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)