def forward(ctx, features: torch.Tensor,
idx: torch.Tensor) -> torch.Tensor:
r"""
Parameters
----------
features : torch.Tensor
(B, C, N) tensor of points to group
idx : torch.Tensor
(B, npoint, nsample) tensor containing the indicies of points to group with
Returns
-------
torch.Tensor
(B, C, npoint, nsample) tensor
"""
assert features.is_contiguous()
assert idx.is_contiguous()
B, nfeatures, nsample = idx.size()
_, C, N = features.size()
#output = torch.cuda.FloatTensor(B, C, nfeatures, nsample)
#pointnet2.group_points_wrapper(
# B, C, N, nfeatures, nsample, features, idx, output
#)
ctx.for_backwards = (idx, N)
return _ext.group_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 of features to group
idx : torch.Tensor
(B, npoint, nsample) tensor containing the indicies of features to group with
Returns
-------
torch.Tensor
(B, C, npoint, nsample) tensor
"""
ctx.save_for_backward(idx, features)
return _ext.group_points(features, idx)
def forward(ctx, features, idx):
B, nfeatures, nsample = idx.size()
_, C, N = features.size()
ctx.for_backwards = (idx, N)
return _ext.group_points(features, idx)