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model.py
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model.py
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import torch
from torch.nn import Linear
import torch.nn.functional as F
from torch_geometric.nn import DenseGCNConv, global_mean_pool
from math import ceil
from typing import Optional, Tuple
from torch import Tensor
def my_dense_diff_pool(x: Tensor, adj: Tensor, s: Tensor, mask: Optional[Tensor] = None) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
x = x.unsqueeze(0) if x.dim() == 2 else x
adj = adj.unsqueeze(0) if adj.dim() == 2 else adj
s = s.unsqueeze(0) if s.dim() == 2 else s
batch_size, num_nodes, _ = x.size()
s = torch.softmax(s, dim=-1)
if mask is not None:
mask = mask.view(batch_size, num_nodes, 1).to(x.dtype)
x, s = x * mask, s * mask
out = torch.matmul(s.transpose(1, 2), x)
out_adj = torch.matmul(torch.matmul(s.transpose(1, 2), adj), s)
link_loss = adj - torch.matmul(s, s.transpose(1, 2))
link_loss = torch.norm(link_loss, p=2)
link_loss = link_loss / adj.numel()
ent_loss = (-s * torch.log(s + 1e-15)).sum(dim=-1).mean()
cluster_assignments = torch.argmax(s, dim=-1)
return out, out_adj, link_loss, ent_loss, cluster_assignments
class GNN(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels,
normalize=False):
super(GNN, self).__init__()
self.convs = torch.nn.ModuleList()
self.bns = torch.nn.ModuleList()
self.convs.append(DenseGCNConv(in_channels, hidden_channels, normalize))
self.bns.append(torch.nn.BatchNorm1d(hidden_channels))
self.convs.append(DenseGCNConv(hidden_channels, hidden_channels, normalize))
self.bns.append(torch.nn.BatchNorm1d(hidden_channels))
self.convs.append(DenseGCNConv(hidden_channels, out_channels, normalize))
self.bns.append(torch.nn.BatchNorm1d(out_channels))
def forward(self, x, adj, mask=None):
for step in range(len(self.convs)):
x = self.convs[step](x, adj, mask)
x = x.permute(0, 2, 1) # Reshape x to (N, C, L) for BatchNorm1d
x = F.relu(self.bns[step](x))
x = x.permute(0, 2, 1) # Reshape back to (N, L, C)
return x
class DiffPoolGNN(torch.nn.Module):
def __init__(self, num_features, num_hidden_unit, num_classes, num_nodes1, num_nodes2):
super(DiffPoolGNN, self).__init__()
self.gnn1_pool = GNN(num_features, num_hidden_unit, num_nodes1)
self.gnn1_embed = GNN(num_features, num_hidden_unit, num_hidden_unit)
self.gnn2_pool = GNN(num_hidden_unit, num_hidden_unit, num_nodes2)
self.gnn2_embed = GNN(num_hidden_unit, num_hidden_unit, num_hidden_unit)
self.gnn3_embed = GNN(num_hidden_unit, num_hidden_unit, num_hidden_unit)
self.lin1 = torch.nn.Linear(num_hidden_unit, num_hidden_unit)
self.lin2 = torch.nn.Linear(num_hidden_unit, num_classes)
def forward(self, x, adj, mask=None):
s = self.gnn1_pool(x, adj, mask)
s_gnn1_pool = s
x = self.gnn1_embed(x, adj, mask)
x_gnn1_embed = x
x, adj, l1, e1, cluster_assignment1 = my_dense_diff_pool(x, adj, s, mask)
out1 = x
new_graph_adj = adj
s = self.gnn2_pool(x, adj)
s_gnn2_pool = s
x = self.gnn2_embed(x, adj)
x_gnn2_embed = x
x, adj, l2, e2, cluster_assignment2 = my_dense_diff_pool(x, adj, s)
out2 = x
x = self.gnn3_embed(x, adj)
x_gnn3_embed = x
x = x.mean(dim=1)
x = F.relu(self.lin1(x))
x = self.lin2(x)
return F.log_softmax(x,dim=-1), l1 + l2, e1 + e2, s_gnn1_pool, x_gnn1_embed, s_gnn2_pool, x_gnn2_embed, x_gnn3_embed, new_graph_adj, cluster_assignment1, cluster_assignment2, out1, out2
class DiffPoolGNN_3DiffPool(torch.nn.Module):
def __init__(self, num_features, num_hidden_unit, num_classes, num_nodes1, num_nodes2, num_nodes3):
super(DiffPoolGNN_3DiffPool, self).__init__()
self.gnn1_pool = GNN(num_features, num_hidden_unit, num_nodes1)
self.gnn1_embed = GNN(num_features, num_hidden_unit, num_hidden_unit)
self.gnn2_pool = GNN(num_hidden_unit, num_hidden_unit, num_nodes2)
self.gnn2_embed = GNN(num_hidden_unit, num_hidden_unit, num_hidden_unit)
self.gnn3_pool = GNN(num_hidden_unit, num_hidden_unit, num_nodes3)
self.gnn3_embed = GNN(num_hidden_unit, num_hidden_unit, num_hidden_unit)
self.lin1 = torch.nn.Linear(num_hidden_unit, num_hidden_unit)
self.lin2 = torch.nn.Linear(num_hidden_unit, num_classes)
def forward(self, x, adj, mask=None):
s = self.gnn1_pool(x, adj, mask)
s_gnn1_pool = s
x = self.gnn1_embed(x, adj, mask)
x_gnn1_embed = x
x, adj, l1, e1, cluster_assignment1 = my_dense_diff_pool(x, adj, s, mask)
out1 = x
new_graph_adj_1 = adj
s = self.gnn2_pool(x, adj)
s_gnn2_pool = s
x = self.gnn2_embed(x, adj)
x_gnn2_embed = x
x, adj, l2, e2, cluster_assignment2 = my_dense_diff_pool(x, adj, s)
out2 = x
new_graph_adj_2 = adj
s = self.gnn3_pool(x, adj)
s_gnn3_pool = s
x = self.gnn3_embed(x, adj)
x_gnn3_embed = x
x, adj, l3, e3, cluster_assignment3 = my_dense_diff_pool(x, adj, s)
new_graph_adj_3 = adj
out3 = x
x = x.mean(dim=1)
x = F.relu(self.lin1(x))
x = self.lin2(x)
return F.log_softmax(x,dim=-1), l1 + l2 + l3, e1 + e2 + e3, s_gnn1_pool, x_gnn1_embed, s_gnn2_pool, x_gnn2_embed, s_gnn3_pool, x_gnn3_embed, new_graph_adj_1, new_graph_adj_2, new_graph_adj_3, cluster_assignment1, cluster_assignment2, cluster_assignment3, out1, out2, out3
class DiffPoolGNN_3DiffPool_addlastembed(torch.nn.Module):
def __init__(self, num_features, num_hidden_unit, num_classes, num_nodes1, num_nodes2, num_nodes3):
super(DiffPoolGNN_3DiffPool_addlastembed, self).__init__()
self.gnn1_pool = GNN(num_features, num_hidden_unit, num_nodes1)
self.gnn1_embed = GNN(num_features, num_hidden_unit, num_hidden_unit)
self.gnn2_pool = GNN(num_hidden_unit, num_hidden_unit, num_nodes2)
self.gnn2_embed = GNN(num_hidden_unit, num_hidden_unit, num_hidden_unit)
self.gnn3_pool = GNN(num_hidden_unit, num_hidden_unit, num_nodes3)
self.gnn3_embed = GNN(num_hidden_unit, num_hidden_unit, num_hidden_unit)
self.gnn4_embed = GNN(num_hidden_unit, num_hidden_unit, num_hidden_unit)
self.lin1 = torch.nn.Linear(num_hidden_unit, num_hidden_unit)
self.lin2 = torch.nn.Linear(num_hidden_unit, num_classes)
def forward(self, x, adj, mask=None):
s = self.gnn1_pool(x, adj, mask)
s_gnn1_pool = s
x = self.gnn1_embed(x, adj, mask)
x_gnn1_embed = x
x, adj, l1, e1, cluster_assignment1 = my_dense_diff_pool(x, adj, s, mask)
out1 = x
new_graph_adj_1 = adj
s = self.gnn2_pool(x, adj)
s_gnn2_pool = s
x = self.gnn2_embed(x, adj)
x_gnn2_embed = x
x, adj, l2, e2, cluster_assignment2 = my_dense_diff_pool(x, adj, s)
out2 = x
new_graph_adj_2 = adj
s = self.gnn3_pool(x, adj)
s_gnn3_pool = s
x = self.gnn3_embed(x, adj)
x_gnn3_embed = x
x, adj, l3, e3, cluster_assignment3 = my_dense_diff_pool(x, adj, s)
new_graph_adj_3 = adj
out3 = x
x = self.gnn4_embed(x, adj)
x_gnn4_embed = x
x = x.mean(dim=1)
x = F.relu(self.lin1(x))
x = self.lin2(x)
return F.log_softmax(x,dim=-1), l1 + l2 + l3, e1 + e2 + e3, s_gnn1_pool, x_gnn1_embed, s_gnn2_pool, x_gnn2_embed, s_gnn3_pool, x_gnn3_embed, x_gnn4_embed, new_graph_adj_1, new_graph_adj_2, new_graph_adj_3, cluster_assignment1, cluster_assignment2, cluster_assignment3, out1, out2, out3
class VanillaGNN(torch.nn.Module):
def __init__(self, num_features, num_hidden_unit, num_classes):
super(VanillaGNN, self).__init__()
self.gnn1_embed = GNN(num_features, num_hidden_unit, num_hidden_unit)
self.lin1 = torch.nn.Linear(num_hidden_unit, num_hidden_unit)
self.lin2 = torch.nn.Linear(num_hidden_unit, num_classes)
def forward(self, x, adj, mask=None):
x = self.gnn1_embed(x, adj, mask)
x_gnn1_embed = x
x = global_mean_pool(x, mask)
x = F.relu(self.lin1(x))
x = self.lin2(x)
return F.log_softmax(x, dim=-1), x_gnn1_embed
class VanillaGNN2(torch.nn.Module):
def __init__(self, num_features, num_hidden_unit, num_classes, num_nodes1):
super(VanillaGNN2, self).__init__()
self.gnn1_pool = GNN(num_features, num_hidden_unit, num_nodes1)
self.gnn1_embed = GNN(num_features, num_hidden_unit, num_hidden_unit)
self.gnn2_embed = GNN(num_hidden_unit, num_hidden_unit, num_hidden_unit)
self.lin1 = torch.nn.Linear(num_hidden_unit, num_hidden_unit)
self.lin2 = torch.nn.Linear(num_hidden_unit, num_classes)
def forward(self, x, adj, mask=None):
s = self.gnn1_pool(x, adj, mask)
x = self.gnn1_embed(x, adj, mask)
x, adj, l, e, cluster_assignment1 = my_dense_diff_pool(x, adj, s, mask)
x = self.gnn2_embed(x, adj)
x_gnn2_embed = x
x = x.mean(dim=1)
x = F.relu(self.lin1(x))
x = self.lin2(x)
return F.log_softmax(x, dim=-1), l, e, x_gnn2_embed, cluster_assignment1
class ChocolateGNN(torch.nn.Module):
def __init__(self, num_features, num_hidden_unit, num_classes, num_nodes1):
super(ChocolateGNN, self).__init__()
self.gnn1_pool = GNN(num_features, num_hidden_unit, num_nodes1)
self.gnn1_embed = GNN(num_features, num_hidden_unit, num_hidden_unit)
self.gnn2_embed = GNN(num_hidden_unit, num_hidden_unit, num_hidden_unit)
self.gnn3_embed = GNN(num_hidden_unit, num_hidden_unit, num_hidden_unit)
self.lin1 = torch.nn.Linear(num_hidden_unit, num_hidden_unit)
self.lin2 = torch.nn.Linear(num_hidden_unit, num_classes)
def forward(self, x, adj, mask=None):
s = self.gnn1_pool(x, adj, mask)
x = self.gnn1_embed(x, adj, mask)
x, adj, l, e, _ = my_dense_diff_pool(x, adj, s, mask)
x = self.gnn2_embed(x, adj)
x = global_mean_pool(x, mask)
x = x.unsqueeze(1).expand(-1, 12, -1)
x = self.gnn3_embed(x, adj)
x = x.mean(dim=1)
x = F.relu(self.lin1(x))
x = self.lin2(x)
return F.log_softmax(x, dim=-1), l, e