pytorch-image-models/scheduler/scheduler.py

from typing import Dict, Any

import torch


class Scheduler:
    """ Parameter Scheduler Base Class
    A scheduler base class that can be used to schedule any optimizer parameter groups.

    Unlike the builtin PyTorch schedulers, this is intended to be consistently called
    * At the END of each epoch, before incrementing the epoch count, to calculate next epoch's value
    * At the END of each optimizer update, after incrementing the update count, to calculate next update's value

    The schedulers built on this should try to remain as stateless as possible (for simplicity).

    This family of schedulers is attempting to avoid the confusion of the meaning of 'last_epoch'
    and -1 values for special behaviour. All epoch and update counts must be tracked in the training
    code and explicitly passed in to the schedulers on the corresponding step or step_update call.

    Based on ideas from:
     * https://github.com/pytorch/fairseq/tree/master/fairseq/optim/lr_scheduler
     * https://github.com/allenai/allennlp/tree/master/allennlp/training/learning_rate_schedulers
    """

    def __init__(self,
                 optimizer: torch.optim.Optimizer,
                 param_group_field: str,
                 initialize: bool = True) -> None:
        self.optimizer = optimizer
        self.param_group_field = param_group_field
        self._initial_param_group_field = f"initial_{param_group_field}"
        if initialize:
            for i, group in enumerate(self.optimizer.param_groups):
                if param_group_field not in group:
                    raise KeyError(f"{param_group_field} missing from param_groups[{i}]")
                group.setdefault(self._initial_param_group_field, group[param_group_field])
        else:
            for i, group in enumerate(self.optimizer.param_groups):
                if self._initial_param_group_field not in group:
                    raise KeyError(f"{self._initial_param_group_field} missing from param_groups[{i}]")
        self.base_values = [group[self._initial_param_group_field] for group in self.optimizer.param_groups]
        self.metric = None  # any point to having this for all?
        self.update_groups(self.base_values)

    def state_dict(self) -> Dict[str, Any]:
        return {key: value for key, value in self.__dict__.items() if key != 'optimizer'}

    def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
        self.__dict__.update(state_dict)

    def get_epoch_values(self, epoch: int):
        return None

    def get_update_values(self, num_updates: int):
        return None

    def step(self, epoch: int, metric: float = None) -> None:
        self.metric = metric
        values = self.get_epoch_values(epoch + 1)  # +1 to calculate for next epoch
        if values is not None:
            self.update_groups(values)

    def step_update(self, num_updates: int, metric: float = None):
        self.metric = metric
        values = self.get_update_values(num_updates)
        if values is not None:
            self.update_groups(values)

    def update_groups(self, values):
        if not isinstance(values, (list, tuple)):
            values = [values] * len(self.optimizer.param_groups)
        for param_group, value in zip(self.optimizer.param_groups, values):
            param_group[self.param_group_field] = value