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
121
122
123
124
125
126
127
128
|
/*
* 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 "internal/Model.h"
namespace internal
{
namespace tflite
{
namespace operand
{
Shape::Shape(uint32_t rank) : nnfw::misc::tensor::Shape(rank)
{
// DO NOTHING
}
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::tensor::Shape Shape::asTensor(void) const
{
return nnfw::misc::tensor::Shape(*this); // this shape represents shape of NNAPI
}
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);
}
// Extended dimension is filled with 1.
void Shape::extendRank(size_t to_rank)
{
for (int i = rank() + 1; i <= to_rank; ++i)
{
prepend(1);
}
}
} // namespace operand
} // namespace tflite
} // namespace internal
namespace internal
{
namespace tflite
{
namespace operand
{
Index Set::append(const Shape &shape, int32_t type, float scale, int32_t zeroPoint)
{
int32_t index = _objects.size();
_objects.emplace_back(new Object{shape, type, scale, zeroPoint});
return Index{index};
}
const Object &Set::at(const Index &index) const { return *(_objects.at(index.asInt())); }
Object &Set::at(const Index &index) { return *(_objects.at(index.asInt())); }
bool Set::exist(const Index &index) const
{
return index.asInt() >= 0 && index.asInt() < _objects.size();
}
} // namespace operand
} // namespace tflite
} // namespace internal
|
