Main Content

calibrate

Simulate and collect ranges of a deep neural network

Since R2020a

Description

calResults = calibrate(quantObj,calData) exercises the network and collects the dynamic ranges of the weights and biases in the convolution and fully connected layers of the network and the dynamic ranges of the activations in all layers of the network specified by dlquantizer object, quantObj, using the data specified by calData.

example

calResults = calibrate(quantObj,calData,Name,Value) calibrates the network with additional options specified by one or more name-value pair arguments.

This function requires Deep Learning Toolbox Model Quantization Library. To learn about the products required to quantize a deep neural network, see Quantization Workflow Prerequisites.

Examples

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This example shows how to quantize learnable parameters in the convolution layers of a neural network for GPU and explore the behavior of the quantized network. In this example, you quantize the squeezenet neural network after retraining the network to classify new images. In this example, the memory required for the network is reduced approximately 75% through quantization while the accuracy of the network is not affected.

Load the pretrained network. net is the output network of the Train Deep Learning Network to Classify New Images example.

load squeezedlnetmerch
net
net = 
  dlnetwork with properties:

         Layers: [67×1 nnet.cnn.layer.Layer]
    Connections: [74×2 table]
     Learnables: [52×3 table]
          State: [0×3 table]
     InputNames: {'data'}
    OutputNames: {'prob'}
    Initialized: 1

  View summary with summary.

Define calibration and validation data to use for quantization.

The calibration data is used to collect the dynamic ranges of the weights and biases in the convolution and fully connected layers of the network and the dynamic ranges of the activations in all layers of the network. For the best quantization results, the calibration data must be representative of inputs to the network.

The validation data is used to test the network after quantization to understand the effects of the limited range and precision of the quantized convolution layers in the network.

In this example, use the images in the MerchData data set. Define an augmentedImageDatastore object to resize the data for the network. Then, split the data into calibration and validation data sets.

unzip('MerchData.zip');
imds = imageDatastore('MerchData', ...
    'IncludeSubfolders',true, ...
    'LabelSource','foldernames');
classes = categories(imds.Labels);
[calData, valData] = splitEachLabel(imds, 0.7, 'randomized');
aug_calData = augmentedImageDatastore([227 227], calData);
aug_valData = augmentedImageDatastore([227 227], valData);

Create a dlquantizer object and specify the network to quantize.

dlquantObj = dlquantizer(net);

Specify the GPU target.

quantOpts = dlquantizationOptions(Target='gpu');
quantOpts.MetricFcn = {@(x)hAccuracy(x,net,aug_valData,classes)}
quantOpts = 
  dlquantizationOptions with properties:

   Validation Metric Info
    MetricFcn: {[@(x)hAccuracy(x,net,aug_valData,classes)]}

   Validation Environment Info
       Target: 'gpu'
    Bitstream: ''

Use the calibrate function to exercise the network with sample inputs and collect range information. The calibrate function exercises the network and collects the dynamic ranges of the weights and biases in the convolution and fully connected layers of the network and the dynamic ranges of the activations in all layers of the network. The function returns a table. Each row of the table contains range information for a learnable parameter of the optimized network.

calResults = calibrate(dlquantObj, aug_calData)
calResults=120×5 table
        Optimized Layer Name         Network Layer Name     Learnables / Activations    MinValue     MaxValue
    ____________________________    ____________________    ________________________    _________    ________

