Create down_r1onevision.py

down_r1onevision.py

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+import datasets
+from tqdm import tqdm
+import os
+import json
+import multiprocessing as mp
+from functools import partial
+
+def process_item(idx_data, dataset_name):
+    idx, d = idx_data
+    conversations = d['conversations']
+    image = d['image']
+    image_path = f"{dataset_name}/{idx}.png"
+    os.makedirs(os.path.dirname(f"data/{image_path}"), exist_ok=True)
+    image.save(f"data/{image_path}")
+    for i, c in enumerate(conversations):
+        conversations[i]['content'] = c['content'].replace("<image>", "")
+    if len(conversations) > 1:
+        conversations[1]['content'] = "<image>" + conversations[1]['content']
+    return {
+        "images": [image_path],
+        "system": SYSTEM_PROMPT,
+        "conversations": conversations
+    }
+
+ds_id = "ahmedheakl/r1_90k_instruct"
+dataset_name = "r1_onevision_90k"
+out_root = "data"  
+SYSTEM_PROMPT = (
+    "A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant "
+    "first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning "
+    "process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., "
+    "<think> reasoning process here </think><answer> answer here </answer>"
+)
+os.makedirs(f"{out_root}/{dataset_name}", exist_ok=True)
+print("Loading dataset...")
+ds = datasets.load_dataset(ds_id, split="train", trust_remote_code=True)
+num_processes = mp.cpu_count() - 6
+print(f"Using {num_processes} processes")
+process_func = partial(process_item, dataset_name=dataset_name)
+with mp.Pool(processes=num_processes) as pool:
+    data = list(tqdm(pool.imap(process_func, enumerate(ds)), total=len(ds), desc="Processing items"))
+output_path = f"{out_root}/{dataset_name}.json"
+print(f"Saving dataset to {output_path}")
+with open(output_path, "w") as f:
+    json.dump(data, f, indent=4, ensure_ascii=False)
+with open(f"{out_root}/dataset_info.json", "r") as f:
+    dataset_info = json.load(f)
+dataset_info[dataset_name] = {
+    "file_name": f"{dataset_name}.json",
+    "formatting": "sharegpt",
+    "columns": {
+        "messages": "conversations",
+        "images": "images",
+        "system": "system"
+    },
+    "tags": {
+        "role_tag": "role",
+        "content_tag": "content",
+        "user_tag": "user",
+        "assistant_tag": "assistant"
+    }
+}
+with open(f"{out_root}/dataset_info.json", "w") as f:
+    json.dump(dataset_info, f, indent=4, ensure_ascii=False)