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103 lines
3.6 KiB
103 lines
3.6 KiB
# 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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from __future__ import print_function
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import math
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import numpy as np
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import unittest
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from op_test import OpTest
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class TestUnfoldOp(OpTest):
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"""
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This is for test on unfold Op
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"""
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def init_data(self):
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self.batch_size = 3
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self.input_channels = 3
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self.input_height = 20
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self.input_width = 20
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self.kernel_sizes = [3, 3]
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self.strides = [1, 1]
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self.paddings = [1, 1, 1, 1]
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self.dilations = [1, 1]
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input_shape = [
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self.batch_size, self.input_channels, self.input_height,
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self.input_width
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]
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self.x = np.random.rand(*input_shape).astype(np.float32)
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def calc_unfold(self):
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output_shape = [0] * 3
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output_shape[0] = self.batch_size
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output_shape[1] = self.input_channels * self.kernel_sizes[
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0] * self.kernel_sizes[1]
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dkernel_h = self.dilations[0] * (self.kernel_sizes[0] - 1) + 1
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dkernel_w = self.dilations[1] * (self.kernel_sizes[1] - 1) + 1
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out_height = int((self.input_height + self.paddings[0] +
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self.paddings[2] - dkernel_h) / self.strides[0]) + 1
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out_width = int((self.input_width + self.paddings[1] + self.paddings[3]
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- dkernel_w) / self.strides[1]) + 1
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output_shape[2] = out_height * out_width
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output = np.zeros(output_shape).astype(np.float32)
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############ calculate output ##############
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for i in range(output_shape[0]):
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for j in range(output_shape[1]):
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for k in range(output_shape[2]):
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h_out = int(k / out_width)
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w_out = k % out_width
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w_offset = j % self.kernel_sizes[1]
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h_offset = int(j /
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self.kernel_sizes[1]) % self.kernel_sizes[0]
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c_in = int(j /
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(self.kernel_sizes[0] * self.kernel_sizes[1]))
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h_in = h_offset * self.dilations[0] + h_out * self.strides[
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0] - self.paddings[0]
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w_in = w_offset * self.dilations[1] + w_out * self.strides[
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1] - self.paddings[1]
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if (h_in>=0 and h_in<self.input_height) and \
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(w_in>=0 and w_in<self.input_width):
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output[i, j, k] = self.x[i, c_in, h_in, w_in]
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self.outputs = output
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def set_data(self):
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self.init_data()
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self.calc_unfold()
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self.inputs = {'X': self.x}
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self.attrs = {
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'kernel_sizes': self.kernel_sizes,
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'paddings': self.paddings,
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'dilations': self.dilations,
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'strides': self.strides
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}
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self.outputs = {'Y': self.outputs}
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def setUp(self):
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self.op_type = 'unfold'
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self.set_data()
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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'], 'Y')
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if __name__ == '__main__':
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unittest.main()
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