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【3D目标检测】OpenPCDet自定义数据集训练_openpcdet 自己数据集
作者：笔触狂放9 | 2024-07-07 07:25:43
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openpcdet 自己数据集
引言
OpenPCDet环境搭建参考：【3D目标检测】环境搭建（OpenPCDet、MMdetection3d）
源码地址：OpenPCDet：https://github.com/open-mmlab/OpenPCDet
1 自定义数据集准备
1.1 标注工具labelCloud
源码地址：https://github.com/ch-sa/labelCloud
建议在Windows上安装，Ubuntu对Qt的版本限制比较麻烦
1.1.1 安装labelCloud
git clone https://github.com/ch-sa/labelCloud.git # 1. Clone repository
pip install -r requirements.txt # 2. Install requirements
# 3. Copy point clouds into `pointclouds` folder.
python labelCloud.py # 4. Start labelCloud
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1.1.2 自定义标签
按照如下格式，修改"./labelCloud/labels/_classes.json"文件，自定义标签
{ "classes": [ { "name": "pedestrian", "id": 0, "color": "#ff0000" }, { "name": "stone", "id": 1, "color": "#7fc4ff" }, { "name": "paperbox", "id": 2, "color": "#00ffc4" }, { "name": "pedestrian_seated", "id": 3, "color": "#00e546" }, { "name": "pedestrian_carrying", "id": 3, "color": "#0046e5" } ], "default": 0, "type": "object_detection", "format": "kitti_untransformed", "created_with": { "name": "labelCloud", "version": "1.1.0" } }
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1.1.3 labelCloud配置
有效的配置，可以是数据标注工作事半功倍！
配置参数定义参考链接：https://ch-sa.github.io/labelCloud/configuration/
接着就开始标注数据吧
笔者这里按kitti格式生成label数据：
1.2 数据集准备和预处理
按照KITTI数据结构规划自定义数据集如下：
custom
├── testing
│ ├── velodyne # 点云数据
├── training
│ ├── label_2 # 标签文件
│ ├── velodyne
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运行分割数据集代码：
""" 2024.03.21 author:alian 数据预处理操作 1.数据集分割 """ import os import random import shutil import numpy as np def get_train_val_txt_kitti(src_path): """ 数据格式:KITTI # For KITTI Dataset └── KITTI_DATASET_ROOT ├── training <-- 7481 train data | ├── image_2 <-- for visualization | ├── calib | ├── label_2 | └── velodyne └── testing <-- 7580 test data ├── image_2 <-- for visualization ├── calib └── velodyne src_path: KITTI_DATASET_ROOT kitti文件夹 """ # 1.自动生成数据集划分文件夹ImageSets set_path = "%s/ImageSets/"%src_path if os.path.exists(set_path): # 如果文件存在 shutil.rmtree(set_path) # 清空原始数据 os.makedirs(set_path) # 重新创建 else: os.makedirs(set_path) # 自动新建文件夹 # 2.训练样本分割 生成train.txt val.txt trainval.txt train_list = os.listdir(os.path.join(src_path,'training','velodyne')) random.shuffle(train_list) # 打乱顺序，随机采样 # 设置训练和验证的比例 train_p = 0.8 # 开始写入分割文件 f_train = open(os.path.join(set_path, "train.txt"), 'w') f_val = open(os.path.join(set_path, "val.txt"), 'w') f_trainval = open(os.path.join(set_path, "trainval.txt"), 'w') for i,src in enumerate(train_list): if i<int(len(train_list)*train_p): # 训练集的数量 f_train.write(src[:-4] + '\n') f_trainval.write(src[:-4] + '\n') else: f_val.write(src[:-4] + '\n') f_trainval.write(src[:-4] + '\n') # 3.测试样本分割 生成test.txt test_list = os.listdir(os.path.join(src_path,'testing','velodyne')) f_test = open(os.path.join(set_path, "test.txt"), 'w') for i,src in enumerate(test_list): f_test.write(src[:-4] + '\n') if __name__=='__main__': """ src_path: 数据目录 """ src_path = './OpenPCDet/data/custom' get_train_val_txt_kitti_det3d(src_path)
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输入：数据集路径
