diff --git a/.gitignore b/.gitignore index 5b4c0dad..df9a22e1 100644 --- a/.gitignore +++ b/.gitignore @@ -442,3 +442,6 @@ pip-selfcheck.json # Docs # docs/**/*.png # docs/**/*.svg +checkpoints/* +outputs_evals/* +output_eval/* diff --git a/docling_eval/dataset_builders/cvat_preannotation_builder.py b/docling_eval/dataset_builders/cvat_preannotation_builder.py index 0974a012..21693aa6 100644 --- a/docling_eval/dataset_builders/cvat_preannotation_builder.py +++ b/docling_eval/dataset_builders/cvat_preannotation_builder.py @@ -47,7 +47,6 @@ def __init__( target: Path, bucket_size: int = 200, use_predictions: bool = False, - sliding_window: int = 2, ): """ Initialize the CvatPreannotationBuilder. @@ -56,13 +55,10 @@ def __init__( dataset_source: Directory containing the source dataset target: Directory where CVAT preannotations will be saved bucket_size: Number of documents per bucket for CVAT tasks - use_predictions: Whether to use predictions instead of ground truth - sliding_window: Size of sliding window for page processing (1 for single pages, >1 for multi-page windows) """ self.source_dir = dataset_source self.target_dir = target self.bucket_size = bucket_size - self.sliding_window = sliding_window self.benchmark_dirs = BenchMarkDirs() self.benchmark_dirs.set_up_directory_structure( source=dataset_source, target=target @@ -803,7 +799,7 @@ def prepare_for_annotation(self) -> None: _log.info(f"Preparing dataset from {self.source_dir} for CVAT annotation") self._create_project_properties() self.overview = self._export_from_dataset() - self._create_preannotation_files(sliding_window=self.sliding_window) + self._create_preannotation_files(sliding_window=1) _log.info(f"CVAT annotation preparation complete in {self.target_dir}") diff --git a/docling_eval/end_to_end/batch_inference_vllm.py b/docling_eval/end_to_end/batch_inference_vllm.py new file mode 100644 index 00000000..28a7bbec --- /dev/null +++ b/docling_eval/end_to_end/batch_inference_vllm.py @@ -0,0 +1,155 @@ +import time +import argparse +import os +from typing import Dict +from transformers import AutoProcessor +from vllm import LLM, SamplingParams +from PIL import Image +from io import BytesIO + +from datasets import load_dataset, load_from_disk, DatasetDict + + +def load_hf_dataset(dataset_path, num_workers, split="test"): + try: + dataset = load_dataset(dataset_path, split=split, num_proc=num_workers) + except: + dataset = load_from_disk(dataset_path) + if isinstance(dataset, DatasetDict) and split in dataset.keys(): + dataset = dataset[split] + elif isinstance(dataset, DatasetDict) and "train" in dataset.keys(): + dataset = dataset["train"] + return dataset + + +def main(): + parser = argparse.ArgumentParser(description="Batch process images using LLM.") + parser.add_argument( + "--model_path", + type=str, + required=True, + help="Path to the model checkpoint directory.", + ) + parser.add_argument( + "--dataset_path", + type=str, + required=True, + help="Dataset path (Hugging Face repo or disk path).", + ) + parser.add_argument("--split", type=str, default="test", help="Split to load.") + parser.add_argument( + "--output_dir", + type=str, + required=True, + help="Directory to save outputs.", + ) + parser.add_argument( + "--prompt", + type=str, + default="Convert this page to docling.", + help="Prompt for the LLM.", + ) + parser.add_argument( + "--batch_size", type=int, default=160, help="Batch size for inference." + ) + parser.add_argument( + "--log_interval", type=int, default=100, help="Logging interval." + ) + parser.add_argument( + "--num_workers", type=int, default=16, help="Number of dataset loading workers." + ) + + args = parser.parse_args() + + os.makedirs(args.output_dir, exist_ok=True) + + dataset = load_hf_dataset(args.dataset_path, args.num_workers, args.split) + print(dataset) + + if "document_id" in dataset.features: + ids = [id for id in dataset["document_id"]] + elif "id" in dataset.features: + ids = [id for id in dataset["id"]] + else: + print( + "๐Ÿšจ Could not find id column in dataset. Falling back to simple enumeration." + ) + ids = list(range(len(dataset))) + + if "GroundTruthPageImages" in dataset.features: + images = [imgs[0] for imgs in dataset["GroundTruthPageImages"]] + elif "images" in dataset.features: + images = [imgs[0] for imgs in dataset["images"]] + elif "image" in dataset.features: + images = [img for img in dataset["image"]] + else: + raise Exception("Image(s) column not found in dataset") + + pil_images = [] + for image in images: + if isinstance(image, Dict) and "bytes" in image: + pil_images.append(Image.open(BytesIO(image["bytes"]))) + elif isinstance(image, Image.Image): + pil_images.append(image) + else: + raise Exception(f"Unsupported image type: {type(image)}") + + assert len(pil_images) == len(ids) + images = pil_images + + llm = LLM(model=args.model_path, limit_mm_per_prompt={"image": 1}, seed=42) + + sampling_params = SamplingParams( + temperature=0.0, + max_tokens=8192, + skip_special_tokens=False, + stop_token_ids=[100338], # change + ) + + messages = [ + { + "role": "user", + "content": [{"type": "image"}, {"type": "text", "text": args.prompt}], + }, + ] + + processor = AutoProcessor.from_pretrained(args.model_path) + prompt = processor.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + + start_time = time.time() + + for i in range(0, len(images), args.batch_size): + batch_images = images[i : i + args.batch_size] + batch_ids = ids[i : i + args.batch_size] + + llm_inputs = [ + {"prompt": prompt, "multi_modal_data": {"image": img}} + for img in batch_images + ] + + outputs = llm.generate(llm_inputs, sampling_params=sampling_params) + + for id_, output in zip(batch_ids, outputs): + output_text = output.outputs[0].text + output_path = os.path.join(args.output_dir, f"{id_}.dt") + with open(output_path, "w", encoding="utf-8") as f: + f.write(output_text) + + del llm_inputs, outputs + + if (i + len(batch_images)) % args.log_interval == 0 or i + len( + batch_images + ) == len(images): + print(f"Processed | {i + len(batch_images)} / {len(images)} images") + + total_time = time.time() - start_time + print(f"Total time: {total_time:.2f} sec") + + +if __name__ == "__main__": + main() + + +# bsub -q normal -n 1 -R "span[hosts=1]" -M 200G -gpu "num=1:mode=exclusive_process" -oo ~/.lsbatch/batch_inference_vllm.stdout -eo ~/.lsbatch/bath_inference_vllm.stderr python3 batch_inference_vllm.py diff --git a/docling_eval/end_to_end/doctag_stast.py b/docling_eval/end_to_end/doctag_stast.py new file mode 100644 index 00000000..df09aa85 --- /dev/null +++ b/docling_eval/end_to_end/doctag_stast.py @@ -0,0 +1,28 @@ +from pathlib import Path + +folder = Path( + "/gpfs/ZuFS1/proj/deep-search/mao/repos/docling-eval/output_eval_no_rl/model_output/ocr" +) # change this +target_string = "" + +total_files = 0 +matching_files = 0 + +for file in folder.iterdir(): + if file.is_file(): + total_files += 1 + try: + content = file.read_text(encoding="utf-8", errors="ignore") + if target_string in content: + matching_files += 1 + except Exception as e: + print(f"Error reading {file}: {e}") + +print("๐Ÿ“Š Stats:") +print(f"Total files checked: {total_files}") +print(f"Files containing tag: {matching_files}") +print( + f"Percentage: {matching_files / total_files * 100:.2f}%" + if total_files + else "N/A" +) diff --git a/docling_eval/end_to_end/equation_config/latex.vocab b/docling_eval/end_to_end/equation_config/latex.vocab new file mode 100644 index 00000000..27c9d6f8 --- /dev/null +++ b/docling_eval/end_to_end/equation_config/latex.vocab @@ -0,0 +1,598 @@ + +! +" +& +( +) +* ++ +, +- +. +/ +0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +; +< += +> +? +A +B +C +D +E +F +G +H +I +J +K +L +M +N +O +P +Q +R +S +T +U +V +W +X +Y +Z +[ +\ +\# +\% +\& +\' +\, +\/ +\AA +\Big +\Box +\Delta +\Diamond +\Doteq +\Downarrow +\Finv +\Gamma +\Hat +\Im +\L +\Lambda +\Leftarrow +\Leftrightarrow +\Longleftarrow +\Longleftrightarrow +\Longrightarrow +\Omega +\P +\Phi +\Pi +\Pr +\Psi +\Re +\Rightarrow +\S +\Sigma +\Subset +\Theta +\Tilde +\Uparrow +\Updownarrow +\Upsilon +\Xi +\\ +\_ +\acute +\aleph +\alpha +\amalg +\approx +\approxeq +\arccos +\arcsin +\arctan +\arg +\ast +\asymp +\backsimeq +\bar +\because +\begin{Bmatrix} +\begin{Vmatrix} +\begin{array} +\begin{bmatrix} +\begin{cases} +\begin{dcases} +\begin{matrix} +\begin{pmatrix} +\begin{psmallmatrix} +\begin{rcases} +\begin{smallmatrix} +\begin{split} +\begin{subarray} +\begin{vmatrix} +\beta +\beth +\bigcap +\bigcirc +\bigcup +\bigoplus +\bigotimes +\bigsqcup +\bigtriangledown +\bigtriangleup +\bigvee +\bigwedge +\binom +\blacklozenge +\blacksquare +\blacktriangle +\blacktriangledown +\blacktriangleleft +\blacktriangleright +\bmatrix +\bmod +\bot +\bowtie +\boxed +\breve +\bullet +\cap +\cdot +\cdots +\centerdot +\cfrac +\check +\chi +\choose +\circ +\circledast +\circledcirc +\circleddash +\clubsuit +\colon +\coloneqq +\complement +\cong +\coprod +\cos +\cosh +\cot +\coth +\csc +\cup +\dag +\dagger +\daleth +\dashleftarrow +\dashrightarrow +\dashv +\ddag +\ddagger +\ddddot +\dddot +\ddot +\ddots +\deg +\delta +\det +\diag +\diagdown +\diagup +\diamond +\diamondsuit +\digamma +\dim +\div +\divideontimes +\dot +\doteq +\dots +\downarrow +\downdownarrows +\downharpoonleft +\downharpoonright +\ell +\emph +\emptyset +\end{Bmatrix} +\end{Vmatrix} +\end{array} +\end{bmatrix} +\end{cases} +\end{dcases} +\end{matrix} +\end{pmatrix} +\end{psmallmatrix} +\end{rcases} +\end{smallmatrix} +\end{split} +\end{subarray} +\end{vmatrix} +\epsilon +\eqqcolon +\eqsim +\equiv +\eta +\exists +\exp +\flat +\forall +\frac +\frown +\gamma +\gcd +\geq +\geqq +\geqslant +\gets +\gg +\gimel +\grave +\gtrapprox +\gtrdot +\gtreqqless +\gtrless +\gtrsim +\gvertneqq +\hat +\hbar +\hdots +\heartsuit +\hom +\hookleftarrow +\hookrightarrow +\hslash +\i +\iff +\iiint +\iint +\imath +\impliedby +\implies +\in +\inf +\infty +\int +\intercal +\iota +\jmath +\kappa +\ker +\lambda +\land +\langle +\lceil +\left +\leftarrow +\leftharpoondown +\leftharpoonup +\leftrightarrow +\leftrightharpoons +\leq +\leqq +\leqslant +\lessapprox +\lesssim +\lfloor +\lgroup +\lhd +\lim +\liminf +\ll +\llbrakcket +\llcorner +\lll +\ln +\lnapprox +\lneq +\lneqq +\lnsim +\log +\longleftarrow +\longleftrightarrow +\longmapsto +\longrightarrow +\lor +\lozenge +\lq +\lrcorner +\ltimes +\lvertneqq +\mapsto +\mathbb +\mathcal +\mathclap +\mathellipsis +\mathfrak +\mathring +\max +\measuredangle +\mho +\min +\mod +\models +\mp +\mu +\nVDash +\nVdash +\nabla +\natural +\ncong +\ne +\nearrow +\neg +\neq +\nexists +\ngeq +\ngeqq +\ngeqslant +\ngtr +\ni +\nleq +\nleqq +\nleqslant +\nless +\nmid +\nolimits +\nonumber +\not +\notin +\nparallel +\nprec +\npreceq +\nrightarrow +\nshortmid +\nshortparallel +\nsim +\nsubseteq +\nsubseteqq +\nsucc +\nsucceq +\nsupseteq +\nsupseteqq +\ntriangleleft +\ntrianglelefteq +\ntriangleright +\ntrianglerighteq +\nu +\null +\nvDash +\nvdash +\nwarrow +\odot +\oint +\omega +\ominus +\oplus +\oslash +\otimes +\overbrace +\overleftarrow +\overline +\overrightarrow +\overset +\overwithdelims +\partial +\perp +\phi +\pi +\pm +\pmod +\pod +\prec +\preceq +\precnapprox +\precneqq +\precnsim +\prescript +\prime +\probability +\prod +\psi +\quad +\rangle +\rceil +\real +\restriction +\rfloor +\rgroup +\rhd +\rho +\right +\rightarrow +\rightharpoondown +\rightharpoonup +\rightleftharpoons +\risingdotseq +\romannumeral +\rq +\rtimes +\searrow +\sec +\shortmid +\shortparallel +\shortvdotswithin +\sideset +\sigma +\sim +\simeq +\sin +\sinh +\sl +\slash +\smallsetminus +\smile +\sqcap +\sqcup +\sqrt +\sqsubset +\sqsubseteq +\sqsubsetneq +\sqsupset +\sqsupseteq +\sqsupsetneq +\square +\stackrel +\strut +\subset +\subseteq +\subseteqq +\subsetneq +\subsetneqq +\substack +\succ +\succeq +\succnapprox +\succneqq +\succnsim +\sum +\sup +\supset +\supseteq +\supseteqq +\supsetneq +\supsetneqq +\surd +\swarrow +\tag +\tan +\tanh +\tau +\tbinom +\text +\therefore +\theta +\tilde +\times +\to +\top +\triangle +\triangleleft +\triangleright +\tt +\twoheadleftarrow +\twoheadrightarrow +\ulcorner +\underbar +\underbrace +\underbracket +\underleftarrow +\underline +\underrightarrow +\underset +\unitlength +\unlhd +\unrhd +\uparrow +\updownarrow +\upharpoonleft +\upharpoonright +\uplus +\upshape +\upsilon +\urcorner +\vDash +\varGamma +\varLambda +\varOmega +\varPhi +\varPi +\varPsi +\varSigma +\varTheta +\varUpsilon +\varXi +\varepsilon +\varinjlim +\varkappa +\varliminf +\varlimsup +\varnothing +\varphi +\varpi +\varprojlim +\varpropto +\varrho +\varsigma +\varsubsetneq +\varsubsetneqq +\varsupsetneq +\varsupsetneqq +\vartheta +\vartriangle +\vartriangleleft +\vartriangleright +\vcentcolon +\vdash +\vdots +\vec +\vee +\veebar +\veqno +\wedge +\widehat +\widetilde +\wp +\wr +\xRightarrow +\xhookleftarrow +\xhookrightarrow +\xi +\xleftarrow +\xrightarrow +\zeta +\{ +\| +\} +] +^ +_ +_END_ +_PAD_ +_START_ +_UNK_ +` +a +b +c +d +e +f +g +h +i +j +k +l +m +n +o +p +q +r +s +t +u +v +w +x +y +z +{ 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new file mode 100644 index 00000000..0e639d08 --- /dev/null +++ b/docling_eval/end_to_end/evaluate_code_equations.py @@ -0,0 +1,247 @@ +from __future__ import annotations + +import argparse +import json +from multiprocessing import cpu_count +from pathlib import Path +import re +from typing import Dict, List, Tuple + +import evaluate +from datasets import load_dataset + +from nltk import download as nltk_download +from nltk import edit_distance, word_tokenize +from nltk.metrics import f_measure, precision, recall +from nltk.translate import meteor_score + +nltk_download("punkt", quiet=True) +bleu_eval = evaluate.load("bleu") + + +class MarkdownTextEvaluator: + """Compute a bundle of stringโ€‘similarity metrics for a pair of snippets.""" + + def __init__(self): + self._bleu_eval = bleu_eval + + def _compute_bleu_score(self, true_txt: str, pred_txt: str) -> float: + try: + result = self._bleu_eval.compute( + predictions=[pred_txt], references=[[true_txt]] + ) + bleu = result["bleu"] + except Exception: + bleu = 0.0 + return bleu + + def compute(self, true_txt: str, pred_txt: str) -> Dict[str, float]: + """Return dict of metrics for *one* (reference, prediction) pair.""" + true_tok = word_tokenize(true_txt) + pred_tok = word_tokenize(pred_txt) + true_set, pred_set = set(true_tok), set(pred_tok) + + bleu = self._compute_bleu_score(true_txt, pred_txt) + + try: + f1_ = f_measure(true_set, pred_set) + except Exception: + f1_ = 0.0 + try: + prec_ = precision(true_set, pred_set) + except Exception: + prec_ = 0.0 + try: + rec_ = recall(true_set, pred_set) + except Exception: + rec_ = 0.0 + try: + edit_dist = edit_distance(pred_tok, true_tok) / max( + len(pred_tok), len(true_tok) + ) + except: + edit_dist = 1.0 + try: + meteor = meteor_score.meteor_score([true_tok], pred_tok) + except Exception: + meteor = 0.0 + + return { + "bleu_score": bleu if bleu else 0.0, + "f1_score": f1_ if f1_ else 0.0, + "precision": prec_ if prec_ else 0.0, + "recall": rec_ if rec_ else 0.0, + "edit_distance": edit_dist if edit_dist is not None else 1.0, + "meteor": meteor if meteor else 0.0, + } + + +def _eval_pair(args: Tuple[int, str, str]) -> Dict[str, float]: + """Worker function executed in a child process. + + Each worker instantiates its own evaluator (lightweight) โ€“ avoids pickling + issues with *evaluate* objects. + """ + + idx, true_txt, pred_txt = args + evaluator = MarkdownTextEvaluator() + metrics = evaluator.compute(true_txt, pred_txt) + metrics["index"] = idx + return metrics + +def sanitize(text: str) -> str: + to_remove = [ + " ", + "", + " ", + "", + "", + "", + "", + ] + for s in to_remove: + text = text.replace(s, "") + + # pattern = r"^<_([^_>]+)_>\s(.