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Paddle/python/paddle/tensor/search.py

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# Copyright (c) 2020 PaddlePaddle 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.
from __future__ import print_function
import numpy as np
from ..fluid.layer_helper import LayerHelper
from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype
from ..fluid import core, layers
# TODO: define searching & indexing functions of a tensor
from ..fluid.layers import argmin #DEFINE_ALIAS
from ..fluid.layers import has_inf #DEFINE_ALIAS
from ..fluid.layers import has_nan #DEFINE_ALIAS
from ..fluid.layers import topk #DEFINE_ALIAS
__all__ = [
'argmax',
'argmin',
'argsort',
'has_inf',
'has_nan',
# 'masked_select',
'topk',
'where',
'index_select',
'nonzero',
'sort',
'index_sample'
]
from paddle.common_ops_import import *
def argsort(x, axis=-1, descending=False, name=None):
"""
:alias_main: paddle.argsort
:alias: paddle.argsort,paddle.tensor.argsort,paddle.tensor.search.argsort
This OP sorts the input along the given axis, and returns the corresponding index tensor for the sorted output values. The default sort algorithm is ascending, if you want the sort algorithm to be descending, you must set the :attr:`descending` as True.
Args:
x(Tensor): An input N-D Tensor with type float32, float64, int16,
int32, int64, uint8.
axis(int, optional): Axis to compute indices along. The effective range
is [-R, R), where R is Rank(x). when axis<0, it works the same way
as axis+R. Default is 0.
descending(bool, optional) : Descending is a flag, if set to true,
algorithm will sort by descending order, else sort by
ascending order. Default is false.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Returns:
Tensor: sorted indices(with the same shape as ``x``
and with data type int64).
Examples:
.. code-block:: python
import paddle
import numpy as np
paddle.disable_static()
input_array = np.array([[[5,8,9,5],
[0,0,1,7],
[6,9,2,4]],
[[5,2,4,2],
[4,7,7,9],
[1,7,0,6]]]).astype(np.float32)
x = paddle.to_variable(input_array)
out1 = paddle.argsort(x=x, axis=-1)
out2 = paddle.argsort(x=x, axis=0)
out3 = paddle.argsort(x=x, axis=1)
print(out1.numpy())
#[[[0 3 1 2]
# [0 1 2 3]
# [2 3 0 1]]
# [[1 3 2 0]
# [0 1 2 3]
# [2 0 3 1]]]
print(out2.numpy())
#[[[0 1 1 1]
# [0 0 0 0]
# [1 1 1 0]]
# [[1 0 0 0]
# [1 1 1 1]
# [0 0 0 1]]]
print(out3.numpy())
#[[[1 1 1 2]
# [0 0 2 0]
# [2 2 0 1]]
# [[2 0 2 0]
# [1 1 0 2]
# [0 2 1 1]]]
"""
if in_dygraph_mode():
_, ids = core.ops.argsort(x, 'axis', axis, 'descending', descending)
return ids
check_variable_and_dtype(
x, 'x', ['float32', 'float64', 'int16', 'int32', 'int64', 'uint8'],
'argsort')
helper = LayerHelper("argsort", **locals())
out = helper.create_variable_for_type_inference(
dtype=x.dtype, stop_gradient=True)
ids = helper.create_variable_for_type_inference(
VarDesc.VarType.INT64, stop_gradient=True)
helper.append_op(
type='argsort',
inputs={'X': x},
outputs={'Out': out,
'Indices': ids},
attrs={'axis': axis,
'descending': descending})
return ids
def argmax(input, axis=None, dtype=None, out=None, keepdims=False, name=None):
"""
:alias_main: paddle.argmax
:alias: paddle.argmax,paddle.tensor.argmax,paddle.tensor.search.argmax
This OP computes the indices of the max elements of the input tensor's
element along the provided axis.
Args:
input(Variable): An input N-D Tensor with type float32, float64, int16,
int32, int64, uint8.
axis(int, optional): Axis to compute indices along. The effective range
is [-R, R), where R is Rank(input). when axis<0, it works the same way
as axis+R. Default is None, it will use the last dim to select indices of max value.
dtype(np.dtype|core.VarDesc.VarType|str): Data type of the output tensor which can
be int32, int64. The default value is None, and it will
return the int64 indices.
out(Variable, optional): Optional output which can be any created
Variable that meets the requirements to store the result of operation.
if out is None, a new Varibale will be create to store the result. Defalut is None.
