未验证 提交 361363c3 编写于 作者: Z Zhong Hui 提交者: GitHub

add pairewise distance for the paddlepaddle api 2.0

add pairewise distance for the paddlepaddle api 2.0
上级 1d870c44
# 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 paddle
import paddle.fluid as fluid
import numpy as np
import unittest
def pairwise_distance(x, y, p=2.0, eps=1e-6, keepdim=False):
return np.linalg.norm(x - y, ord=p, axis=1, keepdims=keepdim)
def test_static(x_np, y_np, p=2.0, eps=1e-6, keepdim=False):
prog = paddle.static.Program()
startup_prog = paddle.static.Program()
place = fluid.CUDAPlace(0) if paddle.fluid.core.is_compiled_with_cuda(
) else fluid.CPUPlace()
with paddle.static.program_guard(prog, startup_prog):
x = paddle.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
y = paddle.data(name='y', shape=y_np.shape, dtype=x_np.dtype)
dist = paddle.nn.layer.distance.PairwiseDistance(
p=p, eps=eps, keepdim=keepdim)
distance = dist(x, y)
exe = paddle.static.Executor(place)
static_ret = exe.run(prog,
feed={'x': x_np,
'y': y_np},
fetch_list=[distance])
static_ret = static_ret[0]
return static_ret
def test_dygraph(x_np, y_np, p=2.0, eps=1e-6, keepdim=False):
paddle.disable_static()
x = paddle.to_variable(x_np)
y = paddle.to_variable(y_np)
dist = paddle.nn.layer.distance.PairwiseDistance(
p=p, eps=eps, keepdim=keepdim)
distance = dist(x, y)
dygraph_ret = distance.numpy()
paddle.enable_static()
return dygraph_ret
class TestPairwiseDistance(unittest.TestCase):
def test_pairwise_distance(self):
all_shape = [[100, 100], [4, 5, 6, 7]]
dtypes = ['float32', 'float64']
keeps = [False, True]
for shape in all_shape:
for dtype in dtypes:
for keepdim in keeps:
x_np = np.random.random(shape).astype(dtype)
y_np = np.random.random(shape).astype(dtype)
static_ret = test_static(x_np, y_np, keepdim=keepdim)
dygraph_ret = test_dygraph(x_np, y_np, keepdim=keepdim)
excepted_value = pairwise_distance(
x_np, y_np, keepdim=keepdim)
self.assertTrue(np.allclose(static_ret, dygraph_ret))
self.assertTrue(np.allclose(static_ret, excepted_value))
self.assertTrue(np.allclose(dygraph_ret, excepted_value))
def test_pairwise_distance_broadcast(self):
shape_x = [100, 100]
shape_y = [100, 1]
keepdim = False
x_np = np.random.random(shape_x).astype('float32')
y_np = np.random.random(shape_y).astype('float32')
static_ret = test_static(x_np, y_np, keepdim=keepdim)
dygraph_ret = test_dygraph(x_np, y_np, keepdim=keepdim)
excepted_value = pairwise_distance(x_np, y_np, keepdim=keepdim)
self.assertTrue(np.allclose(static_ret, dygraph_ret))
self.assertTrue(np.allclose(static_ret, excepted_value))
self.assertTrue(np.allclose(dygraph_ret, excepted_value))
def test_pairwise_distance_different_p(self):
shape = [100, 100]
keepdim = False
p = 3.0
x_np = np.random.random(shape).astype('float32')
y_np = np.random.random(shape).astype('float32')
static_ret = test_static(x_np, y_np, p=p, keepdim=keepdim)
dygraph_ret = test_dygraph(x_np, y_np, p=p, keepdim=keepdim)
excepted_value = pairwise_distance(x_np, y_np, p=p, keepdim=keepdim)
self.assertTrue(np.allclose(static_ret, dygraph_ret))
self.assertTrue(np.allclose(static_ret, excepted_value))
self.assertTrue(np.allclose(dygraph_ret, excepted_value))
if __name__ == "__main__":
unittest.main()
...@@ -93,6 +93,7 @@ from .layer.norm import InstanceNorm #DEFINE_ALIAS ...@@ -93,6 +93,7 @@ from .layer.norm import InstanceNorm #DEFINE_ALIAS
# from .layer.rnn import RNNCell #DEFINE_ALIAS # from .layer.rnn import RNNCell #DEFINE_ALIAS
# from .layer.rnn import GRUCell #DEFINE_ALIAS # from .layer.rnn import GRUCell #DEFINE_ALIAS
# from .layer.rnn import LSTMCell #DEFINE_ALIAS # from .layer.rnn import LSTMCell #DEFINE_ALIAS
from .layer.distance import PairwiseDistance #DEFINE_ALIAS
from .layer import loss #DEFINE_ALIAS from .layer import loss #DEFINE_ALIAS
from .layer import conv #DEFINE_ALIAS from .layer import conv #DEFINE_ALIAS
......
