Test multi-coils in test_autodiff #139
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chaithyagr
requested changes
Jun 26, 2024
src/mrinufft/operators/autodiff.py
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| grid_x = x * grid_r # Element-wise multiplication: x * r | ||
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Lets not add unwanted space.
src/mrinufft/operators/autodiff.py
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| grad_traj = torch.cat( | ||
| [torch.transpose((-1j * torch.conj(dy[:, i, :]) * nufft_dx_dom[:, i, :]), 0, 1)[None, ...] for i in range(dy.shape[1])], | ||
| dim=0, | ||
| ) | ||
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| grad_traj = torch.mean(grad_traj, dim=0).type_as(traj) | ||
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Suggested change
| grad_traj = torch.cat( | |
| [torch.transpose((-1j * torch.conj(dy[:, i, :]) * nufft_dx_dom[:, i, :]), 0, 1)[None, ...] for i in range(dy.shape[1])], | |
| dim=0, | |
| ) | |
| grad_traj = torch.mean(grad_traj, dim=0).type_as(traj) | |
| grad_traj = torch.mean( | |
| [torch.transpose((-1j * torch.conj(dy[:, i, :]) * nufft_dx_dom[:, i, :]), 0, 1)[None, ...] for i in range(dy.shape[1])], | |
| dim=0, | |
| ) |
Maybe just have a single line
src/mrinufft/operators/autodiff.py
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| [torch.transpose((1j * y[i] * inufft_dx_dom[i]), 0, 1)[None, ...] for i in range(y.shape[0])], | ||
| dim=0, | ||
| ) | ||
| grad_traj = torch.mean(grad_traj, dim=0).type_as(traj) |
…ufft into test_autodiff-multicoil
chaithyagr
requested changes
Jun 26, 2024
| @@ -32,14 +31,12 @@ | |||
| (4, 2, True), | |||
| ], | |||
| ) | |||
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| @parametrize_with_cases( | |||
| "kspace_loc, shape", | |||
| cases=[ | |||
| CasesTrajectories.case_nyquist_lowmem_radial3D, | |||
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When did we make this as only 3D test? we need to change this back.
…ufft into test_autodiff-multicoil
chaithyagr
approved these changes
Jun 27, 2024
paquiteau
approved these changes
Jun 27, 2024
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| @with_numpy_cupy | ||
| def data_consistency(self, image_data, obs_data): |
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This will be handled in #140 as we cannot test this right now.
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This will solve #131
Some new configurations about the multi-coils have been set.