import torch import torch.nn as nn import torch.nn.functional as F import torch.fft import math from pytorch_forecasting.models import BaseModel from typing import Dict class PositionalEmbedding(nn.Module): def __init__(self, d_model, max_len=5000): super(PositionalEmbedding, self).__init__() # Compute the positional encodings once in log space. pe = torch.zeros(max_len, d_model).float() pe.require_grad = False position = torch.arange(0, max_len).float().unsqueeze(1) div_term = (torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model)).exp() pe[:, 0::2] = torch.sin(position * div_term) pe[:, 1::2] = torch.cos(position * div_term) pe = pe.unsqueeze(0) self.register_buffer('pe', pe) def forward(self, x): return self.pe[:, :x.size(1)] class TokenEmbedding(nn.Module): def __init__(self, c_in, d_model): super(TokenEmbedding, self).__init__() padding = 1 if torch.__version__ >= '1.5.0' else 2 self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model, kernel_size=3, padding=padding, padding_mode='circular', bias=False) for m in self.modules(): if isinstance(m, nn.Conv1d): nn.init.kaiming_normal_( m.weight, mode='fan_in', nonlinearity='leaky_relu') def forward(self, x): x = self.tokenConv(x.permute(0, 2, 1)).transpose(1, 2) return x class FixedEmbedding(nn.Module): def __init__(self, c_in, d_model): super(FixedEmbedding, self).__init__() w = torch.zeros(c_in, d_model).float() w.require_grad = False position = torch.arange(0, c_in).float().unsqueeze(1) div_term = (torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model)).exp() w[:, 0::2] = torch.sin(position * div_term) w[:, 1::2] = torch.cos(position * div_term) self.emb = nn.Embedding(c_in, d_model) self.emb.weight = nn.Parameter(w, requires_grad=False) def forward(self, x): return self.emb(x).detach() class TemporalEmbedding(nn.Module): def __init__(self, d_model, embed_type='fixed', freq='h'): super(TemporalEmbedding, self).__init__() minute_size = 4 hour_size = 24 weekday_size = 7 day_size = 32 month_size = 13 Embed = FixedEmbedding if embed_type == 'fixed' else nn.Embedding if freq == 't': self.minute_embed = Embed(minute_size, d_model) self.hour_embed = Embed(hour_size, d_model) self.weekday_embed = Embed(weekday_size, d_model) self.day_embed = Embed(day_size, d_model) self.month_embed = Embed(month_size, d_model) def forward(self, x): x = x.long() minute_x = self.minute_embed(x[:, :, 4]) if hasattr( self, 'minute_embed') else 0. hour_x = self.hour_embed(x[:, :, 3]) weekday_x = self.weekday_embed(x[:, :, 2]) day_x = self.day_embed(x[:, :, 1]) month_x = self.month_embed(x[:, :, 0]) return hour_x + weekday_x + day_x + month_x + minute_x class TimeFeatureEmbedding(nn.Module): def __init__(self, d_model, embed_type='timeF', freq='h'): super(TimeFeatureEmbedding, self).__init__() freq_map = {'h': 4, 't': 5, 's': 6, 'm': 1, 'a': 1, 'w': 2, 'd': 3, 'b': 3} d_inp = freq_map[freq] self.embed = nn.Linear(d_inp, d_model, bias=False) def forward(self, x): return self.embed(x) class DataEmbedding(nn.Module): def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1): super(DataEmbedding, self).__init__() self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model) self.position_embedding = PositionalEmbedding(d_model=d_model) self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type, freq=freq) if embed_type != 'timeF' else TimeFeatureEmbedding( d_model=d_model, embed_type=embed_type, freq=freq) self.dropout = nn.Dropout(p=dropout) def forward(self, x, x_mark): if x_mark is None: x = self.value_embedding(x) + self.position_embedding(x) else: x = self.value_embedding( x) + self.temporal_embedding(x_mark) + self.position_embedding(x) return self.dropout(x) class Inception_Block_V1(nn.Module): def __init__(self, in_channels, out_channels, num_kernels=6, init_weight=True): super(Inception_Block_V1, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.num_kernels = num_kernels kernels = [] for i in range(self.num_kernels): kernels.append(nn.Conv2d(in_channels, out_channels, kernel_size=2 * i + 1, padding=i)) self.kernels = nn.ModuleList(kernels) if init_weight: self._initialize_weights() def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') if m.bias is not None: nn.init.constant_(m.bias, 0) def forward(self, x): res_list = [] for i in range(self.num_kernels): res_list.append(self.kernels[i](x)) res = torch.stack(res_list, dim=-1).mean(-1) return res def FFT_for_Period(x, k=2): # [B, T, C] xf = torch.fft.rfft(x, dim=1) # find period by amplitudes frequency_list = abs(xf).mean(0).mean(-1) frequency_list[0] = 0 _, top_list = torch.topk(frequency_list, k) top_list = top_list.detach().cpu().numpy() period = x.shape[1] // top_list return period, abs(xf).mean(-1)[:, top_list] class TimesBlock(nn.Module): def __init__(self, seq_len,pred_len,top_k,d_model,d_ff,num_kernels): super(TimesBlock, self).