fix(ClassifHead): enable Sequential as custom net

Author: meilame-tayebjee

torchTextClassifiers/model/components/classification_head.py

@@ -11,11 +11,43 @@ def __init__(
         num_classes: Optional[int] = None,
         net: Optional[nn.Module] = None,
     ):
+        """
+        Classification head for text classification tasks.
+        It is a nn.Module that can either be a simple Linear layer or a custom neural network module.
+
+        Args:
+            input_dim (int, optional): Dimension of the input features. Required if net is not provided.
+            num_classes (int, optional): Number of output classes. Required if net is not provided.
+            net (nn.Module, optional): Custom neural network module to be used as the classification head.
+                If provided, input_dim and num_classes are inferred from this module.
+                Should be either an nn.Sequential with first and last layers being Linears or nn.Linear.
+        """
         super().__init__()
         if net is not None:
-            self.net = net
-            self.input_dim = net.in_features
-            self.num_classes = net.out_features
+            # --- 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.")
+
+            # --- If Sequential, Check first and last layers are Linear ---
+
+            if isinstance(net, nn.Sequential):
+                first = net[0]
+                last = net[-1]
+
+                if not isinstance(first, nn.Linear):
+                    raise TypeError(f"First layer must be nn.Linear, got {type(first).__name__}.")
+
+                if not isinstance(last, nn.Linear):
+                    raise TypeError(f"Last layer must be nn.Linear, got {type(last).__name__}.")
+
+                # --- 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
+
         else:
             assert (
                 input_dim is not None and num_classes is not None