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import copy |
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import pickle |
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import numpy as np |
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from PIL import Image |
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import torch |
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import torch.nn.functional as F |
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from pathlib import Path |
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from ..dataset import DatasetTemplate |
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from ...ops.roiaware_pool3d import roiaware_pool3d_utils |
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from ...utils import box_utils |
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from .once_toolkits import Octopus |
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class ONCEDataset(DatasetTemplate): |
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def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None): |
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""" |
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Args: |
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root_path: |
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dataset_cfg: |
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class_names: |
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training: |
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logger: |
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""" |
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super().__init__( |
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dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=root_path, logger=logger |
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) |
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self.split = dataset_cfg.DATA_SPLIT['train'] if training else dataset_cfg.DATA_SPLIT['test'] |
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assert self.split in ['train', 'val', 'test', 'raw_small', 'raw_medium', 'raw_large'] |
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split_dir = self.root_path / 'ImageSets' / (self.split + '.txt') |
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self.sample_seq_list = [x.strip() for x in open(split_dir).readlines()] if split_dir.exists() else None |
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self.cam_names = ['cam01', 'cam03', 'cam05', 'cam06', 'cam07', 'cam08', 'cam09'] |
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self.cam_tags = ['top', 'top2', 'left_back', 'left_front', 'right_front', 'right_back', 'back'] |
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self.toolkits = Octopus(self.root_path) |
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self.once_infos = [] |
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self.include_once_data(self.split) |
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def include_once_data(self, split): |
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if self.logger is not None: |
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self.logger.info('Loading ONCE dataset') |
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once_infos = [] |
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for info_path in self.dataset_cfg.INFO_PATH[split]: |
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info_path = self.root_path / info_path |
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if not info_path.exists(): |
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continue |
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with open(info_path, 'rb') as f: |
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infos = pickle.load(f) |
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once_infos.extend(infos) |
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def check_annos(info): |
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return 'annos' in info |
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if self.split != 'raw': |
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once_infos = list(filter(check_annos,once_infos)) |
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self.once_infos.extend(once_infos) |
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if self.logger is not None: |
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self.logger.info('Total samples for ONCE dataset: %d' % (len(once_infos))) |
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def set_split(self, split): |
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super().__init__( |
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dataset_cfg=self.dataset_cfg, class_names=self.class_names, training=self.training, root_path=self.root_path, logger=self.logger |
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) |
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self.split = split |
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split_dir = self.root_path / 'ImageSets' / (self.split + '.txt') |
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self.sample_seq_list = [x.strip() for x in open(split_dir).readlines()] if split_dir.exists() else None |
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def get_lidar(self, sequence_id, frame_id): |
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return self.toolkits.load_point_cloud(sequence_id, frame_id) |
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def get_image(self, sequence_id, frame_id, cam_name): |
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return self.toolkits.load_image(sequence_id, frame_id, cam_name) |
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def project_lidar_to_image(self, sequence_id, frame_id): |
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return self.toolkits.project_lidar_to_image(sequence_id, frame_id) |
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def point_painting(self, points, info): |
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semseg_dir = './' |
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used_classes = [0,1,2,3,4,5] |
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num_classes = len(used_classes) |
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frame_id = str(info['frame_id']) |
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seq_id = str(info['sequence_id']) |
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painted = np.zeros((points.shape[0], num_classes)) |
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for cam_name in self.cam_names: |
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img_path = Path(semseg_dir) / Path(seq_id) / Path(cam_name) / Path(frame_id+'_label.png') |
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calib_info = info['calib'][cam_name] |
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cam_2_velo = calib_info['cam_to_velo'] |
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cam_intri = np.hstack([calib_info['cam_intrinsic'], np.zeros((3, 1), dtype=np.float32)]) |
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point_xyz = points[:, :3] |
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points_homo = np.hstack( |
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[point_xyz, np.ones(point_xyz.shape[0], dtype=np.float32).reshape((-1, 1))]) |
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points_lidar = np.dot(points_homo, np.linalg.inv(cam_2_velo).T) |
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mask = points_lidar[:, 2] > 0 |
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points_lidar = points_lidar[mask] |
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points_img = np.dot(points_lidar, cam_intri.T) |
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points_img = points_img / points_img[:, [2]] |
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uv = points_img[:, [0,1]] |
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seg_map = np.array(Image.open(img_path)) |
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H, W = seg_map.shape |
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seg_feats = np.zeros((H*W, num_classes)) |
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seg_map = seg_map.reshape(-1) |
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for cls_i in used_classes: |
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seg_feats[seg_map==cls_i, cls_i] = 1 |
