Openpcdet训练自己的数据集

一. Openpcdet的安装以及使用

* Openpcdet详细内容请看以下链接：

GitHub - open-mmlab/OpenPCDet: OpenPCDet Toolbox for LiDAR-based 3D Object Detection.

1.首先gitclone原文代码

2. 这里我建议自己按照作者github上的docs/install文件夹下指示一步步安装，(之前根据csdn上教程一直有报错)，然后下载spconv，以及cumm， github链接如下:

GitHub - traveller59/spconv: Spatial Sparse Convolution Library

GitHub - FindDefinition/cumm: CUda Matrix Multiply library.

3. 打开spconv中的readme，并且严格按照readme步骤安装，一般需要编译一段时间。

4. 打开cumm中readme，严格按照上面指示安装。

5. 安装完成之后按照测试数据跑通检验一下。

二. Openpcdet训练自己的数据集

* 本人移植其他的数据集，由于我有自己的image数据，已经按照kitti数据集的格式转换为velodyne, calib, label, image四个文件，并且实现了评估，以及最终的检测结果，所以可能和其他博主不一样。

* 如果你只有velodyne，label，或者数据集格式还不知道如何转换，文件建议参考以下这几个博主的链接：

Training using our own dataset · Issue #771 · open-mmlab/OpenPCDet · GitHub

 OpenPCDet 训练自己的数据集详细教程！_JulyLi2019的博客-CSDN博客_openpcdet 数据集

3D目标检测（4）：OpenPCDet训练篇--自定义数据集 - 知乎

Openpcdet-（2）自数据集训练数据集训练_花花花哇_的博客-CSDN博客

win10 OpenPCDet 训练KITTI以及自己的数据集_树和猫的博客-CSDN博客_openpcdet训练

这里首先总结以下主要涉及到以下三个文件的修改

* pcdet/datasets/custom/custom_dataset.py

* tools/cfgs/custom_models/pointpillar.yaml (也可以是其他模型)

* tools/cfgs/dataset_configs/custom_dataset.yaml

* demo.py

1.pcdet/datasets/custom/custom_dataset.py

其实custom_dataset.py只需要大家去模仿kitti_dataset.py去删改就可以了，而且大部分内容不需要用户修改，这里我修改了：

1）get_lidar函数

* 获取激光雷达数据，其他的get_image也类似

2） __getitem__函数

* 这个函数最重要，是获取数据字典并更新的关键

* 如果有些字典不需要可以删改，如calib，image等

3）get_infos函数

* 生成字典信息infos

infos={'image':xxx,

           'calib': xxx,

           'annos': xxx}

annos = {'name': xxx,

                'truncated': xxx,

                'alpha':xxx,

                .............}

其中annos就是解析你的label文件生成的字典, 如类别名，是否被遮挡，bbox的角度

同理有些字典信息不需要可以增删

3) create_custom_infos函数

这个函数主要用来生成你的数据字典，一般以.pkl后缀，如果你不需要评估，可以将其中的评估部分删除，原理也很简单。

4) main函数中的类别信息

修改后的代码如下：

import copy
import pickle
import os
from skimage import io
import numpy as np

from ..kitti import kitti_utils
from ...ops.roiaware_pool3d import roiaware_pool3d_utils
from ...utils import box_utils, common_utils, calibration_kitti, object3d_custom
from ..dataset import DatasetTemplate


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 = self.root_path / ('training' if self.split != 'test' else 'testing')

        split_dir = os.path.join(self.root_path, 'ImageSets', (self.split + '.txt'))    # custom/ImagSets/xxx.txt
        self.sample_id_list = [x.strip() for x in open(split_dir).readlines()] if os.path.exists(split_dir) else None   # xxx.txt内的内容

        self.custom_infos = []
        self.include_data(self.mode)            # train/val
        self.map_class_to_kitti = self.dataset_cfg.MAP_CLASS_TO_KITTI
        self.ext = ext


    def include_data(self, mode):
        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)

    def get_label(self, idx):
        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=True):
        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 get_image(self, idx):
        """
        Loads image for a sample
        Args:
            idx: int, Sample index
        Returns:
            image: (H, W, 3), RGB Image
        """
        img_file = self.root_split_path / 'image_2' / ('%s.png' % idx)
        assert img_file.exists()
        image = io.imread(img_file)
        image = image.astype(np.float32)
        image /= 255.0
        return image

    def get_image_shape(self, idx):
        img_file = self.root_split_path / 'image_2' / ('%s.png' % idx)
        assert img_file.exists()
        return np.array(io.imread(img_file).shape[:2], dtype=np.int32)

    def get_fov_flag(self, pts_rect, img_shape, calib):
        """
        Args:
            pts_rect:
            img_shape:
            calib:

