209b830f5f6ce1c28e207f46e68f4c431fb71820,torchsample/modules/module_trainer.py,ModuleTrainer,evaluate,#ModuleTrainer#,462

Before Change

            samples_seen += batch_size
            eval_logs["val_loss"] = (samples_seen*eval_logs["val_loss"] + loss.data[0]*batch_size) / (samples_seen+batch_size)

        if self._in_train_loop:
            return eval_logs
        else:
            return eval_logs["val_loss"]
        self.model.train(mode=True)

    def evaluate_loader(self,
                        loader,

After Change

            eval_logs["val_loss"] = (samples_seen*eval_logs["val_loss"] + loss.data[0]*batch_size) / (samples_seen+batch_size)
            
            if self._has_metrics:
                metric_container.reset()
                metrics_logs = metric_container(output_batch, target_batch)
                eval_logs.update(metrics_logs)

        self.model.train(mode=True)

Instances