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Fix some Transform3D -> Transform3d

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Summary: Fix some typos in comments.

Reviewed By: patricklabatut

Differential Revision: D32596645

fbshipit-source-id: 09b6d8c49f4f0301b80df626c6f9a2d5b5d9b1a7
This commit is contained in:
Jeremy Reizenstein 2021-11-23 11:29:49 -08:00 committed by Facebook GitHub Bot
parent a0247ea6bd
commit 7fa333f632
3 changed files with 8 additions and 8 deletions
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6
pytorch3d/renderer/cameras.py
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@ -227,7 +227,7 @@ class CamerasBase(TensorProperties):
eps: If eps!=None, the argument is used to clamp the eps: If eps!=None, the argument is used to clamp the
divisor in the homogeneous normalization of the points divisor in the homogeneous normalization of the points
transformed to the ndc space. Please see transformed to the ndc space. Please see
`transforms.Transform3D.transform_points` for details. `transforms.Transform3d.transform_points` for details.
For `CamerasBase.transform_points`, setting `eps > 0` For `CamerasBase.transform_points`, setting `eps > 0`
stabilizes gradients since it leads to avoiding division stabilizes gradients since it leads to avoiding division
@ -283,7 +283,7 @@ class CamerasBase(TensorProperties):
eps: If eps!=None, the argument is used to clamp the eps: If eps!=None, the argument is used to clamp the
divisor in the homogeneous normalization of the points divisor in the homogeneous normalization of the points
transformed to the ndc space. Please see transformed to the ndc space. Please see
`transforms.Transform3D.transform_points` for details. `transforms.Transform3d.transform_points` for details.
For `CamerasBase.transform_points`, setting `eps > 0` For `CamerasBase.transform_points`, setting `eps > 0`
stabilizes gradients since it leads to avoiding division stabilizes gradients since it leads to avoiding division
@ -313,7 +313,7 @@ class CamerasBase(TensorProperties):
eps: If eps!=None, the argument is used to clamp the eps: If eps!=None, the argument is used to clamp the
divisor in the homogeneous normalization of the points divisor in the homogeneous normalization of the points
transformed to the ndc space. Please see transformed to the ndc space. Please see
`transforms.Transform3D.transform_points` for details. `transforms.Transform3d.transform_points` for details.
For `CamerasBase.transform_points`, setting `eps > 0` For `CamerasBase.transform_points`, setting `eps > 0`
stabilizes gradients since it leads to avoiding division stabilizes gradients since it leads to avoiding division

4
pytorch3d/structures/volumes.py
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@ -124,7 +124,7 @@ class Volumes:
appropriate `world_coordinates` argument. appropriate `world_coordinates` argument.
Internally, the mapping between `x_local` and `x_world` is represented Internally, the mapping between `x_local` and `x_world` is represented
as a `Transform3D` object `Volumes._local_to_world_transform`. as a `Transform3d` object `Volumes._local_to_world_transform`.
Users can access the relevant transformations with the Users can access the relevant transformations with the
`Volumes.get_world_to_local_coords_transform()` and `Volumes.get_world_to_local_coords_transform()` and
`Volumes.get_local_to_world_coords_transform()` `Volumes.get_local_to_world_coords_transform()`
@ -260,7 +260,7 @@ class Volumes:
) -> Transform3d: ) -> Transform3d:
""" """
Converts the `voxel_size` and `volume_translation` constructor arguments Converts the `voxel_size` and `volume_translation` constructor arguments
to the internal `Transform3D` object `local_to_world_transform`. to the internal `Transform3d` object `local_to_world_transform`.
""" """
volume_size_zyx = self.get_grid_sizes().float() volume_size_zyx = self.get_grid_sizes().float()
volume_size_xyz = volume_size_zyx[:, [2, 1, 0]] volume_size_xyz = volume_size_zyx[:, [2, 1, 0]]

6
pytorch3d/transforms/transform3d.py
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@ -32,7 +32,7 @@ class Transform3d:
BROADCASTING BROADCASTING
Transform3d objects supports broadcasting. Suppose that t1 and tN are Transform3d objects supports broadcasting. Suppose that t1 and tN are
Transform3D objects with len(t1) == 1 and len(tN) == N respectively. Then we Transform3d objects with len(t1) == 1 and len(tN) == N respectively. Then we
can broadcast transforms like this: can broadcast transforms like this:
.. code-block:: python .. code-block:: python
@ -248,7 +248,7 @@ class Transform3d:
def inverse(self, invert_composed: bool = False): def inverse(self, invert_composed: bool = False):
""" """
Returns a new Transform3D object that represents an inverse of the Returns a new Transform3d object that represents an inverse of the
current transformation. current transformation.
Args: Args:
@ -262,7 +262,7 @@ class Transform3d:
independently without composing them. independently without composing them.
Returns: Returns:
A new Transform3D object containing the inverse of the original A new Transform3d object containing the inverse of the original
transformation. transformation.
""" """