        Returns:

        """
        pts_img, pts_rect_depth = calib.rect_to_img(pts_rect)
        val_flag_1 = np.logical_and(pts_img[:, 0] >= 0, pts_img[:, 0] < img_shape[1])
        val_flag_2 = np.logical_and(pts_img[:, 1] >= 0, pts_img[:, 1] < img_shape[0])
        val_flag_merge = np.logical_and(val_flag_1, val_flag_2)
        pts_valid_flag = np.logical_and(val_flag_merge, pts_rect_depth >= 0)

        return pts_valid_flag

    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

        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
                custom_infos.extend(infos)

        self.custom_infos.extend(custom_infos)
        self.logger.info('Total samples for CUSTOM dataset: %d' % (len(custom_infos)))


    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):
        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']
        img_shape = info['image']['image_shape']
        calib = self.get_calib(sample_idx)
        get_item_list = self.dataset_cfg.get('GET_ITEM_LIST', ['points'])
        input_dict = {
            'frame_id': self.sample_id_list[index],
            'calib': calib,
        }

        # 如果annos标签存在info的字典里
        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_camera = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1).astype(np.float32)
            gt_boxes_lidar = box_utils.boxes3d_kitti_camera_to_lidar(gt_boxes_camera, calib)

            # 更新gtbox
            input_dict.update({
                'gt_names': gt_names,
                'gt_boxes': gt_boxes_lidar
            })
            if "gt_boxes2d" in get_item_list:
                input_dict['gt_boxes2d'] = annos["bbox"]

        # 获取fov视角的points
        if "points" in get_item_list:
            points = self.get_lidar(sample_idx, False)
            if self.dataset_cfg.FOV_POINTS_ONLY:
                pts_rect = calib.lidar_to_rect(points[:, 0:3])
                fov_flag = self.get_fov_flag(pts_rect, img_shape, calib)
                points = points[fov_flag]
            input_dict['points'] = points

        input_dict['calib'] = calib
        data_dict = self.prepare_data(data_dict=input_dict)

        data_dict['image_shape'] = img_shape
        return data_dict

    def evaluation(self, det_annos, class_names, **kwargs):
        if 'annos' not in self.custom_infos[0].keys():
            return 'No ground-truth boxes for evaluation', {}

        def kitti_eval(eval_det_annos, eval_gt_annos, map_name_to_kitti):
            from ..kitti.kitti_object_eval_python import eval as kitti_eval
            from ..kitti import kitti_utils

            kitti_utils.transform_annotations_to_kitti_format(eval_det_annos, map_name_to_kitti=map_name_to_kitti)
            kitti_utils.transform_annotations_to_kitti_format(
                eval_gt_annos, map_name_to_kitti=map_name_to_kitti,
                info_with_fakelidar=self.dataset_cfg.get('INFO_WITH_FAKELIDAR', False)
            )
            kitti_class_names = [map_name_to_kitti[x] for x in class_names]
            ap_result_str, ap_dict = kitti_eval.get_official_eval_result(
                gt_annos=eval_gt_annos, dt_annos=eval_det_annos, current_classes=kitti_class_names
            )
            return ap_result_str, ap_dict

        eval_det_annos = copy.deepcopy(det_annos)
        eval_gt_annos = [copy.deepcopy(info['annos']) for info in self.custom_infos]

        if kwargs['eval_metric'] == 'kitti':
            ap_result_str, ap_dict = kitti_eval(eval_det_annos, eval_gt_annos, self.map_class_to_kitti)
        else:
            raise NotImplementedError

        return ap_result_str, ap_dict

    def get_calib(self, idx):
        calib_file = self.root_split_path / 'calib' / ('%s.txt' % idx)
        assert calib_file.exists()
        return calibration_kitti.Calibration(calib_file)

    def get_infos(self, num_workers=4, has_label=True, count_inside_pts=True, sample_id_list=None):
        import concurrent.futures as futures

        def process_single_scene(sample_idx):

            # 生成point_cloud字典
            print('%s sample_idx: %s' % (self.split, sample_idx))
            info = {}
            pc_info = {'num_features': 4, 'lidar_idx': sample_idx}
            info['point_cloud'] = pc_info

