| Package | Description |
|---|---|
| org.bytedeco.tensorrt.nvinfer | |
| org.bytedeco.tensorrt.nvparsers |
| Modifier and Type | Class and Description |
|---|---|
class |
Dims2
\class Dims2
\brief Descriptor for two-dimensional data.
|
class |
Dims3
\class Dims3
\brief Descriptor for three-dimensional data.
|
class |
Dims4
\class Dims4
\brief Descriptor for four-dimensional data.
|
class |
DimsHW
\class DimsHW
\brief Descriptor for two-dimensional spatial data.
|
| Modifier and Type | Method and Description |
|---|---|
Dims32 |
Dims32.d(int i,
int setter) |
Dims32 |
Dims32.d(int i,
int setter) |
Dims32 |
PluginTensorDesc.dims()
Dimensions.
|
Dims32 |
PluginTensorDesc.dims()
Dimensions.
|
Dims32 |
ICudaEngine.getBindingDimensions(int bindingIndex)
\brief Get the dimensions of a binding.
|
Dims32 |
IExecutionContext.getBindingDimensions(int bindingIndex)
\brief Get the dynamic dimensions of a binding
If the engine was built with an implicit batch dimension, same as ICudaEngine::getBindingDimensions.
|
Dims32 |
VCudaEngine.getBindingDimensions(int bindingIndex) |
Dims32 |
VExecutionContext.getBindingDimensions(int bindingIndex) |
Dims32 |
VExecutionContext.getBindingDimensions(int bindingIndex) |
Dims32 |
VCudaEngine.getBindingDimensions(int bindingIndex) |
Dims32 |
IExecutionContext.getBindingDimensions(int bindingIndex)
\brief Get the dynamic dimensions of a binding
If the engine was built with an implicit batch dimension, same as ICudaEngine::getBindingDimensions.
|
Dims32 |
ICudaEngine.getBindingDimensions(int bindingIndex)
\brief Get the dimensions of a binding.
|
Dims32 |
IConvolutionLayer.getDilationNd()
\brief Get the multi-dimension dilation of the convolution.
|
Dims32 |
IDeconvolutionLayer.getDilationNd()
\brief Get the multi-dimension dilation of the deconvolution.
|
Dims32 |
VConvolutionLayer.getDilationNd() |
Dims32 |
VDeconvolutionLayer.getDilationNd() |
Dims32 |
VDeconvolutionLayer.getDilationNd() |
Dims32 |
VConvolutionLayer.getDilationNd() |
Dims32 |
IDeconvolutionLayer.getDilationNd()
\brief Get the multi-dimension dilation of the deconvolution.
|
Dims32 |
IConvolutionLayer.getDilationNd()
\brief Get the multi-dimension dilation of the convolution.
|
Dims32 |
IConstantLayer.getDimensions()
\brief Get the dimensions for the layer.
|
Dims32 |
IFillLayer.getDimensions()
\brief Get the output tensor's dimensions.
|
Dims32 |
ITensor.getDimensions()
\brief Get the dimensions of a tensor.
|
Dims32 |
VConstantLayer.getDimensions() |
Dims32 |
VFillLayer.getDimensions() |
Dims32 |
VTensor.getDimensions() |
Dims32 |
VTensor.getDimensions() |
Dims32 |
VFillLayer.getDimensions() |
Dims32 |
VConstantLayer.getDimensions() |
Dims32 |
ITensor.getDimensions()
\brief Get the dimensions of a tensor.
|
Dims32 |
IFillLayer.getDimensions()
\brief Get the output tensor's dimensions.
|
Dims32 |
IConstantLayer.getDimensions()
\brief Get the dimensions for the layer.
|
Dims32 |
IOptimizationProfile.getDimensions(BytePointer inputName,
int select) |
Dims32 |
VOptimizationProfile.getDimensions(BytePointer inputName,
int select) |
Dims32 |
VOptimizationProfile.getDimensions(BytePointer inputName,
int select) |
Dims32 |
IOptimizationProfile.getDimensions(BytePointer inputName,
int select) |
Dims32 |
IAlgorithmContext.getDimensions(int index,
int select) |
Dims32 |
VAlgorithmContext.getDimensions(int index,
int select) |
Dims32 |
VAlgorithmContext.getDimensions(int index,
int select) |
Dims32 |
IAlgorithmContext.getDimensions(int index,
int select) |
Dims32 |
IAlgorithmContext.getDimensions(int index,
nvinfer.OptProfileSelector select)
\brief Get the minimum / optimum / maximum dimensions for input or output tensor.
|
Dims32 |
VAlgorithmContext.getDimensions(int index,
nvinfer.OptProfileSelector select) |
Dims32 |
VAlgorithmContext.getDimensions(int index,
nvinfer.OptProfileSelector select) |
Dims32 |
IAlgorithmContext.getDimensions(int index,
nvinfer.OptProfileSelector select)
\brief Get the minimum / optimum / maximum dimensions for input or output tensor.
|
Dims32 |
IOptimizationProfile.getDimensions(String inputName,
nvinfer.OptProfileSelector select)
\brief Get the minimum / optimum / maximum dimensions for a dynamic input tensor.
|
Dims32 |
VOptimizationProfile.getDimensions(String inputName,
nvinfer.OptProfileSelector select) |
Dims32 |
VOptimizationProfile.getDimensions(String inputName,
nvinfer.OptProfileSelector select) |
Dims32 |
IOptimizationProfile.getDimensions(String inputName,
nvinfer.OptProfileSelector select)
\brief Get the minimum / optimum / maximum dimensions for a dynamic input tensor.
|
Dims32 |
IConvolutionLayer.getKernelSizeNd()
\brief Get the multi-dimension kernel size of the convolution.
|
Dims32 |
IDeconvolutionLayer.getKernelSizeNd()
\brief Get the multi-dimension kernel size of the deconvolution.
|
Dims32 |
VConvolutionLayer.getKernelSizeNd() |
Dims32 |
VDeconvolutionLayer.getKernelSizeNd() |
Dims32 |
VDeconvolutionLayer.getKernelSizeNd() |
Dims32 |
VConvolutionLayer.getKernelSizeNd() |
Dims32 |
IDeconvolutionLayer.getKernelSizeNd()
\brief Get the multi-dimension kernel size of the deconvolution.
|
Dims32 |
IConvolutionLayer.getKernelSizeNd()
\brief Get the multi-dimension kernel size of the convolution.
|
Dims32 |
IResizeLayer.getOutputDimensions()
\brief Get the output dimensions.
|
Dims32 |
VResizeLayer.getOutputDimensions() |
Dims32 |
VResizeLayer.getOutputDimensions() |
Dims32 |
IResizeLayer.getOutputDimensions()
\brief Get the output dimensions.
|
Dims32 |
IPluginV2.getOutputDimensions(int index,
Dims32 inputs,
int nbInputDims)
\brief Get the dimension of an output tensor.
|
Dims32 |
IPluginV2.getOutputDimensions(int index,
Dims32 inputs,
int nbInputDims)
\brief Get the dimension of an output tensor.
|
Dims32 |
IConvolutionLayer.getPaddingNd()
\brief Get the multi-dimension padding of the convolution.
|
Dims32 |
IDeconvolutionLayer.getPaddingNd()
\brief Get the multi-dimension padding of the deconvolution.
|
Dims32 |
IPoolingLayer.getPaddingNd()
\brief Get the multi-dimension padding for pooling.
|
Dims32 |
VConvolutionLayer.getPaddingNd() |
Dims32 |
VDeconvolutionLayer.getPaddingNd() |
Dims32 |
VPoolingLayer.getPaddingNd() |
Dims32 |
VPoolingLayer.getPaddingNd() |
Dims32 |
VDeconvolutionLayer.getPaddingNd() |
Dims32 |
VConvolutionLayer.getPaddingNd() |
Dims32 |
IPoolingLayer.getPaddingNd()
\brief Get the multi-dimension padding for pooling.
|
Dims32 |
IDeconvolutionLayer.getPaddingNd()
\brief Get the multi-dimension padding of the deconvolution.
|
Dims32 |
IConvolutionLayer.getPaddingNd()
\brief Get the multi-dimension padding of the convolution.
|
Dims32 |
Dims32.getPointer(long i) |
Dims32 |
Dims32.getPointer(long i) |
Dims32 |
IConvolutionLayer.getPostPadding()
\brief Get the post-padding.
|
Dims32 |
IDeconvolutionLayer.getPostPadding()
\brief Get the padding.
|
Dims32 |
IPoolingLayer.getPostPadding()
\brief Get the padding.
|
Dims32 |
VConvolutionLayer.getPostPadding() |
Dims32 |
VDeconvolutionLayer.getPostPadding() |
Dims32 |
VPoolingLayer.getPostPadding() |
Dims32 |
VPoolingLayer.getPostPadding() |
Dims32 |
VDeconvolutionLayer.getPostPadding() |
Dims32 |
VConvolutionLayer.getPostPadding() |
Dims32 |
IPoolingLayer.getPostPadding()
\brief Get the padding.
|
Dims32 |
IDeconvolutionLayer.getPostPadding()
\brief Get the padding.
|
Dims32 |
IConvolutionLayer.getPostPadding()
\brief Get the post-padding.
|
Dims32 |
IPaddingLayer.getPostPaddingNd()
\brief Get the padding that is applied at the end of the tensor.
|
Dims32 |
VPaddingLayer.getPostPaddingNd() |
Dims32 |
VPaddingLayer.getPostPaddingNd() |
Dims32 |
IPaddingLayer.getPostPaddingNd()
\brief Get the padding that is applied at the end of the tensor.
|
Dims32 |
IConvolutionLayer.getPrePadding()
\brief Get the pre-padding.
|
Dims32 |
IDeconvolutionLayer.getPrePadding()
\brief Get the pre-padding.
|
Dims32 |
IPoolingLayer.getPrePadding()
\brief Get the pre-padding.
|
Dims32 |
VConvolutionLayer.getPrePadding() |
Dims32 |
VDeconvolutionLayer.getPrePadding() |
Dims32 |
VPoolingLayer.getPrePadding() |
Dims32 |
VPoolingLayer.getPrePadding() |
Dims32 |
VDeconvolutionLayer.getPrePadding() |
Dims32 |
VConvolutionLayer.getPrePadding() |
Dims32 |
IPoolingLayer.getPrePadding()
\brief Get the pre-padding.
|
Dims32 |
IDeconvolutionLayer.getPrePadding()
\brief Get the pre-padding.
|
Dims32 |
IConvolutionLayer.getPrePadding()
\brief Get the pre-padding.
|
Dims32 |
IPaddingLayer.getPrePaddingNd()
\brief Get the padding that is applied at the start of the tensor.
