All the default hyperparameters in the code are set to the settings required to reproduce the results of our paper. We use a single NVIDIA A100 SXM4 40 GB GPU for training. To train your own models run the following commands.
X-AlexNet & X-VGG16:
python train_alexnet_vgg.py -a [model_type]\
--store_path [path_to_store]\
[path_to_imagenet]\
[model_type] can be alexnet, xalexnet, vgg16, or xvgg16.
X-ResNet-50:
python train_resnet.py -a [model_type]\
--store_path [path_to_store]\
[path_to_imagenet]
[model_type] can be fixup_resnet50 or xfixup_resnet50.
After training or downloading the pre-trained models, you can obtain numbers from the experiments in Sec 4.1. and 4.4. by running:
python val.py --model_type [model_type]\
--data_root [path_to_imagenet]\
--model_dict [path_to_trained_model]\
--accuracy\
--compare_attribution\
--evaluate_contrast\
Numbers from the experiments in Sec 4.2. can be obtained from:
python attribution_metrics.py --model_type [model_type]\
--attr_method [attr_method]\
--data_root [path_to_imagenet]\
--model_dict [path_to_trained_models]\
[model_type] can be alexnet, xalexnet, vgg16, xvgg16, fixup_resnet50, or xfixup_resnet50.
[attr_method] can be random, gradient, integrated_gradients128, input_x_gradient, or expected_gradients1.
To visualize attributions run visualize_attribution.ipnyb (make sure to set the path for your model dictionary)