code format for cls class_infer
dongshuilong
parent
3e30214702
commit
e1716ded56
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Before Width: | Height: | Size: 85 KiB After Width: | Height: | Size: 73 KiB |
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@ -35,56 +35,56 @@ using namespace paddle_infer;
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namespace PaddleClas {
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class Classifier {
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public:
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explicit Classifier(const ClsConfig &config) {
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this->use_gpu_ = config.use_gpu;
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this->gpu_id_ = config.gpu_id;
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this->gpu_mem_ = config.gpu_mem;
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this->cpu_math_library_num_threads_ = config.cpu_threads;
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this->use_fp16_ = config.use_fp16;
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this->use_mkldnn_ = config.use_mkldnn;
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this->use_tensorrt_ = config.use_tensorrt;
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this->mean_ = config.mean;
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this->std_ = config.std;
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this->resize_short_size_ = config.resize_short_size;
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this->scale_ = config.scale;
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this->crop_size_ = config.crop_size;
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this->ir_optim_ = config.ir_optim;
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LoadModel(config.cls_model_path, config.cls_params_path);
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}
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class Classifier {
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public:
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explicit Classifier(const ClsConfig &config) {
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this->use_gpu_ = config.use_gpu;
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this->gpu_id_ = config.gpu_id;
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this->gpu_mem_ = config.gpu_mem;
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this->cpu_math_library_num_threads_ = config.cpu_threads;
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this->use_fp16_ = config.use_fp16;
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this->use_mkldnn_ = config.use_mkldnn;
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this->use_tensorrt_ = config.use_tensorrt;
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this->mean_ = config.mean;
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this->std_ = config.std;
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this->resize_short_size_ = config.resize_short_size;
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this->scale_ = config.scale;
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this->crop_size_ = config.crop_size;
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this->ir_optim_ = config.ir_optim;
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LoadModel(config.cls_model_path, config.cls_params_path);
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}
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// Load Paddle inference model
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void LoadModel(const std::string &model_path, const std::string ¶ms_path);
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// Load Paddle inference model
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void LoadModel(const std::string &model_path, const std::string ¶ms_path);
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// Run predictor
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void Run(cv::Mat &img, std::vector<float> &out_data, std::vector<int> &idx,
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std::vector<double> ×);
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// Run predictor
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void Run(cv::Mat &img, std::vector<float> &out_data, std::vector<int> &idx,
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std::vector<double> ×);
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private:
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std::shared_ptr<Predictor> predictor_;
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private:
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std::shared_ptr <Predictor> predictor_;
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bool use_gpu_ = false;
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int gpu_id_ = 0;
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int gpu_mem_ = 4000;
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int cpu_math_library_num_threads_ = 4;
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bool use_mkldnn_ = false;
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bool use_tensorrt_ = false;
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bool use_fp16_ = false;
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bool ir_optim_ = true;
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bool use_gpu_ = false;
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int gpu_id_ = 0;
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int gpu_mem_ = 4000;
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int cpu_math_library_num_threads_ = 4;
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bool use_mkldnn_ = false;
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bool use_tensorrt_ = false;
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bool use_fp16_ = false;
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bool ir_optim_ = true;
