class_docstr: Simplest base roi head including one bbox head and one mask head.
class_docstr_tok: ["Simplest","base","roi","head","including","one","bbox","head","and","one","mask","head","."]
docstr: Test with augmentations.
  
  If rescale is False, then returned bboxes and masks will fit the scale
  of imgs[0].
docstr_tok: ["Test","with","augmentations",".","If","rescale","is","False",",","then","returned","bboxes","and","masks","will","fit","the","scale","of","imgs","[","0","]","."]
code: def aug_test(self, x, proposal_list, img_metas, rescale=False):
      """Test with augmentations.
  
      If rescale is False, then returned bboxes and masks will fit the scale
      of imgs[0].
      """
      det_bboxes, det_labels = self.aug_test_bboxes(
          x, img_metas, proposal_list, self.test_cfg
      )
  
      if rescale:
          _det_bboxes = det_bboxes
      else:
          _det_bboxes = det_bboxes.clone()
          _det_bboxes[:, :4] *= det_bboxes.new_tensor(
              img_metas[0][0]["scale_factor"]
          )
      bbox_results = bbox2result(
          _det_bboxes, det_labels, self.bbox_head.num_classes
      )
  
      # det_bboxes always keep the original scale
      if self.with_mask:
          segm_results = self.aug_test_mask(
              x, img_metas, det_bboxes, det_labels
          )
          return [(bbox_results, segm_results)]
      else:
          return [bbox_results]
  
imports: import torch
  from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler
  from ..builder import HEADS, build_head, build_roi_extractor
  from .base_roi_head import BaseRoIHead
  from .test_mixins import BBoxTestMixin, MaskTestMixin
label_desc: Use image node_id 12 for calling the StandardRoIHead obj's underlying member method code with example usage: obj.aug_test(x, proposal_list, img_metas, rescale) and returns: list, list