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Read the customized model path and parameters that need to run benchn… #4521

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144 changes: 108 additions & 36 deletions benchmark/benchncnn.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,8 @@
#include <float.h>
#include <stdio.h>
#include <string.h>
#include <map>
#include <fstream>

#ifdef _WIN32
#include <algorithm>
Expand Down Expand Up @@ -158,13 +160,53 @@ void benchmark(const char* comment, const ncnn::Mat& _in, const ncnn::Option& op
fprintf(stderr, "%20s min = %7.2f max = %7.2f avg = %7.2f\n", comment, time_min, time_max, time_avg);
}

void trim(std::string& str)
{
if (std::empty(str)) {
return;
}
str.erase(0, str.find_first_not_of(" "));
str.erase(str.find_last_not_of(" ") + 1);
}

int read_model_config(const char* config_path,
std::map<std::string, std::string> &config_map)
{
std::ifstream ifs;
ifs.open(config_path, std::ios::in);
if (!ifs.is_open())
{
return -1;
}
std::string line;
while (!ifs.eof()) {
std::getline(ifs, line);
trim(line);
if (line.empty() || line.find('#') == 0) {
continue;
}
size_t pos = line.find('=');
if (pos == line.npos) {
continue;
}

std::string key = line.substr(0, pos);
std::string value = line.substr(pos + 1);
trim(key);
trim(value);
config_map.insert(std::make_pair(key, value));
}
return 0;
}

int main(int argc, char** argv)
{
int loop_count = 4;
int num_threads = ncnn::get_physical_big_cpu_count();
int powersave = 2;
int gpu_device = -1;
int cooling_down = 1;
char* model_config_path = "";

if (argc >= 2)
{
Expand All @@ -186,6 +228,10 @@ int main(int argc, char** argv)
{
cooling_down = atoi(argv[5]);
}
if (argc >= 7)
{
model_config_path = argv[6];
}

#ifdef __EMSCRIPTEN__
EM_ASM(
Expand Down Expand Up @@ -250,77 +296,103 @@ int main(int argc, char** argv)
fprintf(stderr, "cooling_down = %d\n", (int)g_enable_cooling_down);

// run
benchmark("squeezenet", ncnn::Mat(227, 227, 3), opt);
printf("model_config_path: %s\n", model_config_path);
if (model_config_path != nullptr && model_config_path != "")
{
std::map<std::string, std::string> config_map;
int ret = read_model_config(model_config_path, config_map);
if (ret != 0)
{
printf("open config file filed\n");
return 0;
}

std::string temp = config_map["model_path"];
printf("model path: %s\n", temp.c_str());
size_t pos = temp.rfind(".param");
temp = temp.substr(0, pos);

int width = std::stoi(config_map["width"]);
int height = std::stoi(config_map["height"]);
int channels = std::stoi(config_map["channels"]);
printf("intput width: %d, intput height: %d, intput channels: %d\n",
width, height, channels);

benchmark("squeezenet_int8", ncnn::Mat(227, 227, 3), opt);
benchmark(temp.c_str(),
ncnn::Mat(width, height, channels), opt);
} else {
benchmark("squeezenet", ncnn::Mat(227, 227, 3), opt);

benchmark("mobilenet", ncnn::Mat(224, 224, 3), opt);
benchmark("squeezenet_int8", ncnn::Mat(227, 227, 3), opt);

benchmark("mobilenet_int8", ncnn::Mat(224, 224, 3), opt);
benchmark("mobilenet", ncnn::Mat(224, 224, 3), opt);

benchmark("mobilenet_v2", ncnn::Mat(224, 224, 3), opt);
benchmark("mobilenet_int8", ncnn::Mat(224, 224, 3), opt);

// benchmark("mobilenet_v2_int8", ncnn::Mat(224, 224, 3), opt);
benchmark("mobilenet_v2", ncnn::Mat(224, 224, 3), opt);

benchmark("mobilenet_v3", ncnn::Mat(224, 224, 3), opt);
// benchmark("mobilenet_v2_int8", ncnn::Mat(224, 224, 3), opt);

benchmark("shufflenet", ncnn::Mat(224, 224, 3), opt);
benchmark("mobilenet_v3", ncnn::Mat(224, 224, 3), opt);

benchmark("shufflenet_v2", ncnn::Mat(224, 224, 3), opt);
benchmark("shufflenet", ncnn::Mat(224, 224, 3), opt);

benchmark("mnasnet", ncnn::Mat(224, 224, 3), opt);
benchmark("shufflenet_v2", ncnn::Mat(224, 224, 3), opt);

benchmark("proxylessnasnet", ncnn::Mat(224, 224, 3), opt);
benchmark("mnasnet", ncnn::Mat(224, 224, 3), opt);

