importNetworkFromPyTorch
Syntax
Description
imports a pretrained and traced PyTorch® model from the file net
= importNetworkFromPyTorch(modelfile
)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 might generate a custom layer when
converting a PyTorch layer. For more information, see Algorithms. The function saves
the generated custom layers in the package
+
modelfile
.
imports a pretrained network from PyTorch and saves the generated custom layers and associated functions in the package
net
= importNetworkFromPyTorch(modelfile
,PackageName=CustomLayersPackage
)+
CustomLayersPackage
.
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.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. The function imports the model as an uninitialized dlnetwork
object without an input layer. The software displays a warning that provides you information on 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): inputLayer1 = imageInputLayer(<inputSize1>, Normalization="none"); net = addInputLayer(net, inputLayer1, Initialize=true);
net = dlnetwork with properties: Layers: [152×1 nnet.cnn.layer.Layer] Connections: [163×2 table] Learnables: [210×3 table] State: [104×3 table] InputNames: {'TopLevelModule_layers_0'} OutputNames: {'aten__linear12'} 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 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. Dowload 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. 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): inputLayer1 = imageInputLayer(<inputSize1>, Normalization="none"); net = addInputLayer(net, inputLayer1, Initialize=true);
net = dlnetwork with properties: Layers: [152×1 nnet.cnn.layer.Layer] Connections: [163×2 table] Learnables: [210×3 table] State: [104×3 table] InputNames: {'TopLevelModule_layers_0'} OutputNames: {'aten__linear12'} Initialized: 0 View summary with summary.
Specify the input size of the imported network. Then, 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. Analyze the imported network.
analyzeNetwork(net)
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.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. 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): inputLayer1 = imageInputLayer(<inputSize1>, Normalization="none"); net = addInputLayer(net, inputLayer1, Initialize=true);
net = dlnetwork with properties: Layers: [152×1 nnet.cnn.layer.Layer] Connections: [163×2 table] Learnables: [210×3 table] State: [104×3 table] InputNames: {'TopLevelModule_layers_0'} OutputNames: {'aten__linear12'} 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.
squeezeNet = squeezenet; ClassNames = squeezeNet.Layers(end).Classes;
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 = categorical
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.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. 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): inputLayer1 = imageInputLayer(<inputSize1>, Normalization="none"); net = addInputLayer(net, inputLayer1, Initialize=true);
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 package +mnasnet1_0
in the current folder and the associated functions to the subpackage +ops
. To see the custom layers and associated functions, inspect the package.
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 'aten__add0' Custom Layer mnasnet1_0.aten__add0 2 'aten__add1' Custom Layer mnasnet1_0.aten__add1 3 'aten__add2' Custom Layer mnasnet1_0.aten__add2 4 'aten__add3' Custom Layer mnasnet1_0.aten__add3 5 'aten__add4' Custom Layer mnasnet1_0.aten__add4 6 'aten__add5' Custom Layer mnasnet1_0.aten__add5 7 'aten__add6' Custom Layer mnasnet1_0.aten__add6 8 'aten__add7' Custom Layer mnasnet1_0.aten__add7 9 'aten__add8' Custom Layer mnasnet1_0.aten__add8 10 'aten__add9' Custom Layer mnasnet1_0.aten__add9 11 'aten__dropout_11' Custom Layer mnasnet1_0.aten__dropout_11 12 'aten__linear12' Custom Layer mnasnet1_0.aten__linear12 13 'aten__mean10' Custom Layer mnasnet1_0.aten__mean10
Helper Function
This section provides the findCustomLayers
helper function, which returns the indices
of the custom layers that importNetworkFromPyTorch
automatically generates.
function indices = findCustomLayers(layers,PackageName) s = what(['.\' PackageName]); indices = zeros(1,length(s.m)); for i = 1:length(layers) for j = 1:length(s.m) if strcmpi(class(layers(i)),[PackageName(2:end) '.' s.m{j}(1:end-2)]) indices(j) = i; end end 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 a uninitialized dlnetwork
object. Train the network by using a custom training loop.
This example uses the modelLoss
, modelPredictions
, and preprocessMiniBatchPredictors
helper functions.
This example provides the supporting file new_fcLayer.m
. 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 used in this example requires input images of size 224-by-224-by-3. To automatically resize the training images, use an augmented image datastore. Specify additional augmentation operations to perform on the training images: 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.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 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): inputLayer1 = imageInputLayer(<inputSize1>, Normalization="none"); net = addInputLayer(net, inputLayer1, Initialize=true);
net = dlnetwork with properties: Layers: [152×1 nnet.cnn.layer.Layer] Connections: [163×2 table] Learnables: [210×3 table] State: [104×3 table] InputNames: {'TopLevelModule_layers_0'} OutputNames: {'aten__linear12'} Initialized: 0 View summary with summary.
