import torch import torch.nn as nn import torch.nn.functional as F import numpy as np try: from .TimesNet import DataEmbedding except Exception: from TimesNet import DataEmbedding class _BlockConfig: def __init__(self, seq_len: int, pred_len: int, d_model: int, d_ff: int, num_kernels: int, top_k: int = 2, num_stations: int = 0): self.seq_len = seq_len self.pred_len = pred_len self.d_model = d_model self.d_ff = d_ff self.num_kernels = num_kernels self.top_k = top_k self.num_stations = num_stations 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, kernel in enumerate(self.kernels): res_list.append(kernel(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 TimesBlockStationCond(nn.Module): """TimesBlock with station ID conditioning (one-hot encoded as 1 channel).""" def __init__(self, configs): super(TimesBlockStationCond, self).__init__() self.seq_len = configs.seq_len self.pred_len = configs.pred_len self.k = configs.top_k self.num_stations = getattr(configs, 'num_stations', 0) # Station ID embedding: maps station ID to d_model dimension # This provides richer conditioning information than a single scalar if self.num_stations > 0: self.station_embedding = nn.Embedding(self.num_stations, configs.d_model) # Initialize with small random values nn.init.normal_(self.station_embedding.weight, mean=0.0, std=0.02) # Inception blocks self.conv = nn.Sequential( Inception_Block_V1(configs.d_model, configs.d_ff, num_kernels=configs.num_kernels), nn.GELU(), Inception_Block_V1(configs.d_ff, configs.d_model, num_kernels=configs.num_kernels) ) def forward(self, x, station_ids: torch.Tensor = None): """ Args: x: (B, T, N) input features station_ids: (B,) LongTensor of station IDs (0 to num_stations-1) """ 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 to 2D: (B, N, H, W) out = out.reshape(B, length // period, period, N).permute(0, 3, 1, 2).contiguous() # Inject station ID conditioning via embedding addition # This provides richer conditioning (d_model dimensions) compared to single scalar if station_ids is not None and self.num_stations > 0: # Get station embeddings: (B, d_model) station_ids_flat = station_ids.view(B) station_emb = self.station_embedding(station_ids_flat) # (B, d_model) # out shape: (B, d_model, H, W) # Expand station embedding to spatial dimensions: (B, d_model, H, W) H = out.size(2) W = out.size(3) station_emb_spatial = station_emb.view(B, N, 1, 1).expand(-1, -1, H, W) # Add station embedding to features (element-wise addition) # This allows the model to learn station-specific feature modifications out = out + station_emb_spatial # 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 TimesNetPointCloud(nn.Module): """TimesNet reconstruction with exposed encode/project methods for point-cloud mixing.""" def __init__(self, configs): super().__init__() self.configs = configs self.seq_len = configs.seq_len self.pred_len = getattr(configs, 'pred_len', 0) self.top_k = configs.top_k self.d_model = configs.d_model self.d_ff = configs.d_ff self.num_kernels = configs.num_kernels self.e_layers = configs.e_layers self.dropout = configs.dropout self.c_out = configs.c_out self.num_stations = getattr(configs, 'num_stations', 0) self.enc_embedding = DataEmbedding(configs.enc_in, self.d_model, configs.embed, configs.freq, configs.dropout, configs.seq_len) self.model = nn.ModuleList([ TimesBlockStationCond(_BlockConfig(self.seq_len, 0, self.d_model, self.d_ff, self.num_kernels, self.top_k, self.num_stations)) for _ in range(self.e_layers) ]) self.layer = self.e_layers self.layer_norm = nn.LayerNorm(self.d_model) self.projection = nn.Linear(self.d_model, self.c_out, bias=True) def encode_features_for_reconstruction(self, x_enc: torch.Tensor, station_ids: torch.Tensor = None): """ Encode input with optional station ID conditioning. Args: x_enc: (B, T, C) input signal station_ids: (B,) LongTensor of station IDs (0 to num_stations-1), optional """ means = x_enc.mean(1, keepdim=True).detach() x_norm = x_enc - means stdev = torch.sqrt(torch.var(x_norm, dim=1, keepdim=True, unbiased=False) + 1e-5) x_norm = x_norm / stdev enc_out = self.enc_embedding(x_norm, None) for i in range(self.layer): enc_out = self.layer_norm(self.model[i](enc_out, station_ids)) return enc_out, means, stdev def project_features_for_reconstruction(self, enc_out: torch.Tensor, means: torch.Tensor, stdev: torch.Tensor): dec_out = self.projection(enc_out) dec_out = dec_out * (stdev[:, 0, :].unsqueeze(1).repeat(1, self.pred_len + self.seq_len, 1)) dec_out = dec_out + (means[:, 0, :].unsqueeze(1).repeat(1, self.pred_len + self.seq_len, 1)) return dec_out def anomaly_detection(self, x_enc: torch.Tensor, station_ids: torch.Tensor = None): """Full reconstruction pass with optional station ID conditioning.""" enc_out, means, stdev = self.encode_features_for_reconstruction(x_enc, station_ids) return self.project_features_for_reconstruction(enc_out, means, stdev) def forward(self, x_enc, station_ids=None, x_mark_enc=None, x_dec=None, x_mark_dec=None, mask=None): """ Forward pass compatible with anomaly_detection task. Args: x_enc: (B, T, C) input signal station_ids: (B,) LongTensor of station IDs, optional """ return self.anomaly_detection(x_enc, station_ids)