提交 4dbd85b8 编写于 作者: D dongshuilong

update cls_cpp, use yaml as input

上级 9176a01a
......@@ -14,6 +14,11 @@ SET(TENSORRT_DIR "" CACHE PATH "Compile demo with TensorRT")
set(DEMO_NAME "clas_system")
include(external-cmake/yaml-cpp.cmake)
include_directories("${CMAKE_SOURCE_DIR}/")
include_directories("${CMAKE_CURRENT_BINARY_DIR}/ext/yaml-cpp/src/ext-yaml-cpp/include")
link_directories("${CMAKE_CURRENT_BINARY_DIR}/ext/yaml-cpp/lib")
macro(safe_set_static_flag)
foreach(flag_var
CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE
......@@ -61,7 +66,7 @@ if (WIN32)
add_definitions(-DSTATIC_LIB)
endif()
else()
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -o3 -std=c++11")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -O3 -std=c++11")
set(CMAKE_STATIC_LIBRARY_PREFIX "")
endif()
message("flags" ${CMAKE_CXX_FLAGS})
......@@ -153,7 +158,7 @@ endif(WITH_STATIC_LIB)
if (NOT WIN32)
set(DEPS ${DEPS}
${MATH_LIB} ${MKLDNN_LIB}
glog gflags protobuf z xxhash
glog gflags protobuf z xxhash yaml-cpp
)
if(EXISTS "${PADDLE_LIB}/third_party/install/snappystream/lib")
set(DEPS ${DEPS} snappystream)
......@@ -164,7 +169,7 @@ if (NOT WIN32)
else()
set(DEPS ${DEPS}
${MATH_LIB} ${MKLDNN_LIB}
glog gflags_static libprotobuf xxhash)
glog gflags_static libprotobuf xxhash libyaml-cppmt)
set(DEPS ${DEPS} libcmt shlwapi)
if (EXISTS "${PADDLE_LIB}/third_party/install/snappy/lib")
set(DEPS ${DEPS} snappy)
......@@ -204,6 +209,7 @@ include_directories(${FETCHCONTENT_BASE_DIR}/extern_autolog-src)
AUX_SOURCE_DIRECTORY(./src SRCS)
add_executable(${DEMO_NAME} ${SRCS})
ADD_DEPENDENCIES(${DEMO_NAME} ext-yaml-cpp)
target_link_libraries(${DEMO_NAME} ${DEPS})
......
find_package(Git REQUIRED)
include(ExternalProject)
message("${CMAKE_BUILD_TYPE}")
ExternalProject_Add(
ext-yaml-cpp
URL https://bj.bcebos.com/paddlex/deploy/deps/yaml-cpp.zip
URL_MD5 9542d6de397d1fbd649ed468cb5850e6
CMAKE_ARGS
-DYAML_CPP_BUILD_TESTS=OFF
-DYAML_CPP_BUILD_TOOLS=OFF
-DYAML_CPP_INSTALL=OFF
-DYAML_CPP_BUILD_CONTRIB=OFF
-DMSVC_SHARED_RT=OFF
-DBUILD_SHARED_LIBS=OFF
-DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
-DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=${CMAKE_BINARY_DIR}/ext/yaml-cpp/lib
-DCMAKE_ARCHIVE_OUTPUT_DIRECTORY=${CMAKE_BINARY_DIR}/ext/yaml-cpp/lib
PREFIX "${CMAKE_BINARY_DIR}/ext/yaml-cpp"
# Disable install step
INSTALL_COMMAND ""
LOG_DOWNLOAD ON
LOG_BUILD 1
)
......@@ -28,6 +28,7 @@
#include <fstream>
#include <numeric>
#include "include/cls_config.h"
#include <include/preprocess_op.h>
using namespace paddle_infer;
......@@ -36,25 +37,21 @@ namespace PaddleClas {
class Classifier {
public:
explicit Classifier(const std::string &model_path,
const std::string &params_path, const bool &use_gpu,
const int &gpu_id, const int &gpu_mem,
const int &cpu_math_library_num_threads,
const bool &use_mkldnn, const bool &use_tensorrt,
const bool &use_fp16, const int &resize_short_size,
const int &crop_size) {
this->use_gpu_ = use_gpu;
this->gpu_id_ = gpu_id;
this->gpu_mem_ = gpu_mem;
this->cpu_math_library_num_threads_ = cpu_math_library_num_threads;
this->use_mkldnn_ = use_mkldnn;
this->use_tensorrt_ = use_tensorrt;
this->use_fp16_ = use_fp16;
this->resize_short_size_ = resize_short_size;
