importONNXNetwork
(To be removed) Import pretrained ONNX network
importONNXNetwork
will be removed in a future release. Use importNetworkFromONNX
instead. (since R2023b) For more information about updating
your code, see Version History.
Description
imports a pretrained ONNX™ (Open Neural Network Exchange) network from the file
net
= importONNXNetwork(modelfile
)modelfile
. The function returns the network net
as
a DAGNetwork
or dlnetwork
object.
importONNXNetwork
requires the Deep Learning Toolbox™ Converter for ONNX Model Format support package. If this support package is not installed, then
importONNXNetwork
provides a download link.
Note
By default, importONNXNetwork
tries to generate a custom layer when
the software cannot convert an ONNX operator into an equivalent built-in MATLAB® layer. For a list of operators for which the software supports conversion,
see ONNX Operators Supported for Conversion into Built-In MATLAB Layers.
importONNXNetwork
saves the generated custom layers in the namespace
+
.modelfile
importONNXNetwork
does not automatically generate a custom layer for
each ONNX operator that is not supported for conversion into a built-in MATLAB layer. For more information on how to handle unsupported layers, see Alternative Functionality.
imports a pretrained ONNX network with additional
options specified by one or more name-value arguments. For example,
net
= importONNXNetwork(modelfile
,Name=Value
)OutputLayerType="classification"
imports the network as a
DAGNetwork
object with a classification output layer appended to the end
of the first output branch of the imported network architecture.
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 "simplenet.onnx"
at the command line. If the support package is installed, then the function returns a DAGNetwork
object.
modelfile = "simplenet.onnx";
net = importONNXNetwork(modelfile)
net = DAGNetwork with properties: Layers: [9×1 nnet.cnn.layer.Layer] Connections: [8×2 table] InputNames: {'imageinput'} OutputNames: {'ClassificationLayer_softmax1002'}
Plot the network architecture.
plot(net)
Import ONNX Network as DAGNetwork
Import a pretrained ONNX network as a DAGNetwork
object, and use the imported network to classify an image.
Generate an ONNX model of the squeezenet
convolution neural network.
squeezeNet = squeezenet;
exportONNXNetwork(squeezeNet,"squeezeNet.onnx");
Specify the class names.
ClassNames = squeezeNet.Layers(end).Classes;
Import the pretrained squeezeNet.onnx
model, and specify the classes. By default, importONNXNetwork
imports the network as a DAGNetwork
object.
net = importONNXNetwork("squeezeNet.onnx",Classes=ClassNames)
net = DAGNetwork with properties: Layers: [70×1 nnet.cnn.layer.Layer] Connections: [77×2 table] InputNames: {'data'} OutputNames: {'ClassificationLayer_prob'}
Analyze the imported network.
analyzeNetwork(net)
Read the image you want to classify and display the size of the image. The image is 384-by-512 pixels and has three color channels (RGB).
I = imread("peppers.png");
size(I)
ans = 1×3
384 512 3
Resize the image to the input size of the network. Show the image.
I = imresize(I,[227 227]); imshow(I)
Classify the image using the imported network.
label = classify(net,I)
label = categorical
bell pepper
Import ONNX Network as dlnetwork
Import a pretrained ONNX network as a dlnetwork
object, and use the imported network to classify an image.
Generate an ONNX model of the squeezenet
convolution neural network.
squeezeNet = squeezenet;
exportONNXNetwork(squeezeNet,"squeezeNet.onnx");
Specify the class names.
ClassNames = squeezeNet.Layers(end).Classes;
Import the pretrained squeezeNet.onnx
model as a dlnetwork
object.
net = importONNXNetwork("squeezeNet.onnx",TargetNetwork="dlnetwork")
net = dlnetwork with properties: Layers: [70×1 nnet.cnn.layer.Layer] Connections: [77×2 table] Learnables: [52×3 table] State: [0×3 table] InputNames: {'data'} OutputNames: {'probOutput'} Initialized: 1
Read the image you want to classify and display the size of the image. The image is 384-by-512 pixels and has three color channels (RGB).
