quantizationDetails

R2026b

Display quantization details for neural networks

Since R2022a

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Syntax

qDetails = quantizationDetails(net)

Description

qDetails = quantizationDetails(net) returns a 1-by-1 structure array containing the quantization details for your neural network.

example

Examples

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This example uses:

Open Live Script

This example shows how to display the quantization details for a neural network.

Load the pretrained network. net is a SqueezeNet convolutional neural network that has been retrained using transfer learning to classify images in the MerchData data set.

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.

Use the quantizationDetails function to verify that the network is not quantized.

qDetails_original = quantizationDetails(net)
qDetails_original = struct with fields:
                     IsQuantized: 0
                   TargetLibrary: ""
             QuantizedLayerNames: [0×0 string]
             QuantizedLearnables: [0×3 table]
    NetworkInputEmbeddedDataType: [0×2 table]
     LayerOutputEmbeddedDataType: [0×2 table]

The IsQuantized field returns 0 (false) because the original network uses the single-precision floating-point data type.

Unzip and load the MerchData images as an image data store. Define an augmentedImageDatastore object to resize the data for the network, and split the data into calibration and validation data sets to use for quantization.

unzip('MerchData.zip');
imds = imageDatastore('MerchData', ...
    'IncludeSubfolders',true, ...
    'LabelSource','foldernames');
[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. Set the execution environment to MATLAB. When you use the MATLAB execution environment, quantization is performed using the fi fixed-point data type, which requires a Fixed-Point Designer™ license.

quantObj = dlquantizer(net,'ExecutionEnvironment','MATLAB');

Use the calibrate function to exercise the network with sample inputs and collect range information.

calResults = calibrate(quantObj,aug_calData);

Use the quantize method to quantize the network object and return a simulatable quantized network.

qNet = quantize(quantObj)
qNet = 
  Quantized 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.
  Use the quantizationDetails function to extract quantization details.

Use the quantizationDetails method to extract the quantization details.

qDetails = quantizationDetails(qNet)
qDetails = struct with fields:
                     IsQuantized: 1
                   TargetLibrary: "none"
             QuantizedLayerNames: [66×1 string]
             QuantizedLearnables: [52×3 table]
    NetworkInputEmbeddedDataType: [1×2 table]
     LayerOutputEmbeddedDataType: [66×2 table]

Inspect the QuantizedLayerNames field to see a list of the quantized layers.

qDetails.QuantizedLayerNames
ans = 66×1 string array
    "data"
    "conv1"
    "relu_conv1"
    "pool1"
    "fire2-squeeze1x1"
    "fire2-relu_squeeze1x1"
    "fire2-expand1x1"
    "fire2-relu_expand1x1"
    "fire2-expand3x3"
    "fire2-relu_expand3x3"
      ⋮

Inspect the QuantizedLearnables field to see the quantized values for learnable parameters in the network.

qDetails.QuantizedLearnables
ans = 52×3 table
          Layer           Parameter             Value          
    __________________    _________    ________________________

    "conv1"               "Weights"    {3×3×3×64   embedded.fi}
    "conv1"               "Bias"       {1×1×64     embedded.fi}
    "fire2-squeeze1x1"    "Weights"    {1×1×64×16  embedded.fi}
    "fire2-squeeze1x1"    "Bias"       {1×1×16     embedded.fi}
    "fire2-expand1x1"     "Weights"    {1×1×16×64  embedded.fi}
    "fire2-expand1x1"     "Bias"       {1×1×64     embedded.fi}
    "fire2-expand3x3"     "Weights"    {3×3×16×64  embedded.fi}
    "fire2-expand3x3"     "Bias"       {1×1×64     embedded.fi}
    "fire3-squeeze1x1"    "Weights"    {1×1×128×16 embedded.fi}
    "fire3-squeeze1x1"    "Bias"       {1×1×16     embedded.fi}
    "fire3-expand1x1"     "Weights"    {1×1×16×64  embedded.fi}
    "fire3-expand1x1"     "Bias"       {1×1×64     embedded.fi}
    "fire3-expand3x3"     "Weights"    {3×3×16×64  embedded.fi}
    "fire3-expand3x3"     "Bias"       {1×1×64     embedded.fi}
    "fire4-squeeze1x1"    "Weights"    {1×1×128×32 embedded.fi}
    "fire4-squeeze1x1"    "Bias"       {1×1×32     embedded.fi}
      ⋮

Inspect the NetworkInputEmbeddedDataType field to see the expected data type for the input data. If your input data does not already match the expected type, the quantized network casts input data to this data type before passing it to the input layer.

qDetails.NetworkInputEmbeddedDataType
ans = 1×2 table
    InputNames          DataType       
    __________    _____________________

      "data"      "numerictype(1,10,1)"

Inspect the LayerOutputEmbeddedType field to see the data types of the outputs of each quantized layer in your network.

qDetails.LayerOutputEmbeddedDataType
ans = 66×2 table
              Outputs                    DataType       
    ___________________________    _____________________

    "data/out"                     "numerictype(1,8,-1)"
    "conv1/out"                    "numerictype(1,8,-3)"
    "relu_conv1/out"               "numerictype(1,8,-3)"
    "pool1/out"                    "numerictype(1,8,-3)"
    "fire2-squeeze1x1/out"         "numerictype(1,8,-4)"
    "fire2-relu_squeeze1x1/out"    "numerictype(1,8,-4)"
    "fire2-expand1x1/out"          "numerictype(1,8,-3)"
    "fire2-relu_expand1x1/out"     "numerictype(1,8,-3)"
    "fire2-expand3x3/out"          "numerictype(1,8,-4)"
    "fire2-relu_expand3x3/out"     "numerictype(1,8,-4)"
    "fire2-concat/out"             "numerictype(1,8,-4)"
    "fire3-squeeze1x1/out"         "numerictype(1,8,-4)"
    "fire3-relu_squeeze1x1/out"    "numerictype(1,8,-4)"
    "fire3-expand1x1/out"          "numerictype(1,8,-3)"
    "fire3-relu_expand1x1/out"     "numerictype(1,8,-3)"
    "fire3-expand3x3/out"          "numerictype(1,8,-4)"
      ⋮

Input Arguments

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Quantized neural network, specified as a dlnetwork, SeriesNetwork, or a DAGNetwork object.

Output Arguments

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Quantization details, returned as a 1-by-1 structure array. The structure contains these fields:

  • IsQuantized — Returns 1 (true) if the network is quantized; otherwise, returns 0 (false)

  • TargetLibrary — Target library for code generation

  • QuantizedLayerNames — List of quantized layers

  • QuantizedLearnables — Quantized network learnable parameters

  • NetworkInputEmbeddedDataType — Embedded data types for network inputs, returned as a table with these columns:

    • InputNames — Name of the network input layer.

    • DataType — Embedded data type for the input.

    If your input data does not already match the expected type, the quantized network casts input data to this data type before passing it to the input layer. If the input layer is not quantized, the data type is single.

  • LayerOutputEmbeddedDataType — Embedded data types for outputs of quantized layers, returned as a table with these columns:

    • Outputs — Layer output, in the format "LayerName/PortName".

    • DataType — Embedded data type for the layer output.

    Outputs of layers are represented in fixed-point when the data is passed between quantized layers. Layers that feed into a floating-point layer output single.

Version History

Introduced in R2022a

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See Also

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Functions

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