def validation_step(self, batch, batch_idx):
X, fx = batch
X_compressed = self.conv(self.X_fit)
km = self.cal_km(self.params, X_compressed, X)
alpha_i = torch.abs(self.untreated_coef)
constrainted_alpha_i = (alpha_i - torch.min(alpha_i)) / (torch.max(alpha_i) - torch.min(alpha_i))
coef = constrainted_alpha_i * self.label
fx_hat = torch.sum(coef * km.t(), axis=1)
loss = F.smooth_l1_loss(fx_hat, fx)
var = FM.explained_variance(fx_hat, fx)
mae = FM.mean_absolute_error(fx_hat, fx)
mse = FM.mean_squared_error(fx_hat, fx)
val_metrics = {'val_var': var, 'val_mae': mae, 'val_mse': mse, 'val_loss': loss}
self.log_dict(val_metrics)
return val_metrics
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)