def configure_optimizers()

in dpr_scale/task/dpr_task.py [0:0]

• 27 lines of code
• 3 McCabe index (conditional complexity)

    def configure_optimizers(self):
        self.optimizer = hydra.utils.instantiate(self.optim_conf, self.parameters())
        if self.trainer.max_steps:
            training_steps = self.trainer.max_steps
        else:
            steps_per_epoch = len(self.train_dataloader())
            training_steps = steps_per_epoch * self.trainer.max_epochs
        print(
            f"Configured LR scheduler for total {training_steps} training steps, "
            f"with {self.warmup_steps} warmup steps."
        )

        def lr_lambda(current_step):
            if current_step < self.warmup_steps:
                return float(current_step) / float(max(1, self.warmup_steps))
            return max(
                0.0,
                float(training_steps - current_step)
                / float(max(1, training_steps - self.warmup_steps)),
            )

        scheduler = LambdaLR(self.optimizer, lr_lambda)
        scheduler = {
            "scheduler": LambdaLR(self.optimizer, lr_lambda),
            "name": "learning_rate",
            "interval": "step",
            "frequency": 1,
        }
        return [self.optimizer], [scheduler]