Skip to content

GitLab

Unverified Commit 31a4ef9f authored by vfdev's avatar vfdev Committed by GitHub
Browse files

Updated geometric transforms v2 docstring (#7303) 


Co-authored-by: default avatarNicolas Hug <contact@nicolas-hug.com>
Co-authored-by: default avatarPhilip Meier <github.pmeier@posteo.de>
parent 1dc0318f
Hide whitespace changes
Inline Side-by-side
Showing with 334 additions and 80 deletions
docs/source/transforms.rst
View file @ 31a4ef9f
......@@ -99,10 +99,14 @@ Geometry
Resize
v2.Resize
v2.ScaleJitter
v2.RandomShortestSize
v2.RandomResize
RandomCrop
v2.RandomCrop
RandomResizedCrop
v2.RandomResizedCrop
v2.RandomIoUCrop
CenterCrop
v2.CenterCrop
FiveCrop
......@@ -111,17 +115,21 @@ Geometry
v2.TenCrop
Pad
v2.Pad
v2.RandomZoomOut
RandomRotation
v2.RandomRotation
RandomAffine
v2.RandomAffine
RandomPerspective
v2.RandomPerspective
RandomRotation
v2.RandomRotation
ElasticTransform
v2.ElasticTransform
RandomHorizontalFlip
v2.RandomHorizontalFlip
RandomVerticalFlip
v2.RandomVerticalFlip
Color
-----
......
torchvision/transforms/v2/_geometry.py
View file @ 31a4ef9f
......@@ -26,16 +26,17 @@ from .utils import has_all, has_any, is_simple_tensor, query_bounding_box, query
class RandomHorizontalFlip(_RandomApplyTransform):
"""[BETA] Horizontally flip the given image/box/mask randomly with a given probability.
"""[BETA] Horizontally flip the input with a given probability.
.. betastatus:: RandomHorizontalFlip transform
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading
dimensions
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): probability of the image being flipped. Default value is 0.5
p (float, optional): probability of the input being flipped. Default value is 0.5
"""
_v1_transform_cls = _transforms.RandomHorizontalFlip
......@@ -45,16 +46,17 @@ class RandomHorizontalFlip(_RandomApplyTransform):
class RandomVerticalFlip(_RandomApplyTransform):
"""[BETA] Vertically flip the given image/box/mask randomly with a given probability.
"""[BETA] Vertically flip the input with a given probability.
.. betastatus:: RandomVerticalFlip transform
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading
dimensions
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): probability of the image being flipped. Default value is 0.5
p (float, optional): probability of the input being flipped. Default value is 0.5
"""
_v1_transform_cls = _transforms.RandomVerticalFlip
......@@ -64,12 +66,14 @@ class RandomVerticalFlip(_RandomApplyTransform):
class Resize(Transform):
"""[BETA] Resize the input image/box/mask to the given size.
"""[BETA] Resize the input to the given size.
.. betastatus:: Resize transform
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
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
......@@ -87,7 +91,7 @@ class Resize(Transform):
.. note::
In torchscript mode size as single int is not supported, use a sequence of length 1: ``[size, ]``.
interpolation (InterpolationMode): Desired interpolation enum defined by
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.
......@@ -156,12 +160,15 @@ class Resize(Transform):
class CenterCrop(Transform):
"""[BETA] Crops the given image/box/mask at the center.
"""[BETA] Crop the input at the center.
.. betastatus:: CenterCrop transform
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
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:
......@@ -181,14 +188,16 @@ class CenterCrop(Transform):
class RandomResizedCrop(Transform):
"""[BETA] Crop a random portion of image/box/mask and resize it to a given size.
"""[BETA] Crop a random portion of the input and resize it to a given size.
.. betastatus:: RandomResizedCrop transform
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
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 image is made: the crop has a random area (H * W)
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.
......@@ -199,11 +208,11 @@ class RandomResizedCrop(Transform):
.. note::
In torchscript mode size as single int is not supported, use a sequence of length 1: ``[size, ]``.
scale (tuple of float): Specifies the lower and upper bounds for the random area of the crop,
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): lower and upper bounds for the random aspect ratio of the crop, before
ratio (tuple of float, optional): lower and upper bounds for the random aspect ratio of the crop, before
resizing.
interpolation (InterpolationMode): Desired interpolation enum defined by
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.
......@@ -305,13 +314,13 @@ ImageOrVideoTypeJIT = Union[datapoints._ImageTypeJIT, datapoints._VideoTypeJIT]
class FiveCrop(Transform):
"""[BETA] Crop the given image/box/mask into four corners and the central crop.
"""[BETA] Crop the image or video into four corners and the central crop.
.. betastatus:: FiveCrop transform
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading
dimensions
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
......@@ -367,14 +376,14 @@ class FiveCrop(Transform):
class TenCrop(Transform):
"""[BETA] Crop the given image/box/mask into four corners and the central crop plus the flipped version of
"""[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).
.. betastatus:: TenCrop transform
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading
dimensions.
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.
......@@ -387,7 +396,7 @@ class TenCrop(Transform):
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): Use vertical flipping instead of horizontal
vertical_flip (bool, optional): Use vertical flipping instead of horizontal
"""
_v1_transform_cls = _transforms.TenCrop
......@@ -426,14 +435,14 @@ class TenCrop(Transform):
class Pad(Transform):
"""[BETA] Pad the given image/box/mask on all sides with the given "pad" value.
"""[BETA] Pad the input on all sides with the given "pad" value.
.. betastatus:: Pad transform
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means at most 2 leading dimensions for mode reflect and symmetric,
at most 3 leading dimensions for mode edge,
and an arbitrary number of leading dimensions for mode constant
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
......@@ -444,18 +453,17 @@ class Pad(Transform):
