Fix AdafruitLCD input handling.

CHANGELOG.rst

@@ -29,7 +29,7 @@ Changed
 Bugfix
 ~~~~~
 
-- Nothing
+- ``lensless.hardware.trainable_mask.AdafruitLCD`` input handling.
 
 1.0.6 - (2024-02-21)
 --------------------

lensless/hardware/trainable_mask.py

@@ -116,7 +116,6 @@ def project(self):
 
 
 class AdafruitLCD(TrainableMask):
-    # class AdafruitLCD(torch.nn.Module, TrainableMask):
     def __init__(
         self,
         initial_vals,
@@ -126,7 +125,7 @@ def __init__(
         color_filter=None,
         rotate=None,
         flipud=False,
-        use_waveprop=None,
+        use_waveprop=False,
         vertical_shift=None,
         horizontal_shift=None,
         scene2mask=None,
@@ -145,9 +144,23 @@ def __init__(
         slm_param : :py:class:`~lensless.hardware.slm.SLMParam`
             SLM parameters.
         rotate : float, optional
-            Rotation angle in degrees, by default None
+            Rotation angle in degrees, by default None.
         flipud : bool, optional
-            Whether to flip the mask vertically, by default False
+            Whether to flip the mask vertically, by default False.
+        use_waveprop : bool, optional
+            Whether to use wave propagation for simulating PSF. If False, PSF will simply be intensity of mask pattern, by default False.
+        vertical_shift : int, optional
+            Vertical shift of the mask, by default None.
+        horizontal_shift : int, optional
+            Horizontal shift of the mask, by default None.
+        scene2mask : :py:class:`~torch.Tensor`, optional
+            Distance from scene to mask. Used for wave propagation, by default None.
+        mask2sensor : :py:class:`~torch.Tensor`, optional
+            Distance from mask to sensor. Used for wave propagation, by default None.
+        downsample : int, optional
+            Downsample factor, by default None.
+        min_val : float, optional
+            Minimum value for mask weights, by default 0.
         """
 
         super().__init__(**kwargs)
@@ -158,6 +171,7 @@ def __init__(
         else:
             self._vals = initial_vals
 
+        initial_param = None
         if color_filter is not None:
             self._color_filter = torch.nn.Parameter(color_filter)
             if train_mask_vals:
@@ -168,6 +182,9 @@ def __init__(
             assert (
                 train_mask_vals
             ), "If color filter is not trainable, mask values must be trainable"
+            initial_param = [self._vals]
+            self._color_filter = None
+        assert initial_param is not None, "Initial parameters must be set"
 
         # set optimizer
         self._set_optimizer(initial_param)