REPST #3
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@ -6,7 +6,7 @@ basic:
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seed: 2023
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seed: 2023
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data:
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data:
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batch_size: 64
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batch_size: 16
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column_wise: false
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column_wise: false
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||||||
days_per_week: 7
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days_per_week: 7
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horizon: 24
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horizon: 24
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@ -34,7 +34,7 @@ model:
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train:
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train:
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batch_size: 64
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batch_size: 16
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debug: false
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debug: false
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early_stop: true
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early_stop: true
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early_stop_patience: 15
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early_stop_patience: 15
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@ -6,7 +6,7 @@ basic:
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seed: 2023
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seed: 2023
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||||||
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||||||
data:
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data:
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||||||
batch_size: 64
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batch_size: 16
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||||||
column_wise: false
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column_wise: false
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||||||
days_per_week: 7
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days_per_week: 7
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horizon: 24
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horizon: 24
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@ -34,7 +34,7 @@ model:
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train:
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train:
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batch_size: 64
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batch_size: 16
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debug: false
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debug: false
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early_stop: true
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early_stop: true
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early_stop_patience: 15
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early_stop_patience: 15
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@ -0,0 +1,64 @@
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basic:
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dataset: AirQuality
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device: cuda:0
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mode: train
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model: MTGNN
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seed: 2023
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||||||
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data:
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||||||
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batch_size: 64
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||||||
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column_wise: false
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||||||
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days_per_week: 7
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||||||
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horizon: 24
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||||||
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input_dim: 6
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lag: 24
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normalizer: std
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num_nodes: 35
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steps_per_day: 24
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test_ratio: 0.2
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val_ratio: 0.2
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model:
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gcn_true: True # 是否使用图卷积网络 (bool)
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buildA_true: True # 是否动态构建邻接矩阵 (bool)
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subgraph_size: 20 # 子图大小 (int)
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num_nodes: 35 # 节点数量 (int)
