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author | Nikhil Raj <nikhil.raj@arm.com> | 2023-05-19 11:14:28 +0100 |
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committer | Nikhil Raj <nikhil.raj@arm.com> | 2023-05-19 11:14:28 +0100 |
commit | 8efb48a6847c5cd166c561127ae6611150963ce3 (patch) | |
tree | f4262b7e54d26021a54bbc310f46d16a291463e5 /23.05/parsers.xhtml | |
parent | 023fe66a59414a0a7b337c7be5c3c341eb5b55d2 (diff) | |
download | armnn-8efb48a6847c5cd166c561127ae6611150963ce3.tar.gz |
Update Doxygen docu for 23.05
Signed-off-by: Nikhil Raj <nikhil.raj@arm.com>
Change-Id: I0a992286f14fa68fcc6e5eba31ac39fed003cbbe
Diffstat (limited to '23.05/parsers.xhtml')
-rw-r--r-- | 23.05/parsers.xhtml | 334 |
1 files changed, 334 insertions, 0 deletions
diff --git a/23.05/parsers.xhtml b/23.05/parsers.xhtml new file mode 100644 index 0000000000..0efc63c4eb --- /dev/null +++ b/23.05/parsers.xhtml @@ -0,0 +1,334 @@ +<!-- Copyright (c) 2020 ARM Limited. --> +<!-- --> +<!-- SPDX-License-Identifier: MIT --> +<!-- --> +<!-- HTML header for doxygen 1.8.13--> +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> +<html xmlns="http://www.w3.org/1999/xhtml"> +<head> +<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/> +<meta http-equiv="X-UA-Compatible" content="IE=9"/> +<meta name="generator" content="Doxygen 1.8.17"/> +<meta name="robots" content="NOINDEX, NOFOLLOW" /> +<meta name="viewport" content="width=device-width, initial-scale=1"/> +<title>ArmNN: Parsers</title> +<link href="tabs.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="jquery.js"></script> +<script type="text/javascript" src="dynsections.js"></script> +<link href="navtree.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="resize.js"></script> +<script type="text/javascript" src="navtreedata.js"></script> +<script type="text/javascript" src="navtree.js"></script> +<link href="search/search.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="search/searchdata.js"></script> +<script type="text/javascript" src="search/search.js"></script> +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + extensions: ["tex2jax.js"], + jax: ["input/TeX","output/HTML-CSS"], +}); +</script> +<script type="text/javascript" async="async" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script> +<link href="doxygen.css" rel="stylesheet" type="text/css" /> +<link href="stylesheet.css" rel="stylesheet" type="text/css"/> +</head> +<body> +<div id="top"><!-- do not remove this div, it is closed by doxygen! --> +<div id="titlearea"> +<table cellspacing="0" cellpadding="0"> + <tbody> + <tr style="height: 56px;"> + <img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 10rem; margin-top: .5rem; margin-left 10px"/> + <td style="padding-left: 0.5em;"> + <div id="projectname"> +  <span id="projectnumber">23.05</span> + </div> + </td> + </tr> + </tbody> +</table> +</div> +<!-- end header part --> +<!-- Generated by Doxygen 1.8.17 --> +<script type="text/javascript"> +/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt GPL-v2 */ +var searchBox = new SearchBox("searchBox", "search",false,'Search'); +/* @license-end */ +</script> +<script type="text/javascript" src="menudata.js"></script> +<script type="text/javascript" src="menu.js"></script> +<script type="text/javascript"> +/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt GPL-v2 */ +$(function() { + initMenu('',true,false,'search.php','Search'); + $(document).ready(function() { init_search(); }); +}); +/* @license-end */</script> +<div id="main-nav"></div> +</div><!