def test_hot_diag_norm(self, device, dtype):
input = torch.tensor([[[[
[0., 0., 0., 0., 0.],
[0., 1., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 1., 0.],
[0., 0., 0., 0., 0.],
]]]],
device=device,
dtype=dtype)
softargmax = kornia.ConvSoftArgmax3d((1, 3, 3), (1, 2, 2), (0, 0, 0),
temperature=10.,
normalized_coordinates=True,
output_value=True)
expected_val = torch.tensor([[[[[0.1214, 0.], [0., 0.1214]]]]],
device=device,
dtype=dtype)
expected_coord = torch.tensor(
[[[[[[-1., -1.], [-1., -1.]]], [[[-0.5, 0.5], [-0.5, 0.5]]],
[[[-0.5, -0.5], [0.5, 0.5]]]]]],
device=device,
dtype=dtype)
coords, val = softargmax(input)
assert_allclose(val, expected_val, atol=1e-4, rtol=1e-4)
assert_allclose(coords, expected_coord, atol=1e-4, rtol=1e-4)
def test_cold_diag(self, device, dtype):
input = torch.tensor([[[[
[0., 0., 0., 0., 0.],
[0., 1., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 1., 0.],
[0., 0., 0., 0., 0.],
]]]],
device=device,
dtype=dtype)
softargmax = kornia.ConvSoftArgmax3d((1, 3, 3), (1, 2, 2), (0, 0, 0),
temperature=0.05,
normalized_coordinates=False,
output_value=True)
expected_val = torch.tensor([[[[[1., 0.], [0., 1.]]]]],
device=device,
dtype=dtype)
expected_coord = torch.tensor(
[[[[[[0., 0.], [0., 0.]]], [[[1., 3.], [1., 3.]]],
[[[1., 1.], [3., 3.]]]]]],
device=device,
dtype=dtype)
coords, val = softargmax(input)
assert_allclose(val, expected_val, atol=1e-4, rtol=1e-4)
assert_allclose(coords, expected_coord, atol=1e-4, rtol=1e-4)
def test_cold_diag_norm(self):
input = torch.tensor([[[[
[0., 0., 0., 0., 0.],
[0., 1., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 1., 0.],
[0., 0., 0., 0., 0.],
]]]])
softargmax = kornia.ConvSoftArgmax3d((1, 3, 3), (1, 2, 2), (0, 0, 0),
temperature=0.05,
normalized_coordinates=True,
output_value=True)
expected_val = torch.tensor([[[[[1., 0.], [0., 1.]]]]])
expected_coord = torch.tensor([[[[[[-1., -1.], [-1., -1.]]],
[[[-0.5, 0.5], [-0.5, 0.5]]],
[[[-0.5, -0.5], [0.5, 0.5]]]]]])
coords, val = softargmax(input)
assert_allclose(val, expected_val)
assert_allclose(coords, expected_coord)
def test_hot_diag(self):
input = torch.tensor([[[[
[0., 0., 0., 0., 0.],
[0., 1., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 1., 0.],
[0., 0., 0., 0., 0.],
]]]])
softargmax = kornia.ConvSoftArgmax3d((1, 3, 3), (1, 2, 2), (0, 0, 0),
temperature=10.,
normalized_coordinates=False,
output_value=True)
expected_val = torch.tensor([[[[[0.1214, 0.], [0., 0.1214]]]]])
expected_coord = torch.tensor([[[[[[0., 0.], [0., 0.]]],
[[[1., 3.], [1., 3.]]],
[[[1., 1.], [3., 3.]]]]]])
coords, val = softargmax(input)
assert_allclose(val, expected_val)
assert_allclose(coords, expected_coord)
def test_smoke_with_val(self, device):
input = torch.zeros(1, 1, 3, 3, 3).to(device)
m = kornia.ConvSoftArgmax3d((3, 3, 3), output_value=True)
coords, val = m(input)
assert coords.shape == (1, 1, 3, 3, 3, 3)
assert val.shape == (1, 1, 3, 3, 3)
def test_smoke(self, device):
input = torch.zeros(1, 1, 3, 3, 3).to(device)
m = kornia.ConvSoftArgmax3d((3, 3, 3), output_value=False)
assert m(input).shape == (1, 1, 3, 3, 3, 3)
