Training Object Detection from Simulation in Docker
A common problem in AI is data collection and tagging of training samples. Collecting this data is usually a manual process that is time and resource consuming, and hard to reproduce situations are often not covered. Isaac SDK provides a solution to this problem by generating datasets from simulated environments using Isaac Sim Unity3D.
In this tutorial, you will train an object detection network using Transfer Learning with a dataset from a simulation. We have built a Docker image that contains all the tools you need, as well as a Jupyter notebook that you will use for training.
Using this Docker image, you will be able to quickly start exploring the capabilities of Isaac SDK, but we recommended that you install the complete Isaac SDK on your host machine, as described in the Setup section, for optimal performance.
By setting up a simulation scenario and placing a simulated robot in this environment, you can use the Isaac SDK to extract samples as if there was a real robot taking pictures or video.
Along with images, the simulation environment provides exact information about what the robot is capturing on camera. This information is then used to automatically tag the pictures.
Once a picture is taken, a new scenario is set up randomly, and the process repeats until enough images and tags are generated. You will also learn how to set the size of this dataset.
NVIDIA Pascal GPU or newer. GTX 1080Ti and Titan V are the minimum recommended GPUs.
- Ubuntu 18.04
- Docker 19.03 or newer
- NVIDIA CUDA drivers
- NVIDIA NGC Account and API key
Run the following script to install the software requirements on your Ubuntu 18.04 desktop. You can copy and paste the following commands to a terminal window:
#Docker CE repository sudo apt-get install apt-transport-https ca-certificates curl gnupg-agent software-properties-common curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add - sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu$(lsb_release -cs)stable" #NVIDIA CUDA Drivers wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600 wget http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda-repo-ubuntu1804-10-2-local-10.2.89-440.33.01_1.0-1_amd64.deb sudo dpkg -i cuda-repo-ubuntu1804-10-2-local-10.2.89-440.33.01_1.0-1_amd64.deb sudo apt-key add /var/cuda-repo-10-2-local-10.2.89-440.33.01/7fa2af80.pub #NVIDIA Docker distribution=$(. /etc/os-release;echo $ID$VERSION_ID) curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list #Install packages sudo apt-get update sudo apt-get install docker-ce sudo apt-get -y install cuda sudo apt-get install -y nvidia-container-toolkit #Add yourself to the docker group sudo usermod -a -G docker $(id -nu) echo "All installed"
Once all the software is installed, restart your machine to load the NVIDIA CUDA Drivers:
sudo shutdown -r now
Log back in to your Ubuntu desktop environment and open a new terminal window. Use the following command to verify that the installation went well:
docker run --gpus all nvidia/cuda:10.0-base nvidia-smi
You should see a message indicating some statistics from your GPU and CUDA libraries. The following is an example output from this command.
+-----------------------------------------------------------------------------+ | NVIDIA-SMI 440.33.01 Driver Version: 440.33.01 CUDA Version: 10.2 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 TITAN V On | 00000000:17:00.0 Off | N/A | | 30% 44C P8 26W / 250W | 0MiB / 12066MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 1 TITAN V On | 00000000:65:00.0 On | N/A | | 30% 44C P8 27W / 250W | 404MiB / 12063MiB | 0% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | 1 2983 G /usr/lib/xorg/Xorg 256MiB | | 1 3117 G /usr/bin/gnome-shell 145MiB | +-----------------------------------------------------------------------------+
If you have trouble, please see the Isaac FAQs or visit the forums.
NGC Docker Registry Setup
The NGC registry hosts Docker images for AI as well as models, datasets, and tools for HPC, AI, and other technologies from NVIDIA and partners. To use this tutorial, you will need to have an account and create an API key. This will let you download the isaac-experiments image, as well as pre-trained models for Transfer Learning.
Visit NGC to set up a new account. Once you are logged in, visit the API Key creation page and follow the on-screen instructions.
Keep the API key stored safely; we’ll use it a couple times during setup.
Use the following command, including your API key to log in to the NGC Docker Registry:
docker login -u '$oauthtoken' nvcr.io
The image creates many files to provide you with opportunities to customize and control the behavior of the simulation and training. We recommend creating a separate folder to hold your experiments.
Run the following commands to create an
isaac-experiments folder and create the startup
script for the container.
mkdir ~/isaac-experiments cd ~/isaac-experiments docker run -u $(id -u) -v $PWD:/workspace nvcr.io/nvidia/isaac-ml-training:2020.1 -s
A new script named
start.sh will be created in your
The root password will be required every time you run the
start.sh script, since
it will be used to configure your X-server to allow communication from inside Docker.
To start, run the following command in the same terminal window as above:
start.sh script will not work for docker istallations newer than 19.x because the
flags to enable GPU support have changed.
To enable it to work for newer versions, edit the
start.sh file and delete
the following lines:
IS_19=$(docker -v | grep 19 | wc -l ) if [[ $IS_19 == 1 ]]; then NV_FLAG="--gpus=all" else NV_FLAG="--runtime=nvidia -e CUDA_VISIBLE_DEVICES=all" fi
and replace them with this line:
Finally, you will see the following message:
*************************************************************************************************** * Open your browser and go to: * * http://localhost:8888/notebooks/object_detection_from_sim.ipynb?token=object-detection-from-sim * * * *************************************************************************************************** Press Ctrl-C twice to exit
Control-click on the link to open your browser. This link will take you to a Jupyter notebook that you can run. The Jupyter notebook will be the main way you control dataset generation and the flow of your experiments.
Note that inside the Docker container, your ~/isaac-experiments folder is mounted as /workspace.
Whenever you see this folder being called from inside docker, it actually refers to a folder in your host machine, and any changes you do to files in this folder will be reflected immediately. This is a great way to pass files, executables and scripts between the container and your machine.
Jupyter Variable Setup
At the top of the Jupyter notebook, you have a few variables to configure. These control the way the dataset is generated:
- KEY This can be any string. We’ve provided a default key, but you should set a unique one. You don’t need to change it for every experiment, but you will need to provide it to the apps that make use of your trained model for inference.
- USER_EXPERIMENT_DIR The location where the files generated during training will be saved
- DATA_DIR The location of the dataset will be located.
- SPECS_DIR The folder for your experiment specs, which control all advanced parameters for training
- TRAINING_TESTS The number of test images to generate. Used during evaluation of your trained model.
- TRAINING_SAMPLES The number of training images and tags to generate. Larger sets mean more precision to a point, but take longer to process.
Once you have set up your parameters in the Jupyter notebook, follow the instructions on each cell to run it.
Adding Your Own 3D Models
If you have a 3D model that you would like to include in the image dataset, just place it in the isaac-experiments/models folder in FBX format. The file name, without the extension, will be used as the label on the dataset. Please keep the 3D models at around the same scale as the tennis ball model; a good rule of thumb is to use objects that you would find on a desk or table.
There are some restrictions on the type of models you can add.
- Transparent textures are not supported in this mode. If you have transparent textures in your models, they may not render or be labeled correctly.
- Textures should be embedded in the FBX model. If your textures look like they are missing, try regenerating your model with embedded textures. Adding model textures as additional files is not supported.
Customizing Your Scenarios
The Isaac SDK can run larger simulations and generate datasets for complicated scenarios. Refer to the Isaac Sim Unity3D documentation for more details.
- Accidentally deleted a file and now nothing works: If you have deleted one of the provided files, there is a chance that the notebook will start throwing errors. If this happens, you don’t need to lose all your work. Just restart the Docker container, and the startup script will replace any of the required files with a fresh copy.
- A black window appears when generating the dataset: This is normal, as the simulation needs access to a real display that is connected to the GPU. To provide this, we connect the internal simulation to the real X server on your host. You can safely ignore this window–it will disappear once dataset generation is done.
- When looking at Isaac Websight, there are no graphs or no information is displayed: Try to enable any disabled channels on the left-hand sidebar. This should restore the graphs.
If you want to explore developing robotics applications with Isaac, take a look at other tutorials in this documentation.
You can keep using this Docker image as your development container, but you will have better performance if you perform a full installation on your host machine.