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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 argsort #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 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 name of output variable, normally there is no need for user to set this this property.
Default value is None, the framework set the name of output variable.
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(input, index, dim=0):
"""
:alias_main: paddle.index_select
:alias: paddle.index_select,paddle.tensor.index_select,paddle.tensor.search.index_select
Returns a new tensor which indexes the `input` tensor along dimension `dim` using
the entries in `index` which is a Tensor. The returned tensor has the same number
of dimensions as the original `input` tensor. The dim-th dimension has the same
size as the length of `index`; other dimensions have the same size as in the `input` tensor.
Args:
input (Variable): The input tensor variable.
index (Variable): The 1-D tensor containing the indices to index.
dim (int): The dimension in which we index.
Returns:
Variable: A Tensor with same data type as `input`.
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]])
data_index = np.array([0, 1, 1]).astype('int32')
with fluid.dygraph.guard():
x = fluid.dygraph.to_variable(data)
index = fluid.dygraph.to_variable(data_index)
out_z1 = paddle.index_select(x, index)
print(out_z1.numpy())
#[[1. 2. 3. 4.]
# [5. 6. 7. 8.]
# [5. 6. 7. 8.]]
out_z2 = paddle.index_select(x, index, dim=1)
print(out_z2.numpy())
#[[ 1. 2. 2.]
# [ 5. 6. 6.]
# [ 9. 10. 10.]]
"""
helper = LayerHelper("index_select", **locals())
if in_dygraph_mode():
return core.ops.index_select(input, index, 'dim', dim)
check_variable_and_dtype(input, '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(input.dtype)
helper.append_op(
type='index_select',
inputs={'X': input,
'Index': index},
outputs={'Out': out},
attrs={'dim': dim})
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(input, axis=-1, descending=False, out=None, 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 sorted output
data Varibale and its corresponding index Variable with the same shape as
:attr:`input`.
**NOTICE**: The Variable in the output of this OP has gradient. You could\
set Variable :attr:`stop_gradient`.
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(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.
out(Variable, optional): The default value is None. 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.
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:
tuple: A tuple of sorted data Variable(with the same shape and data
type as input) and the sorted indices(with the same shape as input's
and 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]]]).astype(np.float32)
with fluid.dygraph.guard():
x = fluid.dygraph.to_variable(in1)
out1 = paddle.sort(input=x, axis=-1)
out2 = paddle.sort(input=x, axis=0)
out3 = paddle.sort(input=x, axis=1)
print(out1[0].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(out1[1].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[0].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[0].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.]]]
"""
helper = LayerHelper("sort", **locals())
if out is None:
out = helper.create_variable_for_type_inference(
dtype=input.dtype, stop_gradient=False)
ids = helper.create_variable_for_type_inference(
VarDesc.VarType.INT64, stop_gradient=True)
helper.append_op(
type='argsort',
inputs={'X': input},
outputs={'Out': out,
'Indices': ids},
attrs={'axis': axis,
'descending': descending})
return out, ids
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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