Source code for torchvision.transforms.v2._geometry
import math
import numbers
import warnings
from typing import Any, cast, Dict, List, Literal, Optional, Sequence, Tuple, Type, Union
import PIL.Image
import torch
from torchvision import datapoints, transforms as _transforms
from torchvision.ops.boxes import box_iou
from torchvision.transforms.functional import _get_perspective_coeffs
from torchvision.transforms.v2 import functional as F, InterpolationMode, Transform
from torchvision.transforms.v2.functional._geometry import _check_interpolation
from ._transform import _RandomApplyTransform
from ._utils import (
_check_padding_arg,
_check_padding_mode_arg,
_check_sequence_input,
_setup_angle,
_setup_fill_arg,
_setup_float_or_seq,
_setup_size,
)
from .utils import has_all, has_any, is_simple_tensor, query_bounding_box, query_spatial_size
[docs]class RandomHorizontalFlip(_RandomApplyTransform):
"""[BETA] Horizontally flip the input with a given probability.
.. v2betastatus:: RandomHorizontalFlip transform
If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
:class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
it can have arbitrary number of leading batch dimensions. For example,
the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
Args:
p (float, optional): probability of the input being flipped. Default value is 0.5
"""
_v1_transform_cls = _transforms.RandomHorizontalFlip
def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
return F.horizontal_flip(inpt)
[docs]class RandomVerticalFlip(_RandomApplyTransform):
"""[BETA] Vertically flip the input with a given probability.
.. v2betastatus:: RandomVerticalFlip transform
If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
:class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
it can have arbitrary number of leading batch dimensions. For example,
the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
Args:
p (float, optional): probability of the input being flipped. Default value is 0.5
"""
_v1_transform_cls = _transforms.RandomVerticalFlip
def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
return F.vertical_flip(inpt)
[docs]class Resize(Transform):
"""[BETA] Resize the input to the given size.
.. v2betastatus:: Resize transform
If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
:class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
it can have arbitrary number of leading batch dimensions. For example,
the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
.. warning::
The output image might be different depending on its type: when downsampling, the interpolation of PIL images
and tensors is slightly different, because PIL applies antialiasing. This may lead to significant differences
in the performance of a network. Therefore, it is preferable to train and serve a model with the same input
types. See also below the ``antialias`` parameter, which can help making the output of PIL images and tensors
closer.
Args:
size (sequence or int): Desired output size. If size is a sequence like
(h, w), output size will be matched to this. If size is an int,
smaller edge of the image will be matched to this number.
i.e, if height > width, then image will be rescaled to
(size * height / width, size).
.. note::
In torchscript mode size as single int is not supported, use a sequence of length 1: ``[size, ]``.
interpolation (InterpolationMode, optional): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``,
``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well.
max_size (int, optional): The maximum allowed for the longer edge of
the resized image: if the longer edge of the image is greater
than ``max_size`` after being resized according to ``size``, then
the image is resized again so that the longer edge is equal to
``max_size``. As a result, ``size`` might be overruled, i.e. the
smaller edge may be shorter than ``size``. This is only supported
if ``size`` is an int (or a sequence of length 1 in torchscript
mode).
antialias (bool, optional): Whether to apply antialiasing.
It only affects **tensors** with bilinear or bicubic modes and it is
ignored otherwise: on PIL images, antialiasing is always applied on
bilinear or bicubic modes; on other modes (for PIL images and
tensors), antialiasing makes no sense and this parameter is ignored.
Possible values are:
- ``True``: will apply antialiasing for bilinear or bicubic modes.
Other mode aren't affected. This is probably what you want to use.
- ``False``: will not apply antialiasing for tensors on any mode. PIL
images are still antialiased on bilinear or bicubic modes, because
PIL doesn't support no antialias.
- ``None``: equivalent to ``False`` for tensors and ``True`` for
PIL images. This value exists for legacy reasons and you probably
don't want to use it unless you really know what you are doing.
The current default is ``None`` **but will change to** ``True`` **in
v0.17** for the PIL and Tensor backends to be consistent.
"""
_v1_transform_cls = _transforms.Resize
def __init__(
self,
size: Union[int, Sequence[int]],
interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
max_size: Optional[int] = None,
antialias: Optional[Union[str, bool]] = "warn",
) -> None:
super().__init__()
if isinstance(size, int):
size = [size]
elif isinstance(size, (list, tuple)) and len(size) in {1, 2}:
size = list(size)
else:
raise ValueError(
f"size can either be an integer or a list or tuple of one or two integers, " f"but got {size} instead."
)
self.size = size
self.interpolation = _check_interpolation(interpolation)
self.max_size = max_size
self.antialias = antialias
def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
return F.resize(
inpt,
self.size,
interpolation=self.interpolation,
max_size=self.max_size,
antialias=self.antialias,
)
[docs]class CenterCrop(Transform):
"""[BETA] Crop the input at the center.
.. v2betastatus:: CenterCrop transform
If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
:class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
it can have arbitrary number of leading batch dimensions. For example,
the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
If image size is smaller than output size along any edge, image is padded with 0 and then center cropped.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
"""
_v1_transform_cls = _transforms.CenterCrop
def __init__(self, size: Union[int, Sequence[int]]):
super().__init__()
self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
return F.center_crop(inpt, output_size=self.size)
[docs]class RandomResizedCrop(Transform):
"""[BETA] Crop a random portion of the input and resize it to a given size.
.. v2betastatus:: RandomResizedCrop transform
If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
:class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
it can have arbitrary number of leading batch dimensions. For example,
the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
A crop of the original input is made: the crop has a random area (H * W)
and a random aspect ratio. This crop is finally resized to the given
size. This is popularly used to train the Inception networks.
