transforms.py

import math
import numbers
import random
import warnings
from collections.abc import Sequence
from typing import Tuple, List, Optional

import torch
from PIL import Image
from torch import Tensor

try:
    import accimage
except ImportError:
    accimage = None

from . import functional as F

__all__ = ["Compose", "ToTensor", "PILToTensor", "ConvertImageDtype", "ToPILImage", "Normalize", "Resize", "Scale",
           "CenterCrop", "Pad", "Lambda", "RandomApply", "RandomChoice", "RandomOrder", "RandomCrop",
           "RandomHorizontalFlip", "RandomVerticalFlip", "RandomResizedCrop", "RandomSizedCrop", "FiveCrop", "TenCrop",
           "LinearTransformation", "ColorJitter", "RandomRotation", "RandomAffine", "Grayscale", "RandomGrayscale",
           "RandomPerspective", "RandomErasing", "GaussianBlur"]

_pil_interpolation_to_str = {
    Image.NEAREST: 'PIL.Image.NEAREST',
    Image.BILINEAR: 'PIL.Image.BILINEAR',
    Image.BICUBIC: 'PIL.Image.BICUBIC',
    Image.LANCZOS: 'PIL.Image.LANCZOS',
    Image.HAMMING: 'PIL.Image.HAMMING',
    Image.BOX: 'PIL.Image.BOX',
}


class Compose:
    """Composes several transforms together. This transform does not support torchscript.
    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 ``torch.nn.Sequential`` as below.

        >>> transforms = torch.nn.Sequential(
        >>>     transforms.CenterCrop(10),
        >>>     transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
        >>> )
        >>> scripted_transforms = torch.jit.script(transforms)

        Make sure to use only scriptable transformations, i.e. that work with ``torch.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


class ToTensor:
    """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor. This transform does not support torchscript.

    Converts a PIL Image or numpy.ndarray (H x W x C) in the range
    [0, 255] to a torch.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. See the `references`_ for implementing the transforms for image masks.

    .. _references: https://github.com/pytorch/vision/tree/master/references/segmentation
    """

    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__ + '()'


class PILToTensor:
    """Convert a ``PIL Image`` to a tensor of the same type. This transform does not support torchscript.

    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__ + '()'


class ConvertImageDtype(torch.nn.Module):
    """Convert a tensor image to the given ``dtype`` and scale the values accordingly

    Args:
        dtype (torch.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:`torch.float32` to :class:`torch.int32` or :class:`torch.int64` as
            well as for trying to cast :class:`torch.float64` to :class:`torch.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: torch.dtype) -> None:
        super().__init__()
        self.dtype = dtype

    def forward(self, image):
        return F.convert_image_dtype(image, self.dtype)


class ToPILImage:
    """Convert a tensor or an ndarray to PIL Image. This transform does not support torchscript.

    Converts a torch.*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


class Normalize(torch.nn.Module):
    """Normalize a tensor image with mean and standard deviation.
    Given mean: ``(mean[1],...,mean[n])`` and std: ``(std[1],..,std[n])`` for ``n``
    channels, this transform will normalize each channel of the input
    ``torch.*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

    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)


class Resize(torch.nn.Module):
    """Resize the input image to the given size.
    The image can be a PIL Image or a torch Tensor, in which case 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).
            In torchscript mode padding as single int is not supported, use a tuple or
            list of length 1: ``[size, ]``.
        interpolation (int, optional): Desired interpolation enum defined by `filters`_.
            Default is ``PIL.Image.BILINEAR``. If input is Tensor, only ``PIL.Image.NEAREST``, ``PIL.Image.BILINEAR``
            and ``PIL.Image.BICUBIC`` are supported.
    """

    def __init__(self, size, interpolation=Image.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
        self.interpolation = interpolation

    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 = _pil_interpolation_to_str[self.interpolation]
        return self.__class__.__name__ + '(size={0}, interpolation={1})'.format(self.size, interpolate_str)


class Scale(Resize):
    """
    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)


class CenterCrop(torch.nn.Module):
    """Crops the given image at the center.
    The image can be a PIL Image or a torch Tensor, in which case it is expected
    to have [..., H, W] shape, where ... means an arbitrary number of 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 tuple or list 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.")