I = imread("peppers.png");
size(I)
ans = 1×3
384 512 3
Resize the image to the input size of the network. Show the image.
I = imresize(I,[227 227]); imshow(I)
Convert the image to a dlarray
. Format the images with the dimensions "SSCB"
(spatial, spatial, channel, batch). In this case, the batch size is 1 and you can omit it ("SSC"
).
I_dlarray = dlarray(single(I),"SSCB");
Classify the sample image and find the predicted label.
prob = predict(net,I_dlarray); [~,label] = max(prob);
Display the classification result.
ClassNames(label)
ans = categorical
bell pepper
Import ONNX Network with Multiple Outputs
Import an ONNX network that has multiple outputs as a DAGNetwork
object.
Specify the ONNX model file and import the pretrained ONNX model. By default, importONNXNetwork
imports the network as a DAGNetwork
object.
modelfile = "digitsMIMO.onnx";
net = importONNXNetwork(modelfile)
net = DAGNetwork with properties: Layers: [19×1 nnet.cnn.layer.Layer] Connections: [19×2 table] InputNames: {'input'} OutputNames: {'ClassificationLayer_sm_1' 'RegressionLayer_fc_1_Flatten'}
The network has two output layers: one classification layer (ClassificationLayer_sm_1
) to classify digits and one regression layer (RegressionLayer_fc_1_Flatten
) to compute the mean squared error for the predicted angles of the digits. Plot the network architecture.
plot(net)
title('digitsMIMO Network Architecture')
To make predictions using the imported network, use the predict
function and set the ReturnCategorical
option to true
.
Input Arguments
modelfile
— Name of ONNX model file
character vector | string scalar
Name of the ONNX 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 to the file.
Example: "cifarResNet.onnx"
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: importONNXNetwork(modelfile,TargetNetwork="dagnetwork",GenerateCustomLayers=true,Namespace="CustomLayers")
imports the network in modelfile
as a DAGNetwork
object
and saves the automatically generated custom layers in the namespace
+CustomLayers
in the current folder.
GenerateCustomLayers
— Option for custom layer generation
true
or 1
(default) | false
or 0
Option for custom layer generation, specified as a numeric or logical 1
(true
) or 0
(false
). If you
set GenerateCustomLayers
to true
,
importONNXNetwork
tries to generate a custom layer when the software
cannot convert an ONNX operator into an equivalent built-in MATLAB layer. importONNXNetwork
saves each generated custom layer
to a separate .m
file in
+
. To view or edit a custom layer,
open the associated Namespace
.m
file. For more information on custom layers,
see Custom Layers.
Example: GenerateCustomLayers=false
Namespace
— Name of custom layers namespace
character vector | string scalar
Name of the custom layers namespace in which importONNXNetwork
saves
custom layers, specified as a character vector or string scalar.
importONNXNetwork
saves the custom layers namespace
+
in the current folder. If you
do not specify Namespace
Namespace
, then importONNXNetwork
saves the custom layers in a namespace named
+
in the current folder. For more
information about namespaces, see Create Namespaces.modelfile
Example: Namespace="shufflenet_9"
Example: Namespace="CustomLayers"
TargetNetwork
— Target type of Deep Learning Toolbox network
"dagnetwork"
(default) | "dlnetwork"
Target type of Deep Learning Toolbox network, specified as "dagnetwork"
or
"dlnetwork"
. The function importONNXNetwork
imports the network net
as a DAGNetwork
or
dlnetwork
object.
If you import the network as a
DAGNetwork
object,net
must include input and output layers specified by the ONNX model or that you specify using the name-value argumentsInputDataFormats
,OutputDataFormats
, orOutputLayerType
.If you import the network as a
dlnetwork
object,importONNXNetwork
appends aCustomOutputLayer
at the end of each output branch ofnet
, and might append aCustomInputLayer
at the beginning of an input branch. The function appends aCustomInputLayer
if the input data formats or input image sizes are not known. For network-specific information on the data formats of these layers, see the properties of theCustomInputLayer
andCustomOutputLayer
objects. For information on how to interpret Deep Learning Toolbox input and output data formats, see Conversion of ONNX Input and Output Tensors into Built-In MATLAB Layers.
