1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
|
/*
* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "util/Padding.h"
#include "util/Utils.h"
#include <algorithm>
#include <stdexcept>
namespace neurun
{
namespace util
{
model::ExplicitPadding validPadding(void)
{
//
// ANEURALNETWORKS_PADDING_VALID
//
// VALID padding. No padding.
//
// When the input size is not evenly divisible by the filter size,
// the input at the end that could not fill the whole filter tile
// will simply be ignored.
//
model::ExplicitPadding padding;
padding.top = 0;
padding.bottom = 0;
padding.left = 0;
padding.right = 0;
return padding;
}
model::ExplicitPadding samePaddingUsingIFM(const model::FeatureShape &ifm_shape,
const model::Stride &stride, uint32_t kw, uint32_t kh)
{
model::ExplicitPadding padding;
// ANEURALNETWORKS_PADDING_SAME (from NNAPI spec)
//
// SAME padding. Padding on both ends are the "same":
//
// padding_to_beginning = total_padding / 2
// padding_to_end = (total_padding + 1)/2.
//
const int32_t vertical_expected_output = (ifm_shape.H + stride.vertical - 1) / stride.vertical;
const int32_t horizontal_expected_output =
(ifm_shape.W + stride.horizontal - 1) / stride.horizontal;
const int32_t vertical_needed_input = (vertical_expected_output - 1) * stride.vertical + kh;
const int32_t vertical_total_padding = std::max(0, vertical_needed_input - ifm_shape.H);
const int32_t horizontal_needed_input = (horizontal_expected_output - 1) * stride.horizontal + kw;
const int32_t horizontal_total_padding = std::max(0, horizontal_needed_input - ifm_shape.W);
padding.top = vertical_total_padding / 2;
padding.bottom = (vertical_total_padding + 1) / 2;
padding.left = horizontal_total_padding / 2;
padding.right = (horizontal_total_padding + 1) / 2;
return padding;
}
model::ExplicitPadding samePadding(const model::FeatureShape &ifm_shape,
const model::FeatureShape &ofm_shape,
const model::Stride &stride, uint32_t kw, uint32_t kh)
{
const int32_t vertical_expected_output = (ifm_shape.H + stride.vertical - 1) / stride.vertical;
const int32_t horizontal_expected_output =
(ifm_shape.W + stride.horizontal - 1) / stride.horizontal;
assert(vertical_expected_output == ofm_shape.H);
assert(horizontal_expected_output == ofm_shape.W);
UNUSED_RELEASE(ofm_shape);
UNUSED_RELEASE(vertical_expected_output);
UNUSED_RELEASE(horizontal_expected_output);
return samePaddingUsingIFM(ifm_shape, stride, kw, kh);
}
model::ExplicitPadding calculatePadding(const model::Padding &padding,
const model::FeatureShape &ifm_shape,
const model::FeatureShape &ofm_shape,
const model::Stride &stride, uint32_t kw, uint32_t kh)
{
if (padding.type == model::PaddingType::EXPLICIT)
{
return padding.param;
}
else if (padding.type == model::PaddingType::SAME)
{
return samePadding(ifm_shape, ofm_shape, stride, kw, kh);
}
else if (padding.type == model::PaddingType::VALID)
{
return validPadding();
}
else
{
throw std::runtime_error{"Cannot handle padding type"};
}
}
} // namespace util
} // namespace neurun
|