    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)


class Pad(torch.nn.Module):
    """Pad the given image on all sides with the given "pad" value.
    The image can be a PIL Image or a torch Tensor, in which case it is expected
    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions

    Args:
        padding (int or tuple or list): Padding on each border. If a single int is provided this
            is used to pad all borders. If tuple of length 2 is provided this is the padding
            on left/right and top/bottom respectively. If a tuple of length 4 is provided
            this is the padding for the left, top, right and bottom borders respectively.
            In torchscript mode padding as single int is not supported, use a tuple or
            list of length 1: ``[padding, ]``.
        fill (int 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
        padding_mode (str): Type of padding. Should be: constant, edge, reflect or symmetric.
            Default is constant. Mode symmetric is not yet supported for Tensor inputs.

            - 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]
    """

    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

    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)


class Lambda:
    """Apply a user-defined lambda as a transform. This transform does not support torchscript.

    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__ + '()'


class RandomTransforms:
    """Base class for a list of transformations with randomness

    Args:
        transforms (list or tuple): list of transformations
    """

    def __init__(self, transforms):
        if not isinstance(transforms, Sequence):
            raise TypeError("Argument transforms should be a sequence")
        self.transforms = transforms

    def __call__(self, *args, **kwargs):
        raise NotImplementedError()

    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


class RandomApply(torch.nn.Module):
    """Apply randomly a list of transformations with a given probability.

    .. note::
        In order to script the transformation, please use ``torch.nn.ModuleList`` as input instead of list/tuple of
        transforms as shown below:

        >>> transforms = transforms.RandomApply(torch.nn.ModuleList([
        >>>     transforms.ColorJitter(),
        >>> ]), p=0.3)
        >>> scripted_transforms = torch.jit.script(transforms)

        Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor``, does not require
        `lambda` functions or ``PIL.Image``.

    Args:
        transforms (list or tuple or torch.nn.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 < torch.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


class RandomOrder(RandomTransforms):
    """Apply a list of transformations in a random order. This transform does not support torchscript.
    """
    def __call__(self, img):
        order = list(range(len(self.transforms)))
        random.shuffle(order)
        for i in order:
            img = self.transforms[i](img)
        return img


class RandomChoice(RandomTransforms):
    """Apply single transformation randomly picked from a list. This transform does not support torchscript.
    """
    def __call__(self, img):
        t = random.choice(self.transforms)
        return t(img)


class RandomCrop(torch.nn.Module):
    """Crop the given image at a random location.
    The image can be a PIL Image or a Tensor, in which case it is expected
    to have [..., H, W] shape, where ... means an arbitrary number of 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 tuple or list 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 tuple of length 2 is provided this is the padding
            on left/right and top/bottom respectively. If a tuple of length 4 is provided
            this is the padding for the left, top, right and bottom borders respectively.
            In torchscript mode padding as single int is not supported, use a tuple or
            list of length 1: ``[padding, ]``.
        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 (int 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
        padding_mode (str): Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant.
            Mode symmetric is not yet supported for Tensor inputs.

             - constant: pads with a constant value, this value is specified with fill

             - edge: pads with the last value on the edge of the image

             - reflect: pads with reflection of image (without repeating the last value on the edge)

                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)

                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]

    """

    @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 = torch.randint(0, h - th + 1, size=(1, )).item()
        j = torch.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

    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)


class RandomHorizontalFlip(torch.nn.Module):
    """Horizontally flip the given image randomly with a given probability.
    The image can be a PIL Image or a torch Tensor, in which case 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

    def forward(self, img):
        """
        Args:
            img (PIL Image or Tensor): Image to be flipped.

