Update Doxygen for 23.11

Signed-off-by: Nikhil Raj <nikhil.raj@arm.com> Change-Id: I47cd933f5002cb94a73aa97689d7b3d9c93cb849
author: Nikhil Raj <nikhil.raj@arm.com> 2023-11-22 11:41:15 +0000
committer: Nikhil Raj <nikhil.raj@arm.com> 2023-11-22 11:41:15 +0000
commit: 6f92c8e9f8bb38dcf5dccf8deeff5112ecd8e37c (patch)
tree: 0c076149c03ac45c2617f5e02a77b79287ff5a0f /23.11/parsers.html
parent: 03c7ff3f6188240baaeaeb405a357a0c58195fec (diff)
download: armnn-6f92c8e9f8bb38dcf5dccf8deeff5112ecd8e37c.tar.gz
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+  <div class="headertitle">
+<div class="title">Parsers </div>  </div>
+</div><!--header-->
+<div class="contents">
+<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 (&lt;model_name&gt;.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.html">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.html">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.html">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.html">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>BROADCAST_TO</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>GELU</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>POW</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>REVERSE_V2</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>SQUARE</li>
+<li>SQUARE_DIFFERENCE</li>
+<li>STRIDED_SLICE</li>
+<li>SUB</li>
+<li>SUM</li>
+<li>TANH</li>
+<li>TILE</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 -->
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author	Nikhil Raj <nikhil.raj@arm.com>	2023-11-22 11:41:15 +0000
committer	Nikhil Raj <nikhil.raj@arm.com>	2023-11-22 11:41:15 +0000
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