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 supervised(self, gt, rend):
(gt_rgb, gt_alpha), (rend_rgb, rend_alpha) = self._split(gt, rend)
gt, rend = gt_rgb * gt_alpha, rend_rgb * rend_alpha
return -torch.stack(
[metrics.psnr(r, g, data_range=1) for r, g in zip(rend, gt)]
)
def reconstruction(self, reconst, inputs):
return -torch.stack(
[metrics.psnr(r, i, data_range=1) for r, i in zip(reconst, inputs)]
)
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)