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import numpy as np
import torch
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):
"""
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
"""
self.patience = patience
self.verbose = verbose
self.counter = 0
self.best_score = None
self.early_stop = False
self.val_loss_min = np.Inf
def __call__(self, val_loss, model, epoch, save_path):
score = -val_loss
if self.best_score is None:
self.best_score = score
self.save_checkpoint(val_loss, model, epoch, save_path)
elif score < self.best_score:
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, epoch, save_path)
self.counter = 0
def save_checkpoint(self, val_loss, model, epoch, save_path):
'''Saves model when validation loss decrease.'''
if self.verbose:
print(
f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...'
)
torch.save(
model, save_path + "/" +
"checkpoint_{}_{:.6f}.pth.tar".format(epoch, val_loss))
self.val_loss_min = val_loss
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