class_docstr: Rethinking Atrous Convolution for Semantic Image Segmentation.
  
  This head is the implementation of `DeepLabV3
  <https://arxiv.org/abs/1706.05587>`_.
  
  Args:
      dilations (tuple[int]): Dilation rates for ASPP module.
          Default: (1, 6, 12, 18).
docstr: Forward function.
docstr_tok: ["Forward","function","."]
code: def forward(self, inputs):
      """Forward function."""
      x = self._transform_inputs(inputs)
      aspp_outs = [
          resize(
              self.image_pool(x),
              size=x.size()[2:],
              mode="bilinear",
              align_corners=self.align_corners,
          )
      ]
      aspp_outs.extend(self.aspp_modules(x))
      aspp_outs = torch.cat(aspp_outs, dim=1)
      output = self.bottleneck(aspp_outs)
      output = self.cls_seg(output)
      return output
  
file_path: lavis/lavis/common/annotator/uniformer/mmseg/models/decode_heads/aspp_head.py
imports: import torch
  import torch.nn
  from annotator.uniformer.mmcv.cnn import ConvModule
  from annotator.uniformer.mmseg.ops import resize
  from ..builder import HEADS
  from .decode_head import BaseDecodeHead
label_desc: Use image node_id 2 for calling the ASPPHead obj's underlying member method code with example usage: obj.forward(inputs) and returns: output