Fixed indexing warning of torch meshgrid.

Fixes #406
IMSY-DKFZ · Dec 18, 2024 · 8101182 · 8101182
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commit 8101182
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diff --git a/simpa/core/simulation_modules/reconstruction_module/delay_multiply_and_sum_adapter.py b/simpa/core/simulation_modules/reconstruction_module/delay_multiply_and_sum_adapter.py
@@ -50,7 +50,7 @@ def reconstruction_algorithm(self, time_series_sensor_data, detection_geometry:
             yy, zz, nn, mm = torch.meshgrid(torch.arange(ydim, device=torch_device),
                                             torch.arange(zdim, device=torch_device),
                                             torch.arange(n_sensor_elements, device=torch_device),
-                                            torch.arange(n_sensor_elements, device=torch_device))
+                                            torch.arange(n_sensor_elements, device=torch_device), indexing='ij')
             M = values[x, yy, zz, nn] * values[x, yy, zz, mm]
             M = torch.sign(M) * torch.sqrt(torch.abs(M))
             # only take upper triangle without diagonal and sum up along n and m axis (last two)

diff --git a/simpa/core/simulation_modules/reconstruction_module/reconstruction_utils.py b/simpa/core/simulation_modules/reconstruction_module/reconstruction_utils.py
@@ -542,7 +542,7 @@ def compute_delay_and_sum_values(time_series_sensor_data: Tensor, sensor_positio
         z = zdim_start + torch.arange(zdim, device=torch_device, dtype=torch.float32)
     j = torch.arange(n_sensor_elements, device=torch_device, dtype=torch.float32)
 
-    xx, yy, zz, jj = torch.meshgrid(x, y, z, j)
+    xx, yy, zz, jj = torch.meshgrid(x, y, z, j, indexing='ij')
     jj = jj.long()
 
     delays = torch.sqrt((yy * spacing_in_mm - sensor_positions[:, 2][jj]) ** 2 +

diff --git a/simpa/core/simulation_modules/reconstruction_module/signed_delay_multiply_and_sum_adapter.py b/simpa/core/simulation_modules/reconstruction_module/signed_delay_multiply_and_sum_adapter.py
@@ -53,7 +53,7 @@ def reconstruction_algorithm(self, time_series_sensor_data, detection_geometry:
             yy, zz, nn, mm = torch.meshgrid(torch.arange(ydim, device=torch_device),
                                             torch.arange(zdim, device=torch_device),
                                             torch.arange(n_sensor_elements, device=torch_device),
-                                            torch.arange(n_sensor_elements, device=torch_device))
+                                            torch.arange(n_sensor_elements, device=torch_device), indexing='ij')
             M = values[x, yy, zz, nn] * values[x, yy, zz, mm]
             M = torch.sign(M) * torch.sqrt(torch.abs(M))
             # only take upper triangle without diagonal and sum up along n and m axis (last two)