'resnet101':
lambda: PSPNet(sizes=(1, 2, 3, 6),
psp_size=2048,
deep_features_size=1024,
backend='resnet101'),
'resnet152':
lambda: PSPNet(sizes=(1, 2, 3, 6),
psp_size=2048,
deep_features_size=1024,
backend='resnet152')
}
# Create dataloader
dataset = PanoDataset(root_dir=args.root_dir,
cat_list=[*args.input_cat, 'edge', 'cor'],
flip=False,
rotate=False,
gamma=False,
return_filenames=True)
# Prepare model
backend = args.backend.lower()
net = models[backend]()
net = nn.DataParallel(net).to(device)
net.load_state_dict(torch.load(args.ckpt))
# Start evaluation
test_losses = StatisticDict()
test_pano_losses = StatisticDict()
test_2d3d_losses = StatisticDict()
for ith, datas in enumerate(dataset):
print('processed %d batches out of %d' % (ith, len(dataset)),
type=int,
help='numbers of input channels')
parser.add_argument('--num_workers',
default=6,
type=int,
help='numbers of workers for dataloaders')
parser.add_argument('--batch_size',
default=4,
type=int,
help='mini-batch size')
args = parser.parse_args()
device = torch.device(args.device)
# Create dataloader
dataset = PanoDataset(root_dir=args.root_dir,
cat_list=[*args.input_cat, 'edge', 'cor'],
flip=False,
rotate=False)
loader = DataLoader(dataset,
args.batch_size,
shuffle=False,
drop_last=False,
num_workers=args.num_workers,
pin_memory=args.device.startswith('cuda'))
# Prepare model
encoder = Encoder(args.input_channels).to(device)
edg_decoder = Decoder(skip_num=2, out_planes=3).to(device)
cor_decoder = Decoder(skip_num=3, out_planes=1).to(device)
encoder.load_state_dict(torch.load('%s_encoder.pth' % args.path_prefix))
edg_decoder.load_state_dict(torch.load('%s_edg_decoder.pth' %
args.path_prefix))
default=20,
help='iterations frequency to display')
parser.add_argument('--save_every',
type=int,
default=5,
help='epochs frequency to save state_dict')
args = parser.parse_args()
device = torch.device('cpu' if args.no_cuda else 'cuda')
np.random.seed(args.seed)
torch.manual_seed(args.seed)
os.makedirs(os.path.join(args.ckpt, args.id), exist_ok=True)
# Create dataloader
dataset_train = PanoDataset(root_dir=args.root_dir_train,
cat_list=[*args.input_cat, 'edge', 'cor'],
flip=not args.no_flip,
rotate=not args.no_rotate,
gamma=args.gamma,
noise=args.noise)
dataset_valid = PanoDataset(root_dir=args.root_dir_valid,
cat_list=[*args.input_cat, 'edge', 'cor'],
flip=False,
rotate=False,
gamma=False)
loader_train = DataLoader(dataset_train,
args.batch_size_train,
shuffle=True,
drop_last=True,
num_workers=args.num_workers,
pin_memory=not args.no_cuda)
loader_valid = DataLoader(dataset_valid,
args.batch_size_valid,
help='epochs frequency to save state_dict')
args = parser.parse_args()
device = torch.device(
'cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu')
print('device:{}'.format(device))
np.random.seed(args.seed)
torch.manual_seed(args.seed)
os.makedirs(os.path.join(args.ckpt, args.id), exist_ok=True)
# Create dataloader
dataset_train = PanoDataset(root_dir=args.root_dir_train,
cat_list=[*args.input_cat, 'mfc'],
flip=not args.no_flip,
rotate=not args.no_rotate,
gamma=not args.no_gamma,
noise=args.noise,
contrast=args.contrast)
dataset_valid = PanoDataset(root_dir=args.root_dir_valid,
cat_list=[*args.input_cat, 'mfc'],
flip=False,
rotate=False,
gamma=False,
noise=False,
contrast=False)
loader_train = DataLoader(dataset_train,
args.batch_size_train,
shuffle=True,
drop_last=True,
num_workers=args.num_workers,