| import os
|
| import sys
|
|
|
| import numpy as np
|
| import torch
|
|
|
|
|
| def mkdirs(paths):
|
| if isinstance(paths, list) and not isinstance(paths, str):
|
| for path in paths:
|
| mkdir(path)
|
| else:
|
| mkdir(paths)
|
|
|
|
|
| def mkdir(path):
|
| if not os.path.exists(path):
|
| os.makedirs(path)
|
|
|
|
|
| def unnormalize(tens, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):
|
|
|
| return (
|
| tens * torch.Tensor(std)[None, :, None, None]
|
| + torch.Tensor(mean)[None, :, None, None]
|
| )
|
|
|
|
|
| class Logger(object):
|
| """Log stdout messages."""
|
|
|
| def __init__(self, outfile):
|
| self.terminal = sys.stdout
|
| self.log = open(outfile, 'a')
|
| sys.stdout = self
|
|
|
| def write(self, message):
|
| self.terminal.write(message)
|
| self.log.write(message)
|
|
|
| def flush(self):
|
| self.terminal.flush()
|
|
|
|
|
| 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):
|
| """
|
| 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
|
|
|
| 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
|
| 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):
|
| """Saves model when validation loss decrease."""
|
| if self.verbose:
|
| print(
|
| f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...'
|
| )
|
| self.val_loss_min = val_loss
|
|
|
|
|
| def printSet(set_str):
|
| set_str = str(set_str)
|
| num = len(set_str)
|
| print('=' * num * 3)
|
| print(' ' * num + set_str)
|
| print('=' * num * 3)
|
|
|
