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387f64efab
| Author | SHA1 | Date |
|---|---|---|
 czzhangheng
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387f64efab | |
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czzhangheng
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56a7f309fa |
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@ -0,0 +1,216 @@
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@ -0,0 +1 @@
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Subproject commit e50a1ba6d70528b3e684c85f316aed05bb5085f2
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@ -0,0 +1,60 @@
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||||||
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basic:
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||||||
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device: cuda:0
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||||||
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dataset: PEMS08
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||||||
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model: STGODE
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||||||
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mode: train
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||||||
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seed: 2025
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||||||
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||||||
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data:
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||||||
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dataset_dir: data/PEMS08
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||||||
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val_batch_size: 32
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||||||
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graph_pkl_filename: data/PEMS08/PEMS08_spatial_distance.npy
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||||||
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num_nodes: 170
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||||||
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batch_size: 64
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||||||
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input_dim: 1
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||||||
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lag: 12
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||||||
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horizon: 12
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||||||
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val_ratio: 0.2
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||||||
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test_ratio: 0.2
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||||||
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tod: False
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||||||
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normalizer: std
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||||||
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column_wise: False
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||||||
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default_graph: True
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||||||
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add_time_in_day: True
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||||||
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add_day_in_week: True
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||||||
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steps_per_day: 24
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||||||
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days_per_week: 7
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||||||
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||||||
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model:
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||||||
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input_dim: 1
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||||||
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output_dim: 1
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history: 12
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horizon: 12
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||||||
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num_features: 1
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||||||
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rnn_units: 64
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||||||
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sigma1: 0.1
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||||||
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sigma2: 10
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||||||
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thres1: 0.6
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||||||
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thres2: 0.5
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||||||
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||||||
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||||||
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train:
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||||||
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loss: mae
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||||||
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batch_size: 64
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||||||
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epochs: 100
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||||||
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lr_init: 0.003
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||||||
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mape_thresh: 0.001
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||||||
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mae_thresh: None
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||||||
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debug: False
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||||||
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output_dim: 1
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||||||
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weight_decay: 0
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||||||
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lr_decay: False
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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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early_stop: True
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||||||
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early_stop_patience: 15
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||||||
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grad_norm: False
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||||||
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max_grad_norm: 5
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real_value: True
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log_step: 3000
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@ -0,0 +1,180 @@
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||||||
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import torch
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import math
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import torch.nn as nn
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import torch.nn.functional as F
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from models.STGODE.odegcn import ODEG
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from models.STGODE.adj import get_A_hat
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||||||
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||||||
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class Chomp1d(nn.Module):
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||||||
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"""
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||||||
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extra dimension will be added by padding, remove it
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||||||
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"""
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||||||
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||||||
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def __init__(self, chomp_size):
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||||||
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super(Chomp1d, self).__init__()
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||||||
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self.chomp_size = chomp_size
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||||||
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|
||||||
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def forward(self, x):
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||||||
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return x[:, :, :, :-self.chomp_size].contiguous()
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||||||
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|
||||||
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|
||||||
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class TemporalConvNet(nn.Module):
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||||||
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"""
|
||||||
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time dilation convolution
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||||||
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"""
|
||||||
|
|
||||||
|
def __init__(self, num_inputs, num_channels, kernel_size=2, dropout=0.2):
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||||||
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"""
|
||||||
|
Args:
|
||||||
|
num_inputs : channel's number of input data's feature
|
||||||
|
num_channels : numbers of data feature tranform channels, the last is the output channel
|
||||||
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kernel_size : using 1d convolution, so the real kernel is (1, kernel_size)
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||||||
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"""
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||||||
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super(TemporalConvNet, self).__init__()
|
||||||
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layers = []
|
||||||
|
num_levels = len(num_channels)
|
||||||
|
for i in range(num_levels):
|
||||||
|
dilation_size = 2 ** i
|
||||||
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in_channels = num_inputs if i == 0 else num_channels[i - 1]
|
||||||
|
out_channels = num_channels[i]
|
||||||
|
padding = (kernel_size - 1) * dilation_size
|
||||||
|
self.conv = nn.Conv2d(in_channels, out_channels, (1, kernel_size), dilation=(1, dilation_size),
|
||||||
|
padding=(0, padding))
|
||||||
|
self.conv.weight.data.normal_(0, 0.01)
|
||||||
|
self.chomp = Chomp1d(padding)
|
||||||
|
self.relu = nn.ReLU()
|
||||||
|
self.dropout = nn.Dropout(dropout)
|
||||||
|
|
||||||
|
layers += [nn.Sequential(self.conv, self.chomp, self.relu, self.dropout)]
|
||||||
|
|
||||||
|
self.network = nn.Sequential(*layers)
|
||||||
|
self.downsample = nn.Conv2d(num_inputs, num_channels[-1], (1, 1)) if num_inputs != num_channels[-1] else None
|
||||||
|
if self.downsample:
|
||||||
|
self.downsample.weight.data.normal_(0, 0.01)
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
"""
|
||||||
|
like ResNet
|
||||||
|
Args:
|
||||||
|
X : input data of shape (B, N, T, F)
|
||||||
|
"""
|
||||||
|
# permute shape to (B, F, N, T)
|
||||||
|
y = x.permute(0, 3, 1, 2)
|
||||||
|
y = F.relu(self.network(y) + self.downsample(y) if self.downsample else y)
|
||||||
|
y = y.permute(0, 2, 3, 1)
|
||||||
|
return y
|
||||||
|
|
||||||
|
|
||||||
|
class GCN(nn.Module):
|
||||||
|
def __init__(self, A_hat, in_channels, out_channels, ):
|
||||||
|
super(GCN, self).__init__()
|
||||||
|
self.A_hat = A_hat
|
||||||
|
self.theta = nn.Parameter(torch.FloatTensor(in_channels, out_channels))
|
||||||
|
self.reset()
|
||||||
|
|
||||||
|
def reset(self):
|
||||||
|
stdv = 1. / math.sqrt(self.theta.shape[1])
|
||||||
|
self.theta.data.uniform_(-stdv, stdv)
|
||||||
|
|
||||||
|
def forward(self, X):
|
||||||
|
y = torch.einsum('ij, kjlm-> kilm', self.A_hat, X)
|
||||||
|
return F.relu(torch.einsum('kjlm, mn->kjln', y, self.theta))
|
||||||
|
|
||||||
|
|
||||||
|
class STGCNBlock(nn.Module):
|
||||||
|
def __init__(self, in_channels, out_channels, num_nodes, A_hat):
|
||||||
|
"""
|
||||||
|
Args:
|
||||||
|
in_channels: Number of input features at each node in each time step.
|
||||||
|
out_channels: a list of feature channels in timeblock, the last is output feature channel
|
||||||
|
num_nodes: Number of nodes in the graph
|
||||||
|
A_hat: the normalized adjacency matrix
|
||||||
|
"""
|
||||||
|
super(STGCNBlock, self).__init__()
|
||||||
|
self.A_hat = A_hat
|
||||||
|
self.temporal1 = TemporalConvNet(num_inputs=in_channels,
|
||||||
|
num_channels=out_channels)
|
||||||
|
self.odeg = ODEG(out_channels[-1], 12, A_hat, time=6)
|
||||||
|
self.temporal2 = TemporalConvNet(num_inputs=out_channels[-1],
|
||||||
|
num_channels=out_channels)
|
||||||
|
self.batch_norm = nn.BatchNorm2d(num_nodes)
|
||||||
|
|
||||||
|
def forward(self, X):
|
||||||
|
"""
|
||||||
|
Args:
|
||||||
|
X: Input data of shape (batch_size, num_nodes, num_timesteps, num_features)
|
||||||
|
Return:
|
||||||
|
Output data of shape(batch_size, num_nodes, num_timesteps, out_channels[-1])
|
||||||
|
"""
|
||||||
|
t = self.temporal1(X)
|
||||||
|
t = self.odeg(t)
|
||||||
|
t = self.temporal2(F.relu(t))
|
||||||
|
|
||||||
|
return self.batch_norm(t)
|
||||||
|
|
||||||
|
|
||||||
|
class ODEGCN(nn.Module):
|
||||||
|
""" the overall network framework """
|
||||||
|
|
||||||
|
def __init__(self, config):
|
||||||
|
"""
|
||||||
|
Args:
|
||||||
|
num_nodes : number of nodes in the graph
|
||||||
|
num_features : number of features at each node in each time step
|
||||||
|
num_timesteps_input : number of past time steps fed into the network
|
||||||
|
num_timesteps_output : desired number of future time steps output by the network
|
||||||
|
A_sp_hat : nomarlized adjacency spatial matrix
|
||||||
|
A_se_hat : nomarlized adjacency semantic matrix
|
||||||
|
"""
|
||||||
|
|
||||||
|
super(ODEGCN, self).__init__()
|
||||||
|
args = config['model']
|
||||||
|
num_nodes = config['data']['num_nodes']
|
||||||
|
num_features = args['num_features']
|
||||||
|
num_timesteps_input = args['history']
|
||||||
|
num_timesteps_output = args['horizon']
|
||||||
|
A_sp_hat, A_se_hat = get_A_hat(config)
|
||||||
|
|
||||||
|
# spatial graph
|
||||||
|
self.sp_blocks = nn.ModuleList(
|
||||||
|
[nn.Sequential(
|
||||||
|
STGCNBlock(in_channels=num_features, out_channels=[64, 32, 64],
|
||||||
|
num_nodes=num_nodes, A_hat=A_sp_hat),
|
||||||
|
STGCNBlock(in_channels=64, out_channels=[64, 32, 64],
|
||||||
|
num_nodes=num_nodes, A_hat=A_sp_hat)) for _ in range(3)
|
||||||
|
])
|
||||||
|
# semantic graph
|
||||||
|
self.se_blocks = nn.ModuleList([nn.Sequential(
|
||||||
|
STGCNBlock(in_channels=num_features, out_channels=[64, 32, 64],
|
||||||
|
num_nodes=num_nodes, A_hat=A_se_hat),
|
||||||
|
STGCNBlock(in_channels=64, out_channels=[64, 32, 64],
|
||||||
|
num_nodes=num_nodes, A_hat=A_se_hat)) for _ in range(3)
|
||||||
|
])
|
||||||
|
|
||||||
|
self.pred = nn.Sequential(
|
||||||
|
nn.Linear(num_timesteps_input * 64, num_timesteps_output * 32),
|
||||||
|
nn.ReLU(),
|
||||||
|
nn.Linear(num_timesteps_output * 32, num_timesteps_output)
|
||||||
|
)
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
"""
|
||||||
|
Args:
|
||||||
|
x : input data of shape (batch_size, num_nodes, num_timesteps, num_features) == (B, N, T, F)
|
||||||
|
Returns:
|
||||||
|
prediction for future of shape (batch_size, num_nodes, num_timesteps_output)
|
||||||
|
"""
|
||||||
|
x = x[..., 0:1].permute(0, 2, 1, 3)
|
||||||
|
outs = []
|
||||||
|
# spatial graph
|
||||||
|
for blk in self.sp_blocks:
|
||||||
|
outs.append(blk(x))
|
||||||
|
# semantic graph
|
||||||
|
for blk in self.se_blocks:
|
||||||
|
outs.append(blk(x))
|
||||||
|
outs = torch.stack(outs)
|
||||||
|
x = torch.max(outs, dim=0)[0]
|
||||||
|
x = x.reshape((x.shape[0], x.shape[1], -1))
|
||||||
|
|
||||||
|
return self.pred(x).permute(0,2,1).unsqueeze(dim=-1)
|
||||||
|
|
@ -0,0 +1,132 @@
|
||||||
|
import csv
|
||||||
|
import os
|
||||||
|
import pandas as pd
|
||||||
|
import numpy as np
|
||||||
|
from fastdtw import fastdtw
|
||||||
|
from tqdm import tqdm
|
||||||
|
import torch
|
||||||
|
|
||||||
|
files = {
|
||||||
|
358: ['PEMS03/PEMS03.npz', 'PEMS03/PEMS03.csv'],
|
||||||
|
307: ['PEMS04/PEMS04.npz', 'PEMS04/PEMS04.csv'],
|
||||||
|
883: ['PEMS07/PEMS07.npz', 'PEMS07/PEMS07.csv'],
|
||||||
|
170: ['PEMS08/PEMS08.npz', 'PEMS08/PEMS08.csv'],
|
||||||
|
# 'pemsbay': ['PEMSBAY/pems_bay.npz', 'PEMSBAY/distance.csv'],
|
||||||
|
# 'pemsD7M': ['PEMSD7M/PEMSD7M.npz', 'PEMSD7M/distance.csv'],
|
||||||
|
# 'pemsD7L': ['PEMSD7L/PEMSD7L.npz', 'PEMSD7L/distance.csv']
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def get_A_hat(config):
|
||||||
|
"""read data, generate spatial adjacency matrix and semantic adjacency matrix by dtw
|
||||||
|
|
||||||
|
Args:
|
||||||
|
sigma1: float, default=0.1, sigma for the semantic matrix
|
||||||
|
sigma2: float, default=10, sigma for the spatial matrix
|
||||||
|
thres1: float, default=0.6, the threshold for the semantic matrix
|
||||||
|
thres2: float, default=0.5, the threshold for the spatial matrix
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
data: tensor, T * N * 1
|
||||||
|
dtw_matrix: array, semantic adjacency matrix
|
||||||
|
sp_matrix: array, spatial adjacency matrix
|
||||||
|
"""
|
||||||
|
file_path = config['data']['graph_pkl_filename']
|
||||||
|
filename = config['basic']['dataset']
|
||||||
|
dataset_path = config['data']['dataset_dir']
|
||||||
|
args = config['model']
|
||||||
|
|
||||||
|
data = np.load(file_path)
|
||||||
|
data = np.nan_to_num(data, nan=0.0, posinf=0.0, neginf=0.0)
|
||||||
|
num_node = data.shape[1]
|
||||||
|
mean_value = np.mean(data, axis=(0, 1)).reshape(1, 1, -1)
|
||||||
|
std_value = np.std(data, axis=(0, 1)).reshape(1, 1, -1)
|
||||||
|
data = (data - mean_value) / std_value
|
||||||
|
|
||||||
|
# 计算dtw_distance, 如果存在缓存则直接读取缓存
|
||||||
|
if not os.path.exists(f'data/PEMS0{filename[-1]}/PEMS0{filename[-1]}_dtw_distance.npy'):
|
||||||
|
data_mean = np.mean([data[:, :, 0][24 * 12 * i: 24 * 12 * (i + 1)] for i in range(data.shape[0] // (24 * 12))],
|
||||||
|
axis=0)
|
||||||
|
data_mean = data_mean.squeeze().T
|
||||||
|
dtw_distance = np.zeros((num_node, num_node))
|
||||||
|
for i in tqdm(range(num_node)):
|
||||||
|
for j in range(i, num_node):
|
||||||
|
dtw_distance[i][j] = fastdtw(data_mean[i], data_mean[j], radius=6)[0]
|
||||||
|
for i in range(num_node):
|
||||||
|
for j in range(i):
|
||||||
|
dtw_distance[i][j] = dtw_distance[j][i]
|
||||||
|
np.save(f'data/PEMS0{filename[-1]}/PEMS0{filename[-1]}_dtw_distance.npy', dtw_distance)
|
||||||
|
|
||||||
|
dist_matrix = np.load(f'data/PEMS0{filename[-1]}/PEMS0{filename[-1]}_dtw_distance.npy')
|
||||||
|
|
||||||
|
mean = np.mean(dist_matrix)
|
||||||
|
std = np.std(dist_matrix)
|
||||||
|
dist_matrix = (dist_matrix - mean) / std
|
||||||
|
sigma = args['sigma1']
|
||||||
|
dist_matrix = np.exp(-dist_matrix ** 2 / sigma ** 2)
|
||||||
|
dtw_matrix = np.zeros_like(dist_matrix)
|
||||||
|
dtw_matrix[dist_matrix > args['thres1']] = 1
|
||||||
|
|
||||||
|
# 计算spatial_distance, 如果存在缓存则直接读取缓存
|
||||||
|
if not os.path.exists(f'data/PEMS0{filename[-1]}/PEMS0{filename[-1]}_spatial_distance.npy'):
|
||||||
|
if num_node == 358:
|
||||||
|
with open(f'data/PEMS0{filename[-1]}/PEMS0{filename[-1]}.txt', 'r') as f:
|
||||||
|
id_dict = {int(i): idx for idx, i in enumerate(f.read().strip().split('\n'))} # 建立映射列表
|
||||||
|
# 使用 pandas 读取 CSV 文件，跳过标题行
|
||||||
|
df = pd.read_csv(f'{dataset_path}/{filename}.csv', skiprows=1, header=None)
|
||||||
|
dist_matrix = np.zeros((num_node, num_node)) + float('inf')
|
||||||
|
for _, row in df.iterrows():
|
||||||
|
start = int(id_dict[row[0]])
|
||||||
|
end = int(id_dict[row[1]])
|
||||||
|
dist_matrix[start][end] = float(row[2])
|
||||||
|
dist_matrix[end][start] = float(row[2])
|
||||||
|
np.save(f'data/PEMS0{filename[-1]}/PEMS0{filename[-1]}_spatial_distance.npy', dist_matrix)
|
||||||
|
else:
|
||||||
|
# 使用 pandas 读取 CSV 文件，跳过标题行
|
||||||
|
df = pd.read_csv(f'{dataset_path}/{filename}.csv', skiprows=1, header=None)
|
||||||
|
dist_matrix = np.zeros((num_node, num_node)) + float('inf')
|
||||||
|
for _, row in df.iterrows():
|
||||||
|
start = int(row[0])
|
||||||
|
end = int(row[1])
|
||||||
|
dist_matrix[start][end] = float(row[2])
|
||||||
|
dist_matrix[end][start] = float(row[2])
|
||||||
|
np.save(f'data/PEMS0{filename[-1]}/PEMS0{filename[-1]}_spatial_distance.npy', dist_matrix)
|
||||||
|
# normalization
|
||||||
|
std = np.std(dist_matrix[dist_matrix != float('inf')])
|
||||||
|
mean = np.mean(dist_matrix[dist_matrix != float('inf')])
|
||||||
|
dist_matrix = (dist_matrix - mean) / std
|
||||||
|
sigma = args['sigma2']
|
||||||
|
sp_matrix = np.exp(- dist_matrix ** 2 / sigma ** 2)
|
||||||
|
sp_matrix[sp_matrix < args['thres2']] = 0
|
||||||
|
|
||||||
|
return (get_normalized_adj(dtw_matrix).to(config['basic']['device']),
|
||||||
|
get_normalized_adj(sp_matrix).to(config['basic']['device']))
|
||||||
|
|
||||||
|
|
||||||
|
def get_normalized_adj(A):
|
||||||
|
"""
|
||||||
|
Returns a tensor, the degree normalized adjacency matrix.
|
||||||
|
"""
|
||||||
|
alpha = 0.8
|
||||||
|
D = np.array(np.sum(A, axis=1)).reshape((-1,))
|
||||||
|
D[D <= 10e-5] = 10e-5 # Prevent infs
|
||||||
|
diag = np.reciprocal(np.sqrt(D))
|
||||||
|
A_wave = np.multiply(np.multiply(diag.reshape((-1, 1)), A),
|
||||||
|
diag.reshape((1, -1)))
|
||||||
|
A_reg = alpha / 2 * (np.eye(A.shape[0]) + A_wave)
|
||||||
|
return torch.from_numpy(A_reg.astype(np.float32))
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
config = {
|
||||||
|
'sigma1': 0.1,
|
||||||
|
'sigma2': 10,
|
||||||
|
'thres1': 0.6,
|
||||||
|
'thres2': 0.5,
|
||||||
|
'device': 'cuda:0' if torch.cuda.is_available() else 'cpu'
|
||||||
|
}
|
||||||
|
|
||||||
|
for nodes in [358, 170, 883]:
|
||||||
|
args = {'num_nodes': nodes, **config}
|
||||||
|
get_A_hat(args)
|
||||||
|
|
@ -0,0 +1,74 @@
|
||||||
|
import numpy as np
|
||||||
|
import torch
|
||||||
|
from torch import nn
|
||||||
|
import torch.nn.functional as F
|
||||||
|
|
||||||
|
# Whether use adjoint method or not.
|
||||||
|
adjoint = False
|
||||||
|
if adjoint:
|
||||||
|
from torchdiffeq import odeint_adjoint as odeint
|
||||||
|
else:
|
||||||
|
from torchdiffeq import odeint
|
||||||
|
|
||||||
|
|
||||||
|
# Define the ODE function.
|
||||||
|
# Input:
|
||||||
|
# --- t: A tensor with shape [], meaning the current time.
|
||||||
|
# --- x: A tensor with shape [#batches, dims], meaning the value of x at t.
|
||||||
|
# Output:
|
||||||
|
# --- dx/dt: A tensor with shape [#batches, dims], meaning the derivative of x at t.
|
||||||
|
class ODEFunc(nn.Module):
|
||||||
|
|
||||||
|
def __init__(self, feature_dim, temporal_dim, adj):
|
||||||
|
super(ODEFunc, self).__init__()
|
||||||
|
self.adj = adj
|
||||||
|
self.x0 = None
|
||||||
|
self.alpha = nn.Parameter(0.8 * torch.ones(adj.shape[1]))
|
||||||
|
self.beta = 0.6
|
||||||
|
self.w = nn.Parameter(torch.eye(feature_dim))
|
||||||
|
self.d = nn.Parameter(torch.zeros(feature_dim) + 1)
|
||||||
|
self.w2 = nn.Parameter(torch.eye(temporal_dim))
|
||||||
|
self.d2 = nn.Parameter(torch.zeros(temporal_dim) + 1)
|
||||||
|
|
||||||
|
def forward(self, t, x):
|
||||||
|
alpha = torch.sigmoid(self.alpha).unsqueeze(-1).unsqueeze(-1).unsqueeze(0)
|
||||||
|
xa = torch.einsum('ij, kjlm->kilm', self.adj, x)
|
||||||
|
|
||||||
|
# ensure the eigenvalues to be less than 1
|
||||||
|
d = torch.clamp(self.d, min=0, max=1)
|
||||||
|
w = torch.mm(self.w * d, torch.t(self.w))
|
||||||
|
xw = torch.einsum('ijkl, lm->ijkm', x, w)
|
||||||
|
|
||||||
|
d2 = torch.clamp(self.d2, min=0, max=1)
|
||||||
|
w2 = torch.mm(self.w2 * d2, torch.t(self.w2))
|
||||||
|
xw2 = torch.einsum('ijkl, km->ijml', x, w2)
|
||||||
|
|
||||||
|
f = alpha / 2 * xa - x + xw - x + xw2 - x + self.x0
|
||||||
|
return f
|
||||||
|
|
||||||
|
|
||||||
|
class ODEblock(nn.Module):
|
||||||
|
def __init__(self, odefunc, t=torch.tensor([0,1])):
|
||||||
|
super(ODEblock, self).__init__()
|
||||||
|
self.t = t
|
||||||
|
self.odefunc = odefunc
|
||||||
|
|
||||||
|
def set_x0(self, x0):
|
||||||
|
self.odefunc.x0 = x0.clone().detach()
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
t = self.t.type_as(x)
|
||||||
|
z = odeint(self.odefunc, x, t, method='euler')[1]
|
||||||
|
return z
|
||||||
|
|
||||||
|
|
||||||
|
# Define the ODEGCN model.
|
||||||
|
class ODEG(nn.Module):
|
||||||
|
def __init__(self, feature_dim, temporal_dim, adj, time):
|
||||||
|
super(ODEG, self).__init__()
|
||||||
|
self.odeblock = ODEblock(ODEFunc(feature_dim, temporal_dim, adj), t=torch.tensor([0, time]))
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
self.odeblock.set_x0(x)
|
||||||
|
z = self.odeblock(x)
|
||||||
|
return F.relu(z)
|
||||||
|
|
@ -1,8 +1,12 @@
|
||||||
from models.STDEN.stden_model import STDENModel
|
from models.STDEN.stden_model import STDENModel
|
||||||
|
from models.STGODE.STGODE import ODEGCN
|
||||||
|
|
||||||
def model_selector(config):
|
def model_selector(config):
|
||||||
model_name = config['basic']['model']
|
model_name = config['basic']['model']
|
||||||
model = None
|
model = None
|
||||||
match model_name:
|
match model_name:
|
||||||
case 'STDEN': model = STDENModel(config)
|
case 'STDEN':
|
||||||
|
model = STDENModel(config)
|
||||||
|
case 'STGODE':
|
||||||
|
model = ODEGCN(config)
|
||||||
return model
|
return model
|
||||||
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Reference in New Issue