| /// 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 tensorflow format (<model_name>.pb) use our tensorflow-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. |
| |
| |
| |
| @section S4_caffe_parser Arm NN Caffe Parser |
| |
| `armnnCaffeParser` is a library for loading neural networks defined in Caffe protobuf files into the Arm NN runtime. |
| |
| Please note that certain deprecated Caffe features are not supported by the armnnCaffeParser. If you think that Arm NN |
| should be able to load your model according to the list of supported layers, but you are getting strange error |
| messages, then try upgrading your model to the latest format using Caffe, either by saving it to a new file or using |
| the upgrade utilities in `caffe/tools`. |
| |
| \b NOTE: The Arm NN Caffe Parser is deprecated in Arm NN 21.02 and will be removed in 21.05. |
| |
| ## Caffe layers supported by the Arm NN SDK |
| This reference guide provides a list of Caffe layers the Arm NN SDK currently supports. |
| |
| ### Although some other neural networks might work, Arm tests the Arm NN SDK with Caffe implementations of the following neural networks: |
| |
| - AlexNet. |
| - Cifar10. |
| - Inception-BN. |
| - Resnet_50, Resnet_101 and Resnet_152. |
| - VGG_CNN_S, VGG_16 and VGG_19. |
| - Yolov1_tiny. |
| - Lenet. |
| - MobileNetv1. |
| - SqueezeNet v1.0 and SqueezeNet v1.1 |
| |
| ### The Arm NN SDK supports the following machine learning layers for Caffe networks: |
| |
| - Argmax, excluding the top_k and out_max_val parameters. |
| - BatchNorm, in inference mode. |
| - Convolution, excluding Weight Filler, Bias Filler, Engine, Force nd_im2col, and Axis parameters. |
| - Deconvolution, excluding the Dilation Size, Weight Filler, Bias Filler, Engine, Force nd_im2col, and Axis parameters. |
| |
| Caffe doesn't support depthwise convolution, the equivalent layer is implemented through the notion of groups. ArmNN supports groups this way: |
| - when group=1, it is a normal conv2d |
| - when group=#input_channels, we can replace it by a depthwise convolution |
| - when group>1 && group<#input_channels, we need to split the input into the given number of groups, apply a separate convolution and then merge the results |
| - Concat, along the channel dimension only. |
| - Dropout, in inference mode. |
| - Eltwise, excluding the coeff parameter. |
| - Inner Product, excluding the Weight Filler, Bias Filler, Engine, and Axis parameters. |
| - Input. |
| - LRN, excluding the Engine parameter. |
| - Pooling, excluding the Stochastic Pooling and Engine parameters. |
| - ReLU. |
| - Scale. |
| - Softmax, excluding the Axis and Engine parameters. |
| - Split. |
| |
| More machine learning layers will be supported in future releases. |
| |
| <br/><br/><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: |
| |
| - ADD |
| - 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 |
| - 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 |
| - 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. |
| <br/><br/><br/><br/> |
| |
| |
| |
| |
| @section S7_tf_parser ArmNN Tensorflow Parser |
| |
| `armnnTfParser` is a library for loading neural networks defined by TensorFlow protobuf files into the Arm NN runtime. |
| |
| \b NOTE: The Arm NN Tensorflow Parser is deprecated in Arm NN 21.02 and will be removed in 21.05. |
| |
| ## TensorFlow operators that the Arm NN SDK supports |
| |
| This reference guide provides a list of TensorFlow operators the Arm NN SDK currently supports. |
| |
| The Arm NN SDK TensorFlow parser currently only supports fp32 operators. |
| |
| ### Fully supported |
| |
| - avg_pool |
| - See the TensorFlow [avg_pool documentation](https://www.tensorflow.org/api_docs/python/tf/nn/avg_pool) for more information. |
| - bias_add |
| - See the TensorFlow [bias_add documentation](https://www.tensorflow.org/api_docs/python/tf/nn/bias_add) for more information. |
| - conv2d |
| - See the TensorFlow [conv2d documentation](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d) for more information. |
| - expand_dims |
| - See the TensorFlow [expand_dims documentation](https://www.tensorflow.org/api_docs/python/tf/expand_dims) for more information. |
| - gather |
| - See the TensorFlow [gather documentation](https://www.tensorflow.org/api_docs/python/tf/gather) for more information. |
