I have noticed that the inference speed using the GPU is slower than that of the CPU, and I am uncertain about the underlying issue.

[Jeck-Liu-Create]

I have noticed that the inference speed using the GPU is slower than that of the CPU, and I am uncertain about the underlying issue.

Introduction:

• Operating System: Windows 11
• Memory: 32 GB
• GPU: RTX 3060 with 12 GB Memory

1. Firstly GPU
   I followed the method outlined in the documentation for manual compilation on Windows. The steps are as follows:
   PPLCV

git clone https://github.com/openppl-public/ppl.cv.git  
cd ppl.cv  
git checkout tags/v0.7.0 -b v0.7.0  
$env:PPLCV_DIR = "$pwd"  
mkdir pplcv-build  
cd pplcv-build  
cmake .. -G "Visual Studio 16 2019" -T v142 -A x64 -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=install -DPPLCV_USE_CUDA=ON -DPPLCV_USE_MSVC_STATIC_RUNTIME=OFF  
cmake --install . --config Release  
cd ../..  

CUDA + TensorRT

cd $env:MMDEPLOY_DIR  
mkdir build -ErrorAction SilentlyContinue  
cd build  
cmake .. -G "Visual Studio 16 2019" -A x64 -T v142 `  
  -DMMDEPLOY_BUILD_SDK=ON `  
  -DMMDEPLOY_BUILD_EXAMPLES=ON `  
  -DMMDEPLOY_BUILD_SDK_PYTHON_API=ON `  
  -DMMDEPLOY_TARGET_DEVICES="cuda" `  
  -DMMDEPLOY_TARGET_BACKENDS="ort;trt" `  
  -Dpplcv_DIR="$env:PPLCV_DIR/pplcv-build/install/lib/cmake/ppl" `  
  -DTENSORRT_DIR="$env:TENSORRT_DIR" `  
  -DCUDNN_DIR="$env:CUDNN_DIR"  

cmake --build . --config Release -- /m  
cmake --install . --config Release  

In this process, I modified the official documentation from -DMMDEPLOY_TARGET_BACKENDS="trt" to -DMMDEPLOY_TARGET_BACKENDS="ort;trt" because using -DMMDEPLOY_TARGET_BACKENDS="trt" resulted in an error.

[2024-07-28 20:31:48.312] [mmdeploy] [info] [model.cpp:35] [DirectoryModel] Load model: "d:/my_progarm/mmdeploy/mmdeploy_models/mmocr/dbnet/ort"
[2024-07-28 20:31:48.394] [mmdeploy] [error] [net_module.cpp:47] Net backend not found: onnxruntime, available backends: [("tensorrt", 0)]
[2024-07-28 20:31:48.397] [mmdeploy] [error] [task.cpp:99] error parsing config: {
  "context": {
    "device": "<any>",
    "model": "<any>",
    "stream": "<any>"
  },
  "input": [
    "prep_output"
  ],
  "input_map": {
    "img": "input"
  },
  "is_batched": true,
  "module": "Net",
  "name": "dbnet",
  "output": [
    "infer_output"
  ],
  "output_map": {},
  "type": "Task"
}
DBnet模型加载耗时 85 ms

Code

Below is the modified program I created to perform inference using DBNet independently.

• ocr_test

#include <string>

#include "mmdeploy/text_detector.hpp"
#include "mmdeploy/text_recognizer.hpp"
#include "argparse.h"
#include "mediaio.h"
#include "visualize.h"

DEFINE_ARG_string(det_model, "Text detection model path");
// DEFINE_ARG_string(reg_model, "Text recognition model path");
DEFINE_ARG_string(image, "Input image path");
DEFINE_string(device, "cpu", R"(Device name, e.g. "cpu", "cuda")");
DEFINE_string(output, "text_ocr_output.jpg", "Output image path");

using mmdeploy::TextDetector;
// using mmdeploy::TextRecognizer;

int main(int argc, char* argv[]) {
    if (!utils::ParseArguments(argc, argv)) {
        return -1;
    }

    cv::Mat img = cv::imread(ARGS_image);
    if (img.empty()) {
        fprintf(stderr, "failed to load image: %s\n", ARGS_image.c_str());
        return -1;
    }

    mmdeploy::Device device(FLAGS_device);
    std::chrono::steady_clock::time_point begin = std::chrono::steady_clock::now();
    begin = std::chrono::steady_clock::now();
    TextDetector detector{ mmdeploy::Model(ARGS_det_model), device };
    std::chrono::steady_clock::time_point end = std::chrono::steady_clock::now();
    std::cout << "DBnet模型加载耗时 " << std::chrono::duration_cast<std::chrono::milliseconds>(end - begin).count() << " ms" << std::endl;
    // TextRecognizer recognizer{ mmdeploy::Model(ARGS_reg_model), device };

