def __init__(self, args, activation, device='cpu', skipfeats=False):
super().__init__()
GRCU_args = u.Namespace({})
feats = [args.feats_per_node, args.layer_1_feats, args.layer_2_feats]
self.device = device
self.skipfeats = skipfeats
self.GRCU_layers = []
self._parameters = nn.ParameterList()
# 出力層 FC (ロジスティック回帰用)
self.FC = nn.Sequential(nn.Linear(args.layer_2_feats, args.output_dim),
nn.Sigmoid())
for i in range(1, len(feats)):
GRCU_args = u.Namespace({
'in_feats': feats[i - 1],
'out_feats': feats[i],
'activation': activation
})
grcu_i = GRCU(GRCU_args)
# print (i,'grcu_i', grcu_i)
self.GRCU_layers.append(grcu_i.to(self.device))
self._parameters.extend(list(self.GRCU_layers[-1].parameters()))
def __init__(self, args):
super().__init__()
self.args = args
cell_args = u.Namespace({})
cell_args.rows = args.in_feats
cell_args.cols = args.out_feats
self.evolve_weights = mat_GRU_cell(cell_args)
self.activation = self.args.activation
self.GCN_init_weights = Parameter(torch.Tensor(self.args.in_feats, self.args.out_feats))
self.reset_param(self.GCN_init_weights)
if opt.manualSeed is None:
opt.manualSeed = random.randint(1, 10000)
print("Random Seed: ", opt.manualSeed)
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
opt.dataroot = InputDir
if opt.cuda:
torch.cuda.manual_seed_all(opt.manualSeed)
opt.L = opt.init_L
gcn_parameters = ['feats_per_node', 'feats_per_node_min', 'feats_per_node_max', 'layer_1_feats', 'layer_1_feats_min', 'layer_1_feats_max', 'layer_2_feats', 'layer_2_feats_same_as_l1', 'k_top_grcu', 'num_layers', 'lstm_l1_layers', 'lstm_l1_feats', 'lstm_l1_feats_min', 'lstm_l1_feats_max', 'lstm_l2_layers', 'lstm_l2_feats', 'lstm_l2_feats_same_as_l1', 'cls_feats', 'cls_feats_min', 'cls_feats_max', 'output_dim']
gcn_args = {k:opt.__dict__[k] for k in gcn_parameters}
gcn_args = u.Namespace(gcn_args)
gcn_args.feats_per_node = repeat1_attribute_prediction_exist_PTS_utilize_disappeared_state_dim
opt.device = 'cuda' if opt.cuda else 'cpu'
def main(opt):
train_dataset = BADataset(opt.dataroot, opt.L, True, False, False)
train_dataloader = BADataloader(train_dataset, batch_size=opt.batchSize, \
shuffle=True, num_workers=opt.workers, drop_last=True)
valid_dataset = BADataset(opt.dataroot, opt.L, False, True, False)
valid_dataloader = BADataloader(valid_dataset, batch_size=opt.batchSize, \
shuffle=True, num_workers=opt.workers, drop_last=True)
test_dataset = BADataset(opt.dataroot, opt.L, False, False, True)
test_dataloader = BADataloader(test_dataset, batch_size=opt.batchSize, \
shuffle=True, num_workers=opt.workers, drop_last=True)
