code: def eval_batch(
      frame_data: FrameData,
      implicitron_render: ImplicitronRender,
      bg_color: Union[torch.Tensor, Sequence, str, float] = "black",
      mask_thr: float = 0.5,
      lpips_model=None,
      visualize: bool = False,
      visualize_visdom_env: str = "eval_debug",
      break_after_visualising: bool = True,
  ) -> Dict[str, Any]:
      """
      Produce performance metrics for a single batch of new-view synthesis
      predictions.
  
      Given a set of known views (for which frame_data.frame_type.endswith('known')
      is True), a new-view synthesis method (NVS) is tasked to generate new views
      of the scene from the viewpoint of the target views (for which
      frame_data.frame_type.endswith('known') is False). The resulting
      synthesized new views, stored in `implicitron_render`, are compared to the
      target ground truth in `frame_data` in terms of geometry and appearance
      resulting in a dictionary of metrics returned by the `eval_batch` function.
  
      Args:
          frame_data: A FrameData object containing the input to the new view
              synthesis method.
          implicitron_render: The data describing the synthesized new views.
          bg_color: The background color of the generated new views and the
              ground truth.
          lpips_model: A pre-trained model for evaluating the LPIPS metric.
          visualize: If True, visualizes the results to Visdom.
  
      Returns:
          results: A dictionary holding evaluation metrics.
  
      Throws:
          ValueError if frame_data does not have frame_type, camera, or image_rgb
          ValueError if the batch has a mix of training and test samples
          ValueError if the batch frames are not [unseen, known, known, ...]
          ValueError if one of the required fields in implicitron_render is missing
      """
      frame_type = frame_data.frame_type
      if frame_type is None:
          raise ValueError("Frame type has not been set.")
  
      # we check that all those fields are not None but Pyre can't infer that properly
      # TODO: assign to local variables and simplify the code.
      if frame_data.image_rgb is None:
          raise ValueError("Image is not in the evaluation batch.")
  
      if frame_data.camera is None:
          raise ValueError("Camera is not in the evaluation batch.")
  
      # eval all results in the resolution of the frame_data image
      image_resol = tuple(frame_data.image_rgb.shape[2:])
  
      # Post-process the render:
      # 1) check implicitron_render for Nones,
      # 2) obtain copies to make sure we dont edit the original data,
      # 3) take only the 1st (target) image
      # 4) resize to match ground-truth resolution
      cloned_render: Dict[str, torch.Tensor] = {}
      for k in ["mask_render", "image_render", "depth_render"]:
          field = getattr(implicitron_render, k)
          if field is None:
              raise ValueError(
                  f"A required predicted field {k} is missing"
              )
  
          imode = "bilinear" if k == "image_render" else "nearest"
          cloned_render[k] = (
              F.interpolate(field[:1], size=image_resol, mode=imode)
              .detach()
              .clone()
          )
  
      frame_data = copy.deepcopy(frame_data)
  
      # mask the ground truth depth in case frame_data contains the depth mask
      if (
          frame_data.depth_map is not None
          and frame_data.depth_mask is not None
      ):
          frame_data.depth_map *= frame_data.depth_mask
  
      if not isinstance(frame_type, list):  # not batch FrameData
          frame_type = [frame_type]
  
      is_train = is_train_frame(frame_type)
      if len(is_train) > 1 and (is_train[1] != is_train[1:]).any():
          raise ValueError(
              "All (conditioning) frames in the eval batch have to be either train/test."
          )
  
      for k in [
          "depth_map",
          "image_rgb",
          "fg_probability",
          "mask_crop",
      ]:
          if (
              not hasattr(frame_data, k)
              or getattr(frame_data, k) is None
          ):
              continue
          setattr(frame_data, k, getattr(frame_data, k)[:1])
  
      if (
          frame_data.depth_map is None
          or frame_data.depth_map.sum() <= 0
      ):
          warnings.warn("Empty or missing depth map in evaluation!")
  
      if frame_data.mask_crop is None:
          warnings.warn(
              "mask_crop is None, assuming the whole image is valid."
          )
  
      if frame_data.fg_probability is None:
          warnings.warn(
              "fg_probability is None, assuming the whole image is fg."
          )
  
      # threshold the masks to make ground truth binary masks
      mask_fg = (
          frame_data.fg_probability >= mask_thr
          if frame_data.fg_probability is not None
          # pyre-ignore [16]
          else torch.ones_like(frame_data.image_rgb[:, :1, ...]).bool()
      )
  
      mask_crop = (
          frame_data.mask_crop
          if frame_data.mask_crop is not None
          else torch.ones_like(mask_fg)
      )
  
      # unmasked g.t. image
      image_rgb = frame_data.image_rgb
  
      # fg-masked g.t. image
      image_rgb_masked = mask_background(
          # pyre-fixme[6]: Expected `Tensor` for 1st param but got
          #  `Optional[torch.Tensor]`.
          frame_data.image_rgb,
          mask_fg,
          bg_color=bg_color,
      )
  
