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models.py
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models.py
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import torch
import torch.nn as nn
from layers import TransformerEncoder
class Generator(nn.Module):
"""Generator network."""
def __init__(self, act, vertexes, edges, nodes, dropout, dim, depth, heads, mlp_ratio):
super(Generator, self).__init__()
self.vertexes = vertexes
self.edges = edges
self.nodes = nodes
self.depth = depth
self.dim = dim
self.heads = heads
self.mlp_ratio = mlp_ratio
self.dropout = dropout
if act == "relu":
act = nn.ReLU()
elif act == "leaky":
act = nn.LeakyReLU()
elif act == "sigmoid":
act = nn.Sigmoid()
elif act == "tanh":
act = nn.Tanh()
self.features = vertexes * vertexes * edges + vertexes * nodes
self.transformer_dim = vertexes * vertexes * dim + vertexes * dim
self.pos_enc_dim = 5
self.node_layers = nn.Sequential(nn.Linear(nodes, 64), act, nn.Linear(64, dim), act, nn.Dropout(self.dropout))
self.edge_layers = nn.Sequential(nn.Linear(edges, 64), act, nn.Linear(64, dim), act, nn.Dropout(self.dropout))
self.TransformerEncoder = TransformerEncoder(dim=self.dim, depth=self.depth, heads=self.heads, act = act,
mlp_ratio=self.mlp_ratio, drop_rate=self.dropout)
self.readout_e = nn.Linear(self.dim, edges)
self.readout_n = nn.Linear(self.dim, nodes)
self.softmax = nn.Softmax(dim = -1)
def _generate_square_subsequent_mask(self, sz):
mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
return mask
def laplacian_positional_enc(self, adj):
A = adj
D = torch.diag(torch.count_nonzero(A, dim=-1))
L = torch.eye(A.shape[0], device=A.device) - D * A * D
EigVal, EigVec = torch.linalg.eig(L)
idx = torch.argsort(torch.real(EigVal))
EigVal, EigVec = EigVal[idx], torch.real(EigVec[:,idx])
pos_enc = EigVec[:,1:self.pos_enc_dim + 1]
return pos_enc
def forward(self, z_e, z_n):
b, n, c = z_n.shape
_, _, _ , d = z_e.shape
node = self.node_layers(z_n)
edge = self.edge_layers(z_e)
edge = (edge + edge.permute(0, 2, 1, 3)) / 2
node, edge = self.TransformerEncoder(node, edge)
node_sample = self.readout_n(node)
edge_sample = self.readout_e(edge)
return node, edge, node_sample, edge_sample
class Discriminator(nn.Module):
def __init__(self, act, vertexes, edges, nodes, dropout, dim, depth, heads, mlp_ratio):
super(Discriminator, self).__init__()
self.vertexes = vertexes
self.edges = edges
self.nodes = nodes
self.depth = depth
self.dim = dim
self.heads = heads
self.mlp_ratio = mlp_ratio
self.dropout = dropout
if act == "relu":
act = nn.ReLU()
elif act == "leaky":
act = nn.LeakyReLU()
elif act == "sigmoid":
act = nn.Sigmoid()
elif act == "tanh":
act = nn.Tanh()
self.features = vertexes * vertexes * edges + vertexes * nodes
self.transformer_dim = vertexes * vertexes * dim + vertexes * dim
self.node_layers = nn.Sequential(nn.Linear(nodes, 64), act, nn.Linear(64, dim), act, nn.Dropout(self.dropout))
self.edge_layers = nn.Sequential(nn.Linear(edges, 64), act, nn.Linear(64, dim), act, nn.Dropout(self.dropout))
self.TransformerEncoder = TransformerEncoder(dim=self.dim, depth=self.depth, heads=self.heads, act = act,
mlp_ratio=self.mlp_ratio, drop_rate=self.dropout)
self.node_features = vertexes * dim
self.edge_features = vertexes * vertexes * dim
self.node_mlp = nn.Sequential(nn.Linear(self.node_features, 64), act, nn.Linear(64, 32), act, nn.Linear(32, 16), act, nn.Linear(16, 1))
def forward(self, z_e, z_n):
b, n, c = z_n.shape
_, _, _ , d = z_e.shape
node = self.node_layers(z_n)
edge = self.edge_layers(z_e)
edge = (edge + edge.permute(0, 2, 1, 3)) / 2
node, edge = self.TransformerEncoder(node, edge)
node = node.view(b, -1)
prediction = self.node_mlp(node)
return prediction
class simple_disc(nn.Module):
def __init__(self, act, m_dim, vertexes, b_dim):
super().__init__()
if act == "relu":
act = nn.ReLU()
elif act == "leaky":
act = nn.LeakyReLU()
elif act == "sigmoid":
act = nn.Sigmoid()
elif act == "tanh":
act = nn.Tanh()
else:
raise ValueError("Unsupported activation function: {}".format(act))
features = vertexes * m_dim + vertexes * vertexes * b_dim
self.predictor = nn.Sequential(nn.Linear(features,256), act, nn.Linear(256,128), act, nn.Linear(128,64), act,
nn.Linear(64,32), act, nn.Linear(32,16), act,
nn.Linear(16,1))
def forward(self, x):
prediction = self.predictor(x)
return prediction