Pytorch中mean和std调查的示例分析-创新互联
这篇文章主要介绍了Pytorch中mean和std调查的示例分析，具有一定借鉴价值，感兴趣的朋友可以参考下，希望大家阅读完这篇文章之后大有收获，下面让小编带着大家一起了解一下。
如下所示：
# coding: utf-8 from __future__ import print_function import copy import click import cv2 import numpy as np import torch from torch.autograd import Variable from torchvision import models, transforms import matplotlib.pyplot as plt import load_caffemodel import scipy.io as sio # if model has LSTM # 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
import matplotlib.pyplot as plt
import argparse
import os
import numpy as np
import torchvision
import torchvision.transforms as transforms
dataset_names = ('cifar10','cifar100','mnist')
parser = argparse.ArgumentParser(description='PyTorchLab')
parser.add_argument('-d', '--dataset', metavar='DATA', default='cifar10', choices=dataset_names,
help='dataset to be used: ' + ' | '.join(dataset_names) + ' (default: cifar10)')
args = parser.parse_args()
data_dir = os.path.join('.', args.dataset)
print(args.dataset)
args.dataset = 'cifar10'
if args.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)
# imshow image
train_data = train_set.train_data
ind = 100
img0 = train_data[ind,...]
## test channel number, in total , the correct channel is : RGB,not like BGR in caffe
# error produce
#b,g,r=cv2.split(img0)
#img0=cv2.merge([r,g,b])
print(img0.shape)
print(type(img0))
plt.imshow(img0)
plt.show() # in ship in sea
#img0 = cv2.resize(img0,(224,224))
#cv2.imshow('img0',img0)
#cv2.waitKey()
elif args.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)
elif args.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 Files already downloaded and verified (50000, 32, 32, 3) [ 0.49139968 0.48215841 0.44653091] [ 0.24703223 0.24348513 0.26158784] (32, 32, 3)
使用matlab检测是如何计算mean_file和std_file的：
% load cifar10 dataset
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);
% compute mean_file
mean_val = mean(mean(mean(train_data,1),2),3)/255;
% compute std_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。
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