docstr: validate for whole validation dataset
          
docstr_tok: ["validate","for","whole","validation","dataset"]
code: def validate(self):
      """validate for whole validation dataset"""
      training = self.coarse_net.training
      self.coarse_net.eval()
      if self.fine_net is not None:
          self.fine_net.eval()
      all_loss = {
          "coarse_rgb_loss": [],
          "coarse_empty_space_loss": [],
          "fine_rgb_loss": [],
          "fine_empty_space_loss": [],
          "rgb_loss": [],
          "empty_space_loss": [],
          "total_loss": [],
      }
      for batch_idx, batch in tqdm.tqdm(
          enumerate(self.val_loader),
          total=len(self.val_loader),
          desc="Valid iteration=%d" % self.iteration,
          ncols=80,
          leave=False,
      ):
          cur_loss = self.validate_batch(batch)
          for k, v in cur_loss.items():
              all_loss[k].append(v)
      dummy_cap = self.val_dataset.scene[
          self.val_dataset.inclusions[
              len(self.val_dataset.inclusions) // 2
          ]
      ]
      coarse_render, coarse_depth = render_utils.render_vanilla(
          self.coarse_net,
          dummy_cap,
          fine_net=None,
          rays_per_batch=self.opt.rays_per_batch,
          samples_per_ray=self.opt.samples_per_ray,
          white_bkg=self.opt.white_bkg,
          near_far_source="bkg",
          return_depth=True,
          ablate_nerft=self.opt.ablate_nerft,
      )
      coarse_depth = np.stack([coarse_depth] * 3, -1)
      fine_render, fine_depth = render_utils.render_vanilla(
          self.coarse_net,
          dummy_cap,
          fine_net=self.fine_net,
          rays_per_batch=self.opt.rays_per_batch,
          samples_per_ray=self.opt.samples_per_ray,
          white_bkg=self.opt.white_bkg,
          near_far_source="bkg",
          return_depth=True,
          ablate_nerft=self.opt.ablate_nerft,
      )
      fine_depth = np.stack([fine_depth] * 3, -1)
      validation_data = {
          "coarse_rgb_loss": np.array(
              all_loss["coarse_rgb_loss"]
          ).mean(),
          "coarse_empty_space_loss": np.array(
              all_loss["coarse_empty_space_loss"]
          ).mean(),
          "fine_rgb_loss": np.array(all_loss["fine_rgb_loss"]).mean(),
          "fine_empty_space_loss": np.array(
              all_loss["fine_empty_space_loss"]
          ).mean(),
          "rgb_loss": np.array(all_loss["rgb_loss"]).mean(),
          "empty_space_loss": np.array(
              all_loss["empty_space_loss"]
          ).mean(),
          "total_loss": np.array(all_loss["total_loss"]).mean(),
          "render": utils.np_img_to_torch_img(
              np.stack(
                  [coarse_render, coarse_depth, fine_render, fine_depth]
              )
          ),
      }
      self.push_validation_data(batch, validation_data)
      self.save_model()
      if training:
          self.coarse_net.train()
          if self.fine_net is not None:
              self.fine_net.train()
  
imports: import os
  import time
  from utils.render_utils import render_vanilla
  import tqdm
  import torch
  import torch.nn.functional
  import torchvision.utils
  import numpy
  from utils import utils, render_utils, ray_utils
  from trainers import base_trainer, tensorboard_helper
  from models.vanilla import weight_reset
label_desc: Use image node_id 4 for calling the NeRFTrainer obj's underlying member method code with example usage: obj.validate() without return types