Skip to content

advanced-user-guide.md

Latest commit

 

History

History
1085 lines (737 loc) · 40.1 KB

advanced-user-guide.md

File metadata and controls

1085 lines (737 loc) · 40.1 KB

Advanced User Guide

This document introduces an Intel® software reference implementation (SW RI) for Metro AI Suite Sensor Fusion in Traffic Management. It combines camera and lidar data—referred to as ISF "C+L" or AIO "C+L".

As shown in Fig.1, the end-to-end pipeline includes the following major blocks (workloads):

  • Loading datasets and converting formats
  • Processing lidar signals
  • Running video analytics
  • Fusing data from lidar and camera
  • Visualization

All these tasks run on single Intel SoC processor which provides all the required heterogeneous computing capabilities. To maximize its performance on Intel processors, we optimized this SW RI using Intel SW tool kits in addition to open-source SW libraries.

Case-C+L
Use case: C+L
Figure 1. E2E SW pipelines of sensor fusion C+L(Camera+Lidar).

For prerequisites and system requirements, see Prerequisites and System Requirements.

Architecture Overview

This section describes how to run Intel® Metro AI Suite Sensor Fusion for Traffic Management application.

Intel® Metro AI Suite Sensor Fusion for Traffic Management application can support different pipeline using topology JSON files to describe the pipeline topology. The defined pipeline topology can be found at Resources

There are two steps required for running the sensor fusion application:

  • Start AI Inference service, more details can be found at Service Start
  • Run the application entry program, more details can be found at Entry Program

Additionally, you can test each component (without display) following the guides at 2C+1L Unit Tests, 4C+2L Unit Tests, 12C+2L Unit Tests, 8C+4L Unit Tests, 12C+4L Unit Tests.

Resources

  • Local File Pipeline for Media pipeline

    • Json File: localMediaPipeline.json

      File location: $PROJ_DIR/ai_inference/test/configs/kitti/1C1L/localMediaPipeline.json

    • Pipeline Description:

      input -> decode -> detection -> tracking -> output

  • Local File Pipeline for Lidar pipeline

    • Json File: localLidarPipeline.json

      File location: $PROJ_DIR/ai_inference/test/configs/kitti/1C1L/localLidarPipeline.json

  • Pipeline Description:

    input -> lidar signal processing -> output

  • Local File Pipeline for Camera + Lidar(2C+1L) Sensor fusion pipeline

    • Json File: localFusionPipeline.json

      File location: $PROJ_DIR/ai_inference/test/configs/kitti/2C1L/localFusionPipeline.json

    • Pipeline Description:

             | -> decode     -> detector         -> tracker                  -> |                                    |
input  | -> decode     -> detector         -> tracker                  -> | -> LidarCam2CFusion ->  fusion  -> | -> output
       | ->                lidar signal processing                     -> |                                    |

  • Local File Pipeline for Camera + Lidar(4C+2L) Sensor fusion pipeline

    • Json File: localFusionPipeline.json

      File location: $PROJ_DIR/ai_inference/test/configs/raddet/2C1L/localFusionPipeline.json

    • Pipeline Description:

             | -> decode     -> detector         -> tracker                  -> |                                    |
input  | -> decode     -> detector         -> tracker                  -> | -> LidarCam2CFusion ->  fusion  -> |
       | ->                lidar signal processing                     -> |                                    |
       | -> decode     -> detector         -> tracker                  -> |                                    | -> output
input  | -> decode     -> detector         -> tracker                  -> | -> LidarCam2CFusion ->  fusion  -> |
       | ->                lidar signal processing                     -> |                                    |

  • Local File Pipeline for Camera + Lidar(12C+2L) Sensor fusion pipeline

    • Json File: localFusionPipeline.json File location: ai_inference/test/configs/kitti/6C1L/localFusionPipeline.json

    • Pipeline Description:

             | -> decode     -> detector         -> tracker                  -> |                                    |
       | -> decode     -> detector         -> tracker                  -> |                                    |
       | -> decode     -> detector         -> tracker                  -> |                                    |
input  | -> decode     -> detector         -> tracker                  -> | ->  LidarCam6CFusion -> fusion  -> | -> output
       | -> decode     -> detector         -> tracker                  -> |                                    |
       | -> decode     -> detector         -> tracker                  -> |                                    |
       | ->                lidar signal processing                     -> |                                    |