|
Dims32 |
VPaddingLayer.getPrePaddingNd() |
Dims32 |
VPaddingLayer.getPrePaddingNd() |
Dims32 |
IPaddingLayer.getPrePaddingNd()
\brief Get the padding that is applied at the start of the tensor.
|
Dims32 |
ICudaEngine.getProfileDimensions(int bindingIndex,
int profileIndex,
int select) |
Dims32 |
VCudaEngine.getProfileDimensions(int bindingIndex,
int profileIndex,
int select) |
Dims32 |
VCudaEngine.getProfileDimensions(int bindingIndex,
int profileIndex,
int select) |
Dims32 |
ICudaEngine.getProfileDimensions(int bindingIndex,
int profileIndex,
int select) |
Dims32 |
ICudaEngine.getProfileDimensions(int bindingIndex,
int profileIndex,
nvinfer.OptProfileSelector select)
\brief Get the minimum / optimum / maximum dimensions for a particular binding under an optimization profile.
|
Dims32 |
VCudaEngine.getProfileDimensions(int bindingIndex,
int profileIndex,
nvinfer.OptProfileSelector select) |
Dims32 |
VCudaEngine.getProfileDimensions(int bindingIndex,
int profileIndex,
nvinfer.OptProfileSelector select) |
Dims32 |
ICudaEngine.getProfileDimensions(int bindingIndex,
int profileIndex,
nvinfer.OptProfileSelector select)
\brief Get the minimum / optimum / maximum dimensions for a particular binding under an optimization profile.
|
Dims32 |
IShuffleLayer.getReshapeDimensions()
\brief Get the reshaped dimensions.
|
Dims32 |
VShuffleLayer.getReshapeDimensions() |
Dims32 |
VShuffleLayer.getReshapeDimensions() |
Dims32 |
IShuffleLayer.getReshapeDimensions()
\brief Get the reshaped dimensions.
|
Dims32 |
ISliceLayer.getSize()
\brief Get dimensions of the output slice.
|
Dims32 |
VSliceLayer.getSize() |
Dims32 |
VSliceLayer.getSize() |
Dims32 |
ISliceLayer.getSize()
\brief Get dimensions of the output slice.
|
Dims32 |
ISliceLayer.getStart()
\brief Get the start offset for the slice layer.
|
Dims32 |
VSliceLayer.getStart() |
Dims32 |
VSliceLayer.getStart() |
Dims32 |
ISliceLayer.getStart()
\brief Get the start offset for the slice layer.
|
Dims32 |
ISliceLayer.getStride()
\brief Get the stride for the output slice.
|
Dims32 |
VSliceLayer.getStride() |
Dims32 |
VSliceLayer.getStride() |
Dims32 |
ISliceLayer.getStride()
\brief Get the stride for the output slice.
|
Dims32 |
IConvolutionLayer.getStrideNd()
\brief Get the multi-dimension stride of the convolution.
|
Dims32 |
IDeconvolutionLayer.getStrideNd()
\brief Get the multi-dimension stride of the deconvolution.
|
Dims32 |
IPoolingLayer.getStrideNd()
\brief Get the multi-dimension stride for pooling.
|
Dims32 |
VConvolutionLayer.getStrideNd() |
Dims32 |
VDeconvolutionLayer.getStrideNd() |
Dims32 |
VPoolingLayer.getStrideNd() |
Dims32 |
VPoolingLayer.getStrideNd() |
Dims32 |
VDeconvolutionLayer.getStrideNd() |
Dims32 |
VConvolutionLayer.getStrideNd() |
Dims32 |
IPoolingLayer.getStrideNd()
\brief Get the multi-dimension stride for pooling.
|
Dims32 |
IDeconvolutionLayer.getStrideNd()
\brief Get the multi-dimension stride of the deconvolution.
|
Dims32 |
IConvolutionLayer.getStrideNd()
\brief Get the multi-dimension stride of the convolution.
|
Dims32 |
IAlgorithmIOInfo.getStrides()
\brief Return strides of the input/output tensor of algorithm.
|
Dims32 |
VAlgorithmIOInfo.getStrides() |
Dims32 |
VAlgorithmIOInfo.getStrides() |
Dims32 |
IAlgorithmIOInfo.getStrides()
\brief Return strides of the input/output tensor of algorithm.
|
Dims32 |
IExecutionContext.getStrides(int bindingIndex)
\brief Return the strides of the buffer for the given binding.
|
Dims32 |
VExecutionContext.getStrides(int bindingIndex) |
Dims32 |
VExecutionContext.getStrides(int bindingIndex) |
Dims32 |
IExecutionContext.getStrides(int bindingIndex)
\brief Return the strides of the buffer for the given binding.
|
Dims32 |
IPoolingLayer.getWindowSizeNd()
\brief Get the multi-dimension window size for pooling.
|
Dims32 |
VPoolingLayer.getWindowSizeNd() |
Dims32 |
VPoolingLayer.getWindowSizeNd() |
Dims32 |
IPoolingLayer.getWindowSizeNd()
\brief Get the multi-dimension window size for pooling.