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node_dim: 40 # 节点嵌入维度 (int)
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dilation_exponential: 1 # 膨胀卷积指数 (int)
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conv_channels: 32 # 卷积通道数 (int)
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residual_channels: 32 # 残差通道数 (int)
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skip_channels: 64 # 跳跃连接通道数 (int)
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end_channels: 128 # 输出层通道数 (int)
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seq_len: 24 # 输入序列长度 (int)
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in_dim: 6 # 输入特征维度 (int)
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out_len: 24 # 输出序列长度 (int)
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out_dim: 6 # 输出预测维度 (int)
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layers: 3 # 模型层数 (int)
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propalpha: 0.05 # 图传播参数alpha (float)
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tanhalpha: 3 # tanh激活参数alpha (float)
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layer_norm_affline: True # 层归一化是否使用affine变换 (bool)
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gcn_depth: 2 # 图卷积深度 (int)
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dropout: 0.3 # dropout率 (float)
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predefined_A: null # 预定义邻接矩阵 (optional, None)
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static_feat: null # 静态特征 (optional, None)
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train:
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batch_size: 64
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debug: false
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early_stop: true
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early_stop_patience: 15
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epochs: 100
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grad_norm: false
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log_step: 1000
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loss_func: mae
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lr_decay: true
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lr_decay_rate: 0.3
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lr_decay_step: 5,20,40,70
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lr_init: 0.003
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mae_thresh: None
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mape_thresh: 0.001
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max_grad_norm: 5
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output_dim: 6
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plot: false
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real_value: true
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weight_decay: 0
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@ -0,0 +1,64 @@
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basic:
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dataset: BJTaxi-InFlow
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device: cuda:0
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mode: train
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model: MTGNN
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seed: 2023
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data:
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batch_size: 64
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column_wise: false
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days_per_week: 7
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horizon: 24
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input_dim: 1
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lag: 24
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normalizer: std
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num_nodes: 1024
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steps_per_day: 48
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test_ratio: 0.2
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val_ratio: 0.2
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model:
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gcn_true: True # 是否使用图卷积网络 (bool)
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buildA_true: True # 是否动态构建邻接矩阵 (bool)
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subgraph_size: 20 # 子图大小 (int)
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num_nodes: 1024 # 节点数量 (int)
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node_dim: 40 # 节点嵌入维度 (int)
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dilation_exponential: 1 # 膨胀卷积指数 (int)
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conv_channels: 32 # 卷积通道数 (int)
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residual_channels: 32 # 残差通道数 (int)
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skip_channels: 64 # 跳跃连接通道数 (int)
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end_channels: 128 # 输出层通道数 (int)
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seq_len: 24 # 输入序列长度 (int)
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in_dim: 1 # 输入特征维度 (int)
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out_len: 24 # 输出序列长度 (int)
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out_dim: 1 # 输出预测维度 (int)
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layers: 3 # 模型层数 (int)
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propalpha: 0.05 # 图传播参数alpha (float)
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tanhalpha: 3 # tanh激活参数alpha (float)
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layer_norm_affline: True # 层归一化是否使用affine变换 (bool)
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gcn_depth: 2 # 图卷积深度 (int)
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dropout: 0.3 # dropout率 (float)
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predefined_A: null # 预定义邻接矩阵 (optional, None)
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static_feat: null # 静态特征 (optional, None)
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train:
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batch_size: 64
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debug: false
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early_stop: true
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early_stop_patience: 15
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||||||
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epochs: 100
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grad_norm: false
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log_step: 1000
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||||||
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loss_func: mae
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lr_decay: true
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lr_decay_rate: 0.3
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lr_decay_step: 5,20,40,70
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lr_init: 0.003
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mae_thresh: None
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||||||
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mape_thresh: 0.001
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||||||
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max_grad_norm: 5
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output_dim: 1
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plot: false
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real_value: true
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weight_decay: 0
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@ -0,0 +1,64 @@
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basic:
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||||||
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dataset: BJTaxi-OutFlow
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device: cuda:0
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mode: train
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||||||
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model: MTGNN
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||||||
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seed: 2023
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||||||
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||||||
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data:
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||||||
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batch_size: 64
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||||||
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column_wise: false
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||||||
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days_per_week: 7
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||||||
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horizon: 24
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||||||
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input_dim: 1
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||||||
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lag: 24
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||||||
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normalizer: std
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||||||
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num_nodes: 1024
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||||||
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steps_per_day: 48
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||||||
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test_ratio: 0.2
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||||||
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val_ratio: 0.2
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||||||
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||||||
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model:
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||||||
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gcn_true: True # 是否使用图卷积网络 (bool)
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||||||
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buildA_true: True # 是否动态构建邻接矩阵 (bool)
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||||||
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subgraph_size: 20 # 子图大小 (int)
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||||||
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num_nodes: 1024 # 节点数量 (int)
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||||||
