pytorch 归一化与反归一化教程_F11 - 专业站长和开发者的学习网站

ToTensor中就有转到0-1之间了。

# -*- 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, 0.224, 0.225]
  mean=[0.485, 0.456, 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()

这个测试时间：归一化与反归一化都需要7ms左右，

但是在多路摄像头中，可能比较慢。

std= [0.229, 0.224, 0.225]
 mean=[0.485, 0.456, 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, 2, 0))
 
   print("fan",time.time()-start)
 # torch.set_num_threads(3)
 # img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
 cv2.imshow("sdf", img)
 cv2.waitKeyEx()