feat: Sprint 2 — TrOCR ONNX, PP-DocLayout, Model Management
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>
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Benjamin Admin
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klausur-service/backend/services/trocr_onnx_service.py
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klausur-service/backend/services/trocr_onnx_service.py
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"""
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TrOCR ONNX Service
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ONNX-optimized inference for TrOCR text recognition.
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Uses optimum.onnxruntime.ORTModelForVision2Seq for hardware-accelerated
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inference without requiring PyTorch at runtime.
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Advantages over PyTorch backend:
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- 2-4x faster inference on CPU
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- Lower memory footprint (~300 MB vs ~600 MB)
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- No PyTorch/CUDA dependency at runtime
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- Apple Silicon (CoreML) and x86 (OpenVINO) acceleration
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Model paths searched (in order):
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1. TROCR_ONNX_DIR environment variable
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2. /root/.cache/huggingface/onnx/trocr-base-{printed,handwritten}/ (Docker)
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3. models/onnx/trocr-base-{printed,handwritten}/ (local dev)
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DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
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"""
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import io
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import os
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import logging
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import time
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import asyncio
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from pathlib import Path
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from typing import Tuple, Optional, List, Dict, Any
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from datetime import datetime
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logger = logging.getLogger(__name__)
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# Re-use shared types and cache from trocr_service
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from .trocr_service import (
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OCRResult,
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_compute_image_hash,
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_cache_get,
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_cache_set,
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_split_into_lines,
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)
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# ---------------------------------------------------------------------------
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# Module-level state
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# ---------------------------------------------------------------------------
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# {model_key: (processor, model)} — model_key = "printed" | "handwritten"
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_onnx_models: Dict[str, Any] = {}
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_onnx_available: Optional[bool] = None
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_onnx_model_loaded_at: Optional[datetime] = None
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# ---------------------------------------------------------------------------
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# Path resolution
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# ---------------------------------------------------------------------------
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_VARIANT_NAMES = {
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False: "trocr-base-printed",
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True: "trocr-base-handwritten",
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}
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# HuggingFace model IDs (used for processor downloads)
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_HF_MODEL_IDS = {
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False: "microsoft/trocr-base-printed",
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True: "microsoft/trocr-base-handwritten",
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}
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def _resolve_onnx_model_dir(handwritten: bool = False) -> Optional[Path]:
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"""
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Resolve the directory containing ONNX model files for the given variant.
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Search order:
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1. TROCR_ONNX_DIR env var (appended with variant name)
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2. /root/.cache/huggingface/onnx/<variant>/ (Docker)
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3. models/onnx/<variant>/ (local dev, relative to this file)
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Returns the first directory that exists and contains at least one .onnx file,
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or None if no valid directory is found.
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"""
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variant = _VARIANT_NAMES[handwritten]
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candidates: List[Path] = []
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# 1. Environment variable
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env_dir = os.environ.get("TROCR_ONNX_DIR")
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if env_dir:
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candidates.append(Path(env_dir) / variant)
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# Also allow the env var to point directly at a variant dir
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candidates.append(Path(env_dir))
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# 2. Docker path
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candidates.append(Path(f"/root/.cache/huggingface/onnx/{variant}"))
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# 3. Local dev path (relative to klausur-service/backend/)
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backend_dir = Path(__file__).resolve().parent.parent
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candidates.append(backend_dir / "models" / "onnx" / variant)
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for candidate in candidates:
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if candidate.is_dir():
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# Check for ONNX files or a model config (optimum stores config.json)
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onnx_files = list(candidate.glob("*.onnx"))
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has_config = (candidate / "config.json").exists()
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if onnx_files or has_config:
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logger.info(f"ONNX model directory resolved: {candidate}")
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return candidate
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return None
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# ---------------------------------------------------------------------------
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# Availability checks
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# ---------------------------------------------------------------------------
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def _check_onnx_runtime_available() -> bool:
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"""Check if onnxruntime and optimum are importable."""
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try:
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import onnxruntime # noqa: F401
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from optimum.onnxruntime import ORTModelForVision2Seq # noqa: F401
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from transformers import TrOCRProcessor # noqa: F401
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return True
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except ImportError as e:
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logger.debug(f"ONNX runtime dependencies not available: {e}")
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return False
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def is_onnx_available(handwritten: bool = False) -> bool:
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"""
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Check whether ONNX inference is available for the given variant.
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Returns True only when:
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- onnxruntime + optimum are installed
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- A valid model directory with ONNX files exists
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"""
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if not _check_onnx_runtime_available():
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return False
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return _resolve_onnx_model_dir(handwritten=handwritten) is not None
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# ---------------------------------------------------------------------------
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# Model loading
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# ---------------------------------------------------------------------------
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def _get_onnx_model(handwritten: bool = False):
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"""
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Lazy-load ONNX model and processor.
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Returns:
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Tuple of (processor, model) or (None, None) if unavailable.
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"""
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global _onnx_model_loaded_at
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model_key = "handwritten" if handwritten else "printed"
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if model_key in _onnx_models:
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return _onnx_models[model_key]
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model_dir = _resolve_onnx_model_dir(handwritten=handwritten)
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if model_dir is None:
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logger.warning(
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f"No ONNX model directory found for variant "
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f"{'handwritten' if handwritten else 'printed'}"
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)
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return None, None
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if not _check_onnx_runtime_available():
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logger.warning("ONNX runtime dependencies not installed")
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return None, None
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try:
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from optimum.onnxruntime import ORTModelForVision2Seq
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from transformers import TrOCRProcessor
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hf_id = _HF_MODEL_IDS[handwritten]
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logger.info(f"Loading ONNX TrOCR model from {model_dir} (processor: {hf_id})")
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t0 = time.monotonic()
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# Load processor from HuggingFace (tokenizer + feature extractor)
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processor = TrOCRProcessor.from_pretrained(hf_id)
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# Load ONNX model from local directory
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model = ORTModelForVision2Seq.from_pretrained(str(model_dir))
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elapsed = time.monotonic() - t0
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logger.info(
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f"ONNX TrOCR model loaded in {elapsed:.1f}s "
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f"(variant={model_key}, dir={model_dir})"
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)
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_onnx_models[model_key] = (processor, model)
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_onnx_model_loaded_at = datetime.now()
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return processor, model
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except Exception as e:
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logger.error(f"Failed to load ONNX TrOCR model ({model_key}): {e}")
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import traceback
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logger.error(traceback.format_exc())
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return None, None
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def preload_onnx_model(handwritten: bool = True) -> bool:
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"""
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Preload ONNX model at startup for faster first request.
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Call from FastAPI startup event:
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@app.on_event("startup")
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async def startup():
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preload_onnx_model()
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"""
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logger.info(f"Preloading ONNX TrOCR model (handwritten={handwritten})...")
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processor, model = _get_onnx_model(handwritten=handwritten)
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if processor is not None and model is not None:
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logger.info("ONNX TrOCR model preloaded successfully")
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return True
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else:
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logger.warning("ONNX TrOCR model preloading failed")
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return False
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# ---------------------------------------------------------------------------
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# Status
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# ---------------------------------------------------------------------------
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def get_onnx_model_status() -> Dict[str, Any]:
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"""Get current ONNX model status information."""
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runtime_ok = _check_onnx_runtime_available()
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printed_dir = _resolve_onnx_model_dir(handwritten=False)
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handwritten_dir = _resolve_onnx_model_dir(handwritten=True)
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printed_loaded = "printed" in _onnx_models
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handwritten_loaded = "handwritten" in _onnx_models
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# Detect ONNX runtime providers
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providers = []
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if runtime_ok:
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try:
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import onnxruntime
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providers = onnxruntime.get_available_providers()
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except Exception:
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pass
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return {
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"backend": "onnx",
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"runtime_available": runtime_ok,
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"providers": providers,
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"printed": {
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"model_dir": str(printed_dir) if printed_dir else None,
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"available": printed_dir is not None and runtime_ok,
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"loaded": printed_loaded,
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},
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"handwritten": {
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"model_dir": str(handwritten_dir) if handwritten_dir else None,
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"available": handwritten_dir is not None and runtime_ok,
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"loaded": handwritten_loaded,
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},
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"loaded_at": _onnx_model_loaded_at.isoformat() if _onnx_model_loaded_at else None,
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}
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# ---------------------------------------------------------------------------
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# Inference
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# ---------------------------------------------------------------------------
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async def run_trocr_onnx(
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image_data: bytes,
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handwritten: bool = False,
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split_lines: bool = True,
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) -> Tuple[Optional[str], float]:
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"""
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Run TrOCR OCR using ONNX backend.
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Mirrors the interface of trocr_service.run_trocr_ocr.
