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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torch.fft | |
| from layers.Embed import DataEmbedding | |
| from layers.Conv_Blocks import Inception_Block_V1 | |
| 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, configs): | |
| super(TimesBlock, self).__init__() | |
| self.seq_len = configs.seq_len | |
| self.pred_len = configs.pred_len | |
| self.k = configs.top_k | |
| # parameter-efficient design | |
| 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): | |
| 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 Model(nn.Module): | |
| """ | |
| Paper link: https://openreview.net/pdf?id=ju_Uqw384Oq | |
| """ | |
| def __init__(self, configs): | |
| super(Model, self).__init__() | |
| self.configs = configs | |
| self.task_name = configs.task_name | |
| self.seq_len = configs.seq_len | |
| self.label_len = configs.label_len | |
| self.pred_len = configs.pred_len | |
| self.model = nn.ModuleList([TimesBlock(configs) | |
| for _ in range(configs.e_layers)]) | |
| self.enc_embedding = DataEmbedding(configs.enc_in, configs.d_model, configs.embed, configs.freq, | |
| configs.dropout) | |
| self.layer = configs.e_layers | |
| self.layer_norm = nn.LayerNorm(configs.d_model) | |
| if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast': | |
| self.predict_linear = nn.Linear( | |
| self.seq_len, self.pred_len + self.seq_len) | |
| self.projection = nn.Linear( | |
| configs.d_model, configs.c_out, bias=True) | |
| if self.task_name == 'imputation' or self.task_name == 'anomaly_detection': | |
| self.projection = nn.Linear( | |
| configs.d_model, configs.c_out, bias=True) | |
| if self.task_name == 'classification': | |
| self.act = F.gelu | |
| self.dropout = nn.Dropout(configs.dropout) | |
| self.projection = nn.Linear( | |
| configs.d_model * configs.seq_len, configs.num_class) | |
| def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec): | |
| # Normalization from Non-stationary Transformer | |
| 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 | |
| # embedding | |
| enc_out = self.enc_embedding(x_enc, x_mark_enc) # [B,T,C] | |
| enc_out = self.predict_linear(enc_out.permute(0, 2, 1)).permute( | |
| 0, 2, 1) # align temporal dimension | |
| # TimesNet | |
| for i in range(self.layer): | |
| enc_out = self.layer_norm(self.model[i](enc_out)) | |
| # porject back | |
| dec_out = self.projection(enc_out) | |
| # De-Normalization from Non-stationary Transformer | |
| 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 imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask): | |
| # Normalization from Non-stationary Transformer | |
| means = torch.sum(x_enc, dim=1) / torch.sum(mask == 1, dim=1) | |
| means = means.unsqueeze(1).detach() | |
| x_enc = x_enc - means | |
| x_enc = x_enc.masked_fill(mask == 0, 0) | |
| stdev = torch.sqrt(torch.sum(x_enc * x_enc, dim=1) / | |
| torch.sum(mask == 1, dim=1) + 1e-5) | |
| stdev = stdev.unsqueeze(1).detach() | |
| x_enc /= stdev | |
| # embedding | |
| enc_out = self.enc_embedding(x_enc, x_mark_enc) # [B,T,C] | |
| # TimesNet | |
| for i in range(self.layer): | |
| enc_out = self.layer_norm(self.model[i](enc_out)) | |
| # porject back | |
| dec_out = self.projection(enc_out) | |
| # De-Normalization from Non-stationary Transformer | |
| 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): | |
| # Normalization from Non-stationary Transformer | |
| 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 | |
| # embedding | |
| enc_out = self.enc_embedding(x_enc, None) # [B,T,C] | |
| # TimesNet | |
| for i in range(self.layer): | |
| enc_out = self.layer_norm(self.model[i](enc_out)) | |
| # porject back | |
| dec_out = self.projection(enc_out) | |
| # De-Normalization from Non-stationary Transformer | |
| 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 classification(self, x_enc, x_mark_enc): | |
| # embedding | |
| enc_out = self.enc_embedding(x_enc, None) # [B,T,C] | |
| # TimesNet | |
| for i in range(self.layer): | |
| enc_out = self.layer_norm(self.model[i](enc_out)) | |
| # Output | |
| # the output transformer encoder/decoder embeddings don't include non-linearity | |
| output = self.act(enc_out) | |
| output = self.dropout(output) | |
| # zero-out padding embeddings | |
| output = output * x_mark_enc.unsqueeze(-1) | |
| # (batch_size, seq_length * d_model) | |
| output = output.reshape(output.shape[0], -1) | |
| output = self.projection(output) # (batch_size, num_classes) | |
| return output | |
| def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None): | |
| if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast': | |
| dec_out = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec) | |
| return dec_out[:, -self.pred_len:, :] # [B, L, D] | |
| if self.task_name == 'imputation': | |
| dec_out = self.imputation( | |
| x_enc, x_mark_enc, x_dec, x_mark_dec, mask) | |
| return dec_out # [B, L, D] | |
| if self.task_name == 'anomaly_detection': | |
| dec_out = self.anomaly_detection(x_enc) | |
| return dec_out # [B, L, D] | |
| if self.task_name == 'classification': | |
| dec_out = self.classification(x_enc, x_mark_enc) | |
| return dec_out # [B, N] | |
| return None | |