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import os
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from src.models.tools.jointstypes import JOINTSTYPES
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from .base import ArgumentParser, add_cuda_options, adding_cuda
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from .tools import load_args
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from .dataset import add_dataset_options
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def construct_figname(parameters):
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figname = "fig_{:03d}"
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return figname
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def add_visualize_options(parser):
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group = parser.add_argument_group('Visualization options')
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group.add_argument("--num_actions_to_sample", default=5, type=int, help="num actions to sample")
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group.add_argument("--num_samples_per_action", default=5, type=int, help="num samples per action")
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group.add_argument("--fps", default=20, type=int, help="FPS for the rendering")
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group.add_argument("--appearance_mode", default='motionclip', choices=['motionclip', 'original'], type=str,
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help="How the stick figures will appear")
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group.add_argument("--force_visu_joints", dest='force_visu_joints', action='store_true',
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help="if we want to visualize joints even if it is rotation")
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group.add_argument('--no-force_visu_joints', dest='force_visu_joints', action='store_false',
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help="if we don't want to visualize joints even if it is rotation")
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group.set_defaults(force_visu_joints=True)
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group.add_argument("--jointstype", default="smpl", choices=JOINTSTYPES,
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help="Jointstype for training with xyz")
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group.add_argument('--vertstrans', dest='vertstrans', action='store_true', help="Training with vertex translations")
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group.add_argument('--no-vertstrans', dest='vertstrans', action='store_false',
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help="Training without vertex translations")
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group.set_defaults(vertstrans=False)
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group.add_argument("--noise_same_action", default="random",
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choices=["interpolate", "random", "same"],
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help="inside one action, sample several noise or interpolate it")
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group.add_argument("--noise_diff_action", default="random",
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choices=["random", "same"],
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help="use the same noise or different noise for every actions")
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group.add_argument("--duration_mode", default="mean",
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choices=["mean", "interpolate"],
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help="use the same noise or different noise for every actions")
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group.add_argument("--reconstruction_mode", default="ntf",
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choices=["tf", "ntf", "both"],
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help="reconstruction: teacher forcing or not or both")
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group.add_argument("--decoder_test", default="new",
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choices=["new", "diffaction", "diffduration", "interpolate_action"],
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help="what is the test we want to do")
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group.add_argument("--fact_latent", type=int, default=1,
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help="factor for max latent space")
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group.add_argument("--images_dir", type=str, default='./action_images',
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help="dir with images for clip visualization")
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group.add_argument("--input_file", default=None, help="Input txt/csv file defining generation. For more info, see README.")
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group.add_argument("--zero_global_orient", action='store_true', help="will set global orientation to zero")
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group.add_argument("--ae_after_generation", action='store_true', help="Apply auto encoding after generation to project motion into motion manifold.")
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def parser(checkpoint=True):
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parser = ArgumentParser()
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if checkpoint:
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parser.add_argument("checkpointname")
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else:
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add_dataset_options(parser)
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add_visualize_options(parser)
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add_cuda_options(parser)
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opt = parser.parse_args()
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if checkpoint:
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newparameters = {key: val for key, val in vars(opt).items() if val is not None}
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folder, checkpoint = os.path.split(newparameters["checkpointname"])
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parameters = load_args(os.path.join(folder, "opt.yaml"))
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parameters.update(newparameters)
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else:
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parameters = {key: val for key, val in vars(opt).items() if val is not None}
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adding_cuda(parameters)
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if checkpoint:
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parameters["figname"] = construct_figname(parameters)
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epoch = int(checkpoint.split("_")[-1].split('.')[0])
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return parameters, folder, checkpoint, epoch
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else:
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return parameters |