benchmark("efficientnet_b0", ncnn::Mat(224, 224, 3), opt);
benchmark("proxylessnasnet", ncnn::Mat(224, 224, 3), opt);

benchmark("efficientnetv2_b0", ncnn::Mat(224, 224, 3), opt);
benchmark("efficientnet_b0", ncnn::Mat(224, 224, 3), opt);

benchmark("regnety_400m", ncnn::Mat(224, 224, 3), opt);
benchmark("efficientnetv2_b0", ncnn::Mat(224, 224, 3), opt);

benchmark("blazeface", ncnn::Mat(128, 128, 3), opt);
benchmark("regnety_400m", ncnn::Mat(224, 224, 3), opt);

benchmark("googlenet", ncnn::Mat(224, 224, 3), opt);
benchmark("blazeface", ncnn::Mat(128, 128, 3), opt);

benchmark("googlenet_int8", ncnn::Mat(224, 224, 3), opt);
benchmark("googlenet", ncnn::Mat(224, 224, 3), opt);

benchmark("resnet18", ncnn::Mat(224, 224, 3), opt);
benchmark("googlenet_int8", ncnn::Mat(224, 224, 3), opt);

benchmark("resnet18_int8", ncnn::Mat(224, 224, 3), opt);
benchmark("resnet18", ncnn::Mat(224, 224, 3), opt);

benchmark("alexnet", ncnn::Mat(227, 227, 3), opt);
benchmark("resnet18_int8", ncnn::Mat(224, 224, 3), opt);

benchmark("vgg16", ncnn::Mat(224, 224, 3), opt);
benchmark("alexnet", ncnn::Mat(227, 227, 3), opt);

benchmark("vgg16_int8", ncnn::Mat(224, 224, 3), opt);
benchmark("vgg16", ncnn::Mat(224, 224, 3), opt);

benchmark("resnet50", ncnn::Mat(224, 224, 3), opt);
benchmark("vgg16_int8", ncnn::Mat(224, 224, 3), opt);

benchmark("resnet50_int8", ncnn::Mat(224, 224, 3), opt);
benchmark("resnet50", ncnn::Mat(224, 224, 3), opt);

benchmark("squeezenet_ssd", ncnn::Mat(300, 300, 3), opt);
benchmark("resnet50_int8", ncnn::Mat(224, 224, 3), opt);

benchmark("squeezenet_ssd_int8", ncnn::Mat(300, 300, 3), opt);
benchmark("squeezenet_ssd", ncnn::Mat(300, 300, 3), opt);

benchmark("mobilenet_ssd", ncnn::Mat(300, 300, 3), opt);
benchmark("squeezenet_ssd_int8", ncnn::Mat(300, 300, 3), opt);

benchmark("mobilenet_ssd_int8", ncnn::Mat(300, 300, 3), opt);
benchmark("mobilenet_ssd", ncnn::Mat(300, 300, 3), opt);

benchmark("mobilenet_yolo", ncnn::Mat(416, 416, 3), opt);
benchmark("mobilenet_ssd_int8", ncnn::Mat(300, 300, 3), opt);

benchmark("mobilenetv2_yolov3", ncnn::Mat(352, 352, 3), opt);
benchmark("mobilenet_yolo", ncnn::Mat(416, 416, 3), opt);

benchmark("yolov4-tiny", ncnn::Mat(416, 416, 3), opt);
benchmark("mobilenetv2_yolov3", ncnn::Mat(352, 352, 3), opt);

benchmark("nanodet_m", ncnn::Mat(320, 320, 3), opt);
benchmark("yolov4-tiny", ncnn::Mat(416, 416, 3), opt);

benchmark("yolo-fastest-1.1", ncnn::Mat(320, 320, 3), opt);
benchmark("nanodet_m", ncnn::Mat(320, 320, 3), opt);

benchmark("yolo-fastestv2", ncnn::Mat(352, 352, 3), opt);
benchmark("yolo-fastest-1.1", ncnn::Mat(320, 320, 3), opt);

benchmark("vision_transformer", ncnn::Mat(384, 384, 3), opt);
benchmark("yolo-fastestv2", ncnn::Mat(352, 352, 3), opt);

benchmark("FastestDet", ncnn::Mat(352, 352, 3), opt);
benchmark("vision_transformer", ncnn::Mat(384, 384, 3), opt);

benchmark("FastestDet", ncnn::Mat(352, 352, 3), opt);
}
#if NCNN_VULKAN
delete g_blob_vkallocator;
delete g_staging_vkallocator;
Expand Down
9 changes: 9 additions & 0 deletions benchmark/config
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Original file line number Diff line number Diff line change
@@ -0,0 +1,9 @@

model_path = ./squeezenet.param

width =227

height= 227

channels=3