Fu DaiandClaude Opus 4.8
1a585693be
FastAPI service wrapping the Surya-OCR-2 model (datalab-to) served through vLLM: legacy /v1/api/ai/* endpoints, an OpenAI-compatible /v1/chat/completions endpoint, a coalescing request batcher, a local OCR CLI, Docker packaging, multilingual example outputs, and quantization/concurrency benchmarks. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
"""Speed-vs-accuracy Pareto scatter and per-method bar charts."""
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from __future__ import annotations
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from pathlib import Path
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from typing import Any, Dict, List
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import matplotlib
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matplotlib.use("Agg") # headless
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import matplotlib.pyplot as plt # noqa: E402
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def pareto_points(rows: List[Dict[str, Any]], x_key: str, y_key: str) -> List[Dict[str, Any]]:
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points = []
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for row in rows:
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if row.get("status") != "ok":
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continue
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if row.get(x_key) is None or row.get(y_key) is None:
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continue
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points.append({
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"method": row["method"],
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"x": row[x_key],
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"y": row[y_key],
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"t4_deployable": row.get("t4_deployable"),
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})
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return points
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def _scatter(points, x_label, y_label, title, out_path: Path) -> None:
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fig, ax = plt.subplots(figsize=(7, 5))
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for p in points:
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marker = "o" if p["t4_deployable"] else "x"
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ax.scatter(p["x"], p["y"], marker=marker, s=80)
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ax.annotate(p["method"], (p["x"], p["y"]), textcoords="offset points", xytext=(5, 5))
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ax.set_xlabel(x_label)
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ax.set_ylabel(y_label)
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ax.set_title(title + " (o = T4-deployable, x = A100-only)")
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fig.tight_layout()
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fig.savefig(out_path, dpi=120)
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plt.close(fig)
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def _bar(rows, metric_key, y_label, out_path: Path) -> None:
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ok = [r for r in rows if r.get("status") == "ok" and r.get(metric_key) is not None]
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fig, ax = plt.subplots(figsize=(7, 5))
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ax.bar([r["method"] for r in ok], [r[metric_key] for r in ok])
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ax.set_ylabel(y_label)
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ax.set_title(metric_key)
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fig.tight_layout()
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fig.savefig(out_path, dpi=120)
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plt.close(fig)
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def render_all(rows: List[Dict[str, Any]], out_dir: Path) -> List[Path]:
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out_dir = Path(out_dir)
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out_dir.mkdir(parents=True, exist_ok=True)
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written: List[Path] = []
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p1 = out_dir / "pareto_latency_cer.png"
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_scatter(pareto_points(rows, "mean_latency_s", "mean_cer"),
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"mean latency (s) [lower=faster]", "CER vs BF16 [lower=better]",
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"Speed vs recognition accuracy", p1)
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written.append(p1)
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p2 = out_dir / "pareto_latency_iou.png"
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_scatter(pareto_points(rows, "mean_latency_s", "mean_bbox_iou"),
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"mean latency (s) [lower=faster]", "bbox IoU vs BF16 [higher=better]",
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"Speed vs detection accuracy", p2)
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written.append(p2)
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for metric, label in [("mean_latency_s", "mean latency (s)"),
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("mean_cer", "CER vs BF16"),
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("mean_bbox_iou", "bbox IoU vs BF16")]:
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bp = out_dir / f"bar_{metric}.png"
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_bar(rows, metric, label, bp)
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written.append(bp)
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return written
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