ai-station-code/wudingpv/taihuyuan_roof/schedulers/EarlyStop.py

#!/usr/bin/env python 
# -*- coding: utf-8 -*-
"""
@project: 
@File    : EarlyStop
@Author  : qiqq
@create_time    : 2022/11/5 11:39
"""

import numpy as np
import torch
import os

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=None, patience=10, 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_score):

        score = val_score

        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 ...')
    #     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.val_loss_min = val_loss




#
# 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 ...')
#         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.val_loss_min = val_loss