输出：在输入的数据集路径下生成
└── ImageSets # 数据集划分文件
├── train.txt
├── val.txt
├── test.txt
└── trainval.txt
├── testing
│ ├── velodyne # 点云数据
├── training
│ ├── label_2 # 标签文件
│ ├── velodyne
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1.3 生成标准数据格式
建议复制kitti_dataset.py、kitti_dataset.yaml，重命名为custom_dataset.py、kitti_custom_dataset.yaml，修改文件路径如下：
OpenPCDet/pcdet/datasets/kitti/custom_dataset.py
OpenPCDet/tools/cfgs/dataset_configs/kitti_custom_dataset.yaml
OpenPCDet/tools/cfgs/dataset_configs/kitti_custom_dataset.yaml
DATASET: 'CustomDataset' DATA_PATH: '/media/ll/L/llr/a2023_my_3d/OpenPCDet/data/custom' # 1.绝对路径 # If this config file is modified then pcdet/models/detectors/detector3d_template.py: # Detector3DTemplate::build_networks:model_info_dict needs to be modified. POINT_CLOUD_RANGE: [-70.4, -40, -3, 70.4, 40, 1] # x=[-70.4, 70.4], y=[-40,40], z=[-3,1] 根据自己的标注框进行调整 DATA_SPLIT: { 'train': train, 'test': val } INFO_PATH: { 'train': [custom_infos_train.pkl], 'test': [custom_infos_val.pkl], } GET_ITEM_LIST: ["points"] FOV_POINTS_ONLY: True POINT_FEATURE_ENCODING: { encoding_type: absolute_coordinates_encoding, used_feature_list: ['x', 'y', 'z', 'intensity'], src_feature_list: ['x', 'y', 'z', 'intensity'], } # Same to pv_rcnn[DATA_AUGMENTOR] DATA_AUGMENTOR: DISABLE_AUG_LIST: ['placeholder'] AUG_CONFIG_LIST: - NAME: gt_sampling # Notice that 'USE_ROAD_PLANE' USE_ROAD_PLANE: False DB_INFO_PATH: - custom_dbinfos_train.pkl # pcdet/datasets/augmentor/database_ampler.py:line 26 PREPARE: { filter_by_min_points: ['pedestrian:5', 'stone:5'], # 2.修改类别 # filter_by_difficulty: [-1], # 注释掉，防止训练报错 } SAMPLE_GROUPS: ['pedestrian:15', 'stone:15'] # 3. 修改类别 NUM_POINT_FEATURES: 4 DATABASE_WITH_FAKELIDAR: False REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0] LIMIT_WHOLE_SCENE: True - NAME: random_world_flip ALONG_AXIS_LIST: ['x'] - NAME: random_world_rotation WORLD_ROT_ANGLE: [-0.78539816, 0.78539816] - NAME: random_world_scaling WORLD_SCALE_RANGE: [0.95, 1.05] DATA_PROCESSOR: - NAME: mask_points_and_boxes_outside_range REMOVE_OUTSIDE_BOXES: True - NAME: shuffle_points SHUFFLE_ENABLED: { 'train': True, 'test': False } - NAME: transform_points_to_voxels VOXEL_SIZE: [0.05, 0.05, 0.1] MAX_POINTS_PER_VOXEL: 5 MAX_NUMBER_OF_VOXELS: { 'train': 16000, 'test': 40000 }
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OpenPCDet/pcdet/datasets/kitti/custom_dataset.py
import copy import pickle import os import numpy as np from skimage import io from ...ops.roiaware_pool3d import roiaware_pool3d_utils from ...utils import box_utils, common_utils, object3d_custom from ..dataset import DatasetTemplate # 定义属于自己的数据集，集成数据集模板 class CustomDataset(DatasetTemplate): def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None, ext='.bin'): """ Args: root_path: dataset_cfg: class_names: training: logger: """ super().