tf.reduce_sum(x)
```
Args:
x: Numeric `Tensor`.
ranks: Iterable of scalar `Tensor` objects.
data: The tensors to print out if the condition is False. Defaults to
error message and first few entries of `x`.
summarize: Print this many entries of each tensor.
message: A string to prefix to the default message.
name: A name for this operation (optional).
Defaults to "assert_rank_in".
Returns:
Op raising `InvalidArgumentError` unless rank of `x` is in `ranks`.
If static checks determine `x` has matching rank, a `no_op` is returned.
Raises:
ValueError: If static checks determine `x` has mismatched rank.
"""
with ops.name_scope(
name, 'assert_rank_in', (x,) + tuple(ranks) + tuple(data or [])):
x = ops.convert_to_tensor(x, name='x')
ranks = tuple([ops.convert_to_tensor(rank, name='rank') for rank in ranks])
message = message or ''
if context.executing_eagerly():
name = ''
else:
name = x.name
if data is None:
data = [
message, 'Tensor %s must have rank in' % name
] + list(ranks) + [
'Received shape: ', array_ops.shape(x)
]
try:
assert_op = _assert_ranks_condition(x, ranks, _static_rank_in,
_dynamic_rank_in, data, summarize)
except ValueError as e:
if e.args[0] == 'Static rank condition failed':
raise ValueError(
'%s. Tensor %s must have rank in %s. Received rank %d, '
'shape %s' % (message, name, e.args[2], e.args[1], x.get_shape()))
else:
raise
return assert_op
@tf_export('assert_integer')
def assert_integer(x, message=None, name=None):
"""Assert that `x` is of integer dtype.
Example of adding a dependency to an operation:
```python
with tf.control_dependencies([tf.assert_integer(x)]):
output = tf.reduce_sum(x)
```
Args:
x: `Tensor` whose basetype is integer and is not quantized.
message: A string to prefix to the default message.
name: A name for this operation (optional). Defaults to "assert_integer".
Raises:
TypeError: If `x.dtype` is anything other than non-quantized integer.
Returns:
A `no_op` that does nothing. Type can be determined statically.
"""
message = message or ''
with ops.name_scope(name, 'assert_integer', [x]):
x = ops.convert_to_tensor(x, name='x')
if not x.dtype.is_integer:
if context.executing_eagerly():
name = 'tensor'
else:
name = x.name
err_msg = (
'%s Expected "x" to be integer type. Found: %s of dtype %s'
% (message, name, x.dtype))
raise TypeError(err_msg)
return control_flow_ops.no_op('statically_determined_was_integer')
@tf_export('assert_type')
def assert_type(tensor, tf_type, message=None, name=None):
"""Statically asserts that the given `Tensor` is of the specified type.
Args:
tensor: A tensorflow `Tensor`.
tf_type: A tensorflow type (`dtypes.float32`, `tf.int64`, `dtypes.bool`,
etc).
message: A string to prefix to the default message.
name: A name to give this `Op`. Defaults to "assert_type"
Raises:
TypeError: If the tensors data type doesn't match `tf_type`.
Returns:
A `no_op` that does nothing. Type can be determined statically.
"""
message = message or ''
with ops.name_scope(name, 'assert_type', [tensor]):
tensor = ops.convert_to_tensor(tensor, name='tensor')
if tensor.dtype != tf_type:
if context.executing_eagerly():
raise TypeError('%s tensor must be of type %s' % (message, tf_type))
else:
raise TypeError('%s %s must be of type %s' % (message, tensor.name,
tf_type))
return control_flow_ops.no_op('statically_determined_correct_type')
# pylint: disable=line-too-long
def _get_diff_for_monotonic_comparison(x):
"""Gets the difference x[1:] - x[:-1]."""
x = array_ops.reshape(x, [-1])
if not is_numeric_tensor(x):
raise TypeError('Expected x to be numeric, instead found: %s' % x)
# If x has less than 2 elements, there is nothing to compare. So return [].
is_shorter_than_two = math_ops.less(array_ops.size(x), 2)
short_result = lambda: ops.convert_to_tensor([], dtype=x.dtype)
# With 2 or more elements, return x[1:] - x[:-1]
s_len = array_ops.shape(x) - 1
diff = lambda: array_ops.strided_slice(x, [1], [1] + s_len)- array_ops.strided_slice(x, [0], s_len)
return control_flow_ops.cond(is_shorter_than_two, short_result, diff)
@tf_export('is_numeric_tensor')
def is_numeric_tensor(tensor):
return isinstance(tensor, ops.Tensor) and tensor.dtype in NUMERIC_TYPES
@tf_export('is_non_decreasing')
def is_non_decreasing(x, name=None):
"""Returns `True` if `x` is non-decreasing.
