Breaking Computation Graph

autograd
1

Hello everyone,
I have probably a problem which is probably not a problem… I’m trying to implement a custom nn.Module loss function.

# Here I want to compute repeatability between two sets of points
def repeatability(kp1, kp2, threshold=3):
    if 0 in (len(kp1_), len(kp2_)):
        return 0
    dist = torch.cdist(kp1_, kp2_)
    r_0 = torch.sum(dist.min(dim=0).values <= tau) # this function break the computation graph
    r_1 = torch.sum(dist.min(dim=1).values <= tau)
    rep = torch.div(r_0 + r_1,  len(kp1_) + len(kp2_))
    return rep
  k0 = torch.randn(50, 3)
  k1 = torch.randn(50, 3)
  repeatability_kp(k0, k1)

Is there a problem for the training step if I break the computation graph during loss computation ?
Can i just return like

return torch.tensor(rep, requires_grad=True)

Thank you for your response.

2

Based on your code snippet I don’t think it would be a problem to re-wrap rep into a new tensor, as it shouldn’t be attached to a computation graph in the first place (so you are not detaching it at all).