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提交 29f8fb83 编写于 作者: D dongshuilong
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cpp shitu code format

上级 a96305c2
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Showing with 1324 addition and 1301 deletion
deploy/cpp_shitu/include/feature_extracter.h
浏览文件 @ 29f8fb83
...@@ -35,77 +35,76 @@ using namespace paddle_infer; ...@@ -35,77 +35,76 @@ using namespace paddle_infer;
namespace Feature { namespace Feature {
class FeatureExtracter { class FeatureExtracter {
public: public:
explicit FeatureExtracter(const YAML::Node &config_file) { explicit FeatureExtracter(const YAML::Node &config_file) {
this->use_gpu_ = config_file["Global"]["use_gpu"].as<bool>(); this->use_gpu_ = config_file["Global"]["use_gpu"].as<bool>();
if (config_file["Global"]["gpu_id"].IsDefined()) if (config_file["Global"]["gpu_id"].IsDefined())
this->gpu_id_ = config_file["Global"]["gpu_id"].as<int>(); this->gpu_id_ = config_file["Global"]["gpu_id"].as<int>();
else else
this->gpu_id_ = 0; this->gpu_id_ = 0;
this->gpu_mem_ = config_file["Global"]["gpu_mem"].as<int>(); this->gpu_mem_ = config_file["Global"]["gpu_mem"].as<int>();
this->cpu_math_library_num_threads_ = this->cpu_math_library_num_threads_ =
config_file["Global"]["cpu_num_threads"].as<int>(); config_file["Global"]["cpu_num_threads"].as<int>();
this->use_mkldnn_ = config_file["Global"]["enable_mkldnn"].as<bool>(); this->use_mkldnn_ = config_file["Global"]["enable_mkldnn"].as<bool>();
this->use_tensorrt_ = config_file["Global"]["use_tensorrt"].as<bool>(); this->use_tensorrt_ = config_file["Global"]["use_tensorrt"].as<bool>();
this->use_fp16_ = config_file["Global"]["use_fp16"].as<bool>(); this->use_fp16_ = config_file["Global"]["use_fp16"].as<bool>();
this->cls_model_path_ = this->cls_model_path_ =
config_file["Global"]["rec_inference_model_dir"].as<std::string>() + config_file["Global"]["rec_inference_model_dir"].as<std::string>() +
OS_PATH_SEP + "inference.pdmodel"; OS_PATH_SEP + "inference.pdmodel";
this->cls_params_path_ = this->cls_params_path_ =
config_file["Global"]["rec_inference_model_dir"].as<std::string>() + config_file["Global"]["rec_inference_model_dir"].as<std::string>() +
OS_PATH_SEP + "inference.pdiparams"; OS_PATH_SEP + "inference.pdiparams";
this->resize_size_ = this->resize_size_ =
config_file["RecPreProcess"]["transform_ops"][0]["ResizeImage"]["size"] config_file["RecPreProcess"]["transform_ops"][0]["ResizeImage"]["size"]
.as<int>(); .as<int>();
this->scale_ = config_file["RecPreProcess"]["transform_ops"][1] this->scale_ = config_file["RecPreProcess"]["transform_ops"][1]["NormalizeImage"]["scale"].as<float>();
["NormalizeImage"]["scale"] this->mean_ = config_file["RecPreProcess"]["transform_ops"][1]
.as<float>(); ["NormalizeImage"]["mean"]
this->mean_ = config_file["RecPreProcess"]["transform_ops"][1] .as < std::vector < float >> ();
["NormalizeImage"]["mean"] this->std_ = config_file["RecPreProcess"]["transform_ops"][1]
.as<std::vector<float>>(); ["NormalizeImage"]["std"]
this->std_ = config_file["RecPreProcess"]["transform_ops"][1] .as < std::vector < float >> ();
["NormalizeImage"]["std"] if (config_file["Global"]["rec_feature_normlize"].IsDefined())
.as<std::vector<float>>(); this->feature_norm =
if (config_file["Global"]["rec_feature_normlize"].IsDefined()) config_file["Global"]["rec_feature_normlize"].as<bool>();
this->feature_norm =
config_file["Global"]["rec_feature_normlize"].as<bool>(); LoadModel(cls_model_path_, cls_params_path_);
}
LoadModel(cls_model_path_, cls_params_path_);
} // Load Paddle inference model
void LoadModel(const std::string &model_path, const std::string &params_path);
// Load Paddle inference model
void LoadModel(const std::string &model_path, const std::string &params_path); // Run predictor
void Run(cv::Mat &img, std::vector<float> &out_data,
// Run predictor std::vector<double> &times);
void Run(cv::Mat &img, std::vector<float> &out_data,
std::vector<double> &times); void FeatureNorm(std::vector<float> &feature);
void FeatureNorm(std::vector<float> &feature);
std::shared_ptr <Predictor> predictor_;
std::shared_ptr<Predictor> predictor_;
private:
private: bool use_gpu_ = false;
bool use_gpu_ = false; int gpu_id_ = 0;
int gpu_id_ = 0; int gpu_mem_ = 4000;
int gpu_mem_ = 4000; int cpu_math_library_num_threads_ = 4;
int cpu_math_library_num_threads_ = 4; bool use_mkldnn_ = false;
bool use_mkldnn_ = false; bool use_tensorrt_ = false;
bool use_tensorrt_ = false; bool feature_norm = true;
bool feature_norm = true; bool use_fp16_ = false;
bool use_fp16_ = false; std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
std::vector<float> mean_ = {0.485f, 0.456f, 0.406f}; std::vector<float> std_ = {0.229f, 0.224f, 0.225f};
std::vector<float> std_ = {0.229f, 0.224f, 0.225f}; float scale_ = 0.00392157;
float scale_ = 0.00392157; int resize_size_ = 224;
int resize_size_ = 224; int resize_short_ = 224;
int resize_short_ = 224; std::string cls_model_path_;
std::string cls_model_path_; std::string cls_params_path_;
std::string cls_params_path_;
// pre-process
// pre-process ResizeImg resize_op_;
ResizeImg resize_op_; Normalize normalize_op_;
Normalize normalize_op_; Permute permute_op_;
Permute permute_op_; };
};
} // namespace Feature } // namespace Feature
deploy/cpp_shitu/include/nms.h
浏览文件 @ 29f8fb83
...@@ -17,21 +17,21 @@ ...@@ -17,21 +17,21 @@
#include <algorithm> #include <algorithm>
#include <include/object_detector.h> #include <include/object_detector.h>
template <typename T> template<typename T>
static inline bool SortScorePairDescend(const std::pair<float, T> &pair1, static inline bool SortScorePairDescend(const std::pair<float, T> &pair1,
const std::pair<float, T> &pair2) { const std::pair<float, T> &pair2) {
return pair1.first > pair2.first; return pair1.first > pair2.first;
} }
float RectOverlap(const Detection::ObjectResult &a, float RectOverlap(const Detection::ObjectResult &a,
const Detection::ObjectResult &b) { const Detection::ObjectResult &b) {
float Aa = (a.rect[2] - a.rect[0] + 1) * (a.rect[3] - a.rect[1] + 1); float Aa = (a.rect[2] - a.rect[0] + 1) * (a.rect[3] - a.rect[1] + 1);
float Ab = (b.rect[2] - b.rect[0] + 1) * (b.rect[3] - b.rect[1] + 1); float Ab = (b.rect[2] - b.rect[0] + 1) * (b.rect[3] - b.rect[1] + 1);
int iou_w = max(min(a.rect[2], b.rect[2]) - max(a.rect[0], b.rect[0]) + 1, 0); int iou_w = max(min(a.rect[2], b.rect[2]) - max(a.rect[0], b.rect[0]) + 1, 0);
int iou_h = max(min(a.rect[3], b.rect[3]) - max(a.rect[1], b.rect[1]) + 1, 0); int iou_h = max(min(a.rect[3], b.rect[3]) - max(a.rect[1], b.rect[1]) + 1, 0);
float Aab = iou_w * iou_h; float Aab = iou_w * iou_h;
return Aab / (Aa + Ab - Aab); return Aab / (Aa + Ab - Aab);
} }
// Get max scores with corresponding indices. // Get max scores with corresponding indices.
...@@ -40,46 +40,46 @@ float RectOverlap(const Detection::ObjectResult &a, ...@@ -40,46 +40,46 @@ float RectOverlap(const Detection::ObjectResult &a,
// top_k: if -1, keep all; otherwise, keep at most top_k. // top_k: if -1, keep all; otherwise, keep at most top_k.
// score_index_vec: store the sorted (score, index) pair. // score_index_vec: store the sorted (score, index) pair.
inline void inline void
GetMaxScoreIndex(const std::vector<Detection::ObjectResult> &det_result, GetMaxScoreIndex(const std::vector <Detection::ObjectResult> &det_result,
const float threshold, const float threshold,
std::vector<std::pair<float, int>> &score_index_vec) { std::vector <std::pair<float, int>> &score_index_vec) {
// Generate index score pairs. // Generate index score pairs.
for (size_t i = 0; i < det_result.size(); ++i) { for (size_t i = 0; i < det_result.size(); ++i) {
if (det_result[i].confidence > threshold) { if (det_result[i].confidence > threshold) {
score_index_vec.push_back(std::make_pair(det_result[i].confidence, i)); score_index_vec.push_back(std::make_pair(det_result[i].confidence, i));
}
} }
}
// Sort the score pair according to the scores in descending order // Sort the score pair according to the scores in descending order
std::stable_sort(score_index_vec.begin(), score_index_vec.end(), std::stable_sort(score_index_vec.begin(), score_index_vec.end(),
SortScorePairDescend<int>); SortScorePairDescend<int>);
// // Keep top_k scores if needed. // // Keep top_k scores if needed.
// if (top_k > 0 && top_k < (int)score_index_vec.size()) // if (top_k > 0 && top_k < (int)score_index_vec.size())
// { // {
// score_index_vec.resize(top_k); // score_index_vec.resize(top_k);
// } // }
} }
void NMSBoxes(const std::vector<Detection::ObjectResult> det_result, void NMSBoxes(const std::vector <Detection::ObjectResult> det_result,
const float score_threshold, const float nms_threshold, const float score_threshold, const float nms_threshold,
std::vector<int> &indices) { std::vector<int> &indices) {
int a = 1; int a = 1;
// Get top_k scores (with corresponding indices). // Get top_k scores (with corresponding indices).