Example: TargetNetwork="dlnetwork"
InputDataFormats
— Data format of network inputs
character vector | string scalar | string array
Data format of the network inputs, specified as a character vector, string scalar, or
string array. importONNXNetwork
tries to interpret the input data formats
from the ONNX file. The name-value argument InputDataFormats
is
useful when importONNXNetwork
cannot derive the input data
formats.
Set InputDataFomats
to a data format in the ordering of an
ONNX input tensor. For example, if you specify
InputDataFormats
as "BSSC"
, the imported
network has one imageInputLayer
input. For more information on how
importONNXNetwork
interprets the data format of ONNX input tensors and how to specify InputDataFormats
for
different Deep Learning Toolbox input layers, see Conversion of ONNX Input and Output Tensors into Built-In MATLAB Layers.
If you specify an empty data format ([]
or ""
),
importONNXNetwork
automatically interprets the input data
format.
Example: InputDataFormats='BSSC'
Example: InputDataFormats="BSSC"
Example: InputDataFormats=["BCSS","","BC"]
Example: InputDataFormats={'BCSS',[],'BC'}
Data Types: char
| string
| cell
OutputDataFormats
— Data format of network outputs
character vector | string scalar | string array
Data format of the network outputs, specified as a character vector, string scalar, or
string array. importONNXNetwork
tries to interpret the output data
formats from the ONNX file. The name-value argument OutputDataFormats
is
useful when importONNXNetwork
cannot derive the output data
formats.
Set OutputDataFormats
to a data format in the ordering of an
ONNX output tensor. For example, if you specify
OutputDataFormats
as "BC"
, the imported
network has one classificationLayer
output. For more information on how
importONNXNetwork
interprets the data format of ONNX output tensors and how to specify OutputDataFormats
for different Deep Learning Toolbox output layers, see Conversion of ONNX Input and Output Tensors into Built-In MATLAB Layers.
If you specify an empty data format ([]
or ""
),
importONNXNetwork
automatically interprets the output data
format.
Example: OutputDataFormats='BC'
Example: OutputDataFormats="BC"
Example: OutputDataFormats=["BCSS","","BC"]
Example: OutputDataFormats={'BCSS',[],'BC'}
Data Types: char
| string
| cell
ImageInputSize
— Size of input image for first network input
vector of two or three numerical values
Size of the input image for the first network input, specified as a vector of three or
four numerical values corresponding to [height,width,channels]
for
2-D images and [height,width,depth,channels]
for 3-D images. The
network uses this information only when the ONNX model in modelfile
does not specify the input
size.
Example: ImageInputSize=[28 28 1]
for a 2-D grayscale input
image
Example: ImageInputSize=[224 224 3]
for a 2-D color input
image
Example: ImageInputSize=[28 28 36 3]
for a 3-D color input
image
OutputLayerType
— Layer type for first network output
"classification"
| "regression"
| "pixelclassification"
Layer type for the first network output, specified as
"classification"
, "regression"
, or
"pixelclassification"
. The function
importONNXNetwork
appends a ClassificationOutputLayer
, RegressionOutputLayer
, or pixelClassificationLayer
(Computer Vision Toolbox) object to the end of the first output branch of
the imported network architecture. Appending a pixelClassificationLayer
(Computer Vision Toolbox) object requires Computer Vision Toolbox™. If the ONNX model in modelfile
specifies the output layer type or
you specify TargetNetwork
as "dlnetwork"
,
importONNXNetwork
ignores the name-value argument
OutputLayerType
.
Example: OutputLayerType="regression"
Classes
— Classes of output layer for first network output
"auto"
(default) | categorical vector | string array | cell array of character vectors
Classes of the output layer for the first
network output, specified as a categorical vector, string array, cell array of
character vectors, or "auto"
. If Classes
is
"auto"
, then importONNXNetwork
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 importONNXNetwork
sets the classes of
the output layer to categorical(str,str)
. If you specify
TargetNetwork
as "dlnetwork"
,
importONNXNetwork
ignores the name-value argument
Classes
.
