docstr: Tests yolof head loss when truth is empty and non-empty.
code: def test_yolof_head_loss(self):
      """Tests yolof head loss when truth is empty and non-empty."""
      s = 256
      img_metas = [
          {
              "img_shape": (s, s, 3),
              "scale_factor": 1,
              "pad_shape": (s, s, 3),
          }
      ]
      train_cfg = Config(
          dict(
              assigner=dict(
                  type="UniformAssigner",
                  pos_ignore_thr=0.15,
                  neg_ignore_thr=0.7,
              ),
              allowed_border=-1,
              pos_weight=-1,
              debug=False,
          )
      )
      yolof_head = YOLOFHead(
          num_classes=4,
          in_channels=1,
          feat_channels=1,
          reg_decoded_bbox=True,
          train_cfg=train_cfg,
          anchor_generator=dict(
              type="AnchorGenerator",
              ratios=[1.0],
              scales=[1, 2, 4, 8, 16],
              strides=[32],
          ),
          bbox_coder=dict(
              type="DeltaXYWHBBoxCoder",
              target_means=[0.0, 0.0, 0.0, 0.0],
              target_stds=[1.0, 1.0, 1.0, 1.0],
              add_ctr_clamp=True,
              ctr_clamp=32,
          ),
          loss_cls=dict(
              type="FocalLoss",
              use_sigmoid=True,
              gamma=2.0,
              alpha=0.25,
              loss_weight=1.0,
          ),
          loss_bbox=dict(type="GIoULoss", loss_weight=1.0),
      )
      feat = [torch.rand(1, 1, s // 32, s // 32)]
      cls_scores, bbox_preds = yolof_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 = yolof_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 = yolof_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"
      )
  
file_path: mmdetection/tests/test_models/test_dense_heads/test_yolof_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 YOLOFHead
label_desc: Use image node_id 1 for calling the TestYOLOFHead obj's underlying member method code with example usage: obj.test_yolof_head_loss() without return types