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"""Tests for module gnn"""
# Author: Sonia Mazelet <sonia.mazelet@ens-paris-saclay.fr>
# Rémi Flamary <remi.flamary@unice.fr>
#
# License: MIT License
import pytest
try: # test if pytorch_geometric is installed
import torch_geometric
import torch
from torch_geometric.nn import Linear
from torch_geometric.data import Data as GraphData
from torch_geometric.loader import DataLoader
import torch.nn as nn
from ot.gnn import TFGWPooling, TWPooling
except ImportError:
torch_geometric = False
@pytest.mark.skipif(not torch_geometric, reason="pytorch_geometric not installed")
def test_TFGW_optim():
# Test the TFGW layer by passing two graphs through the layer and doing backpropagation.
class pooling_TFGW(nn.Module):
"""
Pooling architecture using the TFGW layer.
"""
def __init__(self, n_features, n_templates, n_template_nodes):
"""
Pooling architecture using the TFGW layer.
"""
super().__init__()
self.n_features = n_features
self.n_templates = n_templates
self.n_template_nodes = n_template_nodes
self.TFGW = TFGWPooling(
self.n_templates, self.n_template_nodes, self.n_features
)
self.linear = Linear(self.n_templates, 1)
def forward(self, x, edge_index):
x = self.TFGW(x, edge_index)
x = self.linear(x)
return x
torch.manual_seed(0)
n_templates = 3
n_template_nodes = 3
n_nodes = 10
n_features = 3
n_epochs = 3
C1 = torch.randint(0, 2, size=(n_nodes, n_nodes))
C2 = torch.randint(0, 2, size=(n_nodes, n_nodes))
edge_index1 = torch.stack(torch.where(C1 == 1))
edge_index2 = torch.stack(torch.where(C2 == 1))
x1 = torch.rand(n_nodes, n_features)
x2 = torch.rand(n_nodes, n_features)
graph1 = GraphData(x=x1, edge_index=edge_index1, y=torch.tensor([0.0]))
graph2 = GraphData(x=x2, edge_index=edge_index2, y=torch.tensor([1.0]))
dataset = DataLoader([graph1, graph2], batch_size=1)
model_FGW = pooling_TFGW(n_features, n_templates, n_template_nodes)
optimizer = torch.optim.Adam(model_FGW.parameters(), lr=0.01)
criterion = torch.nn.CrossEntropyLoss()
model_FGW.train()
for i in range(n_epochs):
for data in dataset:
out = model_FGW(data.x, data.edge_index)
loss = criterion(out, data.y)
loss.backward()
optimizer.step()
optimizer.zero_grad()
@pytest.mark.skipif(not torch_geometric, reason="pytorch_geometric not installed")
def test_TFGW_variants():
# Test the TFGW layer by passing two graphs through the layer and doing backpropagation.
class GNN_pooling(nn.Module):
"""
Pooling architecture using the TW layer.
"""
def __init__(self, n_features, n_templates, n_template_nodes, pooling_layer):
"""
Pooling architecture using the TW layer.
"""
super().__init__()
self.n_features = n_features
self.n_templates = n_templates
self.n_template_nodes = n_template_nodes
self.TFGW = pooling_layer
self.linear = Linear(self.n_templates, 1)
def forward(self, x, edge_index, batch=None):
x = self.TFGW(x, edge_index, batch=batch)
x = self.linear(x)
return x
n_templates = 3
n_template_nodes = 3
n_nodes = 10
n_features = 3
torch.manual_seed(0)
C1 = torch.randint(0, 2, size=(n_nodes, n_nodes))
edge_index1 = torch.stack(torch.where(C1 == 1))
x1 = torch.rand(n_nodes, n_features)
graph1 = GraphData(x=x1, edge_index=edge_index1, y=torch.tensor([0.0]))
batch1 = torch.tensor([1] * n_nodes)
batch1[: n_nodes // 2] = 0
criterion = torch.nn.CrossEntropyLoss()
for train_node_weights in [True, False]:
for alpha in [None, 0, 0.5]:
for multi_alpha in [True, False]:
model = GNN_pooling(
n_features,
n_templates,
n_template_nodes,
pooling_layer=TFGWPooling(
n_templates,
n_template_nodes,
n_features,
alpha=alpha,
multi_alpha=multi_alpha,
train_node_weights=train_node_weights,
),
)
# predict
out1 = model(graph1.x, graph1.edge_index)
loss = criterion(out1, graph1.y)
loss.backward()
# predict on batch
out1 = model(graph1.x, graph1.edge_index, batch1)
@pytest.mark.skipif(not torch_geometric, reason="pytorch_geometric not installed")
def test_TW_variants():