this->crop_size_ = crop_size;
LoadModel(model_path, params_path);
explicit Classifier(const ClsConfig &config) {
this->use_gpu_ = config.use_gpu;
this->gpu_id_ = config.gpu_id;
this->gpu_mem_ = config.gpu_mem;
this->cpu_math_library_num_threads_ = config.cpu_threads;
this->use_fp16_ = config.use_fp16;
this->use_mkldnn_ = config.use_mkldnn;
this->use_tensorrt_ = config.use_tensorrt;
this->mean_ = config.mean;
this->std_ = config.std;
this->resize_short_size_ = config.resize_short_size;
this->scale_ = config.scale;
this->crop_size_ = config.crop_size;
this->ir_optim_ = config.ir_optim;
LoadModel(config.cls_model_path, config.cls_params_path);
}
// Load Paddle inference model
......@@ -73,10 +70,11 @@ private:
bool use_mkldnn_ = false;
bool use_tensorrt_ = false;
bool use_fp16_ = false;
bool ir_optim_ = true;
std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
std::vector<float> scale_ = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};
bool is_scale_ = true;
std::vector<float> std_ = {0.229f, 0.224f, 0.225f};
float scale_ = 0.00392157;
int resize_short_size_ = 256;
int crop_size_ = 224;
......
......@@ -14,6 +14,14 @@
#pragma once
#ifdef WIN32
#define OS_PATH_SEP "\\"
#else
#define OS_PATH_SEP "/"
#endif
#include "include/utility.h"
#include "yaml-cpp/yaml.h"
#include <iomanip>
#include <iostream>
#include <map>
......@@ -21,70 +29,83 @@
#include <string>
#include <vector>
#include "include/utility.h"
namespace PaddleClas {
class ClsConfig {
public:
explicit ClsConfig(const std::string &config_file) {
config_map_ = LoadConfig(config_file);
this->use_gpu = bool(stoi(config_map_["use_gpu"]));
this->gpu_id = stoi(config_map_["gpu_id"]);
this->gpu_mem = stoi(config_map_["gpu_mem"]);
this->cpu_threads = stoi(config_map_["cpu_threads"]);
this->use_mkldnn = bool(stoi(config_map_["use_mkldnn"]));
this->use_tensorrt = bool(stoi(config_map_["use_tensorrt"]));
this->use_fp16 = bool(stoi(config_map_["use_fp16"]));
this->cls_model_path.assign(config_map_["cls_model_path"]);
this->cls_params_path.assign(config_map_["cls_params_path"]);
this->resize_short_size = stoi(config_map_["resize_short_size"]);
this->crop_size = stoi(config_map_["crop_size"]);
this->benchmark = bool(stoi(config_map_["benchmark"]));
explicit ClsConfig(const std::string &path) {
ReadYamlConfig(path);
this->infer_imgs =
this->config_file["Global"]["infer_imgs"].as<std::string>();
this->batch_size = this->config_file["Global"]["batch_size"].as<int>();
this->use_gpu = this->config_file["Global"]["use_gpu"].as<bool>();
if (this->config_file["Global"]["gpu_id"].IsDefined())
this->gpu_id = this->config_file["Global"]["gpu_id"].as<int>();
else
this->gpu_id = 0;
this->gpu_mem = this->config_file["Global"]["gpu_mem"].as<int>();
this->cpu_threads =
this->config_file["Global"]["cpu_num_threads"].as<int>();
this->use_mkldnn = this->config_file["Global"]["enable_mkldnn"].as<bool>();
this->use_tensorrt = this->config_file["Global"]["use_tensorrt"].as<bool>();
this->use_fp16 = this->config_file["Global"]["use_fp16"].as<bool>();
this->enable_benchmark =
this->config_file["Global"]["enable_benchmark"].as<bool>();
this->ir_optim = this->config_file["Global"]["ir_optim"].as<bool>();
