Masked Multimodal Training Framework

A PyTorch-based framework for masked multimodal training that supports joint training of image and text modalities. This framework utilizes ViT and BERT as backbone encoders, combining contrastive learning with masked pretraining.

Features

• Multimodal training support for images and text
• Patch-level image masking and token-level text masking
• Contrastive learning for multimodal alignment
• Mixed precision training support
• Multi-GPU training support
• Complete training and validation pipeline
• Training monitoring with Tensorboard
• Gradient accumulation and clipping
• Cosine learning rate scheduling

Project Structure

.
├── config/
│ └── training_config.py # Training configuration class
├── data/
│ └── dataset.py # Dataset implementation
├── models/
│ └── encoder.py # Multimodal encoder model
├── trainer/
│ └── trainer.py # Trainer implementation
├── utils/
│ └── training.py # Training utility functions
└── main.py # Main training script

Installation

pip install torch torchvision
pip install transformers
pip install tensorboard
pip install tqdm

Quick Start

1. Prepare your dataset:

   train_dataset = MultimodalDataset(
   image_paths=['path/to/image1.jpg', 'path/to/image2.jpg'],
   texts=['text1', 'text2'],
   transform=your_transform
   )

2. Configure training parameters:

   from config.training_config import TrainingConfig
   config = TrainingConfig(
   num_epochs=10,
   batch_size=32,
   learning_rate=1e-4,
   weight_decay=0.01,
   warmup_steps=1000,
   gradient_accumulation_steps=1,
   max_grad_norm=1.0
   )

3. Start training:

   from main import train_model
   train_model(config, train_dataset)

Core Components

MultimodalEncoder

The multimodal encoder class combines ViT and BERT:

• Feature extraction for both images and text
• Learnable mask tokens
• Projection layers for common space mapping
• Dropout and layer normalization implementation

MaskedMultimodalTrainer

The trainer class implements core training logic:

• Random mask generation
• Contrastive learning loss computation
• Training step handling

MultimodalDataset

The dataset class:

• Support for image-text pair loading
• Customizable data transformations
• Device management support

Training Configuration

Configurable parameters through TrainingConfig:

• num_epochs: Number of training epochs
• batch_size: Batch size
• learning_rate: Learning rate
• weight_decay: Weight decay
• warmup_steps: Number of warmup steps
• gradient_accumulation_steps: Gradient accumulation steps
• max_grad_norm: Maximum gradient norm

Training Monitoring

Monitor training progress with Tensorboard:

tensorboard --logdir runs/

Monitored metrics include:

• Training loss
• Validation loss
• Learning rate changes

Model Checkpointing

Best model is automatically saved during training:

• Save path: best_model.pth
• Saved contents:
  • Model state
  • Optimizer state
  • Training epoch
  • Best loss value

Important Notes

1. Data Preparation:

   • Implement specific image loading logic
   • Ensure correct dataset formatting

2. Hardware Requirements:

   • GPU recommended for training
   • Adjust batch size according to GPU memory

3. Training Optimization:

   • Adjustable masking ratios
   • Configurable temperature parameter
   • Customizable learning rate scheduling

License

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

Contributing

Issues and Pull Requests are welcome!