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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
This commit is contained in:
John Reese
Facebook GitHub Bot
parent
11a9f5ea30
commit
2d39723610
@ -53,7 +53,7 @@ def _compute_alphas(x, c_world):
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def _build_M(y, alphas, weight):
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""" Returns the matrix defining the reprojection equations.
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"""Returns the matrix defining the reprojection equations.
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Args:
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y: projected points in camera coordinates of size B x N x 2
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alphas: barycentric coordinates of size B x N x 4
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@ -90,7 +90,7 @@ def _build_M(y, alphas, weight):
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def _null_space(m, kernel_dim):
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""" Finds the null space (kernel) basis of the matrix
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"""Finds the null space (kernel) basis of the matrix
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Args:
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m: the batch of input matrices, B x N x 12
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kernel_dim: number of dimensions to approximate the kernel
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@ -106,7 +106,7 @@ def _null_space(m, kernel_dim):
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def _reproj_error(y_hat, y, weight, eps=1e-9):
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""" Projects estimated 3D points and computes the reprojection error
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"""Projects estimated 3D points and computes the reprojection error
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Args:
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y_hat: a batch of predicted 2D points in homogeneous coordinates
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y: a batch of ground-truth 2D points
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@ -121,7 +121,7 @@ def _reproj_error(y_hat, y, weight, eps=1e-9):
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def _algebraic_error(x_w_rotated, x_cam, weight):
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""" Computes the residual of Umeyama in 3D.
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"""Computes the residual of Umeyama in 3D.
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Args:
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x_w_rotated: The given 3D points rotated with the predicted camera.
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x_cam: the lifted 2D points y
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@ -135,7 +135,7 @@ def _algebraic_error(x_w_rotated, x_cam, weight):
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def _compute_norm_sign_scaling_factor(c_cam, alphas, x_world, y, weight, eps=1e-9):
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""" Given a solution, adjusts the scale and flip
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"""Given a solution, adjusts the scale and flip
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Args:
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c_cam: control points in camera coordinates
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alphas: barycentric coordinates of the points
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@ -167,7 +167,7 @@ def _compute_norm_sign_scaling_factor(c_cam, alphas, x_world, y, weight, eps=1e-
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def _gen_pairs(input, dim=-2, reducer=lambda a, b: ((a - b) ** 2).sum(dim=-1)):
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""" Generates all pairs of different rows and then applies the reducer
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"""Generates all pairs of different rows and then applies the reducer
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Args:
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input: a tensor
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dim: a dimension to generate pairs across
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@ -184,7 +184,7 @@ def _gen_pairs(input, dim=-2, reducer=lambda a, b: ((a - b) ** 2).sum(dim=-1)):
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def _kernel_vec_distances(v):
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""" Computes the coefficients for linearisation of the quadratic system
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"""Computes the coefficients for linearisation of the quadratic system
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to match all pairwise distances between 4 control points (dim=1).
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The last dimension corresponds to the coefficients for quadratic terms
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Bij = Bi * Bj, where Bi and Bj correspond to kernel vectors.
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@ -208,7 +208,7 @@ def _kernel_vec_distances(v):
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def _solve_lstsq_subcols(rhs, lhs, lhs_col_idx):
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""" Solves an over-determined linear system for selected LHS columns.
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"""Solves an over-determined linear system for selected LHS columns.
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A batched version of `torch.lstsq`.
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Args:
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rhs: right-hand side vectors
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@ -226,7 +226,7 @@ def _binary_sign(t):
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def _find_null_space_coords_1(kernel_dsts, cw_dst, eps=1e-9):
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""" Solves case 1 from the paper [1]; solve for 4 coefficients:
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"""Solves case 1 from the paper [1]; solve for 4 coefficients:
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[B11 B22 B33 B44 B12 B13 B14 B23 B24 B34]
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^ ^ ^ ^
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Args:
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@ -246,7 +246,7 @@ def _find_null_space_coords_1(kernel_dsts, cw_dst, eps=1e-9):
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def _find_null_space_coords_2(kernel_dsts, cw_dst):
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""" Solves case 2 from the paper; solve for 3 coefficients:
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"""Solves case 2 from the paper; solve for 3 coefficients:
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[B11 B22 B33 B44 B12 B13 B14 B23 B24 B34]
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^ ^ ^
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Args:
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@ -270,7 +270,7 @@ def _find_null_space_coords_2(kernel_dsts, cw_dst):
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def _find_null_space_coords_3(kernel_dsts, cw_dst, eps=1e-9):
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""" Solves case 3 from the paper; solve for 5 coefficients:
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"""Solves case 3 from the paper; solve for 5 coefficients:
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[B11 B22 B33 B44 B12 B13 B14 B23 B24 B34]
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^ ^ ^ ^ ^
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Args:
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@ -94,7 +94,7 @@ def convert_pointclouds_to_tensor(pcl: Union[torch.Tensor, "Pointclouds"]):
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def is_pointclouds(pcl: Union[torch.Tensor, "Pointclouds"]):
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""" Checks whether the input `pcl` is an instance of `Pointclouds`
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"""Checks whether the input `pcl` is an instance of `Pointclouds`
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by checking the existence of `points_padded` and `num_points_per_cloud`
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functions.
