GitLab

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

Re-organize fix random seed code; Make saved checkpoint compatible among different torch versions (#986)

* bugfixed: stuck when training with dist_train.sh, support tcp_port

* bugfixed: solve the issue of blocking with dist_train.sh, automatically search tcp ports

* 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

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