Pytorch的mean和std调查实例
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)
<class 'numpy.ndarray'>
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。
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软件开发网。
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