134 :
Dims2(height, width)
144 int&
h() {
return d[0]; }
151 int h()
const {
return d[0]; }
158 int&
w() {
return d[1]; }
165 int w()
const {
return d[1]; }
181 d[0] =
d[1] =
d[2] = 0;
227 :
Dims3(channels, height, width)
238 int&
c() {
return d[0]; }
245 int c()
const {
return d[0]; }
252 int&
h() {
return d[1]; }
259 int h()
const {
return d[1]; }
266 int&
w() {
return d[2]; }
273 int w()
const {
return d[2]; }
289 d[0] =
d[1] =
d[2] =
d[3] = 0;
300 Dims4(
int d0,
int d1,
int d2,
int d3)
338 DimsNCHW(
int batchSize,
int channels,
int height,
int width)
339 :
Dims4(batchSize, channels, height, width)
351 int&
n() {
return d[0]; }
358 int n()
const {
return d[0]; }
365 int&
c() {
return d[1]; }
372 int c()
const {
return d[1]; }
379 int&
h() {
return d[2]; }
386 int h()
const {
return d[2]; }
393 int&
w() {
return d[3]; }
400 int w()
const {
return d[3]; }
472 virtual void setName(
const char* name) TRTNOEXCEPT = 0;
481 virtual const char* getName()
const TRTNOEXCEPT = 0;
497 virtual void setDimensions(
Dims dimensions) TRTNOEXCEPT = 0;
507 virtual Dims getDimensions()
const TRTNOEXCEPT = 0;
528 virtual DataType getType()
const TRTNOEXCEPT = 0;
540 virtual bool setDynamicRange(
float min,
float max) TRTNOEXCEPT = 0;
549 TRT_DEPRECATED
virtual float getDynamicRange()
const TRTNOEXCEPT = 0;
554 virtual bool isNetworkInput()
const TRTNOEXCEPT = 0;
559 virtual bool isNetworkOutput()
const TRTNOEXCEPT = 0;
582 virtual void setBroadcastAcrossBatch(
bool broadcastAcrossBatch) TRTNOEXCEPT = 0;
595 virtual bool getBroadcastAcrossBatch()
const TRTNOEXCEPT = 0;
614 virtual void setLocation(
TensorLocation location) TRTNOEXCEPT = 0;
621 virtual bool dynamicRangeIsSet()
const TRTNOEXCEPT = 0;
626 virtual void resetDynamicRange() TRTNOEXCEPT = 0;
633 virtual float getDynamicRangeMin()
const TRTNOEXCEPT = 0;
640 virtual float getDynamicRangeMax()
const TRTNOEXCEPT = 0;
650 virtual void setAllowedFormats(
TensorFormats formats) TRTNOEXCEPT = 0;
660 virtual TensorFormats getAllowedFormats()
const TRTNOEXCEPT = 0;
672 virtual bool isShapeTensor()
const TRTNOEXCEPT = 0;
688 virtual bool isExecutionTensor()
const TRTNOEXCEPT = 0;
706 virtual LayerType getType()
const TRTNOEXCEPT = 0;
715 virtual void setName(
const char* name) TRTNOEXCEPT = 0;
723 virtual const char* getName()
const TRTNOEXCEPT = 0;
728 virtual int getNbInputs()
const TRTNOEXCEPT = 0;
738 virtual ITensor* getInput(
int index)
const TRTNOEXCEPT = 0;
743 virtual int getNbOutputs()
const TRTNOEXCEPT = 0;
751 virtual ITensor* getOutput(
int index)
const TRTNOEXCEPT = 0;
764 virtual void setInput(
int index,
ITensor& tensor) TRTNOEXCEPT = 0;
781 virtual void setPrecision(
DataType dataType) TRTNOEXCEPT = 0;
790 virtual DataType getPrecision()
const TRTNOEXCEPT = 0;
799 virtual bool precisionIsSet()
const TRTNOEXCEPT = 0;
806 virtual void resetPrecision() TRTNOEXCEPT = 0;
826 virtual void setOutputType(
int index,
DataType dataType) TRTNOEXCEPT = 0;
837 virtual DataType getOutputType(
int index)
const TRTNOEXCEPT = 0;
847 virtual bool outputTypeIsSet(
int index)
const TRTNOEXCEPT = 0;
856 virtual void resetOutputType(
int index) TRTNOEXCEPT = 0;
911 virtual void setKernelSize(
DimsHW kernelSize) TRTNOEXCEPT = 0;
918 virtual DimsHW getKernelSize()
const TRTNOEXCEPT = 0;
927 virtual void setNbOutputMaps(
int nbOutputMaps) TRTNOEXCEPT = 0;
934 virtual int getNbOutputMaps()
const TRTNOEXCEPT = 0;
945 virtual void setStride(
DimsHW stride) TRTNOEXCEPT = 0;
950 virtual DimsHW getStride()
const TRTNOEXCEPT = 0;
964 virtual void setPadding(
DimsHW padding) TRTNOEXCEPT = 0;
971 virtual DimsHW getPadding()
const TRTNOEXCEPT = 0;
986 virtual void setNbGroups(
int nbGroups) TRTNOEXCEPT = 0;
993 virtual int getNbGroups()
const TRTNOEXCEPT = 0;
1004 virtual void setKernelWeights(
Weights weights) TRTNOEXCEPT = 0;
1011 virtual Weights getKernelWeights()
const TRTNOEXCEPT = 0;
1023 virtual void setBiasWeights(
Weights weights) TRTNOEXCEPT = 0;
1030 virtual Weights getBiasWeights()
const TRTNOEXCEPT = 0;
1039 virtual void setDilation(
DimsHW dilation) TRTNOEXCEPT = 0;
1046 virtual DimsHW getDilation()
const TRTNOEXCEPT = 0;
1063 virtual void setPrePadding(
Dims padding) TRTNOEXCEPT = 0;
1070 virtual Dims getPrePadding()
const TRTNOEXCEPT = 0;
1083 virtual void setPostPadding(
Dims padding) TRTNOEXCEPT = 0;
1090 virtual Dims getPostPadding()
const TRTNOEXCEPT = 0;
1101 virtual void setPaddingMode(
PaddingMode paddingMode) TRTNOEXCEPT = 0;
1110 virtual PaddingMode getPaddingMode()
const TRTNOEXCEPT = 0;
1119 virtual void setKernelSizeNd(
Dims kernelSize) TRTNOEXCEPT = 0;
1126 virtual Dims getKernelSizeNd()
const TRTNOEXCEPT = 0;
1137 virtual void setStrideNd(
Dims stride) TRTNOEXCEPT = 0;
1144 virtual Dims getStrideNd()
const TRTNOEXCEPT = 0;
1158 virtual void setPaddingNd(
Dims padding) TRTNOEXCEPT = 0;
1167 virtual Dims getPaddingNd()
const TRTNOEXCEPT = 0;
1176 virtual void setDilationNd(
Dims dilation) TRTNOEXCEPT = 0;
1183 virtual Dims getDilationNd()
const TRTNOEXCEPT = 0;
1225 virtual void setNbOutputChannels(
int nbOutputs) TRTNOEXCEPT = 0;
1232 virtual int getNbOutputChannels()
const TRTNOEXCEPT = 0;
1239 virtual void setKernelWeights(
Weights weights) TRTNOEXCEPT = 0;
1246 virtual Weights getKernelWeights()
const TRTNOEXCEPT = 0;
1255 virtual void setBiasWeights(
Weights weights) TRTNOEXCEPT = 0;
1262 virtual Weights getBiasWeights()
const TRTNOEXCEPT = 0;
1294 virtual ActivationType getActivationType()
const TRTNOEXCEPT = 0;
1309 virtual void setAlpha(
float alpha) TRTNOEXCEPT = 0;
1320 virtual void setBeta(
float beta) TRTNOEXCEPT = 0;
1326 virtual float getAlpha()
const TRTNOEXCEPT = 0;
1332 virtual float getBeta()
const TRTNOEXCEPT = 0;
1344 kMAX_AVERAGE_BLEND = 2
1378 virtual PoolingType getPoolingType()
const TRTNOEXCEPT = 0;
1387 virtual void setWindowSize(
DimsHW windowSize) TRTNOEXCEPT = 0;
1394 virtual DimsHW getWindowSize()
const TRTNOEXCEPT = 0;
1405 virtual void setStride(
DimsHW stride) TRTNOEXCEPT = 0;
1412 virtual DimsHW getStride()
const TRTNOEXCEPT = 0;
1423 virtual void setPadding(
DimsHW padding) TRTNOEXCEPT = 0;
1432 virtual DimsHW getPadding()
const TRTNOEXCEPT = 0;
1442 virtual void setBlendFactor(
float blendFactor) TRTNOEXCEPT = 0;
1452 virtual float getBlendFactor()
const TRTNOEXCEPT = 0;
1463 virtual void setAverageCountExcludesPadding(
bool exclusive) TRTNOEXCEPT = 0;
1471 virtual bool getAverageCountExcludesPadding()
const TRTNOEXCEPT = 0;
1488 virtual void setPrePadding(
Dims padding) TRTNOEXCEPT = 0;
1495 virtual Dims getPrePadding()
const TRTNOEXCEPT = 0;
1508 virtual void setPostPadding(
Dims padding) TRTNOEXCEPT = 0;
1515 virtual Dims getPostPadding()
const TRTNOEXCEPT = 0;
1525 virtual void setPaddingMode(
PaddingMode paddingMode) TRTNOEXCEPT = 0;
1533 virtual PaddingMode getPaddingMode()
const TRTNOEXCEPT = 0;
1542 virtual void setWindowSizeNd(
Dims windowSize) TRTNOEXCEPT = 0;
1549 virtual Dims getWindowSizeNd()
const TRTNOEXCEPT = 0;
1560 virtual void setStrideNd(
Dims stride) TRTNOEXCEPT = 0;
1567 virtual Dims getStrideNd()
const TRTNOEXCEPT = 0;
1581 virtual void setPaddingNd(
Dims padding) TRTNOEXCEPT = 0;
1590 virtual Dims getPaddingNd()
const TRTNOEXCEPT = 0;
1611 virtual void setWindowSize(
int windowSize) TRTNOEXCEPT = 0;
1618 virtual int getWindowSize()
const TRTNOEXCEPT = 0;
1626 virtual void setAlpha(
float alpha) TRTNOEXCEPT = 0;
1633 virtual float getAlpha()
const TRTNOEXCEPT = 0;
1641 virtual void setBeta(
float beta) TRTNOEXCEPT = 0;
1648 virtual float getBeta()
const TRTNOEXCEPT = 0;
1656 virtual void setK(
float k) TRTNOEXCEPT = 0;
1663 virtual float getK()
const TRTNOEXCEPT = 0;
1717 virtual void setMode(
ScaleMode mode) TRTNOEXCEPT = 0;
1724 virtual ScaleMode getMode()
const TRTNOEXCEPT = 0;
1731 virtual void setShift(
Weights shift) TRTNOEXCEPT = 0;
1738 virtual Weights getShift()
const TRTNOEXCEPT = 0;
1745 virtual void setScale(
Weights scale) TRTNOEXCEPT = 0;
1752 virtual Weights getScale()
const TRTNOEXCEPT = 0;
1759 virtual void setPower(
Weights power) TRTNOEXCEPT = 0;
1766 virtual Weights getPower()
const TRTNOEXCEPT = 0;
1784 virtual int getChannelAxis()
const TRTNOEXCEPT = 0;
1832 virtual void setAxes(uint32_t axes) TRTNOEXCEPT = 0;
1839 virtual uint32_t getAxes()
const TRTNOEXCEPT = 0;
1867 virtual void setAxis(
int axis) TRTNOEXCEPT = 0;
1874 virtual int getAxis()
const TRTNOEXCEPT = 0;
1896 virtual void setKernelSize(
DimsHW kernelSize) TRTNOEXCEPT = 0;
1903 virtual DimsHW getKernelSize()
const TRTNOEXCEPT = 0;
1912 virtual void setNbOutputMaps(
int nbOutputMaps) TRTNOEXCEPT = 0;
1919 virtual int getNbOutputMaps()
const TRTNOEXCEPT = 0;
1928 virtual void setStride(
DimsHW stride) TRTNOEXCEPT = 0;
1935 virtual DimsHW getStride()
const TRTNOEXCEPT = 0;
1950 virtual void setPadding(
DimsHW padding) TRTNOEXCEPT = 0;
1957 virtual DimsHW getPadding()
const TRTNOEXCEPT = 0;
1972 virtual void setNbGroups(
int nbGroups) TRTNOEXCEPT = 0;
1979 virtual int getNbGroups()
const TRTNOEXCEPT = 0;
1990 virtual void setKernelWeights(
Weights weights) TRTNOEXCEPT = 0;
1997 virtual Weights getKernelWeights()
const TRTNOEXCEPT = 0;
2009 virtual void setBiasWeights(
Weights weights) TRTNOEXCEPT = 0;
2016 virtual Weights getBiasWeights()
const TRTNOEXCEPT = 0;
2033 virtual void setPrePadding(
Dims padding) TRTNOEXCEPT = 0;
2040 virtual Dims getPrePadding()
const TRTNOEXCEPT = 0;
2053 virtual void setPostPadding(
Dims padding) TRTNOEXCEPT = 0;
2060 virtual Dims getPostPadding()
const TRTNOEXCEPT = 0;
2070 virtual void setPaddingMode(
PaddingMode paddingMode) TRTNOEXCEPT = 0;
2078 virtual PaddingMode getPaddingMode()
const TRTNOEXCEPT = 0;
2087 virtual void setKernelSizeNd(
Dims kernelSize) TRTNOEXCEPT = 0;
2094 virtual Dims getKernelSizeNd()
const TRTNOEXCEPT = 0;
2105 virtual void setStrideNd(
Dims stride) TRTNOEXCEPT = 0;
2112 virtual Dims getStrideNd()
const TRTNOEXCEPT = 0;
2126 virtual void setPaddingNd(
Dims padding) TRTNOEXCEPT = 0;
2135 virtual Dims getPaddingNd()
const TRTNOEXCEPT = 0;
2213 virtual void setGatherAxis(
int axis) TRTNOEXCEPT = 0;
2220 virtual int getGatherAxis()
const TRTNOEXCEPT = 0;
2228 virtual void setNbElementWiseDims(
int k) TRTNOEXCEPT = 0;
2235 virtual int getNbElementWiseDims()
const TRTNOEXCEPT = 0;
2399 virtual unsigned getLayerCount()
const TRTNOEXCEPT = 0;
2409 virtual std::size_t getHiddenSize()
const TRTNOEXCEPT = 0;
2419 virtual int getSeqLength()
const TRTNOEXCEPT = 0;
2426 virtual void setOperation(
RNNOperation op) TRTNOEXCEPT = 0;
2433 virtual RNNOperation getOperation()
const TRTNOEXCEPT = 0;
2440 virtual void setInputMode(
RNNInputMode op) TRTNOEXCEPT = 0;
2447 virtual RNNInputMode getInputMode()
const TRTNOEXCEPT = 0;
2460 virtual void setDirection(
RNNDirection op) TRTNOEXCEPT = 0;
2467 virtual RNNDirection getDirection()
const TRTNOEXCEPT = 0;
2583 virtual void setWeights(
Weights weights) TRTNOEXCEPT = 0;
2590 virtual Weights getWeights()
const TRTNOEXCEPT = 0;
2643 virtual void setBias(
Weights bias) TRTNOEXCEPT = 0;
2650 virtual Weights getBias()
const TRTNOEXCEPT = 0;
2658 virtual int getDataLength()
const TRTNOEXCEPT = 0;
2677 virtual void setHiddenState(
ITensor& hidden) TRTNOEXCEPT = 0;
2684 virtual ITensor* getHiddenState()
const TRTNOEXCEPT = 0;
2705 virtual void setCellState(
ITensor& cell) TRTNOEXCEPT = 0;
2712 virtual ITensor* getCellState()
const TRTNOEXCEPT = 0;
2754 virtual int32_t getLayerCount()
const TRTNOEXCEPT = 0;
2755 virtual int32_t getHiddenSize()
const TRTNOEXCEPT = 0;
2756 virtual int32_t getMaxSeqLength()
const TRTNOEXCEPT = 0;
2757 virtual int32_t getDataLength()
const TRTNOEXCEPT = 0;
2773 virtual void setSequenceLengths(
ITensor& seqLengths) TRTNOEXCEPT = 0;
2782 virtual ITensor* getSequenceLengths()
const TRTNOEXCEPT = 0;
2788 virtual void setOperation(
RNNOperation op) TRTNOEXCEPT = 0;
2794 virtual RNNOperation getOperation()
const TRTNOEXCEPT = 0;
2800 virtual void setInputMode(
RNNInputMode op) TRTNOEXCEPT = 0;
2806 virtual RNNInputMode getInputMode()
const TRTNOEXCEPT = 0;
2812 virtual void setDirection(
RNNDirection op) TRTNOEXCEPT = 0;
2818 virtual RNNDirection getDirection()
const TRTNOEXCEPT = 0;
2837 virtual void setWeightsForGate(
int layerIndex,
RNNGateType gate,
bool isW,
Weights weights) TRTNOEXCEPT = 0;
2843 virtual Weights getWeightsForGate(
int layerIndex,
RNNGateType gate,
bool isW)
const TRTNOEXCEPT = 0;
2860 virtual void setBiasForGate(
int layerIndex,
RNNGateType gate,
bool isW,
Weights bias) TRTNOEXCEPT = 0;
2866 virtual Weights getBiasForGate(
int layerIndex,
RNNGateType gate,
bool isW)
const TRTNOEXCEPT = 0;
2880 virtual void setHiddenState(
ITensor& hidden) TRTNOEXCEPT = 0;
2886 virtual ITensor* getHiddenState()
const TRTNOEXCEPT = 0;
2902 virtual void setCellState(
ITensor& cell) TRTNOEXCEPT = 0;
2908 virtual ITensor* getCellState()
const TRTNOEXCEPT = 0;
2961 virtual IPlugin& getPlugin() TRTNOEXCEPT = 0;
2984 virtual IPluginV2& getPlugin() TRTNOEXCEPT = 0;
3103 virtual void setReduceAxes(uint32_t reduceAxes) TRTNOEXCEPT = 0;
3110 virtual uint32_t getReduceAxes()
const TRTNOEXCEPT = 0;
3117 virtual void setKeepDimensions(
bool keepDimensions) TRTNOEXCEPT = 0;
3124 virtual bool getKeepDimensions()
const TRTNOEXCEPT = 0;
3150 virtual void setPrePadding(
DimsHW padding) TRTNOEXCEPT = 0;
3157 virtual DimsHW getPrePadding()
const TRTNOEXCEPT = 0;
3166 virtual void setPostPadding(
DimsHW padding) TRTNOEXCEPT = 0;
3173 virtual DimsHW getPostPadding()
const TRTNOEXCEPT = 0;
3214 virtual void setFirstTranspose(
Permutation permutation) TRTNOEXCEPT = 0;
3223 virtual Permutation getFirstTranspose()
const TRTNOEXCEPT = 0;
3247 virtual void setReshapeDimensions(
Dims dimensions) TRTNOEXCEPT = 0;
3256 virtual Dims getReshapeDimensions()
const TRTNOEXCEPT = 0;
3281 virtual void setSecondTranspose(
Permutation permutation) TRTNOEXCEPT = 0;
3290 virtual Permutation getSecondTranspose()
const TRTNOEXCEPT = 0;
3333 virtual void setStart(
Dims start) TRTNOEXCEPT = 0;
3345 virtual Dims getStart()
const TRTNOEXCEPT = 0;
3357 virtual void setSize(
Dims size) TRTNOEXCEPT = 0;
3369 virtual Dims getSize()
const TRTNOEXCEPT = 0;
3381 virtual void setStride(
Dims stride) TRTNOEXCEPT = 0;
3393 virtual Dims getStride()
const TRTNOEXCEPT = 0;
3478 virtual void setOperation(
TopKOperation op) TRTNOEXCEPT = 0;
3494 virtual void setK(
int k) TRTNOEXCEPT = 0;
3501 virtual int getK()
const TRTNOEXCEPT = 0;
3508 virtual void setReduceAxes(uint32_t reduceAxes) TRTNOEXCEPT = 0;
3515 virtual uint32_t getReduceAxes()
const TRTNOEXCEPT = 0;
3589 virtual void setOperation(
int index,
MatrixOperation op) TRTNOEXCEPT = 0;
3596 virtual MatrixOperation getOperation(
int index)
const TRTNOEXCEPT = 0;
3606 TRT_DEPRECATED
virtual void setTranspose(
int index,
bool val) TRTNOEXCEPT = 0;
3615 TRT_DEPRECATED
virtual bool getTranspose(
int index)
const TRTNOEXCEPT = 0;
3673 virtual void setWeights(
Weights weights) TRTNOEXCEPT = 0;
3680 virtual Weights getWeights()
const TRTNOEXCEPT = 0;
3689 virtual void setDimensions(
Dims dimensions) TRTNOEXCEPT = 0;
3698 virtual Dims getDimensions()
const TRTNOEXCEPT = 0;
3772 virtual void setOutputDimensions(
Dims dimensions) TRTNOEXCEPT = 0;
3779 virtual Dims getOutputDimensions()
const TRTNOEXCEPT = 0;
3799 virtual void setScales(
const float* scales,
int nbScales) TRTNOEXCEPT = 0;
3815 virtual int getScales(
int size,
float* scales)
const TRTNOEXCEPT = 0;
3824 virtual void setResizeMode(
ResizeMode resizeMode) TRTNOEXCEPT = 0;
3831 virtual ResizeMode getResizeMode()
const TRTNOEXCEPT = 0;
3842 virtual void setAlignCorners(
bool alignCorners) TRTNOEXCEPT = 0;
3849 virtual bool getAlignCorners()
const TRTNOEXCEPT = 0;
3929 virtual void markOutput(
ITensor& tensor) TRTNOEXCEPT = 0;
4012 virtual ILRNLayer* addLRN(
ITensor& input,
int window,
float alpha,
float beta,
float k) TRTNOEXCEPT = 0;
4158 TRT_DEPRECATED
virtual IRNNLayer* addRNN(
ITensor& inputs,
int layerCount, std::size_t hiddenSize,
int maxSeqLen,
4178 ITensor*
const* inputs,
int nbInputs,
IPlugin& plugin) TRTNOEXCEPT = 0;
4256 TRT_DEPRECATED
virtual void setConvolutionOutputDimensionsFormula(
4286 TRT_DEPRECATED
virtual void setDeconvolutionOutputDimensionsFormula(
4309 virtual int getNbLayers()
const TRTNOEXCEPT = 0;
4320 virtual ILayer* getLayer(
int index)
const TRTNOEXCEPT = 0;
4329 virtual int getNbInputs()
const TRTNOEXCEPT = 0;
4340 virtual ITensor* getInput(
int index)
const TRTNOEXCEPT = 0;
4351 virtual int getNbOutputs()
const TRTNOEXCEPT = 0;
4362 virtual ITensor* getOutput(
int index)
const TRTNOEXCEPT = 0;
4367 virtual void destroy() TRTNOEXCEPT = 0;
4486 ITensor& input0,
bool transpose0,
ITensor& input1,
bool transpose1) TRTNOEXCEPT = 0;
4572 ITensor& input, int32_t layerCount, int32_t hiddenSize, int32_t maxSeqLen,
RNNOperation op) TRTNOEXCEPT = 0;
4616 virtual void removeTensor(
ITensor& tensor) TRTNOEXCEPT = 0;
4625 virtual void unmarkOutput(
ITensor& tensor) TRTNOEXCEPT = 0;
4676 virtual void setName(
const char* name) TRTNOEXCEPT = 0;
4687 virtual const char* getName()
const TRTNOEXCEPT = 0;
4718 virtual bool hasImplicitBatchDimension()
const TRTNOEXCEPT = 0;
4733 virtual bool markOutputForShapes(
