delete profiling code

spline_conv.py

@@ -18,8 +18,7 @@ def spline_conv(
         backward_kernel,
         basis_kernel,
         degree=1,
-        bias=None,):
-    t_forward = time.process_time()
+        bias=None, ):
     if input.dim() == 1:
         input = input.unsqueeze(1)
 
@@ -27,49 +26,33 @@ def spline_conv(
     row, col = adj._indices()
 
     # Get features for every end vertex with shape [|E| x M_in].
-    t_gather = time.process_time()
     output = input[col]
-    t_gather = time.process_time() - t_gather
     # Convert to [|E| x M_in] feature matrix and calculate [|E| x M_out].
 
-    t_basis = time.process_time()
-    amount, index = spline(values, kernel_size, is_open_spline, K, degree, basis_kernel)
-    t_basis = time.process_time() - t_basis
+    amount, index = spline(values, kernel_size, is_open_spline, K, degree,
+                           basis_kernel)
 
-    t_conv = time.process_time()
-    output = edgewise_spline_weighting(output, weight[:-1], amount, index, forward_kernel, backward_kernel)
-    t_conv = time.process_time() - t_conv
-    print('t_gather',t_gather,'time_basis:',t_basis,'time_conv:',t_conv)
+    output = edgewise_spline_weighting(output, weight[:-1], amount, index,
+                                       forward_kernel, backward_kernel)
 
     # Convolution via `scatter_add`. Converts [|E| x M_out] feature matrix to
     # [n x M_out] feature matrix.
-    t_scatter_add = time.process_time()
     zero = output.data.new(adj.size(1), output.size(1)).fill_(0.0)
     zero = Variable(zero) if not torch.is_tensor(output) else zero
     r = row.view(-1, 1).expand(row.size(0), output.size(1))
     output = zero.scatter_add_(0, Variable(r), output)
-    t_scatter_add = time.process_time() - t_scatter_add
 
     # Weighten root node features by multiplying with root weight.
-
-    t_root_weight = time.process_time()
     output += torch.mm(input, weight[-1])
-    t_root_weight = time.process_time() - t_root_weight
 
     # Normalize output by degree.
-    t_normalize = time.process_time()
     ones = values.new(values.size(0)).fill_(1)
     zero = values.new(output.size(0)).fill_(0)
     degree = zero.scatter_add_(0, row, ones)
     degree = torch.clamp(degree, min=1)
     output = output / Variable(degree.view(-1, 1))
-    t_normalize = time.process_time() - t_normalize
-
-    print('t_scatter_add:',t_scatter_add,'t_root_weight:',t_root_weight,'t_normalize:',t_normalize)
 
     if bias is not None:
         output += bias
 
-    t_forward = time.process_time()- t_forward
-    print('t_forward',t_forward)
     return output