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"""
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@ -346,7 +346,7 @@ class SoftSilhouetteShader(nn.Module):
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self.blend_params = blend_params if blend_params is not None else BlendParams()
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def forward(self, fragments, meshes, **kwargs) -> torch.Tensor:
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""""
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""" "
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Only want to render the silhouette so RGB values can be ones.
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There is no need for lighting or texturing
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"""
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@ -282,24 +282,24 @@ def Textures(
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verts_rgb: Optional[torch.Tensor] = None,
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) -> TexturesBase:
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"""
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Textures class has been DEPRECATED.
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Preserving Textures as a function for backwards compatibility.
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Textures class has been DEPRECATED.
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Preserving Textures as a function for backwards compatibility.
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Args:
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maps: texture map per mesh. This can either be a list of maps
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[(H, W, 3)] or a padded tensor of shape (N, H, W, 3).
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faces_uvs: (N, F, 3) tensor giving the index into verts_uvs for each
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vertex in the face. Padding value is assumed to be -1.
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verts_uvs: (N, V, 2) tensor giving the uv coordinate per vertex.
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verts_rgb: (N, V, 3) tensor giving the rgb color per vertex. Padding
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value is assumed to be -1.
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Args:
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maps: texture map per mesh. This can either be a list of maps
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[(H, W, 3)] or a padded tensor of shape (N, H, W, 3).
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faces_uvs: (N, F, 3) tensor giving the index into verts_uvs for each
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vertex in the face. Padding value is assumed to be -1.
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verts_uvs: (N, V, 2) tensor giving the uv coordinate per vertex.
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verts_rgb: (N, V, 3) tensor giving the rgb color per vertex. Padding
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value is assumed to be -1.
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Returns:
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a Textures class which is an instance of TexturesBase e.g. TexturesUV,
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TexturesAtlas, TexturesVertex
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Returns:
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a Textures class which is an instance of TexturesBase e.g. TexturesUV,
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TexturesAtlas, TexturesVertex
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"""
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"""
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warnings.warn(
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"""Textures class is deprecated,
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@ -78,68 +78,68 @@ def _try_place_rectangle(
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occupied: List[Tuple[int, int]],
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) -> bool:
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"""
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Try to place rect within the current bounding box.
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Part of the implementation of pack_rectangles.
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Try to place rect within the current bounding box.
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Part of the implementation of pack_rectangles.
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Note that the arguments `placed_so_far` and `occupied` are modified.
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Note that the arguments `placed_so_far` and `occupied` are modified.
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Args:
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rect: rectangle to place
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placed_so_far: the locations decided upon so far - a list of
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(x, y, whether flipped). The nth element is the
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location of the nth rectangle if it has been decided.
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(modified in place)
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occupied: the nodes of the graph of extents of rightmost placed
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rectangles - (modified in place)
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Args:
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rect: rectangle to place
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placed_so_far: the locations decided upon so far - a list of
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(x, y, whether flipped). The nth element is the
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location of the nth rectangle if it has been decided.
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(modified in place)
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occupied: the nodes of the graph of extents of rightmost placed
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rectangles - (modified in place)
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Returns:
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True on success.
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Returns:
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True on success.
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Example:
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(We always have placed the first rectangle horizontally and other
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rectangles above it.)
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Let's say the placed boxes 1-4 are layed out like this.
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The coordinates of the points marked X are stored in occupied.
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It is to the right of the X's that we seek to place rect.
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Example:
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(We always have placed the first rectangle horizontally and other
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rectangles above it.)
