UffPoolPluginV2 Class Reference

Inheritance diagram for UffPoolPluginV2:

Collaboration diagram for UffPoolPluginV2:

Public Member Functions
	UffPoolPluginV2 (const PluginFieldCollection &fc)
	UffPoolPluginV2 (const void *data, size_t length)
	UffPoolPluginV2 ()=delete
virtual	~UffPoolPluginV2 ()
int	getNbOutputs () const override
	Get the number of outputs from the layer. More...
Dims	getOutputDimensions (int index, const Dims *inputs, int nbInputDims) override
int	initialize () override
	Initialize the layer for execution. More...
void	terminate () override
	Release resources acquired during plugin layer initialization. More...
size_t	getWorkspaceSize (int maxBatchSize) const override
int	enqueue (int batchSize, const void *const *inputs, void **outputs, void *workspace, cudaStream_t stream) override
size_t	getSerializationSize () const override
	Find the size of the serialization buffer required. More...
void	serialize (void *buffer) const override
	Serialize the layer. More...
void	configurePlugin (const PluginTensorDesc *in, int nbInput, const PluginTensorDesc *out, int nbOutput) override
bool	supportsFormatCombination (int pos, const PluginTensorDesc *inOut, int nbInputs, int nbOutputs) const override
	The combination of kLINEAR + kINT8/kHALF/kFLOAT is supported. More...
DataType	getOutputDataType (int index, const DataType *inputTypes, int nbInputs) const override
const char *	getPluginType () const override
	Return the plugin type. More...
const char *	getPluginVersion () const override
	Return the plugin version. More...
void	destroy () override
	Destroy the plugin object. More...
IPluginV2Ext *	clone () const override
	Clone the plugin object. More...
void	setPluginNamespace (const char *libNamespace) override
	Set the namespace that this plugin object belongs to. More...
const char *	getPluginNamespace () const override
	Return the namespace of the plugin object. More...
bool	isOutputBroadcastAcrossBatch (int outputIndex, const bool *inputIsBroadcasted, int nbInputs) const override
bool	canBroadcastInputAcrossBatch (int inputIndex) const override
virtual void	configurePlugin (const PluginTensorDesc *in, int32_t nbInput, const PluginTensorDesc *out, int32_t nbOutput)=0
	Configure the layer. More...
virtual void	configurePlugin (const Dims *inputDims, int32_t nbInputs, const Dims *outputDims, int32_t nbOutputs, const DataType *inputTypes, const DataType *outputTypes, const bool *inputIsBroadcast, const bool *outputIsBroadcast, PluginFormat floatFormat, int32_t maxBatchSize)=0
	Configure the layer with input and output data types. More...
virtual bool	supportsFormatCombination (int32_t pos, const PluginTensorDesc *inOut, int32_t nbInputs, int32_t nbOutputs) const =0
	Return true if plugin supports the format and datatype for the input/output indexed by pos. More...
virtual nvinfer1::DataType	getOutputDataType (int32_t index, const nvinfer1::DataType *inputTypes, int32_t nbInputs) const =0
	Return the DataType of the plugin output at the requested index. More...
virtual bool	isOutputBroadcastAcrossBatch (int32_t outputIndex, const bool *inputIsBroadcasted, int32_t nbInputs) const =0
	Return true if output tensor is broadcast across a batch. More...
virtual bool	canBroadcastInputAcrossBatch (int32_t inputIndex) const =0
	Return true if plugin can use input that is broadcast across batch without replication. More...
virtual void	attachToContext (cudnnContext *, cublasContext *, IGpuAllocator *)
	Attach the plugin object to an execution context and grant the plugin the access to some context resource. More...
virtual void	detachFromContext ()
	Detach the plugin object from its execution context. More...
virtual Dims	getOutputDimensions (int32_t index, const Dims *inputs, int32_t nbInputDims)=0
	Get the dimension of an output tensor. More...
virtual bool	supportsFormat (DataType type, PluginFormat format) const =0
	Check format support. More...
virtual size_t	getWorkspaceSize (int32_t maxBatchSize) const =0
	Find the workspace size required by the layer. More...
virtual int32_t	enqueue (int32_t batchSize, const void *const *inputs, void **outputs, void *workspace, cudaStream_t stream)=0
	Execute the layer. More...

Protected Member Functions
	__attribute__ ((deprecated)) int32_t getTensorRTVersion() const
	Return the API version with which this plugin was built. More...
	__attribute__ ((deprecated)) void configureWithFormat(const Dims *
	Deprecated interface inheriting from base class. More...
int32_t	getTensorRTVersion () const
	Return the API version with which this plugin was built. More...
void	configureWithFormat (const Dims *, int32_t, const Dims *, int32_t, DataType, PluginFormat, int32_t)
	Derived classes should not implement this. More...

Protected Attributes
	int32_t
const Dims	int32_t
const Dims	DataType
const Dims	PluginFormat

Private Member Functions
template<typename T >
void	write (char *&buffer, const T &val) const
template<typename T >
T	read (const char *&buffer) const
void	copyDeviceInputToFP32 (const void *src, void *&dst)
void	copyDeviceToInt8Output (const void *src, void *dst)

Private Attributes
cudnnHandle_t	mCudnn
cudnnTensorDescriptor_t	mSrcDescriptor
cudnnTensorDescriptor_t	mDstDescriptor
cudnnPoolingDescriptor_t	mPoolingDesc
PoolParameters	mPoolingParams
cudnnPoolingMode_t	mMode
DataType	mDataType
Dims	mInputDims
Dims	mOutputDims
float	mInHostScale {-1.0f}
float	mOutHostScale {-1.0f}
std::string	mNamespace

Constructor & Destructor Documentation

UffPoolPluginV2() [1/3]

UffPoolPluginV2::UffPoolPluginV2 ( const PluginFieldCollection &  fc )

inline

UffPoolPluginV2() [2/3]

UffPoolPluginV2::UffPoolPluginV2 ( const void *  data, size_t  length  )

inline

UffPoolPluginV2() [3/3]

UffPoolPluginV2::UffPoolPluginV2 ( )

delete

~UffPoolPluginV2()

virtual UffPoolPluginV2::~UffPoolPluginV2 ( )

inlinevirtual

Member Function Documentation

getNbOutputs()

int UffPoolPluginV2::getNbOutputs ( ) const

inlineoverridevirtual

Get the number of outputs from the layer.

Returns

The number of outputs.

This function is called by the implementations of INetworkDefinition and IBuilder. In particular, it is called prior to any call to initialize().

Implements nvinfer1::IPluginV2.

getOutputDimensions() [1/2]

Dims UffPoolPluginV2::getOutputDimensions ( int  index, const Dims *  inputs, int  nbInputDims  )

inlineoverride

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initialize()

int UffPoolPluginV2::initialize ( )

inlineoverridevirtual

Initialize the layer for execution.

This is called when the engine is created.

Returns

0 for success, else non-zero (which will cause engine termination).

Implements nvinfer1::IPluginV2.

terminate()

void UffPoolPluginV2::terminate ( )

inlineoverridevirtual

Release resources acquired during plugin layer initialization.

This is called when the engine is destroyed.

See also

initialize()

Implements nvinfer1::IPluginV2.

getWorkspaceSize() [1/2]

size_t UffPoolPluginV2::getWorkspaceSize ( int  maxBatchSize ) const

inlineoverride

enqueue() [1/2]

int UffPoolPluginV2::enqueue ( int  batchSize, const void *const *  inputs, void **  outputs, void *  workspace, cudaStream_t  stream  )

inlineoverride

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getSerializationSize()

size_t UffPoolPluginV2::getSerializationSize ( ) const

inlineoverridevirtual

Find the size of the serialization buffer required.

Returns

The size of the serialization buffer.

