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0788e19 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | import sys
import os
import torch
import numpy as np
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]):
# assume tensor of shape NxCxHxW
return tens * torch.Tensor(std)[None, :, None, None] + torch.Tensor(
mean)[None, :, None, None]
# 重定向 sys.stdout 输出到一个指定的文件，同时保持在终端显示
class Logger(object):
"""Log stdout messages."""
def __init__(self, outfile):
self.terminal = sys.stdout # 保存当前标准输出流
self.log = open(outfile, "a") # 打开指定的日志文件 outfile，以追加模式 (a) 打开
# 将标准输出流重定向到 Logger 实例，这样所有的 print 语句和标准输出内容都会通过 Logger 的 write 方法
sys.stdout = self
def write(self, message):
self.terminal.write(message) # 将消息写到终端（标准输出）
self.log.write(message) # 将消息写到日志文件
def flush(self):
self.terminal.flush() # 刷新终端的输出缓冲区，确保所有输出都及时显示
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)
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