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araison 2023-03-08 19:01:58 +01:00
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@ -3,15 +3,15 @@ Here is the an example code for using ScoreCAM GNN from the [ScoreCAM GNN : a ge
```python ```python
from torch_geometric.datasets import TUDataset from torch_geometric.datasets import TUDataset
dataset = TUDataset(root="/tmp/ENZYMES", name="ENZYMES") dataset = TUDataset(root="/tmp/ENZYMES", name="ENZYMES")
data = dataset[0] data = dataset[0]
from scgnn.scgnn import SCGNN from scgnn.scgnn import SCGNN
import torch.nn.functional as F import torch.nn.functional as F
from torch_geometric.nn import GCNConv, global_mean_pool from torch_geometric.nn import GCNConv, global_mean_pool
model = Sequential( model = Sequential(
"data", "data",
[ [
( (
@ -22,15 +22,15 @@ from torch_geometric.datasets import TUDataset
(GCNConv(64, dataset.num_classes), "x, edge_index -> x"), (GCNConv(64, dataset.num_classes), "x, edge_index -> x"),
(global_mean_pool, "x, batch -> x"), (global_mean_pool, "x, batch -> x"),
], ],
) )
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device) model = model.to(device)
data = dataset[0].to(device) data = dataset[0].to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4) optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
model.eval() model.eval()
out = model(data) out = model(data)
explainer = SCGNN() explainer = SCGNN()
explained = explainer.forward( explained = explainer.forward(
model, model,
data.x, data.x,
data.edge_index, data.edge_index,