*)" + # match = re.match(pattern, text, flags=re.DOTALL) + # if match: + # text = str(match.group(2)) # everything after the <_language_> + + # text = text.strip() + + return text + + +def compute_scores( + inferred_dir: Path, + gt_dataset_path: Path, + output_dir: Path, + num_workers: int, + split: str = "test", +) -> None: + """Evaluate prediction files in *inferred_dir* against the dataset *split*. + + Work is farmed out to a `concurrent.futures.ProcessPoolExecutor` to utilise + multiple CPU cores for tokenisation & metric computation. + """ + + inferred_dir = inferred_dir.expanduser().resolve() + output_dir = output_dir.expanduser().resolve() + output_dir.mkdir(parents=True, exist_ok=True) + + # Sort numerically by stem so that order matches dataset. + pred_files: List[Path] = sorted( + inferred_dir.iterdir(), key=lambda p: (p.stem.split(".")[0]) + ) + if not pred_files: + raise FileNotFoundError(f"No prediction files found in {inferred_dir}") + + gt_dataset = load_dataset( + str(gt_dataset_path), split=split, num_proc=num_workers or cpu_count() + ) + id_to_gt = { + str(rec["id"]): sanitize(rec["texts"][0]["assistant"]) for rec in gt_dataset + } + if len(pred_files) != len(gt_dataset): + raise ValueError( + "Length mismatch " + f"{len(pred_files)} predictions vs {len(gt_dataset)} references" + ) + + jobs = [] + for pred_path in inferred_dir.iterdir(): + pred_id = pred_path.stem + if pred_id not in id_to_gt: + print(f"โš ๏ธ Warning: {pred_id} not in ground truth dataset. Skipping.") + continue + + pred_txt = sanitize(pred_path.read_text(encoding="utf8", errors="ignore")) + true_txt = id_to_gt[pred_id] + jobs.append( + ( + pred_id, + true_txt, + pred_txt + ) + ) + + from concurrent.futures import ProcessPoolExecutor + from tqdm import tqdm + + per_example: List[Dict[str, float]] = [] + + if num_workers == 1: + # Serial path โ€“ useful for debugging or when requested. + for job in tqdm(jobs, desc="Evaluating", unit="sample"): + per_example.append(_eval_pair(job)) + else: + with ProcessPoolExecutor(max_workers=num_workers) as pool: + for result in tqdm( + pool.map(_eval_pair, jobs), + total=len(jobs), + desc="Evaluating", + unit="sample", + ): + per_example.append(result) + + # ------------------------------------------------------------------ + # Persist results & aggregate means + # ------------------------------------------------------------------ + per_example_path = output_dir / "per_example_scores.json" + mean_scores_path = output_dir / "mean_scores.json" + + with per_example_path.open("w", encoding="utf8") as f: + json.dump(per_example, f, indent=2) + + metric_keys = [ + "bleu_score", + "f1_score", + "precision", + "recall", + "edit_distance", + "meteor", + ] + mean_scores = { + k: sum(d[k] for d in per_example) / len(per_example) for k in metric_keys + } + + with mean_scores_path.open("w", encoding="utf8") as f: + json.dump(mean_scores, f, indent=2) + +def main() -> None: + parser = argparse.ArgumentParser( + description="Compute stringโ€‘similarity metrics for markdown/code recognition tasks (multiprocess)", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument( + "--file_source_path", type=Path, help="Directory with modelโ€‘generated snippet files." + ) + parser.add_argument( + "--gt_dir", type=Path, help="Path to the ๐Ÿค— dataset (local script or repo)." + ) + parser.add_argument( + "--output_dir", type=Path, help="Output directory for JSON results." + ) + parser.add_argument( + "--num_workers", + type=int, + default=cpu_count(), + help="How many processes to spawn (1 โ‡’ serial).", + ) + parser.add_argument( + "--split", type=str, default="test", help="Which dataset split to use." + ) + + args = parser.parse_args() + + compute_scores( + inferred_dir=args.file_source_path, + gt_dataset_path=args.gt_dir, + output_dir=args.output_dir, + num_workers=args.num_workers, + split=args.split, + ) + + +if __name__ == "__main__": + main() diff --git a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh new file mode 100755 index 00000000..ddbd92ae --- /dev/null +++ b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh @@ -0,0 +1,307 @@ +#!/usr/bin/env bash +set -euo pipefail + +source .venv/bin/activate + +timestamp=$(date +"%Y%m%d-%H%M%S") +uuid=$(uuidgen | tr '[:upper:]' '[:lower:]' | cut -c1-8) + +# ------------------------------ +# Default values +# ------------------------------ +model_path="" # if not passed only evaluations will be performed, if inference data already present +# don't pass any of these params to avoid evaluating for this task +layout_dataset_path="" +ocr_dataset_path="" +table_dataset_path="" +equation_dataset_path="" +code_dataset_path="" + +output_dir="" +num_workers=64 + +# ------------------------------ +# Parse args +# ------------------------------ +while [[ $# -gt 0 ]]; do + case $1 in + --model_path) model_path="$2"; shift 2 ;; + --layout_dataset_path) layout_dataset_path="$2"; shift 2 ;; + --ocr_dataset_path) ocr_dataset_path="$2"; shift 2 ;; + --table_dataset_path) table_dataset_path="$2"; shift 2 ;; + --equation_dataset_path) equation_dataset_path="$2"; shift 2 ;; + --code_dataset_path) code_dataset_path="$2"; shift 2 ;; + --output_dir) output_dir="$2"; shift 2 ;; + --num_workers) num_workers="$2"; shift 2 ;; + --help |-h) + echo "Usage: $0 [--model_path path] [--layout_dataset_path path] ... [--output_dir dir] [--num_workers N]" + exit 0 + ;; + *) echo "โŒ Unknown option $1"; exit 1 ;; + esac +done + +printf '%s\n' "-----------------------------------------------" +printf '| %-51s \n' "โœ… Parsed args:" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "model_path" "$model_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "layout_dataset_path" "$layout_dataset_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "ocr_dataset_path" "$ocr_dataset_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "table_dataset_path" "$table_dataset_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "equation_dataset_path" "$equation_dataset_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "code_dataset_path" "$code_dataset_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "output_dir" "$output_dir" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "num_workers" "$num_workers" +printf '%s\n' "-----------------------------------------------" + +if [ -z "$output_dir" ]; then + if [ -n "$model_path" ]; then + # Extract basename from model path, stripping trailing slash + model_name=$(basename "${model_path%/}") + output_dir="./outputs_evals/output_eval_${model_name}_${timestamp}_${uuid}" + else + output_dir="./outputs_evals/output_eval_${timestamp}_${uuid}" + fi +fi + + +skip_layout=false +if [ -z "$layout_dataset_path" ]; then + echo "๐Ÿšจ layout_dataset_path not passed, skipping the evaluation for this