std::vector<std::pair<float, int>> score_index_vec; std::vector <std::pair<float, int>> score_index_vec;
GetMaxScoreIndex(det_result, score_threshold, score_index_vec); GetMaxScoreIndex(det_result, score_threshold, score_index_vec);
// Do nms // Do nms
indices.clear(); indices.clear();
for (size_t i = 0; i < score_index_vec.size(); ++i) { for (size_t i = 0; i < score_index_vec.size(); ++i) {
const int idx = score_index_vec[i].second; const int idx = score_index_vec[i].second;
bool keep = true; bool keep = true;
for (int k = 0; k < (int)indices.size() && keep; ++k) { for (int k = 0; k < (int) indices.size() && keep; ++k) {
const int kept_idx = indices[k]; const int kept_idx = indices[k];
float overlap = RectOverlap(det_result[idx], det_result[kept_idx]); float overlap = RectOverlap(det_result[idx], det_result[kept_idx]);
keep = overlap <= nms_threshold; keep = overlap <= nms_threshold;
}
if (keep)
indices.push_back(idx);
} }
if (keep)
indices.push_back(idx);
}
} }
deploy/cpp_shitu/include/object_detector.h
浏览文件 @ 29f8fb83
...@@ -33,103 +33,106 @@ using namespace paddle_infer; ...@@ -33,103 +33,106 @@ using namespace paddle_infer;
namespace Detection { namespace Detection {
// Object Detection Result // Object Detection Result
struct ObjectResult { struct ObjectResult {
// Rectangle coordinates of detected object: left, right, top, down // Rectangle coordinates of detected object: left, right, top, down
std::vector<int> rect; std::vector<int> rect;
// Class id of detected object // Class id of detected object
int class_id; int class_id;
// Confidence of detected object // Confidence of detected object
float confidence; float confidence;
}; };
// Generate visualization colormap for each class // Generate visualization colormap for each class
std::vector<int> GenerateColorMap(int num_class); std::vector<int> GenerateColorMap(int num_class);
// Visualiztion Detection Result // Visualiztion Detection Result
cv::Mat VisualizeResult(const cv::Mat &img, cv::Mat VisualizeResult(const cv::Mat &img,
const std::vector<ObjectResult> &results, const std::vector <ObjectResult> &results,
const std::vector<std::string> &lables, const std::vector <std::string> &lables,
const std::vector<int> &colormap, const bool is_rbox); const std::vector<int> &colormap, const bool is_rbox);
class ObjectDetector { class ObjectDetector {
public: public:
explicit ObjectDetector(const YAML::Node &config_file) { explicit ObjectDetector(const YAML::Node &config_file) {
this->use_gpu_ = config_file["Global"]["use_gpu"].as<bool>(); this->use_gpu_ = config_file["Global"]["use_gpu"].as<bool>();
if (config_file["Global"]["gpu_id"].IsDefined()) if (config_file["Global"]["gpu_id"].IsDefined())
this->gpu_id_ = config_file["Global"]["gpu_id"].as<int>(); this->gpu_id_ = config_file["Global"]["gpu_id"].as<int>();
this->gpu_mem_ = config_file["Global"]["gpu_mem"].as<int>(); this->gpu_mem_ = config_file["Global"]["gpu_mem"].as<int>();
this->cpu_math_library_num_threads_ = this->cpu_math_library_num_threads_ =
config_file["Global"]["cpu_num_threads"].as<int>(); config_file["Global"]["cpu_num_threads"].as<int>();
this->use_mkldnn_ = config_file["Global"]["enable_mkldnn"].as<bool>(); this->use_mkldnn_ = config_file["Global"]["enable_mkldnn"].as<bool>();
this->use_tensorrt_ = config_file["Global"]["use_tensorrt"].as<bool>(); this->use_tensorrt_ = config_file["Global"]["use_tensorrt"].as<bool>();
this->use_fp16_ = config_file["Global"]["use_fp16"].as<bool>(); this->use_fp16_ = config_file["Global"]["use_fp16"].as<bool>();
this->model_dir_ = this->model_dir_ =
config_file["Global"]["det_inference_model_dir"].as<std::string>(); config_file["Global"]["det_inference_model_dir"].as<std::string>();
this->threshold_ = config_file["Global"]["threshold"].as<float>(); this->threshold_ = config_file["Global"]["threshold"].as<float>();
this->max_det_results_ = config_file["Global"]["max_det_results"].as<int>(); this->max_det_results_ = config_file["Global"]["max_det_results"].as<int>();
this->image_shape_ = this->image_shape_ =
config_file["Global"]["image_shape"].as<std::vector<int>>(); config_file["Global"]["image_shape"].as < std::vector < int >> ();
this->label_list_ = this->label_list_ =
config_file["Global"]["labe_list"].as<std::vector<std::string>>(); config_file["Global"]["labe_list"].as < std::vector < std::string >> ();
this->ir_optim_ = config_file["Global"]["ir_optim"].as<bool>(); this->ir_optim_ = config_file["Global"]["ir_optim"].as<bool>();
this->batch_size_ = config_file["Global"]["batch_size"].as<int>(); this->batch_size_ = config_file["Global"]["batch_size"].as<int>();
preprocessor_.Init(config_file["DetPreProcess"]["transform_ops"]); preprocessor_.Init(config_file["DetPreProcess"]["transform_ops"]);
LoadModel(model_dir_, batch_size_, run_mode); LoadModel(model_dir_, batch_size_, run_mode);
} }
// Load Paddle inference model // Load Paddle inference model
void LoadModel(const std::string &model_dir, const int batch_size = 1, void LoadModel(const std::string &model_dir, const int batch_size = 1,
const std::string &run_mode = "fluid"); const std::string &run_mode = "fluid");
// Run predictor // Run predictor
void Predict(const std::vector<cv::Mat> imgs, const int warmup = 0, void Predict(const std::vector <cv::Mat> imgs, const int warmup = 0,
const int repeats = 1, const int repeats = 1,
std::vector<ObjectResult> *result = nullptr, std::vector <ObjectResult> *result = nullptr,
std::vector<int> *bbox_num = nullptr, std::vector<int> *bbox_num = nullptr,
std::vector<double> *times = nullptr); std::vector<double> *times = nullptr);
const std::vector<std::string> &GetLabelList() const {
return this->label_list_; const std::vector <std::string> &GetLabelList() const {
} return this->label_list_;
const float &GetThreshold() const { return this->threshold_; } }
private: const float &GetThreshold() const { return this->threshold_; }
bool use_gpu_ = true;
int gpu_id_ = 0; private:
int gpu_mem_ = 800; bool use_gpu_ = true;
int cpu_math_library_num_threads_ = 6; int gpu_id_ = 0;
std::string run_mode = "fluid"; int gpu_mem_ = 800;
bool use_mkldnn_ = false; int cpu_math_library_num_threads_ = 6;
bool use_tensorrt_ = false; std::string run_mode = "fluid";
bool batch_size_ = 1; bool use_mkldnn_ = false;
bool use_fp16_ = false; bool use_tensorrt_ = false;
std::string model_dir_; bool batch_size_ = 1;
float threshold_ = 0.5; bool use_fp16_ = false;
float max_det_results_ = 5; std::string model_dir_;
std::vector<int> image_shape_ = {3, 640, 640}; float threshold_ = 0.5;
std::vector<std::string> label_list_; float max_det_results_ = 5;
bool ir_optim_ = true; std::vector<int> image_shape_ = {3, 640, 640};
bool det_permute_ = true; std::vector <std::string> label_list_;
bool det_postprocess_ = true; bool ir_optim_ = true;
int min_subgraph_size_ = 30; bool det_permute_ = true;
bool use_dynamic_shape_ = false; bool det_postprocess_ = true;
int trt_min_shape_ = 1; int min_subgraph_size_ = 30;
int trt_max_shape_ = 1280; bool use_dynamic_shape_ = false;
int trt_opt_shape_ = 640; int trt_min_shape_ = 1;
bool trt_calib_mode_ = false; int trt_max_shape_ = 1280;
int trt_opt_shape_ = 640;
// Preprocess image and copy data to input buffer bool trt_calib_mode_ = false;
void Preprocess(const cv::Mat &image_mat);
// Postprocess result // Preprocess image and copy data to input buffer
void Postprocess(const std::vector<cv::Mat> mats, void Preprocess(const cv::Mat &image_mat);
std::vector<ObjectResult> *result, std::vector<int> bbox_num,
bool is_rbox); // Postprocess result
void Postprocess(const std::vector <cv::Mat> mats,
std::shared_ptr<Predictor> predictor_; std::vector <ObjectResult> *result, std::vector<int> bbox_num,
Preprocessor preprocessor_; bool is_rbox);
ImageBlob inputs_;
std::vector<float> output_data_; std::shared_ptr <Predictor> predictor_;
std::vector<int> out_bbox_num_data_; Preprocessor preprocessor_;
}; ImageBlob inputs_;
std::vector<float> output_data_;
std::vector<int> out_bbox_num_data_;
};
} // namespace Detection } // namespace Detection
deploy/cpp_shitu/include/preprocess_op.h
浏览文件 @ 29f8fb83
...@@ -31,27 +31,27 @@ using namespace std; ...@@ -31,27 +31,27 @@ using namespace std;
namespace Feature { namespace Feature {
class Normalize { class Normalize {
public: public:
virtual void Run(cv::Mat *im, const std::vector<float> &mean, virtual void Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &std, float scale); const std::vector<float> &std, float scale);
}; };
// RGB -> CHW // RGB -> CHW
class Permute { class Permute {
public: public:
virtual void Run(const cv::Mat *im, float *data); virtual void Run(const cv::Mat *im, float *data);
}; };
class CenterCropImg { class CenterCropImg {
public: public:
virtual void Run(cv::Mat &im, const int crop_size = 224); virtual void Run(cv::Mat &im, const int crop_size = 224);
}; };
class ResizeImg { class ResizeImg {
public: public:
virtual void Run(const cv::Mat &img, cv::Mat &resize_img, int max_size_len, virtual void Run(const cv::Mat &img, cv::Mat &resize_img, int max_size_len,
int size = 0); int size = 0);
}; };
} // namespace Feature } // namespace Feature
deploy/cpp_shitu/include/preprocess_op_det.h
浏览文件 @ 29f8fb83
...@@ -31,125 +31,128 @@ ...@@ -31,125 +31,128 @@
namespace Detection { namespace Detection {
// Object for storing all preprocessed data // Object for storing all preprocessed data
class ImageBlob { class ImageBlob {
public: public:
// image width and height // image width and height
std::vector<float> im_shape_; std::vector<float> im_shape_;
// Buffer for image data after preprocessing // Buffer for image data after preprocessing
std::vector<float> im_data_; std::vector<float> im_data_;
// in net data shape(after pad) // in net data shape(after pad)
std::vector<float> in_net_shape_; std::vector<float> in_net_shape_;
// Evaluation image width and height // Evaluation image width and height
// std::vector<float> eval_im_size_f_; // std::vector<float> eval_im_size_f_;
// Scale factor for image size to origin image size // Scale factor for image size to origin image size
std::vector<float> scale_factor_; std::vector<float> scale_factor_;
}; };
// Abstraction of preprocessing opration class // Abstraction of preprocessing opration class
class PreprocessOp { class PreprocessOp {
public: public:
virtual void Init(const YAML::Node &item) = 0; virtual void Init(const YAML::Node &item) = 0;
virtual void Run(cv::Mat *im, ImageBlob *data) = 0;
}; virtual void Run(cv::Mat *im, ImageBlob *data) = 0;
};
class InitInfo : public PreprocessOp {
public: class InitInfo : public PreprocessOp {
virtual void Init(const YAML::Node &item) {} public:
virtual void Run(cv::Mat *im, ImageBlob *data); virtual void Init(const YAML::Node &item) {}
};
virtual void Run(cv::Mat *im, ImageBlob *data);
class NormalizeImage : public PreprocessOp { };
public:
virtual void Init(const YAML::Node &item) { class NormalizeImage : public PreprocessOp {
mean_ = item["mean"].as<std::vector<float>>(); public:
scale_ = item["std"].as<std::vector<float>>(); virtual void Init(const YAML::Node &item) {
is_scale_ = item["is_scale"].as<bool>(); mean_ = item["mean"].as < std::vector < float >> ();
} scale_ = item["std"].as < std::vector < float >> ();
is_scale_ = item["is_scale"].as<bool>();
virtual void Run(cv::Mat *im, ImageBlob *data); }
private: virtual void Run(cv::Mat *im, ImageBlob *data);
// CHW or HWC
std::vector<float> mean_; private:
std::vector<float> scale_; // CHW or HWC
bool is_scale_; std::vector<float> mean_;
}; std::vector<float> scale_;
bool is_scale_;
class Permute : public PreprocessOp { };
public:
virtual void Init(const YAML::Node &item) {} class Permute : public PreprocessOp {
virtual void Run(cv::Mat *im, ImageBlob *data); public:
}; virtual void Init(const YAML::Node &item) {}
class Resize : public PreprocessOp { virtual void Run(cv::Mat *im, ImageBlob *data);
public: };
virtual void Init(const YAML::Node &item) {
interp_ = item["interp"].as<int>(); class Resize : public PreprocessOp {
// max_size_ = item["target_size"].as<int>(); public:
keep_ratio_ = item["keep_ratio"].as<bool>(); virtual void Init(const YAML::Node &item) {
target_size_ = item["target_size"].as<std::vector<int>>(); interp_ = item["interp"].as<int>();
