(self, im, indices)
| 32 | class TestTranspose(unittest.TestCase): |
| 33 | @parameterized.expand(TESTS) |
| 34 | def test_transpose(self, im, indices): |
| 35 | data = {"i": deepcopy(im), "j": deepcopy(im)} |
| 36 | tr = Transposed(["i", "j"], indices) |
| 37 | out_data = tr(data) |
| 38 | out_im1, out_im2 = out_data["i"], out_data["j"] |
| 39 | if isinstance(im, torch.Tensor): |
| 40 | im = im.cpu().numpy() |
| 41 | out_gt = np.transpose(im, indices) |
| 42 | assert_allclose(out_im1, out_gt, type_test=False) |
| 43 | assert_allclose(out_im2, out_gt, type_test=False) |
| 44 | |
| 45 | # test inverse |
| 46 | fwd_inv_data = tr.inverse(out_data) |
| 47 | for i, j in zip(data.values(), fwd_inv_data.values()): |
| 48 | assert_allclose(i, j, type_test=False) |
| 49 | |
| 50 | |
| 51 | if __name__ == "__main__": |
nothing calls this directly
no test coverage detected