-- top --> +<div id="side-nav" class="ui-resizable side-nav-resizable"> + <div id="nav-tree"> + <div id="nav-tree-contents"> + <div id="nav-sync" class="sync"></div> + </div> + </div> + <div id="splitbar" style="-moz-user-select:none;" + class="ui-resizable-handle"> + </div> +</div> +<script type="text/javascript"> +/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt GPL-v2 */ +$(document).ready(function(){initNavTree('parsers.xhtml',''); initResizable(); }); +/* @license-end */ +</script> +<div id="doc-content"> +<!-- window showing the filter options --> +<div id="MSearchSelectWindow" + onmouseover="return searchBox.OnSearchSelectShow()" + onmouseout="return searchBox.OnSearchSelectHide()" + onkeydown="return searchBox.OnSearchSelectKey(event)"> +</div> + +<!-- iframe showing the search results (closed by default) --> +<div id="MSearchResultsWindow"> +<iframe src="javascript:void(0)" frameborder="0" + name="MSearchResults" id="MSearchResults"> +</iframe> +</div> + +<div class="PageDoc"><div class="header"> + <div class="headertitle"> +<div class="title">Parsers </div> </div> +</div><!--header--> +<div class="contents"> +<div class="toc"><h3>Table of Contents</h3> +<ul><li class="level1"><a href="#S5_onnx_parser">Arm NN Onnx Parser</a></li> +<li class="level1"><a href="#S6_tf_lite_parser">Arm NN Tf Lite Parser</a></li> +</ul> +</div> +<div class="textblock"><p>Execute models from different machine learning platforms efficiently with our parsers. Simply choose a parser according to the model you want to run e.g. If you've got a model in onnx format (<model_name>.onnx) use our onnx-parser.</p> +<p>If you would like to run a Tensorflow Lite (TfLite) model you probably also want to take a look at our <a class="el" href="delegate.xhtml">TfLite Delegate</a>.</p> +<p>All parsers are written in C++ but it is also possible to use them in python. For more information on our python bindings take a look into the <a class="el" href="md_python_pyarmnn__r_e_a_d_m_e.xhtml">PyArmNN</a> section.</p> +<p><br /> +<br /> +</p> +<h1><a class="anchor" id="S5_onnx_parser"></a> +Arm NN Onnx Parser</h1> +<p><code><a class="el" href="namespacearmnn_onnx_parser.xhtml">armnnOnnxParser</a></code> is a library for loading neural networks defined in ONNX protobuf files into the Arm NN runtime.</p> +<h2>ONNX operators that the Arm NN SDK supports</h2> +<p>This reference guide provides a list of ONNX operators the Arm NN SDK currently supports.</p> +<p>The Arm NN SDK ONNX parser currently only supports fp32 operators.</p> +<h3>Fully supported</h3> +<ul> +<li>Add<ul> +<li>See the ONNX <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#Add">Add documentation</a> for more information</li> +</ul> +</li> +<li>AveragePool<ul> +<li>See the ONNX <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#AveragePool">AveragePool documentation</a> for more information.</li> +</ul> +</li> +<li>Concat<ul> +<li>See the ONNX <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#Concat">Concat documentation</a> for more information.</li> +</ul> +</li> +<li>Constant<ul> +<li>See the ONNX <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#Constant">Constant documentation</a> for more information.</li> +</ul> +</li> +<li>Clip<ul> +<li>See the ONNX <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#Clip">Clip documentation</a> for more information.</li> +</ul> +</li> +<li>Flatten<ul> +<li>See the ONNX <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#Flatten">Flatten documentation</a> for more information.</li> +</ul> +</li> +<li>Gather<ul> +<li>See the ONNX <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#Gather">Gather documentation</a> for more information.</li> +</ul> +</li> +<li>GlobalAveragePool<ul> +<li>See the ONNX <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#GlobalAveragePool">GlobalAveragePool documentation</a> for more information.</li> +</ul> +</li> +<li>LeakyRelu<ul> +<li>See the ONNX <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#LeakyRelu">LeakyRelu documentation</a> for more information.</li> +</ul> +</li> +<li>MaxPool<ul> +<li>See the ONNX <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#MaxPool">max_pool documentation</a> for more information.</li> +</ul> +</li> +<li>Relu<ul> +<li>See the ONNX <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#Relu">Relu documentation</a> for more information.</li> +</ul> +</li> +<li>Reshape<ul> +<li>See the ONNX <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#Reshape">Reshape documentation</a> for more information.</li> +</ul> +</li> +<li>Shape<ul> +<li>See the ONNX <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#Shape">Shape documentation</a> for more information.</li> +</ul> +</li> +<li>Sigmoid<ul> +<li>See the ONNX <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#Sigmoid">Sigmoid documentation</a> for more information.</li> +</ul> +</li> +<li>Tanh<ul> +<li>See the ONNX <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#Tanh">Tanh documentation</a> for more information.</li> +</ul> +</li> +<li>Unsqueeze<ul> +<li>See the ONNX <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#Unsqueeze">Unsqueeze documentation</a> for more information.</li> +</ul> +</li> +</ul> +<h3>Partially supported</h3> +<ul> +<li>Conv<ul> +<li>The parser only supports 2D convolutions with a group = 1 or group = #Nb_of_channel (depthwise convolution)</li> +</ul> +</li> +<li>BatchNormalization<ul> +<li>The parser does not support training mode. See the ONNX <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#BatchNormalization">BatchNormalization documentation</a> for more information.</li> +</ul> +</li> +<li>Gemm<ul> +<li>The parser only supports constant bias or non-constant bias where bias dimension = 1. See the ONNX <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#Gemm">Gemm documentation</a> for more information.</li> +</ul> +</li> +<li>MatMul<ul> +<li>The parser only supports constant weights in a fully connected layer. See the ONNX <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#MatMul">MatMul documentation</a> for more information.</li> +</ul> +</li> +</ul> +<h2>Tested networks</h2> +<p>Arm tested these operators with the following ONNX fp32 neural networks:</p><ul> +<li>Mobilenet_v2. See the ONNX <a href="https://github.com/onnx/models/tree/master/vision/classification/mobilenet">MobileNet documentation</a> for more information.</li> +<li>Simple MNIST. This is no longer directly documented by ONNX. The model and test data may be downloaded <a href="https://onnxzoo.blob.core.windows.net/models/opset_8/mnist/mnist.tar.gz">from the ONNX model zoo</a>.</li> +</ul> +<p>More machine learning operators will be supported in future releases. <br /> +<br /> +<br /> +<br /> +</p> +<h1><a class="anchor" id="S6_tf_lite_parser"></a> +Arm NN Tf Lite Parser</h1> +<p><code><a class="el" href="namespacearmnn_tf_lite_parser.xhtml">armnnTfLiteParser</a></code> is a library for loading neural networks defined by TensorFlow Lite FlatBuffers files into the Arm NN runtime.</p> +<h2>TensorFlow Lite operators that the Arm NN SDK supports</h2> +<p>This reference guide provides a list of TensorFlow Lite operators the Arm NN SDK currently supports.