Trt10

yolov5/yolov5_trt10/CMakeLists.txt

@@ -27,6 +27,7 @@ include_directories(${PROJECT_SOURCE_DIR}/plugin/)
 file(GLOB_RECURSE SRCS ${PROJECT_SOURCE_DIR}/src/*.cpp ${PROJECT_SOURCE_DIR}/src/*.cu)
 file(GLOB_RECURSE PLUGIN_SRCS ${PROJECT_SOURCE_DIR}/plugin/*.cu)
 
+add_definitions(-DAPI_EXPORTS)
 add_library(myplugins SHARED ${PLUGIN_SRCS})
 target_link_libraries(myplugins nvinfer cudart)
 
@@ -36,4 +37,11 @@ include_directories(${OpenCV_INCLUDE_DIRS})
 add_executable(yolov5_cls yolov5_cls.cpp ${SRCS})
 target_link_libraries(yolov5_cls nvinfer)
 target_link_libraries(yolov5_cls cudart)
+target_link_libraries(yolov5_cls myplugins)
 target_link_libraries(yolov5_cls ${OpenCV_LIBS})
+
+add_executable(yolov5_det yolov5_det.cpp ${SRCS})
+target_link_libraries(yolov5_det nvinfer)
+target_link_libraries(yolov5_det cudart)
+target_link_libraries(yolov5_det myplugins)
+target_link_libraries(yolov5_det ${OpenCV_LIBS})

yolov5/yolov5_trt10/README.md

@@ -11,6 +11,7 @@ TensorRT: TensorRT-10.2.0.19
 ## Support
 
 * [x] YOLOv5-cls support FP32/FP16/INT8 and Python/C++ API
+* [x] YOLOv5-det support FP32/FP16/INT8 and Python/C++ API
 
 ## Config
 
@@ -25,8 +26,10 @@ TensorRT: TensorRT-10.2.0.19
 git clone -b v7.0 https://github.com/ultralytics/yolov5.git
 git clone -b trt10 https://github.com/wang-xinyu/tensorrtx.git
 cd yolov5/
+wget https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt
 wget https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt
 cp [PATH-TO-TENSORRTX]/yolov5/gen_wts.py .
+python gen_wts.py -w yolov5n-cls.pt -o yolov5n-cls.wts -t cls
 python gen_wts.py -w yolov5n.pt -o yolov5n.wts
 # A file 'yolov5n.wts' will be generated.
 ```
@@ -40,7 +43,7 @@ cd [PATH-TO-TENSORRTX]/yolov5/yolov5_trt10
 # Update kNumClass in src/config.h if your model is trained on custom dataset
 mkdir build
 cd build
-cp [PATH-TO-ultralytics-yolov5]/yolov5s.wts .
+cp [PATH-TO-ultralytics-yolov5]/yolov5sn-cls.wts .
 cmake ..
 make
 
@@ -60,6 +63,30 @@ wget https://github.com/joannzhang00/ImageNet-dataset-classes-labels/blob/main/i
 // Install python-tensorrt, pycuda, etc.
 // Ensure the yolov5n-cls.engine
 python yolov5_cls_trt.py
+# faq: in windows bug pycuda._driver.LogicError
+# faq: in linux bug Segmentation fault
+# Add the following code to the py file:
+# import pycuda.autoinit
+# import pycuda.driver as cuda
+```
+
+#### Detection
+
+```shell
+cd [PATH-TO-TENSORRTX]/yolov5/yolov5_trt10
+# Update kNumClass in src/config.h if your model is trained on custom dataset
+mkdir build
+cd build
+cp [PATH-TO-ultralytics-yolov5]/yolov5n.wts .
+cmake ..
+make
+
+# Build and serialize TensorRT engine
+./yolov5_det -s yolov5n.wts yolov5n.engine [n/s/m/l/x]
+
+# Run inference
+./yolov5_det -d yolov5n.engine ../../images
+# The results are displayed in the console
 ```
 
