(self, device, dtype)
| 273 | |
| 274 | @parameterized.expand(TESTS) |
| 275 | def test_collate(self, device, dtype): |
| 276 | numel = 3 |
| 277 | ims = [self.get_im(device=device, dtype=dtype)[0] for _ in range(numel)] |
| 278 | ims = [MetaTensor(im, applied_operations=[f"t{i}"]) for i, im in enumerate(ims)] |
| 279 | collated = list_data_collate(ims) |
| 280 | # tensor |
| 281 | self.assertIsInstance(collated, MetaTensor) |
| 282 | expected_shape = (numel,) + tuple(ims[0].shape) |
| 283 | self.assertTupleEqual(tuple(collated.shape), expected_shape) |
| 284 | for i, im in enumerate(ims): |
| 285 | self.check(im, ims[i], ids=True) |
| 286 | # affine |
| 287 | self.assertIsInstance(collated.affine, torch.Tensor) |
| 288 | expected_shape = (numel,) + tuple(ims[0].affine.shape) |
| 289 | self.assertTupleEqual(tuple(collated.affine.shape), expected_shape) |
| 290 | self.assertEqual(len(collated.applied_operations), numel) |
| 291 | |
| 292 | @parameterized.expand(TESTS) |
| 293 | def test_dataset(self, device, dtype): |
nothing calls this directly
no test coverage detected