first commit

2026-06-03 12:43:14 +08:00
commit ba76cfae28
608 changed files with 120791 additions and 0 deletions
--- a/third_party/trt_yolov8/trt_yolov8_classifier.cpp
+++ b/third_party/trt_yolov8/trt_yolov8_classifier.cpp
@@ -0,0 +1,236 @@
+
+#include "trt_yolov8_classifier.h"
+
+namespace trt_yolov8 {
+    using namespace nvinfer1;
+    void trt_yolov8_classifier::batch_preprocess(std::vector<cv::Mat>& imgs, float* output, int dst_width, int dst_height) {
+        for (size_t b = 0; b < imgs.size(); b++) {
+        int h = imgs[b].rows;
+        int w = imgs[b].cols;
+        int m = std::min(h, w);
+        int top = (h - m) / 2;
+        int left = (w - m) / 2;
+        cv::Mat img = imgs[b](cv::Rect(left, top, m, m));
+        cv::resize(img, img, cv::Size(dst_width, dst_height), 0, 0, cv::INTER_LINEAR);
+        cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
+        img.convertTo(img, CV_32F, 1/255.0);
+
+        std::vector<cv::Mat> channels(3);
+        cv::split(img, channels);
+
+        // CHW format
+        for (int c = 0; c < 3; ++c) {
+        int i = 0;
+        for (int row = 0; row < dst_height; ++row) {
+            for (int col = 0; col < dst_width; ++col) {
+            output[b * 3 * dst_height * dst_width + c * dst_height * dst_width + i] =
+                channels[c].at<float>(row, col);
+            ++i;
+            }
+        }
+        }
+        }
+    }
+
+    std::vector<float> trt_yolov8_classifier::softmax(float *prob, int n) {
+        std::vector<float> res;
+        float sum = 0.0f;
+        float t;
+        for (int i = 0; i < n; i++) {
+            t = expf(prob[i]);
+            res.push_back(t);
+            sum += t;
+        }
+        for (int i = 0; i < n; i++) {
+            res[i] /= sum;
+        }
+        return res;
+    }
+
+    std::vector<int> trt_yolov8_classifier::topk(const std::vector<float>& vec, int k) {
+        std::vector<int> topk_index;
+        std::vector<size_t> vec_index(vec.size());
+        std::iota(vec_index.begin(), vec_index.end(), 0);
+
+        std::sort(vec_index.begin(), vec_index.end(), [&vec](size_t index_1, size_t index_2) { return vec[index_1] > vec[index_2]; });
+
+        int k_num = std::min<int>(vec.size(), k);
+
+        for (int i = 0; i < k_num; ++i) {
+            topk_index.push_back(vec_index[i]);
+        }
+
+        return topk_index;
+    }
+
+    void trt_yolov8_classifier::prepare_buffers(ICudaEngine* engine, float** gpu_input_buffer, float** gpu_output_buffer, float** cpu_input_buffer, float** output_buffer_host) {
+        assert(engine->getNbBindings() == 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()
+        const int inputIndex = engine->getBindingIndex(kInputTensorName);
+        const int outputIndex = engine->getBindingIndex(kOutputTensorName);
+        assert(inputIndex == 0);
+        assert(outputIndex == 1);
+        // Create GPU buffers on device
+        CUDA_CHECK(cudaMalloc((void**)gpu_input_buffer, kBatchSize * 3 * kClsInputH * kClsInputW * sizeof(float)));
+        CUDA_CHECK(cudaMalloc((void**)gpu_output_buffer, kBatchSize * kOutputSize * sizeof(float)));
+
+        *cpu_input_buffer = new float[kBatchSize * 3 * kClsInputH * kClsInputW];
+        *output_buffer_host = new float[kBatchSize * kOutputSize];
+    }
+
+    void trt_yolov8_classifier::infer(IExecutionContext& context, cudaStream_t& stream, void **buffers, float* input, float* output, int batchSize) {
+        CUDA_CHECK(cudaMemcpyAsync(buffers[0], input, batchSize * 3 * kClsInputH * kClsInputW * sizeof(float), cudaMemcpyHostToDevice, stream));
+        context.enqueue(batchSize, buffers, stream, nullptr);
+        CUDA_CHECK(cudaMemcpyAsync(output, buffers[1], batchSize * kOutputSize * sizeof(float), cudaMemcpyDeviceToHost, stream));
+        cudaStreamSynchronize(stream);
+    }
+
+    void trt_yolov8_classifier::serialize_engine(unsigned int max_batchsize, 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;
