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110 lines
3.9 KiB
110 lines
3.9 KiB
# Copyright (c) 2018 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 unittest
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import numpy as np
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from op_test import OpTest
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class TestCosSimOp(OpTest):
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def setUp(self):
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self.op_type = "cos_sim"
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self.inputs = {
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'X': np.random.random((6, 5)).astype("float32"),
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'Y': np.random.random((6, 5)).astype("float32")
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}
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expect_x_norm = np.linalg.norm(self.inputs['X'], axis=1)
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expect_y_norm = np.linalg.norm(self.inputs['Y'], axis=1)
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expect_out = (self.inputs['X'] * self.inputs['Y']).sum(axis=1) / \
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expect_x_norm / expect_y_norm
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self.outputs = {
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'XNorm': np.expand_dims(expect_x_norm, 1),
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'YNorm': np.expand_dims(expect_y_norm, 1),
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'Out': np.expand_dims(expect_out, 1)
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}
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def test_check_output(self):
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self.check_output()
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def test_check_grad_normal(self):
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self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.06)
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def test_check_grad_ingore_x(self):
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self.check_grad(
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['Y'], 'Out', max_relative_error=0.06, no_grad_set=set("X"))
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def test_check_grad_ingore_y(self):
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self.check_grad(
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['X'], 'Out', max_relative_error=0.06, no_grad_set=set('Y'))
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class TestCosSimOp2(TestCosSimOp):
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def setUp(self):
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self.op_type = "cos_sim"
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self.inputs = {
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'X': np.random.random((6, 5)).astype("float32"),
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'Y': np.random.random((1, 5)).astype("float32")
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}
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expect_x_norm = np.linalg.norm(self.inputs['X'], axis=1)
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expect_y_norm = np.linalg.norm(self.inputs['Y'], axis=1)
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expect_out = (self.inputs['X'] * self.inputs['Y']).sum(axis=1) / \
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expect_x_norm / expect_y_norm
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self.outputs = {
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'XNorm': np.expand_dims(expect_x_norm, 1),
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'YNorm': np.expand_dims(expect_y_norm, 1),
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'Out': np.expand_dims(expect_out, 1)
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}
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class TestCosSimOp3(TestCosSimOp):
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def setUp(self):
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self.op_type = "cos_sim"
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self.inputs = {
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'X': np.random.random((6, 5, 2)).astype("float32"),
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'Y': np.random.random((6, 5, 2)).astype("float32")
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}
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expect_x_norm = np.linalg.norm(self.inputs['X'], axis=(1, 2))
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expect_y_norm = np.linalg.norm(self.inputs['Y'], axis=(1, 2))
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expect_out = (self.inputs['X'] * self.inputs['Y']).sum(axis=(1, 2)) / \
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expect_x_norm / expect_y_norm
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self.outputs = {
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'XNorm': np.expand_dims(expect_x_norm, 1),
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'YNorm': np.expand_dims(expect_y_norm, 1),
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'Out': np.expand_dims(expect_out, 1)
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}
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class TestCosSimOp4(TestCosSimOp):
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def setUp(self):
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self.op_type = "cos_sim"
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self.inputs = {
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'X': np.random.random((6, 5, 2)).astype("float32"),
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'Y': np.random.random((1, 5, 2)).astype("float32")
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}
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expect_x_norm = np.linalg.norm(self.inputs['X'], axis=(1, 2))
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expect_y_norm = np.linalg.norm(self.inputs['Y'], axis=(1, 2))
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expect_out = (self.inputs['X'] * self.inputs['Y']).sum(axis=(1, 2)) / \
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expect_x_norm / expect_y_norm
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self.outputs = {
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'XNorm': np.expand_dims(expect_x_norm, 1),
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'YNorm': np.expand_dims(expect_y_norm, 1),
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'Out': np.expand_dims(expect_out, 1)
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}
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if __name__ == '__main__':
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unittest.main()
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