From 4466bf1204cd7c0489315cd5fd384f3594d8e3a8 Mon Sep 17 00:00:00 2001 From: root Date: Thu, 16 Oct 2025 05:24:31 +0000 Subject: [PATCH 1/5] support olmocrbench --- vlmeval/dataset/__init__.py | 3 +- vlmeval/dataset/olmOCRBench/evaluator.py | 467 +++++++ vlmeval/dataset/olmOCRBench/katex/__init__.py | 1 + .../olmOCRBench/katex/auto-render.min.js | 1 + .../dataset/olmOCRBench/katex/katex.min.css | 1 + .../dataset/olmOCRBench/katex/katex.min.js | 1 + vlmeval/dataset/olmOCRBench/katex/render.py | 749 +++++++++++ vlmeval/dataset/olmOCRBench/olmocrbench.py | 47 + vlmeval/dataset/olmOCRBench/repeatdetect.py | 175 +++ vlmeval/dataset/olmOCRBench/tests.py | 1187 +++++++++++++++++ vlmeval/dataset/olmOCRBench/utils.py | 203 +++ 11 files changed, 2834 insertions(+), 1 deletion(-) create mode 100644 vlmeval/dataset/olmOCRBench/evaluator.py create mode 100644 vlmeval/dataset/olmOCRBench/katex/__init__.py create mode 100644 vlmeval/dataset/olmOCRBench/katex/auto-render.min.js create mode 100644 vlmeval/dataset/olmOCRBench/katex/katex.min.css create mode 100644 vlmeval/dataset/olmOCRBench/katex/katex.min.js create mode 100644 vlmeval/dataset/olmOCRBench/katex/render.py create mode 100644 vlmeval/dataset/olmOCRBench/olmocrbench.py create mode 100644 vlmeval/dataset/olmOCRBench/repeatdetect.py create mode 100644 vlmeval/dataset/olmOCRBench/tests.py create mode 100644 vlmeval/dataset/olmOCRBench/utils.py diff --git a/vlmeval/dataset/__init__.py b/vlmeval/dataset/__init__.py index 5737d3370..81a732556 100644 --- a/vlmeval/dataset/__init__.py +++ b/vlmeval/dataset/__init__.py @@ -83,6 +83,7 @@ from .medqbench_mcq import MedqbenchMCQDataset from .medqbench_caption import MedqbenchCaptionDataset from .medqbench_paired_description import MedqbenchPairedDescriptionDataset +from .olmOCRBench.olmocrbench import olmOCRBench class ConcatDataset(ImageBaseDataset): @@ -211,7 +212,7 @@ def evaluate(self, eval_file, **judge_kwargs): MMEReasoning, GOBenchDataset, SFE, ChartMimic, MMVMBench, XLRSBench, OmniEarthMCQBench, VisFactor, OSTDataset, OCRBench_v2, TreeBench, CVQA, M4Bench, AyaVisionBench, TopViewRS, VLMBias, MMHELIX, MedqbenchMCQDataset, - MedqbenchPairedDescriptionDataset, MedqbenchCaptionDataset + MedqbenchPairedDescriptionDataset, MedqbenchCaptionDataset, olmOCRBench ] VIDEO_DATASET = [ diff --git a/vlmeval/dataset/olmOCRBench/evaluator.py b/vlmeval/dataset/olmOCRBench/evaluator.py new file mode 100644 index 000000000..5c3786a83 --- /dev/null +++ b/vlmeval/dataset/olmOCRBench/evaluator.py @@ -0,0 +1,467 @@ +#!/usr/bin/env python3 +""" +This script runs olmocr bench. +It will take as an argument a folder, and scan it for .jsonl files +which contain the various rules and properties that we will check. +It will then validate the JSON files to make sure they are all valid. +Then, each other folder in there (besides /pdfs) represents a pipeline tool that we will evaluate. +We will validate that each one of those contains at least one .md +file (or repeated generations, e.g. _pg{page}_repeat{repeat}.md) +corresponding to its parse for every .pdf in the /pdfs folder. +Then, we will read each one, and check if they pass against all the rules. +If a rule fails on some of the repeats, a short explanation is printed. +The final score is the average of per-JSONL file scores, where each JSONL file's +score is the proportion of tests from that file that pass. +Statistical analysis including bootstrap confidence intervals are provided for the results. +Pairwise permutation tests are conducted between specific candidate pairs. +""" + +import argparse +import glob +import os +import random +import re +import sys +from concurrent.futures import ThreadPoolExecutor, as_completed +from typing import Dict, List, Tuple + +# from pypdf import PdfReader +from tqdm import tqdm + +from .tests import BaselineTest, BasePDFTest, load_tests, save_tests, load_single_test +from .utils import calculate_bootstrap_ci + +import pandas as pd +import json +import tempfile +import shutil +import csv + + +def evaluate_candidate( + candidate_folder: str, + all_tests: List[BasePDFTest], + pdf_basenames: List[str], + force: bool = False, +) -> Tuple[ + float, + int, + List[str], + List[str], + Dict[str, List[float]], + List[float], + Dict[str, Dict[int, List[Tuple[BasePDFTest, bool, str]]]], +]: + """ + For the candidate folder (pipeline tool output), validate that it contains at least one .md file + (i.e. repeated generations like _pg{page}_repeat{repeat}.md) for every PDF in the pdf folder. + Then, run each rule against all corresponding .md files concurrently and average the results. + + Returns a tuple: + (overall_score, total_tests, candidate_errors, test_failures, test_type_breakdown, all_test_scores, test_results) + + - overall_score: Average fraction of tests passed (averaged over repeats and tests). + Note: This is now updated at reporting time to be the average of per-JSONL file scores. + - total_tests: Total number of tests evaluated. + - candidate_errors: List of candidate errors (e.g. missing files). + - test_failures: List of failure messages for tests not passing on all repeats. + - test_type_breakdown: Dictionary mapping test type to list of average pass ratios for tests of that type. + - all_test_scores: List of all individual test scores (used for bootstrapping). + - test_results: Dictionary mapping PDF name to dictionary mapping page number + to list of (test, passed, explanation) tuples. + """ + candidate_errors = [] + test_failures = [] + test_type_breakdown = {} # key: test type, value: list of average pass ratios + all_test_scores = [] # Store all individual test scores for bootstrapping + test_results = {} # Store detailed test results for reporting + candidate_name = os.path.basename(candidate_folder) + + # Map each PDF to its corresponding MD repeats (e.g., doc1_pg1_repeat1.md, doc1_pg2_repeat2.md, etc.) + pdf_to_md_files = {} + all_files = list(glob.glob(os.path.join(candidate_folder, "**/*.md"), recursive=True)) + # print(all_files) + + for pdf_name in pdf_basenames: + md_base = os.path.splitext(pdf_name)[0] + md_regex = re.compile(rf"^{re.escape(md_base)}_pg\d+_repeat\d+\.md$") + md_files = [f for f in all_files if md_regex.match(os.path.relpath(f, candidate_folder))] + + if not md_files and not force: + candidate_errors.append( + f"Candidate '{candidate_name}' is missing MD repeats for {pdf_name} " + f"(expected files matching {md_base}_pg{{page}}_repeat*.md)." + ) + else: + pdf_to_md_files[pdf_name] = md_files + + if candidate_errors: + return (0.0, len(all_tests), candidate_errors, test_failures, + test_type_breakdown, all_test_scores, test_results) + + # Define an inner function to evaluate a single test + def process_test(test: BasePDFTest) -> Tuple[float, str, str, List[str], Tuple[bool, str]]: + local_errors = [] + test_failure = None + pdf_name = test.pdf + + # Initialize the test_results structure if needed + if pdf_name not in test_results: + test_results[pdf_name] = {} + if test.page not in test_results[pdf_name]: + test_results[pdf_name][test.page] = [] + + md_base = os.path.splitext(pdf_name)[0] + md_files = pdf_to_md_files.get(pdf_name, []) + # Filter MD files for the specific page corresponding to the test + page_md_files = [f for f in md_files if re.search(rf"_pg{test.page}_", os.path.basename(f))] + if not page_md_files: + local_errors.append( + f"Candidate '{candidate_name}' is missing MD repeats for {pdf_name} page {test.page} " + f"(expected files matching {md_base}_pg{test.page}_repeat*.md)." + ) + test_results[pdf_name][test.page].append((test, False, "Missing MD files")) + return (0.0, None, test.type, local_errors, (False, "Missing MD files")) + + repeat_passes = 0 + num_repeats = 0 + explanations = [] + for md_path in page_md_files: + num_repeats += 1 + try: + with open(md_path, "r", encoding="utf-8") as f: + md_content = f.read() + except Exception as e: + local_errors.append(f"Error reading {md_path}: {e}") + continue + + try: + passed, explanation = test.run(md_content) + if passed: + repeat_passes += 1 + else: + explanations.append(explanation) + except Exception as e: + local_errors.append(f"Error running test {test.id} on {md_path}: {e}") + explanations.append(str(e)) + + test_avg = repeat_passes / num_repeats if num_repeats > 0 else 0.0 + final_passed = test_avg > 0.5 # Consider test passed if majority of repeats pass + final_explanation = explanations[0] if explanations else "All repeats passed" + + # Store the test result for reporting + test_results[pdf_name][test.page].append((test, final_passed, final_explanation)) + + if test_avg < 1.0: + test_failure = ( + f"Test {test.id} on {md_base} page {test.page} average pass ratio: {test_avg:.3f} " + f"({repeat_passes}/{num_repeats} repeats passed)." + f"Ex: {explanations[0] if explanations else 'No explanation'}" + ) + return (test_avg, test_failure, test.type, local_errors, (final_passed, final_explanation)) + + total_test_score = 0.0 + futures = [] + # Use a thread pool to evaluate each test concurrently. + # with ThreadPoolExecutor(max_workers=min(os.cpu_count() or 1, 64)) as executor: + with ThreadPoolExecutor(max_workers=min(os.cpu_count() or 1, 64)) as executor: + futures = [executor.submit(process_test, test) for test in all_tests] + # tqdm progress bar for this candidate's tests + for future in tqdm( + as_completed(futures), + total=len(futures), + desc=f"Evaluating tests for {candidate_name}", + unit="test", + ): + test_avg, test_failure, test_type, errors, _ = future.result() + all_test_scores.append(test_avg) + total_test_score += test_avg + if test_failure: + test_failures.append(test_failure) + if test_type not in test_type_breakdown: + test_type_breakdown[test_type] = [] + test_type_breakdown[test_type].append(test_avg) + local_errors = errors + if local_errors: + candidate_errors.extend(local_errors) + + overall_score = total_test_score / len(all_tests) if all_tests else 0.0 + return ( + overall_score, + len(all_tests), + candidate_errors, + test_failures, + test_type_breakdown, + all_test_scores, + test_results, + ) + + +def evaluator(tsv_path, eval_file): + force = False + bootstrap_samples = 1000 + confidence_level = 0.95 + n_bootstrap = bootstrap_samples + ci_level = confidence_level + + # 1️⃣ 读取 .xlsx 文件 + df = pd.read_excel(eval_file) + # required_cols = {"index", "pdf", "question", "answer", "prediction"} + # 2️⃣ 解析 answer（构造 tests） + all_tests = [] + pdf_to_tests = {} + pdf_basenames = set() + for _, row in tqdm(df.iterrows(), total=len(df), desc="Parsing XLSX"): + answer_data = row["answer"] + prediction_md = row["prediction"] + + if isinstance(answer_data, str): + answer_data = json.loads(answer_data) + + for test_json in answer_data: + test_obj = load_single_test(test_json) + pdf_path = test_json["pdf"] + pdf_basenames.add(pdf_path) + all_tests.append(test_obj) + + if pdf_path not in pdf_to_tests: + pdf_to_tests[pdf_path] = [] + pdf_to_tests[pdf_path].append({ + "test": test_obj, + "md_content": prediction_md, + }) + + print(f"✅ Loaded {len(all_tests)} tests across {len(pdf_basenames)} pdfs.") + + test_to_jsonl = {} + for test in all_tests: + pdf_cate = test.pdf.split("/")[0] + map_cate_to_jsonl_basename = { + "arxiv_math": "arxiv_math", + "headers_footers": "headers_footers", + "long_tiny_text": "long_tiny_text", + "multi_column": "multi_column", + "old_scans": "old_scans", + "old_scans_math": "old_scans_math", + "tables": "tables", + } + test_to_jsonl[test.id] = map_cate_to_jsonl_basename[pdf_cate] + + for pdf in pdf_basenames: + if not any(t.type == "baseline" for t in all_tests if t.pdf == pdf): + all_tests.append(BaselineTest(id=f"{pdf}_baseline", pdf=pdf, page=1, type="baseline")) + test_to_jsonl[all_tests[-1].id] = "baseline" + + # 3️⃣ 创建临时 candidate 文件夹 + tmp_dir = tempfile.mkdtemp(prefix="olmocr_eval_") + print(f"📂 Creating temporary candidate folder: {tmp_dir}") + + # 按 pdf 写出对应的 markdown + for pdf_path, items in pdf_to_tests.items(): + base = os.path.splitext(os.path.basename(pdf_path))[0] + cate = pdf_path.split("/")[0] + cate_dir = os.path.join(tmp_dir, cate) + os.makedirs(cate_dir, exist_ok=True) # ✅ 若目录不存在则创建 + # repeat 评测 + # for i, entry in enumerate(items): + # md_file = os.path.join(cate_dir, f"{base}_pg{entry['test'].page}_repeat{i+1}.md") + # with open(md_file, "w", encoding="utf-8") as f: + # f.write(entry["md_content"]) + # repeat==1 + for i, entry in enumerate(items): + md_file = os.path.join(cate_dir, f"{base}_pg{entry['test'].page}_repeat{i+1}.md") + with open(md_file, "w", encoding="utf-8") as f: + f.write(entry["md_content"]) + break + + print(f"🧹 Temporary folder: {tmp_dir}") + + summary = [] + test_results_by_candidate = {} + candidate_folders = [tmp_dir] + # Process candidates sequentially so that each candidate's progress bar is distinct. + for candidate in candidate_folders: + candidate_name = os.path.basename(candidate) + print(f"\nEvaluating candidate: {candidate_name}") + ( + overall_score, + total_tests, + candidate_errors, + test_failures, + test_type_breakdown, + all_test_scores, + test_results, + ) = evaluate_candidate( + candidate, + all_tests, + pdf_basenames, + force, + ) + # Always store test results for displaying jsonl file groupings + test_results_by_candidate[candidate_name] = test_results + + # Group results by jsonl file for more accurate CI calculation + jsonl_results = {} + jsonl_scores = [] # List to store scores by jsonl file for CI calculation + jsonl_file_sizes = [] # List to store the number of tests per jsonl file + + for test in all_tests: + # Get the jsonl file this test came from + jsonl_file = test_to_jsonl.get(test.id, "unknown") + + if jsonl_file not in jsonl_results: + jsonl_results[jsonl_file] = {"total": 0, "passed": 0, "scores": []} + + jsonl_results[jsonl_file]["total"] += 1 + + # Get the test result for this candidate if it exists + if not candidate_errors and hasattr(test, "pdf") and hasattr(test, "page"): + pdf_name = test.pdf + page = test.page + if pdf_name in test_results and page in test_results.get(pdf_name, {}): + for t, passed, _ in test_results[pdf_name][page]: + if t.id == test.id: + # Store the test score in its jsonl group + result_score = 1.0 if passed else 0.0 + jsonl_results[jsonl_file]["scores"].append(result_score) + if passed: + jsonl_results[jsonl_file]["passed"] += 