| - identity |
| - See the TensorFlow [identity documentation](https://www.tensorflow.org/api_docs/python/tf/identity) for more information. |
| - local_response_normalization |
| - See the TensorFlow [local_response_normalization documentation](https://www.tensorflow.org/api_docs/python/tf/nn/local_response_normalization) for more information. |
| - max_pool |
| - See the TensorFlow [max_pool documentation](https://www.tensorflow.org/api_docs/python/tf/nn/max_pool) for more information. |
| - placeholder |
| - See the TensorFlow [placeholder documentation](https://www.tensorflow.org/api_docs/python/tf/placeholder) for more information. |
| - reduce_mean |
| - See the TensorFlow [reduce_mean documentation](https://www.tensorflow.org/api_docs/python/tf/reduce_mean) for more information. |
| - relu |
| - See the TensorFlow [relu documentation](https://www.tensorflow.org/api_docs/python/tf/nn/relu) for more information. |
| - relu6 |
| - See the TensorFlow [relu6 documentation](https://www.tensorflow.org/api_docs/python/tf/nn/relu6) for more information. |
| - rsqrt |
| - See the TensorFlow [rsqrt documentation](https://www.tensorflow.org/api_docs/python/tf/math/rsqrt) for more information. |
| - shape |
| - See the TensorFlow [shape documentation](https://www.tensorflow.org/api_docs/python/tf/shape) for more information. |
| - sigmoid |
| - See the TensorFlow [sigmoid documentation](https://www.tensorflow.org/api_docs/python/tf/sigmoid) for more information. |
| - softplus |
| - See the TensorFlow [softplus documentation](https://www.tensorflow.org/api_docs/python/tf/nn/softplus) for more information. |
| - squeeze |
| - See the TensorFlow [squeeze documentation](https://www.tensorflow.org/api_docs/python/tf/squeeze) for more information. |
| - tanh |
| - See the TensorFlow [tanh documentation](https://www.tensorflow.org/api_docs/python/tf/tanh) for more information. |
| - transpose |
| - See the TensorFlow [transpose documentation](https://www.tensorflow.org/api_docs/python/tf/transpose) for more information. |
| |
| ### Partially supported |
| |
| - add |
| - The parser does not support all forms of [broadcast composition](https://www.tensorflow.org/performance/xla/broadcasting), only broadcasting of scalars and 1D tensors. See the TensorFlow [add operator documentation](https://www.tensorflow.org/api_docs/python/tf/add) for more information. |
| - add_n |
| - The parser does not support all forms of [broadcast composition](https://www.tensorflow.org/performance/xla/broadcasting), only broadcasting of scalars and 1D tensors. See the TensorFlow [add operator documentation](https://www.tensorflow.org/api_docs/python/tf/add_n) for more information. |
| - concat |
| - Arm NN supports concatenation along the channel dimension for data formats NHWC and NCHW. |
| - constant |
| - The parser does not support the optional `shape` argument. It always infers the shape of the output tensor from `value`. See the TensorFlow [constant documentation](https://www.tensorflow.org/api_docs/python/tf/constant) for further information. |
| - depthwise_conv2d_native |
| - The parser only supports a dilation rate of (1,1,1,1). See the TensorFlow [depthwise_conv2d_native documentation](https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d_native) for more information. |
| - equal |
| - The parser does not support all forms of [broadcast composition](https://www.tensorflow.org/performance/xla/broadcasting), only broadcasting of 4D and 1D tensors. See the TensorFlow [equal operator documentation](https://www.tensorflow.org/api_docs/python/tf/math/equal) for more information. |
| - fused_batch_norm |
| - The parser does not support training outputs. See the TensorFlow [fused_batch_norm documentation](https://www.tensorflow.org/api_docs/python/tf/nn/fused_batch_norm) for more information. |
| - greater |
| - The parser does not support all forms of [broadcast composition](https://www.tensorflow.org/performance/xla/broadcasting), only broadcasting of 4D and 1D tensors. See the TensorFlow [greater operator documentation](https://www.tensorflow.org/api_docs/python/tf/math/greater) for more information. |
| - matmul |
| - The parser only supports constant weights in a fully connected layer. See the TensorFlow [matmul documentation](https://www.tensorflow.org/api_docs/python/tf/matmul) for more information. |