Implements nvinfer1::IPluginV2.

serialize()

void UffPoolPluginV2::serialize ( void *  buffer ) const

inlineoverridevirtual

Serialize the layer.

Parameters

buffer

A pointer to a buffer to serialize data. Size of buffer must be equal to value returned by getSerializationSize.

See also

getSerializationSize()

Implements nvinfer1::IPluginV2.

configurePlugin() [1/3]

void UffPoolPluginV2::configurePlugin ( const PluginTensorDesc *  in, int  nbInput, const PluginTensorDesc *  out, int  nbOutput  )

inlineoverride

supportsFormatCombination() [1/2]

bool UffPoolPluginV2::supportsFormatCombination ( int  pos, const PluginTensorDesc *  inOut, int  nbInputs, int  nbOutputs  ) const

inlineoverride

The combination of kLINEAR + kINT8/kHALF/kFLOAT is supported.

getOutputDataType() [1/2]

DataType UffPoolPluginV2::getOutputDataType ( int  index, const DataType *  inputTypes, int  nbInputs  ) const

inlineoverride

getPluginType()

const char* UffPoolPluginV2::getPluginType ( ) const

inlineoverridevirtual

Return the plugin type.

Should match the plugin name returned by the corresponding plugin creator

See also

IPluginCreator::getPluginName()

Implements nvinfer1::IPluginV2.

getPluginVersion()

const char* UffPoolPluginV2::getPluginVersion ( ) const

inlineoverridevirtual

Return the plugin version.

Should match the plugin version returned by the corresponding plugin creator

See also

IPluginCreator::getPluginVersion()

Implements nvinfer1::IPluginV2.

destroy()

void UffPoolPluginV2::destroy ( )

inlineoverridevirtual

Destroy the plugin object.

This will be called when the network, builder or engine is destroyed.

Implements nvinfer1::IPluginV2.

clone()

IPluginV2Ext* UffPoolPluginV2::clone ( ) const

inlineoverridevirtual

Clone the plugin object.

This copies over internal plugin parameters as well and returns a new plugin object with these parameters. If the source plugin is pre-configured with configurePlugin(), the returned object should also be pre-configured. The returned object should allow attachToContext() with a new execution context. Cloned plugin objects can share the same per-engine immutable resource (e.g. weights) with the source object (e.g. via ref-counting) to avoid duplication.

Implements nvinfer1::IPluginV2Ext.

setPluginNamespace()

void UffPoolPluginV2::setPluginNamespace ( const char *  pluginNamespace )

inlineoverridevirtual

Set the namespace that this plugin object belongs to.

Ideally, all plugin objects from the same plugin library should have the same namespace.

Implements nvinfer1::IPluginV2.

getPluginNamespace()

const char* UffPoolPluginV2::getPluginNamespace ( ) const

inlineoverridevirtual

Return the namespace of the plugin object.

Implements nvinfer1::IPluginV2.

isOutputBroadcastAcrossBatch() [1/2]

bool UffPoolPluginV2::isOutputBroadcastAcrossBatch ( int  outputIndex, const bool *  inputIsBroadcasted, int  nbInputs  ) const

inlineoverride

canBroadcastInputAcrossBatch() [1/2]

bool UffPoolPluginV2::canBroadcastInputAcrossBatch ( int  inputIndex ) const

inlineoverride

write()

template<typename T >

void UffPoolPluginV2::write ( char *&  buffer, const T &  val  ) const

inlineprivate

read()

template<typename T >

T UffPoolPluginV2::read ( const char *&  buffer ) const

inlineprivate

copyDeviceInputToFP32()

void UffPoolPluginV2::copyDeviceInputToFP32 ( const void *  src, void *&  dst  )

inlineprivate

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copyDeviceToInt8Output()

void UffPoolPluginV2::copyDeviceToInt8Output ( const void *  src, void *  dst  )

inlineprivate

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configurePlugin() [2/3]

virtual void nvinfer1::IPluginV2IOExt::configurePlugin ( const PluginTensorDesc *  in, int32_t  nbInput, const PluginTensorDesc *  out, int32_t  nbOutput  )

pure virtualinherited

Configure the layer.

