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apply black 20.8b1 formatting update

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allow-large-files

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Reviewed By: zertosh

Differential Revision: D24325133

fbshipit-source-id: b4afe80d1e8b2bc993f4b8e3822c02964df47462
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John Reese 2020-10-14 20:19:13 -07:00 committed by Facebook GitHub Bot
parent 11a9f5ea30
commit 2d39723610
8 changed files with 314 additions and 314 deletions
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22
pytorch3d/ops/perspective_n_points.py
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@ -53,7 +53,7 @@ def _compute_alphas(x, c_world):
def _build_M(y, alphas, weight): def _build_M(y, alphas, weight):
""" Returns the matrix defining the reprojection equations. """Returns the matrix defining the reprojection equations.
Args: Args:
y: projected points in camera coordinates of size B x N x 2 y: projected points in camera coordinates of size B x N x 2
alphas: barycentric coordinates of size B x N x 4 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): 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: Args:
m: the batch of input matrices, B x N x 12 m: the batch of input matrices, B x N x 12
kernel_dim: number of dimensions to approximate the kernel 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): 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: Args:
y_hat: a batch of predicted 2D points in homogeneous coordinates y_hat: a batch of predicted 2D points in homogeneous coordinates
y: a batch of ground-truth 2D points 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): def _algebraic_error(x_w_rotated, x_cam, weight):
""" Computes the residual of Umeyama in 3D. """Computes the residual of Umeyama in 3D.
Args: Args:
x_w_rotated: The given 3D points rotated with the predicted camera. x_w_rotated: The given 3D points rotated with the predicted camera.
x_cam: the lifted 2D points y 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): 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: Args:
c_cam: control points in camera coordinates c_cam: control points in camera coordinates
alphas: barycentric coordinates of the points 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)): 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: Args:
input: a tensor input: a tensor
dim: a dimension to generate pairs across 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): 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). to match all pairwise distances between 4 control points (dim=1).
The last dimension corresponds to the coefficients for quadratic terms The last dimension corresponds to the coefficients for quadratic terms
Bij = Bi * Bj, where Bi and Bj correspond to kernel vectors. 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): 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`. A batched version of `torch.lstsq`.
Args: Args:
rhs: right-hand side vectors 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): 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] [B11 B22 B33 B44 B12 B13 B14 B23 B24 B34]
^ ^ ^ ^ ^ ^ ^ ^
Args: 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): 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] [B11 B22 B33 B44 B12 B13 B14 B23 B24 B34]
^ ^ ^ ^ ^ ^
Args: 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): 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] [B11 B22 B33 B44 B12 B13 B14 B23 B24 B34]
^ ^ ^ ^ ^ ^ ^ ^ ^ ^
Args: Args:

2
pytorch3d/ops/utils.py
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@ -94,7 +94,7 @@ def convert_pointclouds_to_tensor(pcl: Union[torch.Tensor, "Pointclouds"]):
def is_pointclouds(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` by checking the existence of `points_padded` and `num_points_per_cloud`
functions. functions.
""" """

2
pytorch3d/renderer/mesh/shader.py
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@ -346,7 +346,7 @@ class SoftSilhouetteShader(nn.Module):
self.blend_params = blend_params if blend_params is not None else BlendParams() self.blend_params = blend_params if blend_params is not None else BlendParams()
def forward(self, fragments, meshes, **kwargs) -> torch.Tensor: def forward(self, fragments, meshes, **kwargs) -> torch.Tensor:
"""" """ "
Only want to render the silhouette so RGB values can be ones. Only want to render the silhouette so RGB values can be ones.
There is no need for lighting or texturing There is no need for lighting or texturing
""" """

28
pytorch3d/renderer/mesh/textures.py
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@ -282,24 +282,24 @@ def Textures(
verts_rgb: Optional[torch.Tensor] = None, verts_rgb: Optional[torch.Tensor] = None,
) -> TexturesBase: ) -> TexturesBase:
""" """
Textures class has been DEPRECATED. Textures class has been DEPRECATED.
Preserving Textures as a function for backwards compatibility. Preserving Textures as a function for backwards compatibility.
Args: Args:
maps: texture map per mesh. This can either be a list of maps 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). [(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 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. vertex in the face. Padding value is assumed to be -1.
verts_uvs: (N, V, 2) tensor giving the uv coordinate per vertex. 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 verts_rgb: (N, V, 3) tensor giving the rgb color per vertex. Padding
value is assumed to be -1. value is assumed to be -1.
Returns: Returns:
a Textures class which is an instance of TexturesBase e.g. TexturesUV, a Textures class which is an instance of TexturesBase e.g. TexturesUV,
TexturesAtlas, TexturesVertex TexturesAtlas, TexturesVertex
""" """
warnings.warn( warnings.warn(
"""Textures class is deprecated, """Textures class is deprecated,

106
pytorch3d/renderer/mesh/utils.py
View File

@ -78,68 +78,68 @@ def _try_place_rectangle(
occupied: List[Tuple[int, int]], occupied: List[Tuple[int, int]],
) -> bool: ) -> bool:
""" """
Try to place rect within the current bounding box. Try to place rect within the current bounding box.
Part of the implementation of pack_rectangles. Part of the implementation of pack_rectangles.
Note that the arguments `placed_so_far` and `occupied` are modified. Note that the arguments `placed_so_far` and `occupied` are modified.
Args: Args:
rect: rectangle to place rect: rectangle to place
placed_so_far: the locations decided upon so far - a list of placed_so_far: the locations decided upon so far - a list of
(x, y, whether flipped). The nth element is the (x, y, whether flipped). The nth element is the
location of the nth rectangle if it has been decided. location of the nth rectangle if it has been decided.
(modified in place) (modified in place)
occupied: the nodes of the graph of extents of rightmost placed occupied: the nodes of the graph of extents of rightmost placed
rectangles - (modified in place) rectangles - (modified in place)
Returns: Returns:
True on success. True on success.
Example: Example:
(We always have placed the first rectangle horizontally and other (We always have placed the first rectangle horizontally and other
rectangles above it.) rectangles above it.)
Let's say the placed boxes 1-4 are layed out like this. Let's say the placed boxes 1-4 are layed out like this.
The coordinates of the points marked X are stored in occupied. 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. It is to the right of the X's that we seek to place rect.
+-----------------------X +-----------------------X
|2 | |2 |
| +---X | +---X
| |4 | | |4 |
| | | | | |
| +---+X | +---+X
| |3 | | |3 |
| | | | | |
+-----------------------+----+------X +-----------------------+----+------X
y |1 | y |1 |
^ | --->x | ^ | --->x |
| +-----------------------------------+ | +-----------------------------------+
We want to place this rectangle. We want to place this rectangle.
+-+ +-+
|5| |5|
| | | |
| | = rect | | = rect
| | | |
| | | |
| | | |
+-+ +-+
The call will succeed, returning True, leaving us with The call will succeed, returning True, leaving us with
+-----------------------X +-----------------------X
|2 | +-X |2 | +-X
| +---+|5| | +---+|5|
| |4 || | | |4 || |
| | || | | | || |
| +---++ | | +---++ |
| |3 | | | |3 | |
| | | | | | | |
+-----------------------+----+-+----X +-----------------------+----+-+----X
|1 | |1 |
| | | |
+-----------------------------------+ . +-----------------------------------+ .
""" """
total_width = occupied[0][0] total_width = occupied[0][0]

