def forward(ctx, features: torch.Tensor,
idx: torch.Tensor) -> torch.Tensor:
r"""
Parameters
----------
features : torch.Tensor
(B, C, N) tensor
idx : torch.Tensor
(B, npoint) tensor of the features to gather
Returns
-------
torch.Tensor
(B, C, npoint) tensor
"""
assert features.is_contiguous()
assert idx.is_contiguous()
B, npoint = idx.size()
_, C, N = features.size()
#output = torch.cuda.FloatTensor(B, C, npoint)
#pointnet2.gather_points_wrapper(
# B, C, N, npoint, features, idx, output
#)
ctx.for_backwards = (idx, C, N)
#return output
return _ext.gather_points(features, idx)
def forward(ctx, features, idx):
# type: (Any, torch.Tensor, torch.Tensor) -> torch.Tensor
r"""
Parameters
----------
features : torch.Tensor
(B, C, N) tensor
idx : torch.Tensor
(B, npoint) tensor of the features to gather
Returns
-------
torch.Tensor
(B, C, npoint) tensor
"""
ctx.save_for_backward(idx, features)
return _ext.gather_points(features, idx)