def backward(ctx, grad_out):
"""
:param ctx:
:param grad_out: (B, C, npoint, nsample) tensor of the gradients of the output from forward
:return:
grad_features: (B, C, N) gradient of the features
"""
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):
# 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):
# 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.saved_tensors.for_backwards
idx, N = ctx.for_backwards
# idx, N = ctx.saved_tensors
grad_features = _ext.group_points_grad(grad_out.contiguous(), idx, N)
# return grad_features, None
return grad_features, torch.zeros_like(idx)