            # 生成image字典
            image_info = {'image_idx': sample_idx, 'image_shape': self.get_image_shape(sample_idx)}
            info['image'] = image_info

            # 生成calib字典
            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:
                # 生成annos字典
                obj_list = self.get_label(sample_idx)
                annotations = {}
                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)

                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]
                loc_lidar = calib.rect_to_lidar(loc)
                l, h, w = dims[:, 0:1], dims[:, 1:2], dims[:, 2:3]
                loc_lidar[:, 2] += h[:, 0] / 2
                gt_boxes_lidar = np.concatenate([loc_lidar, l, w, h, -(np.pi / 2 + rots[..., np.newaxis])], axis=1)
                annotations['gt_boxes_lidar'] = gt_boxes_lidar

                info['annos'] = annotations

                if count_inside_pts:
                    points = self.get_lidar(sample_idx, False)
                    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)

    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]}
                    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 create_label_file_with_name_and_box(class_names, gt_names, gt_boxes, save_label_path):
        with open(save_label_path, 'w') as f:
            for idx in range(gt_boxes.shape[0]):
                boxes = gt_boxes[idx]
                name = gt_names[idx]
                if name not in class_names:
                    continue
                line = "{x} {y} {z} {l} {w} {h} {angle} {name}\n".format(
                    x=boxes[0], y=boxes[1], z=(boxes[2]), l=boxes[3],
                    w=boxes[4], h=boxes[5], angle=boxes[6], name=name
                )
                f.write(line)
    @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_labels: (N), Tensor
            class_names:
            output_path:

        Returns:

        """
        def get_template_prediction(num_samples):
            ret_dict = {
                'name': np.zeros(num_samples), 'truncated': np.zeros(num_samples),
                'occluded': np.zeros(num_samples), 'alpha': np.zeros(num_samples),
                'bbox': np.zeros([num_samples, 4]), 'dimensions': np.zeros([num_samples, 3]),
                'location': np.zeros([num_samples, 3]), 'rotation_y': np.zeros(num_samples),
                'score': np.zeros(num_samples), 'boxes_lidar': np.zeros([num_samples, 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()
            pred_dict = get_template_prediction(pred_scores.shape[0])
            if pred_scores.shape[0] == 0:
                return pred_dict

            calib = batch_dict['calib'][batch_index]
            image_shape = batch_dict['image_shape'][batch_index].cpu().numpy()
            pred_boxes_camera = box_utils.boxes3d_lidar_to_kitti_camera(pred_boxes, calib)
            pred_boxes_img = box_utils.boxes3d_kitti_camera_to_imageboxes(
                pred_boxes_camera, calib, image_shape=image_shape
            )

            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['bbox'] = pred_boxes_img
            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)

            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

                    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 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, logger=common_utils.create_logger()
    )
    train_split, val_split = 'train', 'val'
    num_features = len(dataset_cfg.POINT_FEATURE_ENCODING.src_feature_list)

    train_filename = save_path / ('custom_infos_%s.pkl' % train_split)
    val_filename = save_path / ('custom_infos_%s.pkl' % val_split)

    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 saved to %s' % train_filename)

    dataset.set_split(val_split)
    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 train file is saved to %s' % val_filename)

    print('------------------------Start create groundtruth database for data augmentation------------------------')
    dataset.set_split(train_split)
    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', 'Van'],
            data_path=ROOT_DIR / 'data' / 'custom',
            save_path=ROOT_DIR / 'data' / 'custom',
        )

2. tools/cfgs/custom_models/pointpillar.yaml

这个函数主要是网络模型参数的配置

我主要修改了以下几个点：

1) CLASS_NAMES(替换成你自己的类别信息)

2) _BASE_CONFIFG(custom_dataset.yaml的路径，建议用详细的绝对路径)

3) POINT_CLOUD_RANGE和VOXEL_SIZE

这两者很重要，直接影响后面模型的传播，如果设置不对很容易报错

官方建议 Voxel设置：X,Y方向个数是16的倍数。Z方向为40。

之前尝试设置了一些还是不行，这个我也没太明白到底怎么回事，索性我就不修改

4) ANCHOR_GENERATOR_CONFIG

我修改了自己的类别属性以及feature_map_stride，去除了gt_sampling

完整的代码如下：

CLASS_NAMES: ['Car', 'Pedestrian', 'Van']