|
Dims32 |
DynamicPluginTensorDesc.max()
Upper bounds on tensor’s dimensions
|
Dims32 |
DynamicPluginTensorDesc.max()
Upper bounds on tensor’s dimensions
|
Dims32 |
DynamicPluginTensorDesc.min()
Lower bounds on tensor’s dimensions
|
Dims32 |
DynamicPluginTensorDesc.min()
Lower bounds on tensor’s dimensions
|
Dims32 |
Dims32.nbDims(int setter) |
Dims32 |
Dims32.nbDims(int setter) |
Dims32 |
Dims32.position(long position) |
Dims32 |
Dims32.position(long position) |
| Modifier and Type | Method and Description |
|---|---|
IConstantLayer |
INetworkDefinition.addConstant(Dims32 dimensions,
Weights weights)
\brief Add a constant layer to the network.
|
IConstantLayer |
VNetworkDefinition.addConstant(Dims32 dimensions,
Weights weights) |
IConstantLayer |
VNetworkDefinition.addConstant(Dims32 dimensions,
Weights weights) |
IConstantLayer |
INetworkDefinition.addConstant(Dims32 dimensions,
Weights weights)
\brief Add a constant layer to the network.
|
IConvolutionLayer |
INetworkDefinition.addConvolutionNd(ITensor input,
int nbOutputMaps,
Dims32 kernelSize,
Weights kernelWeights,
Weights biasWeights)
\brief Add a multi-dimension convolution layer to the network.
|
IConvolutionLayer |
VNetworkDefinition.addConvolutionNd(ITensor input,
int nbOutputMaps,
Dims32 kernelSize,
Weights kernelWeights,
Weights biasWeights) |
IConvolutionLayer |
VNetworkDefinition.addConvolutionNd(ITensor input,
int nbOutputMaps,
Dims32 kernelSize,
Weights kernelWeights,
Weights biasWeights) |
IConvolutionLayer |
INetworkDefinition.addConvolutionNd(ITensor input,
int nbOutputMaps,
Dims32 kernelSize,
Weights kernelWeights,
Weights biasWeights)
\brief Add a multi-dimension convolution layer to the network.
|
IDeconvolutionLayer |
INetworkDefinition.addDeconvolutionNd(ITensor input,
int nbOutputMaps,
Dims32 kernelSize,
Weights kernelWeights,
Weights biasWeights) |
IDeconvolutionLayer |
VNetworkDefinition.addDeconvolutionNd(ITensor input,
int nbOutputMaps,
Dims32 kernelSize,
Weights kernelWeights,
Weights biasWeights) |
IDeconvolutionLayer |
VNetworkDefinition.addDeconvolutionNd(ITensor input,
int nbOutputMaps,
Dims32 kernelSize,
Weights kernelWeights,
Weights biasWeights) |
IDeconvolutionLayer |
INetworkDefinition.addDeconvolutionNd(ITensor input,
int nbOutputMaps,
Dims32 kernelSize,
Weights kernelWeights,
Weights biasWeights) |
IFillLayer |
INetworkDefinition.addFill(Dims32 dimensions,
int op) |
IFillLayer |
VNetworkDefinition.addFill(Dims32 dimensions,
int op) |
IFillLayer |
VNetworkDefinition.addFill(Dims32 dimensions,
int op) |
IFillLayer |
INetworkDefinition.addFill(Dims32 dimensions,
int op) |
IFillLayer |
INetworkDefinition.addFill(Dims32 dimensions,
nvinfer.FillOperation op)
\brief Add a fill layer to the network.
|
IFillLayer |
VNetworkDefinition.addFill(Dims32 dimensions,
nvinfer.FillOperation op) |
IFillLayer |
VNetworkDefinition.addFill(Dims32 dimensions,
nvinfer.FillOperation op) |
IFillLayer |
INetworkDefinition.addFill(Dims32 dimensions,
nvinfer.FillOperation op)
\brief Add a fill layer to the network.
|
ITensor |
INetworkDefinition.addInput(BytePointer name,
int type,
Dims32 dimensions) |
ITensor |
VNetworkDefinition.addInput(BytePointer name,
int type,
Dims32 dimensions) |
ITensor |
VNetworkDefinition.addInput(BytePointer name,
int type,
Dims32 dimensions) |
ITensor |
INetworkDefinition.addInput(BytePointer name,
int type,
Dims32 dimensions) |
ITensor |
INetworkDefinition.addInput(String name,
nvinfer.DataType type,
Dims32 dimensions)
\brief Add an input tensor to the network.
|
ITensor |
VNetworkDefinition.addInput(String name,
nvinfer.DataType type,
Dims32 dimensions) |
ITensor |
VNetworkDefinition.addInput(String name,
nvinfer.DataType type,
Dims32 dimensions) |
ITensor |
INetworkDefinition.addInput(String name,
nvinfer.DataType type,
Dims32 dimensions)
\brief Add an input tensor to the network.
|
IPaddingLayer |
INetworkDefinition.addPaddingNd(ITensor input,
Dims32 prePadding,
Dims32 postPadding)
Deprecated.
Superseded by addSlice. Deprecated in TensorRT 8.0
|
IPaddingLayer |
VNetworkDefinition.addPaddingNd(ITensor input,
Dims32 prePadding,
Dims32 postPadding) |
IPaddingLayer |
VNetworkDefinition.addPaddingNd(ITensor input,
Dims32 prePadding,
Dims32 postPadding) |
IPaddingLayer |
INetworkDefinition.addPaddingNd(ITensor input,
Dims32 prePadding,
Dims32 postPadding)
Deprecated.
Superseded by addSlice. Deprecated in TensorRT 8.0
|
IPoolingLayer |
INetworkDefinition.addPoolingNd(ITensor input,
int type,
Dims32 windowSize) |
IPoolingLayer |
VNetworkDefinition.addPoolingNd(ITensor input,
int type,
Dims32 windowSize) |
IPoolingLayer |
VNetworkDefinition.addPoolingNd(ITensor input,
int type,
Dims32 windowSize) |
IPoolingLayer |
INetworkDefinition.addPoolingNd(ITensor input,
int type,
Dims32 windowSize) |
IPoolingLayer |
INetworkDefinition.addPoolingNd(ITensor input,
nvinfer.PoolingType type,
Dims32 windowSize)
\brief Add a multi-dimension pooling layer to the network.
|
IPoolingLayer |
VNetworkDefinition.addPoolingNd(ITensor input,
nvinfer.PoolingType type,
Dims32 windowSize) |
IPoolingLayer |
VNetworkDefinition.addPoolingNd(ITensor input,
nvinfer.PoolingType type,
Dims32 windowSize) |
IPoolingLayer |
INetworkDefinition.addPoolingNd(ITensor input,
nvinfer.PoolingType type,
Dims32 windowSize)
\brief Add a multi-dimension pooling layer to the network.
|
ISliceLayer |
INetworkDefinition.addSlice(ITensor input,
Dims32 start,
Dims32 size,
Dims32 stride)
\brief Add a slice layer to the network.
|
ISliceLayer |
VNetworkDefinition.addSlice(ITensor input,
Dims32 start,
Dims32 size,
Dims32 stride) |
ISliceLayer |
VNetworkDefinition.addSlice(ITensor input,
Dims32 start,
Dims32 size,
Dims32 stride) |
ISliceLayer |
INetworkDefinition.addSlice(ITensor input,
Dims32 start,
Dims32 size,
Dims32 stride)
\brief Add a slice layer to the network.
|
void |
IPluginV2Ext.configurePlugin(Dims32 inputDims,
int nbInputs,
Dims32 outputDims,
int nbOutputs,
int[] inputTypes,
int[] outputTypes,
boolean[] inputIsBroadcast,
boolean[] outputIsBroadcast,
int floatFormat,
int maxBatchSize) |
void |
IPluginV2Ext.configurePlugin(Dims32 inputDims,
int nbInputs,
Dims32 outputDims,
int nbOutputs,
int[] inputTypes,
int[] outputTypes,
boolean[] inputIsBroadcast,
boolean[] outputIsBroadcast,
int floatFormat,
int maxBatchSize) |
void |
IPluginV2Ext.configurePlugin(Dims32 inputDims,
int nbInputs,
Dims32 outputDims,
int nbOutputs,
int[] inputTypes,
int[] outputTypes,
BoolPointer inputIsBroadcast,
BoolPointer outputIsBroadcast,
nvinfer.TensorFormat floatFormat,
int maxBatchSize) |
void |
IPluginV2Ext.configurePlugin(Dims32 inputDims,
int nbInputs,
Dims32 outputDims,
int nbOutputs,
int[] inputTypes,
int[] outputTypes,
BoolPointer inputIsBroadcast,
BoolPointer outputIsBroadcast,
nvinfer.TensorFormat floatFormat,
int maxBatchSize) |
void |
IPluginV2Ext.configurePlugin(Dims32 inputDims,
int nbInputs,
Dims32 outputDims,
int nbOutputs,
IntBuffer inputTypes,
IntBuffer outputTypes,
boolean[] inputIsBroadcast,
boolean[] outputIsBroadcast,
int floatFormat,
int maxBatchSize) |
void |
IPluginV2Ext.configurePlugin(Dims32 inputDims,
int nbInputs,
Dims32 outputDims,
int nbOutputs,
IntBuffer inputTypes,
IntBuffer outputTypes,
boolean[] inputIsBroadcast,
boolean[] outputIsBroadcast,
int floatFormat,
int maxBatchSize) |
void |
IPluginV2Ext.configurePlugin(Dims32 inputDims,
int nbInputs,
Dims32 outputDims,
int nbOutputs,
IntBuffer inputTypes,
IntBuffer outputTypes,
BoolPointer inputIsBroadcast,
BoolPointer outputIsBroadcast,
nvinfer.TensorFormat floatFormat,
int maxBatchSize) |
void |
IPluginV2Ext.configurePlugin(Dims32 inputDims,
int nbInputs,
Dims32 outputDims,
int nbOutputs,
IntBuffer inputTypes,
IntBuffer outputTypes,
BoolPointer inputIsBroadcast,
BoolPointer outputIsBroadcast,
nvinfer.TensorFormat floatFormat,
int maxBatchSize) |
void |
IPluginV2Ext.configurePlugin(Dims32 inputDims,
int nbInputs,
Dims32 outputDims,
int nbOutputs,
IntPointer inputTypes,
IntPointer outputTypes,
boolean[] inputIsBroadcast,
boolean[] outputIsBroadcast,
int floatFormat,
int maxBatchSize) |
void |
IPluginV2Ext.configurePlugin(Dims32 inputDims,
int nbInputs,
Dims32 outputDims,
int nbOutputs,
IntPointer inputTypes,
IntPointer outputTypes,
boolean[] inputIsBroadcast,
boolean[] outputIsBroadcast,
int floatFormat,
int maxBatchSize) |
void |
IPluginV2Ext.configurePlugin(Dims32 inputDims,
int nbInputs,
Dims32 outputDims,
int nbOutputs,
IntPointer inputTypes,
IntPointer outputTypes,
BoolPointer inputIsBroadcast,
BoolPointer outputIsBroadcast,
nvinfer.TensorFormat floatFormat,
int maxBatchSize)
\brief Configure the layer with input and output data types.
