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pyre and lint fixes

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Summary: Fixing several unused imports and pyre/linter warnings.

Reviewed By: bottler

Differential Revision: D22592491

fbshipit-source-id: 463383b9b73a545949475044fb5c531712f8482c
This commit is contained in:
Nikhila Ravi 2020-07-17 10:27:47 -07:00 committed by Facebook GitHub Bot
parent 7e5bad475c
commit 4f78af6170
8 changed files with 53 additions and 65 deletions
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2
pytorch3d/io/obj_io.py
View File

@ -368,7 +368,7 @@ def _load_obj(
# startswith expects each line to be a string. If the file is read in as # startswith expects each line to be a string. If the file is read in as
# bytes then first decode to strings. # bytes then first decode to strings.
if lines and isinstance(lines[0], bytes): if lines and isinstance(lines[0], bytes):
lines = [l.decode("utf-8") for l in lines] lines = [el.decode("utf-8") for el in lines]
for line in lines: for line in lines:
if line.startswith("mtllib"): if line.startswith("mtllib"):

4
pytorch3d/renderer/compositing.py
View File

@ -1,9 +1,5 @@
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
from typing import NamedTuple
import torch import torch
from pytorch3d import _C from pytorch3d import _C

1
pytorch3d/renderer/mesh/rasterizer.py
View File

@ -5,7 +5,6 @@ from typing import NamedTuple, Optional
import torch import torch
import torch.nn as nn import torch.nn as nn
from ..cameras import get_world_to_view_transform
from .rasterize_meshes import rasterize_meshes from .rasterize_meshes import rasterize_meshes

3
pytorch3d/renderer/mesh/renderer.py
View File

@ -4,9 +4,6 @@
import torch import torch
import torch.nn as nn import torch.nn as nn
from .rasterizer import Fragments
from .utils import _clip_barycentric_coordinates, _interpolate_zbuf
# A renderer class should be initialized with a # A renderer class should be initialized with a
# function for rasterization and a function for shading. # function for rasterization and a function for shading.

1
pytorch3d/renderer/points/rasterizer.py
View File

@ -7,7 +7,6 @@ from typing import NamedTuple, Optional
import torch import torch
import torch.nn as nn import torch.nn as nn
from ..cameras import get_world_to_view_transform
from .rasterize_points import rasterize_points from .rasterize_points import rasterize_points

2
pytorch3d/transforms/transform3d.py
View File

@ -375,7 +375,7 @@ class Transform3d:
""" """
other = Transform3d(device=self.device) other = Transform3d(device=self.device)
if self._lu is not None: if self._lu is not None:
other._lu = [l.clone() for l in self._lu] other._lu = [elem.clone() for elem in self._lu]
other._matrix = self._matrix.clone() other._matrix = self._matrix.clone()
other._transforms = [t.clone() for t in self._transforms] other._transforms = [t.clone() for t in self._transforms]
return other return other

5
tests/test_interpolate_face_attributes.py
View File

@ -9,10 +9,7 @@ from pytorch3d.ops.interp_face_attrs import (
interpolate_face_attributes_python, interpolate_face_attributes_python,
) )
from pytorch3d.renderer.mesh.rasterizer import Fragments from pytorch3d.renderer.mesh.rasterizer import Fragments
from pytorch3d.renderer.mesh.texturing import ( from pytorch3d.renderer.mesh.texturing import interpolate_vertex_colors
interpolate_texture_map,
interpolate_vertex_colors,
)
from pytorch3d.structures import Meshes, Textures from pytorch3d.structures import Meshes, Textures