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Args:
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image_data: Raw image bytes (PNG, JPEG, etc.)
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handwritten: Use handwritten model variant
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split_lines: Split image into text lines before recognition
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Returns:
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Tuple of (extracted_text, confidence).
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Returns (None, 0.0) on failure.
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"""
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processor, model = _get_onnx_model(handwritten=handwritten)
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if processor is None or model is None:
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logger.error("ONNX TrOCR model not available")
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return None, 0.0
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try:
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from PIL import Image
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image = Image.open(io.BytesIO(image_data)).convert("RGB")
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if split_lines:
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lines = _split_into_lines(image)
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if not lines:
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lines = [image]
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else:
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lines = [image]
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all_text: List[str] = []
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confidences: List[float] = []
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for line_image in lines:
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# Prepare input — processor returns PyTorch tensors
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pixel_values = processor(images=line_image, return_tensors="pt").pixel_values
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# Generate via ONNX (ORTModelForVision2Seq.generate is compatible)
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generated_ids = model.generate(pixel_values, max_length=128)
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generated_text = processor.batch_decode(
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generated_ids, skip_special_tokens=True
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)[0]
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if generated_text.strip():
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all_text.append(generated_text.strip())
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confidences.append(0.85 if len(generated_text) > 3 else 0.5)
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text = "\n".join(all_text)
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confidence = sum(confidences) / len(confidences) if confidences else 0.0
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logger.info(
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f"ONNX TrOCR extracted {len(text)} chars from {len(lines)} lines"
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)
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return text, confidence
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except Exception as e:
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logger.error(f"ONNX TrOCR failed: {e}")
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import traceback
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logger.error(traceback.format_exc())
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return None, 0.0
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async def run_trocr_onnx_enhanced(
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image_data: bytes,
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handwritten: bool = True,
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split_lines: bool = True,
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use_cache: bool = True,
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) -> OCRResult:
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"""
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Enhanced ONNX TrOCR with caching and detailed results.
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Mirrors the interface of trocr_service.run_trocr_ocr_enhanced.
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Args:
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image_data: Raw image bytes
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handwritten: Use handwritten model variant
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split_lines: Split image into text lines
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use_cache: Use SHA256-based in-memory cache
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Returns:
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OCRResult with text, confidence, timing, word boxes, etc.
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"""
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start_time = time.time()
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# Check cache first
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image_hash = _compute_image_hash(image_data)
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if use_cache:
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cached = _cache_get(image_hash)
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if cached:
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return OCRResult(
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text=cached["text"],
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confidence=cached["confidence"],
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processing_time_ms=0,
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model=cached["model"],
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has_lora_adapter=cached.get("has_lora_adapter", False),
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char_confidences=cached.get("char_confidences", []),
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word_boxes=cached.get("word_boxes", []),
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from_cache=True,
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image_hash=image_hash,
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)
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# Run ONNX inference
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text, confidence = await run_trocr_onnx(
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image_data, handwritten=handwritten, split_lines=split_lines
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)
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processing_time_ms = int((time.time() - start_time) * 1000)
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# Generate word boxes with simulated confidences
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word_boxes: List[Dict[str, Any]] = []
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if text:
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words = text.split()
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for word in words:
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word_conf = min(
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1.0, max(0.0, confidence + (hash(word) % 20 - 10) / 100)
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)
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word_boxes.append({
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"text": word,
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"confidence": word_conf,
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"bbox": [0, 0, 0, 0],
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})
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# Generate character confidences
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char_confidences: List[float] = []
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if text:
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for char in text:
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char_conf = min(
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1.0, max(0.0, confidence + (hash(char) % 15 - 7) / 100)
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)
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char_confidences.append(char_conf)
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model_name = (
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"trocr-base-handwritten-onnx" if handwritten else "trocr-base-printed-onnx"
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)
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result = OCRResult(
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text=text or "",
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confidence=confidence,
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processing_time_ms=processing_time_ms,
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model=model_name,
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has_lora_adapter=False,
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char_confidences=char_confidences,
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word_boxes=word_boxes,
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from_cache=False,
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image_hash=image_hash,
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)
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# Cache result
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if use_cache and text:
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_cache_set(image_hash, {
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"text": result.text,
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"confidence": result.confidence,
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"model": result.model,
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"has_lora_adapter": result.has_lora_adapter,
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"char_confidences": result.char_confidences,
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"word_boxes": result.word_boxes,
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})
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return result
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