normalize arg

README.md

@@ -41,6 +41,7 @@ out = SplineConv.apply(src,
                        kernel_size,
                        is_open_spline,
                        degree=1,
+                       norm=True,
                        root_weight=None,
                        bias=None)
 ```
@@ -66,6 +67,7 @@ The kernel function is defined over the weighted B-spline tensor product basis,
 * **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.
 * **degree** *(int, optional)* - B-spline basis degree. (default: `1`)
+* **norm** *(bool, optional)*: Whether to normalize output by node degree. (default: `True`)
 * **root_weight** *(Tensor, optional)* - Additional shared trainable parameters for each feature of the root node of shape `(in_channels x out_channels)`. (default: `None`)
 * **bias** *(Tensor, optional)* - Optional bias of shape `(out_channels)`. (default: `None`)
 
@@ -86,11 +88,12 @@ weight = torch.rand((25, 2, 4), dtype=torch.float)  # 25 parameters for in_chann
 kernel_size = torch.tensor([5, 5])  # 5 parameters in each edge dimension
 is_open_spline = torch.tensor([1, 1], dtype=torch.uint8)  # only use open B-splines
 degree = 1  # B-spline degree of 1
+norm = True  # Normalize output by node degree.
 root_weight = torch.rand((2, 4), dtype=torch.float)  # separately weight root nodes
 bias = None  # do not apply an additional bias
 
 out = SplineConv.apply(src, edge_index, pseudo, weight, kernel_size,
-                       is_open_spline, degree, root_weight, bias)
+                       is_open_spline, degree, norm root_weight, bias)
 
 print(out.size())
 torch.Size([4, 4])  # 4 nodes with 4 features each

setup.py

@@ -2,7 +2,7 @@
 
 from setuptools import setup, find_packages
 
-__version__ = '1.0.2'
+__version__ = '1.0.3'
 url = 'https://github.com/rusty1s/pytorch_spline_conv'
 
 install_requires = ['cffi']

test/test_conv.py

@@ -31,11 +31,11 @@
     'root_weight': [[12.5], [13]],
     'bias': [1],
     'expected': [
-        1 + (12.5 * 9 + 13 * 10 + 8.5 + 40.5 + 107.5 + 101.5) / 5,
-        1 + 12.5 * 1 + 13 * 2,
-        1 + 12.5 * 3 + 13 * 4,
-        1 + 12.5 * 5 + 13 * 6,
-        1 + 12.5 * 7 + 13 * 8,
+        [1 + 12.5 * 9 + 13 * 10 + (8.5 + 40.5 + 107.5 + 101.5) / 4],
+        [1 + 12.5 * 1 + 13 * 2],
+        [1 + 12.5 * 3 + 13 * 4],
+        [1 + 12.5 * 5 + 13 * 6],
+        [1 + 12.5 * 7 + 13 * 8],
     ]
 }]
 
@@ -52,9 +52,8 @@ def test_spline_conv_forward(test, dtype, device):
     bias = tensor(test['bias'], dtype, device)
 
     out = SplineConv.apply(src, edge_index, pseudo, weight, kernel_size,
-                           is_open_spline, 1, root_weight, bias)
-    assert list(out.size()) == [5, 1]
-    assert pytest.approx(out.view(-1).tolist()) == test['expected']
+                           is_open_spline, 1, True, root_weight, bias)
+    assert out.tolist() == test['expected']
 
 
 @pytest.mark.parametrize('degree,device', product(degrees.keys(), devices))
@@ -74,5 +73,5 @@ def test_spline_basis_backward(degree, device):
     bias.requires_grad_()
 
     data = (src, edge_index, pseudo, weight, kernel_size, is_open_spline,
-            degree, root_weight, bias)
+            degree, True, root_weight, bias)
     assert gradcheck(SplineConv.apply, data, eps=1e-6, atol=1e-4) is True

torch_spline_conv/__init__.py

@@ -2,6 +2,6 @@
 from .weighting import SplineWeighting
 from .conv import SplineConv
 
-__version__ = '1.0.2'
+__version__ = '1.0.3'
 
 __all__ = ['SplineBasis', 'SplineWeighting', 'SplineConv', '__version__']

torch_spline_conv/conv.py

@@ -28,6 +28,8 @@ class SplineConv(object):
         is_open_spline (:class:`ByteTensor`): Whether to use open or closed
             B-spline bases for each dimension.
         degree (int, optional): B-spline basis degree. (default: :obj:`1`)
+        norm (bool, optional): Whether to normalize output by node degree.
+            (default: :obj:`True`)
         root_weight (:class:`Tensor`, optional): Additional shared trainable
             parameters for each feature of the root node of shape
             (in_channels x out_channels). (default: :obj:`None`)
@@ -45,6 +47,7 @@ def apply(src,
               kernel_size,
               is_open_spline,
               degree=1,
+              norm=True,
               root_weight=None,
               bias=None):
 
@@ -62,15 +65,14 @@ def apply(src,
         row_expand = row.unsqueeze(-1).expand_as(out)
         out = src.new_zeros((n, m_out)).scatter_add_(0, row_expand, out)
 
-        deg = node_degree(row, n, out.dtype, out.device)
+        # Normalize out by node degree (if wished).
+        if norm:
+            deg = node_degree(row, n, out.dtype, out.device)
+            out = out / deg.unsqueeze(-1).clamp(min=1)
 
         # Weight root node separately (if wished).
         if root_weight is not None:
             out += torch.mm(src, root_weight)
-            deg += 1
-
-        # Normalize out by node degree.
-        out /= deg.unsqueeze(-1).clamp(min=1)
 
         # Add bias (if wished).
         if bias is not None: