Train and Test Pre-trained ResNet Models

This repository contains a script to train and test pre-trained ResNet models on different datasets using various attribution methods. The script supports adversarial training and provides options for customizing the training and evaluation process.

Requirements

• Python 3.x
• PyTorch
• torchvision
• matplotlib
• argparse

Installation

1. Clone the repository:

   git clone https://github.com/nielseni6/EvalAttAI.git
   cd EvalAttAI

2. Install the required packages:

   pip install -r requirements.txt

Usage

The script can be run from the command line with various arguments to customize the training and evaluation process.

Command Line Arguments

• --dataset: Dataset to use ([cifar10], [cifar100], or [mnist]). Default is [cifar10].
• --device: CUDA device to use (e.g., [0], 0,1,2,3, or [cpu]). Default is [0].
• --train: Flag to indicate whether to train the model. Default is [False].
• --save-folder: Path to save the model weights. Default is [model_weights].
• --load-path: Path to load the model weights. Default is an empty string.
• --train-batch-size: Batch size for training. Default is [128].
• --test-batch-size: Batch size for testing. Default is [32].
• --num-workers: Number of worker threads for data loading. Default is [8].
• --alpha: Alpha value for input modification. Default is [0.1].
• --N: Number of iterations for input modification. Default is [10].
• --num-epochs: Number of epochs for training. Default is [10].
• --early-stop-patience: Number of epochs with no improvement after which training will be stopped. Default is [5].
• --model: Model to use (e.g., [resnet50], [resnet18]). Default is [resnet50].
• --adversarial: Flag to indicate whether to use adversarial training. Default is [False].
• --epsilon: Epsilon value for adversarial attack. Default is [0.1].
• --attack-type: Type of adversarial attack ([fgsm], [gaussian], or [pgd]). Default is [gaussian].
• --pgd-alpha: Alpha value for PGD attack. Default is [0.03].
• --pgd-num-iter: Number of iterations for PGD attack. Default is [10].
• --attr_methods: List of attribution methods to use ([VG], [GB], [IG], [SG], [GC], [random], [gradximage]). Default is ['random', 'VG', 'gradximage', 'GB', 'IG', 'SG', 'GC'].
• --norm: Flag to indicate whether to normalize accuracies to the random method. Default is [True].

Example Usage

To train a ResNet-50 model on CIFAR-10 with adversarial training:

python train_and_test_evalattai.py --dataset cifar10 --train True --adversarial True --model resnet50

To test a pre-trained ResNet-50 model on CIFAR-10:

python train_and_test_evalattai.py --dataset cifar10 --train False --model resnet50 --load-path path/to/weights.pt

Output

The script will save the trained model weights to the specified folder and generate a plot of accuracies vs. number of iterations with confidence intervals for different attribution methods. The plot will be saved in the [figure] folder.

Acknowledgments

• This script uses models from the torchvision library.
• The adversarial training and evaluation methods are inspired by various research papers in the field of machine learning and computer vision.