.. note::
In torchscript mode padding as single int is not supported, use a sequence of
length 1: ``[padding, ]``.
fill (number or tuple): Pixel fill value for constant fill. Default is 0. If a tuple of
length 3, it is used to fill R, G, B channels respectively.
This value is only used when the padding_mode is constant.
Only number is supported for torch Tensor.
Only int or tuple value is supported for PIL Image.
padding_mode (str): Type of padding. Should be: constant, edge, reflect or symmetric.
Default is constant.
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.
If input a 5D torch Tensor, the last 3 dimensions will be padded instead of the last 2
- 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
......@@ -501,6 +509,37 @@ class Pad(Transform):
class RandomZoomOut(_RandomApplyTransform):
"""[BETA] "Zoom out" transformation from
`"SSD: Single Shot MultiBox Detector" <https://arxiv.org/abs/1512.02325>`_.
.. betastatus:: 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,
......@@ -540,18 +579,20 @@ class RandomZoomOut(_RandomApplyTransform):
class RandomRotation(Transform):
"""[BETA] Rotate the image/box/mask by angle.
"""[BETA] Rotate the input by angle.
.. betastatus:: RandomRotation transform
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
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): Desired interpolation enum defined by
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.
......@@ -561,8 +602,11 @@ class RandomRotation(Transform):
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 (sequence or number): Pixel fill value for the area outside the rotated
image. Default is ``0``. If given a number, the value is used for all bands respectively.
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
......@@ -608,12 +652,14 @@ class RandomRotation(Transform):
class RandomAffine(Transform):
"""[BETA] Random affine transformation of the image/box/mask keeping center invariant.
"""[BETA] Random affine transformation the input keeping center invariant.
.. betastatus:: RandomAffine transform
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
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.
......@@ -631,12 +677,15 @@ class RandomAffine(Transform):
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): Desired interpolation enum defined by
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 (sequence or number): Pixel fill value for the area outside the transformed
image. Default is ``0``. If given a number, the value is used for all bands respectively.
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.
......@@ -724,13 +773,14 @@ class RandomAffine(Transform):
class RandomCrop(Transform):
"""[BETA] Crop the given image/box/mask at a random location.
"""[BETA] Crop the input at a random location.
.. betastatus:: RandomCrop transform
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions,
but if non-constant padding is used, the input is expected to have at most 2 leading dimensions
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
......@@ -745,21 +795,20 @@ class RandomCrop(Transform):
.. note::
In torchscript mode padding as single int is not supported, use a sequence of
length 1: ``[padding, ]``.
pad_if_needed (boolean): It will pad the image if smaller than the
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): Pixel fill value for constant fill. Default is 0. If a tuple of
length 3, it is used to fill R, G, B channels respectively.
This value is only used when the padding_mode is constant.
Only number is supported for torch Tensor.
Only int or tuple value is supported for PIL Image.
padding_mode (str): Type of padding. Should be: constant, edge, reflect or symmetric.
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.
If input a 5D torch Tensor, the last 3 dimensions will be padded instead of the last 2
- 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
......@@ -879,23 +928,28 @@ class RandomCrop(Transform):
class RandomPerspective(_RandomApplyTransform):
"""[BETA] Performs a random perspective transformation of the given image/box/mask with a given probability.
"""[BETA] Perform a random perspective transformation of the input with a given probability.
.. betastatus:: RandomPerspective transform
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
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): argument to control the degree of distortion and ranges from 0 to 1.
distortion_scale (float, optional): argument to control the degree of distortion and ranges from 0 to 1.
Default is 0.5.
p (float): probability of the image being transformed. Default is 0.5.
interpolation (InterpolationMode): Desired interpolation enum defined by
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 (sequence or number): Pixel fill value for the area outside the transformed
image. Default is ``0``. If given a number, the value is used for all bands respectively.
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
......@@ -960,6 +1014,46 @@ class RandomPerspective(_RandomApplyTransform):
class ElasticTransform(Transform):
"""[BETA] Transform the input with elastic transformations.
.. betastatus:: 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__(
......@@ -1011,6 +1105,34 @@ class ElasticTransform(Transform):
class RandomIoUCrop(Transform):
"""[BETA] Random IoU crop transformation from
`"SSD: Single Shot MultiBox Detector" <https://arxiv.org/abs/1512.02325>`_.
.. betastatus:: 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.SanitizeBoundingBoxes`, 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,
......@@ -1107,6 +1229,45 @@ class RandomIoUCrop(Transform):
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>`_.
.. betastatus:: 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],
......@@ -1135,6 +1296,43 @@ class ScaleJitter(Transform):
class RandomShortestSize(Transform):
"""[BETA] Randomly resize the input.
.. betastatus:: 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],
......@@ -1166,6 +1364,54 @@ class RandomShortestSize(Transform):
class RandomResize(Transform):
"""[BETA] Randomly resize the input.
.. betastatus:: 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,
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment