57 lines
2.2 KiB
Python
57 lines
2.2 KiB
Python
import numpy as np
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import torch
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import os
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class EarlyStopping:
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"""Early stops the training if validation loss doesn't improve after a given patience."""
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def __init__(self, model_name = 'model_ft', patience=7, verbose=False, delta=0, path='./snapshots/'):
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"""
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Args:
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patience (int): How long to wait after last time validation loss improved.
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Default: 7
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verbose (bool): If True, prints a message for each validation loss improvement.
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Default: False
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delta (float): Minimum change in the monitored quantity to qualify as an improvement.
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Default: 0
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path (str): Path for the checkpoint to be saved to.
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Default: 'checkpoint.pt'
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"""
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self.model_name = model_name
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self.patience = patience
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self.verbose = verbose
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self.counter = 0
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self.best_score = None
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self.early_stop = False
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self.val_loss_min = np.Inf
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self.delta = delta
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self.path = path
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def __call__(self, val_loss, model, epoch_label):
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score = -val_loss
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if self.best_score is None:
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self.best_score = score
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self.save_checkpoint(val_loss, model, epoch_label)
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elif score < self.best_score + self.delta:
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self.counter += 1
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print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
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if self.counter >= self.patience:
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self.early_stop = True
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else:
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self.best_score = score
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self.save_checkpoint(val_loss, model, epoch_label)
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self.counter = 0
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def save_checkpoint(self, val_loss, model, epoch_label):
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'''Saves model when validation loss decrease.'''
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if self.verbose:
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print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...')
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save_filename = self.model_name + '_%s.pth'% epoch_label
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save_path = os.path.join(self.path,save_filename)
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if not os.path.isdir(self.path):
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os.makedirs(self.path)
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torch.save(model.state_dict(), save_path)
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self.val_loss_min = val_loss
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