blob: 63cf29bd307515f0e7dc1875edd45d7c6b1e966b (
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
|
/*
* 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 "Object.h"
namespace neurun
{
namespace model
{
namespace operand
{
size_t Object::operandSize(void) const
{
const uint32_t ranks = _shape.rank();
int32_t elements = 1;
for (uint32_t rank = 0; rank < ranks; rank++)
{
elements *= _shape.dim(rank);
}
DataType type = _type.type();
size_t element_size = 0;
// Value of type is matched with OperandCode enum in NeuralNetworks.h
switch (type)
{
case DataType::SCALAR_FLOAT32:
case DataType::TENSOR_FLOAT32:
element_size = sizeof(float);
break;
case DataType::SCALAR_INT32:
case DataType::TENSOR_INT32:
element_size = sizeof(int32_t);
break;
case DataType::SCALAR_UINT32:
element_size = sizeof(uint32_t);
break;
case DataType::TENSOR_QUANT8_ASYMM:
element_size = sizeof(uint8_t);
break;
default:
throw std::runtime_error{"Unsuppported type size"};
}
return element_size * elements;
}
bool Object::setUsage(const OperandUsage usage)
{
if (usageIsDefined() && (_usage != usage))
{
// Already set as different type
return false;
}
_usage = usage;
return true;
}
void Object::appendUse(const ::neurun::model::operation::Index &idx)
{
assert(_usage != OperandUsage::NOT_DEFINED);
assert(!_uses.contains(idx));
_uses.append(idx);
}
void Object::removeUse(const ::neurun::model::operation::Index &idx)
{
assert(_usage != OperandUsage::NOT_DEFINED);
assert(_uses.contains(idx));
_uses.remove(idx);
}
void Object::appendDef(const ::neurun::model::operation::Index &idx)
{
assert(_usage != OperandUsage::NOT_DEFINED && _usage != OperandUsage::CONSTANT);
assert(_def.size() == 0);
_def.append(idx);
}
void Object::removeDef(const ::neurun::model::operation::Index &idx)
{
assert(_usage != OperandUsage::NOT_DEFINED);
assert(_def.contains(idx));
_def.remove(idx);
}
void Object::lower_info(std::unique_ptr<graph::operand::LowerInfo> &&lower_info)
{
_lower_info = std::move(lower_info);
}
const graph::operand::LowerInfo *Object::lower_info() const { return _lower_info.get(); }
graph::operand::LowerInfo *Object::lower_info() { return _lower_info.get(); }
void Object::parent_info(std::unique_ptr<graph::operand::ParentInfo> &&parent_info)
{
_parent_info = std::move(parent_info);
}
const graph::operand::ParentInfo *Object::parent_info() const { return _parent_info.get(); }
graph::operand::ParentInfo *Object::parent_info() { return _parent_info.get(); }
} // namespace operand
} // namespace model
} // namespace neurun
|
