docstr: Test silhouette blending. Also check that gradient calculation works.
docstr_tok: ["Test","silhouette","blending",".","Also","check","that","gradient","calculation","works","."]
code: def test_silhouette_with_grad(self):
      """
      Test silhouette blending. Also check that gradient calculation works.
      """
      device = torch.device("cuda:0")
      sphere_mesh = ico_sphere(5, device)
      verts, faces = sphere_mesh.get_mesh_verts_faces(0)
      sphere_mesh = Meshes(verts=[verts], faces=[faces])
  
      blend_params = BlendParams(sigma=1e-4, gamma=1e-4)
      raster_settings = RasterizationSettings(
          image_size=512,
          blur_radius=np.log(1.0 / 1e-4 - 1.0) * blend_params.sigma,
          faces_per_pixel=80,
          clip_barycentric_coords=True,
      )
  
      # Init rasterizer settings
      R, T = look_at_view_transform(2.7, 0, 0)
      for cam_type in (
          FoVPerspectiveCameras,
          FoVOrthographicCameras,
          PerspectiveCameras,
          OrthographicCameras,
          FishEyeCameras,
      ):
          if cam_type == FishEyeCameras:
              cameras = cam_type(
                  device=device,
                  R=R,
                  T=T,
                  use_tangential=False,
                  use_radial=False,
                  use_thin_prism=False,
                  world_coordinates=True,
              )
          else:
              cameras = cam_type(device=device, R=R, T=T)
  
          # Init renderer
          renderer = MeshRenderer(
              rasterizer=MeshRasterizer(
                  cameras=cameras, raster_settings=raster_settings
              ),
              shader=SoftSilhouetteShader(blend_params=blend_params),
          )
          images = renderer(sphere_mesh)
          alpha = images[0, ..., 3].squeeze().cpu()
          if DEBUG:
              filename = os.path.join(
                  DATA_DIR,
                  "DEBUG_%s_silhouette.png" % (cam_type.__name__),
              )
              Image.fromarray(
                  (alpha.detach().numpy() * 255).astype(np.uint8)
              ).save(filename)
  
          ref_filename = "test_%s_silhouette.png" % (cam_type.__name__)
          image_ref_filename = DATA_DIR / ref_filename
          with Image.open(image_ref_filename) as raw_image_ref:
              image_ref = torch.from_numpy(np.array(raw_image_ref))
  
          image_ref = image_ref.to(dtype=torch.float32) / 255.0
          self.assertClose(alpha, image_ref, atol=0.055)
  
          # Check grad exist
          verts.requires_grad = True
          sphere_mesh = Meshes(verts=[verts], faces=[faces])
          images = renderer(sphere_mesh)
          images[0, ...].sum().backward()
          self.assertIsNotNone(verts.grad)
  
imports: import os
  import unittest
  from collections import namedtuple
  from itertools import product
  import numpy
  import torch
  from PIL import Image
  from pytorch3d.io import load_obj, load_objs_as_meshes
  from pytorch3d.renderer import AmbientLights, FoVOrthographicCameras, FoVPerspectiveCameras, look_at_view_transform, Materials, MeshRasterizer, MeshRenderer, MeshRendererWithFragments, OrthographicCameras, PerspectiveCameras, PointLights, RasterizationSettings, TexturesAtlas, TexturesUV, TexturesVertex
  from pytorch3d.renderer.fisheyecameras import FishEyeCameras
  from pytorch3d.renderer.mesh.shader import BlendParams, HardFlatShader, HardGouraudShader, HardPhongShader, SoftPhongShader, SoftSilhouetteShader, SplatterPhongShader, TexturedSoftPhongShader
  from pytorch3d.renderer.opengl import MeshRasterizerOpenGL
  from pytorch3d.structures.meshes import join_meshes_as_batch, join_meshes_as_scene, Meshes
  from pytorch3d.utils.ico_sphere import ico_sphere
  from pytorch3d.utils.torus import torus
  from .common_testing import get_pytorch3d_dir, get_tests_dir, load_rgb_image, TestCaseMixin
label_desc: Use image node_id 6 for calling the TestRenderMeshes obj's underlying member method code with example usage: obj.test_silhouette_with_grad() without return types