text: from __future__ import division
  import torch
  import math
  import random
  from PIL import Image, ImageOps, ImageEnhance
  try:
      import accimage
  except ImportError:
      accimage = None
  import numpy as np
  import numbers
  import types
  import collections
  import warnings
  
  from . import functional as F
  
  __all__ = ["Compose", "ToTensor", "ToPILImage", "Normalize", "Resize", "Scale", "CenterCrop", "Pad",
             "Lambda", "RandomApply", "RandomChoice", "RandomOrder", "RandomCrop", "RandomHorizontalFlip",
             "RandomVerticalFlip", "RandomResizedCrop", "RandomSizedCrop", "FiveCrop", "TenCrop", "LinearTransformation",
             "ColorJitter", "RandomRotation", "RandomAffine", "Grayscale", "RandomGrayscale"]
  
  _pil_interpolation_to_str = {
      Image.NEAREST: 'PIL.Image.NEAREST',
      Image.BILINEAR: 'PIL.Image.BILINEAR',
      Image.BICUBIC: 'PIL.Image.BICUBIC',
      Image.LANCZOS: 'PIL.Image.LANCZOS',
  }
  
  
  class Compose(object):
      """Composes several transforms together.
  
      Args:
          transforms (list of ``Transform`` objects): list of transforms to compose.
  
      Example:
          >>> transforms.Compose([
          >>>     transforms.CenterCrop(10),
          >>>     transforms.ToTensor(),
          >>> ])
      """
  
      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(object):
      """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 torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0].
      """
  
      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 ToPILImage(object):
      """Convert a tensor or an ndarray to PIL Image.
  
      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:
              1. If the input has 3 channels, the ``mode`` is assumed to be ``RGB``.
              2. If the input has 4 channels, the ``mode`` is assumed to be ``RGBA``.
              3. If the input has 1 channel, the ``mode`` is determined by the data type (i,e,
              ``int``, ``float``, ``short``).
  
      .. _PIL.Image mode: http://pillow.readthedocs.io/en/3.4.x/handbook/concepts.html#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(object):
      """Normalize a tensor image with mean and standard deviation.
      Given mean: ``(M1,...,Mn)`` and std: ``(S1,..,Sn)`` for ``n`` channels, this transform
      will normalize each channel of the input ``torch.*Tensor`` i.e.
      ``input[channel] = (input[channel] - mean[channel]) / std[channel]``
  
      Args:
          mean (sequence): Sequence of means for each channel.
          std (sequence): Sequence of standard deviations for each channel.
      """
  
      def __init__(self, mean, std):
          self.mean = mean
          self.std = std
  
      def __call__(self, tensor):
          """
          Args:
              tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
  
          Returns:
              Tensor: Normalized Tensor image.
          """
          return F.normalize(tensor, self.mean, self.std)
  
      def __repr__(self):
          return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)
  
  
  class Resize(object):
      """Resize the input PIL Image to the given size.
  
      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 (int, optional): Desired interpolation. Default is
              ``PIL.Image.BILINEAR``
      """
  
      def __init__(self, size, interpolation=Image.BILINEAR):
          assert isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)
          self.size = size
          self.interpolation = interpolation
  
      def __call__(self, img):
          """
          Args:
              img (PIL Image): Image to be scaled.
  
          Returns:
              PIL Image: 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(object):
      """Crops the given PIL Image at the center.
  
      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.
      """
  
      def __init__(self, size):
          if isinstance(size, numbers.Number):
              self.size = (int(size), int(size))
          else:
              self.size = size
  
      def __call__(self, img):
          """
          Args:
              img (PIL Image): Image to be cropped.
  
          Returns:
              PIL Image: Cropped image.
          """
          return F.center_crop(img, self.size)
  
      def __repr__(self):
          return self.__class__.__name__ + '(size={0})'.format(self.size)
  
  
  class Pad(object):
      """Pad the given PIL Image on all sides with the given "pad" value.
  
      Args:
          padding (int or tuple): 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.
          fill: Pixel fill value. Default is 0. If a tuple of
              length 3, it is used to fill R, G, B channels respectively.
      """
  
      def __init__(self, padding, fill=0):
          assert isinstance(padding, (numbers.Number, tuple))
          assert isinstance(fill, (numbers.Number, str, tuple))
          if isinstance(padding, collections.Sequence) and len(padding) not in [2, 4]:
              raise ValueError("Padding must be an int or a 2, or 4 element tuple, not a " +
                               "{} element tuple".format(len(padding)))
  
          self.padding = padding
          self.fill = fill
  
      def __call__(self, img):
          """
          Args:
              img (PIL Image): Image to be padded.
  
          Returns:
              PIL Image: Padded image.
          """
          return F.pad(img, self.padding, self.fill)
  
      def __repr__(self):
          return self.__class__.__name__ + '(padding={0}, fill={1})'.format(self.padding, self.fill)
  
  
  class Lambda(object):
      """Apply a user-defined lambda as a transform.
  
      Args:
          lambd (function): Lambda/function to be used for transform.
      """
  
      def __init__(self, lambd):
          assert isinstance(lambd, types.LambdaType)
          self.lambd = lambd
  
      def __call__(self, img):
          return self.lambd(img)
  
      def __repr__(self):
          return self.__class__.__name__ + '()'
  
  
  class RandomTransforms(object):
      """Base class for a list of transformations with randomness
  
      Args:
          transforms (list or tuple): list of transformations
      """
  
      def __init__(self, transforms):
          assert isinstance(transforms, (list, tuple))
          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(RandomTransforms):
      """Apply randomly a list of transformations with a given probability
  
      Args:
          transforms (list or tuple): list of transformations
          p (float): probability
      """
  
      def __init__(self, transforms, p=0.5):
          super(RandomApply, self).__init__(transforms)
          self.p = p
  
      def __call__(self, img):
          if self.p < random.random():
              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
      """
      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
      """
      def __call__(self, img):
          t = random.choice(self.transforms)
          return t(img)
  
  
  class RandomCrop(object):
      """Crop the given PIL Image at a random location.
  
      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.
          padding (int or sequence, optional): Optional padding on each border
              of the image. Default is 0, i.e no padding. If a sequence of length
              4 is provided, it is used to pad left, top, right, bottom borders
              respectively.
      """
  
      def __init__(self, size, padding=0):
          if isinstance(size, numbers.Number):
              self.size = (int(size), int(size))
          else:
              self.size = size
          self.padding = padding
  
      @staticmethod
      def get_params(img, output_size):
          """Get parameters for ``crop`` for a random crop.
  
          Args:
              img (PIL Image): 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 = img.size
          th, tw = output_size
          if w == tw and h == th:
              return 0, 0, h, w
  
          i = random.randint(0, h - th)
          j = random.randint(0, w - tw)
          return i, j, th, tw
  
      def __call__(self, img):
          """
          Args:
              img (PIL Image): Image to be cropped.
  
          Returns:
              PIL Image: Cropped image.
          """
          if self.padding > 0:
              img = F.pad(img, self.padding)
  
          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(object):
      """Horizontally flip the given PIL Image randomly with a given probability.
  
      Args:
          p (float): probability of the image being flipped. Default value is 0.5
      """
  
      def __init__(self, p=0.5):
          self.p = p
  
      def __call__(self, img):
          """
          Args:
              img (PIL Image): Image to be flipped.
  
          Returns:
              PIL Image: Randomly flipped image.
          """
          if random.random() < self.p:
              return F.hflip(img)
          return img
  
      def __repr__(self):
          return self.__class__.__name__ + '(p={})'.format(self.p)
  
  
  class RandomVerticalFlip(object):
      """Vertically flip the given PIL Image randomly with a given probability.
  
      Args:
          p (float): probability of the image being flipped. Default value is 0.5
      """
  
      def __init__(self, p=0.5):
          self.p = p
  
      def __call__(self, img):
          """
          Args:
              img (PIL Image): Image to be flipped.
  
          Returns:
              PIL Image: Randomly flipped image.
          """
          if random.random() < self.p:
              return F.vflip(img)
          return img
  
      def __repr__(self):
          return self.__class__.__name__ + '(p={})'.format(self.p)
  
  
  class RandomResizedCrop(object):
      """Crop the given PIL Image to random size and aspect ratio.
  
      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: expected output size of each edge
          scale: range of size of the origin size cropped
          ratio: range of aspect ratio of the origin aspect ratio cropped
          interpolation: Default: PIL.Image.BILINEAR
      """
  
      def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), interpolation=Image.BILINEAR):
          self.size = (size, size)
          self.interpolation = interpolation
          self.scale = scale
          self.ratio = ratio
  
      @staticmethod
      def get_params(img, scale, ratio):
          """Get parameters for ``crop`` for a random sized crop.
  
          Args:
              img (PIL Image): Image to be cropped.
              scale (tuple): range of size of the origin size cropped
              ratio (tuple): 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.
          """
          for attempt in range(10):
              area = img.size[0] * img.size[1]
              target_area = random.uniform(*scale) * area
              aspect_ratio = random.uniform(*ratio)
  
              w = int(round(math.sqrt(target_area * aspect_ratio)))
              h = int(round(math.sqrt(target_area / aspect_ratio)))
  
              if random.random() < 0.5:
                  w, h = h, w
  
              if w <= img.size[0] and h <= img.size[1]:
                  i = random.randint(0, img.size[1] - h)
                  j = random.randint(0, img.size[0] - w)
                  return i, j, h, w
  
          # Fallback
          w = min(img.size[0], img.size[1])
          i = (img.size[1] - w) // 2
          j = (img.size[0] - w) // 2
          return i, j, w, w
  
      def __call__(self, img):
          """
          Args:
              img (PIL Image): Image to be cropped and resized.
  
          Returns:
              PIL Image: 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(round(self.scale, 4))
          format_string += ', ratio={0}'.format(round(self.ratio, 4))
          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(object):
      """Crop the given PIL Image into four corners and the central crop
  
      .. 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.
  
      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):
          self.size = size
          if isinstance(size, numbers.Number):
              self.size = (int(size), int(size))
          else:
              assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
              self.size = size
  
      def __call__(self, img):
          return F.five_crop(img, self.size)
  
      def __repr__(self):
          return self.__class__.__name__ + '(size={0})'.format(self.size)
  
  
  class TenCrop(object):
      """Crop the given PIL Image into four corners and the central crop plus the flipped version of
      these (horizontal flipping is used by default)
  
      .. 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.
          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):
          self.size = size
          if isinstance(size, numbers.Number):
              self.size = (int(size), int(size))
          else:
              assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
              self.size = size
          self.vertical_flip = vertical_flip
  
      def __call__(self, img):
          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(object):
      """Transform a tensor image with a square transformation matrix computed
      offline.
  
      Given transformation_matrix, will flatten the torch.*Tensor, compute the dot
      product with the transformation matrix and reshape the tensor to its
      original shape.
  
      Applications:
      - whitening: zero-center the data, compute the data covariance matrix
                   [D x D] with np.dot(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
      """
  
      def __init__(self, transformation_matrix):
          if transformation_matrix.size(0) != transformation_matrix.size(1):
              raise ValueError("transformation_matrix should be square. Got " +
                               "[{} x {}] rectangular matrix.".format(*transformation_matrix.size()))
          self.transformation_matrix = transformation_matrix
  
      def __call__(self, tensor):
          """
          Args:
              tensor (Tensor): Tensor image of size (C, H, W) to be whitened.
  
          Returns:
              Tensor: Transformed image.
          """
          if tensor.size(0) * tensor.size(1) * tensor.size(2) != self.transformation_matrix.size(0):
              raise ValueError("tensor and transformation matrix have incompatible shape." +
                               "[{} x {} x {}] != ".format(*tensor.size()) +
                               "{}".format(self.transformation_matrix.size(0)))
          flat_tensor = tensor.view(1, -1)
          transformed_tensor = torch.mm(flat_tensor, self.transformation_matrix)
          tensor = transformed_tensor.view(tensor.size())
          return tensor
  
      def __repr__(self):
          format_string = self.__class__.__name__ + '('
          format_string += (str(self.transformation_matrix.numpy().tolist()) + ')')
          return format_string
  
  
  class ColorJitter(object):
      """Randomly change the brightness, contrast and saturation of an image.
  
      Args:
          brightness (float): How much to jitter brightness. brightness_factor
              is chosen uniformly from [max(0, 1 - brightness), 1 + brightness].
          contrast (float): How much to jitter contrast. contrast_factor
              is chosen uniformly from [max(0, 1 - contrast), 1 + contrast].
          saturation (float): How much to jitter saturation. saturation_factor
              is chosen uniformly from [max(0, 1 - saturation), 1 + saturation].
          hue(float): How much to jitter hue. hue_factor is chosen uniformly from
              [-hue, hue]. Should be >=0 and <= 0.5.
      """
      def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
          self.brightness = brightness
          self.contrast = contrast
          self.saturation = saturation
          self.hue = hue
  
      @staticmethod
      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 > 0:
              brightness_factor = np.random.uniform(max(0, 1 - brightness), 1 + brightness)
              transforms.append(Lambda(lambda img: F.adjust_brightness(img, brightness_factor)))
  
          if contrast > 0:
              contrast_factor = np.random.uniform(max(0, 1 - contrast), 1 + contrast)
              transforms.append(Lambda(lambda img: F.adjust_contrast(img, contrast_factor)))
  
          if saturation > 0:
              saturation_factor = np.random.uniform(max(0, 1 - saturation), 1 + saturation)
              transforms.append(Lambda(lambda img: F.adjust_saturation(img, saturation_factor)))
  
          if hue > 0:
              hue_factor = np.random.uniform(-hue, hue)
              transforms.append(Lambda(lambda img: F.adjust_hue(img, hue_factor)))
  
          np.random.shuffle(transforms)
          transform = Compose(transforms)
  
          return transform
  
      def __call__(self, img):
          """
          Args:
              img (PIL Image): Input image.
  
          Returns:
              PIL Image: Color jittered image.
          """
          transform = self.get_params(self.brightness, self.contrast,
                                      self.saturation, self.hue)
          return transform(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(object):
      """Rotate the image by angle.
  
      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 ({PIL.Image.NEAREST, PIL.Image.BILINEAR, PIL.Image.BICUBIC}, optional):
              An optional resampling filter.
              See http://pillow.readthedocs.io/en/3.4.x/handbook/concepts.html#filters
              If omitted, or if the image has mode "1" or "P", it is set to PIL.Image.NEAREST.
          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 (2-tuple, optional): Optional center of rotation.
              Origin is the upper left corner.
              Default is the center of the image.
      """
  
      def __init__(self, degrees, resample=False, expand=False, center=None):
          if isinstance(degrees, numbers.Number):
              if degrees < 0:
                  raise ValueError("If degrees is a single number, it must be positive.")
              self.degrees = (-degrees, degrees)
          else:
              if len(degrees) != 2:
                  raise ValueError("If degrees is a sequence, it must be of len 2.")
              self.degrees = degrees
  
          self.resample = resample
          self.expand = expand
          self.center = center
  
      @staticmethod
      def get_params(degrees):
          """Get parameters for ``rotate`` for a random rotation.
  
          Returns:
              sequence: params to be passed to ``rotate`` for random rotation.
          """
          angle = np.random.uniform(degrees[0], degrees[1])
  
          return angle
  
      def __call__(self, img):
          """
              img (PIL Image): Image to be rotated.
  
          Returns:
              PIL Image: Rotated image.
          """
  
          angle = self.get_params(self.degrees)
  
          return F.rotate(img, angle, self.resample, self.expand, self.center)
  
      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)
          format_string += ')'
          return format_string
  
  
  class RandomAffine(object):
      """Random affine transformation of the image keeping center invariant
  
      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 desactivate 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 degrees is a number instead of sequence like (min, max), the range of degrees
              will be (-degrees, +degrees). Will not apply shear by default
          resample ({PIL.Image.NEAREST, PIL.Image.BILINEAR, PIL.Image.BICUBIC}, optional):
              An optional resampling filter.
              See http://pillow.readthedocs.io/en/3.4.x/handbook/concepts.html#filters
              If omitted, or if the image has mode "1" or "P", it is set to PIL.Image.NEAREST.
          fillcolor (int): Optional fill color for the area outside the transform in the output image.
      """
  
      def __init__(self, degrees, translate=None, scale=None, shear=None, resample=False, fillcolor=0):
          if isinstance(degrees, numbers.Number):
              if degrees < 0:
                  raise ValueError("If degrees is a single number, it must be positive.")
              self.degrees = (-degrees, degrees)
          else:
              assert isinstance(degrees, (tuple, list)) and len(degrees) == 2, \
                  "degrees should be a list or tuple and it must be of length 2."
              self.degrees = degrees
  
          if translate is not None:
              assert isinstance(translate, (tuple, list)) and len(translate) == 2, \
                  "translate should be a list or tuple and it must be of length 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:
              assert isinstance(scale, (tuple, list)) and len(scale) == 2, \
                  "scale should be a list or tuple and it must be of length 2."
              for s in scale:
                  if s <= 0:
                      raise ValueError("scale values should be positive")
          self.scale = scale
  
          if shear is not None:
              if isinstance(shear, numbers.Number):
                  if shear < 0:
                      raise ValueError("If shear is a single number, it must be positive.")
                  self.shear = (-shear, shear)
              else:
                  assert isinstance(shear, (tuple, list)) and len(shear) == 2, \
                      "shear should be a list or tuple and it must be of length 2."
                  self.shear = shear
          else:
              self.shear = shear
  
          self.resample = resample
          self.fillcolor = fillcolor
  
      @staticmethod
      def get_params(degrees, translate, scale_ranges, shears, img_size):
          """Get parameters for affine transformation
  
          Returns:
              sequence: params to be passed to the affine transformation
          """
          angle = random.uniform(degrees[0], degrees[1])
          if translate is not None:
              max_dx = translate[0] * img_size[0]
              max_dy = translate[1] * img_size[1]
              translations = (np.round(random.uniform(-max_dx, max_dx)),
                              np.round(random.uniform(-max_dy, max_dy)))
          else:
              translations = (0, 0)
  
          if scale_ranges is not None:
              scale = random.uniform(scale_ranges[0], scale_ranges[1])
          else:
              scale = 1.0
  
          if shears is not None:
              shear = random.uniform(shears[0], shears[1])
          else:
              shear = 0.0
  
          return angle, translations, scale, shear
  
      def __call__(self, img):
          """
              img (PIL Image): Image to be transformed.
  
          Returns:
              PIL Image: Affine transformed image.
          """
          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(object):
      """Convert image to grayscale.
  
      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):
          self.num_output_channels = num_output_channels
  
      def __call__(self, img):
          """
          Args:
              img (PIL Image): Image to be converted to grayscale.
  
          Returns:
              PIL Image: Randomly grayscaled image.
          """
          return F.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(object):
      """Randomly convert image to grayscale with a probability of p (default 0.1).
  
      Args:
          p (float): probability that image should be converted to grayscale.
  
      Returns:
          PIL Image: 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):
          self.p = p
  
      def __call__(self, img):
          """
          Args:
              img (PIL Image): Image to be converted to grayscale.
  
          Returns:
              PIL Image: Randomly grayscaled image.
          """
          num_output_channels = 1 if img.mode == 'L' else 3
          if random.random() < self.p:
              return F.to_grayscale(img, num_output_channels=num_output_channels)
          return img
  
      def __repr__(self):
          return self.__class__.__name__ + '(p={0})'.format(self.p)
  