- attacks/ # Different Adversarial attack methods (PGD, AutoAttack, FGSM, DeepFool, etc. ([Refrence](https://github.com/imrahulr/hat.git)))
- proard/ 
    - classification/ 
        - data_provider/ # The dataset and dataloader definitions for Cifar-10, Cifar-100, and ImageNet.
        - elastic_nn/ 
            - modules/ # The deficnition of dynamic layers 
            - networks/ # The deficnition of dynamic networks 
            - training/ # Progressive training 
        -networks/ # The original networks
        -run_anager/ # The Configs and distributed training   
    - nas 
        - accuracy_predictor/ # The accuracy and robustness predictor
        - efficiency_predictor/ # The efficiency predictor
        - search_algorithm/ # The Multi-Objective Search Engine
    - utils/ # Utility functions
    - model_zoo.py # All the models for evaluation   
- create_acc_rob_pred_dataset.py # Create dataset to train the accuracy-robustness predictor.
- create_acc_rob_pred.py # make the predictor model.
- eval_ofa_net.py # Eval the sub-nets
- search_best.py # Search the best sub-net
- train_ofa_net_WPS.py # train the dynamic network without progressive training.
- train_ofa_net.py # Train the dynamic network with progressive training.
- train_teacher_net.py # Train teacher network for Robust knoweldge distillation. 

python eval_ofa_net.py --path path of dataset  --net Dynamic net name (ResNet50, MBV3) 
                       --dataset (cifar10, cifar100) --robust_mode (True, False)
                       --WPS (True, False) --attack ('fgsm', 'linf-pgd', 'fgm', 'l2-pgd', 'linf-df', 'l2-df', 'linf-apgd', 'l2-apgd','squar_attack','autoattack','apgd_ce')

horovodrun -np 4 python train_teacher_net.py --model_name ("ResNet50", "MBV3") --dataset (cifar10, cifar100)
                                             --robust_mode (True, False) --epsilon 0.031 --num_steps 10
                                             --step_size 0.0078 --distance 'l-inf' --train_criterion 'trades'
                                             --attack_type 'linf-pgd'

horovodrun -np 4 python train_ofa_net.py --task 'width' or 'kernel' --model_name ("ResNet50", "MBV3") --dataset (cifar10, cifar100)
                                             --robust_mode (True, False) --epsilon 0.031 --num_steps 10
                                             --step_size 0.0078 --distance 'l-inf' --train_criterion 'trades'
                                             --attack_type 'linf-pgd' --kd_criterion 'rslad' --phase 1

horovodrun -np 4 python train_ofa_net.py --task 'depth' --model_name ("ResNet50", "MBV3") --dataset (cifar10, cifar100)
                                             --robust_mode (True, False) --epsilon 0.031 --num_steps 10
                                             --step_size 0.0078 --distance 'l-inf' --train_criterion 'trades'
                                             --attack_type 'linf-pgd' --kd_criterion 'rslad' --phase 1

horovodrun -np 4 python train_ofa_net.py --task 'depth' --model_name ("ResNet50", "MBV3") --dataset (cifar10, cifar100)
                                             --robust_mode (True, False) --epsilon 0.031 --num_steps 10
                                             --step_size 0.0078 --distance 'l-inf' --train_criterion 'trades'
                                             --attack_type 'linf-pgd' --kd_criterion 'rslad' --phase 2

horovodrun -np 4 python train_ofa_net.py --task 'expand' --model_name ("ResNet50", "MBV3") --dataset (cifar10, cifar100)
                                             --robust_mode (True, False) --epsilon 0.031 --num_steps 10
                                             --step_size 0.0078 --distance 'l-inf' --train_criterion 'trades'
                                             --attack_type 'linf-pgd' --kd_criterion 'rslad' --phase 1

horovodrun -np 4 python train_ofa_net.py --task 'expand' --model_name ("ResNet50", "MBV3") --dataset (cifar10, cifar100)
                                             --robust_mode (True, False) --epsilon 0.031 --num_steps 10
                                             --step_size 0.0078 --distance 'l-inf' --train_criterion 'trades'
                                             --attack_type 'linf-pgd' --kd_criterion 'rslad' --phase 2