TensorRT
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nvinfer1::plugin::DetectionOutputParameters | The 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: |
nvinfer1::Dims | Structure to define the dimensions of a tensor |
nvinfer1::Dims2 | Descriptor for two-dimensional data |
nvinfer1::DimsHW | Descriptor for two-dimensional spatial data |
nvinfer1::Dims3 | Descriptor for three-dimensional data |
nvinfer1::DimsCHW | Descriptor for data with one channel dimension and two spatial dimensions |
nvinfer1::Dims4 | Descriptor for four-dimensional data |
nvinfer1::DimsNCHW | Descriptor for data with one index dimension, one channel dimension and two spatial dimensions |
nvuffparser::FieldCollection | |
nvuffparser::FieldMap | An array of field params used as a layer parameter for plugin layers |
nvinfer1::plugin::GridAnchorParameters | The Anchor Generator plugin layer generates the prior boxes of designated sizes and aspect ratios across all dimensions . GridAnchorParameters defines a set of parameters for creating the plugin layer for all feature maps. It contains: |
nvcaffeparser1::IBinaryProtoBlob | Object used to store and query data extracted from a binaryproto file using the ICaffeParser |
nvcaffeparser1::IBlobNameToTensor | Object used to store and query Tensors after they have been extracted from a Caffe model using the ICaffeParser |
nvinfer1::IBuilder | Builds an engine from a network definition |
nvcaffeparser1::ICaffeParser | Class used for parsing Caffe models |
nvinfer1::ICudaEngine | An engine for executing inference on a built network |
nvinfer1::IExecutionContext | Context for executing inference using an engine |
nvinfer1::IGpuAllocator | Application-implemented class for controlling allocation on the GPU |
nvinfer1::IHostMemory | Class to handle library allocated memory that is accessible to the user |
nvinfer1::IInt8Calibrator | Application-implemented interface for calibration |
nvinfer1::IInt8EntropyCalibrator | |
nvinfer1::IInt8LegacyCalibrator | |
nvinfer1::ILayer | Base class for all layer classes in a network definition |
nvinfer1::IActivationLayer | An Activation layer in a network definition |
nvinfer1::IConcatenationLayer | A concatenation layer in a network definition |
nvinfer1::IConstantLayer | Layer that represents a constant value |
nvinfer1::IConvolutionLayer | A convolution layer in a network definition |
nvinfer1::IDeconvolutionLayer | A deconvolution layer in a network definition |
nvinfer1::IElementWiseLayer | A elementwise layer in a network definition |
nvinfer1::IFullyConnectedLayer | A 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: |
nvinfer1::IGatherLayer | |
nvinfer1::IIdentityLayer | A layer that represents the identity function |
nvinfer1::ILRNLayer | A LRN layer in a network definition |
nvinfer1::IMatrixMultiplyLayer | Layer that represents a Matrix Multiplication |
nvinfer1::IPaddingLayer | Layer that represents a padding operation |
nvinfer1::IPluginLayer | Layer type for plugins |
nvinfer1::IPoolingLayer | A Pooling layer in a network definition |
nvinfer1::IRaggedSoftMaxLayer | A RaggedSoftmax layer in a network definition |
nvinfer1::IReduceLayer | Layer that represents a reduction operator |
nvinfer1::IRNNLayer | A RNN layer in a network definition |
nvinfer1::IRNNv2Layer | An RNN layer in a network definition, version 2 |
nvinfer1::IScaleLayer | A Scale layer in a network definition |
nvinfer1::IShuffleLayer | Layer type for shuffling data |
nvinfer1::ISoftMaxLayer | A Softmax layer in a network definition |
nvinfer1::ITopKLayer | Layer that represents a TopK reduction |
nvinfer1::IUnaryLayer | Layer that represents an unary operation |
nvinfer1::ILogger | Application-implemented logging interface for the builder, engine and runtime |
nvinfer1::INetworkDefinition | A network definition for input to the builder |
nvonnxparser::IOnnxConfig | Configuration Manager Class |
nvinfer1::IOutputDimensionsFormula | Application-implemented interface to compute layer output sizes |
nvinfer1::IPlugin | Plugin class for user-implemented layers |
nvinfer1::IPluginExt | Plugin class for user-implemented layers |
nvinfer1::plugin::INvPlugin | Common interface for the Nvidia created plugins |
nvinfer1::IPluginCreator | Plugin creator class for user implemented layers |
nvuffparser::IPluginFactory | Plugin factory used to configure plugins |
nvuffparser::IPluginFactoryExt | Plugin factory used to configure plugins with added support for TRT versioning |
nvcaffeparser1::IPluginFactory | Plugin factory used to configure plugins |
nvcaffeparser1::IPluginFactoryExt | Plugin factory used to configure plugins with added support for TRT versioning |
nvinfer1::IPluginFactory | Plugin factory for deserialization |
nvinfer1::IPluginRegistry | |
nvinfer1::IProfiler | Application-implemented interface for profiling |
nvinfer1::IRuntime | Allows a serialized engine to be deserialized |
nvinfer1::ITensor | A tensor in a network definition |
nvuffparser::IUffParser | Class used for parsing models described using the UFF format |
nvinfer1::Permutation | |
nvinfer1::PluginField | Structure containing plugin attribute field names and associated data This information can be parsed to decode necessary plugin metadata |
nvinfer1::PluginFieldCollection | |
nvinfer1::PluginRegistrar< T > | Register the plugin creator to the registry The static registry object will be instantiated when the plugin library is loaded. This static object will register all creators available in the library to the registry |
nvinfer1::plugin::PriorBoxParameters | The PriorBox plugin layer generates the prior boxes of designated sizes and aspect ratios across all dimensions . PriorBoxParameters defines a set of parameters for creating the PriorBox plugin layer. It contains: |
nvinfer1::plugin::Quadruple | The 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 |
nvinfer1::plugin::RegionParameters | |
nvinfer1::plugin::softmaxTree | The 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 |
nvinfer1::Weights | An array of weights used as a layer parameter |