+        //engine = buildEngineYolov8Cls(max_batchsize, builder, config, DataType::kFLOAT, gd, gw, wts_name);
+        serialized_engine = buildEngineYolov8Cls(builder, config, DataType::kFLOAT, wts_name, gd, gw);
+        assert(serialized_engine);
+        // 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 trt_yolov8_classifier::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;
+    }
+
+    trt_yolov8_classifier::trt_yolov8_classifier(std::string model_path) {
+        if (model_path.empty()) {
+            return;
+        }
+
+        cudaSetDevice(kGpuId);
+        deserialize_engine(model_path, &runtime, &engine, &context);
+        CUDA_CHECK(cudaStreamCreate(&stream));
+    }
+
+    trt_yolov8_classifier::~trt_yolov8_classifier() {
+        cudaStreamDestroy(stream);
+        delete context;
+        delete engine;
+        delete runtime;
+    }
+
+    void trt_yolov8_classifier::classify(std::vector<cv::Mat> images, std::vector<std::vector<Classification>>& classifications, int top_k) {
+        // Prepare cpu and gpu buffers
+        float* device_buffers[2];
+        float* cpu_input_buffer = nullptr;
+        float* output_buffer_host = nullptr;
+        prepare_buffers(engine, &device_buffers[0], &device_buffers[1], &cpu_input_buffer, &output_buffer_host);
+
+        // batch predict
+        for (size_t i = 0; i < images.size(); i += kBatchSize) {
+            // Get a batch of images
+            std::vector<cv::Mat> img_batch;
+            for (size_t j = i; j < i + kBatchSize && j < images.size(); j++) {
+                img_batch.push_back(images[j]);
+            }
+
+            // Preprocess
+            batch_preprocess(img_batch, cpu_input_buffer);
+
+            // Run inference
+            auto start = std::chrono::system_clock::now();
+            infer(*context, stream, (void**)device_buffers, cpu_input_buffer, output_buffer_host, 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;
+
+            // Postprocess and get top-k result
+            for (size_t b = 0; b < img_batch.size(); b++) {
+                float* p = &output_buffer_host[b * kOutputSize];
+                auto res = softmax(p, kOutputSize);
+                auto topk_idx = topk(res, top_k);
+
+                std::vector<Classification> classification;
+                for (auto idx: topk_idx) {
+                    classification.push_back(Classification {idx, res[idx]});
+                }
+
+                classifications.push_back(classification);
+            }
+        }
+
+        CUDA_CHECK(cudaFree(device_buffers[0]));
+        CUDA_CHECK(cudaFree(device_buffers[1]));
+        delete[] cpu_input_buffer;
+        delete[] output_buffer_host;      
+    }
+
+    bool trt_yolov8_classifier::wts_2_engine(std::string wts_name, std::string engine_name, std::string sub_type) {
+        int is_p = 0;
+        float gd = 0.0f, gw = 0.0f;
+        int max_channels = 0;
+
+        if (sub_type[0] == 'n') {          // yolov8n
+            gd = 0.33;
+            gw = 0.25;
+            max_channels = 1024;
+        } else if (sub_type[0] == 's') {   // yolov8s
+            gd = 0.33;
+            gw = 0.50;
+            max_channels = 1024;
+        } else if (sub_type[0] == 'm') {   // yolov8m
+            gd = 0.67;
+            gw = 0.75;
+            max_channels = 576;
+        } else if (sub_type[0] == 'l') {   // yolov8l
+            gd = 1.0;
+            gw = 1.0;
+            max_channels = 512;
+        } else if (sub_type[0] == 'x') {   // yolov8x
+            gd = 1.0;
+            gw = 1.25;
+            max_channels = 640;
+        } else {
+            return false;  // not support
+        }
+        
+        if (sub_type.size() == 2 && sub_type[1] == '6') {         // yolov8n6/yolov8s6/yolov8m6/yolov8l6/yolov8x6
+            is_p = 6;
+        } else if (sub_type.size() == 2 && sub_type[1] == '2') {  // yolov8n2/yolov8s2/yolov8m2/yolov8l2/yolov8x2
+            is_p = 2;
+        }
+
+        serialize_engine(kBatchSize, gd, gw, wts_name, engine_name);
+        return true;
+    }
+}