GitLab

* Add basic support for Pandaset dataset

* Add gt_sampling support for pandaset

* Enable training on different classes than raw dataset

* Save pandaset output in the same format as annotations

* Add dataset config file for pandaset

* Make pandaset dataset compatible with test.py

- add 'evaluation' method
- add 'name' key to predictions dict

* Enable selecting lidar device for pandaset

Co-authored-by: xiazhongyu <xiazhongyu@pku.edu.cn>

support FILTER_EMPTY_BOXES_FOR_TRAIN and DISABLE_NLZ_FLAG_ON_POINTS for WOD by default, eval with sample_interval=1

update create_groundtruth_database of WOD to generate global npy file for sharedmemory based GT_database

Improve WOD performance with two LiDAR returns, speed up WOD data pre-processing with multiprocesssing.Pool

Improve WOD performance with two LiDAR returns, speed up WOD data pre-processing with multiprocesssing.Pool

* Added CaDDN detector and support for image, depth map, and 2D GT box
dataloading

* Moved image flip augmentation to augmentor_utils

* Updated default get item list to include points

* Moved utils functions into transform_utils

* Combined FFE + F2V into ImageVFE, renamed FFE to FFN, moved depth downsample into data_processor

* Updated README with updated CaDDN weights

* Updated comments for image vfe

In data_processor.py sample_points, when num_points < far_idx_choice, a negative value is passed into np.random.choice, which cause a ValueError.
In fact the variable `near_idxs_choice` return by the deleted line is never used.

* bugfixed: add num_points_in_gt to the waymo_infos for evaluation

* add clarification for Waymo evaluation

* add kitti_utils.py for evaluating with KITTI metrics