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87 lines
2.8 KiB
87 lines
2.8 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 TestSequenceExpandAs(OpTest):
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def setUp(self):
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self.op_type = 'sequence_expand_as'
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self.set_data()
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self.compute()
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def set_data(self):
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x_data = np.random.uniform(0.1, 1, [3, 1]).astype('float32')
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y_data = np.random.uniform(0.1, 1, [8, 1]).astype('float32')
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y_lod = [[1, 3, 4]]
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self.inputs = {'X': x_data, 'Y': (y_data, y_lod)}
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def compute(self):
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x = self.inputs['X']
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x_data, x_lod = x if type(x) == tuple else (x, None)
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y_data, y_lod = self.inputs['Y']
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assert len(y_lod) == 1 and len(y_lod[0]) == x_data.shape[0]
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repeats = []
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for i in range(len(y_lod[0])):
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repeat_num = y_lod[0][i]
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if repeat_num == 0:
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continue
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repeats.extend([i for _ in range(repeat_num)])
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out_data = x_data[repeats]
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self.outputs = {'Out': (out_data, y_lod)}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(["X"], "Out")
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class TestSequenceExpandAsCase1(TestSequenceExpandAs):
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def set_data(self):
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x_data = np.random.uniform(0.1, 1, [5, 1]).astype('float32')
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x_lod = [[2, 3]]
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y_data = np.random.uniform(0.1, 1, [10, 1]).astype('float32')
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y_lod = [[2, 2, 0, 3, 3]]
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self.inputs = {'X': (x_data, x_lod), 'Y': (y_data, y_lod)}
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class TestSequenceExpandAsCase2(TestSequenceExpandAs):
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def set_data(self):
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x_data = np.random.uniform(0.1, 1, [5, 1]).astype('float32')
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x_lod = [[2, 3]]
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y_data = np.random.uniform(0.1, 1, [10, 1]).astype('float32')
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y_lod = [[0, 4, 0, 6, 0]]
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self.inputs = {'X': (x_data, x_lod), 'Y': (y_data, y_lod)}
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class TestSequenceExpandAsCase3(TestSequenceExpandAs):
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def set_data(self):
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x_data = np.random.uniform(0.1, 1, [1, 2, 2]).astype('float32')
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x_lod = [[1]]
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y_data = np.random.uniform(0.1, 1, [2, 2, 2]).astype('float32')
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y_lod = [[2]]
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self.inputs = {'X': (x_data, x_lod), 'Y': (y_data, y_lod)}
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if __name__ == '__main__':
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unittest.main()
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