Elements of `x` are compared in row-major order. The tensor `[x[0],...]`
is non-decreasing if for every adjacent pair we have `x[i] <= x[i+1]`.
If `x` has less than two elements, it is trivially non-decreasing.
See also: `is_strictly_increasing`
Args:
x: Numeric `Tensor`.
name: A name for this operation (optional). Defaults to "is_non_decreasing"
Returns:
Boolean `Tensor`, equal to `True` iff `x` is non-decreasing.
Raises:
TypeError: if `x` is not a numeric tensor.
"""
with ops.name_scope(name, 'is_non_decreasing', [x]):
diff = _get_diff_for_monotonic_comparison(x)
# When len(x) = 1, diff = [], less_equal = [], and reduce_all([]) = True.
zero = ops.convert_to_tensor(0, dtype=diff.dtype)
return math_ops.reduce_all(math_ops.less_equal(zero, diff))
@tf_export('is_strictly_increasing')
def is_strictly_increasing(x, name=None):
"""Returns `True` if `x` is strictly increasing.
Elements of `x` are compared in row-major order. The tensor `[x[0],...]`
is strictly increasing if for every adjacent pair we have `x[i] < x[i+1]`.
If `x` has less than two elements, it is trivially strictly increasing.
See also: `is_non_decreasing`
Args:
x: Numeric `Tensor`.
name: A name for this operation (optional).
Defaults to "is_strictly_increasing"
Returns:
Boolean `Tensor`, equal to `True` iff `x` is strictly increasing.
Raises:
TypeError: if `x` is not a numeric tensor.
"""
with ops.name_scope(name, 'is_strictly_increasing', [x]):
diff = _get_diff_for_monotonic_comparison(x)
# When len(x) = 1, diff = [], less = [], and reduce_all([]) = True.
zero = ops.convert_to_tensor(0, dtype=diff.dtype)
return math_ops.reduce_all(math_ops.less(zero, diff))
def _assert_same_base_type(items, expected_type=None):
r"""Asserts all items are of the same base type.
Args:
items: List of graph items (e.g., `Variable`, `Tensor`, `SparseTensor`,
`Operation`, or `IndexedSlices`). Can include `None` elements, which
will be ignored.
expected_type: Expected type. If not specified, assert all items are
of the same base type.
Returns:
Validated type, or none if neither expected_type nor items provided.
Raises:
ValueError: If any types do not match.
"""
original_item_str = None
for item in items:
if item is not None:
item_type = item.dtype.base_dtype
if not expected_type:
expected_type = item_type
original_item_str = item.name if hasattr(item, 'name') else str(item)
elif expected_type != item_type:
raise ValueError('%s, type=%s, must be of the same type (%s)%s.' % (
item.name if hasattr(item, 'name') else str(item),
item_type, expected_type,
(' as %s' % original_item_str) if original_item_str else ''))
return expected_type
@tf_export('assert_same_float_dtype')
def assert_same_float_dtype(tensors=None, dtype=None):
"""Validate and return float type based on `tensors` and `dtype`.
For ops such as matrix multiplication, inputs and weights must be of the
same float type. This function validates that all `tensors` are the same type,
validates that type is `dtype` (if supplied), and returns the type. Type must
be a floating point type. If neither `tensors` nor `dtype` is supplied,
the function will return `dtypes.float32`.
Args:
tensors: Tensors of input values. Can include `None` elements, which will be
ignored.
dtype: Expected type.
Returns:
Validated type.
Raises:
ValueError: if neither `tensors` nor `dtype` is supplied, or result is not
float, or the common type of the inputs is not a floating point type.
"""
if tensors:
dtype = _assert_same_base_type(tensors, dtype)
if not dtype:
dtype = dtypes.float32
elif not dtype.is_floating:
raise ValueError('Expected floating point type, got %s.' % dtype)
return dtype
@tf_export('assert_scalar')
def assert_scalar(tensor, name=None):
with ops.name_scope(name, 'assert_scalar', [tensor]) as name_scope:
tensor = ops.convert_to_tensor(tensor, name=name_scope)
shape = tensor.get_shape()
if shape.ndims != 0:
if context.executing_eagerly():
raise ValueError('Expected scalar shape, saw shape: %s.'
% (shape,))
else:
raise ValueError('Expected scalar shape for %s, saw shape: %s.'
% (tensor.name, shape))
return tensor