class_docstr: Hypercolumn head that concatenates all input feature maps to the size of the largest feature map, compute
  a projection (using conv + bn + act) and resize the projected feature map to the original image size.
  
  Reference: https://arxiv.org/pdf/1411.5752.pdf
code: def __init__(
      self,
      input_spec: FeatureMapsSpecification,
      num_classes: int,
      activation: str,
      normalization: str,
      mid_channels: int,
      output_name: Optional[str] = None,
      dropout_rate: float = 0.0,
      dropout_inplace: bool = False,
      interpolation_mode="bilinear",
      interpolation_align_corners=False,
  ):
      super().__init__(input_spec)
      channels = sum(input_spec.channels)
      self.fuse = FPNFuse(
          mode=interpolation_mode,
          align_corners=interpolation_align_corners,
      )
  
      self.projection = nn.Sequential(
          nn.Conv2d(channels, mid_channels, kernel_size=1),
          instantiate_normalization_block(normalization, mid_channels),
          instantiate_activation_block(activation, inplace=True),
          nn.Dropout2d(dropout_rate, inplace=dropout_inplace),
      )
  
      self.final = nn.Conv2d(
          mid_channels, num_classes, kernel_size=3, padding=1
      )
      self.output_name = output_name
      self.output_spec = FeatureMapsSpecification(
          channels=(num_classes,), strides=(1,)
      )
      self.interpolation_mode = interpolation_mode
      self.interpolation_align_corners = interpolation_align_corners
  
file_path: pytorch-toolbelt/pytorch_toolbelt/modules/heads/hypercolumn.py
imports: from typing import Optional, List
  import torch.jit
  from torch import nn, Tensor
  from pytorch_toolbelt.modules import FPNFuse, instantiate_activation_block, instantiate_normalization_block
  from pytorch_toolbelt.modules.interfaces import AbstractHead, FeatureMapsSpecification
label_desc: Use image node_id 1 to create a new HypercolumnHead object from inherited base classes: AbstractHead with example: obj = HypercolumnHead(input_spec, num_classes, activation, normalization, mid_channels, output_name, dropout_rate, dropout_inplace, interpolation_mode, interpolation_align_corners)