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96 lines
3.3 KiB
96 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 unittest
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import numpy as np
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from op_test import OpTest
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import paddle.fluid as fluid
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class TestMultiplexOp(OpTest):
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def setUp(self):
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self.op_type = "multiplex"
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rows = 4
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index = np.arange(0, rows).astype('int32')
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np.random.shuffle(index)
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index = np.reshape(index, (rows, 1))
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ins1 = np.random.random((rows, 25)).astype("float64")
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ins2 = np.random.random((rows, 25)).astype("float64")
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ins3 = np.random.random((rows, 25)).astype("float64")
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ins4 = np.random.random((rows, 25)).astype("float64")
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self.inputs = {
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'Ids': index,
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'X': [('x1', ins1), ('x2', ins2), ('x3', ins3), ('x4', ins4)]
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}
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# multiplex output
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output = np.zeros_like(ins1)
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for i in range(0, rows):
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k = index[i][0]
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output[i] = self.inputs['X'][k][1][i]
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self.outputs = {'Out': output}
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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(['x1', 'x2', 'x3', 'x4'], 'Out')
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def test_check_grad_ignore_x1(self):
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self.check_grad(['x2', 'x3', 'x4'], 'Out', no_grad_set=set('x1'))
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def test_check_grad_ignore_x1_x2(self):
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self.check_grad(['x3', 'x4'], 'Out', no_grad_set=set(['x1', 'x2']))
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def test_check_grad_ignore_x3(self):
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self.check_grad(['x1', 'x2', 'x4'], 'Out', no_grad_set=set('x3'))
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class TestMultiplexOpError(unittest.TestCase):
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def test_errors(self):
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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x1 = fluid.data(name='x1', shape=[None, 2], dtype='int64')
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x2 = fluid.data(name='x2', shape=[None, 2], dtype='int64')
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index = fluid.data(name='index', shape=[None, 1], dtype='int32')
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def test_list():
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# the inputs type must be list
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fluid.layers.multiplex(inputs=x1, index=index)
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self.assertRaises(TypeError, test_list)
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def test_len():
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fluid.layers.multiplex(inputs=[x1], index=index)
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self.assertRaises(ValueError, test_len)
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def test_type():
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y1 = fluid.data(name='y1', shape=[None, 2], dtype='int16')
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y2 = fluid.data(name='y2', shape=[None, 2], dtype='int16')
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fluid.layers.multiplex(inputs=[y1, y2], index=index)
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self.assertRaises(TypeError, test_type)
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def test_type2():
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index2 = fluid.data(
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name='index2', shape=[None, 1], dtype='int16')
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fluid.layers.multiplex(inputs=[x1, x2], index=index2)
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self.assertRaises(TypeError, test_type2)
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if __name__ == '__main__':
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unittest.main()
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