Example: Classes={'0','1','3'}
Example: Classes=categorical({'dog','cat'})
Data Types: char
| categorical
| string
| cell
FoldConstants
— Constant folding optimization
"deep"
(default) | "shallow"
| "none"
Constant folding optimization, specified as "deep"
,
"shallow"
, or "none"
. Constant folding
optimizes the imported network architecture by computing operations on ONNX initializers (initial constant values) during the conversion of
ONNX operators to equivalent built-in MATLAB layers.
If the ONNX network contains operators that the software cannot convert to
equivalent built-in MATLAB layers (see ONNX Operators Supported for Conversion into Built-In MATLAB Layers), constant folding
optimization can reduce the number of unsupported layers. When you set
FoldConstants
to "deep"
, the network has the
same or fewer unsupported layers, compared to when you set the argument to
"shallow"
. However, the network importing time might increase.
Set FoldConstants
to "none"
to disable the
network architecture optimization.
If the network still contains unsupported layers after constant folding
optimization, importONNXNetwork
returns an error. In this case, you can
import the network by using importONNXLayers
or importONNXFunction
. For more information, see Alternative Functionality.
Example: FoldConstants="shallow"
Output Arguments
net
— Pretrained ONNX network
DAGNetwork
object | dlnetwork
object
Pretrained ONNX network, returned as a DAGNetwork
or
dlnetwork
object.
Specify
TargetNetwork
as"dagnetwork"
to import the network as aDAGNetwork
object. On theDAGNetwork
object, you then predict class labels by using theclassify
function.Specify
TargetNetwork
as"dlnetwork"
to import the network as adlnetwork
object. On thedlnetwork
object, you then predict class labels by using thepredict
function. Specify the input data as adlarray
using the correct data format (for more information, see thefmt
argument ofdlarray
).
Limitations
importONNXNetwork
supports these ONNX:ONNX intermediate representation version 9
ONNX operator sets 6–18
Note
If you import an exported network, layers of the reimported network might differ from layers of the original network, and might not be supported.
More About
ONNX Operators Supported for Conversion into Built-In MATLAB Layers
importONNXNetwork
supports these ONNX operators for conversion into built-in MATLAB layers, with some limitations.
* If importONNXNetwork
imports the
Conv
ONNX operator as a convolution2dLayer
object and the
Conv
operator is a vector with only two elements
[p1 p2]
, importONNXNetwork
sets the
Padding
option of convolution2dLayer
to
[p1 p2 p1 p2]
.
ONNX Operator | ONNX Importer Custom Layer |
---|---|
| nnet.onnx.layer.ClipLayer |
| nnet.onnx.layer.ElementwiseAffineLayer |
| nnet.onnx.layer.FlattenLayer or
nnet.onnx.layer.Flatten3dLayer |
| nnet.onnx.layer.ElementwiseAffineLayer |
| nnet.onnx.layer.FlattenLayer |
| nnet.onnx.layer.ElementwiseAffineLayer |
ONNX Operator | Corresponding Image Processing Toolbox™ Layer |
---|---|
DepthToSpace | depthToSpace2dLayer (Image Processing Toolbox) |
Resize | resize2dLayer (Image Processing Toolbox) or resize3dLayer (Image Processing Toolbox) |
SpaceToDepth | spaceToDepthLayer (Image Processing Toolbox) |
Upsample | resize2dLayer (Image Processing Toolbox) or resize3dLayer (Image Processing Toolbox) |
Conversion of ONNX Input and Output Tensors into Built-In MATLAB Layers
importONNXNetwork
tries to interpret the data format of the
ONNX network's input and output tensors, and then convert them into built-in
MATLAB input and output layers. For details on the interpretation, see the tables
Conversion of ONNX Input Tensors into Deep Learning Toolbox Layers and Conversion of ONNX Output Tensors into MATLAB Layers.
