diff --git a/deploy/cpp/docs/linux_build.md b/deploy/cpp/docs/linux_build.md index bb70fc6d5503af5efe20a594f4989964734eea9a..d0667db5c95e19e1063a6315bda2cae59a2a2c23 100644 --- a/deploy/cpp/docs/linux_build.md +++ b/deploy/cpp/docs/linux_build.md @@ -1,7 +1,7 @@ # Linux平台编译指南 ## 说明 -本文档在 `Linux`平台使用`GCC 4.8.5` 和 `GCC 4.9.4`测试过，如果需要使用更高G++版本编译使用，则需要重新编译Paddle预测库，请参考: [从源码编译Paddle预测库](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/advanced_usage/deploy/inference/build_and_install_lib_cn.html#id15)。 +本文档在 `Linux`平台使用`GCC 4.8.5` 和 `GCC 4.9.4`测试过，如果需要使用更高G++版本编译使用，则需要重新编译Paddle预测库，请参考: [从源码编译Paddle预测库](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html)。 ## 前置条件 * G++ 4.8.2 ~ 4.9.4 @@ -19,7 +19,7 @@ ### Step2: 下载PaddlePaddle C++ 预测库 fluid_inference -PaddlePaddle C++ 预测库针对不同的`CPU`和`CUDA`版本提供了不同的预编译版本，请根据实际情况下载: [C++预测库下载列表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/advanced_usage/deploy/inference/build_and_install_lib_cn.html) +PaddlePaddle C++ 预测库针对不同的`CPU`和`CUDA`版本提供了不同的预编译版本，请根据实际情况下载: [C++预测库下载列表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html) 下载并解压后`/root/projects/fluid_inference`目录包含内容为： @@ -39,6 +39,8 @@ fluid_inference 编译`cmake`的命令在`scripts/build.sh`中，请根据实际情况修改主要参数，其主要内容说明如下： +``` + # 是否使用GPU(即是否使用 CUDA) WITH_GPU=OFF # 使用MKL or openblas @@ -93,6 +95,7 @@ make | image_path | 要预测的图片文件路径 | | video_path | 要预测的视频文件路径 | | use_gpu | 是否使用 GPU 预测, 支持值为0或1(默认值为0)| +| --run_mode |使用GPU时，默认为fluid, 可选（fluid/trt_fp32/trt_fp16）| **注意**：如果同时设置了`video_path`和`image_path`，程序仅预测`video_path`。 diff --git a/deploy/cpp/include/object_detector.h b/deploy/cpp/include/object_detector.h index dda91cfc4832ce5599c761f11b6b62ccdd05c0df..328dd458efb4e165d13974415535a6bf3a5c2f65 100644 --- a/deploy/cpp/include/object_detector.h +++ b/deploy/cpp/include/object_detector.h @@ -54,17 +54,21 @@ cv::Mat VisualizeResult(const cv::Mat& img, class ObjectDetector { public: - explicit ObjectDetector(const std::string& model_dir, bool use_gpu = false) { + explicit ObjectDetector(const std::string& model_dir, bool use_gpu = false, + const std::string& run_mode = "fluid") { config_.load_config(model_dir); threshold_ = config_.draw_threshold_; preprocessor_.Init(config_.preprocess_info_, config_.arch_); - LoadModel(model_dir, use_gpu); + LoadModel(model_dir, use_gpu, config_.min_subgraph_size_, 1, run_mode); } // Load Paddle inference model void LoadModel( const std::string& model_dir, - bool use_gpu); + bool use_gpu, + const int min_subgraph_size, + const int batch_size = 1, + const std::string& run_mode = "fluid"); // Run predictor void Predict( diff --git a/deploy/cpp/scripts/build.sh b/deploy/cpp/scripts/build.sh index 72593c5239750fc5e32cdb6f645076526f5b8c87..0cfd8ceb5fce986b2f5fecd9ee5e7c029308ea0a 100644 --- a/deploy/cpp/scripts/build.sh +++ b/deploy/cpp/scripts/build.sh @@ -4,7 +4,7 @@ WITH_GPU=OFF WITH_MKL=ON # 是否集成 TensorRT(仅WITH_GPU=ON 有效) WITH_TENSORRT=OFF -# TensorRT 的lib路径 +# TensorRT 的路径 TENSORRT_DIR=/path/to/TensorRT/ # Paddle 预测库路径 PADDLE_DIR=/path/to/fluid_inference/ diff --git a/deploy/cpp/src/main.cc b/deploy/cpp/src/main.cc index d7eccff0c198e8341aa10d24e6046951e7a52c85..63cd99f0105992c43cf91bbf87769b9f0f1864eb 100644 --- a/deploy/cpp/src/main.cc +++ b/deploy/cpp/src/main.cc @@ -25,6 +25,7 @@ DEFINE_string(model_dir, "", "Path of inference model"); DEFINE_string(image_path, "", "Path of input image"); DEFINE_string(video_path, "", "Path of input video"); DEFINE_bool(use_gpu, false, "Infering with GPU or CPU"); +DEFINE_string(run_mode, "fluid", "mode of running(fluid/trt_fp32/trt_fp16)"); void PredictVideo(const std::string& video_path, PaddleDetection::ObjectDetector* det) { @@ -93,7 +94,10 @@ void PredictImage(const std::string& image_path, auto