def after_eval_step(self, *args, **kwargs):
self.report_dict = filter_dict(
flatten_dict(
self._trainer._meter_interface.tracking_status(
group_name=self._val_groupname)))
self.tqdm_indicator.update()
self.tqdm_indicator.set_postfix(self.report_dict)
def _eval_report_dict(self):
report_dict = flatten_dict({
"average_acc":
self.METERINTERFACE.val_average_acc.summary()["mean"],
"best_acc":
self.METERINTERFACE.val_best_acc.summary()["mean"],
})
return report_dict
def _eval_report_dict(self):
return flatten_dict(
{
"val_loss": self.METERINTERFACE["valloss"].summary()["mean"],
"val_acc": self.METERINTERFACE["valconf"].summary()["acc"],
},
sep="",
)
def _eval_report_dict(self):
return flatten_dict(
{
"val_loss": self.METERINTERFACE["valloss"].summary()["mean"],
"": self.METERINTERFACE["valdice"].summary(),
"b": self.METERINTERFACE["valbdice"].summary(),
},
sep="",
)
def _training_report_dict(self):
return flatten_dict(
{
"tra_loss": self.METERINTERFACE["trloss"].summary()["mean"],
"": self.METERINTERFACE["trdice"].summary(),
"lr": self.METERINTERFACE["lr"].summary()["mean"],
},
sep="_",
)
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 _eval_report_dict(self):
return flatten_dict({'val': self.METERINTERFACE.val_conf.summary()},
sep='_')
