一、导出 onnx 模型
服务器端或者本地电脑 git clone github.com/valeoai/Woo…
进入 omnidet 下的 models 文件夹下面找到 onnx 文件夹，会找到 export_onnx.py 文件。根据文件顶端说明，需要去配置相关的文件。进入 data/params.yaml 中，在配置文件的最下 main 可以找到# -- ONNX MODEL EXPORT --相关配置 。choices 中可以选择你需要的任务，加入你只需要分割，就将 onnx_modelg 更改为 segmentic，假如你的任务是 detection，就将 onnx_model 更改为 detection。这里我们以分割为示例模型。将按照示例得到的训练模型路径写入到 model_path，作为导出 onnx 的输入。然后在 onnx_export_path 写 onnx 的导出路径。保存，终端运行 python . /onnx_export.py --config data/params.yaml。获取到 onnx 模型。
二、校准集准备：
import osimport sysimport argparseimport yamlsys.path.append('./WoodScape-ICCV19/omnidet')import cv2import matplotlib as mplimport matplotlib.cm as cmimport numpy as npimport onnxruntimeimport torchfrom PIL import Imagefrom matplotlib import pyplot as pltfrom horizon_tc_ui.hb_runtime import HBRuntime ALPHA = 0.5 def collect_args() -> argparse.Namespace: """Set command line arguments""" parser = argparse.ArgumentParser() parser.add_argument('--config', help="Config file", type=str, default="params.yaml") args = parser.parse_args() return args class Tupperware(dict): MARKER = object() def __init__(self, value=None): if value is None: pass elif isinstance(value, dict): for key in value: self.__setitem__(key, value[key]) else: raise TypeError('expected dict') def __setitem__(self, key, value): if isinstance(value, dict) and not isinstance(value, Tupperware): value = Tupperware(value) super(Tupperware, self).__setitem__(key, value) def __getitem__(self, key): found = self.get(key, Tupperware.MARKER) if found is Tupperware.MARKER: found = Tupperware() super(Tupperware, self).__setitem__(key, found) return found __setattr__, __getattr__ = __setitem__, __getitem__ def collect_tupperware() -> Tupperware: config = collect_args() params = yaml.safe_load(open(config.config)) args = Tupperware(params) print(args) return args def pre_image_op(args, index, frame_index, cam_side): total_car1_images = 6054 cropped_coords = dict(Car1=dict(FV=(114, 110, 1176, 610), MVL=(343, 5, 1088, 411), MVR=(185, 5, 915, 425), RV=(186, 203, 1105, 630)), Car2=dict(FV=(160, 272, 1030, 677), MVL=(327, 7, 1096, 410), MVR=(175, 4, 935, 404), RV=(285, 187, 1000, 572))) if args.crop: if int(frame_index[1:]) < total_car1_images: cropped_coords = cropped_coords["Car1"][cam_side] else: cropped_coords = cropped_coords["Car2"][cam_side] else: cropped_coords = None cropped_image = get_image(args, index, cropped_coords, frame_index, cam_side) resized_image = cv2.resize(np.array(cropped_image), (args.input_width, args.input_height), cv2.INTER_LANCZOS4).transpose((2, 0, 1)) resized_image = np.expand_dims(resized_image, axis=0).astype(np.float32) return resized_image / 255def get_image(args, index, cropped_coords, frame_index, cam_side): recording_folder = "rgb_images" if index == 0 else "previous_images" file = f"{frame_index}_{cam_side}.png" if index == 0 else f"{frame_index}_{cam_side}_prev.png" path = os.path.join(args.dataset_dir, recording_folder, file) image = Image.open(path).convert('RGB') if args.crop: return image.crop(cropped_coords) return image i = 0def verify_onnx_model(args): image_paths = [line.rstrip('\n') for line in open("./ori_dataset/val.txt")] print(image_paths) i = 0 for path in image_paths: frame_index, cam_side = path.split('.')