1 + break + + # Gather all the scores by jsonl file for CI calculation + for jsonl_file, results in jsonl_results.items(): + if results["scores"]: + jsonl_file_sizes.append(len(results["scores"])) + jsonl_scores.extend(results["scores"]) + + # Calculate CI using the updated function with splits + if jsonl_scores: + ci = calculate_bootstrap_ci( + jsonl_scores, + n_bootstrap=n_bootstrap, + ci_level=ci_level, + splits=jsonl_file_sizes + ) + else: + ci = (0.0, 0.0) + summary.append(( + candidate_name, + overall_score, + total_tests, + candidate_errors, + test_failures, + test_type_breakdown, + ci, + all_test_scores, + )) + print(f"\nCandidate: {candidate_name}") + if candidate_errors: + for err in candidate_errors: + print(f" [ERROR] {err}") + else: + if test_failures: + for fail in test_failures: + print(f" [FAIL] {fail}") + # Calculate and show the per-category average score + jsonl_pass_rates = [] + for _, results in jsonl_results.items(): + if results["total"] > 0: + pass_rate = results["passed"] / results["total"] + jsonl_pass_rates.append(pass_rate) + + per_category_score = sum(jsonl_pass_rates) / len(jsonl_pass_rates) if jsonl_pass_rates else 0.0 + print( + f" Average Score: {per_category_score * 100:.1f}% " + f"(95% CI: [{ci[0] * 100:.1f}%, {ci[1] * 100:.1f}%]) " + f"over {total_tests} tests." + ) + print("\n" + "=" * 60) + print("Final Summary with 95% Confidence Intervals:") + for idx, (candidate_name, _, total_tests, candidate_errors, _, test_type_breakdown, ci, _) in enumerate(summary): + # Group results by jsonl file + jsonl_results = {} + for test in all_tests: + # Get the jsonl file this test came from + jsonl_file = test_to_jsonl.get(test.id, "unknown") + + if jsonl_file not in jsonl_results: + jsonl_results[jsonl_file] = {"total": 0, "passed": 0} + + jsonl_results[jsonl_file]["total"] += 1 + # Get the test result for this candidate if it exists + test_result = None + if not candidate_errors and hasattr(test, "pdf") and hasattr(test, "page"): + pdf_name = test.pdf + page = test.page + if ( + pdf_name in test_results_by_candidate.get(candidate_name, {}) + and page in test_results_by_candidate[candidate_name].get(pdf_name, {}) + ): + for t, passed, _ in test_results_by_candidate[candidate_name][pdf_name][page]: + if t.id == test.id: + test_result = passed + break + + if test_result: + jsonl_results[jsonl_file]["passed"] += 1 + + # Calculate new overall score as average of per-JSONL pass rates + jsonl_pass_rates = [] + for jsonl_file, results in jsonl_results.items(): + if results["total"] > 0: + pass_rate = results["passed"] / results["total"] + jsonl_pass_rates.append(pass_rate) + # New overall score is average of per-JSONL pass rates + new_overall_score = sum(jsonl_pass_rates) / len(jsonl_pass_rates) if jsonl_pass_rates else 0.0 + # Update the overall_score in the summary list for later use (e.g., in permutation tests) + summary[idx] = ( + candidate_name, + new_overall_score, + total_tests, + candidate_errors, + summary[idx][4], + test_type_breakdown, + ci, + summary[idx][7], + ) + + if candidate_errors: + status = "FAILED (errors)" + ciw_str = "" + else: + status = f"{new_overall_score * 100:0.1f}%" + # Use the CI that was calculated with proper category-based bootstrap + half_width = ((ci[1] - ci[0]) / 2) * 100 + ciw_str = f"± {half_width:0.1f}%" + print(f"{candidate_name:20s} : Average Score: {status} {ciw_str} (average of per-JSONL scores)") + # Sort the test types alphabetically + for ttype in sorted(test_type_breakdown.keys()): + scores = test_type_breakdown[ttype] + avg = sum(scores) / len(scores) * 100 if scores else 0.0 + print(f" {ttype:8s}: {avg:0.1f}% average pass rate over {len(scores)} tests") + + print("\n Results by JSONL file:") + for jsonl_file, results in sorted(jsonl_results.items()): + if results["total"] > 0: + pass_rate = (results["passed"] / results["total"]) * 100 + print(f" {jsonl_file:30s}: {pass_rate:0.1f}% ({results['passed']}/{results['total']} tests)") + print("") + + shutil.rmtree(tmp_dir) + + # save + csv_path = os.path.join(os.path.dirname(eval_file), "olmOCRBench_eval_summary.csv") + rows = [["type", "score"]] # CSV 表头 + rows.append(["overall", f"{new_overall_score * 100:.1f}"]) # overall 项 + + for jsonl_file, results in sorted(jsonl_results.items()): + if results["total"] > 0: + pass_rate = (results["passed"] / results["total"]) * 100 + print(f" {jsonl_file:30s}: {pass_rate:0.1f}% ({results['passed']}/{results['total']} tests)") + rows.append([jsonl_file, f"{pass_rate:.1f}"]) + + with open(csv_path, "w", newline="", encoding="utf-8") as f: + writer = csv.writer(f) + writer.writerows(rows) + + print(f"Results save to: {csv_path}") diff --git a/vlmeval/dataset/olmOCRBench/katex/__init__.py b/vlmeval/dataset/olmOCRBench/katex/__init__.py new file mode 100644 index 000000000..f81af1b4b --- /dev/null +++ b/vlmeval/dataset/olmOCRBench/katex/__init__.py @@ -0,0 +1 @@ +from .render import compare_rendered_equations, render_equation diff --git a/vlmeval/dataset/olmOCRBench/katex/auto-render.min.js b/vlmeval/dataset/olmOCRBench/katex/auto-render.min.js new file mode 100644 index 000000000..418ba30dc --- /dev/null +++ b/vlmeval/dataset/olmOCRBench/katex/auto-render.min.js @@ -0,0 +1 @@ +!function(e,t){"object"==typeof exports&&"object"==typeof module?module.exports=t(require("katex")):"function"==typeof define&&define.amd?define(["katex"],t):"object"==typeof exports?exports.renderMathInElement=t(require("katex")):e.renderMathInElement=t(e.katex)}("undefined"!=typeof self?self:this,(function(e){return function(){"use strict";var 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Fn{constructor(e,t){this.mode=void 0,this.gullet=void 0,this.settings=void 0,this.leftrightDepth=void 0,this.nextToken=void 0,this.mode="math",this.gullet=new En(e,t,this.mode),this.settings=t,this.leftrightDepth=0}expect(e,t){if(void 0===t&&(t=!0),this.fetch().text!==e)throw new n("Expected '"+e+"', got '"+this.fetch().text+"'",this.fetch());t&&this.consume()}consume(){this.nextToken=null}fetch(){return null==this.nextToken&&(this.nextToken=this.gullet.expandNextToken()),this.nextToken}switchMode(e){this.mode=e,this.gullet.switchMode(e)}parse(){this.settings.globalGroup||this.gullet.beginGroup(),this.settings.colorIsTextColor&&this.gullet.macros.set("\\color","\\textcolor");try{const e=this.parseExpression(!1);return this.expect("EOF"),this.settings.globalGroup||this.gullet.endGroup(),e}finally{this.gullet.endGroups()}}subparse(e){const t=this.nextToken;this.consume(),this.gullet.pushToken(new Ir("}")),this.gullet.pushTokens(e);const r=this.parseExpression(!1);return this.expect("}"),this.nextToken=t,r}parseExpression(e,t){const r=[];for(;;){"math"===this.mode&&this.consumeSpaces();const n=this.fetch();if(-1!==Fn.endOfExpression.indexOf(n.text))break;if(t&&n.text===t)break;if(e&&wn[n.text]&&wn[n.text].infix)break;const o=this.parseAtom(t);if(!o)break;"internal"!==o.type&&r.push(o)}return"text"===this.mode&&this.formLigatures(r),this.handleInfixNodes(r)}handleInfixNodes(e){let t,r=-1;for(let o=0;o=0&&this.settings.reportNonstrict("unicodeTextInMathMode",'Latin-1/Unicode text character "'+t[0]+'" used in math mode',e);const r=se[this.mode][t].group,n=qr.range(e);let s;if(re.hasOwnProperty(r)){const e=r;s={type:"atom",mode:this.mode,family:e,loc:n,text:t}}else s={type:r,mode:this.mode,loc:n,text:t};o=s}else{if(!(t.charCodeAt(0)>=128))return null;this.settings.strict&&(S(t.charCodeAt(0))?"math"===this.mode&&this.settings.reportNonstrict("unicodeTextInMathMode",'Unicode text character "'+t[0]+'" used in math mode',e):this.settings.reportNonstrict("unknownSymbol",'Unrecognized Unicode character "'+t[0]+'" ('+t.charCodeAt(0)+")",e)),o={type:"textord",mode:"text",loc:qr.range(e),text:t}}if(this.consume(),r)for(let t=0;t Optional["RenderedEquation"]: + with self.lock: + conn = sqlite3.connect(self.db_path) + c = conn.cursor() + c.execute("SELECT mathml, spans, error FROM equations WHERE eq_hash = ?", (eq_hash,)) + row = c.fetchone() + conn.close() + if row: + mathml, spans_json, error = row + if error: + # In error cases, we return an instance with error set and no spans. + return RenderedEquation(mathml=mathml, spans=[], error=error) + else: + spans_data = json.loads(spans_json) + spans = [ + SpanInfo( + text=s["text"], + bounding_box=BoundingBox( + x=s["boundingBox"]["x"], + y=s["boundingBox"]["y"], + width=s["boundingBox"]["width"], + height=s["boundingBox"]["height"], + ), + ) + for s in spans_data + ] + return RenderedEquation(mathml=mathml, spans=spans) + return None + + def save(self, eq_hash: str, rendered_eq: "RenderedEquation"): + spans_data = [ + { + "text": span.text, + "boundingBox": { + "x": span.bounding_box.x, + "y": span.bounding_box.y, + "width": span.bounding_box.width, + "height": span.bounding_box.height, + }, + } + for span in rendered_eq.spans + ] + spans_json = json.dumps(spans_data) + with self.lock: + conn = sqlite3.connect(self.db_path) + c = conn.cursor() + c.execute( + "INSERT OR REPLACE INTO equations (eq_hash, mathml, spans, error) VALUES (?, ?, ?, ?)", + (eq_hash, rendered_eq.mathml, spans_json, rendered_eq.error), + ) + conn.commit() + conn.close() + + def clear(self): + with self.lock: + conn = sqlite3.connect(self.db_path) + c = conn.cursor() + c.execute("DELETE FROM equations") + conn.commit() + conn.close() + + +# Global instance of EquationCache +equation_cache = EquationCache() + +# --- End SQLite Cache Implementation --- + + +# Thread-local storage for Playwright and browser instances +_thread_local = threading.local() + + +@dataclass +class BoundingBox: + x: float + y: float + width: float + height: float + + +@dataclass +class SpanInfo: + text: str + bounding_box: BoundingBox + + +@dataclass +class RenderedEquation: + mathml: str + spans: List[SpanInfo] + error: Optional[str] = None # New field to store error messages if rendering fails + + +def get_equation_hash(equation, bg_color="white", text_color="black", font_size=24): + """ + Calculate SHA1 hash of the equation string and rendering parameters. + """ + params_str = f"{equation}|{bg_color}|{text_color}|{font_size}" + return hashlib.sha1(params_str.encode("utf-8")).hexdigest() + + +def init_browser(): + """ + Initialize the Playwright and browser instance for the current thread if not already done. + """ + if not hasattr(_thread_local, "playwright"): + _thread_local.playwright = sync_playwright().start() + _thread_local.browser = _thread_local.playwright.chromium.launch() + + +def get_browser(): + """ + Return the browser instance for the current thread. + """ + init_browser() + return _thread_local.browser + + +def render_equation( + equation, + bg_color="white", + text_color="black", + font_size=24, + use_cache=True, + debug_dom=False, +): + """ + Render a LaTeX equation using Playwright and KaTeX, extract the inner-most span elements + along with their bounding boxes, and extract the MathML output generated by KaTeX. + """ + # Calculate hash for caching. + eq_hash = get_equation_hash(equation, bg_color, text_color, font_size) + + # Try to load from SQLite cache. + if use_cache: + cached = equation_cache.load(eq_hash) + if cached is not None: + return cached + + # Escape the equation for use in a JavaScript string. + escaped_equation = json.dumps(equation) + + # Get local paths for KaTeX files. + script_dir = os.path.dirname(os.path.abspath(__file__)) + katex_css_path = os.path.join(script_dir, "katex.min.css") + katex_js_path = os.path.join(script_dir, "katex.min.js") + + if not os.path.exists(katex_css_path) or not os.path.exists(katex_js_path): + raise FileNotFoundError( + f"KaTeX files not found. Please ensure katex.min.css and katex.min.js " + f"are in {script_dir}" + ) + + # Get the browser instance for the current thread. + browser = get_browser() + + # Create a new page. + page = browser.new_page(viewport={"width": 800, "height": 400}) + + # Basic HTML structure for rendering. + page_html = f""" + + + + + + +
+ + + """ + page.set_content(page_html) + page.add_style_tag(path=katex_css_path) + page.add_script_tag(path=katex_js_path) + page.wait_for_load_state("networkidle") + + katex_loaded = page.evaluate("typeof katex !== 'undefined'") + if not katex_loaded: + page.close() + raise RuntimeError("KaTeX library failed to load. Check your katex.min.js file.") + + try: + error_message = page.evaluate( + f""" + () => {{ + try {{ + katex.render({escaped_equation}, document.getElementById("equation-container"), {{ + displayMode: true, + throwOnError: true + }}); + return null; + }} catch (error) {{ + console.error("KaTeX error:", error.message); + return error.message; + }} + }} + """ + ) + except PlaywrightError as ex: + print(escaped_equation) + error_message = str(ex) + page.close() + raise + + if error_message: + print(f"Error rendering equation: '{equation}'") + print(error_message) + # Cache the error result so we don't retry it next time. + rendered_eq = RenderedEquation(mathml=error_message, spans=[], error=error_message) + if use_cache: + equation_cache.save(eq_hash, rendered_eq) + page.close() + return rendered_eq + + page.wait_for_selector(".katex", state="attached") + + if debug_dom: + katex_dom_html = page.evaluate( + """ + () => { + return document.getElementById("equation-container").innerHTML; + } + """ + ) + print("\n===== KaTeX DOM HTML =====") + print(katex_dom_html) + + # Extract inner-most spans with non-whitespace text. + spans_info = page.evaluate( + """ + () => { + const spans = Array.from(document.querySelectorAll('span')); + const list = []; + spans.forEach(span => { + if (span.children.length === 0 && /\\S/.test(span.textContent)) { + const rect = span.getBoundingClientRect(); + list.push({ + text: span.textContent.trim(), + boundingBox: { + x: rect.x, + y: rect.y, + width: rect.width, + height: rect.height + } + }); + } + }); + return list; + } + """ + ) + + if debug_dom: + print("\n===== Extracted Span Information =====") + print(spans_info) + + # Extract MathML output (if available) from the KaTeX output. + mathml = page.evaluate( + """ + () => { + const mathElem = document.querySelector('.katex-mathml math'); + return mathElem ? mathElem.outerHTML : ""; + } + """ + ) + + page.close() + + rendered_eq = RenderedEquation( + mathml=mathml, + spans=[ + SpanInfo( + text=s["text"], + bounding_box=BoundingBox( + x=s["boundingBox"]["x"], + y=s["boundingBox"]["y"], + width=s["boundingBox"]["width"], + height=s["boundingBox"]["height"], + ), + ) + for s in spans_info + ], + ) + + # Save the successfully rendered equation to the SQLite cache. + if use_cache: + equation_cache.save(eq_hash, rendered_eq) + return rendered_eq + + +def compare_rendered_equations(reference: RenderedEquation, hypothesis: RenderedEquation) -> bool: + """ + Compare two RenderedEquation objects. + First, check if the normalized MathML of the hypothesis is contained within that of the reference. + If not, perform a neighbor-based matching on the spans. + """ + from bs4 import BeautifulSoup + + def extract_inner(mathml: str) -> str: + try: + soup = BeautifulSoup(mathml, "xml") + semantics = soup.find("semantics") + if semantics: + inner_parts = [ + str(child) + for child in semantics.contents + if getattr(child, "name", None) != "annotation" + ] + return "".join(inner_parts) + else: + return str(soup) + except Exception as e: + print("Error parsing MathML with BeautifulSoup:", e) + print(mathml) + return mathml + + def normalize(s: str) -> str: + return re.sub(r"\s+", "", s) + + reference_inner = normalize(extract_inner(reference.mathml)) + hypothesis_inner = normalize(extract_inner(hypothesis.mathml)) + if reference_inner in hypothesis_inner: + return True + + H, R = reference.spans, hypothesis.spans + H = [span for span in H if span.text != "\u200b"] + R = [span for span in R if span.text != "\u200b"] + + def expand_span_info(span_info: SpanInfo) -> list[SpanInfo]: + total_elems = len(span_info.text) + return [ + SpanInfo( + c, + BoundingBox( + span_info.bounding_box.x + (span_info.bounding_box.width * index) / total_elems, + span_info.bounding_box.y, + span_info.bounding_box.width / total_elems, + span_info.bounding_box.height, + ), + ) + for index, c in enumerate(span_info.text) + ] + + H = [span for sublist in H for span in expand_span_info(sublist)] + R = [span for sublist in R for span in expand_span_info(sublist)] + + candidate_map = {} + for i, hspan in enumerate(H): + candidate_map[i] = [j for j, rsp in enumerate(R) if rsp.text == hspan.text] + if not candidate_map[i]: + return False + + def compute_neighbors(spans, tol=5): + neighbors = {} + for i, span in enumerate(spans): + cx = span.bounding_box.x + span.bounding_box.width / 2 + cy = span.bounding_box.y + span.bounding_box.height / 2 + up = down = left = right = None + up_dist = down_dist = left_dist = right_dist = None + for j, other in enumerate(spans): + if i == j: + continue + ocx = other.bounding_box.x + other.bounding_box.width / 2 + ocy = other.bounding_box.y + other.bounding_box.height / 2 + if ocy < cy and abs(ocx - cx) <= tol: + dist = cy - ocy + if up is None or dist < up_dist: + up = j + up_dist = dist + if ocy > cy and abs(ocx - cx) <= tol: + dist = ocy - cy + if down is None or dist < down_dist: + down = j + down_dist = dist + if ocx < cx and abs(ocy - cy) <= tol: + dist = cx - ocx + if left is None or dist < left_dist: + left = j + left_dist = dist + if ocx > cx and abs(ocy - cy) <= tol: + dist = ocx - cx + if right is None or dist < right_dist: + right = j + right_dist = dist + neighbors[i] = {"up": up, "down": down, "left": left, "right": right} + return neighbors + + hyp_neighbors = compute_neighbors(H) + ref_neighbors = compute_neighbors(R) + + n = len(H) + used = [False] * len(R) + assignment = {} + + def backtrack(i): + if i == n: + return True + for cand in candidate_map[i]: + if used[cand]: + continue + assignment[i] = cand + used[cand] = True + valid = True + for direction in ["up", "down", "left", "right"]: + hyp_nb = hyp_neighbors[i].get(direction) + ref_nb = ref_neighbors[cand].get(direction) + if hyp_nb is not None: + expected_text = H[hyp_nb].text + if ref_nb is None: + valid = False + break + if hyp_nb in assignment: + if assignment[hyp_nb] != ref_nb: + valid = False + break + else: + if R[ref_nb].text != expected_text: + valid = False + break + if valid: + if backtrack(i + 1): + return True + used[cand] = False + del assignment[i] + return False + + return backtrack(0) + + +class TestRenderedEquationComparison(unittest.TestCase): + def test_exact_match(self): + eq1 = render_equation("a+b", use_cache=False) + eq2 = render_equation("a+b", use_cache=False) + self.assertTrue(compare_rendered_equations(eq1, eq2)) + + def test_whitespace_difference(self): + eq1 = render_equation("a+b", use_cache=False) + eq2 = render_equation("a + b", use_cache=False) + self.assertTrue(compare_rendered_equations(eq1, eq2)) + + def test_not_found(self): + eq1 = render_equation("c-d", use_cache=False) + eq2 = render_equation("a+b", use_cache=False) + self.assertFalse(compare_rendered_equations(eq1, eq2)) + + def test_align_block_contains_needle(self): + eq_plain = render_equation("a+b", use_cache=False) + eq_align = render_equation("\\begin{align*}a+b\\end{align*}", use_cache=False) + self.assertTrue(compare_rendered_equations(eq_plain, eq_align)) + + def test_align_block_needle_not_in(self): + eq_align = render_equation("\\begin{align*}a+b\\end{align*}", use_cache=False) + eq_diff = render_equation("c-d", use_cache=False) + self.assertFalse(compare_rendered_equations(eq_diff, eq_align)) + + def test_big(self): + ref_rendered = render_equation( + "\\nabla \\cdot \\mathbf{E} = \\frac{\\rho}{\\varepsilon_0}", + use_cache=False, debug_dom=False + ) + align_rendered = render_equation( + """\\begin{align*}\\nabla \\cdot \\mathbf{E} = \\frac{\\rho}{\\varepsilon_0}\\end{align*}""", + use_cache=False, debug_dom=False + ) + self.assertTrue(compare_rendered_equations(ref_rendered, align_rendered)) + + def test_dot_end1(self): + ref_rendered = render_equation( + "\\lambda_{g}=\\sum_{s \\in S} \\zeta_{n}^{\\psi(g s)}=" + "\\sum_{i=1}^{k}\\left[\\sum_{s, R s=\\mathcal{I}_{i}} " + "\\zeta_{n}^{\\varphi(g s)}\\right]" + ) + align_rendered = render_equation( + "\\lambda_{g}=\\sum_{s \\in S} \\zeta_{n}^{\\psi(g s)}=" + "\\sum_{i=1}^{k}\\left[\\sum_{s, R s=\\mathcal{I}_{i}} " + "\\zeta_{n}^{\\varphi(g s)}\\right]." + ) + self.assertTrue(compare_rendered_equations(ref_rendered, align_rendered)) + + def test_x_vs_textx(self): + ref_rendered = render_equation( + "C_{T}\\left(u_{n}^{T} X_{n}^{\\text {Test }}, \\bar{x}^{\\text {Test }}\\right)" + ) + align_rendered = render_equation( + "C_T \\left(u^T_n X^{\\text{Test}}_n,\\overline{ \\text{x}}^{\\text{Test}}\\right)" + ) + self.assertFalse(compare_rendered_equations(ref_rendered, align_rendered)) + + @unittest.skip("There is a debate whether bar and overline should be the same, currently they are not") + def test_overline(self): + ref_rendered = render_equation( + "C_{T}\\left(u_{n}^{T} X_{n}^{\\text {Test }}, \\bar{x}^{\\text {Test }}\\right)" + ) + align_rendered = render_equation( + "C_T \\left(u^T_n X^{\\text{Test}}_n,\\overline{ x}^{\\text{Test}}\\right)" + ) + self.assertTrue(compare_rendered_equations(ref_rendered, align_rendered)) + + def test_parens(self): + ref_rendered = render_equation("\\left\\{ \\left( 0_{X},0_{Y},-1\\right) \\right\\} ") + align_rendered = render_equation("\\{(0_{X}, 0_{Y}, -1)\\}") + self.assertTrue(compare_rendered_equations(ref_rendered, align_rendered)) + + def test_dot_end2(self): + ref_rendered = render_equation( + "\\lambda_{g}=\\sum_{s \\in S} \\zeta_{n}^{\\psi(g s)}=" + "\\sum_{i=1}^{k}\\left[\\sum_{s, R s=\\mathcal{I}_{i}} " + "\\zeta_{n}^{\\psi(g s)}\\right]" + ) + align_rendered = render_equation( + "\\lambda_g = \\sum_{s \\in S} \\zeta_n^{\\psi(gs)} = " + "\\sum_{i=1}^{k} \\left[ \\sum_{s, Rs = \\mathcal{I}_i} " + "\\zeta_n^{\\psi(gs)} \\right]" + ) + self.assertTrue(compare_rendered_equations(ref_rendered, align_rendered)) + + def test_lambda(self): + ref_rendered = render_equation("\\lambda_g = \\lambda_{g'}") + align_rendered = render_equation("\\lambda_{g}=\\lambda_{g^{\\prime}}") + self.assertTrue(compare_rendered_equations(ref_rendered, align_rendered)) + + def test_gemini(self): + ref_rendered = render_equation("u \\in (R/\\operatorname{Ann}_R(x_i))^{\\times}") + align_rendered = render_equation( + "u \\in\\left(R / \\operatorname{Ann}_{R}\\left(x_{i}\\right)\\right)^{\\times}" + ) + self.assertTrue(compare_rendered_equations(ref_rendered, align_rendered)) + + def test_fraction_vs_divided_by(self): + eq1 = render_equation("\\frac{a}{b}", use_cache=False) + eq2 = render_equation("a / b", use_cache=False) + self.assertFalse(compare_rendered_equations(eq1, eq2)) + + def test_different_bracket_types(self): + eq1 = render_equation("\\left[ a + b \\right]", use_cache=False) + eq2 = render_equation("\\left\\{ a + b \\right\\}", use_cache=False) + self.assertFalse(compare_rendered_equations(eq1, eq2)) + + def test_inline_vs_display_style_fraction(self): + eq1 = render_equation("\\frac{1}{2}", use_cache=False) + eq2 = render_equation("\\displaystyle\\frac{1}{2}", use_cache=False) + self.assertTrue(compare_rendered_equations(eq1, eq2)) + + def test_matrix_equivalent_forms(self): + eq1 = render_equation("\\begin{pmatrix} a & b \\\\ c & d \\end{pmatrix}", use_cache=False) + eq2 = render_equation("\\begin{pmatrix} a & b \\\\ c & d \\end{pmatrix}", use_cache=False) + self.assertTrue(compare_rendered_equations(eq1, eq2)) + + def test_different_matrix_types(self): + eq1 = render_equation("\\begin{pmatrix} a & b \\\\ c & d \\end{pmatrix}", use_cache=False) + eq2 = render_equation("\\begin{bmatrix} a & b \\\\ c & d \\end{bmatrix}", use_cache=False) + self.assertFalse(compare_rendered_equations(eq1, eq2)) + + def test_thinspace_vs_regular_space(self): + eq1 = render_equation("a \\, b", use_cache=False) + eq2 = render_equation("a \\: b", use_cache=False) + self.assertTrue(compare_rendered_equations(eq1, eq2)) + + @unittest.skip( + "Currently these compare to the same thing, " + "because they use the symbol 'x' with a different span class and thus font" + ) + def test_mathbf_vs_boldsymbol(self): + eq1 = render_equation("\\mathbf{x}", use_cache=False) + eq2 = render_equation("\\boldsymbol{x}", use_cache=False) + self.assertFalse(compare_rendered_equations(eq1, eq2)) + + def test_assert_subtle_square_root(self): + eq1 = render_equation( + "A N'P' = \\int \\beta d\\alpha = " + "\\frac{2}{3\\sqrt{3} a}\\int (\\alpha - 2a)^{\\frac{3}{2}} d\\alpha", + use_cache=False, + ) + eq2 = render_equation( + "AN'P' = \\int \\beta \\, d\\alpha = " + "\\frac{2}{3 \\sqrt{3a}} \\int (a - 2a)^{\\frac{3}{2}} d\\alpha", + ) + self.assertFalse(compare_rendered_equations(eq1, eq2)) + + def test_text_added(self): + eq1 = render_equation( + "A N'P' = \\int \\beta d\\alpha = " + "\\frac{2}{3\\sqrt{3} a}\\int (\\alpha - 2a)^{\\frac{3}{2}} d\\alpha", + use_cache=False, + ) + eq2 = render_equation( + "AN'P' = \\int \\beta d\\alpha = " + "\\frac{2}{3 \\sqrt{3} a} \\int (\\alpha - 2a)^{\\frac{3}{2}} d\\alpha", + ) + self.assertTrue(compare_rendered_equations(eq1, eq2)) + + eq1 = render_equation( + "A N'P' = \\int \\beta d\\alpha = " + "\\frac{2}{3\\sqrt{3} a}\\int (\\alpha - 2a)^{\\frac{3}{2}} d\\alpha", + use_cache=False, + ) + eq2 = render_equation( + "\\text{area evolute } AN'P' = \\int \\beta d\\alpha = " + "\\frac{2}{3 \\sqrt{3} a} \\int (\\alpha - 2a)^{\\frac{3}{2}} d\\alpha" + ) + self.assertTrue(compare_rendered_equations(eq1, eq2)) + + def test_tensor_notation_equivalent(self): + eq1 = render_equation("T_{ij}^{kl}", use_cache=False) + eq2 = render_equation("T^{kl}_{ij}", use_cache=False) + self.assertTrue(compare_rendered_equations(eq1, eq2)) + + def test_partial_derivative_forms(self): + eq1 = render_equation("\\frac{\\partial f}{\\partial x}", use_cache=False) + eq2 = render_equation("\\frac{\\partial_f}{\\partial_x}", use_cache=False) + self.assertFalse(compare_rendered_equations(eq1, eq2)) + + def test_equivalent_sin_forms_diff_parens(self): + eq1 = render_equation("\\sin(\\theta)", use_cache=False) + eq2 = render_equation("\\sin \\theta", use_cache=False) + self.assertFalse(compare_rendered_equations(eq1, eq2)) + + def test_aligned_multiline_equation(self): + eq1 = render_equation("\\begin{align*} a &= b \\\\ c &= d \\end{align*}", use_cache=False) + eq2 = render_equation("\\begin{aligned} a &= b \\\\ c &= d \\end{aligned}", use_cache=False) + self.assertTrue(compare_rendered_equations(eq1, eq2)) + + def test_subscript_order_invariance(self): + eq1 = render_equation("x_{i,j}", use_cache=False) + eq2 = render_equation("x_{j,i}", use_cache=False) + self.assertFalse(compare_rendered_equations(eq1, eq2)) + + def test_hat_vs_widehat(self): + eq1 = render_equation("\\hat{x}", use_cache=False) + eq2 = render_equation("\\widehat{x}", use_cache=False) + self.assertFalse(compare_rendered_equations(eq1, eq2)) + + def test_equivalent_integral_bounds(self): + eq1 = render_equation("\\int_{a}^{b} f(x) dx", use_cache=False) + eq2 = render_equation("\\int\\limits_{a}^{b} f(x) dx", use_cache=False) + # Could go either way honestly? + self.assertTrue(compare_rendered_equations(eq1, eq2)) + + def test_equivalent_summation_notation(self): + eq1 = render_equation("\\sum_{i=1}^{n} x_i", use_cache=False) + eq2 = render_equation("\\sum\\limits_{i=1}^{n} x_i", use_cache=False) + self.assertTrue(compare_rendered_equations(eq1, eq2)) + + def test_different_symbol_with_same_appearance(self): + eq1 = render_equation("\\phi", use_cache=False) + eq2 = render_equation("\\varphi", use_cache=False) + self.assertFalse(compare_rendered_equations(eq1, eq2)) + + def test_aligned_vs_gathered(self): + eq1 = render_equation("\\begin{aligned} a &= b \\\\ c &= d \\end{aligned}", use_cache=False) + eq2 = render_equation("\\begin{gathered} a = b \\\\ c = d \\end{gathered}", use_cache=False) + # Different whitespacing, should be invariant to that. + self.assertTrue(compare_rendered_equations(eq1, eq2)) + + def test_identical_but_with_color1(self): + eq1 = render_equation("a + b", use_cache=False) + eq2 = render_equation("\\color{black}{a + b}", use_cache=False) + self.assertTrue(compare_rendered_equations(eq1, eq2)) + + def test_identical_but_with_color2(self): + eq1 = render_equation("a + b", use_cache=False) + eq2 = render_equation("\\color{black}{a} + \\color{black}{b}", use_cache=False) + self.assertTrue(compare_rendered_equations(eq1, eq2)) + + eq1 = render_equation("a + b", use_cache=False) + eq2 = render_equation("\\color{red}{a} + \\color{black}{b}", use_cache=False) + self.assertTrue(compare_rendered_equations(eq1, eq2)) + + def test_newcommand_expansion(self): + eq1 = render_equation("\\alpha + \\beta", use_cache=False) + eq2 = render_equation("\\newcommand{\\ab}{\\alpha + \\beta}\\ab", use_cache=False) + self.assertTrue(compare_rendered_equations(eq1, eq2)) + + +if __name__ == "__main__": + unittest.main() diff --git a/vlmeval/dataset/olmOCRBench/olmocrbench.py b/vlmeval/dataset/olmOCRBench/olmocrbench.py new file mode 100644 index 000000000..417d574b1 --- /dev/null +++ b/vlmeval/dataset/olmOCRBench/olmocrbench.py @@ -0,0 +1,47 @@ +import json +import os +import copy +import pandas as pd +import tempfile +import base64 +import numpy as np +from tqdm import tqdm +import torch.distributed as dist +from ..image_base import ImageBaseDataset +from ...smp import * +from .evaluator import evaluator + + +class olmOCRBench(ImageBaseDataset): + + MODALITY = 'IMAGE' + TYPE = 'QA' + + DATASET_URL = {'olmOCRBench':'./olmOCRBench.tsv'} + DATASET_MD5 = {'olmOCRBench': '25fe250887fee675f887a2a2d24df185'} # 完整版本的tsv文件 + + # base prompt + system_prompt = ( + "Please provide a natural, plain text representation of the document, " + "formatted in Markdown. Skip any headers and footers. " + "For ALL mathematical expressions, use LaTeX notation with " + "\\( and \\) for inline equations and \\[ and \\] for display equations. " + "Convert any tables into Markdown format." + ) + + def __init__(self,dataset='olmOCRBench',**kwargs): + super().