人工智能（Artificial Intelligence，简称AI）是指计算机系统在完成类似人类智力所需的任务时所表现出来的能力。它是一种复杂的技术，通过将大量的数据输入到算法中进行学习，不断调整和改进自己的算法，从而不断优化其性能。
What are the functions of the contract tracking system?
1.High concurrency systems can support tens of millions of TPSs per second,which of course refers to pure ordering.The specific transaction process also includes matchmaking and clearing,among which clearing is the most time-consuming and poses the greatest challenge to the stability of the system.
2.High availability standby system:It is completely consistent with the main system architecture.If the network line fails due to transient high traffic,we can switch to the standby system within a few minutes.
3.API Quota:This is a self stabilizing protection mechanism when the system is overloaded.
合约量化就是利用代码组成的系统以规范的方式进行交易，以高频率进行交易。量化系统会严格按照设定的程序进行交易。交易者在开始交易前，需要设置好盈利点、止损点、回调点等数据。设置启动系统后，系统会自动建仓，实时检测行情，等待行情波动。
当市场价格涨到之前设定的盈利点后，系统会自动平仓，等待市场的下一次波动；如果有亏损，系统就进行止损操作，如果跌到止损点再加仓，等市价回调到相应点位再平仓，如此往复。
There are also multiple modules for contract tracking system development.
1.Tracking module:It can achieve forward,reverse,and multiple tracking effects for multiple firm offer accounts and transaction sub accounts simultaneously.
2.Risk control:The platform will conduct different risk assessments for each trader by analyzing various information of users.
3.Loss Limit:You can limit the maximum loss for each firm offer on the current day,control how much loss you have per day,and add a stop profit based on market trends.
4.Transaction records:Whether it's a new person or an experienced elderly person,what they do and the transactions they clinch will be seen by people,which is an important basis for people to choose the objects to follow
import argparse
import os.path as osp
import sys
sys.path.insert(0,'.')
import torch
from lib.models import model_factory
from configs import set_cfg_from_file
torch.set_grad_enabled(False)
parse=argparse.ArgumentParser()
parse.add_argument('--config',dest='config',type=str,
default='G:/6666Ground_segmentation0813/configs/bisenetv2_city.py',)
parse.add_argument('--weight-path',dest='weight_pth',type=str,
default='G:/6666Ground_segmentation0813/v4_model_final.pth')#最后的pytorch模型
parse.add_argument('--outpath',dest='out_pth',type=str,
default='G:/6666Ground_segmentation0813/model0124.onnx')#转成onnx的路径
args=parse.parse_args()
cfg=set_cfg_from_file(args.config)
if cfg.use_sync_bn:cfg.use_sync_bn=False
net=model_factorycfg.model_type
net.load_state_dict(torch.load(args.weight_pth),strict=False)
net.eval()
#dummy_input=torch.randn(1,3,*cfg.crop_size)
#dummy_input=torch.randn(1,3,1024,2048)
dummy_input=torch.randn(1,3,480,640)#图像的输入尺寸
input_names=['input_image']
output_names=['preds',]
torch.onnx.export(net,dummy_input,args.out_pth,
input_names=input_names,output_names=output_names,
verbose=False,opset_version=11)
