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| import numpy as np | |
| import torch | |
| from os.path import join | |
| import os | |
| class EarlyStopping: | |
| """Early stops the training if validation loss doesn't improve after a given patience.""" | |
| def __init__(self, patience=7, verbose=False, delta=0, save_path="."): | |
| """ | |
| 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 | |
| """ | |
| self.patience = patience | |
| self.verbose = verbose | |
| self.counter = 0 | |
| self.best_score = None | |
| self.early_stop = False | |
| self.val_loss_min = np.Inf | |
| self.delta = delta | |
| self.save_path = save_path | |
| 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 | |
| print(f'EarlyStopping counter: {self.counter} out of {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): | |
| '''Saves model when validation loss decrease.''' | |
| if self.verbose: | |
| print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...') | |
| # save_path = join(self.save_path, "best_model") | |
| # if not os.path.exists(save_path): | |
| # os.mkdir(save_path) | |
| # model_to_save = model.module if hasattr(model, 'module') else model | |
| # model_to_save.save_pretrained(save_path) | |
| self.val_loss_min = val_loss | |