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52 lines
1.6 KiB
52 lines
1.6 KiB
7 years ago
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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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import unittest
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import numpy as np
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from op_test import OpTest
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def maxout_forward_naive(input, groups):
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s0, s1, s2, s3 = input.shape
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return np.ndarray([s0, s1 / groups, groups, s2, s3], \
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buffer = input, dtype=input.dtype).max(axis=(2))
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class TestMaxOutOp(OpTest):
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def setUp(self):
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self.op_type = "maxout"
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self.init_test_case()
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input = np.random.random(self.shape).astype("float32")
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output = self.MaxOut_forward_naive(input, self.groups).astype("float32")
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self.inputs = {'X': input}
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self.attrs = {'groups': self.groups}
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self.outputs = {'Out': output.astype('float32')}
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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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def init_test_case(self):
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self.MaxOut_forward_naive = maxout_forward_naive
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self.shape = [100, 6, 2, 2]
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self.groups = 2
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if __name__ == '__main__':
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unittest.main()
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