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
135
136
137
138
139
140
141
142
143
144
145
146
|
#include <benchmark/benchmark.h>
#include <onnx/onnx_pb.h>
using namespace ONNX_NAMESPACE;
inline void createValueInfo4D(
ValueInfoProto& value_info,
const std::string& name,
int64_t n,
int64_t c,
int64_t h,
int64_t w) {
value_info.set_name(name);
TypeProto_Tensor* tensor_type =
value_info.mutable_type()->mutable_tensor_type();
tensor_type->set_elem_type(TensorProto_DataType_FLOAT);
TensorShapeProto* shape = tensor_type->mutable_shape();
shape->add_dim()->set_dim_value(n);
shape->add_dim()->set_dim_value(c);
shape->add_dim()->set_dim_value(h);
shape->add_dim()->set_dim_value(w);
}
inline void createValueInfo2D(
ValueInfoProto& value_info,
const std::string& name,
int64_t h,
int64_t w) {
value_info.set_name(name);
TypeProto* type = value_info.mutable_type();
TypeProto_Tensor* tensor_type = type->mutable_tensor_type();
tensor_type->set_elem_type(TensorProto_DataType_FLOAT);
TensorShapeProto* shape = tensor_type->mutable_shape();
shape->add_dim()->set_dim_value(h);
shape->add_dim()->set_dim_value(w);
}
inline void createConv2D(
NodeProto& node,
const std::string& input,
const std::string& weights,
const std::string& bias,
const std::string& output,
uint32_t kernel_size) {
node.set_op_type("Conv");
node.add_input(input);
node.add_input(weights);
node.add_input(bias);
node.add_output(output);
{
AttributeProto* kernel = node.add_attribute();
kernel->set_name("kernel_shape");
kernel->set_type(AttributeProto::INTS);
kernel->add_ints(kernel_size);
kernel->add_ints(kernel_size);
}
{
AttributeProto* dilation = node.add_attribute();
dilation->set_name("dilations");
dilation->set_type(AttributeProto::INTS);
dilation->add_ints(1);
dilation->add_ints(1);
}
{
AttributeProto* stride = node.add_attribute();
stride->set_name("strides");
stride->set_type(AttributeProto::INTS);
stride->add_ints(1);
stride->add_ints(1);
}
{
AttributeProto* group = node.add_attribute();
group->set_name("group");
group->set_type(AttributeProto::INTS);
group->set_i(1);
}
{
AttributeProto* padding = node.add_attribute();
padding->set_name("pads");
padding->set_type(AttributeProto::INTS);
/* Use "same" padding */
padding->add_ints(kernel_size / 2);
padding->add_ints(kernel_size / 2);
padding->add_ints(kernel_size - 1 - kernel_size / 2);
padding->add_ints(kernel_size - 1 - kernel_size / 2);
}
}
static void ConvGraph(benchmark::State& state) {
while (state.KeepRunning()) {
std::string data;
GraphProto graph;
createConv2D(*graph.add_node(), "input", "weights", "bias", "output", 3);
createValueInfo4D(*graph.add_input(), "input", 1, 3, 224, 224);
createValueInfo4D(*graph.add_input(), "weights", 16, 16, 3, 3);
createValueInfo2D(*graph.add_input(), "bias", 1, 16);
createValueInfo4D(*graph.add_output(), "output", 16, 3, 224, 224);
graph.SerializeToString(&data);
GraphProto decodedGraph;
decodedGraph.ParseFromString(data);
}
state.SetItemsProcessed(int64_t(state.iterations()));
}
BENCHMARK(ConvGraph)->Unit(benchmark::kMicrosecond);
static void ConvModel(benchmark::State& state) {
while (state.KeepRunning()) {
std::string data;
ModelProto model;
model.set_ir_version(IR_VERSION);
OperatorSetIdProto* op_set_id = model.add_opset_import();
op_set_id->set_domain("");
op_set_id->set_version(4);
GraphProto* graph = model.mutable_graph();
createConv2D(*graph->add_node(), "input", "weights", "bias", "output", 3);
createValueInfo4D(*graph->add_input(), "input", 1, 3, 224, 224);
createValueInfo4D(*graph->add_input(), "weights", 16, 16, 3, 3);
createValueInfo2D(*graph->add_input(), "bias", 1, 16);
createValueInfo4D(*graph->add_output(), "output", 16, 3, 224, 224);
model.SerializeToString(&data);
ModelProto decodedModel;
decodedModel.ParseFromString(data);
}
state.SetItemsProcessed(int64_t(state.iterations()));
}
BENCHMARK(ConvModel)->Unit(benchmark::kMicrosecond);
BENCHMARK_MAIN();
|
