# Copyright 2017 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.
# ==============================================================================
"""Support for sorting tensors."""
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import ops as framework_ops
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.util import dispatch
from tensorflow.python.util.tf_export import tf_export
[文档]@tf_export('sort')
@dispatch.add_dispatch_support
def sort(values, axis=-1, direction='ASCENDING', name=None):
"""Sorts a tensor.
Usage:
>>> a = [1, 10, 26.9, 2.8, 166.32, 62.3]
>>> tf.sort(a).numpy()
array([ 1. , 2.8 , 10. , 26.9 , 62.3 , 166.32], dtype=float32)
>>> tf.sort(a, direction='DESCENDING').numpy()
array([166.32, 62.3 , 26.9 , 10. , 2.8 , 1. ], dtype=float32)
For multidimensional inputs you can control which axis the sort is applied
along. The default `axis=-1` sorts the innermost axis.
>>> mat = [[3,2,1],
... [2,1,3],
... [1,3,2]]
>>> tf.sort(mat, axis=-1).numpy()
array([[1, 2, 3],
[1, 2, 3],
[1, 2, 3]], dtype=int32)
>>> tf.sort(mat, axis=0).numpy()
array([[1, 1, 1],
[2, 2, 2],
[3, 3, 3]], dtype=int32)
See also:
* `tf.argsort`: Like sort, but it returns the sort indices.
* `tf.math.top_k`: A partial sort that returns a fixed number of top values
and corresponding indices.
Args:
values: 1-D or higher **numeric** `Tensor`.
axis: The axis along which to sort. The default is -1, which sorts the last
axis.
direction: The direction in which to sort the values (`'ASCENDING'` or
`'DESCENDING'`).
name: Optional name for the operation.
Returns:
A `Tensor` with the same dtype and shape as `values`, with the elements
sorted along the given `axis`.
Raises:
tf.errors.InvalidArgumentError: If the `values.dtype` is not a `float` or
`int` type.
ValueError: If axis is not a constant scalar, or the direction is invalid.
"""
with framework_ops.name_scope(name, 'sort'):
return _sort_or_argsort(values, axis, direction, return_argsort=False)
[文档]@tf_export('argsort')
@dispatch.add_dispatch_support
def argsort(values, axis=-1, direction='ASCENDING', stable=False, name=None):
"""Returns the indices of a tensor that give its sorted order along an axis.
>>> values = [1, 10, 26.9, 2.8, 166.32, 62.3]
>>> sort_order = tf.argsort(values)
>>> sort_order.numpy()
array([0, 3, 1, 2, 5, 4], dtype=int32)
For a 1D tensor:
>>> sorted = tf.gather(values, sort_order)
>>> assert tf.reduce_all(sorted == tf.sort(values))
For higher dimensions, the output has the same shape as
`values`, but along the given axis, values represent the index of the sorted
element in that slice of the tensor at the given position.
>>> mat = [[30,20,10],
... [20,10,30],
... [10,30,20]]
>>> indices = tf.argsort(mat)
>>> indices.numpy()
array([[2, 1, 0],
[1, 0, 2],
[0, 2, 1]], dtype=int32)
If `axis=-1` these indices can be used to apply a sort using `tf.gather`:
>>> tf.gather(mat, indices, batch_dims=-1).numpy()
array([[10, 20, 30],
[10, 20, 30],
[10, 20, 30]], dtype=int32)
See also:
* `tf.sort`: Sort along an axis.
* `tf.math.top_k`: A partial sort that returns a fixed number of top values
and corresponding indices.
Args:
values: 1-D or higher **numeric** `Tensor`.
axis: The axis along which to sort. The default is -1, which sorts the last
axis.
direction: The direction in which to sort the values (`'ASCENDING'` or
`'DESCENDING'`).
stable: If True, equal elements in the original tensor will not be
re-ordered in the returned order. Unstable sort is not yet implemented,
but will eventually be the default for performance reasons. If you require
a stable order, pass `stable=True` for forwards compatibility.
name: Optional name for the operation.
Returns:
An int32 `Tensor` with the same shape as `values`. The indices that would
sort each slice of the given `values` along the given `axis`.
Raises:
ValueError: If axis is not a constant scalar, or the direction is invalid.
tf.errors.InvalidArgumentError: If the `values.dtype` is not a `float` or
`int` type.
"""
del stable # Unused.
with framework_ops.name_scope(name, 'argsort'):
return _sort_or_argsort(values, axis, direction, return_argsort=True)
def _sort_or_argsort(values, axis, direction, return_argsort):
"""Internal sort/argsort implementation.
