| /// Copyright (c) 2021 ARM Limited and Contributors. All rights reserved. |
| /// |
| /// SPDX-License-Identifier: MIT |
| /// |
| |
| namespace armnn |
| { |
| /** |
| @page parsers Parsers |
| |
| @tableofcontents |
| 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. |
| |
| If you would like to run a Tensorflow Lite (TfLite) model you probably also want to take a look at our @ref delegate. |
| |
| 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 @ref md_python_pyarmnn_README section. |
| |
| <br/><br/> |
| |
| |
| |
| |
| @section S5_onnx_parser ArmNN Onnx Parser |
| |
| `armnnOnnxParser` is a library for loading neural networks defined in ONNX protobuf files into the Arm NN runtime. |
| |
| ## ONNX operators that the Arm NN SDK supports |
| |
| This reference guide provides a list of ONNX operators the Arm NN SDK currently supports. |
| |
| The Arm NN SDK ONNX parser currently only supports fp32 operators. |
| |
| ### Fully supported |
| |
| - Add |
| - See the ONNX [Add documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Add) for more information |
| |
| - AveragePool |
| - See the ONNX [AveragePool documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#AveragePool) for more information. |
| |
| - Constant |
| - See the ONNX [Constant documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Constant) for more information. |
| |
| - Clip |
| - See the ONNX [Clip documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Clip) for more information. |
| |
| - Flatten |
| - See the ONNX [Flatten documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Flatten) for more information. |
| |
| - GlobalAveragePool |
| - See the ONNX [GlobalAveragePool documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#GlobalAveragePool) for more information. |
| |
| - LeakyRelu |
| - See the ONNX [LeakyRelu documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#LeakyRelu) for more information. |
| |
| - MaxPool |
| - See the ONNX [max_pool documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#MaxPool) for more information. |
| |
| - Relu |
| - See the ONNX [Relu documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Relu) for more information. |
| |
| - Reshape |
| - See the ONNX [Reshape documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Reshape) for more information. |
| |
| - Sigmoid |
| - See the ONNX [Sigmoid documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Sigmoid) for more information. |
| |
| - Tanh |
| - See the ONNX [Tanh documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Tanh) for more information. |
| |
| |
| ### Partially supported |
| |
| - Conv |
| - The parser only supports 2D convolutions with a group = 1 or group = #Nb_of_channel (depthwise convolution) |
| - BatchNormalization |
| - The parser does not support training mode. See the ONNX [BatchNormalization documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#BatchNormalization) for more information. |
| - MatMul |
| - The parser only supports constant weights in a fully connected layer. |
| |
| ## Tested networks |
| |
| Arm tested these operators with the following ONNX fp32 neural networks: |
| - Mobilenet_v2. See the ONNX [MobileNet documentation](https://github.com/onnx/models/tree/master/vision/classification/mobilenet) for more information. |
| - Simple MNIST. This is no longer directly documented by ONNX. The model and test data may be downloaded [from the ONNX model zoo](https://onnxzoo.blob.core.windows.net/models/opset_8/mnist/mnist.tar.gz). |
| |
| More machine learning operators will be supported in future releases. |
| <br/><br/><br/><br/> |
| |
| |
| |
| |
| @section S6_tf_lite_parser ArmNN Tf Lite Parser |
| |
| `armnnTfLiteParser` is a library for loading neural networks defined by TensorFlow Lite FlatBuffers files |
| into the Arm NN runtime. |
| |
| ## TensorFlow Lite operators that the Arm NN SDK supports |
| |
| This reference guide provides a list of TensorFlow Lite operators the Arm NN SDK currently supports. |
| |
| ### Fully supported |
| The Arm NN SDK TensorFlow Lite parser currently supports the following operators: |
| |
| - ABS |
| - ADD |
| - ARG_MAX |
| - ARG_MIN |
| - AVERAGE_POOL_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE |
| - BATCH_TO_SPACE |
| - CONCATENATION, Supported Fused Activation: RELU , RELU6 , TANH, NONE |
| - CONV_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE |
| - DEPTH_TO_SPACE |
| - DEPTHWISE_CONV_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE |
| - DEQUANTIZE |
| - DIV |
| - ELU |
| - EXP |
| - FULLY_CONNECTED, Supported Fused Activation: RELU , RELU6 , TANH, NONE |
| - GATHER |
| - HARD_SWISH |
| - LEAKY_RELU |
| - LOGICAL_NOT |
| - LOGISTIC |
| - L2_NORMALIZATION |
| - MAX_POOL_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE |
| - MAXIMUM |
| - MEAN |
| - MINIMUM |
| - MUL |
| - NEG |
| - PACK |
| - PAD |
| - QUANTIZE |
| - RELU |
| - RELU6 |
| - REDUCE_MAX |
| - REDUCE_MIN |
| - RESHAPE |
| - RESIZE_BILINEAR |
| - RESIZE_NEAREST_NEIGHBOR |
| - RSQRT |
| - SLICE |
| - SOFTMAX |
| - SPACE_TO_BATCH |
| - SPLIT |
| - SPLIT_V |
| - SQUEEZE |
| - STRIDED_SLICE |
| - SUB |
| - SUM |
| - TANH |
| - TRANSPOSE |
| - TRANSPOSE_CONV |
| - UNPACK |
| |
| ### Custom Operator |
| - TFLite_Detection_PostProcess |
| |
| ## Tested networks |
| Arm tested these operators with the following TensorFlow Lite neural network: |
| - [Quantized MobileNet](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224_quant.tgz) |
| - [Quantized SSD MobileNet](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18.tar.gz) |
| - DeepSpeech v1 converted from [TensorFlow model](https://github.com/mozilla/DeepSpeech/releases/tag/v0.4.1) |
| - DeepSpeaker |
| - [DeepLab v3+](https://www.tensorflow.org/lite/models/segmentation/overview) |
| - FSRCNN |
| - EfficientNet-lite |
| - RDN converted from [TensorFlow model](https://github.com/hengchuan/RDN-TensorFlow) |
| - Quantized RDN (CpuRef) |
| - [Quantized Inception v3](http://download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz) |
| - [Quantized Inception v4](http://download.tensorflow.org/models/inception_v4_299_quant_20181026.tgz) (CpuRef) |
| - Quantized ResNet v2 50 (CpuRef) |
| - Quantized Yolo v3 (CpuRef) |
| |
| More machine learning operators will be supported in future releases. |
| |
| **/ |
| } |
| |