earlystopping.py

import torch


class EarlyStopping_acc:
    """Early stops the training if test acc doesn't improve after a given patience."""

    def __init__(
        self, patience=7, verbose=False, delta=0, path="checkpoint.pt", trace_func=print
    ):
        """
        Args:
            patience (int): How long to wait after last time validation loss improved.
                            Default: 7
            verbose (bool): If True, prints a message for each validation loss improvement.
                            Default: False
            delta (float): Minimum change in the monitored quantity to qualify as an improvement.
                            Default: 0
            path (str): Path for the checkpoint to be saved to.
                            Default: 'checkpoint.pt'
            trace_func (function): trace print function.
                            Default: print
        """
        self.patience = patience
        self.verbose = verbose
        self.counter = 0
        self.best_score = None
        self.early_stop = False
        self.test_loss_min = 0
        self.delta = delta
        self.path = path
        self.trace_func = trace_func

    def __call__(self, test_loss, model):
        score = test_loss
        if self.best_score is None:
            self.best_score = score
            self.save_checkpoint(test_loss, model)
        elif score <= self.best_score + self.delta:
            self.counter += 1
            self.trace_func(
                f"EarlyStopping counter: {self.counter} out of {self.patience}"
            )
            if self.counter >= self.patience:
                self.early_stop = True
                self.counter = 0
        else:
            self.best_score = score
            self.save_checkpoint(test_loss, model)
            self.counter = 0

    def save_checkpoint(self, test_loss, model):
        """Saves model when test acc increases."""
        if self.verbose:
            self.trace_func(
                f"Test acc increased ({self.test_loss_min:.6f} --> {test_loss:.6f}).  Saving model ..."
            )

        torch.save(model.state_dict(), self.path)
        self.test_loss_min = test_loss