text: import random
  import re
  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, obj_from_dict
  
  from mmdet.core import DistEvalHook, DistOptimizerHook, Fp16OptimizerHook
  from mmdet.datasets import build_dataloader
  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(
                  '{} 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 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):
      logger = get_root_logger(cfg.log_level)
  
      # start training
      if distributed:
          _dist_train(
              model,
              dataset,
              cfg,
              validate=validate,
              logger=logger,
              timestamp=timestamp)
      else:
          _non_dist_train(
              model,
              dataset,
              cfg,
              validate=validate,
              logger=logger,
              timestamp=timestamp)
  
  
  def build_optimizer(model, optimizer_cfg):
      """Build optimizer from configs.
  
      Args:
          model (:obj:`nn.Module`): The model with parameters to be optimized.
          optimizer_cfg (dict): The config dict of the optimizer.
              Positional fields are:
                  - type: class name of the optimizer.
                  - lr: base learning rate.
              Optional fields are:
                  - any arguments of the corresponding optimizer type, e.g.,
                    weight_decay, momentum, etc.
                  - paramwise_options: a dict with 3 accepted fileds
                    (bias_lr_mult, bias_decay_mult, norm_decay_mult).
                    `bias_lr_mult` and `bias_decay_mult` will be multiplied to
                    the lr and weight decay respectively for all bias parameters
                    (except for the normalization layers), and
                    `norm_decay_mult` will be multiplied to the weight decay
                    for all weight and bias parameters of normalization layers.
  
      Returns:
          torch.optim.Optimizer: The initialized optimizer.
  
      Example:
          >>> model = torch.nn.modules.Conv1d(1, 1, 1)
          >>> optimizer_cfg = dict(type='SGD', lr=0.01, momentum=0.9,
          >>>                      weight_decay=0.0001)
          >>> optimizer = build_optimizer(model, optimizer_cfg)
      """
      if hasattr(model, 'module'):
          model = model.module
  
      optimizer_cfg = optimizer_cfg.copy()
      paramwise_options = optimizer_cfg.pop('paramwise_options', None)
      # if no paramwise option is specified, just use the global setting
      if paramwise_options is None:
          return obj_from_dict(optimizer_cfg, torch.optim,
                               dict(params=model.parameters()))
      else:
          assert isinstance(paramwise_options, dict)
          # get base lr and weight decay
          base_lr = optimizer_cfg['lr']
          base_wd = optimizer_cfg.get('weight_decay', None)
          # weight_decay must be explicitly specified if mult is specified
          if ('bias_decay_mult' in paramwise_options
                  or 'norm_decay_mult' in paramwise_options):
              assert base_wd is not None
          # get param-wise options
          bias_lr_mult = paramwise_options.get('bias_lr_mult', 1.)
          bias_decay_mult = paramwise_options.get('bias_decay_mult', 1.)
          norm_decay_mult = paramwise_options.get('norm_decay_mult', 1.)
          # set param-wise lr and weight decay
          params = []
          for name, param in model.named_parameters():
              param_group = {'params': [param]}
              if not param.requires_grad:
                  # FP16 training needs to copy gradient/weight between master
                  # weight copy and model weight, it is convenient to keep all
                  # parameters here to align with model.parameters()
                  params.append(param_group)
                  continue
  
              # for norm layers, overwrite the weight decay of weight and bias
              # TODO: obtain the norm layer prefixes dynamically
              if re.search(r'(bn|gn)(\d+)?.(weight|bias)', name):
                  if base_wd is not None:
                      param_group['weight_decay'] = base_wd * norm_decay_mult
              # for other layers, overwrite both lr and weight decay of bias
              elif name.endswith('.bias'):
                  param_group['lr'] = base_lr * bias_lr_mult
                  if base_wd is not None:
                      param_group['weight_decay'] = base_wd * bias_decay_mult
              # otherwise use the global settings
  
              params.append(param_group)
  
          optimizer_cls = getattr(torch.optim, optimizer_cfg.pop('type'))
          return optimizer_cls(params, **optimizer_cfg)
  
  
  def _dist_train(model,
                  dataset,
                  cfg,
                  validate=False,
                  logger=None,
                  timestamp=None):
      # prepare data loaders
      dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
      data_loaders = [
          build_dataloader(
              ds,
              cfg.data.imgs_per_gpu,
              cfg.data.workers_per_gpu,
              dist=True,
              seed=cfg.seed) for ds in dataset
      ]
      # put model on gpus
      model = MMDistributedDataParallel(model.cuda())
  
      # build runner
      optimizer = build_optimizer(model, cfg.optimizer)
      runner = Runner(
          model, batch_processor, optimizer, cfg.work_dir, logger=logger)
      # 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)
      else:
          optimizer_config = DistOptimizerHook(**cfg.optimizer_config)
  
      # register hooks
      runner.register_training_hooks(cfg.lr_config, optimizer_config,
                                     cfg.checkpoint_config, cfg.log_config)
      runner.register_hook(DistSamplerSeedHook())
      # register eval hooks
      if validate:
          val_dataset_cfg = cfg.data.val
          eval_cfg = cfg.get('evaluation', {})
          runner.register_hook(DistEvalHook(val_dataset_cfg, **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)
  
  
  def _non_dist_train(model,
                      dataset,
                      cfg,
                      validate=False,
                      logger=None,
                      timestamp=None):
      if validate:
          raise NotImplementedError('Built-in validation is not implemented '
                                    'yet in not-distributed training. Use '
                                    'distributed training or test.py and '
                                    '*eval.py scripts instead.')
      # prepare data loaders
      dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
      data_loaders = [
          build_dataloader(
              ds,
              cfg.data.imgs_per_gpu,
              cfg.data.workers_per_gpu,
              cfg.gpus,
              dist=False,
              seed=cfg.seed) for ds in dataset
      ]
      # put model on gpus
      model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()
  
      # build runner
      optimizer = build_optimizer(model, cfg.optimizer)
      runner = Runner(
          model, batch_processor, optimizer, cfg.work_dir, logger=logger)
      # 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=False)
      else:
          optimizer_config = cfg.optimizer_config
      runner.register_training_hooks(cfg.lr_config, 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)
  