__init__( dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=root_path, logger=logger ) self.split = self.dataset_cfg.DATA_SPLIT[self.mode] self.root_split_path = os.path.join(self.root_path, ('training' if self.split != 'test' else 'testing')) split_dir = os.path.join(self.root_path, 'ImageSets',(self.split + '.txt')) self.sample_id_list = [x.strip() for x in open(split_dir).readlines()] if os.path.exists(split_dir) else None self.custom_infos = [] self.include_custom_data(self.mode) self.ext = ext # 用于导入自定义数据 def include_custom_data(self, mode): if self.logger is not None: self.logger.info('Loading Custom dataset.') custom_infos = [] for info_path in self.dataset_cfg.INFO_PATH[mode]: info_path = self.root_path / info_path if not info_path.exists(): continue with open(info_path, 'rb') as f: infos = pickle.load(f) custom_infos.extend(infos) self.custom_infos.extend(custom_infos) if self.logger is not None: self.logger.info('Total samples for CUSTOM dataset: %d' % (len(custom_infos))) # 用于获取标签的标注信息 def get_infos(self, num_workers=4, has_label=True, count_inside_pts=True, sample_id_list=None): import concurrent.futures as futures # 线程函数，主要是为了多线程读取数据，加快处理速度 # 处理一帧 def process_single_scene(sample_idx): print('%s sample_idx: %s' % (self.split, sample_idx)) # 创建一个用于存储一帧信息的空字典 info = {} # 定义该帧点云信息，pointcloud_info pc_info = {'num_features': 4, 'lidar_idx': sample_idx} # 将pc_info这个字典作为info字典里的一个键值对的值，其键名为‘point_cloud’添加到info里去 info['point_cloud'] = pc_info ''' # image信息和calib信息都暂时不需要 # image_info = {'image_idx': sample_idx, 'image_shape': self.get_image_shape(sample_idx)} # info['image'] = image_info # calib = self.get_calib(sample_idx) # P2 = np.concatenate([calib.P2, np.array([[0., 0., 0., 1.]])], axis=0) # R0_4x4 = np.zeros([4, 4], dtype=calib.R0.dtype) # R0_4x4[3, 3] = 1. # R0_4x4[:3, :3] = calib.R0 # V2C_4x4 = np.concatenate([calib.V2C, np.array([[0., 0., 0., 1.]])], axis=0) # calib_info = {'P2': P2, 'R0_rect': R0_4x4, 'Tr_velo_to_cam': V2C_4x4} # info['calib'] = calib_info ''' if has_label: # 通过get_label函数，读取出该帧的标签标注信息 obj_list = self.get_label(sample_idx) # 创建用于存储该帧标注信息的空字典 annotations = {} # 下方根据标注文件里的属性将对应的信息加入到annotations的键值对，可以根据自己的需求取舍 annotations['name'] = np.array([obj.cls_type for obj in obj_list]) # annotations['truncated'] = np.array([obj.truncation for obj in obj_list]) # annotations['occluded'] = np.array([obj.occlusion for obj in obj_list]) # annotations['alpha'] = np.array([obj.alpha for obj in obj_list]) # annotations['bbox'] = np.concatenate([obj.box2d.reshape(1, 4) for obj in obj_list], axis=0) annotations['dimensions'] = np.array([[obj.l, obj.h, obj.w] for obj in obj_list]) # lhw(camera) format annotations['location'] = np.concatenate([obj.loc.reshape(1, 3) for obj in obj_list], axis=0) annotations['rotation_y'] = np.array([obj.ry for obj in obj_list]) annotations['score'] = np.array([obj.score for obj in obj_list]) # annotations['difficulty'] = np.array([obj.level for obj in obj_list], np.int32) # 统计有效物体的个数，即去掉类别名称为“Dontcare”以外的 num_objects = len([obj.cls_type for obj in obj_list if obj.cls_type != 'DontCare']) # 统计物体的总个数，包括了Dontcare num_gt = len(annotations['name']) # 获得当前的index信息 index = list(range(num_objects)) + [-1] * (num_gt - num_objects) annotations['index'] = np.array(index, dtype=np.int32) # 从annotations里提取出从标注信息里获取的location、dims、rots等信息，赋值给对应的变量 loc = annotations['location'][:num_objects] dims = annotations['dimensions'][:num_objects] rots = annotations['rotation_y'][:num_objects] # 由于我们的数据集本来就是基于雷达坐标系标注，所以无需坐标转换 #loc_lidar = calib.rect_to_lidar(loc) loc_lidar = self.get_calib(loc) # 原来的dims排序是高宽长hwl,现在转到pcdet的统一坐标系下,按lhw排布 l, h, w = dims[:, 0:1], dims[:, 1:2], dims[:, 2:3] # 由于我们基于雷达坐标系标注，所以获取的中心点本来就是空间中心，所以无需从底面中心转到空间中心 # bottom center -> object center: no need for loc_lidar[:, 2] += h[:, 0] / 2 # print("sample_idx: ", sample_idx, "loc: ", loc, "loc_lidar: " , sample_idx, loc_lidar) # get gt_boxes_lidar see https://zhuanlan.zhihu.com/p/152120636 # loc_lidar[:, 2] += h[:, 0] / 2 gt_boxes_lidar = np.concatenate([loc_lidar, l, w, h, -(np.pi / 2 + rots[..., np.newaxis])], axis=1) # 将雷达坐标系下的真值框信息存入annotations中 annotations['gt_boxes_lidar'] = gt_boxes_lidar # 将annotations这整个字典作为info字典里的一个键值对的值 info['annos'] = annotations return info # 后续的由于没有calib信息和image信息，所以可以直接注释 ''' # if count_inside_pts: # points = self.get_lidar(sample_idx) # calib = self.get_calib(sample_idx) # pts_rect = calib.lidar_to_rect(points[:, 0:3]) # fov_flag = self.get_fov_flag(pts_rect, info['image']['image_shape'], calib) # pts_fov = points[fov_flag] # corners_lidar = box_utils.boxes_to_corners_3d(gt_boxes_lidar) # num_points_in_gt = -np.ones(num_gt, dtype=np.int32) # for k in range(num_objects): # flag = box_utils.in_hull(pts_fov[:, 0:3], corners_lidar[k]) # num_points_in_gt[k] = flag.sum() # annotations['num_points_in_gt'] = num_points_in_gt # return info ''' sample_id_list = sample_id_list if sample_id_list is not None else self.sample_id_list with futures.ThreadPoolExecutor(num_workers) as executor: infos = executor.map(process_single_scene, sample_id_list) return list(infos) # 此时返回值infos是列表，列表元素为字典类型 # 用于获取标定信息 def get_calib(self, loc): # calib_file = self.root_split_path / 'calib' / ('%s.txt' % idx) # assert calib_file.exists() # return calibration_kitti.Calibration(calib_file) # loc_lidar = np.concatenate([np.array((float(loc_obj[2]),float(-loc_obj[0]),float(loc_obj[1]-2.3)),dtype=np.float32).reshape(1,3) for loc_obj in loc]) # return loc_lidar # 这里做了一个由相机坐标系到雷达坐标系翻转（都遵从右手坐标系），但是 -2.3这个数值具体如何得来需要再看下 # 我们的label中的xyz就是在雷达坐标系下,不用转变,直接赋值 loc_lidar = np.concatenate([np.array((float(loc_obj[0]),float(loc_obj[1]),float(loc_obj[2])),dtype=np.float32).reshape(1,3) for loc_obj in loc]) return loc_lidar # 用于获取标签 def get_label(self, idx): # 从指定路径中提取txt内容 label_file = self.root_split_path / 'label_2' / ('%s.txt' % idx) assert label_file.exists() # 主要就是从这个函数里获取具体的信息 return object3d_custom.get_objects_from_label(label_file) # 用于获取雷达点云信息 def get_lidar(self, idx, getitem): """ Loads point clouds for a sample Args: index (int): Index of the point cloud file to get. Returns: np.array(N, 4): point cloud. """ # get lidar statistics if getitem == True: lidar_file = self.root_split_path + '/velodyne/' + ('%s.bin' % idx) else: lidar_file = self.root_split_path / 'velodyne' / ('%s.bin' % idx) return np.fromfile(str(lidar_file), dtype=np.float32).reshape(-1, 4) # 用于数据集划分 def set_split(self, split): super().