code: def test_csp_darknet_backbone():
      with pytest.raises(ValueError):
          # frozen_stages must in range(-1, len(arch_setting) + 1)
          CSPDarknet(frozen_stages=6)
  
      with pytest.raises(AssertionError):
          # out_indices in range(len(arch_setting) + 1)
          CSPDarknet(out_indices=[6])
  
      # Test CSPDarknet with first stage frozen
      frozen_stages = 1
      model = CSPDarknet(frozen_stages=frozen_stages)
      model.train()
  
      for mod in model.stem.modules():
          for param in mod.parameters():
              assert param.requires_grad is False
      for i in range(1, frozen_stages + 1):
          layer = getattr(model, f"stage{i}")
          for mod in layer.modules():
              if isinstance(mod, _BatchNorm):
                  assert mod.training is False
          for param in layer.parameters():
              assert param.requires_grad is False
  
      # Test CSPDarknet with norm_eval=True
      model = CSPDarknet(norm_eval=True)
      model.train()
  
      assert check_norm_state(model.modules(), False)
  
      # Test CSPDarknet-P5 forward with widen_factor=0.5
      model = CSPDarknet(
          arch="P5", widen_factor=0.25, out_indices=range(0, 5)
      )
      model.train()
  
      imgs = torch.randn(1, 3, 64, 64)
      feat = model(imgs)
      assert len(feat) == 5
      assert feat[0].shape == torch.Size((1, 16, 32, 32))
      assert feat[1].shape == torch.Size((1, 32, 16, 16))
      assert feat[2].shape == torch.Size((1, 64, 8, 8))
      assert feat[3].shape == torch.Size((1, 128, 4, 4))
      assert feat[4].shape == torch.Size((1, 256, 2, 2))
  
      # Test CSPDarknet-P6 forward with widen_factor=0.5
      model = CSPDarknet(
          arch="P6",
          widen_factor=0.25,
          out_indices=range(0, 6),
          spp_kernal_sizes=(3, 5, 7),
      )
      model.train()
  
      imgs = torch.randn(1, 3, 128, 128)
      feat = model(imgs)
      assert feat[0].shape == torch.Size((1, 16, 64, 64))
      assert feat[1].shape == torch.Size((1, 32, 32, 32))
      assert feat[2].shape == torch.Size((1, 64, 16, 16))
      assert feat[3].shape == torch.Size((1, 128, 8, 8))
      assert feat[4].shape == torch.Size((1, 192, 4, 4))
      assert feat[5].shape == torch.Size((1, 256, 2, 2))
  
      # Test CSPDarknet forward with dict(type='ReLU')
      model = CSPDarknet(
          widen_factor=0.125,
          act_cfg=dict(type="ReLU"),
          out_indices=range(0, 5),
      )
      model.train()
  
      imgs = torch.randn(1, 3, 64, 64)
      feat = model(imgs)
      assert len(feat) == 5
      assert feat[0].shape == torch.Size((1, 8, 32, 32))
      assert feat[1].shape == torch.Size((1, 16, 16, 16))
      assert feat[2].shape == torch.Size((1, 32, 8, 8))
      assert feat[3].shape == torch.Size((1, 64, 4, 4))
      assert feat[4].shape == torch.Size((1, 128, 2, 2))
  
      # Test CSPDarknet with BatchNorm forward
      model = CSPDarknet(widen_factor=0.125, out_indices=range(0, 5))
      for m in model.modules():
          if is_norm(m):
              assert isinstance(m, _BatchNorm)
      model.train()
  
      imgs = torch.randn(1, 3, 64, 64)
      feat = model(imgs)
      assert len(feat) == 5
      assert feat[0].shape == torch.Size((1, 8, 32, 32))
      assert feat[1].shape == torch.Size((1, 16, 16, 16))
      assert feat[2].shape == torch.Size((1, 32, 8, 8))
      assert feat[3].shape == torch.Size((1, 64, 4, 4))
      assert feat[4].shape == torch.Size((1, 128, 2, 2))
  
      # Test CSPDarknet with custom arch forward
      arch_ovewrite = [
          [32, 56, 3, True, False],
          [56, 224, 2, True, False],
          [224, 512, 1, True, False],
      ]
      model = CSPDarknet(
          arch_ovewrite=arch_ovewrite,
          widen_factor=0.25,
          out_indices=(0, 1, 2, 3),
      )
      model.train()
  
      imgs = torch.randn(1, 3, 32, 32)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size((1, 8, 16, 16))
      assert feat[1].shape == torch.Size((1, 14, 8, 8))
      assert feat[2].shape == torch.Size((1, 56, 4, 4))
      assert feat[3].shape == torch.Size((1, 128, 2, 2))
  
imports: import pytest
  import torch
  from torch.nn.modules.batchnorm import _BatchNorm
  from mmdet.models.backbones.csp_darknet import CSPDarknet
  from .utils import check_norm_state, is_norm
label_desc: Use image node_id 1 for calling a global function with example usage: test_csp_darknet_backbone() without return types