def test_ConcatTable(self):
input = [
torch.randn(3, 4).float(),
torch.randn(3, 4).float(), [torch.randn(3, 4).float()]
]
_gradOutput = [
torch.randn(3, 3, 4).float(),
torch.randn(3, 3, 4).float(),
torch.randn(3, 3, 4).float()
]
gradOutput = [
[_gradOutput[0][0], _gradOutput[1][0], [_gradOutput[2][0]]],
[_gradOutput[0][1], _gradOutput[1][1], [_gradOutput[2][1]]],
[_gradOutput[0][2], _gradOutput[1][2], [_gradOutput[2][2]]]
]
module = nn.ConcatTable()
module.add(nn.Identity())
module.add(nn.Identity())
module.add(nn.Identity())
module.float()
# Check that these don't raise errors
module.__repr__()
str(module)
output = module.forward(input)
output2 = [input, input, input]
self.assertEqual(output2, output)
gradInput = module.backward(input, gradOutput)
gradInput2 = [
_gradOutput[0].sum(0, keepdim=False),
_gradOutput[1].sum(0, keepdim=False),
[_gradOutput[2].sum(0, keepdim=False)]
]
self.assertTrue(isinstance(gradInput, list))
self.assertFalse(isinstance(gradInput[0], list))
self.assertFalse(isinstance(gradInput[1], list))
self.assertTrue(isinstance(gradInput[2], list))
self.assertEqual(len(gradInput), 3)
self.assertEqual(len(gradInput[2]), 1)
for t1, t2 in zip(iter_tensors(gradInput), iter_tensors(gradInput2)):
self.assertEqual(t1, t2)
# test outputs for variable length inputs
test = nn.ConcatTable()
test.add(nn.Identity())
test.add(nn.Identity())
x = [torch.randn(5), torch.randn(5)]
y = [torch.randn(5)]
o1 = len(test.forward(x))
go1 = len(test.backward(x, [x, x]))
o2 = len(test.forward(y))
go2 = len(test.backward(y, [y, y]))
self.assertEqual(o1, 2)
self.assertEqual(go1, 2)
self.assertEqual(o2, 2)
self.assertEqual(go2, 1)
def test_ConcatTable(self):
input = [
torch.randn(3, 4).float(), torch.randn(3, 4).float(), [torch.randn(3, 4).float()]
]
_gradOutput = [
torch.randn(3, 3, 4).float(), torch.randn(3, 3, 4).float(), torch.randn(3, 3, 4).float()
]
gradOutput = [
[_gradOutput[0][0], _gradOutput[1][0], [_gradOutput[2][0]]],
[_gradOutput[0][1], _gradOutput[1][1], [_gradOutput[2][1]]],
[_gradOutput[0][2], _gradOutput[1][2], [_gradOutput[2][2]]]
]
module = nn.ConcatTable()
module.add(nn.Identity())
module.add(nn.Identity())
module.add(nn.Identity())
module.float()
# Check that these don't raise errors
module.__repr__()
str(module)
output = module.forward(input)
output2 = [input, input, input]
self.assertEqual(output2, output)
gradInput = module.backward(input, gradOutput)
gradInput2 = [_gradOutput[0].sum(0, keepdim=False), _gradOutput[1].sum(
0, keepdim=False), [_gradOutput[2].sum(0, keepdim=False)]]
self.assertTrue(isinstance(gradInput, list))
self.assertFalse(isinstance(gradInput[0], list))
self.assertFalse(isinstance(gradInput[1], list))
self.assertTrue(isinstance(gradInput[2], list))
self.assertEqual(len(gradInput), 3)
self.assertEqual(len(gradInput[2]), 1)
for t1, t2 in zip(iter_tensors(gradInput), iter_tensors(gradInput2)):
self.assertEqual(t1, t2)
# test outputs for variable length inputs
test = nn.ConcatTable()
test.add(nn.Identity())
test.add(nn.Identity())
x = [torch.randn(5), torch.randn(5)]
y = [torch.randn(5)]
o1 = len(test.forward(x))
go1 = len(test.backward(x, [x, x]))
o2 = len(test.forward(y))
go2 = len(test.backward(y, [y, y]))
self.assertEqual(o1, 2)
self.assertEqual(go1, 2)
self.assertEqual(o2, 2)
self.assertEqual(go2, 1)