Method setUp

tests/transforms/inverse/test_inverse.py:367–395

(self)

    """

    def setUp(self):
        if not has_nib:
            self.skipTest("nibabel required for test_inverse")

        set_determinism(seed=0)

        self.all_data = {}

        affine = make_rand_affine()
        affine[0] *= 2

        for size in [10, 11]:
            # pad 5 onto both ends so that cropping can be lossless
            im_1d = np.pad(np.arange(size), 5)[None]
            name = "1D even" if size % 2 == 0 else "1D odd"
            self.all_data[name] = {
                "image": torch.as_tensor(np.array(im_1d, copy=True)),
                "label": torch.as_tensor(np.array(im_1d, copy=True)),
                "other": torch.as_tensor(np.array(im_1d, copy=True)),
            }

        im_2d_fname, seg_2d_fname = (make_nifti_image(i) for i in create_test_image_2d(101, 100))
        im_3d_fname, seg_3d_fname = (make_nifti_image(i, affine) for i in create_test_image_3d(100, 101, 107))

        load_ims = Compose(
            [LoadImaged(KEYS), EnsureChannelFirstd(KEYS, channel_dim="no_channel"), FromMetaTensord(KEYS)]
        )
        self.all_data["2D"] = load_ims({"image": im_2d_fname, "label": seg_2d_fname})
        self.all_data["3D"] = load_ims({"image": im_3d_fname, "label": seg_3d_fname})

    def tearDown(self):
        set_determinism(seed=None)