cognetx 0.0.1

cognetx - Pytorch

CogNetX

CogNetX is an advanced, multimodal neural network architecture inspired by human cognition. It integrates speech, vision, and video processing into one unified framework. Built with PyTorch, CogNetX leverages cutting-edge neural networks such as Transformers, Conformers, and CNNs to handle complex multimodal tasks. The architecture is designed to process inputs like speech, images, and video, and output coherent, human-like text.

Key Features

• Speech Processing: Uses a Conformer network to handle speech inputs with extreme efficiency and accuracy.
• Vision Processing: Employs a ResNet-based Convolutional Neural Network (CNN) for robust image understanding.
• Video Processing: Utilizes a 3D CNN architecture for real-time video analysis and feature extraction.
• Text Generation: Integrates a Transformer model to process and generate human-readable text, combining the features from speech, vision, and video.
• Multimodal Fusion: Combines multiple input streams into a unified architecture, mimicking how humans process various types of sensory information.

Architecture Overview

CogNetX brings together several cutting-edge neural networks:

• Conformer for high-quality speech recognition.
• Transformer for text generation and processing.
• ResNet for vision and image recognition tasks.
• 3D CNN for video stream processing.

The architecture is designed to be highly modular, allowing easy extension and integration of additional modalities.

Neural Networks Used

• Speech: Conformer
• Vision: ResNet50
• Video: 3D CNN (R3D-18)
• Text: Transformer

Installation

To set up and use CogNetX, first clone the repository:

git clone https://github.com/kyegomez/CogNetX
cd CogNetX
pip install -r requirements.txt

Requirements

• Python 3.8+
• PyTorch 1.10+
• Torchvision
• Torchaudio

Install the required packages with:

pip install torch torchvision torchaudio

Usage

Model Architecture

import torch
from cognetx.model import CogNetX

if __name__ == "__main__":
    # Example configuration and usage
    config = {
        "speech_input_dim": 80,  # For example, 80 Mel-filterbank features
        "speech_num_layers": 4,
        "speech_num_heads": 8,
        "encoder_dim": 256,
        "decoder_dim": 512,
        "vocab_size": 10000,
        "embedding_dim": 512,
        "decoder_num_layers": 6,
        "decoder_num_heads": 8,
        "dropout": 0.1,
        "depthwise_conv_kernel_size": 31,
    }

    model = CogNetX(config)

    # Dummy inputs
    batch_size = 2
    speech_input = torch.randn(
        batch_size, 500, config["speech_input_dim"]
    )  # (batch_size, time_steps, feature_dim)
    vision_input = torch.randn(
        batch_size, 3, 224, 224
    )  # (batch_size, 3, H, W)
    video_input = torch.randn(
        batch_size, 3, 16, 112, 112
    )  # (batch_size, 3, time_steps, H, W)
    tgt_input = torch.randint(
        0, config["vocab_size"], (20, batch_size)
    )  # (tgt_seq_len, batch_size)

    # Forward pass
    output = model(speech_input, vision_input, video_input, tgt_input)
    print(
        output.shape
    )  # Expected: (tgt_seq_len, batch_size, vocab_size)

Example Pipeline

1. Speech Input: Provide raw speech data or features extracted via an MFCC filter.
2. Vision Input: Use images or frame snapshots from video.
3. Video Input: Feed the network with video sequences.
4. Text Output: The model will generate a text output based on the combined multimodal input.

Running the Example

To test CogNetX with some example data, run:

python example.py

Code Structure

• cognetx/: Contains the core neural network classes.
  • model: The entire model model architecture.
• example.py: Example script to test the architecture with dummy data.

Future Work

• Add support for additional modalities such as EEG signals or tactile data.
• Optimize the model for real-time performance across edge devices.
• Implement transfer learning and fine-tuning on various datasets.

Contributing

Contributions are welcome! Please submit a pull request or open an issue if you want to suggest an improvement.

Steps to Contribute

1. Fork the repository
2. Create a feature branch (git checkout -b feature/awesome-feature)
3. Commit your changes (git commit -am 'Add awesome feature')
4. Push to the branch (git push origin feature/awesome-feature)
5. Open a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.