File size: 10,769 Bytes
87ce8f2 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 |
import os
import re
import math
import json
import copy
import argparse
import warnings
import traceback
import cv2
import torch
import pysubs2
import numpy as np
import pyarrow.parquet as pq
from tqdm import tqdm
from torch.utils.data import Dataset, DataLoader
import sys
sys.path.append('./')
from videollama2 import model_init, mm_infer
from videollama2.utils import disable_torch_init
# NOTE: Ignore TypedStorage warning, which refers to this link~(https://github.com/pytorch/pytorch/issues/97207#issuecomment-1494781560)
warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')
def split_list(lst, n):
"""Split a list into n (roughly) equal-sized chunks"""
chunk_size = math.ceil(len(lst) / n) # integer division
return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
def get_chunk(lst, n, k):
chunks = split_list(lst, n)
return chunks[k]
def get_seq_frames(total_num_frames, desired_num_frames):
"""
Calculate the indices of frames to extract from a video.
Parameters:
total_num_frames (int): Total number of frames in the video.
desired_num_frames (int): Desired number of frames to extract.
Returns:
list: List of indices of frames to extract.
"""
# Calculate the size of each segment from which a frame will be extracted
seg_size = float(total_num_frames - 1) / desired_num_frames
seq = []
for i in range(desired_num_frames):
# Calculate the start and end indices of each segment
start = int(np.round(seg_size * i))
end = int(np.round(seg_size * (i + 1)))
# Append the middle index of the segment to the list
seq.append((start + end) // 2)
return seq
class VideoMMEDataset(Dataset):
video_formats = ['.mp4', '.avi', '.mov', '.mkv']
def __init__(self, video_folder, subtitle_folder, data_list, processor):
self.video_folder = video_folder
self.subtitle_folder = subtitle_folder
self.data_list = data_list
self.processor = processor
def __len__(self):
return len(self.data_list)
def __getitem__(self, idx):
line = self.data_list[idx]
video_ytid = line['url'].split('watch?v=')[-1]
for fmt in self.video_formats: # Added this line
temp_path = os.path.join(self.video_folder, f'{video_ytid}{fmt}')
if os.path.exists(temp_path):
video_path = temp_path
break
subtitle_path = os.path.join(self.subtitle_folder, f'{video_ytid}.srt')
try:
video_tensor = self.processor(video_path)
num_frames = video_tensor.shape[0]
except:
traceback.print_exc()
print(f'It occurs error when reading {video_ytid}')
video_tensor = None
num_frames = 0
if video_tensor is not None and os.path.exists(subtitle_path):
cv2_vr = cv2.VideoCapture(video_path)
duration = int(cv2_vr.get(cv2.CAP_PROP_FRAME_COUNT))
fps = cv2_vr.get(cv2.CAP_PROP_FPS)
selected_frame_ids = get_seq_frames(duration, num_frames)
subs = pysubs2.load(subtitle_path, encoding="utf-8")
subtitles = []
for seleced_frame_id in selected_frame_ids:
sub_text = ""
cur_time = pysubs2.make_time(fps=fps, frames=seleced_frame_id)
for sub in subs:
if sub.start < cur_time and sub.end > cur_time:
sub_text = sub.text.replace("\\N", " ")
break
if sub_text.strip():
subtitles.append(sub_text)
subtitles = "\n".join(subtitles)
else:
subtitles = ""
return {
'video': video_tensor,
'subtitle': subtitles,
'record': line,
}
def collate_fn(batch):
vid = [x['video'] for x in batch]
sub = [x['subtitle'] for x in batch]
rcs = [x['record'] for x in batch]
return vid, sub, rcs
def load_parquet(parquet_file):
table = pq.read_table(parquet_file)
# Convert PyArrow Table to pandas DataFrame
df = table.to_pandas()
jsons = []
for record in df.itertuples():
if len(jsons) < int(record.video_id):
jsons.append({
"video_id": record.video_id,
"youtube_id": record.videoID,
"url": record.url,
"duration": record.duration,
"domain": record.domain,
"sub_category": record.sub_category,
"questions": [
{
"question_id": record.question_id,
"task_type": record.task_type,
"question": record.question,
"choices": list(record.options),
"answer": record.answer,
}
]
})
else:
jsons[-1]['questions'].append({
"question_id": record.question_id,
"task_type": record.task_type,
"question": record.question,
"choices": list(record.options),
"answer": record.answer,
})
return jsons
def build_videomme_eval(args, processor):
# convert parquet to json
questions = load_parquet(args.question_file)
# questions = json.load(open(args.question_file, "r"))
questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
dataset = VideoMMEDataset(args.video_folder, args.subtitle_folder, questions, processor)
dataloader = DataLoader(dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, collate_fn=collate_fn)
return dataloader
def videomme_dump(record, instruct, options, output):
letters = ['A', 'B', 'C', 'D']
digit2word = {
'1': 'one',
'2': 'two',
'3': 'three',
'4': 'four',
'5': 'five',
'6': 'six',
'7': 'seven',
'8': 'eight',
'9': 'nine',
'0': 'zero',
}
output = output.replace('answer', '')
output = output.replace('Answer', '')
pred_answer = re.findall('[\(\ \[]*([A-D])[\)\.\ \]]*', output)
try:
find_flag = False
if len(pred_answer) == 0:
for idx, opt in enumerate(options):
# Arabic numerals -> English words
opt2 = opt
if opt in digit2word:
opt2 = digit2word[opt]
if opt.lower() in output.lower() or opt2.lower() in output.lower():
pred_idx = idx
find_flag = True
break
else:
pred_answer = pred_answer[0].strip()
pred_answer = pred_answer.strip('()')
pred_idx = letters.index(pred_answer)
find_flag = True
assert find_flag, 'The video \"{}\" instruct: \n\"{}\"\n output: \n\"{}\"\n is not in the expected format'.format(record['youtube_id'], instruct, output)
except:
traceback.print_exc()
pred_idx = 2
return letters[pred_idx]
def run_inference(args):
disable_torch_init()
# Initialize the model
model, processor, tokenizer = model_init(args.model_path)
answer_file = os.path.expanduser(args.answer_file)
answer_sub_file = answer_file.replace('.json', '_sub.json')
os.makedirs(os.path.dirname(answer_file), exist_ok=True)
ans_file = open(answer_file, "w")
ans_sub_file = open(answer_sub_file, "w")
val_loader = build_videomme_eval(args, processor['video'])
# Iterate over each sample in the ground truth file
for i, (videos, subtitles, records) in enumerate(tqdm(val_loader)):
video_tensor = videos[0]
subtitle = subtitles[0]
record = records[0]
new_record = copy.deepcopy(record)
new_record_sub = copy.deepcopy(record)
if video_tensor is None:
new_record['missing'] = True
ans_file.write(json.dumps(new_record) + ",\n")
new_record_sub['missing'] = True
ans_sub_file.write(json.dumps(new_record_sub) + ",\n")
continue
else:
new_record['missing'] = False
new_record_sub['missing'] = False
questions = record['questions']
for idx, question in enumerate(questions):
q = question['question']
choices = question['choices']
options = [re.findall('[A-D]\. (.*).', c)[0] for c in choices]
instruct = "Select the best answer to the following multiple-choice question based on the video. Respond with only the letter (A, B, C, or D) of the correct option.\n"
instruct += f"{q}\n"
for cho_idx, cho in enumerate(choices):
instruct += f"{cho}\n"
# instruct += "The best option is: "
instruct += "Answer with the option\'s letter from the given choices directly and only give the best option. The best answer is: "
output = mm_infer(video_tensor, instruct, model=model, tokenizer=tokenizer, modal='video', do_sample=False)
new_record['questions'][idx]['response'] = videomme_dump(record, instruct, options, output)
instruct = f"This video's subtitles are listed below:\n{subtitle}\n" + instruct
output = mm_infer(video_tensor, instruct, model=model, tokenizer=tokenizer, modal='video', do_sample=False)
new_record_sub['questions'][idx]['response'] = videomme_dump(record, instruct, options, output)
ans_file.write(json.dumps(new_record) + ",\n")
ans_sub_file.write(json.dumps(new_record_sub) + ",\n")
ans_file.close()
ans_sub_file.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--model-path', help='', required=True)
parser.add_argument('--video-folder', help='Directory containing video files.', required=True)
parser.add_argument('--subtitle-folder', help='Directory containing subtitle files.', required=True)
parser.add_argument('--question-file', help='Path to the ground truth file containing question.', required=True)
parser.add_argument('--answer-file', help='Path to the ground truth file containing answers.', required=True)
parser.add_argument("--num-chunks", type=int, default=1)
parser.add_argument("--chunk-idx", type=int, default=0)
parser.add_argument("--device", type=str, required=False, default='cuda:0')
parser.add_argument("--batch-size", type=int, default=1)
parser.add_argument("--num-workers", type=int, default=8)
args = parser.parse_args()
run_inference(args)
|