计算pytorch标准化(Normalize)所需要数据集的均值和方差实例
作者:菜鸟川
今天小编就为大家分享一篇计算pytorch标准化(Normalize)所需要数据集的均值和方差实例,具有很好的参考价值,希望对大家有所帮助。一起跟随小编过来看看吧
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))
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脚本之家。