100
tests/test_rasterize_meshes.py
View File

@ -571,9 +571,9 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
def _test_perspective_correct(self, rasterize_meshes_fn, device, bin_size=None): def _test_perspective_correct(self, rasterize_meshes_fn, device, bin_size=None):
# fmt: off # fmt: off
verts = torch.tensor([ verts = torch.tensor([
[-0.4, -0.4, 10], # noqa: E241, E201 [-0.4, -0.4, 10], # noqa: E241, E201
[ 0.4, -0.4, 10], # noqa: E241, E201 [ 0.4, -0.4, 10], # noqa: E241, E201
[ 0.0, 0.4, 20], # noqa: E241, E201 [ 0.0, 0.4, 20], # noqa: E241, E201
], dtype=torch.float32, device=device) ], dtype=torch.float32, device=device)
# fmt: on # fmt: on
faces = torch.tensor([[0, 1, 2]], device=device) faces = torch.tensor([[0, 1, 2]], device=device)
@ -641,17 +641,17 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
], dtype=torch.float32, device=device).view(1, 11, 11, 1) ], dtype=torch.float32, device=device).view(1, 11, 11, 1)
zbuf_t_expected = torch.tensor([ zbuf_t_expected = torch.tensor([
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201 [-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., -1., 33.8461, 33.8462, 33.8462, -1., -1., -1., -1.], # noqa: E241, E201 [-1., -1., -1., -1., 33.8461, 33.8462, 33.8462, -1., -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., 24.4444, 24.4444, 24.4444, 24.4444, 24.4444, -1., -1., -1.], # noqa: E241, E201 [-1., -1., -1., 24.4444, 24.4444, 24.4444, 24.4444, 24.4444, -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., 19.1304, 19.1304, 19.1304, 19.1304, 19.1304, -1., -1., -1.], # noqa: E241, E201 [-1., -1., -1., 19.1304, 19.1304, 19.1304, 19.1304, 19.1304, -1., -1., -1.], # noqa: E241, E201
[-1., -1., 15.7143, 15.7143, 15.7143, 15.7143, 15.7143, 15.7143, 15.7143, -1., -1.], # noqa: E241, E201 [-1., -1., 15.7143, 15.7143, 15.7143, 15.7143, 15.7143, 15.7143, 15.7143, -1., -1.], # noqa: E241, E201
[-1., -1., 13.3333, 13.3333, 13.3333, 13.3333, 13.3333, 13.3333, 13.3333, -1., -1.], # noqa: E241, E201 [-1., -1., 13.3333, 13.3333, 13.3333, 13.3333, 13.3333, 13.3333, 13.3333, -1., -1.], # noqa: E241, E201
[-1., 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, -1.], # noqa: E241, E201 [-1., 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, -1.], # noqa: E241, E201
[-1., 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, -1.], # noqa: E241, E201 [-1., 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, -1.], # noqa: E241, E201
[-1., 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, -1.], # noqa: E241, E201 [-1., 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, -1.], # noqa: E241, E201
[-1., -1., 8.3019, 8.3019, 8.3019, 8.3019, 8.3019, 8.3019, 8.3019, -1., -1.], # noqa: E241, E201 [-1., -1., 8.3019, 8.3019, 8.3019, 8.3019, 8.3019, 8.3019, 8.3019, -1., -1.], # noqa: E241, E201
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.] # noqa: E241, E201 [-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.] # noqa: E241, E201
], dtype=torch.float32, device=device).view(1, 11, 11, 1) ], dtype=torch.float32, device=device).view(1, 11, 11, 1)
# fmt: on # fmt: on
@ -890,32 +890,32 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
device=device, device=device,
) )
expected_zbuf_k0 = torch.tensor( expected_zbuf_k0 = torch.tensor(
[
[ [
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201 [
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201 [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201 [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, 0.1, -1, -1, -1, -1, -1], # noqa: E241, E201 [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 0.1, 0.1, 0.1, -1, -1, -1, -1], # noqa: E241, E201 [-1, -1, -1, -1, 0.1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, 0.1, 0.1, 0.1, 0.1, -1, -1, -1, -1], # noqa: E241, E201 [-1, -1, -1, 0.1, 0.1, 0.1, -1, -1, -1, -1], # noqa: E241, E201
[-1, 0.1, 0.1, 0.1, 0.1, 0.1, -1, -1, -1, -1], # noqa: E241, E201 [-1, -1, 0.1, 0.1, 0.1, 0.1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201 [-1, 0.1, 0.1, 0.1, 0.1, 0.1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201 [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1] # noqa: E241, E201 [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1] # noqa: E241, E201
],
[
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, 0.1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, 0.5, 0.5, 0.1, 0.1, 0.1, 0.5, -1, -1], # noqa: E241, E201
[-1, -1, -1, 0.1, 0.1, 0.1, 0.1, 0.1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, -1], # noqa: E241, E201
[-1, -1, 0.1, 0.1, 0.1, 0.5, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1] # noqa: E241, E201
]
], ],
[
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, 0.1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, 0.5, 0.5, 0.1, 0.1, 0.1, 0.5, -1, -1], # noqa: E241, E201
[-1, -1, -1, 0.1, 0.1, 0.1, 0.1, 0.1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, -1], # noqa: E241, E201
[-1, -1, 0.1, 0.1, 0.1, 0.5, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1] # noqa: E241, E201
]
],
device=device, device=device,
) )
# fmt: on # fmt: on
@ -928,18 +928,18 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
# fmt: off # fmt: off
expected_p2face_k1[1, :] = torch.tensor( expected_p2face_k1[1, :] = torch.tensor(
[ [
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201 [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201 [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201 [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, 2, 2, 2, -1, -1, -1], # noqa: E241, E201 [-1, -1, -1, -1, 2, 2, 2, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 2, 2, 2, 2, -1, -1, -1], # noqa: E241, E201 [-1, -1, -1, 2, 2, 2, 2, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 2, 2, 2, 2, -1, -1, -1], # noqa: E241, E201 [-1, -1, -1, 2, 2, 2, 2, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, 2, -1, -1, -1, -1, -1], # noqa: E241, E201 [-1, -1, -1, -1, 2, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201 [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201 [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1] # noqa: E241, E201 [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1] # noqa: E241, E201
], ],
dtype=torch.int64, dtype=torch.int64,
device=device, device=device,
) )