add making code

make_ds_coco.py

@@ -0,0 +1,79 @@
+import json
+import os
+from tqdm import tqdm
+
+from datasets import Dataset, load_dataset, Image
+
+
+def load_jsonl(file_path):
+    """
+    Loads a JSONL file and returns a list of Python dictionaries.
+    Each dictionary represents a JSON object from a line in the file.
+    """
+    data = []
+    with open(file_path, 'r', encoding='utf-8') as f:
+        for line in f:
+            try:
+                # Parse each line as a JSON object
+                json_object = json.loads(line.strip())
+                data.append(json_object)
+            except json.JSONDecodeError as e:
+                print(f"Error decoding JSON on line: {line.strip()} - {e}")
+    return data
+
+
+def main():
+    dset = "val"
+    workdir = "./coco"
+
+    # Load Annotions from Official Coco
+    with open(
+        os.path.join(workdir, "annotations", f"captions_{dset}2017.json"),
+        "r",
+        encoding="utf-8"
+    ) as reader:
+        data = json.load(reader)
+
+    # Format dict of image elements
+    images = {}
+    for item in tqdm(data["images"]):
+        _idx = item["id"]
+        images[_idx] = {
+            "file_name": item["file_name"],
+            "height": item["height"],
+            "width": item["width"],
+            "id": _idx,
+            "image": os.path.join(workdir, f"{dset}2017", item["file_name"]),
+            "captions": [],
+            "narratives": []
+        }
+
+    # Assign official annotations
+    for item in tqdm(data["annotations"]):
+        _idx = item["image_id"]
+        images[_idx]["captions"].append(item["caption"])
+
+    # Load Narratives
+    data_narr = load_jsonl(os.path.join(workdir, "localized_narratives", f"coco_{dset}_captions.jsonl"))
+    for item in tqdm(data_narr):
+        _idx = int(item["image_id"])
+        images[_idx]["narratives"].append(item["caption"])
+
+    def gen():
+        for k, v in images.items():
+            yield v
+
+    ds = Dataset.from_generator(gen)
+    ds = ds.cast_column("image", Image())
+    ds.save_to_disk(f"coco/datasets/data/{dset}", max_shard_size="400MB")
+    return
+
+
+def test_coco():
+    ds = load_dataset("./coco/datasets", split="val")
+    print(ds.info)
+    
+
+if __name__ == "__main__":
+    # main()
+    test_coco()