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
417 lines
16 KiB
Python
417 lines
16 KiB
Python
import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from einops import rearrange, repeat, einsum
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# from .layers.Embed import DataEmbedding
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# import torch.nn.functional as F
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from torch.nn.utils import weight_norm
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import math
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class PositionalEmbedding(nn.Module):
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def __init__(self, d_model, max_len=5000):
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super(PositionalEmbedding, self).__init__()
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# Compute the positional encodings once in log space.
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pe = torch.zeros(max_len, d_model).float()
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pe.require_grad = False
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position = torch.arange(0, max_len).float().unsqueeze(1)
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div_term = (torch.arange(0, d_model, 2).float()
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* -(math.log(10000.0) / d_model)).exp()
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pe[:, 0::2] = torch.sin(position * div_term)
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pe[:, 1::2] = torch.cos(position * div_term)
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pe = pe.unsqueeze(0)
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self.register_buffer('pe', pe)
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def forward(self, x):
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return self.pe[:, :x.size(1)]
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class TokenEmbedding(nn.Module):
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def __init__(self, c_in, d_model):
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super(TokenEmbedding, self).__init__()
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padding = 1 if torch.__version__ >= '1.5.0' else 2
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self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model,
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kernel_size=3, padding=padding, padding_mode='circular', bias=False)
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for m in self.modules():
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if isinstance(m, nn.Conv1d):
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nn.init.kaiming_normal_(
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m.weight, mode='fan_in', nonlinearity='leaky_relu')
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def forward(self, x):
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x = self.tokenConv(x.permute(0, 2, 1)).transpose(1, 2)
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return x
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class FixedEmbedding(nn.Module):
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def __init__(self, c_in, d_model):
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super(FixedEmbedding, self).__init__()
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w = torch.zeros(c_in, d_model).float()
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w.require_grad = False
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position = torch.arange(0, c_in).float().unsqueeze(1)
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div_term = (torch.arange(0, d_model, 2).float()
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* -(math.log(10000.0) / d_model)).exp()
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w[:, 0::2] = torch.sin(position * div_term)
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w[:, 1::2] = torch.cos(position * div_term)
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self.emb = nn.Embedding(c_in, d_model)
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self.emb.weight = nn.Parameter(w, requires_grad=False)
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def forward(self, x):
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return self.emb(x).detach()
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class TemporalEmbedding(nn.Module):
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def __init__(self, d_model, embed_type='fixed', freq='h'):
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super(TemporalEmbedding, self).__init__()
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minute_size = 4
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hour_size = 24
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weekday_size = 7
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day_size = 32
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month_size = 13
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Embed = FixedEmbedding if embed_type == 'fixed' else nn.Embedding
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if freq == 't':
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self.minute_embed = Embed(minute_size, d_model)
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self.hour_embed = Embed(hour_size, d_model)
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self.weekday_embed = Embed(weekday_size, d_model)
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self.day_embed = Embed(day_size, d_model)
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self.month_embed = Embed(month_size, d_model)
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def forward(self, x):
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x = x.long()
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minute_x = self.minute_embed(x[:, :, 4]) if hasattr(
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self, 'minute_embed') else 0.
