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Fix the argsort and sort api for the paddle api2.0 (#25514)

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Fix the argsort and sort op for the api2.0, and update the api
fix_copy_if_different
wawltor 5 years ago committed by GitHub
parent 42189be67b
commit dc42e3c4bc
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 278 additions and 83 deletions
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88
python/paddle/fluid/tests/unittests/test_argsort_op.py
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@ -17,6 +17,7 @@ from __future__ import print_function
import unittest
import paddle
import paddle.fluid as fluid
import paddle.imperative as imperative
import paddle.fluid.layers as layers
import numpy as np
import six
@ -321,58 +322,83 @@ class TestArgsortOpDescendingAxisNeg2GPU(TestArgsortOpAxisNeg2GPU):
self.descending = True
class TestSortOnCPU(TestArgsortOpCPU):
def init_place(self):
class TestArgsortErrorOnCPU(unittest.TestCase):
def setUp(self):
self.place = core.CPUPlace()
def test_out(self):
self.init_place()
with fluid.program_guard(fluid.Program()):
input = fluid.data(name="input", shape=[2, 3, 4], dtype="float32")
res = fluid.data(name="output", shape=[2, 3, 4], dtype="float32")
output = paddle.tensor.sort(input=input, out=res)
exe = fluid.Executor(self.place)
data = 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]]],
dtype='float32')
result = exe.run(feed={'input': data}, fetch_list=[res, output[0]])
def test_error(self):
def test_fluid_var_type():
with fluid.program_guard(fluid.Program()):
x = [1]
output = fluid.layers.argsort(input=x)
self.assertRaises(TypeError, test_fluid_var_type)
self.assertEqual((result[0] == result[1]).all(), True)
def test_paddle_var_type():
with fluid.program_guard(fluid.Program()):
x = [1]
output = paddle.argsort(input=x)
self.assertRaises(TypeError, test_paddle_var_type)
class TestSortOnGPU(TestSortOnCPU):
def init_place(self):
class TestArgsortErrorOnGPU(TestArgsortErrorOnCPU):
def setUp(self):
if core.is_compiled_with_cuda():
self.place = core.CUDAPlace(0)
else:
self.place = core.CPUPlace()
class TestArgsortErrorOnCPU(unittest.TestCase):
def init_place(self):
self.place = core.CPUPlace()
class TestArgsort(unittest.TestCase):
def setUp(self):
if core.is_compiled_with_cuda():
self.place = core.CUDAPlace(0)
else:
self.place = core.CPUPlace()
self.data = np.random.rand(2, 3, 4).astype("float32")
def test_error(self):
self.init_place()
def test_api_0(self):
with fluid.program_guard(fluid.Program()):
input = fluid.data(name="input", shape=[2, 3, 4], dtype="float32")
output = paddle.argsort(x=input)
exe = fluid.Executor(self.place)
result, = exe.run(feed={'input': self.data}, fetch_list=[output])
np_result = np.argsort(self.data)
self.assertEqual((result == np_result).all(), True)
def test_input_type():
x = [1]
output = fluid.layers.argsort(input=x)
self.assertRaises(TypeError, test_input_type)
def test_api_1(self):
with fluid.program_guard(fluid.Program()):
input = fluid.data(name="input", shape=[2, 3, 4], dtype="float32")
output = paddle.argsort(x=input, axis=1)
exe = fluid.Executor(self.place)
result, = exe.run(feed={'input': self.data}, fetch_list=[output])
np_result = np.argsort(self.data, axis=1)
self.assertEqual((result == np_result).all(), True)
class TestArgsortErrorOnGPU(TestArgsortErrorOnCPU):
def init_place(self):
class TestArgsortDygraph(unittest.TestCase):
def setUp(self):
self.input_data = np.random.rand(10, 10)
if core.is_compiled_with_cuda():
self.place = core.CUDAPlace(0)
else:
self.place = core.CPUPlace()
def test_api_0(self):
with imperative.guard(self.place):
var_x = imperative.to_variable(self.input_data)
out = paddle.argsort(var_x)
self.assertEqual((np.argsort(self.input_data) == out.numpy()).all(),
True)
def test_api_1(self):
with imperative.guard(self.place):
var_x = imperative.to_variable(self.input_data)
out = paddle.argsort(var_x, axis=-1)
self.assertEqual(
(np.argsort(
self.input_data, axis=-1) == out.numpy()).all(),
True)
if __name__ == "__main__":
unittest.main()

