feat[detection_2d]: Add eval on detection_2d

detection_2d/detection_2d_rt_detr/CMakeLists.txt

@@ -35,3 +35,7 @@ endif()
 if (BUILD_BENCHMARK)
   add_subdirectory(benchmark)
 endif()
+
+if (BUILD_EVAL)
+  add_subdirectory(eval)
+endif()

detection_2d/detection_2d_rt_detr/eval/CMakeLists.txt

@@ -0,0 +1,49 @@
+add_compile_options(-std=c++17)
+add_compile_options(-O3 -Wextra -Wdeprecated -fPIC)
+set(CMAKE_CXX_STANDARD 17)
+
+if(ENABLE_TENSORRT)
+  list(APPEND platform_core_packages trt_core)
+endif()
+
+if(ENABLE_RKNN)
+  list(APPEND platform_core_packages rknn_core)
+endif()
+
+if(ENABLE_ORT)
+  list(APPEND platform_core_packages ort_core)
+endif()
+
+find_package(OpenCV REQUIRED)
+
+set(source_file
+  eval_detection_2d_rt_detr.cpp
+)
+
+include_directories(
+  include
+  ${OpenCV_INCLUDE_DIRS}
+)
+
+add_executable(eval_detection_2d_rt_detr ${source_file})
+
+target_link_libraries(eval_detection_2d_rt_detr PUBLIC
+  ${OpenCV_LIBS}
+  deploy_core
+  image_processing_utils
+  detection_2d_rt_detr
+  eval_utils
+  ${platform_core_packages}
+)
+
+if(ENABLE_TENSORRT)
+  target_compile_definitions(eval_detection_2d_rt_detr PRIVATE ENABLE_TENSORRT)
+endif()
+
+if(ENABLE_RKNN)
+  target_compile_definitions(eval_detection_2d_rt_detr PRIVATE ENABLE_RKNN)
+endif()
+
+if(ENABLE_ORT)
+  target_compile_definitions(eval_detection_2d_rt_detr PRIVATE ENABLE_ORT)
+endif()

detection_2d/detection_2d_rt_detr/eval/eval_detection_2d_rt_detr.cpp

@@ -0,0 +1,74 @@
+#include "eval_utils/detection_2d_eval_utils.hpp"
+#include "detection_2d_util/detection_2d_util.hpp"
+#include "detection_2d_rt_detr/rt_detr.hpp"
+
+using namespace easy_deploy;
+
+#ifdef ENABLE_TENSORRT
+
+#include "trt_core/trt_core.hpp"
+
+class EvalAccuracyRTDetrTensorRTFixture : public EvalAccuracyDetection2DFixture {
+public:
+  SetUpReturnType SetUp() override
+  {
+    std::string                    model_path = "/workspace/models/rt_detr_v2_single_input.engine";
+    const int                      input_height      = 640;
+    const int                      input_width       = 640;
+    const int                      input_channels    = 3;
+    const int                      cls_number        = 80;
+    const std::vector<std::string> input_blobs_name  = {"images"};
+    const std::vector<std::string> output_blobs_name = {"labels", "boxes", "scores"};
+
+    auto infer_core = CreateTrtInferCore(model_path);
+    auto preprocess = CreateCudaDetPreProcess();
+
+    auto rt_detr_model =
+        CreateRTDetrDetectionModel(infer_core, preprocess, input_height, input_width,
+                                   input_channels, cls_number, input_blobs_name, output_blobs_name);
+    const std::string coco_eval_dir_path = "/workspace/test_data/coco2017/coco2017_val";
+    const std::string coco_annotations_path =
+        "/workspace/test_data/coco2017/coco2017_annotations/instances_val2017.json";
+    return {rt_detr_model, coco_eval_dir_path, coco_annotations_path};
+  }
+};
+
+RegisterEvalAccuracyDetection2D(EvalAccuracyRTDetrTensorRTFixture);
+
+#endif
+
+#ifdef ENABLE_ORT
+
+#include "ort_core/ort_core.hpp"
+
+class EvalAccuracyRTDetrOnnxRuntimeFixture : public EvalAccuracyDetection2DFixture {
+public:
+  SetUpReturnType SetUp() override
+  {
+    std::string                    model_path   = "/workspace/models/rt_detr_v2_single_input.onnx";
+    const int                      input_height = 640;
+    const int                      input_width  = 640;
+    const int                      input_channels    = 3;
+    const int                      cls_number        = 80;
+    const std::vector<std::string> input_blobs_name  = {"images"};
+    const std::vector<std::string> output_blobs_name = {"labels", "boxes", "scores"};
+
+    auto infer_core = CreateOrtInferCore(model_path);
+    auto preprocess = CreateCpuDetPreProcess({0, 0, 0}, {255, 255, 255}, true, true);
+
+    auto rt_detr_model =
+        CreateRTDetrDetectionModel(infer_core, preprocess, input_height, input_width,
+                                   input_channels, cls_number, input_blobs_name, output_blobs_name);
+
+    const std::string coco_eval_dir_path = "/workspace/test_data/coco2017/coco2017_val";
+    const std::string coco_annotations_path =
+        "/workspace/test_data/coco2017/coco2017_annotations/instances_val2017.json";
+    return {rt_detr_model, coco_eval_dir_path, coco_annotations_path};
+  }
+};
+
+RegisterEvalAccuracyDetection2D(EvalAccuracyRTDetrOnnxRuntimeFixture);
+
+#endif
+
+EVAL_MAIN()

