import torch import torch.nn as nn import torch.nn.functional as F class LayerNorm(nn.Module): """ LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """ def __init__(self, ndim, bias): super().__init__() self.weight = nn.Parameter(torch.ones(ndim)) self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None def forward(self, input): return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5) class ResBlock(nn.Module): def __init__(self, input_dim, hidden_dim, output_dim, dropout=0.1, bias=True): super().__init__() self.fc1 = nn.Linear(input_dim, hidden_dim, bias=bias) self.fc2 = nn.Linear(hidden_dim, output_dim, bias=bias) self.fc3 = nn.Linear(input_dim, output_dim, bias=bias) self.dropout = nn.Dropout(dropout) self.relu = nn.ReLU() self.ln = LayerNorm(output_dim, bias=bias) def forward(self, x): out = self.fc1(x) out = self.relu(out) out = self.fc2(out) out = self.dropout(out) out = out + self.fc3(x) out = self.ln(out) return out #TiDE class Model(nn.Module): """ paper: https://arxiv.org/pdf/2304.08424.pdf """ def __init__(self, configs, bias=True, feature_encode_dim=2): super(Model, self).__init__() self.configs = configs self.task_name = configs.task_name self.seq_len = configs.seq_len #L self.label_len = configs.label_len self.pred_len = configs.pred_len #H self.hidden_dim=configs.d_model self.res_hidden=configs.d_model self.encoder_num=configs.e_layers self.decoder_num=configs.d_layers self.freq=configs.freq self.feature_encode_dim=feature_encode_dim self.decode_dim = configs.c_out self.temporalDecoderHidden=configs.d_ff dropout=configs.dropout freq_map = {'h': 4, 't': 5, 's': 6, 'm': 1, 'a': 1, 'w': 2, 'd': 3, 'b': 3} self.feature_dim=freq_map[self.freq] flatten_dim = self.seq_len + (self.seq_len + self.pred_len) * self.feature_encode_dim self.feature_encoder = ResBlock(self.feature_dim, self.res_hidden, self.feature_encode_dim, dropout, bias) self.encoders = nn.Sequential(ResBlock(flatten_dim, self.res_hidden, self.hidden_dim, dropout, bias),*([ ResBlock(self.hidden_dim, self.res_hidden, self.hidden_dim, dropout, bias)]*(self.encoder_num-1))) if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast': self.decoders = nn.Sequential(*([ ResBlock(self.hidden_dim, self.res_hidden, self.hidden_dim, dropout, bias)]*(self.decoder_num-1)),ResBlock(self.hidden_dim, self.res_hidden, self.decode_dim * self.pred_len, dropout, bias)) self.temporalDecoder = ResBlock(self.decode_dim + self.feature_encode_dim, self.temporalDecoderHidden, 1, dropout, bias) self.residual_proj = nn.Linear(self.seq_len, self.pred_len, bias=bias) if self.task_name == 'imputation': self.decoders = nn.Sequential(*([ ResBlock(self.hidden_dim, self.res_hidden, self.hidden_dim, dropout, bias)]*(self.decoder_num-1)),ResBlock(self.hidden_dim, self.res_hidden, self.decode_dim * self.seq_len, dropout, bias)) self.temporalDecoder = ResBlock(self.decode_dim + self.feature_encode_dim, self.temporalDecoderHidden, 1, dropout, bias) self.residual_proj = nn.Linear(self.seq_len, self.seq_len, bias=bias) if self.task_name == 'anomaly_detection': self.decoders = nn.Sequential(*([ ResBlock(self.hidden_dim, self.res_hidden, self.hidden_dim, dropout, bias)]*(self.decoder_num-1)),ResBlock(self.hidden_dim, self.res_hidden, self.decode_dim * self.seq_len, dropout, bias)) self.temporalDecoder = ResBlock(self.decode_dim + self.feature_encode_dim, self.temporalDecoderHidden, 1, dropout, bias) self.residual_proj = nn.Linear(self.seq_len, self.seq_len, bias=bias) def forecast(self, x_enc, x_mark_enc, x_dec, batch_y_mark): # Normalization 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 feature = self.feature_encoder(batch_y_mark) hidden = self.encoders(torch.cat([x_enc, feature.reshape(feature.shape[0], -1)], dim=-1)) decoded = self.decoders(hidden).reshape(hidden.shape[0], self.pred_len, self.decode_dim) dec_out = self.temporalDecoder(torch.cat([feature[:,self.seq_len:], decoded], dim=-1)).squeeze(-1) + self.residual_proj(x_enc) # De-Normalization dec_out = dec_out * (stdev[:, 0].unsqueeze(1).repeat(1, self.pred_len)) dec_out = dec_out + (means[:, 0].unsqueeze(1).repeat(1, self.pred_len)) return dec_out def imputation(self, x_enc, x_mark_enc, x_dec, batch_y_mark, mask): # Normalization 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 feature = self.feature_encoder(x_mark_enc) hidden = self.encoders(torch.cat([x_enc, feature.reshape(feature.shape[0], -1)], dim=-1)) decoded = self.decoders(hidden).reshape(hidden.shape[0], self.seq_len, self.decode_dim) dec_out = self.temporalDecoder(torch.cat([feature[:,:self.seq_len], decoded], dim=-1)).squeeze(-1) + self.residual_proj(x_enc) # De-Normalization dec_out = dec_out * (stdev[:, 0].unsqueeze(1).repeat(1, self.seq_len)) dec_out = dec_out + (means[:, 0].unsqueeze(1).repeat(1, self.seq_len)) return dec_out def forward(self, x_enc, x_mark_enc, x_dec, batch_y_mark, mask=None): '''x_mark_enc is the exogenous dynamic feature described in the original paper''' if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast': if batch_y_mark is None: batch_y_mark = torch.zeros((x_enc.shape[0], self.seq_len+self.pred_len, self.feature_dim)).to(x_enc.device).detach() else: batch_y_mark = torch.concat([x_mark_enc, batch_y_mark[:, -self.pred_len:, :]],dim=1) dec_out = torch.stack([self.forecast(x_enc[:, :, feature], x_mark_enc, x_dec, batch_y_mark) for feature in range(x_enc.shape[-1])],dim=-1) return dec_out # [B, L, D] if self.task_name == 'imputation': dec_out = torch.stack([self.imputation(x_enc[:, :, feature], x_mark_enc, x_dec, batch_y_mark, mask) for feature in range(x_enc.shape[-1])],dim=-1) return dec_out # [B, L, D] if self.task_name == 'anomaly_detection': raise NotImplementedError("Task anomaly_detection for Tide is temporarily not supported") if self.task_name == 'classification': raise NotImplementedError("Task classification for Tide is temporarily not supported") return None