260 lines
11 KiB
Python
260 lines
11 KiB
Python
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""where operation for RaggedTensors."""
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import typing
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops.ragged import ragged_concat_ops
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from tensorflow.python.ops.ragged import ragged_functional_ops
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from tensorflow.python.ops.ragged import ragged_gather_ops
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from tensorflow.python.ops.ragged import ragged_tensor
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from tensorflow.python.ops.ragged import ragged_tensor_shape
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from tensorflow.python.util import dispatch
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@dispatch.dispatch_for_api(array_ops.where_v2)
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def where_v2(condition: ragged_tensor.RaggedOrDense,
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x: typing.Optional[ragged_tensor.RaggedOrDense] = None,
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y: typing.Optional[ragged_tensor.RaggedOrDense] = None,
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name=None):
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"""Return the elements where `condition` is `True`.
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: If both `x` and `y` are None: Retrieve indices of true elements.
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Returns the coordinates of true elements of `condition`. The coordinates
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are returned in a 2-D tensor with shape
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`[num_true_values, dim_size(condition)]`, where `result[i]` is the
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coordinates of the `i`th true value (in row-major order).
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: If both `x` and `y` are non-`None`: Multiplex between `x` and `y`.
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Choose an output shape from the shapes of `condition`, `x`, and `y` that
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all three shapes are broadcastable to; and then use the broadcasted
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`condition` tensor as a mask that chooses whether the corredsponding element
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in the output should be taken from `x` (if `condition` is true) or `y` (if
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`condition` is false).
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>>> # Example: retrieve indices of true elements
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>>> tf.where(tf.ragged.constant([[True, False], [True]]))
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<tf.Tensor: shape=(2, 2), dtype=int64, numpy= array([[0, 0], [1, 0]])>
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>>> # Example: multiplex between `x` and `y`
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>>> tf.where(tf.ragged.constant([[True, False], [True, False, True]]),
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... tf.ragged.constant([['A', 'B'], ['C', 'D', 'E']]),
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... tf.ragged.constant([['a', 'b'], ['c', 'd', 'e']]))
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<tf.RaggedTensor [[b'A', b'b'], [b'C', b'd', b'E']]>
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Args:
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condition: A potentially ragged tensor of type `bool`
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x: A potentially ragged tensor (optional).
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y: A potentially ragged tensor (optional). Must be specified if `x` is
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specified. Must have the same rank and type as `x`.
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name: A name of the operation (optional).
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Returns:
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: If both `x` and `y` are `None`:
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A `Tensor` with shape `(num_true, rank(condition))`.
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: Otherwise:
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A potentially ragged tensor with the same type as `x` and `y`, and whose
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shape is broadcast-compatible with `x`, `y`, and `condition`.
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Raises:
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ValueError: When exactly one of `x` or `y` is non-`None`; or when
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`condition`, `x`, and `y` have incompatible shapes.
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"""
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if (x is None) != (y is None):
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raise ValueError('x and y must be either both None or both non-None')
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with ops.name_scope('RaggedWhere', name, [condition, x, y]):
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condition = ragged_tensor.convert_to_tensor_or_ragged_tensor(
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condition, name='condition')
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if x is None:
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return _coordinate_where(condition)
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else:
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x = ragged_tensor.convert_to_tensor_or_ragged_tensor(x, name='x')
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y = ragged_tensor.convert_to_tensor_or_ragged_tensor(y, name='y')
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condition, x, y = ragged_tensor.match_row_splits_dtypes(condition, x, y)
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return _elementwise_where_v2(condition, x, y)
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@dispatch.dispatch_for_api(array_ops.where)
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def where(condition: ragged_tensor.RaggedOrDense,
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x: typing.Optional[ragged_tensor.RaggedOrDense] = None,
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y: typing.Optional[ragged_tensor.RaggedOrDense] = None,
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name=None):
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"""Return the elements, either from `x` or `y`, depending on the `condition`.
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: If both `x` and `y` are `None`:
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Returns the coordinates of true elements of `condition`. The coordinates
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are returned in a 2-D tensor with shape
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`[num_true_values, dim_size(condition)]`, where `result[i]` is the
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coordinates of the `i`th true value (in row-major order).
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: If both `x` and `y` are non-`None`:
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Returns a tensor formed by selecting values from `x` where condition is
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true, and from `y` when condition is false. In particular:
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: If `condition`, `x`, and `y` all have the same shape:
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* `result[i1...iN] = x[i1...iN]` if `condition[i1...iN]` is true.
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* `result[i1...iN] = y[i1...iN]` if `condition[i1...iN]` is false.
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: Otherwise:
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* `condition` must be a vector.
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* `x` and `y` must have the same number of dimensions.
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* The outermost dimensions of `condition`, `x`, and `y` must all have the
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same size.
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* `result[i] = x[i]` if `condition[i]` is true.
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* `result[i] = y[i]` if `condition[i]` is false.
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Args:
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condition: A potentially ragged tensor of type `bool`
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x: A potentially ragged tensor (optional).
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y: A potentially ragged tensor (optional). Must be specified if `x` is
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specified. Must have the same rank and type as `x`.
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name: A name of the operation (optional)
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Returns:
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: If both `x` and `y` are `None`:
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A `Tensor` with shape `(num_true, dim_size(condition))`.
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: Otherwise:
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A potentially ragged tensor with the same type, rank, and outermost
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dimension size as `x` and `y`.
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`result.ragged_rank = max(x.ragged_rank, y.ragged_rank)`.
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Raises:
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ValueError: When exactly one of `x` or `y` is non-`None`; or when
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`condition`, `x`, and `y` have incompatible shapes.
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#### Examples:
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>>> # Coordinates where condition is true.
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>>> condition = tf.ragged.constant([[True, False, True], [False, True]])
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>>> print(where(condition))
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tf.Tensor( [[0 0] [0 2] [1 1]], shape=(3, 2), dtype=int64)
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>>> # Elementwise selection between x and y, based on condition.
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>>> condition = tf.ragged.constant([[True, False, True], [False, True]])
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>>> x = tf.ragged.constant([['A', 'B', 'C'], ['D', 'E']])
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>>> y = tf.ragged.constant([['a', 'b', 'c'], ['d', 'e']])
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>>> print(where(condition, x, y))
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<tf.RaggedTensor [[b'A', b'b', b'C'], [b'd', b'E']]>
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>>> # Row selection between x and y, based on condition.
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>>> condition = [True, False]
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>>> x = tf.ragged.constant([['A', 'B', 'C'], ['D', 'E']])
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>>> y = tf.ragged.constant([['a', 'b', 'c'], ['d', 'e']])
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>>> print(where(condition, x, y))
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<tf.RaggedTensor [[b'A', b'B', b'C'], [b'd', b'e']]>
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"""
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if (x is None) != (y is None):
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raise ValueError('x and y must be either both None or both non-None')
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with ops.name_scope('RaggedWhere', name, [condition, x, y]):
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condition = ragged_tensor.convert_to_tensor_or_ragged_tensor(
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condition, name='condition')
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if x is None:
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return _coordinate_where(condition)
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else:
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x = ragged_tensor.convert_to_tensor_or_ragged_tensor(x, name='x')
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y = ragged_tensor.convert_to_tensor_or_ragged_tensor(y, name='y')
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condition, x, y = ragged_tensor.match_row_splits_dtypes(condition, x, y)
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return _elementwise_where(condition, x, y)
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def _elementwise_where(condition, x, y):
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"""Ragged version of tf.where(condition, x, y)."""
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condition_is_ragged = isinstance(condition, ragged_tensor.RaggedTensor)
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x_is_ragged = isinstance(x, ragged_tensor.RaggedTensor)
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y_is_ragged = isinstance(y, ragged_tensor.RaggedTensor)
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if not (condition_is_ragged or x_is_ragged or y_is_ragged):
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return array_ops.where(condition, x, y)
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elif condition_is_ragged and x_is_ragged and y_is_ragged:
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return ragged_functional_ops.map_flat_values(array_ops.where, condition, x,
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y)
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elif not condition_is_ragged:
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# Concatenate x and y, and then use `gather` to assemble the selected rows.
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condition.shape.assert_has_rank(1)
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x_and_y = ragged_concat_ops.concat([x, y], axis=0)
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x_nrows = _nrows(x, out_type=x_and_y.row_splits.dtype)
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y_nrows = _nrows(y, out_type=x_and_y.row_splits.dtype)
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indices = array_ops.where(condition, math_ops.range(x_nrows),
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x_nrows + math_ops.range(y_nrows))
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return ragged_gather_ops.gather(x_and_y, indices)
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else:
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raise ValueError('Input shapes do not match.')
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def _elementwise_where_v2(condition, x, y):
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"""Ragged version of tf.where_v2(condition, x, y)."""
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# Broadcast x, y, and condition to have the same shape.
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if not (condition.shape.is_fully_defined() and x.shape.is_fully_defined() and
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y.shape.is_fully_defined() and x.shape == y.shape and
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condition.shape == x.shape):
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shape_c = ragged_tensor_shape.RaggedTensorDynamicShape.from_tensor(
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condition)
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shape_x = ragged_tensor_shape.RaggedTensorDynamicShape.from_tensor(x)
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shape_y = ragged_tensor_shape.RaggedTensorDynamicShape.from_tensor(y)
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shape = ragged_tensor_shape.broadcast_dynamic_shape(
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shape_c, ragged_tensor_shape.broadcast_dynamic_shape(shape_x, shape_y))
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condition = ragged_tensor_shape.broadcast_to(condition, shape)
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x = ragged_tensor_shape.broadcast_to(x, shape)
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y = ragged_tensor_shape.broadcast_to(y, shape)
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condition_is_ragged = isinstance(condition, ragged_tensor.RaggedTensor)
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x_is_ragged = isinstance(x, ragged_tensor.RaggedTensor)
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y_is_ragged = isinstance(y, ragged_tensor.RaggedTensor)
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if not (condition_is_ragged or x_is_ragged or y_is_ragged):
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return array_ops.where_v2(condition, x, y)
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return ragged_functional_ops.map_flat_values(array_ops.where_v2, condition, x,
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y)
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def _coordinate_where(condition):
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"""Ragged version of tf.where(condition)."""
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if not isinstance(condition, ragged_tensor.RaggedTensor):
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return array_ops.where(condition)
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# The coordinate for each `true` value in condition.values.
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selected_coords = _coordinate_where(condition.values)
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# Convert the first index in each coordinate to a row index and column index.
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condition = condition.with_row_splits_dtype(selected_coords.dtype)
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first_index = selected_coords[:, 0]
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selected_rows = array_ops.gather(condition.value_rowids(), first_index)
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selected_row_starts = array_ops.gather(condition.row_splits, selected_rows)
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selected_cols = first_index - selected_row_starts
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# Assemble the row & column index with the indices for inner dimensions.
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return array_ops.concat([
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array_ops.expand_dims(selected_rows, 1),
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array_ops.expand_dims(selected_cols, 1), selected_coords[:, 1:]
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],
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axis=1)
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def _nrows(rt_input, out_type):
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if isinstance(rt_input, ragged_tensor.RaggedTensor):
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return rt_input.nrows(out_type=out_type)
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else:
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return array_ops.shape(rt_input, out_type=out_type)[0]
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