</p> +<h3>Fully supported</h3> +<p>The Arm NN SDK TensorFlow Lite parser currently supports the following operators:</p> +<ul> +<li>ABS</li> +<li>ADD</li> +<li>ARG_MAX</li> +<li>ARG_MIN</li> +<li>AVERAGE_POOL_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE</li> +<li>BATCH_TO_SPACE</li> +<li>CAST</li> +<li>CEIL</li> +<li>CONCATENATION, Supported Fused Activation: RELU , RELU6 , TANH, NONE</li> +<li>CONV_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE</li> +<li>CONV_3D, Supported Fused Activation: RELU , RELU6 , TANH, NONE</li> +<li>DEPTH_TO_SPACE</li> +<li>DEPTHWISE_CONV_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE</li> +<li>DEQUANTIZE</li> +<li>DIV</li> +<li>ELU</li> +<li>EQUAL</li> +<li>EXP</li> +<li>EXPAND_DIMS</li> +<li>FLOOR_DIV</li> +<li>FULLY_CONNECTED, Supported Fused Activation: RELU , RELU6 , TANH, NONE</li> +<li>GATHER</li> +<li>GATHER_ND</li> +<li>GREATER</li> +<li>GREATER_EQUAL</li> +<li>HARD_SWISH</li> +<li>LEAKY_RELU</li> +<li>LESS</li> +<li>LESS_EQUAL</li> +<li>LOG</li> +<li>LOGICAL_NOT</li> +<li>LOGISTIC</li> +<li>LOG_SOFTMAX</li> +<li>L2_NORMALIZATION</li> +<li>MAX_POOL_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE</li> +<li>MAXIMUM</li> +<li>MEAN</li> +<li>MINIMUM</li> +<li>MIRROR_PAD</li> +<li>MUL</li> +<li>NEG</li> +<li>NOT_EQUAL</li> +<li>PACK</li> +<li>PAD</li> +<li>PADV2</li> +<li>PRELU</li> +<li>QUANTIZE</li> +<li>RELU</li> +<li>RELU6</li> +<li>REDUCE_MAX</li> +<li>REDUCE_MIN</li> +<li>REDUCE_PROD</li> +<li>RESHAPE</li> +<li>RESIZE_BILINEAR</li> +<li>RESIZE_NEAREST_NEIGHBOR</li> +<li>RSQRT</li> +<li>SHAPE</li> +<li>SIN</li> +<li>SLICE</li> +<li>SOFTMAX</li> +<li>SPACE_TO_BATCH</li> +<li>SPACE_TO_DEPTH</li> +<li>SPLIT</li> +<li>SPLIT_V</li> +<li>SQUEEZE</li> +<li>SQRT</li> +<li>STRIDED_SLICE</li> +<li>SUB</li> +<li>SUM</li> +<li>TANH</li> +<li>TRANSPOSE</li> +<li>TRANSPOSE_CONV</li> +<li>UNIDIRECTIONAL_SEQUENCE_LSTM</li> +<li>UNPACK</li> +</ul> +<h3>Custom Operator</h3> +<ul> +<li>TFLite_Detection_PostProcess</li> +</ul> +<h2>Tested networks</h2> +<p>Arm tested these operators with the following TensorFlow Lite neural network:</p><ul> +<li><a href="http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224_quant.tgz">Quantized MobileNet</a></li> +<li><a href="http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18.tar.gz">Quantized SSD MobileNet</a></li> +<li>DeepSpeech v1 converted from <a href="https://github.com/mozilla/DeepSpeech/releases/tag/v0.4.1">TensorFlow model</a></li> +<li>DeepSpeaker</li> +<li><a href="https://www.tensorflow.org/lite/models/segmentation/overview">DeepLab v3+</a></li> +<li>FSRCNN</li> +<li>EfficientNet-lite</li> +<li>RDN converted from <a href="https://github.com/hengchuan/RDN-TensorFlow">TensorFlow model</a></li> +<li>Quantized RDN (CpuRef)</li> +<li><a href="http://download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz">Quantized Inception v3</a></li> +<li><a href="http://download.tensorflow.org/models/inception_v4_299_quant_20181026.tgz">Quantized Inception v4</a> (CpuRef)</li> +<li>Quantized ResNet v2 50 (CpuRef)</li> +<li>Quantized Yolo v3 (CpuRef)</li> +</ul> +<p>More machine learning operators will be supported in future releases. </p> +</div></div><!-- contents --> +</div><!-- PageDoc --> +</div><!-- doc-content --> +<!-- start footer part --> +<div id="nav-path" class="navpath"><!-- id is needed for treeview function! --> + <ul> + <li class="navelem"><a class="el" href="swtools.xhtml">Software Components</a></li> + <li class="footer">Generated on Thu May 18 2023 10:35:44 for ArmNN by + <a href="http://www.doxygen.org/index.html"> + <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.17 </li> + </ul> +</div> +</body> +</html> |