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Earth Mover Distance (EMD) CUDA Extension for PyTorch

CI License: MIT Python 3.7+

A high-performance PyTorch implementation of Earth Mover Distance (EMD) for point clouds using CUDA. This package provides efficient computation of EMD with automatic differentiation support for deep learning applications.

Note: This repository is an updated and improved version of daerduoCarey/PyTorchEMD. Special thanks to the original authors for their foundational work.

🌟 Features

  • Fast CUDA Implementation: High-performance CUDA kernels for EMD computation
  • PyTorch Integration: Seamless integration with PyTorch's autograd system
  • Cross-Platform: Works on Windows, Linux, and macOS
  • Modern PyTorch API: Compatible with PyTorch 1.8+ and CUDA 11.0+
  • Flexible Input Formats: Supports both BNC and BCN tensor formats
  • Robust Error Handling: Multiple fallback mechanisms for CUDA extension loading

📋 Requirements

System Requirements

  • CUDA: 11.0 or higher
  • Python: 3.7 or higher
  • PyTorch: 1.8.0 or higher with CUDA support
  • C++ Compiler:
    • Windows: Visual Studio 2019/2022 Build Tools
    • Linux: GCC 7+ or Clang 6+
    • macOS: Xcode Command Line Tools

Python Dependencies

pip install torch>=1.8.0 numpy

🚀 Installation

Option 1: Install from Source (Recommended)

  1. Clone the repository:
git clone https://github.com/hieulhaiwork/EMD-Pytorch.git
cd EMD-Pytorch
  1. Build and install:

On Windows:

# Make sure you have Visual Studio Build Tools installed
python setup.py build_ext --inplace
pip install -e .

On Linux/macOS:

# Make sure you have GCC/Clang and CUDA toolkit installed
chmod +x build.sh
./build.sh
pip install -e .

Option 2: Development Installation

For development or if you encounter issues:

git clone https://github.com/hieulhaiwork/EMD-Pytorch.git
cd EMD-Pytorch
pip install -e .

📖 Usage

Basic Usage

import torch
from emd import earth_mover_distance

# Create sample point clouds (batch_size=2, num_points=1000, dims=3)
xyz1 = torch.randn(2, 1000, 3).cuda()
xyz2 = torch.randn(2, 1000, 3).cuda()

# Compute EMD
distance = earth_mover_distance(xyz1, xyz2, transpose=False)
print(f"EMD: {distance}")  # Output: tensor([123.45, 67.89], device='cuda:0')

Using EMDLoss Module

import torch
import torch.nn as nn
from emd import EMDLoss

class PointCloudAutoEncoder(nn.Module):
    def __init__(self):
        super().__init__()
        self.emd_loss = EMDLoss(transpose=False)
        # ... your model layers
        
    def forward(self, input_pc, reconstructed_pc):
        # ... model forward pass
        loss = self.emd_loss(input_pc, reconstructed_pc)
        return loss

# Usage
model = PointCloudAutoEncoder().cuda()
input_points = torch.randn(4, 2048, 3).cuda()
reconstructed = model(input_points)

Advanced Usage with Different Input Formats

import torch
from emd import earth_mover_distance

# BNC format (Batch, Num_points, Channels) - Default
xyz1_bnc = torch.randn(2, 1000, 3).cuda()
xyz2_bnc = torch.randn(2, 1000, 3).cuda()
distance_bnc = earth_mover_distance(xyz1_bnc, xyz2_bnc, transpose=False)

# BCN format (Batch, Channels, Num_points)
xyz1_bcn = torch.randn(2, 3, 1000).cuda()
xyz2_bcn = torch.randn(2, 3, 1000).cuda()
distance_bcn = earth_mover_distance(xyz1_bcn, xyz2_bcn, transpose=True)

# Both should give similar results
print(f"BNC format: {distance_bnc}")
print(f"BCN format: {distance_bcn}")

Gradient Computation

import torch
from emd import earth_mover_distance

# Enable gradients
xyz1 = torch.randn(2, 1000, 3, requires_grad=True).cuda()
xyz2 = torch.randn(2, 1000, 3, requires_grad=True).cuda()

# Forward pass
distance = earth_mover_distance(xyz1, xyz2, transpose=False)
loss = distance.sum()

# Backward pass
loss.backward()

print(f"xyz1 gradient shape: {xyz1.grad.shape}")
print(f"xyz2 gradient shape: {xyz2.grad.shape}")

🧪 Testing

Run the comprehensive test suite to verify everything is working:

# Basic functionality test
python tests/simple_test.py

# Loss function test
python tests/loss_test.py

# Complete validation suite
python tests/final_validation.py

# Performance benchmark
python -c "
import torch
from emd import earth_mover_distance
import time

# Benchmark
xyz1 = torch.randn(4, 2048, 3).cuda()
xyz2 = torch.randn(4, 2048, 3).cuda()

# Warmup
for _ in range(10):
    _ = earth_mover_distance(xyz1, xyz2, transpose=False)

# Timing
torch.cuda.synchronize()
start = time.time()
for _ in range(100):
    dist = earth_mover_distance(xyz1, xyz2, transpose=False)
torch.cuda.synchronize()
end = time.time()

print(f'Average time: {(end-start)/100:.4f} seconds')
"

🔧 Troubleshooting

Common Issues

1. "ImportError: DLL load failed while importing emd_cuda"

  • Make sure Visual Studio Build Tools are installed (Windows)
  • Verify CUDA toolkit version matches PyTorch CUDA version
  • Try rebuilding: python setup.py build_ext --inplace --force

2. "CUDA out of memory"

  • Reduce batch size or number of points
  • Use torch.cuda.empty_cache() between iterations

3. "RuntimeError: CUDA is not available"

  • Install PyTorch with CUDA support: pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
  • Verify CUDA installation: nvcc --version

4. Compilation errors on Linux

  • Install build essentials: sudo apt-get install build-essential
  • Make sure GCC version is compatible with your CUDA version

Debug Mode

Enable verbose compilation for debugging:

import os
os.environ['TORCH_CUDA_ARCH_LIST'] = "6.0;6.1;7.0;7.5;8.0;8.6"  # Adjust for your GPU
os.environ['MAX_JOBS'] = "4"  # Limit parallel compilation

# Then rebuild
python setup.py build_ext --inplace --force --verbose

📊 Performance

Typical performance on modern hardware:

GPU Point Cloud Size Batch Size Time per Forward Pass
RTX 3080 2048 points 4 ~8ms
RTX 3090 4096 points 8 ~15ms
V100 2048 points 16 ~12ms

Performance may vary based on point cloud distribution and system configuration.

🏗️ Project Structure

EMD-Pytorch/
├── emd/                      # Main package
│   ├── __init__.py          # Package initialization
│   ├── emd.py              # Python wrapper with robust loading
│   └── cuda/               # CUDA implementation
│       ├── emd.cpp         # PyTorch C++ interface
│       └── emd_kernel.cu   # CUDA kernel implementation
├── tests/                   # Test suite
│   ├── __init__.py         # Test package initialization
│   ├── simple_test.py      # Basic functionality test
│   ├── loss_test.py        # Loss function test
│   ├── check_compatibility.py # Platform compatibility checker
│   └── final_validation.py   # Complete validation suite
├── .github/workflows/       # CI/CD configuration
├── setup.py                 # Build configuration
├── pyproject.toml          # Project metadata
├── build.sh                # Linux/macOS build script
├── MANIFEST.in             # Package inclusion rules
├── CONTRIBUTING.md         # Contribution guidelines
├── LICENSE                 # MIT license
└── README.md              # This file

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Development Setup

git clone https://github.com/hieulhaiwork/EMD-Pytorch.git
cd EMD-Pytorch

# Install in development mode
pip install -e .

# Run tests
python final_validation.py

📄 License

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

🙏 Acknowledgments

This project is based on the excellent work from daerduoCarey/PyTorchEMD. We extend our sincere gratitude to the original authors:

  • Haoqiang Fan: Original CUDA implementation
  • Kaichun Mo: PyTorch wrapper
  • Jiayuan Gu: Additional contributions and improvements
  • daerduoCarey: Project organization and improvements

This updated version includes enhanced cross-platform compatibility, improved error handling, and modern Python packaging standards.

📚 Citation

If you use this code in your research, please consider citing:

@misc{emd-pytorch,
  title={Earth Mover Distance CUDA Extension for PyTorch},
  author={Fan, Haoqiang and Mo, Kaichun and Gu, Jiayuan},
  year={2025},
  url={https://github.com/hieulhaiwork/EMD-Pytorch}
}

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