Repository containing tools to visualize AWS DeepRacer (training) runs. Currently only a visualization based on GradCam is supported.

• Python 3
• Poetry

First prepare the Python enviroment using poetry install. Available tools can be found in the tools folder. Before running a tool make sure the virtual environment is activated using poetry shell.

You can obtain your model graph in the form of a .pb file either from S3 or from the DeepRacer console.

• S3 -> Go to the S3 folder in which the training run is stored. Here you can download the model files per checkpoint.
• Console -> Select your training run and choose Download model. Inside the obtained .tar.gz you can find your model.pb file.

Can be used to view and store the live feed of a training job. This is the same feed that is shown in the AWS DeepRacer console.

python download_kinesis.py <Kinesis Video Stream Name> -o <output_file.mp4>

Overlays a Gradient-weighted Class Activation Mapping (Grad-CAM, Selvaraju et al.) over an input video for a given action. It takes as input the action number of the action in model_metadata.json or the DeepRacer console for which it then computes the activation map using the model stored in model.pb.

python gradcam.py <input_file.mp4> -m <model.pb file path> -a <action index> -o <output_file.mp4>