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import argparse
import tempfile
import time
from collections import defaultdict
from tqdm import tqdm
import pypdfium2 as pdfium
from marker.convert import convert_single_pdf
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
import json
import os
import subprocess
import shutil
from tabulate import tabulate
import torch
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)
md_file = os.listdir(out_dir)[0]
with open(os.path.join(out_dir, md_file), "r") as f:
data = f.read()
shutil.rmtree(out_dir)
return data
def main():
parser = argparse.ArgumentParser(description="Benchmark PDF to MD conversion. Needs source pdfs, and a refernece 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()
methods = ["marker"]
if args.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")
scores = defaultdict(dict)
benchmark_files = os.listdir(args.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)
with open(reference_filename, "r", encoding="utf-8") as f:
reference = f.read()
pdf_filename = os.path.join(args.in_folder, fname)
doc = pdfium.PdfDocument(pdf_filename)
pages[fname] = len(doc)
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")
elif method == "nougat":
full_text = nougat_prediction(pdf_filename, batch_size=args.nougat_batch_size)
elif method == "naive":
full_text = naive_get_text(doc)
else:
raise ValueError(f"Unknown method {method}")
times[method][fname] = time.time() - start
score = score_text(full_text, reference)
scores[method][fname] = score
if args.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:
f.write(full_text)
total_pages = sum(pages.values())
with open(args.out_file, "w+") as f:
write_data = defaultdict(dict)
for method in methods:
total_time = sum(times[method].values())
file_stats = {
fname:
{
"time": times[method][fname],
"score": scores[method][fname],
"pages": pages[fname]
}
for fname in benchmark_files
}
write_data[method] = {
"files": file_stats,
"avg_score": sum(scores[method].values()) / len(scores[method]),
"time_per_page": total_time / total_pages,
"time_per_doc": total_time / len(scores[method])
}
json.dump(write_data, f, indent=4)
summary_table = []
score_table = []
score_headers = benchmark_files
for method in methods:
summary_table.append([method, write_data[method]["avg_score"], write_data[method]["time_per_page"], write_data[method]["time_per_doc"]])
score_table.append([method, *[write_data[method]["files"][h]["score"] for h in score_headers]])
print(tabulate(summary_table, headers=["Method", "Average Score", "Time per page", "Time per document"]))
print("")
print("Scores by file")
print(tabulate(score_table, headers=["Method", *score_headers]))
if __name__ == "__main__":
main()
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