Upload eval_comet_demo.py with huggingface_hub

eval_comet_demo.py

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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()