GitLab

* 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 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

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

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

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