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NoRELAX: Nonconvex Relaxation Methods for MAP Inference in Discrete Markov Random Fields

This repo aims at re-implementing in Python the algorithms presented in the following paper:

D. Khuê Lê-Huu and Nikos Paragios. Continuous Relaxation of MAP Inference: A Nonconvex Perspective. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

If you use any part of this code, please cite the above paper.

BibTeX:

@inproceedings{lehuu2018norelax,
  title={Continuous Relaxation of MAP Inference: A Nonconvex Perspective},
  author={L{\^e}-Huu, D. Khu{\^e} and Paragios, Nikos},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2018}
}

Usage

Initialization

An MRF can be initialized with a pre-defined number of nodes and number of labels per node (different numbers of labels are currently not supported):

from norelax import MRF
V = 4 # number of nodes
L = 3 # number of labels per node
mrf = MRF(num_nodes=V, num_labels=L, sparse=True)

If one knows in advance the sparseness of the graph then it is beneficial to set the sparse argument (default to True).

Adding node (or unary) potentials

This can be done simply as

mrf.set_node_potentials(pvector=node_potentials)

where node_potentials is a 1D vector of length V*L, which can be seen as the flatten version of a 2D potential matrix of dimensions V x L where each row corresponds to a node. I used a vector instead of a matrix because this would allow an easier extension to the case where the nodes have different numbers of labels.

Adding edge (or pairwise) potentials

An edge (i, j) with its potential matrix edge_potentials_ij (of dimensions L x L) can be added to the model using

mrf.add_edge((i, j), pmatrix=edge_potentials_ij)

Perform MAP inference

Finally, MAP inference can be performed using the optimize() member function that returns the optimal labeling:

labels = mrf.optimize(method='admm', **kwargs)

The variable **kwargs contains optional parameters for method. For example, the default parameters for ADMM are:

kwargs = {"rho_min": 0.01,
          "rho_max": 1000,
          "step": 1.2,
          "precision": 1e-5,
          "iter1": 10,
          "iter2": 50,
          "max_iter": 10000,
          "verbose": True
          }

Notes: these parameters affects greatly the convergence as well as the solution quality of ADMM.

  • For faster convergence: increase rho_min or (preferably) decrease iter2.
  • For better (lower) energy: decrease rho_min and/or increase iter2.

See paper (Section 4.2).

The final discrete energy can be computed using:

E = mrf.energy(labels)
print('Discrete energy = %f'%E)

An complete example of usage is given in example.py. Run python example.py to see the results.

Notes

In the current version:

  • Only pairwise (i.e. first-order) MRFs are supported.
  • Only ADMM with argmax rounding is implemented (which is suboptimal). The other methods and Block Coordinate Descent rounding will be added later.
  • No performance optimization.