(
self,
prob: float = 0.1,
min_zoom: Sequence[float] | float = 0.9,
max_zoom: Sequence[float] | float = 1.1,
mode: str = InterpolateMode.AREA,
padding_mode: str = NumpyPadMode.EDGE,
align_corners: bool | None = None,
dtype: DtypeLike | torch.dtype = torch.float32,
keep_size: bool = True,
lazy: bool = False,
**kwargs,
)
| 1598 | backend = Zoom.backend |
| 1599 | |
| 1600 | def __init__( |
| 1601 | self, |
| 1602 | prob: float = 0.1, |
| 1603 | min_zoom: Sequence[float] | float = 0.9, |
| 1604 | max_zoom: Sequence[float] | float = 1.1, |
| 1605 | mode: str = InterpolateMode.AREA, |
| 1606 | padding_mode: str = NumpyPadMode.EDGE, |
| 1607 | align_corners: bool | None = None, |
| 1608 | dtype: DtypeLike | torch.dtype = torch.float32, |
| 1609 | keep_size: bool = True, |
| 1610 | lazy: bool = False, |
| 1611 | **kwargs, |
| 1612 | ) -> None: |
| 1613 | RandomizableTransform.__init__(self, prob) |
| 1614 | LazyTransform.__init__(self, lazy=lazy) |
| 1615 | self.min_zoom = ensure_tuple(min_zoom) |
| 1616 | self.max_zoom = ensure_tuple(max_zoom) |
| 1617 | if len(self.min_zoom) != len(self.max_zoom): |
| 1618 | raise ValueError( |
| 1619 | f"min_zoom and max_zoom must have same length, got {len(self.min_zoom)} and {len(self.max_zoom)}." |
| 1620 | ) |
| 1621 | self.mode = mode |
| 1622 | self.padding_mode = padding_mode |
| 1623 | self.align_corners = align_corners |
| 1624 | self.dtype = dtype |
| 1625 | self.keep_size = keep_size |
| 1626 | self.kwargs = kwargs |
| 1627 | |
| 1628 | self._zoom: Sequence[float] = [1.0] |
| 1629 | |
| 1630 | def randomize(self, img: NdarrayOrTensor) -> None: |
| 1631 | super().randomize(None) |
nothing calls this directly
no test coverage detected