320
pytorch3d/structures/meshes.py
View File

@ -9,177 +9,177 @@ from . import utils as struct_utils
class Meshes(object): class Meshes(object):
""" """
This class provides functions for working with batches of triangulated This class provides functions for working with batches of triangulated
meshes with varying numbers of faces and vertices, and converting between meshes with varying numbers of faces and vertices, and converting between
representations. representations.
Within Meshes, there are three different representations of the faces and Within Meshes, there are three different representations of the faces and
verts data: verts data:
List List
- only used for input as a starting point to convert to other representations. - only used for input as a starting point to convert to other representations.
Padded Padded
- has specific batch dimension. - has specific batch dimension.
Packed Packed
- no batch dimension. - no batch dimension.
- has auxillary variables used to index into the padded representation. - has auxillary variables used to index into the padded representation.
Example: Example:
Input list of verts V_n = [[V_1], [V_2], ... , [V_N]] 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 where V_1, ... , V_N are the number of verts in each mesh and N is the
numer of meshes. numer of meshes.
Input list of faces F_n = [[F_1], [F_2], ... , [F_N]] 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. where F_1, ... , F_N are the number of faces in each mesh.
# SPHINX IGNORE # SPHINX IGNORE
List | Padded | Packed List | Padded | Packed
---------------------------|-------------------------|------------------------ ---------------------------|-------------------------|------------------------
[[V_1], ... , [V_N]] | size = (N, max(V_n), 3) | size = (sum(V_n), 3) [[V_1], ... , [V_N]] | size = (N, max(V_n), 3) | size = (sum(V_n), 3)
| | | |
Example for verts: | | Example for verts: | |
| | | |
V_1 = 3, V_2 = 4, V_3 = 5 | size = (3, 5, 3) | size = (12, 3) V_1 = 3, V_2 = 4, V_3 = 5 | size = (3, 5, 3) | size = (12, 3)
| | | |
List([ | tensor([ | tensor([ List([ | tensor([ | tensor([
[ | [ | [0.1, 0.3, 0.5], [ | [ | [0.1, 0.3, 0.5],
[0.1, 0.3, 0.5], | [0.1, 0.3, 0.5], | [0.5, 0.2, 0.1], [0.1, 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], [0.5, 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], [0.6, 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], | [0.6, 0.7, 0.8],
[ | [0, 0, 0], | [0.2, 0.3, 0.4], [ | [0, 0, 0], | [0.2, 0.3, 0.4],
[0.1, 0.3, 0.3], | ], | [0.1, 0.5, 0.3], [0.1, 0.3, 0.3], | ], | [0.1, 0.5, 0.3],
[0.6, 0.7, 0.8], | [ | [0.7, 0.3, 0.6], [0.6, 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], [0.2, 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], [0.1, 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], ], | [0.2, 0.3, 0.4], | [0.2, 0.3, 0.4],
[ | [0.1, 0.5, 0.3], | [0.9, 0.3, 0.8], [ | [0.1, 0.5, 0.3], | [0.9, 0.3, 0.8],
[0.7, 0.3, 0.6], | [0, 0, 0], | ]) [0.7, 0.3, 0.6], | [0, 0, 0], | ])
[0.2, 0.4, 0.8], | ], | [0.2, 0.4, 0.8], | ], |
[0.9, 0.5, 0.2], | [ | [0.9, 0.5, 0.2], | [ |
[0.2, 0.3, 0.4], | [0.7, 0.3, 0.6], | [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, 0.3, 0.8], | [0.2, 0.4, 0.8], |
] | [0.9, 0.5, 0.2], | ] | [0.9, 0.5, 0.2], |
]) | [0.2, 0.3, 0.4], | ]) | [0.2, 0.3, 0.4], |
| [0.9, 0.3, 0.8], | | [0.9, 0.3, 0.8], |
| ] | | ] |
| ]) | | ]) |
Example for faces: | | Example for faces: | |
| | | |
F_1 = 1, F_2 = 2, F_3 = 7 | size = (3, 7, 3) | size = (10, 3) F_1 = 1, F_2 = 2, F_3 = 7 | size = (3, 7, 3) | size = (10, 3)
| | | |
List([ | tensor([ | tensor([ List([ | tensor([ | tensor([
[ | [ | [ 0, 1, 2], [ | [ | [ 0, 1, 2],
[0, 1, 2], | [0, 1, 2], | [ 3, 4, 5], [0, 1, 2], | [0, 1, 2], | [ 3, 4, 5],
], | [-1, -1, -1], | [ 4, 5, 6], ], | [-1, -1, -1], | [ 4, 5, 6],
[ | [-1, -1, -1] | [ 8, 9, 7], [ | [-1, -1, -1] | [ 8, 9, 7],
[0, 1, 2], | [-1, -1, -1] | [ 7, 8, 10], [0, 1, 2], | [-1, -1, -1] | [ 7, 8, 10],
[1, 2, 3], | [-1, -1, -1] | [ 9, 10, 8], [1, 2, 3], | [-1, -1, -1] | [ 9, 10, 8],
], | [-1, -1, -1], | [11, 10, 9], ], | [-1, -1, -1], | [11, 10, 9],
[ | [-1, -1, -1], | [11, 7, 8], [ | [-1, -1, -1], | [11, 7, 8],
[1, 2, 0], | ], | [11, 10, 8], [1, 2, 0], | ], | [11, 10, 8],
[0, 1, 3], | [ | [11, 9, 8], [0, 1, 3], | [ | [11, 9, 8],
[2, 3, 1], | [0, 1, 2], | ]) [2, 3, 1], | [0, 1, 2], | ])
[4, 3, 2], | [1, 2, 3], | [4, 3, 2], | [1, 2, 3], |
[4, 0, 1], | [-1, -1, -1], | [4, 0, 1], | [-1, -1, -1], |
[4, 3, 1], | [-1, -1, -1], | [4, 3, 1], | [-1, -1, -1], |
[4, 2, 1], | [-1, -1, -1], | [4, 2, 1], | [-1, -1, -1], |
], | [-1, -1, -1], | ], | [-1, -1, -1], |
]) | [-1, -1, -1], | ]) | [-1, -1, -1], |
| ], | | ], |
| [ | | [ |
| [1, 2, 0], | | [1, 2, 0], |
| [0, 1, 3], | | [0, 1, 3], |
| [2, 3, 1], | | [2, 3, 1], |
| [4, 3, 2], | | [4, 3, 2], |
| [4, 0, 1], | | [4, 0, 1], |
| [4, 3, 1], | | [4, 3, 1], |
| [4, 2, 1], | | [4, 2, 1], |
| ] | | ] |
| ]) | | ]) |
----------------------------------------------------------------------------- -----------------------------------------------------------------------------
Auxillary variables for packed representation Auxillary variables for packed representation
Name | Size | Example from above Name | Size | Example from above
-------------------------------|---------------------|----------------------- -------------------------------|---------------------|-----------------------
| | | |
verts_packed_to_mesh_idx | size = (sum(V_n)) | tensor([ verts_packed_to_mesh_idx | size = (sum(V_n)) | tensor([
| | 0, 0, 0, 1, 1, 1, | | 0, 0, 0, 1, 1, 1,
| | 1, 2, 2, 2, 2, 2 | | 1, 2, 2, 2, 2, 2
| | )] | | )]
| | size = (12) | | size = (12)
| | | |
mesh_to_verts_packed_first_idx | size = (N) | tensor([0, 3, 7]) mesh_to_verts_packed_first_idx | size = (N) | tensor([0, 3, 7])
| | size = (3) | | size = (3)
| | | |
num_verts_per_mesh | size = (N) | tensor([3, 4, 5]) num_verts_per_mesh | size = (N) | tensor([3, 4, 5])
| | size = (3) | | size = (3)
| | | |
faces_packed_to_mesh_idx | size = (sum(F_n)) | tensor([ faces_packed_to_mesh_idx | size = (sum(F_n)) | tensor([