Args:
size (int or sequence): expected output size of the crop, for each edge. If size is an
int instead of sequence like (h, w), a square output size ``(size, size)`` is
made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
.. note::
In torchscript mode size as single int is not supported, use a sequence of length 1: ``[size, ]``.
scale (tuple of float, optional): Specifies the lower and upper bounds for the random area of the crop,
before resizing. The scale is defined with respect to the area of the original image.
ratio (tuple of float, optional): lower and upper bounds for the random aspect ratio of the crop, before
resizing.
interpolation (InterpolationMode, optional): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``,
``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well.
antialias (bool, optional): Whether to apply antialiasing.
It only affects **tensors** with bilinear or bicubic modes and it is
ignored otherwise: on PIL images, antialiasing is always applied on
bilinear or bicubic modes; on other modes (for PIL images and
tensors), antialiasing makes no sense and this parameter is ignored.
Possible values are:
- ``True``: will apply antialiasing for bilinear or bicubic modes.
Other mode aren't affected. This is probably what you want to use.
- ``False``: will not apply antialiasing for tensors on any mode. PIL
images are still antialiased on bilinear or bicubic modes, because
PIL doesn't support no antialias.
- ``None``: equivalent to ``False`` for tensors and ``True`` for
PIL images. This value exists for legacy reasons and you probably
don't want to use it unless you really know what you are doing.
The current default is ``None`` **but will change to** ``True`` **in
v0.17** for the PIL and Tensor backends to be consistent.
"""
_v1_transform_cls = _transforms.RandomResizedCrop
def __init__(
self,
size: Union[int, Sequence[int]],
scale: Tuple[float, float] = (0.08, 1.0),
ratio: Tuple[float, float] = (3.0 / 4.0, 4.0 / 3.0),
interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
antialias: Optional[Union[str, bool]] = "warn",
) -> None:
super().__init__()
self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
if not isinstance(scale, Sequence):
raise TypeError("Scale should be a sequence")
scale = cast(Tuple[float, float], scale)
if not isinstance(ratio, Sequence):
raise TypeError("Ratio should be a sequence")
ratio = cast(Tuple[float, float], ratio)
if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
warnings.warn("Scale and ratio should be of kind (min, max)")
self.scale = scale
self.ratio = ratio
self.interpolation = _check_interpolation(interpolation)
self.antialias = antialias
self._log_ratio = torch.log(torch.tensor(self.ratio))
def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
height, width = query_spatial_size(flat_inputs)
area = height * width
log_ratio = self._log_ratio
for _ in range(10):
target_area = area * torch.empty(1).uniform_(self.scale[0], self.scale[1]).item()
aspect_ratio = torch.exp(
torch.empty(1).uniform_(
log_ratio[0], # type: ignore[arg-type]
log_ratio[1], # type: ignore[arg-type]
)
).item()
w = int(round(math.sqrt(target_area * aspect_ratio)))
h = int(round(math.sqrt(target_area / aspect_ratio)))
if 0 < w <= width and 0 < h <= height:
i = torch.randint(0, height - h + 1, size=(1,)).item()
j = torch.randint(0, width - w + 1, size=(1,)).item()
break
else:
# Fallback to central crop
in_ratio = float(width) / float(height)
if in_ratio < min(self.ratio):
w = width
h = int(round(w / min(self.ratio)))
elif in_ratio > max(self.ratio):
h = height
w = int(round(h * max(self.ratio)))
else: # whole image
w = width
h = height
i = (height - h) // 2
j = (width - w) // 2
return dict(top=i, left=j, height=h, width=w)
def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
return F.resized_crop(
inpt, **params, size=self.size, interpolation=self.interpolation, antialias=self.antialias
)
ImageOrVideoTypeJIT = Union[datapoints._ImageTypeJIT, datapoints._VideoTypeJIT]
[docs]class FiveCrop(Transform):
"""[BETA] Crop the image or video into four corners and the central crop.
.. v2betastatus:: FiveCrop transform
If the input is a :class:`torch.Tensor` or a :class:`~torchvision.datapoints.Image` or a
:class:`~torchvision.datapoints.Video` it can have arbitrary number of leading batch dimensions.
For example, the image can have ``[..., C, H, W]`` shape.
.. Note::
This transform returns a tuple of images and there may be a mismatch in the number of
inputs and targets your Dataset returns. See below for an example of how to deal with
this.
Args:
size (sequence or int): Desired output size of the crop. If size is an ``int``
instead of sequence like (h, w), a square crop of size (size, size) is made.
If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
Example:
>>> class BatchMultiCrop(transforms.Transform):
... def forward(self, sample: Tuple[Tuple[Union[datapoints.Image, datapoints.Video], ...], int]):
... images_or_videos, labels = sample
... batch_size = len(images_or_videos)
... image_or_video = images_or_videos[0]
... images_or_videos = image_or_video.wrap_like(image_or_video, torch.stack(images_or_videos))
... labels = torch.full((batch_size,), label, device=images_or_videos.device)
... return images_or_videos, labels
...
>>> image = datapoints.Image(torch.rand(3, 256, 256))
>>> label = 3
>>> transform = transforms.Compose([transforms.FiveCrop(224), BatchMultiCrop()])
>>> images, labels = transform(image, label)
>>> images.shape
torch.Size([5, 3, 224, 224])
>>> labels
tensor([3, 3, 3, 3, 3])
"""
_v1_transform_cls = _transforms.FiveCrop
_transformed_types = (
datapoints.Image,
PIL.Image.Image,
is_simple_tensor,
datapoints.Video,
)
def __init__(self, size: Union[int, Sequence[int]]) -> None:
super().__init__()
self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
def _transform(
self, inpt: ImageOrVideoTypeJIT, params: Dict[str, Any]
) -> Tuple[ImageOrVideoTypeJIT, ImageOrVideoTypeJIT, ImageOrVideoTypeJIT, ImageOrVideoTypeJIT, ImageOrVideoTypeJIT]:
return F.five_crop(inpt, self.size)
def _check_inputs(self, flat_inputs: List[Any]) -> None:
if has_any(flat_inputs, datapoints.BoundingBox, datapoints.Mask):
raise TypeError(f"BoundingBox'es and Mask's are not supported by {type(self).__name__}()")
[docs]class TenCrop(Transform):
"""[BETA] Crop the image or video into four corners and the central crop plus the flipped version of
these (horizontal flipping is used by default).
