refrakt-core 0.3.0

Run production-ready ML, DL, and fusion pipelines at scale — without writing a single line of code. Refrakt makes research effortless.

About

refrakt_core is a modular deep learning and machine learning research framework for computer vision, designed for rapid experimentation, extensibility, and reproducibility. It now features a robust, thread-safe registry system, dynamic dataset handling, advanced image resizing, flexible hyperparameter overrides, and comprehensive logging and testing. Refrakt supports both classic and modern CV/ML papers, and enables seamless ML/DL/fusion pipelines.

This project aims to unify, extend, and visualize foundational and modern architectures through clean code, clear abstractions, and rigorous logging.

🚀 Key Features

• Safe Registry System: Thread-safe, import-safe, decorator-based registration for models, datasets, losses, trainers, and transforms. Backward compatible with legacy code.
• Dynamic Dataset Loader: Load datasets from custom zip files or torchvision, with automatic format detection (GAN, supervised, contrastive) and size validation.
• Standard Image Resizer/Transforms: Multiple resize strategies (maintain aspect, crop, stretch), size validation, and tensor/PIL support.
• Hyperparameter Overrides: Override any config parameter from the command line or programmatically for fast experimentation.
• Improved Logging: Context-aware logging with better error handling, supporting both TensorBoard and Weights & Biases (W&B).
• Comprehensive Testing: Smoke, sanity, unit, and integration tests for all major features.
• ML/DL/Fusion Pipelines: Support for pure-ML, pure-DL, and hybrid fusion pipelines (e.g., deep feature extraction + ML fusion head).
• Modular YAML Configs: All components (model, trainer, loss, optimizer, scheduler, feature engineering) are defined in modular YAML files.

📚 Implemented Papers

• Vision Transformer (ViT) – An Image is Worth 16x16 Words
• ResNet – Deep Residual Learning for Image Recognition
• Autoencoders – Learning Representations via Reconstruction
• Swin Transformer – Hierarchical Vision Transformer with Shifted Windows
• Attention is All You Need
• ConvNeXt – A ConvNet for the 2020s
• SRGAN – Photo-Realistic Single Image Super-Resolution with GANs
• SimCLR – A Simple Framework for Contrastive Learning
• DINO – Self-Supervised Vision Transformers
• MAE – Masked Autoencoders
• MSN – Masked Siamese Networks

⚙️ Setup

git clone https://github.com/refrakt-hub/refrakt_core.git
cd refrakt_core

# Create and activate a virtual environment
conda create -n refrakt python=3.10 -y
conda activate refrakt

# Install dependencies
pip install -r requirements.txt

🧪 Running Experiments

# Run with a config file
ython -m refrakt_core.api --config refrakt_core/config/vit.yaml

# Or using the CLI
refrakt --config ./refrakt_core/config/resnet.yaml

# Override hyperparameters on-the-fly
python -m refrakt_core.api.train \
    config.model.name=ResNet \
    config.optimizer.lr=0.0005 \
    config.trainer.epochs=20

Supported CLI Flags

Flag	Description
--config	Path to YAML config file
--log_type	Logging backend: tensorboard, wandb, or both
--debug	Enable debug mode with extra verbosity

🔧 Config Structure (YAML)

All components are defined in modular YAML files under refrakt_core/config/.

dataset:
  name: MNIST
  params:
    root: ./data
    train: true
    download: true
  transform:
    - name: Resize
      params: { size: [28, 28] }
    - name: ToTensor
    - name: Normalize
      params:
        mean: [0.1307]
        std: [0.3081]

model:
  name: vit
  params:
    in_channels: 1
    num_classes: 10
    image_size: 28
    patch_size: 7

trainer:
  name: supervised
  params:
    save_dir: "./checkpoints"
    model_name: "vit"
    num_epochs: 1
    device: cuda

🧩 Major Components & Patterns

1. Safe Registry System

Register models, datasets, losses, trainers, and transforms using decorators:

from refrakt_core.registry.safe_registry import register_model, get_model

@register_model("my_model")
class MyModel(torch.nn.Module):
    ...

