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Commit 2d397236 authored by John Reese's avatar John Reese Committed by Facebook GitHub Bot
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apply black 20.8b1 formatting update 

Summary:
allow-large-files

black_any_style

Reviewed By: zertosh

Differential Revision: D24325133

fbshipit-source-id: b4afe80d1e8b2bc993f4b8e3822c02964df47462
parent 11a9f5ea
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Showing with 341 additions and 341 deletions
pytorch3d/ops/perspective_n_points.py
View file @ 2d397236
......@@ -53,7 +53,7 @@ def _compute_alphas(x, c_world):
def _build_M(y, alphas, weight):
""" Returns the matrix defining the reprojection equations.
"""Returns the matrix defining the reprojection equations.
Args:
y: projected points in camera coordinates of size B x N x 2
alphas: barycentric coordinates of size B x N x 4
......@@ -90,7 +90,7 @@ def _build_M(y, alphas, weight):
def _null_space(m, kernel_dim):
""" Finds the null space (kernel) basis of the matrix
"""Finds the null space (kernel) basis of the matrix
Args:
m: the batch of input matrices, B x N x 12
kernel_dim: number of dimensions to approximate the kernel
......@@ -106,7 +106,7 @@ def _null_space(m, kernel_dim):
def _reproj_error(y_hat, y, weight, eps=1e-9):
""" Projects estimated 3D points and computes the reprojection error
"""Projects estimated 3D points and computes the reprojection error
Args:
y_hat: a batch of predicted 2D points in homogeneous coordinates
y: a batch of ground-truth 2D points
......@@ -121,7 +121,7 @@ def _reproj_error(y_hat, y, weight, eps=1e-9):
def _algebraic_error(x_w_rotated, x_cam, weight):
""" Computes the residual of Umeyama in 3D.
"""Computes the residual of Umeyama in 3D.
Args:
x_w_rotated: The given 3D points rotated with the predicted camera.
x_cam: the lifted 2D points y
......@@ -135,7 +135,7 @@ def _algebraic_error(x_w_rotated, x_cam, weight):
def _compute_norm_sign_scaling_factor(c_cam, alphas, x_world, y, weight, eps=1e-9):
""" Given a solution, adjusts the scale and flip
"""Given a solution, adjusts the scale and flip
Args:
c_cam: control points in camera coordinates
alphas: barycentric coordinates of the points
......@@ -167,7 +167,7 @@ def _compute_norm_sign_scaling_factor(c_cam, alphas, x_world, y, weight, eps=1e-
def _gen_pairs(input, dim=-2, reducer=lambda a, b: ((a - b) ** 2).sum(dim=-1)):
""" Generates all pairs of different rows and then applies the reducer
"""Generates all pairs of different rows and then applies the reducer
Args:
input: a tensor
dim: a dimension to generate pairs across
......@@ -184,7 +184,7 @@ def _gen_pairs(input, dim=-2, reducer=lambda a, b: ((a - b) ** 2).sum(dim=-1)):
def _kernel_vec_distances(v):
""" Computes the coefficients for linearisation of the quadratic system
"""Computes the coefficients for linearisation of the quadratic system
to match all pairwise distances between 4 control points (dim=1).
The last dimension corresponds to the coefficients for quadratic terms
Bij = Bi * Bj, where Bi and Bj correspond to kernel vectors.
......@@ -208,7 +208,7 @@ def _kernel_vec_distances(v):
def _solve_lstsq_subcols(rhs, lhs, lhs_col_idx):
""" Solves an over-determined linear system for selected LHS columns.
"""Solves an over-determined linear system for selected LHS columns.
A batched version of `torch.lstsq`.
Args:
rhs: right-hand side vectors
......@@ -226,7 +226,7 @@ def _binary_sign(t):
def _find_null_space_coords_1(kernel_dsts, cw_dst, eps=1e-9):
""" Solves case 1 from the paper [1]; solve for 4 coefficients:
"""Solves case 1 from the paper [1]; solve for 4 coefficients:
[B11 B22 B33 B44 B12 B13 B14 B23 B24 B34]
^ ^ ^ ^
