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Adversarial Masking for Self-Supervised Learning

This is the official PyTorch implementation for Adversarial Masking for Self-Supervised Learning. We propose ADIOS, a masked image modeling (MIM) framework for self-supervised learning, which simultaneously learns a masking function and an image encoder using an adversarial objective.

Our code is developed on the amazing solo-learn repository, a library for self-supervised learning methods. We make use of pytorch-lightning for training pipeline and wandb for tracking experiments.

Installing packages

First clone the repo. Install required packages by

pip3 install .

To install with Dali and/or UMAP support, use:

pip3 install .[dali,umap] --extra-index-url https://developer.download.nvidia.com/compute/redist

The solo-learn repository has more instructions on troubleshooting installing Dali.

Requirements

• torch
• torchvision
• tqdm
• einops
• wandb
• pytorch-lightning
• lightning-bolts
• torchmetrics
• scipy
• timm

Optional:

• nvidia-dali
• matplotlib
• seaborn
• pandas
• umap-learn

Downloading datasets

CIFAR10, CIFAR100 and STL10 datasets are automatically downloaded by running the code.

ImageNet100

First, download the ILSVRC 2012 ImageNet dataset from the official website to <your_data_dir>/imagenet/ (this HAS to be done manually). Then, unzip both .tar files:

cd <your_data_dir>/imagenet
mkdir val
mkdir train
tar -xf ILSVRC2012_img_val.tar --directory val
tar -xf ILSVRC2012_img_train.tar --directory train
cd train
for f in *.tar; do mkdir -p "${f:0:9}" && tar -xvf "$f" -C "${f:0:9}"; done

After all files are unzipped, do the following:

cp <your_code_dir>/adios/bash_files/imagenet.sh <your_data_dir>/imagenet/val/
cd <your_data_dir>/imagenet/val/
./imagenet.sh

This will set up the validation set properly.

After setting up the ImageNet dataset, you can do the following to create the ImageNet100 dataset:

cp <your_code_dir>/adios/bash_files/imagenet100.sh <your_data_dir>/
cd <your_data_dir>
./imagenet100.sh

CLEVR

Download from here, then unzip in <your_data_dir>.

Training

Launching a single experiment

You can find a range of example bash scripts with our selected hyperparameters in bash_files/training. The scripts are categorised by datasets, and then architecture. For example, to train a BYOL_ADIOS model from scratch on ImageNet100S dataset using ResNet18 backbone, you can run the following:

./bash_files/training/imagenet100s/resnet18/byol_adios.sh

Some args related to mask generation to pay attention to are:

• --N: sepcifies the number of masking slots
• --mask_lr: learning rate for the masking model
• --alpha_sparsity: sparsity penalty for masking model, see "sparsity penalty" in section 2.3 of our paper for detail
• --mask_fbase: base channels of masking model
• --unet_norm: normalisation function for masking model, choose from "gn" (group normalisation), "in" (instance normalisation) and "no" (no normalisation).
• --auto_mask: set as true to automatically save generated masks every epoch
• --pretrained_dir: path to pretrained model (if any), set as None to load no pretrained model

Pre-trained models

We offer the following downloadable pre-trained models:

ImageNet100S

ResNet18 backbone

• SimCLR_ADIOS
• SimSiam_ADIOS
• BYOL_ADIOS

Vision Transformer (tiny) backbone

• SimCLR_ADIOS
• SimSiam_ADIOS
• BYOL_ADIOS

ImageNet100

For ImageNet100 we provide pretrained models of the single-forward pass version of ADIOS (ADIOS-s) trained using ResNet18 backbone.

• SimCLR_ADIOS_S
• SimSiam_ADIOS_S
• BYOL_ADIOS_S

To load and run the pre-trained models, specify the path to the downloaded models using --pretrained_dir and make sure to use the same --method as the one used in the pre-trained model. You can run the model on any dataset you desire. For instance, to use the BYOL_ADIOS model trained on ImageNet100S with ResNet18 backbone, download the model from here and run:

python main_pretrain.py \
    --pretrained_dir <download_dir>/byol_adios_imagenet100s/whnjyonq \
    --method byol_adios \
    --dataset imagenet100s \
    --data_dir <your_data_dir> \

Create a sweep

We make use of the sweep function in wandb for hyperparameter search. Here we include a small tutorial for how to do this in this repository. For further detail please refer to their documentations. You can find an example sweep yaml files in bash_files/sweep/example.yaml.

After creating an account with wandb, login in your terminal by:

wandb login

Then follow the insutrctions on screen.

After logging in, you can do the following to initiate a sweep:

wandb sweep bash_files/sweep/example.yaml

Then you will see something like this:

wandb: Creating sweep from: example.yaml
wandb: Created sweep with ID: 0y3kc50c
wandb: View sweep at: https://wandb.ai/yugeten/imagenet/sweeps/0y3kc50c
wandb: Run sweep agent with: wandb agent yugeten/imagenet/0y3kc50c

Then to initiate one run for the sweep, you can simply do:

CUDA_VISIBLE_DEVICES=X wandb agent yugeten/imagenet100/0y3kc50c

After the current run finish, another fresh run will start automatically on the specified GPU.

Evaluation

In bash_files/evaluation you can find several example bash scripts that evaluates trained model. We offer linear evaluation (linear.sh), knn evaluation (knn.sh), finetuning (finetune.sh) and FMI, ARI, NMI clustering (clustering.sh).

Citation

If you found our work helpful, please cite us:

@inproceedings{shi2022adversarial,
  title={Adversarial Masking for Self-Supervised Learning},
  author={Shi, Yuge and Siddharth, N and Torr, Philip HS and Kosiorek, Adam R},
  booktitle = {International Conference on Machine Learning},
  year={2022}
}