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import argparse
import os
import torch
import sys
sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "Evaluation"))
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, \
DEFAULT_VIDEO_TOKEN
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import process_images, tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
from serve.utils import load_image, image_ext, video_ext
from PIL import Image
import requests
from PIL import Image
from io import BytesIO
from transformers import TextStreamer
def main(args):
# Model
disable_torch_init()
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,
args.load_8bit, args.load_4bit,
device=args.device, cache_dir=args.cache_dir)
image_processor, video_processor = processor['image'], processor['video']
if 'llama-2' in model_name.lower():
conv_mode = "llava_llama_2"
elif "v1" in model_name.lower():
conv_mode = "llava_v1"
elif "mpt" in model_name.lower():
conv_mode = "mpt"
else:
conv_mode = "llava_v0"
if args.conv_mode is not None and conv_mode != args.conv_mode:
print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode))
else:
args.conv_mode = conv_mode
conv = conv_templates[args.conv_mode].copy()
if "mpt" in model_name.lower():
roles = ('user', 'assistant')
else:
roles = conv.roles
tensor = []
special_token = []
args.file = args.file if isinstance(args.file, list) else [args.file]
for file in args.file:
if os.path.splitext(file)[-1].lower() in video_ext: # video extension
video_tensor = video_processor(file, return_tensors='pt')['pixel_values'][0].to(model.device, dtype=torch.float16)
special_token += [DEFAULT_IMAGE_TOKEN] * model.get_video_tower().config.num_frames
elif os.path.splitext(os.listdir(file)[0]).lower() in image_ext: # frames folder
vidframes_list = sorted(glob(file + '/*'))
images = load_frames(vidframes_list, model.get_video_tower().config.num_frames)
# Similar operation in model_worker.py
video_tensor = process_images(images, image_processor, args)
video_tensor = video_tensor.to(model.device, dtype=torch.float16)
video_tensor = video_tensor.unsqueeze(0)
special_token += [DEFAULT_IMAGE_TOKEN] * model.get_video_tower().config.num_frames
else:
raise ValueError(f'Support video of {video_ext} and frames of {image_ext}, but found {os.path.splitext(file)[-1].lower()}')
print(video_tensor.shape)
tensor.append(video_tensor)
while True:
try:
inp = input(f"{roles[0]}: ")
except EOFError:
inp = ""
if not inp:
print("exit...")
break
print(f"{roles[1]}: ", end="")
if file is not None:
# first message
if getattr(model.config, "mm_use_im_start_end", False):
inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + inp
# inp = ''.join([DEFAULT_IM_START_TOKEN + i + DEFAULT_IM_END_TOKEN for i in special_token]) + '\n' + inp
else:
inp = DEFAULT_IMAGE_TOKEN + '\n' + inp
# inp = ''.join(special_token) + '\n' + inp
conv.append_message(conv.roles[0], inp)
file = None
else:
# later messages
conv.append_message(conv.roles[0], inp)
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(model.device)
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=tensor, # video as fake images
do_sample=True if args.temperature > 0 else False,
temperature=args.temperature,
max_new_tokens=args.max_new_tokens,
streamer=streamer,
use_cache=True,
stopping_criteria=[stopping_criteria])
outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
conv.messages[-1][-1] = outputs
if args.debug:
print("\n", {"prompt": prompt, "outputs": outputs}, "\n")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="LanguageBind/Video-LLaVA-7B")
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--cache-dir", type=str, default=None)
parser.add_argument("--file", nargs='+', type=str, required=True)
parser.add_argument("--device", type=str, default="cuda")
parser.add_argument("--conv-mode", type=str, default=None)
parser.add_argument("--temperature", type=float, default=0.2)
parser.add_argument("--max-new-tokens", type=int, default=512)
parser.add_argument("--load-8bit", action="store_true")
parser.add_argument("--load-4bit", action="store_true")
parser.add_argument("--debug", action="store_true")
args = parser.parse_args()
main(args)
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