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protobuf-bench.cc
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/*
* SPDX-License-Identifier: Apache-2.0
*/
#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();