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import math
import os
import argparse
import json
import warnings
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 MSVCDataset(Dataset):
video_formats = ['.mp4', '.webm', '.avi', '.mov', '.mkv']
def __init__(self, folder, questions, processor):
self.folder = folder
self.questions = questions
self.processor = processor
def __len__(self):
return len(self.questions)
def __getitem__(self, idx):
sample = self.questions[idx]
video_name = sample['video_path']
question = sample['question']
answer = sample['captions']
video_path = os.path.join(self.folder, video_name)
video_tensor = self.processor(video_path)
return {
'video': video_tensor,
'video_name': video_name,
'question': question,
'answer': answer,
}
def collate_fn(batch):
vid = [x['video'] for x in batch]
v_id = [x['video_name'] for x in batch]
qus = [x['question'] for x in batch]
ans = [x['answer'] for x in batch]
return vid, v_id, qus, ans
def run_inference(args):
disable_torch_init()
model, processor, tokenizer = model_init(args.model_path)
gt_questions = json.load(open(args.question_file, "r"))
gt_questions = get_chunk(gt_questions, args.num_chunks, args.chunk_idx)
answer_file = os.path.join(args.output_file)
os.makedirs(os.path.dirname(args.output_file), exist_ok=True)
ans_file = open(answer_file, "w")
assert args.batch_size == 1, "Batch size must be 1 for inference"
dataset = MSVCDataset(args.video_folder, gt_questions, processor['video'])
dataloader = DataLoader(dataset, shuffle=False, batch_size=args.batch_size, num_workers=args.num_workers, collate_fn=collate_fn)
# Iterate over each sample in the ground truth file
for idx, (video_tensors, video_names, questions, answers) in enumerate(tqdm(dataloader)):
video_tensor = video_tensors[0]
video_name = video_names[0]
question = questions[0]
answer = answers[0]
output = mm_infer(
video_tensor,
question,
model=model,
tokenizer=tokenizer,
modal='video',
do_sample=False,
)
sample_set = {'video_name': video_name, 'question': question, 'answer': answer, 'pred': output}
ans_file.write(json.dumps(sample_set) + "\n")
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('--output-file', help='Directory to save the model results JSON.', 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, required=False, default=1)
parser.add_argument("--num-workers", type=int, required=False, default=8)
args = parser.parse_args()
run_inference(args)
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