fix: Parameterized norm freezing

src/transformers/models/rt_detr/configuration_rt_detr.py

@@ -55,6 +55,8 @@ class RTDetrConfig(PretrainedConfig):
         use_timm_backbone (`bool`, *optional*, defaults to `False`):
             Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers
             library.
+        freeze_backbone_batch_norm (`bool`, *optional*, defaults to `True`):
+            Whether to freeze the batch normalization layers in the backbone.
         backbone_kwargs (`dict`, *optional*):
             Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
             e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
@@ -190,6 +192,7 @@ def __init__(
         backbone=None,
         use_pretrained_backbone=False,
         use_timm_backbone=False,
+        freeze_backbone_batch_norms=True,
         backbone_kwargs=None,
         # encoder HybridEncoder
         encoder_hidden_dim=256,
@@ -280,6 +283,7 @@ def __init__(
         self.backbone = backbone
         self.use_pretrained_backbone = use_pretrained_backbone
         self.use_timm_backbone = use_timm_backbone
+        self.freeze_backbone_batch_norms = freeze_backbone_batch_norms
         self.backbone_kwargs = backbone_kwargs
         # encoder
         self.encoder_hidden_dim = encoder_hidden_dim

src/transformers/models/rt_detr/modeling_rt_detr.py

@@ -559,9 +559,10 @@ def __init__(self, config):
 
         backbone = load_backbone(config)
 
-        # replace batch norm by frozen batch norm
-        with torch.no_grad():
-            replace_batch_norm(backbone)
+        if config.freeze_backbone_batch_norms:
+            # replace batch norm by frozen batch norm
+            with torch.no_grad():
+                replace_batch_norm(backbone)
         self.model = backbone
         self.intermediate_channel_sizes = self.model.channels