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Commit 30e4e891 authored by Jeremy Reizenstein's avatar Jeremy Reizenstein Committed by Facebook GitHub Bot
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linter comment strictnesss 

Summary: The linter has become stricter about the indenting of comments and docstrings. This was accompanied by a codemod. In a few places we can fix the problem nicer than the codemod has.

Reviewed By: gkioxari

Differential Revision: D24363880

fbshipit-source-id: 4cff3bbe3d2a834bc92a490469a2b24fa376e6ab
parent 563d441b
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Showing with 309 additions and 309 deletions
pytorch3d/renderer/mesh/shader.py
View file @ 30e4e891
......@@ -346,7 +346,7 @@ class SoftSilhouetteShader(nn.Module):
self.blend_params = blend_params if blend_params is not None else BlendParams()
def forward(self, fragments, meshes, **kwargs) -> torch.Tensor:
""" "
"""
Only want to render the silhouette so RGB values can be ones.
There is no need for lighting or texturing
"""
......
pytorch3d/renderer/mesh/utils.py
View file @ 30e4e891
......@@ -78,68 +78,68 @@ def _try_place_rectangle(
occupied: List[Tuple[int, int]],
) -> bool:
"""
Try to place rect within the current bounding box.
Part of the implementation of pack_rectangles.
Note that the arguments `placed_so_far` and `occupied` are modified.
Args:
rect: rectangle to place
placed_so_far: the locations decided upon so far - a list of
(x, y, whether flipped). The nth element is the
location of the nth rectangle if it has been decided.
(modified in place)
occupied: the nodes of the graph of extents of rightmost placed
rectangles - (modified in place)
Returns:
True on success.
Example:
(We always have placed the first rectangle horizontally and other
rectangles above it.)
Let's say the placed boxes 1-4 are layed out like this.
The coordinates of the points marked X are stored in occupied.
It is to the right of the X's that we seek to place rect.
+-----------------------X
|2 |
| +---X
| |4 |
| | |
| +---+X
| |3 |
| | |
+-----------------------+----+------X
Try to place rect within the current bounding box.
Part of the implementation of pack_rectangles.
Note that the arguments `placed_so_far` and `occupied` are modified.
Args:
rect: rectangle to place
placed_so_far: the locations decided upon so far - a list of
(x, y, whether flipped). The nth element is the
location of the nth rectangle if it has been decided.
(modified in place)
occupied: the nodes of the graph of extents of rightmost placed
rectangles - (modified in place)
Returns:
True on success.
Example:
(We always have placed the first rectangle horizontally and other
rectangles above it.)
Let's say the placed boxes 1-4 are layed out like this.
The coordinates of the points marked X are stored in occupied.
It is to the right of the X's that we seek to place rect.
+-----------------------X
|2 |
| +---X
| |4 |
| | |
| +---+X
| |3 |
| | |
+-----------------------+----+------X
y |1 |
^ | --->x |
| +-----------------------------------+
We want to place this rectangle.
+-+
|5|
| |
| | = rect
| |
| |
| |
+-+
The call will succeed, returning True, leaving us with
+-----------------------X
|2 | +-X
| +---+|5|
| |4 || |
| | || |
| +---++ |
| |3 | |
| | | |
+-----------------------+----+-+----X
|1 |
| |
+-----------------------------------+ .
We want to place this rectangle.
+-+
|5|
| |
| | = rect
| |
| |
| |
+-+
The call will succeed, returning True, leaving us with
+-----------------------X
|2 | +-X
| +---+|5|
| |4 || |
| | || |
| +---++ |
| |3 | |
| | | |
+-----------------------+----+-+----X
|1 |
| |
+-----------------------------------+ .
"""
total_width = occupied[0][0]
......
pytorch3d/structures/meshes.py
View file @ 30e4e891
This diff is collapsed.
pytorch3d/structures/pointclouds.py
View file @ 30e4e891
......@@ -8,85 +8,85 @@ from . import utils as struct_utils
class Pointclouds(object):
"""
This class provides functions for working with batches of 3d point clouds,
and converting between representations.
Within Pointclouds, there are three different representations of the data.
List
- only used for input as a starting point to convert to other representations.
Padded
- has specific batch dimension.
Packed
- no batch dimension.
- has auxillary variables used to index into the padded representation.
Example
Input list of points = [[P_1], [P_2], ... , [P_N]]
