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Running on Zero
Running on Zero
File size: 1,891 Bytes
e8ae657 | 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 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | import argparse
import torch
from src.training.config import TrainingConfig
from src.training.trainer import Trainer
def parse_args():
parser = argparse.ArgumentParser(
description="Train the multi-task CV pipeline"
)
parser.add_argument(
"--epochs", type=int, default=30,
help="Number of training epochs"
)
parser.add_argument(
"--batch-size", type=int, default=32,
help="Batch size"
)
parser.add_argument(
"--lr", type=float, default=1e-3,
help="Learning rate"
)
parser.add_argument(
"--lambda-cls", type=float, default=1.0,
help="Classification loss weight"
)
parser.add_argument(
"--lambda-det", type=float, default=5.0,
help="Detection loss weight"
)
parser.add_argument(
"--max-train-samples", type=int, default=None,
help="Cap training samples (None = full dataset)"
)
parser.add_argument(
"--max-val-samples", type=int, default=None,
help="Cap validation samples (None = full dataset)"
)
parser.add_argument(
"--experiment-name", type=str, default="multitask_v1",
help="Name for this training run"
)
parser.add_argument(
"--voc-root", type=str, default="data/VOCdevkit/VOC2012",
help="Path to VOC2012 root"
)
return parser.parse_args()
def main():
args = parse_args()
config = TrainingConfig(
voc_root=args.voc_root,
num_epochs=args.epochs,
batch_size=args.batch_size,
learning_rate=args.lr,
lambda_cls=args.lambda_cls,
lambda_det=args.lambda_det,
max_train_samples=args.max_train_samples,
max_val_samples=args.max_val_samples,
experiment_name=args.experiment_name,
)
trainer = Trainer(config)
trainer.train()
if __name__ == "__main__":
main() |