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120 lines
4.1 KiB
120 lines
4.1 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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from op_test import OpTest
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class TestSequenceExpand(OpTest):
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def set_data(self):
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x = [i / 10.0 for i in range(3)]
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y = [i / 10.0 for i in range(8)]
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x_data = np.array(x).reshape(3, 1).astype('float32')
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y_data = np.array(y).reshape(8, 1).astype('float32')
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print(x_data)
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print(y_data)
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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 = [[0, 1, 4, 8]]
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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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if hasattr(self, 'attrs'):
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ref_level = self.attrs['ref_level']
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else:
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ref_level = len(y_lod) - 1
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out = np.zeros(shape=((0, ) + x_data.shape[1:]), dtype=x_data.dtype)
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if x_lod is None:
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x_idx = [i for i in xrange(x_data.shape[0] + 1)]
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else:
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x_idx = x_lod[0]
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out_lod = [[0]]
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for i in xrange(1, len(y_lod[ref_level])):
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repeat_num = y_lod[ref_level][i] - y_lod[ref_level][i - 1]
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x_len = x_idx[i] - x_idx[i - 1]
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if repeat_num > 0:
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x_sub = x_data[x_idx[i - 1]:x_idx[i], :]
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x_sub = np.repeat(x_sub, repeat_num, axis=0)
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out = np.vstack((out, x_sub))
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if x_lod is not None:
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for j in xrange(repeat_num):
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out_lod[0].append(out_lod[0][-1] + x_len)
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if x_lod is None:
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self.outputs = {'Out': out}
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else:
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self.outputs = {'Out': (out, out_lod)}
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def setUp(self):
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self.op_type = 'sequence_expand'
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self.set_data()
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self.compute()
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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 TestSequenceExpandCase1(TestSequenceExpand):
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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 = [[0, 2, 5]]
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y_data = np.random.uniform(0.1, 1, [13, 1]).astype('float32')
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y_lod = [[0, 2, 5], [0, 2, 4, 7, 10, 13]]
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self.inputs = {'X': x_data, 'Y': (y_data, y_lod)}
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self.attrs = {'ref_level': 0}
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class TestSequenceExpandCase2(TestSequenceExpand):
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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 = [[0, 1]]
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y_data = np.random.uniform(0.1, 1, [2, 2, 2]).astype('float32')
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y_lod = [[0, 2], [0, 2]]
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self.inputs = {'X': (x_data, x_lod), 'Y': (y_data, y_lod)}
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self.attrs = {'ref_level': 0}
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class TestSequenceExpandCase3(TestSequenceExpand):
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def set_data(self):
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x_data = np.random.uniform(0.1, 1, [4, 1]).astype('float32')
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x_lod = [[0, 1, 2, 3, 4]]
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y_data = np.random.uniform(0.1, 1, [6, 1]).astype('float32')
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y_lod = [[0, 2, 4, 4, 6]]
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self.inputs = {'X': (x_data, x_lod), 'Y': (y_data, y_lod)}
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class TestSequenceExpandCase4(TestSequenceExpand):
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def set_data(self):
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data = [0.1, 0.3, 0.2, 0.15, 0.25, 0.2, 0.15, 0.25, 0.1, 0.3]
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x_data = np.array(data).reshape([5, 2]).astype('float32')
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x_lod = [[0, 2, 5]]
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y_data = np.random.uniform(0.1, 1, [2, 1]).astype('float32')
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y_lod = [[0, 1, 2], [0, 1, 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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