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 math
  import torch
  from torch.optim import Optimizer
  
  
  class AdaBound(Optimizer):
      """Implements AdaBound algorithm.
      It has been proposed in `Adaptive Gradient Methods with Dynamic Bound of Learning Rate`_.
      Arguments:
          params (iterable): iterable of parameters to optimize or dicts defining
              parameter groups
          lr (float, optional): Adam learning rate (default: 1e-3)
          betas (Tuple[float, float], optional): coefficients used for computing
              running averages of gradient and its square (default: (0.9, 0.999))
          final_lr (float, optional): final (SGD) learning rate (default: 0.1)
          gamma (float, optional): convergence speed of the bound functions (default: 1e-3)
          eps (float, optional): term added to the denominator to improve
              numerical stability (default: 1e-8)
          weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
          amsbound (boolean, optional): whether to use the AMSBound variant of this algorithm
      .. Adaptive Gradient Methods with Dynamic Bound of Learning Rate:
          https://openreview.net/forum?id=Bkg3g2R9FX
  
      Originally taken from https://github.com/Luolc/AdaBound
      NOTE: Has not provided good (or even decent) results on large datasets like ImageNet
      """
  
      def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), final_lr=0.1, gamma=1e-3,
                   eps=1e-8, weight_decay=0, amsbound=False):
          if not 0.0 <= lr:
              raise ValueError("Invalid learning rate: {}".format(lr))
          if not 0.0 <= eps:
              raise ValueError("Invalid epsilon value: {}".format(eps))
          if not 0.0 <= betas[0] < 1.0:
              raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
          if not 0.0 <= betas[1] < 1.0:
              raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
          if not 0.0 <= final_lr:
              raise ValueError("Invalid final learning rate: {}".format(final_lr))
          if not 0.0 <= gamma < 1.0:
              raise ValueError("Invalid gamma parameter: {}".format(gamma))
          defaults = dict(lr=lr, betas=betas, final_lr=final_lr, gamma=gamma, eps=eps,
                          weight_decay=weight_decay, amsbound=amsbound)
          super(AdaBound, self).__init__(params, defaults)
  
          self.base_lrs = list(map(lambda group: group['lr'], self.param_groups))
  
      def __setstate__(self, state):
          super(AdaBound, self).__setstate__(state)
          for group in self.param_groups:
              group.setdefault('amsbound', False)
  
      def step(self, closure=None):
          """Performs a single optimization step.
          Arguments:
              closure (callable, optional): A closure that reevaluates the model
                  and returns the loss.
          """
          loss = None
          if closure is not None:
              loss = closure()
  
          for group, base_lr in zip(self.param_groups, self.base_lrs):
              for p in group['params']:
                  if p.grad is None:
                      continue
                  grad = p.grad.data
                  if grad.is_sparse:
                      raise RuntimeError(
                          'Adam does not support sparse gradients, please consider SparseAdam instead')
                  amsbound = group['amsbound']
  
                  state = self.state[p]
  
                  # State initialization
                  if len(state) == 0:
                      state['step'] = 0
                      # Exponential moving average of gradient values
                      state['exp_avg'] = torch.zeros_like(p.data)
                      # Exponential moving average of squared gradient values
                      state['exp_avg_sq'] = torch.zeros_like(p.data)
                      if amsbound:
                          # Maintains max of all exp. moving avg. of sq. grad. values
                          state['max_exp_avg_sq'] = torch.zeros_like(p.data)
  
                  exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                  if amsbound:
                      max_exp_avg_sq = state['max_exp_avg_sq']
                  beta1, beta2 = group['betas']
  
                  state['step'] += 1
  
                  if group['weight_decay'] != 0:
                      grad = grad.add(group['weight_decay'], p.data)
  
                  # Decay the first and second moment running average coefficient
                  exp_avg.mul_(beta1).add_(1 - beta1, grad)
                  exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                  if amsbound:
                      # Maintains the maximum of all 2nd moment running avg. till now
                      torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
                      # Use the max. for normalizing running avg. of gradient
                      denom = max_exp_avg_sq.sqrt().add_(group['eps'])
                  else:
                      denom = exp_avg_sq.sqrt().add_(group['eps'])
  
                  bias_correction1 = 1 - beta1 ** state['step']
                  bias_correction2 = 1 - beta2 ** state['step']
                  step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
  
                  # Applies bounds on actual learning rate
                  # lr_scheduler cannot affect final_lr, this is a workaround to apply lr decay
                  final_lr = group['final_lr'] * group['lr'] / base_lr
                  lower_bound = final_lr * (1 - 1 / (group['gamma'] * state['step'] + 1))
                  upper_bound = final_lr * (1 + 1 / (group['gamma'] * state['step']))
                  step_size = torch.full_like(denom, step_size)
                  step_size.div_(denom).clamp_(lower_bound, upper_bound).mul_(exp_avg)
  
                  p.data.add_(-step_size)
  
          return loss
  