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Let's say the placed boxes 1-4 are layed out like this.
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The coordinates of the points marked X are stored in occupied.
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It is to the right of the X's that we seek to place rect.
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+-----------------------X
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|2 |
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| +---X
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| |4 |
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| | |
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| +---+X
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| |3 |
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| | |
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+-----------------------+----+------X
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y |1 |
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^ | --->x |
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| +-----------------------------------+
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+-----------------------X
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|2 |
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| +---X
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| |4 |
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| | |
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| +---+X
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| |3 |
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| | |
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+-----------------------+----+------X
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y |1 |
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^ | --->x |
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| +-----------------------------------+
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We want to place this rectangle.
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We want to place this rectangle.
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+-+
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|5|
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| |
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| | = rect
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| |
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| |
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| |
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+-+
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+-+
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|5|
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| |
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| | = rect
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| |
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| |
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| |
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+-+
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The call will succeed, returning True, leaving us with
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The call will succeed, returning True, leaving us with
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+-----------------------X
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|2 | +-X
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| +---+|5|
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| |4 || |
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| | || |
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| +---++ |
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| |3 | |
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| | | |
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+-----------------------+----+-+----X
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|1 |
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| |
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+-----------------------------------+ .
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+-----------------------X
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|2 | +-X
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| +---+|5|
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| |4 || |
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| | || |
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| +---++ |
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| |3 | |
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| | | |
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+-----------------------+----+-+----X
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|1 |
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| |
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+-----------------------------------+ .
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"""
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total_width = occupied[0][0]
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@ -9,177 +9,177 @@ from . import utils as struct_utils
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class Meshes(object):
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"""
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This class provides functions for working with batches of triangulated
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meshes with varying numbers of faces and vertices, and converting between
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representations.
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This class provides functions for working with batches of triangulated
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meshes with varying numbers of faces and vertices, and converting between
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representations.
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Within Meshes, there are three different representations of the faces and
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verts data:
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Within Meshes, there are three different representations of the faces and
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verts data:
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List
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- only used for input as a starting point to convert to other representations.
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Padded
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- has specific batch dimension.
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Packed
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- no batch dimension.
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- has auxillary variables used to index into the padded representation.
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List
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- only used for input as a starting point to convert to other representations.
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Padded
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- has specific batch dimension.
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Packed
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- no batch dimension.
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- has auxillary variables used to index into the padded representation.
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Example:
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Example:
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Input list of verts V_n = [[V_1], [V_2], ... , [V_N]]
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where V_1, ... , V_N are the number of verts in each mesh and N is the
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numer of meshes.
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Input list of verts V_n = [[V_1], [V_2], ... , [V_N]]
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where V_1, ... , V_N are the number of verts in each mesh and N is the
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numer of meshes.
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Input list of faces F_n = [[F_1], [F_2], ... , [F_N]]
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where F_1, ... , F_N are the number of faces in each mesh.
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Input list of faces F_n = [[F_1], [F_2], ... , [F_N]]
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where F_1, ... , F_N are the number of faces in each mesh.
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# SPHINX IGNORE
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List | Padded | Packed
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---------------------------|-------------------------|------------------------
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[[V_1], ... , [V_N]] | size = (N, max(V_n), 3) | size = (sum(V_n), 3)
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| |
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Example for verts: | |
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| |
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V_1 = 3, V_2 = 4, V_3 = 5 | size = (3, 5, 3) | size = (12, 3)
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| |
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List([ | tensor([ | tensor([
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[ | [ | [0.1, 0.3, 0.5],
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[0.1, 0.3, 0.5], | [0.1, 0.3, 0.5], | [0.5, 0.2, 0.1],
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[0.5, 0.2, 0.1], | [0.5, 0.2, 0.1], | [0.6, 0.8, 0.7],
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[0.6, 0.8, 0.7], | [0.6, 0.8, 0.7], | [0.1, 0.3, 0.3],
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], | [0, 0, 0], | [0.6, 0.7, 0.8],
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[ | [0, 0, 0], | [0.2, 0.3, 0.4],
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[0.1, 0.3, 0.3], | ], | [0.1, 0.5, 0.3],
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[0.6, 0.7, 0.8], | [ | [0.7, 0.3, 0.6],
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[0.2, 0.3, 0.4], | [0.1, 0.3, 0.3], | [0.2, 0.4, 0.8],
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[0.1, 0.5, 0.3], | [0.6, 0.7, 0.8], | [0.9, 0.5, 0.2],
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], | [0.2, 0.3, 0.4], | [0.2, 0.3, 0.4],
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[ | [0.1, 0.5, 0.3], | [0.9, 0.3, 0.8],
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[0.7, 0.3, 0.6], | [0, 0, 0], | ])
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[0.2, 0.4, 0.8], | ], |
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[0.9, 0.5, 0.2], | [ |
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[0.2, 0.3, 0.4], | [0.7, 0.3, 0.6], |
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[0.9, 0.3, 0.8], | [0.2, 0.4, 0.8], |
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] | [0.9, 0.5, 0.2], |
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]) | [0.2, 0.3, 0.4], |
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| [0.9, 0.3, 0.8], |
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| ] |
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| ]) |
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Example for faces: | |
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| |
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F_1 = 1, F_2 = 2, F_3 = 7 | size = (3, 7, 3) | size = (10, 3)
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| |
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List([ | tensor([ | tensor([
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[ | [ | [ 0, 1, 2],
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[0, 1, 2], | [0, 1, 2], | [ 3, 4, 5],
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], | [-1, -1, -1], | [ 4, 5, 6],
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[ | [-1, -1, -1] | [ 8, 9, 7],
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[0, 1, 2], | [-1, -1, -1] | [ 7, 8, 10],
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[1, 2, 3], | [-1, -1, -1] | [ 9, 10, 8],
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], | [-1, -1, -1], | [11, 10, 9],
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[ | [-1, -1, -1], | [11, 7, 8],
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[1, 2, 0], | ], | [11, 10, 8],
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[0, 1, 3], | [ | [11, 9, 8],
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[2, 3, 1], | [0, 1, 2], | ])
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[4, 3, 2], | [1, 2, 3], |
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[4, 0, 1], | [-1, -1, -1], |
