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141 lines
4.6 KiB
141 lines
4.6 KiB
# 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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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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import paddle.fluid.core as core
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from paddle.fluid import Program, program_guard, Executor, default_main_program
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class TestCosineSimilarityAPI(unittest.TestCase):
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def setUp(self):
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self.places = [paddle.CPUPlace()]
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if core.is_compiled_with_cuda():
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self.places.append(paddle.CUDAPlace(0))
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def _get_numpy_out(self, x1, x2, axis=1, eps=1e-8):
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w12 = np.sum(x1 * x2, axis=axis)
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w1 = np.sum(x1 * x1, axis=axis)
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w2 = np.sum(x2 * x2, axis=axis)
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n12 = np.sqrt(np.clip(w1 * w2, eps * eps, None))
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cos_sim = w12 / n12
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return cos_sim
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def check_static_result(self, place):
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paddle.enable_static()
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with program_guard(Program(), Program()):
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shape = [10, 15]
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axis = 1
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eps = 1e-8
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np.random.seed(0)
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np_x1 = np.random.rand(*shape).astype(np.float32)
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np_x2 = np.random.rand(*shape).astype(np.float32)
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x1 = paddle.data(name="x1", shape=shape)
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x2 = paddle.data(name="x2", shape=shape)
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result = F.cosine_similarity(x1, x2, axis=axis, eps=eps)
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exe = Executor(place)
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fetches = exe.run(default_main_program(),
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feed={"x1": np_x1,
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"x2": np_x2},
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fetch_list=[result])
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np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
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self.assertTrue(np.allclose(fetches[0], np_out))
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def test_static(self):
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for place in self.places:
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self.check_static_result(place=place)
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def test_dygraph_1(self):
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paddle.disable_static()
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shape = [10, 15]
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axis = 1
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eps = 1e-8
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np.random.seed(1)
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np_x1 = np.random.rand(*shape).astype(np.float32)
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np_x2 = np.random.rand(*shape).astype(np.float32)
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np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
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tesnor_x1 = paddle.to_tensor(np_x1)
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tesnor_x2 = paddle.to_tensor(np_x2)
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y = F.cosine_similarity(tesnor_x1, tesnor_x2, axis=axis, eps=eps)
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self.assertTrue(np.allclose(y.numpy(), np_out))
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def test_dygraph_2(self):
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paddle.disable_static()
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shape = [12, 13]
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axis = 0
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eps = 1e-6
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np.random.seed(1)
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np_x1 = np.random.rand(*shape).astype(np.float32)
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np_x2 = np.random.rand(*shape).astype(np.float32)
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np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
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tesnor_x1 = paddle.to_tensor(np_x1)
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tesnor_x2 = paddle.to_tensor(np_x2)
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y = F.cosine_similarity(tesnor_x1, tesnor_x2, axis=axis, eps=eps)
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self.assertTrue(np.allclose(y.numpy(), np_out))
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def test_dygraph_3(self):
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paddle.disable_static()
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shape1 = [10, 12, 10]
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shape2 = [10, 1, 10]
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axis = 2
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eps = 1e-6
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np.random.seed(1)
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np_x1 = np.random.rand(*shape1).astype(np.float32)
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np_x2 = np.random.rand(*shape2).astype(np.float32)
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np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
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tesnor_x1 = paddle.to_tensor(np_x1)
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tesnor_x2 = paddle.to_tensor(np_x2)
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y = F.cosine_similarity(tesnor_x1, tesnor_x2, axis=axis, eps=eps)
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self.assertTrue(np.allclose(y.numpy(), np_out))
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def test_dygraph_4(self):
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paddle.disable_static()
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shape1 = [23, 12, 1]
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shape2 = [23, 1, 10]
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axis = 2
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eps = 1e-6
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np.random.seed(1)
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np_x1 = np.random.rand(*shape1).astype(np.float32)
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np_x2 = np.random.rand(*shape2).astype(np.float32)
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np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
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cos_sim_func = nn.CosineSimilarity(axis=axis, eps=eps)
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tesnor_x1 = paddle.to_tensor(np_x1)
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tesnor_x2 = paddle.to_tensor(np_x2)
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y = cos_sim_func(tesnor_x1, tesnor_x2)
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self.assertTrue(np.allclose(y.numpy(), np_out))
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if __name__ == '__main__':
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unittest.main()
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