content: import torch
  import torchvision.transforms.functional as F
  import warnings
  import random
  import numpy as np
  import torchvision
  from PIL import Image, ImageOps
  import numbers
  
  
  class GroupRandomCrop(object):
      def __init__(self, size):
          if isinstance(size, numbers.Number):
              self.size = (int(size), int(size))
          else:
              self.size = size
  
      def __call__(self, img_tuple):
          img_group, label = img_tuple
          
          w, h = img_group[0].size
          th, tw = self.size
  
          out_images = list()
  
          x1 = random.randint(0, w - tw)
          y1 = random.randint(0, h - th)
  
          for img in img_group:
              assert(img.size[0] == w and img.size[1] == h)
              if w == tw and h == th:
                  out_images.append(img)
              else:
                  out_images.append(img.crop((x1, y1, x1 + tw, y1 + th)))
  
          return (out_images, label)
  
  
  class GroupCenterCrop(object):
      def __init__(self, size):
          self.worker = torchvision.transforms.CenterCrop(size)
  
      def __call__(self, img_tuple):
          img_group, label = img_tuple
          return ([self.worker(img) for img in img_group], label)
  
  
  class GroupRandomHorizontalFlip(object):
      def __init__(self, flip=False):
          self.flip = flip
  
      def __call__(self, img_tuple):
          v = random.random()
          if self.flip and v < 0.5:
              img_group, label = img_tuple
              ret = [img.transpose(Image.FLIP_LEFT_RIGHT) for img in img_group]
              return (ret, label)
          else:
              return img_tuple
  
  
  class GroupNormalize(object):
      def __init__(self, mean, std):
          self.mean = mean
          self.std = std
  
      def __call__(self, tensor_tuple):
          tensor, label = tensor_tuple
          rep_mean = self.mean * (tensor.size()[0]//len(self.mean))
          rep_std = self.std * (tensor.size()[0]//len(self.std))
          
          # TODO: make efficient
          for t, m, s in zip(tensor, rep_mean, rep_std):
              t.sub_(m).div_(s)
  
          return (tensor,label)
  
  
  class GroupGrayScale(object):
      def __init__(self, size):
          self.worker = torchvision.transforms.Grayscale(size)
  
      def __call__(self, img_tuple):
          img_group, label = img_tuple
          return ([self.worker(img) for img in img_group], label)
  
  
  class GroupColorJitter(object):
      def __init__(self, size):
          self.worker = torchvision.transforms.ColorJitter(
              brightness=size, contrast=size, saturation=size
          )
  
      def __call__(self, img_tuple):
          img_group, label = img_tuple
          return ([self.worker(img) for img in img_group], label)
  
      
  class GroupScale(object):
      """ Rescales the input PIL.Image to the given 'size'.
      'size' will be the size of the smaller edge.
      For example, if height > width, then image will be
      rescaled to (size * height / width, size)
      size: size of the smaller edge
      interpolation: Default: PIL.Image.BILINEAR
      """
  
      def __init__(self, size, interpolation=Image.BILINEAR):
          self.worker = torchvision.transforms.Resize(size, interpolation)
  
      def __call__(self, img_tuple):
          img_group, label = img_tuple
          return ([self.worker(img) for img in img_group], label)
  
  
  class GroupMultiScaleCrop(object):
  
      def __init__(self, input_size, scales=None, max_distort=1, fix_crop=True, more_fix_crop=True):
          self.scales = scales if scales is not None else [1, 875, .75, .66]
          self.max_distort = max_distort
          self.fix_crop = fix_crop
          self.more_fix_crop = more_fix_crop
          self.input_size = input_size if not isinstance(input_size, int) else [input_size, input_size]
          self.interpolation = Image.BILINEAR
  
      def __call__(self, img_tuple):
          img_group, label = img_tuple
          
          im_size = img_group[0].size
  
          crop_w, crop_h, offset_w, offset_h = self._sample_crop_size(im_size)
          crop_img_group = [img.crop((offset_w, offset_h, offset_w + crop_w, offset_h + crop_h)) for img in img_group]
          ret_img_group = [img.resize((self.input_size[0], self.input_size[1]), self.interpolation) for img in crop_img_group]
          return (ret_img_group, label)
  
      def _sample_crop_size(self, im_size):
          image_w, image_h = im_size[0], im_size[1]
  
          # find a crop size
          base_size = min(image_w, image_h)
          crop_sizes = [int(base_size * x) for x in self.scales]
          crop_h = [self.input_size[1] if abs(x - self.input_size[1]) < 3 else x for x in crop_sizes]
          crop_w = [self.input_size[0] if abs(x - self.input_size[0]) < 3 else x for x in crop_sizes]
  
          pairs = []
          for i, h in enumerate(crop_h):
              for j, w in enumerate(crop_w):
                  if abs(i - j) <= self.max_distort:
                      pairs.append((w, h))
  
          crop_pair = random.choice(pairs)
          if not self.fix_crop:
              w_offset = random.randint(0, image_w - crop_pair[0])
              h_offset = random.randint(0, image_h - crop_pair[1])
          else:
              w_offset, h_offset = self._sample_fix_offset(image_w, image_h, crop_pair[0], crop_pair[1])
  
          return crop_pair[0], crop_pair[1], w_offset, h_offset
  
      def _sample_fix_offset(self, image_w, image_h, crop_w, crop_h):
          offsets = self.fill_fix_offset(self.more_fix_crop, image_w, image_h, crop_w, crop_h)
          return random.choice(offsets)
  
      @staticmethod
      def fill_fix_offset(more_fix_crop, image_w, image_h, crop_w, crop_h):
          w_step = (image_w - crop_w) // 4
          h_step = (image_h - crop_h) // 4
  
          ret = list()
          ret.append((0, 0))  # upper left
          ret.append((4 * w_step, 0))  # upper right
          ret.append((0, 4 * h_step))  # lower left
          ret.append((4 * w_step, 4 * h_step))  # lower right
          ret.append((2 * w_step, 2 * h_step))  # center
  
          if more_fix_crop:
              ret.append((0, 2 * h_step))  # center left
              ret.append((4 * w_step, 2 * h_step))  # center right
              ret.append((2 * w_step, 4 * h_step))  # lower center
              ret.append((2 * w_step, 0 * h_step))  # upper center
  
              ret.append((1 * w_step, 1 * h_step))  # upper left quarter
              ret.append((3 * w_step, 1 * h_step))  # upper right quarter
              ret.append((1 * w_step, 3 * h_step))  # lower left quarter
              ret.append((3 * w_step, 3 * h_step))  # lower righ quarter
          return ret
  
  
  class Stack(object):
  
      def __init__(self, roll=False):
          self.roll = roll
  
      def __call__(self, img_tuple):
          img_group, label = img_tuple
          
          if img_group[0].mode == 'L':
              return (np.concatenate([np.expand_dims(x, 2) for x in img_group], axis=2), label)
          elif img_group[0].mode == 'RGB':
              if self.roll:
                  return (np.concatenate([np.array(x)[:, :, ::-1] for x in img_group], axis=2), label)
              else:
                  return (np.concatenate(img_group, axis=2), label)
  
  
  class ToTorchFormatTensor(object):
      """ Converts a PIL.Image (RGB) 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 __init__(self, div=True):
          self.div = div
  
      def __call__(self, pic_tuple):
          pic, label = pic_tuple
          
          if isinstance(pic, np.ndarray):
              # handle numpy array
              img = torch.from_numpy(pic).permute(2, 0, 1).contiguous()
          else:
              # handle PIL Image
              img = torch.ByteTensor(torch.ByteStorage.from_buffer(pic.tobytes()))
              img = img.view(pic.size[1], pic.size[0], len(pic.mode))
              # put it from HWC to CHW format
              # yikes, this transpose takes 80% of the loading time/CPU
              img = img.transpose(0, 1).transpose(0, 2).contiguous()
          return (img.float().div(255.) if self.div else img.float(), label)
  
  
  class IdentityTransform(object):
  
      def __call__(self, data):
          return data
  