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Summary: Collection of spelling things, mostly in docs / tutorials.

Reviewed By: gkioxari

Differential Revision: D26101323

fbshipit-source-id: 652f62bc9d71a4ff872efa21141225e43191353a
This commit is contained in:
Jeremy Reizenstein
2021-04-09 09:57:55 -07:00
committed by Facebook GitHub Bot
parent c2e62a5087
commit 124bb5e391
75 changed files with 220 additions and 217 deletions
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12
pytorch3d/structures/meshes.py
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@@ -22,13 +22,13 @@ class Meshes(object):
- has specific batch dimension.
Packed
- no batch dimension.
- has auxillary variables used to index into the padded representation.
- has auxiliary variables used to index into the padded representation.
Example:
Input list of verts V_n = [[V_1], [V_2], ... , [V_N]]
where V_1, ... , V_N are the number of verts in each mesh and N is the
numer of meshes.
number of meshes.
Input list of faces F_n = [[F_1], [F_2], ... , [F_N]]
where F_1, ... , F_N are the number of faces in each mesh.
@@ -100,7 +100,7 @@ class Meshes(object):
| ]) |
-----------------------------------------------------------------------------
Auxillary variables for packed representation
Auxiliary variables for packed representation
Name | Size | Example from above
-------------------------------|---------------------|-----------------------
@@ -139,7 +139,7 @@ class Meshes(object):
# SPHINX IGNORE
From the faces, edges are computed and have packed and padded
representations with auxillary variables.
representations with auxiliary variables.
E_n = [[E_1], ... , [E_N]]
where E_1, ... , E_N are the number of unique edges in each mesh.
@@ -894,7 +894,7 @@ class Meshes(object):
def _compute_packed(self, refresh: bool = False):
"""
Computes the packed version of the meshes from verts_list and faces_list
and sets the values of auxillary tensors.
and sets the values of auxiliary tensors.
Args:
refresh: Set to True to force recomputation of packed representations.
@@ -1022,7 +1022,7 @@ class Meshes(object):
# Remove duplicate edges: convert each edge (v0, v1) into an
# integer hash = V * v0 + v1; this allows us to use the scalar version of
# unique which is much faster than edges.unique(dim=1) which is very slow.
# After finding the unique elements reconstruct the vertex indicies as:
# After finding the unique elements reconstruct the vertex indices as:
# (v0, v1) = (hash / V, hash % V)
# The inverse maps from unique_edges back to edges:
# unique_edges[inverse_idxs] == edges

16
pytorch3d/structures/pointclouds.py
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@@ -18,7 +18,7 @@ class Pointclouds(object):
- has specific batch dimension.
Packed
- no batch dimension.
- has auxillary variables used to index into the padded representation.
- has auxiliary variables used to index into the padded representation.
Example
@@ -61,7 +61,7 @@ class Pointclouds(object):
| ]) |
-----------------------------------------------------------------------------
Auxillary variables for packed representation
Auxiliary variables for packed representation
Name | Size | Example from above
-------------------------------|---------------------|-----------------------
@@ -265,7 +265,7 @@ class Pointclouds(object):
)
if d.device != self.device:
raise ValueError(
"All auxillary inputs must be on the same device as the points."
"All auxiliary inputs must be on the same device as the points."
)
if p > 0:
if d.dim() != 2:
@@ -291,7 +291,7 @@ class Pointclouds(object):
)
if aux_input.device != self.device:
raise ValueError(
"All auxillary inputs must be on the same device as the points."
"All auxiliary inputs must be on the same device as the points."
)
aux_input_C = aux_input.shape[2]
return None, aux_input, aux_input_C
@@ -508,7 +508,7 @@ class Pointclouds(object):
def padded_to_packed_idx(self):
"""
Return a 1D tensor x with length equal to the total number of points
such that points_packed()[i] is element x[i] of the flattened padded
such that points_packed()[i] is element x[i] of the flattened padded
representation.
The packed representation can be calculated as follows.
@@ -573,7 +573,7 @@ class Pointclouds(object):
def _compute_packed(self, refresh: bool = False):
"""
Computes the packed version from points_list, normals_list and
features_list and sets the values of auxillary tensors.
features_list and sets the values of auxiliary tensors.
Args:
refresh: Set to True to force recomputation of packed
@@ -910,7 +910,7 @@ class Pointclouds(object):
**neighborhood_size**: The size of the neighborhood used to estimate the
geometry around each point.
**disambiguate_directions**: If `True`, uses the algorithm from [1] to
ensure sign consistency of the normals of neigboring points.
ensure sign consistency of the normals of neighboring points.
**normals**: A tensor of normals for each input point
of shape `(minibatch, num_point, 3)`.
If `pointclouds` are of `Pointclouds` class, returns a padded tensor.
@@ -985,7 +985,7 @@ class Pointclouds(object):
Args:
new_points_padded: FloatTensor of shape (N, P, 3)
new_normals_padded: (optional) FloatTensor of shape (N, P, 3)
new_features_padded: (optional) FloatTensors of shape (N, P, C)
new_features_padded: (optional) FloatTensor of shape (N, P, C)
Returns:
Pointcloud with updated padded representations

2
pytorch3d/structures/volumes.py
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@@ -77,7 +77,7 @@ class Volumes(object):
World coordinates:
- These define the locations of the centers of the volume cells
in the world coordinates.
- They are specifiied with the following mapping that converts
- They are specified with the following mapping that converts
points `x_local` in the local coordinates to points `x_world`
in the world coordinates:
```