未验证 提交 ce5e1196 编写于 作者: A Asthestarsfalll 提交者: GitHub

【Hackathon No.27】为 Paddle 新增 frac 数学计算API (#41226)

上级 4819ab4d
......@@ -268,6 +268,7 @@ from .tensor.math import fmax # noqa: F401
from .tensor.math import fmin # noqa: F401
from .tensor.math import inner # noqa: F401
from .tensor.math import outer # noqa: F401
from .tensor.math import frac # noqa: F401
from .tensor.random import bernoulli # noqa: F401
from .tensor.random import poisson # noqa: F401
......@@ -606,6 +607,7 @@ __all__ = [ # noqa
'concat',
'check_shape',
'trunc',
'frac',
'digamma',
'standard_normal',
'diagonal',
......
# Copyright (c) 2018 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 numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid import Program, program_guard
from paddle.fluid.framework import _test_eager_guard
def ref_frac(x):
return x - np.trunc(x)
class TestFracAPI(unittest.TestCase):
"""Test Frac API"""
def set_dtype(self):
self.dtype = 'float64'
def setUp(self):
self.set_dtype()
self.x_np = np.random.uniform(-3, 3, [2, 3]).astype(self.dtype)
self.place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_api_static(self):
paddle.enable_static()
with program_guard(Program()):
input = fluid.data('X', self.x_np.shape, self.x_np.dtype)
out = paddle.frac(input)
place = fluid.CPUPlace()
if fluid.core.is_compiled_with_cuda():
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
out_ref = ref_frac(self.x_np)
self.assertTrue(np.allclose(out_ref, res))
def test_api_dygraph(self):
paddle.disable_static(self.place)
x = paddle.to_tensor(self.x_np)
out = paddle.frac(x)
out_ref = ref_frac(self.x_np)
self.assertTrue(np.allclose(out_ref, out.numpy()))
def test_api_eager(self):
paddle.disable_static(self.place)
with _test_eager_guard():
x_tensor = paddle.to_tensor(self.x_np)
out = paddle.frac(x_tensor)
out_ref = ref_frac(self.x_np)
self.assertTrue(np.allclose(out_ref, out.numpy()))
paddle.enable_static()
def test_api_eager_dygraph(self):
with _test_eager_guard():
self.test_api_dygraph()
class TestFracInt32(TestFracAPI):
"""Test Frac API with data type int32"""
def set_dtype(self):
self.dtype = 'int32'
class TestFracInt64(TestFracAPI):
"""Test Frac API with data type int64"""
def set_dtype(self):
self.dtype = 'int64'
class TestFracFloat32(TestFracAPI):
"""Test Frac API with data type float32"""
def set_dtype(self):
self.dtype = 'float32'
class TestFracError(unittest.TestCase):
"""Test Frac Error"""
def setUp(self):
self.x_np = np.random.uniform(-3, 3, [2, 3]).astype('int16')
self.place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_static_error(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.fluid.data('X', [5, 5], 'bool')
self.assertRaises(TypeError, paddle.frac, x)
def test_dygraph_error(self):
paddle.disable_static(self.place)
x = paddle.to_tensor(self.x_np, dtype='int16')
self.assertRaises(TypeError, paddle.frac, x)
if __name__ == '__main__':
unittest.main()
......@@ -228,6 +228,7 @@ from .math import fmax # noqa: F401
from .math import fmin # noqa: F401
from .math import inner # noqa: F401
from .math import outer # noqa: F401
from .math import frac # noqa: F401
from .random import multinomial # noqa: F401
from .random import standard_normal # noqa: F401
......@@ -454,6 +455,7 @@ tensor_method_func = [ #noqa
'digamma',
'diagonal',
'trunc',
'frac',
'bitwise_and',
'bitwise_or',
'bitwise_xor',
......
......@@ -4385,3 +4385,57 @@ def angle(x, name=None):
outputs = {"Out": out}
helper.append_op(type=op_type, inputs=inputs, outputs=outputs)
return out
def frac(x, name=None):
"""
This API is used to return the fractional portion of each element in input.
Args:
x (Tensor): The input tensor, which data type should be int32, int64, float32, float64.
name: (str, optional): Name for operation (optional, default is None). For more
Returns:
Tensor: The output Tensor of frac.
Examples:
.. code-block:: Python
import paddle
import numpy as np
input = paddle.rand([3, 3], 'float32')
print(input.numpy())
# [[ 1.2203873 -1.0035421 -0.35193074]
# [-0.00928353 0.58917075 -0.8407828 ]
# [-1.5131804 0.5850153 -0.17597814]]
output = paddle.frac(input)
print(output.numpy())
# [[ 0.22038734 -0.00354207 -0.35193074]
# [-0.00928353 0.58917075 -0.8407828 ]
# [-0.5131804 0.5850153 -0.17597814]]
"""
op_type = 'elementwise_sub'
axis = -1
act = None
if x.dtype not in [paddle.int32, paddle.int64, paddle.float32, paddle.float64]:
raise TypeError(
"The data type of input must be one of ['int32', 'int64', 'float32', 'float64'], but got {}".format(x.dtype))
if in_dygraph_mode():
y = _C_ops.final_state_trunc(x)
return _C_ops.final_state_subtract(x, y)
else:
if _in_legacy_dygraph():
y = _C_ops.trunc(x)
return _elementwise_op_in_dygraph(
x, y, axis=axis, act=act, op_name=op_type)
else:
inputs = {"X": x}
attrs = {}
helper = LayerHelper("trunc", **locals())
check_variable_and_dtype(x, "X", ['int32', 'int64', 'float32', 'float64'], 'trunc')
y = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type="trunc", inputs=inputs, attrs=attrs, outputs={"Out": y})
return _elementwise_op(LayerHelper(op_type, **locals()))
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册