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ae419ed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | from __future__ import annotations
import argparse
import json
from common import load_config
from dynafall.train_eval import evaluate_checkpoint, train_model
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--dataset", required=True)
ap.add_argument("--methods", nargs="+", default=["lstm", "stgcn", "agcn", "ctrgcn", "posec3d", "tcnte", "dynafall"])
ap.add_argument("--epochs", type=int, default=None)
ap.add_argument("--config", default="configs/default.yaml")
ap.add_argument("--processed-dataset", default=None)
ap.add_argument("--out-root", default=None)
args = ap.parse_args()
cfg = load_config(args.config)
all_results = []
for method in args.methods:
out_dir = None
if args.out_root:
out_dir = f"{args.out_root}/{args.dataset}/{method}"
print(f"==> Training {method}")
train_model(args.dataset, method, cfg, epochs=args.epochs, out_dir=out_dir, processed_dataset=args.processed_dataset)
result = evaluate_checkpoint(args.dataset, method, cfg, out_dir=out_dir, processed_dataset=args.processed_dataset)
all_results.append(result)
print(json.dumps(result, indent=2))
print(json.dumps(all_results, indent=2))
if __name__ == "__main__":
main()
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