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61 lines
1.9 KiB
61 lines
1.9 KiB
6 years ago
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# Copyright (c) 2019 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 fsp_matrix(a, b):
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batch = a.shape[0]
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a_channel = a.shape[1]
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b_channel = b.shape[1]
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h = a.shape[2]
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w = a.shape[3]
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a_t = a.transpose([0, 2, 3, 1])
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a_t = a_t.reshape([batch, h * w, a_channel])
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b_t = b.transpose([0, 2, 3, 1]).reshape([batch, h * w, b_channel])
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a_r = a_t.repeat(
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b_channel, axis=1).reshape(
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[batch, h * w, b_channel, a_channel]).transpose([0, 1, 3, 2])
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b_r = b_t.repeat(
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a_channel, axis=1).reshape([batch, h * w, a_channel, b_channel])
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return np.mean(a_r * b_r, axis=1)
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class TestFSPOp(OpTest):
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def setUp(self):
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self.op_type = "fsp"
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self.initTestCase()
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feature_map_0 = np.random.uniform(0, 10, self.a_shape).astype('float32')
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feature_map_1 = np.random.uniform(0, 10, self.b_shape).astype('float32')
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self.inputs = {'X': feature_map_0, 'Y': feature_map_1}
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self.outputs = {'Out': fsp_matrix(feature_map_0, feature_map_1)}
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def initTestCase(self):
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self.a_shape = (2, 16, 32, 31)
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self.b_shape = (2, 28, 32, 31)
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def test_check_output(self):
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self.check_output()
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def test_check_grad_normal(self):
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self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.05)
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if __name__ == '__main__':
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unittest.main()
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