def __init__(self, config, ft, num_labels, res, H, device_num):
super(BertGAT, self).__init__(config)
self.device = torch.device('cuda:' + device_num)
self.bert = BertModel.from_pretrained('bert-base-uncased')
self.dropout = nn.Dropout(0.5)
self.ft = ft
self.num_labels = num_labels
self.gat = SpGAT(nfeat=H.shape[1], nclass=num_labels).to(self.device)
self.FCN = nn.Linear(768, num_labels)
self.softmax = nn.Softmax()
self.H = torch.tensor(H).float().to(self.device)
A = torch.tensor(gen_A(num_labels, res)).float().to(self.device)
self.adj = gen_adj(A).detach()
# self.FCN2 = nn.Linear(num_labels, num_labels)
self.apply(self.init_bert_weights)
def __init__(self, config, ft, num_labels, res, H, device_num):
super(BertGCN, self).__init__(config)
self.device = torch.device('cuda:' + device_num)
self.bert = BertModel.from_pretrained('bert-base-uncased')
self.dropout = nn.Dropout(0.5)
self.H = get_tensor(H, self.device)
self.ft = ft
self.num_labels = num_labels
self.FCN = nn.Linear(768, num_labels)
self.softmax = nn.Softmax(dim=1)
self.apply(self.init_bert_weights)
self.gcn_weight1 = Parameter(torch.Tensor(H.shape[1], 1500))
self.gcn_weight2 = Parameter(torch.Tensor(1500, 768))
self.lkrelu = nn.LeakyReLU(0.2)
self.A = torch.tensor(gen_A(num_labels, res)).float().to(self.device)
# self.A = Parameter(torch.from_numpy(gen_A(num_labels, res)).float()).to(self.device)
self.adj = gen_adj(self.A).detach()
def __init__(self, config, ft, num_labels, H, device_num, C, c_adj):
super(BertGCN_Cluster, self).__init__(config)
self.device = torch.device('cuda:' + device_num)
self.bert = BertModel.from_pretrained('bert-base-uncased')
self.dropout = nn.Dropout(0.5)
self.ft = ft
self.H = get_tensor(H, self.device) # m * 3072
self.C = get_tensor(C, self.device) # m * C
self.c_adj = gen_adj(get_tensor(c_adj, self.device)).detach() # C * C
self.num_labels = num_labels
self.FCN = nn.Linear(768, num_labels)
self.actv = nn.LeakyReLU(0.2)
# self.actv = nn.Tanh()
self.softmax = nn.Softmax(dim=1)
self.apply(self.init_bert_weights)
self.W1 = Parameter(torch.Tensor(H.shape[1], 768))
self.W2 = Parameter(torch.Tensor(768, 768))