first commit

models/TimeXer.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from layers.SelfAttention_Family import FullAttention, AttentionLayer
+from layers.Embed import DataEmbedding_inverted, PositionalEmbedding
+import numpy as np
+
+
+class FlattenHead(nn.Module):
+    def __init__(self, n_vars, nf, target_window, head_dropout=0):
+        super().__init__()
+        self.n_vars = n_vars
+        self.flatten = nn.Flatten(start_dim=-2)
+        self.linear = nn.Linear(nf, target_window)
+        self.dropout = nn.Dropout(head_dropout)
+
+    def forward(self, x):  # x: [bs x nvars x d_model x patch_num]
+        x = self.flatten(x)
+        x = self.linear(x)
+        x = self.dropout(x)
+        return x
+
+
+class EnEmbedding(nn.Module):
+    def __init__(self, n_vars, d_model, patch_len, dropout):
+        super(EnEmbedding, self).__init__()
+        # Patching
+        self.patch_len = patch_len
+
+        self.value_embedding = nn.Linear(patch_len, d_model, bias=False)
+        self.glb_token = nn.Parameter(torch.randn(1, n_vars, 1, d_model))
+        self.position_embedding = PositionalEmbedding(d_model)
+
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        # do patching
+        n_vars = x.shape[1]
+        glb = self.glb_token.repeat((x.shape[0], 1, 1, 1))
+
+        x = x.unfold(dimension=-1, size=self.patch_len, step=self.patch_len)
+        x = torch.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3]))
+        # Input encoding
+        x = self.value_embedding(x) + self.position_embedding(x)
+        x = torch.reshape(x, (-1, n_vars, x.shape[-2], x.shape[-1]))
+        x = torch.cat([x, glb], dim=2)
+        x = torch.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3]))
+        return self.dropout(x), n_vars
+
+
+class Encoder(nn.Module):
+    def __init__(self, layers, norm_layer=None, projection=None):
+        super(Encoder, self).__init__()
+        self.layers = nn.ModuleList(layers)
+        self.norm = norm_layer
+        self.projection = projection
+
+    def forward(self, x, cross, x_mask=None, cross_mask=None, tau=None, delta=None):
+        for layer in self.layers:
+            x = layer(x, cross, x_mask=x_mask, cross_mask=cross_mask, tau=tau, delta=delta)
+
+        if self.norm is not None:
+            x = self.norm(x)
+
+        if self.projection is not None:
+            x = self.projection(x)
+        return x
+
+
+class EncoderLayer(nn.Module):
+    def __init__(self, self_attention, cross_attention, d_model, d_ff=None,
+                 dropout=0.1, activation="relu"):
+        super(EncoderLayer, self).__init__()
+        d_ff = d_ff or 4 * d_model
+        self.self_attention = self_attention
+        self.cross_attention = cross_attention
+        self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
+        self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.norm3 = nn.LayerNorm(d_model)
+        self.dropout = nn.Dropout(dropout)
+        self.activation = F.relu if activation == "relu" else F.gelu
+
+    def forward(self, x, cross, x_mask=None, cross_mask=None, tau=None, delta=None):
+        B, L, D = cross.shape
+        x = x + self.dropout(self.self_attention(
+            x, x, x,
+            attn_mask=x_mask,
+            tau=tau, delta=None
+        )[0])
+        x = self.norm1(x)
+
+        x_glb_ori = x[:, -1, :].unsqueeze(1)
+        x_glb = torch.reshape(x_glb_ori, (B, -1, D))
+        x_glb_attn = self.dropout(self.cross_attention(
+            x_glb, cross, cross,
+            attn_mask=cross_mask,
+            tau=tau, delta=delta
+        )[0])
+        x_glb_attn = torch.reshape(x_glb_attn,
+                                   (x_glb_attn.shape[0] * x_glb_attn.shape[1], x_glb_attn.shape[2])).unsqueeze(1)
+        x_glb = x_glb_ori + x_glb_attn
+        x_glb = self.norm2(x_glb)
+
+        y = x = torch.cat([x[:, :-1, :], x_glb], dim=1)
+
+        y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
+        y = self.dropout(self.conv2(y).transpose(-1, 1))
+
+        return self.norm3(x + y)
+
+
+class Model(nn.Module):
+
+    def __init__(self, configs):
+        super(Model, self).__init__()
+        self.task_name = configs.task_name
+        self.features = configs.features
+        self.seq_len = configs.seq_len
+        self.pred_len = configs.pred_len
+        self.use_norm = configs.use_norm
+        self.patch_len = configs.patch_len
+        self.patch_num = int(configs.seq_len // configs.patch_len)
+        self.n_vars = 1 if configs.features == 'MS' else configs.enc_in
+        # Embedding
+        self.en_embedding = EnEmbedding(self.n_vars, configs.d_model, self.patch_len, configs.dropout)
+
+        self.ex_embedding = DataEmbedding_inverted(configs.seq_len, configs.d_model, configs.embed, configs.freq,
+                                                   configs.dropout)
+
+        # Encoder-only architecture
+        self.encoder = Encoder(
+            [
+                EncoderLayer(
+                    AttentionLayer(
+                        FullAttention(False, configs.factor, attention_dropout=configs.dropout,
+                                      output_attention=False),
+                        configs.d_model, configs.n_heads),
+                    AttentionLayer(
+                        FullAttention(False, configs.factor, attention_dropout=configs.dropout,
+                                      output_attention=False),
+                        configs.d_model, configs.n_heads),
+                    configs.d_model,
+                    configs.d_ff,
+                    dropout=configs.dropout,
+                    activation=configs.activation,
+                )
+                for l in range(configs.e_layers)
+            ],
+            norm_layer=torch.nn.LayerNorm(configs.d_model)
+        )
+        self.head_nf = configs.d_model * (self.patch_num + 1)
+        self.head = FlattenHead(configs.enc_in, self.head_nf, configs.pred_len,
+                                head_dropout=configs.dropout)
+
+    def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
+        if self.use_norm:
+            # 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
+
+        _, _, N = x_enc.shape
+
+        en_embed, n_vars = self.en_embedding(x_enc[:, :, -1].unsqueeze(-1).permute(0, 2, 1))
+        ex_embed = self.ex_embedding(x_enc[:, :, :-1], x_mark_enc)
+
+        enc_out = self.encoder(en_embed, ex_embed)
+        enc_out = torch.reshape(
+            enc_out, (-1, n_vars, enc_out.shape[-2], enc_out.shape[-1]))
+        # z: [bs x nvars x d_model x patch_num]
+        enc_out = enc_out.permute(0, 1, 3, 2)
+
+        dec_out = self.head(enc_out)  # z: [bs x nvars x target_window]
+        dec_out = dec_out.permute(0, 2, 1)
+
+        if self.use_norm:
+            # De-Normalization from Non-stationary Transformer
+            dec_out = dec_out * (stdev[:, 0, -1:].unsqueeze(1).repeat(1, self.pred_len, 1))
+            dec_out = dec_out + (means[:, 0, -1:].unsqueeze(1).repeat(1, self.pred_len, 1))
+
+        return dec_out
+
+
+    def forecast_multi(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
+        if self.use_norm:
+            # 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
+
+        _, _, N = x_enc.shape
+
+        en_embed, n_vars = self.en_embedding(x_enc.permute(0, 2, 1))
+        ex_embed = self.ex_embedding(x_enc, x_mark_enc)
+
+        enc_out = self.encoder(en_embed, ex_embed)
+        enc_out = torch.reshape(
+            enc_out, (-1, n_vars, enc_out.shape[-2], enc_out.shape[-1]))
+        # z: [bs x nvars x d_model x patch_num]
+        enc_out = enc_out.permute(0, 1, 3, 2)
+
+        dec_out = self.head(enc_out)  # z: [bs x nvars x target_window]
+        dec_out = dec_out.permute(0, 2, 1)
+
+        if self.use_norm:
+            # De-Normalization from Non-stationary Transformer
+            dec_out = dec_out * (stdev[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1))
+            dec_out = dec_out + (means[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1))
+
+        return dec_out
+
+    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':
+            if self.features == 'M':
+                dec_out = self.forecast_multi(x_enc, x_mark_enc, x_dec, x_mark_dec)
+                return dec_out[:, -self.pred_len:, :]  # [B, L, D]
+            else:
+                dec_out = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)
+                return dec_out[:, -self.pred_len:, :]  # [B, L, D]
+        else:
+            return None