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std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
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std::vector<float> std_ = {0.229f, 0.224f, 0.225f};
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float scale_ = 0.00392157;
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std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
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std::vector<float> std_ = {0.229f, 0.224f, 0.225f};
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float scale_ = 0.00392157;
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int resize_short_size_ = 256;
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int crop_size_ = 224;
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int resize_short_size_ = 256;
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int crop_size_ = 224;
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// pre-process
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ResizeImg resize_op_;
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Normalize normalize_op_;
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Permute permute_op_;
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CenterCropImg crop_op_;
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};
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// pre-process
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ResizeImg resize_op_;
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Normalize normalize_op_;
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Permute permute_op_;
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CenterCropImg crop_op_;
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};
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} // namespace PaddleClas
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@ -31,101 +31,103 @@
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namespace PaddleClas {
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class ClsConfig {
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public:
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explicit ClsConfig(const std::string &path) {
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ReadYamlConfig(path);
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this->infer_imgs =
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this->config_file["Global"]["infer_imgs"].as<std::string>();
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this->batch_size = this->config_file["Global"]["batch_size"].as<int>();
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this->use_gpu = this->config_file["Global"]["use_gpu"].as<bool>();
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if (this->config_file["Global"]["gpu_id"].IsDefined())
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this->gpu_id = this->config_file["Global"]["gpu_id"].as<int>();
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else
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this->gpu_id = 0;
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this->gpu_mem = this->config_file["Global"]["gpu_mem"].as<int>();
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this->cpu_threads =
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this->config_file["Global"]["cpu_num_threads"].as<int>();
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this->use_mkldnn = this->config_file["Global"]["enable_mkldnn"].as<bool>();
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this->use_tensorrt = this->config_file["Global"]["use_tensorrt"].as<bool>();
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this->use_fp16 = this->config_file["Global"]["use_fp16"].as<bool>();
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this->enable_benchmark =
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this->config_file["Global"]["enable_benchmark"].as<bool>();
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this->ir_optim = this->config_file["Global"]["ir_optim"].as<bool>();
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this->enable_profile =
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this->config_file["Global"]["enable_profile"].as<bool>();
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this->cls_model_path =
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this->config_file["Global"]["inference_model_dir"].as<std::string>() +
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OS_PATH_SEP + "inference.pdmodel";
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this->cls_params_path =
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this->config_file["Global"]["inference_model_dir"].as<std::string>() +
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OS_PATH_SEP + "inference.pdiparams";
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this->resize_short_size =
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this->config_file["PreProcess"]["transform_ops"][0]["ResizeImage"]
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["resize_short"]
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.as<int>();
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this->crop_size =
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this->config_file["PreProcess"]["transform_ops"][1]["CropImage"]["size"]
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.as<int>();
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this->scale = this->config_file["PreProcess"]["transform_ops"][2]
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["NormalizeImage"]["scale"]
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.as<float>();
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this->mean = this->config_file["PreProcess"]["transform_ops"][2]
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["NormalizeImage"]["mean"]
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.as<std::vector<float>>();
