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MMTIT-Bench COMET Evaluation Demo
Prediction file format (JSONL):
{"image_id": "Korea_Menu_20843.jpg", "pred": "梅尔街 ..."}
Usage:
python eval_comet_demo.py \
--prediction prediction.jsonl \
--annotation annotation.jsonl \
--direction other2zh \
--batch_size 16 --gpus 0
"""
import json
import argparse
from comet import download_model, load_from_checkpoint
def load_jsonl(path):
with open(path, "r", encoding="utf-8") as f:
return [json.loads(line) for line in f if line.strip()]
def main():
parser = argparse.ArgumentParser(description="MMTIT-Bench COMET Evaluation")
parser.add_argument("--prediction", type=str, required=True, help="Path to prediction JSONL (fields: image_id, pred)")
parser.add_argument("--annotation", type=str, default="annotation.jsonl", help="Path to annotation JSONL")
parser.add_argument("--direction", type=str, required=True, choices=["other2zh", "other2en"], help="Translation direction")
parser.add_argument("--batch_size", type=int, default=16)
parser.add_argument("--gpus", type=int, default=0, help="0 for CPU")
parser.add_argument("--output", type=str, default=None, help="Output path for per-sample scores")
args = parser.parse_args()
if args.output is None:
args.output = f"comet_results_{args.direction}.jsonl"
# Choose reference field based on direction
ref_key = "translation_zh" if args.direction == "other2zh" else "translation_en"
# Load data
annotations = {item["image_id"]: item for item in load_jsonl(args.annotation)}
predictions = load_jsonl(args.prediction)
print(f"Annotations: {len(annotations)}, Predictions: {len(predictions)}")
# Merge by image_id -> build COMET inputs (src / mt / ref)
comet_inputs = []
matched_ids = []
for pred in predictions:
img_id = pred["image_id"]
if img_id in annotations:
ann = annotations[img_id]
comet_inputs.append({
"src": ann["parsing_anno"], # source OCR text
"mt": pred["pred"], # model prediction
"ref": ann[ref_key], # ground-truth translation
})
matched_ids.append(img_id)
print(f"Matched: {len(comet_inputs)} / {len(predictions)}")
assert len(comet_inputs) > 0, "No matching samples found. Check image_id consistency."
# Load COMET model and evaluate
model_path = download_model("Unbabel/wmt22-comet-da")
model = load_from_checkpoint(model_path)
model_output = model.predict(comet_inputs, batch_size=args.batch_size, gpus=args.gpus)
# Print system score
print(f"\n{'='*50}")
print(f" Direction: {args.direction}")
print(f" Samples: {len(comet_inputs)}")
print(f" COMET Score: {model_output.system_score:.4f}")
print(f"{'='*50}")
# Save per-sample results
with open(args.output, "w", encoding="utf-8") as f:
for img_id, score in zip(matched_ids, model_output.scores):
f.write(json.dumps({"image_id": img_id, "comet_score": score}, ensure_ascii=False) + "\n")
print(f"Per-sample scores saved to: {args.output}")
if __name__ == "__main__":
main()
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