importONNXNetwork

Import pretrained ONNX network

Syntax

net = importONNXNetwork(modelfile,'OutputLayerType',outputtype)

net = importONNXNetwork(modelfile,'OutputLayerType',outputtype,'Classes',classes)

Description

net = importONNXNetwork(modelfile,'OutputLayerType',outputtype) imports a pretrained network from the ONNX™ (Open Neural Network Exchange) file modelfile and specifies the output layer type of the imported network.

This function requires the Deep Learning Toolbox™ Converter for ONNX Model Format support package. If this support package is not installed, then the function provides a download link.

net = importONNXNetwork(modelfile,'OutputLayerType',outputtype,'Classes',classes) additionally specifies the classes for a classification network.

Examples

Download and Install Deep Learning Toolbox Converter for ONNX Model Format

Download and install the Deep Learning Toolbox Converter for ONNX Model Format support package.

Type importONNXNetwork at the command line.

importONNXNetwork

If Deep Learning Toolbox Converter for ONNX Model Format is not installed, then the function provides a link to the required support package in the Add-On Explorer. To install the support package, click the link, and then click Install. Check that the installation is successful by importing the network from the model file 'cifarResNet.onnx' at the command line. If the support package is installed, then the function returns a DAGNetwork object.

modelfile = 'cifarResNet.onnx';
classes = ["airplane" "automobile" "bird" "cat" "dee" "dog" "frog" "horse" "ship" "truck"];
net = importONNXNetwork(modelfile,'OutputLayerType','classification','Classes',classes)

net = 

  DAGNetwork with properties:

         Layers: [77×1 nnet.cnn.layer.Layer]
    Connections: [85×2 table]

Import ONNX Network

Import a residual neural network trained on the CIFAR-10 data set. Specify the file containing the ONNX network, its output type, and its output classes.

modelfile = 'cifarResNet.onnx';
classes = ["airplane" "automobile" "bird" "cat" "deer" "dog" "frog" "horse" "ship" "truck"];
net = importONNXNetwork(modelfile, ...
    'OutputLayerType','classification', ...
    'Classes',classes)

net = 
  DAGNetwork with properties:

         Layers: [77×1 nnet.cnn.layer.Layer]
    Connections: [85×2 table]

Analyze the imported network.

analyzeNetwork(net)

Input Arguments

modelfile — Name of ONNX model file

character vector | string scalar

Name of ONNX model file containing the network, specified as a character vector or a string scalar. The file must be in the current folder, in a folder on the MATLAB^® path, or you must include a full or relative path to the file.

Example: 'cifarResNet.onnx'

outputtype — Type of output layer

'classification' | 'regression' | 'pixelclassification'

Type of the output layer that the function appends to the end of the imported network, specified as 'classification', 'regression', or 'pixelclassification'. Using 'pixelclassification' appends a pixelClassificationLayer object (requires Computer Vision Toolbox™).

If a network in modelfile has multiple outputs, then you cannot specify the output layer types using this argument. Use importONNXLayers instead. importONNXLayers inserts placeholder layers for the outputs. After importing, you can find and replace the placeholder layers by using findPlaceholderLayers and replaceLayer, respectively.

Example: 'regression'

classes — Classes of the output layer

'auto' (default) | categorical vector | string array | cell array of character vectors

Classes of the output layer, specified as a categorical vector, string array, cell array of character vectors, or 'auto'. If Classes is 'auto', then the software sets the classes to categorical(1:N), where N is the number of classes. If you specify a string array or cell array of character vectors str, then the software sets the classes of the output layer to categorical(str,str).

Data Types: char | categorical | string | cell

Output Arguments

net — Pretrained network

DAGNetwork object

Pretrained network, returned as DAGNetwork object.

Tips

• If the ONNX network contains a layer that Deep Learning Toolbox Converter for ONNX Model Format does not support, then the function returns an error message. In this case, you can still use importONNXLayers to import the network architecture and weights.

• importONNXNetwork supports the following ONNX layers, with some limitations:

  ONNX Layer	Deep Learning Toolbox Layer
  Add	additionLayer or nnet.onnx.layer.ElementwiseAffineLayer
  AveragePool	averagePooling2dLayer
  BatchNormalization	batchNormalizationLayer
  Clip	nnet.onnx.layer.ClipLayer
  Concat	concatenationLayer
  Constant	None (Imported as weights)
  Conv	convolution2dLayer
  ConvTranspose	transposedConv2dLayer
  Div	nnet.onnx.layer.ElementwiseAffineLayer
  Dropout	dropoutLayer
  Flatten	nnet.onnx.layer.FlattenLayer or nnet.onnx.layer.Flatten3dLayer
  Elu	eluLayer
  Gemm	nnet.onnx.layer.FlattenLayer and fullyConnectedLayer if ONNX network is recurrent, otherwise fullyConnectedLayer
  GlobalAveragePool	globalAveragePooling2dLayer
  GlobalMaxPool	globalMaxPooling2dLayer
  GRU	gruLayer
  Identity	nnet.onnx.layer.IdentityLayer
  ImageScaler	nnet.onnx.layer.ElementwiseAffineLayer
  LeakyRelu	leakyReluLayer
  LRN	CrossChannelNormalizationLayer
  LSTM	lstmLayer or bilstmLayer
  MatMul	fullyConnectedLayer
  MaxPool	maxPooling2dLayer
  Mul	nnet.onnx.layer.ElementwiseAffineLayer
  PRelu	nnet.onnx.layer.PReluLayer
  Relu	reluLayer or clippedReluLayer
  Reshape	nnet.onnx.layer.FlattenLayer
  Sigmoid	nnet.onnx.layer.SigmoidLayer
  Softmax	softmaxLayer
  Sub	nnet.onnx.layer.ElementwiseAffineLayer
  Sum	additionLayer
  Tanh	tanhLayer

• You can import an ONNX network with multiple inputs and a single output using importONNXNetwork. If the network has multiple outputs, use importONNXLayers. The importONNXLayers function inserts placeholder layers for the outputs. After importing, you can find and replace the placeholder layers by using findPlaceholderLayers and replaceLayer, respectively. For an example, see Import ONNX Network with Multiple Outputs. To learn about a deep learning network with multiple inputs and multiple outputs, see Multiple-Input and Multiple-Output Networks.

• To use a pretrained network for prediction or transfer learning on new images, you must preprocess your images in the same way the images that were used to train the imported model were preprocessed. Most common preprocessing steps are resizing images, subtracting image average values, and converting the images from BGR images to RGB.

  • To resize images, use imresize. For example, imresize(image,[227,227,3]).

  • To convert images from RGB to BGR format, use flip. For example, flip(image,3).

  For more information on preprocessing images for training and prediction, see Preprocess Images for Deep Learning.

Compatibility Considerations

'ClassNames' option will be removed

Not recommended starting in R2018b

'ClassNames' will be removed. Use 'Classes' instead. To update your code, replace all instances of 'ClassNames' with 'Classes'. There are some differences between the corresponding properties in classification output layers that require additional updates to your code.

The ClassNames property of a classification output layer is a cell array of character vectors. The Classes property is a categorical array. To use the value of Classes with functions that require cell array input, convert the classes using the cellstr function.

References

[1] Open Neural Network Exchange. https://github.com/onnx/.

[2] ONNX. https://onnx.ai/.