88
python/paddle/fluid/tests/unittests/test_sort_op.py
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@ -0,0 +1,88 @@
# 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 unittest
import paddle
import paddle.fluid as fluid
import paddle.imperative as imperative
import paddle.fluid.layers as layers
import numpy as np
import six
import paddle.fluid.core as core
class TestSortOnCPU(unittest.TestCase):
def setUp(self):
self.place = core.CPUPlace()
def test_api_0(self):
with fluid.program_guard(fluid.Program()):
input = fluid.data(name="input", shape=[2, 3, 4], dtype="float32")
output = paddle.sort(x=input)
exe = fluid.Executor(self.place)
data = 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]]],
dtype='float32')
result, = exe.run(feed={'input': data}, fetch_list=[output[0]])
np_result = np.sort(result)
self.assertEqual((result == np_result).all(), True)
def test_api_1(self):
with fluid.program_guard(fluid.Program()):
input = fluid.data(name="input", shape=[2, 3, 4], dtype="float32")
output = paddle.sort(x=input, axis=1)
exe = fluid.Executor(self.place)
data = 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]]],
dtype='float32')
result, = exe.run(feed={'input': data}, fetch_list=[output[0]])
np_result = np.sort(result, axis=1)
self.assertEqual((result == np_result).all(), True)
class TestSortOnGPU(TestSortOnCPU):
def init_place(self):
if core.is_compiled_with_cuda():
self.place = core.CUDAPlace(0)
else:
self.place = core.CPUPlace()
class TestSortDygraph(unittest.TestCase):
def setUp(self):
self.input_data = np.random.rand(10, 10)
if core.is_compiled_with_cuda():
self.place = core.CUDAPlace(0)
else:
self.place = core.CPUPlace()
def test_api_0(self):
with imperative.guard(self.place):
var_x = imperative.to_variable(self.input_data)
out = paddle.sort(var_x)
self.assertEqual((np.sort(self.input_data) == out[0].numpy()).all(),
True)
def test_api_1(self):
with imperative.guard(self.place):
var_x = imperative.to_variable(self.input_data)
out = paddle.sort(var_x, axis=-1)
self.assertEqual(
(np.sort(
self.input_data, axis=-1) == out[0].numpy()).all(),
True)

185
python/paddle/tensor/search.py
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@ -19,7 +19,6 @@ 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
@ -42,6 +41,92 @@ __all__ = [
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 sorted output
data Varibale and its corresponding index Variable with the same shape as ``x``.
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 paddle.imperative as imperative
import numpy as np
paddle.enable_imperative()
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 = imperative.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
@ -291,19 +376,16 @@ def nonzero(input, as_tuple=False):
return tuple(list_out)
def sort(input, axis=-1, descending=False, out=None, name=None):
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 sorted output
data Varibale and its corresponding index Variable with the same shape as
:attr:`input`.
data Tensor and its corresponding index Tensor with the same shape as ``x``.
**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,
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
@ -311,71 +393,70 @@ def sort(input, axis=-1, descending=False, out=None, name=None):
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
tuple: A tuple of sorted data tensor(with the same shape and data
type as ``x``) and the sorted indices(with the same shape as ``x``
and with data type int64).
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import paddle.imperative as imperative
import numpy as np
in1 = np.array([[[5,8,9,5],
paddle.enable_imperative()
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)
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.]]]
x = imperative.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[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.]]]
"""
if in_dygraph_mode():
out, ids = core.ops.argsort(x, 'axis', axis, 'descending', descending)
return out, ids
helper = LayerHelper("sort", **locals())
if out is None:
out = helper.create_variable_for_type_inference(
dtype=input.dtype, stop_gradient=False)
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': input},
inputs={'X': x},
outputs={'Out': out,
'Indices': ids},
attrs={'axis': axis,

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