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node_dim: 40 # 节点嵌入维度 (int)
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||||||
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dilation_exponential: 1 # 膨胀卷积指数 (int)
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||||||
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conv_channels: 32 # 卷积通道数 (int)
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||||||
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residual_channels: 32 # 残差通道数 (int)
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||||||
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skip_channels: 64 # 跳跃连接通道数 (int)
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||||||
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end_channels: 128 # 输出层通道数 (int)
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||||||
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seq_len: 24 # 输入序列长度 (int)
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||||||
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in_dim: 1 # 输入特征维度 (int)
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||||||
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out_len: 24 # 输出序列长度 (int)
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||||||
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out_dim: 1 # 输出预测维度 (int)
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||||||
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layers: 3 # 模型层数 (int)
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||||||
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propalpha: 0.05 # 图传播参数alpha (float)
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||||||
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tanhalpha: 3 # tanh激活参数alpha (float)
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||||||
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layer_norm_affline: True # 层归一化是否使用affine变换 (bool)
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||||||
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gcn_depth: 2 # 图卷积深度 (int)
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||||||
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dropout: 0.3 # dropout率 (float)
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||||||
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predefined_A: null # 预定义邻接矩阵 (optional, None)
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||||||
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static_feat: null # 静态特征 (optional, None)
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||||||
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||||||
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train:
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||||||
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batch_size: 64
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debug: false
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||||||
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early_stop: true
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||||||
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early_stop_patience: 15
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||||||
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epochs: 100
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||||||
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grad_norm: false
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||||||
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log_step: 1000
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||||||
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loss_func: mae
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||||||
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lr_decay: true
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lr_decay_rate: 0.3
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||||||
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lr_decay_step: 5,20,40,70
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||||||
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lr_init: 0.003
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||||||
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mae_thresh: None
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||||||
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mape_thresh: 0.001
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||||||
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max_grad_norm: 5
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||||||
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output_dim: 1
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plot: false
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real_value: true
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weight_decay: 0
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@ -0,0 +1,52 @@
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basic:
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dataset: METR-LA
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device: cuda:0
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mode: train
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model: STNorm
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seed: 2023
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data:
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||||||
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batch_size: 16
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||||||
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column_wise: false
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||||||
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days_per_week: 7
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||||||
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horizon: 24
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||||||
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input_dim: 1
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||||||
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lag: 24
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||||||
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normalizer: std
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||||||
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num_nodes: 207
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steps_per_day: 288
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test_ratio: 0.2
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val_ratio: 0.2
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model:
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dropout: 0.2
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blocks: 2
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layers: 2
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snorm_bool: True
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tnorm_bool: True
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num_nodes: 207
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in_dim: 1
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out_dim: 24
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channels: 32
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kernel_size: 2
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train:
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batch_size: 16
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debug: false
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||||||
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early_stop: true
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||||||
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early_stop_patience: 15
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||||||
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epochs: 100
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||||||
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grad_norm: false
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||||||
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log_step: 1000
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||||||
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loss_func: mae
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||||||
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lr_decay: true
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||||||
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lr_decay_rate: 0.3
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||||||
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lr_decay_step: 5,20,40,70
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||||||
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lr_init: 0.003
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||||||
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mae_thresh: None
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||||||
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mape_thresh: 0.001
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||||||
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max_grad_norm: 5
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||||||
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output_dim: 1
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||||||
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plot: false
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||||||
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real_value: true
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||||||
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weight_decay: 0
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@ -0,0 +1,64 @@
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basic:
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||||||
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dataset: NYCBike-InFlow
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||||||
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device: cuda:0
|
||||||
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mode: train
|
||||||
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model: MTGNN
|
||||||
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seed: 2023
|
||||||
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|
||||||
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data:
|
||||||
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batch_size: 64
|
||||||
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column_wise: false
|
||||||
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days_per_week: 7
|
||||||
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horizon: 24
