def test_downscale_values_AA(self, device, dtype):
inp_x = torch.arange(20, device=device, dtype=dtype) / 20.
inp = inp_x[None].T @ inp_x[None]
inp = inp[None, None]
out = kornia.rescale(inp, (0.25, 0.25), antialias=True)
expected = torch.tensor([[[[0.0255, 0.0453, 0.0759, 0.1065, 0.1263],
[0.0453, 0.0804, 0.1347, 0.1890, 0.2240],
[0.0759, 0.1347, 0.2256, 0.3166, 0.3753],
[0.1065, 0.1890, 0.3166, 0.4442, 0.5266],
[0.1263, 0.2240, 0.3753, 0.5266, 0.6244]]]],
device=device,
dtype=dtype)
assert_allclose(out, expected, atol=1e-3, rtol=1e-3)
def test_downscale_values(self, device, dtype):
inp_x = torch.arange(20, device=device, dtype=dtype) / 20.
inp = inp_x[None].T @ inp_x[None]
inp = inp[None, None]
out = kornia.rescale(inp, (0.25, 0.25), antialias=False)
expected = torch.tensor([[[[0.0056, 0.0206, 0.0356, 0.0506, 0.0656],
[0.0206, 0.0756, 0.1306, 0.1856, 0.2406],
[0.0356, 0.1306, 0.2256, 0.3206, 0.4156],
[0.0506, 0.1856, 0.3206, 0.4556, 0.5906],
[0.0656, 0.2406, 0.4156, 0.5906, 0.7656]]]],
device=device,
dtype=dtype)
assert_allclose(out, expected, atol=1e-3, rtol=1e-3)
def test_one_param(self, device, dtype):
input = torch.rand(1, 3, 3, 4, device=device, dtype=dtype)
output = kornia.rescale(input, 2.0)
assert output.shape == (1, 3, 6, 8)
def test_downsize(self, device, dtype):
input = torch.rand(1, 3, 9, 8, device=device, dtype=dtype)
output = kornia.rescale(input, (1.0 / 3.0, 1.0 / 2.0))
assert output.shape == (1, 3, 3, 4)
def test_upsize(self, device, dtype):
input = torch.rand(1, 3, 3, 4, device=device, dtype=dtype)
output = kornia.rescale(input, (3.0, 2.0))
assert output.shape == (1, 3, 9, 8)
def test_smoke(self, device, dtype):
input = torch.rand(1, 3, 3, 4, device=device, dtype=dtype)
output = kornia.rescale(input, (1.0, 1.0))
assert_close(input, output, atol=1e-4, rtol=1e-4)
def test_smoke(self, device):
input = torch.rand(1, 3, 3, 4, device=device)
output = kornia.rescale(input, (1.0, 1.0))
assert_allclose(input, output)
