Spaces:
Runtime error
Runtime error
| import torch | |
| from ..utils import ext_loader | |
| ext_module = ext_loader.load_ext('_ext', [ | |
| 'points_in_boxes_part_forward', 'points_in_boxes_cpu_forward', | |
| 'points_in_boxes_all_forward' | |
| ]) | |
| def points_in_boxes_part(points, boxes): | |
| """Find the box in which each point is (CUDA). | |
| Args: | |
| points (torch.Tensor): [B, M, 3], [x, y, z] in LiDAR/DEPTH coordinate | |
| boxes (torch.Tensor): [B, T, 7], | |
| num_valid_boxes <= T, [x, y, z, x_size, y_size, z_size, rz] in | |
| LiDAR/DEPTH coordinate, (x, y, z) is the bottom center | |
| Returns: | |
| box_idxs_of_pts (torch.Tensor): (B, M), default background = -1 | |
| """ | |
| assert points.shape[0] == boxes.shape[0], \ | |
| 'Points and boxes should have the same batch size, ' \ | |
| f'but got {points.shape[0]} and {boxes.shape[0]}' | |
| assert boxes.shape[2] == 7, \ | |
| 'boxes dimension should be 7, ' \ | |
| f'but got unexpected shape {boxes.shape[2]}' | |
| assert points.shape[2] == 3, \ | |
| 'points dimension should be 3, ' \ | |
| f'but got unexpected shape {points.shape[2]}' | |
| batch_size, num_points, _ = points.shape | |
| box_idxs_of_pts = points.new_zeros((batch_size, num_points), | |
| dtype=torch.int).fill_(-1) | |
| # If manually put the tensor 'points' or 'boxes' on a device | |
| # which is not the current device, some temporary variables | |
| # will be created on the current device in the cuda op, | |
| # and the output will be incorrect. | |
| # Therefore, we force the current device to be the same | |
| # as the device of the tensors if it was not. | |
| # Please refer to https://github.com/open-mmlab/mmdetection3d/issues/305 | |
| # for the incorrect output before the fix. | |
| points_device = points.get_device() | |
| assert points_device == boxes.get_device(), \ | |
| 'Points and boxes should be put on the same device' | |
| if torch.cuda.current_device() != points_device: | |
| torch.cuda.set_device(points_device) | |
| ext_module.points_in_boxes_part_forward(boxes.contiguous(), | |
| points.contiguous(), | |
| box_idxs_of_pts) | |
| return box_idxs_of_pts | |
| def points_in_boxes_cpu(points, boxes): | |
| """Find all boxes in which each point is (CPU). The CPU version of | |
| :meth:`points_in_boxes_all`. | |
| Args: | |
| points (torch.Tensor): [B, M, 3], [x, y, z] in | |
| LiDAR/DEPTH coordinate | |
| boxes (torch.Tensor): [B, T, 7], | |
| num_valid_boxes <= T, [x, y, z, x_size, y_size, z_size, rz], | |
| (x, y, z) is the bottom center. | |
| Returns: | |
| box_idxs_of_pts (torch.Tensor): (B, M, T), default background = 0. | |
| """ | |
| assert points.shape[0] == boxes.shape[0], \ | |
| 'Points and boxes should have the same batch size, ' \ | |
| f'but got {points.shape[0]} and {boxes.shape[0]}' | |
| assert boxes.shape[2] == 7, \ | |
| 'boxes dimension should be 7, ' \ | |
| f'but got unexpected shape {boxes.shape[2]}' | |
| assert points.shape[2] == 3, \ | |
| 'points dimension should be 3, ' \ | |
| f'but got unexpected shape {points.shape[2]}' | |
| batch_size, num_points, _ = points.shape | |
| num_boxes = boxes.shape[1] | |
| point_indices = points.new_zeros((batch_size, num_boxes, num_points), | |
| dtype=torch.int) | |
| for b in range(batch_size): | |
| ext_module.points_in_boxes_cpu_forward(boxes[b].float().contiguous(), | |
| points[b].float().contiguous(), | |
| point_indices[b]) | |
| point_indices = point_indices.transpose(1, 2) | |
| return point_indices | |
| def points_in_boxes_all(points, boxes): | |
| """Find all boxes in which each point is (CUDA). | |
| Args: | |
| points (torch.Tensor): [B, M, 3], [x, y, z] in LiDAR/DEPTH coordinate | |
| boxes (torch.Tensor): [B, T, 7], | |
| num_valid_boxes <= T, [x, y, z, x_size, y_size, z_size, rz], | |
| (x, y, z) is the bottom center. | |
| Returns: | |
| box_idxs_of_pts (torch.Tensor): (B, M, T), default background = 0. | |
| """ | |
| assert boxes.shape[0] == points.shape[0], \ | |
| 'Points and boxes should have the same batch size, ' \ | |
| f'but got {boxes.shape[0]} and {boxes.shape[0]}' | |
| assert boxes.shape[2] == 7, \ | |
| 'boxes dimension should be 7, ' \ | |
| f'but got unexpected shape {boxes.shape[2]}' | |
| assert points.shape[2] == 3, \ | |
| 'points dimension should be 3, ' \ | |
| f'but got unexpected shape {points.shape[2]}' | |
| batch_size, num_points, _ = points.shape | |
| num_boxes = boxes.shape[1] | |
| box_idxs_of_pts = points.new_zeros((batch_size, num_points, num_boxes), | |
| dtype=torch.int).fill_(0) | |
| # Same reason as line 25-32 | |
| points_device = points.get_device() | |
| assert points_device == boxes.get_device(), \ | |
| 'Points and boxes should be put on the same device' | |
| if torch.cuda.current_device() != points_device: | |
| torch.cuda.set_device(points_device) | |
| ext_module.points_in_boxes_all_forward(boxes.contiguous(), | |
| points.contiguous(), | |
| box_idxs_of_pts) | |
| return box_idxs_of_pts | |