__init__(dataset,**kwargs) + print(f'self.img_root:{self.img_root}') + + def build_prompt(self, line): + + image_path = self.dump_image(line)[0] + msg = [ + dict(type='image', value=image_path), + dict(type='text', value=self.system_prompt) + ] + return msg + + def evaluate(self, eval_file, **judge_kwargs): + tsv_path = self.data_path + evaluator(tsv_path, eval_file) diff --git a/vlmeval/dataset/olmOCRBench/repeatdetect.py b/vlmeval/dataset/olmOCRBench/repeatdetect.py new file mode 100644 index 000000000..a172312a4 --- /dev/null +++ b/vlmeval/dataset/olmOCRBench/repeatdetect.py @@ -0,0 +1,175 @@ +import random +import re +import string +import time +import unittest + + +class RepeatDetector: + def __init__(self, max_ngram_size: int = 10): + self.max_ngram_size = max_ngram_size + self.data = "" + + def add_letters(self, new_str: str): + self.data += new_str + + def ngram_repeats(self) -> list[int]: + result = [0] * self.max_ngram_size + + if not self.data: + return result + + # Normalize all whitespace to single spaces + text = re.sub(r"\s+", " ", self.data) + + # For each n-gram size + for size in range(1, self.max_ngram_size + 1): + if len(text) < size: + continue + + # Get the last n-gram + target = text[-size:] + + # Count backwards from the end to find repeats + count = 0 + pos = len(text) - size # Start position for previous n-gram + + while pos >= 0: + if text[pos: pos + size] == target: + count += 1 + pos -= size # Move back by the size of the n-gram + else: + break + + result[size - 1] = count + + return result + + +class RepeatDetectorTest(unittest.TestCase): + def test_basicTest1(self): + d = RepeatDetector(max_ngram_size=3) + d.add_letters("a") + self.assertEqual(d.ngram_repeats(), [1, 0, 0]) + + def test_basicTest2(self): + d = RepeatDetector(max_ngram_size=3) + d.add_letters("abab") + self.assertEqual(d.ngram_repeats(), [1, 2, 1]) + + def test_longer_sequence(self): + d = RepeatDetector(max_ngram_size=3) + d.add_letters("aabaabaa") + self.assertEqual(d.ngram_repeats(), [2, 1, 2]) + + def test_no_repeats(self): + d = RepeatDetector(max_ngram_size=3) + d.add_letters("abc") + self.assertEqual(d.ngram_repeats(), [1, 1, 1]) + + def test_empty_data(self): + d = RepeatDetector(max_ngram_size=3) + self.assertEqual(d.ngram_repeats(), [0, 0, 0]) + + def test_max_ngram_greater_than_data_length(self): + d = RepeatDetector(max_ngram_size=5) + d.add_letters("abc") + self.assertEqual(d.ngram_repeats(), [1, 1, 1, 0, 0]) + + def test_large_single_char(self): + d = RepeatDetector(max_ngram_size=5) + d.add_letters("a" * 10000) + self.assertEqual(d.ngram_repeats(), [10000, 5000, 3333, 2500, 2000]) + + def test_repeating_pattern(self): + d = RepeatDetector(max_ngram_size=5) + d.add_letters("abcabcabcabc") + self.assertEqual(d.ngram_repeats(), [1, 1, 4, 1, 1]) + + def test_mixed_characters(self): + d = RepeatDetector(max_ngram_size=4) + d.add_letters("abcdabcabcdabc") + self.assertEqual(d.ngram_repeats(), [1, 1, 1, 1]) + + def test_palindrome(self): + d = RepeatDetector(max_ngram_size=5) + d.add_letters("racecar") + self.assertEqual(d.ngram_repeats(), [1, 1, 1, 1, 1]) + + def test_repeats_not_at_end(self): + d = RepeatDetector(max_ngram_size=3) + d.add_letters("abcabcxyz") + self.assertEqual(d.ngram_repeats(), [1, 1, 1]) + + def test_long_repeat_at_end(self): + d = RepeatDetector(max_ngram_size=5) + d.add_letters("abcabcabcabcabcabcabcabcabcabc") + self.assertEqual(d.ngram_repeats(), [1, 1, 10, 1, 1]) + + def test_large_repeating_pattern(self): + d = RepeatDetector(max_ngram_size=4) + pattern = "abcd" + repeat_count = 1000 + d.add_letters(pattern * repeat_count) + self.assertEqual(d.ngram_repeats(), [1, 1, 1, repeat_count]) + + def test_unicode_characters(self): + d = RepeatDetector(max_ngram_size=3) + d.add_letters("αβγαβγ") + self.assertEqual(d.ngram_repeats(), [1, 1, 2]) + + def test_random_data(self): + random.seed(42) + d = RepeatDetector(max_ngram_size=5) + data = "".join(random.choices(string.ascii_letters, k=10000)) + d.add_letters(data) + counts = d.ngram_repeats() + for count in counts: + self.assertTrue(0 <= count <= len(data)) + + def test_special_characters(self): + d = RepeatDetector(max_ngram_size=4) + d.add_letters("@@##@@##") + self.assertEqual(d.ngram_repeats(), [2, 1, 1, 2]) + + def test_incremental_addition(self): + d = RepeatDetector(max_ngram_size=3) + d.add_letters("abc") + self.assertEqual(d.ngram_repeats(), [1, 1, 1]) + d.add_letters("abc") + self.assertEqual(d.ngram_repeats(), [1, 1, 2]) + d.add_letters("abc") + self.assertEqual(d.ngram_repeats(), [1, 1, 3]) + + def test_long_non_repeating_sequence(self): + d = RepeatDetector(max_ngram_size=5) + d.add_letters("abcdefghijklmnopqrstuvwxyz") + self.assertEqual(d.ngram_repeats(), [1, 1, 1, 1, 1]) + + def test_alternating_characters(self): + d = RepeatDetector(max_ngram_size=4) + d.add_letters("ababababab") + self.assertEqual(d.ngram_repeats(), [1, 5, 1, 2]) + + +class BenchmarkRepeatDetect(unittest.TestCase): + def testLargeRandom(self): + all_data = [] + + for iter in range(1000): + all_data.append("".join(random.choices("a", k=10000))) + + start = time.perf_counter() + + for data in all_data: + d = RepeatDetector(max_ngram_size=20) + d.add_letters(data) + print(d.ngram_repeats()) + + end = time.perf_counter() + + print(f"testLargeRandom took {end-start:0.0001f} seconds") + + +if __name__ == "__main__": + unittest.main() diff --git a/vlmeval/dataset/olmOCRBench/tests.py b/vlmeval/dataset/olmOCRBench/tests.py new file mode 100644 index 000000000..e01d2ba37 --- /dev/null +++ b/vlmeval/dataset/olmOCRBench/tests.py @@ -0,0 +1,1187 @@ +import json +import os +import re +import unicodedata +from concurrent.futures import ThreadPoolExecutor, as_completed +from dataclasses import asdict, dataclass, field +from enum import Enum +from typing import Dict, List, Optional, Set, Tuple, Union + +import numpy as np +from bs4 import BeautifulSoup +from fuzzysearch import find_near_matches +from rapidfuzz import fuzz +from tqdm import tqdm + +from .repeatdetect import RepeatDetector + +from .katex.render import compare_rendered_equations, render_equation + + +@dataclass +class TableData: + """Class to hold table data and metadata about headers.""" + + data: np.ndarray # The actual table data + header_rows: Set[int] = field(default_factory=set) # Indices of rows that are headers + header_cols: Set[int] = field(default_factory=set) # Indices of columns that are headers + col_headers: dict = field(default_factory=dict) # Maps column index to header text, handling colspan + row_headers: dict = field(default_factory=dict) # Maps row index to header text, handling rowspan + + def __repr__(self) -> str: + """Returns a concise representation of the TableData object for debugging.""" + return ( + f"TableData(shape={self.data.shape}, " + f"header_rows={len(self.header_rows)}, " + f"header_cols={len(self.header_cols)})" + ) + + def __str__(self) -> str: + """Returns a pretty string representation of the table with header information.""" + output = [] + + # Table dimensions + output.append(f"Table: {self.data.shape[0]} rows × {self.data.shape[1]} columns") + + # Header info + output.append(f"Header rows: {sorted(self.header_rows)}") + output.append(f"Header columns: {sorted(self.header_cols)}") + + # Table content with formatting + separator = "+" + "+".join(["-" * 17] * self.data.shape[1]) + "+" + + # Add a header for row indices + output.append(separator) + headers = [""] + [f"Column {i}" for i in range(self.data.shape[1])] + output.append("| {:<5} | ".format("Row") + " | ".join(["{:<15}".format(h) for h in headers[1:]]) + " |") + output.append(separator) + + # Format each row + for i in range(min(self.data.shape[0], 15)): # Limit to 15 rows for readability + # Format cells, mark header cells + cells = [] + for j in range(self.data.shape[1]): + cell = str(self.data[i, j]) + if len(cell) > 15: + cell = cell[:12] + "..." + # Mark header cells with * + if i in self.header_rows or j in self.header_cols: + cell = f"*{cell}*" + cells.append(cell) + + row_str = "| {:<5} | ".format(i) + " | ".join(["{:<15}".format(c) for c in cells]) + " |" + output.append(row_str) + output.append(separator) + + # If table is too large, indicate truncation + if self.data.shape[0] > 15: + output.append(f"... {self.data.shape[0] - 15} more rows ...") + + # Column header details if available + if self.col_headers: + output.append("\nColumn header mappings:") + for col, headers in sorted(self.col_headers.items()): + header_strs = [f"({row}, '{text}')" for row, text in headers] + output.append(f" Column {col}: {', '.join(header_strs)}") + + # Row header details if available + if self.row_headers: + output.append("\nRow header mappings:") + for row, headers in sorted(self.row_headers.items()): + header_strs = [f"({col}, '{text}')" for col, text in headers] + output.append(f" Row {row}: {', '.join(header_strs)}") + + return "\n".join(output) + + +class TestType(str, Enum): + BASELINE = "baseline" + PRESENT = "present" + ABSENT = "absent" + ORDER = "order" + TABLE = "table" + MATH = "math" + + +class TestChecked(str, Enum): + VERIFIED = "verified" + REJECTED = "rejected" + + +class ValidationError(Exception): + """Exception raised for validation errors.""" + + pass + + +def normalize_text(md_content: str) -> str: + if md_content is None: + return None + + # Normalize
and
to newlines + md_content = re.sub(r"
", " ", md_content) + + # Normalize whitespace in the md_content + md_content = re.sub(r"\s+", " ", md_content) + + # Remove markdown bold formatting (** or __ for bold) + md_content = re.sub(r"\*\*(.*?)\*\*", r"\1", md_content) + md_content = re.sub(r"__(.*?)__", r"\1", md_content) + md_content = re.sub(r"", "", md_content) # Remove tags if they exist + md_content = re.sub(r"", "", md_content) # Remove tags if they exist + + # Remove markdown italics formatting (* or _ for italics) + md_content = re.sub(r"\*(.*?)\*", r"\1", md_content) + md_content = re.sub(r"_(.*?)_", r"\1", md_content) + + # Convert down to a consistent unicode form, so é == e + accent, unicode forms + md_content = unicodedata.normalize("NFC", md_content) + # Dictionary of characters to replace: keys are fancy characters, + # values are ASCII equivalents, unicode micro with greek mu comes up often enough too + replacements = { + "‘": "'", + "’": "'", + "‚": "'", + "“": '"', + "”": '"', + "„": '"', + "＿": "_", + "–": "-", + "—": "-", + "‑": "-", + "‒": "-", + "−": "-", + "\u00b5": "\u03bc" + } + + # Apply all replacements from the dictionary + for fancy_char, ascii_char in replacements.items(): + md_content = md_content.replace(fancy_char, ascii_char) + + return md_content + + +def parse_markdown_tables(md_content: str) -> List[TableData]: + """ + Extract and parse all markdown tables from the provided content. + Uses a direct approach to find and parse tables, which is more robust for tables + at the end of files or with irregular formatting. + + Args: + md_content: The markdown content containing tables + + Returns: + A list of TableData objects, each containing the table data and header information + """ + # Split the content into lines and process line by line + lines = md_content.strip().split("\n") + + parsed_tables = [] + current_table_lines = [] + in_table = False + + # Identify potential tables by looking for lines with pipe characters + for i, line in enumerate(lines): + # Check if this line has pipe characters (a table row indicator) + if "|" in line: + # If we weren't in a table before, start a new one + if not in_table: + in_table = True + current_table_lines = [line] + else: + # Continue adding to the current table + current_table_lines.append(line) + else: + # No pipes in this line, so if we were in a table, we've reached its end + if in_table: + # Process the completed table if it has at least 2 rows + if len(current_table_lines) >= 2: + table_data = _process_table_lines(current_table_lines) + if table_data and len(table_data) > 0: + # Convert to numpy array for easier manipulation + max_cols = max(len(row) for row in table_data) + padded_data = [row + [""] * (max_cols - len(row)) for row in table_data] + table_array = np.array(padded_data) + + # In markdown tables, the first row is typically a header row + header_rows = {0} if len(table_array) > 0 else set() + + # Set up col_headers with first row headers for each column + col_headers = {} + if len(table_array) > 0: + for col_idx in range(table_array.shape[1]): + if col_idx < len(table_array[0]): + col_headers[col_idx] = [(0, table_array[0, col_idx])] + + # Set up row_headers with first column headers for each row + row_headers = {} + if table_array.shape[1] > 0: + for row_idx in range(1, table_array.shape[0]): # Skip header row + row_headers[row_idx] = [(0, table_array[row_idx, 0])] # First column as heading + + # Create TableData object + parsed_tables.append( + TableData( + data=table_array, + header_rows=header_rows, + header_cols={0} if table_array.shape[1] > 0 else set(), # First column as header + col_headers=col_headers, + row_headers=row_headers, + ) + ) + in_table = False + + # Process the last table if we're still tracking one at the end of the file + if in_table and len(current_table_lines) >= 2: + table_data = _process_table_lines(current_table_lines) + if table_data and len(table_data) > 0: + # Convert to numpy array + max_cols = max(len(row) for row in table_data) + padded_data = [row + [""] * (max_cols - len(row)) for row in table_data] + table_array = np.array(padded_data) + + # In markdown tables, the first row is typically a header row + header_rows = {0} if len(table_array) > 0 else set() + + # Set up col_headers with first row headers for each column + col_headers = {} + if len(table_array) > 0: + for col_idx in range(table_array.shape[1]): + if col_idx < len(table_array[0]): + col_headers[col_idx] = [(0, table_array[0, col_idx])] + + # Set up row_headers with first column headers for each row + row_headers = {} + if table_array.shape[1] > 0: + for row_idx in range(1, table_array.shape[0]): # Skip header row + row_headers[row_idx] = [(0, table_array[row_idx, 0])] # First column as heading + + # Create TableData object + parsed_tables.append( + TableData( + data=table_array, + header_rows=header_rows, + header_cols={0} if table_array.shape[1] > 0 else set(), # First column as header + col_headers=col_headers, + row_headers=row_headers, + ) + ) + + return parsed_tables + + +def _process_table_lines(table_lines: List[str]) -> List[List[str]]: + """ + Process a list of lines that potentially form a markdown table. + + Args: + table_lines: List of strings, each representing a line in a potential markdown table + + Returns: + A list of rows, each a list of cell values + """ + table_data = [] + separator_row_index = None + + # First, identify the separator row (the row with dashes) + for i, line in enumerate(table_lines): + # Check if this looks like a separator row (contains mostly dashes) + content_without_pipes = line.replace("|", "").strip() + if content_without_pipes and all(c in "- :" for c in content_without_pipes): + separator_row_index = i + break + + # Process each line, filtering out the separator row + for i, line in enumerate(table_lines): + # Skip the separator row + if i == separator_row_index: + continue + + # Skip lines that are entirely formatting + if line.strip() and all(c in "- :|" for c in line): + continue + + # Process the cells in this row + cells = [cell.strip() for cell in line.split("|")] + + # Remove empty cells at the beginning and end (caused by leading/trailing pipes) + if cells and cells[0] == "": + cells = cells[1:] + if cells and cells[-1] == "": + cells = cells[:-1] + + if cells: # Only add non-empty rows + table_data.append(cells) + + return table_data + + +def parse_html_tables(html_content: str) -> List[TableData]: + """ + Extract and parse all HTML tables from the provided content. + Identifies header rows and columns, and maps them properly handling rowspan/colspan. + + Args: + html_content: The HTML content containing tables + + Returns: + A list of TableData objects, each containing the table data and header information + """ + soup = BeautifulSoup(html_content, "html.parser") + tables = soup.find_all("table") + + parsed_tables = [] + + for table in tables: + rows = table.find_all(["tr"]) + table_data = [] + header_rows = set() + header_cols = set() + col_headers = {} # Maps column index to all header cells above it + row_headers = {} # Maps row index to all header cells to its left + + # Find rows inside thead tags - these are definitely header rows + thead = table.find("thead") + if thead: + thead_rows = thead.find_all("tr") + for tr in thead_rows: + header_rows.add(rows.index(tr)) + + # Initialize a grid to track filled cells due to rowspan/colspan + cell_grid = {} + col_span_info = {} # Tracks which columns contain headers + row_span_info = {} # Tracks which rows contain headers + + # First pass: process each row to build the raw table data and identify headers + for row_idx, row in enumerate(rows): + cells = row.find_all(["th", "td"]) + row_data = [] + col_idx = 0 + + # If there are th elements in this row, it's likely a header row + if row.find("th"): + header_rows.add(row_idx) + + for cell in cells: + # Skip positions already filled by rowspans from above + while (row_idx, col_idx) in cell_grid: + row_data.append(cell_grid[(row_idx, col_idx)]) + col_idx += 1 + + # Replace
and
tags with newlines before getting text + for br in cell.find_all("br"): + br.replace_with("\n") + cell_text = cell.get_text().strip() + + # Handle rowspan/colspan + rowspan = int(cell.get("rowspan", 1)) + colspan = int(cell.get("colspan", 1)) + + # Add the cell to the row data + row_data.append(cell_text) + + # Fill the grid for this cell and its rowspan/colspan + for i in range(rowspan): + for j in range(colspan): + if i == 0 and j == 0: + continue # Skip the main cell position + # For rowspan cells, preserve the text in all spanned rows + if j == 0 and i > 0: # Only for cells directly below + cell_grid[(row_idx + i, col_idx + j)] = cell_text + else: + cell_grid[(row_idx + i, col_idx + j)] = "" # Mark other spans as empty + + # If this is a header cell (th), mark it and its span + if cell.name == "th": + # Mark columns as header columns + for j in range(colspan): + header_cols.add(col_idx + j) + + # For rowspan, mark spanned rows as part of header + for i in range(1, rowspan): + if row_idx + i < len(rows): + header_rows.add(row_idx + i) + + # Record this header for all spanned columns + for j in range(colspan): + curr_col = col_idx + j + if curr_col not in col_headers: + col_headers[curr_col] = [] + col_headers[curr_col].append((row_idx, cell_text)) + + # Store which columns are covered by this header + if cell_text and colspan > 1: + if cell_text not in col_span_info: + col_span_info[cell_text] = set() + col_span_info[cell_text].add(curr_col) + + # Store which rows are covered by this header for rowspan + if cell_text and rowspan > 1: + if cell_text not in row_span_info: + row_span_info[cell_text] = set() + for i in range(rowspan): + row_span_info[cell_text].add(row_idx + i) + + # Also handle row headers from data cells that have rowspan + if cell.name == "td" and rowspan > 1 and col_idx in header_cols: + for i in range(1, rowspan): + if row_idx + i < len(rows): + if row_idx + i not in row_headers: + row_headers[row_idx + i] = [] + row_headers[row_idx + i].append((col_idx, cell_text)) + + col_idx += colspan + + # Pad the row if needed to handle different row lengths + table_data.append(row_data) + + # Second pass: expand headers to cells that should inherit them + # First handle column headers + for header_text, columns in col_span_info.items(): + for col in columns: + # Add this header to all columns it spans over + for row_idx in range(len(table_data)): + if row_idx not in header_rows: # Only apply to data rows + for j in range(col, len(table_data[row_idx]) if row_idx < len(table_data) else 0): + # Add header info to data cells in these columns + if j not in col_headers: + col_headers[j] = [] + if not any(h[1] == header_text for h in col_headers[j]): + header_row = min([r for r, t in col_headers.get(col, [(0, "")])]) + col_headers[j].append((header_row, header_text)) + + # Handle row headers + for header_text, rows in row_span_info.items(): + for row in rows: + if row < len(table_data): + # Find first header column + header_col = min(header_cols) if header_cols else 0 + if row not in row_headers: + row_headers[row] = [] + if not any(h[1] == header_text for h in row_headers.get(row, [])): + row_headers[row].append((header_col, header_text)) + + # Process regular row headers - each cell in a header column becomes a header for its row + for col_idx in header_cols: + for row_idx, row in enumerate(table_data): + if col_idx < len(row) and row[col_idx].strip(): + if row_idx not in row_headers: + row_headers[row_idx] = [] + if not any(h[1] == row[col_idx] for h in row_headers.get(row_idx, [])): + row_headers[row_idx].append((col_idx, row[col_idx])) + + # Calculate max columns for padding + max_cols = max(len(row) for row in table_data) if table_data else 0 + + # Ensure all rows have the same number of columns + if table_data: + padded_data = [row + [""] * (max_cols - len(row)) for row in table_data] + table_array = np.array(padded_data) + + # Create TableData object with the table and header information + parsed_tables.append( + TableData( + data=table_array, + header_rows=header_rows, + header_cols=header_cols, + col_headers=col_headers, + row_headers=row_headers, + ) + ) + + return parsed_tables + + +@dataclass(kw_only=True) +class BasePDFTest: + """ + Base class for all PDF test types. + + Attributes: + pdf: The PDF filename. + page: The page number for the test. + id: Unique identifier for the test. + type: The type of test. + threshold: A float between 0 and 1 representing the threshold for fuzzy matching. + """ + + pdf: str + page: int + id: str + type: str + max_diffs: int = 0 + checked: Optional[TestChecked] = None + url: Optional[str] = None + + def __post_init__(self): + if not self.pdf: + raise ValidationError("PDF filename cannot be empty") + if not self.id: + raise ValidationError("Test ID cannot be empty") + if not isinstance(self.max_diffs, int) or self.max_diffs < 0: + raise ValidationError("Max diffs must be positive number or 0") + if self.type not in {t.value for t in TestType}: + raise ValidationError(f"Invalid test type: {self.type}") + + def run(self, md_content: str) -> Tuple[bool, str]: + """ + Run the test on the provided markdown content. + + Args: + md_content: The content of the .md file. + + Returns: + A tuple (passed, explanation) where 'passed' is True if the test passes, + and 'explanation' provides details when the test fails. + """ + raise NotImplementedError("Subclasses must implement the run method") + + +@dataclass +class TextPresenceTest(BasePDFTest): + """ + Test to verify the presence or absence of specific text in a PDF. + + Attributes: + text: The text string to search for. + """ + + text: str + case_sensitive: bool = True + first_n: Optional[int] = None + last_n: Optional[int] = None + + def __post_init__(self): + super().__post_init__() + if self.type not in {TestType.PRESENT.value, TestType.ABSENT.value}: + raise ValidationError(f"Invalid type for TextPresenceTest: {self.type}") + self.text = normalize_text(self.text) + if not self.text.strip(): + raise ValidationError("Text field cannot be empty") + + def run(self, md_content: str) -> Tuple[bool, str]: + reference_query = self.text + + # Normalize whitespace in the md_content + md_content = normalize_text(md_content) + + if not self.case_sensitive: + reference_query = reference_query.lower() + md_content = md_content.lower() + + if self.first_n and self.last_n: + md_content = md_content[: self.first_n] + md_content[-self.last_n:] + elif self.first_n: + md_content = md_content[: self.first_n] + elif self.last_n: + md_content = md_content[-self.last_n:] + + # Threshold for fuzzy matching derived from max_diffs + threshold = 1.0 - (self.max_diffs / (len(reference_query) if len(reference_query) > 0 else 1)) + best_ratio = fuzz.partial_ratio(reference_query, md_content) / 100.0 + + if self.type == TestType.PRESENT.value: + if best_ratio >= threshold: + return True, "" + else: + msg = ( + f"Expected '{reference_query[:40]}...' with threshold {threshold} " + f"but best match ratio was {best_ratio:.3f}" + ) + return False, msg + else: # ABSENT + if best_ratio < threshold: + return True, "" + else: + msg = ( + f"Expected absence of '{reference_query[:40]}...' with threshold {threshold} " + f"but best match ratio was {best_ratio:.3f}" + ) + return False, msg + + +@dataclass +class TextOrderTest(BasePDFTest): + """ + Test to verify that one text appears before another in a PDF. + + Attributes: + before: The text expected to appear first. + after: The text expected to appear after the 'before' text. + """ + + before: str + after: str + + def __post_init__(self): + super().__post_init__() + if self.type != TestType.ORDER.value: + raise ValidationError(f"Invalid type for TextOrderTest: {self.type}") + self.before = normalize_text(self.before) + self.after = normalize_text(self.after) + if not self.before.strip(): + raise ValidationError("Before field cannot be empty") + if not self.after.strip(): + raise ValidationError("After field cannot be empty") + if self.max_diffs > len(self.before) // 2 or self.max_diffs > len(self.after) // 2: + raise ValidationError("Max diffs is too large for this test, greater than 50% of the search string") + + def run(self, md_content: str) -> Tuple[bool, str]: + md_content = normalize_text(md_content) + + before_matches = find_near_matches(self.before, md_content, max_l_dist=self.max_diffs) + after_matches = find_near_matches(self.after, md_content, max_l_dist=self.max_diffs) + + if not before_matches: + return False, f"'before' text '{self.before[:40]}...' not found with max_l_dist {self.max_diffs}" + if not after_matches: + return False, f"'after' text '{self.after[:40]}...' not found with max_l_dist {self.max_diffs}" + + for before_match in before_matches: + for after_match in after_matches: + if before_match.start < after_match.start: + return True, "" + return False, ( + f"Could not find a location where '{self.before[:40]}...' appears before " + f"'{self.after[:40]}...'." + ) + + +@dataclass +class TableTest(BasePDFTest): + """ + Test to verify certain properties of a table are held, + namely that some cells appear relative to other cells correctly + """ + + # This is the target cell, which must exist in at least one place in the table + cell: str + + # These properties say that the cell immediately up/down/left/right of the target cell has the string specified + up: str = "" + down: str = "" + left: str = "" + right: str = "" + # These properties say that the cell all the way up, + # or all the way left of the target cell (ex. headings) has the string value specified + top_heading: str = "" + left_heading: str = "" + + def __post_init__(self): + super().__post_init__() + if self.type != TestType.TABLE.value: + raise ValidationError(f"Invalid type for TableTest: {self.type}") + + # Normalize the search text too + self.cell = normalize_text(self.cell) + self.up = normalize_text(self.up) + self.down = normalize_text(self.down) + self.left = normalize_text(self.left) + self.right = normalize_text(self.right) + self.top_heading = normalize_text(self.top_heading) + self.left_heading = normalize_text(self.left_heading) + + def run(self, content: str) -> Tuple[bool, str]: + """ + Run the table test on provided content. + + Finds all tables (markdown and/or HTML based on content_type) and checks if any cell + matches the target cell and satisfies the specified relationships. + + Args: + content: The content containing tables (markdown or HTML) + + Returns: + A tuple (passed, explanation) where 'passed' is True if the test passes, + and 'explanation' provides details when the test fails. + """ + # Initialize variables to track tables and results + tables_to_check = [] + failed_reasons = [] + + # Threshold for fuzzy matching derived from max_diffs + threshold = 1.0 - (self.max_diffs / (len(self.cell) if len(self.cell) > 0 else 1)) + threshold = max(0.5, threshold) + + # Parse tables based on content_type + md_tables = parse_markdown_tables(content) + tables_to_check.extend(md_tables) + + html_tables = parse_html_tables(content) + tables_to_check.extend(html_tables) + + # If no tables found, return failure + if not tables_to_check: + return False, "No tables found in the content" + + # Check each table + for table_data in tables_to_check: + # Removed debug print statement + table_array = table_data.data + header_rows = table_data.header_rows + header_cols = table_data.header_cols + + # Find all cells that match the target cell using fuzzy matching + matches = [] + for i in range(table_array.shape[0]): + for j in range(table_array.shape[1]): + cell_content = normalize_text(table_array[i, j]) + similarity = fuzz.ratio(self.cell, cell_content) / 100.0 + + if similarity >= threshold: + matches.append((i, j)) + + # If no matches found in this table, continue to the next table + if not matches: + continue + + # Check the relationships for each matching cell + for row_idx, col_idx in matches: + all_relationships_satisfied = True + current_failed_reasons = [] + + # Check up relationship + if self.up and row_idx > 0: + up_cell = normalize_text(table_array[row_idx - 1, col_idx]) + up_similarity = fuzz.ratio(self.up, up_cell) / 100.0 + if up_similarity < max( + 0.5, + 1.0 - ( + self.max_diffs / (len(self.up) if len(self.up) > 0 else 1) + ), + ): + all_relationships_satisfied = False + current_failed_reasons.append( + f"Cell above '{up_cell}' doesn't match expected " + f"'{self.up}' (similarity: {up_similarity:.2f})" + ) + + # Check down relationship + if self.down and row_idx < table_array.shape[0] - 1: + down_cell = normalize_text(table_array[row_idx + 1, col_idx]) + down_similarity = fuzz.ratio(self.down, down_cell) / 100.0 + if down_similarity < max( + 0.5, + 1.0 - ( + self.max_diffs / (len(self.down) if len(self.down) > 0 else 1) + ), + ): + all_relationships_satisfied = False + current_failed_reasons.append( + f"Cell below '{down_cell}' doesn't match expected " + f"'{self.down}' (similarity: {down_similarity:.2f})" + ) + + # Check left relationship + if self.left and col_idx > 0: + left_cell = normalize_text(table_array[row_idx, col_idx - 1]) + left_similarity = fuzz.ratio(self.left, left_cell) / 100.0 + if left_similarity < max( + 0.5, + 1.0 - ( + self.max_diffs / (len(self.left) if len(self.left) > 0 else 1) + ), + ): + all_relationships_satisfied = False + current_failed_reasons.append( + f"Cell to the left '{left_cell}' doesn't match expected " + f"'{self.left}' (similarity: {left_similarity:.2f})" + ) + + # Check right relationship + if self.right and col_idx < table_array.shape[1] - 1: + right_cell = normalize_text(table_array[row_idx, col_idx + 1]) + right_similarity = fuzz.ratio(self.right, right_cell) / 100.0 + if right_similarity < max( + 0.5, + 1.0 - (self.max_diffs / (len(self.right) if len(self.right) > 0 else 1)) + ): + all_relationships_satisfied = False + current_failed_reasons.append( + f"Cell to the right '{right_cell}' doesn't match expected " + f"'{self.right}' (similarity: {right_similarity:.2f})" + ) + + # Check top heading relationship + if self.top_heading: + # Try to find a match in the column headers + top_heading_found = False + best_match = "" + best_similarity = 0 + + # Check the col_headers dictionary first (this handles colspan properly) + if col_idx in table_data.col_headers: + for _, header_text in table_data.col_headers[col_idx]: + header_text = normalize_text(header_text) + similarity = fuzz.ratio(self.top_heading, header_text) / 100.0 + if similarity > best_similarity: + best_similarity = similarity + best_match = header_text + if best_similarity >= max( + 0.5, + 1.0 - ( + self.max_diffs / (len(self.top_heading) if len(self.top_heading) > 0 else 1) + ), + ): + top_heading_found = True + break + + # If no match found in col_headers, fall back to checking header rows + if not top_heading_found and header_rows: + for i in sorted(header_rows): + if i < row_idx and table_array[i, col_idx].strip(): + header_text = normalize_text(table_array[i, col_idx]) + similarity = fuzz.ratio(self.top_heading, header_text) / 100.0 + if similarity > best_similarity: + best_similarity = similarity + best_match = header_text + if best_similarity >= max( + 0.5, + 1.0 - ( + self.max_diffs / (len(self.top_heading) if len(self.top_heading) > 0 else 1) + ), + ): + top_heading_found = True + break + + # If still no match, use any non-empty cell above as a last resort + if not top_heading_found and not best_match and row_idx > 0: + for i in range(row_idx): + if table_array[i, col_idx].strip(): + header_text = normalize_text(table_array[i, col_idx]) + similarity = fuzz.ratio(self.top_heading, header_text) / 100.0 + if similarity > best_similarity: + best_similarity = similarity + best_match = header_text + + if not best_match: + all_relationships_satisfied = False + current_failed_reasons.append(f"No top heading found for cell at ({row_idx}, {col_idx})") + elif best_similarity < max( + 0.5, + 1.0 - ( + self.max_diffs / (len(self.top_heading) if len(self.top_heading) > 0 else 1) + ), + ): + all_relationships_satisfied = False + current_failed_reasons.append( + f"Top heading '{best_match}' doesn't match expected " + f"'{self.top_heading}' (similarity: {best_similarity:.2f})" + ) + + # Check left heading relationship + if self.left_heading: + # Try to find a match in the row headers + left_heading_found = False + best_match = "" + best_similarity = 0 + + # Check the row_headers dictionary first (this handles rowspan properly) + if row_idx in table_data.row_headers: + for _, header_text in table_data.row_headers[row_idx]: + header_text = normalize_text(header_text) + similarity = fuzz.ratio(self.left_heading, header_text) / 100.0 + if similarity > best_similarity: + best_similarity = similarity + best_match = header_text + if best_similarity >= max( + 0.5, + 1.0 - ( + self.max_diffs / (len(self.left_heading) if len(self.left_heading) > 0 else 1) + ), + ): + left_heading_found = True + break + + # If no match found in row_headers, fall back to checking header columns + if not left_heading_found and header_cols: + for j in sorted(header_cols): + if j < col_idx and table_array[row_idx, j].strip(): + header_text = normalize_text(table_array[row_idx, j]) + similarity = fuzz.ratio(self.left_heading, header_text) / 100.0 + if similarity > best_similarity: + best_similarity = similarity + best_match = header_text + if best_similarity >= max( + 0.5, + 1.0 - ( + self.max_diffs + / (len(self.left_heading) if len(self.left_heading) > 0 else 1) + ), + ): + left_heading_found = True + break + + # If still no match, use any non-empty cell to the left as a last resort + if not left_heading_found and not best_match and col_idx > 0: + for j in range(col_idx): + if table_array[row_idx, j].strip(): + header_text = normalize_text(table_array[row_idx, j]) + similarity = fuzz.ratio(self.left_heading, header_text) / 100.0 + if similarity > best_similarity: + best_similarity = similarity + best_match = header_text + + if not best_match: + all_relationships_satisfied = False + current_failed_reasons.append(f"No left heading found for cell at ({row_idx}, {col_idx})") + elif best_similarity < max( + 0.5, + 1.0 - ( + self.max_diffs / (len(self.left_heading) if len(self.left_heading) > 0 else 1) + ), + ): + all_relationships_satisfied = False + current_failed_reasons.append( + f"Left heading '{best_match}' doesn't match expected " + f"'{self.left_heading}' (similarity: {best_similarity:.2f})" + ) + + # If all relationships are satisfied for this cell, the test passes + if all_relationships_satisfied: + return True, "" + else: + failed_reasons.extend(current_failed_reasons) + + # If we've gone through all tables and all matching cells and none satisfied all relationships + if not failed_reasons: + return False, f"No cell matching '{self.cell}' found in any table with threshold {threshold}" + else: + return False, ( + f"Found cells matching '{self.cell}' but relationships were not satisfied: " + f"{'; '.join(failed_reasons)}" + ) + + +@dataclass +class BaselineTest(BasePDFTest): + """ + This test makes sure that several baseline quality checks pass for the output generation. + + Namely, the output is not blank, not endlessly repeating, and contains characters of the proper + character sets. + + """ + + max_length: Optional[int] = None # Used to implement blank page checks + + max_repeats: int = 30 + check_disallowed_characters: bool = True + + def run(self, content: str) -> Tuple[bool, str]: + base_content_len = len("".join(c for c in content if c.isalnum()).strip()) + + # If this a blank page check, then it short circuits the rest of the checks + if self.max_length is not None: + if base_content_len > self.max_length: + return False, f"{base_content_len} characters were output for a page we expected to be blank" + else: + return True, "" + + if base_content_len == 0: + return False, "The text contains no alpha numeric characters" + + # Makes sure that the content has no egregious repeated ngrams at the end, + # which indicate a degradation of quality + # Honestly, this test doesn't seem to catch anything at the moment, + # maybe it can be refactored to a "text-quality" + # test or something, that measures repetition, non-blanks, charsets, etc + d = RepeatDetector(max_ngram_size=5) + d.add_letters(content) + repeats = d.ngram_repeats() + + for index, count in enumerate(repeats): + if count > self.max_repeats: + return False, f"Text ends with {count} repeating {index+1}-grams, invalid" + + pattern = re.compile( + r"[" + r"\u4e00-\u9FFF" # CJK Unified Ideographs (Chinese characters) + r"\u3040-\u309F" # Hiragana (Japanese) + r"\u30A0-\u30FF" # Katakana (Japanese) + r"\U0001F600-\U0001F64F" # Emoticons (Emoji) + r"\U0001F300-\U0001F5FF" # Miscellaneous Symbols and Pictographs (Emoji) + r"\U0001F680-\U0001F6FF" # Transport and Map Symbols (Emoji) + r"\U0001F1E0-\U0001F1FF" # Regional Indicator Symbols (flags, Emoji) + r"]", + flags=re.UNICODE, + ) + + matches = pattern.findall(content) + if self.check_disallowed_characters and matches: + return False, f"Text contains disallowed characters {matches}" + + return True, "" + + +@dataclass +class MathTest(BasePDFTest): + math: str + + def __post_init__(self): + super().__post_init__() + if self.type != TestType.MATH.value: + raise ValidationError(f"Invalid type for MathTest: {self.type}") + if len(self.math.strip()) == 0: + raise ValidationError("Math test must have non-empty math expression") + + self.reference_render = render_equation(self.math) + + if self.reference_render is None: + raise ValidationError(f"Math equation {self.math} was not able to render") + + def run(self, content: str) -> Tuple[bool, str]: + # Store both the search pattern and the full pattern to replace + patterns = [ + (r"\$\$(.+?)\$\$", r"\$\$(.+?)\$\$"), # $$...$$ + (r"\\\((.+?)\\\)", r"\\\((.+?)\\\)"), # \(...\) + (r"\\\[(.+?)\\\]", r"\\\[(.+?)\\\]"), # \[...\] + (r"\$(.+?)\$", r"\$(.+?)\$"), # $...