task." + skip_layout=true +fi + +skip_ocr=false +if [ -z "$ocr_dataset_path" ]; then + echo "๐Ÿšจ ocr_dataset_path not passed, skipping the evaluation for this task." + skip_ocr=true +fi + +skip_table=false +if [ -z "$table_dataset_path" ]; then + echo "๐Ÿšจ table_dataset_path not passed, skipping the evaluation for this task." + skip_table=true +fi + +skip_equation=false +if [ -z "$equation_dataset_path" ]; then + echo "๐Ÿšจ equation_dataset_path not passed, skipping the evaluation for this task." + skip_equation=true +fi + +skip_code=false +if [ -z "$code_dataset_path" ]; then + echo "๐Ÿšจ code_dataset_path not passed, skipping the evaluation for this task." + skip_code=true +fi + +mkdir -p "$output_dir" + +skip_inference=false +if [ -z "$model_path" ]; then + skip_inference=true + required=(layout ocr table equation code) + echo "๐Ÿšจ Model path has not been passed. This means that model output should already be present. Skipping Inference." + for name in "${required[@]}"; do + var="skip_${name}" + # if weโ€™re skipping this task, we must have the folder under inferred_path + if [ "${!var}" = "false" ]; then + if [ ! -d "${output_dir}/model_output/${name}" ]; then + echo "๐Ÿšจ Missing required folder '${name}' under '${output_dir}/model_output' for skipped task." + exit 1 + fi + fi + done + echo "โœ… All required inferred sub-folders are present." +fi + +printf '%s\n' "-----------------------------------------------" +printf '| %-18s %s\n' "โญ๏ธ Skip Inference?" "$skip_inference" +printf '| %-16s %s\n' "โญ๏ธ Skip Layout?" "$skip_layout" +printf '| %-16s %s\n' "โญ๏ธ Skip OCR?" "$skip_ocr" +printf '| %-16s %s\n' "โญ๏ธ Skip Table?" "$skip_table" +printf '| %-16s %s\n' "โญ๏ธ Skip Equation?" "$skip_equation" +printf '| %-16s %s\n' "โญ๏ธ Skip Code?" "$skip_code" +printf '%s\n' "-----------------------------------------------" + + + +# --------------------------------------------------------- +# Inference (run only if weโ€™re *not* skipping inference) +# --------------------------------------------------------- +if [ "$skip_inference" = false ]; then + declare -A data_paths=( + [layout]="$layout_dataset_path" + [ocr]="$ocr_dataset_path" + [table]="$table_dataset_path" + [equation]="$equation_dataset_path" + [code]="$code_dataset_path" + ) + + declare -A prompts=( + [layout]="Convert this page to docling." + [ocr]="Convert this page to docling." + [table]="Convert table to OTSL." + [equation]="Convert formula to LaTeX." + [code]="Convert code to text." + ) + + declare -A splits=( + [layout]="test" + [ocr]="test" + [table]="test" + [equation]="test" + [code]="test" + ) + + declare -A dataset_to_task + + for task in layout ocr table equation code; do + skip_var="skip_${task}" + if [ "${!skip_var}" = false ]; then + dataset="${data_paths[$task]}" + if [[ -v 'dataset_to_task["'"$dataset"'"]' ]]; then + # This dataset has already been assigned to another task + original_task="${dataset_to_task[$dataset]}" + echo "๐Ÿ” Detected identical dataset for $task and $original_task." + echo "๐Ÿ”— Creating symlink to reuse output from $original_task for $task." + mkdir -p "${output_dir}/model_output" + ln -sfn "${original_task}" "${output_dir}/model_output/${task}" + else + dataset_to_task[$dataset]="$task" + fi + fi + done + + for task in layout ocr table equation code; do + skip_var="skip_${task}" + if [ "${!skip_var}" = false ]; then + if [ ! -L "${output_dir}/model_output/${task}" ]; then + echo "๐Ÿš€ Performing inference for **${task^^}** on dataset ${data_paths[$task]}" + mkdir -p "${output_dir}/model_output/${task}" + uv run python3 docling_eval/end_to_end/batch_inference_vllm.py \ + --model_path "$model_path" \ + --dataset_path "${data_paths[$task]}" \ + --split "${splits[$task]}" \ + --output_dir "${output_dir}/model_output/${task}" \ + --num_workers "$num_workers" \ + --prompt "${prompts[$task]}" + else + echo "โญ๏ธ Skipping inference for ${task} (already linked to another task)." + fi + fi + done +fi + +# normalize equations +# if [ "$skip_equation" = false ]; then +# echo "๐Ÿงฎ Normalizing equations..." +# mkdir -p "${output_dir}/model_output/equation_normalized" +# uv run python3 docling_eval/end_to_end/normalize_equations.py \ +# --input_dir "${output_dir}/model_output/equation" \ +# --output_dir "${output_dir}/model_output/equation_normalized" \ +# --num_workers "$num_workers" +# printf '%s\n' "-----------------------------------------------" +# fi + +# --------------------------------------------------------- +# Evaluation & visualisation with `docling_eval` +# --------------------------------------------------------- +declare -A modality=( + [layout]="layout" + [ocr]="markdown_text" + [table]="table_structure" +) + +declare -A dataset_paths=( + [layout]="$layout_dataset_path" + [ocr]="$ocr_dataset_path" + [table]="$table_dataset_path" + [equation]="$equation_dataset_path" + [code]="$code_dataset_path" +) + +declare -A output_dirs=( + [layout]="${output_dir}/evals/layout/" + [ocr]="${output_dir}/evals/ocr/" + [table]="${output_dir}/evals/table/" + [equation]="${output_dir}/evals/equation/" + [code]="${output_dir}/evals/code/" +) + +declare -A splits=( + [layout]="test" + [ocr]="test" + [table]="test" + [equation]="test" + [code]="test" +) + +declare -A benchmarks=( + [layout]="DocLayNetV2" + [ocr]="DocLayNetV2" + [table]="FinTabNet" +) + +declare -A file_source_paths=( + [layout]="${output_dir}/model_output/layout" + [ocr]="${output_dir}/model_output/ocr" + [table]="${output_dir}/model_output/table" + [equation]="${output_dir}/model_output/equation" + [code]="${output_dir}/model_output/code" + +) +# evaluate tasks for which you can use docling_eval +for task in layout ocr table; do + skip_var="skip_${task}" + if [ "${!skip_var}" = false ]; then + echo "๐Ÿ“ Evaluating **${task}** (modality: ${modality[$task]}) โ€ฆ" + + echo " ๐Ÿ‘‰ Running create-eval โ€ฆ" + uv run docling-eval \ + create-eval \ + --benchmark ${benchmarks[$task]} \ + --gt-dir ${dataset_paths[$task]}\ + --prediction-provider File \ + --file-prediction-format doctags \ + --file-source-path ${file_source_paths[$task]} \ + --output-dir "${output_dirs[$task]}" + + echo " ๐Ÿ‘‰ Running evaluate โ€ฆ" + uv run docling-eval evaluate \ + --modality "${modality[$task]}" \ + --benchmark "${benchmarks[$task]}" \ + --output-dir "${output_dirs[$task]}" \ + --split "${splits[$task]}" + + echo " ๐Ÿ‘‰ Running visualize โ€ฆ" + uv run docling-eval visualize \ + --modality "${modality[$task]}" \ + --benchmark "${benchmarks[$task]}" \ + --output-dir "${output_dirs[$task]}" \ + --split "${splits[$task]}" + printf '%s\n' "-----------------------------------------------" + else + echo "โญ๏ธ Skipping evaluation for ${task} (skip_${task}=true)" + printf '%s\n' "-----------------------------------------------" + fi +done + + +# evaluate tasks for which you CAN'T use docling_eval +for task in code equation; do + skip_var="skip_${task}" + if [ "${!skip_var}" = false ]; then + echo "๐Ÿ“ Evaluating **${task}** โ€ฆ" + + uv run python3 docling_eval/end_to_end/evaluate_code_equations.py \ + --file_source_path ${file_source_paths[$task]} \ + --gt_dir ${dataset_paths[$task]} \ + --output_dir "${output_dirs[$task]}" \ + --num_workers "$num_workers" \ + --split "${splits[$task]}" + else + echo "โญ๏ธ Skipping evaluation for ${task} (skip_${task}=true)" + fi +done + +# bsub -q normal -n 1 -R "span[hosts=1]" -M 200G -gpu "num=1:mode=exclusive_process" -oo ~/.lsbatch/evaluate_2.stdout -eo ~/.lsbatch/evaluate_2.stderr docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh --model_path /gpfs/ZuFS1/proj/deep-search/mao/repos/docling-eval/checkpoints/granitedocling_v06.5r_stg_4_comon_debug_1_checkpoint-4808 --layout_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --ocr_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --code_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/synth_code_net_test_set_5k --equation_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/im2latex230k_ood_test_set/test_hf_format --table_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/FinTabNet_OTSL_v1.2_doclingdocuments/gt_dataset diff --git a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh new file mode 100644 index 00000000..330b1668 --- /dev/null +++ b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh @@ -0,0 +1,305 @@ +#!