ToTensor中就有轉到0-1之間了。

# -*- coding:utf-8 -*-
 
 
import time
 
import torch
 
from torchvision import transforms
 
import cv2
 
transform_val_list = [
  # transforms.Resize(size=(160,160),interpolation=3),# Image.BICUBIC
  transforms.ToTensor(),transforms.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225])
]
 
trans_compose = transforms.Compose(transform_val_list)
 
 
 
if __name__ == '__main__':
  std= [0.229,0.225]
  mean=[0.485,0.406]
  path="d:/2.jpg"
 
  data=cv2.imread(path)
  t1 = time.time()
  x = trans_compose(data)
  x[0]=x[0]*std[0]+mean[0]
  x[1]=x[1]*std[1]+mean[1]
  x[2]=x[2].mul(std[2])+mean[2]
 
  img = x.mul(255).byte()
  img = img.numpy().transpose((1,2,0))
  # torch.set_num_threads(3)
  # img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
  cv2.imshow("sdf",img)
  cv2.waitKeyEx()
  

這個測試時間：歸一化與反歸一化都需要7ms左右，

但是在多路攝像頭中，可能比較慢。

 std= [0.229,0.406]
  path="d:/2.jpg"
 
  data=cv2.imread(path)
  t1 = time.time()
  start = time.time()
  x = trans_compose(data)
  print("gui",time.time() - start)
  for i in range(10):
    start=time.time()
 
    for i in range(len(mean)):
      # x[i]=x[i]*std[i]+mean[i]
      x[i]=x[i].mul(std[i])+mean[i]
    img = x.mul(255).byte()
    img = img.numpy().transpose((1,0))
 
    print("fan",time.time()-start)
  # torch.set_num_threads(3)
  # img=cv2.cvtColor(img,img)
  cv2.waitKeyEx()

以上這篇pytorch 歸一化與反歸一化例項就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。