pytorch 归一化与反归一化实例

Python  /  管理员 发布于 5年前   463

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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