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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 numpy as np
import pandas as pd
import math
class DCN(nn.Module):
def __init__(self, N, h, k):
super(DCN, self).__init__()
self.h = h
self.k = k
self.W = [nn.Parameter(torch.empty([N,N], dtype=torch.float32, device=device), requires_grad=True) for i in range(k*h)]
for i in range(k*h):
torch.nn.init.xavier_normal_(self.W[i])
def forward(self, a):
p = []
for i in range(self.h):
pi = self.W[i*self.k] * a[0]
for j in range(1,self.k):
pi += self.W[i*self.k + j] * a[j]
p.append(pi)
p = torch.stack(p, 0).unsqueeze(1).unsqueeze(1)
return p
class Encoder_Decoder_Attention(nn.Module):
def __init__(self, outfea, d):
super(Encoder_Decoder_Attention, self).__init__()
self.qff = nn.Linear(outfea, outfea)
self.kff = nn.Linear(outfea, outfea)
self.vff = nn.Linear(outfea, outfea)
self.ff = nn.Sequential(
nn.Linear(outfea, outfea),
nn.GELU(),
nn.Linear(outfea, outfea),
)
self.ln = nn.LayerNorm(outfea)
self.d = d
def forward(self, x, xp):
query = self.qff(x)
key = self.kff(xp)
value = self.vff(xp)
query = torch.cat(torch.split(query, self.d, -1), 0).permute(0,2,1,3)
key = torch.cat(torch.split(key, self.d, -1), 0).permute(0,2,3,1)
value = torch.cat(torch.split(value, self.d, -1), 0).permute(0,2,1,3)
a = torch.matmul(query, key)
a /= (self.d ** 0.5)
a = torch.softmax(a, -1)
value = torch.matmul(a, value)
value = torch.cat(torch.split(value, x.shape[0], 0), -1).permute(0,2,1,3)
value = self.ff(value) + x
return self.ln(value)
class Temporal_Attention(nn.Module):
def __init__(self, outfea, k, d):
super(Temporal_Attention, self).__init__()
self.qff = nn.Linear(outfea, outfea)
self.kff = nn.Linear(outfea, outfea)
self.vff = nn.Linear(outfea, outfea)
self.ff = nn.Sequential(
nn.Linear(outfea, outfea),
nn.GELU(),
nn.Linear(outfea, outfea),
)
self.ln = nn.LayerNorm(outfea)
self.d = d
self.k = k
def forward(self, x, Mask=False):
query = self.qff(x)
key = self.kff(x)
value = self.vff(x)
query = torch.cat(torch.split(query, self.d, -1), 0).permute(0,2,1,3)
key = torch.cat(torch.split(key, self.d, -1), 0).permute(0,2,3,1)
value = torch.cat(torch.split(value, self.d, -1), 0).permute(0,2,1,3)
a = torch.matmul(query, key)
a /= (self.d ** 0.5)
if Mask == True:
batch_size = x.shape[0]
num_steps = x.shape[1]
num_vertexs = x.shape[2]
mask = torch.ones(num_steps, num_steps).to(device) # [T,T]
mask = torch.tril(mask) # [T,T]但是对角线以上的值变成0了
mask = torch.unsqueeze(torch.unsqueeze(mask, dim=0), dim=0) # [1,1,T,T]
mask = mask.repeat(self.k * batch_size, num_vertexs, 1, 1) # [k*B,N,T,T]
mask = mask.to(torch.bool)
zero_vec = (-2 ** 15 + 1)*torch.ones_like(a).to(device) # [k*B,N,T,T]里面元素全是负无穷大
a = torch.where(mask, a, zero_vec)
a = torch.softmax(a, -1)
value = torch.matmul(a, value)
value = torch.cat(torch.split(value, x.shape[0], 0), -1).permute(0,2,1,3)
value = self.ff(value) + x
return self.ln(value)
class Spatial_Attention(nn.Module):
def __init__(self, outfea, N, k, d, K):
super(Spatial_Attention, self).__init__()
self.qff = nn.Linear(outfea, outfea)
self.kff = nn.Linear(outfea, outfea)
self.ksff = nn.Linear(outfea, outfea)
self.vff = nn.Linear(outfea, outfea)
self.vsff = nn.Linear(outfea, outfea)
self.ff = nn.Sequential(
nn.Linear(outfea, outfea),
nn.GELU(),
nn.Linear(outfea, outfea),
)
self.ln = nn.LayerNorm(outfea)
self.ln1 = nn.LayerNorm(outfea)
self.d = d
self.k = k
self.dcni = DCN(N, k//2, K)
self.dcno = DCN(N, k//2, K)
def forward(self, x, A, AT):
query = self.qff(x)
key = self.kff(x)
keys = self.ksff(x)
value = self.vff(x)
values = self.vsff(x)
query = torch.stack(torch.split(query, self.d, -1), 0)
key = torch.stack(torch.split(key, self.d, -1), 0)
keys = torch.stack(torch.split(keys, self.d, -1), 0)
value = torch.stack(torch.split(value, self.d, -1), 0)
values = torch.stack(torch.split(values, self.d, -1), 0)
# inflow
e = torch.matmul(query[:self.k//2], key[:self.k//2].transpose(-1,-2)) / (self.d ** 0.5) + self.dcni(A)
# e = torch.matmul(query[:self.k//2], key[:self.k//2].transpose(-1,-2)) / (self.d ** 0.5)
es = torch.sigmoid((query[:self.k//2] * keys[:self.k//2]).sum(-1, keepdim=True) / (self.d ** 0.5))
