def evaluate_epoch()

in src/train.py [0:0]

• 25 lines of code
• 6 McCabe index (conditional complexity)

    def evaluate_epoch(self, epoch):
        for meter in self.evals_recon:
            meter.reset()
        self.eval_d_right.reset()
        self.eval_total.reset()

        self.encoder.eval()
        self.decoder.eval()
        self.discriminator.eval()

        n_batches = int(np.ceil(self.args.epoch_len / 10))

        with tqdm(total=n_batches) as valid_enum, \
                torch.no_grad():
            for batch_num in range(n_batches):
                if self.args.short and batch_num == 10:
                    break

                if self.args.distributed:
                    assert self.args.rank < self.args.n_datasets, "No. of workers must be equal to #dataset"
                    dset_num = self.args.rank
                else:
                    dset_num = batch_num % self.args.n_datasets

                x, x_aug = next(self.data[dset_num].valid_iter)

                x = wrap(x)
                x_aug = wrap(x_aug)
                batch_loss = self.eval_batch(x, x_aug, dset_num)

                valid_enum.set_description(f'Test (loss: {batch_loss:.2f}) epoch {epoch}')
                valid_enum.update()