def get_save_depth_params():
parser = get_initial_parser()
params = parser.parse_args()
params.net_model = 'all'
params.proceed_step = RunSteps.COLORIZED_DEPTH_SAVE
params = init_save_dirs(params)
return params
def get_finetuned_extraction_params():
parser = get_initial_parser()
parser.add_argument("--batch-size", dest="batch_size", default=64, type=int)
parser.add_argument("--split-no", dest="split_no", default=1, type=int, choices=range(1, 11), help="Split number")
params = parser.parse_args()
params.proceed_step = RunSteps.FINE_EXTRACTION
return params
def get_recursive_params(proceed_step):
parser = get_initial_parser()
parser.add_argument("--split-no",
dest="split_no",
default=1,
type=int,
choices=range(1, 11),
help="Split number")
parser.add_argument("--num-rnn",
dest="num_rnn",
default=128,
type=int,
help="Number of RNN")
parser.add_argument("--save-features",
dest="save_features",
default=0,
type=int,
choices=[0, 1])
parser.add_argument("--batch-size",
dest="batch_size",
default=1000,
type=int)
parser.add_argument(
"--trial",
dest="trial",
default=0,
type=int,
help=
"Trial number is for running the same model with the same params for evaluation "
"the effect of randomness.")
parser.add_argument(
"--reuse-randoms",
dest="reuse_randoms",
default=1,
choices=[0, 1],
type=int,
help="Handles whether the random weights are gonna save/load or not")
parser.add_argument("--fusion-levels",
dest="fusion_levels",
default=0,
choices=[0, 1],
type=int,
help="Handles whether fusion is performed")
parser.add_argument("--load-features",
dest="load_features",
default=0,
type=int,
choices=[0, 1])
parser.add_argument("--pooling",
dest="pooling",
default=Pools.RANDOM,
choices=Pools.ALL,
type=str.lower,
help="Pooling type")
params = parser.parse_args()
params.proceed_step = proceed_step
return params
def get_extraction_params():
parser = get_initial_parser()
parser.add_argument("--batch-size",
dest="batch_size",
default=64,
type=int)
params = parser.parse_args()
params.proceed_step = RunSteps.FIX_EXTRACTION
return params
def get_finetune_params():
parser = get_initial_parser()
parser.add_argument("--split-no", dest="split_no", default=1, type=int, choices=range(1, 11), help="Split number")
parser.add_argument("--batch-size", dest="batch_size", default=16, type=int)
parser.add_argument("--lr", dest="lr", default=0.0001, type=float, help='Initial learning rate')
parser.add_argument("--momentum", dest="momentum", default=0.95, type=float, help='Momentum rate')
parser.add_argument("--step-size", dest="step_size", default=10, type=int,
help='Number of epoch for each learning rate decay')
parser.add_argument("--gamma", dest="gamma", default=0.1, type=float, help="Factor rate of learning rate decay")
parser.add_argument("--num-epochs", dest="num_epochs", default=40, type=int)
parser.add_argument("--trial", dest="trial", default=0, type=int,
help="Trial number is used to run the same model with the same params to evaluate "
"the effect of randomness.")
params = parser.parse_args()
params.proceed_step = RunSteps.FINE_TUNING
return params
def save_sunrgbd_scene():
parser = get_initial_parser()
params = parser.parse_args()
params.debug_mode = 0
params.dataset_path = "../data/sunrgbd/"
if params.data_type == DataTypes.RGB:
params.data_type = DataTypesSUNRGBD.RGB
elif params.data_type == DataTypes.Depth:
params.data_type = DataTypesSUNRGBD.Depth
else:
print('{}{}The parameter {}--data-type{} should be {} RGB or Depth{}!{}'.
format(PrForm.BOLD, PrForm.RED, PrForm.BLUE, PrForm.RED, PrForm.GREEN, PrForm.RED, PrForm.END_FORMAT))
return
params.proceed_step = RunSteps.SAVE_SUNRGBD
logfile_name = params.log_dir + '/' + params.proceed_step + '/' + params.data_type + '_save.log'
init_logger(logfile_name, params)
process_dataset_save(params)