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):
score = psnr(pred=torch.tensor(pred),
target=torch.tensor(target),
data_range=4,
base=2.718281828459045)
sk_score = ski_psnr(np.array(pred), np.array(target), data_range=4) * np.log(10)
assert torch.allclose(score, torch.tensor(sk_score, dtype=torch.float32), atol=1e-3)
def test_psnr_with_skimage(pred, target):
score = psnr(pred=torch.tensor(pred),
target=torch.tensor(target),
data_range=3)
sk_score = ski_psnr(np.array(pred), np.array(target), data_range=3)
assert torch.allclose(score,
torch.tensor(sk_score, dtype=torch.float),
atol=1e-3)
def psnr_lightning(real, fake, return_per_frame=False, normalize_range=True):
if real.dim() == 3:
real = real[None, None]
fake = fake[None, None]
elif real.dim() == 4:
real = real[None]
fake = fake[None]
if normalize_range:
real = (real + 1.) / 2.
fake = (fake + 1.) / 2.
psnr_batch = PF.psnr(fake.reshape(-1, *fake.shape[2:]),
real.reshape(-1, *real.shape[2:])).cpu().numpy()
if return_per_frame:
psnr_per_frame = {
i: PF.psnr(fake[:, i].contiguous(),
real[:, i].contiguous()).cpu().numpy()
for i in range(real.shape[1])
}
return psnr_batch, psnr_per_frame
return psnr_batch
def test_v1_5_metric_regress():
ExplainedVariance.__init__._warned = False
with pytest.deprecated_call(match='It will be removed in v1.5.0'):
ExplainedVariance()
MeanAbsoluteError.__init__._warned = False
with pytest.deprecated_call(match='It will be removed in v1.5.0'):
MeanAbsoluteError()
MeanSquaredError.__init__._warned = False
with pytest.deprecated_call(match='It will be removed in v1.5.0'):
MeanSquaredError()
MeanSquaredLogError.__init__._warned = False
with pytest.deprecated_call(match='It will be removed in v1.5.0'):
MeanSquaredLogError()
target = torch.tensor([3, -0.5, 2, 7])
preds = torch.tensor([2.5, 0.0, 2, 8])
explained_variance._warned = False
with pytest.deprecated_call(match='It will be removed in v1.5.0'):
res = explained_variance(preds, target)
assert torch.allclose(res, torch.tensor(0.9572), atol=1e-4)
x = torch.tensor([0., 1, 2, 3])
y = torch.tensor([0., 1, 2, 2])
mean_absolute_error._warned = False
with pytest.deprecated_call(match='It will be removed in v1.5.0'):
assert mean_absolute_error(x, y) == 0.25
mean_relative_error._warned = False
with pytest.deprecated_call(match='It will be removed in v1.5.0'):
assert mean_relative_error(x, y) == 0.125
mean_squared_error._warned = False
with pytest.deprecated_call(match='It will be removed in v1.5.0'):
assert mean_squared_error(x, y) == 0.25
mean_squared_log_error._warned = False
with pytest.deprecated_call(match='It will be removed in v1.5.0'):
res = mean_squared_log_error(x, y)
assert torch.allclose(res, torch.tensor(0.0207), atol=1e-4)
PSNR.__init__._warned = False
with pytest.deprecated_call(match='It will be removed in v1.5.0'):
PSNR()
R2Score.__init__._warned = False
with pytest.deprecated_call(match='It will be removed in v1.5.0'):
R2Score()
SSIM.__init__._warned = False
with pytest.deprecated_call(match='It will be removed in v1.5.0'):
SSIM()
preds = torch.tensor([[0.0, 1.0], [2.0, 3.0]])
target = torch.tensor([[3.0, 2.0], [1.0, 0.0]])
psnr._warned = False
with pytest.deprecated_call(match='It will be removed in v1.5.0'):
res = psnr(preds, target)
assert torch.allclose(res, torch.tensor(2.5527), atol=1e-4)
target = torch.tensor([3, -0.5, 2, 7])
preds = torch.tensor([2.5, 0.0, 2, 8])
r2score._warned = False
with pytest.deprecated_call(match='It will be removed in v1.5.0'):
res = r2score(preds, target)
assert torch.allclose(res, torch.tensor(0.9486), atol=1e-4)
preds = torch.rand([16, 1, 16, 16])
target = preds * 0.75
ssim._warned = False
with pytest.deprecated_call(match='It will be removed in v1.5.0'):
res = ssim(preds, target)
assert torch.allclose(res, torch.tensor(0.9219), atol=1e-4)
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)
def forward(self, preds, target):
preds = self.unnormalize(preds)
target = self.unnormalize(target)
return psnr(preds, target, data_range=self.scale)
