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
|
/*
* Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2018 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_SELECT_H__
#define __NNFW_CKER_SELECT_H__
#include "cker/Shape.h"
#include "cker/Utils.h"
#include <cmath>
namespace nnfw
{
namespace cker
{
template <typename D, typename T>
void Select(const Shape &input_condition_shape, const D *input_condition_data,
const Shape &input_x_shape, const T *input_x_data, const Shape &input_y_shape,
const T *input_y_data, const Shape &output_shape, T *output_data)
{
const int64_t flatsize =
MatchingFlatSize(input_condition_shape, input_x_shape, input_y_shape, output_shape);
for (int64_t i = 0; i < flatsize; ++i)
{
output_data[i] = (input_condition_data[i] != 0) ? input_x_data[i] : input_y_data[i];
}
}
template <typename D, typename T>
void RankOneSelect(const Shape &input_condition_shape, const D *input_condition_data,
const Shape &input_x_shape, const T *input_x_data, const Shape &input_y_shape,
const T *input_y_data, const Shape &output_shape, T *output_data)
{
const int64_t outer_size = input_condition_shape.FlatSize();
assert(MatchingDim(input_x_shape, 0, input_y_shape, 0, output_shape, 0) == outer_size);
const int64_t inner_size = MatchingFlatSizeSkipDim(input_x_shape, 0, input_y_shape, output_shape);
int64_t offset = 0;
for (int64_t i = 0; i < outer_size; i++)
{
const T *input_data = (input_condition_data[i] != 0) ? input_x_data : input_y_data;
memcpy(output_data + offset, input_data + offset, inner_size * sizeof(T));
offset += inner_size;
}
}
template <typename D, typename T>
void BroadcastSelect4DSlow(const Shape &input_condition_shape, const D *input_condition_data,
const Shape &input_x_shape, const T *input_x_data,
const Shape &input_y_shape, const T *input_y_data,
const Shape &output_shape, T *output_data)
{
assert(input_condition_shape.DimensionsCount() <= 4);
assert(input_x_shape.DimensionsCount() <= 4);
assert(input_y_shape.DimensionsCount() <= 4);
assert(output_shape.DimensionsCount() <= 4);
const Shape extended_output_shape = Shape::ExtendedShape(4, output_shape);
NdArrayDesc<4> desc_condition;
NdArrayDesc<4> desc_x;
NdArrayDesc<4> desc_y;
NdArrayDescsForElementwiseBroadcast(input_condition_shape, input_x_shape, input_y_shape,
&desc_condition, &desc_x, &desc_y);
// In Tensorflow, the dimensions are canonically named (batch_number, row,
// col, channel), with extents (batches, height, width, depth), with the
// trailing dimension changing most rapidly (channels has the smallest
// stride, typically 1 element).
//
// In generated C code, we store arrays with the dimensions reversed. The
// first dimension has smallest stride.
//
// We name our variables by their Tensorflow convention, but generate C code
// nesting loops such that the innermost loop has the smallest stride for
// the best cache behavior.
for (int b = 0; b < extended_output_shape.Dims(0); ++b)
{
for (int y = 0; y < extended_output_shape.Dims(1); ++y)
{
for (int x = 0; x < extended_output_shape.Dims(2); ++x)
{
for (int c = 0; c < extended_output_shape.Dims(3); ++c)
{
const int condition_index = SubscriptToIndex(desc_condition, b, y, x, c);
const int x_index = SubscriptToIndex(desc_x, b, y, x, c);
const int y_index = SubscriptToIndex(desc_y, b, y, x, c);
output_data[Offset(extended_output_shape, b, y, x, c)] =
input_condition_data[condition_index] ? input_x_data[x_index] : input_y_data[y_index];
}
}
}
}
}
} // namespace cker
} // namespace nnfw
#endif // __NNFW_CKER_SELECT_H__
|
