Forked from https://github.com/MaxBourdon/mars/tree/main
This repository contains code for our paper MARS: Masked Automatic Ranks Selection in Tensor Decompositions.
The main files are:
- mars.py — the main module, containing realizations of the MARS wrapper over a tensorized model, the MARS loss and auxiliary functions;
- tensorized_models.py — module, containing realizations of several implemented tensorized models, the base class and auxiliary functions.
The notebooks are:
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MNIST-2FC-soft.ipynb — Jupyter Notebook, replicating the MNIST 2FC-Net experiment using soft compression mode;
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MNIST-2FC-hard.ipynb — Jupyter Notebook, replicating the MNIST 2FC-Net experiment using hard compression mode.
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VAE-AE-Baseline.ipynb — autoencoder and variational autoencoder template of baseline for further experiments
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MNIST-AE.ipynb — Jupyter Notebook, Factorized autoencoder
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MNIST-VAE.ipynb — Jupyter Notebook, Factorized variational autoencoder
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MNIST-VAE-TT.ipynb — Jupyter Notebook, Successful application of tensor train to variational autoencoder
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CIFAR10-ResNet-naive.ipynb — Jupyter Notebook, ResNet-110 on CIFAR10
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CIFAR10-ResNet-base.ipynb — Jupyter Notebook, ResNet-110 on CIFAR10
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CIFAR10-ResNet-proper.ipynb — Jupyter Notebook, ResNet-110 on CIFAR10
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MNIST-LeNet-base.ipynb — Jupyter Notebook, LeNet-5 on MNIST
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MNIST-LeNet-compress.ipynb — Jupyter Notebook, LeNet-5 on MNIST
To run the notebooks, first, install the tt-pytorch library from https://github.com/KhrulkovV/tt-pytorch
System requirements and dependencies are described in https://github.com/KhrulkovV/tt-pytorch/blob/master/README.md
After installing all the dependencies, run the following command to install tt-pytorch from Git via pip: pip install git+https://github.com/KhrulkovV/tt-pytorch.git
Our team:
@sspetya - Petr Sychev
@gurkwe - Petr Kushnir
@xiyori - Foma Shipilov
@MarioAuditore - Elfat Sabitov
@skushneryuk - Sergey Kushneryuk