def __init__(self, c, args):
super(Shallow, self).__init__(c)
self.manifold = getattr(manifolds, args.manifold)()
self.use_feats = args.use_feats
weights = torch.Tensor(args.n_nodes, args.dim)
if not args.pretrained_embeddings:
weights = self.manifold.init_weights(weights, self.c)
trainable = True
else:
weights = torch.Tensor(np.load(args.pretrained_embeddings))
assert weights.shape[
0] == args.n_nodes, "The embeddings you passed seem to be for another dataset."
trainable = False
self.lt = manifolds.ManifoldParameter(weights, trainable,
self.manifold, self.c)
self.all_nodes = torch.LongTensor(list(range(args.n_nodes)))
layers = []
if args.pretrained_embeddings is not None and args.num_layers > 0:
# MLP layers after pre-trained embeddings
dims, acts = get_dim_act(args)
if self.use_feats:
dims[0] = args.feat_dim + weights.shape[1]
else:
dims[0] = weights.shape[1]
for i in range(len(dims) - 1):
in_dim, out_dim = dims[i], dims[i + 1]
act = acts[i]
layers.append(
Linear(in_dim, out_dim, args.dropout, act, args.bias))
self.layers = nn.Sequential(*layers)
self.encode_graph = False
def __init__(self, c, args):
super(MLP, self).__init__(c)
assert args.num_layers > 0
dims, acts = get_dim_act(args)
layers = []
for i in range(len(dims) - 1):
in_dim, out_dim = dims[i], dims[i + 1]
act = acts[i]
layers.append(Linear(in_dim, out_dim, args.dropout, act,
args.bias))
self.layers = nn.Sequential(*layers)
self.encode_graph = False
def __init__(self, c, args):
super(DeepGCN, self).__init__(c)
assert args.num_layers > 0
dims, acts = get_dim_act(args)
gc_layers = []
gc_layers.append(
GraphConvolution(dims[0], dims[1], args.dropout, acts[0],
args.bias))
gc_layers.append(
OriRevLayer(dims, dims[0], dims[1], args.dropout, acts[0],
args.bias))
self.layers = nn.Sequential(*gc_layers)
self.encode_graph = True
def __init__(self, c, args):
super(GCN, self).__init__(c)
assert args.num_layers > 0
dims, acts = get_dim_act(args)
gc_layers = []
for i in range(len(dims) - 1):
in_dim, out_dim = dims[i], dims[i + 1]
act = acts[i]
gc_layers.append(
GraphConvolution(in_dim, out_dim, args.dropout, act,
args.bias))
self.layers = nn.Sequential(*gc_layers)
self.encode_graph = True
def __init__(self, c, args):
super(GAT, self).__init__(c)
assert args.num_layers > 0
dims, acts = get_dim_act(args)
gat_layers = []
for i in range(len(dims) - 1):
in_dim, out_dim = dims[i], dims[i + 1]
act = acts[i]
assert dims[i + 1] % args.n_heads == 0
out_dim = dims[i + 1] // args.n_heads
concat = True
gat_layers.append(
GraphAttentionLayer(in_dim, out_dim, args.dropout, act,
args.alpha, args.n_heads, concat))
self.layers = nn.Sequential(*gat_layers)
self.encode_graph = True