} // max_size_ = item["target_size"].as<int>();
keep_ratio_ = item["keep_ratio"].as<bool>();
// Compute best resize scale for x-dimension, y-dimension target_size_ = item["target_size"].as < std::vector < int >> ();
std::pair<double, double> GenerateScale(const cv::Mat &im); }
virtual void Run(cv::Mat *im, ImageBlob *data); // Compute best resize scale for x-dimension, y-dimension
std::pair<double, double> GenerateScale(const cv::Mat &im);
private:
int interp_ = 2; virtual void Run(cv::Mat *im, ImageBlob *data);
bool keep_ratio_;
std::vector<int> target_size_; private:
std::vector<int> in_net_shape_; int interp_ = 2;
}; bool keep_ratio_;
std::vector<int> target_size_;
std::vector<int> in_net_shape_;
};
// Models with FPN need input shape % stride == 0 // Models with FPN need input shape % stride == 0
class PadStride : public PreprocessOp { class PadStride : public PreprocessOp {
public: public:
virtual void Init(const YAML::Node &item) { virtual void Init(const YAML::Node &item) {
stride_ = item["stride"].as<int>(); stride_ = item["stride"].as<int>();
} }
virtual void Run(cv::Mat *im, ImageBlob *data); virtual void Run(cv::Mat *im, ImageBlob *data);
private: private:
int stride_; int stride_;
}; };
class Preprocessor { class Preprocessor {
public: public:
void Init(const YAML::Node &config_node) { void Init(const YAML::Node &config_node) {
// initialize image info at first // initialize image info at first
ops_["InitInfo"] = std::make_shared<InitInfo>(); ops_["InitInfo"] = std::make_shared<InitInfo>();
for (int i = 0; i < config_node.size(); ++i) { for (int i = 0; i < config_node.size(); ++i) {
if (config_node[i]["DetResize"].IsDefined()) { if (config_node[i]["DetResize"].IsDefined()) {
ops_["Resize"] = std::make_shared<Resize>(); ops_["Resize"] = std::make_shared<Resize>();
ops_["Resize"]->Init(config_node[i]["DetResize"]); ops_["Resize"]->Init(config_node[i]["DetResize"]);
} }
if (config_node[i]["DetNormalizeImage"].IsDefined()) { if (config_node[i]["DetNormalizeImage"].IsDefined()) {
ops_["NormalizeImage"] = std::make_shared<NormalizeImage>(); ops_["NormalizeImage"] = std::make_shared<NormalizeImage>();
ops_["NormalizeImage"]->Init(config_node[i]["DetNormalizeImage"]); ops_["NormalizeImage"]->Init(config_node[i]["DetNormalizeImage"]);
} }
if (config_node[i]["DetPermute"].IsDefined()) { if (config_node[i]["DetPermute"].IsDefined()) {
ops_["Permute"] = std::make_shared<Permute>(); ops_["Permute"] = std::make_shared<Permute>();
ops_["Permute"]->Init(config_node[i]["DetPermute"]); ops_["Permute"]->Init(config_node[i]["DetPermute"]);
} }
if (config_node[i]["DetPadStrid"].IsDefined()) { if (config_node[i]["DetPadStrid"].IsDefined()) {
ops_["PadStride"] = std::make_shared<PadStride>(); ops_["PadStride"] = std::make_shared<PadStride>();
ops_["PadStride"]->Init(config_node[i]["DetPadStrid"]); ops_["PadStride"]->Init(config_node[i]["DetPadStrid"]);
} }
} }
} }
void Run(cv::Mat *im, ImageBlob *data); void Run(cv::Mat *im, ImageBlob *data);
public: public:
static const std::vector<std::string> RUN_ORDER; static const std::vector <std::string> RUN_ORDER;
private: private:
std::unordered_map<std::string, std::shared_ptr<PreprocessOp>> ops_; std::unordered_map <std::string, std::shared_ptr<PreprocessOp>> ops_;
}; };
} // namespace Detection } // namespace Detection
deploy/cpp_shitu/include/vector_search.h
浏览文件 @ 29f8fb83
...@@ -26,40 +26,45 @@ ...@@ -26,40 +26,45 @@
#include <map> #include <map>
struct SearchResult { struct SearchResult {
std::vector<faiss::Index::idx_t> I; std::vector <faiss::Index::idx_t> I;
std::vector<float> D; std::vector<float> D;
int return_k; int return_k;
}; };
class VectorSearch { class VectorSearch {
public: public:
explicit VectorSearch(const YAML::Node &config_file) { explicit VectorSearch(const YAML::Node &config_file) {
// IndexProcess // IndexProcess
this->index_dir = this->index_dir =
config_file["IndexProcess"]["index_dir"].as<std::string>(); config_file["IndexProcess"]["index_dir"].as<std::string>();
this->return_k = config_file["IndexProcess"]["return_k"].as<int>(); this->return_k = config_file["IndexProcess"]["return_k"].as<int>();
this->score_thres = config_file["IndexProcess"]["score_thres"].as<float>(); this->score_thres = config_file["IndexProcess"]["score_thres"].as<float>();
this->max_query_number = this->max_query_number =
config_file["Global"]["max_det_results"].as<int>() + 1; config_file["Global"]["max_det_results"].as<int>() + 1;
LoadIdMap(); LoadIdMap();
LoadIndexFile(); LoadIndexFile();
this->I.resize(this->return_k * this->max_query_number); this->I.resize(this->return_k * this->max_query_number);
this->D.resize(this->return_k * this->max_query_number); this->D.resize(this->return_k * this->max_query_number);
}; };
void LoadIdMap();
void LoadIndexFile(); void LoadIdMap();
const SearchResult &Search(float *feature, int query_number);
const std::string &GetLabel(faiss::Index::idx_t ind); void LoadIndexFile();
const float &GetThreshold() { return this->score_thres; }
const SearchResult &Search(float *feature, int query_number);
const std::string &GetLabel(faiss::Index::idx_t ind);
const float &GetThreshold() { return this->score_thres; }
private: private:
std::string index_dir; std::string index_dir;
int return_k = 5; int return_k = 5;
float score_thres = 0.5; float score_thres = 0.5;
std::map<long int, std::string> id_map; std::map<long int, std::string> id_map;
faiss::Index *index; faiss::Index *index;
int max_query_number = 6; int max_query_number = 6;
std::vector<float> D; std::vector<float> D;
std::vector<faiss::Index::idx_t> I; std::vector <faiss::Index::idx_t> I;
SearchResult sr; SearchResult sr;
}; };
deploy/cpp_shitu/include/yaml_config.h
浏览文件 @ 29f8fb83
...@@ -42,12 +42,17 @@ ...@@ -42,12 +42,17 @@
class YamlConfig { class YamlConfig {
public: public:
explicit YamlConfig(const std::string &path) { explicit YamlConfig(const std::string &path) {
config_file = ReadYamlConfig(path); config_file = ReadYamlConfig(path);
} }
static std::vector<std::string> ReadDict(const std::string &path);
static std::map<int, std::string> ReadIndexId(const std::string &path); static std::vector <std::string> ReadDict(const std::string &path);
static YAML::Node ReadYamlConfig(const std::string &path);
void PrintConfigInfo(); static std::map<int, std::string> ReadIndexId(const std::string &path);
YAML::Node config_file;
static YAML::Node ReadYamlConfig(const std::string &path);
void PrintConfigInfo();
YAML::Node config_file;
}; };
deploy/cpp_shitu/readme.md
浏览文件 @ 29f8fb83
...@@ -6,10 +6,7 @@ ...@@ -6,10 +6,7 @@
## 1. 准备环境 ## 1. 准备环境
### 运行准备 ### 运行准备
- Linux环境，推荐使用docker。 - Linux环境，推荐使用ubuntu docker。
- Windows环境，目前支持基于`Visual Studio 2019 Community`进行编译；此外，如果您希望通过生成`sln解决方案`的方式进行编译，可以参考该文档：[https://zhuanlan.zhihu.com/p/145446681](https://zhuanlan.zhihu.com/p/145446681)
* 该文档主要介绍基于Linux环境下的PaddleClas C++预测流程，如果需要在Windows环境下使用预测库进行C++预测，具体编译方法请参考[Windows下编译教程](./docs/windows_vs2019_build.md)。
### 1.1 编译opencv库 ### 1.1 编译opencv库
...@@ -103,7 +100,7 @@ make -j ...@@ -103,7 +100,7 @@ make -j
make inference_lib_dist make inference_lib_dist
``` ```
更多编译参数选项可以参考Paddle C++预测库官网：[https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16)。 更多编译参数选项可以参考[Paddle C++预测库官网](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16)。
* 编译完成之后，可以在`build/paddle_inference_install_dir/`文件下看到生成了以下文件及文件夹。 * 编译完成之后，可以在`build/paddle_inference_install_dir/`文件下看到生成了以下文件及文件夹。
...@@ -137,29 +134,27 @@ tar -xvf paddle_inference.tgz ...@@ -137,29 +134,27 @@ tar -xvf paddle_inference.tgz
### 1.3 安装faiss库 ### 1.3 安装faiss库
```shell ```shell
# 下载 faiss
git clone https://github.com/facebookresearch/faiss.git git clone https://github.com/facebookresearch/faiss.git
cd faiss cd faiss
cmake -B build . -DFAISS_ENABLE_PYTHON=OFF -DCMAKE_INSTALL_PREFIX=${faiss_install_path} cmake -B build . -DFAISS_ENABLE_PYTHON=OFF -DCMAKE_INSTALL_PREFIX=${faiss_install_path}
make -C build -j faiss make -C build -j faiss
make -C build install make -C build install
``` ```
## 2 开始运行 在安装`faiss`前，请安装`openblas`，`ubuntu`系统中安装命令如下：
### 2.1 将模型导出为inference model ```shell
apt-get install libopenblas-dev
```
* 可以参考[模型导出](../../tools/export_model.py)，导出`inference model`，用于模型预测。得到预测模型后，假设模型文件放在`inference`目录下，则目录结构如下。 注意本教程以安装faiss cpu版本为例，安装时请参考[faiss](https://github.com/facebookresearch/faiss)官网文档，根据需求自行安装。
``` ## 2 代码编译
inference/
|--cls_infer.pdmodel
|--cls_infer.pdiparams
```
**注意**：上述文件中，`cls_infer.pdmodel`文件存储了模型结构信息，`cls_infer.pdiparams`文件存储了模型参数信息。注意两个文件的路径需要与配置文件`tools/config.txt`中的`cls_model_path`和`cls_params_path`参数对应一致。
### 2.2 编译PaddleClas C++预测demo ### 2.2 编译PaddleClas C++预测demo
* 编译命令如下，其中Paddle C++预测库、opencv等其他依赖库的地址需要换成自己机器上的实际地址。 编译命令如下，其中Paddle C++预测库、opencv等其他依赖库的地址需要换成自己机器上的实际地址。同时，编译过程中需要下载编译`yaml-cpp`等C++库，请保持联网环境。
```shell ```shell
...@@ -169,11 +164,12 @@ sh tools/build.sh ...@@ -169,11 +164,12 @@ sh tools/build.sh
具体地，`tools/build.sh`中内容如下。 具体地，`tools/build.sh`中内容如下。
```shell ```shell
OPENCV_DIR=your_opencv_dir OPENCV_DIR=${opencv_install_dir}
LIB_DIR=your_paddle_inference_dir LIB_DIR=${paddle_inference_dir}
CUDA_LIB_DIR=your_cuda_lib_dir CUDA_LIB_DIR=/usr/local/cuda/lib64
CUDNN_LIB_DIR=your_cudnn_lib_dir CUDNN_LIB_DIR=/usr/lib/x86_64-linux-gnu/
TENSORRT_DIR=your_tensorrt_lib_dir FAISS_DIR=${faiss_install_dir}
FAISS_WITH_MKL=OFF
BUILD_DIR=build BUILD_DIR=build
rm -rf ${BUILD_DIR} rm -rf ${BUILD_DIR}
...@@ -182,14 +178,14 @@ cd ${BUILD_DIR} ...@@ -182,14 +178,14 @@ cd ${BUILD_DIR}
cmake .. \ cmake .. \
-DPADDLE_LIB=${LIB_DIR} \ -DPADDLE_LIB=${LIB_DIR} \
-DWITH_MKL=ON \ -DWITH_MKL=ON \
-DDEMO_NAME=clas_system \
-DWITH_GPU=OFF \ -DWITH_GPU=OFF \
-DWITH_STATIC_LIB=OFF \ -DWITH_STATIC_LIB=OFF \
-DWITH_TENSORRT=OFF \ -DUSE_TENSORRT=OFF \
-DTENSORRT_DIR=${TENSORRT_DIR} \
-DOPENCV_DIR=${OPENCV_DIR} \ -DOPENCV_DIR=${OPENCV_DIR} \
-DCUDNN_LIB=${CUDNN_LIB_DIR} \ -DCUDNN_LIB=${CUDNN_LIB_DIR} \
-DCUDA_LIB=${CUDA_LIB_DIR} \ -DCUDA_LIB=${CUDA_LIB_DIR} \
-DFAISS_DIR=${FAISS_DIR} \
-DFAISS_WITH_MKL=${FAISS_WITH_MKL}
make -j make -j
``` ```
...@@ -197,47 +193,75 @@ make -j ...@@ -197,47 +193,75 @@ make -j
上述命令中， 上述命令中，
* `OPENCV_DIR`为opencv编译安装的地址（本例中为`opencv-3.4.7/opencv3`文件夹的路径）； * `OPENCV_DIR`为opencv编译安装的地址（本例中为`opencv-3.4.7/opencv3`文件夹的路径）；
* `LIB_DIR`为下载的Paddle预测库（`paddle_inference`文件夹），或编译生成的Paddle预测库（`build/paddle_inference_install_dir`文件夹）的路径； * `LIB_DIR`为下载的Paddle预测库（`paddle_inference`文件夹），或编译生成的Paddle预测库（`build/paddle_inference_install_dir`文件夹）的路径；
* `CUDA_LIB_DIR`为cuda库文件地址，在docker中为`/usr/local/cuda/lib64`； * `CUDA_LIB_DIR`为cuda库文件地址，在docker中为`/usr/local/cuda/lib64`；
* `CUDNN_LIB_DIR`为cudnn库文件地址，在docker中为`/usr/lib/x86_64-linux-gnu/`。 * `CUDNN_LIB_DIR`为cudnn库文件地址，在docker中为`/usr/lib/x86_64-linux-gnu/`。
* `TENSORRT_DIR`是tensorrt库文件地址，在dokcer中为`/usr/local/TensorRT6-cuda10.0-cudnn7/`，TensorRT需要结合GPU使用。 * `TENSORRT_DIR`是tensorrt库文件地址，在dokcer中为`/usr/local/TensorRT6-cuda10.0-cudnn7/`，TensorRT需要结合GPU使用。
* `FAISS_DIR`是faiss的安装地址
* `FAISS_WITH_MKL`是指在编译faiss的过程中，是否使用了mkldnn，本文档中编译faiss，没有使用，而使用了openblas，故设置为`OFF`，若使用了mkldnn，则为`ON`.
在执行上述命令，编译完成之后，会在当前路径下生成`build`文件夹，其中生成一个名为`clas_system`的可执行文件。
在执行上述命令，编译完成之后，会在当前路径下生成`build`文件夹，其中生成一个名为`pp_shitu`的可执行文件。
### 运行demo ## 3 运行demo
* 首先修改`tools/config.txt`中对应字段：
* use_gpu：是否使用GPU；
* gpu_id：使用的GPU卡号；
* gpu_mem：显存；
* cpu_math_library_num_threads：底层科学计算库所用线程的数量；
* use_mkldnn：是否使用MKLDNN加速；
* use_tensorrt: 是否使用tensorRT进行加速；
* use_fp16：是否使用半精度浮点数进行计算，该选项仅在use_tensorrt为true时有效；
* cls_model_path：预测模型结构文件路径；
* cls_params_path：预测模型参数文件路径；
* resize_short_size：预处理时图像缩放大小；
* crop_size：预处理时图像裁剪后的大小。
* 然后修改`tools/run.sh`： - 请参考[识别快速开始文档](../../docs/zh_CN/quick_start/quick_start_recognition.md)，下载好相应的 轻量级通用主体检测模型、轻量级通用识别模型及瓶装饮料测试数据并解压。
* `./build/clas_system ./tools/config.txt ./docs/imgs/ILSVRC2012_val_00000666.JPEG`
* 上述命令中分别为：编译得到的可执行文件`clas_system`；运行时的配置文件`config.txt`；待预测的图像。
* 最后执行以下命令，完成对一幅图像的分类。 ```shell
mkdir models
cd models
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
tar -xf picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar
tar -xf general_PPLCNet_x2_5_lite_v1.0_infer.tar
cd ..
mkdir data
cd data
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v1.0.tar
tar -xf drink_dataset_v1.0.tar
cd ..
```
```shell - 将相应的yaml文件拷到`test`文件夹下
sh tools/run.sh
``` ```shell
cp ../configs/inference_drink.yaml .