.. v2betastatus:: TenCrop transform
If the input is a :class:`torch.Tensor` or a :class:`~torchvision.datapoints.Image` or a
:class:`~torchvision.datapoints.Video` it can have arbitrary number of leading batch dimensions.
For example, the image can have ``[..., C, H, W]`` shape.
See :class:`~torchvision.transforms.v2.FiveCrop` for an example.
.. Note::
This transform returns a tuple of images and there may be a mismatch in the number of
inputs and targets your Dataset returns. See below for an example of how to deal with
this.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
vertical_flip (bool, optional): Use vertical flipping instead of horizontal
"""
_v1_transform_cls = _transforms.TenCrop
_transformed_types = (
datapoints.Image,
PIL.Image.Image,
is_simple_tensor,
datapoints.Video,
)
def __init__(self, size: Union[int, Sequence[int]], vertical_flip: bool = False) -> None:
super().__init__()
self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
self.vertical_flip = vertical_flip
def _check_inputs(self, flat_inputs: List[Any]) -> None:
if has_any(flat_inputs, datapoints.BoundingBox, datapoints.Mask):
raise TypeError(f"BoundingBox'es and Mask's are not supported by {type(self).__name__}()")
def _transform(
self, inpt: Union[datapoints._ImageType, datapoints._VideoType], params: Dict[str, Any]
) -> Tuple[
ImageOrVideoTypeJIT,
ImageOrVideoTypeJIT,
ImageOrVideoTypeJIT,
ImageOrVideoTypeJIT,
ImageOrVideoTypeJIT,
ImageOrVideoTypeJIT,
ImageOrVideoTypeJIT,
ImageOrVideoTypeJIT,
ImageOrVideoTypeJIT,
ImageOrVideoTypeJIT,
]:
return F.ten_crop(inpt, self.size, vertical_flip=self.vertical_flip)
[docs]class Pad(Transform):
"""[BETA] Pad the input on all sides with the given "pad" value.
.. v2betastatus:: Pad transform
If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
:class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
it can have arbitrary number of leading batch dimensions. For example,
the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
Args:
padding (int or sequence): Padding on each border. If a single int is provided this
is used to pad all borders. If sequence of length 2 is provided this is the padding
on left/right and top/bottom respectively. If a sequence of length 4 is provided
this is the padding for the left, top, right and bottom borders respectively.
.. note::
In torchscript mode padding as single int is not supported, use a sequence of
length 1: ``[padding, ]``.
fill (number or tuple or dict, optional): Pixel fill value used when the ``padding_mode`` is constant.
Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively.
Fill value can be also a dictionary mapping data type to the fill value, e.g.
``fill={datapoints.Image: 127, datapoints.Mask: 0}`` where ``Image`` will be filled with 127 and
``Mask`` will be filled with 0.
padding_mode (str, optional): Type of padding. Should be: constant, edge, reflect or symmetric.
Default is "constant".
- constant: pads with a constant value, this value is specified with fill
- edge: pads with the last value at the edge of the image.
- reflect: pads with reflection of image without repeating the last value on the edge.
For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
will result in [3, 2, 1, 2, 3, 4, 3, 2]
- symmetric: pads with reflection of image repeating the last value on the edge.
For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
will result in [2, 1, 1, 2, 3, 4, 4, 3]
"""
_v1_transform_cls = _transforms.Pad
def _extract_params_for_v1_transform(self) -> Dict[str, Any]:
params = super()._extract_params_for_v1_transform()
if not (params["fill"] is None or isinstance(params["fill"], (int, float))):
raise ValueError(f"{type(self).__name__}() can only be scripted for a scalar `fill`, but got {self.fill}.")
return params
def __init__(
self,
padding: Union[int, Sequence[int]],
fill: Union[datapoints._FillType, Dict[Type, datapoints._FillType]] = 0,
padding_mode: Literal["constant", "edge", "reflect", "symmetric"] = "constant",
) -> None:
super().__init__()
_check_padding_arg(padding)
_check_padding_mode_arg(padding_mode)
# This cast does Sequence[int] -> List[int] and is required to make mypy happy
if not isinstance(padding, int):
padding = list(padding)
self.padding = padding
self.fill = fill
self._fill = _setup_fill_arg(fill)
self.padding_mode = padding_mode
def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
fill = self._fill[type(inpt)]
return F.pad(inpt, padding=self.padding, fill=fill, padding_mode=self.padding_mode) # type: ignore[arg-type]
[docs]class RandomZoomOut(_RandomApplyTransform):
"""[BETA] "Zoom out" transformation from
`"SSD: Single Shot MultiBox Detector" <https://arxiv.org/abs/1512.02325>`_.
.. v2betastatus:: RandomZoomOut transform
This transformation randomly pads images, videos, bounding boxes and masks creating a zoom out effect.
Output spatial size is randomly sampled from original size up to a maximum size configured
with ``side_range`` parameter:
.. code-block:: python
r = uniform_sample(side_range[0], side_range[1])
output_width = input_width * r
output_height = input_height * r
If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
:class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
it can have arbitrary number of leading batch dimensions. For example,
the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
Args:
fill (number or tuple or dict, optional): Pixel fill value used when the ``padding_mode`` is constant.
Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively.
Fill value can be also a dictionary mapping data type to the fill value, e.g.
``fill={datapoints.Image: 127, datapoints.Mask: 0}`` where ``Image`` will be filled with 127 and
``Mask`` will be filled with 0.
side_range (sequence of floats, optional): tuple of two floats defines minimum and maximum factors to
scale the input size.
p (float, optional): probability of the input being flipped. Default value is 0.5
"""
def __init__(
self,
fill: Union[datapoints._FillType, Dict[Type, datapoints._FillType]] = 0,
side_range: Sequence[float] = (1.0, 4.0),
p: float = 0.5,
) -> None:
super().__init__(p=p)
self.fill = fill
self._fill = _setup_fill_arg(fill)
_check_sequence_input(side_range, "side_range", req_sizes=(2,))
self.side_range = side_range
if side_range[0] < 1.0 or side_range[0] > side_range[1]:
raise ValueError(f"Invalid canvas side range provided {side_range}.")
def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
orig_h, orig_w = query_spatial_size(flat_inputs)
r = self.side_range[0] + torch.rand(1) * (self.side_range[1] - self.side_range[0])
canvas_width = int(orig_w * r)
canvas_height = int(orig_h * r)
r = torch.rand(2)
left = int((canvas_width - orig_w) * r[0])
top = int((canvas_height - orig_h) * r[1])
right = canvas_width - (left + orig_w)
bottom = canvas_height - (top + orig_h)
padding = [left, top, right, bottom]
return dict(padding=padding)
def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
fill = self._fill[type(inpt)]
return F.pad(inpt, **params, fill=fill)
[docs]class RandomRotation(Transform):
"""[BETA] Rotate the input by angle.
.. v2betastatus:: RandomRotation transform
If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
:class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
it can have arbitrary number of leading batch dimensions. For example,
the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
Args:
degrees (sequence or number): Range of degrees to select from.
If degrees is a number instead of sequence like (min, max), the range of degrees
will be (-degrees, +degrees).
interpolation (InterpolationMode, optional): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well.
expand (bool, optional): Optional expansion flag.
If true, expands the output to make it large enough to hold the entire rotated image.
If false or omitted, make the output image the same size as the input image.
Note that the expand flag assumes rotation around the center and no translation.
center (sequence, optional): Optional center of rotation, (x, y). Origin is the upper left corner.
Default is the center of the image.
fill (number or tuple or dict, optional): Pixel fill value used when the ``padding_mode`` is constant.
Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively.
Fill value can be also a dictionary mapping data type to the fill value, e.g.
``fill={datapoints.Image: 127, datapoints.Mask: 0}`` where ``Image`` will be filled with 127 and
``Mask`` will be filled with 0.
.. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters
"""
_v1_transform_cls = _transforms.RandomRotation
def __init__(
self,
degrees: Union[numbers.Number, Sequence],
interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
expand: bool = False,
center: Optional[List[float]] = None,
fill: Union[datapoints._FillType, Dict[Type, datapoints._FillType]] = 0,
) -> None:
super().__init__()
self.degrees = _setup_angle(degrees, name="degrees", req_sizes=(2,))
self.interpolation = _check_interpolation(interpolation)
self.expand = expand
self.fill = fill
self._fill = _setup_fill_arg(fill)
if center is not None:
_check_sequence_input(center, "center", req_sizes=(2,))
self.center = center
def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
angle = torch.empty(1).uniform_(self.degrees[0], self.degrees[1]).item()
return dict(angle=angle)
def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
fill = self._fill[type(inpt)]
return F.rotate(
inpt,
**params,
interpolation=self.interpolation,
expand=self.expand,
center=self.center,
fill=fill,
)
[docs]class RandomAffine(Transform):
"""[BETA] Random affine transformation the input keeping center invariant.
.. v2betastatus:: RandomAffine transform
If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
:class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
it can have arbitrary number of leading batch dimensions. For example,
the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
Args:
degrees (sequence or number): Range of degrees to select from.
If degrees is a number instead of sequence like (min, max), the range of degrees
will be (-degrees, +degrees). Set to 0 to deactivate rotations.
translate (tuple, optional): tuple of maximum absolute fraction for horizontal
and vertical translations. For example translate=(a, b), then horizontal shift
is randomly sampled in the range -img_width * a < dx < img_width * a and vertical shift is
randomly sampled in the range -img_height * b < dy < img_height * b. Will not translate by default.
scale (tuple, optional): scaling factor interval, e.g (a, b), then scale is
randomly sampled from the range a <= scale <= b. Will keep original scale by default.
shear (sequence or number, optional): Range of degrees to select from.
If shear is a number, a shear parallel to the x-axis in the range (-shear, +shear)
will be applied. Else if shear is a sequence of 2 values a shear parallel to the x-axis in the
range (shear[0], shear[1]) will be applied. Else if shear is a sequence of 4 values,
an x-axis shear in (shear[0], shear[1]) and y-axis shear in (shear[2], shear[3]) will be applied.
Will not apply shear by default.
interpolation (InterpolationMode, optional): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well.
fill (number or tuple or dict, optional): Pixel fill value used when the ``padding_mode`` is constant.
Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively.
Fill value can be also a dictionary mapping data type to the fill value, e.g.
``fill={datapoints.Image: 127, datapoints.Mask: 0}`` where ``Image`` will be filled with 127 and
``Mask`` will be filled with 0.
center (sequence, optional): Optional center of rotation, (x, y). Origin is the upper left corner.
Default is the center of the image.
.. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters
"""
_v1_transform_cls = _transforms.RandomAffine
def __init__(
self,
degrees: Union[numbers.Number, Sequence],
translate: Optional[Sequence[float]] = None,
scale: Optional[Sequence[float]] = None,
shear: Optional[Union[int, float, Sequence[float]]] = None,
interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
fill: Union[datapoints._FillType, Dict[Type, datapoints._FillType]] = 0,
center: Optional[List[float]] = None,
) -> None:
super().__init__()
self.degrees = _setup_angle(degrees, name="degrees", req_sizes=(2,))
if translate is not None:
_check_sequence_input(translate, "translate", req_sizes=(2,))
for t in translate:
if not (0.0 <= t <= 1.0):
raise ValueError("translation values should be between 0 and 1")
self.translate = translate
if scale is not None:
_check_sequence_input(scale, "scale", req_sizes=(2,))
for s in scale:
if s <= 0:
raise ValueError("scale values should be positive")
self.scale = scale
if shear is not None:
self.shear = _setup_angle(shear, name="shear", req_sizes=(2, 4))
else:
self.shear = shear
self.interpolation = _check_interpolation(interpolation)
self.fill = fill
self._fill = _setup_fill_arg(fill)
if center is not None:
_check_sequence_input(center, "center", req_sizes=(2,))
self.center = center
def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
height, width = query_spatial_size(flat_inputs)
angle = torch.empty(1).uniform_(self.degrees[0], self.degrees[1]).item()
if self.translate is not None:
max_dx = float(self.translate[0] * width)
max_dy = float(self.translate[1] * height)
tx = int(round(torch.empty(1).uniform_(-max_dx, max_dx).item()))
ty = int(round(torch.empty(1).uniform_(-max_dy, max_dy).item()))
translate = (tx, ty)
else:
translate = (0, 0)
if self.scale is not None:
scale = torch.empty(1).uniform_(self.scale[0], self.scale[1]).item()
else:
scale = 1.0
shear_x = shear_y = 0.0
if self.shear is not None:
shear_x = torch.empty(1).uniform_(self.shear[0], self.shear[1]).item()
if len(self.shear) == 4:
shear_y = torch.empty(1).uniform_(self.shear[2], self.shear[3]).item()
shear = (shear_x, shear_y)
return dict(angle=angle, translate=translate, scale=scale, shear=shear)
def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
fill = self._fill[type(inpt)]
return F.affine(
inpt,
**params,
interpolation=self.interpolation,
fill=fill,
center=self.center,
)
[docs]class RandomCrop(Transform):
"""[BETA] Crop the input at a random location.
.. v2betastatus:: RandomCrop transform
If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
:class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
it can have arbitrary number of leading batch dimensions. For example,
the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
padding (int or sequence, optional): Optional padding on each border
of the image. Default is None. If a single int is provided this
is used to pad all borders. If sequence of length 2 is provided this is the padding
on left/right and top/bottom respectively. If a sequence of length 4 is provided
this is the padding for the left, top, right and bottom borders respectively.
.. note::
In torchscript mode padding as single int is not supported, use a sequence of
length 1: ``[padding, ]``.
pad_if_needed (boolean, optional): It will pad the image if smaller than the
desired size to avoid raising an exception. Since cropping is done
after padding, the padding seems to be done at a random offset.
fill (number or tuple or dict, optional): Pixel fill value used when the ``padding_mode`` is constant.
Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively.
Fill value can be also a dictionary mapping data type to the fill value, e.g.
``fill={datapoints.Image: 127, datapoints.Mask: 0}`` where ``Image`` will be filled with 127 and
``Mask`` will be filled with 0.
padding_mode (str, optional): Type of padding. Should be: constant, edge, reflect or symmetric.
Default is constant.
- constant: pads with a constant value, this value is specified with fill
- edge: pads with the last value at the edge of the image.
- reflect: pads with reflection of image without repeating the last value on the edge.
For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
will result in [3, 2, 1, 2, 3, 4, 3, 2]
- symmetric: pads with reflection of image repeating the last value on the edge.
For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
will result in [2, 1, 1, 2, 3, 4, 4, 3]
"""
_v1_transform_cls = _transforms.RandomCrop
def _extract_params_for_v1_transform(self) -> Dict[str, Any]:
params = super()._extract_params_for_v1_transform()
if not (params["fill"] is None or isinstance(params["fill"], (int, float))):
raise ValueError(f"{type(self).__name__}() can only be scripted for a scalar `fill`, but got {self.fill}.")
padding = self.padding
if padding is not None:
pad_left, pad_right, pad_top, pad_bottom = padding
padding = [pad_left, pad_top, pad_right, pad_bottom]
params["padding"] = padding
return params
def __init__(
self,
size: Union[int, Sequence[int]],
padding: Optional[Union[int, Sequence[int]]] = None,
pad_if_needed: bool = False,
fill: Union[datapoints._FillType, Dict[Type, datapoints._FillType]] = 0,
padding_mode: Literal["constant", "edge", "reflect", "symmetric"] = "constant",
) -> None:
super().__init__()
self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
if pad_if_needed or padding is not None:
if padding is not None:
_check_padding_arg(padding)
_check_padding_mode_arg(padding_mode)
self.padding = F._geometry._parse_pad_padding(padding) if padding else None # type: ignore[arg-type]
self.pad_if_needed = pad_if_needed
self.fill = fill
self._fill = _setup_fill_arg(fill)
self.padding_mode = padding_mode
def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
padded_height, padded_width = query_spatial_size(flat_inputs)
if self.padding is not None:
pad_left, pad_right, pad_top, pad_bottom = self.padding
padded_height += pad_top + pad_bottom
padded_width += pad_left + pad_right
else:
pad_left = pad_right = pad_top = pad_bottom = 0
cropped_height, cropped_width = self.size
if self.pad_if_needed:
if padded_height < cropped_height:
diff = cropped_height - padded_height
pad_top += diff
pad_bottom += diff
padded_height += 2 * diff
if padded_width < cropped_width:
diff = cropped_width - padded_width
pad_left += diff
pad_right += diff
padded_width += 2 * diff
if padded_height < cropped_height or padded_width < cropped_width:
raise ValueError(
f"Required crop size {(cropped_height, cropped_width)} is larger than "
f"{'padded ' if self.padding is not None else ''}input image size {(padded_height, padded_width)}."