keepdims(bool, optional): Keep the axis that do the select max.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Returns:
Variable: A Tensor with data type int64.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import numpy as np
in1 = np.array([[[5,8,9,5],
[0,0,1,7],
[6,9,2,4]],
[[5,2,4,2],
[4,7,7,9],
[1,7,0,6]]])
with fluid.dygraph.guard():
x = fluid.dygraph.to_variable(in1)
out1 = paddle.argmax(input=x, axis=-1)
out2 = paddle.argmax(input=x, axis=0)
out3 = paddle.argmax(input=x, axis=1)
out4 = paddle.argmax(input=x, axis=2)
out5 = paddle.argmax(input=x, axis=2, keepdims=True)
print(out1.numpy())
# [[2 3 1]
# [0 3 1]]
print(out2.numpy())
# [[0 0 0 0]
# [1 1 1 1]
# [0 0 0 1]]
print(out3.numpy())
# [[2 2 0 1]
# [0 1 1 1]]
print(out4.numpy())
# [[2 3 1]
# [0 3 1]]
print(out5.numpy())
#array([[[2],
# [3],
# [1]],
# [[0],
# [3],
# [1]]])
"""
helper = LayerHelper("arg_max", **locals())
var_dtype = None
attrs = {}
if dtype is not None:
check_dtype(dtype, 'create data type', ['int32', 'int64'], 'arg_max')
var_dtype = convert_np_dtype_to_dtype_(dtype)
attrs["dtype"] = var_dtype
else:
var_dtype = VarDesc.VarType.INT64
if out is None:
out = helper.create_variable_for_type_inference(var_dtype)
if axis is None:
axis = -1
attrs['keepdims'] = keepdims
attrs['axis'] = axis
helper.append_op(
type='arg_max',
inputs={'X': input},
outputs={'Out': [out]},
attrs=attrs)
out.stop_gradient = True
return out
def index_select(x, index, axis=0, name=None):
"""
:alias_main: paddle.index_select
:alias: paddle.tensor.index_select, paddle.tensor.search.index_select
Returns a new tensor which indexes the ``input`` tensor along dimension ``axis`` using
the entries in ``index`` which is a Tensor. The returned tensor has the same number
of dimensions as the original ``x`` tensor. The dim-th dimension has the same
size as the length of ``index``; other dimensions have the same size as in the ``x`` tensor.
Args:
x (Tensor): The input Tensor to be operated. The data of ``x`` can be one of float32, float64, int32, int64.
index (Tensor): The 1-D Tensor containing the indices to index. The data type of ``index`` must be int32 or int64.
axis (int, optional): The dimension in which we index. Default: if None, the ``axis`` is 0.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Returns:
Tensor: A Tensor with same data type as ``x``.
Raises:
TypeError: ``x`` must be a Tensor and the data type of ``x`` must be one of float32, float64, int32 and int64.
TypeError: ``index`` must be a Tensor and the data type of ``index`` must be int32 or int64.
Examples:
.. code-block:: python
import paddle
import numpy as np
paddle.disable_static() # Now we are in imperative mode
data = np.array([[1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0],
[9.0, 10.0, 11.0, 12.0]])
data_index = np.array([0, 1, 1]).astype('int32')
x = paddle.to_variable(data)
index = paddle.to_variable(data_index)
out_z1 = paddle.index_select(x=x, index=index)
#[[1. 2. 3. 4.]
# [5. 6. 7. 8.]
# [5. 6. 7. 8.]]
out_z2 = paddle.index_select(x=x, index=index, axis=1)
#[[ 1. 2. 2.]
# [ 5. 6. 6.]
# [ 9. 10. 10.]]
"""
if in_dygraph_mode():
return core.ops.index_select(x, index, 'dim', axis)
helper = LayerHelper("index_select", **locals())
check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],
'paddle.tensor.search.index_select')
check_variable_and_dtype(index, 'index', ['int32', 'int64'],
'paddle.tensor.search.index_select')
out = helper.create_variable_for_type_inference(x.dtype)
helper.append_op(
type='index_select',
inputs={'X': x,
'Index': index},
outputs={'Out': out},
attrs={'dim': axis})
return out
def nonzero(input, as_tuple=False):
"""
:alias_main: paddle.nonzero
:alias: paddle.nonzero,paddle.tensor.nonzero,paddle.tensor.search.nonzero
Return a tensor containing the indices of all non-zero elements of the `input`
tensor. If as_tuple is True, return a tuple of 1-D tensors, one for each dimension
in `input`, each containing the indices (in that dimension) of all non-zero elements
of `input`. Given a n-Dimensional `input` tensor with shape [x_1, x_2, ..., x_n], If
as_tuple is False, we can get a output tensor with shape [z, n], where `z` is the
number of all non-zero elements in the `input` tensor. If as_tuple is True, we can get
a 1-D tensor tuple of length `n`, and the shape of each 1-D tensor is [z, 1].
Args:
inputs (Variable): The input tensor variable.
as_tuple (bool): Return type, Tensor or tuple of Tensor.
Returns:
Variable. The data type is int64.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import numpy as np
data1 = np.array([[1.0, 0.0, 0.0],
[0.0, 2.0, 0.0],
[0.0, 0.0, 3.0]])
data2 = np.array([0.0, 1.0, 0.0, 3.0])
data3 = np.array([0.0, 0.0, 0.0])
with fluid.dygraph.guard():
x1 = fluid.dygraph.to_variable(data1)
x2 = fluid.dygraph.to_variable(data2)
x3 = fluid.dygraph.to_variable(data3)
out_z1 = paddle.nonzero(x1)
print(out_z1.numpy())
#[[0 0]
# [1 1]
# [2 2]]
out_z1_tuple = paddle.nonzero(x1, as_tuple=True)
for out in out_z1_tuple:
print(out.numpy())
#[[0]
# [1]
# [2]]
#[[0]
# [1]
# [2]]
out_z2 = paddle.nonzero(x2)
print(out_z2.numpy())
#[[1]
# [3]]
out_z2_tuple = paddle.nonzero(x2, as_tuple=True)
for out in out_z2_tuple:
print(out.numpy())
#[[1]
# [3]]
out_z3 = paddle.nonzero(x3)
print(out_z3.numpy())
#[]
out_z3_tuple = paddle.nonzero(x3, as_tuple=True)
for out in out_z3_tuple:
print(out.numpy())
#[]
"""
list_out = []
shape = input.shape
rank = len(shape)
if in_dygraph_mode():
outs = core.ops.where_index(input)
else:
outs = layers.where(input)
if not as_tuple:
return outs
elif rank == 1:
return tuple([outs])
else:
for i in range(rank):
list_out.append(
layers.slice(
outs, axes=[rank - 1], starts=[i], ends=[i + 1]))
return tuple(list_out)
def sort(x, axis=-1, descending=False, name=None):
"""
:alias_main: paddle.sort
:alias: paddle.sort,paddle.tensor.sort,paddle.tensor.search.sort
This OP sorts the input along the given axis, and returns the sorted output tensor. The default sort algorithm is ascending, if you want the sort algorithm to be descending, you must set the :attr:`descending` as True.
Args:
x(Tensor): An input N-D Tensor with type float32, float64, int16,
int32, int64, uint8.
axis(int, optional): Axis to compute indices along. The effective range
is [-R, R), where R is Rank(x). when axis<0, it works the same way
as axis+R. Default is 0.
descending(bool, optional) : Descending is a flag, if set to true,
algorithm will sort by descending order, else sort by
ascending order. Default is false.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Returns:
Tensor: sorted tensor(with the same shape and data type as ``x``).
Examples:
.. code-block:: python
import paddle
import numpy as np
paddle.disable_static()
input_array = np.array([[[5,8,9,5],
[0,0,1,7],
[6,9,2,4]],
[[5,2,4,2],
[4,7,7,9],
[1,7,0,6]]]).astype(np.float32)
x = paddle.to_variable(input_array)
out1 = paddle.sort(x=x, axis=-1)
out2 = paddle.sort(x=x, axis=0)
out3 = paddle.sort(x=x, axis=1)
print(out1.numpy())
#[[[5. 5. 8. 9.]
# [0. 0. 1. 7.]
# [2. 4. 6. 9.]]
# [[2. 2. 4. 5.]
# [4. 7. 7. 9.]
# [0. 1. 6. 7.]]]
print(out2.numpy())
#[[[5. 2. 4. 2.]
# [0. 0. 1. 7.]
# [1. 7. 0. 4.]]
# [[5. 8. 9. 5.]
# [4. 7. 7. 9.]
# [6. 9. 2. 6.]]]
print(out3.numpy())
#[[[0. 0. 1. 4.]
# [5. 8. 2. 5.]
# [6. 9. 9. 7.]]
# [[1. 2. 0. 2.]
# [4. 7. 4. 6.]
# [5. 7. 7. 9.]]]
"""
if in_dygraph_mode():
out, _ = core.ops.argsort(x, 'axis', axis, 'descending', descending)
return out
helper = LayerHelper("sort", **locals())
out = helper.create_variable_for_type_inference(
dtype=x.dtype, stop_gradient=False)
ids = helper.create_variable_for_type_inference(
VarDesc.VarType.INT64, stop_gradient=True)
helper.append_op(
type='argsort',
inputs={'X': x},
outputs={'Out': out,
'Indices': ids},
attrs={'axis': axis,
'descending': descending})
return out
def where(condition, x, y, name=None):
"""
:alias_main: paddle.where
:alias: paddle.where,paddle.tensor.where,paddle.tensor.search.where
Return a tensor of elements selected from either $x$ or $y$, depending on $condition$.
