code: def test_mobilenetv2_backbone():
      with pytest.raises(ValueError):
          # frozen_stages must in range(-1, 8)
          MobileNetV2(frozen_stages=8)
  
      with pytest.raises(ValueError):
          # out_indices in range(-1, 8)
          MobileNetV2(out_indices=[8])
  
      # Test MobileNetV2 with first stage frozen
      frozen_stages = 1
      model = MobileNetV2(frozen_stages=frozen_stages)
      model.train()
  
      for mod in model.conv1.modules():
          for param in mod.parameters():
              assert param.requires_grad is False
      for i in range(1, frozen_stages + 1):
          layer = getattr(model, f"layer{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 MobileNetV2 with norm_eval=True
      model = MobileNetV2(norm_eval=True)
      model.train()
  
      assert check_norm_state(model.modules(), False)
  
      # Test MobileNetV2 forward with widen_factor=1.0
      model = MobileNetV2(widen_factor=1.0, out_indices=range(0, 8))
      model.train()
  
      assert check_norm_state(model.modules(), True)
  
      imgs = torch.randn(1, 3, 224, 224)
      feat = model(imgs)
      assert len(feat) == 8
      assert feat[0].shape == torch.Size((1, 16, 112, 112))
      assert feat[1].shape == torch.Size((1, 24, 56, 56))
      assert feat[2].shape == torch.Size((1, 32, 28, 28))
      assert feat[3].shape == torch.Size((1, 64, 14, 14))
      assert feat[4].shape == torch.Size((1, 96, 14, 14))
      assert feat[5].shape == torch.Size((1, 160, 7, 7))
      assert feat[6].shape == torch.Size((1, 320, 7, 7))
      assert feat[7].shape == torch.Size((1, 1280, 7, 7))
  
      # Test MobileNetV2 forward with widen_factor=0.5
      model = MobileNetV2(widen_factor=0.5, out_indices=range(0, 7))
      model.train()
  
      imgs = torch.randn(1, 3, 224, 224)
      feat = model(imgs)
      assert len(feat) == 7
      assert feat[0].shape == torch.Size((1, 8, 112, 112))
      assert feat[1].shape == torch.Size((1, 16, 56, 56))
      assert feat[2].shape == torch.Size((1, 16, 28, 28))
      assert feat[3].shape == torch.Size((1, 32, 14, 14))
      assert feat[4].shape == torch.Size((1, 48, 14, 14))
      assert feat[5].shape == torch.Size((1, 80, 7, 7))
      assert feat[6].shape == torch.Size((1, 160, 7, 7))
  
      # Test MobileNetV2 forward with widen_factor=2.0
      model = MobileNetV2(widen_factor=2.0, out_indices=range(0, 8))
      model.train()
  
      imgs = torch.randn(1, 3, 224, 224)
      feat = model(imgs)
      assert feat[0].shape == torch.Size((1, 32, 112, 112))
      assert feat[1].shape == torch.Size((1, 48, 56, 56))
      assert feat[2].shape == torch.Size((1, 64, 28, 28))
      assert feat[3].shape == torch.Size((1, 128, 14, 14))
      assert feat[4].shape == torch.Size((1, 192, 14, 14))
      assert feat[5].shape == torch.Size((1, 320, 7, 7))
      assert feat[6].shape == torch.Size((1, 640, 7, 7))
      assert feat[7].shape == torch.Size((1, 2560, 7, 7))
  
      # Test MobileNetV2 forward with dict(type='ReLU')
      model = MobileNetV2(
          widen_factor=1.0,
          act_cfg=dict(type="ReLU"),
          out_indices=range(0, 7),
      )
      model.train()
  
      imgs = torch.randn(1, 3, 224, 224)
      feat = model(imgs)
      assert len(feat) == 7
      assert feat[0].shape == torch.Size((1, 16, 112, 112))
      assert feat[1].shape == torch.Size((1, 24, 56, 56))
      assert feat[2].shape == torch.Size((1, 32, 28, 28))
      assert feat[3].shape == torch.Size((1, 64, 14, 14))
      assert feat[4].shape == torch.Size((1, 96, 14, 14))
      assert feat[5].shape == torch.Size((1, 160, 7, 7))
      assert feat[6].shape == torch.Size((1, 320, 7, 7))
  