Args:
values: The input values.
axis: The axis along which to sort.
direction: 'ASCENDING' or 'DESCENDING'.
return_argsort: Whether to return the argsort result.
Returns:
Either the sorted values, or the indices of the sorted values in the
original tensor. See the `sort` and `argsort` docstrings.
Raises:
ValueError: If axis is not a constant scalar, or the direction is invalid.
"""
if direction not in _SORT_IMPL:
valid_directions = ', '.join(sorted(_SORT_IMPL.keys()))
raise ValueError(f'Argument `direction` should be one of {valid_directions}'
f'. Received: direction={direction}')
# Axis must be an integer, not a Tensor.
axis = framework_ops.convert_to_tensor(axis, name='axis')
axis_static = tensor_util.constant_value(axis)
if axis.shape.ndims not in (None, 0) or axis_static is None:
raise ValueError(
f'Argument `axis` must be a constant scalar. Received: axis={axis}.')
axis_static = int(axis_static) # Avoids NumPy casting error
values = framework_ops.convert_to_tensor(values, name='values')
return _SORT_IMPL[direction](values, axis_static, return_argsort)
def _descending_sort(values, axis, return_argsort=False):
"""Sorts values in reverse using `top_k`.
Args:
values: Tensor of numeric values.
axis: Index of the axis which values should be sorted along.
return_argsort: If False, return the sorted values. If True, return the
indices that would sort the values.
Returns:
The sorted values.
"""
# TODO(b/190410105): replace with a proper sort kernel.
k = array_ops.shape(values)[axis]
rank = array_ops.rank(values)
static_rank = values.shape.ndims
# Fast path: sorting the last axis.
if axis == -1 or axis + 1 == values.get_shape().ndims:
top_k_input = values
transposition = None
else:
# Otherwise, transpose the array. Swap axes `axis` and `rank - 1`.
if axis < 0:
# Calculate the actual axis index if counting from the end. Use the static
# rank if available, or else make the axis back into a tensor.
axis += static_rank or rank
if static_rank is not None:
# Prefer to calculate the transposition array in NumPy and make it a
# constant.
transposition = constant_op.constant(
np.r_[
# Axes up to axis are unchanged.
np.arange(axis),
# Swap axis and rank - 1.
[static_rank - 1],
# Axes in [axis + 1, rank - 1) are unchanged.
np.arange(axis + 1, static_rank - 1),
# Swap axis and rank - 1.
[axis]],
name='transposition')
else:
# Generate the transposition array from the tensors.
transposition = array_ops.tensor_scatter_update(
math_ops.range(rank), [[axis], [rank-1]], [rank-1, axis])
top_k_input = array_ops.transpose(values, transposition)
values, indices = nn_ops.top_k(top_k_input, k)
return_value = indices if return_argsort else values
if transposition is not None:
# transposition contains a single cycle of length 2 (swapping 2 elements),
# so it is an involution (it is its own inverse).
return_value = array_ops.transpose(return_value, transposition)
return return_value
def _ascending_sort(values, axis, return_argsort=False):
"""Sorts values in ascending order.
Args:
values: Tensor of numeric values.
axis: Index of the axis which values should be sorted along.
return_argsort: If False, return the sorted values. If True, return the
indices that would sort the values.
Returns:
The sorted values.
"""
# TODO(b/190410105): replace with a proper sort kernel.
# If values are integers, we need special handling.
dtype = values.dtype
if dtype.is_unsigned:
# Subtract values from dtype.max to reverse sort order.
offset = dtype.max
values_or_indices = _descending_sort(offset - values, axis, return_argsort)
return values_or_indices if return_argsort else offset - values_or_indices
elif dtype.is_integer:
# Negate and subtract 1 to map dtype.min to dtype.max. Technically this
# will result in signed-integer-overflow UB for dtype.min, though
# practically should produce correct results on all systems.
#
# Casting to unsigned would be better, but uint* subtraction is not
# supported on all devices.
#
# Although more complex and slightly slower than descend+reverse, this
# approach preserves sort stability.
values_or_indices = _descending_sort(-values - 1, axis, return_argsort)
return values_or_indices if return_argsort else -values_or_indices - 1
else:
# Otherwise, negate the values and use descending sort.
values_or_indices = _descending_sort(-values, axis, return_argsort)
# If not argsort, negate the values again.
return values_or_indices if return_argsort else -values_or_indices
_SORT_IMPL = {
'ASCENDING': _ascending_sort,
'DESCENDING': _descending_sort,
}