# Test the TFGW layer by passing two graphs through the layer and doing backpropagation.
class GNN_pooling(nn.Module):
"""
Pooling architecture using the TW layer.
"""
def __init__(self, n_features, n_templates, n_template_nodes, pooling_layer):
"""
Pooling architecture using the TW layer.
"""
super().__init__()
self.n_features = n_features
self.n_templates = n_templates
self.n_template_nodes = n_template_nodes
self.TFGW = pooling_layer
self.linear = Linear(self.n_templates, 1)
def forward(self, x, edge_index, batch=None):
x = self.TFGW(x, edge_index, batch=batch)
x = self.linear(x)
return x
n_templates = 3
n_template_nodes = 3
n_nodes = 10
n_features = 3
torch.manual_seed(0)
C1 = torch.randint(0, 2, size=(n_nodes, n_nodes))
edge_index1 = torch.stack(torch.where(C1 == 1))
x1 = torch.rand(n_nodes, n_features)
graph1 = GraphData(x=x1, edge_index=edge_index1, y=torch.tensor([0.0]))
batch1 = torch.tensor([1] * n_nodes)
batch1[: n_nodes // 2] = 0
criterion = torch.nn.CrossEntropyLoss()
for train_node_weights in [True, False]:
model = GNN_pooling(
n_features,
n_templates,
n_template_nodes,
pooling_layer=TWPooling(
n_templates,
n_template_nodes,
n_features,
train_node_weights=train_node_weights,
),
)
out1 = model(graph1.x, graph1.edge_index)
loss = criterion(out1, graph1.y)
loss.backward()
# predict on batch
out1 = model(graph1.x, graph1.edge_index, batch1)
@pytest.mark.skipif(not torch_geometric, reason="pytorch_geometric not installed")
def test_TW():
# Test the TW layer by passing two graphs through the layer and doing backpropagation.
class pooling_TW(nn.Module):
"""
Pooling architecture using the TW layer.
"""
def __init__(self, n_features, n_templates, n_template_nodes):
"""
Pooling architecture using the TW layer.
"""
super().__init__()
self.n_features = n_features
self.n_templates = n_templates
self.n_template_nodes = n_template_nodes
self.TFGW = TWPooling(
self.n_templates, self.n_template_nodes, self.n_features
)
self.linear = Linear(self.n_templates, 1)
def forward(self, x, edge_index):
x = self.TFGW(x, edge_index)
x = self.linear(x)
return x
torch.manual_seed(0)
n_templates = 3
n_template_nodes = 3
n_nodes = 10
n_features = 3
n_epochs = 3
C1 = torch.randint(0, 2, size=(n_nodes, n_nodes))
C2 = torch.randint(0, 2, size=(n_nodes, n_nodes))
edge_index1 = torch.stack(torch.where(C1 == 1))
edge_index2 = torch.stack(torch.where(C2 == 1))
x1 = torch.rand(n_nodes, n_features)
x2 = torch.rand(n_nodes, n_features)
graph1 = GraphData(x=x1, edge_index=edge_index1, y=torch.tensor([0.0]))
graph2 = GraphData(x=x2, edge_index=edge_index2, y=torch.tensor([1.0]))
dataset = DataLoader([graph1, graph2], batch_size=1)
model_W = pooling_TW(n_features, n_templates, n_template_nodes)
optimizer = torch.optim.Adam(model_W.parameters(), lr=0.01)
criterion = torch.nn.CrossEntropyLoss()
model_W.train()
for i in range(n_epochs):
for data in dataset:
out = model_W(data.x, data.edge_index)
loss = criterion(out, data.y)
loss.backward()
optimizer.step()
optimizer.zero_grad()
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