pytorch 归一化与反归一化实例

ToTensor中就有转到0-1之间了。#-*-coding:utf-8-*-importtimeimporttorchfromtorchvisionimporttransformsimportcv2tr

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()

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pytorch 归一化 反归一化