NVIDIA NvNeural SDK  2022.2
GPU inference framework for NVIDIA Nsight Deep Learning Designer
nvneural::IConvolutionLayer Class Referenceabstract

IConvolutionLayer is an optional interface that marks a layer as a convolution layer. More...

#include <nvneural/layers/IConvolutionLayer.h>

Inheritance diagram for nvneural::IConvolutionLayer:
nvneural::IRefObject nvneural::IConvolutionLayer2

Public Member Functions

virtual NeuralResult applyBias (bool enableBias) noexcept=0
 Turns bias on or off. More...
 
virtual NeuralResult setFeatureCount (std::size_t numFeatures) noexcept=0
 Sets the feature count of the convolution operation. More...
 
virtual NeuralResult setKernelSize (std::size_t kernelSizeW, std::size_t kernelSizeH) noexcept=0
 Sets the kernel size of the convolution operation. More...
 
virtual NeuralResult setStride (std::size_t strideW, std::size_t strideH) noexcept=0
 Sets the stride of the convolution operation. More...
 
virtual NeuralResult setZeroPadding (bool enablePadding) noexcept=0
 Turns zero padding on or off. More...
 
virtual bool zeroPadding () const noexcept=0
 Returns true if zero padding is enabled.
 
- Public Member Functions inherited from nvneural::IRefObject
virtual RefCount addRef () const noexcept=0
 Increments the object's reference count. More...
 
virtual const void * queryInterface (TypeId interface) const noexcept=0
 This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
 
virtual void * queryInterface (TypeId interface) noexcept=0
 Retrieves a new object interface pointer. More...
 
virtual RefCount release () const noexcept=0
 Decrements the object's reference count and destroy the object if the reference count reaches zero. More...
 

Static Public Attributes

static const IRefObject::TypeId typeID = 0x835aee64db0d45baul
 Interface TypeId for InterfaceOf purposes.
 
- Static Public Attributes inherited from nvneural::IRefObject
static const TypeId typeID = 0x14ecc3f9de638e1dul
 Interface TypeId for InterfaceOf purposes.
 

Additional Inherited Members

- Public Types inherited from nvneural::IRefObject
using RefCount = std::uint32_t
 Typedef used to track the number of active references to an object.
 
using TypeId = std::uint64_t
 Every interface must define a unique TypeId. This should be randomized.
 
- Protected Member Functions inherited from nvneural::IRefObject
virtual ~IRefObject ()=default
 A protected destructor prevents accidental stack-allocation of IRefObjects or use with other smart pointer classes like std::unique_ptr.
 

Detailed Description

IConvolutionLayer is an optional interface that marks a layer as a convolution layer.

Networks may fuse convolution layers with batch normalization layers (represented by IBatchNormalizationLayer).

Member Function Documentation

◆ applyBias()

virtual NeuralResult nvneural::IConvolutionLayer::applyBias ( bool  enableBias)
pure virtualnoexcept

Turns bias on or off.

Parameters
enableBiasWhether bias should be applied. If false, bias is implicitly a zero tensor.

◆ setFeatureCount()

virtual NeuralResult nvneural::IConvolutionLayer::setFeatureCount ( std::size_t  numFeatures)
pure virtualnoexcept

Sets the feature count of the convolution operation.

Parameters
numFeaturesNew feature count

◆ setKernelSize()

virtual NeuralResult nvneural::IConvolutionLayer::setKernelSize ( std::size_t  kernelSizeW,
std::size_t  kernelSizeH 
)
pure virtualnoexcept

Sets the kernel size of the convolution operation.

Parameters
kernelSizeWKernel dimension in W
kernelSizeHKernel dimension in H

◆ setStride()

virtual NeuralResult nvneural::IConvolutionLayer::setStride ( std::size_t  strideW,
std::size_t  strideH 
)
pure virtualnoexcept

Sets the stride of the convolution operation.

Parameters
strideWStride dimension in W
strideHStride dimension in H

◆ setZeroPadding()

virtual NeuralResult nvneural::IConvolutionLayer::setZeroPadding ( bool  enablePadding)
pure virtualnoexcept

Turns zero padding on or off.

Parameters
enablePaddingWhether zero padding should be applied

The documentation for this class was generated from the following file: