def read_config():
stream = open("config.yml", "r")
docs = yaml.safe_load(stream)
"""
for doc in docs:
for k,v in doc.items():
print(k, "->", v)
print("\n")
"""
return objectview(docs)
def __init__(self, input_dim, hidden_dim, output_dim, arg, emb=False):
super(ResidualMP, self).__init__()
args = objectview(arg)
self.convs = nn.ModuleList()
self.num_layers = args.num_layers
self.dropout = args.dropout
self.emb = emb
post_hidden = hidden_dim
# May want to change input/hidden/output dim?
self.convs.append(DeeperGraphSage(input_dim, hidden_dim, hidden_dim=args.message_hidden, dropout=args.dropout, first=True))
for l in range(args.num_layers-1):
self.convs.append(
DeeperGraphSage(hidden_dim, hidden_dim, hidden_dim=args.message_hidden, dropout=args.dropout))
self.post_mp = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim),
nn.Dropout(args.dropout),
nn.Linear(hidden_dim, output_dim))
def __init__(self, input_dim, hidden_dim, output_dim, arg, emb=False):
super(GNNStack, self).__init__()
args = objectview(arg)
conv_model = self.build_conv_model(args.model_type)
self.convs = nn.ModuleList()
self.convs.append(conv_model(input_dim, hidden_dim))
assert (args.num_layers >= 1), 'Number of layers is not >=1'
for l in range(args.num_layers - 1):
self.convs.append(conv_model(args.heads * hidden_dim, hidden_dim))
# post-message-passing
self.post_mp = nn.Sequential(
nn.Linear(args.heads * hidden_dim, hidden_dim),
nn.Dropout(args.dropout), nn.Linear(hidden_dim, output_dim))
self.dropout = args.dropout
self.num_layers = args.num_layers
self.emb = emb
def __init__(self, input_dim, hidden_dim, output_dim, arg, emb=False):
super(ResNetPostMP, self).__init__()
args = objectview(arg)
conv_model = GraphSage
self.post_hidden = args.post_hidden
post_hidden = self.post_hidden
self.convs = nn.ModuleList()
self.affine = nn.Linear(input_dim, hidden_dim)
for l in range(args.num_layers):
self.convs.append(conv_model(hidden_dim, hidden_dim))
self.bns = torch.nn.ModuleList([torch.nn.BatchNorm1d(hidden_dim)
for _ in range(args.num_layers)])
# self.post_mp = nn.Sequential(
# nn.Linear(hidden_dim, hidden_dim),
# nn.Dropout(args.dropout),
# nn.Linear(hidden_dim, output_dim))
# post-message-passing
# TODO: Make module list
self.post_mp = nn.Sequential(
ResNetBlock(
nn.Sequential(
nn.Linear(post_hidden, post_hidden),
nn.ReLU(),
nn.BatchNorm1d(post_hidden),
)),
nn.Dropout(args.dropout),
nn.Linear(post_hidden, output_dim)
)
self.dropout = args.dropout
self.num_layers = args.num_layers
self.emb = emb
def read_config():
stream = open("config.yml", "r")
docs = yaml.safe_load(stream)
return objectview(docs)
def read_config():
stream = open("config.yml", "r")
docs = yaml.safe_load(stream)
return objectview(docs)
