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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, patience=7, verbose=False, delta=0, saved_dir='.', save_name=''):
"""
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
"""
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.save_name = save_name
self.saved_dir = saved_dir
def __call__(self, val_loss, model, epoch):
score = -val_loss
if np.mod(epoch, 10) == 0:
self.save_checkpoint(val_loss, model, epoch)
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
if self.verbose:
print(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, epoch='best'):
'''Saves model when validation loss decrease.'''
if self.verbose:
print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...')
torch.save(model.state_dict(), '{}/{}_model_{}.dict'.format(self.saved_dir, self.save_name, epoch))
#torch.save(model, '{}/{}_checkpoint.pt'.format(self.saved_dir, self.save_name))
self.val_loss_min = val_loss
def add_pr_curve_tensorboard(writer, class_index, test_probs, test_preds, names, global_step, prefix):
'''
Takes in a "class_index" from 0 to 9 and plots the corresponding
precision-recall curve
'''
writer.add_pr_curve(prefix + '_' + names[class_index],
test_preds[:,class_index],
test_probs[:,class_index],
global_step=global_step,
num_thresholds=127)
writer.close()