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96d8696 | 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 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | #!/usr/bin/env python
"""Stage 03 — train.
Trains only the projections + fusion + regression head on cached embeddings
(stage 02 output). Backbones are never loaded here.
Usage:
python scripts/03_train.py --config configs/base.yaml
"""
import argparse
import sys
from pathlib import Path
import numpy as np
import torch
from torch.utils.data import DataLoader, random_split
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from src.data.dataset import EmbeddingDataset
from src.models.price_model import PriceModel
from src.training.trainer import Trainer
from src.utils.config import load_config
from src.utils.exceptions import PricePredictorError
from src.utils.logging import get_logger
from src.utils.seed import set_seed
logger = get_logger(__name__)
def run(config_path: str) -> None:
config = load_config(config_path)
set_seed(config["seed"])
data_cfg = config["data"]
embeddings_dir = Path(data_cfg["embeddings_dir"])
price_path = embeddings_dir / "train_price.npy"
if not price_path.exists():
raise PricePredictorError(
f"{price_path} not found — run scripts/02_extract_embeddings.py first"
)
prices = np.load(price_path)
full_dataset = EmbeddingDataset(
text_embeddings_path=str(embeddings_dir / "train_text.npy"),
image_embeddings_path=str(embeddings_dir / "train_image.npy"),
prices=prices,
)
val_split = config["training"].get("val_split", 0.15)
n_val = max(1, int(len(full_dataset) * val_split))
n_train = len(full_dataset) - n_val
if n_train <= 0:
raise PricePredictorError(
f"val_split={val_split} leaves zero training rows for dataset of size {len(full_dataset)}"
)
generator = torch.Generator().manual_seed(config["seed"])
train_ds, val_ds = random_split(full_dataset, [n_train, n_val], generator=generator)
batch_size = config["training"]["batch_size"]
train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, drop_last=True)
val_loader = DataLoader(val_ds, batch_size=batch_size, shuffle=False)
model = PriceModel.from_config(config)
trainer = Trainer(model, config["training"], checkpoint_dir=config["checkpoint_dir"])
history = trainer.fit(train_loader, val_loader)
logger.info("Training complete. Best val_smape=%.4f", trainer.best_val_loss)
return history
def main() -> None:
parser = argparse.ArgumentParser(description="Stage 03: train fusion + head")
parser.add_argument("--config", default="configs/base.yaml")
args = parser.parse_args()
try:
run(args.config)
except PricePredictorError as e:
logger.error("Training failed: %s", e)
sys.exit(1)
except Exception as e:
logger.exception("Unexpected error during training: %s", e)
sys.exit(1)
if __name__ == "__main__":
main()
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