Merge pull request #392 from VikParuchuri/v2

Marker v2

.github/workflows/tests.yml → .github/workflows/benchmark.yml

@@ -7,7 +7,7 @@ env:
   OCR_ENGINE: "surya"
 
 jobs:
-  build:
+  benchmark:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
@@ -28,7 +28,7 @@ jobs:
       - name: Run benchmark test
         run: |
           poetry run python benchmarks/overall.py benchmark_data/pdfs benchmark_data/references report.json
-          poetry run python scripts/verify_benchmark_scores.py report.json --type marker
+          poetry run python benchmarks/verify_scores.py report.json --type marker
         
           
 

.github/workflows/ci.yml

@@ -0,0 +1,25 @@
+name: CI tests
+
+on: [push]
+
+env:
+  TORCH_DEVICE: "cpu"
+  OCR_ENGINE: "surya"
+
+jobs:
+  tests:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.11
+      - name: Install python dependencies
+        run: |
+          pip install poetry
+          poetry install
+      - name: Run tests
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: poetry run pytest

.gitignore

@@ -9,6 +9,9 @@ wandb
 report.json
 benchmark_data
 debug_data
+temp.md
+temp
+conversion_results
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -170,3 +173,5 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 .idea/
+
+.vscode/

benchmarks/overall.py

@@ -1,42 +1,26 @@
-import argparse
 import tempfile
 import time
 from collections import defaultdict
 
+import click
 from tqdm import tqdm
 import pypdfium2 as pdfium
 
-from marker.convert import convert_single_pdf
+from marker.config.parser import ConfigParser
+from marker.converters.pdf import PdfConverter
 from marker.logger import configure_logging
-from marker.models import load_all_models
-from marker.benchmark.scoring import score_text
-from marker.pdf.extract_text import naive_get_text
+from marker.models import create_model_dict
+from pdftext.extraction import plain_text_output
 import json
 import os
 import subprocess
 import shutil
 from tabulate import tabulate
-import torch
+from scoring import score_text
 
 configure_logging()
 
 
-def start_memory_profiling():
-    torch.cuda.memory._record_memory_history(
-        max_entries=100000
-    )
-
-
-def stop_memory_profiling(memory_file):
-    try:
-        torch.cuda.memory._dump_snapshot(memory_file)
-    except Exception as e:
-        logger.error(f"Failed to capture memory snapshot {e}")
-
-        # Stop recording memory snapshot history.
-    torch.cuda.memory._record_memory_history(enabled=None)
-
-
 def nougat_prediction(pdf_filename, batch_size=1):
     out_dir = tempfile.mkdtemp()
     subprocess.run(["nougat", pdf_filename, "-o", out_dir, "--no-skipping", "--recompute", "--batchsize", str(batch_size)], check=True)
@@ -46,62 +30,51 @@ def nougat_prediction(pdf_filename, batch_size=1):
     shutil.rmtree(out_dir)
     return data
 
-
-def main():
-    parser = argparse.ArgumentParser(description="Benchmark PDF to MD conversion.  Needs source pdfs, and a reference folder with the correct markdown.")
-    parser.add_argument("in_folder", help="Input PDF files")
-    parser.add_argument("reference_folder", help="Reference folder with reference markdown files")
-    parser.add_argument("out_file", help="Output filename")
-    parser.add_argument("--nougat", action="store_true", help="Run nougat and compare", default=False)
-    # Nougat batch size 1 uses about as much VRAM as default marker settings
-    parser.add_argument("--marker_batch_multiplier", type=int, default=1, help="Batch size multiplier to use for marker when making predictions.")
-    parser.add_argument("--nougat_batch_size", type=int, default=1, help="Batch size to use for nougat when making predictions.")
-    parser.add_argument("--md_out_path", type=str, default=None, help="Output path for generated markdown files")
-    parser.add_argument("--profile_memory", action="store_true", help="Profile memory usage", default=False)
-
-    args = parser.parse_args()
-
+@click.command(help="Benchmark PDF to MD conversion.")
+@click.argument("in_folder", type=str)
+@click.argument("reference_folder", type=str)
+@click.argument("out_file", type=str)
+@click.option("--nougat", is_flag=True, help="Run nougat and compare")
+@click.option("--md_out_path", type=str, default=None, help="Output path for generated markdown files")
+def main(in_folder: str, reference_folder: str, out_file: str, nougat: bool, md_out_path: str):
     methods = ["marker"]
-    if args.nougat:
+    if nougat:
         methods.append("nougat")
 
-    if args.profile_memory:
-        start_memory_profiling()
-
-    model_lst = load_all_models()
-
-    if args.profile_memory:
-        stop_memory_profiling("model_load.pickle")
+    model_dict = create_model_dict()
 
     scores = defaultdict(dict)
-    benchmark_files = os.listdir(args.in_folder)
+    benchmark_files = os.listdir(in_folder)
     benchmark_files = [b for b in benchmark_files if b.endswith(".pdf")]
     times = defaultdict(dict)
     pages = defaultdict(int)
 
     for idx, fname in tqdm(enumerate(benchmark_files)):
         md_filename = fname.rsplit(".", 1)[0] + ".md"
 
-        reference_filename = os.path.join(args.reference_folder, md_filename)
+        reference_filename = os.path.join(reference_folder, md_filename)
         with open(reference_filename, "r", encoding="utf-8") as f:
             reference = f.read()
 
-        pdf_filename = os.path.join(args.in_folder, fname)
+        pdf_filename = os.path.join(in_folder, fname)
         doc = pdfium.PdfDocument(pdf_filename)
         pages[fname] = len(doc)
 
+        config_parser = ConfigParser({"output_format": "markdown"})
         for method in methods:
             start = time.time()
             if method == "marker":
-                if args.profile_memory:
-                    start_memory_profiling()
-                full_text, _, out_meta = convert_single_pdf(pdf_filename, model_lst, batch_multiplier=args.marker_batch_multiplier)
-                if args.profile_memory:
-                    stop_memory_profiling(f"marker_memory_{idx}.pickle")
+                converter = PdfConverter(
+                    config=config_parser.generate_config_dict(),
+                    artifact_dict=model_dict,
+                    processor_list=None,
+                    renderer=config_parser.get_renderer()
+                )
+                full_text = converter(pdf_filename).markdown
             elif method == "nougat":
-                full_text = nougat_prediction(pdf_filename, batch_size=args.nougat_batch_size)
+                full_text = nougat_prediction(pdf_filename, batch_size=1)
             elif method == "naive":
-                full_text = naive_get_text(doc)
+                full_text = plain_text_output(doc, workers=1)
             else:
                 raise ValueError(f"Unknown method {method}")
 
@@ -110,13 +83,13 @@ def main():
             score = score_text(full_text, reference)
             scores[method][fname] = score
 
-            if args.md_out_path:
+            if md_out_path:
                 md_out_filename = f"{method}_{md_filename}"
-                with open(os.path.join(args.md_out_path, md_out_filename), "w+") as f:
+                with open(os.path.join(md_out_path, md_out_filename), "w+") as f:
                     f.write(full_text)
 
     total_pages = sum(pages.values())
-    with open(args.out_file, "w+") as f:
+    with open(out_file, "w+") as f:
         write_data = defaultdict(dict)
         for method in methods:
             total_time = sum(times[method].values())

marker/benchmark/scoring.py → benchmarks/scoring.py

@@ -1,8 +1,4 @@
-import math
-
 from rapidfuzz import fuzz
-import re
-import regex
 from statistics import mean
 
 CHUNK_MIN_CHARS = 25

chunk_convert.sh

@@ -36,8 +36,6 @@ for (( i=0; i<$NUM_DEVICES; i++ )); do
     export NUM_WORKERS
     echo "Running convert.py on GPU $DEVICE_NUM"
     cmd="CUDA_VISIBLE_DEVICES=$DEVICE_NUM marker $INPUT_FOLDER $OUTPUT_FOLDER --num_chunks $NUM_DEVICES --chunk_idx $DEVICE_NUM --workers $NUM_WORKERS"
-    [[ -n "$METADATA_FILE" ]] && cmd="$cmd --metadata_file $METADATA_FILE"
-    [[ -n "$MIN_LENGTH" ]] && cmd="$cmd --min_length $MIN_LENGTH"
     eval $cmd &
 
     sleep 5