| import numpy as np |
| import torch |
|
|
|
|
| class EarlyStopping: |
| """Early stops the training if validation loss doesn't improve after a given patience.""" |
| def __init__(self, patience=1, verbose=False, delta=0): |
| """ |
| 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.score_max = -np.Inf |
| self.delta = delta |
|
|
| def __call__(self, score, model): |
| if self.best_score is None: |
| self.best_score = score |
| self.save_checkpoint(score, 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(score, model) |
| self.counter = 0 |
|
|
| def save_checkpoint(self, score, model): |
| '''Saves model when validation loss decrease.''' |
| if self.verbose: |
| print(f'Validation accuracy increased ({self.score_max:.6f} --> {score:.6f}). Saving model ...') |
| model.save_networks('best') |
| self.score_max = score |
