docstr: validate for whole validation dataset
          
docstr_tok: ["validate","for","whole","validation","dataset"]
code: def validate(self):
      """validate for whole validation dataset"""
      training = self.net.training
      self.net.eval()
      all_loss = {l: [] for l in LOSS_NAMES}
      all_loss["rgb_loss"] = []
      all_loss["can_loss"] = []
      all_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)
  
      # draw visualizations
      overfit_id = self.train_dataset.scene.fname_to_index_dict[
          self.train_dataset.inclusions[
              len(self.train_dataset.inclusions) // 2
          ]
      ]
      overfit_cap = self.train_dataset.scene.captures[overfit_id]
      verts, _ = self.net.vertex_forward(overfit_id)
      verts = verts[0]
      faces = torch.from_numpy(self.val_dataset.scene.faces[:, :3]).to(
          verts.device
      )
      overlay = render_utils.overlay_smpl(
          overfit_cap.image, verts, faces, overfit_cap
      )
  
      if self.opt.random_view:
          can_cap = random.choice(self.can_caps)
      else:
          can_cap = self.can_caps[0]
      rgb_map, depth_map, acc_map = render_utils.render_smpl_nerf(
          self.net,
          can_cap,
          self.val_dataset.scene.static_vert[0],
          self.val_dataset.scene.faces,
          None,
          rays_per_batch=self.opt.rays_per_batch,
          samples_per_ray=self.opt.samples_per_ray,
          white_bkg=True,
          render_can=True,
          return_mask=True,
          return_depth=True,
          interval_comp=self.interval_comp,
      )
      try:
          alpha_mask = acc_map >= 0.9999999999
          d_min = depth_map[alpha_mask].min()
          d_max = depth_map[alpha_mask].max()
          depth_map[depth_map <= d_min] = d_min
          depth_map[depth_map >= d_max] = d_max
      except:
          pass
      acc_map = np.stack([acc_map] * 3, -1)
      depth_map = np.stack([depth_map] * 3, -1)
  
      validation_data = {
          k: np.array(v).mean() for k, v in all_loss.items()
      }
      validation_data["render"] = utils.np_img_to_torch_img(
          np.stack([rgb_map, depth_map, acc_map, overlay])
      )
      self.push_validation_data(validation_data)
      self.save_model()
      if training:
          self.net.train()
  
imports: import os
  import math
  import time
  import random
  import tqdm
  import torch
  import torch.nn.functional
  import numpy
  import torchvision.utils
  import igl
  import lpips
  from utils import utils, render_utils, ray_utils
  from trainers import tensorboard_helper
  from options import options
  from cameras.captures import ResizedPinholeCapture
  from cameras.pinhole_camera import PinholeCamera
  from models.vanilla import weight_reset
  from utils.constant import HARD_SURFACE_OFFSET, PATCH_SIZE, PATCH_SIZE_SQUARED, CANONICAL_ZOOM_FACTOR, CANONICAL_CAMERA_DIST
label_desc: Use image node_id 12 for calling the HumanNeRFTrainer obj's underlying member method code with example usage: obj.validate() without return types