|
void |
IPluginV2Ext.configurePlugin(Dims32 inputDims,
int nbInputs,
Dims32 outputDims,
int nbOutputs,
IntPointer inputTypes,
IntPointer outputTypes,
BoolPointer inputIsBroadcast,
BoolPointer outputIsBroadcast,
nvinfer.TensorFormat floatFormat,
int maxBatchSize)
\brief Configure the layer with input and output data types.
|
void |
IPluginV2.configureWithFormat(Dims32 inputDims,
int nbInputs,
Dims32 outputDims,
int nbOutputs,
int type,
int format,
int maxBatchSize) |
void |
IPluginV2.configureWithFormat(Dims32 inputDims,
int nbInputs,
Dims32 outputDims,
int nbOutputs,
int type,
int format,
int maxBatchSize) |
void |
IPluginV2.configureWithFormat(Dims32 inputDims,
int nbInputs,
Dims32 outputDims,
int nbOutputs,
nvinfer.DataType type,
nvinfer.TensorFormat format,
int maxBatchSize)
\brief Configure the layer.
|
void |
IPluginV2.configureWithFormat(Dims32 inputDims,
int nbInputs,
Dims32 outputDims,
int nbOutputs,
nvinfer.DataType type,
nvinfer.TensorFormat format,
int maxBatchSize)
\brief Configure the layer.
|
PluginTensorDesc |
PluginTensorDesc.dims(Dims32 setter) |
PluginTensorDesc |
PluginTensorDesc.dims(Dims32 setter) |
Dims32 |
IPluginV2.getOutputDimensions(int index,
Dims32 inputs,
int nbInputDims)
\brief Get the dimension of an output tensor.
|
Dims32 |
IPluginV2.getOutputDimensions(int index,
Dims32 inputs,
int nbInputDims)
\brief Get the dimension of an output tensor.
|
DynamicPluginTensorDesc |
DynamicPluginTensorDesc.max(Dims32 setter) |
DynamicPluginTensorDesc |
DynamicPluginTensorDesc.max(Dims32 setter) |
DynamicPluginTensorDesc |
DynamicPluginTensorDesc.min(Dims32 setter) |
DynamicPluginTensorDesc |
DynamicPluginTensorDesc.min(Dims32 setter) |
boolean |
IExecutionContext.setBindingDimensions(int bindingIndex,
Dims32 dimensions)
\brief Set the dynamic dimensions of a binding
|
boolean |
VExecutionContext.setBindingDimensions(int bindingIndex,
Dims32 dimensions) |
boolean |
VExecutionContext.setBindingDimensions(int bindingIndex,
Dims32 dimensions) |
boolean |
IExecutionContext.setBindingDimensions(int bindingIndex,
Dims32 dimensions)
\brief Set the dynamic dimensions of a binding
|
void |
IConvolutionLayer.setDilationNd(Dims32 dilation)
\brief Set the multi-dimension dilation of the convolution.
|
void |
IDeconvolutionLayer.setDilationNd(Dims32 dilation)
\brief Set the multi-dimension dilation of the deconvolution.
|
void |
VConvolutionLayer.setDilationNd(Dims32 dilation) |
void |
VDeconvolutionLayer.setDilationNd(Dims32 dilation) |
void |
VDeconvolutionLayer.setDilationNd(Dims32 dilation) |
void |
VConvolutionLayer.setDilationNd(Dims32 dilation) |
void |
IDeconvolutionLayer.setDilationNd(Dims32 dilation)
\brief Set the multi-dimension dilation of the deconvolution.
|
void |
IConvolutionLayer.setDilationNd(Dims32 dilation)
\brief Set the multi-dimension dilation of the convolution.
|
boolean |
IOptimizationProfile.setDimensions(BytePointer inputName,
int select,
Dims32 dims) |
boolean |
VOptimizationProfile.setDimensions(BytePointer inputName,
int select,
Dims32 dims) |
boolean |
VOptimizationProfile.setDimensions(BytePointer inputName,
int select,
Dims32 dims) |
boolean |
IOptimizationProfile.setDimensions(BytePointer inputName,
int select,
Dims32 dims) |
void |
IConstantLayer.setDimensions(Dims32 dimensions)
\brief Set the dimensions for the layer.
|
void |
IFillLayer.setDimensions(Dims32 dimensions)
\brief Set the output tensor's dimensions.
|
void |
ITensor.setDimensions(Dims32 dimensions)
\brief Set the dimensions of a tensor.
|
void |
VConstantLayer.setDimensions(Dims32 dimensions) |
void |
VFillLayer.setDimensions(Dims32 dimensions) |
void |
VTensor.setDimensions(Dims32 dimensions) |
void |
VTensor.setDimensions(Dims32 dimensions) |
void |
VFillLayer.setDimensions(Dims32 dimensions) |
void |
VConstantLayer.setDimensions(Dims32 dimensions) |
void |
ITensor.setDimensions(Dims32 dimensions)
\brief Set the dimensions of a tensor.
|
void |
IFillLayer.setDimensions(Dims32 dimensions)
\brief Set the output tensor's dimensions.
