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- # 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.
-
- import unittest
-
- import numpy as np
-
- import paddle
- import paddle.nn.functional as F
- from paddle import nn, static
- from paddle.base import Executor, core
- from paddle.pir_utils import test_with_pir_api
-
-
- class TestCosineSimilarityAPI(unittest.TestCase):
- def setUp(self):
- self.places = [paddle.CPUPlace()]
- if core.is_compiled_with_cuda():
- self.places.append(paddle.CUDAPlace(0))
-
- def _get_numpy_out(self, x1, x2, axis=1, eps=1e-8):
- w12 = np.sum(x1 * x2, axis=axis)
- w1 = np.sum(x1 * x1, axis=axis)
- w2 = np.sum(x2 * x2, axis=axis)
- n12 = np.sqrt(np.clip(w1 * w2, eps * eps, None))
- cos_sim = w12 / n12
- return cos_sim
-
- @test_with_pir_api
- def check_static_result(self, place):
- paddle.enable_static()
-
- main_program = static.Program()
- startup_program = static.Program()
- with static.program_guard(
- main_program=main_program, startup_program=startup_program
- ):
- shape = [10, 15]
- axis = 1
- eps = 1e-8
- np.random.seed(0)
- np_x1 = np.random.rand(*shape).astype(np.float32)
- np_x2 = np.random.rand(*shape).astype(np.float32)
-
- x1 = paddle.static.data(name="x1", shape=shape)
- x2 = paddle.static.data(name="x2", shape=shape)
- result = F.cosine_similarity(x1, x2, axis=axis, eps=eps)
- exe = Executor(place)
- fetches = exe.run(
- feed={"x1": np_x1, "x2": np_x2},
- fetch_list=[result],
- )
-
- np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
- np.testing.assert_allclose(fetches[0], np_out, rtol=1e-05)
-
- def test_static(self):
- for place in self.places:
- self.check_static_result(place=place)
-
- def test_dygraph_1(self):
- paddle.disable_static()
-
- shape = [10, 15]
- axis = 1
- eps = 1e-8
- np.random.seed(1)
- np_x1 = np.random.rand(*shape).astype(np.float32)
- np_x2 = np.random.rand(*shape).astype(np.float32)
- np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
-
- tensor_x1 = paddle.to_tensor(np_x1)
- tensor_x2 = paddle.to_tensor(np_x2)
- y = F.cosine_similarity(tensor_x1, tensor_x2, axis=axis, eps=eps)
-
- np.testing.assert_allclose(y.numpy(), np_out, rtol=1e-05)
-
- def test_dygraph_2(self):
- paddle.disable_static()
-
- shape = [12, 13]
- axis = 0
- eps = 1e-6
- np.random.seed(1)
- np_x1 = np.random.rand(*shape).astype(np.float32)
- np_x2 = np.random.rand(*shape).astype(np.float32)
- np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
-
- tensor_x1 = paddle.to_tensor(np_x1)
- tensor_x2 = paddle.to_tensor(np_x2)
- y = F.cosine_similarity(tensor_x1, tensor_x2, axis=axis, eps=eps)
-
- np.testing.assert_allclose(y.numpy(), np_out, rtol=1e-05)
-
- def test_dygraph_3(self):
- paddle.disable_static()
-
- shape1 = [10, 12, 10]
- shape2 = [10, 1, 10]
- axis = 2
- eps = 1e-6
- np.random.seed(1)
- np_x1 = np.random.rand(*shape1).astype(np.float32)
- np_x2 = np.random.rand(*shape2).astype(np.float32)
- np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
-
- tensor_x1 = paddle.to_tensor(np_x1)
- tensor_x2 = paddle.to_tensor(np_x2)
- y = F.cosine_similarity(tensor_x1, tensor_x2, axis=axis, eps=eps)
-
- np.testing.assert_allclose(y.numpy(), np_out, rtol=1e-05)
-
- def test_dygraph_4(self):
- paddle.disable_static()
-
- shape1 = [23, 12, 1]
- shape2 = [23, 1, 10]
- axis = 2
- eps = 1e-6
- np.random.seed(1)
- np_x1 = np.random.rand(*shape1).astype(np.float32)
- np_x2 = np.random.rand(*shape2).astype(np.float32)
- np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
-
- cos_sim_func = nn.CosineSimilarity(axis=axis, eps=eps)
- tensor_x1 = paddle.to_tensor(np_x1)
- tensor_x2 = paddle.to_tensor(np_x2)
- y = cos_sim_func(tensor_x1, tensor_x2)
-
- np.testing.assert_allclose(y.numpy(), np_out, rtol=1e-05)
-
-
- if __name__ == '__main__':
- unittest.main()
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