class_docstr: Replaceable abstract base for all image generation / rendering models.
  `forward()` method produces a render with a depth map. Derives from Module
  so we can rely on basic functionality provided to torch for model
  optimization.
docstr: Args:
      image_rgb: A tensor of shape `(B, 3, H, W)` containing a batch of rgb images;
          the first `min(B, n_train_target_views)` images are considered targets and
          are used to supervise the renders; the rest corresponding to the source
          viewpoints from which features will be extracted.
      camera: An instance of CamerasBase containing a batch of `B` cameras corresponding
          to the viewpoints of target images, from which the rays will be sampled,
          and source images, which will be used for intersecting with target rays.
      fg_probability: A tensor of shape `(B, 1, H, W)` containing a batch of
          foreground masks.
      mask_crop: A binary tensor of shape `(B, 1, H, W)` deonting valid
          regions in the input images (i.e. regions that do not correspond
          to, e.g., zero-padding). When the `RaySampler`'s sampling mode is set to
          "mask_sample", rays  will be sampled in the non zero regions.
      depth_map: A tensor of shape `(B, 1, H, W)` containing a batch of depth maps.
      sequence_name: A list of `B` strings corresponding to the sequence names
          from which images `image_rgb` were extracted. They are used to match
          target frames with relevant source frames.
      evaluation_mode: one of EvaluationMode.TRAINING or
          EvaluationMode.EVALUATION which determines the settings used for
          rendering.
  
  Returns:
      preds: A dictionary containing all outputs of the forward pass. All models should
          output an instance of `ImplicitronRender` in `preds["implicitron_render"]`.
code: def forward(
      self,
      *,  # force keyword-only arguments
      image_rgb: Optional[torch.Tensor],
      camera: CamerasBase,
      fg_probability: Optional[torch.Tensor],
      mask_crop: Optional[torch.Tensor],
      depth_map: Optional[torch.Tensor],
      sequence_name: Optional[List[str]],
      evaluation_mode: EvaluationMode = EvaluationMode.EVALUATION,
      **kwargs,
  ) -> Dict[str, Any]:
      """
      Args:
          image_rgb: A tensor of shape `(B, 3, H, W)` containing a batch of rgb images;
              the first `min(B, n_train_target_views)` images are considered targets and
              are used to supervise the renders; the rest corresponding to the source
              viewpoints from which features will be extracted.
          camera: An instance of CamerasBase containing a batch of `B` cameras corresponding
              to the viewpoints of target images, from which the rays will be sampled,
              and source images, which will be used for intersecting with target rays.
          fg_probability: A tensor of shape `(B, 1, H, W)` containing a batch of
              foreground masks.
          mask_crop: A binary tensor of shape `(B, 1, H, W)` deonting valid
              regions in the input images (i.e. regions that do not correspond
              to, e.g., zero-padding). When the `RaySampler`'s sampling mode is set to
              "mask_sample", rays  will be sampled in the non zero regions.
          depth_map: A tensor of shape `(B, 1, H, W)` containing a batch of depth maps.
          sequence_name: A list of `B` strings corresponding to the sequence names
              from which images `image_rgb` were extracted. They are used to match
              target frames with relevant source frames.
          evaluation_mode: one of EvaluationMode.TRAINING or
              EvaluationMode.EVALUATION which determines the settings used for
              rendering.
  
      Returns:
          preds: A dictionary containing all outputs of the forward pass. All models should
              output an instance of `ImplicitronRender` in `preds["implicitron_render"]`.
      """
      raise NotImplementedError()
  
file_path: pytorch3d/pytorch3d/implicitron/models/base_model.py
imports: from dataclasses import dataclass, field
  from typing import Any, Dict, List, Optional
  import torch
  from pytorch3d.implicitron.models.renderer.base import EvaluationMode
  from pytorch3d.implicitron.tools.config import ReplaceableBase
  from pytorch3d.renderer.cameras import CamerasBase
label_desc: Use image node_id 1 for calling the ImplicitronModelBase obj's underlying member method code with example usage: obj.forward() without return types