def __init__(self, c, args): #, use_cnn
super(HGCAE, self).__init__(c, use_cnn=True)
self.manifold = getattr(manifolds, args.manifold)()
assert args.num_layers > 0
dims, acts, self.curvatures = hyp_layers.get_dim_act_curv(args)
if args.c_trainable == 1:
self.curvatures.append(
nn.Parameter(torch.Tensor([args.c]).to(args.device)))
else:
self.curvatures.append(torch.tensor([args.c]).to(args.device))
hgc_layers = []
for i in range(len(dims) - 1):
c_in, c_out = self.curvatures[i], self.curvatures[i + 1]
in_dim, out_dim = dims[i], dims[i + 1]
act = acts[i]
hgc_layers.append(
hyp_layers.HyperbolicGraphConvolution(self.manifold,
in_dim,
out_dim,
c_in,
c_out,
args.dropout,
act,
args.bias,
args.use_att,
att_type=args.att_type,
att_logit=args.att_logit,
beta=args.beta))
self.layers = nn.Sequential(*hgc_layers)
self.encode_graph = True
def __init__(self, c, args, task):
super(HGCAEDecoder, self).__init__(c)
self.manifold = getattr(manifolds, args.manifold)()
if task == 'nc':
self.input_dim = args.dim
self.output_dim = args.n_classes
self.bias = args.bias
self.classifier = Linear(self.input_dim, self.output_dim, args.dropout, lambda x: x, self.bias)
self.decode_adj = False
elif task == 'rec':
assert args.num_layers > 0
dims, acts, _ = hyp_layers.get_dim_act_curv(args)
dims = dims[::-1]
acts = acts[::-1][:-1] + [lambda x: x] # Last layer without act
self.curvatures = self.c[::-1]
encdec_share_curvature = False
if not encdec_share_curvature and args.num_layers == args.num_dec_layers: # do not share and enc-dec mirror-shape
num_c = len(self.curvatures)
self.curvatures = self.curvatures[:1]
if args.c_trainable == 1:
self.curvatures += [nn.Parameter(torch.Tensor([args.c]).to(args.device))] * (num_c - 1)
else:
self.curvatures += [torch.tensor([args.c])] * (num_c - 1)
if not args.cuda == -1:
self.curvatures = [curv.to(args.device) for curv in self.curvatures]
self.curvatures = self.curvatures[:-1] + [None]
hgc_layers = []
num_dec_layers = args.num_dec_layers
for i in range(num_dec_layers):
c_in, c_out = self.curvatures[i], self.curvatures[i + 1]
in_dim, out_dim = dims[i], dims[i + 1]
act = acts[i]
hgc_layers.append(
hyp_layers.HyperbolicGraphConvolution(
self.manifold, in_dim, out_dim, c_in, c_out, args.dropout, act, args.bias, args.use_att,
att_type=args.att_type, att_logit=args.att_logit, beta=args.beta, decode=True
)
)
self.decoder = nn.Sequential(*hgc_layers)
self.decode_adj = True
else:
raise RuntimeError('Unknown task')
def __init__(self, c, args):
super(HGCF, self).__init__(c)
self.manifold = getattr(manifolds, "Hyperboloid")()
assert args.num_layers > 1
hgc_layers = []
in_dim = out_dim = args.embedding_dim
hgc_layers.append(
hyp_layers.HyperbolicGraphConvolution(
self.manifold, in_dim, out_dim, self.c, args.network, args.num_layers
)
)
self.layers = nn.Sequential(*hgc_layers)
self.encode_graph = True
def __init__(self,
manifold,
c=1.,
input_dim=10,
hidden_dim=16,
edge_dim=0,
num_layers=2,
dropout=0.05,
use_bias=True,
use_att=True,
agg_direction='in',
**kwargs):
super(HGCN, self).__init__()
self.manifold = manifold
assert num_layers > 1
c = torch.tensor([c]).float()
dims, acts, curvatures = get_dim_act_curv(input_dim=input_dim,
hidden_dim=hidden_dim,
num_layers=num_layers)
curvatures.append(c)
self.curvatures = []
for i, curv in enumerate(curvatures):
curv = nn.Parameter(curv)
self.register_parameter("c_{}".format(i), curv)
self.curvatures.append(manifolds.Curvature(curv))
# https://github.com/HazyResearch/hgcn/blob/25a3701f8dfbab2341bc18091fedcb0e9bf61395/models/base_models.py#L27
hgc_layers = []
for i in range(len(dims) - 1):
c_in, c_out = self.curvatures[i], self.curvatures[i + 1]
in_dim, out_dim = dims[i], dims[i + 1]
act = acts[i]
hgc_layers.append(
hyp_layers.HyperbolicGraphConvolution(
manifold=self.manifold,
in_features=in_dim,
out_features=out_dim,
edge_dim=edge_dim,
c_in=c_in,
c_out=c_out,
dropout=dropout,
act=act,
use_bias=use_bias,
use_att=use_att,
agg_direction=agg_direction))
self.layers = nn.Sequential(*hgc_layers)
self.encode_graph = True
def __init__(self, c, args):
super(HGCN, self).__init__(c)
self.manifold = getattr(manifolds, args.manifold)()
assert args.num_layers > 1
dims, acts, self.curvatures = hyp_layers.get_dim_act_curv(args)
self.curvatures.append(self.c)
hgc_layers = []
for i in range(len(dims) - 1):
c_in, c_out = self.curvatures[i], self.curvatures[i + 1]
in_dim, out_dim = dims[i], dims[i + 1]
act = acts[i]
hgc_layers.append(
hyp_layers.HyperbolicGraphConvolution(self.manifold, in_dim,
out_dim, c_in, c_out,
args.dropout, act,
args.bias))
self.layers = nn.Sequential(*hgc_layers)
self.encode_graph = True
