import copy import pickle from pathlib import Path import numpy as np from tqdm import tqdm from ...ops.roiaware_pool3d import roiaware_pool3d_utils from ...utils import common_utils, box_utils from ..dataset import DatasetTemplate class LyftDataset(DatasetTemplate): def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None): self.root_path = (root_path if root_path is not None else Path(dataset_cfg.DATA_PATH)) / dataset_cfg.VERSION super().__init__( dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=self.root_path, logger=logger ) self.infos = [] self.include_lyft_data(self.mode) def include_lyft_data(self, mode): self.logger.info('Loading lyft dataset') lyft_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) lyft_infos.extend(infos) self.infos.extend(lyft_infos) self.logger.info('Total samples for lyft dataset: %d' % (len(lyft_infos))) @staticmethod def remove_ego_points(points, center_radius=1.0): mask = ~((np.abs(points[:, 0]) < center_radius*1.5) & (np.abs(points[:, 1]) < center_radius)) return points[mask] def get_sweep(self, sweep_info): lidar_path = self.root_path / sweep_info['lidar_path'] points_sweep = np.fromfile(str(lidar_path), dtype=np.float32, count=-1) if points_sweep.shape[0] % 5 != 0: points_sweep = points_sweep[: points_sweep.shape[0] - (points_sweep.shape[0] % 5)] points_sweep = points_sweep.reshape([-1, 5])[:, :4] points_sweep = self.remove_ego_points(points_sweep).T if sweep_info['transform_matrix'] is not None: num_points = points_sweep.shape[1] points_sweep[:3, :] = sweep_info['transform_matrix'].dot( np.vstack((points_sweep[:3, :], np.ones(num_points))))[:3, :] cur_times = sweep_info['time_lag'] * np.ones((1, points_sweep.shape[1])) return points_sweep.T, cur_times.T def get_lidar_with_sweeps(self, index, max_sweeps=1): info = self.infos[index] lidar_path = self.root_path / info['lidar_path'] points = np.fromfile(str(lidar_path), dtype=np.float32, count=-1) if points.shape[0] % 5 != 0: points = points[: points.shape[0] - (points.shape[0] % 5)] points = points.reshape([-1, 5])[:, :4] sweep_points_list = [points] sweep_times_list = [np.zeros((points.shape[0], 1))] for k in np.random.choice(len(info['sweeps']), max_sweeps - 1, replace=False): points_sweep, times_sweep = self.get_sweep(info['sweeps'][k]) sweep_points_list.append(points_sweep) sweep_times_list.append(times_sweep) points = np.concatenate(sweep_points_list, axis=0) times = np.concatenate(sweep_times_list, axis=0).astype(points.dtype) points = np.concatenate((points, times), axis=1) return points def __len__(self): if self._merge_all_iters_to_one_epoch: return len(self.infos) * self.total_epochs return len(self.infos) def __getitem__(self, index): if self._merge_all_iters_to_one_epoch: index = index % len(self.infos) info = copy.deepcopy(self.infos[index]) points = self.get_lidar_with_sweeps(index, max_sweeps=self.dataset_cfg.MAX_SWEEPS) input_dict = { 'points': points, 'frame_id': Path(info['lidar_path']).stem, 'metadata': {'token': info['token']} } if 'gt_boxes' in info: input_dict.update({ 'gt_boxes': info['gt_boxes'], 'gt_names': info['gt_names'] }) data_dict = self.prepare_data(data_dict=input_dict) return data_dict def kitti_eval(self, eval_det_annos, eval_gt_annos, class_names): from ..kitti.kitti_object_eval_python import eval as kitti_eval from ..kitti import kitti_utils map_name_to_kitti = { 'car': 'Car', 'pedestrian': 'Pedestrian', 'truck': 'Truck', 'bicycle': 'Cyclist', 'motorcycle': 'Cyclist' } kitti_utils.transform_to_kitti_format(eval_det_annos, map_name_to_kitti=map_name_to_kitti) kitti_utils.transform_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 def evaluation(self, det_annos, class_names, **kwargs): if kwargs['eval_metric'] == 'kitti': eval_det_annos = copy.deepcopy(det_annos) eval_gt_annos = copy.deepcopy(self.infos) return self.kitti_eval(eval_det_annos, eval_gt_annos, class_names) elif kwargs['eval_metric'] == 'lyft': return self.lyft_eval(det_annos, class_names, iou_thresholds=self.dataset_cfg.EVAL_LYFT_IOU_LIST) else: raise NotImplementedError def lyft_eval(self, det_annos, class_names, iou_thresholds=[0.5]): from lyft_dataset_sdk.lyftdataset import LyftDataset as Lyft from . import lyft_utils # from lyft_dataset_sdk.eval.detection.mAP_evaluation import get_average_precisions from .lyft_mAP_eval.lyft_eval import get_average_precisions lyft = Lyft(json_path=self.root_path / 'data', data_path=self.root_path, verbose=True) det_lyft_boxes, sample_tokens = lyft_utils.convert_det_to_lyft_format(lyft, det_annos) gt_lyft_boxes = lyft_utils.load_lyft_gt_by_tokens(lyft, sample_tokens) average_precisions = get_average_precisions(gt_lyft_boxes, det_lyft_boxes, class_names, iou_thresholds) ap_result_str, ap_dict = lyft_utils.format_lyft_results(average_precisions, class_names, iou_thresholds, version=self.dataset_cfg.VERSION) return ap_result_str, ap_dict def create_groundtruth_database(self, used_classes=None, max_sweeps=10): import