        Returns:
            PIL Image or Tensor: Randomly flipped image.
        """
        if torch.rand(1) < self.p:
            return F.hflip(img)
        return img

    def __repr__(self):
        return self.__class__.__name__ + '(p={})'.format(self.p)


class RandomVerticalFlip(torch.nn.Module):
    """Vertically flip the given image randomly with a given probability.
    The image can be a PIL Image or a torch Tensor, in which case 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

    def forward(self, img):
        """
        Args:
            img (PIL Image or Tensor): Image to be flipped.

        Returns:
            PIL Image or Tensor: Randomly flipped image.
        """
        if torch.rand(1) < self.p:
            return F.vflip(img)
        return img

    def __repr__(self):
        return self.__class__.__name__ + '(p={})'.format(self.p)


class RandomPerspective(torch.nn.Module):
    """Performs a random perspective transformation of the given image with a given probability.
    The image can be a PIL Image or a Tensor, in which case it is expected
    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.

    Args:
        distortion_scale (float): 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 (int): Interpolation type. If input is Tensor, only ``PIL.Image.NEAREST`` and
            ``PIL.Image.BILINEAR`` are supported. Default, ``PIL.Image.BILINEAR`` for PIL images and Tensors.
        fill (n-tuple or int or float): Pixel fill value for area outside the rotated
            image. If int or float, the value is used for all bands respectively. Default is 0.
            This option is only available for ``pillow>=5.0.0``. This option is not supported for Tensor
            input. Fill value for the area outside the transform in the output image is always 0.

    """

    def __init__(self, distortion_scale=0.5, p=0.5, interpolation=Image.BILINEAR, fill=0):
        super().__init__()
        self.p = p
        self.interpolation = interpolation
        self.distortion_scale = distortion_scale
        self.fill = fill

    def forward(self, img):
        """
        Args:
            img (PIL Image or Tensor): Image to be Perspectively transformed.

        Returns:
            PIL Image or Tensor: Randomly transformed image.
        """
        if torch.rand(1) < self.p:
            width, height = F._get_image_size(img)
            startpoints, endpoints = self.get_params(width, height, self.distortion_scale)
            return F.perspective(img, startpoints, endpoints, self.interpolation, self.fill)
        return img

    @staticmethod
    def get_params(width: int, height: int, distortion_scale: float) -> Tuple[List[List[int]], List[List[int]]]:
        """Get parameters for ``perspective`` for a random perspective transform.

        Args:
            width (int): width of the image.
            height (int): height of the image.
            distortion_scale (float): argument to control the degree of distortion and ranges from 0 to 1.

        Returns:
            List containing [top-left, top-right, bottom-right, bottom-left] of the original image,
            List containing [top-left, top-right, bottom-right, bottom-left] of the transformed image.
        """
        half_height = height // 2
        half_width = width // 2
        topleft = [
            int(torch.randint(0, int(distortion_scale * half_width) + 1, size=(1, )).item()),
            int(torch.randint(0, int(distortion_scale * half_height) + 1, size=(1, )).item())
        ]
        topright = [
            int(torch.randint(width - int(distortion_scale * half_width) - 1, width, size=(1, )).item()),
            int(torch.randint(0, int(distortion_scale * half_height) + 1, size=(1, )).item())
        ]
        botright = [
            int(torch.randint(width - int(distortion_scale * half_width) - 1, width, size=(1, )).item()),
            int(torch.randint(height - int(distortion_scale * half_height) - 1, height, size=(1, )).item())
        ]
        botleft = [
            int(torch.randint(0, int(distortion_scale * half_width) + 1, size=(1, )).item()),
            int(torch.randint(height - int(distortion_scale * half_height) - 1, height, size=(1, )).item())
        ]
        startpoints = [[0, 0], [width - 1, 0], [width - 1, height - 1], [0, height - 1]]
        endpoints = [topleft, topright, botright, botleft]
        return startpoints, endpoints

    def __repr__(self):
        return self.__class__.__name__ + '(p={})'.format(self.p)


class RandomResizedCrop(torch.nn.Module):
    """Crop the given image to random size and aspect ratio.
    The image can be a PIL Image or a Tensor, in which case it is expected
    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions

    A crop of random size (default: of 0.08 to 1.0) of the original size and a random
    aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This crop
    is finally resized to given size.
    This is popularly used to train the Inception networks.