  • Local File Pipeline for Camera + Lidar(8C+4L) Sensor fusion pipeline

    • Json File: localFusionPipeline.json File location: ai_inference/test/configs/kitti/2C1L/localFusionPipeline.json

    • Pipeline Description:

             | -> decode     -> detector         -> tracker                  -> |                                    |
input  | -> decode     -> detector         -> tracker                  -> | -> LidarCam2CFusion ->  fusion  -> |
       | ->                lidar signal processing                     -> |                                    |
       | -> decode     -> detector         -> tracker                  -> |                                    |
input  | -> decode     -> detector         -> tracker                  -> | -> LidarCam2CFusion ->  fusion  -> |
       | ->                lidar signal processing                     -> |                                    | -> output
       | -> decode     -> detector         -> tracker                  -> |                                    |
input  | -> decode     -> detector         -> tracker                  -> | -> LidarCam2CFusion ->  fusion  -> |
       | ->                lidar signal processing                     -> |                                    |
       | -> decode     -> detector         -> tracker                  -> |                                    |
input  | -> decode     -> detector         -> tracker                  -> | -> LidarCam2CFusion ->  fusion  -> |
       | ->                lidar signal processing                     -> |                                    |

  • Local File Pipeline for Camera + Lidar(12C+4L) Sensor fusion pipeline

    • Json File: localFusionPipeline.json File location: ai_inference/test/configs/kitti/3C1L/localFusionPipeline.json

    • Pipeline Description:

             | -> decode     -> detector         -> tracker                  -> |                                    |
       | -> decode     -> detector         -> tracker                  -> |                                    |
input  | -> decode     -> detector         -> tracker                  -> | ->  LidarCam3CFusion -> fusion  -> | -> output
       | ->                lidar signal processing                     -> |                                    |

Service Start

Open a terminal, run the following commands:

cd $PROJ_DIR
sudo bash -x run_service_bare.sh

# Output logs:
    [2023-06-26 14:34:42.970] [DualSinks] [info] MaxConcurrentWorkload sets to 1
    [2023-06-26 14:34:42.970] [DualSinks] [info] MaxPipelineLifeTime sets to 300s
    [2023-06-26 14:34:42.970] [DualSinks] [info] Pipeline Manager pool size sets to 1
    [2023-06-26 14:34:42.970] [DualSinks] [trace] [HTTP]: uv loop inited
    [2023-06-26 14:34:42.970] [DualSinks] [trace] [HTTP]: Init completed
    [2023-06-26 14:34:42.971] [DualSinks] [trace] [HTTP]: http server at 0.0.0.0:50051
    [2023-06-26 14:34:42.971] [DualSinks] [trace] [HTTP]: running starts
    [2023-06-26 14:34:42.971] [DualSinks] [info] Server set to listen on 0.0.0.0:50052
    [2023-06-26 14:34:42.972] [DualSinks] [info] Server starts 1 listener. Listening starts
    [2023-06-26 14:34:42.972] [DualSinks] [trace] Connection handle with uid 0 created
    [2023-06-26 14:34:42.972] [DualSinks] [trace] Add connection with uid 0 into the conn pool

NOTE-1: workload (default as 4) can be configured in file: $PROJ_DIR/ai_inference/source/low_latency_server/AiInference.config

...
[Pipeline]
maxConcurrentWorkload=4

NOTE-2 : to stop service, run the following commands:

sudo pkill Hce

Entry Program

Usage

All executable files are located at: $PROJ_DIR/build/bin

entry program with display
Usage: CLSensorFusionDisplay <host> <port> <json_file> <total_stream_num> <repeats> <data_path> <display_type> <visualization_type>    [<save_flag: 0 | 1>] [<pipeline_repeats>] [<cross_stream_num>] [<warmup_flag: 0 | 1>] [<logo_flag: 0 | 1>]
--------------------------------------------------------------------------------
Environment requirement:
   unset http_proxy;unset https_proxy;unset HTTP_PROXY;unset HTTPS_PROXY
  • host: use 127.0.0.1 to call from localhost.
  • port: configured as 50052, can be changed by modifying file: $PROJ_DIR/ai_inference/source/low_latency_server/AiInference.config before starting the service.
  • json_file: AI pipeline topology file.
  • total_stream_num: to control the input streams.
  • repeats: to run tests multiple times, so that we can get more accurate performance.
  • data_path: multi-sensor binary files folder for input.
  • display_type: support for media, lidar, media_lidar, media_fusion currently.
    • media: only show image results in frontview.
    • lidar: only show lidar results in birdview.
    • media_lidar: show image results in frontview and lidar results in birdview separately.
    • media_fusion: show both for image results in frontview and fusion results in birdview.
  • visualization_type: visualization type of different pipelines, currently supports 2C1L, 4C2L, 8C4L, 12C2L.
  • save_flag: whether to save display results into video.
  • pipeline_repeats: pipeline repeats number.
  • cross_stream_num: the stream number that run in a single pipeline.
  • warmup_flag: warm up flag before pipeline start.
  • logo_flag: whether to add Intel logo in display.
entry program without display
Usage: testGRPCCPlusLPipeline <host> <port> <json_file> <total_stream_num> <repeats> <data_path> <media_type> [<pipeline_repeats>] [<cross_stream_num>] [<warmup_flag: 0 | 1>]
--------------------------------------------------------------------------------
Environment requirement:
   unset http_proxy;unset https_proxy;unset HTTP_PROXY;unset HTTPS_PROXY
  • host: use 127.0.0.1 to call from localhost.
  • port: configured as 50052, can be changed by modifying file: $PROJ_DIR/ai_inference/source/low_latency_server/AiInference.config before starting the service.
  • json_file: AI pipeline topology file.
  • total_stream_num: to control the input streams.
  • repeats: to run tests multiple times, so that we can get more accurate performance.
  • data_path: multi-sensor binary files folder for input.
  • media_type: : support for media, multisensor currently.
  • pipeline_repeats: pipeline repeats number.
  • cross_stream_num: the stream number that run in a single pipeline.
  • warmup_flag: warm up flag before pipeline start.

2C+1L

The target platform is Intel® Core™ Ultra 7 265H.

Note: Run with root if you want to get the GPU utilization profiling. Change /path-to-dataset to your data path.

Refer to kitti360_guide.md for data preparation, or just use demo data in kitti360.

  • media_fusion display type

    open another terminal, run the following commands:

    # multi-sensor inputs test-case
sudo -E ./build/bin/CLSensorFusionDisplay 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localFusionPipeline.json 1 1 /path-to-dataset media_fusion 2C1L

    Display type: media_fusion

  • media_lidar display type

    open another terminal, run the following commands:

    # multi-sensor inputs test-case
sudo -E ./build/bin/CLSensorFusionDisplay 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localFusionPipeline.json 1 1 /path-to-dataset media_lidar 2C1L

    Display type: media_lidar

  • media display type

    open another terminal, run the following commands:

    # multi-sensor inputs test-case
sudo -E ./build/bin/CLSensorFusionDisplay 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localMediaPipeline.json 1 1 /path-to-dataset media 2C1L

    Display type: media

  • lidar display type

    open another terminal, run the following commands:

    # multi-sensor inputs test-case
sudo -E ./build/bin/CLSensorFusionDisplay 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localLidarPipeline.json 1 1 /path-to-dataset lidar 2C1L

    Display type: lidar

2C+1L Unit Tests

The target platform is Intel® Core™ Ultra 7 265H.

Note: Run with root if you want to get the GPU utilization profiling. Change /path-to-dataset to your data path.

Refer to kitti360_guide.md for data preparation, or just use demo data in kitti360.

This section describes the unit tests of three major components: media processing, lidar processing, and fusion pipeline without display.

Unit Test: Fusion pipeline without display: Open another terminal, run the following commands:

# fusion test-case
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localFusionPipeline.json 1 1 /path-to-dataset multisensor

Unit Test: Media Processing:

Open another terminal, run the following commands:

# media test-case
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localMediaPipeline.json 1 1 /path-to-dataset media

Unit Test: Lidar Processing:

Open another terminal, run the following commands:

# lidar test-case
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localLidarPipeline.json 1 1 /path-to-dataset multisensor

4C+2L

The target platform is Intel® Core™ Ultra 7 265H.

Note: Run with root if you want to get the GPU utilization profiling. Change /path-to-dataset to your data path.

Refer to kitti360_guide.md for data preparation, or just use demo data in kitti360.

