62 lines
1.7 KiB
62 lines
1.7 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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def conv_shift_forward(x, y):
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out = np.zeros_like(x)
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M = x.shape[1]
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N = y.shape[1]
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y_half_width = (N - 1) // 2
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for i in range(M):
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for j in range(N):
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out[:, i] += x[:, (i + j + M - y_half_width) % M] * y[:, j]
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return out
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class TestConvShiftOp(OpTest):
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def setUp(self):
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self.op_type = "conv_shift"
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batch_size = 10
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x_dim = 17
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y_dim = 11 # must be odd and <= x_dim
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x = np.random.random((batch_size, x_dim)).astype("float32")
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y = np.random.random((batch_size, y_dim)).astype("float32")
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self.inputs = {'X': x, 'Y': y}
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out = conv_shift_forward(x, y)
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self.outputs = {'Out': out}
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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')
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def test_check_grad_ignore_x(self):
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self.check_grad(['Y'], 'Out')
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def test_check_grad_ignore_y(self):
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self.check_grad(['X'], 'Out')
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if __name__ == '__main__':
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unittest.main()
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