def __init__(self, config):
super(MethodGraphBertNodeClassification, self).__init__(config)
self.config = config
self.bert = MethodGraphBert(config)
self.res_h = torch.nn.Linear(config.x_size, config.hidden_size)
self.res_y = torch.nn.Linear(config.x_size, config.y_size)
self.cls_y = torch.nn.Linear(config.hidden_size, config.y_size)
self.init_weights()
def __init__(self, config):
super(MethodGraphBertNodeConstruct, self).__init__(config)
self.place = torch.device('cuda:0')
self.config = config
self.bert = MethodGraphBert(config).to(device=self.place)
self.cls_y = torch.nn.Linear(config.hidden_size, config.x_size).to(device=self.place)
self.init_weights()
def __init__(self, config, pretrained_path):
super(MethodGraphBertGraphRecovery, self).__init__(config)
self.place = torch.device('cuda:0')
self.config = config
self.bert = MethodGraphBert(config).to(self.place)
if pretrained_path is not None:
print("Load pretraiend model from {}".format(pretrained_path))
self.bert.from_pretrained(pretrained_path).to(self.place)
self.init_weights()
def __init__(self, config, pretrained_path, dataset_name):
super(MethodGraphBertNodeClassification, self).__init__(config)
self.place = torch.device('cuda:0')
self.config = config
self.bert = MethodGraphBert(config).to(device=self.place)
# load from pretrained model if necessary
if pretrained_path is not None:
self.bert = self.bert.from_pretrained(pretrained_path).to(device=self.place)
self.res_h = torch.nn.Linear(config.x_size, config.hidden_size).to(device=self.place)
self.res_y = torch.nn.Linear(config.x_size, config.y_size).to(device=self.place)
self.cls_y = torch.nn.Linear(config.hidden_size, config.y_size).to(device=self.place)
self.init_weights()
self.evaluator = Evaluator(dataset_name)
def __init__(self, config, pretrained_path):
super(MethodGraphBertNodeClassification, self).__init__(config)
self.device = torch.device('cuda:0')
self.config = config
self.bert = MethodGraphBert(config).to(self.device)
if pretrained_path is not None:
print("Load pretrained model from {}".format(pretrained_path))
self.bert.from_pretrained(pretrained_path).to(self.device)
self.res_h = torch.nn.Linear(config.x_size,
config.hidden_size).to(self.device)
self.res_y = torch.nn.Linear(config.x_size,
config.y_size).to(self.device)
self.cls_y = torch.nn.Linear(config.hidden_size,
config.y_size).to(self.device)
self.init_weights()
def __init__(self, config):
super(MethodGraphBertGraphClustering, self).__init__(config)
self.config = config
self.bert = MethodGraphBert(config)
self.init_weights()
def __init__(self, config):
super(MethodGraphBertGraphRecovery, self).__init__(config)
self.config = config
self.bert = MethodGraphBert(config)
self.init_weights()