[0].split('_') previous_frame = pre_image_op(args, -1, frame_index, cam_side) current_frame = pre_image_op(args, 0, frame_index, cam_side) np.save(f"calibration_data_rgb/previous_data_npy/previous_index{i}.npy",previous_frame) np.save(f"calibration_data_rgb/rgb_data_npy/current_index{i}.npy",current_frame) i += 1 if __name__ == "__main__": # load your predefined ONNX model args = collect_tupperware() verify_onnx_model(args)eszx数据预处理流程要和训练集合数据处理流程一样。
三、配置文件设置
calibration_parameters: cal_data_dir: ./calibration_data_rgb/previous_data_npy;./calibration_data_rgb/rgb_data_npy cal_data_type: '' calibration_type: default optimization: '' per_channel: false quant_config: '' run_on_bpu: '' run_on_cpu: ''compiler_parameters: advice: 0 balance_factor: 0 compile_mode: latency core_num: 1 debug: true jobs: 16 max_time_per_fc: 0 optimize_level: O2input_parameters: input_layout_rt: '' input_layout_train: NCHW;NCHW input_name: input.1;input.55 input_shape: 1x3x288x544;1x3x288x544 input_type_rt: featuremap;featuremap input_type_train: featuremap;featuremap # mean_value: 0;0 #norm_type: data_mean_and_scale;data_mean_and_scale #scale_value: 0.003921568627451;0.003921568627451 separate_batch: falsemodel_parameters: debug_mode: '' layer_out_dump: false march: nash-e node_info: '' onnx_model: omnidet_float32_opset12.onnx output_model_file_prefix: omnidet_float32_opset12 output_nodes: '' remove_node_name: '' remove_node_type: '' working_dir: ./model_output因为是双输入，所以 cal_data_dir: ./calibration_data_rgb/previous_data_npy;./calibration_data_rgb/rgb_data_npy 这里配置两个校准集路径，后面的一些配置都配置成双份的。
四、模型量化编译
hb_compile --config config.file
编译时间较长，耐心等待即可。
在 model_output 中我们会得到 hbm 模型文件，该模型文件是经过量化之后的。
五、板端编译部署
可以选择在服务器端进行编译，也可以在板端进行编译。NVCC 交叉编译。前提是需要安装好 gcc，g++，可以用 which gcc，which g++查看是否安装了 C++编译器。我们以 Cmake 形式进行工程编译。
CMakeList.txt 如下：
# CMakeLists.txtcmake_minimum_required(VERSION 3.0) project(test_sample)set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wl,-unresolved-symbols=ignore-in-shared-libs") message(STATUS "Build type: ${CMAKE_BUILD_TYPE}") set(CMAKE_CXX_FLAGS_DEBUG "-g -O0")set(CMAKE_C_FLAGS_DEBUG "-g -O0")set(CMAKE_CXX_FLAGS_RELEASE " -O3 ")set(CMAKE_C_FLAGS_RELEASE " -O3 ") set(CMAKE_BUILD_TYPE ${build_type}) set(DEPS_ROOT ${CMAKE_CURRENT_SOURCE_DIR}/deps_aarch64) include_directories(${DEPS_ROOT}/ucp/include)include_directories(/map/haiquan.liu/test2/test_sample/deps_aarch64/opencv/include) link_directories(${DEPS_ROOT}/ucp/lib)# 设置 OpenCV 头文件和库路径add_executable(run_sample src/main.cc)target_link_libraries(run_sample dnn hbucp)target_link_libraries(run_sample "/map/haiquan.liu/test2/test_sample/deps_aarch64/opencv/lib/libopencv_world.so")main函数如下：#include #include #include #include #include #include "hobot/dnn/hb_dnn.h"#include "hobot/hb_ucp.h"#include "hobot/hb_ucp_sys.h"using namespace cv;#define ALIGN(value, alignment) (((value) + ((alignment)-1)) & ~((alignment)-1))#define ALIGN_32(value) ALIGN(value, 32)const char* hbm_path = "omnidet_float32_opset12.hbm";std::string data_path1 = "input_file1.bin";std::string data_path2 = "input_file2.bin";// Read binary input fileint read_binary_file(std::string file_path, char **bin, int *length) { std::ifstream ifs(file_path, std::ios::in | std::ios::binary); ifs.seekg(0, std::ios::end); *length = ifs.tellg(); ifs.seekg(0, std::ios::beg); *bin = new char[sizeof(char) * (*length)]; ifs.read(*bin, *length); ifs.close(); return 0;}// Prepare input tensor and output tensorint prepare_tensor(hbDNNTensor *input_tensor, hbDNNTensor *output_tensor, hbDNNHandle_t dnn_handle) { // Get input and output tensor counts int input_count = 0; int output_count = 0; hbDNNGetInputCount(&input_count, dnn_handle); hbDNNGetOutputCount(&output_count, dnn_handle); hbDNNTensor *input = input_tensor; // Get the properties of the input tensor for (int i = 0; i < input_count; i++) { hbDNNGetInputTensorProperties(&input[i].properties, dnn_handle, i); // Calculate the stride of the input tensor auto dim_len = input[i].properties.validShape.numDimensions; for (int32_t dim_i = dim_len - 1; dim_i >= 0; --dim_i) { if (input[i].properties.stride[dim_i] == -1) { auto cur_stride = input[i].properties.stride[dim_i + 1] * input[i].properties.validShape.dimensionSize[dim_i + 1]; input[i].properties.stride[dim_i] = ALIGN_32(cur_stride); } } // Calculate the memory size of the input tensor and allocate cache memory int input_memSize = input[i].properties.stride[0] * input[i].properties.validShape.dimensionSize[0]; hbUCPMallocCached(&input[i].sysMem, input_memSize, 0); } hbDNNTensor *output = output_tensor; // Get the properties of the input tensor for (int i = 0; i < output_count; i++) { hbDNNGetOutputTensorProperties(&output[i].properties, dnn_handle, i); // Calculate the memory size of the output tensor and allocate cache memory int output_memSize = output[i].properties.alignedByteSize; hbUCPMallocCached(&output[i].sysMem, output_memSize, 0); // Show how to get output name const char *output_name; hbDNNGetOutputName(&output_name, dnn_handle, i); } return 0;}int main() { // 获取模型句柄 hbDNNPackedHandle_t packed_dnn_handle; hbDNNHandle_t dnn_handle; hbDNNInitializeFromFiles(&packed_dnn_handle, &hbm_path, 1); const char **model_name_list; int model_count = 0; hbDNNGetModelNameList(&model_name_list, &model_count, packed_dnn_handle); hbDNNGetModelHandle(&dnn_handle, packed_dnn_handle, model_name_list[0]); // Prepare input and output tensor std::vector<hbDNNTensor> input_tensors; std::vector<hbDNNTensor> output_tensors; int input_count = 0; int output_count = 0; hbDNNGetInputCount(&input_count, dnn_handle); hbDNNGetOutputCount(&output_count, dnn_handle); input_tensors.resize(input_count); output_tensors.resize(output_count); // Initialize and malloc the tensor prepare_tensor(input_tensors.data(), output_tensors.data(), dnn_handle); // 复制输入数据到输入张量 int32_t data_length1 = 0; int32_t data_length2 = 0; char *data1 = nullptr, *data2 = nullptr; // 读取两个输入数据 auto ret1 = read_binary_file(data_path1, &data1, &data_length1); auto ret2 = read_binary_file(data_path2, &data2, &data_length2); // 将数据复制到输入张量 memcpy(reinterpret_cast<char *>(input_tensors[0].sysMem.virAddr), data1, input_tensors[0].sysMem.memSize); memcpy(reinterpret_cast<char *>(input_tensors[1].sysMem.virAddr), data2, input_tensors[1].sysMem.memSize); // 刷新内存，确保数据写入 hbUCPMemFlush(&(input_tensors[0].sysMem), HB_SYS_MEM_CACHE_CLEAN); hbUCPMemFlush(&(input_tensors[1].sysMem), HB_SYS_MEM_CACHE_CLEAN); // 提交推理任务并等待完成 hbUCPTaskHandle_t task_handle{nullptr}; hbDNNTensor *output = output_tensors.data(); hbDNNInferV2(&task_handle, output, input_tensors.data(), dnn_handle); // 等待任务完成 hbUCPSchedParam ctrl_param; HB_UCP_INITIALIZE_SCHED_PARAM(&ctrl_param); ctrl_param.backend = HB_UCP_BPU_CORE_ANY; hbUCPSubmitTask(task_handle, &ctrl_param); hbUCPWaitTaskDone(task_handle, 0); // 解析推理结果并处理每个输出张量 for (int i = 0; i < 4; ++i) { hbUCPMemFlush(&output_tensors[i].sysMem, HB_SYS_MEM_CACHE_INVALIDATE); auto result = reinterpret_cast<float *>(output_tensors[i].sysMem.virAddr); // 处理每个任务的结果 if (i == 0) { // 任务1: 处理 output_tensors[0] // 例如分类任务，分割任务等 } else if (i == 1) { // 任务2: 处理 output_tensors[1] // int height = output_tensors[i].properties.shape[1]; // 输出图像的高度 // int width = output_tensors[i].properties.shape[2]; // 输出图像的宽度 int height = 288; // 输出图像的高度 int width = 544; // 输出图像的宽度 // 假设result是一个(float类型的)指向概率的指针，维度为(10, 288, 544) // 10个类别，对应每个像素的类别概率 float* result = reinterpret_cast<float*>(output_tensors[i].sysMem.virAddr); // 创建一个伪彩色图像用于渲染（将类别索引映射到颜色） cv::Mat rendered_image(height, width, CV_8UC3); // 创建一个颜色映射（例如，10个类别对应不同颜色） std::vector<cv::Vec3b> color_map = { cv::Vec3b(0, 0, 255), // 类别 0 (红色) cv::Vec3b(0, 255, 0), // 类别 1 (绿色) cv::Vec3b(255, 0, 0), // 类别 2 (蓝色) cv::Vec3b(0, 255, 255), // 类别 3 (青色) cv::Vec3b(255, 255, 0), // 类别 4 (黄色) cv::Vec3b(255, 0, 255), // 类别 5 (品红) cv::Vec3b(128, 128, 128), // 类别 6 (灰色) cv::Vec3b(255, 165, 0), // 类别 7 (橙色) cv::Vec3b(255, 20, 147), // 类别 8 (深粉色) cv::Vec3b(0, 191, 255) // 类别 9 (深天蓝) }; // 对每个像素进行argmax操作，选择概率最大类别 for (int h = 0; h < height; ++h) { for (int w = 0; w < width; ++w) { // 每个像素有10个类别的概率，找到最大概率对应的类别索引 int class_id = 0; float max_prob = result[class_id * height * width + h * width + w]; for (int c = 1; c < 10; ++c) { float prob = result[c * height * width + h * width + w]; if (prob > max_prob) { max_prob = prob; class_id = c; } } // 将类别索引映射到颜色 rendered_image.at<cv::Vec3b>(h, w) = color_map[class_id]; } } // 保存渲染图像 cv::imwrite("segmentation_output.png", rendered_image); } else if (i == 2) { // 任务3: 处理 output_tensors[2] } else if (i == 3) { // 任务4: 处理 output_tensors[3] } } return 0;}注意将环境以来迁移过来 ，如在服务器的 docker 环境下，则不必迁移过来。直接在服务器端编译即可。
布局如下
然后 mkdir build，cd build，cmake ..，make 依次操作。
会在 build 文件夹下得到编译过的二进制文件，将输入数据以及模型和二进制文件迁移到同个文件夹下面。运行。/二进制文件即可。分割效果图如下
