Upload DeepTime.py

DeepTime.py

@@ -0,0 +1,147 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+from layers.Transformer_EncDec import Decoder, DecoderLayer, Encoder, EncoderLayer
+from layers.SelfAttention_Family import FullAttention, AttentionLayer
+from layers.Embed import PatchEmbedding
+from collections import Counter
+from layers.SharedWavMoE import WavMoE
+from layers.RevIN import RevIN
+import torch.fft
+from layers.Embed import DataEmbedding
+
+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)
+        # print(self.linear,x.shape)
+        x = self.linear(x)
+        x = self.dropout(x)
+        return x
+
+
+
+class Model(nn.Module):
+    """
+   """
+
+    def __init__(self, configs):
+        super(Model, self).__init__()
+        self.task_name = configs.task_name
+        self.seq_len = configs.seq_len
+        self.patch_len = configs.input_token_len
+        self.stride = self.patch_len
+        self.pred_len = configs.test_pred_len
+        self.test_seq_len = configs.test_seq_len
+        # embedding configs
+        self.output_attention = configs.output_attention
+        self.padding = configs.padding
+        # MoE设置
+        self.hidden_size = configs.hidden_size
+        self.intermediate_size = configs.intermediate_size
+        self.top_k = configs.top_k
+        self.shared_experts = configs.shared_experts
+        self.wavelet = configs.wavelet
+        self.level = configs.shared_experts
+        self.proj_wight = configs.proj_wight
+        # Embedding
+        self.patch_embedding = PatchEmbedding(
+            configs.d_model, self.patch_len, self.stride, self.padding, configs.dropout)
+
+        self.data_embedding = DataEmbedding(configs.enc_in, configs.d_model, configs.embed, configs.freq,
+                                                    configs.dropout)
+
+        self.revin_layer = RevIN(configs.enc_in)
+        self.encoder_patch = Encoder(
+            [
+                EncoderLayer(
+                    AttentionLayer(
+                        FullAttention(False, configs.factor,
+                                      attention_dropout=configs.dropout,
+                                      output_attention=configs.output_attention),
+                        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.encoder_time = Encoder(
+            [
+                EncoderLayer(
+                    AttentionLayer(
+                        FullAttention(False, configs.factor,
+                                      attention_dropout=configs.dropout,
+                                      output_attention=configs.output_attention),
+                        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 * \
+                       int((configs.seq_len - self.patch_len) / self.stride + 1)
+        self.projection = nn.Linear(self.head_nf, int(configs.seq_len*self.proj_wight), bias=True)
+
+        self.data_projection = nn.Linear(configs.d_model, configs.enc_in, bias=True)
+        self.wavmoe = WavMoE(configs)
+        self.head = FlattenHead(configs.enc_in, nf= int(configs.seq_len*self.proj_wight), target_window= self.seq_len,
+                                    head_dropout=configs.dropout)
+        self.gelu = nn.GELU()
+
+
+
+    def main(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
+        # 归一化并且嵌入
+        x_revin = self.revin_layer(x_enc, 'norm').permute(0, 2, 1)
+        # print("x_revin.shape：",x_revin.shape)
+        B, D, S = x_revin.shape
+
+        # 进入注意力机制
+        x_inver=self.data_embedding(x_revin.permute(0, 2, 1), x_mark_enc)
+        nav_out, attn_w = self.encoder_time(x_inver, attn_mask=None)
+        #print("nav_out.shape:", nav_out.shape,self.data_projection)
+        nav_out = self.data_projection(nav_out)
+        #print("nav_out.shape:", nav_out.shape)
+
+        #patch embedding进入多头FullAttention
+
+        # u: [bs * nvars x patch_num x d_model]
+        x_pe, n_vars = self.patch_embedding(x_revin+nav_out.permute(0, 2, 1))
+        #print("x_pe.shape:",x_pe.shape, n_vars)
+        enc_out, attn = self.encoder_patch(x_pe)
+        dec_out = enc_out.reshape(B, D, -1)
+        #print("dec_out.shape:",dec_out.shape, self.head_nf)
+        act_val = self.projection(dec_out)
+        #print("act_val:", act_val.shape)
+
+        # 专家系统
+        moe_out, router_logits = self.wavmoe(act_val + nav_out.permute(0, 2, 1))
+        #print("moe_out", moe_out.shape)
+        head_out = self.head(moe_out)
+
+        # 逆归一化输出
+        x_out = self.revin_layer(head_out.permute(0, 2, 1), 'denorm')
+        #print(x_out.shape)
+        return x_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 == 'forecast':
+            dec_out = self.main(x_enc, x_mark_enc, x_dec, x_mark_dec)
+            return dec_out[:, -self.test_seq_len :, :]  # [B, L, D]
+        if self.task_name == 'anomaly_detection':
+            dec_out = self.main(x_enc, x_mark_enc, x_dec, x_mark_dec)
+            return dec_out  # [B, L, D]
+        return None
+