Updates docstrings

Project-MONAI · Jul 19, 2023 · 89c4e83 · 89c4e83
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diff --git a/docs/source/losses.rst b/docs/source/losses.rst
@@ -99,6 +99,21 @@ Reconstruction Losses
 .. autoclass:: monai.losses.ssim_loss.SSIMLoss
  :members:
 
+`PatchAdversarialLoss`
+~~~~~~~~~~~~~~~~~~~~~~
+.. autoclass:: PatchAdversarialLoss
+ :members:
+
+`PerceptualLoss`
+~~~~~~~~~~~~~~~~~
+.. autoclass:: PerceptualLoss
+ :members:
+
+`JukeboxLoss`
+~~~~~~~~~~~~~~
+.. autoclass:: JukeboxLoss
+ :members:
+
 
 Loss Wrappers
 -------------

diff --git a/monai/losses/adversarial_loss.py b/monai/losses/adversarial_loss.py
@@ -34,14 +34,16 @@ class PatchAdversarialLoss(_Loss):
  mustn't be passed to a final activation layer. That is taken care of internally within the loss.
 
  Args:
- reduction: {``"none"``, ``"mean"``, ``"sum"``} Specifies the reduction to apply to the output.
- Defaults to ``"mean"``.
- - ``"none"``: no reduction will be applied.
- - ``"mean"``: the sum of the output will be divided by the number of elements in the output.
- - ``"sum"``: the output will be summed.
+ reduction: {``"none"``, ``"mean"``, ``"sum"``}
+ Specifies the reduction to apply to the output. Defaults to ``"mean"``.
+
+ - ``"none"``: no reduction will be applied.
+ - ``"mean"``: the sum of the output will be divided by the number of elements in the output.
+ - ``"sum"``: the output will be summed.
+
  criterion: which criterion (hinge, least_squares or bce) you want to use on the discriminators outputs.
- Depending on the criterion, a different activation layer will be used. Make sure you don't run the outputs
- through an activation layer prior to calling the loss.
+  Depending on the criterion, a different activation layer will be used. Make sure you don't run the outputs
+  through an activation layer prior to calling the loss.
  no_activation_leastsq: if True, the activation layer in the case of least-squares is removed.
  """
 
@@ -112,14 +114,14 @@ def forward(
  """
 
  Args:
- input: output of Multi-Scale Patch Discriminator or Patch Discriminator; being a list of
- tensors or a tensor; they shouldn't have gone through an activation layer.
+ input: output of Multi-Scale Patch Discriminator or Patch Discriminator; being a list of tensors
+   or a tensor; they shouldn't have gone through an activation layer.
  target_is_real: whereas the input corresponds to discriminator output for real or fake images
  for_discriminator: whereas this is being calculated for discriminator or generator loss. In the last
- case, target_is_real is set to True, as the generator wants the input to be dimmed as real.
+  case, target_is_real is set to True, as the generator wants the input to be dimmed as real.
  Returns: if reduction is None, returns a list with the loss tensors of each discriminator if multi-scale
- discriminator is active, or the loss tensor if there is just one discriminator. Otherwise, it returns the
- summed or mean loss over the tensor and discriminator/s.
+  discriminator is active, or the loss tensor if there is just one discriminator. Otherwise, it returns the
+  summed or mean loss over the tensor and discriminator/s.
 
  """
 

diff --git a/monai/losses/spectral_loss.py b/monai/losses/spectral_loss.py
@@ -24,7 +24,7 @@ class JukeboxLoss(_Loss):
  Calculate spectral component based on the magnitude of Fast Fourier Transform (FFT).
 
  Based on:
- Dhariwal, et al. 'Jukebox: A generative model for music.'https://arxiv.org/abs/2005.00341
+ Dhariwal, et al. 'Jukebox: A generative model for music.' https://arxiv.org/abs/2005.00341
 
  Args:
  spatial_dims: number of spatial dimensions.