  • media_fusion display type

    open another terminal, run the following commands:

    # multi-sensor inputs test-case
sudo -E ./build/bin/CLSensorFusionDisplay 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localFusionPipeline.json 2 1 /path-to-dataset media_fusion 4C2L

    Display type: media_fusion

  • media_lidar display type

    open another terminal, run the following commands:

    # multi-sensor inputs test-case
sudo -E ./build/bin/CLSensorFusionDisplay 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localFusionPipeline.json 2 1 /path-to-dataset media_lidar 4C2L

    Display type: media_lidar

  • media display type

    open another terminal, run the following commands:

    # multi-sensor inputs test-case
sudo -E ./build/bin/CLSensorFusionDisplay 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localMediaPipeline.json 2 1 /path-to-dataset media 4C2L

    Display type: media

  • lidar display type

    open another terminal, run the following commands:

    # multi-sensor inputs test-case
sudo -E ./build/bin/CLSensorFusionDisplay 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localLidarPipeline.json 2 1 /path-to-dataset lidar 4C2L

    Display type: lidar

4C+2L Unit Tests

The target platform is Intel® Core™ Ultra 7 265H.

Note: Run with root if you want to get the GPU utilization profiling. Change /path-to-dataset to your data path.

Refer to kitti360_guide.md for data preparation, or just use demo data in kitti360.

This section describes the unit tests of three major components: media processing, lidar processing, and fusion pipeline without display.

Unit Test: Fusion pipeline without display:

Open another terminal, run the following commands:

# fusion test-case
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localFusionPipeline.json 2 1 /path-to-dataset multisensor

Unit Test: Media Processing:

Open another terminal, run the following commands:

# media test-case
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localMediaPipeline.json 2 1 /path-to-dataset media

Unit Test: Lidar Processing:

Open another terminal, run the following commands:

# lidar test-case
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localLidarPipeline.json 2 1 /path-to-dataset multisensor

12C+2L

The target platform is Intel® Core™ i7-13700 and Intel® B580 Graphics.

Note: Run with root if you want to get the GPU utilization profiling. Change /path-to-dataset to your data path.

Refer to kitti360_guide.md for data preparation, or just use demo data in kitti360.

  • media_fusion display type

    open another terminal, run the following commands:

    # multi-sensor inputs test-case
sudo -E ./build/bin/CLSensorFusionDisplay 127.0.0.1 50052 ai_inference/test/configs/kitti/6C1L/localFusionPipeline.json 2 1 /path-to-dataset media_fusion 12C2L

    Display type: media_fusion

  • media_lidar display type

    open another terminal, run the following commands:

    # multi-sensor inputs test-case
sudo -E ./build/bin/CLSensorFusionDisplay 127.0.0.1 50052 ai_inference/test/configs/kitti/6C1L/localFusionPipeline.json 2 1 /path-to-dataset media_lidar 12C2L

    Display type: media_lidar

  • media display type

    open another terminal, run the following commands:

    # multi-sensor inputs test-case
sudo -E ./build/bin/CLSensorFusionDisplay 127.0.0.1 50052 ai_inference/test/configs/kitti/6C1L/localMediaPipeline.json 2 1 /path-to-dataset media 12C2L

    Display type: media

  • lidar display type

    open another terminal, run the following commands:

    # multi-sensor inputs test-case
sudo -E ./build/bin/CLSensorFusionDisplay 127.0.0.1 50052 ai_inference/test/configs/kitti/6C1L/localLidarPipeline.json 2 1 /path-to-dataset lidar 12C2L

    Display type: lidar

12C+2L Unit Tests

The target platform is Intel® Core™ i7-13700 and Intel® B580 Graphics.

Note: Run with root if you want to get the GPU utilization profiling. Change /path-to-dataset to your data path.

Refer to kitti360_guide.md for data preparation, or just use demo data in kitti360.

This section describes the unit tests of three major components: media processing, lidar processing, and fusion pipeline without display.

Unit Test: Fusion pipeline without display:

Open another terminal, run the following commands:

# fusion test-case
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/6C1L/localFusionPipeline.json 2 1 /path-to-dataset multisensor

Unit Test: Media Processing::

Open another terminal, run the following commands:

# media test-case
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/6C1L/localMediaPipeline.json 2 1 /path-to-dataset media

Unit Test: Lidar Processing:

Open another terminal, run the following commands:

# lidar test-case
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/6C1L/localLidarPipeline.json 2 1 /path-to-dataset multisensor

8C+4L

The target platform is Intel® Core™ i7-13700 and Intel® B580 Graphics.

