Source code for poutyne.framework.callbacks.lr_scheduler

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import inspect
import sys
from typing import BinaryIO, Dict

import torch.optim.lr_scheduler
from torch.optim import Optimizer

    from torch.optim.lr_scheduler import LRScheduler
except ImportError:
    from torch.optim.lr_scheduler import _LRScheduler as LRScheduler

from poutyne.framework.callbacks.callbacks import Callback

class _PyTorchLRSchedulerWrapper(Callback):
    Default class for the LR scheduling callback. Proposes default comportment for the scheduler
    loading and saving as well as for the epoch end handling.

    def __init__(self, torch_lr_scheduler, *args, **kwargs):
        if len(args) > 0 and isinstance(args[0], Optimizer):
            raise ValueError(
                "In the LR scheduler callbacks, the optimizer is "
                "automatically passed to the PyTorch's LR scheduler. "
                "You must remove it from the arguments."
        self.args = args
        self.kwargs = kwargs
        self.scheduler = None
        self.state_to_load = None
        self.torch_lr_scheduler = torch_lr_scheduler

    def on_epoch_end(self, epoch_number: int, logs: Dict):

    def on_train_begin(self, logs: Dict):
        self.scheduler = self.torch_lr_scheduler(self.model.optimizer, *self.args, **self.kwargs)

        # Load state if the scheduler was not initialized when the user asked
        # to load its state
        if self.state_to_load is not None:
            self.state_to_load = None

    def load_state_dict(self, state_dict):
        if self.scheduler is not None:
            self.state_to_load = state_dict

    def state_dict(self):
        return self.scheduler.state_dict()

    def load_state(self, f: BinaryIO):
        self.load_state_dict(torch.load(f, map_location='cpu'))

    def save_state(self, f: BinaryIO):, f)

def new_init(torch_lr_scheduler):
    def f(self, *args, **kwargs):
        super(type(self), self).__init__(torch_lr_scheduler, *args, **kwargs)

    return f

for name, module_cls in torch.optim.lr_scheduler.__dict__.items():
    if inspect.isclass(module_cls) and issubclass(module_cls, LRScheduler) and module_cls != LRScheduler:
        _new_cls = type(
                '__init__': new_init(module_cls),
                '__doc__': f"""
        setattr(sys.modules[__name__], name, _new_cls)

[docs]class ReduceLROnPlateau(_PyTorchLRSchedulerWrapper): """ Args: monitor (str): The quantity to monitor. (Default value = 'val_loss') See: :class:`~torch.optim.lr_scheduler.ReduceLROnPlateau` """ def __init__(self, *args, monitor: str = 'val_loss', **kwargs): super().__init__(torch_lr_scheduler=torch.optim.lr_scheduler.ReduceLROnPlateau, *args, **kwargs) self.monitor = monitor def on_epoch_end(self, epoch_number: int, logs: Dict): self.scheduler.step(logs[self.monitor])