importNetworkFromPyTorch

Import PyTorch network as MATLAB network

Since R2022b

Syntax

net = importNetworkFromPyTorch(modelfile)

net = importNetworkFromPyTorch(modelfile,Name=Value)

Description

net = importNetworkFromPyTorch(modelfile) imports a pretrained and traced PyTorch^® model from the file modelfile. The function returns the network net as an uninitialized dlnetwork object.

importNetworkFromPyTorch requires the Deep Learning Toolbox™ Converter for PyTorch Models support package. If this support package is not installed, then importNetworkFromPyTorch provides a download link.

Note

The importNetworkFromPyTorch function can generate a custom layer when you import a PyTorch layer. For more information, see Algorithms. The function saves the generated custom layers in the +modelfile namespace.

net = importNetworkFromPyTorch(modelfile,Name=Value) imports a pretrained and traced PyTorch network with additional options specified by one or more name-value arguments. For example, Namespace="CustomLayers" saves any generated custom layers and associated functions in the +CustomLayers namespace in the current folder. If the PyTorchInputSizes name-value argument is specified, then the function may return the network net as an initialized dlnetwork.

For information about how to trace a PyTorch model, see https://pytorch.org/docs/stable/generated/torch.jit.trace.html.

Examples

Import Network from PyTorch and Add Input Layer

Import a pretrained and traced PyTorch model as an uninitialized dlnetwork object. Then, add an input layer to the imported network.

This example imports the MNASNet (Copyright© Soumith Chintala 2016) PyTorch model. MNASNet is an image classification model that is trained with images from the ImageNet database. Download the mnasnet1_0 file, which is approximately 17 MB in size, from the MathWorks website.

modelfile = matlab.internal.examples.downloadSupportFile("nnet", ...
    "data/PyTorchModels/mnasnet1_0.pt");

Import the MNASNet model by using the importNetworkFromPyTorch function. The function imports the model as an uninitialized dlnetwork object without an input layer. The software displays a warning that contains information about the number of input layers, what type of input layer to add, and how to add an input layer.

net = importNetworkFromPyTorch(modelfile)

Warning: Network was imported as an uninitialized dlnetwork. Before using the network, add input layer(s):

% Create imageInputLayer for the network input at index 1:
inputLayer1 = imageInputLayer(<inputSize1>, Normalization="none");

% Add input layers to the network and initialize:
net = addInputLayer(net, inputLayer1, Initialize=true);

net = 
  dlnetwork with properties:

         Layers: [1×1 nnet.cnn.layer.NetworkLayer]
    Connections: [0×2 table]
     Learnables: [210×3 table]
          State: [104×3 table]
     InputNames: {'TopLevelModule'}
    OutputNames: {'TopLevelModule'}
    Initialized: 0

  View summary with summary.

Specify the input size of the imported network and create an image input layer. Then, add the image input layer to the imported network and initialize the network by using the addInputLayer function.

InputSize = [224 224 3];
inputLayer = imageInputLayer(InputSize,Normalization="none");
net = addInputLayer(net,inputLayer,Initialize=true);

Analyze the imported network and view the input layer. The network is ready to use for prediction.

analyzeNetwork(net)

Import Network from PyTorch using PyTorchInputSizes

Import a pretrained and traced PyTorch model as an initialized dlnetwork object using the name-value argument PyTorchInputSizes.

This example imports the MNASNet (Copyright© Soumith Chintala 2016) PyTorch model. MNASNet is an image classification model that is trained with images from the ImageNet database. Download the mnasnet1_0.pt file, which is approximately 17 MB in size, from the MathWorks website.

modelfile = matlab.internal.examples.downloadSupportFile("nnet", ...
    "data/PyTorchModels/mnasnet1_0.pt");

Import the MNASNet model by using the importNetworkFromPyTorch function with the name-value argument PyTorchInputSizes. We know that a 224x224 color image is a valid input size for this PyTorch model. The software automatically creates and adds the input layer for a batch of images. This allows the network to be imported as an initialized network in one line of code.

net = importNetworkFromPyTorch(modelfile,PyTorchInputSizes=[NaN,3,224,224])

net = 
  dlnetwork with properties:

         Layers: [2×1 nnet.cnn.layer.Layer]
    Connections: [1×2 table]
     Learnables: [210×3 table]
          State: [104×3 table]
     InputNames: {'InputLayer1'}
    OutputNames: {'TopLevelModule'}
    Initialized: 1

  View summary with summary.

The network is ready to use for prediction.

Import Network from PyTorch and Initialize

Import a pretrained and traced PyTorch model as an uninitialized dlnetwork object. Then, initialize the imported network.

