added student adapter

src/super_gradients/training/models/kd_modules/kd_module.py

@@ -24,9 +24,10 @@ class implementing Knowledge Distillation logic as an SgModule
         run_teacher_on_eval: bool- whether to run self.teacher at eval mode regardless of self.train(mode)
         arch_params: HpmStruct- Architecture H.P.
 
-        Additionally, by passing teacher_input_adapter (torch.nn.Module) one can modify the teacher net s.t
-        teacher = torch.nn.Sequential(teacher_input_adapter, teacher). This is useful when teacher net expects a
-        different input format from the student (for example different normalization).
+            Additionally, by passing teacher_input_adapter (torch.nn.Module) one can modify the teacher net to act as if
+            teacher = torch.nn.Sequential(teacher_input_adapter, teacher). This is useful when teacher net expects a
+            different input format from the student (for example different normalization).
+            Equivalent arg for the student model, can be passed through student_input_adapter.
 
     """
 
@@ -36,6 +37,7 @@ def __init__(self, arch_params: HpmStruct, student: SgModule, teacher: torch.nn.
         self.student = student
         self.teacher = teacher
         self.teacher_input_adapter = get_param(self.arch_params, "teacher_input_adapter")
+        self.student_input_adapter = get_param(self.arch_params, "student_input_adapter")
         self.run_teacher_on_eval = run_teacher_on_eval
         self._freeze_teacher()
 
@@ -62,10 +64,17 @@ def eval(self):
         self.teacher.eval()
 
     def forward(self, x):
+        if self.student_input_adapter is not None:
+            student_output = self.student(self.student_input_adapter(x))
+        else:
+            student_output = self.student(x)
+
         if self.teacher_input_adapter is not None:
-            return KDOutput(student_output=self.student(x), teacher_output=self.teacher(self.teacher_input_adapter(x)))
+            teacher_output = self.teacher(self.teacher_input_adapter(x))
         else:
-            return KDOutput(student_output=self.student(x), teacher_output=self.teacher(x))
+            teacher_output = self.teacher(x)
+
+        return KDOutput(student_output=student_output, teacher_output=teacher_output)
 
     def initialize_param_groups(self, lr: float, training_params: HpmStruct) -> list:
         return self.student.initialize_param_groups(lr, training_params)