979b8c9efa551e8c948a4aca145367a2d87ac8d6,test/distributions/test_multitask_multivariate_normal.py,TestMultiTaskMultivariateNormal,test_multivariate_normal_correlated_sampels,#TestMultiTaskMultivariateNormal#Any#,112

Before Change

    def test_multivariate_normal_correlated_sampels(self, cuda=False):
        device = torch.device("cuda") if cuda else torch.device("cpu")
        mean = torch.tensor([[0, 1], [2, 3]], dtype=torch.float, device=device)
        variance = 1 + torch.arange(4, dtype=torch.float, device=device)
        covmat = torch.diag(variance)
        mtmvn = MultitaskMultivariateNormal(mean=mean, covariance_matrix=covmat)

        base_samples = mtmvn.get_base_samples(torch.Size((3, 4)))
        self.assertTrue(mtmvn.sample(base_samples=base_samples).shape == torch.Size([3, 4, 2, 2]))

        base_samples = mtmvn.get_base_samples()
        self.assertTrue(mtmvn.sample(base_samples=base_samples).shape == torch.Size([2, 2]))

After Change

    def test_multivariate_normal_correlated_sampels(self, cuda=False):
        device = torch.device("cuda") if cuda else torch.device("cpu")
        for dtype in (torch.float, torch.double):
            mean = torch.tensor([[0, 1], [2, 3]], dtype=dtype, device=device)
            variance = torch.tensor([[1, 2], [3, 4]], dtype=dtype, device=device)
            covmat = variance.view(-1).diag()
            mtmvn = MultitaskMultivariateNormal(mean=mean, covariance_matrix=covmat)
            base_samples = mtmvn.get_base_samples(torch.Size([3, 4]))
            self.assertTrue(mtmvn.sample(base_samples=base_samples).shape == torch.Size([3, 4, 2, 2]))
            base_samples = mtmvn.get_base_samples()
            self.assertTrue(mtmvn.sample(base_samples=base_samples).shape == torch.Size([2, 2]))

    def test_multivariate_normal_correlated_sampels_cuda(self):
        if torch.cuda.is_available():
            with least_used_cuda_device():
                self.test_multivariate_normal_correlated_sampels(cuda=True)

Instances