$ + ] + + equations = [] + modified_content = content + + for search_pattern, replace_pattern in patterns: + # Find all matches for the current pattern + matches = re.findall(search_pattern, modified_content, re.DOTALL) + equations.extend([e.strip() for e in matches]) + + # Replace all instances of this pattern with empty strings + modified_content = re.sub(replace_pattern, "", modified_content, flags=re.DOTALL) + + # If an equation in the markdown exactly matches our math string, then that's good enough + # we don't have to do a more expensive comparison + if any(hyp == self.math for hyp in equations): + return True, "" + + # If not, then let's render the math equation itself and now compare to each hypothesis + # But, to speed things up, since rendering equations is hard, we sort the equations on the page + # by fuzzy similarity to the hypothesis + equations.sort(key=lambda x: -fuzz.ratio(x, self.math)) + for hypothesis in equations: + hypothesis_render = render_equation(hypothesis) + + if not hypothesis_render: + continue + + if compare_rendered_equations(self.reference_render, hypothesis_render): + return True, "" + + # self.reference_render.save(f"maths/{self.id}_ref.png", format="PNG") + # best_match_render.save(f"maths/{self.id}_hyp.png", format="PNG") + + return False, f"No match found for {self.math} anywhere in content" + + +def load_single_test(data: Union[str, Dict]) -> BasePDFTest: + """ + Load a single test from a JSON line string or JSON object. + + Args: + data: Either a JSON string to parse or a dictionary containing test data. + + Returns: + A test object of the appropriate type. + + Raises: + ValidationError: If the test type is unknown or data is invalid. + json.JSONDecodeError: If the string cannot be parsed as JSON. + """ + # Handle JSON string input + if isinstance(data, str): + data = data.strip() + if not data: + raise ValueError("Empty string provided") + data = json.loads(data) + + # Process the test data + test_type = data.get("type") + if test_type in {TestType.PRESENT.value, TestType.ABSENT.value}: + test = TextPresenceTest(**data) + elif test_type == TestType.ORDER.value: + test = TextOrderTest(**data) + elif test_type == TestType.TABLE.value: + test = TableTest(**data) + elif test_type == TestType.MATH.value: + test = MathTest(**data) + elif test_type == TestType.BASELINE.value: + test = BaselineTest(**data) + else: + raise ValidationError(f"Unknown test type: {test_type}") + + return test + + +def load_tests(jsonl_file: str) -> List[BasePDFTest]: + """ + Load tests from a JSONL file using parallel processing with a ThreadPoolExecutor. + + Args: + jsonl_file: Path to the JSONL file containing test definitions. + + Returns: + A list of test objects. + """ + + def process_line_with_number(line_tuple: Tuple[int, str]) -> Optional[Tuple[int, BasePDFTest]]: + """ + Process a single line from the JSONL file and return a tuple of (line_number, test object). + Returns None for empty lines. + """ + line_number, line = line_tuple + line = line.strip() + if not line: + return None + + try: + test = load_single_test(line) + return (line_number, test) + except json.JSONDecodeError as e: + print(f"Error parsing JSON on line {line_number}: {e}") + raise + except (ValidationError, KeyError) as e: + print(f"Error on line {line_number}: {e}") + raise + except Exception as e: + print(f"Unexpected error on line {line_number}: {e}") + raise + + tests = [] + + # Read all lines along with their line numbers. + with open(jsonl_file, "r") as f: + lines = list(enumerate(f, start=1)) + + # Use a ThreadPoolExecutor to process each line in parallel. + with ThreadPoolExecutor(max_workers=min(os.cpu_count() or 1, 64)) as executor: + # Submit all tasks concurrently. + futures = {executor.submit(process_line_with_number, item): item[0] for item in lines} + # Use tqdm to show progress as futures complete. + for future in tqdm(as_completed(futures), total=len(futures), desc="Loading tests"): + result = future.result() + if result is not None: + _, test = result + tests.append(test) + + # Check for duplicate test IDs after parallel processing. + unique_ids = set() + for test in tests: + if test.id in unique_ids: + raise ValidationError(f"Test with duplicate id {test.id} found, error loading tests.") + unique_ids.add(test.id) + + return tests + + +def save_tests(tests: List[BasePDFTest], jsonl_file: str) -> None: + """ + Save tests to a JSONL file using asdict for conversion. + + Args: + tests: A list of test objects. + jsonl_file: Path to the output JSONL file. + """ + with open(jsonl_file, "w") as file: + for test in tests: + file.write(json.dumps(asdict(test)) + "\n") diff --git a/vlmeval/dataset/olmOCRBench/utils.py b/vlmeval/dataset/olmOCRBench/utils.py new file mode 100644 index 000000000..78693f3bd --- /dev/null +++ b/vlmeval/dataset/olmOCRBench/utils.py @@ -0,0 +1,203 @@ +from typing import List, Tuple + +import numpy as np + + +def calculate_bootstrap_ci( + test_scores: List[float], + n_bootstrap: int = 1000, + ci_level: float = 0.95, + splits: List[int] = None, +) -> Tuple[float, float]: + """ + Calculate bootstrap confidence interval for test scores, respecting category splits. + + Args: + test_scores: List of test scores (0.0 to 1.0 for each test) + n_bootstrap: Number of bootstrap samples to generate + ci_level: Confidence interval level (default: 0.95 for 95% CI) + splits: List of sizes for each category. If provided, resampling will be done + within each category independently, and the overall score will be the + average of per-category scores. If None, resampling is done across all tests. + + Returns: + Tuple of (lower_bound, upper_bound) representing the confidence interval + """ + if not test_scores: + return (0.0, 0.0) + + # Convert to numpy array for efficiency + scores = np.array(test_scores) + + # Simple case - no splits provided, use traditional bootstrap + if splits is None: + # Generate bootstrap samples + bootstrap_means = [] + for _ in range(n_bootstrap): + # Sample with replacement + sample = np.random.choice(scores, size=len(scores), replace=True) + bootstrap_means.append(np.mean(sample)) + else: + # Validate splits + if sum(splits) != len(scores): + raise ValueError(f"Sum of splits ({sum(splits)}) must equal length of test_scores ({len(scores)})") + + # Convert flat scores list to a list of category scores + category_scores = [] + start_idx = 0 + for split_size in splits: + category_scores.append(scores[start_idx: start_idx + split_size]) + start_idx += split_size + + # Generate bootstrap samples respecting category structure + bootstrap_means = [] + for _ in range(n_bootstrap): + # Sample within each category independently + category_means = [] + for cat_scores in category_scores: + if len(cat_scores) > 0: + # Sample with replacement within this category + cat_sample = np.random.choice(cat_scores, size=len(cat_scores), replace=True) + category_means.append(np.mean(cat_sample)) + + # Overall score is average of category means (if any categories have scores) + if category_means: + bootstrap_means.append(np.mean(category_means)) + + # Calculate confidence interval + alpha = (1 - ci_level) / 2 + lower_bound = np.percentile(bootstrap_means, alpha * 100) + upper_bound = np.percentile(bootstrap_means, (1 - alpha) * 100) + + return (lower_bound, upper_bound) + + +def perform_permutation_test( + scores_a: List[float], + scores_b: List[float], + n_permutations: int = 10000, + splits_a: List[int] = None, + splits_b: List[int] = None, +) -> Tuple[float, float]: + """ + Perform a permutation test to determine if there's a significant difference + between two sets of test scores. + + Args: + scores_a: List of test scores for candidate A + scores_b: List of test scores for candidate B + n_permutations: Number of permutations to perform + splits_a: List of sizes for each category in scores_a + splits_b: List of sizes for each category in scores_b + + Returns: + Tuple of (observed_difference, p_value) + """ + if not scores_a or not scores_b: + return (0.0, 1.0) + + # Function to calculate mean of means with optional category splits + def mean_of_category_means(scores, splits=None): + if splits is None: + return np.mean(scores) + + category_means = [] + start_idx = 0 + for split_size in splits: + if split_size > 0: + category_scores = scores[start_idx: start_idx + split_size] + category_means.append(np.mean(category_scores)) + start_idx += split_size + + return np.mean(category_means) if category_means else 0.0 + + # Calculate observed difference in means using category structure if provided + mean_a = mean_of_category_means(scores_a, splits_a) + mean_b = mean_of_category_means(scores_b, splits_b) + observed_diff = mean_a - mean_b + + # If no splits are provided, fall back to traditional permutation test + if splits_a is None and splits_b is None: + # Combine all scores + combined = np.concatenate([scores_a, scores_b]) + n_a = len(scores_a) + + # Perform permutation test + count_greater_or_equal = 0 + for _ in range(n_permutations): + # Shuffle the combined array + np.random.shuffle(combined) + + # Split into two groups of original sizes + perm_a = combined[:n_a] + perm_b = combined[n_a:] + + # Calculate difference in means + perm_diff = np.mean(perm_a) - np.mean(perm_b) + + # Count how many permuted differences are >= to observed difference in absolute value + if abs(perm_diff) >= abs(observed_diff): + count_greater_or_equal += 1 + else: + # For category-based permutation test, we need to maintain category structure + # Validate that the splits match the score lengths + if splits_a is not None and sum(splits_a) != len(scores_a): + raise ValueError(f"Sum of splits_a ({sum(splits_a)}) must equal length of scores_a ({len(scores_a)})") + if splits_b is not None and sum(splits_b) != len(scores_b): + raise ValueError(f"Sum of splits_b ({sum(splits_b)}) must equal length of scores_b ({len(scores_b)})") + + # Create category structures + categories_a = [] + categories_b = [] + + if splits_a is not None: + start_idx = 0 + for split_size in splits_a: + categories_a.append(scores_a[start_idx: start_idx + split_size]) + start_idx += split_size + else: + # If no splits for A, treat all scores as one category + categories_a = [scores_a] + + if splits_b is not None: + start_idx = 0 + for split_size in splits_b: + categories_b.append(scores_b[start_idx: start_idx + split_size]) + start_idx += split_size + else: + # If no splits for B, treat all scores as one category + categories_b = [scores_b] + + # Perform permutation test maintaining category structure + count_greater_or_equal = 0 + for _ in range(n_permutations): + # For each category pair, shuffle and redistribute + perm_categories_a = [] + perm_categories_b = [] + + for cat_a, cat_b in zip(categories_a, categories_b): + # Combine and shuffle + combined = np.concatenate([cat_a, cat_b]) + np.random.shuffle(combined) + + # Redistribute maintaining original sizes + perm_categories_a.append(combined[: len(cat_a)]) + perm_categories_b.append(combined[len(cat_a):]) + + # Flatten permuted categories + perm_a = np.concatenate(perm_categories_a) + perm_b = np.concatenate(perm_categories_b) + + # Calculate difference in means respecting category structure + perm_mean_a = mean_of_category_means(perm_a, splits_a) + perm_mean_b = mean_of_category_means(perm_b, splits_b) + perm_diff = perm_mean_a - perm_mean_b + + # Count how many permuted differences are >= to observed difference in absolute value + if abs(perm_diff) >= abs(observed_diff): + count_greater_or_equal += 1 + + # Calculate p-value + p_value = count_greater_or_equal / n_permutations + + return (observed_diff, p_value) From a2c971a4ae78d1b378d2b50a156975abf41e4226 Mon Sep 17 00:00:00 2001 From: root Date: Fri, 17 Oct 2025 06:03:16 +0000 Subject: [PATCH 2/5] fix tsv file path --- vlmeval/dataset/olmOCRBench/olmocrbench.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vlmeval/dataset/olmOCRBench/olmocrbench.py b/vlmeval/dataset/olmOCRBench/olmocrbench.py index 417d574b1..2ab18b6a4 100644 --- a/vlmeval/dataset/olmOCRBench/olmocrbench.py +++ b/vlmeval/dataset/olmOCRBench/olmocrbench.py @@ -17,7 +17,7 @@ class olmOCRBench(ImageBaseDataset): MODALITY = 'IMAGE' TYPE = 'QA' - DATASET_URL = {'olmOCRBench':'./olmOCRBench.tsv'} + DATASET_URL = {'olmOCRBench':'https://opencompass.openxlab.space/utils/VLMEval/olmOCRBench.tsv'} DATASET_MD5 = {'olmOCRBench': '25fe250887fee675f887a2a2d24df185'} # 完整版本的tsv文件 # base prompt From d4f912ff874f8e75304b31200418a6da6b4a37d4 Mon Sep 17 00:00:00 2001 From: root Date: Mon, 20 Oct 2025 12:56:06 +0000 Subject: [PATCH 3/5] support Ocean-OCR and MAT-Bench --- vlmeval/dataset/__init__.py | 4 +- vlmeval/dataset/matbench.py | 182 +++++++++++++++++++++++++++++ vlmeval/dataset/oceanocr.py | 226 ++++++++++++++++++++++++++++++++++++ 3 files changed, 411 insertions(+), 1 deletion(-) create mode 100644 vlmeval/dataset/matbench.py create mode 100644 vlmeval/dataset/oceanocr.py diff --git a/vlmeval/dataset/__init__.py b/vlmeval/dataset/__init__.py index 81a732556..bb6d09a9a 100644 --- a/vlmeval/dataset/__init__.py +++ b/vlmeval/dataset/__init__.py @@ -84,6 +84,8 @@ from .medqbench_caption import MedqbenchCaptionDataset from .medqbench_paired_description import MedqbenchPairedDescriptionDataset from .olmOCRBench.olmocrbench import olmOCRBench +from .oceanocr import OceanOCRBench +from .matbench import MATBench class ConcatDataset(ImageBaseDataset): @@ -212,7 +214,7 @@ def evaluate(self, eval_file, **judge_kwargs): MMEReasoning, GOBenchDataset, SFE, ChartMimic, MMVMBench, XLRSBench, OmniEarthMCQBench, VisFactor, OSTDataset, OCRBench_v2, TreeBench, CVQA, M4Bench, AyaVisionBench, TopViewRS, VLMBias, MMHELIX, MedqbenchMCQDataset, - MedqbenchPairedDescriptionDataset, MedqbenchCaptionDataset, olmOCRBench + MedqbenchPairedDescriptionDataset, MedqbenchCaptionDataset, olmOCRBench, OceanOCRBench, MATBench ] VIDEO_DATASET = [ diff --git a/vlmeval/dataset/matbench.py b/vlmeval/dataset/matbench.py new file mode 100644 index 000000000..b92bd6916 --- /dev/null +++ b/vlmeval/dataset/matbench.py @@ -0,0 +1,182 @@ +import json +import os +import copy +import pandas as pd +import tempfile +import base64 +import numpy as np +from tqdm import tqdm +import torch.distributed as dist +from .image_base import ImageBaseDataset +from ..smp import * +import re + + +def is_chinese(text): + """判断文本是否含中文字符""" + return any('\u4e00' <= ch <= '\u9fff' for ch in text) + + +def normalize(s): + def remove_articles(text): + return re.sub(r"\b(a|an|the)\b", " ", text) + + def white_space_fix(text): + return " ".join(text.split()) + + def remove_punc(text): + chinese_punc = "！？｡＂＃＄％＆＇（）＊＋，－．／：；＜＝＞＠［＼］＾＿｀｛｜｝～“”‘’、。