class_docstr: Gradually warm-up learning rate with cosine annealing in optimizer.
  Proposed in 'Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour'.
class_docstr_tok: ["Gradually","warm-up","learning","rate","with","cosine","annealing","in","optimizer",".","Proposed","in","'Accurate",",","Large","Minibatch","SGD",":","Training","ImageNet","in","1","Hour","'","."]
code: def __init__(
      self,
      optimizer,
      total_epoch,
      eta_min=0,
      warmup_epoch=10,
      last_epoch=-1,
  ):
      self.total_epoch = total_epoch
      self.eta_min = eta_min
      self.warmup_epoch = warmup_epoch
      super(GradualWarmupScheduler, self).__init__(
          optimizer, last_epoch
      )
  
file_path: insightface/parsing/dml_csr/utils/warmup_scheduler.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  from torch.optim.lr_scheduler import _LRScheduler
label_desc: Use image node_id 1 to create a new GradualWarmupScheduler object from inherited base classes: _LRScheduler with example: obj = GradualWarmupScheduler(optimizer, total_epoch, eta_min, warmup_epoch, last_epoch)