def _training_report_dict(self): report_dict = super(IMSATVATIICGeoTrainer, self)._training_report_dict report_dict.update({ "train_head_A": self.METERINTERFACE["train_head_A"].summary()["mean"], "train_head_B": self.METERINTERFACE["train_head_B"].summary()["mean"] }) return dict_filter(report_dict)
def _training_report_dict(self): report_dict = super()._training_report_dict report_dict.update( {"mixup": self.METERINTERFACE["train_mixup"].summary()["mean"]} ) # i do not delete the adv meter but I have dict_filter return dict_filter(report_dict)
def _eval_report_dict(self): report_dict = { "average_acc": self.METERINTERFACE.val_average_acc.summary()["mean"], "best_acc": self.METERINTERFACE.val_best_acc.summary()["mean"], } report_dict = dict_filter(report_dict, lambda k, v: v != 0.0) return report_dict
def _training_report_dict(self): # vat geo report_dict report_dict = super()._training_report_dict report_dict.update( {"train_geo": self.METERINTERFACE["train_geo"].summary()["mean"]} ) # vat geo mixup report_dict return dict_filter(report_dict)
def _training_report_dict(self): report_dict = { "train_MI_A": self.METERINTERFACE["train_head_A"].summary()["mean"], "train_MI_B": self.METERINTERFACE["train_head_B"].summary()["mean"], "train_adv_A": self.METERINTERFACE["train_adv_A"].summary()["mean"], "train_adv_B": self.METERINTERFACE["train_adv_B"].summary()["mean"], } report_dict = dict_filter(report_dict, lambda k, v: v != 0.0) return report_dict
def _eval_report_dict(self): report_dict = dict_filter( flatten_dict({ "val_average_acc": self.METERINTERFACE.val_average_acc.summary()["mean"], "val_best_acc": self.METERINTERFACE.val_best_acc.summary()["mean"], }), lambda k, v: v != 0.0, ) return report_dict
def _training_report_dict(self): report_dict = dict_filter( flatten_dict({ "train_MI": self.METERINTERFACE.train_mi.summary()["mean"], "train_sat": self.METERINTERFACE.train_sat.summary()["mean"], }), lambda k, v: v != 0.0, ) return report_dict
def _training_report_dict(self): return dict_filter({ 'tra_sup_l': self.METERINTERFACE.tra_sup_label.summary()['mean'], 'tra_adv': self.METERINTERFACE.tra_adv.summary()['mean'], 'tra_ent': self.METERINTERFACE.tra_entropy.summary()['mean'], 'tra_acc': self.METERINTERFACE.tra_conf.summary()['acc'] })
def _training_report_dict(self): """ training related meters, including mi, entropy and Centropy. :return: """ report_dict = { "mi": self.METERINTERFACE["train_mi"].summary()["mean"], "entropy": self.METERINTERFACE["train_entropy"].summary()["mean"], "centropy": self.METERINTERFACE["train_centropy"].summary()["mean"], } return dict_filter(report_dict)
def _eval_report_dict(self) -> Dict[str, float]: """ return validation report dict :return: """ report_dict = { "average_acc": self.METERINTERFACE.val_average_acc.summary()["mean"], "best_acc": self.METERINTERFACE.val_best_acc.summary()["mean"], "worst_acc": self.METERINTERFACE.val_worst_acc.summary()["mean"], } report_dict = dict_filter(report_dict) return report_dict
def _training_report_dict(self): report_dict = super()._training_report_dict report_dict.update({"train_cutout": self.METERINTERFACE["train_cutout"].summary()["mean"]}) return dict_filter(report_dict)
def _training_report_dict(self): report_dict = super()._training_report_dict report_dict.update({"train_gaussian": self.METERINTERFACE["train_gaussian"]}) return dict_filter(report_dict)
def _training_report_dict(self): # add vat meter report_dict = super()._training_report_dict report_dict.update({"gaussian": self.METERINTERFACE["train_gaussian"].summary()["mean"]}) return dict_filter(report_dict) # type: ignore