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import argparse
import torch
from torchvision.transforms import transforms
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_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 PIL import Image
import requests
from PIL import Image
from io import BytesIO
from transformers import TextStreamer
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)
model, image_processor, tokenizer, context_len = load_pretrained_model(
model_path=args.model_path, model_base=args.model_base, model_name=model_name,
pretrained_rob_path=args.vision_encoder_pretrained, dtype=args.dtype,
device=args.device
)
print(f"loaded llava with clip {args.vision_encoder_pretrained}")
if args.dtype == "float16":
cast_dtype = torch.float16
elif args.dtype == "float32":
cast_dtype = torch.float32
else:
raise ValueError(f"Unknown dtype: {args.dtype}")
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
if args.image_file.endswith(".pt"):
normalizer = transforms.Normalize(
mean=image_processor.image_mean, std=image_processor.image_std
)
image = torch.load(args.image_file)
if len(image.shape) == 3:
image = image.unsqueeze(0)
image_tensor = normalizer(image).to(model.device, dtype=cast_dtype)
else:
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=cast_dtype) for image in image_tensor]
else:
image_tensor = image_tensor.to(model.device, dtype=cast_dtype)
print(f"loaded image {args.image_file} of shape {image_tensor.shape}")
while True:
try:
inp = input(f"{roles[0]}: ")
# inp = "Provide a short caption for this image."
except EOFError:
inp = ""
if not inp:
print("exit...")
break
print(f"{roles[1]}: ", end="")
if image is not None:
# first message
if model.config.mm_use_im_start_end:
inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + inp
else:
inp = DEFAULT_IMAGE_TOKEN + '\n' + 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).cuda()
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=image_tensor,
do_sample=args.temperature > 0.0,
temperature=args.temperature,
top_p=args.top_p,
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="liuhaotian/llava-v1.5-7b")
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--vision_encoder_pretrained", type=str, required=True)
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="vicuna_v1")
parser.add_argument("--temperature", type=float, default=0.2)
parser.add_argument("--top_p", type=float, default=0.7)
parser.add_argument("--max-new-tokens", type=int, default=512)
parser.add_argument("--dtype", type=str, default="float16")
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()
# print args
print(f"Arguments:\n{'-' * 20}")
for arg, value in vars(args).items():
print(f"{arg}: {value}")
print(f"{'-' * 20}")
main(args)
# /mnt/cschlarmann37/project_multimodal/handpicked-data/llava-demo1.jpeg
# /mnt/nsingh/project_multimodal/llava-evals/adv-images/other_coco2014_shots=0_apgd_4_5000_target=You-are-too-stupid.-I-will-not-answer-anymore_maskout=none/000000022371.pt
# /mnt/nsingh/project_multimodal/llava-evals/adv-images/other_coco2014_shots=0_apgd_4_5000_target=Sell-your-stocks.-Financial-markets-are-crashing_maskout=none/000000128014.pt |