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/**
@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 scripts folder is an example script that shows how to
extract the parameter values from a trained model.
@note complex networks might require altering the script to properly work.
@subsection caffe_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
python 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:
python 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 caffe_result What is the expected output from the script
If the script runs successfully, it prints the names and shapes of each layer onto the standard
output and generates *.npy files containing the weights and biases of each layer.
The arm_compute::utils::load_trained_data shows how one could load
the weights and biases into tensor from the .npy file by the help of Accessor.
@section tensorflow_data_extractor Extract data from pre-trained tensorflow model
The script tensorflow_data_extractor.py extracts trainable parameters (e.g. values of weights and biases) from a
trained tensorflow model. A tensorflow model consists of the following two files:
{model_name}.data-{step}-{global_step}: A binary file containing values of each variable.
{model_name}.meta: A binary file containing a MetaGraph struct which defines the graph structure of the neural
network.
@note Since Tensorflow version 0.11 the binary checkpoint file which contains the values for each parameter has the format of:
{model_name}.data-{step}-of-{max_step}
instead of:
{model_name}.ckpt
When dealing with binary files with version >= 0.11, only pass {model_name} to -m option;
when dealing with binary files with version < 0.11, pass the whole file name {model_name}.ckpt to -m option.
@note This script relies on the parameters to be extracted being in the
'trainable_variables' tensor collection. By default all variables are automatically added to this collection unless
specified otherwise by the user. Thus should a user alter this default behavior and/or want to extract parameters from other
collections, tf.GraphKeys.TRAINABLE_VARIABLES should be replaced accordingly.
@subsection tensorflow_how_to How to use the script
Install tensorflow and numpy.
Download the pre-trained tensorflow model.
Run tensorflow_data_extractor.py with
python tensorflow_data_extractor -m <path_to_binary_checkpoint_file> -n <path_to_metagraph_file>
For example, to extract the data from pre-trained tensorflow Alex model to binary files:
python tensorflow_data_extractor -m /path/to/bvlc_alexnet -n /path/to/bvlc_alexnet.meta
Or for binary checkpoint files before Tensorflow 0.11:
python tensorflow_data_extractor -m /path/to/bvlc_alexnet.ckpt -n /path/to/bvlc_alexnet.meta
@note with versions >= Tensorflow 0.11 only model name is passed to the -m option
The script has been tested with Tensorflow 1.2, 1.3 on Python 2.7.6 and Python 3.4.3.
@subsection tensorflow_result What is the expected output from the script
If the script runs successfully, it prints the names and shapes of each parameter onto the standard output and generates
*.npy files containing the weights and biases of each layer.
The arm_compute::utils::load_trained_data shows how one could load
the weights and biases into tensor from the .npy file by the help of Accessor.
*/
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