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models.py
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import torch
import torch.nn as nn
from torch_geometric.nn import GCNConv, GATConv
class GCN_large(nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels):
super().__init__()
self.conv1 = GCNConv(in_channels, hidden_channels)
self.conv2 = GCNConv(hidden_channels, hidden_channels)
self.conv3 = GCNConv(hidden_channels, hidden_channels)
self.conv4 = GCNConv(hidden_channels, hidden_channels)
self.conv5 = GCNConv(hidden_channels, hidden_channels)
self.linear = nn.Linear(hidden_channels, out_channels)
def forward(self, x, edge_index, edge_attr):
# x: Node feature matrix of shape [num_nodes, in_channels]
# edge_index: Graph connectivity matrix of shape [2, num_edges]
x = self.conv1(x, edge_index).relu()
x = self.conv2(x, edge_index).relu()
x = self.conv3(x, edge_index).relu()
x = self.conv4(x, edge_index).relu()
x = self.conv5(x, edge_index).relu()
x = self.linear(x).relu()
return x
class GAT_medium(nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels, edge_dim, heads=8):
super().__init__()
assert hidden_channels % heads == 0
self.conv1 = GATConv(in_channels, hidden_channels//heads, heads, edge_dim=edge_dim)
self.conv2 = GATConv(hidden_channels, hidden_channels//heads, heads, edge_dim=edge_dim)
self.conv3 = GATConv(hidden_channels, hidden_channels, 1, edge_dim=edge_dim)
self.linear = nn.Linear(hidden_channels, out_channels)
def forward(self, x, edge_index, edge_attr):
# x: Node feature matrix of shape [num_nodes, in_channels]
# edge_index: Graph connectivity matrix of shape [2, num_edges]
x = self.conv1(x, edge_index, edge_attr).relu()
x = self.conv2(x, edge_index, edge_attr).relu()
x = self.conv3(x, edge_index, edge_attr).relu()
x = self.linear(x).relu()
return x
class GAT_large(nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels, edge_dim, heads=8):
super().__init__()
assert hidden_channels % heads == 0
self.conv1 = GATConv(in_channels, hidden_channels//heads, heads, edge_dim=edge_dim)
self.conv2 = GATConv(hidden_channels, hidden_channels//heads, heads, edge_dim=edge_dim)
self.conv3 = GATConv(hidden_channels, hidden_channels//heads, heads, edge_dim=edge_dim)
self.conv4 = GATConv(hidden_channels, hidden_channels//heads, heads, edge_dim=edge_dim)
self.conv5 = GATConv(hidden_channels, hidden_channels, 1, edge_dim=edge_dim)
self.linear = nn.Linear(hidden_channels, out_channels)
def forward(self, x, edge_index, edge_attr):
# x: Node feature matrix of shape [num_nodes, in_channels]
# edge_index: Graph connectivity matrix of shape [2, num_edges]
x = self.conv1(x, edge_index, edge_attr).relu()
x = self.conv2(x, edge_index, edge_attr).relu()
x = self.conv3(x, edge_index, edge_attr).relu()
x = self.conv4(x, edge_index, edge_attr).relu()
x = self.conv5(x, edge_index, edge_attr).relu()
x = self.linear(x).relu()
return x
class GAT_large_no_edge_attr(nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels, edge_dim, heads=8):
super().__init__()
assert hidden_channels % heads == 0
self.conv1 = GATConv(in_channels, hidden_channels//heads, heads)
self.conv2 = GATConv(hidden_channels, hidden_channels//heads, heads)
self.conv3 = GATConv(hidden_channels, hidden_channels//heads, heads)
self.conv4 = GATConv(hidden_channels, hidden_channels//heads, heads)
self.conv5 = GATConv(hidden_channels, hidden_channels, 1)
self.linear = nn.Linear(hidden_channels, out_channels)
def forward(self, x, edge_index, edge_attr):
# x: Node feature matrix of shape [num_nodes, in_channels]
# edge_index: Graph connectivity matrix of shape [2, num_edges]
x = self.conv1(x, edge_index).relu()
