modelV renamed to mediarV

core/models/__init__.py

@@ -2,13 +2,13 @@ from torch import nn
 from typing import Final, Type, Any
 from pydantic import BaseModel
 
-from .model_v import ModelV, ModelVParams
+from .mediar_v import MediarV, MediarVParams
 
 
 __all__ = [
     "ModelRegistry",
-    "ModelV",
+    "MediarV",
-    "ModelVParams"
+    "MediarVParams"
 ]
 
 
@@ -17,9 +17,9 @@ class ModelRegistry:
     
     # Single dictionary storing both model classes and parameter classes.
     __MODELS: Final[dict[str, dict[str, Type[Any]]]] = {
-        "ModelV": {
+        "MediarV": {
-            "class": ModelV,
+            "class": MediarV,
-            "params": ModelVParams,
+            "params": MediarVParams,
         },
     }
     

core/models/mediar_v.py

@@ -7,10 +7,10 @@ from segmentation_models_pytorch.base.modules import Activation
 
 from pydantic import BaseModel, ConfigDict
 
-__all__ = ["ModelV"]
+__all__ = ["MediarV"]
 
 
-class ModelVParams(BaseModel):
+class MediarVParams(BaseModel):
     model_config = ConfigDict(frozen=True)
     
     encoder_name: str = "mit_b5"                               # Default encoder
@@ -26,19 +26,19 @@ class ModelVParams(BaseModel):
     
     def asdict(self) -> dict[str, Any]:
         """
-        Returns a dictionary of valid parameters for `nn.ModelV`.
+        Returns a dictionary of valid parameters for `nn.MediarV`.
 
         Returns:
-            dict(str, Any): Dictionary of parameters for nn.ModelV.
+            dict(str, Any): Dictionary of parameters for nn.MediarV.
         """
         loss_kwargs = self.model_dump()
         return {k: v for k, v in loss_kwargs.items() if v is not None}  # Remove None values
 
 
-class ModelV(MAnet):
+class MediarV(MAnet):
-    """ModelV model"""
+    """MediarV model"""
 
-    def __init__(self, params: ModelVParams) -> None:
+    def __init__(self, params: MediarVParams) -> None:
         # Initialize the MAnet model with provided parameters
         super().__init__(**params.asdict())