Deleted update_params (#2267)

albumentations/augmentations/transforms.py

@@ -2199,10 +2199,6 @@ class GaussNoise(ImageOnlyTransform):
         mean_range (tuple[float, float]): Range for noise mean as a fraction
             of the maximum value (255 for uint8 images or 1.0 for float images).
             Values should be in range [-1, 1]. Default: (0.0, 0.0).
-        var_limit (tuple[float, float] | float): [Deprecated] Variance range for noise.
-            If var_limit is a single float value, the range will be (0, var_limit).
-            Default: (10.0, 50.0).
-        mean (float): [Deprecated] Mean of the noise. Default: 0.
         per_channel (bool): If True, noise will be sampled for each channel independently.
             Otherwise, the noise will be sampled once for all channels. Default: True.
         noise_scale_factor (float): Scaling factor for noise generation. Value should be in the range (0, 1].

albumentations/core/transforms_interface.py

@@ -131,7 +131,7 @@ def __call__(self, *args: Any, force_apply: bool = False, **kwargs: Any) -> Any:
 
         if self.should_apply(force_apply=force_apply):
             params = self.get_params()
-            params = self.update_params_shape(params=params, data=kwargs)
+            params = self.update_transform_params(params=params, data=kwargs)
 
             if self.targets_as_params:  # check if all required targets are in kwargs.
                 missing_keys = set(self.targets_as_params).difference(kwargs.keys())
@@ -173,7 +173,6 @@ def should_apply(self, force_apply: bool = False) -> bool:
 
     def apply_with_params(self, params: dict[str, Any], *args: Any, **kwargs: Any) -> dict[str, Any]:
         """Apply transforms with parameters."""
-        params = self.update_params(params, **kwargs)  # remove after move parameters like interpolation
         res = {}
         for key, arg in kwargs.items():
             if key in self._key2func and arg is not None:
@@ -248,8 +247,16 @@ def get_params(self) -> dict[str, Any]:
         """Returns parameters independent of input."""
         return {}
 
-    def update_params_shape(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, Any]:
-        """Updates parameters with input shape."""
+    def update_transform_params(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, Any]:
+        """Updates parameters with input shape and transform-specific params.
+
+        Args:
+            params: Parameters to be updated
+            data: Input data dictionary containing images/volumes
+
+        Returns:
+            Updated parameters dictionary with shape and transform-specific params
+        """
         # Extract shape from volume, volumes, image, or images
         if "volume" in data:
             shape = data["volume"][0].shape  # Take first slice of volume
@@ -262,6 +269,15 @@ def update_params_shape(self, params: dict[str, Any], data: dict[str, Any]) -> d
 
         # For volumes/images, shape will be either (H, W) or (H, W, C)
         params["shape"] = shape
+
+        # Add transform-specific params
+        if hasattr(self, "interpolation"):
+            params["interpolation"] = self.interpolation
+        if hasattr(self, "fill"):
+            params["fill"] = self.fill
+        if hasattr(self, "fill_mask"):
+            params["fill_mask"] = self.fill_mask
+
         return params
 
     def get_params_dependent_on_data(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, Any]:
@@ -293,22 +309,6 @@ def available_keys(self) -> set[str]:
         """Returns set of available keys."""
         return self._available_keys
 
-    def update_params(self, params: dict[str, Any], **kwargs: Any) -> dict[str, Any]:
-        """Update parameters with transform specific params.
-        This method is deprecated, use:
-        - `get_params` for transform specific params like interpolation and
-        - `update_params_shape` for data like shape.
-        """
-        if hasattr(self, "interpolation"):
-            params["interpolation"] = self.interpolation
-        if hasattr(self, "fill"):
-            params["fill"] = self.fill
-        if hasattr(self, "fill_mask"):
-            params["fill_mask"] = self.fill_mask
-
-        # Use update_params_shape to get shape consistently
-        return self.update_params_shape(params, kwargs)
-
     def add_targets(self, additional_targets: dict[str, str]) -> None:
         """Add targets to transform them the same way as one of existing targets.
         ex: {'target_image': 'image'}

tests/test_transforms.py

@@ -770,7 +770,7 @@ def test_affine_scale_ratio(params):
 
     data = {"image": image}
     call_params = aug.get_params()
-    call_params = aug.update_params_shape(call_params, data)
+    call_params = aug.update_transform_params(call_params, data)
 
     apply_params = aug.get_params_dependent_on_data(params=call_params, data=data)