兼容STAEFormer

config/AEPSA/AirQuality.yaml

@@ -13,7 +13,7 @@ data:
   input_dim: 6
   lag: 24
   normalizer: std
-  num_nodes: 12
+  num_nodes: 35
   steps_per_day: 24
   test_ratio: 0.2
   val_ratio: 0.2
@@ -26,7 +26,7 @@ model:
   gpt_path: ./GPT-2
   input_dim: 6
   n_heads: 1
-  num_nodes: 12
+  num_nodes: 35
   patch_len: 6
   pred_len: 24
   seq_len: 24

config/STAEFormer/AirQuality.yaml

@@ -13,8 +13,8 @@ data:
   input_dim: 6
   lag: 24
   normalizer: std
-  num_nodes: 12
+  num_nodes: 35
-  steps_per_day: 24
+  steps_per_day: 288
   test_ratio: 0.2
   val_ratio: 0.2
 
@@ -28,7 +28,7 @@ model:
   input_embedding_dim: 24
   num_heads: 4
   num_layers: 3
-  num_nodes: 12
+  num_nodes: 35
   out_steps: 24
   output_dim: 6
   spatial_embedding_dim: 0
@@ -41,9 +41,9 @@ train:
   debug: false
   early_stop: true
   early_stop_patience: 15
-  epochs: 300
+  epochs: 100
   grad_norm: false
-  log_step: 200
+  log_step: 20000
   loss_func: mae
   lr_decay: false
   lr_decay_rate: 0.3

config/STAEFormer/PEMS-BAY.yaml

@@ -0,0 +1,58 @@
+basic:
+  dataset: PEMS-BAY
+  device: cuda:0
+  mode: train
+  model: STAEFormer
+  seed: 2023
+
+data:
+  batch_size: 64
+  column_wise: false
+  days_per_week: 7
+  horizon: 24
+  input_dim: 1
+  lag: 24
+  normalizer: std
+  num_nodes: 325
+  steps_per_day: 288
+  test_ratio: 0.2
+  val_ratio: 0.2
+
+model:
+  adaptive_embedding_dim: 80
+  dow_embedding_dim: 24
+  dropout: 0.1
+  feed_forward_dim: 256
+  in_steps: 24
+  input_dim: 1
+  input_embedding_dim: 24
+  num_heads: 4
+  num_layers: 3
+  num_nodes: 325
+  out_steps: 24
+  output_dim: 1
+  spatial_embedding_dim: 0
+  steps_per_day: 288
+  tod_embedding_dim: 24
+  use_mixed_proj: true
+
+train:
+  batch_size: 64
+  debug: false
+  early_stop: true
+  early_stop_patience: 15
+  epochs: 300
+  grad_norm: false
+  log_step: 200
+  loss_func: mae
+  lr_decay: false
+  lr_decay_rate: 0.3
+  lr_decay_step: 5,20,40,70
+  lr_init: 0.003
+  mae_thresh: 0.0
+  mape_thresh: 0.0
+  max_grad_norm: 5
+  output_dim: 1
+  plot: false
+  real_value: true
+  weight_decay: 0

config/STAEFormer/SolarEnergy.yaml

@@ -24,13 +24,13 @@ model:
   dropout: 0.1
   feed_forward_dim: 256
   in_steps: 24
-  input_dim: 137
+  input_dim: 1
   input_embedding_dim: 24
   num_heads: 4
   num_layers: 3
   num_nodes: 137
   out_steps: 24
-  output_dim: 137
+  output_dim: 1
   spatial_embedding_dim: 0
   steps_per_day: 24
   tod_embedding_dim: 24
@@ -41,7 +41,7 @@ train:
   debug: false
   early_stop: true
   early_stop_patience: 15
-  epochs: 300
+  epochs: 100
   grad_norm: false
   log_step: 200
   loss_func: mae
@@ -52,7 +52,7 @@ train:
   mae_thresh: 0.0
   mape_thresh: 0.0
   max_grad_norm: 5
-  output_dim: 137
+  output_dim: 1
   plot: false
   real_value: true
   weight_decay: 0

model/STAEFormer/STAEFormer.py

@@ -187,17 +187,19 @@ class STAEformer(nn.Module):
         batch_size = x.shape[0]
 
         if self.tod_embedding_dim > 0:
-            tod = x[..., 1]
+            tod = x[..., -2]
         if self.dow_embedding_dim > 0:
-            dow = x[..., 2]
+            dow = x[..., -1]
-        x = x[..., 0:1]
+        x = x[..., 0:self.input_dim]
 
         x = self.input_proj(x)  # (batch_size, in_steps, num_nodes, input_embedding_dim)
         features = [x]
         if self.tod_embedding_dim > 0:
-            tod_emb = self.tod_embedding(
+            # 确保索引在有效范围内
-                (tod * self.steps_per_day).long()
+            tod_index = (tod * self.steps_per_day).long()
-            )  # (batch_size, in_steps, num_nodes, tod_embedding_dim)
+            # 防止索引越界
+            tod_index = torch.clamp(tod_index, 0, self.steps_per_day - 1)
+            tod_emb = self.tod_embedding(tod_index)  # (batch_size, in_steps, num_nodes, tod_embedding_dim)
             features.append(tod_emb)
         if self.dow_embedding_dim > 0:
             dow_emb = self.dow_embedding(

run_tests.sh

@@ -1,7 +1,7 @@
 #!/bin/bash
 
 # 设置默认模型名和数据集列表
-MODEL_NAME="GWN"
+MODEL_NAME="STAEFormer"
 DATASETS=(
     "METR-LA"
     "PEMS-BAY"