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import os
import re
import math
import json
import argparse
import warnings
import traceback
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]
class EgoschemaDataset(Dataset):
video_formats = ['.mp4', '.avi', '.mov', '.mkv']
def __init__(self, data_folder, data_list, processor):
self.data_folder = data_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]
q_uid = line['q_uid']
for fmt in self.video_formats: # Added this line
temp_path = os.path.join(self.data_folder, f"{q_uid}{fmt}")
if os.path.exists(temp_path):
video_path = temp_path
break
video_tensor = self.processor(video_path)
question = line['question']
a0 = line['option 0']
a1 = line['option 1']
a2 = line['option 2']
a3 = line['option 3']
a4 = line['option 4']
axs = [a0, a1, a2, a3, a4]
ops = ['(A)', '(B)', '(C)', '(D)', '(E)']
instruct = f'Select the best answer to the following multiple-choice question based on the video.\n{question}\nOptions:\n(A) {a0}\n(B) {a1}\n(C) {a2}\n(D) {a3}\n(E) {a4}\nAnswer with the option\'s letter from the given choices directly and only give the best option. The best answer is: '
return {
'q_uid': q_uid,
'video': video_tensor,
'instruct': instruct,
}
def build_egoschema_eval(args, processor):
questions = json.load(open(args.question_file, "r"))
questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
dataset = EgoschemaDataset(args.video_folder, questions, processor)
dataloader = DataLoader(dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers)
return dataloader
def egoschema_dump(ans_file, line, outputs):
for idx, output in enumerate(outputs):
q_uid = line['q_uid'][idx]
instruct = line['instruct'][idx]
letters = ['A', 'B', 'C', 'D', 'E']
output = output.replace('answer', '')
output = output.replace('Answer', '')
pred_answer = re.findall('[\(\ ]*[A-E][\)\ ]*', output)
try:
assert len(pred_answer) >= 1, 'The video \"{}\" instruct: \n\"{}\"\n output: \n\"{}\"\n is not in the expected format'.format(line['q_uid'], instruct, output)
pred_answer = pred_answer[0].strip()
pred_answer = pred_answer.strip('()')
pred_idx = letters.index(pred_answer)
except:
traceback.print_exc()
pred_idx = 2
ans_file.write(f'{q_uid}, {pred_idx}\n')
def run_inference(args):
disable_torch_init()
model, processor, tokenizer = model_init(args.model_path)
answer_file = os.path.expanduser(args.answer_file)
os.makedirs(os.path.dirname(answer_file), exist_ok=True)
ans_file = open(answer_file, "w")
val_loader = build_egoschema_eval(args, processor['video'])
# Iterate over each sample in the ground truth file
for i, line in enumerate(tqdm(val_loader)):
video_tensor = line['video'][0]
instruct = line['instruct'][0]
try:
pred = mm_infer(
video_tensor,
instruct,
model=model,
tokenizer=tokenizer,
modal='video',
do_sample=False,
)
except:
traceback.print_exc()
pred = 'C'
egoschema_dump(ans_file, line, [pred])
ans_file.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Multiple-Choice Video QA Evaluation Script.')
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("--batch-size", type=int, default=1)
parser.add_argument("--num-workers", type=int, default=8)
args = parser.parse_args()
run_inference(args)
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