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
|
/*
* Copyright (c) 2020 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 "TransposeLayer.h"
#include "OperationUtils.h"
#include <cker/operation/Transpose.h>
#include <numeric>
namespace onert
{
namespace backend
{
namespace cpu
{
namespace ops
{
TransposeLayer::TransposeLayer() : _input(nullptr), _perm(nullptr), _output(nullptr)
{
// DO NOTHING
}
template <typename T> void TransposeLayer::transpose()
{
nnfw::cker::TransposeParams param;
auto perm_shape = _perm->getShape();
assert(perm_shape.rank() == 1);
param.perm_count = _input->getShape().rank();
if (perm_shape.dim(0) == 0) // This means _perm is (n-1...0)
{
const auto begin = param.perm;
const auto end = param.perm + _input->getShape().rank();
std::iota(begin, end, 0);
std::reverse(begin, end);
}
else
{
assert(param.perm_count == static_cast<int>(perm_shape.dim(0)));
for (auto i = 0; i < param.perm_count; i++)
{
param.perm[i] = *(getBuffer<int32_t>(_perm) + i);
}
}
nnfw::cker::Transpose(param, getShape(_input), getBuffer<T>(_input), getShape(_output),
getBuffer<T>(_output));
}
void TransposeLayer::transposeQuant8()
{
if (_input->data_zero_point() != _output->data_zero_point())
{
throw std::runtime_error("TransposeLayer : qassym8 input and output offsets unmatched");
}
if (_input->data_scale() != _output->data_scale())
{
throw std::runtime_error("TransposeLayer : qassym8 input and output scales unmatched");
}
transpose<uint8_t>();
}
void TransposeLayer::configure(const IPortableTensor *input, const IPortableTensor *perm,
IPortableTensor *output)
{
_input = input;
_perm = perm;
_output = output;
}
void TransposeLayer::run()
{
if (_input->data_type() == OperandType::FLOAT32)
{
transpose<float>();
}
else if (_input->data_type() == OperandType::INT32)
{
transpose<int32_t>();
}
else if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM)
{
transposeQuant8();
}
else
{
throw std::runtime_error{"Transpose: unsupported data type"};
}
}
} // namespace ops
} // namespace cpu
} // namespace backend
} // namespace onert
|
