(self, coil_sens_model, refinement_model, num_cascades, input_shape, expected_shape)
| 46 | |
| 47 | @parameterized.expand(TESTS) |
| 48 | def test_script(self, coil_sens_model, refinement_model, num_cascades, input_shape, expected_shape): |
| 49 | net = VariationalNetworkModel(coil_sens_model, refinement_model, num_cascades) |
| 50 | |
| 51 | mask_shape = [1 for _ in input_shape] |
| 52 | mask_shape[-2] = input_shape[-2] |
| 53 | mask = torch.zeros(mask_shape) |
| 54 | mask[..., mask_shape[-2] // 2 - 5 : mask_shape[-2] // 2 + 5, :] = 1 |
| 55 | |
| 56 | test_data = torch.randn(input_shape) |
| 57 | |
| 58 | test_script_save(net, test_data, mask.bool()) |
| 59 | |
| 60 | |
| 61 | if __name__ == "__main__": |
nothing calls this directly
no test coverage detected