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98 lines
3.8 KiB
98 lines
3.8 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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import unittest
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import numpy as np
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from op_test import OpTest
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def unpool2dmax_forward_naive(input, indices, ksize, strides, paddings):
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s0, s1, s2, s3 = input.shape
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out_hsize = (s2 - 1) * strides[0] - 2 * paddings[0] + ksize[0]
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out_wsize = (s2 - 1) * strides[1] - 2 * paddings[1] + ksize[1]
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out = np.zeros((s0, s1, out_hsize, out_wsize))
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for nidx in xrange(s0):
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for cidx in xrange(s1):
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for h in xrange(s2):
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for w in xrange(s3):
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index = indices[nidx, cidx, h, w]
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hidx = (index - index % out_wsize) / out_wsize
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widx = index % out_wsize
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out[nidx, cidx, int(hidx), int(widx)] = \
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input[nidx, cidx, h, w]
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return out
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class TestUnpoolOp(OpTest):
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def setUp(self):
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self.op_type = "unpool"
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self.init_test_case()
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pre_input = np.random.random(self.shape).astype("float32")
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nsize, csize, hsize, wsize = pre_input.shape
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hsize_out = (hsize - self.ksize[0] + 2 * self.paddings[0]) / \
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self.strides[0] + 1
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wsize_out = (wsize - self.ksize[1] + 2 * self.paddings[1]) / \
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self.strides[1] + 1
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input = np.zeros((nsize, csize, hsize_out, wsize_out))
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indices = np.zeros((nsize, csize, hsize_out, wsize_out))
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for i in xrange(hsize_out):
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for j in xrange(wsize_out):
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r_start = np.max((i * self.strides[0] - self.paddings[0], 0))
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r_end = np.min((i * self.strides[0] + self.ksize[0] - \
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self.paddings[0], hsize))
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c_start = np.max((j * self.strides[1] - self.paddings[1], 0))
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c_end = np.min((j * self.strides[1] + self.ksize[1] - \
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self.paddings[1], wsize))
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for nidx in xrange(nsize):
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for cidx in xrange(csize):
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x_masked = pre_input[nidx, cidx, r_start:r_end, \
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c_start:c_end]
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input[nidx, cidx, i, j] = x_masked.max()
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arg = x_masked.argmax()
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indices[nidx, cidx, i, j] = \
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(r_start + arg / self.ksize[1]) * wsize + \
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c_start + arg % self.ksize[1]
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output = self.unpool2d_forward_naive(input, indices, self.ksize, \
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self.strides, self.paddings).astype("float32")
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self.inputs = {
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'X': input.astype('float32'),
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'Indices': indices.astype('int32')
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}
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self.attrs = {
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'strides': self.strides,
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'paddings': self.paddings,
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'ksize': self.ksize,
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'unpooling_type': self.unpooling_type,
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}
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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.unpool2d_forward_naive = unpool2dmax_forward_naive
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self.unpooling_type = "max"
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self.shape = [6, 4, 5, 5]
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self.ksize = [3, 3]
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self.strides = [2, 2]
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self.paddings = [0, 0]
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if __name__ == '__main__':
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unittest.main()
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