File size: 6,020 Bytes
682b227 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 | #!/usr/bin/env python3
"""
Test script to compare llama.cpp mtmd-cli output with HuggingFace reference implementation
for DeepSeek-OCR model using embedding similarity.
"""
import argparse
import subprocess
import sys
from pathlib import Path
from sentence_transformers import SentenceTransformer
from sentence_transformers import util
def run_mtmd_deepseek_ocr(
model_path: str,
mmproj_path: str,
image_path: str,
bin_path: str,
prompt: str = "Free OCR."
) -> str:
"""
Run inference using llama.cpp mtmd-cli.
"""
cmd = [
bin_path,
"-m", model_path,
"--mmproj", mmproj_path,
"--image", image_path,
# "-p", "<|grounding|>Convert the document to markdown.",
"-p", prompt,
"--chat-template", "deepseek-ocr",
"--temp", "0",
"-n", "1024",
# "--verbose"
]
print(f"Running llama.cpp command: {' '.join(cmd)}")
result = subprocess.run(
cmd,
capture_output=True,
text=False,
timeout=300
)
if result.returncode != 0:
stderr = result.stderr.decode('utf-8', errors='replace')
print(f"llama.cpp stderr: {stderr}")
raise RuntimeError(f"llama-mtmd-cli failed with code {result.returncode}")
output = result.stdout.decode('utf-8', errors='replace').strip()
print(f"llama.cpp output length: {len(output)} chars")
return output
def compute_embedding_similarity(text1: str, text2: str, model_name: str) -> float:
"""
Compute cosine similarity between two texts using embedding model.
"""
print(f"Loading embedding model: {model_name}")
# Use sentence-transformers for easier embedding extraction
embed_model = SentenceTransformer(model_name)
print("Computing embeddings...")
embeddings = embed_model.encode([text1, text2], convert_to_numpy=True)
similarity = util.similarity.cos_sim([embeddings[0]], [embeddings[1]])[0][0]
return float(similarity)
def read_expected_output(file_path: str) -> str:
"""
Read expected OCR output from file.
"""
cur_path = Path(__file__).parent
expected_path = str(cur_path / file_path)
with open(expected_path, "r", encoding="utf-8") as f:
return f.read().strip()
def main():
ap = argparse.ArgumentParser(description="Compare llama.cpp and HuggingFace DeepSeek-OCR outputs")
ap.add_argument("--llama-model", default="gguf_models/deepseek-ai/deepseek-ocr-f16.gguf",
help="Path to llama.cpp GGUF model")
ap.add_argument("--mmproj", default="gguf_models/deepseek-ai/mmproj-deepseek-ocr-f16.gguf",
help="Path to mmproj GGUF file")
ap.add_argument("--image", default="test-1.jpeg",
help="Path to test image")
ap.add_argument("--llama-bin", default="build/bin/llama-mtmd-cli",
help="Path to llama-mtmd-cli binary")
ap.add_argument("--embedding-model", default="Qwen/Qwen3-Embedding-0.6B",
help="Embedding model for similarity computation")
ap.add_argument("--threshold", type=float, default=0.7,
help="Minimum similarity threshold for pass")
args = ap.parse_args()
# Validate paths
# script directory + image
mtmd_dir = Path(__file__).parent.parent
args.image = str(mtmd_dir / args.image)
# project directory + llama model
args.llama_model = str(mtmd_dir.parent.parent / args.llama_model)
# project directory + mmproj
args.mmproj = str(mtmd_dir.parent.parent / args.mmproj)
args.llama_bin = str(mtmd_dir.parent.parent / args.llama_bin)
if not Path(args.image).exists():
print(f"Error: Image not found: {args.image}")
sys.exit(1)
if not Path(args.llama_model).exists():
print(f"Error: Model not found: {args.llama_model}")
sys.exit(1)
if not Path(args.mmproj).exists():
print(f"Error: mmproj not found: {args.mmproj}")
sys.exit(1)
print("=" * 60)
print("DeepSeek-OCR: llama.cpp vs HuggingFace Comparison")
print("=" * 60)
# Default paths based on your command
# Run llama.cpp inference
print("\n[2/3] Running llama.cpp implementation...")
llama_free_ocr = run_mtmd_deepseek_ocr(
args.llama_model,
args.mmproj,
args.image,
args.llama_bin
)
llama_md_ocr = run_mtmd_deepseek_ocr(
args.llama_model,
args.mmproj,
args.image,
args.llama_bin,
prompt="<|grounding|>Convert the document to markdown."
)
expected_free_ocr = read_expected_output("test-1-extracted.txt")
expected_md_ocr = read_expected_output("test-1-extracted.md")
# Compute similarity
print("\n[3/3] Computing embedding similarity...")
free_ocr_similarity = compute_embedding_similarity(
expected_free_ocr,
llama_free_ocr,
args.embedding_model
)
md_ocr_similarity = compute_embedding_similarity(
expected_md_ocr,
llama_md_ocr,
args.embedding_model
)
# Results
print("\n" + "=" * 60)
print("RESULTS")
print("=" * 60)
print(f"\nReference Model output:\n{'-' * 40}")
print(expected_free_ocr)
print(f"\nDeepSeek-OCR output:\n{'-' * 40}")
print(llama_free_ocr)
print(f"\n{'=' * 60}")
print(f"Cosine Similarity: {free_ocr_similarity:.4f}")
print(f"Threshold: {args.threshold}")
print(f"Result: {'PASS' if free_ocr_similarity >= args.threshold else 'FAIL'}")
print("=" * 60)
# Markdown OCR results
print(f"\nReference Model Markdown output:\n{'-' * 40}")
print(expected_md_ocr)
print(f"\nDeepSeek-OCR Markdown output:\n{'-' * 40}")
print(llama_md_ocr)
print(f"\n{'=' * 60}")
print(f"Cosine Similarity (Markdown): {md_ocr_similarity:.4f}")
print(f"Threshold: {args.threshold}")
print(f"Result: {'PASS' if md_ocr_similarity >= args.threshold else 'FAIL'}")
print("=" * 60)
if __name__ == "__main__":
main()
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