)
# We need a different order here than we have in self.padding since this padding will be parsed again in `F.pad`
padding = [pad_left, pad_top, pad_right, pad_bottom]
needs_pad = any(padding)
needs_vert_crop, top = (
(True, int(torch.randint(0, padded_height - cropped_height + 1, size=())))
if padded_height > cropped_height
else (False, 0)
)
needs_horz_crop, left = (
(True, int(torch.randint(0, padded_width - cropped_width + 1, size=())))
if padded_width > cropped_width
else (False, 0)
)
return dict(
needs_crop=needs_vert_crop or needs_horz_crop,
top=top,
left=left,
height=cropped_height,
width=cropped_width,
needs_pad=needs_pad,
padding=padding,
)
def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
if params["needs_pad"]:
fill = self._fill[type(inpt)]
inpt = F.pad(inpt, padding=params["padding"], fill=fill, padding_mode=self.padding_mode)
if params["needs_crop"]:
inpt = F.crop(inpt, top=params["top"], left=params["left"], height=params["height"], width=params["width"])
return inpt
[docs]class RandomPerspective(_RandomApplyTransform):
"""[BETA] Perform a random perspective transformation of the input with a given probability.
.. v2betastatus:: RandomPerspective transform
If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
:class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
it can have arbitrary number of leading batch dimensions. For example,
the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
Args:
distortion_scale (float, optional): argument to control the degree of distortion and ranges from 0 to 1.
Default is 0.5.
p (float, optional): probability of the input being transformed. Default is 0.5.
interpolation (InterpolationMode, optional): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well.
fill (number or tuple or dict, optional): Pixel fill value used when the ``padding_mode`` is constant.
Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively.
Fill value can be also a dictionary mapping data type to the fill value, e.g.
``fill={datapoints.Image: 127, datapoints.Mask: 0}`` where ``Image`` will be filled with 127 and
``Mask`` will be filled with 0.
"""
_v1_transform_cls = _transforms.RandomPerspective
def __init__(
self,
distortion_scale: float = 0.5,
p: float = 0.5,
interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
fill: Union[datapoints._FillType, Dict[Type, datapoints._FillType]] = 0,
) -> None:
super().__init__(p=p)
if not (0 <= distortion_scale <= 1):
raise ValueError("Argument distortion_scale value should be between 0 and 1")
self.distortion_scale = distortion_scale
self.interpolation = _check_interpolation(interpolation)
self.fill = fill
self._fill = _setup_fill_arg(fill)
def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
height, width = query_spatial_size(flat_inputs)
distortion_scale = self.distortion_scale
half_height = height // 2
half_width = width // 2
bound_height = int(distortion_scale * half_height) + 1
bound_width = int(distortion_scale * half_width) + 1
topleft = [
int(torch.randint(0, bound_width, size=(1,))),
int(torch.randint(0, bound_height, size=(1,))),
]
topright = [
int(torch.randint(width - bound_width, width, size=(1,))),
int(torch.randint(0, bound_height, size=(1,))),
]
botright = [
int(torch.randint(width - bound_width, width, size=(1,))),
int(torch.randint(height - bound_height, height, size=(1,))),
]
botleft = [
int(torch.randint(0, bound_width, size=(1,))),
int(torch.randint(height - bound_height, height, size=(1,))),
]
startpoints = [[0, 0], [width - 1, 0], [width - 1, height - 1], [0, height - 1]]
endpoints = [topleft, topright, botright, botleft]
perspective_coeffs = _get_perspective_coeffs(startpoints, endpoints)
return dict(coefficients=perspective_coeffs)
def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
fill = self._fill[type(inpt)]
return F.perspective(
inpt,
None,
None,
fill=fill,
interpolation=self.interpolation,
**params,
)
[docs]class ElasticTransform(Transform):
"""[BETA] Transform the input with elastic transformations.
.. v2betastatus:: RandomPerspective transform
If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
:class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
it can have arbitrary number of leading batch dimensions. For example,
the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
Given alpha and sigma, it will generate displacement
vectors for all pixels based on random offsets. Alpha controls the strength
and sigma controls the smoothness of the displacements.
The displacements are added to an identity grid and the resulting grid is
used to transform the input.
.. note::
Implementation to transform bounding boxes is approximative (not exact).
We construct an approximation of the inverse grid as ``inverse_grid = idenity - displacement``.
This is not an exact inverse of the grid used to transform images, i.e. ``grid = identity + displacement``.
Our assumption is that ``displacement * displacement`` is small and can be ignored.
Large displacements would lead to large errors in the approximation.
Applications:
Randomly transforms the morphology of objects in images and produces a
see-through-water-like effect.
Args:
alpha (float or sequence of floats, optional): Magnitude of displacements. Default is 50.0.
sigma (float or sequence of floats, optional): Smoothness of displacements. Default is 5.0.
interpolation (InterpolationMode, optional): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well.
fill (number or tuple or dict, optional): Pixel fill value used when the ``padding_mode`` is constant.
Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively.
Fill value can be also a dictionary mapping data type to the fill value, e.g.
``fill={datapoints.Image: 127, datapoints.Mask: 0}`` where ``Image`` will be filled with 127 and
``Mask`` will be filled with 0.
"""
_v1_transform_cls = _transforms.ElasticTransform
def __init__(
self,
alpha: Union[float, Sequence[float]] = 50.0,
sigma: Union[float, Sequence[float]] = 5.0,
interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
fill: Union[datapoints._FillType, Dict[Type, datapoints._FillType]] = 0,
) -> None:
super().__init__()
self.alpha = _setup_float_or_seq(alpha, "alpha", 2)
self.sigma = _setup_float_or_seq(sigma, "sigma", 2)
self.interpolation = _check_interpolation(interpolation)
self.fill = fill
self._fill = _setup_fill_arg(fill)
def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
size = list(query_spatial_size(flat_inputs))
dx = torch.rand([1, 1] + size) * 2 - 1
if self.sigma[0] > 0.0:
kx = int(8 * self.sigma[0] + 1)
# if kernel size is even we have to make it odd
if kx % 2 == 0:
kx += 1
dx = F.gaussian_blur(dx, [kx, kx], list(self.sigma))
dx = dx * self.alpha[0] / size[0]
dy = torch.rand([1, 1] + size) * 2 - 1
if self.sigma[1] > 0.0:
ky = int(8 * self.sigma[1] + 1)
# if kernel size is even we have to make it odd
if ky % 2 == 0:
ky += 1
dy = F.gaussian_blur(dy, [ky, ky], list(self.sigma))
dy = dy * self.alpha[1] / size[1]
displacement = torch.concat([dx, dy], 1).permute([0, 2, 3, 1]) # 1 x H x W x 2
return dict(displacement=displacement)
def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
fill = self._fill[type(inpt)]
return F.elastic(
inpt,
**params,
fill=fill,
interpolation=self.interpolation,
)
[docs]class RandomIoUCrop(Transform):
"""[BETA] Random IoU crop transformation from
`"SSD: Single Shot MultiBox Detector" <https://arxiv.org/abs/1512.02325>`_.
