100 lines
3.9 KiB
Python
100 lines
3.9 KiB
Python
"""
|
||
SeasonPatch = PatchTST (CI) + ChannelGraphMixer + Linear prediction head
|
||
支持两种编码器：
|
||
- Transformer 编码器路径：PatchTST + GraphMixer + Head
|
||
- Mamba2 编码器路径：Mamba2Encoder（不使用mixer），直接用最后得到的 d_model 走 Head
|
||
"""
|
||
import torch
|
||
import torch.nn as nn
|
||
from layers.TSTEncoder import TSTiEncoder
|
||
from layers.GraphMixer import HierarchicalGraphMixer
|
||
from layers.MambaSeries import Mamba2Encoder
|
||
|
||
|
||
class SeasonPatch(nn.Module):
|
||
def __init__(self,
|
||
c_in: int,
|
||
seq_len: int,
|
||
pred_len: int,
|
||
patch_len: int,
|
||
stride: int,
|
||
k_graph: int = 8,
|
||
encoder_type: str = "Transformer",
|
||
d_model: int = 128,
|
||
n_layers: int = 3,
|
||
n_heads: int = 16,
|
||
# Mamba2 相关可选超参
|
||
d_state: int = 64,
|
||
d_conv: int = 4,
|
||
expand: int = 2,
|
||
headdim: int = 64):
|
||
super().__init__()
|
||
|
||
# Store patch parameters
|
||
self.patch_len = patch_len # patch 长度
|
||
self.stride = stride # patch 步幅
|
||
|
||
# Calculate patch number
|
||
patch_num = (seq_len - patch_len) // stride + 1 # patch_num: int
|
||
|
||
self.encoder_type = encoder_type
|
||
# 只初始化需要的encoder
|
||
|
||
if encoder_type == "Transformer":
|
||
# Transformer (PatchTST) 编码器（channel independent）
|
||
self.encoder = TSTiEncoder(
|
||
c_in=c_in, patch_num=patch_num, patch_len=patch_len,
|
||
d_model=d_model, n_layers=n_layers, n_heads=n_heads
|
||
)
|
||
self.mixer = HierarchicalGraphMixer(c_in, dim=d_model, k=k_graph)
|
||
# Prediction head（Transformer 路径用到，输入维度为 patch_num * d_model）
|
||
self.head = nn.Sequential(
|
||
nn.Linear(patch_num * d_model, patch_num * d_model),
|
||
nn.SiLU(),
|
||
nn.Linear(patch_num * d_model, pred_len)
|
||
)
|
||
elif encoder_type == "Mamba2":
|
||
# Mamba2 编码器（channel independent），返回 [B, C, d_model]
|
||
self.encoder = Mamba2Encoder(
|
||
c_in=c_in, patch_num=patch_num, patch_len=patch_len,
|
||
d_model=d_model, d_state=d_state, d_conv=d_conv,
|
||
expand=expand, headdim=headdim, n_layers=n_layers
|
||
)
|
||
# Prediction head（Mamba2 路径用到，输入维度为 d_model）
|
||
self.head = nn.Sequential(
|
||
nn.Linear(d_model, d_model),
|
||
nn.SiLU(),
|
||
nn.Linear(d_model, pred_len)
|
||
)
|
||
else:
|
||
raise ValueError(f"Unsupported encoder_type: {encoder_type}")
|
||
|
||
def forward(self, x ):
|
||
# x: [B, L, C]
|
||
x = x.permute(0, 2, 1) # x: [B, C, L]
|
||
|
||
# Patch the input
|
||
x_patch = x.unfold(-1, self.patch_len, self.stride) # x_patch: [B, C, patch_num, patch_len]
|
||
|
||
if self.encoder_type == "Transformer":
|
||
# Encode patches (PatchTST)
|
||
z = self.encoder(x_patch) # z: [B, C, d_model, patch_num]
|
||
|
||
# [B, C, d_model, patch_num] -> [B, C, patch_num, d_model]
|
||
B, C, D, N = z.shape # B: batch, C: channels, D: d_model, N: patch_num
|
||
z = z.permute(0, 1, 3, 2) # z: [B, C, patch_num, d_model]
|
||
|
||
# Cross-channel mixing
|
||
z_mix = self.mixer(z) # z_mix: [B, C, patch_num, d_model]
|
||
|
||
# Flatten and predict
|
||
z_mix = z_mix.view(B, C, N * D) # z_mix: [B, C, patch_num * d_model]
|
||
y_pred = self.head(z_mix) # y_pred: [B, C, pred_len]
|
||
|
||
elif self.encoder_type == "Mamba2":
|
||
# 使用 Mamba2 编码器（不使用 mixer）
|
||
z_last = self.encoder(x_patch) # z_last: [B, C, d_model]（仅最后一个时间步）
|
||
y_pred = self.head(z_last) # y_pred: [B, C, pred_len]
|
||
|
||
return y_pred # [B, C, pred_len]
|