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importONNXLayers

Import layers from ONNX network

Description

example

layers = importONNXLayers(modelfile) imports the layers of an ONNX™ (Open Neural Network Exchange) network from the file modelfile. You can train the imported layers on a new data set or assemble the layers into a network ready for prediction. For an example of the workflow of assembling a network, see Assemble Network from Pretrained Keras Layers.

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.

example

layers = importONNXLayers(modelfile,Name,Value) imports the layers from an ONNX network with additional options specified by one or more name-value pair arguments.

For example, importONNXLayers(modelfile,'ImportWeights',false) imports the network architecture without weights from the file modelfile.

Examples

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

Type importONNXLayers at the command line.

importONNXLayers

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';
layers = importONNXLayers(modelfile,'OutputLayerType','classification')
layers = 

  LayerGraph with properties:

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

Import the architecture and weights of a residual neural network trained on the CIFAR-10 data set. Specify the file containing the ONNX network and the type of the output layer to add to the imported network.

modelfile = 'cifarResNet.onnx';
lgraph = importONNXLayers(modelfile, ...
    'OutputLayerType','classification', ...
    'ImportWeights',true)
lgraph = 
  LayerGraph with properties:

         Layers: [77×1 nnet.cnn.layer.Layer]
    Connections: [85×2 table]
     InputNames: {'Input_input'}
    OutputNames: {'ClassificationLayer_softmax'}

Analyze the imported network architecture.

analyzeNetwork(lgraph)

Import an ONNX network that has multiple outputs by using importONNXLayers. The function inserts placeholder layers for the outputs. After importing, you can find and replace the placeholder layers by using findPlaceholderLayers and replaceLayer, respectively.

Specify the network file from which to import layers and weights.

modelfile = 'digitsMIMO.onnx';

The network in digitsMIMO.onnx has two output layers: one classification layer to classify digits and one regression layer to compute the mean squared error for the predicted angles of the digits. Import the layers and weights from modelfile.

layers = importONNXLayers('digitsMIMO.onnx','ImportWeights',true)
Warning: ONNX network has multiple outputs. importONNXLayers inserts placeholder layers for the outputs. Find and replace the layers by using findPlaceholderLayers and replaceLayer, respectively.
layers = 
  LayerGraph with properties:

         Layers: [19×1 nnet.cnn.layer.Layer]
    Connections: [19×2 table]
     InputNames: {'Input_input'}
    OutputNames: {1×0 cell}

importONNXLayers displays a warning and inserts placeholder layers for the output layers.

Plot the layer graph using plot.

plot(layers)

The layer graph has two output layers: Output_fc_1_Flatten and Output_sm_1. These two layers are the placeholders for the outputs. You can check the placeholder layers by viewing the Layers property or by using the findPlaceholderLayers function.

layers.Layers
ans = 
  19x1 Layer array with layers:

     1   'Input_input'           Image Input           28x28x1 images
     2   'conv_1'                Convolution           16 5x5x1 convolutions with stride [1  1] and padding [2  2  2  2]
     3   'BN_1'                  Batch Normalization   Batch normalization with 16 channels
     4   'relu_1'                ReLU                  ReLU
     5   'conv_2'                Convolution           32 1x1x16 convolutions with stride [2  2] and padding [0  0  0  0]
     6   'conv_3'                Convolution           32 3x3x16 convolutions with stride [2  2] and padding [1  1  1  1]
     7   'BN_2'                  Batch Normalization   Batch normalization with 32 channels
     8   'relu_2'                ReLU                  ReLU
     9   'conv_4'                Convolution           32 3x3x32 convolutions with stride [1  1] and padding [1  1  1  1]
    10   'BN_3'                  Batch Normalization   Batch normalization with 32 channels
    11   'relu_3'                ReLU                  ReLU
    12   'plus_1'                Addition              Element-wise addition of 2 inputs
    13   'fc_1'                  Convolution           1 14x14x32 convolutions with stride [1  1] and padding [0  0  0  0]
    14   'fc_2'                  Convolution           10 14x14x32 convolutions with stride [1  1] and padding [0  0  0  0]
    15   'sm_1_Flatten'          ONNX Flatten          Flatten activations into 1-D assuming C-style (row-major) order
    16   'sm_1'                  Softmax               softmax
    17   'Output_sm_1'           PLACEHOLDER LAYER     Placeholder for 'Output' ONNX operator
    18   'fc_1_Flatten'          ONNX Flatten          Flatten activations into 1-D assuming C-style (row-major) order
    19   'Output_fc_1_Flatten'   PLACEHOLDER LAYER     Placeholder for 'Output' ONNX operator
placeholderLayers = findPlaceholderLayers(layers)
placeholderLayers = 
  2x1 PlaceholderOutputLayer array with layers:

     1   'Output_sm_1'           PLACEHOLDER LAYER   Placeholder for 'Output' ONNX operator
     2   'Output_fc_1_Flatten'   PLACEHOLDER LAYER   Placeholder for 'Output' ONNX operator

Create output layers to replace the placeholder layers. First, create a classification layer with the name Output_sm_1. Specify the classes of the output layer as 0, 1, ..., 9. If you do not specify the classes, then the software automatically sets them to 1, 2, ..., N, where N is the number of classes.

output1 = classificationLayer('Name','Output_sm_1','Classes',string(0:9)); 

Create a regression layer with the name Output_fc_1_Flatten.

output2 = regressionLayer('Name','Output_fc_1_Flatten'); 

Replace the placeholder layers with output1 and output2 using replaceLayer.

layers = replaceLayer(layers,'Output_sm_1',output1);
layers = replaceLayer(layers,'Output_fc_1_Flatten',output2);

Display the Layers property of the layer graph to confirm the replacement.

layers.Layers
ans = 
  19x1 Layer array with layers:

     1   'Input_input'           Image Input             28x28x1 images
     2   'conv_1'                Convolution             16 5x5x1 convolutions with stride [1  1] and padding [2  2  2  2]
     3   'BN_1'                  Batch Normalization     Batch normalization with 16 channels
     4   'relu_1'                ReLU                    ReLU
     5   'conv_2'                Convolution             32 1x1x16 convolutions with stride [2  2] and padding [0  0  0  0]
     6   'conv_3'                Convolution             32 3x3x16 convolutions with stride [2  2] and padding [1  1  1  1]
     7   'BN_2'                  Batch Normalization     Batch normalization with 32 channels
     8   'relu_2'                ReLU                    ReLU
     9   'conv_4'                Convolution             32 3x3x32 convolutions with stride [1  1] and padding [1  1  1  1]
    10   'BN_3'                  Batch Normalization     Batch normalization with 32 channels
    11   'relu_3'                ReLU                    ReLU
    12   'plus_1'                Addition                Element-wise addition of 2 inputs
    13   'fc_1'                  Convolution             1 14x14x32 convolutions with stride [1  1] and padding [0  0  0  0]
    14   'fc_2'                  Convolution             10 14x14x32 convolutions with stride [1  1] and padding [0  0  0  0]
    15   'sm_1_Flatten'          ONNX Flatten            Flatten activations into 1-D assuming C-style (row-major) order
    16   'sm_1'                  Softmax                 softmax
    17   'Output_sm_1'           Classification Output   crossentropyex with '0' and 9 other classes
    18   'fc_1_Flatten'          ONNX Flatten            Flatten activations into 1-D assuming C-style (row-major) order
    19   'Output_fc_1_Flatten'   Regression Output       mean-squared-error

Assemble the layer graph using assembleNetwork. The function returns a DAGNetwork object that is ready to use for prediction.

assembledNet = assembleNetwork(layers)
assembledNet = 
  DAGNetwork with properties:

         Layers: [19×1 nnet.cnn.layer.Layer]
    Connections: [19×2 table]
     InputNames: {'Input_input'}
    OutputNames: {'Output_sm_1'  'Output_fc_1_Flatten'}

Input Arguments

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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'

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: importONNXLayers(modelfile,'OutputLayerType','classification') imports the network layers from modelfile and adds an output layer for a classification output layer at the end of the imported layers.

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

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

Example: 'OutputLayerType','regression'

Indicator to import weights as well as the network architecture, specified as either false or true.

Example: 'ImportWeights',true

Data Types: logical

Output Arguments

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Network architecture, returned as a LayerGraph object.

Limitations

  • importONNXLayers supports ONNX versions as follows:

    • importONNXLayers supports ONNX intermediate representation version 6.

    • importONNXLayers fully supports ONNX operator sets 6, 7, 8, and 9.

    • importONNXLayers offers limited support for ONNX operator sets 10 and 11.

Note

If you import an exported network, layers of the reimported network might differ from the original network and might not be supported.

Tips

References

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

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

Introduced in R2018a