```
- 将`inference_drink.yaml`中的相对路径，改成基于本目录的路径或者绝对路径。涉及到的参数有
- Global.infer_imgs ：此参数可以是具体的图像地址，也可以是图像集所在的目录
- Global.det_inference_model_dir ： 检测模型存储目录
- Global.rec_inference_model_dir ： 识别模型存储目录
- IndexProcess.index_dir ： 检索库的存储目录，在示例中，检索库在下载的demo数据中。
- 字典转换
由于python的检索库的字典，使用`pickle`进行的序列化存储，导致C++不方便读取，因此进行转换
```shell
python tools/transform_id_map.py -c inference_drink.yaml
```
转换成功后，在`IndexProcess.index_dir`目录下生成`id_map.txt`，方便c++ 读取。
- 执行程序
```shell
./build/pp_shitu -c inference_drink.yaml
# or
./build/pp_shitu -config inference_drink.yaml
```
若对图像集进行检索，则可能得到，如下结果。注意，此结果只做展示，具体以实际运行结果为准。
同时，需注意的是，由于opencv 版本问题，会导致图像在预处理的过程中，resize产生细微差别，导致python 和c++结果，轻微不同，如bbox相差几个像素，检索结果小数点后3位diff等。但不会改变最终检索label。
* 最终屏幕上会输出结果，如下图所示。 ![](../../docs/images/quick_start/shitu_c++_result.png)
<div align="center"> ## 4 使用自己模型
<img src="./docs/imgs/cpp_infer_result.png" width="600">
</div>
使用自己训练的模型，可以参考[模型导出](../../docs/zh_CN/inference_deployment/export_model.md)，导出`inference model`，用于模型预测。
其中`class id`表示置信度最高的类别对应的id，score表示图片属于该类别的概率。 同时注意修改`yaml`文件中具体参数。
deploy/cpp_shitu/src/feature_extracter.cpp
浏览文件 @ 29f8fb83
...@@ -18,102 +18,102 @@ ...@@ -18,102 +18,102 @@
namespace Feature { namespace Feature {
void FeatureExtracter::LoadModel(const std::string &model_path, void FeatureExtracter::LoadModel(const std::string &model_path,
const std::string &params_path) { const std::string &params_path) {
paddle_infer::Config config; paddle_infer::Config config;
config.SetModel(model_path, params_path); config.SetModel(model_path, params_path);
if (this->use_gpu_) { if (this->use_gpu_) {
config.EnableUseGpu(this->gpu_mem_, this->gpu_id_); config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
if (this->use_tensorrt_) { if (this->use_tensorrt_) {
config.EnableTensorRtEngine( config.EnableTensorRtEngine(
1 << 20, 1, 3, 1 << 20, 1, 3,
this->use_fp16_ ? paddle_infer::Config::Precision::kHalf this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
: paddle_infer::Config::Precision::kFloat32, : paddle_infer::Config::Precision::kFloat32,
false, false); false, false);
}
} else {
config.DisableGpu();
if (this->use_mkldnn_) {
config.EnableMKLDNN();
// cache 10 different shapes for mkldnn to avoid memory leak
config.SetMkldnnCacheCapacity(10);
}
config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
}
config.SwitchUseFeedFetchOps(false);
// true for multiple input
config.SwitchSpecifyInputNames(true);
config.SwitchIrOptim(true);
config.EnableMemoryOptim();
config.DisableGlogInfo();
this->predictor_ = CreatePredictor(config);
} }
} else {
config.DisableGpu(); void FeatureExtracter::Run(cv::Mat &img, std::vector<float> &out_data,
if (this->use_mkldnn_) { std::vector<double> &times) {
config.EnableMKLDNN(); cv::Mat resize_img;
// cache 10 different shapes for mkldnn to avoid memory leak std::vector<double> time;
config.SetMkldnnCacheCapacity(10);
auto preprocess_start = std::chrono::system_clock::now();
this->resize_op_.Run(img, resize_img, this->resize_short_,
this->resize_size_);
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());
auto input_names = this->predictor_->GetInputNames();
auto input_t = this->predictor_->GetInputHandle(input_names[0]);
input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
auto preprocess_end = std::chrono::system_clock::now();
auto infer_start = std::chrono::system_clock::now();
input_t->CopyFromCpu(input.data());
this->predictor_->Run();
auto output_names = this->predictor_->GetOutputNames();
auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
std::vector<int> output_shape = output_t->shape();
int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
std::multiplies<int>());
out_data.resize(out_num);
output_t->CopyToCpu(out_data.data());
auto infer_end = std::chrono::system_clock::now();
auto postprocess_start = std::chrono::system_clock::now();
if (this->feature_norm)
FeatureNorm(out_data);
auto postprocess_end = std::chrono::system_clock::now();
std::chrono::duration<float> preprocess_diff =
preprocess_end - preprocess_start;
time.push_back(double(preprocess_diff.count()));
std::chrono::duration<float> inference_diff = infer_end - infer_start;
double inference_cost_time = double(inference_diff.count());
time.push_back(inference_cost_time);
// std::chrono::duration<float> postprocess_diff =
// postprocess_end - postprocess_start;
time.push_back(0);
// std::cout << "result: " << std::endl;
// std::cout << "\tclass id: " << maxPosition << std::endl;
// std::cout << std::fixed << std::setprecision(10)
// << "\tscore: " << double(out_data[maxPosition]) << std::endl;
times[0] += time[0];
times[1] += time[1];
times[2] += time[2];
}
void FeatureExtracter::FeatureNorm(std::vector<float> &featuer) {
float featuer_sqrt = std::sqrt(std::inner_product(
featuer.begin(), featuer.end(), featuer.begin(), 0.0f));
for (int i = 0; i < featuer.size(); ++i)
featuer[i] /= featuer_sqrt;
} }
config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
}
config.SwitchUseFeedFetchOps(false);
// true for multiple input
config.SwitchSpecifyInputNames(true);
config.SwitchIrOptim(true);
config.EnableMemoryOptim();
config.DisableGlogInfo();
this->predictor_ = CreatePredictor(config);
}
void FeatureExtracter::Run(cv::Mat &img, std::vector<float> &out_data,
std::vector<double> &times) {
cv::Mat resize_img;
std::vector<double> time;
auto preprocess_start = std::chrono::system_clock::now();
this->resize_op_.Run(img, resize_img, this->resize_short_,
this->resize_size_);
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());
auto input_names = this->predictor_->GetInputNames();
auto input_t = this->predictor_->GetInputHandle(input_names[0]);
input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
auto preprocess_end = std::chrono::system_clock::now();
auto infer_start = std::chrono::system_clock::now();
input_t->CopyFromCpu(input.data());
this->predictor_->Run();
auto output_names = this->predictor_->GetOutputNames();
auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
std::vector<int> output_shape = output_t->shape();
int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
std::multiplies<int>());
out_data.resize(out_num);
output_t->CopyToCpu(out_data.data());
auto infer_end = std::chrono::system_clock::now();
auto postprocess_start = std::chrono::system_clock::now();
if (this->feature_norm)
FeatureNorm(out_data);
auto postprocess_end = std::chrono::system_clock::now();
std::chrono::duration<float> preprocess_diff =
preprocess_end - preprocess_start;
time.push_back(double(preprocess_diff.count()));
std::chrono::duration<float> inference_diff = infer_end - infer_start;
double inference_cost_time = double(inference_diff.count());
time.push_back(inference_cost_time);
// std::chrono::duration<float> postprocess_diff =
// postprocess_end - postprocess_start;
time.push_back(0);
// std::cout << "result: " << std::endl;
// std::cout << "\tclass id: " << maxPosition << std::endl;
// std::cout << std::fixed << std::setprecision(10)
// << "\tscore: " << double(out_data[maxPosition]) << std::endl;
times[0] += time[0];
times[1] += time[1];
times[2] += time[2];
}
void FeatureExtracter::FeatureNorm(std::vector<float> &featuer) {
float featuer_sqrt = std::sqrt(std::inner_product(
featuer.begin(), featuer.end(), featuer.begin(), 0.0f));
for (int i = 0; i < featuer.size(); ++i)
featuer[i] /= featuer_sqrt;
}
} // namespace Feature } // namespace Feature
deploy/cpp_shitu/src/main.cpp
浏览文件 @ 29f8fb83
...@@ -37,260 +37,262 @@ ...@@ -37,260 +37,262 @@
using namespace std; using namespace std;
using namespace cv; using namespace cv;
DEFINE_string(config, "", "Path of yaml file"); DEFINE_string(config,
DEFINE_string(c, "", "Path of yaml file"); "", "Path of yaml file");
DEFINE_string(c,
"", "Path of yaml file");
void DetPredictImage(const std::vector<cv::Mat> &batch_imgs, void DetPredictImage(const std::vector <cv::Mat> &batch_imgs,
const std::vector<std::string> &all_img_paths, const std::vector <std::string> &all_img_paths,
const int batch_size, Detection::ObjectDetector *det, const int batch_size, Detection::ObjectDetector *det,
std::vector<Detection::ObjectResult> &im_result, std::vector <Detection::ObjectResult> &im_result,
std::vector<int> &im_bbox_num, std::vector<double> &det_t, std::vector<int> &im_bbox_num, std::vector<double> &det_t,
const bool visual_det = false, const bool visual_det = false,
const bool run_benchmark = false, const bool run_benchmark = false,
const std::string &output_dir = "output") { const std::string &output_dir = "output") {
int steps = ceil(float(all_img_paths.size()) / batch_size); int steps = ceil(float(all_img_paths.size()) / batch_size);
// printf("total images = %d, batch_size = %d, total steps = %d\n", // printf("total images = %d, batch_size = %d, total steps = %d\n",
// all_img_paths.size(), batch_size, steps); // all_img_paths.size(), batch_size, steps);
for (int idx = 0; idx < steps; idx++) { for (int idx = 0; idx < steps; idx++) {
int left_image_cnt = all_img_paths.size() - idx * batch_size; int left_image_cnt = all_img_paths.size() - idx * batch_size;
if (left_image_cnt > batch_size) { if (left_image_cnt > batch_size) {
left_image_cnt = batch_size; left_image_cnt = batch_size;
} }
// for (int bs = 0; bs < left_image_cnt; bs++) { // for (int bs = 0; bs < left_image_cnt; bs++) {
// std::string image_file_path = all_img_paths.at(idx * batch_size+bs); // std::string image_file_path = all_img_paths.at(idx * batch_size+bs);
// cv::Mat im = cv::imread(image_file_path, 1); // cv::Mat im = cv::imread(image_file_path, 1);
// batch_imgs.insert(batch_imgs.end(), im); // batch_imgs.insert(batch_imgs.end(), im);
// } // }
// Store all detected result // Store all detected result
std::vector<Detection::ObjectResult> result; std::vector <Detection::ObjectResult> result;
std::vector<int> bbox_num; std::vector<int> bbox_num;
std::vector<double> det_times; std::vector<double> det_times;
bool is_rbox = false; bool is_rbox = false;
if (run_benchmark) { if (run_benchmark) {
det->Predict(batch_imgs, 10, 10, &result, &bbox_num, &det_times); det->Predict(batch_imgs, 10, 10, &result, &bbox_num, &det_times);
} else { } else {
det->Predict(batch_imgs, 0, 1, &result, &bbox_num, &det_times); det->Predict(batch_imgs, 0, 1, &result, &bbox_num, &det_times);
// get labels and colormap // get labels and colormap
auto labels = det->GetLabelList(); auto labels = det->GetLabelList();
auto colormap = Detection::GenerateColorMap(labels.size()); auto colormap = Detection::GenerateColorMap(labels.size());
int item_start_idx = 0; int item_start_idx = 0;
for (int i = 0; i < left_image_cnt; i++) { for (int i = 0; i < left_image_cnt; i++) {
cv::Mat im = batch_imgs[i]; cv::Mat im = batch_imgs[i];
int detect_num = 0; int detect_num = 0;
for (int j = 0; j < bbox_num[i]; j++) { for (int j = 0; j < bbox_num[i]; j++) {
Detection::ObjectResult item = result[item_start_idx + j]; Detection::ObjectResult item = result[item_start_idx + j];
if (item.confidence < det->GetThreshold() || item.class_id == -1) { if (item.confidence < det->GetThreshold() || item.class_id == -1) {
continue; continue;
} }
detect_num += 1; detect_num += 1;
im_result.push_back(item); im_result.push_back(item);
if (visual_det) { if (visual_det) {
if (item.rect.size() > 6) { if (item.rect.size() > 6) {
is_rbox = true; is_rbox = true;
printf( printf(
"class=%d confidence=%.4f rect=[%d %d %d %d %d %d %d %d]\n", "class=%d confidence=%.4f rect=[%d %d %d %d %d %d %d %d]\n",
item.class_id, item.confidence, item.rect[0], item.rect[1], item.class_id, item.confidence, item.rect[0], item.rect[1],
item.rect[2], item.rect[3], item.rect[4], item.rect[5], item.rect[2], item.rect[3], item.rect[4], item.rect[5],
item.rect[6], item.rect[7]); item.rect[6], item.rect[7]);
} else { } else {
printf("class=%d confidence=%.4f rect=[%d %d %d %d]\n", printf("class=%d confidence=%.4f rect=[%d %d %d %d]\n",
item.class_id, item.confidence, item.rect[0], item.rect[1], item.class_id, item.confidence, item.rect[0], item.rect[1],
item.rect[2], item.rect[3]); item.rect[2], item.rect[3]);
} }
} }
} }
im_bbox_num.push_back(detect_num); im_bbox_num.push_back(detect_num);
item_start_idx = item_start_idx + bbox_num[i]; item_start_idx = item_start_idx + bbox_num[i];
// Visualization result // Visualization result
if (visual_det) { if (visual_det) {
std::cout << all_img_paths.at(idx * batch_size + i) std::cout << all_img_paths.at(idx * batch_size + i)
<< " The number of detected box: " << detect_num << " The number of detected box: " << detect_num
<< std::endl; << std::endl;
cv::Mat vis_img = Detection::VisualizeResult(im, im_result, labels, cv::Mat vis_img = Detection::VisualizeResult(im, im_result, labels,
