python 批量resize性能比较

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torch是最快的，能快1ms

分别是5ms 9ms  6ms，但是torch 占用cpu很高。

import time

import cv2
import torch

img = cv2.imread('d:/guo.jpg')  # modify the image path to yours
img =cv2.cvtColor(img, cv2.COLOR_BGR2RGB)


pic_w=1200
start=time.time()
tensor_in = torch.FloatTensor(img)
for i in range(10):
    pic_w = int(pic_w * 0.8)
    tensor_in = tensor_in.resize_(pic_w,pic_w,3)

print('time0',time.time()-start)
pic_w=1200
start=time.time()
for i in range(10):
    pic_w=int(pic_w*0.8)
    a=cv2.resize(img,(pic_w,pic_w))

print('time1',time.time()-start)

pic_w = 1200
start = time.time()
for i in range(10):
    pic_w = int(pic_w * 0.8)
    img= cv2.resize(img, (pic_w, pic_w))

print('time2', time.time() - start)

torch.Tensor特别占用时间，下面代码需要58ms

pic_w=1200
start=time.time()

for i in range(10):
tensor_in = torch.FloatTensor(img)
pic_w = int(pic_w * 0.8)
tensor_in = tensor_in.resize_(pic_w,pic_w,3)

接着往下看：

这个最牛：几乎不需要时间：

pic_w=1200
start=time.time()

for i in range(10):
    tensor_in = torch.from_numpy(img)
    pic_w = int(pic_w * 0.8)
    tensor_in = tensor_in.resize_(pic_w,pic_w,3)