def build_gcn(args,tasker):
gcn_args = u.Namespace(args.gcn_parameters)
gcn_args.feats_per_node = tasker.feats_per_node
if args.model == 'gcn':
return mls.Sp_GCN(gcn_args,activation = torch.nn.RReLU()).to(args.device)
elif args.model == 'skipgcn':
return mls.Sp_Skip_GCN(gcn_args,activation = torch.nn.RReLU()).to(args.device)
elif args.model == 'skipfeatsgcn':
return mls.Sp_Skip_NodeFeats_GCN(gcn_args,activation = torch.nn.RReLU()).to(args.device)
else:
assert args.num_hist_steps > 0, 'more than one step is necessary to train LSTM'
if args.model == 'lstmA':
return mls.Sp_GCN_LSTM_A(gcn_args,activation = torch.nn.RReLU()).to(args.device)
elif args.model == 'gruA':
return mls.Sp_GCN_GRU_A(gcn_args,activation = torch.nn.RReLU()).to(args.device)
elif args.model == 'lstmB':
return mls.Sp_GCN_LSTM_B(gcn_args,activation = torch.nn.RReLU()).to(args.device)
elif args.model == 'gruB':
return mls.Sp_GCN_GRU_B(gcn_args,activation = torch.nn.RReLU()).to(args.device)
elif args.model == 'egcn':
return egcn.EGCN(gcn_args, activation = torch.nn.RReLU()).to(args.device)
elif args.model == 'egcn_h':
return egcn_h.EGCN(gcn_args, activation = torch.nn.RReLU(), device = args.device)
elif args.model == 'skipfeatsegcn_h':
return egcn_h.EGCN(gcn_args, activation = torch.nn.RReLU(), device = args.device, skipfeats=True)
elif args.model == 'egcn_o':
return egcn_o.EGCN(gcn_args, activation = torch.nn.RReLU(), device = args.device)
else:
raise NotImplementedError('need to finish modifying the models')
def build_gcn(args,tasker):
gcn_args = u.Namespace(args.gcn_parameters)
gcn_args.feats_per_node = tasker.feats_per_node
if args.model == 'gcn':
return mls.Sp_GCN(gcn_args,activation = torch.nn.RReLU()).to(args.device)
else:
#assert args.num_hist_steps > 0, 'more than one step is necessary to train LSTM'
if args.model == 'egcn':
return egcn.EGCN(gcn_args, activation = torch.nn.RReLU()).to(args.device)
elif args.model == 'egcn_h':
return egcn_h.EGCN(gcn_args, activation = torch.nn.RReLU(), device = args.device)
else:
raise NotImplementedError('need to finish modifying the models')