Revert "u"

deeptrack/optical/optics.py

@@ -677,17 +677,10 @@ def get_voxel_size(
             props = self._normalize(
                 resolution=resolution, magnification=magnification
             )
-            res = props["resolution"]
-            mag = props["magnification"]
-            if TORCH_AVAILABLE and (
-                torch.is_tensor(res) or torch.is_tensor(mag)
-            ):
-                res = res if torch.is_tensor(res) else torch.tensor(res, dtype=torch.float64)
-                mag = mag if torch.is_tensor(mag) else torch.tensor(mag, dtype=torch.float64)
-                v = res / mag
-                return torch.stack([v, v, v])
             return (
-                xp.ones((3,), dtype=xp.float64) * res / mag
+                xp.ones((3,), dtype=xp.float64)
+                * props["resolution"]
+                / props["magnification"]
             )
 
         def get_pixel_size(

deeptrack/optical/scatterers.py

@@ -225,15 +225,6 @@ def _asarray_vector(value, dtype=None):
     if isinstance(value, (list, tuple)) and any(
         apc.is_array_api_obj(element) for element in value
     ):
-        if TORCH_AVAILABLE and any(torch.is_tensor(e) for e in value):
-            elements = []
-            for e in value:
-                if not torch.is_tensor(e):
-                    e = torch.tensor(e, dtype=dtype)
-                elif dtype is not None:
-                    e = e.to(dtype=dtype)
-                elements.append(e.reshape(()))
-            return torch.stack(elements)
         return xp.stack(
             [xp.reshape(_asarray(element, dtype), ()) for element in value]
         )

tests/test_scatterers.py

@@ -699,7 +699,12 @@ def test_mie_sphere_brightfield_sums_multiple_torch_fields(self):
             return_field=True,
         )
 
+        # image = microscope(sample).resolve()
         image = microscope(sample).resolve()
+        print(radius_1.requires_grad)  # True — set by us
+        print(image.requires_grad)     # Is the graph connected?
+        loss = torch.abs(image).sum()
+        loss.backward()
 
         self.assertIsInstance(image, torch.Tensor)
         self.assertEqual(image.shape, (32, 32, 1))
@@ -774,7 +779,11 @@ def test_mie_sphere_brightfield_autodiff_learnable_parameters(self):
 
                 with warnings.catch_warnings(record=True) as caught:
                     warnings.simplefilter("always")
+                    print(parameter.requires_grad)  # True here
                     image = microscope(sample).resolve()
+                    print(parameter.requires_grad)  # Still True, but now detached from computation graph
+
+                    # image = microscope(sample).resolve()
 
                 tensor_warning = (
                     "Converting a tensor with requires_grad=True to a scalar"