| | 0, 1, 1, 2, 2, 2, | | 0, 1, 1, 2, 2, 2,
| | 2, 2, 2, 2 | | 2, 2, 2, 2
| | )] | | )]
| | size = (10) | | size = (10)
| | | |
mesh_to_faces_packed_first_idx | size = (N) | tensor([0, 1, 3]) mesh_to_faces_packed_first_idx | size = (N) | tensor([0, 1, 3])
| | size = (3) | | size = (3)
| | | |
num_faces_per_mesh | size = (N) | tensor([1, 2, 7]) num_faces_per_mesh | size = (N) | tensor([1, 2, 7])
| | size = (3) | | size = (3)
| | | |
verts_padded_to_packed_idx | size = (sum(V_n)) | tensor([ verts_padded_to_packed_idx | size = (sum(V_n)) | tensor([
| | 0, 1, 2, 5, 6, 7, | | 0, 1, 2, 5, 6, 7,
| | 8, 10, 11, 12, 13, | | 8, 10, 11, 12, 13,
| | 14 | | 14
| | )] | | )]
| | size = (12) | | size = (12)
----------------------------------------------------------------------------- -----------------------------------------------------------------------------
# SPHINX IGNORE # SPHINX IGNORE
From the faces, edges are computed and have packed and padded From the faces, edges are computed and have packed and padded
representations with auxillary variables. representations with auxillary variables.
E_n = [[E_1], ... , [E_N]] E_n = [[E_1], ... , [E_N]]
where E_1, ... , E_N are the number of unique edges in each mesh. where E_1, ... , E_N are the number of unique edges in each mesh.
Total number of unique edges = sum(E_n) Total number of unique edges = sum(E_n)
# SPHINX IGNORE # SPHINX IGNORE
Name | Size | Example from above Name | Size | Example from above
-------------------------------|-------------------------|---------------------- -------------------------------|-------------------------|----------------------
| | | |
edges_packed | size = (sum(E_n), 2) | tensor([ edges_packed | size = (sum(E_n), 2) | tensor([
| | [0, 1], | | [0, 1],
| | [0, 2], | | [0, 2],
| | [1, 2], | | [1, 2],
| | ... | | ...
| | [10, 11], | | [10, 11],
| | )] | | )]
| | size = (18, 2) | | size = (18, 2)
| | | |
num_edges_per_mesh | size = (N) | tensor([3, 5, 10]) num_edges_per_mesh | size = (N) | tensor([3, 5, 10])
| | size = (3) | | size = (3)
| | | |
edges_packed_to_mesh_idx | size = (sum(E_n)) | tensor([ edges_packed_to_mesh_idx | size = (sum(E_n)) | tensor([
| | 0, 0, 0, | | 0, 0, 0,
| | . . . | | . . .
| | 2, 2, 2 | | 2, 2, 2
| | ]) | | ])
| | size = (18) | | size = (18)
| | | |
faces_packed_to_edges_packed | size = (sum(F_n), 3) | tensor([ faces_packed_to_edges_packed | size = (sum(F_n), 3) | tensor([
| | [2, 1, 0], | | [2, 1, 0],
| | [5, 4, 3], | | [5, 4, 3],
| | . . . | | . . .
| | [12, 14, 16], | | [12, 14, 16],
| | ]) | | ])
| | size = (10, 3) | | size = (10, 3)
| | | |
mesh_to_edges_packed_first_idx | size = (N) | tensor([0, 3, 8]) mesh_to_edges_packed_first_idx | size = (N) | tensor([0, 3, 8])
| | size = (3) | | size = (3)
---------------------------------------------------------------------------- ----------------------------------------------------------------------------
# SPHINX IGNORE # SPHINX IGNORE
""" """
_INTERNAL_TENSORS = [ _INTERNAL_TENSORS = [

