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"""
Sliced OT Distances
"""
# Author: Adrien Corenflos <adrien.corenflos@aalto.fi>
# Nicolas Courty <ncourty@irisa.fr>
# Rémi Flamary <remi.flamary@polytechnique.edu>
#
# License: MIT License
import numpy as np
from .backend import get_backend, NumpyBackend
from .utils import list_to_array
def get_random_projections(d, n_projections, seed=None, backend=None, type_as=None):
r"""
Generates n_projections samples from the uniform on the unit sphere of dimension :math:`d-1`: :math:`\mathcal{U}(\mathcal{S}^{d-1})`
Parameters
----------
d : int
dimension of the space
n_projections : int
number of samples requested
seed: int or RandomState, optional
Seed used for numpy random number generator
backend:
Backend to ue for random generation
Returns
-------
out: ndarray, shape (d, n_projections)
The uniform unit vectors on the sphere
Examples
--------
>>> n_projections = 100
>>> d = 5
>>> projs = get_random_projections(d, n_projections)
>>> np.allclose(np.sum(np.square(projs), 0), 1.) # doctest: +NORMALIZE_WHITESPACE
True
"""
if backend is None:
nx = NumpyBackend()
else:
nx = backend
if isinstance(seed, np.random.RandomState) and str(nx) == 'numpy':
projections = seed.randn(d, n_projections)
else:
if seed is not None:
nx.seed(seed)
projections = nx.randn(d, n_projections, type_as=type_as)
projections = projections / nx.sqrt(nx.sum(projections**2, 0, keepdims=True))
return projections
def sliced_wasserstein_distance(X_s, X_t, a=None, b=None, n_projections=50, p=2,
projections=None, seed=None, log=False):
r"""
Computes a Monte-Carlo approximation of the p-Sliced Wasserstein distance
.. math::
\mathcal{SWD}_p(\mu, \nu) = \underset{\theta \sim \mathcal{U}(\mathbb{S}^{d-1})}{\mathbb{E}}\left(\mathcal{W}_p^p(\theta_\# \mu, \theta_\# \nu)\right)^{\frac{1}{p}}
where :
- :math:`\theta_\# \mu` stands for the pushforwards of the projection :math:`X \in \mathbb{R}^d \mapsto \langle \theta, X \rangle`
Parameters
----------
X_s : ndarray, shape (n_samples_a, dim)
samples in the source domain
X_t : ndarray, shape (n_samples_b, dim)
samples in the target domain
a : ndarray, shape (n_samples_a,), optional
samples weights in the source domain
b : ndarray, shape (n_samples_b,), optional
samples weights in the target domain
n_projections : int, optional
Number of projections used for the Monte-Carlo approximation
p: float, optional =
Power p used for computing the sliced Wasserstein
projections: shape (dim, n_projections), optional
Projection matrix (n_projections and seed are not used in this case)
seed: int or RandomState or None, optional
Seed used for random number generator
log: bool, optional
if True, sliced_wasserstein_distance returns the projections used and their associated EMD.