| - maximum |
| where maximum is used in one of the following ways |
| - max(mul(a, x), x) |
| - max(mul(x, a), x) |
| - max(x, mul(a, x)) |
| - max(x, mul(x, a) |
| This is interpreted as a ActivationLayer with a LeakyRelu activation function. Any other usage of max will result in the insertion of a simple maximum layer. The parser does not support all forms of [broadcast composition](https://www.tensorflow.org/performance/xla/broadcasting). See the TensorFlow [maximum documentation](https://www.tensorflow.org/api_docs/python/tf/maximum) for more information. |
| - minimum |
| - The parser does not support all forms of [broadcast composition](https://www.tensorflow.org/performance/xla/broadcasting), only broadcasting of 4D and 1D tensors. See the TensorFlow [minimum operator documentation](https://www.tensorflow.org/api_docs/python/tf/math/minimum) for more information. |
| - multiply |
| - The parser does not support all forms of [broadcast composition](https://www.tensorflow.org/performance/xla/broadcasting), only broadcasting of scalars and 1D tensors. See the TensorFlow [multiply documentation](https://www.tensorflow.org/api_docs/python/tf/multiply) for more information. |
| - pack/stack |
| - See the TensorFlow [stack documentation](https://www.tensorflow.org/api_docs/python/tf/stack) for more information. |
| - pad |
| - Only supports tf.pad function with mode = 'CONSTANT' and constant_values = 0. See the TensorFlow [pad documentation](https://www.tensorflow.org/api_docs/python/tf/pad) for more information. |
| - realdiv |
| - The parser does not support all forms of [broadcast composition](https://www.tensorflow.org/performance/xla/broadcasting), only broadcasting of scalars and 1D tensors. See the TensorFlow [realdiv documentation](https://www.tensorflow.org/api_docs/python/tf/realdiv) for more information. |
| - reshape |
| - The parser does not support reshaping to or from 4D. See the TensorFlow [reshape documentation](https://www.tensorflow.org/api_docs/python/tf/reshape) for more information. |
| - resize_images |
| - The parser only supports `ResizeMethod.BILINEAR` with `align_corners=False`. See the TensorFlow [resize_images documentation](https://www.tensorflow.org/api_docs/python/tf/image/resize_images) for more information. |
| - softmax |
| - The parser only supports 2D inputs and does not support selecting the `softmax` dimension. See the TensorFlow [softmax documentation](https://www.tensorflow.org/api_docs/python/tf/nn/softmax) for more information. |
| - split |
| - Arm NN supports split along the channel dimension for data formats NHWC and NCHW. |
| - strided_slice |
| - See the TensorFlow [strided_slice documentation](https://www.tensorflow.org/api_docs/python/tf/strided_slice) for more information. |
| - subtract |
| - The parser does not support all forms of broadcasting [broadcast composition](https://www.tensorflow.org/performance/xla/broadcasting), only broadcasting of scalars and 1D tensors. See the TensorFlow [subtract documentation](https://www.tensorflow.org/api_docs/python/tf/math/subtract) for more information. |
| |
| |
| ## Tested networks |
| |
| Arm tests these operators with the following TensorFlow fp32 neural networks: |
| - Lenet |
| - mobilenet_v1_1.0_224. The Arm NN SDK only supports the non-quantized version of the network. See the [MobileNet_v1 documentation](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md) for more information on quantized networks. |
| - inception_v3. The Arm NN SDK only supports the official inception_v3 transformed model. See the TensorFlow documentation on [preparing models for mobile deployment](https://www.tensorflow.org/mobile/prepare_models) for more information on how to transform the inception_v3 network. |
| - Simple MNIST. For more information check out the [tutorial](https://developer.arm.com/technologies/machine-learning-on-arm/developer-material/how-to-guides/deploying-a-tensorflow-mnist-model-on-arm-nn) on the Arm Developer portal. |
| - ResNet v2 50 implementation from the [TF Slim model zoo](https://github.com/tensorflow/models/tree/master/research/slim) |
| |
| More machine learning operators will be supported in future releases. |
| |
| **/ |
| } |
| |