计算pytorch标准化(Normalize)所需要数据集的均值和方差实例

概述计算pytorch标准化(Normalize)所需要数据集的均值和方差实例 pytorch做标准化利用transforms.Normalize(mean_vals, std_vals),其中常用数据集的均值方差有: if 'coco' in args.dataset: mean_vals = [0.471, 0.448, 0.408] std_vals = [0.234, 0.239, 0.242] elif 'imagenet' in args.dataset: mean_vals = [0.485, 0.456, 0.406] std_vals = [0.229,

pytorch做标准化利用transforms.normalize(mean_vals,std_vals)，其中常用数据集的均值方差有：

if 'coco' in args.dataset:  mean_vals = [0.471,0.448,0.408]  std_vals = [0.234,0.239,0.242]elif 'imagenet' in args.dataset:  mean_vals = [0.485,0.456,0.406]  std_vals = [0.229,0.224,0.225]

计算自己数据集图像像素的均值方差：

import numpy as npimport cv2import random# calculate means and stdtrain_txt_path = './train_val_List.txt'CNum = 10000   # 挑选多少图片进行计算img_h,img_w = 32,32imgs = np.zeros([img_w,img_h,3,1])means,stdevs = [],[]with open(train_txt_path,'r') as f:  lines = f.readlines()  random.shuffle(lines)  # shuffle,随机挑选图片  for i in tqdm_notebook(range(CNum)):    img_path = os.path.join('./train',lines[i].rstrip().split()[0])    img = cv2.imread(img_path)    img = cv2.resize(img,(img_h,img_w))    img = img[:,:,np.newaxis]    imgs = np.concatenate((imgs,img),axis=3)#     print(i)imgs = imgs.astype(np.float32)/255.for i in tqdm_notebook(range(3)):  pixels = imgs[:,i,:].ravel() # 拉成一行  means.append(np.mean(pixels))  stdevs.append(np.std(pixels))# cv2 读取的图像格式为BGR，PIL/Skimage读取到的都是RGB不用转means.reverse() # BGR --> RGBstdevs.reverse()print("normMean = {}".format(means))print("normStd = {}".format(stdevs))print('transforms.normalize(normMean = {},normStd = {})'.format(means,stdevs))

以上这篇计算pytorch标准化(normalize)所需要数据集的均值和方差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我们。

总结

以上是内存溢出为你收集整理的计算pytorch标准化(Normalize)所需要数据集的均值和方差实例全部内容，希望文章能够帮你解决计算pytorch标准化(Normalize)所需要数据集的均值和方差实例所遇到的程序开发问题。

如果觉得内存溢出网站内容还不错，欢迎将内存溢出网站推荐给程序员好友。