"""
Modified from https://github.com/pytorch/vision/blob/main/torchvision/transforms/transforms.py
"""
import warnings
import numbers
from collections.abc import Sequence
from typing import Tuple, List, Optional
from PIL import ImageOps
import numpy as np
import random
import math
from . import functional as F
from .functional import InterpolationMode, _interpolation_modes_from_int
import oneflow as flow
from oneflow.nn import Module
from oneflow.framework.tensor import Tensor
class ToNumpy:
def __call__(self, pil_img):
np_img = np.array(pil_img, dtype=np.uint8)
if np_img.ndim < 3:
np_img = np.expand_dims(np_img, axis=-1)
np_img = np.rollaxis(np_img, 2) # HWC to CHW
return np_img
[docs]class Compose:
"""Composes several transforms together.
Please, see the note below.
Args:
transforms (list of ``Transform`` objects): list of transforms to compose.
Example:
>>> transforms.Compose([
>>> transforms.CenterCrop(10),
>>> transforms.ToTensor(),
>>> ])
.. note::
In order to script the transformations, please use ``flow.nn.Sequential`` as below.
>>> transforms = flow.nn.Sequential(
>>> transforms.CenterCrop(10),
>>> transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
>>> )
Make sure to use only scriptable transformations, i.e. that work with ``flow.Tensor``, does not require
`lambda` functions or ``PIL.Image``.
"""
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, img):
for t in self.transforms:
img = t(img)
return img
def __repr__(self):
format_string = self.__class__.__name__ + "("
for t in self.transforms:
format_string += "\n"
format_string += " {0}".format(t)
format_string += "\n)"
return format_string
[docs]class ToTensor:
r"""Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
Converts a PIL Image or numpy.ndarray (H x W x C) in the range
[0, 255] to a flow.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]
if the PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1)
or if the numpy.ndarray has dtype = np.uint8
In the other cases, tensors are returned without scaling.
.. note::
Because the input image is scaled to [0.0, 1.0], this transformation should not be used when
transforming target image masks.
"""
def __call__(self, pic):
"""
Args:
pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
Returns:
Tensor: Converted image.
"""
return F.to_tensor(pic)
def __repr__(self):
return self.__class__.__name__ + "()"
[docs]class PILToTensor:
"""Convert a ``PIL Image`` to a tensor of the same type
Converts a PIL Image (H x W x C) to a Tensor of shape (C x H x W).
"""
def __call__(self, pic):
"""
Args:
pic (PIL Image): Image to be converted to tensor.
Returns:
Tensor: Converted image.
"""
return F.pil_to_tensor(pic)
def __repr__(self):
return self.__class__.__name__ + "()"
[docs]class ConvertImageDtype(Module):
"""Convert a tensor image to the given ``dtype`` and scale the values accordingly
This function does not support PIL Image.
Args:
dtype (flow.dtype): Desired data type of the output
.. note::
When converting from a smaller to a larger integer ``dtype`` the maximum values are **not** mapped exactly.
If converted back and forth, this mismatch has no effect.
Raises:
RuntimeError: When trying to cast :class:`flow.float32` to :class:`flow.int32` or :class:`flow.int64` as
well as for trying to cast :class:`flow.float64` to :class:`flow.int64`. These conversions might lead to
overflow errors since the floating point ``dtype`` cannot store consecutive integers over the whole range
of the integer ``dtype``.
"""
def __init__(self, dtype: flow.dtype) -> None:
super().__init__()
self.dtype = dtype
def forward(self, image):
return F.convert_image_dtype(image, self.dtype)
[docs]class ToPILImage:
"""Convert a tensor or an ndarray to PIL Image.
Converts a flow.Tensor of shape C x H x W or a numpy ndarray of shape
H x W x C to a PIL Image while preserving the value range.
Args:
mode (`PIL.Image mode`_): color space and pixel depth of input data (optional).
If ``mode`` is ``None`` (default) there are some assumptions made about the input data:
- If the input has 4 channels, the ``mode`` is assumed to be ``RGBA``.
- If the input has 3 channels, the ``mode`` is assumed to be ``RGB``.
- If the input has 2 channels, the ``mode`` is assumed to be ``LA``.
- If the input has 1 channel, the ``mode`` is determined by the data type (i.e ``int``, ``float``,
``short``).
.. _PIL.Image mode: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#concept-modes
"""
def __init__(self, mode=None):
self.mode = mode
def __call__(self, pic):
"""
Args:
pic (Tensor or numpy.ndarray): Image to be converted to PIL Image.
Returns:
PIL Image: Image converted to PIL Image.
"""
return F.to_pil_image(pic, self.mode)
def __repr__(self):
format_string = self.__class__.__name__ + "("
if self.mode is not None:
format_string += "mode={0}".format(self.mode)
format_string += ")"
return format_string
[docs]class Normalize(Module):
r"""Normalize a tensor image with mean and standard deviation.
