Refactor amplitude_mse function to include normalization option

The amplitude_mse function in losses.py has been modified to include a new optional parameter, normalization. When set to True, the loss is normalized by the sum of the simulated intensities. This change improves the flexibility and usability of the function.

Add total_variation_loss for images

src/cdtools/tools/losses/losses.py

@@ -11,7 +11,7 @@ import torch as t
 __all__ = ['amplitude_mse', 'intensity_mse', 'poisson_nll']
 
 
-def amplitude_mse(intensities, sim_intensities, mask=None):
+def amplitude_mse(intensities, sim_intensities, mask=None, normalization=None):
     """ Returns the mean squared error of a simulated dataset's amplitudes
 
     Calculates the mean squared error between a given set of 
@@ -40,6 +40,8 @@ def amplitude_mse(intensities, sim_intensities, mask=None):
         A tensor of simulated detector intensities
     mask : torch.Tensor
         A mask with ones for pixels to include and zeros for pixels to exclude
+    normalization : bool
+        If True, the loss is normalized by the sum of the simulated intensities
 
     Returns
     -------
@@ -51,14 +53,23 @@ def amplitude_mse(intensities, sim_intensities, mask=None):
     # amplitudes instead of the intensities, but I want to be consistent
     # with all the errors working off of the same inputs
 
-    if mask is None:
-        return t.sum((t.sqrt(sim_intensities) -
-                      t.sqrt(intensities))**2)
-    else:
-        masked_intensities = intensities.masked_select(mask)
-        return t.sum((t.sqrt(sim_intensities.masked_select(mask)) -
-                      t.sqrt(masked_intensities))**2)
 
+    if mask is None:
+        if normalization is None:
+            return t.sum(t.square(t.sqrt(sim_intensities) - t.sqrt(intensities)))
+        else:
+            return t.sum(t.square(t.sqrt(sim_intensities) -
+                      t.sqrt(intensities)))/t.sum(intensities)
+    else:
+        if normalization is None:
+            masked_intensities = intensities.masked_select(mask)
+            return t.sum(t.square(t.sqrt(sim_intensities.masked_select(mask)) -
+                      t.sqrt(masked_intensities)))
+        else:
+            masked_intensities = intensities.masked_select(mask)
+            masked_sim_intensities = sim_intensities.masked_select(mask)
+            return t.sum(t.square(t.sqrt(masked_sim_intensities) -
+                      t.sqrt(masked_intensities)))/t.sum(masked_intensities)
 
     
 def intensity_mse(intensities, sim_intensities, mask=None):
@@ -249,3 +260,32 @@ def poisson_plus_fixed_nll(
                     / masked_intensities.shape[0]
 
         return nll
+    
+
+def total_variation_loss(image: t.Tensor) -> t.Tensor:
+    """ Returns the Total Variation (TV) loss of an image
+
+    Calculates the Total Variation loss for a given image. This function
+    computes the TV loss by summing the absolute differences between
+    neighboring pixels in the image.
+
+    It can accept image tensors of any shape.
+
+    Parameters
+    ----------
+    image : torch.Tensor
+        A tensor representing the image.
+
+    Returns
+    -------
+    tv_loss : torch.Tensor
+        A single value for the Total Variation loss.
+    """
+
+    # Calculate differences between neighboring pixels
+    loss_h = t.mean(t.abs(image[:-1, :] - image[1:, :]))
+    loss_w = t.mean(t.abs(image[:, :-1] - image[:, 1:]))
+
+    tv_loss = loss_h + loss_w
+
+    return tv_loss