This is a repo to convert deep learning models from .npy format to .ckpt format.

Using Anaconda:

conda create -n npy2ckpt python=3.6
source activate npy2ckpt
pip install tensorflow
conda install -c menpo opencv3

Download GoogleNet model code (.py) and trained variables (.npy) from: http:https://www.deeplearningmodel.net/ (You can also find there the imagenet-classes.txt file)

Move the files to the models folder.

Change first line of the model code (.py):

#from kaffe.tensorflow import Network
from network import Network

Run the converter code, pointing to the .npy file:

python npy2ckpt_GoogleNet.py models/googlenet.npy

Take a look and how I set the parameters (width, height, channels, input_node_name) in npy2ckpt_GoogleNet.py.

Test the results:

python test_GoogleNet.py

Following this blog post, use the covert.py function in the Caffe2Tensorflow repo and move the output files to the models folder.

Change first line of the model code (.py):

#from kaffe.tensorflow import Network
from network import Network

Run the converter code, pointing to the .npy file:

python npy2ckpt_OpenPoseNet.py models/openposenet.npy

Take a look and how I set the parameters (width, height, channels, input_node_name) in npy2ckpt_OpenPoseNet.py.

Amazing job here to convert models from Caffe to TensorFlow: https://github.com/ethereon/caffe-tensorflow . The thing is that the output is in .npy format, and I'm not very comfortable dealing with that.

Most of the code is borrowed from that repo. I changed some things to update it to Python 3 and TensorFlow 1.