def __init__(self, c, args):
super(HNN, self).__init__(c)
self.manifold = getattr(manifolds, args.manifold)()
assert args.num_layers > 1
dims, acts, _ = hyp_layers.get_dim_act_curv(args)
hnn_layers = []
for i in range(len(dims) - 1):
in_dim, out_dim = dims[i], dims[i + 1]
act = acts[i]
hnn_layers.append(
hyp_layers.HNNLayer(self.manifold, in_dim, out_dim, self.c,
args.dropout, act, args.bias))
self.layers = nn.Sequential(*hnn_layers)
self.encode_graph = False
def __init__(self, c, args, task):
super(HNNDecoder, self).__init__(c)
self.manifold = getattr(manifolds, args.manifold)()
if not args.cuda == -1:
c = torch.Tensor([c]).to(args.device)
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
encdec_share_curvature = False
hnn_layers = []
num_dec_layers = args.num_dec_layers
for i in range(num_dec_layers):
in_dim, out_dim = dims[i], dims[i + 1]
act = acts[i]
c_in = c
c_out = None if (i == num_dec_layers - 1) else c
hnn_layers.append(
hyp_layers.HNNLayer(
self.manifold, in_dim, out_dim, c_in, c_out, args.dropout, act, args.bias
)
)
self.decoder = nn.Sequential(*hnn_layers)
self.decode_adj = False
else:
raise RuntimeError('Unknown task')
