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import argparse
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import torch
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from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
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from llava.conversation import conv_templates, SeparatorStyle
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from llava.model.builder import load_pretrained_model
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from llava.utils import disable_torch_init
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from llava.mm_utils import process_images, tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
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from PIL import Image
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import requests
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from PIL import Image
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from io import BytesIO
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from transformers import TextStreamer
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def load_image(image_file):
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if image_file.startswith('http://') or image_file.startswith('https://'):
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response = requests.get(image_file)
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image = Image.open(BytesIO(response.content)).convert('RGB')
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else:
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image = Image.open(image_file).convert('RGB')
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return image
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def main(args):
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disable_torch_init()
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model_name = get_model_name_from_path(args.model_path)
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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)
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if 'llama-2' in model_name.lower():
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conv_mode = "llava_llama_2"
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elif "v1" in model_name.lower():
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conv_mode = "llava_v1"
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elif "mpt" in model_name.lower():
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conv_mode = "mpt"
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else:
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conv_mode = "llava_v0"
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if args.conv_mode is not None and conv_mode != args.conv_mode:
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print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode))
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else:
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args.conv_mode = conv_mode
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conv = conv_templates[args.conv_mode].copy()
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if "mpt" in model_name.lower():
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roles = ('user', 'assistant')
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else:
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roles = conv.roles
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image = load_image(args.image_file)
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image_tensor = process_images([image], image_processor, args)
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if type(image_tensor) is list:
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image_tensor = [image.to(model.device, dtype=torch.float16) for image in image_tensor]
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else:
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image_tensor = image_tensor.to(model.device, dtype=torch.float16)
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while True:
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try:
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inp = input(f"{roles[0]}: ")
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except EOFError:
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inp = ""
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if not inp:
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print("exit...")
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break
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print(f"{roles[1]}: ", end="")
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if image is not None:
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if model.config.mm_use_im_start_end:
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inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + inp
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else:
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inp = DEFAULT_IMAGE_TOKEN + '\n' + inp
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conv.append_message(conv.roles[0], inp)
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image = None
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else:
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conv.append_message(conv.roles[0], inp)
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conv.append_message(conv.roles[1], None)
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prompt = conv.get_prompt()
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input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
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stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
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keywords = [stop_str]
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stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
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with torch.inference_mode():
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output_ids = model.generate(
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input_ids,
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images=image_tensor,
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do_sample=True,
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temperature=args.temperature,
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max_new_tokens=args.max_new_tokens,
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streamer=streamer,
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use_cache=True,
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stopping_criteria=[stopping_criteria])
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outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
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conv.messages[-1][-1] = outputs
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if args.debug:
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print("\n", {"prompt": prompt, "outputs": outputs}, "\n")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
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parser.add_argument("--model-base", type=str, default=None)
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parser.add_argument("--image-file", type=str, required=True)
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parser.add_argument("--device", type=str, default="cuda")
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parser.add_argument("--conv-mode", type=str, default=None)
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parser.add_argument("--temperature", type=float, default=0.2)
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parser.add_argument("--max-new-tokens", type=int, default=512)
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parser.add_argument("--load-8bit", action="store_true")
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parser.add_argument("--load-4bit", action="store_true")
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parser.add_argument("--debug", action="store_true")
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parser.add_argument("--image-aspect-ratio", type=str, default='pad')
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args = parser.parse_args()
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main(args)
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