File size: 9,250 Bytes
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import json
import sys
import time
import traceback
from typing import Dict, Any
from tqdm import tqdm
import torch
import torch.multiprocessing as mp
from transformers import BertTokenizer
worker_model_objects: Dict[str, Any] = {}
def init_worker(model_path: str, bert_path: str, humanomni_project_path: str, device: str):
global worker_model_objects
if humanomni_project_path and humanomni_project_path not in sys.path:
sys.path.append(humanomni_project_path)
try:
from humanomni import model_init, mm_infer
from humanomni.utils import disable_torch_init
except ImportError:
print(f"[Worker PID: {os.getpid()}] ERROR: Failed to import HumanOmni. Ensure the humanomni_path is set correctly.", file=sys.stderr)
return
disable_torch_init()
model, processor, tokenizer = model_init(model_path, device=device)
bert_tokenizer = BertTokenizer.from_pretrained(bert_path)
worker_model_objects = {
"model": model,
"processor": processor,
"tokenizer": tokenizer,
"bert_tokenizer": bert_tokenizer,
"mm_infer": mm_infer,
}
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:
return 'unknown'
def process_single_sample(media_full_path: str, prompt_text: str) -> str:
global worker_model_objects
try:
model = worker_model_objects['model']
processor = worker_model_objects['processor']
tokenizer = worker_model_objects['tokenizer']
bert_tokenizer = worker_model_objects['bert_tokenizer']
mm_infer = worker_model_objects['mm_infer']
media_type = get_media_type(media_full_path)
if media_type == 'unknown':
raise ValueError(f"Unsupported media type for file: {media_full_path}")
clean_prompt = prompt_text.replace("<image>", "").replace("<video>", "").strip()
media_tensor, audio_tensor, modal_str = None, None, ""
if media_type == 'video':
media_tensor = processor['video'](media_full_path)
audio_tensor = processor['audio'](media_full_path)[0]
modal_str = 'video_audio'
elif media_type == 'image':
media_tensor = processor['image'](media_full_path)
if media_tensor.ndim == 3:
media_tensor = media_tensor.unsqueeze(0)
audio_tensor = None
modal_str = 'image'
output = mm_infer(
media_tensor,
instruct=clean_prompt,
model=model,
tokenizer=tokenizer,
modal=modal_str,
question=clean_prompt,
bert_tokeni=bert_tokenizer,
do_sample=False,
audio=audio_tensor
)
return output
except Exception as e:
return f"ERROR: {e}\n{traceback.format_exc()}"
def text_only_fallback(prompt_text: str) -> str:
global worker_model_objects
try:
model = worker_model_objects['model']
tokenizer = worker_model_objects['tokenizer']
clean_prompt = prompt_text.replace("<image>", "").replace("<video>", "").strip()
inputs = tokenizer(clean_prompt, return_tensors="pt").to(model.device)
output_ids = model.generate(
input_ids=inputs.input_ids,
attention_mask=inputs.attention_mask,
max_new_tokens=512,
do_sample=False
)
response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
if response.startswith(clean_prompt):
return response[len(clean_prompt):].strip()
else:
return response
except Exception as e:
return f"ERROR in text-only fallback: {str(e)}"
def run_inference_task(media_full_path, prompt_text):
if not worker_model_objects: return "ERROR: Worker model not initialized."
return process_single_sample(media_full_path, prompt_text)
def run_fallback_task(prompt_text):
if not worker_model_objects: return "ERROR: Worker model not initialized."
return text_only_fallback(prompt_text)
def main():
class Config:
model_path = "example/model/HumanOmni_7B"
bert_model_path = "example/model/bert-base-uncased"
humanomni_path = "example/moodel"
input_dir = "example"
device = "cuda:7"
result_suffix = "_humanomni_result.json"
timeout = 60
config = Config()
os.environ['TRANSFORMERS_OFFLINE'] = '1'
worker_device = config.device
if "cuda" in config.device:
gpu_id = config.device.split(':')[-1]
os.environ['CUDA_VISIBLE_DEVICES'] = gpu_id
worker_device = "cuda:0"
init_args = (config.model_path, config.bert_model_path, config.humanomni_path, worker_device)
pool_ref = [mp.Pool(processes=1, initializer=init_worker, initargs=init_args)]
try:
source_json_files = [
f for f in os.listdir(config.input_dir)
if f.endswith(".json") and not f.endswith(config.result_suffix)
]
if not source_json_files:
print("No source JSON files.")
return
for json_filename in source_json_files:
dataset_json_path = os.path.join(config.input_dir, json_filename)
result_json_path = os.path.join(config.input_dir, f"{os.path.splitext(json_filename)[0]}{config.result_suffix}")
if os.path.exists(result_json_path):
continue
try:
with open(dataset_json_path, "r", encoding="utf-8") as f:
data = json.load(f)
except (json.JSONDecodeError, FileNotFoundError) as e:
continue
all_results = []
for item in tqdm(data, desc=f" Inferring on {json_filename}", unit="item"):
start_time = time.time()
model_output, prompt, ground_truth = "", "", ""
try:
prompt = item["conversations"][0]["value"]
ground_truth = item["conversations"][1]["value"]
media_relative_path = item.get('image') or item.get('video')
if not media_relative_path:
model_output = pool_ref[0].apply(run_fallback_task, args=(prompt,))
else:
media_full_path = os.path.join(config.input_dir, media_relative_path)
if not os.path.exists(media_full_path):
model_output = pool_ref[0].apply(run_fallback_task, args=(prompt,))
else:
media_type = get_media_type(media_full_path)
if media_type == 'image':
model_output = pool_ref[0].apply(run_fallback_task, args=(prompt,))
elif media_type == 'video':
async_result = pool_ref[0].apply_async(run_inference_task, args=(media_full_path, prompt))
try:
model_output = async_result.get(timeout=config.timeout)
except (mp.TimeoutError, Exception) as e:
print(f"\n Worker task failed for item {item.get('id', 'N/A')}. Reason: {type(e).__name__}. Restarting and falling back.", file=sys.stderr)
pool_ref[0].terminate()
pool_ref[0].join()
pool_ref[0] = mp.Pool(processes=1, initializer=init_worker, initargs=init_args)
model_output = pool_ref[0].apply(run_fallback_task, args=(prompt,))
else: # Unknown media type
model_output = pool_ref[0].apply(run_fallback_task, args=(prompt,))
except Exception as e:
model_output = f"ERROR: An unexpected error occurred in the main loop: {e}"
all_results.append({
"id": item.get("id", "N/A"),
"prompt": prompt,
"model_output": model_output,
"ground_truth": ground_truth,
"processing_time_seconds": round(time.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"Results saved to: {result_json_path}")
finally:
if pool_ref and pool_ref[0]:
pool_ref[0].close()
pool_ref[0].join()
if __name__ == "__main__":
if torch.cuda.is_available():
try:
mp.set_start_method('spawn', force=True)
print("Multiprocessing start method set to 'spawn'.")
except RuntimeError:
pass
main() |