| import numpy as np | |
| import torch | |
| class EarlyStoppingLoss: | |
| """Early stops the training if validation loss doesn't improve after a given patience. | |
| Source : https://github.com/Bjarten/early-stopping-pytorch | |
| """ | |
| def __init__(self, patience=7, | |
| verbose=False, | |
| delta=0, | |
| out_folder='.', | |
| path='least_validation_loss.pth', | |
| trace_func=print): | |
| """ | |
| 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 | |
| path (str): Path for the checkpoint to be saved to. | |
| Default: 'checkpoint.pt' | |
| trace_func (function): trace print function. | |
| Default: print | |
| """ | |
| self.patience = patience | |
| self.verbose = verbose | |
| self.counter = 0 | |
| self.best_score = None | |
| self.early_stop = False | |
| self.val_loss_min = np.inf | |
| self.out_folder = out_folder | |
| self.delta = delta | |
| self.path = path | |
| self.trace_func = trace_func | |
| 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 | |
| self.trace_func(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: | |
| self.trace_func( | |
| f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...') | |
| torch.save(model.state_dict(), self.out_folder + 'least_validation_model.pth') | |
| self.val_loss_min = val_loss | |
| class EarlyStopping: | |
| """Early stops the training if validation AUC doesn't improve after a given patience.""" | |
| def __init__(self, patience=7, | |
| verbose=False, | |
| delta=0.0, | |
| out_folder='.', | |
| path='best_auc_model.pth', | |
| trace_func=print): | |
| """ | |
| Args: | |
| patience (int): How long to wait after last time validation AUC improved. | |
| verbose (bool): If True, prints a message for each validation AUC improvement. | |
| delta (float): Minimum change in AUC to qualify as improvement. | |
| out_folder (str): Folder to save checkpoint. | |
| path (str): Filename for saved model. | |
| trace_func (function): Custom print/logging function. | |
| """ | |
| self.patience = patience | |
| self.verbose = verbose | |
| self.delta = delta | |
| self.out_folder = out_folder | |
| self.path = path | |
| self.trace_func = trace_func | |
| self.counter = 0 | |
| self.best_score = None | |
| self.early_stop = False | |
| self.val_auc_max = float('-inf') | |
| def __call__(self, val_auc, model): | |
| """ | |
| Call this at the end of each epoch. | |
| Args: | |
| val_auc (float): Validation AUC from current epoch. | |
| model (nn.Module): PyTorch model to be saved if AUC improves. | |
| """ | |
| score = val_auc | |
| if self.best_score is None: | |
| self.best_score = score | |
| self.save_checkpoint(val_auc, model) | |
| elif score < self.best_score + self.delta: | |
| self.counter += 1 | |
| self.trace_func(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_auc, model) | |
| self.counter = 0 | |
| def save_checkpoint(self, val_auc, model): | |
| """Save model when validation AUC improves.""" | |
| if self.verbose: | |
| self.trace_func( | |
| f'Validation AUC increased ({self.val_auc_max:.6f} --> {val_auc:.6f}). Saving model ...') | |
| torch.save(model.state_dict(), self.out_folder + '/' + self.path) | |
| self.val_auc_max = val_auc | |