blob: 61a160cfb82c87e02755f86de917dd05753e76ff (
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
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
129
130
131
132
133
134
|
/*
* Copyright (c) 2019 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 "TensorPackEnumerator.h"
#include <cassert>
namespace moco
{
void TensorPackEnumerator::shape(const loco::TensorShape &si, const loco::TensorShape &so)
{
_shape_inp = si;
_shape_out = so;
assert(_shape_inp.rank() + 1 == _shape_out.rank());
_rank_out = _shape_out.rank();
}
void TensorPackEnumerator::increment(uint32_t r)
{
_cursor_out.at(r) = _cursor_out.at(r) + 1;
if (_cursor_out.at(r) >= _boundary_out.at(r))
{
if (r > 0)
{
_cursor_out.at(r) = 0;
increment(r - 1);
}
else
{
// reached to the end
}
}
}
void TensorPackEnumerator::start(void)
{
uint32_t rank = _rank_out;
_cursor_out.resize(rank);
_boundary_out.resize(rank);
for (uint32_t r = 0; r < rank; ++r)
{
_cursor_out.at(r) = 0;
_boundary_out.at(r) = _shape_out.dim(r).value();
}
rank = _rank_out - 1;
_cursor_inp.resize(rank);
_boundary_inp.resize(rank);
for (uint32_t r = 0; r < rank; ++r)
{
_cursor_inp.at(r) = 0;
_boundary_inp.at(r) = _shape_inp.dim(r).value();
}
_num_inp = 0;
}
bool TensorPackEnumerator::valid(void)
{
uint32_t rank = _rank_out;
for (uint32_t r = 0; r < rank; ++r)
{
if (_cursor_out.at(r) >= _boundary_out.at(r))
{
return false;
}
}
return true;
}
void TensorPackEnumerator::advance(void)
{
uint32_t r = _rank_out - 1;
increment(r);
// from _cursor_out, set _cursor_inp and _num
for (int32_t r = 0, s = 0; r < _rank_out; ++r)
{
if (r == _axis)
{
_num_inp = _cursor_out.at(r);
}
else
{
_cursor_inp.at(s) = _cursor_out.at(r);
s++;
}
}
}
uint32_t TensorPackEnumerator::inp_num(void) const { return _num_inp; }
uint32_t TensorPackEnumerator::inp_element(void) const
{
uint32_t rank = _rank_out - 1;
uint32_t element = 0;
for (uint32_t r = 0; r < rank; ++r)
{
uint32_t dim = _boundary_inp.at(r);
element = element * dim + _cursor_inp.at(r);
}
return element;
}
uint32_t TensorPackEnumerator::out_element(void) const
{
uint32_t rank = _rank_out;
uint32_t element = 0;
for (uint32_t r = 0; r < rank; ++r)
{
uint32_t dim = _boundary_out.at(r);
element = element * dim + _cursor_out.at(r);
}
return element;
}
} // namespace moco
|
