Method new

tools/train_utils/fastai_optim.py:121–126

Create a new `OptimWrapper` from `self` with another `layer_groups` but the same hyper-parameters.

(self, layer_groups)

        return opt

    def new(self, layer_groups):
        "Create a new `OptimWrapper` from `self` with another `layer_groups` but the same hyper-parameters."
        opt_func = getattr(self, 'opt_func', self.opt.__class__)
        split_groups = split_bn_bias(layer_groups)
        opt = opt_func([{'params': trainable_params(l), 'lr': 0} for l in split_groups])
        return self.create(opt_func, self.lr, layer_groups, wd=self.wd, true_wd=self.true_wd, bn_wd=self.bn_wd)

    def __repr__(self) -> str:
        return f'OptimWrapper over {repr(self.opt)}.\nTrue weight decay: {self.true_wd}'