def test_loading_old_model(self):
train_x, train_y = train_data(cuda=False)
likelihood = GaussianLikelihood()
model = SVGPRegressionModel(
torch.linspace(0, 1, 25),
gpytorch.variational.CholeskyVariationalDistribution)
data_file = Path(__file__).parent.joinpath(
"old_variational_strategy_model.pth").resolve()
state_dicts = torch.load(data_file)
likelihood.load_state_dict(state_dicts["likelihood"], strict=False)
# Ensure we get a warning
with warnings.catch_warnings(record=True) as ws:
# Makes sure warnings we catch don't cause `-w error` to fail
warnings.simplefilter("always", OldVersionWarning)
model.load_state_dict(state_dicts["model"])
self.assertTrue(
any(issubclass(w.category, OldVersionWarning) for w in ws))
with torch.no_grad():
model.eval()
likelihood.eval()
test_preds = likelihood(model(train_x)).mean.squeeze()
mean_abs_error = torch.mean(torch.abs(train_y - test_preds) / 2)
self.assertLess(mean_abs_error.item(), 1e-1)
def test_loading_old_model(self):
train_x, train_y = train_data(cuda=False)
likelihood = GaussianLikelihood()
model = SVGPRegressionModel(
torch.linspace(0, 1, 25),
gpytorch.variational.CholeskyVariationalDistribution)
state_dicts = torch.load(
"test/examples/old_variational_strategy_model.pth")
likelihood.load_state_dict(state_dicts["likelihood"], strict=False)
# Ensure we get a warning
with warnings.catch_warnings(record=True) as ws:
model.load_state_dict(state_dicts["model"])
self.assertTrue(
any(issubclass(w.category, OldVersionWarning) for w in ws))
with torch.no_grad():
model.eval()
likelihood.eval()
test_preds = likelihood(model(train_x)).mean.squeeze()
mean_abs_error = torch.mean(torch.abs(train_y - test_preds) / 2)
self.assertLess(mean_abs_error.item(), 1e-1)