    Args:
        size (int or sequence): expected output size of each edge. If size is an
            int instead of sequence like (h, w), a square output size ``(size, size)`` is
            made. If provided a tuple or list of length 1, it will be interpreted as (size[0], size[0]).
        scale (tuple of float): scale range of the cropped image before resizing, relatively to the origin image.
        ratio (tuple of float): aspect ratio range of the cropped image before resizing.
        interpolation (int): Desired interpolation enum defined by `filters`_.
            Default is ``PIL.Image.BILINEAR``. If input is Tensor, only ``PIL.Image.NEAREST``, ``PIL.Image.BILINEAR``
            and ``PIL.Image.BICUBIC`` are supported.
    """

    def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), interpolation=Image.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)")

        self.interpolation = interpolation
        self.scale = scale
        self.ratio = ratio

    @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

        for _ in range(10):
            target_area = area * torch.empty(1).uniform_(scale[0], scale[1]).item()
            log_ratio = torch.log(torch.tensor(ratio))
            aspect_ratio = torch.exp(
                torch.empty(1).uniform_(log_ratio[0], log_ratio[1])
            ).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()
                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

    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 = _pil_interpolation_to_str[self.interpolation]
        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


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 FiveCrop(torch.nn.Module):
    """Crop the given image into four corners and the central crop.
    The image can be a PIL Image or a Tensor, in which case 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 tuple or list 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: torch.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.")

    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)


class TenCrop(torch.nn.Module):
    """Crop the given image into four corners and the central crop plus the flipped version of
    these (horizontal flipping is used by default).
    The image can be a PIL Image or a Tensor, in which case 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 tuple or list 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: torch.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

    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 LinearTransformation(torch.nn.Module):
    """Transform a tensor image with a square transformation matrix and a mean_vector computed
    offline.
    Given transformation_matrix and mean_vector, will flatten the torch.*Tensor and
    subtract mean_vector from it which is then followed by computing the dot
    product with the transformation matrix and then reshaping the tensor to its
    original shape.

    Applications:
        whitening transformation: Suppose X is a column vector zero-centered data.
        Then compute the data covariance matrix [D x D] with torch.mm(X.t(), X),
        perform SVD on this matrix and pass it as transformation_matrix.

    Args:
        transformation_matrix (Tensor): tensor [D x D], D = C x H x W
        mean_vector (Tensor): tensor [D], D = C x H x W
    """

    def __init__(self, transformation_matrix, mean_vector):
        super().__init__()
        if transformation_matrix.size(0) != transformation_matrix.size(1):
            raise ValueError("transformation_matrix should be square. Got " +
                             "[{} x {}] rectangular matrix.".format(*transformation_matrix.size()))

        if mean_vector.size(0) != transformation_matrix.size(0):
            raise ValueError("mean_vector should have the same length {}".format(mean_vector.size(0)) +
                             " as any one of the dimensions of the transformation_matrix [{}]"
                             .format(tuple(transformation_matrix.size())))

        if transformation_matrix.device != mean_vector.device:
            raise ValueError("Input tensors should be on the same device. Got {} and {}"
                             .format(transformation_matrix.device, mean_vector.device))

        self.transformation_matrix = transformation_matrix
        self.mean_vector = mean_vector

    def forward(self, tensor: Tensor) -> Tensor:
        """
        Args:
            tensor (Tensor): Tensor image to be whitened.

        Returns:
            Tensor: Transformed image.
        """
        shape = tensor.shape
        n = shape[-3] * shape[-2] * shape[-1]
        if n != self.transformation_matrix.shape[0]:
            raise ValueError("Input tensor and transformation matrix have incompatible shape." +
                             "[{} x {} x {}] != ".format(shape[-3], shape[-2], shape[-1]) +
                             "{}".format(self.transformation_matrix.shape[0]))

        if tensor.device.type != self.mean_vector.device.type:
            raise ValueError("Input tensor should be on the same device as transformation matrix and mean vector. "
                             "Got {} vs {}".format(tensor.device, self.mean_vector.device))

        flat_tensor = tensor.view(-1, n) - self.mean_vector
        transformed_tensor = torch.mm(flat_tensor, self.transformation_matrix)
        tensor = transformed_tensor.view(shape)
        return tensor

    def __repr__(self):
        format_string = self.__class__.__name__ + '(transformation_matrix='
        format_string += (str(self.transformation_matrix.tolist()) + ')')
        format_string += (", (mean_vector=" + str(self.mean_vector.tolist()) + ')')
        return format_string


class ColorJitter(torch.nn.Module):
    """Randomly change the brightness, contrast and saturation of an image.

    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)

    @torch.jit.unused
    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 lenght 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
    @torch.jit.unused
    def get_params(brightness, contrast, saturation, hue):
        """Get a randomized transform to be applied on image.

        Arguments are same as that of __init__.

        Returns:
            Transform which randomly adjusts brightness, contrast and
            saturation in a random order.
        """
        transforms = []

        if brightness is not None:
            brightness_factor = random.uniform(brightness[0], brightness[1])
            transforms.append(Lambda(lambda img: F.adjust_brightness(img, brightness_factor)))

        if contrast is not None:
            contrast_factor = random.uniform(contrast[0], contrast[1])
            transforms.append(Lambda(lambda img: F.adjust_contrast(img, contrast_factor)))

        if saturation is not None:
            saturation_factor = random.uniform(saturation[0], saturation[1])
            transforms.append(Lambda(lambda img: F.adjust_saturation(img, saturation_factor)))

        if hue is not None:
            hue_factor = random.uniform(hue[0], hue[1])
            transforms.append(Lambda(lambda img: F.adjust_hue(img, hue_factor)))

        random.shuffle(transforms)
        transform = Compose(transforms)

        return transform

    def forward(self, img):
        """
        Args:
            img (PIL Image or Tensor): Input image.

        Returns:
            PIL Image or Tensor: Color jittered image.
        """
        fn_idx = torch.randperm(4)
        for fn_id in fn_idx:
            if fn_id == 0 and self.brightness is not None:
                brightness = self.brightness
                brightness_factor = torch.tensor(1.0).uniform_(brightness[0], brightness[1]).item()
                img = F.adjust_brightness(img, brightness_factor)

            if fn_id == 1 and self.contrast is not None:
                contrast = self.contrast
                contrast_factor = torch.tensor(1.0).uniform_(contrast[0], contrast[1]).item()
                img = F.adjust_contrast(img, contrast_factor)

            if fn_id == 2 and self.saturation is not None:
                saturation = self.saturation
                saturation_factor = torch.tensor(1.0).uniform_(saturation[0], saturation[1]).item()
                img = F.adjust_saturation(img, saturation_factor)

            if fn_id == 3 and self.hue is not None:
                hue = self.hue
                hue_factor = torch.tensor(1.0).uniform_(hue[0], hue[1]).item()
                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(torch.nn.Module):
    """Rotate the image by angle.
    The image can be a PIL Image or a Tensor, in which case it is expected
    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.

    Args:
        degrees (sequence or float or int): 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).
        resample (int, optional): An optional resampling filter. See `filters`_ for more information.
            If omitted, or if the image has mode "1" or "P", it is set to PIL.Image.NEAREST.
            If input is Tensor, only ``PIL.Image.NEAREST`` and ``PIL.Image.BILINEAR`` are supported.
        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 (list or tuple, optional): Optional center of rotation, (x, y). Origin is the upper left corner.
            Default is the center of the image.
        fill (n-tuple or int or float): Pixel fill value for area outside the rotated
            image. If int or float, the value is used for all bands respectively.
            Defaults to 0 for all bands. This option is only available for Pillow>=5.2.0.
            This option is not supported for Tensor input. Fill value for the area outside the transform in the output
            image is always 0.

    .. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters

    """

    def __init__(self, degrees, resample=False, expand=False, center=None, fill=None):
        super().__init__()
        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 = resample
        self.expand = expand
        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(torch.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.
        """
        angle = self.get_params(self.degrees)
        return F.rotate(img, angle, self.resample, self.expand, self.center, self.fill)

    def __repr__(self):
        format_string = self.__class__.__name__ + '(degrees={0}'.format(self.degrees)
        format_string += ', resample={0}'.format(self.resample)
        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


class RandomAffine(torch.nn.Module):
    """Random affine transformation of the image keeping center invariant.
    The image can be a PIL Image or a Tensor, in which case it is expected
    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.

    Args:
        degrees (sequence or float or int): 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 float or int, 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 tuple or list 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 tuple or list 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.
        resample (int, optional): An optional resampling filter. See `filters`_ for more information.
            If omitted, or if the image has mode "1" or "P", it is set to ``PIL.Image.NEAREST``.
            If input is Tensor, only ``PIL.Image.NEAREST`` and ``PIL.Image.BILINEAR`` are supported.
        fillcolor (tuple or int): Optional fill color (Tuple for RGB Image and int for grayscale) for the area
            outside the transform in the output image (Pillow>=5.0.0). This option is not supported for Tensor
            input. Fill value for the area outside the transform in the output image is always 0.

    .. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters

    """

    def __init__(self, degrees, translate=None, scale=None, shear=None, resample=0, fillcolor=0):
        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.resample = resample
        self.fillcolor = fillcolor

    @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(torch.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(torch.empty(1).uniform_(-max_dx, max_dx).item()))
            ty = int(round(torch.empty(1).uniform_(-max_dy, max_dy).item()))
            translations = (tx, ty)
        else:
            translations = (0, 0)

        if scale_ranges is not None:
            scale = float(torch.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(torch.empty(1).uniform_(shears[0], shears[1]).item())
            if len(shears) == 4:
                shear_y = float(torch.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.
        """

        img_size = F._get_image_size(img)

        ret = self.get_params(self.degrees, self.translate, self.scale, self.shear, img_size)
        return F.affine(img, *ret, resample=self.resample, fillcolor=self.fillcolor)

    def __repr__(self):
        s = '{name}(degrees={degrees}'
        if self.translate is not None:
            s += ', translate={translate}'
        if self.scale is not None:
            s += ', scale={scale}'
        if self.shear is not None:
            s += ', shear={shear}'
        if self.resample > 0:
            s += ', resample={resample}'
        if self.fillcolor != 0:
            s += ', fillcolor={fillcolor}'
        s += ')'
        d = dict(self.__dict__)
        d['resample'] = _pil_interpolation_to_str[d['resample']]
        return s.format(name=self.__class__.__name__, **d)


class Grayscale(torch.nn.Module):
    """Convert image to grayscale.
    The image can be a PIL Image or a Tensor, in which case 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

    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):
        return self.__class__.__name__ + '(num_output_channels={0})'.format(self.num_output_channels)


class RandomGrayscale(torch.nn.Module):
    """Randomly convert image to grayscale with a probability of p (default 0.1).
    The image can be a PIL Image or a Tensor, in which case 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__()
        self.p = p

    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_image_num_channels(img)
        if torch.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)


class RandomErasing(torch.nn.Module):
    """ Randomly selects a rectangle region in an image and erases its pixels.
    'Random Erasing Data Augmentation' by Zhong et al. See https://arxiv.org/abs/1708.04896

    Args:
         p: probability that the random erasing operation will be performed.
         scale: range of proportion of erased area against input image.
         ratio: range of aspect ratio of erased area.
         value: erasing value. Default is 0. If a single int, it is used to
            erase all pixels. If a tuple of length 3, it is used to erase
            R, G, B channels respectively.
            If a str of 'random', erasing each pixel with random values.
         inplace: boolean to make this transform inplace. Default set to False.

    Returns:
        Erased Image.

    Example:
        >>> transform = transforms.Compose([
        >>>   transforms.RandomHorizontalFlip(),
        >>>   transforms.ToTensor(),
        >>>   transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
        >>>   transforms.RandomErasing(),
        >>> ])
    """

    def __init__(self, p=0.5, scale=(0.02, 0.33), ratio=(0.3, 3.3), value=0, inplace=False):
        super().__init__()
        if not isinstance(value, (numbers.Number, str, tuple, list)):
            raise TypeError("Argument value should be either a number or str or a sequence")
        if isinstance(value, str) and value != "random":
            raise ValueError("If value is str, it should be 'random'")
        if not isinstance(scale, (tuple, list)):
            raise TypeError("Scale should be a sequence")
        if not isinstance(ratio, (tuple, list)):
            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)")
        if scale[0] < 0 or scale[1] > 1:
            raise ValueError("Scale should be between 0 and 1")
        if p < 0 or p > 1:
            raise ValueError("Random erasing probability should be between 0 and 1")

        self.p = p
        self.scale = scale
        self.ratio = ratio
        self.value = value
        self.inplace = inplace

    @staticmethod
    def get_params(
            img: Tensor, scale: Tuple[float, float], ratio: Tuple[float, float], value: Optional[List[float]] = None
    ) -> Tuple[int, int, int, int, Tensor]:
        """Get parameters for ``erase`` for a random erasing.

        Args:
            img (Tensor): Tensor image to be erased.
            scale (tuple or list): range of proportion of erased area against input image.
            ratio (tuple or list): range of aspect ratio of erased area.
            value (list, optional): erasing value. If None, it is interpreted as "random"
                (erasing each pixel with random values). If ``len(value)`` is 1, it is interpreted as a number,
                i.e. ``value[0]``.

        Returns:
            tuple: params (i, j, h, w, v) to be passed to ``erase`` for random erasing.
        """
        img_c, img_h, img_w = img.shape[-3], img.shape[-2], img.shape[-1]
        area = img_h * img_w

        for _ in range(10):
            erase_area = area * torch.empty(1).uniform_(scale[0], scale[1]).item()
            aspect_ratio = torch.empty(1).uniform_(ratio[0], ratio[1]).item()

            h = int(round(math.sqrt(erase_area * aspect_ratio)))
            w = int(round(math.sqrt(erase_area / aspect_ratio)))
            if not (h < img_h and w < img_w):
                continue

            if value is None:
                v = torch.empty([img_c, h, w], dtype=torch.float32).normal_()
            else:
                v = torch.tensor(value)[:, None, None]

            i = torch.randint(0, img_h - h + 1, size=(1, )).item()
            j = torch.randint(0, img_w - w + 1, size=(1, )).item()
            return i, j, h, w, v

        # Return original image
        return 0, 0, img_h, img_w, img

    def forward(self, img):
        """
        Args:
            img (Tensor): Tensor image to be erased.

        Returns:
            img (Tensor): Erased Tensor image.
        """
        if torch.rand(1) < self.p:

            # cast self.value to script acceptable type
            if isinstance(self.value, (int, float)):
                value = [self.value, ]
            elif isinstance(self.value, str):
                value = None
            elif isinstance(self.value, tuple):
                value = list(self.value)
            else:
                value = self.value

            if value is not None and not (len(value) in (1, img.shape[-3])):
                raise ValueError(
                    "If value is a sequence, it should have either a single value or "
                    "{} (number of input channels)".format(img.shape[-3])
                )

            x, y, h, w, v = self.get_params(img, scale=self.scale, ratio=self.ratio, value=value)
            return F.erase(img, x, y, h, w, v, self.inplace)
        return img


class GaussianBlur(torch.nn.Module):
    """Blurs image with randomly chosen Gaussian blur.
    The image can be a PIL Image or a Tensor, in which case 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__()
        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. < 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

    @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 torch.empty(1).uniform_(sigma_min, sigma_max).item()

    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):
        s = '(kernel_size={}, '.format(self.kernel_size)
        s += 'sigma={})'.format(self.sigma)
        return self.__class__.__name__ + 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("{} 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]