model_cls = get_model("my_model")
model = model_cls()

2. Dynamic Dataset Loader

Load datasets from zip files or torchvision, with format detection:

from refrakt_core.loaders.dataset_loader import load_dataset
train_dataset, val_dataset = load_dataset("path/to/dataset.zip")
train_dataset, val_dataset = load_dataset("mnist")

3. Standard Image Resizer/Transforms

from refrakt_core.resizers.standard_transforms import create_standard_transform
transform = create_standard_transform(target_size=(224, 224), resize_strategy="maintain_aspect")

4. Hyperparameter Overrides

Override any config value from the command line or programmatically:

python train.py --config config.yaml model.name=ResNet optimizer.lr=0.001

5. ML/DL/Fusion Pipelines

Supports pure-ML, pure-DL, and hybrid fusion pipelines (deep features + ML head):

from refrakt_core.api.builders.model_builder import build_model
model = build_model(cfg=config, modules=modules, device="cuda", overrides=["model.params.lr=0.0005"])

📈 Logging & Monitoring

• TensorBoard: logs in logs/<model_name>/tensorboard/
• Weights & Biases: auto-logged if enabled in config

tensorboard --logdir=./logs/<model_name>/tensorboard/
export WANDB_API_KEY=your_key_here

🧱 Project Structure

refrakt_core/
├── api/                  # CLI: train.py, test.py, inference.py
│   └── builders/         # Builders for models, losses, optimizers, datasets
├── config/               # YAML configurations for each experiment
├── losses/               # Contrastive, GAN, MAE, VAE, etc.
├── models/               # Vision architectures (ViT, ResNet, MAE, etc.)
│   └── templates/        # Base model templates and abstractions
├── trainer/              # Task-specific training logic (SimCLR, SRGAN, etc.)
├── registry/             # Safe, decorator-based plugin system
├── utils/                # Helper modules (encoders, decoders, data classes)
├── resizers/             # Image resizing and standard transforms
├── loaders/              # Dynamic and standard dataset loaders
├── transforms.py         # Data augmentation logic
├── datasets.py           # Dataset definitions and loader helpers
├── logging_config.py     # Logger wrapper for stdout + W&B/TensorBoard

🧪 Testing

Run all tests:

pytest tests/

🧩 Extending Refrakt

Add a New Model

1. Create the architecture in models/your_model.py
2. Inherit from a base class in models/templates/models.py
3. Register it using:

from refrakt_core.registry.model_registry import register_model

@register_model("your_model")
class YourModel(BaseClassifier):
    ...

4. Add a YAML config: config/your_model.yaml
5. Write a custom trainer if needed (trainer/your_model.py)

Add a Custom Dataset Loader or Transform

• Implement in loaders/ or resizers/
• Register with the safe registry

🔍 Example Output

• Progress bar (via tqdm)
• Metrics printed and logged
• ./logs/<model_name>/ with TensorBoard events
• W&B dashboard if enabled

📬 Contributing

1. Clone and install:

   git clone ...
   pip install -r requirements-dev.txt
   pre-commit install
   

2. Follow formatting (black, isort, pylint)
3. Write tests for any new feature
4. Run:

   pytest tests/
   

PRs and issues are welcome!

🔭 Future Scope

Milestone	Description
✅ Stage 1	Paper re-implementations in notebooks
✅ Stage 2	Modular training + model pipelines
✅ Stage 3	Python library (refrakt train, etc.)
🔜 Stage 4	Web app: visual dashboards, interactive inference
🔜 Stage 5	No-code/low-code config engine for multimodal pipelines
🔜 Stage 6	Explainability: saliency maps, attention visualizations

Planned additions:

• Mixed-modality support (image + text)
• More contrastive/self-supervised frameworks
• System-level benchmarks across GPUs
• Downstream task evaluation (detection, segmentation)

📄 License

This repository is licensed under the MIT License. See LICENSE for full details.

👤 Maintainer

Akshath Mangudi If you find issues, raise them. If you learn from this, share it. Built with love and curiosity :)