def backward(ctx, grad_out):
"""idx, C, N = ctx.for_backwards
B, npoint = idx.size()
grad_features = Variable(torch.cuda.FloatTensor(B, C, N).zero_())
grad_out_data = grad_out.data.contiguous()
pointnet2.gather_points_grad_wrapper(
B, C, N, npoint, grad_out_data, idx, grad_features.data
)"""
idx, C, N = ctx.for_backwards
grad_features = _ext.gather_points_grad(grad_out.contiguous(), idx, N)
return grad_features, None
def backward(ctx, grad_out):
idx, features = ctx.saved_tensors
N = features.size(2)
grad_features = _ext.gather_points_grad(grad_out.contiguous(), idx, N)
return grad_features, None
def backward(ctx, grad_out):
idx, C, N = ctx.for_backwards
grad_features = _ext.gather_points_grad(grad_out.contiguous(), idx, N)
return grad_features, None