GitLab

Passing dist_train instead of dist as the to_cpu paremeter to the loading functions.

* Update __init__.py

missing kornia. before image_to_tensor

* Update README.md

add Voxel R-CNN to changelog

* Update README.md

Add Voxel R-CNN as the officially released code

missing kornia. before image_to_tensor

Improve WOD performance with two LiDAR returns, speed up WOD data pre-processing with multiprocesssing.Pool

* 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>

There was some typo mistakes that i fixed in this file.

Co-authored-by: pengfengming <pengfengming@haomo.ai>

* 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 SECOND-IoU

* rename file

* modify readme and fix model name in __init__.py

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.

fixbug: multi_classes_nms and waymo dataset with old version splits

fixbug: modify input args with return for box utils

fixbug: AssertionError features.is_contiguous() in pointnet2_utils.py

fixbug: error of sample_points when num_point < far_idx_choice

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()

In data_processor.py sample_points, when num_points < far_idx_choice, a negative value is passed into np.random.choice, which cause a ValueError.
In fact the variable `near_idxs_choice` return by the deleted line is never used.

* bugfixed: add num_points_in_gt to the waymo_infos for evaluation

* add clarification for Waymo evaluation

* add kitti_utils.py for evaluating with KITTI metrics

* bugfixed: ignore empty boxes in visualization

* add contiguous() after transpose() at pointrcnn_head.py