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.我想生成一个我的caffemodel的numpy表示.

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

附加信息:我安装了python,caffe和python接口等.我显然没有经历过咖啡.

Answer 1

这是一个很好的函数,可以将一个caffe网转换为一个python的字典列表,所以你可以腌制它并随意读取它:

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_