ITensor& tensor) TRTNOEXCEPT = 0;
4742 virtual bool unmarkOutputForShapes(
ITensor& tensor) TRTNOEXCEPT = 0;
4860 virtual bool hasExplicitPrecision()
const TRTNOEXCEPT = 0;
4870 kLEGACY_CALIBRATION = 0,
4871 kENTROPY_CALIBRATION = 1,
4872 kENTROPY_CALIBRATION_2 = 2,
4873 kMINMAX_CALIBRATION = 3,
4901 virtual int getBatchSize()
const TRTNOEXCEPT = 0;
4916 virtual bool getBatch(
void* bindings[],
const char* names[],
int nbBindings) TRTNOEXCEPT = 0;
4932 virtual const void* readCalibrationCache(std::size_t& length) TRTNOEXCEPT = 0;
4942 virtual void writeCalibrationCache(
const void* ptr, std::size_t length) TRTNOEXCEPT = 0;
5016 virtual double getQuantile()
const TRTNOEXCEPT = 0;
5024 virtual double getRegressionCutoff()
const TRTNOEXCEPT = 0;
5038 virtual const void* readHistogramCache(std::size_t& length) TRTNOEXCEPT = 0;
5048 virtual void writeHistogramCache(
const void* ptr, std::size_t length) TRTNOEXCEPT = 0;
5102 virtual void setMinTimingIterations(
int minTiming) TRTNOEXCEPT = 0;
5111 virtual int getMinTimingIterations()
const TRTNOEXCEPT = 0;
5121 virtual void setAvgTimingIterations(
int avgTiming) TRTNOEXCEPT = 0;
5130 virtual int getAvgTimingIterations()
const TRTNOEXCEPT = 0;
5140 virtual void setEngineCapability(
EngineCapability capability) TRTNOEXCEPT = 0;
5156 virtual void setInt8Calibrator(
IInt8Calibrator* calibrator) TRTNOEXCEPT = 0;
5170 virtual void setMaxWorkspaceSize(std::size_t workspaceSize) TRTNOEXCEPT = 0;
5181 virtual std::size_t getMaxWorkspaceSize()
const TRTNOEXCEPT = 0;
5195 virtual void setFlags(BuilderFlags builderFlags) TRTNOEXCEPT = 0;
5204 virtual BuilderFlags getFlags()
const TRTNOEXCEPT = 0;
5213 virtual void clearFlag(
BuilderFlag builderFlag) TRTNOEXCEPT = 0;
5222 virtual void setFlag(
BuilderFlag builderFlag) TRTNOEXCEPT = 0;
5233 virtual bool getFlag(
BuilderFlag builderFlag)
const TRTNOEXCEPT = 0;
5246 virtual void setDeviceType(
const ILayer* layer,
DeviceType deviceType) TRTNOEXCEPT = 0;
5252 virtual DeviceType getDeviceType(
const ILayer* layer)
const TRTNOEXCEPT = 0;
5259 virtual bool isDeviceTypeSet(
const ILayer* layer)
const TRTNOEXCEPT = 0;
5266 virtual void resetDeviceType(
const ILayer* layer) TRTNOEXCEPT = 0;
5272 virtual bool canRunOnDLA(
const ILayer* layer)
const TRTNOEXCEPT = 0;
5282 virtual void setDLACore(
int dlaCore) TRTNOEXCEPT = 0;
5288 virtual int getDLACore()
const TRTNOEXCEPT = 0;
5295 virtual void setDefaultDeviceType(
DeviceType deviceType) TRTNOEXCEPT = 0;
5302 virtual DeviceType getDefaultDeviceType()
const TRTNOEXCEPT = 0;
5309 virtual void reset() TRTNOEXCEPT = 0;
5316 virtual void destroy() TRTNOEXCEPT = 0;
5325 virtual void setProfileStream(
const cudaStream_t stream) TRTNOEXCEPT = 0;
5334 virtual cudaStream_t getProfileStream()
const TRTNOEXCEPT = 0;
5355 virtual int getNbOptimizationProfiles()
const noexcept = 0;
5405 constexpr
inline int EnumMax<NetworkDefinitionCreationFlag>()
5441 virtual void setMaxBatchSize(
int batchSize) TRTNOEXCEPT = 0;
5451 virtual int getMaxBatchSize()
const TRTNOEXCEPT = 0;
5462 TRT_DEPRECATED
virtual void setMaxWorkspaceSize(std::size_t workspaceSize) TRTNOEXCEPT = 0;
5473 TRT_DEPRECATED
virtual std::size_t getMaxWorkspaceSize()
const TRTNOEXCEPT = 0;
5487 TRT_DEPRECATED
virtual void setHalf2Mode(
bool mode) TRTNOEXCEPT = 0;
5496 TRT_DEPRECATED
virtual bool getHalf2Mode()
const TRTNOEXCEPT = 0;
5506 TRT_DEPRECATED
virtual void setDebugSync(
bool sync) TRTNOEXCEPT = 0;
5515 TRT_DEPRECATED
virtual bool getDebugSync()
const TRTNOEXCEPT = 0;
5527 TRT_DEPRECATED
virtual void setMinFindIterations(
int minFind) TRTNOEXCEPT = 0;
5536 TRT_DEPRECATED
virtual int getMinFindIterations()
const TRTNOEXCEPT = 0;
5548 TRT_DEPRECATED
virtual void setAverageFindIterations(
int avgFind) TRTNOEXCEPT = 0;
5557 TRT_DEPRECATED
virtual int getAverageFindIterations()
const TRTNOEXCEPT = 0;
5572 virtual bool platformHasFastFp16()
const TRTNOEXCEPT = 0;
5577 virtual bool platformHasFastInt8()
const TRTNOEXCEPT = 0;
5582 virtual void destroy() TRTNOEXCEPT = 0;
5595 TRT_DEPRECATED
virtual void setInt8Mode(
bool mode) TRTNOEXCEPT = 0;
5604 TRT_DEPRECATED
virtual bool getInt8Mode()
const TRTNOEXCEPT = 0;
5611 TRT_DEPRECATED
virtual void setInt8Calibrator(
IInt8Calibrator* calibrator) TRTNOEXCEPT = 0;
5625 TRT_DEPRECATED
