text: from __future__ import division
  
  import os
  import re
  from collections import OrderedDict
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.optim as optim
  from torch.autograd import Variable
  import torchvision
  
  from mmcv.runner import Runner, DistSamplerSeedHook, obj_from_dict, save_checkpoint, load_checkpoint
  from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
  
  from .env import get_root_logger
  from .utils import build_optimizer
  from ..models import build_loss
  from ..datasets import build_dataloader
  
  
  def parse_losses(losses):
      log_vars = OrderedDict()
      for loss_name, loss_value in losses.items():
          if isinstance(loss_value, torch.Tensor):
              log_vars[loss_name] = loss_value.mean()
          elif isinstance(loss_value, list):
              log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value)
          else:
              raise TypeError(
                  '{} is not a tensor or list of tensors'.format(loss_name))
  
      loss = sum(_value for _key, _value in log_vars.items() if 'loss' in _key)
  
      log_vars['loss'] = loss
      for name in log_vars:
          log_vars[name] = log_vars[name].item()
  
      return loss, log_vars
  
  
  def batch_processor(model, data, train_mode):
      losses = model(**data)
      loss, log_vars = parse_losses(losses)
  
      outputs = dict(loss=loss, log_vars=log_vars, num_samples=len(data))
      return outputs
  
  
  def train_retriever(model,
                      dataset,
                      cfg,
                      distributed=False,
                      validate=False,
                      logger=None):
      if logger is None:
          logger = get_root_logger(cfg.log_level)
  
      # start training predictor
      if distributed:  # to do
          _dist_train(model, dataset, cfg, validate=validate)
      else:
          _non_dist_train(model, dataset, cfg, validate=validate)
  
  
  def _dist_train(model, dataset, cfg, validate=False):
      """ not implemented yet """
      raise NotImplementedError
  
  
  def _non_dist_train(model, dataset, cfg, validate=False):
      # prepare data loaders
      data_loaders = [
          build_dataloader(
              dataset,
              cfg.data.imgs_per_gpu,
              cfg.data.workers_per_gpu,
              len(cfg.gpus.train),
              dist=False)
      ]
      print('dataloader built')
  
      model = MMDataParallel(model, device_ids=cfg.gpus.train).cuda()
      print('model paralleled')
  
      optimizer = build_optimizer(model, cfg.optimizer)
      runner = Runner(model, batch_processor, optimizer, cfg.work_dir,
                      cfg.log_level)
  
      runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
                                     cfg.checkpoint_config, cfg.log_config)
  
      if cfg.resume_from:
          runner.resume(cfg.resume_from)
      elif cfg.load_from:
          runner.load_checkpoint(cfg.load_from)
      runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
  