torch-geometric-median 0.1.1

pytorch-optimized geometric median implementation

torch-geometric-median

A simplified version of the geom-median Python library, updated to be higher performance on Pytorch and with full type-hinting. Thanks to @themachinefan!

Installation

pip install torch-geometric-median

Usage

This library exports a single function, geometric_median, which takes a tensor of shape (N, D) where N is the number of samples, and D is the size of each sample, and returns the geometric median of the points in the tensor .

from torch_geometric_median import geometric_median

# Create a tensor of points
points = torch.tensor([
    [0.0, 0.0],
    [1.0, 1.0],
    [2.0, 2.0],
    [3.0, 3.0],
    [4.0, 4.0],
])

# Compute the geometric median
median = geometric_median(points).median

Backprop

Like the original geom-median library, this library supports backpropagation through the geometric median computation.

median = geometric_median(points).median
torch.linalg.norm(out.median).backward()
# The gradient of the median with respect to the input points is now in `points.grad`

Extra options

The geometric_median function also supports a few extra options:

• maxiter: The maximum number of iterations to run the optimization for. Default is 100.
• ftol: If objective value does not improve by at least this ftol fraction, terminate the algorithm. Default 1e-20.
• weights: A tensor of shape (N,) containing the weights for each point, where N is the number of samples. Default is None, which means all points are weighted equally.
• show_progress: If True, show a progress bar for the optimization. Default is False.
• log_objective_values: If True, log the objective value at each iteration under the key objective_values_log. Default is False.

median = geometric_median(
    points,
    maxiter=1000,
    ftol=1e-10,
    weights=torch.tensor([1.0, 2.0, 1.0, 1.0, 1.0]),
    show_progress=True,
    log_objective_values=True
).median

Why does this library exist?

It appears that the original geom-median library is no longer maintained, and as pointed out by @themachinefan, the original library is not very performant on Pytorch. This library is a repackaging of @themachinefan's improvements to the original geom-median library, simplying the code to just support pytorch, improving torch performance, and adding full type-hinting.

Acknowledgements

This library is a repackaging of the work done by the original geom-median library, and @themachinefan in their PR, and as such, all credit goes to these incredible authors. If you use this library, you should cite the original geom-median paper.

License

This library is licensed under a GPL license, as per the original geom-median library.

Contributing

Contributions are welcome! Please open an issue or a PR if you have any suggestions or improvements. This library uses PDM for dependency management, Ruff for linting, Pyright for type-checking, and Pytest for tests.