utils.py

import torch


class EarlyStopping:
    def __init__(self, patience=10, delta=0, path='checkpoint.pt', verbose=False, trace_func=print):
        self.patience = patience
        self.delta = delta
        self.counter = 0
        self.best_score = None
        self.early_stop = False
        self.val_loss_min = float('inf')
        self.path = path
        self.verbose = verbose
        self.trace_func = trace_func

    def __call__(self, val_loss, model):
        score = -val_loss

        if self.best_score is None:
            self.best_score = score
            self.save_checkpoint(val_loss, model)
        elif score < self.best_score + self.delta:
            self.counter += 1
            self.trace_func(f'EarlyStopping counter: {self.counter} / {self.patience}')
            if self.counter >= self.patience:
                self.early_stop = True
        else:
            self.best_score = score
            self.save_checkpoint(val_loss, model)
            self.counter = 0

    def save_checkpoint(self, val_loss, model):
        if self.verbose:
            cmd = 'Val loss decreased ({:.6f} --> {:.6f}). Saving model.'.format(self.val_loss_min, val_loss)
            self.trace_func(cmd)
        torch.save(model.state_dict(), self.path)
        self.val_loss_min = val_loss