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| import os | |
| import torch | |
| from transformers import Qwen2_5OmniForConditionalGeneration, Qwen2_5OmniProcessor | |
| from qwen_omni_utils import process_mm_info | |
| import argparse | |
| import json | |
| from tqdm import tqdm | |
| from pathlib import Path | |
| VIDEO_MAX_PIXELS = 401408 # 512*28*28 | |
| VIDEO_TOTAL_PIXELS = 20070400 # 512*28*28*50 | |
| USE_AUDIO_IN_VIDEO = True | |
| os.environ['VIDEO_MAX_PIXELS'] = str(VIDEO_TOTAL_PIXELS) | |
| script_dir = Path(__file__).resolve().parent | |
| fin_path = script_dir / "video_salmonn2_test.json" | |
| video_dir = "" # TODO | |
| parser = argparse.ArgumentParser(description="Evaluate a model and save results.") | |
| parser.add_argument("--model_path", type=str, required=True, help="Path to the model checkpoint.") | |
| parser.add_argument("--save_path", type=str, required=True, help="Path to save the evaluation results.") | |
| args = parser.parse_args() | |
| model_path = args.model_path | |
| fout_path = args.save_path | |
| fin = open(fin_path, 'r', encoding='utf-8') | |
| fout = open(fout_path, 'w', encoding='utf-8') | |
| model = Qwen2_5OmniForConditionalGeneration.from_pretrained( | |
| model_path, | |
| torch_dtype=torch.bfloat16, | |
| device_map="auto", | |
| attn_implementation="flash_attention_2", | |
| ) | |
| model.disable_talker() | |
| processor = Qwen2_5OmniProcessor.from_pretrained(model_path) | |
| def chat(file_path, prompt): | |
| conversation = [ | |
| { | |
| "role": "system", | |
| "content": [ | |
| {"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."} | |
| ], | |
| }, | |
| { | |
| "role": "user", | |
| "content": [ | |
| { | |
| "type": "video", | |
| "video": file_path, | |
| "max_pixels": VIDEO_MAX_PIXELS, | |
| }, | |
| { | |
| "type": "text", | |
| "text": prompt | |
| }, | |
| ], | |
| }, | |
| ] | |
| text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False) | |
| audios, images, videos = process_mm_info(conversation, use_audio_in_video=USE_AUDIO_IN_VIDEO) | |
| inputs = processor(text=text, audio=audios, images=images, videos=videos, return_tensors="pt", padding=True, use_audio_in_video=USE_AUDIO_IN_VIDEO) | |
| inputs = inputs.to(model.device).to(model.dtype) | |
| text_ids = model.generate(**inputs, use_audio_in_video=USE_AUDIO_IN_VIDEO, do_sample=False, thinker_max_new_tokens=2048) | |
| text = processor.batch_decode(text_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] | |
| model_generation = text.split("\nassistant\n")[-1] | |
| return model_generation | |
| annotations = json.load(fin) | |
| updated_annos = [] | |
| for idx, anno in tqdm(enumerate(annotations, start=1)): | |
| video_path = os.path.join(video_dir, anno["video"]) | |
| prompt = anno["conversations"][0]["value"].replace("<image>\n", "") | |
| model_generation = chat(video_path, prompt) | |
| out_data = { | |
| "id": [video_path], | |
| "prompt": prompt, | |
| "pred": model_generation, | |
| } | |
| updated_annos.append(out_data) | |
| json.dump(updated_annos, fout, indent=4, ensure_ascii=False) |