docstr: Naive iterative implementation of nearest neighbor and chamfer distance.
  Returns lists of the unreduced loss and loss_normals. This naive
  version only supports homogeneous pointcouds in a batch.
docstr_tok: ["Naive","iterative","implementation","of","nearest","neighbor","and","chamfer","distance",".","Returns","lists","of","the","unreduced","loss","and","loss_normals",".","This","naive","version","only","supports","homogeneous","pointcouds","in","a","batch","."]
code: def chamfer_distance_naive(
      x,
      y,
      x_normals=None,
      y_normals=None,
      norm: int = 2,
      abs_cosine=True,
  ):
      """
      Naive iterative implementation of nearest neighbor and chamfer distance.
      Returns lists of the unreduced loss and loss_normals. This naive
      version only supports homogeneous pointcouds in a batch.
      """
      N, P1, D = x.shape
      P2 = y.size(1)
      device = x.device
      return_normals = x_normals is not None and y_normals is not None
      dist = torch.zeros(
          (N, P1, P2), dtype=torch.float32, device=device
      )
  
      for n in range(N):
          for i1 in range(P1):
              for i2 in range(P2):
                  if norm == 2:
                      dist[n, i1, i2] = torch.sum(
                          (x[n, i1, :] - y[n, i2, :]) ** 2
                      )
                  elif norm == 1:
                      dist[n, i1, i2] = torch.sum(
                          torch.abs(x[n, i1, :] - y[n, i2, :])
                      )
                  else:
                      raise ValueError(
                          "No support for norm %d" % (norm)
                      )
  
      loss = [
          torch.min(dist, dim=2)[0],  # (N, P1)
          torch.min(dist, dim=1)[0],  # (N, P2)
      ]
      lnorm = [x.new_zeros(()), x.new_zeros(())]
  
      if return_normals:
          x_index = dist.argmin(2).view(N, P1, 1).expand(N, P1, 3)
          y_index = dist.argmin(1).view(N, P2, 1).expand(N, P2, 3)
  
          cosine_sim1 = F.cosine_similarity(
              x_normals, y_normals.gather(1, x_index), dim=2, eps=1e-6
          )
          cosine_sim2 = F.cosine_similarity(
              y_normals, x_normals.gather(1, y_index), dim=2, eps=1e-6
          )
  
          if abs_cosine:
              lnorm1 = 1 - torch.abs(cosine_sim1)
              lnorm2 = 1 - torch.abs(cosine_sim2)
          else:
              lnorm1 = 1 - cosine_sim1
              lnorm2 = 1 - cosine_sim2
  
          lnorm = [lnorm1, lnorm2]  # [(N, P1), (N, P2)]
  
      return loss, lnorm
  
imports: import unittest
  from collections import namedtuple
  import numpy
  import torch
  import torch.nn.functional
  from pytorch3d.loss import chamfer_distance
  from pytorch3d.structures.pointclouds import Pointclouds
  from .common_testing import get_random_cuda_device, TestCaseMixin
label_desc: Use image node_id 4 for calling the TestChamfer obj's underlying member method code with example usage: obj.chamfer_distance_naive(x, y, x_normals, y_normals, norm, abs_cosine) and returns: loss,  lnorm