class_docstr: Encoder Decoder depther.
  
  EncoderDecoder typically consists of backbone and decode_head.
class_docstr_tok: ["Encoder","Decoder","depther",".","EncoderDecoder","typically","consists","of","backbone","and","decode_head","."]
docstr: Test with augmentations.
  
  Only rescale=True is supported.
docstr_tok: ["Test","with","augmentations",".","Only","rescale=True","is","supported","."]
code: def aug_test(self, imgs, img_metas, rescale=True):
      """Test with augmentations.
  
      Only rescale=True is supported.
      """
      # aug_test rescale all imgs back to ori_shape for now
      assert rescale
      # to save memory, we get augmented depth logit inplace
      depth_pred = self.inference(imgs[0], img_metas[0], rescale)
      for i in range(1, len(imgs)):
          cur_depth_pred = self.inference(
              imgs[i], img_metas[i], rescale, size=depth_pred.shape[-2:]
          )
          depth_pred += cur_depth_pred
      depth_pred /= len(imgs)
      depth_pred = depth_pred.cpu().numpy()
      # unravel batch dim
      depth_pred = list(depth_pred)
      return depth_pred
  
imports: from collections import OrderedDict
  import torch
  import torch.nn
  import torch.nn.functional
  from .ops import resize
label_desc: Use image node_id 12 for calling the DepthEncoderDecoder obj's underlying member method code with example usage: obj.aug_test(imgs, img_metas, rescale) and returns: depth_pred