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

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 np
import cv2
import random
 
# calculate means and std
train_txt_path = './train_val_list.txt'
 
CNum = 10000 # 挑选多少图片进行计算
 
img_h, img_w = 32, 32
imgs = 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 --> RGB
stdevs.reverse()
 
print("normMean = {}".format(means))
print("normStd = {}".format(stdevs))
print('transforms.Normalize(normMean = {}, normStd = {})'.format(means, stdevs))

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pytorch 标准化 数据集 均值 方差