This example imports the MNASNet (Copyright© Soumith Chintal 2016) PyTorch model. MNASNet is an image classification model that is trained with images from the ImageNet database. Download the mnasnet1_0 file, which is approximately 17 MB in size, from the MathWorks website.

modelfile = matlab.internal.examples.downloadSupportFile("nnet", ...
    "data/PyTorchModels/mnasnet1_0.pt");

Import the MNASNet model by using the importNetworkFromPyTorch function. The function imports the model as an uninitialized dlnetwork object.

net = importNetworkFromPyTorch(modelfile)

Warning: Network was imported as an uninitialized dlnetwork. Before using the network, add input layer(s):

% Create imageInputLayer for the network input at index 1:
inputLayer1 = imageInputLayer(<inputSize1>, Normalization="none");

% Add input layers to the network and initialize:
net = addInputLayer(net, inputLayer1, Initialize=true);

net = 
  dlnetwork with properties:

         Layers: [1×1 nnet.cnn.layer.NetworkLayer]
    Connections: [0×2 table]
     Learnables: [210×3 table]
          State: [104×3 table]
     InputNames: {'TopLevelModule'}
    OutputNames: {'TopLevelModule'}
    Initialized: 0

  View summary with summary.

net is a dlnetwork object consisting of a single networkLayer layer that contains a nested network. Specify the input size for net and create a random dlarray object that represents the input to the network. The data format of the dlarray object must have the dimensions "SSCB" (spatial, spatial, channel, batch) to represent a 2-D image input. For more information, see Data Formats for Prediction with dlnetwork.

InputSize = [224 224 3];
X = dlarray(rand(InputSize),"SSCB");

Initialize the learnable parameters of the imported network by using the initialize function.

net = initialize(net,X);

Now the imported network is ready to use for prediction. Expand the networkLayer using the expandLayers function and analyze the imported network.

netExpanded = expandLayers(net)

netExpanded = 
  dlnetwork with properties:

         Layers: [152×1 nnet.cnn.layer.Layer]
    Connections: [161×2 table]
     Learnables: [210×3 table]
          State: [104×3 table]
     InputNames: {'TopLevelModule:layers:0'}
    OutputNames: {'TopLevelModule:classifier:1'}
    Initialized: 1

  View summary with summary.

analyzeNetwork(netExpanded)

Import Network from PyTorch and Classify Image

Import a pretrained and traced PyTorch model as an uninitialized dlnetwork object to classify an image.

This example imports the MNASNet (Copyright© Soumith Chintala 2016) PyTorch model. MNASNet is an image classification model that is trained with images from the ImageNet database. Download the mnasnet1_0 file, which is approximately 17 MB in size, from the MathWorks website.

modelfile = matlab.internal.examples.downloadSupportFile("nnet", ...
    "data/PyTorchModels/mnasnet1_0.pt");

Import the MNASNet model by using the importNetworkFromPyTorch function. The function imports the model as an uninitialized dlnetwork object.

net = importNetworkFromPyTorch(modelfile)

Warning: Network was imported as an uninitialized dlnetwork. Before using the network, add input layer(s):

% Create imageInputLayer for the network input at index 1:
inputLayer1 = imageInputLayer(<inputSize1>, Normalization="none");

% Add input layers to the network and initialize:
net = addInputLayer(net, inputLayer1, Initialize=true);

net = 
  dlnetwork with properties:

         Layers: [1×1 nnet.cnn.layer.NetworkLayer]
    Connections: [0×2 table]
     Learnables: [210×3 table]
          State: [104×3 table]
     InputNames: {'TopLevelModule'}
    OutputNames: {'TopLevelModule'}
    Initialized: 0

  View summary with summary.

Specify the input size of the imported network and create an image input layer. Then, add the image input layer to the imported network and initialize the network by using the addInputLayer function.

InputSize = [224 224 3];
inputLayer = imageInputLayer(InputSize,Normalization="none");
net = addInputLayer(net,inputLayer,Initialize=true);

Read the image you want to classify.

Im = imread("peppers.png");

Resize the image to the input size of the network. Show the image.

InputSize = [224 224 3];
Im = imresize(Im,InputSize(1:2));
imshow(Im)

The inputs to MNASNet require further preprocessing. Rescale the image. Then, normalize the image by subtracting the training images mean and dividing by the training images standard deviation. For more information, see Input Data Preprocessing.

Im = rescale(Im,0,1);

meanIm = [0.485 0.456 0.406];
stdIm = [0.229 0.224 0.225];
Im = (Im - reshape(meanIm,[1 1 3]))./reshape(stdIm,[1 1 3]);

Convert the image to a dlarray object. Format the image with the dimensions "SSCB" (spatial, spatial, channel, batch).

Im_dlarray = dlarray(single(Im),"SSCB");

Get the class names from squeezenet, which is also trained with ImageNet images.

[~,ClassNames] = imagePretrainedNetwork("squeezenet");

Classify the image and find the predicted label.

prob = predict(net,Im_dlarray);
[~,label_ind] = max(prob);

Display the classification result.

ClassNames(label_ind)

ans = 
"bell pepper"

Import Network from PyTorch and Find Generated Custom Layers

Import a pretrained and traced PyTorch model as an uninitialized dlnetwork object. Then, find the custom layers that the software generates.

This example uses the findCustomLayers helper function.