.. math::
out_i =
\\begin{cases}
x_i, \quad \\text{if} \\ condition_i \\ is \\ True \\\\
y_i, \quad \\text{if} \\ condition_i \\ is \\ False \\\\
\\end{cases}
Args:
condition(Variable): The condition to choose x or y.
x(Variable): x is a Tensor Variable with data type float32, float64, int32, int64.
y(Variable): y is a Tensor Variable with data type float32, float64, int32, int64.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Returns:
Variable: A Tensor with the same data dype as x.
Examples:
.. code-block:: python
import paddle
import numpy as np
import paddle.fluid as fluid
x_i = np.array([0.9383, 0.1983, 3.2, 1.2]).astype("float32")
y_i = np.array([1.0, 1.0, 1.0, 1.0]).astype("float32")
with fluid.dygraph.guard():
x = fluid.dygraph.to_variable(x_i)
y = fluid.dygraph.to_variable(y_i)
out = paddle.where(x>1, x, y)
print(out.numpy())
#out: [1.0, 1.0, 3.2, 1.2]
"""
if not in_dygraph_mode():
check_variable_and_dtype(condition, 'condition', ['bool'], 'where')
check_variable_and_dtype(
x, 'x', ['float32', 'float64', 'int32', 'int64'], 'where')
check_variable_and_dtype(
y, 'y', ['float32', 'float64', 'int32', 'int64'], 'where')
x_shape = list(x.shape)
y_shape = list(y.shape)
if x_shape == y_shape:
if in_dygraph_mode():
return core.ops.where(condition, x, y)
else:
helper = LayerHelper("where", **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='where',
inputs={'Condition': condition,
'X': x,
'Y': y},
outputs={'Out': [out]})
return out
else:
cond_int = layers.cast(condition, x.dtype)
cond_not_int = layers.cast(layers.logical_not(condition), x.dtype)
out1 = layers.elementwise_mul(x, cond_int)
out2 = layers.elementwise_mul(y, cond_not_int)
out = layers.elementwise_add(out1, out2)
return out
def index_sample(x, index):
"""
:alias_main: paddle.index_sample
:alias: paddle.index_sample,paddle.tensor.index_sample,paddle.tensor.search.index_sample
**IndexSample Layer**
IndexSample OP returns the element of the specified location of X,
and the location is specified by Index.
.. code-block:: text
Given:
X = [[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10]]
Index = [[0, 1, 3],
[0, 2, 4]]
Then:
Out = [[1, 2, 4],
[6, 8, 10]]
Args:
x (Variable): The source input tensor with 2-D shape. Supported data type is
int32, int64, float32, float64.
index (Variable): The index input tensor with 2-D shape, first dimension should be same with X.
Data type is int32 or int64.
Returns:
output (Variable): The output is a tensor with the same shape as index.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import numpy as np
data = np.array([[1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0],
[9.0, 10.0, 11.0, 12.0]]).astype('float32')
data_index = np.array([[0, 1, 2],
[1, 2, 3],
[0, 0, 0]]).astype('int32')
target_data = np.array([[100, 200, 300, 400],
[500, 600, 700, 800],
[900, 1000, 1100, 1200]]).astype('int32')
with fluid.dygraph.guard():
x = fluid.dygraph.to_variable(data)
index = fluid.dygraph.to_variable(data_index)
target = fluid.dygraph.to_variable(target_data)
out_z1 = paddle.index_sample(x, index)
print(out_z1.numpy())
#[[1. 2. 3.]
# [6. 7. 8.]
# [9. 9. 9.]]
# Use the index of the maximum value by topk op
# get the value of the element of the corresponding index in other tensors
top_value, top_index = fluid.layers.topk(x, k=2)
out_z2 = paddle.index_sample(target, top_index)
print(top_value.numpy())
#[[ 4. 3.]
# [ 8. 7.]
# [12. 11.]]
print(top_index.numpy())
#[[3 2]
# [3 2]
# [3 2]]
print(out_z2.numpy())
#[[ 400 300]
# [ 800 700]
# [1200 1100]]
"""
helper = LayerHelper("index_sample", **locals())
check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],
'paddle.tensor.search.index_sample')
check_variable_and_dtype(index, 'index', ['int32', 'int64'],
'paddle.tensor.search.index_sample')
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='index_sample',
inputs={'X': x,
'Index': index},
outputs={'Out': out})
return out
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