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this->std = this->config_file["PreProcess"]["transform_ops"][2]
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["NormalizeImage"]["std"]
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.as<std::vector<float>>();
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if (this->config_file["Global"]["benchmark"].IsDefined())
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this->benchmark = this->config_file["Global"]["benchmark"].as<bool>();
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else
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this->benchmark = false;
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class ClsConfig {
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public:
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explicit ClsConfig(const std::string &path) {
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ReadYamlConfig(path);
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this->infer_imgs =
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this->config_file["Global"]["infer_imgs"].as<std::string>();
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this->batch_size = this->config_file["Global"]["batch_size"].as<int>();
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this->use_gpu = this->config_file["Global"]["use_gpu"].as<bool>();
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if (this->config_file["Global"]["gpu_id"].IsDefined())
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this->gpu_id = this->config_file["Global"]["gpu_id"].as<int>();
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else
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this->gpu_id = 0;
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this->gpu_mem = this->config_file["Global"]["gpu_mem"].as<int>();
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this->cpu_threads =
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this->config_file["Global"]["cpu_num_threads"].as<int>();
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this->use_mkldnn = this->config_file["Global"]["enable_mkldnn"].as<bool>();
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this->use_tensorrt = this->config_file["Global"]["use_tensorrt"].as<bool>();
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this->use_fp16 = this->config_file["Global"]["use_fp16"].as<bool>();
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this->enable_benchmark =
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this->config_file["Global"]["enable_benchmark"].as<bool>();
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this->ir_optim = this->config_file["Global"]["ir_optim"].as<bool>();
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this->enable_profile =
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this->config_file["Global"]["enable_profile"].as<bool>();
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this->cls_model_path =
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this->config_file["Global"]["inference_model_dir"].as<std::string>() +
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OS_PATH_SEP + "inference.pdmodel";
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this->cls_params_path =
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this->config_file["Global"]["inference_model_dir"].as<std::string>() +
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OS_PATH_SEP + "inference.pdiparams";
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this->resize_short_size =
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this->config_file["PreProcess"]["transform_ops"][0]["ResizeImage"]
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["resize_short"]
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.as<int>();
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this->crop_size =
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this->config_file["PreProcess"]["transform_ops"][1]["CropImage"]["size"]
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.as<int>();
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this->scale = this->config_file["PreProcess"]["transform_ops"][2]
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["NormalizeImage"]["scale"]
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.as<float>();
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this->mean = this->config_file["PreProcess"]["transform_ops"][2]
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["NormalizeImage"]["mean"]
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.as < std::vector < float >> ();
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this->std = this->config_file["PreProcess"]["transform_ops"][2]
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["NormalizeImage"]["std"]
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.as < std::vector < float >> ();
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if (this->config_file["Global"]["benchmark"].IsDefined())
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this->benchmark = this->config_file["Global"]["benchmark"].as<bool>();
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else
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this->benchmark = false;
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if (this->config_file["PostProcess"]["Topk"]["topk"].IsDefined())
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this->topk = this->config_file["PostProcess"]["Topk"]["topk"].as<int>();
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if (this->config_file["PostProcess"]["Topk"]["class_id_map_file"]
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.IsDefined())
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this->class_id_map_path =
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this->config_file["PostProcess"]["Topk"]["class_id_map_file"]
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.as<std::string>();
