typos

README.md

@@ -48,8 +48,8 @@ The kernel function *g* is defined over the weighted B-spline tensor product bas
 ### Parameters
 
 * **src** *(Tensor or Variable)* - Input node features of shape `(number_of_nodes x in_channels)`
-* **edge_idex** *(LongTensor)* - Graph edges, given by source and target indices, of shape `(2 x number_of_edges)`
-* **pseudo** *(Tensor or Variable)* - Edge attributes, ie. pseudo coordinates, of shape `(number_of_edges x number_of_edge_attributes)`
+* **edge_index** *(LongTensor)* - Graph edges, given by source and target indices, of shape `(2 x number_of_edges)`
+* **pseudo** *(Tensor or Variable)* - Edge attributes, ie. pseudo coordinates, of shape `(number_of_edges x number_of_edge_attributes)` in the fixed interval [0, 1]
 * **weight** *(Tensor or Variable)* - Trainable weight parameters of shape `(kernel_size x in_channels x out_channels)`
 * **kernel_size** *(LongTensor)* - Number of trainable weight parameters in each edge dimension
 * **is_open_spline** *(ByteTensor)* - Whether to use open or closed B-spline bases for each dimension
@@ -65,16 +65,16 @@ from torch_spline_conv import spline_conv
 
 src = torch.Tensor(4, 2)  # 4 nodes with 2 features
 edge_index = torch.LongTensor([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]])  # 6 edges
-pseudo = torch.Tensor(6, 2)  # 2-dimensional edge attributes, restricted to [0, 1]
+pseudo = torch.Tensor(6, 2)  # two-dimensional edge attributes
 weight = torch.Tensor(25, 2, 4)  # 25 trainable parameters for each in_channels x out_channels combination
 kernel_size = torch.LongTensor([5, 5])  # 5 trainable parameters in each edge dimension
-is_open_spline = torch.ByteTensor([1, 1])  # Both B-spline bases should be open
-degree = 1  # B-spline degree of 1 (implemented: 1, 2, 3)
-root_weight = torch.Tensor(2, 4)  # Weight root nodes in addition
-bias = torch.Tensor(4)  # Add bias
+is_open_spline = torch.ByteTensor([1, 1])  # only use open B-splines
+degree = 1  # B-spline degree of 1
+root_weight = torch.Tensor(2, 4)  # Weight root nodes separatly
+bias = None  # No additional bias
 
 output = spline_conv(src, edge_index, pseudo, weight, kernel_size,
-                     is_open_spline, degree=1, root_weight=None, bias=None)
+                     is_open_spline, degree, root_weight, bias)
 
 print(output.size())
 torch.Size([4, 4])  # 4 nodes with 4 features

torch_spline_conv/conv.py

@@ -26,8 +26,9 @@ def spline_conv(src,
     Args:
         src (Tensor or Variable): Input node features of shape
             (number_of_nodes x in_channels)
-        edge_idex (LongTensor): Graph edges, given by source and target
-            indices, of shape (2 x number_of_edges)
+        edge_index (LongTensor): Graph edges, given by source and target
+            indices, of shape (2 x number_of_edges) in the fixed interval
+            [0, 1]
         pseudo (Tensor or Variable): Edge attributes, ie. pseudo coordinates,
             of shape (number_of_edges x number_of_edge_attributes)
         weight (Tensor or Variable): Trainable weight parameters of shape