def backward(ctx, grad_out):
# type: (any, torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]
r"""
Parameters
----------
grad_out : torch.Tensor
(B, c, n) tensor with gradients of ouputs
Returns
-------
grad_features : torch.Tensor
(B, c, m) tensor with gradients of features
None
None
"""
idx, weight, m = ctx.three_interpolate_for_backward
# idx, weight, m = ctx.saved_tensors
grad_features = _ext.three_interpolate_grad(
grad_out.contiguous(), idx, weight, m
)
# return grad_features, None, None
return grad_features, torch.zeros_like(idx), torch.zeros_like(weight)
def backward(ctx, grad_out):
"""
:param ctx:
:param grad_out: (B, C, N) tensor with gradients of outputs
:return:
grad_features: (B, C, M) tensor with gradients of features
None:
None:
"""
idx, weight, m = ctx.three_interpolate_for_backward
grad_features = _ext.three_interpolate_grad(grad_out.contiguous(), idx,
weight, m)
return grad_features, None, None
def backward(ctx, grad_out):
# type: (Any, torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
r"""
Parameters
----------
grad_out : torch.Tensor
(B, c, n) tensor with gradients of ouputs
Returns
-------
grad_features : torch.Tensor
(B, c, m) tensor with gradients of features
None
None
"""
idx, weight, m = ctx.three_interpolate_for_backward
grad_features = _ext.three_interpolate_grad(grad_out.contiguous(), idx,
weight, m)
return grad_features, None, None
