CDL: some requested changes. Remove redundant variable, remove unused…

… dtype arg, rename set_device, etc

pose_evaluation/metrics/embedding_distance_metric.py

@@ -30,13 +30,11 @@ def __init__(
         self,
         kind: ValidDistanceKinds = "cosine",
         device: Union[torch.device, str] = None,
-        dtype=torch.float32,
+        dtype=None,
     ):
         """
-        Initialize the embedding distance metric.
-
         Args:
-            kind (ValidDistanceKinds): The type of distance metric.
+            kind (ValidDistanceKinds): The type of distance metric, e.g. "cosine", or "euclidean".
             device (Union[torch.device, str]): The device to use for computation.
                 If None, automatically detects.
             dtype (torch.dtype): The data type to use for tensors.
@@ -52,6 +50,8 @@ def __init__(
         if dtype is None:
             dtype = torch.get_default_dtype()
 
+        self.dtype = dtype
+
         # Dispatch table for metric computations
         self._metric_dispatch = {
             "cosine": self.cosine_distances,
@@ -60,9 +60,7 @@ def __init__(
             "manhattan": self.manhattan_distances,
         }
 
-        self.dtype = dtype
-
-    def set_device(self, device: Union[torch.device, str]) -> None:
+    def to(self, device: Union[torch.device, str]) -> None:
         """
         Explicitly set the device used for tensors.
 
@@ -72,35 +70,16 @@ def set_device(self, device: Union[torch.device, str]) -> None:
         self.device = torch.device(device)
         logger.info(f"Device set to: {self.device}")
 
-    def _to_tensor_on_device(self, data: TensorConvertableType, dtype=None) -> Tensor:
-        """
-        Convert input data to a tensor on the specified device.
-
-        Args:
-            data (TensorConvertableType: The input data to convert.
-            dtype (torch.dtype): The data type for the tensor.
-
-        Returns:
-            Tensor: Tensor representation of the data on the specified device.
-        """
-        if dtype is None:
-            dtype = self.dtype
-        return st_util._convert_to_tensor(data).to(device=self.device, dtype=dtype)
-
-    def _to_batch_tensor_on_device(self, data: TensorConvertableType, dtype=None) -> Tensor:
+    def _to_batch_tensor_on_device(self, data: TensorConvertableType) -> Tensor:
         """
         Convert input data to a batch tensor on the specified device.
 
         Args:
             data (TensorConvertableType): The input data to convert.
-            dtype (torch.dtype): The data type for the tensor.
 
         Returns:
             Tensor: Batch tensor representation of the data on the specified device.
         """
-        if dtype is None:
-            dtype = self.dtype
-
         # better performance this way, see https://github.com/pytorch/pytorch/issues/13918
         if isinstance(data, list) and all(isinstance(x, np.ndarray) for x in data):
             data = np.asanyarray(data)
@@ -110,7 +89,7 @@ def _to_batch_tensor_on_device(self, data: TensorConvertableType, dtype=None) ->
             # https://stackoverflow.com/questions/55050717/converting-list-of-tensors-to-tensors-pytorch
             data = torch.stack(data)
 
-        return st_util._convert_to_batch_tensor(data).to(device=self.device, dtype=dtype)
+        return st_util._convert_to_batch_tensor(data).to(device=self.device)
 
     def score(
         self,
@@ -128,7 +107,6 @@ def score(
             Number: The calculated distance.
 
         """
-
         return self.score_all(hypothesis, reference).item()
 
     def score_all(
@@ -163,12 +141,7 @@ def score_all(
         except RuntimeError as e:
             raise TypeError(f"Inputs must support conversion to device tensors: {e}") from e
 
-        if self.kind not in self._metric_dispatch:
-            logger.error(f"Unsupported distance metric: {self.kind}")
-            raise ValueError(f"Unsupported distance metric: {self.kind}")
-
-        distance_matrix = self._metric_dispatch[self.kind](hypotheses, references)
-        return distance_matrix
+        return self._metric_dispatch[self.kind](hypotheses, references)
 
     def dot_product(self, hypotheses: TensorConvertableType, references: TensorConvertableType) -> Tensor:
         """