Spaces:
Running
on
Zero
Running
on
Zero
File size: 8,403 Bytes
e428df4 |
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 |
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_videos
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 PerceptionTestMCQADataset(Dataset):
video_formats = ['.mp4', '.avi', '.mov', '.mkv']
def __init__(self, data_list, processor, num_segments=8):
self.data_list = data_list
self.processor = processor
self.num_segments = num_segments
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
decord_vr = VideoReader(uri=video_path, ctx=cpu(0))
frames = decord_vr.get_batch(np.linspace(0, len(decord_vr) - 1, self.num_segments, dtype=int)).asnumpy()
video_tensor = self.processor.preprocess(frames, return_tensors='pt')['pixel_values'] # do not pad for video frames
qs = []
qids = []
ops = []
for q in mc_questions:
question = q['question']
qid = q['id']
options = q['options']
option_question = 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.'
qs.append(option_question)
qids.append(qid)
ops.append(options)
return {
'video': video_tensor,
'video_id': video_name,
'questions': qs,
'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]
qs = [x['questions'] 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, qs, q_ids, ops
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):
# Initialize the model
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)
questions = json.load(open(args.question_file, "r"))
questions = list(questions.values())
questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
num_frames = model.config.num_frames if hasattr(model.config, "num_frames") else NUM_FRAMES
assert args.batch_size == 1, "Batch size must be 1 for inference"
dataset = PerceptionTestMCQADataset(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_tensor, video_id, questions, question_ids, options) in enumerate(tqdm(dataloader)):
# reduce batch dimension
video_tensor = video_tensor[0]
video_id = video_id[0]
questions = questions[0]
question_ids = question_ids[0]
options = options[0]
qas = []
for idx, question in enumerate(questions):
letters = ['(A)', '(B)', '(C)']
question_id = question_ids[idx]
_options = options[idx]
output = get_model_output(model, tokenizer, question, video_tensor, args)
pred_answer = re.findall('\(*[A-C]\)*', output)
if len(pred_answer) == 0:
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
else:
pred_answer = pred_answer[0].strip()
if not pred_answer.startswith('('):
pred_answer = f'({pred_answer})'
pred_idx = letters.index(pred_answer)
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()
# 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)
|