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 gradient([0.], kernel)
with pytest.raises(TypeError):
assert gradient(input, [0.])
with pytest.raises(ValueError):
test = torch.ones(2, 3, 4, device=device, dtype=dtype)
assert gradient(test, kernel)
with pytest.raises(ValueError):
test = torch.ones(2, 3, 4, device=device, dtype=dtype)
assert gradient(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 gradient(test, kernel)
with pytest.raises(ValueError):
test = ivy.ones((2, 3, 4), dev_str=dev_str, dtype_str=dtype_str)
assert gradient(input_, test)
if call is not helpers.torch_call:
return
with pytest.raises(TypeError):
assert gradient([0.], kernel)
with pytest.raises(TypeError):
assert gradient(input_, [0.])
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(
[[0.5, 0.7, 0.7], [0.4, 0.7, 0.6], [0.5, 0.7, 0.7]],
device=device,
dtype=dtype)[None, None, :, :]
assert_allclose(gradient(input, kernel), expected)
def test_value(self, device, dtype):
tensor = torch.tensor(
[[0.5, 1.0, 0.3], [0.7, 0.3, 0.8], [0.4, 0.9, 0.2]],
device=device,
dtype=dtype)[None, None, :, :]
kernel = torch.tensor(
[[-1.0, 0.0, -1.0], [0.0, 0.0, 0.0], [-1.0, 0.0, -1.0]],
device=device,
dtype=dtype)
expected = torch.tensor(
[[0.5, 0.7, 0.7], [0.4, 0.7, 0.6], [0.5, 0.7, 0.7]],
device=device,
dtype=dtype)[None, None, :, :]
assert_allclose(gradient(tensor, kernel), expected)
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 gradient(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 gradient(img, krnl).shape == shape