Args:
......@@ -246,7 +246,7 @@ def _find_null_space_coords_1(kernel_dsts, cw_dst, eps=1e-9):
def _find_null_space_coords_2(kernel_dsts, cw_dst):
""" Solves case 2 from the paper; solve for 3 coefficients:
"""Solves case 2 from the paper; solve for 3 coefficients:
[B11 B22 B33 B44 B12 B13 B14 B23 B24 B34]
^ ^ ^
Args:
......@@ -270,7 +270,7 @@ def _find_null_space_coords_2(kernel_dsts, cw_dst):
def _find_null_space_coords_3(kernel_dsts, cw_dst, eps=1e-9):
""" Solves case 3 from the paper; solve for 5 coefficients:
"""Solves case 3 from the paper; solve for 5 coefficients:
[B11 B22 B33 B44 B12 B13 B14 B23 B24 B34]
^ ^ ^ ^ ^
Args:
......
pytorch3d/ops/utils.py
View file @ 2d397236
......@@ -94,7 +94,7 @@ def convert_pointclouds_to_tensor(pcl: Union[torch.Tensor, "Pointclouds"]):
def is_pointclouds(pcl: Union[torch.Tensor, "Pointclouds"]):
""" Checks whether the input `pcl` is an instance of `Pointclouds`
"""Checks whether the input `pcl` is an instance of `Pointclouds`
by checking the existence of `points_padded` and `num_points_per_cloud`
functions.
"""
......
pytorch3d/renderer/mesh/shader.py
View file @ 2d397236
......@@ -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/textures.py
View file @ 2d397236
......@@ -282,24 +282,24 @@ def Textures(
verts_rgb: Optional[torch.Tensor] = None,
) -> TexturesBase:
"""
Textures class has been DEPRECATED.
Preserving Textures as a function for backwards compatibility.
Textures class has been DEPRECATED.
Preserving Textures as a function for backwards compatibility.
Args:
maps: texture map per mesh. This can either be a list of maps
[(H, W, 3)] or a padded tensor of shape (N, H, W, 3).
faces_uvs: (N, F, 3) tensor giving the index into verts_uvs for each
vertex in the face. Padding value is assumed to be -1.
verts_uvs: (N, V, 2) tensor giving the uv coordinate per vertex.
verts_rgb: (N, V, 3) tensor giving the rgb color per vertex. Padding
value is assumed to be -1.
Args:
maps: texture map per mesh. This can either be a list of maps
[(H, W, 3)] or a padded tensor of shape (N, H, W, 3).
faces_uvs: (N, F, 3) tensor giving the index into verts_uvs for each
vertex in the face. Padding value is assumed to be -1.
verts_uvs: (N, V, 2) tensor giving the uv coordinate per vertex.
verts_rgb: (N, V, 3) tensor giving the rgb color per vertex. Padding
value is assumed to be -1.
Returns:
a Textures class which is an instance of TexturesBase e.g. TexturesUV,
TexturesAtlas, TexturesVertex
Returns:
a Textures class which is an instance of TexturesBase e.g. TexturesUV,
TexturesAtlas, TexturesVertex
"""
"""
warnings.warn(
"""Textures class is deprecated,
......
pytorch3d/renderer/mesh/utils.py
View file @ 2d397236
......@@ -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
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 |
| |
+-----------------------------------+ .
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 |
| |
+-----------------------------------+ .
"""
total_width = occupied[0][0]
......
pytorch3d/structures/meshes.py
View file @ 2d397236
This diff is collapsed.
pytorch3d/structures/pointclouds.py
View file @ 2d397236
......@@ -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 = [
......
tests/test_blending.py
View file @ 2d397236
......@@ -410,8 +410,8 @@ class TestBlending(TestCaseMixin, unittest.TestCase):
def test_blend_params(self):
"""Test color parameter of BlendParams().
Assert passed value overrides default value.
"""
Assert passed value overrides default value.
"""
bp_default = BlendParams()
bp_new = BlendParams(background_color=(0.5, 0.5, 0.5))
self.assertEqual(bp_new.background_color, (0.5, 0.5, 0.5))
......
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