...@@ -20,6 +20,7 @@ from . import conv ...@@ -20,6 +20,7 @@ from . import conv
from . import extension from . import extension
from . import activation from . import activation
from . import norm from . import norm
from . import distance
from .activation import * from .activation import *
from .loss import * from .loss import *
...@@ -69,3 +70,4 @@ from .norm import InstanceNorm #DEFINE_ALIAS ...@@ -69,3 +70,4 @@ from .norm import InstanceNorm #DEFINE_ALIAS
# from .rnn import RNNCell #DEFINE_ALIAS # from .rnn import RNNCell #DEFINE_ALIAS
# from .rnn import GRUCell #DEFINE_ALIAS # from .rnn import GRUCell #DEFINE_ALIAS
# from .rnn import LSTMCell #DEFINE_ALIAS # from .rnn import LSTMCell #DEFINE_ALIAS
from .distance import PairwiseDistance #DEFINE_ALIAS
# 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.
__all__ = ['PairwiseDistance']
import numpy as np
import paddle
from ...fluid.dygraph import layers
from ...fluid.framework import core, in_dygraph_mode
from ...fluid.data_feeder import check_variable_and_dtype, check_type
from ...fluid.layer_helper import LayerHelper
class PairwiseDistance(layers.Layer):
"""
This operator computes the pairwise distance between two vectors. The
distance is calculated by p-oreder norm:
.. math::
\Vert x \Vert _p = \left( \sum_{i=1}^n \vert x_i \vert ^ p \right) ^ {1/p}.
Parameters:
p (float): The order of norm. The default value is 2.
eps (float, optional): Add small value to avoid division by zero,
default value is 1e-6.
keepdim (bool, optional): Whether to reserve the reduced dimension
in the output Tensor. The result tensor is one dimension less than
the result of ``'x-y'`` unless :attr:`keepdim` is True, default
value is False.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Shape:
x: :math:`(N, D)` where `D` is the dimension of vector, available dtype
is float32, float64.
y: :math:`(N, D)`, y have the same shape and dtype as x.
out: :math:`(N)`. If :attr:`keepdim` is ``True``, the out shape is :math:`(N, 1)`.
The same dtype as input tensor.
Examples:
.. code-block:: python
import paddle
import numpy as np
paddle.disable_static()
x_np = np.array([[1., 3.], [3., 5.]]).astype(np.float64)
y_np = np.array([[5., 6.], [7., 8.]]).astype(np.float64)
x = paddle.to_variable(x_np)
y = paddle.to_variable(y_np)
dist = paddle.nn.PairwiseDistance()
distance = dist(x, y)
print(distance.numpy()) # [5. 5.]
"""
def __init__(self, p=2., eps=1e-6, keepdim=False, name=None):
super(PairwiseDistance, self).__init__()
self.p = p
self.eps = eps
self.keepdim = keepdim
self.name = name
check_type(self.p, 'porder', (float, int), 'PairwiseDistance')
check_type(self.eps, 'epsilon', (float), 'PairwiseDistance')
check_type(self.keepdim, 'keepdim', (bool), 'PairwiseDistance')
def forward(self, x, y):
if in_dygraph_mode():
sub = core.ops.elementwise_sub(x, y)
return core.ops.p_norm(sub, 'axis', 1, 'porder', self.p, 'keepdim',
self.keepdim, 'epsilon', self.eps)
check_variable_and_dtype(x, 'x', ['float32', 'float64'],
'PairwiseDistance')
check_variable_and_dtype(y, 'y', ['float32', 'float64'],
'PairwiseDistance')
sub = paddle.elementwise_sub(x, y)
helper = LayerHelper("p_norm", name=self.name)
attrs = {
'axis': 1,
'porder': self.p,
'keepdim': self.keepdim,
'epsilon': self.eps,
}
out = helper.create_variable_for_type_inference(
dtype=self._helper.input_dtype(x))
helper.append_op(
type='p_norm', inputs={'X': sub}, outputs={'Out': out}, attrs=attrs)
return out
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