def test_gated_graph_conv():
x = torch.randn(4, 16)
edge_index = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]])
row, col = edge_index
value = torch.rand(row.size(0))
adj2 = SparseTensor(row=row, col=col, value=value, sparse_sizes=(4, 4))
adj1 = adj2.set_value(None)
conv = GatedGraphConv(32, num_layers=3)
assert conv.__repr__() == 'GatedGraphConv(32, num_layers=3)'
out1 = conv(x, edge_index)
assert out1.size() == (4, 32)
assert torch.allclose(conv(x, adj1.t()), out1, atol=1e-6)
out2 = conv(x, edge_index, value)
assert out2.size() == (4, 32)
assert torch.allclose(conv(x, adj2.t()), out2, atol=1e-6)
t = '(Tensor, Tensor, OptTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit(x, edge_index).tolist() == out1.tolist()
assert jit(x, edge_index, value).tolist() == out2.tolist()
t = '(Tensor, SparseTensor, OptTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert torch.allclose(jit(x, adj1.t()), out1, atol=1e-6)
assert torch.allclose(jit(x, adj2.t()), out2, atol=1e-6)
def __init__(self, in_feats, hid_feats, out_feats):
super(BUrumorGGCN, self).__init__()
self.conv1 = GatedGraphConv(in_feats, hid_feats, aggr='add', bias=True)
self.conv2 = GatedGraphConv(hid_feats + in_feats,
out_feats,
aggr='add',
bias=True)
def test_gcn_conv():
in_channels, out_channels = (16, 32)
edge_index = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]])
num_nodes = edge_index.max().item() + 1
x = torch.randn((num_nodes, in_channels))
conv = GatedGraphConv(out_channels, num_layers=3)
assert conv.__repr__() == 'GatedGraphConv(32, num_layers=3)'
assert conv(x, edge_index).size() == (num_nodes, out_channels)
def __init__(self,
features_num=16,
num_class=2,
dropout=0.2,
num_layers=2,
hidden=16):
super(GGC, self).__init__()
self.conv1 = GatedGraphConv(features_num, hidden)
self.conv2 = GatedGraphConv(hidden, num_class)
self.dropout = dropout
def __init__(self, embeddings, word_vec_size, gnn_type, gnn_layers,
rnn_size):
super(GraphEncoder, self).__init__()
self.is_graph_encoder = True
self.gnn_type = gnn_type
self.gnn_layers = gnn_layers
self.embeddings = embeddings
self.dropout = nn.Dropout(0.3)
self.num_inputs = word_vec_size
self.rnn_size = rnn_size
if self.gnn_type == 'ggnn':
self.gnn_td = GatedGraphConv(self.num_inputs, self.gnn_layers)
self.gnn_bu = GatedGraphConv(self.num_inputs, self.gnn_layers)
elif self.gnn_type == 'gat':
self.gnn_td = GATConv(self.num_inputs,
self.num_inputs,
heads=self.gnn_layers,
concat=False,
dropout=0.3)
self.gnn_bu = GATConv(self.num_inputs,
self.num_inputs,
heads=self.gnn_layers,
concat=False,
dropout=0.3)
else:
self.gins_td = []
self.gins_bu = []
num_layers = self.gnn_layers
nn_td = Sequential(Linear(self.num_inputs,
self.num_inputs), ReLU(),
Linear(self.num_inputs, self.num_inputs))
nn_tb = Sequential(Linear(self.num_inputs,
self.num_inputs), ReLU(),
Linear(self.num_inputs, self.num_inputs))
