add pairewise distance for the paddlepaddle api 2.0revert-24895-update_cub
Zhong Hui
5 years ago
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GitHub
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1d870c442e
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import paddle
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import paddle.fluid as fluid
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import numpy as np
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import unittest
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def pairwise_distance(x, y, p=2.0, eps=1e-6, keepdim=False):
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return np.linalg.norm(x - y, ord=p, axis=1, keepdims=keepdim)
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def test_static(x_np, y_np, p=2.0, eps=1e-6, keepdim=False):
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prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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place = fluid.CUDAPlace(0) if paddle.fluid.core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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with paddle.static.program_guard(prog, startup_prog):
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x = paddle.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
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y = paddle.data(name='y', shape=y_np.shape, dtype=x_np.dtype)
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dist = paddle.nn.layer.distance.PairwiseDistance(
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p=p, eps=eps, keepdim=keepdim)
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distance = dist(x, y)
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exe = paddle.static.Executor(place)
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static_ret = exe.run(prog,
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feed={'x': x_np,
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'y': y_np},
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fetch_list=[distance])
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static_ret = static_ret[0]
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return static_ret
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def test_dygraph(x_np, y_np, p=2.0, eps=1e-6, keepdim=False):
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paddle.disable_static()
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x = paddle.to_variable(x_np)
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y = paddle.to_variable(y_np)
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dist = paddle.nn.layer.distance.PairwiseDistance(
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p=p, eps=eps, keepdim=keepdim)
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distance = dist(x, y)
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dygraph_ret = distance.numpy()
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paddle.enable_static()
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return dygraph_ret
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class TestPairwiseDistance(unittest.TestCase):
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def test_pairwise_distance(self):
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all_shape = [[100, 100], [4, 5, 6, 7]]
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dtypes = ['float32', 'float64']
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keeps = [False, True]
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for shape in all_shape:
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for dtype in dtypes:
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for keepdim in keeps:
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x_np = np.random.random(shape).astype(dtype)
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y_np = np.random.random(shape).astype(dtype)
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static_ret = test_static(x_np, y_np, keepdim=keepdim)
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dygraph_ret = test_dygraph(x_np, y_np, keepdim=keepdim)
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excepted_value = pairwise_distance(
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x_np, y_np, keepdim=keepdim)
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self.assertTrue(np.allclose(static_ret, dygraph_ret))
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self.assertTrue(np.allclose(static_ret, excepted_value))
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self.assertTrue(np.allclose(dygraph_ret, excepted_value))
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def test_pairwise_distance_broadcast(self):
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shape_x = [100, 100]
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shape_y = [100, 1]
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keepdim = False
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x_np = np.random.random(shape_x).astype('float32')
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y_np = np.random.random(shape_y).astype('float32')
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static_ret = test_static(x_np, y_np, keepdim=keepdim)
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dygraph_ret = test_dygraph(x_np, y_np, keepdim=keepdim)
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excepted_value = pairwise_distance(x_np, y_np, keepdim=keepdim)
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self.assertTrue(np.allclose(static_ret, dygraph_ret))
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self.assertTrue(np.allclose(static_ret, excepted_value))
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self.assertTrue(np.allclose(dygraph_ret, excepted_value))
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def test_pairwise_distance_different_p(self):
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shape = [100, 100]
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keepdim = False
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p = 3.0
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x_np = np.random.random(shape).astype('float32')
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y_np = np.random.random(shape).astype('float32')
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static_ret = test_static(x_np, y_np, p=p, keepdim=keepdim)
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dygraph_ret = test_dygraph(x_np, y_np, p=p, keepdim=keepdim)
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excepted_value = pairwise_distance(x_np, y_np, p=p, keepdim=keepdim)
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self.assertTrue(np.allclose(static_ret, dygraph_ret))
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self.assertTrue(np.allclose(static_ret, excepted_value))
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self.assertTrue(np.allclose(dygraph_ret, excepted_value))
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if __name__ == "__main__":
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unittest.main()
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@ -0,0 +1,103 @@
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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__all__ = ['PairwiseDistance']
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import numpy as np
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import paddle
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from ...fluid.dygraph import layers
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from ...fluid.framework import core, in_dygraph_mode
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from ...fluid.data_feeder import check_variable_and_dtype, check_type
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from ...fluid.layer_helper import LayerHelper
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class PairwiseDistance(layers.Layer):
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"""
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This operator computes the pairwise distance between two vectors. The
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distance is calculated by p-oreder norm:
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.. math::
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\Vert x \Vert _p = \left( \sum_{i=1}^n \vert x_i \vert ^ p \right) ^ {1/p}.
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Parameters:
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p (float): The order of norm. The default value is 2.
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eps (float, optional): Add small value to avoid division by zero,
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default value is 1e-6.
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keepdim (bool, optional): Whether to reserve the reduced dimension
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in the output Tensor. The result tensor is one dimension less than
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the result of ``'x-y'`` unless :attr:`keepdim` is True, default
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value is False.
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name (str, optional): Name for the operation (optional, default is None).
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For more information, please refer to :ref:`api_guide_Name`.
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Shape:
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x: :math:`(N, D)` where `D` is the dimension of vector, available dtype
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is float32, float64.
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y: :math:`(N, D)`, y have the same shape and dtype as x.
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out: :math:`(N)`. If :attr:`keepdim` is ``True``, the out shape is :math:`(N, 1)`.
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The same dtype as input tensor.
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Examples:
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.. code-block:: python
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import paddle
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import numpy as np
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paddle.disable_static()
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x_np = np.array([[1., 3.], [3., 5.]]).astype(np.float64)
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y_np = np.array([[5., 6.], [7., 8.]]).astype(np.float64)
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x = paddle.to_variable(x_np)
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y = paddle.to_variable(y_np)
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dist = paddle.nn.PairwiseDistance()
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distance = dist(x, y)
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print(distance.numpy()) # [5. 5.]
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"""
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def __init__(self, p=2., eps=1e-6, keepdim=False, name=None):
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super(PairwiseDistance, self).__init__()
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self.p = p
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self.eps = eps
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self.keepdim = keepdim
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self.name = name
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check_type(self.p, 'porder', (float, int), 'PairwiseDistance')
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check_type(self.eps, 'epsilon', (float), 'PairwiseDistance')
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check_type(self.keepdim, 'keepdim', (bool), 'PairwiseDistance')
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def forward(self, x, y):
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if in_dygraph_mode():
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sub = core.ops.elementwise_sub(x, y)
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return core.ops.p_norm(sub, 'axis', 1, 'porder', self.p, 'keepdim',
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self.keepdim, 'epsilon', self.eps)
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check_variable_and_dtype(x, 'x', ['float32', 'float64'],
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'PairwiseDistance')
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check_variable_and_dtype(y, 'y', ['float32', 'float64'],
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'PairwiseDistance')
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sub = paddle.elementwise_sub(x, y)
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helper = LayerHelper("p_norm", name=self.name)
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attrs = {
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'axis': 1,
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'porder': self.p,
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'keepdim': self.keepdim,
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'epsilon': self.eps,
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}
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out = helper.create_variable_for_type_inference(
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dtype=self._helper.input_dtype(x))
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helper.append_op(
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type='p_norm', inputs={'X': sub}, outputs={'Out': out}, attrs=attrs)
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return out
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