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seg_feats = seg_feats.reshape(H, W, num_classes).transpose(2, 0, 1) |
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uv[:, 0] = (uv[:, 0] - W / 2) / (W / 2) |
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uv[:, 1] = (uv[:, 1] - H / 2) / (H / 2) |
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uv_tensor = torch.from_numpy(uv).unsqueeze(0).unsqueeze(0) |
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seg_feats = torch.from_numpy(seg_feats).unsqueeze(0) |
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proj_scores = F.grid_sample(seg_feats, uv_tensor, mode='bilinear', padding_mode='zeros') |
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proj_scores = proj_scores.squeeze(0).squeeze(1).transpose(0, 1).contiguous() |
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painted[mask] = proj_scores.numpy() |
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return np.concatenate([points, painted], axis=1) |
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def __len__(self): |
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if self._merge_all_iters_to_one_epoch: |
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return len(self.once_infos) * self.total_epochs |
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return len(self.once_infos) |
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def __getitem__(self, index): |
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if self._merge_all_iters_to_one_epoch: |
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index = index % len(self.once_infos) |
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info = copy.deepcopy(self.once_infos[index]) |
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frame_id = info['frame_id'] |
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seq_id = info['sequence_id'] |
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points = self.get_lidar(seq_id, frame_id) |
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if self.dataset_cfg.get('POINT_PAINTING', False): |
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points = self.point_painting(points, info) |
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input_dict = { |
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'points': points, |
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'frame_id': frame_id, |
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} |
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if 'annos' in info: |
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annos = info['annos'] |
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input_dict.update({ |
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'gt_names': annos['name'], |
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'gt_boxes': annos['boxes_3d'], |
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'num_points_in_gt': annos.get('num_points_in_gt', None) |
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}) |
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data_dict = self.prepare_data(data_dict=input_dict) |
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data_dict.pop('num_points_in_gt', None) |
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return data_dict |
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def get_infos(self, num_workers=4, sample_seq_list=None): |
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import concurrent.futures as futures |
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import json |
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root_path = self.root_path |
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cam_names = self.cam_names |
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""" |
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# dataset json format |
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{ |
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'meta_info': |
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'calib': { |
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'cam01': { |
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'cam_to_velo': list |
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'cam_intrinsic': list |
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'distortion': list |
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} |
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... |
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} |
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'frames': [ |
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{ |
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'frame_id': timestamp, |
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'annos': { |
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'names': list |
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'boxes_3d': list of list |
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'boxes_2d': { |
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'cam01': list of list |
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... |
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} |
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} |
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'pose': list |
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}, |
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... |
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] |
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} |
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# open pcdet format |
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{ |
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'meta_info': |
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'sequence_id': seq_idx |
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'frame_id': timestamp |
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'timestamp': timestamp |
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'lidar': path |
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'cam01': path |
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... |
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'calib': { |
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'cam01': { |
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'cam_to_velo': np.array |
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'cam_intrinsic': np.array |
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'distortion': np.array |
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} |
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... |
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} |
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'pose': np.array |
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'annos': { |
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'name': np.array |
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'boxes_3d': np.array |
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'boxes_2d': { |
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'cam01': np.array |
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.... |
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} |
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} |
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} |
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""" |
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def process_single_sequence(seq_idx): |
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print('%s seq_idx: %s' % (self.split, seq_idx)) |
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seq_infos = [] |
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seq_path = Path(root_path) / 'data' / seq_idx |
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json_path = seq_path / ('%s.json' % seq_idx) |
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with open(json_path, 'r') as f: |
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info_this_seq = json.load(f) |
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meta_info = info_this_seq['meta_info'] |
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calib = info_this_seq['calib'] |
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for f_idx, frame in enumerate(info_this_seq['frames']): |
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frame_id = frame['frame_id'] |
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if f_idx == 0: |
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prev_id = None |
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else: |
