GitLab

* Add files via upload

* Delete cbgs_voxel01_voxelnext_headkernel3.yaml

* Delete voxelnext_ioubranch.yaml

* VoxelNeXt

* New CenterPoint parameters and scores

*  the offical support of ONCE dataset from ONCE team
Co-authored-by: JiagengMao <maojg@zju.edu.cn>
Co-authored-by: JiagengMao <maojiageng@gmail.com>

* update readme of mppnet

* fixbug of get_sequence_data when generate gt_database

* update centerpoint_4frames yaml for using all training datasets

* add mppnet in openpcdet

* add mppnet yamls

* add IOU_WEIGHT Flag

* add IOU_WEIGHT Flag

* add IOU_WEIGHT Flag

* add 16 frame effi_test

* add ctrans

* use effi crop

* update mppnet_head.py

* update mppnet_16frame.yaml

* update test.py

* add mppnet_4frame.yaml

* update mppnet_4frame.yaml

* update deted_template

* update det3d_template

* update yaml and clean mppnet head

* rm unused py and yaml

* update yamls

* fixbug of bs 2 eval

* fixbug of bs>1 eval

* update mppnet training code

* update training code

* rm unused file

* rm unused file

* reorganzie code

* reorginaze code

* add transformer.py with paper name

* add transformer.py with paper name

* add transformer.py with paper name

* reorganize code

* reorganize code

* reorganize code

* reorganize code

* reorganize code

* reorganize code

* reorganize code

* reorganize code

* rm unused code

* rm unused code

* format codes

* support save_to_file for WOD to save model predicted results

* fix small bug in generate_single_sample_dict

* support to load pred_boxes from result.pkl to avoid massive small object loading

* bugfixed: train with MPPNet

* bugfixed: remove num_frames in transformer.forward()

* bugfixed: remove num_frames in transformer.forward(), continue

* support to configure train/val result.pkl for ROI_BOXES_PATH for MPPNet

* update MPPNet codes

* bugfixed to support float32/float64 GT database

* update document
Co-authored-by: Shaoshuai Shi <shaoshuaics@gmail.com>

support MultiFrame setting for Waymo Open Dataset, add its config, needs to re-preprocess the Waymo Dataset to save speeds/accels, save new GT database

refactor database_sampler.py to support extending to other multimodal datasets (in addition to kitti)

* add cbgs_dyn_pp_centerpoint.yaml

* add cbgs_voxel01_res3d_centerpoint.yaml

* add support of centerpoint on Nuscenes in README

* add translation augmentation, re-implement the performance of CenterPoints with 0.075m voxels

* cover the original world translation augmentation

* modify random_translation_along_X

* add cbgs_dyn_pp_centerpoint.yaml

* add cbgs_voxel01_res3d_centerpoint.yaml

* add support of centerpoint on Nuscenes in README

* add dynamic pillar vfe

* make the upperbound unaccessible

* add place holder for voxel generation

* add DynPillarVFE

* add PFNLayerV2

* add try except for torch_scatter package

* add dynamic pillar in readme

* add the cfg file of centerpoint with dynamic pillar

* recover original mask_points_by_range

* masking points with points_coords out of grid_size

* add dynamic voxelization and config for voxel_rcnn

* add dynamic pillar vfe

* make the upperbound unaccessible

* add place holder for voxel generation

* add DynPillarVFE

* add PFNLayerV2

* add try except for torch_scatter package

* add dynamic pillar in readme

* add the cfg file of centerpoint with dynamic pillar

* add pyramid augmentation

* add pyramid augmentation

* format the modified file
Co-authored-by: Shaoshuai Shi <shaoshuaics@gmail.com>

* support PointPillar (anchor-based, anchor-free) for WOD datasets

* update OpenPCDet to v0.5.1

Added new augmentation methods and the option of augmentation by addition instead of replacement (#653)

* NOTEBOOK FOR DATA AUGMENTATION

* SMALL SAMPLE DATASET

* TRANSLACTIONS GLOBAL AND LOCAL

* LOCAL AUGMENTATIONS FINISHED

* ADJUSTED DATA FORMATS TO PCDET YAML COMPATIBILITY

* BUGFIX

* ADDED TITLES

* ADAPTED OPENPCDET TO FIT NEW AUGMENTATIONS

* SAVE IMAGES OF THE DATA AUGMENTATION EXAMPLES

* LOCAL TRANSFORMATION RANDOM VALUES VARY WITH OBJECT AND NOT SCENE

* FIXED YAML

* FIXED BUGS IN PARAMETERS

* FIX IN LOCAL BOXES

* NEW BOX FILTER

* FRUSTUM DROPOUT

* FRUSTUM DROPOUT IMPLEMENTED ON OPENPCDET

* REMOVE FIGURES FROM VERSION CONTROL

* MORE EFFICIENT LOCAL METHOD

* LOCAL TRANSFORMATION WITH NUMPY SPED UP

* BUGFIX ON AUGMENTATION FUNCTION

* GET PREDICTED LABELS

* AUGMENTATION DONE BY ADDITION

* RANDOM SELECTION OF REMAINIG FRAMES

* LOGGER WITH TRANSFORMATION DETAILS

* CHANGED COUNTER TO SAMPLE IDX SELECTION METHOD

* BUGFIX ON KITTY_DATABASE

* BUGFIX

* BUGFIX

* FIX ON GENERATOR

* DEBUG LOGS TO FILE

* FIXED LOGGER

* SAVE POINT CLOUDS TO OBJ FILE

* UPDATED NOTEBOOK

* CLEANUP

* .gitignore is now working

* fixed .gitignore

* REMOVED YAML FILES FROM .gitignore

* parse results to a csv

* yaml changed

* fixed gitignore

* FIXED BUG THAT DUPLICATED THE TEST SET

* small fixes on yaml

* fix gitignore

* updated generate_sets.py

* fixed csv_parser.py

* Delete output directory

* Delete output directory

* saving figures with better resolution

* implemented a way to chose to augment the raw pcs

* updated csv_parser.py

* fixed yamls to include the new selection system

* notebook updated

* mosaic added

* new models

* fixed gitignore

* fixed gitignore

* applied changes

* Delete data_aumentation.ipynb

* Delete pointpillar_fullaug.yaml

* updated gitignore

* bugfix

* updated training and notebook

* updated gitignore

* cleanup code

* updated yamls

* reset

* new augmentation methods

* example conf for pointpillars

* updated example config

* bugfix on the config file

* fixed get_points_in_box function

* fixed local frustum dropout

* fixed pointpillar_newaugs.yaml

* bugfixed: import math for some augmentations

* increased margin in point selection for local augmentation

* bugfixed: velocity should not be translated for translation augs. Add warning to check the correctness before using

* bugfixed: add back the aug of random_image_flip
Co-authored-by: Shaoshuai Shi <shaoshuaics@gmail.com>

* 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