be7f5f1872
D2: TrOCR ONNX export script (printed + handwritten, int8 quantization) D3: PP-DocLayout ONNX export script (download or Docker-based conversion) B3: Model Management admin page (PyTorch vs ONNX status, benchmarks, config) A4: TrOCR ONNX service with runtime routing (auto/pytorch/onnx via TROCR_BACKEND) A5: PP-DocLayout ONNX detection with OpenCV fallback (via GRAPHIC_DETECT_BACKEND) B4: Structure Detection UI toggle (OpenCV vs PP-DocLayout) with class color coding C3: TrOCR-ONNX.md documentation C4: OCR-Pipeline.md ONNX section added C5: mkdocs.yml nav updated, optimum added to requirements.txt Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
547 lines
18 KiB
Python
Executable File
547 lines
18 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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PP-DocLayout ONNX Export — exports PP-DocLayout model to ONNX for document layout detection.
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PP-DocLayout detects: table, figure, title, text, list regions on document pages.
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Since PaddlePaddle doesn't work natively on ARM Mac, this script either:
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1. Downloads a pre-exported ONNX model
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2. Uses Docker (linux/amd64) for the conversion
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Usage:
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python scripts/export-doclayout-onnx.py
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python scripts/export-doclayout-onnx.py --method docker
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"""
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import argparse
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import hashlib
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import json
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import logging
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import os
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import shutil
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import subprocess
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import sys
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import tempfile
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import urllib.request
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from pathlib import Path
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(message)s",
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datefmt="%H:%M:%S",
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)
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log = logging.getLogger("export-doclayout")
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# ---------------------------------------------------------------------------
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# Constants
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# ---------------------------------------------------------------------------
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# 10 PP-DocLayout class labels in standard order
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CLASS_LABELS = [
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"table",
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"figure",
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"title",
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"text",
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"list",
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"header",
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"footer",
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"equation",
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"reference",
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"abstract",
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]
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# Known download sources for pre-exported ONNX models.
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# Ordered by preference — first successful download wins.
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DOWNLOAD_SOURCES = [
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{
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"name": "PaddleOCR PP-DocLayout (ppyoloe_plus_sod, HuggingFace)",
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"url": "https://huggingface.co/SWHL/PP-DocLayout/resolve/main/pp_doclayout_onnx/model.onnx",
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"filename": "model.onnx",
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"sha256": None, # populated once a known-good hash is available
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},
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{
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"name": "PaddleOCR PP-DocLayout (RapidOCR mirror)",
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"url": "https://huggingface.co/SWHL/PP-DocLayout/resolve/main/pp_doclayout_onnx/model.onnx",
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"filename": "model.onnx",
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"sha256": None,
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},
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]
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# Paddle inference model URLs (for Docker-based conversion).
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PADDLE_MODEL_URL = (
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"https://paddleocr.bj.bcebos.com/PP-DocLayout/PP-DocLayout_plus.tar"
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)
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# Expected input shape for the model (batch, channels, height, width).
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MODEL_INPUT_SHAPE = (1, 3, 800, 800)
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# Docker image name used for conversion.
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DOCKER_IMAGE_TAG = "breakpilot/paddle2onnx-converter:latest"
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def sha256_file(path: Path) -> str:
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"""Compute SHA-256 hex digest for a file."""
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h = hashlib.sha256()
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with open(path, "rb") as f:
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for chunk in iter(lambda: f.read(1 << 20), b""):
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h.update(chunk)
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return h.hexdigest()
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def download_file(url: str, dest: Path, desc: str = "") -> bool:
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"""Download a file with progress reporting. Returns True on success."""
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label = desc or url.split("/")[-1]
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log.info("Downloading %s ...", label)
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log.info(" URL: %s", url)
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try:
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req = urllib.request.Request(url, headers={"User-Agent": "breakpilot-export/1.0"})
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with urllib.request.urlopen(req, timeout=120) as resp:
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total = resp.headers.get("Content-Length")
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total = int(total) if total else None
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downloaded = 0
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dest.parent.mkdir(parents=True, exist_ok=True)
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with open(dest, "wb") as f:
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while True:
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chunk = resp.read(1 << 18) # 256 KB
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if not chunk:
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break
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f.write(chunk)
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downloaded += len(chunk)
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if total:
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pct = downloaded * 100 / total
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mb = downloaded / (1 << 20)
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total_mb = total / (1 << 20)
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print(
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f"\r {mb:.1f}/{total_mb:.1f} MB ({pct:.0f}%)",
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end="",
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flush=True,
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)
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if total:
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print() # newline after progress
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size_mb = dest.stat().st_size / (1 << 20)
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log.info(" Downloaded %.1f MB -> %s", size_mb, dest)
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return True
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except Exception as exc:
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log.warning(" Download failed: %s", exc)
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if dest.exists():
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dest.unlink()
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return False
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def verify_onnx(model_path: Path) -> bool:
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"""Load the ONNX model with onnxruntime, run a dummy inference, check outputs."""
