chore: import upstream snapshot with attribution

examples/pytorch/rect/utils.py

@@ -0,0 +1,51 @@
+import dgl
+import torch
+from dgl.data import CiteseerGraphDataset, CoraGraphDataset
+
+
+def load_data(args):
+    if args.dataset == "cora":
+        data = CoraGraphDataset()
+    elif args.dataset == "citeseer":
+        data = CiteseerGraphDataset()
+    else:
+        raise ValueError("Unknown dataset: {}".format(args.dataset))
+    g = data[0]
+    if args.gpu < 0:
+        cuda = False
+    else:
+        cuda = True
+        g = g.int().to(args.gpu)
+    features = g.ndata["feat"]
+    labels = g.ndata["label"]
+    train_mask = g.ndata["train_mask"]
+    test_mask = g.ndata["test_mask"]
+    g = dgl.add_self_loop(g)
+    return g, features, labels, train_mask, test_mask, data.num_classes, cuda
+
+
+def svd_feature(features, d=200):
+    """Get 200-dimensional node features, to avoid curse of dimensionality"""
+    if features.shape[1] <= d:
+        return features
+    U, S, VT = torch.svd(features)
+    res = torch.mm(U[:, 0:d], torch.diag(S[0:d]))
+    return res
+
+
+def process_classids(labels_temp):
+    """Reorder the remaining classes with unseen classes removed.
+    Input: the label only removing unseen classes
+    Output: the label with reordered classes
+    """
+    labeldict = {}
+    num = 0
+    for i in labels_temp:
+        labeldict[int(i)] = 1
+    labellist = sorted(labeldict)
+    for label in labellist:
+        labeldict[int(label)] = num
+        num = num + 1
+    for i in range(labels_temp.numel()):
+        labels_temp[i] = labeldict[int(labels_temp[i])]
+    return labels_temp