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demo_multichat_few_shot.py
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import argparse
import os
import random
import sys
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import glob
from minigpt4.common.config import Config
from minigpt4.common.dist_utils import get_rank
from minigpt4.common.registry import registry
from minigpt4.conversation.conversation import Chat, Conversation
from minigpt4.datasets.builders import *
from minigpt4.models import *
from minigpt4.processors import *
from minigpt4.runners import *
from minigpt4.tasks import *
def parse_args():
parser = argparse.ArgumentParser(description="Demo")
parser.add_argument("--cfg-path", default='minigptv2_eval.yaml', help="path to configuration file.")
parser.add_argument("--gpu-id", type=int, default=0, help="specify the gpu to load the model.")
parser.add_argument("--num-beams", type=int, default=1, help="specify the gpu to load the model.")
parser.add_argument("--temperature", type=int, default=0.6, help="specify the gpu to load the model.")
parser.add_argument("--prompt", type=str, default="/nfs/volume-512-1/wangchang/MiniGPT-4-2/multi_chat/1.txt", help="第一个few shot样本的query，这里将query写到了txt文本当中，便于读取")
parser.add_argument("--answer-txt", type=str, default="/nfs/volume-512-1/wangchang/MiniGPT-4-2/multi_chat/answer_84_2.txt", help="将文件夹内的图像批量测试的推理结果写入到这个txt文本文件当中")
parser.add_argument(
"--options",
nargs="+",
help="override some settings in the used config, the key-value pair "
"in xxx=yyy format will be merged into config file (deprecate), "
"change to --cfg-options instead.",
)
args = parser.parse_args()
return args
def setup_seeds(config):
seed = config.run_cfg.seed + get_rank()
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
cudnn.benchmark = False
cudnn.deterministic = True
# ========================================
# Model Initialization
# ========================================
CONV_VISION_minigptv2 = Conversation(
system="Give the following image: <Img>ImageContent</Img>. "
"You will be able to see the image once I provide it to you, Please answer my questions.",
roles=("<s>[INST] ", " [/INST]"),
messages=[],
offset=2,
sep_style=SeparatorStyle.SINGLE,
sep="",
)
print('Initializing Chat')
args = parse_args()
cfg = Config(args)
model_config = cfg.model_cfg
model_config.device_8bit = args.gpu_id
model_cls = registry.get_model_class(model_config.arch)
model = model_cls.from_config(model_config).to('cuda:{}'.format(args.gpu_id))
vis_processor_cfg = cfg.datasets_cfg.cc_sbu_align.vis_processor.train
vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
chat = Chat(model, vis_processor, device='cuda:{}'.format(args.gpu_id))
print('Initialization Finished')
nums = 0
while True:
image_path = "/nfs/dataset-libstdm_situation_awareness/wangchang/testcase_17_69/img_guiji_1/858140270.png"
# 第一张few-shot图像
if image_path == 'stop':
break
if len(image_path) > 0:
query = args.prompt
chat_state = CONV_VISION_minigptv2.copy()
img_list = []
img_list2 = []
chat.upload_img(image_path, chat_state, img_list)
img_list2.append(img_list)
txt_path = args.answer_txt
while True:
if nums == 90:
sys.exit(0)
# nums == 90的时候，标志着文件夹的图像已经批量推理完成
if 90 > nums >= 6:
# 一共是6个few shot的案例样本，所以nums >= 6,
# 需要批量测试的图像共有84张，所以nums <= 84+6
query = "question.txt"
# 需要批量测试的文件夹内的图像的prompt，写入到一个txt文本文件中，方便读取
image_path = "/img/"
# 需要批量测试的文件夹内的图像路径
imgs = glob.glob(os.path.join(image_path, '*'))
image_path = imgs[nums-6]
img_list = []
chat_state = chat_state
chat.upload_img(image_path, chat_state, img_list)
img_list2.append(img_list)
if nums == 1:
query = "2.txt"
# 第2个few shot样本的query，这里将query写到了txt文本当中，便于读取
image_path = "/8582632.png"
# 第2个few shot样本的图像
image_path = image_path
img_list = []
chat_state = chat_state
chat.upload_img(image_path, chat_state, img_list)
img_list2.append(img_list)
if nums == 2:
query = "3.txt"
# 第3个few shot样本的query，这里将query写到了txt文本当中，便于读取
image_path = "/858263.png"
# 第3个few shot样本的图像
image_path = image_path
img_list = []
chat_state = chat_state
chat.upload_img(image_path, chat_state, img_list)
img_list2.append(img_list)
if nums == 3:
query = "4.txt"
# 第4个few shot样本的query，这里将query写到了txt文本当中，便于读取
image_path = "/85826.png"
# 第4个few shot样本的图像
image_path = image_path
img_list = []
chat_state = chat_state
chat.upload_img(image_path, chat_state, img_list)
img_list2.append(img_list)
if nums == 4:
query = "5.txt"
# 第5个few shot样本的query，这里将query写到了txt文本当中，便于读取
image_path = "/8582.png"
# 第5个few shot样本的图像
image_path = image_path
img_list = []
chat_state = chat_state
chat.upload_img(image_path, chat_state, img_list)
img_list2.append(img_list)
if nums == 5:
query = "6.txt"
# 第6个few shot样本的query，这里将query写到了txt文本当中，便于读取
image_path = "/85.png"
# 第6个few shot样本的图像
image_path = image_path
img_list = []
chat_state = chat_state
chat.upload_img(image_path, chat_state, img_list)
img_list2.append(img_list)
# 以上一共是6个few shot的案例样本
image_path = image_path
img_list = img_list
chat_state = chat_state
f3 = open(query, 'r', encoding='utf-8')
lines = f3.read()
query = str(lines)
chat.ask(query, chat_state)
chat.encode_img(img_list2[-1])
if nums >= 6:
llm_message = chat.answer(
conv=chat_state,
img_list=img_list2[-1],
num_beams=args.num_beams,
temperature=args.temperature,
max_new_tokens=500,
max_length=2000
)[0]
f1 = open(txt_path, 'a')
x = image_path.split("/")[-1]
f1.write(x + ':' + llm_message + '\t')
f1.write('\n')
nums += 1