first commit

models/Autoformer.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from layers.Embed import DataEmbedding, DataEmbedding_wo_pos
+from layers.AutoCorrelation import AutoCorrelation, AutoCorrelationLayer
+from layers.Autoformer_EncDec import Encoder, Decoder, EncoderLayer, DecoderLayer, my_Layernorm, series_decomp
+import math
+import numpy as np
+
+
+class Model(nn.Module):
+    """
+    Autoformer is the first method to achieve the series-wise connection,
+    with inherent O(LlogL) complexity
+    Paper link: https://openreview.net/pdf?id=I55UqU-M11y
+    """
+
+    def __init__(self, configs):
+        super(Model, self).__init__()
+        self.task_name = configs.task_name
+        self.seq_len = configs.seq_len
+        self.label_len = configs.label_len
+        self.pred_len = configs.pred_len
+
+        # Decomp
+        kernel_size = configs.moving_avg
+        self.decomp = series_decomp(kernel_size)
+
+        # Embedding
+        self.enc_embedding = DataEmbedding_wo_pos(configs.enc_in, configs.d_model, configs.embed, configs.freq,
+                                                  configs.dropout)
+        # Encoder
+        self.encoder = Encoder(
+            [
+                EncoderLayer(
+                    AutoCorrelationLayer(
+                        AutoCorrelation(False, configs.factor, attention_dropout=configs.dropout,
+                                        output_attention=False),
+                        configs.d_model, configs.n_heads),
+                    configs.d_model,
+                    configs.d_ff,
+                    moving_avg=configs.moving_avg,
+                    dropout=configs.dropout,
+                    activation=configs.activation
+                ) for l in range(configs.e_layers)
+            ],
+            norm_layer=my_Layernorm(configs.d_model)
+        )
+        # Decoder
+        if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast':
+            self.dec_embedding = DataEmbedding_wo_pos(configs.dec_in, configs.d_model, configs.embed, configs.freq,
+                                                      configs.dropout)
+            self.decoder = Decoder(
+                [
+                    DecoderLayer(
+                        AutoCorrelationLayer(
+                            AutoCorrelation(True, configs.factor, attention_dropout=configs.dropout,
+                                            output_attention=False),
+                            configs.d_model, configs.n_heads),
+                        AutoCorrelationLayer(
+                            AutoCorrelation(False, configs.factor, attention_dropout=configs.dropout,
+                                            output_attention=False),
+                            configs.d_model, configs.n_heads),
+                        configs.d_model,
+                        configs.c_out,
+                        configs.d_ff,
+                        moving_avg=configs.moving_avg,
+                        dropout=configs.dropout,
+                        activation=configs.activation,
+                    )
+                    for l in range(configs.d_layers)
+                ],
+                norm_layer=my_Layernorm(configs.d_model),
+                projection=nn.Linear(configs.d_model, configs.c_out, bias=True)
+            )
+        if self.task_name == 'imputation':
+            self.projection = nn.Linear(
+                configs.d_model, configs.c_out, bias=True)
+        if 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):
+        # decomp init
+        mean = torch.mean(x_enc, dim=1).unsqueeze(
+            1).repeat(1, self.pred_len, 1)
+        zeros = torch.zeros([x_dec.shape[0], self.pred_len,
+                             x_dec.shape[2]], device=x_enc.device)
+        seasonal_init, trend_init = self.decomp(x_enc)
+        # decoder input
+        trend_init = torch.cat(
+            [trend_init[:, -self.label_len:, :], mean], dim=1)
+        seasonal_init = torch.cat(
+            [seasonal_init[:, -self.label_len:, :], zeros], dim=1)
+        # enc
+        enc_out = self.enc_embedding(x_enc, x_mark_enc)
+        enc_out, attns = self.encoder(enc_out, attn_mask=None)
+        # dec
+        dec_out = self.dec_embedding(seasonal_init, x_mark_dec)
+        seasonal_part, trend_part = self.decoder(dec_out, enc_out, x_mask=None, cross_mask=None,
+                                                 trend=trend_init)
+        # final
+        dec_out = trend_part + seasonal_part
+        return dec_out
+
+    def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask):
+        # enc
+        enc_out = self.enc_embedding(x_enc, x_mark_enc)
+        enc_out, attns = self.encoder(enc_out, attn_mask=None)
+        # final
+        dec_out = self.projection(enc_out)
+        return dec_out
+
+    def anomaly_detection(self, x_enc):
+        # enc
+        enc_out = self.enc_embedding(x_enc, None)
+        enc_out, attns = self.encoder(enc_out, attn_mask=None)
+        # final
+        dec_out = self.projection(enc_out)
+        return dec_out
+
+    def classification(self, x_enc, x_mark_enc):
+        # enc
+        enc_out = self.enc_embedding(x_enc, None)
+        enc_out, attns = self.encoder(enc_out, attn_mask=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' 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