TensorRT 8.2.5
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A RaggedSoftmax layer in a network definition. More...
#include <NvInfer.h>
Protected Attributes | |
apiv::VRaggedSoftMaxLayer * | mImpl |
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apiv::VLayer * | mLayer |
Additional Inherited Members | |
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LayerType | getType () const noexcept |
Return the type of a layer. More... | |
void | setName (const char *name) noexcept |
Set the name of a layer. More... | |
const char * | getName () const noexcept |
Return the name of a layer. More... | |
int32_t | getNbInputs () const noexcept |
Get the number of inputs of a layer. | |
ITensor * | getInput (int32_t index) const noexcept |
Get the layer input corresponding to the given index. More... | |
int32_t | getNbOutputs () const noexcept |
Get the number of outputs of a layer. | |
ITensor * | getOutput (int32_t index) const noexcept |
Get the layer output corresponding to the given index. More... | |
void | setInput (int32_t index, ITensor &tensor) noexcept |
Replace an input of this layer with a specific tensor. More... | |
void | setPrecision (DataType dataType) noexcept |
Set the computational precision of this layer. More... | |
DataType | getPrecision () const noexcept |
get the computational precision of this layer More... | |
bool | precisionIsSet () const noexcept |
whether the computational precision has been set for this layer More... | |
void | resetPrecision () noexcept |
reset the computational precision for this layer More... | |
void | setOutputType (int32_t index, DataType dataType) noexcept |
Set the output type of this layer. More... | |
DataType | getOutputType (int32_t index) const noexcept |
get the output type of this layer More... | |
bool | outputTypeIsSet (int32_t index) const noexcept |
whether the output type has been set for this layer More... | |
void | resetOutputType (int32_t index) noexcept |
reset the output type for this layer More... | |
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INoCopy (const INoCopy &other)=delete | |
INoCopy & | operator= (const INoCopy &other)=delete |
INoCopy (INoCopy &&other)=delete | |
INoCopy & | operator= (INoCopy &&other)=delete |
A RaggedSoftmax layer in a network definition.
This layer takes a ZxS input tensor and an additional Zx1 bounds tensor holding the lengths of the Z sequences.
This layer computes a softmax across each of the Z sequences.
The output tensor is of the same size as the input tensor.
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