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parser = argparse.ArgumentParser()
parser.add_argument("--conf",
help="path to json config file for hyperparameters",
type=str,
default='../config/config.json')
parser.add_argument("--debug",
help="disable all disk writing processes.",
action='store_true')
parser.add_argument("--preprocess_workers",
help="number of processes to spawn for preprocessing",
type=int,
default=0)
# Model Parameters
parser.add_argument("--offline_scene_graph",
help="whether to precompute the scene graphs offline, options are 'no' and 'yes'",
type=str,
default='yes')
parser.add_argument("--dynamic_edges",
help="whether to use dynamic edges or not, options are 'no' and 'yes'",
type=str,
default='yes')
parser.add_argument("--edge_state_combine_method",
help="the method to use for combining edges of the same type",
type=str,
default='sum')
parser.add_argument("--edge_influence_combine_method",
help="the method to use for combining edge influences",
type=str,
default='attention')
parser.add_argument('--edge_addition_filter',
nargs='+',
help="what scaling to use for edges as they're created",
type=float,
default=[0.25, 0.5, 0.75, 1.0]) # We don't automatically pad left with 0.0, if you want a sharp
# and short edge addition, then you need to have a 0.0 at the
# beginning, e.g. [0.0, 1.0].
parser.add_argument('--edge_removal_filter',
nargs='+',
help="what scaling to use for edges as they're removed",
type=float,
default=[1.0, 0.0]) # We don't automatically pad right with 0.0, if you want a sharp drop off like
# the default, then you need to have a 0.0 at the end.
parser.add_argument('--override_attention_radius',
action='append',
help='Specify one attention radius to override. E.g. "PEDESTRIAN VEHICLE 10.0"',
default=[])
parser.add_argument('--incl_robot_node',
help="whether to include a robot node in the graph or simply model all agents",
action='store_true')
parser.add_argument('--map_encoding',
help="Whether to use map encoding or not",
action='store_true')
parser.add_argument('--augment',
help="Whether to augment the scene during training",
action='store_true')
parser.add_argument('--node_freq_mult_train',
help="Whether to use frequency multiplying of nodes during training",
action='store_true')
parser.add_argument('--node_freq_mult_eval',
help="Whether to use frequency multiplying of nodes during evaluation",
action='store_true')
parser.add_argument('--scene_freq_mult_train',
help="Whether to use frequency multiplying of nodes during training",
action='store_true')
parser.add_argument('--scene_freq_mult_eval',
help="Whether to use frequency multiplying of nodes during evaluation",
action='store_true')
parser.add_argument('--scene_freq_mult_viz',
help="Whether to use frequency multiplying of nodes during evaluation",
action='store_true')
parser.add_argument('--no_edge_encoding',
help="Whether to use neighbors edge encoding",
action='store_true')
# Data Parameters
parser.add_argument("--data_dir",
help="what dir to look in for data",
type=str,
default='../experiments/processed')
parser.add_argument("--train_data_dict",
help="what file to load for training data",
type=str,
default='train.pkl')
parser.add_argument("--eval_data_dict",
help="what file to load for evaluation data",
type=str,
default='val.pkl')
parser.add_argument("--log_dir",
help="what dir to save training information (i.e., saved models, logs, etc)",
type=str,
default='../experiments/logs')
parser.add_argument("--log_tag",
help="tag for the log folder",
type=str,
default='')
parser.add_argument('--device',
help='what device to perform training on',
type=str,
default='cuda:0')
parser.add_argument("--eval_device",
help="what device to use during evaluation",
type=str,
default=None)
# Training Parameters
parser.add_argument("--train_epochs",
help="number of iterations to train for",
type=int,
default=1)
parser.add_argument('--batch_size',
help='training batch size',
type=int,
default=256)
parser.add_argument('--eval_batch_size',
help='evaluation batch size',
type=int,
default=256)
parser.add_argument('--k_eval',
help='how many samples to take during evaluation',
type=int,
default=25)
parser.add_argument('--seed',
help='manual seed to use, default is 123',
type=int,
default=123)
parser.add_argument('--eval_every',
help='how often to evaluate during training, never if None',
type=int,
default=1)
parser.add_argument('--vis_every',
help='how often to visualize during training, never if None',
type=int,
default=1)
parser.add_argument('--save_every',
help='how often to save during training, never if None',
type=int,
default=1)
args = parser.parse_args() |