Skip to content

PyTorch implementation of [1412.6553] and [1511.06530] tensor decomposition methods for convolutional layers.

Notifications You must be signed in to change notification settings

jacobgil/pytorch-tensor-decompositions

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

10 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

PyTorch Tensor Decompositions

This is an implementation of Tucker and CP decomposition of convolutional layers. A blog post about this can be found here.

It depends on TensorLy for performing tensor decompositions.

Usage

  • Train a model based on fine tuning VGG16: python main.py --train.

  • There should be a dataset with two categories. One directory for each category. Training data should go into a directory called 'train'. Testing data should go into a directory called 'test'. This can be controlled with the flags --train_path and --test_path.

  • I used the Kaggle Cats/Dogs dataset.

  • The model is then saved into a file called "model".

  • Perform a decomposition: python main.py --decompose This saves the new model into "decomposed_model". It uses the Tucker decomposition by default. To use CP decomposition, pass --cp.

  • Fine tune the decomposed model: python main.py --fine_tune

References

About

PyTorch implementation of [1412.6553] and [1511.06530] tensor decomposition methods for convolutional layers.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages