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Improve memory efficiency in VolumeSampler

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Summary: Avoids use of `torch.cat` operation when rendering a volume by instead issuing multiple calls to `torch.nn.functional.grid_sample`. Density and color tensors can be large.

Reviewed By: bottler

Differential Revision: D40072399

fbshipit-source-id: eb4cd34f6171d54972bbf2877065f973db497de0
This commit is contained in:
Kyle Hundman 2022-10-06 12:10:26 -07:00 committed by Facebook GitHub Bot
parent 0d8608b9f9
commit 4c8338b00f
1 changed files with 22 additions and 17 deletions
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39
pytorch3d/renderer/implicit/renderer.py
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@ -363,35 +363,40 @@ class VolumeSampler(torch.nn.Module):
volumes_densities = self._volumes.densities() volumes_densities = self._volumes.densities()
dim_density = volumes_densities.shape[1] dim_density = volumes_densities.shape[1]
volumes_features = self._volumes.features() volumes_features = self._volumes.features()
# adjust the volumes_features variable in case we have a feature-less volume
if volumes_features is None:
dim_feature = 0
data_to_sample = volumes_densities
else:
dim_feature = volumes_features.shape[1]
data_to_sample = torch.cat((volumes_densities, volumes_features), dim=1)
# reshape to a size which grid_sample likes # reshape to a size which grid_sample likes
rays_points_local_flat = rays_points_local.view( rays_points_local_flat = rays_points_local.view(
rays_points_local.shape[0], -1, 1, 1, 3 rays_points_local.shape[0], -1, 1, 1, 3
) )
# run the grid sampler # run the grid sampler on the volumes densities
data_sampled = torch.nn.functional.grid_sample( rays_densities = torch.nn.functional.grid_sample(
data_to_sample, volumes_densities,
rays_points_local_flat, rays_points_local_flat,
align_corners=True, align_corners=True,
mode=self._sample_mode, mode=self._sample_mode,
) )
# permute the dimensions & reshape after sampling # permute the dimensions & reshape densities after sampling
data_sampled = data_sampled.permute(0, 2, 3, 4, 1).view( rays_densities = rays_densities.permute(0, 2, 3, 4, 1).view(
*rays_points_local.shape[:-1], data_sampled.shape[1] *rays_points_local.shape[:-1], volumes_densities.shape[1]
) )
# split back to densities and features # if features exist, run grid sampler again on the features densities
rays_densities, rays_features = data_sampled.split( if volumes_features is None:
[dim_density, dim_feature], dim=-1 dim_feature = 0
) _, rays_features = rays_densities.split([dim_density, dim_feature], dim=-1)
else:
rays_features = torch.nn.functional.grid_sample(
volumes_features,
rays_points_local_flat,
align_corners=True,
mode=self._sample_mode,
)
# permute the dimensions & reshape features after sampling
rays_features = rays_features.permute(0, 2, 3, 4, 1).view(
*rays_points_local.shape[:-1], volumes_features.shape[1]
)
return rays_densities, rays_features return rays_densities, rays_features