for x in range(num_layers):
gin = GINConv(nn_td)
self.gins_td.append(gin.cuda())
gin = GINConv(nn_tb)
self.gins_bu.append(gin.cuda())
self.bilstm = nn.LSTM(self.rnn_size,
self.rnn_size // 2,
num_layers=2,
bidirectional=True,
dropout=0.3)
if self.gnn_type == 'gin':
self.layers_seq = nn.Sequential(*self.gins_td, *self.gins_bu,
self.bilstm)
def __init__(self, device: torch.device, *,
embedding_dim: int = 64,
no_shortcut: bool = False,
**kwargs
):
super().__init__(device)
self.embedding_dim = embedding_dim
self.no_shortcut = no_shortcut
# self.aggregator_type = aggregator_type
self.embed = GatedGraphConv(embedding_dim, 2, aggr='mean')
self.conv2= GatedGraphConv(embedding_dim, 2, aggr='mean')
self.fc = nn.Linear(embedding_dim, 2)
self.activate = nn.ELU()
def __init__(self, net_params):
super().__init__()
in_dim_node = net_params['in_dim'] # node_dim (feat is an integer)
in_dim_edge = 1 # edge_dim (feat is a float)
hidden_dim = net_params['hidden_dim']
n_classes = net_params['n_classes']
self.dropout = net_params['dropout']
n_layers = net_params['L']
self.readout = net_params['readout']
self.batch_norm = net_params['batch_norm']
self.residual = net_params['residual']
self.n_classes = n_classes
self.device = net_params['device']
self.pos_enc = net_params['pos_enc']
if self.pos_enc:
pos_enc_dim = net_params['pos_enc_dim']
self.embedding_pos_enc = nn.Linear(pos_enc_dim, hidden_dim)
self.embedding_h = nn.Linear(in_dim_node,
hidden_dim) # node feat is an integer
# self.embedding_e = nn.Linear(in_dim_edge, hidden_dim) # edge feat is a float
self.layers = nn.ModuleList(
[GatedGraphConv(hidden_dim, n_layers, aggr='add')])
# self.layers = nn.ModuleList([GatedGCNLayer(hidden_dim, hidden_dim, dropout,
# self.batch_norm, self.residual) for _ in range(n_layers)])
if self.batch_norm:
self.normlayers = nn.ModuleList([nn.BatchNorm1d(hidden_dim)])
# self.MLP_layer = MLPReadout(hidden_dim, n_classes)
self.MLP_layer = nn.Linear(hidden_dim, n_classes, bias=True)
def __init__(self, hidden_size, opt, n_node, step=1):
super(GNN, self).__init__()
self.step = step
self.hidden_size = hidden_size
self.input_size = hidden_size * 2
self.gate_size = 3 * hidden_size
self.w_ih = Parameter(torch.Tensor(self.gate_size, self.input_size))
self.w_hh = Parameter(torch.Tensor(self.gate_size, self.hidden_size))
self.b_ih = Parameter(torch.Tensor(self.gate_size))
self.b_hh = Parameter(torch.Tensor(self.gate_size))
self.b_iah = Parameter(torch.Tensor(self.hidden_size))
self.b_oah = Parameter(torch.Tensor(self.hidden_size))
self.linear_edge_in = nn.Linear(self.hidden_size,
self.hidden_size,
bias=True)
self.linear_edge_out = nn.Linear(self.hidden_size,
self.hidden_size,
bias=True)
self.linear_edge_f = nn.Linear(self.hidden_size,
self.hidden_size,
bias=True)
heads = 1
self.conv1 = GATConv(self.hidden_size,
self.hidden_size,
heads=heads,
dropout=0.6)
# On the Pubmed dataset, use heads=8 in conv2.
self.conv2 = GATConv(heads * self.hidden_size,
self.hidden_size,
heads=1,
concat=False,
dropout=0.6)
self.ggnn = GatedGraphConv(self.hidden_size, step + 1)
def __init__(self, input_size, hidden_size, output_size, seq_len,
edge_embedding_dim, sampling, recurrent):
super(Decoder, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.seq_len = seq_len
self.edge_embedding_dim = edge_embedding_dim
self.sampling = sampling
self.recurrent = recurrent
self.node_transform = Sequential(
Linear(self.input_size, self.output_size), ReLU())
if self.recurrent == True:
self.rnn_graph_conv = GatedGraphConv(out_channels=self.input_size,
num_layers=self.num_layers)
else:
self.MLP = Sequential(
Linear(self.edge_embedding_dim, self.hidden_size), ReLU(),
Linear(self.hidden_size,
2 * self.input_size * self.input_size))
self.graph_conv_1 = MLPGraphConv(in_channels=self.input_size,
out_channels=self.input_size,
nn=self.MLP,
root_weight=True,
bias=True,
aggr='add')
self.graph_conv_2 = MLPGraphConv(in_channels=self.input_size,
out_channels=self.input_size,
nn=self.MLP,
root_weight=True,
bias=True,
aggr='add')
self.graph_conv_list = ModuleList(
[self.graph_conv_1, self.graph_conv_2])
def get_layer(self, in_dim, out_dim):
if self is GNN_TYPE.GCN:
return GCNConv(in_channels=in_dim, out_channels=out_dim)
elif self is GNN_TYPE.GGNN:
return GatedGraphConv(out_channels=out_dim, num_layers=1)
elif self is GNN_TYPE.GIN:
return GINConv(
nn.Sequential(nn.Linear(in_dim, out_dim),
nn.BatchNorm1d(out_dim), nn.ReLU(),
nn.Linear(out_dim, out_dim),
nn.BatchNorm1d(out_dim), nn.ReLU()))
elif self is GNN_TYPE.GAT:
# 4-heads, although the paper by Velickovic et al. had used 6-8 heads.
# The output will be the concatenation of the heads, yielding a vector of size out_dim
num_heads = 4
return GATConv(in_dim, out_dim // num_heads, heads=num_heads)
elif self is GNN_TYPE.GAT2:
# 4-heads, although the paper by Velickovic et al. had used 6-8 heads.
# The output will be the concatenation of the heads, yielding a vector of size out_dim
num_heads = 4
return GAT2Conv(in_dim, out_dim // num_heads, heads=num_heads)
elif self is GNN_TYPE.LUONG_GAT:
# 4-heads, although the paper by Velickovic et al. had used 6-8 heads.
# The output will be the concatenation of the heads, yielding a vector of size out_dim
num_heads = 4
return LuongGATConv(in_dim, out_dim // num_heads, heads=num_heads)
elif self is GNN_TYPE.DP_GAT:
# 4-heads, although the paper by Velickovic et al. had used 6-8 heads.
# The output will be the concatenation of the heads, yielding a vector of size out_dim
num_heads = 4
return DotProductGATConv(in_dim,
out_dim // num_heads,
heads=num_heads)
def __init__(self, num_vocab, max_seq_len, node_encoder, emb_dim,
layer_timesteps=[5], num_class=0):
super(GGNN_Simple, self).__init__()
self.num_class = num_class # if we do classification
self.layer_timesteps = layer_timesteps
# self.residual_connections = residual_connections
# 'use_edge_bias': False,
self.emb_dim = emb_dim
self.num_vocab = num_vocab
self.max_seq_len = max_seq_len
self.node_encoder = node_encoder
self.convs = []
for layer_idx, t in enumerate(layer_timesteps):
self.convs += [GatedGraphConv(emb_dim, t)]
self.convs = nn.ModuleList(self.convs)
self.classifier_l = nn.Sequential(
nn.Linear(2*emb_dim, emb_dim),
nn.Sigmoid()
)
self.classifier_r = nn.Sequential(
nn.Linear(2*emb_dim, emb_dim),
nn.Tanh()
)
if self.num_class > 0:
self.graph_pred_linear = torch.nn.Linear(emb_dim, self.num_class)
else:
self.graph_pred_linear_list = torch.nn.ModuleList()
for i in range(max_seq_len):
self.graph_pred_linear_list.append(torch.nn.Linear(emb_dim, self.num_vocab))
def __init__(self, embed_size, vocab_size, keyword_vocab_size, hidden_size, output_size, n_layers, gnn, aggregation,
n_heads=0, dropout=0, bidirectional=False, \
utterance_encoder="", keywordid2wordid=None, keyword_mask_matrix=None, nodeid2wordid=None,
keywordid2nodeid=None, concept_encoder="mean", \
combine_node_emb="mean"):
super(KW_GNN, self).__init__()
self.embed_size = embed_size
self.vocab_size = vocab_size
self.keyword_vocab_size = keyword_vocab_size
self.hidden_size = hidden_size
self.output_size = output_size
self.n_layers = n_layers
self.gnn = gnn
self.aggregation = aggregation
self.n_heads = n_heads
self.dropout = dropout
self.bidirectional = bidirectional
self.utterance_encoder_name = utterance_encoder
self.keywordid2wordid = keywordid2wordid
self.keyword_mask_matrix = keyword_mask_matrix
self.nodeid2wordid = nodeid2wordid
self.keywordid2nodeid = keywordid2nodeid
self.concept_encoder = concept_encoder
self.combine_node_emb = combine_node_emb
self.num_nodes = nodeid2wordid.shape[0]
self.embedding = nn.Embedding(vocab_size, embed_size)
# GNN learning
if gnn == "GatedGraphConv":
self.conv1 = GatedGraphConv(hidden_size, num_layers=n_layers)
output_size = hidden_size
if n_layers == 1:
output_size = hidden_size
# aggregation
if aggregation in ["mean", "max"]:
output_size = output_size
# utterance encoder
if self.utterance_encoder_name == "HierGRU":
self.utterance_encoder = nn.GRU(embed_size,
hidden_size,
1,
batch_first=True,
dropout=dropout,
bidirectional=bidirectional)
self.context_encoder = nn.GRU(
2 * hidden_size if bidirectional else hidden_size,
hidden_size,
1,
batch_first=True,
dropout=dropout,
bidirectional=bidirectional)
output_size = output_size + 2 * hidden_size if bidirectional else output_size + hidden_size
# final linear layer
self.mlp = nn.Linear(output_size, keyword_vocab_size)
def __init__(self, hidden_size, n_node):
super(GNNModel, self).__init__()
self.hidden_size, self.n_node = hidden_size, n_node
self.embedding = nn.Embedding(self.n_node, self.hidden_size)
self.gated = GatedGraphConv(self.hidden_size, num_layers=1)
self.e2s = Embedding2Score(self.hidden_size)
self.loss_function = nn.CrossEntropyLoss()
self.reset_parameters()
def _create_layers(self):
self.conv_layer = GatedGraphConv(out_channels=self.conv_out_channels,
num_layers=self.conv_num_layers,
aggr=self.conv_aggr,
bias=True)
self.recurrent_layer = LSTM(input_size=self.conv_out_channels,
hidden_size=self.lstm_out_channels,
num_layers=self.lstm_num_layers)
def __init__(self, node_size):
super(GatedGCN, self).__init__()
self.node_per_graph = node_size
self.linprev = EdgeConv(MLP([4*2, 64, 64, 64]), aggr = 'max')
self.conv1 = GatedGraphConv(256, 2)
self.bn1 = torch.nn.BatchNorm1d(256)
self.conv2 = GatedGraphConv(256, 2)
self.bn2 = torch.nn.BatchNorm1d(256)
self.conv3 = GatedGraphConv(256, 2)
self.bn3 = torch.nn.BatchNorm1d(256)
self.conv4 = GatedGraphConv(256, 3)
self.bn4 = torch.nn.BatchNorm1d(256)
self.mlp = Seq(
MLP([256*2*4, 512]), Dropout(0.4), MLP([512, 128]), Dropout(0.4),
Lin(128, 40))
def test_gated_graph_conv():
in_channels, out_channels = (16, 32)
edge_index = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]])
edge_weight = torch.randn(edge_index.size(1))
