Export: TensorRT¶

Export type: CPP_TensorRT

C++ export using TensorRT.

n2d2 MobileNet_ONNX.ini -seed 1 -w /dev/null -export CPP_TensorRT -nbbits -32

Warning

The calibration for this export is done using the tools provided by NVIDIA. For this reason, you cannot calibrate when exporting your network. You need to use the export to calibrate your network.

Informations¶

In order to exploit TensorRT optimizations, the N2D2 Framework provide a code generator linked with a C++/Python API that gives access to TensorRT methods, I/O handling and specifics control. The generated code is provided as a standalone code with its own compilation environment under a Makefile format. Moreover a benchmark environment with stimuli from the test dataset is given to evaluates execution time performances of your model.

This allow a low level of dependency, only TensorRT, CUDA, cuDNN, cuBLAS and GCC are needed. We recommended you to ensure the correct compatibility of your installation by referring to the TensorRT archive page: https://docs.nvidia.com/deeplearning/tensorrt/archives/index.html Follow the support matrix section of your TensorRT version, notice that TensorRT export have been tested from TensorRT 2.1 to TensorRT 8.2.3 versions.

The TensorRT library includes implementation for the most common deep learning layers, but strong limitations are known depending of the TensorRT version. For example, TensorRT provide a support to the well-known resize layer since version 6.0.1. This layer is widely use for decoder, segmentation and detector tasks. For inferior version a support have been integrated under a plugin layer. The TensorRT plugin layers allow the application to implement not supported layers. You can find additional informations about how to implements new plugin layers here : https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#add_custom_layer

The plugin layers that N2D2 TensorRT generator implements are available in the folder export/CPP_TensorRT/include/plugins/. These layers are used by the N2D2 TensorRT generator when TensorRT doesn’t provide support to a requested layers.

Export parameters¶

Extra parameters can be passed during export using the -export-parameters params.ini command line argument. The parameters must be saved in an INI-like file.

List of available parameters:

Argument [default value]	Description
`GenStimuliCalib` [1]	If true (1), generate calibration files, necessary for 8-bits precision. Beware that calibration files may take a lot of disk space!

Benchmark your TensorRT Model - C++ Benchmark¶

The TensorRT export is given with a C++ benchmark ready to be used. The benchmark program is able to evaluates the applicative performances of your model on the test dataset exported under the stimuli folder at export time. A per-layer execution time analysis is also performed to evaluates your mode latency and identify potential bottleneck. Moreover different numerical precision supporter by NVIDIA GPU can be evaluates in order to assess potential acceleration factor and eventual applicative performances losses.

When numerical precision is set to 8 bits for benchmark, the program will use the calibration files exported under the batches_calib folder at export time. The calibration files also corresponds to the test stimuli pre-processes for the IInt8EntropyCalibrator2 that implement TensorRT. You can find more informations about the INT8 calibration procedure with TensorRT here : https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#optimizing_int8_c

The command to compile and execute the C++ TensorRT Benchmark under a FP32 precision is :

make
cd export_CPP_TensorRT_float32/
./bin/n2d2_tensorRT_test -nbbits -32

To launch the Benchmark in FP16 (half precision) use this command :

./bin/n2d2_tensorRT_test -nbbits -16

To launch the Benchmark in INT8 use this command :

./bin/n2d2_tensorRT_test -nbbits 8

List of the program option related to the TensorRT C++ benchmark:

Option [default value]	Description
`-batch` [1]	Size of the batch to use
`-dev` [0]	CUDA Device ID selection
`-stimulus` [NULL]	Path to a specific input stimulus to test. For example: -stimulus \({/stimulus/env0000.pgm}\) command will test the file env0000.pgm of the stimulus folder.
`-prof`	Activates the layer wise profiling mechanism. This option can decrease execution time performance.
`-iter-build` [1]	Sets the number of minimization build iterations done by the tensorRT builder to find the best layer tactics.
`-nbbits` [-32]	Number of bits used for computation. Value -32 for Full FP32 bits configuration, -16 for Half FP16 bits configuration and 8 for INT8 bits configuration. When running INT8 mode for the first time, the TensorRT calibration process can be very long. Once generated the generated calibration table will be automatically reused. Supported compute mode in function of the compute capability are provided here: https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#compute-capabilities .
`-calib-cache`	Path and name to the calibration file generated by TensorRT calibrator when precision is INT8. Must be compatible with the TensorRT Entropy Calibrator version used to calibrate.
`-calib-folder` [batches_calib]	Path to the calibration data samples. This is mandatory when precision is set to INT8 and if no calibration file cache is load.

Analyse the execution performances of your TensorRT Model (FP32)¶

Here is a small example that described how to report the per-layer analysis on execution time.

Launch the Benchmark with the -prof argument :

./bin/n2d2_tensorRT_test -prof

At the end of the execution the performances analysis is displayed in your screen :

(19%)  **************************************** CONV1 + CONV1_ACTIVATION: 0.0219467 ms
(05%)  ************ POOL1: 0.00675573 ms
(13%)  **************************** CONV2 + CONV2_ACTIVATION: 0.0159089 ms
(05%)  ************ POOL2: 0.00616047 ms
(14%)  ****************************** CONV3 + CONV3_ACTIVATION: 0.0159713 ms
(19%)  **************************************** FC1 + FC1_ACTIVATION: 0.0222242 ms
(13%)  **************************** FC2: 0.0149013 ms
(08%)  ****************** SOFTMAX: 0.0100633 ms
Average profiled tensorRT process time per stimulus = 0.113932 ms

You can evaluates impact of the performances for various batch size and the different numerical precision supported.

Deploy your TensorRT Model in Application¶

The TensorRT export is provided with a C++ and a python interface. The python interface is accessible through a wrapper to the C++ API method and linked with the libboost-python librabry.

You can integrates your model in your application environment as a library thanks to this API.

The command to compile the TensorRT export as a C++ library is :

make WRAPPER_CPP=1

The library of your TensorRT model is provided under the name libn2d2_tensorRT_inference.so locate at bin/ folder.

The command to compile your TensorRT export as a Python3.6m library is :

make WRAPPER_PYTHON=3.6m

The python library of your TensorRT model is then provided under the name N2D2.so locate at bin/ folder.

Method accessible through C++ or Python API are listed and detailled here:

Return Type	C++ API	Python API	Args Type	ArgsName(Default Value)	Description	Comments
	N2D2::Network()	N2D2_Network()			TensorRT DNN object creation
`void`	setMaxBatchSize	setMaxBatchSize	`size_t`	batchsize(`1`)	Maximum batchsize for setting the internal tensorrt graph memory usage limit	Use before run initialize()
`void`	setDeviceID	setDeviceID	`size_t`	device(`0`)	Device ID on which run the TensorRT model	Use before run initialize()
`void`	setPrecision	setPrecision	`int`	precision(`-32`)	Numerical Precision to use: `-32` for float, `-16` for half float, `8` for int8	Use before run initialize()
`void`	useDLA	useDLA	`bool`	useDla(`False`)	If True, use the first DLA core for every possible layers	Use before run initialize()
`void`	setMaxWorkSpaceSize	setMaxWorkSpaceSize	`int64_t`	maxWorkSpaceSize(`1073741824`)	Size of the workspace, influence the optimisations done by NVIDIA	Use before run initialize()
`void`	setDetectorThresholds	setDetectorThresholds	`float*`, `uint`	thresholds, lengthThreshold	Set the confidences thresholds of a detector output. Bypass the internal thresholds from the exported model	Use before run initialize()
`void`	setDetectorNMS	setDetectorNMS	`double`	thresholdNms	Set the threshold for non-maxima suppression range (from 0.0 to 1.0) of a detector output. Bypass the internal thresholds from the exported model	Use before run initialize()
`void`	setInputEngine	setInputEngine	`string`	enginePath	Path of a serialized and optimized TensorRT plan file. The serialized plan file are not portable across platforms or TensorRT versions and are specific to the exact GPU model they were built on	Use before run initialize()
`void`	setOutputEngine	setOutputEngine	`string`	enginePath	Path and name of where to save your serialized plan file. The serialized plan file are not portable across platforms or TensorRT versions and are specific to the exact GPU model they were built on	Use before run initialize()
`void`	setCalibCache	setCalibCache	`string`	calibPath	Path and name to the calibration file generated by TensorRT calibrator when precision is INT8. Must be compatible with the TensorRT version used to calibrate.	Use before run initialize()
`void`	setCalibFolder	setCalibFolder	`string`	folderPath(`batches_calib/`)	Path to the calibration data samples. This is mandatory when precision is set to INT8 and if no calibration file cache is load.	Use before run initialize()
`void`	setParamPath	setParamPath	`string`	parmPath(`dnn/`)	Path to the DNN parameters generated by N2D2	Use before run initialize()
`void`	initialize	initialize			Initialize the TensorRT engine following the specified options. This function allocates the memory workspace I/O, set the precision, create the builder, create the network topology from the Network generated with N2D2 and initialize the TensorRT context
`void`	setProfiling	setProfiling			Initialize the profiler in the TensorRT engine	Use after run initialize()
`uint`	getInputDimX	getInputDimX			Return the dimension X of the input
`uint`	getInputDimY	getInputDimY			Return the dimension Y of the input
`uint`	getInputDimZ	getInputDimZ			Return the dimension Z of the input
`uint`	getOutputNbTargets	getOutputNbTargets			Return the number of outputs
`uint`	getOutputDimX	getOutputDimX	`uint`	outputNumber	Return the dimension X of a specified output
`uint`	getOutputDimY	getOutputDimY	`uint`	outputNumber	Return the dimension Y of a specified output
`uint`	getOutputDimZ	getOutputDimZ	`uint`	outputNumber	Return the dimension Z of a specified output
`uint`	getOutputTarget	getOutputTarget	`uint`	outputNumber	Return the number of labels if a specified output
`void`	syncExe	syncExe	`float*`, `uint`	inputData,batchsize	Synchronously execute inference on a batch of the specified size	Use after run initialize()
`void`	log_output	cpyOutput	`float*`, `uint`	inputData,outputID	Copy the full batch of the output of a DNN	Use after run initialize()
`void`	estimated	estimated	`float*`, `uint`, `bool`, `float`	inputData,outputID, useGPU, threshold	Copy per output pixel estimated labels of a specified output. UseGpu is recommended and threshold value allow to clip the outputs values before classification	Use after run initialize()