102123eef25500a39160412eca7e8cb9f53104a6,trainer/trainer.py,Trainer,_train_epoch,#Trainer#Any#,36

Before Change

        self.model.train()

        total_loss = 0
        total_metrics = np.zeros(len(self.metrics))
        for batch_idx, (data, target) in enumerate(self.data_loader):
            data, target = data.to(self.device), target.to(self.device)

            self.optimizer.zero_grad()
            output = self.model(data)
            loss = self.criterion(output, target)
            loss.backward()
            self.optimizer.step()

            self.writer.set_step((epoch - 1) * self.len_epoch + batch_idx)
            self.writer.add_scalar("loss", loss.item())
            total_loss += loss.item()
            total_metrics += self._eval_metrics(output, target)

            if batch_idx % self.log_step == 0:
                self.logger.debug("Train Epoch: {} {} Loss: {:.6f}".format(
                    epoch,

After Change

        if self.do_validation:
            val_log = self._valid_epoch(epoch)
            log.update(**{"val_"+k : v for k, v in val_log.items()})

        if self.lr_scheduler is not None:
            self.lr_scheduler.step()

Instances