num_nodes = edge_index.max().item() + 1
x = torch.randn((num_nodes, in_channels))
conv = GatedGraphConv(out_channels, num_layers=3)
assert conv.__repr__() == 'GatedGraphConv(32, num_layers=3)'
out1 = conv(x, edge_index)
assert out1.size() == (num_nodes, out_channels)
out2 = conv(x, edge_index, edge_weight)
assert out2.size() == (num_nodes, out_channels)
jit_conv = conv.jittable(x=x, edge_index=edge_index)
jit_conv = torch.jit.script(jit_conv)
assert jit_conv(x, edge_index).tolist() == out1.tolist()
assert jit_conv(x, edge_index, edge_weight).tolist() == out2.tolist()
def __init__(self, vocablen, embedding_dim, num_layers, device):
super(GGNN, self).__init__()
self.device = device
#self.num_layers=num_layers
self.embed = nn.Embedding(vocablen, embedding_dim)
self.edge_embed = nn.Embedding(20, embedding_dim)
#self.gmn=nn.ModuleList([GMNlayer(embedding_dim,embedding_dim) for i in range(num_layers)])
self.ggnnlayer = GatedGraphConv(embedding_dim, num_layers)
self.mlp_gate = nn.Sequential(nn.Linear(embedding_dim, 1),
nn.Sigmoid())
self.pool = GlobalAttention(gate_nn=self.mlp_gate)
def gnn_map(gnn_name, in_dim, out_dim, concat=False, bias=True) -> nn.Module:
"""
:param gnn_name:
:param in_dim:
:param out_dim:
:param concat: for gat, concat multi-head output or not
:return: GNN model
"""
if gnn_name == "gat_8":
return GATConv(in_dim, out_dim, 8, concat=concat, bias=bias)
elif gnn_name == "gat_6":
return GATConv(in_dim, out_dim, 6, concat=concat, bias=bias)
elif gnn_name == "gat_4":
return GATConv(in_dim, out_dim, 4, concat=concat, bias=bias)
elif gnn_name == "gat_2":
return GATConv(in_dim, out_dim, 2, concat=concat, bias=bias)
elif gnn_name in ["gat_1", "gat"]:
return GATConv(in_dim, out_dim, 1, concat=concat, bias=bias)
elif gnn_name == "gcn":
return GCNConv(in_dim, out_dim)
elif gnn_name == "cheb":
return ChebConv(in_dim, out_dim, K=2, bias=bias)
elif gnn_name == "sage":
return SAGEConv(in_dim, out_dim, bias=bias)
elif gnn_name == "gated":
return GatedGraphConv(in_dim, out_dim, bias=bias)
elif gnn_name == "arma":
return ARMAConv(in_dim, out_dim, bias=bias)
elif gnn_name == "sg":
return SGConv(in_dim, out_dim, bias=bias)
elif gnn_name == "linear":
return LinearConv(in_dim, out_dim, bias=bias)
elif gnn_name == "zero":
return ZeroConv()
elif gnn_name == "identity":
return Identity()
elif hasattr(torch_geometric.nn, gnn_name):
cls = getattr(torch_geometric.nn, gnn_name)
assert isinstance(cls,
type), "Only support modules, get %s" % (gnn_name)
kwargs = {
"in_channels": in_dim,
"out_channels": out_dim,
"concat": concat,
"bias": bias,
}
kwargs = {
key: kwargs[key]
for key in cls.__init__.__code__.co_varnames if key in kwargs
}
return cls(**kwargs)
raise KeyError("Cannot parse key %s" % (gnn_name))
def __init__(self, args, trained=True, bn=False, backbone_type='res101'):
super(Vrd_Graph_GA_v4, self).__init__()
self.n_obj = args.num_classes
self.n_rel = args.num_relations
global res101
if trained and backbone_type == 'res101':
res101 = resnet.__dict__['resnet101'](pretrained=False, norm_layer=FrozenBatchNorm2d)
weight_path = "../models/resnet101-5d3b4d8f.pth"
state = torch.load(weight_path)
res101.load_state_dict(state)
layers_res = OrderedDict()
for k, v in res101.named_children():
if k in ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3']:
layers_res[k] = v
backbone = nn.Sequential(layers_res)
# 2048
self.features = backbone
else:
res101 = resnet.__dict__['resnet101'](pretrained=False, norm_layer=FrozenBatchNorm2d)