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log.info("Verifying ONNX model: %s", model_path)
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try:
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import numpy as np
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except ImportError:
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log.error("numpy is required for verification: pip install numpy")
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return False
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try:
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import onnxruntime as ort
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except ImportError:
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log.error("onnxruntime is required for verification: pip install onnxruntime")
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return False
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try:
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# Load the model
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opts = ort.SessionOptions()
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opts.log_severity_level = 3 # suppress verbose logs
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session = ort.InferenceSession(str(model_path), sess_options=opts)
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# Inspect inputs
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inputs = session.get_inputs()
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log.info(" Model inputs:")
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for inp in inputs:
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log.info(" %s: shape=%s dtype=%s", inp.name, inp.shape, inp.type)
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# Inspect outputs
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outputs = session.get_outputs()
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log.info(" Model outputs:")
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for out in outputs:
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log.info(" %s: shape=%s dtype=%s", out.name, out.shape, out.type)
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# Build dummy input — use the first input's name and expected shape.
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input_name = inputs[0].name
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input_shape = inputs[0].shape
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# Replace dynamic dims (strings or None) with concrete sizes.
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concrete_shape = []
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for i, dim in enumerate(input_shape):
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if isinstance(dim, (int,)) and dim > 0:
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concrete_shape.append(dim)
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elif i == 0:
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concrete_shape.append(1) # batch
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elif i == 1:
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concrete_shape.append(3) # channels
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else:
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concrete_shape.append(800) # spatial
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concrete_shape = tuple(concrete_shape)
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# Fallback if shape looks wrong — use standard MODEL_INPUT_SHAPE.
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if len(concrete_shape) != 4:
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concrete_shape = MODEL_INPUT_SHAPE
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log.info(" Running dummy inference with shape %s ...", concrete_shape)
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dummy = np.random.randn(*concrete_shape).astype(np.float32)
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result = session.run(None, {input_name: dummy})
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log.info(" Inference succeeded — %d output tensors:", len(result))
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for i, r in enumerate(result):
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arr = np.asarray(r)
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log.info(" output[%d]: shape=%s dtype=%s", i, arr.shape, arr.dtype)
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# Basic sanity checks
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if len(result) == 0:
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log.error(" Model produced no outputs!")
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return False
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# Check for at least one output with a bounding-box-like shape (N, 4) or
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# a detection-like structure. Be lenient — different ONNX exports vary.
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has_plausible_output = False
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for r in result:
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arr = np.asarray(r)
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# Common detection output shapes: (1, N, 6), (N, 4), (N, 6), (1, N, 5+C), etc.
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if arr.ndim >= 2 and any(d >= 4 for d in arr.shape):
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has_plausible_output = True
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# Some models output (N,) labels or scores
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if arr.ndim >= 1 and arr.size > 0:
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has_plausible_output = True
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if has_plausible_output:
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log.info(" Verification PASSED")
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return True
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else:
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log.warning(" Output shapes look unexpected, but model loaded OK.")
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log.warning(" Treating as PASSED (shapes may differ by export variant).")
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return True
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except Exception as exc:
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log.error(" Verification FAILED: %s", exc)
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return False
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# ---------------------------------------------------------------------------
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# Method: Download
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# ---------------------------------------------------------------------------
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def try_download(output_dir: Path) -> bool:
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"""Attempt to download a pre-exported ONNX model. Returns True on success."""
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log.info("=== Method: DOWNLOAD ===")
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output_dir.mkdir(parents=True, exist_ok=True)
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model_path = output_dir / "model.onnx"
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for source in DOWNLOAD_SOURCES:
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log.info("Trying source: %s", source["name"])
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tmp_path = output_dir / f".{source['filename']}.tmp"
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if not download_file(source["url"], tmp_path, desc=source["name"]):
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continue
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# Check SHA-256 if known.
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if source["sha256"]:
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actual_hash = sha256_file(tmp_path)
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if actual_hash != source["sha256"]:
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log.warning(
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" SHA-256 mismatch: expected %s, got %s",
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source["sha256"],
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actual_hash,
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)
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tmp_path.unlink()
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continue
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# Basic sanity: file should be > 1 MB (a real ONNX model, not an error page).
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size = tmp_path.stat().st_size
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if size < 1 << 20:
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log.warning(" File too small (%.1f KB) — probably not a valid model.", size / 1024)
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tmp_path.unlink()
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continue
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# Move into place.
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shutil.move(str(tmp_path), str(model_path))
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log.info("Model saved to %s (%.1f MB)", model_path, model_path.stat().st_size / (1 << 20))
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return True
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log.warning("All download sources failed.")
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return False
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# ---------------------------------------------------------------------------
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# Method: Docker
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# ---------------------------------------------------------------------------
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DOCKERFILE_CONTENT = r"""
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FROM --platform=linux/amd64 python:3.11-slim
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RUN pip install --no-cache-dir \
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paddlepaddle==3.0.0 \
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paddle2onnx==1.3.1 \
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onnx==1.17.0 \
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requests
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WORKDIR /work
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# Download + extract the PP-DocLayout Paddle inference model.
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RUN python3 -c "
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import urllib.request, tarfile, os
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url = 'PADDLE_MODEL_URL_PLACEHOLDER'
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print(f'Downloading {url} ...')
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dest = '/work/pp_doclayout.tar'
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urllib.request.urlretrieve(url, dest)
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print('Extracting ...')
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with tarfile.open(dest) as t:
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t.extractall('/work/paddle_model')
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os.remove(dest)
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# List what we extracted
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for root, dirs, files in os.walk('/work/paddle_model'):
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for f in files:
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fp = os.path.join(root, f)
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sz = os.path.getsize(fp)
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print(f' {fp} ({sz} bytes)')
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"
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# Convert Paddle model to ONNX.
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# paddle2onnx expects model_dir with model.pdmodel + model.pdiparams
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RUN python3 -c "
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import os, glob, subprocess
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# Find the inference model files
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model_dir = '/work/paddle_model'
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pdmodel_files = glob.glob(os.path.join(model_dir, '**', '*.pdmodel'), recursive=True)
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pdiparams_files = glob.glob(os.path.join(model_dir, '**', '*.pdiparams'), recursive=True)
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if not pdmodel_files:
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raise FileNotFoundError('No .pdmodel file found in extracted archive')
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pdmodel = pdmodel_files[0]
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pdiparams = pdiparams_files[0] if pdiparams_files else None
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model_dir_actual = os.path.dirname(pdmodel)
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pdmodel_name = os.path.basename(pdmodel).replace('.pdmodel', '')
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print(f'Found model: {pdmodel}')
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print(f'Found params: {pdiparams}')
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print(f'Model dir: {model_dir_actual}')
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print(f'Model name prefix: {pdmodel_name}')
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cmd = [
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'paddle2onnx',
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'--model_dir', model_dir_actual,
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'--model_filename', os.path.basename(pdmodel),
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]
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if pdiparams:
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cmd += ['--params_filename', os.path.basename(pdiparams)]
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cmd += [
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'--save_file', '/work/output/model.onnx',
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'--opset_version', '14',
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'--enable_onnx_checker', 'True',
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]
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os.makedirs('/work/output', exist_ok=True)
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print(f'Running: {\" \".join(cmd)}')
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subprocess.run(cmd, check=True)
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out_size = os.path.getsize('/work/output/model.onnx')
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print(f'Conversion done: /work/output/model.onnx ({out_size} bytes)')
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"
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CMD ["cp", "-v", "/work/output/model.onnx", "/output/model.onnx"]
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""".replace(
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"PADDLE_MODEL_URL_PLACEHOLDER", PADDLE_MODEL_URL
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)
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def try_docker(output_dir: Path) -> bool:
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"""Build a Docker image to convert the Paddle model to ONNX. Returns True on success."""
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log.info("=== Method: DOCKER (linux/amd64) ===")
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# Check Docker is available.
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docker_bin = shutil.which("docker") or "/usr/local/bin/docker"
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try:
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subprocess.run(
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[docker_bin, "version"],
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capture_output=True,
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check=True,
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timeout=15,
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)
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except (subprocess.CalledProcessError, FileNotFoundError, subprocess.TimeoutExpired) as exc:
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log.error("Docker is not available: %s", exc)
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return False
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output_dir.mkdir(parents=True, exist_ok=True)
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with tempfile.TemporaryDirectory(prefix="doclayout-export-") as tmpdir:
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tmpdir = Path(tmpdir)
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# Write Dockerfile.
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dockerfile_path = tmpdir / "Dockerfile"
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dockerfile_path.write_text(DOCKERFILE_CONTENT)
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log.info("Wrote Dockerfile to %s", dockerfile_path)
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# Build image.
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log.info("Building Docker image (this downloads ~2 GB, may take a while) ...")
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build_cmd = [
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docker_bin, "build",
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"--platform", "linux/amd64",
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"-t", DOCKER_IMAGE_TAG,
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"-f", str(dockerfile_path),
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str(tmpdir),
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]
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log.info(" %s", " ".join(build_cmd))
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build_result = subprocess.run(
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build_cmd,
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capture_output=False, # stream output to terminal
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timeout=1200, # 20 min
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)
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if build_result.returncode != 0:
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log.error("Docker build failed (exit code %d).", build_result.returncode)
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return False
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# Run container — mount output_dir as /output, the CMD copies model.onnx there.
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log.info("Running conversion container ...")
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run_cmd = [
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docker_bin, "run",
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"--rm",
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"--platform", "linux/amd64",
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"-v", f"{output_dir.resolve()}:/output",
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DOCKER_IMAGE_TAG,
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]
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log.info(" %s", " ".join(run_cmd))
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run_result = subprocess.run(
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run_cmd,
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capture_output=False,
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timeout=300,
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)
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if run_result.returncode != 0:
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log.error("Docker run failed (exit code %d).", run_result.returncode)
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return False
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model_path = output_dir / "model.onnx"
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if model_path.exists():
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size_mb = model_path.stat().st_size / (1 << 20)
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log.info("Model exported: %s (%.1f MB)", model_path, size_mb)
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return True
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else:
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log.error("Expected output file not found: %s", model_path)
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return False
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# ---------------------------------------------------------------------------
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# Write metadata
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# ---------------------------------------------------------------------------
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def write_metadata(output_dir: Path, method: str) -> None:
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"""Write a metadata JSON next to the model for provenance tracking."""
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model_path = output_dir / "model.onnx"
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if not model_path.exists():
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return
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meta = {
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"model": "PP-DocLayout",
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"format": "ONNX",
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"export_method": method,
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"class_labels": CLASS_LABELS,
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"input_shape": list(MODEL_INPUT_SHAPE),
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"file_size_bytes": model_path.stat().st_size,
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"sha256": sha256_file(model_path),
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}
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meta_path = output_dir / "metadata.json"
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with open(meta_path, "w") as f:
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json.dump(meta, f, indent=2)
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log.info("Metadata written to %s", meta_path)
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# ---------------------------------------------------------------------------
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# Main
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# ---------------------------------------------------------------------------
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def main() -> int:
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parser = argparse.ArgumentParser(
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description="Export PP-DocLayout model to ONNX for document layout detection.",
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)
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parser.add_argument(
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"--output-dir",
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type=Path,
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default=Path("models/onnx/pp-doclayout"),
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help="Directory for the exported ONNX model (default: models/onnx/pp-doclayout/)",
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)
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parser.add_argument(
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"--method",
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choices=["auto", "download", "docker"],
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default="auto",
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help="Export method: auto (try download then docker), download, or docker.",
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)
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parser.add_argument(
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"--skip-verify",
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action="store_true",
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help="Skip ONNX model verification after export.",
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)
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args = parser.parse_args()
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output_dir: Path = args.output_dir
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model_path = output_dir / "model.onnx"
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# Check if model already exists.
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if model_path.exists():
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size_mb = model_path.stat().st_size / (1 << 20)
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log.info("Model already exists: %s (%.1f MB)", model_path, size_mb)
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log.info("Delete it first if you want to re-export.")
|
|
if not args.skip_verify:
|
|
if not verify_onnx(model_path):
|
|
log.error("Existing model failed verification!")
|
|
return 1
|
|
return 0
|
|
|
|
success = False
|
|
used_method = None
|
|
|
|
if args.method in ("auto", "download"):
|
|
success = try_download(output_dir)
|
|
if success:
|
|
used_method = "download"
|
|
|
|
if not success and args.method in ("auto", "docker"):
|
|
success = try_docker(output_dir)
|
|
if success:
|
|
used_method = "docker"
|
|
|
|
if not success:
|
|
log.error("All export methods failed.")
|
|
if args.method == "download":
|
|
log.info("Hint: try --method docker to convert via Docker (linux/amd64).")
|
|
elif args.method == "docker":
|
|
log.info("Hint: ensure Docker is running and has internet access.")
|
|
else:
|
|
log.info("Hint: check your internet connection and Docker installation.")
|
|
return 1
|
|
|
|
# Write metadata.
|
|
write_metadata(output_dir, used_method)
|
|
|
|
# Verify.
|
|
if not args.skip_verify:
|
|
if not verify_onnx(model_path):
|
|
log.error("Exported model failed verification!")
|
|
log.info("The file is kept at %s — inspect manually.", model_path)
|
|
return 1
|
|
else:
|
|
log.info("Skipping verification (--skip-verify).")
|
|
|
|
log.info("Done. Model ready at %s", model_path)
|
|
return 0
|
|
|
|
|
|
if __name__ == "__main__":
|
|
sys.exit(main())
|