def test_fails_Sphere():
with pytest.raises(ValueError):
manifold = geoopt.Sphere()
manifold.origin(())
with pytest.raises(ValueError):
manifold = geoopt.Sphere()
manifold.origin(1)
def test_fails_Sphere():
with pytest.raises(ValueError):
manifold = geoopt.Sphere()
manifold.random_uniform(())
with pytest.raises(ValueError):
manifold = geoopt.Sphere()
manifold.random_uniform(1)
def product_case():
torch.manual_seed(42)
ex = [
torch.randn(10),
torch.randn(3) / 10,
torch.randn(3, 2),
torch.randn(())
]
ev = [
torch.randn(10),
torch.randn(3) / 10,
torch.randn(3, 2),
torch.randn(())
]
manifolds = [
geoopt.Sphere(),
geoopt.PoincareBall(),
geoopt.Stiefel(),
geoopt.Euclidean(),
]
x = [manifolds[i].projx(ex[i]) for i in range(len(manifolds))]
v = [manifolds[i].proju(x[i], ev[i]) for i in range(len(manifolds))]
product_manifold = geoopt.ProductManifold(*((manifolds[i], ex[i].shape)
for i in range(len(ex))))
yield UnaryCase(
manifold_shapes[geoopt.ProductManifold],
product_manifold.pack_point(*x),
product_manifold.pack_point(*ex),
product_manifold.pack_point(*v),
product_manifold.pack_point(*ev),
product_manifold,
)
# + 1 case without stiefel
torch.manual_seed(42)
ex = [torch.randn(10), torch.randn(3) / 10, torch.randn(())]
ev = [torch.randn(10), torch.randn(3) / 10, torch.randn(())]
manifolds = [
geoopt.Sphere(),
geoopt.PoincareBall(),
# geoopt.Stiefel(),
geoopt.Euclidean(),
]
x = [manifolds[i].projx(ex[i]) for i in range(len(manifolds))]
v = [manifolds[i].proju(x[i], ev[i]) for i in range(len(manifolds))]
product_manifold = geoopt.ProductManifold(*((manifolds[i], ex[i].shape)
for i in range(len(ex))))
yield UnaryCase(
manifold_shapes[geoopt.ProductManifold],
product_manifold.pack_point(*x),
product_manifold.pack_point(*ex),
product_manifold.pack_point(*v),
product_manifold.pack_point(*ev),
product_manifold,
)
class ManifoldFactory:
geoopt_manifolds = {
"euclidean": lambda dims: gt.Euclidean(1),
"poincare": lambda dims: gt.PoincareBall(),
"lorentz": lambda dims: gt.Lorentz(),
"sphere": lambda dims: gt.Sphere(),
"prod-hysph": get_prod_hysph_manifold,
"prod-hyhy": get_prod_hyhy_manifold,
"prod-hyeu": get_prod_hyeu_manifold,
"prod-sphsph": get_prod_sphsph_manifold,
"spd": lambda dims: SymmetricPositiveDefinite(),
}
sympa_manifolds = {
"upper": UpperHalfManifold,
"bounded": BoundedDomainManifold,
"dual": CompactDualManifold
}
@classmethod
def get_manifold(cls, manifold_name, metric_name, dims):
if manifold_name in cls.geoopt_manifolds:
return cls.geoopt_manifolds[manifold_name](dims)
manifold = cls.sympa_manifolds[manifold_name]
metric = MetricType.from_str(metric_name)
return manifold(dims=dims, metric=metric)
def test_knn():
man = geoopt.Sphere()
layer = geoopt_layers.KNN(manifold=man, dim=0, k=5)
points = man.random(10, 9)
knn_points = layer(points)
assert knn_points.shape == (10, 5, 9)
man.assert_check_point_on_manifold(knn_points)
def test_expmap():
sphere = geoopt.Sphere()
layer = geoopt_layers.Expmap(manifold=sphere, origin=sphere.origin(10))
smth = torch.randn(12, 3, 10)
out = layer(smth)
sphere.assert_check_point_on_manifold(out)
def test_product():
manifold = geoopt.ProductManifold(
(geoopt.Sphere(), 10),
(geoopt.PoincareBall(), 3),
(geoopt.Stiefel(), (20, 2)),
(geoopt.Euclidean(), 43),
)
sample = manifold.random(20, manifold.n_elements)
manifold.assert_check_point_on_manifold(sample)
def test_knn_unroll():
man = geoopt.Sphere()
layer = geoopt_layers.KNN(manifold=man, dim=1, k=5)
layer_u = geoopt_layers.KNN(manifold=man, dim=1, k=5, unroll=0)
points = man.random(7, 10, 9)
knn_points1 = layer(points)
knn_points2 = layer_u(points)
assert knn_points1.shape == (7, 10, 5, 9)
assert knn_points1.shape == knn_points2.shape
np.testing.assert_allclose(knn_points1, knn_points2)
