Consider the first frame as target ignoring subset labels in evaluator

Summary:
Aligning the logic with the official CO3Dv2 evaluation: https://github.com/facebookresearch/co3d/blob/92283c43688c3be685eea35f2eaf3ce58b5bbf0b/co3d/dataset/utils.py#L7

This will make the evaluator work with the datasets that do not define known/unseen subsets.

Reviewed By: bottler

Differential Revision: D42803136

fbshipit-source-id: cfac389eab010c32d2e33b40fc7f6ed845c327ef

pytorch3d/implicitron/evaluation/evaluate_new_view_synthesis.py

@@ -15,7 +15,7 @@ import numpy as np
 import torch
 import torch.nn.functional as F
 from pytorch3d.implicitron.dataset.dataset_base import FrameData
-from pytorch3d.implicitron.dataset.utils import is_known_frame, is_train_frame
+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

pytorch3d/implicitron/evaluation/evaluator.py

@@ -149,14 +149,11 @@ def _dump_to_json(
 
 def _get_eval_frame_data(frame_data: Any) -> Any:
     """
-    Masks the unknown image data to make sure we cannot use it at model evaluation time.
+    Masks the target image data to make sure we cannot use it at model evaluation
+    time. Assumes the first batch element is target, the rest are source.
     """
     frame_data_for_eval = copy.deepcopy(frame_data)
-    is_known = ds_utils.is_known_frame(frame_data.frame_type).type_as(
-        frame_data.image_rgb
-    )[:, None, None, None]
     for k in ("image_rgb", "depth_map", "fg_probability", "mask_crop"):
         value = getattr(frame_data_for_eval, k)
-        value_masked = value.clone() * is_known if value is not None else None
-        setattr(frame_data_for_eval, k, value_masked)
+        value[0].zero_()
     return frame_data_for_eval