virtual void setDeviceType(
ILayer* layer,
DeviceType deviceType) TRTNOEXCEPT = 0;
5633 TRT_DEPRECATED
virtual DeviceType getDeviceType(
const ILayer* layer)
const TRTNOEXCEPT = 0;
5642 TRT_DEPRECATED
virtual bool isDeviceTypeSet(
const ILayer* layer)
const TRTNOEXCEPT = 0;
5651 TRT_DEPRECATED
virtual void resetDeviceType(
ILayer* layer) TRTNOEXCEPT = 0;
5657 TRT_DEPRECATED
virtual bool canRunOnDLA(
const ILayer* layer)
const TRTNOEXCEPT = 0;
5666 TRT_DEPRECATED
virtual void setDefaultDeviceType(
DeviceType deviceType) TRTNOEXCEPT = 0;
5673 TRT_DEPRECATED
virtual DeviceType getDefaultDeviceType()
const TRTNOEXCEPT = 0;
5682 virtual int getMaxDLABatchSize()
const TRTNOEXCEPT = 0;
5693 TRT_DEPRECATED
virtual void allowGPUFallback(
bool setFallBackMode) TRTNOEXCEPT = 0;
5698 virtual int getNbDLACores()
const TRTNOEXCEPT = 0;
5710 TRT_DEPRECATED
virtual void setDLACore(
int dlaCore) TRTNOEXCEPT = 0;
5718 TRT_DEPRECATED
virtual int getDLACore()
const TRTNOEXCEPT = 0;
5744 virtual void setGpuAllocator(
IGpuAllocator* allocator) TRTNOEXCEPT = 0;
5757 TRT_DEPRECATED
virtual void setFp16Mode(
bool mode) TRTNOEXCEPT = 0;
5766 TRT_DEPRECATED
virtual bool getFp16Mode()
const TRTNOEXCEPT = 0;
5786 TRT_DEPRECATED
virtual void setStrictTypeConstraints(
bool mode) TRTNOEXCEPT = 0;
5795 TRT_DEPRECATED
virtual bool getStrictTypeConstraints()
const TRTNOEXCEPT = 0;
5802 TRT_DEPRECATED
virtual void setRefittable(
bool canRefit) TRTNOEXCEPT = 0;
5811 TRT_DEPRECATED
virtual bool getRefittable()
const TRTNOEXCEPT = 0;
5818 TRT_DEPRECATED
virtual void setEngineCapability(
EngineCapability capability) TRTNOEXCEPT = 0;
5827 TRT_DEPRECATED
virtual EngineCapability getEngineCapability()
const TRTNOEXCEPT = 0;
5880 virtual void setErrorRecorder(
IErrorRecorder* recorder) TRTNOEXCEPT = 0;
5892 virtual IErrorRecorder* getErrorRecorder()
const TRTNOEXCEPT = 0;
5897 virtual void reset() TRTNOEXCEPT = 0;
Use SAME padding with prePadding <= postPadding.
An engine for executing inference on a built network, with functionally unsafe features.
Definition: NvInferRuntime.h:1108
Substract the second element from the first.
Perform the normal matrix multiplication in the first recurrent layer.
DataType
The type of weights and tensors.
Definition: NvInferRuntimeCommon.h:162
PaddingMode
Enumerates the modes of padding to perform in convolution, deconvolution and pooling layer...
Definition: NvInfer.h:873
NetworkDefinitionCreationFlag
List of immutable network properties expressed at network creation time. NetworkDefinitionCreationFla...
Definition: NvInfer.h:5382
DimsNCHW(int batchSize, int channels, int height, int width)
Construct a DimsNCHW given batch size, channel count, height and width.
Definition: NvInfer.h:338
constexpr int EnumMax< CalibrationAlgoType >()
Maximum number of elements in CalibrationAlgoType enum.
Definition: NvInfer.h:4877
RNNOperation
Enumerates the RNN operations that may be performed by an RNN layer.
Definition: NvInfer.h:2320
A Softmax layer in a network definition.
Definition: NvInfer.h:1798
MatrixOperation
Enumerates the operations that may be performed on a tensor by IMatrixMultiplyLayer before multiplica...
Definition: NvInfer.h:3527
Definition: NvInfer.h:3179
Plugin class for user-implemented layers.
Definition: NvInferRuntimeCommon.h:343
constexpr int EnumMax< RNNDirection >()
Maximum number of elements in RNNDirection enum.
Definition: NvInfer.h:2348
Single gate RNN w/ TANH activation function.
Layer that represents an unary operation.
Definition: NvInfer.h:3033
struct CUstream_st * cudaStream_t
Forward declaration of cudaStream_t.
Definition: NvInferRuntimeCommon.h:112
An Activation layer in a network definition.
Definition: NvInfer.h:1279
BuilderFlag
Definition: NvInfer.h:5068
constexpr int EnumMax< PaddingMode >()
Maximum number of elements in PaddingMode enum.
Definition: NvInfer.h:884
int w() const
Get the width.
Definition: NvInfer.h:400
RNNDirection
Enumerates the RNN direction that may be performed by an RNN layer.
Definition: NvInfer.h:2341
Layer that represents a Matrix Multiplication.
Definition: NvInfer.h:3580
DimsCHW()
Construct an empty DimsCHW object.
Definition: NvInfer.h:212
No operation is performed on the first recurrent layer.
Holds properties for configuring a builder to produce an engine.
Definition: NvInfer.h:5089
CalibrationAlgoType getAlgorithm() override
Definition: NvInfer.h:4979
constexpr int EnumMax< RNNOperation >()
Maximum number of elements in RNNOperation enum.
Definition: NvInfer.h:2329
A convolution layer in a network definition.
Definition: NvInfer.h:901
A RaggedSoftmax layer in a network definition.
Definition: NvInfer.h:3635
ScaleMode
Controls how shift, scale and power are applied in a Scale layer.
Definition: NvInfer.h:1674
Plugin class for user-implemented layers.
Definition: NvInferRuntime.h:134
Layer that represents a constant value.
Definition: NvInfer.h:3661
A Scale layer in a network definition.
Definition: NvInfer.h:1709
Layer type for getting shape of a tensor.
Definition: NvInfer.h:3440
Enable FP16 layer selection.
Use SAME padding, with prePadding >= postPadding.
static const int MAX_DIMS
The maximum number of dimensions supported for a tensor.
Definition: NvInferRuntimeCommon.h:208
A fully connected layer in a network definition. This layer expects an input tensor of three or more ...
Definition: NvInfer.h:1215
Use CAFFE padding, rounding output size down.
Layer that represents a parametric ReLU operation.
Definition: NvInfer.h:3711
ReduceOperation
Enumerates the reduce operations that may be performed by a Reduce layer.
Definition: NvInfer.h:3059
A LRN layer in a network definition.
Definition: NvInfer.h:1602
Descriptor for three-dimensional data.
Definition: NvInfer.h:172
constexpr int EnumMax< ElementWiseOperation >()
Maximum number of elements in ElementWiseOperation enum.
Definition: NvInfer.h:2158
uint32_t BuilderFlags
It is capable of representing one or more BuilderFlags by binary OR operations, e.g., 1U << BuilderFlag::kFP16 | 1U << BuilderFlag::kDEBUG.
Definition: NvInfer.h:5059
TensorLocation
The location for tensor data storage, device or host.
Definition: NvInferRuntimeCommon.h:925
Definition: NvInfer.h:2204
Builds an engine from a network definition.
Definition: NvInfer.h:5417
Layer that represents a TopK reduction.
Definition: NvInfer.h:3470
int n() const
Get the index count.
Definition: NvInfer.h:358
An RNN layer in a network definition, version 2.
Definition: NvInfer.h:2751
int & h()
Get the height.
Definition: NvInfer.h:252
Per-channel coefficients.
Elements correspond to different spatial data.
The TensorRT API version 1 namespace.
constexpr int EnumMax< LayerType >()
Maximum number of elements in LayerType enum.
Definition: NvInfer.h:443
CalibrationAlgoType getAlgorithm() override
Definition: NvInfer.h:4994
int & n()
Get the index count.
Definition: NvInfer.h:351
int & w()
Get the width.
Definition: NvInfer.h:266
Layer that represents a reduction operator.
Definition: NvInfer.h:3081
Definition: NvInfer.h:5002
PoolingType
The type of pooling to perform in a pooling layer.
Definition: NvInfer.h:1340
int h() const
Get the height.
Definition: NvInfer.h:151
Inverse hyperbolic tangent.
int & w()
Get the width.
Definition: NvInfer.h:158
CalibrationAlgoType getAlgorithm() override
Definition: NvInfer.h:5008
int h() const
Get the height.
Definition: NvInfer.h:259
void * createInferBuilder_INTERNAL(void *logger, int version)
Internal C entry point for creating IBuilder.
Descriptor for data with one channel dimension and two spatial dimensions.
Definition: NvInfer.h:206
The first element to the power of the second element.
A network definition for input to the builder.
Definition: NvInfer.h:3891
Like kNONE, but transpose the matrix dimensions.
int w() const
Get the width.
Definition: NvInfer.h:165
Divide the first element by the second.
Layer type for pluginV2.
Definition: NvInfer.h:2976
Product of the two elements.
int c() const
Get the channel count.
Definition: NvInfer.h:372
Dims2()
Construct an empty Dims2 object.
Definition: NvInfer.h:91
TopKOperation
Enumerates the operations that may be performed by a TopK layer.
Definition: NvInfer.h:3451
Dims3(int d0, int d1, int d2)
Construct a Dims3 from 3 elements.
Definition: NvInfer.h:191
Descriptor for four-dimensional data.
Definition: NvInfer.h:280
ActivationType
Forward declare IGpuAllocator for use in other interfaces.
Definition: NvInferRuntimeCommon.h:133
Descriptor for data with one index dimension, one channel dimension and two spatial dimensions...
Definition: NvInfer.h:316
TRT_DEPRECATED DimensionType type[MAX_DIMS]
The type of each dimension.
Definition: NvInferRuntimeCommon.h:211
Optimization profile for dynamic input dimensions and shape tensors.
Definition: NvInferRuntime.h:990
Inverse hyperbolic cosine.
A resize layer in a network definition.
Definition: NvInfer.h:3754
constexpr int EnumMax< BuilderFlag >()
Maximum number of builder flags in BuilderFlag enum.
Definition: NvInfer.h:5079
DimsHW()
Construct an empty DimsHW object.
Definition: NvInfer.h:121
Use explicit padding, rounding output size up.
Base class for all layer classes in a network definition.
Definition: NvInfer.h:698
Reference counted application-implemented error reporting interface for TensorRT objects.
Definition: NvInferRuntimeCommon.h:1141
Dims4(int d0, int d1, int d2, int d3)
Construct a Dims4 from 4 elements.
Definition: NvInfer.h:300
Slices an input tensor into an output tensor based on the offset and strides.
Definition: NvInfer.h:3320
int & h()
Get the height.
Definition: NvInfer.h:379
int & h()
Get the height.
Definition: NvInfer.h:144
Mark the network to be an explicit batch network.
Enables strict type constraints.
int & c()
Get the channel count.
Definition: NvInfer.h:365
Structure to define the dimensions of a tensor.
Definition: NvInferRuntimeCommon.h:205
Network iterates from first to last and vice versa and outputs concatenated.
Layer type for shuffling data.
Definition: NvInfer.h:3202
DimsCHW(int channels, int height, int width)
Construct a DimsCHW given channel count, height and width.
Definition: NvInfer.h:226
Definition: NvInfer.h:4973
A elementwise layer in a network definition.
Definition: NvInfer.h:2174
A Pooling layer in a network definition.
Definition: NvInfer.h:1361
constexpr int EnumMax< UnaryOperation >()
Maximum number of elements in UnaryOperation enum.
Definition: NvInfer.h:3021
int d[MAX_DIMS]
The extent of each dimension.
Definition: NvInferRuntimeCommon.h:210
Minimum of the two elements.
ElementWiseOperation
Enumerates the binary operations that may be performed by an ElementWise layer.
Definition: NvInfer.h:2145
Three-gate network consisting of Gated Recurrent Units.
RNNGateType
Identifies an individual gate within an RNN cell.
Definition: NvInfer.h:2725
Network iterations from first input to last input.
int & c()
Get the channel count.
Definition: NvInfer.h:238
Single gate RNN w/ ReLU activation function.
Dims3()
Construct an empty Dims3 object.
Definition: NvInfer.h:178
uint32_t TensorFormats
It is capable of representing one or more TensorFormat by binary OR operations, e.g., 1U << TensorFormats::kCHW4 | 1U << TensorFormats::kCHW32.
Definition: NvInferRuntimeCommon.h:220
A tensor in a network definition.
Definition: NvInfer.h:457
An array of weights used as a layer parameter.
Definition: NvInferRuntime.h:98
int & w()
Get the width.
Definition: NvInfer.h:393
Enable Int8 layer selection.
Mark the network to be an explicit precision network.
int c() const
Get the channel count.
Definition: NvInfer.h:245
Dims2(int d0, int d1)
Construct a Dims2 from 2 elements.
Definition: NvInfer.h:103
Enable building a refittable engine.
Dims4()
Construct an empty Dims2 object.
Definition: NvInfer.h:286
int nbDims
The number of dimensions.
Definition: NvInferRuntimeCommon.h:209
Application-implemented interface for calibration.
Definition: NvInfer.h:4893
Application-implemented logging interface for the builder, engine and runtime.
Definition: NvInferRuntimeCommon.h:985
DimsHW(int height, int width)
Construct a DimsHW given height and width.
Definition: NvInfer.h:133
constexpr int EnumMax< ReduceOperation >()
Maximum number of elements in ReduceOperation enum.
Definition: NvInfer.h:3069
int w() const
Get the width.
Definition: NvInfer.h:273
Identical coefficients across all elements of the tensor.
Plugin class for user-implemented layers.
Definition: NvInferRuntime.h:238
Layer that represents a padding operation.
Definition: NvInfer.h:3140
Floor division of the first element by the second.
Definition: NvInfer.h:4988
constexpr int EnumMax< RNNGateType >()
Maximum number of elements in RNNGateType enum.
Definition: NvInfer.h:2737
constexpr int EnumMax< RNNInputMode >()
Maximum number of elements in RNNInputMode enum.
Definition: NvInfer.h:2375
Enable debugging of layers via synchronizing after every layer.
CalibrationAlgoType
Version of calibration algorithm to use.
Definition: NvInfer.h:4868
EngineCapability
Forward declaration of IPluginFactory for use by other interfaces.
Definition: NvInferRuntime.h:76
Enable layers marked to execute on GPU if layer cannot execute on DLA.
Use explicit padding, rounding output size down.
DimsNCHW()
Construct an empty DimsNCHW object.
Definition: NvInfer.h:322
LayerType
The type values of layer classes.
Definition: NvInfer.h:410
A layer that represents the identity function.
Definition: NvInfer.h:3649
RNNInputMode
Enumerates the RNN input modes that may occur with an RNN layer.
Definition: NvInfer.h:2368
A RNN layer in a network definition.
Definition: NvInfer.h:2391
Layer type for plugins.
Definition: NvInfer.h:2953
int h() const
Get the height.
Definition: NvInfer.h:386
uint32_t NetworkDefinitionCreationFlags
This bitset is capable of representing one or more NetworkDefinitionCreationFlag flags constructed wi...
Definition: NvInfer.h:5371
Four-gate LSTM network w/o peephole connections.
constexpr int EnumMax< MatrixOperation >()
Maximum number of elements in MatrixOperation enum.
Definition: NvInfer.h:3550
#define _TENSORRT_OVERRIDE
Items that are marked as deprecated will be removed in a future release.
Definition: NvInferRuntimeCommon.h:62
Application-implemented class for controlling allocation on the GPU.
Definition: NvInferRuntimeCommon.h:942
constexpr int EnumMax< TopKOperation >()
Maximum number of elements in TopKOperation enum.
Definition: NvInfer.h:3458
constexpr int EnumMax< PoolingType >()
Maximum number of elements in PoolingType enum.
Definition: NvInfer.h:1348
constexpr int EnumMax< ScaleMode >()
Maximum number of elements in ScaleMode enum.
Definition: NvInfer.h:1682
Descriptor for two-dimensional spatial data.
Definition: NvInfer.h:115
UnaryOperation
Enumerates the unary operations that may be performed by a Unary layer.
Definition: NvInfer.h:2997
constexpr int EnumMax< ResizeMode >()
Maximum number of elements in ResizeMode enum.
Definition: NvInfer.h:3729
DeviceType
The device that this layer/network will execute on.
Definition: NvInferRuntime.h:672
Elements correspond to different batch index.
Descriptor for two-dimensional data.
Definition: NvInfer.h:85
A concatenation layer in a network definition.
Definition: NvInfer.h:1853
ResizeMode
Enumerates various modes of resize in the resize layer. Resize mode set using setResizeMode().
Definition: NvInfer.h:3722
CalibrationAlgoType getAlgorithm() override
Definition: NvInfer.h:4964
A deconvolution layer in a network definition.
Definition: NvInfer.h:1886
Use CAFFE padding, rounding output size up.
Definition: NvInfer.h:4958