      # clamp predicted images
      image_render = cloned_render["image_render"].clamp(0.0, 1.0)
  
      if visualize:
          visualizer = _Visualizer(
              image_render=image_render,
              image_rgb_masked=image_rgb_masked,
              depth_render=cloned_render["depth_render"],
              depth_map=frame_data.depth_map,
              depth_mask=frame_data.depth_mask[:1]
              if frame_data.depth_mask is not None
              else None,
              visdom_env=visualize_visdom_env,
          )
  
      results: Dict[str, Any] = {}
  
      results["iou"] = iou(
          cloned_render["mask_render"],
          mask_fg,
          mask=mask_crop,
      )
  
      for loss_fg_mask, name_postfix in zip(
          (mask_crop, mask_fg), ("_masked", "_fg")
      ):
          loss_mask_now = mask_crop * loss_fg_mask
  
          for rgb_metric_name, rgb_metric_fun in zip(
              ("psnr", "rgb_l1"), (calc_psnr, rgb_l1)
          ):
              metric_name = rgb_metric_name + name_postfix
              results[metric_name] = rgb_metric_fun(
                  image_render,
                  image_rgb_masked,
                  mask=loss_mask_now,
              )
  
              if visualize:
                  visualizer.show_rgb(
                      results[metric_name].item(),
                      metric_name,
                      loss_mask_now,
                  )
  
          if name_postfix == "_fg" and frame_data.depth_map is not None:
              # only record depth metrics for the foreground
              _, abs_ = eval_depth(
                  cloned_render["depth_render"],
                  # pyre-fixme[6]: For 2nd param expected `Tensor` but got
                  #  `Optional[Tensor]`.
                  frame_data.depth_map,
                  get_best_scale=True,
                  mask=loss_mask_now,
                  crop=5,
              )
              results["depth_abs" + name_postfix] = abs_.mean()
  
              if visualize:
                  visualizer.show_depth(
                      abs_.mean().item(), name_postfix, loss_mask_now
                  )
                  if break_after_visualising:
                      breakpoint()  # noqa: B601
  
      # add the rgb metrics between the render and the unmasked image
      for rgb_metric_name, rgb_metric_fun in zip(
          ("psnr_full_image", "rgb_l1_full_image"), (calc_psnr, rgb_l1)
      ):
          results[rgb_metric_name] = rgb_metric_fun(
              image_render,
              # pyre-fixme[6]: For 2nd argument expected `Tensor` but got
              #  `Optional[Tensor]`.
              image_rgb,
              mask=mask_crop,
          )
  
      if lpips_model is not None:
          for gt_image_type in ("_full_image", "_masked"):
              im1, im2 = [
                  2.0 * im.clamp(0.0, 1.0) - 1.0  # pyre-ignore[16]
                  for im in (
                      image_rgb_masked
                      if gt_image_type == "_masked"
                      else image_rgb,
                      cloned_render["image_render"],
                  )
              ]
              results["lpips" + gt_image_type] = lpips_model.forward(
                  im1, im2
              ).item()
  
      # convert all metrics to floats
      results = {k: float(v) for k, v in results.items()}
  
      results["meta"] = {
          # store the size of the batch (corresponds to n_src_views+1)
          "batch_size": len(frame_type),
          # store the type of the target frame
          # pyre-fixme[16]: `None` has no attribute `__getitem__`.
          "frame_type": str(frame_data.frame_type[0]),
      }
  
      return results
  
imports: import copy
  import warnings
  from collections import OrderedDict
  from dataclasses import dataclass, field
  from typing import Any, Dict, List, Optional, Sequence, Tuple, TYPE_CHECKING, Union
  import numpy
  import torch
  import torch.nn.functional
  from pytorch3d.implicitron.dataset.frame_data import FrameData
  from pytorch3d.implicitron.dataset.utils import is_train_frame
  from pytorch3d.implicitron.models.base_model import ImplicitronRender
  from pytorch3d.implicitron.tools import vis_utils
  from pytorch3d.implicitron.tools.image_utils import mask_background
  from pytorch3d.implicitron.tools.metric_utils import calc_psnr, eval_depth, iou, rgb_l1
  from pytorch3d.implicitron.tools.point_cloud_utils import get_rgbd_point_cloud
  from pytorch3d.implicitron.tools.vis_utils import make_depth_image
  from pytorch3d.renderer.cameras import PerspectiveCameras
  from pytorch3d.vis.plotly_vis import plot_scene
  from tabulate import tabulate
label_desc: Use image node_id 1 for calling a global function with example usage: eval_batch(frame_data, implicitron_render, bg_color, mask_thr, lpips_model, visualize, visualize_visdom_env, break_after_visualising) and returns: results