目标检测多模态大模型实践:貌似是全网唯一Shikra的部署和测试教程,内含各种踩坑以及demo代码

原文:

Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic

代码:

https://github.com/shikras/shikra

模型:

https://huggingface.co/shikras/shikra-7b-delta-v1

https://huggingface.co/shikras/shikra7b-delta-v1-0708

第一个是论文用的,第二个会有迭代。

本人的shikra论文解读,逐行解读,非常详细!

多模态大模型目标检测,精读,Shikra

部署:

  1. 下载GitHub工程,和shikras的模型参数,注意,还要下载LLaMA-7b的模型;

  2. 创建环境:

    conda create -n shikra python=3.10
    conda activate shikra
    pip install -r requirements.txt

后面我运行的时候报缺包了,又pip install了以下包,不过每个人情况不同:

复制代码
pip install uvicorn
pip install mmengine

然后还会报错:

复制代码
File "/usr/local/lib/python3.10/dist-packages/cv2/typing/__init__.py", line 171, in <module>
    LayerId = cv2.dnn.DictValue
AttributeError: module 'cv2.dnn' has no attribute 'DictValue'

解决方案:

修改/usr/local/lib/python3.10/dist-packages/cv2/typing/init .py

注释掉LayerId = cv2.dnn.DictValue这行即可。

  1. 权重下载和合并
    shikra官方提供的模型权重需要和llama1-7b合并之后才能用,然而llama1需要申请,比较麻烦,在hf上找到了平替(这一步我走了好久QwQ):
    https://huggingface.co/huggyllama/llama-7b
    大家自己下载,然后运行官方提供的合并代码:

    python mllm/models/shikra/apply_delta.py
    --base /path/to/llama-7b
    --target /output/path/to/shikra-7b-merge
    --delta shikras/shikra-7b-delta-v1

得到了可用的模型参数shikra-7b-merge。

注意要把参数文件夹里config里的模型路径改成merge版的。

此外还需要下载clip 模型参数:

https://huggingface.co/openai/clip-vit-large-patch14

代码和配置文件中有多处调用/openai/clip-vit-large-patch14,要改成本地版本。如果不预先下载,应该会在运行时自动下载,大家看网络情况自行选择。

  1. 我写的demo文件,用于在命令行测试模型效果,主要是为了不用gradiofastapi这些东西。
python 复制代码
import argparse
import os
import sys
import base64
import logging
import time
from pathlib import Path
from io import BytesIO

import torch
import uvicorn
import transformers
from PIL import Image
from mmengine import Config
from transformers import BitsAndBytesConfig

sys.path.append(str(Path(__file__).parent.parent.parent))

from mllm.dataset.process_function import PlainBoxFormatter
from mllm.dataset.builder import prepare_interactive
from mllm.models.builder.build_shikra import load_pretrained_shikra
from mllm.dataset.utils.transform import expand2square, box_xyxy_expand2square

# Set up logging
log_level = logging.DEBUG
transformers.logging.set_verbosity(log_level)
transformers.logging.enable_default_handler()
transformers.logging.enable_explicit_format()

# prompt for coco

# Argument parsing
parser = argparse.ArgumentParser("Shikra Local Demo")
parser.add_argument('--model_path', default = "xxx/shikra-merge", help="Path to the model")
parser.add_argument('--load_in_8bit', action='store_true', help="Load model in 8-bit precision")
parser.add_argument('--image_path', default = "xxx/shikra-main/mllm/demo/assets/ball.jpg", help="Path to the image file")
parser.add_argument('--text', default="What do you see in this image? Please mention the objects and their locations using the format [x1,y1,x2,y2].", help="Text prompt")
parser.add_argument('--boxes_value', nargs='+', type=int, default=[], help="Bounding box values (x1, y1, x2, y2)")
parser.add_argument('--boxes_seq', nargs='+', type=int, default=[], help="Sequence of bounding boxes")
parser.add_argument('--do_sample', action='store_true', help="Use sampling during generation")
parser.add_argument('--max_length', type=int, default=512, help="Maximum length of the output")
parser.add_argument('--top_p', type=float, default=1.0, help="Top-p value for sampling")
parser.add_argument('--temperature', type=float, default=1.0, help="Temperature for sampling")

args = parser.parse_args()
model_name_or_path = args.model_path
# Model initialization
model_args = Config(dict(
    type='shikra',
    version='v1',

    # checkpoint config
    cache_dir=None,
    model_name_or_path=model_name_or_path,
    vision_tower=r'xxx/clip-vit-large-patch14',
    pretrain_mm_mlp_adapter=None,

