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86 lines
2.6 KiB
86 lines
2.6 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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import unittest
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import numpy as np
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import math
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from op_test import OpTest
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class TestSequenceReshape(OpTest):
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def setUp(self):
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self.op_type = 'sequence_reshape'
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dimension = 12
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x_lod = [[0, 4, 5, 8, 11]]
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x = np.random.uniform(0.1, 1, [11, 24]).astype('float32')
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self.inputs = {'X': (x, x_lod)}
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self.attrs = {'new_dim': dimension}
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out, out_lod = self.compute_output(x, x_lod, dimension)
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self.outputs = {'Out': (out, out_lod)}
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def compute_output(self, x, x_lod, dimension):
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x_width = x.shape[1]
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out_lod = [[0]]
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for i in xrange(len(x_lod[0]) - 1):
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seq_len = x_lod[0][i + 1] - x_lod[0][i]
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offset = (seq_len * x_width) / dimension
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assert int(offset) * dimension == seq_len * x_width
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out_lod[0].append(out_lod[0][-1] + int(offset))
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out = np.zeros(shape=(out_lod[0][-1], dimension)).astype('float32')
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out.ravel()[:] = x.ravel()[:]
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return out, out_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 TestSequenceReshape_reduce(TestSequenceReshape):
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def setUp(self):
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self.op_type = 'sequence_reshape'
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dimension = 24
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x_lod = [[0, 4, 6, 8, 12]]
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x = np.random.uniform(0.1, 1, [12, 12]).astype('float32')
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self.inputs = {'X': (x, x_lod)}
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self.attrs = {'new_dim': dimension}
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out, out_lod = self.compute_output(x, x_lod, dimension)
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self.outputs = {'Out': (out, out_lod)}
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class TestSequenceReshape_same(TestSequenceReshape):
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def setUp(self):
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self.op_type = 'sequence_reshape'
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dimension = 12
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x_lod = [[0, 4, 6, 8, 12]]
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x = np.random.uniform(0.1, 1, [12, 12]).astype('float32')
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self.inputs = {'X': (x, x_lod)}
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self.attrs = {'new_dim': dimension}
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out, out_lod = self.compute_output(x, x_lod, dimension)
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self.outputs = {'Out': (out, out_lod)}
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if __name__ == '__main__':
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unittest.main()
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