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hour_x = self.hour_embed(x[:, :, 3])
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weekday_x = self.weekday_embed(x[:, :, 2])
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day_x = self.day_embed(x[:, :, 1])
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month_x = self.month_embed(x[:, :, 0])
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return hour_x + weekday_x + day_x + month_x + minute_x
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class TimeFeatureEmbedding(nn.Module):
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def __init__(self, d_model, embed_type='timeF', freq='h'):
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super(TimeFeatureEmbedding, self).__init__()
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freq_map = {'h': 4, 't': 5, 's': 6,
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'm': 1, 'a': 1, 'w': 2, 'd': 3, 'b': 3}
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d_inp = freq_map[freq]
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self.embed = nn.Linear(d_inp, d_model, bias=False)
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def forward(self, x):
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return self.embed(x)
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class DataEmbedding(nn.Module):
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def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
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super(DataEmbedding, self).__init__()
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self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
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self.position_embedding = PositionalEmbedding(d_model=d_model)
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self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type,
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freq=freq) if embed_type != 'timeF' else TimeFeatureEmbedding(
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d_model=d_model, embed_type=embed_type, freq=freq)
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self.dropout = nn.Dropout(p=dropout)
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def forward(self, x, x_mark):
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if x_mark is None:
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x = self.value_embedding(x) + self.position_embedding(x)
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else:
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x = self.value_embedding(
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x) + self.temporal_embedding(x_mark) + self.position_embedding(x)
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return self.dropout(x)
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class DataEmbedding_inverted(nn.Module):
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def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
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super(DataEmbedding_inverted, self).__init__()
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self.value_embedding = nn.Linear(c_in, d_model)
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self.dropout = nn.Dropout(p=dropout)
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def forward(self, x, x_mark):
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x = x.permute(0, 2, 1)
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# x: [Batch Variate Time]
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if x_mark is None:
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x = self.value_embedding(x)
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else:
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x = self.value_embedding(torch.cat([x, x_mark.permute(0, 2, 1)], 1))
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# x: [Batch Variate d_model]
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return self.dropout(x)
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class DataEmbedding_wo_pos(nn.Module):
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def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
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super(DataEmbedding_wo_pos, self).__init__()
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self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
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self.position_embedding = PositionalEmbedding(d_model=d_model)
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self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type,
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freq=freq) if embed_type != 'timeF' else TimeFeatureEmbedding(
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d_model=d_model, embed_type=embed_type, freq=freq)
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self.dropout = nn.Dropout(p=dropout)
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def forward(self, x, x_mark):
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if x_mark is None:
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x = self.value_embedding(x)
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else:
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x = self.value_embedding(x) + self.temporal_embedding(x_mark)
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return self.dropout(x)
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class PatchEmbedding(nn.Module):
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def __init__(self, d_model, patch_len, stride, padding, dropout):
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super(PatchEmbedding, self).__init__()
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# Patching
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self.patch_len = patch_len
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self.stride = stride
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self.padding_patch_layer = nn.ReplicationPad1d((0, padding))
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# Backbone, Input encoding: projection of feature vectors onto a d-dim vector space
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self.value_embedding = nn.Linear(patch_len, d_model, bias=False)
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# Positional embedding
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self.position_embedding = PositionalEmbedding(d_model)
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# Residual dropout
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self.dropout = nn.Dropout(dropout)
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def forward(self, x):
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# do patching
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n_vars = x.shape[1]
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x = self.padding_patch_layer(x)
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x = x.unfold(dimension=-1, size=self.patch_len, step=self.stride)
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x = torch.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3]))
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# Input encoding
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x = self.value_embedding(x) + self.position_embedding(x)
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return self.dropout(x), n_vars
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class ResidualBlock(nn.Module):
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def __init__(self, d_inner, dt_rank, d_model=16, d_ff=32,d_conv=4, top_k=5,):
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super(ResidualBlock, self).__init__()
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self.mixer = MambaBlock(d_inner, dt_rank, d_model=d_model, d_ff=d_ff,d_conv=d_conv, top_k=top_k)
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self.norm = RMSNorm(d_model)
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def forward(self, x):
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output = self.mixer(self.norm(x)) + x
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return output
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class MambaBlock(nn.Module):
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def __init__(self, d_inner, dt_rank, d_model=16, d_ff=32,d_conv=4, top_k=5,):
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super(MambaBlock, self).__init__()
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self.d_inner = d_inner
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self.dt_rank = dt_rank
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self.in_proj = nn.Linear(d_model, self.d_inner * 2, bias=False)
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self.conv1d = nn.Conv1d(
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in_channels = self.d_inner,
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out_channels = self.d_inner,
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bias = True,
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kernel_size = d_conv,
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padding = d_conv - 1,
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groups = self.d_inner
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)
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# takes in x and outputs the input-specific delta, B, C
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self.x_proj = nn.Linear(self.d_inner, self.dt_rank + d_ff * 2, bias=False)
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# projects delta
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self.dt_proj = nn.Linear(self.dt_rank, self.d_inner, bias=True)
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A = repeat(torch.arange(1, d_ff + 1), "n -> d n", d=self.d_inner)
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self.A_log = nn.Parameter(torch.log(A))
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self.D = nn.Parameter(torch.ones(self.d_inner))
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self.out_proj = nn.Linear(self.d_inner, d_model, bias=False)
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def forward(self, x):
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"""
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Figure 3 in Section 3.4 in the paper
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"""
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(b, l, d) = x.shape
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x_and_res = self.in_proj(x) # [B, L, 2 * d_inner]
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(x, res) = x_and_res.split(split_size=[self.d_inner, self.d_inner], dim=-1)
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x = rearrange(x, "b l d -> b d l")
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x = self.conv1d(x)[:, :, :l]
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x = rearrange(x, "b d l -> b l d")
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x = F.silu(x)
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y = self.ssm(x)
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y = y * F.silu(res)
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output = self.out_proj(y)
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return output
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def ssm(self, x):
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"""
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Algorithm 2 in Section 3.2 in the paper
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"""
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(d_in, n) = self.A_log.shape
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A = -torch.exp(self.A_log.float()) # [d_in, n]
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D = self.D.float() # [d_in]
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x_dbl = self.x_proj(x) # [B, L, d_rank + 2 * d_ff]
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(delta, B, C) = x_dbl.split(split_size=[self.dt_rank, n, n], dim=-1) # delta: [B, L, d_rank]; B, C: [B, L, n]
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delta = F.softplus(self.dt_proj(delta)) # [B, L, d_in]
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y = self.selective_scan(x, delta, A, B, C, D)
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return y
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def selective_scan(self, u, delta, A, B, C, D):
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(b, l, d_in) = u.shape
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n = A.shape[1]
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deltaA = torch.exp(einsum(delta, A, "b l d, d n -> b l d n")) # A is discretized using zero-order hold (ZOH) discretization
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deltaB_u = einsum(delta, B, u, "b l d, b l n, b l d -> b l d n") # B is discretized using a simplified Euler discretization instead of ZOH. From a discussion with authors: "A is the more important term and the performance doesn't change much with the simplification on B"
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# selective scan, sequential instead of parallel
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x = torch.zeros((b, d_in, n), device=deltaA.device)
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ys = []
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for i in range(l):
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x = deltaA[:, i] * x + deltaB_u[:, i]
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y = einsum(x, C[:, i, :], "b d n, b n -> b d")
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ys.append(y)
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y = torch.stack(ys, dim=1) # [B, L, d_in]
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y = y + u * D
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return y
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class RMSNorm(nn.Module):
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def __init__(self, d_model, eps=1e-5):
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super(RMSNorm, self).__init__()
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self.eps = eps
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self.weight = nn.Parameter(torch.ones(d_model))
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def forward(self, x):
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output = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) * self.weight
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return output
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class MambaSimple(nn.Module):
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"""
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Mamba, linear-time sequence modeling with selective state spaces O(L)
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Paper link: https://arxiv.org/abs/2312.00752
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Implementation refernce: https://github.com/johnma2006/mamba-minimal/
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"""
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def __init__(self, task_name='short_term_forecast',seq_len=96, label_len=48, pred_len=96, enc_in=7, dec_in=7, c_out=1, e_layers=2, d_layers=1, n_heads=8,factor=3,
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d_model=16, d_ff=32, des='Exp', expand=2, d_conv=4, top_k=5, embed='timeF',freq='h', dropout=0.1,num_kernels=6,
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moving_avg=25,channel_independence=1, decomp_method='moving_avg', use_norm=1,
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version='fourier', mode_select='random', modes=32, activation='gelu',seasonal_patterns='Monthly',
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inverse=False, mask_rate=0.25, anomaly_ratio=0.25,output_attention=False,down_sampling_layers=0, down_sampling_window=1, down_sampling_method=None,
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seg_len=48, num_workers=0, itr=1, train_epochs=100, batch_size=32, patience=3, learning_rate=0.0001, loss='MSE',
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lradj='type1', use_amp=False, use_gpu=True, gpu=0, use_multi_gpu=False, devices='0,1,2,3', p_hidden_dims=[128, 128],
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p_hidden_layers=2, use_dtw=False, augmentation_ratio=0, seed=2, jitter=False, scaling=False, permutation=False,
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randompermutation=False, magwarp=False, timewarp=False, windowslice=False, windowwarp=False, rotation=False,
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spawner=False, dtwwarp=False, shapedtwwarp=False, wdba=False, discdtw=False, discsdtw=False, extra_tag='',
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**kwargs):
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super(MambaSimple, self).__init__()
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self.task_name = task_name
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self.pred_len = pred_len
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self.d_inner = d_model * expand
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self.dt_rank = math.ceil(d_model / 16)
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self.embedding = DataEmbedding(enc_in, d_model, embed, freq, dropout)
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self.layers = nn.ModuleList([ResidualBlock(self.d_inner, self.dt_rank,d_model=d_model, d_ff=d_ff,d_conv=d_conv, top_k=top_k) for _ in range(e_layers)])
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self.norm = RMSNorm(d_model)
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self.out_layer = nn.Linear(d_model, c_out, bias=False)
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self.projection_final = nn.Linear(pred_len*enc_in, pred_len*c_out, bias=True)
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# def short_term_forecast(self, x_enc, x_mark_enc):
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def forecast(self, x_enc, x_mark_enc):
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mean_enc = x_enc.mean(1, keepdim=True).detach()
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x_enc = x_enc - mean_enc
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std_enc = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5).detach()
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x_enc = x_enc / std_enc
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x = self.embedding(x_enc, x_mark_enc)
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for layer in self.layers:
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x = layer(x)
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x = self.norm(x)
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x_out = self.out_layer(x)
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x_out = x_out * std_enc + mean_enc
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return x_out
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# def long_term_forecast(self, x_enc, x_mark_enc):
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# x = self.embedding(x_enc, x_mark_enc)
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# for layer in self.layers:
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# x = layer(x)
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# x = self.norm(x)
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# x_out = self.out_layer(x)
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# return x_out
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def forward(self, x_enc, x_mark_enc, x_dec=None, x_mark_dec=None, mask=None):
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if self.task_name in ['short_term_forecast', 'long_term_forecast']:
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x_out = self.forecast(x_enc, x_mark_enc)
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x_out=x_out[:, -self.pred_len:, :]
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x_out = self.projection_final(x_out.view(x_out.shape[0], -1))
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return x_out
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# other tasks not implemented
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from pytorch_forecasting.models import BaseModel
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from typing import Dict
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class MambaSimpleNetModel(BaseModel):
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def __init__(self,seq_len=24, label_len=0, pred_len=1, enc_in=7, dec_in=7, c_out=1, e_layers=2, d_layers=1, factor=3,
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d_model=16, d_ff=32, des='Exp', itr=1, top_k=5,embed='timeF',freq='h', dropout=0.1,num_kernels=6, **kwargs):
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# saves arguments in signature to `.hparams` attribute, mandatory call - do not skip this
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self.save_hyperparameters()
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# pass additional arguments to BaseModel.__init__, mandatory call - do not skip this
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super().__init__(**kwargs)
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self.network = MambaSimple(
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seq_len=seq_len, label_len=label_len, pred_len=pred_len, enc_in=enc_in, dec_in=dec_in, c_out=c_out, e_layers=e_layers, d_layers=d_layers, factor=factor,
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d_model=d_model, d_ff=d_ff, des=des, itr=itr, top_k=top_k, embed=embed, freq=freq, dropout=dropout, num_kernels=num_kernels
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)
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# 修改，锂电池预测
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def forward(self, x: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
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x_enc = x["encoder_cont"][:,:,:-1]
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# 输出
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prediction = self.network(x_enc, x_mark_enc=None)
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# 输出rescale， rescale predictions into target space
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prediction = self.transform_output(prediction, target_scale=x["target_scale"])
|
||
|
||
# 返回一个字典，包含输出结果（prediction）
|
||
return self.to_network_output(prediction=prediction)
|
||
|
||
|
||
if __name__=='__main__':
|
||
N,L,C=100,24,11
|
||
label_len = 0
|
||
c_out = 1
|
||
pred_len=1
|
||
x_enc=torch.ones((N,L,C))
|
||
x_mark_enc=torch.ones((N, L, 4))
|
||
x_dec = torch.ones((N, pred_len, C))
|
||
x_mark_dec=torch.ones((N, pred_len, 4))
|
||
model=MambaSimple(seq_len=L, enc_in=C, dec_in=C, label_len = label_len, pred_len=pred_len, c_out=1) # pred_len 被限制了
|
||
out=model(x_enc=x_enc, x_mark_enc=x_mark_enc, x_dec=x_dec, x_mark_dec=x_mark_dec)
|
||
print(out.shape) |