layers_res = OrderedDict()
for k, v in res101.named_children():
if k in ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3']:
layers_res[k] = v
backbone = nn.Sequential(layers_res)
# 2048
self.features = backbone
network.set_trainable(self.features, requires_grad=False)
self.roi_pool = RoIPool((14, 14), 1.0 / 16)
self.inter_layer = res101.layer4
network.set_trainable(self.inter_layer, requires_grad=False)
self.pool = nn.AdaptiveAvgPool2d(output_size=(1, 1))
self.fc6 = nn.Linear(2048, 256)
self.fc_obj = nn.Linear(2048, self.n_obj)
self.gat_conv_rel1 = GatedGraphConv(out_channels=256, num_layers=2)
self.rel_1 = nn.Linear(768, 256)
self.rel_2 = nn.Linear(256, self.n_rel)
self.fc_lov = nn.Linear(8, 256)
self.fc_sub_obj = nn.Linear(2 * 300, 256)
self.initialize_param()
def __init__(self,
embeddings,
edge_embeddings,
num_inputs,
num_units,
num_layers=1,
geometric_layer="rgcn"):
super(DualGraphEncoder, self).__init__()
self.embeddings = embeddings
self.edge_embeddings = edge_embeddings
self.tanh = nn.Tanh()
self.dropout = nn.Dropout(0.3)
# The number of expected features in the input x
self.num_layers = num_layers
self.num_inputs = num_inputs
# The number of features in the hidden state h
self.num_units = num_units
self.geometric_layer = geometric_layer
self.gated_gcn_in = GatedGraphConv(num_inputs, 5)
self.gated_gcn_out = GatedGraphConv(num_inputs, 5)
self.gated_gat_in = GATConv(num_inputs,
num_units,
heads=3,
concat=False,
dropout=0.3)
#self.gated_gat_out = GATConv(num_inputs, num_units, heads=3, concat=False, dropout=0.3)
hidden_size = self.num_units * 3 // 2
self.bilstm = nn.LSTM(self.num_inputs * 3,
hidden_size,
num_layers=2,
bidirectional=True,
dropout=0.3)
def __init__(self, config):
super(Net, self).__init__()
annotation_size = config["hidden_size_orig"]
hidden_size = config["gnn_h_size"]
n_steps = config["num_timesteps"]
num_cls = 2
self.reduce = nn.Linear(annotation_size, hidden_size)
self.conv = GatedGraphConv(hidden_size, n_steps)
self.agg = GlobalAttention(nn.Linear(hidden_size, 1),
nn.Linear(hidden_size, 2))
self.lin = nn.Linear(hidden_size, num_cls)
def __init__(
self,
feat_size=19,
gather_width=64,
k=2,
neighbor_threshold=None,
output_pool_result=False,
bn_track_running_stats=False,
):
super(PotentialNetPropagation, self).__init__()
assert neighbor_threshold is not None
self.neighbor_threshold = neighbor_threshold
self.bn_track_running_stats = bn_track_running_stats
self.edge_attr_size = 1
self.k = k
self.gather_width = gather_width
self.feat_size = feat_size
self.edge_network_nn = nn.Sequential(
nn.Linear(self.edge_attr_size, int(self.feat_size / 2)),
nn.Softsign(),
nn.Linear(int(self.feat_size / 2), self.feat_size),
nn.Softsign(),
)
self.edge_network = NNConv(
self.feat_size,
self.edge_attr_size * self.feat_size,
nn=self.edge_network_nn,
root_weight=True,
aggr="add",
)
self.gate = GatedGraphConv(self.feat_size,
self.k,
edge_network=self.edge_network)
self.attention = PotentialNetAttention(
net_i=nn.Sequential(
nn.Linear(self.feat_size * 2, self.feat_size),
nn.Softsign(),
nn.Linear(self.feat_size, self.gather_width),
nn.Softsign(),
),
net_j=nn.Sequential(nn.Linear(self.feat_size, self.gather_width),
nn.Softsign()),
)
self.output_pool_result = output_pool_result
if self.output_pool_result:
self.global_add_pool = global_add_pool
def __init__(self, config: DictConfig, vocab: Vocabulary,
vocabulary_size: int, pad_idx: int):