x = self.conv2(x, edge_index).relu()
x = self.conv3(x, edge_index).relu()
x = self.conv4(x, edge_index).relu()
x = self.conv5(x, edge_index).relu()
x = self.linear(x).relu()
return x
class NodeClassifier(nn.Module):
def __init__(self, encoder, dim_embeddings, num_classes):
super().__init__()
self.encoder = encoder
self.dim_embeddings = dim_embeddings
self.num_classes = num_classes
self.cls = nn.Linear(self.dim_embeddings, num_classes)
def forward(self, nodes, edge_index, edge_attr):
embeddings = self.encoder(nodes, edge_index, edge_attr)
y = self.cls(embeddings)
return y
class cnn_5layer(nn.Module):
def __init__(self, in_ch=1, width=64, linear_size=512, in_dim=8):
super().__init__()
self.encoder = nn.Sequential(
nn.Conv2d(in_ch, width, 3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(width, width, 3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(width, 2 * width, 3, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(2 * width, 2 * width, 3, stride=1, padding=1),
nn.ReLU(),
nn.Flatten(),
nn.Linear((in_dim//2) * (in_dim//2) * 2 * width, linear_size),
)
def forward(self, x):
return self.encoder(x)
class PureVisionNodeClassifier(nn.Module):
def __init__(self, num_classes, dim_embeddings=512):
super().__init__()
self.encoder = cnn_5layer(linear_size=dim_embeddings)
self.dim_embeddings = dim_embeddings
self.num_classes = num_classes
self.cls = nn.Linear(dim_embeddings, num_classes)
def forward(self, x):
x = x.view(x.size(0), 1, 8, 8)
embeddings = self.encoder(x)
y = self.cls(embeddings)
return y
class CombinedNodeClassifier(nn.Module):
def __init__(self, model_graph, num_classes, dim_embeddings=512):
super().__init__()
self.encoder_graph = model_graph
self.encoder_image = cnn_5layer(linear_size=dim_embeddings)
self.dim_embeddings = dim_embeddings
self.num_classes = num_classes
self.cls = nn.Linear(dim_embeddings * 2, num_classes)
def forward(self, nodes, edge_index, edge_attr, x_vision):
embeddings_graph = self.encoder_graph(nodes, edge_index, edge_attr)
embeddings_graph = embeddings_graph[nodes.sum(dim=-1) == 0]
x_vision = x_vision.view(x_vision.size(0), 1, 8, 8)
embeddings_image = self.encoder_image(x_vision)
y = self.cls(torch.cat([embeddings_graph, embeddings_image], dim=-1))
return y
class LinkClassification(nn.Module):
def __init__(self, encoder, dim_embeddings, num_classes):
super().__init__()
self.encoder = encoder
self.dim_embeddings = dim_embeddings
self.num_classes = num_classes
self.cls = nn.Linear(self.dim_embeddings*2, num_classes)
def forward(self, nodes, edges, attrib):
embeddings = self.encoder(nodes, edges, attrib)
new_embeddings = torch.cat([embeddings[edges[0]], embeddings[edges[1]]], dim=-1)
y = self.cls(new_embeddings)
return y
class LinkClassifier(nn.Module):
def __init__(self, encoder, dim_embeddings, num_classes):
super().__init__()
self.encoder = encoder
self.dim_embeddings = dim_embeddings
self.num_classes = num_classes
# link existence, only contain true or false
self.cls = nn.Linear(self.dim_embeddings*2, 2)
def forward(self, nodes, edges):
embeddings = self.encoder(nodes, edges)
Nedges = edges.size(dim=0)
Nnodes = nodes.size(dim=0)
newembeddings = torch.zeros(Nnodes*(Nnodes-1)//2,embeddings.size(dim=1)*2)
y_true = torch.zeros(Nnodes*(Nnodes-1)//2,1,dtype=torch.int8)
count = 0
# FIXME replace this for-loop as it is very slow (see `LinkClassification`).
for source in range(Nnodes-1):
for sink in range(source+1,Nnodes):
newembeddings[count] = torch.cat((embeddings[source],embeddings[sink]),dim=0)
if torch.any((edges[0]==source)&(edges[1]==sink)):
y_true[count] = 1
count += 1
y = self.cls(newembeddings)
self.y_true = y_true.flatten().long()
return y