code: def __init__(
      self,
      num_classes=80,
      proposal_embedding_dim=256,
      dynamic_feature_channels=64,
      roi_resolution=14,
      num_convs=4,
      conv_kernel_size=3,
      conv_channels=256,
      upsample_method="deconv",
      upsample_scale_factor=2,
      norm_type="bn",
  ):
      super(DynamicMaskHead, self).__init__()
  
      self.d_model = proposal_embedding_dim
  
      self.instance_interactive_conv = DynamicConv(
          self.d_model,
          dynamic_feature_channels,
          roi_resolution=roi_resolution,
          with_proj=False,
      )
  
      self.convs = nn.LayerList()
      for i in range(num_convs):
          self.convs.append(
              nn.Sequential(
                  nn.Conv2D(
                      self.d_model if i == 0 else conv_channels,
                      conv_channels,
                      conv_kernel_size,
                      padding="same",
                      bias_attr=False,
                  ),
                  nn.BatchNorm2D(conv_channels),
                  nn.ReLU(),
              )
          )
      if norm_type == "sync_bn":
          self.convs = nn.SyncBatchNorm.convert_sync_batchnorm(
              self.convs
          )
  
      self.upsample_method = upsample_method
      if upsample_method is None:
          self.upsample = None
      elif upsample_method == "deconv":
          self.upsample = nn.Conv2DTranspose(
              conv_channels if num_convs > 0 else self.d_model,
              conv_channels,
              upsample_scale_factor,
              stride=upsample_scale_factor,
          )
          self.relu = nn.ReLU()
      else:
          self.upsample = nn.Upsample(None, upsample_scale_factor)
  
      cls_in_channels = conv_channels if num_convs > 0 else self.d_model
      cls_in_channels = (
          conv_channels
          if upsample_method == "deconv"
          else cls_in_channels
      )
      self.conv_cls = nn.Conv2D(cls_in_channels, num_classes, 1)
  
      self._init_weights()
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import paddle
  from paddle import nn
  from ppdet.core.workspace import register
  from ppdet.modeling import initializer
  from .roi_extractor import RoIAlign
  from ..bbox_utils import delta2bbox_v2
  from ..cls_utils import _get_class_default_kwargs
  from ..layers import MultiHeadAttention
label_desc: Use image node_id 1 to create a new DynamicMaskHead object from inherited base classes: nn with example: obj = DynamicMaskHead(num_classes, proposal_embedding_dim, dynamic_feature_channels, roi_resolution, num_convs, conv_kernel_size, conv_channels, upsample_method, upsample_scale_factor, norm_type)