    // apply the detector, the result is an array-like class holding references to
    // `mmdeploy_text_detection_t`, will be released automatically on destruction
    // 应用检测器，结果是一个类数组，持有对 mmdeploy_text_detection_t 的引用，释放将在销毁时自动进行
    
    begin = std::chrono::steady_clock::now();
    // 执行推理
    TextDetector::Result bboxes = detector.Apply(img);
    end = std::chrono::steady_clock::now();
    std::cout << "推理耗时: " << std::chrono::duration_cast<std::chrono::milliseconds>(end - begin).count() << " ms" << std::endl;
   

    // apply recognizer, if no bboxes are provided, full image will be used; the result is an
    // array-like class holding references to `mmdeploy_text_recognition_t`, will be released
    // automatically on destruction
    // 应用识别器，如果未提供边界框，将使用整个图像；结果是一个类数组，持有对 mmdeploy_text_recognition_t 的引用，释放将在销毁时自动进行。
    // TextRecognizer::Result texts = recognizer.Apply(img, { bboxes.begin(), bboxes.size() });

    // visualize results
    // 可视化结果
    utils::Visualize v;
    auto sess = v.get_session(img);
    for (size_t i = 0; i < bboxes.size(); ++i) {
        mmdeploy_text_detection_t& bbox = bboxes[i];
        // mmdeploy_text_recognition_t& text = texts[i];
        sess.add_text_dbnet(bbox.bbox, bbox.score, i);
    }

    if (!FLAGS_output.empty()) {
        cv::imwrite(FLAGS_output, sess.get());
    }

    return 0;
}

debug d:/my_progarm/mmdeploy/mmdeploy_models/mmocr/dbnet/ortc:/Users/12994/source/repos/TextDetection/x64/Release/demo_text_det.jpg --device cuda
output