super(GatedGraphConvEncoder, self).__init__()
self.__config = config
self.__pad_idx = pad_idx
self.__st_embedding = STEncoder(config, vocab, vocabulary_size,
pad_idx)
self.input_GCL = GatedGraphConv(out_channels=config.hidden_size,
num_layers=config.n_gru)
self.input_GPL = TopKPooling(config.hidden_size,
ratio=config.pooling_ratio)
for i in range(config.n_hidden_layers - 1):
setattr(
self, f"hidden_GCL{i}",
GatedGraphConv(out_channels=config.hidden_size,
num_layers=config.n_gru))
setattr(
self, f"hidden_GPL{i}",
TopKPooling(config.hidden_size, ratio=config.pooling_ratio))
self.attpool = GlobalAttention(torch.nn.Linear(config.hidden_size, 1))
def __init__(
self,
embed_dim: int,
num_layers: int,
heads: int = 8,
normalization: str = "batch",
feed_forward_hidden: int = 512,
pooling_method: str = "add",
) -> None:
super().__init__()
self.embed_dim = embed_dim
self.num_layers = num_layers
self.heads = heads
assert (self.embed_dim % self.heads) == 0
self.pooling_func = get_pooling_func(pooling_method)
self.norm_class = get_normalization_class(normalization)
self.norm_out = GraphNorm(self.embed_dim)
self.gnn_layer = GatedGraphConv(out_channels=self.embed_dim,
num_layers=1)
self.reversed_gnn_layer = GatedGraphConv(out_channels=self.embed_dim,
num_layers=1)
self.edge_extractor = EdgeFeatureExtractor(in_channels=self.embed_dim,
edge_dim=self.embed_dim)
def __init__(self, hidden_size, n_node):
super(testGNN, self).__init__()
self.hidden_size, self.n_node = hidden_size, n_node
self.embedding = torch.nn.Embedding(self.n_node, self.hidden_size)
self.gcn = GCNConv(self.hidden_size, self.hidden_size)
self.gcn2 = GCNConv(self.hidden_size, self.hidden_size)
self.ggcn = GatedGraphConv(self.hidden_size, self.hidden_size)
self.gat1 = GATConv(self.hidden_size,
self.hidden_size,
heads=8,
negative_slope=0.2)
self.gat2 = GATConv(8 * self.hidden_size,
self.hidden_size,
heads=1,
negative_slope=0.2)
self.e2s = Embedding2Score(self.hidden_size)
def __init__(self, embed_size, vocab_size, gnn_hidden_size, gnn_layers, encoder_hidden_size, encoder_layers, n_heads, gnn, encoder, matching, \
aggregation, use_keywords, keyword_encoder, keyword_score_weight=1, dropout=0, CN_hopk_edge_matrix_mask=None, nodeid2wordid=None, \
keywordid2wordid=None, keywordid2nodeid=None, concept_encoder="mean", combine_word_concepts="concat"):
super(CoGraphMatcher, self).__init__()
self.embed_size = embed_size
self.vocab_size = vocab_size
self.gnn_hidden_size = gnn_hidden_size
self.encoder_hidden_size = encoder_hidden_size
self.gnn_layers = gnn_layers
self.encoder_layers = encoder_layers
self.n_heads = n_heads
self.gnn = gnn
self.encoder = encoder
self.aggregation = aggregation
self.use_keywords = use_keywords
self.keyword_score_weight = keyword_score_weight
self.keyword_encoder = keyword_encoder
self.dropout = dropout
self.CN_hopk_edge_matrix_mask = CN_hopk_edge_matrix_mask
self.nodeid2wordid = nodeid2wordid
self.keywordid2wordid = keywordid2wordid
self.keywordid2nodeid = keywordid2nodeid
self.concept_encoder = concept_encoder
self.combine_word_concepts = combine_word_concepts
self.num_nodes = nodeid2wordid.shape[0]
self.embedding = nn.Embedding(vocab_size, embed_size)
# GNN learning
encoder_input_size = gnn_hidden_size
if gnn == "GatedGraphConv":
self.conv1 = GatedGraphConv(gnn_hidden_size, num_layers=gnn_layers)
if self.encoder == "GRU":
self.utterance_encoder = nn.GRU(encoder_input_size,
encoder_hidden_size,
encoder_layers,
