From bee466b5eac4ec39d4032d946c9a4aee051f2b31 Mon Sep 17 00:00:00 2001
From: steniu01 <steven.niu@arm.com>
Date: Wed, 21 Jun 2017 16:45:41 +0100
Subject: COMPID-345 Add caffe_data_extractor.py script and the instructions

Change-Id: Ibb84b2060c4d6362be9ce4b1757e273e013de618
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/78630
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
---
 docs/03_scripts.dox | 40 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 40 insertions(+)
 create mode 100644 docs/03_scripts.dox

(limited to 'docs/03_scripts.dox')

diff --git a/docs/03_scripts.dox b/docs/03_scripts.dox
new file mode 100644
index 0000000000..a91a93166b
--- /dev/null
+++ b/docs/03_scripts.dox
@@ -0,0 +1,40 @@
+/**
+@page data_import Importing data from existing models
+
+@tableofcontents
+
+@section caffe_data_extractor Extract data from pre-trained caffe model
+
+One can find caffe <a href="https://github.com/BVLC/caffe/wiki/Model-Zoo">pre-trained models</a> on
+caffe's official github repository.
+
+The caffe_data_extractor.py provided in the @ref scripts folder is an example script that shows how to
+extract the hyperparameter values from a trained model.
+
+@note complex networks might require alter the script to properly work.
+
+@subsection how_to How to use the script
+
+Install caffe following <a href="http://caffe.berkeleyvision.org/installation.html">caffe's document</a>.
+Make sure the pycaffe has been added into the PYTHONPATH.
+
+Download the pre-trained caffe model.
+
+Run the caffe_data_extractor.py script by
+
+         ./caffe_data_extractor.py -m <caffe model> -n <caffe netlist>
+
+For example, to extract the data from pre-trained caffe Alex model to binary file:
+
+        ./caffe_data_extractor.py -m /path/to/bvlc_alexnet.caffemodel -n /path/to/caffe/models/bvlc_alexnet/deploy.prototxt
+
+The script has been tested under Python2.7.
+
+@subsection result  What is the expected ouput from the script
+
+If the script run succesfully, it prints the shapes of each layer onto the standard
+output and generates *.dat files containing the weights and biases of each layer.
+
+The @ref arm_compute::utils::load_trained_data shows how one could load
+the weights and biases into tensor from the .dat file by the help of Accessor.
+*/
-- 
cgit v1.2.1