this->enable_profile =
this->config_file["Global"]["enable_profile"].as<bool>();
this->cls_model_path =
this->config_file["Global"]["inference_model_dir"].as<std::string>() +
OS_PATH_SEP + "inference.pdmodel";
this->cls_params_path =
this->config_file["Global"]["inference_model_dir"].as<std::string>() +
OS_PATH_SEP + "inference.pdiparams";
this->resize_short_size =
this->config_file["PreProcess"]["transform_ops"][0]["ResizeImage"]
["resize_short"]
.as<int>();
this->crop_size =
this->config_file["PreProcess"]["transform_ops"][1]["CropImage"]["size"]
.as<int>();
this->scale = this->config_file["PreProcess"]["transform_ops"][2]
["NormalizeImage"]["scale"]
.as<float>();
this->mean = this->config_file["PreProcess"]["transform_ops"][2]
["NormalizeImage"]["mean"]
.as<std::vector<float>>();
this->std = this->config_file["PreProcess"]["transform_ops"][2]
["NormalizeImage"]["std"]
.as<std::vector<float>>();
if (this->config_file["Global"]["benchmark"].IsDefined())
this->benchmark = this->config_file["Global"]["benchmark"].as<bool>();
else
this->benchmark = false;
}
YAML::Node config_file;
bool use_gpu = false;
int gpu_id = 0;
int gpu_mem = 4000;
int cpu_threads = 1;
bool use_mkldnn = false;
bool use_tensorrt = false;
bool use_fp16 = false;
bool benchmark = false;
int batch_size = 1;
bool enable_benchmark = false;
bool ir_optim = true;
bool enable_profile = false;
std::string cls_model_path;
std::string cls_params_path;
std::string infer_imgs;
int resize_short_size = 256;
int crop_size = 224;
float scale = 0.00392157;
std::vector<float> mean = {0.485, 0.456, 0.406};
std::vector<float> std = {0.229, 0.224, 0.225};
void PrintConfigInfo();
private:
// Load configuration
std::map<std::string, std::string> LoadConfig(const std::string &config_file);
std::vector<std::string> split(const std::string &str,
const std::string &delim);
std::map<std::string, std::string> config_map_;
void ReadYamlConfig(const std::string &path);
};
} // namespace PaddleClas
// Copyright (c) 2020 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.
// 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
......@@ -34,7 +34,7 @@ namespace PaddleClas {
class Normalize {
public:
virtual void Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &scale, const bool is_scale = true);
const std::vector<float> &std, float &scale);
};
// RGB -> CHW
......@@ -53,4 +53,4 @@ public:
virtual void Run(const cv::Mat &img, cv::Mat &resize_img, int max_size_len);
};
} // namespace PaddleClas
\ No newline at end of file
} // namespace PaddleClas
......@@ -44,7 +44,7 @@ void Classifier::LoadModel(const std::string &model_path,
// true for multiple input
config.SwitchSpecifyInputNames(true);
config.SwitchIrOptim(true);
config.SwitchIrOptim(this->ir_optim_);
config.EnableMemoryOptim();
config.DisableGlogInfo();
......@@ -62,8 +62,7 @@ double Classifier::Run(cv::Mat &img, std::vector<double> *times) {
this->crop_op_.Run(resize_img, this->crop_size_);
this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
this->is_scale_);
this->normalize_op_.Run(&resize_img, this->mean_, this->std_, this->scale_);
std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
this->permute_op_.Run(&resize_img, input.data());
......