In Deep Learning Toolbox, each data format character must be one of these labels:
S
— SpatialC
— ChannelB
— Batch observationsT
— Time or sequenceU
— Unspecified
Conversion of ONNX Input Tensors into Deep Learning Toolbox Layers
Data Formats | Data Interpretation | Deep Learning Toolbox Layer | ||
---|---|---|---|---|
ONNX Input Tensor | MATLAB Input Format | Shape | Type | |
BC | CB | c-by-n array, where c is the number of features and n is the number of observations | Features | featureInputLayer |
BCSS , BSSC , CSS , SSC | SSCB | h-by-w-by-c-by-n numeric array, where h, w, c and n are the height, width, number of channels of the images, and number of observations, respectively | 2-D image | imageInputLayer |
BCSSS , BSSSC , CSSS , SSSC | SSSCB | h-by-w-by-d-by-c-by-n numeric array, where h, w, d, c and n are the height, width, depth, number of channels of the images, and number of image observations, respectively | 3-D image | image3dInputLayer |
TBC | CBT | c-by-s-by-n matrix, where c is the number of features of the sequence, s is the sequence length, and n is the number of sequence observations | Vector sequence | sequenceInputLayer |
TBCSS | SSCBT | h-by-w-by-c-by-s-by-n array, where h, w, c and n correspond to the height, width, and number of channels of the image, respectively, s is the sequence length, and n is the number of image sequence observations | 2-D image sequence | sequenceInputLayer |
TBCSSS | SSSCBT | h-by-w-by-d-by-c-by-s-by-n array, where h, w, d, and c correspond to the height, width, depth, and number of channels of the image, respectively, s is the sequence length, and n is the number of image sequence observations | 3-D image sequence | sequenceInputLayer |
Conversion of ONNX Output Tensors into MATLAB Layers
Data Formats | MATLAB Layer | |
---|---|---|
ONNX Output Tensor | MATLAB Output Format | |
BC , TBC | CB , CBT | classificationLayer |
BCSS , BSSC , CSS , SSC , BCSSS , BSSSC , CSSS , SSSC | SSCB , SSSCB | pixelClassificationLayer (Computer Vision Toolbox) |
TBCSS , TBCSSS | SSCBT , SSSCBT | regressionLayer |
Generate Code for Imported Network
You can use MATLAB Coder™ or GPU Coder™ together with Deep Learning Toolbox to generate MEX, standalone CPU, CUDA® MEX, or standalone CUDA code for an imported network. For more information, see Code Generation.
Use MATLAB Coder with Deep Learning Toolbox to generate MEX or standalone CPU code that runs on desktop or embedded targets. You can deploy generated standalone code that uses the Intel® MKL-DNN library or the ARM® Compute library. Alternatively, you can generate generic C or C++ code that does not call third-party library functions. For more information, see Deep Learning with MATLAB Coder (MATLAB Coder).
Use GPU Coder with Deep Learning Toolbox to generate CUDA MEX or standalone CUDA code that runs on desktop or embedded targets. You can deploy generated standalone CUDA code that uses the CUDA deep neural network library (cuDNN), the TensorRT™ high performance inference library, or the ARM Compute library for Mali GPU. For more information, see Deep Learning with GPU Coder (GPU Coder).
importONNXNetwork
returns the network
net
as a DAGNetwork
or dlnetwork
object. Both these objects support code generation. For more information on MATLAB
Coder and GPU Coder support for Deep Learning Toolbox objects, see Supported Classes (MATLAB Coder) and Supported Classes (GPU Coder), respectively.
You can generate code for any imported network whose layers support code generation. For lists
of the layers that support code generation with MATLAB
Coder and GPU Coder, see Supported Layers (MATLAB Coder) and Supported Layers (GPU Coder), respectively. For
more information on the code generation capabilities and limitations of each built-in
MATLAB layer, see the Extended Capabilities section of the layer. For example, see
Code Generation and GPU Code Generation of imageInputLayer
.
