102123eef25500a39160412eca7e8cb9f53104a6,base/base_trainer.py,BaseTrainer,train,#BaseTrainer#,60

Before Change

            // save logged informations into log dict
            log = {"epoch": epoch}
            for key, value in result.items():
                if key == "metrics":
                    log.update({mtr.__name__: value[i] for i, mtr in enumerate(self.metrics)})
                elif key == "val_metrics":
                    log.update({"val_" + mtr.__name__: value[i] for i, mtr in enumerate(self.metrics)})
                else:
                    log[key] = value

            // print logged informations to the screen
            for key, value in log.items():
                self.logger.info("    {:15s}: {}".format(str(key), value))

            // evaluate model performance according to configured metric, save best checkpoint as model_best

After Change

            // save logged informations into log dict
            log = {"epoch": epoch}
            log.update(result)

            // print logged informations to the screen
            for key, value in log.items():
                self.logger.info("    {:15s}: {}".format(str(key), value))

Instances