where P_1, ... , P_N are the number of points in each cloud and N is the
number of clouds.
# SPHINX IGNORE
List | Padded | Packed
---------------------------|-------------------------|------------------------
[[P_1], ... , [P_N]] | size = (N, max(P_n), 3) | size = (sum(P_n), 3)
| |
Example for locations | |
or colors: | |
| |
P_1 = 3, P_2 = 4, P_3 = 5 | size = (3, 5, 3) | size = (12, 3)
| |
List([ | tensor([ | tensor([
[ | [ | [0.1, 0.3, 0.5],
[0.1, 0.3, 0.5], | [0.1, 0.3, 0.5], | [0.5, 0.2, 0.1],
[0.5, 0.2, 0.1], | [0.5, 0.2, 0.1], | [0.6, 0.8, 0.7],
[0.6, 0.8, 0.7] | [0.6, 0.8, 0.7], | [0.1, 0.3, 0.3],
], | [0, 0, 0], | [0.6, 0.7, 0.8],
[ | [0, 0, 0] | [0.2, 0.3, 0.4],
[0.1, 0.3, 0.3], | ], | [0.1, 0.5, 0.3],
[0.6, 0.7, 0.8], | [ | [0.7, 0.3, 0.6],
[0.2, 0.3, 0.4], | [0.1, 0.3, 0.3], | [0.2, 0.4, 0.8],
[0.1, 0.5, 0.3] | [0.6, 0.7, 0.8], | [0.9, 0.5, 0.2],
], | [0.2, 0.3, 0.4], | [0.2, 0.3, 0.4],
[ | [0.1, 0.5, 0.3], | [0.9, 0.3, 0.8],
[0.7, 0.3, 0.6], | [0, 0, 0] | ])
[0.2, 0.4, 0.8], | ], |
[0.9, 0.5, 0.2], | [ |
[0.2, 0.3, 0.4], | [0.7, 0.3, 0.6], |
[0.9, 0.3, 0.8], | [0.2, 0.4, 0.8], |
] | [0.9, 0.5, 0.2], |
]) | [0.2, 0.3, 0.4], |
| [0.9, 0.3, 0.8] |
| ] |
| ]) |
-----------------------------------------------------------------------------
Auxillary variables for packed representation
Name | Size | Example from above
-------------------------------|---------------------|-----------------------
| |
packed_to_cloud_idx | size = (sum(P_n)) | tensor([
| | 0, 0, 0, 1, 1, 1,
| | 1, 2, 2, 2, 2, 2
| | )]
| | size = (12)
| |
cloud_to_packed_first_idx | size = (N) | tensor([0, 3, 7])
| | size = (3)
| |
num_points_per_cloud | size = (N) | tensor([3, 4, 5])
| | size = (3)
| |
padded_to_packed_idx | size = (sum(P_n)) | tensor([
| | 0, 1, 2, 5, 6, 7,
| | 8, 10, 11, 12, 13,
| | 14
| | )]
| | size = (12)
-----------------------------------------------------------------------------
# SPHINX IGNORE
This class provides functions for working with batches of 3d point clouds,
and converting between representations.
Within Pointclouds, there are three different representations of the data.
List
- only used for input as a starting point to convert to other representations.
Padded
- has specific batch dimension.
Packed
- no batch dimension.
- has auxillary variables used to index into the padded representation.
Example
Input list of points = [[P_1], [P_2], ... , [P_N]]
where P_1, ... , P_N are the number of points in each cloud and N is the
number of clouds.
# SPHINX IGNORE
List | Padded | Packed
---------------------------|-------------------------|------------------------
[[P_1], ... , [P_N]] | size = (N, max(P_n), 3) | size = (sum(P_n), 3)
| |
Example for locations | |
or colors: | |
| |
P_1 = 3, P_2 = 4, P_3 = 5 | size = (3, 5, 3) | size = (12, 3)
| |
List([ | tensor([ | tensor([
[ | [ | [0.1, 0.3, 0.5],
[0.1, 0.3, 0.5], | [0.1, 0.3, 0.5], | [0.5, 0.2, 0.1],
[0.5, 0.2, 0.1], | [0.5, 0.2, 0.1], | [0.6, 0.8, 0.7],
[0.6, 0.8, 0.7] | [0.6, 0.8, 0.7], | [0.1, 0.3, 0.3],
], | [0, 0, 0], | [0.6, 0.7, 0.8],
[ | [0, 0, 0] | [0.2, 0.3, 0.4],
[0.1, 0.3, 0.3], | ], | [0.1, 0.5, 0.3],
[0.6, 0.7, 0.8], | [ | [0.7, 0.3, 0.6],
[0.2, 0.3, 0.4], | [0.1, 0.3, 0.3], | [0.2, 0.4, 0.8],
[0.1, 0.5, 0.3] | [0.6, 0.7, 0.8], | [0.9, 0.5, 0.2],
], | [0.2, 0.3, 0.4], | [0.2, 0.3, 0.4],
[ | [0.1, 0.5, 0.3], | [0.9, 0.3, 0.8],
[0.7, 0.3, 0.6], | [0, 0, 0] | ])
[0.2, 0.4, 0.8], | ], |
[0.9, 0.5, 0.2], | [ |
[0.2, 0.3, 0.4], | [0.7, 0.3, 0.6], |
[0.9, 0.3, 0.8], | [0.2, 0.4, 0.8], |
] | [0.9, 0.5, 0.2], |
]) | [0.2, 0.3, 0.4], |
| [0.9, 0.3, 0.8] |
| ] |
| ]) |
-----------------------------------------------------------------------------
Auxillary variables for packed representation
Name | Size | Example from above
-------------------------------|---------------------|-----------------------
| |
packed_to_cloud_idx | size = (sum(P_n)) | tensor([
| | 0, 0, 0, 1, 1, 1,
| | 1, 2, 2, 2, 2, 2
| | )]
| | size = (12)
| |
cloud_to_packed_first_idx | size = (N) | tensor([0, 3, 7])
| | size = (3)
| |
num_points_per_cloud | size = (N) | tensor([3, 4, 5])
| | size = (3)
| |
padded_to_packed_idx | size = (sum(P_n)) | tensor([
| | 0, 1, 2, 5, 6, 7,
| | 8, 10, 11, 12, 13,
| | 14
| | )]
| | size = (12)
-----------------------------------------------------------------------------
# SPHINX IGNORE
"""
_INTERNAL_TENSORS = [
......
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