423A35C7/pytorch3d
1
0
Fork 0
mirror of https://github.com/facebookresearch/pytorch3d.git synced 2025-08-02 03:42:50 +08:00
Code Issues Packages Projects Releases Wiki Activity

Annotate dunder functions

Browse Source 
Summary: Annotate the (return type of the) following dunder functions across the codebase: `__init__()`, `__len__()`, `__getitem__()`

Reviewed By: nikhilaravi

Differential Revision: D29001801

fbshipit-source-id: 928d9e1c417ffe01ab8c0445311287786e997c7c
This commit is contained in:
Patrick Labatut 2021-06-24 15:18:19 -07:00 committed by Facebook GitHub Bot
parent 35855bf860
commit 64289a491d
35 changed files with 79 additions and 79 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
projects/nerf/nerf/dataset.py
View File

@ -36,7 +36,7 @@ class ListDataset(Dataset):
A simple dataset made of a list of entries.
"""
def __init__(self, entries: List):
def __init__(self, entries: List) -> None:
"""
Args:
entries: The list of dataset entries.
@ -45,7 +45,7 @@ class ListDataset(Dataset):
def __len__(
self,
):
) -> int:
return len(self._entries)
def __getitem__(self, index):

4
projects/nerf/nerf/stats.py
View File

@ -22,7 +22,7 @@ class AverageMeter:
Tracks the exact history of the added values in every epoch.
"""
def __init__(self):
def __init__(self) -> None:
"""
Initialize the structure with empty history and zero-ed moving average.
"""
@ -110,7 +110,7 @@ class Stats:
verbose: bool = False,
epoch: int = -1,
plot_file: Optional[str] = None,
):
) -> None:
"""
Args:
log_vars: The list of variable names to be logged.

2
pytorch3d/datasets/r2n2/r2n2.py
View File

@ -64,7 +64,7 @@ class R2N2(ShapeNetBase): # pragma: no cover
voxels_rel_path: str = "ShapeNetVoxels",
load_textures: bool = True,
texture_resolution: int = 4,
):
) -> None:
"""
Store each object's synset id and models id the given directories.

2
pytorch3d/datasets/r2n2/utils.py
View File

@ -437,7 +437,7 @@ class BlenderCamera(CamerasBase): # pragma: no cover
(which uses Blender for rendering the views for each model).
"""
def __init__(self, R=r, T=t, K=k, device: Device = "cpu"):
def __init__(self, R=r, T=t, K=k, device: Device = "cpu") -> None:
"""
Args:
R: Rotation matrix of shape (N, 3, 3).

2
pytorch3d/datasets/shapenet/shapenet_core.py
View File

@ -31,7 +31,7 @@ class ShapeNetCore(ShapeNetBase): # pragma: no cover
version: int = 1,
load_textures: bool = True,
texture_resolution: int = 4,
):
) -> None:
"""
Store each object's synset id and models id from data_dir.

4
pytorch3d/datasets/shapenet_base.py
View File

@ -30,7 +30,7 @@ class ShapeNetBase(torch.utils.data.Dataset): # pragma: no cover
and __getitem__ need to be implemented.
"""
def __init__(self):
def __init__(self) -> None:
"""
Set up lists of synset_ids and model_ids.
"""
@ -44,7 +44,7 @@ class ShapeNetBase(torch.utils.data.Dataset): # pragma: no cover
self.load_textures = True
self.texture_resolution = 4
def __len__(self):
def __len__(self) -> int:
"""
Return number of total models in the loaded dataset.
"""

4
pytorch3d/io/experimental_gltf_io.py
View File

@ -187,7 +187,7 @@ def _make_node_transform(node: Dict[str, Any]) -> Transform3d:
class _GLTFLoader:
def __init__(self, stream: BinaryIO):
def __init__(self, stream: BinaryIO) -> None:
self._json_data = None
# Map from buffer index to (decoded) binary data
self._binary_data = {}
@ -539,7 +539,7 @@ class MeshGlbFormat(MeshFormatInterpreter):
used which does not match the semantics of the standard.
"""
def __init__(self):
def __init__(self) -> None:
self.known_suffixes = (".glb",)
def read(

