def backward(ctx,
grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
r"""
Parameters
----------
grad_out : torch.Tensor
(B, C, npoint, nsample) tensor of the gradients of the output from forward
Returns
-------
torch.Tensor
(B, C, N) gradient of the features
None
"""
idx, N = ctx.for_backwards
#B, C, npoint, nsample = grad_out.size()
#grad_features = Variable(torch.cuda.FloatTensor(B, C, N).zero_())
#grad_out_data = grad_out.data.contiguous()
#pointnet2.group_points_grad_wrapper(
# B, C, N, npoint, nsample, grad_out_data, idx, grad_features.data
#)
grad_features = _ext.group_points_grad(grad_out.contiguous(), idx, N)
return grad_features, None
def backward(ctx, grad_out):
# type: (Any, torch.tensor) -> Tuple[torch.Tensor, torch.Tensor]
r"""
Parameters
----------
grad_out : torch.Tensor
(B, C, npoint, nsample) tensor of the gradients of the output from forward
Returns
-------
torch.Tensor
(B, C, N) gradient of the features
None
"""
idx, N = ctx.for_backwards
grad_features = _ext.group_points_grad(grad_out.contiguous(), idx, N)
return grad_features, None
def backward(ctx, grad_out):
idx, N = ctx.for_backwards
grad_features = _ext.group_points_grad(grad_out.contiguous(), idx, N)
return grad_features, None