Updated documentation.

dect/ect.py

@@ -13,6 +13,25 @@
 class ECTConfig:
     """
     Configuration of the ECT Layer.
+
+    Parameters
+    ----------
+    bump_steps : int
+        The number of steps to discretize the ECT into.
+    radius : float
+        The radius of the circle the directions lie on. Usually this is a bit
+        larger than the objects we wish to compute the ECT for, which in most
+        cases have radius 1. For now it defaults to 1 as well.
+    ect_type : str
+        The type of ECT we wish to compute. Can be "points" for point clouds,
+        "edges" for graphs or "faces" for meshes.
+    normalized: bool
+        Whether or not to normalize the ECT. Only work with ect_type set to
+        points and normalized the ECT to the interval [0,1].
+    fixed: bool
+        Option to keep the directions fixed or not. In case the directions are
+        learnable, we can use backpropagation to optimize over a set of
+        directions. See notebooks for examples.
     """
 
     bump_steps: int = 32
@@ -192,7 +211,35 @@ def normalize(ect):
 
 
 class ECTLayer(nn.Module):
-    """Machine learning layer for computing the ECT."""
+    """Machine learning layer for computing the ECT.
+
+    Parameters
+    ----------
+    v: torch.FloatTensor
+        The direction vector that contains the directions. The shape of the
+        tensor v is either [ndims, num_thetas] or [n_channels, ndims, num_thetas].
+    config: ECTConfig
+        The configuration config of the ECT layer.
+
+    Config Parameters
+    ----------
+    bump_steps : int
+        The number of steps to discretize the ECT into.
+    radius : float
+        The radius of the circle the directions lie on. Usually this is a bit
+        larger than the objects we wish to compute the ECT for, which in most
+        cases have radius 1. For now it defaults to 1 as well.
+    ect_type : str
+        The type of ECT we wish to compute. Can be "points" for point clouds,
+        "edges" for graphs or "faces" for meshes.
+    normalized: bool
+        Whether or not to normalize the ECT. Only work with ect_type set to
+        points and normalized the ECT to the interval [0,1].
+    fixed: bool
+        Option to keep the directions fixed or not. In case the directions are
+        learnable, we can use backpropagation to optimize over a set of
+        directions. See notebooks for examples.
+    """
 
     def __init__(self, config: ECTConfig, v=None):
         super().__init__()
@@ -210,7 +257,6 @@ def __init__(self, config: ECTConfig, v=None):
             v.unsqueeze(0)
 
         # The set of directions is added
-        # TODO: Requires testing.
         if config.fixed:
             self.v = nn.Parameter(v.movedim(-1, -2), requires_grad=False)
         else:
@@ -230,7 +276,24 @@ def __init__(self, config: ECTConfig, v=None):
             self.compute_ect = compute_ect_faces
 
     def forward(self, batch: Batch):
-        """Forward method for the ECT Layer."""
+        """Forward method for the ECT Layer.
+
+
+        Parameters
+        ----------
+        batch : Batch
+            A batch of data containing the node coordinates, edges, faces and
+            batch index. It should follow the pytorch geometric conventions.
+
+        Returns
+        ----------
+        ect: torch.FloatTensor
+            Returns the ECT of each data object in the batch. If the layer is
+            initialized with v of the shape [ndims,num_thetas], the returned ECT
+            has shape [batch,num_thetas,bump_steps]. In case the layer is
+            initialized with v of the form [n_channels, ndims, num_thetas] the
+            returned ECT has the shape [batch,n_channels,num_thetas,bump_steps]
+        """
         # Movedim for geotorch.
         ect = self.compute_ect(batch, self.v.movedim(-1, -2), self.lin)
         if self.config.normalized: