Remove self.device to prevent import requirement of torch for predict…

…ion (#617)

cornac/models/lightgcn/lightgcn.py

@@ -62,11 +62,10 @@ def forward(self, g, feat_dict):
 
 
 class Model(nn.Module):
- def __init__(self, g, in_size, num_layers, lambda_reg, device=None):
+ def __init__(self, g, in_size, num_layers, lambda_reg):
  super(Model, self).__init__()
  self.norm_dict = dict()
  self.lambda_reg = lambda_reg
- self.device = device
 
  self.layers = nn.ModuleList([GCNLayer() for _ in range(num_layers)])
 

cornac/models/lightgcn/recom_lightgcn.py

@@ -124,21 +124,21 @@ def fit(self, train_set, val_set=None):
  from .lightgcn import Model
  from .lightgcn import construct_graph
 
- self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  if self.seed is not None:
  torch.manual_seed(self.seed)
  if torch.cuda.is_available():
  torch.cuda.manual_seed_all(self.seed)
 
  graph = construct_graph(train_set, self.total_users, self.total_items).to(
- self.device
+ device
  )
  model = Model(
  graph,
  self.emb_size,
  self.num_layers,
  self.lambda_reg,
- ).to(self.device)
+ ).to(device)
 
  optimizer = torch.optim.Adam(model.parameters(), lr=self.learning_rate)