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
|
/*
* 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 <cassert>
#include "Shape.h"
namespace neurun
{
namespace model
{
namespace operand
{
Shape::Shape(uint32_t rank) { _dims.resize(rank); }
int32_t Shape::asVector(void) const
{
assert(rank() == 1);
return dim(0);
}
nnfw::misc::matrix::Shape Shape::asMatrix(void) const
{
assert(rank() == 2);
const auto height = dim(0);
const auto width = dim(1);
return nnfw::misc::matrix::Shape(height, width);
}
nnfw::misc::feature::Shape Shape::asFeature(void) const
{
assert(rank() == 4);
// Feature Map in NNAPI
// - Dimension(0) -> Batch
// - Dimension(1) -> Height
// - Dimension(2) -> Width
// - Dimension(3) -> Depth
const auto batch = dim(0);
const auto depth = dim(3);
const auto height = dim(1);
const auto width = dim(2);
return nnfw::misc::feature::Shape(batch, depth, height, width);
}
nnfw::misc::kernel::Shape Shape::asKernel(void) const
{
assert(rank() == 4);
// Convolution Kernel in NNAPI
// - Dimension(0) -> Count
// - Dimension(1) -> Height
// - Dimension(2) -> Width
// - Dimension(3) -> Depth
const auto count = dim(0);
const auto depth = dim(3);
const auto height = dim(1);
const auto width = dim(2);
return nnfw::misc::kernel::Shape(count, depth, height, width);
}
nnfw::misc::tensor::Shape Shape::asTensor(void) const
{
nnfw::misc::tensor::Shape shape{};
for (uint32_t i = 0; i < rank(); ++i)
{
shape.append(dim(i));
}
return shape; // this shape represents shape of NNAPI
}
} // namespace operand
} // namespace model
} // namespace neurun
|