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prev_id = info_this_seq['frames'][f_idx-1]['frame_id'] |
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if f_idx == len(info_this_seq['frames'])-1: |
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next_id = None |
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else: |
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next_id = info_this_seq['frames'][f_idx+1]['frame_id'] |
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pc_path = str(seq_path / 'lidar_roof' / ('%s.bin' % frame_id)) |
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pose = np.array(frame['pose']) |
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frame_dict = { |
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'sequence_id': seq_idx, |
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'frame_id': frame_id, |
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'timestamp': int(frame_id), |
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'prev_id': prev_id, |
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'next_id': next_id, |
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'meta_info': meta_info, |
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'lidar': pc_path, |
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'pose': pose |
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} |
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calib_dict = {} |
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for cam_name in cam_names: |
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cam_path = str(seq_path / cam_name / ('%s.jpg' % frame_id)) |
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frame_dict.update({cam_name: cam_path}) |
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calib_dict[cam_name] = {} |
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calib_dict[cam_name]['cam_to_velo'] = np.array(calib[cam_name]['cam_to_velo']) |
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calib_dict[cam_name]['cam_intrinsic'] = np.array(calib[cam_name]['cam_intrinsic']) |
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calib_dict[cam_name]['distortion'] = np.array(calib[cam_name]['distortion']) |
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frame_dict.update({'calib': calib_dict}) |
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if 'annos' in frame: |
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annos = frame['annos'] |
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boxes_3d = np.array(annos['boxes_3d']) |
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if boxes_3d.shape[0] == 0: |
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print(frame_id) |
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continue |
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boxes_2d_dict = {} |
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for cam_name in cam_names: |
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boxes_2d_dict[cam_name] = np.array(annos['boxes_2d'][cam_name]) |
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annos_dict = { |
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'name': np.array(annos['names']), |
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'boxes_3d': boxes_3d, |
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'boxes_2d': boxes_2d_dict |
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} |
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points = self.get_lidar(seq_idx, frame_id) |
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corners_lidar = box_utils.boxes_to_corners_3d(np.array(annos['boxes_3d'])) |
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num_gt = boxes_3d.shape[0] |
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num_points_in_gt = -np.ones(num_gt, dtype=np.int32) |
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for k in range(num_gt): |
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flag = box_utils.in_hull(points[:, 0:3], corners_lidar[k]) |
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num_points_in_gt[k] = flag.sum() |
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annos_dict['num_points_in_gt'] = num_points_in_gt |
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frame_dict.update({'annos': annos_dict}) |
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seq_infos.append(frame_dict) |
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return seq_infos |
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sample_seq_list = sample_seq_list if sample_seq_list is not None else self.sample_seq_list |
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with futures.ThreadPoolExecutor(num_workers) as executor: |
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infos = executor.map(process_single_sequence, sample_seq_list) |
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all_infos = [] |
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for info in infos: |
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all_infos.extend(info) |
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return all_infos |
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def create_groundtruth_database(self, info_path=None, used_classes=None, split='train'): |
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import torch |
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database_save_path = Path(self.root_path) / ('gt_database' if split == 'train' else ('gt_database_%s' % split)) |
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db_info_save_path = Path(self.root_path) / ('once_dbinfos_%s.pkl' % split) |
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database_save_path.mkdir(parents=True, exist_ok=True) |
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all_db_infos = {} |
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with open(info_path, 'rb') as f: |
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infos = pickle.load(f) |
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for k in range(len(infos)): |
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if 'annos' not in infos[k]: |
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continue |
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print('gt_database sample: %d' % (k + 1)) |
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info = infos[k] |
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frame_id = info['frame_id'] |
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seq_id = info['sequence_id'] |
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points = self.get_lidar(seq_id, frame_id) |
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annos = info['annos'] |
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names = annos['name'] |
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gt_boxes = annos['boxes_3d'] |
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num_obj = gt_boxes.shape[0] |
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point_indices = roiaware_pool3d_utils.points_in_boxes_cpu( |
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torch.from_numpy(points[:, 0:3]), torch.from_numpy(gt_boxes) |
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).numpy() |
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for i in range(num_obj): |
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filename = '%s_%s_%d.bin' % (frame_id, names[i], i) |
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filepath = database_save_path / filename |
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gt_points = points[point_indices[i] > 0] |
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gt_points[:, :3] -= gt_boxes[i, :3] |
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with open(filepath, 'w') as f: |
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gt_points.tofile(f) |
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db_path = str(filepath.relative_to(self.root_path)) |
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db_info = {'name': names[i], 'path': db_path, 'gt_idx': i, |
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'box3d_lidar': gt_boxes[i], 'num_points_in_gt': gt_points.shape[0]} |
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if names[i] in all_db_infos: |
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all_db_infos[names[i]].append(db_info) |
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else: |
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all_db_infos[names[i]] = [db_info] |
