first commit

models/Crossformer.py

@@ -0,0 +1,145 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from layers.Crossformer_EncDec import scale_block, Encoder, Decoder, DecoderLayer
+from layers.Embed import PatchEmbedding
+from layers.SelfAttention_Family import AttentionLayer, FullAttention, TwoStageAttentionLayer
+from models.PatchTST import FlattenHead
+
+
+from math import ceil
+
+
+class Model(nn.Module):
+    """
+    Paper link: https://openreview.net/pdf?id=vSVLM2j9eie
+    """
+    def __init__(self, configs):
+        super(Model, self).__init__()
+        self.enc_in = configs.enc_in
+        self.seq_len = configs.seq_len
+        self.pred_len = configs.pred_len
+        self.seg_len = 12
+        self.win_size = 2
+        self.task_name = configs.task_name
+
+        # The padding operation to handle invisible sgemnet length
+        self.pad_in_len = ceil(1.0 * configs.seq_len / self.seg_len) * self.seg_len
+        self.pad_out_len = ceil(1.0 * configs.pred_len / self.seg_len) * self.seg_len
+        self.in_seg_num = self.pad_in_len // self.seg_len
+        self.out_seg_num = ceil(self.in_seg_num / (self.win_size ** (configs.e_layers - 1)))
+        self.head_nf = configs.d_model * self.out_seg_num
+
+        # Embedding
+        self.enc_value_embedding = PatchEmbedding(configs.d_model, self.seg_len, self.seg_len, self.pad_in_len - configs.seq_len, 0)
+        self.enc_pos_embedding = nn.Parameter(
+            torch.randn(1, configs.enc_in, self.in_seg_num, configs.d_model))
+        self.pre_norm = nn.LayerNorm(configs.d_model)
+
+        # Encoder
+        self.encoder = Encoder(
+            [
+                scale_block(configs, 1 if l == 0 else self.win_size, configs.d_model, configs.n_heads, configs.d_ff,
+                            1, configs.dropout,
+                            self.in_seg_num if l == 0 else ceil(self.in_seg_num / self.win_size ** l), configs.factor
+                            ) for l in range(configs.e_layers)
+            ]
+        )
+        # Decoder
+        self.dec_pos_embedding = nn.Parameter(
+            torch.randn(1, configs.enc_in, (self.pad_out_len // self.seg_len), configs.d_model))
+
+        self.decoder = Decoder(
+            [
+                DecoderLayer(
+                    TwoStageAttentionLayer(configs, (self.pad_out_len // self.seg_len), configs.factor, configs.d_model, configs.n_heads,
+                                           configs.d_ff, configs.dropout),
+                    AttentionLayer(
+                        FullAttention(False, configs.factor, attention_dropout=configs.dropout,
+                                      output_attention=False),
+                        configs.d_model, configs.n_heads),
+                    self.seg_len,
+                    configs.d_model,
+                    configs.d_ff,
+                    dropout=configs.dropout,
+                    # activation=configs.activation,
+                )
+                for l in range(configs.e_layers + 1)
+            ],
+        )
+        if self.task_name == 'imputation' or self.task_name == 'anomaly_detection':
+            self.head = FlattenHead(configs.enc_in, self.head_nf, configs.seq_len,
+                                    head_dropout=configs.dropout)
+        elif self.task_name == 'classification':
+            self.flatten = nn.Flatten(start_dim=-2)
+            self.dropout = nn.Dropout(configs.dropout)
+            self.projection = nn.Linear(
+                self.head_nf * configs.enc_in, configs.num_class)
+
+
+
+    def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
+        # embedding
+        x_enc, n_vars = self.enc_value_embedding(x_enc.permute(0, 2, 1))
+        x_enc = rearrange(x_enc, '(b d) seg_num d_model -> b d seg_num d_model', d = n_vars)
+        x_enc += self.enc_pos_embedding
+        x_enc = self.pre_norm(x_enc)
+        enc_out, attns = self.encoder(x_enc)
+
+        dec_in = repeat(self.dec_pos_embedding, 'b ts_d l d -> (repeat b) ts_d l d', repeat=x_enc.shape[0])
+        dec_out = self.decoder(dec_in, enc_out)
+        return dec_out
+
+    def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask):
+        # embedding
+        x_enc, n_vars = self.enc_value_embedding(x_enc.permute(0, 2, 1))
+        x_enc = rearrange(x_enc, '(b d) seg_num d_model -> b d seg_num d_model', d=n_vars)
+        x_enc += self.enc_pos_embedding
+        x_enc = self.pre_norm(x_enc)
+        enc_out, attns = self.encoder(x_enc)
+
+        dec_out = self.head(enc_out[-1].permute(0, 1, 3, 2)).permute(0, 2, 1)
+
+        return dec_out
+
+    def anomaly_detection(self, x_enc):
+        # embedding
+        x_enc, n_vars = self.enc_value_embedding(x_enc.permute(0, 2, 1))
+        x_enc = rearrange(x_enc, '(b d) seg_num d_model -> b d seg_num d_model', d=n_vars)
+        x_enc += self.enc_pos_embedding
+        x_enc = self.pre_norm(x_enc)
+        enc_out, attns = self.encoder(x_enc)
+
+        dec_out = self.head(enc_out[-1].permute(0, 1, 3, 2)).permute(0, 2, 1)
+        return dec_out
+
+    def classification(self, x_enc, x_mark_enc):
+        # embedding
+        x_enc, n_vars = self.enc_value_embedding(x_enc.permute(0, 2, 1))
+
+        x_enc = rearrange(x_enc, '(b d) seg_num d_model -> b d seg_num d_model', d=n_vars)
+        x_enc += self.enc_pos_embedding
+        x_enc = self.pre_norm(x_enc)
+        enc_out, attns = self.encoder(x_enc)
+        # Output from Non-stationary Transformer
+        output = self.flatten(enc_out[-1].permute(0, 1, 3, 2))
+        output = self.dropout(output)
+        output = output.reshape(output.shape[0], -1)
+        output = self.projection(output)
+        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