File size: 3,651 Bytes
babffc8 | 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 | from __future__ import annotations
import argparse
import sys
from pathlib import Path
import torch
from tqdm import tqdm
sys.path.append(str(Path(__file__).resolve().parents[1]))
from src.io_utils import apply_overrides, load_config, output_dir, read_jsonl, sample_rows, write_json, write_jsonl
from src.label_utils import normalize_label, normalize_text
from src.metrics import metrics_for_task
from src.qwen_video_utils import generate_one, load_model_for_training, load_processor
from src.train_utils import oom_help, quantization_config_from_config
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--config", required=True)
parser.add_argument("--max_samples", type=int, default=None)
parser.add_argument("--model_name", default=None)
parser.add_argument("--output_dir", default=None)
args = parser.parse_args()
cfg = apply_overrides(load_config(args.config), model_name=args.model_name, output_dir=args.output_dir)
out_dir = output_dir(cfg)
out_dir.mkdir(parents=True, exist_ok=True)
if torch.cuda.is_available() is False:
print("Warning: CUDA is unavailable. This may be very slow and 4-bit loading may not work on CPU.", file=sys.stderr)
rows = sample_rows(read_jsonl(cfg["val_jsonl"]), args.max_samples or int(cfg.get("baseline_max_eval_samples", 0)))
processor = load_processor(cfg["model_name"])
model = load_model_for_training(
cfg["model_name"],
quantization_config=quantization_config_from_config(cfg),
device_map="auto",
dtype="bfloat16" if cfg.get("bf16", True) else "float16",
)
model.eval()
preds = []
references = []
predictions = []
task = cfg["task_name"]
target_column = cfg["target_column"]
video_column = cfg["video_column"]
gen_cfg = cfg.get("generation", {})
try:
for row in tqdm(rows, desc="baseline"):
reference = str(row.get(target_column, ""))
if not reference:
raise ValueError(f"Missing target '{target_column}' in row id={row.get('id')}")
prediction = generate_one(
model,
processor,
row[video_column],
cfg["prompt"],
gen_cfg,
video_fps=cfg.get("video_fps"),
max_frames=cfg.get("max_frames"),
)
norm_ref = normalize_label(reference) if task == "asl_citizen" else normalize_text(reference)
norm_pred = normalize_label(prediction) if task == "asl_citizen" else normalize_text(prediction)
correct = norm_ref == norm_pred if task == "asl_citizen" else None
preds.append(
{
"id": row.get("id"),
"video_path": row[video_column],
"reference": reference,
"prediction": prediction,
"normalized_reference": norm_ref,
"normalized_prediction": norm_pred,
"correct": correct,
}
)
references.append(reference)
predictions.append(prediction)
except torch.cuda.OutOfMemoryError as exc:
raise RuntimeError(oom_help()) from exc
metrics = metrics_for_task(task, references, predictions)
write_jsonl(out_dir / "baseline_predictions.jsonl", preds)
write_json(out_dir / "baseline_metrics.json", metrics)
print(metrics)
print(f"Saved predictions to {out_dir / 'baseline_predictions.jsonl'}")
print(f"Saved metrics to {out_dir / 'baseline_metrics.json'}")
if __name__ == "__main__":
main()
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