def __init__(self, regression=False):
super(Classifier, self).__init__()
self.regression = regression
if cmd_args.gm == 'DGCNN':
model = DGCNN
else:
print('unknown gm %s' % cmd_args.gm)
sys.exit()
if cmd_args.gm == 'DGCNN':
self.gnn = model(latent_dim=cmd_args.latent_dim,
output_dim=cmd_args.out_dim,
num_node_feats=cmd_args.feat_dim +
cmd_args.attr_dim,
num_edge_feats=cmd_args.edge_feat_dim,
k=cmd_args.sortpooling_k,
conv1d_activation=cmd_args.conv1d_activation)
out_dim = cmd_args.out_dim
if out_dim == 0:
if cmd_args.gm == 'DGCNN':
out_dim = self.gnn.dense_dim
else:
out_dim = cmd_args.latent_dim
self.mlp = MLPClassifier(input_size=out_dim,
hidden_size=cmd_args.hidden,
num_class=cmd_args.num_class,
with_dropout=cmd_args.dropout)
if regression:
self.mlp = MLPRegression(input_size=out_dim,
hidden_size=cmd_args.hidden,
with_dropout=cmd_args.dropout)
def __init__(self, num_node_feats, num_class, num_edge_feats=0, regression=False, with_dropout=False):
super(DGCNN, self).__init__()
self.regression = regression
self.gnn = DGCNNEmbedding(output_dim=1024,
num_node_feats=num_node_feats)
self.mlp = MLPClassifier(input_size=1024, hidden_size=100, num_class=num_class, with_dropout=with_dropout)
if regression:
self.mlp = MLPRegression(input_size=1024, hidden_size=100, with_dropout=with_dropout)
def __init__(self, regression=False):
super(Classifier, self).__init__()
self.regression = regression
model = Enet
self.gnn = model(latent_dim=cmd_args.latent_dim,
output_dim=cmd_args.out_dim,
num_node_feats=cmd_args.feat_dim + cmd_args.attr_dim,
num_edge_feats=cmd_args.edge_feat_dim,
total_num_nodes=cmd_args.total_num_nodes,
total_num_tag=cmd_args.feat_dim,
k=cmd_args.sortpooling_k,
conv1d_activation=cmd_args.conv1d_activation,
alpha=0.1,
sumsort=cmd_args.sumsort,
noise_matrix=cmd_args.noise_matrix,
reg_smooth=cmd_args.reg_smooth,
smooth_coef=cmd_args.smooth_coef,
trainable_noise=cmd_args.trainable_noise,
use_sig=cmd_args.use_sig,
use_soft=cmd_args.use_soft,
noise_bias=cmd_args.noise_bias,
noise_init=cmd_args.noise_init)
out_dim = cmd_args.out_dim
if out_dim == 0:
out_dim = self.gnn.dense_dim
if cmd_args.nodefeat_lp:
out_dim += (2 * cmd_args.attr_dim)
self.mlp = MLPClassifier(input_size=out_dim,
hidden_size=cmd_args.hidden,
num_class=cmd_args.num_class,
with_dropout=cmd_args.dropout)
if regression:
self.mlp = MLPRegression(input_size=out_dim,
hidden_size=cmd_args.hidden,
with_dropout=cmd_args.dropout)