GitLab

* 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>

bugfixed: weights transformation between spconv 1.x and spconv 2.x, should consider both native/implicit spconv 2.x

- as per the spconv documentation, the order of channels in weights is now changed
- we need to get all the affected weights, using the util function in spconv_utils
- we need to check the version with the version checker in __init__.py
- we need to transpose last two dimensions of existing weights, from older models
- to avoid duplication a single _load_state_dict method is added

* add voxel roi pooling

* add voxel r-cnn head

* add voxel query

* add voxel r-cnn

* add Voxel R-CNN

* add infos about Voxel R-CNN

* add voxel_rcnn_car.yaml

* add infos about Voxel R-CNN
Co-authored-by: Shaoshuai Shi <shaoshuaics@gmail.com>

* 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

all model codes refactoring for training/testing, support multiple 3D detectors (SECOND, PartA2-Net (official release), PV-RCNN (official release))