def test_hot_diag_norm(self, device, dtype):
input = torch.tensor(
[[[
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 1.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
]]],
device=device,
dtype=dtype,
)
softargmax = kornia.ConvSoftArgmax2d((3, 3), (2, 2), (0, 0),
temperature=10.0,
normalized_coordinates=True,
output_value=True)
expected_val = torch.tensor([[[[0.1214, 0.0], [0.0, 0.1214]]]],
device=device,
dtype=dtype)
expected_coord = torch.tensor(
[[[[[-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_close(val, expected_val, atol=1e-4, rtol=1e-4)
assert_close(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, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 1.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
]]],
device=device,
dtype=dtype,
)
softargmax = kornia.ConvSoftArgmax2d((3, 3), (2, 2), (0, 0),
temperature=0.05,
normalized_coordinates=False,
output_value=True)
expected_val = torch.tensor([[[[1.0, 0.0], [0.0, 1.0]]]],
device=device,
dtype=dtype)
expected_coord = torch.tensor(
[[[[[1.0, 3.0], [1.0, 3.0]], [[1.0, 1.0], [3.0, 3.0]]]]],
device=device,
dtype=dtype)
coords, val = softargmax(input)
assert_close(val, expected_val, atol=1e-4, rtol=1e-4)
assert_close(coords, expected_coord, atol=1e-4, rtol=1e-4)
def test_cold_diag(self, device):
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.],
]]]).to(device)
softargmax = kornia.ConvSoftArgmax2d((3, 3), (2, 2), (0, 0),
temperature=0.05,
normalized_coordinates=False,
output_value=True)
expected_val = torch.tensor([[[[1., 0.], [0., 1.]]]]).to(device)
expected_coord = torch.tensor([[[[[1., 3.], [1., 3.]],
[[1., 1.], [3., 3.]]]]]).to(device)
coords, val = softargmax(input)
assert_allclose(val, expected_val)
assert_allclose(coords, expected_coord)
def test_hot_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.ConvSoftArgmax2d((3, 3), (2, 2), (0, 0),
temperature=10.,
normalized_coordinates=True,
output_value=True)
expected_val = torch.tensor([[[[0.1214, 0.], [0., 0.1214]]]])
expected_coord = torch.tensor([[[[[-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_smoke_batch_with_val(self, device):
input = torch.zeros(2, 5, 3, 3).to(device)
m = kornia.ConvSoftArgmax2d((3, 3), output_value=True)
coords, val = m(input)
assert coords.shape == (2, 5, 2, 3, 3)
assert val.shape == (2, 5, 3, 3)
def test_smoke_batch(self, device):
input = torch.zeros(2, 5, 3, 3).to(device)
m = kornia.ConvSoftArgmax2d()
assert m(input).shape == (2, 5, 2, 3, 3)
def test_smoke(self, device):
input = torch.zeros(1, 1, 3, 3).to(device)
m = kornia.ConvSoftArgmax2d((3, 3))
assert m(input).shape == (1, 1, 2, 3, 3)
def test_smoke_with_val(self, device, dtype):
input = torch.zeros(1, 1, 3, 3, device=device, dtype=dtype)
m = kornia.ConvSoftArgmax2d((3, 3), output_value=True)
coords, val = m(input)
assert coords.shape == (1, 1, 2, 3, 3)
assert val.shape == (1, 1, 3, 3)