Un-fix double conditioning. It wasn't a problem before. (#7)

- Un-fix double conditioning. It wasn't a problem before. (ad55e7d211daf46175749326844418f84524d743)

config.json

@@ -347,7 +347,6 @@
     "AutoConfig": "hf_model.RADIOConfig",
     "AutoModel": "hf_model.RADIOModel"
   },
-  "external_conditioner": false,
   "max_resolution": 2048,
   "patch_size": 16,
   "preferred_resolution": [

hf_model.py

@@ -45,7 +45,6 @@ class RADIOConfig(PretrainedConfig):
         preferred_resolution: Optional[Resolution] = None,
         adaptor_names: Union[str, List[str]] = None,
         vitdet_window_size: Optional[int] = None,
-        external_conditioner: Optional[bool] = False,
         **kwargs,
     ):
         self.args = args
@@ -64,7 +63,6 @@ class RADIOConfig(PretrainedConfig):
         )
         self.adaptor_names = adaptor_names
         self.vitdet_window_size = vitdet_window_size
-        self.external_conditioner = external_conditioner
         super().__init__(**kwargs)
 
 
@@ -117,7 +115,6 @@ class RADIOModel(PreTrainedModel):
             preferred_resolution=config.preferred_resolution,
             adaptors=adaptors,
         )
-        self.radio_model._external_conditioner = config.external_conditioner
 
     @property
     def adaptors(self) -> nn.ModuleDict:

radio_model.py

@@ -51,12 +51,6 @@ class RADIOModel(nn.Module):
         self._patch_size = patch_size
         self._max_resolution = max_resolution
         self._window_size = window_size
-        # This is a hack workaround for huggingface, since their
-        # data prep is annoying and complicated. If set to true,
-        # then will not call `self.input_conditioner` on the
-        # input tensor. This will be set in `hf_model.RADIOModel`
-        # where appropriate.
-        self._external_conditioner = False
 
         adaptors = adaptors or dict()
         self.adaptors = nn.ModuleDict(adaptors)
@@ -119,8 +113,7 @@ class RADIOModel(nn.Module):
                              '`self.get_nearest_supported_resolution(<height>, <width>) is provided as a convenience API. '
                              f'Input: {x.shape[-2:]}, Nearest: {self.get_nearest_supported_resolution(*x.shape[-2:])}')
 
-        if not self._external_conditioner:
-            x = self.input_conditioner(x)
+        x = self.input_conditioner(x)
         y = self.model.forward_features(x)
 
         if isinstance(self.model, VisionTransformer):