def test_wrapped_normal_StereographicProductManifold():
manifold = geoopt.StereographicProductManifold(
(geoopt.PoincareBall(), 2),
(geoopt.SphereProjection(), 2),
(geoopt.Stereographic(), 2),
)
mean = manifold.random(6)
point = manifold.wrapped_normal(4, 6, mean=mean)
manifold.assert_check_point_on_manifold(point)
assert point.manifold is manifold
def __init__(self, curvature: float, dim_hidden: int, **kwargs):
print("Constructing PoincareProbeNoSquare")
super().__init__(**kwargs)
self.ball = gt.Stereographic(curvature)
self.dim_hidden = dim_hidden
self.proj = nn.Parameter(data=th.zeros(self.dim_in, self.dim_hidden))
self.trans = nn.Parameter(data=th.zeros(self.dim_out, self.dim_hidden))
nn.init.uniform_(self.proj, -0.05, 0.05)
nn.init.uniform_(self.trans, -0.05, 0.05)
def stereographic_case():
torch.manual_seed(42)
shape = manifold_shapes[geoopt.manifolds.Stereographic]
ex = torch.randn(*shape, dtype=torch.float64) / 3
ev = torch.randn(*shape, dtype=torch.float64) / 3
x = ex # default curvature = 0
ex = x.clone()
v = ev.clone()
manifold = geoopt.Stereographic().to(dtype=torch.float64)
x = geoopt.ManifoldTensor(x, manifold=manifold)
case = UnaryCase(shape, x, ex, v, ev, manifold)
yield case
manifold = geoopt.StereographicExact().to(dtype=torch.float64)
x = geoopt.ManifoldTensor(x, manifold=manifold)
case = UnaryCase(shape, x, ex, v, ev, manifold)
yield case
def __init__(self, db_dir='data/database', db_content=True):
super(Preprocessor, self).__init__()
self.db_dir = db_dir
self.db_content = db_content
self.nlp = stanza.Pipeline(
'en', processors='tokenize,pos,lemma') #, use_gpu=False)
self.stopwords = stopwords.words("english")
self.device = torch.device("cuda:0")
self.hidden_size = 1024
self.max_batch_size = 8
self.plm_model = AutoModel.from_pretrained(
os.path.join('./pretrained_models',
'electra-large-discriminator')).to(self.device)
self.plm_tokenizer = AutoTokenizer.from_pretrained(
os.path.join('./pretrained_models', 'electra-large-discriminator'))
self.config = self.plm_model.config
self.ball = gt.Stereographic(-1)
self.threshold = 0.4
def test_adam_StereographicProductManifold(params):
torch.manual_seed(42)
pman = geoopt.StereographicProductManifold(
(geoopt.PoincareBall(), 2),
(geoopt.SphereProjection(), 2),
(geoopt.Stereographic(k=0), 2),
)
ideal = pman.random(6)
start = geoopt.ManifoldParameter(pman.random(6), manifold=pman)
def closure():
optim.zero_grad()
loss = pman.dist(start, ideal)**2
loss.backward()
return loss.item()
optim = geoopt.optim.RiemannianAdam([start], **params)
for _ in range(2000):
optim.step(closure)
np.testing.assert_allclose(start.data, ideal, atol=1e-5, rtol=1e-5)
def __init__(self, curvature: float, dim_hidden: int, **kwargs):
super().__init__(**kwargs)
self.ball = gt.Stereographic(curvature)
self.dim_hidden = dim_hidden