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if (this->config_file["PostProcess"]["SavePreLabel"]["save_dir"]
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.IsDefined())
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this->label_save_dir =
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this->config_file["PostProcess"]["SavePreLabel"]["save_dir"]
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.as<std::string>();
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ReadLabelMap();
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}
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if (this->config_file["PostProcess"]["Topk"]["topk"].IsDefined())
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this->topk = this->config_file["PostProcess"]["Topk"]["topk"].as<int>();
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if (this->config_file["PostProcess"]["Topk"]["class_id_map_file"]
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.IsDefined())
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this->class_id_map_path =
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this->config_file["PostProcess"]["Topk"]["class_id_map_file"]
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.as<std::string>();
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if (this->config_file["PostProcess"]["SavePreLabel"]["save_dir"]
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.IsDefined())
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this->label_save_dir =
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this->config_file["PostProcess"]["SavePreLabel"]["save_dir"]
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.as<std::string>();
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ReadLabelMap();
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}
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YAML::Node config_file;
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bool use_gpu = false;
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int gpu_id = 0;
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int gpu_mem = 4000;
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int cpu_threads = 1;
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bool use_mkldnn = false;
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bool use_tensorrt = false;
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bool use_fp16 = false;
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bool benchmark = false;
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int batch_size = 1;
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bool enable_benchmark = false;
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bool ir_optim = true;
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bool enable_profile = false;
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YAML::Node config_file;
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bool use_gpu = false;
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int gpu_id = 0;
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int gpu_mem = 4000;
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int cpu_threads = 1;
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bool use_mkldnn = false;
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bool use_tensorrt = false;
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bool use_fp16 = false;
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bool benchmark = false;
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int batch_size = 1;
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bool enable_benchmark = false;
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bool ir_optim = true;
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bool enable_profile = false;
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std::string cls_model_path;
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std::string cls_params_path;
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std::string infer_imgs;
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std::string cls_model_path;
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std::string cls_params_path;
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std::string infer_imgs;
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int resize_short_size = 256;
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int crop_size = 224;
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float scale = 0.00392157;
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std::vector<float> mean = {0.485, 0.456, 0.406};
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std::vector<float> std = {0.229, 0.224, 0.225};
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int topk = 5;
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std::string class_id_map_path;
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std::map<int, std::string> id_map;
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std::string label_save_dir;
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int resize_short_size = 256;
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int crop_size = 224;
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float scale = 0.00392157;
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std::vector<float> mean = {0.485, 0.456, 0.406};
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std::vector<float> std = {0.229, 0.224, 0.225};
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int topk = 5;
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std::string class_id_map_path;
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std::map<int, std::string> id_map;
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std::string label_save_dir;
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void PrintConfigInfo();
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void ReadLabelMap();
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void ReadYamlConfig(const std::string &path);