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[4, 3, 1], | [-1, -1, -1], |
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[4, 2, 1], | [-1, -1, -1], |
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], | [-1, -1, -1], |
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]) | [-1, -1, -1], |
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| ], |
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| [ |
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| [1, 2, 0], |
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| [0, 1, 3], |
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| [2, 3, 1], |
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| [4, 3, 2], |
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| [4, 0, 1], |
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| [4, 3, 1], |
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| [4, 2, 1], |
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| ] |
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| ]) |
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-----------------------------------------------------------------------------
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# SPHINX IGNORE
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List | Padded | Packed
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---------------------------|-------------------------|------------------------
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[[V_1], ... , [V_N]] | size = (N, max(V_n), 3) | size = (sum(V_n), 3)
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| |
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Example for verts: | |
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| |
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V_1 = 3, V_2 = 4, V_3 = 5 | size = (3, 5, 3) | size = (12, 3)
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| |
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List([ | tensor([ | tensor([
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[ | [ | [0.1, 0.3, 0.5],
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[0.1, 0.3, 0.5], | [0.1, 0.3, 0.5], | [0.5, 0.2, 0.1],
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[0.5, 0.2, 0.1], | [0.5, 0.2, 0.1], | [0.6, 0.8, 0.7],
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[0.6, 0.8, 0.7], | [0.6, 0.8, 0.7], | [0.1, 0.3, 0.3],
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], | [0, 0, 0], | [0.6, 0.7, 0.8],
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[ | [0, 0, 0], | [0.2, 0.3, 0.4],
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[0.1, 0.3, 0.3], | ], | [0.1, 0.5, 0.3],
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[0.6, 0.7, 0.8], | [ | [0.7, 0.3, 0.6],
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[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], |
|
||||
| ] |
|
||||
| ]) |
|
||||
Example for faces: | |
|
||||
| |
|
||||
F_1 = 1, F_2 = 2, F_3 = 7 | size = (3, 7, 3) | size = (10, 3)
|
||||
| |
|
||||
List([ | tensor([ | tensor([
|
||||
[ | [ | [ 0, 1, 2],
|
||||
[0, 1, 2], | [0, 1, 2], | [ 3, 4, 5],
|
||||
], | [-1, -1, -1], | [ 4, 5, 6],
|
||||
[ | [-1, -1, -1] | [ 8, 9, 7],
|
||||
[0, 1, 2], | [-1, -1, -1] | [ 7, 8, 10],
|
||||
[1, 2, 3], | [-1, -1, -1] | [ 9, 10, 8],
|
||||
], | [-1, -1, -1], | [11, 10, 9],
|
||||
[ | [-1, -1, -1], | [11, 7, 8],
|
||||
[1, 2, 0], | ], | [11, 10, 8],
|
||||
[0, 1, 3], | [ | [11, 9, 8],
|
||||
[2, 3, 1], | [0, 1, 2], | ])
|
||||
[4, 3, 2], | [1, 2, 3], |
|
||||
[4, 0, 1], | [-1, -1, -1], |
|
||||
[4, 3, 1], | [-1, -1, -1], |
|
||||
[4, 2, 1], | [-1, -1, -1], |
|
||||
], | [-1, -1, -1], |
|
||||
]) | [-1, -1, -1], |
|
||||
| ], |
|
||||
| [ |
|
||||
| [1, 2, 0], |
|
||||
| [0, 1, 3], |
|
||||
| [2, 3, 1], |
|
||||
| [4, 3, 2], |
|
||||
| [4, 0, 1], |
|
||||
| [4, 3, 1], |
|
||||
| [4, 2, 1], |
|
||||
| ] |
|
||||
| ]) |
|
||||
-----------------------------------------------------------------------------
|
||||
|
||||
Auxillary variables for packed representation
|
||||
Auxillary variables for packed representation
|
||||
|
||||
Name | Size | Example from above
|
||||
-------------------------------|---------------------|-----------------------
|
||||
| |
|
||||
verts_packed_to_mesh_idx | size = (sum(V_n)) | tensor([
|
||||
| | 0, 0, 0, 1, 1, 1,
|
||||
| | 1, 2, 2, 2, 2, 2
|
||||
| | )]
|
||||
| | size = (12)
|
||||
| |
|
||||
mesh_to_verts_packed_first_idx | size = (N) | tensor([0, 3, 7])
|
||||
| | size = (3)
|
||||
| |
|
||||
num_verts_per_mesh | size = (N) | tensor([3, 4, 5])
|
||||
| | size = (3)
|
||||
| |
|
||||
faces_packed_to_mesh_idx | size = (sum(F_n)) | tensor([
|
||||
| | 0, 1, 1, 2, 2, 2,
|
||||
| | 2, 2, 2, 2
|
||||
| | )]
|
||||
| | size = (10)
|
||||
| |
|
||||
mesh_to_faces_packed_first_idx | size = (N) | tensor([0, 1, 3])
|
||||
| | size = (3)
|
||||
| |
|
||||
num_faces_per_mesh | size = (N) | tensor([1, 2, 7])
|
||||
| | size = (3)
|
||||
| |
|
||||
verts_padded_to_packed_idx | size = (sum(V_n)) | tensor([
|
||||
| | 0, 1, 2, 5, 6, 7,
|
||||
| | 8, 10, 11, 12, 13,
|
||||
| | 14
|
||||
| | )]
|
||||
| | size = (12)
|
||||
-----------------------------------------------------------------------------
|
||||
# SPHINX IGNORE
|
||||
Name | Size | Example from above
|
||||
-------------------------------|---------------------|-----------------------
|
||||
| |
|
||||
verts_packed_to_mesh_idx | size = (sum(V_n)) | tensor([
|
||||
| | 0, 0, 0, 1, 1, 1,
|
||||
| | 1, 2, 2, 2, 2, 2
|
||||
| | )]
|
||||
| | size = (12)
|
||||
| |
|
||||
mesh_to_verts_packed_first_idx | size = (N) | tensor([0, 3, 7])
|
||||
| | size = (3)
|
||||
| |
|
||||
num_verts_per_mesh | size = (N) | tensor([3, 4, 5])
|
||||
| | size = (3)
|
||||
| |
|
||||
faces_packed_to_mesh_idx | size = (sum(F_n)) | tensor([
|
||||
| | 0, 1, 1, 2, 2, 2,
|
||||
| | 2, 2, 2, 2
|
||||
| | )]
|
||||
| | size = (10)
|
||||
| |
|
||||
mesh_to_faces_packed_first_idx | size = (N) | tensor([0, 1, 3])
|
||||
| | size = (3)
|
||||
| |
|
||||
num_faces_per_mesh | size = (N) | tensor([1, 2, 7])
|
||||
| | size = (3)
|
||||
| |
|
||||
verts_padded_to_packed_idx | size = (sum(V_n)) | tensor([
|
||||
| | 0, 1, 2, 5, 6, 7,
|
||||
| | 8, 10, 11, 12, 13,
|
||||
| | 14
|
||||
| | )]
|
||||
| | size = (12)
|
||||
-----------------------------------------------------------------------------
|
||||
# SPHINX IGNORE
|
||||
|
||||
From the faces, edges are computed and have packed and padded
|
||||
representations with auxillary variables.
|
||||
From the faces, edges are computed and have packed and padded
|
||||
representations with auxillary variables.
|
||||
|
||||
E_n = [[E_1], ... , [E_N]]
|
||||
where E_1, ... , E_N are the number of unique edges in each mesh.
|
||||
Total number of unique edges = sum(E_n)
|
||||
E_n = [[E_1], ... , [E_N]]
|
||||
where E_1, ... , E_N are the number of unique edges in each mesh.
|
||||
Total number of unique edges = sum(E_n)
|
||||
|
||||
# SPHINX IGNORE
|
||||
Name | Size | Example from above
|
||||
-------------------------------|-------------------------|----------------------
|
||||
| |
|
||||
edges_packed | size = (sum(E_n), 2) | tensor([
|
||||
| | [0, 1],
|
||||
| | [0, 2],
|
||||
| | [1, 2],
|
||||
| | ...
|
||||
| | [10, 11],
|
||||
| | )]
|
||||
| | size = (18, 2)
|
||||
| |
|
||||
num_edges_per_mesh | size = (N) | tensor([3, 5, 10])
|
||||
| | size = (3)
|
||||
| |
|
||||
edges_packed_to_mesh_idx | size = (sum(E_n)) | tensor([
|
||||
| | 0, 0, 0,
|
||||
| | . . .
|
||||
| | 2, 2, 2
|
||||
| | ])
|
||||
| | size = (18)
|
||||
| |
|
||||
faces_packed_to_edges_packed | size = (sum(F_n), 3) | tensor([
|
||||
| | [2, 1, 0],
|
||||
| | [5, 4, 3],
|
||||
| | . . .
|
||||
| | [12, 14, 16],
|
||||
| | ])
|
||||
| | size = (10, 3)
|
||||
| |
|
||||
mesh_to_edges_packed_first_idx | size = (N) | tensor([0, 3, 8])
|
||||
| | size = (3)
|
||||
----------------------------------------------------------------------------
|
||||
# SPHINX IGNORE
|
||||
# SPHINX IGNORE
|
||||
Name | Size | Example from above
|
||||
-------------------------------|-------------------------|----------------------
|
||||
| |
|
||||
edges_packed | size = (sum(E_n), 2) | tensor([
|
||||
| | [0, 1],
|
||||
| | [0, 2],
|
||||
| | [1, 2],
|
||||
| | ...
|
||||
| | [10, 11],
|
||||
| | )]
|
||||
| | size = (18, 2)
|
||||
| |
|
||||
num_edges_per_mesh | size = (N) | tensor([3, 5, 10])
|
||||
| | size = (3)
|
||||
| |
|
||||
edges_packed_to_mesh_idx | size = (sum(E_n)) | tensor([
|
||||
| | 0, 0, 0,
|
||||
| | . . .
|
||||
| | 2, 2, 2
|
||||
| | ])
|
||||
| | size = (18)
|
||||
| |
|
||||
faces_packed_to_edges_packed | size = (sum(F_n), 3) | tensor([
|
||||
| | [2, 1, 0],
|
||||
| | [5, 4, 3],
|
||||
| | . . .
|
||||
| | [12, 14, 16],
|
||||
| | ])
|
||||
| | size = (10, 3)
|
||||
| |
|
||||
mesh_to_edges_packed_first_idx | size = (N) | tensor([0, 3, 8])
|
||||
| | size = (3)
|
||||
----------------------------------------------------------------------------
|
||||
# SPHINX IGNORE
|
||||
"""
|
||||
|
||||
_INTERNAL_TENSORS = [
|
||||
|
||||
@ -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.
|
||||
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.
|
||||
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.
|
||||
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
|
||||
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.
|
||||
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] |
|
||||
| ] |
|
||||
| ]) |
|
||||
-----------------------------------------------------------------------------
|
||||
# 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
|
||||
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
|
||||
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 = [
|
||||
|
||||
@ -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))
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user