OpenPCDet 训练自己的数据集详细教程！

1024程序员节 python 点云 3d目标检测人工智能 2023-09-05 15:09:31 475人浏览薄情痞子

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摘要

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/

文章目录

前言
一、pcd转bin
二、labelCloud 工具安装与使用
三、训练
四、总结

前言

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：
在这里插入图片描述
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

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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

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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

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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