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replicate_last_interval in raymarcher

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Summary: Add option to flat pad the last delta. Might to help when training on rgb only.

Reviewed By: shapovalov

Differential Revision: D40587475

fbshipit-source-id: c763fa38948600ea532c730538dc4ff29d2c3e0a
This commit is contained in:
Jeremy Reizenstein 2022-10-23 02:47:09 -07:00 committed by Facebook GitHub Bot
parent ff933ab82b
commit 611aba9a20
2 changed files with 16 additions and 9 deletions
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2
projects/implicitron_trainer/tests/experiment.yaml
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@ -235,6 +235,7 @@ model_factory_ImplicitronModelFactory_args:
surface_thickness: 1 surface_thickness: 1
bg_color: bg_color:
- 0.0 - 0.0
replicate_last_interval: False
background_opacity: 0.0 background_opacity: 0.0
density_relu: true density_relu: true
blend_output: false blend_output: false
@ -242,6 +243,7 @@ model_factory_ImplicitronModelFactory_args:
surface_thickness: 1 surface_thickness: 1
bg_color: bg_color:
- 0.0 - 0.0
replicate_last_interval: False
background_opacity: 10000000000.0 background_opacity: 10000000000.0
density_relu: true density_relu: true
blend_output: false blend_output: false

23
pytorch3d/implicitron/models/renderer/raymarcher.py
View File

@ -55,8 +55,11 @@ class AccumulativeRaymarcherBase(RaymarcherBase, torch.nn.Module):
surface_thickness: The thickness of the raymarched surface. surface_thickness: The thickness of the raymarched surface.
bg_color: The background color. A tuple of either 1 element or of D elements, bg_color: The background color. A tuple of either 1 element or of D elements,
where D matches the feature dimensionality; it is broadcast when necessary. where D matches the feature dimensionality; it is broadcast when necessary.
background_opacity: The raw opacity value (i.e. before exponentiation) replicate_last_interval: If True, the ray length assigned to the last interval
of the background. for the opacity delta calculation is copied from the penultimate interval.
background_opacity: The length over which the last raw opacity value
(i.e. before exponentiation) is considered to apply, for the delta
calculation. Ignored if replicate_last_interval=True.
density_relu: If `True`, passes the input density through ReLU before density_relu: If `True`, passes the input density through ReLU before
raymarching. raymarching.
blend_output: If `True`, alpha-blends the output renders with the blend_output: If `True`, alpha-blends the output renders with the
@ -76,6 +79,7 @@ class AccumulativeRaymarcherBase(RaymarcherBase, torch.nn.Module):
surface_thickness: int = 1 surface_thickness: int = 1
bg_color: Tuple[float, ...] = (0.0,) bg_color: Tuple[float, ...] = (0.0,)
replicate_last_interval: bool = False
background_opacity: float = 0.0 background_opacity: float = 0.0
density_relu: bool = True density_relu: bool = True
blend_output: bool = False blend_output: bool = False
@ -151,13 +155,14 @@ class AccumulativeRaymarcherBase(RaymarcherBase, torch.nn.Module):
density_1d=True, density_1d=True,
) )
deltas = torch.cat( ray_lengths_diffs = ray_lengths[..., 1:] - ray_lengths[..., :-1]
( if self.replicate_last_interval:
ray_lengths[..., 1:] - ray_lengths[..., :-1], last_interval = ray_lengths_diffs[..., -1:]
self.background_opacity * torch.ones_like(ray_lengths[..., :1]), else:
), last_interval = torch.full_like(
dim=-1, ray_lengths[..., :1], self.background_opacity
) )
deltas = torch.cat((ray_lengths_diffs, last_interval), dim=-1)
rays_densities = rays_densities[..., 0] rays_densities = rays_densities[..., 0]