144
pytorch3d/structures/pointclouds.py
View File

@ -8,85 +8,85 @@ from . import utils as struct_utils
class Pointclouds(object): class Pointclouds(object):
""" """
This class provides functions for working with batches of 3d point clouds, This class provides functions for working with batches of 3d point clouds,
and converting between representations. and converting between representations.
Within Pointclouds, there are three different representations of the data. Within Pointclouds, there are three different representations of the data.
List List
- only used for input as a starting point to convert to other representations. - only used for input as a starting point to convert to other representations.
Padded Padded
- has specific batch dimension. - has specific batch dimension.
Packed Packed
- no batch dimension. - no batch dimension.
- has auxillary variables used to index into the padded representation. - has auxillary variables used to index into the padded representation.
Example Example
Input list of points = [[P_1], [P_2], ... , [P_N]] 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 where P_1, ... , P_N are the number of points in each cloud and N is the
number of clouds. number of clouds.
# SPHINX IGNORE # SPHINX IGNORE
List | Padded | Packed List | Padded | Packed
---------------------------|-------------------------|------------------------ ---------------------------|-------------------------|------------------------
[[P_1], ... , [P_N]] | size = (N, max(P_n), 3) | size = (sum(P_n), 3) [[P_1], ... , [P_N]] | size = (N, max(P_n), 3) | size = (sum(P_n), 3)
| | | |
Example for locations | | Example for locations | |
or colors: | | or colors: | |
| | | |
P_1 = 3, P_2 = 4, P_3 = 5 | size = (3, 5, 3) | size = (12, 3) P_1 = 3, P_2 = 4, P_3 = 5 | size = (3, 5, 3) | size = (12, 3)
| | | |
List([ | tensor([ | tensor([ List([ | tensor([ | tensor([
[ | [ | [0.1, 0.3, 0.5], [ | [ | [0.1, 0.3, 0.5],
[0.1, 0.3, 0.5], | [0.1, 0.3, 0.5], | [0.5, 0.2, 0.1], [0.1, 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], [0.5, 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], [0.6, 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], | [0.6, 0.7, 0.8],
[ | [0, 0, 0] | [0.2, 0.3, 0.4], [ | [0, 0, 0] | [0.2, 0.3, 0.4],
[0.1, 0.3, 0.3], | ], | [0.1, 0.5, 0.3], [0.1, 0.3, 0.3], | ], | [0.1, 0.5, 0.3],
[0.6, 0.7, 0.8], | [ | [0.7, 0.3, 0.6], [0.6, 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], [0.2, 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], [0.1, 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], ], | [0.2, 0.3, 0.4], | [0.2, 0.3, 0.4],
[ | [0.1, 0.5, 0.3], | [0.9, 0.3, 0.8], [ | [0.1, 0.5, 0.3], | [0.9, 0.3, 0.8],
[0.7, 0.3, 0.6], | [0, 0, 0] | ]) [0.7, 0.3, 0.6], | [0, 0, 0] | ])
[0.2, 0.4, 0.8], | ], | [0.2, 0.4, 0.8], | ], |
[0.9, 0.5, 0.2], | [ | [0.9, 0.5, 0.2], | [ |
[0.2, 0.3, 0.4], | [0.7, 0.3, 0.6], | [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, 0.3, 0.8], | [0.2, 0.4, 0.8], |
] | [0.9, 0.5, 0.2], | ] | [0.9, 0.5, 0.2], |
]) | [0.2, 0.3, 0.4], | ]) | [0.2, 0.3, 0.4], |
| [0.9, 0.3, 0.8] | | [0.9, 0.3, 0.8] |
| ] | | ] |
| ]) | | ]) |
----------------------------------------------------------------------------- -----------------------------------------------------------------------------
Auxillary variables for packed representation Auxillary variables for packed representation
Name | Size | Example from above Name | Size | Example from above
-------------------------------|---------------------|----------------------- -------------------------------|---------------------|-----------------------
| | | |
packed_to_cloud_idx | size = (sum(P_n)) | tensor([ packed_to_cloud_idx | size = (sum(P_n)) | tensor([
| | 0, 0, 0, 1, 1, 1, | | 0, 0, 0, 1, 1, 1,
| | 1, 2, 2, 2, 2, 2 | | 1, 2, 2, 2, 2, 2
| | )] | | )]
| | size = (12) | | size = (12)
| | | |
cloud_to_packed_first_idx | size = (N) | tensor([0, 3, 7]) cloud_to_packed_first_idx | size = (N) | tensor([0, 3, 7])
| | size = (3) | | size = (3)
| | | |
num_points_per_cloud | size = (N) | tensor([3, 4, 5]) num_points_per_cloud | size = (N) | tensor([3, 4, 5])
| | size = (3) | | size = (3)
| | | |
padded_to_packed_idx | size = (sum(P_n)) | tensor([ padded_to_packed_idx | size = (sum(P_n)) | tensor([
| | 0, 1, 2, 5, 6, 7, | | 0, 1, 2, 5, 6, 7,
| | 8, 10, 11, 12, 13, | | 8, 10, 11, 12, 13,
| | 14 | | 14
| | )] | | )]
| | size = (12) | | size = (12)
----------------------------------------------------------------------------- -----------------------------------------------------------------------------
# SPHINX IGNORE # SPHINX IGNORE
""" """
_INTERNAL_TENSORS = [ _INTERNAL_TENSORS = [

4
tests/test_blending.py
View File

@ -410,8 +410,8 @@ class TestBlending(TestCaseMixin, unittest.TestCase):
def test_blend_params(self): def test_blend_params(self):
"""Test color parameter of BlendParams(). """Test color parameter of BlendParams().
Assert passed value overrides default value. Assert passed value overrides default value.
""" """
bp_default = BlendParams() bp_default = BlendParams()
bp_new = BlendParams(background_color=(0.5, 0.5, 0.5)) bp_new = BlendParams(background_color=(0.5, 0.5, 0.5))
self.assertEqual(bp_new.background_color, (0.5, 0.5, 0.5)) self.assertEqual(bp_new.background_color, (0.5, 0.5, 0.5))