def test_lambda_one_origin():
sphere = geoopt.Sphere()
point = sphere.random(1, 10)
func = torch.nn.Linear(10, 10)
with pytest.raises(ValueError):
geoopt_layers.TangentLambda(func, manifold=sphere)
layer = geoopt_layers.TangentLambda(func, manifold=sphere, origin_shape=10)
out = layer(point)
sphere.assert_check_point_on_manifold(out)
def test_lambda_no_dim_change_origins():
sphere = geoopt.Sphere()
func = torch.nn.Linear(10, 11)
with pytest.raises(ValueError):
geoopt_layers.TangentLambda(
func,
manifold=sphere,
origin=sphere.origin(10),
out_origin=sphere.origin(11),
)
def test_lambda_two_origins():
sphere = geoopt.Sphere()
point = sphere.random(1, 10)
func = torch.nn.Linear(10, 10)
layer = geoopt_layers.TangentLambda(
func, manifold=sphere, origin_shape=10, same_origin=False
)
out = layer(point)
sphere.assert_check_point_on_manifold(out)
def test_distance_pairwise_auto():
man = geoopt.Sphere()
layer = geoopt_layers.PairwiseDistances(manifold=man, dim=0)
points = man.random(10, 9)
distances = layer(points)
assert distances.shape == (10, 10)
layer = geoopt_layers.PairwiseDistances(manifold=man, dim=-2)
points = man.random(10, 3, 9)
distances = layer(points.detach())
assert distances.shape == (10, 3, 3)
def test_pickle2():
t = torch.ones(10)
p = geoopt.ManifoldParameter(t, manifold=geoopt.Sphere())
with tempfile.TemporaryDirectory() as path:
torch.save(p, os.path.join(path, "tens.t7"))
p1 = torch.load(os.path.join(path, "tens.t7"))
assert isinstance(p1, geoopt.ManifoldParameter)
assert p.stride() == p1.stride()
assert p.storage_offset() == p1.storage_offset()
assert p.requires_grad == p1.requires_grad
np.testing.assert_allclose(p.detach(), p1.detach())
assert isinstance(p.manifold, type(p1.manifold))
def test_distance_pairwise_paired():
man = geoopt.Sphere()
layer = geoopt_layers.PairwiseDistances(manifold=man, dim=0)
points = man.random(10, 9)
points1 = man.random(7, 9)
distances = layer(points, points1)
assert distances.shape == (10, 7)
layer = geoopt_layers.PairwiseDistances(manifold=man, dim=-3)
points = man.random(10, 3, 9)
points1 = man.random(7, 3, 9)
distances = layer(points, points1)
assert distances.shape == (10, 7, 3)
def test_knn_permutations():
man = geoopt.Sphere()
layer = geoopt_layers.KNN(manifold=man, dim=-2, k=7)
points = man.random(1, 2, 3, 9, 5)
knn_points_1 = layer(points)
assert knn_points_1.shape == (1, 2, 3, 9, 7, 5)
perm1 = geoopt_layers.shape.Permute(3, 0, 1, 2, 4, contiguous=True)
layer = geoopt_layers.KNN(manifold=man, dim=0, k=7)
knn_points_2 = layer(perm1(points))
assert knn_points_2.shape == (9, 7, 1, 2, 3, 5)
knn_points_2 = knn_points_2.permute(2, 3, 4, 0, 1, 5)
man.assert_check_point_on_manifold(knn_points_1)
np.testing.assert_allclose(knn_points_1, knn_points_2)
def test_remap_provided_origin():
sphere = geoopt.Sphere()
poincare = geoopt.PoincareBall()
point = sphere.random(1, 10)
func = torch.nn.Linear(10, 13)
layer = geoopt_layers.RemapLambda(
func,
source_manifold=sphere,
target_manifold=poincare,
source_origin=sphere.origin(10),
target_origin=poincare.origin(13),
)
out = layer(point)
poincare.assert_check_point_on_manifold(out)
def sphere_case():
torch.manual_seed(42)
shape = manifold_shapes[geoopt.manifolds.Sphere]
ex = torch.randn(*shape, dtype=torch.float64)
ev = torch.randn(*shape, dtype=torch.float64)
x = ex / torch.norm(ex)
v = ev - (x @ ev) * x
manifold = geoopt.Sphere()
x = geoopt.ManifoldTensor(x, manifold=manifold)
case = UnaryCase(shape, x, ex, v, ev, manifold)
yield case
manifold = geoopt.SphereExact()
x = geoopt.ManifoldTensor(x, manifold=manifold)
case = UnaryCase(shape, x, ex, v, ev, manifold)
yield case
def test_remap_request_shapes():
sphere = geoopt.Sphere()
