def run_epoch()

in Dassl.pytorch/dassl/engine/trainer.py [0:0]

• 36 lines of code
• 5 McCabe index (conditional complexity)

    def run_epoch(self):
        self.set_model_mode("train")
        losses = MetricMeter()
        batch_time = AverageMeter()
        data_time = AverageMeter()
        self.num_batches = len(self.train_loader_x)

        end = time.time()
        for self.batch_idx, batch in enumerate(self.train_loader_x):
            data_time.update(time.time() - end)
            loss_summary = self.forward_backward(batch)
            batch_time.update(time.time() - end)
            losses.update(loss_summary)

            meet_freq = (self.batch_idx + 1) % self.cfg.TRAIN.PRINT_FREQ == 0
            only_few_batches = self.num_batches < self.cfg.TRAIN.PRINT_FREQ
            if meet_freq or only_few_batches:
                nb_remain = 0
                nb_remain += self.num_batches - self.batch_idx - 1
                nb_remain += (
                    self.max_epoch - self.epoch - 1
                ) * self.num_batches
                eta_seconds = batch_time.avg * nb_remain
                eta = str(datetime.timedelta(seconds=int(eta_seconds)))

                info = []
                info += [f"epoch [{self.epoch + 1}/{self.max_epoch}]"]
                info += [f"batch [{self.batch_idx + 1}/{self.num_batches}]"]
                info += [f"time {batch_time.val:.3f} ({batch_time.avg:.3f})"]
                info += [f"data {data_time.val:.3f} ({data_time.avg:.3f})"]
                info += [f"{losses}"]
                info += [f"lr {self.get_current_lr():.4e}"]
                info += [f"eta {eta}"]
                print(" ".join(info))

            n_iter = self.epoch * self.num_batches + self.batch_idx
            for name, meter in losses.meters.items():
                self.write_scalar("train/" + name, meter.avg, n_iter)
            self.write_scalar("train/lr", self.get_current_lr(), n_iter)

            end = time.time()