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};
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void PrintConfigInfo();
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void ReadLabelMap();
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void ReadYamlConfig(const std::string &path);
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};
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} // namespace PaddleClas
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@ -209,7 +209,7 @@ cp ../configs/inference_cls.yaml tools/
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根据[python预测推理](../../docs/zh_CN/inference_deployment/python_deploy.md)的`图像分类推理`部分修改好`tools`目录下`inference_cls.yaml`文件。`yaml`文件的参数说明详见[python预测推理](../../docs/zh_CN/inference_deployment/python_deploy.md)。
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请根据实际存放文件，修改好`Global.infer_imgs`、`Global.inference_model_dir`等参数。
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请根据实际存放文件，修改好`Global.infer_imgs`、`Global.inference_model_dir`、`PostProcess.Topk.topk`、`PostProcess.Topk.class_id_map_file`等参数。
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#### 2.3.2 执行
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@ -18,99 +18,99 @@
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namespace PaddleClas {
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void Classifier::LoadModel(const std::string &model_path,
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const std::string ¶ms_path) {
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paddle_infer::Config config;
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config.SetModel(model_path, params_path);
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void Classifier::LoadModel(const std::string &model_path,
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const std::string ¶ms_path) {
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paddle_infer::Config config;
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config.SetModel(model_path, params_path);
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if (this->use_gpu_) {
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config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
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if (this->use_tensorrt_) {
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config.EnableTensorRtEngine(
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1 << 20, 1, 3,
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this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
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: paddle_infer::Config::Precision::kFloat32,
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false, false);
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if (this->use_gpu_) {
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config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
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if (this->use_tensorrt_) {
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config.EnableTensorRtEngine(
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1 << 20, 1, 3,
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this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
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: paddle_infer::Config::Precision::kFloat32,
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false, false);
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}
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} else {
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config.DisableGpu();
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if (this->use_mkldnn_) {
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config.EnableMKLDNN();
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// cache 10 different shapes for mkldnn to avoid memory leak
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config.SetMkldnnCacheCapacity(10);
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}
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config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
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}
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config.SwitchUseFeedFetchOps(false);
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// true for multiple input
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config.SwitchSpecifyInputNames(true);
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config.SwitchIrOptim(this->ir_optim_);
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config.EnableMemoryOptim();
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config.DisableGlogInfo();
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this->predictor_ = CreatePredictor(config);
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}
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} else {
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config.DisableGpu();
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if (this->use_mkldnn_) {
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config.EnableMKLDNN();
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// cache 10 different shapes for mkldnn to avoid memory leak
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config.SetMkldnnCacheCapacity(10);
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void Classifier::Run(cv::Mat &img, std::vector<float> &out_data,
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std::vector<int> &idx, std::vector<double> ×) {
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cv::Mat srcimg;
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cv::Mat resize_img;
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img.copyTo(srcimg);
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auto preprocess_start = std::chrono::system_clock::now();
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this->resize_op_.Run(img, resize_img, this->resize_short_size_);
