73b2221b5eb4fd21802e6bf41e21d5df8ef9bf2c,src/sdk/pynni/nni/nas/pytorch/darts/trainer.py,DartsTrainer,_compute_hessian,#DartsTrainer#Any#Any#Any#Any#,119

Before Change

        hessian = (dalpha { L_trn(w+, alpha) } - dalpha { L_trn(w-, alpha) }) / (2*eps)
        eps = 0.01 / ||dw||
        
        self.model.load_state_dict(model)

        norm = torch.cat([w.view(-1) for w in dw]).norm()
        eps = 0.01 / norm

After Change

                for p, d in zip(self.model.parameters(), dw):
                    p += e * d

            _, loss = self._logits_and_loss(trn_X, trn_y)
            dalphas.append(torch.autograd.grad(loss, self.mutator.parameters()))

        dalpha_pos, dalpha_neg = dalphas  // dalpha { L_trn(w+) }, // dalpha { L_trn(w-) }

Instances