This function is called by the builder prior to initialize(). It provides an opportunity for the layer to make algorithm choices on the basis of I/O PluginTensorDesc and the maximum batch size.

Parameters

in	The input tensors attributes that are used for configuration.
nbInput	Number of input tensors.
out	The output tensors attributes that are used for configuration.
nbOutput	Number of output tensors.

configurePlugin() [3/3]

virtual void nvinfer1::IPluginV2Ext::configurePlugin ( const Dims *  inputDims, int32_t  nbInputs, const Dims *  outputDims, int32_t  nbOutputs, const DataType *  inputTypes, const DataType *  outputTypes, const bool *  inputIsBroadcast, const bool *  outputIsBroadcast, PluginFormat  floatFormat, int32_t  maxBatchSize  )

pure virtualinherited

Configure the layer with input and output data types.

This function is called by the builder prior to initialize(). It provides an opportunity for the layer to make algorithm choices on the basis of its weights, dimensions, data types and maximum batch size.

Parameters

inputDims	The input tensor dimensions.
nbInputs	The number of inputs.
outputDims	The output tensor dimensions.
nbOutputs	The number of outputs.
inputTypes	The data types selected for the plugin inputs.
outputTypes	The data types selected for the plugin outputs.
inputIsBroadcast	True for each input that the plugin must broadcast across the batch.
outputIsBroadcast	True for each output that TensorRT will broadcast across the batch.
floatFormat	The format selected for the engine for the floating point inputs/outputs.
maxBatchSize	The maximum batch size.

The dimensions passed here do not include the outermost batch size (i.e. for 2-D image networks, they will be 3-dimensional CHW dimensions). When inputIsBroadcast or outputIsBroadcast is true, the outermost batch size for that input or output should be treated as if it is one. inputIsBroadcast[i] is true only if the input is semantically broadcast across the batch and canBroadcastInputAcrossBatch(i) returned true. outputIsBroadcast[i] is true only if isOutputBroadcastAcrossBatch(i) returned true.

Warning

for the floatFormat field, the values PluginFormat::kCHW4, PluginFormat::kCHW16, and PluginFormat::kCHW32 will not be passed in, this is to keep backward compatibility with TensorRT 5.x series. Use PluginV2IOExt or PluginV2DynamicExt for other PluginFormats.

supportsFormatCombination() [2/2]

virtual bool nvinfer1::IPluginV2IOExt::supportsFormatCombination ( int32_t  pos, const PluginTensorDesc *  inOut, int32_t  nbInputs, int32_t  nbOutputs  ) const

pure virtualinherited

Return true if plugin supports the format and datatype for the input/output indexed by pos.

For this method inputs are numbered 0..(nbInputs-1) and outputs are numbered nbInputs..(nbInputs+nbOutputs-1). Using this numbering, pos is an index into InOut, where 0 <= pos < nbInputs+nbOutputs-1.

TensorRT invokes this method to ask if the input/output indexed by pos supports the format/datatype specified by inOut[pos].format and inOut[pos].type. The override should return true if that format/datatype at inOut[pos] are supported by the plugin. If support is conditional on other input/output formats/datatypes, the plugin can make its result conditional on the formats/datatypes in inOut[0..pos-1], which will be set to values that the plugin supports. The override should not inspect inOut[pos+1..nbInputs+nbOutputs-1], which will have invalid values. In other words, the decision for pos must be based on inOut[0..pos] only.

Some examples:

• A definition for a plugin that supports only FP16 NCHW:

    return inOut.format[pos] == TensorFormat::kLINEAR && inOut.type[pos] == DataType::kHALF;
  

• A definition for a plugin that supports only FP16 NCHW for its two inputs, and FP32 NCHW for its single output:

    return inOut.format[pos] == TensorFormat::kLINEAR && (inOut.type[pos] == pos < 2 ?  DataType::kHALF :
    DataType::kFLOAT);
  

• A definition for a "polymorphic" plugin with two inputs and one output that supports any format or type, but the inputs and output must have the same format and type:

    return pos == 0 || (inOut.format[pos] == inOut.format[0] && inOut.type[pos] == inOut.type[0]);
  

Warning: TensorRT will stop asking for formats once it finds kFORMAT_COMBINATION_LIMIT on combinations.

__attribute__() [1/2]

nvinfer1::IPluginV2IOExt::__attribute__ ( (deprecated)  ) const

inlineprotectedinherited

Return the API version with which this plugin was built.

The upper byte is reserved by TensorRT and is used to differentiate this from IPlguinV2 and IPluginV2Ext.

Do not override this method as it is used by the TensorRT library to maintain backwards-compatibility with plugins.

Deprecated:

Deprecated interface will be removed in TensorRT 8.0.

__attribute__() [2/2]

nvinfer1::IPluginV2IOExt::__attribute__ ( (deprecated)  ) const

protectedinherited

Deprecated interface inheriting from base class.

Derived classes should not implement this. In a C++11 API it would be override final.

Deprecated:

Deprecated interface will be removed in TensorRT 8.0.

getOutputDataType() [2/2]

virtual nvinfer1::DataType nvinfer1::IPluginV2Ext::getOutputDataType ( int32_t  index, const nvinfer1::DataType *  inputTypes, int32_t  nbInputs  ) const

pure virtualinherited

Return the DataType of the plugin output at the requested index.

The default behavior should be to return the type of the first input, or DataType::kFLOAT if the layer has no inputs. The returned data type must have a format that is supported by the plugin.

See also

supportsFormat()

Warning

DataType:kBOOL not supported.

isOutputBroadcastAcrossBatch() [2/2]

virtual bool nvinfer1::IPluginV2Ext::isOutputBroadcastAcrossBatch ( int32_t  outputIndex, const bool *  inputIsBroadcasted, int32_t  nbInputs  ) const

pure virtualinherited

Return true if output tensor is broadcast across a batch.

Parameters

outputIndex	The index of the output
inputIsBroadcasted	The ith element is true if the tensor for the ith input is broadcast across a batch.
nbInputs	The number of inputs

The values in inputIsBroadcasted refer to broadcasting at the semantic level, i.e. are unaffected by whether method canBroadcastInputAcrossBatch requests physical replication of the values.

canBroadcastInputAcrossBatch() [2/2]

virtual bool nvinfer1::IPluginV2Ext::canBroadcastInputAcrossBatch ( int32_t  inputIndex ) const

pure virtualinherited

Return true if plugin can use input that is broadcast across batch without replication.

Parameters

inputIndex

Index of input that could be broadcast.

For each input whose tensor is semantically broadcast across a batch, TensorRT calls this method before calling configurePlugin. If canBroadcastInputAcrossBatch returns true, TensorRT will not replicate the input tensor; i.e., there will be a single copy that the plugin should share across the batch. If it returns false, TensorRT will replicate the input tensor so that it appears like a non-broadcasted tensor.

This method is called only for inputs that can be broadcast.

attachToContext()

virtual void nvinfer1::IPluginV2Ext::attachToContext ( cudnnContext *  , cublasContext *  , IGpuAllocator *    )

inlinevirtualinherited

Attach the plugin object to an execution context and grant the plugin the access to some context resource.

Parameters

cudnn	The cudnn context handle of the execution context
cublas	The cublas context handle of the execution context
allocator	The allocator used by the execution context

This function is called automatically for each plugin when a new execution context is created. If the plugin needs per-context resource, it can be allocated here. The plugin can also get context-owned CUDNN and CUBLAS context here.

