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models.py
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models.py
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import torch
import math
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Linear, Conv1d
from torch_geometric.nn import GCNConv, RGCNConv, global_sort_pool, global_add_pool
from torch_geometric.utils import dropout_adj
from util_functions import *
import pdb
import time
class GNN(torch.nn.Module):
# a base GNN class, GCN message passing + sum_pooling
def __init__(self, dataset, gconv=GCNConv, latent_dim=[32, 32, 32, 1],
regression=False, adj_dropout=0.2, force_undirected=False):
super(GNN, self).__init__()
self.regression = regression
self.adj_dropout = adj_dropout
self.force_undirected = force_undirected
self.convs = torch.nn.ModuleList()
self.convs.append(gconv(dataset.num_features, latent_dim[0]))
for i in range(0, len(latent_dim)-1):
self.convs.append(gconv(latent_dim[i], latent_dim[i+1]))
self.lin1 = Linear(sum(latent_dim), 128)
if self.regression:
self.lin2 = Linear(128, 1)
else:
self.lin2 = Linear(128, dataset.num_classes)
def reset_parameters(self):
for conv in self.convs:
conv.reset_parameters()
self.lin1.reset_parameters()
self.lin2.reset_parameters()
def forward(self, data):
x, edge_index, batch = data.x, data.edge_index, data.batch
if self.adj_dropout > 0:
edge_index, edge_type = dropout_adj(
edge_index, edge_type, p=self.adj_dropout,
force_undirected=self.force_undirected, num_nodes=len(x),
training=self.training
)
concat_states = []
for conv in self.convs:
x = torch.tanh(conv(x, edge_index))
concat_states.append(x)
concat_states = torch.cat(concat_states, 1)
x = global_add_pool(concat_states, batch)
x = F.relu(self.lin1(x))
x = F.dropout(x, p=0.5, training=self.training)
x = self.lin2(x)
if self.regression:
return x[:, 0]
else:
return F.log_softmax(x, dim=-1)
def __repr__(self):
return self.__class__.__name__
class DGCNN(GNN):
# DGCNN from [Zhang et al. AAAI 2018], GCN message passing + SortPooling
def __init__(self, dataset, gconv=GCNConv, latent_dim=[32, 32, 32, 1], k=30,
regression=False, adj_dropout=0.2, force_undirected=False):
super(DGCNN, self).__init__(
dataset, gconv, latent_dim, regression, adj_dropout, force_undirected
)
if k < 1: # transform percentile to number
node_nums = sorted([g.num_nodes for g in dataset])
k = node_nums[int(math.ceil(k * len(node_nums)))-1]
k = max(10, k) # no smaller than 10
self.k = int(k)
print('k used in sortpooling is:', self.k)
conv1d_channels = [16, 32]
conv1d_activation = nn.ReLU()
self.total_latent_dim = sum(latent_dim)
conv1d_kws = [self.total_latent_dim, 5]
self.conv1d_params1 = Conv1d(1, conv1d_channels[0], conv1d_kws[0], conv1d_kws[0])
self.maxpool1d = nn.MaxPool1d(2, 2)
self.conv1d_params2 = Conv1d(conv1d_channels[0], conv1d_channels[1], conv1d_kws[1], 1)
dense_dim = int((k - 2) / 2 + 1)
self.dense_dim = (dense_dim - conv1d_kws[1] + 1) * conv1d_channels[1]
self.lin1 = Linear(self.dense_dim, 128)
def reset_parameters(self):
for conv in self.convs:
conv.reset_parameters()
self.conv1d_params1.reset_parameters()
self.conv1d_params2.reset_parameters()
self.lin1.reset_parameters()
self.lin2.reset_parameters()
def forward(self, data):
x, edge_index, batch = data.x, data.edge_index, data.batch
if self.adj_dropout > 0:
edge_index, edge_type = dropout_adj(
edge_index, edge_type, p=self.adj_dropout,
force_undirected=self.force_undirected, num_nodes=len(x),
training=self.training
)
concat_states = []
for conv in self.convs:
x = torch.tanh(conv(x, edge_index))
concat_states.append(x)
concat_states = torch.cat(concat_states, 1)
x = global_sort_pool(concat_states, batch, self.k) # batch * (k*hidden)
x = x.unsqueeze(1) # batch * 1 * (k*hidden)
x = F.relu(self.conv1d_params1(x))
x = self.maxpool1d(x)
x = F.relu(self.conv1d_params2(x))
x = x.view(len(x), -1) # flatten
x = F.relu(self.lin1(x))
x = F.dropout(x, p=0.5, training=self.training)
x = self.lin2(x)
if self.regression:
return x[:, 0]
else:
return F.log_softmax(x, dim=-1)
class DGCNN_RS(DGCNN):
# A DGCNN model using RGCN convolution to take consideration of edge types.
def __init__(self, dataset, gconv=RGCNConv, latent_dim=[32, 32, 32, 1], k=30,
num_relations=5, num_bases=2, regression=False, adj_dropout=0.2,
force_undirected=False):
super(DGCNN_RS, self).__init__(
dataset,
GCNConv,
latent_dim,
k,
regression,
adj_dropout=adj_dropout,
force_undirected=force_undirected
)
self.convs = torch.nn.ModuleList()
self.convs.append(gconv(dataset.num_features, latent_dim[0], num_relations, num_bases))
for i in range(0, len(latent_dim)-1):
self.convs.append(gconv(latent_dim[i], latent_dim[i+1], num_relations, num_bases))
def forward(self, data):
x, edge_index, edge_type, batch = data.x, data.edge_index, data.edge_type, data.batch
if self.adj_dropout > 0:
edge_index, edge_type = dropout_adj(
edge_index, edge_type, p=self.adj_dropout,
force_undirected=self.force_undirected, num_nodes=len(x),
training=self.training
)
concat_states = []
for conv in self.convs:
x = torch.tanh(conv(x, edge_index, edge_type))
concat_states.append(x)
concat_states = torch.cat(concat_states, 1)
x = global_sort_pool(concat_states, batch, self.k) # batch * (k*hidden)
x = x.unsqueeze(1) # batch * 1 * (k*hidden)
x = F.relu(self.conv1d_params1(x))
x = self.maxpool1d(x)
x = F.relu(self.conv1d_params2(x))
x = x.view(len(x), -1) # flatten
x = F.relu(self.lin1(x))
x = F.dropout(x, p=0.5, training=self.training)
x = self.lin2(x)
if self.regression:
return x[:, 0]
else:
return F.log_softmax(x, dim=-1)
class IGMC(GNN):
# The GNN model of Inductive Graph-based Matrix Completion.
# Use RGCN convolution + center-nodes readout.
def __init__(self, dataset, gconv=RGCNConv, latent_dim=[32, 32, 32, 32],
num_relations=5, num_bases=2, regression=False, adj_dropout=0.2,
force_undirected=False, side_features=False, n_side_features=0,
multiply_by=1):
super(IGMC, self).__init__(
dataset, GCNConv, latent_dim, regression, adj_dropout, force_undirected
)
self.multiply_by = multiply_by
self.convs = torch.nn.ModuleList()
self.convs.append(gconv(dataset.num_features, latent_dim[0], num_relations, num_bases))
for i in range(0, len(latent_dim)-1):
self.convs.append(gconv(latent_dim[i], latent_dim[i+1], num_relations, num_bases))
self.lin1 = Linear(2*sum(latent_dim), 128)
self.side_features = side_features
if side_features:
self.lin1 = Linear(2*sum(latent_dim)+n_side_features, 128)
def forward(self, data):
start = time.time()
x, edge_index, edge_type, batch = data.x, data.edge_index, data.edge_type, data.batch
if self.adj_dropout > 0:
edge_index, edge_type = dropout_adj(
edge_index, edge_type, p=self.adj_dropout,
force_undirected=self.force_undirected, num_nodes=len(x),
training=self.training
)
concat_states = []
for conv in self.convs:
x = torch.tanh(conv(x, edge_index, edge_type))
concat_states.append(x)
concat_states = torch.cat(concat_states, 1)
users = data.x[:, 0] == 1
items = data.x[:, 1] == 1
x = torch.cat([concat_states[users], concat_states[items]], 1)
if self.side_features:
x = torch.cat([x, data.u_feature, data.v_feature], 1)
x = F.relu(self.lin1(x))
x = F.dropout(x, p=0.5, training=self.training)
x = self.lin2(x)
if self.regression:
return x[:, 0] * self.multiply_by
else:
return F.log_softmax(x, dim=-1)