text: import pytest
  import torch
  from torch.nn.modules.batchnorm import _BatchNorm
  
  from mmpose.models.backbones import SCNet
  from mmpose.models.backbones.scnet import SCBottleneck, SCConv
  
  
  def is_block(modules):
      """Check if is SCNet building block."""
      if isinstance(modules, (SCBottleneck, )):
          return True
      return False
  
  
  def is_norm(modules):
      """Check if is one of the norms."""
      if isinstance(modules, (_BatchNorm, )):
          return True
      return False
  
  
  def all_zeros(modules):
      """Check if the weight(and bias) is all zero."""
      weight_zero = torch.equal(modules.weight.data,
                                torch.zeros_like(modules.weight.data))
      if hasattr(modules, 'bias'):
          bias_zero = torch.equal(modules.bias.data,
                                  torch.zeros_like(modules.bias.data))
      else:
          bias_zero = True
  
      return weight_zero and bias_zero
  
  
  def check_norm_state(modules, train_state):
      """Check if norm layer is in correct train state."""
      for mod in modules:
          if isinstance(mod, _BatchNorm):
              if mod.training != train_state:
                  return False
      return True
  
  
  def test_scnet_scconv():
      # Test scconv forward
      layer = SCConv(64, 64, 1, 4)
      x = torch.randn(1, 64, 56, 56)
      x_out = layer(x)
      assert x_out.shape == torch.Size([1, 64, 56, 56])
  
  
  def test_scnet_bottleneck():
      # Test Bottleneck forward
      block = SCBottleneck(64, 64)
      x = torch.randn(1, 64, 56, 56)
      x_out = block(x)
      assert x_out.shape == torch.Size([1, 64, 56, 56])
  
  
  def test_scnet_backbone():
      """Test scnet backbone."""
      with pytest.raises(KeyError):
          # SCNet depth should be in [50, 101]
          SCNet(20)
  
      with pytest.raises(TypeError):
          # pretrained must be a string path
          model = SCNet(50)
          model.init_weights(pretrained=0)
  
      # Test SCNet norm_eval=True
      model = SCNet(50, norm_eval=True)
      model.init_weights()
      model.train()
      assert check_norm_state(model.modules(), False)
  
      # Test SCNet50 with first stage frozen
      frozen_stages = 1
      model = SCNet(50, frozen_stages=frozen_stages)
      model.init_weights()
      model.train()
      assert model.norm1.training is False
      for layer in [model.conv1, model.norm1]:
          for param in layer.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 SCNet with BatchNorm forward
      model = SCNet(50, out_indices=(0, 1, 2, 3))
      for m in model.modules():
          if is_norm(m):
              assert isinstance(m, _BatchNorm)
      model.init_weights()
      model.train()
  
      imgs = torch.randn(2, 3, 224, 224)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size([2, 256, 56, 56])
      assert feat[1].shape == torch.Size([2, 512, 28, 28])
      assert feat[2].shape == torch.Size([2, 1024, 14, 14])
      assert feat[3].shape == torch.Size([2, 2048, 7, 7])
  
      # Test SCNet with layers 1, 2, 3 out forward
      model = SCNet(50, out_indices=(0, 1, 2))
      model.init_weights()
      model.train()
  
      imgs = torch.randn(2, 3, 224, 224)
      feat = model(imgs)
      assert len(feat) == 3
      assert feat[0].shape == torch.Size([2, 256, 56, 56])
      assert feat[1].shape == torch.Size([2, 512, 28, 28])
      assert feat[2].shape == torch.Size([2, 1024, 14, 14])
  
      # Test SEResNet50 with layers 3 (top feature maps) out forward
      model = SCNet(50, out_indices=(3, ))
      model.init_weights()
      model.train()
  
      imgs = torch.randn(2, 3, 224, 224)
      feat = model(imgs)
      assert feat.shape == torch.Size([2, 2048, 7, 7])
  
      # Test SEResNet50 with checkpoint forward
      model = SCNet(50, out_indices=(0, 1, 2, 3), with_cp=True)
      for m in model.modules():
          if is_block(m):
              assert m.with_cp
      model.init_weights()
      model.train()
  
      imgs = torch.randn(2, 3, 224, 224)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size([2, 256, 56, 56])
      assert feat[1].shape == torch.Size([2, 512, 28, 28])
      assert feat[2].shape == torch.Size([2, 1024, 14, 14])
      assert feat[3].shape == torch.Size([2, 2048, 7, 7])
  
      # Test SCNet zero initialization of residual
      model = SCNet(50, out_indices=(0, 1, 2, 3), zero_init_residual=True)
      model.init_weights()
      for m in model.modules():
          if isinstance(m, SCBottleneck):
              assert all_zeros(m.norm3)
      model.train()
  
      imgs = torch.randn(2, 3, 224, 224)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size([2, 256, 56, 56])
      assert feat[1].shape == torch.Size([2, 512, 28, 28])
      assert feat[2].shape == torch.Size([2, 1024, 14, 14])
      assert feat[3].shape == torch.Size([2, 2048, 7, 7])
  