colormap, is_rbox); colormap, is_rbox);
std::vector<int> compression_params; std::vector<int> compression_params;
compression_params.push_back(CV_IMWRITE_JPEG_QUALITY); compression_params.push_back(CV_IMWRITE_JPEG_QUALITY);
compression_params.push_back(95); compression_params.push_back(95);
std::string output_path(output_dir); std::string output_path(output_dir);
if (output_dir.rfind(OS_PATH_SEP) != output_dir.size() - 1) { if (output_dir.rfind(OS_PATH_SEP) != output_dir.size() - 1) {
output_path += OS_PATH_SEP; output_path += OS_PATH_SEP;
} }
std::string image_file_path = all_img_paths.at(idx * batch_size + i); std::string image_file_path = all_img_paths.at(idx * batch_size + i);
output_path += output_path +=
image_file_path.substr(image_file_path.find_last_of('/') + 1); image_file_path.substr(image_file_path.find_last_of('/') + 1);
cv::imwrite(output_path, vis_img, compression_params); cv::imwrite(output_path, vis_img, compression_params);
printf("Visualized output saved as %s\n", output_path.c_str()); printf("Visualized output saved as %s\n", output_path.c_str());
}
}
} }
} det_t[0] += det_times[0];
det_t[1] += det_times[1];
det_t[2] += det_times[2];
} }
det_t[0] += det_times[0];
det_t[1] += det_times[1];
det_t[2] += det_times[2];
}
} }
void PrintResult(std::string &img_path, void PrintResult(std::string &img_path,
std::vector<Detection::ObjectResult> &det_result, std::vector <Detection::ObjectResult> &det_result,
std::vector<int> &indeices, VectorSearch &vector_search, std::vector<int> &indeices, VectorSearch &vector_search,
SearchResult &search_result) { SearchResult &search_result) {
printf("%s:\n", img_path.c_str()); printf("%s:\n", img_path.c_str());
for (int i = 0; i < indeices.size(); ++i) { for (int i = 0; i < indeices.size(); ++i) {
int t = indeices[i]; int t = indeices[i];
printf("\tresult%d: bbox[%d, %d, %d, %d], score: %f, label: %s\n", i, printf("\tresult%d: bbox[%d, %d, %d, %d], score: %f, label: %s\n", i,
det_result[t].rect[0], det_result[t].rect[1], det_result[t].rect[2], det_result[t].rect[0], det_result[t].rect[1], det_result[t].rect[2],
det_result[t].rect[3], det_result[t].confidence, det_result[t].rect[3], det_result[t].confidence,
vector_search.GetLabel(search_result.I[search_result.return_k * t]) vector_search.GetLabel(search_result.I[search_result.return_k * t])
.c_str()); .c_str());
} }
} }
int main(int argc, char **argv) { int main(int argc, char **argv) {
google::ParseCommandLineFlags(&argc, &argv, true); google::ParseCommandLineFlags(&argc, &argv, true);
std::string yaml_path = ""; std::string yaml_path = "";
if (FLAGS_config == "" && FLAGS_c == "") { if (FLAGS_config == "" && FLAGS_c == "") {
std::cerr << "[ERROR] usage: " << std::endl std::cerr << "[ERROR] usage: " << std::endl
<< argv[0] << " -c $yaml_path" << std::endl << argv[0] << " -c $yaml_path" << std::endl
<< "or:" << std::endl << "or:" << std::endl
<< argv[0] << " -config $yaml_path" << std::endl; << argv[0] << " -config $yaml_path" << std::endl;
exit(1); exit(1);
} else if (FLAGS_config != "") { } else if (FLAGS_config != "") {
yaml_path = FLAGS_config; yaml_path = FLAGS_config;
} else { } else {
yaml_path = FLAGS_c; yaml_path = FLAGS_c;
} }
YamlConfig config(yaml_path);
config.PrintConfigInfo();
// initialize detector, rec_Model, vector_search YamlConfig config(yaml_path);
Feature::FeatureExtracter feature_extracter(config.config_file); config.PrintConfigInfo();
Detection::ObjectDetector detector(config.config_file);
VectorSearch searcher(config.config_file);
// config // initialize detector, rec_Model, vector_search
const int batch_size = config.config_file["Global"]["batch_size"].as<int>(); Feature::FeatureExtracter feature_extracter(config.config_file);
bool visual_det = false; Detection::ObjectDetector detector(config.config_file);
if (config.config_file["Global"]["visual_det"].IsDefined()) { VectorSearch searcher(config.config_file);
visual_det = config.config_file["Global"]["visual_det"].as<bool>();
}
bool run_benchmark = false;
if (config.config_file["Global"]["benchmark"].IsDefined()) {
run_benchmark = config.config_file["Global"]["benchmark"].as<bool>();
}
int max_det_results = 5;
if (config.config_file["Global"]["max_det_results"].IsDefined()) {
max_det_results = config.config_file["Global"]["max_det_results"].as<int>();
}
float rec_nms_thresold = 0.05;
if (config.config_file["Global"]["rec_nms_thresold"].IsDefined()) {
rec_nms_thresold =
config.config_file["Global"]["rec_nms_thresold"].as<float>();
}
// load image_file_path // config
std::string path = const int batch_size = config.config_file["Global"]["batch_size"].as<int>();
config.config_file["Global"]["infer_imgs"].as<std::string>(); bool visual_det = false;
std::vector<std::string> img_files_list; if (config.config_file["Global"]["visual_det"].IsDefined()) {
if (cv::utils::fs::isDirectory(path)) { visual_det = config.config_file["Global"]["visual_det"].as<bool>();
std::vector<cv::String> filenames; }
cv::glob(path, filenames); bool run_benchmark = false;
for (auto f : filenames) { if (config.config_file["Global"]["benchmark"].IsDefined()) {
img_files_list.push_back(f); run_benchmark = config.config_file["Global"]["benchmark"].as<bool>();
}
int max_det_results = 5;
if (config.config_file["Global"]["max_det_results"].IsDefined()) {
max_det_results = config.config_file["Global"]["max_det_results"].as<int>();
}
float rec_nms_thresold = 0.05;
if (config.config_file["Global"]["rec_nms_thresold"].IsDefined()) {
rec_nms_thresold =
config.config_file["Global"]["rec_nms_thresold"].as<float>();
} }
} else {
img_files_list.push_back(path);
}
std::cout << "img_file_list length: " << img_files_list.size() << std::endl;
// for time log
std::vector<double> cls_times = {0, 0, 0};
std::vector<double> det_times = {0, 0, 0};
// for read images
std::vector<cv::Mat> batch_imgs;
std::vector<std::string> img_paths;
// for detection
std::vector<Detection::ObjectResult> det_result;
std::vector<int> det_bbox_num;
// for vector search
std::vector<float> features;
std::vector<float> feature;
// for nms
std::vector<int> indeices;
int warmup_iter = img_files_list.size() > 5 ? 5 : 0; // load image_file_path
for (int idx = 0; idx < img_files_list.size(); ++idx) { std::string path =
std::string img_path = img_files_list[idx]; config.config_file["Global"]["infer_imgs"].as<std::string>();
cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR); std::vector <std::string> img_files_list;
if (!srcimg.data) { if (cv::utils::fs::isDirectory(path)) {
std::cerr << "[ERROR] image read failed! image path: " << img_path std::vector <cv::String> filenames;
<< "\n"; cv::glob(path, filenames);
exit(-1); for (auto f : filenames) {
img_files_list.push_back(f);
}
} else {
img_files_list.push_back(path);
} }
cv::cvtColor(srcimg, srcimg, cv::COLOR_BGR2RGB); std::cout << "img_file_list length: " << img_files_list.size() << std::endl;
// for time log
std::vector<double> cls_times = {0, 0, 0};
std::vector<double> det_times = {0, 0, 0};
// for read images
std::vector <cv::Mat> batch_imgs;
std::vector <std::string> img_paths;
// for detection
std::vector <Detection::ObjectResult> det_result;
std::vector<int> det_bbox_num;
// for vector search
std::vector<float> features;
std::vector<float> feature;
// for nms
std::vector<int> indeices;
batch_imgs.push_back(srcimg); int warmup_iter = img_files_list.size() > 5 ? 5 : 0;
img_paths.push_back(img_path); for (int idx = 0; idx < img_files_list.size(); ++idx) {
std::string img_path = img_files_list[idx];
cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
if (!srcimg.data) {
std::cerr << "[ERROR] image read failed! image path: " << img_path
<< "\n";
exit(-1);
}
cv::cvtColor(srcimg, srcimg, cv::COLOR_BGR2RGB);
// step1: get all detection results batch_imgs.push_back(srcimg);
DetPredictImage(batch_imgs, img_paths, batch_size, &detector, det_result, img_paths.push_back(img_path);
det_bbox_num, det_times, visual_det, run_benchmark);
// select max_det_results bbox // step1: get all detection results
if (det_result.size() > max_det_results) { DetPredictImage(batch_imgs, img_paths, batch_size, &detector, det_result,
det_result.resize(max_det_results); det_bbox_num, det_times, visual_det, run_benchmark);
}
// step2: add the whole image for recognition to improve recall
Detection::ObjectResult result_whole_img = {
{0, 0, srcimg.cols - 1, srcimg.rows - 1}, 0, 1.0};
det_result.push_back(result_whole_img);
det_bbox_num[0] = det_result.size() + 1;
// step3: extract feature for all boxes in an inmage // select max_det_results bbox
SearchResult search_result; if (det_result.size() > max_det_results) {
for (int j = 0; j < det_result.size(); ++j) { det_result.resize(max_det_results);
int w = det_result[j].rect[2] - det_result[j].rect[0]; }
int h = det_result[j].rect[3] - det_result[j].rect[1]; // step2: add the whole image for recognition to improve recall
cv::Rect rect(det_result[j].rect[0], det_result[j].rect[1], w, h); Detection::ObjectResult result_whole_img = {
cv::Mat crop_img = srcimg(rect); {0, 0, srcimg.cols - 1, srcimg.rows - 1}, 0, 1.0};
feature_extracter.Run(crop_img, feature, cls_times); det_result.push_back(result_whole_img);
features.insert(features.end(), feature.begin(), feature.end()); det_bbox_num[0] = det_result.size() + 1;
}
// step4: get search result // step3: extract feature for all boxes in an inmage
search_result = searcher.Search(features.data(), det_result.size()); SearchResult search_result;
for (int j = 0; j < det_result.size(); ++j) {
int w = det_result[j].rect[2] - det_result[j].rect[0];
int h = det_result[j].rect[3] - det_result[j].rect[1];
cv::Rect rect(det_result[j].rect[0], det_result[j].rect[1], w, h);
cv::Mat crop_img = srcimg(rect);
feature_extracter.Run(crop_img, feature, cls_times);
features.insert(features.end(), feature.begin(), feature.end());
}
// nms for search result // step4: get search result
for (int i = 0; i < det_result.size(); ++i) { search_result = searcher.Search(features.data(), det_result.size());
det_result[i].confidence = search_result.D[search_result.return_k * i];
} // nms for search result
NMSBoxes(det_result, searcher.GetThreshold(), rec_nms_thresold, indeices); for (int i = 0; i < det_result.size(); ++i) {
det_result[i].confidence = search_result.D[search_result.return_k * i];
}
NMSBoxes(det_result, searcher.GetThreshold(), rec_nms_thresold, indeices);
// print result // print result
PrintResult(img_path, det_result, indeices, searcher, search_result); PrintResult(img_path, det_result, indeices, searcher, search_result);
// for postprocess // for postprocess
batch_imgs.clear(); batch_imgs.clear();
img_paths.clear(); img_paths.clear();
det_bbox_num.clear(); det_bbox_num.clear();
det_result.clear(); det_result.clear();
feature.clear(); feature.clear();
features.clear(); features.clear();
indeices.clear(); indeices.clear();
} }
std::string presion = "fp32"; std::string presion = "fp32";
// if (config.use_fp16) // if (config.use_fp16)
// presion = "fp16"; // presion = "fp16";
// if (config.benchmark) { // if (config.benchmark) {
// AutoLogger autolog("Classification", config.use_gpu, config.use_tensorrt, // AutoLogger autolog("Classification", config.use_gpu, config.use_tensorrt,
// config.use_mkldnn, config.cpu_threads, 1, // config.use_mkldnn, config.cpu_threads, 1,
// "1, 3, 224, 224", presion, cls_times, // "1, 3, 224, 224", presion, cls_times,
// img_files_list.size()); // img_files_list.size());
// autolog.report(); // autolog.report();
// } // }
return 0; return 0;
} }
deploy/cpp_shitu/src/object_detector.cpp
浏览文件 @ 29f8fb83
...@@ -22,344 +22,344 @@ using namespace paddle_infer; ...@@ -22,344 +22,344 @@ using namespace paddle_infer;
namespace Detection { namespace Detection {
// Load Model and create model predictor // Load Model and create model predictor
void ObjectDetector::LoadModel(const std::string &model_dir, void ObjectDetector::LoadModel(const std::string &model_dir,
const int batch_size, const int batch_size,
const std::string &run_mode) { const std::string &run_mode) {
paddle_infer::Config config; paddle_infer::Config config;
std::string prog_file = model_dir + OS_PATH_SEP + "inference.pdmodel"; std::string prog_file = model_dir + OS_PATH_SEP + "inference.pdmodel";
std::string params_file = model_dir + OS_PATH_SEP + "inference.pdiparams"; std::string params_file = model_dir + OS_PATH_SEP + "inference.pdiparams";
config.SetModel(prog_file, params_file); config.SetModel(prog_file, params_file);
if (this->use_gpu_) { if (this->use_gpu_) {
config.EnableUseGpu(this->gpu_mem_, this->gpu_id_); config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
config.SwitchIrOptim(this->ir_optim_); config.SwitchIrOptim(this->ir_optim_);
// // use tensorrt // // use tensorrt
// if (run_mode != "fluid") { // if (run_mode != "fluid") {
// auto precision = paddle_infer::Config::Precision::kFloat32; // auto precision = paddle_infer::Config::Precision::kFloat32;
// if (run_mode == "trt_fp32") { // if (run_mode == "trt_fp32") {
// precision = paddle_infer::Config::Precision::kFloat32; // precision = paddle_infer::Config::Precision::kFloat32;
// } // }
// else if (run_mode == "trt_fp16") { // else if (run_mode == "trt_fp16") {
// precision = paddle_infer::Config::Precision::kHalf; // precision = paddle_infer::Config::Precision::kHalf;
// } // }
// else if (run_mode == "trt_int8") { // else if (run_mode == "trt_int8") {
// precision = paddle_infer::Config::Precision::kInt8; // precision = paddle_infer::Config::Precision::kInt8;
// } else { // } else {
// printf("run_mode should be 'fluid', 'trt_fp32', 'trt_fp16' or // printf("run_mode should be 'fluid', 'trt_fp32', 'trt_fp16' or
// 'trt_int8'"); // 'trt_int8'");
// } // }
// set tensorrt // set tensorrt
if (this->use_tensorrt_) { if (this->use_tensorrt_) {
config.EnableTensorRtEngine( config.EnableTensorRtEngine(
1 << 30, batch_size, this->min_subgraph_size_, 1 << 30, batch_size, this->min_subgraph_size_,
this->use_fp16_ ? paddle_infer::Config::Precision::kHalf this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
: paddle_infer::Config::Precision::kFloat32, : paddle_infer::Config::Precision::kFloat32,
false, this->trt_calib_mode_); false, this->trt_calib_mode_);
// set use dynamic shape // set use dynamic shape
if (this->use_dynamic_shape_) { if (this->use_dynamic_shape_) {
// set DynamicShsape for image tensor // set DynamicShsape for image tensor
const std::vector<int> min_input_shape = {1, 3, this->trt_min_shape_, const std::vector<int> min_input_shape = {1, 3, this->trt_min_shape_,
this->trt_min_shape_}; this->trt_min_shape_};
const std::vector<int> max_input_shape = {1, 3, this->trt_max_shape_, const std::vector<int> max_input_shape = {1, 3, this->trt_max_shape_,
this->trt_max_shape_}; this->trt_max_shape_};
const std::vector<int> opt_input_shape = {1, 3, this->trt_opt_shape_, const std::vector<int> opt_input_shape = {1, 3, this->trt_opt_shape_,
this->trt_opt_shape_}; this->trt_opt_shape_};
const std::map<std::string, std::vector<int>> map_min_input_shape = { const std::map <std::string, std::vector<int>> map_min_input_shape = {
{"image", min_input_shape}}; {"image", min_input_shape}};
const std::map<std::string, std::vector<int>> map_max_input_shape = { const std::map <std::string, std::vector<int>> map_max_input_shape = {
{"image", max_input_shape}}; {"image", max_input_shape}};
const std::map<std::string, std::vector<int>> map_opt_input_shape = { const std::map <std::string, std::vector<int>> map_opt_input_shape = {
{"image", opt_input_shape}}; {"image", opt_input_shape}};
config.SetTRTDynamicShapeInfo(map_min_input_shape, map_max_input_shape, config.SetTRTDynamicShapeInfo(map_min_input_shape, map_max_input_shape,
map_opt_input_shape); map_opt_input_shape);
std::cout << "TensorRT dynamic shape enabled" << std::endl; std::cout << "TensorRT dynamic shape enabled" << std::endl;
} }
} }
// } else if (this->device_ == "XPU"){ // } else if (this->device_ == "XPU"){
// config.EnableXpu(10*1024*1024); // config.EnableXpu(10*1024*1024);
} else { } else {
config.DisableGpu(); config.DisableGpu();
if (this->use_mkldnn_) { if (this->use_mkldnn_) {
config.EnableMKLDNN(); config.EnableMKLDNN();
// cache 10 different shapes for mkldnn to avoid memory leak // cache 10 different shapes for mkldnn to avoid memory leak
config.SetMkldnnCacheCapacity(10); config.SetMkldnnCacheCapacity(10);
}
config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
}
config.SwitchUseFeedFetchOps(false);
config.SwitchIrOptim(this->ir_optim_);
config.DisableGlogInfo();
// Memory optimization
config.EnableMemoryOptim();
predictor_ = std::move(CreatePredictor(config));
} }
config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
}
config.SwitchUseFeedFetchOps(false);
config.SwitchIrOptim(this->ir_optim_);
config.DisableGlogInfo();
// Memory optimization
config.EnableMemoryOptim();
predictor_ = std::move(CreatePredictor(config));
}
// Visualiztion MaskDetector results // Visualiztion MaskDetector results
cv::Mat VisualizeResult(const cv::Mat &img, cv::Mat VisualizeResult(const cv::Mat &img,
const std::vector<ObjectResult> &results, const std::vector <ObjectResult> &results,
const std::vector<std::string> &lables, const std::vector <std::string> &lables,
const std::vector<int> &colormap, const std::vector<int> &colormap,
const bool is_rbox = false) { const bool is_rbox = false) {
cv::Mat vis_img = img.clone(); cv::Mat vis_img = img.clone();
for (int i = 0; i < results.size(); ++i) { for (int i = 0; i < results.size(); ++i) {
// Configure color and text size // Configure color and text size
std::ostringstream oss; std::ostringstream oss;
oss << std::setiosflags(std::ios::fixed) << std::setprecision(4); oss << std::setiosflags(std::ios::fixed) << std::setprecision(4);
oss << lables[results[i].class_id] << " "; oss << lables[results[i].class_id] << " ";
oss << results[i].confidence; oss << results[i].confidence;
std::string text = oss.str(); std::string text = oss.str();
int c1 = colormap[3 * results[i].class_id + 0]; int c1 = colormap[3 * results[i].class_id + 0];
int c2 = colormap[3 * results[i].class_id + 1]; int c2 = colormap[3 * results[i].class_id + 1];
int c3 = colormap[3 * results[i].class_id + 2]; int c3 = colormap[3 * results[i].class_id + 2];
cv::Scalar roi_color = cv::Scalar(c1, c2, c3); cv::Scalar roi_color = cv::Scalar(c1, c2, c3);
int font_face = cv::FONT_HERSHEY_COMPLEX_SMALL; int font_face = cv::FONT_HERSHEY_COMPLEX_SMALL;
double font_scale = 0.5f; double font_scale = 0.5f;
float thickness = 0.5; float thickness = 0.5;
cv::Size text_size = cv::Size text_size =
cv::getTextSize(text, font_face, font_scale, thickness, nullptr); cv::getTextSize(text, font_face, font_scale, thickness, nullptr);
cv::Point origin; cv::Point origin;
if (is_rbox) { if (is_rbox) {
// Draw object, text, and background // Draw object, text, and background
for (int k = 0; k < 4; k++) { for (int k = 0; k < 4; k++) {
cv::Point pt1 = cv::Point(results[i].rect[(k * 2) % 8], cv::Point pt1 = cv::Point(results[i].rect[(k * 2) % 8],
results[i].rect[(k * 2 + 1) % 8]); results[i].rect[(k * 2 + 1) % 8]);
cv::Point pt2 = cv::Point(results[i].rect[(k * 2 + 2) % 8], cv::Point pt2 = cv::Point(results[i].rect[(k * 2 + 2) % 8],
results[i].rect[(k * 2 + 3) % 8]); results[i].rect[(k * 2 + 3) % 8]);
cv::line(vis_img, pt1, pt2, roi_color, 2); cv::line(vis_img, pt1, pt2, roi_color, 2);
} }
} else { } else {
int w = results[i].rect[2] - results[i].rect[0]; int w = results[i].rect[2] - results[i].rect[0];
int h = results[i].rect[3] - results[i].rect[1]; int h = results[i].rect[3] - results[i].rect[1];
cv::Rect roi = cv::Rect(results[i].rect[0], results[i].rect[1], w, h); cv::Rect roi = cv::Rect(results[i].rect[0], results[i].rect[1], w, h);
// Draw roi object, text, and background // Draw roi object, text, and background
cv::rectangle(vis_img, roi, roi_color, 2); cv::rectangle(vis_img, roi, roi_color, 2);
} }
origin.x = results[i].rect[0]; origin.x = results[i].rect[0];
origin.y = results[i].rect[1]; origin.y = results[i].rect[1];
// Configure text background // Configure text background
cv::Rect text_back = cv::Rect text_back =
cv::Rect(results[i].rect[0], results[i].rect[1] - text_size.height, cv::Rect(results[i].rect[0], results[i].rect[1] - text_size.height,
text_size.width, text_size.height); text_size.width, text_size.height);
// Draw text, and background // Draw text, and background
cv::rectangle(vis_img, text_back, roi_color, -1); cv::rectangle(vis_img, text_back, roi_color, -1);
cv::putText(vis_img, text, origin, font_face, font_scale, cv::putText(vis_img, text, origin, font_face, font_scale,
cv::Scalar(255, 255, 255), thickness); cv::Scalar(255, 255, 255), thickness);
} }
return vis_img; return vis_img;
} }
void ObjectDetector::Preprocess(const cv::Mat &ori_im) { void ObjectDetector::Preprocess(const cv::Mat &ori_im) {
// Clone the image : keep the original mat for postprocess // Clone the image : keep the original mat for postprocess
cv::Mat im = ori_im.clone(); cv::Mat im = ori_im.clone();
cv::cvtColor(im, im, cv::COLOR_BGR2RGB); cv::cvtColor(im, im, cv::COLOR_BGR2RGB);
preprocessor_.Run(&im, &inputs_); preprocessor_.Run(&im, &inputs_);
} }
void ObjectDetector::Postprocess(const std::vector<cv::Mat> mats, void ObjectDetector::Postprocess(const std::vector <cv::Mat> mats,
std::vector<ObjectResult> *result, std::vector <ObjectResult> *result,
std::vector<int> bbox_num, std::vector<int> bbox_num,
bool is_rbox = false) { bool is_rbox = false) {
result->clear(); result->clear();
int start_idx = 0; int start_idx = 0;
for (int im_id = 0; im_id < mats.size(); im_id++) { for (int im_id = 0; im_id < mats.size(); im_id++) {
cv::Mat raw_mat = mats[im_id]; cv::Mat raw_mat = mats[im_id];
int rh = 1; int rh = 1;
int rw = 1; int rw = 1;
// if (config_.arch_ == "Face") { // if (config_.arch_ == "Face") {
// rh = raw_mat.rows; // rh = raw_mat.rows;
// rw = raw_mat.cols; // rw = raw_mat.cols;
// } // }
for (int j = start_idx; j < start_idx + bbox_num[im_id]; j++) { for (int j = start_idx; j < start_idx + bbox_num[im_id]; j++) {
if (is_rbox) { if (is_rbox) {
// Class id // Class id
int class_id = static_cast<int>(round(output_data_[0 + j * 10])); int class_id = static_cast<int>(round(output_data_[0 + j * 10]));
// Confidence score // Confidence score
float score = output_data_[1 + j * 10]; float score = output_data_[1 + j * 10];
int x1 = (output_data_[2 + j * 10] * rw); int x1 = (output_data_[2 + j * 10] * rw);
int y1 = (output_data_[3 + j * 10] * rh); int y1 = (output_data_[3 + j * 10] * rh);
int x2 = (output_data_[4 + j * 10] * rw); int x2 = (output_data_[4 + j * 10] * rw);
int y2 = (output_data_[5 + j * 10] * rh); int y2 = (output_data_[5 + j * 10] * rh);
int x3 = (output_data_[6 + j * 10] * rw); int x3 = (output_data_[6 + j * 10] * rw);
int y3 = (output_data_[7 + j * 10] * rh); int y3 = (output_data_[7 + j * 10] * rh);
int x4 = (output_data_[8 + j * 10] * rw); int x4 = (output_data_[8 + j * 10] * rw);
int y4 = (output_data_[9 + j * 10] * rh); int y4 = (output_data_[9 + j * 10] * rh);
ObjectResult result_item; ObjectResult result_item;
result_item.rect = {x1, y1, x2, y2, x3, y3, x4, y4}; result_item.rect = {x1, y1, x2, y2, x3, y3, x4, y4};
result_item.class_id = class_id; result_item.class_id = class_id;
result_item.confidence = score; result_item.confidence = score;
result->push_back(result_item); result->push_back(result_item);
} else { } else {
// Class id // Class id
int class_id = static_cast<int>(round(output_data_[0 + j * 6])); int class_id = static_cast<int>(round(output_data_[0 + j * 6]));
// Confidence score // Confidence score
float score = output_data_[1 + j * 6]; float score = output_data_[1 + j * 6];
int xmin = (output_data_[2 + j * 6] * rw); int xmin = (output_data_[2 + j * 6] * rw);
int ymin = (output_data_[3 + j * 6] * rh); int ymin = (output_data_[3 + j * 6] * rh);
int xmax = (output_data_[4 + j * 6] * rw); int xmax = (output_data_[4 + j * 6] * rw);
int ymax = (output_data_[5 + j * 6] * rh); int ymax = (output_data_[5 + j * 6] * rh);
int wd = xmax - xmin; int wd = xmax - xmin;
int hd = ymax - ymin; int hd = ymax - ymin;
ObjectResult result_item; ObjectResult result_item;
result_item.rect = {xmin, ymin, xmax, ymax}; result_item.rect = {xmin, ymin, xmax, ymax};
result_item.class_id = class_id; result_item.class_id = class_id;
result_item.confidence = score; result_item.confidence = score;
result->push_back(result_item); result->push_back(result_item);
} }
}
start_idx += bbox_num[im_id];
}
} }
start_idx += bbox_num[im_id];
}
}
void ObjectDetector::Predict(const std::vector<cv::Mat> imgs, const int warmup, void ObjectDetector::Predict(const std::vector <cv::Mat> imgs, const int warmup,
const int repeats, const int repeats,
std::vector<ObjectResult> *result, std::vector <ObjectResult> *result,
std::vector<int> *bbox_num, std::vector<int> *bbox_num,
std::vector<double> *times) { std::vector<double> *times) {
auto preprocess_start = std::chrono::steady_clock::now(); auto preprocess_start = std::chrono::steady_clock::now();
int batch_size = imgs.size(); int batch_size = imgs.size();
// in_data_batch // in_data_batch
std::vector<float> in_data_all; std::vector<float> in_data_all;
std::vector<float> im_shape_all(batch_size * 2); std::vector<float> im_shape_all(batch_size * 2);
