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run/openai_api.py

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+import os
+import openai
+from openai import OpenAI
+import base64
+from tqdm import tqdm
+import time
+import json
+from pathlib import Path
+from threading import Lock
+from typing import Any
+
+
+_json_write_lock = Lock()
+
+def save_json_file(
+    data: Any,
+    file_path: str,
+    indent: int = 4,
+    temp_suffix: str = ".tmp"
+) -> None:
+    """
+
+    """
+    path = Path(file_path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+
+    temp_path = f"{file_path}{temp_suffix}"
+
+    with _json_write_lock:
+        try:
+
+            with open(temp_path, "w", encoding="utf-8") as f:
+                json.dump(data, f, ensure_ascii=False, indent=indent)
+
+
+                f.flush()
+                os.fsync(f.fileno())
+
+
+            os.replace(temp_path, file_path)
+
+        except Exception as e:
+            # 出错则删除临时文件
+            try:
+                if os.path.exists(temp_path):
+                    os.remove(temp_path)
+            except OSError:
+                pass
+            raise RuntimeError(f"save json failed: {e}") from e
+
+def read_json_file(file_path):
+    """
+    Reads a JSON file and returns the parsed data as a Python object.
+
+    :param file_path: The path to the JSON file
+    :return: The data parsed from the JSON file
+    """
+    with open(file_path, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+    return data
+
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.standard_b64encode(image_file.read()).decode("utf-8")
+
+def merge_json_lists(folder_path):
+    """
+    """
+
+    json_list = [
+        os.path.join(folder_path, f)
+        for f in os.listdir(folder_path)
+        if f.lower().endswith('.json') and os.path.isfile(os.path.join(folder_path, f))
+    ]
+
+    merged_list = []
+
+    for file_path in json_list:
+        try:
+            with open(file_path, 'r', encoding='utf-8') as f:
+                data = json.load(f)
+                if isinstance(data, list):
+                    merged_list.extend(data)
+                else:
+                    print(f"waring: {file_path} is not list. skipped")
+        except Exception as e:
+            print(f"processing {file_path} error: {str(e)}")
+
+    return merged_list
+
+
+def openai_api(image_path, prompt = None):
+    if prompt == None:
+        prompt = "What's in this image?"
+
+    base64_image = encode_image(image_path)
+    client = OpenAI(
+        base_url='your_url',
+        api_key='your_key'
+    )
+    response = client.chat.completions.create(
+        model="claude-3-7-sonnet-20250219",
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": prompt},
+                    {
+                        "type": "image_url",
+                        "image_url": {
+                             "url": f"data:image/jpeg;base64,{base64_image}"
+                        }
+                    },
+                ],
+            }
+        ],
+        max_tokens=5000,
+    )
+
+    return response
+
+if __name__ == "__main__":
+    folder_path = r"your_path/poster/data"
+    save_dir = r"your_path/poster/result"
+
+    saved_josn = os.path.join(save_dir,"result.json")
+    if not os.path.exists(saved_josn):
+        tasks = merge_json_lists(folder_path)
+        save_json_file(tasks, saved_josn)
+
+    tasks = read_json_file(saved_josn)
+    max_retries = 4
+    retry_wait = 10
+    for item in tqdm(tasks):
+        if "response" in item: continue
+
+        prompt = item["prompt"]
+        image_path = os.path.join(folder_path, item["path"])
+        for attempt in range(max_retries):
+            try:
+                response = openai_api(image_path, prompt)
+                item["response"] = response.choices[0].message.content
+                print(item["response"])
+                save_json_file(tasks, saved_josn)
+                break  
+            except Exception as e:
+                print(f"[Warning] Request failed: {e}")
+                if attempt < max_retries - 1:
+                    print(f"Retrying in {retry_wait} seconds... (attempt {attempt + 1})")
+                    time.sleep(retry_wait)
+                else:
+                    print("[Error] Reached max retries. Skipping this item.")
+                    item["error"] = str(e)
+
+
+
+
+

run/qwen_32b.py

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+from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+from tqdm import tqdm
+import json
+import os
+from pathlib import Path
+from threading import Lock
+from typing import Any
+
+
+
+
+_json_write_lock = Lock()
+
+def save_json_file(
+    data: Any,
+    file_path: str,
+    indent: int = 4,
+    temp_suffix: str = ".tmp"
+) -> None:
+    """
+    """
+    path = Path(file_path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+
+    temp_path = f"{file_path}{temp_suffix}"
+
+    with _json_write_lock:
+        try:
+
+            with open(temp_path, "w", encoding="utf-8") as f:
+                json.dump(data, f, ensure_ascii=False, indent=indent)
+
+                f.flush()
+                os.fsync(f.fileno())
+
+            os.replace(temp_path, file_path)
+
+        except Exception as e:
+
+            try:
+                if os.path.exists(temp_path):
+                    os.remove(temp_path)
+            except OSError:
+                pass
+            raise RuntimeError(f"save json failed: {e}") from e
+
+def read_json_file(file_path):
+    """
+    Reads a JSON file and returns the parsed data as a Python object.
+
+    :param file_path: The path to the JSON file
+    :return: The data parsed from the JSON file
+    """
+    with open(file_path, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+    return data
+
+
+def merge_json_lists(folder_path):
+
+
+    json_list = [
+        os.path.join(folder_path, f)
+        for f in os.listdir(folder_path)
+        if f.lower().endswith('.json') and os.path.isfile(os.path.join(folder_path, f))
+    ]
+
+    merged_list = []
+
+    for file_path in json_list:
+        try:
+            with open(file_path, 'r', encoding='utf-8') as f:
+                data = json.load(f)
+                if isinstance(data, list):
+                    merged_list.extend(data)
+                else:
+                    print(f"警告: {file_path} 的内容不是列表，已跳过")
+        except Exception as e:
+            print(f"处理文件 {file_path} 时出错: {str(e)}")
+
+    return merged_list
+
+
+def build():
+        #cache_dir="/workspace/huggingface"
+    # default: Load the model on the available device(s)
+    model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2.5-VL-32B-Instruct", torch_dtype="auto", device_map="auto")
+
+    # default processer
+    processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-32B-Instruct")
+    
+
+    return model,processor
+
+
+def conversation(model, processor, image_path, prompt):
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image",
+                    "image": image_path,
+                },
+                {"type": "text", "text": prompt},
+            ],
+        }
+    ]
+    
+    # Preparation for inference
+    text = processor.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    image_inputs, video_inputs = process_vision_info(messages)
+    inputs = processor(
+        text=[text],
+        images=image_inputs,
+        videos=video_inputs,
+        padding=True,
+        return_tensors="pt",
+    )
+    inputs = inputs.to("cuda")
+    
+    # Inference: Generation of the output
+    generated_ids = model.generate(**inputs, max_new_tokens=128)
+    generated_ids_trimmed = [
+        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+    ]
+    output_text = processor.batch_decode(
+        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+    )
+    print(output_text)
+    return output_text
+
+
+if __name__ == "__main__":
+    folder_path = r"your_path/poster/data"
+    save_dir = r"your_path/poster/result"
+
+    saved_josn = os.path.join(save_dir,"qwen_32b_bench.json")
+    if not os.path.exists(saved_josn):
+        tasks = merge_json_lists(folder_path)
+        save_json_file(tasks, saved_josn)
+
+    tasks = read_json_file(saved_josn)
+    model,processor = build()
+    print("build model successful")
+    for item in tqdm(tasks):
+        if "response" in item: continue
+
+        prompt = item["prompt"]
+        image_path = os.path.join(folder_path, item["path"])
+        try:
+            item["response"] = conversation(model, processor, image_path, prompt)
+            print(item["response"])
+            save_json_file(tasks, saved_josn)
+            break
+        except Exception as e:
+            print(f"[Warning]  failed: {e}")
+
+
+
+