：《》【】" + exclude = set(string.punctuation + chinese_punc) + return "".join(ch for ch in text if ch not in exclude) + + def lower(text): + return text.lower() + + s = lower(s) + s = remove_punc(s) + + if is_chinese(s): + s = s.replace(" ", "") # 中文一般去除所有空白 + else: + s = remove_articles(s) + s = white_space_fix(s) + + return s + + +def compute_f1(prediction, ground_truth): + if prediction is None: + return 0.0 + + norm_pred = normalize(prediction) + norm_gt = normalize(ground_truth) + + # 中文使用字符级，英文使用词级 + if is_chinese(norm_pred) or is_chinese(norm_gt): + pred_tokens = list(norm_pred) + gt_tokens = list(norm_gt) + else: + pred_tokens = norm_pred.split() + gt_tokens = norm_gt.split() + + common = set(pred_tokens) & set(gt_tokens) + num_same = len(common) + + if num_same == 0: + return 0.0 + + precision = num_same / len(pred_tokens) + recall = num_same / len(gt_tokens) + f1 = 2 * precision * recall / (precision + recall) + return f1 + + +def exact_match_score(prediction, ground_truth): + if prediction is None: + return 0.0 + return int(normalize(prediction) == normalize(ground_truth)) + + +def mat_evaluate(tsv_path, eval_file): + # pass + print("\nStarting evaluation Ocean-OCR Bench...") + df = pd.read_excel(eval_file) + + print(df.iloc[300]) + if "id" in df.iloc[0].values: + print("⚠️ 检测到首行是header，跳过该行") + df = df.iloc[1:].reset_index(drop=True) + + results = df.to_dict(orient='records') + print(results[0]) + + eval_results = { + "code_simple_f1": [], + "code_simple_em": [], + "code_hard_f1": [], + "code_hard_em": [], + "search_simple_f1": [], + "search_simple_em": [], + "search_hard_f1": [], + "search_hard_em": [], + } + for item in tqdm(results): + try: + pred = item['prediction'] + gts = item['answer'] + split = item["split"] + category = item["category"] + # 若gt是str，统一转换为列表处理 + if isinstance(gts, str): + gts = [gts] + f1 = max([compute_f1(pred, gt) for gt in gts]) + em = max([exact_match_score(pred, gt) for gt in gts]) + if em == 1: + f1 = 1 + keys_f1 = f"{category}_{split}_f1" + keys_em = f"{category}_{split}_em" + eval_results[keys_f1].append(f1) + eval_results[keys_em].append(em) + except Exception as e: + print("Error:", {e}) + + # 计算各项指标均值 + mean_results = {} + for key, values in eval_results.items(): + if len(values) > 0: + mean_results[key] = sum(values) / len(values) + else: + mean_results[key] = 0.0 + + # 计算 code / search / overall 的平均值 + code_f1_values = eval_results["code_simple_f1"] + eval_results["code_hard_f1"] + code_em_values = eval_results["code_simple_em"] + eval_results["code_hard_em"] + search_f1_values = eval_results["search_simple_f1"] + eval_results["search_hard_f1"] + search_em_values = eval_results["search_simple_em"] + eval_results["search_hard_em"] + all_f1_values = code_f1_values + search_f1_values + all_em_values = code_em_values + search_em_values + + def safe_mean(values): + return sum(values) / len(values) if len(values) > 0 else 0.0 + + mean_results["code_f1_avg"] = safe_mean(code_f1_values) + mean_results["code_em_avg"] = safe_mean(code_em_values) + mean_results["search_f1_avg"] = safe_mean(search_f1_values) + mean_results["search_em_avg"] = safe_mean(search_em_values) + mean_results["overall_f1_avg"] = safe_mean(all_f1_values) + mean_results["overall_em_avg"] = safe_mean(all_em_values) + + # 打印结果 + for key, value in mean_results.items(): + print(f"{key}: {value:.4f}") + + # 保存为 CSV 文件 + csv_path = os.path.join(os.path.dirname(eval_file), "MATBench_eval_summary.csv") + df_mean = pd.DataFrame(list(mean_results.items()), columns=['metric', 'mean_value']) + df_mean.to_csv(csv_path, index=False) + print(f"\n✅ saved to {csv_path}") + + +class MATBench(ImageBaseDataset): + MODALITY = 'IMAGE' + TYPE = 'QA' + + DATASET_URL = {'MATBench':'https://opencompass.openxlab.space/utils/VLMEval/MATBench.tsv'} + DATASET_MD5 = {'MATBench': '8c79a75ade70384c9918fad1c2a146cb'} # 测试版本的tsv文件 4e1f4f80f753325f6a471d2ae0f9654e + + def __init__(self,dataset='MATBench',**kwargs): + super().__init__(dataset,**kwargs) + print(f'self.img_root:{self.img_root}') + + def build_prompt(self, line): + input_question = line[2] + image_path = self.dump_image(line)[0] + msg = [ + dict(type='image', value=image_path), + dict(type='text', value=input_question + "\nPlease provide a brief answer directly.") + ] + return msg + + def evaluate(self, eval_file, **judge_kwargs): + tsv_path = self.data_path + mat_evaluate(tsv_path, eval_file) diff --git a/vlmeval/dataset/oceanocr.py b/vlmeval/dataset/oceanocr.py new file mode 100644 index 000000000..2aee85384 --- /dev/null +++ b/vlmeval/dataset/oceanocr.py @@ -0,0 +1,226 @@ +import json +import os +import copy +import pandas as pd +import tempfile +import base64 +import numpy as np +from tqdm import tqdm +import torch.distributed as dist +from .image_base import ImageBaseDataset +from ..smp import * + +import glob +import random +import re +from PIL import Image +import nltk +from nltk.metrics import precision, recall, f_measure +import jieba +from nltk.translate import meteor_score +from concurrent.futures import ProcessPoolExecutor, as_completed + + +def contain_chinese_string(text): + chinese_pattern = re.compile(r'[\u4e00-\u9fa5]') + return bool(chinese_pattern.search(text)) + + +def cal_per_metrics(pred, gt): + + metrics = {} + + if contain_chinese_string(gt) or contain_chinese_string(pred): + reference = jieba.lcut(gt) + hypothesis = jieba.lcut(pred) + else: + reference = gt.split() + hypothesis = pred.split() + + metrics["bleu"] = nltk.translate.bleu([reference], hypothesis) + metrics["meteor"] = meteor_score.meteor_score([reference], hypothesis) + + reference = set(reference) + hypothesis = set(hypothesis) + metrics["f_measure"] = f_measure(reference, hypothesis) + + metrics["precision"] = precision(reference, hypothesis) + metrics["recall"] = recall(reference, hypothesis) + metrics["edit_dist"] = nltk.edit_distance(pred, gt) / max(len(pred), len(gt)) + return metrics + + +def lable_norm(eval_type, pred): + if 'scene' in eval_type: + # 去除一些额外的符号 + pred = pred.replace('\\text{', '').replace('}', '').replace('\n', ' ').replace('-', ' ').replace('*', ' ') + pred = pred.strip() + # 根据一些符号划分开并排序 + pred = re.split(r'[（）()，,\s]+', pred) + pred.sort() + pred = [p for p in pred if (p != ' ') and (p != '')] + pred = ' '.join(pred) + if 'handwritten' in eval_type: + pred = pred.replace('*', ' ').replace('\n', ' ') + return pred + + +def doc_text_eval(eval_type, predicts): + + result = [] + for ann in tqdm(predicts): + ann['label'] = lable_norm(eval_type, ann['label']) + ann['answer'] = lable_norm(eval_type, ann['answer']) + ans = cal_per_metrics(ann["label"], ann["answer"]) + result.append(ans) + + mean_dict = {} + + mean_dict["eval question num"] = len(result) + for k, v in result[0].items(): + mean_dict[k] = 0 + + for each in result: + for k, v in each.items(): + if v is None: + v = 0 + mean_dict[k] += v + + for k, v in mean_dict.items(): + if k == "eval question num": + continue + mean_dict[k] /= len(result) + # print(json.dumps(mean_dict, indent=4)) + return mean_dict + + +def avg_metric(results): + if not results: + return {} + keys = ["bleu", "meteor", "f_measure", "precision", "recall", "edit_dist"] + avg = {} + for k in keys: + values = [r[k] for r in results if k in r] + avg[k] = float(np.mean(values)) if values else None + avg["eval question num"] = int(np.sum([r["eval question num"] for r in results])) + return avg + + +def run_eval(eval_type, results): + """评估单个结果文件""" + print(f"\n📊 Evaluating {eval_type} ...") + results_single_domain = doc_text_eval(eval_type, results) + results_single_domain["eval_type"] = eval_type + print(f"✅ {eval_type} evaluation done.") + return results_single_domain + + +def run_eval_wrapper(args): + eval_type, result_list = args + print(f"🚀 启动评测: {eval_type}, 样本数={len(result_list)}") + res = run_eval(eval_type, result_list) + return eval_type, res + + +def oceanocr_evaluate(tsv_path, eval_file): + # pass + print("\nStarting evaluation Ocean-OCR Bench...") + df = pd.read_excel(eval_file) + if "id" in df.iloc[0].values: + print("⚠️ 检测到首行是header，跳过该行") + df = df.iloc[1:].reset_index(drop=True) + eval_types = ["document_en", "document_zh", "scene_text_rec", "handwritten_en", "handwritten_zh"] + # eval_types = ["document_en"] + results = {name: [] for name in eval_types} + for i, row in df.iterrows(): + try: + id_name = row["id"] + answer_json = json.loads(row["answer"]) + label_value = answer_json[1]["value"] + prediction = row.get("prediction", "") + results[id_name].append({ + "label": label_value, + "answer": prediction + }) + except Exception as e: + print(f"⚠️ 第{i}行解析失败: {e}") + continue + # single evaluator + # eval_results = [] + # for eval_type in eval_types: + # results_single_domain = run_eval(eval_type, results[eval_type]) + # eval_results.append(results_single_domain) + + tasks = [(eval_type, results[eval_type]) for eval_type in eval_types] + eval_results = [] + with ProcessPoolExecutor(max_workers=len(tasks)) as executor: + futures = [executor.submit(run_eval_wrapper, task) for task in tasks] + for future in as_completed(futures): + eval_type, res = future.result() + eval_results.append(res) + print(f"✅ {eval_type} 完成") + + print(eval_results) + print("\n🎯 All inference + evaluation finished!") + + # 按中英文分类 + scene_results = [r for r in eval_results if r["eval_type"] == "scene_text_rec"] + zh_results = [r for r in eval_results if r["eval_type"].endswith("_zh")] + en_results = [r for r in eval_results if r["eval_type"].endswith("_en")] + + zh_avg = avg_metric(zh_results) + en_avg = avg_metric(en_results) + scene_avg = avg_metric(scene_results) + + print("\n================== Average (ZH) ==================") + for k, v in zh_avg.items(): + print(f"{k:<20}: {v}") + + print("\n================== Average (EN) ==================") + for k, v in en_avg.items(): + print(f"{k:<20}: {v}") + + print("\n================== Average (scene_text_rec) ==================") + for k, v in en_avg.items(): + print(f"{k:<20}: {v}") + + csv_path = os.path.join(os.path.dirname(eval_file), "OceanOCRBench_eval_summary.csv") + rows = [] + for name, dct in [ + ("ZH", zh_avg), + ("EN", en_avg), + ("scene_text_rec", scene_avg) + ]: + for k, v in dct.items(): + rows.append({"Category": name, "Metric": k, "Value": v}) + + df_summary = pd.DataFrame(rows) + df_summary.to_csv(csv_path, index=False) + print(f"\n✅ Results save to: {csv_path}") + + +class OceanOCRBench(ImageBaseDataset): + MODALITY = 'IMAGE' + TYPE = 'QA' + + DATASET_URL = {'OceanOCRBench':'https://opencompass.openxlab.space/utils/VLMEval/OceanOCRBench.tsv'} + DATASET_MD5 = {'OceanOCRBench': '482553a1bcf5cbcadcb31d847e6e01c8'} # 测试版本的tsv文件 50243d62799e3467149195980cd7d660 + + system_prompt = "Can you pull all textual information from the image?" + + def __init__(self,dataset='OceanOCRBench',**kwargs): + super().__init__(dataset,**kwargs) + print(f'self.img_root:{self.img_root}') + + def build_prompt(self, line): + + image_path = self.dump_image(line)[0] + msg = [ + dict(type='image', value=image_path), + dict(type='text', value=self.system_prompt) + ] + return msg + + def evaluate(self, eval_file, **judge_kwargs): + tsv_path = self.data_path + oceanocr_evaluate(tsv_path, eval_file) From 37fa5bd485541dd28216998e7c0a7141d5f424e8 Mon Sep 17 00:00:00 2001 From: root Date: Tue, 28 Oct 2025 11:09:17 +0000 Subject: [PATCH 4/5] fixed prompt for MATBench and envs for OlmOCRBench --- vlmeval/dataset/matbench.py | 10 +++++++++- vlmeval/dataset/olmOCRBench/eval_req.txt | 6 ++++++ vlmeval/dataset/olmOCRBench/olmocrbench.py | 9 ++++++++- 3 files changed, 23 insertions(+), 2 deletions(-) create mode 100644 vlmeval/dataset/olmOCRBench/eval_req.txt diff --git a/vlmeval/dataset/matbench.py b/vlmeval/dataset/matbench.py index b92bd6916..8830f5e67 100644 --- a/vlmeval/dataset/matbench.py +++ b/vlmeval/dataset/matbench.py @@ -173,7 +173,15 @@ def build_prompt(self, line): image_path = self.dump_image(line)[0] msg = [ dict(type='image', value=image_path), - dict(type='text', value=input_question + "\nPlease provide a brief answer directly.") + dict( + type='text', + value=( + input_question + + '\n' + + "Answer the question directly. " + "The answer should be very brief." + ), + ), ] return msg diff --git a/vlmeval/dataset/olmOCRBench/eval_req.txt b/vlmeval/dataset/olmOCRBench/eval_req.txt new file mode 100644 index 000000000..a10a94a2c --- /dev/null +++ b/vlmeval/dataset/olmOCRBench/eval_req.txt @@ -0,0 +1,6 @@ +fuzzysearch +rapidfuzz +bs4 +dataclasses +unicodedata +unittest diff --git a/vlmeval/dataset/olmOCRBench/olmocrbench.py b/vlmeval/dataset/olmOCRBench/olmocrbench.py index 2ab18b6a4..3a0cd34c1 100644 --- a/vlmeval/dataset/olmOCRBench/olmocrbench.py +++ b/vlmeval/dataset/olmOCRBench/olmocrbench.py @@ -9,7 +9,6 @@ import torch.distributed as dist from ..image_base import ImageBaseDataset from ...smp import * -from .evaluator import evaluator class olmOCRBench(ImageBaseDataset): @@ -43,5 +42,13 @@ def build_prompt(self, line): return msg def evaluate(self, eval_file, **judge_kwargs): + try: + from .evaluator import evaluator + except ImportError as e: + logging.critical( + "Please follow the requirements (see vlmeval/dataset/olmOCRBench/eval_req.txt) \ + to install dependency package for chartmimic evaluation." + ) + raise e tsv_path = self.data_path evaluator(tsv_path, eval_file) From e64aff635ae54bba511b0c5733e8d1d6f3f4c32c Mon Sep 17 00:00:00 2001 From: root Date: Tue, 28 Oct 2025 13:44:56 +0000 Subject: [PATCH 5/5] fix OceanocrBench --- vlmeval/dataset/__init__.py | 3 ++- vlmeval/dataset/oceanocr.py | 18 ++++++++++++------ .../dataset/utils/oceanoctbench/eval_req.txt | 2 ++ 3 files changed, 16 insertions(+), 7 deletions(-) create mode 100644 vlmeval/dataset/utils/oceanoctbench/eval_req.txt diff --git a/vlmeval/dataset/__init__.py b/vlmeval/dataset/__init__.py index b278c3359..0aedf083d 100644 --- a/vlmeval/dataset/__init__.py +++ b/vlmeval/dataset/__init__.py @@ -215,7 +215,8 @@ def evaluate(self, eval_file, **judge_kwargs): MMEReasoning, GOBenchDataset, SFE, ChartMimic, MMVMBench, XLRSBench, OmniEarthMCQBench, VisFactor, OSTDataset, OCRBench_v2, TreeBench, CVQA, M4Bench, AyaVisionBench, TopViewRS, VLMBias, MMHELIX, MedqbenchMCQDataset, - MedqbenchPairedDescriptionDataset, MedqbenchCaptionDataset, ChartMuseum, ChartQAPro, olmOCRBench, OceanOCRBench, MATBench + MedqbenchPairedDescriptionDataset, MedqbenchCaptionDataset, ChartMuseum, ChartQAPro, + olmOCRBench, OceanOCRBench, MATBench ] VIDEO_DATASET = [ diff --git a/vlmeval/dataset/oceanocr.py b/vlmeval/dataset/oceanocr.py index 2aee85384..60c5bd285 100644 --- a/vlmeval/dataset/oceanocr.py +++ b/vlmeval/dataset/oceanocr.py @@ -2,7 +2,6 @@ import os import copy import pandas as pd -import tempfile import base64 import numpy as np from tqdm import tqdm @@ -14,11 +13,6 @@ import random import re from PIL import Image -import nltk -from nltk.metrics import precision, recall, f_measure -import jieba -from nltk.translate import meteor_score -from concurrent.futures import ProcessPoolExecutor, as_completed def contain_chinese_string(text): @@ -222,5 +216,17 @@ def build_prompt(self, line): return msg def evaluate(self, eval_file, **judge_kwargs): + try: + import nltk + from nltk.metrics import precision, recall, f_measure + import jieba + from nltk.translate import meteor_score + from concurrent.futures import ProcessPoolExecutor, as_completed + except ImportError as e: + logging.critical( + "Please follow the requirements (see vlmeval/dataset/utils/oceanoctbench/eval_req.txt) \ + to install dependency package for chartmimic evaluation." + ) + raise e tsv_path = self.data_path oceanocr_evaluate(tsv_path, eval_file) diff --git a/vlmeval/dataset/utils/oceanoctbench/eval_req.txt b/vlmeval/dataset/utils/oceanoctbench/eval_req.txt new file mode 100644 index 000000000..08877b132 --- /dev/null +++ b/vlmeval/dataset/utils/oceanoctbench/eval_req.txt @@ -0,0 +1,2 @@ +nltk +jieba