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Running
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Zero
File size: 6,661 Bytes
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import os
import re
import math
import json
import argparse
import warnings
from tqdm import tqdm
import torch
import decord
import numpy as np
import transformers
from decord import VideoReader, cpu
from torch.utils.data import Dataset, DataLoader
import sys
sys.path.append('./')
from videollama2.conversation import conv_templates, SeparatorStyle
from videollama2.constants import NUM_FRAMES, DEFAULT_MMODAL_TOKEN, DEFAULT_MMODAL_START_TOKEN, DEFAULT_MMODAL_END_TOKEN, MMODAL_TOKEN_INDEX
from videollama2.mm_utils import get_model_name_from_path, tokenizer_MMODAL_token, KeywordsStoppingCriteria, process_video
from videollama2.model.builder import load_pretrained_model
# 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')
default_mm_token = DEFAULT_MMODAL_TOKEN["VIDEO"]
default_mm_start_token = DEFAULT_MMODAL_START_TOKEN["VIDEO"]
default_mm_end_token = DEFAULT_MMODAL_END_TOKEN["VIDEO"]
modal_token_index = MMODAL_TOKEN_INDEX["VIDEO"]
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 VCGPTDataset(Dataset):
video_formats = ['.mp4', '.avi', '.mov', '.mkv']
def __init__(self, data_list, processor, num_frames):
self.data_list = data_list
self.processor = processor
self.num_frames = num_frames
def __len__(self):
return len(self.data_list)
def __getitem__(self, idx):
line = self.data_list[idx]
question = line['Q']
answer = line['A']
video_name = line['video_name']
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 = process_video(video_path, self.processor, aspect_ratio=None, sample_scheme='uniform', num_frames=self.num_frames)
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]
vid = torch.stack(vid, dim=0)
return vid, v_id, qus, ans
def get_model_output(model, tokenizer, qs, video_tensor, args):
if model.config.mm_use_im_start_end:
qs = default_mm_start_token + default_mm_token + default_mm_end_token + "\n" + qs
else:
qs = default_mm_token + "\n" + qs
conv = conv_templates[args.conv_mode].copy()
conv.append_message(conv.roles[0], qs)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
# input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(args.device)
input_ids = tokenizer_MMODAL_token(prompt, tokenizer, modal_token_index, return_tensors='pt').to(args.device)
attention_mask=input_ids.ne(tokenizer.pad_token_id).to(args.device)
modal_list = ["video"]
video_tensor = video_tensor.to(dtype=torch.float16, device=args.device, non_blocking=True)
with torch.inference_mode():
output_ids = model.generate(
input_ids.unsqueeze(0),
attention_mask=attention_mask.unsqueeze(0),
images_or_videos=[video_tensor],
modal_list=modal_list,
do_sample=False,
max_new_tokens=1024,
use_cache=True,
pad_token_id=tokenizer.eos_token_id)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
return outputs
def run_inference(args):
model_name = get_model_name_from_path(args.model_path)
tokenizer, model, processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name)
num_frames = model.config.num_frames if hasattr(model.config, "num_frames") else NUM_FRAMES
questions = json.load(open(args.question_file, "r"))
questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
assert args.batch_size == 1, "Batch size must be 1 for inference"
dataset = VCGPTDataset(questions, processor, num_frames)
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")
output_list = [] # List to store the output results
# Iterate over each sample in the ground truth file
for i, (video_tensors, video_names, questions, answers) in enumerate(tqdm(dataloader)):
# reduce batch dimension
video_tensor = video_tensors[0]
video_name = video_names[0]
question = questions[0]
answer = answers[0]
output = get_model_output(model, tokenizer, question, video_tensor, args)
qa = {'video_name': video_name, 'Q': question, 'A': answer, 'P': output}
ans_file.write(json.dumps(qa) + "\n")
ans_file.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Define the command-line arguments
parser.add_argument('--model-path', help='', required=True)
parser.add_argument('--model_base', help='', default=None, type=str, required=False)
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("--conv-mode", type=str, default="llava_v1")
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)
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