This example imports the MNASNet (Copyright© Soumith Chintala 2016) PyTorch model. MNASNet is an image classification model that is trained with images from the ImageNet database. Download the mnasnet1_0 file, which is approximately 17 MB in size, from the MathWorks website.

modelfile = matlab.internal.examples.downloadSupportFile("nnet", ...
    "data/PyTorchModels/mnasnet1_0.pt");

Import the MNASNet model by using the importNetworkFromPyTorch function. The function imports the model as an uninitialized dlnetwork object.

net = importNetworkFromPyTorch(modelfile)

Warning: Network was imported as an uninitialized dlnetwork. Before using the network, add input layer(s):

% Create imageInputLayer for the network input at index 1:
inputLayer1 = imageInputLayer(<inputSize1>, Normalization="none");

% Add input layers to the network and initialize:
net = addInputLayer(net, inputLayer1, Initialize=true);

net = 
  dlnetwork with properties:

         Layers: [1×1 nnet.cnn.layer.NetworkLayer]
    Connections: [0×2 table]
     Learnables: [210×3 table]
          State: [104×3 table]
     InputNames: {'TopLevelModule'}
    OutputNames: {'TopLevelModule'}
    Initialized: 0

  View summary with summary.

net is a dlnetwork object consisting of a single networkLayer layer that contains a nested network. Expand the nested network layers using the expandLayers function.

net = expandLayers(net);

The importNetworkFromPyTorch function generates custom layers for the PyTorch layers that the function cannot convert to built-in MATLAB layers or functions. For more information, see Algorithms. The software saves the automatically generated custom layers to the +mnasnet1_0 namespace in the current folder and the associated functions to the +ops inner namespace. To see the custom layers and associated functions, inspect the namespace.

You can also find the indices of the generated custom layers by using the findCustomLayers helper function. Display the custom layers.

ind = findCustomLayers(net.Layers,'+mnasnet1_0');
net.Layers(ind)

ans = 
  13×1 Layer array with layers:

     1   'TopLevelModule:layers:0'              2-D Convolution           32 3×3×3 convolutions with stride [2  2] and padding [1  1  1  1]
     2   'TopLevelModule:layers:1'              Batch Normalization       Batch normalization with 32 channels
     3   'TopLevelModule:layers:2'              ReLU                      ReLU
     4   'TopLevelModule:layers:3'              2-D Grouped Convolution   32 groups of 1 3×3×1 convolutions with stride [1  1] and padding [1  1  1  1]
     5   'TopLevelModule:layers:4'              Batch Normalization       Batch normalization with 32 channels
     6   'TopLevelModule:layers:5'              ReLU                      ReLU
     7   'TopLevelModule:layers:6'              2-D Convolution           16 1×1×32 convolutions with stride [1  1] and padding [0  0  0  0]
     8   'TopLevelModule:layers:7'              Batch Normalization       Batch normalization with 16 channels
     9   'TopLevelModule:layers:8:0:layers:0'   2-D Convolution           48 1×1×16 convolutions with stride [1  1] and padding [0  0  0  0]
    10   'TopLevelModule:layers:8:0:layers:1'   Batch Normalization       Batch normalization with 48 channels
    11   'TopLevelModule:layers:8:0:layers:2'   ReLU                      ReLU
    12   'TopLevelModule:layers:8:0:layers:6'   2-D Convolution           24 1×1×48 convolutions with stride [1  1] and padding [0  0  0  0]
    13   'TopLevelModule:layers:8:0:layers:7'   Batch Normalization       Batch normalization with 24 channels

Helper Function

The findCustomLayers helper function returns a logical vector corresponding to the indices of the custom layers that importNetworkFromPyTorch automatically generates.

function indices = findCustomLayers(layers,Namespace)

s = what(['.' filesep Namespace]);

indices = zeros(1,length(s.m));
for i = 1:length(layers)
    for j = 1:length(s.m)
        if strcmpi(class(layers(i)),[Namespace(2:end) '.' s.m{j}(1:end-2)])
            indices(j) = i;
        end
    end
    indices = logical(indices);
end

end

Train Network Imported from PyTorch to Classify New Images

This example shows how to import a network from PyTorch and train the network to classify new images. Use the importNetworkFromPytorch function to import the network as an uninitialized dlnetwork object. Train the network by using a custom training loop.

This example uses the modelLoss, modelPredictions, and preprocessMiniBatchPredictors helper functions.

This example also uses the supporting file new_fcLayer. To access the supporting file, open the example in Live Editor.

Load Data

Unzip the MerchData data set, which contains 75 images. Load the new images as an image datastore. The imageDatastore function automatically labels the images based on folder names and stores the data as an ImageDatastore object. Divide the data into training and validation data sets. Use 70% of the images for training and 30% for validation.

unzip("MerchData.zip");
imds = imageDatastore("MerchData", ...
    IncludeSubfolders=true, ...
    LabelSource="foldernames"); 
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7);

The network you use in this example requires input images with a size of 224-by-224-by-3. To automatically resize the training images, use an augmented image datastore. Randomly translate the images up to 30 pixels in the horizontal and vertical axes. Data augmentation helps prevent the network from overfitting and memorizing the exact details of the training images.

inputSize = [224 224 3];

pixelRange = [-30 30];
scaleRange = [0.9 1.1];
imageAugmenter = imageDataAugmenter(...
    RandXReflection=true, ...
    RandXTranslation=pixelRange, ...
    RandYTranslation=pixelRange, ...
    RandXScale=scaleRange, ...
    RandYScale=scaleRange);
augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain, ...
    DataAugmentation=imageAugmenter);

To automatically resize the validation images without performing further data augmentation, use an augmented image datastore without specifying any additional preprocessing operations.

augimdsValidation = augmentedImageDatastore(inputSize(1:2),imdsValidation);

Determine the number of classes in the training data.

classes = categories(imdsTrain.Labels);
numClasses = numel(classes);

Import Network

Download the MNASNet (Copyright© Soumith Chintala 2016) PyTorch model. MNASNet is an image classification model that is trained with images from the ImageNet database. Download the mnasnet1_0 file, which is approximately 17 MB in size, from the MathWorks website.

