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8a11f7f | 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 | 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) |