HPC TuSimple Lane Detection

This repository contains a high-performance computing (HPC) implementation of lane detection using the TuSimple dataset. The implementation focuses on performance optimization through various parallelization techniques and provides comprehensive benchmarking tools.

Table of Contents

1. Features
2. Project Structure
3. Installation
4. Configuration
5. Code Organization
6. Parallelization Techniques
7. Profiling and Benchmarking
8. Visualization Tools
9. Training Pipeline
10. Performance Optimization

Features

• Modular implementation of TuSimple lane detection
• Multiple parallelization strategies:
  • Data Parallelism (DistributedDataParallel)
  • Model Parallelism
  • Hybrid Parallelism capabilities
• Comprehensive system profiling
• Performance benchmarking
• Resource utilization tracking
• Visualization tools
• Configurable training pipeline

Project Structure

hpc-tusimple/
├── configs/                 # Configuration files
│   ├── base_config.yaml    # Base configuration template
│   ├── cpu_config.yaml     # CPU-specific settings
│   └── gpu_config.yaml     # GPU-specific settings
├── src/
│   ├── data/               # Data handling
│   │   ├── dataset.py      # TuSimple dataset implementation
│   │   └── transforms.py   # Data transformations
│   ├── models/             # Model architectures
│   │   ├── attention.py    # Coordinate Attention mechanism
│   │   └── lane_detection.py # Main model implementation
│   ├── training/           # Training components
│   │   ├── trainer.py      # Training loop implementation
│   │   ├── losses.py       # Loss functions
│   │   ├── metrics.py      # Performance metrics
│   │   └── distributed.py  # Distributed training utilities
│   └── utils/              # Utility functions
│       ├── profiling.py    # System profiling tools
│       └── visualization.py # Visualization utilities
├── benchmark.py            # Benchmarking script
├── main.py                # Main training script
├── setup.py               # Package setup
└── README.md              # Documentation

Installation

1. Clone the repository:

git clone https://github.com/yourusername/hpc-tusimple.git
cd hpc-tusimple

2. Install dependencies:

pip install -e .

For development installation:

pip install -e ".[dev]"

Configuration

Base Configuration (base_config.yaml)

dataset:
  path: './dataset/TUSimple'
  image_size: [800, 360]
  batch_size: 8
  num_workers: 4

model:
  name: 'LaneDetectionModel'
  num_classes: 2
  backbone: 'resnet50'
  pretrained: true

training:
  epochs: 10
  learning_rate: 0.001
  weight_decay: 0.0001
  optimizer: 'adamw'

system:
  device: 'auto'  # 'auto', 'cpu', or 'cuda'
  precision: 'float32'

CPU Configuration (cpu_config.yaml)

system:
  device: 'cpu'
  num_threads: 4
  pin_memory: false
  
training:
  batch_size: 4  # Reduced for CPU

GPU Configuration (gpu_config.yaml)

system:
  device: 'cuda'
  cuda_devices: [0]
  pin_memory: true
  
training:
  batch_size: 16  # Increased for GPU
  
optimization:
  cudnn_benchmark: true
  mixed_precision: true

Code Organization

1. Data Module (src/data/)

• dataset.py: TuSimple dataset implementation
  • Custom dataset class
  • Data loading and preprocessing
  • Augmentation pipeline

2. Models (src/models/)

• attention.py: Coordinate Attention implementation

  • Spatial and channel attention mechanism
  • Adaptive pooling and feature refinement

• lane_detection.py: Main model architecture

  • ResNet50 backbone
  • Coordinate Attention integration
  • Decoder with upsampling blocks

3. Training (src/training/)

• trainer.py: Training loop implementation

  • Epoch management
  • Loss computation
  • Optimization steps
  • Checkpoint handling

• losses.py: Loss functions

  • Dice Loss
  • IoU Loss
  • Combined Loss

• metrics.py: Performance metrics

  • Accuracy calculation
  • IoU computation
  • System metrics tracking

• distributed.py: Distributed training utilities

  • DDP wrapper
  • Model parallel wrapper
  • Process group management

4. Utils (src/utils/)

• profiling.py: System profiling

  • CPU/GPU utilization tracking
  • Memory usage monitoring
  • Training time profiling

• visualization.py: Visualization tools

  • Training metrics plots
  • System utilization graphs
  • Model predictions visualization

Parallelization Techniques

1. Data Parallelism (DDP)

• Implementation in distributed.py
• Features:
  • Process group initialization
  • Gradient synchronization
  • Batch size scaling
  • Multi-GPU data distribution

2. Model Parallelism

• Implementation in distributed.py
• Features:
  • Model partitioning
  • Pipeline parallelism
  • Memory optimization
  • Cross-GPU communication

3. Hybrid Parallelism

• Combination of data and model parallelism
• Dynamic switching based on:
  • Model size
  • Batch size
  • Available resources

Profiling and Benchmarking

System Profiling (profiling.py)

• Metrics tracked:
  • CPU utilization
  • Memory usage
  • GPU utilization
  • Training time
  • I/O operations

Benchmark Runner (benchmark.py)

• Benchmarking features:
  • Single GPU training
  • Multi-GPU DDP training
  • Model parallel training
  • CPU vs GPU comparison
  • Resource utilization analysis

Visualization Tools

Training Metrics (visualization.py)

• Plots available:
  • Loss curves
  • Accuracy metrics
  • Learning rate schedules
  • IoU progression

System Metrics

• Visualizations:
  • Resource utilization over time
  • Training speed comparison
  • Memory usage patterns
  • GPU utilization graphs

Model Predictions

• Visualization types:
  • Original images
  • Predicted lane markings
  • Ground truth comparison
  • Error analysis

Training Pipeline

1. Data Loading:

   dataset = LaneDataset(config['dataset']['path'])
   dataloader = DataLoader(dataset, batch_size=config['batch_size'])

2. Model Initialization:

   model = LaneDetectionModel(config['model'])
   model = model.to(device)

3. Training Loop:

   trainer = Trainer(config)
   trainer.train(model, train_loader, val_loader)

Performance Optimization

Resource Management

1. Memory Optimization:

   • Gradient accumulation
   • Mixed precision training
   • Memory-efficient backprop

2. CPU Optimization:

   • Thread management
   • Pinned memory
   • Efficient data loading

3. GPU Optimization:

   • CUDA graphs
   • Async data transfer
   • Kernel optimization

Usage Examples

1. Single GPU Training:

python main.py --config configs/gpu_config.yaml

2. Multi-GPU Training:

python main.py --config configs/gpu_config.yaml --distributed

3. Run Benchmarks:

python benchmark.py --config configs/base_config.yaml --gpu-configs 1 2 4

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a pull request

License

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