type=int,
default=0,
help='index of the image to visualize activations for')
parser.add_argument('--vis-alayer',
type=int,
default=0,
help='index of the layer to visualize (0-23)')
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)
val_ds = CarvanaDataset()
val_ds.initialize(args, phase='val')
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
val_loader = DataLoader(val_ds,
batch_size=args.batch_size,
drop_last=True,
**kwargs)
model = CarvanaFvbNet()
print(model)
print('\nloading model params')
model.load_state_dict(
torch.load('./checkpoints/latest_{}.pth'.format(args.which_epoch)))
if args.cuda:
model.cuda()
print('\nload complete!')
help='SGD momentum (default: 0.5)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
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')
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)
test_ds = CarvanaDataset()
test_ds.initialize(args, phase='test')
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
test_loader = DataLoader(test_ds, batch_size=args.batch_size, drop_last=True, **kwargs)
model = CarvanaFvbNet()
model.load_state_dict(torch.load('./checkpoints/latest_{}.pth'.format(args.which_epoch)))
if args.cuda:
model.cuda()
def test():
model.eval()
print(model)
test_loss = 0
correct = 0
for data, target, dsidx in test_loader:
if args.cuda:
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')
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)
train_ds = CarvanaDataset()
train_ds.initialize(args, phase='train')
test_ds = CarvanaDataset()
test_ds.initialize(args, phase='test')
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
train_loader = DataLoader(train_ds,
batch_size=args.batch_size,
drop_last=True,
shuffle=True,
**kwargs)
test_loader = DataLoader(test_ds,
batch_size=args.batch_size,
drop_last=True,
**kwargs)
model = CarvanaFvbNet()
if args.cuda: