How reconstruct the caffe net by using pycaffe

可紊 提交于 2019-12-21 05:05:00

问题


What I want is, After loading a net, I will decompose some certain layers and save the new net. For example

Orignial net:

data -> conv1 -> conv2 -> fc1 -> fc2 -> softmax;

New net:

data -> conv1_1 -> conv1_2 -> conv2_1 -> conv2_2 -> fc1 -> fc2 -> softmax

Therefore, during this process, I stuck in the following situation:
1. How to new a certain layer with specified layer parameters in pycaffe?
2. How to copy the layer parameters from existing layers(such as fc1 and fc2 above)?

I know by using caffe::net_spec, we can define a new net manually. But caffe::net_spec can not specify a layer from a existing one(e.g: fc1).


回答1:


I didn't see how to load in previous nets with net_spec but you can always use the protobuf objects directly. (I use your network structure as an example)

import caffe.proto.caffe_pb2 as caffe_pb2
import google.protobuf as pb
from caffe import layers as L

net = caffe_pb2.NetParameter()
with open('net.prototxt', 'r') as f:
    pb.text_format.Merge(f.read(), net)

#example of modifing the network:
net.layer[1].name = 'conv1_1'
net.layer[1].top[0] = 'conv1_1'
net.layer[2].name = 'conv1_2'
net.layer[2].top[0] = 'conv1_2'
net.layer[2].bottom[0] = 'conv1_1'

net.layer[3].bottom[0] = 'conv2_2'

#example of adding new layers (using net_spec):
conv2_1 = net.layer.add()
conv2_1.CopyFrom(L.Convolution(kernel_size=7, stride=1, num_output=48, pad=0).to_proto().layer[0])
conv2_1.name = 'conv2_1'
conv2_1.top[0] = 'conv2_1'
conv2_1.bottom.add('conv1_2')

conv2_2 = net.layer.add()
conv2_2.CopyFrom(L.Convolution(kernel_size=7, stride=1, num_output=48, pad=0).to_proto().layer[0])
conv2_2.name = 'conv2_2'
conv2_2.top[0] = 'conv2_2'
conv2_2.bottom.add('conv2_1')

# then write back out:
with open('net2.prototxt, 'w') as f:
    f.write(pb.text_format.MessageToString(net))

Also see here as a guide on protocol buffers in python and here for the current caffe message formats.



来源:https://stackoverflow.com/questions/35423309/how-reconstruct-the-caffe-net-by-using-pycaffe

易学教程内所有资源均来自网络或用户发布的内容,如有违反法律规定的内容欢迎反馈
该文章没有解决你所遇到的问题?点击提问,说说你的问题,让更多的人一起探讨吧!