    {'conv1_Weights'           }    {'conv1'           }           "Weights"             -0.91985     0.88489
    {'conv1_Bias'              }    {'conv1'           }           "Bias"                -0.07925     0.26343
    {'fire2-squeeze1x1_Weights'}    {'fire2-squeeze1x1'}           "Weights"                -1.38      1.2477
    {'fire2-squeeze1x1_Bias'   }    {'fire2-squeeze1x1'}           "Bias"                -0.11641     0.24273
    {'fire2-expand1x1_Weights' }    {'fire2-expand1x1' }           "Weights"              -0.7406     0.90982
    {'fire2-expand1x1_Bias'    }    {'fire2-expand1x1' }           "Bias"               -0.060056     0.14602
    {'fire2-expand3x3_Weights' }    {'fire2-expand3x3' }           "Weights"             -0.74397     0.66905
    {'fire2-expand3x3_Bias'    }    {'fire2-expand3x3' }           "Bias"               -0.051778    0.074239
    {'fire3-squeeze1x1_Weights'}    {'fire3-squeeze1x1'}           "Weights"              -0.7712     0.68917
    {'fire3-squeeze1x1_Bias'   }    {'fire3-squeeze1x1'}           "Bias"                -0.10138     0.32675
    {'fire3-expand1x1_Weights' }    {'fire3-expand1x1' }           "Weights"             -0.72035      0.9743
    {'fire3-expand1x1_Bias'    }    {'fire3-expand1x1' }           "Bias"               -0.067029     0.30425
    {'fire3-expand3x3_Weights' }    {'fire3-expand3x3' }           "Weights"             -0.61443      0.7741
    {'fire3-expand3x3_Bias'    }    {'fire3-expand3x3' }           "Bias"               -0.053613     0.10329
    {'fire4-squeeze1x1_Weights'}    {'fire4-squeeze1x1'}           "Weights"              -0.7422      1.0877
    {'fire4-squeeze1x1_Bias'   }    {'fire4-squeeze1x1'}           "Bias"                -0.10885     0.13881
      ⋮

Use the validate function to quantize the learnable parameters in the convolution layers of the network and exercise the network. The function uses the metric function defined in the dlquantizationOptions object to compare the results of the network before and after quantization.

valResults = validate(dlquantObj, aug_valData, quantOpts)
valResults = struct with fields:
       NumSamples: 20
    MetricResults: [1×1 struct]
       Statistics: [2×2 table]

Examine the validation output to see the performance of the quantized network.

valResults.MetricResults.Result
ans=2×2 table
    NetworkImplementation    MetricOutput
    _____________________    ____________

     {'Floating-Point'}           1      
     {'Quantized'     }           1      

valResults.Statistics
ans=2×2 table
    NetworkImplementation    LearnableParameterMemory(bytes)
    _____________________    _______________________________

     {'Floating-Point'}                2.9003e+06           
     {'Quantized'     }                7.3393e+05           

In this example, the memory required for the network was reduced approximately 75% through quantization. The accuracy of the network is not affected.

The weights, biases, and activations of the convolution layers of the network specified in the dlquantizer object now use scaled 8-bit integer data types.

Reduce the memory footprint of a deep neural network by quantizing the weights, biases, and activations of convolution layers to 8-bit scaled integer data types. This example shows how to use Deep Learning Toolbox Model Quantization Library and Deep Learning HDL Toolbox to deploy the int8 network to a target FPGA board.

For this example, you need:

  • Deep Learning Toolbox™

  • Deep Learning HDL Toolbox™

  • Deep Learning Toolbox Model Quantization Library

  • Deep Learning HDL Toolbox Support Package for Xilinx® FPGA and SoC Devices

  • MATLAB® Coder™ Interface for Deep Learning.

Load Pretrained Network

Load the pretrained LogoNet network and analyze the network architecture.

snet = getLogoNetwork;
deepNetworkDesigner(snet);

Set random number generator for reproducibility.

rng(0);

Load Data

This example uses the logos_dataset data set. The data set consists of 320 images. Each image is 227-by-227 in size and has three color channels (RGB). Create an augmentedImageDatastore object for calibration and validation.

curDir = pwd;
unzip("logos_dataset.zip");
imageData = imageDatastore(fullfile(curDir,'logos_dataset'),...
'IncludeSubfolders',true,'FileExtensions','.JPG','LabelSource','foldernames');
[calibrationData, validationData] = splitEachLabel(imageData, 0.5,'randomized');

Generate Calibration Result File for the Network

Create a dlquantizer (Deep Learning HDL Toolbox) object and specify the network to quantize. Specify the execution environment as FPGA.

dlQuantObj = dlquantizer(snet,'ExecutionEnvironment',"FPGA");

Use the calibrate (Deep Learning HDL Toolbox) function to exercise the network with sample inputs and collect the range information. The calibrate function collects the dynamic ranges of the weights and biases. The calibrate function returns a table. Each row of the table contains range information for a learnable parameter of the quantized network.

calibrate(dlQuantObj,calibrationData)
ans=35×5 table
        Optimized Layer Name        Network Layer Name    Learnables / Activations     MinValue       MaxValue 
    ____________________________    __________________    ________________________    ___________    __________

    {'conv_1_Weights'          }      {'conv_1'    }           "Weights"                -0.048978      0.039352
    {'conv_1_Bias'             }      {'conv_1'    }           "Bias"                     0.99996        1.0028
    {'conv_2_Weights'          }      {'conv_2'    }           "Weights"                -0.055518      0.061901
    {'conv_2_Bias'             }      {'conv_2'    }           "Bias"                 -0.00061171       0.00227
    {'conv_3_Weights'          }      {'conv_3'    }           "Weights"                -0.045942      0.046927
    {'conv_3_Bias'             }      {'conv_3'    }           "Bias"                  -0.0013998     0.0015218
    {'conv_4_Weights'          }      {'conv_4'    }           "Weights"                -0.045967         0.051
    {'conv_4_Bias'             }      {'conv_4'    }           "Bias"                    -0.00164     0.0037892
    {'fc_1_Weights'            }      {'fc_1'      }           "Weights"                -0.051394      0.054344
    {'fc_1_Bias'               }      {'fc_1'      }           "Bias"                 -0.00052319    0.00084454
    {'fc_2_Weights'            }      {'fc_2'      }           "Weights"                 -0.05016      0.051557
    {'fc_2_Bias'               }      {'fc_2'      }           "Bias"                  -0.0017564     0.0018502
    {'fc_3_Weights'            }      {'fc_3'      }           "Weights"                -0.050706       0.04678
    {'fc_3_Bias'               }      {'fc_3'      }           "Bias"                    -0.02951      0.024855
    {'imageinput'              }      {'imageinput'}           "Activations"                    0           255
    {'imageinput_normalization'}      {'imageinput'}           "Activations"              -139.34        198.72
      ⋮

Create Target Object

Create a target object with a custom name for your target device and an interface to connect your target device to the host computer. Interface options are JTAG and Ethernet. Interface options are JTAG and Ethernet. To use JTAG, install Xilinx Vivado® Design Suite 2022.1. To set the Xilinx Vivado toolpath, enter:

hdlsetuptoolpath('ToolName', 'Xilinx Vivado', 'ToolPath', 'C:\Xilinx\Vivado\2022.1\bin\vivado.bat');

To create the target object, enter:

hTarget = dlhdl.Target('Xilinx','Interface','Ethernet','IPAddress','10.10.10.15');

Alternatively, you can also use the JTAG interface.

% hTarget = dlhdl.Target('Xilinx', 'Interface', 'JTAG');

Create dlQuantizationOptions Object

Create a dlquantizationOptions object. Specify the target bitstream and target board interface. The default metric function is a Top-1 accuracy metric function.

options_FPGA = dlquantizationOptions('Bitstream','zcu102_int8','Target',hTarget);
options_emulation = dlquantizationOptions('Target','host');

To use a custom metric function, specify the metric function in the dlquantizationOptions object.

options_FPGA = dlquantizationOptions('MetricFcn',{@(x)hComputeAccuracy(x,snet,validationData)},'Bitstream','zcu102_int8','Target',hTarget);
options_emulation = dlquantizationOptions('MetricFcn',{@(x)hComputeAccuracy(x,snet,validationData)})

Validate Quantized Network

Use the validate function to quantize the learnable parameters in the convolution layers of the network. The validate function simulates the quantized network in MATLAB. The validate function uses the metric function defined in the dlquantizationOptions object to compare the results of the single-data-type network object to the results of the quantized network object.

prediction_emulation = dlQuantObj.validate(validationData,options_emulation)
prediction_emulation = struct with fields:
       NumSamples: 160
    MetricResults: [1×1 struct]
       Statistics: []

For validation on an FPGA, the validate function:

  • Programs the FPGA board by using the output of the compile method and the programming file

