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>

* To make different spconv version compatible for focalsconv

* Reflect the changes of the default behavior of grid_sample and affine_grid. If PyTorch > 1.3.0 align_corners will be set to False by default. This is undesired behavior.

* Support grid_sample and affine_grid for PyTorch<1.3 as well.
Co-authored-by: Dusan Malic <dusan.malic@icg.tugraz.at>

* Fix missing __init__.py

* Add further missing __init__.py

* 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 multi_scale_3d_strides in resbackbone

* 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

* recover original mask_points_by_range

* masking points with points_coords out of grid_size

* 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 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>

* Download DeepLabV3 if not available yet

* Fix dtype issue

Fix RuntimeError: expected backend CPU and dtype Float but got backend CPU and dtype Long

* 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

If there are less than half of model_cfg.NUM_KEYPOINTS it will lead to 
RuntimeError: unsupported operation: some elements of the input tensor and the written-to tensor refer to a single memory location. 
Please clone() the tensor before performing the operation.

caused by `features` in pointnet2_backbone.py:78, which is possible to be a non-contiguous tensor, being directly passed into SA_modules without calling .contiguous()

support more flexible setting of conv head; slice inputs when batch size is too large in PFNLayer to avoid bugs (#124)

* support more flexible setting

* slice inputs of nn.Linear when batch size is too large