我正在努力使我的分类过程更快一些。我想增加我的 deploy.prototxt 中的第一个 input_dim，但这似乎不起作用。它甚至比一张一张地分类要慢一点。
部署.prototxt
input: "data"
input_dim: 128
input_dim: 1
input_dim: 120
input_dim: 160
... net description ...
python网络初始化
net=caffe.Net( 'deploy.prototxt', 'model.caffemodel', caffe.TEST)
net.blobs['data'].reshape(128, 1, 120, 160)
transformer = caffe.io.Transformer({'data':net.blobs['data'].data.shape})
#transformer settings
python分类
images=[None]*128
for i in range(len(images)):
images[i]=caffe.io.load_image('image_path', False)
for j in range(len(images)):
net.blobs['data'].data[j,:,:,:] = transformer.preprocess('data',images[j])
out = net.forward()['prob']
我跳过了一些细节，但应该给出重要的东西。我尝试了不同的批量大小，例如 32、64、...、1024，但都几乎相同。所以我的问题是，如果有人知道我做错了什么或需要改变什么？ 感谢您的帮助!
编辑:
一些计时结果，avg-times只是总时间除以处理图像(1044)。
批量大小:1
2016-05-04 10:51:20,721 - detector - INFO - data shape: (1, 1, 120, 160)
2016-05-04 10:51:35,149 - main - INFO - GPU timings:
2016-05-04 10:51:35,149 - main - INFO - processed images: 1044
2016-05-04 10:51:35,149 - main - INFO - total-time: 14.43s
2016-05-04 10:51:35,149 - main - INFO - avg-time: 13.82ms
2016-05-04 10:51:35,149 - main - INFO - load-time: 8.31s
2016-05-04 10:51:35,149 - main - INFO - avg-load-time: 7.96ms
2016-05-04 10:51:35,149 - main - INFO - classify-time: 5.99s
2016-05-04 10:51:35,149 - main - INFO - avg-classify-time: 5.74ms
批量大小:32
2016-05-04 10:52:30,773 - detector - INFO - data shape: (32, 1, 120, 160)
2016-05-04 10:52:45,135 - main - INFO - GPU timings:
2016-05-04 10:52:45,135 - main - INFO - processed images: 1044
2016-05-04 10:52:45,135 - main - INFO - total-time: 14.36s
2016-05-04 10:52:45,136 - main - INFO - avg-time: 13.76ms
2016-05-04 10:52:45,136 - main - INFO - load-time: 7.13s
2016-05-04 10:52:45,136 - main - INFO - avg-load-time: 6.83ms
2016-05-04 10:52:45,136 - main - INFO - classify-time: 7.13s
2016-05-04 10:52:45,136 - main - INFO - avg-classify-time: 6.83ms
批量大小:128
2016-05-04 10:53:17,478 - detector - INFO - data shape: (128, 1, 120, 160)
2016-05-04 10:53:31,299 - main - INFO - GPU timings:
2016-05-04 10:53:31,299 - main - INFO - processed images: 1044
2016-05-04 10:53:31,299 - main - INFO - total-time: 13.82s
2016-05-04 10:53:31,299 - main - INFO - avg-time: 13.24ms
2016-05-04 10:53:31,299 - main - INFO - load-time: 7.06s
2016-05-04 10:53:31,299 - main - INFO - avg-load-time: 6.77ms
2016-05-04 10:53:31,299 - main - INFO - classify-time: 6.66s
2016-05-04 10:53:31,299 - main - INFO - avg-classify-time: 6.38ms
批量大小:1024
2016-05-04 10:54:11,546 - detector - INFO - data shape: (1024, 1, 120, 160)
2016-05-04 10:54:25,316 - main - INFO - GPU timings:
2016-05-04 10:54:25,316 - main - INFO - processed images: 1044
2016-05-04 10:54:25,316 - main - INFO - total-time: 13.77s
2016-05-04 10:54:25,316 - main - INFO - avg-time: 13.19ms
2016-05-04 10:54:25,316 - main - INFO - load-time: 7.04s
2016-05-04 10:54:25,316 - main - INFO - avg-load-time: 6.75ms
2016-05-04 10:54:25,316 - main - INFO - classify-time: 6.63s
2016-05-04 10:54:25,316 - main - INFO - avg-classify-time: 6.35ms
最佳答案
我很确定问题出在行上
for j in range(len(images)):
net.blobs['data'].data[j,:,:,:] = transformer.preprocess('data',images[j])
out = net.forward()['prob']
这样做只会将 for 循环的最后一次迭代中的单个图像数据设置为网络的唯一输入。尝试预先堆叠 N 图像(例如 stackedimages)并仅调用一次该行，例如
for j in range(len(images)):
stackedimages <- transformer.preprocess('data',images[j])
然后调用，
net.blobs['data'].data[...] = stackedimages
关于neural-network - 批量大小不适用于带有 deploy.prototxt 的 caffe，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/37003575/