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.
Add
See the ONNX Add documentation for more information
AveragePool
See the ONNX AveragePool documentation for more information.
Constant
See the ONNX Constant documentation for more information.
Clip
See the ONNX Clip documentation for more information.
Flatten
See the ONNX Flatten documentation for more information.
GlobalAveragePool
See the ONNX GlobalAveragePool documentation for more information.
LeakyRelu
See the ONNX LeakyRelu documentation for more information.
MaxPool
See the ONNX max_pool documentation for more information.
Relu
See the ONNX Relu documentation for more information.
Reshape
See the ONNX Reshape documentation for more information.
Sigmoid
See the ONNX Sigmoid documentation for more information.
Tanh
See the ONNX Tanh documentation for more information.
Conv
The parser only supports 2D convolutions with a dilation rate of [1, 1] and group = 1 or group = #Nb_of_channel (depthwise convolution) See the ONNX Conv documentation for more information.
BatchNormalization
The parser does not support training mode. See the ONNX BatchNormalization documentation for more information.
MatMul
The parser only supports constant weights in a fully connected layer.
Arm tested these operators with the following ONNX fp32 neural networks:
Mobilenet_v2. See the ONNX MobileNet documentation 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.
More machine learning operators will be supported in future releases as time allows. If you require specific operator support contribution are welcome.