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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 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | #!/usr/bin/env python
"""Stage 05 — predict on the test set, from cached embeddings.
The test set was already fully embedded by stage 02 (test_text.npy,
test_image.npy) — re-running EmbeddingGemma/SigLIP2 here would repeat that
expensive GPU work for identical output. This script loads the cached
embeddings and runs only the small trained model (projections + fusion +
head), the same way stages 03/04 do. That's why this is fast: no encoder
loading, no HTTP calls to Hugging Face, no per-row image decoding.
The live encoder chain (src/inference/predictor.py) still exists and is
still correct — but only for genuinely new, never-cached inputs, e.g. a
live API request for a brand-new product. See api/main.py / frontend/app.py
for that path. Don't use Predictor for a batch that was already embedded.
Usage:
python scripts/05_predict.py --config configs/base.yaml
"""
import argparse
import sys
from pathlib import Path
import pandas as pd
import torch
from torch.utils.data import DataLoader
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.utils.config import load_config
from src.utils.exceptions import CheckpointError, PricePredictorError
from src.utils.logging import get_logger
logger = get_logger(__name__)
def run(config_path: str, output_path: str) -> None:
config = load_config(config_path)
embeddings_dir = Path(config["data"]["embeddings_dir"])
text_path = embeddings_dir / "test_text.npy"
image_path = embeddings_dir / "test_image.npy"
ids_path = embeddings_dir / "test_ids.csv"
for p in (text_path, image_path, ids_path):
if not p.exists():
raise PricePredictorError(
f"{p} not found — run scripts/02_extract_embeddings.py first. "
"This script predicts from cached test embeddings; it does not "
"re-run the encoders."
)
dataset = EmbeddingDataset(str(text_path), str(image_path))
ids_df = pd.read_csv(ids_path)
if len(ids_df) != len(dataset):
raise PricePredictorError(
f"test_ids.csv has {len(ids_df)} rows but the embeddings have {len(dataset)} — "
"these came from different runs. Re-run scripts/02_extract_embeddings.py "
"so both are regenerated together."
)
loader = DataLoader(dataset, batch_size=256, shuffle=False)
model = PriceModel.from_config(config)
checkpoint_path = Path(config["checkpoint_dir"]) / "best.pt"
if not checkpoint_path.exists():
raise CheckpointError(f"No checkpoint found at {checkpoint_path} — run scripts/03_train.py first")
checkpoint = torch.load(checkpoint_path, map_location="cpu")
try:
model.load_state_dict(checkpoint["model_state_dict"])
except RuntimeError as e:
raise CheckpointError(
f"Checkpoint at {checkpoint_path} does not match the current model config "
f"(e.g. a fusion.type or head.hidden_dims mismatch): {e}"
) from e
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
model.eval()
logger.info("Predicting %d rows from cached embeddings on %s (no encoder loading needed)", len(dataset), device)
all_prices = []
n_done = 0
with torch.no_grad():
for text_emb, image_emb in loader:
text_emb, image_emb = text_emb.to(device), image_emb.to(device)
raw_price = model(text_emb, image_emb)
price = torch.clamp(raw_price, min=0.01)
all_prices.extend(price.cpu().tolist())
n_done += text_emb.shape[0]
logger.info("Predicted %d/%d rows", n_done, len(dataset))
out_df = pd.DataFrame({
"sample_id": ids_df["sample_id"],
"price": [round(float(p), 2) for p in all_prices],
})
out_df.to_csv(output_path, index=False)
logger.info("Wrote predictions to %s (%d rows)", output_path, len(out_df))
def main() -> None:
parser = argparse.ArgumentParser(description="Stage 05: predict on the test set from cached embeddings")
parser.add_argument("--config", default="configs/base.yaml")
parser.add_argument("--output", default="data/raw/test_out.csv")
args = parser.parse_args()
try:
run(args.config, args.output)
except PricePredictorError as e:
logger.error("Prediction failed: %s", e)
sys.exit(1)
except Exception as e:
logger.exception("Unexpected error during prediction: %s", e)
sys.exit(1)
if __name__ == "__main__":
main() |