|
void |
IConstantLayer.setDimensions(Dims32 dimensions)
\brief Set the dimensions for the layer.
|
boolean |
IOptimizationProfile.setDimensions(String inputName,
nvinfer.OptProfileSelector select,
Dims32 dims)
\brief Set the minimum / optimum / maximum dimensions for a dynamic input tensor.
|
boolean |
VOptimizationProfile.setDimensions(String inputName,
nvinfer.OptProfileSelector select,
Dims32 dims) |
boolean |
VOptimizationProfile.setDimensions(String inputName,
nvinfer.OptProfileSelector select,
Dims32 dims) |
boolean |
IOptimizationProfile.setDimensions(String inputName,
nvinfer.OptProfileSelector select,
Dims32 dims)
\brief Set the minimum / optimum / maximum dimensions for a dynamic input tensor.
|
void |
IConvolutionLayer.setKernelSizeNd(Dims32 kernelSize)
\brief Set the multi-dimension kernel size of the convolution.
|
void |
IDeconvolutionLayer.setKernelSizeNd(Dims32 kernelSize)
\brief Set the multi-dimension kernel size of the deconvolution.
|
void |
VConvolutionLayer.setKernelSizeNd(Dims32 kernelSize) |
void |
VDeconvolutionLayer.setKernelSizeNd(Dims32 kernelSize) |
void |
VDeconvolutionLayer.setKernelSizeNd(Dims32 kernelSize) |
void |
VConvolutionLayer.setKernelSizeNd(Dims32 kernelSize) |
void |
IDeconvolutionLayer.setKernelSizeNd(Dims32 kernelSize)
\brief Set the multi-dimension kernel size of the deconvolution.
|
void |
IConvolutionLayer.setKernelSizeNd(Dims32 kernelSize)
\brief Set the multi-dimension kernel size of the convolution.
|
void |
IResizeLayer.setOutputDimensions(Dims32 dimensions)
\brief Set the output dimensions.
|
void |
VResizeLayer.setOutputDimensions(Dims32 dimensions) |
void |
VResizeLayer.setOutputDimensions(Dims32 dimensions) |
void |
IResizeLayer.setOutputDimensions(Dims32 dimensions)
\brief Set the output dimensions.
|
void |
IConvolutionLayer.setPaddingNd(Dims32 padding)
\brief Set the multi-dimension padding of the convolution.
|
void |
IDeconvolutionLayer.setPaddingNd(Dims32 padding)
\brief Set the multi-dimension padding of the deconvolution.
|
void |
IPoolingLayer.setPaddingNd(Dims32 padding)
\brief Set the multi-dimension padding for pooling.
|
void |
VConvolutionLayer.setPaddingNd(Dims32 padding) |
void |
VDeconvolutionLayer.setPaddingNd(Dims32 padding) |
void |
VPoolingLayer.setPaddingNd(Dims32 padding) |
void |
VPoolingLayer.setPaddingNd(Dims32 padding) |
void |
VDeconvolutionLayer.setPaddingNd(Dims32 padding) |
void |
VConvolutionLayer.setPaddingNd(Dims32 padding) |
void |
IPoolingLayer.setPaddingNd(Dims32 padding)
\brief Set the multi-dimension padding for pooling.
|
void |
IDeconvolutionLayer.setPaddingNd(Dims32 padding)
\brief Set the multi-dimension padding of the deconvolution.
|
void |
IConvolutionLayer.setPaddingNd(Dims32 padding)
\brief Set the multi-dimension padding of the convolution.
|
void |
IConvolutionLayer.setPostPadding(Dims32 padding)
\brief Set the multi-dimension post-padding of the convolution.
|
void |
IDeconvolutionLayer.setPostPadding(Dims32 padding)
\brief Set the multi-dimension post-padding of the deconvolution.
|
void |
IPoolingLayer.setPostPadding(Dims32 padding)
\brief Set the multi-dimension post-padding for pooling.
|
void |
VConvolutionLayer.setPostPadding(Dims32 padding) |
void |
VDeconvolutionLayer.setPostPadding(Dims32 padding) |
void |
VPoolingLayer.setPostPadding(Dims32 padding) |
void |
VPoolingLayer.setPostPadding(Dims32 padding) |
void |
VDeconvolutionLayer.setPostPadding(Dims32 padding) |
void |
VConvolutionLayer.setPostPadding(Dims32 padding) |
void |
IPoolingLayer.setPostPadding(Dims32 padding)
\brief Set the multi-dimension post-padding for pooling.
|
void |
IDeconvolutionLayer.setPostPadding(Dims32 padding)
\brief Set the multi-dimension post-padding of the deconvolution.
|
void |
IConvolutionLayer.setPostPadding(Dims32 padding)
\brief Set the multi-dimension post-padding of the convolution.
|
void |
IPaddingLayer.setPostPaddingNd(Dims32 padding)
\brief Set the padding that is applied at the end of the tensor.
|
void |
VPaddingLayer.setPostPaddingNd(Dims32 padding) |
void |
VPaddingLayer.setPostPaddingNd(Dims32 padding) |
void |
IPaddingLayer.setPostPaddingNd(Dims32 padding)
\brief Set the padding that is applied at the end of the tensor.
|
void |
IConvolutionLayer.setPrePadding(Dims32 padding)
\brief Set the multi-dimension pre-padding of the convolution.