modelfile = matlab.internal.examples.downloadSupportFile("nnet", ...
    "data/PyTorchModels/mnasnet1_0.pt");

Import the MNASNet model as an uninitialized dlnetwork object by using the importNetworkFromPyTorch function.

net = importNetworkFromPyTorch(modelfile)

Warning: Network was imported as an uninitialized dlnetwork. Before using the network, add input layer(s):

% Create imageInputLayer for the network input at index 1:
inputLayer1 = imageInputLayer(<inputSize1>, Normalization="none");

% Add input layers to the network and initialize:
net = addInputLayer(net, inputLayer1, Initialize=true);

net = 
  dlnetwork with properties:

         Layers: [1×1 nnet.cnn.layer.NetworkLayer]
    Connections: [0×2 table]
     Learnables: [210×3 table]
          State: [104×3 table]
     InputNames: {'TopLevelModule'}
    OutputNames: {'TopLevelModule'}
    Initialized: 0

  View summary with summary.

net is a dlnetwork object consisting of a single networkLayer layer that contains a nested network. Expand the networkLayer using the expandLayers function. Display the final layer of the imported network using the analyzeNetwork function.

net = expandLayers(net);
analyzeNetwork(net)

The TopLevelModule:classifier:1 layer is a custom layer generated by the importNetworkFromPyTorch function and the last learnable layer of the imported network. This layer contains information about how to combine the features that the network extracts into class probabilities and a loss value.

Replace Final Layer

To retrain the imported network to classify new images, replace the final layers with a new fully connected layer. The new layer new_fclayer is adapted to the new data set and must also be a custom layer because it has two inputs.

Initialize the new_fcLayer layer and replace the TopLevelModule:classifier:1 layer with new_fcLayer.

newLayer = new_fcLayer("TopLevelModule:classifier:fc1","Custom Layer", ...
    {'in'},{'out'},numClasses);
net = replaceLayer(net,"TopLevelModule:classifier:1",newLayer);

Add a softmax layer to the network and connect the softmax layer to the new fully connected layer.

net = addLayers(net,softmaxLayer(Name="sm1"));
net = connectLayers(net,"TopLevelModule:classifier:fc1","sm1");

Add Input Layer

Add an image input layer to the network and initialize the network.

inputLayer = imageInputLayer(inputSize,Normalization="none");
net = addInputLayer(net,inputLayer,Initialize=true);

Analyze the network. View the first layer and the final layers.

analyzeNetwork(net)

Define Model Loss Function

Training a deep neural network is an optimization task. By treating a neural network as a function $$f(X;\theta )$$, where $$X$$ is the network input and $$\theta$$ is the set of learnable parameters, you can optimize $$\theta$$ so that it minimizes some loss value based on the training data. For example, optimize the learnable parameters $$\theta$$ such that, for inputs $$X$$ with corresponding targets $$T$$, they minimize the error between the predictions $$Y=f(X;\theta )$$ and $$T$$.

Create the modelLoss function, listed in the Model Loss Function section of the example, which takes as input the dlnetwork object and a mini-batch of input data with corresponding targets. The function returns the loss, the gradients of the loss with respect to the learnable parameters, and the network state.

Specify Training Options

Train for 15 epochs with a mini-batch size of 20.

numEpochs = 15;
miniBatchSize = 20;

Specify the options for SGDM optimization. Specify an initial learning rate of 0.001 with a decay of 0.005, and a momentum of 0.9.

initialLearnRate = 0.001;
decay = 0.005;
momentum = 0.9;

Train Network

Create a minibatchqueue object that processes and manages mini-batches of images during training. For each mini-batch, perform these steps:

1. Use the custom mini-batch preprocessing function preprocessMiniBatch (defined at the end of this example) to convert the labels to one-hot encoded variables.

2. Format the image data with the dimension labels "SSCB" (spatial, spatial, channel, batch). By default, the minibatchqueue object converts the data to dlarray objects with the underlying type single. Do not format the class labels.

3. Train on a GPU if one is available. By default, the minibatchqueue object converts each output to a gpuArray object if a GPU is available. Using a GPU requires a Parallel Computing Toolbox™ license and a supported GPU device. For information about supported devices, see GPU Computing Requirements (Parallel Computing Toolbox).

mbq = minibatchqueue(augimdsTrain,...
    MiniBatchSize=miniBatchSize,...
    MiniBatchFcn=@preprocessMiniBatch,...
    MiniBatchFormat=["SSCB" ""]);

Initialize the velocity parameter for the gradient descent with momentum (SGDM) solver.

velocity = [];

Calculate the total number of iterations for the training progress monitor.

numObservationsTrain = numel(imdsTrain.Files);
numIterationsPerEpoch = ceil(numObservationsTrain/miniBatchSize);
numIterations = numEpochs*numIterationsPerEpoch;

Initialize the trainingProgressMonitor object. Because the timer starts when you create the monitor object, create the object immediately after the training loop.

monitor = trainingProgressMonitor(Metrics="Loss",Info=["Epoch","LearnRate"],XLabel="Iteration");

Train the network using a custom training loop. For each epoch, shuffle the data and loop over mini-batches of data. For each mini-batch, perform these steps:

1. Evaluate the model loss, gradients, and state using the dlfeval and modelLoss functions and then update the network state.

