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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
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