Completely wrong implementation. Missing weight attribute

src/skelcast/models/gcn/tg.py

@@ -0,0 +1,50 @@
+"""Typed Graph model modules"""
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class GraphLinear(nn.Module):
+    """
+    N: number of nodes (joints)
+    
+    """
+    def __init__(self, in_features: int, out_features: int):
+        super().__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+
+    def reset_parameters(self) -> None:
+        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+        #stdv = 1. / math.sqrt(self.weight.size(1))
+        #self.weight.data.uniform_(-stdv, stdv)
+        #if self.learn_influence:
+        #    self.G.data.uniform_(-stdv, stdv)
+        if len(self.weight.shape) == 3:
+            self.weight.data[1:] = self.weight.data[0]
+        if self.bias is not None:
+            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
+            bound = 1 / math.sqrt(fan_in)
+            nn.init.uniform_(self.bias, -bound, bound)
+
+    def forward(self, input: torch.Tensor, g: Optional[torch.Tensor] = None) -> torch.Tensor:
+        if g is None and self.learn_influence:
+            g = torch.nn.functional.normalize(self.G, p=1., dim=1)
+            #g = torch.softmax(self.G, dim=1)
+        elif g is None:
+            g = self.G
+        w = self.weight[self.node_type_index]
+        output = self.mm(input, w.transpose(-2, -1))
+        if self.bias is not None:
+            bias = self.bias[self.node_type_index]
+            output += bias
+        output = g.matmul(output)
+
+        return output
+
+if __name__ == '__main__':
+    gl = GraphLinear(10, 10)
+    gl.reset_parameters()