text: # Copyright (c) OpenMMLab. All rights reserved.
  import numpy as np
  import pytest
  import torch
  
  from mmpose.models.necks import GlobalAveragePooling
  
  
  def test_gap():
      """Test GlobalAveragePooling neck."""
      gap = GlobalAveragePooling()
  
      with pytest.raises(TypeError):
          gap(1)
  
      x0_shape = (32, 1024, 4, 4)
      x1_shape = (32, 2048, 2, 2)
      x0 = _demo_inputs(x0_shape)
      x1 = _demo_inputs(x1_shape)
  
      y = gap(x0)
      assert y.shape == torch.Size([32, 1024])
  
      y = gap([x0, x1])
      assert y[0].shape == torch.Size([32, 1024])
      assert y[1].shape == torch.Size([32, 2048])
  
      y = gap((x0, x1))
      assert y[0].shape == torch.Size([32, 1024])
      assert y[1].shape == torch.Size([32, 2048])
  
  
  def _demo_inputs(input_shape=(1, 3, 64, 64)):
      """Create a superset of inputs needed to run backbone.
  
      Args:
          input_shape (tuple): input batch dimensions.
              Default: (1, 3, 64, 64).
      """
      imgs = np.random.random(input_shape)
      imgs = torch.FloatTensor(imgs)
  
      return imgs
  