docstr: Tests fsaf head loss when truth is empty and non-empty.
code: def test_fsaf_head_loss(self):
      """Tests fsaf head loss when truth is empty and non-empty."""
      s = 300
      img_metas = [
          {
              "img_shape": (s, s),
              "pad_shape": (s, s),
              "scale_factor": 1,
          }
      ]
      cfg = Config(
          dict(
              assigner=dict(
                  type="CenterRegionAssigner",
                  pos_scale=0.2,
                  neg_scale=0.2,
                  min_pos_iof=0.01,
              ),
              allowed_border=-1,
              pos_weight=-1,
              debug=False,
          )
      )
      fsaf_head = FSAFHead(
          num_classes=4,
          in_channels=1,
          stacked_convs=1,
          feat_channels=1,
          reg_decoded_bbox=True,
          anchor_generator=dict(
              type="AnchorGenerator",
              octave_base_scale=1,
              scales_per_octave=1,
              ratios=[1.0],
              strides=[8, 16, 32, 64, 128],
          ),
          bbox_coder=dict(type="TBLRBBoxCoder", normalizer=4.0),
          loss_cls=dict(
              type="FocalLoss",
              use_sigmoid=True,
              gamma=2.0,
              alpha=0.25,
              loss_weight=1.0,
              reduction="none",
          ),
          loss_bbox=dict(
              type="IoULoss",
              eps=1e-6,
              loss_weight=1.0,
              reduction="none",
          ),
          train_cfg=cfg,
      )
  
      # FSAF head expects a multiple levels of features per image
      feats = (
          torch.rand(1, 1, ceil(s / stride[0]), ceil(s / stride[0]))
          for stride in fsaf_head.prior_generator.strides
      )
      cls_scores, bbox_preds = fsaf_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 = fsaf_head.loss_by_feat(
          cls_scores, bbox_preds, [gt_instances], img_metas
      )
      # When there is no truth, the cls loss should be nonzero but
      # box loss should be zero
      empty_cls_loss = sum(empty_gt_losses["loss_cls"])
      empty_box_loss = sum(empty_gt_losses["loss_bbox"])
      self.assertGreater(
          empty_cls_loss, 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 = fsaf_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"])
      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_fsaf_head.py
imports: from math import ceil
  from unittest import TestCase
  import torch
  from mmengine import Config
  from mmengine.structures import InstanceData
  from mmdet.models.dense_heads import FSAFHead
label_desc: Use image node_id 1 for calling the TestFSAFHead obj's underlying member method code with example usage: obj.test_fsaf_head_loss() without return types