class_docstr: Faster R-CNN training task that can be scheduled concurrently using Parallel.
  Parameters
  ----------
  net : gluon.HybridBlock
      Faster R-CNN network.
  optimizer : gluon.Trainer
      Optimizer for the training.
  rpn_cls_loss : gluon.loss
      RPN box classification loss.
  rpn_box_loss : gluon.loss
      RPN box regression loss.
  rcnn_cls_loss : gluon.loss
      R-CNN box head classification loss.
  rcnn_box_loss : gluon.loss
      R-CNN box head regression loss.
  mix_ratio : float
      Object detection mixup ratio.
  amp_enabled : bool
      Whether to enable Automatic Mixed Precision.
class_docstr_tok: ["Faster","R-CNN","training","task","that","can","be","scheduled","concurrently","using","Parallel",".","Parameters","--","--","--","--","--","net",":","gluon.HybridBlock","Faster","R-CNN","network",".","optimizer",":","gluon.Trainer","Optimizer","for","the","training",".","rpn_cls_loss",":","gluon.loss","RPN","box","classification","loss",".","rpn_box_loss",":","gluon.loss","RPN","box","regression","loss",".","rcnn_cls_loss",":","gluon.loss","R-CNN","box","head","classification","loss",".","rcnn_box_loss",":","gluon.loss","R-CNN","box","head","regression","loss",".","mix_ratio",":","float","Object","detection","mixup","ratio",".","amp_enabled",":","bool","Whether","to","enable","Automatic","Mixed","Precision","."]
code: def __init__(
      self,
      net,
      optimizer,
      rpn_cls_loss,
      rpn_box_loss,
      rcnn_cls_loss,
      rcnn_box_loss,
      mix_ratio,
      amp_enabled,
  ):
      super(ForwardBackwardTask, self).__init__()
      self.net = net
      ctx = list(net.collect_params().values())[0].list_ctx()
      self.net.target_generator.collect_params().reset_ctx(ctx)
      self._optimizer = optimizer
      self.rpn_cls_loss = rpn_cls_loss
      self.rpn_box_loss = rpn_box_loss
      self.rcnn_cls_loss = rcnn_cls_loss
      self.rcnn_box_loss = rcnn_box_loss
      self.mix_ratio = mix_ratio
      self.amp_enabled = amp_enabled
  
imports: from mxnet import autograd
  from gluoncv.utils.parallel import Parallelizable
label_desc: Use image node_id 1 to create a new ForwardBackwardTask object from inherited base classes: Parallelizable with example: obj = ForwardBackwardTask(net, optimizer, rpn_cls_loss, rpn_box_loss, rcnn_cls_loss, rcnn_box_loss, mix_ratio, amp_enabled)