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::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 |
nvonnxparser::IONNXParser | ONNX Parser 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 |
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::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::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 |