code: def forward(self, input):
      # Parse model input
      points_predicted = None
  
      intrinsics = input["intrinsics"]
      uv = input["uv"]
      pose = input["pose"]
      object_mask = input["object_mask"].reshape(-1)
  
      ray_dirs, cam_loc = rend_util.get_camera_params(
          uv, pose, intrinsics
      )
      batch_size, num_pixels, _ = ray_dirs.shape
  
      self.implicit_network.eval()
      with torch.no_grad():
          points, network_object_mask, dists = self.ray_tracer(
              sdf=lambda x: self.implicit_network(x)[:, 0],
              cam_loc=cam_loc,
              object_mask=object_mask,
              ray_directions=ray_dirs,
          )
      self.implicit_network.train()
  
      points = (
          cam_loc.unsqueeze(1)
          + dists.reshape(batch_size, num_pixels, 1) * ray_dirs
      ).reshape(-1, 3)
      points_normal = self.implicit_network.gradient(points)
      sdf_output = self.implicit_network(points)[:, 0:1]
      ray_dirs = ray_dirs.reshape(-1, 3)
  
      ray_dirs_np = ray_dirs.cpu().numpy()
      cam_loc_np = np.concatenate(
          [cam_loc.cpu().numpy()] * len(ray_dirs_np), axis=0
      )
      # points_mesh_ray: may have the more points than surface mask points,
      # Need an Index for the Points_Mesh_Ray
      (
          points_mesh_ray,
          index_ray,
          index_tri,
      ) = self.mesh.ray.intersects_location(
          ray_origins=cam_loc_np,
          ray_directions=ray_dirs_np,
          multiple_hits=False,
      )
      # Index ray: total 2048 / ~1200
      MeshRay_mask = torch.tensor(
          [
              True if i in index_ray else False
              for i in range(len(cam_loc_np))
          ],
          dtype=torch.bool,
      ).to(points.device)
      network_object_mask = network_object_mask & MeshRay_mask
  
      if self.training:
          surface_mask = network_object_mask & object_mask
  
          listA = surface_mask.cpu().detach().numpy()
          A = [int(a) for a in listA]
          AA = [
              i for i, a in enumerate(A) if a == 1
          ]  # surface mask 的 index
          MeshRay_Index = np.array(
              [i for i, a in enumerate(index_ray) if a in AA], dtype=int
          )
  
          face_points_index = self.faces[index_tri][MeshRay_Index]
          select_vertex_normals = self.vertex_normals[face_points_index]
          select_vertices = self.vertices[face_points_index]
  
          points_mesh_ray = points_mesh_ray[MeshRay_Index]
          bcoords = barycentric_coordinates(
              points_mesh_ray, select_vertices
          )
          resampled_normals = np.sum(
              np.expand_dims(bcoords, -1) * select_vertex_normals, 1
          )
  
          # Mesh Pull
          resampled_normals = torch.tensor(resampled_normals).to(points)
          points_mesh_ray = torch.tensor(points_mesh_ray).to(points)
          sdf_points_mesh_ray = self.implicit_network(points_mesh_ray)[
              :, 0:1
          ]
          g_points_mesh_ray = self.implicit_network.gradient(
              points_mesh_ray
          )
          points_predicted = (
              points_mesh_ray
              - g_points_mesh_ray.squeeze() * sdf_points_mesh_ray
          )
  
          surface_points = points[surface_mask]
          surface_dists = dists[surface_mask].unsqueeze(-1)
          surface_ray_dirs = ray_dirs[surface_mask]
          surface_cam_loc = (
              cam_loc.unsqueeze(1)
              .repeat(1, num_pixels, 1)
              .reshape(-1, 3)[surface_mask]
          )
          surface_output = sdf_output[surface_mask]
          N = surface_points.shape[0]
  
          # Sample points for the eikonal loss
          eik_bounding_box = self.object_bounding_sphere
          n_eik_points = batch_size * num_pixels // 2
          eikonal_points = (
              torch.empty(n_eik_points, 3)
              .uniform_(-eik_bounding_box, eik_bounding_box)
              .cuda()
          )
          eikonal_pixel_points = points.clone()
          eikonal_pixel_points = eikonal_pixel_points.detach()
          eikonal_points = torch.cat(
              [eikonal_points, eikonal_pixel_points], 0
          )
  
          points_all = torch.cat(
              [surface_points, eikonal_points], dim=0
          )
  
          output = self.implicit_network(surface_points)
          surface_sdf_values = output[:N, 0:1].detach()
  
          g = self.implicit_network.gradient(points_all)
          surface_points_grad = g[:N, 0, :].clone().detach()
          grad_theta = g[N:, 0, :]
  
          differentiable_surface_points = self.sample_network(
              surface_output,
              surface_sdf_values,
              surface_points_grad,
              surface_dists,
              surface_cam_loc,
              surface_ray_dirs,
          )
  
      else:
          surface_mask = network_object_mask
          differentiable_surface_points = points[surface_mask]
          grad_theta = None
  
          listA = surface_mask.cpu().detach().numpy()
          A = [int(a) for a in listA]
          AA = [
              i for i, a in enumerate(A) if a == 1
          ]  # surface mask 的 index
          MeshRay_Index = np.array(
              [i for i, a in enumerate(index_ray) if a in AA], dtype=int
          )
  
          face_points_index = self.faces[index_tri][MeshRay_Index]
          select_vertex_normals = self.vertex_normals[face_points_index]
          select_vertices = self.vertices[face_points_index]
  
          points_mesh_ray = points_mesh_ray[MeshRay_Index]
          bcoords = barycentric_coordinates(
              points_mesh_ray, select_vertices
          )
          resampled_normals = np.sum(
              np.expand_dims(bcoords, -1) * select_vertex_normals, 1
          )
          resampled_normals = torch.tensor(resampled_normals).to(points)
  
      view = -ray_dirs[surface_mask]
  
      rgb_values = torch.ones_like(points).float().cuda()
      diffuse_values = torch.ones_like(points).float().cuda()
      specular_values = torch.ones_like(points).float().cuda()
      albedo_values = torch.ones_like(points).float().cuda()
  
      if differentiable_surface_points.shape[0] > 0:
          rgb_values[surface_mask] = self.get_rbg_value(
              differentiable_surface_points, view, resampled_normals
          )
          diffuse_values[surface_mask] = self.get_diffuse_value(
              differentiable_surface_points, view, resampled_normals
          )
  
          specular_values[surface_mask] = self.get_specular_value(
              differentiable_surface_points, view
          )
          albedo_values[surface_mask] = self.get_albedo_value(
              differentiable_surface_points, view
          )
  
      output = {
          "points": points,
          "points_pre": points_predicted,
          "points_mesh_ray_gt": points[surface_mask],
          "points_mesh_ray_normals": resampled_normals,
          "surface_normals": points_normal[surface_mask].reshape(
              [-1, 3]
          ),
          "rgb_values": rgb_values,
          "diffuse_values": diffuse_values,
          "specular_values": specular_values,
          "albedo_values": albedo_values,
          "sdf_output": sdf_output,
          "network_object_mask": network_object_mask,
          "object_mask": object_mask,
          "grad_theta": grad_theta,
      }
  
      return output
  
imports: import torch
  import torch.nn
  import numpy
  import trimesh
  import os
  from utils import rend_util
  from model.embedder import *
  from model.ray_tracing import RayTracing
  from model.sample_network import SampleNetwork
label_desc: Use image node_id 2 for calling the IFNetwork obj's underlying member method code with example usage: obj.forward(input) and returns: output