2. Determine the learning rate for the time-based decay learning rate schedule.

3. Update the network parameters using the sgdmupdate function.

4. Update the loss, learning rate, and epoch values in the training progress monitor.

5. Stop if the Stop property is true. The Stop property value of the TrainingProgressMonitor object changes to true when you click the Stop button.

epoch = 0;
iteration = 0;

% Loop over epochs.
while epoch < numEpochs && ~monitor.Stop
    
    epoch = epoch + 1;

    % Shuffle data.
    shuffle(mbq);
    
    % Loop over mini-batches.
    while hasdata(mbq) && ~monitor.Stop

        iteration = iteration + 1;
        
        % Read mini-batch of data.
        [X,T] = next(mbq);
        
        % Evaluate the model gradients, state, and loss using dlfeval and the
        % modelLoss function and update the network state.
        [loss,gradients,state] = dlfeval(@modelLoss,net,X,T);
        net.State = state;
        
        % Determine learning rate for time-based decay learning rate schedule.
        learnRate = initialLearnRate/(1 + decay*iteration);
        
        % Update the network parameters using the SGDM optimizer.
        [net,velocity] = sgdmupdate(net,gradients,velocity,learnRate,momentum);
        
        % Update the training progress monitor.
        recordMetrics(monitor,iteration,Loss=loss);
        updateInfo(monitor,Epoch=epoch,LearnRate=learnRate);
        monitor.Progress = 100*iteration/numIterations;
    end
end

Classify Validation Images

Test the classification accuracy of the model by comparing the predictions on the validation set with the true labels.

After training, making predictions on new data does not require the labels. Create a minibatchqueue object containing only the predictors of the test data:

• To ignore the labels for testing, set the number of outputs of the mini-batch queue to 1.

• Specify the same mini-batch size that you use for training.

• Preprocess the predictors using the preprocessMiniBatchPredictors function, listed at the end of the example.

• For the single output of the datastore, specify the mini-batch format "SSCB" (spatial, spatial, channel, batch).

numOutputs = 1;

mbqTest = minibatchqueue(augimdsValidation,numOutputs, ...
    MiniBatchSize=miniBatchSize, ...
    MiniBatchFcn=@preprocessMiniBatchPredictors, ...
    MiniBatchFormat="SSCB");

Loop over the mini-batches and classify the images using the modelPredictions function, listed at the end of the example.

YTest = modelPredictions(net,mbqTest,classes);

Evaluate the classification accuracy.

TTest = imdsValidation.Labels;
accuracy = mean(TTest == YTest)

accuracy = 0.9500

Visualize the predictions in a confusion chart. Large values on the diagonal indicate accurate predictions for the corresponding class. Large values on the off-diagonal indicate strong confusion between the corresponding classes.

figure
confusionchart(TTest,YTest)

Helper Functions

Model Loss Function

The modelLoss function takes as input a dlnetwork object net and a mini-batch of input data X with corresponding targets T. The function returns the loss, the gradients of the loss with respect to the learnable parameters in net, and the network state. To compute the gradients automatically, use the dlgradient function.

function [loss,gradients,state] = modelLoss(net,X,T)

% Forward data through network.
[Y,state] = forward(net,X);

% Calculate cross-entropy loss.
loss = crossentropy(Y,T);

% Calculate gradients of loss with respect to learnable parameters.
gradients = dlgradient(loss,net.Learnables);

end

Model Predictions Function

The modelPredictions function takes as input a dlnetwork object net, a minibatchqueue of input data mbq, and the network classes. The function computes the model predictions by iterating over all the data in the minibatchqueue object. The function uses the onehotdecode function to find the predicted class with the highest score.

function Y = modelPredictions(net,mbq,classes)

Y = [];

% Loop over mini-batches.
while hasdata(mbq)
    X = next(mbq);

    % Make prediction.
    scores = predict(net,X);

    % Decode labels and append to output.
    labels = onehotdecode(scores,classes,1)';
    Y = [Y; labels];
end

end

Mini Batch Preprocessing Function

The preprocessMiniBatch function preprocesses a mini-batch of predictors and labels using these steps:

1. Preprocess the images using the preprocessMiniBatchPredictors function.

2. Extract the label data from the incoming cell array and concatenate the result into a categorical array along the second dimension.

3. One-hot encode the categorical labels into numeric arrays. Encoding into the first dimension produces an encoded array that matches the shape of the network output.

function [X,T] = preprocessMiniBatch(dataX,dataT)

% Preprocess predictors.
X = preprocessMiniBatchPredictors(dataX);

% Extract label data from cell and concatenate.
T = cat(2,dataT{1:end});

% One-hot encode labels.
T = onehotencode(T,1);

end

Mini-Batch Predictors Preprocessing Function

The preprocessMiniBatchPredictors function preprocesses a mini-batch of predictors by extracting the image data from the input cell array and concatenating the result into a numeric array. For grayscale input, concatenating over the fourth dimension adds a third dimension to each image to use as a singleton channel dimension.

function X = preprocessMiniBatchPredictors(dataX)

% Concatenate.
X = cat(4,dataX{1:end});

end

Input Arguments

modelfile — Name of PyTorch model file

character vector | string scalar

Name of the PyTorch model file, specified as a character vector or string scalar. modelfile must be in the current folder, or you must include a full or relative path to the file. The PyTorch model must be pretrained and traced over one inference iteration.

