content: ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
  ## Created by: Hang Zhang
  ## ECE Department, Rutgers University
  ## Email: zhang.hang@rutgers.edu
  ## Copyright (c) 2017
  ##
  ## This source code is licensed under the MIT-style license found in the
  ## LICENSE file in the root directory of this source tree
  ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
  
  import math
  
  class LR_Scheduler(object):
      """Learning Rate Scheduler
  
      Step mode: ``lr = baselr * 0.1 ^ {floor(epoch-1 / lr_step)}``
  
      Cosine mode: ``lr = baselr * 0.5 * (1 + cos(iter/maxiter))``
  
      Poly mode: ``lr = baselr * (1 - iter/maxiter) ^ 0.9``
  
      Args:
          args:
            :attr:`args.lr_scheduler` lr scheduler mode (`cos`, `poly`),
            :attr:`args.lr` base learning rate, :attr:`args.epochs` number of epochs,
            :attr:`args.lr_step`
  
          iters_per_epoch: number of iterations per epoch
      """
      def __init__(self, mode, base_lr, num_epochs, iters_per_epoch=0,
                   lr_step=0, warmup_epochs=0):
          self.mode = mode
          print('Using {} LR Scheduler!'.format(self.mode))
          self.lr = base_lr
          if mode == 'step':
              assert lr_step
          self.lr_step = lr_step
          self.iters_per_epoch = iters_per_epoch
          self.N = num_epochs * iters_per_epoch
          self.epoch = -1
          self.warmup_iters = warmup_epochs * iters_per_epoch
  
      def __call__(self, optimizer, i, epoch, best_pred):
          T = epoch * self.iters_per_epoch + i
          if self.mode == 'cos':
              lr = 0.5 * self.lr * (1 + math.cos(1.0 * T / self.N * math.pi))
          elif self.mode == 'poly':
              lr = self.lr * pow((1 - 1.0 * T / self.N), 0.9)
          elif self.mode == 'step':
              lr = self.lr * (0.1 ** (epoch // self.lr_step))
          else:
              raise NotImplemented
          # warm up lr schedule
          if self.warmup_iters > 0 and T < self.warmup_iters:
              lr = lr * 1.0 * T / self.warmup_iters
          if epoch > self.epoch:
              print('\n=>Epoches %i, learning rate = %.4f, \
                  previous best = %.4f' % (epoch, lr, best_pred))
              self.epoch = epoch
          assert lr >= 0
          self._adjust_learning_rate(optimizer, lr)
  
      def _adjust_learning_rate(self, optimizer, lr):
          if len(optimizer.param_groups) == 1:
              optimizer.param_groups[0]['lr'] = lr
          else:
              # enlarge the lr at the head
              optimizer.param_groups[0]['lr'] = lr
              for i in range(1, len(optimizer.param_groups)):
                  optimizer.param_groups[i]['lr'] = lr * 10
  