......@@ -16,49 +16,20 @@
namespace PaddleClas {
std::vector<std::string> ClsConfig::split(const std::string &str,
const std::string &delim) {
std::vector<std::string> res;
if ("" == str)
return res;
char *strs = new char[str.length() + 1];
std::strcpy(strs, str.c_str());
char *d = new char[delim.length() + 1];
std::strcpy(d, delim.c_str());
char *p = std::strtok(strs, d);
while (p) {
std::string s = p;
res.push_back(s);
p = std::strtok(NULL, d);
}
return res;
}
std::map<std::string, std::string>
ClsConfig::LoadConfig(const std::string &config_path) {
auto config = Utility::ReadDict(config_path);
std::map<std::string, std::string> dict;
for (int i = 0; i < config.size(); i++) {
// pass for empty line or comment
if (config[i].size() <= 1 || config[i][0] == '#') {
continue;
}
std::vector<std::string> res = split(config[i], " ");
dict[res[0]] = res[1];
}
return dict;
}
void ClsConfig::PrintConfigInfo() {
std::cout << "=======Paddle Class inference config======" << std::endl;
for (auto iter = config_map_.begin(); iter != config_map_.end(); iter++) {
std::cout << iter->first << " : " << iter->second << std::endl;
}
std::cout << this->config_file << std::endl;
std::cout << "=======End of Paddle Class inference config======" << std::endl;
}
} // namespace PaddleClas
\ No newline at end of file
void ClsConfig::ReadYamlConfig(const std::string &path) {
try {
this->config_file = YAML::LoadFile(path);
} catch (YAML::BadFile &e) {
std::cout << "Something wrong in yaml file, please check yaml file"
<< std::endl;
exit(1);
}
}
}; // namespace PaddleClas
......@@ -27,6 +27,7 @@
#include <numeric>
#include <auto_log/autolog.h>
#include <gflags/gflags.h>
#include <include/cls.h>
#include <include/cls_config.h>
......@@ -34,18 +35,27 @@ using namespace std;
using namespace cv;
using namespace PaddleClas;
DEFINE_string(config, "", "Path of yaml file");
DEFINE_string(c, "", "Path of yaml file");
int main(int argc, char **argv) {
if (argc < 3) {
std::cerr << "[ERROR] usage: " << argv[0]
<< " configure_filepath image_path\n";
google::ParseCommandLineFlags(&argc, &argv, true);
std::string yaml_path = "";
if (FLAGS_config == "" && FLAGS_c == "") {
std::cerr << "[ERROR] usage: " << std::endl
<< argv[0] << " -c $yaml_path" << std::endl
<< "or:" << std::endl
<< argv[0] << " -config $yaml_path" << std::endl;
exit(1);
} else if (FLAGS_config != "") {
yaml_path = FLAGS_config;
} else {
yaml_path = FLAGS_c;
}
ClsConfig config(argv[1]);
ClsConfig config(yaml_path);
config.PrintConfigInfo();
std::string path(argv[2]);
std::string path(config.infer_imgs);
std::vector<std::string> img_files_list;
if (cv::utils::fs::isDirectory(path)) {
......@@ -60,11 +70,7 @@ int main(int argc, char **argv) {
std::cout << "img_file_list length: " << img_files_list.size() << std::endl;
Classifier classifier(config.cls_model_path, config.cls_params_path,
config.use_gpu, config.gpu_id, config.gpu_mem,
config.cpu_threads, config.use_mkldnn,
config.use_tensorrt, config.use_fp16,
config.resize_short_size, config.crop_size);
Classifier classifier(config);
double elapsed_time = 0.0;
std::vector<double> cls_times;
......
......@@ -42,20 +42,18 @@ void Permute::Run(const cv::Mat *im, float *data) {
}
void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &scale, const bool is_scale) {
double e = 1.0;
if (is_scale) {
e /= 255.0;
const std::vector<float> &std, float &scale) {
if (scale) {
(*im).convertTo(*im, CV_32FC3, scale);
}
(*im).convertTo(*im, CV_32FC3, e);
for (int h = 0; h < im->rows; h++) {
for (int w = 0; w < im->cols; w++) {
im->at<cv::Vec3f>(h, w)[0] =
(im->at<cv::Vec3f>(h, w)[0] - mean[0]) * scale[0];
(im->at<cv::Vec3f>(h, w)[0] - mean[0]) / std[0];
im->at<cv::Vec3f>(h, w)[1] =
(im->at<cv::Vec3f>(h, w)[1] - mean[1]) * scale[1];
(im->at<cv::Vec3f>(h, w)[1] - mean[1]) / std[1];
im->at<cv::Vec3f>(h, w)[2] =
(im->at<cv::Vec3f>(h, w)[2] - mean[2]) * scale[2];
(im->at<cv::Vec3f>(h, w)[2] - mean[2]) / std[2];
}
}
}
......@@ -87,4 +85,4 @@ void ResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
}
} // namespace PaddleClas
\ No newline at end of file
} // namespace PaddleClas
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