content: import torch.nn as nn
  import torch
  import torch.nn.functional as F
  
  import numpy as np
  
  
  from pathlib import Path
  import cv2
  import trimesh
  import nvdiffrast.torch as dr
  
  from CRM_T2I_V3.model.archs.decoders.shape_texture_net import TetTexNet
  from CRM_T2I_V3.model.archs.unet import UNetPP
  from CRM_T2I_V3.util.renderer import Renderer
  from CRM_T2I_V3.model.archs.mlp_head import SdfMlp, RgbMlp
  import xatlas
  
  
  class Dummy:
      pass
  
  class ConvolutionalReconstructionModel(nn.Module):
      def __init__(self, specs):
          super(ConvolutionalReconstructionModel, self).__init__()
  
          self.specs = specs
          # configs
          input_specs = specs["Input"]
          self.input = Dummy()
          self.input.scale = input_specs['scale']
          self.input.resolution = input_specs['resolution']
          self.tet_grid_size = input_specs['tet_grid_size']
          self.camera_angle_num = input_specs['camera_angle_num']
  
          self.arch = Dummy()
          self.arch.fea_concat = specs["ArchSpecs"]["fea_concat"]
          self.arch.mlp_bias = specs["ArchSpecs"]["mlp_bias"]
  
          self.dec = Dummy()
          self.dec.c_dim = specs["DecoderSpecs"]["c_dim"]
          self.dec.plane_resolution = specs["DecoderSpecs"]["plane_resolution"]
  
          self.geo_type = specs["Train"].get("geo_type", "flex") # "dmtet" or "flex"
  
          self.unet2 = UNetPP(in_channels=self.dec.c_dim)
  
          mlp_chnl_s = 3 if self.arch.fea_concat else 1  # 3 for queried triplane feature concatenation
          self.decoder = TetTexNet(plane_reso=self.dec.plane_resolution, fea_concat=self.arch.fea_concat)
  
          if self.geo_type == "flex":
              self.weightMlp = nn.Sequential(
                              nn.Linear(mlp_chnl_s * 32 * 8, 512),
                              nn.SiLU(),
                              nn.Linear(512, 21))         
              
          self.sdfMlp = SdfMlp(mlp_chnl_s * 32, 512, bias=self.arch.mlp_bias) 
          self.rgbMlp = RgbMlp(mlp_chnl_s * 32, 512, bias=self.arch.mlp_bias)
          self.renderer = Renderer(tet_grid_size=self.tet_grid_size, camera_angle_num=self.camera_angle_num,
                                   scale=self.input.scale, geo_type = self.geo_type)
  
  
          self.spob = True if specs['Pretrain']['mode'] is None else False  # whether to add sphere
          self.radius = specs['Pretrain']['radius']  # used when spob
  
          self.denoising = True
          from diffusers import DDIMScheduler
          self.scheduler = DDIMScheduler.from_pretrained("stabilityai/stable-diffusion-2-1-base", subfolder="scheduler")
  
      def decode(self, data, triplane_feature2):
          if self.geo_type == "flex":
              tet_verts = self.renderer.flexicubes.verts.unsqueeze(0)
              tet_indices = self.renderer.flexicubes.indices
  
          dec_verts = self.decoder(triplane_feature2, tet_verts)
          out = self.sdfMlp(dec_verts)
  
          weight = None
          if self.geo_type == "flex":
              grid_feat = torch.index_select(input=dec_verts, index=self.renderer.flexicubes.indices.reshape(-1),dim=1)
              grid_feat = grid_feat.reshape(dec_verts.shape[0], self.renderer.flexicubes.indices.shape[0], self.renderer.flexicubes.indices.shape[1] * dec_verts.shape[-1])
              weight = self.weightMlp(grid_feat)
              weight = weight * 0.1
  
          pred_sdf, deformation = out[..., 0], out[..., 1:]
          if self.spob:
              pred_sdf = pred_sdf + self.radius - torch.sqrt((tet_verts**2).sum(-1))
  
          _, verts, faces = self.renderer(data, pred_sdf, deformation, tet_verts, tet_indices, weight= weight)
          return verts[0].unsqueeze(0), faces[0].int()
  
      def export_mesh(self, data, out_dir, ind, device=None, tri_fea_2 = None):
          verts = data['verts']
          faces = data['faces']
  
          dec_verts = self.decoder(tri_fea_2, verts.unsqueeze(0))
          colors = self.rgbMlp(dec_verts).squeeze().detach().cpu().numpy()
          # Expect predicted colors value range from [-1, 1]
          colors = (colors * 0.5 + 0.5).clip(0, 1)
  
          verts = verts.squeeze().cpu().numpy()
          faces = faces[..., [2, 1, 0]].squeeze().cpu().numpy()
  
          # export the final mesh
          with torch.no_grad():
              mesh = trimesh.Trimesh(verts, faces, vertex_colors=colors, process=False) # important, process=True leads to seg fault...
              mesh.export(out_dir / f'{ind}.obj')
  
      def export_mesh_wt_uv(self, ctx, data, out_dir, ind, device, res, tri_fea_2=None):
  
          mesh_v = data['verts'].squeeze().cpu().numpy()
          mesh_pos_idx = data['faces'].squeeze().cpu().numpy()
  
          def interpolate(attr, rast, attr_idx, rast_db=None):
              return dr.interpolate(attr.contiguous(), rast, attr_idx, rast_db=rast_db,
                                    diff_attrs=None if rast_db is None else 'all')
  
          vmapping, indices, uvs = xatlas.parametrize(mesh_v, mesh_pos_idx)
  
          mesh_v = torch.tensor(mesh_v, dtype=torch.float32, device=device)
          mesh_pos_idx = torch.tensor(mesh_pos_idx, dtype=torch.int64, device=device)
  
          # Convert to tensors
          indices_int64 = indices.astype(np.uint64, casting='same_kind').view(np.int64)
  
          uvs = torch.tensor(uvs, dtype=torch.float32, device=mesh_v.device)
          mesh_tex_idx = torch.tensor(indices_int64, dtype=torch.int64, device=mesh_v.device)
          # mesh_v_tex. ture
          uv_clip = uvs[None, ...] * 2.0 - 1.0
  
          # pad to four component coordinate
          uv_clip4 = torch.cat((uv_clip, torch.zeros_like(uv_clip[..., 0:1]), torch.ones_like(uv_clip[..., 0:1])), dim=-1)
  
          # rasterize
          rast, _ = dr.rasterize(ctx, uv_clip4, mesh_tex_idx.int(), res)
  
          # Interpolate world space position
          gb_pos, _ = interpolate(mesh_v[None, ...], rast, mesh_pos_idx.int())
          mask = rast[..., 3:4] > 0
  
          # return uvs, mesh_tex_idx, gb_pos, mask
          gb_pos_unsqz = gb_pos.view(-1, 3)
          mask_unsqz = mask.view(-1)
          tex_unsqz = torch.zeros_like(gb_pos_unsqz) + 1
  
          gb_mask_pos = gb_pos_unsqz[mask_unsqz]
  
          gb_mask_pos = gb_mask_pos[None, ]
  
          with torch.no_grad():
  
              dec_verts = self.decoder(tri_fea_2, gb_mask_pos)
              colors = self.rgbMlp(dec_verts).squeeze()
  
          # Expect predicted colors value range from [-1, 1]
          lo, hi = (-1, 1)
          colors = (colors - lo) * (255 / (hi - lo))
          colors = colors.clip(0, 255)
  
          tex_unsqz[mask_unsqz] = colors
  
          tex = tex_unsqz.view(res + (3,))
  
          verts = mesh_v.squeeze().cpu().numpy()
          faces = mesh_pos_idx[..., [2, 1, 0]].squeeze().cpu().numpy()
          # faces = mesh_pos_idx
          # faces = faces.detach().cpu().numpy()
          # faces = faces[..., [2, 1, 0]]
          indices = indices[..., [2, 1, 0]]
  
          # xatlas.export(f"{out_dir}/{ind}.obj", verts[vmapping], indices, uvs)
          matname = f'{out_dir}.mtl'
          # matname = f'{out_dir}/{ind}.mtl'
          fid = open(matname, 'w')
          fid.write('newmtl material_0\n')
          fid.write('Kd 1 1 1\n')
          fid.write('Ka 1 1 1\n')
          # fid.write('Ks 0 0 0\n')
          fid.write('Ks 0.4 0.4 0.4\n')
          fid.write('Ns 10\n')
          fid.write('illum 2\n')
          fid.write(f'map_Kd {out_dir.split("/")[-1]}.png\n')
          fid.close()
  
          fid = open(f'{out_dir}.obj', 'w')
          # fid = open(f'{out_dir}/{ind}.obj', 'w')
          fid.write('mtllib %s.mtl\n' % out_dir.split("/")[-1])
  
          for pidx, p in enumerate(verts):
              pp = p
              fid.write('v %f %f %f\n' % (pp[0], pp[2], - pp[1]))
  
          for pidx, p in enumerate(uvs):
              pp = p
              fid.write('vt %f %f\n' % (pp[0], 1 - pp[1]))
  
          fid.write('usemtl material_0\n')
          for i, f in enumerate(faces):
              f1 = f + 1
              f2 = indices[i] + 1
              fid.write('f %d/%d %d/%d %d/%d\n' % (f1[0], f2[0], f1[1], f2[1], f1[2], f2[2]))
          fid.close()
  
          img = np.asarray(tex.data.cpu().numpy(), dtype=np.float32)
          mask = np.sum(img.astype(float), axis=-1, keepdims=True)
          mask = (mask <= 3.0).astype(float)
          kernel = np.ones((3, 3), 'uint8')
          dilate_img = cv2.dilate(img, kernel, iterations=1)
          img = img * (1 - mask) + dilate_img * mask
          img = img.clip(0, 255).astype(np.uint8)
  
          cv2.imwrite(f'{out_dir}.png', img[..., [2, 1, 0]])
          # cv2.imwrite(f'{out_dir}/{ind}.png', img[..., [2, 1, 0]])
  