hunk.code_window:     tfl = [
           transforms.RandomResizedCrop(
               img_size, scale=scale,
               interpolation=Image.BILINEAR if interpolation == 'bilinear' else Image.BICUBIC),
           transforms.RandomHorizontalFlip(),
           transforms.ColorJitter(*color_jitter),
       ]
  
hunk.labels: keep keep keep keep replace keep keep keep keep
hunk.after_edit:             interpolation=_pil_interp(interpolation)),
  
hunk.file_path: data/transforms.py
hunk.type: replace
file: import torch.nn as nn
  import torch.nn.functional as F
  
  
  class LabelSmoothingCrossEntropy(nn.Module):
      """
      NLL loss with label smoothing.
      """
      def __init__(self, smoothing=0.1):
          """
          Constructor for the LabelSmoothing module.
          :param smoothing: label smoothing factor
          """
          super(LabelSmoothingCrossEntropy, self).__init__()
          assert smoothing < 1.0
          self.smoothing = smoothing
          self.confidence = 1. - smoothing
  
      def forward(self, x, target):
          logprobs = F.log_softmax(x, dim=-1)
          nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1))
          nll_loss = nll_loss.squeeze(1)
          smooth_loss = -logprobs.mean(dim=-1)
          loss = self.confidence * nll_loss + self.smoothing * smooth_loss
          return loss.mean()
  
  