  • Downloads the network weights and biases

  • Compares the performance of the network before and after quantization

prediction_FPGA = dlQuantObj.validate(validationData,options_FPGA)
### Compiling network for Deep Learning FPGA prototyping ...
### Targeting FPGA bitstream zcu102_int8.
### The network includes the following layers:
     1   'imageinput'    Image Input             227×227×3 images with 'zerocenter' normalization and 'randfliplr' augmentations  (SW Layer)
     2   'conv_1'        2-D Convolution         96 5×5×3 convolutions with stride [1  1] and padding [0  0  0  0]                (HW Layer)
     3   'relu_1'        ReLU                    ReLU                                                                             (HW Layer)
     4   'maxpool_1'     2-D Max Pooling         3×3 max pooling with stride [2  2] and padding [0  0  0  0]                      (HW Layer)
     5   'conv_2'        2-D Convolution         128 3×3×96 convolutions with stride [1  1] and padding [0  0  0  0]              (HW Layer)
     6   'relu_2'        ReLU                    ReLU                                                                             (HW Layer)
     7   'maxpool_2'     2-D Max Pooling         3×3 max pooling with stride [2  2] and padding [0  0  0  0]                      (HW Layer)
     8   'conv_3'        2-D Convolution         384 3×3×128 convolutions with stride [1  1] and padding [0  0  0  0]             (HW Layer)
     9   'relu_3'        ReLU                    ReLU                                                                             (HW Layer)
    10   'maxpool_3'     2-D Max Pooling         3×3 max pooling with stride [2  2] and padding [0  0  0  0]                      (HW Layer)
    11   'conv_4'        2-D Convolution         128 3×3×384 convolutions with stride [2  2] and padding [0  0  0  0]             (HW Layer)
    12   'relu_4'        ReLU                    ReLU                                                                             (HW Layer)
    13   'maxpool_4'     2-D Max Pooling         3×3 max pooling with stride [2  2] and padding [0  0  0  0]                      (HW Layer)
    14   'fc_1'          Fully Connected         2048 fully connected layer                                                       (HW Layer)
    15   'relu_5'        ReLU                    ReLU                                                                             (HW Layer)
    16   'fc_2'          Fully Connected         2048 fully connected layer                                                       (HW Layer)
    17   'relu_6'        ReLU                    ReLU                                                                             (HW Layer)
    18   'fc_3'          Fully Connected         32 fully connected layer                                                         (HW Layer)
    19   'softmax'       Softmax                 softmax                                                                          (SW Layer)
    20   'classoutput'   Classification Output   crossentropyex with 'adidas' and 31 other classes                                (SW Layer)
                                                                                                                                
### Notice: The layer 'imageinput' with type 'nnet.cnn.layer.ImageInputLayer' is implemented in software.
### Notice: The layer 'softmax' with type 'nnet.cnn.layer.SoftmaxLayer' is implemented in software.
### Notice: The layer 'classoutput' with type 'nnet.cnn.layer.ClassificationOutputLayer' is implemented in software.
### Compiling layer group: conv_1>>relu_4 ...
### Compiling layer group: conv_1>>relu_4 ... complete.
### Compiling layer group: maxpool_4 ...
### Compiling layer group: maxpool_4 ... complete.
### Compiling layer group: fc_1>>fc_3 ...
### Compiling layer group: fc_1>>fc_3 ... complete.

### Allocating external memory buffers:

          offset_name          offset_address    allocated_space 
    _______________________    ______________    ________________

    "InputDataOffset"           "0x00000000"     "11.9 MB"       
    "OutputResultOffset"        "0x00be0000"     "128.0 kB"      
    "SchedulerDataOffset"       "0x00c00000"     "128.0 kB"      
    "SystemBufferOffset"        "0x00c20000"     "9.9 MB"        
    "InstructionDataOffset"     "0x01600000"     "4.6 MB"        
    "ConvWeightDataOffset"      "0x01aa0000"     "8.2 MB"        
    "FCWeightDataOffset"        "0x022e0000"     "10.4 MB"       
    "EndOffset"                 "0x02d40000"     "Total: 45.2 MB"

### Network compilation complete.

### FPGA bitstream programming has been skipped as the same bitstream is already loaded on the target FPGA.
### Deep learning network programming has been skipped as the same network is already loaded on the target FPGA.
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### Notice: The layer 'imageinput' of type 'ImageInputLayer' is split into an image input layer 'imageinput' and an addition layer 'imageinput_norm' for normalization on hardware.
### The network includes the following layers:
     1   'imageinput'    Image Input             227×227×3 images with 'zerocenter' normalization and 'randfliplr' augmentations  (SW Layer)
     2   'conv_1'        2-D Convolution         96 5×5×3 convolutions with stride [1  1] and padding [0  0  0  0]                (HW Layer)
     3   'relu_1'        ReLU                    ReLU                                                                             (HW Layer)
     4   'maxpool_1'     2-D Max Pooling         3×3 max pooling with stride [2  2] and padding [0  0  0  0]                      (HW Layer)
     5   'conv_2'        2-D Convolution         128 3×3×96 convolutions with stride [1  1] and padding [0  0  0  0]              (HW Layer)
     6   'relu_2'        ReLU                    ReLU                                                                             (HW Layer)
     7   'maxpool_2'     2-D Max Pooling         3×3 max pooling with stride [2  2] and padding [0  0  0  0]                      (HW Layer)
     8   'conv_3'        2-D Convolution         384 3×3×128 convolutions with stride [1  1] and padding [0  0  0  0]             (HW Layer)
     9   'relu_3'        ReLU                    ReLU                                                                             (HW Layer)
    10   'maxpool_3'     2-D Max Pooling         3×3 max pooling with stride [2  2] and padding [0  0  0  0]                      (HW Layer)
    11   'conv_4'        2-D Convolution         128 3×3×384 convolutions with stride [2  2] and padding [0  0  0  0]             (HW Layer)
    12   'relu_4'        ReLU                    ReLU                                                                             (HW Layer)
    13   'maxpool_4'     2-D Max Pooling         3×3 max pooling with stride [2  2] and padding [0  0  0  0]                      (HW Layer)
    14   'fc_1'          Fully Connected         2048 fully connected layer                                                       (HW Layer)
    15   'relu_5'        ReLU                    ReLU                                                                             (HW Layer)
    16   'fc_2'          Fully Connected         2048 fully connected layer                                                       (HW Layer)
    17   'relu_6'        ReLU                    ReLU                                                                             (HW Layer)
    18   'fc_3'          Fully Connected         32 fully connected layer                                                         (HW Layer)
    19   'softmax'       Softmax                 softmax                                                                          (SW Layer)
    20   'classoutput'   Classification Output   crossentropyex with 'adidas' and 31 other classes                                (SW Layer)
                                                                                                                                
### Notice: The layer 'softmax' with type 'nnet.cnn.layer.SoftmaxLayer' is implemented in software.
### Notice: The layer 'classoutput' with type 'nnet.cnn.layer.ClassificationOutputLayer' is implemented in software.


              Deep Learning Processor Estimator Performance Results

                   LastFrameLatency(cycles)   LastFrameLatency(seconds)       FramesNum      Total Latency     Frames/s
                         -------------             -------------              ---------        ---------       ---------
Network                   39136574                  0.17789                       1           39136574              5.6
    imageinput_norm         216472                  0.00098 
    conv_1                 6832680                  0.03106 
    maxpool_1              3705912                  0.01685 
    conv_2                10454501                  0.04752 
    maxpool_2              1173810                  0.00534 
    conv_3                 9364533                  0.04257 
    maxpool_3              1229970                  0.00559 
    conv_4                 1759348                  0.00800 
    maxpool_4                24450                  0.00011 
    fc_1                   2651288                  0.01205 
    fc_2                   1696632                  0.00771 
    fc_3                     26978                  0.00012 
 * The clock frequency of the DL processor is: 220MHz


### Finished writing input activations.
### Running single input activation.
prediction_FPGA = struct with fields:
       NumSamples: 160
    MetricResults: [1×1 struct]
       Statistics: [2×7 table]

View Performance of Quantized Neural Network

Display the accuracy of the quantized network.

prediction_emulation.MetricResults.Result
ans=2×2 table
    NetworkImplementation    MetricOutput
    _____________________    ____________

     {'Floating-Point'}         0.9875   
     {'Quantized'     }         0.9875   

prediction_FPGA.MetricResults.Result
ans=2×2 table
    NetworkImplementation    MetricOutput
    _____________________    ____________

     {'Floating-Point'}         0.9875   
     {'Quantized'     }         0.9875   

Display the performance of the quantized network in frames per second.

prediction_FPGA.Statistics
ans=2×7 table
    NetworkImplementation    FramesPerSecond    Number of Threads (Convolution)    Number of Threads (Fully Connected)    LUT Utilization (%)    BlockRAM Utilization (%)    DSP Utilization (%)
    _____________________    _______________    _______________________________    ___________________________________    ___________________    ________________________    ___________________

     {'Floating-Point'}          5.6213                       16                                    4                           93.198                    63.925                   15.595       
     {'Quantized'     }          19.433                       64                                   16                            62.31                     50.11                   32.103       

This example shows how to quantize and validate a neural network for a CPU target. This workflow is similar to other execution environments, but before validating you must establish a raspi connection and specify it as target using dlquantizationOptions.

First, load your network. This example uses the pretrained network squeezenet.

load squeezedlnetmerch
net
net = 
  dlnetwork with properties:

         Layers: [67×1 nnet.cnn.layer.Layer]
    Connections: [74×2 table]
     Learnables: [52×3 table]
          State: [0×3 table]
     InputNames: {'data'}
    OutputNames: {'prob'}
    Initialized: 1

  View summary with summary.

Then define your calibration and validation data, calDS and valDS respectively.

unzip('MerchData.zip');
imds = imageDatastore('MerchData', ...
    'IncludeSubfolders',true, ...
    'LabelSource','foldernames');
classes = categories(imds.Labels);
[calData, valData] = splitEachLabel(imds, 0.7, 'randomized');
aug_calData = augmentedImageDatastore([227 227],calData);
aug_valData = augmentedImageDatastore([227 227],valData);

Create the dlquantizer object and specify a CPU execution environment.

dq =  dlquantizer(net,'ExecutionEnvironment','CPU') 
dq = 
  dlquantizer with properties:

           NetworkObject: [1×1 dlnetwork]
    ExecutionEnvironment: 'CPU'

Calibrate the network.

calResults = calibrate(dq,aug_calData,'UseGPU','off')
calResults=120×5 table
       Optimized Layer Name        Network Layer Name     Learnables / Activations    MinValue     MaxValue
    __________________________    ____________________    ________________________    _________    ________

    "conv1_Weights"               {'conv1'           }           "Weights"             -0.91985     0.88489
    "conv1_Bias"                  {'conv1'           }           "Bias"                -0.07925     0.26343
    "fire2-squeeze1x1_Weights"    {'fire2-squeeze1x1'}           "Weights"                -1.38      1.2477
    "fire2-squeeze1x1_Bias"       {'fire2-squeeze1x1'}           "Bias"                -0.11641     0.24273
    "fire2-expand1x1_Weights"     {'fire2-expand1x1' }           "Weights"              -0.7406     0.90982
    "fire2-expand1x1_Bias"        {'fire2-expand1x1' }           "Bias"               -0.060056     0.14602
    "fire2-expand3x3_Weights"     {'fire2-expand3x3' }           "Weights"             -0.74397     0.66905
    "fire2-expand3x3_Bias"        {'fire2-expand3x3' }           "Bias"               -0.051778    0.074239
    "fire3-squeeze1x1_Weights"    {'fire3-squeeze1x1'}           "Weights"              -0.7712     0.68917
    "fire3-squeeze1x1_Bias"       {'fire3-squeeze1x1'}           "Bias"                -0.10138     0.32675
    "fire3-expand1x1_Weights"     {'fire3-expand1x1' }           "Weights"             -0.72035      0.9743
    "fire3-expand1x1_Bias"        {'fire3-expand1x1' }           "Bias"               -0.067029     0.30425
    "fire3-expand3x3_Weights"     {'fire3-expand3x3' }           "Weights"             -0.61443      0.7741
    "fire3-expand3x3_Bias"        {'fire3-expand3x3' }           "Bias"               -0.053613     0.10329
    "fire4-squeeze1x1_Weights"    {'fire4-squeeze1x1'}           "Weights"              -0.7422      1.0877
    "fire4-squeeze1x1_Bias"       {'fire4-squeeze1x1'}           "Bias"                -0.10885     0.13881
      ⋮

Use the MATLAB Support Package for Raspberry Pi Hardware function, raspi, to create a connection to the Raspberry Pi. In the following code, replace:

  • raspiname with the name or address of your Raspberry Pi

  • username with your user name

  • password with your password

% r = raspi('raspiname','username','password')

For example,

r = raspi('gpucoder-raspberrypi-8','pi','matlab')
r = 
  raspi with properties:

         DeviceAddress: 'gpucoder-raspberrypi-8'      
                  Port: 18734                         
             BoardName: 'Raspberry Pi 3 Model B+'     
         AvailableLEDs: {'led0'}                      
  AvailableDigitalPins: [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]
  AvailableSPIChannels: {}                            
     AvailableI2CBuses: {}                            
      AvailableWebcams: {}                            
           I2CBusSpeed:                               
AvailableCANInterfaces: {}                            

  Supported peripherals

Specify raspi object as the target for the quantized network.

opts = dlquantizationOptions('Target',r);
opts.MetricFcn = {@(x)hAccuracy(x,net,aug_valData,classes)}
opts = 
  dlquantizationOptions with properties:

   Validation Metric Info
    MetricFcn: {[@(x)hAccuracy(x,net,aug_valData,classes)]}

   Validation Environment Info
       Target: [1×1 raspi]
    Bitstream: ''

Validate the quantized network with the validate function.

valResults = validate(dq,aug_valData,opts)
### Starting application: 'codegen/lib/validate_predict_int8/pil/validate_predict_int8.elf'
    To terminate execution: clear validate_predict_int8_pil
### Launching application validate_predict_int8.elf...
### Host application produced the following standard output (stdout) and standard error (stderr) messages:
valResults = struct with fields:
       NumSamples: 20
    MetricResults: [1×1 struct]
       Statistics: []

Examine the validation output to see the performance of the quantized network.

valResults.MetricResults.Result
ans=2×2 table
    NetworkImplementation    MetricOutput
    _____________________    ____________

     {'Floating-Point'}           1      
     {'Quantized'     }           1      

This example shows how to quantize a yolov3ObjectDetector (Computer Vision Toolbox) object using preprocessed calibration and validation data.

First, download a pretrained YOLO v3 object detector.

detector = downloadPretrainedNetwork();

This example uses a small labeled data set that contains one or two labeled instances of a vehicle. Many of these images come from the Caltech Cars 1999 and 2001 data sets, created by Pietro Perona and used with permission.

Unzip the vehicle images and load the vehicle ground truth data.

unzip vehicleDatasetImages.zip
data = load('vehicleDatasetGroundTruth.mat');
vehicleDataset = data.vehicleDataset;

Add the full path to the local vehicle data folder.

vehicleDataset.imageFilename = fullfile(pwd, vehicleDataset.imageFilename);

Create an imageDatastore for loading the images and a boxLabelDatastore (Computer Vision Toolbox) for the ground truth bounding boxes.

imds = imageDatastore(vehicleDataset.imageFilename);
blds = boxLabelDatastore(vehicleDataset(:,2));

Use the combine function to combine both the datastores into a CombinedDatastore.

combinedDS = combine(imds, blds);

Split the data into calibration and validation data.

calData = combinedDS.subset(1:32);
valData = combinedDS.subset(33:64);

Use the preprocess (Computer Vision Toolbox) method of yolov3ObjectDetector (Computer Vision Toolbox) object with transform function to prepare the data for calibration and validation.

The transform function returns a TransformedDatastore object.

processedCalData = transform(calData, @(data)preprocess(detector,data));
processedValData = transform(valData, @(data)preprocess(detector,data));

Create the dlquantizer object. When you use the MATLAB execution environment, quantization is performed using the fi fixed-point data type which requires a Fixed-Point Designer™ license.

dq = dlquantizer(detector, 'ExecutionEnvironment', 'MATLAB');

Calibrate the network.

calResults = calibrate(dq, processedCalData,'UseGPU','off')
calResults=135×5 table
        Optimized Layer Name         Network Layer Name     Learnables / Activations    MinValue     MaxValue
    ____________________________    ____________________    ________________________    _________    ________

    {'conv1_Weights'           }    {'conv1'           }           "Weights"             -0.92189     0.85687
    {'conv1_Bias'              }    {'conv1'           }           "Bias"               -0.096271     0.26628
    {'fire2-squeeze1x1_Weights'}    {'fire2-squeeze1x1'}           "Weights"              -1.3751      1.2444
    {'fire2-squeeze1x1_Bias'   }    {'fire2-squeeze1x1'}           "Bias"                -0.12068     0.23104
    {'fire2-expand1x1_Weights' }    {'fire2-expand1x1' }           "Weights"             -0.75275     0.91615
    {'fire2-expand1x1_Bias'    }    {'fire2-expand1x1' }           "Bias"               -0.059252     0.14035
    {'fire2-expand3x3_Weights' }    {'fire2-expand3x3' }           "Weights"             -0.75271      0.6774
    {'fire2-expand3x3_Bias'    }    {'fire2-expand3x3' }           "Bias"               -0.062214    0.088242
    {'fire3-squeeze1x1_Weights'}    {'fire3-squeeze1x1'}           "Weights"              -0.7586     0.68772
    {'fire3-squeeze1x1_Bias'   }    {'fire3-squeeze1x1'}           "Bias"                -0.10206     0.31645
    {'fire3-expand1x1_Weights' }    {'fire3-expand1x1' }           "Weights"             -0.71566     0.97678
    {'fire3-expand1x1_Bias'    }    {'fire3-expand1x1' }           "Bias"               -0.069313     0.32881
    {'fire3-expand3x3_Weights' }    {'fire3-expand3x3' }           "Weights"             -0.60079     0.77642
    {'fire3-expand3x3_Bias'    }    {'fire3-expand3x3' }           "Bias"               -0.058045     0.11229
    {'fire4-squeeze1x1_Weights'}    {'fire4-squeeze1x1'}           "Weights"               -0.738      1.0805
    {'fire4-squeeze1x1_Bias'   }    {'fire4-squeeze1x1'}           "Bias"                -0.11189     0.13698
      ⋮

Validate the quantized network with the validate function.

valResults = validate(dq, processedValData)
valResults = struct with fields:
       NumSamples: 32
    MetricResults: [1×1 struct]
       Statistics: []

function detector = downloadPretrainedNetwork()
   pretrainedURL = 'https://ssd.mathworks.com/supportfiles/vision/data/yolov3SqueezeNetVehicleExample_21aSPKG.zip';
   websave('yolov3SqueezeNetVehicleExample_21aSPKG.zip', pretrainedURL);

   unzip('yolov3SqueezeNetVehicleExample_21aSPKG.zip');

   pretrained = load("yolov3SqueezeNetVehicleExample_21aSPKG.mat");
   detector = pretrained.detector;
end    

Input Arguments

collapse all

Network to quantize, specified as a dlquantizer object.

Data to use for calibration of quantized network, specified as one of these values:

When using a dlarray object, format the dlarray to assure the data has the appropriate shape. Unformatted dlarray objects are treated in the same way as numeric arrays.

You must preprocess the data used for calibration of a yolov3ObjectDetector (Computer Vision Toolbox) object using the preprocess (Computer Vision Toolbox) function. For an example of using preprocessed data for calibration of a yolov3ObjectDetector, see Quantize YOLO v3 Object Detector.

For more information on supported datastores, see Prepare Data for Quantizing Networks.

Data Types: single | double | int8 | int16 | int32 | uint8 | uint16 | uint32

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.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: calResults = calibrate(quantObj,calData,'UseGPU','on')

Size of the mini-batches to use for calibration, specified as a positive integer. Larger mini-batch sizes require more memory, but can lead to faster calibration.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Whether to use host GPU for calibration, specified as one of the following:

  • 'auto' — Use host GPU for calibration if one is available. Otherwise, use host CPU for calibration.

  • 'on' — Use host GPU for calibration.

  • 'off' — Use host CPU for calibration.

Data Types: char

Output Arguments

collapse all

Dynamic ranges of layers of the network, returned as a table. Each row in the table displays the minimum and maximum values of a learnable parameter of a convolution layer of the optimized network. The software uses these minimum and maximum values to determine the scaling for the data type of the quantized parameter.

Version History

Introduced in R2020a

expand all