This transform does not support PIL Image.
Given mean: ``(mean[1],...,mean[n])`` and std: ``(std[1],..,std[n])`` for ``n``
channels, this transform will normalize each channel of the input
``flow.*Tensor`` i.e.,
``output[channel] = (input[channel] - mean[channel]) / std[channel]``
.. note::
This transform acts out of place, i.e., it does not mutate the input tensor.
Args:
mean (sequence): Sequence of means for each channel.
std (sequence): Sequence of standard deviations for each channel.
inplace(bool,optional): Bool to make this operation in-place.
"""
def __init__(self, mean, std, inplace=False):
super().__init__()
self.mean = mean
self.std = std
self.inplace = inplace
[docs] def forward(self, tensor: Tensor) -> Tensor:
"""
Args:
tensor (Tensor): Tensor image to be normalized.
Returns:
Tensor: Normalized Tensor image.
"""
return F.normalize(tensor, self.mean, self.std, self.inplace)
def __repr__(self):
return self.__class__.__name__ + "(mean={0}, std={1})".format(
self.mean, self.std
)
[docs]class Resize(Module):
r"""Resize the input image to the given size.
If the image is oneflow Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
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).
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`flowvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` and
``InterpolationMode.BICUBIC`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
"""
def __init__(self, size, interpolation=InterpolationMode.BILINEAR):
super().__init__()
if not isinstance(size, (int, Sequence)):
raise TypeError("Size should be int or sequence. Got {}".format(type(size)))
if isinstance(size, Sequence) and len(size) not in (1, 2):
raise ValueError("If size is a sequence, it should have 1 or 2 values")
self.size = size
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument interpolation should be of type InterpolationMode instead of int. "
"Please, use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
self.interpolation = interpolation
[docs] def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be scaled.
Returns:
PIL Image or Tensor: Rescaled image.
"""
return F.resize(img, self.size, self.interpolation)
def __repr__(self):
interpolate_str = self.interpolation.value
return self.__class__.__name__ + "(size={0}, interpolation={1})".format(
self.size, interpolate_str
)
[docs]class Scale(Resize):
r"""
Note: This transform is deprecated in favor of Resize.
"""
def __init__(self, *args, **kwargs):
warnings.warn(
"The use of the transforms.Scale transform is deprecated, "
+ "please use transforms.Resize instead."
)
super(Scale, self).__init__(*args, **kwargs)
[docs]class CenterCrop(Module):
r"""Crops the given image at the center.
If the image is oneflow Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
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]).
"""
def __init__(self, size):
super().__init__()
self.size = _setup_size(
size, error_msg="Please provide only two dimensions (h, w) for size."
)
[docs] def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be cropped.
Returns:
PIL Image or Tensor: Cropped image.
"""
return F.center_crop(img, self.size)
def __repr__(self):
return self.__class__.__name__ + "(size={0})".format(self.size)
[docs]class Pad(Module):
r"""Pad the given image on all sides with the given "pad" value.
If the image is oneflow 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
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.
fill (number or str 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 oneflow Tensor.
Only int or str or tuple value is supported for PIL Image.
padding_mode (str): 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 oneflow 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
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]
"""
def __init__(self, padding, fill=0, padding_mode="constant"):
super().__init__()
if not isinstance(padding, (numbers.Number, tuple, list)):
raise TypeError("Got inappropriate padding arg")
if not isinstance(fill, (numbers.Number, str, tuple)):
raise TypeError("Got inappropriate fill arg")
if padding_mode not in ["constant", "edge", "reflect", "symmetric"]:
raise ValueError(
"Padding mode should be either constant, edge, reflect or symmetric"
)
if isinstance(padding, Sequence) and len(padding) not in [1, 2, 4]:
raise ValueError(
"Padding must be an int or a 1, 2, or 4 element tuple, not a "
+ "{} element tuple".format(len(padding))
)
self.padding = padding
self.fill = fill
self.padding_mode = padding_mode
[docs] def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be padded.
Returns:
PIL Image or Tensor: Padded image.
"""
return F.pad(img, self.padding, self.fill, self.padding_mode)
def __repr__(self):
return (
self.__class__.__name__
+ "(padding={0}, fill={1}, padding_mode={2})".format(
self.padding, self.fill, self.padding_mode
)
)
[docs]class Lambda:
r"""Apply a user-defined lambda as a transform.
Args:
lambd (function): Lambda/function to be used for transform.
