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.
 See also
 IBuilderConfig::addOptimizationProfile()
bool nvinfer1::IOptimizationProfile::isValid 
( 
 ) 
const 

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.
 Returns
 true if the optimization profile is valid and may be passed to an IBuilderConfig, else false
bool nvinfer1::IOptimizationProfile::setDimensions 
( 
char const * 
inputName, 


OptProfileSelector 
select, 


Dims 
dims 

) 
 

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.
 Parameters

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 
 Returns
 false if an inconsistency was detected (e.g. the rank does not match another dimension that was previously set for the same input), true if no inconsistency was detected. Note that inputs can be validated only partially; a full validation is performed at engine build time.
 Warning
 If run on DLA, minimum, optimum, and maximum dimensions must to be the same.
bool nvinfer1::IOptimizationProfile::setShapeValues 
( 
char const * 
inputName, 


OptProfileSelector 
select, 


int32_t const * 
values, 


int32_t 
nbValues 

) 
 

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 datatype of t is DataType::kINT32, the rank is either 0 or 1, and the dimensions of t are fixed at network definition time. 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, and can contain any int32_t values appropriate for the network. Examples:
 A shape tensor used as the second input to IShuffleLayer can contain a 1 wildcard. The corresponding minVal[i] should be 1.
 A shape tensor used as the stride input to ISliceLayer can contain any valid strides. The values could be positive, negative, or zero.
 A shape tensor subtracted from zero to compute the size input of an ISliceLayer can contain any nonpositive values that yield a valid slice operation.
Tightening the minVals and maxVals bounds to cover only values that are necessary may help optimization.
 Parameters

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. 
nbValues  The length of the value array, which must equal the number of shape tensor elements (>= 1) 
 Returns
 false if an inconsistency was detected (e.g. nbValues does not match a previous call for the same tensor), else true. As for setDimensions(), a full validation can only be performed at engine build time.
 Warning
 If run on DLA, minimum, optimum, and maximum shape values must to be the same.