docstr: Tests gfl head loss when truth is empty and non-empty.
code: def test_gfl_head_loss(self):
      """Tests gfl 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,
          }
      ]
      train_cfg = Config(
          dict(
              assigner=dict(type="ATSSAssigner", topk=9),
              allowed_border=-1,
              pos_weight=-1,
              debug=False,
          )
      )
      gfl_head = GFLHead(
          num_classes=4,
          in_channels=1,
          stacked_convs=1,
          train_cfg=train_cfg,
          anchor_generator=dict(
              type="AnchorGenerator",
              ratios=[1.0],
              octave_base_scale=8,
              scales_per_octave=1,
              strides=[8, 16, 32, 64, 128],
          ),
          loss_cls=dict(
              type="QualityFocalLoss",
              use_sigmoid=True,
              beta=2.0,
              loss_weight=1.0,
          ),
          loss_bbox=dict(type="GIoULoss", loss_weight=2.0),
      )
      feat = [
          torch.rand(1, 1, s // feat_size, s // feat_size)
          for feat_size in [4, 8, 16, 32, 64]
      ]
      cls_scores, bbox_preds = gfl_head.forward(feat)
  
      # 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 = gfl_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 = sum(empty_gt_losses["loss_cls"])
      empty_box_loss = sum(empty_gt_losses["loss_bbox"])
      empty_dfl_loss = sum(empty_gt_losses["loss_dfl"])
      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",
      )
      self.assertEqual(
          empty_dfl_loss.item(),
          0,
          "there should be no dfl 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 = gfl_head.loss_by_feat(
          cls_scores, bbox_preds, [gt_instances], img_metas
      )
      onegt_cls_loss = sum(one_gt_losses["loss_cls"])
      onegt_box_loss = sum(one_gt_losses["loss_bbox"])
      onegt_dfl_loss = sum(one_gt_losses["loss_dfl"])
      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"
      )
      self.assertGreater(
          onegt_dfl_loss.item(), 0, "dfl loss should be non-zero"
      )
  
file_path: mmdetection/tests/test_models/test_dense_heads/test_gfl_head.py
imports: from unittest import TestCase
  import torch
  from mmengine import Config
  from mmengine.structures import InstanceData
  from mmdet import *
  from mmdet.models.dense_heads import GFLHead
label_desc: Use image node_id 1 for calling the TestGFLHead obj's underlying member method code with example usage: obj.test_gfl_head_loss() without return types