def test_value(self, device, dtype):
input = torch.tensor([[0.5, 1., 0.3], [0.7, 0.3, 0.8], [0.4, 0.9, 0.2]],
device=device, dtype=dtype)[None, None, :, :]
kernel = torch.tensor([[0., 1., 0.], [1., 1., 1.], [0., 1., 0.]], device=device, dtype=dtype)
expected = torch.tensor([[0., 0.5, 0.], [0.2, 0., 0.5], [0., 0.5, 0.]],
device=device, dtype=dtype)[None, None, :, :]
assert_allclose(top_hat(input, kernel), expected)
def test_exception(self, device, dtype):
input = torch.ones(1, 1, 3, 4, device=device, dtype=dtype)
kernel = torch.ones(3, 3, device=device, dtype=dtype)
with pytest.raises(TypeError):
assert top_hat([0.0], kernel)
with pytest.raises(TypeError):
assert top_hat(input, [0.0])
with pytest.raises(ValueError):
test = torch.ones(2, 3, 4, device=device, dtype=dtype)
assert top_hat(test, kernel)
with pytest.raises(ValueError):
test = torch.ones(2, 3, 4, device=device, dtype=dtype)
assert top_hat(input, test)
def test_exception(self, dev_str, dtype_str, call):
input_ = ivy.ones((1, 1, 3, 4), dev_str=dev_str, dtype_str=dtype_str)
kernel = ivy.ones((3, 3), dev_str=dev_str, dtype_str=dtype_str)
with pytest.raises(ValueError):
test = ivy.ones((2, 3, 4), dev_str=dev_str, dtype_str=dtype_str)
assert top_hat(test, kernel)
with pytest.raises(ValueError):
test = ivy.ones((2, 3, 4), dev_str=dev_str, dtype_str=dtype_str)
assert top_hat(input_, test)
if call is not helpers.torch_call:
return
with pytest.raises(TypeError):
assert top_hat([0.], kernel)
with pytest.raises(TypeError):
assert top_hat(input_, [0.])
def test_cardinality(self, dev_str, dtype_str, call, shape, kernel):
img = ivy.ones(shape, dtype_str=dtype_str, dev_str=dev_str)
krnl = ivy.ones(kernel, dtype_str=dtype_str, dev_str=dev_str)
assert top_hat(img, krnl).shape == shape
def test_cardinality(self, device, dtype, shape, kernel):
img = torch.ones(shape, device=device, dtype=dtype)
krnl = torch.ones(kernel, device=device, dtype=dtype)
assert top_hat(img, krnl).shape == shape