def test_Concat(self):
input = torch.randn(4, 2)
num_modules = random.randint(2, 5)
linears = [nn.Linear(2, 5) for i in range(num_modules)]
m = nn.Concat(0)
for l in linears:
m.add(l)
l.zeroGradParameters()
l.weight.fill_(1)
l.bias.fill_(0)
# Check that these don't raise errors
m.__repr__()
str(m)
output = m.forward(input)
output2 = input.sum(1).expand(4, 5).repeat(num_modules, 1)
self.assertEqual(output2, output)
gradInput = m.backward(input, torch.ones(output2.size()))
gradInput2 = torch.ones(4, 2).fill_(num_modules * 5)
self.assertEqual(gradInput, gradInput2)
gradWeight = input.sum(0).expand(5, 2)
for l in linears:
self.assertEqual(gradWeight, l.gradWeight)
def _build_net(self):
return (nn.Sequential()
.add(nn.Concat(0)
.add(nn.Linear(2, 5))
.add(nn.Linear(2, 5)))
.add(nn.ReLU())
.add(nn.Linear(10, 20)))
