ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter6/rgb-sim2real-grasping/validate_evidence.py

@@ -0,0 +1,81 @@
+#!/usr/bin/env python3
+"""Validate local GPU RGB domain-transfer evidence for Experiment 6-13."""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import sys
+from pathlib import Path
+from typing import Any
+
+
+def file_sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as handle:
+        for chunk in iter(lambda: handle.read(1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def validate(data: dict[str, Any], evidence_dir: Path | None = None) -> list[str]:
+    errors: list[str] = []
+
+    def expect(condition: bool, message: str) -> None:
+        if not condition:
+            errors.append(message)
+
+    expect(data.get("schema_version") == "3.0", "schema_version must be 3.0")
+    expect(data.get("experiment_id") == "6-13", "experiment_id must be 6-13")
+    expect(data.get("kind") == "local_gpu_rgb_domain_transfer", "wrong evidence kind")
+    expect(data.get("status") == "complete", "local evidence must be complete")
+    metrics = data.get("metrics", {})
+    expect(metrics.get("device", {}).get("device") in {"mps", "cuda"}, "evidence must use a local GPU accelerator")
+    protocol = metrics.get("protocol", {})
+    expect(len(protocol.get("seeds", [])) >= 3, "at least three training seeds are required")
+    expect(set(protocol.get("variants", [])) == {"source_clean", "source_background", "source_appearance", "source_full"}, "all four randomization conditions are required")
+    expect(len(protocol.get("target_domains", [])) >= 2, "at least two target visual domains are required")
+    expect(protocol.get("total_training_examples", 0) >= 20000, "at least 20000 training examples are required")
+    summary = metrics.get("summary", {})
+    expect(summary.get("source_clean", {}).get("source", {}).get("mean", 0) > 0.85, "clean source accuracy must exceed 0.85")
+    for domain in protocol.get("target_domains", []):
+        clean = summary.get("source_clean", {}).get(domain, {}).get("mean", 0)
+        full = summary.get("source_full", {}).get(domain, {}).get("mean", 0)
+        expect(full > 0.65, f"full randomization target accuracy is too low for {domain}")
+        expect(full > clean, f"full randomization must improve target accuracy for {domain}")
+    expect(metrics.get("dataset_replay_match") is True, "repeating a fixed dataset seed must reproduce the exact data")
+    artifacts = data.get("artifacts", [])
+    expect(len(artifacts) == 5, "checkpoint, metrics, matrix and two preview artifacts are required")
+    if evidence_dir is not None:
+        for index, artifact in enumerate(artifacts):
+            path = evidence_dir / str(artifact.get("path", ""))
+            expect(path.is_file(), f"artifact[{index}] does not exist")
+            if path.is_file():
+                expect(file_sha256(path) == artifact.get("sha256"), f"artifact[{index}] hash mismatch")
+    extension = data.get("hardware_extension", {})
+    expect(extension.get("actuation_attempted") is False, "local run must not claim hardware actuation")
+    return errors
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("evidence", type=Path)
+    args = parser.parse_args()
+    try:
+        data = json.loads(args.evidence.read_text(encoding="utf-8"))
+    except (OSError, json.JSONDecodeError) as exc:
+        print(f"INVALID: {exc}", file=sys.stderr)
+        return 2
+    errors = validate(data, args.evidence.resolve().parent)
+    if errors:
+        print("INVALID")
+        for error in errors:
+            print(f"- {error}")
+        return 1
+    print("VALID: experiment 6-13 local GPU evidence")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())