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utils.py
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utils.py
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# -*- coding:utf-8-*-
import os
import math
import shutil #shutil则就是对os中文件操作的补充。--移动 复制 打包 压缩 解压
import logging
import numpy as np
import torch
import torch.utils.data as Data
import torchvision
import torchvision.transforms as transforms
#https://www.cnblogs.com/CJOKER/p/8295272.html
class Logger(object):
def __init__(self,log_file_name,log_level,logger_name):
#第一步,创建一个logger
self.__logger = logging.getLogger(logger_name)
self.__logger.setLevel(log_level)
#第二步,创建一个handler
file_handler = logging.FileHandler(log_file_name)
console_handler = logging.StreamHandler()
#第三步,定义handler的输出格式
formatter = logging.Formatter(
'[%(asctime)s]-[%(filename)s line:%(lineno)d]:%(message)s '
)
file_handler.setFormatter(formatter)
console_handler.setFormatter(formatter)
#第四步,将Hander添加到logger中
self.__logger.addHandler(file_handler)
self.__logger.addHandler(console_handler)
def get_log(self):
return self.__logger
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad) #numel()函数:返回数组中元素的个数
def data_augmentation(config,is_train=True):
aug = []
if is_train:
#random crop
if config.augmentation.random_crop:
aug.append(transforms.RandomCrop(config.input_size,padding=4))
#horizontal flip
if config.augmentation.random_horizontal_flip:
aug.append(transforms.RandomHorizontalFlip())
aug.append(transforms.ToTensor())
#normalize [-mean/std]
if config.augmentation.normalize:
if config.dataset =='cifar10':
aug.append(transforms.Normalize(
(0.4914,0.4822,0.4465),(0.2023,0.1994,0.2010)
))
else:
aug.append(transforms.Normalize(
(0.5071,0.4867,0.4408),(0.2675,0.2565,0.2761)
))
if is_train and config.augmentation.cutout:
#cutout
aug.append(Cutout(n_holes=config.augmentation.holes,
length=config.augmentation.length))
return aug
class Cutout(object):
def __init__(self,n_holes,length):
self.n_holes = n_holes
self.length = length
def __call__(self, img):
h = img.size(1)
w = img.size(2)
mask = np.ones((h,w),np.float32)
for n in range(self.n_holes):
y = np.random.randint(h)
x = np.random.randint(w)
#np.clip()函数将将数组中的元素限制在a_min, a_max之间
y1 = np.clip(y-self.length//2,0,h) #" / " 表示浮点数除法,返回浮点结果," // " 表示整数除法,返回不大于结果的一个最大的整数
y2 = np.clip(y+self.length//2,0,h)
x1 = np.clip(x-self.length//2,0,w)
x2 = np.clip(x+self.length//2,0,w)
mask[y1:y2, x1:x2] = 0
mask = torch.from_numpy(mask)
mask = mask.expand_as(img) #把一个tensor变成和函数括号内一样形状的tensor,用法与expand()类似
img = img*mask
return img
def save_checkpoint(state,is_best,filename):
torch.save(state,filename+'.pth.tar')
if is_best:
shutil.copyfile(filename+'.pth.tar',filename+'_best.pth.tar')
def load_checkpoint(path,model,optimizer=None):
if os.path.isfile(path):
logging.info("=== loading checkpoint '{}' ===".format(path))
checkpoint = torch.load(path)
model.load_state_dict(checkpoint['state_dict'],strict=False)
if optimizer != None:
best_prec = checkpoint['best_prec']
last_epoch = checkpoint['last_epoch']
optimizer.load_state_dict(checkpoint['optimizer'])
logging.info("=== load state dict done.also load optimizer from checkpoint'{}'(epoch{})".format(
path,last_epoch+1
))
return best_prec,last_epoch
def get_data_loader(transform_train, transform_test, config):
assert config.dataset == 'cifar10' or config.dataset == 'cifar100'
if config.dataset == 'cifar10':
trainset = torchvision.datasets.CIFAR10(
root=config.data_path, train=True, transform=transform_train, download=True
)
testset = torchvision.datasets.CIFAR10(
root=config.data_path, train=False, transform=transform_test, download=True)
else:
trainset = torchvision.datasets.CIFAR100(
root=config.data_path, train=True, transform=transform_train, download=True
)
testset = torchvision.datasets.CIFAR100(
root=config.data_path, train=False, transform=transform_test, download=True
)
train_loader = Data.DataLoader(
trainset, batch_size=config.batch_size, shuffle=True, num_workers=config.workers)
test_loader = Data.DataLoader(
testset, batch_size=config.test_batch, shuffle=True, num_workers=config.workers)
return train_loader, test_loader
def mixup_data(x,y,alpha,device):
'''Returns mixed inputs,pairs of targets,and lambda'''
if alpha > 0:
lam = np.random.beta(alpha,alpha)
else:
lam = 1
batch_size = x.size()[0]
index = torch.randperm(batch_size).to(device) #返回一个0到n-1的数组
mixed_x = lam*x + (1-lam)*x[index,:]
y_a,y_b = y,y[index]
return mixed_x,y_a,y_b,lam
def mixup_criterion(criterion,pred,y_a,y_b,lam):
return lam*criterion(pred,y_a)+(1-lam)*criterion(pred,y_b)
def get_current_lr(optimizer):
for param_group in optimizer.param_groups:
return param_group['lr']
def adjust_learning_rate(optimizer,epoch,config):
lr = get_current_lr(optimizer)
if config.lr_scheduler.type == 'STEP':
if epoch in config.lr_scheduler.lr_epochs:
lr *= config.lr_scheduler.lr_mults
elif config.lr_scheduler.type == 'COSINE':
ratio = epoch / config.epochs
lr = config.lr_scheduler.min_lr + \
(config.lr_scheduler.base_lr - config.lr_scheduler.min_lr)*\
(1.0+math.cos(math.pi*ratio))/2.0
elif config.lr_scheduler.type == 'HTD':
ratio = epoch / config.epochs
lr = config.lr_schedule.min_lr + \
(config.lr_scheduler.base_lr-config.lr_scheduler.min_lr)*\
(1.0-math.tanh(config.lr_scheduler.lower_bound+
(config.lr_scheduler.upper_bound - config.lr_scheduler.lower_bound)*ratio))/2.0
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return lr