chengduoZH
7 years ago
parent
284519561d
commit
b57a2b989b
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# 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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import sys
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from op_test import OpTest
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def to_abs_offset_lod(lod):
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offset_lod = [[0] for i in lod]
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for i, level in enumerate(lod):
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for seq_len in level:
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offset_lod[i].append(offset_lod[i][-1] + seq_len)
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if len(offset_lod) == 0 or len(offset_lod) == 1:
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return offset_lod
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import copy
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new_offset_lod = copy.deepcopy(offset_lod)
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for idx, val in enumerate(offset_lod[0]):
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new_offset_lod[0][idx] = offset_lod[1][val]
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return new_offset_lod
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def seq_concat(inputs, level):
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lod0 = inputs['X'][0][1][1]
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lod1 = inputs['X'][1][1][1]
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x0 = inputs['X'][0][1][0]
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x1 = inputs['X'][1][1][0]
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level_idx = len(lod0) - level - 1
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outs = []
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for i in range(len(lod0[level_idx])):
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sub_x0 = x0[to_abs_offset_lod(lod0)[level_idx][i]:to_abs_offset_lod(
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lod0)[level_idx][i + 1], :]
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sub_x1 = x1[to_abs_offset_lod(lod1)[level_idx][i]:to_abs_offset_lod(
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lod1)[level_idx][i + 1], :]
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outs.append(np.concatenate((sub_x0, sub_x1), axis=0))
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return np.concatenate(outs, axis=0)
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class TestSeqConcatOp(OpTest):
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def set_data(self):
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# two level, batch size is 3
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x0 = np.random.random((4, 6, 3)).astype('float32')
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lod0 = [[2, 2], [1, 1, 1, 1]]
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x1 = np.random.random((4, 8, 3)).astype('float32')
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lod1 = [[2, 2], [1, 1, 1, 1]]
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axis = 1
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level = 1
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self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
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self.attrs = {'axis': axis, 'level': level}
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self.outputs = {'Out': (np.concatenate([x0, x1], axis=1), lod0)}
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def setUp(self):
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self.op_type = "sequence_concat"
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self.set_data()
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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(['x0'], 'Out')
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class TestSeqConcatOpLevelZeroNestedSequence(TestSeqConcatOp):
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def set_data(self):
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# two level, batch size is 3
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x0 = np.random.random((4, 6, 3)).astype('float32')
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lod0 = [[2, 2], [1, 1, 1, 1]]
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x1 = np.random.random((7, 6, 3)).astype('float32')
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lod1 = [[2, 2], [1, 2, 2, 2]]
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axis = 0
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level = 0
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self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
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self.attrs = {'axis': axis, 'level': level}
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out_lod = [[2, 2], [2, 3, 3, 3]]
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self.outputs = {'Out': (seq_concat(self.inputs, level), out_lod)}
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class TestSeqConcatOplevelOneNestedSequence(TestSeqConcatOp):
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def set_data(self):
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# two level, batch size is 3
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x0 = np.random.random((4, 6, 3)).astype('float32')
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lod0 = [[2, 2], [1, 1, 1, 1]]
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x1 = np.random.random((7, 6, 3)).astype('float32')
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lod1 = [[3, 1], [1, 2, 2, 2]]
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axis = 0
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level = 1
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self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
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self.attrs = {'axis': axis, 'level': level}
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out_lod = [[5, 3], [1, 1, 1, 2, 2, 1, 1, 2]]
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self.outputs = {'Out': (seq_concat(self.inputs, level), out_lod)}
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class TestSeqConcatOpLevelZeroSequence(TestSeqConcatOp):
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def set_data(self):
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# two level, batch size is 3
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x0 = np.random.random((4, 3, 4)).astype('float32')
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lod0 = [[1, 1, 1, 1]]
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x1 = np.random.random((7, 3, 4)).astype('float32')
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lod1 = [[1, 2, 2, 2]]
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axis = 0
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level = 0
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self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
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self.attrs = {'axis': axis, 'level': level}
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out_lod = [[2, 3, 3, 3]]
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self.outputs = {'Out': (seq_concat(self.inputs, level), out_lod)}
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if __name__ == '__main__':
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unittest.main()
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