124 lines
4.0 KiB
Python
124 lines
4.0 KiB
Python
#!/usr/bin/env python3
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"""
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Test checkpoint + replay flow end-to-end.
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1. Runs the pipeline with checkpointing enabled on a test video
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2. Lists available checkpoints
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3. Replays from run_ocr with different config
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4. Compares detection counts
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Usage:
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MPR_CHECKPOINT=1 INFERENCE_URL=http://mcrndeb:8000 python tests/detect/manual/test_replay.py [--job JOB_ID]
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Requires: inference server running, MinIO/S3 running, test video available
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"""
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import argparse
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import logging
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import os
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import sys
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from pathlib import Path
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# Load ctrl/.env
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env_file = Path(__file__).resolve().parents[3] / "ctrl" / ".env"
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if env_file.exists():
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for line in env_file.read_text().splitlines():
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line = line.strip()
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if line and not line.startswith("#") and "=" in line:
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key, _, value = line.partition("=")
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os.environ.setdefault(key.strip(), value.strip())
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sys.path.insert(0, ".")
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logging.basicConfig(level=logging.INFO, format="%(levelname)-7s %(name)s — %(message)s")
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logger = logging.getLogger(__name__)
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# Force checkpointing on
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os.environ["MPR_CHECKPOINT"] = "1"
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def main():
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parser = argparse.ArgumentParser()
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import time
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default_job = f"replay-{int(time.time()) % 100000}"
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parser.add_argument("--job", default=default_job)
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parser.add_argument("--port", type=int, default=6382)
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args = parser.parse_args()
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# Override Redis to localhost (ctrl/.env has k8s hostname)
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os.environ["REDIS_URL"] = f"redis://localhost:{args.port}/0"
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from core.detect.graph import get_pipeline, NODES
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from core.detect.checkpoint import list_checkpoints
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from core.detect.checkpoint import replay_from
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from core.detect.state import DetectState
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VIDEO = "media/out/chunks/95043d50-4df6-4ac8-bbd5-2ba873117c6e/chunk_0000.mp4"
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logger.info("Job: %s", args.job)
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logger.info("Checkpoint: enabled")
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logger.info("Video: %s", VIDEO)
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logger.info("Open: http://mpr.local.ar/detection/?job=%s", args.job)
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input("\nPress Enter to run initial pipeline...")
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# --- Initial run ---
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pipeline = get_pipeline(checkpoint=True)
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initial_state = DetectState(
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video_path=VIDEO,
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job_id=args.job,
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profile_name="soccer_broadcast",
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)
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logger.info("Running initial pipeline...")
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result = pipeline.invoke(initial_state)
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detections = result.get("detections", [])
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report = result.get("report")
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logger.info("Initial run: %d detections, %d brands",
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len(detections), len(report.brands) if report else 0)
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# --- List checkpoints ---
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stages = list_checkpoints(args.job)
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logger.info("Available checkpoints: %s", stages)
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if "detect_objects" not in stages:
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logger.error("Expected checkpoint for detect_objects — aborting replay test")
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return
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input("\nPress Enter to replay from run_ocr with different config...")
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# --- Replay with different OCR config ---
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overrides = {"ocr": {"min_confidence": 0.3, "languages": ["en", "es"]}}
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logger.info("Replaying from run_ocr with overrides: %s", overrides)
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replay_result = replay_from(
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job_id=args.job,
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start_stage="run_ocr",
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config_overrides=overrides,
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)
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replay_detections = replay_result.get("detections", [])
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replay_report = replay_result.get("report")
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logger.info("Replay run: %d detections, %d brands",
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len(replay_detections),
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len(replay_report.brands) if replay_report else 0)
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# --- Compare ---
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logger.info("--- Comparison ---")
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logger.info("Initial: %d detections", len(detections))
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logger.info("Replay: %d detections (min_confidence 0.5 → 0.3)", len(replay_detections))
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diff = len(replay_detections) - len(detections)
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if diff > 0:
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logger.info("Replay found %d more detections with lower threshold", diff)
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elif diff == 0:
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logger.info("Same count — threshold change didn't affect this video")
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else:
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logger.warning("Replay found fewer detections — unexpected")
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logger.info("Done.")
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if __name__ == "__main__":
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main()
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