chore: import upstream snapshot with attribution

2026-07-13 12:36:30 +08:00
commit 55ab4e4a73
473 changed files with 72932 additions and 0 deletions
@@ -0,0 +1,70 @@
+import torch
+import torch.nn as nn
+
+
+class DHNE(nn.Module):
+    r"""The DHNE model proposed in `Structural Deep Embedding for Hyper-Networks <https://arxiv.org/abs/1711.10146>`_ paper (AAAI 2018).
+
+    Parameters:
+        ``dim_feature`` (``int``): : feature dimension list ( len = 3)
+        ``embedding_size`` (``int``): :The embedding dimension size
+        ``hidden_size`` (``int``): The hidden full connected layer size.
+
+    """
+
+    def __init__(self, dim_feature, embedding_size, hidden_size):
+        super(DHNE, self).__init__()
+        self.dim_feature = dim_feature
+        self.embedding_size = embedding_size
+        self.hidden_size = hidden_size
+        self.encode0 = nn.Sequential(
+            nn.Linear(
+                in_features=self.dim_feature[0], out_features=self.embedding_size[0]
+            )
+        )
+        self.encode1 = nn.Sequential(
+            nn.Linear(
+                in_features=self.dim_feature[1], out_features=self.embedding_size[1]
+            )
+        )
+        self.encode2 = nn.Sequential(
+            nn.Linear(
+                in_features=self.dim_feature[2], out_features=self.embedding_size[2]
+            )
+        )
+        self.decode_layer0 = nn.Linear(
+            in_features=self.embedding_size[0], out_features=self.dim_feature[0]
+        )
+        self.decode_layer1 = nn.Linear(
+            in_features=self.embedding_size[1], out_features=self.dim_feature[1]
+        )
+        self.decode_layer2 = nn.Linear(
+            in_features=self.embedding_size[2], out_features=self.dim_feature[2]
+        )
+
+        self.hidden_layer = nn.Linear(
+            in_features=sum(self.embedding_size), out_features=self.hidden_size
+        )
+        self.ouput_layer = nn.Linear(in_features=self.hidden_size, out_features=1)
+
+    def forward(self, input0, input1, input2):
+        input0 = self.encode0(input0)
+        input0 = torch.tanh(input0)
+        decode0 = self.decode_layer0(input0)
+        decode0 = torch.sigmoid(decode0)
+
+        input1 = self.encode1(input1)
+        input1 = torch.tanh(input1)
+        decode1 = self.decode_layer1(input1)
+        decode1 = torch.sigmoid(decode1)
+
+        input2 = self.encode2(input2)
+        input2 = torch.tanh(input2)
+        decode2 = self.decode_layer2(input2)
+        decode2 = torch.sigmoid(decode2)
+
+        merged = torch.tanh(torch.cat((input0, input1, input2), dim=1))
+        merged = self.hidden_layer(merged)
+        merged = self.ouput_layer(merged)
+        merged = torch.sigmoid(merged)
+        return [decode0, decode1, decode2, merged]