EBC Exercise 39 Setting Up tidl on X15

Here are instructions on how to run TI's Deep Learning (tidl) examples on a BeagleBoard-X15.

Install
Get Robert's tidl repo

x15$ git clone https://github.com/rcn-ee/tidl-api

Now follow the instructions in the readme.md file.

x15$ sudo apt update x15$ sudo apt install ti-opencl libboost-dev libopencv-core-dev libopencv-imgproc-dev libopencv-highgui-dev libjson-c-dev

Most were already installed and up to date. Install time 38s.

Checkout the most current branch and compile. Use -j2 since we have 2 cores.

x15$ cd tidl-api/ x15$ git checkout origin/v01.02.02-bb.org -b v01.02.02-bb.org x15$ make -j2 build-api     # 1m31s The next build puts things in /usr/share/ti/tidl so create it and assume give user 1000 (should be debian) permission to read/write it.

x15$ sudo mkdir -p /usr/share/ti/tidl x15$ sudo chown -R 1000:1000 /usr/share/ti/tidl/ x15$ make -j2 build-examples  # 2m28s

Extras to install
Here are a few other handy extras to install.

If you get a cmemk error: x15$ cd /opt/scripts/tools/ ; git pull ; sudo ./update_kernel.sh ; sudo apt upgrade

Fix a path error with x15$ cd /usr/share/ti/tidl x15$ '''sudo ln -s /tidl-api/examples.

The x15 runs a bit hot. A fan is suggested. You can check the CPU temp with x15$ cat /sys/class/thermal/*/temp 36600 36200 35800 35400 36200 25625

The units are millidegrees C. A fan will drop the temp some 20 Deg C.

If you get Gtk-Message: Failed to load module "canberra-gtk-module", run x15$ sudo apt install libcanberra-gtk-module libcanberra-gtk3-module

Install the image viewer "eye of gnome" for viewing images on the x15. x15$ sudo apt install eog

Run Examples
Here's how to run some of the examples. From the host computer you need to ssh with the -XC flags so the x15 can access the host's X-windows to display things. You need to ssh as root for the X-Windows authentication to work. Here are instructions for setting a root password, etc. host$ ssh -XC root@x15

classification
The imagenet demo is looking for one object out of a list of 1000 things. The classification demo is looking for one (or two if you set TWO_ROIs) object out of a small list of 12 or so things. You need to login to the x15 as root for the X-Windows authentication to work.

root@x15$ cd classification root@x15$ ls avg_fps_window.h imagenet1001.txt  Makefile                        stream_config_mobilenet.txt classlist.txt    imagenet.txt      readme.md                       tidl_classification clips            images            stream_config_inceptionnet.txt  tidl-sw-stack-small.png findclasses.cpp  main.cpp          stream_config_j11_v2.txt

seems to have a file missing.

runs but gets the error "Corrupt JPEG data: 2 extraneous bytes before marker 0xd4". So I send stderr to /dev/null

runs but it looks like the color channels are switched

The following takes live video from a camera (/dev/video0) and displays it on the host. It also displays a list of objects it is looking for and highlights the last object it found. See readme.md for more details.

root@x15$ ./tidl_classification -g 1 -d 2 -e 2 -l ./imagenet.txt -s ./classlist.txt -i 0 -c ./stream_config_j11_v2.txt 2> /dev/null

This will play a video and classify it. Note: The readme.md referenced test50.mp4, but I couldn't find it so I'm using test10.mp4.

root@x15$ ls clips test10.mp4 test1.mp4  test2.mp4 root@x15$ ./tidl_classification -g 1 -d 2 -e 2 -l ./imagenet.txt -s ./classlist.txt -i ./clips/test10.mp4 -c ./stream_config_j11_v2.txt See readme.md for more examples

main.cpp, line 55, uncomment to have two Regions of Interest.

Look in imagenet.txt to see what can be recognized and add them to classlist.txt.

imagenet
Run the imagenet demo to recognize any of the 1000 images.

root@x15$ cd tidl-api/examples/imagenet root@x15$ ls imagenet imagenet_objects.json  main.cpp  Makefile Processing live video from /dev/video0 ./imagenet -i camera0 2> /dev/null # Redirect the errors to ignore a message

Processing a still image. ./imagenet -d 2 -e2 -i IMG_3806.jpg

segmentation
The segmentation example takes an image as input and performs pixel-level classification according to pre-trained categories.

root@x15 '''cd /tidl-api/examples/segmentation root@x15$ ./segmentation -d 2 -e 2 -i camera0 -w 1200 2> /dev/null

ssd_multibox
SSD is the abbreviation for Single Shot multi-box Detector. The ssd_multibox example takes an image as input and detects multiple objects with bounding boxes according to pre-trained categories.

root@x15$ cd /tidl-api/examples/ssd_multibox root@x15$ ./ssd_multibox -d 2 -e 2 -i camera0 -w 1200 2> /dev/null

Others
layer_ouput and mcbench look like handy tools.