std::vector<float> scale_factor_all(batch_size * 2); std::vector<float> scale_factor_all(batch_size * 2);
// Preprocess image // Preprocess image
for (int bs_idx = 0; bs_idx < batch_size; bs_idx++) { for (int bs_idx = 0; bs_idx < batch_size; bs_idx++) {
cv::Mat im = imgs.at(bs_idx); cv::Mat im = imgs.at(bs_idx);
Preprocess(im); Preprocess(im);
im_shape_all[bs_idx * 2] = inputs_.im_shape_[0]; im_shape_all[bs_idx * 2] = inputs_.im_shape_[0];
im_shape_all[bs_idx * 2 + 1] = inputs_.im_shape_[1]; im_shape_all[bs_idx * 2 + 1] = inputs_.im_shape_[1];
scale_factor_all[bs_idx * 2] = inputs_.scale_factor_[0]; scale_factor_all[bs_idx * 2] = inputs_.scale_factor_[0];
scale_factor_all[bs_idx * 2 + 1] = inputs_.scale_factor_[1]; scale_factor_all[bs_idx * 2 + 1] = inputs_.scale_factor_[1];
// TODO: reduce cost time // TODO: reduce cost time
in_data_all.insert(in_data_all.end(), inputs_.im_data_.begin(), in_data_all.insert(in_data_all.end(), inputs_.im_data_.begin(),
inputs_.im_data_.end()); inputs_.im_data_.end());
} }
// Prepare input tensor // Prepare input tensor
auto input_names = predictor_->GetInputNames(); auto input_names = predictor_->GetInputNames();
for (const auto &tensor_name : input_names) { for (const auto &tensor_name : input_names) {
auto in_tensor = predictor_->GetInputHandle(tensor_name); auto in_tensor = predictor_->GetInputHandle(tensor_name);
if (tensor_name == "image") { if (tensor_name == "image") {
int rh = inputs_.in_net_shape_[0]; int rh = inputs_.in_net_shape_[0];
int rw = inputs_.in_net_shape_[1]; int rw = inputs_.in_net_shape_[1];
in_tensor->Reshape({batch_size, 3, rh, rw}); in_tensor->Reshape({batch_size, 3, rh, rw});
in_tensor->CopyFromCpu(in_data_all.data()); in_tensor->CopyFromCpu(in_data_all.data());
} else if (tensor_name == "im_shape") { } else if (tensor_name == "im_shape") {
in_tensor->Reshape({batch_size, 2}); in_tensor->Reshape({batch_size, 2});
in_tensor->CopyFromCpu(im_shape_all.data()); in_tensor->CopyFromCpu(im_shape_all.data());
} else if (tensor_name == "scale_factor") { } else if (tensor_name == "scale_factor") {
in_tensor->Reshape({batch_size, 2}); in_tensor->Reshape({batch_size, 2});
in_tensor->CopyFromCpu(scale_factor_all.data()); in_tensor->CopyFromCpu(scale_factor_all.data());
} }
} }
auto preprocess_end = std::chrono::steady_clock::now(); auto preprocess_end = std::chrono::steady_clock::now();
// Run predictor // Run predictor
// warmup // warmup
for (int i = 0; i < warmup; i++) { for (int i = 0; i < warmup; i++) {
predictor_->Run(); predictor_->Run();
// Get output tensor // Get output tensor
auto output_names = predictor_->GetOutputNames(); auto output_names = predictor_->GetOutputNames();
auto out_tensor = predictor_->GetOutputHandle(output_names[0]); auto out_tensor = predictor_->GetOutputHandle(output_names[0]);
std::vector<int> output_shape = out_tensor->shape(); std::vector<int> output_shape = out_tensor->shape();
auto out_bbox_num = predictor_->GetOutputHandle(output_names[1]); auto out_bbox_num = predictor_->GetOutputHandle(output_names[1]);
std::vector<int> out_bbox_num_shape = out_bbox_num->shape(); std::vector<int> out_bbox_num_shape = out_bbox_num->shape();
// Calculate output length // Calculate output length
int output_size = 1; int output_size = 1;
for (int j = 0; j < output_shape.size(); ++j) { for (int j = 0; j < output_shape.size(); ++j) {
output_size *= output_shape[j]; output_size *= output_shape[j];
} }
if (output_size < 6) { if (output_size < 6) {
std::cerr << "[WARNING] No object detected." << std::endl; std::cerr << "[WARNING] No object detected." << std::endl;
} }
output_data_.resize(output_size); output_data_.resize(output_size);
out_tensor->CopyToCpu(output_data_.data()); out_tensor->CopyToCpu(output_data_.data());
int out_bbox_num_size = 1; int out_bbox_num_size = 1;
for (int j = 0; j < out_bbox_num_shape.size(); ++j) { for (int j = 0; j < out_bbox_num_shape.size(); ++j) {
out_bbox_num_size *= out_bbox_num_shape[j]; out_bbox_num_size *= out_bbox_num_shape[j];
} }
out_bbox_num_data_.resize(out_bbox_num_size); out_bbox_num_data_.resize(out_bbox_num_size);
out_bbox_num->CopyToCpu(out_bbox_num_data_.data()); out_bbox_num->CopyToCpu(out_bbox_num_data_.data());
} }
bool is_rbox = false; bool is_rbox = false;
auto inference_start = std::chrono::steady_clock::now(); auto inference_start = std::chrono::steady_clock::now();
for (int i = 0; i < repeats; i++) { for (int i = 0; i < repeats; i++) {
predictor_->Run(); predictor_->Run();
// Get output tensor // Get output tensor
auto output_names = predictor_->GetOutputNames(); auto output_names = predictor_->GetOutputNames();
auto out_tensor = predictor_->GetOutputHandle(output_names[0]); auto out_tensor = predictor_->GetOutputHandle(output_names[0]);
std::vector<int> output_shape = out_tensor->shape(); std::vector<int> output_shape = out_tensor->shape();
auto out_bbox_num = predictor_->GetOutputHandle(output_names[1]); auto out_bbox_num = predictor_->GetOutputHandle(output_names[1]);
std::vector<int> out_bbox_num_shape = out_bbox_num->shape(); std::vector<int> out_bbox_num_shape = out_bbox_num->shape();
// Calculate output length // Calculate output length
int output_size = 1; int output_size = 1;
for (int j = 0; j < output_shape.size(); ++j) { for (int j = 0; j < output_shape.size(); ++j) {
output_size *= output_shape[j]; output_size *= output_shape[j];
} }
is_rbox = output_shape[output_shape.size() - 1] % 10 == 0; is_rbox = output_shape[output_shape.size() - 1] % 10 == 0;
if (output_size < 6) { if (output_size < 6) {
std::cerr << "[WARNING] No object detected." << std::endl; std::cerr << "[WARNING] No object detected." << std::endl;
} }
output_data_.resize(output_size); output_data_.resize(output_size);
out_tensor->CopyToCpu(output_data_.data()); out_tensor->CopyToCpu(output_data_.data());
int out_bbox_num_size = 1; int out_bbox_num_size = 1;
for (int j = 0; j < out_bbox_num_shape.size(); ++j) { for (int j = 0; j < out_bbox_num_shape.size(); ++j) {
out_bbox_num_size *= out_bbox_num_shape[j]; out_bbox_num_size *= out_bbox_num_shape[j];
} }
out_bbox_num_data_.resize(out_bbox_num_size); out_bbox_num_data_.resize(out_bbox_num_size);
out_bbox_num->CopyToCpu(out_bbox_num_data_.data()); out_bbox_num->CopyToCpu(out_bbox_num_data_.data());
} }
auto inference_end = std::chrono::steady_clock::now(); auto inference_end = std::chrono::steady_clock::now();
auto postprocess_start = std::chrono::steady_clock::now(); auto postprocess_start = std::chrono::steady_clock::now();
// Postprocessing result // Postprocessing result
result->clear(); result->clear();
Postprocess(imgs, result, out_bbox_num_data_, is_rbox); Postprocess(imgs, result, out_bbox_num_data_, is_rbox);
bbox_num->clear(); bbox_num->clear();
for (int k = 0; k < out_bbox_num_data_.size(); k++) { for (int k = 0; k < out_bbox_num_data_.size(); k++) {
int tmp = out_bbox_num_data_[k]; int tmp = out_bbox_num_data_[k];
bbox_num->push_back(tmp); bbox_num->push_back(tmp);
} }
auto postprocess_end = std::chrono::steady_clock::now(); auto postprocess_end = std::chrono::steady_clock::now();
std::chrono::duration<float> preprocess_diff = std::chrono::duration<float> preprocess_diff =
preprocess_end - preprocess_start; preprocess_end - preprocess_start;
times->push_back(double(preprocess_diff.count() * 1000)); times->push_back(double(preprocess_diff.count() * 1000));
std::chrono::duration<float> inference_diff = inference_end - inference_start; std::chrono::duration<float> inference_diff = inference_end - inference_start;
times->push_back(double(inference_diff.count() / repeats * 1000)); times->push_back(double(inference_diff.count() / repeats * 1000));
std::chrono::duration<float> postprocess_diff = std::chrono::duration<float> postprocess_diff =
postprocess_end - postprocess_start; postprocess_end - postprocess_start;
times->push_back(double(postprocess_diff.count() * 1000)); times->push_back(double(postprocess_diff.count() * 1000));
} }
std::vector<int> GenerateColorMap(int num_class) { std::vector<int> GenerateColorMap(int num_class) {
auto colormap = std::vector<int>(3 * num_class, 0); auto colormap = std::vector<int>(3 * num_class, 0);
for (int i = 0; i < num_class; ++i) { for (int i = 0; i < num_class; ++i) {
int j = 0; int j = 0;
int lab = i; int lab = i;
while (lab) { while (lab) {
colormap[i * 3] |= (((lab >> 0) & 1) << (7 - j)); colormap[i * 3] |= (((lab >> 0) & 1) << (7 - j));
colormap[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j)); colormap[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j));
colormap[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j)); colormap[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j));
++j; ++j;
lab >>= 3; lab >>= 3;
}
}
return colormap;
} }
}
return colormap;
}
} // namespace Detection } // namespace Detection
deploy/cpp_shitu/src/preprocess_op.cpp
浏览文件 @ 29f8fb83
...@@ -32,60 +32,60 @@ ...@@ -32,60 +32,60 @@
namespace Feature { namespace Feature {
void Permute::Run(const cv::Mat *im, float *data) { void Permute::Run(const cv::Mat *im, float *data) {
int rh = im->rows; int rh = im->rows;
int rw = im->cols; int rw = im->cols;
int rc = im->channels(); int rc = im->channels();
for (int i = 0; i < rc; ++i) { for (int i = 0; i < rc; ++i) {
cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw), i); cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw), i);
} }
} }
void Normalize::Run(cv::Mat *im, const std::vector<float> &mean, void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &std, float scale) { const std::vector<float> &std, float scale) {
(*im).convertTo(*im, CV_32FC3, scale); (*im).convertTo(*im, CV_32FC3, scale);
for (int h = 0; h < im->rows; h++) { for (int h = 0; h < im->rows; h++) {
for (int w = 0; w < im->cols; w++) { for (int w = 0; w < im->cols; w++) {
im->at<cv::Vec3f>(h, w)[0] = im->at<cv::Vec3f>(h, w)[0] =
(im->at<cv::Vec3f>(h, w)[0] - mean[0]) / std[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] =
(im->at<cv::Vec3f>(h, w)[1] - mean[1]) / std[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] =
(im->at<cv::Vec3f>(h, w)[2] - mean[2]) / std[2]; (im->at<cv::Vec3f>(h, w)[2] - mean[2]) / std[2];
}
}
} }
}
}
void CenterCropImg::Run(cv::Mat &img, const int crop_size) { void CenterCropImg::Run(cv::Mat &img, const int crop_size) {
int resize_w = img.cols; int resize_w = img.cols;
int resize_h = img.rows; int resize_h = img.rows;
int w_start = int((resize_w - crop_size) / 2); int w_start = int((resize_w - crop_size) / 2);
int h_start = int((resize_h - crop_size) / 2); int h_start = int((resize_h - crop_size) / 2);
cv::Rect rect(w_start, h_start, crop_size, crop_size); cv::Rect rect(w_start, h_start, crop_size, crop_size);
img = img(rect); img = img(rect);
} }
void ResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img, void ResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
int resize_short_size, int size) { int resize_short_size, int size) {
int resize_h = 0; int resize_h = 0;
int resize_w = 0; int resize_w = 0;
if (size > 0) { if (size > 0) {
resize_h = size; resize_h = size;
resize_w = size; resize_w = size;
} else { } else {
int w = img.cols; int w = img.cols;
int h = img.rows; int h = img.rows;
float ratio = 1.f; float ratio = 1.f;
if (h < w) { if (h < w) {
ratio = float(resize_short_size) / float(h); ratio = float(resize_short_size) / float(h);
} else { } else {
ratio = float(resize_short_size) / float(w); ratio = float(resize_short_size) / float(w);
}
resize_h = round(float(h) * ratio);
resize_w = round(float(w) * ratio);
}
cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
} }
resize_h = round(float(h) * ratio);
resize_w = round(float(w) * ratio);
}
cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
}
} // namespace Feature } // namespace Feature
deploy/cpp_shitu/src/preprocess_op_det.cpp
浏览文件 @ 29f8fb83
...@@ -19,112 +19,112 @@ ...@@ -19,112 +19,112 @@
namespace Detection { namespace Detection {
void InitInfo::Run(cv::Mat *im, ImageBlob *data) { void InitInfo::Run(cv::Mat *im, ImageBlob *data) {
data->im_shape_ = {static_cast<float>(im->rows), data->im_shape_ = {static_cast<float>(im->rows),
static_cast<float>(im->cols)}; static_cast<float>(im->cols)};
data->scale_factor_ = {1., 1.}; data->scale_factor_ = {1., 1.};
data->in_net_shape_ = {static_cast<float>(im->rows), data->in_net_shape_ = {static_cast<float>(im->rows),
static_cast<float>(im->cols)}; static_cast<float>(im->cols)};
} }
void NormalizeImage::Run(cv::Mat *im, ImageBlob *data) { void NormalizeImage::Run(cv::Mat *im, ImageBlob *data) {
double e = 1.0; double e = 1.0;
if (is_scale_) { if (is_scale_) {
e /= 255.0; e /= 255.0;
} }
(*im).convertTo(*im, CV_32FC3, e); (*im).convertTo(*im, CV_32FC3, e);