Use Imported Network on GPU
importONNXNetwork
does not execute on a GPU. However, importONNXNetwork
imports a pretrained neural network for deep learning as a DAGNetwork
or dlnetwork
object, which you can use on a GPU.
If you import the network as a
DAGNetwork
object, you can make predictions with the imported network on either a CPU or GPU by usingclassify
. Specify the hardware requirements using the name-value argumentExecutionEnvironment
. For networks with multiple outputs, use thepredict
function forDAGNetwork
objects.If you import the network as a
DAGNetwork
object, you can make predictions with the imported network on either a CPU or GPU by usingpredict
. Specify the hardware requirements using the name-value argumentExecutionEnvironment
. If the network has multiple outputs, specify the name-value argumentReturnCategorical
astrue
.If you import the network as a
dlnetwork
object, you can make predictions with the imported network on either a CPU or GPU by usingpredict
. The functionpredict
executes on the GPU if either the input data or network parameters are stored on the GPU.If you use
minibatchqueue
to process and manage the mini-batches of input data, theminibatchqueue
object converts the output to a GPU array by default if a GPU is available.Use
dlupdate
to convert the learnable parameters of adlnetwork
object to GPU arrays.net = dlupdate(@gpuArray,net)
You can train the imported network on either a CPU or GPU by using the
trainnet
andtrainNetwork
functions. To specify training options, including options for the execution environment, use thetrainingOptions
function. Specify the hardware requirements using the name-value argumentExecutionEnvironment
. For more information on how to accelerate training, see Scale Up Deep Learning in Parallel, on GPUs, and in the Cloud.
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).
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.
For more information about preprocessing images for training and prediction, see Preprocess Images for Deep Learning.
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 toind+1
.For more tips, see Tips on Importing Models from TensorFlow, PyTorch, and ONNX.
Alternative Functionality
Deep Learning Toolbox Converter for ONNX Model Format provides three functions to import a pretrained ONNX network: importONNXNetwork
, importONNXLayers
,
and importONNXFunction
.
If the imported network contains an ONNX operator not supported for conversion into a built-in MATLAB layer (see ONNX Operators Supported for Conversion into Built-In MATLAB Layers) and
importONNXNetwork
does not generate a custom layer, then
importONNXNetwork
returns an error. In this case, you can still use
importONNXLayers
to import the network architecture and weights or
importONNXFunction
to import the network as an ONNXParameters
object
and a model function.
Version History
Introduced in R2018aR2023b: importONNXNetwork
will be removed
Starting in R2023b, the importONNXNetwork
function warns. Use importNetworkFromONNX
instead. The importNetworkFromONNX
function has these advantages over importONNXNetwork
:
Imports an ONNX model into a
dlnetwork
object in a single stepProvides a simplified workflow for importing models with unknown input and output information
Has improved name-value arguments that you can use to more easily specify import options
R2021b: ClassNames
option has been removed
ClassNames
has been removed. Use Classes
instead. To update your code, replace all instances of ClassNames
with
Classes
.
R2021b: importONNXNetwork
cannot create input and output layers from ONNX file information
If you import an ONNX model as a DAGNetwork
object, the imported network must
include input and output layers. importONNXNetwork
tries to convert the input
and output ONNX tensors into built-in MATLAB layers. When importing some networks, which importONNXNetwork
could previously import with input and output built-in MATLAB layers, importONNXNetwork
might now return an error. In this
case, do one of the following to update your code:
Specify the name-value argument
TargetNetwork
as"dlnetwork"
to import the network as adlnetwork
object.Use the name-value arguments
InputDataFormats
,OutputDataFormats
, andOutputLayerType
to specify the imported network's inputs and outputs.Use
importONNXLayers
to import the network as a layer graph with placeholder layers.Use
importONNXFunction
to import the network as a model function and anONNXParameters
object.
R2021b: Layer names of imported network might differ
The layer names of an imported network might differ from previous releases. To update
your code, replace the existing name of a layer with the new name or
net.Layers(n).Name
.
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