For information about how to trace a PyTorch model, see https://pytorch.org/docs/stable/generated/torch.jit.trace.html.

Example: "mobilenet_v3.pt"

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: importNetworkFromPyTorch(modelfile,Namespace="CustomLayers") imports the network in modelfile and saves the custom layers namespace +Namespace in the current folder.

Namespace — Name of custom layers namespace

character vector | string scalar

Name of the custom layers namespace in which importNetworkFromPyTorch saves custom layers, specified as a character vector or string scalar. importNetworkFromPyTorch saves the custom layers +Namespace namespace in the current folder. If you do not specify Namespace, then importNetworkFromPyTorch saves the custom layers in the +modelfile namespace in the current folder. For more information about namespaces, see Create Namespaces.

importNetworkFromPyTorch tries to generate a custom layer when you import a custom PyTorch layer or when the software cannot convert a PyTorch layer into an equivalent built-in MATLAB^® layer. importNetworkFromPyTorch saves each generated custom layer to a separate MATLAB code file in +Namespace. To view or edit a custom layer, open the associated MATLAB code file. For more information about custom layers, see Custom Layers.

The +Namespace namespace can also contain the +ops inner namespace. This inner namespace contains MATLAB functions corresponding to PyTorch operators that the automatically generated custom layers use. importNetworkFromPyTorch saves the associated MATLAB function for each operator in a separate MATLAB code file in the +ops inner namespace. The object functions of dlnetwork, such as the predict function, use these operators when it interacts with the custom layers. The +ops inner namespace can also contain placeholder functions. For more information, see Placeholder Functions.

Example: Namespace="mobilenet_v3"

PyTorchInputSizes — Dimension sizes of network inputs

numeric array | string scalar | cell array

Dimension sizes of the PyTorch network inputs, specified as a numeric array, string scalar, or cell array. The dimension input order is the same as in the PyTorch network. You can specify PyTorchInputSizes as a numeric array only when the network has a single nonscalar input. If the network has multiple inputs, PyTorchInputSizes must be a cell array of the input sizes. For an input whose size or shape is not known specify PyTorchInputSize as "unknown". For an input that corresponds to a 0-dimensional scalar in PyTorch, specify PyTorchInputSize as "scalar".

The standard input layers that importNetworkFromPyTorch supports are ImageInputLayer (SSCB), FeatureInputLayer (CB), ImageInputLayer3D (SSSCB), and SequenceInputLayer (CBT). Here, S is spatial, C is channel, B is batch, and T is time. importNetworkFromPyTorch also supports nonstandard inputs using PyTorchInputSizes. For example, import the network and specify the input dimension sizes with this function call: net = importNetworkFromPyTorch("nonStandardModel.pt",PyTorchInputSizes=[1 3 224]). Then, initialize the network with a U-labelled dlarray object, where U is unknown, with these function calls: X = dlarray(rand(1 3 224),"UUU") and net = initialize(net,X). The software interprets the U-labelled dlarray as data in PyTorch order.

Example: PyTorchInputSizes=[NaN 3 224 224] is a network with one input that is a batch of images.

Example: PyTorchInputSizes={[NaN 3 224 224],"unknown"} is a network with two inputs. The first input is a batch of images and the second input has unknown size.

Data Types: numeric array | string | cell array

PreferredNestingType — Network composition representation

"networklayer" (default) | "customlayer"

Network composition representation, specified as one of the following values:

• "networklayer" — Represents network composition in the imported network using networkLayer layer objects.

• "customlayer" — Represents network composition in the imported network using nested custom layers. For more information about custom layers, see Define Custom Deep Learning Layers.

Example: PreferredNestingType="customlayer"

Data Types: char | string

Output Arguments

net — Pretrained PyTorch network

dlnetwork object

Pretrained PyTorch network, returned as an uninitialized dlnetwork object. Before using the imported network, you must add an input layer or initialize the network. For examples, see Import Network from PyTorch and Add Input Layer and Import Network from PyTorch and Initialize.

Limitations

• The importNetworkFromPyTorch function fully supports PyTorch version 2.0. The function can import most models created in other PyTorch versions.

• The importNetworkFromPyTorch function can import only image classification and segmentation models.

More About

Conversion of PyTorch Layers and Functions into Built-In MATLAB Layers and Functions

The importNetworkFromPyTorch function supports the PyTorch layers, functions, and operators listed in this section for conversion into built-in MATLAB layers and functions with dlarray support. For more information about functions that operate on dlarray objects, see List of Functions with dlarray Support. The conversion process often has limitations.

Conversion of PyTorch Layers

This table shows the correspondence between PyTorch layers and Deep Learning Toolbox layers. In some cases, when importNetworkFromPyTorch cannot convert a PyTorch layer into a MATLAB layer, the software converts the PyTorch layer into a Deep Learning Toolbox function with dlarray support.