[2024-07-28 20:46:29.791] [mmdeploy] [info] [model.cpp:35] [DirectoryModel] Load model: "d:/my_progarm/mmdeploy/mmdeploy_models/mmocr/dbnet/ort"
DBnet模型加载耗时 353 ms
推理耗时: 6633 ms
[ INFO:0@7.057] global c:\build\master_winpack-build-win64-vc15\opencv\modules\core\src\parallel\registry_parallel.impl.hpp (96) cv::parallel::ParallelBackendRegistry::ParallelBackendRegistry core(parallel): Enabled backends(3, sorted by priority): ONETBB(1000); TBB(990); OPENMP(980)
[ INFO:0@7.058] global c:\build\master_winpack-build-win64-vc15\opencv\modules\core\src\utils\plugin_loader.impl.hpp (67) cv::plugin::impl::DynamicLib::libraryLoad load D:\nvidia\opencv4.5.5\opencv\build\x64\vc15\bin\opencv_core_parallel_onetbb455_64d.dll => FAILED
[ INFO:0@7.060] global c:\build\master_winpack-build-win64-vc15\opencv\modules\core\src\utils\plugin_loader.impl.hpp (67) cv::plugin::impl::DynamicLib::libraryLoad load opencv_core_parallel_onetbb455_64d.dll => FAILED
[ INFO:0@7.060] global c:\build\master_winpack-build-win64-vc15\opencv\modules\core\src\utils\plugin_loader.impl.hpp (67) cv::plugin::impl::DynamicLib::libraryLoad load D:\nvidia\opencv4.5.5\opencv\build\x64\vc15\bin\opencv_core_parallel_tbb455_64d.dll => FAILED
[ INFO:0@7.062] global c:\build\master_winpack-build-win64-vc15\opencv\modules\core\src\utils\plugin_loader.impl.hpp (67) cv::plugin::impl::DynamicLib::libraryLoad load opencv_core_parallel_tbb455_64d.dll => FAILED
[ INFO:0@7.063] global c:\build\master_winpack-build-win64-vc15\opencv\modules\core\src\utils\plugin_loader.impl.hpp (67) cv::plugin::impl::DynamicLib::libraryLoad load D:\nvidia\opencv4.5.5\opencv\build\x64\vc15\bin\opencv_core_parallel_openmp455_64d.dll => FAILED
[ INFO:0@7.065] global c:\build\master_winpack-build-win64-vc15\opencv\modules\core\src\utils\plugin_loader.impl.hpp (67) cv::plugin::impl::DynamicLib::libraryLoad load opencv_core_parallel_openmp455_64d.dll => FAILED
bbox[0]: (192.00, 38.00), (238.00, 32.00), (240.00, 44.00), (193.00, 50.00), 0.84
bbox[1]: (240.00, 45.00), (240.00, 35.00), (253.00, 35.00), (253.00, 45.00), 0.75
bbox[2]: (251.00, 44.00), (258.00, 33.00), (309.00, 64.00), (301.00, 76.00), 0.82
bbox[3]: (152.00, 67.00), (186.00, 41.00), (193.00, 51.00), (159.00, 76.00), 0.83
bbox[4]: (164.00, 98.00), (164.00, 79.00), (247.00, 79.00), (247.00, 98.00), 0.91
bbox[5]: (251.00, 76.00), (296.00, 76.00), (297.00, 98.00), (251.00, 98.00), 0.90
bbox[6]: (164.00, 121.00), (164.00, 104.00), (215.00, 105.00), (215.00, 122.00), 0.90
bbox[7]: (219.00, 121.00), (219.00, 105.00), (293.00, 105.00), (293.00, 121.00), 0.90
bbox[8]: (260.00, 141.00), (260.00, 127.00), (310.00, 127.00), (310.00, 141.00), 0.86
bbox[9]: (230.00, 140.00), (230.00, 126.00), (256.00, 126.00), (256.00, 140.00), 0.86
bbox[10]: (145.00, 141.00), (145.00, 126.00), (228.00, 126.00), (228.00, 141.00), 0.89
bbox[11]: (199.00, 156.00), (199.00, 143.00), (225.00, 143.00), (225.00, 156.00), 0.84
bbox[12]: (226.00, 157.00), (226.00, 144.00), (287.00, 146.00), (287.00, 159.00), 0.83
bbox[13]: (167.00, 156.00), (167.00, 142.00), (196.00, 142.00), (196.00, 156.00), 0.87
bbox[14]: (227.00, 188.00), (227.00, 175.00), (275.00, 175.00), (275.00, 188.00), 0.88
bbox[15]: (180.00, 174.00), (180.00, 158.00), (208.00, 158.00), (208.00, 174.00), 0.85
bbox[16]: (210.00, 160.00), (279.00, 159.00), (279.00, 173.00), (210.00, 173.00), 0.85
bbox[17]: (181.00, 189.00), (181.00, 175.00), (223.00, 175.00), (223.00, 190.00), 0.85
bbox[18]: (199.00, 206.00), (199.00, 190.00), (260.00, 190.00), (260.00, 206.00), 0.87
bbox[19]: (172.00, 212.00), (177.00, 197.00), (211.00, 210.00), (205.00, 225.00), 0.87
bbox[20]: (241.00, 214.00), (277.00, 199.00), (283.00, 212.00), (246.00, 227.00), 0.86
bbox[21]: (206.00, 227.00), (206.00, 211.00), (244.00, 212.00), (244.00, 228.00), 0.82

2. Second CPU
   Using the CPU with ONNX Runtime as outlined in the documentation.

cd $env:MMDEPLOY_DIR
mkdir build -ErrorAction SilentlyContinue
cd build
cmake .. -G "Visual Studio 16 2019" -A x64 -T v142 `
    -DMMDEPLOY_BUILD_SDK=ON `
    -DMMDEPLOY_BUILD_EXAMPLES=ON `
    -DMMDEPLOY_BUILD_SDK_PYTHON_API=ON `
    -DMMDEPLOY_TARGET_DEVICES="cpu" `
    -DMMDEPLOY_TARGET_BACKENDS="ort" `
    -DONNXRUNTIME_DIR="$env:ONNXRUNTIME_DIR"

