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add a new op paddle.sort() to API 2.0 test=develop (#23470)

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* add a new op paddle.tensor.sort(), test=develop
* add a new op paddle.sort(), test=develop
revert-23830-2.0-beta
mamingjie-China 6 years ago committed by GitHub
parent 915341e3de
commit 1a8d6ee371
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 139 additions and 13 deletions
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3
python/paddle/__init__.py
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@ -34,6 +34,7 @@ import paddle.distributed
batch = batch.batch
import paddle.sysconfig
import paddle.nn
import paddle.tensor
#TODO: define alias in tensor and framework directory
# from .tensor.creation import create_.tensor #DEFINE_ALIAS
@ -191,7 +192,7 @@ from .tensor.manipulation import flip #DEFINE_ALIAS
# from .tensor.search import where #DEFINE_ALIAS
# from .tensor.search import index_select #DEFINE_ALIAS
# from .tensor.search import nonzero #DEFINE_ALIAS
# from .tensor.search import sort #DEFINE_ALIAS
from .tensor.search import sort #DEFINE_ALIAS
# from .framework.framework import set_default_dtype #DEFINE_ALIAS
# from .framework.framework import get_default_dtype #DEFINE_ALIAS
# from .framework.random import manual_seed #DEFINE_ALIAS

31
python/paddle/fluid/tests/unittests/test_argsort_op.py
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@ -15,6 +15,7 @@
from __future__ import print_function
import unittest
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import numpy as np
@ -320,5 +321,35 @@ class TestArgsortOpDescendingAxisNeg2GPU(TestArgsortOpAxisNeg2GPU):
self.descending = True
class TestSortOnCPU(TestArgsortOpCPU):
def init_place(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]])
self.assertEqual((result[0] == result[1]).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()
if __name__ == "__main__":
unittest.main()

2
python/paddle/tensor/__init__.py
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@ -168,4 +168,4 @@ from .manipulation import flip #DEFINE_ALIAS
# from .search import where #DEFINE_ALIAS
# from .search import index_select #DEFINE_ALIAS
# from .search import nonzero #DEFINE_ALIAS
# from .search import sort #DEFINE_ALIAS
from .search import sort #DEFINE_ALIAS

116
python/paddle/tensor/search.py
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@ -13,14 +13,108 @@
# limitations under the License.
# TODO: define searching & indexing functions of a tensor
# __all__ = ['argmax',
# 'argmin',
# 'argsort',
# 'has_inf',
# 'has_nan',
# 'masked_select',
# 'topk',
# 'where',
# 'index_select',
# 'nonzero',
# 'sort']
__all__ = [
'argmax',
# 'argmin',
# 'argsort',
# 'has_inf',
# 'has_nan',
# 'masked_select',
# 'topk',
# 'where',
# 'index_select',
# 'nonzero',
'sort'
]
from paddle.common_ops_import import *
import warnings
def sort(input, axis=-1, descending=False, out=None, name=None):
"""
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

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