docs/03_scripts.dox

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namespace arm_compute
{
/**
@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.

@section tf_frozen_model_extractor Extract data from pre-trained frozen tensorflow model

The script tf_frozen_model_extractor.py extracts trainable parameters (e.g. values of weights and biases) from a
frozen trained Tensorflow model.

@subsection tensorflow_frozen_how_to How to use the script

Install Tensorflow and NumPy.

Download the pre-trained Tensorflow model and freeze the model using the architecture and the checkpoint file.

Run tf_frozen_model_extractor.py with

        python tf_frozen_model_extractor -m <path_to_frozen_pb_model_file> -d <path_to_store_parameters>

For example, to extract the data from pre-trained Tensorflow model to binary files:

        python tf_frozen_model_extractor -m /path/to/inceptionv3.pb -d ./data

@subsection tensorflow_frozen_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.

@section validate_examples Validating examples
Using one of the provided scripts will generate files containing the trainable parameters.

You can validate a given graph example on a list of inputs by running:

    LD_LIBRARY_PATH=lib ./<graph_example> --validation-range='<validation_range>' --validation-file='<validation_file>' --validation-path='/path/to/test/images/' --data='/path/to/weights/'

e.g:

LD_LIBRARY_PATH=lib ./bin/graph_alexnet --target=CL --layout=NHWC --type=F32 --threads=4 --validation-range='16666,24998' --validation-file='val.txt' --validation-path='images/' --data='data/'

where:
    validation file is a plain document containing a list of images along with their expected label value.
    e.g:

        val_00000001.JPEG 65
        val_00000002.JPEG 970
        val_00000003.JPEG 230
        val_00000004.JPEG 809
        val_00000005.JPEG 516

    --validation-range is the index range of the images within the validation file you want to check:
    e.g:

       --validation-range='100,200' will validate 100 images starting from 100th one in the validation file.

    This can be useful when parallelizing the validation process is needed.
*/
}