hunk.code_window: @register_model
   def vit_relpos_base_patch32_plus_rpn_256(pretrained=False, **kwargs):
       """ ViT-Base (ViT-B/32+) w/ relative log-coord position  and residual post-norm, no class token
       """
       model_kwargs = dict(
  
hunk.labels: keep keep keep keep replace keep keep
hunk.after_edit:     """ ViT-Base (ViT-B/32+) w/ relative log-coord position and residual post-norm, no class token
  
hunk.file_path: timm/models/vision_transformer_relpos.py
hunk.type: replace
file: """ Random Erasing (Cutout)
  
  Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
  Copyright Zhun Zhong & Liang Zheng
  
  Hacked together by / Copyright 2019, Ross Wightman
  """
  import random
  import math
  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'):
          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
          self.mode = mode.lower()
          self.rand_color = False
          self.per_pixel = False
          if self.mode == 'rand':
              self.rand_color = True  # per block random normal
          elif self.mode == 'pixel':
              self.per_pixel = True  # per pixel random normal
          else:
              assert not self.mode or self.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 attempt 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 __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
              for i in range(batch_start, batch_size):
                  self._erase(input[i], chan, img_h, img_w, input.dtype)
          return input
  
      def __repr__(self):
          # NOTE simplified state for repr
          fs = self.__class__.__name__ + f'(p={self.probability}, mode={self.mode}'
          fs += f', count=({self.min_count}, {self.max_count}))'
          return fs
  