Caffe小玩意(2)-从caffemodel中导出参数

最近读到一篇paper非常有意思，他们把caffe里训练好的模型的参数导出来了，然后…弄到了torch里。所以，今天就来看看怎么导出参数吧。
为了简单，这次我选的是LeNet

import numpy as npimport scipy.io as sioimport caffedef load():    # Load the net    caffe.set_mode_cpu()    # You may need to train this caffemodel first    # There should be script to help you do the training    net = caffe.Net(root + 'lenet.prototxt', root + 'lenet_iter_10000.caffemodel',\        caffe.TEST)    conv1_w = net.params['conv1'][0].data    conv1_b = net.params['conv1'][1].data    conv2_w = net.params['conv2'][0].data    conv2_b = net.params['conv2'][1].data    ip1_w = net.params['ip1'][0].data    ip1_b = net.params['ip1'][1].data    ip2_w = net.params['ip2'][0].data    ip2_b = net.params['ip2'][1].data    sio.savemat('conv1_w', {'conv1_w':conv1_w})    sio.savemat('conv1_b', {'conv1_b':conv1_b})    sio.savemat('conv2_w', {'conv2_w':conv2_w})    sio.savemat('conv2_b', {'conv2_b':conv2_b})    sio.savemat('ip1_w', {'ip1_w':ip1_w})    sio.savemat('ip1_b', {'ip1_b':ip1_b})    sio.savemat('ip2_w', {'ip2_w':ip2_w})    sio.savemat('ip2_b', {'ip2_b':ip2_b})if __name__ == "__main__":    # You will need to change this path    root = '/Users/yuliangzou/caffe-rc3/examples/mnist/'    load()    print 'Caffemodel loaded and written to .mat files successfully!'

从代码里可以看得很清楚啦，首先导入模型，然后利用net.params就可以获取参数了，另外你也可以利用net.data导出数据进行可视化。当然，在导出参数之前…你必须要跑过一遍，不然你没有这个caffemodel…
最后…要说一下我最近无聊的时候在github上开了个Naive-CNN的项目，就是….把Caffe里的模型参数导出来，用Matlab或者Python写一遍。目前只做了LeNet。欢迎大家也来玩:
https://github.com/Yuliang-Zou/Naive-CNN