parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--resume', type=str,
help='resume from model stored')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
if args.model == 'CNN_MLP':
model = CNN_MLP(args)
else:
model = RN(args)
model_dirs = './model'
bs = args.batch_size
input_img = torch.FloatTensor(bs, 3, 75, 75) #画像の大きさをテンソル化
input_qst = torch.FloatTensor(bs, 11) #input_qstのベクトルをテンソル化
label = torch.LongTensor(bs) #長さ64のテンソル，ダミーのテストラベル
if args.cuda:
model.cuda()
input_img = input_img.cuda()
input_qst = input_qst.cuda()
label = label.cuda()
args.dtype = torch.cuda.FloatTensor if args.cuda else torch.FloatTensor
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed) # Safe even if no GPU
from model import RN, CNN_MLP
from model_vigil import RFES, RFESH
if args.model == 'RFES':
model = RFES(args)
elif args.model == 'RFESH':
model = RFESH(args)
elif args.model == 'CNN_MLP':
model = CNN_MLP(args)
else:
model = RN(args)
print(args)
# For loading the data (possibly a symlink to relational-networks/data)
data_dirs = './data'
bs = args.batch_size
input_img = torch.FloatTensor(bs, 3, 75, 75)
input_qst = torch.FloatTensor(bs, 11)
label = torch.LongTensor(bs)
if args.cuda:
model.cuda()
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--resume', type=str,
help='resume from model stored')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
if args.model=='CNN_MLP':
model = CNN_MLP(args)
else:
model = RN(args)
model_dirs = './model'
bs = args.batch_size
input_img = torch.FloatTensor(bs, 3, 75, 75)
input_qst = torch.FloatTensor(bs, 11)
label = torch.LongTensor(bs)
if args.cuda:
model.cuda()
input_img = input_img.cuda()
input_qst = input_qst.cuda()
label = label.cuda()