Method test_inverse

tests/transforms/inverse/test_inverse.py:426–453

(self, _, data_name, acceptable_diff, is_meta, *transforms)

    @parameterized.expand(TESTS)
    def test_inverse(self, _, data_name, acceptable_diff, is_meta, *transforms):
        name = _

        data = self.all_data[data_name]
        if is_meta:
            data = ToMetaTensord(KEYS)(data)

        forwards = [data.copy()]

        # Apply forwards
        for t in transforms:
            if isinstance(t, Randomizable):
                t.set_random_state(seed=get_seed())
            forwards.append(t(forwards[-1]))

        # Apply inverses
        fwd_bck = forwards[-1].copy()
        for i, t in enumerate(reversed(transforms)):
            if isinstance(t, InvertibleTransform):
                if isinstance(fwd_bck, list):
                    for j, _fwd_bck in enumerate(fwd_bck):
                        fwd_bck = t.inverse(_fwd_bck)
                        self.check_inverse(
                            name, data.keys(), forwards[-i - 2], fwd_bck, forwards[-1][j], acceptable_diff
                        )
                else:
                    fwd_bck = t.inverse(fwd_bck)
                    self.check_inverse(name, data.keys(), forwards[-i - 2], fwd_bck, forwards[-1], acceptable_diff)

    # skip this test if multiprocessing uses 'spawn', as the check is only basic anyway
    @skipUnless(torch.multiprocessing.get_start_method() == "spawn", "requires spawn")