2
pytorch3d/io/obj_io.py
View File

@ -291,7 +291,7 @@ def load_objs_as_meshes(
class MeshObjFormat(MeshFormatInterpreter):
def __init__(self):
def __init__(self) -> None:
self.known_suffixes = (".obj",)
def read(

2
pytorch3d/io/off_io.py
View File

@ -419,7 +419,7 @@ class MeshOffFormat(MeshFormatInterpreter):
"""
def __init__(self):
def __init__(self) -> None:
self.known_suffixes = (".off",)
def read(

2
pytorch3d/io/pluggable.py
View File

@ -63,7 +63,7 @@ class IO:
self,
include_default_formats: bool = True,
path_manager: Optional[PathManager] = None,
):
) -> None:
if path_manager is None:
self.path_manager = PathManager()
else:

8
pytorch3d/io/ply_io.py
View File

@ -66,7 +66,7 @@ class _PlyElementType:
self.name: (str) name of the element
"""
def __init__(self, name: str, count: int):
def __init__(self, name: str, count: int) -> None:
self.name = name
self.count = count
self.properties: List[_Property] = []
@ -130,7 +130,7 @@ class _PlyElementType:
class _PlyHeader:
def __init__(self, f):
def __init__(self, f) -> None:
"""
Load a header of a Ply file from a file-like object.
Members:
@ -1232,7 +1232,7 @@ def save_ply(
class MeshPlyFormat(MeshFormatInterpreter):
def __init__(self):
def __init__(self) -> None:
self.known_suffixes = (".ply",)
def read(
@ -1313,7 +1313,7 @@ class MeshPlyFormat(MeshFormatInterpreter):
class PointcloudPlyFormat(PointcloudFormatInterpreter):
def __init__(self):
def __init__(self) -> None:
self.known_suffixes = (".ply",)
def read(

2
pytorch3d/ops/graph_conv.py
View File

@ -21,7 +21,7 @@ class GraphConv(nn.Module):
output_dim: int,
init: str = "normal",
directed: bool = False,
):
) -> None:
"""
Args:
input_dim: Number of input features per vertex.

2
pytorch3d/ops/marching_cubes.py
View File

@ -15,7 +15,7 @@ EPS = 0.00001
class Cube:
def __init__(self, bfl_vertex: Tuple[int, int, int], spacing: int = 1):
def __init__(self, bfl_vertex: Tuple[int, int, int], spacing: int = 1) -> None:
"""
Initializes a cube given the bottom front left vertex coordinate
and the cube spacing

2
pytorch3d/ops/subdivide_meshes.py
View File

@ -26,7 +26,7 @@ class SubdivideMeshes(nn.Module):
but different vertex positions.
"""
def __init__(self, meshes=None):
def __init__(self, meshes=None) -> None:
"""
Args:
meshes: Meshes object or None. If a meshes object is provided,

6
pytorch3d/renderer/cameras.py
View File

@ -364,7 +364,7 @@ class FoVPerspectiveCameras(CamerasBase):
T=_T,
K=None,
device: Device = "cpu",
):
) -> None:
"""
Args:
@ -848,7 +848,7 @@ class PerspectiveCameras(CamerasBase):
K=None,
device="cpu",
image_size=((-1, -1),),
):
) -> None:
"""
Args:
@ -1013,7 +1013,7 @@ class OrthographicCameras(CamerasBase):
K=None,
device="cpu",
image_size=((-1, -1),),
):
) -> None:
"""
Args:

4
pytorch3d/renderer/implicit/raymarching.py
View File

@ -49,7 +49,7 @@ class EmissionAbsorptionRaymarcher(torch.nn.Module):
elements along the ray direction.
"""
def __init__(self, surface_thickness: int = 1):
def __init__(self, surface_thickness: int = 1) -> None:
"""
Args:
surface_thickness: Denotes the overlap between the absorption
@ -128,7 +128,7 @@ class AbsorptionOnlyRaymarcher(torch.nn.Module):
It then returns `opacities = 1 - total_transmission`.
"""
def __init__(self):
def __init__(self) -> None:
super().__init__()
def forward(

