chore: import upstream snapshot with attribution

2026-07-13 13:35:51 +08:00
commit c36a561cd8
2172 changed files with 455595 additions and 0 deletions
@@ -0,0 +1,88 @@
+from collections import defaultdict
+
+import numpy as np
+import torch
+
+
+def remove_unseen_classes_from_training(train_mask, labels, removed_class):
+    """Remove the unseen classes (the first three classes by default) to get the zero-shot (i.e., completely imbalanced) label setting
+    Input: train_mask, labels, removed_class
+    Output: train_mask_zs: the bool list only containing seen classes
+    """
+    train_mask_zs = train_mask.clone()
+    for i in range(train_mask_zs.numel()):
+        if train_mask_zs[i] == 1 and (labels[i].item() in removed_class):
+            train_mask_zs[i] = 0
+    return train_mask_zs
+
+
+def get_class_set(labels):
+    """Get the class set.
+    Input: labels [l, [c1, c2, ..]]
+    Output：the labeled class set dict_keys([k1, k2, ..])
+    """
+    mydict = {}
+    for y in labels:
+        for label in y:
+            mydict[int(label)] = 1
+    return mydict.keys()
+
+
+def get_label_attributes(train_mask_zs, nodeids, labellist, features):
+    """Get the class-center (semanic knowledge) of each seen class.
+    Suppose a node i is labeled as c, then attribute[c] += node_i_attribute, finally mean(attribute[c])
+    Input: train_mask_zs, nodeids, labellist, features
+    Output: label_attribute{}: label -> average_labeled_node_features (class centers)
+    """
+    _, feat_num = features.shape
+    labels = get_class_set(labellist)
+    label_attribute_nodes = defaultdict(list)
+    for nodeid, labels in zip(nodeids, labellist):
+        for label in labels:
+            label_attribute_nodes[int(label)].append(int(nodeid))
+    label_attribute = {}
+    for label in label_attribute_nodes.keys():
+        nodes = label_attribute_nodes[int(label)]
+        selected_features = features[nodes, :]
+        label_attribute[int(label)] = np.mean(selected_features, axis=0)
+    return label_attribute
+
+
+def get_labeled_nodes_label_attribute(train_mask_zs, labels, features, cuda):
+    """Replace the original labels by their class-centers.
+    For each label c in the training set, the following operations will be performed:
+    Get label_attribute{} through function get_label_attributes, then res[i, :] = label_attribute[c]
+    Input: train_mask_zs, labels, features
+    Output: Y_{semantic} [l, ft]: tensor
+    """
+    X = torch.LongTensor(range(features.shape[0]))
+    nodeids = []
+    labellist = []
+    for i in X[train_mask_zs].numpy().tolist():
+        nodeids.append(str(i))
+    for i in labels[train_mask_zs].cpu().numpy().tolist():
+        labellist.append([str(i)])
+
+    # 1. get the semantic knowledge (class centers) of all seen classes
+    label_attribute = get_label_attributes(
+        train_mask_zs=train_mask_zs,
+        nodeids=nodeids,
+        labellist=labellist,
+        features=features.cpu().numpy(),
+    )
+
+    # 2. replace original labels by their class centers (semantic knowledge)
+    res = np.zeros([len(nodeids), features.shape[1]])
+    for i, labels in enumerate(labellist):
+        # support mutiple labels
+        c = len(labels)
+        temp = np.zeros([c, features.shape[1]])
+        for ii, label in enumerate(labels):
+            temp[ii, :] = label_attribute[int(label)]
+        temp = np.mean(temp, axis=0)
+        res[i, :] = temp
+    if cuda:
+        res = torch.FloatTensor(res).cuda()
+    else:
+        res = torch.FloatTensor(res)
+    return res