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from llava.model.builder import load_pretrained_model
from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
from llava.conversation import conv_templates, SeparatorStyle
from PIL import Image
import requests
import copy
import torch
import sys
import warnings
import os
import re
from decord import VideoReader, cpu
import numpy as np
import json
import argparse
from vbench2.utils import load_dimension_info
from tqdm import tqdm
warnings.filterwarnings("ignore")
def load_video(video_path, max_frames_num,fps=1,force_sample=False):
if max_frames_num == 0:
return np.zeros((1, 336, 336, 3))
vr = VideoReader(video_path, ctx=cpu(0),num_threads=1)
total_frame_num = len(vr)
video_time = total_frame_num / vr.get_avg_fps()
fps = round(vr.get_avg_fps()/fps)
frame_idx = [i for i in range(0, len(vr), fps)]
frame_time = [i/fps for i in frame_idx]
if len(frame_idx) > max_frames_num or force_sample:
sample_fps = max_frames_num
uniform_sampled_frames = np.linspace(0, total_frame_num - 1, sample_fps, dtype=int)
frame_idx = uniform_sampled_frames.tolist()
frame_time = [i/vr.get_avg_fps() for i in frame_idx]
frame_time = ",".join([f"{i:.2f}s" for i in frame_time])
spare_frames = vr.get_batch(frame_idx).asnumpy()
return spare_frames,frame_time,video_time
def LLaVA_Video(prompt_dict_ls, model, tokenizer, image_processor, device):
final_score = 0
valid_num = 0
processed_json = []
for prompt_dict in tqdm(prompt_dict_ls):
base_question = prompt_dict['auxiliary_info']
question_num = len(base_question)
video_paths = prompt_dict['video_list']
for video_path in video_paths:
max_frames_num = 64
video,frame_time,video_time = load_video(video_path, max_frames_num, 1, force_sample=True)
video = image_processor.preprocess(video, return_tensors="pt")["pixel_values"].cuda().bfloat16()
video = [video]
conv_template = "qwen_1_5" # Make sure you use correct chat template for different models
time_instruciton = f"The video lasts for {video_time:.2f} seconds, and {len(video[0])} frames are uniformly sampled from it. These frames are located at {frame_time}. Please answer yes or no only for the following questions"
flag=True
valid=True
new_item = {
'video_path':video_path,
}
if len(base_question)==1:
question = DEFAULT_IMAGE_TOKEN + f"{time_instruciton}\n{base_question[0]}"
conv = copy.deepcopy(conv_templates[conv_template])
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt_question = conv.get_prompt()
input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
cont = model.generate(
input_ids,
images=video,
modalities= ["video"],
do_sample=False,
temperature=0,
max_new_tokens=4096,
)
text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)[0]
if "yes" not in text_outputs.lower():
flag=False
else:
for i in range(len(base_question)):
question = DEFAULT_IMAGE_TOKEN + f"{time_instruciton}\n{base_question[i]}"
conv = copy.deepcopy(conv_templates[conv_template])
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt_question = conv.get_prompt()
input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
cont = model.generate(
input_ids,
images=video,
modalities= ["video"],
do_sample=False,
temperature=0,
max_new_tokens=4096,
)
text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)[0]
if i==0 and "yes" not in text_outputs.lower():
valid=False
elif i==1 and "yes" not in text_outputs.lower():
flag=False
if not valid:
new_item["video_results"]=-1
processed_json.append(new_item)
continue
elif valid and flag:
final_score+=1
valid_num+=1
new_item["video_results"]=1
elif valid and not flag:
valid_num+=1
new_item["video_results"]=0
processed_json.append(new_item)
return final_score/valid_num, processed_json
def compute_material(json_dir, device, submodules_dict, **kwargs):
_, prompt_dict_ls = load_dimension_info(json_dir, dimension='material', lang='en')
model_name = "llava_qwen"
device_map = "auto"
try:
pretrained = submodules_dict['llava']
llava_tokenizer, llava_model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, torch_dtype="bfloat16", device_map=device_map) # Add any other thing you want to pass in llava_model_args
except:
pretrained = "lmms-lab/LLaVA-Video-7B-Qwen2"
llava_tokenizer, llava_model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, torch_dtype="bfloat16", device_map=device_map) # Add any other thing you want to pass in llava_model_args
llava_model.eval()
all_results, video_results = LLaVA_Video(prompt_dict_ls, llava_model, llava_tokenizer, image_processor, device)
score=0
num=0
for d in video_results:
if d['video_results']!=-1:
num+=1
score+= d['video_results']
all_results = score/num
return all_results, video_results |