removed benchmark

benchmark/benchmark.py

-import time
-import sys
-
-import torch
-from torch.autograd import Variable
-from torch_spline_conv import spline_conv
-
-sys.path.insert(0, '../../pytorch_geometric')
-
-from torch_geometric.nn.modules import SplineConv  # noqa
-
-n = 600
-m_in = 64
-m_out = 64
-d = 4
-x = torch.FloatTensor(n, m_in).uniform_(-1, 1)
-row = torch.arange(0, n).view(-1, 1).repeat(1, n).view(-1).long()
-col = torch.arange(0, n).repeat(n).long()
-edge_index = torch.stack([row, col], dim=0)
-pseudo = torch.FloatTensor(n * n, d).uniform_(0, 1)
-kernel_size = torch.LongTensor(d).fill_(5)
-is_open_spline = torch.ByteTensor(d).fill_(1)
-K = kernel_size.prod()
-weight = torch.FloatTensor(K, m_in, m_out).uniform_(-1, 1)
-
-# t = time.perf_counter()
-# out = spline_conv(x, edge_index, pseudo, weight, kernel_size, is_open_spline)
-# t = time.perf_counter() - t
-# print('CPU:', t)
-
-x = x.cuda()
-edge_index = edge_index.cuda()
-pseudo = pseudo.cuda()
-weight = weight.cuda()
-kernel_size = kernel_size.cuda()
-is_open_spline = is_open_spline.cuda()
-
-out = spline_conv(x, edge_index, pseudo, weight, kernel_size, is_open_spline)
-torch.cuda.synchronize()
-t = time.perf_counter()
-out = spline_conv(x, edge_index, pseudo, weight, kernel_size, is_open_spline)
-torch.cuda.synchronize()
-t = time.perf_counter() - t
-print('GPU:', t)
-
-conv = SplineConv(m_in, m_out, d, kernel_size, is_open_spline.long()).cuda()
-adj = {'indices': edge_index, 'values': pseudo, 'size': torch.Size([n, n, d])}
-x = Variable(x)
-conv(adj, x)
-torch.cuda.synchronize()
-t = time.perf_counter()
-conv(adj, x)
-torch.cuda.synchronize()
-t = time.perf_counter() - t
-print('GPU old:', t)