class_docstr: Encoder Decoder segmentors.
  
  EncoderDecoder typically consists of backbone, decode_head, auxiliary_head.
  Note that auxiliary_head is only used for deep supervision during training,
  which could be dumped during inference.
class_docstr_tok: ["Encoder","Decoder","segmentors",".","EncoderDecoder","typically","consists","of","backbone",",","decode_head",",","auxiliary_head",".","Note","that","auxiliary_head","is","only","used","for","deep","supervision","during","training",",","which","could","be","dumped","during","inference","."]
docstr: Simple test with single image.
docstr_tok: ["Simple","test","with","single","image","."]
code: def simple_test(self, img, img_meta, rescale=True):
      """Simple test with single image."""
      seg_logit = self.inference(img, img_meta, rescale)
      seg_pred = seg_logit.argmax(dim=1)
      if torch.onnx.is_in_onnx_export():
          # our inference backend only support 4D output
          seg_pred = seg_pred.unsqueeze(0)
          return seg_pred
      seg_pred = seg_pred.cpu().numpy()
      # unravel batch dim
      seg_pred = list(seg_pred)
      return seg_pred
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from annotator.uniformer.mmseg.core import add_prefix
  from annotator.uniformer.mmseg.ops import resize
  from ..None import builder
  from ..builder import SEGMENTORS
  from .base import BaseSegmentor
label_desc: Use image node_id 15 for calling the EncoderDecoder obj's underlying member method code with example usage: obj.simple_test(img, img_meta, rescale) and returns: seg_pred, seg_pred