benderthon
==========
Set of utilities to work easier with
`Bender <https://github.com/xmartlabs/Bender>`__.
Currently there's support for TensorFlow and Caffe, but we are working
on more stuff!
Works on Python 2.7.+ and 3.+, with Tensorflow 1.2+.
To install:
.. code:: bash
pip install benderthon
TensorFlow is required too. The simplest way to install it is:
.. code:: bash
pip install tensorflow
There are other ways, see `Installing
Tensorflow <https://www.tensorflow.org/install/>`__. Benderthon does not
install it by default to let the usage of a custom installation.
tf-freeze
---------
Utility to convert **TensorFlow** checkpoints into minimal frozen
**graphs**.
Usage
~~~~~
To take the checkpoint in ``checkpoint_path.ckpt``, whose output is
yielded by the node named ``Tanh``, and save it to
``graph_with_weights.pb``:
.. code:: bash
benderthon tf-freeze checkpoint_path.ckpt graph_with_weights.pb Tanh
Sample
~~~~~~
The file ``sample.py`` contains a network example for MNIST dataset with
2 convolutional layers and 2 dens layers. If you run it, it will
generate checkpoints files with prefix ``checkpoints/mnist.ckpt``:
.. code:: bash
./sample.py
Then you can get a minimal protobuf version with the weights frozen:
.. code:: bash
benderthon tf-freeze checkpoints/mnist.ckpt output/mnist.pb Prediction
The generated file occupies **half** the original checkpoints (26MB to
13MB).
You can also get only the graph, which occupies just **13kB**:
.. code:: bash
benderthon tf-freeze --no-weights checkpoints/mnist.ckpt output/mnist_only_graph.pb Prediction
To save the weights in a separate path for later processing:
.. code:: bash
benderthon tf-freeze --only-weights checkpoints/mnist.ckpt weights/ Prediction
caffe-freeze
------------
This module cannot be accessed from the command line utility, it should
be used from Python code, importing ``benderthon.caffe_freeze``.
You need ``caffeflow`` package installed first:
.. code:: bash
pip install -e git://github.com/xmartlabs/caffeflow.git@4618f89#egg=caffeflow
Development
-----------
This utility is under development and the API **is not stable**. So, do
not heavily rely on it.
To install locally you should do ``./setup.py install``, but first have
`pandoc <http://pandoc.org/>`__ and
`pypandoc <https://github.com/bebraw/pypandoc>`__ installed.
License
-------
::
Copyright 2017 Xmartlabs SRL.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
