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add enable benchmark for lite (#503) 

* add enable benchmark for lite

* fix config
pull/505/head
littletomatodonkey 2020-12-18 20:17:50 +08:00 committed by GitHub
parent 70877e91e5
commit e5cc8d463c
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2 changed files with 37 additions and 24 deletions
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1
deploy/lite/config.txt
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@ -3,3 +3,4 @@ label_path ./imagenet1k_label_list.txt
resize_short_size 256
crop_size 224
visualize 0
enable_benchmark 1

60
deploy/lite/image_classfication.cpp
View File

@ -149,7 +149,7 @@ cv::Mat CenterCropImg(const cv::Mat &img, const int &crop_size) {
std::vector<RESULT>
RunClasModel(std::shared_ptr<PaddlePredictor> predictor, const cv::Mat &img,
const std::map<std::string, std::string> &config,
const std::vector<std::string> &word_labels) {
const std::vector<std::string> &word_labels, double &cost_time) {
// Read img
int resize_short_size = stoi(config.at("resize_short_size"));
int crop_size = stoi(config.at("crop_size"));
@ -173,6 +173,7 @@ RunClasModel(std::shared_ptr<PaddlePredictor> predictor, const cv::Mat &img,
const float *dimg = reinterpret_cast<const float *>(img_fp.data);
NeonMeanScale(dimg, data0, img_fp.rows * img_fp.cols, mean, scale);
auto start = std::chrono::system_clock::now();
// Run predictor
predictor->Run();
@ -180,6 +181,12 @@ RunClasModel(std::shared_ptr<PaddlePredictor> predictor, const cv::Mat &img,
std::unique_ptr<const Tensor> output_tensor(
std::move(predictor->GetOutput(0)));
auto *output_data = output_tensor->data<float>();
auto end = std::chrono::system_clock::now();
auto duration =
std::chrono::duration_cast<std::chrono::microseconds>(end - start);
cost_time = double(duration.count()) *
std::chrono::microseconds::period::num /
std::chrono::microseconds::period::den;
int output_size = 1;
for (auto dim : output_tensor->shape()) {
@ -294,6 +301,12 @@ int main(int argc, char **argv) {
auto config = LoadConfigTxt(config_path);
PrintConfig(config);
double elapsed_time = 0.0;
int warmup_iter = 10;
bool enable_benchmark = bool(stoi(config.at("enable_benchmark")));
int total_cnt = enable_benchmark ? 1000 : 1;
std::string clas_model_file = config.at("clas_model_file");
std::string label_path = config.at("label_path");
@ -301,32 +314,31 @@ int main(int argc, char **argv) {
std::vector<std::string> word_labels = LoadLabels(label_path);
auto clas_predictor = LoadModel(clas_model_file);
for (int j = 0; j < total_cnt; ++j) {
cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
cv::cvtColor(srcimg, srcimg, cv::COLOR_BGR2RGB);
auto start = std::chrono::system_clock::now();
double run_time = 0;
std::vector<RESULT> results =
RunClasModel(clas_predictor, srcimg, config, word_labels, run_time);
cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
cv::cvtColor(srcimg, srcimg, cv::COLOR_BGR2RGB);
std::vector<RESULT> results =
RunClasModel(clas_predictor, srcimg, config, word_labels);
std::cout << "===clas result for image: " << img_path << "===" << std::endl;
for (int i = 0; i < results.size(); i++) {
std::cout << "\t"
<< "Top-" << i + 1 << ", class_id: " << results[i].class_id
<< ", class_name: " << results[i].class_name
<< ", score: " << results[i].score << std::endl;
std::cout << "===clas result for image: " << img_path << "===" << std::endl;
for (int i = 0; i < results.size(); i++) {
std::cout << "\t"
<< "Top-" << i + 1 << ", class_id: " << results[i].class_id
<< ", class_name: " << results[i].class_name
<< ", score: " << results[i].score << std::endl;
}
if (j >= warmup_iter) {
elapsed_time += run_time;
std::cout << "Current image path: " << img_path << std::endl;
std::cout << "Current time cost: " << run_time << " s, "
<< "average time cost in all: "
<< elapsed_time / (j + 1 - warmup_iter) << " s." << std::endl;
} else {
std::cout << "Current time cost: " << run_time << " s." << std::endl;
}
}
auto end = std::chrono::system_clock::now();
auto duration =
std::chrono::duration_cast<std::chrono::microseconds>(end - start);
std::cout << "Cost "
<< double(duration.count()) *
std::chrono::microseconds::period::num /
std::chrono::microseconds::period::den
<< " s" << std::endl;
return 0;
}