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egcn_h.py
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egcn_h.py
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import utils as u
import torch
from torch.nn.parameter import Parameter
import torch.nn as nn
import math
class EGCN(torch.nn.Module):
def __init__(self, args, activation, device='cpu', skipfeats=False):
super().__init__()
GRCU_args = u.Namespace({})
feats = [args.feats_per_node,
args.layer_1_feats,
args.layer_2_feats]
self.device = device
self.skipfeats = skipfeats
self.GRCU_layers = []
self._parameters = nn.ParameterList()
for i in range(1,len(feats)):
GRCU_args = u.Namespace({'in_feats' : feats[i-1],
'out_feats': feats[i],
'activation': activation})
grcu_i = GRCU(GRCU_args)
#print (i,'grcu_i', grcu_i)
self.GRCU_layers.append(grcu_i.to(self.device))
self._parameters.extend(list(self.GRCU_layers[-1].parameters()))
def parameters(self):
return self._parameters
def forward(self,A_list, Nodes_list,nodes_mask_list):
node_feats= Nodes_list[-1]
for unit in self.GRCU_layers:
Nodes_list = unit(A_list,Nodes_list,nodes_mask_list)
out = Nodes_list[-1]
if self.skipfeats:
out = torch.cat((out,node_feats), dim=1) # use node_feats.to_dense() if 2hot encoded input
return out
class GRCU(torch.nn.Module):
def __init__(self,args):
super().__init__()
self.args = args
cell_args = u.Namespace({})
cell_args.rows = args.in_feats
cell_args.cols = args.out_feats
self.evolve_weights = mat_GRU_cell(cell_args)
self.activation = self.args.activation
self.GCN_init_weights = Parameter(torch.Tensor(self.args.in_feats,self.args.out_feats))
self.reset_param(self.GCN_init_weights)
def reset_param(self,t):
#Initialize based on the number of columns
stdv = 1. / math.sqrt(t.size(1))
t.data.uniform_(-stdv,stdv)
def forward(self,A_list,node_embs_list,mask_list):
GCN_weights = self.GCN_init_weights
out_seq = []
for t,Ahat in enumerate(A_list):
node_embs = node_embs_list[t]
#first evolve the weights from the initial and use the new weights with the node_embs
GCN_weights = self.evolve_weights(GCN_weights,node_embs,mask_list[t])
node_embs = self.activation(Ahat.matmul(node_embs.matmul(GCN_weights)))
out_seq.append(node_embs)
return out_seq
class mat_GRU_cell(torch.nn.Module):
def __init__(self,args):
super().__init__()
self.args = args
self.update = mat_GRU_gate(args.rows,
args.cols,
torch.nn.Sigmoid())
self.reset = mat_GRU_gate(args.rows,
args.cols,
torch.nn.Sigmoid())
self.htilda = mat_GRU_gate(args.rows,
args.cols,
torch.nn.Tanh())
self.choose_topk = TopK(feats = args.rows,
k = args.cols)
def forward(self,prev_Q,prev_Z,mask):
z_topk = self.choose_topk(prev_Z,mask)
update = self.update(z_topk,prev_Q)
reset = self.reset(z_topk,prev_Q)
h_cap = reset * prev_Q
h_cap = self.htilda(z_topk, h_cap)
new_Q = (1 - update) * prev_Q + update * h_cap
return new_Q
class mat_GRU_gate(torch.nn.Module):
def __init__(self,rows,cols,activation):
super().__init__()
self.activation = activation
#the k here should be in_feats which is actually the rows
self.W = Parameter(torch.Tensor(rows,rows))
self.reset_param(self.W)
self.U = Parameter(torch.Tensor(rows,rows))
self.reset_param(self.U)
self.bias = Parameter(torch.zeros(rows,cols))
def reset_param(self,t):
#Initialize based on the number of columns
stdv = 1. / math.sqrt(t.size(1))
t.data.uniform_(-stdv,stdv)
def forward(self,x,hidden):
out = self.activation(self.W.matmul(x) + \
self.U.matmul(hidden) + \
self.bias)
return out
class TopK(torch.nn.Module):
def __init__(self,feats,k):
super().__init__()
self.scorer = Parameter(torch.Tensor(feats,1))
self.reset_param(self.scorer)
self.k = k
def reset_param(self,t):
#Initialize based on the number of rows
stdv = 1. / math.sqrt(t.size(0))
t.data.uniform_(-stdv,stdv)
def forward(self,node_embs,mask):
scores = node_embs.matmul(self.scorer) / self.scorer.norm()
scores = scores + mask
vals, topk_indices = scores.view(-1).topk(self.k)
topk_indices = topk_indices[vals > -float("Inf")]
if topk_indices.size(0) < self.k:
topk_indices = u.pad_with_last_val(topk_indices,self.k)
tanh = torch.nn.Tanh()
if isinstance(node_embs, torch.sparse.FloatTensor) or \
isinstance(node_embs, torch.cuda.sparse.FloatTensor):
node_embs = node_embs.to_dense()
out = node_embs[topk_indices] * tanh(scores[topk_indices].view(-1,1))
#we need to transpose the output
return out.t()