/usr/bin/env bash + +timestamp=$(date +"%Y%m%d-%H%M%S") +uuid=$(uuidgen | tr '[:upper:]' '[:lower:]' | cut -c1-8) + +# ------------------------------ +# Default values +# ------------------------------ +model_path="" # if not passed only evaluations will be performed, if inference data already present +# don't pass any of these params to avoid evaluating for this task +layout_dataset_path="" +ocr_dataset_path="" +table_dataset_path="" +equation_dataset_path="" +code_dataset_path="" + +output_dir="" +num_workers=8 + +# ------------------------------ +# Parse args +# ------------------------------ +while [[ $# -gt 0 ]]; do + case $1 in + --model_path) model_path="$2"; shift 2 ;; + --layout_dataset_path) layout_dataset_path="$2"; shift 2 ;; + --ocr_dataset_path) ocr_dataset_path="$2"; shift 2 ;; + --table_dataset_path) table_dataset_path="$2"; shift 2 ;; + --equation_dataset_path) equation_dataset_path="$2"; shift 2 ;; + --code_dataset_path) code_dataset_path="$2"; shift 2 ;; + --output_dir) output_dir="$2"; shift 2 ;; + --num_workers) num_workers="$2"; shift 2 ;; + --help |-h) + echo "Usage: $0 [--model_path path] [--layout_dataset_path path] ... [--output_dir dir] [--num_workers N]" + exit 0 + ;; + *) echo "โŒ Unknown option $1"; exit 1 ;; + esac +done + +# if the ouput_dir is not passed and the model_path is passed then +# the output_dir is set to the name of the model +if [ -z "$output_dir" ]; then + if [ -n "$model_path" ]; then + # Extract basename from model path, stripping trailing slash + model_name=$(basename "${model_path%/}") + output_dir="./outputs_evals/output_eval_${model_name}_${timestamp}_${uuid}" + else + output_dir="./outputs_evals/output_eval_${timestamp}_${uuid}" + fi +fi + +printf '%s\n' "-----------------------------------------------" +printf '| %-51s \n' "โœ… Parsed args:" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "model_path" "$model_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "layout_dataset_path" "$layout_dataset_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "ocr_dataset_path" "$ocr_dataset_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "table_dataset_path" "$table_dataset_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "equation_dataset_path" "$equation_dataset_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "code_dataset_path" "$code_dataset_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "output_dir" "$output_dir" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "num_workers" "$num_workers" +printf '%s\n' "-----------------------------------------------" + + + +skip_layout=false +if [ -z "$layout_dataset_path" ]; then + echo "๐Ÿšจ layout_dataset_path not passed, skipping the evaluation for this task." + skip_layout=true +fi + +skip_ocr=false +if [ -z "$ocr_dataset_path" ]; then + echo "๐Ÿšจ ocr_dataset_path not passed, skipping the evaluation for this task." + skip_ocr=true +fi + +skip_table=false +if [ -z "$table_dataset_path" ]; then + echo "๐Ÿšจ table_dataset_path not passed, skipping the evaluation for this task." + skip_table=true +fi + +skip_equation=false +if [ -z "$equation_dataset_path" ]; then + echo "๐Ÿšจ equation_dataset_path not passed, skipping the evaluation for this task." + skip_equation=true +fi + +skip_code=false +if [ -z "$code_dataset_path" ]; then + echo "๐Ÿšจ code_dataset_path not passed, skipping the evaluation for this task." + skip_code=true +fi + +mkdir -p "$output_dir" + +skip_inference=false +if [ -z "$model_path" ]; then + skip_inference=true + required=(layout ocr table equation code) + echo "๐Ÿšจ Model path has not been passed. This means that model output should already be present. Skipping Inference." + for name in "${required[@]}"; do + var="skip_${name}" + # if weโ€™re skipping this task, we must have the folder under inferred_path + if [ "${!var}" = "false" ]; then + if [ ! -d "${output_dir}/model_output/${name}" ]; then + echo "๐Ÿšจ Missing required folder '${name}' under '${output_dir}/model_output' for skipped task." + exit 1 + fi + fi + done + echo "โœ… All required inferred sub-folders are present." +fi + +printf '%s\n' "-----------------------------------------------" +printf '| %-18s %s\n' "โญ๏ธ Skip Inference?" "$skip_inference" +printf '| %-16s %s\n' "โญ๏ธ Skip Layout?" "$skip_layout" +printf '| %-16s %s\n' "โญ๏ธ Skip OCR?" "$skip_ocr" +printf '| %-16s %s\n' "โญ๏ธ Skip Table?" "$skip_table" +printf '| %-16s %s\n' "โญ๏ธ Skip Equation?" "$skip_equation" +printf '| %-16s %s\n' "โญ๏ธ Skip Code?" "$skip_code" +printf '%s\n' "-----------------------------------------------" + + + +# --------------------------------------------------------- +# Inference (run only if weโ€™re *not* skipping inference) +# --------------------------------------------------------- +if [ "$skip_inference" = false ]; then + declare -A data_paths=( + [layout]="$layout_dataset_path" + [ocr]="$ocr_dataset_path" + [table]="$table_dataset_path" + [equation]="$equation_dataset_path" + [code]="$code_dataset_path" + ) + + declare -A prompts=( + [layout]="Convert this page to docling." + [ocr]="Convert this page to docling." + [table]="Convert table to OTSL." + [equation]="Convert formula to LaTeX." + [code]="Convert code to text." + ) + + declare -A splits=( + [layout]="test" + [ocr]="test" + [table]="test" + [equation]="test" + [code]="test" + ) + + declare -A dataset_to_task + + for task in layout ocr table equation code; do + skip_var="skip_${task}" + if [ "${!skip_var}" = false ]; then + dataset="${data_paths[$task]}" + if [[ -v 'dataset_to_task["'"$dataset"'"]' ]]; then + # This dataset has already been assigned to another task + original_task="${dataset_to_task[$dataset]}" + echo "๐Ÿ” Detected identical dataset for $task and $original_task." + echo "๐Ÿ”— Creating symlink to reuse output from $original_task for $task." + mkdir -p "${output_dir}/model_output" + ln -sfn "${original_task}" "${output_dir}/model_output/${task}" + else + dataset_to_task[$dataset]="$task" + fi + fi + done + + for task in layout ocr table equation code; do + skip_var="skip_${task}" + if [ "${!skip_var}" = false ]; then + if [ ! -L "${output_dir}/model_output/${task}" ]; then + echo "๐Ÿš€ Performing inference for **${task^^}** on dataset ${data_paths[$task]}" + mkdir -p "${output_dir}/model_output/${task}" + python3 docling_eval/end_to_end/batch_inference_vllm.py \ + --model_path "$model_path" \ + --dataset_path "${data_paths[$task]}" \ + --split "${splits[$task]}" \ + --output_dir "${output_dir}/model_output/${task}" \ + --num_workers "$num_workers" \ + --prompt "${prompts[$task]}" + else + echo "โญ๏ธ Skipping inference for ${task} (already linked to another task)." + fi + fi + done +fi + +# normalize equations +# if [ "$skip_equation" = false ]; then +# echo "๐Ÿงฎ Normalizing equations..." +# mkdir -p "${output_dir}/model_output/equation_normalized" +# python3 docling_eval/end_to_end/normalize_equations.py \ +# --input_dir "${output_dir}/model_output/equation" \ +# --output_dir "${output_dir}/model_output/equation_normalized" \ +# --num_workers "$num_workers" +# printf '%s\n' "-----------------------------------------------" +# fi + +# --------------------------------------------------------- +# Evaluation & visualisation with `docling_eval` +# --------------------------------------------------------- +declare -A modality=( + [layout]="layout" + [ocr]="markdown_text" + [table]="table_structure" +) + +declare -A dataset_paths=( + [layout]="$layout_dataset_path" + [ocr]="$ocr_dataset_path" + [table]="$table_dataset_path" + [equation]="$equation_dataset_path" + [code]="$code_dataset_path" +) + +declare -A output_dirs=( + [layout]="${output_dir}/evals/layout/" + [ocr]="${output_dir}/evals/ocr/" + [table]="${output_dir}/evals/table/" + [equation]="${output_dir}/evals/equation/" + [code]="${output_dir}/evals/code/" +) + +declare -A splits=( + [layout]="test" + [ocr]="test" + [table]="test" + [equation]="test" + [code]="test" +) + +declare -A benchmarks=( + [layout]="DocLayNetV2" + [ocr]="DocLayNetV2" + [table]="FinTabNet" +) + +declare -A file_source_paths=( + [layout]="${output_dir}/model_output/layout" + [ocr]="${output_dir}/model_output/ocr" + [table]="${output_dir}/model_output/table" + [equation]="${output_dir}/model_output/equation" + [code]="${output_dir}/model_output/code" + +) +# evaluate tasks for which you can use docling_eval +for task in layout ocr table; do + skip_var="skip_${task}" + if [ "${!skip_var}" = false ]; then + echo "๐Ÿ“ Evaluating **${task}** (modality: ${modality[$task]}) โ€ฆ" + + echo " ๐Ÿ‘‰ Running create-eval โ€ฆ" + docling-eval \ + create-eval \ + --benchmark ${benchmarks[$task]} \ + --gt-dir ${dataset_paths[$task]}\ + --prediction-provider File \ + --file-prediction-format doctags \ + --file-source-path ${file_source_paths[$task]} \ + --output-dir "${output_dirs[$task]}" + + echo " ๐Ÿ‘‰ Running evaluate โ€ฆ" + docling-eval evaluate \ + --modality "${modality[$task]}" \ + --benchmark "${benchmarks[$task]}" \ + --output-dir "${output_dirs[$task]}" \ + --split "${splits[$task]}" + + echo " ๐Ÿ‘‰ Running visualize โ€ฆ" + docling-eval visualize \ + --modality "${modality[$task]}" \ + --benchmark "${benchmarks[$task]}" \ + --output-dir "${output_dirs[$task]}" \ + --split "${splits[$task]}" + printf '%s\n' "-----------------------------------------------" + else + echo "โญ๏ธ Skipping evaluation for ${task} (skip_${task}=true)" + printf '%s\n' "-----------------------------------------------" + fi +done + + +# evaluate tasks for which you CAN'T use docling_eval +for task in code equation; do + skip_var="skip_${task}" + if [ "${!skip_var}" = false ]; then + echo "๐Ÿ“ Evaluating **${task}** โ€ฆ" + + python3 docling_eval/end_to_end/evaluate_code_equations.py \ + --file_source_path ${file_source_paths[$task]} \ + --gt_dir ${dataset_paths[$task]} \ + --output_dir "${output_dirs[$task]}" \ + --num_workers "$num_workers" \ + --split "${splits[$task]}" + else + echo "โญ๏ธ Skipping evaluation for ${task} (skip_${task}=true)" + fi +done diff --git a/docling_eval/end_to_end/normalize_equations.py b/docling_eval/end_to_end/normalize_equations.py new file mode 100644 index 00000000..b0abf222 --- /dev/null +++ b/docling_eval/end_to_end/normalize_equations.py @@ -0,0 +1,517 @@ +from functools import partial +import pandas as pd +import random +import re + +random.seed(45) +from typing import List, Dict +from multiprocessing import Pool, cpu_count +import argparse +from pathlib import Path + + +class RegexTokenizer: + def __init__(self, tokens: List[str]): + # Escape tokens to ensure special characters are treated as literals + escaped_tokens = [ + re.escape(token) for token in sorted(tokens, key=len, reverse=True) + ] + # Create a regex pattern that matches any of the tokens + self.pattern = re.compile("|".join(escaped_tokens)) + # Create mapping dictionaries + self.token_to_id = {token: idx for idx, token in enumerate(tokens)} + self.id_to_token = {idx: token for token, idx in self.token_to_id.items()} + + def encode(self, text: str) -> List[int]: + matches = self.pattern.findall(text) + return [ + self.token_to_id[match] for match in matches if match in self.token_to_id + ] + + def decode(self, token_ids: List[int]) -> str: + return "".join( + [ + self.id_to_token[token_id] + for token_id in token_ids + if token_id in self.id_to_token + ] + ) + + def tokenize(self, text: str) -> List[str]: + tokens = self.pattern.findall(text) + return tokens + +tokens = pd.read_csv("docling_eval/end_to_end/equation_config/tokens.csv") +all_tokens = set(tokens["token"].tolist()) +tokens_to_keep = set(tokens.loc[tokens["policy"].isin([0])]["token"].tolist()) +tokens_to_drop = set(tokens.loc[tokens["policy"] == 1]["token"].tolist()) +tokens_to_replace = dict( + zip( + tokens.loc[tokens["policy"] == 2, "token"], + tokens.loc[tokens["policy"] == 2, "replace"].fillna(""), + ) +) +remove_with_braces_tokens = set(tokens.loc[tokens["policy"] == 3]["token"].tolist()) +one_braces_tokens = set(tokens.loc[tokens["policy"] == 4]["token"].tolist()) +two_braces_tokens = set(tokens.loc[tokens["policy"] == 5]["token"].tolist()) +remove_with_braces_tokens_keep_tokens = set( + tokens.loc[tokens["policy"] == 6]["token"].tolist() +) +remove_content_tokens = set(tokens.loc[tokens["policy"] == 7]["token"].tolist()) +tokenizer = RegexTokenizer(list(all_tokens)) + + +def handle_spaces(tokenized_equation): + in_text = False + braces_counter = 0 + new_equation = [] + for token in tokenized_equation: + if not in_text and token == " ": + continue + + if token == r"\text": + in_text = True + + if in_text and token == r"{": + braces_counter += 1 + elif in_text and token == r"}": + braces_counter -= 1 + if braces_counter == 0: + in_text = False + + new_equation.append(token) + + return new_equation + +def equation_join(split_equation: List[str]) -> str: + """ + Joins a list of LaTeX tokens into a single string, adding spaces between tokens except + when inside a '\\text{...}' block, where tokens are joined without spaces. + + Parameters: + - split_equation (list of str): A list of LaTeX tokens. + + Returns: + - str: The joined LaTeX equation as a single string. + + The function handles '\\text{...