def test_psnr():
psnr = PSNR()
assert psnr.name == 'psnr'
pred = torch.tensor([0., 1, 2, 3])
target = torch.tensor([0., 1, 2, 2])
score = psnr(pred, target)
assert isinstance(score, torch.Tensor)
def test_reduction_for_dim_none(reduction):
match = f"The `reduction={reduction}` will not have any effect when `dim` is None."
with pytest.warns(UserWarning, match=match):
PSNR(reduction=reduction, dim=None)
with pytest.warns(UserWarning, match=match):
psnr(_inputs[0].preds,
_inputs[0].target,
reduction=reduction,
dim=None)
def test_psnr_base_e_wider_range(pred, target, exp):
psnr = PSNR(data_range=4, base=2.718281828459045)
assert psnr.name == 'psnr'
score = psnr(pred=torch.tensor(pred), target=torch.tensor(target))
assert isinstance(score, torch.Tensor)
assert pytest.approx(score.item(), rel=1e-3) == exp
def test_psnr(pred, target, exp):
psnr = PSNR()
assert psnr.name == 'psnr'
score = psnr(pred=torch.tensor(pred), target=torch.tensor(target))
assert isinstance(score, torch.Tensor)
assert pytest.approx(score.item(), rel=1e-3) == exp
def test_missing_data_range():
with pytest.raises(ValueError):
PSNR(data_range=None, dim=0)
with pytest.raises(ValueError):
psnr(_inputs[0].preds, _inputs[0].target, data_range=None, dim=0)