Pytorch的mean和std调查实例
如下所示：
#coding:utf-8 from__future__importprint_function importcopy importclick importcv2 importnumpyasnp importtorch fromtorch.autogradimportVariable fromtorchvisionimportmodels,transforms importmatplotlib.pyplotasplt importload_caffemodel importscipy.ioassio #ifmodelhasLSTM #torch.backends.cudnn.enabled=False imgpath='D:/ck/files_detected_face224/' imgname='S055_002_00000025.png'#anger image_path=imgpath+imgname mean_file=[0.485,0.456,0.406] std_file=[0.229,0.224,0.225] raw_image=cv2.imread(image_path)[...,::-1] print(raw_image.shape) raw_image=cv2.resize(raw_image,(224,)*2) image=transforms.Compose([ transforms.ToTensor(), transforms.Normalize( mean=mean_file, std=std_file, #mean=mean_file, #std=std_file, ) ])(raw_image).unsqueeze(0) print(image.shape) convert_image1=image.numpy() convert_image1=np.squeeze(convert_image1)#3*224*224,C*H*W convert_image1=convert_image1*np.reshape(std_file,(3,1,1))+np.reshape(mean_file,(3,1,1)) convert_image1=np.transpose(convert_image1,(1,2,0))#H*W*C print(convert_image1.shape) convert_image1=convert_image1*255 diff=raw_image-convert_image1 err=np.max(diff) print(err) plt.imshow(np.uint8(convert_image1)) plt.show()
结论：
input_image=(raw_image/255-mean)./std
下面调查均值文件和方差文件是如何生成的：
mean_file=[0.485,0.456,0.406] std_file=[0.229,0.224,0.225]
#coding:utf-8
importmatplotlib.pyplotasplt
importargparse
importos
importnumpyasnp
importtorchvision
importtorchvision.transformsastransforms
dataset_names=('cifar10','cifar100','mnist')
parser=argparse.ArgumentParser(description='PyTorchLab')
parser.add_argument('-d','--dataset',metavar='DATA',default='cifar10',choices=dataset_names,
help='datasettobeused:'+'|'.join(dataset_names)+'(default:cifar10)')
args=parser.parse_args()
data_dir=os.path.join('.',args.dataset)
print(args.dataset)
args.dataset='cifar10'
ifargs.dataset=="cifar10":
train_transform=transforms.Compose([transforms.ToTensor()])
train_set=torchvision.datasets.CIFAR10(root=data_dir,train=True,download=True,transform=train_transform)
#print(vars(train_set))
print(train_set.train_data.shape)
print(train_set.train_data.mean(axis=(0,1,2))/255)
print(train_set.train_data.std(axis=(0,1,2))/255)
#imshowimage
train_data=train_set.train_data
ind=100
img0=train_data[ind,...]
##testchannelnumber,intotal,thecorrectchannelis:RGB,notlikeBGRincaffe
#errorproduce
#b,g,r=cv2.split(img0)
#img0=cv2.merge([r,g,b])
print(img0.shape)
print(type(img0))
plt.imshow(img0)
plt.show()#inshipinsea
#img0=cv2.resize(img0,(224,224))
#cv2.imshow('img0',img0)
#cv2.waitKey()
elifargs.dataset=="cifar100":
train_transform=transforms.Compose([transforms.ToTensor()])
train_set=torchvision.datasets.CIFAR100(root=data_dir,train=True,download=True,transform=train_transform)
#print(vars(train_set))
print(train_set.train_data.shape)
print(np.mean(train_set.train_data,axis=(0,1,2))/255)
print(np.std(train_set.train_data,axis=(0,1,2))/255)
elifargs.dataset=="mnist":
train_transform=transforms.Compose([transforms.ToTensor()])
train_set=torchvision.datasets.MNIST(root=data_dir,train=True,download=True,transform=train_transform)
#print(vars(train_set))
print(list(train_set.train_data.size()))
print(train_set.train_data.float().mean()/255)
print(train_set.train_data.float().std()/255)
结果：
cifar10 Filesalreadydownloadedandverified (50000,32,32,3) [0.491399680.482158410.44653091] [0.247032230.243485130.26158784] (32,32,3)
使用matlab检测是如何计算mean_file和std_file的：
%loadcifar10dataset
data=load('cifar10_train_data.mat');
train_data=data.train_data;
disp(size(train_data));
temp=mean(train_data,1);
disp(size(temp));
train_data=double(train_data);
%computemean_file
mean_val=mean(mean(mean(train_data,1),2),3)/255;
%computestd_file
temp1=train_data(:,:,:,1);
std_val1=std(temp1(:))/255;
temp2=train_data(:,:,:,2);
std_val2=std(temp2(:))/255;
temp3=train_data(:,:,:,3);
std_val3=std(temp3(:))/255;
mean_val=squeeze(mean_val);
std_val=[std_val1,std_val2,std_val3];
disp(mean_val);
disp(std_val);
%result:mean_val:[0.4914,0.4822,0.4465]
%std_val:[0.2470,0.2435,0.2616]
均值计算的过程也可以遵循标准差的计算过程。为了简单，例如对于一个矩阵，所有元素的均值，等于两个方向上先后均值。所以会直接采用如下的形式：
mean_val=mean(mean(mean(train_data,1),2),3)/255;
标准差的计算是每一个通道的对所有样本的求标准差。然后再除以255。
以上这篇Pytorch的mean和std调查实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持毛票票。
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