def __init__(self, net, hidden_dim, num_gc_layers, alpha=0.5, beta=1.,
gamma=.1, projection_size = 256, projection_hidden_size = 4096, moving_average_decay = 0.99):
super(CLSA, self).__init__()
self.alpha = alpha
self.beta = beta
self.gamma = gamma
args = arg_parse()
self.prior = args.prior
self.embedding_dim = hidden_dim * num_gc_layers
self.online_encoder = net
self.target_encoder = None
self.target_ema_updater = EMA(moving_average_decay)
self.online_predictor = MLP(projection_size, projection_size, projection_hidden_size)
self.init_emb()
def seed_everything(seed=1234):
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
if __name__ == '__main__':
seed_everything()
from model import Net
from arguments import arg_parse
args = arg_parse()
target = args.target
dim = 64
epochs = 500
batch_size = 20
lamda = args.lamda
use_unsup_loss = args.use_unsup_loss
separate_encoder = args.separate_encoder
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'QM9')
transform = T.Compose([MyTransform(), Complete(), T.Distance(norm=False)])
dataset = QM9(path, transform=transform).shuffle()
print('num_features : {}\n'.format(dataset.num_features))
# Normalize targets to mean = 0 and std = 1.
