docs: improve cross-referencing for literals

src/aidsorb/_torch_utils.py

@@ -26,7 +26,7 @@ def get_activation(config=None):
     Parameters
     ----------
     config : dict, default=None
-        Dictionary for configuring activation function. If ``None``, the
+        Dictionary for configuring activation function. If :obj:`None`, the
         :class:`~torch.nn.modules.activation.ReLU` activation is used.
 
         * ``'name'`` activation's class name :class:`str`
@@ -109,7 +109,7 @@ def get_optimizers(params, config_optim=None, config_lrs=None):
     params : iterable
         Iterable containing the parameters to be optimized.
     config_optim : dict, default=None
-        Dictionary for configuring optimizer. If ``None``, the
+        Dictionary for configuring optimizer. If :obj:`None`, the
         :class:`~torch.optim.Adam` optimizer with default hyperparameters is
         used.
 

src/aidsorb/data.py

@@ -52,7 +52,7 @@ def prepare_data(source: str, split_ratio: Sequence = None, seed: int = 1):
         Absolute or relative path to the directory holding the point clouds.
     split_ratio : sequence, default=None
         Absolute sizes or fractions of splits of the form ``(train, val,
-        test)``. If ``None``, it is set to ``(0.8, 0.1, 0.1)``.
+        test)``. If :obj:`None`, it is set to ``(0.8, 0.1, 0.1)``.
 
     seed : int, default=1
         Controls randomness of the ``rng`` used for splitting.
@@ -128,6 +128,16 @@ def pad_pcds(pcds, channels_first, mode='upsample', return_mask=False):
 
     Each point cloud must have shape ``(N_i, C)``.
 
+    .. rubric:: Shapes
+
+    * ``batch`` tensor of shape ``(B, T, C)`` if ``channels_first=False``,
+      else ``(B, C, T)``.
+    * ``mask`` boolean tensor of shape ``(B, T)`` where :obj:`True` indicates
+      padding.
+
+    ``B`` is the batch size and ``T`` is the size of the largest point
+    cloud in the sequence.
+
     Parameters
     ----------
     pcds : sequence of tensors
@@ -140,14 +150,6 @@ def pad_pcds(pcds, channels_first, mode='upsample', return_mask=False):
     tensor or tuple of length 2
         ``batch`` if ``return_mask=False``, else ``(batch, mask)``.
 
-        * ``batch`` tensor of shape ``(B, T, C)`` if ``channels_first=False``,
-          else ``(B, C, T)``.
-        * ``mask`` boolean tensor of shape ``(B, T)`` where ``True`` indicates
-          padding.
-
-        ``B`` is the batch size and ``T`` is the size of the largest point
-        cloud in the sequence.
-         
     See Also
     --------
     :func:`~.upsample_pcd` : For a description of ``'upsample'`` mode.
@@ -246,15 +248,15 @@ class Collator:
         Each sample is a tuple of ``(pcd, label)``.
 
         * ``pcd`` tensor of shape ``(N_i, C)``.
-        * ``label`` tensor of shape ``(n_outputs,)``, ``()`` or ``None``.
+        * ``label`` tensor of shape ``(n_outputs,)``, ``()`` or :obj:`None`.
 
     * Output: tuple of length 2
 
         If ``return_mask=False``, then output is ``(x, y)``, else ``((x, mask), y)``.
 
         * ``x`` tensor of shape ``(B, C, T)`` if ``channels_first=True``, else ``(B, T, C)``.
-        * ``y`` tensor of  shape ``(B, n_outputs)``, ``(B,)`` or ``None``.
-        * ``mask`` boolean tensor of shape ``(B, T)`` where ``True`` indicates padding.
+        * ``y`` tensor of  shape ``(B, n_outputs)``, ``(B,)`` or :obj:`None`.
+        * ``mask`` boolean tensor of shape ``(B, T)`` where :obj:`True` indicates padding.
 
      ``B`` is the batch size and ``T`` is the size of the largest point cloud in the
      sequence.

src/aidsorb/datamodules.py

@@ -71,7 +71,7 @@ class PCDDataModule(L.LightningDataModule):
         Column names of the ``.csv`` file containing the properties to be
         predicted.
     train_size : int, optional
-        Number of training samples. If ``None``, all training samples are used.
+        Number of training samples. If :obj:`None`, all training samples are used.
     train_transform_x : callable, optional
         Transformation to apply to point cloud during training.
     eval_transform_x : callable, optional

src/aidsorb/litmodels.py

@@ -54,7 +54,7 @@ class PCDLit(L.LightningModule):
     metric : :class:`~torchmetrics.MetricCollection`
         Metric(s) to be logged and optionally monitored.
     config_optimizer : dict, default=None
-        Dictionary for configuring optimizer. If ``None``, the
+        Dictionary for configuring optimizer. If :obj:`None`, the
         :class:`~torch.optim.Adam` optimizer with default hyperparameters is
         used.
         

src/aidsorb/modules.py

@@ -50,10 +50,10 @@ def conv1d_block(in_channels, out_channels, config_activation=None, **kwargs):
     Parameters
     ----------
     in_channels : int or None
-        If ``None``, the ``conv_layer`` is lazy initialized.
+        If :obj:`None`, the ``conv_layer`` is lazy initialized.
     out_channels : int
     config_activation : dict, default=None
-        Dictionary for configuring activation function. If ``None``, the
+        Dictionary for configuring activation function. If :obj:`None`, the
         :class:`~torch.nn.modules.activation.ReLU` activation is used.
 
         * ``'name'`` activation's class name :class:`str`
@@ -119,10 +119,10 @@ def dense_block(in_features, out_features, config_activation=None, **kwargs):
     Parameters
     ----------
     in_features : int or None
-        If ``None``, the ``linear_layer`` is lazy initialized.
+        If :obj:`None`, the ``linear_layer`` is lazy initialized.
     out_features : int
     config_activation : dict, default=None
-        Dictionary for configuring activation function. If ``None``, the
+        Dictionary for configuring activation function. If :obj:`None`, the
         :class:`~torch.nn.modules.activation.ReLU` activation is used.
 
         * ``'name'`` activation's class name :class:`str`
@@ -258,7 +258,7 @@ class PointNetBackbone(nn.Module):
     Parameters
     ----------
     local_feats : bool, default=False
-        If ``True``, the returned features are a concatenation of local features
+        If :obj:`True`, the returned features are a concatenation of local features
         and global features. Otherwise, the global features are returned.
     n_global_feats : int, default=1024
         Number of global features.

src/aidsorb/visualize.py

@@ -95,7 +95,7 @@ def draw_pcd(
 
     * The size of each point is determined by its atomic number ``pcd[i, 3]``.
 
-    * The color of each point is determined by ``feature_to_color``. If ``None``,
+    * The color of each point is determined by ``feature_to_color``. If :obj:`None`,
       the atomic number of each point determines the color. Otherwise, ``pcd[i,
       feature_to_color[0]]`` value determines the color.
 
@@ -168,7 +168,8 @@ def draw_pcd_from_file(filename: str, render: bool = True, **kwargs):
 
     Returns
     -------
-    render : :class:`~plotly.graph_objects.Figure` if ``render=False``, else ``None``.
+    :class:`~plotly.graph_objects.Figure` or :obj:`None`
+        :class:`~plotly.graph_objects.Figure` if ``render=False``, else :obj:`None`.
     """
     if filename.endswith('.npy'):
         pcd = np.load(filename)