blob: 242aadf46d3f741cf79402e717d5cf554979791b (
plain)
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
|
/*
* Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2017 The TensorFlow Authors. 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.
*/
#ifndef __NNFW_CKER_UNPACK_H__
#define __NNFW_CKER_UNPACK_H__
#include "cker/Shape.h"
#include "cker/Types.h"
namespace nnfw
{
namespace cker
{
template <typename Scalar>
void Unpack(const UnpackParams ¶ms, const Shape &input_shape, const Scalar *input_data,
const Shape &output_shape, Scalar *const *output_datas)
{
const int dimensions = input_shape.DimensionsCount();
const int outputs_count = params.num_split;
int outer_size = 1;
for (int i = 0; i < params.axis; i++)
{
outer_size *= input_shape.Dims(i);
}
int copy_size = 1;
for (int i = params.axis + 1; i < dimensions; i++)
{
copy_size *= input_shape.Dims(i);
}
assert(output_shape.FlatSize() == copy_size * outer_size);
UNUSED_RELEASE(output_shape);
for (int i = 0; i < outputs_count; ++i)
{
for (int k = 0; k < outer_size; k++)
{
Scalar *output_ptr = output_datas[i] + copy_size * k;
int loc = k * outputs_count * copy_size + i * copy_size;
memcpy(output_ptr, input_data + loc, copy_size * sizeof(Scalar));
}
}
}
} // namespace cker
} // namespace nnfw
#endif // __NNFW_CKER_UNPACK_H__
|