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for k, v in all_db_infos.items(): |
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print('Database %s: %d' % (k, len(v))) |
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with open(db_info_save_path, 'wb') as f: |
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pickle.dump(all_db_infos, f) |
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@staticmethod |
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def generate_prediction_dicts(batch_dict, pred_dicts, class_names, output_path=None): |
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def get_template_prediction(num_samples): |
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ret_dict = { |
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'name': np.zeros(num_samples), 'score': np.zeros(num_samples), |
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'boxes_3d': np.zeros((num_samples, 7)) |
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} |
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return ret_dict |
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|
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def generate_single_sample_dict(box_dict): |
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pred_scores = box_dict['pred_scores'].cpu().numpy() |
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pred_boxes = box_dict['pred_boxes'].cpu().numpy() |
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pred_labels = box_dict['pred_labels'].cpu().numpy() |
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pred_dict = get_template_prediction(pred_scores.shape[0]) |
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if pred_scores.shape[0] == 0: |
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return pred_dict |
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pred_dict['name'] = np.array(class_names)[pred_labels - 1] |
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pred_dict['score'] = pred_scores |
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pred_dict['boxes_3d'] = pred_boxes |
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return pred_dict |
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annos = [] |
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for index, box_dict in enumerate(pred_dicts): |
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frame_id = batch_dict['frame_id'][index] |
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single_pred_dict = generate_single_sample_dict(box_dict) |
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single_pred_dict['frame_id'] = frame_id |
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annos.append(single_pred_dict) |
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if output_path is not None: |
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raise NotImplementedError |
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return annos |
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def evaluation(self, det_annos, class_names, **kwargs): |
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from .once_eval.evaluation import get_evaluation_results |
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eval_det_annos = copy.deepcopy(det_annos) |
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eval_gt_annos = [copy.deepcopy(info['annos']) for info in self.once_infos] |
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ap_result_str, ap_dict = get_evaluation_results(eval_gt_annos, eval_det_annos, class_names) |
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return ap_result_str, ap_dict |
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|
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def create_once_infos(dataset_cfg, class_names, data_path, save_path, workers=4): |
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dataset = ONCEDataset(dataset_cfg=dataset_cfg, class_names=class_names, root_path=data_path, training=False) |
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splits = ['train', 'val', 'test', 'raw_small', 'raw_medium', 'raw_large'] |
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ignore = ['test'] |
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print('---------------Start to generate data infos---------------') |
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for split in splits: |
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if split in ignore: |
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continue |
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|
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filename = 'once_infos_%s.pkl' % split |
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filename = save_path / Path(filename) |
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dataset.set_split(split) |
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once_infos = dataset.get_infos(num_workers=workers) |
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with open(filename, 'wb') as f: |
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pickle.dump(once_infos, f) |
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print('ONCE info %s file is saved to %s' % (split, filename)) |
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|
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train_filename = save_path / 'once_infos_train.pkl' |
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print('---------------Start create groundtruth database for data augmentation---------------') |
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dataset.set_split('train') |
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dataset.create_groundtruth_database(train_filename, split='train') |
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print('---------------Data preparation Done---------------') |
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|
|
|
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if __name__ == '__main__': |
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import argparse |
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|
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parser = argparse.ArgumentParser(description='arg parser') |
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parser.add_argument('--cfg_file', type=str, default=None, help='specify the config of dataset') |
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parser.add_argument('--func', type=str, default='create_waymo_infos', help='') |
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parser.add_argument('--runs_on', type=str, default='server', help='') |
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args = parser.parse_args() |
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|
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if args.func == 'create_once_infos': |
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import yaml |
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from pathlib import Path |
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from easydict import EasyDict |
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dataset_cfg = EasyDict(yaml.load(open(args.cfg_file))) |
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|
|
|
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ROOT_DIR = (Path(__file__).resolve().parent / '../../../').resolve() |
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once_data_path = ROOT_DIR / 'data' / 'once' |
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once_save_path = ROOT_DIR / 'data' / 'once' |
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|
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if args.runs_on == 'cloud': |
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once_data_path = Path('/cache/once/') |
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once_save_path = Path('/cache/once/') |
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dataset_cfg.DATA_PATH = dataset_cfg.CLOUD_DATA_PATH |
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|
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create_once_infos( |
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dataset_cfg=dataset_cfg, |
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class_names=['Car', 'Bus', 'Truck', 'Pedestrian', 'Bicycle'], |
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data_path=once_data_path, |
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save_path=once_save_path |
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) |