code: import argparse
  import json
  import copy
  import torch
  import torch.nn.functional as F
  from torch.utils.data import DataLoader
  from datasets.shapenet import build_shapenet
  from models.nerf import build_nerf
  from models.rendering import get_rays_shapenet, sample_points, volume_render
  
  
  def inner_loop(model, optim, imgs, poses, hwf, bound, num_samples, raybatch_size, inner_steps):
      """
      train the inner model for a specified number of iterations
      """
      pixels = imgs.reshape(-1, 3)
  
      rays_o, rays_d = get_rays_shapenet(hwf, poses)
      rays_o, rays_d = rays_o.reshape(-1, 3), rays_d.reshape(-1, 3)
  
      num_rays = rays_d.shape[0]
      for step in range(inner_steps):
          indices = torch.randint(num_rays, size=[raybatch_size])
          raybatch_o, raybatch_d = rays_o[indices], rays_d[indices]
          pixelbatch = pixels[indices] 
          t_vals, xyz = sample_points(raybatch_o, raybatch_d, bound[0], bound[1],
                                      num_samples, perturb=True)
          
          optim.zero_grad()
          rgbs, sigmas = model(xyz)
          colors = volume_render(rgbs, sigmas, t_vals, white_bkgd=True)
          loss = F.mse_loss(colors, pixelbatch)
          loss.backward()
          optim.step()
  
  
  def train_meta(args, meta_model, meta_optim, data_loader, device):
      """
      train the meta_model for one epoch using reptile meta learning
      https://arxiv.org/abs/1803.02999
      """
      for imgs, poses, hwf, bound in data_loader:
          imgs, poses, hwf, bound = imgs.to(device), poses.to(device), hwf.to(device), bound.to(device)
          imgs, poses, hwf, bound = imgs.squeeze(), poses.squeeze(), hwf.squeeze(), bound.squeeze()
  
          meta_optim.zero_grad()
  
          inner_model = copy.deepcopy(meta_model)
          inner_optim = torch.optim.SGD(inner_model.parameters(), args.inner_lr)
  
          inner_loop(inner_model, inner_optim, imgs, poses,
                      hwf, bound, args.num_samples,
                      args.train_batchsize, args.inner_steps)
          
          with torch.no_grad():
              for meta_param, inner_param in zip(meta_model.parameters(), inner_model.parameters()):
                  meta_param.grad = meta_param - inner_param
          
          meta_optim.step()
  
  
  def report_result(model, imgs, poses, hwf, bound, num_samples, raybatch_size):
      """
      report view-synthesis result on heldout views
      """
      ray_origins, ray_directions = get_rays_shapenet(hwf, poses)
  
      view_psnrs = []
      for img, rays_o, rays_d in zip(imgs, ray_origins, ray_directions):
          rays_o, rays_d = rays_o.reshape(-1, 3), rays_d.reshape(-1, 3)
          t_vals, xyz = sample_points(rays_o, rays_d, bound[0], bound[1],
                                      num_samples, perturb=False)
          
          synth = []
          num_rays = rays_d.shape[0]
          with torch.no_grad():
              for i in range(0, num_rays, raybatch_size):
                  rgbs_batch, sigmas_batch = model(xyz[i:i+raybatch_size])
                  color_batch = volume_render(rgbs_batch, sigmas_batch, 
                                              t_vals[i:i+raybatch_size],
                                              white_bkgd=True)
                  synth.append(color_batch)
              synth = torch.cat(synth, dim=0).reshape_as(img)
              error = F.mse_loss(img, synth)
              psnr = -10*torch.log10(error)
              view_psnrs.append(psnr)
      
      scene_psnr = torch.stack(view_psnrs).mean()
      return scene_psnr
  
  
  def val_meta(args, model, val_loader, device):
      """
      validate the meta trained model for few-shot view synthesis
      """
      meta_trained_state = model.state_dict()
      val_model = copy.deepcopy(model)
      
      val_psnrs = []
      for imgs, poses, hwf, bound in val_loader:
          imgs, poses, hwf, bound = imgs.to(device), poses.to(device), hwf.to(device), bound.to(device)
          imgs, poses, hwf, bound = imgs.squeeze(), poses.squeeze(), hwf.squeeze(), bound.squeeze()
  
          tto_imgs, test_imgs = torch.split(imgs, [args.tto_views, args.test_views], dim=0)
          tto_poses, test_poses = torch.split(poses, [args.tto_views, args.test_views], dim=0)
  
          val_model.load_state_dict(meta_trained_state)
          val_optim = torch.optim.SGD(val_model.parameters(), args.tto_lr)
  
          inner_loop(val_model, val_optim, tto_imgs, tto_poses, hwf,
                      bound, args.num_samples, args.tto_batchsize, args.tto_steps)
          
          scene_psnr = report_result(val_model, test_imgs, test_poses, hwf, bound, 
                                      args.num_samples, args.test_batchsize)
          val_psnrs.append(scene_psnr)
  
      val_psnr = torch.stack(val_psnrs).mean()
      return val_psnr
  
  
  def main():
      parser = argparse.ArgumentParser(description='shapenet few-shot view synthesis')
      parser.add_argument('--config', type=str, required=True,
                          help='config file for the shape class (cars, chairs or lamps)')
      args = parser.parse_args()
  
      with open(args.config) as config:
          info = json.load(config)
          for key, value in info.items():
              args.__dict__[key] = value
  
      device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  
      train_set = build_shapenet(image_set="train", dataset_root=args.dataset_root,
                                  splits_path=args.splits_path, num_views=args.train_views)
      train_loader = DataLoader(train_set, batch_size=1, shuffle=True)
  
      val_set = build_shapenet(image_set="val", dataset_root=args.dataset_root,
                              splits_path=args.splits_path,
                              num_views=args.tto_views+args.test_views)
      val_loader = DataLoader(val_set, batch_size=1, shuffle=False)
  
      meta_model = build_nerf(args)
      meta_model.to(device)
  
      meta_optim = torch.optim.Adam(meta_model.parameters(), lr=args.meta_lr)
  
      for epoch in range(1, args.meta_epochs+1):
          train_meta(args, meta_model, meta_optim, train_loader, device)
          val_psnr = val_meta(args, meta_model, val_loader, device)
          print(f"Epoch: {epoch}, val psnr: {val_psnr:0.3f}")
  
          torch.save({
              'epoch': epoch,
              'meta_model_state_dict': meta_model.state_dict(),
              'meta_optim_state_dict': meta_optim.state_dict(),
              }, f'meta_epoch{epoch}.pth')
  
  
  if __name__ == '__main__':
      main()
apis: torch.utils.data.DataLoader torch.nn.functional.mse_loss torch.stack torch.randint torch.split torch.no_grad torch.cuda.is_available torch.log10 torch.cat