6
pytorch3d/renderer/implicit/raysampling.py
View File

@ -66,7 +66,7 @@ class GridRaysampler(torch.nn.Module):
n_pts_per_ray: int,
min_depth: float,
max_depth: float,
):
) -> None:
"""
Args:
min_x: The leftmost x-coordinate of each ray's source pixel's center.
@ -150,7 +150,7 @@ class NDCGridRaysampler(GridRaysampler):
n_pts_per_ray: int,
min_depth: float,
max_depth: float,
):
) -> None:
"""
Args:
image_width: The horizontal size of the image grid.
@ -192,7 +192,7 @@ class MonteCarloRaysampler(torch.nn.Module):
n_pts_per_ray: int,
min_depth: float,
max_depth: float,
):
) -> None:
"""
Args:
min_x: The smallest x-coordinate of each ray's source pixel.

6
pytorch3d/renderer/implicit/renderer.py
View File

@ -105,7 +105,7 @@ class ImplicitRenderer(torch.nn.Module):
```
"""
def __init__(self, raysampler: Callable, raymarcher: Callable):
def __init__(self, raysampler: Callable, raymarcher: Callable) -> None:
"""
Args:
raysampler: A `Callable` that takes as input scene cameras
@ -206,7 +206,7 @@ class VolumeRenderer(torch.nn.Module):
def __init__(
self, raysampler: Callable, raymarcher: Callable, sample_mode: str = "bilinear"
):
) -> None:
"""
Args:
raysampler: A `Callable` that takes as input scene cameras
@ -256,7 +256,7 @@ class VolumeSampler(torch.nn.Module):
at 3D points sampled along projection rays.
"""
def __init__(self, volumes: Volumes, sample_mode: str = "bilinear"):
def __init__(self, volumes: Volumes, sample_mode: str = "bilinear") -> None:
"""
Args:
volumes: An instance of the `Volumes` class representing a

6
pytorch3d/renderer/lighting.py
View File

@ -164,7 +164,7 @@ class DirectionalLights(TensorProperties):
specular_color=((0.2, 0.2, 0.2),),
direction=((0, 1, 0),),
device: Device = "cpu",
):
) -> None:
"""
Args:
ambient_color: RGB color of the ambient component.
@ -225,7 +225,7 @@ class PointLights(TensorProperties):
specular_color=((0.2, 0.2, 0.2),),
location=((0, 1, 0),),
device: Device = "cpu",
):
) -> None:
"""
Args:
ambient_color: RGB color of the ambient component
@ -294,7 +294,7 @@ class AmbientLights(TensorProperties):
not used in rendering.
"""
def __init__(self, *, ambient_color=None, device: Device = "cpu"):
def __init__(self, *, ambient_color=None, device: Device = "cpu") -> None:
"""
If ambient_color is provided, it should be a sequence of
triples of floats.

2
pytorch3d/renderer/materials.py
View File

@ -24,7 +24,7 @@ class Materials(TensorProperties):
specular_color=((1, 1, 1),),
shininess=64,
device: Device = "cpu",
):
) -> None:
"""
Args:
ambient_color: RGB ambient reflectivity of the material

4
pytorch3d/renderer/mesh/clip.py
View File

@ -84,7 +84,7 @@ class ClippedFaces:
barycentric_conversion: Optional[torch.Tensor] = None,
faces_clipped_to_conversion_idx: Optional[torch.Tensor] = None,
clipped_faces_neighbor_idx: Optional[torch.Tensor] = None,
):
) -> None:
self.face_verts = face_verts
self.mesh_to_face_first_idx = mesh_to_face_first_idx
self.num_faces_per_mesh = num_faces_per_mesh
@ -139,7 +139,7 @@ class ClipFrustum:
perspective_correct: bool = False,
cull: bool = True,
z_clip_value: Optional[float] = None,
):
) -> None:
self.left = left
self.right = right
self.top = top

4
pytorch3d/renderer/mesh/rasterizer.py
View File

@ -49,7 +49,7 @@ class RasterizationSettings:
cull_backfaces: bool = False,
z_clip_value: Optional[float] = None,
cull_to_frustum: bool = False,
):
) -> None:
self.image_size = image_size
self.blur_radius = blur_radius
self.faces_per_pixel = faces_per_pixel