    # model config
    mm_vision_select_layer=-2,
    model_max_length=2048,

    # finetune config
    freeze_backbone=False,
    tune_mm_mlp_adapter=False,
    freeze_mm_mlp_adapter=False,

    # data process config
    is_multimodal=True,
    sep_image_conv_front=False,
    image_token_len=256,
    mm_use_im_start_end=True,

    target_processor=dict(
        boxes=dict(type='PlainBoxFormatter'),
    ),

    process_func_args=dict(
        conv=dict(type='ShikraConvProcess'),
        target=dict(type='BoxFormatProcess'),
        text=dict(type='ShikraTextProcess'),
        image=dict(type='ShikraImageProcessor'),
    ),

    conv_args=dict(
        conv_template='vicuna_v1.1',
        transforms=dict(type='Expand2square'),
        tokenize_kwargs=dict(truncation_size=None),
    ),

    gen_kwargs_set_pad_token_id=True,
    gen_kwargs_set_bos_token_id=True,
    gen_kwargs_set_eos_token_id=True,
))
training_args = Config(dict(
    bf16=False,
    fp16=True,
    device='cuda',
    fsdp=None,
))

quantization_kwargs = dict(
    quantization_config=BitsAndBytesConfig(
        load_in_8bit=args.load_in_8bit,
    )
) if args.load_in_8bit else dict()

model, preprocessor = load_pretrained_shikra(model_args, training_args, **quantization_kwargs)

# Convert the model and vision tower to float16
if not getattr(model, 'is_quantized', False):
    model.to(dtype=torch.float16, device=torch.device('cuda'))
if not getattr(model.model.vision_tower[0], 'is_quantized', False):
    model.model.vision_tower[0].to(dtype=torch.float16, device=torch.device('cuda'))

preprocessor['target'] = {'boxes': PlainBoxFormatter()}
tokenizer = preprocessor['text']

# Load and preprocess the image
pil_image = Image.open(args.image_path).convert("RGB")
ds = prepare_interactive(model_args, preprocessor)

image = expand2square(pil_image)
boxes_value = [box_xyxy_expand2square(box, w=pil_image.width, h=pil_image.height) for box in zip(args.boxes_value[::2], args.boxes_value[1::2], args.boxes_value[2::2], args.boxes_value[3::2])]

ds.set_image(image)
ds.append_message(role=ds.roles[0], message=args.text, boxes=boxes_value, boxes_seq=args.boxes_seq)
model_inputs = ds.to_model_input()
model_inputs['images'] = model_inputs['images'].to(torch.float16)

# Generate
gen_kwargs = dict(
    use_cache=True,
    do_sample=args.do_sample,
    pad_token_id=tokenizer.pad_token_id,
    bos_token_id=tokenizer.bos_token_id,
    eos_token_id=tokenizer.eos_token_id,
    max_new_tokens=args.max_length,
    top_p=args.top_p,
    temperature=args.temperature,
)

input_ids = model_inputs['input_ids']
st_time = time.time()
with torch.inference_mode():
    with torch.autocast(device_type='cuda', dtype=torch.float16):
        output_ids = model.generate(**model_inputs, **gen_kwargs)
print(f"Generated in {time.time() - st_time} seconds")

input_token_len = input_ids.shape[-1]
response = tokenizer.batch_decode(output_ids[:, input_token_len:])[0]
print(f"Response: {response}")

这么良心,点个关注吧,会持续更新多模态大模型相关内容。

相关推荐
墨风如雪16 分钟前
DeepSeek OCR:用'眼睛'阅读长文本,AI记忆新纪元?
aigc
WWZZ20251 小时前
快速上手大模型:机器学习2(一元线性回归、代价函数、梯度下降法)
人工智能·算法·机器学习·计算机视觉·机器人·大模型·slam
2401_858869802 小时前
目标检测2
人工智能·目标检测·计算机视觉
ARM+FPGA+AI工业主板定制专家2 小时前
基于ZYNQ的目标检测算法硬件加速器优化设计
人工智能·目标检测·计算机视觉·fpga开发·自动驾驶
格林威3 小时前
UV紫外相机的简单介绍和场景应用
人工智能·数码相机·计算机视觉·视觉检测·制造·uv·工业相机
Blossom.1184 小时前
把AI“撒”进农田:基于极值量化与状态机的1KB边缘灌溉决策树
人工智能·python·深度学习·算法·目标检测·决策树·机器学习
算家计算5 小时前
SAIL-VL2本地部署教程:2B/8B参数媲美大规模模型,为轻量级设备量身打造的多模态大脑
人工智能·开源·aigc
Python智慧行囊5 小时前
图像处理-opencv(二)-形态学
人工智能·计算机视觉
zenRRan6 小时前
用中等难度prompt做高效post training
人工智能·深度学习·机器学习·计算机视觉·prompt
格林威7 小时前
短波红外相机的简单介绍和场景应用
人工智能·数码相机·计算机视觉·目标跟踪·视觉检测·工业相机·工业镜头