Net Surgery

swap_input_channels.py

import sys
import numpy as np
import scipy.io

import caffe

# take args for model arch (prototxt), input/output models (binaryproto ),
# layer name, and channel permutation.
arch_f, in_f, out_f = sys.argv[1:4]
layer = sys.argv[4]
channel_order = sys.argv[5:]

# load model
net = caffe.Net(arch_f, in_f)

# map channels from in to out for weights and bias
remapped_W = net.params[layer][0].data[:, channel_order, :, :]
net.params[layer][0].data[...] = remapped_W

# save model
net.save(out_f)

transplant.py

from __future__ import division
import caffe
import numpy as np

def transplant(new_net, net):
    for p in net.params:
        if p not in new_net.params:
            print 'dropping', p
            continue
        for i in range(len(net.params[p])):
            if net.params[p][i].data.shape != new_net.params[p][i].data.shape:
                print 'coercing', p, i, 'from', net.params[p][i].data.shape, 'to', new_net.params[p][i].data.shape
            else:
                print 'copying', p, i
            new_net.params[p][i].data.flat = net.params[p][i].data.flat