Note: Run with root if you want to get the GPU utilization profiling. Change /path-to-dataset to your data path.

Refer to kitti360_guide.md for data preparation, or just use demo data in kitti360.

  • media_fusion display type

    open another terminal, run the following commands:

    # multi-sensor inputs test-case
sudo -E ./build/bin/CLSensorFusionDisplay 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localFusionPipeline.json 4 1 /path-to-dataset media_fusion 8C4L

    Display type: media_fusion

  • media_lidar display type

    open another terminal, run the following commands:

    # multi-sensor inputs test-case
sudo -E ./build/bin/CLSensorFusionDisplay 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localFusionPipeline.json 4 1 /path-to-dataset media_lidar 8C4L

    Display type: media_lidar

  • media display type

    open another terminal, run the following commands:

    # multi-sensor inputs test-case
sudo -E ./build/bin/CLSensorFusionDisplay 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localMediaPipeline.json 4 1 /path-to-dataset media 8C4L

    Display type: media

  • lidar display type

    open another terminal, run the following commands:

    # multi-sensor inputs test-case
sudo -E ./build/bin/CLSensorFusionDisplay 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localLidarPipeline.json 4 1 /path-to-dataset lidar 8C4L

    Display type: lidar

8C+4L Unit Tests

The target platform is Intel® Core™ i7-13700 and Intel® B580 Graphics.

Note: Run with root if you want to get the GPU utilization profiling. Change /path-to-dataset to your data path.

Refer to kitti360_guide.md for data preparation, or just use demo data in kitti360.

This section describes the unit tests of three major components: media processing, lidar processing, and fusion pipeline without display.

Unit Test: Fusion pipeline without display:

Open another terminal, run the following commands:

# fusion test-case
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localFusionPipeline.json 4 1 /path-to-dataset multisensor

Unit Test: Media Processing:

Open another terminal, run the following commands:

# media test-case
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localMediaPipeline.json 4 1 /path-to-dataset media

Unit Test: Lidar Processing:

Open another terminal, run the following commands:

# lidar test-case
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localLidarPipeline.json 4 1 /path-to-dataset multisensor

12C+4L

*The target platform is Intel® Core™ i7-13700 and Intel® B580 Graphics.

Note: Run with root if you want to get the GPU utilization profiling. Change /path-to-dataset to your data path.

Refer to kitti360_guide.md for data preparation, or just use demo data in kitti360.

  • media_fusion display type

    open another terminal, run the following commands:

    # multi-sensor inputs test-case
sudo -E ./build/bin/CLSensorFusionDisplay 127.0.0.1 50052 ai_inference/test/configs/kitti/3C1L/localFusionPipeline.json 4 1 /path-to-dataset media_fusion 12C4L

    Display type: media_fusion

  • media_lidar display type

    open another terminal, run the following commands:

    # multi-sensor inputs test-case
sudo -E ./build/bin/CLSensorFusionDisplay 127.0.0.1 50052 ai_inference/test/configs/kitti/3C1L/localFusionPipeline.json 4 1 /path-to-dataset media_lidar 12C4L

    Display type: media_lidar

  • media display type

    open another terminal, run the following commands:

    # multi-sensor inputs test-case
sudo -E ./build/bin/CLSensorFusionDisplay 127.0.0.1 50052 ai_inference/test/configs/kitti/3C1L/localMediaPipeline.json 4 1 /path-to-dataset media 12C4L

    Display type: media

  • lidar display type

    open another terminal, run the following commands:

    # multi-sensor inputs test-case
sudo -E ./build/bin/CLSensorFusionDisplay 127.0.0.1 50052 ai_inference/test/configs/kitti/3C1L/localLidarPipeline.json 4 1 /path-to-dataset lidar 12C4L

    Display type: lidar

12C+4L Unit Tests

The target platform is Intel® Core™ i7-13700 and Intel® B580 Graphics.

Note: Run with root if you want to get the GPU utilization profiling. Change /path-to-dataset to your data path.

Refer to kitti360_guide.md for data preparation, or just use demo data in kitti360.

This section describes the unit tests of three major components: media processing, lidar processing, and fusion pipeline without display.

