With GPU Coder™, you can generate optimized code for Simulink® models containing a variety of trained deep learning networks. You can implement deep learning functionality in Simulink by using MATLAB Function blocks or by using blocks from the Deep Neural Networks library. The Deep Neural Networks block library includes:
Predict (Deep Learning Toolbox) block — Predict responses using the trained network specified through the block parameter. This block enables loading of a pretrained network into the Simulink model from a MAT-file or from a MATLAB® function.
For more information about working with the Predict block, see Lane and Vehicle Detection in Simulink Using Deep Learning (Deep Learning Toolbox).
Image Classifier (Deep Learning Toolbox) block — Classify data using a trained deep learning neural network specified through the block parameter. This block enables loading of a pretrained network into the Simulink model from a MAT-file or from a MATLAB function.
For more information about working with the Image Classifier block, see Classify ECG Signals in Simulink Using Deep Learning (Deep Learning Toolbox).
You can configure the code generator to take advantage of the NVIDIA® CUDA® deep neural network library (cuDNN) and TensorRT™ high performance inference libraries for NVIDIA GPUs. The generated code implements the deep convolutional neural network (CNN) by using the architecture, the layers, and parameters that you specify in the network object.
GoogLeNet has been trained on over a million images and can classify images into 1000
object categories (such as keyboard, coffee mug, pencil, and animals). The network takes an
image as input, and then outputs a label for the object in the image with the probabilities
for each of the object categories. This example shows how to perform simulation and generate
CUDA code for the pretrained googlenet
deep convolutional
neural network and classify an image. The pretrained networks are available as support
packages from the Deep Learning Toolbox™.
Load the pretrained GoogLeNet network.
net = googlenet;
The object net
contains the DAGNetwork
object.
Use the analyzeNetwork
function to display an interactive
visualization of the network architecture, to detect errors and issues in the network,
and to display detailed information about the network layers. The layer information
includes the sizes of layer activations and learnable parameters, the total number of
learnable parameters, and the sizes of state parameters of recurrent layers.
analyzeNetwork(net);
The image that you want to classify must have the same size as the input size of the
network. For GoogLeNet, the size of the imageInputLayer
is
224-by-224-by-3. The Classes
property of the output
classificationLayer
contains the names of the classes learned by
the network. View 10 random class names out of the total of 1000.
classNames = net.Layers(end).Classes; numClasses = numel(classNames); disp(classNames(randperm(numClasses,10)))
'speedboat' 'window screen' 'isopod' 'wooden spoon' 'lipstick' 'drake' 'hyena' 'dumbbell' 'strawberry' 'custard apple'
Create a Simulink model and insert a Predict block from the Deep Neural Networks library.
Add an Image From File block from the Computer Vision Toolbox™ library and set the File name
parameter to
peppers.png
. Add a Resize block from the
Computer Vision Toolbox library to the model. Set the Specify
parameter of the Resize block to Number of output rows and
columns
and enter [224 224]
as the value for
Number of output rows and columns. The resize block resizes the
input image to that of the input layer of the network. Add a To Workspace
to the model and change the variable name to yPred
.
Open the Block Parameters (subsystem) of the
Predict block. Select Network from MATLAB
function
for Network and
googlenet for MATLAB function.
Connect these blocks as shown in the diagram. Save the model as
googlenetModel.slx
.
Model configuration parameters determine the acceleration method used during simulation.
Open the Configuration Parameters dialog box. Open the Solver pane. To compile your model for acceleration and generate CUDA code, configure the model to use a fixed-step solver. This table shows the solver configuration for this example.
Parameter | Setting | Effect on Generated Code |
---|---|---|
Type | Fixed-step | Maintains a constant (fixed) step size, which is required for code generation |
Solver | discrete (no continuous states) | Applies a fixed-step integration technique for computing the state derivative of the model |
Fixed-step size | auto | Simulink chooses the step size |
Select the Simulation Target pane. Set the
Language to C++
.
Select GPU acceleration. Options specific to GPU Coder are now visible in the Simulation Target > GPU Acceleration pane. For this example, you can use the default values of these parameters.
On the Simulation Target pane, set the Target
Library in the Deep learning group to
cuDNN
. You can also select
TensorRT
.
Click OK to save and close the Configuration Parameters dialog box.
You can use set_param
to configure the model parameter
programmatically in the MATLAB Command
Window.
set_param('googlenetModel','GPUAcceleration','on');
To build and simulate the GPU accelerated model, select Run on the Simulation tab or use the command:
out = sim('googlenetModel');
The software first checks to see if CUDA/C++ code was previously compiled for your model. If code was created
previously, the software runs the model. If code was not previously built, the
software first generates and compiles the CUDA/C++ code, and then runs the model. The code generation tool places the
generated code in a subfolder of the working folder called
slprj/_slprj/googlenetModel
.
Display the top five predicted labels and their associated probabilities as a histogram. Because the network classifies images into so many object categories, and many categories are similar, it is common to consider the top-five accuracy when evaluating networks. The network classifies the image as a bell pepper with a high probability.
im = imread('peppers.png'); predict_scores = out.yPred.Data(:,:,1); [scores,indx] = sort(predict_scores,'descend'); topScores = scores(1:5); classNamesTop = classNames(indx(1:5)) h = figure; h.Position(3) = 2*h.Position(3); ax1 = subplot(1,2,1); ax2 = subplot(1,2,2); image(ax1,im); barh(ax2,topScores) xlabel(ax2,'Probability') yticklabels(ax2,classNamesTop(5:-1:1)) ax2.YAxisLocation = 'right'; sgtitle('Top 5 predictions using GoogLeNet')
The model configuration parameters provide many options for the code generation and build process.
In Configuration Parameters dialog box, select Code Generation
pane. Set the System target file to
grt.tlc
.
You can also use the Embedded Coder® target file ert.tlc
.
Set the Language to C++
.
Select Generate GPU code.
Select Generate code only.
Select the Toolchain. For Linux® platforms, select NVIDIA CUDA | gmake (64-bit
Linux)
. For Windows® systems, select NVIDIA CUDA (w/Microsoft Visual C++ 20XX) |
nmake (64-bit windows)
.
On the Code Generation > Report pane, select Create code generation report and Open report automatically.
On the Code Generation > Interface pane, set the
Target Library in the Deep learning group to
cuDNN
. You can also select
TensorRT
.
Options specific to GPU Coder are in the Code Generation > GPU Code pane. For this example, you can use the default values of the GPU-specific parameters in Code Generation > GPU Code pane.
Click OK to save and close the Configuration Parameters dialog box.
You can also use set_param
to configure the model parameter
programmatically in the MATLAB Command
Window.
set_param('googlenetModel','GenerateGPUCode','CUDA');
In the Simulink Editor, open the Simulink Coder app.
Generate code.
Messages appear in the Diagnostics Viewer. The code generator produces CUDA source and header files, and an HTML code generation report. The code
generator places the files in a build folder, a subfolder
named googlenetModel_grt_rtw
under your current working folder.
Code generation for a deep learning network with custom layer is not supported in Simulink.
GPU code generation for MATLAB Function blocks in Stateflow® charts is not supported.
The code generator does not support all the data types from the MATLAB language. For supported data types, refer to the block documentation.
bdclose
(Simulink) | close_system
(Simulink) | get_param
(Simulink) | load_system
(Simulink) | open_system
(Simulink) | save_system
(Simulink) | set_param
(Simulink) | sim
(Simulink) | slbuild
(Simulink)