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this->crop_op_.Run(resize_img, this->crop_size_);
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this->normalize_op_.Run(&resize_img, this->mean_, this->std_, this->scale_);
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std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
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this->permute_op_.Run(&resize_img, input.data());
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auto input_names = this->predictor_->GetInputNames();
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auto input_t = this->predictor_->GetInputHandle(input_names[0]);
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input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
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auto preprocess_end = std::chrono::system_clock::now();
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auto infer_start = std::chrono::system_clock::now();
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input_t->CopyFromCpu(input.data());
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this->predictor_->Run();
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auto output_names = this->predictor_->GetOutputNames();
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auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
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std::vector<int> output_shape = output_t->shape();
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int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
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std::multiplies<int>());
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out_data.resize(out_num);
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idx.resize(out_num);
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output_t->CopyToCpu(out_data.data());
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auto infer_end = std::chrono::system_clock::now();
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auto postprocess_start = std::chrono::system_clock::now();
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// int maxPosition =
|
||||
// max_element(out_data.begin(), out_data.end()) - out_data.begin();
|
||||
iota(idx.begin(), idx.end(), 0);
|
||||
stable_sort(idx.begin(), idx.end(), [&out_data](int i1, int i2) {
|
||||
return out_data[i1] > out_data[i2];
|
||||
});
|
||||
auto postprocess_end = std::chrono::system_clock::now();
|
||||
|
||||
std::chrono::duration<float> preprocess_diff =
|
||||
preprocess_end - preprocess_start;
|
||||
times[0] = double(preprocess_diff.count() * 1000);
|
||||
std::chrono::duration<float> inference_diff = infer_end - infer_start;
|
||||
double inference_cost_time = double(inference_diff.count() * 1000);
|
||||
times[1] = inference_cost_time;
|
||||
std::chrono::duration<float> postprocess_diff =
|
||||
postprocess_end - postprocess_start;
|
||||
times[2] = double(postprocess_diff.count() * 1000);
|
||||
|
||||
/* 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; */
|
||||
}
|
||||
config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
|
||||
}
|
||||
|
||||
config.SwitchUseFeedFetchOps(false);
|
||||
// true for multiple input
|
||||
config.SwitchSpecifyInputNames(true);
|
||||
|
||||
config.SwitchIrOptim(this->ir_optim_);
|
||||
|
||||
config.EnableMemoryOptim();
|
||||
config.DisableGlogInfo();
|
||||
|
||||
this->predictor_ = CreatePredictor(config);
|
||||
}
|
||||
|
||||
void Classifier::Run(cv::Mat &img, std::vector<float> &out_data,
|
||||
std::vector<int> &idx, std::vector<double> ×) {
|
||||
cv::Mat srcimg;
|
||||
cv::Mat resize_img;
|
||||
img.copyTo(srcimg);
|
||||
|
||||
auto preprocess_start = std::chrono::system_clock::now();
|
||||
this->resize_op_.Run(img, resize_img, this->resize_short_size_);
|
||||
|
||||
this->crop_op_.Run(resize_img, this->crop_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);
|
||||
idx.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();
|
||||
// int maxPosition =
|
||||
// max_element(out_data.begin(), out_data.end()) - out_data.begin();
|
||||
iota(idx.begin(), idx.end(), 0);
|
||||
stable_sort(idx.begin(), idx.end(), [&out_data](int i1, int i2) {
|
||||
return out_data[i1] > out_data[i2];
|
||||
});
|
||||
auto postprocess_end = std::chrono::system_clock::now();
|
||||
|
||||
std::chrono::duration<float> preprocess_diff =
|
||||
preprocess_end - preprocess_start;
|
||||
times[0] = double(preprocess_diff.count() * 1000);
|
||||
std::chrono::duration<float> inference_diff = infer_end - infer_start;
|
||||
double inference_cost_time = double(inference_diff.count() * 1000);
|
||||
times[1] = inference_cost_time;
|
||||
std::chrono::duration<float> postprocess_diff =
|
||||
postprocess_end - postprocess_start;
|
||||
times[2] = double(postprocess_diff.count() * 1000);
|
||||
|
||||
/* 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; */
|
||||
}
|
||||
} // namespace PaddleClas
|
||||
|
|
|
|||
|
|
@ -17,36 +17,36 @@
|
|||
|
||||
namespace PaddleClas {
|
||||
|
||||
void ClsConfig::PrintConfigInfo() {
|
||||
std::cout << "=======Paddle Class inference config======" << std::endl;
|
||||
std::cout << this->config_file << std::endl;
|
||||
std::cout << "=======End of Paddle Class inference config======" << std::endl;
|
||||
}
|
||||
|
||||
void ClsConfig::ReadYamlConfig(const std::string &path) {
|
||||
|
||||
try {
|
||||
this->config_file = YAML::LoadFile(path);
|
||||
} catch (YAML::BadFile &e) {
|
||||
std::cout << "Something wrong in yaml file, please check yaml file"
|
||||
<< std::endl;
|
||||
exit(1);
|
||||
}
|
||||
}
|
||||
|
||||
void ClsConfig::ReadLabelMap() {
|
||||
if (this->class_id_map_path.empty()) {
|
||||
std::cout << "The Class Label file dose not input" << std::endl;
|
||||
return;
|
||||
}
|
||||
std::ifstream in(this->class_id_map_path);
|
||||
std::string line;
|
||||
if (in) {
|
||||
while (getline(in, line)) {
|
||||
int split_flag = line.find_first_of(" ");