Unit Test: Fusion pipeline without display:

Open another terminal, run the following commands:

# fusion test-case
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/3C1L/localFusionPipeline.json 4 1 /path-to-dataset multisensor

Unit Test: Media Processing:

Open another terminal, run the following commands:

# media test-case
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/3C1L/localMediaPipeline.json 4 1 /path-to-dataset media

Unit Test: Lidar Processing:

Open another terminal, run the following commands:

# lidar test-case
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/3C1L/localLidarPipeline.json 4 1 /path-to-dataset multisensor

KPI test

2C+1L KPI test

# Run service with the following command:
sudo bash run_service_bare_log.sh
# Open another terminal, run the command below:
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localFusionPipeline.json 1 10 /path-to-dataset multisensor

Fps and average latency will be calculated.

4C+2L KPI test

# Run service with the following command:
sudo bash run_service_bare_log.sh
# Open another terminal, run the command below:
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localFusionPipeline.json 2 10 /path-to-dataset multisensor

Fps and average latency will be calculated.

12C+2L KPI test

# Run service with the following command:
sudo bash run_service_bare_log.sh
# Open another terminal, run the command below:
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/6C1L/localFusionPipeline.json 2 10 /path-to-dataset multisensor

Fps and average latency will be calculated.

8C+4L KPI test

# Run service with the following command:
sudo bash run_service_bare_log.sh
# Open another terminal, run the command below:
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localFusionPipeline.json 4 10 /path-to-dataset multisensor

Fps and average latency will be calculated.

12C+4L KPI test

# Run service with the following command:
sudo bash run_service_bare_log.sh
# Open another terminal, run the command below:
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/3C1L/localFusionPipeline.json 4 10 /path-to-dataset multisensor

Fps and average latency will be calculated.

Stability test

2C+1L stability test

Note: change workload configuration to 1 in file: $PROJ_DIR/ai_inference/source/low_latency_server/AiInference.config

...
[Pipeline]
numOfProcess=1
maxConcurrentWorkload=1

Run the service first, and open another terminal, run the command below:

# 2C1L without display
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localFusionPipeline.json 1 100 /path-to-dataset multisensor 100

4C+2L stability test

Note: change workload configuration to 2 in file: $PROJ_DIR/ai_inference/source/low_latency_server/AiInference.config

...
[Pipeline]
numOfProcess=2
maxConcurrentWorkload=2

Run the service first, and open another terminal, run the command below:

# 4C2L without display
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localFusionPipeline.json 2 100 /path-to-dataset multisensor 100

12C+2L stability test

Note: change workload configuration to 2 in file: $PROJ_DIR/ai_inference/source/low_latency_server/AiInference.config

...
[Pipeline]
numOfProcess=2
maxConcurrentWorkload=2

Run the service first, and open another terminal, run the command below:

# 12C2L without display
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/6C1L/localFusionPipeline.json 2 100 /path-to-dataset multisensor 100

8C+4L stability test

Note: change workload configuration to 4 in file: $PROJ_DIR/ai_inference/source/low_latency_server/AiInference.config

...
[Pipeline]
numOfProcess=4
maxConcurrentWorkload=4

Run the service first, and open another terminal, run the command below:

# 8C4L without display
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/2C1L/localFusionPipeline.json 4 100 /path-to-dataset multisensor 100

12C+4L stability test

Note: change workload configuration to 4 in file: $PROJ_DIR/ai_inference/source/low_latency_server/AiInference.config

...
[Pipeline]
numOfProcess=4
maxConcurrentWorkload=4

Run the service first, and open another terminal, run the command below:

# 8C4L without display
sudo -E ./build/bin/testGRPCCPlusLPipeline 127.0.0.1 50052 ai_inference/test/configs/kitti/3C1L/localFusionPipeline.json 4 100 /path-to-dataset multisensor 100

Build Docker image

Install Docker Engine and Docker Compose on Ubuntu

Install Docker Engine and Docker Compose according to the guide on the official website.

Before you install Docker Engine for the first time on a new host machine, you need to set up the Docker apt repository. Afterward, you can install and update Docker from the repository.

  1. Set up Docker's apt repository.

    # Add Docker's official GPG key:
sudo -E apt-get update
sudo -E apt-get install ca-certificates curl
sudo -E install -m 0755 -d /etc/apt/keyrings
sudo -E curl -fsSL https://download.docker.com/linux/ubuntu/gpg -o /etc/apt/keyrings/docker.asc
sudo chmod a+r /etc/apt/keyrings/docker.asc

# Add the repository to Apt sources:
echo \
  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/ubuntu \
  $(. /etc/os-release && echo "${UBUNTU_CODENAME:-$VERSION_CODENAME}") stable" | \
  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo -E apt-get update

  2. Install the Docker packages.

    To install the latest version, run:

    sudo -E apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

  3. Set proxy(Optional).

    Note you may need to set proxy for docker.

    sudo mkdir -p /etc/systemd/system/docker.service.d
sudo vim /etc/systemd/system/docker.service.d/http-proxy.conf

# Modify the file contents as follows
[Service]
Environment="HTTP_PROXY=http://proxy.example.com:8080"
Environment="HTTPS_PROXY=http://proxy.example.com:8080"
Environment="NO_PROXY=localhost,127.0.0.1"

    Then restart docker:

    sudo systemctl daemon-reload
sudo systemctl restart docker

  4. Verify that the installation is successful by running the hello-world image:

    sudo docker run hello-world

    This command downloads a test image and runs it in a container. When the container runs, it prints a confirmation message and exits.

  5. Add user to group

    sudo usermod -aG docker $USER
newgrp docker

  6. Then pull base image

    docker pull ubuntu:24.04

Install the corresponding driver on the host

bash install_driver_related_libs.sh

If driver are already installed on the machine, you don't need to do this step.

Pull docker image through docker hub

You can pull latest tfcc docker image through intel/tfcc - Docker Image.

For example:

docker pull intel/tfcc:latest

Build and run docker image through scripts

Note: The default username is tfcc and password is intel in docker image.

Build docker image:

Usage:

bash build_docker.sh <IMAGE_TAG, default tfcc:latest> <DOCKERFILE, default Dockerfile_TFCC.dockerfile>  <BASE, default ubuntu> <BASE_VERSION, default 24.04>

Example:

cd $PROJ_DIR/docker
bash build_docker.sh tfcc:latest Dockerfile_TFCC.dockerfile

Run docker image:

Usage:

bash run_docker.sh <DOCKER_IMAGE, default tfcc:latest> <NPU_ON, default false>

Example:

cd $PROJ_DIR/docker
bash run_docker.sh tfcc:latest false
# After the run is complete, the container ID will be output, or you can view it through docker ps

Enter docker:

Get the container id using the command below:

docker ps -a

And then enter docker using the command below:

docker exec -it <container id> /bin/bash

Copy dataset:

If you want to copy the dataset or other files to docker, you can refer to the command below:

docker cp /path/to/dataset <container id>:/path/to/dataset

Build and run docker image through docker compose

Note: The default username is tfcc and password is intel in docker image.

Modify proxy, VIDEO_GROUP_ID and RENDER_GROUP_ID in .env file.

# proxy settings
https_proxy=
http_proxy=
# base image settings
BASE=ubuntu
BASE_VERSION=24.04
# group IDs for various services
VIDEO_GROUP_ID=44
RENDER_GROUP_ID=110
# display settings
DISPLAY=$DISPLAY

You can get VIDEO_GROUP_ID and RENDER_GROUP_ID with the following command:

# VIDEO_GROUP_ID
echo $(getent group video | awk -F: '{printf "%s\n", $3}')
# RENDER_GROUP_ID
echo $(getent group render | awk -F: '{printf "%s\n", $3}')

Build and run docker image:

Usage:

cd $PROJ_DIR/docker
docker compose up <services-name> -d # tfcc and tfcc-npu. tfcc-npu means with NPU support

Example:

cd $PROJ_DIR/docker
docker compose up tfcc -d

If you need NPU support, for example, on MTL platform, run the command below:

cd $PROJ_DIR/docker
docker compose up tfcc-npu -d

Enter docker:

Usage:

docker compose exec <services-name> /bin/bash

Example:

docker compose exec tfcc /bin/bash

Copy dataset:

Find the container name or ID:

docker compose ps

Sample output:

NAME                IMAGE      COMMAND       SERVICE    CREATED         STATUS         PORTS
docker-tfcc-1    tfcc:latest   "/bin/bash"     tfcc   4 minutes ago   Up 9 seconds

Copy dataset:

docker cp /path/to/dataset docker-tfcc-1:/path/to/dataset

Running inside docker

Follow the guide from Get Started to run sensor fusion application.

Code References

Some of the code is referenced from the following projects: