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94 lines
3.5 KiB
94 lines
3.5 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 paddle.fluid as fluid
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from op_test import OpTest
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class TestReorgOp(OpTest):
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@staticmethod
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def helper(in_, width, height, channel, batch, stride, forward, out_):
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channel_out = channel // (stride * stride)
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for b in range(batch):
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for k in range(channel):
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for j in range(height):
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for i in range(width):
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in_index = i + width * (j + height * (k + channel * b))
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channel2 = k % channel_out
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offset = k // channel_out
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width2 = i * stride + offset % stride
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height2 = j * stride + offset // stride
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out_index = width2 + width * stride * (
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height2 + height * stride *
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(channel2 + channel_out * b))
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if forward:
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out_[out_index] = in_[in_index]
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else:
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out_[in_index] = in_[out_index]
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def setUp(self):
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self.init_data()
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self.op_type = "reorg"
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self.inputs = {"X": self.x}
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self.helper(self.x_1d, self.x.shape[3], self.x.shape[2],
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self.x.shape[1], self.x.shape[0], self.stride, self.forward,
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self.out_1d)
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self.out = np.reshape(self.out_1d, self.infered_shape)
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self.attrs = {"stride": long(self.stride)}
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self.outputs = {"Out": self.out}
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def init_data(self):
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self.ori_shape = (32, 12, 6, 6)
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self.infered_shape = (32, 48, 3, 3)
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self.one_d_len = 32 * 48 * 3 * 3
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self.stride = 2
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self.x = np.random.random(self.ori_shape).astype('float32')
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self.x_1d = np.reshape(self.x, self.one_d_len)
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self.out = np.zeros(self.infered_shape).astype('float32')
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self.out_1d = np.reshape(self.out, self.one_d_len)
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self.forward = 1
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def test_check_output(self):
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place = fluid.core.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
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) else fluid.core.CPUPlace()
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self.check_output_with_place(place, 1e-5, None, False)
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def test_check_grad(self):
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place = fluid.core.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
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) else fluid.core.CPUPlace()
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self.check_grad_with_place(place, ['X'], 'Out')
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class TestReorgOp2(TestReorgOp):
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def init_data(self):
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self.ori_shape = (32, 9, 6, 6)
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self.infered_shape = (32, 81, 2, 2)
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self.one_d_len = 32 * 81 * 2 * 2
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self.stride = 3
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self.x = np.random.random(self.ori_shape).astype('float32')
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self.x_1d = np.reshape(self.x, self.one_d_len)
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self.out = np.zeros(self.infered_shape).astype('float32')
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self.out_1d = np.reshape(self.out, self.one_d_len)
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self.forward = 1
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if __name__ == '__main__':
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unittest.main()
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