Docker support and new models (GPU fixing)

Autoencoder Project - Release 2.1.3

Release Date: October 16, 2023.

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Differences from the previous version

• Changing the Dockerfile to allow GPU use;

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v2.1.x

Highlights:

• Docker integration, offering a seamless and reproducible way to set up the project environment across different systems.
• Addition of new autoencoder models, enhancing the diversity of the architectures available to the users.
• Auxiliary scripts for easy dataset management, including resetting the dataset and random image copying.

Features:

• Docker Support: With the inclusion of a Dockerfile, users can easily containerize the project, ensuring consistency across various platforms and eliminating the "works on my machine" problem.
• Extended Autoencoder Models: Further expanding on the available architectures, this release introduces additional autoencoder models, catering to advanced use-cases and research requirements.
• Dataset Management Tools: Two new scripts simplify dataset handling:
  • Resetting the dataset: Deletes and reconstructs the dataset structure.
  • Random image copier: Facilitates copying random images from a source folder to the dataset.

Enhancements:

• Docker implementation ensures a consistent environment setup, eliminating potential discrepancies arising from different system configurations.
• Newly added autoencoder models are integrated seamlessly into the existing codebase, making the training process straightforward.
• Modular design continues to be a priority, ensuring that the project remains scalable and easy to understand.

Usage:

1. Clone the repository and navigate to the project directory.

2. To set up the Docker environment:

   • Build the Docker image: docker build -t autoencoder_project .
   • Run the Docker container: docker run -it --rm -v $(pwd):/app autoencoder_project bash

3. Install the necessary dependencies using pip install -r requirements.txt (if not using Docker).

4. Adjust data paths and settings in settings/settings.py based on your dataset.

5. Decide on the autoencoder type and adjust the json/params.json file.

6. Run the main script with python run.py.

7. Utilize the dataset management scripts as needed for resetting the dataset or copying random images.

8. Post-training, visualize the reconstructed results displayed, which will now cater to the chosen autoencoder type.