|
||||
this->id_map[std::stoi(line.substr(0, split_flag))] =
|
||||
line.substr(split_flag + 1, line.size());
|
||||
void ClsConfig::PrintConfigInfo() {
|
||||
std::cout << "=======Paddle Class inference config======" << std::endl;
|
||||
std::cout << this->config_file << std::endl;
|
||||
std::cout << "=======End of Paddle Class inference config======" << std::endl;
|
||||
}
|
||||
|
||||
void ClsConfig::ReadYamlConfig(const std::string &path) {
|
||||
|
||||
try {
|
||||
this->config_file = YAML::LoadFile(path);
|
||||
} catch (YAML::BadFile &e) {
|
||||
std::cout << "Something wrong in yaml file, please check yaml file"
|
||||
<< std::endl;
|
||||
exit(1);
|
||||
}
|
||||
}
|
||||
|
||||
void ClsConfig::ReadLabelMap() {
|
||||
if (this->class_id_map_path.empty()) {
|
||||
std::cout << "The Class Label file dose not input" << std::endl;
|
||||
return;
|
||||
}
|
||||
std::ifstream in(this->class_id_map_path);
|
||||
std::string line;
|
||||
if (in) {
|
||||
while (getline(in, line)) {
|
||||
int split_flag = line.find_first_of(" ");
|
||||
this->id_map[std::stoi(line.substr(0, split_flag))] =
|
||||
line.substr(split_flag + 1, line.size());
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}; // namespace PaddleClas
|
||||
|
|
|
|||
|
|
@ -36,100 +36,102 @@ using namespace std;
|
|||
using namespace cv;
|
||||
using namespace PaddleClas;
|
||||
|
||||
DEFINE_string(config, "", "Path of yaml file");
|
||||
DEFINE_string(c, "", "Path of yaml file");
|
||||
DEFINE_string(config,
|
||||
"", "Path of yaml file");
|
||||
DEFINE_string(c,
|
||||
"", "Path of yaml file");
|
||||
|
||||
int main(int argc, char **argv) {
|
||||
google::ParseCommandLineFlags(&argc, &argv, true);
|
||||
std::string yaml_path = "";
|
||||
if (FLAGS_config == "" && FLAGS_c == "") {
|
||||
std::cerr << "[ERROR] usage: " << std::endl
|
||||
<< argv[0] << " -c $yaml_path" << std::endl
|
||||
<< "or:" << std::endl
|
||||
<< argv[0] << " -config $yaml_path" << std::endl;
|
||||
exit(1);
|
||||
} else if (FLAGS_config != "") {
|
||||
yaml_path = FLAGS_config;
|
||||
} else {
|
||||
yaml_path = FLAGS_c;
|
||||
}
|
||||
ClsConfig config(yaml_path);
|
||||
config.PrintConfigInfo();
|
||||
|
||||
std::string path(config.infer_imgs);
|
||||
|
||||
std::vector<std::string> img_files_list;
|
||||
if (cv::utils::fs::isDirectory(path)) {
|
||||
std::vector<cv::String> filenames;
|
||||
cv::glob(path, filenames);
|
||||
for (auto f : filenames) {
|
||||
img_files_list.push_back(f);
|
||||
google::ParseCommandLineFlags(&argc, &argv, true);
|
||||
std::string yaml_path = "";
|
||||
if (FLAGS_config == "" && FLAGS_c == "") {
|
||||
std::cerr << "[ERROR] usage: " << std::endl
|
||||
<< argv[0] << " -c $yaml_path" << std::endl
|
||||
<< "or:" << std::endl
|
||||
<< argv[0] << " -config $yaml_path" << std::endl;
|
||||
exit(1);
|
||||
} else if (FLAGS_config != "") {
|
||||
yaml_path = FLAGS_config;
|
||||
} else {
|
||||
yaml_path = FLAGS_c;
|
||||
}
|
||||
} else {
|
||||
img_files_list.push_back(path);
|
||||
}
|
||||
ClsConfig config(yaml_path);
|
||||
config.PrintConfigInfo();
|
||||
|
||||
std::cout << "img_file_list length: " << img_files_list.size() << std::endl;
|
||||
std::string path(config.infer_imgs);
|
||||
|
||||
Classifier classifier(config);
|
||||
|
||||
std::vector<double> cls_times = {0, 0, 0};
|
||||
std::vector<double> cls_times_total = {0, 0, 0};
|
||||
double infer_time;
|
||||
std::vector<float> out_data;
|
||||
std::vector<int> result_index;
|
||||
int warmup_iter = 5;
|
||||
bool label_output_equal_flag = true;
|
||||
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);
|
||||
std::vector <std::string> img_files_list;
|
||||
if (cv::utils::fs::isDirectory(path)) {
|
||||
std::vector <cv::String> filenames;
|
||||
cv::glob(path, filenames);
|
||||
for (auto f : filenames) {
|
||||
img_files_list.push_back(f);
|
||||
}
|
||||
} else {
|
||||
img_files_list.push_back(path);
|
||||
}
|
||||
|
||||
cv::cvtColor(srcimg, srcimg, cv::COLOR_BGR2RGB);
|
||||
classifier.Run(srcimg, out_data, result_index, cls_times);
|
||||
if (label_output_equal_flag and out_data.size() != config.id_map.size()) {
|
||||
std::cout << "Warning: the label size is not equal to output size!"
|
||||
<< std::endl;
|
||||
label_output_equal_flag = false;
|
||||
std::cout << "img_file_list length: " << img_files_list.size() << std::endl;
|
||||
|
||||
Classifier classifier(config);
|
||||
|
||||
std::vector<double> cls_times = {0, 0, 0};
|
||||
std::vector<double> cls_times_total = {0, 0, 0};
|
||||
double infer_time;
|
||||
std::vector<float> out_data;
|
||||
std::vector<int> result_index;
|
||||
int warmup_iter = 5;
|
||||
bool label_output_equal_flag = true;
|
||||
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);
|
||||
classifier.Run(srcimg, out_data, result_index, cls_times);
|
||||
if (label_output_equal_flag and out_data.size() != config.id_map.size()) {
|
||||
std::cout << "Warning: the label size is not equal to output size!"
|
||||
<< std::endl;
|
||||
label_output_equal_flag = false;
|
||||
}
|
||||
|
||||
int max_len = std::min(config.topk, int(out_data.size()));
|
||||
std::cout << "Current image path: " << img_path << std::endl;
|
||||
infer_time = cls_times[0] + cls_times[1] + cls_times[2];
|
||||
std::cout << "Current total inferen time cost: " << infer_time << " ms."
|
||||
<< std::endl;
|
||||
for (int i = 0; i < max_len; ++i) {
|
||||
printf("\tTop%d: class_id: %d, score: %.4f, ", i + 1, result_index[i],
|
||||
out_data[result_index[i]]);
|
||||
if (label_output_equal_flag)
|
||||
printf("label: %s\n", config.id_map[result_index[i]].c_str());
|
||||
}
|
||||
if (idx >= warmup_iter) {
|
||||
for (int i = 0; i < cls_times.size(); ++i)
|
||||
cls_times_total[i] += cls_times[i];
|
||||
}
|
||||
}
|
||||
if (img_files_list.size() > warmup_iter) {
|
||||
|
||||
infer_time = cls_times_total[0] + cls_times_total[1] + cls_times_total[2];
|
||||
std::cout << "average time cost in all: "
|
||||
<< infer_time / (img_files_list.size() - warmup_iter) << " ms."
