fusion_data_generation_script

    parser.add_argument('--image_downsampling', type=float, default=4.0,
                        help='image downsampling rate to speed up training and reduce overfitting')
    parser.add_argument('--network_downsampling', type=int, default=64,
                        help='network downsampling rate')
    parser.add_argument('--input_size', nargs='+', type=int,
                        help='input size for the network')
    parser.add_argument('--batch_size', type=int, default=8, help='batch size for testing')
    parser.add_argument('--num_workers', type=int, default=8, help='number of workers for input data loader')
    parser.add_argument('--visible_interval', type=int, default=5,
                        help='range for propagating point visibility information')
    parser.add_argument('--inlier_percentage', type=float, default=0.998,
                        help='percentage of inliers of SfM point clouds (for pruning some outliers)')
    parser.add_argument('--load_intermediate_data', action='store_true', help='whether to load intermediate data')
    parser.add_argument('--display_architecture', action='store_true', help='display the network architecture')
    parser.add_argument('--trained_model_path', type=str, required=True,
                        help='path to the trained model')
    parser.add_argument('--data_root', type=str, required=True,
                        help='root storing the video and sparse reconstruction data')
    parser.add_argument('--sequence_root', type=str, default=None,
                        help='root of the sequence')
    parser.add_argument('--patient_id', nargs='+', type=int,
                        help='list patient id')
    parser.add_argument('--precompute_root', type=str, required=True,
                        help='root of the precompute data')


--image_downsampling
4.0
--network_downsampling
64
--input_size
256 320
--batch_size
1
--num_workers
1
--visible_interval
5
--inlier_percentage
0.9
--load_intermediate_data
--trained_model_path
"D:\Data\example_training_data_root\Train\depth_train_6_28_23_53\checkpoint_model_epoch_26_-6.794628364205362_-4.715754954576502_-2.534272967725988_0.4329575656577947_0.022441990300314515.pt"
--data_root
"D:\Data\example_training_data_root\example_training_data_root_2"
--sequence_root
"D:\Data\example_training_data_root\example_training_data_root_2\1\_start_002603_end_002984_stride_1000_segment_00"
--patient_id
1
--precompute_root
"D:\Data\example_training_data_root\Train\precompute"