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torchcallback.py
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torchcallback.py
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import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
"""
model check point and early stopping modules
"""
# We set check point to save model for getting best validation loss
# this module is stored when the validation loss decreases for each epoch
# and, if validation loss does not decrease in the next epoch in training, do not store it
class CheckPoint:
def __init__(self, verbose=False, path='checkpoint.pt', trace_func=print):
self.verbose = verbose
self.path = path
self.trace_func = trace_func
self.val_loss_min = np.Inf
self.best_score = None
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.best_score = score
self.save_checkpoint(val_loss, model)
def save_checkpoint(self, val_loss, model):
if self.verbose:
self.trace_func(f'Validation loss decreased ({self.val_loss_min:.3f} --> {val_loss:.3f}). Saving model ...')
torch.save(model.state_dict(), self.path)
self.val_loss_min = val_loss
# We set early stopping to check over fitting of model
# this module can be perform above checkpoint with early stopping
class EarlyStopping:
def __init__(self, patience=7, verbose=False, delta=0, path='es_checkpoint.pt', trace_func=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.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_model(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_model(val_loss, model)
self.counter = 0
def save_model(self, val_loss, model):
'''Saves model when validation loss decrease.'''
if self.verbose:
self.trace_func(f'Validation loss decreased ({self.val_loss_min:.3f} --> {val_loss:.3f}). Saving model ...')
torch.save(model.state_dict(), self.path)
self.val_loss_min = val_loss