这是一个很好的函数，可以将caffe net转换为python词典列表，因此您可以按需要对其进行腌制和读取：
import caffedef shai_net_to_py_readable(prototxt_filename, caffemodel_filename): net = caffe.Net(prototxt_filename, caffemodel_filename, caffe.TEST) # read the net + weights pynet_ = [] for li in xrange(len(net.layers)): # for each layer in the net layer = {} # store layer's information layer['name'] = net._layer_names[li] # for each input to the layer (aka "bottom") store its name and shape layer['bottoms'] = [(net._blob_names[bi], net.blobs[net._blob_names[bi]].data.shape) for bi in list(net._bottom_ids(li))] # for each output of the layer (aka "top") store its name and shape layer['tops'] = [(net._blob_names[bi], net.blobs[net._blob_names[bi]].data.shape) for bi in list(net._top_ids(li))] layer['type'] = net.layers[li].type # type of the layer # the internal parameters of the layer. not all layers has weights. layer['weights'] = [net.layers[li].blobs[bi].data[...] for bi in xrange(len(net.layers[li].blobs))] pynet_.append(layer) return pynet_