__init__( dataset_cfg=self.dataset_cfg, class_names=self.class_names, training=self.training, root_path=self.root_path, logger=self.logger ) self.split = split self.root_split_path = self.root_path / ('training' if self.split != 'test' else 'testing') split_dir = self.root_path / 'ImageSets' / (self.split + '.txt') self.sample_id_list = [x.strip() for x in open(split_dir).readlines()] if split_dir.exists() else None # 创建真值数据库 # Create gt database for data augmentation def create_groundtruth_database(self, info_path=None, used_classes=None, split='train'): import torch database_save_path = Path(self.root_path) / ('gt_database' if split == 'train' else ('gt_database_%s' % split)) db_info_save_path = Path(self.root_path) / ('custom_dbinfos_%s.pkl' % split) database_save_path.mkdir(parents=True, exist_ok=True) all_db_infos = {} with open(info_path, 'rb') as f: infos = pickle.load(f) for k in range(len(infos)): print('gt_database sample: %d/%d' % (k + 1, len(infos))) info = infos[k] sample_idx = info['point_cloud']['lidar_idx'] points = self.get_lidar(sample_idx,False) annos = info['annos'] names = annos['name'] # difficulty = annos['difficulty'] # bbox = annos['bbox'] gt_boxes = annos['gt_boxes_lidar'] num_obj = gt_boxes.shape[0] point_indices = roiaware_pool3d_utils.points_in_boxes_cpu( torch.from_numpy(points[:, 0:3]), torch.from_numpy(gt_boxes) ).numpy() # (nboxes, npoints) for i in range(num_obj): filename = '%s_%s_%d.bin' % (sample_idx, names[i], i) filepath = database_save_path / filename gt_points = points[point_indices[i] > 0] gt_points[:, :3] -= gt_boxes[i, :3] with open(filepath, 'w') as f: gt_points.tofile(f) if (used_classes is None) or names[i] in used_classes: db_path = str(filepath.relative_to(self.root_path)) # gt_database/xxxxx.bin # db_info = {'name': names[i], 'path': db_path, 'image_idx': sample_idx, 'gt_idx': i, # 'box3d_lidar': gt_boxes[i], 'num_points_in_gt': gt_points.shape[0], # 'difficulty': difficulty[i], 'bbox': bbox[i], 'score': annos['score'][i]} db_info = {'name': names[i], 'path': db_path, 'gt_idx': i, 'box3d_lidar': gt_boxes[i], 'num_points_in_gt': gt_points.shape[0], 'score': annos['score'][i]} if names[i] in all_db_infos: all_db_infos[names[i]].append(db_info) else: all_db_infos[names[i]] = [db_info] for k, v in all_db_infos.items(): print('Database %s: %d' % (k, len(v))) with open(db_info_save_path, 'wb') as f: pickle.dump(all_db_infos, f) # 生成预测字典信息 @staticmethod def generate_prediction_dicts(batch_dict, pred_dicts, class_names, output_path=None): """ Args: batch_dict: frame_id: pred_dicts: list of pred_dicts pred_boxes: (N,7), Tensor pred_scores: (N), Tensor pred_lables: (N), Tensor class_names: output_path: Returns: """ def get_template_prediction(num_smaples): ret_dict = { 'name': np.zeros(num_smaples), 'alpha' : np.zeros(num_smaples), 'dimensions': np.zeros([num_smaples, 3]), 'location': np.zeros([num_smaples, 3]), 'rotation_y': np.zeros(num_smaples), 'score': np.zeros(num_smaples), 'boxes_lidar': np.zeros([num_smaples, 7]) } return ret_dict def generate_single_sample_dict(batch_index, box_dict): pred_scores = box_dict['pred_scores'].cpu().numpy() pred_boxes = box_dict['pred_boxes'].cpu().numpy() pred_labels = box_dict['pred_labels'].cpu().numpy() # Define an empty template dict to store the prediction information, 'pred_scores.shape[0]' means 'num_samples' pred_dict = get_template_prediction(pred_scores.shape[0]) # If num_samples equals zero then return the empty dict if pred_scores.shape[0] == 0: return pred_dict # No calibration files # pred_boxes_camera = box_utils.boxes3d_lidar_to_kitti_camera(pred_boxes,None) pred_dict['name'] = np.array(class_names)[pred_labels - 1] # pred_dict['alpha'] = -np.arctan2(-pred_boxes[:, 1], pred_boxes[:, 0]) + pred_boxes_camera[:, 6] # pred_dict['dimensions'] = pred_boxes_camera[:, 3:6] # pred_dict['location'] = pred_boxes_camera[:, 0:3] # pred_dict['rotation_y'] = pred_boxes_camera[:, 6] pred_dict['score'] = pred_scores pred_dict['boxes_lidar'] = pred_boxes return pred_dict annos = [] for index, box_dict in enumerate(pred_dicts): frame_id = batch_dict['frame_id'][index] single_pred_dict = generate_single_sample_dict(index, box_dict) single_pred_dict['frame_id'] = frame_id annos.append(single_pred_dict) # Output pred results to Output-path in .txt file if output_path is not None: cur_det_file = output_path / ('%s.txt' % frame_id) with open(cur_det_file, 'w') as f: bbox = single_pred_dict['bbox'] loc = single_pred_dict['location'] dims = single_pred_dict['dimensions'] # lhw -> hwl: lidar -> camera for idx in range(len(bbox)): print('%s -1 -1 %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f' % (single_pred_dict['name'][idx], single_pred_dict['alpha'][idx], bbox[idx][0], bbox[idx][1], bbox[idx][2], bbox[idx][3], dims[idx][1], dims[idx][2], dims[idx][0], loc[idx][0], loc[idx][1], loc[idx][2], single_pred_dict['rotation_y'][idx], single_pred_dict['score'][idx]), file=f) return annos def evaluation(self, det_annos, class_names, **kwargs): if 'annos' not in self.custom_infos[0].keys(): return None, {} from .kitti_object_eval_python import eval as kitti_eval eval_det_annos = copy.deepcopy(det_annos) eval_gt_annos = [copy.deepcopy(info['annos']) for info in self.kitti_infos] ap_result_str, ap_dict = kitti_eval.get_official_eval_result(eval_gt_annos, eval_det_annos, class_names) return ap_result_str, ap_dict # 用于返回训练帧的总个数 def __len__(self): if self._merge_all_iters_to_one_epoch: return len(self.sample_id_list) * self.total_epochs return len(self.custom_infos) # 用于将点云与3D标注框均转至前述统一坐标定义下，送入数据基类提供的self.prepare_data() def __getitem__(self, index): ## 修改如下 if self._merge_all_iters_to_one_epoch: index = index % len(self.custom_infos) info = copy.deepcopy(self.custom_infos[index]) sample_idx = info['point_cloud']['lidar_idx'] points = self.get_lidar(sample_idx, True) input_dict = { 'frame_id': self.sample_id_list[index], 'points': points } if 'annos' in info: annos = info['annos'] annos = common_utils.drop_info_with_name(annos, name='DontCare') gt_names = annos['name'] gt_boxes_lidar = annos['gt_boxes_lidar'] input_dict.update({ 'gt_names': gt_names, 'gt_boxes': gt_boxes_lidar }) data_dict = self.prepare_data(data_dict=input_dict) return data_dict # 用于创建自定义数据集的信息 def create_custom_infos(dataset_cfg, class_names, data_path, save_path, workers=4): dataset = CustomDataset(dataset_cfg=dataset_cfg, class_names=class_names, root_path=data_path, training=False) train_split, val_split = 'train', 'val' # 定义文件的路径和名称 train_filename = save_path / ('custom_infos_%s.pkl' % train_split) val_filename = save_path / ('custom_infos_%s.pkl' % val_split) trainval_filename = save_path / 'custom_infos_trainval.pkl' test_filename = save_path / 'custom_infos_test.pkl' print('---------------Start to generate data infos---------------') dataset.set_split(train_split) # 执行完上一步，得到train相关的保存文件，以及sample_id_list的值为train.txt文件下的数字 # 下面是得到train.txt中序列相关的所有点云数据的信息，并且进行保存 custom_infos_train = dataset.get_infos(num_workers=workers, has_label=True, count_inside_pts=True) with open(train_filename, 'wb') as f: pickle.dump(custom_infos_train, f) print('Custom info train file is saved to %s' % train_filename) dataset.set_split(val_split) # 对验证集的数据进行信息统计并保存 custom_infos_val = dataset.get_infos(num_workers=workers, has_label=True, count_inside_pts=True) with open(val_filename, 'wb') as f: pickle.dump(custom_infos_val, f) print('Custom info val file is saved to %s' % val_filename) with open(trainval_filename, 'wb') as f: pickle.dump(custom_infos_train + custom_infos_val, f) print('Custom info trainval file is saved to %s' % trainval_filename) dataset.set_split('test') # kitti_infos_test = dataset.get_infos(num_workers=workers, has_label=False, count_inside_pts=False) custom_infos_test = dataset.get_infos(num_workers=workers, has_label=False, count_inside_pts=False) with open(test_filename, 'wb') as f: pickle.dump(custom_infos_test, f) print('Custom info test file is saved to %s' % test_filename) print('---------------Start create groundtruth database for data augmentation---------------') # 用trainfile产生groundtruth_database # 只保存训练数据中的gt_box及其包围点的信息，用于数据增强 dataset.set_split(train_split) dataset.create_groundtruth_database(info_path=train_filename, split=train_split) print('---------------Data preparation Done---------------') if __name__=='__main__': import sys if sys.argv.__len__() > 1 and sys.argv[1] == 'create_custom_infos': import yaml from pathlib import Path from easydict import EasyDict dataset_cfg = EasyDict(yaml.safe_load(open(sys.argv[2]))) ROOT_DIR = (Path(__file__).resolve().parent / '../../../').resolve() create_custom_infos( dataset_cfg=dataset_cfg, class_names=['pedestrian', 'stone'], # 1.修改类别 data_path=ROOT_DIR / 'data' / 'custom', save_path=ROOT_DIR / 'data' / 'custom' )
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注：源码中已存在custom的相关文件，因为数据标注格式以kitti为标准，所以笔者是基于kitti文件的格式进行修改
生成标注数据指令
python -m pcdet.datasets.kitti.custom_dataset create_custom_infos tools/cfgs/dataset_configs/kitti_custom_dataset.yaml
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2 模型训练
笔者选用模型为potinpillar，其他模型以此类推
修改文件如下：
OpenPCDet/tools/cfgs/kitti_models/pointpillar.yaml
# CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist'] # 修改类别 CLASS_NAMES: ['pedestrian', 'stone'] DATA_CONFIG: _BASE_CONFIG_: /media/ll/L/llr/a2023_my_3d/OpenPCDet/tools/cfgs/dataset_configs/alian_dataset.yaml POINT_CLOUD_RANGE: [0, -39.68, -3, 69.12, 39.68, 1] DATA_PROCESSOR: - NAME: mask_points_and_boxes_outside_range REMOVE_OUTSIDE_BOXES: True - NAME: shuffle_points SHUFFLE_ENABLED: { 'train': True, 'test': False } - NAME: transform_points_to_voxels VOXEL_SIZE: [0.16, 0.16, 4] MAX_POINTS_PER_VOXEL: 32 MAX_NUMBER_OF_VOXELS: { 'train': 16000, 'test': 40000 } DATA_AUGMENTOR: DISABLE_AUG_LIST: ['placeholder'] AUG_CONFIG_LIST: - NAME: gt_sampling USE_ROAD_PLANE: True DB_INFO_PATH: - kitti_dbinfos_train.pkl PREPARE: { filter_by_min_points: ['pedestrian:5', 'stone:5'], filter_by_difficulty: [-1], } SAMPLE_GROUPS: ['pedestrian:5', 'stone:5'] NUM_POINT_FEATURES: 4 DATABASE_WITH_FAKELIDAR: False REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0] LIMIT_WHOLE_SCENE: False - NAME: random_world_flip ALONG_AXIS_LIST: ['x'] - NAME: random_world_rotation WORLD_ROT_ANGLE: [-0.78539816, 0.78539816] - NAME: random_world_scaling WORLD_SCALE_RANGE: [0.95, 1.05] MODEL: NAME: PointPillar VFE: NAME: PillarVFE WITH_DISTANCE: False USE_ABSLOTE_XYZ: True USE_NORM: True NUM_FILTERS: [64] MAP_TO_BEV: NAME: PointPillarScatter NUM_BEV_FEATURES: 64 BACKBONE_2D: NAME: BaseBEVBackbone LAYER_NUMS: [3, 5, 5] LAYER_STRIDES: [2, 2, 2] NUM_FILTERS: [64, 128, 256] UPSAMPLE_STRIDES: [1, 2, 4] NUM_UPSAMPLE_FILTERS: [128, 128, 128] DENSE_HEAD: NAME: AnchorHeadSingle CLASS_AGNOSTIC: False USE_DIRECTION_CLASSIFIER: True DIR_OFFSET: 0.78539 DIR_LIMIT_OFFSET: 0.0 NUM_DIR_BINS: 2 # anchor配置，需要适配自己的数据集 ANCHOR_GENERATOR_CONFIG: [ # { # 'class_name': 'Car', # 'anchor_sizes': [[3.9, 1.6, 1.56]], # 'anchor_rotations': [0, 1.57], # 'anchor_bottom_heights': [-1.78], # 'align_center': False, # 'feature_map_stride': 2, # 'matched_threshold': 0.6, # 'unmatched_threshold': 0.45 # }, { 'class_name': 'pedestrian', 'anchor_sizes': [[0.8, 0.6, 1.9]], 'anchor_rotations': [0, 1.57], 'anchor_bottom_heights': [-0.6], 'align_center': False, 'feature_map_stride': 2, 'matched_threshold': 0.5, 'unmatched_threshold': 0.35 }, { 'class_name': 'stone', 'anchor_sizes': [[1.0, 1.0, 0.73]], 'anchor_rotations': [0, 1.57], 'anchor_bottom_heights': [-0.6], 'align_center': False, 'feature_map_stride': 2, 'matched_threshold': 0.5, 'unmatched_threshold': 0.35 } ] TARGET_ASSIGNER_CONFIG: NAME: AxisAlignedTargetAssigner POS_FRACTION: -1.0 SAMPLE_SIZE: 512 NORM_BY_NUM_EXAMPLES: False MATCH_HEIGHT: False BOX_CODER: ResidualCoder LOSS_CONFIG: LOSS_WEIGHTS: { 'cls_weight': 1.0, 'loc_weight': 2.0, 'dir_weight': 0.2, 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] } POST_PROCESSING: RECALL_THRESH_LIST: [0.3, 0.5, 0.7] SCORE_THRESH: 0.1 OUTPUT_RAW_SCORE: False EVAL_METRIC: kitti NMS_CONFIG: MULTI_CLASSES_NMS: False NMS_TYPE: nms_gpu NMS_THRESH: 0.01 NMS_PRE_MAXSIZE: 4096 NMS_POST_MAXSIZE: 500 OPTIMIZATION: BATCH_SIZE_PER_GPU: 4 NUM_EPOCHS: 80 OPTIMIZER: adam_onecycle LR: 0.003 WEIGHT_DECAY: 0.01 MOMENTUM: 0.9 MOMS: [0.95, 0.85] PCT_START: 0.4 DIV_FACTOR: 10 DECAY_STEP_LIST: [35, 45] LR_DECAY: 0.1 LR_CLIP: 0.0000001 LR_WARMUP: False WARMUP_EPOCH: 1 GRAD_NORM_CLIP: 10
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训练指令：
python tools/train.py --cfg_file tools/cfgs/kitti_models/pointpillar.yaml --batch_size=2 --epochs=300
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成功训练如下：
3 自定义模型测试
测试demo代码：
python tools/demo.py --cfg_file /media/ll/L/llr/a2023_my_3d/OpenPCDet/tools/cfgs/kitti_models/pointpillar.yaml --data_path /media/ll/L/llr/a2023_my_3d/OpenPCDet/data/custom/testing/velodyne/ --ckpt /media/ll/L/llr/a2023_my_3d/OpenPCDet/output/cfgs/kitti_models/pointpillar/default/ckpt/checkpoint_epoch_300.pth
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参考链接：
1.OpenPCDet 训练自己的数据集详细教程！
2.基于OpenPCDet实现自定义数据集的训练
3.OpenPCDet安装、使用方式及自定义数据集训练
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