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model.py
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import torch
import torch.nn.functional as F
from torch.nn import Linear, BatchNorm1d, ModuleList
from torch_geometric.nn import TransformerConv, TopKPooling, GINEConv
from torch_geometric.nn import global_mean_pool as gap, global_max_pool as gmp
torch.manual_seed(42)
# class GNN(torch.nn.Module):
# def __init__(self, feature_size, model_params):
# super(GNN, self).__init__()
# embedding_size = model_params["model_embedding_size"]
# n_heads = model_params["model_attention_heads"]
# self.n_layers = model_params["model_layers"]
# dropout_rate = model_params["model_dropout_rate"]
# top_k_ratio = model_params["model_top_k_ratio"]
# self.top_k_every_n = model_params["model_top_k_every_n"]
# dense_neurons = model_params["model_dense_neurons"]
# edge_dim = model_params["model_edge_dim"]
# node_dim = model_params["model_node_dim"]
# self.conv_layers = ModuleList([])
# self.transf_layers = ModuleList([])
# self.pooling_layers = ModuleList([])
# self.bn_layers = ModuleList([])
# self.lin1 = Linear(node_dim, node_dim)
# self.conv1 = GINEConv(self.lin1, edge_dim=edge_dim)
# self.transf1 = Linear(node_dim, embedding_size)
# self.bn1 = BatchNorm1d(embedding_size)
# # Other layers
# for i in range(self.n_layers):
# self.conv_layers.append(GINEConv(Linear(embedding_size, embedding_size), edge_dim=edge_dim))
# self.transf_layers.append(Linear(embedding_size, embedding_size))
# self.bn_layers.append(BatchNorm1d(embedding_size))
# if i % self.top_k_every_n == 0:
# self.pooling_layers.append(TopKPooling(embedding_size, ratio=top_k_ratio))
# # Linear layers
# self.linear1 = Linear(embedding_size*2, dense_neurons)
# self.linear2 = Linear(dense_neurons, int(dense_neurons/2))
# self.linear3 = Linear(int(dense_neurons/2), 1)
# def forward(self, x, edge_attr, edge_index, batch_index):
# # Initial transformation
# x = self.conv1(x, edge_index, edge_attr)
# x = torch.relu(self.transf1(x))
# x = self.bn1(x)
# # Holds the intermediate graph representations
# global_representation = []
# for i in range(self.n_layers):
# x = self.conv_layers[i](x, edge_index, edge_attr)
# x = torch.relu(self.transf_layers[i](x))
# x = self.bn_layers[i](x)
# # Always aggregate last layer
# if i % self.top_k_every_n == 0 or i == self.n_layers:
# x , edge_index, edge_attr, batch_index, _, _ = self.pooling_layers[int(i/self.top_k_every_n)](
# x, edge_index, edge_attr, batch_index
# )
# # Add current representation
# global_representation.append(torch.cat([gmp(x, batch_index), gap(x, batch_index)], dim=1))
# x = sum(global_representation)
# # Output block
# x = torch.relu(self.linear1(x))
# x = F.dropout(x, p=0.8, training=self.training)
# x = torch.relu(self.linear2(x))
# x = F.dropout(x, p=0.8, training=self.training)
# x = self.linear3(x)
# return x
# Transformer GNN
class GNN(torch.nn.Module):
def __init__(self, feature_size, model_params):
super(GNN, self).__init__()
embedding_size = model_params["model_embedding_size"]
n_heads = model_params["model_attention_heads"]
self.n_layers = model_params["model_layers"]
dropout_rate = model_params["model_dropout_rate"]
top_k_ratio = model_params["model_top_k_ratio"]
self.top_k_every_n = model_params["model_top_k_every_n"]
dense_neurons = model_params["model_dense_neurons"]
edge_dim = model_params["model_edge_dim"]
self.conv_layers = ModuleList([])
self.transf_layers = ModuleList([])
self.pooling_layers = ModuleList([])
self.bn_layers = ModuleList([])
# Transformation layer
self.conv1 = TransformerConv(feature_size,
embedding_size,
heads=n_heads,
dropout=dropout_rate,
edge_dim=edge_dim,
beta=True)
self.transf1 = Linear(embedding_size*n_heads, embedding_size)
self.bn1 = BatchNorm1d(embedding_size)
# Other layers
for i in range(self.n_layers):
self.conv_layers.append(TransformerConv(embedding_size,
embedding_size,
heads=n_heads,
dropout=dropout_rate,
edge_dim=edge_dim,
beta=True))
self.transf_layers.append(Linear(embedding_size*n_heads, embedding_size))
self.bn_layers.append(BatchNorm1d(embedding_size))
if i % self.top_k_every_n == 0:
self.pooling_layers.append(TopKPooling(embedding_size, ratio=top_k_ratio))
# Linear layers
self.linear1 = Linear(embedding_size*2, dense_neurons)
self.linear2 = Linear(dense_neurons, int(dense_neurons/2))
self.linear3 = Linear(int(dense_neurons/2), 1)
def forward(self, x, edge_attr, edge_index, batch_index):
# Initial transformation
x = self.conv1(x, edge_index, edge_attr)
x = torch.relu(self.transf1(x))
x = self.bn1(x)
# Holds the intermediate graph representations
global_representation = []
for i in range(self.n_layers):
x = self.conv_layers[i](x, edge_index, edge_attr)
x = torch.relu(self.transf_layers[i](x))
x = self.bn_layers[i](x)
# Always aggregate last layer
if i % self.top_k_every_n == 0 or i == self.n_layers:
x , edge_index, edge_attr, batch_index, _, _ = self.pooling_layers[int(i/self.top_k_every_n)](
x, edge_index, edge_attr, batch_index
)
# Add current representation
global_representation.append(torch.cat([gmp(x, batch_index), gap(x, batch_index)], dim=1))
x = sum(global_representation)
# Output block
x = torch.relu(self.linear1(x))
x = F.dropout(x, p=0.8, training=self.training)
x = torch.relu(self.linear2(x))
x = F.dropout(x, p=0.8, training=self.training)
x = self.linear3(x)
return x