| from typing import Tuple |
|
|
| import torch |
| from torch.autograd import Function |
|
|
| from ..utils import ext_loader |
|
|
| ext_module = ext_loader.load_ext('_ext', ['three_nn_forward']) |
|
|
|
|
| class ThreeNN(Function): |
| """Find the top-3 nearest neighbors of the target set from the source set. |
| |
| Please refer to `Paper of PointNet++ <https://arxiv.org/abs/1706.02413>`_ |
| for more details. |
| """ |
|
|
| @staticmethod |
| def forward(ctx, target: torch.Tensor, |
| source: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: |
| """ |
| Args: |
| target (Tensor): shape (B, N, 3), points set that needs to |
| find the nearest neighbors. |
| source (Tensor): shape (B, M, 3), points set that is used |
| to find the nearest neighbors of points in target set. |
| |
| Returns: |
| Tensor: shape (B, N, 3), L2 distance of each point in target |
| set to their corresponding nearest neighbors. |
| """ |
| target = target.contiguous() |
| source = source.contiguous() |
|
|
| B, N, _ = target.size() |
| m = source.size(1) |
| dist2 = torch.cuda.FloatTensor(B, N, 3) |
| idx = torch.cuda.IntTensor(B, N, 3) |
|
|
| ext_module.three_nn_forward(target, source, dist2, idx, b=B, n=N, m=m) |
| if torch.__version__ != 'parrots': |
| ctx.mark_non_differentiable(idx) |
|
|
| return torch.sqrt(dist2), idx |
|
|
| @staticmethod |
| def backward(ctx, a=None, b=None): |
| return None, None |
|
|
|
|
| three_nn = ThreeNN.apply |
|
|
