text: import random
  from collections import OrderedDict
  
  import numpy as np
  import torch
  import torch.distributed as dist
  from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
  from mmcv.runner import DistSamplerSeedHook, Runner
  
  from mmdet.core import (DistEvalHook, DistOptimizerHook, EvalHook,
                          Fp16OptimizerHook, build_optimizer)
  from mmdet.datasets import build_dataloader, build_dataset
  from mmdet.utils import get_root_logger
  
  
  def set_random_seed(seed, deterministic=False):
      """Set random seed.
  
      Args:
          seed (int): Seed to be used.
          deterministic (bool): Whether to set the deterministic option for
              CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
              to True and `torch.backends.cudnn.benchmark` to False.
              Default: False.
      """
      random.seed(seed)
      np.random.seed(seed)
      torch.manual_seed(seed)
      torch.cuda.manual_seed_all(seed)
      if deterministic:
          torch.backends.cudnn.deterministic = True
          torch.backends.cudnn.benchmark = False
  
  
  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(f'{loss_name} is not a tensor or list of tensors')
  
      loss = sum(_value for _key, _value in log_vars.items() if 'loss' in _key)
  
      log_vars['loss'] = loss
      for loss_name, loss_value in log_vars.items():
          # reduce loss when distributed training
          if dist.is_available() and dist.is_initialized():
              loss_value = loss_value.data.clone()
              dist.all_reduce(loss_value.div_(dist.get_world_size()))
          log_vars[loss_name] = loss_value.item()
  
      return loss, log_vars
  
  
  def batch_processor(model, data, train_mode):
      """Process a data batch.
  
      This method is required as an argument of Runner, which defines how to
      process a data batch and obtain proper outputs. The first 3 arguments of
      batch_processor are fixed.
  
      Args:
          model (nn.Module): A PyTorch model.
          data (dict): The data batch in a dict.
          train_mode (bool): Training mode or not. It may be useless for some
              models.
  
      Returns:
          dict: A dict containing losses and log vars.
      """
      losses = model(**data)
      loss, log_vars = parse_losses(losses)
  
      outputs = dict(
          loss=loss, log_vars=log_vars, num_samples=len(data['img'].data))
  
      return outputs
  
  
  def train_detector(model,
                     dataset,
                     cfg,
                     distributed=False,
                     validate=False,
                     timestamp=None,
                     meta=None):
      logger = get_root_logger(cfg.log_level)
  
      # prepare data loaders
      dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
      data_loaders = [
          build_dataloader(
              ds,
              cfg.data.samples_per_gpu,
              cfg.data.workers_per_gpu,
              # cfg.gpus will be ignored if distributed
              len(cfg.gpu_ids),
              dist=distributed,
              seed=cfg.seed) for ds in dataset
      ]
  
      # put model on gpus
      if distributed:
          find_unused_parameters = cfg.get('find_unused_parameters', True)
          # Sets the `find_unused_parameters` parameter in
          # torch.nn.parallel.DistributedDataParallel
          model = MMDistributedDataParallel(
              model.cuda(),
              device_ids=[torch.cuda.current_device()],
              broadcast_buffers=False,
              find_unused_parameters=find_unused_parameters)
      else:
          model = MMDataParallel(
              model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids)
  
      # build runner
      optimizer = build_optimizer(model, cfg.optimizer)
      runner = Runner(
          model,
          batch_processor,
          optimizer,
          cfg.work_dir,
          logger=logger,
          meta=meta)
      # an ugly walkaround to make the .log and .log.json filenames the same
      runner.timestamp = timestamp
  
      # fp16 setting
      fp16_cfg = cfg.get('fp16', None)
      if fp16_cfg is not None:
          optimizer_config = Fp16OptimizerHook(
              **cfg.optimizer_config, **fp16_cfg, distributed=distributed)
      elif distributed and 'type' not in cfg.optimizer_config:
          optimizer_config = DistOptimizerHook(**cfg.optimizer_config)
      else:
          optimizer_config = cfg.optimizer_config
  
      # register hooks
      runner.register_training_hooks(cfg.lr_config, optimizer_config,
                                     cfg.checkpoint_config, cfg.log_config,
                                     cfg.get('momentum_config', None))
      if distributed:
          runner.register_hook(DistSamplerSeedHook())
  
      # register eval hooks
      if validate:
          val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
          val_dataloader = build_dataloader(
              val_dataset,
              samples_per_gpu=1,
              workers_per_gpu=cfg.data.workers_per_gpu,
              dist=distributed,
              shuffle=False)
          eval_cfg = cfg.get('evaluation', {})
          eval_hook = DistEvalHook if distributed else EvalHook
          runner.register_hook(eval_hook(val_dataloader, **eval_cfg))
  
      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)
  