DeepRegNet / DeepReg

        )

    ]

    def test_build_model(self, model, labeled, backbone):

        expected_outputs_len = 3 if labeled else 2

        if backbone == "global":

            expected_outputs_len += 1

            theta = model._outputs["theta"]

            assert theta.shape == (batch_size, 4, 3)

        assert len(model._outputs) == expected_outputs_len

        ddf = model._outputs["ddf"]

        pred_fixed_image = model._outputs["pred_fixed_image"]

        assert ddf.shape == (batch_size, *fixed_image_size, 3)

        assert pred_fixed_image.shape == (batch_size, *fixed_image_size)

        if labeled:

            pred_fixed_label = model._outputs["pred_fixed_label"]

            assert pred_fixed_label.shape == (batch_size, *fixed_image_size)

    def test_build_loss(self, model, labeled, backbone):

        expected = 3 if labeled else 2

        )

    ]

    def test_build_model(self, model, labeled, backbone):

        expected_outputs_len = 4 if labeled else 3

        assert len(model._outputs) == expected_outputs_len

        dvf = model._outputs["dvf"]

        ddf = model._outputs["ddf"]

        pred_fixed_image = model._outputs["pred_fixed_image"]

        assert dvf.shape == (batch_size, *fixed_image_size, 3)

        assert ddf.shape == (batch_size, *fixed_image_size, 3)

        assert pred_fixed_image.shape == (batch_size, *fixed_image_size)

        if labeled:

            pred_fixed_label = model._outputs["pred_fixed_label"]

            assert pred_fixed_label.shape == (batch_size, *fixed_image_size)

    def test_build_loss(self, model, labeled, backbone):

        expected = 3 if labeled else 2