 ## INT8 Quantization
@@ -69,4 +96,4 @@ python yolov5_cls_trt.py
 4. serialize the model and test
 
 ## More Information
-See the readme in [home page.](https://github.com/wang-xinyu/tensorrtx)
+See the readme in [home page.](https://github.com/wang-xinyu/tensorrtx)

yolov5/yolov5_trt10/plugin/yololayer.h

@@ -11,7 +11,9 @@ class API YoloLayerPlugin : public IPluginV2IOExt {
    public:
     YoloLayerPlugin(int classCount, int netWidth, int netHeight, int maxOut, bool is_segmentation,
                     const std::vector<YoloKernel>& vYoloKernel);
+
     YoloLayerPlugin(const void* data, size_t length);
+
     ~YoloLayerPlugin();
 
     int getNbOutputs() const TRT_NOEXCEPT override { return 1; }
@@ -66,6 +68,7 @@ class API YoloLayerPlugin : public IPluginV2IOExt {
 
    private:
     void forwardGpu(const float* const* inputs, float* output, cudaStream_t stream, int batchSize = 1);
+
     int mThreadCount = 256;
     const char* mPluginNamespace;
     int mKernelCount;
@@ -104,5 +107,6 @@ class API YoloPluginCreator : public IPluginCreator {
     static PluginFieldCollection mFC;
     static std::vector<PluginField> mPluginAttributes;
 };
+
 REGISTER_TENSORRT_PLUGIN(YoloPluginCreator);
 };  // namespace nvinfer1

yolov5/yolov5_trt10/src/model.h

@@ -6,3 +6,11 @@
 nvinfer1::IHostMemory* build_cls_engine(unsigned int maxBatchSize, nvinfer1::IBuilder* builder,
                                         nvinfer1::IBuilderConfig* config, nvinfer1::DataType dt, float& gd, float& gw,
                                         std::string& wts_name);
+
+nvinfer1::IHostMemory* build_det_engine(unsigned int maxBatchSize, nvinfer1::IBuilder* builder,
+                                        nvinfer1::IBuilderConfig* config, nvinfer1::DataType dt, float& gd, float& gw,
+                                        std::string& wts_name);
+
+nvinfer1::IHostMemory* build_det_p6_engine(unsigned int maxBatchSize, nvinfer1::IBuilder* builder,
+                                           nvinfer1::IBuilderConfig* config, nvinfer1::DataType dt, float& gd,
+                                           float& gw, std::string& wts_name);

yolov5/yolov5_trt10/yolov5_cls_trt.py

@@ -9,6 +9,7 @@
 import cv2
 import numpy as np
 import torch
+import pycuda.autoinit  # noqa: F401
 import pycuda.driver as cuda
 import tensorrt as trt
 

