class GNNp(nn.Module):
def __init__(self, opt, adj):
super(GNNp, self).__init__()
self.opt = opt
self.adj = adj
opt_ = dict([('in', opt['num_class']), ('out', opt['hidden_dim'])])
self.m1 = GraphConvolution(opt_, adj)
opt_ = dict([('in', opt['hidden_dim']), ('out', opt['num_class'])])
self.m2 = GraphConvolution(opt_, adj)
if opt['cuda']:
self.cuda()
def reset(self):
self.m1.reset_parameters()
self.m2.reset_parameters()
def forward(self, x):
x = F.dropout(x, self.opt['input_dropout'], training=self.training)
x = self.m1(x)
x = F.relu(x)
x = F.dropout(x, self.opt['dropout'], training=self.training)
x = self.m2(x)
return x
class GNN(nn.Module):
def __init__(self, opt, adj):
super(GNN, self).__init__()
self.opt = opt
self.adj = adj
opt_ = dict([('in', opt['num_feature']), ('out', opt['hidden_dim'])])
self.m1 = GraphConvolution(opt_, adj)
opt_ = dict([('in', opt['hidden_dim']), ('out', opt['num_class'])])
self.m2 = GraphConvolution(opt_, adj)
if opt['cuda']:
self.cuda()
def reset(self):
self.m1.reset_parameters()
self.m2.reset_parameters()
def forward(self, x):
x = F.dropout(x, self.opt['input_dropout'], training=self.training)
x = self.m1(x)
x = F.relu(x)
x = F.dropout(x, self.opt['dropout'], training=self.training)
x = self.m2(x)
return x
def forward_partition(self, x, adj_ss):
x = F.dropout(x, self.opt['input_dropout'], training=self.training)
x = self.m1(x, adj_ss)
x = F.relu(x)
x = F.dropout(x, self.opt['dropout'], training=self.training)
x = self.m2.forward_partition(x, adj_ss)
return x
def forward_mix(self, x, target, target_discrete, idx, opt, mixup_layer):
layer = random.choice(mixup_layer)
if layer == 0:
x, target, idx = get_augmented_network_input(
self, x, target, target_discrete, idx, opt)
x = F.dropout(x, self.opt['input_dropout'], training=self.training)
x = self.m1(x)
x = F.relu(x)
if layer == 1:
x, target, idx = get_augmented_network_input(
self, x, target, target_discrete, idx, opt)
x = F.dropout(x, self.opt['dropout'], training=self.training)
x = self.m2(x)
return x, target, idx
def forward_aux(self,
x,
target=None,
train_idx=None,
mixup_input=False,
mixup_hidden=False,
mixup_alpha=0.0,
layer_mix=None):
if mixup_hidden == True or mixup_input == True:
if mixup_hidden == True:
layer_mix = random.choice(layer_mix)
elif mixup_input == True:
layer_mix = 0
if layer_mix == 0:
x, target_a, target_b, lam = mixup_gnn_hidden(
x, target, train_idx, mixup_alpha)
x = F.dropout(x, self.opt['input_dropout'], training=self.training)
x = self.m1.forward_aux(x)
x = F.relu(x)
if layer_mix == 1:
x, target_a, target_b, lam = mixup_gnn_hidden(
x, target, train_idx, mixup_alpha)
x = F.dropout(x, self.opt['dropout'], training=self.training)
x = self.m2.forward_aux(x)
return x, target_a, target_b, lam
else:
x = F.dropout(x, self.opt['input_dropout'], training=self.training)
x = self.m1.forward_aux(x)
x = F.relu(x)
x = F.dropout(x, self.opt['dropout'], training=self.training)
x = self.m2.forward_aux(x)
return x
class GNN_mix(nn.Module):
def __init__(self, opt, adj):
super(GNN_mix, self).__init__()
self.opt = opt
self.adj = adj
opt_ = dict([('in', opt['num_feature']), ('out', 1000)])
self.m1 = GraphConvolution(opt_, adj)
#self.linear_m1_1 = nn.Linear(1000,500)
#self.linear_m1_2 = nn.Linear(50,opt['num_class'] )
opt_ = dict([('in', 1000), ('out', 500)])
self.m2 = GraphConvolution(opt_, adj)
#self.linear_m2_1 = nn.Linear(50,20)
#self.linear_m2_2 = nn.Linear(20,opt['num_class'] )
opt_ = dict([('in', 500), ('out', 100)])
self.m3 = GraphConvolution(opt_, adj)
#self.linear_m3_1 = nn.Linear(10,5 )
#self.linear_m3_2 = nn.Linear(5,opt['num_class'] )
opt_ = dict([('in', 100), ('out', opt['num_class'])])
self.m4 = GraphConvolution(opt_, adj)
"""
opt_ = dict([('in', opt['num_feature']), ('out', opt['hidden_dim'])])
self.m1 = GraphConvolution(opt_, adj)
opt_ = dict([('in', opt['hidden_dim']), ('out', opt['num_class'])])
self.m2 = GraphConvolution(opt_, adj)
"""
if opt['cuda']:
self.cuda()
def reset(self):
self.m1.reset_parameters()
self.m2.reset_parameters()
def forward(self,
x,
target=None,
train_idx=None,
mixup_input=False,
mixup_hidden=False,
mixup_alpha=0.0,
layer_mix=None):
"""
#import pdb; pdb.set_trace()
if target is not None:
x, target_a, target_b, lam = mixup_gnn_hidden(x, target, train_idx, mixup_alpha)
x = F.dropout(x, self.opt['input_dropout'], training=self.training)
x = self.m1(x)
x = F.relu(x)
if target is not None:
x, target_a, target_b, lam = mixup_gnn_hidden(x, target, train_idx, mixup_alpha)
x = F.dropout(x, self.opt['dropout'], training=self.training)
x = self.m2(x)
if target is not None:
return x, target_a, target_b, lam
else:
return x
"""
#import pdb; pdb.set_trace()
if mixup_hidden == True or mixup_input == True:
if mixup_hidden == True:
layer_mix = random.randint(1, layer_mix)
elif mixup_input == True:
layer_mix = 0
if layer_mix == 0:
x, target_a, target_b, lam = mixup_gnn_hidden(
x, target, train_idx, mixup_alpha)
x = F.dropout(x, self.opt['input_dropout'], training=self.training)
x = self.m1(x)
x = F.relu(x)
if layer_mix == 1:
x, target_a, target_b, lam = mixup_gnn_hidden(
x, target, train_idx, mixup_alpha)
x = F.dropout(x, self.opt['dropout'], training=self.training)
x = self.m2(x)
x = F.relu(x)
if layer_mix == 2:
x, target_a, target_b, lam = mixup_gnn_hidden(
x, target, train_idx, mixup_alpha)
x = F.dropout(x, self.opt['dropout'], training=self.training)
x = self.m3(x)
x = F.relu(x)
if layer_mix == 3:
x, target_a, target_b, lam = mixup_gnn_hidden(
x, target, train_idx, mixup_alpha)
x = F.dropout(x, self.opt['dropout'], training=self.training)
x = self.m4(x)
return x, target_a, target_b, lam
else:
x = F.dropout(x, self.opt['input_dropout'], training=self.training)
x = self.m1(x)
x = F.relu(x)
x = F.dropout(x, self.opt['dropout'], training=self.training)
x = self.m2(x)
x = F.relu(x)
x = F.dropout(x, self.opt['input_dropout'], training=self.training)
x = self.m3(x)
x = F.relu(x)
x = F.dropout(x, self.opt['dropout'], training=self.training)
x = self.m4(x)
return x
"""