DeepStream SDK: Towards Large Scale Deployment of Intelligent … · 2017. 11. 2. · DeepStream...

November 1, 2017

DeepStream SDK:Towards Large Scale Deployment of Intelligent Video Analytics Systems

Jeremy Furtek

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MOTIVATION

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Proliferation of Video Data

Content Filtering

Retail Analytics

Traffic Engineering

Law Enforcement

Ad Injection

Securing Critical Infrastructure

Parking Management

In-Vehicle Analytics

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Advances in Deep Learning: Classification and Beyond

Image Classification(AlexNet, 2012)

Object Detection(R-FCN, 2016)

Semantic Image Segmentation(ENet, 2016)

Image Compression(WaveOne, 2017)

Structure From Motion(SfM-Net, 2017)

Pose-Aware Face Recognition(USC, 2017)

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WHAT IS DEEPSTREAM?

HOW IT WORKS

DEEP LEARNING

HOW IT WORKS

VIDEO

DEEP LEARNING WITH DEEPSTREAM

DeepStream EnabledApp or Service

VIDEO ANALYTICS WITH DEEP LEARNING ON NVIDIA GPUS: AN OVERVIEW

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From Video to Analytics With Deep Learning…

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Video Decode

Fully-accelerated hardware decoding on NVIDIAGPUs (NVDEC) is accessible via the Video DecodeAPI.

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bitstream

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BGRimage

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Image

Conversion

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Image Conversion

Custom CUDA kernels and/or optimized libraries (e.g. NVIDIA Performance Primitives, or NPP) enable high performance image conversions.

__global__ void resize(uint8_t* dst,

const uint8_t* src,

int dst_w,

int dst_h,

int src_w,

int src_h)

{

…

}

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inferenceresults

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Inference

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Inference

InferenceTensorRT* provides a neural network optimizer and a high performance runtime library for inference deployment on NVIDIA GPUs.

Step 1: Optimize a trained neural network Step 2: Perform real-time inference with the TensorRT runtime

*The current DeepStream release supports TensorRT 2.1.

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Display/StorageProcessing of inference output is application dependent.

Some common options are:

• Real-time display with overlay• Alert generation• Database insertion• Augmented video encoding• “Slew-to-Cue” camera control• Tracker update• Custom post-processing algorithms (CUDA, OpenCV, …)

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Video to Analytics With the DeepStream SDK

DeepStream runtime library (libdeepstream)

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USING THE DEEPSTREAM LIBRARY

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DeepStream from the Ground Up

DeepStream tensors are objects that implement the IStreamTensor interface and represent a 4-dimensional array of values stored in NCHW order.

Tensors

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C

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typedef enum {

FLOAT_TENSOR, // float32

NV12_FRAME, // decoded YCbCr

OBJ_COORD, // detection bounding box

CUSTOMER_TYPE // user defined

} TENSOR_TYPE;

typedef enum {

GPU_DATA, // device memory

CPU_DATA, // host memory

} MEMORY_TYPE;

IStreamTensor* createStreamTensor(int maxlen,

size_t elemsize,

TENSOR_TYPE Ttype,

MEMORY_TYPE Mtype,

int deviceID);

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DeepStream modules are objects that implement the IModule interface and operate on one or more input tensors to produce one or more output tensors.

Modules

void IModule::execute(const ModuleContext& ctx,

const std::vector& in,

const std::vector& out) = 0;

DeepStream

Module

input[0]

input[1]

input[M -1]

…

output[0]

output[1]

output[N -1]

…

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DeepStream modules maintain connectivity information using a std::vector of pairs that represent the inputs to the module.

Building the DeepStream dataflow graph

Module CModule A A.output[0]

A.output[1]

Module B

output[0]

B.output[0]

B.output[1]

C.input[0]

C.input[1]

IModule* index

&A 1

&B 0

Inputs:

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Using the DeepStream Library

// Create a device worker

IDeviceWorker* pDW = createDeviceWorker(1, // num channels

0); // GPU device ID

Step 1: Create a device worker object

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IDeviceWorker* pDW = createDeviceWorker(1, 0);

// Add a decode task, specifying the CODEC type

pDW->addDecodeTask(cudaVideoCodec_H264);

Step 2: Add a decode task

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IDeviceWorker* pDW = createDeviceWorker(1, 0);

// Add a decode task, specifying the CODEC type

pDW->addDecodeTask(cudaVideoCodec_H264);

// Add a module for color conversion.

// Color conversion module outputs:

// 0: BGR_PLANAR

// 1: NV12 (YCbCr)

IModule* pCC = pDW->addColorSpaceConvertorTask(BGR_PLANAR);

Step 3: Add an image conversion module

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Caffe uses BGR ordering(not RGB)

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Inference in DeepStream: CAFFE and TensorRT

solver

parameters

network

description

(training)

CAFFE

caffe train –solver solver.prototxt

trained

weightssolver.prototxt

network.prototxt

trained.caffemodel(BINARYPROTO)

TensorRT

network

description

(deploy)

deploy.protoxt

CUDA

Engine

in-memory(serializable)

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// Add a module for color conversion.

// Color conversion module outputs:

// 0: BGR_PLANAR

// 1: NV12 (YCbCr)

IModule* pCC = pDW->addColorSpaceConvertorTask(BGR_PLANAR);

// Add an inference module using output 0 from color conversion

std::vector inf_inputs{std::make_pair(pCC, 0)};

IModule* pInf = pDW->addInferenceTask(inf_inputs,

prototxt_file.c_str(),

model_file.c_str(),

input_layer_name,

output_layer_names_vector,

num_channels,

&inference_params);

Step 4: Add an inference module

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// Add a parser module using output 0 from inference

std::vector parser_inputs;

parser_inputs.push_back(std::make_pair(pInf, 0));

IModule* pParse = new UserInferenceParser(parser_inputs);

pDW->addCustomerTask(pParse);

// Add a display module using image and parser outputs

std::vector display_inputs;

display_inputs.push_back(std::make_pair(pCC, 0));

display_inputs.push_back(std::make_pair(pParse, 0));

IModule* pDisp = new UserDisplayModule(display_inputs);

pDW->addCustomerTask(pDisp);

Step 5: Add custom output processor(s)

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Display /

Storage

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// Add a display module using image conversion and parser outputs

std::vector display_inputs;

display_inputs.push_back(std::make_pair(pCC, 0)); // BGR output

display_inputs.push_back(std::make_pair(pParse, 0));

IModule* pDisp = new UserDisplayModule(display_inputs);

pDW->addCustomerTask(pDisp);

// Start the device worker...

pDW->start();

Step 6: Start the device worker

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// Start the device worker...

pDW->start();

// Initiate data input by pushing data to the device worker

while(1)

{

// Read data from file/socket/database...

pDW->pushPacket(data, num_bytes, channel_num);

}

Step 7: Initiate data input

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SCALING WITH DEEPSTREAM

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DECODE PERFORMANCE ON NVIDIA GPUS

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Scaling Video Analytics With DeepStream: Multiple Channels

GPU 0DeepStream runtime library (libdeepstream)

Display /

Storage

Image Conversion InferenceVideo Decode




… … … ……

channel 0

channel 1

channel 2

channel 3

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Faster Inference with INT8 Integers

GPUs with Compute Capability 6.1 and higher support dot product instructions with 8-bit operands. (Examples of GPUs with this capability are Tesla P4 and P40.)

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Inference with INT8: Accuracy

From: “8-bit Inference with TensorRT,” Szymon Migacz, NVIDIA, GTC2017.

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Inference with INT8: Performance

From: “8-bit Inference with TensorRT,” Szymon Migacz, NVIDIA, GTC2017.

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Using INT8 Inference in DeepStreamStep 1: Run TensorRT offline to generate a calibration table file

trained

weights

trained.caffemodel(BINARYPROTO)

TensorRT

network

description

(deploy)

deploy.protoxt

CUDA

Engine

calibration

table

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Using int8 Inference in DeepStreamStep 2: Provide the path to the calibration file when creating the

inference task

// Add an inference module using output 0 from color conversion

std::vector inf_inputs{std::make_pair(pCC, 0)};

inference_params.calibrationTableFile = calibration_file.c_str();

IModule* pInf = pDW->addInferenceTask(inf_inputs,

prototxt_file.c_str(),

model_file.c_str(),

input_layer_name,

output_layer_names_vector,

num_channels,

&inference_params);

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GPU 1

Scaling Video Analytics With DeepStream: Multiple GPUs

GPU 0DeepStream device worker (device ID = 0)

Display /

Storage



… … … ……

channel 0

channel 1

…

DeepStream device worker (device ID = 1)



… … … ……

channel 0

channel 1

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Examples (Videos)

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DeepStream Resources• DeepStream SDK Download

https://developer.nvidia.com/deepstream-sdk

• NVIDIA Parallel For All Blog:

DeepStream: Next Generation Video Analytics for Smart Citieshttps://devblogs.nvidia.com/parallelforall/deepstream-video-analytics-smart-cities/

• NVIDIA Developer Forums

https://devtalk.nvidia.com/

• Caffe Model Zoo

https://github.com/BVLC/caffe/wiki/Model-Zoo

• NVIDIA Video Encode and Decode GPU Support Matrix

https://developer.nvidia.com/video-encode-decode-gpu-support-matrix

https://developer.nvidia.com/deepstream-sdkhttps://devblogs.nvidia.com/parallelforall/deepstream-video-analytics-smart-cities/https://devtalk.nvidia.com/https://github.com/BVLC/caffe/wiki/Model-Zoohttps://developer.nvidia.com/video-encode-decode-gpu-support-matrix

Fundamentals

Parallel Computing

Game Development & Digital Content

Finance

NVIDIA DEEP LEARNING INSTITUTE

Training available as online self-paced labs and instructor-led workshops

Take self-paced labs at www.nvidia.com/dlilabs

Find or request an instructor-led workshop at www.nvidia.com/dli

Educators can download the Teaching Kit at developer.nvidia.com/teaching-kit and contact [email protected] for info on the University Ambassador Program

Intelligent Video Analytics

Healthcare

Robotics

Autonomous Vehicles

Virtual Reality

DeepStream SDK: Towards Large Scale Deployment of Intelligent … · 2017. 11. 2. · DeepStream...

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