diff --git a/src/cdtools/models/fancy_ptycho.py b/src/cdtools/models/fancy_ptycho.py
index 0b3d499..3a580e6 100644
--- a/src/cdtools/models/fancy_ptycho.py
+++ b/src/cdtools/models/fancy_ptycho.py
@@ -459,7 +459,8 @@ def from_dataset(cls,
         if hasattr(dataset, 'intensities') and dataset.intensities is not None:
             intensities = dataset.intensities.to(dtype=Ws.dtype)[:,...]
             weights = t.sqrt(intensities)
-            Ws *= (weights / t.mean(weights))
+            Ws *= (weights / t.mean(weights)).reshape(
+                (len(weights),) + (1,)*(Ws.ndim - 1))
 
         if hasattr(dataset, 'mask') and dataset.mask is not None:
             mask = dataset.mask.to(t.bool)
diff --git a/tests/models/test_fancy_ptycho.py b/tests/models/test_fancy_ptycho.py
index db05a75..c398be0 100644
--- a/tests/models/test_fancy_ptycho.py
+++ b/tests/models/test_fancy_ptycho.py
@@ -45,6 +45,28 @@ def test_center_probe(lab_ptycho_cxi):
         rtol=1e-3
     )
 
+def test_lab_ptycho_data_loading(lab_ptycho_cxi):
+    
+    print('\nTesting a few unusual data loading scenarios.')
+    dataset = cdtools.datasets.Ptycho2DDataset.from_cxi(lab_ptycho_cxi)
+    
+    # Test that it will properly load an initialization for the weights
+    # from the intensities with OPRP on
+    dataset.intensities = t.rand(len(dataset))
+    
+    model = cdtools.models.FancyPtycho.from_dataset(
+        dataset,
+        n_modes=4,
+        dm_rank=1,
+    )
+
+    # And test the case without OPRP    
+    model = cdtools.models.FancyPtycho.from_dataset(
+        dataset,
+        n_modes=2,
+    )
+
+    
 
 @pytest.mark.slow
 def test_lab_ptycho(lab_ptycho_cxi, reconstruction_device, show_plot):
@@ -71,8 +93,8 @@ def test_lab_ptycho(lab_ptycho_cxi, reconstruction_device, show_plot):
 
     print('Running reconstruction on provided reconstruction_device,',
           reconstruction_device)
-    model.to(device=reconstruction_device)
-    dataset.get_as(device=reconstruction_device)
+    #model.to(device=reconstruction_device)
+    #dataset.get_as(device=reconstruction_device)
 
     for loss in model.Adam_optimize(50, dataset, lr=0.02, batch_size=10):
         print(model.report())