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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, save_path, patience=7, verbose=False, delta=0):
"""
Args:
save_path : 模型保存文件夹
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.save_path = save_path
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
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 ... best_network.pth ...')
path = os.path.join(self.save_path, 'best_network.pth')
# torch.save(model.state_dict(), path) # 这里会存储迄今最优模型的参数
self.save_networks(path, model)
self.val_loss_min = val_loss
def save_networks(self, save_path, model):
# serialize model and optimizer to dict
state_dict = {
'model': model.state_dict(),
}
torch.save(state_dict, save_path) |