help='number of training images per object category')
parser.add_argument('--num_val',
type=int,
default=100,
help='number of validation images per object category')
parser.add_argument('--loaders',
type=int,
default=4,
help='number of parallel data loading processes')
parser.add_argument('--batch_size', type=int, default=32)
args = parser.parse_args()
pipeline.initialize(args)
## load model : image --> reflectance x normals x depth x lighting
model = models.Decomposer().cuda()
## get a data loader for train and val sets
train_set = pipeline.IntrinsicDataset(args.data_path,
args.train_sets,
args.intrinsics,
array=args.array,
size_per_dataset=args.num_train)
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=args.batch_size,
num_workers=args.loaders,
shuffle=True)
val_set = pipeline.IntrinsicDataset(args.data_path,
args.val_sets,
args.intrinsics,
default=100,
help='number of training images per object category')
parser.add_argument('--num_val',
type=int,
default=100,
help='number of validation images per object category')
parser.add_argument('--loaders',
type=int,
default=4,
help='number of parallel data loading processes')
parser.add_argument('--batch_size', type=int, default=32)
args = parser.parse_args()
print("inside ai function")
## load model : image --> reflectance x normals x depth x lighting
model = models.Decomposer()
train_set = pipeline.IntrinsicDataset(args.data_path,
args.train_sets,
args.intrinsics,
array=args.array,
size_per_dataset=args.num_train)
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=args.batch_size,
num_workers=args.loaders,
shuffle=True)
val_set = pipeline.IntrinsicDataset(args.data_path,
args.val_sets,
args.intrinsics,
array=args.array,
size_per_dataset=args.num_val)