@ -68,7 +68,7 @@ class MeshRasterizer(nn.Module):
Meshes.
"""
def __init__(self, cameras=None, raster_settings=None):
def __init__(self, cameras=None, raster_settings=None) -> None:
"""
Args:
cameras: A cameras object which has a `transform_points` method

4
pytorch3d/renderer/mesh/renderer.py
View File

@ -32,7 +32,7 @@ class MeshRenderer(nn.Module):
function.
"""
def __init__(self, rasterizer, shader):
def __init__(self, rasterizer, shader) -> None:
super().__init__()
self.rasterizer = rasterizer
self.shader = shader
@ -76,7 +76,7 @@ class MeshRendererWithFragments(nn.Module):
depth = fragments.zbuf
"""
def __init__(self, rasterizer, shader):
def __init__(self, rasterizer, shader) -> None:
super().__init__()
self.rasterizer = rasterizer
self.shader = shader

12
pytorch3d/renderer/mesh/shader.py
View File

@ -51,7 +51,7 @@ class HardPhongShader(nn.Module):
lights=None,
materials=None,
blend_params=None,
):
) -> None:
super().__init__()
self.lights = lights if lights is not None else PointLights(device=device)
self.materials = (
@ -112,7 +112,7 @@ class SoftPhongShader(nn.Module):
lights=None,
materials=None,
blend_params=None,
):
) -> None:
super().__init__()
self.lights = lights if lights is not None else PointLights(device=device)
self.materials = (
@ -178,7 +178,7 @@ class HardGouraudShader(nn.Module):
lights=None,
materials=None,
blend_params=None,
):
) -> None:
super().__init__()
self.lights = lights if lights is not None else PointLights(device=device)
self.materials = (
@ -243,7 +243,7 @@ class SoftGouraudShader(nn.Module):
lights=None,
materials=None,
blend_params=None,
):
) -> None:
super().__init__()
self.lights = lights if lights is not None else PointLights(device=device)
self.materials = (
@ -325,7 +325,7 @@ class HardFlatShader(nn.Module):
lights=None,
materials=None,
blend_params=None,
):
) -> None:
super().__init__()
self.lights = lights if lights is not None else PointLights(device=device)
self.materials = (
@ -381,7 +381,7 @@ class SoftSilhouetteShader(nn.Module):
3D Reasoning', ICCV 2019
"""
def __init__(self, blend_params=None):
def __init__(self, blend_params=None) -> None:
super().__init__()
self.blend_params = blend_params if blend_params is not None else BlendParams()

12
pytorch3d/renderer/mesh/textures.py
View File

@ -262,7 +262,7 @@ class TexturesBase:
"""
raise NotImplementedError()
def __getitem__(self, index):
def __getitem__(self, index) -> "TexturesBase":
"""
Each texture class should implement a method
to get the texture properties for the
@ -321,7 +321,7 @@ def Textures(
class TexturesAtlas(TexturesBase):
def __init__(self, atlas: Union[torch.Tensor, List[torch.Tensor]]):
def __init__(self, atlas: Union[torch.Tensor, List[torch.Tensor]]) -> None:
"""
A texture representation where each face has a square texture map.
This is based on the implementation from SoftRasterizer [1].
@ -420,7 +420,7 @@ class TexturesAtlas(TexturesBase):
tex._num_faces_per_mesh = num_faces
return tex
def __getitem__(self, index):
def __getitem__(self, index) -> "TexturesAtlas":
props = ["atlas_list", "_num_faces_per_mesh"]
new_props = self._getitem(index, props=props)
atlas = new_props["atlas_list"]
@ -596,7 +596,7 @@ class TexturesUV(TexturesBase):
verts_uvs: Union[torch.Tensor, List[torch.Tensor], Tuple[torch.Tensor]],
padding_mode: str = "border",
align_corners: bool = True,
):
) -> None:
"""
Textures are represented as a per mesh texture map and uv coordinates for each
vertex in each face. NOTE: this class only supports one texture map per mesh.
@ -786,7 +786,7 @@ class TexturesUV(TexturesBase):
tex.valid = self.valid.detach()
return tex
def __getitem__(self, index):
def __getitem__(self, index) -> "TexturesUV":
props = ["verts_uvs_list", "faces_uvs_list", "maps_list", "_num_faces_per_mesh"]
new_props = self._getitem(index, props)
faces_uvs = new_props["faces_uvs_list"]
@ -1257,7 +1257,7 @@ class TexturesVertex(TexturesBase):
def __init__(
self,
verts_features: Union[torch.Tensor, List[torch.Tensor], Tuple[torch.Tensor]],
):
) -> None:
"""
Batched texture representation where each vertex in a mesh
has a C dimensional feature vector.

4
pytorch3d/renderer/points/compositor.py
View File

@ -25,7 +25,7 @@ class AlphaCompositor(nn.Module):
def __init__(
self, background_color: Optional[Union[Tuple, List, torch.Tensor]] = None
):
) -> None:
super().__init__()
self.background_color = background_color
@ -47,7 +47,7 @@ class NormWeightedCompositor(nn.Module):
def __init__(
self, background_color: Optional[Union[Tuple, List, torch.Tensor]] = None
):
) -> None:
super().__init__()
self.background_color = background_color

2
pytorch3d/renderer/points/pulsar/renderer.py
View File

@ -326,7 +326,7 @@ class Renderer(torch.nn.Module):
background_normalized_depth: float = _C.EPS,
n_channels: int = 3,
n_track: int = 5,
):
) -> None:
super(Renderer, self).__init__()
# pyre-fixme[16]: Module `pytorch3d` has no attribute `_C`.
self._renderer = _C.PulsarRenderer(

2
pytorch3d/renderer/points/pulsar/unified.py
View File

@ -51,7 +51,7 @@ class PulsarPointsRenderer(nn.Module):
n_channels: int = 3,
max_num_spheres: int = int(1e6), # noqa: B008
**kwargs,
):
) -> None:
"""
rasterizer (PointsRasterizer): An object encapsulating rasterization parameters.
compositor (ignored): Only keeping this for interface consistency. Default: None.

4
pytorch3d/renderer/points/rasterizer.py
View File

@ -38,7 +38,7 @@ class PointsRasterizationSettings:
points_per_pixel: int = 8,
bin_size: Optional[int] = None,
max_points_per_bin: Optional[int] = None,
):
) -> None:
self.image_size = image_size
self.radius = radius
self.points_per_pixel = points_per_pixel
@ -51,7 +51,7 @@ class PointsRasterizer(nn.Module):
This class implements methods for rasterizing a batch of pointclouds.
"""
def __init__(self, cameras=None, raster_settings=None):
def __init__(self, cameras=None, raster_settings=None) -> None:
"""
cameras: A cameras object which has a `transform_points` method
which returns the transformed points after applying the

2
pytorch3d/renderer/points/renderer.py
View File

@ -32,7 +32,7 @@ class PointsRenderer(nn.Module):
function.
"""
def __init__(self, rasterizer, compositor):
def __init__(self, rasterizer, compositor) -> None:
super().__init__()
self.rasterizer = rasterizer
self.compositor = compositor

6
pytorch3d/renderer/utils.py
View File

@ -25,7 +25,7 @@ class TensorAccessor(nn.Module):
and one element in the batch needs to be modified.
"""
def __init__(self, class_object, index: Union[int, slice]):
def __init__(self, class_object, index: Union[int, slice]) -> None:
"""
Args:
class_object: this should be an instance of a class which has
@ -96,7 +96,7 @@ class TensorProperties(nn.Module):
def __init__(
self, dtype: torch.dtype = torch.float32, device: Device = "cpu", **kwargs
):
) -> None:
"""
Args:
dtype: data type to set for the inputs
@ -143,7 +143,7 @@ class TensorProperties(nn.Module):
def isempty(self) -> bool:
return self._N == 0
def __getitem__(self, index: Union[int, slice]):
def __getitem__(self, index: Union[int, slice]) -> TensorAccessor:
"""
Args:

8
pytorch3d/structures/meshes.py
View File

@ -219,7 +219,7 @@ class Meshes:
textures=None,
*,
verts_normals=None,
):
) -> None:
"""
Args:
verts:
@ -469,10 +469,10 @@ class Meshes:
else:
raise ValueError("verts_normals must be a list or tensor")
def __len__(self):
def __len__(self) -> int:
return self._N
def __getitem__(self, index):
def __getitem__(self, index) -> "Meshes":
"""
Args:
index: Specifying the index of the mesh to retrieve.
@ -493,7 +493,7 @@ class Meshes:
# NOTE consider converting index to cpu for efficiency
if index.dtype == torch.bool:
# advanced indexing on a single dimension
index = index.nonzero()
index = index.nonzero() # pyre-ignore
index = index.squeeze(1) if index.numel() > 0 else index
index = index.tolist()
verts = [self.verts_list()[i] for i in index]

8
pytorch3d/structures/pointclouds.py
View File

@ -108,7 +108,7 @@ class Pointclouds:
"equisized",
]
def __init__(self, points, normals=None, features=None):
def __init__(self, points, normals=None, features=None) -> None:
"""
Args:
points:
@ -306,10 +306,10 @@ class Pointclouds:
points in a cloud."
)
def __len__(self):
def __len__(self) -> int:
return self._N
def __getitem__(self, index):
def __getitem__(self, index) -> "Pointclouds":
"""
Args:
index: Specifying the index of the cloud to retrieve.
@ -343,7 +343,7 @@ class Pointclouds:
# NOTE consider converting index to cpu for efficiency
if index.dtype == torch.bool:
# advanced indexing on a single dimension
index = index.nonzero()
index = index.nonzero() # pyre-ignore
index = index.squeeze(1) if index.numel() > 0 else index
index = index.tolist()
points = [self.points_list()[i] for i in index]

2
pytorch3d/structures/volumes.py
View File

@ -155,7 +155,7 @@ class Volumes:
features: Optional[_TensorBatch] = None,
voxel_size: _VoxelSize = 1.0,
volume_translation: _Translation = (0.0, 0.0, 0.0),
):
) -> None:
"""
Args:
**densities**: Batch of input feature volume occupancies of shape

12
pytorch3d/transforms/transform3d.py
View File

@ -144,7 +144,7 @@ class Transform3d:
dtype: torch.dtype = torch.float32,
device: Device = "cpu",
matrix: Optional[torch.Tensor] = None,
):
) -> None:
"""
Args:
dtype: The data type of the transformation matrix.
@ -176,7 +176,7 @@ class Transform3d:
self.device = make_device(device)
self.dtype = dtype
def __len__(self):
def __len__(self) -> int:
return self.get_matrix().shape[0]
def __getitem__(
@ -462,7 +462,7 @@ class Translate(Transform3d):
z=None,
dtype: torch.dtype = torch.float32,
device: Optional[Device] = None,
):
) -> None:
"""
Create a new Transform3d representing 3D translations.
@ -503,7 +503,7 @@ class Scale(Transform3d):
z=None,
dtype: torch.dtype = torch.float32,
device: Optional[Device] = None,
):
) -> None:
"""
A Transform3d representing a scaling operation, with different scale
factors along each coordinate axis.
@ -549,7 +549,7 @@ class Rotate(Transform3d):
dtype: torch.dtype = torch.float32,
device: Optional[Device] = None,
orthogonal_tol: float = 1e-5,
):
) -> None:
"""
Create a new Transform3d representing 3D rotation using a rotation
matrix as the input.
@ -589,7 +589,7 @@ class RotateAxisAngle(Rotate):
degrees: bool = True,
dtype: torch.dtype = torch.float64,
device: Optional[Device] = None,
):
) -> None:
"""
Create a new Transform3d representing 3D rotation about an axis
by an angle.