Datasets:
File size: 18,818 Bytes
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"""Evaluate Cosmos3-Super Reasoner on target-backed retrieval probes.
This runner mirrors the Qwen3-Omni retrieval-task contract, but calls an
OpenAI-compatible Cosmos3-Super server. It is intentionally metrics-only: it
does not fine-tune weights, invent targets, or fill matrix cells unless the
task writes a real held-out metrics.json artifact.
"""
from __future__ import annotations
import argparse
import csv
import json
import time
import urllib.error
import urllib.request
from pathlib import Path
from typing import Any
from eval_qwen3_omni_retrieval_task_probes import (
SENSOR_TARGET_TASKS,
TASK_SPECS,
SensorFeatureCache,
answer,
artifact_query_text,
build_candidate_indices,
build_messages,
extract_ranking,
future_index_map,
has_camera_view_pair,
has_sensor_feature,
media_video_path,
prediction_id,
read_jsonl_if_exists,
row_end,
row_start,
score_retrieval,
select_eval_indices,
select_tasks,
write_json,
write_jsonl,
)
from qwen3_omni_dataset_utils import load_jsonl
SYSTEM_PROMPT = (
"You are an embodied episode-understanding model for Ropedia/Xperience-10M. "
"Return exactly one compact valid JSON object and no markdown, prose, code "
"fences, explanations, or repeated text."
)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--dataset-jsonl", type=Path, required=True)
parser.add_argument("--run-id", default="xperience10m_cosmos3_super_retrieval_task_probes")
parser.add_argument("--output-dir", type=Path)
parser.add_argument("--base-url", default="http://127.0.0.1:8000/v1")
parser.add_argument("--model", default="cosmos3-super-local")
parser.add_argument("--eval-split", default="test")
parser.add_argument("--tasks", default="cross_modal_retrieval")
parser.add_argument("--candidate-count", type=int, default=4)
parser.add_argument("--future-frames", type=int, default=100)
parser.add_argument("--sample-limit", type=int, default=0)
parser.add_argument("--sample-offset", type=int, default=0)
parser.add_argument("--sample-stride", type=int, default=1)
parser.add_argument("--max-tokens", type=int, default=96)
parser.add_argument("--temperature", type=float, default=0.0)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--request-timeout", type=float, default=900.0)
parser.add_argument("--media-mode", choices=["video_url", "text_only"], default="video_url")
parser.add_argument("--resume", action=argparse.BooleanOptionalAction, default=True)
parser.add_argument("--progress-jsonl", type=Path)
return parser.parse_args()
def append_jsonl(path: Path, row: dict[str, Any]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("a", encoding="utf-8") as handle:
handle.write(json.dumps(row, ensure_ascii=False, sort_keys=True) + "\n")
def write_csv(path: Path, rows: list[dict[str, Any]], fieldnames: list[str]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("w", newline="", encoding="utf-8") as handle:
writer = csv.DictWriter(handle, fieldnames=fieldnames, extrasaction="ignore", lineterminator="\n")
writer.writeheader()
writer.writerows(rows)
def normalize_base_url(base_url: str) -> str:
return base_url.rstrip("/")
def file_url(path_text: str) -> str:
path = Path(path_text).expanduser()
if not path.is_absolute():
path = path.resolve()
return path.as_uri()
def http_json(method: str, url: str, payload: dict[str, Any] | None, timeout: float) -> dict[str, Any]:
data = None if payload is None else json.dumps(payload).encode("utf-8")
request = urllib.request.Request(
url,
data=data,
method=method,
headers={"Content-Type": "application/json", "Accept": "application/json"},
)
try:
with urllib.request.urlopen(request, timeout=timeout) as response:
body = response.read().decode("utf-8")
except urllib.error.HTTPError as exc:
detail = exc.read().decode("utf-8", errors="replace")
raise RuntimeError(f"HTTP {exc.code} from {url}: {detail}") from exc
return json.loads(body) if body else {}
def server_info(args: argparse.Namespace) -> dict[str, Any]:
try:
return http_json("GET", f"{normalize_base_url(args.base_url)}/models", None, min(args.request_timeout, 30.0))
except Exception as exc: # noqa: BLE001 - diagnostic only.
