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import numpy as np
from utils1.trainer import Trainer
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: float, trainer: Trainer):
if self.best_score is None:
self.best_score = score
self.save_checkpoint(score, trainer)
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, trainer)
self.counter = 0
def save_checkpoint(self, score: float, trainer: Trainer):
"""Saves model when validation loss decrease."""
if self.verbose:
print(f"Validation accuracy increased ({self.score_max:.6f} --> {score:.6f}). Saving model ...")
trainer.save_networks("best")
self.score_max = score
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