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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
from torch_geometric.datasets import TUDataset
dataset = TUDataset(root="/tmp/ENZYMES", name="ENZYMES")
data = dataset[0]
from scgnn.scgnn import SCGNN
dataset = TUDataset(root="/tmp/ENZYMES", name="ENZYMES")
data = dataset[0]
from scgnn.scgnn import SCGNN
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, global_mean_pool
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, global_mean_pool
model = Sequential(
model = Sequential(
"data",
[
(
@ -22,15 +22,15 @@ from torch_geometric.datasets import TUDataset
(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(
)
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,