return {"error": f"{type(exc).__name__}: {exc}"}
def qwen_content_to_openai(content: list[dict[str, Any]], args: argparse.Namespace) -> list[dict[str, Any]]:
converted: list[dict[str, Any]] = []
for item in content:
kind = item.get("type")
if kind == "text":
converted.append({"type": "text", "text": str(item.get("text", ""))})
elif kind == "video":
path = str(item.get("video") or "")
if args.media_mode == "video_url" and path:
converted.append({"type": "video_url", "video_url": {"url": file_url(path)}})
elif path:
converted.append({"type": "text", "text": f"[video omitted in text_only mode: {path}]"})
return converted
def openai_messages(qwen_messages: list[dict[str, Any]], args: argparse.Namespace) -> list[dict[str, Any]]:
messages: list[dict[str, Any]] = [{"role": "system", "content": SYSTEM_PROMPT}]
for message in qwen_messages:
role = str(message.get("role") or "user")
content = message.get("content")
if isinstance(content, list):
messages.append({"role": role, "content": qwen_content_to_openai(content, args)})
else:
messages.append({"role": role, "content": str(content or "")})
return messages
def chat_completion(qwen_messages: list[dict[str, Any]], args: argparse.Namespace) -> tuple[str, dict[str, Any], float]:
payload = {
"model": args.model,
"messages": openai_messages(qwen_messages, args),
"max_tokens": args.max_tokens,
"temperature": args.temperature,
"seed": args.seed,
}
started = time.time()
response = http_json(
"POST",
f"{normalize_base_url(args.base_url)}/chat/completions",
payload,
args.request_timeout,
)
choices = response.get("choices") if isinstance(response.get("choices"), list) else []
message = choices[0].get("message") if choices and isinstance(choices[0], dict) else {}
content = message.get("content") if isinstance(message, dict) else ""
if isinstance(content, list):
text = "\n".join(str(item.get("text", "")) for item in content if isinstance(item, dict))
else:
text = str(content or "")
return text, response, time.time() - started
def score_task(task_id: str, spec: dict[str, Any], rows: list[dict[str, Any]], output_dir: Path, args: argparse.Namespace) -> dict[str, Any]:
task_dir = output_dir / task_id
task_dir.mkdir(parents=True, exist_ok=True)
write_jsonl(task_dir / "predictions.jsonl", rows)
write_csv(
task_dir / "predictions.csv",
[
{
"id": row["id"],
"episode_id": row["episode_id"],
"split": row["split"],
"start_frame": row["start_frame"],
"end_frame": row["end_frame"],
"target_id": row.get("target_id"),
"target_start_frame": row.get("target_start_frame"),
"target_end_frame": row.get("target_end_frame"),
"true_letter": row["true_letter"],
"predicted_ranking": json.dumps(row["predicted_ranking"], ensure_ascii=False),
"reciprocal_rank": row["reciprocal_rank"],
"top1_correct": row["top1_correct"],
"latency_seconds": row.get("latency_seconds"),
"raw_prediction": row["raw_prediction"],
}
for row in rows
],
[
"id",
"episode_id",
"split",
"start_frame",
"end_frame",
"target_id",
"target_start_frame",
"target_end_frame",
"true_letter",
"predicted_ranking",
"reciprocal_rank",
"top1_correct",
"latency_seconds",
"raw_prediction",
],
)
metrics = score_retrieval(rows)
primary_score = metrics[spec["metric_key"]]
metrics.update(
{
"title": f"Cosmos3-Super Reasoner {spec['label']}",
"status": "pass",
"run_id": args.run_id,
"task_id": task_id,
"task_number": spec["task_number"],
"task_label": spec["label"],
"metric_key": spec["metric_key"],
"primary_metric": spec["metric_key"],
"primary_score": primary_score,
"model": args.model,
"base_url": args.base_url,
"dataset_jsonl": str(args.dataset_jsonl),
"eval_split": args.eval_split,
"candidate_count": args.candidate_count,
"future_frames": args.future_frames,
"sample_offset": args.sample_offset,
"sample_stride": args.sample_stride,
"media_mode": args.media_mode,
"scope": "held_out_test_cosmos3_super_retrieval_task_probe",
"score_policy": (
"GPU-backed Cosmos3-Super Reasoner retrieval probe over real held-out "
"candidate windows or staged sensor targets. The score is MRR of the "
"true candidate; no labels are fabricated and no weights are updated."
),
}
)
write_json(task_dir / "metrics.json", metrics)
return metrics
def main() -> int:
args = parse_args()
if args.output_dir is None:
args.output_dir = Path(__file__).resolve().parents[2] / "results" / "omni_finetune" / args.run_id
args.output_dir.mkdir(parents=True, exist_ok=True)
args.progress_jsonl = args.progress_jsonl or args.output_dir / "progress.jsonl"
selected_tasks = select_tasks(args.tasks)
samples = load_jsonl(args.dataset_jsonl)
eval_pool = [idx for idx, sample in enumerate(samples) if sample.get("split") == args.eval_split and media_video_path(sample)]
eval_indices = select_eval_indices(samples, args)
if "cross_modal_retrieval" in selected_tasks:
eval_indices = [idx for idx in eval_indices if has_sensor_feature(samples[idx])]
eval_pool = [idx for idx in eval_pool if has_sensor_feature(samples[idx])]
if any(task_id in SENSOR_TARGET_TASKS for task_id in selected_tasks):
eval_indices = [idx for idx in eval_indices if has_sensor_feature(samples[idx])]
eval_pool = [idx for idx in eval_pool if has_sensor_feature(samples[idx])]
future_targets = future_index_map(samples, args.future_frames) if "hand_trajectory_forecast" in selected_tasks else {}
if "hand_trajectory_forecast" in selected_tasks:
eval_indices = [
idx
for idx in eval_indices
if idx in future_targets and has_sensor_feature(samples[future_targets[idx]])
]
if "camera_view_sync_retrieval" in selected_tasks:
eval_indices = [idx for idx in eval_indices if has_camera_view_pair(samples[idx])]
eval_pool = [idx for idx in eval_pool if has_camera_view_pair(samples[idx])]
if not eval_indices:
raise ValueError("No evaluation samples with retrieval candidates selected.")
write_json(args.output_dir / "server_info.json", server_info(args))
append_jsonl(
args.progress_jsonl,
{
"event": "eval_start",
"timestamp": time.time(),
"run_id": args.run_id,
"tasks": selected_tasks,
"num_eval_samples": len(eval_indices),
"sample_offset": args.sample_offset,
"sample_stride": args.sample_stride,
"candidate_count": args.candidate_count,
"future_frames": args.future_frames,
"model": args.model,
"base_url": args.base_url,
"media_mode": args.media_mode,
},
)
sensor_cache = (
SensorFeatureCache()
if "cross_modal_retrieval" in selected_tasks
or any(task_id in SENSOR_TARGET_TASKS for task_id in selected_tasks)
else None
)
camera_clip_dir = args.output_dir / "camera_view_sync_clips" if "camera_view_sync_retrieval" in selected_tasks else None
partial_by_task = {
task_id: {
row.get("prediction_id"): row
for row in read_jsonl_if_exists(args.output_dir / task_id / "predictions.partial.jsonl")
if row.get("prediction_id")
}
for task_id in selected_tasks
}
for task_id in selected_tasks:
spec = TASK_SPECS[task_id]
partial_path = args.output_dir / task_id / "predictions.partial.jsonl"
for local_pos, sample_idx in enumerate(eval_indices, start=1):
sample = samples[sample_idx]
pred_id = prediction_id(task_id, sample)
if args.resume and pred_id in partial_by_task[task_id]:
continue
started = time.time()
target_idx = future_targets[sample_idx] if task_id == "hand_trajectory_forecast" else sample_idx
candidate_indices = build_candidate_indices(
samples,
eval_pool,
sample_idx,
task_id,
args.candidate_count,
target_idx=target_idx,
)
qwen_messages, true_letter, candidate_records = build_messages(
samples,
sample_idx,
target_idx,
candidate_indices,
task_id,
spec,
sensor_cache=sensor_cache,
camera_clip_dir=camera_clip_dir,
future_frames=args.future_frames,
)
raw, response, latency = chat_completion(qwen_messages, args)
valid_letters = [record["letter"] for record in candidate_records]
ranking = extract_ranking(raw, valid_letters)
rank = ranking.index(true_letter) + 1 if true_letter in ranking else len(ranking) + 1
usage = response.get("usage") if isinstance(response.get("usage"), dict) else {}
row = {
"prediction_id": pred_id,
"id": sample.get("id"),
"task_id": task_id,
"task_label": spec["label"],
"split": sample.get("split"),
"episode_id": sample.get("episode_id"),
"start_frame": row_start(sample),
"end_frame": row_end(sample),
"query_text": artifact_query_text(task_id, sample, sensor_cache, future_frames=args.future_frames),
"target_id": samples[target_idx].get("id"),
"target_start_frame": row_start(samples[target_idx]),
"target_end_frame": row_end(samples[target_idx]),
"candidates": candidate_records,
"true_letter": true_letter,
"predicted_ranking": ranking,
"reciprocal_rank": 1.0 / rank,
"top1_correct": int(bool(ranking) and ranking[0] == true_letter),
"latency_seconds": round(latency, 3),
"prompt_tokens": usage.get("prompt_tokens"),
"completion_tokens": usage.get("completion_tokens"),
"total_tokens": usage.get("total_tokens"),
"raw_prediction": raw,
}
partial_by_task[task_id][pred_id] = row
append_jsonl(partial_path, row)
append_jsonl(
args.progress_jsonl,
{
"event": "sample_done",
"timestamp": time.time(),
"task_id": task_id,
"sample_index": local_pos,
"num_eval_samples": len(eval_indices),
"completed_samples_for_task": len(partial_by_task[task_id]),
"sample_id": sample.get("id"),
"seconds": round(time.time() - started, 3),
},
)
task_metrics = {}
for task_id in selected_tasks:
rows = [partial_by_task[task_id][prediction_id(task_id, samples[idx])] for idx in eval_indices]
task_metrics[task_id] = score_task(task_id, TASK_SPECS[task_id], rows, args.output_dir, args)
summary = {
"title": "Cosmos3-Super Reasoner Retrieval Task Probes",
"status": "pass",
"run_id": args.run_id,
"model": args.model,
"base_url": args.base_url,
"dataset_jsonl": str(args.dataset_jsonl),
"eval_split": args.eval_split,
"candidate_count": args.candidate_count,
"future_frames": args.future_frames,
"sample_offset": args.sample_offset,
"sample_stride": args.sample_stride,
"media_mode": args.media_mode,
"tasks": {
task_id: {
"task_number": metrics["task_number"],
"task_label": metrics["task_label"],
"metric_key": metrics["metric_key"],
"primary_score": metrics["primary_score"],
"num_samples": metrics["num_samples"],
"metrics_json": str(args.output_dir / task_id / "metrics.json"),
}
for task_id, metrics in task_metrics.items()
},
}
write_json(args.output_dir / "summary.json", summary)
report_lines = [
"# Cosmos3-Super Reasoner Retrieval Task Probes",
"",
f"- Run ID: `{args.run_id}`",
f"- Model: `{args.model}`",
f"- API base URL: `{args.base_url}`",
f"- Dataset: `{args.dataset_jsonl}`",
f"- Candidate count: `{args.candidate_count}`",
f"- Shard: offset `{args.sample_offset}` / stride `{args.sample_stride}`",
"",
"| Task | Metric | Score | Samples |",
"| --- | --- | ---: | ---: |",
]
for task_id, metrics in task_metrics.items():
report_lines.append(
f"| {metrics['task_label']} | {metrics['metric_key']} | {metrics['primary_score']:.6f} | {metrics['num_samples']} |"
)
(args.output_dir / "RUN_REPORT.md").write_text("\n".join(report_lines) + "\n", encoding="utf-8")
append_jsonl(args.progress_jsonl, {"event": "eval_complete", "timestamp": time.time(), "run_id": args.run_id})
print(json.dumps(summary, indent=2, sort_keys=True))
return 0
if __name__ == "__main__":
raise SystemExit(main())
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