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101 lines
3.3 KiB
101 lines
3.3 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 logging
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from paddle.fluid.op import Operator, DynamicRecurrentOp
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import paddle.fluid.core as core
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import unittest
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import numpy as np
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def create_tensor(scope, name, np_data):
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tensor = scope.var(name).get_tensor()
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tensor.set(np_data, core.CPUPlace())
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return tensor
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class BeamSearchOpTester(unittest.TestCase):
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"""unittest of beam_search_op"""
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def setUp(self):
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self.scope = core.Scope()
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self._create_ids()
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self._create_pre_scores()
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self._create_scores()
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self._create_pre_ids()
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self.scope.var('selected_ids')
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self.scope.var('selected_scores')
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self.scope.var('parent_idx')
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def test_run(self):
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op = Operator(
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'beam_search',
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pre_ids='pre_ids',
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pre_scores='pre_scores',
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ids='ids',
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scores='scores',
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selected_ids='selected_ids',
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selected_scores='selected_scores',
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parent_idx='parent_idx',
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level=0,
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beam_size=2,
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end_id=0, )
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op.run(self.scope, core.CPUPlace())
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selected_ids = self.scope.find_var("selected_ids").get_tensor()
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selected_scores = self.scope.find_var("selected_scores").get_tensor()
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parent_idx = self.scope.find_var("parent_idx").get_tensor()
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self.assertTrue(
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np.allclose(
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np.array(selected_ids), np.array([4, 2, 3, 8])[:, np.newaxis]))
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self.assertTrue(
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np.allclose(
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np.array(selected_scores),
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np.array([0.5, 0.6, 0.9, 0.7])[:, np.newaxis]))
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self.assertEqual(selected_ids.lod(), [[0, 2, 4], [0, 1, 2, 3, 4]])
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self.assertTrue(
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np.allclose(np.array(parent_idx), np.array([0, 1, 2, 3])))
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def _create_pre_ids(self):
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np_data = np.array([[1, 2, 3, 4]], dtype='int64')
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tensor = create_tensor(self.scope, 'pre_ids', np_data)
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def _create_pre_scores(self):
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np_data = np.array([[0.1, 0.2, 0.3, 0.4]], dtype='float32')
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tensor = create_tensor(self.scope, 'pre_scores', np_data)
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def _create_ids(self):
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self.lod = [[0, 2, 4], [0, 1, 2, 3, 4]]
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np_data = np.array(
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[[4, 2, 5], [2, 1, 3], [3, 5, 2], [8, 2, 1]], dtype='int64')
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tensor = create_tensor(self.scope, "ids", np_data)
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tensor.set_lod(self.lod)
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def _create_scores(self):
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np_data = np.array(
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[
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[0.5, 0.3, 0.2],
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[0.6, 0.3, 0.1],
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[0.9, 0.5, 0.1],
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[0.7, 0.5, 0.1],
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],
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dtype='float32')
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tensor = create_tensor(self.scope, "scores", np_data)
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tensor.set_lod(self.lod)
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if __name__ == '__main__':
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unittest.main()
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