docstr: Test different ways of calling the `Volumes` constructor
docstr_tok: ["Test","different","ways","of","calling","the","`","Volumes","`","constructor"]
code: def test_constructor(
      self,
      num_volumes=3,
      num_channels=4,
      size=(6, 8, 10),
      dtype=torch.float32,
  ):
      """
      Test different ways of calling the `Volumes` constructor
      """
  
      device = torch.device("cuda:0")
  
      # all ways to define features
      features = [
          torch.randn(
              size=[num_volumes, num_channels, *size],
              device=device,
              dtype=torch.float32,
          ),  # padded tensor
          torch.randn(
              size=[num_volumes, num_channels, *size],
              device=device,
              dtype=torch.float32,
          ).unbind(
              0
          ),  # list of features
          None,  # no features
      ]
  
      # bad ways to define features
      bad_features = [
          torch.randn(
              size=[num_volumes, num_channels, 2, *size],
              device=device,
              dtype=torch.float32,
          ),  # 6 dims
          torch.randn(
              size=[num_volumes, *size],
              device=device,
              dtype=torch.float32,
          ),  # 4 dims
          torch.randn(
              size=[num_volumes, *size],
              device=device,
              dtype=torch.float32,
          ).unbind(
              0
          ),  # list of 4 dim tensors
      ]
  
      # all ways to define densities
      densities = [
          torch.randn(
              size=[num_volumes, 1, *size],
              device=device,
              dtype=torch.float32,
          ),  # padded tensor
          torch.randn(
              size=[num_volumes, 1, *size],
              device=device,
              dtype=torch.float32,
          ).unbind(
              0
          ),  # list of densities
      ]
  
      # bad ways to define densities
      bad_densities = [
          None,  # omitted
          torch.randn(
              size=[num_volumes, 1, 1, *size],
              device=device,
              dtype=torch.float32,
          ),  # 6-dim tensor
          torch.randn(
              size=[num_volumes, 1, 1, *size],
              device=device,
              dtype=torch.float32,
          ).unbind(
              0
          ),  # list of 5-dim densities
      ]
  
      # all possible ways to define the voxels sizes
      vox_sizes = [
          torch.Tensor([1.0, 1.0, 1.0]),
          [1.0, 1.0, 1.0],
          torch.Tensor([1.0, 1.0, 1.0])[None].repeat(num_volumes, 1),
          torch.Tensor([1.0])[None].repeat(num_volumes, 1),
          1.0,
          torch.Tensor([1.0]),
      ]
  
      # all possible ways to define the volume translations
      vol_translations = [
          torch.Tensor([1.0, 1.0, 1.0]),
          [1.0, 1.0, 1.0],
          torch.Tensor([1.0, 1.0, 1.0])[None].repeat(num_volumes, 1),
      ]
  
      # wrong ways to define voxel sizes
      bad_vox_sizes = [
          torch.Tensor([1.0, 1.0, 1.0, 1.0]),
          [1.0, 1.0, 1.0, 1.0],
          torch.Tensor([]),
          None,
      ]
  
      # wrong ways to define the volume translations
      bad_vol_translations = [
          torch.Tensor([1.0, 1.0]),
          [1.0, 1.0],
          1.0,
          torch.Tensor([1.0, 1.0, 1.0])[None].repeat(
              num_volumes + 1, 1
          ),
      ]
  
      def zip_with_ok_indicator(good, bad):
          return zip(
              [*good, *bad],
              [*([True] * len(good)), *([False] * len(bad))],
          )
  
      for features_, features_ok in zip_with_ok_indicator(
          features, bad_features
      ):
          for densities_, densities_ok in zip_with_ok_indicator(
              densities, bad_densities
          ):
              for vox_size, size_ok in zip_with_ok_indicator(
                  vox_sizes, bad_vox_sizes
              ):
                  for (
                      vol_translation,
                      trans_ok,
                  ) in zip_with_ok_indicator(
                      vol_translations, bad_vol_translations
                  ):
                      if (
                          size_ok
                          and trans_ok
                          and features_ok
                          and densities_ok
                      ):  # if all entries are good we check that this doesnt throw
                          Volumes(
                              features=features_,
                              densities=densities_,
                              voxel_size=vox_size,
                              volume_translation=vol_translation,
                          )
  
                      else:  # otherwise we check for ValueError
                          self.assertRaises(
                              ValueError,
                              Volumes,
                              features=features_,
                              densities=densities_,
                              voxel_size=vox_size,
                              volume_translation=vol_translation,
                          )
  
imports: import copy
  import itertools
  import random
  import unittest
  import numpy
  import torch
  from pytorch3d.structures.volumes import VolumeLocator, Volumes
  from pytorch3d.transforms import Scale
  from .common_testing import TestCaseMixin
label_desc: Use image node_id 16 for calling the TestVolumes obj's underlying member method code with example usage: obj.test_constructor(num_volumes, num_channels, size, dtype) and returns: zip