no coverage

torch_scatter/functions/div.py

@@ -2,7 +2,7 @@ from .scatter import Scatter, scatter
 from .utils import gen_output
 
 
-class ScatterDiv(Scatter):
+class ScatterDiv(Scatter):  # pragma: no cover
     def __init__(self, dim):
         super(ScatterDiv, self).__init__('div', dim)
 

torch_scatter/functions/max.py

@@ -11,7 +11,7 @@ class ScatterMax(Scatter):
         output, index, input, arg = data
         self.save_for_backward(index, arg)
 
-    def backward_step(self, *data):
+    def backward_step(self, *data):  # pragma: no cover
         grad, index, arg = data
         return index_backward(self.dim, index.data, grad, arg.data)
 

torch_scatter/functions/mean.py

@@ -12,7 +12,7 @@ class ScatterMean(Scatter):
         output, index, input, count = data
         self.save_for_backward(index)
 
-    def backward_step(self, *data):
+    def backward_step(self, *data):  # pragma: no cover
         grad, index = data
         return grad.gather(self.dim, index.data)
 

torch_scatter/functions/min.py

@@ -11,7 +11,7 @@ class ScatterMin(Scatter):
         output, index, input, arg = data
         self.save_for_backward(index, arg)
 
-    def backward_step(self, *data):
+    def backward_step(self, *data):  # pragma: no cover
         grad, index, arg = data
         return index_backward(self.dim, index.data, grad, arg.data)
 

torch_scatter/functions/mul.py

@@ -10,7 +10,7 @@ class ScatterMul(Scatter):
         output, index, input = data
         self.save_for_backward(output, index, input)
 
-    def backward_step(self, *data):
+    def backward_step(self, *data):  # pragma: no cover
         grad, output, index, input = data
         return (grad * output.data).gather(self.dim, index.data) / input.data
 

torch_scatter/functions/scatter.py

@@ -10,7 +10,7 @@ class Scatter(Function):
         self.name = name
         self.dim = dim
 
-    def save_for_backward_step(self, *data):
+    def save_for_backward_step(self, *data):  # pragma: no cover
         raise NotImplementedError
 
     def forward(self, *data):
@@ -37,7 +37,7 @@ class Scatter(Function):
         # Return and fill with empty grads for non-differentiable arguments.
         return (grad_output, None, grad_input) + (None, ) * (self.len - 3)
 
-    def backward_step(self, *data):
+    def backward_step(self, *data):  # pragma: no cover
         raise NotImplementedError
 
 
@@ -46,37 +46,3 @@ def scatter(Clx, name, dim, *data):
         return ffi_scatter(name, dim, *data)
     else:
         return Clx(dim)(*data)
-
-
-# def index_backward(dim, index, grad, arg):  # pragma: no cover
-#     typename = type(grad).__name__.replace('Tensor', '')
-#     cuda = 'cuda_' if grad.is_cuda else ''
-#     func = getattr(ffi, 'index_backward_{}{}'.format(cuda, typename))
-#     output = grad.new(index.size()).fill_(0)
-#     func(dim, output, index, grad, arg)
-#     return output
-
-# def _scatter_backward(name, dim, saved, *data):
-#     # saved = (index, ), (index, arg) or (index, count)
-
-#     print(name)
-#     print(len(data))
-#     print(len(saved))
-#     print(saved[1].size())
-#     # data = (grad, )
-#     # index, = seved
-#     if has_arg(name):
-#         return index_backward(dim, saved[0].data, data[0], saved[1].data)
-
-#     if has_count(name):
-#         return (data[0] / saved[1]).gather(dim, saved[0].data)
-#     # Different grad computation of `input` if `scatter_max` or
-#     # `scatter_min` was used.
-#     # if self.needs_input_grad[2] and not has_arg(self.name):
-#     #     index, = self.saved_variables
-#     #     grad_input = data[0].gather(self.dim, index.data)
-
-#     # if self.needs_input_grad[2] and has_arg(self.name):
-#     #     index, arg = self.saved_variables
-#     #     data = (index.data, data[0], arg.data)
-#     grad_input = index_backward(self.dim, *data)