今天终于吧caffe配好了，迫不及待的测试了两个例子：
一个是caffe自带的mnist例子。
另一个是网上有人改了caffe的例子cpp_classification，功能是识别猫。
配的比较困难，因为用到了opencv、boost和cuda的各种库，最终终于配好了。下面是测试数据（图像大小216kb），跑多次程序，提速基本保持在1.5-2倍
CPU_ONLY=1
---------- Prediction for cat1.jpg ----------
start()
0.9282 - "n02129604 tiger, Panthera tigris"
0.0718 - "n02123159 tiger cat"
0.0001 - "n02128925 jaguar, panther, Panthera onca, Felis onca"
0.0000 - "n02128385 leopard, Panthera pardus"
0.0000 - "n02127052 lynx, catamount"
t =155.918186
CPU_ONLY=0
---------- Prediction for cat.jpg ----------
start()
0.9282 - "n02129604 tiger, Panthera tigris"
0.0718 - "n02123159 tiger cat"
0.0001 - "n02128925 jaguar, panther, Panthera onca, Felis onca"
0.0000 - "n02128385 leopard, Panthera pardus"
0.0000 - "n02127052 lynx, catamount"
t =70.072037最开始怀疑图像大小问题，是不是这个大小的图像就是这么慢，换图片大小，没用。后来又怀疑会不会我的opencv已经做了cuda加速（cpu_only=1的情况下）（因为我配的opencv3.2_with_cuda），但是一想，这么慢肯定没做过加速，毕竟用的GTX1050的显卡，不至于这么慢，而且查了一些资料，虽然没有明确证据证明opencv_with_cuda的普通算法代码没有加速，但一般用到加速的都需要包含一个带gpu字眼的库，并且要包含相关头文件，因此认为cpu_only=1版本并没有cuda加速，思考良久。后来想到可能是代码第一次运行时cuda未初始化，第一次运行较慢，于是修改了代码跑了四张图片，证实了我的想法。结果如下，第一帧图片提速与上面测试一样，后面都提速约10倍左右（图片大小如图所示）
CPU_ONLY=1
---------- Prediction for cat1.jpg ----------
start()
0.9282 - "n02129604 tiger, Panthera tigris"
0.0718 - "n02123159 tiger cat"
0.0001 - "n02128925 jaguar, panther, Panthera onca, Felis onca"
0.0000 - "n02128385 leopard, Panthera pardus"
0.0000 - "n02127052 lynx, catamount"
t =155.918186
---------- Prediction for cat2.jpg ----------
start()
0.2427 - "n02124075 Egyptian cat"
0.1550 - "n02123045 tabby, tabby cat"
0.1420 - "n02085620 Chihuahua"
0.0870 - "n02113023 Pembroke, Pembroke Welsh corgi"
0.0808 - "n02123159 tiger cat"
t =119.027723
---------- Prediction for cat3.jpg ----------
start()
0.9448 - "n02124075 Egyptian cat"
0.0229 - "n02123045 tabby, tabby cat"
0.0170 - "n02128757 snow leopard, ounce, Panthera uncia"
0.0084 - "n02128385 leopard, Panthera pardus"
0.0033 - "n02128925 jaguar, panther, Panthera onca, Felis onca"
t =125.195330
---------- Prediction for cat4.jpg ----------
start()
0.9781 - "n02130308 cheetah, chetah, Acinonyx jubatus"
0.0210 - "n02128385 leopard, Panthera pardus"
0.0009 - "n02128925 jaguar, panther, Panthera onca, Felis onca"
0.0000 - "n02128757 snow leopard, ounce, Panthera uncia"
0.0000 - "n02127052 lynx, catamount"
t =134.427212
请按任意键继续. . .
CPU_ONLY=0
---------- Prediction for cat1.jpg ----------
start()
0.9282 - "n02129604 tiger, Panthera tigris"
0.0718 - "n02123159 tiger cat"
0.0001 - "n02128925 jaguar, panther, Panthera onca, Felis onca"
0.0000 - "n02128385 leopard, Panthera pardus"
0.0000 - "n02127052 lynx, catamount"
t =70.072037
---------- Prediction for cat2.jpg ----------
start()
0.2427 - "n02124075 Egyptian cat"
0.1550 - "n02123045 tabby, tabby cat"
0.1420 - "n02085620 Chihuahua"
0.0870 - "n02113023 Pembroke, Pembroke Welsh corgi"
0.0808 - "n02123159 tiger cat"
t =13.625015
---------- Prediction for cat3.jpg ----------
start()
0.9448 - "n02124075 Egyptian cat"
0.0229 - "n02123045 tabby, tabby cat"
0.0170 - "n02128757 snow leopard, ounce, Panthera uncia"
0.0084 - "n02128385 leopard, Panthera pardus"
0.0033 - "n02128925 jaguar, panther, Panthera onca, Felis onca"
t =12.377125
---------- Prediction for cat4.jpg ----------
start()
0.9781 - "n02130308 cheetah, chetah, Acinonyx jubatus"
0.0210 - "n02128385 leopard, Panthera pardus"
0.0009 - "n02128925 jaguar, panther, Panthera onca, Felis onca"
0.0000 - "n02128757 snow leopard, ounce, Panthera uncia"
0.0000 - "n02127052 lynx, catamount"
t =13.749035
请按任意键继续. . .