Changes to support DINOv2 in HF

hf_hub.py

@@ -21,7 +21,7 @@
 from radio.adaptor_base import RadioOutput
 from radio.adaptor_registry import adaptor_registry
 from radio.adaptor_mlp import get_mlp_info_from_state
-from radio.hf_model import RADIOConfig, RADIOModel
+from radio.hf_model import RADIOConfig, RADIOModel, rename_all_gamma_to_weight_with_proxy
 from test_hf import deterministic_grid_init
 
 
@@ -164,7 +164,7 @@ def main():
 
     feat_norm_sd = get_prefix_state_dict(state_dict, '_feature_normalizer.')
     feature_normalizer_config = None
-    if feat_norm_sd is not None:
+    if feat_norm_sd:
         feature_normalizer_config = {
             "embed_dim": feat_norm_sd['mean'].shape[0]
         }
@@ -219,6 +219,10 @@ def main():
     if inter_feat_norm_sd:
         radio_model.radio_model.inter_feature_normalizer.load_state_dict(inter_feat_norm_sd)
 
+    # Rename "gamma" parameters to "weight"
+    rename_all_gamma_to_weight_with_proxy(radio_model.radio_model)
+    radio_config.rename_gamma_to_weight = True
+
     radio_model.eval().cuda()
 
     # Sample inference with deterministic values.
@@ -240,7 +244,7 @@ def main():
         hf_summary, hf_features = v.summary, v.features
 
         print(
-            f"[{k}] Sample inference on tensor shape {x.shape} returned summary ",
+            f"[{k}] HF inference on tensor shape {x.shape} returned summary ",
             f"with shape={hf_summary.shape} and std={hf_summary.std().item():.3}, ",
             f"features with shape={hf_features.shape} and std={hf_features.std().item():.3}",
         )
@@ -288,6 +292,12 @@ def main():
             torchhub_output[k].features,
         )
 
+        print(
+            f"[{k}] TorchHub inference on tensor shape {x.shape} returned summary ",
+            f"with shape={torchhub_summary.shape} and std={torchhub_summary.std().item():.3}, ",
+            f"features with shape={torchhub_features.shape} and std={torchhub_features.std().item():.3}",
+        )
+
         # Make sure the shapes are the same.
         assert (
             hf_summary.shape == torchhub_summary.shape

radio/common.py

@@ -87,6 +87,13 @@ class RadioResource:
         max_resolution=2048,
         preferred_resolution=Resolution(512, 512),
     ),
+    # RADIO-DINOv2
+    "radio_dinov2-g": RadioResource(
+        None,  # TODO: add URL for DINOv2 student.
+        patch_size=14,
+        max_resolution=2044,
+        preferred_resolution=Resolution(518, 518),
+    ),
 }
 
 DEFAULT_VERSION = "radio_v2.5-h"

radio/hf_model.py

@@ -43,6 +43,33 @@
 from .extra_timm_models import *
 
 
+
+def rename_all_gamma_to_weight_with_proxy(module):
+    """
+    Renames all parameters named 'gamma' in a module (including submodules)
+    to 'weight' and sets up a property so that accesses to 'gamma' still work.
+    """
+    # Recursively iterate through submodules
+    for submodule_name, submodule in module.named_modules():
+        # Get all parameters within the current submodule
+        for param_name, param in list(submodule.named_parameters(recurse=False)):
+            if 'gamma' in param_name:
+                # Generate the new name by replacing 'gamma' with 'weight'
+                new_name = param_name.replace('gamma', 'weight')
+
+                # Remove the old parameter and assign it with the new name
+                delattr(submodule, param_name)
+                setattr(submodule, new_name, nn.Parameter(param.data))
+
+                # Define a property to proxy access to the renamed parameter
+                def make_property(old_name, new_name):
+                    return property(lambda self: getattr(self, new_name),
+                                    lambda self, value: setattr(self, new_name, value))
+
+                # Add the property to the submodule to proxy access to 'gamma'
+                setattr(submodule.__class__, param_name, make_property(param_name, new_name))
+
+
 class RADIOConfig(PretrainedConfig):
     """Pretrained Hugging Face configuration for RADIO models."""
 
@@ -58,6 +85,7 @@ def __init__(
         vitdet_window_size: Optional[int] = None,
         feature_normalizer_config: Optional[dict] = None,
         inter_feature_normalizer_config: Optional[dict] = None,
+        rename_gamma_to_weight: bool = False,
         **kwargs,
     ):
         self.args = args
@@ -79,9 +107,11 @@ def __init__(
         self.vitdet_window_size = vitdet_window_size
         self.feature_normalizer_config = feature_normalizer_config
         self.inter_feature_normalizer_config = inter_feature_normalizer_config
+        self.rename_gamma_to_weight = rename_gamma_to_weight
         super().__init__(**kwargs)
 
 
+
 class RADIOModel(PreTrainedModel):
     """Pretrained Hugging Face model for RADIO.
 
@@ -149,6 +179,9 @@ def __init__(self, config: RADIOConfig):
             inter_feature_normalizer=inter_feature_normalizer,
         )
 
+        if config.rename_gamma_to_weight:
+            rename_all_gamma_to_weight_with_proxy(self.radio_model)
+
     @property
     def adaptors(self) -> nn.ModuleDict:
         return self.radio_model.adaptors