AUKUS Container

To support users that require tools for an ML T&E workflow, we define a container that would accept an input dataset and apply perturbations to the entire dataset. The perturbed images should be saved to disk and then the container will shut down. In order to support this workflow, the AUKUS container was created.

Given a COCO dataset and an NRTK factory configuration file, the AUKUS container is able to generate perturbed images for each image in the dataset. Each perturbed image will be saved to a given output directory as an individual image file. Once all perturbed images are saved, the container will terminate.

How to Use

To run the AUKUS container, use the following command: docker run -v /path/to/input:/root/input/:ro -v /path/to/output:/root/output/ nrtk-perturber This will mount the inputs to the correct locations and use the nrtk-perturber CLI script with the default args. The CLI script will attempt to load a COCO dataset from the dataset directory, save perturbed images to /root/output, and load a config file named nrtk_config.json. The dataset directory and nrtk_config.json file must be in the directory mounted to /root/input/.

If the user wants to use different input paths, the container expects the following arguments:

  • dataset_dir: input COCO dataset

  • output_dir: directory to store the generated saliency maps

  • config_file: configuration file specifying the PerturbImageFactory params for image perturbation

Note: The values for dataset_dir and config_file should be written from the perspective of the container (i.e. /path/on/container/dataset_dir/ instead of /path/on/local/machine/dataset_dir/)

Limitations

Currently, the AUKUS container supports the loading of only COCO datasets. Any existing dataset must be converted to a COCO dataset before using the AUKUS container. Please see KWCOCO documentation for more information on COCO datasets.