renamings

tests/test_convolution.py

@@ -39,7 +39,7 @@ from torch.autograd import gradcheck
 from torch_harmonics import quadrature, DiscreteContinuousConvS2, DiscreteContinuousConvTransposeS2
 
 from torch_harmonics.quadrature import _precompute_grid, _precompute_latitudes, _precompute_longitudes
-
+from torch_harmonics.convolution import _precompute_convolution_tensor_s2
 
 _devices = [(torch.device("cpu"),)]
 if torch.cuda.is_available():
@@ -199,9 +199,10 @@ class TestDiscreteContinuousConvolution(unittest.TestCase):
             [8, 4, 2, (8, 16), (16, 32), (5), "piecewise linear", "mean", "equiangular", "legendre-gauss", True, 1e-4, False],
             [8, 4, 2, (8, 16), (16, 32), (5), "piecewise linear", "mean", "legendre-gauss", "equiangular", True, 1e-4, False],
             [8, 4, 2, (8, 16), (16, 32), (5), "piecewise linear", "mean", "legendre-gauss", "legendre-gauss", True, 1e-4, False],
-        ]
+        ],
+        skip_on_empty=True,
     )
-    def test_disco_convolution(
+    def test_forward_backward(
         self,
         batch_size,
         in_channels,
@@ -321,7 +322,8 @@ class TestDiscreteContinuousConvolution(unittest.TestCase):
             [8, 4, 2, (16, 32), (8, 16), (5), "piecewise linear", "mean", "legendre-gauss", "legendre-gauss", False, 1e-4, False],
             [8, 4, 2, (16, 32), (16, 32), (3), "piecewise linear", "mean", "equiangular", "equiangular", True, 1e-4, False],
             [8, 4, 2, (8, 16), (16, 32), (5), "piecewise linear", "mean", "legendre-gauss", "legendre-gauss", True, 1e-4, False],
-        ]
+        ],
+        skip_on_empty=True,
     )
     @unittest.skipIf(not torch.cuda.is_available(), "CUDA is not available")
     def test_device_instantiation(self, batch_size, in_channels, out_channels, in_shape, out_shape, kernel_shape, basis_type, basis_norm_mode, grid_in, grid_out, transpose, tol, verbose):
@@ -334,7 +336,10 @@ class TestDiscreteContinuousConvolution(unittest.TestCase):
         else:
             theta_cutoff = (kernel_shape[0] + 1) * torch.pi / float(nlat_in - 1)
 
+        # get handle
         Conv = DiscreteContinuousConvTransposeS2 if transpose else DiscreteContinuousConvS2
+
+        # init on cpu
         conv_host = Conv(
             in_channels,
             out_channels,
@@ -350,8 +355,8 @@ class TestDiscreteContinuousConvolution(unittest.TestCase):
             theta_cutoff=theta_cutoff,
         )
 
-        torch.set_default_device(self.device)
-        #with(self.device):
+        #torch.set_default_device(self.device)
+        with torch.device(self.device):
             conv_device = Conv(
                 in_channels,
                 out_channels,
@@ -367,8 +372,8 @@ class TestDiscreteContinuousConvolution(unittest.TestCase):
                 theta_cutoff=theta_cutoff,
             )
 
-        print(conv_host.psi_col_idx.device, conv_device.psi_col_idx.device)
-
+        # since we specified the device specifier everywhere, it should always
+        # use the cpu and it should be the same everywhere
         self.assertTrue(torch.allclose(conv_host.psi_col_idx.cpu(), conv_device.psi_col_idx.cpu()))
         self.assertTrue(torch.allclose(conv_host.psi_row_idx.cpu(), conv_device.psi_row_idx.cpu()))
         self.assertTrue(torch.allclose(conv_host.psi_roff_idx.cpu(), conv_device.psi_roff_idx.cpu()))

tests/test_sht.py

@@ -107,9 +107,10 @@ class TestSphericalHarmonicTransform(unittest.TestCase):
             [33, 64, 32, "schmidt", "equiangular", 1e-9, False],
             [33, 64, 32, "schmidt", "legendre-gauss", 1e-9, False],
             [33, 64, 32, "schmidt", "lobatto", 1e-9, False],
-        ]
+        ],
+        skip_on_empty=True,
     )
-    def test_sht(self, nlat, nlon, batch_size, norm, grid, tol, verbose):
+    def test_forward_inverse(self, nlat, nlon, batch_size, norm, grid, tol, verbose):
         if verbose:
             print(f"Testing real-valued SHT on {nlat}x{nlon} {grid} grid with {norm} normalization on {self.device.type} device")
 
@@ -168,9 +169,10 @@ class TestSphericalHarmonicTransform(unittest.TestCase):
             [15, 30, 2, "schmidt", "equiangular", 1e-5, False],
             [15, 30, 2, "schmidt", "legendre-gauss", 1e-5, False],
             [15, 30, 2, "schmidt", "lobatto", 1e-5, False],
-        ]
+        ],
+        skip_on_empty=True,
     )
-    def test_sht_grads(self, nlat, nlon, batch_size, norm, grid, tol, verbose):
+    def test_grads(self, nlat, nlon, batch_size, norm, grid, tol, verbose):
         if verbose:
             print(f"Testing gradients of real-valued SHT on {nlat}x{nlon} {grid} grid with {norm} normalization")
 
@@ -202,6 +204,40 @@ class TestSphericalHarmonicTransform(unittest.TestCase):
         test_result = gradcheck(err_handle, grad_input, eps=1e-6, atol=tol)
         self.assertTrue(test_result)
 
+    @parameterized.expand(
+        [
+            # even-even
+            [12, 24, 2, "ortho", "equiangular", 1e-5, False],
+            [12, 24, 2, "ortho", "legendre-gauss", 1e-5, False],
+            [12, 24, 2, "ortho", "lobatto", 1e-5, False],
+        ],
+        skip_on_empty=True,
+    )
+    @unittest.skipIf(not torch.cuda.is_available(), "CUDA is not available")
+    def test_device_instantiation(self, nlat, nlon, batch_size, norm, grid, tol, verbose):
+        if verbose:
+            print(f"Testing device instantiation of real-valued SHT on {nlat}x{nlon} {grid} grid with {norm} normalization")
+
+        if grid == "equiangular":
+            mmax = nlat // 2
+        elif grid == "lobatto":
+            mmax = nlat - 1
+        else:
+            mmax = nlat
+        lmax = mmax
+
+        # init on cpu
+        sht_host = th.RealSHT(nlat, nlon, mmax=mmax, lmax=lmax, grid=grid, norm=norm)
+        isht_host = th.InverseRealSHT(nlat, nlon, mmax=mmax, lmax=lmax, grid=grid, norm=norm)
+
+        # init on device
+        with torch.device(self.device):
+            sht_device = th.RealSHT(nlat, nlon, mmax=mmax, lmax=lmax, grid=grid, norm=norm)
+            isht_device = th.InverseRealSHT(nlat, nlon, mmax=mmax, lmax=lmax, grid=grid, norm=norm)
+
+        self.assertTrue(torch.allclose(sht_host.weights.cpu(), sht_device.weights.cpu()))
+        self.assertTrue(torch.allclose(isht_host.pct.cpu(), isht_device.pct.cpu()))
+
 
 if __name__ == "__main__":
     unittest.main()