"""
def __init__(self, lambd):
if not callable(lambd):
raise TypeError(
"Argument lambd should be callable, got {}".format(
repr(type(lambd).__name__)
)
)
self.lambd = lambd
def __call__(self, img):
return self.lambd(img)
def __repr__(self):
return self.__class__.__name__ + "()"
def _setup_size(size, error_msg):
if isinstance(size, numbers.Number):
return int(size), int(size)
if isinstance(size, Sequence) and len(size) == 1:
return size[0], size[0]
if len(size) != 2:
raise ValueError(error_msg)
return size
[docs]class RandomApply(Module):
"""Apply randomly a list of transformations with a given probability.
.. note::
In order to script the transformation, please use ``flow.nn.ModuleList`` as input instead of list/tuple of
transforms as shown below:
>>> transforms = transforms.RandomApply(flow.nn.ModuleList([
>>> transforms.ColorJitter(),
>>> ]), p=0.3)
Make sure to use only scriptable transformations, i.e. that work with ``flow.Tensor``, does not require
`lambda` functions or ``PIL.Image``.
Args:
transforms (sequence or Module): list of transformations
p (float): probability
"""
def __init__(self, transforms, p=0.5):
super().__init__()
self.transforms = transforms
self.p = p
def forward(self, img):
if self.p < flow.rand(1):
return img
for t in self.transforms:
img = t(img)
return img
def __repr__(self):
format_string = self.__class__.__name__ + "("
format_string += "\n p={}".format(self.p)
for t in self.transforms:
format_string += "\n"
format_string += " {0}".format(t)
format_string += "\n)"
return format_string
[docs]class RandomOrder(RandomTransforms):
"""Apply a list of transformations in a random order.
"""
def __call__(self, img):
order = list(range(len(self.transforms)))
random.shuffle(order)
for i in order:
img = self.transforms[i](img)
return img
[docs]class RandomChoice(RandomTransforms):
"""Apply single transformation randomly picked from a list.
"""
def __call__(self, img):
t = random.choice(self.transforms)
return t(img)
[docs]class RandomCrop(Module):
"""Crop the given image at a random location.
If the image is oneflow 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
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.
pad_if_needed (boolean): 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 str 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 flow Tensor.
Only int or str or tuple value is supported for PIL Image.
padding_mode (str): 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 flow 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
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]
"""
[docs] @staticmethod
def get_params(
img: Tensor, output_size: Tuple[int, int]
) -> Tuple[int, int, int, int]:
"""Get parameters for ``crop`` for a random crop.
Args:
img (PIL Image or Tensor): Image to be cropped.
output_size (tuple): Expected output size of the crop.
Returns:
tuple: params (i, j, h, w) to be passed to ``crop`` for random crop.
"""
w, h = F._get_image_size(img)
th, tw = output_size
if h + 1 < th or w + 1 < tw:
raise ValueError(
"Required crop size {} is larger then input image size {}".format(
(th, tw), (h, w)
)
)
if w == tw and h == th:
return 0, 0, h, w
i = np.random.randint(0, h - th + 1, size=(1,)).item()
j = np.random.randint(0, w - tw + 1, size=(1,)).item()
return i, j, th, tw
def __init__(
self, size, padding=None, pad_if_needed=False, fill=0, padding_mode="constant"
):
super().__init__()
self.size = tuple(
_setup_size(
size, error_msg="Please provide only two dimensions (h, w) for size."
)
)
self.padding = padding
self.pad_if_needed = pad_if_needed
self.fill = fill
self.padding_mode = padding_mode
[docs] def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be cropped.
Returns:
PIL Image or Tensor: Cropped image.
"""
if self.padding is not None:
img = F.pad(img, self.padding, self.fill, self.padding_mode)
width, height = F._get_image_size(img)
# pad the width if needed
if self.pad_if_needed and width < self.size[1]:
padding = [self.size[1] - width, 0]
img = F.pad(img, padding, self.fill, self.padding_mode)
# pad the height if needed
if self.pad_if_needed and height < self.size[0]:
padding = [0, self.size[0] - height]
img = F.pad(img, padding, self.fill, self.padding_mode)
i, j, h, w = self.get_params(img, self.size)
return F.crop(img, i, j, h, w)
def __repr__(self):
return self.__class__.__name__ + "(size={0}, padding={1})".format(
self.size, self.padding
)
[docs]class RandomHorizontalFlip(Module):
"""Horizontally flip the given image randomly with a given probability.
If the image is flow Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading
dimensions
Args:
p (float): probability of the image being flipped. Default value is 0.5
"""
def __init__(self, p=0.5):
super().__init__()
self.p = p
[docs] def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be flipped.
Returns:
PIL Image or Tensor: Randomly flipped image.
"""
if flow.rand(1) < self.p:
return F.hflip(img)
return img
def __repr__(self):
return self.__class__.__name__ + "(p={})".format(self.p)
[docs]class RandomVerticalFlip(Module):
"""Vertically flip the given image randomly with a given probability.
If the image is flow Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading
dimensions
Args:
p (float): probability of the image being flipped. Default value is 0.5
"""
def __init__(self, p=0.5):
super().__init__()
self.p = p
[docs] def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be flipped.
Returns:
PIL Image or Tensor: Randomly flipped image.
"""
if flow.rand(1) < self.p:
return F.vflip(img)
return img
def __repr__(self):
return self.__class__.__name__ + "(p={})".format(self.p)
[docs]class RandomResizedCrop(Module):
"""Crop a random portion of image and resize it to a given size.
If the image is flow Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
A crop of the original image 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]).
scale (tuple of float): 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
resizing.
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`flowvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` and
``InterpolationMode.BICUBIC`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
"""
def __init__(
self,
size,
scale=(0.08, 1.0),
ratio=(3.0 / 4.0, 4.0 / 3.0),
interpolation=InterpolationMode.BILINEAR,
):
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")
if not isinstance(ratio, Sequence):
raise TypeError("Ratio should be a sequence")
if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
warnings.warn("Scale and ratio should be of kind (min, max)")
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument interpolation should be of type InterpolationMode instead of int. "
"Please, use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
self.interpolation = interpolation
self.scale = scale
self.ratio = ratio
[docs] @staticmethod
def get_params(
img: Tensor, scale: List[float], ratio: List[float]
) -> Tuple[int, int, int, int]:
"""Get parameters for ``crop`` for a random sized crop.
Args:
img (PIL Image or Tensor): Input image.
scale (list): range of scale of the origin size cropped
ratio (list): range of aspect ratio of the origin aspect ratio cropped
Returns:
tuple: params (i, j, h, w) to be passed to ``crop`` for a random
sized crop.
"""
width, height = F._get_image_size(img)
area = height * width
log_ratio = np.log(np.array(ratio))
for _ in range(10):
target_area = area * np.random.uniform(scale[0], scale[1])
aspect_ratio = np.exp(np.random.uniform(log_ratio[0], log_ratio[1]))
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 = np.random.randint(0, height - h + 1)
j = np.random.randint(0, width - w + 1)
return i, j, h, w
# Fallback to central crop
in_ratio = float(width) / float(height)
if in_ratio < min(ratio):
w = width
h = int(round(w / min(ratio)))
elif in_ratio > max(ratio):
h = height
w = int(round(h * max(ratio)))
else: # whole image
w = width
h = height
i = (height - h) // 2
j = (width - w) // 2
return i, j, h, w
[docs] def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be cropped and resized.
Returns:
PIL Image or Tensor: Randomly cropped and resized image.
"""
i, j, h, w = self.get_params(img, self.scale, self.ratio)
return F.resized_crop(img, i, j, h, w, self.size, self.interpolation)
def __repr__(self):
interpolate_str = self.interpolation.value
format_string = self.__class__.__name__ + "(size={0}".format(self.size)
format_string += ", scale={0}".format(tuple(round(s, 4) for s in self.scale))
format_string += ", ratio={0}".format(tuple(round(r, 4) for r in self.ratio))
format_string += ", interpolation={0})".format(interpolate_str)
return format_string
[docs]class RandomSizedCrop(RandomResizedCrop):
"""
Note: This transform is deprecated in favor of RandomResizedCrop.
"""
def __init__(self, *args, **kwargs):
warnings.warn(
"The use of the transforms.RandomSizedCrop transform is deprecated, "
+ "please use transforms.RandomResizedCrop instead."
)
super(RandomSizedCrop, self).__init__(*args, **kwargs)
class RandomAffine(Module):
"""Random affine transformation of the image keeping center invariant.
If the image is oneflow Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
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,
a 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
:class:`flowvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
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.
fillcolor (sequence or number, optional):
.. warning::
This parameter was deprecated in ``0.12`` and will be removed in ``0.14``. Please use ``fill`` instead.
resample (int, optional):
.. warning::
This parameter was deprecated in ``0.12`` and will be removed in ``0.14``. Please use ``interpolation``
instead.
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
"""
def __init__(
self,
degrees,
translate=None,
scale=None,
shear=None,
interpolation=InterpolationMode.NEAREST,
fill=0,
fillcolor=None,
resample=None,
center=None,
):
super().__init__()
if resample is not None:
warnings.warn(
"The parameter 'resample' is deprecated since 0.12 and will be removed in 0.14. "
"Please use 'interpolation' instead."
)
interpolation = _interpolation_modes_from_int(resample)
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
"Please use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
if fillcolor is not None:
warnings.warn(
"The parameter 'fillcolor' is deprecated since 0.12 and will be removed in 0.14. "
"Please use 'fill' instead."
)
fill = fillcolor
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.resample = self.interpolation = interpolation
if fill is None:
fill = 0
elif not isinstance(fill, (Sequence, numbers.Number)):
raise TypeError("Fill should be either a sequence or a number.")
self.fillcolor = self.fill = fill
if center is not None:
_check_sequence_input(center, "center", req_sizes=(2,))
self.center = center
@staticmethod
def get_params(
degrees: List[float],
translate: Optional[List[float]],
scale_ranges: Optional[List[float]],
shears: Optional[List[float]],
img_size: List[int],
) -> Tuple[float, Tuple[int, int], float, Tuple[float, float]]:
"""Get parameters for affine transformation
Returns:
params to be passed to the affine transformation
"""
angle = float(
flow.empty(1).uniform_(float(degrees[0]), float(degrees[1])).item()
)
if translate is not None:
max_dx = float(translate[0] * img_size[0])
max_dy = float(translate[1] * img_size[1])
tx = int(round(flow.empty(1).uniform_(-max_dx, max_dx).item()))
ty = int(round(flow.empty(1).uniform_(-max_dy, max_dy).item()))
translations = (tx, ty)
else:
translations = (0, 0)
if scale_ranges is not None:
scale = float(
flow.empty(1).uniform_(scale_ranges[0], scale_ranges[1]).item()
)
else:
scale = 1.0
shear_x = shear_y = 0.0
if shears is not None:
shear_x = float(flow.empty(1).uniform_(shears[0], shears[1]).item())
if len(shears) == 4:
shear_y = float(flow.empty(1).uniform_(shears[2], shears[3]).item())
shear = (shear_x, shear_y)
return angle, translations, scale, shear
def forward(self, img):
"""
img (PIL Image or Tensor): Image to be transformed.
Returns:
PIL Image or Tensor: Affine transformed image.
"""
fill = self.fill
channels, height, width = F.get_dimensions(img)
if isinstance(img, Tensor):
if isinstance(fill, (int, float)):
fill = [float(fill)] * channels
else:
fill = [float(f) for f in fill]
img_size = [width, height] # flip for keeping BC on get_params call
ret = self.get_params(
self.degrees, self.translate, self.scale, self.shear, img_size
)
return F.affine(
img, *ret, interpolation=self.interpolation, fill=fill, center=self.center
)
def __repr__(self) -> str:
s = f"{self.__class__.__name__}(degrees={self.degrees}"
s += f", translate={self.translate}" if self.translate is not None else ""
s += f", scale={self.scale}" if self.scale is not None else ""
s += f", shear={self.shear}" if self.shear is not None else ""
s += (
f", interpolation={self.interpolation.value}"
if self.interpolation != InterpolationMode.NEAREST
else ""
)
s += f", fill={self.fill}" if self.fill != 0 else ""
s += f", center={self.center}" if self.center is not None else ""
s += ")"
return s
[docs]class FiveCrop(Module):
"""Crop the given image into four corners and the central crop.
If the image is flow Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading
dimensions
.. 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:
>>> transform = Compose([
>>> FiveCrop(size), # this is a list of PIL Images
>>> Lambda(lambda crops: flow.stack([ToTensor()(crop) for crop in crops])) # returns a 4D tensor
>>> ])
>>> #In your test loop you can do the following:
>>> input, target = batch # input is a 5d tensor, target is 2d
>>> bs, ncrops, c, h, w = input.size()
>>> result = model(input.view(-1, c, h, w)) # fuse batch size and ncrops
>>> result_avg = result.view(bs, ncrops, -1).mean(1) # avg over crops
"""
def __init__(self, size):
super().__init__()
self.size = _setup_size(
size, error_msg="Please provide only two dimensions (h, w) for size."
)
[docs] def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be cropped.
Returns:
tuple of 5 images. Image can be PIL Image or Tensor
"""
return F.five_crop(img, self.size)
def __repr__(self):
return self.__class__.__name__ + "(size={0})".format(self.size)
[docs]class TenCrop(Module):
"""Crop the given image into four corners and the central crop plus the flipped version of
these (horizontal flipping is used by default).
If the image is flow Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading
dimensions
.. 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): Use vertical flipping instead of horizontal
Example:
>>> transform = Compose([
>>> TenCrop(size), # this is a list of PIL Images
>>> Lambda(lambda crops: flow.stack([ToTensor()(crop) for crop in crops])) # returns a 4D tensor
>>> ])
>>> #In your test loop you can do the following:
>>> input, target = batch # input is a 5d tensor, target is 2d
>>> bs, ncrops, c, h, w = input.size()
>>> result = model(input.view(-1, c, h, w)) # fuse batch size and ncrops
>>> result_avg = result.view(bs, ncrops, -1).mean(1) # avg over crops
"""
def __init__(self, size, vertical_flip=False):
super().__init__()
self.size = _setup_size(
size, error_msg="Please provide only two dimensions (h, w) for size."
)
self.vertical_flip = vertical_flip
[docs] def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be cropped.
Returns:
tuple of 10 images. Image can be PIL Image or Tensor
"""
return F.ten_crop(img, self.size, self.vertical_flip)
def __repr__(self):
return self.__class__.__name__ + "(size={0}, vertical_flip={1})".format(
self.size, self.vertical_flip
)
class ColorJitter(Module):
"""Randomly change the brightness, contrast, saturation and hue of an image.
If the image is flow Tensor, it is expected
to have [..., 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
If img is PIL Image, mode "1", "L", "I", "F" and modes with transparency (alpha channel) are not supported.
Args:
brightness (float or tuple of float (min, max)): How much to jitter brightness.
brightness_factor is chosen uniformly from [max(0, 1 - brightness), 1 + brightness]
or the given [min, max]. Should be non negative numbers.
contrast (float or tuple of float (min, max)): How much to jitter contrast.
contrast_factor is chosen uniformly from [max(0, 1 - contrast), 1 + contrast]
or the given [min, max]. Should be non negative numbers.
saturation (float or tuple of float (min, max)): How much to jitter saturation.
saturation_factor is chosen uniformly from [max(0, 1 - saturation), 1 + saturation]
or the given [min, max]. Should be non negative numbers.
hue (float or tuple of float (min, max)): How much to jitter hue.
hue_factor is chosen uniformly from [-hue, hue] or the given [min, max].
Should have 0<= hue <= 0.5 or -0.5 <= min <= max <= 0.5.
"""
def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
super().__init__()
self.brightness = self._check_input(brightness, "brightness")
self.contrast = self._check_input(contrast, "contrast")
self.saturation = self._check_input(saturation, "saturation")
self.hue = self._check_input(
hue, "hue", center=0, bound=(-0.5, 0.5), clip_first_on_zero=False
)
def _check_input(
self, value, name, center=1, bound=(0, float("inf")), clip_first_on_zero=True
):
if isinstance(value, numbers.Number):
if value < 0:
raise ValueError(
"If {} is a single number, it must be non negative.".format(name)
)
value = [center - float(value), center + float(value)]
if clip_first_on_zero:
value[0] = max(value[0], 0.0)
elif isinstance(value, (tuple, list)) and len(value) == 2:
if not bound[0] <= value[0] <= value[1] <= bound[1]:
raise ValueError("{} values should be between {}".format(name, bound))
else:
raise TypeError(
"{} should be a single number or a list/tuple with length 2.".format(
name
)
)
# if value is 0 or (1., 1.) for brightness/contrast/saturation
# or (0., 0.) for hue, do nothing
if value[0] == value[1] == center:
value = None
return value
@staticmethod
def get_params(
brightness: Optional[List[float]],
contrast: Optional[List[float]],
saturation: Optional[List[float]],
hue: Optional[List[float]],
) -> Tuple[
Tensor, Optional[float], Optional[float], Optional[float], Optional[float]
]:
"""Get the parameters for the randomized transform to be applied on image.
Args:
brightness (tuple of float (min, max), optional): The range from which the brightness_factor is chosen
uniformly. Pass None to turn off the transformation.
contrast (tuple of float (min, max), optional): The range from which the contrast_factor is chosen
uniformly. Pass None to turn off the transformation.
saturation (tuple of float (min, max), optional): The range from which the saturation_factor is chosen
uniformly. Pass None to turn off the transformation.
hue (tuple of float (min, max), optional): The range from which the hue_factor is chosen uniformly.
Pass None to turn off the transformation.
Returns:
tuple: The parameters used to apply the randomized transform
along with their random order.
"""
fn_idx = flow.randperm(4)
b = (
None
if brightness is None
else float(flow.empty(1).uniform_(brightness[0], brightness[1]))
)
c = (
None
if contrast is None
else float(flow.empty(1).uniform_(contrast[0], contrast[1]))
)
s = (
None
if saturation is None
else float(flow.empty(1).uniform_(saturation[0], saturation[1]))
)
h = None if hue is None else float(flow.empty(1).uniform_(hue[0], hue[1]))
return fn_idx, b, c, s, h
def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Input image.
Returns:
PIL Image or Tensor: Color jittered image.
"""
(
fn_idx,
brightness_factor,
contrast_factor,
saturation_factor,
hue_factor,
) = self.get_params(self.brightness, self.contrast, self.saturation, self.hue)
for fn_id in fn_idx:
if fn_id == 0 and brightness_factor is not None:
img = F.adjust_brightness(img, brightness_factor)
elif fn_id == 1 and contrast_factor is not None:
img = F.adjust_contrast(img, contrast_factor)
elif fn_id == 2 and saturation_factor is not None:
img = F.adjust_saturation(img, saturation_factor)
elif fn_id == 3 and hue_factor is not None:
img = F.adjust_hue(img, hue_factor)
return img
def __repr__(self):
format_string = self.__class__.__name__ + "("
format_string += "brightness={0}".format(self.brightness)
format_string += ", contrast={0}".format(self.contrast)
format_string += ", saturation={0}".format(self.saturation)
format_string += ", hue={0})".format(self.hue)
return format_string
class RandomRotation(Module):
"""Rotate the image by angle.
If the image is flow Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
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
:class:`flowvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
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 (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.
resample (int, optional): deprecated argument and will be removed since v0.10.0.
Please use the ``interpolation`` parameter instead.
.. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters
"""
def __init__(
self,
degrees,
interpolation=InterpolationMode.NEAREST,
expand=False,
center=None,
fill=0,
resample=None,
):
super().__init__()
if resample is not None:
warnings.warn(
"Argument resample is deprecated and will be removed since v0.10.0. Please, use interpolation instead"
)
interpolation = _interpolation_modes_from_int(resample)
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument interpolation should be of type InterpolationMode instead of int. "
"Please, use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
self.degrees = _setup_angle(degrees, name="degrees", req_sizes=(2,))
if center is not None:
_check_sequence_input(center, "center", req_sizes=(2,))
self.center = center
self.resample = self.interpolation = interpolation
self.expand = expand
if fill is None:
fill = 0
elif not isinstance(fill, (Sequence, numbers.Number)):
raise TypeError("Fill should be either a sequence or a number.")
self.fill = fill
@staticmethod
def get_params(degrees: List[float]) -> float:
"""Get parameters for ``rotate`` for a random rotation.
Returns:
float: angle parameter to be passed to ``rotate`` for random rotation.
"""
angle = float(
flow.empty(1).uniform_(float(degrees[0]), float(degrees[1])).item()
)
return angle
def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be rotated.
Returns:
PIL Image or Tensor: Rotated image.
"""
fill = self.fill
if isinstance(img, Tensor):
if isinstance(fill, (int, float)):
fill = [float(fill)] * F._get_image_num_channels(img)
else:
fill = [float(f) for f in fill]
angle = self.get_params(self.degrees)
return F.rotate(img, angle, self.resample, self.expand, self.center, fill)
def __repr__(self):
interpolate_str = self.interpolation.value
format_string = self.__class__.__name__ + "(degrees={0}".format(self.degrees)
format_string += ", interpolation={0}".format(interpolate_str)
format_string += ", expand={0}".format(self.expand)
if self.center is not None:
format_string += ", center={0}".format(self.center)
if self.fill is not None:
format_string += ", fill={0}".format(self.fill)
format_string += ")"
return format_string
[docs]class Grayscale(Module):
"""Convert image to grayscale.
If the image is oneflow Tensor, it is expected
to have [..., 3, H, W] shape, where ... means an arbitrary number of leading dimensions
Args:
num_output_channels (int): (1 or 3) number of channels desired for output image
Returns:
PIL Image: Grayscale version of the input.
- If ``num_output_channels == 1`` : returned image is single channel
- If ``num_output_channels == 3`` : returned image is 3 channel with r == g == b
"""
def __init__(self, num_output_channels=1):
super().__init__()
self.num_output_channels = num_output_channels
[docs] def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be converted to grayscale.
Returns:
PIL Image or Tensor: Grayscaled image.
"""
return F.rgb_to_grayscale(img, num_output_channels=self.num_output_channels)
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}(num_output_channels={self.num_output_channels})"
)
[docs]class RandomGrayscale(Module):
"""Randomly convert image to grayscale with a probability of p (default 0.1).
If the image is flow Tensor, it is expected
to have [..., 3, H, W] shape, where ... means an arbitrary number of leading dimensions
Args:
p (float): probability that image should be converted to grayscale.
Returns:
PIL Image or Tensor: Grayscale version of the input image with probability p and unchanged
with probability (1-p).
- If input image is 1 channel: grayscale version is 1 channel
- If input image is 3 channel: grayscale version is 3 channel with r == g == b
"""
def __init__(self, p=0.1):
super().__init__()
assert 0.0 <= p <= 1.0
self.p = p
[docs] def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be converted to grayscale.
Returns:
PIL Image or Tensor: Randomly grayscaled image.
"""
num_output_channels, _, _ = F.get_dimensions(img)
if flow.rand(1) < self.p:
return F.rgb_to_grayscale(img, num_output_channels=num_output_channels)
return img
def __repr__(self):
return self.__class__.__name__ + "(p={0})".format(self.p)
[docs]class GaussianBlur(Module):
"""Blurs image with randomly chosen Gaussian blur.
If the image is oneflow Tensor, it is expected
to have [..., C, H, W] shape, where ... means an arbitrary number of leading dimensions.
Args:
kernel_size (int or sequence): Size of the Gaussian kernel.
sigma (float or tuple of float (min, max)): Standard deviation to be used for
creating kernel to perform blurring. If float, sigma is fixed. If it is tuple
of float (min, max), sigma is chosen uniformly at random to lie in the
given range.
Returns:
PIL Image or Tensor: Gaussian blurred version of the input image.
"""
def __init__(self, kernel_size, sigma=(0.1, 2.0)):
super().__init__()
# TODO: implement the following code with oneflow
# _log_api_usage_once(self)
self.kernel_size = _setup_size(
kernel_size, "Kernel size should be a tuple/list of two integers"
)
for ks in self.kernel_size:
if ks <= 0 or ks % 2 == 0:
raise ValueError(
"Kernel size value should be an odd and positive number."
)
if isinstance(sigma, numbers.Number):
if sigma <= 0:
raise ValueError("If sigma is a single number, it must be positive.")
sigma = (sigma, sigma)
elif isinstance(sigma, Sequence) and len(sigma) == 2:
if not 0.0 < sigma[0] <= sigma[1]:
raise ValueError(
"sigma values should be positive and of the form (min, max)."
)
else:
raise ValueError(
"sigma should be a single number or a list/tuple with length 2."
)
self.sigma = sigma
[docs] @staticmethod
def get_params(sigma_min: float, sigma_max: float) -> float:
"""Choose sigma for random gaussian blurring.
Args:
sigma_min (float): Minimum standard deviation that can be chosen for blurring kernel.
sigma_max (float): Maximum standard deviation that can be chosen for blurring kernel.
Returns:
float: Standard deviation to be passed to calculate kernel for gaussian blurring.
"""
return flow.empty(1).uniform_(sigma_min, sigma_max).item()
[docs] def forward(self, img: Tensor) -> Tensor:
"""
Args:
img (PIL Image or Tensor): image to be blurred.
Returns:
PIL Image or Tensor: Gaussian blurred image
"""
sigma = self.get_params(self.sigma[0], self.sigma[1])
return F.gaussian_blur(img, self.kernel_size, [sigma, sigma])
def __repr__(self) -> str:
s = f"{self.__class__.__name__}(kernel_size={self.kernel_size}, sigma={self.sigma})"
return s
[docs]class Solarization(Module):
"""
Apply Solarization to the input PIL Image.
Args:
p (float): probability that image should be applied with solarization operation.
"""
def __init__(self, p=0.1):
super().__init__()
self.p = p
if p > 1:
warnings.warn("Probability is larger than 1, return the original image.")
[docs] def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be applied with solarization operation.
Returns:
PIL Image or Tensor: selorization image.
"""
if flow.rand(1) < self.p:
return ImageOps.solarize(img)
else:
return img
def __repr__(self) -> str:
s = f"{self.__class__.__name__}(p={self.p})"
return s
def _setup_size(size, error_msg):
if isinstance(size, numbers.Number):
return int(size), int(size)
if isinstance(size, Sequence) and len(size) == 1:
return size[0], size[0]
if len(size) != 2:
raise ValueError(error_msg)
return size
def _check_sequence_input(x, name, req_sizes):
msg = (
req_sizes[0] if len(req_sizes) < 2 else " or ".join([str(s) for s in req_sizes])
)
if not isinstance(x, Sequence):
raise TypeError(f"{name} should be a sequence of length {msg}.")
if len(x) not in req_sizes:
raise ValueError(f"{name} should be sequence of length {msg}.")
def _setup_angle(x, name, req_sizes=(2,)):
if isinstance(x, numbers.Number):
if x < 0:
raise ValueError(f"If {name} is a single number, it must be positive.")
x = [-x, x]
else:
_check_sequence_input(x, name, req_sizes)
return [float(d) for d in x]
def _setup_size(size, error_msg):
if isinstance(size, numbers.Number):
return int(size), int(size)
if isinstance(size, Sequence) and len(size) == 1:
return size[0], size[0]
if len(size) != 2:
raise ValueError(error_msg)
return size
def _check_sequence_input(x, name, req_sizes):
msg = (
req_sizes[0] if len(req_sizes) < 2 else " or ".join([str(s) for s in req_sizes])
)
if not isinstance(x, Sequence):
raise TypeError("{} should be a sequence of length {}.".format(name, msg))
if len(x) not in req_sizes:
raise ValueError("{} should be sequence of length {}.".format(name, msg))
def _setup_angle(x, name, req_sizes=(2,)):
if isinstance(x, numbers.Number):
if x < 0:
raise ValueError(
"If {} is a single number, it must be positive.".format(name)
)
x = [-x, x]
else:
_check_sequence_input(x, name, req_sizes)
return [float(d) for d in x]