docs/03_scripts.dox - ml/ComputeLibrary - Gitiles

 ///
 /// Copyright (c) 2017-2018 ARM Limited.
 ///
 /// SPDX-License-Identifier: MIT
 ///
 /// Permission is hereby granted, free of charge, to any person obtaining a copy
 /// of this software and associated documentation files (the "Software"), to
 /// deal in the Software without restriction, including without limitation the
 /// rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
 /// sell copies of the Software, and to permit persons to whom the Software is
 /// furnished to do so, subject to the following conditions:
 ///
 /// The above copyright notice and this permission notice shall be included in all
 /// copies or substantial portions of the Software.
 ///
 /// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 /// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 /// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 /// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 /// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 /// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 /// SOFTWARE.
 ///
 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.
 */
 }
	///
	/// Copyright (c) 2017-2018 ARM Limited.
	///
	/// SPDX-License-Identifier: MIT
	///
	/// Permission is hereby granted, free of charge, to any person obtaining a copy
	/// of this software and associated documentation files (the "Software"), to
	/// deal in the Software without restriction, including without limitation the
	/// rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
	/// sell copies of the Software, and to permit persons to whom the Software is
	/// furnished to do so, subject to the following conditions:
	///
	/// The above copyright notice and this permission notice shall be included in all
	/// copies or substantial portions of the Software.
	///
	/// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
	/// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
	/// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
	/// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
	/// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
	/// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
	/// SOFTWARE.
	///
	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.
	*/
	}