class_docstr: Predict the density map with DenseNet
  
  Example:
      >>> import torch
      >>> from towhee.models.transrac import DenseMap
      >>>
      >>> dummy_input = torch.rand(3)
      >>> dense_map = DenseMap(input_dim=3, hidden_dim_1=8, hidden_dim_2=8, out_dim=5)
      >>> out = dense_map(dummy_input)
      >>> print(out.shape)
      torch.Size([5])
code: def __init__(
      self, input_dim, hidden_dim_1, hidden_dim_2, out_dim, dropout=0.25
  ):
      super().__init__()
      self.layers = nn.Sequential(
          nn.Linear(input_dim, hidden_dim_1),
          nn.LayerNorm(hidden_dim_1),
          nn.Dropout(p=dropout, inplace=False),
          nn.ReLU(True),
          nn.Linear(hidden_dim_1, hidden_dim_2),
          nn.ReLU(True),
          nn.Dropout(p=dropout, inplace=False),
          nn.Linear(hidden_dim_2, out_dim),
      )
  
file_path: towhee/towhee/models/transrac/utils.py
imports: from torch import nn
  from towhee.models.layers.attention import Attention
label_desc: Use image node_id 1 to create a new DenseMap object from inherited base classes: nn with example: obj = DenseMap(input_dim, hidden_dim_1, hidden_dim_2, out_dim, dropout)