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'''
@Description:
@Author: jiajunlong
@Date: 2024-06-19 19:30:17
@LastEditTime: 2024-06-19 19:32:47
@LastEditors: jiajunlong
'''
import argparse
import requests
from PIL import Image
from io import BytesIO
import torch
from transformers import TextStreamer
from tinyllava.utils import *
from tinyllava.data import *
from tinyllava.model import *
def load_image(image_file):
if image_file.startswith('http://') or image_file.startswith('https://'):
response = requests.get(image_file)
image = Image.open(BytesIO(response.content)).convert('RGB')
else:
image = Image.open(image_file).convert('RGB')
return image
def main(args):
# Model
disable_torch_init()
if args.model_path is not None:
model, tokenizer, image_processor, context_len = load_pretrained_model(model_name_or_path=args.model_path, load_8bit=args.load_8bit, load_4bit=args.load_4bit, device=args.device)
else:
assert args.model is not None, 'model_path or model must be provided'
model = args.model
if hasattr(model.config, "max_sequence_length"):
context_len = model.config.max_sequence_length
else:
context_len = 2048
tokenizer = model.tokenizer
image_processor = model.vision_tower._image_processor
text_processor = TextPreprocess(tokenizer, args.conv_mode)
data_args = model.config
image_processor = ImagePreprocess(image_processor, data_args)
model.to(args.device)
if getattr(text_processor.template, 'role', None) is None:
roles = ['USER', 'ASSISTANT']
else:
roles = text_processor.template.role.apply()
msg = Message()
image = load_image(args.image_file)
# Similar operation in model_worker.py
image_tensor = image_processor(image)
image_tensor = image_tensor.unsqueeze(0).to(model.device, dtype=torch.float16)
while True:
try:
inp = input(f"{roles[0]}: ")
except EOFError:
inp = ""
if not inp:
print("exit...")
break
print(f"{roles[1]}: ", end="")
if image is not None:
# first message
inp = DEFAULT_IMAGE_TOKEN + '\n' + inp
msg.add_message(inp)
image = None
else:
# later messages
msg.add_message(inp)
result = text_processor(msg.messages, mode='eval')
prompt = result['prompt']
input_ids = result['input_ids'].unsqueeze(0).to(model.device)
# stop_str = text_processor.template.separator.apply()[1]
# 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=image_tensor,
do_sample=True if args.temperature > 0 else False,
temperature=args.temperature,
max_new_tokens=args.max_new_tokens,
streamer=streamer,
use_cache=True,
pad_token_id = tokenizer.eos_token_id,
# stopping_criteria=[stopping_criteria]
)
outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
msg.messages[-1]['value'] = 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="tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B")
parser.add_argument("--model", type=str, default=None)
parser.add_argument("--image-file", type=str, required=True)
parser.add_argument("--device", type=str, default="cuda")
parser.add_argument("--conv-mode", type=str, default='phi')
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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