def _train_epoch()

in sing/fondation/trainer.py [0:0]

• 20 lines of code
• 4 McCabe index (conditional complexity)

    def _train_epoch(self, dataset, epoch):
        """
        Train a single epoch on the given dataset and displays
        statistics from time to time.
        """
        loader = DataLoader(
            dataset,
            batch_size=self.batch_size,
            shuffle=True,
            collate_fn=collate)
        iterator = utils.progress_iterator(loader, divisions=20)

        total_loss = 0
        with tqdm.tqdm(total=len(dataset), unit="ex") as bar:
            for idx, (progress, batch) in enumerate(iterator):
                if self.cuda:
                    batch.cuda_()
                total_loss += self._train_batch(batch)
                bar.update(len(batch))
                if progress:
                    tqdm.tqdm.write(
                        "[train{}][{:03d}] {:.1f}%, loss {:.6f}".format(
                            self.suffix, epoch, progress,
                            total_loss / (idx + 1)))
        return total_loss