Upload convert_dataset_v2.py with huggingface_hub

convert_dataset_v2.py

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+import json
+from tqdm import tqdm
+import os
+
+# GAIA
+data_path = "data/mat_train.json"
+
+with open(data_path, "r") as f:
+    dataset = json.load(f)
+
+def _convert(image_path_map, conversations):
+    output = []
+    for turn in conversations:
+        role = turn["role"]
+        content = turn["content"]
+        turn_new = dict()
+        turn_new["from"] = role
+        pid = 1
+        keys = sorted(list(image_path_map.keys()))
+        for k in keys:
+            v = image_path_map[k]
+            if k in content:
+                content = content.replace(k, f"Picture {pid}: <img>{v}</img>\n")
+                content = content.replace(f"</img>\n\n", "</img>\n")
+                pid += 1
+        turn_new["value"] = content
+        output.append(turn_new)
+    return output
+
+
+for item in tqdm(dataset):
+    #print(item["image"])
+    #print(item.keys())
+    conversations = item["conversations"]
+    #print(len(conversations), conversations[1])
+    image_path_map = dict()
+    if "image" not in item:
+        pass
+    elif type(item["image"]) == str:
+        image_path_map["<image>"] = item["image"]
+        item['image'] = f"{os.getcwd()}/data/{item['image']}"
+    else:
+        for k, v in item["image"].items():
+            image_path_map[k] = v
+            item["image"][k] = f"{os.getcwd()}/data/{v}"
+    item["conversations"] = _convert(image_path_map, conversations)
+
+from datetime import datetime
+import json
+
+now = "20241209_1731"
+print("write to", f"data/train_{now}.json")
+with open(f"data/train_{now}.json", "w") as f:
+    json.dump(dataset, f, indent=4, ensure_ascii=False)
+
+import random
+with open(f"data/train_{now}_subset.json", "w") as f:
+    random.shuffle(dataset)
+    json.dump(dataset[:1000], f, indent=4, ensure_ascii=False)