colormap = PaddleDetection::GenerateColorMap(labels.size()); cv::Mat vis_img = PaddleDetection::VisualizeResult( im, result, labels, colormap); - cv::imwrite("output.jpeg", vis_img); + std::vector compression_params; + compression_params.push_back(CV_IMWRITE_JPEG_QUALITY); + compression_params.push_back(95); + cv::imwrite("output.jpeg", vis_img, compression_params); printf("Visualized output saved as output.jpeg\n"); } @@ -102,13 +106,19 @@ int main(int argc, char** argv) { google::ParseCommandLineFlags(&argc, &argv, true); if (FLAGS_model_dir.empty() || (FLAGS_image_path.empty() && FLAGS_video_path.empty())) { - std::cout << "Usage: ./main --model_dir=/PATH/TO/INFERENCE_MODEL/ " + std::cout << "Usage: ./main --model_dir=/PATH/TO/INFERENCE_MODEL/ " << "--image_path=/PATH/TO/INPUT/IMAGE/" << std::endl; - return -1; + return -1; + } + if (!(FLAGS_run_mode == "fluid" || FLAGS_run_mode == "trt_fp32" + || FLAGS_run_mode == "trt_fp16")) { + std::cout << "run_mode should be 'fluid', 'trt_fp32' or 'trt_fp16'."; + return -1; } // Load model and create a object detector - PaddleDetection::ObjectDetector det(FLAGS_model_dir, FLAGS_use_gpu); + PaddleDetection::ObjectDetector det(FLAGS_model_dir, FLAGS_use_gpu, + FLAGS_run_mode); // Do inference on input video or image if (!FLAGS_video_path.empty()) { PredictVideo(FLAGS_video_path, &det); diff --git a/deploy/cpp/src/object_detector.cc b/deploy/cpp/src/object_detector.cc index 172b22d2c5e63d97d5580ea093024a3a411e7685..65d5d4e1f1f68255a66a55c0be73922ba00c9d06 100644 --- a/deploy/cpp/src/object_detector.cc +++ b/deploy/cpp/src/object_detector.cc @@ -17,15 +17,38 @@ namespace PaddleDetection { // Load Model and create model predictor -void ObjectDetector::LoadModel(const std::string& model_dir, bool use_gpu) { +void ObjectDetector::LoadModel(const std::string& model_dir, + bool use_gpu, + const int batch_size, + const int min_subgraph_size, + const std::string& run_mode) { paddle::AnalysisConfig config; std::string prog_file = model_dir + OS_PATH_SEP + "__model__"; std::string params_file = model_dir + OS_PATH_SEP + "__params__"; config.SetModel(prog_file, params_file); if (use_gpu) { - config.EnableUseGpu(100, 0); + config.EnableUseGpu(100, 0); + if (run_mode != "fluid") { + auto precision = paddle::AnalysisConfig::Precision::kFloat32; + if (run_mode == "trt_fp16") { + precision = paddle::AnalysisConfig::Precision::kHalf; + } else if (run_mode == "trt_int8") { + precision = paddle::AnalysisConfig::Precision::kInt8; + } else { + if (run_mode != "trt_32") { + printf("run_mode should be 'fluid', 'trt_fp32' or 'trt_fp16'"); + } + } + config.EnableTensorRtEngine( + 1 << 10, + batch_size, + min_subgraph_size, + precision, + false, + run_mode == "trt_int8"); + } } else { - config.DisableGpu(); + config.DisableGpu(); } config.SwitchUseFeedFetchOps(false); config.SwitchSpecifyInputNames(true); @@ -51,6 +74,7 @@ cv::Mat VisualizeResult(const cv::Mat& img, int c2 = colormap[3 * results[i].class_id + 1]; int c3 = colormap[3 * results[i].class_id + 2]; cv::Scalar roi_color = cv::Scalar(c1, c2, c3); + text += " "; text += std::to_string(static_cast(results[i].confidence * 100)) + "%"; int font_face = cv::FONT_HERSHEY_COMPLEX_SMALL; double font_scale = 0.5f; @@ -60,12 +84,6 @@ cv::Mat VisualizeResult(const cv::Mat& img, font_scale, thickness, nullptr); - float new_font_scale = roi.width * font_scale / text_size.width; - text_size = cv::getTextSize(text, - font_face, - new_font_scale, - thickness, - nullptr); cv::Point origin; origin.x = roi.x; origin.y = roi.y; @@ -83,7 +101,7 @@ cv::Mat VisualizeResult(const cv::Mat& img, text, origin, font_face, - new_font_scale, + font_scale, cv::Scalar(255, 255, 255), thickness); }