.. v2betastatus:: RandomIoUCrop transform
This transformation requires an image or video data and ``datapoints.BoundingBox`` in the input.
.. warning::
In order to properly remove the bounding boxes below the IoU threshold, `RandomIoUCrop`
must be followed by :class:`~torchvision.transforms.v2.SanitizeBoundingBox`, either immediately
after or later in the transforms pipeline.
If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
:class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
it can have arbitrary number of leading batch dimensions. For example,
the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
Args:
min_scale (float, optional): Minimum factors to scale the input size.
max_scale (float, optional): Maximum factors to scale the input size.
min_aspect_ratio (float, optional): Minimum aspect ratio for the cropped image or video.
max_aspect_ratio (float, optional): Maximum aspect ratio for the cropped image or video.
sampler_options (list of float, optional): List of minimal IoU (Jaccard) overlap between all the boxes and
a cropped image or video. Default, ``None`` which corresponds to ``[0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0]``
trials (int, optional): Number of trials to find a crop for a given value of minimal IoU (Jaccard) overlap.
Default, 40.
"""
def __init__(
self,
min_scale: float = 0.3,
max_scale: float = 1.0,
min_aspect_ratio: float = 0.5,
max_aspect_ratio: float = 2.0,
sampler_options: Optional[List[float]] = None,
trials: int = 40,
):
super().__init__()
# Configuration similar to https://github.com/weiliu89/caffe/blob/ssd/examples/ssd/ssd_coco.py#L89-L174
self.min_scale = min_scale
self.max_scale = max_scale
self.min_aspect_ratio = min_aspect_ratio
self.max_aspect_ratio = max_aspect_ratio
if sampler_options is None:
sampler_options = [0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0]
self.options = sampler_options
self.trials = trials
def _check_inputs(self, flat_inputs: List[Any]) -> None:
if not (
has_all(flat_inputs, datapoints.BoundingBox)
and has_any(flat_inputs, PIL.Image.Image, datapoints.Image, is_simple_tensor)
):
raise TypeError(
f"{type(self).__name__}() requires input sample to contain tensor or PIL images "
"and bounding boxes. Sample can also contain masks."
)
def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
orig_h, orig_w = query_spatial_size(flat_inputs)
bboxes = query_bounding_box(flat_inputs)
while True:
# sample an option
idx = int(torch.randint(low=0, high=len(self.options), size=(1,)))
min_jaccard_overlap = self.options[idx]
if min_jaccard_overlap >= 1.0: # a value larger than 1 encodes the leave as-is option
return dict()
for _ in range(self.trials):
# check the aspect ratio limitations
r = self.min_scale + (self.max_scale - self.min_scale) * torch.rand(2)
new_w = int(orig_w * r[0])
new_h = int(orig_h * r[1])
aspect_ratio = new_w / new_h
if not (self.min_aspect_ratio <= aspect_ratio <= self.max_aspect_ratio):
continue
# check for 0 area crops
r = torch.rand(2)
left = int((orig_w - new_w) * r[0])
top = int((orig_h - new_h) * r[1])
right = left + new_w
bottom = top + new_h
if left == right or top == bottom:
continue
# check for any valid boxes with centers within the crop area
xyxy_bboxes = F.convert_format_bounding_box(
bboxes.as_subclass(torch.Tensor), bboxes.format, datapoints.BoundingBoxFormat.XYXY
)
cx = 0.5 * (xyxy_bboxes[..., 0] + xyxy_bboxes[..., 2])
cy = 0.5 * (xyxy_bboxes[..., 1] + xyxy_bboxes[..., 3])
is_within_crop_area = (left < cx) & (cx < right) & (top < cy) & (cy < bottom)
if not is_within_crop_area.any():
continue
# check at least 1 box with jaccard limitations
xyxy_bboxes = xyxy_bboxes[is_within_crop_area]
ious = box_iou(
xyxy_bboxes,
torch.tensor([[left, top, right, bottom]], dtype=xyxy_bboxes.dtype, device=xyxy_bboxes.device),
)
if ious.max() < min_jaccard_overlap:
continue
return dict(top=top, left=left, height=new_h, width=new_w, is_within_crop_area=is_within_crop_area)
def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
if len(params) < 1:
return inpt
output = F.crop(inpt, top=params["top"], left=params["left"], height=params["height"], width=params["width"])
if isinstance(output, datapoints.BoundingBox):
# We "mark" the invalid boxes as degenreate, and they can be
# removed by a later call to SanitizeBoundingBox()
output[~params["is_within_crop_area"]] = 0
return output
[docs]class ScaleJitter(Transform):
"""[BETA] Perform Large Scale Jitter on the input according to
`"Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation" <https://arxiv.org/abs/2012.07177>`_.
.. v2betastatus:: ScaleJitter transform
If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
:class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
it can have arbitrary number of leading batch dimensions. For example,
the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
Args:
target_size (tuple of int): Target size. This parameter defines base scale for jittering,
e.g. ``min(target_size[0] / width, target_size[1] / height)``.
scale_range (tuple of float, optional): Minimum and maximum of the scale range. Default, ``(0.1, 2.0)``.
interpolation (InterpolationMode, optional): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``,
``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well.
antialias (bool, optional): Whether to apply antialiasing.
