OpenPCDet 训练自己的数据集详细教程！(opencv制作训练数据集)

OpenPCDet 训练自己的数据集详细教程！ 文章目录前言一、pcd转bin二、labelCloud 工具安装与使用三、训练仿写代码对pcdet/datasets/custom/custom_dataset.py进行改写新建tools/cfgs/dataset_configs/custom_dataset.yaml并修改新建tools/cfgs/custom_models/pointrcnn.yaml并修改其他调整事项数据集预处理数据集训练可视化测试获取尺寸四、总结前言

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这些天一直在尝试通过OpenPCDet平台训练自己的数据集（非kitti格式），好在最后终于跑通了，特此记录一下训练过程。

一、pcd转bin

笔者自己的点云数据是pcd格式的，参照kitti训练过程是需要转成bin格式的。 下面给出转换代码：

# -*- coding: utf-8 -*- # @Time : 2022/7/25 11:30 # @Author : JulyLi# @File : pcd2bin.pyimport numpy as npimport osimport argparsefrom pypcd import pypcdimport csvfrom tqdm import tqdmdef main(): ## Add parser parser = argparse.ArgumentParser(description="Convert .pcd to .bin") parser.add_argument( "--pcd_path", help=".pcd file path.", type=str, default="pcd_raw1" ) parser.add_argument( "--bin_path", help=".bin file path.", type=str, default="bin" ) parser.add_argument( "--file_name", help="File name.", type=str, default="file_name" ) args = parser.parse_args() ## Find all pcd files pcd_files = [] for (path, dir, files) in os.walk(args.pcd_path): for filename in files: # print(filename) ext = os.path.splitext(filename)[-1] if ext == '.pcd': pcd_files.append(path + "/" + filename) ## Sort pcd files by file name pcd_files.sort() print("Finish to load point clouds!") ## Make bin_path directory try: if not (os.path.isdir(args.bin_path)): os.makedirs(os.path.join(args.bin_path)) except OSError as e: # if e.errno != errno.EEXIST: # print("Failed to create directory!!!!!") raise ## Generate csv meta file csv_file_path = os.path.join(args.bin_path, "meta.csv") csv_file = open(csv_file_path, "w") meta_file = csv.writer( csv_file, delimiter=",", quotechar="|", quoting=csv.QUOTE_MINIMAL ) ## Write csv meta file header meta_file.writerow( [ "pcd file name", "bin file name", ] ) print("Finish to generate csv meta file") ## Converting Process print("Converting Start!") seq = 0 for pcd_file in tqdm(pcd_files): ## Get pcd file pc = pypcd.PointCloud.from_path(pcd_file) ## Generate bin file name # bin_file_name = "{}_{:05d}.bin".format(args.file_name, seq) bin_file_name = "{:05d}.bin".format(seq) bin_file_path = os.path.join(args.bin_path, bin_file_name) ## Get data from pcd (x, y, z, intensity, ring, time) np_x = (np.array(pc.pc_data['x'], dtype=np.float32)).astype(np.float32) np_y = (np.array(pc.pc_data['y'], dtype=np.float32)).astype(np.float32) np_z = (np.array(pc.pc_data['z'], dtype=np.float32)).astype(np.float32) np_i = (np.array(pc.pc_data['intensity'], dtype=np.float32)).astype(np.float32) / 256 # np_r = (np.array(pc.pc_data['ring'], dtype=np.float32)).astype(np.float32) # np_t = (np.array(pc.pc_data['time'], dtype=np.float32)).astype(np.float32) ## Stack all data points_32 = np.transpose(np.vstack((np_x, np_y, np_z, np_i))) ## Save bin file points_32.tofile(bin_file_path) ## Write csv meta file meta_file.writerow( [os.path.split(pcd_file)[-1], bin_file_name] ) seq = seq + 1if __name__ == "__main__": main()二、labelCloud 工具安装与使用

拉取源码

git clone https://github.com/ch-sa/labelCloud.git

安装依赖

pip install -r requirements.txt

启动程序

python labelCloud.py

启动后出现如下界面： 在setting界面按需设置，笔者这里按kitti格式生成label数据： 标注完成后会在对应目录下生成标签： 标签内容大致如下：

三、训练仿写代码

把pcdet/datasets/kitti文件夹复制并改名为pcdet/datasets/custom，然后把pcdet/utils/object3d_kitti.py复制为pcdet/utils/object3d_custom.py 把data/kitti文件夹复制并改名为data/custom，然后修改训练信息，结构如下：

custom├── ImageSets│ ├── test.txt│ ├── train.txt├── testing│ ├── velodyne├── training│ ├── label_2│ ├── velodyne对pcdet/datasets/custom/custom_dataset.py进行改写import copyimport pickleimport osimport numpy as npfrom skimage import iofrom . import custom_utilsfrom ...ops.roiaware_pool3d import roiaware_pool3d_utilsfrom ...utils import box_utils, common_utils, object3d_customfrom ..dataset import DatasetTemplateclass 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 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))) def get_infos(self, num_workers=16, has_label=True, count_inside_pts=True, sample_id_list=None): import concurrent.futures as futures # Process single scene def process_single_scene(sample_idx): print('%s sample_idx: %s' % (self.split, sample_idx)) # define an empty dict info = {} # pts infos: dimention and idx pc_info = {'num_features': 4, 'lidar_idx': sample_idx} # add to pts infos info['point_cloud'] = pc_info # no images, calibs are need to transform the labels type_to_id = {'Car': 1, 'Pedestrian': 2, 'Cyclist': 3} if has_label: # read labels to build object list according to idx obj_list = self.get_label(sample_idx) # build an empty annotations dict annotations = {} # add to annotations ==> refer to 'object3d_custom' (no truncated,occluded,alpha,bbox) annotations['name'] = np.array([obj.cls_type for obj in obj_list]) # 1-dimension # hwl(camera) format 2-dimension: The kitti-labels are in camera-coord # h,w,l -> 0.21,0.22,0.33 (see object3d_custom.py h=label[8], w=label[9], l=label[10]) annotations['dimensions'] = np.array([[obj.l, obj.h, obj.w] for obj in obj_list]) annotations['location'] = np.concatenate([obj.loc.reshape(1,3) for obj in obj_list]) annotations['rotation_y'] = np.array([obj.ry for obj in obj_list]) # 1-dimension num_objects = len([obj.cls_type for obj in obj_list if obj.cls_type != 'DontCare']) num_gt = len(annotations['name']) index = list(range(num_objects)) + [-1] * (num_gt - num_objects) annotations['index'] = np.array(index, dtype=np.int32) loc = annotations['location'][:num_objects] dims = annotations['dimensions'][:num_objects] rots = annotations['rotation_y'][:num_objects] # camera -> lidar: The points of custom_dataset are already in lidar-coord # But the labels are in camera-coord and need to transform loc_lidar = self.get_calib(loc) l, h, w = dims[:, 0:1], dims[:, 1:2], dims[:, 2:3] # 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 gt_boxes_lidar = np.concatenate([loc_lidar, l, w, h, (np.pi / 2 - rots[..., np.newaxis])], axis=1) # 2-dimension array annotations['gt_boxes_lidar'] = gt_boxes_lidar # add annotation info info['annos'] = annotations return info sample_id_list = sample_id_list if sample_id_list is not None else self.sample_id_list # create a thread pool to improve the velocity with futures.ThreadPoolExecutor(num_workers) as executor: infos = executor.map(process_single_scene, sample_id_list) # infos is a list that each element represents per frame return list(infos) def get_calib(self, loc): """ This calibration is different from the kitti dataset. The transform formual of labelCloud: ROOT/labelCloud/io/labels/kitti.py: import labels if self.transformed: centroid = centroid[2], -centroid[0], centroid[1] - 2.3 dimensions = [float(v) for v in line_elements[8:11]] if self.transformed: dimensions = dimensions[2], dimensions[1], dimensions[0] bbox = BBox(*centroid, *dimensions) """ 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 def get_label(self, idx): # get labels label_file = self.root_split_path / 'label_2' / ('%s.txt' % idx) assert label_file.exists() return object3d_custom.get_objects_from_label(label_file) 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) 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 # Create gt database for data augmentation def create_groundtruth_database(self, info_path=None, used_classes=None, split='train'): import torch # Specify the direction 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 = {} # Open 'custom_train_info.pkl' with open(info_path, 'rb') as f: infos = pickle.load(f) # For each .bin file for k in range(len(infos)): print('gt_database sample: %d/%d' % (k + 1, len(infos))) # Get current scene info info = infos[k] sample_idx = info['point_cloud']['lidar_idx'] points = self.get_lidar(sample_idx, False) annos = info['annos'] names = annos['name'] 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, 'gt_idx': i, 'box3d_lidar': gt_boxes[i], 'num_points_in_gt': gt_points.shape[0]} if names[i] in all_db_infos: all_db_infos[names[i]].append(db_info) else: all_db_infos[names[i]] = [db_info] # Output the num of all classes in database 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) @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.zero(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] 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 __len__(self): if self._merge_all_iters_to_one_epoch: return len(self.sample_id_list) * self.total_epochs return len(self.custom_infos) def __getitem__(self, index): """ Function: Read 'velodyne' folder as pointclouds Read 'label_2' folder as labels Return type 'dict' """ 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'] get_item_list = self.dataset_cfg.get('GET_ITEM_LIST', ['points']) input_dict = { 'frame_id': self.sample_id_list[index], } """ Here infos was generated by get_infos """ if 'annos' in info: annos = info['annos'] annos = common_utils.drop_info_with_name(annos, name='DontCare') loc, dims, rots = annos['location'], annos['dimensions'], annos['rotation_y'] gt_names = annos['name'] gt_boxes_lidar = annos['gt_boxes_lidar'] if 'points' in get_item_list: points = self.get_lidar(sample_idx, True) # import time # print(points.shape) # if points.shape[0] == 0: # print("**********************************") # print("sample_idx: ", sample_idx) # time.sleep(999999) # print("**********************************") # 000099, 000009 input_dict['points'] = points input_dict.update({ 'gt_names': gt_names, 'gt_boxes': gt_boxes_lidar }) data_dict = self.prepare_data(data_dict=input_dict) return data_dictdef 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' # No evaluation train_filename = save_path / ('custom_infos_%s.pkl' % train_split) val_filenmae = 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) 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 save to %s' % train_filename) dataset.set_split('test') 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------------------------') dataset.set_split(train_split) # Input the 'custom_train_info.pkl' to generate gt_database dataset.create_groundtruth_database(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=['Car', 'Pedestrian', 'Cyclist'], data_path=ROOT_DIR / 'data' / 'custom', save_path=ROOT_DIR / 'data' / 'custom' )新建tools/cfgs/dataset_configs/custom_dataset.yaml并修改DATASET: 'CustomDataset'DATA_PATH: '../data/custom'# 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]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: TruePOINT_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: ['Car:5', 'Pedestrian:5', 'Cyclist:5'], filter_by_difficulty: [-1], } SAMPLE_GROUPS: ['Car:20','Pedestrian:15', 'Cyclist:15'] 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 }新建tools/cfgs/custom_models/pointrcnn.yaml并修改CLASS_NAMES: ['Car']# CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist']DATA_CONFIG: _BASE_CONFIG_: /home/zonlin/CRLFnet/src/site_model/src/LidCamFusion/OpenPCDet/tools/cfgs/dataset_configs/custom_dataset.yaml _BASE_CONFIG_RT_: /home/zonlin/CRLFnet/src/site_model/src/LidCamFusion/OpenPCDet/tools/cfgs/dataset_configs/custom_dataset.yaml DATA_PROCESSOR: - NAME: mask_points_and_boxes_outside_range REMOVE_OUTSIDE_BOXES: True - NAME: sample_points NUM_POINTS: { 'train': 16384, 'test': 16384 } - NAME: shuffle_points SHUFFLE_ENABLED: { 'train': True, 'test': False }MODEL: NAME: PointRCNN BACKBONE_3D: NAME: PointNet2MSG SA_CONFIG: NPOINTS: [4096, 1024, 256, 64] RADIUS: [[0.1, 0.5], [0.5, 1.0], [1.0, 2.0], [2.0, 4.0]] NSAMPLE: [[16, 32], [16, 32], [16, 32], [16, 32]] MLPS: [[[16, 16, 32], [32, 32, 64]], [[64, 64, 128], [64, 96, 128]], [[128, 196, 256], [128, 196, 256]], [[256, 256, 512], [256, 384, 512]]] FP_MLPS: [[128, 128], [256, 256], [512, 512], [512, 512]] POINT_HEAD: NAME: PointHeadBox CLS_FC: [256, 256] REG_FC: [256, 256] CLASS_AGNOSTIC: False USE_POINT_FEATURES_BEFORE_FUSION: False TARGET_CONFIG: GT_EXTRA_WIDTH: [0.2, 0.2, 0.2] BOX_CODER: PointResidualCoder BOX_CODER_CONFIG: { 'use_mean_size': True, 'mean_size': [ [3.9, 1.6, 1.56], [0.8, 0.6, 1.73], [1.76, 0.6, 1.73] ] } LOSS_CONFIG: LOSS_REG: WeightedSmoothL1Loss LOSS_WEIGHTS: { 'point_cls_weight': 1.0, 'point_box_weight': 1.0, 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] } ROI_HEAD: NAME: PointRCNNHead CLASS_AGNOSTIC: True ROI_POINT_POOL: POOL_EXTRA_WIDTH: [0.0, 0.0, 0.0] NUM_SAMPLED_POINTS: 512 DEPTH_NORMALIZER: 70.0 XYZ_UP_LAYER: [128, 128] CLS_FC: [256, 256] REG_FC: [256, 256] DP_RATIO: 0.0 USE_BN: False SA_CONFIG: NPOINTS: [128, 32, -1] RADIUS: [0.2, 0.4, 100] NSAMPLE: [16, 16, 16] MLPS: [[128, 128, 128], [128, 128, 256], [256, 256, 512]] NMS_CONFIG: TRAIN: NMS_TYPE: nms_gpu MULTI_CLASSES_NMS: False NMS_PRE_MAXSIZE: 9000 NMS_POST_MAXSIZE: 512 NMS_THRESH: 0.8 TEST: NMS_TYPE: nms_gpu MULTI_CLASSES_NMS: False NMS_PRE_MAXSIZE: 9000 NMS_POST_MAXSIZE: 100 NMS_THRESH: 0.85 TARGET_CONFIG: BOX_CODER: ResidualCoder ROI_PER_IMAGE: 128 FG_RATIO: 0.5 SAMPLE_ROI_BY_EACH_CLASS: True CLS_SCORE_TYPE: cls CLS_FG_THRESH: 0.6 CLS_BG_THRESH: 0.45 CLS_BG_THRESH_LO: 0.1 HARD_BG_RATIO: 0.8 REG_FG_THRESH: 0.55 LOSS_CONFIG: CLS_LOSS: BinaryCrossEntropy REG_LOSS: smooth-l1 CORNER_LOSS_REGULARIZATION: True LOSS_WEIGHTS: { 'rcnn_cls_weight': 1.0, 'rcnn_reg_weight': 1.0, 'rcnn_corner_weight': 1.0, '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: 0.1 NMS_PRE_MAXSIZE: 4096 NMS_POST_MAXSIZE: 500OPTIMIZATION: BATCH_SIZE_PER_GPU: 2 NUM_EPOCHS: 80 OPTIMIZER: adam_onecycle LR: 0.01 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其他调整事项

需要对以上文件中的类别信息，数据集路径，点云范围POINT_CLOUD_RANGE进行更改 在 pcdet/datasets/init.py文件，增加以下代码

from .custom.custom_dataset import CustomDataset# 在__all__ = 中增加'CustomDataset': CustomDataset

完成以上就可以开始对数据集进行预处理和训练了

数据集预处理python -m pcdet.datasets.custom.custom_dataset create_custom_infos tools/cfgs/dataset_configs/custom_dataset.yaml

同时在gt_database文件夹下生成的.bin文件，data/custom文件夹结构变为如下：

custom├── ImageSets│ ├── test.txt│ ├── train.txt├── testing│ ├── velodyne├── training│ ├── label_2│ ├── velodyne├── gt_database│ ├── xxxxx.bin├── custom_infos_train.pkl├── custom_infos_val.pkl├── custom_dbinfos_train.pkl数据集训练python tools/train.py --cfg_file tools/cfgs/custom_models/pointrcnn.yaml --batch_size=2 --epochs=300

可视化测试

cd到tools文件夹下，运行：

python demo.py --cfg_file cfgs/custom_models/pointrcnn.yaml --data_path ../data/custom/testinging/velodyne/ --ckpt ../output/custom_models/pointrcnn/default/ckpt/checkpoint_epoch_300.pth

此处根据自己的文件路径进行修改，推理效果如下（笔者标注50多张闸口船舶的点云数据）： 看起来效果还是挺不错。

获取尺寸

OpenPCDet平台下根据kitti格式推理得到的bbox的dx、dy、dz就是约等于实际的物体的尺寸。

对于我们的点云数据而言，上述数据对应船的高宽长。（这里不理解的可以去看下OpenPCDet的坐标定义）

四、总结

至此，基于OpenPCDet平台的自定义数据集的训练基本完成了，这里要特别感谢下树和猫，对于自定义数据集的训练我们交流了很多，之前他是通过我写的yolov5系列文章关注的我，现在我通过OpenPCDet 训练自己的数据集系列关注了他，着实让我感觉到了技术分享是一个圈。

参考文档： https://blog.csdn.net/m0_68312479/article/details/126201450 https://blog.csdn.net/jin15203846657/article/details/122949271 https://blog.csdn.net/hihui1231/article/details/124903276 https://github.com/OrangeSodahub/CRLFnet/tree/master/src/site_model/src/LidCamFusion/OpenPCDet https://blog.csdn.net/weixin_43464623/article/details/116718451

如果阅读本文对你有用，欢迎一键三连呀！！！ 2022年10月24日11:12:53