Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/sayakpaul/paws-tf

Minimal implementation of PAWS (https://arxiv.org/abs/2104.13963) in TensorFlow.
https://github.com/sayakpaul/paws-tf

keras multi-crop representation-learning semi-supervised-learning soft-similarity tensorflow vision

Last synced: about 13 hours ago
JSON representation

Minimal implementation of PAWS (https://arxiv.org/abs/2104.13963) in TensorFlow.

Awesome Lists containing this project

README 

        
# PAWS-TF 🐾

Implementation of [Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples](https://arxiv.org/abs/2104.13963)

 (PAWS) in TensorFlow (2.4.1). 



PAWS introduces a simple way to combine a very small fraction of labeled data with a comparatively larger corpus of unlabeled data _during_ pre-training. With its approach, it sets the state-of-the-art in semi-supervised learning (as of May 2021) beating methods like [SimCLRV2](https://arxiv.org/abs/2006.10029), [Meta Pseudo Labels](https://arxiv.org/abs/2003.10580) that too with **fewer parameters and a smaller pre-training schedule**. For details, I recommend checking out the original paper as well as [this blog post](https://ai.facebook.com/blog/dino-paws-computer-vision-with-self-supervised-transformers-and-10x-more-efficient-training/) by the authors. 



This repository implements and includes all the major bits proposed in PAWS in TensorFlow. The only major difference is that the pre-training and subsequent fine-tuning weren't run for the original number of epochs (600 and 30 respectively) to save compute. I have reused the utility components for PAWS loss from the [original implementation](https://github.com/facebookresearch/suncet/).



## Dataset βŒ—



The current code works with CIFAR10 and uses 4000 labeled samples (**8%**) during pre-training (along with the unlabeled samples).



## Features ✨



- [x] Multi-crop augmentation strategy (originally introduced in [SwAV](https://arxiv.org/abs/2006.09882))

- [x] Class stratified sampler (common in few-shot classification problems)

- [x] WarmUpCosine learning rate schedule (which is typical for self-supervised and semi-supervised pre-training)

- [x] LARS optimizer (comes from [TensorFlow Model Garden](https://github.com/tensorflow/models/blob/master/official/modeling/optimization/lars_optimizer.py))



The trunk portion (all, except the last classification layer) of a WideResNet-28-2 is used inside the encoder for CIFAR10. All the experimental configurations were followed from the Appendix C of the paper. 



## Setup and code structure πŸ’»



A GCP VM ([`n1-standard-8`](https://cloud.google.com/compute/docs/machine-types)) with a single V100 GPU was used for executing the code. 



* `paws_train.py` runs the pre-training as introduced in PAWS.

* `fine_tune.py` runs the fine-tuning part as suggested in Appendix C. Note that this is only required for CIFAR10.

* `nn_eval.py` runs the soft nearest neighbor classification on CIFAR10 test set.



Pre-training and fine-tuning total take **1.4 hours** to complete. All the logs are available in [`misc/logs.txt`](https://github.com/sayakpaul/PAWS-TF/blob/main/misc/logs.txt). Additionally, the indices that were used to sample the labeled examples from the CIFAR10 training set are available [here](https://github.com/sayakpaul/PAWS-TF/blob/main/misc/random_idx.npy).



## Results πŸ“Š



### Pre-training



PAWS minimizes the cross-entropy loss (as well as maximizes mean-entropy) during pre-training. This is what the training plot indicates too:










To evaluate the effectivity of the pre-training, PAWS performs soft nearest neighbor classification to report the top-1 accuracy score on a given test set.



### Top-1 Accuracy



This repository gets to **73.46%** top-1 accuracy on the CIFAR10 test set. Again, **note** that I only pre-trained for 50 epochs (as opposed to 600) and fine-tuned for 10 epochs (as opposed to 30). With the original schedule this score should be around 96.0%. 



In the following PCA projection plot, we see that the embeddings of images (computed after fine-tuning) of PAWS are starting to be well separated:










## Notebooks πŸ“˜



There are two Colab Notebooks:



* [`colabs/data_prep.ipynb`](https://github.com/sayakpaul/PAWS-TF/blob/main/colabs/data_prep.ipynb): It walks through the process of constructing a multi-crop dataset with CIFAR10.

* [`colabs/visualization_paws_projections.ipynb`](https://github.com/sayakpaul/PAWS-TF/blob/main/colabs/visualization_paws_projections.ipynb): Visualizes the PCA projections of pre-computed embeddings. 



## Misc ⺟



* Model weights are available [here](https://github.com/sayakpaul/PAWS-TF/releases/tag/v1.0.0) for reproducibility. 

* With mixed-precision training, the performance can further be improved. I am open to accepting contributions that would implement mixed-precision training in the current code.



## Acknowledgements



* Huge amount of thanks to [Mahmoud Assran](https://www.linkedin.com/in/midoassran/) (first author of PAWS) for patiently resolving my doubts.

* [ML-GDE program](https://developers.google.com/programs/experts/) for providing GCP credit support. 



## Paper Citation



```

@misc{assran2021semisupervised,

      title={Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples}, 

      author={Mahmoud Assran and Mathilde Caron and Ishan Misra and Piotr Bojanowski and Armand Joulin and Nicolas Ballas and Michael Rabbat},

      year={2021},

      eprint={2104.13963},

      archivePrefix={arXiv},

      primaryClass={cs.CV}

}

```