IConvolutionLayer is an optional interface that marks a layer as a convolution layer.
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#include <nvneural/layers/IConvolutionLayer.h>
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using | RefCount = std::uint32_t |
| | Typedef used to track the number of active references to an object.
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using | TypeId = std::uint64_t |
| | Every interface must define a unique TypeId. This should be randomized.
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virtual | ~IRefObject ()=default |
| | A protected destructor prevents accidental stack-allocation of IRefObjects or use with other smart pointer classes like std::unique_ptr.
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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).
◆ applyBias()
| virtual NeuralResult nvneural::IConvolutionLayer::applyBias |
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bool |
enableBias | ) |
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pure virtualnoexcept |
Turns bias on or off.
- Parameters
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| enableBias | Whether bias should be applied. If false, bias is implicitly a zero tensor. |
◆ setFeatureCount()
| virtual NeuralResult nvneural::IConvolutionLayer::setFeatureCount |
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std::size_t |
numFeatures | ) |
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pure virtualnoexcept |
Sets the feature count of the convolution operation.
- Parameters
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| numFeatures | New feature count |
◆ setKernelSize()
| virtual NeuralResult nvneural::IConvolutionLayer::setKernelSize |
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std::size_t |
kernelSizeW, |
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std::size_t |
kernelSizeH |
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pure virtualnoexcept |
Sets the kernel size of the convolution operation.
- Parameters
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| kernelSizeW | Kernel dimension in W |
| kernelSizeH | Kernel dimension in H |
◆ setStride()
| virtual NeuralResult nvneural::IConvolutionLayer::setStride |
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std::size_t |
strideW, |
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std::size_t |
strideH |
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pure virtualnoexcept |
Sets the stride of the convolution operation.
- Parameters
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| strideW | Stride dimension in W |
| strideH | Stride dimension in H |
◆ setZeroPadding()
| virtual NeuralResult nvneural::IConvolutionLayer::setZeroPadding |
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bool |
enablePadding | ) |
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pure virtualnoexcept |
Turns zero padding on or off.
- Parameters
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| enablePadding | Whether zero padding should be applied |
The documentation for this class was generated from the following file: