1. Install the RISE package (to use Causal Metrics)

cd RISE
pip install -e .

2. Install the explainableAI env

cd RExL
pip install -e .

3. Note the location of the dataset and the base models. Update the paths for the base models accordingly in RExL/explainableAI/utils/models.py. The pretrained models are taken from here. These models are in caffe so these must be converted to pytorch.

Run RExL/train.py with the following arguments:

-rp or --root_path : Root path for the Dataset

-m or --model : Model type resnet or vgg

-ci or --class_index : Class index for the class on which the agent is to be trained (-1 if training Dataset Specific)

-d or --dataset : Dataset Name eg: PASCAL, MSCOCO or IMAGENET

-dt or --dataset_type: Dataset type, eg: train or val or test

-tl or --tensorboard_log_dir: Path for tensorboard logs

-sp or --save_path: Path to save the trained policy

-nt or --num_timesteps: Number of training steps

-si or --save_interval: Interval to periodically save the policy

-lp or --load_path: Path to load a previously trained policy, Default: None i.e not applicable

-vp or --video_path: Path to store the images for each step. Default: None i.e. not applicable

-i or --idx: Id of a specific image to be trained on (RExL-IS)

Run RExL/evaluate.py with the following arguments:

-rp or --root_path : Root path for the Dataset

-m or --model : Model type resnet or vgg

-ci or --class_index : Class index for the class on which the agent is to be trained (-1 if training Dataset Specific)

-d or --dataset : Dataset Name eg: PASCAL, MSCOCO or IMAGENET

-dt or --dataset_type: Dataset type, eg: train or val or test

-lp or --load_path: Path to load a previously trained policy, Default: None i.e not applicable

-bs or --batch_size: Batch size for running causal metrics

-v or --verbose: 0 (Default) if saliency maps are not saved and 1 to save saliency maps. -v=1 works with -bs=1 only.

-ip or --image_path: Path to save the images

-log or --log_path: Path to save the image wise logs

-i or --idx: Id of a specific image to be trained on (RExL-IS)