}' blocks by tracking the nesting of braces to ensure + that all content within the braces is joined without additional spaces. This preserves + the intended formatting of text within LaTeX equations. + + Example: + >>> split_equation = [ + ... "A", "_", "7", "+", "A", "_", "8", "-", "2", r"\text", "{", + ... "A", " ", "i", "s", " ", "a", " ", "p", "a", "r", "a", "m", "e", "t", "e", "r", "}" + ... ] + >>> equation_join(split_equation) + 'A _ 7 + A _ 8 - 2 \\text{A is a parameter}' + """ + in_text = False + braces_counter = 0 + joined_equation = "" + for token in split_equation: + if token == r"\text": + in_text = True + joined_equation += token + " " + continue + + if in_text and token == r"{": + braces_counter += 1 + elif in_text and token == r"}": + braces_counter -= 1 + if braces_counter == 0: + in_text = False + + if in_text: + joined_equation += token + else: + joined_equation += token + " " + + return joined_equation.strip() + + +def replace_tokens( + equation_tokenized: List[str], to_replace_tokens: Dict[str, str] +) -> List[str]: + equation_tokenized = [ + to_replace_tokens[token] if token in to_replace_tokens else token + for token in equation_tokenized + ] + equation_tokenized = [token for token in equation_tokenized if token] + + return equation_tokenized + + +def remove_with_braces( + equation: str, remove_with_braces_tokens: List[str], keep_tokens: bool +): + for token in remove_with_braces_tokens: + pattern = rf"\{token}\s*\{{" + output = "" + pos = 0 + while True: + match = re.search(pattern, equation[pos:]) + if not match: + output += equation[pos:] + break + start = pos + match.start() + end = pos + match.end() + if keep_tokens: + output += ( + equation[pos : start + len(token)] + " " + ) # Append text before and including \command + else: + output += equation[pos:start] # Append text before \command{ + # Now find the matching closing brace + depth = 1 + i = end + while i < len(equation) and depth > 0: + if equation[i] == "{": + depth += 1 + elif equation[i] == "}": + depth -= 1 + i += 1 + if depth != 0: + # print(f"Unbalanced braces after position {end}") + return None + content = equation[end : i - 1] # Extract content inside braces + output += content # Append the content without \command{ and } + pos = i # Move the position forward + + equation = output + + return equation + + +def remove_tokens_and_content( + equation: str, + remove_content_tokens: List[str], +): + for token in remove_content_tokens: + pattern = rf"\{token}\s*\{{" + output = "" + pos = 0 + while True: + match = re.search(pattern, equation[pos:]) + if not match: + output += equation[pos:] + break + start = pos + match.start() + end = pos + match.end() + + output += equation[pos:start] # Append text before \command{ + # Now find the matching closing brace + depth = 1 + i = end + while i < len(equation) and depth > 0: + if equation[i] == "{": + depth += 1 + elif equation[i] == "}": + depth -= 1 + i += 1 + if depth != 0: + # print(f"Unbalanced braces after position {end}") + return None + pos = i # Move the position forward + + equation = output + + return equation + + +def normalize_one_braces_tokens(equation_tokenized: List[str], one_braces_tokens): + new_equation = [] + i = 0 + while i < len(equation_tokenized): + token = equation_tokenized[i] + new_equation.append(token) + i = i + 1 + if i < len(equation_tokenized): + next_token = equation_tokenized[i] + if token in one_braces_tokens and next_token != r"{": + new_equation.extend(["{", next_token, "}"]) + i = i + 1 + + return new_equation + + +def normalize_two_braces_tokens(equation_tokenized: List[str], two_braces_tokens): + new_equation = [] + i = 0 + while i < len(equation_tokenized): + token = equation_tokenized[i] + new_equation.append(token) + i = i + 1 + if i < len(equation_tokenized) - 1: + next_token = equation_tokenized[i] + next_next_token = equation_tokenized[i + 1] + if token in two_braces_tokens and next_token != r"{": + new_equation.extend(["{", next_token, "}", "{", next_next_token, "}"]) + i = i + 2 + + return new_equation + + +def normalize_left_right(equation: str) -> str: + left_delimiters = [ + "(", + "[", + "\\{", + "|", + "\\|", + "\\lceil", + "\\lfloor", + "\\langle", + ] + right_delimiters = [ + ")", + "]", + "\\}", + "|", + "\\|", + "\\rceil", + "\\rfloor", + "\\rangle", + ] + + words_pattern = "|".join(map(re.escape, left_delimiters)) + pattern = rf"\{{\s*\\left\s*({words_pattern})\s*\}}" + tmp_equation = re.sub(pattern, r"\\left \1", equation) + + words_pattern = "|".join(map(re.escape, right_delimiters)) + pattern = rf"\{{\s*\\right\s*({words_pattern})\s*\}}" + new_equation = re.sub(pattern, r"\\right \1", tmp_equation) + + return new_equation + + +def handle_big(equation_tokenized: List[str]) -> List[str]: + """ + Replaces instances of \\Big followed by valid tokens ( (, [, {, ), ], } ) with \\left or \\right. + """ + new_equation = [] + i = 0 + seen_left_bar = False + counter_left = 0 + counter_right = 0 + while i < len(equation_tokenized) - 1: + token = equation_tokenized[i] + next_token = equation_tokenized[i + 1] + + if ( + token == "\\Big" + and (next_token == "|" or next_token == "\\|") + and not seen_left_bar + ): + new_equation.append("\\left") + seen_left_bar = True + counter_left += 1 + elif ( + token == "\\Big" + and (next_token == "|" or next_token == "\\|") + and seen_left_bar + ): + new_equation.append("\\right") + seen_left_bar = False + counter_right += 1 + elif token == "\\Big" and next_token in [ + "(", + "[", + r"\{", + r"\lceil", + r"\lfloor", + r"\langle", + ]: + new_equation.append("\\left") + counter_left += 1 + elif token == "\\Big" and next_token in [ + ")", + "]", + r"\}", + r"\rceil", + r"\rfloor", + r"\rangle", + ]: + new_equation.append("\\right") + counter_right += 1 + else: + new_equation.append(token) + + i += 1 + # Add the last token + if equation_tokenized and equation_tokenized[-1] != "\\Big": + new_equation.append(equation_tokenized[-1]) + + if counter_left != counter_right: + return equation_tokenized + + return new_equation + + +def handle_prime(equation): + pattern = r"(?:'\s*)+" + + def replacement(match): + count = match.group().count("'") + return "^{" + "\\prime" * count + "}" + + return re.sub(pattern, replacement, equation) + + +def handle_multiple_subsequent_braces(equation): + """ + Simplify a LaTeX equation by removing unnecessary multiple curly braces. + + Args: + equation (str): The LaTeX equation to simplify. + + Returns: + str: The simplified LaTeX equation. + """ + pattern = r"\{\s*\{([^{}]+)\}\s*\}" + prev_equation = None + while equation != prev_equation: + prev_equation = equation + # Replace redundant multiple braces with a single brace + equation = re.sub(pattern, r"{\1}", equation) + return equation + +def handle_multiple_subsequent_verts(equation): + """ + Simplify a LaTeX equation by removing unnecessary multiple curly braces. + + Args: + equation (str): The LaTeX equation to simplify. + + Returns: + str: The simplified LaTeX equation. + """ + pattern = r"\\\|\s*\\\|([^\\\|]+)\\\|\s*\\\|" + prev_equation = None + while equation != prev_equation: + prev_equation = equation + # Replace redundant multiple braces with a single brace + equation = re.sub(pattern, r"\\|\1\\|", equation) + return equation + + +def normalize(equation: str) -> str: + equation = ( + equation.replace("", "") + .replace("", "") + .replace("", "") + .replace("", "") + .replace("", "") + ) + equation = remove_with_braces(equation, remove_with_braces_tokens, False) + if not equation: + return None + equation = remove_tokens_and_content(equation, remove_content_tokens) + if not equation: + return None + equation = remove_with_braces(equation, remove_with_braces_tokens_keep_tokens, True) + if not equation: + return None + equation = normalize_left_right(equation) + + equation = handle_multiple_subsequent_braces(equation) + equation = handle_multiple_subsequent_verts(equation) + equation = handle_prime(equation) + + if not equation: + return None + + equation = equation.strip() + equation_tokenized = tokenizer.tokenize(equation) + + if len(set(equation_tokenized).intersection(tokens_to_drop)) > 0: + return None + + equation_tokenized = handle_spaces(equation_tokenized) + + if not equation_tokenized: + return None + + equation_tokenized = replace_tokens(equation_tokenized, tokens_to_replace) + equation_tokenized = normalize_one_braces_tokens( + equation_tokenized, one_braces_tokens + ) + equation_tokenized = normalize_two_braces_tokens( + equation_tokenized, two_braces_tokens + ) + equation_tokenized = handle_big(equation_tokenized) + + equation = equation_join(equation_tokenized) + + equation = equation.replace(" ", " ").replace(r"\text {", r"\text{").strip() + + equation_tokenized = tokenizer.tokenize(equation) + equation_tokenized = handle_spaces(equation_tokenized) + return equation_join(equation_tokenized) + + +def _process_file(file_path: Path, out_dir: Path) -> tuple[str, bool]: + """ + Read one-line equation from *file_path*, normalize it, and write the result + to *out_dir/file_path.name*. + Returns (filename, True) on success, (filename, False) if normalization + returned None. + """ + try: + eq = file_path.read_text(encoding="utf-8").strip() + except Exception as e: + return (file_path.name, False) + + normalized_equation = normalize(eq) # may be None + flag = True + if normalized_equation is None: + normalized_equation = eq + flag = False + (out_dir / file_path.name).write_text(normalized_equation, encoding="utf-8") + + return (file_path.name, flag) + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Normalize LaTeX-equation files in parallel." + ) + parser.add_argument( + "--input_dir", + type=Path, + help="Directory containing text files, each with a single equation.", + ) + parser.add_argument( + "--output_dir", + type=Path, + help="Directory to receive normalized files (created if absent).", + ) + parser.add_argument( + "-w", + "--num_workers", + type=int, + default=cpu_count(), + metavar="N", + help="Number of worker processes (default: all logical cores).", + ) + args = parser.parse_args() + + if not args.input_dir.is_dir(): + parser.error(f"{args.input_dir} is not a directory.") + args.output_dir.mkdir(parents=True, exist_ok=True) + + files = sorted(p for p in args.input_dir.iterdir() if p.is_file()) + if not files: + parser.error(f"No files found in {args.input_dir}") + + with Pool(processes=min(args.num_workers, cpu_count())) as pool: + process = partial(_process_file, out_dir=args.output_dir) + results = pool.map(process, files) + + ok = sum(flag for _, flag in results) + fail = len(results) - ok + print(f"โœ“ Normalized {ok} file(s); โœ— skipped {fail} file(s).") + + +if __name__ == "__main__": + main() diff --git a/docling_eval/end_to_end/read_dataset.py b/docling_eval/end_to_end/read_dataset.py new file mode 100644 index 00000000..0864629b --- /dev/null +++ b/docling_eval/end_to_end/read_dataset.py @@ -0,0 +1,94 @@ +from pathlib import Path +import pandas as pd + +import os +import math +from multiprocessing import Pool +from datasets.utils.py_utils import convert_file_size_to_int +from PIL import Image +from datasets import load_dataset + +def save_shard(shard_info): + """ + shard_info is a tuple of (index, shard, shard_path). + We call to_parquet on the shard, writing it out to shard_path. + """ + index, shard, shard_path = shard_info + shard.to_parquet(shard_path) + + +def to_parquet(ds, output_dir, num_proc): + os.makedirs(output_dir, exist_ok=True) + + dataset_nbytes = ds._estimate_nbytes() + max_shard_size = convert_file_size_to_int("500MB") + num_shards = max(math.ceil(dataset_nbytes / max_shard_size), 1) + + # Build all shard subsets in a list + shard_infos = [] + for i in range(num_shards): + shard = ds.shard(num_shards=num_shards, index=i, contiguous=True) + shard_path = os.path.join( + output_dir, f"{i:05d}-of-{(num_shards - 1):05d}.parquet" + ) + shard_infos.append((i, shard, shard_path)) + + # Start a worker pool and map the shards + with Pool(processes=num_proc) as pool: + pool.map(save_shard, shard_infos) + + +# Load the dataset from a local directory containing .parquet files +dataset = load_dataset( + "/gpfs/ZuFS1/proj/deep-search/mao/datasets/FinTabNet_OTSL_v1.2_doclingdocuments/gt_dataset", + split="test", +) +print(dataset) +for i, row in enumerate(dataset): + if i == 1: + break + print(row) + + +# df = pd.read_csv( +# "/gpfs/ZuFS1/proj/deep-search/mao/datasets/im2latex230k_ood_test_set/test.csv" +# ) +# df = df.sample(frac=1, random_state=42).reset_index(drop=True) # shuffle +# df = df.iloc[:10000] # select first 10k rows +# print(df) +# images_path = Path( +# "/gpfs/ZuFS1/proj/deep-search/mao/datasets/im2latex230k_ood_test_set/images" +# ) + +# rows = [] +# for i, row in df.iterrows(): +# formula = row["formula"] + +# texts = [ +# {"assistant": f"{formula}", +# "user": "Convert formula to LaTeX." +# } +# ] +# filename = row["filename"] +# images = [Image.open(images_path / filename).convert("RGB")] + +# rows.append( +# { +# "texts": texts, +# "images": images, +# "id": filename.replace(".png", "") +# } +# ) + +# from datasets import Dataset +# from datasets.features import Features, Value, Sequence, Image as HFImage + +# # Convert rows to Hugging Face dataset +# ds = Dataset.from_list(rows) + +# # Save as sharded parquet +# to_parquet( +# ds, +# "/gpfs/ZuFS1/proj/deep-search/mao/datasets/im2latex230k_ood_test_set/test", +# num_proc=128, +# ) diff --git a/docling_eval/evaluators/table_evaluator.py b/docling_eval/evaluators/table_evaluator.py index 3082b2cc..f2c19bbc 100644 --- a/docling_eval/evaluators/table_evaluator.py +++ b/docling_eval/evaluators/table_evaluator.py @@ -163,6 +163,14 @@ def __call__( ): data_record = DatasetRecordWithPrediction.model_validate(data) doc_id = data_record.doc_id + + if data_record.status not in self._accepted_status: + _log.error( + "Skipping record without successfull conversion status: %s", doc_id + ) + rejected_samples[EvaluationRejectionType.INVALID_CONVERSION_STATUS] += 1 + continue + gt_doc = data_record.ground_truth_doc pred_doc = self._get_pred_doc(data_record) if not pred_doc: diff --git a/pyproject.toml b/pyproject.toml index 1e6a6440..ce2291cd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "docling-eval" -version = "0.6.0" # DO NOT EDIT, updated automatically +version = "0.5.0" # DO NOT EDIT, updated automatically description = "Evaluation of Docling" license = "MIT" keywords= ["docling", "evaluation", "convert", "document", "pdf", "docx", "html", "markdown", "layout model", "segmentation", "table structure", "table former"] @@ -38,9 +38,10 @@ dependencies = [ 'pycocotools (>=2.0.8,<3.0.0)', 'tabulate (>=0.9.0,<0.10.0)', 'tqdm (>=4.67.1,<5.0.0)', - 'pillow (>=10.3.0,<12.0.0)', + 'pillow (>=10.3.0,<11.0.0)', 'evaluate (>=0.4.3,<0.5.0)', 'nltk (>=3.9.1,<4.0.0)', + 'ibm-cos-sdk (>=2.1.40,<3.0.0)', 'beautifulsoup4 (>=4.12.3,<5.0.0)', 'jiwer (>=3.1.0,<4.0.0)', 'urllib3 (>=1.24.2,<2.0.0)', @@ -49,9 +50,7 @@ dependencies = [ 'pypdf2 (>=3.0.1,<4.0.0)', "xmltodict (>=0.14.2,<0.15.0)", 'edit-distance (>=1.0.6,<2.0.0)', - 'mlx-vlm<0.2; sys_platform == "darwin" and platform_machine == "arm64"', - "pyarrow>=20.0.0", - "typer>=0.16.0", + "mlx-vlm<0.2", ] [project.urls] @@ -70,7 +69,6 @@ hyperscalers = [ 'azure-core (>=1.33.0,<2.0.0)', 'boto3 (>=1.37.8,<2.0.0)', 'google-cloud-documentai (>=3.2.0,<4.0.0)', - 'ibm-cos-sdk (>=2.1.40,<3.0.0)', ] cvat_tools = [ "cvat-sdk>=2.40.0", @@ -143,8 +141,6 @@ module = [ "tqdm.*", "azure.*", "google.*", - "pycocotools.*", - "pyarrow.*", "ocrmac.*", ] ignore_missing_imports = true diff --git a/uv.lock b/uv.lock index d7f9ee38..e24695e0 100644 --- a/uv.lock +++ b/uv.lock @@ -3,10 +3,10 @@ revision = 2 requires-python = ">=3.10, <4.0" resolution-markers = [ "python_full_version >= '3.13' and sys_platform == 'darwin'", - "python_full_version == '3.12.*' and sys_platform == 'darwin'", "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", "(python_full_version >= '3.13' and 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