poincare = geoopt.PoincareBall()
func = torch.nn.Linear(10, 13)
with pytest.raises(ValueError):
geoopt_layers.RemapLambda(
func,
source_manifold=sphere,
target_manifold=poincare,
source_origin_shape=None,
target_origin_shape=10,
)
with pytest.raises(ValueError):
geoopt_layers.RemapLambda(
func,
source_manifold=sphere,
target_manifold=poincare,
source_origin_shape=10,
target_origin_shape=None,
)
def sphere_subspace_case():
torch.manual_seed(42)
shape = manifold_shapes[geoopt.manifolds.Sphere]
subspace = torch.rand(shape[-1], 2, dtype=torch.float64)
Q, _ = geoopt.linalg.batch_linalg.qr(subspace, "reduced")
P = Q @ Q.t()
ex = torch.randn(*shape, dtype=torch.float64)
ev = torch.randn(*shape, dtype=torch.float64)
x = (ex @ P.t()) / torch.norm(ex @ P.t())
v = (ev - (x @ ev) * x) @ P.t()
manifold = geoopt.Sphere(intersection=subspace)
x = geoopt.ManifoldTensor(x, manifold=manifold)
case = UnaryCase(shape, x, ex, v, ev, manifold)
yield case
manifold = geoopt.SphereExact(intersection=subspace)
x = geoopt.ManifoldTensor(x, manifold=manifold)
case = UnaryCase(shape, x, ex, v, ev, manifold)
yield case
def sphere_compliment_case():
torch.manual_seed(42)
shape = manifold_shapes[geoopt.manifolds.Sphere]
complement = torch.rand(shape[-1], 1, dtype=torch.float64)
Q, _ = geoopt.linalg.batch_linalg.qr(complement, "reduced")
P = -Q @ Q.transpose(-1, -2)
P[..., torch.arange(P.shape[-2]), torch.arange(P.shape[-2])] += 1
ex = torch.randn(*shape, dtype=torch.float64)
ev = torch.randn(*shape, dtype=torch.float64)
x = (ex @ P.t()) / torch.norm(ex @ P.t())
v = (ev - (x @ ev) * x) @ P.t()
manifold = geoopt.Sphere(complement=complement)
x = geoopt.ManifoldTensor(x, manifold=manifold)
case = UnaryCase(shape, x, ex, v, ev, manifold)
yield case
manifold = geoopt.SphereExact(complement=complement)
x = geoopt.ManifoldTensor(x, manifold=manifold)
case = UnaryCase(shape, x, ex, v, ev, manifold)
yield case
def test_knn_index():
man = geoopt.Sphere()
layer = geoopt_layers.KNNIndex(manifold=man, dim=0, k=5)
points = man.random(10, 9)
idx = layer(points)
assert idx.shape == (10, 5)
def test_remap_init():
sphere = geoopt.Sphere()
layer = geoopt_layers.Remap(source_manifold=sphere, source_origin=sphere.origin(10))
assert layer.target_manifold is sphere
def test_fails_SphereProjection():
subspace = torch.rand(10, 2, dtype=torch.float64)
manifold = geoopt.Sphere(intersection=subspace)
with pytest.raises(ValueError):
manifold.random_uniform(50)
def test_random_Sphere():
manifold = geoopt.Sphere()
point = manifold.random_uniform(3, 10, 10)
manifold.assert_check_point_on_manifold(point)
assert point.manifold is manifold
def test_random_SphereProjection():
subspace = torch.rand(10, 2, dtype=torch.float64)
manifold = geoopt.Sphere(intersection=subspace)
point = manifold.random_uniform(3, 10, 10)
manifold.assert_check_point_on_manifold(point)
assert point.manifold is manifold
def get_prod_hysph_manifold(dims):
poincare = gt.PoincareBall()
sphere = gt.Sphere()
return gt.ProductManifold((poincare, dims // 2), (sphere, dims // 2))
def get_prod_sphsph_manifold(dims):
sphere = gt.Sphere()
return gt.ProductManifold((sphere, dims // 2), (sphere, dims // 2))
def test_no_type_promotion():
p = geoopt.Sphere().random(10)
t = p.manifold.proju(p, torch.randn(10))
assert not isinstance(t, type(p))
def test_logmap():
sphere = geoopt.Sphere()
layer = geoopt_layers.Logmap(manifold=sphere, origin=sphere.origin(10))
out = layer(sphere.random(2, 30, 10))
assert isinstance(out, torch.Tensor)
assert not torch.isnan(out).any()
def test_distance2centroids():
man = geoopt.Sphere()
layer = geoopt_layers.Distance2Centroids(man, 9, 256)
points = man.random(10, 9)
distances = layer(points)
assert distances.shape == (10, 256)