Reimplemented in nvinfer1::plugin::ProposalPlugin, nvinfer1::plugin::CropAndResizePlugin, nvinfer1::plugin::SpecialSlice, nvinfer1::plugin::FlattenConcat, nvinfer1::plugin::ProposalLayer, nvinfer1::plugin::GenerateDetection, nvinfer1::plugin::MultilevelCropAndResize, nvinfer1::plugin::PyramidROIAlign, nvinfer1::plugin::DetectionLayer, nvinfer1::plugin::MultilevelProposeROI, nvinfer1::plugin::Normalize, nvinfer1::plugin::ResizeNearest, nvinfer1::plugin::DetectionOutput, nvinfer1::plugin::RPROIPlugin, nvinfer1::plugin::PriorBox, nvinfer1::plugin::Region, nvinfer1::plugin::Reorg, nvinfer1::plugin::GridAnchorGenerator, nvinfer1::plugin::GroupNormalizationPlugin, nvinfer1::plugin::InstanceNormalizationPlugin, and nvinfer1::plugin::SplitPlugin.

detachFromContext()

virtual void nvinfer1::IPluginV2Ext::detachFromContext ( )

inlinevirtualinherited

Detach the plugin object from its execution context.

This function is called automatically for each plugin when a execution context is destroyed. If the plugin owns per-context resource, it can be released here.

Reimplemented in nvinfer1::plugin::SplitPlugin, nvinfer1::plugin::ProposalPlugin, nvinfer1::plugin::CropAndResizePlugin, nvinfer1::plugin::SpecialSlice, nvinfer1::plugin::ProposalLayer, nvinfer1::plugin::GenerateDetection, nvinfer1::plugin::MultilevelCropAndResize, nvinfer1::plugin::PyramidROIAlign, nvinfer1::plugin::DetectionLayer, nvinfer1::plugin::MultilevelProposeROI, nvinfer1::plugin::Normalize, nvinfer1::plugin::ResizeNearest, nvinfer1::plugin::DetectionOutput, nvinfer1::plugin::RPROIPlugin, nvinfer1::plugin::GroupNormalizationPlugin, nvinfer1::plugin::PriorBox, nvinfer1::plugin::Region, nvinfer1::plugin::InstanceNormalizationPlugin, nvinfer1::plugin::Reorg, nvinfer1::plugin::GridAnchorGenerator, and nvinfer1::plugin::FlattenConcat.

getTensorRTVersion()

int32_t nvinfer1::IPluginV2Ext::getTensorRTVersion ( ) const

inlineprotectedvirtualinherited

Return the API version with which this plugin was built.

The upper byte reserved by TensorRT and is used to differentiate this from IPlguinV2.

Do not override this method as it is used by the TensorRT library to maintain backwards-compatibility with plugins.

Reimplemented from nvinfer1::IPluginV2.

configureWithFormat()

void nvinfer1::IPluginV2Ext::configureWithFormat ( const Dims *  , int32_t  , const Dims *  , int32_t  , DataType  , PluginFormat  , int32_t    )

inlineprotectedvirtualinherited

Derived classes should not implement this.

In a C++11 API it would be override final.

Implements nvinfer1::IPluginV2.

getOutputDimensions() [2/2]

virtual Dims nvinfer1::IPluginV2::getOutputDimensions ( int32_t  index, const Dims *  inputs, int32_t  nbInputDims  )

pure virtualinherited

Get the dimension of an output tensor.

Parameters

index	The index of the output tensor.
inputs	The input tensors.
nbInputDims	The number of input tensors.

This function is called by the implementations of INetworkDefinition and IBuilder. In particular, it is called prior to any call to initialize().

supportsFormat()

virtual bool nvinfer1::IPluginV2::supportsFormat ( DataType  type, PluginFormat  format  ) const

pure virtualinherited

Check format support.

Parameters

type	DataType requested.
format	PluginFormat requested.

Returns

true if the plugin supports the type-format combination.

This function is called by the implementations of INetworkDefinition, IBuilder, and safe::ICudaEngine/ICudaEngine. In particular, it is called when creating an engine and when deserializing an engine.

