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pytorchtools.py
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pytorchtools.py
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# import numpy as np
# import torch
# class EarlyStopping:
# """Early stops the training if validation loss doesn't improve after a given patience."""
# def __init__(self, checkpointfilename, patience=7, verbose=False):
# """
# 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
# """
# self.patience = patience
# self.verbose = verbose
# self.counter = 0
# self.best_score = None
# self.early_stop = False
# self.val_loss_min = np.Inf
# self.checkpointfilename = checkpointfilename
# 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.counter += 1
# print('EarlyStopping counter: {} out of {}'.format(self.counter,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('Validation loss decreased ({} --> {}). Saving model ...'.format(self.val_loss_min,val_loss))
# torch.save(model.state_dict(), self.checkpointfilename)
# self.val_loss_min = val_loss
import numpy as np
import torch
import pickle
import os
class EarlyStopping:
"""Early stops the training if validation loss doesn't improve after a given patience."""
def __init__(self, patience=7, verbose=False):
"""
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
"""
self.patience = patience
self.verbose = verbose
self.counter = 0
self.best_score = None
self.early_stop = False
self.val_loss_min = np.Inf
# self.checkpointfilename = checkpointfilename
def __call__(self, val_loss, model, modelname, df, results_dict, parentfolder):
score = -val_loss
if self.best_score is None:
self.best_score = score
self.save_checkpoint(val_loss, model,modelname, df, results_dict, parentfolder)
elif score < self.best_score:
self.counter += 1
print('EarlyStopping counter: {} out of {}'.format(self.counter,self.patience))
if self.counter >= self.patience:
self.early_stop = True
else:
self.best_score = score
self.save_checkpoint(val_loss, model, modelname, df, results_dict,parentfolder)
self.counter = 0
def save_checkpoint(self, val_loss, model, modelname, df , results_dict, parentfolder):
# parentfolder = r"/mnt/ccipd/home/axh672/testretest/Tesla/"
parentfolder = r"Data/"
'''Saves model when validation loss decrease.'''
if self.verbose:
print('Validation loss decreased ({} --> {}). Saving model ...'.format(self.val_loss_min,val_loss))
if not os.path.exists("{}modelcheckpoints/{}".format(parentfolder,modelname)):
os.makedirs("{}modelcheckpoints/{}".format(parentfolder,modelname))
torch.save(model.state_dict(), "{}modelcheckpoints/{}/checkpoint.pt".format(parentfolder,modelname))
df.to_csv("{}modelcheckpoints/{}/predictions.csv".format(parentfolder,modelname))
with open("{}modelcheckpoints/{}/aucs.pkl".format(parentfolder,modelname),"wb") as outfile:
pickle.dump(results_dict,outfile,protocol = 2)
self.val_loss_min = val_loss