text: import torch
  
  from mmcls.models.classifiers import ImageClassifier
  
  
  def test_image_classifier():
  
      # Test mixup in ImageClassifier
      model_cfg = dict(
          backbone=dict(
              type='ResNet_CIFAR',
              depth=50,
              num_stages=4,
              out_indices=(3, ),
              style='pytorch'),
          neck=dict(type='GlobalAveragePooling'),
          head=dict(
              type='MultiLabelLinearClsHead',
              num_classes=10,
              in_channels=2048,
              loss=dict(type='CrossEntropyLoss', loss_weight=1.0,
                        use_soft=True)),
          train_cfg=dict(mixup=dict(alpha=1.0, num_classes=10)))
      img_classifier = ImageClassifier(**model_cfg)
      img_classifier.init_weights()
      imgs = torch.randn(16, 3, 32, 32)
      label = torch.randint(0, 10, (16, ))
  
      losses = img_classifier.forward_train(imgs, label)
      assert losses['loss'].item() > 0
  
  
  def test_image_classifier_with_cutmix():
  
      # Test cutmix in ImageClassifier
      model_cfg = dict(
          backbone=dict(
              type='ResNet_CIFAR',
              depth=50,
              num_stages=4,
              out_indices=(3, ),
              style='pytorch'),
          neck=dict(type='GlobalAveragePooling'),
          head=dict(
              type='MultiLabelLinearClsHead',
              num_classes=10,
              in_channels=2048,
              loss=dict(type='CrossEntropyLoss', loss_weight=1.0,
                        use_soft=True)),
          train_cfg=dict(
              cutmix=dict(alpha=1.0, num_classes=10, cutmix_prob=1.0)))
      img_classifier = ImageClassifier(**model_cfg)
      img_classifier.init_weights()
      imgs = torch.randn(16, 3, 32, 32)
      label = torch.randint(0, 10, (16, ))
  
      losses = img_classifier.forward_train(imgs, label)
      assert losses['loss'].item() > 0
  
  
  def test_image_classifier_with_label_smooth_loss():
  
      # Test mixup in ImageClassifier
      model_cfg = dict(
          backbone=dict(
              type='ResNet_CIFAR',
              depth=50,
              num_stages=4,
              out_indices=(3, ),
              style='pytorch'),
          neck=dict(type='GlobalAveragePooling'),
          head=dict(
              type='MultiLabelLinearClsHead',
              num_classes=10,
              in_channels=2048,
              loss=dict(type='LabelSmoothLoss', label_smooth_val=0.1)),
          train_cfg=dict(mixup=dict(alpha=1.0, num_classes=10)))
      img_classifier = ImageClassifier(**model_cfg)
      img_classifier.init_weights()
      imgs = torch.randn(16, 3, 32, 32)
      label = torch.randint(0, 10, (16, ))
  
      losses = img_classifier.forward_train(imgs, label)
      assert losses['loss'].item() > 0
  
  
  def test_image_classifier_vit():
  
      model_cfg = dict(
          backbone=dict(
              type='VisionTransformer',
              num_layers=12,
              embed_dim=768,
              num_heads=12,
              img_size=224,
              patch_size=16,
              in_channels=3,
              feedforward_channels=3072,
              drop_rate=0.1,
              attn_drop_rate=0.),
          neck=None,
          head=dict(
              type='VisionTransformerClsHead',
              num_classes=1000,
              in_channels=768,
              hidden_dim=3072,
              loss=dict(type='CrossEntropyLoss', loss_weight=1.0, use_soft=True),
              topk=(1, 5),
          ),
          train_cfg=dict(mixup=dict(alpha=0.2, num_classes=1000)))
      img_classifier = ImageClassifier(**model_cfg)
      img_classifier.init_weights()
      imgs = torch.randn(3, 3, 224, 224)
      label = torch.randint(0, 1000, (3, ))
  
      losses = img_classifier.forward_train(imgs, label)
      assert losses['loss'].item() > 0
  