File size: 5,967 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 |
import os
import re
import math
import json
import argparse
import warnings
import traceback
from tqdm import tqdm
import torch
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
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]
class PerceptionTestMCQADataset(Dataset):
video_formats = ['.mp4', '.avi', '.mov', '.mkv']
def __init__(self, data_list, processor):
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_name = line['metadata']['video_id']
mc_questions = line['mc_question']
for fmt in self.video_formats: # Added this line
temp_path = os.path.join(args.video_folder, f"{video_name}{fmt}")
if os.path.exists(temp_path):
video_path = temp_path
break
video_tensor = self.processor(video_path)
instructs = []
qids = []
ops = []
for q in mc_questions:
question = q['question']
qid = q['id']
options = q['options']
instruct = f'Question: {question}\nOptions:\n(A) {options[0]}\n(B) {options[1]}\n(C) {options[2]}\nAnswer with the option\'s letter from the given choices directly and only give the best option.'
instructs.append(instruct)
qids.append(qid)
ops.append(options)
return {
'video': video_tensor,
'video_id': video_name,
'instructs': instructs,
'question_ids': qids,
'options': ops,
}
def collate_fn(batch):
vid = [x['video'] for x in batch]
v_id = [x['video_id'] for x in batch]
ins = [x['instructs'] for x in batch]
q_ids = [x['question_ids'] for x in batch]
ops = [x['options'] for x in batch]
vid = torch.stack(vid, dim=0)
return vid, v_id, ins, q_ids, ops
def run_inference(args):
disable_torch_init()
model, processor, tokenizer = model_init(args.model_path)
questions = json.load(open(args.question_file, "r"))
questions = list(questions.values())
questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
assert args.batch_size == 1, "Batch size must be 1 for inference"
dataset = PerceptionTestMCQADataset(questions, processor['video'])
dataloader = DataLoader(dataset, shuffle=False, batch_size=args.batch_size, num_workers=args.num_workers, collate_fn=collate_fn)
answer_file = os.path.expanduser(args.answer_file)
os.makedirs(os.path.dirname(answer_file), exist_ok=True)
ans_file = open(answer_file, "w")
# Iterate over each sample in the ground truth file
for i, (video_tensor, video_id, instructs, question_ids, options) in enumerate(tqdm(dataloader)):
# reduce batch dimension
video_tensor = video_tensor[0]
video_id = video_id[0]
instructs = instructs[0]
question_ids = question_ids[0]
options = options[0]
qas = []
for idx, instruct in enumerate(instructs):
letters = ['(A)', '(B)', '(C)']
question_id = question_ids[idx]
_options = options[idx]
output = mm_infer(
video_tensor,
instruct,
model=model,
tokenizer=tokenizer,
modal='video',
do_sample=False,
)
output = output.replace('answer', '')
output = output.replace('Answer', '')
pred_answer = re.findall('\(*[A-C]\)*', output)
try:
assert len(pred_answer) >= 1, 'The video \"{}\" instruct: \n\"{}\"\n output: \n\"{}\"\n is not in the expected format'.format(video_id, instruct, output)
pred_answer = pred_answer[0].strip()
# if not pred_answer.startswith('('):
pred_answer = pred_answer.strip('()')
pred_answer = f'({pred_answer})'
pred_idx = letters.index(pred_answer)
except:
traceback.print_exc()
tmp_options = [x.lower() for x in _options]
if output.lower() in tmp_options:
tmp_options = [x.lower() for x in _options]
pred_idx = tmp_options.index(output.lower())
else:
pred_idx = 2
qas.append({'id': question_id, 'answer_id': pred_idx, 'answer': _options[pred_idx]})
ans_file.write('\"{}\": {},\n'.format(video_id, json.dumps(qas)))
ans_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('--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("--model_max_length", type=int, required=False, default=2048)
parser.add_argument("--batch-size", type=int, required=False, default=1)
parser.add_argument("--num-workers", type=int, required=False, default=8)
args = parser.parse_args()
run_inference(args)
|