Remove variable_values()

phiml/math/_magic_ops.py

@@ -809,15 +809,6 @@ def value_attributes(obj) -> Tuple[str, ...]:
     raise ValueError(f"{type(obj).__name__} must implement '__value_attrs__()' or be a dataclass to be used with value functions.")
 
 
-def variable_values(obj) -> Tuple[str, ...]:
-    if hasattr(obj, '__variable_attrs__'):
-        values = obj.__value_attrs__()
-        variables = obj.__variable_attrs__()
-        return tuple([a for a in values if a in variables])
-    else:
-        return obj.__value_attrs__()  # this takes care of dataclasses as well
-
-
 def all_attributes(obj, assert_any=False) -> Tuple[str, ...]:
     if hasattr(obj, '__all_attrs__'):
         result = obj.__all_attrs__()

phiml/math/_nd.py

@@ -4,7 +4,7 @@
 
 from . import _ops as math
 from . import extrapolation as extrapolation
-from ._magic_ops import stack, rename_dims, concat, variable_values, tree_map
+from ._magic_ops import stack, rename_dims, concat, tree_map, value_attributes
 from ._ops import choose_backend_t, reshaped_native, reshaped_tensor
 from ._shape import Shape, channel, batch, spatial, DimFilter, parse_dim_order, instance, dual, auto, non_batch
 from ._tensors import Tensor, wrap, tensor, reshaped_numpy
@@ -251,7 +251,7 @@ def l1_loss(x, reduce: DimFilter = math.non_batch) -> Tensor:
     if isinstance(x, Tensor):
         return math.sum_(abs(x), reduce)
     elif isinstance(x, PhiTreeNode):
-        return sum([l1_loss(getattr(x, a), reduce) for a in variable_values(x)])
+        return sum([l1_loss(getattr(x, a), reduce) for a in value_attributes(x)])
     else:
         try:
             backend = math.choose_backend(x)
@@ -283,7 +283,7 @@ def l2_loss(x, reduce: DimFilter = math.non_batch) -> Tensor:
             x = abs(x)
         return math.sum_(x ** 2, reduce) * 0.5
     elif isinstance(x, PhiTreeNode):
-        return sum([l2_loss(getattr(x, a), reduce) for a in variable_values(x)])
+        return sum([l2_loss(getattr(x, a), reduce) for a in value_attributes(x)])
     else:
         try:
             backend = math.choose_backend(x)
@@ -329,7 +329,7 @@ def frequency_loss(x,
         diff_fft = math.sqrt(math.maximum(diff_fft, threshold))
         return l2_loss(diff_fft) if n == 2 else l1_loss(diff_fft)
     elif isinstance(x, PhiTreeNode):
-        losses = [frequency_loss(getattr(x, a), frequency_falloff, threshold, ignore_mean, n) for a in variable_values(x)]
+        losses = [frequency_loss(getattr(x, a), frequency_falloff, threshold, ignore_mean, n) for a in value_attributes(x)]
         return sum(losses)
     else:
         raise ValueError(x)