|
||||||
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input_dim: 1
|
||||||
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lag: 24
|
||||||
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normalizer: std
|
||||||
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num_nodes: 128
|
||||||
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steps_per_day: 48
|
||||||
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test_ratio: 0.2
|
||||||
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val_ratio: 0.2
|
||||||
|
|
||||||
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model:
|
||||||
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gcn_true: True # 是否使用图卷积网络 (bool)
|
||||||
|
buildA_true: True # 是否动态构建邻接矩阵 (bool)
|
||||||
|
subgraph_size: 20 # 子图大小 (int)
|
||||||
|
num_nodes: 128 # 节点数量 (int)
|
||||||
|
node_dim: 40 # 节点嵌入维度 (int)
|
||||||
|
dilation_exponential: 1 # 膨胀卷积指数 (int)
|
||||||
|
conv_channels: 32 # 卷积通道数 (int)
|
||||||
|
residual_channels: 32 # 残差通道数 (int)
|
||||||
|
skip_channels: 64 # 跳跃连接通道数 (int)
|
||||||
|
end_channels: 128 # 输出层通道数 (int)
|
||||||
|
seq_len: 24 # 输入序列长度 (int)
|
||||||
|
in_dim: 1 # 输入特征维度 (int)
|
||||||
|
out_len: 24 # 输出序列长度 (int)
|
||||||
|
out_dim: 1 # 输出预测维度 (int)
|
||||||
|
layers: 3 # 模型层数 (int)
|
||||||
|
propalpha: 0.05 # 图传播参数alpha (float)
|
||||||
|
tanhalpha: 3 # tanh激活参数alpha (float)
|
||||||
|
layer_norm_affline: True # 层归一化是否使用affine变换 (bool)
|
||||||
|
gcn_depth: 2 # 图卷积深度 (int)
|
||||||
|
dropout: 0.3 # dropout率 (float)
|
||||||
|
predefined_A: null # 预定义邻接矩阵 (optional, None)
|
||||||
|
static_feat: null # 静态特征 (optional, None)
|
||||||
|
|
||||||
|
train:
|
||||||
|
batch_size: 64
|
||||||
|
debug: false
|
||||||
|
early_stop: true
|
||||||
|
early_stop_patience: 15
|
||||||
|
epochs: 100
|
||||||
|
grad_norm: false
|
||||||
|
log_step: 1000
|
||||||
|
loss_func: mae
|
||||||
|
lr_decay: true
|
||||||
|
lr_decay_rate: 0.3
|
||||||
|
lr_decay_step: 5,20,40,70
|
||||||
|
lr_init: 0.003
|
||||||
|
mae_thresh: None
|
||||||
|
mape_thresh: 0.001
|
||||||
|
max_grad_norm: 5
|
||||||
|
output_dim: 1
|
||||||
|
plot: false
|
||||||
|
real_value: true
|
||||||
|
weight_decay: 0
|
||||||
|
|
@ -0,0 +1,64 @@
|
||||||
|
basic:
|
||||||
|
dataset: NYCBike-OutFlow
|
||||||
|
device: cuda:0
|
||||||
|
mode: train
|
||||||
|
model: MTGNN
|
||||||
|
seed: 2023
|
||||||
|
|
||||||
|
data:
|
||||||
|
batch_size: 64
|
||||||
|
column_wise: false
|
||||||
|
days_per_week: 7
|
||||||
|
horizon: 24
|
||||||
|
input_dim: 1
|
||||||
|
lag: 24
|
||||||
|
normalizer: std
|
||||||
|
num_nodes: 128
|
||||||
|
steps_per_day: 48
|
||||||
|
test_ratio: 0.2
|
||||||
|
val_ratio: 0.2
|
||||||
|
|
||||||
|
model:
|
||||||
|
gcn_true: True # 是否使用图卷积网络 (bool)
|
||||||
|
buildA_true: True # 是否动态构建邻接矩阵 (bool)
|
||||||
|
subgraph_size: 20 # 子图大小 (int)
|
||||||
|
num_nodes: 128 # 节点数量 (int)
|
||||||
|
node_dim: 40 # 节点嵌入维度 (int)
|
||||||
|
dilation_exponential: 1 # 膨胀卷积指数 (int)
|
||||||
|
conv_channels: 32 # 卷积通道数 (int)
|
||||||
|
residual_channels: 32 # 残差通道数 (int)
|
||||||
|
skip_channels: 64 # 跳跃连接通道数 (int)
|
||||||
|
end_channels: 128 # 输出层通道数 (int)
|
||||||
|
seq_len: 24 # 输入序列长度 (int)
|
||||||
|
in_dim: 1 # 输入特征维度 (int)
|
||||||
|
out_len: 24 # 输出序列长度 (int)
|
||||||
|
out_dim: 1 # 输出预测维度 (int)
|
||||||
|
layers: 3 # 模型层数 (int)
|
||||||
|
propalpha: 0.05 # 图传播参数alpha (float)
|
||||||
|
tanhalpha: 3 # tanh激活参数alpha (float)
|
||||||
|
layer_norm_affline: True # 层归一化是否使用affine变换 (bool)
|
||||||
|
gcn_depth: 2 # 图卷积深度 (int)
|
||||||
|
dropout: 0.3 # dropout率 (float)
|
||||||
|
predefined_A: null # 预定义邻接矩阵 (optional, None)
|
||||||
|
static_feat: null # 静态特征 (optional, None)
|
||||||
|
|
||||||
|
train:
|
||||||
|
batch_size: 64
|
||||||
|
debug: false
|
||||||
|
early_stop: true
|
||||||
|
early_stop_patience: 15
|
||||||
|
epochs: 100
|
||||||
|
grad_norm: false
|
||||||
|
log_step: 1000
|
||||||
|
loss_func: mae
|
||||||
|
lr_decay: true
|
||||||
|
lr_decay_rate: 0.3
|
||||||
|
lr_decay_step: 5,20,40,70
|
||||||
|
lr_init: 0.003
|
||||||
|
mae_thresh: None
|
||||||
|
mape_thresh: 0.001
|
||||||
|
max_grad_norm: 5
|
||||||
|
output_dim: 1
|
||||||
|
plot: false
|
||||||
|
real_value: true
|
||||||
|
weight_decay: 0
|
||||||
|
|
@ -0,0 +1,64 @@
|
||||||
|
basic:
|
||||||
|
dataset: PEMS-BAY
|
||||||
|
device: cuda:0
|
||||||
|
mode: train
|
||||||
|
model: MTGNN
|
||||||
|
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:
|
||||||
|
gcn_true: True # 是否使用图卷积网络 (bool)
|
||||||
|
buildA_true: True # 是否动态构建邻接矩阵 (bool)
|
||||||
|
subgraph_size: 20 # 子图大小 (int)
|
||||||
|
num_nodes: 325 # 节点数量 (int)
|
||||||
|
node_dim: 40 # 节点嵌入维度 (int)
|
||||||
|
dilation_exponential: 1 # 膨胀卷积指数 (int)
|
||||||
|
conv_channels: 32 # 卷积通道数 (int)
|
||||||
|
residual_channels: 32 # 残差通道数 (int)
|
||||||
|
skip_channels: 64 # 跳跃连接通道数 (int)
|
||||||
|
end_channels: 128 # 输出层通道数 (int)
|
||||||
|
seq_len: 24 # 输入序列长度 (int)
|
||||||
|
in_dim: 1 # 输入特征维度 (int)
|
||||||
|
out_len: 24 # 输出序列长度 (int)
|
||||||
|
out_dim: 1 # 输出预测维度 (int)
|
||||||
|
layers: 3 # 模型层数 (int)
|
||||||
|
propalpha: 0.05 # 图传播参数alpha (float)
|
||||||
|
tanhalpha: 3 # tanh激活参数alpha (float)
|
||||||
|
layer_norm_affline: True # 层归一化是否使用affine变换 (bool)
|
||||||
|
gcn_depth: 2 # 图卷积深度 (int)
|
||||||
|
dropout: 0.3 # dropout率 (float)
|
||||||
|
predefined_A: null # 预定义邻接矩阵 (optional, None)
|
||||||
|
static_feat: null # 静态特征 (optional, None)
|
||||||
|
|
||||||
|
train:
|
||||||
|
batch_size: 64
|
||||||
|
debug: false
|
||||||
|
early_stop: true
|
||||||
|
early_stop_patience: 15
|
||||||
|
epochs: 100
|
||||||
|
grad_norm: false
|
||||||
|
log_step: 1000
|
||||||
|
loss_func: mae
|
||||||
|
lr_decay: true
|
||||||
|
lr_decay_rate: 0.3
|
||||||
|
lr_decay_step: 5,20,40,70
|
||||||
|
lr_init: 0.003
|
||||||
|
mae_thresh: None
|
||||||
|
mape_thresh: 0.001
|
||||||
|
max_grad_norm: 5
|
||||||
|
output_dim: 1
|
||||||
|
plot: false
|
||||||
|
real_value: true
|
||||||
|
weight_decay: 0
|
||||||
|
|
@ -0,0 +1,64 @@
|
||||||
|
basic:
|
||||||
|
dataset: SolarEnergy
|
||||||
|
device: cuda:0
|
||||||
|
mode: train
|
||||||
|
model: MTGNN
|
||||||
|
seed: 2023
|
||||||
|
|
||||||
|
data:
|
||||||
|
batch_size: 64
|
||||||
|
column_wise: false
|
||||||
|
days_per_week: 7
|
||||||
|
horizon: 24
|
||||||
|
input_dim: 1
|
||||||
|
lag: 24
|
||||||
|
normalizer: std
|
||||||
|
num_nodes: 137
|
||||||
|
steps_per_day: 24
|
||||||
|
test_ratio: 0.2
|
||||||
|
val_ratio: 0.2
|
||||||
|
|
||||||
|
model:
|
||||||
|
gcn_true: True # 是否使用图卷积网络 (bool)
|
||||||
|
buildA_true: True # 是否动态构建邻接矩阵 (bool)
|
||||||
|
subgraph_size: 20 # 子图大小 (int)
|
||||||
|
num_nodes: 137 # 节点数量 (int)
|
||||||
|
node_dim: 40 # 节点嵌入维度 (int)
|
||||||
|
dilation_exponential: 1 # 膨胀卷积指数 (int)
|
||||||
|
conv_channels: 32 # 卷积通道数 (int)
|
||||||
|
residual_channels: 32 # 残差通道数 (int)
|
||||||
|
skip_channels: 64 # 跳跃连接通道数 (int)
|
||||||
|
end_channels: 128 # 输出层通道数 (int)
|
||||||
|
seq_len: 24 # 输入序列长度 (int)
|
||||||
|
in_dim: 1 # 输入特征维度 (int)
|
||||||
|
out_len: 24 # 输出序列长度 (int)
|
||||||
|
out_dim: 1 # 输出预测维度 (int)
|
||||||
|
layers: 3 # 模型层数 (int)
|
||||||
|
propalpha: 0.05 # 图传播参数alpha (float)
|
||||||
|
tanhalpha: 3 # tanh激活参数alpha (float)
|
||||||
|
layer_norm_affline: True # 层归一化是否使用affine变换 (bool)
|
||||||
|
gcn_depth: 2 # 图卷积深度 (int)
|
||||||
|
dropout: 0.3 # dropout率 (float)
|
||||||
|
predefined_A: null # 预定义邻接矩阵 (optional, None)
|
||||||
|
static_feat: null # 静态特征 (optional, None)
|
||||||
|
|
||||||
|
train:
|
||||||
|
batch_size: 64
|
||||||
|
debug: false
|
||||||
|
early_stop: true
|
||||||
|
early_stop_patience: 15
|
||||||
|
epochs: 100
|
||||||
|
grad_norm: false
|
||||||
|
log_step: 1000
|
||||||
|
loss_func: mae
|
||||||
|
lr_decay: true
|
||||||
|
lr_decay_rate: 0.3
|
||||||
|
lr_decay_step: 5,20,40,70
|
||||||
|
lr_init: 0.003
|
||||||
|
mae_thresh: None
|
||||||
|
mape_thresh: 0.001
|
||||||
|
max_grad_norm: 5
|
||||||
|
output_dim: 1
|
||||||
|
plot: false
|
||||||
|
real_value: true
|
||||||
|
weight_decay: 0
|
||||||
|
|
@ -0,0 +1,140 @@
|
||||||
|
import torch
|
||||||
|
import torch.nn as nn
|
||||||
|
import torch.nn.functional as F
|
||||||
|
|
||||||
|
class SNorm(nn.Module):
|
||||||
|
def __init__(self, channels):
|
||||||
|
super().__init__()
|
||||||
|
self.beta = nn.Parameter(torch.zeros(channels))
|
||||||
|
self.gamma = nn.Parameter(torch.ones(channels))
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
x_norm = (x - x.mean(2, keepdims=True)) / (x.var(2, keepdims=True, unbiased=True) + 1e-5) ** 0.5
|
||||||
|
return x_norm * self.gamma.view(1, -1, 1, 1) + self.beta.view(1, -1, 1, 1)
|
||||||
|
|
||||||
|
class TNorm(nn.Module):
|
||||||
|
def __init__(self, num_nodes, channels, track_running_stats=True, momentum=0.1):
|
||||||
|
super().__init__()
|
||||||
|
self.track_running_stats = track_running_stats
|
||||||
|
self.beta = nn.Parameter(torch.zeros(1, channels, num_nodes, 1))
|
||||||
|
self.gamma = nn.Parameter(torch.ones(1, channels, num_nodes, 1))
|
||||||
|
self.register_buffer('running_mean', torch.zeros(1, channels, num_nodes, 1))
|
||||||
|
self.register_buffer('running_var', torch.ones(1, channels, num_nodes, 1))
|
||||||
|
self.momentum = momentum
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
if self.track_running_stats:
|
||||||
|
mean = x.mean((0, 3), keepdims=True)
|
||||||
|
var = x.var((0, 3), keepdims=True, unbiased=False)
|
||||||
|
if self.training:
|
||||||
|
n = x.shape[3] * x.shape[0]
|
||||||
|
with torch.no_grad():
|
||||||
|
self.running_mean = self.momentum * mean + (1 - self.momentum) * self.running_mean
|
||||||
|
self.running_var = self.momentum * var * n / (n - 1) + (1 - self.momentum) * self.running_var
|
||||||
|
else:
|
||||||
|
mean = self.running_mean
|
||||||
|
var = self.running_var
|
||||||
|
else:
|
||||||
|
mean = x.mean(3, keepdims=True)
|
||||||
|
var = x.var(3, keepdims=True, unbiased=True)
|
||||||
|
x_norm = (x - mean) / (var + 1e-5) ** 0.5
|
||||||
|
return x_norm * self.gamma + self.beta
|
||||||
|
|
||||||
|
class stnorm(nn.Module):
|
||||||
|
def __init__(self, args):
|
||||||
|
super().__init__()
|
||||||
|
self.dropout = args["dropout"]
|
||||||
|
self.blocks = args["blocks"]
|
||||||
|
self.layers = args["layers"]
|
||||||
|
self.snorm_bool = args["snorm_bool"]
|
||||||
|
self.tnorm_bool = args["tnorm_bool"]
|
||||||
|
self.num_nodes = args["num_nodes"]
|
||||||
|
in_dim = args["in_dim"]
|
||||||
|
out_dim = args["out_dim"]
|
||||||
|
channels = args["channels"]
|
||||||
|
kernel_size = args["kernel_size"]
|
||||||
|
|
||||||
|
# 初始化卷积层
|
||||||
|
self.start_conv = nn.Conv2d(in_channels=in_dim, out_channels=channels, kernel_size=(1, 1))
|
||||||
|
|
||||||
|
# 初始化模块列表
|
||||||
|
self.filter_convs = nn.ModuleList()
|
||||||
|
self.gate_convs = nn.ModuleList()
|
||||||
|
self.residual_convs = nn.ModuleList()
|
||||||
|
self.skip_convs = nn.ModuleList()
|
||||||
|
self.sn = nn.ModuleList() if self.snorm_bool else None
|
||||||
|
self.tn = nn.ModuleList() if self.tnorm_bool else None
|
||||||
|
|
||||||
|
# 计算感受野
|
||||||
|
self.receptive_field = 1
|
||||||
|
additional_scope = kernel_size - 1
|
||||||
|
|
||||||
|
# 构建网络层
|
||||||
|
for b in range(self.blocks):
|
||||||
|
new_dilation = 1
|
||||||
|
for i in range(self.layers):
|
||||||
|
if self.tnorm_bool:
|
||||||
|
self.tn.append(TNorm(self.num_nodes, channels))
|
||||||
|
if self.snorm_bool:
|
||||||
|
self.sn.append(SNorm(channels))
|
||||||
|
|
||||||
|
# 膨胀卷积 - 直接使用channels作为输入通道，不再拼接多个特征
|
||||||
|
self.filter_convs.append(nn.Conv2d(in_channels=channels, out_channels=channels,
|
||||||
|
kernel_size=(1, kernel_size), dilation=new_dilation))
|
||||||
|
self.gate_convs.append(nn.Conv2d(in_channels=channels, out_channels=channels,
|
||||||
|
kernel_size=(1, kernel_size), dilation=new_dilation))
|
||||||
|
|
||||||
|
# 残差连接和跳跃连接
|
||||||
|
self.residual_convs.append(nn.Conv2d(in_channels=channels, out_channels=channels, kernel_size=(1, 1)))
|
||||||
|
self.skip_convs.append(nn.Conv2d(in_channels=channels, out_channels=channels, kernel_size=(1, 1)))
|
||||||
|
|
||||||
|
# 更新感受野
|
||||||
|
self.receptive_field += additional_scope
|
||||||
|
additional_scope *= 2
|
||||||
|
new_dilation *= 2
|
||||||
|
|
||||||
|
# 输出层
|
||||||
|
self.end_conv_1 = nn.Conv2d(in_channels=channels, out_channels=channels, kernel_size=(1, 1), bias=True)
|
||||||
|
self.end_conv_2 = nn.Conv2d(in_channels=channels, out_channels=out_dim, kernel_size=(1, 1), bias=True)
|
||||||
|
|
||||||
|
def forward(self, input):
|
||||||
|
# 输入处理：与GWN保持一致 (bs, features, n_nodes, n_timesteps)
|
||||||
|
x = input[..., 0:1].transpose(1, 3)
|
||||||
|
|
||||||
|
# 处理感受野
|
||||||
|
in_len = x.size(3)
|
||||||
|
if in_len < self.receptive_field:
|
||||||
|
x = nn.functional.pad(x, (self.receptive_field - in_len, 0, 0, 0))
|
||||||
|
|
||||||
|
# 起始卷积
|
||||||
|
x = self.start_conv(x)
|
||||||
|
skip = 0
|
||||||
|
|
||||||
|
# WaveNet层
|
||||||
|
for i in range(self.blocks * self.layers):
|
||||||
|
residual = x
|
||||||
|
|
||||||
|
# 添加空间和时间归一化（直接叠加到原始特征上，而不是拼接）
|
||||||
|
x_norm = x
|
||||||
|
if self.tnorm_bool:
|
||||||
|
x_norm += self.tn[i](x)
|
||||||
|
if self.snorm_bool:
|
||||||
|
x_norm += self.sn[i](x)
|
||||||
|
|
||||||
|
# 膨胀卷积
|
||||||
|
filter = torch.tanh(self.filter_convs[i](x_norm))
|
||||||
|
gate = torch.sigmoid(self.gate_convs[i](x_norm))
|
||||||
|
x = filter * gate
|
||||||
|
|
||||||
|
# 跳跃连接
|
||||||
|
s = self.skip_convs[i](x)
|
||||||
|
skip = s + (skip[:, :, :, -s.size(3):] if isinstance(skip, torch.Tensor) else 0)
|
||||||
|
|
||||||
|
# 残差连接
|
||||||
|
x = self.residual_convs[i](x) + residual[:, :, :, -x.size(3):]
|
||||||
|
|
||||||
|
# 输出处理
|
||||||
|
x = F.relu(skip)
|
||||||
|
x = F.relu(self.end_conv_1(x))
|
||||||
|
x = self.end_conv_2(x)
|
||||||
|
return x
|
||||||
|
|
@ -0,0 +1,7 @@
|
||||||
|
[
|
||||||
|
{
|
||||||
|
"name": "STNorm",
|
||||||
|
"module": "model.STNorm.STNorm",
|
||||||
|
"entry": "stnorm"
|
||||||
|
}
|
||||||
|
]
|
||||||
10
train.py
10
train.py
|
|
@ -90,9 +90,9 @@ def main(model, data, debug=False):
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
# 调试用
|
# 调试用
|
||||||
# model_list = ["iTransformer", "PatchTST", "HI"]
|
# model_list = ["iTransformer", "PatchTST", "HI"]
|
||||||
model_list = ["ASTRA_v3"]
|
# model_list = ["ASTRA_v3"]
|
||||||
# model_list = ["MTGNN"]
|
model_list = ["PatchTST"]
|
||||||
# dataset_list = ["AirQuality", "SolarEnergy", "PEMS-BAY", "METR-LA", "BJTaxi-InFlow", "BJTaxi-OutFlow", "NYCBike-InFlow", "NYCBike-OutFlow"]
|
dataset_list = ["AirQuality", "SolarEnergy", "PEMS-BAY", "METR-LA", "BJTaxi-InFlow", "BJTaxi-OutFlow", "NYCBike-InFlow", "NYCBike-OutFlow"]
|
||||||
# dataset_list = ["AirQuality"]
|
# dataset_list = ["AirQuality"]
|
||||||
dataset_list = ["AirQuality", "SolarEnergy", "METR-LA", "NYCBike-InFlow", "NYCBike-OutFlow"]
|
# dataset_list = ["METR-LA"]
|
||||||
main(model_list, dataset_list, debug = True)
|
main(model_list, dataset_list, debug = False)
|
||||||
|
|
@ -102,7 +102,9 @@ class Trainer:
|
||||||
for data, target in self.test_loader:
|
for data, target in self.test_loader:
|
||||||
data, target = data.to(self.device), target.to(self.device)
|
data, target = data.to(self.device), target.to(self.device)
|
||||||
label = target[..., :self.args["output_dim"]]
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label = target[..., :self.args["output_dim"]]
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||||||
y_pred.append(self.model(data).cpu())
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x, shp = self.pack(data)
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||||||
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out = self.unpack(self.model(x), shp)
|
||||||
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y_pred.append(out.cpu())
|
||||||
y_true.append(label.cpu())
|
y_true.append(label.cpu())
|
||||||
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|
||||||
d_pred, d_true = self.inv(torch.cat(y_pred)), self.inv(torch.cat(y_true)) # 反归一化
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d_pred, d_true = self.inv(torch.cat(y_pred)), self.inv(torch.cat(y_true)) # 反归一化
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue