TensorRT
8.0.2

Layer type for shuffling data. More...
#include <NvInfer.h>
Public Member Functions  
void  setFirstTranspose (Permutation permutation) noexcept 
Set the permutation applied by the first transpose operation. More...  
Permutation  getFirstTranspose () const noexcept 
Get the permutation applied by the first transpose operation. More...  
void  setReshapeDimensions (Dims dimensions) noexcept 
Set the reshaped dimensions. More...  
Dims  getReshapeDimensions () const noexcept 
Get the reshaped dimensions. More...  
void  setSecondTranspose (Permutation permutation) noexcept 
Set the permutation applied by the second transpose operation. More...  
Permutation  getSecondTranspose () const noexcept 
Get the permutation applied by the second transpose operation. More...  
void  setZeroIsPlaceholder (bool zeroIsPlaceholder) noexcept 
Set meaning of 0 in reshape dimensions. More...  
bool  getZeroIsPlaceholder () const noexcept 
Get meaning of 0 in reshape dimensions. More...  
void  setInput (int32_t index, ITensor &tensor) noexcept 
Append or replace an input of this layer with a specific tensor. More...  
Public Member Functions inherited from nvinfer1::ILayer  
LayerType  getType () const noexcept 
Return the type of a layer. More...  
void  setName (const char *name) noexcept 
Set the name of a layer. More...  
const char *  getName () const noexcept 
Return the name of a layer. More...  
int32_t  getNbInputs () const noexcept 
Get the number of inputs of a layer.  
ITensor *  getInput (int32_t index) const noexcept 
Get the layer input corresponding to the given index. More...  
int32_t  getNbOutputs () const noexcept 
Get the number of outputs of a layer.  
ITensor *  getOutput (int32_t index) const noexcept 
Get the layer output corresponding to the given index. More...  
void  setInput (int32_t index, ITensor &tensor) noexcept 
Replace an input of this layer with a specific tensor. More...  
void  setPrecision (DataType dataType) noexcept 
Set the computational precision of this layer. More...  
DataType  getPrecision () const noexcept 
get the computational precision of this layer More...  
bool  precisionIsSet () const noexcept 
whether the computational precision has been set for this layer More...  
void  resetPrecision () noexcept 
reset the computational precision for this layer More...  
void  setOutputType (int32_t index, DataType dataType) noexcept 
Set the output type of this layer. More...  
DataType  getOutputType (int32_t index) const noexcept 
get the output type of this layer More...  
bool  outputTypeIsSet (int32_t index) const noexcept 
whether the output type has been set for this layer More...  
void  resetOutputType (int32_t index) noexcept 
reset the output type for this layer More...  
Protected Attributes  
apiv::VShuffleLayer *  mImpl 
Protected Attributes inherited from nvinfer1::ILayer  
apiv::VLayer *  mLayer 
Additional Inherited Members  
Protected Member Functions inherited from nvinfer1::INoCopy  
INoCopy (const INoCopy &other)=delete  
INoCopy &  operator= (const INoCopy &other)=delete 
INoCopy (INoCopy &&other)=delete  
INoCopy &  operator= (INoCopy &&other)=delete 
Layer type for shuffling data.
This layer shuffles data by applying in sequence: a transpose operation, a reshape operation and a second transpose operation. The dimension types of the output are those of the reshape dimension.
The layer has an optional second input. If present, it must be a 1D Int32 shape tensor, and the reshape dimensions are taken from it.

inlinenoexcept 
Get the permutation applied by the first transpose operation.

inlinenoexcept 
Get the reshaped dimensions.
If a second input is present and nonnull, or setReshapeDimensions has not yet been called, this function returns Dims with nbDims == 1.

inlinenoexcept 
Get the permutation applied by the second transpose operation.

inlinenoexcept 
Get meaning of 0 in reshape dimensions.

inlinenoexcept 
Set the permutation applied by the first transpose operation.
permutation  The dimension permutation applied before the reshape. 
The default is the identity permutation.

inlinenoexcept 
Append or replace an input of this layer with a specific tensor.
index  the index of the input to modify. 
tensor  the new input tensor Sets the input tensor for the given index. The index must be 0 for a static shuffle layer. A static shuffle layer is converted to a dynamic shuffle layer by calling setInput with an index 1. A dynamic shuffle layer cannot be converted back to a static shuffle layer. 
For a dynamic shuffle layer, the values 0 and 1 are valid. The indices in the dynamic case are as follows:
If this function is called with a value 1, then the function getNbInputs() changes from returning 1 to 2.
The reshape dimensions are treated identically to how they are treated if set statically via setReshapeDimensions. In particular, a 1 is treated as a wildcard even if dynamically supplied at runtime, and a 0 is treated as a placeholder if getZeroIsPlaceholder() = true, which is the default. If the placeholder interpretation of 0 is unwanted because the runtime dimension should be 0 when the reshape dimension is 0, be sure to call setZeroIsPlacholder(false) on the IShuffleLayer.

inlinenoexcept 
Set the reshaped dimensions.
dimensions  The reshaped dimensions. 
Two special values can be used as dimensions.
Value 0 copies the corresponding dimension from input. This special value can be used more than once in the dimensions. If number of reshape dimensions is less than input, 0s are resolved by aligning the most significant dimensions of input.
Value 1 infers that particular dimension by looking at input and rest of the reshape dimensions. Note that only a maximum of one dimension is permitted to be specified as 1.
The product of the new dimensions must be equal to the product of the old.
If a second input had been used to create this layer, that input is reset to null by this method.

inlinenoexcept 
Set the permutation applied by the second transpose operation.
permutation  The dimension permutation applied after the reshape. 
The default is the identity permutation.
The permutation is applied as outputDimensionIndex = permutation.order[inputDimensionIndex], so to permute from CHW order to HWC order, the required permutation is [1, 2, 0].

inlinenoexcept 
Set meaning of 0 in reshape dimensions.
If true, then a 0 in the reshape dimensions denotes copying the corresponding dimension from the first input tensor. If false, then a 0 in the reshape dimensions denotes a zerolength dimension.
Default: true