adding option in LSNO to select between upsampling methods

examples/train_sfno.py

@@ -445,7 +445,8 @@ def count_parameters(model):
         normalization_layer="none",
         kernel_shape=[4, 4],
         encoder_kernel_shape=[4, 4],
-        filter_basis_type="morlet"
+        filter_basis_type="morlet",
+        upsample_sht = True,
     )
 
     models[f"lsno_sc2_layers4_e32_zernike"] = partial(
@@ -463,7 +464,8 @@ def count_parameters(model):
         normalization_layer="none",
         kernel_shape=[4],
         encoder_kernel_shape=[4],
-        filter_basis_type="zernike"
+        filter_basis_type="zernike",
+        upsample_sht = True,
     )
 
     # iterate over models and train each model

torch_harmonics/examples/models/lsno.py

@@ -70,7 +70,7 @@ def __init__(
             grid_out=grid_out,
             groups=groups,
             bias=bias,
-            theta_cutoff=1.0 * torch.pi / float(out_shape[0] - 1),
+            theta_cutoff=4.0 * torch.pi / float(out_shape[0] - 1),
         )
 
     def forward(self, x):
@@ -97,13 +97,17 @@ def __init__(
         basis_type="piecewise linear",
         groups=1,
         bias=False,
+        upsample_sht=False
     ):
         super().__init__()
 
-        # # set up
-        self.sht = RealSHT(*in_shape, grid=grid_in).float()
-        self.isht = InverseRealSHT(*out_shape, lmax=self.sht.lmax, mmax=self.sht.mmax, grid=grid_out).float()
-        self.upscale = ResampleS2(*in_shape, *out_shape, grid_in=grid_in, grid_out=grid_out)
+        # set up upsampling
+        if upsample_sht:
+            self.sht = RealSHT(*in_shape, grid=grid_in).float()
+            self.isht = InverseRealSHT(*out_shape, lmax=self.sht.lmax, mmax=self.sht.mmax, grid=grid_out).float()
+            self.upsample = nn.Sequential(self.sht, self.isht)
+        else:
+            self.upsample = ResampleS2(*in_shape, *out_shape, grid_in=grid_in, grid_out=grid_out)
 
         # set up DISCO convolution
         self.conv = DiscreteContinuousConvS2(
@@ -117,19 +121,15 @@ def __init__(
             grid_out=grid_out,
             groups=groups,
             bias=False,
-            theta_cutoff=1.0 * torch.pi / float(in_shape[0] - 1),
+            theta_cutoff=4.0 * torch.pi / float(in_shape[0] - 1),
         )
 
-    def upscale_sht(self, x: torch.Tensor):
-        return self.isht(self.sht(x))
-
     def forward(self, x):
         dtype = x.dtype
-        x = self.upscale(x)
 
         with amp.autocast(device_type="cuda", enabled=False):
             x = x.float()
-            # x = self.upscale_sht(x)
+            x = self.upsample(x)
             x = self.conv(x)
             x = x.to(dtype=dtype)
 
@@ -182,7 +182,7 @@ def __init__(
                 grid_in=forward_transform.grid,
                 grid_out=inverse_transform.grid,
                 bias=False,
-                theta_cutoff=1.0 * (disco_kernel_shape[0] + 1) * torch.pi / float(inverse_transform.nlat - 1),
+                theta_cutoff=4.0 * (disco_kernel_shape[0] + 1) * torch.pi / float(inverse_transform.nlat - 1),
             )
         elif conv_type == "global":
             self.global_conv = SpectralConvS2(forward_transform, inverse_transform, input_dim, output_dim, gain=gain_factor, operator_type=operator_type, bias=False)
@@ -309,6 +309,8 @@ class LocalSphericalNeuralOperatorNet(nn.Module):
         Whether to add a single large skip connection, by default True
     pos_embed : bool, optional
         Whether to use positional embedding, by default True
+    upsample_sht : bool, optional
+        Use SHT upsampling if true, else linear interpolation
 
     Example
     -----------
@@ -359,6 +361,7 @@ def __init__(
         use_complex_kernels=True,
         big_skip=False,
         pos_embed=False,
+        upsample_sht=False,
     ):
         super().__init__()
 
@@ -491,6 +494,7 @@ def __init__(
             basis_type=filter_basis_type,
             groups=1,
             bias=False,
+            upsample_sht=upsample_sht
         )
 
         # # residual prediction