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parser.py
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parser.py
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from torch import optim, nn
import torch.nn.functional as F
from models import (
GCN,
GAT,
NTPoolGCN,
GIN,
HGT,
HEATNet2,
HEATNet4,
HeteroRGCN,
)
def parse_optimizer(config_optim, model):
opt_method = config_optim["opt_method"].lower()
alpha = config_optim["lr"]
weight_decay = config_optim["weight_decay"]
if opt_method == "adagrad":
optimizer = optim.Adagrad(
model.parameters(),
lr=alpha,
lr_decay=weight_decay,
weight_decay=weight_decay,
)
elif opt_method == "adadelta":
optimizer = optim.Adadelta(
model.parameters(),
lr=alpha,
weight_decay=weight_decay,
)
elif opt_method == "adam":
optimizer = optim.Adam(
model.parameters(),
lr=alpha,
weight_decay=weight_decay,
)
else:
optimizer = optim.SGD(
model.parameters(),
lr=alpha,
weight_decay=weight_decay,
)
return optimizer
def parse_gnn_model(config_gnn):
gnn_name = config_gnn["name"]
if gnn_name == "GAT":
n_layers = config_gnn["num_layers"]
n_heads = config_gnn["num_heads"]
n_out_heads = config_gnn["num_out_heads"]
heads = ([n_heads] * n_layers) + [n_out_heads]
return GAT(
n_layers=config_gnn["num_layers"],
in_dim=config_gnn["in_dim"],
hidden_dim=config_gnn["hidden_dim"],
out_dim=config_gnn["out_dim"],
heads=heads,
activation=F.leaky_relu,
feat_drop=config_gnn["feat_drop"],
attn_drop=config_gnn["attn_drop"],
negative_slope=config_gnn["negative_slope"],
residual=False,
graph_pooling_type=config_gnn["graph_pooling_type"]
)
elif gnn_name == "GCN":
return GCN(
in_dim=config_gnn["in_dim"],
hidden_dim=config_gnn["hidden_dim"],
out_dim=config_gnn["out_dim"],
n_layers=config_gnn["num_layers"],
activation=F.relu,
dropout=config_gnn["feat_drop"],
graph_pooling_type=config_gnn["graph_pooling_type"]
)
elif gnn_name == "GCN_NTPool":
n_node_types = config_gnn["n_node_types"]
node_dict = {str(i): i for i in range(n_node_types)}
return NTPoolGCN(
in_dim=config_gnn["in_dim"],
hidden_dim=config_gnn["hidden_dim"],
out_dim=config_gnn["out_dim"],
node_dict=node_dict,
n_layers=config_gnn["num_layers"],
activation=F.relu,
dropout=config_gnn["feat_drop"],
graph_pooling_type=config_gnn["graph_pooling_type"]
)
elif gnn_name == "GIN":
return GIN(
input_dim=config_gnn["in_dim"],
hidden_dim=config_gnn["hidden_dim"],
out_dim=config_gnn["out_dim"],
num_layers=config_gnn["num_layers"],
num_mlp_layers=config_gnn["num_mlp_layers"],
final_dropout=config_gnn["feat_drop"],
graph_pooling_type=config_gnn["graph_pooling_type"],
neighbor_pooling_type=config_gnn["neighbor_pooling_type"]
)
elif gnn_name == "HetRGCN":
n_node_types = config_gnn["n_node_types"]
etypes = config_gnn["edge_types"]
canonical_etypes = [
(str(s), r, str(t))
for r in etypes
for s in range(n_node_types)
for t in range(n_node_types)
]
node_dict = {str(i): i for i in range(n_node_types)}
canonical_etypes = {et: str(i) for i, et in enumerate(canonical_etypes)}
return HeteroRGCN(
in_dim=config_gnn["in_dim"],
hidden_dim=config_gnn["hidden_dim"],
out_dim=config_gnn["out_dim"],
n_layers=config_gnn["num_layers"],
etypes=canonical_etypes,
node_dict=node_dict,
graph_pooling_type=config_gnn["graph_pooling_type"],
)
elif gnn_name == "HGT":
n_node_types = config_gnn["n_node_types"]
etypes = config_gnn["edge_types"]
canonical_etypes = [
(str(s), r, str(t))
for r in etypes
for s in range(n_node_types)
for t in range(n_node_types)
]
node_dict = {str(i): i for i in range(n_node_types)}
edge_dict = {et: i for i, et in enumerate(canonical_etypes)}
return HGT(
node_dict,
edge_dict,
in_dim=config_gnn["in_dim"],
hidden_dim=config_gnn["hidden_dim"],
out_dim=config_gnn["out_dim"],
n_layers=config_gnn["num_layers"],
n_heads=config_gnn["num_heads"]
)
elif gnn_name == "HEAT2":
n_node_types = config_gnn["n_node_types"]
node_dict = {str(i): i for i in range(n_node_types)}
return HEATNet2(
in_dim=config_gnn["in_dim"],
hidden_dim=config_gnn["hidden_dim"],
out_dim=config_gnn["out_dim"],
n_layers=config_gnn["num_layers"],
n_heads=config_gnn["n_heads"],
node_dict=node_dict,
dropuout=config_gnn["feat_drop"],
graph_pooling_type=config_gnn["graph_pooling_type"]
)
elif gnn_name == "HEAT4":
n_node_types = config_gnn["n_node_types"]
node_dict = {str(i): i for i in range(n_node_types)}
return HEATNet4(
in_dim=config_gnn["in_dim"],
hidden_dim=config_gnn["hidden_dim"],
out_dim=config_gnn["out_dim"],
n_layers=config_gnn["num_layers"],
n_heads=config_gnn["n_heads"],
node_dict=node_dict,
dropuout=config_gnn["feat_drop"],
graph_pooling_type=config_gnn["graph_pooling_type"]
)
else:
raise NotImplementedError("This GNN model is not implemented")
def parse_loss(config_train):
loss_name = config_train["loss"]
if loss_name == "BCE":
return nn.BCELoss()
elif loss_name == "CE":
return nn.CrossEntropyLoss()
else:
raise NotImplementedError("This Loss is not implemented")