first commit

models/Informer.py

@@ -0,0 +1,147 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from layers.Transformer_EncDec import Decoder, DecoderLayer, Encoder, EncoderLayer, ConvLayer
+from layers.SelfAttention_Family import ProbAttention, AttentionLayer
+from layers.Embed import DataEmbedding
+
+
+class Model(nn.Module):
+    """
+    Informer with Propspare attention in O(LlogL) complexity
+    Paper link: https://ojs.aaai.org/index.php/AAAI/article/view/17325/17132
+    """
+
+    def __init__(self, configs):
+        super(Model, self).__init__()
+        self.task_name = configs.task_name
+        self.pred_len = configs.pred_len
+        self.label_len = configs.label_len
+
+        # Embedding
+        self.enc_embedding = DataEmbedding(configs.enc_in, configs.d_model, configs.embed, configs.freq,
+                                           configs.dropout)
+        self.dec_embedding = DataEmbedding(configs.dec_in, configs.d_model, configs.embed, configs.freq,
+                                           configs.dropout)
+
+        # Encoder
+        self.encoder = Encoder(
+            [
+                EncoderLayer(
+                    AttentionLayer(
+                        ProbAttention(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)
+            ],
+            [
+                ConvLayer(
+                    configs.d_model
+                ) for l in range(configs.e_layers - 1)
+            ] if configs.distil and ('forecast' in configs.task_name) else None,
+            norm_layer=torch.nn.LayerNorm(configs.d_model)
+        )
+        # Decoder
+        self.decoder = Decoder(
+            [
+                DecoderLayer(
+                    AttentionLayer(
+                        ProbAttention(True, configs.factor, attention_dropout=configs.dropout, output_attention=False),
+                        configs.d_model, configs.n_heads),
+                    AttentionLayer(
+                        ProbAttention(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.d_layers)
+            ],
+            norm_layer=torch.nn.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 long_forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
+        enc_out = self.enc_embedding(x_enc, x_mark_enc)
+        dec_out = self.dec_embedding(x_dec, x_mark_dec)
+        enc_out, attns = self.encoder(enc_out, attn_mask=None)
+
+        dec_out = self.decoder(dec_out, enc_out, x_mask=None, cross_mask=None)
+
+        return dec_out  # [B, L, D]
+    
+    def short_forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
+        # 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.enc_embedding(x_enc, x_mark_enc)
+        dec_out = self.dec_embedding(x_dec, x_mark_dec)
+        enc_out, attns = self.encoder(enc_out, attn_mask=None)
+
+        dec_out = self.decoder(dec_out, enc_out, x_mask=None, cross_mask=None)
+
+        dec_out = dec_out * std_enc + mean_enc
+        return dec_out  # [B, L, D]
+
+    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
+        output = self.act(enc_out)  # the output transformer encoder/decoder embeddings don't include non-linearity
+        output = self.dropout(output)
+        output = output * x_mark_enc.unsqueeze(-1)  # zero-out padding embeddings
+        output = output.reshape(output.shape[0], -1)  # (batch_size, seq_length * d_model)
+        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)
+            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