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import pytest
import torch
from torch_geometric.data import Data, HeteroData
from torch_geometric.explain.algorithm.captum import to_captum_input
from torch_geometric.nn import GAT, GCN, SAGEConv
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.models import to_captum_model
from torch_geometric.testing import withPackage
x = torch.randn(8, 3, requires_grad=True)
edge_index = torch.tensor([[0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7],
[1, 0, 2, 1, 3, 2, 4, 3, 5, 4, 6, 5, 7, 6]])
GCN = GCN(3, 16, 2, 7, dropout=0.5)
GAT = GAT(3, 16, 2, 7, heads=2, concat=False)
mask_types = ['edge', 'node_and_edge', 'node']
methods = [
'Saliency',
'InputXGradient',
'Deconvolution',
'FeatureAblation',
'ShapleyValueSampling',
'IntegratedGradients',
'GradientShap',
'Occlusion',
'GuidedBackprop',
'KernelShap',
'Lime',
]
@pytest.mark.parametrize('mask_type', mask_types)
@pytest.mark.parametrize('model', [GCN, GAT])
@pytest.mark.parametrize('output_idx', [None, 1])
def test_to_captum(model, mask_type, output_idx):
captum_model = to_captum_model(model, mask_type=mask_type,
output_idx=output_idx)
pre_out = model(x, edge_index)
if mask_type == 'node':
mask = x * 0.0
out = captum_model(mask.unsqueeze(0), edge_index)
elif mask_type == 'edge':
mask = torch.ones(edge_index.shape[1], dtype=torch.float,
requires_grad=True) * 0.5
out = captum_model(mask.unsqueeze(0), x, edge_index)
elif mask_type == 'node_and_edge':
node_mask = x * 0.0
edge_mask = torch.ones(edge_index.shape[1], dtype=torch.float,
requires_grad=True) * 0.5
out = captum_model(node_mask.unsqueeze(0), edge_mask.unsqueeze(0),
edge_index)
if output_idx is not None:
assert out.shape == (1, 7)
assert torch.any(out != pre_out[[output_idx]])
else:
assert out.shape == (8, 7)
assert torch.any(out != pre_out)
@withPackage('captum')
@pytest.mark.parametrize('mask_type', mask_types)
@pytest.mark.parametrize('method', methods)
def test_captum_attribution_methods(mask_type, method):
from captum import attr # noqa
captum_model = to_captum_model(GCN, mask_type, 0)
explainer = getattr(attr, method)(captum_model)
data = Data(x, edge_index)
input, additional_forward_args = to_captum_input(data.x, data.edge_index,
mask_type)
if mask_type == 'node':
sliding_window_shapes = (3, 3)
elif mask_type == 'edge':
sliding_window_shapes = (5, )
elif mask_type == 'node_and_edge':
sliding_window_shapes = ((3, 3), (5, ))
if method == 'IntegratedGradients':
attributions, delta = explainer.attribute(
input, target=0, internal_batch_size=1,
additional_forward_args=additional_forward_args,
return_convergence_delta=True)
elif method == 'GradientShap':
attributions, delta = explainer.attribute(
input, target=0, return_convergence_delta=True, baselines=input,
n_samples=1, additional_forward_args=additional_forward_args)
elif method == 'DeepLiftShap' or method == 'DeepLift':
attributions, delta = explainer.attribute(
input, target=0, return_convergence_delta=True, baselines=input,
additional_forward_args=additional_forward_args)
elif method == 'Occlusion':
attributions = explainer.attribute(
input, target=0, sliding_window_shapes=sliding_window_shapes,
additional_forward_args=additional_forward_args)
else:
attributions = explainer.attribute(
input, target=0, additional_forward_args=additional_forward_args)
if mask_type == 'node':
assert attributions[0].shape == (1, 8, 3)
elif mask_type == 'edge':
assert attributions[0].shape == (1, 14)
else:
assert attributions[0].shape == (1, 8, 3)
assert attributions[1].shape == (1, 14)
def test_custom_explain_message():
x = torch.randn(4, 8)
edge_index = torch.tensor([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]])
conv = SAGEConv(8, 32)
def explain_message(self, inputs, x_i, x_j):
assert isinstance(self, SAGEConv)
assert inputs.size() == (6, 8)
assert inputs.size() == x_i.size() == x_j.size()
assert torch.allclose(inputs, x_j)
self.x_i = x_i
self.x_j = x_j
return inputs
conv.explain_message = explain_message.__get__(conv, MessagePassing)
conv.explain = True
conv(x, edge_index)
assert torch.allclose(conv.x_i, x[edge_index[1]])
assert torch.allclose(conv.x_j, x[edge_index[0]])
@withPackage('captum')
@pytest.mark.parametrize('mask_type', ['node', 'edge', 'node_and_edge'])
def test_to_captum_input(mask_type):
num_nodes = x.shape[0]
num_node_feats = x.shape[1]
num_edges = edge_index.shape[1]
# Check for Data:
data = Data(x, edge_index)
args = 'test_args'
inputs, additional_forward_args = to_captum_input(data.x, data.edge_index,
mask_type, args)
if mask_type == 'node':
assert len(inputs) == 1
assert inputs[0].shape == (1, num_nodes, num_node_feats)
assert len(additional_forward_args) == 2
assert torch.allclose(additional_forward_args[0], edge_index)
elif mask_type == 'edge':
assert len(inputs) == 1
assert inputs[0].shape == (1, num_edges)
assert inputs[0].sum() == num_edges
assert len(additional_forward_args) == 3
assert torch.allclose(additional_forward_args[0], x)
assert torch.allclose(additional_forward_args[1], edge_index)
else:
assert len(inputs) == 2
assert inputs[0].shape == (1, num_nodes, num_node_feats)
assert inputs[1].shape == (1, num_edges)
assert inputs[1].sum() == num_edges
assert len(additional_forward_args) == 2
assert torch.allclose(additional_forward_args[0], edge_index)
# Check for HeteroData:
data = HeteroData()
x2 = torch.rand(8, 3)
data['paper'].x = x
data['author'].x = x2
data['paper', 'to', 'author'].edge_index = edge_index
data['author', 'to', 'paper'].edge_index = edge_index.flip([0])
inputs, additional_forward_args = to_captum_input(data.x_dict,
data.edge_index_dict,
mask_type, args)
if mask_type == 'node':
assert len(inputs) == 2
assert inputs[0].shape == (1, num_nodes, num_node_feats)
assert inputs[1].shape == (1, num_nodes, num_node_feats)
assert len(additional_forward_args) == 2
for key in data.edge_types:
torch.allclose(additional_forward_args[0][key],
data[key].edge_index)
elif mask_type == 'edge':
assert len(inputs) == 2
assert inputs[0].shape == (1, num_edges)
assert inputs[1].shape == (1, num_edges)
assert inputs[1].sum() == inputs[0].sum() == num_edges
assert len(additional_forward_args) == 3
for key in data.node_types:
torch.allclose(additional_forward_args[0][key], data[key].x)
for key in data.edge_types:
torch.allclose(additional_forward_args[1][key],
data[key].edge_index)
else:
assert len(inputs) == 4
assert inputs[0].shape == (1, num_nodes, num_node_feats)
assert inputs[1].shape == (1, num_nodes, num_node_feats)
assert inputs[2].shape == (1, num_edges)
assert inputs[3].shape == (1, num_edges)
assert inputs[3].sum() == inputs[2].sum() == num_edges
assert len(additional_forward_args) == 2
for key in data.edge_types:
torch.allclose(additional_forward_args[0][key],
data[key].edge_index)
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