Method load

python/paddle/hapi/model.py:1379–1454

(self, param_state_pairs, optim_state, scaler_state=None)

                paddle.save(scaler, path + '.pdscaler')

    def load(self, param_state_pairs, optim_state, scaler_state=None):
        # restore parameter states
        for param, state in param_state_pairs:
            param.set_value(state)

        if hasattr(self.model, '_scaler') and self.model._scaler is not None:
            if scaler_state:
                self.model._scaler.load_state_dict(scaler_state)

        # restore optimizer states
        if not self.model._optimizer or not optim_state:
            return

        # If optimizer performs set_state_dict when state vars haven't been created,
        # which would happen when set_state_dict before minimize, the state would be
        # stored in optimizer._accumulators_holder and loaded lazily.
        # To contrive this when loading from static-graph saved states, extend
        # state dict to include keys named according to dygraph naming rules.
        # TODO: if len(self.model._optimizer._accumulators) > 0
        converted_state = dict(optim_state)
        opt_unq_name = self.model._optimizer._name
        if opt_unq_name is None:
            opt_unq_name = ''

        opt_cls_name = self.model._optimizer.__class__.__name__
        opt_name = opt_unq_name[: opt_unq_name.rfind("_")]  # remove suffix idx
        param_names = [param.name for param in self.model.network.parameters()]
        for var_name, state_var in sorted(
            optim_state.items(), key=lambda x: len(x[0]), reverse=True
        ):
            if var_name in ["@LR_DECAY_COUNTER@", "global_step"]:
                # NOTE: dygraph saved global_step is 1 larger than that in
                # static-graph, since the time of global_step to increase is
                # different.
                if var_name == "@LR_DECAY_COUNTER@":
                    converted_state["global_step"] = (
                        np.array(converted_state.pop("@LR_DECAY_COUNTER@")) + 1
                    )
            else:
                # moment and other accumulators
                # extend state dict to include promising dygraph names
                for param_name in param_names:
                    if var_name.startswith(param_name + "_" + opt_name):
                        # when init optimizer with name
                        accum_name = var_name[
                            len(param_name + "_" + opt_name + "_") :
                        ]
                    elif (
                        var_name.startswith(param_name + "_")
                        and opt_name == opt_cls_name
                    ):
                        # when init optimizer without name
                        accum_name = var_name[len(param_name + "_") :]
                    else:
                        continue
                    # remove suffix idx
                    accum_name = accum_name[: accum_name.rfind("_")]
                    # state names always end with "_0" in dygraph because of the