text: # Copyright (c) OpenMMLab. All rights reserved.
  import platform
  
  import pytest
  import torch
  
  from mmselfsup.models.algorithms import NPID
  
  backbone = dict(
      type='ResNet',
      depth=50,
      in_channels=3,
      out_indices=[4],  # 0: conv-1, x: stage-x
      norm_cfg=dict(type='BN'))
  neck = dict(
      type='LinearNeck', in_channels=2048, out_channels=4, with_avg_pool=True)
  head = dict(type='ContrastiveHead', temperature=0.07)
  memory_bank = dict(type='SimpleMemory', length=8, feat_dim=4, momentum=0.5)
  
  
  @pytest.mark.skipif(
      not torch.cuda.is_available() or platform.system() == 'Windows',
      reason='CUDA is not available or Windows mem limit')
  def test_npid():
      with pytest.raises(AssertionError):
          alg = NPID(backbone=backbone, neck=neck, head=head, memory_bank=None)
      with pytest.raises(AssertionError):
          alg = NPID(
              backbone=backbone, neck=neck, head=None, memory_bank=memory_bank)
  
      alg = NPID(
          backbone=backbone, neck=neck, head=head, memory_bank=memory_bank)
      fake_input = torch.randn((16, 3, 224, 224))
      fake_backbone_out = alg.extract_feat(fake_input)
      assert fake_backbone_out[0].size() == torch.Size([16, 2048, 7, 7])
  