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119 lines
3.6 KiB
119 lines
3.6 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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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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sys.path.append("../")
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from op_test import OpTest
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from paddle import fluid
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class TestSequenceConcat(OpTest):
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def setLoD(self):
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self.lod1 = [7, 3]
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self.lod2 = [12, 8]
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self.out_lod = [19, 11]
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def setUp(self):
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x1 = np.random.random(size=(10, 80)).astype('float64')
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x2 = np.random.random(size=(20, 80)).astype('float64')
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self.setLoD()
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out = np.concatenate((x1[0:self.lod1[0]], x2[0:self.lod2[0]],
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x1[self.lod1[0]:], x2[self.lod2[0]:]))
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self.op_type = "sequence_concat"
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self.inputs = {
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'X': [("x1", (x1, [self.lod1])), ("x2", (x2, [self.lod2]))]
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}
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self.outputs = {"Out": (out, [self.out_lod])}
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def test_output(self):
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self.check_output()
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def test_dx(self):
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self.check_grad(inputs_to_check=['x1', 'x2'], output_names="Out")
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class TestSequenceConcatCase2(TestSequenceConcat):
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def setLoD(self):
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self.lod1 = [10, 0]
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self.lod2 = [12, 8]
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self.out_lod = [22, 8]
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class TestSequenceConcatCase3(TestSequenceConcat):
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def setLoD(self):
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self.lod1 = [10, 0]
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self.lod2 = [20, 0]
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self.out_lod = [30, 0]
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class TestSequenceConcatCase4(TestSequenceConcat):
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def setLoD(self):
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self.lod1 = [0, 10]
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self.lod2 = [0, 20]
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self.out_lod = [0, 30]
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class TestSequenceConcatCase5(TestSequenceConcat):
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def setLoD(self):
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self.lod1 = [0, 10]
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self.lod2 = [20, 0]
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self.out_lod = [20, 10]
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class TestSequenceConcatOpError(unittest.TestCase):
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def test_errors(self):
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def test_input_list():
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# the input type must be list
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x_data = fluid.layers.data(name='x', shape=[4], dtype='float32')
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fluid.layers.sequence_concat(input=x_data)
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self.assertRaises(TypeError, test_input_list)
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def test_variable1():
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# the input element type must be Variable
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x1_data = np.array([[3, 5]]).astype('float32')
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y1_data = fluid.layers.data(name='y1', shape=[4], dtype='float32')
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fluid.layers.sequence_concat(input=[x1_data, y1_data])
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def test_variable2():
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x2_data = np.array([[3, 5]]).astype('float32')
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y2_data = fluid.layers.data(name='y2', shape=[4], dtype='float32')
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fluid.layers.sequence_concat(input=[y2_data, x2_data])
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for i in range(2):
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if i == 0:
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self.assertRaises(TypeError, test_variable1)
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else:
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self.assertRaises(TypeError, test_variable2)
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def test_dtype():
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# dtype must be 'float32', 'float64', 'int64'
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x3_data = fluid.layers.data(name="x3", shape=[3, 5], dtype='int32')
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y3_data = fluid.layers.data(name="y3", shape=[3, 5], dtype='int16')
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input_list = [x3_data, y3_data]
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fluid.layers.sequence_concat(input=input_list)
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self.assertRaises(TypeError, test_dtype)
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if __name__ == '__main__':
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unittest.main()
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