mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 08:37:06 +08:00
[Benchmark] Add macros for benchmark (#1301)
* add GPL lisence * add GPL-3.0 lisence * add GPL-3.0 lisence * add GPL-3.0 lisence * support yolov8 * add pybind for yolov8 * add yolov8 readme * add cpp benchmark * add cpu and gpu mem * public part split * add runtime mode * fixed bugs * add cpu_thread_nums * deal with comments * deal with comments * deal with comments * rm useless code * add FASTDEPLOY_DECL * add FASTDEPLOY_DECL * fixed for windows * mv rss to pss * mv rss to pss * Update utils.cc * use thread to collect mem * Add ResourceUsageMonitor * rm useless code * fixed bug * fixed typo * update ResourceUsageMonitor * fixed bug * fixed bug * add note for ResourceUsageMonitor * deal with comments * add macros * deal with comments * deal with comments * deal with comments * re-lint --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
This commit is contained in:
WJJ1995
@@ -12,9 +12,9 @@
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#include "fastdeploy/benchmark/utils.h"
|
||||
#include "fastdeploy/vision.h"
|
||||
#include "flags.h"
|
||||
#include "macros.h"
|
||||
#include "option.h"
|
||||
|
||||
#ifdef WIN32
|
||||
const char sep = '\\';
|
||||
@@ -22,104 +22,24 @@ const char sep = '\\';
|
||||
const char sep = '/';
|
||||
#endif
|
||||
|
||||
bool RunModel(std::string model_dir, std::string image_file, size_t warmup,
|
||||
size_t repeats, size_t dump_period, std::string cpu_mem_file_name,
|
||||
std::string gpu_mem_file_name) {
|
||||
int main(int argc, char* argv[]) {
|
||||
google::ParseCommandLineFlags(&argc, &argv, true);
|
||||
auto im = cv::imread(FLAGS_image);
|
||||
// Initialization
|
||||
auto option = fastdeploy::RuntimeOption();
|
||||
if (!CreateRuntimeOption(&option)) {
|
||||
PrintUsage();
|
||||
return false;
|
||||
}
|
||||
auto model_file = model_dir + sep + "model.pdmodel";
|
||||
auto params_file = model_dir + sep + "model.pdiparams";
|
||||
auto config_file = model_dir + sep + "infer_cfg.yml";
|
||||
|
||||
if (FLAGS_profile_mode == "runtime") {
|
||||
option.EnableProfiling(FLAGS_include_h2d_d2h, repeats, warmup);
|
||||
}
|
||||
auto model = fastdeploy::vision::detection::PaddleYOLOv8(
|
||||
auto model_file = FLAGS_model + sep + "model.pdmodel";
|
||||
auto params_file = FLAGS_model + sep + "model.pdiparams";
|
||||
auto config_file = FLAGS_model + sep + "infer_cfg.yml";
|
||||
auto model_ppyolov8 = fastdeploy::vision::detection::PaddleYOLOv8(
|
||||
model_file, params_file, config_file, option);
|
||||
if (!model.Initialized()) {
|
||||
std::cerr << "Failed to initialize." << std::endl;
|
||||
return false;
|
||||
}
|
||||
auto im = cv::imread(image_file);
|
||||
// For Runtime
|
||||
if (FLAGS_profile_mode == "runtime") {
|
||||
fastdeploy::vision::DetectionResult res;
|
||||
if (!model.Predict(im, &res)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return false;
|
||||
}
|
||||
double profile_time = model.GetProfileTime() * 1000;
|
||||
std::cout << "Runtime(ms): " << profile_time << "ms." << std::endl;
|
||||
auto vis_im = fastdeploy::vision::VisDetection(im, res);
|
||||
cv::imwrite("vis_result.jpg", vis_im);
|
||||
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||
} else {
|
||||
// For End2End
|
||||
// Step1: warm up for warmup times
|
||||
std::cout << "Warmup " << warmup << " times..." << std::endl;
|
||||
for (int i = 0; i < warmup; i++) {
|
||||
fastdeploy::vision::DetectionResult res;
|
||||
if (!model.Predict(im, &res)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return false;
|
||||
}
|
||||
}
|
||||
std::vector<float> end2end_statis;
|
||||
// Step2: repeat for repeats times
|
||||
std::cout << "Counting time..." << std::endl;
|
||||
fastdeploy::TimeCounter tc;
|
||||
fastdeploy::vision::DetectionResult res;
|
||||
for (int i = 0; i < repeats; i++) {
|
||||
if (FLAGS_collect_memory_info && i % dump_period == 0) {
|
||||
fastdeploy::benchmark::DumpCurrentCpuMemoryUsage(cpu_mem_file_name);
|
||||
#if defined(WITH_GPU)
|
||||
fastdeploy::benchmark::DumpCurrentGpuMemoryUsage(gpu_mem_file_name,
|
||||
FLAGS_device_id);
|
||||
#endif
|
||||
}
|
||||
tc.Start();
|
||||
if (!model.Predict(im, &res)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return false;
|
||||
}
|
||||
tc.End();
|
||||
end2end_statis.push_back(tc.Duration() * 1000);
|
||||
}
|
||||
float end2end = std::accumulate(end2end_statis.end() - repeats,
|
||||
end2end_statis.end(), 0.f) /
|
||||
repeats;
|
||||
std::cout << "End2End(ms): " << end2end << "ms." << std::endl;
|
||||
auto vis_im = fastdeploy::vision::VisDetection(im, res);
|
||||
cv::imwrite("vis_result.jpg", vis_im);
|
||||
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
int main(int argc, char* argv[]) {
|
||||
google::ParseCommandLineFlags(&argc, &argv, true);
|
||||
int repeats = FLAGS_repeat;
|
||||
int warmup = FLAGS_warmup;
|
||||
int dump_period = FLAGS_dump_period;
|
||||
std::string cpu_mem_file_name = "result_cpu.txt";
|
||||
std::string gpu_mem_file_name = "result_gpu.txt";
|
||||
// Run model
|
||||
if (RunModel(FLAGS_model, FLAGS_image, warmup, repeats, dump_period,
|
||||
cpu_mem_file_name, gpu_mem_file_name) != true) {
|
||||
exit(1);
|
||||
}
|
||||
if (FLAGS_collect_memory_info) {
|
||||
float cpu_mem = fastdeploy::benchmark::GetCpuMemoryUsage(cpu_mem_file_name);
|
||||
std::cout << "cpu_pss_mb: " << cpu_mem << "MB." << std::endl;
|
||||
#if defined(WITH_GPU)
|
||||
float gpu_mem = fastdeploy::benchmark::GetGpuMemoryUsage(gpu_mem_file_name);
|
||||
std::cout << "gpu_pss_mb: " << gpu_mem << "MB." << std::endl;
|
||||
#endif
|
||||
}
|
||||
fastdeploy::vision::DetectionResult res;
|
||||
BENCHMARK_MODEL(model_ppyolov8, model_ppyolov8.Predict(im, &res))
|
||||
auto vis_im = fastdeploy::vision::VisDetection(im, res);
|
||||
cv::imwrite("vis_result.jpg", vis_im);
|
||||
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||
return 0;
|
||||
}
|
||||
95
benchmark/cpp/benchmark_yolov5.cc
Executable file → Normal file
95
benchmark/cpp/benchmark_yolov5.cc
Executable file → Normal file
@@ -12,96 +12,25 @@
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#include "fastdeploy/benchmark/utils.h"
|
||||
#include "fastdeploy/vision.h"
|
||||
#include "flags.h"
|
||||
#include "macros.h"
|
||||
#include "option.h"
|
||||
|
||||
bool RunModel(std::string model_file, std::string image_file, size_t warmup,
|
||||
size_t repeats, size_t sampling_interval) {
|
||||
int main(int argc, char* argv[]) {
|
||||
google::ParseCommandLineFlags(&argc, &argv, true);
|
||||
auto im = cv::imread(FLAGS_image);
|
||||
// Initialization
|
||||
auto option = fastdeploy::RuntimeOption();
|
||||
if (!CreateRuntimeOption(&option)) {
|
||||
PrintUsage();
|
||||
return false;
|
||||
}
|
||||
if (FLAGS_profile_mode == "runtime") {
|
||||
option.EnableProfiling(FLAGS_include_h2d_d2h, repeats, warmup);
|
||||
}
|
||||
auto model = fastdeploy::vision::detection::YOLOv5(model_file, "", option);
|
||||
if (!model.Initialized()) {
|
||||
std::cerr << "Failed to initialize." << std::endl;
|
||||
return false;
|
||||
}
|
||||
auto im = cv::imread(image_file);
|
||||
// For collect memory info
|
||||
fastdeploy::benchmark::ResourceUsageMonitor resource_moniter(
|
||||
sampling_interval, FLAGS_device_id);
|
||||
if (FLAGS_collect_memory_info) {
|
||||
resource_moniter.Start();
|
||||
}
|
||||
// For Runtime
|
||||
if (FLAGS_profile_mode == "runtime") {
|
||||
fastdeploy::vision::DetectionResult res;
|
||||
if (!model.Predict(im, &res)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return false;
|
||||
}
|
||||
double profile_time = model.GetProfileTime() * 1000;
|
||||
std::cout << "Runtime(ms): " << profile_time << "ms." << std::endl;
|
||||
auto vis_im = fastdeploy::vision::VisDetection(im, res);
|
||||
cv::imwrite("vis_result.jpg", vis_im);
|
||||
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||
} else {
|
||||
// For End2End
|
||||
// Step1: warm up for warmup times
|
||||
std::cout << "Warmup " << warmup << " times..." << std::endl;
|
||||
for (int i = 0; i < warmup; i++) {
|
||||
fastdeploy::vision::DetectionResult res;
|
||||
if (!model.Predict(im, &res)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return false;
|
||||
}
|
||||
}
|
||||
// Step2: repeat for repeats times
|
||||
std::cout << "Counting time..." << std::endl;
|
||||
std::cout << "Repeat " << repeats << " times..." << std::endl;
|
||||
fastdeploy::vision::DetectionResult res;
|
||||
fastdeploy::TimeCounter tc;
|
||||
tc.Start();
|
||||
for (int i = 0; i < repeats; i++) {
|
||||
if (!model.Predict(im, &res)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return false;
|
||||
}
|
||||
}
|
||||
tc.End();
|
||||
double end2end = tc.Duration() / repeats * 1000;
|
||||
std::cout << "End2End(ms): " << end2end << "ms." << std::endl;
|
||||
auto vis_im = fastdeploy::vision::VisDetection(im, res);
|
||||
cv::imwrite("vis_result.jpg", vis_im);
|
||||
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||
}
|
||||
if (FLAGS_collect_memory_info) {
|
||||
float cpu_mem = resource_moniter.GetMaxCpuMem();
|
||||
float gpu_mem = resource_moniter.GetMaxGpuMem();
|
||||
float gpu_util = resource_moniter.GetMaxGpuUtil();
|
||||
std::cout << "cpu_pss_mb: " << cpu_mem << "MB." << std::endl;
|
||||
std::cout << "gpu_pss_mb: " << gpu_mem << "MB." << std::endl;
|
||||
std::cout << "gpu_util: " << gpu_util << std::endl;
|
||||
resource_moniter.Stop();
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
int main(int argc, char* argv[]) {
|
||||
google::ParseCommandLineFlags(&argc, &argv, true);
|
||||
int repeats = FLAGS_repeat;
|
||||
int warmup = FLAGS_warmup;
|
||||
int sampling_interval = FLAGS_sampling_interval;
|
||||
// Run model
|
||||
if (!RunModel(FLAGS_model, FLAGS_image, warmup, repeats, sampling_interval)) {
|
||||
exit(1);
|
||||
}
|
||||
auto model_yolov5 =
|
||||
fastdeploy::vision::detection::YOLOv5(FLAGS_model, "", option);
|
||||
fastdeploy::vision::DetectionResult res;
|
||||
BENCHMARK_MODEL(model_yolov5, model_yolov5.Predict(im, &res))
|
||||
auto vis_im = fastdeploy::vision::VisDetection(im, res);
|
||||
cv::imwrite("vis_result.jpg", vis_im);
|
||||
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||
return 0;
|
||||
}
|
||||
@@ -15,7 +15,6 @@
|
||||
#pragma once
|
||||
|
||||
#include "gflags/gflags.h"
|
||||
#include "fastdeploy/utils/perf.h"
|
||||
|
||||
DEFINE_string(model, "", "Directory of the inference model.");
|
||||
DEFINE_string(image, "", "Path of the image file.");
|
||||
@@ -49,75 +48,3 @@ void PrintUsage() {
|
||||
std::cout << "Default value of backend: default" << std::endl;
|
||||
std::cout << "Default value of use_fp16: false" << std::endl;
|
||||
}
|
||||
|
||||
bool CreateRuntimeOption(fastdeploy::RuntimeOption* option) {
|
||||
if (FLAGS_device == "gpu") {
|
||||
option->UseGpu(FLAGS_device_id);
|
||||
if (FLAGS_backend == "ort") {
|
||||
option->UseOrtBackend();
|
||||
} else if (FLAGS_backend == "paddle") {
|
||||
option->UsePaddleInferBackend();
|
||||
} else if (FLAGS_backend == "trt" || FLAGS_backend == "paddle_trt") {
|
||||
option->UseTrtBackend();
|
||||
if (FLAGS_backend == "paddle_trt") {
|
||||
option->EnablePaddleToTrt();
|
||||
}
|
||||
if (FLAGS_use_fp16) {
|
||||
option->EnableTrtFP16();
|
||||
}
|
||||
} else if (FLAGS_backend == "default") {
|
||||
return true;
|
||||
} else {
|
||||
std::cout << "While inference with GPU, only support "
|
||||
"default/ort/paddle/trt/paddle_trt now, "
|
||||
<< FLAGS_backend << " is not supported." << std::endl;
|
||||
return false;
|
||||
}
|
||||
} else if (FLAGS_device == "cpu") {
|
||||
option->SetCpuThreadNum(FLAGS_cpu_thread_nums);
|
||||
if (FLAGS_backend == "ort") {
|
||||
option->UseOrtBackend();
|
||||
} else if (FLAGS_backend == "ov") {
|
||||
option->UseOpenVINOBackend();
|
||||
} else if (FLAGS_backend == "paddle") {
|
||||
option->UsePaddleInferBackend();
|
||||
} else if (FLAGS_backend == "lite") {
|
||||
option->UsePaddleLiteBackend();
|
||||
if (FLAGS_use_fp16) {
|
||||
option->EnableLiteFP16();
|
||||
}
|
||||
} else if (FLAGS_backend == "default") {
|
||||
return true;
|
||||
} else {
|
||||
std::cout << "While inference with CPU, only support "
|
||||
"default/ort/ov/paddle/lite now, "
|
||||
<< FLAGS_backend << " is not supported." << std::endl;
|
||||
return false;
|
||||
}
|
||||
} else if (FLAGS_device == "xpu") {
|
||||
option->UseKunlunXin(FLAGS_device_id);
|
||||
if (FLAGS_backend == "ort") {
|
||||
option->UseOrtBackend();
|
||||
} else if (FLAGS_backend == "paddle") {
|
||||
option->UsePaddleInferBackend();
|
||||
} else if (FLAGS_backend == "lite") {
|
||||
option->UsePaddleLiteBackend();
|
||||
if (FLAGS_use_fp16) {
|
||||
option->EnableLiteFP16();
|
||||
}
|
||||
} else if (FLAGS_backend == "default") {
|
||||
return true;
|
||||
} else {
|
||||
std::cout << "While inference with XPU, only support "
|
||||
"default/ort/paddle/lite now, "
|
||||
<< FLAGS_backend << " is not supported." << std::endl;
|
||||
return false;
|
||||
}
|
||||
} else {
|
||||
std::cerr << "Only support device CPU/GPU/XPU now, " << FLAGS_device
|
||||
<< " is not supported." << std::endl;
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
70
benchmark/cpp/macros.h
Executable file
70
benchmark/cpp/macros.h
Executable file
@@ -0,0 +1,70 @@
|
||||
// Copyright (c) 2022 PaddlePaddle Authors. 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.
|
||||
#pragma once
|
||||
|
||||
#include "fastdeploy/benchmark/utils.h"
|
||||
#include "fastdeploy/utils/perf.h"
|
||||
|
||||
#define BENCHMARK_MODEL(MODEL_NAME, BENCHMARK_FUNC) \
|
||||
{ \
|
||||
std::cout << "====" << #MODEL_NAME << "====" << std::endl; \
|
||||
if (!MODEL_NAME.Initialized()) { \
|
||||
std::cerr << "Failed to initialize." << std::endl; \
|
||||
return 0; \
|
||||
} \
|
||||
auto __im__ = cv::imread(FLAGS_image); \
|
||||
fastdeploy::benchmark::ResourceUsageMonitor __resource_moniter__( \
|
||||
FLAGS_sampling_interval, FLAGS_device_id); \
|
||||
if (FLAGS_collect_memory_info) { \
|
||||
__resource_moniter__.Start(); \
|
||||
} \
|
||||
if (FLAGS_profile_mode == "runtime") { \
|
||||
if (!BENCHMARK_FUNC) { \
|
||||
std::cerr << "Failed to predict." << std::endl; \
|
||||
return 0; \
|
||||
} \
|
||||
double __profile_time__ = MODEL_NAME.GetProfileTime() * 1000; \
|
||||
std::cout << "Runtime(ms): " << __profile_time__ << "ms." << std::endl; \
|
||||
} else { \
|
||||
std::cout << "Warmup " << FLAGS_warmup << " times..." << std::endl; \
|
||||
for (int __i__ = 0; __i__ < FLAGS_warmup; __i__++) { \
|
||||
if (!BENCHMARK_FUNC) { \
|
||||
std::cerr << "Failed to predict." << std::endl; \
|
||||
return 0; \
|
||||
} \
|
||||
} \
|
||||
std::cout << "Counting time..." << std::endl; \
|
||||
std::cout << "Repeat " << FLAGS_repeat << " times..." << std::endl; \
|
||||
fastdeploy::TimeCounter __tc__; \
|
||||
__tc__.Start(); \
|
||||
for (int __i__ = 0; __i__ < FLAGS_repeat; __i__++) { \
|
||||
if (!BENCHMARK_FUNC) { \
|
||||
std::cerr << "Failed to predict." << std::endl; \
|
||||
return 0; \
|
||||
} \
|
||||
} \
|
||||
__tc__.End(); \
|
||||
double __end2end__ = __tc__.Duration() / FLAGS_repeat * 1000; \
|
||||
std::cout << "End2End(ms): " << __end2end__ << "ms." << std::endl; \
|
||||
} \
|
||||
if (FLAGS_collect_memory_info) { \
|
||||
float __cpu_mem__ = __resource_moniter__.GetMaxCpuMem(); \
|
||||
float __gpu_mem__ = __resource_moniter__.GetMaxGpuMem(); \
|
||||
float __gpu_util__ = __resource_moniter__.GetMaxGpuUtil(); \
|
||||
std::cout << "cpu_pss_mb: " << __cpu_mem__ << "MB." << std::endl; \
|
||||
std::cout << "gpu_pss_mb: " << __gpu_mem__ << "MB." << std::endl; \
|
||||
std::cout << "gpu_util: " << __gpu_util__ << std::endl; \
|
||||
__resource_moniter__.Stop(); \
|
||||
} \
|
||||
}
|
||||
92
benchmark/cpp/option.h
Executable file
92
benchmark/cpp/option.h
Executable file
@@ -0,0 +1,92 @@
|
||||
// Copyright (c) 2023 PaddlePaddle Authors. 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.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "fastdeploy/vision.h"
|
||||
|
||||
static bool CreateRuntimeOption(fastdeploy::RuntimeOption* option) {
|
||||
if (FLAGS_profile_mode == "runtime") {
|
||||
option->EnableProfiling(FLAGS_include_h2d_d2h, FLAGS_repeat, FLAGS_warmup);
|
||||
}
|
||||
if (FLAGS_device == "gpu") {
|
||||
option->UseGpu(FLAGS_device_id);
|
||||
if (FLAGS_backend == "ort") {
|
||||
option->UseOrtBackend();
|
||||
} else if (FLAGS_backend == "paddle") {
|
||||
option->UsePaddleInferBackend();
|
||||
} else if (FLAGS_backend == "trt" || FLAGS_backend == "paddle_trt") {
|
||||
option->UseTrtBackend();
|
||||
if (FLAGS_backend == "paddle_trt") {
|
||||
option->EnablePaddleToTrt();
|
||||
}
|
||||
if (FLAGS_use_fp16) {
|
||||
option->EnableTrtFP16();
|
||||
}
|
||||
} else if (FLAGS_backend == "default") {
|
||||
return true;
|
||||
} else {
|
||||
std::cout << "While inference with GPU, only support "
|
||||
"default/ort/paddle/trt/paddle_trt now, "
|
||||
<< FLAGS_backend << " is not supported." << std::endl;
|
||||
return false;
|
||||
}
|
||||
} else if (FLAGS_device == "cpu") {
|
||||
option->SetCpuThreadNum(FLAGS_cpu_thread_nums);
|
||||
if (FLAGS_backend == "ort") {
|
||||
option->UseOrtBackend();
|
||||
} else if (FLAGS_backend == "ov") {
|
||||
option->UseOpenVINOBackend();
|
||||
} else if (FLAGS_backend == "paddle") {
|
||||
option->UsePaddleInferBackend();
|
||||
} else if (FLAGS_backend == "lite") {
|
||||
option->UsePaddleLiteBackend();
|
||||
if (FLAGS_use_fp16) {
|
||||
option->EnableLiteFP16();
|
||||
}
|
||||
} else if (FLAGS_backend == "default") {
|
||||
return true;
|
||||
} else {
|
||||
std::cout << "While inference with CPU, only support "
|
||||
"default/ort/ov/paddle/lite now, "
|
||||
<< FLAGS_backend << " is not supported." << std::endl;
|
||||
return false;
|
||||
}
|
||||
} else if (FLAGS_device == "xpu") {
|
||||
option->UseKunlunXin(FLAGS_device_id);
|
||||
if (FLAGS_backend == "ort") {
|
||||
option->UseOrtBackend();
|
||||
} else if (FLAGS_backend == "paddle") {
|
||||
option->UsePaddleInferBackend();
|
||||
} else if (FLAGS_backend == "lite") {
|
||||
option->UsePaddleLiteBackend();
|
||||
if (FLAGS_use_fp16) {
|
||||
option->EnableLiteFP16();
|
||||
}
|
||||
} else if (FLAGS_backend == "default") {
|
||||
return true;
|
||||
} else {
|
||||
std::cout << "While inference with XPU, only support "
|
||||
"default/ort/paddle/lite now, "
|
||||
<< FLAGS_backend << " is not supported." << std::endl;
|
||||
return false;
|
||||
}
|
||||
} else {
|
||||
std::cerr << "Only support device CPU/GPU/XPU now, " << FLAGS_device
|
||||
<< " is not supported." << std::endl;
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
Reference in New Issue
Block a user