It only affects **tensors** with bilinear or bicubic modes and it is
ignored otherwise: on PIL images, antialiasing is always applied on
bilinear or bicubic modes; on other modes (for PIL images and
tensors), antialiasing makes no sense and this parameter is ignored.
Possible values are:
- ``True``: will apply antialiasing for bilinear or bicubic modes.
Other mode aren't affected. This is probably what you want to use.
- ``False``: will not apply antialiasing for tensors on any mode. PIL
images are still antialiased on bilinear or bicubic modes, because
PIL doesn't support no antialias.
- ``None``: equivalent to ``False`` for tensors and ``True`` for
PIL images. This value exists for legacy reasons and you probably
don't want to use it unless you really know what you are doing.
The current default is ``None`` **but will change to** ``True`` **in
v0.17** for the PIL and Tensor backends to be consistent.
"""
def __init__(
self,
target_size: Tuple[int, int],
scale_range: Tuple[float, float] = (0.1, 2.0),
interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
antialias: Optional[Union[str, bool]] = "warn",
):
super().__init__()
self.target_size = target_size
self.scale_range = scale_range
self.interpolation = _check_interpolation(interpolation)
self.antialias = antialias
def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
orig_height, orig_width = query_spatial_size(flat_inputs)
scale = self.scale_range[0] + torch.rand(1) * (self.scale_range[1] - self.scale_range[0])
r = min(self.target_size[1] / orig_height, self.target_size[0] / orig_width) * scale
new_width = int(orig_width * r)
new_height = int(orig_height * r)
return dict(size=(new_height, new_width))
def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
return F.resize(inpt, size=params["size"], interpolation=self.interpolation, antialias=self.antialias)
[docs]class RandomShortestSize(Transform):
"""[BETA] Randomly resize the input.
.. v2betastatus:: RandomShortestSize transform
If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
:class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
it can have arbitrary number of leading batch dimensions. For example,
the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
Args:
min_size (int or sequence of int): Minimum spatial size. Single integer value or a sequence of integer values.
max_size (int, optional): Maximum spatial size. Default, None.
interpolation (InterpolationMode, optional): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``,
``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well.
antialias (bool, optional): Whether to apply antialiasing.
It only affects **tensors** with bilinear or bicubic modes and it is
ignored otherwise: on PIL images, antialiasing is always applied on
bilinear or bicubic modes; on other modes (for PIL images and
tensors), antialiasing makes no sense and this parameter is ignored.
Possible values are:
- ``True``: will apply antialiasing for bilinear or bicubic modes.
Other mode aren't affected. This is probably what you want to use.
- ``False``: will not apply antialiasing for tensors on any mode. PIL
images are still antialiased on bilinear or bicubic modes, because
PIL doesn't support no antialias.
- ``None``: equivalent to ``False`` for tensors and ``True`` for
PIL images. This value exists for legacy reasons and you probably
don't want to use it unless you really know what you are doing.
The current default is ``None`` **but will change to** ``True`` **in
v0.17** for the PIL and Tensor backends to be consistent.
"""
def __init__(
self,
min_size: Union[List[int], Tuple[int], int],
max_size: Optional[int] = None,
interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
antialias: Optional[Union[str, bool]] = "warn",
):
super().__init__()
self.min_size = [min_size] if isinstance(min_size, int) else list(min_size)
self.max_size = max_size
self.interpolation = _check_interpolation(interpolation)
self.antialias = antialias
def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
orig_height, orig_width = query_spatial_size(flat_inputs)
min_size = self.min_size[int(torch.randint(len(self.min_size), ()))]
r = min_size / min(orig_height, orig_width)
if self.max_size is not None:
r = min(r, self.max_size / max(orig_height, orig_width))
new_width = int(orig_width * r)
new_height = int(orig_height * r)
return dict(size=(new_height, new_width))
def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
return F.resize(inpt, size=params["size"], interpolation=self.interpolation, antialias=self.antialias)
[docs]class RandomResize(Transform):
"""[BETA] Randomly resize the input.
.. v2betastatus:: RandomResize transform
This transformation can be used together with ``RandomCrop`` as data augmentations to train
models on image segmentation task.
Output spatial size is randomly sampled from the interval ``[min_size, max_size]``:
.. code-block:: python
size = uniform_sample(min_size, max_size)
output_width = size
output_height = size
If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
:class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
it can have arbitrary number of leading batch dimensions. For example,
the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
Args:
min_size (int): Minimum output size for random sampling
max_size (int): Maximum output size for random sampling
interpolation (InterpolationMode, optional): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``,
``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well.
antialias (bool, optional): Whether to apply antialiasing.
It only affects **tensors** with bilinear or bicubic modes and it is
ignored otherwise: on PIL images, antialiasing is always applied on
bilinear or bicubic modes; on other modes (for PIL images and
tensors), antialiasing makes no sense and this parameter is ignored.
Possible values are:
- ``True``: will apply antialiasing for bilinear or bicubic modes.
Other mode aren't affected. This is probably what you want to use.
- ``False``: will not apply antialiasing for tensors on any mode. PIL
images are still antialiased on bilinear or bicubic modes, because
PIL doesn't support no antialias.
- ``None``: equivalent to ``False`` for tensors and ``True`` for
PIL images. This value exists for legacy reasons and you probably
don't want to use it unless you really know what you are doing.
The current default is ``None`` **but will change to** ``True`` **in
v0.17** for the PIL and Tensor backends to be consistent.
"""
def __init__(
self,
min_size: int,
max_size: int,
interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
antialias: Optional[Union[str, bool]] = "warn",
) -> None:
super().__init__()
self.min_size = min_size
self.max_size = max_size
self.interpolation = _check_interpolation(interpolation)
self.antialias = antialias
def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
size = int(torch.randint(self.min_size, self.max_size, ()))
return dict(size=[size])
def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
return F.resize(inpt, params["size"], interpolation=self.interpolation, antialias=self.antialias)
