text: # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
  from enum import Enum
  from typing import Any, Callable, Dict, Iterable, List, Set, Type, Union
  import torch
  
  from detectron2.config import CfgNode
  
  from .lr_scheduler import WarmupCosineLR, WarmupMultiStepLR
  
  _GradientClipperInput = Union[torch.Tensor, Iterable[torch.Tensor]]
  _GradientClipper = Callable[[_GradientClipperInput], None]
  
  
  class GradientClipType(Enum):
      VALUE = "value"
      NORM = "norm"
  
  
  def _create_gradient_clipper(cfg: CfgNode) -> _GradientClipper:
      """
      Creates gradient clipping closure to clip by value or by norm,
      according to the provided config.
      """
      cfg = cfg.clone()
  
      def clip_grad_norm(p: _GradientClipperInput):
          torch.nn.utils.clip_grad_norm_(p, cfg.CLIP_VALUE, cfg.NORM_TYPE)
  
      def clip_grad_value(p: _GradientClipperInput):
          torch.nn.utils.clip_grad_value_(p, cfg.CLIP_VALUE)
  
      _GRADIENT_CLIP_TYPE_TO_CLIPPER = {
          GradientClipType.VALUE: clip_grad_value,
          GradientClipType.NORM: clip_grad_norm,
      }
      return _GRADIENT_CLIP_TYPE_TO_CLIPPER[GradientClipType(cfg.CLIP_TYPE)]
  
  
  def _generate_optimizer_class_with_gradient_clipping(
      optimizer_type: Type[torch.optim.Optimizer], gradient_clipper: _GradientClipper
  ) -> Type[torch.optim.Optimizer]:
      """
      Dynamically creates a new type that inherits the type of a given instance
      and overrides the `step` method to add gradient clipping
      """
  
      def optimizer_wgc_step(self, closure=None):
          for group in self.param_groups:
              for p in group["params"]:
                  gradient_clipper(p)
          super(type(self), self).step(closure)
  
      OptimizerWithGradientClip = type(
          optimizer_type.__name__ + "WithGradientClip",
          (optimizer_type,),
          {"step": optimizer_wgc_step},
      )
      return OptimizerWithGradientClip
  
  
  def maybe_add_gradient_clipping(
      cfg: CfgNode, optimizer: torch.optim.Optimizer
  ) -> torch.optim.Optimizer:
      """
      If gradient clipping is enabled through config options, wraps the existing
      optimizer instance of some type OptimizerType to become an instance
      of the new dynamically created class OptimizerTypeWithGradientClip
      that inherits OptimizerType and overrides the `step` method to
      include gradient clipping.
  
      Args:
          cfg: CfgNode
              configuration options
          optimizer: torch.optim.Optimizer
              existing optimizer instance
  
      Return:
          optimizer: torch.optim.Optimizer
              either the unmodified optimizer instance (if gradient clipping is
              disabled), or the same instance with adjusted __class__ to override
              the `step` method and include gradient clipping
      """
      if not cfg.SOLVER.CLIP_GRADIENTS.ENABLED:
          return optimizer
      grad_clipper = _create_gradient_clipper(cfg.SOLVER.CLIP_GRADIENTS)
      OptimizerWithGradientClip = _generate_optimizer_class_with_gradient_clipping(
          type(optimizer), grad_clipper
      )
      optimizer.__class__ = OptimizerWithGradientClip
      return optimizer
  
  
  def build_optimizer(cfg: CfgNode, model: torch.nn.Module) -> torch.optim.Optimizer:
      """
      Build an optimizer from config.
      """
      norm_module_types = (
          torch.nn.BatchNorm1d,
          torch.nn.BatchNorm2d,
          torch.nn.BatchNorm3d,
          torch.nn.SyncBatchNorm,
          # NaiveSyncBatchNorm inherits from BatchNorm2d
          torch.nn.GroupNorm,
          torch.nn.InstanceNorm1d,
          torch.nn.InstanceNorm2d,
          torch.nn.InstanceNorm3d,
          torch.nn.LayerNorm,
          torch.nn.LocalResponseNorm,
      )
      params: List[Dict[str, Any]] = []
      memo: Set[torch.nn.parameter.Parameter] = set()
      for module in model.modules():
          for key, value in module.named_parameters(recurse=False):
              if not value.requires_grad:
                  continue
              # Avoid duplicating parameters
              if value in memo:
                  continue
              memo.add(value)
              lr = cfg.SOLVER.BASE_LR
              weight_decay = cfg.SOLVER.WEIGHT_DECAY
              if isinstance(module, norm_module_types):
                  weight_decay = cfg.SOLVER.WEIGHT_DECAY_NORM
              elif key == "bias":
                  # NOTE: unlike Detectron v1, we now default BIAS_LR_FACTOR to 1.0
                  # and WEIGHT_DECAY_BIAS to WEIGHT_DECAY so that bias optimizer
                  # hyperparameters are by default exactly the same as for regular
                  # weights.
                  lr = cfg.SOLVER.BASE_LR * cfg.SOLVER.BIAS_LR_FACTOR
                  weight_decay = cfg.SOLVER.WEIGHT_DECAY_BIAS
              params += [{"params": [value], "lr": lr, "weight_decay": weight_decay}]
  
      optimizer = torch.optim.SGD(
          params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM, nesterov=cfg.SOLVER.NESTEROV
      )
  
      # zjw TODO:Adam
      if cfg.SOLVER.OPTIM == "Adam":
          masks = [p for n, p in model.named_parameters() if 'mask' in n]
          nomask_params = [p for n, p in model.named_parameters() if 'mask' not in n]
          optimizer = torch.optim.Adam([
              {'params': masks, 'lr': 1e-5, 'weight_decay':cfg.SOLVER.WEIGHT_DECAY_BIAS},
              {'params': nomask_params, 'lr': 1e-4, 'weight_decay':cfg.SOLVER.WEIGHT_DECAY_BIAS}
          ])
      # zjw TODO END
  
      optimizer = maybe_add_gradient_clipping(cfg, optimizer)
      return optimizer
  
  
  def build_lr_scheduler(
      cfg: CfgNode, optimizer: torch.optim.Optimizer
  ) -> torch.optim.lr_scheduler._LRScheduler:
      """
      Build a LR scheduler from config.
      """
      name = cfg.SOLVER.LR_SCHEDULER_NAME
      if name == "WarmupMultiStepLR":
          return WarmupMultiStepLR(
              optimizer,
              cfg.SOLVER.STEPS,
              cfg.SOLVER.GAMMA,
              warmup_factor=cfg.SOLVER.WARMUP_FACTOR,
              warmup_iters=cfg.SOLVER.WARMUP_ITERS,
              warmup_method=cfg.SOLVER.WARMUP_METHOD,
          )
      elif name == "WarmupCosineLR":
          return WarmupCosineLR(
              optimizer,
              cfg.SOLVER.MAX_ITER,
              warmup_factor=cfg.SOLVER.WARMUP_FACTOR,
              warmup_iters=cfg.SOLVER.WARMUP_ITERS,
              warmup_method=cfg.SOLVER.WARMUP_METHOD,
          )
      else:
          raise ValueError("Unknown LR scheduler: {}".format(name))
  