RobustViT

Official PyTorch implementation of Optimizing Relevance Maps of Vision Transformers Improves Robustness. This code allows to finetune the explainability maps of Vision Transformers to enhance robustness.

The method employs loss functions directly to the explainability maps to ensure that the model is focused mostly on the foreground of the image:

Using a short finetuning process with only 3 labeled examples from 500 classes, our method imrpoves robustness of ViT models across different model sizes and training techniques, even when data augmentations/ regularization are applied.

Producing Segmenataion Data

Using ImageNet-S

To use the ImageNet-S labeled data, download the ImageNetS919 dataset

Using TokenCut for unsupervised segmentation

Clone the TokenCut project

git clone https://github.com/YangtaoWANG95/TokenCut.git

Install the dependencies Python 3.7, PyTorch 1.7.1 and CUDA 11.2. Please refer to the official installation. If CUDA 10.2 has been properly installed:
```
pip install torch==1.7.1 torchvision==0.8.2
```
Followed by
```
pip install -r TokenCut/requirements.txt
```

Use the following command to extract the segmentation maps:

python tokencut_generate_segmentation.py --img_path <PATH_TO_IMAGE> --out_dir <PATH_TO_OUTPUT_DIRECTORY>

Finetuning ViT models

To finetune a pretrained ViT model use the imagenet_finetune.py script. Notice to uncomment the import line containing the pretrained model you wish to finetune.

Usage example:

python imagenet_finetune.py --seg_data <PATH_TO_SEGMENTATION_DATA> --data <PATH_TO_IMAGENET> --gpu 0  --lr <LR> --lambda_seg <SEG> --lambda_acc <ACC> --lambda_background <BACK> --lambda_foreground <FORE>

Notes:

For all models we use :
- lambda_seg=0.8
- lambda_acc=0.2
- lambda_background=2
- lambda_foreground=0.3
For DeiT models, a temprature is required as follows:
- temprature=0.65 for DeiT-B
- temprature=0.55 for DeiT-S
The learning rates per model are:
- ViT-B: 3e-6
- ViT-L: 9e-7
- AR-S: 2e-6
- AR-B: 6e-7
- AR-L: 9e-7
- DeiT-S: 1e-6
- DeiT-B: 8e-7

Baseline methods

Notice to uncomment the import line containing the pretrained model you wish to finetune in the code.

GradMask

Run the following command:

python imagenet_finetune_gradmask.py --seg_data <PATH_TO_SEGMENTATION_DATA> --data <PATH_TO_IMAGENET> --gpu 0  --lr <LR> --lambda_seg <SEG> --lambda_acc <ACC>

All hyperparameters for the different models can be found in section D of the supplementary material.

Right for the Right Reasons

Run the following command:

python imagenet_finetune_rrr.py --seg_data <PATH_TO_SEGMENTATION_DATA> --data <PATH_TO_IMAGENET> --gpu 0  --lr <LR> --lambda_seg <SEG> --lambda_acc <ACC>

All hyperparameters for the different models can be found in section D of the supplementary material.

Evaluation

Robustness Evaluation

Download the evaluation datasets:
- INet-A
- INet-R
- INet-v2
- ObjectNet
- SI-Score
Run the following script to evaluate:

python imagenet_eval_robustness.py --data <PATH_TO_ROBUSTNESS_DATASET> --batch-size <BATCH_SIZE> --evaluate --checkpoint <PATH_TO_FINETUNED_CHECKPOINT>

Notice to uncomment the import line containing the pretrained model you wish to evaluate in the code.
To evaluate the original model simply omit the checkpoint parameter.
For the INet-v2 dataset add --isV2.
For the ObjectNet dataset add --isObjectNet.
For the SI datasets add --isSI.

Segmentation Evaluation

Our segmentation tests are based on the test in the official implementation of Transformer Interpretability Beyond Attention Visualization.

Download the ImageNet segmentation test set.
Run the following script to evaluate:

PYTHONPATH=./:$PYTHONPATH python SegmentationTest/imagenet_seg_eval.py  --imagenet-seg-path <PATH_TO_gtsegs_ijcv.mat>

Notice to uncomment the import line containing the pretrained model you wish to evaluate in the code.

Credits

The TokenCut code is built on top of LOST, DINO, Segswap, and Bilateral_Sovlver.
Our ViT code is based on the pytorch-image-models repository.
Our ImageNet finetuning code is based on code from the official PyTorch repo.
The code to convert ObjectNet classes to ImageNet classes was taken from the torchprune repo.
The code to convert SI-Score classes to ImageNet classes was taken from the official implementation.

We would like to sincerely thank the authors for their great works.