(self, coil_sens_model, refinement_model, num_cascades, input_shape, expected_shape)
| 34 | class TestVarNet(unittest.TestCase): |
| 35 | @parameterized.expand(TESTS) |
| 36 | def test_shape(self, coil_sens_model, refinement_model, num_cascades, input_shape, expected_shape): |
| 37 | net = VariationalNetworkModel(coil_sens_model, refinement_model, num_cascades).to(device) |
| 38 | mask_shape = [1 for _ in input_shape] |
| 39 | mask_shape[-2] = input_shape[-2] |
| 40 | mask = torch.zeros(mask_shape) |
| 41 | mask[..., mask_shape[-2] // 2 - 5 : mask_shape[-2] // 2 + 5, :] = 1 |
| 42 | |
| 43 | with eval_mode(net): |
| 44 | result = net(torch.randn(input_shape).to(device), mask.bool().to(device)) |
| 45 | self.assertEqual(result.shape, expected_shape) |
| 46 | |
| 47 | @parameterized.expand(TESTS) |
| 48 | def test_script(self, coil_sens_model, refinement_model, num_cascades, input_shape, expected_shape): |
nothing calls this directly
no test coverage detected