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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
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