TensorRT
 All Classes Namespaces Files Functions Variables Typedefs Enumerations Enumerator Macros Pages
Class Hierarchy
This inheritance list is sorted roughly, but not completely, alphabetically:
[detail level 123]
oCnvinfer1::plugin::DetectionOutputParametersThe DetectionOutput plugin layer generates the detection output based on location and confidence predictions by doing non maximum suppression. DetectionOutputParameters defines a set of parameters for creating the DetectionOutput plugin layer. It contains:
oCnvinfer1::DimsStructure to define the dimensions of a tensor
|oCnvinfer1::Dims2Descriptor for two-dimensional data
||\Cnvinfer1::DimsHWDescriptor for two-dimensional spatial data
|oCnvinfer1::Dims3Descriptor for three-dimensional data
||\Cnvinfer1::DimsCHWDescriptor for data with one channel dimension and two spatial dimensions
|\Cnvinfer1::Dims4Descriptor for four-dimensional data
| \Cnvinfer1::DimsNCHWDescriptor for data with one index dimension, one channel dimension and two spatial dimensions
oCnvuffparser::FieldCollection
oCnvuffparser::FieldMapAn array of field params used as a layer parameter for plugin layers
oCnvinfer1::plugin::GridAnchorParametersThe Anchor Generator plugin layer generates the prior boxes of designated sizes and aspect ratios across all dimensions $ (H \times W) $. GridAnchorParameters defines a set of parameters for creating the plugin layer for all feature maps. It contains:
oCnvcaffeparser1::IBinaryProtoBlobObject used to store and query data extracted from a binaryproto file using the ICaffeParser
oCnvcaffeparser1::IBlobNameToTensorObject used to store and query Tensors after they have been extracted from a Caffe model using the ICaffeParser
oCnvinfer1::IBuilderBuilds an engine from a network definition
oCnvcaffeparser1::ICaffeParserClass used for parsing Caffe models
oCnvinfer1::ICudaEngineAn engine for executing inference on a built network
oCnvinfer1::IExecutionContextContext for executing inference using an engine
oCnvinfer1::IGpuAllocatorApplication-implemented class for controlling allocation on the GPU
oCnvinfer1::IHostMemoryClass to handle library allocated memory that is accessible to the user
oCnvinfer1::IInt8CalibratorApplication-implemented interface for calibration
|oCnvinfer1::IInt8EntropyCalibrator
|\Cnvinfer1::IInt8LegacyCalibrator
oCnvinfer1::ILayerBase class for all layer classes in a network definition
|oCnvinfer1::IActivationLayerAn Activation layer in a network definition
|oCnvinfer1::IConcatenationLayerA concatenation layer in a network definition
|oCnvinfer1::IConstantLayerLayer that represents a constant value
|oCnvinfer1::IConvolutionLayerA convolution layer in a network definition
|oCnvinfer1::IDeconvolutionLayerA deconvolution layer in a network definition
|oCnvinfer1::IElementWiseLayerA elementwise layer in a network definition
|oCnvinfer1::IFullyConnectedLayerA fully connected layer in a network definition. This layer expects an input tensor of three or more non-batch dimensions. The input is automatically reshaped into an MxV tensor X, where V is a product of the last three dimensions and M is a product of the remaining dimensions (where the product over 0 dimensions is defined as 1). For example:
|oCnvinfer1::IGatherLayer
|oCnvinfer1::ILRNLayerA LRN layer in a network definition
|oCnvinfer1::IMatrixMultiplyLayerLayer that represents a Matrix Multiplication
|oCnvinfer1::IPaddingLayerLayer that represents a padding operation
|oCnvinfer1::IPluginLayerLayer type for plugins
|oCnvinfer1::IPoolingLayerA Pooling layer in a network definition
|oCnvinfer1::IRaggedSoftMaxLayerA RaggedSoftmax layer in a network definition
|oCnvinfer1::IReduceLayerLayer that represents a reduction operator
|oCnvinfer1::IRNNLayerA RNN layer in a network definition
|oCnvinfer1::IRNNv2LayerAn RNN layer in a network definition, version 2
|oCnvinfer1::IScaleLayerA Scale layer in a network definition
|oCnvinfer1::IShuffleLayerLayer type for shuffling data
|oCnvinfer1::ISoftMaxLayerA Softmax layer in a network definition
|oCnvinfer1::ITopKLayerLayer that represents a TopK reduction
|\Cnvinfer1::IUnaryLayerLayer that represents an unary operation
oCnvinfer1::ILoggerApplication-implemented logging interface for the builder, engine and runtime
oCnvinfer1::INetworkDefinitionA network definition for input to the builder
oCnvonnxparser::IOnnxConfigConfiguration Manager Class
oCnvonnxparser::IONNXParserONNX Parser Class
oCnvinfer1::IOutputDimensionsFormulaApplication-implemented interface to compute layer output sizes
oCnvinfer1::IPluginPlugin class for user-implemented layers
|oCnvinfer1::IPluginExtPlugin class for user-implemented layers
|\Cnvinfer1::plugin::INvPluginCommon interface for the Nvidia created plugins
oCnvuffparser::IPluginFactoryPlugin factory used to configure plugins
|\Cnvuffparser::IPluginFactoryExtPlugin factory used to configure plugins with added support for TRT versioning
oCnvcaffeparser1::IPluginFactoryPlugin factory used to configure plugins
|\Cnvcaffeparser1::IPluginFactoryExtPlugin factory used to configure plugins with added support for TRT versioning
oCnvinfer1::IPluginFactoryPlugin factory for deserialization
oCnvinfer1::IProfilerApplication-implemented interface for profiling
oCnvinfer1::IRuntimeAllows a serialized engine to be deserialized
oCnvinfer1::ITensorA tensor in a network definition
oCnvuffparser::IUffParserClass used for parsing models described using the UFF format
oCnvinfer1::Permutation
oCnvinfer1::plugin::PriorBoxParametersThe PriorBox plugin layer generates the prior boxes of designated sizes and aspect ratios across all dimensions $ (H \times W) $. PriorBoxParameters defines a set of parameters for creating the PriorBox plugin layer. It contains:
oCnvinfer1::plugin::QuadrupleThe Permute plugin layer permutes the input tensor by changing the memory order of the data. Quadruple defines a structure that contains an array of 4 integers. They can represent the permute orders or the strides in each dimension
oCnvinfer1::plugin::RegionParameters
oCnvinfer1::plugin::softmaxTreeThe Region plugin layer performs region proposal calculation: generate 5 bounding boxes per cell (for yolo9000, generate 3 bounding boxes per cell). For each box, calculating its probablities of objects detections from 80 pre-defined classifications (yolo9000 has 9416 pre-defined classifications, and these 9416 items are organized as work-tree structure). RegionParameters defines a set of parameters for creating the Region plugin layer
\Cnvinfer1::WeightsAn array of weights used as a layer parameter