Display the final layer of the imported network.
net.Layers(end)
ans = aten__linear12 with properties: Name: 'aten__linear12' NumInputs: 2 InputNames: {'in' 'in_rank'} Learnable Parameters TopLevelModule_classifier_1_weight: [1280×1000 single] TopLevelModule_classifier_1_bias: [0.0493 -0.0804 -0.0906 -0.1006 0.1332 -0.0767 -0.0788 -0.0026 -0.0525 -0.1215 -0.1635 -0.1147 -0.1421 -0.1148 -0.0586 -0.2150 -0.0970 -0.0798 -5.4136e-04 -0.0968 0.0479 0.0780 0.0015 -0.1375 -0.0485 -0.1223 … ] State Parameters No properties. Show all properties
The aten__linear12
layer is a custom layer generated by the importNetworkFromPyTorch
function and the last learnable layer of the imported network. This layer contains information on 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 aten__linear12
layer with new_fcLayer
.
newLayer = new_fcLayer("fc1","Custom Layer", ... {'in' 'in_rank'},{'out'},numClasses); net = replaceLayer(net,"aten__linear12",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,"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 considering a neural network as a function , where is the network input, and is the set of learnable parameters, you can optimize so that it minimizes some loss value based on the training data. For example, optimize the learnable parameters such that, for given inputs with corresponding targets , they minimize the error between the predictions and .
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 learn 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 Model
Create a minibatchqueue
object that processes and manages mini-batches of images during training. For each mini-batch:
Use the custom mini-batch preprocessing function
preprocessMiniBatch
(defined at the end of this example) to convert the labels to one-hot encoded variables.Format the image data with the dimension labels
"SSCB"
(spatial, spatial, channel, batch). By default, theminibatchqueue
object converts the data todlarray
objects with the underlying typesingle
. Do not format the class labels.Train on a GPU if one is available. By default, the
minibatchqueue
object converts each output to agpuArray
object if a GPU is available. Using a GPU requires Parallel Computing Toolbox™ and a supported GPU device. For information on 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:
Evaluate the model loss, gradients, and state using the
dlfeval
andmodelLoss
functions and update the network state.Determine the learning rate for the time-based decay learning rate schedule.
Update the network parameters using the
sgdmupdate
function.Update the loss, learn rate, and epoch values in the training progress monitor.
Stop if the
Stop
property is true. TheStop
property value of theTrainingProgressMonitor
object changes totrue
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 used 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:
Preprocess the images using the
preprocessMiniBatchPredictors
function.Extract the label data from the incoming cell array and concatenate into a categorical array along the second dimension.
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 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 containing the network, specified as a character vector or string scalar. The file must be in the current folder or in a folder on the MATLAB® path, or you must include a full or relative path of the file. The PyTorch model must be pretrained and traced over one inference iteration.
For information on how to trace a PyTorch model, see https://pytorch.org/docs/stable/generated/torch.jit.trace.html.
Example: "mobilenet_v3.pt"
CustomLayersPackage
— Name of custom layers package
character vector | string scalar
Name of the package in which importNetworkFromPyTorch
saves custom layers,
specified as a character vector or string scalar. importNetworkFromPyTorch
saves
the custom layers package +CustomLayersPackage
in the current folder.
If you do not specify CustomLayersPackage
, then
importNetworkFromPyTorch
saves the custom layers in a package named
+
modelfile
in the current folder. For more
information on packages, see Packages Create Namespaces.
See Algorithms about information on
when the importNetworkFromPyTorch
function generates a custom layer. The
function saves each generated custom layer to a separate program file in
+
. To view or edit a
custom layer, open the associated program file. For more information on custom layers,
see Custom Layers.CustomLayersPackage
The package +
can also
contain the subpackage CustomLayersPackage
+ops
. This subpackage contains MATLAB functions that the automatically generated custom layers use.
importNetworkFromPyTorch
saves each MATLAB function in a separate program file in the subpackage
+ops
. The object functions of dlnetwork
, such as
the predict
function, use
these functions when interacting with the custom layers. The subpackage
+ops
might also contain Placeholder Functions.
Example: "mobilenet_v3"
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 1.10.0. The function can import most models created in other PyTorch versions.The
importNetworkFromPyTorch
function can import only image classification and segmentation models.You can run
importNetworkFromPyTorch
on a Windows® or Mac OS platform.
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 on functions that operate on dlarray
objects, see
List of Functions with dlarray Support. You might observe
limitations in the conversion.
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 | nnet.pytorch.layer.AdaptiveAveragePoolingLayer | 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.Dropout | dropoutLayer | Not applicable |
torch.nn.GroupNorm | groupNormalizationLayer | Not applicable |
torch.nn.LayerNorm | layerNormalizationLayer | Not applicable |
torch.nn.Linear | fullyConnectedLayer | pyLinear |
torch.nn.MaxPool2d | maxPooling2dLayer | Not applicable |
torch.nn.PReLU | nnet.pytorch.layer.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.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) |
This table shows the correspondence between PyTorch functions and Deep Learning Toolbox functions.
PyTorch Function | Corresponding Deep Learning Toolbox Function |
---|---|
torch.nn.functional.avg_pool2d | pyAvgPool2d |
torch.nn.functional.conv1d | pyConvolution |
torch.nn.functional.conv2d | pyConvolution |
torch.nn.functional.hardsigmoid | pyAdaptiveAvgPool2d |
torch.nn.functional.dropout | pyDropout |
torch.nn.functional.hardsigmoid | pyHardSigmoid |
torch.nn.functional.hardswish | pyHardSwish |
torch.nn.functional.linear | pyLinear |
torch.nn.functional.log_softmax | pyLogSoftmax |
torch.nn.functional.max_pool2d | pyMaxPool2d |
torch.nn.functional.relu | pyHardTanh |
torch.nn.functional.silu | pySilu |
torch.nn.functional.softmax | pySoftmax |
This table shows the correspondence between PyTorch mathematical operators and Deep Learning Toolbox functions. For the cat
PyTorch operator, importNetworkFromPyTorch
first tries to convert it to a
concatenation layer and alternatively to a function.
PyTorch Operator | Corresponding Deep Learning Toolbox Layer or Function | Alternative Deep Learning Toolbox Function |
---|---|---|
+ , - , * ,
/ | pyElementwiseBinary | Not applicable |
torch.argmax | pyArgMax | Not applicable |
torch.bmm | pyMatMul | Not applicable |
torch.cat | concatenationLayer | pyConcat |
torch.chunk | pyChunk | Not applicable |
torch.concat | pyConcat | Not applicable |
torch.matmul | pyMatMul | Not applicable |
torch.max | pyMaxBinary/pyMaxUnary | Not applicable |
torch.mean | pyMean | Not applicable |
torch.permute | pyPermute | Not applicable |
torch.pow | pyElementwiseBinary | Not applicable |
torch.reshape | pyView | Not applicable |
torch.size | pySize | Not applicable |
torch.split | pySplitWithSizes | Not applicable |
torch.stack | pyStack | Not applicable |
torch.sum | pySum | Not applicable |
torch.squeeze | pySqueeze | Not applicable |
torch.transpose | pyTranspose | Not applicable |
torch.unsqueeze | pyUnsqueeze | Not applicable |
torch.zeros | pyZeros | Not applicable |
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.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 shows the definition of a custom layer with the
placeholder function pyAtenUnsupportedOperator
.
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 the images that were used to train the imported model were preprocessed. The most common preprocessing steps are resizing images, subtracting image average values, and converting the images from BGR format to RGB format.
For more information on preprocessing images for training and prediction, see Preprocess Images for Deep Learning.
The members of the package
+
are not accessible if the package parent folder is not on the MATLAB path. For more information, see Packages and the MATLAB Path.PackageName
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 on MATLAB indexing, see Array Indexing. In MATLAB, to use an array of indices (
ind
) created in Python, convert the array toind+1
.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:
The function tries to import the PyTorch layer as a built-in MATLAB layer. For more information, see Conversion of PyTorch Layers.
The function tries to import the PyTorch layer as a built-in MATLAB function. For more information, see Conversion of PyTorch Layers.
The function tries to import the PyTorch layer as a custom layer.
importNetworkFromPyTorch
saves the generated custom layers and the associated functions in the package+
modelfile
. For an example, see Import Network from PyTorch and Find Generated Custom Layers.The function imports the PyTorch layer as a custom layer with a placeholder function. For more information, see Placeholder Functions.
In the first three cases, the imported network is ready for prediction after you initialize it.
Version History
Introduced in R2022b
Abrir ejemplo
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