Avoid referencing non-existing dims

phiml/math/_deprecated.py

@@ -55,16 +55,16 @@ def cross_product(vec1: Tensor, vec2: Tensor) -> Tensor:
     vec2 = tensor(vec2)
     spatial_rank = vec1.vector.size if 'vector' in vec1.shape else vec2.vector.size
     if spatial_rank == 2:  # Curl in 2D
-        assert vec2.vector.exists
-        if vec1.vector.exists:
+        assert 'vector' in vec2.shape
+        if 'vector' in vec1.shape:
             v1_x, v1_y = vec1.vector
             v2_x, v2_y = vec2.vector
             return v1_x * v2_y - v1_y * v2_x
         else:
             v2_x, v2_y = vec2.vector
             return vec1 * stack_tensors([-v2_y, v2_x], channel(vec2))
     elif spatial_rank == 3:  # Curl in 3D
-        assert vec1.vector.exists and vec2.vector.exists, f"Both vectors must have a 'vector' dimension but got shapes {vec1.shape}, {vec2.shape}"
+        assert 'vector' in vec1.shape and 'vector' in vec2.shape, f"Both vectors must have a 'vector' dimension but got shapes {vec1.shape}, {vec2.shape}"
         v1_x, v1_y, v1_z = vec1.vector
         v2_x, v2_y, v2_z = vec2.vector
         return stack_tensors([

phiml/math/_nd.py

@@ -662,7 +662,7 @@ def spatial_gradient(grid: Tensor,
         grid = grid[{stack_dim.name: 0}]
     dims = grid.shape.only(dims)
     dx = wrap(dx)
-    if dx.vector.exists:
+    if 'vector' in dx.shape:
         dx = dx.vector[dims]
         if dx.vector.size in (None, 1):
             dx = dx.vector[0]
@@ -706,7 +706,7 @@ def laplace(x: Tensor,
     """
     if isinstance(dx, (tuple, list)):
         dx = wrap(dx, batch('_laplace'))
-    elif isinstance(dx, Tensor) and dx.vector.exists:
+    elif isinstance(dx, Tensor) and 'vector' in dx.shape:
         dx = rename_dims(dx, 'vector', batch('_laplace'))
     if isinstance(x, Extrapolation):
         return x.spatial_gradient()