| |
| |
| |
|
|
| """Stage 1: run a VLM over every (benchmark sample, prompt type) through an API. |
| |
| Writes <output-dir>/inference/<model_slug>/<prompt_type>/idx_NNNN_<side>.json. |
| """ |
| from __future__ import annotations |
|
|
| import argparse |
| import datetime |
| import os |
| import sys |
|
|
| from . import _api |
| from ._shared import ( |
| PROMPT_TYPE_NAMES, |
| build_question, |
| load_eval_items, |
| result_path, |
| save_result, |
| ) |
|
|
|
|
| def cmd_infer(args) -> None: |
| """Run inference on every benchmark sample.""" |
| items = load_eval_items(args.scenarios) |
| scenarios_dir = os.path.dirname(os.path.abspath(args.scenarios)) |
| if not items: |
| print("No valid scenarios found.", file=sys.stderr) |
| sys.exit(1) |
|
|
| prompt_types = args.prompt_type |
| total = len(items) * len(prompt_types) |
| done = 0 |
| skipped = 0 |
| print( |
| f"Inference: {len(items)} eval items x {len(prompt_types)} prompt type(s) " |
| f"= {total} calls | model={args.model}" |
| ) |
|
|
| for pt in prompt_types: |
| for item in items: |
| idx = item["scenario_index"] |
| side = item["side"] |
| rpath = result_path(args.output_dir, "inference", args.model, pt, idx, side) |
|
|
| |
| if not args.no_resume and os.path.isfile(rpath): |
| skipped += 1 |
| continue |
|
|
| question = build_question(pt, item["task"]) |
| |
| video = item["video_path"] |
| if not os.path.isabs(video): |
| video = os.path.join(scenarios_dir, video) |
| if not os.path.isfile(video): |
| print(f" Video not found, skipping: {video}", file=sys.stderr) |
| continue |
|
|
| done += 1 |
| print( |
| f" [{done + skipped}/{total}] idx={idx} side={side} pt={pt}", |
| end="", |
| flush=True, |
| ) |
|
|
| try: |
| answer = _api.query_model( |
| video, question, args.model, args.api_url, args.api_key, |
| args.input_mode, args.max_frames, robot_type=item["robot"], |
| ) |
| except SystemExit: |
| print(" [FAILED]") |
| continue |
|
|
| save_result(rpath, { |
| "scenario_index": idx, |
| "side": side, |
| "prompt_type": pt, |
| "model": args.model, |
| "question": question, |
| "video_path": item["video_path"], |
| "model_response": answer, |
| "timestamp": datetime.datetime.now(datetime.timezone.utc).isoformat(), |
| }) |
| print(f" [OK] {len(answer)} chars") |
|
|
| print(f"Inference done: {done} new, {skipped} resumed, {total} total.") |
|
|
|
|
| def main() -> None: |
| parser = argparse.ArgumentParser( |
| description="Run VLM inference on the benchmark videos through an API.", |
| ) |
| parser.add_argument("--scenarios", required=True, help="Path to benchmark.json") |
| parser.add_argument( |
| "--model", required=True, help="Model name as your API endpoint knows it", |
| ) |
| parser.add_argument( |
| "--prompt-type", nargs="+", default=list(PROMPT_TYPE_NAMES), |
| choices=PROMPT_TYPE_NAMES, |
| help="Prompt type(s) to evaluate (default: all four)", |
| ) |
| parser.add_argument("--output-dir", default="eval_results", help="Output directory") |
| parser.add_argument( |
| "--api-url", default=_api.CHAT_COMPLETIONS_URL, |
| help="Full chat-completions URL (default: $VLM_API_BASE/v1/chat/completions)", |
| ) |
| parser.add_argument( |
| "--input-mode", choices=("auto", "gemini_video", "openai_frames"), default="auto", |
| help="Send the whole clip ('gemini_video') or sampled frames ('openai_frames'). " |
| "'auto' sends frames to GPT models and the clip to others.", |
| ) |
| parser.add_argument( |
| "--max-frames", type=int, default=16, |
| help="Frames sent in 'openai_frames' mode, spread evenly over the clip", |
| ) |
| parser.add_argument("--no-resume", action="store_true", help="Re-run all, ignore existing") |
|
|
| args = parser.parse_args() |
| args.api_key = os.environ.get("VLM_API_KEY", "") |
| if args.max_frames < 1: |
| parser.error("--max-frames must be at least 1") |
| if not args.api_key.strip(): |
| print("Missing API key: set VLM_API_KEY.", file=sys.stderr) |
| sys.exit(1) |
| if not args.api_url: |
| print("Missing endpoint: set VLM_API_BASE or pass --api-url.", file=sys.stderr) |
| sys.exit(1) |
| cmd_infer(args) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
