def test_subset_computes_active_downsampling_function_batch(self):
a = torch.tensor([[0.1, 0.2, 0.2], [0.3, 0.4, 0.2],
[0.5, 0.5, 0.5]]).view(3, 3, 1)
a_p = torch.tensor([[0.1, 0.2, 0.2], [0.3, 0.4, 0.2],
[0.5, 0.5, 0.5]]).view(3, 3, 1)
a = torch.cat((a, a_p), 2)
b = torch.tensor([[0.5, 0.6, 0.1], [0.7, 0.8, 0.2],
[0.6, 0.6, 0.5]]).view(3, 3, 1)
power = 1
offset = 1
kernel = DownsamplingKernel(batch_shape=torch.Size([3]),
active_dims=[0])
kernel.initialize(power=power, offset=offset)
kernel.eval()
res = kernel(a, b).evaluate()
actual = torch.zeros(3, 3, 3)
diff = torch.tensor([[0.45, 0.36, 0.81], [0.4, 0.32, 0.72],
[0.4, 0.32, 0.72]])
actual[0, :, :] = offset + diff.pow(1 + power)
diff = torch.tensor([[0.21, 0.14, 0.56], [0.18, 0.12, 0.48],
[0.24, 0.16, 0.64]])
actual[1, :, :] = offset + diff.pow(1 + power)
diff = torch.tensor([[0.2, 0.2, 0.25], [0.2, 0.2, 0.25],
[0.2, 0.2, 0.25]])
actual[2, :, :] = offset + diff.pow(1 + power)
self.assertLess(torch.norm(res - actual), 1e-5)
def test_diag_calculation(self):
a = torch.tensor([0.1, 0.2]).view(2, 1)
b = torch.tensor([0.2, 0.4]).view(2, 1)
power = 1
offset = 1
kernel = DownsamplingKernel()
kernel.initialize(power=power, offset=offset)
kernel.eval()
diff = torch.tensor([[0.72, 0.54], [0.64, 0.48]])
actual = offset + diff.pow(1 + power)
res = kernel(a, b, diag=True)
self.assertLess(torch.norm(res - torch.diag(actual)), 1e-5)
def test_computes_downsampling_function(self):
a = torch.tensor([0.1, 0.2]).view(2, 1)
b = torch.tensor([0.2, 0.4]).view(2, 1)
power = 1
offset = 1
kernel = DownsamplingKernel()
kernel.initialize(power=power, offset=offset)
kernel.eval()
diff = torch.tensor([[0.72, 0.54], [0.64, 0.48]])
actual = offset + diff.pow(1 + power)
res = kernel(a, b).evaluate()
self.assertLess(torch.norm(res - actual), 1e-5)
def test_computes_downsampling_function_batch(self):
a = torch.tensor([[0.1, 0.2], [0.3, 0.4], [0.5, 0.5]]).view(3, 2, 1)
b = torch.tensor([[0.5, 0.6], [0.7, 0.8], [0.6, 0.6]]).view(3, 2, 1)
power = 1
offset = 1
kernel = DownsamplingKernel(batch_shape=torch.Size([3]))
kernel.initialize(power=power, offset=offset)
kernel.eval()
res = kernel(a, b).evaluate()
actual = torch.zeros(3, 2, 2)
diff = torch.tensor([[0.45, 0.36], [0.4, 0.32]])
actual[0, :, :] = offset + diff.pow(1 + power)
diff = torch.tensor([[0.21, 0.14], [0.18, 0.12]])
actual[1, :, :] = offset + diff.pow(1 + power)
diff = torch.tensor([[0.2, 0.2], [0.2, 0.2]])
actual[2, :, :] = offset + diff.pow(1 + power)
self.assertLess(torch.norm(res - actual), 1e-5)
def test_last_dim_is_batch(self):
a = (torch.tensor([[0.1, 0.2], [0.3, 0.4],
[0.5, 0.5]]).view(3, 2).transpose(-1, -2))
b = (torch.tensor([[0.5, 0.6], [0.7, 0.8],
[0.6, 0.6]]).view(3, 2).transpose(-1, -2))
power = 1
offset = 1
kernel = DownsamplingKernel()
kernel.initialize(power=power, offset=offset)
kernel.eval()
res = kernel(a, b, last_dim_is_batch=True).evaluate()
actual = torch.zeros(3, 2, 2)
diff = torch.tensor([[0.45, 0.36], [0.4, 0.32]])
actual[0, :, :] = offset + diff.pow(1 + power)
diff = torch.tensor([[0.21, 0.14], [0.18, 0.12]])
actual[1, :, :] = offset + diff.pow(1 + power)
diff = torch.tensor([[0.2, 0.2], [0.2, 0.2]])
actual[2, :, :] = offset + diff.pow(1 + power)
self.assertLess(torch.norm(res - actual), 1e-5)