batch_first=True,
dropout=dropout,
bidirectional=True)
self.candidate_encoder = nn.GRU(encoder_input_size,
encoder_hidden_size,
encoder_layers,
batch_first=True,
dropout=dropout,
bidirectional=True)
def __init__(self, hidden_size, n_node):
super(GNNModel, self).__init__()
self.hidden_size, self.n_node = hidden_size, n_node
self.embedding = nn.Embedding(self.n_node, self.hidden_size)
self.gat1 = GATConv(self.hidden_size,
self.hidden_size,
heads=8,
negative_slope=0.2)
self.gat2 = GATConv(8 * self.hidden_size,
self.hidden_size,
heads=1,
negative_slope=0.2)
self.sage1 = SAGEConv(self.hidden_size, self.hidden_size)
self.sage2 = SAGEConv(self.hidden_size, self.hidden_size)
self.gated = GatedGraphConv(self.hidden_size, num_layers=2)
self.e2s = Embedding2Score(self.hidden_size)
self.loss_function = nn.CrossEntropyLoss()
self.reset_parameters()
def __init__(self, node_dim: int, num_convs: int,
num_layers: Union[int, List[int]], num_edge_types: int):
super().__init__()
self.convs = []
if isinstance(num_layers, int):
num_layers = [num_layers] * num_convs
for i in range(num_convs):
self.convs.append(GatedGraphConv(node_dim, num_layers=1))
# Weight given to each edge type
self.edge_type_weights = torch.nn.Parameter(
torch.FloatTensor((1.0, ) * num_edge_types))
# Simple linear layer for the output of conv3
self.lin1 = torch.nn.Linear(node_dim, node_dim)
# Slope for leaky relus
self.neg_slope = 0.01
def init_model(self, n_class, feature_num):
num_layers = self.hyperparameters['num_layers']
hidden_size = int(2**self.hyperparameters['hidden'])
lr = self.hyperparameters['lr']
gated_conv_layers = int(self.hyperparameters['gated_conv_layers'])
self.input_linear = Linear(feature_num, hidden_size)
self.convs = torch.nn.ModuleList()
for i in range(num_layers - 1):
self.convs.append(
GatedGraphConv(out_channels=hidden_size,
num_layers=gated_conv_layers))
self.output_linear = Linear(hidden_size, n_class)
self.optimizer = torch.optim.Adam(self.parameters(),
lr=lr,
weight_decay=5e-4)
self = self.to('cuda')
torch.cuda.empty_cache()
def __init__(self, poi_size, embedding_matrix, struct_topology, user_history_data_dict):
super(Net, self).__init__()
self.verbose = True
self.use_cuda = True
self.embedding_dim = Parameters.type_embedding_dim
self.embedding_matrix = embedding_matrix
self.struct_topology = struct_topology
self.hidden_dim = Parameters.HIDDEN_SIZE
self.linear_size = 30
self.batch_size = 4
self.poi_size = poi_size
self.drop_out = Parameters.DROPOUT_RATE
self.num_classes = 5
self.n_epochs = 3
self.poi_size = embedding_matrix.shape[0]
self.user_history_data_dict = user_history_data_dict
self.conv3 = GatedGraphConv(self.hidden_dim, 3)
# self.conv4 = ChebConv(self.hidden_dim, 56, 2)
# self.conv5 = ChebConv(16, self.num_classes, 2)
self.fc1 = nn.Sequential(
nn.BatchNorm1d(self.hidden_dim * 2),
nn.Linear(self.hidden_dim * 2, self.linear_size),
nn.ReLU(),
nn.BatchNorm1d(self.linear_size),
nn.Dropout(self.drop_out)
)
self.fc2 = nn.Sequential(
nn.BatchNorm1d(self.hidden_dim * 2),
nn.Linear(self.hidden_dim * 2, self.linear_size),
nn.ReLU(),
nn.BatchNorm1d(self.linear_size),
nn.Dropout(self.drop_out),
nn.Linear(self.linear_size, self.num_classes)
)
self.embed = nn.Embedding(self.poi_size, self.embedding_dim, padding_idx=0)
self.embed.weight.data.copy_(torch.from_numpy(embedding_matrix))
self.bat_nor_embed = nn.BatchNorm1d(self.embedding_dim)
self.lstm = nn.LSTM(self.hidden_dim, self.hidden_dim, batch_first=True)