DATA_CONFIG: 
    _BASE_CONFIG_: /home/gmm/下载/OpenPCDet/tools/cfgs/dataset_configs/custom_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:
#                  - custom_dbinfos_train.pkl
#              PREPARE: {
#                 filter_by_min_points: ['Car:5', 'Pedestrian:5', 'Van:5']
#              }
#
#              SAMPLE_GROUPS: ['Car:15', 'Pedestrian:15', 'Van:15']
#              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_GENERATOR_CONFIG: [
            {
                'class_name': 'Car',
                'anchor_sizes': [[1.8, 4.7, 1.8]],
                'anchor_rotations': [0, 1.57],
                'anchor_bottom_heights': [0],
                'align_center': False,
                'feature_map_stride': 2,
                'matched_threshold': 0.55,
                'unmatched_threshold': 0.45
            },
            {
                'class_name': 'Pedestrian',
                'anchor_sizes': [[0.77, 0.92, 1.83]],
                'anchor_rotations': [0, 1.57],
                'anchor_bottom_heights': [0],
                'align_center': False,
                'feature_map_stride': 2,
                'matched_threshold': 0.5,
                'unmatched_threshold': 0.45
            },
            {
                'class_name': 'Van',
                'anchor_sizes': [[2.5, 5.7, 1.9]],
                'anchor_rotations': [0, 1.57],
                'anchor_bottom_heights': [0],
                'align_center': False,
                'feature_map_stride': 2,
                'matched_threshold': 0.5,
                'unmatched_threshold': 0.45
            },
        ]

        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

3. tools/cfgs/dataset_configs/custom_dataset.yaml

修改了DATA_PATH, POINT_CLOUD_RANGE和MAP_CLASS_TO_KITTI还有其他的一些类别属性。

修改后的代码如下：

DATASET: 'CustomDataset'
DATA_PATH: '/home/gmm/下载/OpenPCDet/data/custom'

POINT_CLOUD_RANGE: [0, -40, -3, 70.4, 40, 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: True

MAP_CLASS_TO_KITTI: {
    'Car': 'Car',
    'Pedestrian': 'Pedestrian',
    'Van': 'Cyclist',
}


DATA_AUGMENTOR:
    DISABLE_AUG_LIST: ['placeholder']
    AUG_CONFIG_LIST:
        - NAME: gt_sampling
          USE_ROAD_PLANE: False
          DB_INFO_PATH:
              - custom_dbinfos_train.pkl
          PREPARE: {
             filter_by_min_points: ['Car:5', 'Pedestrian:5', 'Van:5'],
          }

          SAMPLE_GROUPS: ['Car:20', 'Pedestrian:15', 'Van:20']
          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]


POINT_FEATURE_ENCODING: {
    encoding_type: absolute_coordinates_encoding,
    used_feature_list: ['x', 'y', 'z', 'intensity'],
    src_feature_list: ['x', 'y', 'z', 'intensity'],
}

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
      }

4. demo.py

之前训练之后检测框并没有出来，后来我才发现可能是自己的数据集太少，出来的检测框精度太低，于是我在V.draw_scenes部分作了一点修改，并在之前加入一个mask限制条件，结果果然出来检测框了。

demo.py修改部分的代码：

with torch.no_grad():
        for idx, data_dict in enumerate(demo_dataset):
            logger.info(f'Visualized sample index: \t{idx + 1}')
            data_dict = demo_dataset.collate_batch([data_dict])
            load_data_to_gpu(data_dict)
            pred_dicts, _ = model.forward(data_dict)

            scores = pred_dicts[0]['pred_scores'].detach().cpu().numpy()
            mask = scores > 0.3

            V.draw_scenes(
                points=data_dict['points'][:, 1:], ref_boxes=pred_dicts[0]['pred_boxes'][mask],
                ref_scores=pred_dicts[0]['pred_scores'], ref_labels=pred_dicts[0]['pred_labels'],
            )

            if not OPEN3D_FLAG:
                mlab.show(stop=True)

三. 运行过程

1. 生成数据字典

python -m pcdet.datasets.custom.custom_dataset create_custom_infos tools/cfgs/dataset_configs/custom_dataset.yaml

 

 2. 训练

这里我偷懒只训练10轮，自己可以自定义

python tools/train.py --cfg_file tools/cfgs/custom_models/pointpillar.yaml --batch_size=1 --epochs=10

 这里有个警告不知道怎么回事，暂时忽略[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)

3. 评估

由于数据集样本设置比较少，而且训练次数比较少，可以看出评估结果较差

 4. 结果

 还好能有显示，如果没有出现检测框可以把demo.py的score调低