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model.py
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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import sys
import math
class nconv(nn.Module):
def __init__(self):
super(nconv,self).__init__()
def forward(self,x, A):
x = torch.einsum('ncvl,vw->ncwl',(x,A))
return x.contiguous()
class linear(nn.Module):
def __init__(self,c_in,c_out):
super(linear,self).__init__()
self.mlp = torch.nn.Conv2d(c_in, c_out, kernel_size=(1, 1), padding=(0,0), stride=(1,1), bias=True)
def forward(self,x):
return self.mlp(x)
class gcn(nn.Module):
def __init__(self,c_in,c_out,dropout,support_len=3,order=2):
super(gcn,self).__init__()
self.nconv = nconv()
c_in = (order*support_len+1)*c_in
self.mlp = linear(c_in,c_out)
self.dropout = dropout
self.order = order
def forward(self,x,support):
out = [x]
for a in support:
x1 = self.nconv(x,a)
out.append(x1)
for k in range(2, self.order + 1):
x2 = self.nconv(x1,a)
out.append(x2)
x1 = x2
h = torch.cat(out,dim=1)
h = self.mlp(h)
h = F.dropout(h, self.dropout, training=self.training)
return h
class Bi_directional_spatial_relation_learner(nn.Module):
def __init__(self,c_in,c_out,dropout,support_len=3):
super(Bi_directional_spatial_relation_learner,self).__init__()
self.gcn1 = gcn(c_in=c_in,c_out=c_out,dropout=dropout,support_len=support_len)
self.gcn2 = gcn(c_in=c_in,c_out=c_out,dropout=dropout,support_len=support_len)
def forward(self,x,support):
inverse = 1/(x+100) # 10为平滑项，防止出现nan值
x = self.gcn1(x,support)
inverse = self.gcn2(inverse,support)
out = x+0.01*inverse #k0x k1x k2x 中的系数k0 k1 k2，都在各自的gcn中的mlp实现
return out
class Decomposed_temporal_evolution_extractor(nn.Module):
def __init__(self):
super(Decomposed_temporal_evolution_extractor,self).__init__()
self.seasonal_layer = nn.ModuleList()
self.seasonal_number = 3
# 卷积核大小
for i in range(self.seasonal_number):
# 计算 padding 的大小
kernel_size = 2*i+1
padding = math.floor((kernel_size - 1) // 2)
# 定义卷积层，并设置 padding 参数
self.seasonal_layer.append(nn.Conv2d(in_channels=1,
out_channels=1,
kernel_size=(1, kernel_size),
padding=(0,padding)))
self.trend_layer = nn.Linear(12,12)
def forward(self,x):# x B 1 N 12
y_trend,y_seasonal=0.0,0.0
for i in range(self.seasonal_number):
#print(self.seasonal_layer[i](x).shape)
y_seasonal = y_seasonal + self.seasonal_layer[i](x)
y_trend = self.trend_layer(x)
return y_trend.transpose(1,3),y_seasonal.transpose(1,3) # B 1 N 12-> B 12 N 1
class CauSTG(nn.Module):
def __init__(self, device, num_nodes, dropout=0.3, supports=None, gcn_bool=True, addaptadj=True, aptinit=None, in_dim=2,out_dim=12,residual_channels=32,dilation_channels=32,skip_channels=256,end_channels=512,kernel_size=2,blocks=4,layers=2):
super(CauSTG, self).__init__()
self.dropout = dropout
self.blocks = blocks
self.layers = layers
self.gcn_bool = gcn_bool
self.addaptadj = addaptadj
self.filter_convs = nn.ModuleList()
self.gate_convs = nn.ModuleList()
self.residual_convs = nn.ModuleList()
self.skip_convs = nn.ModuleList()
self.bn = nn.ModuleList()
self.gconv = nn.ModuleList()
self.start_conv = nn.Conv2d(in_channels=in_dim,
out_channels=residual_channels,
kernel_size=(1,1))
self.supports = supports
receptive_field = 1
self.supports_len = 0
if supports is not None:
self.supports_len += len(supports)
if gcn_bool and addaptadj:
if aptinit is None:
if supports is None:
self.supports = []
self.nodevec1 = nn.Parameter(torch.randn(num_nodes, 10).to(device), requires_grad=True).to(device)
self.nodevec2 = nn.Parameter(torch.randn(10, num_nodes).to(device), requires_grad=True).to(device)
self.supports_len +=1
else:
if supports is None:
self.supports = []
m, p, n = torch.svd(aptinit)
initemb1 = torch.mm(m[:, :10], torch.diag(p[:10] ** 0.5))
initemb2 = torch.mm(torch.diag(p[:10] ** 0.5), n[:, :10].t())
self.nodevec1 = nn.Parameter(initemb1, requires_grad=True).to(device)
self.nodevec2 = nn.Parameter(initemb2, requires_grad=True).to(device)
self.supports_len += 1
for b in range(blocks):
additional_scope = kernel_size - 1
new_dilation = 1
for i in range(layers):
# dilated convolutions
self.filter_convs.append(nn.Conv2d(in_channels=residual_channels,
out_channels=dilation_channels,
kernel_size=(1,kernel_size),dilation=new_dilation))
self.gate_convs.append(nn.Conv1d(in_channels=residual_channels,
out_channels=dilation_channels,
kernel_size=(1, kernel_size), dilation=new_dilation))
# 1x1 convolution for residual connection
self.residual_convs.append(nn.Conv1d(in_channels=dilation_channels,
out_channels=residual_channels,
kernel_size=(1, 1)))
# 1x1 convolution for skip connection
self.skip_convs.append(nn.Conv1d(in_channels=dilation_channels,
out_channels=skip_channels,
kernel_size=(1, 1)))
self.bn.append(nn.BatchNorm2d(residual_channels))
new_dilation *=2
receptive_field += additional_scope
additional_scope *= 2
if self.gcn_bool:
self.gconv.append(Bi_directional_spatial_relation_learner(dilation_channels,residual_channels,dropout,support_len=self.supports_len))
self.end_conv_1 = nn.Conv2d(in_channels=skip_channels,
out_channels=end_channels,
kernel_size=(1,1),
bias=True)
self.end_conv_2 = nn.Conv2d(in_channels=end_channels,
out_channels=out_dim,
kernel_size=(1,1),
bias=True)
self.receptive_field = receptive_field
self.decomposed_sea_tre = Decomposed_temporal_evolution_extractor()
def forward(self, input):
in_len = input.size(3)
if in_len<self.receptive_field:
x = nn.functional.pad(input,(self.receptive_field-in_len,0,0,0))
else:
x = input
x = self.start_conv(x)
skip = 0
# calculate the current adaptive adj matrix once per iteration
new_supports = None
if self.gcn_bool and self.addaptadj and self.supports is not None:
adp = F.softmax(F.relu(torch.mm(self.nodevec1, self.nodevec2)), dim=1)
new_supports = self.supports + [adp]
# encoder
for i in range(self.blocks * self.layers):
#(dilation, init_dilation) = self.dilations[i]
#residual = dilation_func(x, dilation, init_dilation, i)
residual = x
# dilated convolution
filter = self.filter_convs[i](residual)
filter = torch.tanh(filter)
gate = self.gate_convs[i](residual)
gate = torch.sigmoid(gate)
x = filter * gate
# parametrized skip connection
s = x
s = self.skip_convs[i](s)
try:
skip = skip[:, :, :, -s.size(3):]
except:
skip = 0
skip = s + skip
if self.gcn_bool and self.supports is not None:
if self.addaptadj:
x = self.gconv[i](x, new_supports)
else:
x = self.gconv[i](x,self.supports)
else:
x = self.residual_convs[i](x)
x = x + residual[:, :, :, -x.size(3):]
# B C N T
x = self.bn[i](x)
x = F.relu(skip)
x = F.relu(self.end_conv_1(x))
x = self.end_conv_2(x)
trend,season = self.decomposed_sea_tre(x.transpose(1,3)) # 输入是 B 12 N 1
return x,trend,season