PyTorch Layer	Corresponding Deep Learning Toolbox Layer	Alternative Deep Learning Toolbox Function
torch.nn.AdaptiveAvgPool2d	adaptiveAveragePooling2dLayer	pyAdaptiveAvgPool2d
torch.nn.AvgPool2d	averagePooling2dLayer	Not applicable
torch.nn.BatchNorm2d	batchNormalizationLayer	Not applicable
torch.nn.Conv1d	convolution1dLayer	pyConvolution
torch.nn.Conv2d	convolution2dLayer	Not applicable
torch.nn.ConvTranspose1d	transposedConv1dLayer	pyConvolution
torch.nn.ConvTranspose2d	transposedConv2dLayer	pyConvolution
torch.nn.Dropout	dropoutLayer	Not applicable
torch.nn.Dropout2d	spatialDropoutLayer	pyFeatureDropout
torch.nn.Embedding	Not applicable	pyEmbedding
torch.nn.GELU	geluLayer	pyGelu
torch.nn.GLU	Not applicable	pyGLU
torch.nn.GroupNorm	groupNormalizationLayer	Not applicable
torch.nn.LayerNorm	layerNormalizationLayer	Not applicable
torch.nn.LeakyReLU	leakyReluLayer	pyLeakyRelu
torch.nn.Linear	fullyConnectedLayer	pyLinear
torch.nn.MaxPool2d	maxPooling2dLayer	Not applicable
torch.nn.PReLU	preluLayer	Not applicable
torch.nn.ReLU	reluLayer	relu
torch.nn.SiLU	swishLayer	pySilu
torch.nn.Sigmoid	sigmoidLayer	pySigmoid
torch.nn.Softmax	nnet.pytorch.layer.SoftmaxLayer	pySoftmax
torch.nn.Tanh	tanhLayer	tanh
torch.nn.Upsample	resize2dLayer (Image Processing Toolbox)	pyUpsample (requires Image Processing Toolbox™)
torch.nn.UpsamplingNearest2d	resize2dLayer (Image Processing Toolbox)	pyUpsample (requires Image Processing Toolbox)
torch.nn.UpsamplingBilinear2d	resize2dLayer (Image Processing Toolbox)	pyUpsample (requires Image Processing Toolbox)

Conversion of PyTorch Functions

This table shows the correspondence between PyTorch functions and Deep Learning Toolbox functions.

PyTorch Function	Corresponding Deep Learning Toolbox Function
torch.nn.functional.adaptive_avg_pool2d	pyAdaptiveAvgPool2d
torch.nn.functional.avg_pool2d	pyAvgPool2d
torch.nn.functional.conv1d	pyConvolution
torch.nn.functional.conv2d	pyConvolution
torch.nn.functional.dropout	pyDropout
torch.nn.functional.embedding	pyEmbedding
torch.nn.functional.gelu	pyGelu
torch.nn.functional.glu	pyGLU
torch.nn.functional.hardsigmoid	pyHardSigmoid
torch.nn.functional.hardswish	pyHardSwish
torch.nn.functional.layer_norm	pyLayerNorm
torch.nn.functional.leaky_relu	pyLeakyRelu
torch.nn.functional.linear	pyLinear
torch.nn.functional.log_softmax	pyLogSoftmax
torch.nn.functional.pad	pyPad
torch.nn.functional.max_pool2d	pyMaxPool2d
torch.nn.functional.relu	relu
torch.nn.functional.silu	pySilu
torch.nn.functional.softmax	pySoftmax
torch.nn.functional.tanh	tanh

Conversion of PyTorch Mathematical Operators

This table shows the correspondence between PyTorch mathematical operators and Deep Learning Toolbox functions. The importNetworkFromPyTorch first tries to convert the cat PyTorch operator to a concatenation layer, then to a function.

PyTorch Operator	Corresponding Deep Learning Toolbox Layer or Function	Alternative Deep Learning Toolbox Function
+, -, /	pyElementwiseBinary	Not applicable
torch.abs	pyAbs	Not applicable
torch.arange	pyArange	Not applicable
torch.argmax	pyArgMax	Not applicable
torch.baddbmm	pyBaddbmm	Not applicable
torch.bitwise_not	pyBitwiseNot	No applicable
torch.bmm	pyMatMul	Not applicable
torch.cat	concatenationLayer	pyConcat
torch.chunk	pyChunk	Not applicable
torch.clamp_min	pyClampMin	Not applicable
torch.clone	identityLayer	Not applicable
torch.concat	pyConcat	Not applicable
torch.cos	pyCos	Not applicable
torch.cumsum	pyCumsum	Not applicable
torch.detach	pyDetach	Not applicable
torch.eq	pyEq	Not applicable
torch.floor_div	pyElementwiseBinary	Not applicable
torch.gather	pyGather	Not applicable
torch.ge	pyGe	Not applicable
torch.matmul	pyMatMul	Not applicable
torch.max	pyMaxBinary/pyMaxUnary	Not applicable
torch.mean	pyMean	Not applicable
torch.mul, *	multiplicationLayer	pyElementwiseBinary
torch.norm	pyNorm	Not applicable
torch.permute	pyPermute	Not applicable
torch.pow	pyElementwiseBinary	Not applicable
torch.remainder	pyRemainder	Not applicable
torch.repeat	pyRepeat	Not applicable
torch.repeat_interleave	pyRepeatInterleave	Not applicable
torch.reshape	pyView	Not applicable
torch.rsqrt	pyRsqrt	Not applicable
torch.size	pySize	Not applicable
torch.sin	pySin	Not applicable
torch.split	pySplitWithSizes	Not applicable
torch.sqrt	pyElementwiseBinary	Not applicable
torch.square	pySquare	Not applicable
torch.squeeze	pySqueeze	Not applicable
torch.stack	pyStack	Not applicable
torch.sum	pySum	Not applicable
torch.t	pyT	Not applicable
torch.to	pyTo	Not applicable
torch.transpose	pyTranspose	Not applicable
torch.unsqueeze	pyUnsqueeze	Not applicable
torch.zeros	pyZeros	Not applicable
torch.zeros_like	pyZerosLike	Not applicable

Conversion of PyTorch Matrix Operators

This table shows the correspondence between PyTorch matrix operators and Deep Learning Toolbox functions.

PyTorch Operator	Corresponding Deep Learning Toolbox Function or Operator
Indexing (for example, X[:,1])	pySlice
torch.tensor.contiguous	=
torch.tensor.expand	pyExpand
torch.tensor.expand_as	pyExpandAs
torch.tensor.masked_fill	pyMaskedFill
torch.tensor.select	pySlice
torch.tensor.view	pyView

Placeholder Functions

When the importNetworkFromPyTorch function cannot convert a PyTorch layer into a built-in MATLAB layer or generate a custom layer with associated MATLAB functions, the function creates a custom layer with a placeholder function. You must complete the placeholder function before you can use the network.

This code snippet defines a custom layer with the pyAtenUnsupportedOperator placeholder function.

classdef UnsupportedOperator < nnet.layer.Layer

function [output] = predict(obj,arg1)
% Placeholder function for aten::<unsupportedOperator>
output= pyAtenUnsupportedOperator(arg1,params);
end

end

Tips

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

  • 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 about preprocessing images for training and prediction, see Preprocess Images for Deep Learning.

• The members of the +Namespace namespace are not accessible if the namespace parent folder is not on the MATLAB path. For more information, see Namespaces and the MATLAB Path.

• MATLAB uses one-based indexing, whereas Python^® uses zero-based indexing. In other words, the first element in an array has an index of 1 and 0 in MATLAB and Python, respectively. For more information about MATLAB indexing, see Array Indexing. In MATLAB, to use an array of indices (ind) created in Python, convert the array to ind+1.

• If you encounter a Python library conflict, use the pyenv function to specify the ExecutionMode name-value argument as "OutOfProcess".

• For more tips, see Tips on Importing Models from TensorFlow, PyTorch, and ONNX.

Algorithms

The importNetworkFromPyTorch function imports a PyTorch layer into MATLAB by trying these steps in order:

1. The function tries to import the PyTorch layer as a built-in MATLAB layer. For more information, see Conversion of PyTorch Layers.

2. The function tries to import the PyTorch layer as a built-in MATLAB function. For more information, see Conversion of PyTorch Layers.

3. The function tries to import the PyTorch layer as a custom layer. importNetworkFromPyTorch saves the generated custom layers and the associated functions in the +Namespace namespace. For an example, see Import Network from PyTorch and Find Generated Custom Layers.

4. The function imports the PyTorch layer as a custom layer with a placeholder function. You must complete the placeholder function before you can use the network, see Placeholder Functions.

In the first three cases, the imported network is ready for prediction after you initialize it.

Alternative Functionality

App

You can also import models from external platforms using the Deep Network Designer app. The app uses the importNetworkFromPyTorch function to import the network. On import, the app shows an import report with details about any issues that require attention.

Version History

Introduced in R2022b

R2024b: Represent network composition using networkLayer

You can import a network that uses networkLayer objects to represent network composition. To specify whether the imported network represents composition using networkLayer or custom layer objects, use the PreferredNestingType name-value argument. For more information, see Deep Learning Network Composition.

R2024b: Import networks with new layers, operators, and functions

You can import the following PyTorch operator and layers into Deep Learning Toolbox layers:

• torch.clone

• torch.nn.AdaptiveAvgPool2d

• torch.nn.Dropout2D

• torch.nn.PReLU

You can also import the following PyTorch operators, functions, and layers into custom layers:

• torch.abs

• torch.arange

• torch.baddbmm

• torch.bitwise_not

• torch.cos

• torch.cumsum

• torch.ge

• torch.remainder

• torch.repeat_interleave

• torch.sin

• torch.rsqrt

• torch.zeros_like

• torch.nn.functional.pad

• torch.nn.functional.glu and torch.nn.GLU

R2024b: Import traced networks from PyTorch 2.0

You can import a traced network from PyTorch 2.0. Previously, importNetworkFromPyTorch supported importing networks created using PyTorch versions 1.10.0 and earlier.

R2024a: Import networks that include embedding and hyperbolic tangent layers

You can now import a PyTorch network that includes the torch.nn.Embedding and torch.nn.tanh layers.

R2024a: Import networks that include embedding and hyperbolic tangent functions

You can now import a PyTorch network that includes the torch.functional.embedding and torch.functional.tanh functions.

R2024a: Import networks that include element-wise equality and masked fill operators

You can now import a PyTorch network that includes the torch.eq and torch.tensor.masked_fill operators.

R2024a: Import networks with weight tying

importNetworkFromPyTorch supports importing PyTorch models with weight tying.

R2024a: Import networks with weight sharing

importNetworkFromPyTorch supports importing PyTorch models with weight sharing.

R2023b: Support for dimension sizes of network inputs

importNetworkFromPyTorch supports the specification of dimension sizes of the PyTorch network inputs. Specify the input sizes using the PyTorchInputSizes name-value argument.