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import argparse
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
from pathlib import Path
from toolbox import Toolbox
from utils.argutils import print_args
from utils.default_models import ensure_default_models
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description="Runs the toolbox.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("--run_id", type=str, default="20230609", help= \
"Name for this model. By default, training outputs will be stored to saved_models/<run_id>/. If a model state "
"from the same run ID was previously saved, the training will restart from there. Pass -f to overwrite saved "
"states and restart from scratch.")
parser.add_argument("-d", "--datasets_root", type=Path, help= \
"Path to the directory containing your datasets. See toolbox/__init__.py for a list of "
"supported datasets.", default=None)
parser.add_argument("-m", "--models_dir", type=Path, default="saved_models",
help="Directory containing all saved models")
parser.add_argument("--cpu", action="store_true", help=\
"If True, all inference will be done on CPU")
parser.add_argument("--seed", type=int, default=None, help=\
"Optional random number seed value to make toolbox deterministic.")
args = parser.parse_args()
arg_dict = vars(args)
print_args(args, parser)
# Hide GPUs from Pytorch to force CPU processing
if arg_dict.pop("cpu"):
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
# Remind the user to download pretrained models if needed
ensure_default_models(args.run_id, args.models_dir)
# Launch the toolbox
Toolbox(**arg_dict)
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