chore: import upstream snapshot with attribution

examples/pytorch/ogc/utils.py

@@ -0,0 +1,35 @@
+import dgl.sparse as dglsp
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class LinearNeuralNetwork(nn.Module):
+    def __init__(self, nfeat, nclass, bias=True):
+        super(LinearNeuralNetwork, self).__init__()
+        self.W = nn.Linear(nfeat, nclass, bias=bias)
+
+    def forward(self, x):
+        return self.W(x)
+
+
+def symmetric_normalize_adjacency(graph):
+    """Symmetric normalize graph adjacency matrix."""
+    indices = torch.stack(graph.edges())
+    n = graph.num_nodes()
+    adj = dglsp.spmatrix(indices, shape=(n, n))
+    deg_invsqrt = dglsp.diag(adj.sum(0)) ** -0.5
+    return deg_invsqrt @ adj @ deg_invsqrt
+
+
+def model_test(model, embeds):
+    model.eval()
+    with torch.no_grad():
+        output = model(embeds)
+        pred = output.argmax(dim=-1)
+        test_mask, tv_mask = model.test_mask, model.tv_mask
+        loss_tv = F.mse_loss(output[tv_mask], model.label_one_hot[tv_mask])
+    accs = []
+    for mask in [tv_mask, test_mask]:
+        accs.append(float((pred[mask] == model.label[mask]).sum() / mask.sum()))
+    return loss_tv.item(), accs[0], accs[1], pred