for (int h = 0; h < im->rows; h++) { for (int h = 0; h < im->rows; h++) {
for (int w = 0; w < im->cols; w++) { for (int w = 0; w < im->cols; w++) {
im->at<cv::Vec3f>(h, w)[0] = 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]) / scale_[0];
im->at<cv::Vec3f>(h, w)[1] = 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]) / scale_[1];
im->at<cv::Vec3f>(h, w)[2] = 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]) / scale_[2];
}
}
} }
}
}
void Permute::Run(cv::Mat *im, ImageBlob *data) { void Permute::Run(cv::Mat *im, ImageBlob *data) {
int rh = im->rows; int rh = im->rows;
int rw = im->cols; int rw = im->cols;
int rc = im->channels(); int rc = im->channels();
(data->im_data_).resize(rc * rh * rw); (data->im_data_).resize(rc * rh * rw);
float *base = (data->im_data_).data(); float *base = (data->im_data_).data();
for (int i = 0; i < rc; ++i) { for (int i = 0; i < rc; ++i) {
cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, base + i * rh * rw), i); cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, base + i * rh * rw), i);
} }
} }
void Resize::Run(cv::Mat *im, ImageBlob *data) { void Resize::Run(cv::Mat *im, ImageBlob *data) {
auto resize_scale = GenerateScale(*im); auto resize_scale = GenerateScale(*im);
data->im_shape_ = {static_cast<float>(im->cols * resize_scale.first), data->im_shape_ = {static_cast<float>(im->cols * resize_scale.first),
static_cast<float>(im->rows * resize_scale.second)}; static_cast<float>(im->rows * resize_scale.second)};
data->in_net_shape_ = {static_cast<float>(im->cols * resize_scale.first), data->in_net_shape_ = {static_cast<float>(im->cols * resize_scale.first),
static_cast<float>(im->rows * resize_scale.second)}; static_cast<float>(im->rows * resize_scale.second)};
cv::resize(*im, *im, cv::Size(), resize_scale.first, resize_scale.second, cv::resize(*im, *im, cv::Size(), resize_scale.first, resize_scale.second,
interp_); interp_);
data->im_shape_ = { data->im_shape_ = {
static_cast<float>(im->rows), static_cast<float>(im->cols), static_cast<float>(im->rows), static_cast<float>(im->cols),
}; };
data->scale_factor_ = { data->scale_factor_ = {
resize_scale.second, resize_scale.first, resize_scale.second, resize_scale.first,
}; };
} }
std::pair<double, double> Resize::GenerateScale(const cv::Mat &im) { std::pair<double, double> Resize::GenerateScale(const cv::Mat &im) {
std::pair<double, double> resize_scale; std::pair<double, double> resize_scale;
int origin_w = im.cols; int origin_w = im.cols;
int origin_h = im.rows; int origin_h = im.rows;
if (keep_ratio_) { if (keep_ratio_) {
int im_size_max = std::max(origin_w, origin_h); int im_size_max = std::max(origin_w, origin_h);
int im_size_min = std::min(origin_w, origin_h); int im_size_min = std::min(origin_w, origin_h);
int target_size_max = int target_size_max =
*std::max_element(target_size_.begin(), target_size_.end()); *std::max_element(target_size_.begin(), target_size_.end());
int target_size_min = int target_size_min =
*std::min_element(target_size_.begin(), target_size_.end()); *std::min_element(target_size_.begin(), target_size_.end());
double scale_min = double scale_min =
static_cast<double>(target_size_min) / static_cast<double>(im_size_min); static_cast<double>(target_size_min) / static_cast<double>(im_size_min);
double scale_max = double scale_max =
static_cast<double>(target_size_max) / static_cast<double>(im_size_max); static_cast<double>(target_size_max) / static_cast<double>(im_size_max);
double scale_ratio = std::min(scale_min, scale_max); double scale_ratio = std::min(scale_min, scale_max);
resize_scale = {scale_ratio, scale_ratio}; resize_scale = {scale_ratio, scale_ratio};
} else { } else {
resize_scale.first = resize_scale.first =
static_cast<double>(target_size_[1]) / static_cast<double>(origin_w); static_cast<double>(target_size_[1]) / static_cast<double>(origin_w);
resize_scale.second = resize_scale.second =
static_cast<double>(target_size_[0]) / static_cast<double>(origin_h); static_cast<double>(target_size_[0]) / static_cast<double>(origin_h);
} }
return resize_scale; return resize_scale;
} }
void PadStride::Run(cv::Mat *im, ImageBlob *data) { void PadStride::Run(cv::Mat *im, ImageBlob *data) {
if (stride_ <= 0) { if (stride_ <= 0) {
return; return;
} }
int rc = im->channels(); int rc = im->channels();
int rh = im->rows; int rh = im->rows;
int rw = im->cols; int rw = im->cols;
int nh = (rh / stride_) * stride_ + (rh % stride_ != 0) * stride_; int nh = (rh / stride_) * stride_ + (rh % stride_ != 0) * stride_;
int nw = (rw / stride_) * stride_ + (rw % stride_ != 0) * stride_; int nw = (rw / stride_) * stride_ + (rw % stride_ != 0) * stride_;
cv::copyMakeBorder(*im, *im, 0, nh - rh, 0, nw - rw, cv::BORDER_CONSTANT, cv::copyMakeBorder(*im, *im, 0, nh - rh, 0, nw - rw, cv::BORDER_CONSTANT,
cv::Scalar(0)); cv::Scalar(0));
data->in_net_shape_ = { data->in_net_shape_ = {
static_cast<float>(im->rows), static_cast<float>(im->cols), static_cast<float>(im->rows), static_cast<float>(im->cols),
}; };
} }
// Preprocessor op running order // Preprocessor op running order
const std::vector<std::string> Preprocessor::RUN_ORDER = { const std::vector <std::string> Preprocessor::RUN_ORDER = {
"InitInfo", "Resize", "NormalizeImage", "PadStride", "Permute"}; "InitInfo", "Resize", "NormalizeImage", "PadStride", "Permute"};
void Preprocessor::Run(cv::Mat *im, ImageBlob *data) { void Preprocessor::Run(cv::Mat *im, ImageBlob *data) {
for (const auto &name : RUN_ORDER) { for (const auto &name : RUN_ORDER) {
if (ops_.find(name) != ops_.end()) { if (ops_.find(name) != ops_.end()) {
ops_[name]->Run(im, data); ops_[name]->Run(im, data);
}
}
} }
}
}
} // namespace Detection } // namespace Detection
deploy/cpp_shitu/src/vector_search.cpp
浏览文件 @ 29f8fb83
...@@ -20,43 +20,43 @@ ...@@ -20,43 +20,43 @@
#include <regex> #include <regex>
void VectorSearch::LoadIndexFile() { void VectorSearch::LoadIndexFile() {
std::string file_path = this->index_dir + OS_PATH_SEP + "vector.index"; std::string file_path = this->index_dir + OS_PATH_SEP + "vector.index";
const char *fname = file_path.c_str(); const char *fname = file_path.c_str();
this->index = faiss::read_index(fname, 0); this->index = faiss::read_index(fname, 0);
} }
void VectorSearch::LoadIdMap() { void VectorSearch::LoadIdMap() {
std::string file_path = this->index_dir + OS_PATH_SEP + "id_map.txt"; std::string file_path = this->index_dir + OS_PATH_SEP + "id_map.txt";
std::ifstream in(file_path); std::ifstream in(file_path);
std::string line; std::string line;
std::vector<std::string> m_vec; std::vector <std::string> m_vec;
if (in) { if (in) {
while (getline(in, line)) { while (getline(in, line)) {
std::regex ws_re("\\s+"); std::regex ws_re("\\s+");
std::vector<std::string> v( std::vector <std::string> v(
std::sregex_token_iterator(line.begin(), line.end(), ws_re, -1), std::sregex_token_iterator(line.begin(), line.end(), ws_re, -1),
std::sregex_token_iterator()); std::sregex_token_iterator());
if (v.size() != 2) { if (v.size() != 2) {
std::cout << "The number of element for each line in : " << file_path std::cout << "The number of element for each line in : " << file_path
<< "must be 2, exit the program..." << std::endl; << "must be 2, exit the program..." << std::endl;
exit(1); exit(1);
} else } else
this->id_map.insert(std::pair<long int, std::string>( this->id_map.insert(std::pair<long int, std::string>(
std::stol(v[0], nullptr, 10), v[1])); std::stol(v[0], nullptr, 10), v[1]));
}
} }
}
} }
const SearchResult &VectorSearch::Search(float *feature, int query_number) { const SearchResult &VectorSearch::Search(float *feature, int query_number) {
this->D.resize(this->return_k * query_number); this->D.resize(this->return_k * query_number);
this->I.resize(this->return_k * query_number); this->I.resize(this->return_k * query_number);
this->index->search(query_number, feature, return_k, D.data(), I.data()); this->index->search(query_number, feature, return_k, D.data(), I.data());
this->sr.return_k = this->return_k; this->sr.return_k = this->return_k;
this->sr.D = this->D; this->sr.D = this->D;
this->sr.I = this->I; this->sr.I = this->I;
return this->sr; return this->sr;
} }
const std::string &VectorSearch::GetLabel(faiss::Index::idx_t ind) { const std::string &VectorSearch::GetLabel(faiss::Index::idx_t ind) {
return this->id_map.at(ind); return this->id_map.at(ind);
} }
deploy/cpp_shitu/src/yaml_config.cpp
浏览文件 @ 29f8fb83
...@@ -19,60 +19,60 @@ ...@@ -19,60 +19,60 @@
#include <include/yaml_config.h> #include <include/yaml_config.h>
std::vector<std::string> YamlConfig::ReadDict(const std::string &path) { std::vector <std::string> YamlConfig::ReadDict(const std::string &path) {
std::ifstream in(path); std::ifstream in(path);
std::string line; std::string line;
std::vector<std::string> m_vec; std::vector <std::string> m_vec;
if (in) { if (in) {
while (getline(in, line)) { while (getline(in, line)) {
m_vec.push_back(line); m_vec.push_back(line);
}
} else {
std::cout << "no such label file: " << path << ", exit the program..."
<< std::endl;
exit(1);
} }
} else { return m_vec;
std::cout << "no such label file: " << path << ", exit the program..."
<< std::endl;
exit(1);
}
return m_vec;
} }
std::map<int, std::string> YamlConfig::ReadIndexId(const std::string &path) { std::map<int, std::string> YamlConfig::ReadIndexId(const std::string &path) {
std::ifstream in(path); std::ifstream in(path);
std::string line; std::string line;
std::map<int, std::string> m_vec; std::map<int, std::string> m_vec;
if (in) { if (in) {
while (getline(in, line)) { while (getline(in, line)) {
std::regex ws_re("\\s+"); std::regex ws_re("\\s+");
std::vector<std::string> v( std::vector <std::string> v(
std::sregex_token_iterator(line.begin(), line.end(), ws_re, -1), std::sregex_token_iterator(line.begin(), line.end(), ws_re, -1),
std::sregex_token_iterator()); std::sregex_token_iterator());
if (v.size() != 3) { if (v.size() != 3) {
std::cout << "The number of element for each line in : " << path std::cout << "The number of element for each line in : " << path
<< "must be 3, exit the program..." << std::endl; << "must be 3, exit the program..." << std::endl;
exit(1); exit(1);
} else } else
m_vec.insert(std::pair<int, std::string>(stoi(v[0]), v[2])); m_vec.insert(std::pair<int, std::string>(stoi(v[0]), v[2]));
}
} }
} return m_vec;
return m_vec;
} }
YAML::Node YamlConfig::ReadYamlConfig(const std::string &path) { YAML::Node YamlConfig::ReadYamlConfig(const std::string &path) {
YAML::Node config; YAML::Node config;
try { try {
config = YAML::LoadFile(path); config = YAML::LoadFile(path);
} catch (YAML::BadFile &e) { } catch (YAML::BadFile &e) {
std::cout << "Something wrong in yaml file, please check yaml file" std::cout << "Something wrong in yaml file, please check yaml file"
<< std::endl; << std::endl;
exit(1); exit(1);
} }
return config; return config;
} }
void YamlConfig::PrintConfigInfo() { void YamlConfig::PrintConfigInfo() {
std::cout << this->config_file << std::endl; std::cout << this->config_file << std::endl;
// for (YAML::const_iterator // for (YAML::const_iterator
// it=config_file.begin();it!=config_file.end();++it) // it=config_file.begin();it!=config_file.end();++it)
// { // {
// std::cout << it->as<std::string>() << "\n"; // std::cout << it->as<std::string>() << "\n";
// } // }
} }
deploy/cpp_shitu/tools/build.sh
浏览文件 @ 29f8fb83
OPENCV_DIR=/work/project/project/cpp_infer/opencv-3.4.7/opencv3 OPENCV_DIR=${opencv_install_dir}
LIB_DIR=/work/project/project/cpp_infer/paddle_inference/ LIB_DIR=${paddle_inference_dir}
CUDA_LIB_DIR=/usr/local/cuda/lib64 CUDA_LIB_DIR=/usr/local/cuda/lib64
CUDNN_LIB_DIR=/usr/lib/x86_64-linux-gnu/ CUDNN_LIB_DIR=/usr/lib/x86_64-linux-gnu/
FAISS_DIR=/work/project/project/cpp_infer/faiss/faiss_install FAISS_DIR=${faiss_install_dir}
FAISS_WITH_MKL=OFF FAISS_WITH_MKL=OFF
BUILD_DIR=build BUILD_DIR=build
...@@ -21,4 +21,4 @@ cmake .. \ ...@@ -21,4 +21,4 @@ cmake .. \
-DFAISS_DIR=${FAISS_DIR} \ -DFAISS_DIR=${FAISS_DIR} \
-DFAISS_WITH_MKL=${FAISS_WITH_MKL} -DFAISS_WITH_MKL=${FAISS_WITH_MKL}
make -j make -j
\ No newline at end of file
deploy/cpp_shitu/tools/config.txt 已删除 100755 → 0
浏览文件 @ a96305c2
# model load config
use_gpu 0
gpu_id 0
gpu_mem 4000
cpu_threads 10
use_mkldnn 1
use_tensorrt 0
use_fp16 0
# cls config
cls_model_path /PaddleClas/inference/cls_infer.pdmodel
cls_params_path /PaddleClas/inference/cls_infer.pdiparams
resize_short_size 256
crop_size 224
# for log env info
benchmark 0
deploy/cpp_shitu/tools/run.sh 已删除 100755 → 0
浏览文件 @ a96305c2
./build/clas_system ../configs/inference_rec.yaml
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