yolov5/yolov5_trt10/yolov5_det.cpp

@@ -0,0 +1,259 @@
+#include "cuda_utils.h"
+#include "logging.h"
+#include "model.h"
+#include "postprocess.h"
+#include "preprocess.h"
+#include "utils.h"
+
+#include <chrono>
+#include <cmath>
+#include <iostream>
+
+using namespace nvinfer1;
+
+static Logger gLogger;
+const static int kOutputSize = kMaxNumOutputBbox * sizeof(Detection) / sizeof(float) + 1;
+
+bool parse_args(int argc, char** argv, std::string& wts, std::string& engine, bool& is_p6, float& gd, float& gw,
+                std::string& img_dir) {
+    if (argc < 4)
+        return false;
+    if (std::string(argv[1]) == "-s" && (argc == 5 || argc == 7)) {
+        wts = std::string(argv[2]);
+        engine = std::string(argv[3]);
+        auto net = std::string(argv[4]);
+        if (net[0] == 'n') {
+            gd = 0.33;
+            gw = 0.25;
+        } else if (net[0] == 's') {
+            gd = 0.33;
+            gw = 0.50;
+        } else if (net[0] == 'm') {
+            gd = 0.67;
+            gw = 0.75;
+        } else if (net[0] == 'l') {
+            gd = 1.0;
+            gw = 1.0;
+        } else if (net[0] == 'x') {
+            gd = 1.33;
+            gw = 1.25;
+        } else if (net[0] == 'c' && argc == 7) {
+            gd = atof(argv[5]);
+            gw = atof(argv[6]);
+        } else {
+            return false;
+        }
+        if (net.size() == 2 && net[1] == '6') {
+            is_p6 = true;
+        }
+    } else if (std::string(argv[1]) == "-d" && argc == 4) {
+        engine = std::string(argv[2]);
+        img_dir = std::string(argv[3]);
+    } else {
+        return false;
+    }
+    return true;
+}
+
+void prepare_buffers(ICudaEngine* engine, float** gpu_input_buffer, float** gpu_output_buffer,
+                     float** cpu_output_buffer) {
+    assert(engine->getNbIOTensors() == 2);
+    // In order to bind the buffers, we need to know the names of the input and output tensors.
+    // Note that indices are guaranteed to be less than IEngine::getNbBindings()
+    TensorIOMode input_mode = engine->getTensorIOMode(kInputTensorName);
+    if (input_mode != TensorIOMode::kINPUT) {
+        std::cerr << kInputTensorName << " should be input tensor" << std::endl;
+        assert(false);
+    }
+    TensorIOMode output_mode = engine->getTensorIOMode(kOutputTensorName);
+    if (output_mode != TensorIOMode::kOUTPUT) {
+        std::cerr << kOutputTensorName << " should be output tensor" << std::endl;
+        assert(false);
+    }
+    // Create GPU buffers on device
+    CUDA_CHECK(cudaMalloc((void**)gpu_input_buffer, kBatchSize * 3 * kInputH * kInputW * sizeof(float)));
+    CUDA_CHECK(cudaMalloc((void**)gpu_output_buffer, kBatchSize * kOutputSize * sizeof(float)));
+
+    *cpu_output_buffer = new float[kBatchSize * kOutputSize];
+}
+
+void infer(IExecutionContext& context, cudaStream_t& stream, void** gpu_buffers, float* output, int batchsize) {
+    context.setInputTensorAddress(kInputTensorName, gpu_buffers[0]);
+    context.setOutputTensorAddress(kOutputTensorName, gpu_buffers[1]);
+    context.enqueueV3(stream);
+    CUDA_CHECK(cudaMemcpyAsync(output, gpu_buffers[1], batchsize * kOutputSize * sizeof(float), cudaMemcpyDeviceToHost,
+                               stream));
+    cudaStreamSynchronize(stream);
+}
+
+void serialize_engine(unsigned int max_batchsize, bool& is_p6, float& gd, float& gw, std::string& wts_name,
+                      std::string& engine_name) {
+    // Create builder
+    IBuilder* builder = createInferBuilder(gLogger);
+    IBuilderConfig* config = builder->createBuilderConfig();
+
+    // Create model to populate the network, then set the outputs and create an engine
+    IHostMemory* serialized_engine = nullptr;
+    if (is_p6) {
+        serialized_engine = build_det_p6_engine(max_batchsize, builder, config, DataType::kFLOAT, gd, gw, wts_name);
+    } else {
+        serialized_engine = build_det_engine(max_batchsize, builder, config, DataType::kFLOAT, gd, gw, wts_name);
+    }
+    assert(serialized_engine != nullptr);
+
+    // Serialize the engine
+    assert(serialized_engine != nullptr);
+
+    // Save engine to file
+    std::ofstream p(engine_name, std::ios::binary);
+    if (!p) {
+        std::cerr << "Could not open plan output file" << std::endl;
+        assert(false);
+    }
+    p.write(reinterpret_cast<const char*>(serialized_engine->data()), serialized_engine->size());
+
+    // Close everything down
+    delete serialized_engine;
+    delete config;
+    delete builder;
+}
+
+void deserialize_engine(std::string& engine_name, IRuntime** runtime, ICudaEngine** engine,
+                        IExecutionContext** context) {
+    std::ifstream file(engine_name, std::ios::binary);
+    if (!file.good()) {
+        std::cerr << "read " << engine_name << " error!" << std::endl;
+        assert(false);
+    }
+    size_t size = 0;
+    file.seekg(0, file.end);
+    size = file.tellg();
+    file.seekg(0, file.beg);
+    char* serialized_engine = new char[size];
+    assert(serialized_engine);
+    file.read(serialized_engine, size);
+    file.close();
+
+    *runtime = createInferRuntime(gLogger);
+    assert(*runtime);
+    *engine = (*runtime)->deserializeCudaEngine(serialized_engine, size);
+    assert(*engine);
+    *context = (*engine)->createExecutionContext();
+    assert(*context);
+    delete[] serialized_engine;
+}
+
+int main(int argc, char** argv) {
+    // -s ../models/yolov5n.wts ../models/yolov5n.fp32.trt n
+    // -d ../models/yolov5n.fp32.trt ../images
+    cudaSetDevice(kGpuId);
+
+    std::string wts_name = "";
+    std::string engine_name = "";
+    bool is_p6 = false;
+    float gd = 0.0f, gw = 0.0f;
+    std::string img_dir;
+
+    if (!parse_args(argc, argv, wts_name, engine_name, is_p6, gd, gw, img_dir)) {
+        std::cerr << "arguments not right!" << std::endl;
+        std::cerr << "./yolov5_det -s [.wts] [.engine] [n/s/m/l/x/n6/s6/m6/l6/x6 or c/c6 gd gw]  // serialize model to "
+                     "plan file"
+                  << std::endl;
+        std::cerr << "./yolov5_det -d [.engine] ../images  // deserialize plan file and run inference" << std::endl;
+        return -1;
+    }
+
+    // Create a model using the API directly and serialize it to a file
+    if (!wts_name.empty()) {
+        serialize_engine(kBatchSize, is_p6, gd, gw, wts_name, engine_name);
+        return 0;
+    }
+
+    // Deserialize the engine from file
+    IRuntime* runtime = nullptr;
+    ICudaEngine* engine = nullptr;
+    IExecutionContext* context = nullptr;
+    deserialize_engine(engine_name, &runtime, &engine, &context);
+    cudaStream_t stream;
+    CUDA_CHECK(cudaStreamCreate(&stream));
+
+    // Init CUDA preprocessing
+    cuda_preprocess_init(kMaxInputImageSize);
+
+    // Prepare cpu and gpu buffers
+    float* gpu_buffers[2];
+    float* cpu_output_buffer = nullptr;
+    prepare_buffers(engine, &gpu_buffers[0], &gpu_buffers[1], &cpu_output_buffer);
+
+    // Read images from directory
+    std::vector<std::string> file_names;
+    if (read_files_in_dir(img_dir.c_str(), file_names) < 0) {
+        std::cerr << "read_files_in_dir failed." << std::endl;
+        return -1;
+    }
+
+    // batch predict
+    for (size_t i = 0; i < file_names.size(); i += kBatchSize) {
+        // Get a batch of images
+        std::vector<cv::Mat> img_batch;
+        std::vector<std::string> img_name_batch;
+        for (size_t j = i; j < i + kBatchSize && j < file_names.size(); j++) {
+            cv::Mat img = cv::imread(img_dir + "/" + file_names[j]);
+            img_batch.push_back(img);
+            img_name_batch.push_back(file_names[j]);
+        }
+
+        // Preprocess
+        cuda_batch_preprocess(img_batch, gpu_buffers[0], kInputW, kInputH, stream);
+
+        // Run inference
+        auto start = std::chrono::system_clock::now();
+        infer(*context, stream, (void**)gpu_buffers, cpu_output_buffer, kBatchSize);
+        auto end = std::chrono::system_clock::now();
+        std::cout << "inference time: " << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count()
+                  << "ms" << std::endl;
+
+        // NMS
+        std::vector<std::vector<Detection>> res_batch;
+        batch_nms(res_batch, cpu_output_buffer, img_batch.size(), kOutputSize, kConfThresh, kNmsThresh);
+
+        // print results
+        for (size_t j = 0; j < res_batch.size(); j++) {
+            for (size_t k = 0; k < res_batch[j].size(); k++) {
+                std::cout << "image: " << img_name_batch[j] << ", bbox: " << res_batch[j][k].bbox[0] << ", "
+                          << res_batch[j][k].bbox[1] << ", " << res_batch[j][k].bbox[2] << ", "
+                          << res_batch[j][k].bbox[3] << ", conf: " << res_batch[j][k].conf
+                          << ", class_id: " << res_batch[j][k].class_id << std::endl;
+            }
+        }
+
+        // Draw bounding boxes
+        draw_bbox(img_batch, res_batch);
+
+        // Save images
+        for (size_t j = 0; j < img_batch.size(); j++) {
+            cv::imwrite("_" + img_name_batch[j], img_batch[j]);
+        }
+    }
+
+    // Release stream and buffers
+    cudaStreamDestroy(stream);
+    CUDA_CHECK(cudaFree(gpu_buffers[0]));
+    CUDA_CHECK(cudaFree(gpu_buffers[1]));
+    delete[] cpu_output_buffer;
+    cuda_preprocess_destroy();
+    // Destroy the engine
+    delete context;
+    delete engine;
+    delete runtime;
+
+    // Print histogram of the output distribution
+    // std::cout << "\nOutput:\n\n";
+    // for (unsigned int i = 0; i < kOutputSize; i++) {
+    //   std::cout << prob[i] << ", ";
+    //   if (i % 10 == 0) std::cout << std::endl;
+    // }
+    // std::cout << std::endl;
+
+    return 0;
+}