cmake --build . --config Release -- /m
cmake --install . --config Release

output

[2024-07-28 21:16:54.240] [mmdeploy] [info] [model.cpp:35] [DirectoryModel] Load model: "d:/my_progarm/mmdeploy/mmdeploy_models/mmocr/dbnet/ort"
DBnet模型加载耗时 201 ms
推理耗时: 510 ms
[ INFO:0@0.786] global c:\build\master_winpack-build-win64-vc15\opencv\modules\core\src\parallel\registry_parallel.impl.hpp (96) cv::parallel::ParallelBackendRegistry::ParallelBackendRegistry core(parallel): Enabled backends(3, sorted by priority): ONETBB(1000); TBB(990); OPENMP(980)
[ INFO:0@0.786] global c:\build\master_winpack-build-win64-vc15\opencv\modules\core\src\utils\plugin_loader.impl.hpp (67) cv::plugin::impl::DynamicLib::libraryLoad load D:\nvidia\opencv4.5.5\opencv\build\x64\vc15\bin\opencv_core_parallel_onetbb455_64d.dll => FAILED
[ INFO:0@0.789] global c:\build\master_winpack-build-win64-vc15\opencv\modules\core\src\utils\plugin_loader.impl.hpp (67) cv::plugin::impl::DynamicLib::libraryLoad load opencv_core_parallel_onetbb455_64d.dll => FAILED
[ INFO:0@0.789] global c:\build\master_winpack-build-win64-vc15\opencv\modules\core\src\utils\plugin_loader.impl.hpp (67) cv::plugin::impl::DynamicLib::libraryLoad load D:\nvidia\opencv4.5.5\opencv\build\x64\vc15\bin\opencv_core_parallel_tbb455_64d.dll => FAILED
[ INFO:0@0.792] global c:\build\master_winpack-build-win64-vc15\opencv\modules\core\src\utils\plugin_loader.impl.hpp (67) cv::plugin::impl::DynamicLib::libraryLoad load opencv_core_parallel_tbb455_64d.dll => FAILED
[ INFO:0@0.792] global c:\build\master_winpack-build-win64-vc15\opencv\modules\core\src\utils\plugin_loader.impl.hpp (67) cv::plugin::impl::DynamicLib::libraryLoad load D:\nvidia\opencv4.5.5\opencv\build\x64\vc15\bin\opencv_core_parallel_openmp455_64d.dll => FAILED
[ INFO:0@0.795] global c:\build\master_winpack-build-win64-vc15\opencv\modules\core\src\utils\plugin_loader.impl.hpp (67) cv::plugin::impl::DynamicLib::libraryLoad load opencv_core_parallel_openmp455_64d.dll => FAILED
bbox[0]: (208.00, 226.00), (208.00, 213.00), (242.00, 213.00), (242.00, 226.00), 0.82
bbox[1]: (241.00, 214.00), (277.00, 200.00), (282.00, 212.00), (246.00, 226.00), 0.86
bbox[2]: (173.00, 212.00), (178.00, 198.00), (210.00, 210.00), (205.00, 224.00), 0.86
bbox[3]: (200.00, 204.00), (200.00, 189.00), (260.00, 191.00), (259.00, 206.00), 0.86
bbox[4]: (181.00, 189.00), (182.00, 175.00), (223.00, 175.00), (223.00, 189.00), 0.86
bbox[5]: (228.00, 187.00), (228.00, 176.00), (274.00, 176.00), (274.00, 187.00), 0.93
bbox[6]: (210.00, 173.00), (210.00, 160.00), (279.00, 160.00), (279.00, 173.00), 0.79
bbox[7]: (180.00, 174.00), (180.00, 158.00), (207.00, 158.00), (207.00, 174.00), 0.84
bbox[8]: (227.00, 157.00), (227.00, 145.00), (287.00, 146.00), (286.00, 158.00), 0.87
bbox[9]: (199.00, 156.00), (199.00, 143.00), (225.00, 144.00), (224.00, 156.00), 0.83
bbox[10]: (167.00, 142.00), (195.00, 141.00), (196.00, 155.00), (167.00, 156.00), 0.89
bbox[11]: (260.00, 127.00), (310.00, 126.00), (310.00, 141.00), (260.00, 141.00), 0.83
bbox[12]: (145.00, 140.00), (145.00, 126.00), (227.00, 126.00), (227.00, 140.00), 0.87
bbox[13]: (230.00, 140.00), (230.00, 126.00), (256.00, 126.00), (256.00, 140.00), 0.84
bbox[14]: (220.00, 119.00), (220.00, 106.00), (292.00, 106.00), (292.00, 119.00), 0.88
bbox[15]: (164.00, 120.00), (164.00, 104.00), (215.00, 105.00), (214.00, 121.00), 0.89
bbox[16]: (165.00, 81.00), (246.00, 80.00), (246.00, 97.00), (165.00, 98.00), 0.92
bbox[17]: (251.00, 77.00), (296.00, 77.00), (296.00, 97.00), (252.00, 98.00), 0.92
bbox[18]: (153.00, 67.00), (186.00, 42.00), (192.00, 51.00), (159.00, 75.00), 0.85
bbox[19]: (253.00, 44.00), (259.00, 35.00), (307.00, 64.00), (301.00, 74.00), 0.83
bbox[20]: (241.00, 45.00), (241.00, 35.00), (253.00, 35.00), (253.00, 45.00), 0.75
bbox[21]: (192.00, 38.00), (238.00, 33.00), (239.00, 43.00), (193.00, 49.00), 0.81