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Consider the first frame as target ignoring subset labels in evaluator

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Summary:
Aligning the logic with the official CO3Dv2 evaluation: 92283c4368/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
This commit is contained in:
Roman Shapovalov 2023-01-27 08:05:42 -08:00 committed by Facebook GitHub Bot
parent 9540c29023
commit a7256e4034
2 changed files with 4 additions and 7 deletions
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2
pytorch3d/implicitron/evaluation/evaluate_new_view_synthesis.py
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@ -15,7 +15,7 @@ import numpy as np
import torch import torch
import torch.nn.functional as F import torch.nn.functional as F
from pytorch3d.implicitron.dataset.dataset_base import FrameData 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.models.base_model import ImplicitronRender
from pytorch3d.implicitron.tools import vis_utils from pytorch3d.implicitron.tools import vis_utils
from pytorch3d.implicitron.tools.image_utils import mask_background from pytorch3d.implicitron.tools.image_utils import mask_background

9
pytorch3d/implicitron/evaluation/evaluator.py
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@ -149,14 +149,11 @@ def _dump_to_json(
def _get_eval_frame_data(frame_data: Any) -> Any: 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) 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"): for k in ("image_rgb", "depth_map", "fg_probability", "mask_crop"):
value = getattr(frame_data_for_eval, k) value = getattr(frame_data_for_eval, k)
value_masked = value.clone() * is_known if value is not None else None value[0].zero_()
setattr(frame_data_for_eval, k, value_masked)
return frame_data_for_eval return frame_data_for_eval