e = e - torch.max(e, -1, keepdim=True)[0]
a = torch.exp(e) / (es + torch.exp(e).sum(-1, keepdim=True))
# a = torch.softmax(e, -1)
valuei = torch.matmul(a, value[:self.k//2]) + values[:self.k//2] - a.sum(-1, keepdim=True)*values[:self.k//2]
# outflow
eo = torch.matmul(query[self.k//2:], key[self.k//2:].transpose(-1,-2)) / (self.d ** 0.5) + self.dcno(AT)
# eo = torch.matmul(query[self.k//2:], key[self.k//2:].transpose(-1,-2)) / (self.d ** 0.5)
eso = torch.sigmoid((query[self.k//2:] * keys[self.k//2:]).sum(-1, keepdim=True) / (self.d ** 0.5))
eo = eo - torch.max(eo, -1, keepdim=True)[0]
ao = torch.exp(eo) / (eso + torch.exp(eo).sum(-1, keepdim=True))
# ao = torch.softmax(eo, -1)
valueo = torch.matmul(ao, value[self.k//2:]) + values[self.k//2:] - ao.sum(-1, keepdim=True)*values[self.k//2:]
# cat
value = torch.cat([valuei, valueo], 0)
value = torch.cat(torch.split(value, 1, 0), -1).squeeze(0)
value = self.ff(value) + x
return self.ln(value)
class FeedForward(nn.Module):
def __init__(self, outfea):
super(FeedForward, self).__init__()
self.ff = nn.Sequential(
nn.Linear(outfea, outfea),
nn.GELU(),
nn.Linear(outfea, outfea)
)
self.ln = nn.LayerNorm(outfea)
def forward(self, x, xl):
x = self.ff(x) + xl
return self.ln(x)
class Emb(nn.Module):
def __init__(self, outfea, max_len=12):
super(Emb, self).__init__()
self.ff = nn.Linear(outfea+64, outfea)
pe = torch.zeros(max_len, outfea)
for pos in range(max_len):
for i in range(0, outfea, 2):
pe[pos, i] = math.sin(pos / (10000 ** ((2 * i)/outfea)))
pe[pos, i+1] = math.cos(pos / (10000 ** ((2 * (i + 1)) / outfea)))
pe = pe.unsqueeze(0).unsqueeze(2) #[1,T,1,F]
self.register_buffer('pe', pe)
def forward(self, x, se):
# se = torch.from_numpy(se).to(device)
se = se.unsqueeze(0).unsqueeze(1).repeat(x.shape[0],x.shape[1],1,1)
x = torch.cat([x, se], -1)
x = self.ff(x)
return x + Variable(self.pe[:,:x.shape[1],:,:], requires_grad=False)
class Decoder(nn.Module):
def __init__(self, outfea, L, N, k, d, K):
super(Decoder, self).__init__()
self.spatial_attention = nn.ModuleList([Spatial_Attention(outfea, N,k,d,K) for i in range(L)])
self.masked_temporal_attention = nn.ModuleList([Temporal_Attention(outfea, k, d) for i in range(L)])
self.encoder_decoder_attention = nn.ModuleList([Encoder_Decoder_Attention(outfea, d) for i in range(L)])
self.ff = nn.ModuleList([FeedForward(outfea) for i in range(L)])
self.L = L
def forward(self, x, xp, A, AT):
for i in range(self.L):
xl = x
x = self.spatial_attention[i](x, A, AT)
x = self.masked_temporal_attention[i](x, True)
x = self.encoder_decoder_attention[i](x, xp)
x = self.ff[i](x, xl)
return x
class Encoder(nn.Module):
def __init__(self, outfea, L, N, k, d, K):
super(Encoder, self).__init__()
self.spatial_attention = nn.ModuleList([Spatial_Attention(outfea, N,k,d,K) for i in range(L)])
self.temporal_attention = nn.ModuleList([Temporal_Attention(outfea, k, d) for i in range(L)])
self.ff = nn.ModuleList([FeedForward(outfea) for i in range(L)])
self.L = L
def forward(self, x, A, AT):
for i in range(self.L):
xl = x
x = self.spatial_attention[i](x, A, AT)
x = self.temporal_attention[i](x)
x = self.ff[i](x, xl)
return x
class STGRAT(nn.Module):
def __init__(self, SE, infea, outfea, L, N, k, d, K, A, AT, dev):
super(STGRAT, self).__init__()
global device
device = dev
self.encoder = Encoder(outfea, L, N, k, d, K)
self.decoder = Decoder(outfea, L, N, k, d, K)
self.encode_start_emb = nn.Linear(infea, outfea)
self.decode_start_emb = nn.Linear(infea, outfea)
self.end_emb = nn.Linear(outfea, infea)
self.src_emb = Emb(outfea)
self.tgt_emb = Emb(outfea)
self.se = SE
self.A = A
self.AT = AT
def forward(self, xp, x):
return self.decode(x, self.encode(xp))
def encode(self, x):
x = x.unsqueeze(-1)
x = self.encode_start_emb(x)
x = self.src_emb(x, self.se)
x = self.encoder(x, self.A, self.AT)
return x
def decode(self, x, xp):
x = x.unsqueeze(-1)
x = self.decode_start_emb(x)
x = self.tgt_emb(x, self.se)
x = self.decoder(x, xp, self.A, self.AT)
x = self.end_emb(x)
return x.squeeze(-1)