def __call__()

in src/diarizers/utils.py [0:0]

• 15 lines of code
• 2 McCabe index (conditional complexity)

    def __call__(self, eval_pred):

        logits, labels = eval_pred

        if self.powerset:
            predictions = self.model_powerset.to_multilabel(torch.tensor(logits))
        else:
            predictions = torch.tensor(logits)

        labels = torch.tensor(labels)

        predictions = torch.transpose(predictions, 1, 2)
        labels = torch.transpose(labels, 1, 2)

        metrics = {"der": 0, "false_alarm": 0, "missed_detection": 0, "confusion": 0}

        metrics["der"] += self.metrics["der"](predictions, labels).cpu().numpy()
        metrics["false_alarm"] += self.metrics["false_alarm"](predictions, labels).cpu().numpy()
        metrics["missed_detection"] += self.metrics["missed_detection"](predictions, labels).cpu().numpy()
        metrics["confusion"] += self.metrics["confusion"](predictions, labels).cpu().numpy()

        return metrics