first timesnet try

models/TimesNet/TimesNet.py

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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.sub(means)
+        stdev = torch.sqrt(
+            torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)
+        x_enc = x_enc.div(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))
+        # project back
+        dec_out = self.projection(enc_out)
+
+        # De-Normalization from Non-stationary Transformer
+        dec_out = dec_out.mul(
+                  (stdev[:, 0, :].unsqueeze(1).repeat(
+                      1, self.pred_len + self.seq_len, 1)))
+        dec_out = dec_out.add(
+                  (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.sub(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 = x_enc.div(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))
+        # project back
+        dec_out = self.projection(enc_out)
+
+        # De-Normalization from Non-stationary Transformer
+        dec_out = dec_out.mul(
+                  (stdev[:, 0, :].unsqueeze(1).repeat(
+                      1, self.pred_len + self.seq_len, 1)))
+        dec_out = dec_out.add(
+                  (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.sub(means)
+        stdev = torch.sqrt(
+            torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)
+        x_enc = x_enc.div(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))
+        # project back
+        dec_out = self.projection(enc_out)
+
+        # De-Normalization from Non-stationary Transformer
+        dec_out = dec_out.mul(
+                  (stdev[:, 0, :].unsqueeze(1).repeat(
+                      1, self.pred_len + self.seq_len, 1)))
+        dec_out = dec_out.add(
+                  (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=None, x_dec=None, x_mark_dec=None, 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