计算pytorch标准化(Normalize)所需要数据集的均值和方差实例

Python  /  管理员 发布于 5年前   492

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))

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