llama.cpp/tools/mtmd/tests/test-deepseek-ocr.py
Saba Fallah da3f990a47
mtmd: Add DeepSeekOCR 2 Support (#20975)
* mtmd: DeepSeek-OCR 2 support, with multi-tile dynamic resolution

* introduced clip_image_f32::add_viewsep

* address PR review

- drop redundant ggml_cpy ops in both deepseekocr versions build
- drop no-op ggml_cont in build_sam
- assert num_image_tokens deepseekocr2
- view_seperator as (1, n_embd) at conversion (for both versions)
- drop redundant ggml_reshape_2d

* Update tools/mtmd/models/deepseekocr2.cpp

Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>

---------

Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>
2026-05-29 16:13:51 +02:00

294 lines
10 KiB
Python

#!/usr/bin/env python3
"""
Evaluates llama.cpp's DeepSeek-OCR by comparing its output for a test
image to the actual text in part of that image.
Runs each test image through mtmd-cli, calculates CER and chrF for
its output, and holds them against the HF model's scores.
"""
import argparse
import logging
import subprocess
import sys
import unicodedata
from dataclasses import dataclass
from pathlib import Path
logger = logging.getLogger("deepseek-ocr-test")
RUN_TIMEOUT = 300
@dataclass
class ModelSpec:
key: str
label: str
model_arg: str
mmproj_arg: str
model_default: str
mmproj_default: str
@dataclass
class TestCase:
model_key: str
label: str
image: str
ground_truth: str
hf_cer: float
hf_chrf: float
cer_tol: float
chrf_tol: float
@property
def cer_max(self) -> float:
return self.hf_cer + self.cer_tol
@property
def chrf_min(self) -> float:
return self.hf_chrf - self.chrf_tol
MODELS = {
"v1": ModelSpec(
key="v1", label="DeepSeek-OCR",
model_arg="--llama-model", mmproj_arg="--mmproj",
model_default="gguf_models/deepseek-ai/deepseek-ocr-bf16.gguf",
mmproj_default="gguf_models/deepseek-ai/mmproj-deepseek-ocr-bf16.gguf",
),
"v2": ModelSpec(
key="v2", label="DeepSeek-OCR-2",
model_arg="--llama-model-2", mmproj_arg="--mmproj-2",
model_default="gguf_models/deepseek-ai/deepseek-ocr-2-bf16.gguf",
mmproj_default="gguf_models/deepseek-ai/mmproj-deepseek-ocr-2-bf16.gguf",
),
}
CASES = [
TestCase(
model_key="v1", label="single-view scan",
image="tools/mtmd/test-1.jpeg",
ground_truth="tools/mtmd/tests/test-1-ground-truth.txt",
hf_cer=0.3030, hf_chrf=67.52, cer_tol=0.02, chrf_tol=2.0,
),
TestCase(
model_key="v2", label="single-view scan",
image="tools/mtmd/test-1.jpeg",
ground_truth="tools/mtmd/tests/test-1-ground-truth.txt",
# 640x488 is below the 768 tiling threshold -- single 1024 global view.
# hf_cer/hf_chrf are the deepseek-ai repo's own scores (ImageOps.pad);
# the transformers HF processor is *not* the reference -- its pad_to_square
# is one pixel off and lands at ~0.69 instead.
hf_cer=0.7761, hf_chrf=28.70, cer_tol=0.12, chrf_tol=8.0,
),
]
def arg_dest(flag: str) -> str:
return flag.lstrip("-").replace("-", "_")
def verdict(ok: bool) -> str:
return "PASS" if ok else "FAIL"
def normalize_text(text: str) -> str:
"""NFC-normalize and collapse whitespace, so line-wrap and spacing
don't count as CER errors."""
return " ".join(unicodedata.normalize("NFC", text).split())
def locally_align(expected: str, ocr_out: str) -> str:
"""Return the span of `ocr_out` that best matches `expected`.
The ground truth covers part of the article body.
But the test image includes half of the newspaper's front page.
Fuzzy partial-ratio matching picks out
the body so the unrelated text doesn't disturb CER / chrF.
"""
from rapidfuzz import fuzz
alignment = fuzz.partial_ratio_alignment(expected, ocr_out)
if alignment is None or alignment.dest_end <= alignment.dest_start:
return ocr_out
return ocr_out[alignment.dest_start:alignment.dest_end]
def compute_cer(expected: str, ocr_out: str) -> float:
"""Character Error Rate. Lower is better.
CER: fraction of characters you'd insert/delete/substitute to fix the output; 0 = perfect."""
import jiwer
return jiwer.cer(expected, ocr_out)
def compute_chrf(expected: str, ocr_out: str) -> float:
"""chrF score on 0-100. Higher is better.
chrF: F-score over shared character n-grams; more forgiving of small word/spacing drift than CER.
"""
from sacrebleu.metrics import CHRF
return CHRF().sentence_score(ocr_out, [expected]).score
def run_mtmd_cli(model_path, mmproj_path, image_path, bin_path) -> str:
"""Run mtmd-cli on the image and return its output."""
cmd = [
str(bin_path),
"-m", str(model_path),
"--mmproj", str(mmproj_path),
"--image", str(image_path),
"-p", "Free OCR. ",
"--chat-template", "deepseek-ocr",
"--temp", "0",
"--flash-attn", "off", # match the HF "eager" attention reference
"--no-warmup",
"-n", "512", # cap loops on hard images (KV would otherwise fill)
# HF decodes with no_repeat_ngram_size; llama.cpp's analog is DRY.
# Default DRY breakers include "\n", so they are cleared below.
"--dry-multiplier", "0.8",
"--dry-base", "1.75",
"--dry-allowed-length", "2",
"--dry-penalty-last-n", "-1",
"--dry-sequence-breaker", "none",
]
logger.debug(f" command: {' '.join(cmd)}")
try:
result = subprocess.run(cmd, capture_output=True, text=False, timeout=RUN_TIMEOUT)
except subprocess.TimeoutExpired as e:
if e.stderr:
logger.error("llama.cpp stderr:\n%s", e.stderr.decode("utf-8", errors="replace"))
raise RuntimeError(f"llama-mtmd-cli timed out after {RUN_TIMEOUT}s")
if result.returncode != 0:
logger.error("llama.cpp stderr:\n%s", result.stderr.decode("utf-8", errors="replace"))
raise RuntimeError(f"llama-mtmd-cli failed with code {result.returncode}")
output = result.stdout.decode("utf-8", errors="replace").strip()
if not output:
raise RuntimeError("llama-mtmd-cli produced no output on stdout")
logger.info(f" output: {len(output)} chars")
return output
def read_expected_text(file_path: Path) -> str:
with open(file_path, "r", encoding="utf-8") as f:
return f.read().strip()
def evaluate(case: "TestCase", expected: str, ocr_out: str) -> bool:
expected = normalize_text(expected)
ocr_out = normalize_text(ocr_out)
aligned = locally_align(expected, ocr_out)
logger.debug(f"\n--- expected (normalized) ---\n{expected}")
logger.debug(f"\n--- OCR output (normalized) ---\n{ocr_out}")
logger.debug(f"\n--- aligned span ---\n{aligned}")
cer = compute_cer(expected, aligned)
chrf = compute_chrf(expected, aligned)
cer_pass = cer <= case.cer_max
chrf_pass = chrf >= case.chrf_min
passed = cer_pass and chrf_pass
logger.info("")
logger.info("=" * 60)
logger.info("Free OCR evaluation:")
logger.info("=" * 60)
logger.info(f" CER {cer:>7.4f} (HF {case.hf_cer:.4f}, <= {case.cer_max:>7.4f} -> {verdict(cer_pass)})")
logger.info(f" chrF (0-100) {chrf:>7.2f} (HF {case.hf_chrf:.2f}, >= {case.chrf_min:>7.2f} -> {verdict(chrf_pass)})")
logger.info(f" Expected chars {len(expected):>7}")
logger.info(f" Aligned chars {len(aligned):>7} (of {len(ocr_out)} OCR chars)")
logger.info("")
logger.info(f" Result: {verdict(passed)}")
logger.info("=" * 60)
return passed
def argument_parser() -> argparse.ArgumentParser:
ap = argparse.ArgumentParser(description="Compare llama.cpp DeepSeek-OCR output with a ground-truth transcript")
ap.add_argument("--llama-bin", default="build/bin/llama-mtmd-cli",
help="Path to llama-mtmd-cli binary (relative to repo root or absolute)")
for spec in MODELS.values():
ap.add_argument(spec.model_arg, default=spec.model_default,
help=f"Path to the {spec.label} GGUF model (relative to repo root or absolute)")
ap.add_argument(spec.mmproj_arg, default=spec.mmproj_default,
help=f"Path to the {spec.label} mmproj GGUF file (relative to repo root or absolute)")
ap.add_argument("--verbose", action="store_true",
help="Also log the expected, OCR, and aligned text")
return ap
def configure_logging(verbose: bool) -> None:
logging.basicConfig(level=logging.DEBUG if verbose else logging.INFO,
format="%(message)s")
def resolve_path(path: str, base: Path) -> Path:
p = Path(path)
return p if p.is_absolute() else base / p
def main() -> int:
args = argument_parser().parse_args()
configure_logging(args.verbose)
repo_root = Path(__file__).resolve().parents[3] # tests -> mtmd -> tools -> repo root
binary = resolve_path(args.llama_bin, repo_root)
if not binary.exists():
logger.error(f"Error: binary not found: {binary}")
return 1
logger.info("=" * 60)
logger.info("DeepSeek-OCR: llama.cpp vs HF parity check")
logger.info("=" * 60)
results = {}
for case in CASES:
model_spec = MODELS[case.model_key]
title = f"{model_spec.label} -- {case.label}"
logger.info("")
logger.info(f"=== {title} ===")
model = resolve_path(getattr(args, arg_dest(model_spec.model_arg)), repo_root)
mmproj = resolve_path(getattr(args, arg_dest(model_spec.mmproj_arg)), repo_root)
image = resolve_path(case.image, repo_root)
ground_truth = resolve_path(case.ground_truth, repo_root)
missing = [(lbl, p) for lbl, p in [("model", model), ("mmproj", mmproj),
("image", image), ("ground-truth", ground_truth)]
if not p.exists()]
if missing:
for lbl, p in missing:
logger.error(f" Error: {lbl} not found: {p}")
results[title] = False
continue
expected = read_expected_text(ground_truth)
logger.info(f" Image: {case.image}")
logger.info(f" Expected text: {len(expected)} chars")
logger.info(" Running llama.cpp 'Free OCR'")
try:
ocr_out = run_mtmd_cli(model, mmproj, image, binary)
except RuntimeError as e:
logger.error(f" Error: {e}")
results[title] = False
continue
results[title] = evaluate(case, expected, ocr_out)
logger.info("")
logger.info("=== Summary ===")
for title, ok in results.items():
logger.info(f" {title:<48} {verdict(ok)}")
all_passed = all(results.values())
logger.info(f"Overall: {verdict(all_passed)}")
return 0 if all_passed else 1
if __name__ == "__main__":
sys.exit(main())