|
||||
<< std::endl;
|
||||
}
|
||||
|
||||
int max_len = std::min(config.topk, int(out_data.size()));
|
||||
std::cout << "Current image path: " << img_path << std::endl;
|
||||
infer_time = cls_times[0] + cls_times[1] + cls_times[2];
|
||||
std::cout << "Current total inferen time cost: " << infer_time << " ms."
|
||||
<< std::endl;
|
||||
for (int i = 0; i < max_len; ++i) {
|
||||
printf("\tTop%d: class_id: %d, score: %.4f, ", i + 1, result_index[i],
|
||||
out_data[result_index[i]]);
|
||||
if (label_output_equal_flag)
|
||||
printf("label: %s\n", config.id_map[result_index[i]].c_str());
|
||||
std::string presion = "fp32";
|
||||
if (config.use_fp16)
|
||||
presion = "fp16";
|
||||
if (config.benchmark) {
|
||||
AutoLogger autolog("Classification", config.use_gpu, config.use_tensorrt,
|
||||
config.use_mkldnn, config.cpu_threads, 1,
|
||||
"1, 3, 224, 224", presion, cls_times_total,
|
||||
img_files_list.size());
|
||||
autolog.report();
|
||||
}
|
||||
if (idx >= warmup_iter) {
|
||||
for (int i = 0; i < cls_times.size(); ++i)
|
||||
cls_times_total[i] += cls_times[i];
|
||||
}
|
||||
}
|
||||
if (img_files_list.size() > warmup_iter) {
|
||||
|
||||
infer_time = cls_times_total[0] + cls_times_total[1] + cls_times_total[2];
|
||||
std::cout << "average time cost in all: "
|
||||
<< infer_time / (img_files_list.size() - warmup_iter) << " ms."
|
||||
<< std::endl;
|
||||
}
|
||||
|
||||
std::string presion = "fp32";
|
||||
if (config.use_fp16)
|
||||
presion = "fp16";
|
||||
if (config.benchmark) {
|
||||
AutoLogger autolog("Classification", config.use_gpu, config.use_tensorrt,
|
||||
config.use_mkldnn, config.cpu_threads, 1,
|
||||
"1, 3, 224, 224", presion, cls_times_total,
|
||||
img_files_list.size());
|
||||
autolog.report();
|
||||
}
|
||||
return 0;
|
||||
return 0;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -20,20 +20,20 @@
|
|||
|
||||
namespace PaddleClas {
|
||||
|
||||
std::vector<std::string> Utility::ReadDict(const std::string &path) {
|
||||
std::ifstream in(path);
|
||||
std::string line;
|
||||
std::vector<std::string> m_vec;
|
||||
if (in) {
|
||||
while (getline(in, line)) {
|
||||
m_vec.push_back(line);
|
||||
std::vector <std::string> Utility::ReadDict(const std::string &path) {
|
||||
std::ifstream in(path);
|
||||
std::string line;
|
||||
std::vector <std::string> m_vec;
|
||||
if (in) {
|
||||
while (getline(in, line)) {
|
||||
m_vec.push_back(line);
|
||||
}
|
||||
} else {
|
||||
std::cout << "no such label file: " << path << ", exit the program..."
|
||||
<< std::endl;
|
||||
exit(1);
|
||||
}
|
||||
return m_vec;
|
||||
}
|
||||
} else {
|
||||
std::cout << "no such label file: " << path << ", exit the program..."
|
||||
<< std::endl;
|
||||
exit(1);
|
||||
}
|
||||
return m_vec;
|
||||
}
|
||||
|
||||
} // namespace PaddleClas
|
||||
Loading…
Reference in New Issue