eason
79db6e5c96
新增锂电池剩余使用寿命预测模型RUL-Mamba，包含以下主要组件： 1. 添加Mamba模块作为核心时序建模组件 2. 实现特征注意力网络(FAN)和门控残差网络(GRN) 3. 新增数据预处理和归一化层 4. 添加模型训练和评估脚本 5. 补充README文档说明使用方法 6. 添加可视化辅助工具Helper_Plot.py 7. 实现多种时间序列处理层(Embedding、AutoCorrelation等) 8. 添加配置文件requirements.txt 9. 补充测试数据集TJU battery data
47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
import random
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import torch
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import shutil
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import numpy as np
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import time
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import copy
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import os
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import argparse
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import subprocess
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import torch.backends.cudnn as cudnn
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import glob
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import logging
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import sys
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import yaml
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import json
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# 寻找现存最大的显卡编号
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def get_gpus_memory_info():
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"""Get the maximum free usage memory of gpu"""
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rst = subprocess.run('nvidia-smi -q -d Memory', stdout=subprocess.PIPE, shell=True).stdout.decode('utf-8')
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rst = rst.strip().split('\n')
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memory_available = [int(line.split(':')[1].split(' ')[1]) for line in rst if 'Free' in line][::2]
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id = int(np.argmax(memory_available))
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return id, memory_available
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# 设置random, numpy， torch（cpu和cuda）的seed
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def set_seed(seed):
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"""
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set seed of numpy and torch
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:param seed:
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:return:
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"""
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if seed is None:
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seed = np.random.randint(1e6)
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random.seed(seed)
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np.random.seed(seed)
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os.environ['PYTHONHASHSEED'] = str(seed) # 为了禁止hash随机化，使得实验可复现。
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torch.manual_seed(seed) # 为CPU设置随机种子
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if torch.cuda.is_available():
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torch.cuda.manual_seed(seed) # 为当前GPU设置随机种子
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torch.cuda.manual_seed_all(seed) # if you are using multi-GPU，为所有GPU设置随机种子
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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return seed
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