Source code for catalyst.core.callbacks.scheduler

from typing import Tuple

import torch

from catalyst.contrib.nn.schedulers import BatchScheduler, OneCycleLRWithWarmup
from catalyst.core import utils
from catalyst.core.callback import Callback, CallbackNode, CallbackOrder
from catalyst.core.runner import IRunner


class SchedulerCallback(Callback):
    """@TODO: Docs. Contribution is welcome."""

    def __init__(
        self,
        scheduler_key: str = None,
        mode: str = None,
        reduced_metric: str = None,
    ):
        """@TODO: Docs. Contribution is welcome."""
        super().__init__(order=CallbackOrder.Scheduler, node=CallbackNode.All)
        self.scheduler_key = scheduler_key
        self.mode = mode
        self.reduced_metric = reduced_metric

    @staticmethod
    def _scheduler_step(
        scheduler, reduced_metric=None,
    ):
        if isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
            scheduler.step(reduced_metric)
            lr = scheduler.optimizer.param_groups[0]["lr"]
        else:
            scheduler.step()
            lr = scheduler.get_lr()[0]

        momentum = utils.get_optimizer_momentum(scheduler.optimizer)

        return lr, momentum

    def step_batch(self, runner: IRunner) -> None:
        """@TODO: Docs. Contribution is welcome.

        Args:
            runner (IRunner): current runner
        """
        lr, momentum = self._scheduler_step(scheduler=self._scheduler)

        if self.scheduler_key is not None:
            runner.batch_metrics[f"lr/{self.scheduler_key}"] = lr
            if momentum is not None:
                runner.batch_metrics[
                    f"momentum/{self.scheduler_key}"
                ] = momentum
        else:
            runner.batch_metrics["lr"] = lr
            if momentum is not None:
                runner.batch_metrics["momentum"] = momentum

    def step_epoch(self, runner: IRunner) -> None:
        """@TODO: Docs. Contribution is welcome.

        Args:
            runner (IRunner): current runner
        """
        reduced_metric = runner.valid_metrics[self.reduced_metric]
        lr, momentum = self._scheduler_step(
            scheduler=self._scheduler, reduced_metric=reduced_metric
        )

        if self.scheduler_key is not None:
            runner.epoch_metrics[f"lr/{self.scheduler_key}"] = lr
            if momentum is not None:
                runner.epoch_metrics[
                    f"momentum/{self.scheduler_key}"
                ] = momentum
        else:
            runner.epoch_metrics["lr"] = lr
            if momentum is not None:
                runner.epoch_metrics["momentum"] = momentum

    def on_stage_start(self, runner: IRunner) -> None:
        """Stage start hook.

        Args:
            runner (IRunner): current runner
        """
        self.reduced_metric = self.reduced_metric or runner.main_metric

        scheduler = runner.get_attr(
            key="scheduler", inner_key=self.scheduler_key
        )
        assert scheduler is not None
        self._scheduler = scheduler

        if self.mode is None:
            if isinstance(scheduler, BatchScheduler):
                self.mode = "batch"
            else:
                self.mode = "epoch"

        if (
            isinstance(scheduler, OneCycleLRWithWarmup)
            and self.mode == "batch"
        ):
            scheduler.reset()
        assert self.mode is not None

    def on_loader_start(self, runner: IRunner) -> None:
        """Loader start hook.

        Args:
            runner (IRunner): current runner
        """
        if (
            runner.is_train_loader
            and isinstance(self._scheduler, OneCycleLRWithWarmup)
            and self.mode == "batch"
        ):
            self._scheduler.recalculate(
                loader_len=runner.loader_len, current_step=runner.epoch
            )

    def on_batch_end(self, runner: IRunner) -> None:
        """Batch end hook.

        Args:
            runner (IRunner): current runner
        """
        if runner.is_train_loader and self.mode == "batch":
            self.step_batch(runner=runner)

    def on_epoch_end(self, runner: IRunner) -> None:
        """Epoch end hook.

        Args:
            runner (IRunner): current runner
        """
        if self.mode == "epoch":
            self.step_epoch(runner=runner)


class LRUpdater(Callback):
    """Basic class that all Lr updaters inherit from."""

    def __init__(self, optimizer_key: str = None):
        """
        Args:
            optimizer_key (str): which optimizer key to use
                for learning rate scheduling
        """
        super().__init__(order=CallbackOrder.Scheduler, node=CallbackNode.All)
        self.init_lr = 0
        self.optimizer_key = optimizer_key

    def calc_lr(self):
        """@TODO: Docs. Contribution is welcome."""
        return None

    def calc_momentum(self):
        """@TODO: Docs. Contribution is welcome."""
        return None

    @staticmethod
    def _update_lr(optimizer, new_lr) -> None:
        for pg in optimizer.param_groups:
            pg["lr"] = new_lr

    @staticmethod
    def _update_momentum(optimizer, new_momentum) -> None:
        if "betas" in optimizer.param_groups[0]:
            for pg in optimizer.param_groups:
                pg["betas"] = (new_momentum, pg["betas"][1])
        else:
            for pg in optimizer.param_groups:
                pg["momentum"] = new_momentum

    def _update_optimizer(self, optimizer) -> Tuple[float, float]:
        new_lr = self.calc_lr()
        if new_lr is not None:
            self._update_lr(optimizer, new_lr)

        new_momentum = self.calc_momentum()
        if new_momentum is not None:
            self._update_momentum(optimizer, new_momentum)
        else:
            new_momentum = utils.get_optimizer_momentum(optimizer)

        return new_lr, new_momentum

    def update_optimizer(self, runner: IRunner) -> None:
        """@TODO: Docs. Contribution is welcome.

        Args:
            runner (IRunner): current runner
        """
        lr, momentum = self._update_optimizer(optimizer=self._optimizer)

        if self.optimizer_key is not None:
            runner.batch_metrics[f"lr_{self.optimizer_key}"] = lr
            runner.batch_metrics[f"momentum_{self.optimizer_key}"] = momentum
        else:
            runner.batch_metrics["lr"] = lr
            runner.batch_metrics["momentum"] = momentum

    def on_stage_start(self, runner: IRunner) -> None:
        """Stage start hook.

        Args:
            runner (IRunner): current runner
        """
        optimizer = runner.get_attr(
            key="optimizer", inner_key=self.optimizer_key
        )
        assert optimizer is not None
        self._optimizer = optimizer
        self.init_lr = optimizer.defaults["lr"]

    def on_loader_start(self, runner: IRunner) -> None:
        """Loader start hook.

        Args:
            runner (IRunner): current runner
        """
        if runner.is_train_loader:
            self.update_optimizer(runner=runner)

    def on_batch_end(self, runner: IRunner) -> None:
        """Batch end hook.

        Args:
            runner (IRunner): current runner
        """
        if runner.is_train_loader:
            self.update_optimizer(runner=runner)


__all__ = ["SchedulerCallback", "LRUpdater"]