docstr: Same spirit than test_simple but use bins in the ImplicitronRayBunle.
  It has been duplicated to avoid cognitive overload while reading the
  test (lot of if else).
docstr_tok: ["Same","spirit","than","test_simple","but","use","bins","in","the","ImplicitronRayBunle",".","It","has","been","duplicated","to","avoid","cognitive","overload","while","reading","the","test","(","lot","of","if","else",")","."]
code: def test_simple_use_bins(self):
      """
      Same spirit than test_simple but use bins in the ImplicitronRayBunle.
      It has been duplicated to avoid cognitive overload while reading the
      test (lot of if else).
      """
      length = 15
      n_pts_per_ray = 10
  
      for add_input_samples, use_blurpool in product(
          [False, True], [False, True]
      ):
          ray_point_refiner = RayPointRefiner(
              n_pts_per_ray=n_pts_per_ray,
              random_sampling=False,
              add_input_samples=add_input_samples,
          )
  
          bundle = ImplicitronRayBundle(
              lengths=None,
              bins=torch.arange(length + 1, dtype=torch.float32).expand(
                  3, 25, length + 1
              ),
              origins=None,
              directions=None,
              xys=None,
              camera_ids=None,
              camera_counts=None,
          )
          weights = torch.ones(3, 25, length)
          refined = ray_point_refiner(
              bundle, weights, blurpool_weights=use_blurpool
          )
  
          self.assertIsNone(refined.directions)
          self.assertIsNone(refined.origins)
          self.assertIsNone(refined.xys)
          expected_bins = torch.linspace(0, length, n_pts_per_ray + 1)
          expected_bins = expected_bins.expand(3, 25, n_pts_per_ray + 1)
          if add_input_samples:
              expected_bins = torch.cat(
                  (bundle.bins, expected_bins), dim=-1
              ).sort()[0]
          full_expected = torch.lerp(
              expected_bins[..., :-1], expected_bins[..., 1:], 0.5
          )
  
          self.assertClose(refined.lengths, full_expected)
  
          ray_point_refiner_random = RayPointRefiner(
              n_pts_per_ray=n_pts_per_ray,
              random_sampling=True,
              add_input_samples=add_input_samples,
          )
  
          refined_random = ray_point_refiner_random(
              bundle, weights, blurpool_weights=use_blurpool
          )
          lengths_random = refined_random.lengths
          self.assertEqual(lengths_random.shape, full_expected.shape)
          if not add_input_samples:
              self.assertGreater(lengths_random.min().item(), 0)
              self.assertLess(lengths_random.max().item(), length)
  
          # Check sorted
          self.assertTrue(
              (
                  lengths_random[..., 1:] - lengths_random[..., :-1] > 0
              ).all()
          )
  
file_path: pytorch3d/tests/implicitron/test_ray_point_refiner.py
imports: import unittest
  from itertools import product
  import torch
  from pytorch3d.implicitron.models.renderer.ray_point_refiner import apply_blurpool_on_weights, RayPointRefiner
  from pytorch3d.implicitron.models.renderer.ray_sampler import ImplicitronRayBundle
  from tests.common_testing import TestCaseMixin
label_desc: Use image node_id 2 for calling the TestRayPointRefiner obj's underlying member method code with example usage: obj.test_simple_use_bins() without return types