text: """Numpy version of euclidean distance, etc.
  Notice the input/output shape of methods, so that you can better understand
  the meaning of these methods."""
  
  import torch
  import numpy as np
  
  
  def normalize(nparray, order=2, axis=0):
      """Normalize a N-D numpy array along the specified axis."""
      norm = np.linalg.norm(nparray, ord=order, axis=axis, keepdims=True)
      return nparray / (norm + np.finfo(np.float32).eps)
  
  
  def compute_dsr_dist(array1, array2, distmat, scores, topk=30):
      """ Compute the sptial feature reconstruction of all pairs
       array: [M, N, C] M: the number of query, N: the number of spatial feature, C: the dimension of each spatial feature
       array2: [M, N, C] M: the number of gallery
      :return:
      numpy array with shape [m1, m2]
      """
  
      dist = 100 * torch.ones(len(array1), len(array2))
      dist = dist.cuda()
      index = np.argsort(distmat, axis=1)
  
      for i in range(0, len(array1)):
          q = torch.FloatTensor(array1[i])
          q = q.view(q.size(0), q.size(1))
          q = q.cuda()
          score = scores[i]
          for j in range(topk):
              g = array2[index[i, j]]
              g = torch.FloatTensor(g)
              g = g.view(g.size(0), g.size(1))
              g = g.cuda()
              sim = torch.matmul(q.t(), g)
              min_value, min_index = (1 - sim).min(1)
              dist[i, index[i, j]] = (min_value * score).sum()
      dist = dist.cpu()
      dist = dist.numpy()
  
      return dist
  