179 lines
7.7 KiB
Plaintext
179 lines
7.7 KiB
Plaintext
///
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/// Copyright (c) 2017-2019 Arm Limited.
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///
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/// SPDX-License-Identifier: MIT
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///
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/// Permission is hereby granted, free of charge, to any person obtaining a copy
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/// of this software and associated documentation files (the "Software"), to
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/// deal in the Software without restriction, including without limitation the
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/// rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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/// sell copies of the Software, and to permit persons to whom the Software is
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/// furnished to do so, subject to the following conditions:
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///
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/// The above copyright notice and this permission notice shall be included in all
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/// copies or substantial portions of the Software.
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///
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/// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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/// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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/// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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/// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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/// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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/// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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/// SOFTWARE.
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///
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namespace arm_compute
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{
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/**
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@page data_import Importing data from existing models
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@tableofcontents
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@section caffe_data_extractor Extract data from pre-trained caffe model
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One can find caffe <a href="https://github.com/BVLC/caffe/wiki/Model-Zoo">pre-trained models</a> on
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caffe's official github repository.
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The caffe_data_extractor.py provided in the scripts folder is an example script that shows how to
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extract the parameter values from a trained model.
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@note complex networks might require altering the script to properly work.
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@subsection caffe_how_to How to use the script
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Install caffe following <a href="http://caffe.berkeleyvision.org/installation.html">caffe's document</a>.
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Make sure the pycaffe has been added into the PYTHONPATH.
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Download the pre-trained caffe model.
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Run the caffe_data_extractor.py script by
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python caffe_data_extractor.py -m <caffe model> -n <caffe netlist>
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For example, to extract the data from pre-trained caffe Alex model to binary file:
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python caffe_data_extractor.py -m /path/to/bvlc_alexnet.caffemodel -n /path/to/caffe/models/bvlc_alexnet/deploy.prototxt
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The script has been tested under Python2.7.
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@subsection caffe_result What is the expected output from the script
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If the script runs successfully, it prints the names and shapes of each layer onto the standard
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output and generates *.npy files containing the weights and biases of each layer.
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The arm_compute::utils::load_trained_data shows how one could load
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the weights and biases into tensor from the .npy file by the help of Accessor.
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@section tensorflow_data_extractor Extract data from pre-trained tensorflow model
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The script tensorflow_data_extractor.py extracts trainable parameters (e.g. values of weights and biases) from a
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trained tensorflow model. A tensorflow model consists of the following two files:
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{model_name}.data-{step}-{global_step}: A binary file containing values of each variable.
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{model_name}.meta: A binary file containing a MetaGraph struct which defines the graph structure of the neural
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network.
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@note Since Tensorflow version 0.11 the binary checkpoint file which contains the values for each parameter has the format of:
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{model_name}.data-{step}-of-{max_step}
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instead of:
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{model_name}.ckpt
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When dealing with binary files with version >= 0.11, only pass {model_name} to -m option;
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when dealing with binary files with version < 0.11, pass the whole file name {model_name}.ckpt to -m option.
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@note This script relies on the parameters to be extracted being in the
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'trainable_variables' tensor collection. By default all variables are automatically added to this collection unless
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specified otherwise by the user. Thus should a user alter this default behavior and/or want to extract parameters from other
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collections, tf.GraphKeys.TRAINABLE_VARIABLES should be replaced accordingly.
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@subsection tensorflow_how_to How to use the script
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Install tensorflow and numpy.
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Download the pre-trained tensorflow model.
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Run tensorflow_data_extractor.py with
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python tensorflow_data_extractor -m <path_to_binary_checkpoint_file> -n <path_to_metagraph_file>
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For example, to extract the data from pre-trained tensorflow Alex model to binary files:
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python tensorflow_data_extractor -m /path/to/bvlc_alexnet -n /path/to/bvlc_alexnet.meta
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Or for binary checkpoint files before Tensorflow 0.11:
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python tensorflow_data_extractor -m /path/to/bvlc_alexnet.ckpt -n /path/to/bvlc_alexnet.meta
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@note with versions >= Tensorflow 0.11 only model name is passed to the -m option
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The script has been tested with Tensorflow 1.2, 1.3 on Python 2.7.6 and Python 3.4.3.
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@subsection tensorflow_result What is the expected output from the script
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If the script runs successfully, it prints the names and shapes of each parameter onto the standard output and generates
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*.npy files containing the weights and biases of each layer.
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The arm_compute::utils::load_trained_data shows how one could load
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the weights and biases into tensor from the .npy file by the help of Accessor.
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@section tf_frozen_model_extractor Extract data from pre-trained frozen tensorflow model
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The script tf_frozen_model_extractor.py extracts trainable parameters (e.g. values of weights and biases) from a
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frozen trained Tensorflow model.
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@subsection tensorflow_frozen_how_to How to use the script
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Install Tensorflow and NumPy.
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Download the pre-trained Tensorflow model and freeze the model using the architecture and the checkpoint file.
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Run tf_frozen_model_extractor.py with
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python tf_frozen_model_extractor -m <path_to_frozen_pb_model_file> -d <path_to_store_parameters>
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For example, to extract the data from pre-trained Tensorflow model to binary files:
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python tf_frozen_model_extractor -m /path/to/inceptionv3.pb -d ./data
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@subsection tensorflow_frozen_result What is the expected output from the script
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If the script runs successfully, it prints the names and shapes of each parameter onto the standard output and generates
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*.npy files containing the weights and biases of each layer.
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The arm_compute::utils::load_trained_data shows how one could load
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the weights and biases into tensor from the .npy file by the help of Accessor.
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@section validate_examples Validating examples
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Compute Library provides a list of graph examples that are used in the context of integration and performance testing.
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The provenance of each model is part of its documentation and no structural or data alterations have been applied to any
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of them unless explicitly specified otherwise in the documentation.
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Using one of the provided scripts will generate files containing the trainable parameters.
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You can validate a given graph example on a list of inputs by running:
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LD_LIBRARY_PATH=lib ./<graph_example> --validation-range='<validation_range>' --validation-file='<validation_file>' --validation-path='/path/to/test/images/' --data='/path/to/weights/'
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e.g:
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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/'
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where:
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validation file is a plain document containing a list of images along with their expected label value.
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e.g:
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val_00000001.JPEG 65
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val_00000002.JPEG 970
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val_00000003.JPEG 230
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val_00000004.JPEG 809
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val_00000005.JPEG 516
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--validation-range is the index range of the images within the validation file you want to check:
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e.g:
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--validation-range='100,200' will validate 100 images starting from 100th one in the validation file.
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This can be useful when parallelizing the validation process is needed.
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*/
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}
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