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import argparse
import torch
from mplug_owl2.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
from mplug_owl2.conversation import conv_templates, SeparatorStyle
from mplug_owl2.model.builder import load_pretrained_model
from mplug_owl2.mm_utils import process_images, tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
from PIL import Image
import requests
from PIL import Image
from io import BytesIO
from transformers import TextStreamer
def disable_torch_init():
"""
Disable the redundant torch default initialization to accelerate model creation.
"""
import torch
setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
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()
model_name = get_model_name_from_path(args.model_path)
tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit, device=args.device)
conv_mode = "mplug_owl2"
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()
roles = conv.roles
image = load_image(args.image_file)
# Similar operation in model_worker.py
image_tensor = process_images([image], image_processor, args)
if type(image_tensor) is list:
image_tensor = [image.to(model.device, dtype=torch.float16) for image in image_tensor]
else:
image_tensor = image_tensor.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 + inp
conv.append_message(conv.roles[0], inp)
image = 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 not in [SeparatorStyle.TWO, SeparatorStyle.TWO_NO_SYS] 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=image_tensor,
do_sample=True,
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="facebook/opt-350m")
parser.add_argument("--model-base", 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=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")
parser.add_argument("--image-aspect-ratio", type=str, default='pad')
args = parser.parse_args()
main(args) |