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