fix(ClassifHead): simplify the net validation

torchTextClassifiers/model/components/classification_head.py

@@ -24,6 +24,8 @@ def __init__(
         """
         super().__init__()
         if net is not None:
+            self.net = net
+
             # --- Custom net should either be a Sequential or a Linear ---
             if not (isinstance(net, nn.Sequential) or isinstance(net, nn.Linear)):
                 raise ValueError("net must be an nn.Sequential when provided.")
@@ -43,7 +45,6 @@ def __init__(
                 # --- Extract features ---
                 self.input_dim = first.in_features
                 self.num_classes = last.out_features
-                self.net = net
             else:  # if not Sequential, it is a Linear
                 self.input_dim = net.in_features
                 self.num_classes = net.out_features
@@ -53,23 +54,8 @@ def __init__(
                 input_dim is not None and num_classes is not None
             ), "Either net or both input_dim and num_classes must be provided."
             self.net = nn.Linear(input_dim, num_classes)
-            self.input_dim, self.num_classes = self._get_linear_input_output_dims(self.net)
+            self.input_dim = input_dim
+            self.num_classes = num_classes
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         return self.net(x)
-
-    @staticmethod
-    def _get_linear_input_output_dims(module: nn.Module):
-        """
-        Returns (input_dim, output_dim) for any module containing Linear layers.
-        Works for Linear, Sequential, or nested models.
-        """
-        # Collect all Linear layers recursively
-        linears = [m for m in module.modules() if isinstance(m, nn.Linear)]
-
-        if not linears:
-            raise ValueError("No Linear layers found in the given module.")
-
-        input_dim = linears[0].in_features
-        output_dim = linears[-1].out_features
-        return input_dim, output_dim