parser.add_argument('--weightdecay', type=float, default=0.0)
parser.add_argument('--model', type=str, default='vgg')
parser.add_argument('--resume', type=str, default=None)
parser.add_argument('--datadir',
type=str,
default='/Users/wjf/datasets/SVHN/train25k_test70k')
parser.add_argument('--logdir', type=str, default='logs/GD')
args = parser.parse_args()
logger = LogSaver(args.logdir)
logger.save(str(args), 'args')
# data
dataset = SVHN(args.datadir)
logger.save(str(dataset), 'dataset')
train_list = dataset.getTrainList(args.batchsize, True)
test_list = dataset.getTestList(1000, True)
# model
start_iter = 0
lr = args.lr
if args.model == 'resnet':
from resnet import ResNet18
model = ResNet18().cuda()
elif args.model == 'vgg':
from vgg import vgg11
model = vgg11().cuda()
else:
raise NotImplementedError()
criterion = torch.nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(),
parser.add_argument('--list-size', type=int, default=5000)
parser.add_argument('--sigma', type=float, default=1e-3)
parser.add_argument('--resume', type=str, default=None)
parser.add_argument('--datadir',
type=str,
default='/home/wjf/datasets/SVHN/train25000_test70000')
parser.add_argument('--logdir', type=str, default='logs/GLD')
args = parser.parse_args()
logger = LogSaver(args.logdir)
logger.save(str(args), 'args')
# data
dataset = SVHN(args.datadir)
logger.save(str(dataset), 'dataset')
train_list = dataset.getTrainList(args.list_size, True)
test_list = dataset.getTestList(1000, True)
# model
start_iter = 0
model = vgg11().cuda()
logger.save(str(model), 'classifier')
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr)
logger.save(str(optimizer), 'optimizer')
if args.resume:
checkpoint = torch.load(args.resume)
start_iter = checkpoint['iter']
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
parser.add_argument('--ckptdir', type=str, default=None)
parser.add_argument('--start', type=int, default=3)
parser.add_argument('--end', type=int, default=16)
parser.add_argument('--datadir',
type=str,
default='/home/wjf/datasets/SVHN/train25000_test70000')
parser.add_argument('--logdir', type=str, default='flat_logs/GD')
args = parser.parse_args()
logger = LogSaver(args.logdir)
logger.save(str(args), 'args')
# data
dataset = SVHN(args.datadir)
logger.save(str(dataset), 'dataset')
train_list = dataset.getTrainList(5000, True)
test_list = dataset.getTestList(5000, True)
# model
model = vgg11().cuda()
logger.save(str(model), 'classifier')
criterion = nn.CrossEntropyLoss().cuda()
# writer
writer = SummaryWriter(args.logdir)
# eval flatness
torch.backends.cudnn.benchmark = True
for i in range(args.start, args.end):
ckpt_file = os.path.join(args.ckptdir,
'iter-' + str(i * 1000) + '.pth.tar')
