利用pycaffe提取caffe model中的参数

import sysimport os# Add caffe packagecaffe_python_dir = "your/caffe-master/python"sys.path.append(caffe_python_dir)if not os.path.exists(caffe_python_dir):    print "python caffe not found."    exit(-1)import caffeimport numpy as npfrom PIL import Image# load image, switch to BGR, subtract mean, and make dims C x H x W for Caffeim = Image.open('your/2007_000129.jpg')in_ = np.array(im, dtype=np.float32)in_ = in_[:,:,::-1]in_ -= np.array((104.00698793,116.66876762,122.67891434))in_ = in_.transpose((2,0,1))# load netnet = caffe.Net('your/deploy.prototxt',                'your.caffemodel', caffe.TEST)# shape for input (data blob is N x C x H x W), set datanet.blobs['data'].reshape(1, *in_.shape)net.blobs['data'].data[...] = in_# run net and take argmax for predictionnet.forward()out = net.blobs['score'].data[0].argmax(axis=0)print netprint('net.params: {0}'.format(net.params))t = net.params["conv1_1"]  #这个就是net.params字典里面的一个层,"conv1_1"是层的名字。print("conv1_1 length: {0}".format(len(t))) #看看这个有多少个Blob，这里输出的是2t0 = t[0] #这个就是卷积核weight的参数t1 = t[1] #这个就是biasprint(t1)