Returns
-------
cost: float
Sliced Wasserstein Cost
log : dict, optional
log dictionary return only if log==True in parameters
Examples
--------
>>> n_samples_a = 20
>>> reg = 0.1
>>> X = np.random.normal(0., 1., (n_samples_a, 5))
>>> sliced_wasserstein_distance(X, X, seed=0) # doctest: +NORMALIZE_WHITESPACE
0.0
References
----------
.. [31] Bonneel, Nicolas, et al. "Sliced and radon wasserstein barycenters of measures." Journal of Mathematical Imaging and Vision 51.1 (2015): 22-45
"""
from .lp import wasserstein_1d
X_s, X_t = list_to_array(X_s, X_t)
if a is not None and b is not None and projections is None:
nx = get_backend(X_s, X_t, a, b)
elif a is not None and b is not None and projections is not None:
nx = get_backend(X_s, X_t, a, b, projections)
elif a is None and b is None and projections is not None:
nx = get_backend(X_s, X_t, projections)
else:
nx = get_backend(X_s, X_t)
n = X_s.shape[0]
m = X_t.shape[0]
if X_s.shape[1] != X_t.shape[1]:
raise ValueError(
"X_s and X_t must have the same number of dimensions {} and {} respectively given".format(X_s.shape[1],
X_t.shape[1]))
if a is None:
a = nx.full(n, 1 / n, type_as=X_s)
if b is None:
b = nx.full(m, 1 / m, type_as=X_s)
d = X_s.shape[1]
if projections is None:
projections = get_random_projections(d, n_projections, seed, backend=nx, type_as=X_s)
X_s_projections = nx.dot(X_s, projections)
X_t_projections = nx.dot(X_t, projections)
projected_emd = wasserstein_1d(X_s_projections, X_t_projections, a, b, p=p)
res = (nx.sum(projected_emd) / n_projections) ** (1.0 / p)
if log:
return res, {"projections": projections, "projected_emds": projected_emd}
return res
def max_sliced_wasserstein_distance(X_s, X_t, a=None, b=None, n_projections=50, p=2,
projections=None, seed=None, log=False):
r"""
Computes a Monte-Carlo approximation of the max p-Sliced Wasserstein distance
.. math::
\mathcal{Max-SWD}_p(\mu, \nu) = \underset{\theta _in
\mathcal{U}(\mathbb{S}^{d-1})}{\max} [\mathcal{W}_p^p(\theta_\#
\mu, \theta_\# \nu)]^{\frac{1}{p}}
where :
- :math:`\theta_\# \mu` stands for the pushforwars of the projection :math:`\mathbb{R}^d \ni X \mapsto \langle \theta, X \rangle`
Parameters
----------
X_s : ndarray, shape (n_samples_a, dim)
samples in the source domain
X_t : ndarray, shape (n_samples_b, dim)
samples in the target domain
a : ndarray, shape (n_samples_a,), optional
samples weights in the source domain
b : ndarray, shape (n_samples_b,), optional
samples weights in the target domain
n_projections : int, optional
Number of projections used for the Monte-Carlo approximation
p: float, optional =
Power p used for computing the sliced Wasserstein
projections: shape (dim, n_projections), optional
Projection matrix (n_projections and seed are not used in this case)
seed: int or RandomState or None, optional
Seed used for random number generator
log: bool, optional
if True, sliced_wasserstein_distance returns the projections used and their associated EMD.
Returns
-------
cost: float
Sliced Wasserstein Cost
log : dict, optional
log dictionary return only if log==True in parameters
Examples
--------
>>> n_samples_a = 20
>>> reg = 0.1
>>> X = np.random.normal(0., 1., (n_samples_a, 5))
>>> sliced_wasserstein_distance(X, X, seed=0) # doctest: +NORMALIZE_WHITESPACE
0.0
References
----------
.. [35] Deshpande, I., Hu, Y. T., Sun, R., Pyrros, A., Siddiqui, N., Koyejo, S., ... & Schwing, A. G. (2019). Max-sliced wasserstein distance and its use for gans. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10648-10656).
"""
from .lp import wasserstein_1d
X_s, X_t = list_to_array(X_s, X_t)
if a is not None and b is not None and projections is None:
nx = get_backend(X_s, X_t, a, b)
elif a is not None and b is not None and projections is not None:
nx = get_backend(X_s, X_t, a, b, projections)
elif a is None and b is None and projections is not None:
nx = get_backend(X_s, X_t, projections)
else:
nx = get_backend(X_s, X_t)
n = X_s.shape[0]
m = X_t.shape[0]
if X_s.shape[1] != X_t.shape[1]:
raise ValueError(
"X_s and X_t must have the same number of dimensions {} and {} respectively given".format(X_s.shape[1],
X_t.shape[1]))
if a is None:
a = nx.full(n, 1 / n, type_as=X_s)
if b is None:
b = nx.full(m, 1 / m, type_as=X_s)
d = X_s.shape[1]
if projections is None:
projections = get_random_projections(d, n_projections, seed, backend=nx, type_as=X_s)
X_s_projections = nx.dot(X_s, projections)
X_t_projections = nx.dot(X_t, projections)
projected_emd = wasserstein_1d(X_s_projections, X_t_projections, a, b, p=p)
res = nx.max(projected_emd) ** (1.0 / p)
if log:
return res, {"projections": projections, "projected_emds": projected_emd}
return res
|
