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hierarchical_graph_conv.py
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hierarchical_graph_conv.py
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import math
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
"""CxtConv and PropConv"""
class SpGraphAttentionLayer(nn.Module):
def __init__(self, in_features, out_features, dropout, alpha, ret_adj=False, pa_prop=False):
super(SpGraphAttentionLayer, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.pa_prop = pa_prop
self.w_key = nn.Linear(in_features, out_features, bias=True)
self.w_value = nn.Linear(in_features, out_features, bias=True)
self.leakyrelu = nn.LeakyReLU(alpha)
self.cosinesimilarity = nn.CosineSimilarity(dim=-1, eps=1e-8)
def edge_attention(self, edges):
# edge UDF
# dot-product attention
att_sim = torch.sum(torch.mul(edges.src['h_key'], edges.dst['h_key']),dim=-1)
# att_sim = self.cosinesimilarity(edges.src['h_key'], edges.dst['h_key'])
return {'att_sim': att_sim}
def message_func(self, edges):
# message UDF
return {'h_value': edges.src['h_value'], 'att_sim': edges.data['att_sim']}
def reduce_func(self, nodes):
# reduce UDF
alpha = F.softmax(nodes.mailbox['att_sim'], dim=1) # (# of nodes, # of neibors)
alpha = alpha.unsqueeze(-1)
h_att = torch.sum(alpha * nodes.mailbox['h_value'], dim=1)
return {'h_att': h_att}
def forward(self, X_key, X_value, g):
"""
:param X_key: X_key data of shape (batch_size(B), num_nodes(N), in_features_1).
:param X_value: X_value dasta of shape (batch_size, num_nodes(N), in_features_2).
:param g: sparse graph.
:return: Output data of shape (batch_size, num_nodes(N), out_features).
"""
B,N,in_features = X_key.size()
h_key = self.w_key(X_key) # (B,N,out_features)
h_key = h_key.view(B*N,-1) # (B*N,out_features)
h_value = X_value if(self.pa_prop == True) else self.w_value(X_value)
h_value = h_value.view(B*N,-1)
g.ndata['h_key'] = h_key
g.ndata['h_value']= h_value
g.apply_edges(self.edge_attention)
g.update_all(self.message_func, self.reduce_func)
h_att = g.ndata.pop('h_att').view(B,N,-1) # (B,N,out_features)
h_conv = h_att if(self.pa_prop == True) else self.leakyrelu(h_att)
return h_conv
class GAT(nn.Module):
def __init__(self, in_feat, nhid=32, dropout=0, alpha=0.2, hopnum=2, pa_prop=False):
"""sparse GAT."""
super(GAT, self).__init__()
self.pa_prop = pa_prop
self.dropout = nn.Dropout(dropout)
if(pa_prop == True): hopnum = 1
print('hopnum_gat:',hopnum)
self.gat_stacks = nn.ModuleList()
for i in range(hopnum):
if(i > 0): in_feat = nhid
att_layer = SpGraphAttentionLayer(in_feat, nhid, dropout=dropout, alpha=alpha, pa_prop=pa_prop)
self.gat_stacks.append(att_layer)
def forward(self, X_key, X_value, adj):
out = X_key
for att_layer in self.gat_stacks:
if(self.pa_prop == True):
out = att_layer(out, X_value, adj)
else:
out = att_layer(out, out, adj)
return out
"""SCConv"""
class SCConv(nn.Module):
def __init__(self, in_features, out_features, dropout, alpha, latend_num, gcn_hop):
super(SCConv, self).__init__()
self.in_features = in_features
self.dropout = nn.Dropout(dropout)
self.leakyrelu = nn.LeakyReLU(alpha)
self.conv_block_after_pool = GCN(in_features=self.in_features, out_features=out_features, \
dropout=dropout, alpha=alpha, hop = gcn_hop)
self.w_classify = nn.Linear(self.in_features, latend_num, bias=True)
def apply_bn(self, x):
# Batch normalization of 3D tensor x
bn_module = nn.BatchNorm1d(x.size()[1]).cuda()
x = bn_module(x)
return x
def forward(self, X_lots, adj):
"""
:param X_lots: Concat of the outputs of CxtConv and PA_approximation (batch_size, N, in_features).
:param adj: adj_merge (N, N).
:return: Output soft clustering representation for each parking lot of shape (batch_size, N, out_features).
"""
B, N, in_features = X_lots.size()
h_now = self.dropout(X_lots) # (B, N, F)
S = self.w_classify(h_now) # (B, N, latend_num(K))
S = F.softmax(S,dim=-1) # (B, N, K)
h_c = torch.bmm(S.permute(0,2,1),h_now) # (B, K, F)
h_c = self.apply_bn(h_c)
adj = torch.bmm(torch.bmm(S.permute(0,2,1),adj),S) # (B, K, K)
# GCN
h_latent = self.dropout(self.conv_block_after_pool(h_c,adj)) # (B, K, F)
h_sc = torch.bmm(S,h_latent)
return h_sc
class GCN(nn.Module):
def __init__(self, in_features, out_features, dropout, alpha, hop = 1):
super(GCN, self).__init__()
self.in_features = in_features
self.hop = hop
self.dropout = nn.Dropout(dropout)
self.leakyrelu = nn.LeakyReLU(alpha)
self.w_lot = nn.ModuleList()
for i in range(hop):
in_features = (self.in_features) if(i==0) else out_features
self.w_lot.append(nn.Linear(in_features, out_features, bias=True))
def forward(self, h_c, adj):
# adj normalize
adj_rowsum = torch.sum(adj,dim=-1,keepdim=True)
adj = adj.div(torch.where(adj_rowsum>1e-8, adj_rowsum, 1e-8*torch.ones(1,1).cuda())) # row normalize
# weight aggregate
for i in range(self.hop):
h_c = torch.bmm(adj,h_c)
h_c = self.leakyrelu(self.w_lot[i](h_c)) #(B, N, F)
return h_c