| import argparse
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| import torch
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|
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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 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)
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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 = image_processor.preprocess(image, return_tensors="pt")["pixel_values"].half().cuda()
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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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|
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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(input_ids, images=image_tensor, do_sample=True, temperature=0.2, max_new_tokens=1024, streamer=streamer, use_cache=True, 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("--num-gpus", type=int, default=1)
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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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| args = parser.parse_args()
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| main(args)
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|
|
