Revert "[Feature] Add connector components and FitNet"

mmrazor/models/algorithms/distill/configurable/single_teacher_distill.py

@@ -5,7 +5,6 @@
 from mmcv.runner import load_checkpoint
 from mmengine import BaseDataElement
 from mmengine.model import BaseModel
-from torch import nn
 from torch.nn.modules.batchnorm import _BatchNorm
 
 from mmrazor.models.utils import add_prefix
@@ -19,7 +18,6 @@ class SingleTeacherDistill(BaseAlgorithm):
     only use one teacher.
 
     Args:
-        distiller (dict): The config dict for built distiller.
         teacher (dict | BaseModel): The config dict for teacher model or built
             teacher model.
         teacher_ckpt (str): The path of teacher's checkpoint. Defaults to None.
@@ -28,10 +26,6 @@ class SingleTeacherDistill(BaseAlgorithm):
         teacher_norm_eval (bool): Whether to set teacher's norm layers to eval
             mode, namely, freeze running stats (mean and var). Note: Effect on
             Batch Norm and its variants only. Defaults to True.
-        student_trainable (bool): Whether the student is trainable. Defaults
-            to True.
-        calculate_student_loss (bool): Whether to calculate student loss
-            (original task loss) to update student model. Defaults to True.
     """
 
     def __init__(self,
@@ -40,9 +34,7 @@ def __init__(self,
                  teacher_ckpt: Optional[str] = None,
                  teacher_trainable: bool = False,
                  teacher_norm_eval: bool = True,
-                 student_trainable: bool = True,
-                 calculate_student_loss: bool = True,
-                 **kwargs) -> None:
+                 **kwargs):
         super().__init__(**kwargs)
 
         self.distiller = MODELS.build(distiller)
@@ -63,21 +55,13 @@ def __init__(self,
         self.teacher_trainable = teacher_trainable
         self.teacher_norm_eval = teacher_norm_eval
 
-        # The student model will not calculate gradients and update parameters
-        # in some pretraining process.
-        self.student_trainable = student_trainable
-
-        # The student loss will not be updated into ``losses`` in some
-        # pretraining process.
-        self.calculate_student_loss = calculate_student_loss
-
         # In ``ConfigurableDistller``, the recorder manager is just
         # constructed, but not really initialized yet.
         self.distiller.prepare_from_student(self.student)
         self.distiller.prepare_from_teacher(self.teacher)
 
     @property
-    def student(self) -> nn.Module:
+    def student(self):
         """Alias for ``architecture``."""
         return self.architecture
 
@@ -102,25 +86,16 @@ def loss(
         else:
             with self.distiller.teacher_recorders, self.distiller.deliveries:
                 with torch.no_grad():
+
                     _ = self.teacher(batch_inputs, data_samples, mode='loss')
 
         # If the `override_data` of a delivery is True, the delivery will
         # override the origin data with the recorded data.
         self.distiller.set_deliveries_override(True)
-        # Original task loss will not be used during some pretraining process.
-        if self.calculate_student_loss:
-            with self.distiller.student_recorders, self.distiller.deliveries:
-                student_losses = self.student(
-                    batch_inputs, data_samples, mode='loss')
-            losses.update(add_prefix(student_losses, 'student'))
-        else:
-            with self.distiller.student_recorders, self.distiller.deliveries:
-                if self.student_trainable:
-                    _ = self.student(batch_inputs, data_samples, mode='loss')
-                else:
-                    with torch.no_grad():
-                        _ = self.student(
-                            batch_inputs, data_samples, mode='loss')
+        with self.distiller.student_recorders, self.distiller.deliveries:
+            student_losses = self.student(
+                batch_inputs, data_samples, mode='loss')
+        losses.update(add_prefix(student_losses, 'student'))
 
         # Automatically compute distill losses based on `loss_forward_mappings`
         # The required data already exists in the recorders.
@@ -129,7 +104,7 @@ def loss(
 
         return losses
 
-    def train(self, mode: bool = True) -> None:
+    def train(self, mode=True):
         """Set distiller's forward mode."""
         super().train(mode)
         if mode and self.teacher_norm_eval:

mmrazor/models/architectures/__init__.py

@@ -1,5 +1,4 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .backbones import *  # noqa: F401,F403
-from .connectors import *  # noqa: F401,F403
 from .dynamic_op import *  # noqa: F401,F403
 from .heads import *  # noqa: F401,F403