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MSN Masked Siamese Networks

This repo provides a PyTorch implementation of MSN (Masked Siamese Networks), as described in the paper Masked Siamese Networks for Label-Efficient Learning.

MSN is a self-supervised learning framework that leverages the idea of mask-denoising while avoiding pixel and token-level reconstruction. Given two views of an image, MSN randomly masks patches from one view while leaving the other view unchanged. The objective is to train a neural network encoder, parametrized with a vision transformer (ViT), to output similar embeddings for the two views. In this procedure, MSN does not predict the masked patches at the input level, but rather performs the denoising step implicitly at the representation level by ensuring that the representation of the masked input matches the representation of the unmasked one.

Low-shot evaluation on ImageNet-1K

Visualizations

We can use the RCDM framework of Bordes et al., 2021 to qualitatively demonstrates the effectiveness of the MSN denoising process.

Pre-trained models

Running MSN self-supervised pre-training

Config files

All experiment parameters are specified in config files (as opposed to command-line-arguments). Config files make it easier to keep track of different experiments, as well as launch batches of jobs at a time. See the configs/ directory for example config files.

Requirements

• Python 3.8 (or newer)
• PyTorch install 1.11.0 (older versions may work too)
• torchvision
• Other dependencies: PyYaml, numpy, opencv, submitit, cyanure

Single-GPU training

Our implementation starts from the main.py, which parses the experiment config file and runs the msn pre-training locally on a multi-GPU (or single-GPU) machine. For example, to run on GPUs "0","1", and "2" on a local machine, use the command:

python main.py \
  --fname configs/pretrain/msn_vits16.yaml \
  --devices cuda:0 cuda:1 cuda:2

Multi-GPU training

In the multi-GPU setting, the implementation starts from main_distributed.py, which, in addition to parsing the config file, also allows for specifying details about distributed training. For distributed training, we use the popular open-source submitit tool and provide examples for a SLURM cluster. Feel free to edit main_distributed.py for your purposes to specify a different procedure for launching a multi-GPU job on a cluster.

For example, to pre-train with MSN on 16 GPUs using the pre-training experiment configs specificed inside configs/pretrain/msn_vits16.yaml, run:

python main_distributed.py \
  --fname configs/pretrain/msn_vits16.yaml \
  --folder $path_to_save_submitit_logs \
  --partition $slurm_partition \
  --nodes 2 --tasks-per-node 8 \
  --time 1000

ImageNet-1K Logistic Regression Evaluation

Labeled Training Splits

For reproducibilty, we have pre-specified the labeled training images as .txt files in the imagenet_subsets/ directory. Based on your specifications in your experiment's config file, our implementation will automatically use the images specified in one of these .txt files as the set of labeled images.

To run logistic regression on a pre-trained model using some labeled training split you can directly call the script from the command line:

python logistic_eval.py \
  --subset-path imagenet_subsets1/5imgs_class.txt \
  --root-path /datasets/ --image-folder imagenet_full_size/061417/ \
  --device cuda:0 \
  --pretrained $directory_containing_your_model \
  --fname $model_filename \
  --model-name deit_small \
  --penalty l2 \
  --lambd 0.0025

ImageNet-1K Linear Evaluation

To run linear evaluation on the entire ImageNet-1K dataset, use the main_distributed.py script and specify the --linear-eval flag.

For example, to evaluate MSN on 32 GPUs using the linear evaluation config specificed inside configs/eval/lineval_msn_vits16.yaml, run:

python main_distributed.py \
  --linear-eval \
  --fname configs/eval/lineval_msn_vits16.yaml \
  --folder $path_to_save_submitit_logs \
  --partition $slurm_partition \
  --nodes 4 --tasks-per-node 8 \
  --time 1000

ImageNet-1K Fine-Tuning Evaluation

For fine-tuning evaluation, we use the MAE codebase.

License

See the LICENSE file for details about the license under which this code is made available.

Citation

If you find this repository useful in your research, please consider giving a star :star: and a citation

@article{assran2022masked,
  title={Masked Siamese Networks for Label-Efficient Learning}, 
  author={Assran, Mahmoud, and Caron, Mathilde, and Misra, Ishan, and Bojanowski, Piotr, and Bordes, Florian and Vincent, Pascal, and Joulin, Armand, and Rabbat, Michael, and Ballas, Nicolas},
  journal={arXiv preprint arXiv:2204.07141},
  year={2022}
}