__init__() self.seq_len = seq_len self.pred_len = pred_len self.k = top_k # parameter-efficient design self.conv = nn.Sequential( Inception_Block_V1(d_model, d_ff, num_kernels=num_kernels), nn.GELU(), Inception_Block_V1(d_ff, d_model, num_kernels=num_kernels) ) def forward(self, x): B, T, N = x.size() period_list, period_weight = FFT_for_Period(x, self.k) res = [] for i in range(self.k): period = period_list[i] # padding if (self.seq_len + self.pred_len) % period != 0: length = (((self.seq_len + self.pred_len) // period) + 1) * period padding = torch.zeros([x.shape[0], (length - (self.seq_len + self.pred_len)), x.shape[2]]).to(x.device) out = torch.cat([x, padding], dim=1) else: length = (self.seq_len + self.pred_len) out = x # reshape out = out.reshape(B, length // period, period, N).permute(0, 3, 1, 2).contiguous() # 2D conv: from 1d Variation to 2d Variation out = self.conv(out) # reshape back out = out.permute(0, 2, 3, 1).reshape(B, -1, N) res.append(out[:, :(self.seq_len + self.pred_len), :]) res = torch.stack(res, dim=-1) # adaptive aggregation period_weight = F.softmax(period_weight, dim=1) period_weight = period_weight.unsqueeze( 1).unsqueeze(1).repeat(1, T, N, 1) res = torch.sum(res * period_weight, -1) # residual connection res = res + x return res class TimesNet(nn.Module): """ Paper link: https://openreview.net/pdf?id=ju_Uqw384Oq """ 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, d_model=16, d_ff=32, des='Exp', itr=1, top_k=5,embed='timeF',freq='h', dropout=0.1,num_kernels=6 ): super(TimesNet, self).__init__() self.task_name = 'TimesNet' self.seq_len = seq_len self.label_len = label_len self.pred_len = pred_len self.model = nn.ModuleList([TimesBlock(seq_len,pred_len,top_k,d_model,d_ff,num_kernels) for _ in range(e_layers)]) self.enc_embedding = DataEmbedding(enc_in, d_model, embed, freq, dropout) self.layer = e_layers self.layer_norm = nn.LayerNorm(d_model) self.predict_linear = nn.Linear(self.seq_len, self.pred_len+ self.seq_len) self.projection = nn.Linear(d_model, c_out, bias=True) def forward(self, x_enc, x_mark_enc=None): ''' :param x_enc: torch.Size([32, 96, 7]) :param x_mark_enc: torch.Size([32, 96, 4]) :param x_dec: torch.Size([32, 144, 7]) :param x_mark_dec: torch.Size([32, 144, 4]) :return: ''' # Normalization from Non-stationary Transformer 减均值除方差 means = x_enc.mean(1, keepdim=True).detach() x_enc = x_enc - means stdev = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5) x_enc /= stdev target_mean = means[:, :, -1] target_stdev = stdev[:, :, -1] # embedding ''' self.enc_embedding: DataEmbedding( (value_embedding): TokenEmbedding( (tokenConv): Conv1d(7, 16, kernel_size=(3,), stride=(1,), padding=(1,), bias=False, padding_mode=circular) ) (position_embedding): PositionalEmbedding() (temporal_embedding): TimeFeatureEmbedding( (embed): Linear(in_features=4, out_features=16, bias=False) ) (dropout): Dropout(p=0.1, inplace=False) ) ''' enc_out = self.enc_embedding(x_enc, x_mark_enc) # [B,T,C] x_mark_enc=None ''' self.predict_linear: Linear(in_features=96, out_features=192, bias=True) enc_out :[100,96,16] -> [100,16,96]-> [100,16,192]->[100,192,16] ''' enc_out = self.predict_linear(enc_out.permute(0, 2, 1)).permute(0, 2, 1) # align temporal dimension # TimesNet ''' self.model[i]: TimesBlock( (conv): Sequential( (0): Inception_Block_V1( (kernels): ModuleList( (0): Conv2d(16, 32, kernel_size=(1, 1), stride=(1, 1)) (1): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)) (3): Conv2d(16, 32, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) (4): Conv2d(16, 32, kernel_size=(9, 9), stride=(1, 1), padding=(4, 4)) (5): Conv2d(16, 32, kernel_size=(11, 11), stride=(1, 1), padding=(5, 5)) ) ) (1): GELU(approximate=none) (2): Inception_Block_V1( (kernels): ModuleList( (0): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1)) (1): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): Conv2d(32, 16, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)) (3): Conv2d(32, 16, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3)) (4): Conv2d(32, 16, kernel_size=(9, 9), stride=(1, 1), padding=(4, 4)) (5): Conv2d(32, 16, kernel_size=(11, 11), stride=(1, 1), padding=(5, 5)) ) ) self.layer_norm: LayerNorm((16,), eps=1e-05, elementwise_affine=True) ''' for i in range(self.layer): enc_out = self.layer_norm(self.model[i](enc_out)) # porject back ''' self.projection: Linear(in_features=16, out_features=7, bias=True) enc_out: [100, 97, 16]-> [100, 97, 1] ''' dec_out = self.projection(enc_out) # torch.Size([100, 34, 16])-> torch.Size([100, 34, 1]) dec_out=dec_out*target_stdev.unsqueeze(1).repeat(1, self.pred_len + self.seq_len, 1)+target_mean.unsqueeze(1).repeat(1, self.pred_len + self.seq_len, 1) return dec_out[:, -self.pred_len:, :] class TimesNetModel(BaseModel): 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, d_model=16, d_ff=32, des='Exp', itr=1, top_k=5,embed='timeF',freq='h', dropout=0.1,num_kernels=6, **kwargs): # saves arguments in signature to `.hparams` attribute, mandatory call - do not skip this self.save_hyperparameters() # pass additional arguments to BaseModel.__init__, mandatory call - do not skip this super().__init__(**kwargs) self.network = TimesNet( 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, 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 ) # 修改，锂电池预测 def forward(self, x: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: x_enc = x["encoder_cont"][:,:,:-1] # torch.Size([100, 10, 9]) # 输出 prediction = self.network(x_enc) # 输出rescale， rescale predictions into target space 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=100,96,5 input=torch.ones((N,L,C)) model=TimesNet(seq_len=L, enc_in=C, pred_len=1) out=model(input) print(out.shape)