def build_hidden_layer(self):
return RGCNLayer(self.h_dim,
self.h_dim,
self.num_rels,
self.num_bases,
dropout=self.dropout,
activation=F.relu)
def build_input_layer(self):
return RGCNLayer(self.num_nodes,
self.h_dim,
self.num_rels,
self.num_bases,
activation=F.relu,
is_input_layer=True)
def build_hidden_layer(self, idx):
act = F.relu if idx < self.num_hidden_layers - 1 else None
return RGCNLayer(self.h_dim,
self.h_dim,
self.num_rels,
self.num_bases,
activation=act,
rank=2)
def build_input_layer(self):
return RGCNLayer(self.in_feat,
self.h_dim,
self.num_rels,
self.num_bases,
activation=F.relu,
is_input_layer=True,
node_features=self.features)
def build_hidden_layer(self, idx):
act = F.relu if idx < self.num_hidden_layers - 1 else None
return RGCNLayer(self.h_dim,
self.h_dim,
self.num_rels,
self.num_bases,
activation=act,
self_loop=True,
dropout=self.dropout)
def build_hidden_layer(self, idx):
act = F.relu if idx < self.num_hidden_layers - 1 else None
concat = True if idx < self.num_hidden_layers - 1 else False
if USE_ATTN:
return RGCNLayer(self.h_dim,
self.h_dim,
self.num_rels,
self.num_bases,
num_heads=self.num_heads,
activation=act,
self_loop=True,
dropout=self.dropout,
concat_attn=concat,
relation_type=self.relation_type,
relation_size=self.relation_size)
else:
return RGCNLayer(self.h_dim,
self.h_dim,
self.num_rels,
self.num_bases,
activation=act,
self_loop=True,
dropout=self.dropout)
def build_output_layer(self):
return RGCNLayer(self.h_dim, self.out_dim, self.num_rels,self.num_bases,
activation=partial(F.softmax, axis=1))
def build_hidden_layer(self, idx):
return RGCNLayer(self.h_dim, self.h_dim, self.num_rels, self.num_bases,
activation=F.relu)
def build_output_layer(self):
return RGCNLayer(self.h_dim,
self.out_dim,
self.num_rels,
self.num_bases,
activation=None)
