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models.py
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models.py
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import torch
import utils as u
from argparse import Namespace
from torch.nn.parameter import Parameter
from torch.nn import functional as F
import torch.nn as nn
import math
class Sp_GCN(torch.nn.Module):
def __init__(self,args,activation):
super().__init__()
self.activation = activation
self.num_layers = args.num_layers
self.w_list = nn.ParameterList()
for i in range(self.num_layers):
if i==0:
w_i = Parameter(torch.Tensor(args.feats_per_node, args.layer_1_feats))
u.reset_param(w_i)
else:
w_i = Parameter(torch.Tensor(args.layer_1_feats, args.layer_2_feats))
u.reset_param(w_i)
self.w_list.append(w_i)
def forward(self,A_list, Nodes_list, nodes_mask_list):
node_feats = Nodes_list[-1]
#A_list: T, each element sparse tensor
#take only last adj matrix in time
Ahat = A_list[-1]
#Ahat: NxN ~ 30k
#sparse multiplication
# Ahat NxN
# self.node_embs = Nxk
#
# note(bwheatman, tfk): change order of matrix multiply
last_l = self.activation(Ahat.matmul(node_feats.matmul(self.w_list[0])))
for i in range(1, self.num_layers):
last_l = self.activation(Ahat.matmul(last_l.matmul(self.w_list[i])))
return last_l
class Sp_Skip_GCN(Sp_GCN):
def __init__(self,args,activation):
super().__init__(args,activation)
self.W_feat = Parameter(torch.Tensor(args.feats_per_node, args.layer_1_feats))
def forward(self,A_list, Nodes_list = None):
node_feats = Nodes_list[-1]
#A_list: T, each element sparse tensor
#take only last adj matrix in time
Ahat = A_list[-1]
#Ahat: NxN ~ 30k
#sparse multiplication
# Ahat NxN
# self.node_feats = Nxk
#
# note(bwheatman, tfk): change order of matrix multiply
l1 = self.activation(Ahat.matmul(node_feats.matmul(self.W1)))
l2 = self.activation(Ahat.matmul(l1.matmul(self.W2)) + (node_feats.matmul(self.W3)))
return l2
class Sp_Skip_NodeFeats_GCN(Sp_GCN):
def __init__(self,args,activation):
super().__init__(args,activation)
def forward(self,A_list, Nodes_list = None):
node_feats = Nodes_list[-1]
Ahat = A_list[-1]
last_l = self.activation(Ahat.matmul(node_feats.matmul(self.w_list[0])))
for i in range(1, self.num_layers):
last_l = self.activation(Ahat.matmul(last_l.matmul(self.w_list[i])))
skip_last_l = torch.cat((last_l,node_feats), dim=1) # use node_feats.to_dense() if 2hot encoded input
return skip_last_l
class Sp_GCN_LSTM_A(Sp_GCN):
def __init__(self,args,activation):
super().__init__(args,activation)
self.rnn = nn.LSTM(
input_size=args.layer_2_feats,
hidden_size=args.lstm_l2_feats,
num_layers=args.lstm_l2_layers
)
def forward(self,A_list, Nodes_list = None, nodes_mask_list = None):
last_l_seq=[]
for t,Ahat in enumerate(A_list):
node_feats = Nodes_list[t]
#A_list: T, each element sparse tensor
#note(bwheatman, tfk): change order of matrix multiply
last_l = self.activation(Ahat.matmul(node_feats.matmul(self.w_list[0])))
for i in range(1, self.num_layers):
last_l = self.activation(Ahat.matmul(last_l.matmul(self.w_list[i])))
last_l_seq.append(last_l)
last_l_seq = torch.stack(last_l_seq)
out, _ = self.rnn(last_l_seq, None)
return out[-1]
class Sp_GCN_GRU_A(Sp_GCN_LSTM_A):
def __init__(self,args,activation):
super().__init__(args,activation)
self.rnn = nn.GRU(
input_size=args.layer_2_feats,
hidden_size=args.lstm_l2_feats,
num_layers=args.lstm_l2_layers
)
class Sp_GCN_LSTM_B(Sp_GCN):
def __init__(self,args,activation):
super().__init__(args,activation)
assert args.num_layers == 2, 'GCN-LSTM and GCN-GRU requires 2 conv layers.'
self.rnn_l1 = nn.LSTM(
input_size=args.layer_1_feats,
hidden_size=args.lstm_l1_feats,
num_layers=args.lstm_l1_layers
)
self.rnn_l2 = nn.LSTM(
input_size=args.layer_2_feats,
hidden_size=args.lstm_l2_feats,
num_layers=args.lstm_l2_layers
)
self.W2 = Parameter(torch.Tensor(args.lstm_l1_feats, args.layer_2_feats))
u.reset_param(self.W2)
def forward(self,A_list, Nodes_list = None, nodes_mask_list = None):
l1_seq=[]
l2_seq=[]
for t,Ahat in enumerate(A_list):
node_feats = Nodes_list[t]
l1 = self.activation(Ahat.matmul(node_feats.matmul(self.w_list[0])))
l1_seq.append(l1)
l1_seq = torch.stack(l1_seq)
out_l1, _ = self.rnn_l1(l1_seq, None)
for i in range(len(A_list)):
Ahat = A_list[i]
out_t_l1 = out_l1[i]
#A_list: T, each element sparse tensor
l2 = self.activation(Ahat.matmul(out_t_l1).matmul(self.w_list[1]))
l2_seq.append(l2)
l2_seq = torch.stack(l2_seq)
out, _ = self.rnn_l2(l2_seq, None)
return out[-1]
class Sp_GCN_GRU_B(Sp_GCN_LSTM_B):
def __init__(self,args,activation):
super().__init__(args,activation)
self.rnn_l1 = nn.GRU(
input_size=args.layer_1_feats,
hidden_size=args.lstm_l1_feats,
num_layers=args.lstm_l1_layers
)
self.rnn_l2 = nn.GRU(
input_size=args.layer_2_feats,
hidden_size=args.lstm_l2_feats,
num_layers=args.lstm_l2_layers
)
class Classifier(torch.nn.Module):
def __init__(self,args,out_features=2, in_features = None):
super(Classifier,self).__init__()
activation = torch.nn.ReLU()
if in_features is not None:
num_feats = in_features
elif args.experiment_type in ['sp_lstm_A_trainer', 'sp_lstm_B_trainer',
'sp_weighted_lstm_A', 'sp_weighted_lstm_B'] :
num_feats = args.gcn_parameters['lstm_l2_feats'] * 2
else:
num_feats = args.gcn_parameters['layer_2_feats'] * 2
print ('CLS num_feats',num_feats)
self.mlp = torch.nn.Sequential(torch.nn.Linear(in_features = num_feats,
out_features =args.gcn_parameters['cls_feats']),
activation,
torch.nn.Linear(in_features = args.gcn_parameters['cls_feats'],
out_features = out_features))
def forward(self,x):
return self.mlp(x)