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REPST #3

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czzhangheng merged 42 commits from REPST into main 2025-12-20 16:03:22 +08:00
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4
config/PatchTST/BJTaxi-InFlow.yaml
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@ -6,7 +6,7 @@ basic:
seed: 2023
data:
batch_size: 64
batch_size: 16
column_wise: false
days_per_week: 7
horizon: 24
@ -34,7 +34,7 @@ model:
train:
batch_size: 64
batch_size: 16
debug: false
early_stop: true
early_stop_patience: 15

4
config/PatchTST/BJTaxi-OutFlow.yaml
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@ -6,7 +6,7 @@ basic:
seed: 2023
data:
batch_size: 64
batch_size: 16
column_wise: false
days_per_week: 7
horizon: 24
@ -34,7 +34,7 @@ model:
train:
batch_size: 64
batch_size: 16
debug: false
early_stop: true
early_stop_patience: 15

64
config/STNorm/AirQuality.yaml Normal file
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@ -0,0 +1,64 @@
basic:
dataset: AirQuality
device: cuda:0
mode: train
model: MTGNN
seed: 2023
data:
batch_size: 64
column_wise: false
days_per_week: 7
horizon: 24
input_dim: 6
lag: 24
normalizer: std
num_nodes: 35
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: 35 # 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: 6 # 输入特征维度 (int)
out_len: 24 # 输出序列长度 (int)
out_dim: 6 # 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: 6
plot: false
real_value: true
weight_decay: 0

64
config/STNorm/BJTaxi-InFlow.yaml Normal file
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@ -0,0 +1,64 @@
basic:
dataset: BJTaxi-InFlow
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: 1024
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: 1024 # 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

64
config/STNorm/BJTaxi-OutFlow.yaml Normal file
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@ -0,0 +1,64 @@
basic:
dataset: BJTaxi-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: 1024
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: 1024 # 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

52
config/STNorm/METR-LA.yaml Normal file
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@ -0,0 +1,52 @@
basic:
dataset: METR-LA
device: cuda:0
mode: train
model: STNorm
seed: 2023
data:
batch_size: 16
column_wise: false
days_per_week: 7
horizon: 24
input_dim: 1
lag: 24
normalizer: std
num_nodes: 207
steps_per_day: 288
test_ratio: 0.2
val_ratio: 0.2
model:
dropout: 0.2
blocks: 2
layers: 2
snorm_bool: True
tnorm_bool: True
num_nodes: 207
in_dim: 1
out_dim: 24
channels: 32
kernel_size: 2
train:
batch_size: 16
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

64
config/STNorm/NYCBike-InFlow.yaml Normal file
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@ -0,0 +1,64 @@
basic:
dataset: NYCBike-InFlow
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

64
config/STNorm/NYCBike-OutFlow.yaml Normal file
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@ -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

64
config/STNorm/PEMS-BAY.yaml Normal file
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@ -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

64
config/STNorm/SolarEnergy.yaml Normal file
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@ -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

140
model/STNorm/STNorm.py Normal file
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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

7
model/STNorm/model_config.json Normal file
View File

@ -0,0 +1,7 @@
[
{
"name": "STNorm",
"module": "model.STNorm.STNorm",
"entry": "stnorm"
}
]

10
train.py
View File

@ -90,9 +90,9 @@ def main(model, data, debug=False):
if __name__ == "__main__":
# 调试用
# model_list = ["iTransformer", "PatchTST", "HI"]
model_list = ["ASTRA_v3"]
# model_list = ["MTGNN"]
# dataset_list = ["AirQuality", "SolarEnergy", "PEMS-BAY", "METR-LA", "BJTaxi-InFlow", "BJTaxi-OutFlow", "NYCBike-InFlow", "NYCBike-OutFlow"]
# model_list = ["ASTRA_v3"]
model_list = ["PatchTST"]
dataset_list = ["AirQuality", "SolarEnergy", "PEMS-BAY", "METR-LA", "BJTaxi-InFlow", "BJTaxi-OutFlow", "NYCBike-InFlow", "NYCBike-OutFlow"]
# dataset_list = ["AirQuality"]
dataset_list = ["AirQuality", "SolarEnergy", "METR-LA", "NYCBike-InFlow", "NYCBike-OutFlow"]
main(model_list, dataset_list, debug = True)
# dataset_list = ["METR-LA"]
main(model_list, dataset_list, debug = False)

4
trainer/TSTrainer.py
View File

@ -102,7 +102,9 @@ class Trainer:
for data, target in self.test_loader:
data, target = data.to(self.device), target.to(self.device)
label = target[..., :self.args["output_dim"]]
y_pred.append(self.model(data).cpu())
x, shp = self.pack(data)
out = self.unpack(self.model(x), shp)
y_pred.append(out.cpu())
y_true.append(label.cpu())
d_pred, d_true = self.inv(torch.cat(y_pred)), self.inv(torch.cat(y_true)) # 反归一化