Difference between revisions of "Ubuntu L4T User Guide"

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(Created page with "= Getting Started = == <span style="color:#0070c0">Host Environment</span> == == <span style="color:#0070c0">Force Recovery Mode</span> == == <span style="color:#0070c0">Fl...")
 
 
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== <span style="color:#0070c0">Host Environment</span> ==
 
== <span style="color:#0070c0">Host Environment</span> ==
 +
 +
Ubuntu 18.04 (recommended) or 16.04
 +
 +
'''[Prerequisite]'''
 +
 +
$ sudo apt-get install python
  
 
== <span style="color:#0070c0">Force Recovery Mode</span> ==
 
== <span style="color:#0070c0">Force Recovery Mode</span> ==
  
−
== <span style="color:#0070c0">Flash Ubuntu Image</span> ==
+
To enter force recovery mode, you can do:
 +
 
 +
:1. Hold the '''Recovery '''key
 +
:2. Power on device
 +
:3. Wait for 5 seconds and you can release the '''Recovery '''key
 +
 
 +
Once it enters recovery mode successfully, the HDMI output should be disabled. Then, you have to connect a USB cable with TX2 device and PC. A new "''nvidia apx''" device will be detected on PC.
 +
 
 +
$ lsusb
 +
Bus 003 Device 125: ID '''<span style="color:#0000F0">0955:7c18</span> NVidia Corp'''.
 +
 
 +
== <span style="color:#0070c0">Flash Pre-built Image</span> ==
 +
 
 +
First, make sure your TX2 device is already in Force Recover mode, and USB cable is connected.
 +
 
 +
Then, execute the '''TX2_flash.sh''' script which you can find it in the release folder.
 +
 
 +
$ sudo ./TX2_flash.sh
 +
 
 +
After script is done, the target device will boot into OS automatically.
  
 
== <span style="color:#0070c0">Install SDK Components</span> ==
 
== <span style="color:#0070c0">Install SDK Components</span> ==
 +
 +
Download the SDK Manager for '''Jetson TX2 series''' from [https://developer.nvidia.com/embedded/jetpack JetPack website].
 +
 +
'''Note:''' You will need a nVidia developer account for access.
 +
 +
After download complete, double click the DEB file to install, or you can run `sudo dpkg -i sdkmanager-xxx.deb`
 +
 +
[[File:Sdkmamanger install.png|800px|Sdkmamanger_install]]
 +
 +
Then, you're able to run SDK manager.
 +
 +
$ sdkmanager
 +
 +
Log in with your nVidia developer account, and you can see the STEP 01 page.
 +
 +
'''STEP 01:''' Set "''Target Hardware''" to "''Jetson TX2 P3310''", and select target OS you want to install. Here, we choose 4.2.2.
 +
 +
[[File:Tx2-sdk-step1.png|800px|Tx2-sdk-step1]]
 +
 +
'''STEP 02: '''Check the components you want, and continue.
 +
 +
[[File:Tx2-sdk-step2.png|800px|Tx2-sdk-step2]]
 +
 +
'''Note: '''Please <span style="color:#FF0000">DO NOT</span> check the "''Jetson OS''" item. It will generate and flash TX2 demo image into your device.
 +
 +
'''Note: '''You may need minimum 120GB free storage and 8GB RAM as prerequisite.
 +
 +
'''STEP 03:''' After download process is done, you need to input the IP address of your TX2 device. It will install SDK via network.
 +
 +
[[File:Tx2-sdk-step3-ip.png|500px|Tx2-sdk-step3-ip]]
 +
 +
It will take several minutes to finish the installation.
 +
 +
[[File:Tx2-sdk-step3-sdk.png|800px|Tx2-sdk-step3-sdk]]
 +
 +
'''Note: '''If you see the warning dialog said it's taking longer than expected, please press '''Yes '''to continue installing.
 +
 +
[[File:Install take longer.png|300px|Install_take_longer]]
 +
 +
'''STEP 04: '''When you go to this step, it's done!
 +
 +
[[File:Tx2-sdk-step4.png|800px|Tx2-sdk-step4]]
  
 
= Demo =
 
= Demo =
 +
 +
In this section, we setup and run demo applications on TX2 target device.
 +
 +
Open '''Terminal '''program and export deepstream sdk root first.
 +
 +
$ export DS_SDK_ROOT="/opt/nvidia/deepstream/deepstream-4.0"
  
 
== <span style="color:#0070c0">Deepstream Samples</span> ==
 
== <span style="color:#0070c0">Deepstream Samples</span> ==
 +
 +
There are 3 kinds of object detector demos in deepstream SDK.
 +
 +
To replace the video file, you can modify the corresponding config files. For example,
 +
 +
$ vim deepstream_app_config_yoloV3.txt
 +
uri=[file:///home/advrisc/Videos/test.mp4 file:///home/advrisc/Videos/test.mp4]
 +
 +
=== <span style="color:#0070c0">FasterRCNN</span> ===
 +
 +
Setup:
 +
 +
$ cd $DS_SDK_ROOT/sources/objectDetector_FasterRCNN
 +
$ wget --no-check-certificate  [https://dl.dropboxusercontent.com/s/o6ii098bu51d139/faster_rcnn_models.tgz?dl=0 https://dl.dropboxusercontent.com/s/o6ii098bu51d139/faster_rcnn_models.tgz?dl=0]  -O faster-rcnn.tgz
 +
$ tar zxvf  faster-rcnn.tgz  -C .  --strip-components=1  --exclude=ZF_*
 +
$ cp /usr/src/tensorrt/data/faster-rcnn/faster_rcnn_test_iplugin.prototxt .
 +
 +
$ make -C nvdsinfer_custom_impl_fasterRCNN
 +
 +
Run:
 +
 +
$ deepstream-app -c deepstream_app_config_fasterRCNN.txt
 +
 +
=== <span style="color:#0070c0">SSD</span> ===
 +
 +
Setup:
 +
 +
$ cd $DS_SDK_ROOT/sources/objectDetector_SSD
 +
 +
$ cp /usr/src/tensorrt/data/ssd/ssd_coco_labels.txt .
 +
$ sudo apt-get install python-protobuf
 +
$ pip install tensorflow-gpu
 +
 +
$ wget [http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2017_11_17.tar.gz http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2017_11_17.tar.gz]
 +
$ tar zxvf ssd_inception_v2_coco_2017_11_17.tar.gz
 +
$ cd ssd_inception_v2_coco_2017_11_17
 +
$ python3 /usr/lib/python3.6/dist-packages/uff/bin/convert_to_uff.py \
 +
    frozen_inference_graph.pb  -O NMS  -p /usr/src/tensorrt/samples/sampleUffSSD/config.py  -o sample_ssd_relu6.uff
 +
$ cp sample_ssd_relu6.uff ../
 +
 +
$ cd ..
 +
$ export CUDA_VER=10.0
 +
$ make -C nvdsinfer_custom_impl_ssd
 +
 +
Run:
 +
 +
$ deepstream-app -c deepstream_app_config_ssd.txt
 +
 +
=== <span style="color:#0070c0">Yolo</span> ===
 +
 +
Setup:
 +
 +
$ cd $DS_SDK_ROOT/sources/objectDetector_Yolo
 +
$ ./prebuild.sh
 +
$ export CUDA_VER=10.0
 +
$ make -C nvdsinfer_custom_impl_Yolo
 +
 +
Run:
 +
 +
$ deepstream-app -c deepstream_app_config_yoloV3.txt
 +
-OR-
 +
$ deepstream-app -c deepstream_app_config_yoloV3_tiny.txt
  
 
== <span style="color:#0070c0">Deepstream Reference Apps</span> ==
 
== <span style="color:#0070c0">Deepstream Reference Apps</span> ==
 +
 +
In this repository, it provides some reference applications for video analytics tasks using TensorRT and DeepSTream SDK 4.0.
 +
 +
$ cd $DS_SDK_ROOT/sources/apps/sample_apps/
 +
$ git clone [https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps.git https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps.git]
 +
$ cd deepstream_reference_apps
 +
 +
=== <span style="color:#0070c0">back-to-back-detectors & anomaly</span> ===
 +
 +
These two applications only support elementary h264 stream, not mp4 video file.
 +
 +
=== <span style="color:#0070c0">runtime_source_add_delete</span> ===
 +
 +
Setup:
 +
 +
$ cd runtime_source_add_delete
 +
$ make
 +
 +
Run:
 +
 +
$ ./deepstream-test-rt-src-add-del <uri>
 +
$ ./deepstream-test-rt-src-add-del [file://$DS_SDK_ROOT/samples/streams/sample_1080p_h265.mp4 file://$DS_SDK_ROOT/samples/streams/sample_1080p_h265.mp4]
 +
$ ./deepstream-test-rt-src-add-del rtsp://127.0.0.1/video

Latest revision as of 08:35, 6 May 2020

Getting Started

Host Environment

Ubuntu 18.04 (recommended) or 16.04

[Prerequisite] 

$ sudo apt-get install python

Force Recovery Mode

To enter force recovery mode, you can do:

1. Hold the Recovery key
2. Power on device
3. Wait for 5 seconds and you can release the Recovery key

Once it enters recovery mode successfully, the HDMI output should be disabled. Then, you have to connect a USB cable with TX2 device and PC. A new "nvidia apx" device will be detected on PC. 

$ lsusb
Bus 003 Device 125: ID 0955:7c18 NVidia Corp.

Flash Pre-built Image

First, make sure your TX2 device is already in Force Recover mode, and USB cable is connected.

Then, execute the TX2_flash.sh script which you can find it in the release folder. 

$ sudo ./TX2_flash.sh

After script is done, the target device will boot into OS automatically.

Install SDK Components

Download the SDK Manager for Jetson TX2 series from JetPack website.

Note: You will need a nVidia developer account for access.

After download complete, double click the DEB file to install, or you can run `sudo dpkg -i sdkmanager-xxx.deb`

Sdkmamanger_install

Then, you're able to run SDK manager. 

$ sdkmanager

Log in with your nVidia developer account, and you can see the STEP 01 page.

STEP 01: Set "Target Hardware" to "Jetson TX2 P3310", and select target OS you want to install. Here, we choose 4.2.2.

Tx2-sdk-step1

STEP 02: Check the components you want, and continue.

Tx2-sdk-step2

Note: Please DO NOT check the "Jetson OS" item. It will generate and flash TX2 demo image into your device.

Note: You may need minimum 120GB free storage and 8GB RAM as prerequisite.

STEP 03: After download process is done, you need to input the IP address of your TX2 device. It will install SDK via network.

Tx2-sdk-step3-ip

It will take several minutes to finish the installation.

Tx2-sdk-step3-sdk

Note: If you see the warning dialog said it's taking longer than expected, please press Yes to continue installing.

Install_take_longer

STEP 04: When you go to this step, it's done!

Tx2-sdk-step4

Demo

In this section, we setup and run demo applications on TX2 target device.

Open Terminal program and export deepstream sdk root first. 

$ export DS_SDK_ROOT="/opt/nvidia/deepstream/deepstream-4.0"

Deepstream Samples

There are 3 kinds of object detector demos in deepstream SDK.

To replace the video file, you can modify the corresponding config files. For example, 

$ vim deepstream_app_config_yoloV3.txt
uri=file:///home/advrisc/Videos/test.mp4

FasterRCNN

Setup: 

$ cd $DS_SDK_ROOT/sources/objectDetector_FasterRCNN
$ wget --no-check-certificate  https://dl.dropboxusercontent.com/s/o6ii098bu51d139/faster_rcnn_models.tgz?dl=0  -O faster-rcnn.tgz
$ tar zxvf  faster-rcnn.tgz  -C .  --strip-components=1  --exclude=ZF_*
$ cp /usr/src/tensorrt/data/faster-rcnn/faster_rcnn_test_iplugin.prototxt .

$ make -C nvdsinfer_custom_impl_fasterRCNN

Run: 

$ deepstream-app -c deepstream_app_config_fasterRCNN.txt

SSD

Setup: 

$ cd $DS_SDK_ROOT/sources/objectDetector_SSD

$ cp /usr/src/tensorrt/data/ssd/ssd_coco_labels.txt .
$ sudo apt-get install python-protobuf
$ pip install tensorflow-gpu

$ wget http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2017_11_17.tar.gz
$ tar zxvf ssd_inception_v2_coco_2017_11_17.tar.gz
$ cd ssd_inception_v2_coco_2017_11_17
$ python3 /usr/lib/python3.6/dist-packages/uff/bin/convert_to_uff.py \
    frozen_inference_graph.pb  -O NMS  -p /usr/src/tensorrt/samples/sampleUffSSD/config.py  -o sample_ssd_relu6.uff
$ cp sample_ssd_relu6.uff ../

$ cd ..
$ export CUDA_VER=10.0
$ make -C nvdsinfer_custom_impl_ssd

Run: 

$ deepstream-app -c deepstream_app_config_ssd.txt

Yolo

Setup: 

$ cd $DS_SDK_ROOT/sources/objectDetector_Yolo
$ ./prebuild.sh
$ export CUDA_VER=10.0
$ make -C nvdsinfer_custom_impl_Yolo

Run: 

$ deepstream-app -c deepstream_app_config_yoloV3.txt
-OR-
$ deepstream-app -c deepstream_app_config_yoloV3_tiny.txt

Deepstream Reference Apps

In this repository, it provides some reference applications for video analytics tasks using TensorRT and DeepSTream SDK 4.0. 

$ cd $DS_SDK_ROOT/sources/apps/sample_apps/
$ git clone https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps.git
$ cd deepstream_reference_apps

back-to-back-detectors & anomaly

These two applications only support elementary h264 stream, not mp4 video file.

runtime_source_add_delete

Setup: 

$ cd runtime_source_add_delete
$ make

Run: 

$ ./deepstream-test-rt-src-add-del <uri>
$ ./deepstream-test-rt-src-add-del file://$DS_SDK_ROOT/samples/streams/sample_1080p_h265.mp4
$ ./deepstream-test-rt-src-add-del rtsp://127.0.0.1/video
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