text: """
  Copyright 2020 Nvidia Corporation
  
  Redistribution and use in source and binary forms, with or without
  modification, are permitted provided that the following conditions are met:
  
  1. Redistributions of source code must retain the above copyright notice, this
     list of conditions and the following disclaimer.
  
  2. Redistributions in binary form must reproduce the above copyright notice,
     this list of conditions and the following disclaimer in the documentation
     and/or other materials provided with the distribution.
  
  3. Neither the name of the copyright holder nor the names of its contributors
     may be used to endorse or promote products derived from this software
     without specific prior written permission.
  
  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
  POSSIBILITY OF SUCH DAMAGE.
  """
  
  # Optimizer and scheduler related tasks
  
  import math
  import torch
  
  from torch import optim
  from runx.logx import logx
  
  from config import cfg
  from loss.radam import RAdam
  
  
  def get_optimizer(args, net):
      """
      Decide Optimizer (Adam or SGD)
      """
      param_groups = net.parameters()
  
      if args.optimizer == 'sgd':
          optimizer = optim.SGD(param_groups,
                                lr=args.lr,
                                weight_decay=args.weight_decay,
                                momentum=args.momentum,
                                nesterov=False)
      elif args.optimizer == 'adam':
          optimizer = optim.Adam(param_groups,
                                 lr=args.lr,
                                 weight_decay=args.weight_decay,
                                 amsgrad=args.amsgrad)
      elif args.optimizer == 'radam':
          optimizer = RAdam(param_groups,
                            lr=args.lr,
                            weight_decay=args.weight_decay)
      else:
          raise ValueError('Not a valid optimizer')
  
      def poly_schd(epoch):
          return math.pow(1 - epoch / args.max_epoch, args.poly_exp)
  
      def poly2_schd(epoch):
          if epoch < args.poly_step:
              poly_exp = args.poly_exp
          else:
              poly_exp = 2 * args.poly_exp
          return math.pow(1 - epoch / args.max_epoch, poly_exp)
  
      if args.lr_schedule == 'scl-poly':
          if cfg.REDUCE_BORDER_EPOCH == -1:
              raise ValueError('ERROR Cannot Do Scale Poly')
  
          rescale_thresh = cfg.REDUCE_BORDER_EPOCH
          scale_value = args.rescale
          lambda1 = lambda epoch: \
               math.pow(1 - epoch / args.max_epoch,
                        args.poly_exp) if epoch < rescale_thresh else scale_value * math.pow(
                            1 - (epoch - rescale_thresh) / (args.max_epoch - rescale_thresh),
                            args.repoly)
          scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)
      elif args.lr_schedule == 'poly2':
          scheduler = optim.lr_scheduler.LambdaLR(optimizer,
                                                  lr_lambda=poly2_schd)
      elif args.lr_schedule == 'poly':
          scheduler = optim.lr_scheduler.LambdaLR(optimizer,
                                                  lr_lambda=poly_schd)
      else:
          raise ValueError('unknown lr schedule {}'.format(args.lr_schedule))
  
      return optimizer, scheduler
  
  
  def load_weights(net, optimizer, snapshot_file, restore_optimizer_bool=False):
      """
      Load weights from snapshot file
      """
      logx.msg("Loading weights from model {}".format(snapshot_file))
      net, optimizer = restore_snapshot(net, optimizer, snapshot_file, restore_optimizer_bool)
      return net, optimizer
  
  
  def restore_snapshot(net, optimizer, snapshot, restore_optimizer_bool):
      """
      Restore weights and optimizer (if needed ) for resuming job.
      """
      checkpoint = torch.load(snapshot, map_location=torch.device('cpu'))
      logx.msg("Checkpoint Load Compelete")
      if optimizer is not None and 'optimizer' in checkpoint and restore_optimizer_bool:
          optimizer.load_state_dict(checkpoint['optimizer'])
  
      if 'state_dict' in checkpoint:
          net = forgiving_state_restore(net, checkpoint['state_dict'])
      else:
          net = forgiving_state_restore(net, checkpoint)
  
      return net, optimizer
  
  
  def restore_opt(optimizer, checkpoint):
      assert 'optimizer' in checkpoint, 'cant find optimizer in checkpoint'
      optimizer.load_state_dict(checkpoint['optimizer'])
  
  def restore_net_onnx(net, checkpoint):
      assert 'state_dict' in checkpoint, 'cant find state_dict in checkpoint'
      forgiving_state_restore_onnx(net, checkpoint['state_dict'])
  
  def restore_net(net, checkpoint):
      assert 'state_dict' in checkpoint, 'cant find state_dict in checkpoint'
      forgiving_state_restore(net, checkpoint['state_dict'])
  
  
  def forgiving_state_restore(net, loaded_dict):
      """
      Handle partial loading when some tensors don't match up in size.
      Because we want to use models that were trained off a different
      number of classes.
      """
  
      net_state_dict = net.state_dict()
      new_loaded_dict = {}
      for k in net_state_dict:
          new_k = k
          if new_k in loaded_dict and net_state_dict[k].size() == loaded_dict[new_k].size():
              new_loaded_dict[k] = loaded_dict[new_k]
          else:            
              logx.msg("Skipped loading parameter {}".format(k))
      net_state_dict.update(new_loaded_dict)
      net.load_state_dict(net_state_dict)
      return net
  
  def forgiving_state_restore_onnx(net, loaded_dict):
      """
      Handle partial loading when some tensors don't match up in size.
      Because we want to use models that were trained off a different
      number of classes.
      """
  
      net_state_dict = net.state_dict()
      new_loaded_dict = {}
      for k in net_state_dict:
          new_k = "module."+k
          if new_k in loaded_dict and net_state_dict[k].size() == loaded_dict[new_k].size():
              new_loaded_dict[k] = loaded_dict[new_k]
          else:
              print("Skipped loading parameter {}".format(k))
              # logx.msg("Skipped loading parameter {}".format(k))
      net_state_dict.update(new_loaded_dict)
      net.load_state_dict(net_state_dict)
      return net