Workspace Overview

This workspace is structured to support a Python-based machine learning or deep learning project, leveraging PyTorch and PyTorch Lightning for model development and training. It includes configurations for various neural network architectures and setups, data handling, and optimization processes.

Structure

The workspace is organized into several directories and files, each serving a specific purpose in the project lifecycle:

• .gitignore: Specifies intentionally untracked files to ignore.
• config/: Hydra configuration files for experiment training and plotting. The important files are: config_train.yaml for training autoencoder models with different architectures and seeds with models/train.py, config_map.yaml for training mappings with optimization/fit_mapping.py, and config_plot.py for creating the final box plots shown in the report and presentation using optimization/run_stitching.py.
• data/: Intended for storing datasets used in the project.
• models/: Contains Python scripts for model definitions and training routines.
• optimization/, results/: Directories for storing optimization processes and final results respectively. Here the mappings Linear, Affine, Decouple, MLP, MLP + Linear can be calculated. The mappings are specified in optimization/optimizer.py. They can be calculated in fit_mapping.py with hydra or for running multiple configurations (different lamda, number of samples, combination between models) optimization/optimization_run_all.ipynb can be used.
• setup.py: A setup script for installing the project as a package.
• stitching/: In this script the stitching can be performed with the mapping previously calculated in optimization.
• utils/: Include utility scripts and modules for data processing, model loading, sampling strategys and other helper functions.

Key Components

• Model Definitions and Training: The models/ directory contains scripts like models/definitions/vit.py and models/train.py (models/training_models) for defining and training neural network models.
• Configuration Management: Various YAML files in the config/ directory allow for flexible configuration of models and experiments.
• Mapping Creation: The optimization/optimization_run_all.ipynb creates all the mappings (for now, linear, affine, decouple and NN) between the models saved in one folder. The mappings are stored in the results/mapping_files/{name_model3} with name convention '{name_filenamemodel1}>{name_filenamemodel2}>{mapping}{num_samples}{lamda}_{sampling_stragey}' e.g. 'FMNIST_PCKTAEClASS01234_10_1>FMNIST_PCKTAEClASS01234_10_2>Affine_100_0.001_equally'.
• Measuring Performance: In stitching/stitching_run_all.ipynb the stitching for all models specified in one folder is performed. The performance is measured in a csv file. Then you can plot dependent on the number of samples, lambda, mapping type and also analyse the behavior for different classes.
• Data Processing: The script stitching/create_calculation_databases.py handles data processing and preparation for visualization, creating three databases from which one can get different information regarding the performance of the different mapping under different settings.
• Visualization: The notebook stitching/visualize_perfomances.ipynb takes as input these databases, and generates various plots for future visualization

Installation

To set up this project, first clone this repository:

git clone git@github.com:kaieberl/latent-communication.git

Next, create a new virtual environment (e.g. using conda), then run

cd latent-communication
pip install -e .

This will install all required dependencies.

Usage

For usage instructions, refer to the documentation at the top of each file.