docstr: Tests anchor head loss when truth is empty and non-empty.
docstr_tok: ["Tests","anchor","head","loss","when","truth","is","empty","and","non-empty","."]
code: def test_fovea_head_loss(self):
      """Tests anchor head loss when truth is empty and non-empty."""
      s = 256
      img_metas = [
          {
              "img_shape": (s, s, 3),
              "pad_shape": (s, s, 3),
              "scale_factor": 1,
          }
      ]
  
      fovea_head = FoveaHead(num_classes=4, in_channels=1)
  
      # Anchor head expects a multiple levels of features per image
      feats = (
          torch.rand(1, 1, s // (2 ** (i + 2)), s // (2 ** (i + 2)))
          for i in range(len(fovea_head.prior_generator.strides))
      )
      cls_scores, bbox_preds = fovea_head.forward(feats)
  
      # Test that empty ground truth encourages the network to
      # predict background
      gt_instances = InstanceData()
      gt_instances.bboxes = torch.empty((0, 4))
      gt_instances.labels = torch.LongTensor([])
  
      empty_gt_losses = fovea_head.loss_by_feat(
          cls_scores, bbox_preds, [gt_instances], img_metas
      )
      # When there is no truth, the cls loss should be nonzero but
      # there should be no box loss.
      empty_cls_loss = empty_gt_losses["loss_cls"]
      empty_box_loss = empty_gt_losses["loss_bbox"]
      self.assertGreater(
          empty_cls_loss.item(), 0, "cls loss should be non-zero"
      )
      self.assertEqual(
          empty_box_loss.item(),
          0,
          "there should be no box loss when there are no true boxes",
      )
  
      # When truth is non-empty then both cls and box loss
      # should be nonzero for random inputs
      gt_instances = InstanceData()
      gt_instances.bboxes = torch.Tensor(
          [[23.6667, 23.8757, 238.6326, 151.8874]]
      )
      gt_instances.labels = torch.LongTensor([2])
  
      one_gt_losses = fovea_head.loss_by_feat(
          cls_scores, bbox_preds, [gt_instances], img_metas
      )
      onegt_cls_loss = one_gt_losses["loss_cls"]
      onegt_box_loss = one_gt_losses["loss_bbox"]
      self.assertGreater(
          onegt_cls_loss.item(), 0, "cls loss should be non-zero"
      )
      self.assertGreater(
          onegt_box_loss.item(), 0, "box loss should be non-zero"
      )
  
imports: from unittest import TestCase
  import torch
  from mmengine.structures import InstanceData
  from mmdet.models.dense_heads import FoveaHead
label_desc: Use image node_id 1 for calling the TestFOVEAHead obj's underlying member method code with example usage: obj.test_fovea_head_loss() without return types