|
void |
IDeconvolutionLayer.setPrePadding(Dims32 padding)
\brief Set the multi-dimension pre-padding of the deconvolution.
|
void |
IPoolingLayer.setPrePadding(Dims32 padding)
\brief Set the multi-dimension pre-padding for pooling.
|
void |
VConvolutionLayer.setPrePadding(Dims32 padding) |
void |
VDeconvolutionLayer.setPrePadding(Dims32 padding) |
void |
VPoolingLayer.setPrePadding(Dims32 padding) |
void |
VPoolingLayer.setPrePadding(Dims32 padding) |
void |
VDeconvolutionLayer.setPrePadding(Dims32 padding) |
void |
VConvolutionLayer.setPrePadding(Dims32 padding) |
void |
IPoolingLayer.setPrePadding(Dims32 padding)
\brief Set the multi-dimension pre-padding for pooling.
|
void |
IDeconvolutionLayer.setPrePadding(Dims32 padding)
\brief Set the multi-dimension pre-padding of the deconvolution.
|
void |
IConvolutionLayer.setPrePadding(Dims32 padding)
\brief Set the multi-dimension pre-padding of the convolution.
|
void |
IPaddingLayer.setPrePaddingNd(Dims32 padding)
\brief Set the padding that is applied at the start of the tensor.
|
void |
VPaddingLayer.setPrePaddingNd(Dims32 padding) |
void |
VPaddingLayer.setPrePaddingNd(Dims32 padding) |
void |
IPaddingLayer.setPrePaddingNd(Dims32 padding)
\brief Set the padding that is applied at the start of the tensor.
|
void |
IShuffleLayer.setReshapeDimensions(Dims32 dimensions)
\brief Set the reshaped dimensions.
|
void |
VShuffleLayer.setReshapeDimensions(Dims32 dimensions) |
void |
VShuffleLayer.setReshapeDimensions(Dims32 dimensions) |
void |
IShuffleLayer.setReshapeDimensions(Dims32 dimensions)
\brief Set the reshaped dimensions.
|
void |
ISliceLayer.setSize(Dims32 size)
\brief Set the dimensions of the output slice.
|
void |
VSliceLayer.setSize(Dims32 size) |
void |
VSliceLayer.setSize(Dims32 size) |
void |
ISliceLayer.setSize(Dims32 size)
\brief Set the dimensions of the output slice.
|
void |
ISliceLayer.setStart(Dims32 start)
\brief Set the start offset that the slice layer uses to create the output slice.
|
void |
VSliceLayer.setStart(Dims32 start) |
void |
VSliceLayer.setStart(Dims32 start) |
void |
ISliceLayer.setStart(Dims32 start)
\brief Set the start offset that the slice layer uses to create the output slice.
|
void |
ISliceLayer.setStride(Dims32 stride)
\brief Set the stride for computing the output slice data.
|
void |
VSliceLayer.setStride(Dims32 stride) |
void |
VSliceLayer.setStride(Dims32 stride) |
void |
ISliceLayer.setStride(Dims32 stride)
\brief Set the stride for computing the output slice data.
|
void |
IConvolutionLayer.setStrideNd(Dims32 stride)
\brief Set the multi-dimension stride of the convolution.
|
void |
IDeconvolutionLayer.setStrideNd(Dims32 stride)
\brief Set the multi-dimension stride of the deconvolution.
|
void |
IPoolingLayer.setStrideNd(Dims32 stride)
\brief Set the multi-dimension stride for pooling.
|
void |
VConvolutionLayer.setStrideNd(Dims32 stride) |
void |
VDeconvolutionLayer.setStrideNd(Dims32 stride) |
void |
VPoolingLayer.setStrideNd(Dims32 stride) |
void |
VPoolingLayer.setStrideNd(Dims32 stride) |
void |
VDeconvolutionLayer.setStrideNd(Dims32 stride) |
void |
VConvolutionLayer.setStrideNd(Dims32 stride) |
void |
IPoolingLayer.setStrideNd(Dims32 stride)
\brief Set the multi-dimension stride for pooling.
|
void |
IDeconvolutionLayer.setStrideNd(Dims32 stride)
\brief Set the multi-dimension stride of the deconvolution.
|
void |
IConvolutionLayer.setStrideNd(Dims32 stride)
\brief Set the multi-dimension stride of the convolution.
|
void |
IPoolingLayer.setWindowSizeNd(Dims32 windowSize)
\brief Set the multi-dimension window size for pooling.
|
void |
VPoolingLayer.setWindowSizeNd(Dims32 windowSize) |
void |
VPoolingLayer.setWindowSizeNd(Dims32 windowSize) |
void |
IPoolingLayer.setWindowSizeNd(Dims32 windowSize)
\brief Set the multi-dimension window size for pooling.
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
IUffParser.registerInput(BytePointer inputName,
Dims32 inputDims,
int inputOrder) |
boolean |
IUffParser.registerInput(BytePointer inputName,
Dims32 inputDims,
int inputOrder) |
boolean |
IUffParser.registerInput(String inputName,
Dims32 inputDims,
nvparsers.UffInputOrder inputOrder)
\brief Register an input name of a UFF network with the associated Dimensions.
|
boolean |
IUffParser.registerInput(String inputName,
Dims32 inputDims,
nvparsers.UffInputOrder inputOrder)
\brief Register an input name of a UFF network with the associated Dimensions.
|
Copyright © 2022. All rights reserved.