如何加载caffe模型并转换为numpy数组？

Question

我有一个caffemodel文件，其中包含ethereon的caffe-tensorflow转换实用程序不支持的图层。我想生成一个我caffemodel的代表性。

我的问题是，如何将一个caffemodel文件（我也有prototxt，如果有用的话）转换为一个numpy文件？

附加信息：我有python，安装了python接口等的caffe。我显然没有经历过咖啡。

Answer 1

这里是一个不错的功能，一个朱古力净转换成词典的蟒列表，这样就可以泡制它，反正读它，你想：

import caffe 

def 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_