TensorRT 10.5.0

Optimization profile for dynamic input dimensions and shape tensors. More...
#include <NvInferRuntime.h>
Public Member Functions  
bool  setDimensions (char const *inputName, OptProfileSelector select, Dims const &dims) noexcept 
Set the minimum / optimum / maximum dimensions for a dynamic input tensor. More...  
Dims  getDimensions (char const *inputName, OptProfileSelector select) const noexcept 
Get the minimum / optimum / maximum dimensions for a dynamic input tensor. More...  
bool  setShapeValues (char const *inputName, OptProfileSelector select, int32_t const *values, int32_t nbValues) noexcept 
Set the minimum / optimum / maximum values for an input shape tensor. More...  
int32_t  getNbShapeValues (char const *inputName) const noexcept 
Get the number of values for an input shape tensor. More...  
int32_t const *  getShapeValues (char const *inputName, OptProfileSelector select) const noexcept 
Get the minimum / optimum / maximum values for an input shape tensor. More...  
bool  setExtraMemoryTarget (float target) noexcept 
Set a target for extra GPU memory that may be used by this profile. More...  
float  getExtraMemoryTarget () const noexcept 
Get the extra memory target that has been defined for this profile. More...  
bool  isValid () const noexcept 
Check whether the optimization profile can be passed to an IBuilderConfig object. More...  
Protected Member Functions  
virtual  ~IOptimizationProfile () noexcept=default 
Protected Member Functions inherited from nvinfer1::INoCopy  
INoCopy ()=default  
virtual  ~INoCopy ()=default 
INoCopy (INoCopy const &other)=delete  
INoCopy &  operator= (INoCopy const &other)=delete 
INoCopy (INoCopy &&other)=delete  
INoCopy &  operator= (INoCopy &&other)=delete 
Protected Attributes  
apiv::VOptimizationProfile *  mImpl 
Optimization profile for dynamic input dimensions and shape tensors.
When building an ICudaEngine from an INetworkDefinition that has dynamically resizable inputs (at least one input tensor has one or more of its dimensions specified as 1) or shape input tensors, users need to specify at least one optimization profile. Optimization profiles are numbered 0, 1, ... The first optimization profile that has been defined (with index 0) will be used by the ICudaEngine whenever no optimization profile has been selected explicitly. If none of the inputs are dynamic, the default optimization profile will be generated automatically unless it is explicitly provided by the user (this is possible but not required in this case). If more than a single optimization profile is defined, users may set a target how much additional weight space should be maximally allocated to each additional profile (as a fraction of the maximum, unconstrained memory).
Users set optimum input tensor dimensions, as well as minimum and maximum input tensor dimensions. The builder selects the kernels that result in the lowest runtime for the optimum input tensor dimensions, and are valid for all input tensor sizes in the valid range between minimum and maximum dimensions. A runtime error will be raised if the input tensor dimensions fall outside the valid range for this profile. Likewise, users provide minimum, optimum, and maximum values for all shape tensor input values.

protectedvirtualdefaultnoexcept 

inlinenoexcept 
Get the minimum / optimum / maximum dimensions for a dynamic input tensor.
If the dimensions have not been previously set via setDimensions(), return an invalid Dims with nbDims == 1.

inlinenoexcept 
Get the extra memory target that has been defined for this profile.
This defaults to 1.0F.

inlinenoexcept 
Get the number of values for an input shape tensor.
This will return the number of shape values if setShapeValues() has been called before for this input tensor. Otherwise, return 1.

inlinenoexcept 
Get the minimum / optimum / maximum values for an input shape tensor.
If the shape values have not been set previously with setShapeValues(), this returns nullptr.

inlinenoexcept 
Check whether the optimization profile can be passed to an IBuilderConfig object.
This function performs partial validation, by e.g. checking that whenever one of the minimum, optimum, or maximum dimensions of a tensor have been set, the others have also been set and have the same rank, as well as checking that the optimum dimensions are always as least as large as the minimum dimensions, and that the maximum dimensions are at least as large as the optimum dimensions. Some validation steps require knowledge of the network definition and are deferred to engine build time.

inlinenoexcept 
Set the minimum / optimum / maximum dimensions for a dynamic input tensor.
This function must be called three times (for the minimum, optimum, and maximum) for any network input tensor that has dynamic dimensions. If minDims, optDims, and maxDims are the minimum, optimum, and maximum dimensions, and networkDims are the dimensions for this input tensor that are provided to the INetworkDefinition object, then the following conditions must all hold:
(1) minDims.nbDims == optDims.nbDims == maxDims.nbDims == networkDims.nbDims (2) 0 <= minDims.d[i] <= optDims.d[i] <= maxDims.d[i] for i = 0, ..., networkDims.nbDims1 (3) if networkDims.d[i] != 1, then minDims.d[i] == optDims.d[i] == maxDims.d[i] == networkDims.d[i]
This function may (but need not be) called for an input tensor that does not have dynamic dimensions. In this case, the third argument must always equal networkDims.
inputName  The input tensor name 
select  Whether to set the minimum, optimum, or maximum dimensions 
dims  The minimum, optimum, or maximum dimensions for this input tensor 

inlinenoexcept 
Set a target for extra GPU memory that may be used by this profile.
target  Additional memory that the builder should aim to maximally allocate for this profile, as a fraction of the memory it would use if the user did not impose any constraints on memory. This unconstrained case is the default; it corresponds to target == 1.0. If target == 0.0, the builder aims to create the new optimization profile without allocating any additional weight memory. Valid inputs lie between 0.0 and 1.0. This parameter is only a hint, and TensorRT does not guarantee that the target will be reached. This parameter is ignored for the first (default) optimization profile that is defined. 

inlinenoexcept 
Set the minimum / optimum / maximum values for an input shape tensor.
This function must be called three times for every input tensor t that is a shape tensor (t.isShape() == true). This implies that the dimensions of t are fixed at network definition time and the volume does not exceed 64. This function must not be called for any input tensor that is not a shape tensor.
Each time this function is called for the same input tensor, the same nbValues must be supplied (either 1 if the tensor rank is 0, or dims.d[0] if the rank is 1). Furthermore, if minVals, optVals, maxVals are the minimum, optimum, and maximum values, it must be true that minVals[i] <= optVals[i] <= maxVals[i] for i = 0, ..., nbValues  1. Execution of the network must be valid for the optVals.
Shape tensors are tensors that contribute to shape calculations in some way. While input shape tensors can be type kINT32 or kINT64, the values used to set the minimum, optimium, and maximum values must fit in int32_t.
Examples:
Tightening the minVals and maxVals bounds to cover only values that are necessary may help optimization.
inputName  The input tensor name 
select  Whether to set the minimum, optimum, or maximum input values. 
values  An array of length nbValues containing the minimum, optimum, or maximum shape tensor elements. For multidimensional tensors, the array is in rowmajor order. 
nbValues  The length of the value array, which must equal the number of shape tensor elements (>= 1) 

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