Warning

for the format field, the values PluginFormat::kCHW4, PluginFormat::kCHW16, and PluginFormat::kCHW32 will not be passed in, this is to keep backward compatibility with TensorRT 5.x series. Use PluginV2IOExt or PluginV2DynamicExt for other PluginFormats.

DataType:kBOOL not supported.

Implemented in nvinfer1::plugin::FlattenConcat, nvinfer1::plugin::ProposalPlugin, nvinfer1::plugin::CropAndResizePlugin, nvinfer1::plugin::SpecialSlice, nvinfer1::plugin::ProposalLayer, nvinfer1::plugin::BatchTilePlugin, nvinfer1::plugin::CoordConvACPlugin, nvinfer1::plugin::GenerateDetection, nvinfer1::plugin::MultilevelCropAndResize, nvinfer1::plugin::PyramidROIAlign, nvinfer1::plugin::DetectionLayer, nvinfer1::plugin::MultilevelProposeROI, nvinfer1::plugin::ResizeNearest, nvinfer1::plugin::LReLU, nvinfer1::plugin::Normalize, nvinfer1::plugin::DetectionOutput, nvinfer1::plugin::RPROIPlugin, nvinfer1::plugin::PriorBox, nvinfer1::plugin::Region, nvinfer1::plugin::Reorg, nvinfer1::plugin::GridAnchorGenerator, and nvinfer1::plugin::BatchedNMSPlugin.

getWorkspaceSize() [2/2]

virtual size_t nvinfer1::IPluginV2::getWorkspaceSize ( int32_t  maxBatchSize ) const

pure virtualinherited

Find the workspace size required by the layer.

This function is called during engine startup, after initialize(). The workspace size returned should be sufficient for any batch size up to the maximum.

Returns

The workspace size.

enqueue() [2/2]

virtual int32_t nvinfer1::IPluginV2::enqueue ( int32_t  batchSize, const void *const *  inputs, void **  outputs, void *  workspace, cudaStream_t  stream  )

pure virtualinherited

Execute the layer.

Parameters

batchSize	The number of inputs in the batch.
inputs	The memory for the input tensors.
outputs	The memory for the output tensors.
workspace	Workspace for execution.
stream	The stream in which to execute the kernels.

Returns

0 for success, else non-zero (which will cause engine termination).

Member Data Documentation

mCudnn

cudnnHandle_t UffPoolPluginV2::mCudnn

private

mSrcDescriptor

cudnnTensorDescriptor_t UffPoolPluginV2::mSrcDescriptor

private

mDstDescriptor

cudnnTensorDescriptor_t UffPoolPluginV2::mDstDescriptor

private

mPoolingDesc

cudnnPoolingDescriptor_t UffPoolPluginV2::mPoolingDesc

private

mPoolingParams

PoolParameters UffPoolPluginV2::mPoolingParams

private

mMode

cudnnPoolingMode_t UffPoolPluginV2::mMode

private

mDataType

DataType UffPoolPluginV2::mDataType

private

mInputDims

Dims UffPoolPluginV2::mInputDims

private

mOutputDims

Dims UffPoolPluginV2::mOutputDims

private

mInHostScale

float UffPoolPluginV2::mInHostScale {-1.0f}

private

mOutHostScale

float UffPoolPluginV2::mOutHostScale {-1.0f}

private

mNamespace

std::string UffPoolPluginV2::mNamespace

private

int32_t [1/2]

nvinfer1::IPluginV2IOExt::int32_t

protectedinherited

int32_t [2/2]

const Dims nvinfer1::IPluginV2IOExt::int32_t

protectedinherited

DataType

const Dims nvinfer1::IPluginV2IOExt::DataType

protectedinherited

PluginFormat

const Dims nvinfer1::IPluginV2IOExt::PluginFormat

protectedinherited

---

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

• sampleUffPluginV2Ext.cpp