getUnpaddedOutputData
Class: dlhdl.Processor
Namespace: dlhdl
Description
returns the unpadded output data for the padded data, unpaddedOutput
= getUnpaddedOutputData(hProc
,paddedData
,numofFrames
,activationLayer
)paddedData
, for the
specified number of frames numofFrames
, and activation layer of the
network in the deep learning processor hProc
.
Input Arguments
hProc
— Deep learning processor
dlhdl.Processor
object
Deep learning processor, specified as a dlhdl.Processor
object.
paddeddata
— Padded output data
numeric column vector
Padded output data of the deep learning processor IP core, specified as a numeric m-by-1 column vector. The padded output contains padded zeros depending upon the convolution thread number value.
numofFrames
— Number of frames
scalar integer
Number of frames of data, specified as a scalar integer. The number of frames is typically the same value as the last dimension of the padded output data.
activationLayer
— Activation layer
char
Activation layer, specified as a character vector. The layer name must correspond to the layer for which the padded output data is retrieved from the deep learning processor IP core.
Output Arguments
unpaddedOutput
— Output data with padding removed
numeric array
Output data with padding removed, returned as a numeric array. The size of the
output data corresponds to the size of the output of the layer specified by
activationLayer
.
Examples
Convert Padded Data to Unpadded Output Data
Retrieve the padded input data for a network with an input layer of size 10-by-10-by-5. The convolution thread number is nine and the expected padded input data should be an array of size 10-by-10-by-8.
Create a network with an input layer of size 10-by-10-by-5.
layers = [imageInputLayer([10,10,5],'Normalization','none') convolution2dLayer(3,5,'Padding','same') regressionLayer]; layers(2).Weights = ones(3,3,5,5); layers(2).Bias = ones(1,1,5); net = assembleNetwork(layers);
Create a processor configuration object and set the convolution thread number as nine.
hPC = dlhdl.ProcessorConfig; hPC.setModuleProperty('conv','ConvThreadNumber',9);
hPC = Processing Module "conv" ModuleGeneration: 'on' LRNBlockGeneration: 'off' SegmentationBlockGeneration: 'on' ConvThreadNumber: 9 InputMemorySize: [227 227 3] OutputMemorySize: [227 227 3] FeatureSizeLimit: 2048 Processing Module "fc" ModuleGeneration: 'on' SoftmaxBlockGeneration: 'off' FCThreadNumber: 4 InputMemorySize: 25088 OutputMemorySize: 4096 Processing Module "custom" ModuleGeneration: 'on' Sigmoid: 'off' TanhLayer: 'off' Addition: 'on' MishLayer: 'off' Multiplication: 'on' Resize2D: 'off' SwishLayer: 'off' InputMemorySize: 40 OutputMemorySize: 120 Processor Top Level Properties RunTimeControl: 'register' RunTimeStatus: 'register' InputStreamControl: 'register' OutputStreamControl: 'register' SetupControl: 'register' ProcessorDataType: 'single' System Level Properties TargetPlatform: 'Xilinx Zynq UltraScale+ MPSoC ZCU102 Evaluation Kit' TargetFrequency: 200 SynthesisTool: 'Xilinx Vivado' ReferenceDesign: 'AXI-Stream DDR Memory Access : 3-AXIM' SynthesisToolChipFamily: 'Zynq UltraScale+' SynthesisToolDeviceName: 'xczu9eg-ffvb1156-2-e' SynthesisToolPackageName: '' SynthesisToolSpeedValue: ''
Create a processor object and a random input array of size 10-by-10-by-5.
hProc = dlhdl.Processor(Network=net,ProcessorConfig=hPC); im = rand(10,10,5);
Retrieve the padded input data by using the
getExpectedPaddedInputData
method. The size of the
output
matrix is 10-by-10-by-8. Reshape output
to an 800-by-1 matrix.
output = getExpectedPaddedInputData(hProc,im); paddedData = dnnfpga.format.convert3DInputToDDRVectorFormatConv4(output, 4);
Retrieve the unpadded output data by using getUnpaddedOutputData
.
The size of the unpadded output data corresponds to the size of the
conv
layer, which is 10-by-10-by-5.
outH = getUnpaddedOutputData(hProc,paddedData',1,'conv');
Version History
Introduced in R2023b
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