first commit

models/Pyraformer.py

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+import torch
+import torch.nn as nn
+from layers.Pyraformer_EncDec import Encoder
+
+
+class Model(nn.Module):
+    """ 
+    Pyraformer: Pyramidal attention to reduce complexity
+    Paper link: https://openreview.net/pdf?id=0EXmFzUn5I
+    """
+
+    def __init__(self, configs, window_size=[4,4], inner_size=5):
+        """
+        window_size: list, the downsample window size in pyramidal attention.
+        inner_size: int, the size of neighbour attention
+        """
+        super().__init__()
+        self.task_name = configs.task_name
+        self.pred_len = configs.pred_len
+        self.d_model = configs.d_model
+
+        if self.task_name == 'short_term_forecast':
+            window_size = [2,2]
+        self.encoder = Encoder(configs, window_size, inner_size)
+
+        if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast':
+            self.projection = nn.Linear(
+                (len(window_size)+1)*self.d_model, self.pred_len * configs.enc_in)
+        elif self.task_name == 'imputation' or self.task_name == 'anomaly_detection':
+            self.projection = nn.Linear(
+                (len(window_size)+1)*self.d_model, configs.enc_in, bias=True)
+        elif self.task_name == 'classification':
+            self.act = torch.nn.functional.gelu
+            self.dropout = nn.Dropout(configs.dropout)
+            self.projection = nn.Linear(
+                (len(window_size)+1)*self.d_model * configs.seq_len, configs.num_class)
+
+    def long_forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
+        enc_out = self.encoder(x_enc, x_mark_enc)[:, -1, :]
+        dec_out = self.projection(enc_out).view(
+            enc_out.size(0), self.pred_len, -1)
+        return dec_out
+    
+    def short_forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
+        # Normalization
+        mean_enc = x_enc.mean(1, keepdim=True).detach()  # B x 1 x E
+        x_enc = x_enc - mean_enc
+        std_enc = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5).detach()  # B x 1 x E
+        x_enc = x_enc / std_enc
+
+        enc_out = self.encoder(x_enc, x_mark_enc)[:, -1, :]
+        dec_out = self.projection(enc_out).view(
+            enc_out.size(0), self.pred_len, -1)
+        
+        dec_out = dec_out * std_enc + mean_enc
+        return dec_out
+
+    def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask):
+        enc_out = self.encoder(x_enc, x_mark_enc)
+        dec_out = self.projection(enc_out)
+        return dec_out
+
+    def anomaly_detection(self, x_enc, x_mark_enc):
+        enc_out = self.encoder(x_enc, x_mark_enc)
+        dec_out = self.projection(enc_out)
+        return dec_out
+
+    def classification(self, x_enc, x_mark_enc):
+        # enc
+        enc_out = self.encoder(x_enc, x_mark_enc=None)
+
+        # 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':
+            dec_out = self.long_forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)
+            return dec_out[:, -self.pred_len:, :]  # [B, L, D]
+        if self.task_name == 'short_term_forecast':
+            dec_out = self.short_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, x_mark_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