earlystopping.py

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=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.accs=0
		self.F1=0
		self.F2 = 0
		self.F3 = 0
		self.F4 = 0
		self.val_loss_min = np.Inf

	def __call__(self, val_loss, accs,F1,F2,F3,F4,model,modelname,str):

		score = -val_loss

		if self.best_score is None:
			self.best_score = score
			self.accs = accs
			self.F1 = F1
			self.F2 = F2
			self.F3 = F3
			self.F4 = F4
			self.save_checkpoint(val_loss, model,modelname,str)
		elif score < self.best_score:
			self.counter += 1
			# print('EarlyStopping counter: {} out of {}'.format(self.counter,self.patience))
			if self.counter >= self.patience:
				self.early_stop = True
				print("BEST Accuracy: {:.4f}|NR F1: {:.4f}|FR F1: {:.4f}|TR F1: {:.4f}|UR F1: {:.4f}"
					  .format(self.accs,self.F1,self.F2,self.F3,self.F4))
		else:
			self.best_score = score
			self.accs = accs
			self.F1 = F1
			self.F2 = F2
			self.F3 = F3
			self.F4 = F4
			self.save_checkpoint(val_loss, model,modelname,str)
			self.counter = 0

	def save_checkpoint(self, val_loss, model,modelname,str):
		'''Saves model when validation loss decrease.'''
		# if self.verbose:
		#     print('Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...'.format(self.val_loss_min,val_loss))
		torch.save(model.state_dict(),modelname+str+'.m')
		self.val_loss_min = val_loss