detection_2d/detection_2d_yolov8/CMakeLists.txt

@@ -35,3 +35,7 @@ endif()
 if (BUILD_BENCHMARK)
   add_subdirectory(benchmark)
 endif()
+
+if (BUILD_EVAL)
+  add_subdirectory(eval)
+endif()

detection_2d/detection_2d_yolov8/benchmark/CMakeLists.txt

@@ -14,7 +14,6 @@ if(ENABLE_ORT)
   list(APPEND platform_core_packages ort_core)
 endif()
 
-find_package(glog REQUIRED)
 find_package(OpenCV REQUIRED)
 find_package(benchmark REQUIRED)
 
@@ -31,7 +30,6 @@ add_executable(benchmark_detection_2d_yolov8 ${source_file})
 
 target_link_libraries(benchmark_detection_2d_yolov8 PUBLIC
   benchmark::benchmark
-  glog::glog
   ${OpenCV_LIBS}
   deploy_core
   image_processing_utils

detection_2d/detection_2d_yolov8/eval/CMakeLists.txt

@@ -0,0 +1,49 @@
+add_compile_options(-std=c++17)
+add_compile_options(-O3 -Wextra -Wdeprecated -fPIC)
+set(CMAKE_CXX_STANDARD 17)
+
+if(ENABLE_TENSORRT)
+  list(APPEND platform_core_packages trt_core)
+endif()
+
+if(ENABLE_RKNN)
+  list(APPEND platform_core_packages rknn_core)
+endif()
+
+if(ENABLE_ORT)
+  list(APPEND platform_core_packages ort_core)
+endif()
+
+find_package(OpenCV REQUIRED)
+
+set(source_file
+  eval_detection_2d_yolov8.cpp
+)
+
+include_directories(
+  include
+  ${OpenCV_INCLUDE_DIRS}
+)
+
+add_executable(eval_detection_2d_yolov8 ${source_file})
+
+target_link_libraries(eval_detection_2d_yolov8 PUBLIC
+  ${OpenCV_LIBS}
+  deploy_core
+  image_processing_utils
+  detection_2d_yolov8
+  eval_utils
+  ${platform_core_packages}
+)
+
+if(ENABLE_TENSORRT)
+  target_compile_definitions(eval_detection_2d_yolov8 PRIVATE ENABLE_TENSORRT)
+endif()
+
+if(ENABLE_RKNN)
+  target_compile_definitions(eval_detection_2d_yolov8 PRIVATE ENABLE_RKNN)
+endif()
+
+if(ENABLE_ORT)
+  target_compile_definitions(eval_detection_2d_yolov8 PRIVATE ENABLE_ORT)
+endif()

detection_2d/detection_2d_yolov8/eval/eval_detection_2d_yolov8.cpp

@@ -0,0 +1,114 @@
+#include "eval_utils/detection_2d_eval_utils.hpp"
+#include "detection_2d_util/detection_2d_util.hpp"
+#include "detection_2d_yolov8/yolov8.hpp"
+
+using namespace easy_deploy;
+
+#ifdef ENABLE_TENSORRT
+
+#include "trt_core/trt_core.hpp"
+
+class EvalAccuracyYolov8TensorRTFixture : public EvalAccuracyDetection2DFixture {
+public:
+  SetUpReturnType SetUp() override
+  {
+    std::string                    model_path        = "/workspace/models/yolov8n.engine";
+    const int                      input_height      = 640;
+    const int                      input_width       = 640;
+    const int                      input_channels    = 3;
+    const int                      cls_number        = 80;
+    const std::vector<std::string> input_blobs_name  = {"images"};
+    const std::vector<std::string> output_blobs_name = {"output0"};
+
+    auto infer_core  = CreateTrtInferCore(model_path);
+    auto preprocess  = CreateCudaDetPreProcess();
+    auto postprocess = CreateYolov8PostProcessCpuOrigin(input_height, input_width, cls_number);
+
+    auto yolov8_model =
+        CreateYolov8DetectionModel(infer_core, preprocess, postprocess, input_height, input_width,
+                                   input_channels, cls_number, input_blobs_name, output_blobs_name);
+
+    const std::string coco_eval_dir_path = "/workspace/test_data/coco2017/coco2017_val";
+    const std::string coco_annotations_path =
+        "/workspace/test_data/coco2017/coco2017_annotations/instances_val2017.json";
+    return {yolov8_model, coco_eval_dir_path, coco_annotations_path};
+  }
+};
+
+RegisterEvalAccuracyDetection2D(EvalAccuracyYolov8TensorRTFixture);
+
+#endif
+
+#ifdef ENABLE_ORT
+
+#include "ort_core/ort_core.hpp"
+
+class EvalAccuracyYolov8OnnxRuntimeFixture : public EvalAccuracyDetection2DFixture {
+public:
+  SetUpReturnType SetUp() override
+  {
+    std::string                    model_path        = "/workspace/models/yolov8n.onnx";
+    const int                      input_height      = 640;
+    const int                      input_width       = 640;
+    const int                      input_channels    = 3;
+    const int                      cls_number        = 80;
+    const std::vector<std::string> input_blobs_name  = {"images"};
+    const std::vector<std::string> output_blobs_name = {"output0"};
+
+    auto infer_core  = CreateOrtInferCore(model_path);
+    auto preprocess  = CreateCpuDetPreProcess({0, 0, 0}, {255, 255, 255}, true, true);
+    auto postprocess = CreateYolov8PostProcessCpuOrigin(input_height, input_width, cls_number);
+
+    auto yolov8_model =
+        CreateYolov8DetectionModel(infer_core, preprocess, postprocess, input_height, input_width,
+                                   input_channels, cls_number, input_blobs_name, output_blobs_name);
+
+    const std::string coco_eval_dir_path = "/workspace/test_data/coco2017/coco2017_val";
+    const std::string coco_annotations_path =
+        "/workspace/test_data/coco2017/coco2017_annotations/instances_val2017.json";
+    return {yolov8_model, coco_eval_dir_path, coco_annotations_path};
+  }
+};
+
+RegisterEvalAccuracyDetection2D(EvalAccuracyYolov8OnnxRuntimeFixture);
+
+#endif
+
+#ifdef ENABLE_RKNN
+
+#include "rknn_core/rknn_core.hpp"
+
+class EvalAccuracyYolov8RknnFixture : public EvalAccuracyDetection2DFixture {
+public:
+  SetUpReturnType SetUp() override
+  {
+    std::string                    model_path     = "/workspace/models/yolov8n_divide_opset11.rknn";
+    const int                      input_height   = 640;
+    const int                      input_width    = 640;
+    const int                      input_channels = 3;
+    const int                      cls_number     = 80;
+    const std::vector<std::string> input_blobs_name  = {"images"};
+    const std::vector<std::string> output_blobs_name = {"318", "onnx::ReduceSum_326", "331",
+                                                        "338", "onnx::ReduceSum_346", "350",
+                                                        "357", "onnx::ReduceSum_365", "369"};
+
+    auto infer_core  = CreateRknnInferCore(model_path, {{"images", RknnInputTensorType::RK_UINT8}});
+    auto preprocess  = CreateCpuDetPreProcess({0, 0, 0}, {1, 1, 1}, false, false);
+    auto postprocess = CreateYolov8PostProcessCpuDivide(input_height, input_width, cls_number);
+
+    auto yolov8_model =
+        CreateYolov8DetectionModel(infer_core, preprocess, postprocess, input_height, input_width,
+                                   input_channels, cls_number, input_blobs_name, output_blobs_name);
+
+    const std::string coco_eval_dir_path = "/workspace/test_data/coco2017/coco2017_val";
+    const std::string coco_annotations_path =
+        "/workspace/test_data/coco2017/coco2017_annotations/instances_val2017.json";
+    return {yolov8_model, coco_eval_dir_path, coco_annotations_path};
+  }
+};
+
+RegisterEvalAccuracyDetection2D(EvalAccuracyYolov8RknnFixture);
+
+#endif
+
+EVAL_MAIN()