torch database_save_path = self.root_path / f'gt_database' db_info_save_path = self.root_path / f'lyft_dbinfos_{max_sweeps}sweeps.pkl' database_save_path.mkdir(parents=True, exist_ok=True) all_db_infos = {} for idx in tqdm(range(len(self.infos))): sample_idx = idx info = self.infos[idx] points = self.get_lidar_with_sweeps(idx, max_sweeps=max_sweeps) gt_boxes = info['gt_boxes'] gt_names = info['gt_names'] box_idxs_of_pts = roiaware_pool3d_utils.points_in_boxes_gpu( torch.from_numpy(points[:, 0:3]).unsqueeze(dim=0).float().cuda(), torch.from_numpy(gt_boxes[:, 0:7]).unsqueeze(dim=0).float().cuda() ).long().squeeze(dim=0).cpu().numpy() for i in range(gt_boxes.shape[0]): filename = '%s_%s_%d.bin' % (sample_idx, gt_names[i], i) filepath = database_save_path / filename gt_points = points[box_idxs_of_pts == i] gt_points[:, :3] -= gt_boxes[i, :3] with open(filepath, 'w') as f: gt_points.tofile(f) if (used_classes is None) or gt_names[i] in used_classes: db_path = str(filepath.relative_to(self.root_path)) # gt_database/xxxxx.bin db_info = {'name': gt_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]} if gt_names[i] in all_db_infos: all_db_infos[gt_names[i]].append(db_info) else: all_db_infos[gt_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) def create_lyft_info(version, data_path, save_path, split, max_sweeps=10): from lyft_dataset_sdk.lyftdataset import LyftDataset from . import lyft_utils data_path = data_path / version save_path = save_path / version split_path = data_path.parent / 'ImageSets' if split is not None: save_path = save_path / split split_path = split_path / split save_path.mkdir(exist_ok=True) assert version in ['trainval', 'one_scene', 'test'] if version == 'trainval': train_split_path = split_path / 'train.txt' val_split_path = split_path / 'val.txt' elif version == 'test': train_split_path = split_path / 'test.txt' val_split_path = None elif version == 'one_scene': train_split_path = split_path / 'one_scene.txt' val_split_path = split_path / 'one_scene.txt' else: raise NotImplementedError train_scenes = [x.strip() for x in open(train_split_path).readlines()] if train_split_path.exists() else [] val_scenes = [x.strip() for x in open(val_split_path).readlines()] if val_split_path is not None and val_split_path.exists() else [] lyft = LyftDataset(json_path=data_path / 'data', data_path=data_path, verbose=True) available_scenes = lyft_utils.get_available_scenes(lyft) available_scene_names = [s['name'] for s in available_scenes] train_scenes = list(filter(lambda x: x in available_scene_names, train_scenes)) val_scenes = list(filter(lambda x: x in available_scene_names, val_scenes)) train_scenes = set([available_scenes[available_scene_names.index(s)]['token'] for s in train_scenes]) val_scenes = set([available_scenes[available_scene_names.index(s)]['token'] for s in val_scenes]) print('%s: train scene(%d), val scene(%d)' % (version, len(train_scenes), len(val_scenes))) train_lyft_infos, val_lyft_infos = lyft_utils.fill_trainval_infos( data_path=data_path, lyft=lyft, train_scenes=train_scenes, val_scenes=val_scenes, test='test' in version, max_sweeps=max_sweeps ) if version == 'test': print('test sample: %d' % len(train_lyft_infos)) with open(save_path / f'lyft_infos_test.pkl', 'wb') as f: pickle.dump(train_lyft_infos, f) else: print('train sample: %d, val sample: %d' % (len(train_lyft_infos), len(val_lyft_infos))) with open(save_path / f'lyft_infos_train.pkl', 'wb') as f: pickle.dump(train_lyft_infos, f) with open(save_path / f'lyft_infos_val.pkl', 'wb') as f: pickle.dump(val_lyft_infos, f) if __name__ == '__main__': import yaml import argparse from pathlib import Path from easydict import EasyDict parser = argparse.ArgumentParser(description='arg parser') parser.add_argument('--cfg_file', type=str, default=None, help='specify the config of dataset') parser.add_argument('--func', type=str, default='create_lyft_infos', help='') parser.add_argument('--version', type=str, default='trainval', help='') parser.add_argument('--split', type=str, default=None, help='') parser.add_argument('--max_sweeps', type=int, default=10, help='') args = parser.parse_args() if args.func == 'create_lyft_infos': try: yaml_config = yaml.safe_load(open(args.cfg_file), Loader=yaml.FullLoader) except: yaml_config = yaml.safe_load(open(args.cfg_file)) dataset_cfg = EasyDict(yaml_config) ROOT_DIR = (Path(__file__).resolve().parent / '../../../').resolve() dataset_cfg.VERSION = args.version dataset_cfg.MAX_SWEEPS = args.max_sweeps create_lyft_info( version=dataset_cfg.VERSION, data_path=ROOT_DIR / 'data' / 'lyft', save_path=ROOT_DIR / 'data' / 'lyft', split=args.split, max_sweeps=dataset_cfg.MAX_SWEEPS ) lyft_dataset = LyftDataset( dataset_cfg=dataset_cfg, class_names=None, root_path=ROOT_DIR / 'data' / 'lyft', logger=common_utils.create_logger(), training=True ) if args.version != 'test': lyft_dataset.create_groundtruth_database(max_sweeps=dataset_cfg.MAX_SWEEPS)