File size: 5,107 Bytes
ba1d61a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 | import os
import json
import argparse
import base64
from openai import OpenAI
from tqdm import tqdm
import time
GENERIC_RESULT_PATTERN = "_result.json"
def get_media_type(file_path: str) -> str:
ext = os.path.splitext(file_path)[1].lower()
if ext in ['.mp4', '.avi', '.mov', '.mkv', '.webm']:
return 'video'
elif ext in ['.jpg', '.jpeg', '.png', '.bmp', '.gif', '.webp']:
return 'image'
else:
raise ValueError(f"Unsupported file format: {ext}")
def encode_media_to_base64(media_path: str) -> str:
try:
with open(media_path, "rb") as media_file:
return base64.b64encode(media_file.read()).decode('utf-8')
except FileNotFoundError:
raise
except Exception as e:
raise IOError(f"Failed to read or encode file {media_path}: {e}")
def process_file(dataset_json_path: str, client: OpenAI, model_name: str, result_suffix: str):
json_filename = os.path.basename(dataset_json_path)
result_json_path = os.path.join(
os.path.dirname(dataset_json_path),
f"{os.path.splitext(json_filename)[0]}{result_suffix}"
)
if os.path.exists(result_json_path):
print(f"Result file '{os.path.basename(result_json_path)}' already exists. Skipping.")
return
try:
with open(dataset_json_path, 'r', encoding='utf-8') as f:
data = json.load(f)
except (json.JSONDecodeError, FileNotFoundError) as e:
print(f"Failed to read or parse JSON file {dataset_json_path}: {e}")
return
all_results = []
base_path = os.path.dirname(dataset_json_path)
for item in tqdm(data, desc=f" Querying API for {json_filename}"):
start_time = time.time()
model_output = "N/A"
try:
prompt = item['conversations'][0]['value']
ground_truth = item['conversations'][1]['value']
media_path_key = 'image' if 'image' in item else 'video'
media_relative_path = item.get(media_path_key)
if not media_relative_path:
raise ValueError("JSON item is missing 'image' or 'video' key.")
media_full_path = os.path.join(base_path, media_relative_path)
if not os.path.exists(media_full_path):
raise FileNotFoundError(f"Media file not found: {media_full_path}")
media_type = get_media_type(media_full_path)
media_base64 = encode_media_to_base64(media_full_path)
clean_prompt = prompt.replace("<image>", "").replace("<video>", "").strip()
if media_type == 'image':
messages = [{"role": "user", "content": [{"type": "text", "text": clean_prompt}, {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{media_base64}"}}]}]
else:
messages = [{"role": "user", "content": [{"type": "text", "text": clean_prompt}, {"type": "video_url", "video_url": {"url": f"data:video/mp4;base64,{media_base64}"}}]}]
response = client.chat.completions.create(model=model_name, messages=messages, max_tokens=1024, temperature=0.0)
model_output = response.choices[0].message.content
except Exception as e:
model_output = f"ERROR: {str(e)}"
end_time = time.time()
all_results.append({
"id": item.get('id', 'N/A'),
"prompt": prompt,
"model_output": model_output,
"ground_truth": ground_truth,
"processing_time_seconds": round(end_time - start_time, 2)
})
with open(result_json_path, 'w', encoding='utf-8') as f:
json.dump(all_results, f, indent=4, ensure_ascii=False)
print(f" [SUCCESS] Processing complete. Results saved to: {result_json_path}")
def main():
parser = argparse.ArgumentParser(description="Batch inference for multimodal models using an OpenAI-compatible API.")
parser.add_argument("--model-endpoint", default="http://localhost:8004/v1", help="The API endpoint of the model server.")
parser.add_argument("--model-name", default="llavaonevision7b", help="The name of the model to use.")
parser.add_argument("--result-suffix", default="_result.json", help="Suffix for the generated result files.")
args = parser.parse_args()
try:
client = OpenAI(base_url=args.model_endpoint, api_key="EMPTY")
except Exception as e:
print(f"Could not initialize OpenAI client: {e}")
return
current_dir = os.getcwd()
source_json_files = [
f for f in os.listdir(current_dir)
if f.endswith('.json') and not f.endswith(GENERIC_RESULT_PATTERN)
]
if not source_json_files:
print(f"\nNo source JSON files: {current_dir}")
else:
for json_filename in sorted(source_json_files):
process_file(
dataset_json_path=os.path.join(current_dir, json_filename),
client=client,
model_name=args.model_name,
result_suffix=args.result_suffix
)
if __name__ == "__main__":
main() |