24.02/parsers.html - ml/armnn - Gitiles

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 <div class="title">Parsers </div>  </div>
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 <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>
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	<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 (<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.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>
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