content: # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
  
  """
  This implementation is based on
  https://github.com/rwightman/pytorch-image-models/blob/master/timm/data/random_erasing.py
  pulished under an Apache License 2.0.
  
  COMMENT FROM ORIGINAL:
  Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
  Copyright Zhun Zhong & Liang Zheng
  Hacked together by / Copyright 2020 Ross Wightman
  """
  import math
  import random
  import torch
  
  
  def _get_pixels(
      per_pixel, rand_color, patch_size, dtype=torch.float32, device="cuda"
  ):
      # NOTE I've seen CUDA illegal memory access errors being caused by the normal_()
      # paths, flip the order so normal is run on CPU if this becomes a problem
      # Issue has been fixed in master https://github.com/pytorch/pytorch/issues/19508
      if per_pixel:
          return torch.empty(patch_size, dtype=dtype, device=device).normal_()
      elif rand_color:
          return torch.empty(
              (patch_size[0], 1, 1), dtype=dtype, device=device
          ).normal_()
      else:
          return torch.zeros((patch_size[0], 1, 1), dtype=dtype, device=device)
  
  
  class RandomErasing:
      """Randomly selects a rectangle region in an image and erases its pixels.
          'Random Erasing Data Augmentation' by Zhong et al.
          See https://arxiv.org/pdf/1708.04896.pdf
          This variant of RandomErasing is intended to be applied to either a batch
          or single image tensor after it has been normalized by dataset mean and std.
      Args:
           probability: Probability that the Random Erasing operation will be performed.
           min_area: Minimum percentage of erased area wrt input image area.
           max_area: Maximum percentage of erased area wrt input image area.
           min_aspect: Minimum aspect ratio of erased area.
           mode: pixel color mode, one of 'const', 'rand', or 'pixel'
              'const' - erase block is constant color of 0 for all channels
              'rand'  - erase block is same per-channel random (normal) color
              'pixel' - erase block is per-pixel random (normal) color
          max_count: maximum number of erasing blocks per image, area per box is scaled by count.
              per-image count is randomly chosen between 1 and this value.
      """
  
      def __init__(
          self,
          probability=0.5,
          min_area=0.02,
          max_area=1 / 3,
          min_aspect=0.3,
          max_aspect=None,
          mode="const",
          min_count=1,
          max_count=None,
          num_splits=0,
          device="cuda",
          cube=True,
      ):
          self.probability = probability
          self.min_area = min_area
          self.max_area = max_area
          max_aspect = max_aspect or 1 / min_aspect
          self.log_aspect_ratio = (math.log(min_aspect), math.log(max_aspect))
          self.min_count = min_count
          self.max_count = max_count or min_count
          self.num_splits = num_splits
          mode = mode.lower()
          self.rand_color = False
          self.per_pixel = False
          self.cube = cube
          if mode == "rand":
              self.rand_color = True  # per block random normal
          elif mode == "pixel":
              self.per_pixel = True  # per pixel random normal
          else:
              assert not mode or mode == "const"
          self.device = device
  
      def _erase(self, img, chan, img_h, img_w, dtype):
          if random.random() > self.probability:
              return
          area = img_h * img_w
          count = (
              self.min_count
              if self.min_count == self.max_count
              else random.randint(self.min_count, self.max_count)
          )
          for _ in range(count):
              for _ in range(10):
                  target_area = (
                      random.uniform(self.min_area, self.max_area) * area / count
                  )
                  aspect_ratio = math.exp(random.uniform(*self.log_aspect_ratio))
                  h = int(round(math.sqrt(target_area * aspect_ratio)))
                  w = int(round(math.sqrt(target_area / aspect_ratio)))
                  if w < img_w and h < img_h:
                      top = random.randint(0, img_h - h)
                      left = random.randint(0, img_w - w)
                      img[:, top : top + h, left : left + w] = _get_pixels(
                          self.per_pixel,
                          self.rand_color,
                          (chan, h, w),
                          dtype=dtype,
                          device=self.device,
                      )
                      break
  
      def _erase_cube(
          self,
          img,
          batch_start,
          batch_size,
          chan,
          img_h,
          img_w,
          dtype,
      ):
          if random.random() > self.probability:
              return
          area = img_h * img_w
          count = (
              self.min_count
              if self.min_count == self.max_count
              else random.randint(self.min_count, self.max_count)
          )
          for _ in range(count):
              for _ in range(100):
                  target_area = (
                      random.uniform(self.min_area, self.max_area) * area / count
                  )
                  aspect_ratio = math.exp(random.uniform(*self.log_aspect_ratio))
                  h = int(round(math.sqrt(target_area * aspect_ratio)))
                  w = int(round(math.sqrt(target_area / aspect_ratio)))
                  if w < img_w and h < img_h:
                      top = random.randint(0, img_h - h)
                      left = random.randint(0, img_w - w)
                      for i in range(batch_start, batch_size):
                          img_instance = img[i]
                          img_instance[
                              :, top : top + h, left : left + w
                          ] = _get_pixels(
                              self.per_pixel,
                              self.rand_color,
                              (chan, h, w),
                              dtype=dtype,
                              device=self.device,
                          )
                      break
  
      def __call__(self, input):
          if len(input.size()) == 3:
              self._erase(input, *input.size(), input.dtype)
          else:
              batch_size, chan, img_h, img_w = input.size()
              # skip first slice of batch if num_splits is set (for clean portion of samples)
              batch_start = (
                  batch_size // self.num_splits if self.num_splits > 1 else 0
              )
              if self.cube:
                  self._erase_cube(
                      input,
                      batch_start,
                      batch_size,
                      chan,
                      img_h,
                      img_w,
                      input.dtype,
                  )
              else:
                  for i in range(batch_start, batch_size):
                      self._erase(input[i], chan, img_h, img_w, input.dtype)
          return input
  