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征程 6M 部署 Omnidet 感知模型

news 2025/7/11 8:56:57

一、导出 onnx 模型

服务器端或者本地电脑 git clone https://github.com/valeoai/WoodScape.git

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 os
import sys
import argparse
import yaml
sys.path.append('./WoodScape-ICCV19/omnidet')
import cv2
import matplotlib as mpl
import matplotlib.cm as cm
import numpy as np
import onnxruntime
import torch
from PIL import Image
from matplotlib import pyplot as plt
from horizon_tc_ui.hb_runtime import HBRuntimeALPHA = 0.5def 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 argsclass Tupperware(dict):MARKER = object()def __init__(self, value=None):if value is None:passelif 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 argsdef pre_image_op(args, index, frame_index, cam_side):total_car1_images = 6054cropped_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 = Nonecropped_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 imagei = 0
def verify_onnx_model(args):image_paths = [line.rstrip('\n') for line in open("./ori_dataset/val.txt")]print(image_paths)i = 0for 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 += 1if __name__ == "__main__":# load your predefined ONNX modelargs = 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_npycal_data_type: ''calibration_type: defaultoptimization: ''per_channel: falsequant_config: ''run_on_bpu: ''run_on_cpu: ''
compiler_parameters:advice: 0balance_factor: 0compile_mode: latencycore_num: 1debug: truejobs: 16max_time_per_fc: 0optimize_level: O2
input_parameters:input_layout_rt: ''input_layout_train: NCHW;NCHWinput_name: input.1;input.55input_shape: 1x3x288x544;1x3x288x544input_type_rt: featuremap;featuremapinput_type_train: featuremap;featuremap# mean_value: 0;0#norm_type: data_mean_and_scale;data_mean_and_scale#scale_value: 0.003921568627451;0.003921568627451separate_batch: false
model_parameters:debug_mode: ''layer_out_dump: falsemarch: nash-enode_info: ''onnx_model: omnidet_float32_opset12.onnxoutput_model_file_prefix: omnidet_float32_opset12output_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.txt
cmake_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 file
int 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 tensor
int prepare_tensor(hbDNNTensor *input_tensor, hbDNNTensor *output_tensor,hbDNNHandle_t dnn_handle) {// Get input and output tensor countsint 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 tensorfor (int i = 0; i < input_count; i++) {hbDNNGetInputTensorProperties(&input[i].properties, dnn_handle, i);// Calculate the stride of the input tensorauto 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 memoryint 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 tensorfor (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 memoryint output_memSize = output[i].properties.alignedByteSize;hbUCPMallocCached(&output[i].sysMem, output_memSize, 0);// Show how to get output nameconst 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 tensorstd::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 tensorprepare_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。

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