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