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diff --git a/docs/03_scripts.dox b/docs/03_scripts.dox deleted file mode 100644 index e66bb402fe..0000000000 --- a/docs/03_scripts.dox +++ /dev/null @@ -1,178 +0,0 @@ -/// -/// Copyright (c) 2017-2020 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 - -Compute Library provides a list of graph examples that are used in the context of integration and performance testing. -The provenance of each model is part of its documentation and no structural or data alterations have been applied to any -of them unless explicitly specified otherwise in the documentation. - -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. -*/ -} |