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scorecamgnn
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araison 2023-03-08 20:01:07 +01:00
parent 352edb7df6
commit 9d4a132b25
1 changed files with 0 additions and 47 deletions
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scgnn/scgnn.py
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@ -159,55 +159,8 @@ class SCGNN(ExplainerAlgorithm):
score_map = score_map + val * feat score_map = score_map + val * feat
score_map = F.relu(score_map) score_map = F.relu(score_map)
if self.score_map_norm and score_map.min() != score_map.max(): if self.score_map_norm and score_map.min() != score_map.max():
score_map = (score_map - score_map.min()).div( score_map = (score_map - score_map.min()).div(
score_map.max() - score_map.min() score_map.max() - score_map.min()
) )
return score_map return score_map
if __name__ == "__main__":
from torch_geometric.datasets import TUDataset
dataset = TUDataset(root="/tmp/ENZYMES", name="ENZYMES")
data = dataset[0]
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, global_mean_pool
# model = torch.nn.ModuleDict(
# {
# "conv1": GCNConv(dataset.num_node_features, 64),
# "conv2": GCNConv(64, dataset.num_classes),
# "gmp": global_mean_pool,
# }
# )
model = Sequential(
"data",
[
(
lambda data: (data.x, data.edge_index, data.batch),
"data -> x, edge_index, batch",
),
(GCNConv(dataset.num_node_features, 64), "x, edge_index -> x"),
(GCNConv(64, dataset.num_classes), "x, edge_index -> x"),
(global_mean_pool, "x, batch -> x"),
],
)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
data = dataset[0].to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
model.eval()
out = model(data)
explainer = SCGNN()
explained = explainer.forward(
model,
data.x,
data.edge_index,
target=2,
interest_map_norm=True,
score_map_norm=True,
)