Docker support and new models (GPU fixing)

Autoencoder Project - Release 2.1.3

Release Date: October 16, 2023.

---

Differences from the previous version

• Changing the Dockerfile to allow GPU use;

---

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.

Docker support and new models (bugfix)

Autoencoder Project - Release 2.1.2

Release Date: October 16, 2023.

---

Differences from the previous version

• bug fix with dataset path for copying files by running copy_randomic_files.py.

---

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.

Docker support and new models (settings updated)

Autoencoder Project - Release 2.1.1

Release Date: October 16, 2023.

---

Differences from the previous version (v2.1.0)

• Improved version of settings/settings.py
• Refactoring the file for a smarter copy copy_randomic_file.py

---

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.

Docker support and new models

Autoencoder Project - Release 2.1.0

Release Date: October 16, 2023.

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.

New Arquitectures and features

Autoencoder Project - Release 2.0.0

Release Date: October 13, 2023.

Highlights:

• Expanded model architectures, introducing Convolutional Autoencoder and Variational Autoencoder, including a combination of both.
• Implementation of checkpointing functionality, providing an advanced and seamless way to save and continue model training.
• Enhanced evaluation and visualization mechanisms to cater to different autoencoder architectures.

Features:

• Diverse Model Architectures: Users now have the flexibility to train not just a simple Autoencoder but also a Convolutional Autoencoder, a Variational Autoencoder, and a Convolutional Variational Autoencoder.
• Checkpointing: Advanced training control with checkpointing, allowing users to save intermediate states of training and resume from them whenever required.
• Enhanced Visualization: With the introduction of new models, visualization capabilities have been expanded to provide a clearer understanding of how different architectures perform.
• General Code Improvements: Refactoring for cleaner code, optimized imports, and better modularization.

Enhancements:

• Extended run.py to detect and handle different autoencoder architectures seamlessly.
• Updated trainer.py to handle the training nuances of the newly introduced autoencoder architectures.
• Modular design ensures easy extensibility to accommodate more sophisticated models in the future.

Usage:

1. Clone the repository and navigate to the project directory.
2. Install the necessary dependencies using pip install -r requirements.txt.
3. Adjust data paths and settings in settings/settings.py based on your dataset.
4. Decide on the autoencoder type and adjust the main method in run.py.
5. Run the main script with python run.py.
6. Post-training, visualize the reconstructed results displayed, which will now cater to the chosen autoencoder type.
7. Utilize the checkpointing feature to save intermediate training states and resume from them whenever required.

Initial Release

Autoencoder Project - Release 1.0.0

Release Date: October 13, 2023.

Highlights:

• Introduction of an end-to-end autoencoder training pipeline for 64x64 images.
• Efficient data handling using PyTorch's DataLoader for streamlined batching and preprocessing.
• Visual reconstruction comparison, showcasing original, encoded, and decoded images side-by-side.

Features:

• End-to-end Training: Seamlessly load data, train an autoencoder model, evaluate its performance, and visualize its reconstructions with a simple command.
• Modular Structure: Organized structure with separate modules for model definitions, data loading, and training utilities, making the project expandable and maintainable.
• Visualization Capabilities: After training, the model's capability to encode and decode is demonstrated with a visual comparison of original and reconstructed images.
• Model Saving & Loading: Easily save trained model weights to a file and reload them for later use, avoiding the need to retrain frequently.

Usage:

1. Clone the repository and navigate to the project directory.
2. Install the necessary dependencies using pip install -r requirements.txt.
3. Adjust data paths and settings in settings/settings.py based on your dataset.
4. Run the main script with python run.py.
5. Post-training, visualize the reconstructed results displayed, showcasing original, encoded, and decoded images.
6. Trained models are saved automatically to a predefined path for future usage.