diff --git a/rising/transforms/functional/intensity.py b/rising/transforms/functional/intensity.py
index 8f01063..5db1256 100644
--- a/rising/transforms/functional/intensity.py
+++ b/rising/transforms/functional/intensity.py
@@ -283,12 +283,12 @@ def scale_by_value(data: torch.Tensor, value: float, out: Optional[torch.Tensor]
 def bezier_3rd_order(
     data: torch.Tensor, maxv: float = 1.0, minv: float = 0.0, out: Optional[torch.Tensor] = None
 ) -> torch.Tensor:
-    p0 = torch.zeros((1, 2))
-    p1 = torch.rand((1, 2))
-    p2 = torch.rand((1, 2))
-    p3 = torch.ones((1, 2))
+    p0 = torch.zeros((1, 2)).to(data.device)
+    p1 = torch.rand((1, 2)).to(data.device)
+    p2 = torch.rand((1, 2)).to(data.device)
+    p3 = torch.ones((1, 2)).to(data.device)
 
-    t = torch.linspace(0.0, 1.0, 1000).unsqueeze(1)
+    t = torch.linspace(0.0, 1.0, 1000).unsqueeze(1).to(data.device)
 
     points = (1 - t * t * t) * p0 + 3 * (1 - t) * (1 - t) * t * p1 + 3 * (1 - t) * t * t * p2 + t * t * t * p3
 
@@ -298,7 +298,7 @@ def bezier_3rd_order(
     xvals = points[:, 0]
     yvals = points[:, 1]
 
-    out_flat = Interp1d.apply(xvals, yvals, data.view(-1))
+    out_flat = Interp1d.apply(xvals, yvals, data.contiguous().view(-1))
 
     return out_flat.view(data.shape)
 
diff --git a/rising/transforms/kernel.py b/rising/transforms/kernel.py
index 2a7646e..26c070d 100644
--- a/rising/transforms/kernel.py
+++ b/rising/transforms/kernel.py
@@ -107,8 +107,8 @@ def forward(self, **data) -> dict:
         Returns:
             dict: dict with transformed data
         """
-        # dtype, device = data[self.keys[0]].dtype, data[self.keys[0]].device
-        # self.to(dtype)
+        dtype, device = data[self.keys[0]].dtype, data[self.keys[0]].device
+        self.to(dtype)
         for key, padding_mode in zip(self.keys, self.padding_mode):
             inp_pad = F.pad(data[key], self.padding, mode=padding_mode)
 
diff --git a/rising/transforms/spatial.py b/rising/transforms/spatial.py
index b9b5b17..ff1fa12 100644
--- a/rising/transforms/spatial.py
+++ b/rising/transforms/spatial.py
@@ -73,9 +73,10 @@ def forward(self, **data) -> dict:
             prob = self.prob
 
         seed = torch.random.get_rng_state()
+        dims = self.dims
         for key in self.keys:
             torch.random.set_rng_state(seed)
-            for dim, p in zip(self.dims, prob):
+            for dim, p in zip(dims, prob):
                 if torch.rand(1) < p:
                     data[key] = mirror(data[key], dim)
         return data