(
self,
range_x: tuple[float, float] | float = 0.0,
range_y: tuple[float, float] | float = 0.0,
range_z: tuple[float, float] | float = 0.0,
prob: float = 0.1,
keep_size: bool = True,
mode: str = GridSampleMode.BILINEAR,
padding_mode: str = GridSamplePadMode.BORDER,
align_corners: bool = False,
dtype: DtypeLike | torch.dtype = np.float32,
lazy: bool = False,
)
| 1337 | backend = Rotate.backend |
| 1338 | |
| 1339 | def __init__( |
| 1340 | self, |
| 1341 | range_x: tuple[float, float] | float = 0.0, |
| 1342 | range_y: tuple[float, float] | float = 0.0, |
| 1343 | range_z: tuple[float, float] | float = 0.0, |
| 1344 | prob: float = 0.1, |
| 1345 | keep_size: bool = True, |
| 1346 | mode: str = GridSampleMode.BILINEAR, |
| 1347 | padding_mode: str = GridSamplePadMode.BORDER, |
| 1348 | align_corners: bool = False, |
| 1349 | dtype: DtypeLike | torch.dtype = np.float32, |
| 1350 | lazy: bool = False, |
| 1351 | ) -> None: |
| 1352 | RandomizableTransform.__init__(self, prob) |
| 1353 | LazyTransform.__init__(self, lazy=lazy) |
| 1354 | self.range_x = ensure_tuple(range_x) |
| 1355 | if len(self.range_x) == 1: |
| 1356 | self.range_x = tuple(sorted([-self.range_x[0], self.range_x[0]])) |
| 1357 | self.range_y = ensure_tuple(range_y) |
| 1358 | if len(self.range_y) == 1: |
| 1359 | self.range_y = tuple(sorted([-self.range_y[0], self.range_y[0]])) |
| 1360 | self.range_z = ensure_tuple(range_z) |
| 1361 | if len(self.range_z) == 1: |
| 1362 | self.range_z = tuple(sorted([-self.range_z[0], self.range_z[0]])) |
| 1363 | |
| 1364 | self.keep_size = keep_size |
| 1365 | self.mode: str = mode |
| 1366 | self.padding_mode: str = padding_mode |
| 1367 | self.align_corners = align_corners |
| 1368 | self.dtype = dtype |
| 1369 | |
| 1370 | self.x = 0.0 |
| 1371 | self.y = 0.0 |
| 1372 | self.z = 0.0 |
| 1373 | |
| 1374 | def randomize(self, data: Any | None = None) -> None: |
| 1375 | super().randomize(None) |
nothing calls this directly
no test coverage detected