      # Test MobileNetV2 with BatchNorm forward
      model = MobileNetV2(widen_factor=1.0, out_indices=range(0, 7))
      for m in model.modules():
          if is_norm(m):
              assert isinstance(m, _BatchNorm)
      model.train()
  
      imgs = torch.randn(1, 3, 224, 224)
      feat = model(imgs)
      assert len(feat) == 7
      assert feat[0].shape == torch.Size((1, 16, 112, 112))
      assert feat[1].shape == torch.Size((1, 24, 56, 56))
      assert feat[2].shape == torch.Size((1, 32, 28, 28))
      assert feat[3].shape == torch.Size((1, 64, 14, 14))
      assert feat[4].shape == torch.Size((1, 96, 14, 14))
      assert feat[5].shape == torch.Size((1, 160, 7, 7))
      assert feat[6].shape == torch.Size((1, 320, 7, 7))
  
      # Test MobileNetV2 with GroupNorm forward
      model = MobileNetV2(
          widen_factor=1.0,
          norm_cfg=dict(type="GN", num_groups=2, requires_grad=True),
          out_indices=range(0, 7),
      )
      for m in model.modules():
          if is_norm(m):
              assert isinstance(m, GroupNorm)
      model.train()
  
      imgs = torch.randn(1, 3, 224, 224)
      feat = model(imgs)
      assert len(feat) == 7
      assert feat[0].shape == torch.Size((1, 16, 112, 112))
      assert feat[1].shape == torch.Size((1, 24, 56, 56))
      assert feat[2].shape == torch.Size((1, 32, 28, 28))
      assert feat[3].shape == torch.Size((1, 64, 14, 14))
      assert feat[4].shape == torch.Size((1, 96, 14, 14))
      assert feat[5].shape == torch.Size((1, 160, 7, 7))
      assert feat[6].shape == torch.Size((1, 320, 7, 7))
  
      # Test MobileNetV2 with layers 1, 3, 5 out forward
      model = MobileNetV2(widen_factor=1.0, out_indices=(0, 2, 4))
      model.train()
  
      imgs = torch.randn(1, 3, 224, 224)
      feat = model(imgs)
      assert len(feat) == 3
      assert feat[0].shape == torch.Size((1, 16, 112, 112))
      assert feat[1].shape == torch.Size((1, 32, 28, 28))
      assert feat[2].shape == torch.Size((1, 96, 14, 14))
  
      # Test MobileNetV2 with checkpoint forward
      model = MobileNetV2(
          widen_factor=1.0, with_cp=True, out_indices=range(0, 7)
      )
      for m in model.modules():
          if is_block(m):
              assert m.with_cp
      model.train()
  
      imgs = torch.randn(1, 3, 224, 224)
      feat = model(imgs)
      assert len(feat) == 7
      assert feat[0].shape == torch.Size((1, 16, 112, 112))
      assert feat[1].shape == torch.Size((1, 24, 56, 56))
      assert feat[2].shape == torch.Size((1, 32, 28, 28))
      assert feat[3].shape == torch.Size((1, 64, 14, 14))
      assert feat[4].shape == torch.Size((1, 96, 14, 14))
      assert feat[5].shape == torch.Size((1, 160, 7, 7))
      assert feat[6].shape == torch.Size((1, 320, 7, 7))
  
imports: import pytest
  import torch
  from torch.nn.modules import GroupNorm
  from torch.nn.modules.batchnorm import _BatchNorm
  from mmdet.models.backbones.mobilenet_v2 import MobileNetV2
  from .utils import check_norm_state, is_block, is_norm
label_desc: Use image node_id 1 for calling a global function with example usage: test_mobilenetv2_backbone() without return types