!diff imagenet/imagenet_full_conv.prototxt imagenet/imagenet_deploy.prototxt

# Make sure that caffe is on the python path:
caffe_root = '../'  # this file is expected to be in {caffe_root}/examples
import sys
sys.path.insert(0, caffe_root + 'python')

import caffe

# Load the original network and extract the fully-connected layers' parameters.
net = caffe.Net('imagenet/imagenet_deploy.prototxt', 'imagenet/caffe_reference_imagenet_model')
params = ['fc6', 'fc7', 'fc8']
# fc_params = {name: (weights, biases)}
fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}

for fc in params:
    print '{} weights are {} dimensional and biases are {} dimensional'.format(fc, fc_params[fc][0].shape, fc_params[fc][1].shape)

# Load the fully-convolutional network to transplant the parameters.
net_full_conv = caffe.Net('imagenet/imagenet_full_conv.prototxt', 'imagenet/caffe_reference_imagenet_model')
params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']
# conv_params = {name: (weights, biases)}
conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}

for conv in params_full_conv:
    print '{} weights are {} dimensional and biases are {} dimensional'.format(conv, conv_params[conv][0].shape, conv_params[conv][1].shape)

for pr, pr_conv in zip(params, params_full_conv):
    conv_params[pr_conv][1][...] = fc_params[pr][1]

for pr, pr_conv in zip(params, params_full_conv):
    out, in_, h, w = conv_params[pr_conv][0].shape
    W = fc_params[pr][0].reshape((out, in_, h, w))
    conv_params[pr_conv][0][...] = W

net_full_conv.save('imagenet/caffe_imagenet_full_conv')

# load input and configure preprocessing
im = caffe.io.load_image('images/cat.jpg')
plt.imshow(im)
net_full_conv.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy'))
net_full_conv.set_channel_swap('data', (2,1,0))
net_full_conv.set_raw_scale('data', 255.0)
# make classification map by forward pass and show top prediction index per location
out = net_full_conv.forward_all(data=np.asarray([net_full_conv.preprocess('data', im)]))
out['prob'][0].argmax(axis=0)